diff --git a/.gitignore b/.gitignore
index 41d1561..eb9f39c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -501,8 +501,6 @@ notebooks/word-embedding/
 notebooks/temp/
 notebooks/*.pdf
 
-notebooks/word-embedding
-notebooks/*.pdf
 
 
 
diff --git a/notebooks/notebook_fair-profession_bert.ipynb b/notebooks/notebook_fair-profession_bert.ipynb
deleted file mode 100644
index a2f1f9e..0000000
--- a/notebooks/notebook_fair-profession_bert.ipynb
+++ /dev/null
@@ -1,1662 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%load_ext autoreload\n",
-    "%autoreload 2\n",
-    "import sys\n",
-    "sys.path.append(\"../src\")\n",
-    "sys.path.append(\"../data/embeddings\")\n",
-    "sys.path.append(\"../data/biasbios\")\n",
-    "sys.path.append(\"../data/embeddings/biasbios\")\n",
-    "import classifier\n",
-    "import debias\n",
-    "import gensim\n",
-    "import codecs\n",
-    "import json\n",
-    "from gensim.models.keyedvectors import Word2VecKeyedVectors\n",
-    "from gensim.models import KeyedVectors\n",
-    "import numpy as np\n",
-    "import random\n",
-    "import sklearn\n",
-    "from sklearn import model_selection\n",
-    "from sklearn import cluster\n",
-    "from sklearn import metrics\n",
-    "from sklearn.manifold import TSNE\n",
-    "from sklearn.svm import LinearSVC, SVC\n",
-    "from sklearn.neural_network import MLPClassifier\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from sklearn.feature_extraction import DictVectorizer\n",
-    "from pytorch_transformers import BertTokenizer, BertModel, BertForMaskedLM\n",
-    "\n",
-    "import scipy\n",
-    "from scipy import linalg\n",
-    "from scipy import sparse\n",
-    "from scipy.stats.stats import pearsonr\n",
-    "import tqdm\n",
-    "import matplotlib\n",
-    "import matplotlib.pyplot as plt\n",
-    "from sklearn.linear_model import SGDClassifier, SGDRegressor, Perceptron, LogisticRegression\n",
-    "from sklearn.utils import shuffle\n",
-    "\n",
-    "%matplotlib inline\n",
-    "matplotlib.rcParams['agg.path.chunksize'] = 10000\n",
-    "\n",
-    "import warnings\n",
-    "warnings.filterwarnings(\"ignore\")\n",
-    "\n",
-    "import pickle\n",
-    "from collections import defaultdict, Counter\n",
-    "from typing import List, Dict\n",
-    "\n",
-    "import torch\n",
-    "from torch import utils\n",
-    "\n",
-    "# import pytorch_lightning as pl\n",
-    "# from pytorch_lightning import Trainer\n",
-    "import copy\n",
-    "import pandas as pd\n",
-    "from gensim.models import FastText\n",
-    "import time\n",
-    "from gensim.scripts.glove2word2vec import glove2word2vec\n",
-    "\n",
-    "STOPWORDS = set([\"i\", \"me\", \"my\", \"myself\", \"we\", \"our\", \"ours\", \"ourselves\", \"you\", \"your\", \"yours\", \"yourself\", \"yourselves\", \"he\", \"him\", \"his\", \"himself\", \"she\", \"her\", \"hers\", \"herself\", \"it\", \"its\", \"itself\", \"they\", \"them\", \"their\", \"theirs\", \"themselves\", \"what\", \"which\", \"who\", \"whom\", \"this\", \"that\", \"these\", \"those\", \"am\", \"is\", \"are\", \"was\", \"were\", \"be\", \"been\", \"being\", \"have\", \"has\", \"had\", \"having\", \"do\", \"does\", \"did\", \"doing\", \"a\", \"an\", \"the\", \"and\", \"but\", \"if\", \"or\", \"because\", \"as\", \"until\", \"while\", \"of\", \"at\", \"by\", \"for\", \"with\", \"about\", \"against\", \"between\", \"into\", \"through\", \"during\", \"before\", \"after\", \"above\", \"below\", \"to\", \"from\", \"up\", \"down\", \"in\", \"out\", \"on\", \"off\", \"over\", \"under\", \"again\", \"further\", \"then\", \"once\", \"here\", \"there\", \"when\", \"where\", \"why\", \"how\", \"all\", \"any\", \"both\", \"each\", \"few\", \"more\", \"most\", \"other\", \"some\", \"such\", \"no\", \"nor\", \"not\", \"only\", \"own\", \"same\", \"so\", \"than\", \"too\", \"very\", \"s\", \"t\", \"can\", \"will\", \"just\", \"don\", \"should\", \"now\"])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "\n",
-    "def load_dataset(path):\n",
-    "    \n",
-    "    with open(path, \"rb\") as f:\n",
-    "        \n",
-    "        data = pickle.load(f)\n",
-    "    return data\n",
-    "\n",
-    "def load_dictionary(path):\n",
-    "    \n",
-    "    with open(path, \"r\", encoding = \"utf-8\") as f:\n",
-    "        \n",
-    "        lines = f.readlines()\n",
-    "        \n",
-    "    k2v, v2k = {}, {}\n",
-    "    for line in lines:\n",
-    "        \n",
-    "        k,v = line.strip().split(\"\\t\")\n",
-    "        v = int(v)\n",
-    "        k2v[k] = v\n",
-    "        v2k[v] = k\n",
-    "    \n",
-    "    return k2v, v2k\n",
-    "    \n",
-    "def count_profs_and_gender(data: List[dict]):\n",
-    "    \n",
-    "    counter = defaultdict(Counter)\n",
-    "    for entry in data:\n",
-    "        gender, prof = entry[\"g\"], entry[\"p\"]\n",
-    "        counter[prof][gender] += 1\n",
-    "        \n",
-    "    return counter\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train = load_dataset(\"../data/biasbios/train.pickle\")\n",
-    "dev = load_dataset(\"../data/biasbios/dev.pickle\")\n",
-    "test = load_dataset(\"../data/biasbios/test.pickle\")\n",
-    "\n",
-    "p2i, i2p = load_dictionary(\"../data/biasbios/profession2index.txt\")\n",
-    "g2i, i2g = load_dictionary(\"../data/biasbios/gender2index.txt\")\n",
-    "counter = count_profs_and_gender(train+dev+test)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'g': 'f',\n",
-       " 'p': 'professor',\n",
-       " 'text': 'Dr. Jessica Orlofske is an Assistant Professor of Biology at the University of Wisconsin-Parkside. Her research program areas include invertebrate biodiversity and conservation and biomonitoring and assessment of ecological integrity. She obtained her PhD from the University of New Brunswick under the direction of Dr. Donald Baird. Dr. Orlofske was also a post-doctoral fellow with the CRI before taking up her current position, making her the first CRI student to become a Science Director.',\n",
-       " 'start': 98,\n",
-       " 'hard_text': 'Her research program areas include invertebrate biodiversity and conservation and biomonitoring and assessment of ecological integrity. She obtained her PhD from the University of New Brunswick under the direction of Dr. Donald Baird. Dr. Orlofske was also a post-doctoral fellow with the CRI before taking up her current position, making her the first CRI student to become a Science Director.',\n",
-       " 'text_without_gender': '_ research program areas include invertebrate biodiversity and conservation and biomonitoring and assessment of ecological integrity. _ obtained _ PhD from the University of New Brunswick under the direction of Dr. Donald Baird. Dr. _ was also a post-doctoral fellow with the CRI before taking up _ current position, making _ the first CRI student to become a Science Director.',\n",
-       " 'hard_text_tokenized': 'Her research program areas include invertebrate biodiversity and conservation and biomonitoring and assessment of ecological integrity . She obtained her PhD from the University of New Brunswick under the direction of Dr. Donald Baird . Dr. Orlofske was also a post - doctoral fellow with the CRI before taking up her current position , making her the first CRI student to become a Science Director .'}"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "train[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'accountant': 0, 'architect': 1, 'attorney': 2, 'chiropractor': 3, 'comedian': 4, 'composer': 5, 'dentist': 6, 'dietitian': 7, 'dj': 8, 'filmmaker': 9, 'interior_designer': 10, 'journalist': 11, 'model': 12, 'nurse': 13, 'painter': 14, 'paralegal': 15, 'pastor': 16, 'personal_trainer': 17, 'photographer': 18, 'physician': 19, 'poet': 20, 'professor': 21, 'psychologist': 22, 'rapper': 23, 'software_engineer': 24, 'surgeon': 25, 'teacher': 26, 'yoga_teacher': 27}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(p2i)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.44541700643683746\n",
-      "{'professor': 0.4533699535765501, 'psychologist': 0.627327974906881, 'pastor': 0.2583668005354752, 'comedian': 0.21605667060212513, 'nurse': 0.9126009126009126, 'yoga_teacher': 0.825, 'attorney': 0.3766122913505311, 'photographer': 0.3563658099222953, 'composer': 0.16329625884732052, 'model': 0.7858407079646018, 'surgeon': 0.12832108535895986, 'physician': 0.4158485273492286, 'software_engineer': 0.17130434782608694, 'poet': 0.5133630289532294, 'painter': 0.47116788321167885, 'dj': 0.1636828644501279, 'journalist': 0.5159589626674266, 'architect': 0.23372781065088757, 'paralegal': 0.8618677042801557, 'dentist': 0.3672911787665886, 'personal_trainer': 0.4410377358490566, 'teacher': 0.5943396226415094, 'accountant': 0.3950091296409008, 'interior_designer': 0.7874493927125507, 'dietitian': 0.934412955465587, 'filmmaker': 0.3533007334963325, 'chiropractor': 0.3069908814589666, 'rapper': 0.09438775510204081}\n"
-     ]
-    }
-   ],
-   "source": [
-    "counter = count_profs_and_gender(train+dev+test)\n",
-    "f,m = 0., 0.\n",
-    "prof2fem = dict()\n",
-    "\n",
-    "for k, values in counter.items():\n",
-    "    f += values['f']\n",
-    "    m += values['m']\n",
-    "    prof2fem[k] = values['f']/(values['f'] + values['m'])\n",
-    "\n",
-    "print(f / (f + m))\n",
-    "print(prof2fem)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "{'accountant': 0, 'architect': 1, 'attorney': 2, 'chiropractor': 3, 'comedian': 4, 'composer': 5, 'dentist': 6, 'dietitian': 7, 'dj': 8, 'filmmaker': 9, 'interior_designer': 10, 'journalist': 11, 'model': 12, 'nurse': 13, 'painter': 14, 'paralegal': 15, 'pastor': 16, 'personal_trainer': 17, 'photographer': 18, 'physician': 19, 'poet': 20, 'professor': 21, 'psychologist': 22, 'rapper': 23, 'software_engineer': 24, 'surgeon': 25, 'teacher': 26, 'yoga_teacher': 27}\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(p2i)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### get input representatons "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "def load_word_vectors(fname):\n",
-    "    \n",
-    "    model = KeyedVectors.load_word2vec_format(fname, binary=False)\n",
-    "    vecs = model.vectors\n",
-    "    words = list(model.vocab.keys())\n",
-    "    return model, vecs, words\n",
-    "\n",
-    "\n",
-    "def get_embeddings_based_dataset(data: List[dict], word2vec_model, p2i, filter_stopwords = False):\n",
-    "    \n",
-    "    X, Y = [], []\n",
-    "    unk, total = 0., 0.\n",
-    "    unknown = []\n",
-    "    vocab_counter = Counter()\n",
-    "    \n",
-    "    for entry in tqdm.tqdm_notebook(data, total = len(data)):\n",
-    "        \n",
-    "        y = p2i[entry[\"p\"]]\n",
-    "        words = entry[\"hard_text_tokenized\"].split(\" \")\n",
-    "        if filter_stopwords:\n",
-    "            words = [w for w in words if w.lower() not in STOPWORDS]\n",
-    "            \n",
-    "        vocab_counter.update(words) \n",
-    "        bagofwords = np.sum([word2vec_model[w] if w in word2vec_model else word2vec_model[\"unk\"] for w in words], axis = 0)\n",
-    "        #print(bagofwords.shape)\n",
-    "        X.append(bagofwords)\n",
-    "        Y.append(y)\n",
-    "        total += len(words)\n",
-    "        \n",
-    "        unknown_entry = [w for w in words if w not in word2vec_model]\n",
-    "        unknown.extend(unknown_entry)\n",
-    "        unk += len(unknown_entry)\n",
-    "    \n",
-    "    X = np.array(X)\n",
-    "    Y = np.array(Y)\n",
-    "    print(\"% unknown: {}\".format(unk/total))\n",
-    "    return X,Y,unknown,vocab_counter\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "word2vec, vecs, words = load_word_vectors(\"../data/embeddings/crawl-300d-2M.vec\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "126757347998435c9ef77686b565b727",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/74882 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "% unknown: 0.010167678378823583\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "5f8a4c87dd214faf8567f15fb7aec4b9",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/11550 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "% unknown: 0.010116209992979287\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ed317762b9134e9ca709e745ef65232b",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/28842 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "% unknown: 0.01018950257509165\n"
-     ]
-    }
-   ],
-   "source": [
-    "x_train, y_train, unknown_train, vocab_counter_train = get_embeddings_based_dataset(train, word2vec, p2i)\n",
-    "x_dev, y_dev, unknown_dev, vocab_counter_dev =  get_embeddings_based_dataset(dev, word2vec, p2i)\n",
-    "x_test, y_test, unknown_test, vocab_counter_test =  get_embeddings_based_dataset(test, word2vec, p2i)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# def save_in_word2vec_format(vecs: np.ndarray, words: np.ndarray, fname: str):\n",
-    "\n",
-    "\n",
-    "#     with open(fname, \"w\", encoding = \"utf-8\") as f:\n",
-    "\n",
-    "#         f.write(str(len(vecs)) + \" \" + \"300\" + \"\\n\")\n",
-    "#         for i, (v,w) in tqdm.tqdm_notebook(enumerate(zip(vecs, words))):\n",
-    "\n",
-    "#             vec_as_str = \" \".join([str(x) for x in v])\n",
-    "#             f.write(w + \" \" + vec_as_str + \"\\n\")\n",
-    "\n",
-    "\n",
-    "# vocab_bios, _ = list(zip(*vocab_counter_train.most_common(120000)))\n",
-    "# words_set = set(words)\n",
-    "# vocab_bios = [w for w in tqdm.tqdm_notebook(vocab_bios) if w in words_set]\n",
-    "# vecs_for_vocab = np.array([word2vec[w] for w in tqdm.tqdm_notebook(vocab_bios)])\n",
-    "# print(\"here\")\n",
-    "# save_in_word2vec_format(vecs_for_vocab, vocab_bios, \"vecs.vocab.bios.txt\")\n",
-    "# word2vec_bios, _, _ = load_word_vectors(\"vecs.vocab.bios.txt\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "len train: 74882; len dev: 11550; len test: 28842\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"len train: {}; len dev: {}; len test: {}\".format(len(train), len(dev), len(test)))\n",
-    "mean_train = np.mean(x_train, axis = 0, keepdims = True)\n",
-    "mean_dev = np.mean(x_dev, axis = 0, keepdims = True)\n",
-    "mean_test = np.mean(x_test, axis = 0, keepdims = True)\n",
-    "\n",
-    "#X_train -= mean_train\n",
-    "#X_dev -= mean_dev\n",
-    "#X_test -= mean_test"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "len train: 74882; len dev: 11550; len test: 28842\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"len train: {}; len dev: {}; len test: {}\".format(len(train), len(dev), len(test)))\n",
-    "mean_train = np.mean(x_train, axis = 0, keepdims = True)\n",
-    "mean_dev = np.mean(x_dev, axis = 0, keepdims = True)\n",
-    "mean_test = np.mean(x_test, axis = 0, keepdims = True)\n",
-    "\n",
-    "#X_train -= mean_train\n",
-    "#X_dev -= mean_dev\n",
-    "#X_test -= mean_test"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Classify by profession"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=64)]: Using backend ThreadingBackend with 64 concurrent workers.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "max_iter reached after 17 seconds\n",
-      "time: 17.493760347366333\n",
-      "0.7554112554112554\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=64)]: Done   1 tasks      | elapsed:   17.3s\n",
-      "[Parallel(n_jobs=64)]: Done   1 out of   1 | elapsed:   17.3s finished\n"
-     ]
-    }
-   ],
-   "source": [
-    "#clf = LinearSVC(max_iter = 10) #LogisticRegression()\n",
-    "#clf = SGDClassifier(warm_start = True, loss = 'log', n_jobs = 64, max_iter = 250, random_state = 0)\n",
-    "\n",
-    "clf = LogisticRegression(warm_start = True, penalty = 'l2',\n",
-    "                         solver = \"sag\", multi_class = 'multinomial', fit_intercept = True,\n",
-    "                         verbose = 10, max_iter = 4, n_jobs = 64, random_state = 1)\n",
-    "#clf = SGDClassifier(loss= 'log', max_iter= 3000, fit_intercept= True, class_weight= None, n_jobs= 100)\n",
-    "#clf = LinearSVC(max_iter = 50) #LogisticRegression()\n",
-    "\n",
-    "start = time.time()\n",
-    "idx = np.random.rand(x_train.shape[0]) < 1.0\n",
-    "clf.fit(x_train[idx], y_train[idx])\n",
-    "print(\"time: {}\".format(time.time() - start))\n",
-    "print(clf.score(x_dev, y_dev))\n",
-    "#print(clf.score(X_test, Y_test))\n",
-    "clf_original = copy.deepcopy(clf)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.7553914430344636\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(clf.score(x_test, y_test))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### perform debiasing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y_dev_gender = np.array([g2i[d[\"g\"]] for d in dev])\n",
-    "y_test_gender = np.array([g2i[d[\"g\"]] for d in test])\n",
-    "y_train_gender = np.array([g2i[d[\"g\"]] for d in train])\n",
-    "y_train_gender_2d = np.asarray([y_train_gender, - y_train_gender + 1]).T"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "((11550,), (74882,), (28842,))"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "y_dev_gender.shape, y_train_gender.shape, y_test_gender.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t = time.time()\n",
-    "u, s, vh = np.linalg.svd(A, full_matrices=True)\n",
-    "print(f\"u has shape {u.shape}, s has shape {s.shape}, vh has shape {vh.shape}\")\n",
-    "elapsed = time.time() - t\n",
-    "print(f\"vSVD took {elapsed} seconds\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "sal took 0.1637871265411377 seconds\n",
-      "u has shape (300, 300), s has shape (2,), vh has shape (2, 2)\n"
-     ]
-    }
-   ],
-   "source": [
-    "t = time.time()\n",
-    "A = np.dot(x_train.T, y_train_gender_2d) / x_train.shape[0]\n",
-    "u, s, vh = np.linalg.svd(A, full_matrices=True)\n",
-    "elapsed = time.time() - t\n",
-    "print(f\"sal took {elapsed} seconds\")\n",
-    "print(f\"u has shape {u.shape}, s has shape {s.shape}, vh has shape {vh.shape}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# removal = 1\n",
-    "# u_r = u[:, removal:]\n",
-    "# proj = u_r @ u_r.T\n",
-    "# P = proj\n",
-    "\n",
-    "# debiased_x_train_1 = P.dot(x_train.T).T\n",
-    "# debiased_x_dev_1 = P.dot(x_dev.T).T\n",
-    "# debiased_x_test_1 = P.dot(x_test.T).T\n",
-    "\n",
-    "# removal = 2\n",
-    "# u_r = u[:, removal:]\n",
-    "# proj = u_r @ u_r.T\n",
-    "# P = proj\n",
-    "\n",
-    "# debiased_x_train_2 = P.dot(x_train.T).T\n",
-    "# debiased_x_dev_2 = P.dot(x_dev.T).T\n",
-    "# debiased_x_test_2 = P.dot(x_test.T).T"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# rm -rf FastText.npz"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# np.savez(\"FastText.npz\", u = u, s = s, vh = vh, \n",
-    "#          x_train = x_train, x_test = x_test, \n",
-    "#          debiased_x_train_1 = debiased_x_train_1, debiased_x_test_1 = debiased_x_test_1, \n",
-    "#          debiased_x_train_2 = debiased_x_train_2, debiased_x_test_2 = debiased_x_test_2, \n",
-    "#          y_train_gender = y_train_gender, y_train = y_train, \n",
-    "#          y_test_gender = y_test_gender, y_test = y_test)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# gender_clf = LogisticRegression(warm_start = True, penalty = 'l2',\n",
-    "#                          solver = \"sag\", multi_class = 'multinomial', fit_intercept = True,\n",
-    "#                          verbose = 10, max_iter = 7, n_jobs = 64, random_state = 1)\n",
-    "\n",
-    "# gender_clf.fit(debiased_x_train, y_train_gender)\n",
-    "# gender_clf.score(debiased_x_dev, y_dev_gender)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# gender_clf.score(debiased_x_train, y_train_gender)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Perform finetuning"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "# clf = LogisticRegression(warm_start = True, penalty = 'l2',\n",
-    "#                          solver = \"sag\", multi_class = 'multinomial', fit_intercept = True,\n",
-    "#                          verbose = 10, max_iter = 6, n_jobs = 64, random_state = 0, class_weight = None)\n",
-    "\n",
-    "# clf.fit(debiased_x_train, y_train)\n",
-    "\n",
-    "# print(clf.score(debiased_x_test, y_test))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "#### test again"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_TPR(y_pred, y_true, p2i, i2p, gender):\n",
-    "    \n",
-    "    scores = defaultdict(Counter)\n",
-    "    prof_count_total = defaultdict(Counter)\n",
-    "    \n",
-    "    assert len(y_pred) == len(y_true) == len(gender)\n",
-    "               \n",
-    "    for y_hat, y, g in zip(y_pred, y_true, gender):\n",
-    "                \n",
-    "        if y == y_hat:\n",
-    "            \n",
-    "            scores[i2p[y]][g] += 1\n",
-    "        \n",
-    "        prof_count_total[i2p[y]][g] += 1\n",
-    "    \n",
-    "    tprs = defaultdict(dict)\n",
-    "    tprs_change = dict()\n",
-    "    tprs_ratio = []\n",
-    "    \n",
-    "    for profession, scores_dict in scores.items():\n",
-    "        \n",
-    "        good_m, good_f = scores_dict[\"m\"], scores_dict[\"f\"]\n",
-    "        prof_total_f = prof_count_total[profession][\"f\"]\n",
-    "        prof_total_m = prof_count_total[profession][\"m\"]\n",
-    "        tpr_m = (good_m) / prof_total_m\n",
-    "        tpr_f = (good_f) / prof_total_f\n",
-    "        \n",
-    "        tprs[profession][\"m\"] = tpr_m\n",
-    "        tprs[profession][\"f\"] = tpr_f\n",
-    "        tprs_ratio.append(0)\n",
-    "        tprs_change[profession] = tpr_f - tpr_m\n",
-    "        \n",
-    "    return tprs, tprs_change, np.mean(np.abs(tprs_ratio))\n",
-    "\n",
-    "def get_FPR(y_pred, y_true, p2i, i2p, gender):\n",
-    "    \n",
-    "    fp = defaultdict(Counter)\n",
-    "    neg_count_total = defaultdict(Counter)\n",
-    "    pos_count_total = defaultdict(Counter)\n",
-    "    \n",
-    "    \n",
-    "               \n",
-    "    for y_hat, y, g in zip(y_pred, y_true, gender):\n",
-    "        \n",
-    "        if y != y_hat:\n",
-    "            \n",
-    "            fp[i2p[y_hat]][g] += 1 # count false positives for y_hat\n",
-    "        \n",
-    "        neg_count_total[i2p[y_hat]][g] += 1 # count \"not y_hat\"\n",
-    "                \n",
-    "    fprs = defaultdict(dict)\n",
-    "    fprs_change = dict()\n",
-    "    fprs_ratio = []\n",
-    "    \n",
-    "    #print(prof_count_total)\n",
-    "    \n",
-    "    for profession, scores_dict in fp.items():\n",
-    "        \n",
-    "        good_m, good_f = scores_dict[\"m\"], scores_dict[\"f\"]\n",
-    "        prof_total_f = neg_count_total[profession][\"f\"]\n",
-    "        prof_total_m = neg_count_total[profession][\"m\"]\n",
-    "        print(profession)\n",
-    "        fpr_m = (good_m+good_f) / prof_total_m\n",
-    "        fpr_f = (good_m+good_f) / prof_total_f\n",
-    "        \n",
-    "        fprs[profession][\"m\"] = fpr_m\n",
-    "        fprs[profession][\"f\"] = fpr_f\n",
-    "        fprs_ratio.append(0)\n",
-    "        fprs_change[profession] = fpr_m - fpr_f\n",
-    "        \n",
-    "    return fprs, fprs_change, np.mean(np.abs(fprs_ratio))    \n",
-    "    \n",
-    "def similarity_vs_tpr(tprs, word2vec, title, measure, prof2fem):\n",
-    "    \n",
-    "    professions = list(tprs.keys())\n",
-    "    #\n",
-    "    \"\"\" \n",
-    "    sims = dict()\n",
-    "    gender_direction = word2vec[\"he\"] - word2vec[\"she\"]\n",
-    "    \n",
-    "    for p in professions:\n",
-    "        sim = word2vec.cosine_similarities(word2vec[p], [gender_direction])[0]\n",
-    "        sims[p] = sim\n",
-    "    \"\"\"\n",
-    "    tpr_lst = [tprs[p] for p in professions]\n",
-    "    sim_lst = [prof2fem[p] for p in professions]\n",
-    "\n",
-    "    #professions = [p.replace(\"_\", \" \") for p in professions if p in word2vec]\n",
-    "    \n",
-    "    plt.plot(sim_lst, tpr_lst, marker = \"o\", linestyle = \"none\")\n",
-    "    plt.xlabel(\"% women\", fontsize = 13)\n",
-    "    plt.ylabel(r'$GAP_{female,y}^{TPR}$', fontsize = 13)\n",
-    "    for p in professions:\n",
-    "        x,y = prof2fem[p], tprs[p]\n",
-    "        plt.annotate(p , (x,y), size = 7, color = \"red\")\n",
-    "    \n",
-    "    plt.ylim(-0.4,0.55)\n",
-    "    z = np.polyfit(sim_lst, tpr_lst, 1)\n",
-    "    p = np.poly1d(z)\n",
-    "    plt.plot(sim_lst,p(sim_lst),\"b--\")\n",
-    "\n",
-    "    plt.savefig(\"{}_vs_bias_{}_fasttext\".format(measure, title), dpi = 600)\n",
-    "    print(\"Correlation: {}; p-value: {}\".format(*pearsonr(sim_lst, tpr_lst)))\n",
-    "    plt.show()\n",
-    "    \n",
-    "def rms_diff(tpr_diff):\n",
-    "    \n",
-    "    return np.sqrt(np.mean(tpr_diff**2))\n",
-    "    \n",
-    "def save_vecs_and_words(vecs, words):\n",
-    "    def to_string(arr):\n",
-    "        return \"\\t\".join([str(x) for x in arr])\n",
-    "    \n",
-    "    with open(\"vecs.txt\", \"w\") as f:\n",
-    "        for v in vecs:\n",
-    "            assert len(v) == 300\n",
-    "            f.write(to_string(v) + \"\\n\")\n",
-    "    \n",
-    "    with open(\"labels.txt\", \"w\") as f:\n",
-    "            f.write(\"Profession\\n\")\n",
-    "            for w in words:\n",
-    "                f.write(w + \"\\n\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "removal = 1\n",
-    "u_r = u[:, removal:]\n",
-    "proj = u_r @ u_r.T\n",
-    "P = proj\n",
-    "\n",
-    "debiased_x_train = P.dot(x_train.T).T\n",
-    "debiased_x_dev = P.dot(x_dev.T).T\n",
-    "debiased_x_test = P.dot(x_test.T).T"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Correlation: 0.8229158334674403; p-value: 7.657770186244778e-08\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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xrm0bY42JTu+/n74YtlejRloa7mQF3kMCiC0Sw9PdGwavuhBJxo3TxkulSun0Yg6I1NJBbwBLnXNJfsfeFZE5Ybo+aEZUze9xVWBb4Eki0gh4HeialWBkjIk+ofYOffyxzkKFi38JoG37EqkSF8vQUCWAwuHTT+H7732Phw8Pnt1kxX33hWdMuShHSweJSLxzLiFM1yoMrAMuBhKAn4AbnHOr/c6pDnwB3BxwPylTliEZE/kSEqBqiLvCtncob0RV6aBwBSPPtY4Cg4D5wBrgPefcahEZKCIDPaf9H1AOeElEVoiIRRljotxLL2lGFBiMbr899N4hE53CniGJSDU0aMxzzh1nF1hksAzJmMgTalpu3TqoXTt3x2KCi9R7SMc45/4C6otIlXBf2xiTv4XqOxQXp9ttTP4Wtik7EYkXkWNJtXMuw4IDY4wJ5rbbNCMKDEavvaZTchaMCoZwlQ56BqgH1BCRc4Huzrn3w3FtY0z+lJoaen/QgQPazdsULOHKkFo6564A9jjnUoH+YbquMSa3TZmi66ez66uvYOLE45723HNQSNIyBKPLL/ctUrBgVDCF6x5SsogUBbwrJIqE6brGmNy2aBEcPqyR4fPPtSLA1VdDfDy89RZs365VAJo1gzvu0MKcDRtCzZrw2WdaQDQpSYPTu+/CkiXw77+0fX8wFx+cQ1n20JsmTKEvAN99p3VFjQlXQBoLLADKiUgPIMT6GGNMpJqzPIEx89fS8u+SSPla3DfmeSpd1FpXFPz4o1aLPnJEy+RMn643dpo10/I2oBlSixbaevW66wA4OmEiTy65mKLEchY/AvAu17GY1qSlhV5JZwqmkwpIItIHWI4Go/XAdUAj4KaTHpkxJtf4l8hJE2HfoWTW/r2fHy7ty5XNauhJd92l9dec87UtCCyl7SnO+e13wgUC84nlcUYce/rDc0cwYmppyKR8mym4TjZDOhUYDDRAp+tWowGqGukLoRpjIph/m4U1p9XizsXv8X79C7lk4ABo1wCaNtUlcKNHa4YEWsDzzjt1Y1CjRhAfz6C74MW74B3Pdf9LLyYxgJ59Yyl5/aWQ5foppiAK28ZYESmCBqYmwDnOubvDcuFcYBtjTUFXa/g8gn0SCLBxVObF4TZsgDPOCP6cVVHI3yK2dJBzLsU5t8I592Y0BSNjjLZZyM5x0HYPIhmD0dixVtLHnJhw7UMaCVwKpAEr0crfL4bj2saYnDe0c92gbRaGdq6b7rzM2oEfPAglSuTkKE1+F64MqSs6VbcfmAe0DNN1jTG5oFuTeJ7u3pD4uFgEiI+LTdfzJyvtwC0YmZMVrmXfO51zTkTSnHMzRaR7mK5rjMkl3ZrEZ+jzE2pZ9qefQufOYfrBU6ZA+fJw2WWhz+nTR/c1ZWXH7NChMGZM9n+GyXPhCkgfi0gJYJ2I3IJ2jzXGRKFDh0J/7qem+rIk776lbfsS6bf+a246spHqbZrqCT/9pBtlL7tMW3rff78GhH79NLAMHqyP27XTi733nka5ihW1u+njj+s+p/37td2319y58NFHuh9qxAjtT/7441C3Lvzwg15j40bYuVNXAHrH4N3sCxaUIlhYApJz7gUAERkOPAQMC8d1jTHZNGIE9OgBZ2d/o8+IEfrZHkzgAoXA1t57D6cwsUgtWnXqRbf7b4bTToNu3aBlS6hSBV59FU4/XTfXvvUW9OoFHTvqxaZM0XTrxht1Q+2//8KmTfDmmzB1qlZ/8Jo2DWbNgs2b4YUXdGCjR2sVCf+U7cgRiInxjWHDBsuQokDIgCQiFYBuwDrn3NeeYzFAW+fcVwHnlkCLq250zlkwMiZcpkyBL7+E+vX1A3bLFq1ImpKiH8h+pXkYPFgzgR074IYboE2bLP2IUNNya9ZAvXrBn/Pft+SVlpLCmPlr6RYXB88/D7Nnw4IFuol22za94IwZ8PLLITfUZhhMqMF5jzun3weeV61a+jHUqhX8OiaiZJYhzQF+BHqKyBzgPKAt2jr8q4BzFwFrgbIiUg7Y4Jy7JtyDNaagOFbG59uVlDy1Co079aLbyLvhrLPgiSc0lfn1V53+uvhiiI3V8j5t2mQpQ/rjD6hTJ/hzWVmuvW1fYoZjbTcup96OjXBZExg5UrOUDh30yYsvhr//1kB0ww1w770aKNq2zXjxUqWgRg247z7Ys0en7GbO1Od69dJeFYcP69ReaioMG6a/jP8846pVMHmybwy1a+uYjh7VrMlEpMwCUqpz7l4RKQZsA3oDfVzwnbS7nHPXex+IiP05YswJCpwOO3DwCA/OXkXzvYep4s0svBlBbKzOtXn95z+ZXrtlS/j++4zH27bVlXRZVSUuloSAoDS3fjvWNm1Hv+EXpT/5u+80U5k8WR+XKaOZXzAzZuhX/98JfOdfdZX+89qzR4PNrl3Qu7ce8wav8ePTX+O9947zW5m8FrJSg4isAK52zq0XkS+dc0H6OB4793pgr3Nufs4MM2dZpQYTSVqP+uLYh32PVQtpu3E5O0vEkVy+AnecUVSDUGKiZkfTpuk0XWwsXHqpZgwzZsAttxxbMJDZ3qGdO/XWSnYFBk3QfUv+S8VN/pebLcy/Al4VkdOBMiIyAVgF/OqcC/wbqxvQQkT2A7+jG2PHhmuQxhQkgdNhc+u344szmyPAHYFlfG6+Wf/569oVgE8+gUsuCf4zTraKgjfoeFfZVYmLZWjnuhaMzEnJLCA9ClwDxKBFU+OAxsDdQGBAinfO1QIQkTro/SZjzAnwnw6b2bBDuuNZEWodwNixelsmXILtWzLmZGRWqeEj4HzgDOBlwDnnnnbO9Qxy7uciUh09aZ1z7p0g5xhjsmBo57rEFolJdyxYGR9/hw4FX2wG2ivPufAGI2NyQmYBKcY5N8A5Nxxoh7aZCOVS4CcReUtE7hGRrK03NSan9Ohx/HP8W26/8gqsX39i1wmz45Xx8TdhggahYBtZvSV9Tjkl58dsTDhkNmVXUkQaAuudc/tEJOR/1s65ZiJSFagFnIlO9S0K71CNCc67RLrY+nU89MO7VG3RmLoHD+oy35074cABXXHVsye0b6/LpYcM0cUAixdrR9R//tGFAuPG6abL0qV1k+Zvv+mKr/vvz1rZmjA53nRYqGm577/Xpq3GRKPMMqQvgfHAZhHZCjQRkcdFJMNqOxF5BngJmAhMA77LgbEak4F3tVfCvkSuXzmfx1r14urTOrJz97+6jjkuTlOENWv0BUOG6NzVvHm6Z+eSS3Rvi9c//2gzusGDdS9P/foakHIxGIWycWPoaTlvNmTBqABKS8t4rE8fLb9+IvJgVsArswzpEefcIQARiUcXNDQGBqDByl9L51xbz/LwVBHpB9iif5Pj/CsGiHOkxBTm4FHHpn1JVGh7fvr9LMWK6frnIkX0xkqwtdCjR2sdtr594e23Q6ciuahdu+B7hFq21ATP5B8h6wPGxGgGv2sXVKoEDz6oZZBatYJmzWDtWi2PVKiQrl7xWrBA//hKTISrr9YKFoG1/554Iv1189DxFjUA4JxLcM79L5NFDckiUhSONZ0sEs5BGhOK/xLpdxp3YdCS9xjw42yOHPVsvhkyBG6/Hf76K+OL69SBhQu1ZprXM8/A++9D2bJQvLgW+xw+/MT/2jwJ3mwoMBglJGg2ZMEof/HP9h2e+oAxtZjTqZdW4UhL0yoWs2frC9LS4IEHtCZgaqruRVuyREtHeT3/vM4SVK6s13j9df2j69FHtfxUWlrG6+ahzDKk7PxpOBZYAJQTkR7ZfK0xJ8x/ifT68tV4tNMdAMzr3IvvAisGeHfw16vny5zmzEl/TmDJHe+ih1zy/vtw7bXBn7MOrPlbpvUBd+7UA088ofc+Qf9gKlwYdu+GlSvhjTc0s/dWNQcNNo88oueB/oHmP++7fLl+73/dPJRZQKorIv+HdoD9xTm3MfAEETnPObfMOfeJiKwHrgMaATflzHCNSS+rnU4jXaiZwf/8R/+YNflf5vUB28Mvv+h0nDc4ecXFaRAaOxbWrUv/3ODB2vKjbFm9N9q/f/raf2eeqcEs2HXzQGalg1YCL+K7d1QD2IQGpzs953zpnLtQRB53zj2WO0MOPysdFN38592jqWJAUpLe1grG2oEXPP4lo0DLRu2JLcXapu0yZvsnas8eXXG6e7cWvO1+cr1Uw106KLOAlKF+nadoamPn3BzP44/QDOoSoJtzbku4BpabLCCZ3DR4sHaOCMam5QquaKwPmJu17DJUW/BM2/lP3V0NdAFuBp711L3bCfzsnMvb5RrGRJhQ03ILF+ofq6Zgs/qAmQQk59yrx3uxcy4Z+FBEtjjnVgB4+iFZLTtjgO3bdTVtMGlp2VhV/uGH2rYhWP+gUBcPVeLba9MmXbQxZIg2+rv33vTPn0T3WXNiCnp9wOP8F+sjImeKyAci8omI9PJ/zhuMPN/vds59luECxoTT+vVwxRW+TqDh3My3aZNWZsiKEHNszZppsAkWjLybWI8XjOYsT6D1qC+oNXweI6cvYuXHX+sm3n79YO7c9OMcPlwf9+mjgeSDD3Tj77PPakO8bdtg9Wp46CG49VZdHuyVnKxrydes0RbjDzyg1SwWLYKXXoqI1VemYMhsyi7Qa8ApaAuKiSKS4px7N2eGZUxw3gUMfWZP5Oz92zmzfBUq+J/Qrh1ccIFuFGzfXje5dugArVvrh3Xz5rpJsHJl3Rg4apRmE2+9pelMv376HMDSpdpv6IkntA23c1CyJDz5JDRoADfdpDeF/Vqvhgoyv/+uexGz83v630/YeziFmOnT+OzVqXS6rCVccw00bhz8xf37Q3y87jkZMgQ++gi+/lqj5JEjurdq+nQNPP7279cs7IYbNCvKYvdZY8IlyxkScC7QxTk3EK1Vd2fODMlEpAi42+6/cfDzM5vxSfUm/HxQmLM8wXdS0aIaQFq3hqpVdW/G3Ln6XJ06uvF161YYNEiXv376qb7G/4MatCjcO+/oiqS339YgVqaM7oZPToYqVTQrqVOH1auPX9InO8EIgu9JcWlpTPrWcwtXRMd99Kg+PnTId2Lp0vrVu0zPW5liwgS45x5fC/BA55+vGdScOTB16vGn/IwJs+xkSOKcO+D5/gvgzcxONifPmw3I5s088v3bnHn26Zx5203w2mv6KXfHHfpB+txzcMYZ+sETG6t/2b/5Jrz4Ivz7r/5Vf8YZOh1z2236gVW2rH6g9u6tH9xt2mhGkcVMIC8E+5BOTXO6cdB7oFQp/Vq0qH4v4qv1FfjcKaf4PqiHDtXf+zHP7oXKlXXH++HD+vpLL9UpQq/SpUNmQ507a5w7GYF7UsQ5Zp19Mb3mvQ77vtZCsZUqaVbz7LO6R+V4LrxQd+lXrBj8+S+/1Gxqzx7d/X/aaTBmTLrus8bkpOwEpMIiUts594enXl3xHBtVCCLSBZiANg183Tk3KuB58Tx/CXAY6OOc+zm3xxkO/lM2w3/+mFHNrmF7xep89tQ4qs32LIDs31/vB7RooX/ZduwIn32mf9l75/27d9dgc+21GoQuuEB3c996q/7Vf+SIdhi94AINdN7prHXr0mcCESDYxsHMjmdZsA/qGjXgzjth4EAN7IMGwbffknYkmZiJE3g/yGX27tU9iuHgX4Gi7OH9dF63mEc63cmCDtdxlf+eFG/ZoyFD9OuUKb7nvJUpunTxHQvcd+Kte+b9emFA7WRP91ljckN2AtI+4HcR2YN2jC0qIhcDq5xzOzJ9ZRiISAy6UbcjsBXtv/Shc+43v9O6ArU9/1qgjQWjsv5xuqKhQJoUIjEllTXb/qVa4J/m3r/8y5XTjMD7lz/4pnRSUjLeSRfRD7DPPtMP3IYNg2YCkcL/Q/r76o34vnojpp13OfFxsb4PX+/XgQN9L/Qe837ozpihX5s21X8Q+oP6v//Vl1w6nZ5+VRyvYeax7487mxlsBUPv3prhjB4d9CX+FSj2FC9Nvx6P6Z6UUBUosrKqzpgIl+WA5JyrIiJVgGaef6egFb3jRGQHWsGhc84ME4DmwJ/OuQ0AIjIDuBLwD0hXAtOc7vb9XkTiRKSyc+7vHBxXjvD/q396k0u497u32VGiLJPO7kon74ft7bdrFpOZ997TJb1XXQWdOukH9apVWvX30CF46imtJNygga7g8mQCJCfrVFYEyYsyQaGm5caN06REV8KFqM785ps63Xnhhbq8ulQpaNRI3+ufftL3evly/aPg6FEt3121KkyeTLdKlSjasSe7nhpL8qHDHDqtMtWffIRub46G6YX1D4wXXkhf8bljxxx7H4zJDSErNWT5AiJnoAGqqXMui2tlT+jn9EAXVfTzPL4JaOGcG+R3zsfAKOfcIs/jz4FhzrkMZRhEZADaSoPq1auft3nz5pwa+gkJLCPiFR8Xm/UyIvlwH0lulAnasSP0bZbUVF8iErgSrseqhRQqUoRWj91Nt9H3aXmWBQs0GDVooIHpmmu0gmqPHpq53XSTLmHv1EkbCbZooX8w3HSTLtV+6CHdH9S5M2zZoq99/HH9d/XVeu/ro498xTONyUW5WanheAOJRzfAnuv3L8cCEsEriAdG06ycowd14++roKWDTm5o4ReWbMC/F1A+kZMbB7t31+07wQT7uy3T6swpKWxLK8I1o75g6PRZVE6ZyqldO9Jg7VpdBbhiBTz9tGai5cvr/1aXXab371av1oqqJUrolOmKFbrar2ZNvbdXu7buObrjDl2AEAE9m4wJh+xsjO0uIiM9G2O3A1uAD4BHgGRgXA6N0WsrUM3vcVVg2wmcExW6NYnn6e4NiY+LRdDM6FhNqx499C9jExbeJduBwejbb33LtoMJVZ2576znWV21Lqu2/UvCvkR+rlKHQocP8fOS1exLcXq/p0gR7T8zbJgGnPvu0yBTvbqucFy7Fi66SKfmkpK0gVrt2jrQ8eNh3z7NmurV0+lBY/KB7GRIM4HdwAxgDvAL8CuwDrgjFxY2/ATU9hR4TQCuB24IOOdDYJDn/lILYH803j8KpvqOLTQdNh5aNNZS0BszdAMx2fDFF6Hrx2V1Ftt/kYXX3PrtWNu0HW8CCVe2BuDwKcV5t3FnZjbsQIP3H+Zc0KoIF14I556rm3XHjYPNm3U1ZKNGuvCienXo1k3vNf31F9x9ty/r/eorraYwcybG5BfZWZYzCigJVAcWOOeW+O1LynHOuaPAIGA+sAZ4zzm3WkQGioh3SdX/gA3An2hliTtya3zhFtg98qJFc+lTpztzr+h3/IUMJiRvNhQYjFq3zjwbCmZo57rEFvFlJzMbdmDJWS0Z2rluhuyp7cblPPL5a3xduUHovjaB/DvaDhsGd92lmVSoUuHGRLlsLWoQkbrARKA18CzwFLAebUmR40u/c0oktp8IXNTwyOev8VrzqyhctSrffTlaV2zZX8dZ4lzoFdGbN2sicqJCLbLw/9/P29fmizObZ29RijERLk8XNTjn1gIdRaQn2ra8N5o1mTAL/Av7ncZdGLTkPbaWrgiFM/mENce88oqujAeowSYGMZGhjKU3U5jyUXldRBBKFvf1hFpk4b8oZWbDDkB0drI1Jjed0Co759w7nuZ8T6I17SaLSF/n3K6wjq4AC7w/sb58NR7tdAfxcbE8eHSxVl0I9O23usS4Y0et5und3JkdObXBcuhQLUNzMrIwtjnLE7jqXA0Q9VnNSN6iItvZRE16nb6Y+++dqIsI3vbUcktN1WXTR47o/ZlFi3SarGlTPa92ba2E3a2bLjJ4+GFdZJCaqhUsxo7V0k3jxum83/nnA9bbxpgTccLLvp1zB4F7ROQNtCLCr0CIzi8mu4It+xbgvMWfMPefNTSoG8+ZMZP0fsS//+rKqwkTtErDtm2weLEGpz/+0GXGQ4boTfRTTtF/hQtrBegdO7QW2ocf+j6IGzSAefO0jNDVV+seGX8vv6zLj/fu1Xp3bdpoGaP339fipRdcoKWJatWCTz7Rn+NdhHHZZVoz79dfdUw1amSsnzdypN5fOXBAf6+ePY+7+TMxEYoXB/B94CdRlGIcoXXv4ty6YwnUb6ULBKZM0aXWl12mv9+sWTp398ILumera1e48UatDt6vn174/vs1CG3aBGedpeenpmpw2rtXN7red1+6MRX03jbGZFeWA5KIVALOcs596X/cOfcL0FpE+oZ7cAWZ/1/YCfsSEXRDVeUDu1hWuirjy7TmrXffpsoXng/8d97RD9jy5fVD9d9/9cP3/ffh22/5MymGH179kFP27KasHOWs8sWpclop+PFH/eu/XTsNDl9+Cc8/r/thihTRG+mLFmmF7ClT9JP/iy+08sPixbpp89Ah/eD+6Sf9cJ81SzeFVq6s5XFuu00ztjvu0EAzZIjutfn4Yy3+5l8/b+VK+OYbrVqQlKSr0dLStFVCkM2fd9yh8TGDmFQeaXwvr7bozqKSxZi9ZbXvuWBZlv9eHv9ySSVK6CbV0qV1HK1baw9yr/79NWDe4LfgM1Q2GI4s0Zh8LDsZ0mCgLPAlgIj8g5btWer599+wj66A8/6F7X+DfFKLHpy1YwNDFrzGuhioAhk3Rvp/4LZvz7bre/PfomdQJTGFZn+vY0D3R3j06zdJLVaUag96Os2/+SZcfrlWCjh6VIPBggW+Qq2zZ2vF8OLF9frVq8OkSZp1VayoH9wi+roiRTQLW7LEm7bops7Ro7WHT6FCvpYIgZW0V6/WDM1/U2/x4hmCUai9oB/VaM+g63WP1pK1jRj4/Sx2lYiDapXgzz81gpUqpW0kjh7Vckm33cZfW3cxtN4VVH1/Bc32baHlpKlU37ZBs84NGzQb2rNHp0rPPluztcce0/dt40b9o+DttzX4erPBadPgu++0AvvYsb7jEydqdYaYGD3ep0/6qcHAjNSYAiI7AekSwL8CZXFgEVqt4RZ0Rska9uUA/wUOPVd8Ss2920iTQvxUrgbtBw/WKaPnntOMAzQr+fNPnYobMoTE1b/z7eVX0C4piSOFi5ImhVhbtirVl/8MpYvpvRGvUqU0Axg4UKegUlP1vkhysu6DKVNG21tMmaJBpnRp/WB9+GF9/eef6/VKldJprX37NPspXFiDkLcVREKCFnX95JOM9fMKFdIsKjFRS+d47N6tCaC/3kzhQr6k96j6TPx6I6cv28Kwr6Zw5u6/GHTFA+w4tSwDVs+HwpX0ns/ChZrZ3XCDvk8vv8yW3Yd44LQ2/JFcmF4bllE0JQm3ZgtzPl9Kt46N9Xd+6y0NQIcP6/tx/vm6MbV0aR33aadB/fq6u3bFCp0m/flnDS7r1mkGmZioweeffzSA//OPTplC+qlBC0imgMpOQKrqLWzqkeqc+z8AEbkMrQtnASkH+C9weOccXyuB+LhYhvovIe7Tx/f9nDnHvu3Q5wUcsL5cNT6r3eJYodbhF9/OrCKr2fzRQp5q3JOS367nmj9mUanBmdQ44wztqFqzplYJOHBA7+M0bqzBpUkTDSJjxmiLioQEDYynnqofxPHxmgXUrq3TcN99Bw8+qB+6zz0HFTx9XqdP9zXF8wpYjNHn1JlMDZIRFa+3jfKnr2R1kYrM6dSLbl/cwZYScYxu34dbfppLgx0b6Lt8Himvvw7nxPvadcTFaWZ0440weTK9x3zFXe8+w8rKdZje5BKq7d/O6d/rcu5uhQrpRtWmnoKpRYvq75OUpO/J6tX6tX9/ncq89loNWr/+qsF82jQNXKCBqWRJ/fkzZmgbEG+jvBIl9H31Vmk3pgDKTkBKEpEizrkUz2P/O7hfAuPDNiqTzsnWtfMPaFvjKnHfpdo7Jz4uljmdex67do1qxai3cxMrdsKVkkal+vX1Rv/FF/sqCpx/vnZT7dhRW2Rfd51mTD16aPUAb9HQl1/We1Lee0MbNmgw+s1TnH3TJs0kBg0KOubM9g5VveszYop7/jNc5asf992phSlUqxLxcbEkFS5CldhCnFOtNNWaVPW92P+inrYQ2/YfAbQJnvPMBZY+cpC+s57X6UgRrcotogHt8cd1WnHVKg06kyfr4pHYWA3OoOd27arB9fBhDdzXX69TmYcP6/F167L0v58xBUV2AtJaoBMwD8A594b3CefcIRGpEOaxGY+TXUKcWUDzLxDad+mHvNqiO+Kg2o8zuKR0aShWTDOjgQP1HhJoQPLe+A+8meN9HOzekH8wCBFtfvlFk7BgnINaw+dlqJbbduNy6u3YCD1aUuWnn3Tjadw6rfN2yvm+3ki33w6nn65TmSVKaDAcOJBnV/3N5HMuYXOZyoxYOImiR5PYXbw0b149mH5LX9IAu3+/BpOlSzW4rl6trSKefFKD0fTpmhmWLKnnX3+9/rwDB7Tszz336PTj88/rdOepp+r0HKRvquf/vTEFTHYC0mvAeBH50TmXruaJpwXFnrCOzKRzMkuIMwto97674th5S6r7FgEcTvarYn3HHbrIoHLl9M37gilSRCtV339/xntDiYm60qxGDRgwIN19rqZNYdmyjJd76630C9gyqx/Xb5jf9KV/g75WrdJf1G86k1atSFmewJrZq0hKSmZD2aqcmnyYJbWbaQY63FMNo3RpXT0IvkDSo4feK/Jq1Mj3vbcJ4AMPpP/ZFnCMCSm7pYMmA5cDTwBvOef2eJaDvwlsds4NzPQCESoSSwfllrD0XTpBycnwVNERzKQHq/H1bHqGodyTPIYiRTwH/DquBvYgAs32jlVCz8rG3j59dKXbqaceO5ShBFDH2nQ7r1roaxhj8rwfUj9gKFqhYbyIJAKxwArg5nANyuSe3OzC6v3QP7wwkSGfz2AHp9GGlZRlDyU5wF81L+D/NvaFHhuhCHqfJqDjardGjaD7ZTTu0oppDTtz7v6tMGECl094WBdgNGyomdiKFboX68UX9V7P+PG6RK9PH33uqac09Zo1C/bupdv+/XSbNEkzt2I1YdNesIBkTK7Kbi07BzwjIi8A7YHKwCbgW7/FDiaK5FaJmznLE+jevBLuaDyjeYD/8H/8SW0eYwRXvdiRxne01gUS+O2vrlJFl2pPnAi33HKs42q3226DhrV5bOGrusx6sye77d9fV/f997+6Yi0hQRcjPP88vPqqb0/UOefo/Zy0NF1c8eabMHWqLkP3v44xJledaC27ROCTMI/F5JGcLHGza5d3hbfv+oIjjULUGDaPaks30viCEAskvAsn/Kbsjjl6VL+mpPie857/3ntaCunxx3VFW+Drs1OpwRiTa6xktMkRDz2kn/EVAtZelmr5B3Nvq8czZw9g2FdTqLN+1fEv1rOnLq0ePFizJNBdsg89pBnS5ZenP79yZa2p9+OP+vjee3VhxvDhWsKoZUtdbLBpky6wuO8+LZlkG1KNyVPZWtSQXxXkRQ3hFqqkz7lDF7O70N4Mx0948YR3v5MxJs+Ee1GDZUjmpHn3jAYLRt4urI/2rJGuuyqc5OIJC0bG5DsWkMwJu+46DULnnpv++IwZGduBd2sSz9PdGxIfF4ugmdGxpdrGGMNJ9EMyBVNqatAuEAAcPKgFEELJD/2BQrUsT2fTJl0ZOHbsCe+LSuerr7Q2XmCZpWDTlsEWgJxI24tg1zEmh1lAMlkyZYrWAg3UvLnWYI0G3mBS4o/f6bVhEe1Kp1Hjvju17E/JkrqH6aqrdPFE+fLaI6p+fa1SUawY3zdoxQsbYuj9w1zWVahB/x8/4JsPm7N3/1rKVDkNtm/XpepXX62LMNau1QUX48drYdZevXThRJcuvjJM8+fDu+9qeaGiRXVf1M8/axfbf/6BRx4J/sts3ap1AUeM0ErhnTvrnq1LLtFVhvv2aQ2m/v19bS/atIHu3bX80dixukJx3DgNPmecoa0vbrxRx3zrrRlXpBiTw2zKzmSqVSv9QzkwGP3wg36ORVMwenD2KhL2JZJUuAjJBw/z6U441LOX9jYaN04zlbfe0sAxbpyWSXrlFS0P9PrrHH5jKklHfRuI/yp9GlMbdyFx23YtO3HppbBli36QlymjKwBbtdLnGjbUwPDhh9pzavFiXRN/8CCUKwdz52ox1hUrtGZecrLum5o9O/gvVLWqL1hWrerbs1W7ti6JL1tWA5O/kiW1rUfPntq/6aWXtCBsuXK6eRj0msOHWzAyecIyJJPBgQPaziiYtLTonMkJVUT22pWfUiKwAniQiuAAiSlpJMUUoXCaXqfY0WQ9JTVNA85jj8Hff+tNtcKF9d+kSVo775tvtJ5fz54aBEB/TlKSbw7U27Rw3DhtjLh4sS5HDyXY3qn//U+Dys03+5bIe3nnU/2bI950k68G36ZNtgfL5CkLSOaYW27RogWBHn0U/vOf3B9POPk3OfQvIvt1jXPo9sMP2gqiUSMtJ3Tvvdott21b7QD76KNQvDiLmnVkZ4kylEw6TNsNy6i3cxODFs/gQMk4DS4PPKBdclNSNPN59lmtCnHDDRoM4uI063rvPZ0WrF5dB1SunL62bFmtUH7BBRrcDh3STCuUBg200Ovdd/uONWmiGc7ff+sNv8wMGqR7uSpX1uypd+8TfXuNCQvbh4TtQwqV8fz1l84G5QfhKCLrX9i1x6qF7IktxZKzWtpqQVNg5XVxVZNP7NypXbeDyY9/o4SjiKx/3b9ZDTtQJS6Wp3Og7l9Io0bBEW0mSL162nPJmHzEMiQKVoZ01VXp2wF5TZ6sU3b5WZaWbBtjsswyJJNtmbUDT0rSRV0FQX7YB2VMfmbLvvOxtWv1/lBgMIqN9VVSKCjByBgT+Swg5UO1amkgqlcv/fEPPtAgdPhw3ozLGGMyY1N2+URioq//nL/ixXUFcjTuHTLGFCyWIUW5b77RYBMYjK66SrOhQ4csGBljooNlSFGqaFGtLhNo5UrfxntjjIkmliFFkU2bfH2H/IPRhRf6FilYMDLGRCsLSFFg2TINQrVqpT8+aZIGoS++yJtxGWNMONmUXYRyDq65RrsRBNq5U7sjGGNMfmIZUoTZsMG3d8g/GE2f7puWs2BkjMmPLCBFiE2btEizt2+b1x9/aBDq1StPhmWMMbkmaqbsRKQs8C5QE9gEXOuc2xtwTjVgGlAJSANedc5NyN2RZl1qKjRtqj3ZQLOi6tW1GWhysjYZNcaYgiKaMqThwOfOudrA557HgY4C9znnzgLOB+4Ukfq5OMYs+eEHnZYrXNgXjEAbim7erIHKgpExpqCJpoB0JTDV8/1UoFvgCc65v51zP3u+PwCsASKmmuaaNXDnnXD++emP79ih03Lefm35xqZN2kDueKzivDGGKJqyAyo65/4GDTwiEqKbjxKRmkAT4IcQzw8ABgBUz8FIcPQofPQRPPUULF2qG1pB9wutXJljPzbnTJmibbXr19c0LjERdu2CSpXgwQdhwADtf96ggbbKXrwYJk6ELl1gxAgoVgwuvxwaN4Ybb9Tvb70VKlTI69/MGJPHIiogichC9P5PoIezeZ1TgVnAPc65f4Od45x7FXgVtB9SNod6XL/9ppnQgQP6uFo17RZ9zz3R99nr30eo3/rf6Nz4PJoOGwzXXquBp1QpmD1bA9LevXDllXDxxfDPPzonOWiQZkpPPKGbqa65RgNS/fq6ksMYY4iwKTvnXAfn3NlB/s0FtotIZQDP1x3BriEiRdBg9JZzbnbujV799BM0aaKf095gNHu2LuceOTI6g9GDs1eRsC8RB+w9nMKsHzczZ3mCbogCDTSnnqrfT5umN8j69Enf98I5X1E979fSpXPr1zDGRIGIypCO40OgNzDK83Vu4AkiIsBkYI1z7tncGlhSErz3Hjz5JKxb5yt0OnMmXH11bo0iZ4yZvzZd22+A8/9cRuJdd0PH9vDLLzB2rAanlBS47z5tuFSnjk7j/fknPPss3HYbPPqovjk9e+bNL2OMiWhR08JcRMoB7wHVgS3ANc65PSJSBXjdOXeJiLQBvgVWocu+AR5yzv0vs2ufaAvz1avh7LN9jytX1lmr3r11Fis/qDV8Hv7/hfRYtZA9saX48szmbBx1aZ6NyxiT9wpsC3Pn3G7g4iDHtwGXeL5fBORaswX/YLRggd42yW+tHqrExZKwL/HY45kNOwAQHxebV0MyxuRTEXUPKdps2aKFT52DDh3yXzACGNq5LrFF0m+Kii0Sw9DOdfNoRMaY/CpqMqRIVK2a/svPujXRbVzeVXZV4mIZ2rnusePGGBMuFpDMcXVrEm8ByBiT42zKzhhjTESwgGSMMSYiWEAyxhgTESwgRaoo2R9mjDHhYosacol/PbjemxbTJ3kTNWtV0ppCH3wAv/8OM2ZA+/YwZgy0bq3/Xn9dqx6sXKnPP/ywloZITYXx47U8xN69sH8/TJqkxU1r14aEBOjWDTp1yuPf3BhjssYypFwQWA+u6PZtTD9cmvkdrg/e+KhVK63EOmmSBqSBA/X4ggXa0iEuDg4e1KCzaZMGpvbt4bPP9Lx+/WD06PQ90I0xJsJZhpQLAuvBTWrRg7N2bKDl8Afg9Dg9eOiQ7wX+RUdFfDtu09I0axo8WB//+2/687xKlNDuf0lJ4f1FjDEmB1lAygXb/ErvAPRc8Sk1927jUIqDNm00G0pJ0UDib+BAzXZq1tRq2p0767GhQ2HfPnjhBahRQwua7tmjGdXMmbn2exljTDhFTXHVnHSixVWzqvWoL9LVg/OKj4vlu+EXhX7hhg3wxhuwfbu2c2jdOsfGaIwx2VVgi6tGs6Gd6/Lg7FXppu2yVA/u9NN10YIxxhQAFpBygdWDM8aY47OAlEusHpwxxmTOln0bY4yJCBaQjDHGRAQLSMYYYyKCBSRjjDERwQKSMcaYiGAByRhjTESwgGSMMSYiWEAyxhgTESwgGWOMiQgWkIwxxkQEC0jGGGMiggUkY4wxEcECkjHGmIhgAckYY0xEsIBkjDEmIlhAMsYYExEsIBljjIkIFpCMMcZEBAtIxhhjIoIFJGOMMRHBApIxxpiIYAHJGGNMRIiagCQiZUVkgYj84flaJpNzY0RkuYh8nJtjNMYYc+KiJiABw4HPnXO1gc89j0O5G1iTK6MyxhgTFtEUkK4Epnq+nwp0C3aSiFQFLgVez51hGWOMCYfCeT2AbKjonPsbwDn3t4icFuK88cADQMnMLiYiA4ABnocHRWRtuAYaIcoDu/J6EBHO3qPM2ftzfAX9PaoRzotFVEASkYVApSBPPZzF118G7HDOLROR9pmd65x7FXg1u2OMFiKy1DnXNK/HEcnsPcqcvT/HZ+9ReEVUQHLOdQj1nIhsF5HKnuyoMrAjyGmtgStE5BKgGFBKRP7rnOuVQ0M2xhgTJtF0D+lDoLfn+97A3MATnHMPOueqOudqAtcDX1gwMsaY6BBNAWkU0FFE/gA6eh4jIlVE5H95OrLIlG+nI8PI3qPM2ftzfPYehZE45/J6DMYYY0xUZUjGGGPyMQtIxhhjIoIFpCgnIl1EZK2I/CkiGapXiMiNIvKL599iEWmcF+PMK8d7f/zOayYiqSLSIzfHFwmy8h6JSHsRWSEiq0Xk69weY17Kwv/HSovIRyKy0vP+9M2LceYHdg8piolIDLAOXeSxFfgJ6Omc+83vnFbAGufcXhHpCoxwzrXIkwHnsqy8P37nLQCOAG8452bm9ljzShb/G4oDFgNdnHNbROQ051ywbRf5Thbfn4eA0s65YSJSAVgLVHLOJefFmKOZZUjRrTnwp3Nug+c//hloiaVjnHOLnXN7PQ+/B6rm8hjz0nHfH4+7gFkE39uW32XlPboBmO2c2wJQUIKRR1beHweUFBEBTgX2AEdzd5j5gwWk6BYP/OX3eKvnWCi3Ap/k6Igiy3HfHxGJB64CXsnFcUWSrPw3VAcoIyJficgyEbk510aX97Ly/kwEzgK2AauAu51zabkzvPwloio1mGyTIMeCzsGKyIVoQGqToyOKLFl5f8YDw5xzqfoHboGTlfeoMHAecDEQCywRke+dc+tyenARICvvT2dgBXARcAawQES+dc79m8Njy3csIEW3rUA1v8dV0b/S0hGRRmj1867Oud25NLZIkJX3pykwwxOMygOXiMhR59ycXBlh3svKe7QV2OWcOwQcEpFvgMbovZX8LivvT19glNMb8n+KyEagHvBj7gwx/7Apu+j2E1BbRGqJyClouaQP/U8QkerAbOCmAvIXrb/jvj/OuVrOuZqeclMzgTsKUDCCLLxHaJmutiJSWESKAy0oOP3GsvL+bEGzR0SkIlAX2JCro8wnLEOKYs65oyIyCJgPxKArxFaLyEDP868A/weUA17yZAFHC0p14iy+PwVaVt4j59waEfkU+AVIA153zv2ad6POPVn8b+gJYIqIrEKn+IY55wpyS4oTZsu+jTHGRASbsjPGGBMRLCAZY4yJCBaQjDHGRAQLSMYYYyKCBSRjjDERwQKSMcaYiGAByZjjEJHbRWS7iPwlIt0Cnvsk8Jgx5sTYPiRjMuHZeb8aOAeoBHwKVPTUvuuNlmO6Pg+HaEy+YRmSMZmrAfzhnNvqnFuKthUo5wlUD6OtKzIlIqtEpKfn+1gROSIiU/2e/0REhnq+Ly4iEzzZ2C4RmeMp/+Q99ysReVZEPhCRAyKyXkQuFpEOIvKriPzrea6k32vKichkzzV3ish7nvF7n98kIg+JyOcictBznVZheO+MyRYLSMZk7k+glojUEJHmaEDaCbyENjvcmYVrLEQbvAFcgLYz6AAgIkWAtp5zAJ4Dzvf8qwHsAj7yNIrzugkYDcQB7wLTgQGea9dEa6nd5bm+AHPQCtVne655AHg7YIy3AIOB0mizwqkYk8tsys6Y4xCR7sBwINnztRLQG7gReB44HVgGDHXOZWjMJiKXAi8552qIyBg0INwIdEcrjM8ETkProB0CrnDOLfC81tvwrZ1zbomIfAWsds7d6Xm+Pjql2Nw595Pn2DNAbefcVSLSFPgGKOOcS/I8Xw4NdNWcc1tFZBPwonNujOf5BsCvQJxzbn+Y3kZjjssyJGOOwzk32znX3DnXBvgNGAkMBB4ENjvnLkADSt8Ql/gaqCwiddDMaAG+rKkD8IWndUEFoBh+laKdcwfRTrb+LRD+9vv+cIhj3im7WkBRYLuI7BORfcB6tF17db/X+L/+kOdrSYzJRVbt25jsGQ+Mdc4liEhjYILn+LdAk2AvcM4dFJEf0NYFNdA+OZWAfkBZ4A3PqTuBJDSIrIdjGdJppO9amh2b0QBT1rqYmkhnGZIxWSQiXYF459xrnkPrgS4iUhjtGvpnJi9fCNwHfO2cSwW+RO8dNfU8hydgTAOeEJEqnt5D44DfOfFmb0vRbqYTPFN1iEgFEbGVgSbiWEAyJgs8q9aeBfr7HX4abe29G73/MymTSywASnm+4pzbhwaaLc65jX7n3YsGkZ/Qxm+V0XtKqScybk+Q64b+f32ZiBwAfgDan8j1jMlJtqjBGGNMRLAMyRhjTESwgGSMMSYiWEAyxhgTESwgGWOMiQgWkIwxxkQEC0jGGGMiggUkY4wxEcECkjHGmIjw/6Qi+K+fNdY4AAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=64)]: Using backend ThreadingBackend with 64 concurrent workers.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "max_iter reached after 26 seconds\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=64)]: Done   1 tasks      | elapsed:   25.9s\n",
-      "[Parallel(n_jobs=64)]: Done   1 out of   1 | elapsed:   25.9s finished\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Correlation: 0.8414221239242293; p-value: 2.0380691845191047e-08\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "rms-diff before: 0.20125138721368724; rms-diff after: 0.18630195968662291\n"
-     ]
-    }
-   ],
-   "source": [
-    "y_pred_before = clf_original.predict(x_test)\n",
-    "test_gender = [d[\"g\"] for d in test]\n",
-    "dev_gender = [d[\"g\"] for d in dev]\n",
-    "train_gender = [d[\"g\"] for d in train]\n",
-    "\n",
-    "tprs_before, tprs_change_before, mean_ratio_before = get_TPR(y_pred_before, y_test, p2i, i2p, test_gender)\n",
-    "similarity_vs_tpr(tprs_change_before, word2vec, \"before\", \"TPR\", prof2fem)\n",
-    "\n",
-    "clf = LogisticRegression(warm_start = True, penalty = 'l2',\n",
-    "                         solver = \"sag\", multi_class = 'multinomial', fit_intercept = True,\n",
-    "                         verbose = 10, max_iter = 6, n_jobs = 64, random_state = 0, class_weight = None)\n",
-    "clf.fit(debiased_x_train, y_train)\n",
-    "y_pred_after = clf.predict(debiased_x_test)\n",
-    "\n",
-    "tprs, tprs_change_after, mean_ratio_after = get_TPR(y_pred_after, y_test, p2i, i2p, test_gender)\n",
-    "similarity_vs_tpr(tprs_change_after, word2vec, \"after\", \"TPR\", prof2fem)\n",
-    "\n",
-    "change_vals_before = np.array(list((tprs_change_before.values())))\n",
-    "change_vals_after = np.array(list(tprs_change_after.values()))\n",
-    "\n",
-    "print(\"rms-diff before: {}; rms-diff after: {}\".format(rms_diff(change_vals_before), rms_diff(change_vals_after)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=64)]: Using backend ThreadingBackend with 64 concurrent workers.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "max_iter reached after 26 seconds\n",
-      "0.7596907287982803\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=64)]: Done   1 tasks      | elapsed:   26.2s\n",
-      "[Parallel(n_jobs=64)]: Done   1 out of   1 | elapsed:   26.2s finished\n"
-     ]
-    }
-   ],
-   "source": [
-    "clf = LogisticRegression(warm_start = True, penalty = 'l2',\n",
-    "                         solver = \"sag\", multi_class = 'multinomial', fit_intercept = True,\n",
-    "                         verbose = 10, max_iter = 6, n_jobs = 64, random_state = 0, class_weight = None)\n",
-    "\n",
-    "clf.fit(debiased_x_train, y_train)\n",
-    "\n",
-    "print(clf.score(debiased_x_test, y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "removal = 2\n",
-    "u_r = u[:, removal:]\n",
-    "proj = u_r @ u_r.T\n",
-    "P = proj\n",
-    "\n",
-    "debiased_x_train = P.dot(x_train.T).T\n",
-    "debiased_x_dev = P.dot(x_dev.T).T\n",
-    "debiased_x_test = P.dot(x_test.T).T"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Correlation: 0.8229158334674403; p-value: 7.657770186244778e-08\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=64)]: Using backend ThreadingBackend with 64 concurrent workers.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "max_iter reached after 26 seconds\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=64)]: Done   1 tasks      | elapsed:   25.9s\n",
-      "[Parallel(n_jobs=64)]: Done   1 out of   1 | elapsed:   25.9s finished\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Correlation: 0.5988793708459228; p-value: 0.0007596910522041412\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "rms-diff before: 0.20125138721368724; rms-diff after: 0.11933701391761586\n"
-     ]
-    }
-   ],
-   "source": [
-    "y_pred_before = clf_original.predict(x_test)\n",
-    "test_gender = [d[\"g\"] for d in test]\n",
-    "dev_gender = [d[\"g\"] for d in dev]\n",
-    "train_gender = [d[\"g\"] for d in train]\n",
-    "\n",
-    "tprs_before, tprs_change_before, mean_ratio_before = get_TPR(y_pred_before, y_test, p2i, i2p, test_gender)\n",
-    "similarity_vs_tpr(tprs_change_before, word2vec, \"before\", \"TPR\", prof2fem)\n",
-    "\n",
-    "clf = LogisticRegression(warm_start = True, penalty = 'l2',\n",
-    "                         solver = \"sag\", multi_class = 'multinomial', fit_intercept = True,\n",
-    "                         verbose = 10, max_iter = 6, n_jobs = 64, random_state = 0, class_weight = None)\n",
-    "clf.fit(debiased_x_train, y_train)\n",
-    "y_pred_after = clf.predict(debiased_x_test)\n",
-    "\n",
-    "tprs, tprs_change_after, mean_ratio_after = get_TPR(y_pred_after, y_test, p2i, i2p, test_gender)\n",
-    "similarity_vs_tpr(tprs_change_after, word2vec, \"after\", \"TPR\", prof2fem)\n",
-    "\n",
-    "change_vals_before = np.array(list((tprs_change_before.values())))\n",
-    "change_vals_after = np.array(list(tprs_change_after.values()))\n",
-    "\n",
-    "print(\"rms-diff before: {}; rms-diff after: {}\".format(rms_diff(change_vals_before), rms_diff(change_vals_after)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=64)]: Using backend ThreadingBackend with 64 concurrent workers.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "max_iter reached after 26 seconds\n",
-      "0.7558075029470911\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=64)]: Done   1 tasks      | elapsed:   25.8s\n",
-      "[Parallel(n_jobs=64)]: Done   1 out of   1 | elapsed:   25.8s finished\n"
-     ]
-    }
-   ],
-   "source": [
-    "clf = LogisticRegression(warm_start = True, penalty = 'l2',\n",
-    "                         solver = \"sag\", multi_class = 'multinomial', fit_intercept = True,\n",
-    "                         verbose = 10, max_iter = 6, n_jobs = 64, random_state = 0, class_weight = None)\n",
-    "\n",
-    "clf.fit(debiased_x_train, y_train)\n",
-    "\n",
-    "print(clf.score(debiased_x_test, y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# gender_clf = LogisticRegression(warm_start = True, penalty = 'l2',\n",
-    "#                          solver = \"sag\", multi_class = 'multinomial', fit_intercept = True,\n",
-    "#                          verbose = 10, max_iter = 7, n_jobs = 64, random_state = 1)\n",
-    "\n",
-    "# gender_clf.fit(debiased_x_train, y_train_gender)\n",
-    "# gender_clf.score(debiased_x_dev, y_dev_gender)\n",
-    "# gender_clf.score(debiased_x_train, y_train_gender)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# clf = LogisticRegression(warm_start = True, penalty = 'l2',\n",
-    "#                          solver = \"sag\", multi_class = 'multinomial', fit_intercept = True,\n",
-    "#                          verbose = 10, max_iter = 6, n_jobs = 64, random_state = 0, class_weight = None)\n",
-    "\n",
-    "# clf.fit(x_train, y_train)\n",
-    "\n",
-    "# print(f\"Biased Train accuracy {clf.score(x_train, y_train)}\")\n",
-    "# print(f\"Biased Test accuracy {clf.score(x_test, y_test)}\")\n",
-    "# print(f\"Train accuracy {clf.score(debiased_x_train, y_train)}\")\n",
-    "# print(f\"Test accuracy {clf.score(debiased_x_test, y_test)}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "removal = 20\n",
-    "u_r = u[:, removal:]\n",
-    "proj = u_r @ u_r.T\n",
-    "P = proj\n",
-    "\n",
-    "debiased_x_train = P.dot(x_train.T).T\n",
-    "debiased_x_dev = P.dot(x_dev.T).T\n",
-    "debiased_x_test = P.dot(x_test.T).T"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y_pred_before = clf_original.predict(x_test)\n",
-    "test_gender = [d[\"g\"] for d in test]\n",
-    "dev_gender = [d[\"g\"] for d in dev]\n",
-    "train_gender = [d[\"g\"] for d in train]\n",
-    "\n",
-    "tprs_before, tprs_change_before, mean_ratio_before = get_TPR(y_pred_before, y_test, p2i, i2p, test_gender)\n",
-    "similarity_vs_tpr(tprs_change_before, word2vec, \"before\", \"TPR\", prof2fem)\n",
-    "#y_pred_after = clf.predict(X_test.dot(P))\n",
-    "y_pred_after = clf.predict(debiased_x_test)\n",
-    "tprs, tprs_change_after, mean_ratio_after = get_TPR(y_pred_after, y_test, p2i, i2p, test_gender)\n",
-    "similarity_vs_tpr(tprs_change_after, word2vec, \"after\", \"TPR\", prof2fem)\n",
-    "\n",
-    "change_vals_before = np.array(list((tprs_change_before.values())))\n",
-    "change_vals_after = np.array(list(tprs_change_after.values()))\n",
-    "\n",
-    "print(\"rms-diff before: {}; rms-diff after: {}\".format(rms_diff(change_vals_before), rms_diff(change_vals_after)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "clf = LogisticRegression(warm_start = True, penalty = 'l2',\n",
-    "                         solver = \"sag\", multi_class = 'multinomial', fit_intercept = True,\n",
-    "                         verbose = 10, max_iter = 6, n_jobs = 64, random_state = 0, class_weight = None)\n",
-    "\n",
-    "clf.fit(debiased_x_train, y_train)\n",
-    "\n",
-    "print(clf.score(debiased_x_test, y_test))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "clf = LogisticRegression(warm_start = True, penalty = 'l2',\n",
-    "                         solver = \"sag\", multi_class = 'multinomial', fit_intercept = True,\n",
-    "                         verbose = 10, max_iter = 6, n_jobs = 64, random_state = 0, class_weight = None)\n",
-    "\n",
-    "clf.fit(x_train, y_train)\n",
-    "\n",
-    "print(f\"Biased Train accuracy {clf.score(x_train, y_train)}\")\n",
-    "print(f\"Biased Test accuracy {clf.score(x_test, y_test)}\")\n",
-    "print(f\"Train accuracy {clf.score(debiased_x_train, y_train)}\")\n",
-    "print(f\"Test accuracy {clf.score(debiased_x_test, y_test)}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tsne_by_gender(vecs, labels, title, words = None):\n",
-    "\n",
-    "  tsne = TSNE(n_components=2, random_state=0)\n",
-    "  vecs_2d = tsne.fit_transform(vecs)\n",
-    "  num_labels = len(set(labels.tolist()))\n",
-    "\n",
-    "  names = [\"class {}\".format(i) for i in range(num_labels)]\n",
-    "  plt.figure(figsize=(6, 5))\n",
-    "  colors = 'r', 'b', 'orange'\n",
-    "  for i, c, label in zip(set(labels.tolist()), colors, names):\n",
-    "    print(len(vecs_2d[labels == i, 0]))\n",
-    "    plt.scatter(vecs_2d[labels == i, 0], vecs_2d[labels == i, 1], c=c,\n",
-    "                label=label, alpha = 0.6)\n",
-    "  plt.legend(loc = \"upper right\")\n",
-    "  plt.title(title)\n",
-    "  \n",
-    "  if words is not None:\n",
-    "        k = 60\n",
-    "        for i in range(k):\n",
-    "            \n",
-    "            j = np.random.choice(range(len(words)))\n",
-    "            label = labels[i]\n",
-    "            w = words[j]\n",
-    "            x,y = vecs_2d[i]\n",
-    "            plt.annotate(w , (x,y), size = 10, color = \"black\" if label == 1 else \"black\")\n",
-    "            \n",
-    "  plt.show()\n",
-    "  return vecs_2d"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "prof = \"professor\"\n",
-    "idx = np.random.rand(x_dev.shape[0]) < 0.1\n",
-    "prof_idx = y_dev == p2i[prof]\n",
-    "n = 800\n",
-    "tsne_by_gender(x_dev[prof_idx][:n], np.array(dev_gender)[prof_idx][:n], \"tsne by gender, before, {}\".format(prof))\n",
-    "tsne_by_gender((debiased_x_dev[prof_idx])[:n], np.array(dev_gender)[prof_idx][:n], \"tsne by gender, after. {}\".format(prof))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def count_profs(data_y, i2p):\n",
-    "    d = Counter()\n",
-    "    for y in data_y:\n",
-    "        d[i2p[y]] += 1\n",
-    "    return d"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "count_profs(Y_train, i2p)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "count_profs(Y_dev, i2p)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p0 = all_Ps[0]\n",
-    "p1 = all_Ps[1]\n",
-    "p2 = all_Ps[2]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p2.dot(p2.T)<"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p0 = all_Ps[0]\n",
-    "p1 = all_Ps[1]\n",
-    "p2 = all_Ps[2]\n",
-    "\n",
-    "p2.dot(p2.T)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_common_words(data: List[dict], word2vec_model):\n",
-    "    \n",
-    "    words_counter = Counter()\n",
-    "    vecs = []\n",
-    "    all_words = []\n",
-    "    \n",
-    "    \n",
-    "    for entry in tqdm.tqdm(data, total = len(data)):\n",
-    "        \n",
-    "        y = p2i[entry[\"p\"]]\n",
-    "        words = entry[\"hard_text\"].split(\" \")\n",
-    "        all_words.extend(words)\n",
-    "            \n",
-    "    \n",
-    "    words_counter = Counter(all_words)\n",
-    "    common_words = [w for w in words_counter if words_counter[w] > 10 and (w in word2vec_model)]\n",
-    "    common_vecs = [word2vec_model[w] for w in common_words]\n",
-    "    \n",
-    "    return common_words, common_vecs\n",
-    "        \n",
-    "        "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "words, vecs = get_common_words(train,word2vec)\n",
-    "\n",
-    "vecs_normed = vecs / np.linalg.norm(vecs, keepdims = True)\n",
-    "ws_normed = [w/np.linalg.norm(w) for w in all_ws]\n",
-    "k = 1000\n",
-    "groups, labels = [], []\n",
-    "\n",
-    "for i,w in enumerate(ws_normed):\n",
-    "    print(\"INLP ITERATION: {}\".format(i))\n",
-    "    sims_i = w.dot(vecs_normed.T).squeeze(0)\n",
-    "    zipped = zip(words, vecs_normed, sims_i)\n",
-    "    zipped = sorted(list(zipped), key = lambda tuple: -abs(tuple[2]))\n",
-    "    ws,vs, sims = list(zip(*zipped))\n",
-    "    print(ws[:k])\n",
-    "    groups.append(vs[:k])\n",
-    "    labels.append(i)\n",
-    "    print(\"------------------------------------------------------------------\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def visualize_gender_subspace(vecs, labels):\n",
-    "    \n",
-    "        N = len(labels)\n",
-    "        \n",
-    "        all_vecs = []\n",
-    "        all_labels = []\n",
-    "        \n",
-    "        for vecs_list,l in zip(vecs, labels):\n",
-    "            \n",
-    "            all_labels.append(np.ones(len(vecs_list)) * l)\n",
-    "        \n",
-    "        all_vecs_np = np.concatenate(vecs, axis = 0)\n",
-    "        all_labels_np = np.concatenate(all_labels, axis = 0)\n",
-    "        tsne = TSNE(n_components=2, random_state=0)\n",
-    "        vecs_2d = tsne.fit_transform(all_vecs_np)\n",
-    "    \n",
-    "        fig, ax = plt.subplots()\n",
-    "        # define the colormap\n",
-    "        cmap = plt.cm.jet\n",
-    "        # extract all colors from the .jet map\n",
-    "        cmaplist = [cmap(i) for i in range(cmap.N)]\n",
-    "        # create the new map\n",
-    "        cmap = cmap.from_list('Custom cmap', cmaplist, cmap.N)\n",
-    "        # define the bins and normalize\n",
-    "        bounds = np.linspace(0, N, N + 1)\n",
-    "        norm = matplotlib.colors.BoundaryNorm(bounds, cmap.N)\n",
-    "        print(\"here\")\n",
-    "        print(all_labels_np.shape)\n",
-    "        scat = ax.scatter(vecs_2d[:,0], vecs_2d[:,1], c=all_labels_np, cmap=cmap, norm=norm, alpha=0.15)\n",
-    "        cb = plt.colorbar(scat, spacing='proportional')#, ticks=bounds)\n",
-    "        cb.set_label(\"INLP iteration number\")\n",
-    "        plt.savefig(\"INLP progress\", dpi = 600)\n",
-    "        plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#visualize_gender_subspace(groups, labels)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "word2vec"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "i = 0\n",
-    "for w in Ws[:150]:\n",
-    "    \n",
-    "    #sims = word2vec.similar_by_vector(-w.squeeze(), topn = 60, restrict_vocab=None)\n",
-    "    most_similar_f, _ =  list(zip(*word2vec_bios.similar_by_vector(w.squeeze(), topn = 20, restrict_vocab=None)))\n",
-    "    most_similar_m, _ =  list(zip(*word2vec_bios.similar_by_vector(-w.squeeze(), topn = 20, restrict_vocab=None)))\n",
-    "    print(\"gender direction {}.\\n most_similar male: {}\\n; most_similar female: {}\".format(i, most_similar_m, most_similar_f))\n",
-    "    print(\"=====================================\")\n",
-    "    i += 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nurse_train = [r for r in train if r[\"p\"] == \"nurse\"]\n",
-    "nurse_train[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "profs_train_str = np.array([i2p[y] for y in Y_train[:]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_train.shape, profs_train_str.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for p in p2i.keys():\n",
-    "    x_train_nurse = X_train[profs_train_str == p]\n",
-    "    y_train_nurse = Y_train[profs_train_str == p]\n",
-    "    print(p,len(x_train_nurse)/len(X_train)*100,\"%\", clf_original.score(x_train_nurse, y_train_nurse), clf.score(P.dot(x_train_nurse.T).T, y_train_nurse))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x_train_nurse.shape, y_train_nurse.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "{'professor': 0.45118956904580476, 'chiropractor': 0.26558891454965355, 'psychologist': 0.6223011751844766, 'architect': 0.23712053792148718, 'physician': 0.507688318423441, 'nurse': 0.9085446207369142, 'dentist': 0.35589474411216243, 'surgeon': 0.14857228961048746, 'rapper': 0.09665955934612651, 'model': 0.8283124500133298, 'photographer': 0.35721920736720936, 'composer': 0.16392857142857142, 'comedian': 0.21150410861021793, 'filmmaker': 0.3295762590954487, 'paralegal': 0.8483305036785512, 'journalist': 0.49488721804511276, 'personal_trainer': 0.45670391061452514, 'teacher': 0.603111879476414, 'painter': 0.4579886246122027, 'attorney': 0.38316925813475633, 'accountant': 0.36818825194621374, 'software_engineer': 0.1576889661164205, 'poet': 0.49080017115960634, 'dj': 0.1420875420875421, 'pastor': 0.24052132701421802, 'yoga_teacher': 0.8454600120264583, 'dietitian': 0.9273504273504274, 'interior_designer': 0.8086124401913876}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebooks/notebook_fair-profession_bert_SAL.ipynb b/notebooks/notebook_fair-profession_bert_SAL.ipynb
index a2f1f9e..2fb5be1 100644
--- a/notebooks/notebook_fair-profession_bert_SAL.ipynb
+++ b/notebooks/notebook_fair-profession_bert_SAL.ipynb
@@ -542,19 +542,6 @@
     "y_dev_gender.shape, y_train_gender.shape, y_test_gender.shape"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "t = time.time()\n",
-    "u, s, vh = np.linalg.svd(A, full_matrices=True)\n",
-    "print(f\"u has shape {u.shape}, s has shape {s.shape}, vh has shape {vh.shape}\")\n",
-    "elapsed = time.time() - t\n",
-    "print(f\"vSVD took {elapsed} seconds\")"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 18,
@@ -578,12 +565,28 @@
     "print(f\"u has shape {u.shape}, s has shape {s.shape}, vh has shape {vh.shape}\")"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
+    "# To save the model\n",
+    "\n",
     "# removal = 1\n",
     "# u_r = u[:, removal:]\n",
     "# proj = u_r @ u_r.T\n",
@@ -600,24 +603,10 @@
     "\n",
     "# debiased_x_train_2 = P.dot(x_train.T).T\n",
     "# debiased_x_dev_2 = P.dot(x_dev.T).T\n",
-    "# debiased_x_test_2 = P.dot(x_test.T).T"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# rm -rf FastText.npz"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
+    "# debiased_x_test_2 = P.dot(x_test.T).T\n",
+    "\n",
+    "# rm -rf FastText.npz\n",
+    "\n",
     "# np.savez(\"FastText.npz\", u = u, s = s, vh = vh, \n",
     "#          x_train = x_train, x_test = x_test, \n",
     "#          debiased_x_train_1 = debiased_x_train_1, debiased_x_test_1 = debiased_x_test_1, \n",
@@ -626,29 +615,6 @@
     "#          y_test_gender = y_test_gender, y_test = y_test)\n"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# gender_clf = LogisticRegression(warm_start = True, penalty = 'l2',\n",
-    "#                          solver = \"sag\", multi_class = 'multinomial', fit_intercept = True,\n",
-    "#                          verbose = 10, max_iter = 7, n_jobs = 64, random_state = 1)\n",
-    "\n",
-    "# gender_clf.fit(debiased_x_train, y_train_gender)\n",
-    "# gender_clf.score(debiased_x_dev, y_dev_gender)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# gender_clf.score(debiased_x_train, y_train_gender)"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1164,39 +1130,6 @@
     "print(clf.score(debiased_x_test, y_test))"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# gender_clf = LogisticRegression(warm_start = True, penalty = 'l2',\n",
-    "#                          solver = \"sag\", multi_class = 'multinomial', fit_intercept = True,\n",
-    "#                          verbose = 10, max_iter = 7, n_jobs = 64, random_state = 1)\n",
-    "\n",
-    "# gender_clf.fit(debiased_x_train, y_train_gender)\n",
-    "# gender_clf.score(debiased_x_dev, y_dev_gender)\n",
-    "# gender_clf.score(debiased_x_train, y_train_gender)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# clf = LogisticRegression(warm_start = True, penalty = 'l2',\n",
-    "#                          solver = \"sag\", multi_class = 'multinomial', fit_intercept = True,\n",
-    "#                          verbose = 10, max_iter = 6, n_jobs = 64, random_state = 0, class_weight = None)\n",
-    "\n",
-    "# clf.fit(x_train, y_train)\n",
-    "\n",
-    "# print(f\"Biased Train accuracy {clf.score(x_train, y_train)}\")\n",
-    "# print(f\"Biased Test accuracy {clf.score(x_test, y_test)}\")\n",
-    "# print(f\"Train accuracy {clf.score(debiased_x_train, y_train)}\")\n",
-    "# print(f\"Test accuracy {clf.score(debiased_x_test, y_test)}\")"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -1284,352 +1217,6 @@
    "outputs": [],
    "source": []
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tsne_by_gender(vecs, labels, title, words = None):\n",
-    "\n",
-    "  tsne = TSNE(n_components=2, random_state=0)\n",
-    "  vecs_2d = tsne.fit_transform(vecs)\n",
-    "  num_labels = len(set(labels.tolist()))\n",
-    "\n",
-    "  names = [\"class {}\".format(i) for i in range(num_labels)]\n",
-    "  plt.figure(figsize=(6, 5))\n",
-    "  colors = 'r', 'b', 'orange'\n",
-    "  for i, c, label in zip(set(labels.tolist()), colors, names):\n",
-    "    print(len(vecs_2d[labels == i, 0]))\n",
-    "    plt.scatter(vecs_2d[labels == i, 0], vecs_2d[labels == i, 1], c=c,\n",
-    "                label=label, alpha = 0.6)\n",
-    "  plt.legend(loc = \"upper right\")\n",
-    "  plt.title(title)\n",
-    "  \n",
-    "  if words is not None:\n",
-    "        k = 60\n",
-    "        for i in range(k):\n",
-    "            \n",
-    "            j = np.random.choice(range(len(words)))\n",
-    "            label = labels[i]\n",
-    "            w = words[j]\n",
-    "            x,y = vecs_2d[i]\n",
-    "            plt.annotate(w , (x,y), size = 10, color = \"black\" if label == 1 else \"black\")\n",
-    "            \n",
-    "  plt.show()\n",
-    "  return vecs_2d"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "prof = \"professor\"\n",
-    "idx = np.random.rand(x_dev.shape[0]) < 0.1\n",
-    "prof_idx = y_dev == p2i[prof]\n",
-    "n = 800\n",
-    "tsne_by_gender(x_dev[prof_idx][:n], np.array(dev_gender)[prof_idx][:n], \"tsne by gender, before, {}\".format(prof))\n",
-    "tsne_by_gender((debiased_x_dev[prof_idx])[:n], np.array(dev_gender)[prof_idx][:n], \"tsne by gender, after. {}\".format(prof))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def count_profs(data_y, i2p):\n",
-    "    d = Counter()\n",
-    "    for y in data_y:\n",
-    "        d[i2p[y]] += 1\n",
-    "    return d"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "count_profs(Y_train, i2p)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "count_profs(Y_dev, i2p)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p0 = all_Ps[0]\n",
-    "p1 = all_Ps[1]\n",
-    "p2 = all_Ps[2]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p2.dot(p2.T)<"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p0 = all_Ps[0]\n",
-    "p1 = all_Ps[1]\n",
-    "p2 = all_Ps[2]\n",
-    "\n",
-    "p2.dot(p2.T)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_common_words(data: List[dict], word2vec_model):\n",
-    "    \n",
-    "    words_counter = Counter()\n",
-    "    vecs = []\n",
-    "    all_words = []\n",
-    "    \n",
-    "    \n",
-    "    for entry in tqdm.tqdm(data, total = len(data)):\n",
-    "        \n",
-    "        y = p2i[entry[\"p\"]]\n",
-    "        words = entry[\"hard_text\"].split(\" \")\n",
-    "        all_words.extend(words)\n",
-    "            \n",
-    "    \n",
-    "    words_counter = Counter(all_words)\n",
-    "    common_words = [w for w in words_counter if words_counter[w] > 10 and (w in word2vec_model)]\n",
-    "    common_vecs = [word2vec_model[w] for w in common_words]\n",
-    "    \n",
-    "    return common_words, common_vecs\n",
-    "        \n",
-    "        "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "words, vecs = get_common_words(train,word2vec)\n",
-    "\n",
-    "vecs_normed = vecs / np.linalg.norm(vecs, keepdims = True)\n",
-    "ws_normed = [w/np.linalg.norm(w) for w in all_ws]\n",
-    "k = 1000\n",
-    "groups, labels = [], []\n",
-    "\n",
-    "for i,w in enumerate(ws_normed):\n",
-    "    print(\"INLP ITERATION: {}\".format(i))\n",
-    "    sims_i = w.dot(vecs_normed.T).squeeze(0)\n",
-    "    zipped = zip(words, vecs_normed, sims_i)\n",
-    "    zipped = sorted(list(zipped), key = lambda tuple: -abs(tuple[2]))\n",
-    "    ws,vs, sims = list(zip(*zipped))\n",
-    "    print(ws[:k])\n",
-    "    groups.append(vs[:k])\n",
-    "    labels.append(i)\n",
-    "    print(\"------------------------------------------------------------------\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def visualize_gender_subspace(vecs, labels):\n",
-    "    \n",
-    "        N = len(labels)\n",
-    "        \n",
-    "        all_vecs = []\n",
-    "        all_labels = []\n",
-    "        \n",
-    "        for vecs_list,l in zip(vecs, labels):\n",
-    "            \n",
-    "            all_labels.append(np.ones(len(vecs_list)) * l)\n",
-    "        \n",
-    "        all_vecs_np = np.concatenate(vecs, axis = 0)\n",
-    "        all_labels_np = np.concatenate(all_labels, axis = 0)\n",
-    "        tsne = TSNE(n_components=2, random_state=0)\n",
-    "        vecs_2d = tsne.fit_transform(all_vecs_np)\n",
-    "    \n",
-    "        fig, ax = plt.subplots()\n",
-    "        # define the colormap\n",
-    "        cmap = plt.cm.jet\n",
-    "        # extract all colors from the .jet map\n",
-    "        cmaplist = [cmap(i) for i in range(cmap.N)]\n",
-    "        # create the new map\n",
-    "        cmap = cmap.from_list('Custom cmap', cmaplist, cmap.N)\n",
-    "        # define the bins and normalize\n",
-    "        bounds = np.linspace(0, N, N + 1)\n",
-    "        norm = matplotlib.colors.BoundaryNorm(bounds, cmap.N)\n",
-    "        print(\"here\")\n",
-    "        print(all_labels_np.shape)\n",
-    "        scat = ax.scatter(vecs_2d[:,0], vecs_2d[:,1], c=all_labels_np, cmap=cmap, norm=norm, alpha=0.15)\n",
-    "        cb = plt.colorbar(scat, spacing='proportional')#, ticks=bounds)\n",
-    "        cb.set_label(\"INLP iteration number\")\n",
-    "        plt.savefig(\"INLP progress\", dpi = 600)\n",
-    "        plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "#visualize_gender_subspace(groups, labels)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "word2vec"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "i = 0\n",
-    "for w in Ws[:150]:\n",
-    "    \n",
-    "    #sims = word2vec.similar_by_vector(-w.squeeze(), topn = 60, restrict_vocab=None)\n",
-    "    most_similar_f, _ =  list(zip(*word2vec_bios.similar_by_vector(w.squeeze(), topn = 20, restrict_vocab=None)))\n",
-    "    most_similar_m, _ =  list(zip(*word2vec_bios.similar_by_vector(-w.squeeze(), topn = 20, restrict_vocab=None)))\n",
-    "    print(\"gender direction {}.\\n most_similar male: {}\\n; most_similar female: {}\".format(i, most_similar_m, most_similar_f))\n",
-    "    print(\"=====================================\")\n",
-    "    i += 1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "nurse_train = [r for r in train if r[\"p\"] == \"nurse\"]\n",
-    "nurse_train[0]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "profs_train_str = np.array([i2p[y] for y in Y_train[:]])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_train.shape, profs_train_str.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for p in p2i.keys():\n",
-    "    x_train_nurse = X_train[profs_train_str == p]\n",
-    "    y_train_nurse = Y_train[profs_train_str == p]\n",
-    "    print(p,len(x_train_nurse)/len(X_train)*100,\"%\", clf_original.score(x_train_nurse, y_train_nurse), clf.score(P.dot(x_train_nurse.T).T, y_train_nurse))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x_train_nurse.shape, y_train_nurse.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "{'professor': 0.45118956904580476, 'chiropractor': 0.26558891454965355, 'psychologist': 0.6223011751844766, 'architect': 0.23712053792148718, 'physician': 0.507688318423441, 'nurse': 0.9085446207369142, 'dentist': 0.35589474411216243, 'surgeon': 0.14857228961048746, 'rapper': 0.09665955934612651, 'model': 0.8283124500133298, 'photographer': 0.35721920736720936, 'composer': 0.16392857142857142, 'comedian': 0.21150410861021793, 'filmmaker': 0.3295762590954487, 'paralegal': 0.8483305036785512, 'journalist': 0.49488721804511276, 'personal_trainer': 0.45670391061452514, 'teacher': 0.603111879476414, 'painter': 0.4579886246122027, 'attorney': 0.38316925813475633, 'accountant': 0.36818825194621374, 'software_engineer': 0.1576889661164205, 'poet': 0.49080017115960634, 'dj': 0.1420875420875421, 'pastor': 0.24052132701421802, 'yoga_teacher': 0.8454600120264583, 'dietitian': 0.9273504273504274, 'interior_designer': 0.8086124401913876}"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/notebooks/notebook_fair-profession_bert_kSAL.ipynb b/notebooks/notebook_fair-profession_bert_kSAL.ipynb
index 31ad7dc..7fc3b64 100644
--- a/notebooks/notebook_fair-profession_bert_kSAL.ipynb
+++ b/notebooks/notebook_fair-profession_bert_kSAL.ipynb
@@ -1396,200 +1396,6 @@
     "tpr_exp(x_test, debiased_x_train_2, y_m_train, debiased_x_test_2, y_m_test, \\\n",
     "        y_p_test, N_test, p2i, i2p, clf_original)"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 4. Polynomial at degree=5"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kernel_fn = \"poly\"\n",
-    "kernel_coef = 5\n",
-    "\n",
-    "saved_model = np.load(f\"../data/saved_models/fair_biography_prof_gender/{ratio}/cleaned_{kernel_fn}_{kernel_coef}_Train{N}_Test{N_test}.npz\")\n",
-    "debiased_x_train_0 = saved_model['debiased_x_train_0']\n",
-    "debiased_x_test_0 = saved_model['debiased_x_test_0']\n",
-    "debiased_x_train_1 = saved_model['debiased_x_train_1']\n",
-    "debiased_x_test_1 = saved_model['debiased_x_test_1']\n",
-    "debiased_x_train_2 = saved_model['debiased_x_train_2']\n",
-    "debiased_x_test_2 = saved_model['debiased_x_test_2']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "# Removal 0 BERT Poly 5\n",
-    "tpr_exp(x_test, debiased_x_train_0, y_m_train, debiased_x_test_0, y_m_test, \\\n",
-    "        y_p_test, N_test, p2i, i2p, clf_original)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Removal 1 BERT Poly 5\n",
-    "tpr_exp(x_test, debiased_x_train_1, y_m_train, debiased_x_test_1, y_m_test, \\\n",
-    "        y_p_test, N_test, p2i, i2p, clf_original)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Removal 2 BERT Poly 5\n",
-    "tpr_exp(x_test, debiased_x_train_2, y_m_train, debiased_x_test_2, y_m_test, \\\n",
-    "        y_p_test, N_test, p2i, i2p, clf_original)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### t-SNE by gender"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tsne_by_gender(vecs, labels, title, words = None):\n",
-    "\n",
-    "  tsne = TSNE(n_components=2, random_state=0)\n",
-    "  vecs_2d = tsne.fit_transform(vecs)\n",
-    "  num_labels = len(set(labels.tolist()))\n",
-    "\n",
-    "  names = list(set(labels)) # [\"class {}\".format(i) for i in range(num_labels)]\n",
-    "  plt.figure(figsize=(6, 5))\n",
-    "  colors = 'b', 'r', 'orange'\n",
-    "  markers = [\"o\", \"s\"]\n",
-    "\n",
-    "  for i, c, label, marker in zip(set(labels.tolist()), colors, names, markers):\n",
-    "    print(len(vecs_2d[labels == i, 0]))\n",
-    "    plt.scatter(vecs_2d[labels == i, 0], vecs_2d[labels == i, 1], c=c,\n",
-    "                label=\"Female\" if label == 1 else \"Male\", alpha = 0.45, marker = marker)\n",
-    "  plt.legend(fontsize = 15, loc = \"upper right\")\n",
-    "  plt.title(title, fontsize = 15)\n",
-    "  \n",
-    "  if words is not None:\n",
-    "        k = 60\n",
-    "        for i in range(k):\n",
-    "            \n",
-    "            j = np.random.choice(range(len(words)))\n",
-    "            label = labels[i]\n",
-    "            w = words[j]\n",
-    "            x,y = vecs_2d[i]\n",
-    "            plt.annotate(w , (x,y), size = 10, color = \"black\" if label == 1 else \"black\")\n",
-    "  plt.savefig(title, dpi = 600)          \n",
-    "  plt.show()\n",
-    "  return vecs_2d"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y_test_gender = np.array([d[\"g\"] for d in dev])\n",
-    "n = 2000\n",
-    "for prof in [\"nurse\", \"professor\", \"physician\", \"accountant\", \"dj\", \"dietitian\"]:\n",
-    "    \n",
-    "    idx = np.random.rand(x_test.shape[0]) < 0.1\n",
-    "    prof_idx = y_dev == p2i[prof]  \n",
-    "    prof_upper = prof[0].upper() + prof[1:]\n",
-    "    tsne_by_gender(x_dev[prof_idx][:n], y_dev_gender[prof_idx][:n], \"{} (Original)\".format(prof_upper))\n",
-    "    tsne_by_gender(debiased_x_dev[prof_idx][:n], y_dev_gender[prof_idx][:n], \"{} (Projected)\".format(prof_upper))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "tsne_by_gender(x_dev[:n], y_dev_gender[:n], \"All (Original)\".format(prof_upper))\n",
-    "tsne_by_gender(debiased_x_dev[:n], y_dev_gender[:n], \"All (Projected)\".format(prof_upper))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def visualize_gender_subspace(proj_matrices, vecs):\n",
-    "    \n",
-    "        labels = range(len(proj_matrices))\n",
-    "        N = len(labels)\n",
-    "        \n",
-    "        all_vecs = []\n",
-    "        all_labels = []\n",
-    "        \n",
-    "        for i,p in enumerate(proj_matrices):\n",
-    "            \n",
-    "            vecs_proj = vecs - vecs.dot(p)\n",
-    "            all_vecs.append(vecs_proj)\n",
-    "            all_labels.append(np.ones(vecs.shape[0]) * i)\n",
-    "        \n",
-    "        all_vecs_np = np.concatenate(all_vecs, axis = 0)\n",
-    "        all_labels_np = np.concatenate(all_labels, axis = 0)\n",
-    "        tsne = TSNE(n_components=2, random_state=0)\n",
-    "        vecs_2d = tsne.fit_transform(all_vecs_np)\n",
-    "    \n",
-    "        fig, ax = plt.subplots()\n",
-    "        # define the colormap\n",
-    "        cmap = plt.cm.jet\n",
-    "        # extract all colors from the .jet map\n",
-    "        cmaplist = [cmap(i) for i in range(cmap.N)]\n",
-    "        # create the new map\n",
-    "        cmap = cmap.from_list('Custom cmap', cmaplist, cmap.N)\n",
-    "        # define the bins and normalize\n",
-    "        bounds = np.linspace(0, N, N + 1)\n",
-    "        norm = matplotlib.colors.BoundaryNorm(bounds, cmap.N)\n",
-    "        print(\"here\")\n",
-    "        print(all_labels_np.shape)\n",
-    "        scat = ax.scatter(vecs_2d[:,0], vecs_2d[:,1], c=all_labels_np, cmap=cmap, norm=norm, alpha=0.4)\n",
-    "        cb = plt.colorbar(scat, spacing='proportional')#, ticks=bounds)\n",
-    "        cb.set_label(\"INLP iteration number\")\n",
-    "        plt.savefig(\"INLP progress\", dpi = 600)\n",
-    "        plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "visualize_gender_subspace(all_Ps[:25], x_dev[:5000])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
diff --git a/notebooks/biasbios_fair-profession_fasttext.ipynb b/notebooks/notebook_fair-profession_fasttext_INLP.ipynb
similarity index 100%
rename from notebooks/biasbios_fair-profession_fasttext.ipynb
rename to notebooks/notebook_fair-profession_fasttext_INLP.ipynb
diff --git a/notebooks/notebook_fair-profession_fasttext_SAL.ipynb b/notebooks/notebook_fair-profession_fasttext_SAL.ipynb
index 0f3315e..bd5c9c9 100644
--- a/notebooks/notebook_fair-profession_fasttext_SAL.ipynb
+++ b/notebooks/notebook_fair-profession_fasttext_SAL.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -56,8 +56,8 @@
     "import torch\n",
     "from torch import utils\n",
     "\n",
-    "# import pytorch_lightning as pl\n",
-    "# from pytorch_lightning import Trainer\n",
+    "import pytorch_lightning as pl\n",
+    "from pytorch_lightning import Trainer\n",
     "import copy\n",
     "import pandas as pd\n",
     "from gensim.models import FastText\n",
@@ -73,7 +73,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
     "\n",
     "def load_dataset(path):\n",
     "    \n",
@@ -224,7 +223,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -271,7 +270,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -280,13 +279,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "d82c5b5735b94948a205af1c9bc31045",
+       "model_id": "8c75943cd66f4564adb92f5eedaf1899",
        "version_major": 2,
        "version_minor": 0
       },
@@ -301,13 +300,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "% unknown: 0.010167678378823583\n"
+      "% unknown: 0.010212038730551916\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "758f6e69806e4f1783d8d59b2b35d425",
+       "model_id": "1287c81c30e847d8bad6b5ff363619ce",
        "version_major": 2,
        "version_minor": 0
       },
@@ -322,13 +321,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "% unknown: 0.010116209992979287\n"
+      "% unknown: 0.01017481830681595\n"
      ]
     },
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "3f5a9ad077a0417baac596d738a3332d",
+       "model_id": "34092e9ee71f4cf09207acc6403c36de",
        "version_major": 2,
        "version_minor": 0
       },
@@ -343,23 +342,22 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "% unknown: 0.01018950257509165\n"
+      "% unknown: 0.01024306743581662\n"
      ]
     }
    ],
    "source": [
-    "X_train, Y_train, unknown_train, vocab_counter_train = get_embeddings_based_dataset(train, word2vec, p2i)\n",
-    "X_dev, Y_dev, unknown_dev, vocab_counter_dev =  get_embeddings_based_dataset(dev, word2vec, p2i)\n",
-    "X_test, Y_test, unknown_test, vocab_counter_test =  get_embeddings_based_dataset(test, word2vec, p2i)"
+    "x_train, y_train, unknown_train, vocab_counter_train = get_embeddings_based_dataset(train, word2vec, p2i)\n",
+    "x_dev, y_dev, unknown_dev, vocab_counter_dev =  get_embeddings_based_dataset(dev, word2vec, p2i)\n",
+    "x_test, y_test, unknown_test, vocab_counter_test =  get_embeddings_based_dataset(test, word2vec, p2i)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Run only at initialization\n",
     "# def save_in_word2vec_format(vecs: np.ndarray, words: np.ndarray, fname: str):\n",
     "\n",
     "\n",
@@ -378,13 +376,36 @@
     "# vecs_for_vocab = np.array([word2vec[w] for w in tqdm.tqdm_notebook(vocab_bios)])\n",
     "# print(\"here\")\n",
     "# save_in_word2vec_format(vecs_for_vocab, vocab_bios, \"vecs.vocab.bios.txt\")\n",
+    "word2vec_bios, _, _ = load_word_vectors(\"vecs.vocab.bios.txt\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "len train: 74882; len dev: 11550; len test: 28842\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"len train: {}; len dev: {}; len test: {}\".format(len(train), len(dev), len(test)))\n",
+    "mean_train = np.mean(x_train, axis = 0, keepdims = True)\n",
+    "mean_dev = np.mean(x_dev, axis = 0, keepdims = True)\n",
+    "mean_test = np.mean(x_test, axis = 0, keepdims = True)\n",
     "\n",
-    "# word2vec_bios, _, _ = load_word_vectors(\"vecs.vocab.bios.txt\")"
+    "#X_train -= mean_train\n",
+    "#X_dev -= mean_dev\n",
+    "#X_test -= mean_test"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -397,9 +418,9 @@
    ],
    "source": [
     "print(\"len train: {}; len dev: {}; len test: {}\".format(len(train), len(dev), len(test)))\n",
-    "mean_train = np.mean(X_train, axis = 0, keepdims = True)\n",
-    "mean_dev = np.mean(X_dev, axis = 0, keepdims = True)\n",
-    "mean_test = np.mean(X_test, axis = 0, keepdims = True)\n",
+    "mean_train = np.mean(x_train, axis = 0, keepdims = True)\n",
+    "mean_dev = np.mean(x_dev, axis = 0, keepdims = True)\n",
+    "mean_test = np.mean(x_test, axis = 0, keepdims = True)\n",
     "\n",
     "#X_train -= mean_train\n",
     "#X_dev -= mean_dev\n",
@@ -431,23 +452,17 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "max_iter reached after 17 seconds\n",
-      "time: 17.3991961479187\n"
+      "max_iter reached after 10 seconds\n",
+      "time: 10.01023244857788\n",
+      "0.7555844155844156\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "[Parallel(n_jobs=64)]: Done   1 tasks      | elapsed:   17.3s\n",
-      "[Parallel(n_jobs=64)]: Done   1 out of   1 | elapsed:   17.3s finished\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.7554112554112554\n"
+      "[Parallel(n_jobs=64)]: Done   1 tasks      | elapsed:    9.9s\n",
+      "[Parallel(n_jobs=64)]: Done   1 out of   1 | elapsed:    9.9s finished\n"
      ]
     }
    ],
@@ -462,10 +477,10 @@
     "#clf = LinearSVC(max_iter = 50) #LogisticRegression()\n",
     "\n",
     "start = time.time()\n",
-    "idx = np.random.rand(X_train.shape[0]) < 1.0\n",
-    "clf.fit(X_train[idx], Y_train[idx])\n",
+    "idx = np.random.rand(x_train.shape[0]) < 1.0\n",
+    "clf.fit(x_train[idx], y_train[idx])\n",
     "print(\"time: {}\".format(time.time() - start))\n",
-    "print(clf.score(X_dev, Y_dev))\n",
+    "print(clf.score(x_dev, y_dev))\n",
     "#print(clf.score(X_test, Y_test))\n",
     "clf_original = copy.deepcopy(clf)"
    ]
@@ -479,12 +494,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "0.7553914430344636\n"
+      "0.7557381596283198\n"
      ]
     }
    ],
    "source": [
-    "print(clf.score(X_test, Y_test))"
+    "print(clf.score(x_test, y_test))"
    ]
   },
   {
@@ -496,182 +511,56 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "iteration: 149, accuracy: 0.6567164179104478: 100%|█████████████████████████████████████████████████████████████████████████████████████| 150/150 [05:30<00:00,  2.20s/it]\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "time: 330.5958001613617\n"
-     ]
-    }
-   ],
-   "source": [
-    "def get_projection_matrix(num_clfs, X_train, Y_train, X_dev, Y_dev, Y_train_task, Y_dev_task, dim, all_data_prob, by_class = False):\n",
-    "\n",
-    "    is_autoregressive = True\n",
-    "    min_acc = 0.\n",
-    "    dim = 300\n",
-    "    n = num_clfs\n",
-    "    random_subset = 1\n",
-    "    start = time.time()\n",
-    "    TYPE= \"svm\"\n",
-    "    penalty = \"l2\"\n",
-    "    MLP = False\n",
-    "    \n",
-    "    if MLP:\n",
-    "        x_train_gender = np.matmul(X_train, clf.coefs_[0]) + clf.intercepts_[0]\n",
-    "        x_dev_gender = np.matmul(X_dev, clf.coefs_[0]) + clf.intercepts_[0]\n",
-    "    else:\n",
-    "        x_train_gender = X_train.copy()\n",
-    "        x_dev_gender = X_dev.copy()\n",
-    "        \n",
-    "    \n",
-    "    if TYPE == \"sgd\":\n",
-    "        print(\"using sgd\")\n",
-    "        gender_clf = SGDClassifier\n",
-    "        params = {'alpha': 0.01, 'penalty': penalty, 'loss': 'hinge', 'fit_intercept': True, 'class_weight': \"balanced\", 'n_jobs': 16}\n",
-    "    elif TYPE == \"svm\":\n",
-    "        gender_clf = LinearSVC\n",
-    "        params = {'fit_intercept': True, 'C': 0.3, 'class_weight': None, \"dual\": False}\n",
-    "    elif TYPE == \"perceptron\":\n",
-    "        gender_clf = Perceptron\n",
-    "        params = {'max_iter': 1000, 'fit_intercept': True, 'class_weight': None}\n",
-    "    elif TYPE == \"logistic\":\n",
-    "        gender_clf = LogisticRegression\n",
-    "        params = {}\n",
-    "        \n",
-    "    result = debias.get_debiasing_projection(gender_clf, params, n, dim, is_autoregressive, min_acc,\n",
-    "                                              x_train_gender, Y_train, x_dev_gender, Y_dev,\n",
-    "                                       Y_train_main=Y_train_task, Y_dev_main=Y_dev_task, \n",
-    "                                        by_class = by_class)\n",
-    "    print(\"time: {}\".format(time.time() - start))\n",
-    "    return result\n",
-    "\n",
-    "# was c=0.15, num_clfs=130\n",
-    "num_clfs = 150\n",
-    "Y_dev_gender = np.array([g2i[d[\"g\"]] for d in dev])\n",
-    "Y_test_gender = np.array([g2i[d[\"g\"]] for d in test])\n",
-    "Y_train_gender = np.array([g2i[d[\"g\"]] for d in train])\n",
-    "P, rowspace_projs, Ws = get_projection_matrix(num_clfs, X_train, Y_train_gender, X_dev, Y_dev_gender, Y_train, Y_dev, 300, 0.0, by_class= True)\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# np.savez(\"P_FastText.npz\", P = P, rowspace_projs = rowspace_projs, Ws = Ws)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# with open(\"P.bios.fasttext.{}.svm.pickle\".format(num_clfs), \"wb\") as f:\n",
-    "    \n",
-    "#     pickle.dump(P, f)\n",
-    "    \n",
-    "# np.linalg.norm(Ws[-1])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "# Ws[-50].dot(Ws[0].T)"
+    "y_dev_gender = np.array([g2i[d[\"g\"]] for d in dev])\n",
+    "y_test_gender = np.array([g2i[d[\"g\"]] for d in test])\n",
+    "y_train_gender = np.array([g2i[d[\"g\"]] for d in train])\n",
+    "y_train_gender_2d = np.asarray([y_train_gender, - y_train_gender + 1]).T"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=64)]: Using backend ThreadingBackend with 64 concurrent workers.\n"
-     ]
-    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "max_iter reached after 4 seconds\n"
+      "u has shape (300, 300), s has shape (2,), vh has shape (2, 2)\n"
      ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=64)]: Done   1 tasks      | elapsed:    4.2s\n",
-      "[Parallel(n_jobs=64)]: Done   1 out of   1 | elapsed:    4.2s finished\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "0.5925541125541125"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
     }
    ],
+   "source": [
+    "A = np.dot(x_train.T, y_train_gender_2d) / x_train.shape[0]\n",
+    "u, s, vh = np.linalg.svd(A, full_matrices=True)\n",
+    "print(f\"u has shape {u.shape}, s has shape {s.shape}, vh has shape {vh.shape}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
    "source": [
     "gender_clf = LogisticRegression(warm_start = True, penalty = 'l2',\n",
     "                         solver = \"sag\", multi_class = 'multinomial', fit_intercept = True,\n",
     "                         verbose = 10, max_iter = 7, n_jobs = 64, random_state = 1)\n",
     "\n",
-    "gender_clf.fit(P.dot(X_train.T).T, Y_train_gender)\n",
-    "gender_clf.score(P.dot(X_dev.T).T, Y_dev_gender)"
+    "gender_clf.fit(debiased_x_train, y_train_gender)\n",
+    "gender_clf.score(debiased_x_dev, y_dev_gender)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.5894741059266579"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
-    "gender_clf.score(P.dot(X_train.T).T, Y_train_gender)"
+    "gender_clf.score(debiased_x_train, y_train_gender)"
    ]
   },
   {
@@ -683,86 +572,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=64)]: Using backend ThreadingBackend with 64 concurrent workers.\n"
-     ]
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "max_iter reached after 27 seconds\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "[Parallel(n_jobs=64)]: Done   1 tasks      | elapsed:   26.8s\n",
-      "[Parallel(n_jobs=64)]: Done   1 out of   1 | elapsed:   26.8s finished\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "LogisticRegression(max_iter=6, multi_class='multinomial', n_jobs=64,\n",
-       "                   random_state=0, solver='sag', verbose=10, warm_start=True)"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
+    "\n",
     "clf = LogisticRegression(warm_start = True, penalty = 'l2',\n",
     "                         solver = \"sag\", multi_class = 'multinomial', fit_intercept = True,\n",
     "                         verbose = 10, max_iter = 6, n_jobs = 64, random_state = 0, class_weight = None)\n",
     "\n",
-    "clf.fit(P.dot(X_train.T).T, Y_train)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train accuracy 0.7203466787746053\n",
-      "\n",
-      "Test accuracy 0.7149989598502184\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(f\"Train accuracy {clf.score(P.dot(X_train.T).T, Y_train)}\\n\")\n",
-    "print(f\"Test accuracy {clf.score((P.dot(X_test.T)).T, Y_test)}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# clf = LogisticRegression(warm_start = True, penalty = 'l2',\n",
-    "#                          solver = \"sag\", multi_class = 'multinomial', fit_intercept = True,\n",
-    "#                          verbose = 10, max_iter = 3, n_jobs = 64, random_state = 1)\n",
-    "# clf.fit(X_train, Y_train)\n",
+    "clf.fit(debiased_x_train, y_train)\n",
     "\n",
-    "# print(f\"Biased Train accuracy {clf.score(X_train, Y_train)}\")\n",
-    "# print(f\"Biased Train accuracy {clf.score(X_test, Y_test)}\")\n",
-    "# print(f\"Train accuracy {clf.score(P.dot(X_train.T).T, Y_train)}\")\n",
-    "# print(f\"Test accuracy {clf.score((P.dot(X_test.T)).T, Y_test)}\")"
+    "print(clf.score(debiased_x_test, y_test))"
    ]
   },
   {
@@ -774,7 +595,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -904,7 +725,51 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "removal = 1\n",
+    "u_r = u[:, removal:]\n",
+    "proj = u_r @ u_r.T\n",
+    "P = proj\n",
+    "\n",
+    "debiased_x_train = P.dot(x_train.T).T\n",
+    "debiased_x_dev = P.dot(x_dev.T).T\n",
+    "debiased_x_test = P.dot(x_test.T).T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
    "metadata": {
     "scrolled": true
    },
@@ -913,12 +778,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation: 0.8229158334674403; p-value: 7.657770186244778e-08\n"
+      "Correlation: 0.8238598514929694; p-value: 7.184529134104979e-08\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -932,12 +797,12 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Correlation: 0.4625654647200439; p-value: 0.015122273151764936\n"
+      "Correlation: 0.7218103837718111; p-value: 1.454744766070305e-05\n"
      ]
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -951,21 +816,21 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "rms-diff before: 0.20125138721368724; rms-diff after: 0.09203048936545488\n"
+      "rms-diff before: 0.20243245189797604; rms-diff after: 0.19520309695779406\n"
      ]
     }
    ],
    "source": [
-    "y_pred_before = clf_original.predict(X_test)\n",
+    "y_pred_before = clf_original.predict(x_test)\n",
     "test_gender = [d[\"g\"] for d in test]\n",
     "dev_gender = [d[\"g\"] for d in dev]\n",
     "train_gender = [d[\"g\"] for d in train]\n",
     "\n",
-    "tprs_before, tprs_change_before, mean_ratio_before = get_TPR(y_pred_before, Y_test, p2i, i2p, test_gender)\n",
+    "tprs_before, tprs_change_before, mean_ratio_before = get_TPR(y_pred_before, y_test, p2i, i2p, test_gender)\n",
     "similarity_vs_tpr(tprs_change_before, word2vec, \"before\", \"TPR\", prof2fem)\n",
     "#y_pred_after = clf.predict(X_test.dot(P))\n",
-    "y_pred_after = clf.predict(X_test.dot(P))\n",
-    "tprs, tprs_change_after, mean_ratio_after = get_TPR(y_pred_after, Y_test, p2i, i2p, test_gender)\n",
+    "y_pred_after = clf.predict(debiased_x_test)\n",
+    "tprs, tprs_change_after, mean_ratio_after = get_TPR(y_pred_after, y_test, p2i, i2p, test_gender)\n",
     "similarity_vs_tpr(tprs_change_after, word2vec, \"after\", \"TPR\", prof2fem)\n",
     "\n",
     "change_vals_before = np.array(list((tprs_change_before.values())))\n",
@@ -976,74 +841,86 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=64)]: Using backend ThreadingBackend with 64 concurrent workers.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "max_iter reached after 14 seconds\n",
+      "0.7593440122044242\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=64)]: Done   1 tasks      | elapsed:   14.2s\n",
+      "[Parallel(n_jobs=64)]: Done   1 out of   1 | elapsed:   14.2s finished\n"
+     ]
+    }
+   ],
    "source": [
-    "def tsne_by_gender(vecs, labels, title, words = None):\n",
+    "clf = LogisticRegression(warm_start = True, penalty = 'l2',\n",
+    "                         solver = \"sag\", multi_class = 'multinomial', fit_intercept = True,\n",
+    "                         verbose = 10, max_iter = 6, n_jobs = 64, random_state = 0, class_weight = None)\n",
     "\n",
-    "  tsne = TSNE(n_components=2, random_state=0)\n",
-    "  vecs_2d = tsne.fit_transform(vecs)\n",
-    "  num_labels = len(set(labels.tolist()))\n",
+    "clf.fit(debiased_x_train, y_train)\n",
     "\n",
-    "  names = [\"class {}\".format(i) for i in range(num_labels)]\n",
-    "  plt.figure(figsize=(6, 5))\n",
-    "  colors = 'r', 'b', 'orange'\n",
-    "  for i, c, label in zip(set(labels.tolist()), colors, names):\n",
-    "    print(len(vecs_2d[labels == i, 0]))\n",
-    "    plt.scatter(vecs_2d[labels == i, 0], vecs_2d[labels == i, 1], c=c,\n",
-    "                label=label, alpha = 0.6)\n",
-    "  plt.legend(loc = \"upper right\")\n",
-    "  plt.title(title)\n",
-    "  \n",
-    "  if words is not None:\n",
-    "        k = 60\n",
-    "        for i in range(k):\n",
-    "            \n",
-    "            j = np.random.choice(range(len(words)))\n",
-    "            label = labels[i]\n",
-    "            w = words[j]\n",
-    "            x,y = vecs_2d[i]\n",
-    "            plt.annotate(w , (x,y), size = 10, color = \"black\" if label == 1 else \"black\")\n",
-    "            \n",
-    "  plt.show()\n",
-    "  return vecs_2d"
+    "print(clf.score(debiased_x_test, y_test))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "prof = \"professor\"\n",
-    "idx = np.random.rand(X_dev.shape[0]) < 0.1\n",
-    "prof_idx = Y_dev == p2i[prof]\n",
-    "n = 800\n",
-    "tsne_by_gender(X_dev[prof_idx][:n], np.array(dev_gender)[prof_idx][:n], \"tsne by gender, before, {}\".format(prof))\n",
-    "tsne_by_gender((X_dev[prof_idx].dot(P))[:n], np.array(dev_gender)[prof_idx][:n], \"tsne by gender, after. {}\".format(prof))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def count_profs(data_y, i2p):\n",
-    "    d = Counter()\n",
-    "    for y in data_y:\n",
-    "        d[i2p[y]] += 1\n",
-    "    return d"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=64)]: Using backend ThreadingBackend with 64 concurrent workers.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "max_iter reached after 15 seconds\n",
+      "Biased Train accuracy 0.7716273603803318\n",
+      "Biased Test accuracy 0.7656889258719922\n",
+      "Train accuracy 0.603603002056569\n",
+      "Test accuracy 0.5986755426114694\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=64)]: Done   1 tasks      | elapsed:   14.8s\n",
+      "[Parallel(n_jobs=64)]: Done   1 out of   1 | elapsed:   14.8s finished\n"
+     ]
+    }
+   ],
    "source": [
-    "count_profs(Y_train, i2p)"
+    "clf = LogisticRegression(warm_start = True, penalty = 'l2',\n",
+    "                         solver = \"sag\", multi_class = 'multinomial', fit_intercept = True,\n",
+    "                         verbose = 10, max_iter = 6, n_jobs = 64, random_state = 0, class_weight = None)\n",
+    "\n",
+    "clf.fit(x_train, y_train)\n",
+    "\n",
+    "print(f\"Biased Train accuracy {clf.score(x_train, y_train)}\")\n",
+    "print(f\"Biased Test accuracy {clf.score(x_test, y_test)}\")\n",
+    "print(f\"Train accuracy {clf.score(debiased_x_train, y_train)}\")\n",
+    "print(f\"Test accuracy {clf.score(debiased_x_test, y_test)}\")"
    ]
   },
   {
@@ -1051,147 +928,176 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "count_profs(Y_dev, i2p)"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
-    "p0 = all_Ps[0]\n",
-    "p1 = all_Ps[1]\n",
-    "p2 = all_Ps[2]"
+    "removal = 2\n",
+    "u_r = u[:, removal:]\n",
+    "proj = u_r @ u_r.T\n",
+    "P = proj\n",
+    "\n",
+    "debiased_x_train = P.dot(x_train.T).T\n",
+    "debiased_x_dev = P.dot(x_dev.T).T\n",
+    "debiased_x_test = P.dot(x_test.T).T"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
-     "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-34-bb1006037f2f>, line 1)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-34-bb1006037f2f>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    p2.dot(p2.T)<\u001b[0m\n\u001b[0m                 ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Correlation: 0.8238598514929694; p-value: 7.184529134104979e-08\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Correlation: 0.6193537761733974; p-value: 0.00044087774258949516\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "rms-diff before: 0.20243245189797604; rms-diff after: 0.11733802587024243\n"
      ]
     }
    ],
    "source": [
-    "p2.dot(p2.T)<"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "p0 = all_Ps[0]\n",
-    "p1 = all_Ps[1]\n",
-    "p2 = all_Ps[2]\n",
+    "y_pred_before = clf_original.predict(x_test)\n",
+    "test_gender = [d[\"g\"] for d in test]\n",
+    "dev_gender = [d[\"g\"] for d in dev]\n",
+    "train_gender = [d[\"g\"] for d in train]\n",
+    "\n",
+    "tprs_before, tprs_change_before, mean_ratio_before = get_TPR(y_pred_before, y_test, p2i, i2p, test_gender)\n",
+    "similarity_vs_tpr(tprs_change_before, word2vec, \"before\", \"TPR\", prof2fem)\n",
+    "#y_pred_after = clf.predict(X_test.dot(P))\n",
+    "y_pred_after = clf.predict(debiased_x_test)\n",
+    "tprs, tprs_change_after, mean_ratio_after = get_TPR(y_pred_after, y_test, p2i, i2p, test_gender)\n",
+    "similarity_vs_tpr(tprs_change_after, word2vec, \"after\", \"TPR\", prof2fem)\n",
+    "\n",
+    "change_vals_before = np.array(list((tprs_change_before.values())))\n",
+    "change_vals_after = np.array(list(tprs_change_after.values()))\n",
     "\n",
-    "p2.dot(p2.T)"
+    "print(\"rms-diff before: {}; rms-diff after: {}\".format(rms_diff(change_vals_before), rms_diff(change_vals_after)))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 24,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_common_words(data: List[dict], word2vec_model):\n",
-    "    \n",
-    "    words_counter = Counter()\n",
-    "    vecs = []\n",
-    "    all_words = []\n",
-    "    \n",
-    "    \n",
-    "    for entry in tqdm.tqdm(data, total = len(data)):\n",
-    "        \n",
-    "        y = p2i[entry[\"p\"]]\n",
-    "        words = entry[\"hard_text\"].split(\" \")\n",
-    "        all_words.extend(words)\n",
-    "            \n",
-    "    \n",
-    "    words_counter = Counter(all_words)\n",
-    "    common_words = [w for w in words_counter if words_counter[w] > 10 and (w in word2vec_model)]\n",
-    "    common_vecs = [word2vec_model[w] for w in common_words]\n",
-    "    \n",
-    "    return common_words, common_vecs\n",
-    "        \n",
-    "        "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=64)]: Using backend ThreadingBackend with 64 concurrent workers.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "max_iter reached after 14 seconds\n",
+      "0.7554607863532349\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=64)]: Done   1 tasks      | elapsed:   14.2s\n",
+      "[Parallel(n_jobs=64)]: Done   1 out of   1 | elapsed:   14.2s finished\n"
+     ]
+    }
+   ],
    "source": [
-    "words, vecs = get_common_words(train,word2vec)\n",
+    "clf = LogisticRegression(warm_start = True, penalty = 'l2',\n",
+    "                         solver = \"sag\", multi_class = 'multinomial', fit_intercept = True,\n",
+    "                         verbose = 10, max_iter = 6, n_jobs = 64, random_state = 0, class_weight = None)\n",
     "\n",
-    "vecs_normed = vecs / np.linalg.norm(vecs, keepdims = True)\n",
-    "ws_normed = [w/np.linalg.norm(w) for w in all_ws]\n",
-    "k = 1000\n",
-    "groups, labels = [], []\n",
+    "clf.fit(debiased_x_train, y_train)\n",
     "\n",
-    "for i,w in enumerate(ws_normed):\n",
-    "    print(\"INLP ITERATION: {}\".format(i))\n",
-    "    sims_i = w.dot(vecs_normed.T).squeeze(0)\n",
-    "    zipped = zip(words, vecs_normed, sims_i)\n",
-    "    zipped = sorted(list(zipped), key = lambda tuple: -abs(tuple[2]))\n",
-    "    ws,vs, sims = list(zip(*zipped))\n",
-    "    print(ws[:k])\n",
-    "    groups.append(vs[:k])\n",
-    "    labels.append(i)\n",
-    "    print(\"------------------------------------------------------------------\")"
+    "print(clf.score(debiased_x_test, y_test))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 22,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=64)]: Using backend ThreadingBackend with 64 concurrent workers.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "max_iter reached after 14 seconds\n",
+      "Biased Train accuracy 0.7716273603803318\n",
+      "Biased Test accuracy 0.7656889258719922\n",
+      "Train accuracy 0.6091183461980182\n",
+      "Test accuracy 0.60515914291658\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=64)]: Done   1 tasks      | elapsed:   14.8s\n",
+      "[Parallel(n_jobs=64)]: Done   1 out of   1 | elapsed:   14.8s finished\n"
+     ]
+    }
+   ],
    "source": [
-    "def visualize_gender_subspace(vecs, labels):\n",
-    "    \n",
-    "        N = len(labels)\n",
-    "        \n",
-    "        all_vecs = []\n",
-    "        all_labels = []\n",
-    "        \n",
-    "        for vecs_list,l in zip(vecs, labels):\n",
-    "            \n",
-    "            all_labels.append(np.ones(len(vecs_list)) * l)\n",
-    "        \n",
-    "        all_vecs_np = np.concatenate(vecs, axis = 0)\n",
-    "        all_labels_np = np.concatenate(all_labels, axis = 0)\n",
-    "        tsne = TSNE(n_components=2, random_state=0)\n",
-    "        vecs_2d = tsne.fit_transform(all_vecs_np)\n",
-    "    \n",
-    "        fig, ax = plt.subplots()\n",
-    "        # define the colormap\n",
-    "        cmap = plt.cm.jet\n",
-    "        # extract all colors from the .jet map\n",
-    "        cmaplist = [cmap(i) for i in range(cmap.N)]\n",
-    "        # create the new map\n",
-    "        cmap = cmap.from_list('Custom cmap', cmaplist, cmap.N)\n",
-    "        # define the bins and normalize\n",
-    "        bounds = np.linspace(0, N, N + 1)\n",
-    "        norm = matplotlib.colors.BoundaryNorm(bounds, cmap.N)\n",
-    "        print(\"here\")\n",
-    "        print(all_labels_np.shape)\n",
-    "        scat = ax.scatter(vecs_2d[:,0], vecs_2d[:,1], c=all_labels_np, cmap=cmap, norm=norm, alpha=0.15)\n",
-    "        cb = plt.colorbar(scat, spacing='proportional')#, ticks=bounds)\n",
-    "        cb.set_label(\"INLP iteration number\")\n",
-    "        plt.savefig(\"INLP progress\", dpi = 600)\n",
-    "        plt.show()"
+    "clf = LogisticRegression(warm_start = True, penalty = 'l2',\n",
+    "                         solver = \"sag\", multi_class = 'multinomial', fit_intercept = True,\n",
+    "                         verbose = 10, max_iter = 6, n_jobs = 64, random_state = 0, class_weight = None)\n",
+    "\n",
+    "clf.fit(x_train, y_train)\n",
+    "\n",
+    "print(f\"Biased Train accuracy {clf.score(x_train, y_train)}\")\n",
+    "print(f\"Biased Test accuracy {clf.score(x_test, y_test)}\")\n",
+    "print(f\"Train accuracy {clf.score(debiased_x_train, y_train)}\")\n",
+    "print(f\"Test accuracy {clf.score(debiased_x_test, y_test)}\")"
    ]
   },
   {
@@ -1199,683 +1105,176 @@
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "#visualize_gender_subspace(groups, labels)"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
-    "word2vec"
+    "removal = 20\n",
+    "u_r = u[:, removal:]\n",
+    "proj = u_r @ u_r.T\n",
+    "P = proj\n",
+    "\n",
+    "debiased_x_train = P.dot(x_train.T).T\n",
+    "debiased_x_dev = P.dot(x_dev.T).T\n",
+    "debiased_x_test = P.dot(x_test.T).T"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 362,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "gender direction 0.\n",
-      " most_similar male: ('himself', 'his', 'he', 'Fatherhood', 'His', 'vExpert', 'Freemasonry', 'He', 'Topman', 'fatherhood', 'Grails', 'homoerotic', 'everyman', 'FanDuel', 'Masonry', 'VMworld', 'varicocele', '.His', 'journeyman', 'APEX')\n",
-      "; most_similar female: ('herself', 'she', 'her', 'She', 'Her', 'hers', 'Carolyn', 'Colleen', 'Susan', 'Diane', '.She', 'Ms.', 'Kathleen', 'Chairwoman', 'Kathryn', 'Laura', 'Kathy', 'Alyssa', 'Karen', 'Tricia')\n",
-      "=====================================\n",
-      "gender direction 1.\n",
-      " most_similar male: ('shortwave', 'E.F.', 'Henceforth', 'R.T.', 'Rattling', 'CMG', 'SGM', 'GMR', 'microstructure', 'faithfully', 'periodically', 'Wyner', 'SPT', 'impulse', 'W.R.', 'henceforth', 'J.E.', 'mesoscale', 'maestro', 'urges')\n",
-      "; most_similar female: ('Childbirth', 'Yoga', 'Hatha', 'Midwifery', 'Pregnancy', 'pregnancy', 'yoga', 'Maternity', 'yoginis', 'Childbearing', 'Asana', 'YOGA', 'childbirth', 'midwifery', 'YogaWorks', 'Intimates', 'Jivamukti', 'AcroYoga', 'motherhood', 'maternity')\n",
-      "=====================================\n",
-      "gender direction 2.\n",
-      " most_similar male: ('2410', 'Ichimura', 'Br', 'Bienkowski', 'Hedden', 'Fr', 'Furuya', 'Tsutsui', 'Uchiyama', 'Streu', 'Tirone', 'Eisch', 'Desper', 'Kawamoto', 'Oberdick', 'Sma', 'Demler', 'Bollinger', 'Dubos', 'Hepato')\n",
-      "; most_similar female: ('Feminists', 'Whoopi', 'Herstory', 'feminists', 'Hillary', 'Latinos', 'dressage', 'Hispanics', 'Skechers', 'Footwear', 'Portia', 'blacks', 'Africans', 'gays', 'shoe', 'victimization', 'transgenders', 'Women', 'victimisation', 'Nkechi')\n",
-      "=====================================\n",
-      "gender direction 3.\n",
-      " most_similar male: ('talking', 'talk', 'talked', 'saying', 'engineer', 'Gordon', 'electromagnetics', 'secondly', 'stressing', 'arguing', 'CTO', 'Electric', 'loner', 'said', 'speaking', 'explaining', 'Electromagnetics', 'electromagnetism', 'Aldous', 'Swendsen')\n",
-      "; most_similar female: ('Lentils', 'Kayte', 'PopSugar', 'Blessings', 'endowments', 'heirlooms', 'ornaments', 'VAL', 'KYM', 'Scarves', 'Peppers', 'Halftime', 'Culinary', '//', 'Peas', 'Aashirwad', 'bustlines', 'Portfolios', 'Hers', 'ornament')\n",
-      "=====================================\n",
-      "gender direction 4.\n",
-      " most_similar male: ('RSA', 'O', 'Mantle', 'softly', 'unattended', 'hushed', 'alone', 'WSC', 'undisturbed', 'Reverie', 'Lament', 'serenely', 'preindustrial', 'slower', 'multiplied', 'slowly', 'Goodnight', 'unguarded', 'Rexroth', 'Alice')\n",
-      "; most_similar female: ('Pacitti', 'Athletica', 'bronchitis', 'yoga', 'myofascial', 'Paltrow', 'Chloë', 'Kurti', 'Drumline', 'Corburn', 'Mattocks', 'Sweatpants', 'Adames', 'Kesha', 'shetty', 'Vitalia', 'Antimicrobials', 'Activism', 'Fighter', '9244')\n",
-      "=====================================\n",
-      "gender direction 5.\n",
-      " most_similar male: ('Uwe', 'Hoctor', '−', 'Ralf', 'Sadly', 'Susanne', 'Unfortunately', 'M3', '64', 'Rostock', '7154', 'Bernd', 'Jochen', '4029', 'Günter', '6914', 'Torben', 'Jürgen', 'Mickey', 'drivers')\n",
-      "; most_similar female: ('CPMC', 'NEDO', 'Polyclinics', 'valorem', 'propos', 'telepresence', 'HBS', 'Hermeneutics', 'clinic-', 'Vietnam', 'tug', 'FDI', 'Manchuria', 'Sarang', 'adjudged', 'Shreveport', 'ICBC', 'valuation', 'Studies-', 'antimonopoly')\n",
-      "=====================================\n",
-      "gender direction 6.\n",
-      " most_similar male: ('Davidians', 'Davidian', 'ensuring', 'cans', 'OD', 'persecuted', 'insuring', 'TOD', 'VO', 'knob', 'vowel', 'ameliorating', 'Michalak', 'Lip', 'ignition', 'Pedal', 'EIN', 'Tung', 'correct', 'assuring')\n",
-      "; most_similar female: ('php', 'ruby', 'Embedded', 'surf', 'RPC', 'webs', 'defences', 'Hypervisor', 'Guilt', 'TCL', 'marvel', 'mommy', 'sentinel', 'ICD', 'backend', 'daydream', 'PHP', 'antivirus', 'Moorestown', 'domesticity')\n",
-      "=====================================\n",
-      "gender direction 7.\n",
-      " most_similar male: ('whilst', 'ITS', 'ISSUES', 'FRUIT', 'Jurisdictions', 'size', 'oppressions', 'DIFFERENT', 'Heresies', 'Histories', 'inequality', 'Essays', 'Patriarchy', 'parcel', 'Myths', 'circumnavigating', 'Lavinia', 'Unpacking', 'Wherein', 'fleet')\n",
-      "; most_similar female: ('Minneapolis', 'Wayzata', 'Lucado', 'Bemidji', 'Metrodome', 'HealthPartners', 'AHCA', 'Politte', 'Chanhassen', 'Novocaine', 'Wichita', 'Robbinsdale', 'KC', 'Lindstrom', 'Hazanavicius', 'SLP', 'Owatonna', 'Minn.', 'audiologists', 'otolaryngologist')\n",
-      "=====================================\n",
-      "gender direction 8.\n",
-      " most_similar male: ('Masur', 'Clemens', 'bark', 'Gibbon', 'rowers', 'commands', 'mute', 'bin', 'pauses', 'Olberding', 'rower', 'wood', 'Holmgren', 'CHORD', 'chord', 'rowing', 'WASH', 'geese', 'Sacchi', 'SFS')\n",
-      "; most_similar female: ('Scoreland', '34DD', 'Striptease', 'Curvy', 'Adalynn', 'Alona', 'Brazzers', 'Voluptuous', 'Glamorous', 'Busty', 'Sexy', 'Redhead', 'Heels', 'Boobs', 'bombshell', 'Optometrists', '36D', 'Blonde', 'Disclosure', 'Brunette')\n",
-      "=====================================\n",
-      "gender direction 9.\n",
-      " most_similar male: ('92', '82', '84', '86', '91', '83', '76', '66', '78', '79', '94', '87', '68', '89', '88', '74', '63', '93', '71', '67')\n",
-      "; most_similar female: ('OpenID', 'ITRC', 'Malware', 'Symantec', 'OSCON', 'committer', 'Xeon', 'Undertow', 'Greenplum', 'cybercrime', 'Itanium', 'Solr', 'Zeitgeist', 'PostgreSQL', 'filmography', 'committers', 'Sideshow', 'URLs', 'CERIAS', 'MySpace')\n",
-      "=====================================\n",
-      "gender direction 10.\n",
-      " most_similar male: ('Fog', 'E-40', 'DMZ', 'Rade', 'Shaper', 'Philips', 'Profoto', 'fog', 'Sharp', 'grinder', 'Elster', 'sharpness', 'magnifies', 'Ree', 'Quit', 'Mifune', 'macros', 'Bogie', 'Doc', 'Kirk')\n",
-      "; most_similar female: ('RBD', 'POMCO', 'Storytime', 'caregivers', 'Associati', 'Builders', 'preschool', 'Populus', 'GPR', 'schizotypy', 'completion', 'Nursery', 'Constructions', 'nursery', 'Pawsitively', 'Debnam', 'Paez', 'Aadya', 'BCMG', 'Tlc')\n",
-      "=====================================\n",
-      "gender direction 11.\n",
-      " most_similar male: ('Bug', 'gamma', 'Scissorhands', 'Redmond', 'X-', 'D.from', 'America-', 'Gizmo', 'hyperactive', 'Fauvist', 'tyrosine', 'messes', 'scolded', 'Gamma', 'Hulk', 'Georgia-', 'Ladybug', 'Merah', 'bakes', 'CNET')\n",
-      "; most_similar female: ('polyamory', 'intersectional', 'PrEP', 'ECHS', 'ODU', 'Equality', 'endourology', 'Archivaria', '7x7', 'Mutuality', 'photovoice', 'bisexuality', 'GBV', 'epidemiology', 'SFA', 'fabricating', 'PhotoVoice', 'GIPA', 'EdM', 'JAAPA')\n",
-      "=====================================\n",
-      "gender direction 12.\n",
-      " most_similar male: ('Schumpeter', 'thought', 'GST', 'occurred', 'Krakatoa', 'remark', 'Question', 'INSET', 'encountered', 'What', 'HST', 'abolished', 'Pentecost', 'remembered', 'Bathtub', 'wondered', 'Remarks', 'assumed', 'Sugata', 'Exactly')\n",
-      "; most_similar female: ('Stalley', 'Hartgrove', 'Ravens', 'ravens', 'falcons', 'Exempla', 'Hemby', 'caseworker', 'consignment', 'Consignment', 'Nightjar', 'Gourds', 'MCSA', 'staking', 'Waxwing', 'Stonebridge', 'Skins', 'BRIT', 'Coldplay', 'Patience')\n",
-      "=====================================\n",
-      "gender direction 13.\n",
-      " most_similar male: ('Largent', 'Ensminger', 'Whorley', 'Kouns', 'Gooch', 'Mcquaid', 'uniforms', 'Hass', 'jerseys', 'cunt', 'glove', 'McElfresh', 'Narus', 'Moby', 'SOE', 'Pihos', 'Musil', 'sigmoidoscopy', \"'em\", 'cloth')\n",
-      "; most_similar female: ('bachelors', 'newlyweds', 'Spousal', 'Brides.com', 'Kiara', 'build', 'UPR', 'couples', 'Meera', 'bachelor', 'Couples', 'Pi', 'variable', 'Camila', 'Essence.com', '⠀', 'Lifespan', 'regenerating', 'healthily', 'DIY')\n",
-      "=====================================\n",
-      "gender direction 14.\n",
-      " most_similar male: ('mid-80s', 'EPD', 'Fuji', 'definition', 'podium', 'crews', 'Corning', 'Schindler', 'glossary', 'stairs', 'Libbey', 'Dow', 'mid-1980', 'Ballinger', 'Schwing', 'Near', 'Gorge', 'USGS', 'quarry', 'cetera')\n",
-      "; most_similar female: ('untrusted', 'Abyei', 'MLB', 'Antinomianism', 'WeddingWire', 'ligament', 'Scrappy', 'cupcake', 'Vodou', 'Broncos', 'Somaliland', 'Premal', 'LLM', 'CCM', 'PURCHASE', 'Criminology', 'Yankees', 'Him', 'Ruminate', 'Peacebuilding')\n",
-      "=====================================\n",
-      "gender direction 15.\n",
-      " most_similar male: ('flaw', 'HIDE', 'closeness', 'Owosso', 'Him', 'Lint', '\\u200b', 'predictability', 'Bonifas', 'underwear', 'Beaufort', 'THEN', 'HE', 'Allegan', 'INSTEAD', 'Audit', 'SHE', 'embolism', 'Petoskey', 'COST')\n",
-      "; most_similar female: ('Mixcloud', 'XLR8R', 'soundcloud', 'dietetic', 'Vocalo', 'ill', 'Southasian', 'reportage', 'undertaking', 'Bookslut', 'modding', 'earthbound', 'hemisphere', 'PopMatters', 'JVC', 'KCRW', 'videogaming', 'febrile', 'Kerrang', 'Populous')\n",
-      "=====================================\n"
+      "Correlation: 0.8238598514929694; p-value: 7.184529134104979e-08\n"
      ]
     },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "gender direction 16.\n",
-      " most_similar male: ('Azab', 'Assi', 'Headliners', 'rammed', 'main', 'Loconte', 'AFP', 'Faux', 'Antiwar.com', 'millimeter', 'Zig', 'expounds', 'Yad', 'animating', 'MERS', 'Propagation', 'rails', 'propagation', 'extrema', 'collapsed')\n",
-      "; most_similar female: ('Nest', 'poems', 'Haiku', 'Poems', 'poem', 'POEM', 'solitude', 'PANK', 'poet', 'LARK', 'Reveries', 'sweetheart', 'elegies', 'moniker', 'Hawthornden', 'Peranakan', 'sobriquet', 'Ramanujan', 'mama', 'smart')\n",
-      "=====================================\n",
-      "gender direction 17.\n",
-      " most_similar male: ('Idiom', 'Raiders', 'Rodel', 'Swalwell', 'Raka', 'McNair', 'Spindrift', 'Mcnair', 'Vikings', 'Whiz', 'Dunmore', 'Hampton', 'OJ', 'Merriman', 'NHBA', '7011', 'Baffert', 'Tulley', 'Harlene', 'Excelsior')\n",
-      "; most_similar female: ('Lambda', 'RNAs', 'Granade', 'VDI', 'Curves', 'MIRA', 'Glamour', 'LiDAR', 'Inked', 'PLOS', 'heterosexual', 'lobe', 'carnivore', 'Ceph', 'MAV', 'sexes', 'Palimpsest', 'Complutense', 'MATLAB', 'Département')\n",
-      "=====================================\n",
-      "gender direction 18.\n",
-      " most_similar male: ('Neurofeedback', 'Ratatouille', 'pizza', 'amusement', 'XD', 'Berardinelli', 'Koontz', 'cheesy', 'Lucifer', 'neurofeedback', 'salad', 'Voted', 'Timpano', 'Drago', 'behaviorists', 'chaos', 'blaze', 'Geauga', '\\ufeff', 'Dragonfall')\n",
-      "; most_similar female: ('Marginalized', 'ADU', 'BME', '0280', 'Intimacies', '0279', '28304', 'Pasifika', '54401', 'intimacies', 'VUP', '53233', 'Marshallese', 'Identities', 'Hyphen', 'macro-', 'data', 'linkages', 'Herstory', 'rowing')\n",
-      "=====================================\n",
-      "gender direction 19.\n",
-      " most_similar male: ('Wex', 'Sherkin', 'Isley', 'Rooks', 'vultures', 'Vultures', 'Cafaro', 'externing', 'Blackfish', 'Manos', 'Gulfcoast', 'Nordlund', 'Roda', 'Buffett', 'HHC', 'Graul', 'Oddball', 'mainland', 'Healthsouth', 'Kurre')\n",
-      "; most_similar female: ('cardiomyocyte', 'cystectomy', 'Querying', 'terrified', 'frustrated', 'sympathectomy', 'contractility', 'cardiomyocytes', 'scared', 'myocytes', 'amenorrhea', 'PCOS', 'Arabe', 'dysmenorrhea', 'hypertrophy', 'EHESS', 'intimidated', 'CIBSE', 'radicalised', 'intravesical')\n",
-      "=====================================\n",
-      "gender direction 20.\n",
-      " most_similar male: ('Juraj', 'Lalande', 'Halina', 'Szent', 'Jerold', 'Keven', 'Simma', 'Luc', 'Gyorgy', 'Praha', 'Endre', 'Orlin', 'Laci', 'Floriano', 'Wery', 'Malec', 'Sonnenburg', 'Steenbergen', 'unneeded', 'Lebar')\n",
-      "; most_similar female: ('100-Mile', 'MEND', 'AFSP', 'Fairey', 'Creatives', 'PlayMakers', 'hydrogel', 'Bravery', 'Tees', 'hydrogels', 'polymer', 'Toughest', 'Activism', 'mettle', 'nonviolence', 'paddleboarding', 'militancy', 'Activists', 'activism', 'flyer')\n",
-      "=====================================\n",
-      "gender direction 21.\n",
-      " most_similar male: ('oro', 'Sumitomo', 'tution', 'immunity', 'Riken', 'Trembling', 'Cooling', 'mint', 'Below', 'Kuro', 'Mitsubishi', 'Mint', 'cooling', 'vivos', 'victor', 'Voltage', 'dispositive', 'compensation', 'Mamiya', 'orchid')\n",
-      "; most_similar female: ('Mongo', 'nesting', 'charting', 'Wayfarer', 'Saundra', 'Fb', 'Angular', 'HTML5', 'Coasters', 'Greasemonkey', 'Bhagwat', 'gestation', 'Chabon', 'Shona', 'concurrently', 'Jaswinder', 'populating', 'jQuery', 'JavaScript', 'CIDR')\n",
-      "=====================================\n",
-      "gender direction 22.\n",
-      " most_similar male: ('Impossibility', 'EMMA', 'con-', 'hus', 'Sketch', 'LIM', 'AMOS', 'in-', 'Fancy', 'the-', 'Stitch', 're-', 'REX', 'SIR', 'silico', 'tion', 'sharply', 'securely', 'sketch', 'of-')\n",
-      "; most_similar female: ('Regardless', 'K-6', 'mastery', 'cyberharassment', 'geared', 'mystique', 'fan', 'sourcebook', 'resurgence', 'melee', 'Shadowrun', 'MAcc', 'Celene', 'intrigue', 'MSLS', 'prefer', 'allure', 'Internists', 'Jaclyn', 'reignite')\n",
-      "=====================================\n",
-      "gender direction 23.\n",
-      " most_similar male: ('MoreRead', '\\u200b', 'reservations', 'consults', 'presides', 'immobilization', 'hikes', 'steps', 'Dipl', 'Hendersonville', 'walks', 'conducts', 'negotiations', 'Sandals', 'perishables', 'consultative', 'treks', 'Ca', 'Saturdays', 'weekends')\n",
-      "; most_similar female: ('youtube', 'username', 'microsoft', 'Youtube', 'censored', 'yours', 'muppet', 'flickr', 'creationists', 'machinima', 'haters', 'Orkut', 'barbie', 'alright', 'deviantART', 'deniers', 'mr', 'Diddy', 'Blogging', 'xxxx')\n",
-      "=====================================\n",
-      "gender direction 24.\n",
-      " most_similar male: ('Allegro', 'move', 'Nathans', 'Schild', 'Motta', 'typing', 'Morsani', 'VBA', 'Chromatin', 'Sparr', 'PeopleSoft', 'VB', 'Traurig', 'Nucleus', 'strings', 'handles', 'Seibel', 'IMG', 'moves', 'Weitzenhoffer')\n",
-      "; most_similar female: ('Khoon', 'interventionism', 'fanciful', 'bailouts', 'Tidepools', 'Catwalk', 'fantastical', 'Keynesian', 'empiric', 'bazaars', 'Vientiane', 'Mun', 'Spooked', 'Thuy', 'TARP', 'DSGE', 'Blithe', 'Traveller', 'Selfie', 'storybooks')\n",
-      "=====================================\n",
-      "gender direction 25.\n",
-      " most_similar male: ('Venturi', 'Orlando', 'cluttering', 'Ugo', 'scrutinized', 'McGruff', 'Nürnberg', 'Textron', 'AMG', 'Bucerius', 'loitering', 'HSV', 'DSM', 'Ricciardelli', 'Nuremberg', 'Deltona', 'Chirico', 'Bosch', 'Gotti', 'Schnee')\n",
-      "; most_similar female: ('Carnatic', 'flautist', 'erhu', 'caving', 'Rupee', 'Karnatic', 'marimba', 'quartets', '1857', 'Govinda', 'NIC', 'Bansuri', 'sitar', '8-bit', 'Selah', 'Sharath', 'Metroid', 'Saira', 'rock', 'Sibelius')\n",
-      "=====================================\n",
-      "gender direction 26.\n",
-      " most_similar male: ('Bertram', 'Bramwell', '1927', 'novelty', 'anachronism', 'foolishness', 'Cheriton', 'Lovegren', '1928', 'p', 'Fairbanks', '1908', 'inventory', 'Hirschberg', 'Blois', 'Chesterton', '1936', 'historicism', 'beehives', 'Blondel')\n",
-      "; most_similar female: ('abdominoplasty', 'Banglalink', 'Pawsitively', 'Tummy', 'PYP', 'Beaconhouse', 'NOSM', 'Anam', 'VIVO', 'Colaiste', 'Seo', 'Sprouts', 'Squeeze', 'DIEP', 'inking', 'POSTECH', 'Tuna', 'Pak', 'cholangiocarcinoma', 'Critters')\n",
-      "=====================================\n",
-      "gender direction 27.\n",
-      " most_similar male: ('stints', 'Trammell', 'presumably', 'Shoals', 'Garrigues', 'post-1980', 'Housecall', '41.2', 'orthopedist', '2009-present', 'Ambulatory', 'Emmy-', 'propelling', 'averaged', '12-time', 'dutifully', 'ESPN.com', 'tilts', 'nods', 'showrunner')\n",
-      "; most_similar female: ('food', 'childish', 'sensitization', 'greedy', 'weather', 'firework', 'dermatologic', 'rain', 'hunger', 'hungry', 'science', 'itchy', 'preparedness', 'cancer', 'bitter', 'sad', 'anticancer', 'chemical', 'rainy', 'eczema')\n",
-      "=====================================\n",
-      "gender direction 28.\n",
-      " most_similar male: ('alarmed', 'alarm', 'Convinced', 'Puzzled', 'Worried', 'postman', 'Upset', 'rung', 'clanking', 'dismay', 'shaken', 'checked', 'Initially', 'Enabled', 'sceptical', 'Undaunted', 'ringing', 'suitcases', 'suitcase', 'worried')\n",
-      "; most_similar female: ('excels', 'wanna', 'transcends', 'PL', 'laude', 'Femme', 'objectively', 'did', 'idolize', 'NEED', 'exposures', '1-on-1', 'chose', 'manages', 'gender-', 'strive', 'exists', 'CF', 'crave', 'excelling')\n",
-      "=====================================\n",
-      "gender direction 29.\n",
-      " most_similar male: ('Papierski', 'folklorist', 'ENSA', 'Wartman', 'Nogowski', 'muralist', 'WKBW', 'Kandy', 'Ngoi', 'MidAmerica', 'SPUR', 'Shereen', 'accordionist', 'Tongan', 'Raekwon', 'regionalism', 'griot', 'Castagnola', 'Dafford', 'locals')\n",
-      "; most_similar female: ('Kamchatka', 'Parenthood', 'Admit', '6560', 'Concern', 'Omsk', 'Natacha', 'Watcher', 'Novosibirsk', 'Responsibility', 'FOF', 'observed', 'România', '7733', '6339', 'stalked', 'Creation', 'Numéro', 'Males', 'Females')\n",
-      "=====================================\n",
-      "gender direction 30.\n",
-      " most_similar male: ('pic', 'mani', 'Popeye', 'hill', 'CG', 'layout', 'Charmed', 'Mcgraw', 'devour', 'eat', 'devours', 'TCM', 'Hercules', 'Thirumala', 'pics', 'Jaws', 'pickiest', 'movie', 'Hooters', 'Maxim')\n",
-      "; most_similar female: ('RCAH', 'CLIR', 'P-16', 'WEFT', 'caucuses', 'Junctures', 'EGAP', 'StoryCorps', 'ELMCIP', 'Conversations', 'Pambazuka', 'Undercurrents', 'countercultures', 'Oikocredit', 'futures', 'Futures', 'DAAD', 'AICGS', 'WOLA', 'KUOW')\n",
-      "=====================================\n",
-      "gender direction 31.\n",
-      " most_similar male: ('Maccabees', 'tank', 'crater', 'Phantoms', 'deeds', 'JNF', 'shadow', 'Avigdor', 'Bombers', 'tanks', 'spotter', 'Pravda', 'Menachem', 'Wiesel', 'Lancer', 'jets', 'Jets', 'Squadrons', 'faint', 'afford')\n",
-      "; most_similar female: ('introduces', 'welcomes', 'Pinterest', 'introducing', 'imposes', 'kidnaps', 'Hardy', 'proposes', 'derails', 'Wiggs', 'teases', 'Banga', 'establishes', 'pinterest', 'adopts', '}', '{', 'appropriates', 'poses', 'proposing')\n",
-      "=====================================\n",
-      "gender direction 32.\n",
-      " most_similar male: ('Delventhal', 'Baking', 'Littles', 'Coop', 'brainstormed', 'Crafting', 'Duesterhaus', 'Shivar', 'Grains', 'Telling', 'Trottier', 'DuVernay', 'Chaffin', 'Viticulture', 'Baranowski', 'DeMeo', 'Ashworth', 'Boydston', '@', 'Hagopian')\n",
-      "; most_similar female: ('nude', 'BNA', 'iPSC', 'Venezuelan', 'BPMN', 'EPO', 'Ilia', 'Macedonian', 'humpback', 'Slovenian', 'Serbian', 'Revlon', 'Ruffian', 'Maracaibo', 'UTP', 'macaque', 'premature', 'autologous', 'PPP', 'Republic')\n",
-      "=====================================\n",
-      "gender direction 33.\n",
-      " most_similar male: ('5871', 'Discolored', '9131', 'blistering', '6177', '6659', '7787', '4483', '7664', '2523', 'Repair', '6564', 'Boil', '6426', '5588', '2584', 'Topsham', 'Distinguish', '4888', '9059')\n",
-      "; most_similar female: ('AVID', 'Anglicare', 'EMAP', 'ACAP', 'Shelter', 'cages', 'ACTF', 'Caritas', 'LSS', 'ZSL', 'SPM', 'Takahe', 'INGOs', 'cage', 'DIVE', 'shifters', 'MassHealth', 'welfare', 'Nari', 'SAS')\n",
-      "=====================================\n",
-      "gender direction 34.\n",
-      " most_similar male: ('POLA', 'heuristic', 'Sunga', 'webmasters', 'surfers', 'heuristics', 'scripts', 'Ahdoot', 'Asian', 'roberto', 'BitTorrent', 'ruthless', 'empires', 'inexperienced', 'Umeko', 'Sapan', 'actresses', 'Atsuko', 'Savalia', 'Vipul')\n",
-      "; most_similar female: ('celebrating', 'nuptials', 'Watertown', 'wedding', 'Moraine', 'Mass', 'celebratory', 'celebration', 'vouchers', 'MSP', 'Celebrate', 'celebrate', 'AF', 'tethered', 'festivities', 'baptized', 'Stillwater', 'jubilant', 'voucher', 'captured')\n",
-      "=====================================\n"
+      "Correlation: 0.5965726469096496; p-value: 0.0008058843493053067\n"
      ]
     },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "gender direction 35.\n",
-      " most_similar male: ('poodle', 'fluffy', 'poodles', 'Goldendoodle', 'schnauzer', 'ditzy', 'darling', 'dreamy', 'delicate', 'dachshunds', 'Javaheri', 'leggy', 'angelic', 'Farshid', 'Julee', 'Zilker', 'Chelsi', 'scrumptious', 'Heidi', 'embellishments')\n",
-      "; most_similar female: ('Ss', 'Corman', 'Handed', 'Sell', 'Fly', 'LD', 'Pay', 'dimorphism', 'Eynde', 'indicator', 'ACP', 'Coffin', 'Ca', 'Aware', 'Paid', 'Bottle', 'Differently', 'rites', 'pay', 'Detect')\n",
-      "=====================================\n",
-      "gender direction 36.\n",
-      " most_similar male: ('Papanikolaou', 'Bersani', 'Chihara', 'Violi', 'Bernardi', 'Risch', 'Hamori', 'Appearing', 'Fabius', 'aplomb', 'Kokas', 'invitations', 'Papaconstantinou', 'Bartschi', 'Camilleri', 'Noblet', 'Yokomizo', 'Albanese', 'premiers', 'Frattini')\n",
-      "; most_similar female: ('SAI', 'Burrough', 'MMS', 'RISC', 'OI', 'MMU', 'SLF', 'FLL', 'HMP', 'shorted', 'BAH', 'LEF', 'CMP', 'VCO', 'Tektronix', 'HUM', 'BPC', 'EME', 'DFN', 'NI')\n",
-      "=====================================\n",
-      "gender direction 37.\n",
-      " most_similar male: ('Bang', 'Money', '6x6', '1015', '1017', 'mattered', 'Whole', 'Sufi', 'Picasso', 'Creativity', 'bang', 'Imagination', 'disclosed', 'Door', 'probed', 'Indiatimes', 'MPS', 'Tv', 'stimulated', 'Led')\n",
-      "; most_similar female: ('Addison', 'Kingwood', 'Trish', 'ARMS', 'Ophthalmologist', 'Oncologist', 'Olmsted', 'birthmark', 'Skeeters', 'brachioplasty', 'Phoebe', 'Elissa', 'Machemer', 'SouthEast', 'hyperhidrosis', 'Histiocytosis', 'Pearland', 'Radburn', 'Litchman', 'Settlers')\n",
-      "=====================================\n",
-      "gender direction 38.\n",
-      " most_similar male: ('Fatales', 'Adrien', 'Jackal', 'Vargas', 'Maldonado', 'colonia', 'Madara', 'Decree', 'Racecourse', 'Diaz', 'Alphas', 'ingrained', 'Minardi', 'ruins', 'Mayweather', 'stables', '1787', 'Velasquez', '1777', 'Adonis')\n",
-      "; most_similar female: ('segmental', 'Siemens', 'Vinyasa', 'hatha', 'Hatha', 'Falun', 'Timpe', 'Davita', 'Raychem', 'interrelatedness', 'Gaiam', 'Nortel', 'CorePower', 'G.E.', 'Sears', 'Conveyor', 'offshoring', 'Wipro', 'Cathay', 'HPB')\n",
-      "=====================================\n",
-      "gender direction 39.\n",
-      " most_similar male: ('Bar', 'CMJ', 'Porthole', 'Scribes', 'Bands', 'Jockey', 'Jukebox', 'Dein', 'Skull', 'Tribe', 'Medicus', 'Slippers', 'Morrisey', 'Fedora', 'Fan', 'Slack', 'Lesion', 'Cantor', 'Congressional', 'radiologists')\n",
-      "; most_similar female: ('̶', 'ending', 'canola', 'frying', 'demystifying', 'repurposing', 'lifecycle', 'underbanked', 'personne', 'GLA', 'maki', 'Waititi', 'honey', 'noncommunicable', 'fluffy', 'countering', '\\u200b', 'mixture', 'aHUS', 'dripping')\n",
-      "=====================================\n",
-      "gender direction 40.\n",
-      " most_similar male: ('Resurgent', 'wagons', 'formations', 'post-2003', 'Chargers', 'cowboys', 'Confederate', 'dismantled', 'marshal', 'subnational', 'Yeovil', 'Haverfordwest', 'Batteries', 'postbellum', 'Callies', 'Division', '01655', 'dyads', 'declining', 'institutionalization')\n",
-      "; most_similar female: ('Gain', 'Achieve', 'Lifestyle', 'lifestyle', 'Ideal', 'Perfect', 'Faizan', 'Striving', 'IDEAL', 'Helping', 'Livelihood', 'Upscale', 'Worldly', 'Tangible', 'taste', 'Helps', 'comfort', 'Able', 'Viterbi', 'degrees')\n",
-      "=====================================\n",
-      "gender direction 41.\n",
-      " most_similar male: ('Woon', 'investigating', 'Quek', 'Sims', 'Wait', 'Wadley', 'Balazs', 'Hmm', 'Tue', 'Rowell', 'Posted', 'Thu', 'glitch', 'Flux', 'dragging', 'Teo', 'underwater', 'investigate', 'Yawn', 'Josey')\n",
-      "; most_similar female: ('donna', 'ArtScroll', 'ings', 'reprinted', 'Twiztid', 'jerseys', 'Bioidentical', 'telugu', 'bioidentical', 'marathi', 'Tradizionale', 'Sorelle', 'nce', 'Champe', 'clit', 'ISLS', 'cation', 'inserts', 'convent', '34DD')\n",
-      "=====================================\n",
-      "gender direction 42.\n",
-      " most_similar male: ('broadsides', 'Kimmel', 'hammered', 'rapped', 'podium', 'Guggenheim', 'premieres', 'Premiered', 'emcee', 'NYSE', 'amNewYork', 'bearer', 'Verizon', 'Shorenstein', 'emcees', 'date', 'WNYC', 'news', 'engraved', 'syphilis')\n",
-      "; most_similar female: ('Annai', 'River', 'gradient', 'Samiti', 'Territory', 'PATHS', 'Sturt', 'Unschooling', 'gradients', 'NLP', 'Warlpiri', 'Godavari', 'Sharavathi', 'Shivpuri', 'Siang', 'Shipibo', 'ILP', 'Rivers', 'Swamp', 'Mahavidyalaya')\n",
-      "=====================================\n",
-      "gender direction 43.\n",
-      " most_similar male: ('Was', 'Tired', 'Got', 'Not', 'Did', 'Can', 'Wanted', 'Became', 'Want', 'Are', 'Should', 'Thrilled', 'Need', 'Reluctant', 'Largely', 'Never', 'Shall', 'sat', 'Hated', 'Would')\n",
-      "; most_similar female: ('worded', 'generalize', 'generalization', 'classifies', 'contextualized', 'Senge', 'operationalize', 'Boutique', 'behave', 'heading', 'Mr.', 'implement', 'Ms.', 'Fashions', 'accordance', 'spelled', 'contextually', 'actionable', 'categorize', 'interpretable')\n",
-      "=====================================\n",
-      "gender direction 44.\n",
-      " most_similar male: ('monotonous', 'restrictive', 'monotonic', 'furthermore', 'leukemic', 'stratified', 'TRIPS', 'hurry', 'Malawian', 'shapeless', 'STAM', 'Noth', 'deepness', 'abolish', 'PEL', 'burdensome', 'quantity', 'Andriessen', 'psychedelic', 'eternal')\n",
-      "; most_similar female: ('athlete', 'ProActive', 'Jaguar', 'GoPro', 'sports', 'geolocation', 'botnets', 'Dasher', 'Giffin', 'analytics', 'Proactive', 'operations', 'arthroscopy', 'Analytics', 'Citi', 'USAA', 'botnet', 'Nike', 'KeyBank', 'Hoppes')\n",
-      "=====================================\n",
-      "gender direction 45.\n",
-      " most_similar male: ('Recep', 'Foxworthy', 'Grainger', 'Tracie', '18th', 'Shanna', '19th', 'McKnight', 'Dorman', 'Seine', 'Ferrell', 'Rudisill', '20th', 'Selz', 'Gena', 'Garey', 'Shaun', 'Ronna', 'Ferguson', 'Shellie')\n",
-      "; most_similar female: ('Populi', 'Portfolios', 'Modest', 'Espacio', 'applets', 'Impetus', 'substantial', 'Abierta', 'Ercol', 'educative', 'reforms', 'Magma', 'Veldt', 'compulsory', 'Finale', 'sizeable', 'ETFs', 'movable', 'Constructive', 'Nimbus')\n",
-      "=====================================\n",
-      "gender direction 46.\n",
-      " most_similar male: ('Fade', 'Rated', 'Statistical', 'corrections', 'Rating', 'Correction', 'CCTV', 'Revealing', 'fugitive', 'handcuffed', 'Flash', 'aid', 'Imager', 'fading', 'aided', 'hiding', 'Exterior', 'handcuffs', 'Interior', 'manipulated')\n",
-      "; most_similar female: ('Essig', 'Ellzey', 'Nespresso', 'Teeter', 'Zachman', 'Coles', 'Brockmeier', 'Bombeck', 'Herweg', 'Diedrich', 'coworking', 'Bendel', 'Malouf', 'kubernetes', 'sustainer', 'RBD', 'Friese', 'apt', 'savannas', 'Specht')\n",
-      "=====================================\n",
-      "gender direction 47.\n",
-      " most_similar male: ('fraught', 'UCD', 'tense', 'earlier', 'Globes', 'metre', 'metres', 'Holi', '9NEWS', 'calmer', 'Firstpost', 'Starry', 'hackathon', 'Festive', 'Moments', 'contentious', 'tantrums', 'improvised', 'Sawant', 'Cochabamba')\n",
-      "; most_similar female: ('handyman', 'Handyman', 'presses', 'Woodbury', 'Chipley', 'woodworker', 'Chubb', 'Brooksville', 'Woodworker', 'Wendell', 'Grandy', 'Batesville', 'Homeowner', 'watercolorist', 'Pawling', 'insures', 'carpenter', 'Bookkeeper', 'Clewiston', 'jobbing')\n",
-      "=====================================\n",
-      "gender direction 48.\n",
-      " most_similar male: ('Elvira', 'juvenile', 'juveniles', 'PPU', 'Adolf', 'stork', 'Englund', 'Seghers', 'Francoist', 'Komala', 'Polanski', 'BAD', 'Kuczynski', 'Malathi', 'Showgirls', 'inmates', 'Böll', 'Picazo', 'homosexual', 'lesbianism')\n",
-      "; most_similar female: ('Haptic', 'ADAS', 'Embedded', 'QTI', 'webcasts', 'Adaptive', 'Modality', 'Stream', 'webcast', 'podcasts', 'Planner', 'Cutoff', 'dominate', 'Sensory', 'Quizlet', 'podcast', 'mLearning', 'Cognitive', 'MassCUE', 'Analyzer')\n",
-      "=====================================\n",
-      "gender direction 49.\n",
-      " most_similar male: ('Navodaya', 'Bahraini', 'Saudi', 'Hardinge', 'Corea', 'Amal', 'Mayas', 'Yucatan', 'Bahrain', 'Emirati', 'Filipinas', 'HBSC', 'Bengaluru', 'aspirants', 'Deccan', 'satires', 'Naipaul', 'Malabar', 'Sidhwa', 'Lakdawala')\n",
-      "; most_similar female: ('Stimulus', 'inventory', 'Infusion', 'Wubs', 'increases', 'Sunnyview', 'quantities', 'Bonus', 'Yarn', 'infusions', 'overdoses', 'irrationally', 'NETA', 'Omega-3', 'Artie', 'Organismic', 'regenerating', 'decrease', 'baby', 'Collectibles')\n",
-      "=====================================\n",
-      "gender direction 50.\n",
-      " most_similar male: ('reformulation', '', 'Celaya', 'neurotoxin', 'Cengage', 'Shh', 'option', 'Ayyash', 'explicit', 'Alembic', 'adding', 'adrenalectomy', 'adobe', 'panicked', 'Schrager', 'smell', 'AJE', 'D.E.A.', 'rhinoplasties', 'Answer')\n",
-      "; most_similar female: ('imagers', 'Hitachi', 'GMD', 'Mirka', 'Portable', 'Imke', 'imager', 'Miniature', 'Appreciation', 'polymath', 'Princesses', 'Outings', 'Ribbons', 'Tennis', 'ODD', 'CTO', 'Roundtables', 'breakthroughs', 'holders', 'Slippers')\n",
-      "=====================================\n",
-      "gender direction 51.\n",
-      " most_similar male: ('fats', '4100', '5900', '5650', '2555', '2562', '7050', '4231', 'body', '5500', '6700', '5600', '8200', 'Matkin', 'bodys', '1725', 'valves', '1820', '9200', 'Thomassen')\n",
-      "; most_similar female: ('Guerilla', 'Posters', 'Peep', 'Penned', 'Guerrilla', 'Ads', 'Superbowl', 'guerrilla', 'Advertisements', 'Tagline', 'Informer', 'Poster', 'Political', 'guerilla', 'NOON', 'Apologies', 'TYPO', 'Monocle', 'Dumbest', 'glimpsed')\n",
-      "=====================================\n",
-      "gender direction 52.\n",
-      " most_similar male: ('applets', 'implementations', 'pixie', 'Atomics', 'Atol', 'SSA', 'LineRate', 'SoCs', 'caching', 'carousel', 'arbitrarily', 'Semiconductor', 'Chubby', 'Architecting', 'FTF', 'workforces', 'Daiichi', 'Vanger', 'ActiveX', '1637')\n",
-      "; most_similar female: ('M.d', 'HH', 'upcycling', 'canvass', 'URBZ', 'reserach', 'tous', 'Ranawat', 'C.S', 'lifestyle', 'eco-', 'Rav', 'passions', 'Studying', 'Kolb', 'leather', 'Choma', 'Involvement', 'Shailee', 'ArtLab')\n",
-      "=====================================\n",
-      "gender direction 53.\n",
-      " most_similar male: ('blocked', 'injured', 'totaled', 'brakes', 'Nobis', 'Coffman', 'Gutterman', 'redirected', 'Majus', 'Meyers', 'Garns', 'Kaster', 'Karman', 'Bahr', 'ligaments', 'Alman', 'Amster', 'Shoulder', 'Lumbar', 'angles')\n",
-      "; most_similar female: ('Piagetian', 'blackboard', 'Melanesian', 'Unpacking', 'SciPy', 'precollege', 'eResearch', 'numeracy', 'Statistical', 'macrobiotic', 'ISME', 'Nonparametric', 'Honiara', 'Behaviours', 'Dietary', 'Inference', 'lice', 'Representational', 'powerpoint', 'schoolwide')\n",
-      "=====================================\n",
-      "gender direction 54.\n",
-      " most_similar male: ('Linings', 'threaded', 'Drenner', 'scrapers', 'donning', 'gray', 'newsreaders', 'Keaton', 'Fracturing', 'CFTC', 'opaque', 'Geoscientists', 'Ridgeway', 'Carbide', 'piecing', 'CJA', 'tungsten', 'dominate', 'processors', 'Drilling')\n",
-      "; most_similar female: ('wanderlust', 'Francophile', 'Philosophie', 'hazard', 'Condé', 'Toots', 'concomitance', 'Docent', 'Lettres', 'una', 'Voltaire', 'Gallico', 'Passi', 'CNAP', 'VD', 'foodie', 'Montesquieu', 'Stendhal', 'Giannuzzi', 'Galatians')\n",
-      "=====================================\n"
+      "rms-diff before: 0.20243245189797604; rms-diff after: 0.11083594611306065\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "y_pred_before = clf_original.predict(x_test)\n",
+    "test_gender = [d[\"g\"] for d in test]\n",
+    "dev_gender = [d[\"g\"] for d in dev]\n",
+    "train_gender = [d[\"g\"] for d in train]\n",
+    "\n",
+    "tprs_before, tprs_change_before, mean_ratio_before = get_TPR(y_pred_before, y_test, p2i, i2p, test_gender)\n",
+    "similarity_vs_tpr(tprs_change_before, word2vec, \"before\", \"TPR\", prof2fem)\n",
+    "#y_pred_after = clf.predict(X_test.dot(P))\n",
+    "y_pred_after = clf.predict(debiased_x_test)\n",
+    "tprs, tprs_change_after, mean_ratio_after = get_TPR(y_pred_after, y_test, p2i, i2p, test_gender)\n",
+    "similarity_vs_tpr(tprs_change_after, word2vec, \"after\", \"TPR\", prof2fem)\n",
+    "\n",
+    "change_vals_before = np.array(list((tprs_change_before.values())))\n",
+    "change_vals_after = np.array(list(tprs_change_after.values()))\n",
+    "\n",
+    "print(\"rms-diff before: {}; rms-diff after: {}\".format(rms_diff(change_vals_before), rms_diff(change_vals_after)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "gender direction 55.\n",
-      " most_similar male: ('Cenac', 'Gilfillan', 'Ghouse', 'Rothberg', 'Talwar', 'McGillivray', 'Loughlin', 'Maclean', 'Purdey', 'Schorr', 'Spinella', 'Saperstein', 'manifold', 'Carrell', 'Mazzei', 'Valiant', 'Rigg', 'Chisholm', 'Advani', 'Ruffalo')\n",
-      "; most_similar female: ('AUP', 'cyberpunk', 'tos', 'Troopers', 'interlocal', 'assignement', 'netlabel', 'GNU', 'PED', 'Aomori', 'Paintsville', 'po', 'intermedia', 'carpal', 'github', 'Symfony2', 'ville', 'Lona', 'Kaiapoi', 'parapsychological')\n",
-      "=====================================\n",
-      "gender direction 56.\n",
-      " most_similar male: ('Palatka', 'Slocumb', 'Wests', 'Opdyke', 'LaCrosse', 'Wasco', 'Designers', 'Kucha', 'Dowdy', 'Kaukauna', 'Westfield', 'Lamson', 'Should', 'Brookville', 'Goodyear', 'Wainscott', 'Bechers', 'Elco', 'Allworth', 'Millville')\n",
-      "; most_similar female: ('caseworker', 'Sociale', 'penpal', 'Volunteering', 'monitor', 'adjust', 'Reintegration', 'LGBTI', 'monitoring', 'Aftercare', 'clinic-', 'volunteering', 'see', 'waitlists', 'reintegration', 'VSO', 'culturale', 'TPR', 'organise', 'EDGI')\n",
-      "=====================================\n",
-      "gender direction 57.\n",
-      " most_similar male: ('Sketchbook', 'Binders', 'Sketchbooks', 'Manuscript', 'Entries', 'Prose', 'Templates', 'Bookbinding', 'Inventories', 'Handal', 'Pages', 'Zine', 'Everyday', 'Conscientious', 'Calendars', 'Jernej', 'Page', 'Blurb.com', 'Critiques', 'Binder')\n",
-      "; most_similar female: ('Afrobeats', 'Nigerian', 'landmine', 'Ukranian', 'Cameroonian', 'kidnapping', 'Congolese', 'scaling', 'grappling', 'collision', 'abduction', 'dodging', 'explosion', 'Brazillian', 'grapples', 'survivor', 'Ukrainian', 'homing', 'dual', 'shapeshifting')\n",
-      "=====================================\n",
-      "gender direction 58.\n",
-      " most_similar male: ('AskMen', 'Mitochondrion', 'apostasy', 'Hashemi', 'Jackets', 'uterus', 'Women', 'sexism', 'misogyny', 'Ghaddar', 'imports', 'womens', 'Polarize', 'andropause', 'equivalents', 'famine', 'breadwinner', 'post-1970s', 'huh', 'women')\n",
-      "; most_similar female: ('preschoolers', 'BES', 'PreK', 'preschool', 'identified', 'SEP', 'Briarwood', 'PreK-3', 'planted', 'MAR', 'Montessori', 'disabled', 'Playworld', 'belonging', 'NCSS', 'kindergarten', '0022', '0280', 'ILD', 'Educare')\n",
-      "=====================================\n",
-      "gender direction 59.\n",
-      " most_similar male: ('Chafee', 'Lasell', 'Babson', 'CFMA', 'Applicability', 'Subsection', 'Heuristics', 'loopholes', 'Shepard', 'Predoctoral', 'G.S.', 'Paragraph', 'Greylock', 'Incivility', 'Shepherds', 'Arbitrator', 'ECE', 'MANETs', 'Quarters', 'Sentence')\n",
-      "; most_similar female: ('FreeOnes', 'nudes', 'Garters', 'striptease', 'Playboy', 'softcore', 'ladyboy', 'Bayamon', 'Dansk', 'pictorials', 'GGurls', 'Kata', 'annexation', 'arousing', 'porno', 'Nudes', 'babe', 'FHM', 'pornographic', 'Kmart')\n",
-      "=====================================\n",
-      "gender direction 60.\n",
-      " most_similar male: ('whipped', 'Moore', 'Calvin', 'Stringfellow', 'Gray', 'Lacroix', 'unquestionably', 'Switzer', 'Heade', 'Ingels', 'Mayhew', 'Katt', 'cornerstones', 'Hobbes', 'Hendricks', 'Mugler', 'screenshots', 'Mapp', 'McCray', 'stripes')\n",
-      "; most_similar female: ('4th-8th', '4th-6th', 'Seatown', 'ureter', '10th-12th', 'chondrocytes', 'Raise', 'Inguinal', 'Carpal', 'Fermoy', 'carpel', 'Gorkem', 'Iraqi', 'microtia', 'Cartilage', 'DePuy', 'Zilvinas', 'cartilage', 'Rachna', 'orthopaedic')\n",
-      "=====================================\n",
-      "gender direction 61.\n",
-      " most_similar male: ('choline', 'Tau', 'UAlbany', 'Estrich', 'Brahma', 'Tok', 'theologically', 'QSM', 'xoJane', 'theorize', 'Gok', 'Apatow', 'ecofeminism', 'exegesis', 'Misogyny', 'eggs', 'Har', 'chant', 'Kra', 'Ecofeminism')\n",
-      "; most_similar female: ('Children', 'children', 'braces', 'Activity', 'marionettes', 'kids', 'Activities', 'kid', 'kiddos', 'Kids', 'Interests', 'Adults', 'childrens', 'Braces', 'impairments', 'LOVED', 'child', 'strabismus', 'rates', 'minimum')\n",
-      "=====================================\n",
-      "gender direction 62.\n",
-      " most_similar male: ('utilizing', 'Merriman', 'Traumatology', 'incorporates', 'utilizes', 'AAOS', 'cardiothoracic', 'knee', 'VATS', 'spinal', 'thoracic', 'echos', 'rearfoot', 'clavicle', 'articular', 'Employing', 'patellofemoral', 'UAA', 'dislocated', 'preexisting')\n",
-      "; most_similar female: ('Bus', 'Stonehenge', 'Nasik', 'bus', 'Siddhi', 'Hezbollah', 'Harihareshwar', 'Gangapur', 'Poona', 'Kingfisher', 'Satguru', 'P1', 'Porsche', '004', 'Haridwar', 'Holi', 'Hitech', 'Kalam', 'Belur', 'Atheists')\n",
-      "=====================================\n",
-      "gender direction 63.\n",
-      " most_similar male: ('Gird', 'forecasting', 'Sanghi', 'OND', 'Ahya', 'GBM', 'someday', 'EEE', 'sis', 'A380', 'engraftment', 'B.E', 'Gab', 'IBRAHIM', 'Fratello', 'Isetan', 'Spencers', 'plotting', 'Shifa', 'Defy')\n",
-      "; most_similar female: ('contested', 'Symbolism', 'marginalised', 'devalued', 'Comprehensive', 'Engaging', 'Critically', 'Chaplin', 'Importance', 'unrepresented', 'Meanings', 'Cultural', 'grappled', 'minoritized', 'Communities', 'disempowered', 'Problematizing', 'stigmatized', 'Reframing', 'Ethnography')\n",
-      "=====================================\n",
-      "gender direction 64.\n",
-      " most_similar male: ('depraved', 'uncivilized', 'barbarism', 'dandyism', 'civilized', 'barbaric', 'Abdellah', 'atrocity', 'decadence', 'heinous', 'Sade', 'abomination', 'southerner', 'Moslem', 'squalid', 'disgusting', 'Timbuktu', 'mutilated', 'Libertines', 'stigmatized')\n",
-      "; most_similar female: ('Busines', 'teambuilding', 'Business', 'Collider', 'clients', 'Agility', 'Pros', 'pipeline', 'strategy', 'solopreneurs', 'Dragon', 'Teamwork', 'hurdles', 'skillsets', 'chances', 'queries', 'Propeller', 'business', 'strategies', 'workstations')\n",
-      "=====================================\n",
-      "gender direction 65.\n",
-      " most_similar male: ('Khar', 'Firmly', 'Mangal', 'Aashirwad', 'Kuber', 'Essel', 'Shahi', 'tenement', 'Deal', 'mogul', 'victimisation', 'Kashi', 'Atta', 'Ansari', 'Panchsheel', 'Jayshree', 'Ambika', 'Khurshid', 'Poddar', 'Listed')\n",
-      "; most_similar female: ('Liaisons', 'Wartburg', 'Overtime', 'downtime', 'UGA', 'Chattahoochee', 'clinicals', 'CLCE', 'uniforms', 'exam', 'Cerner', 'Coding', 'CCHS', 'Knoxville', 'Corrections', 'evolves', 'Revolutions', 'labors', 'Benefis', 'Portneuf')\n",
-      "=====================================\n",
-      "gender direction 66.\n",
-      " most_similar male: ('This', 'Biannual', 'Such', 'That', 'AMA', 'Panchali', 'Ruchira', 'Generally', 'It', 'ISCM', 'Certain', 'One', 'Another', 'Having', 'VONA', 'Aosdána', 'Biopics', 'lifetime', 'The', 'MELUS')\n",
-      "; most_similar female: ('overtones', 'silliness', 'profitably', 'continued', 'absurdity', 'resumed', 'misadventures', 'difficulties', 'aggressively', 'supersymmetric', 'Carlinville', 'begin', 'excels', 'Qs', 'familiar', 'singled', 'resorting', 'strangeness', 'commonplaces', 'resorted')\n",
-      "=====================================\n",
-      "gender direction 67.\n",
-      " most_similar male: ('ColorLines', 'Kondabolu', 'undo', 'MLJ', 'peep', '0170', 'Transgress', 'Colorlines', 'diff', 'disproportionality', 'Theol', 'Mic', 'Gest', 'Tuebingen', 'Deutsche', 'injustices', 'Hochschule', 'Tanaz', 'gap', '0066')\n",
-      "; most_similar female: ('occupants', 'perishable', 'warm', 'possessions', 'warmer', 'blankets', 'transient', 'responders', 'dependable', 'Oliviero', 'Mustang', 'Statesville', 'nonspecific', 'Warm', 'Fernandina', 'linens', 'Dependable', 'porous', 'Ormond', 'Kennealy')\n",
-      "=====================================\n",
-      "gender direction 68.\n",
-      " most_similar male: ('copyright', 'copyrights', 'copyleft', 'chemokines', 'dementias', 'guilds', 'ribosome', 'copyrighted', 'Faeries', 'Sigcomm', 'URBZ', 'pathogenic', 'KONAMI', 'Legalities', 'chemotaxis', 'mafias', 'Crytek', 'goblins', 'Giants', 'AIAS')\n",
-      "; most_similar female: ('hs', 'pediatrician', '12-years', 'Worth', 'Children', 'Coroner', 'child', 'recommended', 'Diagnostician', 'P.E.', 'Pediatrician', 'willingness', 'Jett', 'Breckinridge', 'tire', 'physician', 'Tilden', 'Holabird', 'Northshore', 'Snorkel')\n",
-      "=====================================\n",
-      "gender direction 69.\n",
-      " most_similar male: ('Sociedad', 'Torcuato', 'lexical', 'Quechua', 'Akademi', 'psycholinguistic', 'Musa', 'Azerbaijani', 'Emiri', 'Iñupiat', 'Maulana', 'GLS', 'Sangeet', 'Vygotsky', 'Republika', 'Sahrawi', 'Cristóbal', 'IALA', 'Uzbekistan', 'Circulo')\n",
-      "; most_similar female: ('repaired', 'Lyme', 'Doylestown', 'enough', 'Weld', 'civilly', 'repair', 'fix', 'Herkimer', 'DHMC', 'Sherbourne', 'EDAC', 'Fishkill', 'Paltz', 'Bullett', 'Fries', 'inexpensively', 'Cozen', 'Ober', 'reconfigure')\n",
-      "=====================================\n",
-      "gender direction 70.\n",
-      " most_similar male: ('IDG', 'Piaget', 'undisputed', 'elevates', 'catwalk', 'outlets', 'disputed', 'ADN', 'neuroanatomical', 'Huffington', 'Enquirer', 'MoMa', 'claims', 'MLB.com', 'Radar', 'catwalks', 'tuatara', 'celeb', 'elevated', 'mainstream')\n",
-      "; most_similar female: ('Compiled', 'environnment', 'sands', 'Theme', 'Coins', 'Winds', 'Cleansing', 'Runes', 'Used', 'Wishing', 'Horseman', 'percussions', 'Seaside', 'Conducting', 'Rider', 'Liberating', 'Crusade', 'Bountiful', 'Prosperous', 'Kindler')\n",
-      "=====================================\n",
-      "gender direction 71.\n",
-      " most_similar male: ('estimations', 'Suono', 'BBC3', 'Yankers', 'Bomba', 'interference', 'genus', 'linguists', 'PCS', 'Mosca', 'hermaphrodite', 'Milli', 'Armonica', 'ranking', 'unscientific', 'Grub', 'spin', 'Scienza', 'Squad', 'MIMO')\n",
-      "; most_similar female: ('Monadnock', 'Timeline', 'fairmont', 'Amiel', 'Wiser', 'overnight', 'AJN', 'lade', 'Pilgrim', 'Sendai', 'Staying', 'Fairmont', 'mindful', 'vigilant', 'Badertscher', 'cookies', 'Lurene', 'Carmel', 'awhile', 'HIE')\n",
-      "=====================================\n",
-      "gender direction 72.\n",
-      " most_similar male: ('Karr', 'MacIntyre', 'Shrout', 'Laird', 'Heit', 'Bluedorn', 'MacLean', 'Empirically', 'Braden', 'Rossman', 'Melnyk', 'Aitken', 'Tatman', 'Shefrin', 'Kornfield', 'Kendall', 'Russell', 'Laughing', 'Cowen', 'Krupnick')\n",
-      "; most_similar female: ('sweltering', 'humid', 'stopover', 'showpiece', 'spiders', 'knockout', 'migrant', 'pulsating', 'bays', 'torrid', 'humidity', 'dusky', 'fixture', 'ingress', 'hotbed', 'injection', 'pivotal', 'afterhour', 'spider', 'teeming')\n",
-      "=====================================\n",
-      "gender direction 73.\n",
-      " most_similar male: ('optioned', 'SMOKE', 'unmarketable', 'jobbing', 'ASPECT', 'emblazoned', 'Backlash', 'REGION', 'MuscleMag', 'veto', 'Relapse', 'flexes', 'noticeable', 'Hellraiser', 'occasional', 'début', 'Kerrang', 'flung', 'birthmark', 'pisses')\n",
-      "; most_similar female: ('coastal', 'riverine', 'Vital', 'Nurul', 'Nasrul', 'calming', 'analytics', 'sophisticated', 'upazila', 'mangrove', 'Erudite', 'modernizing', 'cardiothoracic', 'defense', 'Pantai', 'Tarik', 'Anam', 'wetlands', 'mangroves', 'Boko')\n",
-      "=====================================\n"
+      "[Parallel(n_jobs=64)]: Using backend ThreadingBackend with 64 concurrent workers.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "gender direction 74.\n",
-      " most_similar male: ('entice', 'not', 'Peirce', 'Durgin', 'teaser', 'Courant', 'vagueness', 'adjectives', 'descriptive', 'Rendell', 'persuade', 'ought', 'Propositions', 'Culliton', 'description', 'EPPA', 'Lussier', 'coin', 'might', 'propositions')\n",
-      "; most_similar female: ('Zhanna', 'choreographing', 'logistical', 'Eurosport', 'YMT', 'choreographs', 'Rostov', 'Baku', 'aerials', 'upmost', 'spectating', 'Shanghainese', 'epicentre', 'parental', 'hora', 'T1D', 'bespoke', 'orchestrates', 'Dynastic', 'interruptions')\n",
-      "=====================================\n",
-      "gender direction 75.\n",
-      " most_similar male: ('mythological', 'Maratha', 'ideological', 'nationalistic', 'historical', 'nationalist', 'militarized', 'microbiological', 'heritage', 'historiographic', 'bureaucratic', 'Ossetian', 'scientific', 'mythical', 'Bicentenary', 'Odissi', 'missionary', 'Afrikaner', 'Kuka', 'delegations')\n",
-      "; most_similar female: ('admit', 'Bornstein', 'LL', 'Mcgee', 'Kaz', 'Biddle', 'Attell', 'Wiz', 'Hebb', 'Reggie', 'Golson', 'McNally', 'Ullman', 'Burrell', 'Macklin', 'Ivey', 'Darnell', 'Holzman', 'Spades', 'Sacks')\n",
-      "=====================================\n",
-      "gender direction 76.\n",
-      " most_similar male: ('Mazzaglia', 'Plale', 'Crosbie', 'Gallaway', 'cents', 'indictment', 'Heifner', 'twenties', 'Wardrobe', 'Keg', 'Flinn', 'lb', 'barge', 'Geddings', 'storesThe', 'comment', 'Reckless', 'Shields', 'ANY', 'Boon')\n",
-      "; most_similar female: ('CGS', 'Endocrinology', 'Shaare', 'HIAS', 'Kedar', 'Openhouse', 'SFVAMC', 'KCRW', 'Outpatient', 'institutionalization', 'asylum', 'Terman', 'Shalem', 'Yeshurun', 'Rivka', 'CARECEN', 'cardiology', 'Schwartzman', 'Asylum', 'Perlin')\n",
-      "=====================================\n",
-      "gender direction 77.\n",
-      " most_similar male: ('Nines', 'Dirksen', 'humiliation', '2441', 'disfigurement', 'Lehnert', 'embarrassment', 'Notes', 'Pietrangelo', 'legs', 'meningioma', '2147', 'Luebbert', 'memories', '4144', 'Boram', 'Yusaf', 'Rhinos', 'Aamer', 'iceberg')\n",
-      "; most_similar female: ('scrutinizing', 'UPV', 'scrutinize', 'Perform', 'Regulate', 'naturopathic', 'naturopath', 'MovNat', 'Inverse', 'lauded', 'conducting', 'Undertaking', 'surprised', 'parapsychologist', 'Conducting', 'prover', 'Proactive', 'scrutinized', 'correlating', 'Prove')\n",
-      "=====================================\n",
-      "gender direction 78.\n",
-      " most_similar male: ('VxWorks', 'WRL', 'Uludağ', 'panoramas', 'HiRISE', 'Norikura', 'Keweenaw', 'panorama', 'Stegner', 'Yangtze', 'Alexanderplatz', 'PanoTools', 'Sharp', 'windsurfing', 'daylighting', 'stitching', 'AOFAS', 'CME', 'Dolin', 'hydroelectric')\n",
-      "; most_similar female: ('Idols', 'Politiques', 'normalities', 'crushes', 'Valedictorian', 'princes', 'Mentality', 'precepts', 'outcasts', 'mantras', 'buddies', 'vows', 'idols', 'misfits', 'acts', 'Ideals', 'pranks', 'brothers', 'Ideologies', 'kings')\n",
-      "=====================================\n",
-      "gender direction 79.\n",
-      " most_similar male: ('Korsakov', 'shipbuilding', 'Tula', 'Grandin', 'Georgi', 'neuroprotection', 'Calva', 'Vor', 'Perm', 'Nizhny', 'neuroprotective', 'arts', 'soy', 'NMS', 'gar', 'Canman', 'Bast', 'novo', 'Crucible', 'polyaromatic')\n",
-      "; most_similar female: ('privacy', 'Knesset', 'smile', 'Privacy', 'Ridges', 'knob', 'millimeter', 'wholeness', 'consent', 'Erlbaum', 'unbearable', 'smiles', 'Dharamsala', 'obstacle', 'apart', 'Hearing', 'continuously', 'Smiling', 'Dharamshala', 'Osho')\n",
-      "=====================================\n",
-      "gender direction 80.\n",
-      " most_similar male: ('Atol', 'thieves', 'jewelery', 'shielding', 'theft', 'dwarfs', 'Hoppé', 'Bielik', 'Stieg', 'generators', 'jewellery', 'Hugo', 'thefts', 'Ekstrand', 'jewllery', 'Koscielniak', 'masks', 'Oppermann', 'crooks', 'Steffan')\n",
-      "; most_similar female: ('Refresh', 'Commanding', 'readmission', 'YLD', 'reminiscing', 'Rotation', 'Nittany', 'GSAPP', 'Transitioning', 'Dining', 'Forward', 'commanding', 'converged', 'Transgress', 'Admissions', 'Moderation', 'Pivot', 'Readmissions', 'Command', 'Healthy')\n",
-      "=====================================\n",
-      "gender direction 81.\n",
-      " most_similar male: ('Outsiders', 'unsurprisingly', 'Retailers', 'assortment', 'Bigger', 'ACEP', 'lineup', 'fanfare', 'Issue', 'traditionally', 'pervade', 'Hoops', 'retailers', 'fewer', 'Freshmen', 'lotta', 'Princesses', 'Mavericks', 'wraparound', 'seemingly')\n",
-      "; most_similar female: ('crucis', 'Bacillus', 'artemisinin', 'Basil', 'cerevisiae', 'Mathan', 'Edapally', 'whistleblower', 'whistleblowing', 'Hanbury', 'Perumbavoor', 'Shoba', 'multiferroic', 'Dhanya', 'Sreenivasan', 'SAPA', 'Soham', 'Katherine', 'Huy', 'testimonio')\n",
-      "=====================================\n",
-      "gender direction 82.\n",
-      " most_similar male: ('Sabah', 'Huay', 'Sarawak', 'Khai', 'Tok', 'Topaz', 'Dian', 'Yue', 'Fen', 'ranger', 'Amulet', 'Lao', 'Kedah', 'Haji', 'Kalimantan', 'Tabin', 'Idris', 'Joss', 'Shou', 'Shang')\n",
-      "; most_similar female: ('shootings', 'Victimology', 'Obsessive', 'Futurism', 'Nihilism', 'Shootings', 'femicide', 'fixation', 'casein', 'Fascists', 'gynecomastia', 'molestation', 'Interventions', 'Lacanian', 'PCI', 'infill', 'Compulsive', 'Psychopathy', 'Postmodern', 'autodidact')\n",
-      "=====================================\n",
-      "gender direction 83.\n",
-      " most_similar male: ('epitomized', 'modeled', 'seasons', 'furthered', 'variation', 'explored', 'variations', 'phenology', 'demonstrated', 'facilitated', 'reinterpretation', 'explore', 'characterized', 'NCHA', 'summarized', 'season', 'identified', 'characterize', 'host', 'oversaw')\n",
-      "; most_similar female: ('e-', 'pos', 'photosensitive', '0', 'Godrej', 'Akansha', 'Thio', 'HCL', 'Narmada', 'Veldt', 'l', 'Crawshaw', 'Lesen', 'orang', 'Akshaya', '10.0', 'Arnab', 'Akruti', 'Vats', 'Amitav')\n",
-      "=====================================\n",
-      "gender direction 84.\n",
-      " most_similar male: ('MPC', 'Starfighter', 'Cranberry', 'pilots', 'touched', 'glide', 'Landings', 'landings', 'Pilots', 'Eke', 'Drosophila', 'SPB', 'AJK', 'fondled', 'Aikins', 'Neonatal', 'PAs', 'FCS', 'gliders', 'nurses')\n",
-      "; most_similar female: ('skepticism', 'Zócalo', 'disagreement', 'Corbis', 'edX', 'cultura', 'mistrust', 'Postmodernity', 'distrust', 'cocina', 'originates', 'belief', 'Environmentalism', 'agrees', 'indemnification', 'clause', 'environmentalism', 'Belief', 'wealthy', 'presuppositions')\n",
-      "=====================================\n",
-      "gender direction 85.\n",
-      " most_similar male: ('widow', 'Nicu', 'gure', 'heroic', '90th', 'prince', 'RAF', 'Molar', 'child-', 'tenth', 'Hardest', 'Hedi', 'fresco', 'molar', 'Heimlich', 'infant', 'twentieth', 'Airman', 'selfless', '0.7')\n",
-      "; most_similar female: ('EVP', 'TRIP', 'expands', 'BUILD', 'Bioenergy', 'energy', 'SHIFT', 'generated', 'soundscapes', 'INNS', 'REC', 'MAKE', 'Trek', 'mapped', 'unleashes', 'Geographics', 'Quantum', 'contracts', 'generates', 'MIND')\n",
-      "=====================================\n",
-      "gender direction 86.\n",
-      " most_similar male: ('Handwritten', 'Mornington', 'alphabet', 'alphabets', 'handwritten', 'Phrases', 'letters', 'Hawkesbury', 'Complicated', 'Solitaire', 'Typing', 'sums', 'Entertaining', 'Valentines', 'scribbling', 'Ab', 'variations', 'verses', 'Manly', 'emoji')\n",
-      "; most_similar female: ('FLACSO', 'UHM', 'MESA', 'generalist', 'spatialization', 'Schimmelpfennig', 'rehabilitates', 'interdisciplinary', 'coordinates', 'colloquium', 'restores', 'phenomenologist', 'MSSW', 'Commonweal', 'NAU', 'Codirector', 'gathers', 'UABC', 'SDI', 'EHESS')\n",
-      "=====================================\n",
-      "gender direction 87.\n",
-      " most_similar male: ('grown', 'aggregated', 'Velvet', 'cultivars', 'Charcoal', 'matured', 'Ash', 'gotten', 'beetle', 'pest', 'beetles', 'darker', 'Pug', 'grained', 'mite', 'Bark', 'Marl', 'ash', 'Beetles', 'mature')\n",
-      "; most_similar female: ('emerita', 'RAICES', 'Iker', '\"The', 'speechwriter', 'Migrantes', 'veteran', 'Macri', 'veterans', 'CARECEN', 'lector', 'Sojourners', 'newfound', 'pedagogue', 'sainted', 'Agustín', 'LIRS', 'embattled', 'reassignment', 'reinsertion')\n",
-      "=====================================\n",
-      "gender direction 88.\n",
-      " most_similar male: ('Amongst', 'cobalt', 'aqua', 'Buchenwald', 'dyes', 'Plaisir', 'Among', 'Arkansan', 'pistachio', 'Graduating', 'abalone', 'scrolls', 'Brevis', 'augmentative', 'polka', 'alphabetical', 'din', 'Theresienstadt', 'conjugation', 'Etoiles')\n",
-      "; most_similar female: ('sanctioning', '7420', 'partnering', 'don’t', 'consider', 'Gateway', 'rethink', 'reconsider', 'Saldana', 'partnership', 'reinterpreting', 'Northlake', 'Watts', 'accusing', 'Partners', 'Windermere', 'reexamine', 'Wellmont', 'ProMedica', 'upfront')\n",
-      "=====================================\n",
-      "gender direction 89.\n",
-      " most_similar male: ('conveniently', 'contemporaneously', 'paperless', 'effectively', 'Orwellian', 'unopposed', 'Zeitgeist', 'occurring', 'circulate', 'obtain', 'robotically', 'Conveniently', 'erasure', 'alternatively', 'TDSB', 'damning', 'Korydallos', 'vacate', 'Nightline', 'simultaneously')\n",
-      "; most_similar female: ('lakeside', 'neuroendocrinology', 'Bassmaster', 'Sig', 'camaraderie', 'luthier', 'picnic', 'waterfowl', 'AKC', 'boating', 'trad', 'motorcycling', 'SUT', 'wheeler', 'motorsports', 'Mountaineering', '4-wheeling', 'Bavarian', 'bluegrass', 'family')\n",
-      "=====================================\n",
-      "gender direction 90.\n",
-      " most_similar male: ('Fahmi', 'SOAS', 'Ishai', 'Yunan', 'Rachman', 'Shenkar', 'Icelandic', 'depths', 'BookFest', 'thee', 'Sadia', 'Barkat', 'Badawi', 'Sallam', 'Gan', 'Yiddishland', 'internationalization', 'Aku', 'Demain', 'Arab')\n",
-      "; most_similar female: ('timeshare', 'Hepatitis', 'HCV', 'Homefront', 'hepatitis', 'DSG', 'rubella', 'HTLV', 'cytomegalovirus', 'Escalera', 'immaculate', 'Heizer', 'HSV', 'Telfair', 'sniper', 'HSV-2', 'Fluor', 'HCA', 'mesothelioma', 'Hartmayer')\n",
-      "=====================================\n",
-      "gender direction 91.\n",
-      " most_similar male: ('homelessness', 'consolidations', 'metamorphosis', 'Allegiance', 'bullying', 'Leaps', 'WLRN', 'reshape', 'Menino', 'reinvention', 'transformation', 'redistricting', 'reform', 'reshaped', 'reshapes', 'Homelessness', 'jowl', 'Newhart', 'Philanthropies', 'leaps')\n",
-      "; most_similar female: ('instructing', 'impregnated', 'Aymar', 'DF', 'camping', 'B.V.', 'visiting', 'Savasta', 'PF', 'Haemostasis', 'applicable', 'responsible', 'Découverte', 'seducing', 'für', 'C.G', 'guarding', 'Shobo', 'Camping', 'LifeForce')\n",
-      "=====================================\n",
-      "gender direction 92.\n",
-      " most_similar male: ('jumpsuit', 'wallpaper', 'aqua', 'Estella', 'Snakeskin', 'Lenor', 'longing', 'Anthropologie', 'blouse', 'Barbarella', 'retro', 'maxi', 'saloon', 'Voyeur', 'strapless', 'frills', 'costar', 'love', 'tunic', 'neon')\n",
-      "; most_similar female: ('FRONTLINE', 'Churchill', 'DOCUMENTARY', 'Documentary', 'Shatz', 'Grodzinski', 'historiography', 'WhoWhatWhy', 'ICH', 'sovereign', 'historiographies', 'RUSI', 'SPOTLIGHT', 'Frankfurter', 'Gatekeepers', 'MRC', 'OZY', 'Katzenstein', 'Rosenfeld', 'Kelner')\n",
-      "=====================================\n"
+      "max_iter reached after 14 seconds\n",
+      "0.7528257402399279\n"
      ]
     },
     {
-     "name": "stdout",
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=64)]: Done   1 tasks      | elapsed:   14.2s\n",
+      "[Parallel(n_jobs=64)]: Done   1 out of   1 | elapsed:   14.2s finished\n"
+     ]
+    }
+   ],
+   "source": [
+    "clf = LogisticRegression(warm_start = True, penalty = 'l2',\n",
+    "                         solver = \"sag\", multi_class = 'multinomial', fit_intercept = True,\n",
+    "                         verbose = 10, max_iter = 6, n_jobs = 64, random_state = 0, class_weight = None)\n",
+    "\n",
+    "clf.fit(debiased_x_train, y_train)\n",
+    "\n",
+    "print(clf.score(debiased_x_test, y_test))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "gender direction 93.\n",
-      " most_similar male: ('shoulders', 'weakened', 'modifier', 'competed', 'compete', 'shoulder', 'grappled', 'braces', 'bows', 'decorate', 'weaker', 'qualifiers', 'solidify', 'Braces', 'PRODUCTS', 'top', 'icing', 'piping', 'grabs', 'squeezed')\n",
-      "; most_similar female: ('reincarnation', 'Airpark', 'Ctr', 'madness', 'painless', 'Tun', 'eugenics', 'resort', 'millenials', 'martinis', 'Deming', 'Spira', 'Tread', 'folly', 'Th', 'fusion', 'Interval', 'Reynolda', 'paradoxes', 'quaint')\n",
-      "=====================================\n",
-      "gender direction 94.\n",
-      " most_similar male: ('cup', 'kissed', 'Shade', 'kiss', 'adoration', 'Sailor', 'Lalah', 'stroll', 'infatuation', 'download', 'escort', 'Prince', 'Nerds', 'shake', 'Ishq', 'Alto', 'Morning', 'sake', 'Sake', 'Nylon')\n",
-      "; most_similar female: ('ACLA', 'RFE', 'extracorporeal', 'ILSI', 'NAACL', 'AstraZeneca', 'Paleontological', 'Mallinckrodt', 'EMNLP', 'Medstat', 'CJA', 'ILP', 'Iterative', 'IPPE', 'AHFMR', 'CDCI', 'nongovernmental', 'CIGNA', 'LINGUIST', 'CRNI')\n",
-      "=====================================\n",
-      "gender direction 95.\n",
-      " most_similar male: ('reporter', 'Lasswell', 'columnist', 'ambushes', 'insurgents', 'guerrillas', 'snipers', 'Laird', 'covertly', 'Irregulars', 'contributor', 'subversive', 'Flanagin', 'editor', 'columnists', 'intrepid', 'ink', 'UWEX', 'obscure', 'ambush')\n",
-      "; most_similar female: ('Bugatti', 'orofacial', 'shoulders', 'MB', 'Cycladic', 'Bardot', 'EMST', 'Chiron', 'soreness', 'motions', 'Rearing', 'Motions', 'torque', 'Heals', 'dystocia', 'Porsche', 'pain', '600k', 'Karting', 'Transgenerational')\n",
-      "=====================================\n",
-      "gender direction 96.\n",
-      " most_similar male: ('shortages', 'cosmic', 'shortage', 'unexplained', 'slowdown', 'inexplicable', 'ECCC', 'Lightning', 'enclave', 'finish', 'suffered', 'Lucky', 'cluster', 'Zodiac', 'experiencing', 'pieced', 'LepreCon', 'glitch', 'sweater', 'pavilion')\n",
-      "; most_similar female: ('Ranges', 'Ultrasonography', 'ESEM', 'OpenLayers', 'AOI', 'ABPP', 'Depth', 'Agar', 'provably', 'DKI', 'VASER', 'AI2', 'caudatum', 'Instants', 'Paramecium', 'elegans', 'Transanal', 'Blacher', 'reflectometry', 'LESS')\n",
-      "=====================================\n",
-      "gender direction 97.\n",
-      " most_similar male: ('roasting', 'Interregional', 'NMT', 'Dierks', 'Bulk', 'harvesting', 'lymphadenopathy', 'bulk', 'transplanting', 'cultivation', 'CO2', 'DWI', 'DeCordova', 'epidural', 'stimulation', 'interregional', 'intrathecal', 'transnasal', 'FCI', 'Biochar')\n",
-      "; most_similar female: ('Prophecy', 'castle', 'Sectarianism', 'Aaj', 'Shahrzad', 'kingdom', 'Hazar', 'Shades', 'sworn', 'Foscari', 'dynasty', 'Synopsys', 'ruins', 'Naagin', 'torn', 'Loyalty', 'Number', 'Malahide', 'Tide', 'Qubool')\n",
-      "=====================================\n",
-      "gender direction 98.\n",
-      " most_similar male: ('Internationaux', 'des', 'Mille', 'éditions', 'Des', 'Héros', 'Nouvelle', 'Ritz', 'Seuil', 'historique', 'Figues', 'flags', 'Etoiles', 'Nemours', 'Cues', 'Sequins', 'décoratifs', 'Souper', 'Au', 'accrued')\n",
-      "; most_similar female: ('K-20', 'IT', 'sane', '587', 'interoperability', 'ARUP', 'standardize', 'screwball', 'SOME', 'OF', 'practicality', 'standardization', '0595', 'TO', 'PG', 'consensual', '3566', '787', 'OUT', '0694')\n",
-      "=====================================\n",
-      "gender direction 99.\n",
-      " most_similar male: ('Cela', 'Lancet', 'last.fm', 'Asterism', 'detract', 'Kritya', 'Aosdána', 'Permafrost', 'Juste', 'Societas', 'cru', 'Gilius', 'icicles', 'eFiction', 'Exaudi', 'lowlands', 'Requiem', 'Klinikum', 'Ploughshares', 'Vita')\n",
-      "; most_similar female: ('K-12', 'classroom', 'Toddler', 'nanny', 'instructional', 'Kids', 'schooling', 'Top', 'laminate', 'sitter', 'education', 'prekindergarten', 'Whitson', 'Teacher', 'kids', 'TEACHER', 'daycare', 'Chalkboard', 'Parents', 'preK')\n",
-      "=====================================\n",
-      "gender direction 100.\n",
-      " most_similar male: ('sigma', 'Studien', 'Husserl', 'Parmenides', 'ab', 'magna', 'Epistemological', 'Lateral', 'venia', 'nos', 'docendi', 'Angle', 'DEXA', 'Extremity', 'Sigma', 'lateral', 'Gadamer', 'Hegel', 'Ser', 'Noven')\n",
-      "; most_similar female: ('entertainer', 'lifestyle', 'consumption', 'vacationing', 'writer', 'rainforests', 'wordsmith', 'raconteur', 'newsman', 'florist', 'pollution', 'TODAY.com', 'Lifestyles', 'pollutant', 'Journatic', 'Madang', 'Rotarian', 'vaping', 'lifestyles', 'gossip')\n",
-      "=====================================\n",
-      "gender direction 101.\n",
-      " most_similar male: ('Feaga', 'Farivar', 'Jurden', 'Horrocks', 'woodwork', 'Moylan', 'Csaky', 'inbreeding', 'loyalism', 'Bowyer', 'Swilley', 'Broadhead', 'Waddell', 'Partin', 'attracts', 'interstellar', 'Barwick', 'PMTrump', 'cholangitis', 'Vrana')\n",
-      "; most_similar female: ('methodologies', 'supplier', 'modalities', 'authenticate', 'Standard', 'franchised', 'technologies', 'Presses', 'Centric', 'anew', 'Agencia', 'APress', 'STANDARD', 'GHD', 'validated', 'Supplier', 'Eastern', 'OCM', 'BPM', 'Presently')\n",
-      "=====================================\n",
-      "gender direction 102.\n",
-      " most_similar male: ('Cenegenics', 'adulthood', 'Olympics', 'academy', 'IAE', 'Boz', 'intertemporal', 'storefront', 'Academy', 'widowhood', 'promotion', 'Paralympics', 'Phelps', 'NCQA', 'Viator', 'Menopause', 'adolescence', 'unmanaged', 'bandwagon', 'Grasshopper')\n",
-      "; most_similar female: ('Charged', 'Arrest', 'Mesna', 'rinse', 'Needed', 'Nd', 'Meth', 'Convicted', 'washed', 'ore', 'n', 'syringe', 'în', 'fue', 'Florida1', 'cleaned', 'wiping', 'Arrestee', 'wash', 'Laundering')\n",
-      "=====================================\n",
-      "gender direction 103.\n",
-      " most_similar male: ('Below', 'Warning', 'lengths', '10-Year', 'Above', '7-year', 'perils', 'BAC', 'dystopias', '5-year', 'above', 'NWSA', 'dangers', 'DEF', '25-year', 'Passengers', 'predicting', '10-year', 'below', 'UOWD')\n",
-      "; most_similar female: ('Senat', 'Chatter', 'dello', '·', 'Pancake', 'informally', 'mojo', 'tele', 'Small', 'Boutique', 'Vall', 'Bayard', 'refocused', 'Dudy', 'Tele', 'Longuet', 'informal', 'Arbogast', 'Mon', 'Kermit')\n",
-      "=====================================\n",
-      "gender direction 104.\n",
-      " most_similar male: ('Cressy', 'Groene', 'Gregoire', 'pitting', 'hardening', 'knot', 'Nationals', 'pitted', 'Stier', 'pits', 'Dufour', 'Sevigny', 'DeVos', 'bulging', 'Fournier', 'Pulled', 'PHOTOS', 'Kolbe', 'VSBA', 'Mises')\n",
-      "; most_similar female: ('assistive', 'sedentary', 'Coltrane', 'downside', '77030', 'Ornette', 'bebop', 'R01', 'Sobh', '61801', 'cognitive-', 'neurorehabilitation', 'ergonomic', 'Nasrullah', 'paraplegic', 'underserved', 'accelerometry', 'upside', 'ambulatory', 'UCB')\n",
-      "=====================================\n",
-      "gender direction 105.\n",
-      " most_similar male: ('altitude', 'topographic', 'potty', 'glacier', 'woodland', 'subalpine', 'crater', 'glaciers', 'glaciated', 'Garmin', 'snowpack', 'Polar', 'Fitbit', 'meadow', 'carb', 'U.S.-led', 'vegetation', 'Snowdon', 'elliptical', 'forecasters')\n",
-      "; most_similar female: ('Karpal', 'demands', 'essence', 'Onstage', 'spirit', 'Sabai', 'DeVita', 'expressions', 'Jayan', 'theatrics', 'Playwright', 'Danley', 'pathos', 'Sincerity', 'Silat', 'epitome', 'expression', 'Jax', 'alive', 'playwright')\n",
-      "=====================================\n",
-      "gender direction 106.\n",
-      " most_similar male: ('campfire', 'bonfire', 'alight', 'Tiene', 'shag', 'Farewell', 'fiddle', 'Primates', 'pants', 'rockin', 'shirtless', 'chop', 'underwear', 'flannel', 'escribir', 'willy', 'baggy', 'pee', 'Thriller', 'scent')\n",
-      "; most_similar female: ('Commerce', 'Mercatus', 'FNB', 'Corporation', 'Marchenko', 'Kharkov', 'Yaroslav', 'Kiron', 'Blum', 'Sigel', 'Starkman', 'Thaler', 'DDA', 'CEC', 'JDC', 'voter', 'KMC', 'IFAS', 'TVM', 'Maltz')\n",
-      "=====================================\n",
-      "gender direction 107.\n",
-      " most_similar male: ('Liceo', 'Primaria', 'Bonum', 'Colegio', 'liceo', 'Cinco', 'Tétouan', 'Admiral', 'Sabancı', 'Fondo', 'Gulbenkian', 'Shorenstein', 'acromegaly', 'Infantil', '1516', 'Stomatology', 'toreros', 'prognosis', 'leukemia', 'divorced')\n",
-      "; most_similar female: ('Anywhere', 'Everywhere', 'raves', 'Bump', 'bump', 'stream', 'Requests', 'Devotees', 'Ventured', 'logs', 'Raves', 'Recommendations', 'butters', 'streams', 'Massive', 'speedups', 'snowpack', 'suggestions', 'Utube', 'Deeper')\n",
-      "=====================================\n",
-      "gender direction 108.\n",
-      " most_similar male: ('Bringing', 'Rumor', 'rediscovers', 'fetched', 'contention', 'Stays', 'revived', 'stokes', 'benchmark', 'tumbled', 'GALLERY', 'bringing', 'Feature', 'Reviving', 'Refreshing', 'News', 'tumbling', 'Quite', 'alive', 'gone')\n",
-      "; most_similar female: ('NJMS', 'Preprofessional', 'Muhimbili', 'Oculoplastics', 'Sokoine', 'Trabalhadores', 'sonography', 'dotting', '01089', 'ARDMS', 'S.U.N.Y.', 'InterAmerican', 'PUCRS', 'USCIS', 'NYCDOE', 'Sonography', '02115', 'UCEDD', 'Javits', '02114')\n",
-      "=====================================\n",
-      "gender direction 109.\n",
-      " most_similar male: ('probationary', 'unreasonable', 'reasonable', 'qualify', 'prohibitive', 'subsidize', 'unfair', 'justify', 'fairer', 'gyms', 'rid', 'ranks', 'Disciplined', 'preferable', 'rank', 'accustomed', 'tiered', 'normal', 'memberships', 'fair')\n",
-      "; most_similar female: ('Depuy', 'Duralde', 'Karlheinz', 'Lore', 'Gallenberger', 'Lado', 'Calva', 'Bayona', 'Morell', 'Ramón', 'McNeel', 'folklore', 'produced', 'Himmat', 'Paldi', 'Rickman', 'Gérard', 'Kirschstein', 'woodcarver', 'Brochet')\n",
-      "=====================================\n",
-      "gender direction 110.\n",
-      " most_similar male: ('structure-', 'Preacher', 'buckle', 'Cites', 'Scarface', 'Xzibit', 'mLearning', 'Poker', 'theÂ', 'ACCU', '85-year', 'Reparations', 'Grudge', 'Kidnapped', 'Executions', 'W.Va', 'Hakes', 'Heckler', 'Nazi', 'bloodline')\n",
-      "; most_similar female: ('Sagar', 'vernal', 'Ganymede', 'COMMUNICATION', 'Saptarshi', 'Slava', 'oneiric', 'Krishan', 'Vasant', 'moon', 'Gagan', 'Kripa', 'Vitebsk', 'Shveta', 'sublimation', 'Neeti', 'Anu', 'Metamorphoses', 'Jivan', 'Kenora')\n",
-      "=====================================\n",
-      "gender direction 111.\n",
-      " most_similar male: ('Leawood', 'covenant', 'HASSELL', 'walkers', 'equating', 'zombies', 'sorghum', 'L.L.M.', 'conveyancer', 'Mahayana', 'RapidResponse', 'restatements', 'Shaara', 'MDes', 'mindless', 'Courtyard', 'grit', 'CADRE', 'NRP', 'pointillism')\n",
-      "; most_similar female: ('Bald', 'XXX', 'Stenn', 'cam', 'webcam', 'FreeOnes', 'Compilation', 'Vixon', 'webcams', 'Cheeks', 'Tapes', 'pornstar', 'Deshon', 'Booty', 'ModelMayhem', 'Hotties', 'Playboy', 'Cam', 'Playgirl', 'Starr')\n",
-      "=====================================\n"
+      "[Parallel(n_jobs=64)]: Using backend ThreadingBackend with 64 concurrent workers.\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "gender direction 112.\n",
-      " most_similar male: ('securitized', 'numerics', 'topologies', 'topology', 'topological', 'syllabic', 'hygiene', 'securitizations', 'notated', 'insulated', 'multicenter', 'SCNM', 'superconducting', 'loops', 'maintained', 'essentialized', 'modulated', 'collateralized', 'DownBeat', 'tamed')\n",
-      "; most_similar female: ('usher', 'Marketing', 'Labor', 'scraping', 'Ministry', 'Gribble', 'Capitalist', 'Rafat', 'Shuck', 'Winstanley', 'scouring', 'poking', 'graveyard', 'dawn', 'Victim', 'Brogden', 'Scraping', 'Deese', 'pit', 'Usher')\n",
-      "=====================================\n",
-      "gender direction 113.\n",
-      " most_similar male: ('GAP', 'DSM', 'Outback', 'Voyagers', 'MPI', 'Numero', 'GEO', 'TSI', 'ION', 'exposing', '8410', 'Rorvik', 'Eggert', 'OnEarth', 'scanning', 'metadata', 'loader', 'offending', 'scanned', 'Vagabond')\n",
-      "; most_similar female: ('Jimi', 'denies', 'B.Ed', 'lawful', 'Hanafi', 'faculties', 'Faculties', 'Singhania', 'Hendrix', 'Subha', 'M.M', 'Truvy', 'Bisexual', 'say', 'B.D.S', 'M.ed', 'Piercings', 'recollection', 'pupil', 'Boitnott')\n",
-      "=====================================\n",
-      "gender direction 114.\n",
-      " most_similar male: ('perturb', 'regression', 'Mantle', 'amplification', 'reproducible', 'Stochastic', 'progenitors', 'Reproducing', 'Kidner', 'purify', 'ethnoarchaeology', 'Progeny', 'variants', 'generative', 'bless', 'emulation', 'hybridisation', 'dilute', 'Gush', 'variant')\n",
-      "; most_similar female: ('nut', 'Compliance', 'Backpacker', '2013-', 'UNI', 'Priorities', 'traineeship', 'obligations', 'COOKIE', 'priorities', 'Dietitian', 'FISMA', 'PROFILE', 'taxes', 'Resolutions', 'FEDERAL', 'Budget', 'compliance', 'Worksite', 'reimbursements')\n",
-      "=====================================\n",
-      "gender direction 115.\n",
-      " most_similar male: ('rehearsing', 'frantic', 'afternoon', 'tuck', 'Shortly', 'chore', 'nervous', 'habit', 'Cue', 'hurried', 'prologue', 'Zoot', 'beastly', 'finish', 'frantically', 'Lunaris', 'Queue', 'dreaded', 'Mallinger', 'Queuing')\n",
-      "; most_similar female: ('ministries', 'churches', 'NTUA', 'Churches', 'regionalization', 'MRE', 'decentralization', 'Geodesy', 'MEG', 'ACN', 'TEC', 'DYS', 'dioceses', 'Regionalization', 'Steubenville', 'denominational', 'CREC', 'groundwater', 'generalizability', 'Ministries')\n",
-      "=====================================\n",
-      "gender direction 116.\n",
-      " most_similar male: ('Defensive', 'Conestoga', 'Grossmont', 'EHow', 'Bears', 'Claiborne', 'Evangelista', 'polarity', 'Ledyard', 'Diggs', 'Lyndsie', 'Offense', 'Bollier', 'Nichelson', 'Thiel', 'Ayinde', 'offense', 'Tice', 'Dighton', 'Kalyn')\n",
-      "; most_similar female: ('SPR', 'glazed', 'nan', 'begs', 'core', 'HMC', 'infill', 'Supercomputing', 'Enhancements', 'Endemic', 'Kielder', 'biomedically', 'abstraction', 'oncologists', 'Gaeilge', 'Owler', 'parallelization', 'shaders', 'Aged', 'sip')\n",
-      "=====================================\n",
-      "gender direction 117.\n",
-      " most_similar male: ('3686', 'Alder', 'Blazer', '4064', 'Millican', 'occasion', 'Nephi', 'reads', 'fervour', 'disappoint', 'nan', 'votes', 'Temuco', 'interpretation', 'Lafferty', 'Duff', 'Carrico', 'nothing', 'Vizenor', 'Smithville')\n",
-      "; most_similar female: ('wealthiest', 'externalizing', 'Externalization', 'Disappearing', 'richest', 'Benefits', 'Ruining', 'Decline', 'downloadable', 'Rethinking', 'Maximizing', 'earners', 'reconceptualizing', 'Strides', 'Profits', 'Interdependent', 'Effects', 'maximizing', 'benefits', 'Minimized')\n",
-      "=====================================\n",
-      "gender direction 118.\n",
-      " most_similar male: ('swollen', 'debt', 'benign', 'recovers', 'Ganges', 'warlike', 'cognates', 'Metaphilosophy', 'debts', 'Bharata', 'recuperates', 'Douro', 'doubtful', 'securitized', 'entangled', 'withdraws', 'Persianate', 'republicanism', 'reorganize', 'degraded')\n",
-      "; most_similar female: ('Keys', 'Mims', 'Wiggs', 'classroom', 'FOUR', 'Leichman', 'Pointer', 'Crayton', 'SIX', 'Sherling', 'Row', 'Nevermind', 'KIRA', 'WARD', 'keys', 'Angello', 'row', 'Parkview', 'Jonell', 'Moshi')\n",
-      "=====================================\n",
-      "gender direction 119.\n",
-      " most_similar male: ('watched', 'Kalamazoo', 'Rapids', 'Kalida', 'Benda', 'Saginaw', 'deciding', 'faced', 'Openness', 'Dowagiac', 'Allegan', 'Chauncey', 'Farmington', 'Troy', 'unfold', 'Marquette', 'MI', 'WDIV', 'MAKE', 'Jalen')\n",
-      "; most_similar female: ('pointillism', 'Belotero', 'immunohistochemistry', 'Juvederm', 'Radiesse', 'brushwork', 'monochromes', 'anatomical', 'Byun', 'Smilex', 'Joo', 'Volbella', 'museological', 'Precise', 'Shevchenko', 'MCZ', 'Choo', 'Ishida', 'Kybella', 'Swelling')\n",
-      "=====================================\n",
-      "gender direction 120.\n",
-      " most_similar male: ('TSC2', 'terpenoid', 'mysteriously', 'Slack', 'remote', 'JVM', 'conf', 'headaches', 'middleware', 'Headaches', 'bullied', 'RxJS', 'multiplatform', 'Confederated', 'streaming', 'binds', 'Piscataway', 'inexplicably', 'Fios', 'integrations')\n",
-      "; most_similar female: ('Strauss', 'Eisenstein', 'Ethical', 'ethically', 'Artistic', 'Bucharest', 'Braun', 'Domestic', 'Urban', 'Museology', 'Ettinger', 'responsibly', 'Oster', 'Oberholzer', 'Disegno', 'Metropolis', 'Curatorship', 'portray', 'Ethically', 'Singer')\n",
-      "=====================================\n",
-      "gender direction 121.\n",
-      " most_similar male: ('actionable', 'achieving', 'Graduating', 'attaining', 'graduating', 'earning', 'garnering', 'achievable', 'constructive', 'stabilizing', '4228', 'brightest', 'implementers', 'implementable', 'MCP', 'CPIP', 'feedback', 'smartest', 'sabotaging', 'rejoining')\n",
-      "; most_similar female: ('hymnals', 'Passenger', 'bilingually', 'Englishes', 'Trapp', 'Houten', 'Sharan', 'Ahlstrom', 'Weiter', 'Dindy', 'van', 'Wyk', 'vocabulary', 'Dass', 'Proulx', 'B.V.', 'Vistar', 'Luso', 'Homero', 'Uden')\n",
-      "=====================================\n",
-      "gender direction 122.\n",
-      " most_similar male: ('DF', 'parametric', 'SSG', 'EOS', 'Eos', 'ROC', 'TOR', 'TAI', 'LX', 'LIV', 'RES', 'Ruina', 'MCF', 'MF', 'DSG', 'DFC', 'BEG', 'SM', 'GK', 'APG')\n",
-      "; most_similar female: ('Educationally', 'selfhood', 'psychologies', 'portrayals', 'antidotes', 'undermined', 'enactments', 'authentically', 'confrontations', 'injurious', 'Positively', 'transpersonal', 'Americanization', 'educationally', 'selves', 'detrimental', 'Mäori', 'harmful', 'undesirable', 'Māori')\n",
-      "=====================================\n",
-      "gender direction 123.\n",
-      " most_similar male: ('Vision', 'Syntha', 'Adagio', 'Harmony', 'K7', 'Solace', 'potentials', 'Bandy', 'intentions', 'Concerto', 'Astell', 'Gleneagles', 'Revelation', 'irons', 'lifes', 'twin', 'No.2', 'GH4', 'Swope', '4AD')\n",
-      "; most_similar female: ('Smithsonian', 'Testudo', 'Polynesian', 'Wendy', 'mammal', 'SCBWI', 'crocodile', 'Capitals', 'penguins', 'D.C.', 'je', 'PowerPoint', 'Kissing', 'penguin', 'Penguins', 'WTOP', 'Disneyland', 'Cafeteria', 'doughnut', 'Jafar')\n",
-      "=====================================\n",
-      "gender direction 124.\n",
-      " most_similar male: ('internalize', 'suffer', 'perish', 'TIRR', 'Inflammation', 'Immunodeficiency', 'Thrombophilia', 'Immunobiology', 'Retardation', 'sit', 'Sperm', 'Ischemia', 'perpetuate', 'Syndrom', 'Psychophysiology', 'jumble', 'Supremacist', 'plastered', 'Thrombosis', 'Tumor')\n",
-      "; most_similar female: ('Cudahy', 'Kildare', 'discreet', 'Las', 'Meath', 'Tipperary', 'Cesar', 'Broadbent', 'g.', 'stepson', 'informal', 'involving', 'Clare', 'Daly', 'Calabasas', 'Hacienda', 'Fiore', 'attending', 'Richie', 'Wicklow')\n",
-      "=====================================\n",
-      "gender direction 125.\n",
-      " most_similar male: ('worldwide', 'adventurers', 'globally', 'arises', 'trachomatis', 'Discontinuity', 'rift', 'Keenly', 'Globally', 'motorcyclists', 'Ahmadabad', 'Mpumalanga', 'Gauteng', 'polarized', 'Maharastra', '%', 'region', 'rifts', 'Raigad', 'wherein')\n",
-      "; most_similar female: ('Adelphi', 'iSchool', 'Widener', 'REU', 'ODU', 'ORNL', 'Towson', 'EdD', 'SU', 'UMBC', 'NYIT', '4-volume', 'SJSU', 'instructorship', 'ORAU', 'UMD', 'assistantship', 'JHU', 'Michener', 'UConn')\n",
-      "=====================================\n",
-      "gender direction 126.\n",
-      " most_similar male: ('Tiered', 'vertically', 'discipled', 'Align', 'concentric', 'approx', 'Kukatpally', '3-tier', 'horizontally', '2x', '946', 'catalyze', 'interconnects', '2223', 'catalyse', 'mentored', 'spaced', 'labelled', 'Ø', 'tiered')\n",
-      "; most_similar female: ('contraband', 'rhinovirus', 'Harpold', 'smuggling', 'animating', 'NMU', 'foreignness', 'impersonations', 'objectionable', 'weariness', 'Orientalist', 'impersonation', 'Bribery', 'enforcing', 'customs', 'Bers', 'asthma', 'Wassenaar', 'Mounties', 'vacationing')\n",
-      "=====================================\n",
-      "gender direction 127.\n",
-      " most_similar male: ('propeller', 'AIJ', '2035', 'propellers', 'impossible', '2025', 'computationally', 'Goncharov', 'Farooqui', 'Abidi', 'Shestakov', 'Mehndiratta', 'nonlinearities', 'microfluidics', 'nonconvex', 'Ruchita', 'metabarcoding', '2030', 'MFC', 'deuterium')\n",
-      "; most_similar female: ('OTHER', 'wondering', 'sowing', 'referring', 'Ranting', 'homophobic', 'COURSE', 'sore', 'courses', 'turf', 'preacher', 'Heartland', 'concerned', 'horsewoman', 'teaching', 'bigot', 'See', 'unaccredited', 'Episcopalian', 'offended')\n",
-      "=====================================\n",
-      "gender direction 128.\n",
-      " most_similar male: ('Mois', 'implode', 'CPN', 'Kettler', 'infect', 'Sitbon', 'CDMS', 'psychotherapeutic', 'Brayton', 'infecting', 'Nicoloff', 'Kadmon', 'psychoanalyst', 'Spaniol', 'estranged', 'Dyadic', 'psychopathological', 'Barkin', 'psychoses', 'CPNP')\n",
-      "; most_similar female: ('Transportation', 'Talent', 'guesswork', 'Restaurants', 'Tip', 'section', 'Ambassadors', 'jug', 'Motoring', 'recommendations', 'tips', 'ambassadors', 'Musicians', 'Generosity', 'Abundance', 'Shaykh', 'itinerary', 'rule', 'wineries', 'Tell')\n",
-      "=====================================\n",
-      "gender direction 129.\n",
-      " most_similar male: ('Wiens', 'Denice', 'Haag', 'Kolar', 'Jonelle', 'Leiva', 'Milam', 'Kosak', 'Huberty', 'Wiese', 'Tanja', 'Whitten', 'Tollefson', 'Darci', 'Ibarra', 'Killeen', 'Jann', 'Haggard', 'Davidsonville', 'Jeannie')\n",
-      "; most_similar female: ('irritation', 'rivalry', 'Papyrus', 'prophylaxis', 'rivalries', 'impact-', 'rule-', 'religiously', 'Rivalry', 'Obstetric', 'hangover', 'continuous', 'enmity', 'Acute', 'prophylactic', 'Prophylaxis', 'Traction', 'Mild', 'PPD', 'preference')\n",
-      "=====================================\n",
-      "gender direction 130.\n",
-      " most_similar male: ('cultures', 'PCI', 'differences', 'polarity', 'Colombians', 'Ideals', 'genders', 'discs', 'polarities', 'hours', 'NDAs', 'Quidditch', 'ideals', 'loyalties', 'commonality', 'uncommon', 'Initech', 'peaceful', 'commonalities', 'faiths')\n",
-      "; most_similar female: ('LINE', 'Introducing', 'Communications', 'ala', 'Multimedia', 'ported', 'ann', 'arsenal', 'Telephony', 'Telecommunication', 'tempt', 'esque', 'iPhone', 'telecommunications', 'iphone', 'N8', 'veto', 'cf', 'leake', 'hurled')\n",
-      "=====================================\n",
-      "gender direction 131.\n",
-      " most_similar male: ('VMs', 'VMware', 'Eleazar', 'filesystem', 'filesystems', 'HDFS', 'aggregates', 'EMFStore', 'ClearCase', 'QRadar', 'virtualization', 'NVM', 'vSphere', 'Win32', 'hyperconverged', 'VM', 'HCS', 'debugger', 'Crashlytics', 'MidoNet')\n",
-      "; most_similar female: ('Challenging', 'Rediscover', 'Mornings', 'dare', 'exhilarating', 'flair', 'urban', 'mornings', 'invigorating', 'Towards', 'Contemporary', 'rediscover', 'challenging', 'contemporary', 'Rabat', 'novelty', 'Urban', 'Marrakesh', 'Someday', 'Copenhagen')\n",
-      "=====================================\n"
+      "max_iter reached after 15 seconds\n",
+      "Biased Train accuracy 0.7716273603803318\n",
+      "Biased Test accuracy 0.7656889258719922\n",
+      "Train accuracy 0.6010790310087871\n",
+      "Test accuracy 0.5974273628735871\n"
      ]
     },
     {
-     "name": "stdout",
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "gender direction 132.\n",
-      " most_similar male: ('Realists', 'australis', 'potted', 'Raphaels', 'Columba', 'Aristote', 'Boathouse', 'Seagull', 'borealis', 'neo', 'Fireweed', 'Swans', 'emanated', 'Herter', 'Augustine', 'literati', 'Expressionists', 'Basking', 'asemic', 'lighthouse')\n",
-      "; most_similar female: ('multiple', 'assignments', 'specialisation', 'specialization', 'assigments', 'selective', 'coverages', 'seniority', 'Selective', 'specialist', 'Specialist', 'AIT', 'arbitrations', 'specialisations', 'job', 'Tuba', 'reservist', 'Tez', 'specializations', 'IBN7')\n",
-      "=====================================\n",
-      "gender direction 133.\n",
-      " most_similar male: ('conventionally', 'andhas', 'well-', 'classically', 'ruff', 'francophone', 'lotta', 'asa', 'Kortright', 'sizeable', 'clubhouse', 'provincially', 'large-', 'dyke', 'nonformal', 'a', 'Rideau', 'civically', 'TCC', 'Lisgar')\n",
-      "; most_similar female: ('Devices', 'Sorcery', 'saddened', 'heading', 'Demise', 'Elder', 'Intent', 'Falling', 'disclosure', 'closeness', 'heartbeats', 'passing', 'idolizes', 'discontinuing', 'Controller', 'Obsession', 'Orson', 'Monopolies', 'Presidency', 'Hardship')\n",
-      "=====================================\n",
-      "gender direction 134.\n",
-      " most_similar male: ('Microbiologists', 'Ragtime', 'Niccolai', 'Kubernetes', 'Docker', 'Meyerson', 'Lerone', 'Narcisi', 'Novello', 'Chemists', 'Astronautical', 'Hortonworks', 'Garvey', 'Organists', 'Rhysling', 'Buttigieg', 'Alumnae', 'musicology', 'Aretha', 'Ayyar')\n",
-      "; most_similar female: ('SZ', 'uncertainties', 'trembling', 'depiction', 'TM', 'stress', 'bearing', 'reliefs', 'outflow', 'numbness', 'flashes', 'uncertainty', 'springs', 'flickering', 'TS', 'animations', 'Ts', 'sublimation', 'displays', 'KL')\n",
-      "=====================================\n",
-      "gender direction 135.\n",
-      " most_similar male: ('interiority', 'HAL', 'ATM', 'Grief', 'Cleansing', 'Ergonomic', 'PCOS', 'Disappeared', 'CPAP', 'PML', 'Enquiry', 'Personhood', 'personhood', 'CART', 'OI', 'Ondaatje', 'Intimacies', 'CARD', 'Activa', 'Facilitation')\n",
-      "; most_similar female: ('freshmen', 'freshman', 'sophomores', 'chancellor', 'college', 'dean', 'grades', 'semesters', 'headmaster', 'classmates', 'deans', 'WCU', 'school', 'colleges', 'students', 'tom', 'graduate', 'roommates', 'varsity', 'graduates')\n",
-      "=====================================\n",
-      "gender direction 136.\n",
-      " most_similar male: ('Thema', 'Approximately', 'foreigners', 'wie', 'Match', 'Mariel', 'Saidy', '�', 'Ich', 'Pairs', 'Euros', 'Following', 'Tag', 'Lucerne', 'Maxi', 'Simple', 'hier', 'Loose', 'Eder', 'Freestyle')\n",
-      "; most_similar female: ('technoscience', 'medicolegal', 'zooming', 'docket', 'subspeciality', 'litigative', '98105', 'paleoclimate', 'io9', 'conferencing', 'reinventing', 'dc', '15224', 'redeveloping', 'Horsham', 'sci', 'neglecting', 'otosclerosis', 'VR', 'rheumatology')\n",
-      "=====================================\n",
-      "gender direction 137.\n",
-      " most_similar male: ('Lohmann', 'INRS', 'Heuer', 'CIRANO', 'Bernick', 'Hofmann', 'Hauger', 'Rohling', 'OTREC', 'Goller', 'Eawag', 'Lalive', 'FNRS', 'Foth', 'EUROGRAPHICS', 'Kallmeyer', 'flexography', 'Hasselberg', 'IHDP', 'Kling')\n",
-      "; most_similar female: ('Far', 'dreaded', 'deemed', 'Sword', 'deems', 'weathers', 'sickle', 'Anvil', 'biblical', 'Warrior', 'Tyranny', 'tolerable', 'sacred', 'heath', 'truths', 'encountered', 'Cairn', 'mighty', 'Mornings', 'none')\n",
-      "=====================================\n",
-      "gender direction 138.\n",
-      " most_similar male: ('syndromes', 'Gamewell', '-omics', 'farce', 'Taymor', 'braking', 'cytogenetics', 'MCC', 'Aster', 'assays', 'Sibley', 'bells', 'redundant', 'farcical', 'occlusive', 'STEMI', 'misusing', 'Heights', 'jeopardy', 'yield')\n",
-      "; most_similar female: ('contributor', 'URL', 'tanned', 'models.com', 'Rusher', 'Juxtapoz', '1413', 'Rippy', '15203', 'Academia.edu', 'XLR8R', 'academia.edu', 'DLD', 'Influencers', 'SSRN', 'http', '4577', '8796', 'Jelen', 'surfs')\n",
-      "=====================================\n",
-      "gender direction 139.\n",
-      " most_similar male: ('imagination', 'Hypochondria', 'Reveal', 'Depicting', 'Regard', 'Bartók', 'fascination', 'resides', 'Heracles', 'Revealing', 'ailment', 'nature', 'Geographical', 'Fairies', 'persistence', 'inner', 'Ravel', 'mythical', 'tranquility', 'fixated')\n",
-      "; most_similar female: ('BMT', 'LSC', 'grading', 'IPS', 'grad', 'WMU', 'CU', 'seminary', 'MDX', 'IMRT', 'TSX', 'flotation', 'CGS', 'BCU', 'cadets', 'Vantage', 'grade', 'ELPS', 'UHV', 'shootouts')\n",
-      "=====================================\n",
-      "gender direction 140.\n",
-      " most_similar male: ('Concertino', 'Affinities', 'chances', 'stats', 'Kindle', 'Defender', 'IMDb', 'Transcendent', 'Civilisation', 'Iolani', 'Artifact', 'Notables', '200th', 'historicity', 'IMDB', 'Masterpieces', 'Civilization', 'category', 'Literati', 'Quintet')\n",
-      "; most_similar female: ('CALM', 'calm', 'applicator', 'sage', 'hazel', 'lavender', 'Nicosia', 'Tangier', 'beck', 'counselors', 'supervisors', 'spill', 'calmness', 'RCRA', 'hotline', 'biomonitoring', 'consultants', 'gray', 'regulators', 'Willner')\n",
-      "=====================================\n",
-      "gender direction 141.\n",
-      " most_similar male: ('9400', '757', '9550', '8007', '1070', '1061', '1144', '7080', 'FEES', '1150', '4600', '1250', '9850', '1045', '980', '7000', '5250', '4200', '990', '8950')\n",
-      "; most_similar female: ('intertwine', 'meld', 'intermingle', 'harmonize', 'melds', 'counterbalance', 'aligns', 'resonates', 'detective', 'interlinking', 'coexist', 'sleuth', 'personified', 'imbues', 'emanate', 'pinpoints', 'spotlights', 'juxtapose', 'align', 'bureaus')\n",
-      "=====================================\n",
-      "gender direction 142.\n",
-      " most_similar male: ('imprint', 'chancery', 'digest', 'unsettled', 'salivary', 'whether', 'Biopsy', 'rereleased', 'bullet', 'superannuation', 'elastomer', 'gland', 'biopsy', 'release', 'roost', 'reissue', 'Doubt', 'seminal', 'environs', 'unsettling')\n",
-      "; most_similar female: ('Teller', 'Peoples', 'Dornsife', 'Basset', 'Online', 'AJS', 'Bonanza', 'Wayuu', 'Poker', 'Morongo', 'Maidu', 'Fortney', 'Biker', 'Agoura', 'Eckley', 'BBW', 'Hippie', 'Iroquois', 'Javan', 'Lovers')\n",
-      "=====================================\n",
-      "gender direction 143.\n",
-      " most_similar male: ('breakers', 'reached', 'Ameriprise', 'Protection', 'DSL', 'Ngoepe', 'Sash', 'Comission', 'Hallandale', 'consulation', 'Breakers', 'Pretoria', 'zoned', 'Counselor', 'Appt', 'Rang', 'Nedbank', 'advise', 'Barrier', 'Fikile')\n",
-      "; most_similar female: ('egos', 'wrangles', '\\x97', 'backstage', 'musicians', 'creativities', 'wrangling', 'restless', 'biopharma', 'enriches', 'collaborating', 'hyperactive', 'juggles', 'overactive', 'dicks', 'collaboration', 'teamwork', 'insatiable', 'cocks', 'colossal')\n",
-      "=====================================\n",
-      "gender direction 144.\n",
-      " most_similar male: ('church', 'pharmacy', 'coffee', 'latte', 'milk', 'Finnegan', 'Presti', 'Nicks', 'league', 'wifi', 'Collinsworth', 'Starbucks', 'concussions', 'candlelight', 'Conformation', 'meatballs', 'NFL', 'lattes', 'surfed', 'AFTRA')\n",
-      "; most_similar female: ('Shahdara', '1541', '1546', 'Adugodi', '1483', 'Banashankari', '1185', 'MRTA', '7780', '14203', '1276', '14215', '1591', '1255', 'BAU', '1386', '10400', '1231', 'Domlur', '1617')\n",
-      "=====================================\n",
-      "gender direction 145.\n",
-      " most_similar male: ('coils', 'Webs', 'coil', 'rods', 'webs', 'exhibit', 'rugs', 'loops', 'impulsively', 'nanotube', 'ducts', 'transgression', 'rug', 'exhibits', 'spirals', 'fibers', 'reticulum', 'cascades', 'exhibited', 'Zanjan')\n",
-      "; most_similar female: ('MPS', 'iSeries', 'KODAK', 'CEREC', 'OpenEmbedded', 'Socialists', 'COA', 'Austerity', 'Convenience', 'relying', 'Relying', 'Bolstering', 'OPS', 'SAD', 'ELITE', 'Ricoh', 'GPC', 'Tories', 'OPC', 'electing')\n",
-      "=====================================\n",
-      "gender direction 146.\n",
-      " most_similar male: ('Marathwada', 'Sangh', 'Orissa', 'Odisha', 'rainfall', 'clothed', 'Ujjain', 'fed', 'Bihar', 'Berhampur', 'Jharkhand', 'Utkal', 'Vicenza', 'Gorakhpur', 'Pontificial', 'Chattisgarh', 'Todi', 'Arunachal', 'Pontifical', 'Govt')\n",
-      "; most_similar female: ('Litigator', 'Cruising', 'blepharoplasty', 'Kravis', 'Hellraiser', 'Painkiller', 'Zofia', 'multisport', 'Epix', 'Eye', 'ILW.COM', 'POZ', 'Dentons', 'LeClairRyan', 'genderqueer', 'Beholder', 'Jinx', 'Swix', 'Cozen', 'gel')\n",
-      "=====================================\n",
-      "gender direction 147.\n",
-      " most_similar male: ('Shrek', 'Lancome', '50.000', 'prize', 'lemmings', 'Harrods', 'Huntsman', 'Fairytale', 'Frozen', 'giveaway', 'wishes', 'Disney', 'Pixar', 'Lancôme', 'refused', 'beens', 'price', 'Apple', '5000', 'Miliband')\n",
-      "; most_similar female: ('Started', 'Conceived', 'Incorporating', 'breeze', 'Formed', 'aegis', '90.1', 'Propagation', '90.7', 'Recorded', 'Maddoux', 'Separating', 'Getting', 'Deriving', 'Adherence', 'Implementing', 'ASCA', 'breezy', '88.5', 'Baselios')\n",
-      "=====================================\n",
-      "gender direction 148.\n",
-      " most_similar male: ('3121', 'biorefinery', 'B.S.E.E.', 'Hilfinger', '3090', '2801', 'Kanbar', 'coinventor', '2980', 'NREL', 'Bellcore', 'Fachhochschule', '3045', 'Outfest', '2901', 'BAVC', 'CITRIS', '2701', '2360', 'CEEE')\n",
-      "; most_similar female: ('explanations', 'descriptions', 'helpful', 'explanation', 'heartbreaking', 'spoiling', 'understandable', 'exaggeration', 'complicating', 'unhelpful', 'colouring', 'drastically', 'tremendously', 'mood', 'Heartbreaking', 'greatly', 'Easter', 'distressing', 'upsetting', 'prayers')\n",
-      "=====================================\n",
-      "gender direction 149.\n",
-      " most_similar male: ('30-year', '20-year', '10-year', 'MEDEX', 'trialled', 'Wanderings', 'Scandinavia', '30-day', 'commemorate', 'Arbour', '25-year', 'Maritimes', 'Saltspring', '15-year', 'Pangnirtung', 'NorthShore', '5-year', 'laminates', '40-year', 'laminate')\n",
-      "; most_similar female: ('Hayek', 'optional', 'Barbi', 'Libertarian', 'Poonam', 'Laxmi', 'Fed', 'Paz', 'libertarian', 'extraneous', 'Wataru', 'Salaz', 'Krit', 'F.A.', 'Shinji', 'Ram', 'Kavya', 'Gakuen', 'Twombly', 'fork')\n",
-      "=====================================\n"
+      "[Parallel(n_jobs=64)]: Done   1 tasks      | elapsed:   14.7s\n",
+      "[Parallel(n_jobs=64)]: Done   1 out of   1 | elapsed:   14.7s finished\n"
      ]
     }
    ],
    "source": [
-    "i = 0\n",
-    "for w in Ws[:150]:\n",
-    "    \n",
-    "    #sims = word2vec.similar_by_vector(-w.squeeze(), topn = 60, restrict_vocab=None)\n",
-    "    most_similar_f, _ =  list(zip(*word2vec_bios.similar_by_vector(w.squeeze(), topn = 20, restrict_vocab=None)))\n",
-    "    most_similar_m, _ =  list(zip(*word2vec_bios.similar_by_vector(-w.squeeze(), topn = 20, restrict_vocab=None)))\n",
-    "    print(\"gender direction {}.\\n most_similar male: {}\\n; most_similar female: {}\".format(i, most_similar_m, most_similar_f))\n",
-    "    print(\"=====================================\")\n",
-    "    i += 1"
+    "clf = LogisticRegression(warm_start = True, penalty = 'l2',\n",
+    "                         solver = \"sag\", multi_class = 'multinomial', fit_intercept = True,\n",
+    "                         verbose = 10, max_iter = 6, n_jobs = 64, random_state = 0, class_weight = None)\n",
+    "\n",
+    "clf.fit(x_train, y_train)\n",
+    "\n",
+    "print(f\"Biased Train accuracy {clf.score(x_train, y_train)}\")\n",
+    "print(f\"Biased Test accuracy {clf.score(x_test, y_test)}\")\n",
+    "print(f\"Train accuracy {clf.score(debiased_x_train, y_train)}\")\n",
+    "print(f\"Test accuracy {clf.score(debiased_x_test, y_test)}\")"
    ]
   },
   {
@@ -1887,209 +1286,124 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 243,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'g': 'f',\n",
-       " 'p': 'nurse',\n",
-       " 'text': 'Esther E Brown is a Nurse Practitioner Specialist in Salem, Oregon. She graduated with honors in 2002. Having more than 15 years of diverse experiences, especially in NURSE PRACTITIONER, Esther E Brown affiliates with Salem Hospital, and cooperates with other doctors and specialists in medical group Salem Clinic, Pc. Call Esther E Brown on phone number (503) 399-2424 for more information and advises or to book an appointment.',\n",
-       " 'start': 67,\n",
-       " 'hard_text': 'She graduated with honors in 2002. Having more than 15 years of diverse experiences, especially in NURSE PRACTITIONER, Esther E Brown affiliates with Salem Hospital, and cooperates with other doctors and specialists in medical group Salem Clinic, Pc. Call Esther E Brown on phone number (503) 399-2424 for more information and advises or to book an appointment.',\n",
-       " 'text_without_gender': '_ graduated with honors in 2002. Having more than 15 years of diverse experiences, especially in NURSE PRACTITIONER, _ _ _ affiliates with Salem Hospital, and cooperates with other doctors and specialists in medical group Salem Clinic, Pc. Call _ _ _ on phone number (503) 399-2424 for more information and advises or to book an appointment.',\n",
-       " 'hard_text_tokenized': 'She graduated with honors in 2002 . Having more than 15 years of diverse experiences , especially in NURSE PRACTITIONER , Esther E Brown affiliates with Salem Hospital , and cooperates with other doctors and specialists in medical group Salem Clinic , Pc . Call Esther E Brown on phone number ( 503 ) 399 - 2424 for more information and advises or to book an appointment .'}"
-      ]
-     },
-     "execution_count": 243,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "nurse_train = [r for r in train if r[\"p\"] == \"nurse\"]\n",
-    "nurse_train[0]"
-   ]
+   "outputs": [],
+   "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": 128,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [],
    "source": [
-    "profs_train_str = np.array([i2p[y] for y in Y_train[:]])"
+    "def tsne_by_gender(vecs, labels, title, words = None):\n",
+    "\n",
+    "  tsne = TSNE(n_components=2, random_state=0)\n",
+    "  vecs_2d = tsne.fit_transform(vecs)\n",
+    "  num_labels = len(set(labels.tolist()))\n",
+    "\n",
+    "  names = [\"class {}\".format(i) for i in range(num_labels)]\n",
+    "  plt.figure(figsize=(6, 5))\n",
+    "  colors = 'r', 'b', 'orange'\n",
+    "  for i, c, label in zip(set(labels.tolist()), colors, names):\n",
+    "    print(len(vecs_2d[labels == i, 0]))\n",
+    "    plt.scatter(vecs_2d[labels == i, 0], vecs_2d[labels == i, 1], c=c,\n",
+    "                label=label, alpha = 0.6)\n",
+    "  plt.legend(loc = \"upper right\")\n",
+    "  plt.title(title)\n",
+    "  \n",
+    "  if words is not None:\n",
+    "        k = 60\n",
+    "        for i in range(k):\n",
+    "            \n",
+    "            j = np.random.choice(range(len(words)))\n",
+    "            label = labels[i]\n",
+    "            w = words[j]\n",
+    "            x,y = vecs_2d[i]\n",
+    "            plt.annotate(w , (x,y), size = 10, color = \"black\" if label == 1 else \"black\")\n",
+    "            \n",
+    "  plt.show()\n",
+    "  return vecs_2d"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 131,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "438\n",
+      "362\n"
+     ]
+    },
     {
      "data": {
+      "image/png": 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\n",
       "text/plain": [
-       "((255682, 300), (255682,))"
+       "<Figure size 432x360 with 1 Axes>"
       ]
      },
-     "execution_count": 131,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X_train.shape, profs_train_str.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 332,
-   "metadata": {},
-   "outputs": [
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "accountant 1.4361589787313929 % 0.6947167755991286 0.6636710239651417\n",
-      "architect 2.570380394396164 % 0.615642118076689 0.5348447961046866\n",
-      "attorney 8.288420772678561 % 0.8696677991694979 0.8259720649301623\n",
-      "chiropractor 0.6601950860834944 % 0.47334123222748814 0.44016587677725116\n",
-      "comedian 0.7110394943719152 % 0.7442244224422442 0.7046204620462047\n",
-      "composer 1.422861210409806 % 0.7660802638812534 0.7218251786695987\n",
-      "dentist 3.6799618275827006 % 0.8686364119460092 0.8567329152938675\n",
-      "dietitian 1.01063039244061 % 0.7430340557275542 0.6385448916408669\n",
-      "dj 0.37663973216730157 % 0.6542056074766355 0.6105919003115264\n",
-      "filmmaker 1.7815098442596662 % 0.7369923161361142 0.706476399560922\n",
-      "interior_designer 0.3715552913384595 % 0.5452631578947369 0.43894736842105264\n",
-      "journalist 5.070751949687502 % 0.7170844581565754 0.6748168145005785\n",
-      "model 1.9066653108157792 % 0.7577435897435898 0.683076923076923\n",
-      "nurse 4.822396570740216 % 0.7705596107055961 0.7046228710462287\n",
-      "painter 1.9657230465969444 % 0.7600477516912058 0.7278153601273378\n",
-      "paralegal 0.44899523627005417 % 0.27439024390243905 0.2029616724738676\n",
-      "pastor 0.6429862094320288 % 0.5395377128953771 0.4811435523114355\n",
-      "personal_trainer 0.3633419638457146 % 0.6027987082884823 0.573735199138859\n",
-      "photographer 6.183071158704954 % 0.8666582326522867 0.8209247896767664\n",
-      "physician 9.8035841396735 % 0.8291310939120722 0.7955796696720657\n",
-      "poet 1.7819009550926541 % 0.7047848990342406 0.6202809482001755\n",
-      "professor 30.025969759310396 % 0.8593609566112204 0.8587487462713785\n",
-      "psychologist 4.6503078042255614 % 0.6747687132043734 0.6119428090832633\n",
-      "rapper 0.35708419051790896 % 0.7053669222343921 0.6232201533406353\n",
-      "software_engineer 1.7549143076164926 % 0.6469801649208825 0.5910407844885224\n",
-      "surgeon 3.373722045353212 % 0.6291444470206353 0.5503130071875725\n",
-      "teacher 4.117223738863119 % 0.5994110382825116 0.47829391089579176\n",
-      "yoga_teacher 0.4220085887938924 % 0.6200185356811863 0.5987025023169601\n"
+      "438\n",
+      "362\n"
      ]
-    }
-   ],
-   "source": [
-    "for p in p2i.keys():\n",
-    "    x_train_nurse = X_train[profs_train_str == p]\n",
-    "    y_train_nurse = Y_train[profs_train_str == p]\n",
-    "    print(p,len(x_train_nurse)/len(X_train)*100,\"%\", clf_original.score(x_train_nurse, y_train_nurse), clf.score(P.dot(x_train_nurse.T).T, y_train_nurse))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 266,
-   "metadata": {},
-   "outputs": [
+    },
     {
      "data": {
+      "image/png": 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\n",
       "text/plain": [
-       "((4556, 300), (4556,))"
+       "<Figure size 432x360 with 1 Axes>"
       ]
      },
-     "execution_count": 266,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "x_train_nurse.shape, y_train_nurse.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 267,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "0.6248902546093064"
-      ]
+     "metadata": {
+      "needs_background": "light"
      },
-     "execution_count": 267,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 268,
-   "metadata": {},
-   "outputs": [
+     "output_type": "display_data"
+    },
     {
      "data": {
       "text/plain": [
-       "0.7047848990342406"
+       "array([[  7.3815756 ,   0.2581286 ],\n",
+       "       [-10.514421  ,  -0.7842676 ],\n",
+       "       [  0.9040104 ,   6.0851326 ],\n",
+       "       ...,\n",
+       "       [ -4.084077  ,  14.131436  ],\n",
+       "       [  1.2464001 ,   8.642483  ],\n",
+       "       [-15.642866  ,   0.47921807]], dtype=float32)"
       ]
      },
-     "execution_count": 268,
+     "execution_count": 55,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": []
+   "source": [
+    "prof = \"professor\"\n",
+    "idx = np.random.rand(x_dev.shape[0]) < 0.1\n",
+    "prof_idx = y_dev == p2i[prof]\n",
+    "n = 800\n",
+    "tsne_by_gender(x_dev[prof_idx][:n], np.array(dev_gender)[prof_idx][:n], \"tsne by gender, before, {}\".format(prof))\n",
+    "tsne_by_gender((debiased_x_dev[prof_idx])[:n], np.array(dev_gender)[prof_idx][:n], \"tsne by gender, after. {}\".format(prof))"
+   ]
   },
   {
    "cell_type": "code",
-   "execution_count": 306,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'professor': 0.45118956904580476,\n",
-       " 'chiropractor': 0.26558891454965355,\n",
-       " 'psychologist': 0.6223011751844766,\n",
-       " 'architect': 0.23712053792148718,\n",
-       " 'physician': 0.507688318423441,\n",
-       " 'nurse': 0.9085446207369142,\n",
-       " 'dentist': 0.35589474411216243,\n",
-       " 'surgeon': 0.14857228961048746,\n",
-       " 'rapper': 0.09665955934612651,\n",
-       " 'model': 0.8283124500133298,\n",
-       " 'photographer': 0.35721920736720936,\n",
-       " 'composer': 0.16392857142857142,\n",
-       " 'comedian': 0.21150410861021793,\n",
-       " 'filmmaker': 0.3295762590954487,\n",
-       " 'paralegal': 0.8483305036785512,\n",
-       " 'journalist': 0.49488721804511276,\n",
-       " 'personal_trainer': 0.45670391061452514,\n",
-       " 'teacher': 0.603111879476414,\n",
-       " 'painter': 0.4579886246122027,\n",
-       " 'attorney': 0.38316925813475633,\n",
-       " 'accountant': 0.36818825194621374,\n",
-       " 'software_engineer': 0.1576889661164205,\n",
-       " 'poet': 0.49080017115960634,\n",
-       " 'dj': 0.1420875420875421,\n",
-       " 'pastor': 0.24052132701421802,\n",
-       " 'yoga_teacher': 0.8454600120264583,\n",
-       " 'dietitian': 0.9273504273504274,\n",
-       " 'interior_designer': 0.8086124401913876}"
-      ]
-     },
-     "execution_count": 306,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "{'professor': 0.45118956904580476, 'chiropractor': 0.26558891454965355, 'psychologist': 0.6223011751844766, 'architect': 0.23712053792148718, 'physician': 0.507688318423441, 'nurse': 0.9085446207369142, 'dentist': 0.35589474411216243, 'surgeon': 0.14857228961048746, 'rapper': 0.09665955934612651, 'model': 0.8283124500133298, 'photographer': 0.35721920736720936, 'composer': 0.16392857142857142, 'comedian': 0.21150410861021793, 'filmmaker': 0.3295762590954487, 'paralegal': 0.8483305036785512, 'journalist': 0.49488721804511276, 'personal_trainer': 0.45670391061452514, 'teacher': 0.603111879476414, 'painter': 0.4579886246122027, 'attorney': 0.38316925813475633, 'accountant': 0.36818825194621374, 'software_engineer': 0.1576889661164205, 'poet': 0.49080017115960634, 'dj': 0.1420875420875421, 'pastor': 0.24052132701421802, 'yoga_teacher': 0.8454600120264583, 'dietitian': 0.9273504273504274, 'interior_designer': 0.8086124401913876}"
-   ]
+   "outputs": [],
+   "source": []
   },
   {
    "cell_type": "code",
diff --git a/notebooks/notebook_fair-profession_fasttext_kSAL.ipynb b/notebooks/notebook_fair-profession_fasttext_kSAL.ipynb
new file mode 100644
index 0000000..ec0b54c
--- /dev/null
+++ b/notebooks/notebook_fair-profession_fasttext_kSAL.ipynb
@@ -0,0 +1,1086 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from scipy import linalg\n",
+    "from sklearn.metrics.pairwise import rbf_kernel, polynomial_kernel\n",
+    "import random\n",
+    "import time\n",
+    "import copy\n",
+    "from collections import defaultdict, Counter\n",
+    "\n",
+    "from typing import List\n",
+    "import pickle\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.stats.stats import pearsonr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_TPR(y_pred, y_true, p2i, i2p, y_p_test):\n",
+    "    gender = np.where(y_p_test == 1, 'f', 'm')\n",
+    "    scores = defaultdict(Counter)\n",
+    "    prof_count_total = defaultdict(Counter)\n",
+    "    \n",
+    "    for y_hat, y, g in zip(y_pred, y_true, gender):\n",
+    "        \n",
+    "        if y == y_hat:\n",
+    "            \n",
+    "            scores[i2p[y]][g] += 1\n",
+    "        \n",
+    "        prof_count_total[i2p[y]][g] += 1\n",
+    "    \n",
+    "    tprs = defaultdict(dict)\n",
+    "    tprs_change = dict()\n",
+    "    tprs_ratio = []\n",
+    "    \n",
+    "    for profession, scores_dict in scores.items():\n",
+    "        \n",
+    "        good_m, good_f = scores_dict[\"m\"], scores_dict[\"f\"]\n",
+    "        prof_total_f = prof_count_total[profession][\"f\"]\n",
+    "        prof_total_m = prof_count_total[profession][\"m\"]\n",
+    "        if prof_total_m > 0 and prof_total_f > 0:\n",
+    "            tpr_m = (good_m) / prof_total_m\n",
+    "            tpr_f = (good_f) / prof_total_f\n",
+    "\n",
+    "            tprs[profession][\"m\"] = tpr_m\n",
+    "            tprs[profession][\"f\"] = tpr_f\n",
+    "            tprs_ratio.append(0)\n",
+    "            tprs_change[profession] = tpr_f - tpr_m\n",
+    "        else:\n",
+    "            print(f\"profession {profession} missed in tpr-calc:\\n\\\n",
+    "                  number of {profession} male in test set {prof_total_m}\\n\\\n",
+    "                  number of {profession} female in test set {prof_total_f}\\n\\n\")\n",
+    "        \n",
+    "    return tprs, tprs_change, np.mean(np.abs(tprs_ratio))\n",
+    "    \n",
+    "def similarity_vs_tpr(tprs, word2vec, title, measure, prof2fem):\n",
+    "    \n",
+    "    professions = list(tprs.keys())\n",
+    "    #\n",
+    "    \"\"\" \n",
+    "    sims = dict()\n",
+    "    gender_direction = word2vec[\"he\"] - word2vec[\"she\"]\n",
+    "    \n",
+    "    for p in professions:\n",
+    "        sim = word2vec.cosine_similarities(word2vec[p], [gender_direction])[0]\n",
+    "        sims[p] = sim\n",
+    "    \"\"\"\n",
+    "    tpr_lst = [tprs[p] for p in professions]\n",
+    "    sim_lst = [prof2fem[p] for p in professions]\n",
+    "\n",
+    "    #professions = [p.replace(\"_\", \" \") for p in professions if p in word2vec]\n",
+    "    \n",
+    "    plt.plot(sim_lst, tpr_lst, marker = \"o\", linestyle = \"none\")\n",
+    "    plt.xlabel(\"% women\", fontsize = 13)\n",
+    "    plt.ylabel(r'$GAP_{female,y}^{TPR}$', fontsize = 13)\n",
+    "    for p in professions:\n",
+    "        x,y = prof2fem[p], tprs[p]\n",
+    "        plt.annotate(p , (x,y), size = 7, color = \"red\")\n",
+    "    plt.ylim(-0.4, 0.55)\n",
+    "    z = np.polyfit(sim_lst, tpr_lst, 1)\n",
+    "    p = np.poly1d(z)\n",
+    "    plt.plot(sim_lst,p(sim_lst),\"r--\")\n",
+    "    plt.savefig(\"{}_vs_bias_{}_bert\".format(measure, title), dpi = 600)\n",
+    "    print(\"Correlation: {}; p-value: {}\".format(*pearsonr(sim_lst, tpr_lst)))\n",
+    "    plt.show()\n",
+    "\n",
+    "def rms_diff(tpr_diff):\n",
+    "    \n",
+    "    return np.sqrt(np.mean(tpr_diff**2))\n",
+    "    \n",
+    "# def save_vecs_and_words(vecs, words):\n",
+    "#     def to_string(arr):\n",
+    "#         return \"\\t\".join([str(x) for x in arr])\n",
+    "    \n",
+    "#     with open(\"vecs.txt\", \"w\") as f:\n",
+    "#         for v in vecs:\n",
+    "#             assert len(v) == 300\n",
+    "#             f.write(to_string(v) + \"\\n\")\n",
+    "    \n",
+    "#     with open(\"labels.txt\", \"w\") as f:\n",
+    "#             f.write(\"Profession\\n\")\n",
+    "#             for w in words:\n",
+    "#                 f.write(w + \"\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_dataset(path):\n",
+    "    \n",
+    "    with open(path, \"rb\") as f:\n",
+    "        \n",
+    "        data = pickle.load(f)\n",
+    "    return data\n",
+    "\n",
+    "def load_dictionary(path):\n",
+    "    \n",
+    "    with open(path, \"r\", encoding = \"utf-8\") as f:\n",
+    "        \n",
+    "        lines = f.readlines()\n",
+    "        \n",
+    "    k2v, v2k = {}, {}\n",
+    "    for line in lines:\n",
+    "        \n",
+    "        k,v = line.strip().split(\"\\t\")\n",
+    "        v = int(v)\n",
+    "        k2v[k] = v\n",
+    "        v2k[v] = k\n",
+    "    \n",
+    "    return k2v, v2k\n",
+    "    \n",
+    "def count_profs_and_gender(data: List[dict]):\n",
+    "    \n",
+    "    counter = defaultdict(Counter)\n",
+    "    for entry in data:\n",
+    "        gender, prof = entry[\"g\"], entry[\"p\"]\n",
+    "        counter[prof][gender] += 1\n",
+    "        \n",
+    "    return counter"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Load original data (train.pickle etc.) and pre-calculated BERT CLS states (\"train_cls.npy\" etc.)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.44541700643683746\n",
+      "{'professor': 0.4533699535765501, 'psychologist': 0.627327974906881, 'pastor': 0.2583668005354752, 'comedian': 0.21605667060212513, 'nurse': 0.9126009126009126, 'yoga_teacher': 0.825, 'attorney': 0.3766122913505311, 'photographer': 0.3563658099222953, 'composer': 0.16329625884732052, 'model': 0.7858407079646018, 'surgeon': 0.12832108535895986, 'physician': 0.4158485273492286, 'software_engineer': 0.17130434782608694, 'poet': 0.5133630289532294, 'painter': 0.47116788321167885, 'dj': 0.1636828644501279, 'journalist': 0.5159589626674266, 'architect': 0.23372781065088757, 'paralegal': 0.8618677042801557, 'dentist': 0.3672911787665886, 'personal_trainer': 0.4410377358490566, 'teacher': 0.5943396226415094, 'accountant': 0.3950091296409008, 'interior_designer': 0.7874493927125507, 'dietitian': 0.934412955465587, 'filmmaker': 0.3533007334963325, 'chiropractor': 0.3069908814589666, 'rapper': 0.09438775510204081}\n"
+     ]
+    }
+   ],
+   "source": [
+    "train = load_dataset(\"../data/biasbios/train.pickle\")\n",
+    "dev = load_dataset(\"../data/biasbios/dev.pickle\")\n",
+    "test = load_dataset(\"../data/biasbios/test.pickle\")\n",
+    "counter = count_profs_and_gender(train+dev+test)\n",
+    "prof2fem = dict()\n",
+    "\n",
+    "f,m = 0., 0.\n",
+    "for k, values in counter.items():\n",
+    "    f += values['f']\n",
+    "    m += values['m']\n",
+    "    prof2fem[k] = values['f']/(values['f'] + values['m'])\n",
+    "\n",
+    "print(f / (f + m))\n",
+    "print(prof2fem)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "saved_dataset = np.load(f\"../data/saved_models/fair_biography_prof_gender/FastText/all.npz\")\n",
+    "\n",
+    "x_train = saved_dataset['x_train']\n",
+    "y_m_train = saved_dataset['y_m_train']\n",
+    "y_p_train = saved_dataset['y_p_train']\n",
+    "\n",
+    "x_dev = saved_dataset['x_dev']\n",
+    "y_p_dev = saved_dataset['y_p_dev']\n",
+    "y_m_dev = saved_dataset['y_m_dev']\n",
+    "\n",
+    "x_test = saved_dataset['x_test']\n",
+    "y_p_test = saved_dataset['y_p_test']\n",
+    "y_m_test = saved_dataset['y_m_test']\n",
+    "\n",
+    "y_p_train_2d = np.asarray([y_p_train, - y_p_train + 1]).T"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p2i, i2p = load_dictionary(\"../data/biasbios/profession2index.txt\")\n",
+    "g2i, i2g = load_dictionary(\"../data/biasbios/gender2index.txt\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Option 1: Train the model from scratch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# N = 15000\n",
+    "# N_test = 5000\n",
+    "N = 4000\n",
+    "N_test = 1000\n",
+    "\n",
+    "ratio = \"FastText\"\n",
+    "\n",
+    "# kernel_fn = \"rbf\"\n",
+    "# kernel_coef = 0.1\n",
+    "kernel_fn = \"poly\"\n",
+    "kernel_coef = 2\n",
+    "\n",
+    "assert x_train[:N, :].shape[1] == x_train[:N, :].shape[1]\n",
+    "\n",
+    "K_y = y_p_train_2d[:N, :] @ y_p_train_2d[:N, :].T\n",
+    "if kernel_fn == \"linear\":\n",
+    "    K_x = x_train[:N, :] @ x_train[:N, :].T\n",
+    "    K_x_test_tr = x_test[:N_test, :] @ x_train[:N, :].T\n",
+    "elif kernel_fn == \"rbf\":\n",
+    "    K_x = rbf_kernel(x_train[:N, :], x_train[:N, :], kernel_coef)\n",
+    "    K_x_test_tr = rbf_kernel(x_test[:N_test, :], x_train[:N, :], kernel_coef)\n",
+    "elif kernel_fn == \"poly\":\n",
+    "    n_features = x_train[:N, :].shape[1]\n",
+    "    K_x = rbf_kernel(x_train[:N, :], x_train[:N, :], kernel_coef)\n",
+    "    K_x_test_tr = polynomial_kernel(x_test[:N_test, :], x_train[:N, :], degree = kernel_coef, gamma = n_features, coef0=1)\n",
+    "\n",
+    "print(f\"K_x.shape, K_x_test_tr.shape {K_x.shape, K_x_test_tr.shape}\")\n",
+    "eigenval, W = np.linalg.eig(np.dot(K_y, K_x))\n",
+    "order = np.argsort(np.abs(np.real(eigenval)))\n",
+    "W = W[:, order]\n",
+    "W = np.real(W)\n",
+    "A = np.concatenate((K_x, K_x_test_tr), axis=0)\n",
+    "u, s, vh = np.linalg.svd(A)\n",
+    "s = s**(0.5)\n",
+    "s = np.diag(s)\n",
+    "print(f\"A.shape, u.shape, s.shape, vh.shape {A.shape, u.shape, s.shape, vh.shape}\")\n",
+    "\n",
+    "K_x_sqrt_all = np.dot(np.dot(u[:, :s.shape[0]], s), vh[:s.shape[1], :])\n",
+    "K_x_sqrt2 = K_x_sqrt_all[:N, :]\n",
+    "K_x_test_sqrt2 = K_x_sqrt_all[N:, :]\n",
+    "print(f\"K_x.shape, K_x_test_sqrt2.shape, K_x_sqrt_all.shape {K_x.shape, K_x_test_sqrt2.shape, K_x_sqrt_all.shape}\")\n",
+    "\n",
+    "remove_eigen_num = 2\n",
+    "U_proj_ker = linalg.null_space((K_x_sqrt2 @ W[:, :remove_eigen_num]).T)\n",
+    "debiased_x_train = np.dot(K_x_sqrt2, U_proj_ker)\n",
+    "debiased_x_test = np.dot(K_x_test_sqrt2, U_proj_ker)\n",
+    "print(f\"debiased_x_train.shape, debiased_x_test.shape {debiased_x_train.shape, debiased_x_test.shape}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "random.seed(0)\n",
+    "np.random.seed(0)\n",
+    "\n",
+    "clf_original = LogisticRegression(warm_start = True, penalty = 'l2',\n",
+    "                         solver = \"saga\", multi_class = 'multinomial', fit_intercept = False,\n",
+    "                         verbose = 5, n_jobs = 90, random_state = 1, max_iter = 7)\n",
+    "                         \n",
+    "\n",
+    "clf_original.fit(x_train[:N, :], y_m_train[:N])\n",
+    "print(f\"Score of profession classifier on original(biased) dataset \\n{clf_original.score(x_test[:N_test, :], y_m_test[:N_test])}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "clf_debiased = LogisticRegression(warm_start = True, penalty = 'l2',\n",
+    "                         solver = \"saga\", multi_class = 'multinomial', fit_intercept = False,\n",
+    "                         verbose = 5, n_jobs = 90, random_state = 1, max_iter = 7)\n",
+    "\n",
+    "clf_debiased.fit(debiased_x_train, y_m_train[:N])\n",
+    "print(f\"Score of profession classifier on debiased dataset \\n{clf_debiased.score(debiased_x_test, y_m_test[:N_test])}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred_before = clf_original.predict(x_test[:N_test])\n",
+    "tprs_before, tprs_change_before, mean_ratio_before = get_TPR(y_pred_before, y_m_test[:N_test], p2i, i2p, y_p_test[:N_test])\n",
+    "similarity_vs_tpr(tprs_change_before, None, \"before\", \"TPR\", prof2fem)\n",
+    "\n",
+    "y_pred_after = clf_debiased.predict(debiased_x_test)\n",
+    "tprs, tprs_change_after, mean_ratio_after = get_TPR(y_pred_after, y_m_test[:N_test], p2i, i2p, y_p_test[:N_test])\n",
+    "similarity_vs_tpr(tprs_change_after, None, \"after\", \"TPR\", prof2fem)\n",
+    "\n",
+    "change_vals_before = np.array(list((tprs_change_before.values())))\n",
+    "change_vals_after = np.array(list(tprs_change_after.values()))\n",
+    "\n",
+    "print(\"rms-diff before: {}; rms-diff after: {}\".format(rms_diff(change_vals_before), rms_diff(change_vals_after)))\n",
+    "\n",
+    "# np.savez(f\"../../data/saved_models/fair_biography_prof_gender/{ratio}/cleaned_Train{N}_Test{N_test}.npz\", debiased_x_train = debiased_x_train, debiased_x_test = debiased_x_test)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Option 2: load directly"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Train and save the model by src/ksal/ksal.py \n",
+    "\n",
+    "Then load it to the notebook"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def tpr_exp(x_test, debiased_x_train, y_m_train, debiased_x_test, y_m_test, y_p_test, N_test, p2i, i2p, clf_original):\n",
+    "    y_p_test_string = np.where(y_p_test == 1, 'f', 'm')\n",
+    "\n",
+    "    y_pred_before = clf_original.predict(x_test[:N_test])\n",
+    "    tprs_before, tprs_change_before, mean_ratio_before = get_TPR(y_pred_before, y_m_test[:N_test], p2i, i2p, y_p_test[:N_test])\n",
+    "    similarity_vs_tpr(tprs_change_before, None, \"before\", \"TPR\", prof2fem)\n",
+    "\n",
+    "    clf_debiased = LogisticRegression(warm_start = True, penalty = 'l2',\n",
+    "                             solver = \"saga\", multi_class = 'multinomial', fit_intercept = False,\n",
+    "                             verbose = 5, n_jobs = 90, random_state = 1, max_iter = 7)\n",
+    "\n",
+    "    clf_debiased.fit(debiased_x_train, y_m_train[:N])\n",
+    "    print(f\"\\n\\n\\n================================================\\\n",
+    "                \\nSCORE: of profession classifier on debiased dataset\\\n",
+    "                \\n{clf_debiased.score(debiased_x_test, y_m_test[:N_test])}\\\n",
+    "                \\n================================================\\n\\n\\n\")\n",
+    "\n",
+    "    y_pred_after = clf_debiased.predict(debiased_x_test)\n",
+    "    tprs, tprs_change_after, mean_ratio_after = get_TPR(y_pred_after, y_m_test[:N_test], p2i, i2p, y_p_test[:N_test])\n",
+    "    similarity_vs_tpr(tprs_change_after, None, \"after\", \"TPR\", prof2fem)\n",
+    "\n",
+    "    change_vals_before = np.array(list((tprs_change_before.values())))\n",
+    "    change_vals_after = np.array(list(tprs_change_after.values()))\n",
+    "\n",
+    "    print(\"rms-diff before: {}; rms-diff after: {}\".format(rms_diff(change_vals_before), rms_diff(change_vals_after)))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "N = 15000\n",
+    "N_test = 5000\n",
+    "ratio = \"FastText\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=90)]: Using backend ThreadingBackend with 90 concurrent workers.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "max_iter reached after 5 seconds\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/lustre/home/sc066/shunshao/miniconda3/envs/cca/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
+      "[Parallel(n_jobs=90)]: Done   1 out of   1 | elapsed:    5.4s finished\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Score of profession classifier on original(biased) dataset \n",
+      "0.7022\n"
+     ]
+    }
+   ],
+   "source": [
+    "random.seed(0)\n",
+    "np.random.seed(0)\n",
+    "\n",
+    "clf_original = LogisticRegression(warm_start = True, penalty = 'l2',\n",
+    "                         solver = \"saga\", multi_class = 'multinomial', fit_intercept = False,\n",
+    "                         verbose = 5, n_jobs = 90, random_state = 1, max_iter = 7)\n",
+    "                         \n",
+    "\n",
+    "clf_original.fit(x_train[:N, :], y_m_train[:N])\n",
+    "print(f\"Score of profession classifier on original(biased) dataset \\n{clf_original.score(x_test[:N_test, :], y_m_test[:N_test])}\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 1. Linear Kernel"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kernel_fn = \"linear\"\n",
+    "kernel_coef = 0\n",
+    "\n",
+    "saved_model = np.load(f\"../data/saved_models/fair_biography_prof_gender/{ratio}/cleaned_{kernel_fn}_{kernel_coef}_Train{N}_Test{N_test}.npz\")\n",
+    "debiased_x_train_0 = saved_model['debiased_x_train_0']\n",
+    "debiased_x_test_0 = saved_model['debiased_x_test_0']\n",
+    "debiased_x_train_1 = saved_model['debiased_x_train_1']\n",
+    "debiased_x_test_1 = saved_model['debiased_x_test_1']\n",
+    "debiased_x_train_2 = saved_model['debiased_x_train_2']\n",
+    "debiased_x_test_2 = saved_model['debiased_x_test_2']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Removal 0 BERT Linear\n",
+    "tpr_exp(x_test, debiased_x_train_0, y_m_train, debiased_x_test_0, y_m_test, \\\n",
+    "        y_p_test, N_test, p2i, i2p, clf_original)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Removal 1 BERT Linear\n",
+    "tpr_exp(x_test, debiased_x_train_1, y_m_train, debiased_x_test_1, y_m_test, \\\n",
+    "        y_p_test, N_test, p2i, i2p, clf_original)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "profession dietitian missed in tpr-calc:\n",
+      "                  number of dietitian male in test set 0\n",
+      "                  number of dietitian female in test set 59\n",
+      "\n",
+      "\n",
+      "Correlation: 0.773655072810786; p-value: 6.29340039229481e-05\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=90)]: Using backend ThreadingBackend with 90 concurrent workers.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "max_iter reached after 298 seconds\n",
+      "\n",
+      "\n",
+      "\n",
+      "================================================                \n",
+      "SCORE: of profession classifier on debiased dataset                \n",
+      "0.6974                \n",
+      "================================================\n",
+      "\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/lustre/home/sc066/shunshao/miniconda3/envs/cca/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
+      "[Parallel(n_jobs=90)]: Done   1 out of   1 | elapsed:  5.0min finished\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "profession dietitian missed in tpr-calc:\n",
+      "                  number of dietitian male in test set 0\n",
+      "                  number of dietitian female in test set 59\n",
+      "\n",
+      "\n",
+      "Correlation: 0.28151941939644465; p-value: 0.21635439221736918\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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6NKbVVhGZw2TdV37HZUumsaMtg8GDOXvc9SytVZdPm7VhYod9OO79iTRe/jO9Zr4O/Q6Iz/XUUyMYNG4cgaRTp1jkcc6cyNCvXh3mz49a7z/8EPXj27WDgQNj+GynneLxxYtjpeG+feP16Yz4UaNiW5HNSE6CTfr6jJkNAy4GLsrFfjcl2zWuW2hq6zeNm3PBb86nReO6nLnvvrDvvoVf0KNHfB83Lr6nL1wPHx4BKZUp/ti0uYzc8Xjsyy+59MX7af/Zt7Q//fi4BvTjj5F/ccopBdeA/vKXeG3fvnEN6JtvYiHENWviekSvXrH8Sfv28Rxs0PWHipI9TPbNL2t44+O50LEFO554Il/uejT/6BBTiQe99xTvbvdr3mrdmU+7HUiv75+OXkzbtvDb30a53QcegO++i58Toody3XUFw2APPhify6hR0dO59dboFdWtG5/TMcfEYxdcEL3UI4+M3+EWW8TnLrKZKDbYmFlz4LfATHefmHqsJnBw+n7Gtg2AzsBn7v6XBNtbZW1MOdlCMrLGMw+sw957khF7/JbvtmnNc9fcQKtH/hsbre8a0COPRBACmDYtlnU/44yYWPDyy4Xfuwpcf8geJhv87hPctld/JjSowyM/j6N35225O786y1fnYTgvtdudaTvsHlOM//NU4Uz92rVjja7RoyMAp9WvH+uUrVxZeIHHm26KXqB7TMpIbwsFw2ZaEFIqwooV8f97xowY4WjQIFbO/uCDOGEqByX1bB4BXgOONLOdgd2AfYA3iGqZmV4mZqE1SwWpue5+RALtrbKSyLbOPLAakG/VWL46j//N+4lW6YPm+q4BrVoF554LW24Zz998czwHhS+Ap68/3HlnTEaYOTNmyGUeeIuSfU0pYdnDlW+07sIZbz7M9/UbQ69edP12Jo+v+pz/Lm/Mwx17MOKVOxledx4d5uXFBIDLLovgOXAgNG8es9BuvDH+UxalefOYwXbjjRGgr7suarIUZ5994KKLIqB36ZK7H1w2L6tXw/ffx//J6tWjfs/ll8ffYu3a0KdPnBC+8Ub83//pp4pucfErCJjZS+5+oJnVBr4F/uDuzxSz7XPu3ivj/nZVqZ5NVV1BoO2wp0j/9lounh/XgOo3YVL7vXi45ox44rTTIqB89FFcuznuuBiaGz8e1qxhcn5jVo0cxdc1G9Bq9c90alaXlksWxAHz3Xdj+O6FF+KPukOHmFI9b14M5V17bQzL9ekTw0MHHQRHHAG77BIr+x5xRPSqmjUrt88kOxM/bZ3FIUUqk7y8GIb94IP4/7XtthFA7rorhsIXLIhRiNLq0gW23homTYqTyWbN4JlnYjj4gANiksvWW0cPZwNPBnO5gkAjM/uVu39mZu8XF2hS7jOzI9z9CVDhtPKSeR0ofQ0I4sDKsKwZUkVcA1o7DNf7vHjow0m8ULMm+173D/pde16svbVmTcH1h06d4uz/kEPiAvncuXEt4sIL4486fa1jl12gY8cKmV6ds+FKkY2RlxfXOl94IXogDRvGwf6Z1GF0t93isSlTIhB8/33xSdZffbXuCdv//V88NnduFI1r1ix6NK1bx//VolxySc5+vA1RUrB5HrjVzNoBTc3sFuBD4CN3fzVr2yOAXc3sKmJBzqnuPiKRFstaG3tgLXGl4YUL44znyisL8nseeAAmT46JAddfX/Ci7FWJocKmV2txSEmEe1T7fPzxOMGCGJL+4Ye43axZ9ERK6733Cmr59O8fvYx33okh3N12iwk3zZpFHl2NTSOTpKSf4ipijbPqwEfAFsAuwFlAdrBp5e7tAcysPbBH7psq2Tb2wFriSsN9e8TQ2ahR8Z9o9er4T1a3bgynZV6ryL7WUcGUiS+lMmMG/L//F7Mut9giirtNnlz616cDDRQdaNq0iWuZxx8f+Vpt28YwdL16G930qqikazYvE6s4f08sRXO5uz9ZzLZXA7e7+5yE2pmoqnrNZmNtEisNp3OBSjJlSsE1q3/8I2bkpWfglWU/UrktWhQzLc88M27nWufOkc8GsOuuMGIE7LlnweSazUwur9mYu5+Z2ulIosxzkcEG6AWcamYvAG8Tw2jZvR+pZKraSsPpZM06n83k4rceoOVeu7Dj0qWRof/99/Dzz5FPNHBgXKP66CM4//wYBnz99UjSnD8/lu+54YaY+tmoERx7bKyFNnx4XH/aYosK/kkFiJ7D/fdH7tPSpbD77pGwnISLLoq/gyZNokciOVdSsGlgZuncmZ/MrFZxG7r7Hqnhs4bEUNtvWXeoTSqZqnR9IzOn6JL3J3L5voNYvFVzJk9/m2YvvxxTilesgP/9L15w/vkxTPLkk7GOXOPGsZpCOk9pzpyY5XPYYTE23rFj4cqXknurV8dKFtdcExfOs7VvH0OzxZmYnXGR5dhj47oiRF7TH/9YkJyr/KYKt74JAjcAu5jZaqCemV0JvOzuz2duaGY3AG2BXxH5OOteDJBKqapc38iczHDI7Le4Y49+LF3jzFm8kmb77104UPz8c4zF9+xZfCLlmDFxQfakk+Ls2SzWkVvfQemuu6Bp01iBYX1Ks7+qbNWqWKninnuiR/C738VU9w315ZfFP9e3b6xYvmoVdO1a/EXz9GxLqXRKumazpbsvSt3eFtiV6LV0cfffZ237srsfYGaT3f2g7Lybym5zvWZTmWWXAfj1J+8ya6vW5Fk1jv1gIitq1qbWmjUs3GJLuu/bMVZozsuLWXIXXxyZ/z16RPLpkiXw5ptRemHlyhh3X7UqekGHHhpn0+4x8aFVqwhWzZtHqYG+fSN/aPbsuBbw3nvw4osxbXWbbWJixK23xvssXhyz9y69NIZiOneO5WqqCvf4nC66qGAGYi6deCLcfXfB/QEDIkD16VOwAKpUGWW9ZlPSadfD6Rvu/q27P+PuI7IDTcoqM6sDa3MMix1yE1mf9JDZ3MXLcaIMwKttuvKPvQfQZf4svm3QjL6Db2LcLr25+7BTolfy3nsxe278+Kj5c/jhcdBcvjxWwZ41K86cW7eOIPLiixFc7rknVkF4441YK65mzQgk6QS6vLxIirvmmtgvRF7D2LFxTWjp0thHo0YxVPfee7HNaadVfKBxj2Gl9Iri6/uqVi3W6NuQQLPbbvD885FvtXhxvHf21113Fb7/0EMxzKVAs1kocYJAGfYzCngO2MrMjtu4Jsnmrqj8n+r5eRhQIz+PX2rXBSC/dm1O2K05TM7I83EvPHTlWc9lys4JmjZt3dyi/PwIOKtXF7wuvX26JESLFoWH8R55JLk8o6+/junmo0cns/9MNWvGtN1zzolEXZGNUFKw+bWZXQG8D3zg7rOyNzCzvdz9LXd/1sxmAQOBTsDxyTRXNgcl5f980a4jbT//mBaN69Kvawt23aEZdDq7cImGdu3igFy/fkx3znyuffs4eL7wwrrBp337wrlFEOvEXXVV9Iwuvrig55LWoEFMf/3Tn2J/J59cth/23XehW6lHIjZO3bqxWOhee8UQn0g5KumazXTgJuI6zS5AO2AuEXhOT23zvLsfamZXu3vFroWwETbbazbZi2QOHx7j6KnyBUBBLZvS5qHMmVOw5P4GSjT/Jy8vei5LlsQw2plnlrx9WfNvfvghkl8zr00kbdQoOO+8TXsyglQ6ucyzWeTud2btvBUReNKWmtk1wGFm9i93/7xszZUkpS+y15/1CYM+f5UDG+Wz/V/OjVySI46IfJRLL42lMo5OVfK+7ba4QJ4uNf3FFzGk9PHHcYb/pz/F6rLucVZ/1VURkNasieshzZpFTsvYsdGr2ACJ5v9Ur162Kc633x6LIs6fv/HvXRpNmsS6V6eeGnVxRDYRJQWb+7MfcPevga8zHvodkdA5CLg+lWuzAHjX3f+ay4ZK2WTmpbSpXoNVS5fx7Ko6HDX0EpqnF8m86CL4298KCoNNmhS5Ct27x/eTTorH99sv8lAuvTRmXi1fHgfgmTNjldpatQoqec6ZEzPDNjDQQML5P3l5MZR0/vkbv6/S6tgxckRatiy/9xSpZIoNNu5++/pe7O6rgafM7Eh3nw5gZlsBu+eshbJBMi+ypwuImUOrt8dxePridfbFdCh88TtT+n5+PvzmN1FSACJxMnMfORrKKXX+j3v0VK64IifvWypHHRWz0zp2LL/3FKniSr2caKrXMhKoA9zn7v9JP5cONKnbPxAz06QCZV5kzywgtmxVxiyvs86KA/W22xYEj+LUrBk5JRdeGL2WV16JXJUxY6KnM3Ro5LYMGVKwQOeG9h6+/TYO5mPHbtjrN8R//hM1eEQkEcVOEFhnQ7PJRP7Mh8BxwOnu/kCCbSs3m+IEgUpVRGzWrAhGjz8eQ26rVxdfuyMXDj00Amm/fsm9h8hmLpdJndl2A/q4+xnE2md/LGvjpPwM7b0jdWsWLqKUsyJiy5ZFAmRpkwU7dIhAA9Eb2pBAc9llcb2lqGTB7K/nnlOgEalkylKVx9z959TtF4F/J9AeyZEyXWRftixWTv7732NIrG3bmIWWlNtugx13jAUSa2mxCZHNQVmCTQ0z28HdZ7l7nplVSAUgM+sDjCGKut2RXRHUzGoD9xCTFH4Ajq2qdXY2WH4+vPce/Xr2pN+SJQWPl3Z+4IYEmvPOi+s/DRuW/bUisskryzDaYuATM1toZk8Atc3sEDPbOpmmrcvMqgO3AIcBHYGBZpY9JegUIkeoPfB34Lryal9i3CPX5bzzYKedImO9pGGr6tVj+fzMQFMWI0bAhAlR37w0w1buMSFAgab0hg4t+vEpU9Y/MSLJ610iCSl1z8bdtzOz7YiSz3sQkwUeBBqb2QJiZYHeyTRzrT2B2enkUTMbBxwFfJyxzVHA8NTt8cBYMzMv7UyI8rJoEUydGjO7Zs5M/v0OPDAOYpmrA1R2n30WAfbDD6O3lctqmmVZ6SB7pYVirF2petEyTv38ZY5f8QWt9+sWwf++++L7McfEQqDp3mPfvsUXeuvUKRavXLMm6vW0bBn5TN27R7CqWXMjPgCR8lWWYTTcfR7weOoLADP7FRF8ymOBpxYUTir9BtiruG3cfY2ZLQG2Ispbr2VmQ4AhAK1bt974lv3yS2TZz5gR10DSS9C/+GJM5c3l8iUXXABdukTvZaedcrffSiJ90B78yFh2XvId7ZtuR7PMDQ48MFY4+PTTOFC/807UruneHQYPjp5fOvH0rbeip5afHwf8776L7Pxtt419TZ0aqzZfeeW6KyN06hQLUfbvH5McWLf0QTqgfDR/KTv/5x6O7HQQb7TuQu/Xn+CbWnX4sUs3dn3j6ahz361bTLGePz8W/fzPf2J5m/ffj0A0blwEweXLYcGCSASdOTNyl15/Pa6pzZ4dP+eUKTHrTqSKKFOwyWRmLYjrIrtlfF2Yo3Ylzt1vA26DmPq8wTvaddc4QBQ1ZDVwYMzCaty48ONNm0ZVyZYt4ZNP4rG+fSOjf7/9SnUWvanKXPnghfZ78PXCren+zUcsnzaXfumNateO4DBmTHyGZ55ZsPJBhw5R0+boo2PSQ48e8Oyz8fmuWBEnAPfeG5/1m29GcBk9Gv75z8IrI6xaBdttFystFNE2iNIHY2u2Zd9eg9hi8CBq12/CrXv/lhPefYJ3Wnbis6atOHzC09C0WiwJ9MwzUSdnyy3jZ5g2LWbYtWkTS/u/9VaUt27XLgLi734XgfX662MJmy++iKJhF11UfPEwkUqqLEmdx1AQVLrC2pNNBx4jqnombS7QKuN+y9RjRW3zjZnVABoREwWSsd9+cZb90ENxoNp553isR484sNx332YdPMqqqPICefnOyImfFgSb9LWh2rXjdrrKZlHP1aoVBdNuuimGntyjBwPx+/ruu+iJZq+MAOuUCSiqbfmrVzNy4qdcumIVa1KlDyz1z/5fTKPt/NkwYEjMwBs1KgLauefGsNkNN8TQ4AknRJtq1oR//StOUvbbDy65JAJmXl4UemvVKrZRoJEqqCx/tQ8RB+1xRHD5APgImAmc5e4Lct66db0D7GBmbYmgchyQXcxtAnAi8AYwAHgx0es16Yu5N92U2FtsTooqL1DS46V20EFw3XXRs0nbfvuoU3/66VFGOntlhFK0IbP0QZvP49LhhJ0O4PaHr2RR3YY8vP8ALrz88rjudOGFcPDBsZjp9tvDzTfDccdFr8os1p67+eaYEr5iRfTWXnklhtHatYvyBU8/vXGfg0gFKcsKAtcA5wMTgfMyLtJ/C+xSTsEGMzscGE1Mfb7T3a9O1d2Z6u4TUhVD7yV6Xz8Cx61vNepNcQWBqqpSrXyQpaTSB9krVUMk0V7bv3NuFhAVqWQSW0HA3S8GdgXqAx+Z2VUVkWvj7k+7ewd3/5W7X5167G/uPiF1e4W7/9bd27v7nip7ULUkuvLBRspu2/jOPXljp33WJste278zLRrXxYjgqEAjUqDUPZtCLzIbSJSCzge2BNqVV88mCerZVC6ZM75yWl4gBypz20TKU1l7NhsUbFJv1AC4klgj7RngZHf/vuRXVU4KNiIiZZPkQpyFuPvP7v5nIr9mK2KygEjFmjMnLsRDJEQ++WTJ26dnsYlIosoy9bk5sJO7T8583N3fB7qb2Um5bpxIqcyYUZCw2aZNQVnq6dNjWjPE9OEnnohZXsOHR6b+pEmRaDl9elQrnTs3Vos++OCYdrxyZbxu2LDI3B89Opbl2Wef+BKRUivL1OdzgCbAZAAzm08sEzM19fWf4l8qkluZ1072XPMDV8//nvatt4E33ogEybPPjp5N06aR0HnMMfDww/DllzG9eOed4bDDomDa4MGRRFmvXvSK8vKih7TTTrF9Xl5Mh160CN5+u3xLSotsIsoSbA4H+mfcrwe8SqwicDKRy7ZJFFOTyi07k/+wyQ8xpPtv+Uv3Hen95psFGxZVotqsYCHLzKTN+vUjWXLlyhha694dzjmn4PnTTotky99np3WJSGmUJdi0zJpGnOfufwMws77EOmMKNpK47Ez+N1p3YfCrD/L1/5rGqg3pstSHHBIZ+GvWwKBBkby5bFkUYnv99eLfoHdvOOOMWHFg8eLoCXXtGq899tjkf0CRTVBZkjqXAE3dfXXq/snufmfqdn3g/dSy/lWOZqNVLW2HPUVRf7UGfDHiN8m86bBhsVzMH1WgVgTKPhutLD2b94FewFMA6UCTuv2LmTUr7oUiubRd47pFrjKwXeO6yb3piBHr30ZEilWWqc+3AqOLCiqpMgM/5qxVIiWozKsM5NSECbE2WmmVZhp3emr4vHlRsiDb8OFRXmF9U8aLUprCb2macl71bOTvrCzF0/5rZj2BGWZ2JXCfu/+YmhI9llgzTSRx6Yz9TT6T/8cf4auvYqHOOnXgiCNgl10Kir4NGxbXloYPjynfnTtHAOnfP+r0jBoVM+iKquOzalVM9f7f/6J8QevWvLBHbxr+5wkaL17A4i23Jm/29+w9Z3qsVH3MMdCiReF97bEHnHVW1P/p3Dna8NxzUQph5cpo5wMPRCmHn36KBUgfeyx+rq5d4SRlS1SU7LpMg1Z+yfZ77ByTaqZNiyKFzz4btZegIE0gLy9maKYn15idCXQAGgOX4f5Nce9Z1qTOU4llaq4CFprZUmL15a2By8q4r83eY9Pm0n3Ei7Qd9hTdR7zIY9OyqyUQZ7bHHgt33FGQrFhWSZ1FFlfauCw2sG39urbgtWEH88WI3/DasIM3mUCT+Tdx9VMfs+j/3R61e+64I4qtFee00yIgNGgQU7MHDoSXXooSC5l1fLItWABbbsnk3Xpy9gerea35jjzWsQe37tKX5f/vVj5ZXi0C1Ntvr7uv556LgHPDDTF9HGCvveL+woVxf+zYODBtvXXsA+LvWYGmwqRnc85dvBwn6jKNqdGOx/qeHKUvinLYYfDnP0fPuFs3OOccGkb8OAFYAiwmys8Uq6yVOh243sxuBnoA2wJzgFfSEwekdDKn7x758RT2/upDFj1Wn+mH7Mmui7+KM8HRo2Op+1q1Ytgjnaw4e3acjV54YRTXqlMnpu3WrBkHmAULYjbWhAkFZySdOsFTTxWcpfbqVbhBt94aRcMWL46D26WXFk50POAAOOUUaNs2ioC99FLxpY23337dqpdXXx2FwX7+Oc6+Bw6MfJg99lDFyZSiirPNW7SMdz7+jl5t28a07dq1Y3YdRHXYtPSZZv368b1mzeLr+GQ68EBo147PT7qUw2tvRb5Vw6MiD56Xz5DWh/HyJanfz5/+VHhf7utOLy84443vdetGzytt+PB16gRJ+SqqLtPiarWiZlTm77Sov68xY6Iy7kknUSPm5MzFfXhp3neDqjC5+3JiPTTZQJm/8O0Xfcsnzdrw/A57M+Lxf8FHr8TB/L//jQN506aRhPjTT5GsOH589HgaNoySwitXxn/it9+OnsLq1RFkoCBx8YgjYPfdo2ro228XDjZLl0Zp5N694/5778X3zETHlSvjNSeeGN3sbOefH5n8Tz4Z75FZ9fL99+HllyPrfsWKGLrJz1fFySzZBwFz56FOB7P3pZfBhHYRoJs3j6qwN94IH3yw/p0WVccn0+TJ8MQTNPpuLnN22Zkf6jVi0LSn2H/OdO7rehjnjLsOfngmTliy99WrV8zOmzkzypS3KKJ3OWgQDBkSQec3Cc0UlDJZb82oLl3gmmvgs89g//0Lb3T99XHS2KQJP0Ee8DbR+TDgTtzfK+599T+9gmT+wm/uPpCdFnzOxZPvZEVe6qw1M/kQCp9BHnhg5Iz06gWzZsUYeJMm0Tt5/HG4++6CZVrSZyT5+dFbKerg7h4Hiswz0EceKZzomCm78midOtG+9Nl0dtXLGTOiZ5W5/3r1FGiyZP5NNFm2hN4zX+fSXn/k7oZHFp7S/e9/x/f0SgZ33VXw3Pjx8b1Pn4LH+mfmYhPXcjK/H3QQf8+o1TPlVwWzWaft05sBmXWEsvd1222F7/foEd/HjYvvJ5wQX2nZPWopd9mzOcd37glEWYy1v7fiZJRJX3PvveB+fWnfd4MX4pSNkzlNd+D0Z+k3Ywr5Vo05bTtG5vrtt8eZbFpmsmKzZhFkevSI3I9mqQmCHTvGcNXzz6/7huecEz2V88+H++8v/FyDBrDnnjFMcvbZBT2bTIceGvu97LIY0qtTp/gfbtCgKJM9dGiUQO7UKYLR+efDmWfC11+X/oPajGT+TfxYrxGnDric+Q2bJjulO2WzmeEnFfa73uASA5uSikjqzB6fhypQ2fGGG2J2SsOGEXQkpyr6b0K1ejYfufhdl1s9m01JRa0gUGH/uVesKJykuPfehYddpMLogC9VhYLNBtByNSIiZVNuxdNERERKS8FGREQSp2AjIiKJU7AREZHEKdiIiEjiFGxERCRxCjYiIpI4BRsREUmcgo2IiCROwUZERBKnYCMiIolTsBERkcQp2IiISOIUbEREJHEKNiIikjgFGxERSZyCjYiIJE7BRkREEldlgo2ZNTGz581sVur7lkVss6uZvWFmM8zsAzM7tiLaKiIihVWZYAMMA15w9x2AF1L3sy0DTnD3TkAfYLSZNS6/JoqISFGqUrA5Crg7dftuoF/2Bu4+091npW7PAxYAzcqrgSIiUrSqFGy2cfdvU7fnA9uUtLGZ7QnUAj4r5vkhZjbVzKYuXLgwty3N5p7s/kVEKrkaFd2ATGY2CWhexFOXZN5xdzezYo/gZrYtcC9worvnF7WNu98G3AbQrVu3MkeDx6bNZeTET+n2+jMcPP9/dNqxBe2XLoBHH4VPPoFx46BHDxg5Erp3j6877oAOHeD99+P5Sy6BlSshLw9Gj4arroJFi2DJEvjnP2HIENhhB5g7F/r1g169ytpMEZFKoVL1bNy9p7vvXMTX48B3qSCSDiYLitqHmTUEngIucfc3k2jnY9Pm8tdHPmTu4uW0XvQt7zZqyWlb7sfcn1etu/G++8LFF0fwuOMOOOOMePz552HOHGjcGJYujYAyZ04EnR494LnnYrtTT4XrroOHH07iRxERKReVqmezHhOAE4ERqe+PZ29gZrWAR4F73H18Ug0ZOfFTlq/OA+Dm7gPZacHnnP/87cyulk8LgF9+Kdi4UaPMBsYXQH5+9HbOOSfu//RT4e3SQ2/160ONGtEDEhGpoipVz2Y9RgCHmtksoGfqPmbWzczuSG3zO+AAYLCZTU997ZrrhsxbvHzt7YHTn6XfjCnkWzVe3WbH6MWMG7fui844I3opN90EW2wBvXvDBx/A0KFw2mlQqxZsvz1ccAFMnhzPi4hsIsx18Zpu3br51KlTS7199xEvMjcj4KS1aFyX14YdXPSLPv8c7rwTvvsOBg+OXo2ISBVlZu+6e7fSbl+VhtEqjaG9d+Svj3y4digNoG7N6gztvWPxL2rXLiYAiIhshhRsNkC/ri2AuHYzb/Fytmtcl6G9d1z7uIiIFKZgs4H6dW2h4CIiUkpVaYKAiIhUUQo2IiKSOAUbERFJnIKNiIgkTsFGREQSp2AjIiKJU7AREZHEKdiIiEjiFGxERCRxCjYiIpI4BRsREUmcgo2IiCROwUZERBKnYCMiIolTsBERkcQp2IiISOIUbEREJHEKNiIikjgFGxERSZyCjYiIJE7BRkREEqdgIyIiiVOwERGRxCnYiIhI4hRsREQkcQo2IiKSOAUbERFJnIKNiIgkTsFGREQSp2AjIiKJU7AREZHEKdiIiEjiFGxERCRxCjYiIpK4KhNszKyJmT1vZrNS37csYduGZvaNmY0tzzaKiEjRqkywAYYBL7j7DsALqfvFuRJ4uVxaJSIi61WVgs1RwN2p23cD/YrayMx2B7YBniufZomIyPrUqOgGlME27v5t6vZ8IqAUYmbVgBuAQUDPknZmZkOAIam7S83s04ynmwLfb3SLk6G2lV1lbReobRtKbSu7XLdr+7JsXKmCjZlNApoX8dQlmXfc3c3Mi9juLOBpd//GzEp8L3e/DbitmHZMdfdupWt1+VLbyq6ytgvUtg2ltpVdRberUgUbdy+2N2Jm35nZtu7+rZltCywoYrN9gP3N7CxgC6CWmS1195Ku74iISMIqVbBZjwnAicCI1PfHszdw9z+kb5vZYKCbAo2ISMWrShMERgCHmtks4nrMCAAz62Zmd+T4vYocXqsk1Layq6ztArVtQ6ltZVeh7TL3oi59iIiI5E5V6tmIiEgVpWAjIiKJ26yDjZn1MbNPzWy2ma0zkcDMDjCz98xsjZkNqETtOt/MPjazD8zsBTMr03z3hNt2hpl9aGbTzexVM+tYWdqWsd0xZuZmVm7TQEvxuQ02s4Wpz226mZ1aWdqW2uZ3qb+5GWZ2f2Vol5n9PePzmmlmi8ujXaVsW2szm2xm01L/Tw+vRG3bPnXc+MDMpphZy3JpmLtvll9AdeAzoB1QC3gf6Ji1TRugC3APMKAStesgoF7q9pnAA5WobQ0zbh8JPFtZ2pbargGxlNGbxGzFStE2YDAwtjzaswFt2wGYBmyZur91ZWhX1vZ/Au6sRJ/ZbcCZqdsdgTmVqG0PASembh8M3FsebducezZ7ArPd/XN3XwWMI5bEWcvd57j7B0B+JWvXZHdflrr7JlA+Zyala9tPGXfrA+U1A2W9bUu5ErgOWFFO7SpL2ypCadp2GnCLuy8CcPeictwqol2ZBgL/LYd2Qena5kDD1O1GwLxK1LaOwIup25OLeD4Rm3OwaQF8nXH/m9RjFa2s7ToFeCbRFhUoVdvM7I9m9hlwPXBOZWmbme0GtHL3p8qpTWml/Z0ekxraGG9mrcqnaaVqWwegg5m9ZmZvmlmfStIuIIaFgLYUHECTVpq2DQcGmdk3wNNEz6s8lKZt7wP9U7ePBhqY2VZJN2xzDjZVnpkNAroBIyu6LZnc/RZ3/xXwF+DSim4PrF0370bggopuSzGeANq4exfgeQoWna0MahBDaT2IHsTtZta4IhuU5ThgvLvnVXRDMgwE7nL3lsDhwL2pv8HK4ELgQDObBhwIzAUS/+wqyw9fEeYCmWePLVOPVbRStcvMehJrxh3p7isrU9syjKOY1bkTsL62NQB2BqaY2Rxgb2BCOU0SWO/n5u4/ZPwe7wB2L4d2laptxNnxBHdf7e5fADOJ4FPR7Uo7jvIbQoPSte0U4EEAd38DqEMshFnhbXP3ee7e3927klp30t0XJ96y8rgwVBm/iLO1z4nud/pCWqditr2L8psgsN52AV2Ji4A7VLbPLLNNwBHA1MrStqztp1B+EwRK87ltm3H7aODNStS2PsDdqdtNiWGarSq6Xantfg3MIZWgXok+s2eAwanbOxHXbBJvYynb1hSolrp9NXBFuXxu5fULqoxfRPd2ZurAfUnqsSuI3gLAHsRZ3S/AD8CMStKuScB3wPTU14RK9JmNAWak2jW5pAN+ebcta9tyCzal/NyuTX1u76c+t19XorYZMQT5MfAhcFxlaFfq/nBgRHl9VmX4zDoCr6V+n9OBXpWobQOAWalt7gBql0e7tFyNiIgkbnO+ZiMiIuVEwUZERBKnYCMiIolTsBERkcQp2IiISOIUbEREJHEKNiLrYWZnmtl3Zva1mfXLeu6Z7MdEZF3KsxEpgZltQyRb7go0B54FtnH3PDM7ETjM3Y+rwCaKVAnq2YiUbHtglrt/4+5TgTXAVmbWnFhXar2r+aZWcv596nZdM1thZvdkPP+0mV2Uul3PzMakelHfm9ljZtY6Y9spZnajmT1qZj+b2WdmdoiZ9TSzj8zsp9RzDTJes5WZ/Su1z4Vm9mAqiKafn2NmF6cKai1N7WffHHx2Imsp2IiUbDbQNlXdcE8i2CwEbgGGu/vCUuxjEtAzdfsAYm2xQwDMrFbqsUmp5/9OLBK6NxHovgeeMLPqGfs7HhgBNAYeAO4FhqT20wbYkVRpBzMz4DGivsrOqX3+DGRX2zw59ZpGVL5Vp2UToGE0kfUws/7AMGBV6ntz4ETgD8BNRFXEd4Gh7r6miNcfDtzq7tub2UjiYP8HoqZIU+BhoBmxBtky4Ah3fz712i2AH4ED3f0NM5tCrNH3x9TzHYlhvj3d/Z3UY9cTC6IenVrV+mWiyubK1PNbEUGslbt/k1oF+xZ3H5l6vhPwEdDY3Zfk6GOUzZx6NiLr4e6PuPue7r4fsRjl1cAZwF+BL939AGBr4KRidvESsK2ZdSB6OM8TPZlDU/df9DjrawbUBr7IeO+lwAIKLxv/bcbtZcU8lh5Ga5va53dmttjMFhMLNK4AWme8JvP1v6S+N0AkR2pUdANEqpjRwCh3n2tmuxCrXAO8QpR+WIe7/2JmbxJ1V9oAbxO9o1OBJsC/U5suBFamtpkNa3s2W1O4+mJZfEkEjybuXp7lzUUKUc9GpJTM7DCghbvfnnroM6CPmdUEepEKEMWYRFQJneJRUXIysD9RaXUSQCoY3ANcaWbbmVk94AbgEyJAbYipxDL3N6VL/5pZMzPTDDopVwo2IqWQmt11I3BaxsPXEhU1vyf+L/2zhF1MAhoSQ2h4VEb8BPja3T/P2O48IkC8A3wFbEvUIdmgsr2pAHYUcT3oXTP7GXiTKPEsUm40QUBERBKnno2IiCROwUZERBKnYCMiIolTsBERkcQp2IiISOIUbEREJHEKNiIikjgFGxERSdz/B8PNKTmYPFd1AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "rms-diff before: 0.18337572303336608; rms-diff after: 0.11473460987575866\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Removal 2 BERT Linear\n",
+    "tpr_exp(x_test, debiased_x_train_2, y_m_train, debiased_x_test_2, y_m_test, \\\n",
+    "        y_p_test, N_test, p2i, i2p, clf_original)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2. RBF kernel at gamma = 0.1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kernel_fn = \"rbf\"\n",
+    "kernel_coef = 0.1\n",
+    "\n",
+    "saved_model = np.load(f\"../data/saved_models/fair_biography_prof_gender/{ratio}/cleaned_{kernel_fn}_{kernel_coef}_Train{N}_Test{N_test}.npz\")\n",
+    "debiased_x_train_0 = saved_model['debiased_x_train_0']\n",
+    "debiased_x_test_0 = saved_model['debiased_x_test_0']\n",
+    "debiased_x_train_1 = saved_model['debiased_x_train_1']\n",
+    "debiased_x_test_1 = saved_model['debiased_x_test_1']\n",
+    "debiased_x_train_2 = saved_model['debiased_x_train_2']\n",
+    "debiased_x_test_2 = saved_model['debiased_x_test_2']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Removal 0 BERT RBF at gamma = 0.1\n",
+    "tpr_exp(x_test, debiased_x_train_0, y_m_train, debiased_x_test_0, y_m_test, \\\n",
+    "        y_p_test, N_test, p2i, i2p, clf_original)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Removal 1 BERT RBF at gamma = 0.1\n",
+    "tpr_exp(x_test, debiased_x_train_1, y_m_train, debiased_x_test_1, y_m_test, \\\n",
+    "        y_p_test, N_test, p2i, i2p, clf_original)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "profession dietitian missed in tpr-calc:\n",
+      "                  number of dietitian male in test set 0\n",
+      "                  number of dietitian female in test set 59\n",
+      "\n",
+      "\n",
+      "Correlation: 0.773655072810786; p-value: 6.29340039229481e-05\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=90)]: Using backend ThreadingBackend with 90 concurrent workers.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "max_iter reached after 287 seconds\n",
+      "\n",
+      "\n",
+      "\n",
+      "================================================                \n",
+      "SCORE: of profession classifier on debiased dataset                \n",
+      "0.1372                \n",
+      "================================================\n",
+      "\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/lustre/home/sc066/shunshao/miniconda3/envs/cca/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
+      "[Parallel(n_jobs=90)]: Done   1 out of   1 | elapsed:  4.8min finished\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "profession dietitian missed in tpr-calc:\n",
+      "                  number of dietitian male in test set 0\n",
+      "                  number of dietitian female in test set 59\n",
+      "\n",
+      "\n",
+      "Correlation: 0.450143871049959; p-value: 0.04059786566020312\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "rms-diff before: 0.18337572303336608; rms-diff after: 0.08420485250044037\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Removal 2 BERT RBF at gamma = 0.1\n",
+    "tpr_exp(x_test, debiased_x_train_2, y_m_train, debiased_x_test_2, y_m_test, \\\n",
+    "        y_p_test, N_test, p2i, i2p, clf_original)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 3. Polynomial kernel at degree=2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "kernel_fn = \"poly\"\n",
+    "kernel_coef = 2\n",
+    "\n",
+    "saved_model = np.load(f\"../data/saved_models/fair_biography_prof_gender/{ratio}/cleaned_{kernel_fn}_{kernel_coef}_Train{N}_Test{N_test}.npz\")\n",
+    "debiased_x_train_0 = saved_model['debiased_x_train_0']\n",
+    "debiased_x_test_0 = saved_model['debiased_x_test_0']\n",
+    "debiased_x_train_1 = saved_model['debiased_x_train_1']\n",
+    "debiased_x_test_1 = saved_model['debiased_x_test_1']\n",
+    "debiased_x_train_2 = saved_model['debiased_x_train_2']\n",
+    "debiased_x_test_2 = saved_model['debiased_x_test_2']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "profession dietitian missed in tpr-calc:\n",
+      "                  number of dietitian male in test set 0\n",
+      "                  number of dietitian female in test set 59\n",
+      "\n",
+      "\n",
+      "Correlation: 0.773655072810786; p-value: 6.29340039229481e-05\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=90)]: Using backend ThreadingBackend with 90 concurrent workers.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "max_iter reached after 298 seconds\n",
+      "\n",
+      "\n",
+      "\n",
+      "================================================                \n",
+      "SCORE: of profession classifier on debiased dataset                \n",
+      "0.4952                \n",
+      "================================================\n",
+      "\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/lustre/home/sc066/shunshao/miniconda3/envs/cca/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
+      "[Parallel(n_jobs=90)]: Done   1 out of   1 | elapsed:  5.0min finished\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Correlation: 0.576632539140049; p-value: 0.08098230117672431\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "rms-diff before: 0.18337572303336608; rms-diff after: 0.04961962564035084\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Removal 0 BERT Poly 2\n",
+    "tpr_exp(x_test, debiased_x_train_0, y_m_train, debiased_x_test_0, y_m_test, \\\n",
+    "        y_p_test, N_test, p2i, i2p, clf_original)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "profession dietitian missed in tpr-calc:\n",
+      "                  number of dietitian male in test set 0\n",
+      "                  number of dietitian female in test set 59\n",
+      "\n",
+      "\n",
+      "Correlation: 0.773655072810786; p-value: 6.29340039229481e-05\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=90)]: Using backend ThreadingBackend with 90 concurrent workers.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "max_iter reached after 301 seconds\n",
+      "\n",
+      "\n",
+      "\n",
+      "================================================                \n",
+      "SCORE: of profession classifier on debiased dataset                \n",
+      "0.5806                \n",
+      "================================================\n",
+      "\n",
+      "\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/lustre/home/sc066/shunshao/miniconda3/envs/cca/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
+      "[Parallel(n_jobs=90)]: Done   1 out of   1 | elapsed:  5.0min finished\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Correlation: 0.3789776883508932; p-value: 0.14772867602704037\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "rms-diff before: 0.18337572303336608; rms-diff after: 0.1492207928515152\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Removal 1 BERT Poly 2\n",
+    "tpr_exp(x_test, debiased_x_train_1, y_m_train, debiased_x_test_1, y_m_test, \\\n",
+    "        y_p_test, N_test, p2i, i2p, clf_original)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "profession dietitian missed in tpr-calc:\n",
+      "                  number of dietitian male in test set 0\n",
+      "                  number of dietitian female in test set 59\n",
+      "\n",
+      "\n",
+      "Correlation: 0.773655072810786; p-value: 6.29340039229481e-05\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=90)]: Using backend ThreadingBackend with 90 concurrent workers.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "max_iter reached after 296 seconds\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/lustre/home/sc066/shunshao/miniconda3/envs/cca/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:329: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
+      "  \"the coef_ did not converge\", ConvergenceWarning)\n",
+      "[Parallel(n_jobs=90)]: Done   1 out of   1 | elapsed:  4.9min finished\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "\n",
+      "\n",
+      "================================================                \n",
+      "SCORE: of profession classifier on debiased dataset                \n",
+      "0.5762                \n",
+      "================================================\n",
+      "\n",
+      "\n",
+      "\n",
+      "Correlation: 0.08446044696320024; p-value: 0.7472326984514821\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "rms-diff before: 0.18337572303336608; rms-diff after: 0.10341280718742507\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Removal 2 BERT Poly 2\n",
+    "tpr_exp(x_test, debiased_x_train_2, y_m_train, debiased_x_test_2, y_m_test, \\\n",
+    "        y_p_test, N_test, p2i, i2p, clf_original)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/notebook_fair-sentiment.ipynb b/notebooks/notebook_fair-sentiment.ipynb
deleted file mode 100644
index 2dbedbf..0000000
--- a/notebooks/notebook_fair-sentiment.ipynb
+++ /dev/null
@@ -1,2528 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "sys.path.append(\"../src\")\n",
-    "sys.path.append(\"../data/embeddings\")\n",
-    "import classifier\n",
-    "import debias\n",
-    "#import debias_old as debias\n",
-    "import gensim\n",
-    "import codecs\n",
-    "import json\n",
-    "from gensim.models.keyedvectors import Word2VecKeyedVectors\n",
-    "from gensim.models import KeyedVectors\n",
-    "import numpy as np\n",
-    "import random\n",
-    "import sklearn\n",
-    "from sklearn import model_selection\n",
-    "from sklearn import cluster\n",
-    "from sklearn import metrics\n",
-    "from sklearn.manifold import TSNE\n",
-    "from sklearn.svm import LinearSVC, SVC\n",
-    "from sklearn.linear_model import SGDClassifier, Perceptron, LogisticRegression, PassiveAggressiveClassifier\n",
-    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
-    "from sklearn.neural_network import MLPClassifier\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from sklearn.decomposition import PCA\n",
-    "import scipy\n",
-    "from scipy import linalg\n",
-    "from scipy.stats.stats import pearsonr\n",
-    "import tqdm\n",
-    "import matplotlib\n",
-    "import matplotlib.pyplot as plt\n",
-    "# %matplotlib inline\n",
-    "# matplotlib.rcParams['agg.path.chunksize'] = 10000\n",
-    "from sklearn.utils import shuffle\n",
-    "\n",
-    "# import warnings\n",
-    "# warnings.filterwarnings(\"ignore\")\n",
-    "# %load_ext autoreload\n",
-    "# %autoreload\n",
-    "\n",
-    "from collections import Counter, defaultdict\n",
-    "import seaborn as sns\n",
-    "# sns.set_style(\"whitegrid\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Function"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def load_data(path, size, ratio=0.5):\n",
-    "    fnames = [\"neg_neg.npy\", \"neg_pos.npy\", \"pos_neg.npy\", \"pos_pos.npy\"]\n",
-    "    protected_labels = [0, 1, 0, 1]\n",
-    "    main_labels = [0, 0, 1, 1]\n",
-    "    X, Y_p, Y_m = [], [], []\n",
-    "    n1 = int(size * ratio / 2)\n",
-    "    n2 = int(size * (1 - ratio) / 2)\n",
-    "#     print(n1, n2)\n",
-    "\n",
-    "    for fname, p_label, m_label, n in zip(fnames, protected_labels, main_labels, [n1, n2, n2, n1]):\n",
-    "#         print(path + '/' + fname)\n",
-    "#         print(np.load(path + '/' + fname).shape)\n",
-    "        data = np.load(path + '/' + fname)[:n]\n",
-    "        for x in data:\n",
-    "            X.append(x)\n",
-    "        for _ in data:\n",
-    "            Y_p.append(p_label)\n",
-    "        for _ in data:\n",
-    "            Y_m.append(m_label)\n",
-    "\n",
-    "    Y_p = np.array(Y_p)\n",
-    "    Y_m = np.array(Y_m)\n",
-    "    X = np.array(X)\n",
-    "    X, Y_p, Y_m = shuffle(X, Y_p, Y_m, random_state=0)\n",
-    "    return X, Y_p, Y_m"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_TPR(y_main, y_hat_main, y_protected):\n",
-    "    \n",
-    "    all_y = list(Counter(y_main).keys())\n",
-    "    \n",
-    "    protected_vals = defaultdict(dict)\n",
-    "    for label in all_y:\n",
-    "        for i in range(2):\n",
-    "            used_vals = (y_main == label) & (y_protected == i)\n",
-    "            y_label = y_main[used_vals]\n",
-    "            y_hat_label = y_hat_main[used_vals]\n",
-    "            protected_vals['y:{}'.format(label)]['p:{}'.format(i)] = (y_label == y_hat_label).mean()\n",
-    "            \n",
-    "    diffs = {}\n",
-    "    for k, v in protected_vals.items():\n",
-    "        vals = list(v.values())\n",
-    "        diffs[k] = vals[0] - vals[1]\n",
-    "    return protected_vals, diffs"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def rms(arr):\n",
-    "    return np.sqrt(np.mean(np.square(arr)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def calc_plot_tpr(P):\n",
-    "    \n",
-    "    results = defaultdict(dict)\n",
-    "    for ratio in [0.5, 0.6, 0.7, 0.8]:\n",
-    "\n",
-    "        x_train, y_p_train, y_m_train = load_data(\n",
-    "            '../data/emoji_sent_race_{}/train/'.format(ratio),\n",
-    "            size=100000, ratio=ratio)\n",
-    "        x_dev, y_p_dev, y_m_dev = load_data(\n",
-    "            '../data/emoji_sent_race_{}/test/'.format(ratio),\n",
-    "            size=100000, ratio=0.5)\n",
-    "\n",
-    "        biased_classifier = LinearSVC(fit_intercept=True, class_weight='balanced', dual=False, C=0.1, max_iter=10000)\n",
-    "\n",
-    "        biased_classifier.fit(x_train, y_m_train)\n",
-    "        biased_score = biased_classifier.score(x_dev, y_m_dev)\n",
-    "\n",
-    "    #     P = np.load('../data/emoji_sent_race_{}/P_svm.num-clfs=300.npy'.format(ratio), allow_pickle=True)\n",
-    "    #     P = P[1]\n",
-    "\n",
-    "    #     n_dims = 120\n",
-    "    # #     n_dims = 70\n",
-    "    #     if ratio == 0.5:\n",
-    "    #         n_dims = 200\n",
-    "    #     elif ratio == 0.6:\n",
-    "    #         n_dims = 100\n",
-    "    #     elif ratio == 0.7:\n",
-    "    #         n_dims = 115\n",
-    "    #     elif ratio == 0.8:\n",
-    "    #         n_dims = 200\n",
-    "\n",
-    "    #     P = debias.get_projection_to_intersection_of_nullspaces(P[:n_dims], input_dim=300)\n",
-    "\n",
-    "        debiased_x_train = P.dot(x_train.T).T\n",
-    "        debiased_x_dev = P.dot(x_dev.T).T\n",
-    "\n",
-    "        classifier = LinearSVC(fit_intercept=True, class_weight='balanced', dual=False, C=0.1, max_iter=10000)\n",
-    "\n",
-    "        classifier.fit(debiased_x_train, y_m_train)\n",
-    "        debiased_score = classifier.score(debiased_x_dev, y_m_dev)\n",
-    "\n",
-    "        p_classifier = SGDClassifier(warm_start=True, loss='log', n_jobs=64, max_iter=10000, random_state=0, tol=1e-3)\n",
-    "        p_classifier.fit(debiased_x_train, y_p_train)\n",
-    "        p_score = p_classifier.score(debiased_x_dev, y_p_dev)\n",
-    "        results[ratio]['p_acc'] = p_score\n",
-    "\n",
-    "        _, biased_diffs = get_TPR(y_m_dev, biased_classifier.predict(x_dev), y_p_dev)\n",
-    "\n",
-    "        _, debiased_diffs = get_TPR(y_m_dev, classifier.predict(debiased_x_dev), y_p_dev)\n",
-    "\n",
-    "    #     results[ratio]['biased_diff_tpr'] = biased_diffs['y:0']\n",
-    "        results[ratio]['biased_diff_tpr'] = rms(list(biased_diffs.values()))\n",
-    "    #     results[ratio]['debiased_diff_tpr'] = debiased_diffs['y:0']\n",
-    "        results[ratio]['debiased_diff_tpr'] = rms(list(debiased_diffs.values()))\n",
-    "\n",
-    "        results[ratio]['biased_acc'] = biased_score\n",
-    "        results[ratio]['debiased_acc'] = debiased_score\n",
-    "\n",
-    "    plot_results = defaultdict(list)\n",
-    "    for r in [0.5, 0.6, 0.7, 0.8]:\n",
-    "        plot_results['biased_diff_tpr'].append(results[r]['biased_diff_tpr'])\n",
-    "        plot_results['debiased_diff_tpr'].append(results[r]['debiased_diff_tpr'])\n",
-    "        plot_results['biased_acc'].append(results[r]['biased_acc'])\n",
-    "        plot_results['debiased_acc'].append(results[r]['debiased_acc'])\n",
-    "\n",
-    "    return results, plot_results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def data2plot(results, removal_range):\n",
-    "    x = removal_range\n",
-    "\n",
-    "    fig, ax1 = plt.subplots()\n",
-    "\n",
-    "    color = 'tab:red'\n",
-    "    ax1.set_xlabel('ratio')\n",
-    "    ax1.set_ylabel('diff tpr', color=color)\n",
-    "    ax1.plot(x, results['biased_diff_tpr'], '.--', label='biased tpr diff', color=color)\n",
-    "    ax1.plot(x, results['debiased_diff_tpr'], '*:', label='debiased tpr diff', color=color)\n",
-    "    ax1.tick_params(axis='y', labelcolor=color)\n",
-    "    plt.ylim(0.,0.6)\n",
-    "\n",
-    "    ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n",
-    "\n",
-    "    color = 'tab:blue'\n",
-    "    ax2.set_ylabel('acc', color=color)  # we already handled the x-label with ax1\n",
-    "    ax2.plot(x, results['biased_acc'], '^-', label='biased acc', color=color)\n",
-    "    ax2.plot(x, results['debiased_acc'], 'o--', label='debiased acc', color=color)\n",
-    "    ax2.tick_params(axis='y', labelcolor=color)\n",
-    "    \n",
-    "    # ask matplotlib for the plotted objects and their labels\n",
-    "    lines, labels = ax1.get_legend_handles_labels()\n",
-    "    lines2, labels2 = ax2.get_legend_handles_labels()\n",
-    "    ax2.legend(lines, labels, loc='lower right')\n",
-    "    \n",
-    "    from matplotlib.legend import Legend\n",
-    "    leg = Legend(ax2, lines2, labels2,\n",
-    "             loc='upper left', frameon=False)\n",
-    "    ax2.add_artist(leg);\n",
-    "\n",
-    "    plt.title('TPR rates as a function of the ratio')\n",
-    "    plt.ylim(0.5,.85)\n",
-    "#     plt.legend()\n",
-    "#     plt.savefig('tpr_rates_ratio.png', dpi=1000)\n",
-    "    plt.show()\n",
-    "    \n",
-    "# data2plot(plot_results)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Cross-validate"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def check_result(u, removel_range, x_train, x_dev, y_m_train, y_m_dev, y_p_train, y_p_dev):\n",
-    "    results = defaultdict(dict)\n",
-    "\n",
-    "    biased_classifier = LinearSVC(fit_intercept=True, class_weight='balanced', dual=False, C=0.1, max_iter=10000)\n",
-    "\n",
-    "    biased_classifier.fit(x_train, y_m_train)\n",
-    "    biased_score = biased_classifier.score(x_dev, y_m_dev)\n",
-    "    _, biased_diffs = get_TPR(y_m_dev, biased_classifier.predict(x_dev), y_p_dev)\n",
-    "    \n",
-    "    for removal in removel_range:\n",
-    "        u_r = u[:, removal:]\n",
-    "        proj = u_r @ u_r.T\n",
-    "        P = proj\n",
-    "\n",
-    "        debiased_x_train = P.dot(x_train.T).T\n",
-    "        debiased_x_dev = P.dot(x_dev.T).T\n",
-    "\n",
-    "        classifier = LinearSVC(fit_intercept=True, class_weight='balanced', dual=False, C=0.1, max_iter=10000)\n",
-    "\n",
-    "        classifier.fit(debiased_x_train, y_m_train)\n",
-    "        debiased_score = classifier.score(debiased_x_dev, y_m_dev)\n",
-    "\n",
-    "        p_classifier = SGDClassifier(warm_start=True, loss='log', n_jobs=64, max_iter=10000, random_state=0, tol=1e-3)\n",
-    "        p_classifier.fit(debiased_x_train, y_p_train)\n",
-    "        p_score = p_classifier.score(debiased_x_dev, y_p_dev)\n",
-    "\n",
-    "        _, debiased_diffs = get_TPR(y_m_dev, classifier.predict(debiased_x_dev), y_p_dev)\n",
-    "\n",
-    "\n",
-    "        results[removal]['p_acc'] = p_score\n",
-    "        #     results[ratio]['biased_diff_tpr'] = biased_diffs['y:0']\n",
-    "        results[removal]['biased_diff_tpr'] = rms(list(biased_diffs.values()))\n",
-    "        #     results[ratio]['debiased_diff_tpr'] = debiased_diffs['y:0']\n",
-    "        results[removal]['debiased_diff_tpr'] = rms(list(debiased_diffs.values()))\n",
-    "\n",
-    "        results[removal]['biased_acc'] = biased_score\n",
-    "        results[removal]['debiased_acc'] = debiased_score\n",
-    "        \n",
-    "    return results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def check_result_ksvd(W, K_x_sqrt2, U_Kx, K_x_dv_sqrt2, U_Kx_dv, removel_range, x_train, x_dev, y_m_train, y_m_dev, y_p_train, y_p_dev):\n",
-    "    results = defaultdict(dict)\n",
-    "\n",
-    "    biased_classifier = LinearSVC(fit_intercept=True, class_weight='balanced', dual=False, C=0.1, max_iter=10000)\n",
-    "\n",
-    "    biased_classifier.fit(x_train, y_m_train)\n",
-    "    biased_score = biased_classifier.score(x_dev, y_m_dev)\n",
-    "    _, biased_diffs = get_TPR(y_m_dev, biased_classifier.predict(x_dev), y_p_dev)\n",
-    "    \n",
-    "    for removal in removel_range:\n",
-    "        W2 = W[:, :removal]\n",
-    "        Wprime = W2\n",
-    "        U_proj_ker = linalg.null_space((K_x_sqrt2 @ Wprime).T)\n",
-    "        \n",
-    "        debiased_x_train = (U_proj_ker.T @ K_x_sqrt2).T\n",
-    "        debiased_x_dev = (U_proj_ker.T @ U_Kx  @ U_Kx_dv.T @ K_x_dv_sqrt2).T\n",
-    "\n",
-    "        classifier = LinearSVC(fit_intercept=True, class_weight='balanced', dual=False, C=0.1, max_iter=10000)\n",
-    "\n",
-    "        classifier.fit(debiased_x_train, y_m_train)\n",
-    "        debiased_score = classifier.score(debiased_x_dev, y_m_dev)\n",
-    "\n",
-    "        p_classifier = SGDClassifier(warm_start=True, loss='log', n_jobs=64, max_iter=10000, random_state=0, tol=1e-3)\n",
-    "        p_classifier.fit(debiased_x_train, y_p_train)\n",
-    "        p_score = p_classifier.score(debiased_x_dev, y_p_dev)\n",
-    "\n",
-    "        _, debiased_diffs = get_TPR(y_m_dev, classifier.predict(debiased_x_dev), y_p_dev)\n",
-    "\n",
-    "\n",
-    "        results[removal]['p_acc'] = p_score\n",
-    "        #     results[ratio]['biased_diff_tpr'] = biased_diffs['y:0']\n",
-    "        results[removal]['biased_diff_tpr'] = rms(list(biased_diffs.values()))\n",
-    "        #     results[ratio]['debiased_diff_tpr'] = debiased_diffs['y:0']\n",
-    "        results[removal]['debiased_diff_tpr'] = rms(list(debiased_diffs.values()))\n",
-    "\n",
-    "        results[removal]['biased_acc'] = biased_score\n",
-    "        results[removal]['debiased_acc'] = debiased_score\n",
-    "        \n",
-    "    return results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def convert_to_plot_results(results, removel_range):\n",
-    "    plot_results = defaultdict(list)\n",
-    "    for r in removel_range:\n",
-    "        plot_results['biased_diff_tpr'].append(results[r]['biased_diff_tpr'])\n",
-    "        plot_results['debiased_diff_tpr'].append(results[r]['debiased_diff_tpr'])\n",
-    "        plot_results['biased_acc'].append(results[r]['biased_acc'])\n",
-    "        plot_results['debiased_acc'].append(results[r]['debiased_acc'])\n",
-    "    return plot_results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# ratio = 0.5\n",
-    "\n",
-    "# x_train, y_p_train, y_m_train = load_data(\n",
-    "#     '../data/emoji_sent_race_{}/train/'.format(ratio),\n",
-    "#     size=100000, ratio=ratio)\n",
-    "# x_dev, y_p_dev, y_m_dev = load_data(\n",
-    "#     '../data/emoji_sent_race_{}/test/'.format(ratio),\n",
-    "#     size=100000, ratio=0.5)\n",
-    "# np.savetxt(f\"../data/saved_models/fair_emoji_sent_race/{ratio}/x_train.csv\", x_train, delimiter=\",\")\n",
-    "# np.savetxt(f\"../data/saved_models/fair_emoji_sent_race/{ratio}/y_p_train.csv\", y_p_train, delimiter=\",\")\n",
-    "# np.savetxt(f\"../data/saved_models/fair_emoji_sent_race/{ratio}/y_m_train.csv\", y_m_train, delimiter=\",\")\n",
-    "# np.savetxt(f\"../data/saved_models/fair_emoji_sent_race/{ratio}/x_dev.csv\", x_dev, delimiter=\",\")\n",
-    "# np.savetxt(f\"../data/saved_models/fair_emoji_sent_race/{ratio}/y_p_dev.csv\", y_p_dev, delimiter=\",\")\n",
-    "# np.savetxt(f\"../data/saved_models/fair_emoji_sent_race/{ratio}/y_m_dev.csv\", y_m_dev, delimiter=\",\")\n",
-    "# np.savez(f\"../data/saved_models/fair_emoji_sent_race/{ratio}/all.npz\", x_train = x_train, y_p_train = y_p_train, y_m_train = y_m_train, x_dev = x_dev, y_p_dev = y_p_dev, y_m_dev = y_m_dev)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Null it out"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# ratio = 0.5\n",
-    "\n",
-    "# saved_dataset = np.load(f\"../data/saved_models/fair_emoji_sent_race/{ratio}/all.npz\")\n",
-    "\n",
-    "# x_train = saved_dataset['x_train']\n",
-    "# y_m_train = saved_dataset['y_m_train']\n",
-    "# y_p_train = saved_dataset['y_p_train']\n",
-    "\n",
-    "# x_dev = saved_dataset['x_dev']\n",
-    "# y_p_dev = saved_dataset['y_p_dev']\n",
-    "# y_m_dev = saved_dataset['y_m_dev']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "ratio = 0.5, train size (100000, 300), dev size (7998, 300)\n",
-      "\n",
-      "\n",
-      "\n",
-      "ratio = 0.6, train size (100000, 300), dev size (7998, 300)\n",
-      "\n",
-      "\n",
-      "\n",
-      "ratio = 0.7, train size (100000, 300), dev size (7998, 300)\n",
-      "\n",
-      "\n",
-      "\n",
-      "ratio = 0.8, train size (99998, 300), dev size (7998, 300)\n",
-      "\n",
-      "\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "for ratio in [0.5, 0.6, 0.7, 0.8]:\n",
-    "\n",
-    "#     x_train, y_p_train, y_m_train = load_data(\n",
-    "#         '../data/emoji_sent_race_{}/train/'.format(ratio),\n",
-    "#         size=100000, ratio=ratio)\n",
-    "#     x_dev, y_p_dev, y_m_dev = load_data(\n",
-    "#         '../data/emoji_sent_race_{}/test/'.format(ratio),\n",
-    "#         size=100000, ratio=0.5)\n",
-    "    saved_dataset = np.load(f\"../data/saved_models/fair_emoji_sent_race/{ratio}/all.npz\")\n",
-    "\n",
-    "    x_train = saved_dataset['x_train']\n",
-    "    y_m_train = saved_dataset['y_m_train']\n",
-    "    y_p_train = saved_dataset['y_p_train']\n",
-    "\n",
-    "    x_dev = saved_dataset['x_dev']\n",
-    "    y_p_dev = saved_dataset['y_p_dev']\n",
-    "    y_m_dev = saved_dataset['y_m_dev']\n",
-    "    \n",
-    "    print(f\"ratio = {ratio}, train size {x_train.shape}, dev size {x_dev.shape}\\n\\n\\n\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results = defaultdict(dict)\n",
-    "\n",
-    "for ratio in [0.5, 0.6, 0.7, 0.8]:\n",
-    "\n",
-    "#     x_train, y_p_train, y_m_train = load_data(\n",
-    "#         '../data/emoji_sent_race_{}/train/'.format(ratio),\n",
-    "#         size=100000, ratio=ratio)\n",
-    "#     x_dev, y_p_dev, y_m_dev = load_data(\n",
-    "#         '../data/emoji_sent_race_{}/test/'.format(ratio),\n",
-    "#         size=100000, ratio=0.5)\n",
-    "    saved_dataset = np.load(f\"../data/saved_models/fair_emoji_sent_race/{ratio}/all.npz\")\n",
-    "\n",
-    "    x_train = saved_dataset['x_train']\n",
-    "    y_m_train = saved_dataset['y_m_train']\n",
-    "    y_p_train = saved_dataset['y_p_train']\n",
-    "\n",
-    "    x_dev = saved_dataset['x_dev']\n",
-    "    y_p_dev = saved_dataset['y_p_dev']\n",
-    "    y_m_dev = saved_dataset['y_m_dev']\n",
-    "    \n",
-    "    biased_classifier = LinearSVC(fit_intercept=True, class_weight='balanced', dual=False, C=0.1, max_iter=10000)\n",
-    "\n",
-    "    biased_classifier.fit(x_train, y_m_train)\n",
-    "    biased_score = biased_classifier.score(x_dev, y_m_dev)\n",
-    "    \n",
-    "    P = np.load('../data/emoji_sent_race_{}/P_svm.num-clfs=300.npy'.format(ratio), allow_pickle=True)\n",
-    "    P = P[1]\n",
-    "    n_dims = 120\n",
-    "#     n_dims = 70\n",
-    "    if ratio == 0.5:\n",
-    "        n_dims = 200\n",
-    "    elif ratio == 0.6:\n",
-    "        n_dims = 100\n",
-    "    elif ratio == 0.7:\n",
-    "        n_dims = 115\n",
-    "    elif ratio == 0.8:\n",
-    "        n_dims = 200\n",
-    "    P = debias.get_projection_to_intersection_of_nullspaces(P[:n_dims], input_dim=300)\n",
-    "    \n",
-    "    debiased_x_train = P.dot(x_train.T).T\n",
-    "    debiased_x_dev = P.dot(x_dev.T).T\n",
-    "\n",
-    "    classifier = LinearSVC(fit_intercept=True, class_weight='balanced', dual=False, C=0.1, max_iter=10000)\n",
-    "\n",
-    "    classifier.fit(debiased_x_train, y_m_train)\n",
-    "    debiased_score = classifier.score(debiased_x_dev, y_m_dev)\n",
-    "    \n",
-    "    p_classifier = SGDClassifier(warm_start=True, loss='log', n_jobs=64, max_iter=10000, random_state=0, tol=1e-3)\n",
-    "    p_classifier.fit(debiased_x_train, y_p_train)\n",
-    "    p_score = p_classifier.score(debiased_x_dev, y_p_dev)\n",
-    "    results[ratio]['p_acc'] = p_score\n",
-    "    \n",
-    "    _, biased_diffs = get_TPR(y_m_dev, biased_classifier.predict(x_dev), y_p_dev)\n",
-    "    \n",
-    "    _, debiased_diffs = get_TPR(y_m_dev, classifier.predict(debiased_x_dev), y_p_dev)\n",
-    "    \n",
-    "#     results[ratio]['biased_diff_tpr'] = biased_diffs['y:0']\n",
-    "    results[ratio]['biased_diff_tpr'] = rms(list(biased_diffs.values()))\n",
-    "#     results[ratio]['debiased_diff_tpr'] = debiased_diffs['y:0']\n",
-    "    results[ratio]['debiased_diff_tpr'] = rms(list(debiased_diffs.values()))\n",
-    "    \n",
-    "    results[ratio]['biased_acc'] = biased_score\n",
-    "    results[ratio]['debiased_acc'] = debiased_score"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "defaultdict(dict,\n",
-       "            {0.5: {'p_acc': 0.5396349087271818,\n",
-       "              'biased_diff_tpr': 0.14560632483708713,\n",
-       "              'debiased_diff_tpr': 0.11974302439852863,\n",
-       "              'biased_acc': 0.7653163290822705,\n",
-       "              'debiased_acc': 0.7625656414103525},\n",
-       "             0.6: {'p_acc': 0.5946486621655414,\n",
-       "              'biased_diff_tpr': 0.22630502371727185,\n",
-       "              'debiased_diff_tpr': 0.19067232927585404,\n",
-       "              'biased_acc': 0.7531882970742686,\n",
-       "              'debiased_acc': 0.7579394848712178},\n",
-       "             0.7: {'p_acc': 0.6257814453613403,\n",
-       "              'biased_diff_tpr': 0.31864557675474753,\n",
-       "              'debiased_diff_tpr': 0.26266284955843594,\n",
-       "              'biased_acc': 0.741185296324081,\n",
-       "              'debiased_acc': 0.7478119529882471},\n",
-       "             0.8: {'p_acc': 0.5043760940235059,\n",
-       "              'biased_diff_tpr': 0.4043010400370602,\n",
-       "              'debiased_diff_tpr': 0.010947167516829342,\n",
-       "              'biased_acc': 0.7200550137534384,\n",
-       "              'debiased_acc': 0.5290072518129533}})"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "defaultdict(list,\n",
-       "            {'biased_diff_tpr': [0.14560632483708713,\n",
-       "              0.22630502371727185,\n",
-       "              0.31864557675474753,\n",
-       "              0.4043010400370602],\n",
-       "             'debiased_diff_tpr': [0.11974302439852863,\n",
-       "              0.19067232927585404,\n",
-       "              0.26266284955843594,\n",
-       "              0.010947167516829342],\n",
-       "             'biased_acc': [0.7653163290822705,\n",
-       "              0.7531882970742686,\n",
-       "              0.741185296324081,\n",
-       "              0.7200550137534384],\n",
-       "             'debiased_acc': [0.7625656414103525,\n",
-       "              0.7579394848712178,\n",
-       "              0.7478119529882471,\n",
-       "              0.5290072518129533]})"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "plot_results = defaultdict(list)\n",
-    "for r in [0.5, 0.6, 0.7, 0.8]:\n",
-    "    plot_results['biased_diff_tpr'].append(results[r]['biased_diff_tpr'])\n",
-    "    plot_results['debiased_diff_tpr'].append(results[r]['debiased_diff_tpr'])\n",
-    "    plot_results['biased_acc'].append(results[r]['biased_acc'])\n",
-    "    plot_results['debiased_acc'].append(results[r]['debiased_acc'])\n",
-    "plot_results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "removel_range = [0, 1, 2, 5, 30, 50, 100, 200, 220, 240, 260, 280, 290, 295]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import time"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "sal took 0.37548398971557617 seconds\n",
-      "u has shape (300, 300), s has shape (2,), vh has shape (2, 2)\n"
-     ]
-    }
-   ],
-   "source": [
-    "ratio = 0.5\n",
-    "\n",
-    "saved_dataset = np.load(f\"../data/saved_models/fair_emoji_sent_race/{ratio}/all.npz\")\n",
-    "\n",
-    "x_train = saved_dataset['x_train']\n",
-    "y_m_train = saved_dataset['y_m_train']\n",
-    "y_p_train = saved_dataset['y_p_train']\n",
-    "y_p_train_2d = np.asarray([y_p_train, - y_p_train + 1]).T\n",
-    "\n",
-    "x_dev = saved_dataset['x_dev']\n",
-    "y_p_dev = saved_dataset['y_p_dev']\n",
-    "y_m_dev = saved_dataset['y_m_dev']\n",
-    "\n",
-    "t = time.time()\n",
-    "A = np.dot(x_train.T, y_p_train_2d) / x_train.shape[0]\n",
-    "u, s, vh = np.linalg.svd(A, full_matrices=True)\n",
-    "elapsed = time.time() - t\n",
-    "print(f\"sal took {elapsed} seconds\")\n",
-    "print(f\"u has shape {u.shape}, s has shape {s.shape}, vh has shape {vh.shape}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "defaultdict(dict,\n",
-       "            {0: {'p_acc': 0.8714678669667417,\n",
-       "              'biased_diff_tpr': 0.14560632483708713,\n",
-       "              'debiased_diff_tpr': 0.14560632483708713,\n",
-       "              'biased_acc': 0.7653163290822705,\n",
-       "              'debiased_acc': 0.7653163290822705},\n",
-       "             1: {'p_acc': 0.8630907726931732,\n",
-       "              'biased_diff_tpr': 0.14560632483708713,\n",
-       "              'debiased_diff_tpr': 0.1445020833238553,\n",
-       "              'biased_acc': 0.7653163290822705,\n",
-       "              'debiased_acc': 0.7653163290822705},\n",
-       "             2: {'p_acc': 0.49699924981245314,\n",
-       "              'biased_diff_tpr': 0.14560632483708713,\n",
-       "              'debiased_diff_tpr': 0.11020173553336259,\n",
-       "              'biased_acc': 0.7653163290822705,\n",
-       "              'debiased_acc': 0.766816704176044},\n",
-       "             5: {'p_acc': 0.4959989997499375,\n",
-       "              'biased_diff_tpr': 0.14560632483708713,\n",
-       "              'debiased_diff_tpr': 0.11007656356661116,\n",
-       "              'biased_acc': 0.7653163290822705,\n",
-       "              'debiased_acc': 0.7669417354338585},\n",
-       "             30: {'p_acc': 0.4948737184296074,\n",
-       "              'biased_diff_tpr': 0.14560632483708713,\n",
-       "              'debiased_diff_tpr': 0.10621266898739956,\n",
-       "              'biased_acc': 0.7653163290822705,\n",
-       "              'debiased_acc': 0.7660665166291573},\n",
-       "             50: {'p_acc': 0.4973743435858965,\n",
-       "              'biased_diff_tpr': 0.14560632483708713,\n",
-       "              'debiased_diff_tpr': 0.10841554967602068,\n",
-       "              'biased_acc': 0.7653163290822705,\n",
-       "              'debiased_acc': 0.766816704176044},\n",
-       "             100: {'p_acc': 0.49637409352338085,\n",
-       "              'biased_diff_tpr': 0.14560632483708713,\n",
-       "              'debiased_diff_tpr': 0.10844931019709282,\n",
-       "              'biased_acc': 0.7653163290822705,\n",
-       "              'debiased_acc': 0.7664416104026006},\n",
-       "             200: {'p_acc': 0.5248812203050762,\n",
-       "              'biased_diff_tpr': 0.14560632483708713,\n",
-       "              'debiased_diff_tpr': 0.1042793080915506,\n",
-       "              'biased_acc': 0.7653163290822705,\n",
-       "              'debiased_acc': 0.7656914228557139},\n",
-       "             220: {'p_acc': 0.44711177794448614,\n",
-       "              'biased_diff_tpr': 0.14560632483708713,\n",
-       "              'debiased_diff_tpr': 0.09987399516901178,\n",
-       "              'biased_acc': 0.7653163290822705,\n",
-       "              'debiased_acc': 0.7661915478869717},\n",
-       "             240: {'p_acc': 0.45023755938984744,\n",
-       "              'biased_diff_tpr': 0.14560632483708713,\n",
-       "              'debiased_diff_tpr': 0.1032924184615246,\n",
-       "              'biased_acc': 0.7653163290822705,\n",
-       "              'debiased_acc': 0.7656914228557139},\n",
-       "             260: {'p_acc': 0.450112528132033,\n",
-       "              'biased_diff_tpr': 0.14560632483708713,\n",
-       "              'debiased_diff_tpr': 0.10346416206559751,\n",
-       "              'biased_acc': 0.7653163290822705,\n",
-       "              'debiased_acc': 0.7656914228557139},\n",
-       "             280: {'p_acc': 0.4587396849212303,\n",
-       "              'biased_diff_tpr': 0.14560632483708713,\n",
-       "              'debiased_diff_tpr': 0.1043429019092808,\n",
-       "              'biased_acc': 0.7653163290822705,\n",
-       "              'debiased_acc': 0.7591897974493623},\n",
-       "             290: {'p_acc': 0.5001250312578145,\n",
-       "              'biased_diff_tpr': 0.14560632483708713,\n",
-       "              'debiased_diff_tpr': 0.10178012864537783,\n",
-       "              'biased_acc': 0.7653163290822705,\n",
-       "              'debiased_acc': 0.7596899224806202},\n",
-       "             295: {'p_acc': 0.49987496874218557,\n",
-       "              'biased_diff_tpr': 0.14560632483708713,\n",
-       "              'debiased_diff_tpr': 0.09851762574167189,\n",
-       "              'biased_acc': 0.7653163290822705,\n",
-       "              'debiased_acc': 0.7548137034258565}})"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results = check_result(u, removel_range, x_train, x_dev, y_m_train, y_m_dev, y_p_train, y_p_dev)\n",
-    "results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "defaultdict(list,\n",
-       "            {'biased_diff_tpr': [0.14560632483708713,\n",
-       "              0.14560632483708713,\n",
-       "              0.14560632483708713,\n",
-       "              0.14560632483708713,\n",
-       "              0.14560632483708713,\n",
-       "              0.14560632483708713,\n",
-       "              0.14560632483708713,\n",
-       "              0.14560632483708713,\n",
-       "              0.14560632483708713,\n",
-       "              0.14560632483708713,\n",
-       "              0.14560632483708713,\n",
-       "              0.14560632483708713,\n",
-       "              0.14560632483708713,\n",
-       "              0.14560632483708713],\n",
-       "             'debiased_diff_tpr': [0.14560632483708713,\n",
-       "              0.1445020833238553,\n",
-       "              0.11020173553336259,\n",
-       "              0.11007656356661116,\n",
-       "              0.10621266898739956,\n",
-       "              0.10841554967602068,\n",
-       "              0.10844931019709282,\n",
-       "              0.1042793080915506,\n",
-       "              0.09987399516901178,\n",
-       "              0.1032924184615246,\n",
-       "              0.10346416206559751,\n",
-       "              0.1043429019092808,\n",
-       "              0.10178012864537783,\n",
-       "              0.09851762574167189],\n",
-       "             'biased_acc': [0.7653163290822705,\n",
-       "              0.7653163290822705,\n",
-       "              0.7653163290822705,\n",
-       "              0.7653163290822705,\n",
-       "              0.7653163290822705,\n",
-       "              0.7653163290822705,\n",
-       "              0.7653163290822705,\n",
-       "              0.7653163290822705,\n",
-       "              0.7653163290822705,\n",
-       "              0.7653163290822705,\n",
-       "              0.7653163290822705,\n",
-       "              0.7653163290822705,\n",
-       "              0.7653163290822705,\n",
-       "              0.7653163290822705],\n",
-       "             'debiased_acc': [0.7653163290822705,\n",
-       "              0.7653163290822705,\n",
-       "              0.766816704176044,\n",
-       "              0.7669417354338585,\n",
-       "              0.7660665166291573,\n",
-       "              0.766816704176044,\n",
-       "              0.7664416104026006,\n",
-       "              0.7656914228557139,\n",
-       "              0.7661915478869717,\n",
-       "              0.7656914228557139,\n",
-       "              0.7656914228557139,\n",
-       "              0.7591897974493623,\n",
-       "              0.7596899224806202,\n",
-       "              0.7548137034258565]})"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "plot_results = convert_to_plot_results(results, removel_range)\n",
-    "plot_results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "data2plot(plot_results, removel_range)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "u has shape (300, 300), s has shape (2,), vh has shape (2, 2)\n"
-     ]
-    }
-   ],
-   "source": [
-    "ratio = 0.6\n",
-    "\n",
-    "saved_dataset = np.load(f\"../data/saved_models/fair_emoji_sent_race/{ratio}/all.npz\")\n",
-    "\n",
-    "x_train = saved_dataset['x_train']\n",
-    "y_m_train = saved_dataset['y_m_train']\n",
-    "y_p_train = saved_dataset['y_p_train']\n",
-    "y_p_train_2d = np.asarray([y_p_train, - y_p_train + 1]).T\n",
-    "\n",
-    "x_dev = saved_dataset['x_dev']\n",
-    "y_p_dev = saved_dataset['y_p_dev']\n",
-    "y_m_dev = saved_dataset['y_m_dev']\n",
-    "\n",
-    "A = np.dot(x_train.T, y_p_train_2d) / x_train.shape[0]\n",
-    "u, s, vh = np.linalg.svd(A, full_matrices=True)\n",
-    "print(f\"u has shape {u.shape}, s has shape {s.shape}, vh has shape {vh.shape}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "defaultdict(dict,\n",
-       "            {0: {'p_acc': 0.840960240060015,\n",
-       "              'biased_diff_tpr': 0.22630502371727185,\n",
-       "              'debiased_diff_tpr': 0.22630502371727185,\n",
-       "              'biased_acc': 0.7531882970742686,\n",
-       "              'debiased_acc': 0.7531882970742686},\n",
-       "             1: {'p_acc': 0.8508377094273568,\n",
-       "              'biased_diff_tpr': 0.22630502371727185,\n",
-       "              'debiased_diff_tpr': 0.22803630357189353,\n",
-       "              'biased_acc': 0.7531882970742686,\n",
-       "              'debiased_acc': 0.7530632658164541},\n",
-       "             2: {'p_acc': 0.4711177794448612,\n",
-       "              'biased_diff_tpr': 0.22630502371727185,\n",
-       "              'debiased_diff_tpr': 0.08988024196274066,\n",
-       "              'biased_acc': 0.7531882970742686,\n",
-       "              'debiased_acc': 0.7531882970742686},\n",
-       "             5: {'p_acc': 0.4708677169292323,\n",
-       "              'biased_diff_tpr': 0.22630502371727185,\n",
-       "              'debiased_diff_tpr': 0.09037932830110916,\n",
-       "              'biased_acc': 0.7531882970742686,\n",
-       "              'debiased_acc': 0.7526881720430108},\n",
-       "             30: {'p_acc': 0.4723680920230057,\n",
-       "              'biased_diff_tpr': 0.22630502371727185,\n",
-       "              'debiased_diff_tpr': 0.09126866357639556,\n",
-       "              'biased_acc': 0.7531882970742686,\n",
-       "              'debiased_acc': 0.7530632658164541},\n",
-       "             50: {'p_acc': 0.5066266566641661,\n",
-       "              'biased_diff_tpr': 0.22630502371727185,\n",
-       "              'debiased_diff_tpr': 0.09258461867418033,\n",
-       "              'biased_acc': 0.7531882970742686,\n",
-       "              'debiased_acc': 0.7511877969492373},\n",
-       "             100: {'p_acc': 0.5031257814453614,\n",
-       "              'biased_diff_tpr': 0.22630502371727185,\n",
-       "              'debiased_diff_tpr': 0.09496617559255267,\n",
-       "              'biased_acc': 0.7531882970742686,\n",
-       "              'debiased_acc': 0.7484371092773193},\n",
-       "             200: {'p_acc': 0.5007501875468867,\n",
-       "              'biased_diff_tpr': 0.22630502371727185,\n",
-       "              'debiased_diff_tpr': 0.10150799279833236,\n",
-       "              'biased_acc': 0.7531882970742686,\n",
-       "              'debiased_acc': 0.7403100775193798},\n",
-       "             220: {'p_acc': 0.5002500625156289,\n",
-       "              'biased_diff_tpr': 0.22630502371727185,\n",
-       "              'debiased_diff_tpr': 0.10921997885015855,\n",
-       "              'biased_acc': 0.7531882970742686,\n",
-       "              'debiased_acc': 0.7353088272068017},\n",
-       "             240: {'p_acc': 0.5001250312578145,\n",
-       "              'biased_diff_tpr': 0.22630502371727185,\n",
-       "              'debiased_diff_tpr': 0.10635797301822242,\n",
-       "              'biased_acc': 0.7531882970742686,\n",
-       "              'debiased_acc': 0.733183295823956},\n",
-       "             260: {'p_acc': 0.5002500625156289,\n",
-       "              'biased_diff_tpr': 0.22630502371727185,\n",
-       "              'debiased_diff_tpr': 0.10776873695187088,\n",
-       "              'biased_acc': 0.7531882970742686,\n",
-       "              'debiased_acc': 0.729307326831708},\n",
-       "             280: {'p_acc': 0.4981245311327832,\n",
-       "              'biased_diff_tpr': 0.22630502371727185,\n",
-       "              'debiased_diff_tpr': 0.11257271707339944,\n",
-       "              'biased_acc': 0.7531882970742686,\n",
-       "              'debiased_acc': 0.7215553888472118},\n",
-       "             290: {'p_acc': 0.5036259064766192,\n",
-       "              'biased_diff_tpr': 0.22630502371727185,\n",
-       "              'debiased_diff_tpr': 0.1257355117153975,\n",
-       "              'biased_acc': 0.7531882970742686,\n",
-       "              'debiased_acc': 0.7189297324331083},\n",
-       "             295: {'p_acc': 0.49987496874218557,\n",
-       "              'biased_diff_tpr': 0.22630502371727185,\n",
-       "              'debiased_diff_tpr': 0.11164602462363725,\n",
-       "              'biased_acc': 0.7531882970742686,\n",
-       "              'debiased_acc': 0.6765441360340085}})"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results = check_result(u, removel_range, x_train, x_dev, y_m_train, y_m_dev, y_p_train, y_p_dev)\n",
-    "results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "defaultdict(list,\n",
-       "            {'biased_diff_tpr': [0.22630502371727185,\n",
-       "              0.22630502371727185,\n",
-       "              0.22630502371727185,\n",
-       "              0.22630502371727185,\n",
-       "              0.22630502371727185,\n",
-       "              0.22630502371727185,\n",
-       "              0.22630502371727185,\n",
-       "              0.22630502371727185,\n",
-       "              0.22630502371727185,\n",
-       "              0.22630502371727185,\n",
-       "              0.22630502371727185,\n",
-       "              0.22630502371727185,\n",
-       "              0.22630502371727185,\n",
-       "              0.22630502371727185],\n",
-       "             'debiased_diff_tpr': [0.22630502371727185,\n",
-       "              0.22803630357189353,\n",
-       "              0.08988024196274066,\n",
-       "              0.09037932830110916,\n",
-       "              0.09126866357639556,\n",
-       "              0.09258461867418033,\n",
-       "              0.09496617559255267,\n",
-       "              0.10150799279833236,\n",
-       "              0.10921997885015855,\n",
-       "              0.10635797301822242,\n",
-       "              0.10776873695187088,\n",
-       "              0.11257271707339944,\n",
-       "              0.1257355117153975,\n",
-       "              0.11164602462363725],\n",
-       "             'biased_acc': [0.7531882970742686,\n",
-       "              0.7531882970742686,\n",
-       "              0.7531882970742686,\n",
-       "              0.7531882970742686,\n",
-       "              0.7531882970742686,\n",
-       "              0.7531882970742686,\n",
-       "              0.7531882970742686,\n",
-       "              0.7531882970742686,\n",
-       "              0.7531882970742686,\n",
-       "              0.7531882970742686,\n",
-       "              0.7531882970742686,\n",
-       "              0.7531882970742686,\n",
-       "              0.7531882970742686,\n",
-       "              0.7531882970742686],\n",
-       "             'debiased_acc': [0.7531882970742686,\n",
-       "              0.7530632658164541,\n",
-       "              0.7531882970742686,\n",
-       "              0.7526881720430108,\n",
-       "              0.7530632658164541,\n",
-       "              0.7511877969492373,\n",
-       "              0.7484371092773193,\n",
-       "              0.7403100775193798,\n",
-       "              0.7353088272068017,\n",
-       "              0.733183295823956,\n",
-       "              0.729307326831708,\n",
-       "              0.7215553888472118,\n",
-       "              0.7189297324331083,\n",
-       "              0.6765441360340085]})"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "plot_results = convert_to_plot_results(results, removel_range)\n",
-    "plot_results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "data2plot(plot_results, removel_range)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "u has shape (300, 300), s has shape (2,), vh has shape (2, 2)\n"
-     ]
-    }
-   ],
-   "source": [
-    "ratio = 0.7\n",
-    "\n",
-    "saved_dataset = np.load(f\"../data/saved_models/fair_emoji_sent_race/{ratio}/all.npz\")\n",
-    "\n",
-    "x_train = saved_dataset['x_train']\n",
-    "y_m_train = saved_dataset['y_m_train']\n",
-    "y_p_train = saved_dataset['y_p_train']\n",
-    "y_p_train_2d = np.asarray([y_p_train, - y_p_train + 1]).T\n",
-    "\n",
-    "x_dev = saved_dataset['x_dev']\n",
-    "y_p_dev = saved_dataset['y_p_dev']\n",
-    "y_m_dev = saved_dataset['y_m_dev']\n",
-    "\n",
-    "A = np.dot(x_train.T, y_p_train_2d) / x_train.shape[0]\n",
-    "u, s, vh = np.linalg.svd(A, full_matrices=True)\n",
-    "print(f\"u has shape {u.shape}, s has shape {s.shape}, vh has shape {vh.shape}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "defaultdict(dict,\n",
-       "            {0: {'p_acc': 0.8199549887471868,\n",
-       "              'biased_diff_tpr': 0.31864557675474753,\n",
-       "              'debiased_diff_tpr': 0.31864557675474753,\n",
-       "              'biased_acc': 0.741185296324081,\n",
-       "              'debiased_acc': 0.741185296324081},\n",
-       "             1: {'p_acc': 0.8438359589897474,\n",
-       "              'biased_diff_tpr': 0.31864557675474753,\n",
-       "              'debiased_diff_tpr': 0.31575482093361984,\n",
-       "              'biased_acc': 0.741185296324081,\n",
-       "              'debiased_acc': 0.7346836709177295},\n",
-       "             2: {'p_acc': 0.463615903975994,\n",
-       "              'biased_diff_tpr': 0.31864557675474753,\n",
-       "              'debiased_diff_tpr': 0.11635856368415173,\n",
-       "              'biased_acc': 0.741185296324081,\n",
-       "              'debiased_acc': 0.7363090772693174},\n",
-       "             5: {'p_acc': 0.46399099774943736,\n",
-       "              'biased_diff_tpr': 0.31864557675474753,\n",
-       "              'debiased_diff_tpr': 0.11674792676509015,\n",
-       "              'biased_acc': 0.741185296324081,\n",
-       "              'debiased_acc': 0.734558639659915},\n",
-       "             30: {'p_acc': 0.5030007501875469,\n",
-       "              'biased_diff_tpr': 0.31864557675474753,\n",
-       "              'debiased_diff_tpr': 0.12023899899338386,\n",
-       "              'biased_acc': 0.741185296324081,\n",
-       "              'debiased_acc': 0.7366841710427607},\n",
-       "             50: {'p_acc': 0.5018754688672168,\n",
-       "              'biased_diff_tpr': 0.31864557675474753,\n",
-       "              'debiased_diff_tpr': 0.12100513006482054,\n",
-       "              'biased_acc': 0.741185296324081,\n",
-       "              'debiased_acc': 0.7349337334333583},\n",
-       "             100: {'p_acc': 0.4981245311327832,\n",
-       "              'biased_diff_tpr': 0.31864557675474753,\n",
-       "              'debiased_diff_tpr': 0.12715509044451567,\n",
-       "              'biased_acc': 0.741185296324081,\n",
-       "              'debiased_acc': 0.7334333583395849},\n",
-       "             200: {'p_acc': 0.47999499874968743,\n",
-       "              'biased_diff_tpr': 0.31864557675474753,\n",
-       "              'debiased_diff_tpr': 0.15675297512285796,\n",
-       "              'biased_acc': 0.741185296324081,\n",
-       "              'debiased_acc': 0.7150537634408602},\n",
-       "             220: {'p_acc': 0.4673668417104276,\n",
-       "              'biased_diff_tpr': 0.31864557675474753,\n",
-       "              'debiased_diff_tpr': 0.1525863232511454,\n",
-       "              'biased_acc': 0.741185296324081,\n",
-       "              'debiased_acc': 0.7150537634408602},\n",
-       "             240: {'p_acc': 0.5385096274068517,\n",
-       "              'biased_diff_tpr': 0.31864557675474753,\n",
-       "              'debiased_diff_tpr': 0.15004721659847078,\n",
-       "              'biased_acc': 0.741185296324081,\n",
-       "              'debiased_acc': 0.7125531382845711},\n",
-       "             260: {'p_acc': 0.5482620655163791,\n",
-       "              'biased_diff_tpr': 0.31864557675474753,\n",
-       "              'debiased_diff_tpr': 0.1537984908912681,\n",
-       "              'biased_acc': 0.741185296324081,\n",
-       "              'debiased_acc': 0.7040510127531883},\n",
-       "             280: {'p_acc': 0.5118779694923731,\n",
-       "              'biased_diff_tpr': 0.31864557675474753,\n",
-       "              'debiased_diff_tpr': 0.15392966588017956,\n",
-       "              'biased_acc': 0.741185296324081,\n",
-       "              'debiased_acc': 0.6950487621905477},\n",
-       "             290: {'p_acc': 0.5113778444611152,\n",
-       "              'biased_diff_tpr': 0.31864557675474753,\n",
-       "              'debiased_diff_tpr': 0.1504312844224986,\n",
-       "              'biased_acc': 0.741185296324081,\n",
-       "              'debiased_acc': 0.691297824456114},\n",
-       "             295: {'p_acc': 0.49987496874218557,\n",
-       "              'biased_diff_tpr': 0.31864557675474753,\n",
-       "              'debiased_diff_tpr': 0.1759445240878502,\n",
-       "              'biased_acc': 0.741185296324081,\n",
-       "              'debiased_acc': 0.661790447611903}})"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results = check_result(u, removel_range, x_train, x_dev, y_m_train, y_m_dev, y_p_train, y_p_dev)\n",
-    "results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "defaultdict(list,\n",
-       "            {'biased_diff_tpr': [0.31864557675474753,\n",
-       "              0.31864557675474753,\n",
-       "              0.31864557675474753,\n",
-       "              0.31864557675474753,\n",
-       "              0.31864557675474753,\n",
-       "              0.31864557675474753,\n",
-       "              0.31864557675474753,\n",
-       "              0.31864557675474753,\n",
-       "              0.31864557675474753,\n",
-       "              0.31864557675474753,\n",
-       "              0.31864557675474753,\n",
-       "              0.31864557675474753,\n",
-       "              0.31864557675474753,\n",
-       "              0.31864557675474753],\n",
-       "             'debiased_diff_tpr': [0.31864557675474753,\n",
-       "              0.31575482093361984,\n",
-       "              0.11635856368415173,\n",
-       "              0.11674792676509015,\n",
-       "              0.12023899899338386,\n",
-       "              0.12100513006482054,\n",
-       "              0.12715509044451567,\n",
-       "              0.15675297512285796,\n",
-       "              0.1525863232511454,\n",
-       "              0.15004721659847078,\n",
-       "              0.1537984908912681,\n",
-       "              0.15392966588017956,\n",
-       "              0.1504312844224986,\n",
-       "              0.1759445240878502],\n",
-       "             'biased_acc': [0.741185296324081,\n",
-       "              0.741185296324081,\n",
-       "              0.741185296324081,\n",
-       "              0.741185296324081,\n",
-       "              0.741185296324081,\n",
-       "              0.741185296324081,\n",
-       "              0.741185296324081,\n",
-       "              0.741185296324081,\n",
-       "              0.741185296324081,\n",
-       "              0.741185296324081,\n",
-       "              0.741185296324081,\n",
-       "              0.741185296324081,\n",
-       "              0.741185296324081,\n",
-       "              0.741185296324081],\n",
-       "             'debiased_acc': [0.741185296324081,\n",
-       "              0.7346836709177295,\n",
-       "              0.7363090772693174,\n",
-       "              0.734558639659915,\n",
-       "              0.7366841710427607,\n",
-       "              0.7349337334333583,\n",
-       "              0.7334333583395849,\n",
-       "              0.7150537634408602,\n",
-       "              0.7150537634408602,\n",
-       "              0.7125531382845711,\n",
-       "              0.7040510127531883,\n",
-       "              0.6950487621905477,\n",
-       "              0.691297824456114,\n",
-       "              0.661790447611903]})"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "plot_results = convert_to_plot_results(results, removel_range)\n",
-    "plot_results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "data2plot(plot_results, removel_range)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "u has shape (300, 300), s has shape (2,), vh has shape (2, 2)\n"
-     ]
-    }
-   ],
-   "source": [
-    "ratio = 0.8\n",
-    "\n",
-    "saved_dataset = np.load(f\"../data/saved_models/fair_emoji_sent_race/{ratio}/all.npz\")\n",
-    "\n",
-    "x_train = saved_dataset['x_train']\n",
-    "y_m_train = saved_dataset['y_m_train']\n",
-    "y_p_train = saved_dataset['y_p_train']\n",
-    "y_p_train_2d = np.asarray([y_p_train, - y_p_train + 1]).T\n",
-    "\n",
-    "x_dev = saved_dataset['x_dev']\n",
-    "y_p_dev = saved_dataset['y_p_dev']\n",
-    "y_m_dev = saved_dataset['y_m_dev']\n",
-    "\n",
-    "A = np.dot(x_train.T, y_p_train_2d) / x_train.shape[0]\n",
-    "u, s, vh = np.linalg.svd(A, full_matrices=True)\n",
-    "print(f\"u has shape {u.shape}, s has shape {s.shape}, vh has shape {vh.shape}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "defaultdict(dict,\n",
-       "            {0: {'p_acc': 0.8379594898724682,\n",
-       "              'biased_diff_tpr': 0.4043010400370602,\n",
-       "              'debiased_diff_tpr': 0.40411721363762787,\n",
-       "              'biased_acc': 0.7200550137534384,\n",
-       "              'debiased_acc': 0.7201800450112528},\n",
-       "             1: {'p_acc': 0.7973243310827707,\n",
-       "              'biased_diff_tpr': 0.4043010400370602,\n",
-       "              'debiased_diff_tpr': 0.3654118278211388,\n",
-       "              'biased_acc': 0.7200550137534384,\n",
-       "              'debiased_acc': 0.7148037009252313},\n",
-       "             2: {'p_acc': 0.47186796699174793,\n",
-       "              'biased_diff_tpr': 0.4043010400370602,\n",
-       "              'debiased_diff_tpr': 0.18610177633438116,\n",
-       "              'biased_acc': 0.7200550137534384,\n",
-       "              'debiased_acc': 0.7191797949487372},\n",
-       "             5: {'p_acc': 0.47199299824956237,\n",
-       "              'biased_diff_tpr': 0.4043010400370602,\n",
-       "              'debiased_diff_tpr': 0.184739631028025,\n",
-       "              'biased_acc': 0.7200550137534384,\n",
-       "              'debiased_acc': 0.7193048262065517},\n",
-       "             30: {'p_acc': 0.4668667166791698,\n",
-       "              'biased_diff_tpr': 0.4043010400370602,\n",
-       "              'debiased_diff_tpr': 0.18506971632421484,\n",
-       "              'biased_acc': 0.7200550137534384,\n",
-       "              'debiased_acc': 0.7191797949487372},\n",
-       "             50: {'p_acc': 0.46524131032758187,\n",
-       "              'biased_diff_tpr': 0.4043010400370602,\n",
-       "              'debiased_diff_tpr': 0.1867917631297146,\n",
-       "              'biased_acc': 0.7200550137534384,\n",
-       "              'debiased_acc': 0.7181795448862216},\n",
-       "             100: {'p_acc': 0.4736184046011503,\n",
-       "              'biased_diff_tpr': 0.4043010400370602,\n",
-       "              'debiased_diff_tpr': 0.18737210776237648,\n",
-       "              'biased_acc': 0.7200550137534384,\n",
-       "              'debiased_acc': 0.7146786696674169},\n",
-       "             200: {'p_acc': 0.5230057514378594,\n",
-       "              'biased_diff_tpr': 0.4043010400370602,\n",
-       "              'debiased_diff_tpr': 0.19752038343148395,\n",
-       "              'biased_acc': 0.7200550137534384,\n",
-       "              'debiased_acc': 0.6956739184796199},\n",
-       "             220: {'p_acc': 0.5100025006251563,\n",
-       "              'biased_diff_tpr': 0.4043010400370602,\n",
-       "              'debiased_diff_tpr': 0.202949662154134,\n",
-       "              'biased_acc': 0.7200550137534384,\n",
-       "              'debiased_acc': 0.6937984496124031},\n",
-       "             240: {'p_acc': 0.5075018754688673,\n",
-       "              'biased_diff_tpr': 0.4043010400370602,\n",
-       "              'debiased_diff_tpr': 0.20801803316486164,\n",
-       "              'biased_acc': 0.7200550137534384,\n",
-       "              'debiased_acc': 0.6820455113778444},\n",
-       "             260: {'p_acc': 0.502750687671918,\n",
-       "              'biased_diff_tpr': 0.4043010400370602,\n",
-       "              'debiased_diff_tpr': 0.18813856463946912,\n",
-       "              'biased_acc': 0.7200550137534384,\n",
-       "              'debiased_acc': 0.6614153538384596},\n",
-       "             280: {'p_acc': 0.49987496874218557,\n",
-       "              'biased_diff_tpr': 0.4043010400370602,\n",
-       "              'debiased_diff_tpr': 0.19263016753347223,\n",
-       "              'biased_acc': 0.7200550137534384,\n",
-       "              'debiased_acc': 0.6404101025256314},\n",
-       "             290: {'p_acc': 0.49987496874218557,\n",
-       "              'biased_diff_tpr': 0.4043010400370602,\n",
-       "              'debiased_diff_tpr': 0.1721118417919755,\n",
-       "              'biased_acc': 0.7200550137534384,\n",
-       "              'debiased_acc': 0.6280320080020005},\n",
-       "             295: {'p_acc': 0.49987496874218557,\n",
-       "              'biased_diff_tpr': 0.4043010400370602,\n",
-       "              'debiased_diff_tpr': 0.0834654770143427,\n",
-       "              'biased_acc': 0.7200550137534384,\n",
-       "              'debiased_acc': 0.5681420355088772}})"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results = check_result(u, removel_range, x_train, x_dev, y_m_train, y_m_dev, y_p_train, y_p_dev)\n",
-    "results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "defaultdict(list,\n",
-       "            {'biased_diff_tpr': [0.4043010400370602,\n",
-       "              0.4043010400370602,\n",
-       "              0.4043010400370602,\n",
-       "              0.4043010400370602,\n",
-       "              0.4043010400370602,\n",
-       "              0.4043010400370602,\n",
-       "              0.4043010400370602,\n",
-       "              0.4043010400370602,\n",
-       "              0.4043010400370602,\n",
-       "              0.4043010400370602,\n",
-       "              0.4043010400370602,\n",
-       "              0.4043010400370602,\n",
-       "              0.4043010400370602,\n",
-       "              0.4043010400370602],\n",
-       "             'debiased_diff_tpr': [0.40411721363762787,\n",
-       "              0.3654118278211388,\n",
-       "              0.18610177633438116,\n",
-       "              0.184739631028025,\n",
-       "              0.18506971632421484,\n",
-       "              0.1867917631297146,\n",
-       "              0.18737210776237648,\n",
-       "              0.19752038343148395,\n",
-       "              0.202949662154134,\n",
-       "              0.20801803316486164,\n",
-       "              0.18813856463946912,\n",
-       "              0.19263016753347223,\n",
-       "              0.1721118417919755,\n",
-       "              0.0834654770143427],\n",
-       "             'biased_acc': [0.7200550137534384,\n",
-       "              0.7200550137534384,\n",
-       "              0.7200550137534384,\n",
-       "              0.7200550137534384,\n",
-       "              0.7200550137534384,\n",
-       "              0.7200550137534384,\n",
-       "              0.7200550137534384,\n",
-       "              0.7200550137534384,\n",
-       "              0.7200550137534384,\n",
-       "              0.7200550137534384,\n",
-       "              0.7200550137534384,\n",
-       "              0.7200550137534384,\n",
-       "              0.7200550137534384,\n",
-       "              0.7200550137534384],\n",
-       "             'debiased_acc': [0.7201800450112528,\n",
-       "              0.7148037009252313,\n",
-       "              0.7191797949487372,\n",
-       "              0.7193048262065517,\n",
-       "              0.7191797949487372,\n",
-       "              0.7181795448862216,\n",
-       "              0.7146786696674169,\n",
-       "              0.6956739184796199,\n",
-       "              0.6937984496124031,\n",
-       "              0.6820455113778444,\n",
-       "              0.6614153538384596,\n",
-       "              0.6404101025256314,\n",
-       "              0.6280320080020005,\n",
-       "              0.5681420355088772]})"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "plot_results = convert_to_plot_results(results, removel_range)\n",
-    "plot_results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "data2plot(plot_results, removel_range)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Old"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "removel_range = [0, 1, 2, 5, 30, 50, 100, 200, 220, 240, 260, 280, 290, 295]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "u has shape (300, 300), s has shape (2,), vh has shape (2, 2)\n"
-     ]
-    }
-   ],
-   "source": [
-    "ratio = 0.5\n",
-    "\n",
-    "x_train, y_p_train, y_m_train = load_data(\n",
-    "    '../data/emoji_sent_race_{}/train/'.format(ratio),\n",
-    "    size=100000, ratio=ratio)\n",
-    "x_dev, y_p_dev, y_m_dev = load_data(\n",
-    "    '../data/emoji_sent_race_{}/test/'.format(ratio),\n",
-    "    size=100000, ratio=0.5)\n",
-    "\n",
-    "# y_p_train_2d = np.asarray([y_p_train, y_p_train*-1 +1]).T\n",
-    "\n",
-    "# A = np.dot(x_train.T, y_p_train_2d) / x_train.shape[0]\n",
-    "# u, s, vh = np.linalg.svd(A, full_matrices=True)\n",
-    "# print(f\"u has shape {u.shape}, s has shape {s.shape}, vh has shape {vh.shape}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "defaultdict(dict,\n",
-       "            {0: {'p_acc': 0.8714678669667417,\n",
-       "              'biased_diff_tpr': 0.14560632483708713,\n",
-       "              'debiased_diff_tpr': 0.14560632483708713,\n",
-       "              'biased_acc': 0.7653163290822705,\n",
-       "              'debiased_acc': 0.7653163290822705},\n",
-       "             1: {'p_acc': 0.8630907726931732,\n",
-       "              'biased_diff_tpr': 0.14560632483708713,\n",
-       "              'debiased_diff_tpr': 0.1445020833238553,\n",
-       "              'biased_acc': 0.7653163290822705,\n",
-       "              'debiased_acc': 0.7653163290822705},\n",
-       "             2: {'p_acc': 0.49699924981245314,\n",
-       "              'biased_diff_tpr': 0.14560632483708713,\n",
-       "              'debiased_diff_tpr': 0.11020173553336259,\n",
-       "              'biased_acc': 0.7653163290822705,\n",
-       "              'debiased_acc': 0.766816704176044},\n",
-       "             5: {'p_acc': 0.4959989997499375,\n",
-       "              'biased_diff_tpr': 0.14560632483708713,\n",
-       "              'debiased_diff_tpr': 0.11007656356661116,\n",
-       "              'biased_acc': 0.7653163290822705,\n",
-       "              'debiased_acc': 0.7669417354338585},\n",
-       "             30: {'p_acc': 0.4948737184296074,\n",
-       "              'biased_diff_tpr': 0.14560632483708713,\n",
-       "              'debiased_diff_tpr': 0.10621266898739956,\n",
-       "              'biased_acc': 0.7653163290822705,\n",
-       "              'debiased_acc': 0.7660665166291573},\n",
-       "             50: {'p_acc': 0.4973743435858965,\n",
-       "              'biased_diff_tpr': 0.14560632483708713,\n",
-       "              'debiased_diff_tpr': 0.10841554967602068,\n",
-       "              'biased_acc': 0.7653163290822705,\n",
-       "              'debiased_acc': 0.766816704176044},\n",
-       "             100: {'p_acc': 0.49637409352338085,\n",
-       "              'biased_diff_tpr': 0.14560632483708713,\n",
-       "              'debiased_diff_tpr': 0.10844931019709282,\n",
-       "              'biased_acc': 0.7653163290822705,\n",
-       "              'debiased_acc': 0.7664416104026006},\n",
-       "             200: {'p_acc': 0.5248812203050762,\n",
-       "              'biased_diff_tpr': 0.14560632483708713,\n",
-       "              'debiased_diff_tpr': 0.1042793080915506,\n",
-       "              'biased_acc': 0.7653163290822705,\n",
-       "              'debiased_acc': 0.7656914228557139},\n",
-       "             220: {'p_acc': 0.44711177794448614,\n",
-       "              'biased_diff_tpr': 0.14560632483708713,\n",
-       "              'debiased_diff_tpr': 0.09987399516901178,\n",
-       "              'biased_acc': 0.7653163290822705,\n",
-       "              'debiased_acc': 0.7661915478869717},\n",
-       "             240: {'p_acc': 0.45023755938984744,\n",
-       "              'biased_diff_tpr': 0.14560632483708713,\n",
-       "              'debiased_diff_tpr': 0.1032924184615246,\n",
-       "              'biased_acc': 0.7653163290822705,\n",
-       "              'debiased_acc': 0.7656914228557139},\n",
-       "             260: {'p_acc': 0.450112528132033,\n",
-       "              'biased_diff_tpr': 0.14560632483708713,\n",
-       "              'debiased_diff_tpr': 0.10346416206559751,\n",
-       "              'biased_acc': 0.7653163290822705,\n",
-       "              'debiased_acc': 0.7656914228557139},\n",
-       "             280: {'p_acc': 0.4587396849212303,\n",
-       "              'biased_diff_tpr': 0.14560632483708713,\n",
-       "              'debiased_diff_tpr': 0.1043429019092808,\n",
-       "              'biased_acc': 0.7653163290822705,\n",
-       "              'debiased_acc': 0.7591897974493623},\n",
-       "             290: {'p_acc': 0.5001250312578145,\n",
-       "              'biased_diff_tpr': 0.14560632483708713,\n",
-       "              'debiased_diff_tpr': 0.10178012864537783,\n",
-       "              'biased_acc': 0.7653163290822705,\n",
-       "              'debiased_acc': 0.7596899224806202},\n",
-       "             295: {'p_acc': 0.49987496874218557,\n",
-       "              'biased_diff_tpr': 0.14560632483708713,\n",
-       "              'debiased_diff_tpr': 0.09851762574167189,\n",
-       "              'biased_acc': 0.7653163290822705,\n",
-       "              'debiased_acc': 0.7548137034258565}})"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results = check_result(u, removel_range, x_train, x_dev, y_m_train, y_m_dev, y_p_train, y_p_dev)\n",
-    "results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "defaultdict(list,\n",
-       "            {'biased_diff_tpr': [0.14560632483708713,\n",
-       "              0.14560632483708713,\n",
-       "              0.14560632483708713,\n",
-       "              0.14560632483708713,\n",
-       "              0.14560632483708713,\n",
-       "              0.14560632483708713,\n",
-       "              0.14560632483708713,\n",
-       "              0.14560632483708713,\n",
-       "              0.14560632483708713,\n",
-       "              0.14560632483708713,\n",
-       "              0.14560632483708713,\n",
-       "              0.14560632483708713,\n",
-       "              0.14560632483708713,\n",
-       "              0.14560632483708713],\n",
-       "             'debiased_diff_tpr': [0.14560632483708713,\n",
-       "              0.1445020833238553,\n",
-       "              0.11020173553336259,\n",
-       "              0.11007656356661116,\n",
-       "              0.10621266898739956,\n",
-       "              0.10841554967602068,\n",
-       "              0.10844931019709282,\n",
-       "              0.1042793080915506,\n",
-       "              0.09987399516901178,\n",
-       "              0.1032924184615246,\n",
-       "              0.10346416206559751,\n",
-       "              0.1043429019092808,\n",
-       "              0.10178012864537783,\n",
-       "              0.09851762574167189],\n",
-       "             'biased_acc': [0.7653163290822705,\n",
-       "              0.7653163290822705,\n",
-       "              0.7653163290822705,\n",
-       "              0.7653163290822705,\n",
-       "              0.7653163290822705,\n",
-       "              0.7653163290822705,\n",
-       "              0.7653163290822705,\n",
-       "              0.7653163290822705,\n",
-       "              0.7653163290822705,\n",
-       "              0.7653163290822705,\n",
-       "              0.7653163290822705,\n",
-       "              0.7653163290822705,\n",
-       "              0.7653163290822705,\n",
-       "              0.7653163290822705],\n",
-       "             'debiased_acc': [0.7653163290822705,\n",
-       "              0.7653163290822705,\n",
-       "              0.766816704176044,\n",
-       "              0.7669417354338585,\n",
-       "              0.7660665166291573,\n",
-       "              0.766816704176044,\n",
-       "              0.7664416104026006,\n",
-       "              0.7656914228557139,\n",
-       "              0.7661915478869717,\n",
-       "              0.7656914228557139,\n",
-       "              0.7656914228557139,\n",
-       "              0.7591897974493623,\n",
-       "              0.7596899224806202,\n",
-       "              0.7548137034258565]})"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "plot_results = convert_to_plot_results(results, removel_range)\n",
-    "plot_results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "data2plot(plot_results, removel_range)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "u has shape (300, 300), s has shape (2,), vh has shape (2, 2)\n"
-     ]
-    }
-   ],
-   "source": [
-    "ratio = 0.6\n",
-    "\n",
-    "x_train, y_p_train, y_m_train = load_data(\n",
-    "    '../data/emoji_sent_race_{}/train/'.format(ratio),\n",
-    "    size=100000, ratio=ratio)\n",
-    "x_dev, y_p_dev, y_m_dev = load_data(\n",
-    "    '../data/emoji_sent_race_{}/test/'.format(ratio),\n",
-    "    size=100000, ratio=0.5)\n",
-    "\n",
-    "y_p_train_2d = np.asarray([y_p_train, y_p_train*-1 +1]).T\n",
-    "\n",
-    "A = np.dot(x_train.T, y_p_train_2d) / x_train.shape[0]\n",
-    "u, s, vh = np.linalg.svd(A, full_matrices=True)\n",
-    "print(f\"u has shape {u.shape}, s has shape {s.shape}, vh has shape {vh.shape}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "defaultdict(dict,\n",
-       "            {0: {'p_acc': 0.840960240060015,\n",
-       "              'biased_diff_tpr': 0.22630502371727185,\n",
-       "              'debiased_diff_tpr': 0.22630502371727185,\n",
-       "              'biased_acc': 0.7531882970742686,\n",
-       "              'debiased_acc': 0.7531882970742686},\n",
-       "             1: {'p_acc': 0.8508377094273568,\n",
-       "              'biased_diff_tpr': 0.22630502371727185,\n",
-       "              'debiased_diff_tpr': 0.22803630357189353,\n",
-       "              'biased_acc': 0.7531882970742686,\n",
-       "              'debiased_acc': 0.7530632658164541},\n",
-       "             2: {'p_acc': 0.4711177794448612,\n",
-       "              'biased_diff_tpr': 0.22630502371727185,\n",
-       "              'debiased_diff_tpr': 0.08988024196274066,\n",
-       "              'biased_acc': 0.7531882970742686,\n",
-       "              'debiased_acc': 0.7531882970742686},\n",
-       "             5: {'p_acc': 0.4708677169292323,\n",
-       "              'biased_diff_tpr': 0.22630502371727185,\n",
-       "              'debiased_diff_tpr': 0.09037932830110916,\n",
-       "              'biased_acc': 0.7531882970742686,\n",
-       "              'debiased_acc': 0.7526881720430108},\n",
-       "             30: {'p_acc': 0.4723680920230057,\n",
-       "              'biased_diff_tpr': 0.22630502371727185,\n",
-       "              'debiased_diff_tpr': 0.09126866357639556,\n",
-       "              'biased_acc': 0.7531882970742686,\n",
-       "              'debiased_acc': 0.7530632658164541},\n",
-       "             50: {'p_acc': 0.5066266566641661,\n",
-       "              'biased_diff_tpr': 0.22630502371727185,\n",
-       "              'debiased_diff_tpr': 0.09258461867418033,\n",
-       "              'biased_acc': 0.7531882970742686,\n",
-       "              'debiased_acc': 0.7511877969492373},\n",
-       "             100: {'p_acc': 0.5031257814453614,\n",
-       "              'biased_diff_tpr': 0.22630502371727185,\n",
-       "              'debiased_diff_tpr': 0.09496617559255267,\n",
-       "              'biased_acc': 0.7531882970742686,\n",
-       "              'debiased_acc': 0.7484371092773193},\n",
-       "             200: {'p_acc': 0.5007501875468867,\n",
-       "              'biased_diff_tpr': 0.22630502371727185,\n",
-       "              'debiased_diff_tpr': 0.10150799279833236,\n",
-       "              'biased_acc': 0.7531882970742686,\n",
-       "              'debiased_acc': 0.7403100775193798},\n",
-       "             220: {'p_acc': 0.5002500625156289,\n",
-       "              'biased_diff_tpr': 0.22630502371727185,\n",
-       "              'debiased_diff_tpr': 0.10921997885015855,\n",
-       "              'biased_acc': 0.7531882970742686,\n",
-       "              'debiased_acc': 0.7353088272068017},\n",
-       "             240: {'p_acc': 0.5001250312578145,\n",
-       "              'biased_diff_tpr': 0.22630502371727185,\n",
-       "              'debiased_diff_tpr': 0.10635797301822242,\n",
-       "              'biased_acc': 0.7531882970742686,\n",
-       "              'debiased_acc': 0.733183295823956},\n",
-       "             260: {'p_acc': 0.5002500625156289,\n",
-       "              'biased_diff_tpr': 0.22630502371727185,\n",
-       "              'debiased_diff_tpr': 0.10776873695187088,\n",
-       "              'biased_acc': 0.7531882970742686,\n",
-       "              'debiased_acc': 0.729307326831708},\n",
-       "             280: {'p_acc': 0.4981245311327832,\n",
-       "              'biased_diff_tpr': 0.22630502371727185,\n",
-       "              'debiased_diff_tpr': 0.11257271707339944,\n",
-       "              'biased_acc': 0.7531882970742686,\n",
-       "              'debiased_acc': 0.7215553888472118},\n",
-       "             290: {'p_acc': 0.5036259064766192,\n",
-       "              'biased_diff_tpr': 0.22630502371727185,\n",
-       "              'debiased_diff_tpr': 0.1257355117153975,\n",
-       "              'biased_acc': 0.7531882970742686,\n",
-       "              'debiased_acc': 0.7189297324331083},\n",
-       "             295: {'p_acc': 0.49987496874218557,\n",
-       "              'biased_diff_tpr': 0.22630502371727185,\n",
-       "              'debiased_diff_tpr': 0.11164602462363725,\n",
-       "              'biased_acc': 0.7531882970742686,\n",
-       "              'debiased_acc': 0.6765441360340085}})"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results = check_result(u, removel_range, x_train, x_dev, y_m_train, y_m_dev, y_p_train, y_p_dev)\n",
-    "results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "defaultdict(list,\n",
-       "            {'biased_diff_tpr': [0.22630502371727185,\n",
-       "              0.22630502371727185,\n",
-       "              0.22630502371727185,\n",
-       "              0.22630502371727185,\n",
-       "              0.22630502371727185,\n",
-       "              0.22630502371727185,\n",
-       "              0.22630502371727185,\n",
-       "              0.22630502371727185,\n",
-       "              0.22630502371727185,\n",
-       "              0.22630502371727185,\n",
-       "              0.22630502371727185,\n",
-       "              0.22630502371727185,\n",
-       "              0.22630502371727185,\n",
-       "              0.22630502371727185],\n",
-       "             'debiased_diff_tpr': [0.22630502371727185,\n",
-       "              0.22803630357189353,\n",
-       "              0.08988024196274066,\n",
-       "              0.09037932830110916,\n",
-       "              0.09126866357639556,\n",
-       "              0.09258461867418033,\n",
-       "              0.09496617559255267,\n",
-       "              0.10150799279833236,\n",
-       "              0.10921997885015855,\n",
-       "              0.10635797301822242,\n",
-       "              0.10776873695187088,\n",
-       "              0.11257271707339944,\n",
-       "              0.1257355117153975,\n",
-       "              0.11164602462363725],\n",
-       "             'biased_acc': [0.7531882970742686,\n",
-       "              0.7531882970742686,\n",
-       "              0.7531882970742686,\n",
-       "              0.7531882970742686,\n",
-       "              0.7531882970742686,\n",
-       "              0.7531882970742686,\n",
-       "              0.7531882970742686,\n",
-       "              0.7531882970742686,\n",
-       "              0.7531882970742686,\n",
-       "              0.7531882970742686,\n",
-       "              0.7531882970742686,\n",
-       "              0.7531882970742686,\n",
-       "              0.7531882970742686,\n",
-       "              0.7531882970742686],\n",
-       "             'debiased_acc': [0.7531882970742686,\n",
-       "              0.7530632658164541,\n",
-       "              0.7531882970742686,\n",
-       "              0.7526881720430108,\n",
-       "              0.7530632658164541,\n",
-       "              0.7511877969492373,\n",
-       "              0.7484371092773193,\n",
-       "              0.7403100775193798,\n",
-       "              0.7353088272068017,\n",
-       "              0.733183295823956,\n",
-       "              0.729307326831708,\n",
-       "              0.7215553888472118,\n",
-       "              0.7189297324331083,\n",
-       "              0.6765441360340085]})"
-      ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "plot_results = convert_to_plot_results(results, removel_range)\n",
-    "plot_results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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HWFjYTYddXdSm9tSmtoBsT3VWm9oC5W9PTEwMa9asqfBx1h9OYXTnYB69qzkHErKZuvIQ2567Cz8vF3ZHR1HP3ZmjSbk8tmw/26behafGqcLHuh6HXR5U+/thupBqWzampqG+5o50dUAAnn2iUDg54RwcjHPTphgSEhwVkiRJklQCfy8NKblFtuULuTr8vTR2ZVbsS2Rwh0AAOjeph95kJktrwEWtop679d7B8GBvGvu6cS6z0GGxOixpuYaHY0hIwJCUhDAYyNu0Cc8o+4cOeva9G+2lSUtN2dkY4uNxCg52VEiSJElSCToGexN/sZDELC0Gk4UNh1Po19a+kxHk48pfpzMBOJ2ej95oob67MxcL9Jgt1tt9z1/UEn+xkMa+bg6L1WGXBxVqNQGz/o/ESY9Yh7zfOwqXVq3IWLAATfv2eEZF4d6rF4V//sWZwUNQKJX4vfgC6nr1HBWSJEmSVAK1SsmcYe2ZuHQvZotgbJdgWvt78v62k4QH+9CvrT//NziM6DVH+eLPc9Z5Psd0RKFQsPdcFu//8i9qlRKlAuaOCMfH7cZnbSl3rA6rGfCIjMQjMtJuXcMpU2w/KxQK/GdEI6exlCRJqlp9Qv3oE+pnt+75/m1sP7fy92T1k8WnwhoYHsjA8ECHx3eZnBFDkiRJqjFk0pIkSZJqDJm0JEmSpBpDJi1JkiSpxpBJqxIkJSUxZMiQEre9/PLLnD592mHHjoqKIisry2H1S5IkVSd1Nmml5+kY++ke0vN1ZRe+CXPnzqVly5ZlF5QkSZLKVLXTOFWhBdtPsS8+iwXbT/PGiPY3XZ/JZGLatGnExsbSqlUr3n77bVxdXZkwYQIvvfQS4eHhvPrqqxw9ehS9Xs+AAQOYcmn4//z58/ntt99QqVT06tWL6dOnk5WVxauvvkpKSgpgnXOxc+fOZGdnM23aNNLS0mjatCmlPcOztGMdOXKEefPmodVqcXZ25quvvsLV1ZX58+eza9cuFAoFY8eOZcKECTf9mkiSJFW2Wpe0Vh9IYuX+xBK3abVa3P7IwWCycCgpByFg+T8JHE/OxVldeqdzbJcQ7u18/Zk6zp07x9y5c+ncuTMzZszgu+++Y9KkSXZlpk6dio+PD2azmYceeogTJ07g7+/PL7/8wpYtW1AoFOTl5QHWHtp//vMfunTpQkpKCpMmTWLz5s189NFH3HbbbUyePJlly5bx66+/lhhPScdq3rw5U6dO5YMPPqBDhw4UFBSg0WhYsWIFycnJrF27FrVaTU5OznXbKkmSVFVqXdIqj+ScIrjcQRHW5WYN3G+qzsDAQDp37gzAsGHDWLZsWbGktXnzZlauXInJZCIjI4MzZ87QsmVLXFxcmDlzJn369KF3794A7N692+67sIKCAgoLC9m3bx+LFi0CoEuXLnhfeqzLtUo6lkKhoGHDhnTo0AEADw/rQ9v27NnD+PHjUautbwcfH5+bei0kSZIcpdYlrXs7B5faK4qLi6N+o2bc+c7vV+cs8oqMLLw/Aj9PTYn7lce1j0u5djkxMZGlS5eyatUqvL29iY6ORq/Xo1arWbVqFXv27GHLli18++23fPPNN1gsFlauXImLy40/s6a0Y0mSJNV0dW4gxoLtp7Bc8z2QWQgWbL+5EX4pKSnExMQA8PPPP9t6XZcVFhbi6uqKp6cnmZmZ/PHHH7b1+fn5REZGMnPmTE6ePAlAr169WLZsmW3/uLg4ALp27cqGDRsAOHDgALm5ucViKe1YzZo1IyMjgyNHrM81KygowGQy0bNnT1asWIHJZH3Am7w8KElSdVXrelplOXg+B6PZPmkZzYKDCdk3VW+zZs1Yvnw5M2fOpGXLltx3331220NDQ2nbti0DBw4kICCA2267DbAmmKeeesrWE4qOjgasQ+XnzJnD0KFDMZvNdOnShTlz5vD0008zbdo0Bg8eTLNmzQgKCioWS2nHcnZ25oMPPuCNN95Ap9Oh0Wj48ssvGTNmDPHx8QwbNgy1Ws3YsWN58MEHb+r1kCRJcgSFKG34WTUlHwJ5RW1qT21qC8j2VGe1qS1wYw+BrOhnZ3VS5y4PSpIkSTWXTFqSJElSjSGTliRJklRjyKQlSZIk1RgyaUmSJEk1hkxakiRJUo0hk5YDLFy4kC+++OK6ZaKjo9myZUux9UePHuWNN95wVGisWbOGOXPmOKx+SZIkR6pzNxcDrI1J5t2tJ0nJKSLIx5UXB7RhRESjqg4LgPDwcMLDw6s6DEmSpGqpzvW01sYkM2PNUZJzihBYJ8udseYoa2OSb6rexYsXM2DAAO677z7OnTtnW3/+/HkmTZrEqFGjuP/++zlz5oxt2+7duxk1ahQDBgzg999/B+Cff/7h8ccfB6yPERk3bhwjRoxg/PjxnD17FoBTp04xevRonnvuOYYOHUp8fDwA69atY/To0QwfPpxXXnkFs9kMwOrVqxkwYACjR4/m4MGDJcZf2rHMZjNvv/02Q4YMYejQobappY4cOcL48eMZNmwYo0ePpqCg4KZeP0mSpPKolT2tcZ/uKbZuSIdAuvjAO1tOUGQ0220rMpp5bcNxRkQ0IqvQwJPfHrDbvuLx2697vGPHjrFp0ybWrl2L2Wxm5MiRtGvXDoBZs2Yxe/ZsmjZtyuHDh5k9ezbffPMNAMnJyaxatYrz588zceJEevbsaVdv8+bNWb58OWq1mt27d/PBBx+wcOFCfvjhByZOnEirVq1o0aIFFouFM2fOsHnzZr7//nucnJx47bXX2LBhAz179mThwoWsWbMGDw8PJk6cSNu2bYu1obRjlfTYEoPBUOIjTiRJqrl2nExnzoZYzEIwrmsIT/W2f3htck4R01YeIq/IhEUIpt8TSp9QPwA++v00K/cnolIoeHVYOyJbN3RYnLUyaV3PhdySn1ScozVWuM79+/fTt29fXF1dAYiKigKs8wrGxMTw7LPP2soaDAbbzwMHDkSpVNK0aVNCQkJsvZvL8vPzmT59OgkJCSgUCoxGa4ydOnXik08+oWfPnjzwwAM0bdqUPXv2cOzYMUaPHg2ATqejfv36HDlyhG7duuHr6wvAoEGDbD2z8hyrpMeWnDx5ssRHnEiSVDOZLYJX1h3n20ndCfDWMGzRn/QL86eVv6etzKLfTjG4QxATejThVFo+D325j7+ioziVls+Gwylsm3oX6Xl6Hvj8H35/oTcqpeI6R6y4Wpm0SusZxcXFEeTjan2e1jUa+VgTjq+7c5k9q/ISQuDl5cW6detK3F7W40w+/PBDunfvzkcffURSUhITJ04EYOjQoXTs2JEVK1bw2GOPMXv2bIQQjBw5kmnTptnVUdpDIq9V2rEkSar9DiXm0KS+G43ruwEwtGMQ22LT7JIWKCjQWZ8Ekacz4e9lfWzSttg0hnYMwkWtIsTXjSb13TiUmEPnJvUcEmuNS1oWi8X2mI4bpdPpuD/cgwW7deivmundRaXg/nCPCtfboEEDvvvuOyIjI7FYLGzdupUBAwaQmJiIr68vn3/+OXfccQdCCOLj42nWrBk5OTmsWrWK0NBQ0tLSOHfuHHq9noSEBAoKCoiLiyMlJYVWrVoRFxfH999/j8FgIC4ujtTUVPz9/enbty8ZGRn88ccfRERE8MUXX9CzZ098fHzIz8+nqKgIV1dXdu/ezT///IObmxtr1qyhWbNmxdpa2rGaN2/OZ599hre3NyqVivz8fDQaDSkpKaxfv55WrVpRVFSEs7MzKpWqQq/f5XNT0de/OpLtqb5qU1ug/O3Jzc1l1KhRtuVx48Yxbtw4ANLydAR5u9q2BXprOJSYY7f/1L6tmPDFXr7eHY/WYGL5Iz1s+0Y09rHbNy2v5CtalaHGJS2lUlnhGZrj4uJ4enAEjYIqd/RgWFgYiYmJTJ8+HV9fXzp37oyfnx9hYWF89NFHvPbaa6xfvx6TycSgQYMYNGgQPj4++Pn5MWvWLAoLC5k7dy4dO3ZEp9Ph4eFBWFgYU6dOJTo6mg0bNhAZGYmzszNhYWHs2rWL+fPnYzKZCA4OZubMmfj4+GAymXjrrbewWCw4OTnxyiuv0KlTJ6ZOncorr7yCp6cn4eHhODk5FXsNSztWq1atePfdd3nppZfsHluyaNGiYo84cXev+NOf6+rM2zVFbWpPbWoLlL89Op2ONWvWVPg46w+nMLpzMI/e1ZwDCdlMXXmIbc/dVeH6Kkw4UP4ff4jTA+4Rp/r1FxmfLim2PXv1GnGyx+3izPAR4szwESJr5coy6zx48GCF44mNja3wvtVRbWpPbWqLELI91VltaosQ5W/P9T4798dniQc//9u2vOi3U2LRb6fsyvR9b4dIztbalnu9vV1k5OuKlX3w87/F/vis8oZ/wxw25F2YzaTOeZ2Qz5bQ4ucN5G3ciP508acDew0cSPO1P9F87U/UGzPGUeFIkiRJpegY7E38xUISs7QYTBY2HE6hX1t/uzJBPq78dToTgNPp+eiNFuq7O9OvrT8bDqegN5lJzNISf7GQTiE+DovVYZcHi44cwblxY5xDQgDwGjSI/O2/4dKyZRl7SpIkSbeSWqVkzrD2TFy6F7NFMLZLMK39PXl/20nCg33o19af/xscRvSao3zx5zkUCgXzx3REoVDQ2t+TIR0C6ff+H6iVCuYMb++wkYPgwKRlSktHHRhgW3YK8Kfo8JFi5fJ+2YZ2/36cmzbFf0Y0ToGBjgpJkiRJKkWfUD/bfVeXPd+/je3nVv6erH6y57W7ATA5qhWTo1o5NL7LqnQghkef3ngNGYzS2ZnsH1aQEj2DJl9/VazcihUrWLFiBWCds+9mRg/WxVFDNUFtagvI9lRntaktUPvaUxaHJS21vx+mC6m2ZWNqGmp/+2uk6npXxvH7jBlN+vz5JdZ19dDMmJiYmxo9WBdHDdUEtaktINtTndWmtkD52xMTE3MLonE8hw3EcA0Px5CQgCEpCWEwkLdpE55RfezKGNPTbT/n//Ybzi2aOyocSZIkqRZwWE9LoVYTMOv/SJz0CMJiwefeUbi0akXGggVo2rfHMyqK7GXfkv/7byhUalTe3gS9+aajwpEkSZJqAYd+p+URGYlHZKTduoZTpth+9pv2PH7TnndkCJIkSVItUuceTSJJkiTVXDJpSZIkSTWGTFqSJElSjSGTliRJklRjyKQlSZIk1RgyaUmSJEk1hkxakiRJUo0hk5YkSZJUY8ikJUmSJNUYMmlJkiRJNYZMWpIkSVKNIZOWJEmSVGPIpCVJkiTVGDJpSZIkSTWGTFqSJElSjSGTliRJklRj1ImktTYmmQ6vbWXg12dpGr2RptEbCZu1iY6zt9I0eiM95m1nbUyyw47dY972Sj9Oep6OF7ekkJ6vq5T6qlJtakttVNnnJz1Px9hP91Tq+a4JdZa3vtLKOaKNNVGtT1prY5J58cfD5OlMduuLjILcIuu61DwdM9YcLZZQhBBYLMK2rDOaKdSbyNcZyS0ykl1oIE9ntG1Pz9ORnFNEYpaWhIuFfPbHWaJXHyE1T2c7TvSaI/x0MOmm29Rn/g6Openo8+4OhyXcW2XB9lMcT9OxYPvpqg5FKkFln58F20+xLz6rUs93TaizvPWVVs4RbbzajpPpRM3fQeS7v/PxjuLHmLMhloEf7mLgh7voM38H4a9ttW1rPmOjbdsjX+9zSHyXKYQQouxi1UdMTAwRERHlLn/HW7+RnFNUrrKKS/9f/YJ4uKhpF+QFwJGkXIqMZrt9vF2dCA3wtMZ2PgeD2VKuY6mUClRKBQ3cnQnxdQPgTEYBSoXCtk2lVODurMZTo0YIgdZgJrfISFJOEVefNYUCmtd3p4GnS7mOXZ2k5haRkHXl/DTxdSXA27UKI6ocWq0WNze3qg7jphlMFg4l5SCE9X3WKdgHZ3XF/9at7PoqUmd5zk1Vtbu0clev16iV/DG9D36eGgDi4uIICwsrM4brfXaaLYI+83fw7aTuBHhrGLboTxbeF0Erf88Sy3/11zmOp+Tx7piOALR9ZQuxc+4p78txU9S35ChVKKWcCQusySrQW2NNXgprEnNWq2zbA7w1mMwWFJcKKACXq954jX1dMYsrye9sZmGpx2rg4YzZImz7CyHI15kwWwRmIWxJKdBbg6dGjVnAsZS8kuMWcD5bS3JOEUqlAoUClAoFSoUCP08XfN2dMZgs1u1XbVMqrUnXzVmNyWwhT2dCqQCFQmEr5+KkRK1UYhECi8C6HWuZm5WZr7dLWAAJWUWolcoamYBro+Scoit/xQnrcrMG7tWmvppSZ3nrK63c1evNQrBg+2neGNG+wvFc61BiDk3qu9G4vjWZD+0YxLbYtFKT1vrDKUzt17rSjn8jalzSslgsxMXFlbt8fTcVmVpz2QWBBm4qlo4IqmhoxUz4MaHEYzdwU/HVyEYl7FHP9pPBLNAaLagU4OmiwmAWHGzuzOzf00o8ltEsuKOZG3qTwGgR6E0Cg1kwMtSNyGYeJOQYeGlLHkazQG+2JiCA5+/woF9LT2LTdUzbnFKs3pd7+9GriQcHkrX836+pgDVxOakUuKgUvNzbnw4BrsSkaPnyYDYuKoV1m1qBs0rBhE71CPZ25t9MPX8lFOJ81fZlySX/QXGxoIjZferj6qTEVa2olAR5q+l0GjQaTVWHcVOytCYeXpN99WcoOVoDU7r64+t64x8dlV1fRess69xUVbtLKzehfQOe33RlvdEsWLnvPAMbC3xd1eh0unJ9Jubm5jJq1Cjb8rhx4xg3bhwAaXk6gq66whHoreFQYk6J9SRla0nMLqJniwa2dXqThaEL/0SlVPBk7xYMaBdQZjwVVeOSllKpLFdX+LI2gTlknskqZ1mfG6q7oseu6HE6tof5f22h0FA8Ebo7q/jq8d6l7hsG3HN7R9uyyWxBZ7JcSiAqmrQw0bZ1C3RGM3qTBb3Jgs5opmOwDwHeGjz8tfyfSz3rNqMZ3aX/u7RvSouGHuS5XKTReTN6owWdyUye0YJOZyakaTNCA7w4uj+RtXEXynX5NEdn4cEfzwOwOzqKIB9Xlv55jm/2xOPuosbdWY27iwo3FzXvju6Am7OaXacyOJach4eLCjdnNe4uajxc1NzRsj4KhYJcrRGBwM1ZfdOXo8qjvJdsqrP/++mo3aVysH6Qbj6v4I0RN962yq6vonWWdW6qqt2llfvwn9zr7l/e95pOp2PNmjU3HP+1Nhy+wKD2AaiUV/6Y/Gt6FAHeGs5f1HLfZ38TGuBJk/o319stTY1LWjcqW2squ5CtrLHsQpVw7Js5jrerU4lJy9vV6YbqUauUeKiufHi7OasJC/QqtXyIrxuP3Nm81O3dm9ene/P6pW4f2yWEsV1CMFsEhksJsevcXzFZrv11BJVCwZwR7SjUm6jn5gxAkI+GDsE+FOpNFBpMZBYYKMzS2n5xfo1N4+s9CfZtVCo4NXcgAG9sjOXHA9YBMM4qJW4uKvw9NWydehcAH/1+miNJOZcSotq2/b+9mgGwLz6LAp3Jus1ZhYeL9bvG+h619zLmwfM5GM3258doFhxMyK4W9dWUOstbX2nlzmdpK72N1/L30pCSe+XKx4VcHf5eJfdGNxxO4fUR7ezWBXhbyzau70aP5vU5npLnsKRV6wdiXK02/PUL1tGD7249SUpOEUE+rrw4oA0jIkq63Fi9rY1JZsaao3aDW1ydVLw5KvyG2yOEQGe0UKA3oTWYKNCb0BnNdG7iC8DuM5mcTM2nUG+iQG9GazChVCh4bZj1l2/uxlj++DeTQoPpUmI0E+StYceLfQAYv2QPf5+17zWHBniy5Tlr0rtvyd+cySjA41LCU5gM9GwTxIxB1vfbJzvPoDWYcXdWWXuLLioa+7rZ4ovPLMRZrbT1ItWq6jWwt7b87kDtagtUzkAMk9lCn/d28N0jPfD3sg7EWHBfBK2v+U7rdHoB/1m6lz+n97Fdts/VGtE4K3FRq8gqNDDq47/4bGKXUr8Pu1m1vqdVG42IaMSIiEY1/pfvcmKqjASsUChwdVbh6qwCivd+erZoYHcN/lovD27Ly4Pt15muupT5zr0duViop1BvtiU2N+crg3Tuat2QJvXdLiVNM+nZBrRX9YhXH0jiVHqBXf392/qzZKI1ad27eDcXCw22bS5qJfd2DmbeyHAA7v/sb1RKhTUpOqvxcFFxe4v63NM+EItFsOpgki3hXb6EGuCtwdfdmct/l1bku0H7P5Au1Ng/kKTrU6uUzBnWnolL92K2CMZ2Caa1vyfvbztJeLAP/dr6A9Ze1tCOQXbvpdMZ+cxccwyFwjoo7MneLRyWsEAmLamKVecEfHVvp/FVI6tK8mTvFnbL17bnl+cjsVgERZfu9Ss0mFFf9Z3AGyPak1tkpNBgtl0CbXvpcq0QApVSQb7ORGquDq3BTIHehMZJxT3tA9GZzLy06kixmJ6Jasm0/m24WGig+7ztuDmr7BLbf+9oxoiIRmQW6Hlv2792vUB3FzUFOhP/+/WUrSecnFPEjDVHAWTiqoX6hPrRJ9TPbt3z/dvYLZc0YrBzE1/bZfZbQSYtSbpFlErFpaRQ/NduYHhgqfspFAqWTepe6naNWsWul/rYkpn2Uk+wWQMPAJxUSp6MbHHl0uel3qLGyZqUc4uM/BKbRqHeZHep1sfVqdh9iUVGM9FrjvBvWj6jOwfTvKEHQogaOcJTqplk0pKkGk6pVNhuUC+Jt6sTLwxoU+r2Fg092P9/fQHrTaZag/USZ49520ssrzNaWPLHWe5s1ZDmDT3YciyVmT8dpVkDd5o39KB5Q3eaN3DnjpYN8NTYDxCqLd/HSlXHoUmrYNcu0ubOQ1gs+IweTYPHHi2xXN7WbSQ/+yxNf/wR1/DKu2FOkqQbo1Iq8NQ44alxIsjHtcTZZBr5uLLzxd625QBvDfe0D+RcZgF//JvBqkujNHe80BtPjROrDiSx/nAKQgj+PnvRNhKusi43ykRYtzgsaQmzmdQ5r9N46Rc4+ftzbsxYPKP64NKypV05c0EhWcu+QdOxg6NCkSSpAl4c0KbE0Z0vDmhj931fRON6RDS+cmN8gd7EuYxCgutZb1Y1WyxkFeo5npxX7H6jIqOZ/1t7jPnbTuKlccLLVY23qxO+7i68Oco6COWv05mk5+vw0jjh7eqEl6sTPq5O+Hlpio1Ald+71X4OS1pFR47g3LgxziEhAHgNGkT+9t+KJa2MBR9S/5FHyPpiqaNCkSSpAio6utPDRU14sLdteVzXxozr2phm0RtLLF+gN9G/rT+5RUbydEbiM7Wcv2p6r693x7Mt1n4mmEY+rvwVHcW7W0+W+L3b7A3HbXHqjGY0TipuhOy9VV8OS1qmtHTUgVem8nAK8KfosP0Ip6LjxzFdSMWzd+/rJq0VK1awYsUKAKKjo29oGqerlXe6k5qiNrWnNrUFak972mjg8+GB6HS6S1Mf5REXV/IcmGVp6K4mvbD4Dfd+7moe7eDCtbcqXH79HumgYWybEAr0ZgoMFgqNFhSXtpc2t2i21mjb/6n1SaQXmGjoocbPXU09Fwg/k8/dLazDsrO0Jrw0Kttozt/O5rNgdyb6qy5jTl91mOSUZKKaO24od0XVlvdaeVXZQAxhsZD+1tsEvvlmmWWvniMrJiamwkOjq+Ow6ptRm9pTm9oCsj0lmTnEq8TLjTOHtCcsrGK9mCCfCyV+7xborbHF+3COK2fSC0jO0XEht4gTSQU4uXsz+dL2jrO3ka8z4u+lIcjHldiUPFvCukxvFnx3tICnB3erUJyOdCM3F9cGDktaan8/TBdSbcvG1DTU/v62ZUthIfpTpzg/cSIApsxMkp56iuCPP5aDMSSpFqrMm8kvK+17t+n3hNqWJ97e1G6fuLg4WrexbhdCMHNQKMk5OlJyikjJKSp2ufGy5Jwi/vg3g4jGPsVGRUq3jsOSlmt4OIaEBAxJSTj5+ZG3aRON5r9r267y9KT133tsywkTJuL30ksyYUlSLXb5ZvLKrA9uPBFenrNSoVAwrmtju23XewbfxKV7USrg3dEdubdzMIV6E3k6I4G14BlwNYXDkpZCrSZg1v+ROOkR65D3e0fh0qoVGQsWoGnfHs+oKEcdWpKkOqSyE2FpvbdXh7YluJ4b++Kz6BhiHWiy42QGT393kEY+rnRtWo8uTX3p2tSXVn4eKK+a8UQO7Kg8Dv1OyyMyEo/ISLt1DadMKbFsk2XfODIUSZKkcimr99ar1ZU5LDsEe/PKkLbsT8jirzMXWXvI+ky6nS/2pkl9d44l57Ll2AU+//McOqN1Lks5LP/myBkxJEmSrlHe3luIrxv/7dWM//ZqhhCCxKwiYhKzaXxphpIv/jzHTzHJxfYrMpp5d+vJYseQPbKyyaQlSZJUCRQKRbGJlV8Z0rbEpAWQklPEk98eILNAT4uGHhQZzGw+lmp7UKrskZWsej20R5IkqRap5+5MI5+SB2kE+bjStIE7ChT8EpvGusMpxZ7sfblHJl0he1qSJEkOdL3psK7uQTWL3lhsmiug1Buo6yrZ05IkSXKgERGNeHNUOI18XFFgnYKqpKdzB12nRyZdIXtakiRJDlaegR3X65FJV8ikJUmSVA1cPdT+8s3Nb4xoJwdhXENeHpQkSaomRkQ04q/oKBbcFwFAWKB3GXvUPTJpSZIkVTPtg7wAOJaSW8WRVD8yaUmSJFUzTeu7ExrgicVS0njCuk1+pyVJklTNKJUKtjx3V1WHUS3JpCVJkiSx42Q6czbEYhaCcV1DeKq3/VPm52yIZc/Zi4D1adCZBXqOvjYAgFUHklj02ykAJke1YnTnYIfFKZOWJElSNbT7TCYvrDzM1//tRit/xz4x2WwRvLLuON9O6k6At4Zhi/6kX5i/3XFfGdrW9vNXf53jeIr1CdY5WgMfbv+XDZN7oUDBkEW76Bfmj7ebY545Jr/TkiRJqoYaeLiQkqu7JYMxDiXm0OTSvInOaiVDOwaxLTat1PLrD6cwrFMQADv/zaBXy4b4uDnj7eZEr5YN2fFvusNilUlLkiSpGmrewB2Nk5JjyXkOP1Zano6gqx5kGeitIS1PV2LZpGwtidlF9GzR4Kp9NeXatzJc9/KgMJs5//B/afLN1w4L4EZZLBbi4uIqtK9Op6vwvtVRbWpPbWoLyPZUZzWpLU19nNh76gJxcYpSy5S3Pbm5uYwaNcq2PG7cOMaNG3fDMW04fIFB7QNsT3++1a6btBQqFSiVmPPzUXk69ppqeSmVSsLCwiq0b1xcXIX3rY5qU3tqU1tAtqc6q0lt6XrSzE8xybRpE2r3JOSrlbc9Op2ONWvWlLjN30tDSu6ViXkv5Orw99KUWHbD4RReH9HObt+/z2bZ7dujuW+Z8VRUmQMxlG5unB02HPeet6N0vfKcmID/e9lhQUmSJEnQu01DFArrI0rcXRw3bq5jsDfxFwtJzNLi76Vhw+EU26wcVzudXkBukZHbGtezrYts3ZB3t54kV2sEYNepDKbf47j5Est8FTz79cOzXz+HBSBJkiSV7O4wf+4O83f4cdQqJXOGtWfi0r2YLYKxXYJp7e/J+9tOEh7sQ7+21hg2HE5haMcgFIorvT4fN2emRLVi2Ed/AjDl7lb4uDk7LtayCviMHIEwGNCfOwcocGnWFIWz4wKSJEmSrjCZLeQUGWng4eLQ4/QJ9aNPqJ/duuf72/eYpvZrXeK+Y7uGMLZriMNiu1qZSatg504uvPoaziEhIASG5GQCZ7+Gx13ybm1JkiRHG/vpHtyc1Xz7SPeqDqVaKDNppb31Nk2+/grnJk0AMJw/T+LjT8ikJUmSdAu0CfBk87FUhBB2l+XqqjLv01K6u9sSFoBTSAhKd3eHBiVJkiRZtQvyJkdrJCm7qOzCdUCZPS1N+3acf+wxvO4ZCAoF+Vu2oAlvT962bQB49e/v8CAlSZLqqvaNrM/UOp6SS4ivWxmla78ye1pCb0BdvwHaffvQ7t2LytcXodNT8PsOCnbsvBUxSpIk1VmhAZ6olIpbMjPGrbLlWCp5OqNtObfIyNbjqeXat+zRg2NG43bbbXbrtAcPFlsnSZIkVT6Nk4rZw9oR3qj2PMX4w+2nuKd9gG3Z29WJD389xYB2AdfZy6rMnlbqG2+Ua50kSZLkGB4uap5afpBm0Ru5463fWBuTXNUh3RQhij/c0lzOB16W2tPSxsRQFHMIc1Y2F7/8yrbeUlAAZku5Ki/YtYu0ufMQFgs+o0fT4LFH7bZn//AD2cu/A5UKpZsbgXNm49KyZSm1SZIk1T1rY5KJXnMEndH6uZucU8SMNUcBGBHRqCpDq7DwRt68/nMsE2+3DvL7Zk+C7bu7spTa0xJGIxatFmE2YSkstP1TengQ/OH/yqxYmM2kznmdkM+W0OLnDeRt3Ij+9Gm7Ml5DhtB8w3qar/2J+o9MIu2tt8sVtCRJUl3x7taTtoR1WZHRzLtbT1ZRRDdv9vB2OKmUTP4uhme+j8FFrbSbz/B6Su1puXfrhnu3bviMHIFToxvP5kVHjuDcuLH1pmTAa9Ag8rf/ZteTUnl42H62aItA3oMgSZJkJyWn5KHupa2vCdyc1UQPDK3QvmUOxKhIwgIwpaWjDrzypZpTgD9Fh48UK5e1fDlZX32NMBpp8tWXJda1YsUKVqxYAUB0dLR8NMkltak9taktINtTndW0tjR0V5NeaCpxfVxcXI1rD8CDn//DRw/chrer9enGuVojk78/yLJJZc/64bhpg8vJ94EH8H3gAXI3/Ezm4k8IevutYmWufu5LTEyMfDTJJbWpPbWpLSDbU53VtLbMHOLFjDVHKTKabetcnVTMHNKesLBG5W5PTEyMI8O8IVmFBlvCAvB2c+JigaFc+5b6nVb6/PkA5G3ZUqGg1P5+mC5cGXdvTE1D7V/6bMVegweRv317hY4lSZJUW42IaMSbo8Jp5OOKAmjk48qbo8Jr7CAMAKXSOqDkssQsbbm/HSq1p1Ww8w8aTptG5pIleN1zzw0H5RoejiEhAUNSEk5+fuRt2kSj+e/alTHEx+PctKn1eDt22k0XJUmSJFmNiGhUo5PUtV7o34Yxi3fTvXl9hBDsi89m3qjwcu1b+kCMO+/k327dsWi1nOzc5coGIUChoM2B/detWKFWEzDr/0ic9Ih1yPu9o3Bp1YqMBQvQtG+PZ1QUWcu/o3DPbhRqJ1ReXgS99Wb5WixJkiTVWL3b+LH+mV58/8952jXyon+7ADTqMm8bBq6TtBo+9yz+L71I4lNPE/LxRxUKzCMyEo/ISPt6p0yx/Rzw8swK1StJkiTVXD/sPc+Xf8VzIbeItkFexJzP4bbG9fj+sfpl7ltqaosfP95awEPO6C5JkiRVni//imfd5DtoVM+NHx67nY1T7sTLtXzjAksvZTSSu+FnimIO2WZ0v5qc3V2SJEmqCBcnJRonFQB6k5mWfh6czSgs176lJq2A114jd8MGLHl5FPy+w36jQiGTliRJklQhAV4acouM9G/rz4TP9+Ll6kSjeq7l2rfUpOXWuTNunTvj2r49PqNHV1qwkiRJUt22ZKJ1cN/Ufq25vcVF8nUmIls3LNe+pSatwr//xr1HD5ReXvLyoCRJkuQQPZqXPfjiaqXP8r53H+49ehS/NAjy8qAkSZJUJUof8j7lGQCC3px3y4KRJEmSqsaOk+nM2RCLWQjGdQ3hqd7FHxP185EU/vfrKRRAWKAXC+6LAKD5jI20CfACoJGPhs//09VhcZaatK5+hlZJ6j/8UCWHIkmSJFUFs0XwyrrjfDupOwHeGoYt+pN+Yf608ve0lTmXWcjHv59h9RM98XZzIrNAb9umcVKx+dk7b0mspSYtS6F1+KHh3DmKjh3Fs08UAAW//46mQ4dbEpwkSZLkeIcSc2hS343G9d0AGNoxiG2xaXZJ64e955l4exO83awT3TbwcKmSWEu/PDj5aQDiH3yQZqvXoLp0k3GDyZNJfOLxWxOdJEmS5HBpeTqCvK8MOQ/01nAoMceuzNlMa0fm3sW7MVsEz/VtRe82fgDoTRaGLvwTlVLBk71bMKBdAI5S5i3I5syLKJyvTCGvcHbCnHnRYQGVxWKxyOdpXVKb2lOb2gKyPdVZbWoLlL89ubm5jBo1yrZ89SOfysNsEZzLLOSHx3qQmqtj7Kd72PLcXXi7OvHX9CgCvDWcv6jlvs/+JjTAkyb1HTObUplJy3vEcOLHjMWzb18A8rdvx3vkSIcEUx5KpVI+T+uS2tSe2tQWkO2pzmpTW6D87dHpdKxZs6bEbf5eGlJyrzwq5EKuDn8vjV2ZAG8NnUJ8cFIpCfF1o1kDd+IzC+kY4kOAt7Vs4/pu9Ghen+MpeQ5LWmVOq9vgiScInDcXlbcXKm8vgubNpcHjjzkkGEmSJOnW6xjsTfzFQhKztBhMFjYcTqFfW/vnH/Zv68/fZ61X2bIKDZzLLKSxrxu5WiN6k9m2/kBCFq38PBwWa7lmKHRt1w7Xdu0cFoQkSZJUddQqJXOGtWfi0r2YLYKxXYJp7e/J+9tOEh7sQ7+2/kS2bsiuU5n0fX8nKoWCGYPCqOfuzIGELGauOYZCYX1y1ZO9W9gN4Kj0WB1WsyRJklRj9An1o0+on9265/u3sf2sUCiYNaQts67Zr3MTX7ZOvesWRGhVvqduSZIkSVI1IJOWJEmSVGPUraSVlUX8gxMwZWRUdSSSJElSBdSZpGVMT4cXXqTowAEyPvq4qsORJEmSKqBOJK0THTtx+q5IyM4GIcj54QfiQsM4Ed6BzE+XoI2JcejxtTExlX4cbUwMrF7t8NhvhdrUltqoss+Po34fqnud5a2vtHKOaGNNpBBCiKoO4kbExMQQERFR7vInOnZC6PWlF1AoULi4EPLJYjJL6IF5jxyJz6iRmLKzSZ7ybLHt9e4bj9egQRgvXCDlpenFtrtHRpK5aBHCYAAhcAkNReVx5R6GBk8+gXvPnuji4kib92ax/RtOnYrbbRFoD8aQ8cEHAJgLCtCfOAFCoHB2pvHXXyGKishc/Emx/QNmz8aleTPyf/udrC+/LLY96J23cQoMJG/TJrK//6HY9kYLPkRdrx45a34i96efim0PWfIpSldXsr77jvzNW4ptb7LsGwAufrGUgh077LYpNBoaPPUk5x/+L0KnA4XC7vVR+fgQvHABAOnvvU/RoUN2+6sDAmj07jsApM6bhz7uhN1256ZNCXx9DgAXZr2CIT7ebrtLWCgBM2cCkPziS5hSU+22u3bqhN+05wFIemYK5pwcu+1ut/eg4VNPAXD+0cesbbhEq9XiN2gQ9Sf9F4CECROLvTaeA+/B9/77sRQVkfhY8anRbva95/vww3hG9UF/9hypr75abHt53nso4Px/HrK+f685P/4zZ6AJC6Nw9+5yv/fs3rsuLjT+6ktMFy7c1Hsv9e13yP7qK+t466tiLO29p9Vqcff1pfFnSwDI+PhjtHv+LlZ/0dGjV9rdurXd7+2NvveKjh2ztRuFAs97BhB86ff56vee7fUB6+vz5VKyln6JISnJul6hsP7Of7kUt0ufg+W9ufhGPzurq1rf02rxyzZc2rQpvYAQCKMR7f4DDjm+/t9/rW98iwWEwJKXd9N1WvLyrG9+QJhMaPfuu+k6q4p27z7r6wOV9vpIlUe7dx/CZLIuVML5sXvvGo2V8t41JsTb6qys95ApO/vK763FUqntRgiM1/yBVKzc5c+lS6+Pbb3FUmmvW01V63taAImTJ1Pw63b7lQoFCicnhNmMwsnJ7i+XyqSNibH2JIzGSjuOrU6DodhfXTVNbWrL1WrLVEGVfX4c+vtQzjrLc24qO87y1ldauevtX9d6WnUkaT0DCig4dw5On0Ht70+j/1m75tq9+3Dr1tWhH5TamJhKP442JoaEjRtpMnhwjf+Qr01tuay2JC2o/PPjqN+H8tZZ3nNT2XGWt77SypW2Xiatau5mXvi4X3/FPzUN76FDUHl7V3Jkt15t+mCsTW0B2Z7qrDa1Bepe0qpb0zg1aoTvpdnqJUmSpJqn1g/EsGM0YkxLx3L5i39JkiSpRnFo0irYtYsz9wzkdP8BZC75rNj2i19+xZnBQzg7bDgJDz2MMTnZkeHAsWOcjoxEd+y4Y48jSZIkOYTDkpYwm0md8zohny2hxc8byNu4Ef3p03ZlNGFhNFv1I83Xr8NrQH/S5s93VDhWISEEzJ6Nc0iwY48jSZIkOYTDklbRkSM4N26Mc0gICmdnvAYNIn/7b3Zl3Ht0R+nqCoBrx46YUtMcFY5VgwbUGzcWdcOGjj2OJEmS5BAOG4hhSktHHRhgW3YK8Kfo8JFSy+esWo3HXXeWuG3FihWsWLECgOjoaOLi4ioUk66ggLgdO8DbGzSaMstXdzqdrsKvRXVTm9oCsj3VWW1qC9S+9pSlWowezF2/nqLjx2iybFmJ28eNG8e4ceMA67DNig5Xjfv5Z3jhRYI/WoTn3XdXON7qojYN3a1NbQHZnuqsNrUFbmzIe23gsKSl9vfDdOHKVCXG1DTU/v7FyhXu3k3mJ5/SZNk3KJ2dHRWOlZ8fgfPmoalFb1hJkqS6xGHfabmGh2NISMCQlIQwGMjbtAnPqD52ZXSxsVx49TVCPv4Idf36jgrlCk9PfEaNxCkoyPHHkiRJkiqdw3paCrWagFn/R+KkRxAWCz73jsKlVSsyFixA0749nlFRpL37LhatlqTnpgLgFBhIyGIHPuvKZEJ/9hzqBvVReXk57jiSJEmSQzj0Oy2PyEg8IiPt1jWcMsX2c5MSHpXhUDk5nB0zloDX51BvzJhbe2xJkiTpplWLgRi3jKcnQe++i2vHDlUdiSRJklQBdStpubjgPXRIVUchSZIkVVDdSloWC7qT/6Ku74u6QYOqjkaSJKna2HEynTkbYjELwbiuITzVu2WxMj8fSeF/v55CAYQFerHgPuus8asOJLHot1MATI5qxejOjpt1qG4lLbOZc/eOpuFzz9HgieKPN5ckSaqLzBbBK+uO8+2k7gR4axi26E/6hfnTyt/TVuZcZiEf/36G1U/0xNvNicwCPQA5WgMfbv+XDZN7oUDBkEW76Bfmj7ebk0NirVuzvKtUNPrf//Ds36+qI5EkSao2DiXm0KS+G43ru+GsVjK0YxDbYu2n1fth73km3t7ElowaeLgAsPPfDHq1bIiPmzPebk70atmQHf+mOyzWGtfTslgsFZ/GyWAguUlj0OuhFkx7Upumb6lNbQHZnuqsNrUFyt+e3NxcRo0aZVu+eqahtDwdQd6utm2B3hoOJebY7X82sxCAexfvxmwRPNe3Fb3b+F3aV2O3b1qe7maadF01LmkplcqKT+MUF0czIVD5+NSKG4xr03Q0taktINtTndWmtkD526PT6VizZk2Fj2O2CM5lFvLDYz1IzdUx9tM9bHnurgrXV1F16/IgkDBhIllff1PVYUiSJFUb/l4aUnKLbMsXcnX4e9lPKh7graFvW3+cVEpCfN1o1sCd+MzCS/vqrrtvZapzSavRB+/jM2Z0VYchSZJUbXQM9ib+YiGJWVoMJgsbDqfQr639XLH92/rz99mLAGQVGjiXWUhjXzciWzdk16kMcrVGcrVGdp3KILK14x7/VOMuD94sj7tufXdWkiSpOlOrlMwZ1p6JS/ditgjGdgmmtb8n7287SXiwD/3a+l9KTpn0fX8nKoWCGYPCqOduneR8SlQrhn30p/Xnu1vh4+a4yc/rXNIqOnYcpZsbLs2bVXUokiRJ1UafUD/6hPrZrXu+fxvbzwqFgllD2jKrhH3Hdg1hbNcQB0doVecuDyZPnUrmJ4urOgxJkiSpAupcTyvorTdRenqWXVCSJEmqdupc0nLr3LmqQ5AkSZIqqM5dHiw6dhxdLbqxUJIkqS6pc0kr9fU5pL/3flWHIUmSJFVAnbs8GPDKKyjUjpnIUZIkSXKsOpe0XNu1q+oQJEmSpAqqc5cHi44fR3vwYFWHIUmSVCJjejrxD07AlJFR1aFUS3UuaWUuXETaG3OrOgxJkqQSZX68mKIDB8j46OOqDqVaqnOXB/1eehFhMlV1GJIkSXZOdOyE0Ottyzk//EDODz+gcHEh9PChqgusmqlzPS2X5s3RtG5d1WFIkiTZabRwIZ6DBqHQXJohXaHAa8hgWv76S9UGVs3UuaSli4uj4M+/qjoMSZIkm8J/9pL02GOYc3KsvS219SKY0sMTdUPHzZheE9W5pJW17FsuzCppykdJkiTHuXqAhRCC3PXryduyBQC3Lp0JmD0bpYsLPuPH02zVj/iMH48pM5Oio8cw5+bKARqX1LnvtBo89RS+D/2nqsOQpDrLmJ5O8vPTCP7g/TrVi7h6gEXAq6+Q/d33qLy98brnHhQqFfXGjaXeuLG28oGvvoK5oJAzffvi3rMnSi8v2/6Br71ahS2pWnWup+Uc3Eh+pyVJVcgRo+Mc0Qu50TqF0YgxLQ1hNAKgP3uWi199xYkOHYkLDSPnhx9ACHJ++IETYW3RxcYSvPj6r4HKwx1zYSF5mzbZ7R8XGsaJjp1utok1Up1LWrqTJ8nbuq2qw5CkOudEx07FPrzjQsM4Ed7BVqbo2HGMaWm25fKO9HVEIsz86GOKDhwgfcFCAIypqWR9s8wWnzYmhvj7H0B/9iwA+b/8wunI3ujPnQNAFxtH+ltvE/LF53gNGYzC2ToTj8LFBa+hQ2i5/VcUyrI/gltu/9W6/6UBGgqNxrp/HR2g4dCkVbBrF2fuGcjp/gPIXPJZse3affs4O2oUce3ak7dlqyNDscnbsIGUl166JceSpNrAmJ4OL/9fuXocuthYig4dsi0nPTeVtDffAqDFL9tQuLqCSgVYP3xxcsJr6FBb+fMTJ5K19Evb8smI20h//wMAhBCcHTmK7BUrrctmM3Ft25WcCDt2wpybS/JLL1H4998AmDIySHj4Ybg0uYAhKYkzQ4aQ//vvAOhPneJkt+7EtQ+31rliBQhB7o8/Ehcaxpl+/UmbNw/9v6es8Ts5o3ByQhitiVXToQMBr72GukEDADzvjqL1P3/j1qULSg8PhNGEwsUFYTCgdPco96VRJz8/6/56vXV/vf6G9q9tHJa0hNlM6pzXCflsCS1+3kDexo3oT5+2K6MODCLozTfxHjLYUWEU4/uf/9BszepbdjxJqukyP14McXFkfPQxxtRUio4dt21L/9//uPDKle9XUufOI33+e7ZldcOGqHx9AeuHr0fv3mCx2D58vYYMwX/6lT8iG334IT6j7wWsSarB00/h3vN260aTCadGQag8PazbdTpUDRrg0r79lWHigKZ9e1r++gvCYqHoYAymjEzrBoUCodODxQKA0sUFlxYtUXlY61P5+OA9bBghn3xi7dm4uFh3u9Qzar51C6327Mb9jp4AuLZvR5Ovv0LTxvp1g3NwMPXGj0N9qb1KV1dU3t4oFApMmRfxGT+epit+sA2wuBE3u39tohBCCEdUrI2JIXPRRzT+4nMAMj9dAkCDxx8rVjYlegYevXvjdc+AMuuNiYkhIiKiQjHFxcURFhZWoX2ro9rSHmN6OqefeJJWn35Sa/56rMxzc/lXVKFQYNHrseTlofL1RaFSYcrOxpSWhkvLlijUaozJyejPxeN+ew8UKhW6f/9FFxuL97BhKJRKtAdjKIo5SP1JkzAajSQcPozOZELl7Q2ARVuEMOhR+fhgTLkAlPzx4BQUBIA5Lw8sFlQ+PtZYjUZQKFCoSx7jZcrKQqFUonR3x1JYiLBYbB/yFWXOycGi1QIKQKB0c7PFUxKj0YiT0/Unzb7ROqvSte3RaDQEBwcXa+PNfHZWK8JBcjdvEckvv2xbzlm7VlyYPafEssnTo0Xu5i3lqvfgwYMVjik2NlboTp0S2T/9JCxGY4XrqQ4MaWkiduQoYUxPr+pQblrKq6+J2NAwkfLqa7fsmBaLxfq/ySSMFy8Kc1GREEIIs14vik6eFKacHOtyQYEo+Osv2+tsys4WOevWCUNKihBCCENqmsj8YqnQJyYKIYTQnz8vUt95R8Ru3y6EEKLo5EmR/NJLQnf2rBBCCO2hQyLhv5NsywW7d4szw4bblvN++UWc7HmH0MfHCyGEyF7zk4htEyr0iUlCCCGyflghYtuECkNqmhBCiIvfLBOxbUKFMStLCCFE5hdLRWybUGHKLxBCCJHx6RIR2yZUmHU6IYQQ6QsXidg2ocJiNouzZ8+K1NOnhfb4cdvrYkhLF7p/T1nbbjAI3alTQnv0qPXfseNCd+6cMOXmVtZpqBT6hAShT04WZq1W6JOThT4h4brltVptpddZla5uj8ViERkZGeLspffT1W7ms7M6qRFD3lesWMGKFSsAiI6OJq6CD3HU6XSc/e47+HEVF7y8wMUF3nsfXpgG9epVZsj2srIq/ziffoqIi+PU3Lnw+OOVU2d5mc3WSyyX/5LLz7f+7+lp/T8tzfq9xaVr+5w9Cy4aaGT965yjR8HDA6ZHw6WRVnBl2hpUKnjlFegQbt3w3XfQujV06WJdXrgIOt8GPXtaY5k7D+66C3pHgsEAM2bAoEFw991QWAjPTIHx46B/f8jJgUmPwCOTYOBAyMyERx+Dp5+Cvn3hwgV46ml4dgr07g2JSTBlCkx7Hnr1gnPn4KXpMP0l6NED/j0F77xDurMTdO4MJ07Asm8xBAZa36dxcfD3P+TecQfodNbXIiODs//+a11OTwdvb87Gx1uXi4qgc2fOJCWBVgsuzjBuLGdSkiE/D+r5wOOPczo5CbIuQqNGMP0lTp0/D6mp0LoVvDmPf+PPWV/HTh1h8cecPH0alEq4sxfc0ZMTJ05gNJnwb9kSFAqKioounUMP8PS4suzsbI1LoQBhwaJUYnByssZZXVzqnZvB9vtVdJ34hBDX3V6ROqvSte1xc3MjJSWlwp+T1Z6jsmHhwYMi4b+TbMsZn3wqMj75tMSyt7KnlTxj5qW/6l+9ZX/hp7zyqt1xTPn5Qp+YZPtr35CaJrQxMbbyujNnRN6lv9SFsP51nr1qtRBCiLgOHUVsm9Di/9q2s5XPWrlSpM5707ac+cVSkfLKq7bltP/9TyS/NN22fGHO6yLx2edsy0nTXhDnn3jStpww6RG7c3lu7Di75bMjR4nzjz9hWz4zZIhInPyMbfl0/wEi6flptuVTfaJE8kvThSEtTSRNm2ZrQ1zHTiLphRfEia7d7HrlJ7v3EGnz51/Zv39/kfn550II61+WZ8eMFVkrV1qXjUZx/oknbe8ns04nUv5vlij46y/rslYr0v73P6E9dMi6XFgoLi77Vuj+/de2nLt5izAkJdnKF+7bJ4wXL1qX9Xqhj48X5sJC2/FM+QXCYjKJq8XGxorqrjwxXu5xaHNyqn2Po7zK09OqSUpqT0nnVva0yuAaHo4hIQFDUhJOfn7kbdpEo/nvOupwZTrRsRPo9eReWs75YYVtm21iSicnQo8eoXDvXtLnv0fQ22/h0qwZBbt2kfb224R8/DHOjRuTt20baW++RdNvl+HUqBG569eT+sZcmm9Yj5O/P9k//EDqG3NRKJUIg6HYcVCpwGymzeFDKFxcyFn1I5kLFxF6/BgKlYrc9eu5+NnnhB0/BkDetm1kL/8On3tH0eKXbSRM/A/G+HjAOgLLKTAQi/HKcQzn4ik6fNi2bM7JsRv5pVCqbCO4ANR+fiicnW3LmvbtrF9YX+J5d5Tda+lz33i7B2nWf+wxlK5Xvgj3e/FFlO7utuXAuW+gvNwLA4I/WoTSw8M2KgqFAtRq26ioltt/tX0JDtD67z12x2+59cpIU4VCQbOVV86lQq0m5Kp7X5QuLgS+PufKsqsrfs8+e2XZzQ3fBx+wW776u1Wlqytul3t4gNLZGecmTeyOp/KoERcsKsS5cWPA2stwvvS9l1Q77TiZzpwNsZiFYFzXEJ7q3dJu+4/7E3lz8wn8vay/6/+5vQnju1nfH81nbKRNgBcAjXw0fP6fro4L1JEZMX/HDnG6/wBxqm8/kbF4sRBCiPQPP7T1IrRHjoh/74oUcZ0ixMlu3cXpwYPLrLOify0Y0tJE7COPiriOna70TsLaWv/CD+8gTva4XeT//bcQQojCAwdFwqRHbN8rFB44KBKfmWL7HqPwwEGRHD1DGDMyrMsHD4oLr78hTNnZ1nYdOiTS3v9A6M7Fi6Rp00RceAe7nkT+nj0ie/UaYTEYhBBC6M+dE/l//CEsZrM11tRUUXT8uK0nZsrNtfvuKmXWLBEbGiZi24ff8u+CKtv5pyeLlNdmi9iNG0XKa7PF+acnV3VIlaK29LQuc0TvJDExUQwu5Xd+5syZ4tSpU5V+TCGsbenTp4+4eKn3fLXFlz6nKlOnTp2EEEKkpqaKZ565cgVi6tSpYsiQIeLLL78Up0+fFsOGDRPDhw8XCTfYm62MnpbJbBF3vv2bSMgsFHqjWQz4YKf4NzXPrszKfefFrLVHS9w/bNbmG4r5Zjj0T0SPyEg8IiPt1jWcMsX2s2t4OK127nBkCDZOfn7g5mZ3r4Nt6K3BgOeAAXh07w6A220RNP78yn1lbrdF4HZbROnLERG4XTUqx7VjR1w7dgS4dH+G0e7+Co8ePexic27aFOemTa/E6u+Pk7+/bVnl5WVX3pSVjc/48eR07YLPvv01ei6ykEXWGzdz4uIIHDSoiqORyqKNiUG7dx9u3bravecr29y5VfPMu08//ZQnnnjihvYxm82orrpyURp/f38WLFgAQEZGBkePHuWXX6w3CC9ZsoQBAwbw1FNP3XjQleBQYg5N6rvRuL4bAEM7BrEtNo1W/p5l7Hnr1bjrGhaLpeIDMQb0RzVsKCpfXwznzwPWyx/mrCzSTSZyHPDFpeH221HceScqX9/KO87T1je2TqcjZ+wYgBr/patOp6vxbbhaTWiP0Wi0+wL/wiOPFivj3r8fXmPHYtZqOT3+Poz//gtCgEKBU+vWeN1/H57DhmHOzib9Rfub9gM/Lz6hwNV0Oh1Go5HnnnuOEydO0KJFC15//XVcXV2ZNGkSzz//PO3atWPu3LkcP34cnU5H3759bR/sH374ITt37kSlUnH77bfz/PPPk5WVxdy5c7lw4QIAL774IhEREeTk5BAdHU16ejodOnTAYrGg0+ns2v/hhx+i0+kYOnQoLVq0YPLkyTz99NOEhYUVi2/gwIEMGDCAv//+m4ceeoh77rnHVk9ycjIzZsxAq9XSu3dv20CJ5ORkpkyZwurVq3n44YdJS0tj6NChREVF8eOPP6JUKvnrr7/4/PPPb+g8ihIGlhiNxmLvv9zcXEaNGmVbHjduHOPGjQMgLU9HkLerbVugt4ZDiTnFjrX5WCp7z2XRrIE7s4a0JcjHuo/eZGHowj9RKRU82bsFA9oF3FAbbkSNS1pKpbLC97/EwZV9b9X9Cg68j6q23KcFtastUDPaExcXh6vrlQ8qVQlTCjk5OeHq6kphUREUFFgTFlj/LyjA2ckZV1dXTDpdsf2vrrskGo2G+Ph45s2bR+fOnZkxYwY//fQTkyZNQqVS4eLigqurKy+88AI+Pj6YzWYeeughEhIS8Pf35/fff2fLli0oFAry8vJwdXXl/fff57///S9dunQhJSWFSZMmsXnzZt577z26du3K5MmT2bp1K2vXrkWj0djFGB0dzYoVK9iwYQMASUlJpcanVCpp0KAB69atK9au+fPn88ADDzBixAiWL1+OQqHA1dUVjUaDUqnE1dWVTz75hCeeeMJ2LLVajZubG5MmTSrfybtKUVFRsdfaycmp2PtPp9OxZs2aG67/sr5h/gzrFISLWsXyfxKYtvIw3z9mvWr01/QoArw1nL+o5b7P/iY0wJMm9d3LqLFialzSkiTJMZos+6bUbUpXV4Lmv8v5h/9rvdzt5ETQ/HdtlwjV9epdd//SBAYG0rlzZwCGDRvGsmXLin1wb968mZUrV2IymcjIyODMmTO0bNkSFxcXZs6cSZ8+fejduzcAu3fv5vRVM+8UFBRQWFjIvn37WLRoEQB33XUX3uUcVHK9+AaVcjk7JiaGhQutl72HDx/O/Pnzy/lqVB1/Lw0puVf1unN1tgEXl9VzvzJYa3zXxry16YRtOcDbWrZxfTd6NK/P8ZQ8mbQkSapabhERNP5yaaV+p6VQKK67nJiYyNKlS1m1ahXe3t5ER0ej1+tRq9WsWrWKPXv2sGXLFr799lu++eYbLBYLK1euxOWq0aeOiu96Pclr96vuOgZ7E3+xkMQsLf5eGjYcTmHBffbnNz1Ph9+lRPZLbBot/KzTX+VqjWiclbioVWQVGjiQkMUTkc0dFqtMWpIkldu1g45uVkpKim16oZ9//tnWq7mssLAQV1dXPD09yczM5I8//qBbt24UFhai0+mIjIzktttuo2/fvgD06tWLZcuW8cgjjwBXLtN27dqVDRs28NRTT/Hnn3+Sm5tbLBawXqa7elqksuIrSUREBBs3bmT48OGsX7/+Zl6eW0atUjJnWHsmLt2L2SIY2yWY1v6evL/tJOHBPvRr68+Xu+P5NTYNlVKBj5sT88dYB5udzshn5ppj1vvPBTzZu4VDB3DIpCVJUpVp1qwZy5cvZ+bMmbRs2ZL77rvPbntoaCht27Zl4MCBBAQEcNtttwHWZPbUU0+h11vvJ4yOjgbg5ZdfZs6cOQwdOhSz2UyXLl2YM2cOTz/9NNOmTWPw4MGEh4cTdGnuxGuNHTuWYcOG0bZtW6ZOnVpmfCV5+eWXeeGFF/j888+Jiooqs3x10SfUjz6hfnbrnu/fxvbz9HtCmX5PaLH9OjfxZevUuxwe32UOmzDXUeSEuVfUpvbUprZAzWjPjcRY0pf9NVV525KUlMQTTzzBzz//fAuiqriS2lPSua0tE+bWuYdASpIkSTWXTFqSJEklCA4Orva9rLpIJi1JkiSpxpBJS5IkSaoxZNKSJEmSagyZtCRJkqQaQyYtSZLKzZieTvyDExzyZIGFCxfyxRdfXLdMdHQ0W7ZsKbb+6NGjvPHGG5Ue02Vr1qxhzpw5xdb/888/HDx4sFKPdfXr8OGHH7J7924A9u/fz+DBgxk+fDg6nY63336bwYMH8/7771fq8as7eXOxJEnllvnxYooOHCDjo48JfO3Vqg7HJjw8nPDw8Ft+3L179+Lm5ma76bk8TCYTanX5PnqfveqBpevXr+exxx5j+PDhAKxcuZK9e/diuOpBs3WBTFqSJAGQMGEi3iNH4jNqJMJo5Px/J+EzZjTew4YR370HlPQUbrWasGNHMWVnkzzlWXwffhjPqD6YMjJQN2xY5jEXL17M2rVr8fX1JTAwkHbt2gFw/vx5Zs+eTXZ2NhqNhtdff50WLVoA1klxlyxZQmFhIdHR0fTp04d//vmHpUuX8umnn3LkyBHmzp2LXq9Ho9Ewb948mjdvzqlTp5gxY4ZtFo2FCxfStGlT1q1bx7JlyzAajXTs2JFXX30VlUrF6tWrWbJkCZ6enoSGhuJ81dO9wXrz8Q8//IBSqWT9+vXMmjWLVatW4ezszLFjx+ziW7NmDdu2bUOr1WKxWPj222/L9TpER0fTu3dv8vPz2bJlC3/++Sd//PEHhYWFaLVaRo0axcMPP8yIESMqfN5rGpm0JEkqU6NVP5L60MOY8/Ph0kNNle7uNHx+aoXrPHbsGJs2bWLt2rWYzWZGjhxp+7CeNWsWs2fPpmnTphw+fJjZs2fzzTfWWeSTk5NZtWoV58+fZ+LEifTs2dOu3ubNm7N8+XLUajW7d+/mgw8+YOHChfzwww9MnDiRfv36oVKpsFgsnDlzhs2bN/P999/j5OTEa6+9xoYNG+jZsycLFy5kzZo1eHh4MHHiRNq2bWt3nODgYMaPH2/3SJFVq1aVGl9sbCzr16/Hx8en3K/DZWPGjOHAgQP07t3b9uyuiIgI1q1bV+xZWrWdTFqSJAH2jyZRODnZLTuFhODZvx85K1Zeedr3yJHUGz0aKP5okvL0svbv30/fvn1tUxBdnqevsLCQmJgYu0tjV18CGzhwIEqlkqZNmxISEsLZs2ft6s3Pz2f69OkkJCSgUCgwGo0AdOrUiU8++YSkpCQGDRpE06ZN2bNnD8eOHWP0pXbodDrq16/PkSNH6NatG76+voD1MSTx8fFlv4jXie+OO+4olrCu9zpIJZNJS5KkcjFlXsRn/HjqjRtL9oqVDhmMAdYn8Xp5eZX4gEUo+3EmH374Id27d+ejjz4iKSmJiRMnAjB06FA6duzIL7/8wmOPPcbs2bMRQjBy5EimTZtmV8evv/5a4fhLi6+2zN1Y1eToQUmSyiVk0UICX30FTWgoga++QsiihTdVX9euXfn111/R6XQUFBTw+++/A+Dh4UFwcDCbN28GrEnsxIkrDxzcsmULFouF8+fPk5iYSLNmzezqzc/Px9/fH4CffvrJtj4xMZGQkBDuv/9+7r77bk6ePMntt9/O1q1buXjxIgA5OTkkJyfToUMH9u3bR3Z2NkajscQRiwDu7u4UFhbarSsrvvK+DlLJZE9LkqQq0a5dOwYNGsTw4cPx9fW1G/337rvv8tprr7F48WJMJhODBg0iNNT6WIzAwEBGjx5NYWEhs2fPLvbAx0ceeYTo6GgWL15MZGSkbf3mzZtZt24dSqUSPz8/Hn/8cXx8fHjuuef473//i8ViwcnJiVdeeYVOnToxefJkxo8fj6enZ6mz4ffp04cpU6awfft2Zs2aVa74buR1kIqTjyapwWpTe2pTW6BmtEc+mqTyXR7td3mwxK0gH00iSZIkSdWUvDwoSZJUSd56662qDqHWkz0tSarDati3A1I51PZzKpOWJNVRGo2Gixcv1voPubpECMHFixfRaDRVHYrDyMuDklRHBQcHk5SUREY57rcyGo04OTndgqgcrza1BYq3R6PREBwcXIUROZZMWpJURzk5OZV5D9FlNWE0ZHnVprZA7WtPWRyatAp27SJt7jyExYLP6NE0eOxRu+0Wg4GU6dPRHY9F5eNDo/ffxzm4kSNDkiRJkkqw42Q6czbEYhaCcV1DeKp3S7vtP+5P5M3NJ/D3sl56/M/tTRjfrTEAqw4ksei3UwBMjmrF6M6O6+k5LGkJs5nUOa/TeOkXOPn7c27MWDyj+uDS8soLkbNqFSovb1pu20ruxo2kvzef4A8+cFRIkiRJUgnMFsEr647z7aTuBHhrGLboT/qF+dPK39Ou3JAOgcwZ3t5uXY7WwIfb/2XD5F4oUDBk0S76hfnj7eaYS7AOG4hRdOQIzo0b4xwSgsLZGa9Bg8jf/ptdmYLtv+E9wvpsGK8BA9Du+Vt+KSxJknSLHUrMoUl9NxrXd8NZrWRoxyC2xaaVa9+d/2bQq2VDfNyc8XZzolfLhuz4N91hsTqsp2VKS0cdGGBbdgrwp+jwEfsy6Wk4BQYCoFCrUXp6Ys7JQV2vnl25FStWsGLFCgBmzpxJTExMheO6mX2ro9rUntrUFpDtqc5qU1ugfO3R6XSMGjXKtjxu3DjGjRsHQFqejiDvK7NqBHprOJSYU6yOzcdS2Xsui2YN3Jk1pC1BPq6X9tXY7ZuWp7uJ1lxfjRiIcfWLK0mSJFXMmjVrKrxv3zB/hnUKwkWtYvk/CUxbeZjvH+tRidGVj8MuD6r9/TBdSLUtG1PTUF+aedlWxs8f44ULAAiTCUt+PqoSnjcjSZIkOY6/l4aU3CsPk7yQq7MNuLisnrszLmoVAOO7NuZYcu5V++quu29lcljScg0Px5CQgCEpCWEwkLdpE55RfezKeET1IXet9Zk5eVu34tajR7Fn0UiSJEmO1THYm/iLhSRmaTGYLGw4nEK/tvadjPSrLvn9EptGCz8PACJbN2TXqQxytUZytUZ2ncogsnXZDwGtKIfO8l6wcydp8960Dnm/dxQNnniCjAUL0LRvj2dUFBa9npSXpqOLi0Pl7U2j99/DOSTEUeFIkiRJpfj9RDpzfo7FbBGM7RLM5KhWvL/tJOHBPvRr68/bW07wa2waKqUCHzcn3hgRTstLiWvlvkQ+2nEagKf7tGRsF8d9jte4R5NIkiRJdZece1CSJEmqMWTSkiRJkmqMOpO0/vjjDwYMGEC/fv1YsmRJVYdzw6Kiohg6dCjDhw+33WuRk5PDww8/TP/+/Xn44YfJzc2t4ihLN2PGDG6//XaGDBliW1da/EII3njjDfr168fQoUM5fvx4VYVdqpLas3DhQu68806GDx/O8OHD2blzp23bp59+Sr9+/RgwYAC7du2qipBLdeHCBSZMmMCgQYMYPHgwX3/9NVAzz09pbamp50av1zN69GiGDRvG4MGDWbBgAQCJiYmMGTOGfv368dxzz2EwGAAwGAw899xz9OvXjzFjxpCUlFSV4TuGqANMJpO4++67xfnz54VerxdDhw4Vp06dquqwbkifPn3ExYsX7da9/fbb4tNPPxVCCPHpp5+Kd955pypCK5e9e/eKY8eOicGDB9vWlRb/jh07xKRJk4TFYhExMTFi9OjRVRLz9ZTUngULFojPP/+8WNlTp06JoUOHCr1eL86fPy/uvvtuYTKZbmW415WWliaOHTsmhBAiPz9f9O/fX5w6dapGnp/S2lJTz43FYhEFBQVCCCEMBoMYPXq0iImJEVOmTBE///yzEEKIWbNmieXLlwshhPj222/FrFmzhBBC/Pzzz+LZZ5+tkrgdqU70tI4cOUKTJk0ICQnB2dmZwYMHs3379qoO66Zt376dESNGADBixAh+/fXXqg3oOrp27Yq3t7fdutLiv7xeoVDQqVMn8vLySE933LQwFVFSe0qzfft2Bg8ejLOzMyEhITRp0oQjR46UveMt4ufnR7t27QDw8PCgefPmpKWl1cjzU1pbSlPdz41CocDd3R0Ak8mEyWRCoVDw999/M2DAAABGjhxp+zz77bffGDlyJAADBgxgz549tW5qvDqRtNLS0ggIuDKllL+//3XfyNXVpEmTGDVqlG1Kq4sXL+Ln5wdAw4YNuXjxYlWGd8NKi//a8xUQEFBjztfy5csZOnQoM2bMsF1Oq0nvv6SkJOLi4ujYsWONPz9XtwVq7rkxm80MHz6cnj170rNnT0JCQvDy8kKttk5odPXrn5aWRuClqfHUajWenp5kZ2dXWeyOUCeSVm3w/fff89NPP/HZZ5+xfPly9u3bZ7ddoVDU6Buza3r8APfddx+//PIL69atw8/Pj7feequqQ7ohhYWFTJkyhZkzZ+Lh4WG3raadn2vbUpPPjUqlYt26dezcuZMjR45w9uzZqg6pStWJpOXv709q6pUppdLS0vC/Zkqp6u5yvPXr16dfv34cOXKE+vXr2y7LpKen4+vrW5Uh3rDS4r/2fKWmptaI89WgQQNUKhVKpZIxY8Zw9OhRoGa8/4xGI1OmTGHo0KH0798fqLnnp6S21ORzc5mXlxfdu3fn0KFD5OXlYTKZAPvX39/fnwuXpsYzmUzk5+dT75oJyGu6OpG0wsPDiY+PJzExEYPBwMaNG4mKiqrqsMpNq9VSUFBg+/mvv/6iVatWREVFsXbtWgDWrl3L3XffXYVR3rjS4r+8XgjBoUOH8PT0tF2mqs6u/l7n119/pVWrVoC1PRs3bsRgMJCYmEh8fDwdOnSoqjCLEULw8ssv07x5cx5++GHb+pp4fkprS009N1lZWeTl5QHWWdp3795NixYt6N69O1u3bgXgp59+sn2eRUVF8dNPPwGwdetWetTCqfHqzIwYO3fuZN68eZjNZu69916efPLJqg6p3BITE3n66acB6/XtIUOG8OSTT5Kdnc1zzz3HhQsXCAoK4n//+x8+1XTC4eeff569e/eSnZ1N/fr1eeaZZ+jbt2+J8QshmDNnDrt27cLV1ZV58+YRHh5e1U2wU1J79u7dy4kTJwBo1KgRc+bMsX2YL168mNWrV6NSqZg5cyaRkZFVGb6d/fv388ADD9C6dWuUSuvfsc8//zwdOnSoceentLb8/PPPNfLcnDhxgujoaMxmM0II7rnnHiZPnkxiYiJTp04lNzeXsLAw5s+fj7OzM3q9nhdffJG4uDi8vb354IMPCKllU+PVmaQlSZIk1Xx14vKgJEmSVDvIpCVJkiTVGDJpSZIkSTWGTFqSJElSjSGTliRJklRjyKQlSZXkq6++oqioyLb86KOP2u6xkSSpcsgh75J0A4QQCCFs9wBdLSoqilWrVtW4mUkkqSZRV3UAklTdJSUlMWnSJDp27Mjx48fp0KEDJ0+eRK/XM2DAAKZMmcI333xDeno6//nPf/Dx8WHZsmV2SezLL79k9erVAIwePZqHHnqoahslSTWUTFqSVA4JCQm8/fbbdOrUiZycHHx8fDCbzTz00EOcOHGCiRMn8tVXX/H1118X62kdO3aMNWvWsHLlSoQQjB07lm7dutG2bdsqao0k1VwyaUlSOQQFBdGpUycANm/ezMqVKzGZTGRkZHDmzBlCQ0NL3ffAgQP07dsXNzc3APr168f+/ftl0pKkCpBJS5LK4XLCSUxMZOnSpaxatQpvb2+io6PR6/VVHJ0k1R1y9KAk3YDCwkJcXV3x9PQkMzOTP/74w7bN3d2dwsLCYvt06dKFX3/9laKiIrRaLb/++itdunS5lWFLUq0he1qSdANCQ0Np27YtAwcOJCAggNtuu822bezYsTzyyCP4+fmxbNky2/p27doxatQoxowZA1gHYshLg5JUMXLIuyRJklRjyMuDkiRJUo0hk5YkSZJUY8ikJUmSJNUYMmlJkiRJNYZMWpIkSVKNIZOWJEmSVGPIpCVJkiTVGP8Pq1Pu0StP5JoAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "data2plot(plot_results, removel_range)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "u has shape (300, 300), s has shape (2,), vh has shape (2, 2)\n"
-     ]
-    }
-   ],
-   "source": [
-    "ratio = 0.7\n",
-    "\n",
-    "x_train, y_p_train, y_m_train = load_data(\n",
-    "    '../data/emoji_sent_race_{}/train/'.format(ratio),\n",
-    "    size=100000, ratio=ratio)\n",
-    "x_dev, y_p_dev, y_m_dev = load_data(\n",
-    "    '../data/emoji_sent_race_{}/test/'.format(ratio),\n",
-    "    size=100000, ratio=0.5)\n",
-    "\n",
-    "y_p_train_2d = np.asarray([y_p_train, y_p_train*-1 +1]).T\n",
-    "\n",
-    "A = np.dot(x_train.T, y_p_train_2d) / x_train.shape[0]\n",
-    "u, s, vh = np.linalg.svd(A, full_matrices=True)\n",
-    "print(f\"u has shape {u.shape}, s has shape {s.shape}, vh has shape {vh.shape}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "defaultdict(dict,\n",
-       "            {0: {'p_acc': 0.8199549887471868,\n",
-       "              'biased_diff_tpr': 0.31864557675474753,\n",
-       "              'debiased_diff_tpr': 0.31864557675474753,\n",
-       "              'biased_acc': 0.741185296324081,\n",
-       "              'debiased_acc': 0.741185296324081},\n",
-       "             1: {'p_acc': 0.8438359589897474,\n",
-       "              'biased_diff_tpr': 0.31864557675474753,\n",
-       "              'debiased_diff_tpr': 0.31575482093361984,\n",
-       "              'biased_acc': 0.741185296324081,\n",
-       "              'debiased_acc': 0.7346836709177295},\n",
-       "             2: {'p_acc': 0.463615903975994,\n",
-       "              'biased_diff_tpr': 0.31864557675474753,\n",
-       "              'debiased_diff_tpr': 0.11635856368415173,\n",
-       "              'biased_acc': 0.741185296324081,\n",
-       "              'debiased_acc': 0.7363090772693174},\n",
-       "             5: {'p_acc': 0.46399099774943736,\n",
-       "              'biased_diff_tpr': 0.31864557675474753,\n",
-       "              'debiased_diff_tpr': 0.11674792676509015,\n",
-       "              'biased_acc': 0.741185296324081,\n",
-       "              'debiased_acc': 0.734558639659915},\n",
-       "             30: {'p_acc': 0.5030007501875469,\n",
-       "              'biased_diff_tpr': 0.31864557675474753,\n",
-       "              'debiased_diff_tpr': 0.12023899899338386,\n",
-       "              'biased_acc': 0.741185296324081,\n",
-       "              'debiased_acc': 0.7366841710427607},\n",
-       "             50: {'p_acc': 0.5018754688672168,\n",
-       "              'biased_diff_tpr': 0.31864557675474753,\n",
-       "              'debiased_diff_tpr': 0.12100513006482054,\n",
-       "              'biased_acc': 0.741185296324081,\n",
-       "              'debiased_acc': 0.7349337334333583},\n",
-       "             100: {'p_acc': 0.4981245311327832,\n",
-       "              'biased_diff_tpr': 0.31864557675474753,\n",
-       "              'debiased_diff_tpr': 0.12715509044451567,\n",
-       "              'biased_acc': 0.741185296324081,\n",
-       "              'debiased_acc': 0.7334333583395849},\n",
-       "             200: {'p_acc': 0.47999499874968743,\n",
-       "              'biased_diff_tpr': 0.31864557675474753,\n",
-       "              'debiased_diff_tpr': 0.15675297512285796,\n",
-       "              'biased_acc': 0.741185296324081,\n",
-       "              'debiased_acc': 0.7150537634408602},\n",
-       "             220: {'p_acc': 0.4673668417104276,\n",
-       "              'biased_diff_tpr': 0.31864557675474753,\n",
-       "              'debiased_diff_tpr': 0.1525863232511454,\n",
-       "              'biased_acc': 0.741185296324081,\n",
-       "              'debiased_acc': 0.7150537634408602},\n",
-       "             240: {'p_acc': 0.5385096274068517,\n",
-       "              'biased_diff_tpr': 0.31864557675474753,\n",
-       "              'debiased_diff_tpr': 0.15004721659847078,\n",
-       "              'biased_acc': 0.741185296324081,\n",
-       "              'debiased_acc': 0.7125531382845711},\n",
-       "             260: {'p_acc': 0.5482620655163791,\n",
-       "              'biased_diff_tpr': 0.31864557675474753,\n",
-       "              'debiased_diff_tpr': 0.1537984908912681,\n",
-       "              'biased_acc': 0.741185296324081,\n",
-       "              'debiased_acc': 0.7040510127531883},\n",
-       "             280: {'p_acc': 0.5118779694923731,\n",
-       "              'biased_diff_tpr': 0.31864557675474753,\n",
-       "              'debiased_diff_tpr': 0.15392966588017956,\n",
-       "              'biased_acc': 0.741185296324081,\n",
-       "              'debiased_acc': 0.6950487621905477},\n",
-       "             290: {'p_acc': 0.5113778444611152,\n",
-       "              'biased_diff_tpr': 0.31864557675474753,\n",
-       "              'debiased_diff_tpr': 0.1504312844224986,\n",
-       "              'biased_acc': 0.741185296324081,\n",
-       "              'debiased_acc': 0.691297824456114},\n",
-       "             295: {'p_acc': 0.49987496874218557,\n",
-       "              'biased_diff_tpr': 0.31864557675474753,\n",
-       "              'debiased_diff_tpr': 0.1759445240878502,\n",
-       "              'biased_acc': 0.741185296324081,\n",
-       "              'debiased_acc': 0.661790447611903}})"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results = check_result(u, removel_range, x_train, x_dev, y_m_train, y_m_dev, y_p_train, y_p_dev)\n",
-    "results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "defaultdict(list,\n",
-       "            {'biased_diff_tpr': [0.31864557675474753,\n",
-       "              0.31864557675474753,\n",
-       "              0.31864557675474753,\n",
-       "              0.31864557675474753,\n",
-       "              0.31864557675474753,\n",
-       "              0.31864557675474753,\n",
-       "              0.31864557675474753,\n",
-       "              0.31864557675474753,\n",
-       "              0.31864557675474753,\n",
-       "              0.31864557675474753,\n",
-       "              0.31864557675474753,\n",
-       "              0.31864557675474753,\n",
-       "              0.31864557675474753,\n",
-       "              0.31864557675474753],\n",
-       "             'debiased_diff_tpr': [0.31864557675474753,\n",
-       "              0.31575482093361984,\n",
-       "              0.11635856368415173,\n",
-       "              0.11674792676509015,\n",
-       "              0.12023899899338386,\n",
-       "              0.12100513006482054,\n",
-       "              0.12715509044451567,\n",
-       "              0.15675297512285796,\n",
-       "              0.1525863232511454,\n",
-       "              0.15004721659847078,\n",
-       "              0.1537984908912681,\n",
-       "              0.15392966588017956,\n",
-       "              0.1504312844224986,\n",
-       "              0.1759445240878502],\n",
-       "             'biased_acc': [0.741185296324081,\n",
-       "              0.741185296324081,\n",
-       "              0.741185296324081,\n",
-       "              0.741185296324081,\n",
-       "              0.741185296324081,\n",
-       "              0.741185296324081,\n",
-       "              0.741185296324081,\n",
-       "              0.741185296324081,\n",
-       "              0.741185296324081,\n",
-       "              0.741185296324081,\n",
-       "              0.741185296324081,\n",
-       "              0.741185296324081,\n",
-       "              0.741185296324081,\n",
-       "              0.741185296324081],\n",
-       "             'debiased_acc': [0.741185296324081,\n",
-       "              0.7346836709177295,\n",
-       "              0.7363090772693174,\n",
-       "              0.734558639659915,\n",
-       "              0.7366841710427607,\n",
-       "              0.7349337334333583,\n",
-       "              0.7334333583395849,\n",
-       "              0.7150537634408602,\n",
-       "              0.7150537634408602,\n",
-       "              0.7125531382845711,\n",
-       "              0.7040510127531883,\n",
-       "              0.6950487621905477,\n",
-       "              0.691297824456114,\n",
-       "              0.661790447611903]})"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "plot_results = convert_to_plot_results(results, removel_range)\n",
-    "plot_results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "data2plot(plot_results, removel_range)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "u has shape (300, 300), s has shape (2,), vh has shape (2, 2)\n"
-     ]
-    }
-   ],
-   "source": [
-    "ratio = 0.8\n",
-    "\n",
-    "x_train, y_p_train, y_m_train = load_data(\n",
-    "    '../data/emoji_sent_race_{}/train/'.format(ratio),\n",
-    "    size=100000, ratio=ratio)\n",
-    "x_dev, y_p_dev, y_m_dev = load_data(\n",
-    "    '../data/emoji_sent_race_{}/test/'.format(ratio),\n",
-    "    size=100000, ratio=0.5)\n",
-    "\n",
-    "y_p_train_2d = np.asarray([y_p_train, y_p_train*-1 +1]).T\n",
-    "\n",
-    "A = np.dot(x_train.T, y_p_train_2d) / x_train.shape[0]\n",
-    "u, s, vh = np.linalg.svd(A, full_matrices=True)\n",
-    "print(f\"u has shape {u.shape}, s has shape {s.shape}, vh has shape {vh.shape}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "defaultdict(dict,\n",
-       "            {0: {'p_acc': 0.8379594898724682,\n",
-       "              'biased_diff_tpr': 0.4043010400370602,\n",
-       "              'debiased_diff_tpr': 0.40411721363762787,\n",
-       "              'biased_acc': 0.7200550137534384,\n",
-       "              'debiased_acc': 0.7201800450112528},\n",
-       "             1: {'p_acc': 0.7973243310827707,\n",
-       "              'biased_diff_tpr': 0.4043010400370602,\n",
-       "              'debiased_diff_tpr': 0.3654118278211388,\n",
-       "              'biased_acc': 0.7200550137534384,\n",
-       "              'debiased_acc': 0.7148037009252313},\n",
-       "             2: {'p_acc': 0.47186796699174793,\n",
-       "              'biased_diff_tpr': 0.4043010400370602,\n",
-       "              'debiased_diff_tpr': 0.18610177633438116,\n",
-       "              'biased_acc': 0.7200550137534384,\n",
-       "              'debiased_acc': 0.7191797949487372},\n",
-       "             5: {'p_acc': 0.47199299824956237,\n",
-       "              'biased_diff_tpr': 0.4043010400370602,\n",
-       "              'debiased_diff_tpr': 0.184739631028025,\n",
-       "              'biased_acc': 0.7200550137534384,\n",
-       "              'debiased_acc': 0.7193048262065517},\n",
-       "             30: {'p_acc': 0.4668667166791698,\n",
-       "              'biased_diff_tpr': 0.4043010400370602,\n",
-       "              'debiased_diff_tpr': 0.18506971632421484,\n",
-       "              'biased_acc': 0.7200550137534384,\n",
-       "              'debiased_acc': 0.7191797949487372},\n",
-       "             50: {'p_acc': 0.46524131032758187,\n",
-       "              'biased_diff_tpr': 0.4043010400370602,\n",
-       "              'debiased_diff_tpr': 0.1867917631297146,\n",
-       "              'biased_acc': 0.7200550137534384,\n",
-       "              'debiased_acc': 0.7181795448862216},\n",
-       "             100: {'p_acc': 0.4736184046011503,\n",
-       "              'biased_diff_tpr': 0.4043010400370602,\n",
-       "              'debiased_diff_tpr': 0.18737210776237648,\n",
-       "              'biased_acc': 0.7200550137534384,\n",
-       "              'debiased_acc': 0.7146786696674169},\n",
-       "             200: {'p_acc': 0.5230057514378594,\n",
-       "              'biased_diff_tpr': 0.4043010400370602,\n",
-       "              'debiased_diff_tpr': 0.19752038343148395,\n",
-       "              'biased_acc': 0.7200550137534384,\n",
-       "              'debiased_acc': 0.6956739184796199},\n",
-       "             220: {'p_acc': 0.5100025006251563,\n",
-       "              'biased_diff_tpr': 0.4043010400370602,\n",
-       "              'debiased_diff_tpr': 0.202949662154134,\n",
-       "              'biased_acc': 0.7200550137534384,\n",
-       "              'debiased_acc': 0.6937984496124031},\n",
-       "             240: {'p_acc': 0.5075018754688673,\n",
-       "              'biased_diff_tpr': 0.4043010400370602,\n",
-       "              'debiased_diff_tpr': 0.20801803316486164,\n",
-       "              'biased_acc': 0.7200550137534384,\n",
-       "              'debiased_acc': 0.6820455113778444},\n",
-       "             260: {'p_acc': 0.502750687671918,\n",
-       "              'biased_diff_tpr': 0.4043010400370602,\n",
-       "              'debiased_diff_tpr': 0.18813856463946912,\n",
-       "              'biased_acc': 0.7200550137534384,\n",
-       "              'debiased_acc': 0.6614153538384596},\n",
-       "             280: {'p_acc': 0.49987496874218557,\n",
-       "              'biased_diff_tpr': 0.4043010400370602,\n",
-       "              'debiased_diff_tpr': 0.19263016753347223,\n",
-       "              'biased_acc': 0.7200550137534384,\n",
-       "              'debiased_acc': 0.6404101025256314},\n",
-       "             290: {'p_acc': 0.49987496874218557,\n",
-       "              'biased_diff_tpr': 0.4043010400370602,\n",
-       "              'debiased_diff_tpr': 0.1721118417919755,\n",
-       "              'biased_acc': 0.7200550137534384,\n",
-       "              'debiased_acc': 0.6280320080020005},\n",
-       "             295: {'p_acc': 0.49987496874218557,\n",
-       "              'biased_diff_tpr': 0.4043010400370602,\n",
-       "              'debiased_diff_tpr': 0.0834654770143427,\n",
-       "              'biased_acc': 0.7200550137534384,\n",
-       "              'debiased_acc': 0.5681420355088772}})"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "results = check_result(u, removel_range, x_train, x_dev, y_m_train, y_m_dev, y_p_train, y_p_dev)\n",
-    "results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "defaultdict(list,\n",
-       "            {'biased_diff_tpr': [0.4043010400370602,\n",
-       "              0.4043010400370602,\n",
-       "              0.4043010400370602,\n",
-       "              0.4043010400370602,\n",
-       "              0.4043010400370602,\n",
-       "              0.4043010400370602,\n",
-       "              0.4043010400370602,\n",
-       "              0.4043010400370602,\n",
-       "              0.4043010400370602,\n",
-       "              0.4043010400370602,\n",
-       "              0.4043010400370602,\n",
-       "              0.4043010400370602,\n",
-       "              0.4043010400370602,\n",
-       "              0.4043010400370602],\n",
-       "             'debiased_diff_tpr': [0.40411721363762787,\n",
-       "              0.3654118278211388,\n",
-       "              0.18610177633438116,\n",
-       "              0.184739631028025,\n",
-       "              0.18506971632421484,\n",
-       "              0.1867917631297146,\n",
-       "              0.18737210776237648,\n",
-       "              0.19752038343148395,\n",
-       "              0.202949662154134,\n",
-       "              0.20801803316486164,\n",
-       "              0.18813856463946912,\n",
-       "              0.19263016753347223,\n",
-       "              0.1721118417919755,\n",
-       "              0.0834654770143427],\n",
-       "             'biased_acc': [0.7200550137534384,\n",
-       "              0.7200550137534384,\n",
-       "              0.7200550137534384,\n",
-       "              0.7200550137534384,\n",
-       "              0.7200550137534384,\n",
-       "              0.7200550137534384,\n",
-       "              0.7200550137534384,\n",
-       "              0.7200550137534384,\n",
-       "              0.7200550137534384,\n",
-       "              0.7200550137534384,\n",
-       "              0.7200550137534384,\n",
-       "              0.7200550137534384,\n",
-       "              0.7200550137534384,\n",
-       "              0.7200550137534384],\n",
-       "             'debiased_acc': [0.7201800450112528,\n",
-       "              0.7148037009252313,\n",
-       "              0.7191797949487372,\n",
-       "              0.7193048262065517,\n",
-       "              0.7191797949487372,\n",
-       "              0.7181795448862216,\n",
-       "              0.7146786696674169,\n",
-       "              0.6956739184796199,\n",
-       "              0.6937984496124031,\n",
-       "              0.6820455113778444,\n",
-       "              0.6614153538384596,\n",
-       "              0.6404101025256314,\n",
-       "              0.6280320080020005,\n",
-       "              0.5681420355088772]})"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "plot_results = convert_to_plot_results(results, removel_range)\n",
-    "plot_results"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "data2plot(plot_results, removel_range)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.11"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/notebooks/notebook_word-embedding.ipynb b/notebooks/notebook_word-embedding.ipynb
deleted file mode 100644
index 0832946..0000000
--- a/notebooks/notebook_word-embedding.ipynb
+++ /dev/null
@@ -1,1794 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import sys\n",
-    "sys.path.append(\"../src\")\n",
-    "sys.path.append(\"../data/embeddings\")\n",
-    "import classifier\n",
-    "import debias\n",
-    "#import debias_old as debias\n",
-    "import gensim\n",
-    "import codecs\n",
-    "import json\n",
-    "from gensim.models.keyedvectors import Word2VecKeyedVectors\n",
-    "from gensim.models import KeyedVectors\n",
-    "import numpy as np\n",
-    "import random\n",
-    "import sklearn\n",
-    "from sklearn import model_selection\n",
-    "from sklearn import cluster\n",
-    "from sklearn import metrics\n",
-    "from sklearn.manifold import TSNE\n",
-    "from sklearn.svm import LinearSVC, SVC\n",
-    "from sklearn.linear_model import SGDClassifier, Perceptron, LogisticRegression, PassiveAggressiveClassifier\n",
-    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
-    "from sklearn.neural_network import MLPClassifier\n",
-    "from sklearn.metrics.pairwise import cosine_similarity\n",
-    "from sklearn.decomposition import PCA\n",
-    "import scipy\n",
-    "from scipy import linalg\n",
-    "from scipy.stats.stats import pearsonr\n",
-    "import tqdm\n",
-    "import matplotlib\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib inline\n",
-    "matplotlib.rcParams['agg.path.chunksize'] = 10000\n",
-    "from sklearn.utils import shuffle\n",
-    "\n",
-    "#import warnings\n",
-    "#warnings.filterwarnings(\"ignore\")\n",
-    "%load_ext autoreload\n",
-    "%autoreload"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def tsne(vecs, labels, title=\"\", ind2label = None, words = None, metric = \"l2\"):\n",
-    "\n",
-    "  tsne = TSNE(n_components=2)#, angle = 0.5, perplexity = 20)\n",
-    "  vecs_2d = tsne.fit_transform(vecs)\n",
-    "  label_names = sorted(list(set(labels.tolist())))\n",
-    "  num_labels = len(label_names)\n",
-    "\n",
-    "  names = sorted(set(labels.tolist()))\n",
-    "\n",
-    "  plt.figure(figsize=(6, 5))\n",
-    "  colors = \"red\", \"blue\"\n",
-    "  for i, c, label in zip(sorted(set(labels.tolist())), colors, names):\n",
-    "     plt.scatter(vecs_2d[labels == i, 0], vecs_2d[labels == i, 1], c=c,\n",
-    "                label=label if ind2label is None else ind2label[label], alpha = 0.3, marker = \"s\" if i==0 else \"o\")\n",
-    "     plt.legend(loc = \"upper right\")\n",
-    "\n",
-    "  plt.title(title)\n",
-    "  plt.savefig(\"embeddings.{}.png\".format(title), dpi=600)\n",
-    "  plt.show()\n",
-    "  return vecs_2d"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Data loading & processing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def load_word_vectors(fname):\n",
-    "    \n",
-    "    model = KeyedVectors.load_word2vec_format(fname, binary=False)\n",
-    "    vecs = model.vectors\n",
-    "    words = list(model.vocab.keys())\n",
-    "    return model, vecs, words\n",
-    "\n",
-    "def project_on_gender_subspaces(gender_vector, model: Word2VecKeyedVectors, n = 2500):\n",
-    "    \n",
-    "    group1 = model.similar_by_vector(gender_vector, topn = n, restrict_vocab=None)\n",
-    "    group2 = model.similar_by_vector(-gender_vector, topn = n, restrict_vocab=None)\n",
-    "    \n",
-    "    all_sims = model.similar_by_vector(gender_vector, topn = len(model.vectors), restrict_vocab=None)\n",
-    "    eps = 0.03\n",
-    "    idx = [i for i in range(len(all_sims)) if abs(all_sims[i][1]) < eps]\n",
-    "    samp = set(np.random.choice(idx, size = n))\n",
-    "    neut = [s for i,s in enumerate(all_sims) if i in samp]\n",
-    "    return group1, group2, neut\n",
-    "\n",
-    "def get_vectors(word_list: list, model: Word2VecKeyedVectors):\n",
-    "    \n",
-    "    vecs = []\n",
-    "    for w in word_list:\n",
-    "        \n",
-    "        vecs.append(model[w])\n",
-    "    \n",
-    "    vecs = np.array(vecs)\n",
-    "\n",
-    "    return vecs\n",
-    "    \n",
-    "def get_bias_by_neighbors(model, v, gender_direction, topn): \n",
-    "    \n",
-    "    neighbors = model.similar_by_vector(v, topn = topn) \n",
-    "    neighbors_words = [n for n, _ in neighbors]\n",
-    "    \n",
-    "    #bias = len([n for n in neighbors_words if n in gendered_words])\n",
-    "    bias = len([n for n in neighbors_words if model.cosine_similarities(model[n], [gender_direction])[0] > 0])\n",
-    "    bias /= (1.*topn)\n",
-    "    return bias\n",
-    "\n",
-    "\n",
-    "def save_in_word2vec_format(vecs: np.ndarray, words: np.ndarray, fname: str):\n",
-    "\n",
-    "\n",
-    "    with open(fname, \"w\", encoding = \"utf-8\") as f:\n",
-    "\n",
-    "        f.write(str(len(vecs)) + \" \" + \"300\" + \"\\n\")\n",
-    "        for i, (v,w) in tqdm.tqdm_notebook(enumerate(zip(vecs, words))):\n",
-    "\n",
-    "            vec_as_str = \" \".join([str(x) for x in v])\n",
-    "            f.write(w + \" \" + vec_as_str + \"\\n\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load word vectors"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 150k top vectors (with gender-typical words) - used for training\n",
-    "\n",
-    "model, vecs, words = load_word_vectors(fname = \"../data/embeddings/vecs.filtered.txt\")\n",
-    "\n",
-    "# only gendered vectors\n",
-    "\n",
-    "model_gendered, _, _ = load_word_vectors(fname = \"../data/embeddings/vecs.gendered.txt\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def load_word_vectors(fname):\n",
-    "    \n",
-    "    model = KeyedVectors.load_word2vec_format(fname, binary=False)\n",
-    "    vecs = model.vectors\n",
-    "    words = list(model.vocab.keys())\n",
-    "    return model, vecs, words\n",
-    "\n",
-    "# 150k top vectors (with gender-typical words) - used for training\n",
-    "\n",
-    "model, vecs, words = load_word_vectors(fname = \"../data/embeddings/vecs.filtered.txt\")\n",
-    "\n",
-    "gender_direction = model[\"he\"]-model[\"she\"] \n",
-    "\n",
-    "    group1 = model.similar_by_vector(gender_vector, topn = n, restrict_vocab=None)\n",
-    "    group2 = model.similar_by_vector(-gender_vector, topn = n, restrict_vocab=None)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def project_on_gender_subspaces(gender_vector, model: Word2VecKeyedVectors, n = 2500):\n",
-    "    \n",
-    "    group1 = model.similar_by_vector(gender_vector, topn = n, restrict_vocab=None)\n",
-    "    group2 = model.similar_by_vector(-gender_vector, topn = n, restrict_vocab=None)\n",
-    "    \n",
-    "    all_sims = model.similar_by_vector(gender_vector, topn = len(model.vectors), restrict_vocab=None)\n",
-    "    eps = 0.03\n",
-    "    idx = [i for i in range(len(all_sims)) if abs(all_sims[i][1]) < eps]\n",
-    "    samp = set(np.random.choice(idx, size = n))\n",
-    "    neut = [s for i,s in enumerate(all_sims) if i in samp]\n",
-    "    return group1, group2, neut\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Collect biased words"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "TOP MASC\n",
-      "('drafted', 'qb', 'soriano', 'himself', 'cardinals', 'giants', 'he', 'bullpen', 'sabean', 'jagr', 'muhammad', 'alou', 'rangers', 'defensive', 'draft', 'belichick', 'rookie', 'ratzinger', 'obp', 'santonio', 'muhammed', 'yankees', 'outfielder', 'preached', 'playmaker', 'lineman', 'offensive', 'wr', 'steelers', 'redskins', 'rushers', 'his', 'punter', 'anquan', 'boldin', 'bochy', 'caesarea', 'nfl', 'umenyiora', 'laurinaitis', 'flacco', 'rc', 'eusebius', 'packers', 'lhp', 'homers', 'mitre', 'linebacker', 'rhp', 'manny')\n",
-      "-------------------------\n",
-      "TOP FEM\n",
-      "('nichole', 'ftv', 'renee', 'sophie', 'christina', 'marie', 'pregnant', 'nicole', 'samantha', 'denise', 'sassy', 'madeline', 'alicia', 'lynette', 'xoxo', 'melanie', 'michelle', 'missy', 'kimberly', 'melissa', 'kayla', 'angelina', 'kristin', 'jennifer', 'emma', 'katie', 'pregnancy', 'jessica', 'heidi', 'tina', 'mandy', 'erika', 'maggie', 'shes', 'elaine', 'julie', 'vanessa', 'actress', 'leanne', 'kristina', 'faye', 'alexandra', 'tanya', 'fiona', 'rebecca', 'cassie', 'cindy', 'janice', 'danielle', 'ballerina')\n",
-      "-------------------------\n",
-      "('dxb', 'stereophonic', 'flyby', 'hound', 'clubs', 'turtles', 'rechargable', 'fits', 'provo', 'bcv', 'frittered', 'guster', 'asker', 'embankment', 'jsessionid', 'cigarettes', 'commuters', 'aligning', 'youngsters', 'attleborough', 'kirton', 'quoth', 'loader', 'capoeira', 'hepatocytes', 'cosworth', 'blisteringly', 'muenster', 'replete', 'ited', 'clarksburg', 'burrito', 'pmlast', 'jillette', 'ddt', 'garni', 'stuffit', 'razorlight', 'horning', 'tenochtitlan', 'gars', 'stapler', 'placard', 'regains', 'immokalee', 'grosseto', 'dravidian', 'pumpernickel', 'halifax', 'missionary')\n"
-     ]
-    }
-   ],
-   "source": [
-    "num_vectors_per_class = 7500\n",
-    "\n",
-    "by_pca = False\n",
-    "if by_pca:\n",
-    "    pairs = [(\"male\", \"female\"), (\"masculine\", \"feminine\"), (\"he\", \"she\"), (\"him\", \"her\")]\n",
-    "    gender_vecs = [model[p[0]] - model[p[1]] for p in pairs]\n",
-    "    pca = PCA(n_components=1)\n",
-    "    pca.fit(gender_vecs)\n",
-    "    gender_direction = pca.components_[0]\n",
-    "    \n",
-    "else:\n",
-    "    gender_direction = model[\"he\"]-model[\"she\"] \n",
-    "\n",
-    "\n",
-    "gender_unit_vec = gender_direction/np.linalg.norm(gender_direction)\n",
-    "masc_words_and_scores, fem_words_and_scores, neut_words_and_scores = project_on_gender_subspaces(gender_direction, model, n = num_vectors_per_class)\n",
-    "\n",
-    "masc_words, masc_scores = list(zip(*masc_words_and_scores))\n",
-    "neut_words, neut_scores = list(zip(*neut_words_and_scores))\n",
-    "fem_words, fem_scores = list(zip(*fem_words_and_scores))\n",
-    "masc_vecs, fem_vecs = get_vectors(masc_words, model), get_vectors(fem_words, model)\n",
-    "neut_vecs = get_vectors(neut_words, model)\n",
-    "\n",
-    "n = min(3000, num_vectors_per_class)\n",
-    "all_significantly_biased_words = masc_words[:n] + fem_words[:n]\n",
-    "all_significantly_biased_vecs =  np.concatenate((masc_vecs[:n], fem_vecs[:n]))\n",
-    "all_significantly_biased_labels = np.concatenate((np.ones(n, dtype = int),\n",
-    "                                                  np.zeros(n, dtype = int)))\n",
-    "\n",
-    "all_significantly_biased_words, all_significantly_biased_vecs, all_significantly_biased_labels = sklearn.utils.shuffle(\n",
-    "all_significantly_biased_words, all_significantly_biased_vecs, all_significantly_biased_labels)\n",
-    "#print(np.random.choice(masc_words, size = 75))\n",
-    "print(\"TOP MASC\")\n",
-    "print(masc_words[:50])\n",
-    "#print(\"LAST MASC\")\n",
-    "#print(masc_words[-120:])\n",
-    "print(\"-------------------------\")\n",
-    "#print(np.random.choice(fem_words, size = 75))\n",
-    "print(\"TOP FEM\")\n",
-    "print(fem_words[:50])\n",
-    "#print(\"LAST FEM\")\n",
-    "#print(fem_words[-120:])\n",
-    "print(\"-------------------------\")\n",
-    "#print(np.random.choice(neut_words, size = 75))\n",
-    "print(neut_words[:50])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(0.3077116906642914, 0.2944546043872833, 0.2902606725692749, 0.2880435585975647, 0.28346988558769226, 0.2792784869670868, 0.27867886424064636, 0.2778513729572296, 0.2777228057384491, 0.2761598825454712)\n",
-      "(0.12253306061029434, 0.1225176751613617, 0.1225091889500618, 0.1225038692355156, 0.12250120937824249, 0.12250052392482758, 0.12249962985515594, 0.12249766290187836, 0.12249290198087692, 0.12249206751585007)\n",
-      "(0.029992904514074326, 0.02998550981283188, 0.029980672523379326, 0.02998044341802597, 0.029976919293403625, 0.029973600059747696, 0.02996787615120411, 0.0299593023955822, 0.029956789687275887, 0.029918069019913673)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(masc_scores[:10])\n",
-    "print(masc_scores[-10:])\n",
-    "print(neut_scores[:10])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# 1. Dataset"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1.1 Load Directly from saved"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "saved_dataset = np.load(\"../data/saved_models/general/all.npz\")\n",
-    "X_dev = saved_dataset['x_dev']\n",
-    "X_train = saved_dataset['x_train']\n",
-    "X_test = saved_dataset['x_test']\n",
-    "\n",
-    "Y_dev = saved_dataset['y_p_dev']\n",
-    "Y_train = saved_dataset['y_p_train']\n",
-    "Y_test = saved_dataset['y_p_test']\n",
-    "\n",
-    "# Y_dev_label = Y_dev\n",
-    "# Y_train_label = Y_train\n",
-    "# Y_test_label = Y_test\n",
-    "\n",
-    "Y_dev_2d = np.asarray([Y_dev, -Y_dev + 1]).T\n",
-    "Y_train_2d = np.asarray([Y_train, -Y_train + 1]).T\n",
-    "Y_test_2d = np.asarray([Y_test, -Y_test + 1]).T"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 1.2 Create the dataset Skip if load from saved "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Perform train-dev-test split"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train size: 7350; Dev size: 3150; Test size: 4500\n"
-     ]
-    }
-   ],
-   "source": [
-    "random.seed(0)\n",
-    "np.random.seed(0)\n",
-    "\n",
-    "X = np.concatenate((masc_vecs, fem_vecs), axis = 0)\n",
-    "#X = (X - np.mean(X, axis = 0, keepdims = True)) / np.std(X, axis = 0)\n",
-    "y_masc = np.ones(masc_vecs.shape[0], dtype = int)\n",
-    "y_fem = np.zeros(fem_vecs.shape[0], dtype = int)\n",
-    "#y = np.concatenate((masc_scores, fem_scores, neut_scores))#np.concatenate((y_masc, y_fem))\n",
-    "y = np.concatenate((y_masc, y_fem))\n",
-    "X_train_dev, X_test, y_train_dev, Y_test = sklearn.model_selection.train_test_split(X, y, test_size = 0.3, random_state = 0)\n",
-    "X_train, X_dev, Y_train, Y_dev = sklearn.model_selection.train_test_split(X_train_dev, y_train_dev, test_size = 0.3, random_state = 0)\n",
-    "print(\"Train size: {}; Dev size: {}; Test size: {}\".format(X_train.shape[0], X_dev.shape[0], X_test.shape[0]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Train size: 7350; Dev size: 3150; Test size: 4500\n"
-     ]
-    }
-   ],
-   "source": [
-    "# remove neutral class, keep only male and female biased\n",
-    "\n",
-    "X_dev = X_dev[Y_dev != -1]\n",
-    "X_train = X_train[Y_train != -1]\n",
-    "X_test = X_test[Y_test != -1]\n",
-    "\n",
-    "\n",
-    "Y_dev = Y_dev[Y_dev != -1]\n",
-    "Y_train = Y_train[Y_train != -1]\n",
-    "Y_test = Y_test[Y_test != -1]\n",
-    "\n",
-    "print(\"Train size: {}; Dev size: {}; Test size: {}\".format(X_train.shape[0], X_dev.shape[0], X_test.shape[0]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_dev = np.asarray(X_dev)\n",
-    "X_train = np.asarray(X_train)\n",
-    "X_test = np.asarray(X_test)\n",
-    "\n",
-    "Y_dev = np.asarray(Y_dev)\n",
-    "Y_train = np.asarray(Y_train)\n",
-    "Y_test = np.asarray(Y_test)\n",
-    "\n",
-    "# Y_dev_label = Y_dev\n",
-    "# Y_train_label = Y_train\n",
-    "# Y_test_label = Y_test\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Save\n",
-    "np.savez(\"../data/saved_models/general/all.npz\", x_train = X_train, x_dev = X_dev, x_test = X_test, y_p_train = Y_train, y_p_dev = Y_dev, y_p_test = Y_test)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## 2 Debiasing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(7350,)"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "Y_train.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import time"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "time: 0.024875879287719727\n",
-      "u has shape (300, 300), s has shape (2,), vh has shape (2, 2)\n"
-     ]
-    }
-   ],
-   "source": [
-    "start = time.time()\n",
-    "A = np.dot(X_train.T, Y_train_2d) / X_train.shape[0]\n",
-    "u, s, vh = np.linalg.svd(A, full_matrices=True)\n",
-    "print(\"time: {}\".format(time.time() - start))\n",
-    "print(f\"u has shape {u.shape}, s has shape {s.shape}, vh has shape {vh.shape}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Save model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "np.savez(\"../data/saved_models/general/USV.npz\", u = u, s = s, vh = vh)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Load model"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "saved_model = np.load(\"../data/saved_models/general/USV.npz\")\n",
-    "u = saved_model['u']\n",
-    "s = saved_model['s']\n",
-    "vh = saved_model['vh']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Example: Update U to minimize the correlation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "r = 2\n",
-    "u_r = u[:, r:]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "(7350, 300)\n"
-     ]
-    }
-   ],
-   "source": [
-    "proj = u_r @ u_r.T\n",
-    "X_debiased = proj.dot(X_train.T).T\n",
-    "print(f\"{X_debiased.shape}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Test 1.1 : Perform the T-SNE test on different level of removal"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "u_r has the shape of (300, 298)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "V-measure-before (TSNE space): 0.7238705067634674\n",
-      "V-measure-after (TSNE space): 0.004238804751062806\n",
-      "V-measure-before (original space): 1.0\n",
-      "V-measure-after (original space): 0.0005728149041390694\n",
-      "Rank before: 300; Rank after: 298\n"
-     ]
-    }
-   ],
-   "source": [
-    "# def perform_purity_test(vecs, k, labels_true):\n",
-    "#         np.random.seed(0)\n",
-    "#         clustering = sklearn.cluster.KMeans(n_clusters = k)\n",
-    "#         clustering.fit(vecs)\n",
-    "#         labels_pred = clustering.labels_\n",
-    "#         score = sklearn.metrics.homogeneity_score(labels_true, labels_pred)\n",
-    "#         return score\n",
-    "\n",
-    "def compute_v_measure(vecs, labels_true, k=2):\n",
-    "    \n",
-    "        np.random.seed(0)\n",
-    "        clustering = sklearn.cluster.KMeans(n_clusters = k)\n",
-    "        clustering.fit(vecs)\n",
-    "        labels_pred = clustering.labels_\n",
-    "        return sklearn.metrics.v_measure_score(labels_true, labels_pred)\n",
-    "    \n",
-    "\n",
-    "# remove neutral class, keep only male and female biased\n",
-    "\n",
-    "# X_dev = X_dev[Y_dev != -1]\n",
-    "# X_train = X_train[Y_train != -1]\n",
-    "# X_test = X_test[Y_test != -1]\n",
-    "\n",
-    "\n",
-    "# Y_dev = Y_dev[Y_dev != -1]\n",
-    "# Y_train = Y_train[Y_train != -1]\n",
-    "# Y_test = Y_test[Y_test != -1]\n",
-    "\n",
-    "\n",
-    "M =  2000\n",
-    "ind2label =  {1: \"Male-biased\", 0: \"Female-biased\"}\n",
-    "#tsne_before = tsne(all_significantly_biased_vecs[:M], all_significantly_biased_labels[:M], title = \"Original (t=0)\", ind2label =ind2label )\n",
-    "tsne_before = tsne(all_significantly_biased_vecs[:M], all_significantly_biased_labels[:M], title = \"Original (r=0)\", ind2label =ind2label )\n",
-    "\n",
-    "r = 2\n",
-    "u_r = u[:, r:]\n",
-    "print(f\"u_r has the shape of {u_r.shape}\")\n",
-    "proj = u_r @ u_r.T\n",
-    "X_dev_cleaned = proj.dot(X_dev.T).T\n",
-    "X_test_cleaned = proj.dot(X_test.T).T\n",
-    "X_trained_cleaned = proj.dot(X_train.T).T\n",
-    "all_significantly_biased_cleaned = proj.dot(all_significantly_biased_vecs.T).T\n",
-    "\n",
-    "#tsne_after = tsne_by_gender(all_significantly_biased_cleaned[:M], all_significantly_biased_labels[:M], title = \"Projected (t = {})\".format(n))\n",
-    "tsne_after = tsne(all_significantly_biased_cleaned[:M], all_significantly_biased_labels[:M], title = \"Projected (r={})\".format(r), ind2label =ind2label )\n",
-    "\n",
-    "#tsne_projection = tsne_by_gender(all_biased_cleaned, all_significantly_biased_labels,title = \"after (all)\", words = all_significantly_biased_words)\n",
-    "\n",
-    "print(\"V-measure-before (TSNE space): {}\".format(compute_v_measure(tsne_before, all_significantly_biased_labels[:M])))\n",
-    "print(\"V-measure-after (TSNE space): {}\".format(compute_v_measure(tsne_after, all_significantly_biased_labels[:M])))\n",
-    "\n",
-    "#print(\"V-measure-before (original space): {}\".format(compute_v_measure(all_significantly_biased_vecs[:M], all_significantly_biased_labels[:M]), k = 2))\n",
-    "#print(\"V-measure-after (original space): {}\".format(compute_v_measure(all_significantly_biased_cleaned[:M], all_significantly_biased_labels[:M]), k = 2))\n",
-    "print(\"V-measure-before (original space): {}\".format(compute_v_measure(all_significantly_biased_vecs[:M], all_significantly_biased_labels[:M]), k = 2))\n",
-    "print(\"V-measure-after (original space): {}\".format(compute_v_measure(X_test_cleaned[:M], Y_test[:M]), k = 2))\n",
-    "\n",
-    "rank_before = np.linalg.matrix_rank(X_train)\n",
-    "rank_after = np.linalg.matrix_rank(X_trained_cleaned)\n",
-    "print(\"Rank before: {}; Rank after: {}\".format(rank_before, rank_after))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "u_r has the shape of (300, 300)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "u_r has the shape of (300, 299)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "u_r has the shape of (300, 298)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "u_r has the shape of (300, 297)\n"
-     ]
-    },
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\n",
-      "text/plain": [
-       "<Figure size 432x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "M =  2000\n",
-    "\n",
-    "for r in [0, 1, 2]:\n",
-    "    u_r = u[:, r:]\n",
-    "    print(f\"u_r has the shape of {u_r.shape}\")\n",
-    "    proj = u_r @ u_r.T\n",
-    "    tsne_after = tsne(proj.dot(all_significantly_biased_vecs[:M].T).T , all_significantly_biased_labels[:M], title = \"Projected (r={})\".format(r), ind2label =ind2label )"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "u_r has the shape of (300, 300)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "u_r has the shape of (300, 299)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "u_r has the shape of (300, 298)\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x360 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "M =  2000\n",
-    "\n",
-    "for r in [0, 1, 2]:\n",
-    "    u_r = u[:, r:]\n",
-    "    print(f\"u_r has the shape of {u_r.shape}\")\n",
-    "    proj = u_r @ u_r.T\n",
-    "    tsne_after = tsne(proj.dot(all_significantly_biased_vecs[:M].T).T , all_significantly_biased_labels[:M], ind2label =ind2label )"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Test 1.2 Gender prediction by linear and non-linear classifier"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "============== remove 1 canonical components ==============\n",
-      "Before, linear:\n",
-      "X_test_cleaned score: 1.0\n",
-      "After, linear:\n",
-      "X_test_cleaned score: 1.0,           X_dev_cleaned score: 1.0\n",
-      "After, rbf-svm:\n",
-      "X_dev_cleaned score: 1.0\n",
-      "After, mlp:\n",
-      "X_dev_cleaned score: 1.0\n",
-      "============== remove 2 canonical components ==============\n",
-      "Before, linear:\n",
-      "X_test_cleaned score: 1.0\n",
-      "After, linear:\n",
-      "X_test_cleaned score: 0.49422222222222223,           X_dev_cleaned score: 0.4946031746031746\n",
-      "After, rbf-svm:\n",
-      "X_dev_cleaned score: 0.9676190476190476\n",
-      "After, mlp:\n",
-      "X_dev_cleaned score: 0.966984126984127\n",
-      "============== remove 3 canonical components ==============\n",
-      "Before, linear:\n",
-      "X_test_cleaned score: 1.0\n",
-      "After, linear:\n",
-      "X_test_cleaned score: 0.49422222222222223,           X_dev_cleaned score: 0.4946031746031746\n",
-      "After, rbf-svm:\n",
-      "X_dev_cleaned score: 0.9682539682539683\n",
-      "After, mlp:\n",
-      "X_dev_cleaned score: 0.9704761904761905\n"
-     ]
-    }
-   ],
-   "source": [
-    "\"\"\"\n",
-    "    The Y_train has been transformed to 1D to fit with the classifier, what if we define it to be \n",
-    "\"\"\"\n",
-    "\n",
-    "\n",
-    "\"\"\"\n",
-    "nonlinear_clf = SVC(kernel = \"rbf\")\n",
-    "print(\"Before, rbf-svm:\")\n",
-    "nonlinear_clf.fit(X_train, Y_train)\n",
-    "print(nonlinear_clf.score(X_dev, Y_dev))\n",
-    "\n",
-    "\"\"\" \n",
-    "\n",
-    "\n",
-    "# for r in [1, 2, 3]:\n",
-    "for r in [1, 2, 3]:\n",
-    "\n",
-    "    u_r = u[:, r:]\n",
-    "    proj = u_r @ u_r.T\n",
-    "    X_dev_cleaned = proj.dot(X_dev.T).T\n",
-    "    X_test_cleaned = proj.dot(X_test.T).T\n",
-    "    X_trained_cleaned = proj.dot(X_train.T).T\n",
-    "    \n",
-    "    print(f\"============== remove {r} canonical components ==============\")\n",
-    "    print(\"Before, linear:\")\n",
-    "    linear_clf = LinearSVC(dual=False, max_iter = 1500)\n",
-    "    linear_clf.fit(X_train, Y_train)\n",
-    "    print(f\"X_test_cleaned score: {linear_clf.score(X_test, Y_test)}\")\n",
-    "\n",
-    "    print(\"After, linear:\")\n",
-    "    linear_clf = LinearSVC(dual=False, max_iter = 1500)\n",
-    "    linear_clf.fit(X_trained_cleaned, Y_train)\n",
-    "    print(f\"X_test_cleaned score: {linear_clf.score(X_test_cleaned, Y_test)}, \\\n",
-    "          X_dev_cleaned score: {linear_clf.score(X_dev_cleaned, Y_dev)}\")\n",
-    "\n",
-    "    print(\"After, rbf-svm:\")\n",
-    "    nonlinear_clf = SVC(kernel = \"rbf\")\n",
-    "    nonlinear_clf.fit(X_trained_cleaned, Y_train)\n",
-    "    print(f\"X_dev_cleaned score: {nonlinear_clf.score(X_dev_cleaned, Y_dev)}\")\n",
-    "\n",
-    "    print(\"After, mlp:\")\n",
-    "    nonlinear_clf = MLPClassifier(hidden_layer_sizes = 256, activation = \"relu\")\n",
-    "    nonlinear_clf.fit(X_trained_cleaned, Y_train)\n",
-    "    print(f\"X_dev_cleaned score: {nonlinear_clf.score(X_dev_cleaned, Y_dev)}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "<font color='darkorchid' size=5>Interesting fact that whatever we remove from the canonical components, it can still be recoverable from non-linear calssifier</font>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Test 1.3 Project on the gender direction"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "masc-bias-before: 1.0073015689849854\n",
-      "masc-bias-after: 0.003109228071241853\n",
-      "fem-bias-before: -0.9732301831245422\n",
-      "fem-bias-after: -0.001174279573870251\n"
-     ]
-    }
-   ],
-   "source": [
-    "r = 2\n",
-    "u_r = u[:, r:]\n",
-    "proj = u_r @ u_r.T\n",
-    "masc_vecs_cleaned = proj.dot(masc_vecs.T).T\n",
-    "fem_vecs_cleaned = proj.dot(fem_vecs.T).T\n",
-    "\n",
-    "print(\"masc-bias-before: {}\".format(masc_vecs.dot(gender_unit_vec).mean()))\n",
-    "print(\"masc-bias-after: {}\".format(masc_vecs_cleaned.dot(gender_unit_vec.dot(proj)).mean()))\n",
-    "print(\"fem-bias-before: {}\".format(fem_vecs.dot(gender_unit_vec).mean()))\n",
-    "print(\"fem-bias-after: {}\".format(fem_vecs_cleaned.dot(gender_unit_vec.dot(proj)).mean()))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# SAVE THE RESULTS"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/lustre/home/sc066/shunshao/miniconda3/envs/cca/lib/python3.7/site-packages/ipykernel_launcher.py:48: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
-      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "93d67227b815429293fafa6c55b4ad58",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "0it [00:00, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# r is the number of canonical components to remove\n",
-    "r = 2\n",
-    "u_r = u[:, r:]\n",
-    "proj = u_r @ u_r.T\n",
-    "\n",
-    "vecs_cleaned = proj.dot(vecs.T).T\n",
-    "save_in_word2vec_format(vecs_cleaned, words, \"../data/embeddings/vecs.cca.cleaned.txt\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "model_cleaned, _, _ = load_word_vectors(fname = \"../data/embeddings/vecs.cca.cleaned.txt\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Test 1.4.1 calcualte the similarity of male-stereotyped and female-stereotyped words to 'girlish', before and after"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Similarity of female-stereotyped words to 'girlish' before: 0.09480048716068268; similarity after: -8.931956108426675e-05\n",
-      "Similarity of male-stereotyped words to 'girlish' before: 0.010917985811829567; similarity after: 0.0012107666116207838\n"
-     ]
-    }
-   ],
-   "source": [
-    "w = \"girlish\"\n",
-    "k = 5000\n",
-    "\n",
-    "random_fem_words = np.random.choice(fem_words, size = k)\n",
-    "sim_to_girlish_before = [model.similarity(w,w2) for w2 in random_fem_words]\n",
-    "sim_to_girlish_after = [model_cleaned.similarity(w,w2) for w2 in random_fem_words]\n",
-    "\n",
-    "print(\"Similarity of female-stereotyped words to 'girlish' before: {}; similarity after: {}\".format(np.mean(sim_to_girlish_before), np.mean(sim_to_girlish_after)))\n",
-    "\n",
-    "\n",
-    "w = \"girlish\"\n",
-    "random_masc_words = np.random.choice(masc_words, size = k)\n",
-    "sim_to_girlish_before = [model.similarity(w,w2) for w2 in random_masc_words]\n",
-    "sim_to_girlish_after = [model_cleaned.similarity(w,w2) for w2 in random_masc_words]\n",
-    "\n",
-    "print(\"Similarity of male-stereotyped words to 'girlish' before: {}; similarity after: {}\".format(np.mean(sim_to_girlish_before), np.mean(sim_to_girlish_after)))\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Test 1.4.2 print the most similar words to random words before and after (to make sure we didn't damage the space too much)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "w: elaine\n",
-      " most-similar-before: ('karen', 'kathy', 'joanne')\n",
-      " most-similar-after: ('karen', 'kathy', 'carol')\n",
-      "----------------------------------\n",
-      "w: lobbying\n",
-      " most-similar-before: ('lobbyists', 'lobbyist', 'campaigning')\n",
-      " most-similar-after: ('lobbyists', 'lobbyist', 'campaigning')\n",
-      "----------------------------------\n",
-      "w: once\n",
-      " most-similar-before: ('again', 'then', 'when')\n",
-      " most-similar-after: ('again', 'then', 'when')\n",
-      "----------------------------------\n",
-      "w: romney\n",
-      " most-similar-before: ('mitt', 'mccain', 'obama')\n",
-      " most-similar-after: ('mitt', 'mccain', 'obama')\n",
-      "----------------------------------\n",
-      "w: parliament\n",
-      " most-similar-before: ('parliamentary', 'mps', 'elections')\n",
-      " most-similar-after: ('parliamentary', 'mps', 'elections')\n",
-      "----------------------------------\n",
-      "w: dashboard\n",
-      " most-similar-before: ('dashboards', 'smf', 'powered')\n",
-      " most-similar-after: ('dashboards', 'smf', 'powered')\n",
-      "----------------------------------\n",
-      "w: cumulative\n",
-      " most-similar-before: ('gpa', 'accumulative', 'aggregate')\n",
-      " most-similar-after: ('gpa', 'accumulative', 'aggregate')\n",
-      "----------------------------------\n",
-      "w: foam\n",
-      " most-similar-before: ('rubber', 'mattress', 'polyurethane')\n",
-      " most-similar-after: ('rubber', 'mattress', 'polyurethane')\n",
-      "----------------------------------\n",
-      "w: rh\n",
-      " most-similar-before: ('lh', 'bl', 'r')\n",
-      " most-similar-after: ('lh', 'bl', 'graphite')\n",
-      "----------------------------------\n",
-      "w: genetically\n",
-      " most-similar-before: ('gmo', 'gmos', 'genetic')\n",
-      " most-similar-after: ('gmo', 'gmos', 'genetic')\n",
-      "----------------------------------\n",
-      "w: inner\n",
-      " most-similar-before: ('outer', 'inside', 'innermost')\n",
-      " most-similar-after: ('outer', 'inside', 'innermost')\n",
-      "----------------------------------\n",
-      "w: harvest\n",
-      " most-similar-before: ('harvesting', 'harvests', 'harvested')\n",
-      " most-similar-after: ('harvesting', 'harvests', 'harvested')\n",
-      "----------------------------------\n",
-      "w: forge\n",
-      " most-similar-before: ('gatlinburg', 'blacksmith', 'forging')\n",
-      " most-similar-after: ('gatlinburg', 'blacksmith', 'forges')\n",
-      "----------------------------------\n",
-      "w: collapsed\n",
-      " most-similar-before: ('collapsing', 'collapses', 'collapse')\n",
-      " most-similar-after: ('collapsing', 'collapses', 'collapse')\n",
-      "----------------------------------\n",
-      "w: bears\n",
-      " most-similar-before: ('bear', 'lions', 'panthers')\n",
-      " most-similar-after: ('bear', 'lions', 'panthers')\n",
-      "----------------------------------\n",
-      "w: gender\n",
-      " most-similar-before: ('ethnicity', 'sexuality', 'racial')\n",
-      " most-similar-after: ('ethnicity', 'racial', 'sexuality')\n",
-      "----------------------------------\n",
-      "w: thrill\n",
-      " most-similar-before: ('thrills', 'excitement', 'thrilling')\n",
-      " most-similar-after: ('thrills', 'excitement', 'thrilling')\n",
-      "----------------------------------\n",
-      "w: xvid\n",
-      " most-similar-before: ('dvdrip', 'brrip', 'avi')\n",
-      " most-similar-after: ('dvdrip', 'brrip', 'avi')\n",
-      "----------------------------------\n",
-      "w: ja\n",
-      " most-similar-before: ('auch', 'das', 'jm')\n",
-      " most-similar-after: ('auch', 'das', 'jm')\n",
-      "----------------------------------\n",
-      "w: cookie\n",
-      " most-similar-before: ('cookies', 'cake', 'candy')\n",
-      " most-similar-after: ('cookies', 'cake', 'candy')\n",
-      "----------------------------------\n",
-      "w: sidney\n",
-      " most-similar-before: ('crosby', 'vernon', 'sheldon')\n",
-      " most-similar-after: ('crosby', 'vernon', 'sheldon')\n",
-      "----------------------------------\n",
-      "w: curves\n",
-      " most-similar-before: ('curve', 'contours', 'curvy')\n",
-      " most-similar-after: ('curve', 'contours', 'angles')\n",
-      "----------------------------------\n",
-      "w: realistic\n",
-      " most-similar-before: ('realistically', 'lifelike', 'unrealistic')\n",
-      " most-similar-after: ('realistically', 'lifelike', 'unrealistic')\n",
-      "----------------------------------\n",
-      "w: rookie\n",
-      " most-similar-before: ('rookies', 'quarterback', 'qb')\n",
-      " most-similar-after: ('rookies', 'freshman', 'quarterback')\n",
-      "----------------------------------\n",
-      "w: officers\n",
-      " most-similar-before: ('police', 'officer', 'personnel')\n",
-      " most-similar-after: ('police', 'officer', 'personnel')\n",
-      "----------------------------------\n",
-      "w: spreadsheet\n",
-      " most-similar-before: ('spreadsheets', 'excel', 'worksheet')\n",
-      " most-similar-after: ('spreadsheets', 'excel', 'worksheet')\n",
-      "----------------------------------\n",
-      "w: hwy\n",
-      " most-similar-before: ('highway', 'rd', 'blvd')\n",
-      " most-similar-after: ('highway', 'rd', 'blvd')\n",
-      "----------------------------------\n",
-      "w: burning\n",
-      " most-similar-before: ('burn', 'burned', 'burns')\n",
-      " most-similar-after: ('burn', 'burned', 'burns')\n",
-      "----------------------------------\n",
-      "w: v\n",
-      " most-similar-before: ('vs', 'b', 'h')\n",
-      " most-similar-after: ('vs', 'b', 'l')\n",
-      "----------------------------------\n",
-      "w: chasing\n",
-      " most-similar-before: ('chased', 'catching', 'chase')\n",
-      " most-similar-after: ('chased', 'catching', 'chase')\n",
-      "----------------------------------\n",
-      "w: trapped\n",
-      " most-similar-before: ('stuck', 'escaping', 'escape')\n",
-      " most-similar-after: ('stuck', 'escaping', 'escape')\n",
-      "----------------------------------\n",
-      "w: demolition\n",
-      " most-similar-before: ('excavation', 'construction', 'renovation')\n",
-      " most-similar-after: ('excavation', 'renovation', 'construction')\n",
-      "----------------------------------\n",
-      "w: mercy\n",
-      " most-similar-before: ('grace', 'god', 'compassion')\n",
-      " most-similar-after: ('grace', 'compassion', 'god')\n",
-      "----------------------------------\n",
-      "w: stores\n",
-      " most-similar-before: ('store', 'shops', 'retailers')\n",
-      " most-similar-after: ('store', 'shops', 'retailers')\n",
-      "----------------------------------\n",
-      "w: willis\n",
-      " most-similar-before: ('smith', 'bruce', 'harris')\n",
-      " most-similar-after: ('bruce', 'smith', 'harris')\n",
-      "----------------------------------\n",
-      "w: excel\n",
-      " most-similar-before: ('spreadsheet', 'spreadsheets', 'vba')\n",
-      " most-similar-after: ('spreadsheet', 'spreadsheets', 'vba')\n",
-      "----------------------------------\n",
-      "w: licensing\n",
-      " most-similar-before: ('license', 'licenses', 'licence')\n",
-      " most-similar-after: ('license', 'licenses', 'licence')\n",
-      "----------------------------------\n",
-      "w: keyless\n",
-      " most-similar-before: ('wipers', 'immobilizer', 'locks')\n",
-      " most-similar-after: ('wipers', 'immobilizer', 'locks')\n",
-      "----------------------------------\n",
-      "w: prefer\n",
-      " most-similar-before: ('want', 'tend', 'choose')\n",
-      " most-similar-after: ('want', 'tend', 'think')\n",
-      "----------------------------------\n",
-      "w: education\n",
-      " most-similar-before: ('educational', 'schools', 'curriculum')\n",
-      " most-similar-after: ('educational', 'schools', 'curriculum')\n",
-      "----------------------------------\n",
-      "====================================================================\n",
-      "\n",
-      "\n",
-      "gendered words:\n",
-      "w: ruth\n",
-      " most-similar-before: ('helen', 'esther', 'margaret')\n",
-      " most-similar-after: ('helen', 'esther', 'margaret')\n",
-      "----------------------------------\n",
-      "w: charlotte\n",
-      " most-similar-before: ('raleigh', 'nc', 'atlanta')\n",
-      " most-similar-after: ('raleigh', 'nc', 'atlanta')\n",
-      "----------------------------------\n",
-      "w: abigail\n",
-      " most-similar-before: ('hannah', 'lydia', 'eliza')\n",
-      " most-similar-after: ('hannah', 'lydia', 'samuel')\n",
-      "----------------------------------\n",
-      "w: sophie\n",
-      " most-similar-before: ('julia', 'marie', 'lucy')\n",
-      " most-similar-after: ('julia', 'lucy', 'claire')\n",
-      "----------------------------------\n",
-      "w: nichole\n",
-      " most-similar-before: ('nicole', 'kimberly', 'kayla')\n",
-      " most-similar-after: ('nicole', 'kimberly', 'mya')\n",
-      "----------------------------------\n",
-      "w: emma\n",
-      " most-similar-before: ('emily', 'lucy', 'sarah')\n",
-      " most-similar-after: ('emily', 'watson', 'sarah')\n",
-      "----------------------------------\n",
-      "w: olivia\n",
-      " most-similar-before: ('emma', 'rachel', 'kate')\n",
-      " most-similar-after: ('wilde', 'emma', 'rachel')\n",
-      "----------------------------------\n",
-      "w: ava\n",
-      " most-similar-before: ('devine', 'zoe', 'isabella')\n",
-      " most-similar-after: ('devine', 'isabella', 'zoe')\n",
-      "----------------------------------\n",
-      "w: isabella\n",
-      " most-similar-before: ('sophia', 'josephine', 'isabel')\n",
-      " most-similar-after: ('isabel', 'henry', 'josephine')\n",
-      "----------------------------------\n",
-      "w: sophia\n",
-      " most-similar-before: ('anna', 'lydia', 'julia')\n",
-      " most-similar-after: ('hagia', 'anna', 'sofia')\n",
-      "----------------------------------\n",
-      "w: charlotte\n",
-      " most-similar-before: ('raleigh', 'nc', 'atlanta')\n",
-      " most-similar-after: ('raleigh', 'nc', 'atlanta')\n",
-      "----------------------------------\n",
-      "w: mia\n",
-      " most-similar-before: ('bella', 'mamma', 'mama')\n",
-      " most-similar-after: ('bella', 'mamma', 'che')\n",
-      "----------------------------------\n",
-      "w: amelia\n",
-      " most-similar-before: ('earhart', 'louisa', 'caroline')\n",
-      " most-similar-after: ('earhart', 'louisa', 'caroline')\n",
-      "----------------------------------\n",
-      "w: james\n",
-      " most-similar-before: ('john', 'william', 'thomas')\n",
-      " most-similar-after: ('william', 'john', 'thomas')\n",
-      "----------------------------------\n",
-      "w: john\n",
-      " most-similar-before: ('james', 'william', 'paul')\n",
-      " most-similar-after: ('james', 'william', 'mary')\n",
-      "----------------------------------\n",
-      "w: robert\n",
-      " most-similar-before: ('richard', 'william', 'james')\n",
-      " most-similar-after: ('richard', 'william', 'james')\n",
-      "----------------------------------\n",
-      "w: michael\n",
-      " most-similar-before: ('david', 'mike', 'brian')\n",
-      " most-similar-after: ('david', 'jackson', 'mike')\n",
-      "----------------------------------\n",
-      "w: william\n",
-      " most-similar-before: ('henry', 'edward', 'james')\n",
-      " most-similar-after: ('henry', 'edward', 'charles')\n",
-      "----------------------------------\n",
-      "w: david\n",
-      " most-similar-before: ('stephen', 'richard', 'michael')\n",
-      " most-similar-after: ('alan', 'stephen', 'richard')\n",
-      "----------------------------------\n",
-      "w: richard\n",
-      " most-similar-before: ('robert', 'william', 'david')\n",
-      " most-similar-after: ('robert', 'william', 'david')\n",
-      "----------------------------------\n",
-      "w: joseph\n",
-      " most-similar-before: ('francis', 'charles', 'thomas')\n",
-      " most-similar-after: ('mary', 'francis', 'charles')\n",
-      "----------------------------------\n",
-      "w: thomas\n",
-      " most-similar-before: ('james', 'william', 'john')\n",
-      " most-similar-after: ('james', 'william', 'henry')\n",
-      "----------------------------------\n",
-      "w: ariel\n",
-      " most-similar-before: ('sharon', 'alexis', 'hanna')\n",
-      " most-similar-after: ('sharon', 'israel', 'israeli')\n",
-      "----------------------------------\n",
-      "w: mike\n",
-      " most-similar-before: ('brian', 'chris', 'dave')\n",
-      " most-similar-after: ('dave', 'brian', 'chris')\n",
-      "----------------------------------\n",
-      "w: nurse\n",
-      " most-similar-before: ('nurses', 'nursing', 'physician')\n",
-      " most-similar-after: ('nurses', 'nursing', 'physician')\n",
-      "----------------------------------\n",
-      "w: mom\n",
-      " most-similar-before: ('dad', 'mother', 'mommy')\n",
-      " most-similar-after: ('dad', 'mother', 'mommy')\n",
-      "----------------------------------\n",
-      "w: secretary\n",
-      " most-similar-before: ('deputy', 'minister', 'treasurer')\n",
-      " most-similar-after: ('deputy', 'minister', 'secretaries')\n",
-      "----------------------------------\n",
-      "w: nursery\n",
-      " most-similar-before: ('preschool', 'bedding', 'nurseries')\n",
-      " most-similar-after: ('preschool', 'bedding', 'nurseries')\n",
-      "----------------------------------\n"
-     ]
-    }
-   ],
-   "source": [
-    "from collections import defaultdict\n",
-    "import pickle\n",
-    "\n",
-    "words_chosen = np.random.choice(words[:15000] , size = 40)\n",
-    "topn = 3\n",
-    "words_before_and_after = defaultdict(dict)\n",
-    "gendered_words_before_and_after = defaultdict(dict)\n",
-    "\n",
-    "for w in words_chosen:\n",
-    "    words_and_sims_before = model.most_similar(w, topn = topn)\n",
-    "    words__and_sims_after = model_cleaned.most_similar(w, topn = topn)\n",
-    "    words_before, sims_before = zip(*words_and_sims_before)\n",
-    "    words_after, sims_after = zip(*words__and_sims_after)\n",
-    "    words_before_and_after[w][\"before\"] = words_before\n",
-    "    words_before_and_after[w][\"after\"] = words_after\n",
-    "    print(\"w: {}\\n most-similar-before: {}\\n most-similar-after: {}\".format(w,words_before, words_after))\n",
-    "    print(\"----------------------------------\")\n",
-    "\n",
-    "print(\"====================================================================\\n\\n\")\n",
-    "print(\"gendered words:\")\n",
-    "words_chosen = [\"miss\", \"mrs\", \"mr\", \"john\", \"rachel\", \"wife\", \"mom\", \"family\", \"father\", \"lady\", \"he\", \"she\"]\n",
-    "words_chosen = [\"ruth\", \"charlotte\", \"abigail\", \"sophie\", \"nichole\", \"emma\", \"olivia\", \"ava\", \"isabella\", \"sophia\", \"charlotte\", \"mia\", \"amelia\",\n",
-    "                \"james\", \"john\", \"robert\", \"michael\", \"william\", \"david\", \"richard\", \"joseph\", \"thomas\", \"ariel\", \"mike\", \"nurse\", \"mom\", \"secretary\", \"nursery\"]\n",
-    "for w in words_chosen:\n",
-    "    \n",
-    "    words_and_sims_before = model.most_similar(w, topn = topn)\n",
-    "    words__and_sims_after = model_cleaned.most_similar(w, topn = topn)\n",
-    "    words_before, _ = zip(*words_and_sims_before)\n",
-    "    words_after, _ = zip(*words__and_sims_after)\n",
-    "    gendered_words_before_and_after[w][\"before\"] = words_before\n",
-    "    gendered_words_before_and_after[w][\"after\"] = words_after\n",
-    "    print(\"w: {}\\n most-similar-before: {}\\n most-similar-after: {}\".format(w,words_before, words_after))\n",
-    "    print(\"----------------------------------\")    \n",
-    "    \n",
-    "with open(\"words_before_and_after.pickle\", \"wb\") as f:\n",
-    "    pickle.dump(words_before_and_after, f)\n",
-    "    \n",
-    "with open(\"words_before_and_after_gendered.pickle\", \"wb\") as f:\n",
-    "    pickle.dump(gendered_words_before_and_after, f)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "((0.8115034103393555, 0.7444023489952087, 0.7280417084693909),\n",
-       " (0.8143391013145447, 0.7359080910682678, 0.7278944253921509))"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "sims_before, sims_after"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Test 1.5 bias by profession experiment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Correlation before: 0.8524125255370698, p-value: 2.2680766648262084e-83\n",
-      "Correlation after: 0.7484998774662416, p-value: 1.7326473393063508e-53\n"
-     ]
-    }
-   ],
-   "source": [
-    "def get_bias_by_neighbors(model, model_cleaned, gendered_words, v, gender_direction): \n",
-    "    \n",
-    "    neighbors = model_cleaned.similar_by_vector(v, topn=100) \n",
-    "    neighbors_words = [n for n, _ in neighbors]\n",
-    "    \n",
-    "    #bias = len([n for n in neighbors_words if n in gendered_words])\n",
-    "    bias = len([n for n in neighbors_words if model.cosine_similarities(model[n], [gender_direction])[0] > 0])\n",
-    "    return bias\n",
-    "\n",
-    "def bias_by_profession(model, model_cleaned, gender_direction, masc_words):\n",
-    "    \n",
-    "    with codecs.open(\"../data/lists/professions.json\") as f:\n",
-    "        professions_and_scores = json.load(f)\n",
-    "\n",
-    "    professions = [p[0] for p in professions_and_scores]\n",
-    "    #print(professions)\n",
-    "    professions = list(filter(lambda p: p in model, professions))\n",
-    "    vecs = np.array([model[p] for p in professions])\n",
-    "    r = 2\n",
-    "    u_r = u[:, r:]\n",
-    "    proj = u_r @ u_r.T\n",
-    "#     vecs_cleaned = vecs.dot(proj)\n",
-    "    vecs_cleaned = proj.dot(vecs.T).T\n",
-    "#     vecs_cleaned = vecs.dot(P)\n",
-    "    bias_vals = np.array([model.cosine_similarities(gender_direction,vecs)])[0]\n",
-    "    #bias_vals_after = np.array([model.cosine_similarities(gender_direction,vecs_cleaned)])[0]\n",
-    "    bias_by_neighbors_after = np.array([get_bias_by_neighbors(model, model_cleaned, masc_words, v, gender_direction) for v in vecs_cleaned])\n",
-    "    bias_by_neighbors_before = np.array([get_bias_by_neighbors(model, model, masc_words, v, gender_direction) for v in vecs])\n",
-    "\n",
-    "    #plt.ylim([np.min(bias_vals), np.max(bias_vals)])\n",
-    "    plt.plot(bias_vals, bias_by_neighbors_after, marker = \"o\", linestyle = \"none\", color = \"red\", label = \"after\", alpha = 0.25)\n",
-    "    plt.plot(bias_vals, bias_by_neighbors_before, marker = \"o\", linestyle = \"none\", color = \"blue\", label = \"before\", alpha = 0.25)\n",
-    "    \n",
-    "    word_idx_high = np.argsort(bias_vals)[:4] \n",
-    "    word_idx_low = np.argsort(bias_vals)[-4:]\n",
-    "    word_idx_middle_low = np.argsort(bias_vals)[-55:-51]\n",
-    "    word_idx_middle_high = np.argsort(bias_vals)[51:55]\n",
-    "    words_biased_fem = [professions[i] for i in word_idx_high]\n",
-    "    words_biased_masc = [professions[i] for i in word_idx_low]\n",
-    "    mid_low = [professions[i] for i in word_idx_middle_low]\n",
-    "    mid_high = [professions[i] for i in word_idx_middle_high]\n",
-    "    words = words_biased_masc + words_biased_fem + mid_low + mid_high\n",
-    "    \n",
-    "    for w in words:\n",
-    "        i = professions.index(w)\n",
-    "        x1,y1 = bias_vals[i],bias_by_neighbors_after[i]\n",
-    "        plt.annotate(w , (x1,y1), size = 8, color = \"red\")\n",
-    "        x2,y2 = bias_vals[i],bias_by_neighbors_before[i]\n",
-    "        plt.annotate(w, (x2,y2), size = 8, color = \"blue\")\n",
-    "        #plt.arrow(x2,y2,x1-x2,y1-y2, width = 0.0005)\n",
-    "        \n",
-    "    plt.legend()\n",
-    "    plt.xlabel(\"bias-by-PROJECTION of the professions before\")\n",
-    "    plt.ylabel(\"bias-by-NEIGHBORS\")\n",
-    "    plt.title(\"projection bias before vs. neighbors bias before/after \\n(# neighbors closer to 'she' then 'he')\")\n",
-    "    plt.show()\n",
-    "    \n",
-    "    print(\"Correlation before: {}, p-value: {}\".format(*pearsonr(bias_vals, bias_by_neighbors_before)))\n",
-    "    print(\"Correlation after: {}, p-value: {}\".format(*pearsonr(bias_vals, bias_by_neighbors_after)))\n",
-    "\n",
-    "        \n",
-    "bias_by_profession(model, model_cleaned, gender_direction, None)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Test 1.6 word association tests"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Auxiliary functions for experiments by Caliskan et al.\n",
-    "\n",
-    "import scipy\n",
-    "import scipy.misc as misc\n",
-    "import itertools\n",
-    "\n",
-    "\n",
-    "def s_word(w, A, B, model, all_s_words):\n",
-    "    \n",
-    "    if w in all_s_words:\n",
-    "        return all_s_words[w]\n",
-    "    \n",
-    "    mean_a = []\n",
-    "    mean_b = []\n",
-    "    \n",
-    "    for a in A:\n",
-    "        mean_a.append(model.similarity(w,a))\n",
-    "    for b in B:\n",
-    "        mean_b.append(model.similarity(w,b))\n",
-    "        \n",
-    "    mean_a = sum(mean_a)/float(len(mean_a))\n",
-    "    mean_b = sum(mean_b)/float(len(mean_b))\n",
-    "    \n",
-    "    all_s_words[w] = mean_a - mean_b\n",
-    "\n",
-    "    return all_s_words[w]\n",
-    "\n",
-    "\n",
-    "def s_group(X, Y, A, B, model, all_s_words):\n",
-    "    \n",
-    "    total = 0\n",
-    "    for x in X:\n",
-    "        x_sim = s_word(x, A, B, model, all_s_words)\n",
-    "        total += x_sim\n",
-    "    for y in Y:\n",
-    "        y_sim =  s_word(y, A, B, model, all_s_words)\n",
-    "        total -= y_sim\n",
-    "    \n",
-    "    #print(x_sim, y_sim)\n",
-    "        \n",
-    "    return total\n",
-    "\n",
-    "\n",
-    "def p_value_exhust(X, Y, A, B, model):\n",
-    "    \n",
-    "    if len(X) > 10:\n",
-    "        print ('might take too long, use sampled version: p_value')\n",
-    "        return\n",
-    "    \n",
-    "    assert(len(X) == len(Y))\n",
-    "    \n",
-    "    all_s_words = {}\n",
-    "    s_orig = s_group(X, Y, A, B, model, all_s_words)\n",
-    "    #print(\"s-orig: {}\".format(s_orig))\n",
-    "    \n",
-    "    union = set(X+Y)\n",
-    "    subset_size = int(len(union)/2)\n",
-    "    \n",
-    "    larger = 0\n",
-    "    total = 0\n",
-    "    #all_subs = set(itertools.combinations(union, subset_size))\n",
-    "    #print(all_subs)\n",
-    "    for subset in tqdm.tqdm_notebook(set(itertools.combinations(union, subset_size))):\n",
-    "        total += 1\n",
-    "        Xi = list(set(subset))\n",
-    "        Yi = list(union - set(subset))\n",
-    "        if s_group(Xi, Yi, A, B, model, all_s_words) > s_orig:\n",
-    "            larger += 1\n",
-    "    #print ('num of samples', total)\n",
-    "    return larger/float(total)\n",
-    "\n",
-    "\n",
-    "def p_value_sample(X, Y, A, B, model):\n",
-    "    \n",
-    "    random.seed(10)\n",
-    "    np.random.seed(10)\n",
-    "    all_s_words = {}\n",
-    "    \n",
-    "    assert(len(X) == len(Y))\n",
-    "    length = len(X)\n",
-    "    \n",
-    "    s_orig = s_group(X, Y, A, B, model, all_s_words) \n",
-    "    \n",
-    "    num_of_samples = min(10000, int(scipy.special.comb(length*2,length)*100))\n",
-    "    print ('num of samples', num_of_samples)\n",
-    "    larger = 0\n",
-    "    for i in range(num_of_samples):\n",
-    "        permute = np.random.permutation(X+Y)\n",
-    "        Xi = permute[:length]\n",
-    "        Yi = permute[length:]\n",
-    "        if s_group(Xi, Yi, A, B, model, all_s_words) > s_orig:\n",
-    "            larger += 1\n",
-    "    \n",
-    "    return larger/float(num_of_samples)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/lustre/home/sc066/shunshao/miniconda3/envs/cca/lib/python3.7/site-packages/ipykernel_launcher.py:63: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
-      "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c49cb37ca0f047a5a7bd3ab5c98d07b5",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/12870 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.06596736596736597\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "110854a4dd434a138f8469e9a162dbc7",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/12870 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.04825174825174825\n"
-     ]
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "154eea306252465eb6bb5f4e24dd78d6",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/12870 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "0.10007770007770007\n"
-     ]
-    }
-   ],
-   "source": [
-    "# Experiment 1\n",
-    "\n",
-    "A = ['john', 'paul', 'mike', 'kevin', 'steve', 'greg', 'jeff', 'bill']\n",
-    "B = ['amy', 'joan', 'lisa', 'sarah', 'diana', 'kate', 'ann', 'donna']\n",
-    "\n",
-    "C = ['executive', 'management', 'professional', 'corporation', 'salary', 'office', 'business', 'career']\n",
-    "D = ['home', 'parents', 'children', 'family', 'cousins', 'marriage', 'wedding', 'relatives']\n",
-    "\n",
-    "print (p_value_exhust(A, B, C, D, model_cleaned))\n",
-    "\n",
-    "# Experiment 2\n",
-    "\n",
-    "E = ['math', 'algebra', 'geometry', 'calculus', 'equations', 'computation', 'numbers', 'addition']\n",
-    "F = ['poetry', 'art', 'dance', 'literature', 'novel', 'symphony', 'drama', 'sculpture']\n",
-    "\n",
-    "print (p_value_exhust(A, B, E, F, model_cleaned))\n",
-    "\n",
-    "\n",
-    "# Experiment 3\n",
-    "\n",
-    "G = ['science', 'technology', 'physics', 'chemistry', 'einstein', 'nasa', 'experiment', 'astronomy']\n",
-    "H = ['poetry', 'art', 'shakespeare', 'dance', 'literature', 'novel', 'symphony', 'drama']\n",
-    "\n",
-    "print (p_value_exhust(A, B, G, H, model_cleaned))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.11"
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