notebooks/notebook_fair-profession_fasttext_SAL.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "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",
    "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": 5,
   "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": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "word2vec, vecs, words = load_word_vectors(\"../data/embeddings/crawl-300d-2M.vec\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8c75943cd66f4564adb92f5eedaf1899",
       "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: 0.010212038730551916\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1287c81c30e847d8bad6b5ff363619ce",
       "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: 0.01017481830681595\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "34092e9ee71f4cf09207acc6403c36de",
       "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: 0.01024306743581662\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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 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",
    "#X_train -= mean_train\n",
    "#X_dev -= mean_dev\n",
    "#X_test -= mean_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": 13,
   "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 10 seconds\n",
      "time: 10.01023244857788\n",
      "0.7555844155844156\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[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"
     ]
    }
   ],
   "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": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7557381596283198\n"
     ]
    }
   ],
   "source": [
    "print(clf.score(x_test, y_test))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### perform debiasing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "u has shape (300, 300), s has shape (2,), vh has shape (2, 2)\n"
     ]
    }
   ],
   "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(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": 12,
   "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": 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
   },
   "outputs": [
    {
     "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: 0.7218103837718111; p-value: 1.454744766070305e-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": "stdout",
     "output_type": "stream",
     "text": [
      "rms-diff before: 0.20243245189797604; rms-diff after: 0.19520309695779406\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": 21,
   "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 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": [
    "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": 20,
   "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 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": [
    "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": 21,
   "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": 23,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "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": 24,
   "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 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": [
    "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": 22,
   "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 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": [
    "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": 23,
   "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": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation: 0.8238598514929694; p-value: 7.184529134104979e-08\n"
     ]
    },
    {
     "data": {
      "image/png": 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oNWCx35f/5yLyRZDOD5AAVPO5X9XTlo6ItAMeIYfByBgTWRo00Esn/oYMgReCfNXYt0jq1n2JVImNYUhmRVKD4ZtvYOHCtPtDhwbObnLivvuC06cQytPiqiIS55zLEDRyea7CwBrgCjQQ/Qrc5Jz7w+eYJuhkhk7OubU5PbdlSMaEv8yG5Q4dghIlQtsXkzcZUp6uQwpWMPKc6zg6DDcDWAV86pz7Q0SeEhHvVbmRwBno5IqlIjI1WK9vjAm9deuyny1nwajgCHqlBhGphgaNac65bFaBnRzn3HRgul/b4z63A2yvaIyJNJllQ6f7lg8FXdADknNuM9BARDK53GiMMRlltQHe0aNQtGho+2NCL2hDdiJSQ0RO7C7vnNsarHMbYwqulSs1IwoUjLzDchaMTg9BCUgiMhp4EZgsIlEickMwzmuMKbi814YaNkzf/umnaYHInF6ClSFd5JzrCuzxVGjIYpN4Y0xYGz8evvrq5J/37bfwyitZHpKU5MmGJGMR0ePHNQhdf/3Jv7QpGIJ1DSlJRIoB3r9pimR1sDEmjP3wAxw5otFhzhytCHDddRAXBx9+CP/8o1UALroIbr9dC3M2agQ1a8LMmVpA9NgxDU6ffAILF7Jl5QGunnkX8UzmZfawhCaMpy9gmZBJE6yANAqYCZQVkRuDdE5jTAhNXpLAyBmrab6tJFKuFveNHEulti20UOgvv8ANN+jsgooV4f33Ye9eDUr9++sJvv0WLr4YHnlEjwXm3/gKc7iCohSjGb8A8Ak38OSsFrxjc2KNn1MKSCLSF/gNmA2sBbqjlbh7nXrXjDGh4lsiJ1WEfYeTWL1tPz937su1F9XQg+66S8shOJe2bYH/TITSpTl4EKZ9KnT/FGYQw5MMO/Fw6uPDGHZ9aTg3dO/NRI5TzZBKAIOAhuhw3R/oFhDVSF8I1RgTxny3WVhVoRZ3/PQpnzW4nKtuHQCtG0LTprrFwfPPp1Ur7dAB7rhDNxZq3Jhv18Yx8Un4z13wsee8H9CTL8oPIL57jFaW/il/3p+JDEErHSQi0WhgagKc75y7OygnDgErHWROd7WGTiPQN4EAG0Z0zvK5mS1iXboUzjvvVHtmwlU4F1fFOZcMLPX8GGMiSGbbLFSJjQl4/JYtUK1awIdskoLJtWCtQ3rWUztumYi8LyIRkx0ZY2BIx/rEREela4uJjmJIx/rp2t55RzMi/2B09dW2dsicumBlSB2dc+eLyDxgEvCvIJ3XGBMC2W2zkNmw3MaNUKNGiDppCrxgLYzd5fmd6pz7gjyokWeMyVvxTeL4cWhbNozozI9D21KvaFzAStsVKqRlQ0EJRjlZiNunj+4zkRNDAtR0zu1iXxNSwQoc00WkBLBGRHqjExuMMRHokUfg2Wcztt9/P4wcmXbfu25p675E+q3/jl5HN1C9ZVOdCv7rr7pQ9uqrYfJk3SyuXDldUPvKKzBokN5v3VpP9umnujldxYq6u+mTT+o6p/37dbtvrylT4MsvdT3UsGGQnKzH1quna6W++UYX5u7cqTMAvX3wLvYFvW/CUlACknPuZQARGQo8DDwQjPMaY0Ijq0rbe/bAmWemb/Pf2nvvkWReia7FpR16Ev/M3RAdDfHx0Ly57jP+xhtQuzbceKNWe+jZE9q315ONHw8dO0KPHrqg9sABHQt85x14912t/uD13nvw+eewaROMG6cdf/55rSLRsWPacfv3Q1RUWh/++ksDoAWjsJbpkJ2IVBKRu0Sko09btO99n/aSInIpUMw596Bz7ss86q8xJivDhsGKFTk+fOrUwJW269ZNG5bzD0aQft2SV2pyMiNnrNYnvfQSLF8OTz0FderA1q0wbRpceWWmC2qBjOODIoFnSvi3+48t1qmjmyd5+5BZtDVhJasMaRLwI9BFRM4FLgCaAwvQDfh8fQ/8DpQXkUpAgnPumjzorzGnhRNlfOZ/yRXb/qB6q6Y0rFYG/v4bChfWSqRjx56oFceBA1pJ4YcfYMcOuOkmaNky0/M3aACrVmVs/+9/4bbbsu/f1gBTxFttWMLZOzbA1U3gued0WK2dpz7QFVfAtm0aGHr0gHvugVmzoFWrjCcvVUovTt13n6Znr7+uWRFoZjVwoA6/PfYYpKTA0KE6ZOe7dezy5fDWW2l9qFsXnnlGP7f4+OzfoMkXmS6MFZHvnHOtRaQosA3o4Zz7OpNjZzrnOvjcrxJJ+yHZwlgTTnyHw7otn83xQlHMOL8dsxeMI675BTB8uF43ue46jR5XXKHFTGvV0kykWzc4N2NtnpQUjWWBHDkCMYGXHAXUYsTcdOuWui2fzZ6YUqxu2pofh7ZNf/CPP8LLL2uAKFky5y+SE3v2aDa2e7d+Dl27Bvf8JlOhXhhbWkTOcs6tF5FlmQUjjw9F5BrvUF0kBSNjwo3/cFjh1BQSk1NYu/0gcd5hKe+QVUyMDtN5PfVUhvP95z9w550ZX6dEiZxPXPM3pGP9dNeQJjZqR0x0FM/5rVsCoEUL/ckLZcoEfM8mMmUVkGYBr4pIbaCciPwHWA6scM794HfsNcD5IvI0WmR1kXNuRJ702JgCzn84zDsUtrDcWbTZs0enNScm6pYPPXvCgAEamDp31urbI0fCzTcjbVoHPP+cOdC2bcCHciy7dUvG5EZWQ3algRuAKGAFcAZwHtDYOXeT37E/O+cu9tyug27Y9zERwobsTDjxHQ7zDoXNrdOMuNiYjMNhfhIToXjxwI8dP64Tz4wJhrwYsstq6smX6HqiKsBLQJRzboR/MPKYLSI1AZxz6yIpGBkTbnzL+Exs1I65dZoFLOPj6+67dRTPPxjVqpU2W86CkQl3WQUkcc7d5px7DLgcuCOLYzsAP4vIRyJyj4hkPr3HmFDo1i37Y3y33H7tNVi/PnfnCbL4JnE817URcbExCBAXG8NzXRsFHA7zznYeOzZ9+9KlGoT++iskXTYmKLK6hlRSRBoB651zB0Qk023JnXMXeYbqSqHDetcD/teZjMkT3inSxdav4eGfP6HqxedR/9Ahnea7axccPAgvvgjdu0ObNrpOZ/BgnSL900+6I+r27TreNXq0LrosXVoXaa5cqZMG7r8fzjgjZO8pvklcptdj9u/XLgdixU1NJMsqQ5oFjAY2iMgWoImIDBeR9v4Hisho4AXgHeA9dK2SMXnOO0U6YV8iNy6bwROX9uS6Cu3ZufsAfP+9Bpbo6LRFN4MH6/qWadN0nc5VV+nEAK+NG3UzukGDdOp0gwYakEIYjDLTsqVmQ/7BqHFjq7R9WktNzdh2MrX//OXDqIBXVhnSs865vQAiUhk4H81++qLBytdFzrnLRGSecy5FRG4GJuRFh43x5T9FOjmqMIeOOzbuO0b5VpeknxJdrJguzIyO1nU7gVbvv/yy1mHr2xc++ijzMtchlFkXdu7Uajim4Mi0PmBUlGbwu3ZBpUrw0ENaBunSS3Vm5erVOuQcFQWjRqWdcNYs/eMrMVHXrVWrlrH23/Dh6c+bj7LKkD733nDObXPOfZ3FpIYkESkGJzadzHR4z5hg8p0iPaFxR+5c8Cn9f/mCo8c95WkGD9bFo5s3Z3xyvXowe7bWTPN64QX47DNd31K8uBb7HDo0939t5tLmzRmr4Xh5syELRgWLb7bv8NQHjKrF5A49NXikpmoVi0mT9AmpqfDAA1oTMCVF/70uWKCVOrzGjtWUunJlPcebb2rtv8cf18K0qakZz5uPssqQTuZPw1HATKCsiNx4al0yJud8dzpdX64aj3W4HYDpHXtknCI9caL+PvvstMxp8uT0x/hXOPBOegiRzLKhTp3g66yWppuIl1V9wPidO/Ufx/Dheu0TNAAVLqxVKpYtg7ff1szeW9UcNNg8+mhaiY777tPf3r92lizJeN58lFVAOltEngKWAb8759b6HyAiFzvnfnbOfSMia4HuQEOgV95015j0/CsGQOCdTsOdDcuZrOsDttGgM2qU/qPwFRurQWjUKFizJv1jgwbplh9lyui10X790tf+q1Mn8/Pmg6wWxi4FxqLXjc4DagMJaHAa6DlmlnOuvYg845x7JDRdDj5bGBvZfMfdI6liwKZNul1PIDZB4fRzUvUBcyuItf/yYmFsVgFpnnPucr+2asB5zrmvPPe/AFYBVwLXOecictWDBSQTSpllQ23awLx5Ie2KCSP+e0yBZvuZrUHLb6EurvqRf4NzbjPge3X43+ii2J7AC561SDuAxc65/J2uYUwYyWoDvMOHMy/3Y04fVh8wiwzppE4icr5zbqnndlngQufczKyfFT4sQzJ55eef4ZJLAj9mw3ImkoW6lp3/i9cRkS9E5GsR6en7mDcYeW7vjqRgZCLU+vXQpYsWa4PgLubbuFErM+REpkPe+uMfjAYMyMUi1qlTYf78nB8faKGkP+973LpVq1j4O8mdZ40JhqyG7Pz9D11ftBx4RUSSnXOf5E23jAnMO4Ghz6RXOHf/P9QpV4Xyvge0bg2XXaYLBdu00UWu7drpfjx9+kCzZrpIsHJlTV9GjNAv8A8/hH/+0VlIlSvruRYtgvfe0ymxTzyhUaRkSXj6aWjYEHr10ovC9eoBuqyjSCYr8JKSdD1ubt5r8/nzqBKVwvXPvUi1KmXgmmvgvPN0SvqoUTpr6tZbNYjUrKnbUrz4ovZt0SI9Zu/ewO8xKQkSErSSxZNPQvXq8H//l+OdZ40JppPZaP4CoJNz7la0Vl1WxVaNCTrfhYNz6lzE19Wb8NshYfKShLSDihbVANKiBVStqmszpkzRx+rV04WvW7bojnUPPqgr1YsU0a2uK1aE99/XYxcuhI8/1hlJH32kQezMM7VaaVISVKlyYvrsnDmaDQUKRt5sKDfByPteAdr9+jV9a3dh8h1PwgcfZP7E/v11RX7JkroouHt3+O67wO/R144d+v5uuknXYrVsCbffbsHIhNTJZEjinDvouT0XrVtnThfO5XsZnUALB1NSnS4c9DaUKqW/ixbV2yJpQ1j+jxUpoiWExo7VTe+c00wINIP45x9d35GaqpvfdemS9sKlS2f6cbz1Ftx8c3DfqzjH0ZRUfa8i+h6OH9cHDx9O1y9A15hAWpmkQO/RV+vWULu2dn7ZssxnYBiTh04mIBUWkbrOubWeenX5Mi9IRDoBL6MbB77pvzOtiBRFC7xeCOwGbnDObQx1P4PBO2Qjmzbx6MKPqHNubeoM7AVvvKEH3H67/tX74otw1ln65RMTo8M077yje1cfOKB/LZ91Ftx4IwwcqF9aZcroX/i9e2sm0aIFXH55joam8kughYNZtefY5ZdrOZWKFdPaatSAO+7Qz+u//9WMav58jh1MotjrL/NZgNOkpgYvZvu+J3GOz8+9gsHzP+DogqLwxACtO7Z/P4wZA7//nv0JA71HX/PmwZdf6jqV9u2hQoUTO8/SOvDOs8YEW45n2YnIVqAisAdYCLQFugDLnXM7snpusIhIFLAGaA9sAX4FujvnVvocczu6q+2tnjJG/3LO3ZDVecNxlp3vmoSh895mwnkd+adidWYufp1qkzz7H/bvD7fcolsoPPywfpHMnKlDTTEx+pduu3Y67PLvf+u1gZ07tbzILbfol1mvXnDPPXrd5X//0410KlfWFd/jx2tmMMu/lm7+8F846JWTnVRP1dtv60cWSLb/CwXKLnv31qDy/PMBn+J9r2WO7OeF6S/xaIc72F6qXObvNTXVshoTUqFeh5SOc66KiFQBLvL8FAE+BWJFZAdawaFjMDsXQDNgnXcBrohMAK4FVvoccy0wzHN7IjoBQ1ww5reHkO+QjQCpUojE5BRWbT1ANe+Xm/cteYeiypZNu5hx7Ji2eYd1kpMzfjGK6PWEmTM1A2jUKODQVLjIjzJBmWU833wDHf3/tY8fr5lGgwZadfmddzTgX365TkAoVUr3imjYUCdb3Hmn1hLr10+n4zVvfuK613iKM+5IObotnsbacjXo/Od8PmrRjfeWfQCDv9L/rmPH6n+vFi204nP7DDvDGBNRTmbIDufcVmCK5wcAETkLDVBBjZSZiCP9wtwtwMWZHeOcOy4i+4GywC7fg0RkADAAoHr16nnV31zzHbJ5v8lV3PvjR+woUYbXz72SDrfeqg/cfrteYM/Kp5/CJ5/Av/4FHTrobKzly7UM/eHD8Oyz+uXZsKHuC+QZmiIpSbdiCCOhWji4cWPabHJ//n/WpN8uYCUdz7uQpg8OYkvHa9mWVIx/72vEq7c+SpNiSVSMv0qHUa+/Xj9f75BbYiKUL68VmC++GFaupO7w4dw64wfK797MX2WqklD3XGYufoNqOzdD57u1Svlll+l1rkmT9JqSMREu1wtjRSQOvU5zgffHOVc1iH0L9Jrd0Jl+/Tz3ewEXO+fu9DlmheeYLZ776z3H7Ap0TgjPIbugDE8NG6brc/wrWJuABg5Muzznq2hRvVTnz7/US7fls4mOKkTUzX247KHbSE1N5dZ/PcKLX47in9iKVHplNPFdLtGMaMIE3fSvdm0NSFOn6v42U6fC9Ol6/e6GG3QY7rzzoEcPzVxXrYILL9RrPWecAdu2pVUxNyaE8nth7HUi8oxnYex24G/gC+BRIAndXTavJQDVfO5X9bQFPEZECgOl0ckNEWVIx/rEREela4uJjuLT2aN1tlRODBtmwSgHvItY/YPR4sWaEQUKRhB41t8l6xZT69nH+a1iXVI9O7gsqVKPphuXkXzHXXptT0Qz0jPO0KnnS5fqtgBbtmi28/nnmr1WrKgTDb76SmsLPfus/vcsVAj27YP6kVXR3JjsnMyQ3WfoF/sEYDLwO7ACnWRwe4gmNvwK1BWRWmjguRHw3zBwKtAb3Ua9GzA30q4fgQ5PLdq0h49/3kzNXX8z+IePKN7oHOIKp8CGDfndvYj3ww/QqlXgx3L6ryXQ7L4pDVozt06zdG1HihQnoXRF9ienaqazbJleLxo/Hi64QBfrjh6t2aw3o50wAdq21SHXhg11fdBdd2mQql5drx2tWGHZkSlQTmaW3bPAYGAGcK/PxIJtaAXwUM20uwp4CZ32/bZz7hnPvk2LnHNTPTvXvg80QWcE3phdFfJwHLLzHQ56ZO6bvHvB1ewrW4l5M5+lfI3K9kWUS61aBd6HrG7djFvJZCezYdUoEVJ8/r/Kk20EjMln+Tpk55x7GDgfKAGsEJGn82MtknNuunOunnPuLOfcM562x51zUz23jzrnrnfO1XHONYvULTH8h4OSowpz6Lhjw75j+diryOUdlvMPRuvXa0Z0ssEIMh9W7X5xtXTtExu1Y8E5zSNu00BjQu2kFi445/50zrUDbgH6AquBknnRsdOd73DQhMYduXPBp/T/5QuSklNsvUkOff55WiCqwUZGogVTezMe9+VXOKdzCgLKQYHS+CZxPNe1EXGxMQg64eS5ro14Or5RwPbTaRsBY3LjVGbZlQSGozXtvgZuzmomWzgLxyG7zIaD7vxpApxZhjrDHsj4BTd/vq53ad8e/vxTi2qerLxaYDlkiK78PxU56NvkJQn07FyKw9tK0oA/6MGHVOQfNlKTAVW/ptqDN+kkgiNH9LpMSopOHDh6VCeB/PCDTqlu2lSPq1tXi4/Gx+s1nUce0TVeKSla6WLkSK13N2aMXhdq3vzU3qMxESJfF8b689S1u0dE3gH+i05wqBSsjp3uAi0CBfg7thKXbFrO3tsGsfSKZpy/728tD/TSS7puqEgR3VLgp580OK1bp1Wc779fS8AUKwaFC2uZoe++06KaY8bodGPvF3HDhjBtmk5Hvu46Xb/k69VXdYxr3z4tZNqypVaN+OwzLV562WVa1qBWLfj6a30d70SMq6/WKtwrVmjxzxo1MpYreuYZ2LULDh7Uskjdu8Oll2a5+DM1VZdT6TI0lUQRinGUi3sU55Y9C6DBpbrOavx4KFdO+3LddZpKbdoE48bphIIrr9TJB3366BTt4sX180tJ0UVK55yjx6ek6HqivXvhl1/0/Rhjci3HAUlEKgHnOOfSbbLsnFsGtBCRvsHu3OnMdxGob6ZUY+82/ixfk1l1L2HElLdgxXz9wv/4Y/2CLVdOv1QPHNAv34kTYf581h0txC9vfEn03t2UkeOcU644VSqU0i0Y4uM1WNWooZUGxo7V8kHR0Tqz64cfdEry+PEapObO1YW2P/4I996rU5T79dPqA59/rj+7duk5KlXSBT5//qkLeXfu1C/uP/7Q6cyxsWnbQaxZozPQvv9eM42jR3XdTWoqPPCABlI/48bBoEF6+zO6cT1pkz0euWAwb1zclR9KFmPS1j9g8+a0ign+RNKm1/lWpyhRQqstlC6t/WjRIu0FQQNx9+660Pirr/S/QWbZoLfdyvwYE9DJZEiDgDLAPADPWqSVwCLPTxY18U1uxDeJI75JHLWGTsM7sDquRXfO2fEXD897m6MpnrJAvl+mkP7LrnVrtt7wf3xQrA5xR5NovW0NA7o+ymPfvUNKsaJUe/hhPe6dd3SfnYsv1rI0x45pDbuPP9ahwC++0AKtxYtrRlC4MLz+upbFqVhR18iIwIwZmsUUKQILFqTtzV2zptZtO+887d+OHTpU1qNH+nJFf/yhGdqwYWnvoXjxdMFo8pIE/nVBHL0Zz+XM4wEakEIUdVjHy2f35pzjq7mzywP8s6gs7094jGOFo+H8ulqE9PfftSLFu++mDWnGx5Nw+Dib/t7J4Sk/0OSftRxpcwXV532t2eaGDRrgv/9et2h44w3NDnv31szy0CEdAnzgAc0sv/9eA0/v3vDbb5pJTZ+uGWSJEhrcS5bUlG7UKM3EfIcG/TNSY04TJxOQrgK6+twvDvyAVmu4GS25Zhv25YEqsTEnsqTuS7+h5t6tpEohNtZqoH+t792rQ1tffaVPqFRJh+rGjIHBg0lcuZr511xL62PHOFq4KKlSiNVlqlJ9yW9QupheG3EOtm/XjEQEVq6EuDgddrvxRg1KpUppNuDduO6jj7RQa1SUDmHt2aM/ixZpZfCFCzUbiIvT2QOPP66Z04svam29zZv1i/yzz9KXKypUSLOoxEQtGuuRtgFe2rDcLNrzIT35u/21bN8QxZhr/83Nv06h4Y6/qL9rI1fdPI4qpWOYt+ZDPdeKFZo5rlypWy0Am6/rwbtRlVl+YUt+rt6IOf8byOtRtbj/3AuoUKaMvodWrTQj3LxZy/8MGqQZ5Hnnad+rV9c1RIMGabZ06JAG3EGDtESQtyJ3//6ate7YAd9+q78h/dCgBSRzmjqZgFTVbwp1inPucQARuRqtC2cBKQ/4Xk/6+PxOgE4vfq5rI/Cd2NCnT9rtyZNP3GzXZxwOWF+2GjPrXnyiLt7QK25jzIFfWfH657x99nU03r6b2/cepnz71lrvrnNnrSD67rt6jWriRM2UvNnR1Kn6OlWqaFuFCpodRUdrpnPkiBZs3bFDJwB88YVuY3HvvXotZseOwFUn/CZjvNJmIncFKHJaqFgSldouo0ajM1n/1V7qVatATHQUxwpHUyQlGXFQNDqKezvUg9UufeboU2h25db9JJdLt+8sqcnJ/HEYKiQnp+0r0bUrvPaaBvA9e3RX2pdfhsce06A7caJef/KePyYmfaY3aJBmn8uWafnwvn31MwLNnAoXTiuKa8xp6GQCUrSIRDvnkj337/N5bB66WNXkgVMtKuqbYW2JrcR9nfXi+5nFo+mUfC6JDXTixDWzXuOGFgN5sEV9Om7dqtdNrr5aM625c6FOHa1KXbKkDsG98YZmTxMmaHYwcaIWDp0wQbOIatXSD8UVKaLXoSBH11Ayq7Qdd9scCpc6Spnlq2i1YQln79jAwnJn0abMQZ7r2oi1K2YAML31dXz5x4fU2F5cr1/Vrq3vpUQJzZI8RWrfaNiJjWdWZtjs17l00zIAWm1YQs1t66BnZ50gskzb2b9ff5curcFl0CDNBhcs0A6XLauPlyypEzEaN9ZA89pr2h4bq0Fo1KjcLX4ypgA7mUoN3wPPO+emZfL4fudc+OxVcBLCcdp3MPkXAQXNsIoWLsS+xOQTbR1X/0SLTctIKluOfnHoBIlRo/QayrBhOvGgSxfdrsJb4sY/IN10k15ruv9+/dKvVCltKO7++z2LgmrAgAG6R9Nll6WbnZaYmHbZyV9qKrR8Pm06vLcCwtw6zU5pT6QWI+aybc8hBv00gZLHjhCbeJBpZ7fMvLLCl1/qe9uwwSYnmNNWXkz7PpmA1B14CrjUObfT77GzgNnOuUyK9oe3gh6QIP02Cd4M695PlhLov74AG0Z0Dmn/hgwJvGyqbVuYMyftfmbB9VQWnubFOY0p6PJ7g76PRaQd8IeIDAc+dM7t8UwHfwWtcWfClHfGni//KeVeVWJjQtUthskwJtKNP0irSv4CQ7h518gTo1++13uyHb7MyZTqPn10+vcZZ2R+zvZ1LRgZE2InuzC2HzAEeBp4SUQSgRhgKfB/we2ayWuh3oHVm6W5dZu57a3Z7KACLVlGGfZQkoN8z2W84/pCtw26pWLDhgF3XI0fOJD4Hu11fdPy5XDXyxpkatbUSRSJiboT68GDei1r+XKdlFGunB63dKlOU7/pJl0ztXcv8fv3E//66zqUWKwmbNwLF1bL4t0YY4LtZHeMdcALIjIOaANUBjYC830mO5gIEaodWEGDUa+bojj0Z1ue5wGe4nHWUZcnGEaxq9sz6MsW9L3hBrREokeVKjo775VX4OabNTBdf70GoipV4O67debe1Kl6fP/+Otvtgw90pl9CggamsWN1Aob34tT55+sU8NRUne33zjs6k3DmzPTnMcaEVK5KBznnEtH6dSbCBRrKCzYdbUt7DcGRSiGq3fMN1ZZv4JbnSvsemMZbMcFnyO6E455FwcnJGY//9FMNUk8+qTPa/J8faEgvs0oNxpiQsSlCJk/s3p1WadvflIFn88K5A3howVvUW788+5N1766LWAcN0izJ+wIPP6wZkndquVflylpT75df9P699+q076FDtYRR8+ZaVWHjRp3xd999WjKpY8dTes/GmFOT62rfBcnpMMsuVFq21IIG/qq23UDURSsztOd6urZ3mrkxJl/k6wZ9xmTGOyImkjEYJSbq4+NGFQm4mV2uJ1BYMDKmwMn19hPG/P23jngF4p94h3IChTEmMlmGZE5amTJpBRd8jRungSizUeD4JnH8OLQtG0Z05sehbSMuGE1ekkCLEXOpNXQaLUbMZfKShMAHbtyoVSkgRzvP0qePFmPNzLff6kxDf926ZWwL9OEHqheYHRvKN/nAMiSTIykpAbcjAnTCW1RU4MfCiXcdVIm1f9Lzrx9oXTqVGvfdAe+/r7XnGjXSfZ4GDdI1S61bQ4MGMGwYGw+nMCu6HlK2Bg8t/oo15WvQYMzNrOncmXrLf9ZtKXbs0OnoXbvqJIzVq3VLj5de0g+oRw+dONGpk5ZXAt2u45NPtIp40aK6Luq337Q80fbt8Oijgd/Mli1asXzYMK0U3rGjrtm66iqdZbhvn1Yi798/bXPEli21b4sWaVmM5GQYPVqDz1ln6dYXPXpon2+5BcqXD/zaxuQRy5BMllau1GwoUDDyZkOREowemrSchH2JJEUVJunQEb7ZCYe799SdaEeP1kzlww91v6TRo3X23muvwfDh9Li4P1f9PifdOf8uVYGHKrTQGX9JSRoMNm3SL/Izz9QZgBddpBW8GzXSzGnqVN1z6qefdCuOQ4e0IOuUKVopfOlSLUKblKTrpiZNCvyGqlY9ESypWjVtzVbduvoXQpkyGph8lSypdQO7d9dNHf/7X61IXrasLh4GPefQoRaMTL6wgGQC8k5SaNgwffvkyVkPy4WrkTNWn6hI0Wfxl7x90bW817gjSfv2Z9yWIsA2FVv3JeIQjkVFUzhVz1PseBL/HDiq6WOjRroVe7NmcMEFGsELF9ZtJqZM0exk3ry0bSlAX+fYsbRoHx2t959/Xn9uuCFte4pAfOfUe9dOTZ+uQeWpp9LWanmVKJH+dVJTNSMaNuzE3lC2BsvkJxuyMyccOZL2neWreHH9Qz6z7SAiwVafmn0Lqjfm1oWfs6tELN/VOJ/4n3/WrSAaN9Yv6Hvu0d1yW7XSqhCPPcaLa/byZYPL2FniTEoeO0KrvxZz9s6NPLhoomYThQrp2qYFC3Qo7NAh3eri/PO1RFGJErr1RJcumrkMGqSb+oFmKA88oFnN2WfrUOETT+jW8GeemfmbathQr1XdfXdaW5MmmuFs26aBMit33qlruSpX1uypd+/cfrzGBIWtQ8LWIf35J5xzTsb2OnVg7drQ9ycvtBgxN2Ah2Zyug/KvCN5t+WwOl4yl44P9Im5yhjHBkK/Vvk3BU66cXv7w9+OPurdcQXKqhWT9p60vaHVN6KetjxgBR4/q7bPP1r2ojClALEPi9MqQ9u0LPAoUH6/X4AuyQHtCWXZjTO5YhmRybeFCLeHm75FH4OmnQ9+f/BCKQrLGmNyzgFTAVami17f9bdqUdk3dGGPCgU37LoASEtKmbfsGox490qZsWzAyxoQbC0gFyNtvaxCqWjV9+1tvaRD64IP86ZcxxuSEDdkVALVrp1WH8bV7ty5tMcaYSGAZUoRatSptWM43GF15ZdqwnAUjY0wksYAUYYYO1SDUoEH69p9+0iA0fXr+9MsYY06VDdlFAOd0B+7XXsv42OHDWtrHGGMinQWkMLZ1K8QFWDZz+eUwd27o+2OMMXnJhuzC0HPP6bCcfzDauFGzJQtGxpiCyAJSmHAOxo/XQPTww2ntl12mRZudy3y7cGOMKQgiJiCJSBkRmSUiaz2/M1RkE5HzRWSBiPwhIr+LyA350deT8ddfGoQKFYK+fdPaR4zQIPTdd+m35zHGmIIqkr7qhgJznHN1gTme+/6OAP/nnGsIdAJeEpHY0HUx5+69VwORdydrgIce0kkKzsGDD+Zf34wxJj9E0qSGa4E2ntvvAt8C6b62nXNrfG5vFZEdQHlgX0h6mI3UVA00o0alb+/WTfdsi+QN8Iwx5lRFUkCq6JzzVmbbDlTM6mARaQYUAdZn8vgAYABA9Twu7LZ/vwahTz5Jv+Hdm2/CLbfk6UsbY0zECKshOxGZLSIrAvxc63uc002cMt3ISUQqA+8DfZ1zqYGOcc694Zxr6pxrWr58+aC+D6/PP9ddsOPidIuHqCiduHDwoA7LFfhgtHGjbrGdHduTyxhDmGVIzrl2mT0mIv+ISGXn3DZPwNmRyXGlgGnAI865hXnU1UylpOi07cceS2vr3RvuugsuvDDUvQkO343t+q3/jl5HN1C9ZVONsImJsGsXVKqkF8EGDIBSpaBhQ4iO1hISr7wCnTrBsGFQrBhccw2cd56WH7/mGo3MefRHgTEmcoRVhpSNqUBvz+3ewBT/A0SkCPAF8J5zbmII+8bu3Tpd+6yz0oJRyZKwcqVmRZEcjB6atJyEfYk4YO+RZF6JqsXkDj3hl1/0wlipUjBpkj5h+3Zdudu9O7RsqXuh33mnlpkYPlzHKb1lxxs00FpIFoyMMURWQBoBtBeRtUA7z31EpKmIvOk55t/AZUAfEVnq+Tk/Lzv17rvQq5du+fDcc1Crlg7VJSXBgQNwzjl5+ep5b+SM1SQmp6RrS01OZuSM1bBzp87EGD4czjhDH/zkE23r0yf9fHXn0mZteH+XLp33b8AYEzHCasguK8653cAVAdoXAf08tz8AQrbrz0UXwaJFenvAALjjDmjcOFSvHhpb9yVmaGu1YQln79gAV7eBZct0xsbOnZCcDPfdBzExUK+eDuOtWwdjxsDAgZo6Fi+u2ZMxxvgRZxeUadq0qVvkjSwn4eOP4amnYNo03ZOoIGoxYi4JPkGp2/LZ7IkpxeqmrflxaNt87JkxJj+JyGLnXNNgnjOShuzCTvfuui9RQQ1GAEM61icmOurE/YmN2rHgnOYM6Vg/H3tljCmIImbIzuSP+CZa4dU7y65KbAxDOtY/0W6MMcFiAclkK75JnAUgY0yesyE7Y4wxYcECkjHGmLBgAckYY0xYsIBkjDEmLFhACle2PswYc5qxWXYh4lugtPfGn+iTtJGatSrplrFffAF//gkTJkCbNjByJLRooT9vvqlVD5Yt08cfeQSOHdMqri+9pGXE9+7VPS5ef11LRtStCwkJEB8PHTrk8zs3xpicsQwpBPwLlJZM2MT7R0ozo92NWjHb36WXaqXW11/XgHTrrdo+a5Zu6RAbC4cOadDZuFEDU5s2MHOmHtevHzz/vBbVM8aYCGEZUgj4Fygd16I75+z4i+ZDH4Dasdp4+HDaE3yLjoqkFSNNTdWsadAgvX/gQPrjvMN8JUpA4cKaSRljTISwgBQC/gVKuy/9hpp7t3I42ekWDQ8/rIVJS5RI/8Rbb9Vsp2ZNrabdsaO2DRkC+/bBuHFQo4YWNN2zRzMqy4qMMRHKiquS++KqOeVfoNQrLjYm6wKlf/0Fb78N//yj2zm0aJFnfTTGmJORF8VVLUMKgSEd6/PQpOXphu1ioqOyL1Bau7ZOWjDGmNOABaQQsAKlxhiTPQtIIWIFSo0xJms27dsYY0xYsIBkjDEmLFhAMsYYExYsIBljjAkLFpCMMcaEBQtIxhhjwoIFJGOMMWHBApIxxpiwYAHJGGNMWLCAZIwxJixYQDLGGBMWLCAZY4wJCxaQjDHGhAULSMYYY8KCBSRjjDFhwQKSMcaYsGAByRhjTFiwgGSMMSYsWEAyxhgTFiwgGWOMCQsRE5BEpIyIzBKRtZ7fZ2ZxbCkR2SIir4Syj8YYY3IvYgISMBSY45yrC8zx3M/McOD7kPTKGGNMUERSQLoWeNdz+10gPtBBInIhUBGYGZpuGWOMCYbC+d2Bk1DRObfNc3s7GnTSEZFCwGigJ9Auq5OJyABggOfuIRFZHcS+hoNywK787kSYs88oa/b5ZO90/oxqBPuEYRWQRGQ2UCnAQ4/43nHOORFxAY67HZjunNsiIlm+lnPuDeCN3PY13InIIudc0/zuRzizzyhr9vlkzz6j4AqrgOScyzSrEZF/RKSyc26biFQGdgQ4rDnQSkRuB84AiojIIedcVtebjDHGhIGwCkjZmAr0BkZ4fk/xP8A518N7W0T6AE0tGBljTGSIpEkNI4D2IrIWvT40AkBEmorIm/nas/BUYIcjg8g+o6zZ55M9+4yCSJwLdCnGGGOMCa1IypCMMcYUYBaQjDHGhAULSBFORDqJyGoRWSciGSZwiMhgEVkpIr+LyBwRCfragXCW3efjc9x1IuJE5LSbwpuTz0hE/u35d/SHiHwU6j7mpxz8P1ZdROaJyBLP/2dX5Uc/CwK7hhTBRCQKWAO0B7YAvwLdnXMrfY65HPjZOXdERG4D2jjnbsiXDodYTj4fz3ElgWlAEeBO59yiUPc1v+Tw31Bd4FOgrXNur4hUcM4FWnZR4OTw83kDWOKce1VEGqBrIWvmR38jnWVIka0ZsM4595dzLgmYgJZYOsE5N885d8RzdyFQNcR9zE/Zfj4ew4HngaOh7FyYyMln1B/4j3NuL8DpEow8cvL5OKCU53ZpYGsI+1egWECKbHHAZp/7WzxtmbkF+DpPexResv18ROQCoJpzblooOxZGcvJvqB5QT0R+FJGFItIpZL3Lfzn5fIYBPUVkCzAduCs0XSt4ImlhrDkFItITaAq0zu++hAtP7cMxQJ987kq4KwzUBdqgGfb3ItLIObcvPzsVRroD451zo0WkOfC+iJzrnEvN745FGsuQIlsCUM3nflVPWzoi0g6tB9jFOXcsRH0LB9l9PiWBc4FvRWQjcAkw9TSb2JCTf0NbgKnOuWTn3Ab0mkrdEPUvv+Xk87kFvcaGc24BUAwtumpOkgWkyPYrUFdEaolIEeBGtMTSCSLSBHgdDUan09g/ZPP5OOf2O+fKOedqei5CL0Q/p9NmUgM5+DcETEazI0SkHDqE91cI+5ifcvL5/A1cASAi56ABaWdIe1lAWECKYM6548CdwAxgFfCpc+4PEXlKRLp4DhuJFpr9TESWioj//0wFVg4/n9NaDj+jGcBuEVkJzAOGOOd250+PQyuHn899QH8RWQZ8DPRxNn05V2zatzHGmLBgGZIxxpiwYAHJGGNMWLCAZIwxJixYQDLGGBMWLCAZY4wJCxaQjDHGhAULSMZkQ0RuE5F/RGSziMT7Pfa1f5sxJndsHZIxWRCRisAfwPlAJeAboKJzLkVEegNXOuduzMcuGlNgWIZkTNZqAGudc1s8JYWOA2VFpBJaHzDbys6eTdtu8tyOEZGjIvKez+PTReQBz+3iIvKyJxvbJSKTRaS6z7HfisgYEflCRA6KyHoRuUJE2onIChE54HmspM9zyorIW55z7hSRTz2B1vv4RhF52LOB4yHPeS4NwmdnzEmxgGRM1tYBtUSkhog0QwPSTuA/wDDnXE5qls0G2nluX4ZuZ+CtfVbE0zbb8/iLaJHXS9BguAv40rNRnFcvYAQQC3wCvA8M8JynJlAfGOQ5v6C16BxaSLYGcBDw3/X1Zs9zSgOzgHdz8L6MCSobsjMmGyLSFRgKJHl+VwJ6Az2AsUBtYDFa4+14gOdfBbzqnKshIiPRgNAD6IpWhf4cKA8IcAS4xjk3y/PcM4A9QGvn3AIR+Rb4wzl3h+fxBuiQYjPn3K+etheAus65f3kql38PnOmt9C4iZdFAV805t8VT6fw/zrmRnscbAiuAWOfc/iB9jMZkyzIkY7LhnJvknGvmnGsJrASeAW4FHgI2OecuAyoAfTM5xXdAZRGph2ZKs9CMqL3n/lxPMc7yQFFgg89rHwJ2kH4LhG0+t49k0uYdsqvlOec/IrJPRPYB69Hdcav7PMf3+Yc9v0tiTAjZBn3GnJyXgFHOuQQROQ942dM+H2gS6AnOucMishDduqAm8AuaZfUDygDveA7dCRzzHLMOTmRIFUi/a+nJ2IQGmDK2YZwJd5YhGZNDInIlEOec+5+naT3QSUSigQ54gkgmZqPbFHzrnEtBt3Fohe7iOxvAEzDeA4aLSBURKQ6MBv5Eg1huLAKWAWM9Q3WISHkRsZmBJuxYQDImBzyz1sYA/X2anwMuRK/HFEI3QszMbKAUOlyHZ/vvP4HNzjnfze7uRYPIr+jGb5XRTQNTctNvT5C7Fr0+tVhEDqIbEbbJzfmMyUs2qcEYY0xYsAzJGGNMWLCAZIwxJixYQDLGGBMWLCAZY4wJCxaQjDHGhAULSMYYY8KCBSRjjDFhwQKSMcaYsPD/aPi0bC/DAagAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation: 0.5965726469096496; 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": [
      "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": "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",
      "0.7528257402399279\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": [
    "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": [
      "[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 0.6010790310087871\n",
      "Test accuracy 0.5974273628735871\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[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": [
    "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": 54,
   "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": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "438\n",
      "362\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": [
      "438\n",
      "362\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"
    },
    {
     "data": {
      "text/plain": [
       "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": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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": []
  }
 ],
 "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
}