notebooks/notebook_fair-profession_bert_kSAL.ipynb

{
 "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/BERT/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 = \"BERT\"\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": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tpr_exp(x_test, debiased_x_train, debiased_x_test, y_m_train, y_m_test, y_p_test, y_pred_before, p2i, i2p):\n",
    "\n",
    "    tprs_before, tprs_change_before, mean_ratio_before = get_TPR(y_pred_before, y_m_test, p2i, i2p, y_p_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 = 100)\n",
    "\n",
    "    clf_debiased.fit(debiased_x_train, y_m_train)\n",
    "    print(f\"\\n\\n\\n================================================\\\n",
    "                \\nSCORE: of profession classifier on debiased train dataset\\\n",
    "                \\n{clf_debiased.score(debiased_x_train, y_m_train)}\\\n",
    "                \\nSCORE: of profession classifier on debiased test dataset\\\n",
    "                \\n{clf_debiased.score(debiased_x_test, y_m_test)}\\\n",
    "                \\n================================================\\n\\n\\n\")\n",
    "    y_pred_after_train = clf_debiased.predict(debiased_x_train)\n",
    "    tprs, tprs_change_after_train, mean_ratio_after = get_TPR(y_pred_after_train, y_m_test, p2i, i2p, y_p_test)\n",
    "    similarity_vs_tpr(tprs_change_after_train, None, \"after\", \"TPR\", prof2fem)\n",
    "    \n",
    "    y_pred_after_test = clf_debiased.predict(debiased_x_test)\n",
    "    tprs, tprs_change_after_test, mean_ratio_after = get_TPR(y_pred_after_test, y_m_test, p2i, i2p, y_p_test)\n",
    "    similarity_vs_tpr(tprs_change_after_test, None, \"after\", \"TPR\", prof2fem)\n",
    "\n",
    "    change_vals_before = np.array(list((tprs_change_before.values())))\n",
    "    change_vals_after_train = np.array(list(tprs_change_after_train.values()))\n",
    "    change_vals_after_test = np.array(list(tprs_change_after_test.values()))\n",
    "\n",
    "    print(\"rms-diff before: {}; rms-diff after for train set: {}; rms-diff after for test set: {}\".format(rms_diff(change_vals_before), rms_diff(change_vals_after_train), rms_diff(change_vals_after_test)))\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": [
    "# BERT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# clf = 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",
    "# 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_test, y_test))\n",
    "# clf_original = copy.deepcopy(clf)"
   ]
  },
  {
   "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",
    "\n",
    "# P_rowspace = np.eye(768) - P\n",
    "# mean_gender_vec = np.mean(P_rowspace.dot(x_train.T).T, axis = 0)\n",
    "\n",
    "# print(clf.fit((P.dot(x_train.T)).T, y_train))\n",
    "# print(clf.score((P.dot(x_test.T)).T, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# FastText"
   ]
  },
  {
   "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 = 4, n_jobs = 64, random_state = 1)\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",
    "# clf_original = copy.deepcopy(clf)\n",
    "# print(clf.score(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(P.dot(X_train.T).T, Y_train)\n",
    "\n",
    "# print(clf.score(P.dot(X_test.T).T, 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": 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": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 15000\n",
    "N_test = 5000\n",
    "ratio = \"BERT\""
   ]
  },
  {
   "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 54 seconds\n",
      "Score of profession classifier on original(biased) dataset \n",
      "0.7744\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/s1711677/anaconda3/envs/ksal/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  ConvergenceWarning,\n",
      "[Parallel(n_jobs=90)]: Done   1 out of   1 | elapsed:   54.8s finished\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": null,
   "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": null,
   "metadata": {},
   "outputs": [],
   "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": 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": 10,
   "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']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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: 0.8012729079196022; p-value: 5.119513015554055e-07\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": [
      "convergence after 24 epochs took 3894 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=90)]: Done   1 out of   1 | elapsed: 64.9min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "================================================                \n",
      "SCORE: of profession classifier on debiased train dataset                \n",
      "0.9414                \n",
      "SCORE: of profession classifier on debiased test dataset                \n",
      "0.616                \n",
      "================================================\n",
      "\n",
      "\n",
      "\n",
      "Correlation: -0.05492985095638916; p-value: 0.8341456440301055\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": [
      "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: 0.8646924579158608; p-value: 1.030113205737928e-07\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: 0.31762255736322154; rms-diff after for train set: 0.030108648788419543; rms-diff after for test set: 0.2384915602268884\n"
     ]
    }
   ],
   "source": [
    "N = 15000\n",
    "N_test = 5000\n",
    "\n",
    "y_pred_before = clf_original.predict(x_test[:N_test])\n",
    "\n",
    "# Removal 0 BERT RBF at gamma = 0.1\n",
    "tpr_exp(x_test[:N_test, :], debiased_x_train_0[:N, :], debiased_x_test_0[:N_test, :], \\\n",
    "        y_m_train[:N], y_m_test[:N_test], y_p_test[:N_test], y_pred_before, p2i, i2p)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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: 0.8012729079196022; p-value: 5.119513015554055e-07\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": [
      "convergence after 24 epochs took 3895 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=90)]: Done   1 out of   1 | elapsed: 64.9min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "================================================                \n",
      "SCORE: of profession classifier on debiased train dataset                \n",
      "0.9646                \n",
      "SCORE: of profession classifier on debiased test dataset                \n",
      "0.6822                \n",
      "================================================\n",
      "\n",
      "\n",
      "\n",
      "Correlation: -0.14323457415693489; p-value: 0.5833974880770244\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": [
      "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: 0.9078917096040395; p-value: 9.064735663446359e-10\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: 0.31762255736322154; rms-diff after for train set: 0.031157255007608457; rms-diff after for test set: 0.3451425938625001\n"
     ]
    }
   ],
   "source": [
    "# Removal 1 BERT RBF at gamma = 0.1\n",
    "tpr_exp(x_test[:N_test, :], debiased_x_train_1[:N, :], debiased_x_test_1[:N_test, :], \\\n",
    "        y_m_train[:N], y_m_test[:N_test], y_p_test[:N_test], y_pred_before, p2i, i2p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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: 0.8012729079196022; p-value: 5.119513015554055e-07\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": [
      "convergence after 24 epochs took 3896 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=90)]: Done   1 out of   1 | elapsed: 64.9min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "================================================                \n",
      "SCORE: of profession classifier on debiased train dataset                \n",
      "0.9666                \n",
      "SCORE: of profession classifier on debiased test dataset                \n",
      "0.6844                \n",
      "================================================\n",
      "\n",
      "\n",
      "\n",
      "Correlation: -0.1350915794158646; p-value: 0.6051905673506155\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": [
      "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: 0.8910265190798715; p-value: 2.361955676720526e-09\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: 0.31762255736322154; rms-diff after for train set: 0.03110437681454351; rms-diff after for test set: 0.3112198773585039\n"
     ]
    }
   ],
   "source": [
    "# Removal 2 BERT RBF at gamma = 0.1\n",
    "tpr_exp(x_test[:N_test, :], debiased_x_train_2[:N, :], debiased_x_test_2[:N_test, :], \\\n",
    "        y_m_train[:N], y_m_test[:N_test], y_p_test[:N_test], y_pred_before, p2i, i2p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Polynomial kernel at degree=2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": 16,
   "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: 0.8012729079196022; p-value: 5.119513015554055e-07\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 3245 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/s1711677/anaconda3/envs/ksal/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  ConvergenceWarning,\n",
      "[Parallel(n_jobs=90)]: Done   1 out of   1 | elapsed: 54.1min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "================================================                \n",
      "SCORE: of profession classifier on debiased dataset                \n",
      "0.7734                \n",
      "================================================\n",
      "\n",
      "\n",
      "\n",
      "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: 0.779017416104506; p-value: 1.6939916829396618e-06\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: 0.31762255736322154; rms-diff after: 0.33501350003684505\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": 17,
   "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: 0.8012729079196022; p-value: 5.119513015554055e-07\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 3249 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/s1711677/anaconda3/envs/ksal/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  ConvergenceWarning,\n",
      "[Parallel(n_jobs=90)]: Done   1 out of   1 | elapsed: 54.2min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "================================================                \n",
      "SCORE: of profession classifier on debiased dataset                \n",
      "0.771                \n",
      "================================================\n",
      "\n",
      "\n",
      "\n",
      "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: 0.7676523745639011; p-value: 2.9651191851158273e-06\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: 0.31762255736322154; rms-diff after: 0.2854043227477506\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": 18,
   "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: 0.8012729079196022; p-value: 5.119513015554055e-07\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 3243 seconds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/s1711677/anaconda3/envs/ksal/lib/python3.7/site-packages/sklearn/linear_model/_sag.py:354: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  ConvergenceWarning,\n",
      "[Parallel(n_jobs=90)]: Done   1 out of   1 | elapsed: 54.1min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\n",
      "================================================                \n",
      "SCORE: of profession classifier on debiased dataset                \n",
      "0.771                \n",
      "================================================\n",
      "\n",
      "\n",
      "\n",
      "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: 0.777966444085103; p-value: 1.786413107330186e-06\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: 0.31762255736322154; rms-diff after: 0.25079756868832964\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)"
   ]
  }
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