notebooks/notebook_fair-sentiment_SAL.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append(\"../src\")\n",
    "sys.path.append(\"../data/embeddings\")\n",
    "import classifier\n",
    "import debias\n",
    "#import debias_old as debias\n",
    "import gensim\n",
    "import codecs\n",
    "import json\n",
    "from gensim.models.keyedvectors import Word2VecKeyedVectors\n",
    "from gensim.models import KeyedVectors\n",
    "import numpy as np\n",
    "import random\n",
    "import sklearn\n",
    "from sklearn import model_selection\n",
    "from sklearn import cluster\n",
    "from sklearn import metrics\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.svm import LinearSVC, SVC\n",
    "from sklearn.linear_model import SGDClassifier, Perceptron, LogisticRegression, PassiveAggressiveClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "from sklearn.decomposition import PCA\n",
    "import scipy\n",
    "from scipy import linalg\n",
    "from scipy.stats.stats import pearsonr\n",
    "import tqdm\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "# %matplotlib inline\n",
    "# matplotlib.rcParams['agg.path.chunksize'] = 10000\n",
    "from sklearn.utils import shuffle\n",
    "\n",
    "# import warnings\n",
    "# warnings.filterwarnings(\"ignore\")\n",
    "# %load_ext autoreload\n",
    "# %autoreload\n",
    "\n",
    "from collections import Counter, defaultdict\n",
    "import seaborn as sns\n",
    "# sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_data(path, size, ratio=0.5):\n",
    "    fnames = [\"neg_neg.npy\", \"neg_pos.npy\", \"pos_neg.npy\", \"pos_pos.npy\"]\n",
    "    protected_labels = [0, 1, 0, 1]\n",
    "    main_labels = [0, 0, 1, 1]\n",
    "    X, Y_p, Y_m = [], [], []\n",
    "    n1 = int(size * ratio / 2)\n",
    "    n2 = int(size * (1 - ratio) / 2)\n",
    "#     print(n1, n2)\n",
    "\n",
    "    for fname, p_label, m_label, n in zip(fnames, protected_labels, main_labels, [n1, n2, n2, n1]):\n",
    "#         print(path + '/' + fname)\n",
    "#         print(np.load(path + '/' + fname).shape)\n",
    "        data = np.load(path + '/' + fname)[:n]\n",
    "        for x in data:\n",
    "            X.append(x)\n",
    "        for _ in data:\n",
    "            Y_p.append(p_label)\n",
    "        for _ in data:\n",
    "            Y_m.append(m_label)\n",
    "\n",
    "    Y_p = np.array(Y_p)\n",
    "    Y_m = np.array(Y_m)\n",
    "    X = np.array(X)\n",
    "    X, Y_p, Y_m = shuffle(X, Y_p, Y_m, random_state=0)\n",
    "    return X, Y_p, Y_m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_TPR(y_main, y_hat_main, y_protected):\n",
    "    \n",
    "    all_y = list(Counter(y_main).keys())\n",
    "    \n",
    "    protected_vals = defaultdict(dict)\n",
    "    for label in all_y:\n",
    "        for i in range(2):\n",
    "            used_vals = (y_main == label) & (y_protected == i)\n",
    "            y_label = y_main[used_vals]\n",
    "            y_hat_label = y_hat_main[used_vals]\n",
    "            protected_vals['y:{}'.format(label)]['p:{}'.format(i)] = (y_label == y_hat_label).mean()\n",
    "            \n",
    "    diffs = {}\n",
    "    for k, v in protected_vals.items():\n",
    "        vals = list(v.values())\n",
    "        diffs[k] = vals[0] - vals[1]\n",
    "    return protected_vals, diffs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rms(arr):\n",
    "    return np.sqrt(np.mean(np.square(arr)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_plot_tpr(P):\n",
    "    \n",
    "    results = defaultdict(dict)\n",
    "    for ratio in [0.5, 0.6, 0.7, 0.8]:\n",
    "\n",
    "        x_train, y_p_train, y_m_train = load_data(\n",
    "            '../data/emoji_sent_race_{}/train/'.format(ratio),\n",
    "            size=100000, ratio=ratio)\n",
    "        x_dev, y_p_dev, y_m_dev = load_data(\n",
    "            '../data/emoji_sent_race_{}/test/'.format(ratio),\n",
    "            size=100000, ratio=0.5)\n",
    "\n",
    "        biased_classifier = LinearSVC(fit_intercept=True, class_weight='balanced', dual=False, C=0.1, max_iter=10000)\n",
    "\n",
    "        biased_classifier.fit(x_train, y_m_train)\n",
    "        biased_score = biased_classifier.score(x_dev, y_m_dev)\n",
    "\n",
    "    #     P = np.load('../data/emoji_sent_race_{}/P_svm.num-clfs=300.npy'.format(ratio), allow_pickle=True)\n",
    "    #     P = P[1]\n",
    "\n",
    "    #     n_dims = 120\n",
    "    # #     n_dims = 70\n",
    "    #     if ratio == 0.5:\n",
    "    #         n_dims = 200\n",
    "    #     elif ratio == 0.6:\n",
    "    #         n_dims = 100\n",
    "    #     elif ratio == 0.7:\n",
    "    #         n_dims = 115\n",
    "    #     elif ratio == 0.8:\n",
    "    #         n_dims = 200\n",
    "\n",
    "    #     P = debias.get_projection_to_intersection_of_nullspaces(P[:n_dims], input_dim=300)\n",
    "\n",
    "        debiased_x_train = P.dot(x_train.T).T\n",
    "        debiased_x_dev = P.dot(x_dev.T).T\n",
    "\n",
    "        classifier = LinearSVC(fit_intercept=True, class_weight='balanced', dual=False, C=0.1, max_iter=10000)\n",
    "\n",
    "        classifier.fit(debiased_x_train, y_m_train)\n",
    "        debiased_score = classifier.score(debiased_x_dev, y_m_dev)\n",
    "\n",
    "        p_classifier = SGDClassifier(warm_start=True, loss='log', n_jobs=64, max_iter=10000, random_state=0, tol=1e-3)\n",
    "        p_classifier.fit(debiased_x_train, y_p_train)\n",
    "        p_score = p_classifier.score(debiased_x_dev, y_p_dev)\n",
    "        results[ratio]['p_acc'] = p_score\n",
    "\n",
    "        _, biased_diffs = get_TPR(y_m_dev, biased_classifier.predict(x_dev), y_p_dev)\n",
    "\n",
    "        _, debiased_diffs = get_TPR(y_m_dev, classifier.predict(debiased_x_dev), y_p_dev)\n",
    "\n",
    "    #     results[ratio]['biased_diff_tpr'] = biased_diffs['y:0']\n",
    "        results[ratio]['biased_diff_tpr'] = rms(list(biased_diffs.values()))\n",
    "    #     results[ratio]['debiased_diff_tpr'] = debiased_diffs['y:0']\n",
    "        results[ratio]['debiased_diff_tpr'] = rms(list(debiased_diffs.values()))\n",
    "\n",
    "        results[ratio]['biased_acc'] = biased_score\n",
    "        results[ratio]['debiased_acc'] = debiased_score\n",
    "\n",
    "    plot_results = defaultdict(list)\n",
    "    for r in [0.5, 0.6, 0.7, 0.8]:\n",
    "        plot_results['biased_diff_tpr'].append(results[r]['biased_diff_tpr'])\n",
    "        plot_results['debiased_diff_tpr'].append(results[r]['debiased_diff_tpr'])\n",
    "        plot_results['biased_acc'].append(results[r]['biased_acc'])\n",
    "        plot_results['debiased_acc'].append(results[r]['debiased_acc'])\n",
    "\n",
    "    return results, plot_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data2plot(results, removal_range):\n",
    "    x = removal_range\n",
    "\n",
    "    fig, ax1 = plt.subplots()\n",
    "\n",
    "    color = 'tab:red'\n",
    "    ax1.set_xlabel('ratio')\n",
    "    ax1.set_ylabel('diff tpr', color=color)\n",
    "    ax1.plot(x, results['biased_diff_tpr'], '.--', label='biased tpr diff', color=color)\n",
    "    ax1.plot(x, results['debiased_diff_tpr'], '*:', label='debiased tpr diff', color=color)\n",
    "    ax1.tick_params(axis='y', labelcolor=color)\n",
    "    plt.ylim(0.,0.6)\n",
    "\n",
    "    ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n",
    "\n",
    "    color = 'tab:blue'\n",
    "    ax2.set_ylabel('acc', color=color)  # we already handled the x-label with ax1\n",
    "    ax2.plot(x, results['biased_acc'], '^-', label='biased acc', color=color)\n",
    "    ax2.plot(x, results['debiased_acc'], 'o--', label='debiased acc', color=color)\n",
    "    ax2.tick_params(axis='y', labelcolor=color)\n",
    "    \n",
    "    # ask matplotlib for the plotted objects and their labels\n",
    "    lines, labels = ax1.get_legend_handles_labels()\n",
    "    lines2, labels2 = ax2.get_legend_handles_labels()\n",
    "    ax2.legend(lines, labels, loc='lower right')\n",
    "    \n",
    "    from matplotlib.legend import Legend\n",
    "    leg = Legend(ax2, lines2, labels2,\n",
    "             loc='upper left', frameon=False)\n",
    "    ax2.add_artist(leg);\n",
    "\n",
    "    plt.title('TPR rates as a function of the ratio')\n",
    "    plt.ylim(0.5,.85)\n",
    "#     plt.legend()\n",
    "#     plt.savefig('tpr_rates_ratio.png', dpi=1000)\n",
    "    plt.show()\n",
    "    \n",
    "# data2plot(plot_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cross-validate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_result(u, removel_range, x_train, x_dev, y_m_train, y_m_dev, y_p_train, y_p_dev):\n",
    "    results = defaultdict(dict)\n",
    "\n",
    "    biased_classifier = LinearSVC(fit_intercept=True, class_weight='balanced', dual=False, C=0.1, max_iter=10000)\n",
    "\n",
    "    biased_classifier.fit(x_train, y_m_train)\n",
    "    biased_score = biased_classifier.score(x_dev, y_m_dev)\n",
    "    _, biased_diffs = get_TPR(y_m_dev, biased_classifier.predict(x_dev), y_p_dev)\n",
    "    \n",
    "    for removal in removel_range:\n",
    "        u_r = u[:, removal:]\n",
    "        proj = u_r @ u_r.T\n",
    "        P = proj\n",
    "\n",
    "        debiased_x_train = P.dot(x_train.T).T\n",
    "        debiased_x_dev = P.dot(x_dev.T).T\n",
    "\n",
    "        classifier = LinearSVC(fit_intercept=True, class_weight='balanced', dual=False, C=0.1, max_iter=10000)\n",
    "\n",
    "        classifier.fit(debiased_x_train, y_m_train)\n",
    "        debiased_score = classifier.score(debiased_x_dev, y_m_dev)\n",
    "\n",
    "        p_classifier = SGDClassifier(warm_start=True, loss='log', n_jobs=64, max_iter=10000, random_state=0, tol=1e-3)\n",
    "        p_classifier.fit(debiased_x_train, y_p_train)\n",
    "        p_score = p_classifier.score(debiased_x_dev, y_p_dev)\n",
    "\n",
    "        _, debiased_diffs = get_TPR(y_m_dev, classifier.predict(debiased_x_dev), y_p_dev)\n",
    "\n",
    "\n",
    "        results[removal]['p_acc'] = p_score\n",
    "        #     results[ratio]['biased_diff_tpr'] = biased_diffs['y:0']\n",
    "        results[removal]['biased_diff_tpr'] = rms(list(biased_diffs.values()))\n",
    "        #     results[ratio]['debiased_diff_tpr'] = debiased_diffs['y:0']\n",
    "        results[removal]['debiased_diff_tpr'] = rms(list(debiased_diffs.values()))\n",
    "\n",
    "        results[removal]['biased_acc'] = biased_score\n",
    "        results[removal]['debiased_acc'] = debiased_score\n",
    "        \n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def check_result_ksvd(W, K_x_sqrt2, U_Kx, K_x_dv_sqrt2, U_Kx_dv, removel_range, x_train, x_dev, y_m_train, y_m_dev, y_p_train, y_p_dev):\n",
    "    results = defaultdict(dict)\n",
    "\n",
    "    biased_classifier = LinearSVC(fit_intercept=True, class_weight='balanced', dual=False, C=0.1, max_iter=10000)\n",
    "\n",
    "    biased_classifier.fit(x_train, y_m_train)\n",
    "    biased_score = biased_classifier.score(x_dev, y_m_dev)\n",
    "    _, biased_diffs = get_TPR(y_m_dev, biased_classifier.predict(x_dev), y_p_dev)\n",
    "    \n",
    "    for removal in removel_range:\n",
    "        W2 = W[:, :removal]\n",
    "        Wprime = W2\n",
    "        U_proj_ker = linalg.null_space((K_x_sqrt2 @ Wprime).T)\n",
    "        \n",
    "        debiased_x_train = (U_proj_ker.T @ K_x_sqrt2).T\n",
    "        debiased_x_dev = (U_proj_ker.T @ U_Kx  @ U_Kx_dv.T @ K_x_dv_sqrt2).T\n",
    "\n",
    "        classifier = LinearSVC(fit_intercept=True, class_weight='balanced', dual=False, C=0.1, max_iter=10000)\n",
    "\n",
    "        classifier.fit(debiased_x_train, y_m_train)\n",
    "        debiased_score = classifier.score(debiased_x_dev, y_m_dev)\n",
    "\n",
    "        p_classifier = SGDClassifier(warm_start=True, loss='log', n_jobs=64, max_iter=10000, random_state=0, tol=1e-3)\n",
    "        p_classifier.fit(debiased_x_train, y_p_train)\n",
    "        p_score = p_classifier.score(debiased_x_dev, y_p_dev)\n",
    "\n",
    "        _, debiased_diffs = get_TPR(y_m_dev, classifier.predict(debiased_x_dev), y_p_dev)\n",
    "\n",
    "\n",
    "        results[removal]['p_acc'] = p_score\n",
    "        #     results[ratio]['biased_diff_tpr'] = biased_diffs['y:0']\n",
    "        results[removal]['biased_diff_tpr'] = rms(list(biased_diffs.values()))\n",
    "        #     results[ratio]['debiased_diff_tpr'] = debiased_diffs['y:0']\n",
    "        results[removal]['debiased_diff_tpr'] = rms(list(debiased_diffs.values()))\n",
    "\n",
    "        results[removal]['biased_acc'] = biased_score\n",
    "        results[removal]['debiased_acc'] = debiased_score\n",
    "        \n",
    "    return results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_to_plot_results(results, removel_range):\n",
    "    plot_results = defaultdict(list)\n",
    "    for r in removel_range:\n",
    "        plot_results['biased_diff_tpr'].append(results[r]['biased_diff_tpr'])\n",
    "        plot_results['debiased_diff_tpr'].append(results[r]['debiased_diff_tpr'])\n",
    "        plot_results['biased_acc'].append(results[r]['biased_acc'])\n",
    "        plot_results['debiased_acc'].append(results[r]['debiased_acc'])\n",
    "    return plot_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ratio = 0.5\n",
    "\n",
    "# x_train, y_p_train, y_m_train = load_data(\n",
    "#     '../data/emoji_sent_race_{}/train/'.format(ratio),\n",
    "#     size=100000, ratio=ratio)\n",
    "# x_dev, y_p_dev, y_m_dev = load_data(\n",
    "#     '../data/emoji_sent_race_{}/test/'.format(ratio),\n",
    "#     size=100000, ratio=0.5)\n",
    "# np.savetxt(f\"../data/saved_models/fair_emoji_sent_race/{ratio}/x_train.csv\", x_train, delimiter=\",\")\n",
    "# np.savetxt(f\"../data/saved_models/fair_emoji_sent_race/{ratio}/y_p_train.csv\", y_p_train, delimiter=\",\")\n",
    "# np.savetxt(f\"../data/saved_models/fair_emoji_sent_race/{ratio}/y_m_train.csv\", y_m_train, delimiter=\",\")\n",
    "# np.savetxt(f\"../data/saved_models/fair_emoji_sent_race/{ratio}/x_dev.csv\", x_dev, delimiter=\",\")\n",
    "# np.savetxt(f\"../data/saved_models/fair_emoji_sent_race/{ratio}/y_p_dev.csv\", y_p_dev, delimiter=\",\")\n",
    "# np.savetxt(f\"../data/saved_models/fair_emoji_sent_race/{ratio}/y_m_dev.csv\", y_m_dev, delimiter=\",\")\n",
    "# np.savez(f\"../data/saved_models/fair_emoji_sent_race/{ratio}/all.npz\", x_train = x_train, y_p_train = y_p_train, y_m_train = y_m_train, x_dev = x_dev, y_p_dev = y_p_dev, y_m_dev = y_m_dev)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Null it out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# ratio = 0.5\n",
    "\n",
    "# saved_dataset = np.load(f\"../data/saved_models/fair_emoji_sent_race/{ratio}/all.npz\")\n",
    "\n",
    "# x_train = saved_dataset['x_train']\n",
    "# y_m_train = saved_dataset['y_m_train']\n",
    "# y_p_train = saved_dataset['y_p_train']\n",
    "\n",
    "# x_dev = saved_dataset['x_dev']\n",
    "# y_p_dev = saved_dataset['y_p_dev']\n",
    "# y_m_dev = saved_dataset['y_m_dev']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ratio = 0.5, train size (100000, 300), dev size (7998, 300)\n",
      "\n",
      "\n",
      "\n",
      "ratio = 0.6, train size (100000, 300), dev size (7998, 300)\n",
      "\n",
      "\n",
      "\n",
      "ratio = 0.7, train size (100000, 300), dev size (7998, 300)\n",
      "\n",
      "\n",
      "\n",
      "ratio = 0.8, train size (99998, 300), dev size (7998, 300)\n",
      "\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for ratio in [0.5, 0.6, 0.7, 0.8]:\n",
    "\n",
    "#     x_train, y_p_train, y_m_train = load_data(\n",
    "#         '../data/emoji_sent_race_{}/train/'.format(ratio),\n",
    "#         size=100000, ratio=ratio)\n",
    "#     x_dev, y_p_dev, y_m_dev = load_data(\n",
    "#         '../data/emoji_sent_race_{}/test/'.format(ratio),\n",
    "#         size=100000, ratio=0.5)\n",
    "    saved_dataset = np.load(f\"../data/saved_models/fair_emoji_sent_race/{ratio}/all.npz\")\n",
    "\n",
    "    x_train = saved_dataset['x_train']\n",
    "    y_m_train = saved_dataset['y_m_train']\n",
    "    y_p_train = saved_dataset['y_p_train']\n",
    "\n",
    "    x_dev = saved_dataset['x_dev']\n",
    "    y_p_dev = saved_dataset['y_p_dev']\n",
    "    y_m_dev = saved_dataset['y_m_dev']\n",
    "    \n",
    "    print(f\"ratio = {ratio}, train size {x_train.shape}, dev size {x_dev.shape}\\n\\n\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = defaultdict(dict)\n",
    "\n",
    "for ratio in [0.5, 0.6, 0.7, 0.8]:\n",
    "\n",
    "#     x_train, y_p_train, y_m_train = load_data(\n",
    "#         '../data/emoji_sent_race_{}/train/'.format(ratio),\n",
    "#         size=100000, ratio=ratio)\n",
    "#     x_dev, y_p_dev, y_m_dev = load_data(\n",
    "#         '../data/emoji_sent_race_{}/test/'.format(ratio),\n",
    "#         size=100000, ratio=0.5)\n",
    "    saved_dataset = np.load(f\"../data/saved_models/fair_emoji_sent_race/{ratio}/all.npz\")\n",
    "\n",
    "    x_train = saved_dataset['x_train']\n",
    "    y_m_train = saved_dataset['y_m_train']\n",
    "    y_p_train = saved_dataset['y_p_train']\n",
    "\n",
    "    x_dev = saved_dataset['x_dev']\n",
    "    y_p_dev = saved_dataset['y_p_dev']\n",
    "    y_m_dev = saved_dataset['y_m_dev']\n",
    "    \n",
    "    biased_classifier = LinearSVC(fit_intercept=True, class_weight='balanced', dual=False, C=0.1, max_iter=10000)\n",
    "\n",
    "    biased_classifier.fit(x_train, y_m_train)\n",
    "    biased_score = biased_classifier.score(x_dev, y_m_dev)\n",
    "    \n",
    "    P = np.load('../data/emoji_sent_race_{}/P_svm.num-clfs=300.npy'.format(ratio), allow_pickle=True)\n",
    "    P = P[1]\n",
    "    n_dims = 120\n",
    "#     n_dims = 70\n",
    "    if ratio == 0.5:\n",
    "        n_dims = 200\n",
    "    elif ratio == 0.6:\n",
    "        n_dims = 100\n",
    "    elif ratio == 0.7:\n",
    "        n_dims = 115\n",
    "    elif ratio == 0.8:\n",
    "        n_dims = 200\n",
    "    P = debias.get_projection_to_intersection_of_nullspaces(P[:n_dims], input_dim=300)\n",
    "    \n",
    "    debiased_x_train = P.dot(x_train.T).T\n",
    "    debiased_x_dev = P.dot(x_dev.T).T\n",
    "\n",
    "    classifier = LinearSVC(fit_intercept=True, class_weight='balanced', dual=False, C=0.1, max_iter=10000)\n",
    "\n",
    "    classifier.fit(debiased_x_train, y_m_train)\n",
    "    debiased_score = classifier.score(debiased_x_dev, y_m_dev)\n",
    "    \n",
    "    p_classifier = SGDClassifier(warm_start=True, loss='log', n_jobs=64, max_iter=10000, random_state=0, tol=1e-3)\n",
    "    p_classifier.fit(debiased_x_train, y_p_train)\n",
    "    p_score = p_classifier.score(debiased_x_dev, y_p_dev)\n",
    "    results[ratio]['p_acc'] = p_score\n",
    "    \n",
    "    _, biased_diffs = get_TPR(y_m_dev, biased_classifier.predict(x_dev), y_p_dev)\n",
    "    \n",
    "    _, debiased_diffs = get_TPR(y_m_dev, classifier.predict(debiased_x_dev), y_p_dev)\n",
    "    \n",
    "#     results[ratio]['biased_diff_tpr'] = biased_diffs['y:0']\n",
    "    results[ratio]['biased_diff_tpr'] = rms(list(biased_diffs.values()))\n",
    "#     results[ratio]['debiased_diff_tpr'] = debiased_diffs['y:0']\n",
    "    results[ratio]['debiased_diff_tpr'] = rms(list(debiased_diffs.values()))\n",
    "    \n",
    "    results[ratio]['biased_acc'] = biased_score\n",
    "    results[ratio]['debiased_acc'] = debiased_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(dict,\n",
       "            {0.5: {'p_acc': 0.5396349087271818,\n",
       "              'biased_diff_tpr': 0.14560632483708713,\n",
       "              'debiased_diff_tpr': 0.11974302439852863,\n",
       "              'biased_acc': 0.7653163290822705,\n",
       "              'debiased_acc': 0.7625656414103525},\n",
       "             0.6: {'p_acc': 0.5946486621655414,\n",
       "              'biased_diff_tpr': 0.22630502371727185,\n",
       "              'debiased_diff_tpr': 0.19067232927585404,\n",
       "              'biased_acc': 0.7531882970742686,\n",
       "              'debiased_acc': 0.7579394848712178},\n",
       "             0.7: {'p_acc': 0.6257814453613403,\n",
       "              'biased_diff_tpr': 0.31864557675474753,\n",
       "              'debiased_diff_tpr': 0.26266284955843594,\n",
       "              'biased_acc': 0.741185296324081,\n",
       "              'debiased_acc': 0.7478119529882471},\n",
       "             0.8: {'p_acc': 0.5043760940235059,\n",
       "              'biased_diff_tpr': 0.4043010400370602,\n",
       "              'debiased_diff_tpr': 0.010947167516829342,\n",
       "              'biased_acc': 0.7200550137534384,\n",
       "              'debiased_acc': 0.5290072518129533}})"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(list,\n",
       "            {'biased_diff_tpr': [0.14560632483708713,\n",
       "              0.22630502371727185,\n",
       "              0.31864557675474753,\n",
       "              0.4043010400370602],\n",
       "             'debiased_diff_tpr': [0.11974302439852863,\n",
       "              0.19067232927585404,\n",
       "              0.26266284955843594,\n",
       "              0.010947167516829342],\n",
       "             'biased_acc': [0.7653163290822705,\n",
       "              0.7531882970742686,\n",
       "              0.741185296324081,\n",
       "              0.7200550137534384],\n",
       "             'debiased_acc': [0.7625656414103525,\n",
       "              0.7579394848712178,\n",
       "              0.7478119529882471,\n",
       "              0.5290072518129533]})"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_results = defaultdict(list)\n",
    "for r in [0.5, 0.6, 0.7, 0.8]:\n",
    "    plot_results['biased_diff_tpr'].append(results[r]['biased_diff_tpr'])\n",
    "    plot_results['debiased_diff_tpr'].append(results[r]['debiased_diff_tpr'])\n",
    "    plot_results['biased_acc'].append(results[r]['biased_acc'])\n",
    "    plot_results['debiased_acc'].append(results[r]['debiased_acc'])\n",
    "plot_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "removel_range = [0, 1, 2, 5, 30, 50, 100, 200, 220, 240, 260, 280, 290, 295]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sal took 0.37548398971557617 seconds\n",
      "u has shape (300, 300), s has shape (2,), vh has shape (2, 2)\n"
     ]
    }
   ],
   "source": [
    "ratio = 0.5\n",
    "\n",
    "saved_dataset = np.load(f\"../data/saved_models/fair_emoji_sent_race/{ratio}/all.npz\")\n",
    "\n",
    "x_train = saved_dataset['x_train']\n",
    "y_m_train = saved_dataset['y_m_train']\n",
    "y_p_train = saved_dataset['y_p_train']\n",
    "y_p_train_2d = np.asarray([y_p_train, - y_p_train + 1]).T\n",
    "\n",
    "x_dev = saved_dataset['x_dev']\n",
    "y_p_dev = saved_dataset['y_p_dev']\n",
    "y_m_dev = saved_dataset['y_m_dev']\n",
    "\n",
    "t = time.time()\n",
    "A = np.dot(x_train.T, y_p_train_2d) / x_train.shape[0]\n",
    "u, s, vh = np.linalg.svd(A, full_matrices=True)\n",
    "elapsed = time.time() - t\n",
    "print(f\"sal took {elapsed} seconds\")\n",
    "print(f\"u has shape {u.shape}, s has shape {s.shape}, vh has shape {vh.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(dict,\n",
       "            {0: {'p_acc': 0.8714678669667417,\n",
       "              'biased_diff_tpr': 0.14560632483708713,\n",
       "              'debiased_diff_tpr': 0.14560632483708713,\n",
       "              'biased_acc': 0.7653163290822705,\n",
       "              'debiased_acc': 0.7653163290822705},\n",
       "             1: {'p_acc': 0.8630907726931732,\n",
       "              'biased_diff_tpr': 0.14560632483708713,\n",
       "              'debiased_diff_tpr': 0.1445020833238553,\n",
       "              'biased_acc': 0.7653163290822705,\n",
       "              'debiased_acc': 0.7653163290822705},\n",
       "             2: {'p_acc': 0.49699924981245314,\n",
       "              'biased_diff_tpr': 0.14560632483708713,\n",
       "              'debiased_diff_tpr': 0.11020173553336259,\n",
       "              'biased_acc': 0.7653163290822705,\n",
       "              'debiased_acc': 0.766816704176044},\n",
       "             5: {'p_acc': 0.4959989997499375,\n",
       "              'biased_diff_tpr': 0.14560632483708713,\n",
       "              'debiased_diff_tpr': 0.11007656356661116,\n",
       "              'biased_acc': 0.7653163290822705,\n",
       "              'debiased_acc': 0.7669417354338585},\n",
       "             30: {'p_acc': 0.4948737184296074,\n",
       "              'biased_diff_tpr': 0.14560632483708713,\n",
       "              'debiased_diff_tpr': 0.10621266898739956,\n",
       "              'biased_acc': 0.7653163290822705,\n",
       "              'debiased_acc': 0.7660665166291573},\n",
       "             50: {'p_acc': 0.4973743435858965,\n",
       "              'biased_diff_tpr': 0.14560632483708713,\n",
       "              'debiased_diff_tpr': 0.10841554967602068,\n",
       "              'biased_acc': 0.7653163290822705,\n",
       "              'debiased_acc': 0.766816704176044},\n",
       "             100: {'p_acc': 0.49637409352338085,\n",
       "              'biased_diff_tpr': 0.14560632483708713,\n",
       "              'debiased_diff_tpr': 0.10844931019709282,\n",
       "              'biased_acc': 0.7653163290822705,\n",
       "              'debiased_acc': 0.7664416104026006},\n",
       "             200: {'p_acc': 0.5248812203050762,\n",
       "              'biased_diff_tpr': 0.14560632483708713,\n",
       "              'debiased_diff_tpr': 0.1042793080915506,\n",
       "              'biased_acc': 0.7653163290822705,\n",
       "              'debiased_acc': 0.7656914228557139},\n",
       "             220: {'p_acc': 0.44711177794448614,\n",
       "              'biased_diff_tpr': 0.14560632483708713,\n",
       "              'debiased_diff_tpr': 0.09987399516901178,\n",
       "              'biased_acc': 0.7653163290822705,\n",
       "              'debiased_acc': 0.7661915478869717},\n",
       "             240: {'p_acc': 0.45023755938984744,\n",
       "              'biased_diff_tpr': 0.14560632483708713,\n",
       "              'debiased_diff_tpr': 0.1032924184615246,\n",
       "              'biased_acc': 0.7653163290822705,\n",
       "              'debiased_acc': 0.7656914228557139},\n",
       "             260: {'p_acc': 0.450112528132033,\n",
       "              'biased_diff_tpr': 0.14560632483708713,\n",
       "              'debiased_diff_tpr': 0.10346416206559751,\n",
       "              'biased_acc': 0.7653163290822705,\n",
       "              'debiased_acc': 0.7656914228557139},\n",
       "             280: {'p_acc': 0.4587396849212303,\n",
       "              'biased_diff_tpr': 0.14560632483708713,\n",
       "              'debiased_diff_tpr': 0.1043429019092808,\n",
       "              'biased_acc': 0.7653163290822705,\n",
       "              'debiased_acc': 0.7591897974493623},\n",
       "             290: {'p_acc': 0.5001250312578145,\n",
       "              'biased_diff_tpr': 0.14560632483708713,\n",
       "              'debiased_diff_tpr': 0.10178012864537783,\n",
       "              'biased_acc': 0.7653163290822705,\n",
       "              'debiased_acc': 0.7596899224806202},\n",
       "             295: {'p_acc': 0.49987496874218557,\n",
       "              'biased_diff_tpr': 0.14560632483708713,\n",
       "              'debiased_diff_tpr': 0.09851762574167189,\n",
       "              'biased_acc': 0.7653163290822705,\n",
       "              'debiased_acc': 0.7548137034258565}})"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = check_result(u, removel_range, x_train, x_dev, y_m_train, y_m_dev, y_p_train, y_p_dev)\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(list,\n",
       "            {'biased_diff_tpr': [0.14560632483708713,\n",
       "              0.14560632483708713,\n",
       "              0.14560632483708713,\n",
       "              0.14560632483708713,\n",
       "              0.14560632483708713,\n",
       "              0.14560632483708713,\n",
       "              0.14560632483708713,\n",
       "              0.14560632483708713,\n",
       "              0.14560632483708713,\n",
       "              0.14560632483708713,\n",
       "              0.14560632483708713,\n",
       "              0.14560632483708713,\n",
       "              0.14560632483708713,\n",
       "              0.14560632483708713],\n",
       "             'debiased_diff_tpr': [0.14560632483708713,\n",
       "              0.1445020833238553,\n",
       "              0.11020173553336259,\n",
       "              0.11007656356661116,\n",
       "              0.10621266898739956,\n",
       "              0.10841554967602068,\n",
       "              0.10844931019709282,\n",
       "              0.1042793080915506,\n",
       "              0.09987399516901178,\n",
       "              0.1032924184615246,\n",
       "              0.10346416206559751,\n",
       "              0.1043429019092808,\n",
       "              0.10178012864537783,\n",
       "              0.09851762574167189],\n",
       "             'biased_acc': [0.7653163290822705,\n",
       "              0.7653163290822705,\n",
       "              0.7653163290822705,\n",
       "              0.7653163290822705,\n",
       "              0.7653163290822705,\n",
       "              0.7653163290822705,\n",
       "              0.7653163290822705,\n",
       "              0.7653163290822705,\n",
       "              0.7653163290822705,\n",
       "              0.7653163290822705,\n",
       "              0.7653163290822705,\n",
       "              0.7653163290822705,\n",
       "              0.7653163290822705,\n",
       "              0.7653163290822705],\n",
       "             'debiased_acc': [0.7653163290822705,\n",
       "              0.7653163290822705,\n",
       "              0.766816704176044,\n",
       "              0.7669417354338585,\n",
       "              0.7660665166291573,\n",
       "              0.766816704176044,\n",
       "              0.7664416104026006,\n",
       "              0.7656914228557139,\n",
       "              0.7661915478869717,\n",
       "              0.7656914228557139,\n",
       "              0.7656914228557139,\n",
       "              0.7591897974493623,\n",
       "              0.7596899224806202,\n",
       "              0.7548137034258565]})"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_results = convert_to_plot_results(results, removel_range)\n",
    "plot_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2plot(plot_results, removel_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "u has shape (300, 300), s has shape (2,), vh has shape (2, 2)\n"
     ]
    }
   ],
   "source": [
    "ratio = 0.6\n",
    "\n",
    "saved_dataset = np.load(f\"../data/saved_models/fair_emoji_sent_race/{ratio}/all.npz\")\n",
    "\n",
    "x_train = saved_dataset['x_train']\n",
    "y_m_train = saved_dataset['y_m_train']\n",
    "y_p_train = saved_dataset['y_p_train']\n",
    "y_p_train_2d = np.asarray([y_p_train, - y_p_train + 1]).T\n",
    "\n",
    "x_dev = saved_dataset['x_dev']\n",
    "y_p_dev = saved_dataset['y_p_dev']\n",
    "y_m_dev = saved_dataset['y_m_dev']\n",
    "\n",
    "A = np.dot(x_train.T, y_p_train_2d) / x_train.shape[0]\n",
    "u, s, vh = np.linalg.svd(A, full_matrices=True)\n",
    "print(f\"u has shape {u.shape}, s has shape {s.shape}, vh has shape {vh.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(dict,\n",
       "            {0: {'p_acc': 0.840960240060015,\n",
       "              'biased_diff_tpr': 0.22630502371727185,\n",
       "              'debiased_diff_tpr': 0.22630502371727185,\n",
       "              'biased_acc': 0.7531882970742686,\n",
       "              'debiased_acc': 0.7531882970742686},\n",
       "             1: {'p_acc': 0.8508377094273568,\n",
       "              'biased_diff_tpr': 0.22630502371727185,\n",
       "              'debiased_diff_tpr': 0.22803630357189353,\n",
       "              'biased_acc': 0.7531882970742686,\n",
       "              'debiased_acc': 0.7530632658164541},\n",
       "             2: {'p_acc': 0.4711177794448612,\n",
       "              'biased_diff_tpr': 0.22630502371727185,\n",
       "              'debiased_diff_tpr': 0.08988024196274066,\n",
       "              'biased_acc': 0.7531882970742686,\n",
       "              'debiased_acc': 0.7531882970742686},\n",
       "             5: {'p_acc': 0.4708677169292323,\n",
       "              'biased_diff_tpr': 0.22630502371727185,\n",
       "              'debiased_diff_tpr': 0.09037932830110916,\n",
       "              'biased_acc': 0.7531882970742686,\n",
       "              'debiased_acc': 0.7526881720430108},\n",
       "             30: {'p_acc': 0.4723680920230057,\n",
       "              'biased_diff_tpr': 0.22630502371727185,\n",
       "              'debiased_diff_tpr': 0.09126866357639556,\n",
       "              'biased_acc': 0.7531882970742686,\n",
       "              'debiased_acc': 0.7530632658164541},\n",
       "             50: {'p_acc': 0.5066266566641661,\n",
       "              'biased_diff_tpr': 0.22630502371727185,\n",
       "              'debiased_diff_tpr': 0.09258461867418033,\n",
       "              'biased_acc': 0.7531882970742686,\n",
       "              'debiased_acc': 0.7511877969492373},\n",
       "             100: {'p_acc': 0.5031257814453614,\n",
       "              'biased_diff_tpr': 0.22630502371727185,\n",
       "              'debiased_diff_tpr': 0.09496617559255267,\n",
       "              'biased_acc': 0.7531882970742686,\n",
       "              'debiased_acc': 0.7484371092773193},\n",
       "             200: {'p_acc': 0.5007501875468867,\n",
       "              'biased_diff_tpr': 0.22630502371727185,\n",
       "              'debiased_diff_tpr': 0.10150799279833236,\n",
       "              'biased_acc': 0.7531882970742686,\n",
       "              'debiased_acc': 0.7403100775193798},\n",
       "             220: {'p_acc': 0.5002500625156289,\n",
       "              'biased_diff_tpr': 0.22630502371727185,\n",
       "              'debiased_diff_tpr': 0.10921997885015855,\n",
       "              'biased_acc': 0.7531882970742686,\n",
       "              'debiased_acc': 0.7353088272068017},\n",
       "             240: {'p_acc': 0.5001250312578145,\n",
       "              'biased_diff_tpr': 0.22630502371727185,\n",
       "              'debiased_diff_tpr': 0.10635797301822242,\n",
       "              'biased_acc': 0.7531882970742686,\n",
       "              'debiased_acc': 0.733183295823956},\n",
       "             260: {'p_acc': 0.5002500625156289,\n",
       "              'biased_diff_tpr': 0.22630502371727185,\n",
       "              'debiased_diff_tpr': 0.10776873695187088,\n",
       "              'biased_acc': 0.7531882970742686,\n",
       "              'debiased_acc': 0.729307326831708},\n",
       "             280: {'p_acc': 0.4981245311327832,\n",
       "              'biased_diff_tpr': 0.22630502371727185,\n",
       "              'debiased_diff_tpr': 0.11257271707339944,\n",
       "              'biased_acc': 0.7531882970742686,\n",
       "              'debiased_acc': 0.7215553888472118},\n",
       "             290: {'p_acc': 0.5036259064766192,\n",
       "              'biased_diff_tpr': 0.22630502371727185,\n",
       "              'debiased_diff_tpr': 0.1257355117153975,\n",
       "              'biased_acc': 0.7531882970742686,\n",
       "              'debiased_acc': 0.7189297324331083},\n",
       "             295: {'p_acc': 0.49987496874218557,\n",
       "              'biased_diff_tpr': 0.22630502371727185,\n",
       "              'debiased_diff_tpr': 0.11164602462363725,\n",
       "              'biased_acc': 0.7531882970742686,\n",
       "              'debiased_acc': 0.6765441360340085}})"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = check_result(u, removel_range, x_train, x_dev, y_m_train, y_m_dev, y_p_train, y_p_dev)\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(list,\n",
       "            {'biased_diff_tpr': [0.22630502371727185,\n",
       "              0.22630502371727185,\n",
       "              0.22630502371727185,\n",
       "              0.22630502371727185,\n",
       "              0.22630502371727185,\n",
       "              0.22630502371727185,\n",
       "              0.22630502371727185,\n",
       "              0.22630502371727185,\n",
       "              0.22630502371727185,\n",
       "              0.22630502371727185,\n",
       "              0.22630502371727185,\n",
       "              0.22630502371727185,\n",
       "              0.22630502371727185,\n",
       "              0.22630502371727185],\n",
       "             'debiased_diff_tpr': [0.22630502371727185,\n",
       "              0.22803630357189353,\n",
       "              0.08988024196274066,\n",
       "              0.09037932830110916,\n",
       "              0.09126866357639556,\n",
       "              0.09258461867418033,\n",
       "              0.09496617559255267,\n",
       "              0.10150799279833236,\n",
       "              0.10921997885015855,\n",
       "              0.10635797301822242,\n",
       "              0.10776873695187088,\n",
       "              0.11257271707339944,\n",
       "              0.1257355117153975,\n",
       "              0.11164602462363725],\n",
       "             'biased_acc': [0.7531882970742686,\n",
       "              0.7531882970742686,\n",
       "              0.7531882970742686,\n",
       "              0.7531882970742686,\n",
       "              0.7531882970742686,\n",
       "              0.7531882970742686,\n",
       "              0.7531882970742686,\n",
       "              0.7531882970742686,\n",
       "              0.7531882970742686,\n",
       "              0.7531882970742686,\n",
       "              0.7531882970742686,\n",
       "              0.7531882970742686,\n",
       "              0.7531882970742686,\n",
       "              0.7531882970742686],\n",
       "             'debiased_acc': [0.7531882970742686,\n",
       "              0.7530632658164541,\n",
       "              0.7531882970742686,\n",
       "              0.7526881720430108,\n",
       "              0.7530632658164541,\n",
       "              0.7511877969492373,\n",
       "              0.7484371092773193,\n",
       "              0.7403100775193798,\n",
       "              0.7353088272068017,\n",
       "              0.733183295823956,\n",
       "              0.729307326831708,\n",
       "              0.7215553888472118,\n",
       "              0.7189297324331083,\n",
       "              0.6765441360340085]})"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_results = convert_to_plot_results(results, removel_range)\n",
    "plot_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAa0AAAEWCAYAAADVW8iBAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAABUO0lEQVR4nO3dd3hUVfrA8e9Jb5DQSwAHFJgBBEKVIou6IDIKtpViYy3Ydd1Vd/Cngq7oWNZekAV1dWUBWWFxZy2AdW10FJihhQEJvSQhpE5yfn/cm2HSE8iQTPJ+nidP5vb3zkzum3PuuecorTVCCCFEKAir6wCEEEKI6pKkJYQQImRI0hJCCBEyJGkJIYQIGZK0hBBChAxJWkIIIUKGJC0hyqGUukMpdUAplaWUanEGj/uwUmrOmTpewHGvUEr9ap5vSjXWH6mU2nMmYqsOpVQnM/bwuo5FBJckrRBh/kEW/xQppXICpq9VSs1QShWY0+lKqe+VUkPMbacopQrNZZlKqQ1KqUtrOb6vlFK31OY+64pSKhJ4ARittU7QWh8J0nHKXPi11k9previfXweuNs833WlFyqltFLqnDqIq1xKKa9S6rfF01rr3WbshXUZlwg+SVohwvyDTNBaJwC7gcsC5n1grrbAXN4K+B/wkVJKmct+MJclAW8A85VSSdU5tlIqojbPJQS0AWKATXUdyBl0FvXkfBvh903UgCStBkhrXQD8HWgLtCi1rAh4H4gHupa3fXEJQCn1Z6XUfuAdpVQzpdR/lFKHlFLHzNcdzPVnAucDr5mludfM+Val1DKl1FGl1Bal1DUBxxirlNqslDqulEpTSj1QQSxnK6W+UEodUUodVkp9EJhszRjTzP1sUUpdVMF+7EqpdWZJ81el1IwK1usGbDEn081jW8ySRkTAev6SpVmS/Z9S6nnzvdmplLokYN3mSql3lFJ7zeVLlFLxwCdA+4ASc3uzxPyPgG3HKaU2maXnr5RStoBlXqXUA0qpn5VSGUqpBUqpmArOK0wp9YhSapdS6qBS6j2lVKJSKloplQWEAxuUUjvK2fYb8+UGM84JAcv+ZO5vn1Lq9wHzo833Y7cyqllnKaViK4htilLqO6XUi0qpI8CMyj53pdT7QCfgYzOeh0p/RuZ7udT87m1XSt1a3rFFCNJay0+I/QBe4Lel5s0A/mG+jgaeA3ab01OA/5mvw4G7gHygdQX7Hwn4gGfMfcViJL+rgDigCfAhsCRgm6+AWwKm44Ffgd8DEUAKcBjoYS7fB5xvvm4G9KsglnOAUWYcrYBvgJfMZd3NY7Q3py3A2ZWc07kY/6j1Bg4Al1ewrgXQQER506XP13x/C4Bbzff3DmAvoMzlLmCBeZ6RwG8CYtpTyefYDThhnn8k8BCwHYgK+B6sBNoDzQE3cHsF53STuW0XIAH4CHg/YLkGzqnkO1diecB35AkztrFANtDMXP4isNSMqwnwMfB0BfueYu7rHvO7ElvZ517e30A5n9k3GDUKMUBf4BBwYV3/7crP6f/UeQDycwofWsVJKx9IBw4CXwD9zWXFF4V08+KaA1xTyf5HmvuKqWSdvsCxgOmvKJm0JgDfltrmLWC6+Xo3cBvQtIbnfjmwznx9jnmuvwUia7ifl4AXK1hW+gJYYrr0+Zrv7/aAZXHm+m2BdkBR8cW8nPe5sqT1KLAwYFkYkAaMDPgeXBew/FlgVgXntAK4M2C6u/ldKD7HU0laOaXek4PAeYDCSLZnBywbAuysYN9TMP/Bqs7nXt7fQOBnBHQECoEmAcufBt49lb83+alfP1I92LAs1Fonaa1ba60v1FqvCVj2o9Y6CeO//aUY1XmVOaS1zi2eUErFKaXeMquXMjH+k01SFbfWOgsYbFZrpSul0oFrMS7kYJTaxgK7lFJfK7PRSGlKqTZKqflmFWAm8A+gJYDWejvwB4wL/UFzvfYV7GewUupLZVRvZgC3F++nluwvfqG1zjZfJmBcQI9qrY+dwj7bA7sC9luEUbJMLu+4GCWdhOrsy3wdgXH/7lQd0Vr7yjl+K4zEvSbgs//UnF+RXwMnKvvcq6E9xnt+PGDeLkq+byJESdJqZLTWWRjVV9eryps2l+7+/08Y/50P1lo3BUaY81UF6/8KfG0m0eKfBK31HWYcq7TW44HWwBJgYQVxPGXu+1zzuNcFHBOt9Tyt9XCMJKkxqjTLMw8jWXfUWicCswL3U4UT5u+4gHlty1uxHL8CzVX5jV6qGmJhL8Z5AaCUUhhJMK2ax65wXxj3hHwY1aS17TBGKaxnwGefqI2GQBUp/V5U+rmXs36gvRjveZOAeZ04tfdN1DOStBohrfVRYA7wWA02a4JxIUpXSjUHppdafgDjfkmx/wDdlFLXK6UizZ+BSimbUipKGc30E7XRaCQTowqtouNmARlKqWTgweIFSqnuSqkLlVLRQK4ZX2X7Oaq1zlVKDQImV/fEtdaHMC541ymlwpVSNwFnV3PbfRgNLt5QRmOWSKVUccI/ALRQSiVWsPlCwK6UukgZzfD/BOQB31c39gD/BO5XSnVWSiVgJIUFpUpKlSn9+VbILBH+DXhRKdUaQCmVrJS6uAbxVvi5VxWP1vpXjPfoaaVUjFKqN3AzRmlNhDhJWo3XS8BY8w+6uuvHYvwX/SNGdU+gl4GrldE67hWzamY0MBHjP9/9nGzYAXA94DWrfm7HqDosz+NAPyADo0HDRwHLogGnGdN+jFLbtAr2cyfwhFLqOEayrqhkV5FbMS6cR4Ce1CxxXI9x/8iDcd/nDwBaaw9GMkk1q9FKVG1qrbdglDBexTjHyzAedcivYewAb2O0Gv0G2ImR5O+pwfYzgL+bcV5T1crAnzEafvxofsbLMUrq1VXZ5w7GPapHzHjKa3k6CeM+115gMca91OU1OL6op4pbNwkhhBD1npS0hBBChIygPnnuttrGYFQbhQNzbB63s5x1rsGoetDABpvHXe17DUIIIWqHxeEqcb32Ou3OUss7YXRakGSu4/A67f+1OFwWjGcEix/K/9HrtN8erDiDVtJyW23hwOvAJUAPYJLbautRap2uGPcghtk87p6Ydf1CCCHOHIvDVeZ6bXG4epRa7RFgoddpT8G4V/1GwLIdXqe9r/kTtIQFwa0eHARst3ncqTaPOx+YD4wvtc6twOs2j/sYgM3jPhjEeIQQQpRvELDd67Snep32iq7XGmhqvk7EaORyxgWzejCZkg8M7gEGl1qnG4DbavsOo7g5w+Zxl26VhlJqKjDVnOwfFxdXehUhhBCVyM7O1sDagFmztdazzdfVuV7PAD63OFz3YHTT9tuAZZ0tDtc6jMdXHvE67d/WZuyB6ro35QiMTltHAh2Ab9xW27k2jzs9cCXzjZ0NEB8fr0+cOIEQQojqU0rlaK0HnMYuJgHvep32v1ocriHA+xaHqxdGP6KdvE77EYvD1R9YYnG4enqd9szaiLu0YFYPpmE8vV+sA2WfSN8DLLV53AU2j3snsJUKeh4XQggRNNW5Xt+M+Xyj12n/AaMz4pZepz3P67QfMeevAXZg1qIFQzBLWquArm6rrTPGyU+kbC8ESzCy9ztuq60lxommBjEmIYQQZa0Culocrsqu17uBi4B3LQ6XDSNpHbI4XK2Ao16nvdDicHXBKHgE7ToetJKWzeP2AXcDn2E0h1xo87g3ua22J9xW2zhztc+AI26rbTPwJfCgzeMOyiixQgghyud12stcr71O+yaLw/WExeEqvl7/CbjV4nBtwOjJZYrXadcY/ZD+bHG41gOLgNu9TvvRYMUacj1iyD0tIYSoOaVUttY6vq7jOF3SI4YQQoiQIUlLCCFEyJCkJYQQImRI0hJCCBEyJGnVAq/XS69evcpddsstt7B58+agHdtisXD48OGg7V8IIeqTRpu0Dmbmcs1bP3DweG5QjzNnzhx69Cjd76QQQohT0WiT1isrtrHKe5RXVmyvlf35fD6uvfZabDYbV199NdnZ2QCMHDmS1atXA3DHHXcwYMAAevbsyfTpJ0erdzgc9OjRg969e/PAA8YgrIcOHeKqq65i4MCBDBw4kO+++w6AI0eOMHr0aHr27Mktt9xCRY8sVHSsVatWMXToUPr06cOgQYM4fvw4hYWFPPDAA/Tq1YvevXvz6quv1sp7IoQQta2u+x6sdY9/vInNeyvv8irfV8T6PeloDR/8tItNaRlERVScv3u0b8r0y3pWus8tW7Ywd+5chg0bxk033cQbb7zhT0DFZs6cSfPmzSksLOSiiy7i559/Jjk5mcWLF+PxeFBKkZ6eDsB9993H/fffz/Dhw9m9ezcXX3wxbrebxx9/nOHDh/PYY4/hcrmYO3duufGUdyyr1cqECRNYsGABAwcOJDMzk9jYWGbPno3X62X9+vVERERw9GjQngsUQojT0ihLWmnpOUYn+wDanD5NHTt2ZNiwYQBcd911/O9//yuzzsKFC+nXrx8pKSls2rSJzZs3k5iYSExMDDfffDMfffQRxT3YL1++nLvvvpu+ffsybtw4MjMzycrK4ptvvuG6664DwG6306xZs3LjKe9YW7ZsoV27dgwcOBCApk2bEhERwfLly7ntttuIiDD+h2nevPlpvx9CCBEMDa6kVVWJ6GBmLuc/+2VgziIzp4BXJ6fQuknMKR9XKVXp9M6dO3n++edZtWoVzZo1Y8qUKeTm5hIREcHKlStZsWIFixYt4rXXXuOLL76gqKiIH3/8kZiYmsdU0bGEECLUNbqS1isrtlFU6j5QodanfW9r9+7d/PDDDwDMmzeP4cOHl1iemZlJfHw8iYmJHDhwgE8++QSArKwsMjIyGDt2LC+++CIbNmwAYPTo0SXuLa1fvx6AESNGMG/ePAA++eQTjh07ViaWio7VvXt39u3bx6pVqwA4fvw4Pp+PUaNG8dZbb+Hz+QCkelAIUW81uJJWVdbuTqegsGTSKijUrN1V9uJfE927d+f111/npptuokePHtxxxx0llvfp04eUlBSsVmuJqsTjx48zfvx4cnNz0VrzwgsvAPDKK69w11130bt3b3w+HyNGjGDWrFlMnz6dSZMm0bNnT4YOHUqnTp3KxFLRsaKioliwYAH33HMPOTk5xMbGsnz5cm655Ra2bt1K7969iYyM5NZbb+Xuu+8+rfdDCCGCQTrMFUKIRkA6zBVCCCHOMElaQgghQoYkLSGEECFDkpYQQoiQIUlLCCFEyJCkJYQQImRI0gqCGTNm8Pzzz1e6zpQpU1i0aFGZ+atXr+bee+8NVmi8++678gyWECJkNbqHiwGWrEvjuc+2sDc9h/ZJsTx4cXcuT0mu67AAGDBgAAMGDKjrMIQQol5qdCWtJevSmPbRL6Sl56AxOsud9tEvLFmXdlr7nTlzJt26dWP48OFs2bLFP3/Hjh2MGTOG/v37c/755+PxePzLli9fzoABA+jWrRv/+c9/APjqq6+49NJLAVi5ciVDhgwhJSWFoUOH+ve7adMmBg0aRN++fenduzfbtm0D4B//+Id//m233UZhYSEA77zzDt26dWPQoEH+IU5Kq+hYFQ1bUt4QJ0IIEWwNsqQ14a0fysy7tHc7rh9i4dlPPeQUFJZYllNQyIyPN3F5SjJHT+Rzxz/WlFi+4LYhlR5vzZo1zJ8/n/Xr1+Pz+ejXrx/9+/cHYOrUqcyaNYuuXbvy008/ceedd/LFF18AxojHK1euZMeOHVxwwQVs316y/0Or1cq3337r74n94Ycf5l//+hezZs3ivvvu49prryU/P5/CwkLcbjcLFizgu+++IzIykjvvvJMPPviAUaNGMX36dNasWUNiYiIXXHABKSkpZc6homOVN2xJfn5+uUOcCCFCl8XhGgO8DIQDc7xOu7PU8k7A34Ekcx2H12n/r7lsGnAzUAjc63XaPwtWnA0yaVVmX0b5vZ2nZxec8j6//fZbrrjiCv+wIuPGjQOMznC///57fve73/nXzcvL87++5pprCAsLo2vXrnTp0qVEKQwgIyODG2+8kW3btqGUoqDAiHHIkCHMnDmTPXv2cOWVV9K1a1dWrFjBmjVr/MOO5OTk0Lp1a3766SdGjhxJq1atAJgwYQJbt24tcw4VHWv58uXcfvvtJYYt+eWXX8oMcSKECF0WhysceB0YBewBVlkcrqVep31zwGqPAAu9TvubFoerB/BfwGK+ngj0BNoDyy0OVzev016ydFBLGmTSqqxk1D4pttzxs5KTjJJC8/ioKktW1VVUVERSUpK/h/bSqhrO5NFHH+WCCy5g8eLFeL1eRo4cCcDkyZMZPHgwLpeLsWPH8tZbb6G15sYbb+Tpp58usY8lS5ZUK9aKjiWEaBQGAdu9TnsqgMXhmg+MBwKTlgaK/0NNBPaar8cD871Oex6w0+JwbTf3V7bKqxY0untaD17cndjI8BLzYiPDefDi7qe8zxEjRrBkyRJycnI4fvw4H3/8MWCUQDp37syHH34IgNbaP/QIwIcffkhRURE7duwgNTWV7t1LxpCRkUFystFA5N133/XPT01NpUuXLtx7772MHz+en3/+mYsuuohFixZx8OBBwBheZNeuXQwePJivv/6aI0eOUFBQ4I+ltIqOVd6wJRUNcSKEqNcilFKrA36mBixLBn4NmN5jzgs0A7jO4nDtwShl3VODbWtNo0tal6ck8/SV55KcFIvCKGE9feW5p9V6sF+/fkyYMIE+ffpwySWX+KvNAD744APmzp1Lnz596NmzJ//+97/9yzp16sSgQYO45JJLmDVrVpkBHx966CGmTZtGSkpKiaSwcOFCevXqRd++fdm4cSM33HADPXr04Mknn2T06NH07t2bUaNGsW/fPtq1a8eMGTMYMmQIw4YNw2azlXsOFR3rlltuoVOnTvTu3Zs+ffowb968EkOc9OnTh1GjRskgk0LUfz6t9YCAn9k13H4S8K7Xae8AjAXetzhcZzyHBHVoErfVVuLGns3jdpZaPgV4DihuuveazeOeU9k+ZWgSIYSoucqGJrE4XEOAGV6n/WJzehqA12l/OmCdTcAYr9P+qzmdCpyH0QDDv67F4frM3FdQqgeDdk/LbbWVubHnttqW2jzuzaVWXWDzuOVpVyGEqDurgK4Wh6szRiFiIjC51Dq7gYuAdy0Olw2IAQ4BS4F5FofrBYyGGF2BlcEKNJhFu0HAdpvHnWrzuPOB4ht7Qggh6hGv0+4D7gY+A9wYrQQ3WRyuJywO1zhztT8Bt1ocrg3AP4EpXqdde532TcBCjEYbnwJ3BavlIASxetBttV0NjLF53LeY09cDgwNLVWb14NMY2XorcL/N4/61nN35SfWgEELUnIxcXDs+Biw2j7s3sAzjwbUylFJTi1u8SCs1IYRovIL5nFYa0DFgugMnG1wAYPO4jwRMzgGeLW9HZiuX2WCUtGo3TCGEEKEimCWtVUBXt9XW2W21RWHc2FsauILbamsXMDkOoy5VCCGEKFfQSlo2j9vnttqKb+yFA2/bPO5NbqvtCWC1zeNeCtzrttrGAT7gKDAlWPEIIYQIfUF9TisYpCGGEELUnDTEEEIIIc4wSVpCCCFChiQtIYQQIUOSlhBCiJAhSUsIIUTIkKQlhBAiZEjSEkIIETIkaQkhhAgZkrSEEEKEDElaQgghQoYkLSGEECFDkpYQQoiQIUlLCCFEyJCkJYQQImRI0hJCCBEyJGkJIYQIGY0iaS1Zl0bvGZ9hcbj8P7ZH/0ufx4155z21giXr0oJ27POeWlHrxzmYmcs1b/3AweO5tbK/utSQzqUhqu3PJxifdyjss7r7q2g9+TsxNPiktWRdGg9+uIHMXF+J+TkFmowcY97+zFymffRLmYSitaao6OTIzrkFhZzI83E8t4CMnAKOncgnM7fAv/xgZi5p6Tn8ejSbXUdO8LdvUnH862f2Z+b6j+P46GcWr91z2ud0wfNfsXLnUS547qugJdwz5ZUV21jlPcorK7bXdSiiHLX9+QTj8w6FfVZ3fxWtF+y/E4vDNcbicG2xOFzbLQ6Xo5zlL1ocrvXmz1aLw5UesKwwYNnSoARoUlrrqteqR+Lj4/WJEyeqvf4w5xekpedUa11l/g58RxKiI+jZvikAP+/JIKegsMQ2ibGRWNs2AWDd7nTyC4uqdazwMEV4mKJlfBQdm8cBsONQFmFK+ZeFhynioyJoEhOB1prs/EIycgrYk55D4MemFHRpEU/LJtHVOnZ9sj8jh11HT34+ZzWPpW1ibB1GJALl+4pYvycdrY3vWd8OSURFnPr/urW9v1DZZ3X3V9F6gfNjIsL45s8X0LpJTI1iUEpla63jy1tmcbjCga3AKGAPsAqY5HXaN1ew/j1Aitdpv8mczvI67Qk1CugURZyJg9SlvdVMWGAkq3aJMUbyUkYSi4oI9y9vmxiDr7AIZa6ggOiAL16n5rEU6pPJL/Vwxcm1ZUIUhUXav73WmuO5PgqLNIVa+5NSu8QYmsREUKhh497M8uPWsPtYNmnpOYSFKZSCMKUIU4rWTaJpHh9Fvq/IWB6wLCzMSLpxURH4CovIzPURpkAp5V8vOjKMiLAwirSmSGMsx1jndB0+nlciYQHsOppDRFhYSCbghigtPefkf3HamO7cstzrXp3sL1T2Wd39VbRe4PxCrXllxXaevLzXKcdTjkHAdq/TngpgcbjmA+OBcpMWMAmYXpsBVFeDT1ptmsb4q+eq0rZpDD9Mu6jWjn3eUyvKPXbbpjH8+HDlx8nzFZKV6yMiLIzEuEjyfIV8s/Uwt763utz1fYWaS/q0I7egkDxfkf/3zcM7c1mf9mw7cJwJs38kr6CQXF8RhWa15/O/68bV/TuwZtdRrnrzhzL7ffPaflxybju+2XqIG95eCRiJKzoinJjIMN64tj9Dzm7B/7Yd5tnPPMREhBMdGUa0+ftPo7rRpVUCP+9J55ON+4mOCPNv++KyreWeS0ZuAfOmnkd8dATxUeG1kiBFzR3MzOX8Z78MvIaSmVPAq5NTavxffjD2Fyr7rO7+Klpv+mU9uOKN7/3zCwo1i1b/yr0XnVPTeCKUUoEXkNla69nm62Tg14Ble4DB5e3E4nCdBXQGvgiYHWNxuFYDPsDpddqX1CSwmmjwSevsVnHVTlpntzq9/86qe+zqHCc6IpzohPAS06N6tCE+KpwT+YVl1o+LCueVSSkV7q9rmyasfXSUf9pXWESur4jIcCMh2No15ZP7zvcnu+LE16dDEgCdW8bziN1mLDMTX15BIa2bGiWiyHBF8/go8gqKyMrzcSQrn1xfob+61LP/OHO/3Vmt6tPDWfkMfmoFAN87LqR9Uixv/28n7/3gNRNZBPHR4cRFR/Dc1b2Ji4rg222H2JiWSUJ0OHFREcRHR5AQHcGwc1qglCIjuwCNJi4q4rSrjhqLV1Zso6jU7YPT+S+/tvcXKvus7v4qWu+++etrKx6f1npATeMvx0RgkddpD7wQneV12tMsDlcX4AuLw/WL12nfUQvHKqPBJ61j2b6qV/KvW1D1SrVw7NM5TmJsZLlJKzE2skb7iQgPIyH85MU7LioCW7umFa7fsXkct5zfpcLlg7u0YHCXFhUuv2ZAR64Z0JHCIk2+mRAHzlyOr0iXWTdcKZ64vCcn8nw0i4sCoH1SDL07JHEiz8eJfB+Hs/I5cTSb8DAj6S7ffIC//7Cr5DmGKbbNvASAJ12b+XCN0QAmKjyMuOhw2jSJ4bP7RwDw+pfb+XlPupkQI/zLbxreGYBV3qNk5fqMZVHhJEQb9xpbJDTcasy1u9MpKCz5+RQUatbuOlYv9hcq+6zu/ipab/fR7Fo/x3KkAR0DpjuY88ozEbgrcIbXaU8zf6daHK6vgBQgKEmrwTfEaIiWrEvjuc+2sDc9h/ZJsTx4cXcuT0mu67BqbMm6NKZ99EuJxi2xkeE8feW5NT4frTW5ZikvO99HVp6P3IJC+p/VHIDvdxxmy/7jnMjzkZVXSHa+jzClmDGuJwAzXZv5ZuthTuT7zMRYSPvEGL568AIAJs7+gR9Tj5Y4prVtEz79g5H0Js3+kR2HskgwE158VAR9OyYxbawNgFlf7yA7v5D4qHCjtBgdTqfmcf74vIdPEBUR5i9FRoRLaVDUrioaYkRgNMS4CCNZrQIme532TaXWswKfAp29Trs25zUDsr1Oe57F4WoJ/ACMr6gRx2mfhyQtUZfqcwL2FRb5k8fuI9kcOZHHibxCf2KLiwpnTK92ALz51Q52HTlhJs1CsvJ8dG/ThL+Y1TejXviabQezSux/dI82zL7BqK3p/5dlHDmR718WHRHGVf078NQV5wIw+W8/Eh6mjKQYFUFCdDhDzm7BmF7tKCrSLFq7x5/wiqtQ2ybG0Dw+iuK/8VO5N1ifPx9RM5UlLQCLwzUWeAkIB972Ou0zLQ7XE8Bqr9O+1FxnBhDjddodAdsNBd4CijAeo3rJ67TPDdp5SNIS4swoKtLkmM/6ncgvJCJM+R93+OSXfWTkFHAiv9BfBdqjXVPG901Ga80Nb6/keK6RLIuT4sSBHZk21kZ2vo8ej31W5nj3XHgOfxrdncNZeQx+agVxUeElEttNwzpzeUoyh7Py+OvnW0uUAuOjI8jK9fHS8m21UhIWda+qpBUqJGkJEeKKijRp6Tn+ZJZtlgQ7t0yge9smZOQU8LdvUk9WfZqlxYkDOzKmVzt2HMpiwls/ciLPVyJBJcVGkp5T9v5rTGQYNw3rzNX9O9ClVQJaa2nhGQIkadURSVpCBE9hkSY73yjNnffUCiq6OkSEKd6/eTBDzm7BJ7/s4+HFv9C5ZTxdWiXQpVU8XVrGM+ycljSJKdlASKob605DSVpBbT3ottrGAC9j1JHOsXnczgrWuwpYBAy0edzlP4gkhAi68DBFk5hImsRE0j4pttzeZJKTYvn6wZH+6baJMYzp1Y6dh7P4ZushFpmtNL96YCRNYiJZtGYPSzfsRWvNj6lH/C3h0tJzmPbRLwCnlbgkETYuQStpua22crsFsXncm0ut1wRwAVHA3VUlLSlpCXFmnGrrzqw8HzsPncDWrgkR4WEsWLWb93/cxaa0zHJLbgnRESTFRdI0JpKmsREkxkbSPD6ap680GqF8t/0wB4/n0jQmksTYSJrGRpIUG0nrpjG12gK1oZOSVtUGAdttHncqgNtqq6hbkL8AzwAPBjEWIUQNFV/0a1qKSYiO4NwOif7pCQM7MWFgJzo7XOWun5XnY3SPNmTkFJCZW4D3cDa7A7r3+vv3Xj7ffKDENslJsXznuJDnPttSpj/QnIJCHv94kz/O3IJCYiLDqQkpvdVfwUxaVXYL4rba+gEdbR63y221VZi0lFJTgakAUVFRQQhVCFGey1OSa+1iXVl14wsT+la43XNX92Fadr6R1MzEpswePivqWzTwAf7LX/+OtPQckpNiaZ8US7vEGPqf1Ywr+3UAjO6TmsVHEWk+3lC69FZb1ZiidtRZjxhuqy0MeAGYUtW6Zv9Ys8GoHgxuZEKIYHjw4u7lVuU9eHH3SrdLjIskMa78Hl8qSoTtEk/2yTd5cCd2HMwiLT2XfRk5rNt9jIycAn/SGvXiNxzPLaBN0xjaJ8WyeW9muaW35z7bIkmrHghm0qqqW5AmQC/gK7fVBtAWWOq22sZJYwwhGp5TrW6sTEWJ8M9jrP7pG4ZYymxX3GG01pqHx1pJS89lb3oOe9NzyiSsYmnpOXyz9RApnZLKtIoUZ04wG2KU2y2IzePeVMH6XwEPSEMMIURN1Pb9p6rG4AtTRpXlVf07cCLPR2ZuAe1CYAw4aYhRBZvH7XNbbXcDn2F2C2LzuDe5rbYngNU2jzuoo1sKIRqH2rzvBhWX3qZf1oMOzeJY5T1Kn45GQ5OvthzirnlrSU6KZaClGQMszRloaU7X1gmEhZ184FoadtQeebhYCCFKqW6S+fVoNss2H2D1rqOs8h7j0PE8AL5+cCRntYhnY1oGn27cx5z/7SS34OSwPHXRLL+hlLQkaQkhRC3QWvPr0RzW/XqMcX3ao5Ti/gXrWbyu/BE+ipvtBwpmiUySVh2RpCWECBXHTuST8pdl5S5TwJhebTmclcfZrRLIyS/kk437SwyUWpslsoaStGTQHiGECJJm8VEkJ5XfSKN9UiyWlvEoFMs2H+DfG/aWGdm7uKm9OKnBj1wshBB1qbLn0wJLUJ0drnK7uaroAerGSkpaQggRRJenJPP0leeSnBSLwriXVV6VX/tKSmTiJLmnJYQQ9UCwO/9tKPe0pHpQCCHqgcAeQ4ofbn7y8p7yPFcpUj0ohBD1xOUpyXznuJBXJqUAYGuXWMUWjY8kLSGEqGd6tW8KwMa9GXUcSf0jSUsIIeoZS4t4rG2bUFQUWm0OzgRpiCGEEI2ANMQQQgjRYFgcrjHAyxgdnM/xOu3OUstfBC4wJ+OA1l6nPclcdiPwiLnsSa/T/vdgxSklLSGEqIe+33GYBxZu4O83DaJrmyanvb/KSloWhyscYyipURijzK8CJnmd9s0VrH8PkOJ12m+yOFzNgdXAAEADa4D+Xqf92GkHXQ65pyWEEPVQy4Ro9mbknqnGGIOA7V6nPdXrtOcD84Hxlaw/Cfin+fpiYJnXaT9qJqplwJhgBSpJSwgh6qEuLeOJiQxjY1rmmThcMvBrwPQec14ZFofrLKAz8EVNt60NlSYtt9UW7rbavgzWwYUQQpQvIjwMW7umbEyrtZJWhFJqdcDP1FPcz0RgkddpL6xyzSCoNGnZPO5CoMhttckTbkIIcYb1ap/Ipr2ZtdX03ae1HhDwMztgWRrQMWC6gzmvPBM5WTVY021PW3VaD2YBv7ittmWAvwWEzeO+N1hBCSGEgJHdW6GUMURJfHRQG3uvArpaHK7OGAlnIjC59EoWh8sKNAN+CJj9GfCUxeFqZk6PBqYFK9Dq3NP6CHgU+AajVUjxjxBCiCC6yNaGJ8b3CnbCwuu0+4C7MRKQG1joddo3WRyuJywO17iAVScC871Ouw7Y9ijwF4zEtwp4wpwXFNVq8u622qIAK0Zzxi02jzs/WAFVRZq8CyEaE19hEek5BbRMiD6t/TSUh4urLGm5rbaxwA7gFeA1YLvbarsk2IEJIYSAa976gT/MX1/XYdQb1SlzvgBcYPO4twO4rbazARfwSTADE0IIAd3bNuGTjfvRWqOUqutw6lx17mkdL05YplTgeJDiEUIIEaBn+0TSswvYcyynrkOpF6pT0lrtttr+CyzEuKf1O2CV22q7EsDmcX8UxPiEEKJR65VsPHG0aW8GHZvH1XE0da86Ja0Y4ADwG2AkcAiIBS4DLg1aZEIIIbC2bUJ4mDpTPWOcERaH6wqLw5UYMJ1kcbgur8621SlpzbF53N8FznBbbcNKzxNCCFH7YiLDeXxcT85NblB9PEz3Ou2Liye8Tnu6xeGaDiypasPqJK1XgX7VmCeEECIIEqIjuPODtexNz6F9UiwPXtydy1OC1r3fmVBeLV+1HkarcCW31TYEGAq0clttfwxY1BRjvJUqua22EuOz2DxuZ6nltwN3AYUYPW9MtXnc5XaFL4QQjdGSdWk4PvqZ3IIiANLSc5j20S8AoZy4VlscrheA183pu6hmpxWV3dOKAhIwEluTgJ9M4Oqqduy22sLNgC4BegCT3FZbj1KrzbN53OfaPO6+wLMYzeuFEEKYnvtsiz9hFcspKOS5z7bUUUS14h4gH1iAMQxKLkbiqlKFJS2bx/018LXbanvX5nHvOoWgBgHbbR53KoDbaisen8VfkrJ53IF3FuMxWicKIYQw7U0vv6l7RfNDgddpPwE4TmXbKlsPnmLCgmqOseK22u5yW207MEpa5XbCq5SaWtydvs/nO8VwhBAi9LRPiq3R/FBgcbiWWRyupIDpZhaH67PqbFvng0DaPO7XbR732cCfgUfKW0drPbu4O/2IiOB2HCmEEPXJgxd3JzayZDOC2MhwHry4ex1FVCtaep329OIJc8Tj1tXZsMKk5bbanjF//+4Ug6rpGCvzgctP8VhCCNEgXZ6SzNNXnktyUiwKSE6K5ekrzw3lRhgARRaHq1PxhMXhslDN20OVFVvGuq02B8a4KB+eQlCrgK5uq63C8VncVltXm8e9zZy0A9sQQghRwuUpyaGepEr7P+B/Fofra0AB5wPVGkm5sqT1KXAMSHBbbYENJhSgbR5308p2bPO4fW6rrXh8lnDgbZvHvclttT0BrLZ53EuBu91W22+BAvNYN1YnaCGEEKHL67R/anG4BmAkqnUYDxVXq2VJheNpua22aJvHnee22v5t87jH11awp0vG0xJCiJqrT+NpWRyuW4D7MG4brQfOA37wOu0XVrVtZQ0xiodTbjgdXgkhhKgP7gMGAru8TvsFQAqQXp0NK6sejHJbbZOBocU9ugeS3t2FEEKcolyv055rcbiwOFzRXqfdY3G4qtUcsrKkdTtwLZCE0aN7IA1I0hJCCHEq9pjPaS0BllkcrmNAtZ4JrvCeVjG31XazzeOee7oR1ha5pyWEEDVXn+5pBbI4XL8BEoFPvU57flXrV9YQ40Kbx/1FeVWDUHfVg5K0hBCi5upr0qqpyqoHfwN8QdmqQZDqQSGEEHWgyurB+kZKWkIIUXNVlbQsDleJoaS8TruznHWuAWZgFFw2eJ32yeb8QuAXc7XdXqd9XC2H71fZeFp/rGgZgM3jlmFEhBCiAbA4XMVDSY3C6Nx8lcXhWup12jcHrNMVo4ekYV6n/ZjF4QrsKzDH67T3PROxVlY92MT83R2jPf1Sc/oyYGUwgxJCCHFGDQK2e532VACLw1VmKCngVuB1s3NbvE77wTMeJZWPp/U4gNtq+wboZ/O4j5vTMwDXGYlOCCHEmVDeUFKDS63TDcDicH2HUYU4w+u0f2oui7E4XKsBH+D0Ou1LghVodYYmaYMxwmSxfHOeEEKI0BFRPC6h+VOtDmoDtwe6AiOBScDfAsbEOsvrtA/A6BT9JYvDdXZtBV1eEFV5D1jpttoWm9OXA+8GKyAhhBBB4dNaD6hgWXWGktoD/OR12guAnRaHaytGElvlddrTALxOe6rF4foKo1umHbUZfLHqjFw8E/g9Ri/sx4Df2zzup4MRjBBCiDqxCuhqcbg6WxyuKIyhpJaWWmcJRikLi8PVEqO6MNUcdTg6YP4wSt4Lq1XS5F0IIRqBajR5Hwu8hDmUlNdpn2lxuJ4AVnud9qUWh0sBfwXGAIXATK/TPt/icA0F3gKKMApCL3md9qD1oiRJSwghGoGG0iNGdRpiCCGEEPWCJC0hhBAho1ElrYKDB/Fedz2+Q4fqOhQhhBCnoNEkrYKDB9l51dXkrFnDodffqOtwhBBCnIJG0RDD06cvOi+vzHwVGUnLu+8mbtBA4lJSaivEMrLXrSN75apaPU4w9llXGtK5NES1/fmEyt9DXZ13ReudbjwNpSFGg09aFSUsP6VQ0dF0nPUmh8spgSVecQVJV16B79gx0u69r8zyZpMm0nTsWAr27WPvQ38uG+9vfsPh115D5+eD1kRbrYQnJPiXt7zjduKHDiXX7ebAU2Uff2t1//3E9Ushe+06Dr34IgCFWVnkeTygNSoqik5/fxedk8PhN2eV2b7t448T3aUzx7/4kqPvvFNmeftnnyGyXTsy//tfjv1zfpnlya+8TESzZqR/tJiMxYvLLO84+y3CYmM5Om8exz/5tMzys95/D4Ajc98m66uvSixTMTG0vPMOdv/+Jv9nFPj+hCcl0eHVVwA4+NcXyFm/vsT2EW3bkvzcswDsf+op8tyeEsujLBba/eUJAPY9+hj5Xm+J5dE2K20ffhiAtAcfwrd/f4nlsX370vpPRr/Re+65l8L09BLL44acR6s77wRg961T0bm5JZYnjBxJi5tvAmDX9TeUeW+aXDKG5pMnU5STw69Tbyuz/HS/e81//3uaXHgBeak72T99epnl1fnuoWD3jVOM769SJT6fNg9PI8Zm48T331f7u1fiuxsdTad338G3b99pfff2P/Msx959F7QuEWNV371Of5sNwKE33iD7hx/L7D/nl19Onne3biX+bmv63cvZuNF/3ihFkzEX08H8ew787vnfHzDen3fe5ujb75C/Z48xXynjb/6dt2ucuBpK0mrw1YNnL/uc6O7dK15Ba3RBAdmr1wTl+Hlbtxpf/KIi0JqizMzT3mdRZqbx5Qe0z0f2ylWnvc+6kr1ylT+h19b7I2pP9spVaJ/PmKiFz6fEd7egoFa+uwW7vP591tZ3yHfs2Mm/26KiWj1vtKag1D9IZdYrvi6Z749/flFRrb1voarBl7QAfr37brKWryg5UylUZCS6sBAVGXlK/7lUR/a6dUZJoqCg1o4TjH3WlYZ0Lg1RbX8+ofL3UFfnXdF6tRFPQylpNZKkdQ8o8O3bT+7GjUS0aUPyS0bR/EzcSwmF+va61JDOpSGSe1pyT6s+aRRJq1he6k5OfP89iZddSnhiYi1HJoQQ9ZckrToi3TgJIUTNNZSk1eAbYgQqys+n4MBBivLzq15ZCCFEvVOd8bROmdtqGwO8jNFr8Bybx+0stfyPwC0Yo10eAm6yedy7ghVP9k8r+fXWWzlr3jzi+sm9EyGECDVBK2m5rbZw4HXgEqAHMMlttfUotdo6YIDN4+4NLAKeDVY8ANFdz6Ht448T1bFDMA8jhBAiSIJZ0hoEbLd53KkAbqttPjCegMHBbB73lwHr/whcF8R4iGzblmYTrgnmIYQQQgRRMO9pJQO/BkzvMedV5Gbgk/IWKKWmKqVWK6VW+4ofdDwFOj+f/D17KMrOPuV9CCGEqDv1oiGG22q7DhgAPFfecq31bK31AK31gIiIUy8c5m7bxo7fjuLEDz+c8j6EEELUnWBWD6YBHQOmO5jzSnBbbb8F/g/4jc3jrqSTwNMXlZxMu6eeIsZmC+ZhhBBCBEnQntNyW20RwFbgIoxktQqYbPO4NwWsk4LRAGOMzePeVp39ynNaQghRc/KcVhVsHrcPuBv4DHADC20e9ya31faE22obZ672HJAAfOi22ta7rbalwYoHjA4681J3UiidsgohREhqVD1iFOzfz/aRF9D2L0/Q7He/q+XIhBCi/mooJa2gPlxc34QnJdH+ueeI7dO7rkMRQghxChpVSUsIIRorKWmFIF1URN627US0aE5Ey5Z1HY4QQtQbFoerRLd7XqfdWc461wAzAA1s8Drtk835NwKPmKs96XXa/x6sOOvFc1pnivb52Dl+POmL/lXXoQghRL1hcbjKdLtncbh6lFqnKzANGOZ12nsCfzDnNwemA4MxekKabnG4mgUr1kaVtFREBMkvvUST0aPqOhQhhKhPBgHbvU57qtdpzweKu90LdCvwutdpPwbgddoPmvMvBpZ5nfaj5rJlwJhgBdqoqgdVWBhNx1xc12EIIURdiFBKrQ6Ynq21nm2+Lq/bvcGltu8GYHG4vsOoQpzhddo/rWDbyrrsOy2NKmkB5G7eTHhSEpHt29d1KEIIcSb5tNYDTmP7CKArMBKjh6NvLA7XubURWE00qupBgF3X38DRv79X12EIIUR9Up1u9/YAS71Oe4HXad+J0eNR12puW2saXUkr+cUXpJQlhBAlrQK6WhyuzhgJZyIwudQ6S4BJwDsWh6slRnVhKrADeCqg8cVojAYbQdHoSloJI0YQfc45dR2GEELUG16nvUy3e16nfZPF4XrC4nAVd7v3GXDE4nBtBr4EHvQ67Ue8TvtR4C8YiW8V8IQ5Lyga3cPFORs3ERYXR3SXzrUYlRBC1G8N5eHiRlfSSrv/fg7PerOuwxBCCHEKGt09rfbOpwlr0qSuwxBCCHEKGl31oBBCNEZSPRiicjZuItftruswhBBCnIJGl7T2/+UJDv71hboOQwghxClodPe02j72GCoisq7DEEIIcQoaXdKK7dmzrkMQQghxihpd9WDOpk1kr11b12EIIUS5Cg4exHvd9fgOHarrUOqlRpe0Dr/6GgeenFnXYQghRLkOv/EmOWvWcOj1N+o6lHqp0TV5z0tNRft8xHTrVotRCSHE6fH06YvOyyszX0VHY92w/rT3L03eQ1R0ly6SsIQQ9U7yq6/SZOxYVEyMMUMpml5q55zly+o2sHqm0SWtXLebrP99V9dhCCGE34mfVrJn6lQK09ON0laE0UYuLKEJEa1a1XF09UujS1pH3/8H+x59tK7DEEI0MoENLLTWZCxdSuannwIQN6A/bR9/nLDoaJImTqTzog9JmjgR3+HD5PyykcKMDGmgYWp097Ty96RRlH1CqgiFqCMFBw+S9sc/0eHFFxpVKWLfjMdJX7CApAkTaDv9MXZNmkx4YiId35pV4TaFWSfY8dvfEj90KGFNm/q3bzdjeo2P31DuaTW6pCWEqFuBF+9TufiWJxiJsKb71AUF+I4eJaJ5c1RkJHmpqWR98w2HXngRnZ9fZn0VFUX39etQYZVXeLnP7Q0FBWW3r2EDjYaStBpd9WDuli1kfvZ5XYchRKPj6dMXt9VG+vz5oDXp8+fjttrwnNvbv07Oxk0UHDjgn9Y+X7X2HYxm4odff4OcNWs4+MqrABTs38/R9973x5e9bh3eydeSl5oKwPFly9j+m5Hk7dwJQO5mNwedz9Bx7hyaXmpHRRk98ajoaJpedinnrFheZcICOGfFcmN7s4GGiokxtm+kDTSCmrTcVtsYt9W2xW21bXdbbY5ylo9wW21r3Vabz221XR3MWIplfvwxex966EwcSogGoSb3UnI3byZn/Xr/9J4/3M+Bp50AnL3sc1RsLISHA8bFl8hIml52mX/93TfcwNG33/FPb0npx8EXXgRAa03qFVdybMFCY7qwEHePnuUnwj59KczIIO2hhzjx448A+A4dYtfvf0/Wt98CkL9nDzsuvZTjX34JQN62bWwZNBh3r3ONfS5YAFqT8eGHuK02dowazYGnniJv6zYj/sgoVGQkusBIrDG9e9N2xgwiWrYEoMlFF9Ltpx+JGzCAsIQEdIEPFR2Nzs8nLD6h2iXCyNatje3z8ozt8/JqtH1DE7Sk5bbawoHXgUuAHsAkt9XWo9Rqu4EpwLxgxVFa8xtvpPNH/zpThxMi5AWWYgr27ydn4yb/soMvvcS+x05W8e2f+RQHn/+rfzqiVSvCmzcHjItvwsiRUFTkv/g2vfRS2vz55D+RyS+/TNLVVwFGkmp5153EDx1iLPT5iExuT3iTBGN5bi7hLVsS3avXyWbiQEyvXpyzfBm6qIictevwHTpsLFAKnZuHLiwEICw6muizzyE8wdhfeFISiePG0XHWLKNkEx1tbGaWjLp89ildf/ie+GFDAYjt1ZOz/v4uMd2N++NRHTrQbOIEIszzDYuNJTwxEaUUvsNHSJo4EcuC+f4GFjVxuts3JEG7p+W22oYAM2we98Xm9DQAm8f9dDnrvgv8x+ZxL6pqv3JPq+FprDfmq6v4b1QpRVFeHkWZmYQ3b44KD8d37Bi+AweIPuccVEQEBWlp5O30Ej/kPFR4OLlbt5K7eTOJ48ahwsLIXruOnHVraXHzzRQUFLBrwwZyfT7CExMBKMrOQefnEZ6URMHefUD514fI9u0BKMzMhKIiwpOSjFgLCkApVET53Zr6jh5FhYURFh9P0YkT6KIi/0X+VBWmp1OUnQ0oQBMWF+ePpz7t80yJiYmhQ4cOREaW7Bi8odzTQmsdlJ/N3a1Xb+5unRMwff3m7tbXKlj33c3drVdXZ79xcXH6dORu26aPLV6siwoKTms/dS3/wAG989rrdMHBg3UdymnbO32G3my16b3TZ5yxYxYVFRm/fT5dcOSILszJ0VprXZiXp3O2bNG+9HRjOitLZ333nf999h07ptP//W+dv3ev1lrr/P0H9OG5b+u8X3/VWmudt3u33v/sszrP69Vaa52zZYtOe+ghnZuaqrXWOnv9er3rppv901nff693jBvvn85ctkxvGTrMv/2xjxbrzd2tOu/XPVprrY/OX6A3d7fq/P0HtNZaH3nvfb25u1UXHD2qtdb68Ny39ebuVu07nqW11vrQW7P15u5WXZibq7XW+uCrr+nN3a26qLBQp6am6v3bt+vsTZv870v+gYM6d+s249zz83Xutm06+5dfjJ+Nm3Tuzp3al5FRWx9DrcjbtUvnpaXpwuxsnZeWpvN27aqX+zwTioqK9KFDh3Sq+X0KBJzQQbren8mfkGiIoZSaqpRarZRa7avmjdmKZLj+yz7HNPL3pJ2x5x6CcZy67J9M+3wlWkMVpqdTmJ7un87fs4eC/fv907mbN/tvTgOc+PEncj2eCm/Mu3ud678PAXDw5Zc5/tVX/um9D/8fmZ9+ZsRSWMjuW6eSsXQpAEV5eaReeSXp//rIiO34cbadP8J/H8R35AjuHj059s9/GtMHD7Jt6DAy//MfY3rfPnaOG0+WebyC/fvZfdPNZK9a5Z/e+9Cfydm40Vj/wH4OPvss+Tt2GNOHDnPsg3kU7DPOvygri+w1aynKyjLjLTJem9/jsLg4Ijt08JdMItq0pclvf2vc+wFirN1pedddhCcY/yDH9e9H2xnT/dMJvxlB8quvEBYXB0BT+1jOmjePsBijaqvZNb8z7iWZ/3W3uPUWuq9dA0qRm5tL6y5diO1xstY+snUrorueY8QWGYky94tSoItQUVGEN21KfRLVqRNR7dsTFhtLVPv2RHXqVC/3eSYopWjRogW5ubl1HUrQBHNokjSgY8B0B3NejWmtZwOzwagePJ2gfAcOgFIcffcdQPkv/LXV9LY8xa2Qio9TmJVFYXoGkcntUUpRcOAgvn17ie3bFzD6R8z3emly4YUA5GzYQN72HSRddWWZ/snS5883Lvrh4dg2GRfSYx9+SP72HbSZZrR9OfL2O+Tv2kW7x2cARhLw7d1H+2eMG+T7//IkviNH6PCSccM77YEHKTpxgo5vGglx9y23gtZ0mjsHgF3XXkdYQoJ/evdNNxPRujUdZ70JwJ477iDK0pkOr75i7O/+PxLTqxfJf30egH0PP0zcwIGcvexzDj77LJn/cQHGjfkmo35L1tffcPzzZcSfd55xjv+cDz4fTUaOBCB7zWqiz+5ivAFhYRRmZFBkvicqPJzINm0Jizcu6ioqioSRI4nq2MFYPS6OFrdN9Q9RE56YSJtHHiG2Tx/AuAeT/NJLxJ7bCzCqwc76x/tEdTGOF9WlC2d/9qm/GjOmRw+6rV5NWKxxTyWuXwrW9ev8n09cv34lWnnF9UvBsmC+fzq2Tx86vv7ayelze/mPDRBjsxFjs/mno885h+hzzvFPR3XqVOKCGtmmDZFt2vinw5OSSlRrhZn3aYoppaiUz0d48+ZENGuG79gxf7IV9VeVn2mIC2bSWgV0dVttnTGS1URgchCPV6myF/sFAa+NC7+KjMT6y8+cWLmSg8//lfbPOInu3Jmsb7/lwDPP0PGNN4jq1InMzz/nwNNOLP94n8jkZDKWLmX/kzPp8vFSItu04dj8+ex/ciYqLKxEiSQwwVBYSPcN61HR0aQv+pDDr76GddNGVHg4GUuXcuRvc/xJKPPzzzn2wTySrrqSs5d9zq4bbqTA6wWMC31ku3YUFZw8Tv5OLzkbNvinC9PTS5TyVFi4vwUXQETr1qioKP90TK+e6NyT71WTiy4s8V4mTZpYYiDNFlOn+i/aAK0ffNCfNADazXySsCZN/NMdXn+NsIQEf6solDJaYZmtos5Zsdx/Exyg248/lDj+OZ99dvJclKLzwpOfpYqI8CdbMC7S7f7yxMnp2Fha33ffyem4OJpfd22J6aZjLi6xftyAASeno6KIOuusEscLT2i4w9IFJsQos/QnGiaLwzUGeBkIB+Z4nXZnqeVTgOc4Wfh4zeu0zzGXFQK/mPN3e532ccGKM2h/bTaP2+e22u4GPsN4E962edyb3FbbE8Bqm8e91G21DQQWA82Ay9xW2+M2jzsoozQW/1d/fPkKdHHROSzMaMkUFUVYfDztzZKGiogkvGlT/zMUYfEJRHc521/FEtGyFfHnnee/sEZ27EjiuHH+/2JjbDZa3HwziVdcweHXXuX458vQ+fn+kkTiVVfh27vPv/9Eu53Y3r2NKhig2aRJNB09Gq01Sila3nYbLaZMMY7VujXxgwaSvmsXKioKnZdH3ODBJUqKbR56sMS5t/7j/SWmW91zd4nplrdNLTFdfKxizSZNKjGddPnlJaYDL/IACSNGlJgOvOgDJUoOxa2imk24hmMLFuI7dIjwgAQnGi6v18ull17KRrOqNdAtt9zCH//4R3r0KN3guHZYLBZWr15NS7N5erGnnnqKhx9+uFaPlZCQQFZWFnv37uXee+9l0SKjvdmkSZPYtGkTv//977nkkkuYOHEiSikWLVrE2WefXasxVMXicBW39h4F7AFWWRyupV6nfXOpVRd4nfa7y+wAcrxOe98ghwkEeeRim8f9X+C/peY9FvB6FUa1YdCV96yDv+ltfj5NLr6YhMGDAaMKp9Ocv/m3jeuXQly/lIqnU1KISzk5Hdunj7+6yXg+o6DE8xUJZrVXsSiLhSiL5WSspat4St1D8B09VuZCH6o6vvaq/3W76Y9VsqaoD7LXrSN75SriBg0s8Z2vbXPmzAnavitzKkmrsLCQ8ICai4q0b9/en7D279/PqlWr2L59OwBOp5Orr76aRx55pOZB145BwHav054KYHG45gPjgdJJq8413HqNcgT+V//r3feA1nR8/bWgXvjLK0mcLrnQi2DYdf0NZeY1uWQMzSdPpignx+j9weMBrUEpoq1Wml9/PUlXXoHv2DHS7r2vxLZnvf9elcf0+Xxce+21rF27lp49e/Lee+8RFxfHyJEjef755xkwYAB33HEHq1atIicnh6uvvprHH38cAIfDwdKlS4mIiGD06NE8//zzHDp0iNtvv53du3cD8NJLLzFs2DCOHDnCpEmTSEtLY8iQIf7HCAI5HA5ycnLo27cvPXv2ZObMmYwZM4b+/fuXic9isTBhwgSWLVvGQw89xMSJE/372blzJ5MnTyYrK4vx48f75weWLEePHk1aWhp9+/bliiuu4M033yQ8PJwVK1bwpfmwcxBEKKVWB0zPNtsLACQDvwYs2wMMLmcfV1kcrhHAVuB+r9NevE2MxeFaDfgAp9dpX1K7oZ/UqJJW4MW+a8DN8WBe+CXBiIaiKDPTSFgAWhvTp2nLli3MnTuXYcOGcdNNN/HGG2/wwAMPlFhn5syZNG/enMLCQi666CJ+/vlnkpOTWbx4MR6PB6UU6Wbr1fvuu4/777+f4cOHs3v3bi6++GLcbjePP/44w4cP57HHHsPlcjF37twysTidTl577TXWmz16eL3eSuNr0aIFa9euLbOf++67jzvuuIMbbriB119/vdzzXrp0KZdeeqn/WFprEhISypx7LfNprQdUvVqFPgb+6XXa8ywO123A34HiG95neZ32NIvD1QX4wuJw/eJ12necbsDlaVRJSwhRscpKRmGxsbR//jl2//4mo7o7MpL2zz/nryKMaNasWiWr0jp27MiwYcMAuO6663jllVfKXLgXLlzI7Nmz8fl87Nu3j82bN9OjRw9iYmK4+eabufTSS7n00ksBWL58OZs3n6zRyszMJCsri2+++YaPPjIeg7Db7TRr1uy045swYUK523z33Xf8619GrzvXX389f/7zn6v7dtSlKlt7e532IwGTc4BnA5almb9TLQ7XV0AKIElLCFF34lJS6PTO27V6T6t08+zS0zt37uT5559n1apVNGvWjClTppCbm0tERAQrV65kxYoVLFq0iNdee40vvviCoqIifvzxR2ICunUKVnzx8RV3LhGCzc5XAV0tDleFrb0tDlc7r9O+z5wcB7jN+c2AbLME1hIYRkBCq20h8XCxEKJ+iEtJoeVtU2utEcbu3bv54QfjkYZ58+YxfPjwEsszMzOJj48nMTGRAwcO8MknnwCQlZVFRkYGY8eO5cUXX2SD+YjH6NGjefXVk1XyxdVvI0aMYN48o4vTTz75hGPHjpUbT2RkJAUBw4BUFV95hg0bxvz5xrN4H3zwQZXr1wdep90HFLf2dgMLvU77JovD9YTF4Spuvn6vxeHaZHG4NgD3YvQbC2ADVpvzv8S4pxW0BhxS0hJC1Jnu3bvz+uuvc9NNN9GjRw/uuOOOEsv79OlDSkoKVqu1RFXd8ePHGT9+PLm5uWiteeGFFwB45ZVXuOuuu+jduzc+n48RI0Ywa9Yspk+fzqRJk+jZsydDhw6lUwU9XEydOpXevXvTr18/Zs6cWWV85Xn55ZeZPHkyzzzzTImGGPWd12kv09rb67Q/FvB6GjCtnO2+B84NeoAmGQRSiEbK7XZjC3hmTpRU2XNk9V15n21D6TBXqgeFEEKEDElaQghRDovFEpKlrIZOkpYQQoiQIUlLCCFEyJCkJYQQImRI0hJCCBEyJGkJIaotmKN9z5gxg+eff77SdaZMmeLvKT3Q6tWruffee2s9pmLvvvsud99ddkSOr776iu+//75WjxX4Pjz22GMsX74cgG+//ZaePXvSt29fcnJyePDBB+nZsycPPvhgZbtrcOThYiFEtR1+480zMtp3TQ0YMIABA06nL9hT89VXX5GQkMDQoUOrvY3P5yMionqX3ieeODmA6QcffMC0adO47rrrAJg9ezZHjx6t1rAoDYmUtIQQgDE0SfpHiwHQBQXsuv4GMpYuBcDTuw9uq80YeVtr0ufPx2214e5ldITgO3aMXdffwPEvjGE1qlsSmzlzJt26dWP48OFs2bLFP3/Hjh3+YUHOP/98PB6Pf9ny5csZMGAA3bp14z//+Q9gJI/iTnNXrlzJkCFDSElJYejQof79btq0iUGDBtG3b1969+7Ntm3bAPjHP/7hn3/bbbdRWFgIwDvvvEO3bt0YNGgQ3333XZnYvV4vs2bN4sUXX6Rv3758++23TJkyhdtvv71MfO+++y7jxo3jwgsv5KKLLqr2+1BcspwzZw4LFy7k0Ucf5dprr2XcuHFkZWXRv39/FixYUGZ/DZmUtIQQVeryn4/xTpxE4fHjYA5qGhYfT6tSo2LXxJo1a5g/fz7r16/H5/PRr18/+vfvDxjdKc2aNYuuXbvy008/ceedd/LFF18ARrJYuXIlO3bs4IILLvAPpFjMarXy7bffEhERwfLly3n44Yf517/+xaxZs7jvvvu49tpryc/Pp7CwELfbzYIFC/juu++IjIzkzjvv5IMPPmDUqFFMnz6dNWvWkJiYyAUXXEBKqf4WLRYLt99+e4khRebOnVthfGvXruXnn3+mefPm1X4fit1yyy3873//49JLL+Xqq68GjBGRi/tWbEwkaQkhgJJDk6jIyBLTUZ060WT0KNIXLDw52vcVV9DMvICWHpokolWrKo/37bffcsUVVxAXFwfAuHFGv6xZWVl8//33/O53v/Ovm5eX5399zTXXEBYWRteuXenSpUuJUhhARkYGN954I9u2bUMp5e8Ad8iQIcycOZM9e/Zw5ZVX0rVrV1asWMGaNWsYOHAgADk5ObRu3ZqffvqJkSNH0so8jwkTJrB169ZqvIsVxzdq1KgyCauy90GUT5KWEKJagjEKd3mKiopISkqqsBRR1XAmjz76KBdccAGLFy/G6/UycuRIACZPnszgwYNxuVyMHTuWt956C601N954I08//XSJfSxZsuSU468ovsqGMhHVJ/e0hBDV0vG1V2k3/TFirFbaTX+sxKjcp2LEiBEsWbKEnJwcjh8/zscffwxA06ZN6dy5Mx9++CFgjOpbPPQIwIcffkhRURE7duwgNTWV7t27l9hvRkYGycnJgHEvqVhqaipdunTh3nvvZfz48fz8889cdNFFLFq0iIMHDwJw9OhRdu3axeDBg/n66685cuQIBQUF/lhKa9KkCcePHy8xr6r4qvs+iPJJ0hJC1Il+/foxYcIE+vTpwyWXXOKvogOjpdzcuXPp06cPPXv25N///rd/WadOnRg0aBCXXHIJs2bNKjPg40MPPcS0adNISUnB5/P55y9cuJBevXrRt29fNm7cyA033ECPHj148sknGT16NL1792bUqFHs27ePdu3aMWPGDIYMGcKwYcMq7A3/sssuY/Hixf6GGNWJrybvgyhLhiYRopGSoUlq35QpU0o0lqgrMjSJEEIIUQ9IQwwhhKglgffQRHBISUuIRizUbg+IqjX0z1SSlhCNVExMDEeOHGnwF7nGRGvNkSNHqmz8EcqkelCIRqpDhw7s2bOHQ0F63krUjZiYGDp06FDXYQSNtB4UQohGoKG0HgxqSctttY0BXgbCgTk2j9tZank08B7QHzgCTLB53N5gxiSEEKIsi8NV4nrtddqdpZZPAZ4D0sxZr3md9jnmshuBR8z5T3qd9r8HK86g3dNyW23hwOvAJUAPYJLbautRarWbgWM2j/sc4EXgmWDFI4QQonwWh6vM9dricJW+XgMs8Drtfc2f4oTVHJgODAYGAdMtDlezYMUazIYYg4DtNo871eZx5wPzgfGl1hkPFGfkRcBFbqtNIYQQ4kwaBGz3Ou2pXqe9out1RS4Glnmd9qNep/0YsAwYE6Q4g1o9mAz8GjC9ByMTl7uOzeP2ua22DKAFcDhwJaXUVGCqOamVUjmnGFME4KtyrdDRkM6nIZ0LyPnUZw3pXKD65xOrlFodMD1baz3bfF2d6zXAVRaHawSwFbjf67T/WsG2ydUNvqZCovWg+cbOrnLFKiilVmutz/zwpkHSkM6nIZ0LyPnUZw3pXOCMns/HwD+9TnuexeG6DaOW7MIzcNwSgpm00oCOAdMdOHkDr/Q6e9xWWwSQiNEgQwghxJlT5fXa67QHXpvnAM8GbDuy1LZf1XqEpmAmrVVAV7fV1hnjpCYCk0utsxS4EfgBuBr4wuZxh1YbfCGECH2rgK4Wh6vC67XF4Wrnddr3mZPjALf5+jPgqYDGF6OBacEKNGgNMWwetw+4G+OE3MBCm8e9yW21PeG22oqH5pwLtHBbbduBPwKOYMVjOu0qxnqmIZ1PQzoXkPOpzxrSuUAtnI/XaS9zvfY67ZssDtcTFoer+Hp9r8Xh2mRxuDYA9wJTzG2PAn/BSHyrgCfMeUERcg8XCyGEaLyk70EhhBAhQ5KWEEKIkNFokpZSaoxSaotSartSKtj3zmqdUsqrlPpFKbW++FkLpVRzpdQypdQ283fQnkI/XUqpt5VSB5VSGwPmlRu/MrxiflY/K6X61V3k5avgfGYopdLMz2i9UmpswLJp5vlsUUpdXDdRl08p1VEp9aVSarNSapNS6j5zfsh9PpWcS6h+NjFKqZVKqQ3m+Txuzu+slPrJjHuBUirKnB9tTm83l1vq9ASCQWvd4H8w+tLaAXQBooANQI+6jquG5+AFWpaa9yzgMF87gGfqOs5K4h8B9AM2VhU/MBb4BFDAecBPdR1/Nc9nBvBAOev2ML9z0UBn87sYXtfnEBBfO6Cf+boJxoOjPULx86nkXEL1s1FAgvk6EvjJfM8XAhPN+bOAO8zXdwKzzNcTgQV1fQ61/dNYSlqDgO1a61StdU27KKnPArvB+jtwed2FUjmt9TdA6RZFFcU/HnhPG34EkpRS7c5IoNVUwflUZDwwX2udp7XeCWzH+E7WC1rrfVrrtebr4xitx5IJwc+nknOpSH3/bLTWOsucjDR/NMZDvYvM+aU/mxJd4ymlGlTXeI0laZ3RbkaCRAOfK6XWmN1aAbTRWhc/N7EfaFM3oZ2yiuIP5c/rbrPK7O2A6tqQOR+zOikF4z/6kP58Sp0LhOhno5QKV0qtBw5i9Ou3A0jXWhd33RQYs/98zOXFXeM1GI0laTUEw7XW/TB6Yb5LKTUicKE26gNC9vmFUI/f9CZwNtAX2Af8tU6jqSGlVALwL+APWuvMwGWh9vmUcy4h+9lorQu11n0xepoYBFjrNqK61ViSVnW6lKrXtNZp5u+DwGKML++B4moZ8/fBuovwlFQUf0h+XlrrA+YFpgj4Gyermer9+SilIjEu8h9orT8yZ4fk51PeuYTyZ1NMa50OfAkMwaiSLe7RKDBm//mYyxtc13iNJWmtArqaLW6iMG5QLq3jmKpNKRWvlGpS/Bqjm5SNnOwGC/P3v+smwlNWUfxLgRvMVmrnARkB1VT1Vqn7OldgfEZgnM9Es2VXZ6ArsPJMx1cR857HXMCttX4hYFHIfT4VnUsIfzatlFJJ5utYYBTGfbovMbq+g7KfTfFndjXwhVlKbjjquiXImfrBaPG0FaM++P/qOp4axt4Fo4XTBmBTcfwYddUrgG3AcqB5XcdayTn8E6NapgCjDv7miuLHaDH1uvlZ/QIMqOv4q3k+75vx/oxx8WgXsP7/meezBbikruMvdS7DMar+fgbWmz9jQ/HzqeRcQvWz6Q2sM+PeCDxmzu+CkVy3Ax8C0eb8GHN6u7m8S12fQ23/SDdOQgghQkZjqR4UQgjRAEjSEkIIETIkaQkhhAgZkrSEEEKEDElaQgghQoYkLSFqiVLqD0qpuIDp/xY/YyOEqB3S5F2IGjAfXlXa6Fmh9DIvxjNLh894YEI0ElLSEqIKSimLOdbSexgPeM5VSq0uNb7RvUB74Eul1JfmPK9SqqX5+o9KqY3mzx/q6FSECHlS0hKiCmZv4anAUK31j0qp5lrro0qpcIweI+7VWv9cuqRVPA2cBbyLMQ6Swuh1/Dqt9bozfS5ChDopaQlRPbu0MXYUwDVKqbUY3ev0xBhIsDLDgcVa6xPaGBvpI+D84IUqRMMVUfUqQgjgBBjDnAMPAAO11seUUu9i9PcmhDgDpKQlRM00xUhgGUqpNhjjmxU7jjHEe2nfApcrpeLMXvqvMOcJIWpISlpC1IDWeoNSah3gwRgh9ruAxbOBT5VSe7XWFwRss9YskRUPeTFH7mcJcWqkIYYQQoiQIdWDQgghQoYkLSGEECFDkpYQQoiQIUlLCCFEyJCkJYQQImRI0hJCCBEyJGkJIYQIGf8PGggiVeiaXWYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2plot(plot_results, removel_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "u has shape (300, 300), s has shape (2,), vh has shape (2, 2)\n"
     ]
    }
   ],
   "source": [
    "ratio = 0.7\n",
    "\n",
    "saved_dataset = np.load(f\"../data/saved_models/fair_emoji_sent_race/{ratio}/all.npz\")\n",
    "\n",
    "x_train = saved_dataset['x_train']\n",
    "y_m_train = saved_dataset['y_m_train']\n",
    "y_p_train = saved_dataset['y_p_train']\n",
    "y_p_train_2d = np.asarray([y_p_train, - y_p_train + 1]).T\n",
    "\n",
    "x_dev = saved_dataset['x_dev']\n",
    "y_p_dev = saved_dataset['y_p_dev']\n",
    "y_m_dev = saved_dataset['y_m_dev']\n",
    "\n",
    "A = np.dot(x_train.T, y_p_train_2d) / x_train.shape[0]\n",
    "u, s, vh = np.linalg.svd(A, full_matrices=True)\n",
    "print(f\"u has shape {u.shape}, s has shape {s.shape}, vh has shape {vh.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(dict,\n",
       "            {0: {'p_acc': 0.8199549887471868,\n",
       "              'biased_diff_tpr': 0.31864557675474753,\n",
       "              'debiased_diff_tpr': 0.31864557675474753,\n",
       "              'biased_acc': 0.741185296324081,\n",
       "              'debiased_acc': 0.741185296324081},\n",
       "             1: {'p_acc': 0.8438359589897474,\n",
       "              'biased_diff_tpr': 0.31864557675474753,\n",
       "              'debiased_diff_tpr': 0.31575482093361984,\n",
       "              'biased_acc': 0.741185296324081,\n",
       "              'debiased_acc': 0.7346836709177295},\n",
       "             2: {'p_acc': 0.463615903975994,\n",
       "              'biased_diff_tpr': 0.31864557675474753,\n",
       "              'debiased_diff_tpr': 0.11635856368415173,\n",
       "              'biased_acc': 0.741185296324081,\n",
       "              'debiased_acc': 0.7363090772693174},\n",
       "             5: {'p_acc': 0.46399099774943736,\n",
       "              'biased_diff_tpr': 0.31864557675474753,\n",
       "              'debiased_diff_tpr': 0.11674792676509015,\n",
       "              'biased_acc': 0.741185296324081,\n",
       "              'debiased_acc': 0.734558639659915},\n",
       "             30: {'p_acc': 0.5030007501875469,\n",
       "              'biased_diff_tpr': 0.31864557675474753,\n",
       "              'debiased_diff_tpr': 0.12023899899338386,\n",
       "              'biased_acc': 0.741185296324081,\n",
       "              'debiased_acc': 0.7366841710427607},\n",
       "             50: {'p_acc': 0.5018754688672168,\n",
       "              'biased_diff_tpr': 0.31864557675474753,\n",
       "              'debiased_diff_tpr': 0.12100513006482054,\n",
       "              'biased_acc': 0.741185296324081,\n",
       "              'debiased_acc': 0.7349337334333583},\n",
       "             100: {'p_acc': 0.4981245311327832,\n",
       "              'biased_diff_tpr': 0.31864557675474753,\n",
       "              'debiased_diff_tpr': 0.12715509044451567,\n",
       "              'biased_acc': 0.741185296324081,\n",
       "              'debiased_acc': 0.7334333583395849},\n",
       "             200: {'p_acc': 0.47999499874968743,\n",
       "              'biased_diff_tpr': 0.31864557675474753,\n",
       "              'debiased_diff_tpr': 0.15675297512285796,\n",
       "              'biased_acc': 0.741185296324081,\n",
       "              'debiased_acc': 0.7150537634408602},\n",
       "             220: {'p_acc': 0.4673668417104276,\n",
       "              'biased_diff_tpr': 0.31864557675474753,\n",
       "              'debiased_diff_tpr': 0.1525863232511454,\n",
       "              'biased_acc': 0.741185296324081,\n",
       "              'debiased_acc': 0.7150537634408602},\n",
       "             240: {'p_acc': 0.5385096274068517,\n",
       "              'biased_diff_tpr': 0.31864557675474753,\n",
       "              'debiased_diff_tpr': 0.15004721659847078,\n",
       "              'biased_acc': 0.741185296324081,\n",
       "              'debiased_acc': 0.7125531382845711},\n",
       "             260: {'p_acc': 0.5482620655163791,\n",
       "              'biased_diff_tpr': 0.31864557675474753,\n",
       "              'debiased_diff_tpr': 0.1537984908912681,\n",
       "              'biased_acc': 0.741185296324081,\n",
       "              'debiased_acc': 0.7040510127531883},\n",
       "             280: {'p_acc': 0.5118779694923731,\n",
       "              'biased_diff_tpr': 0.31864557675474753,\n",
       "              'debiased_diff_tpr': 0.15392966588017956,\n",
       "              'biased_acc': 0.741185296324081,\n",
       "              'debiased_acc': 0.6950487621905477},\n",
       "             290: {'p_acc': 0.5113778444611152,\n",
       "              'biased_diff_tpr': 0.31864557675474753,\n",
       "              'debiased_diff_tpr': 0.1504312844224986,\n",
       "              'biased_acc': 0.741185296324081,\n",
       "              'debiased_acc': 0.691297824456114},\n",
       "             295: {'p_acc': 0.49987496874218557,\n",
       "              'biased_diff_tpr': 0.31864557675474753,\n",
       "              'debiased_diff_tpr': 0.1759445240878502,\n",
       "              'biased_acc': 0.741185296324081,\n",
       "              'debiased_acc': 0.661790447611903}})"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = check_result(u, removel_range, x_train, x_dev, y_m_train, y_m_dev, y_p_train, y_p_dev)\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(list,\n",
       "            {'biased_diff_tpr': [0.31864557675474753,\n",
       "              0.31864557675474753,\n",
       "              0.31864557675474753,\n",
       "              0.31864557675474753,\n",
       "              0.31864557675474753,\n",
       "              0.31864557675474753,\n",
       "              0.31864557675474753,\n",
       "              0.31864557675474753,\n",
       "              0.31864557675474753,\n",
       "              0.31864557675474753,\n",
       "              0.31864557675474753,\n",
       "              0.31864557675474753,\n",
       "              0.31864557675474753,\n",
       "              0.31864557675474753],\n",
       "             'debiased_diff_tpr': [0.31864557675474753,\n",
       "              0.31575482093361984,\n",
       "              0.11635856368415173,\n",
       "              0.11674792676509015,\n",
       "              0.12023899899338386,\n",
       "              0.12100513006482054,\n",
       "              0.12715509044451567,\n",
       "              0.15675297512285796,\n",
       "              0.1525863232511454,\n",
       "              0.15004721659847078,\n",
       "              0.1537984908912681,\n",
       "              0.15392966588017956,\n",
       "              0.1504312844224986,\n",
       "              0.1759445240878502],\n",
       "             'biased_acc': [0.741185296324081,\n",
       "              0.741185296324081,\n",
       "              0.741185296324081,\n",
       "              0.741185296324081,\n",
       "              0.741185296324081,\n",
       "              0.741185296324081,\n",
       "              0.741185296324081,\n",
       "              0.741185296324081,\n",
       "              0.741185296324081,\n",
       "              0.741185296324081,\n",
       "              0.741185296324081,\n",
       "              0.741185296324081,\n",
       "              0.741185296324081,\n",
       "              0.741185296324081],\n",
       "             'debiased_acc': [0.741185296324081,\n",
       "              0.7346836709177295,\n",
       "              0.7363090772693174,\n",
       "              0.734558639659915,\n",
       "              0.7366841710427607,\n",
       "              0.7349337334333583,\n",
       "              0.7334333583395849,\n",
       "              0.7150537634408602,\n",
       "              0.7150537634408602,\n",
       "              0.7125531382845711,\n",
       "              0.7040510127531883,\n",
       "              0.6950487621905477,\n",
       "              0.691297824456114,\n",
       "              0.661790447611903]})"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_results = convert_to_plot_results(results, removel_range)\n",
    "plot_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2plot(plot_results, removel_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "u has shape (300, 300), s has shape (2,), vh has shape (2, 2)\n"
     ]
    }
   ],
   "source": [
    "ratio = 0.8\n",
    "\n",
    "saved_dataset = np.load(f\"../data/saved_models/fair_emoji_sent_race/{ratio}/all.npz\")\n",
    "\n",
    "x_train = saved_dataset['x_train']\n",
    "y_m_train = saved_dataset['y_m_train']\n",
    "y_p_train = saved_dataset['y_p_train']\n",
    "y_p_train_2d = np.asarray([y_p_train, - y_p_train + 1]).T\n",
    "\n",
    "x_dev = saved_dataset['x_dev']\n",
    "y_p_dev = saved_dataset['y_p_dev']\n",
    "y_m_dev = saved_dataset['y_m_dev']\n",
    "\n",
    "A = np.dot(x_train.T, y_p_train_2d) / x_train.shape[0]\n",
    "u, s, vh = np.linalg.svd(A, full_matrices=True)\n",
    "print(f\"u has shape {u.shape}, s has shape {s.shape}, vh has shape {vh.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(dict,\n",
       "            {0: {'p_acc': 0.8379594898724682,\n",
       "              'biased_diff_tpr': 0.4043010400370602,\n",
       "              'debiased_diff_tpr': 0.40411721363762787,\n",
       "              'biased_acc': 0.7200550137534384,\n",
       "              'debiased_acc': 0.7201800450112528},\n",
       "             1: {'p_acc': 0.7973243310827707,\n",
       "              'biased_diff_tpr': 0.4043010400370602,\n",
       "              'debiased_diff_tpr': 0.3654118278211388,\n",
       "              'biased_acc': 0.7200550137534384,\n",
       "              'debiased_acc': 0.7148037009252313},\n",
       "             2: {'p_acc': 0.47186796699174793,\n",
       "              'biased_diff_tpr': 0.4043010400370602,\n",
       "              'debiased_diff_tpr': 0.18610177633438116,\n",
       "              'biased_acc': 0.7200550137534384,\n",
       "              'debiased_acc': 0.7191797949487372},\n",
       "             5: {'p_acc': 0.47199299824956237,\n",
       "              'biased_diff_tpr': 0.4043010400370602,\n",
       "              'debiased_diff_tpr': 0.184739631028025,\n",
       "              'biased_acc': 0.7200550137534384,\n",
       "              'debiased_acc': 0.7193048262065517},\n",
       "             30: {'p_acc': 0.4668667166791698,\n",
       "              'biased_diff_tpr': 0.4043010400370602,\n",
       "              'debiased_diff_tpr': 0.18506971632421484,\n",
       "              'biased_acc': 0.7200550137534384,\n",
       "              'debiased_acc': 0.7191797949487372},\n",
       "             50: {'p_acc': 0.46524131032758187,\n",
       "              'biased_diff_tpr': 0.4043010400370602,\n",
       "              'debiased_diff_tpr': 0.1867917631297146,\n",
       "              'biased_acc': 0.7200550137534384,\n",
       "              'debiased_acc': 0.7181795448862216},\n",
       "             100: {'p_acc': 0.4736184046011503,\n",
       "              'biased_diff_tpr': 0.4043010400370602,\n",
       "              'debiased_diff_tpr': 0.18737210776237648,\n",
       "              'biased_acc': 0.7200550137534384,\n",
       "              'debiased_acc': 0.7146786696674169},\n",
       "             200: {'p_acc': 0.5230057514378594,\n",
       "              'biased_diff_tpr': 0.4043010400370602,\n",
       "              'debiased_diff_tpr': 0.19752038343148395,\n",
       "              'biased_acc': 0.7200550137534384,\n",
       "              'debiased_acc': 0.6956739184796199},\n",
       "             220: {'p_acc': 0.5100025006251563,\n",
       "              'biased_diff_tpr': 0.4043010400370602,\n",
       "              'debiased_diff_tpr': 0.202949662154134,\n",
       "              'biased_acc': 0.7200550137534384,\n",
       "              'debiased_acc': 0.6937984496124031},\n",
       "             240: {'p_acc': 0.5075018754688673,\n",
       "              'biased_diff_tpr': 0.4043010400370602,\n",
       "              'debiased_diff_tpr': 0.20801803316486164,\n",
       "              'biased_acc': 0.7200550137534384,\n",
       "              'debiased_acc': 0.6820455113778444},\n",
       "             260: {'p_acc': 0.502750687671918,\n",
       "              'biased_diff_tpr': 0.4043010400370602,\n",
       "              'debiased_diff_tpr': 0.18813856463946912,\n",
       "              'biased_acc': 0.7200550137534384,\n",
       "              'debiased_acc': 0.6614153538384596},\n",
       "             280: {'p_acc': 0.49987496874218557,\n",
       "              'biased_diff_tpr': 0.4043010400370602,\n",
       "              'debiased_diff_tpr': 0.19263016753347223,\n",
       "              'biased_acc': 0.7200550137534384,\n",
       "              'debiased_acc': 0.6404101025256314},\n",
       "             290: {'p_acc': 0.49987496874218557,\n",
       "              'biased_diff_tpr': 0.4043010400370602,\n",
       "              'debiased_diff_tpr': 0.1721118417919755,\n",
       "              'biased_acc': 0.7200550137534384,\n",
       "              'debiased_acc': 0.6280320080020005},\n",
       "             295: {'p_acc': 0.49987496874218557,\n",
       "              'biased_diff_tpr': 0.4043010400370602,\n",
       "              'debiased_diff_tpr': 0.0834654770143427,\n",
       "              'biased_acc': 0.7200550137534384,\n",
       "              'debiased_acc': 0.5681420355088772}})"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = check_result(u, removel_range, x_train, x_dev, y_m_train, y_m_dev, y_p_train, y_p_dev)\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(list,\n",
       "            {'biased_diff_tpr': [0.4043010400370602,\n",
       "              0.4043010400370602,\n",
       "              0.4043010400370602,\n",
       "              0.4043010400370602,\n",
       "              0.4043010400370602,\n",
       "              0.4043010400370602,\n",
       "              0.4043010400370602,\n",
       "              0.4043010400370602,\n",
       "              0.4043010400370602,\n",
       "              0.4043010400370602,\n",
       "              0.4043010400370602,\n",
       "              0.4043010400370602,\n",
       "              0.4043010400370602,\n",
       "              0.4043010400370602],\n",
       "             'debiased_diff_tpr': [0.40411721363762787,\n",
       "              0.3654118278211388,\n",
       "              0.18610177633438116,\n",
       "              0.184739631028025,\n",
       "              0.18506971632421484,\n",
       "              0.1867917631297146,\n",
       "              0.18737210776237648,\n",
       "              0.19752038343148395,\n",
       "              0.202949662154134,\n",
       "              0.20801803316486164,\n",
       "              0.18813856463946912,\n",
       "              0.19263016753347223,\n",
       "              0.1721118417919755,\n",
       "              0.0834654770143427],\n",
       "             'biased_acc': [0.7200550137534384,\n",
       "              0.7200550137534384,\n",
       "              0.7200550137534384,\n",
       "              0.7200550137534384,\n",
       "              0.7200550137534384,\n",
       "              0.7200550137534384,\n",
       "              0.7200550137534384,\n",
       "              0.7200550137534384,\n",
       "              0.7200550137534384,\n",
       "              0.7200550137534384,\n",
       "              0.7200550137534384,\n",
       "              0.7200550137534384,\n",
       "              0.7200550137534384,\n",
       "              0.7200550137534384],\n",
       "             'debiased_acc': [0.7201800450112528,\n",
       "              0.7148037009252313,\n",
       "              0.7191797949487372,\n",
       "              0.7193048262065517,\n",
       "              0.7191797949487372,\n",
       "              0.7181795448862216,\n",
       "              0.7146786696674169,\n",
       "              0.6956739184796199,\n",
       "              0.6937984496124031,\n",
       "              0.6820455113778444,\n",
       "              0.6614153538384596,\n",
       "              0.6404101025256314,\n",
       "              0.6280320080020005,\n",
       "              0.5681420355088772]})"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_results = convert_to_plot_results(results, removel_range)\n",
    "plot_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2plot(plot_results, removel_range)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Old"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "removel_range = [0, 1, 2, 5, 30, 50, 100, 200, 220, 240, 260, 280, 290, 295]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "u has shape (300, 300), s has shape (2,), vh has shape (2, 2)\n"
     ]
    }
   ],
   "source": [
    "ratio = 0.5\n",
    "\n",
    "x_train, y_p_train, y_m_train = load_data(\n",
    "    '../data/emoji_sent_race_{}/train/'.format(ratio),\n",
    "    size=100000, ratio=ratio)\n",
    "x_dev, y_p_dev, y_m_dev = load_data(\n",
    "    '../data/emoji_sent_race_{}/test/'.format(ratio),\n",
    "    size=100000, ratio=0.5)\n",
    "\n",
    "# y_p_train_2d = np.asarray([y_p_train, y_p_train*-1 +1]).T\n",
    "\n",
    "# A = np.dot(x_train.T, y_p_train_2d) / x_train.shape[0]\n",
    "# u, s, vh = np.linalg.svd(A, full_matrices=True)\n",
    "# print(f\"u has shape {u.shape}, s has shape {s.shape}, vh has shape {vh.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(dict,\n",
       "            {0: {'p_acc': 0.8714678669667417,\n",
       "              'biased_diff_tpr': 0.14560632483708713,\n",
       "              'debiased_diff_tpr': 0.14560632483708713,\n",
       "              'biased_acc': 0.7653163290822705,\n",
       "              'debiased_acc': 0.7653163290822705},\n",
       "             1: {'p_acc': 0.8630907726931732,\n",
       "              'biased_diff_tpr': 0.14560632483708713,\n",
       "              'debiased_diff_tpr': 0.1445020833238553,\n",
       "              'biased_acc': 0.7653163290822705,\n",
       "              'debiased_acc': 0.7653163290822705},\n",
       "             2: {'p_acc': 0.49699924981245314,\n",
       "              'biased_diff_tpr': 0.14560632483708713,\n",
       "              'debiased_diff_tpr': 0.11020173553336259,\n",
       "              'biased_acc': 0.7653163290822705,\n",
       "              'debiased_acc': 0.766816704176044},\n",
       "             5: {'p_acc': 0.4959989997499375,\n",
       "              'biased_diff_tpr': 0.14560632483708713,\n",
       "              'debiased_diff_tpr': 0.11007656356661116,\n",
       "              'biased_acc': 0.7653163290822705,\n",
       "              'debiased_acc': 0.7669417354338585},\n",
       "             30: {'p_acc': 0.4948737184296074,\n",
       "              'biased_diff_tpr': 0.14560632483708713,\n",
       "              'debiased_diff_tpr': 0.10621266898739956,\n",
       "              'biased_acc': 0.7653163290822705,\n",
       "              'debiased_acc': 0.7660665166291573},\n",
       "             50: {'p_acc': 0.4973743435858965,\n",
       "              'biased_diff_tpr': 0.14560632483708713,\n",
       "              'debiased_diff_tpr': 0.10841554967602068,\n",
       "              'biased_acc': 0.7653163290822705,\n",
       "              'debiased_acc': 0.766816704176044},\n",
       "             100: {'p_acc': 0.49637409352338085,\n",
       "              'biased_diff_tpr': 0.14560632483708713,\n",
       "              'debiased_diff_tpr': 0.10844931019709282,\n",
       "              'biased_acc': 0.7653163290822705,\n",
       "              'debiased_acc': 0.7664416104026006},\n",
       "             200: {'p_acc': 0.5248812203050762,\n",
       "              'biased_diff_tpr': 0.14560632483708713,\n",
       "              'debiased_diff_tpr': 0.1042793080915506,\n",
       "              'biased_acc': 0.7653163290822705,\n",
       "              'debiased_acc': 0.7656914228557139},\n",
       "             220: {'p_acc': 0.44711177794448614,\n",
       "              'biased_diff_tpr': 0.14560632483708713,\n",
       "              'debiased_diff_tpr': 0.09987399516901178,\n",
       "              'biased_acc': 0.7653163290822705,\n",
       "              'debiased_acc': 0.7661915478869717},\n",
       "             240: {'p_acc': 0.45023755938984744,\n",
       "              'biased_diff_tpr': 0.14560632483708713,\n",
       "              'debiased_diff_tpr': 0.1032924184615246,\n",
       "              'biased_acc': 0.7653163290822705,\n",
       "              'debiased_acc': 0.7656914228557139},\n",
       "             260: {'p_acc': 0.450112528132033,\n",
       "              'biased_diff_tpr': 0.14560632483708713,\n",
       "              'debiased_diff_tpr': 0.10346416206559751,\n",
       "              'biased_acc': 0.7653163290822705,\n",
       "              'debiased_acc': 0.7656914228557139},\n",
       "             280: {'p_acc': 0.4587396849212303,\n",
       "              'biased_diff_tpr': 0.14560632483708713,\n",
       "              'debiased_diff_tpr': 0.1043429019092808,\n",
       "              'biased_acc': 0.7653163290822705,\n",
       "              'debiased_acc': 0.7591897974493623},\n",
       "             290: {'p_acc': 0.5001250312578145,\n",
       "              'biased_diff_tpr': 0.14560632483708713,\n",
       "              'debiased_diff_tpr': 0.10178012864537783,\n",
       "              'biased_acc': 0.7653163290822705,\n",
       "              'debiased_acc': 0.7596899224806202},\n",
       "             295: {'p_acc': 0.49987496874218557,\n",
       "              'biased_diff_tpr': 0.14560632483708713,\n",
       "              'debiased_diff_tpr': 0.09851762574167189,\n",
       "              'biased_acc': 0.7653163290822705,\n",
       "              'debiased_acc': 0.7548137034258565}})"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = check_result(u, removel_range, x_train, x_dev, y_m_train, y_m_dev, y_p_train, y_p_dev)\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(list,\n",
       "            {'biased_diff_tpr': [0.14560632483708713,\n",
       "              0.14560632483708713,\n",
       "              0.14560632483708713,\n",
       "              0.14560632483708713,\n",
       "              0.14560632483708713,\n",
       "              0.14560632483708713,\n",
       "              0.14560632483708713,\n",
       "              0.14560632483708713,\n",
       "              0.14560632483708713,\n",
       "              0.14560632483708713,\n",
       "              0.14560632483708713,\n",
       "              0.14560632483708713,\n",
       "              0.14560632483708713,\n",
       "              0.14560632483708713],\n",
       "             'debiased_diff_tpr': [0.14560632483708713,\n",
       "              0.1445020833238553,\n",
       "              0.11020173553336259,\n",
       "              0.11007656356661116,\n",
       "              0.10621266898739956,\n",
       "              0.10841554967602068,\n",
       "              0.10844931019709282,\n",
       "              0.1042793080915506,\n",
       "              0.09987399516901178,\n",
       "              0.1032924184615246,\n",
       "              0.10346416206559751,\n",
       "              0.1043429019092808,\n",
       "              0.10178012864537783,\n",
       "              0.09851762574167189],\n",
       "             'biased_acc': [0.7653163290822705,\n",
       "              0.7653163290822705,\n",
       "              0.7653163290822705,\n",
       "              0.7653163290822705,\n",
       "              0.7653163290822705,\n",
       "              0.7653163290822705,\n",
       "              0.7653163290822705,\n",
       "              0.7653163290822705,\n",
       "              0.7653163290822705,\n",
       "              0.7653163290822705,\n",
       "              0.7653163290822705,\n",
       "              0.7653163290822705,\n",
       "              0.7653163290822705,\n",
       "              0.7653163290822705],\n",
       "             'debiased_acc': [0.7653163290822705,\n",
       "              0.7653163290822705,\n",
       "              0.766816704176044,\n",
       "              0.7669417354338585,\n",
       "              0.7660665166291573,\n",
       "              0.766816704176044,\n",
       "              0.7664416104026006,\n",
       "              0.7656914228557139,\n",
       "              0.7661915478869717,\n",
       "              0.7656914228557139,\n",
       "              0.7656914228557139,\n",
       "              0.7591897974493623,\n",
       "              0.7596899224806202,\n",
       "              0.7548137034258565]})"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_results = convert_to_plot_results(results, removel_range)\n",
    "plot_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAa0AAAEWCAYAAADVW8iBAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAABg6ElEQVR4nO3dd3gU1frA8e/WbHpoaSR0MHRCVUQDEUR6kWYB8YfXikixBLyooFi5egW8KioWRAUBQVBARUUUlGIglIC0hIRAek822+b3x8KSJQmEkCVseD8PPE+mnXnPzO6+e2ZnzlEpiqIghBBCuAF1TQcghBBCVJYkLSGEEG5DkpYQQgi3IUlLCCGE25CkJYQQwm1I0hJCCOE2JGkJUY4vvviCnj17EhkZSXZ29lXb73vvvcezzz571fZ3zo8//khUVBSRkZEcPHjwkuv/9ddf3HrrrVchsspJSUkhMjISq9Va06EIF1PJc1ruITIy0vF3cXExer0ejUYDwJw5c0hMTOS9995zzG/RogXPPPMMkZGRrF69mmeffRaDwYBKpSIsLIxp06bRp0+faotv/PjxDB06lNGjR1dbmTXFbDbTpUsXVqxYQUREhMv289dff/HUU0/x22+/uWwfldW3b19iYmLo27dvuctvuOEGfvjhBxo3bgzUfOzR0dG89NJL9OzZs0b2L2qOtLTcRGxsrON/aGgo7733nmN66NChAAwYMIDY2Fi2b99O586defzxxzn3naRTp07Exsaya9cu7r77bqZPn05eXl6l9m2xWFxWr2tRZmYmJSUltGjRoqZDuWpSUlJo2bJlTYcBXH+vN3F5JGnVQjqdjhEjRpCenl7m0pZarWbYsGEUFRWRkJBQ7vbnLv0sXryYm2++mZkzZ5Kbm8tDDz3EjTfeSLdu3XjooYc4c+YMAG+99Ra7du1i7ty5REZGMnfuXACOHTvG/fffT/fu3enfvz/ff/+9Yx9btmxh4MCBREZGcsstt/DRRx+VG8vJkyeZMGECPXr0oEePHsyYMcMp2S5evJhbbrmFyMhI+vfvz/bt28st59dff2X48OF07tyZqKgoFi5cWO56J06c4I477gCgW7duTJgwgeTkZG644QanD9Px48fz9ddfA7B69WruuusuXnvtNbp160Z0dDRbtmxxrJuTk8PMmTPp1asX3bp149FHH6WoqIh//etfpKWlERkZSWRkJKmpqSxcuJAnn3zSse3mzZsZNGgQXbt2Zfz48Rw7dsyxLDo6mo8++oghQ4bQpUsXpk6dSklJSbn1stls/O9//6NPnz7cdNNNPP300+Tn52MymRyX1YYNG1ZuS+uee+4BYNiwYURGRjqdxyVLlnDTTTfRq1cvVq1a5ZhvMpl47bXX6N27Nz179uS5557DaDSWG9vq1asZN24cL7/8Mj169GDhwoUXPe9PPfUUKSkpPPzww0RGRvLBBx+UOUepqak8/PDDdO/enX79+rFixYpy9y3ckCLcTp8+fZQ//vjDad6CBQuUGTNmKIqiKCUlJcqrr76qREVFKYqiKKtWrVLGjRunKIqiWCwW5fPPP1fatm2rZGRklFv+n3/+qbRu3Vp5/fXXlZKSEqW4uFjJyspSNm7cqBQVFSn5+fnK448/rjzyyCOObe69915lxYoVjunCwkLl1ltvVVauXKmYzWblwIEDSvfu3ZUjR44oiqIoN998s7Jz505FURQlJydH2b9/f7mxJCQkKL///rtSUlKiZGZmKnfffbfy0ksvKYqiKMeOHVNuvfVW5cyZM4qiKEpSUpKSmJhYYZ0OHTqkWK1WJT4+XrnpppuUH3/8sdx1k5KSlFatWilms7nc6Qvru2rVKqVNmzbK8uXLFYvFoixbtky5+eabFZvNpiiKovzrX/9SnnjiCSUnJ0cxmUzKX3/95Yjplltucdp36fN4/PhxpWPHjsrvv/+umEwmZfHixUrfvn2VkpISRVHsr4M777xTOXPmjJKdna3ccccdyhdffFFunb7++mulb9++ysmTJ5WCggLlscceU5588knH8latWikJCQnlblve8nOvkf/+97+KyWRSfv31V6VDhw5KTk6OoiiKMm/ePOWhhx5SsrOzlfz8fOWhhx5S5s+fX27Zq1atUlq3bq189tlnitlsVoqLiy963s/VvfR74MJzdPfddyvPP/+8YjQalYMHDyo9evRQtm3bVmH9hPuQllYtsnHjRrp27UpUVBQHDhxg0aJFjmV79+6la9eudOjQgddee43XX3+devXqVViWWq1mypQp6PV6DAYDderUoX///nh6euLj48MjjzzCzp07K9z+119/pWHDhtx5551otVratGlD//792bhxIwBarZajR49SUFCAv78/bdu2Lbecxo0bc/PNN6PX66lbty7333+/Y78ajQaTycSxY8cwm82EhYXRqFGjcsvp0aMHN9xwA2q1moiICAYNGsSOHTsueUwrKzQ0lDFjxqDRaByt3IyMDNLS0vjtt9+YM2cO/v7+6HQ6unfvXqkyv//+e6Kiorj55pvR6XRMmjQJo9FIbGysY53x48cTFBREQEAAffr0IT4+vtyy1q1bx8SJEwkPD8fb25vp06fz/fffX9GlOK1Wy2OPPYZOpyMqKgovLy9OnDiBoiisWLGCWbNmERAQgI+PDw899BDfffddhWUFBgYyfvx4tFotBoPhouf9Uk6fPs3ff//Nk08+iYeHB61bt2b06NGsXbu2ynUV1w5tTQcgqs8dd9zB/Pnzy13WsWNHvvzySwoLC3n22WfZvXs3AwcOrLCsOnXq4OHh4ZguLi7mlVdeYevWreTm5gJQWFiI1Wp13BBS2qlTp4iLi6Nr166OeVar1fH724IFC3j33Xf5z3/+ww033MCMGTOcbjY5JyMjg3nz5rFr1y4KCwtRFAU/Pz/AntBmzZrFwoULOXr0KL169SImJoagoKAy5ezdu5f58+dz5MgRzGYzJpPJcRmwOtSvX9/xt6enJwBFRUXk5ubi7++Pv7//ZZeZlpZGaGioY1qtVhMSEkJqaqpjXoMGDZz2m5aWVmFZDRs2dEw3bNgQi8VCZmZmucerMgICAtBqz3+EeHp6UlRURFZWFsXFxYwcOdKxTFEUbDZbhWUFBwc7TV/svF9KWloa/v7++Pj4OOaFhoayf//+ylZNXMOkpXWd8fb25oUXXmDt2rUXvbVZpVI5TS9ZsoQTJ06wYsUK/v77b5YtWwbguNHjQiEhIXTr1o1du3Y5/sfGxjJnzhwAOnTowLvvvsu2bdvo27cvU6dOLbecN998E5VKxbp16/j777954403nPY5ZMgQvvzyS3755RdUKlWFSXvGjBncdtttbNmyhd27dzNu3LgKY7+Ql5cXgNNvMunp6ZXaNjg4mNzc3HJvernwGF8oMDCQlJQUx7SiKJw+fbpKSSYwMJBTp045plNSUtBqtRdtbVdVnTp1MBgMfPfdd45zv3v3bqcW4oUuPBaXOu8XExgYSG5uLgUFBY55VT1u4tojSes6FBAQwOjRo3nnnXcqvU1hYSEeHh74+fmRk5PjdOkR7C2NpKQkx3Tv3r1JSEhgzZo1mM1mzGYzcXFxHDt2DJPJxLfffkt+fj46nQ5vb2/U6vJfioWFhXh5eeHr60tqaioffvihY9nx48fZvn07JpMJvV6Ph4fHRcvx9/fHw8ODuLg41q9fX+m6161bl6CgINauXYvVamXlypVOdb2YwMBAbr31VubMmUNubi5ms9lxmatevXrk5OSQn59f7rYDBgxgy5YtbN++HbPZzJIlS9Dr9eW2SC9l8ODBfPrppyQlJVFYWMhbb73FgAEDnFpKF3Ph+b0YtVrN6NGjefnll8nMzATsN0Zs3bq10vFe7LxfKp6QkBAiIyN58803KSkp4dChQ6xcudLRyhfuTZLWdeq+++5jy5YtHDp0qNLrl5SUcOONNzJ27FhuueUWp+UTJkxg06ZNdOvWjZdeegkfHx8++ugjvv/+e2655RZ69erF/PnzMZlMAKxdu5bo6Gg6d+7MV199xRtvvFHufidPnszBgwfp2rUrDz74ILfffrtjmclk4j//+Q89evSgV69eZGVlMX369HLLef7551mwYAGRkZG88847DBgwoFL1PufFF1/ko48+okePHhw9evSyEsfrr7+OVqtlwIAB9OzZk08//RSA5s2bM2jQIPr27UvXrl2dLvsBNGvWjDfeeIMXX3yRG2+8kV9++cXxLN7luvPOOxk6dCj33nsvt912G3q9ntmzZ1d6+8mTJxMTE0PXrl2d7h6syFNPPUXjxo0ZM2YMnTt3ZuLEiZw4ceKy9lfReQd48MEHeffdd+natWu5d56++eabnDp1iltuuYXJkyfz+OOPyzNdtYQ8XCyEEMJtSEtLCCGE23Dp3YMFW7eSOu9lFJuNgFGjqP/gv8qsk7dhA+mL3gEVGG6IoOF/yv8hXQghhOv8ejiNuesOYlUUxnYL59Hezj3CnMopZsaKPeQVW7ApCs/cEUGfiECSsoro++YWmjWw360Z2SiAl0e0d1mcLktaitXKmbkv0mjJR+iCgjgxegy+0X3wKNU1jikhgYzFH9Dki2Vo/P2xnP3RVgghxNVjtSk8t/YAn0/qQbC/gaGLfqdf6yBaBvk61ln08xEGdQhl/I2NOZKaz8SPd/JHTDQAjet5seGJWyoqvlq57PJgcVwc+kaN0IeHo9Lr8Rs4kPzNPzutk/3119S5+y40Z59h0brg9lshhBAXtycph8b1vGhUzwu9Vs2QjqH8cDD1grVUFBjtD6PnGS0E+XmULegqcFlLy5Kahjbk/AODuuAgivfGOa1jSkgEIOGuu1FsVhpMnozPLWWz9fLly1m+fDkAs2bNuuTzLUIIIZyZTCanu3THjh3L2LFjAUjNMxLq7+lYFuJvYE9SjtP20/q2ZPxHO/h0WwJFJgvLHrjRsSwpq5iBb2/Fx6DlydtvoHvTui6rR832iGGxYEpMpPFnn2JOTSXx3vE0+3YtmguefC99cGNjY6v0nApAfHw8rVu3vuKwrxW1qT61qS4g9bmW1aa6QOXrExsby+rVq6u8n2/3pjCqSxj/urUZuxOzmbZiDz9MvZVAPw+2xURTx1vPvuRcHly6ix+m3YqvQVflfV2Myy4PaoMCsZw+45g2n0lFe8ET6drgYHz7RKPS6dCHhaFv0gRTYqKrQhJCCFGOID8DKbnFjunTuUaC/AxO6yzfmcSgDiEAdGlchxKLlawiEx5aDXW87c8Otg/zp1FdL05kFLosVpclLc/27TElJmJKTkYxmcj7/nt8o50HHfTtextFZzsttWRnY0pIQBcW5qqQhBBClKNjmD8JmYUkZRVhsthYtzeFfm2cGxmhAZ78cTQDgKNp+ZSYbdTz1pNZUILVZn/c92RmEQmZhTSq6+WyWF12eVCl1RI8+98kTXrAfsv7nSPxaNmS9AULMLRrh290NN69elH4+x8cGzQYlVpN4FNPoq1Tx1UhCSGEKIdWo2bu0HZMWLIDq01hTNcwWgX58uYPh2kfFkC/NkH8e1BrYlbv46PfT9j7+RzdEZVKxY4TWbz54z9oNWrUKpg3vD0BXpffa0ulY3VZyYBPVBQ+UVFO8xpMmeL4W6VSETQzBunGUgghalafiED6RAQ6zZt++w2Ov1sG+bLqkbJdYQ1oH8KA9iEuj+8c6RFDCCGE25CkJYQQwm1I0hJCCOE2JGkJIYRwG5K0qkFycjKDBw8ud9mzzz7L0aNHXbbv6OhosrKyXFa+EEJcS67bpJWWZ2TM+9tJyzdeeuUrMG/ePFq0aHHpFYUQQlxSzXbjVIMWbD7CzoQsFmw+ykvD211xeRaLhRkzZnDw4EFatmzJa6+9hqenJ+PHj+fpp5+mffv2PP/88+zbt4+SkhL69+/PlLO3/8+fP5+ff/4ZjUZDr169eOaZZ8jKyuL5558nJSUFsPe52KVLF7Kzs5kxYwapqak0adKEisbwrGhfcXFxvPzyyxQVFaHX6/nkk0/w9PRk/vz5bN26FZVKxZgxYxg/fvwVHxMhhKhutS5prdqdzIpdSeUuKyoqwuu3HEwWG3uSc1AUWPZXIgdO5aLXVtzoHNM1nDu7XLynjhMnTjBv3jy6dOnCzJkz+eKLL5g0aZLTOtOmTSMgIACr1crEiRM5dOgQQUFB/Pjjj2zcuBGVSkVeXh5gb6Hdd999dO3alZSUFCZNmsSGDRt455136Ny5M5MnT2bp0qX89NNP5cZT3r6aNWvGtGnTeOutt+jQoQMFBQUYDAaWL1/OqVOnWLNmDVqtlpycnIvWVQghakqtS1qVcSqnGM41UBT7dNP63ldUZkhICF26dAFg6NChLF26tEzS2rBhAytWrMBisZCens6xY8do0aIFHh4ezJo1iz59+tC7d28Atm3b5vRbWEFBAYWFhezcuZNFixYB0LVrV/zPDutyofL2pVKpaNCgAR06dADAx8c+aNv27dsZN24cWq395RAQEHBFx0IIIVyl1iWtO7uEVdgqio+Pp17Dptzy+i+lcxZ5xWYW3h1JoK+h3O0q48LhUi6cTkpKYsmSJaxcuRJ/f39iYmIoKSlBq9WycuVKtm/fzsaNG/n888/57LPPsNlsrFixAg+Pyx+zpqJ9CSGEu7vubsRYsPkItgt+B7IqCgs2X9kdfikpKcTGxgKwfv16R6vrnMLCQjw9PfH19SUjI4PffvvNMT8/P5+oqChmzZrF4cOHAejVqxdLly51bB8fHw9At27dWLduHQC7d+8mNze3TCwV7atp06akp6cTF2cf16ygoACLxULPnj1Zvnw5Fot9gDe5PCiEuFbVupbWpfx9Mgez1Tlpma0KfydmX1G5TZs2ZdmyZcyaNYsWLVpw1113OS2PiIigTZs2DBgwgODgYDp37gzYE8yjjz7qaAnFxMQA9lvl586dy5AhQ7BarXTt2pW5c+fy2GOPMWPGDAYNGkTTpk0JDQ0tE0tF+9Lr9bz11lu89NJLGI1GDAYDH3/8MaNHjyYhIYGhQ4ei1WoZM2YM99577xUdDyGEcAWVUtHtZ9coGQTyvNpUn9pUF5D6XMtqU13g8gaBrOpn57Xkurs8KIQQwn1J0hJCCOE2JGkJIYRwG5K0hBBCuA1JWkIIIdyGJC0hhBBuQ5KWCyxcuJCPPvroouvExMSwcePGMvP37dvHSy+95KrQWL16NXPnznVZ+UII4UrX3cPFAGtiT/HGpsOk5BQTGuDJU/1vYHhkw5oOC4D27dvTvn37mg5DCCGuSdddS2tN7Clmrt7HqZxiFOyd5c5cvY81saeuqNx3332X/v37c9ddd3HixAnH/JMnTzJp0iRGjhzJ3XffzbFjxxzLtm3bxsiRI+nfvz+//PILAH/99RcPPfQQYB9GZOzYsQwfPpxx48Zx/PhxAI4cOcKoUaOYOnUqQ4YMISEhAYC1a9cyatQohg0bxnPPPYfVagVg1apV9O/fn1GjRvH333+XG39F+7Jarbz22msMHjyYIUOGOLqWiouLY9y4cQwdOpRRo0ZRUFBwRcdPCCEqo1a2tMa+v73MvMEdQugaAK9vPESx2eq0rNhs5YV1Bxge2ZCsQhOPfL7bafnyh2666P7279/P999/z5o1a7BarYwYMYK2bdsCMHv2bObMmUOTJk3Yu3cvc+bM4bPPPgPg1KlTrFy5kpMnTzJhwgR69uzpVG6zZs1YtmwZWq2Wbdu28dZbb7Fw4UK++uorJkyYQMuWLWnevDk2m41jx46xYcMGvvzyS3Q6HS+88ALr1q2jZ8+eLFy4kNWrV+Pj48OECRNo06ZNmTpUtK/yhi0xmUzlDnEihHBfvx5OY+66g1gVhbHdwnm0t/PgtadyipmxYg95xRZsisIzd0TQJyIQgHd+OcqKXUloVCqeH9qWqFYNXBZnrUxaF3M6t/yRinOKzFUuc9euXfTt2xdPT08AoqOjAXu/grGxsTzxxBOOdU0mk+PvAQMGoFaradKkCeHh4Y7WzTn5+fk888wzJCYmolKpMJvtMXbq1In33nuPnj17cs8999CkSRO2b9/O/v37GTVqFABGo5F69eoRFxdH9+7dqVu3LgADBw50tMwqs6/yhi05fPhwuUOcCCHck9Wm8NzaA3w+qQfB/gaGLvqdfq2DaBnk61hn0c9HGNQhlPE3NuZIaj4TP97JHzHRHEnNZ93eFH6YditpeSXc8+Ff/PJkbzRq1UX2WHW1MmlV1DKKj48nNMDTPp7WBRoG2BNOXW/9JVtWlaUoCn5+fqxdu7bc5ZcazuTtt9+mR48evPPOOyQnJzNhwgQAhgwZQseOHVm+fDkPPvggc+bMQVEURowYwYwZM5zKqGiQyAtVtC8hRO23JymHxvW8aFTPC4AhHUP54WCqU9ICFQVG+0gQeUYLQX72YZN+OJjKkI6heGg1hNf1onE9L/Yk5dClcR2XxOp2SctmszmG6bhcRqORu9v7sGCbkZJSPb17aFTc3d6nyuXWr1+fL774gqioKGw2G5s2baJ///4kJSVRt25dPvzwQ26++WYURSEhIYGmTZuSk5PDypUriYiIIDU1lRMnTlBSUkJiYiIFBQXEx8eTkpJCy5YtiY+P58svv8RkMhEfH8+ZM2cICgqib9++pKen89tvvxEZGclHH31Ez549CQgIID8/n+LiYjw9Pdm2bRt//fUXXl5erF69mqZNm5apa0X7atasGR988AH+/v5oNBry8/MxGAykpKTw7bff0rJlS4qLi9Hr9Wg0miodv3PnpqrH/1ok9bl21aa6QOXrk5uby8iRIx3TY8eOZezYsQCk5hkJ9fd0LAvxN7AnKcdp+2l9WzL+ox18ui2BIpOFZQ/c6Ng2slGA07apeeVf0aoObpe01Gp1lXtojo+P57FBkTQMrd67B1u3bk1SUhLPPPMMdevWpUuXLgQGBtK6dWveeecdXnjhBb799lssFgsDBw5k4MCBBAQEEBgYyOzZsyksLGTevHl07NgRo9GIj48PrVu3Ztq0acTExLBu3TqioqLQ6/W0bt2arVu3Mn/+fCwWC2FhYcyaNYuAgAAsFguvvvoqNpsNnU7Hc889R6dOnZg2bRrPPfccvr6+tG/fHp1OV+YYVrSvli1b8sYbb/D00087DVuyaNGiMkOceHtXffTn67XnbXdRm+pTm+oCla+P0Whk9erVVd7Pt3tTGNUljH/d2ozdidlMW7GHH6beWuXyqkxxofzfflOO9r9DOdLvdiX9/cVllmevWq0cvvEm5diw4cqxYcOVrBUrLlnm33//XeV4Dh48WOVtr0W1qT61qS6KIvW5ltWmuihK5etzsc/OXQlZyr0f/umYXvTzEWXRz0ec1un7n1+VU9lFjuler21W0vONZda998M/lV0JWZUN/7K57JZ3xWrlzNwXCf9gMc3XryPvu+8oOVp2dGC/AQNotuYbmq35hjqjR7sqHCGEEBXoGOZPQmYhSVlFmCw21u1NoV+bIKd1QgM8+eNoBgBH0/IpMduo562nX5sg1u1NocRiJSmriITMQjqFB7gsVpddHiyOi0PfqBH68HAA/AYOJH/zz3i0aHGJLYUQQlxNWo2auUPbMWHJDqw2hTFdw2gV5MubPxymfVgA/doE8e9BrYlZvY+Pfj+BSqVi/uiOqFQqWgX5MrhDCP3e/A2tWsXcYe1cducguDBpWVLT0IYEO6Z1wUEU740rs17ejz9QtGsX+iZNCJoZgy4kxFUhCSGEqECfiEDHc1fnTL/9BsffLYN8WfVIzws3A2BydEsmR7d0aXzn1OiNGD59euM3eBBqvZ7sr5aTEjOTxp9+Uma95cuXs3z5csDeZ9+V3D14Pd415A5qU11A6nMtq011gdpXn0txWdLSBgViOX3GMW0+k4o2yPkaqbbO+fv4A0aPIm3+/HLLKn1rZmxs7BXdPXg93jXkDmpTXUDqcy2rTXWBytcnNjb2KkTjei67EcOzfXtMiYmYkpNRTCbyvv8e3+g+TuuY09Icf+f//DP65s1cFY4QQohawGUtLZVWS/Dsf5M06QEUm42AO0fi0bIl6QsWYGjXDt/oaLKXfk7+Lz+j0mjR+PsT+sorrgpHCCFELeDS37R8oqLwiYpymtdgyhTH34EzphM4Y7orQxBCCFGLXHdDkwghhHBfkrSEEEK4DUlaQggh3IYkLSGEEG5DkpYQQgi3IUlLCCGE25CkJYQQwm243SCQV+Ln4/k8sPZnUnKK8ffUoVJBTpG5WgaCvJrWxJYexPK0W8V+odpUF3Fpzufbvd534tpwXSStNbGniFm1F6NFcczLKTY7/j6VU8zM1fsAqv0NtCb2FK9uOMSZPCPBfgZiBkRc0T7WxJ5i5up9FJutgGtjd7XaVJfayvn1m3JFr19Xne/qfo+5qsy0PCOTv4xl0d2RBPoaLnu9ym5f29X6pLUm9hRPfb0Xs0256HrFZivPrIpj7Z5TaDVq9Bo1Wo2KkZ3DiGrVgLR8I+/9ehydRoXu7DKdRk10RCCtQ/zIKCjhp4OpaDVqxzq7E7NZ9mciRosNgDN5Rp5eGcfepGwiG9fFbLHRq2V9gvwMHE8v4MeDqZitNkxWBbPVhtliY+LNTQir48WfxzP5asdJNh04Q7HZVib2uesPsj4uBVChUoEKUKlg3oj21PfxYOP+M6yLS0GtUjmWqbAv9/bQ8l3caX4+lIZadW6ZvZyXhrdDq1Gzbm8Kf53IdMxXYR+DZ/bgNgCs3XOKfcm59mVn9+Gl1/JE35aO5UdSC0rFpuKTbSccH2Cl6/Lc2v2czjVSz0fPmK7hju3T80scZatVEORnYEB7+1A26+NSyCky2+t3dh/B/gZ632AfauH7facpNlnPxmevX2iAJ92b1gVg04EzWKyK07FrGOBF+zB/AH4+lIqi4FS/sDqetAj0xWZT2HYs06luJ88U4xNURHhdL8xWG3HJORecGxWh/gYC/QyUWKyljo3KEWOQr4E63nqMZivJ2cVO26qA+r4e+HhoMZqtZBTYj426VBn+njoMOg0lFiv5RsvZ46Zy7MfLQ4NOo8ZitVFisTntH2BD3GlmrdnvOEdn8ozErI6jsMTC7W2DCfDSodOoyS02k1lQgtWmYLEpWM/+jwjxxUOrISmriMTMIl5cf7Dc8/3Khnga+HqgUavQqlVozv5vF+qPWq0iPb+EwhKLfblGdXY9NXW99fYvpKvjMJrPv8dmro7DZlMY2SUMAOsF7/1z8Z0b8+nC5WtjT/Hsmn2O95m9zCtPrgs2H2FnQhYLNh/lpeHtLnu9ym5f26kURbn4p/k1JjY2lsjIyEqvf/OrP3Mqp7jS63vrNSiATVFQFGgY4EkDXw+KTFYOpuRhwz7/nGb1vWng60G+0czB0/mXURO7G4J8CPDSk1Vo4khagWP+uQ/OiGA/fA1aMgtNJGUVUWKxVViWl17j+PtcjBHBvui1alLzjJzJMzotA2gX6odWoyYlp5jUvBL7cs6v0Ck8ALVKxcmsItLzS86Xjz1xdG5k76n/eEYhmQXOy7VqlWP5P6n5ZBedb91WhqdOQ4ezSWN/Si6FJc4feD4eWtqG+gEQl5xb5gPR31NLRLB9eezJHExW52NXx0tHqyBfAHYlZpf58Krvo6d5Ax8AdpzI4sI3SpCfB03qeWOzKexMzC4Tf6i/wZG0/j6ZU2Z5WB1PGgZ4YjRb2ZucW2Z543peBPsZKCyxsD8lr8zyS732Wgb6UNdbT06RicOpBWWWRwT74u+pI7PQxNG0ssu1ahWWi3zZa9/QDy+9ljO5RhKzisos7xTuj4dWw6mcYpKzK/8ePKdbkzqoVSpOZBSSVuq1B/b3Rvcmdcs9r+f0OPuFpLzXnodW7RhdN/50HnlGyyXj0apVdAzzR6u5/FsBTBYbe5JzHF98OoUFoNeWLaei9UrPN2jV/PZMH0dr63J6eb+cz85rVa1PWk1jvivzYVMRvUZNZKOASq2rKPaP9nPffG2KvXWkKPakoKCw71TZD5pzOjT0R6Wy71OtVjmSZOlv0+Wp6E16ObFfCxRFYU9SboV1OZesSn8bVlA4dzLPHftzHyDnjv25ZaCgVtlbvAAlFqtTslYAjUrl+OAoNlmdkrWigFajwkNr/yJQWGI5v/Ts+dVp1Bh0GhRFIb/EQukXmrHEiJ+3FwadBpuikFfs/KGpYE/KBp0Gq00ht5zl3nr7covV5nQ5+1x8vgYtBp0Gs9VW6kNZOffPqaXlWF7qGNX10uGh01BsspJdZCqz/4slmib1vKjrrUenUVNsslJosji9blUq8DPo0KhVlFismCw2/kktKDcJ6jQqWgT6nD9/Z99bAZ46VCoVBSUWjGar4311br0gPwN/nciqMMZzSSuzoMTREgMwmc14eugJ9rd/6GcUlFBSannyJb7kemjV1PfRE1bHCwCL1XbJRHYio5D0/BLH67aBrwdN63uXWe9gSi75pb6c+XpoaBPq77S9TqNibLdGjtbW9Za0av3lwSA/g6OFcSn/HhzBhJuaVtu+b3x5c7n7DvYz8O3jvapU5mfbTvDctwfLzK/u2K+G2lSXC9WGMZsu9vr99ak+5WxxcRWd79mDW1f5fF8sxuUP3VTuNpc6NxWVWcdLx79ubcaBU3m0bejHo71bYLLYaPv8Rur7eNA21J92Df1oF+pPZKMA6vl4APbfom56ZbPjO40CZBWUsH5KL6ffpj7bdqJMEs4vsXJrq/rsScpxbG+2KqzclcSU21pcl79t1fpb3ps38KrUemoV/JNaeFX23bxB2W9YlfVPagHqCxphroj9aqhNdamNqvv164rz7Yr3WEVltgmxJ6p37unMo71bAGCx2Xi6fwTdm9blREYBb28+wgOf7eLr3cmAvZU38n9/YL2ggWlRYNpXeygyWUjMLOSf1Hxe3nCo3P2+9eMRbBdcELMqCgs2H61yHd1ZrW9pZRdd+lo1gE2Bv8v5XcIV+77c33ZK+/tkDhdeYXFF7FdDbapLbVTdr19XnG9XvMcup0wvvZZ/3Xp+8NrCEgvxp/Mclx6PpBWQnFP+lZ6/TmTx86E0Jn9x8RGFy7ukarYq1+37pNb/plVabbhkU1ptqk9tqgtIfa5lV7suFf2urgK2PtOHHSey0GvVPLf2AFmFpjLr6c7eTWksdROWp07DKyPbMzyyofymJYQQovqEBniWewdzaIAnYXW8St3QoTg9xwb2OxbNNqXMIzvFZitvbDp8XT7PKElLCCFc6Kn+N5RJRp46DU/1v8FpvXMJ6MIeQ6Yt31NuSy3lMh7lqYxfD6cxd91BrIrC2G7hjt/tzpm77iDbj2cCOJ4N3PdCfwCazfyOG84+XtIwwMCH93Wr1thKk6QlhBAuVFEyKq+VNDyyYZn5b2w6XGFLrbpYbQrPrT3A55N6EOxvYOii3+nXOoiWZ59jBHhuSBvH35/8cYIDpZ4dNOg0bHjilmqL52IkaQkhhIuVl4wqq7IttSuxJymHxvW8aFTPfqlySMdQfjiY6pS0Svt2bwrT+rWqtv1fDrdLWjabjfj4+CptazQaq7zttag21ac21QWkPtcyd6vLDQaYfGNdPv07m/RCCw28tdzXuQ43GPKIj8+rdH1yc3MZOXKkY3rs2LGMHTsWgNQ8I6H+51tuIf4G9iTllFtOcnYRSdnF9Gxe3zGvxGJjyMLf0ahVPNK7Of3bBlextpfmdklLrVZX+c6f2nQHFNSu+tSmuoDU51rmjnVp3RoeG1T+ssrWx2g0snr16iuOZd3e0wxsF+zorQbgj2eiCfY3cDKziLs++JOIYF8a16v6s3IXU+sfLhZCCHFxQX4GUnLP/252OtdIkF/5vW2s25vC0E6hTvPOPZfWqJ4XNzar5/R7V3WTpCWEENe5jmH+JGQWkpRVhMliY93eFPq1CSqz3tG0AnKLzY6OsAFyi8yUWOy/t2UVmtidmEXLQB+Xxep2lweFEEJUL61Gzdyh7ZiwZAdWm8KYrmG0CvLlzR8O0z4swJHA1u1NYUjHUKcOvY+m5zNr9X5UKntHzo/0bl7hDRzVEqvLShZCCOE2+kQE0ici0Gne9Nud71As747BLo3rsmnarS6NrTS5PCiEEMJtSNISQgjhNlyatAq2buXYHQM4ent/MhZ/UOF6eZt+ID6iNcX79rsyHCGEEG7OZUlLsVo5M/dFwj9YTPP168j77jtKjpYd/8VaUEjW0s8wdOzgqlCEEELUEi5LWsVxcegbNUIfHo5Kr8dv4EDyN/9cZr30BW9T74EHUOs9XBWKEEKIWsJldw9aUtPQhpzvykMXHETx3jindYoPHMBy+gy+vXuT9dGSCstavnw5y5cvByAmJka6cTqrNtWnNtUFpD7XstpUF6h99bmUGrvlXbHZSHv1NUJeeeWS65buIys2Nla6cTqrNtWnNtUFpD7XstpUF6h8fWJjLz5Csrtw2eVBbVAgltNnHNPmM6log84/YW0rLKTkyBFOTpjA0ejbKN67l+RHH5WbMYQQQlTIZS0tz/btMSUmYkpORhcYSN7339Nw/huO5RpfX1r9ud0xnTh+AoFPP41n+3auCkkIIYSbc1nSUmm1BM/+N0mTHkCx2Qi4cyQeLVuSvmABhnbt8I2OdtWuhRBC1FIu/U3LJyoKn6gop3kNpkwpd93GSz9zZShCCCFqAekRQwghhNuQpCWEEMJtSNISQgjhNiRpCSGEcBuStIQQQrgNSVpCCCHchiQtIYQQbkOSlhBCCLchSUsIIYTbkKQlhBDCbUjSEkII4TZqbDwtIYQQ145fD6cxd91BrIrC2G7hPNq7hdPyuesOsv14JgBGs5WMghL2vdAfgJW7k1n08xEAJke3ZFSXMJfFKUlLCCGuc1abwnNrD/D5pB4E+xsYuuh3+rUOomWQr2Od54a0cfz9yR8nOJCSB0BOkYm3N//Dusm9UKFi8KKt9GsdhL+XziWxyuVBIYS4zu1JyqFxPS8a1fNCr1UzpGMoPxxMrXD9b/emMLRTKABb/kmnV4sGBHjp8ffS0atFA379J81lsUrSEkKI61xqnpFQf0/HdIi/gdQ8Y7nrJmcXkZRdTM/m9Utta6jUttXhopcHFauVk/f/H40/+9RlAVwum81GfHx8lbY1Go1V3vZaVJvqU5vqAlKfa1ltqgtUvj65ubmMHDnSMT127FjGjh172ftbt/c0A9sFo1GrLnvb6nDRpKXSaECtxpqfj8bX92KrXjVqtZrWrVtXadv4+Pgqb3stqk31qU11AanPtaw21QUqXx+j0cjq1avLXRbkZyAlt9gxfTrXSJCfodx11+1N4cXhbZ22/fN4ltO2NzarW9nwL9slb8RQe3lxfOgwvHvehNrTyzE/+N/PuiwoIYQQV0/HMH8SMgtJyioiyM/Aur0pLLgrssx6R9MKyC0207lRHce8qFYNeGPTYXKLzABsPZLOM3fc4LJYL5m0fPv1w7dfP5cFIIQQomZpNWrmDm3HhCU7sNoUxnQNo1WQL2/+cJj2YQH0axME2FtZQzqGolKdvzQY4KVnSnRLhr7zOwBTbmtJgJfedbFeaoWAEcNRTCZKTpwAVHg0bYJK77qAhBBCXH19IgLpExHoNG/67c4tpmn9WpW77Zhu4YzpFu6y2Eq7ZNIq2LKF08+/gD48HBQF06lThMx5AZ9bb70a8QkhhBAOl0xaqa++RuNPP0HfuDEAppMnSXroYUlaQgghrrpLPqel9vZ2JCwAXXg4am9vlwYlhBBClOeSLS1Du7acfPBB/O4YACoV+Rs3YmjfjrwffgDA7/bbXR6kEEIIAZVoaSklJrT16lO0cydFO3agqVsXxVhCwS+/UvDrlqsRoxBCiFpk4/4z5BnNjuncYjObDpyp1LaXvntw9Ci8Ond2mlf0999l5gkhhBCV8fbmI9zRLtgx7e+p4+2fjtC/bfBFtrK7ZEvrzEsvVWqeEEIIURmKopSZZ7WVnVeeCltaRbGxFMfuwZqVTebHnzjm2woKwGqrVOEFW7eSOu9lFJuNgFGjqP/gv5yWZ3/1FdnLvgCNBrWXFyFz5+DRokUFpQkhhKgN2jf058X1B5lwk/0mv8+2J9KuoX+ltq2wpaWYzdiKilCsFmyFhY7/ah8fwt7+7yULVqxWzsx9kfAPFtN8/TryvvuOkqNHndbxGzyYZuu+pdmab6j3wCRSX32tUkELIYRwX3OGtUWnUTP5i1ge/zIWD63aqT/Di6mwpeXdvTve3bsTMGI4uoYNLzuo4rg49I0a2R9KBvwGDiR/889OLSmNj4/jb1tRMahqptdgIYQQV4+XXkvMgIgqbXvJGzGqkrAALKlpaEPO/6imCw6ieG9cmfWyli0j65NPUcxmGn/ycbllLV++nOXLlwMQExMjQ5OcVZvqU5vqAlKfa1ltqgu4Z33u/fAv3rmnM/6e9tGNc4vMTP7yb5ZO6nHJbS+ZtFyt7j33UPeee8hdt56Md98j9LVXy6xTetyX2NhYGZrkrNpUn9pUF5D6XMtqU12g8vWJjY29CtFUTlahyZGwAPy9dGQWmCq1bYW/aaXNnw9A3saNVQpKGxSI5fT5++7NZ1LRBgVVuL7foIHkb95cpX0JIYRwH2o1nMo5P35XUlZRpX8dqjBpFWz5DUVRyFi8uEpBebZvjykxEVNyMorJRN733+Mb3cdpHVNCwvn9/brFqbsoIYQQtdOTt9/A6He3MW35HqZ+Fcu4xX/y9B2V+42r4hsxbrmFf7r3wFZUxOEuXc8vUBRQqbhh966LFqzSagme/W+SJj1gv+X9zpF4tGxJ+oIFGNq1wzc6mqxlX1C4fRsqrQ6Nnx+hr75SuRoLIYRwW71vCOTbx3vx5V8nadvQj9vbBmPQXvKxYeAiSavB1CcIevopkh59jPD/vVOlwHyiovCJinIud8oUx9/Bz86qUrlCCCHc11c7TvLxHwmczi2mTagfsSdz6NyoDl8+WO+S21aY2hLGjbOv4CM9ugshhKg+H/+RwNrJN9OwjhdfPXgT3025BT/Pyt0XWPFaZjO569ZTHLvH0aN7adK7uxBCiKrw0Kkx6DQAlFistAj04Xh6YaW2rTBpBb/wArnr1mHLy6Pgl1+dF6pUkrSEEEJUSbCfgdxiM7e3CWL8hzvw89TRsI5npbatMGl5demCV5cueLZrR8CoUdUWrBBCiOvb4gn2m/um9WvFTc0zyTdaiGrVoFLbVpi0Cv/8E+8bb0Tt5yeXB4UQQrjEjc0uffNFaRX38r5jJ9433lj20iDI5UEhhBA1ouJb3qc8DkDoKy9ftWCEEELUjF8PpzF33UGsisLYbuE82rvsMFHr41L4709HUAGtQ/xYcFckAM1mfscNwX4ANAww8OF93VwWZ4VJq/QYWuWpd//Eag5FCCFETbDaFJ5be4DPJ/Ug2N/A0EW/0691EC2DfB3rnMgo5H+/HGPVwz3x99KRUVDiWGbQadjwxC1XJdYKk5at0H77oenECYr378O3TzQABb/8gqFDh6sSnBBCCNfbk5RD43peNKrnBcCQjqH8cDDVKWl9teMkE25qjL+XvaPb+j4eNRJrxZcHJz8GQMK999J01Wo0Zx8yrj95MkkPP3R1ohNCCOFyqXlGQv3P33Ie4m9gT1KO0zrHM+wNmTvf3YbVpjC1b0t63xAIQInFxpCFv6NRq3ikd3P6tw3GVS75CLI1IxOV/nwX8iq9DmtGpssCuhSbzSbjaZ1Vm+pTm+oCUp9rWW2qC1S+Prm5uYwcOdIxXXrIp8qw2hROZBTy1YM3cibXyJj3t7Nx6q34e+r445logv0NnMws4q4P/iQi2JfG9VzTm9Ilk5b/8GEkjB6Db9++AORv3oz/iBEuCaYy1Gq1jKd1Vm2qT22qC0h9rmW1qS5Q+foYjUZWr15d7rIgPwMpueeHCjmdayTIz+C0TrC/gU7hAeg0asLretG0vjcJGYV0DA8g2N++bqN6XtzYrB4HUvJclrQu2a1u/YcfJuTleWj8/dD4+xH68jzqP/SgS4IRQghx9XUM8ychs5CkrCJMFhvr9qbQr43z+Ie3twniz+P2q2xZhSZOZBTSqK4XuUVmSixWx/zdiVm0DPRxWayV6qHQs21bPNu2dVkQQgghao5Wo2bu0HZMWLIDq01hTNcwWgX58uYPh2kfFkC/NkFEtWrA1iMZ9H1zCxqVipkDW1PHW8/uxCxmrd6PSmUfueqR3s2dbuCo9lhdVrIQQgi30ScikD4RgU7zpt9+g+NvlUrF7MFtmH3Bdl0a12XTtFuvQoR2lRt1SwghhLgGSNISQgjhNiRpCSGEcBuStIQQQrgNSVpCCCHchiQtIYQQbkOSlhBCCLchSUsIIYTbkKQlhBDCbUjSEkII4TYkaQkhhHAbkrSEEEK4DZcmrYKtWzl2xwCO3t6fjMUflFme+fEnHBs0mONDh5E48X7Mp065MhwhhBBuzmVJS7FaOTP3RcI/WEzz9evI++47So4edVrH0Lo1TVd+TbNv1+LX/3ZS5893VThCCCFqAZclreK4OPSNGqEPD0el1+M3cCD5m392Wsf7xh6oPT0B8OzYEcuZVFeFI4QQohZw2XhaltQ0tCHBjmldcBDFe+MqXD9n5Sp8br2l3GXLly9n+fLlAMTExBAfH1+lmIxGY5W3vRbVpvrUprqA1OdaVpvqArWvPpdyTQwCmfvttxQf2E/jpUvLXT527FjGjh0LQGxsLK1bt67SfuLj46u87bWoNtWnNtUFpD7XstpUF6h8fWJjY69CNK7nsqSlDQrEcvqMY9p8JhVtUFCZ9Qq3bSPjvfdpvPQz1Hq9q8IRQghRC7jsNy3P9u0xJSZiSk5GMZnI+/57fKP7OK1jPHiQ08+/QPj/3kFbr56rQhFCCFFLuKylpdJqCZ79b5ImPYBisxFw50g8WrYkfcECDO3a4RsdTeobb2ArKiJ56jQAdCEhhL/7P1eFJIQQws259Dctn6gofKKinOY1mDLF8Xfjjz925e6FEELUMtIjhhBCCLchSUsIIYTbuCZueRdCCFGzfj2cxtx1B7EqCmO7hfNo7xZl1lkfl8J/fzqCCmgd4seCuyIBWLk7mUU/HwFgcnRLRnUJc1mckrSEEOI6Z7UpPLf2AJ9P6kGwv4Ghi36nX+sgWgb5OtY5kVHI/345xqqHe+LvpSOjoASAnCITb2/+h3WTe6FCxeBFW+nXOgh/L51LYpXLg0IIcZ3bk5RD43peNKrnhV6rZkjHUH446Nyt3lc7TjLhpsaOZFTfxwOALf+k06tFAwK89Ph76ejVogG//pPmsljdrqVls9mkG6ezalN9alNdQOpzLatNdYHK1yc3N5eRI0c6pkv3NJSaZyTU39OxLMTfwJ6kHKftj2cUAnDnu9uw2hSm9m1J7xsCz25rcNo2Nc94JVW6KLdLWmq1WrpxOqs21ac21QWkPtey2lQXqHx9jEYjq1evrvJ+rDaFExmFfPXgjZzJNTLm/e1snHprlcurKrk8KIQQ17kgPwMpucWO6dO5RoL8DE7rBPsb6NsmCJ1GTXhdL5rW9yYho/DstsaLbludJGkJIcR1rmOYPwmZhSRlFWGy2Fi3N4V+bZz7ir29TRB/Hs8EIKvQxImMQhrV9SKqVQO2Hkknt8hMbpGZrUfSiWrVwGWxut3lQSGEENVLq1Ezd2g7JizZgdWmMKZrGK2CfHnzh8O0DwugX5ugs8kpg75vbkGjUjFzYGvqeNs7OZ8S3ZKh7/xu//u2lgR4ua7zc0laQggh6BMRSJ+IQKd502+/wfG3SqVi9uA2zC5n2zHdwhnTLdzFEdpdN5cHzWlp8PQznBg7Dkt6ek2HI4QQogqum6R1Zu6LcOQIxr17SZx4P0WxsRTFxpLx/mKKXDw4miv2UxQbC6tWuTz2q6E21aU2qu7z46r3w7VeZmXLq2i9q/V5da1TKYqi1HQQlyM2NpbIyMhKr3+oYyeUkpKKV1CpUHl4EP7eu2S8U3ZYFP8RIwgYOQJLdjanpjxRZnmdu8bhN3Ag5tOnSXn6mTLLvaOiyFi0CMVkAkXBIyICjY+PY3n9Rx7Gu2dPjPHxpL78SpntG0ybhlfnSIr+jiX9rbcAsBYUUHLoECgKKr2eRp9+glJcTMa775XZPnjOHDyaNSX/51/IKqdX/dDXX0MXEkLe99+T/eVXZZY3XPA22jp1yFn9DbnffFNmefji91F7epL1xRfkb9hYZnnjpZ8BkPnREgp+/dVpmcpgoP6jj3Dy/v9DMRpBpXI6PpqAAMIWLgAg7T9vUrxnj9P22uBgGr7xOgBnXn6ZkvhDTsv1TZoQ8uJcAE7Pfg5TQoLTco/WEQTPmgXAqaeexnLmjNNyz06dCJwxHYDkx6dgzclxWu510400ePRRAE7+60F7Hc4qKioicOBA6k36PwASx08oc2x8B9xB3bvvxlZcTNKDD5VZfqWvvbr3349vdB9Kjp/gzPPPl1lemdceKjh530T76/eC8xM0ayaG1q3tA7lW8rXn9Nr18KDRJx9jOX36il57Z157nexPPgFFcYqxotdeUVER3nXr0uiDxQCk/+9/FG3/s0z5xfv2na93q1ZO79vLfe0V79/vqDcqFb539Cfs7Pu59GvPcXzAfnw+XkLWko8xJSfb56tU9vf8x0vwOvs5eDkjF1/OZ+e1qta3tJr/+AP65s0rXkFRUMxminbtdsn+S/75x/7Ct9lAUbDl5V1xmba8PPuLH1AsFop27LziMmtK0Y6d9uMD1XZ8RPUp2rETxWKxT1TD+XF67ZrN1fLaNScmOMqsrteQJTv7/PvWZqvWeqMomC/4glRmvXOfS2ePj2O+zVZtx81d1fqWFkDS5MkU/LTZeaZKhUqnQ7FaUel0Tt9cqlNRbKy9JWE2V9t+HGWaTGW+dbmb2lSX0mrLA6zVfX5c+n6oZJmVOTfVHWdly6tovYttf721tK6TpPU4xXv3YlWr0SgKtsJCDG3aEDh9GkU7duLVvZtLPyiLYmOrfT9FsbEkfvcdjQcNcvsP+dpUl3NqS9KC6j8/rno/VLbMyp6b6o6zsuVVtF5F8yVpXeOu5MDH//473kuXUm/iRLxvuqmaI7v6atMHY22qC0h9rmW1qS5w/SWtWv+blhONBmt6BrZi13XmKIQQwnWur4eLAwJounpVTUchhBCiiq6vlpYQQgi3dt0lrdOznyPtP2/WdBhCCCGq4LpLWqhUNR2BEEKIKrq+ftMCQubOqekQhBBCVNF119Iyp6WRcO94LOnpTn+LmmNOS4Nn/y3nQQhxSddd0kp56mmKd+0ifdE7ZPzvXYp37ya9nD4Hq5MrkmNt+qDP+N+7EB/v8vMghHB/103SOtSxE4wYSdFffwGQs3w5OV99BYpCzldfER/RmkPtO5ASMxNzahoAJceOkf3ll1gLCgCwZGRgPHgQxWwGoLLPZV9Jciy9D0t2NuZTpxxlEh/P6Rdfciw3/vMPxQcOnJ8+fBhjfPz56UOHMB4637Gn8eBBjIf/cUwX7z/gPL1vHyVHjpyfjouj5Nix89N79lBy/LhjuujvWEqOnzg/vXu3Uye1Rbt2YTp50jEd374D8RGty56Hjp1QFAXj4cNYsrLsx8Fmw3zqFNaCQse0NS/P0W+hoiiVPh/i8rjDFyRXfTGszjLlyk71cGnSKti6lWN3DODo7f3JWPxBmeVFO3dyfORI4tu2I2/jJleGQvMff4BbbkFlMNhnqNX2/9h7U/YbMpigF56ncMdfYLV3EFq0azdn5szFVmj/oMzbtIkTI+/EerbzzOzPPiO+fQesubkA5KxZQ+K947Gd7VX+Yh/KADmrVpHyTIwjxvR33iFxwn2O6ZRnn+XY7f0d06kvvsjRvv2cyiz44QdHmen/eZMzz53vzTvttdc5M2fu+e1fmufUm/eZOXNJe/3189PPPefoSR7g9KxZpC9cdD6ep562J8uzkqdPJ/ODD89PPzGFrE8+OT/96GNkLf3cMX3ywYece/NWFPTNm58/J4C+VSta/PQjitnMiWHDyfl6JQC2oiKO3taXnBUrALDm5vJP9x5kr/gaAEtaGodatyF7uX25+dQpDnfpSu669QCYTp7kSO8+5G+290FZcuIExwYPpuCPP+zTx46RMO4uiv7+2z599CgnH3zQ8SWg5MgRTk2f4UjaJUeOcPr5FzAlJTnWT5s/H3Nqqr0iSclkvPc+lkz78OQlx0+QtWyZ47VjSkwk99tvHa8tU/Ip8n/+xfHaMZ85Q+GOHY4vSJaMDIzx8ShWq6P+puRkR6K2FRc7XocAitWKYrNRHdyhJeyKqybVXWZly5PkdnEuS1qK1cqZuS8S/sFimq9fR95331Fy9KjTOtqQUEJfeQX/wYNcFYaDLjAQvLxQSkpQeXg4em9WeXigmEyovX2oc+edtPz5Z3ShoQD4jxhOy62/oa1fHwCfqN6ELVqIxs8PAEO7dtSbOBG1tzcAKo0W1GpUevtQ03XvmwBa7fkPZY0GNBpa/PQjAJbMLEyJieePR7366BqdH/3T55ZbqTN2jGO6zl13EfLSi/gNHuQoU6XX4zdkMC1++pEG06YS/ML5pBX41JMEPfvs+emZMQQ+87RjOujf/ybwyRmO6eA5L9Bg2lTHdMi8edSf/JhjOvS1V6n/8PkhNBrO/w/1HpjkmA57ewF1J048P/2/d6g7/l7HdKPF71PnrnHnp5d8hEfrCPvQMTodqFQYWrVC26ABKo2GhgvexrdfXwDUej0h8+bh3etm+7TBQNDMGLy6dLZPe3tTf/JkDG3b2o+LlxcBo0ahb9LYPm0w4H1zT8e5VOn0eDRrfn64CZUKtZcnKo0GsPdAbs3OgXNJoqAA48GD2IqKALBkZpK/efP5JJSURNbSz7GebRlyMpH0//4Xa3Y2AMZ9caS++JJjumjnTlKefsaxfcFvW0h+9FFsZ1v1+Zs2cXLCfY795a5Zw4kRIx0ty+wvvuBY336O+DIWL+afm3o6jm36W29xuNP5LntS33iDI7dGOabT/vMmx4cOOz/93/+SeN/5c5e+cBHxbdpW+KWraNcuCv/a4VjfVlJS6ZbulX4oK4qCKfkUlqwsDnXsVG6M8e3aO64y2Ewmsr/8EuPhw/YCSkrI+nyZ46qCtaCQrM8+c1xVONShY/n1bt/BHv+ZM6S+8qpje9PJk6Q8+6xjuuToUZIfn+KYPlTRl9cOHQH7FZTi/QewnR3apqLkJsnMzmV9DxbFxpKx6B0afWT/Jp7xvn3smvoPPVhm3ZSYmfj07o3fHf3LLLvQFfU9OHEiAU2bUWfsGJImPw6KQvg7i8hevgJLejrhixZWqdyLOf3CC+QsX4FKr0cxmQgYO5aQF8qObVSVMtFqwWKpljJrStLkx9E2aEBOt64E7NzlsvNwtcUfOEBEy5b2Ly1qNTaTCVtBARo/P1RaLdaCAqyZmehCQ1HpdFiysjCnnMZwQytUOh3m06cxJSbi1bUrKq0WU0ICxiNH8I2ORqXR2C/1HozHf8RwVCoVRbGxGPcfcHxJKNy2jeJ9+x3vt/zNmynet4/AqVMByF23jsJ//qFk5EiMRiO2wkIUsxlNQAAA1vx8eytPpbKPE3b2Y0Ll6YnGz88+tpjNhrZBA8CexLEpaBvYvxRYc3NBpXJ8wbMVF4NKjdrggTUnB1tRESpPT7R16tiXFxWBRoPaw8O+fU4OKr0etZcXAObUVNSeXmj8fO3TKSmofXxQe3vbLxMXF9sP/Nnx8RSjEbWvLxpfXxSbDcuZM2j8/FD7+GAuKYHMTDT+/qi9vVGsViypqWgCAlB7eWEzmbBmZJQ6mypAOb++2YwlIwNNnTqoDQb7dFaWfXsPDxSTGWtONpqAAFR6PTaj0XG8StPUq4faw8PeSj77ZaYiupAQrLm52IqKUHt5Oc4TgNlsRqfTOaYNBgNhYWFO86D29D3oslveLalpaEOCHdO64CCK98a5aneV88wzhJztWLLl2dYOQMjzz7lsl5aMTALGjaPO2DGO5FhdZZb+oHdX5xJUTnw8IQMH1nA01ahUixvsLUV13bqOaY2Pj/OggnXroi21XBcSgi4kxDGtb9IEfZMmjmlDRASGiAjHtFdkpFPP3949e+Ld83zLy/e22/C97TbHtP+QIWSdOIGvry9NmjRBVcHzi6aUFHvrUaUCRUFTty760FBsZwc1PZdkLDk5oCiOJGRKPoVKrUYXaq+D8cgRe4vaagVfX/t/sE+rVKjOJiB9WBgAJcePo/bxsV8hAcxnE4bG39++v5AQ1B4eqD09y41R26ABKrUalUZjbwG2amU/JxoNxcXFGHQ6+7RabV8eEeE0bU5JsSeSC+pdVeXFqAsJQaVSYTObUYqLUXl4YElLs7e+FQVUajR+vlhz8+wJr5zj5tm2LcXFxXh6egL2VmhmZibJyck0bdq0yvFey9ziOa3ly5ezfPlyAGJiYogvdXPB5TAajVXetsoes49sm6MoMGY0wJXHcLZMo9FITnWVWcNq5Ny4kDvUx2w2ExQUhNF4kQ6kS0rAzx/F1wdVfgHWkhKKz7VqAM79fTZ5mc9N17MnYMu56dBQsFggOxsKCx0j+OLtA/XroQBWtfp82SEh2EpvfzYZXrg/iosdMeLvB7l5WEtKsJ4buLIciqJgPPtbYYVMpjJlOtX7cpUXY+njrtM5Bop1HBvFhlVRICjQftzM5jLHrbi4GEVRnGLz8vIiJSXlmn/9VZXLkpY2KBDL6fOjc5rPpKINCqpSWWPHjmXs2LGAvYlb1WEFrtchCdxBbaoLuEd94uPj8Tp7+a1CZ7+tFxcX43kucVwBU2Gh/W7ccy0OvQ59qRZnlZRuUZxtiV1M6ZZJdZV5SZUszwRQty7aOnWwZGeDxYK+QQNMZrNzS63UcSuvPjqdrszrLzY29srrcQ1wWdLybN8eU2IipuRkdIGB5H3/PQ3nv+Gq3Qkh3IHFYr98V+pDWZynb9To/N+lE9FVOG6/Hk5j7rqDWBWFsd3CebR3C6flX+9K4pUNhwjys98Edt9NjRnX3R5vs5nfcUOw/ffLhgEGPryvW7XHd47L7h5UabUEz/43SZMe4NigwfgNuAOPli1JX7CA/J9/BuzPAR2J6k3epk2cef55jg0e7KpwhBDXAH2jRuhDQ1F7epJmszHy0UfLXe/ZZ5/l6AV3G1en6Ohoss7d6VnKe++9V+37OnfzQ2pqKlOmTHHMnz59OkOGDOGTTz7h2LFjDBs2jOHDh3Oy1LOM55Q+bvrQUKfkVh2sNoXn1h7gk/u78+O0KL7dk8KR1Pwy6w3uEMKGJ25hwxO3OBIWgEGnccx3ZcICF/+m5RMVhU9UlNO8BqVOmmf79rTc8qsrQxBCVKPqHoK+IvPmzXNZ2Rfz/vvv8/DDD1/WNlarFc3ZRyUuJigoiAULFgCQnp7Ovn37+PFH+w1hixcvpn///jxaQRJ3tT1JOTSu50WjevbLxUM6hvLDwVRaBvnWSDwX4xY3YpRms9nc60YMF6pN9alNdQH3qI/ZbHb6Af/0A/8qs4737f3wGzMGa1ERR8fdhfmffxw3A+hatcLv7rvwHToUa3Y2aU897bRtyIdlOxQozWg0YjabmTp1KocOHaJ58+a8+OKLeHp6MmnSJKZPn07btm2ZN28eBw4cwGg00rdvX8cH+9tvv82WLVvQaDTcdNNNTJ8+naysLObNm8fp06cBeOqpp4iMjCQnJ4eYmBjS0tLo0KEDNpsNo9HoVP+3334bo9HIkCFDaN68OZMnT+axxx6jdevWZeIbMGAA/fv3588//2TixInccccdjnJOnTrFzJkzKSoqonfv3o4bJU6dOsWUKVNYtWoV999/P6mpqQwZMoTo6Gi+/vpr1Go1f/zxBx9++CGX48IbMcB+bi98/eXm5jJy5EjHdOl7BVLzjIT6n78cGeJvYE9STpl9bdh/hh0nsmha35vZg9sQGmDfpsRiY8jC39GoVTzSuzn92waX2ba6uF3SUqvVciPGWbWpPrWpLuAe9YmPj3f6AV+jLvtrgU6nw9PTk8LiYigocDyvhaJAQQF6nR5PT08sRmOZ7S91s4PBYCAhIYGXX36ZLl26MHPmTL755hsmTZqERqPBw8MDT09PnnzySQICArBarUycOJHExESCgoL45Zdf2LhxIyqViry8PDw9PXnzzTf5v//7P7p27UpKSgqTJk1iw4YN/Oc//6Fbt25MnjyZTZs2sWbNGgwGg1OMMTExLF++nHXr1gGQnJxcYXxqtZr69euzdu3aMvWaP38+99xzD8OHD2fZsmWoVCo8PT0xGAyo1Wo8PT157733ePjhhx370mq1eHl5MWnSpDLlXUplb8QwGo2sXr36sss/p2/rIIZ2CsVDq2HZX4nMWLGXLx+8EYA/nokm2N/Aycwi7vrgTyKCfWlcz7vK+7oYt0taQgjXaLz0swqXqT09CZ3/Bifv/z8UsxmVTkfo/Dcclwi1depcdPuKhISE0KVLFwCGDh3K0qVLy3xwb9iwgRUrVmCxWEhPT+fYsWO0aNECDw8PZs2aRZ8+fejduzcA27Ztc/otrKCggMLCQnbu3MmiRfYuyW699Vb8K3lH4MXiG1jBc4WxsbEsXGh//nDYsGHMnz+/kkej5gT5GUjJLdXqzjU6brg4p473+ecOx3VrxKvfn+/HNNjfvm6jel7c2KweB1LyJGkJIWqWV2QkjT5eUq2/aV34UPOF00lJSSxZsoSVK1fi7+9PTEwMJSUlaLVaVq5cyfbt29m4cSOff/45n332GTabjRUrVuBx7jkuF8Z3sZZkRQ9rX6s6hvmTkFlIUlYRQX4G1u1NYcFdzuc3Lc9I4NlE9uPBVJoH2m+5zy0yY9Cr8dBqyCo0sTsxi4ejmrksVklaQohKu7DnjSuVkpLi6F5o/fr1jlbNOYWFhXh6euLr60tGRga//fYb3bt3p7CwEKPRSFRUFJ07d6ZvX3sflb169WLp0qU88MADwPnLtN26dWPdunU8+uij/P777+SW6ly4NK1W69Qt0qXiK09kZCTfffcdw4YN49tvv72Sw3PVaDVq5g5tx4QlO7DaFMZ0DaNVkC9v/nCY9mEB9GsTxMfbEvjpYCoatYoALx3zR9v7Tjyans+s1fvPPULGI72bu/QGDklaQoga07RpU5YtW8asWbNo0aIFd911l9PyiIgI2rRpw4ABAwgODqZzZ3sHyYWFhTz66KOUnO0VPybGPlrCs88+y9y5cxkyZAhWq5WuXbsyd+5cHnvsMWbMmMGgQYNo3749oRV0yTRmzBiGDh1KmzZtmDZt2iXjK8+zzz7Lk08+yYcffkh0dPSVHJ6rqk9EIH0iAp3mTb/9Bsffz9wRwTN3RFy4GV0a12XTtFtdHt85Lusw11WuqMNcN/hx/HLUpvrUprqAe9TncmKsVC8SbqKydUlOTubhhx9m/fr1VyGqqiuvPuWd29rSYe51MwikEEII9ydJSwghyhEWFnbNt7KuR5K0hBBCuA1JWkIIIdyGJC0hhBBuQ5KWEEIItyFJSwhRaea0NBLuHY8lPb3ay164cCEfffTRRdeJiYlh48aNZebv27ePl156qdpjOmf16tXMnTu3zPy//vqLv//+u1r3Vfo4vP3222zbtg2AXbt2MWjQIIYNG4bRaOS1115j0KBBvPnmm9W6/2udPFwshKi0jP+9S/Hu3aS/8z9CXni+psNxaN++Pe3bt7/q+92xYwdeXl6Oh54rw2KxoNVW7qP3iSeecPz97bff8uCDDzJs2DAAVqxYwY4dOzCZTJcXtJuTpCWEACBx/AT8R4wgYOQIFLOZk/83iYDRo/AfOpSEHjdCqQ/HnK++Iuerr0CrpfX+fViyszk15Qnq3n8/vtF9sKSno23Q4JL7fPfdd1mzZg1169YlJCSEtm3bAnDy5EnmzJlDdnY2BoOBF198kebNmwP2TnEXL15MYWEhMTEx9OnTh7/++oslS5bw/vvvExcXx7x58ygpKcFgMPDyyy/TrFkzjhw5wsyZMx29aCxcuJAmTZqwdu1ali5ditlspmPHjjz//PNoNBpWrVrF4sWL8fX1JSIiAr1e7xR7cnIyX331FWq1mm+//ZbZs2ezcuVK9Ho9+/fvd4pv9erV/PDDDxQVFWGz2fj8888rdRxiYmLo3bs3+fn5bNy4kd9//53ffvuNwsJCioqKGDlyJPfffz/Dhw+v8nl3N5K0hBCX1HDl15yZeD/W/Hwwm1F5eKD29qbB9GlVLnP//v18//33rFmzBqvVyogRIxwf1rNnz2bOnDk0adKEvXv3MmfOHD77zN6L/KlTp1i5ciUnT55kwoQJ9OzZ06ncZs2asWzZMrRaLdu2beOtt95i4cKFfPXVV0yYMIF+/fqh0Wiw2WwcO3aMDRs28OWXX6LT6XjhhRdYt24dPXv2ZOHChaxevRofHx8mTJhAmzZtnPYTFhbGuHHjnIYUWblyZYXxHTx4kG+//ZaAgIBKH4dzRo8eze7du+ndu7dj7K7IyEjWrl1bZiyt2k6SlhACcB6aRKXTOU3rwsPxvb0fOctXoPLwQDGZ8B0xgjqjRgFlhyapTCtr165d9O3b19EF0bl++goLC4mNjXW6NFb6EtiAAQNQq9U0adKE8PBwjh8/7lRufn4+zzzzDImJiahUKsxmMwCdOnXivffeIzk5mYEDB9KkSRO2b9/O/v37GXW2HkajkXr16hEXF0f37t2pW7cuYB+GJCEh4dIH8SLx3XzzzWUS1sWOgyifJC0hRKVYMjIJGDeOOmPHkL18hUtuxgD7SLx+fn7lDrAIlx7O5O2336ZHjx688847JCcnM2HCBACGDBlCx44d+fHHH3nwwQeZM2cOiqIwYsQIZsyY4VTGTz/9VOX4K4qvtvTdWNPk7kEhRKWEL1pIyPPPYYiIIOT55whftPCKyuvWrRs//fQTRqORgoICfvnlFwB8fHwICwtjw4YNgD2JHTp0fsDBjRs3YrPZOHnyJElJSTRt2tSp3Pz8fIKCggD45ptvHPOTkpIIDw/n7rvv5rbbbuPw4cPcdNNNbNq0iczMTABycnI4deoUHTp0YOfOnWRnZ2M2m8u9YxHA29ubwsJCp3mXiq+yx0GUT1paQoga0bZtWwYOHMiwYcOoW7eu091/b7zxBi+88ALvvvsuFouFgQMHEhFhHxYjJCSEUaNGUVhYyJw5c8oM+PjAAw8QExPDu+++S1RUlGP+hg0bWLt2LWq1msDAQB566CECAgKYOnUq//d//4fNZkOn0/Hcc8/RqVMnJk+ezLhx4/D19a2wN/w+ffowZcoUNm/ezOzZsysV3+UcB1GWDE3ixmpTfWpTXcA96iNDk1S/c3f7nbtZ4mqQoUmEEEKIa5RcHhRCiGry6quv1nQItZ60tIS4jrnZrwOiEmr7OZWkJcR1ymAwkJmZWes/5K4niqKQmZmJwWCo6VBcRi4PCnGdCgsLIzk5mfRKPG9lNpvR6XRXISrXq011gbL1MRgMhIWF1WBEriVJS4jrlE6nu+QzROe4w92QlVWb6gK1rz6X4tKkVbB1K6nzXkax2QgYNYr6D/7LabnNZCLlmWcwHjiIJiCAhm++iT6soStDEkIIUY5fD6cxd91BrIrC2G7hPNq7hdPyr3cl8cqGQwT52S893ndTY8Z1bwTAyt3JLPr5CACTo1syqovrWnouS1qK1cqZuS/SaMlH6IKCODF6DL7RffBocf5A5KxcicbPnxY/bCL3u+9I+898wt56y1UhCSGEKIfVpvDc2gN8PqkHwf4Ghi76nX6tg2gZ5Ou03uAOIcwd1s5pXk6Ribc3/8O6yb1QoWLwoq30ax2Ev5drLsG67EaM4rg49I0aoQ8PR6XX4zdwIPmbf3Zap2Dzz/gPt48N49e/P0Xb/5QfhYUQ4irbk5RD43peNKrnhV6rZkjHUH44mFqpbbf8k06vFg0I8NLj76WjV4sG/PpPmstidVlLy5KahjYk2DGtCw6ieG+c8zppqehCQgBQabWofX2x5uSgrVPHab3ly5ezfPlyAGbNmkVsbGyV47qSba9Ftak+takuIPW5ltWmukDl6mM0Ghk5cqRjeuzYsYwdOxaA1Dwjof7ne9UI8TewJymnTBkb9p9hx4ksmtb3ZvbgNoQGeJ7d1uC0bWqe8Qpqc3FucSNG6YMrhBCialavXl3lbfu2DmJop1A8tBqW/ZXIjBV7+fLBG6sxuspx2eVBbVAgltNnHNPmM6loz/a87FgnMAjz6dMAKBYLtvx8NOWMNyOEEMJ1gvwMpOSeH0zydK7RccPFOXW89XhoNQCM69aI/adyS21rvOi21cllScuzfXtMiYmYkpNRTCbyvv8e3+g+Tuv4RPchd419zJy8TZvwuvHGMmPRCCGEcK2OYf4kZBaSlFWEyWJj3d4U+rVxbmSklbrk9+PBVJoH+gAQ1aoBW4+kk1tkJrfIzNYj6US1uvQgoFXl0l7eC7ZsIfXlV+y3vN85kvoPP0z6ggUY2rXDNzoaW0kJKU8/gzE+Ho2/Pw3f/A/68HBXhSOEEKICvxxKY+76g1htCmO6hjE5uiVv/nCY9mEB9GsTxGsbD/HTwVQ0ahUBXjpeGt6eFmcT14qdSbzz61EAHuvTgjFdXfc57nZDkwghhLh+Sd+DQggh3IYkLSGEEG7juklav/32G/3796dfv34sXry4psO5bNHR0QwZMoRhw4Y5nrXIycnh/vvv5/bbb+f+++8nNze3hqOs2MyZM7npppsYPHiwY15F8SuKwksvvUS/fv0YMmQIBw4cqKmwK1RefRYuXMgtt9zCsGHDGDZsGFu2bHEse//99+nXrx/9+/dn69atNRFyhU6fPs348eMZOHAggwYN4tNPPwXc8/xUVBd3PTclJSWMGjWKoUOHMmjQIBYsWABAUlISo0ePpl+/fkydOhWTyQSAyWRi6tSp9OvXj9GjR5OcnFyT4buGch2wWCzKbbfdppw8eVIpKSlRhgwZohw5cqSmw7osffr0UTIzM53mvfbaa8r777+vKIqivP/++8rrr79eE6FVyo4dO5T9+/crgwYNcsyrKP5ff/1VmTRpkmKz2ZTY2Fhl1KhRNRLzxZRXnwULFigffvhhmXWPHDmiDBkyRCkpKVFOnjyp3HbbbYrFYrma4V5Uamqqsn//fkVRFCU/P1+5/fbblSNHjrjl+amoLu56bmw2m1JQUKAoiqKYTCZl1KhRSmxsrDJlyhRl/fr1iqIoyuzZs5Vly5YpiqIon3/+uTJ79mxFURRl/fr1yhNPPFEjcbvSddHSiouLo3HjxoSHh6PX6xk0aBCbN2+u6bCu2ObNmxk+fDgAw4cP56effqrZgC6iW7du+Pv7O82rKP5z81UqFZ06dSIvL4+0NNd1C1MV5dWnIps3b2bQoEHo9XrCw8Np3LgxcXFxl97wKgkMDKRt27YA+Pj40KxZM1JTU93y/FRUl4pc6+dGpVLh7e0NgMViwWKxoFKp+PPPP+nfvz8AI0aMcHye/fzzz4wYMQKA/v37s3379lrXNd51kbRSU1MJDj7fpVRQUNBFX8jXqkmTJjFy5EhHl1aZmZkEBgYC0KBBAzIzM2syvMtWUfwXnq/g4GC3OV/Lli1jyJAhzJw503E5zZ1ef8nJycTHx9OxY0e3Pz+l6wLue26sVivDhg2jZ8+e9OzZk/DwcPz8/NBq7R0alT7+qamphJztGk+r1eLr60t2dnaNxe4K10XSqg2+/PJLvvnmGz744AOWLVvGzp07nZarVCq3fjDb3eMHuOuuu/jxxx9Zu3YtgYGBvPrqqzUd0mUpLCxkypQpzJo1Cx8fH6dl7nZ+LqyLO58bjUbD2rVr2bJlC3FxcRw/frymQ6pR10XSCgoK4syZ811KpaamEnRBl1LXunPx1qtXj379+hEXF0e9evUcl2XS0tKoW7duTYZ42SqK/8LzdebMGbc4X/Xr10ej0aBWqxk9ejT79u0D3OP1ZzabmTJlCkOGDOH2228H3Pf8lFcXdz435/j5+dGjRw/27NlDXl4eFosFcD7+QUFBnD7bNZ7FYiE/P586F3RA7u6ui6TVvn17EhISSEpKwmQy8d133xEdHV3TYVVaUVERBQUFjr//+OMPWrZsSXR0NGvWrAFgzZo13HbbbTUY5eWrKP5z8xVFYc+ePfj6+jouU13LSv+u89NPP9GyZUvAXp/vvvsOk8lEUlISCQkJdOjQoabCLENRFJ599lmaNWvG/fff75jvjuenorq467nJysoiLy8PsPfSvm3bNpo3b06PHj3YtGkTAN98843j8yw6OppvvvkGgE2bNnFjLewa77rpEWPLli28/PLLWK1W7rzzTh555JGaDqnSkpKSeOyxxwD79e3BgwfzyCOPkJ2dzdSpUzl9+jShoaH897//JeAa7XB4+vTp7Nixg+zsbOrVq8fjjz9O3759y41fURTmzp3L1q1b8fT05OWXX6Z9+/Y1XQUn5dVnx44dHDp0CICGDRsyd+5cx4f5u+++y6pVq9BoNMyaNYuoqKiaDN/Jrl27uOeee2jVqhVqtf177PTp0+nQoYPbnZ+K6rJ+/Xq3PDeHDh0iJiYGq9WKoijccccdTJ48maSkJKZNm0Zubi6tW7dm/vz56PV6SkpKeOqpp4iPj8ff35+33nqL8FrWNd51k7SEEEK4v+vi8qAQQojaQZKWEEIItyFJSwghhNuQpCWEEMJtSNISQgjhNiRpCVFNPvnkE4qLix3T//rXvxzP2Aghqofc8i7EZVAUBUVRHM8AlRYdHc3KlSvdrmcSIdyJtqYDEOJal5yczKRJk+jYsSMHDhygQ4cOHD58mJKSEvr378+UKVP47LPPSEtL47777iMgIIClS5c6JbGPP/6YVatWATBq1CgmTpxYs5USwk1J0hKiEhITE3nttdfo1KkTOTk5BAQEYLVamThxIocOHWLChAl88sknfPrpp2VaWvv372f16tWsWLECRVEYM2YM3bt3p02bNjVUGyHclyQtISohNDSUTp06AbBhwwZWrFiBxWIhPT2dY8eOERERUeG2u3fvpm/fvnh5eQHQr18/du3aJUlLiCqQpCVEJZxLOElJSSxZsoSVK1fi7+9PTEwMJSUlNRydENcPuXtQiMtQWFiIp6cnvr6+ZGRk8NtvvzmWeXt7U1hYWGabrl278tNPP1FcXExRURE//fQTXbt2vZphC1FrSEtLiMsQERFBmzZtGDBgAMHBwXTu3NmxbMyYMTzwwAMEBgaydOlSx/y2bdsycuRIRo8eDdhvxJBLg0JUjdzyLoQQwm3I5UEhhBBuQ5KWEEIItyFJSwghhNuQpCWEEMJtSNISQgjhNiRpCSGEcBuStIQQQriN/weAPOlJ/CwBEwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2plot(plot_results, removel_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "u has shape (300, 300), s has shape (2,), vh has shape (2, 2)\n"
     ]
    }
   ],
   "source": [
    "ratio = 0.6\n",
    "\n",
    "x_train, y_p_train, y_m_train = load_data(\n",
    "    '../data/emoji_sent_race_{}/train/'.format(ratio),\n",
    "    size=100000, ratio=ratio)\n",
    "x_dev, y_p_dev, y_m_dev = load_data(\n",
    "    '../data/emoji_sent_race_{}/test/'.format(ratio),\n",
    "    size=100000, ratio=0.5)\n",
    "\n",
    "y_p_train_2d = np.asarray([y_p_train, y_p_train*-1 +1]).T\n",
    "\n",
    "A = np.dot(x_train.T, y_p_train_2d) / x_train.shape[0]\n",
    "u, s, vh = np.linalg.svd(A, full_matrices=True)\n",
    "print(f\"u has shape {u.shape}, s has shape {s.shape}, vh has shape {vh.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(dict,\n",
       "            {0: {'p_acc': 0.840960240060015,\n",
       "              'biased_diff_tpr': 0.22630502371727185,\n",
       "              'debiased_diff_tpr': 0.22630502371727185,\n",
       "              'biased_acc': 0.7531882970742686,\n",
       "              'debiased_acc': 0.7531882970742686},\n",
       "             1: {'p_acc': 0.8508377094273568,\n",
       "              'biased_diff_tpr': 0.22630502371727185,\n",
       "              'debiased_diff_tpr': 0.22803630357189353,\n",
       "              'biased_acc': 0.7531882970742686,\n",
       "              'debiased_acc': 0.7530632658164541},\n",
       "             2: {'p_acc': 0.4711177794448612,\n",
       "              'biased_diff_tpr': 0.22630502371727185,\n",
       "              'debiased_diff_tpr': 0.08988024196274066,\n",
       "              'biased_acc': 0.7531882970742686,\n",
       "              'debiased_acc': 0.7531882970742686},\n",
       "             5: {'p_acc': 0.4708677169292323,\n",
       "              'biased_diff_tpr': 0.22630502371727185,\n",
       "              'debiased_diff_tpr': 0.09037932830110916,\n",
       "              'biased_acc': 0.7531882970742686,\n",
       "              'debiased_acc': 0.7526881720430108},\n",
       "             30: {'p_acc': 0.4723680920230057,\n",
       "              'biased_diff_tpr': 0.22630502371727185,\n",
       "              'debiased_diff_tpr': 0.09126866357639556,\n",
       "              'biased_acc': 0.7531882970742686,\n",
       "              'debiased_acc': 0.7530632658164541},\n",
       "             50: {'p_acc': 0.5066266566641661,\n",
       "              'biased_diff_tpr': 0.22630502371727185,\n",
       "              'debiased_diff_tpr': 0.09258461867418033,\n",
       "              'biased_acc': 0.7531882970742686,\n",
       "              'debiased_acc': 0.7511877969492373},\n",
       "             100: {'p_acc': 0.5031257814453614,\n",
       "              'biased_diff_tpr': 0.22630502371727185,\n",
       "              'debiased_diff_tpr': 0.09496617559255267,\n",
       "              'biased_acc': 0.7531882970742686,\n",
       "              'debiased_acc': 0.7484371092773193},\n",
       "             200: {'p_acc': 0.5007501875468867,\n",
       "              'biased_diff_tpr': 0.22630502371727185,\n",
       "              'debiased_diff_tpr': 0.10150799279833236,\n",
       "              'biased_acc': 0.7531882970742686,\n",
       "              'debiased_acc': 0.7403100775193798},\n",
       "             220: {'p_acc': 0.5002500625156289,\n",
       "              'biased_diff_tpr': 0.22630502371727185,\n",
       "              'debiased_diff_tpr': 0.10921997885015855,\n",
       "              'biased_acc': 0.7531882970742686,\n",
       "              'debiased_acc': 0.7353088272068017},\n",
       "             240: {'p_acc': 0.5001250312578145,\n",
       "              'biased_diff_tpr': 0.22630502371727185,\n",
       "              'debiased_diff_tpr': 0.10635797301822242,\n",
       "              'biased_acc': 0.7531882970742686,\n",
       "              'debiased_acc': 0.733183295823956},\n",
       "             260: {'p_acc': 0.5002500625156289,\n",
       "              'biased_diff_tpr': 0.22630502371727185,\n",
       "              'debiased_diff_tpr': 0.10776873695187088,\n",
       "              'biased_acc': 0.7531882970742686,\n",
       "              'debiased_acc': 0.729307326831708},\n",
       "             280: {'p_acc': 0.4981245311327832,\n",
       "              'biased_diff_tpr': 0.22630502371727185,\n",
       "              'debiased_diff_tpr': 0.11257271707339944,\n",
       "              'biased_acc': 0.7531882970742686,\n",
       "              'debiased_acc': 0.7215553888472118},\n",
       "             290: {'p_acc': 0.5036259064766192,\n",
       "              'biased_diff_tpr': 0.22630502371727185,\n",
       "              'debiased_diff_tpr': 0.1257355117153975,\n",
       "              'biased_acc': 0.7531882970742686,\n",
       "              'debiased_acc': 0.7189297324331083},\n",
       "             295: {'p_acc': 0.49987496874218557,\n",
       "              'biased_diff_tpr': 0.22630502371727185,\n",
       "              'debiased_diff_tpr': 0.11164602462363725,\n",
       "              'biased_acc': 0.7531882970742686,\n",
       "              'debiased_acc': 0.6765441360340085}})"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = check_result(u, removel_range, x_train, x_dev, y_m_train, y_m_dev, y_p_train, y_p_dev)\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(list,\n",
       "            {'biased_diff_tpr': [0.22630502371727185,\n",
       "              0.22630502371727185,\n",
       "              0.22630502371727185,\n",
       "              0.22630502371727185,\n",
       "              0.22630502371727185,\n",
       "              0.22630502371727185,\n",
       "              0.22630502371727185,\n",
       "              0.22630502371727185,\n",
       "              0.22630502371727185,\n",
       "              0.22630502371727185,\n",
       "              0.22630502371727185,\n",
       "              0.22630502371727185,\n",
       "              0.22630502371727185,\n",
       "              0.22630502371727185],\n",
       "             'debiased_diff_tpr': [0.22630502371727185,\n",
       "              0.22803630357189353,\n",
       "              0.08988024196274066,\n",
       "              0.09037932830110916,\n",
       "              0.09126866357639556,\n",
       "              0.09258461867418033,\n",
       "              0.09496617559255267,\n",
       "              0.10150799279833236,\n",
       "              0.10921997885015855,\n",
       "              0.10635797301822242,\n",
       "              0.10776873695187088,\n",
       "              0.11257271707339944,\n",
       "              0.1257355117153975,\n",
       "              0.11164602462363725],\n",
       "             'biased_acc': [0.7531882970742686,\n",
       "              0.7531882970742686,\n",
       "              0.7531882970742686,\n",
       "              0.7531882970742686,\n",
       "              0.7531882970742686,\n",
       "              0.7531882970742686,\n",
       "              0.7531882970742686,\n",
       "              0.7531882970742686,\n",
       "              0.7531882970742686,\n",
       "              0.7531882970742686,\n",
       "              0.7531882970742686,\n",
       "              0.7531882970742686,\n",
       "              0.7531882970742686,\n",
       "              0.7531882970742686],\n",
       "             'debiased_acc': [0.7531882970742686,\n",
       "              0.7530632658164541,\n",
       "              0.7531882970742686,\n",
       "              0.7526881720430108,\n",
       "              0.7530632658164541,\n",
       "              0.7511877969492373,\n",
       "              0.7484371092773193,\n",
       "              0.7403100775193798,\n",
       "              0.7353088272068017,\n",
       "              0.733183295823956,\n",
       "              0.729307326831708,\n",
       "              0.7215553888472118,\n",
       "              0.7189297324331083,\n",
       "              0.6765441360340085]})"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_results = convert_to_plot_results(results, removel_range)\n",
    "plot_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2plot(plot_results, removel_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "u has shape (300, 300), s has shape (2,), vh has shape (2, 2)\n"
     ]
    }
   ],
   "source": [
    "ratio = 0.7\n",
    "\n",
    "x_train, y_p_train, y_m_train = load_data(\n",
    "    '../data/emoji_sent_race_{}/train/'.format(ratio),\n",
    "    size=100000, ratio=ratio)\n",
    "x_dev, y_p_dev, y_m_dev = load_data(\n",
    "    '../data/emoji_sent_race_{}/test/'.format(ratio),\n",
    "    size=100000, ratio=0.5)\n",
    "\n",
    "y_p_train_2d = np.asarray([y_p_train, y_p_train*-1 +1]).T\n",
    "\n",
    "A = np.dot(x_train.T, y_p_train_2d) / x_train.shape[0]\n",
    "u, s, vh = np.linalg.svd(A, full_matrices=True)\n",
    "print(f\"u has shape {u.shape}, s has shape {s.shape}, vh has shape {vh.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(dict,\n",
       "            {0: {'p_acc': 0.8199549887471868,\n",
       "              'biased_diff_tpr': 0.31864557675474753,\n",
       "              'debiased_diff_tpr': 0.31864557675474753,\n",
       "              'biased_acc': 0.741185296324081,\n",
       "              'debiased_acc': 0.741185296324081},\n",
       "             1: {'p_acc': 0.8438359589897474,\n",
       "              'biased_diff_tpr': 0.31864557675474753,\n",
       "              'debiased_diff_tpr': 0.31575482093361984,\n",
       "              'biased_acc': 0.741185296324081,\n",
       "              'debiased_acc': 0.7346836709177295},\n",
       "             2: {'p_acc': 0.463615903975994,\n",
       "              'biased_diff_tpr': 0.31864557675474753,\n",
       "              'debiased_diff_tpr': 0.11635856368415173,\n",
       "              'biased_acc': 0.741185296324081,\n",
       "              'debiased_acc': 0.7363090772693174},\n",
       "             5: {'p_acc': 0.46399099774943736,\n",
       "              'biased_diff_tpr': 0.31864557675474753,\n",
       "              'debiased_diff_tpr': 0.11674792676509015,\n",
       "              'biased_acc': 0.741185296324081,\n",
       "              'debiased_acc': 0.734558639659915},\n",
       "             30: {'p_acc': 0.5030007501875469,\n",
       "              'biased_diff_tpr': 0.31864557675474753,\n",
       "              'debiased_diff_tpr': 0.12023899899338386,\n",
       "              'biased_acc': 0.741185296324081,\n",
       "              'debiased_acc': 0.7366841710427607},\n",
       "             50: {'p_acc': 0.5018754688672168,\n",
       "              'biased_diff_tpr': 0.31864557675474753,\n",
       "              'debiased_diff_tpr': 0.12100513006482054,\n",
       "              'biased_acc': 0.741185296324081,\n",
       "              'debiased_acc': 0.7349337334333583},\n",
       "             100: {'p_acc': 0.4981245311327832,\n",
       "              'biased_diff_tpr': 0.31864557675474753,\n",
       "              'debiased_diff_tpr': 0.12715509044451567,\n",
       "              'biased_acc': 0.741185296324081,\n",
       "              'debiased_acc': 0.7334333583395849},\n",
       "             200: {'p_acc': 0.47999499874968743,\n",
       "              'biased_diff_tpr': 0.31864557675474753,\n",
       "              'debiased_diff_tpr': 0.15675297512285796,\n",
       "              'biased_acc': 0.741185296324081,\n",
       "              'debiased_acc': 0.7150537634408602},\n",
       "             220: {'p_acc': 0.4673668417104276,\n",
       "              'biased_diff_tpr': 0.31864557675474753,\n",
       "              'debiased_diff_tpr': 0.1525863232511454,\n",
       "              'biased_acc': 0.741185296324081,\n",
       "              'debiased_acc': 0.7150537634408602},\n",
       "             240: {'p_acc': 0.5385096274068517,\n",
       "              'biased_diff_tpr': 0.31864557675474753,\n",
       "              'debiased_diff_tpr': 0.15004721659847078,\n",
       "              'biased_acc': 0.741185296324081,\n",
       "              'debiased_acc': 0.7125531382845711},\n",
       "             260: {'p_acc': 0.5482620655163791,\n",
       "              'biased_diff_tpr': 0.31864557675474753,\n",
       "              'debiased_diff_tpr': 0.1537984908912681,\n",
       "              'biased_acc': 0.741185296324081,\n",
       "              'debiased_acc': 0.7040510127531883},\n",
       "             280: {'p_acc': 0.5118779694923731,\n",
       "              'biased_diff_tpr': 0.31864557675474753,\n",
       "              'debiased_diff_tpr': 0.15392966588017956,\n",
       "              'biased_acc': 0.741185296324081,\n",
       "              'debiased_acc': 0.6950487621905477},\n",
       "             290: {'p_acc': 0.5113778444611152,\n",
       "              'biased_diff_tpr': 0.31864557675474753,\n",
       "              'debiased_diff_tpr': 0.1504312844224986,\n",
       "              'biased_acc': 0.741185296324081,\n",
       "              'debiased_acc': 0.691297824456114},\n",
       "             295: {'p_acc': 0.49987496874218557,\n",
       "              'biased_diff_tpr': 0.31864557675474753,\n",
       "              'debiased_diff_tpr': 0.1759445240878502,\n",
       "              'biased_acc': 0.741185296324081,\n",
       "              'debiased_acc': 0.661790447611903}})"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = check_result(u, removel_range, x_train, x_dev, y_m_train, y_m_dev, y_p_train, y_p_dev)\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(list,\n",
       "            {'biased_diff_tpr': [0.31864557675474753,\n",
       "              0.31864557675474753,\n",
       "              0.31864557675474753,\n",
       "              0.31864557675474753,\n",
       "              0.31864557675474753,\n",
       "              0.31864557675474753,\n",
       "              0.31864557675474753,\n",
       "              0.31864557675474753,\n",
       "              0.31864557675474753,\n",
       "              0.31864557675474753,\n",
       "              0.31864557675474753,\n",
       "              0.31864557675474753,\n",
       "              0.31864557675474753,\n",
       "              0.31864557675474753],\n",
       "             'debiased_diff_tpr': [0.31864557675474753,\n",
       "              0.31575482093361984,\n",
       "              0.11635856368415173,\n",
       "              0.11674792676509015,\n",
       "              0.12023899899338386,\n",
       "              0.12100513006482054,\n",
       "              0.12715509044451567,\n",
       "              0.15675297512285796,\n",
       "              0.1525863232511454,\n",
       "              0.15004721659847078,\n",
       "              0.1537984908912681,\n",
       "              0.15392966588017956,\n",
       "              0.1504312844224986,\n",
       "              0.1759445240878502],\n",
       "             'biased_acc': [0.741185296324081,\n",
       "              0.741185296324081,\n",
       "              0.741185296324081,\n",
       "              0.741185296324081,\n",
       "              0.741185296324081,\n",
       "              0.741185296324081,\n",
       "              0.741185296324081,\n",
       "              0.741185296324081,\n",
       "              0.741185296324081,\n",
       "              0.741185296324081,\n",
       "              0.741185296324081,\n",
       "              0.741185296324081,\n",
       "              0.741185296324081,\n",
       "              0.741185296324081],\n",
       "             'debiased_acc': [0.741185296324081,\n",
       "              0.7346836709177295,\n",
       "              0.7363090772693174,\n",
       "              0.734558639659915,\n",
       "              0.7366841710427607,\n",
       "              0.7349337334333583,\n",
       "              0.7334333583395849,\n",
       "              0.7150537634408602,\n",
       "              0.7150537634408602,\n",
       "              0.7125531382845711,\n",
       "              0.7040510127531883,\n",
       "              0.6950487621905477,\n",
       "              0.691297824456114,\n",
       "              0.661790447611903]})"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_results = convert_to_plot_results(results, removel_range)\n",
    "plot_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2plot(plot_results, removel_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "u has shape (300, 300), s has shape (2,), vh has shape (2, 2)\n"
     ]
    }
   ],
   "source": [
    "ratio = 0.8\n",
    "\n",
    "x_train, y_p_train, y_m_train = load_data(\n",
    "    '../data/emoji_sent_race_{}/train/'.format(ratio),\n",
    "    size=100000, ratio=ratio)\n",
    "x_dev, y_p_dev, y_m_dev = load_data(\n",
    "    '../data/emoji_sent_race_{}/test/'.format(ratio),\n",
    "    size=100000, ratio=0.5)\n",
    "\n",
    "y_p_train_2d = np.asarray([y_p_train, y_p_train*-1 +1]).T\n",
    "\n",
    "A = np.dot(x_train.T, y_p_train_2d) / x_train.shape[0]\n",
    "u, s, vh = np.linalg.svd(A, full_matrices=True)\n",
    "print(f\"u has shape {u.shape}, s has shape {s.shape}, vh has shape {vh.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(dict,\n",
       "            {0: {'p_acc': 0.8379594898724682,\n",
       "              'biased_diff_tpr': 0.4043010400370602,\n",
       "              'debiased_diff_tpr': 0.40411721363762787,\n",
       "              'biased_acc': 0.7200550137534384,\n",
       "              'debiased_acc': 0.7201800450112528},\n",
       "             1: {'p_acc': 0.7973243310827707,\n",
       "              'biased_diff_tpr': 0.4043010400370602,\n",
       "              'debiased_diff_tpr': 0.3654118278211388,\n",
       "              'biased_acc': 0.7200550137534384,\n",
       "              'debiased_acc': 0.7148037009252313},\n",
       "             2: {'p_acc': 0.47186796699174793,\n",
       "              'biased_diff_tpr': 0.4043010400370602,\n",
       "              'debiased_diff_tpr': 0.18610177633438116,\n",
       "              'biased_acc': 0.7200550137534384,\n",
       "              'debiased_acc': 0.7191797949487372},\n",
       "             5: {'p_acc': 0.47199299824956237,\n",
       "              'biased_diff_tpr': 0.4043010400370602,\n",
       "              'debiased_diff_tpr': 0.184739631028025,\n",
       "              'biased_acc': 0.7200550137534384,\n",
       "              'debiased_acc': 0.7193048262065517},\n",
       "             30: {'p_acc': 0.4668667166791698,\n",
       "              'biased_diff_tpr': 0.4043010400370602,\n",
       "              'debiased_diff_tpr': 0.18506971632421484,\n",
       "              'biased_acc': 0.7200550137534384,\n",
       "              'debiased_acc': 0.7191797949487372},\n",
       "             50: {'p_acc': 0.46524131032758187,\n",
       "              'biased_diff_tpr': 0.4043010400370602,\n",
       "              'debiased_diff_tpr': 0.1867917631297146,\n",
       "              'biased_acc': 0.7200550137534384,\n",
       "              'debiased_acc': 0.7181795448862216},\n",
       "             100: {'p_acc': 0.4736184046011503,\n",
       "              'biased_diff_tpr': 0.4043010400370602,\n",
       "              'debiased_diff_tpr': 0.18737210776237648,\n",
       "              'biased_acc': 0.7200550137534384,\n",
       "              'debiased_acc': 0.7146786696674169},\n",
       "             200: {'p_acc': 0.5230057514378594,\n",
       "              'biased_diff_tpr': 0.4043010400370602,\n",
       "              'debiased_diff_tpr': 0.19752038343148395,\n",
       "              'biased_acc': 0.7200550137534384,\n",
       "              'debiased_acc': 0.6956739184796199},\n",
       "             220: {'p_acc': 0.5100025006251563,\n",
       "              'biased_diff_tpr': 0.4043010400370602,\n",
       "              'debiased_diff_tpr': 0.202949662154134,\n",
       "              'biased_acc': 0.7200550137534384,\n",
       "              'debiased_acc': 0.6937984496124031},\n",
       "             240: {'p_acc': 0.5075018754688673,\n",
       "              'biased_diff_tpr': 0.4043010400370602,\n",
       "              'debiased_diff_tpr': 0.20801803316486164,\n",
       "              'biased_acc': 0.7200550137534384,\n",
       "              'debiased_acc': 0.6820455113778444},\n",
       "             260: {'p_acc': 0.502750687671918,\n",
       "              'biased_diff_tpr': 0.4043010400370602,\n",
       "              'debiased_diff_tpr': 0.18813856463946912,\n",
       "              'biased_acc': 0.7200550137534384,\n",
       "              'debiased_acc': 0.6614153538384596},\n",
       "             280: {'p_acc': 0.49987496874218557,\n",
       "              'biased_diff_tpr': 0.4043010400370602,\n",
       "              'debiased_diff_tpr': 0.19263016753347223,\n",
       "              'biased_acc': 0.7200550137534384,\n",
       "              'debiased_acc': 0.6404101025256314},\n",
       "             290: {'p_acc': 0.49987496874218557,\n",
       "              'biased_diff_tpr': 0.4043010400370602,\n",
       "              'debiased_diff_tpr': 0.1721118417919755,\n",
       "              'biased_acc': 0.7200550137534384,\n",
       "              'debiased_acc': 0.6280320080020005},\n",
       "             295: {'p_acc': 0.49987496874218557,\n",
       "              'biased_diff_tpr': 0.4043010400370602,\n",
       "              'debiased_diff_tpr': 0.0834654770143427,\n",
       "              'biased_acc': 0.7200550137534384,\n",
       "              'debiased_acc': 0.5681420355088772}})"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = check_result(u, removel_range, x_train, x_dev, y_m_train, y_m_dev, y_p_train, y_p_dev)\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(list,\n",
       "            {'biased_diff_tpr': [0.4043010400370602,\n",
       "              0.4043010400370602,\n",
       "              0.4043010400370602,\n",
       "              0.4043010400370602,\n",
       "              0.4043010400370602,\n",
       "              0.4043010400370602,\n",
       "              0.4043010400370602,\n",
       "              0.4043010400370602,\n",
       "              0.4043010400370602,\n",
       "              0.4043010400370602,\n",
       "              0.4043010400370602,\n",
       "              0.4043010400370602,\n",
       "              0.4043010400370602,\n",
       "              0.4043010400370602],\n",
       "             'debiased_diff_tpr': [0.40411721363762787,\n",
       "              0.3654118278211388,\n",
       "              0.18610177633438116,\n",
       "              0.184739631028025,\n",
       "              0.18506971632421484,\n",
       "              0.1867917631297146,\n",
       "              0.18737210776237648,\n",
       "              0.19752038343148395,\n",
       "              0.202949662154134,\n",
       "              0.20801803316486164,\n",
       "              0.18813856463946912,\n",
       "              0.19263016753347223,\n",
       "              0.1721118417919755,\n",
       "              0.0834654770143427],\n",
       "             'biased_acc': [0.7200550137534384,\n",
       "              0.7200550137534384,\n",
       "              0.7200550137534384,\n",
       "              0.7200550137534384,\n",
       "              0.7200550137534384,\n",
       "              0.7200550137534384,\n",
       "              0.7200550137534384,\n",
       "              0.7200550137534384,\n",
       "              0.7200550137534384,\n",
       "              0.7200550137534384,\n",
       "              0.7200550137534384,\n",
       "              0.7200550137534384,\n",
       "              0.7200550137534384,\n",
       "              0.7200550137534384],\n",
       "             'debiased_acc': [0.7201800450112528,\n",
       "              0.7148037009252313,\n",
       "              0.7191797949487372,\n",
       "              0.7193048262065517,\n",
       "              0.7191797949487372,\n",
       "              0.7181795448862216,\n",
       "              0.7146786696674169,\n",
       "              0.6956739184796199,\n",
       "              0.6937984496124031,\n",
       "              0.6820455113778444,\n",
       "              0.6614153538384596,\n",
       "              0.6404101025256314,\n",
       "              0.6280320080020005,\n",
       "              0.5681420355088772]})"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_results = convert_to_plot_results(results, removel_range)\n",
    "plot_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2plot(plot_results, removel_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}