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"# residuals_2=tmp[:, 1])\n",
"\n",
"# # Regress Y on X and residuals from step 2.\n",
"# lr_y, __ = fitPredictiveModel(\n",
"# train_labeled.dropna()[['X', 'residuals_1', 'residuals_2']],\n",
"# train_labeled.dropna().result_Y, np.ones((1, 3)), 0)\n",
"# # With the test data, predict Y by\n",
"# # repeating steps 1 and 2\n",
"# # (Regress T on X)\n",
"# lr_t, __ = fitPredictiveModel(test.X,\n",
"# test.decision_T, np.ones(1),\n",
"# 1)\n",
"\n",
"# # (Calculate the residuals from previous regression)\n",
"# residuals_T = test.decision_T - \\\n",
"# lr_t.predict(test.X.values.reshape(-1, 1))\n",
"# test = test.assign(residuals_T=residuals_T)\n",
"\n",
"# # (Convert residuals from -1, 0 and 1 values to one-hot-encoded.\n",
"# # this way there will be separate betas for each type of residual.)\n",
"# enc = OneHotEncoder(categories='auto')\n",
"# resid_tf = test.residuals_T.values.reshape(-1, 1)\n",
"# tmp = enc.fit_transform(resid_tf).toarray()\n",
"# test = test.assign(residuals_1=tmp[:, 0], residuals_2=tmp[:, 1])\n",
"\n",
"# # by using the model from step 3 with X and the residuals from 4.a. as input\n",
"\n",
"# preds = getProbabilityForClass(\n",
"# test[['X', 'residuals_1', 'residuals_2']], lr_y, 0)\n",
"\n",
"# test = test.assign(preds=preds)\n",
"\n",
" # True evaluation\n",
" #\n",
" # Sort by failure probabilities, subjects with the smallest risk are first.\n",
" test.sort_values(by='B_prob_0_model', inplace=True, ascending=True)\n",
"\n",
" to_release = int(round(test.shape[0] * r / 10))\n",
"\n",
" # Calculate failure rate as the ratio of failures to those who were given a\n",
" # positive decision, i.e. those whose probability of negative outcome was\n",
" # low enough.\n",
" f_rate_true[i] = np.sum(\n",
" test.result_Y[0:to_release] == 0) / test.shape[0]\n",
"\n",
" # Labeled outcomes only\n",
" #\n",
" # Sort by failure probabilities, subjects with the smallest risk are first.\n",
" test_labeled.sort_values(by='B_prob_0_model',\n",
" inplace=True,\n",
" ascending=True)\n",
"\n",
" to_release = int(round(test_labeled.shape[0] * r / 10))\n",
"\n",
" f_rate_label[i] = np.sum(\n",
" test_labeled.result_Y[0:to_release] == 0) / test_labeled.shape[0]\n",
"\n",
" # Human evaluation\n",
" #\n",
" # Get judges with correct leniency as list\n",
" correct_leniency_list = test_labeled.judgeID_J[\n",
" test_labeled['acceptanceRate_R'].round(1) == r / 10].values\n",
"\n",
" # Released are the people they judged and released, T = 1\n",
" released = test_labeled[\n",
" test_labeled.judgeID_J.isin(correct_leniency_list)\n",
" & (test_labeled.decision_T == 1)]\n",
"\n",
" # Get their failure rate, aka ratio of reoffenders to number of people judged in total\n",
" f_rate_human[i] = np.sum(\n",
" released.result_Y == 0) / correct_leniency_list.shape[0]\n",
"\n",
" # Contraction, logistic regression\n",
" #\n",
" f_rate_cont[i] = contraction(test_labeled, 'judgeID_J', 'decision_T',\n",
" 'result_Y', 'B_prob_0_model',\n",
" 'acceptanceRate_R', r / 10)\n",
"\n",
" # Causal model - empirical performance\n",
"\n",
"# released = bailIndicator(\n",
"# r * 10, lr_y, train_labeled[['X', 'residuals_1', 'residuals_2']],\n",
"# test[['X', 'residuals_1', 'residuals_2']])\n",
" \n",
" released = bailIndicator(r * 10, logreg, train_labeled.X, test.X)\n",
" \n",
" #released = cdf(test.X, logreg, 0) < r / 10\n",
"\n",
"# released = npr.choice([True, False], size = test.X.shape, p=[r/10, 1-r/10])\n",
" f_rate_caus[i] = np.mean(test.B_prob_0_model * released)\n",
"\n",
" #percentiles = estimatePercentiles(train_labeled.X, logreg, N_sample=train_labeled.shape[0])\n",
"\n",
" # def releaseProbability(x):\n",
" # return calcReleaseProbabilities(r*10, train_labeled.X, x, logreg, percentileMatrix=percentiles)\n",
"\n",
" # def integraali(x):\n",
" # p_y0 = logreg.predict_proba(x.reshape(-1, 1))[:, 0]\n",
"\n",
" # p_t1 = releaseProbability(x)\n",
"\n",
" # p_x = scs.norm.pdf(x)\n",
"\n",
" # return p_y0 * p_t1 * p_x\n",
"\n",
" #f_rate_caus[i] = si.quad(lambda x: integraali(np.ones((1, 1))*x), -10, 10)[0]\n",
"\n",
" failure_rates[r - 1, 0] = np.mean(f_rate_true)\n",
" failure_rates[r - 1, 1] = np.mean(f_rate_label)\n",
" failure_rates[r - 1, 2] = np.mean(f_rate_human)\n",
" failure_rates[r - 1, 3] = np.mean(f_rate_cont)\n",
" failure_rates[r - 1, 4] = np.mean(f_rate_caus)\n",
"\n",
" failure_sems[r - 1, 0] = scs.sem(f_rate_true)\n",
" failure_sems[r - 1, 1] = scs.sem(f_rate_label)\n",
" failure_sems[r - 1, 2] = scs.sem(f_rate_human)\n",
" failure_sems[r - 1, 3] = scs.sem(f_rate_cont)\n",
" failure_sems[r - 1, 4] = scs.sem(f_rate_caus)\n",
"\n",
"x_ax = np.arange(0.1, 0.9, 0.1)\n",
"\n",
"plt.errorbar(x_ax,\n",
" failure_rates[:, 0],\n",
" label='True Evaluation',\n",
" c='green',\n",
" yerr=failure_sems[:, 0])\n",
"plt.errorbar(x_ax,\n",
" failure_rates[:, 1],\n",
" label='Labeled outcomes',\n",
" c='magenta',\n",
" yerr=failure_sems[:, 1])\n",
"plt.errorbar(x_ax,\n",
" failure_rates[:, 2],\n",
" label='Human evaluation',\n",
" c='red',\n",
" yerr=failure_sems[:, 2])\n",
"plt.errorbar(x_ax,\n",
" failure_rates[:, 3],\n",
" label='Contraction, log.',\n",
" c='blue',\n",
" yerr=failure_sems[:, 3])\n",
"plt.errorbar(x_ax,\n",
" failure_rates[:, 4],\n",
" label='Causal model, ep',\n",
" c='black',\n",
" yerr=failure_sems[:, 4])\n",
"\n",
"plt.title('Failure rate vs. Acceptance rate with unobservables')\n",
"plt.xlabel('Acceptance rate')\n",
"plt.ylabel('Failure rate')\n",
"plt.legend()\n",
"plt.grid()\n",
"plt.show()\n",
"\n",
"print(failure_rates)\n",
"print(\"\\nMean absolute errors:\")\n",
"for i in range(1, failure_rates.shape[1]):\n",
" print(np.mean(np.abs(failure_rates[:, 0] - failure_rates[:, i])))"
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