Analysis_07MAY2019_new.ipynb

    "\n",
    "        #released = cdf(s_test.X, s_logreg, 0) < r/10\n",
    "\n",
    "        s_f_rate_caus[i] = np.mean(s_test.B_prob_0_logreg * released)\n",
    "\n",
    "        ########################\n",
    "        #percentiles = estimatePercentiles(s_train_labeled.X, s_logreg)\n",
    "\n",
    "        #def releaseProbability(x):\n",
    "        #    return calcReleaseProbabilities(r * 10,\n",
    "        #                                     s_train_labeled.X,\n",
    "        #                                     x,\n",
    "        #                                     s_logreg,\n",
    "        #                                     percentileMatrix=percentiles)\n",
    "\n",
    "        #def integrand(x):\n",
    "        #    p_y0 = s_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",
    "        #s_f_rate_caus[i] = si.quad(lambda x: integrand(np.ones((1, 1)) * x),\n",
    "        #                           -10, 10)[0]\n",
    "\n",
    "    f_rates[r - 1, 0] = np.mean(s_f_rate_true)\n",
    "    f_rates[r - 1, 1] = np.mean(s_f_rate_labeled)\n",
    "    f_rates[r - 1, 2] = np.mean(s_f_rate_human)\n",
    "    f_rates[r - 1, 3] = np.mean(s_f_rate_cont)\n",
    "    f_rates[r - 1, 4] = np.mean(s_f_rate_caus)\n",
    "\n",
    "    f_sems[r - 1, 0] = scs.sem(s_f_rate_true)\n",
    "    f_sems[r - 1, 1] = scs.sem(s_f_rate_labeled)\n",
    "    f_sems[r - 1, 2] = scs.sem(s_f_rate_human)\n",
    "    f_sems[r - 1, 3] = scs.sem(s_f_rate_cont)\n",
    "    f_sems[r - 1, 4] = scs.sem(s_f_rate_caus)\n",
    "\n",
    "x_ax = np.arange(0.1, 0.9, 0.1)\n",
    "\n",
    "plt.figure(figsize=(14, 8))\n",
    "plt.errorbar(x_ax,\n",
    "             f_rates[:, 0],\n",
    "             label='True Evaluation',\n",
    "             c='green',\n",
    "             yerr=f_sems[:, 0])\n",
    "plt.errorbar(x_ax,\n",
    "             f_rates[:, 1],\n",
    "             label='Labeled outcomes',\n",
    "             c='magenta',\n",
    "             yerr=f_sems[:, 1])\n",
    "plt.errorbar(x_ax,\n",
    "             f_rates[:, 2],\n",
    "             label='Human evaluation',\n",
    "             c='red',\n",
    "             yerr=f_sems[:, 2])\n",
    "plt.errorbar(x_ax,\n",
    "             f_rates[:, 3],\n",
    "             label='Contraction, log.',\n",
    "             c='blue',\n",
    "             yerr=f_sems[:, 3])\n",
    "plt.errorbar(x_ax,\n",
    "             f_rates[:, 4],\n",
    "             label='Causal model, ep',\n",
    "             c='black',\n",
    "             yerr=f_sems[:, 4])\n",
    "\n",
    "plt.title('Failure rate vs. Acceptance rate without unobservables')\n",
    "plt.xlabel('Acceptance rate')\n",
    "plt.ylabel('Failure rate')\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()\n",
    "\n",
    "print(f_rates)\n",
    "print(\"\\nMean absolute errors:\")\n",
    "for i in range(1, f_rates.shape[1]):\n",
    "    print(np.mean(np.abs(f_rates[:, 0] - f_rates[:, i])))"
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