{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import synapseclient\n", "import os\n", "import sys\n", "import pandas\n", "import pylab as pl\n", "\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "import numpy as np\n", "import pylab as pl\n", "from sklearn import svm, datasets\n", "from sklearn.utils import shuffle\n", "from sklearn.metrics import roc_curve, auc, precision_recall_curve\n", "\n", "import warnings\n", "warnings.filterwarnings('ignore')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Welcome, Abhishek Pratap!\n", "\n" ] } ], "source": [ "syn = synapseclient.login()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def get_truePhenotype_testSamples_RA_challenge(syn):\n", " \"\"\"\n", " function to get the read the true phenotype values for the testSamples\n", " \"\"\"\n", " #get the phenotype data\n", " phenotype_data_synId = 'syn2324939'\n", " phenotype_data_entity = syn.get(phenotype_data_synId)\n", " phenotype_df = pandas.DataFrame.from_csv(header=0, sep=\" \", path=phenotype_data_entity.path)\n", " phenotype_df = phenotype_df.reset_index()\n", " #renaming column 'ID_1' to 'ID'\n", " phenotype_df['ID'] = phenotype_df.ID_1\n", " phenotype_df = phenotype_df.drop(['ID_1'], axis=1)\n", "\n", " #get the partitions and extract the testSamples ID (test1 == 1)\n", " partition_data_synId = 'syn2351768'\n", " partition_data_entity = syn.get(partition_data_synId)\n", " partitions_df = pandas.DataFrame.from_csv(partition_data_entity.path,header=0,sep=\" \")\n", " partitions_df = partitions_df.reset_index()\n", "\n", " #the following generates warning \n", " #testSamples_ID = partitions_df.query('test1 == 1')['ID']\n", " testSamples_ID = partitions_df[partitions_df['test1'] == 1]['ID']\n", "\n", " #return only the phenotypes for the testSamples set\n", " #testSamples_true_phenotype = phenotype_df.query('ID == testSamples_ID')\n", " testSamples_true_phenotype = phenotype_df[phenotype_df.ID.isin(testSamples_ID)]\n", " return (testSamples_true_phenotype)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "truth_df = get_truePhenotype_testSamples_RA_challenge(syn)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "####creating binary class for Precision Recall calculation \n", "* 1 = non responder\n", "* 0 = responder" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ "truth_df['true_class'] = np.nan\n", "truth_df.true_class[truth_df.Response.isin(['Intermediate', 'Good'])] = 0\n", "truth_df.true_class[truth_df.Response.isin(['Non', 'Supernon'])] = 1" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def __get_blockWise_stats(sub_stats):\n", " \n", " #group to calculate group wise stats for each block\n", " grouped = sub_stats.groupby(['predict'], sort=False)\n", " \n", " #instantiate a pandas dataframe to store the results for each group (tied values)\n", " result = pandas.DataFrame.from_dict({'block':xrange(len(grouped)),\n", " 'block_numElements' : np.nan,\n", " 'block_truePos_density' : np.nan,\n", " 'block_truePos' : np.nan,\n", " 'blockValue' : np.nan\n", " })\n", " \n", " for block,grp in enumerate(grouped):\n", " name,grp = grp[0],grp[1]\n", " truePositive = sum(grp.truth == 1)\n", " grp_truePositive_density = truePositive / float(len(grp))\n", " idxs = result.block == block\n", " result.block_truePos_density[idxs] = grp_truePositive_density\n", " result.block_numElements[idxs] = len(grp)\n", " result.block_truePos[idxs] = truePositive\n", " result.blockValue[idxs] = grp.predict.unique()\n", " result.block = result.block + 1\n", " result['cum_numElements'] = result.block_numElements.cumsum()\n", " result['cum_truePos'] = result.block_truePos.cumsum()\n", " \n", " return(result)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def get_precision_recall_fpr(truth, pred):\n", " \n", " sub_stats = pandas.DataFrame.from_dict({'predict':pred, 'truth':truth}, dtype='float64')\n", " sub_stats = sub_stats.sort_values(by=['predict'],ascending=False)\n", "\n", " blockWise_stats = __get_blockWise_stats(sub_stats)\n", " grouped = sub_stats.groupby(['predict'],sort=False)\n", " sub_stats = grouped.apply(__nonlinear_interpolated_evalStats,blockWise_stats)\n", "# sub_stats = sub_stats.sort(columns=['precision'])\n", " precision, recall, fpr, threshold = sub_stats.precision.values, sub_stats.recall.values, sub_stats.fpr.values, sub_stats.predict.values \n", " \n", " #YFG suggestion - for the case when Truth == Prediction\n", " # REF - https://github.com/Sage-Bionetworks/DARPA_Challenge/blob/master/challenge_config.py#L162\n", " #PR curve AUC (Fixes error when prediction == truth)\n", " #recall_new=list(recall)\n", " #precision_new=list(precision)\n", " #recall_new.reverse()\n", " #recall_new.append(0)\n", " #recall_new.reverse()\n", " #precision_new.reverse()\n", " #precision_new.append(precision_new[len(precision_new)-1])\n", " #precision_new.reverse()\n", " \n", " ### Implementing the change using numpy style // Abhishek Pratap - 08/31/2016\n", " recall_mod = np.insert(recall,0,0) ## adding 0 at the beginning\n", " precision_mod = np.insert(precision,0,precision[0]) ## adding corresponding value at the beginning \n", " fpr_mod = np.insert(fpr,0,fpr[0]) ## adding corresponding value at the beginning \n", "\n", " return(precision_mod, recall_mod, fpr_mod, threshold)\n", "\n", "\n", "def __nonlinear_interpolated_evalStats(block_df, blockWise_stats):\n", " \"\"\"\n", " //needs to be updated\n", " \"\"\"\n", " \n", " blockValue = block_df.predict.unique()\n", " if len(blockValue) != 1:\n", " raise Exception(\"grouping by predict column doesnt yield unique predict vals per group..WIERD\")\n", " blockValue = blockValue[0]\n", " blockStats = blockWise_stats[blockWise_stats.blockValue == blockValue].squeeze() #squeeze will convert one row df to series\n", " \n", " block_precision = []\n", " block_recall = []\n", " block_fpr = []\n", " test_FP = []\n", " test_TP = []\n", " total_elements = blockWise_stats.cum_numElements.max()\n", " total_truePos = blockWise_stats.cum_truePos.max()\n", " total_trueNeg = total_elements - total_truePos\n", " for block_depth,row in enumerate(block_df.iterrows()):\n", " block_depth += 1 #increase block depth by 1 \n", " #calculate the cumulative true positives seen till the last block from the current active block\n", " # and the total number of elements(cumulative) seen till the last block\n", " if blockStats.block == 1: #no previous obviously\n", " cum_truePos_till_lastBlock = 0\n", " cum_numElements_till_lastBlock = 0\n", " cum_trueNeg_till_lastBlock = 0\n", " elif blockStats.block > 1:\n", " last_blockStats = blockWise_stats[blockWise_stats.block == (blockStats.block-1)].squeeze()\n", " cum_truePos_till_lastBlock = last_blockStats['cum_truePos']\n", " cum_numElements_till_lastBlock = last_blockStats['cum_numElements']\n", " cum_trueNeg_till_lastBlock = cum_numElements_till_lastBlock - cum_truePos_till_lastBlock\n", " \n", " truePos = cum_truePos_till_lastBlock + (blockStats.block_truePos_density*block_depth)\n", " falsePos = cum_trueNeg_till_lastBlock + ((1 - blockStats.block_truePos_density ) * block_depth)\n", " test_FP.append(falsePos)\n", " test_TP.append(truePos)\n", " #precision\n", " interpolated_precision = truePos / float((cum_numElements_till_lastBlock+block_depth))\n", " block_precision.append(interpolated_precision)\n", " #recall == true positive rate\n", " interpolated_recall = truePos / float(total_truePos)\n", " block_recall.append(interpolated_recall)\n", " #fpr == false positive rate\n", " interpolated_fpr = falsePos / float(total_trueNeg)\n", " block_fpr.append(interpolated_fpr)\n", " \n", " block_df['precision'] = block_precision\n", " block_df['recall'] = block_recall\n", " block_df['fpr'] = block_fpr\n", " block_df['block_depth'] = np.arange(1,block_df.shape[0]+1)\n", " block_df['block'] = blockStats.block\n", " return(block_df)" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def __temp_plot(truth, pred, debug=False):\n", " \n", " fpr1, tpr1, threshold1 = roc_curve(truth, pred)\n", " frp1 = sorted(fpr1)\n", " precision1, recall1, thresholds = precision_recall_curve(truth, pred)\n", " print 'linear interpolation (AUC: %0.3f)' % auc(recall1,precision1, reorder=True)\n", "\n", " precision2, recall2, fpr2, threshold2= get_precision_recall_fpr(truth, pred)\n", " print 'Non-linear interpolation (AUC: %0.3f)' % auc(recall2,precision2, reorder=True)\n", "\n", " plt.clf()\n", " fig = plt.figure(figsize=(8,6))\n", " fig.subplots_adjust(hspace=.5)\n", " ax1 = fig.add_subplot(211)\n", " ax1.plot(recall1, precision1, label='linear (AUC: %0.3f)' % auc(recall1,precision1, reorder=True) )\n", " ax1.plot(recall2, precision2, label='Non linear (AUC: %0.3f)' % auc(recall2,precision2, reorder=True) )\n", " ax1.set_xlabel('Recall')\n", " ax1.set_ylabel('Precision')\n", " ax1.set_ylim([0.0, 1.2])\n", " ax1.set_xlim([0.0, 1.2])\n", " ax1.set_title('Precision-Recall')\n", " ax1.legend(loc=\"upper right\",fontsize='small' )\n", "\n", " ax2 = fig.add_subplot(212)\n", " ax2.plot(fpr1,tpr1, label='linear (AUC: %0.3f)' % auc(fpr1,tpr1, reorder=True) )\n", " ax2.plot(fpr2, recall2, label='Non linear (AUC: %0.3f)' % auc(fpr2,recall2, reorder=True) )\n", " ax2.plot(np.arange(0,1.1,.1), np.arange(0,1.1,.1),'--', label='TPR=FPR')\n", " ax2.set_xlabel('FPR')\n", " ax2.set_ylabel('TPR')\n", " ax2.set_ylim([0.0, 1.2])\n", " ax2.set_xlim([0.0, 1.2])\n", " ax2.set_title('ROC')\n", " ax2.legend(loc=\"lower right\",fontsize='small' )\n", " plt.show()\n", " \n", " if(debug == True):\n", " sub_stats = pandas.DataFrame.from_dict({'predict':pred, 'truth':truth}, dtype='float64')\n", " sub_stats = sub_stats.sort_values(by=['predict'],ascending=False)\n", " blockWise_stats = __get_blockWise_stats(sub_stats)\n", " grouped = sub_stats.groupby(['predict'],sort=False)\n", " \n", " print('-- Block wise true positive densities-----\\n')\n", " print(blockWise_stats)\n", " \n", " print('\\n\\n --- Stats per observation---\\n')\n", " sub_stats = grouped.apply(__nonlinear_interpolated_evalStats,blockWise_stats)\n", " print(sub_stats)\n", " \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Examples" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1 : single belief score" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "linear interpolation (AUC: 0.613)\n", "Non-linear interpolation (AUC: 0.225)\n" ] }, { "data": { "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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pX/bsCQI+elT+D34Qe93+9NM1m55IUeId7rMJppDNCj/69STgJXcv+IS3uFG4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\nHnuMHj16kJaWRkFBAXv37uXuu++29nH27FmrL+p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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sub = syn.getSubmission(2363293)\n", "df = pandas.DataFrame.from_csv(sub.filePath).reset_index()\n", "df = df.merge(truth_df[['true_class','ID', 'Response']], left_on=\"ID\", right_on=\"ID\", how=\"outer\")\n", "__temp_plot(df.true_class, df.belief_gen)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example 2 : binary predictions" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "linear interpolation (AUC: 0.457)\n", "Non-linear interpolation (AUC: 0.275)\n" ] }, { "data": { "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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1qiq5uzun/t+p/KHPH7jsuMti/nlS/e3eva/qL+l8f2TVX9K5flX9IlKaWCf3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\nTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sub = syn.getSubmission(2363303)\n", "df = pandas.DataFrame.from_csv(sub.filePath).reset_index()\n", "df = df.merge(truth_df[['true_class','ID', 'Response']], left_on=\"ID\", right_on=\"ID\", how=\"outer\")\n", "__temp_plot(df.true_class, df.belief_gen)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example 3 : less skewed scores" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "linear interpolation (AUC: 0.261)\n", "Non-linear interpolation (AUC: 0.258)\n" ] }, { "data": { "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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XX89dd91V2rKNRqPceeedvP/++4wfP54//elPpeXPOussJk+ezMyZM2t8mhvA2rVreeCB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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sub = syn.getSubmission(2364533)\n", "df = pandas.DataFrame.from_csv(sub.filePath).reset_index()\n", "df = df.merge(truth_df[['true_class','ID', 'Response']], left_on=\"ID\", right_on=\"ID\", how=\"outer\")\n", "__temp_plot(df.true_class, df.belief_gen)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Example 4 : 670 uniq scores" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "linear interpolation (AUC: 0.381)\n", "Non-linear interpolation (AUC: 0.381)\n" ] }, { "data": { "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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ldg99+G5acRSP/ubXNRBh5Jx5XPnZfwuLSnjsvYk8/eWL/Jj8AS0LsvjdkcN5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\nl9CwYUOys7Np0aJFmXW/4oor7Of6//nPf6hTp06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NTQ11FSKCtpN/tJ38V5G2atKkCSKi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\nUUaDu1JKKRVlNLgrpZRSUeb/AWKuqSxw9/xFAAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sub = syn.getSubmission(2368533)\n", "df = pandas.DataFrame.from_csv(sub.filePath).reset_index()\n", "df = df.merge(truth_df[['true_class','ID', 'Response']], left_on=\"ID\", right_on=\"ID\", how=\"outer\")\n", "__temp_plot(df.true_class, df.belief_gen)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Testing -- When truth == prediction (May 9, 2016)\n", "\n", "reported case by Tom " ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "linear interpolation (AUC: 1.000)\n", "Non-linear interpolation (AUC: 0.989)\n" ] }, { "data": { "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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T+2Z2fhLCTDoze8TMtprZe2WUqfXf5VD+tarU97m7p8yD4MfGf4G2QH3gHaBT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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "pred = np.concatenate([np.repeat(1,25), np.repeat(0,30)])\n", "truth = pred\n", "__temp_plot(truth, pred)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "#### 8/9/2016 - Testing Another case \n", "reported by Tom - possible bug" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sub1 = syn.getSubmission(7180273)\n", "sub1 = pandas.DataFrame.from_csv(sub1.filePath).reset_index()\n", "sub1['pred'] = sub1.SHEDDING_SC1\n", "\n", "sub2 = syn.getSubmission(7154754)\n", "sub2 = pandas.DataFrame.from_csv(sub2.filePath).reset_index()\n", "sub2['pred'] = sub2.SHEDDING_SC1\n", "\n", "truth = syn.get('syn5705153')\n", "truth = pandas.DataFrame.from_csv(truth.path).reset_index()\n", "truth['true_class'] = truth.SHEDDING_SC1" ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "linear interpolation (AUC: 0.873)\n", "Non-linear interpolation (AUC: 0.852)\n" ] }, { "data": { "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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MMkJzCXRN6UpWaibZzeYTyMnMJLdrJj2yMumRnUn3rl3IyDB1XI0RBQUFFBQU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\n9O7dm7CwMD766KMs73HkyBHnojI///wzlSpVYsi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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-- Block wise true positive densities-----\n", "\n", " block blockValue block_numElements block_truePos block_truePos_density \\\n", "0 1 1.000000 5.0 4.0 0.800000 \n", "1 2 0.857143 1.0 1.0 1.000000 \n", "2 3 0.750000 4.0 4.0 1.000000 \n", "3 4 0.600000 2.0 2.0 1.000000 \n", "4 5 0.571429 1.0 1.0 1.000000 \n", "5 6 0.500000 4.0 3.0 0.750000 \n", "6 7 0.375000 1.0 0.0 0.000000 \n", "7 8 0.285714 1.0 0.0 0.000000 \n", "8 9 0.250000 3.0 2.0 0.666667 \n", "9 10 0.000000 1.0 0.0 0.000000 \n", "\n", " cum_numElements cum_truePos \n", "0 5.0 4.0 \n", "1 6.0 5.0 \n", "2 10.0 9.0 \n", "3 12.0 11.0 \n", "4 13.0 12.0 \n", "5 17.0 15.0 \n", "6 18.0 15.0 \n", "7 19.0 15.0 \n", "8 22.0 17.0 \n", "9 23.0 17.0 \n", "\n", "\n", " --- Stats per observation---\n", "\n", " predict truth precision recall fpr block_depth block\n", "16 1.000000 1.0 0.800000 0.047059 0.033333 1 1.0\n", "2 1.000000 1.0 0.800000 0.094118 0.066667 2 1.0\n", "4 1.000000 1.0 0.800000 0.141176 0.100000 3 1.0\n", "6 1.000000 1.0 0.800000 0.188235 0.133333 4 1.0\n", "1 1.000000 0.0 0.800000 0.235294 0.166667 5 1.0\n", "19 0.857143 1.0 0.833333 0.294118 0.166667 1 2.0\n", "5 0.750000 1.0 0.857143 0.352941 0.166667 1 3.0\n", "21 0.750000 1.0 0.875000 0.411765 0.166667 2 3.0\n", "8 0.750000 1.0 0.888889 0.470588 0.166667 3 3.0\n", "17 0.750000 1.0 0.900000 0.529412 0.166667 4 3.0\n", "0 0.600000 1.0 0.909091 0.588235 0.166667 1 4.0\n", "3 0.600000 1.0 0.916667 0.647059 0.166667 2 4.0\n", "22 0.571429 1.0 0.923077 0.705882 0.166667 1 5.0\n", "13 0.500000 1.0 0.910714 0.750000 0.208333 1 6.0\n", "10 0.500000 0.0 0.900000 0.794118 0.250000 2 6.0\n", "9 0.500000 1.0 0.890625 0.838235 0.291667 3 6.0\n", "20 0.500000 1.0 0.882353 0.882353 0.333333 4 6.0\n", "18 0.375000 0.0 0.833333 0.882353 0.500000 1 7.0\n", "15 0.285714 0.0 0.789474 0.882353 0.666667 1 8.0\n", "12 0.250000 0.0 0.783333 0.921569 0.722222 1 9.0\n", "7 0.250000 1.0 0.777778 0.960784 0.777778 2 9.0\n", "11 0.250000 1.0 0.772727 1.000000 0.833333 3 9.0\n", "14 0.000000 0.0 0.739130 1.000000 1.000000 1 10.0\n" ] } ], "source": [ "data = sub1.merge(truth, left_on=\"SUBJECTID\", right_on=\"SUBJECTID\", how=\"outer\")\n", "__temp_plot(data.true_class, data.pred, debug=True)" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "linear interpolation (AUC: 0.887)\n", "Non-linear interpolation (AUC: 0.867)\n" ] }, { "data": { "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "image/png": 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fbqceJXcRkSRrjjbz+HtzuXPuo7y9aTbNGwexR/1X+doB07n8jC8xYoTuiNmd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\nQ7t27ViyZAkA6enpREdHExkZ6Z5ydf/+/dhsNiI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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "-- Block wise true positive densities-----\n", "\n", " block blockValue block_numElements block_truePos \\\n", "0 1 1.000000 6.0 5.0 \n", "1 2 0.875000 1.0 1.0 \n", "2 3 0.857143 1.0 1.0 \n", "3 4 0.800000 1.0 1.0 \n", "4 5 0.750000 2.0 2.0 \n", "5 6 0.625000 1.0 1.0 \n", "6 7 0.571429 1.0 1.0 \n", "7 8 0.500000 3.0 3.0 \n", "8 9 0.428571 1.0 0.0 \n", "9 10 0.375000 1.0 0.0 \n", "10 11 0.250000 2.0 1.0 \n", "11 12 0.000000 3.0 1.0 \n", "\n", " block_truePos_density cum_numElements cum_truePos \n", "0 0.833333 6.0 5.0 \n", "1 1.000000 7.0 6.0 \n", "2 1.000000 8.0 7.0 \n", "3 1.000000 9.0 8.0 \n", "4 1.000000 11.0 10.0 \n", "5 1.000000 12.0 11.0 \n", "6 1.000000 13.0 12.0 \n", "7 1.000000 16.0 15.0 \n", "8 0.000000 17.0 15.0 \n", "9 0.000000 18.0 15.0 \n", "10 0.500000 20.0 16.0 \n", "11 0.333333 23.0 17.0 \n", "\n", "\n", " --- Stats per observation---\n", "\n", " predict truth precision recall fpr block_depth block\n", "0 1.000000 1.0 0.833333 0.049020 0.027778 1 1.0\n", "16 1.000000 1.0 0.833333 0.098039 0.055556 2 1.0\n", "2 1.000000 1.0 0.833333 0.147059 0.083333 3 1.0\n", "4 1.000000 1.0 0.833333 0.196078 0.111111 4 1.0\n", "6 1.000000 1.0 0.833333 0.245098 0.138889 5 1.0\n", "1 1.000000 0.0 0.833333 0.294118 0.166667 6 1.0\n", "17 0.875000 1.0 0.857143 0.352941 0.166667 1 2.0\n", "19 0.857143 1.0 0.875000 0.411765 0.166667 1 3.0\n", "3 0.800000 1.0 0.888889 0.470588 0.166667 1 4.0\n", "5 0.750000 1.0 0.900000 0.529412 0.166667 1 5.0\n", "9 0.750000 1.0 0.909091 0.588235 0.166667 2 5.0\n", "21 0.625000 1.0 0.916667 0.647059 0.166667 1 6.0\n", "22 0.571429 1.0 0.923077 0.705882 0.166667 1 7.0\n", "13 0.500000 1.0 0.928571 0.764706 0.166667 1 8.0\n", "8 0.500000 1.0 0.933333 0.823529 0.166667 2 8.0\n", "20 0.500000 1.0 0.937500 0.882353 0.166667 3 8.0\n", "15 0.428571 0.0 0.882353 0.882353 0.333333 1 9.0\n", "18 0.375000 0.0 0.833333 0.882353 0.500000 1 10.0\n", "12 0.250000 0.0 0.815789 0.911765 0.583333 1 11.0\n", "7 0.250000 1.0 0.800000 0.941176 0.666667 2 11.0\n", "14 0.000000 0.0 0.777778 0.960784 0.777778 1 12.0\n", "10 0.000000 0.0 0.757576 0.980392 0.888889 2 12.0\n", "11 0.000000 1.0 0.739130 1.000000 1.000000 3 12.0\n" ] } ], "source": [ "data = sub2.merge(truth, left_on=\"SUBJECTID\", right_on=\"SUBJECTID\", how=\"outer\")\n", "__temp_plot(data.true_class, data.pred, debug=True)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.11" } }, "nbformat": 4, "nbformat_minor": 0 }