{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Machine Learning for Telecom with RandomForestClassifer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Machine Learning for Telecom with Randomforestclassifer is a supervised learning algorithm for classifying Call Detail Records in Telecom data. This notebook demonstrates exploration of data, feature Selection, auto feature importances selection and Evalution of CDR dataset to classify CallDisconnectReason with Spark ML RandomForestClassifier. \n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%config IPCompleter.greedy=True" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from pyspark.sql.types import *\n", "from pyspark.sql import SparkSession\n", "from sagemaker import get_execution_role\n", "import sagemaker_pyspark\n", "\n", "role = get_execution_role()\n", "\n", "# Configure Spark to use the SageMaker Spark dependency jars\n", "jars = sagemaker_pyspark.classpath_jars()\n", "\n", "classpath = \":\".join(sagemaker_pyspark.classpath_jars())\n", "\n", "spark = SparkSession.builder.config(\"spark.driver.extraClassPath\", classpath)\\\n", " .master(\"local[*]\").getOrCreate()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using S3 Select, enables applications to retrieve only a subset of data from an object by using simple SQL expressions. By using S3 Select to retrieve only the data, you can achieve drastic performance increases – in many cases you can get as much as a 400% improvement." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "cdr_start_loc = \"<%CDRStartFile%>\"\n", "cdr_stop_loc = \"<%CDRStopFile%>\"\n", "cdr_start_sample_loc = \"<%CDRStartSampleFile%>\"\n", "cdr_stop_sample_loc = \"<%CDRStopSampleFile%>\"\n", "\n", "df = spark.read.format(\"s3select\").parquet(cdr_stop_sample_loc)\n", "\n", "df.createOrReplaceTempView(\"cdr\")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def build_schema():\n", " \"\"\"Build and return a schema to use for the sample data.\"\"\"\n", " schema = StructType(\n", " [\n", " StructField(\"Accounting_ID\", StringType(), True),\n", " StructField(\"Start_Time_MM_DD_YYYY\", StringType(), True),\n", " StructField(\"Start_Time_HH_MM_SS_s\", StringType(), True),\n", " StructField(\"Call_Service_Duration\", StringType(), True),\n", " StructField(\"Call_Disconnect_Reason\", StringType(), True),\n", " StructField(\"Calling_Number\", StringType(), True),\n", " StructField(\"Called_Number\", StringType(), True)\n", " ]\n", " )\n", " return schema" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Exploratary Data Analysis" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Index(['_c0', '_c1', '_c2', '_c3', '_c4', '_c5', '_c6', '_c7', '_c8', '_c9',\n", " '_c10', '_c11', '_c12', '_c13', '_c14', '_c15', '_c16', '_c17', '_c19',\n", " '_c20', '_c22', '_c24', '_c26', '_c27', '_c28', '_c29', '_c30', '_c31',\n", " '_c32', '_c33', '_c35', '_c37', '_c42', '_c45', '_c49', '_c50', '_c51',\n", " '_c52', '_c53', '_c54', '_c55', '_c58', '_c59', '_c60', '_c61', '_c63',\n", " '_c64', '_c65', '_c66', '_c67', '_c68', '_c69', '_c70', '_c73', '_c74',\n", " '_c77', '_c78', '_c79', '_c80', '_c81', '_c84', '_c90', '_c94', '_c101',\n", " '_c105', '_c106', '_c107', '_c108', '_c109', '_c110', '_c112', '_c113',\n", " '_c114', '_c115', '_c116', '_c117', '_c118', '_c119', '_c124', '_c125',\n", " '_c126', '_c127', '_c128', '_c137', '_c140', '_c143', '_c148', '_c149',\n", " '_c150', '_c159', '_c160', '_c181', '_c182'],\n", " dtype='object')\n", "Index([], dtype='object')\n" ] } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "stopDF = spark.sql(\"SELECT * from cdr where _c0 = 'STOP'\")\n", "newStopPd = stopDF.toPandas()\n", "stopPd = newStopPd.dropna(axis=1)\n", "print(stopPd.columns)\n", "\n", "# Filter out Numeric Columns\n", "stopNCdf = stopPd._get_numeric_data()\n", "stopNCdf.columns \n", "print(stopNCdf.columns)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Feature Selection" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "dataDF = spark.sql(\"SELECT _c2,_c5,_c6,_c13,_c14,_c19,_c20 from cdr where _c0 = 'STOP'\")\n", "dataPanda = dataDF.toPandas()\n", "\n", "newDataDF = spark.createDataFrame(dataPanda.dropna(),build_schema())\n", "dataPd = newDataDF.toPandas()\n", "\n", "integerColumns = [\"Call_Service_Duration\" , \"Call_Disconnect_Reason\", \"Calling_Number\", \"Called_Number\"]\n", "for col in integerColumns:\n", " dataPd[col] = dataPd[col].astype(int)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Correlation HeatMap" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pandas.plotting import scatter_matrix\n", "stopNumPd = dataPd._get_numeric_data()\n", "stopNumPd.columns \n", "stopNumPd.head\n", "\n", "import seaborn as sns\n", "\n", "# calculate the correlation matrix\n", "corr = stopNumPd.corr()\n", "# plot the heatmap\n", "\n", "sns.heatmap(corr, \n", " xticklabels=corr.columns,\n", " yticklabels=corr.columns,annot=True)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Correlation Matrix" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import seaborn as sns\n", "sns.set(style='whitegrid',context='notebook')\n", "sns.pairplot(dataPd[['Accounting_ID','Start_Time_MM_DD_YYYY','Start_Time_HH_MM_SS_s','Call_Service_Duration','Call_Disconnect_Reason','Calling_Number','Called_Number']])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "- _The CDR dataset used posseses records with NORMAL CALL CLEARING(16), we are mocking the dataset with a random seed of > 0.5 % of records to represent as NOT NORMAL CALL CLEARING and hence injects 17._" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "44826" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Mock Data\n", "addDF = newDataDF\n", "unionDF = addDF.union(newDataDF)\n", "df = unionDF.drop('Call_Disconnect_Reason')\n", "\n", "from pyspark.sql.functions import rand,when\n", "df1 = df.withColumn('Call_Disconnect_Reason', when(rand(seed=1234) > 0.5, 16).otherwise(17))\n", "df1.count()" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(31385, 13441)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pyspark.sql.functions import rand\n", "\n", "trainingFraction = 0.70; testingFraction = (1-trainingFraction);\n", "seed = 1234;\n", "\n", "trainData, testData = df1.randomSplit([trainingFraction, testingFraction], seed=seed);\n", "\n", "# # CACHE TRAIN AND TEST DATA\n", "trainData.cache()\n", "testData.cache()\n", "trainData.count(),testData.count()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "from pyspark.ml.feature import StringIndexer\n", "columns_list = list(set(newDataDF.columns)-set(['Call_Disconnect_Reason']) ) \n", "indexers = []\n", "for column in columns_list:\n", " indexer = StringIndexer(inputCol=column, outputCol=column+\"_index\")\n", " indexer.setHandleInvalid(\"skip\")\n", " indexers.append(indexer)\n" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "StringIndexer_446c9c6b4a0a5026447b" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from pyspark.ml.feature import StringIndexer\n", "# Convert target into numerical categories\n", "labelIndexer = StringIndexer(inputCol=\"Call_Disconnect_Reason\", outputCol=\"label\")\n", "labelIndexer.setHandleInvalid(\"skip\")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "['Start_Time_HH_MM_SS_s_index', 'Call_Service_Duration_index', 'Called_Number_index', 'Start_Time_MM_DD_YYYY_index', 'Accounting_ID_index', 'Calling_Number_index']\n" ] } ], "source": [ "from pyspark.ml.feature import VectorAssembler\n", "from array import array\n", "\n", "inputcolsIndexer = []\n", "for col in columns_list:\n", " inputcolsIndexer.append(col+\"_index\")\n", "print(inputcolsIndexer)\n", "\n", "vecAssembler = VectorAssembler(inputCols=inputcolsIndexer, outputCol=\"features\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "```\n", "Random Forest Classifier: Random forests are a popular family of classification and regression methods. Random forests are ensembles of decision trees. Random forests combine many decision trees in order to reduce the risk of overfitting. The spark.ml implementation supports random forests for binary and multiclass classification and for regression, using both continuous and categorical features.\n", "\n", "```" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "from pyspark.ml.classification import RandomForestClassifier\n", "from pyspark.ml.evaluation import MulticlassClassificationEvaluator\n", "\n", "# Train a RandomForest model.\n", "rf = RandomForestClassifier(labelCol=\"label\", featuresCol=\"features\", maxDepth=8, maxBins=2400000, numTrees=128,impurity=\"gini\")\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Feature Importances" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "__ChiSqSelector__ implements Chi-Squared feature selection. It operates on labeled data with categorical features. ChiSqSelector uses the Chi-Squared test of independence to decide which features to choose. It supports five selection methods: numTopFeatures, percentile, fpr, fdr, fwe:\n", "\n", "- _numTopFeatures chooses a fixed number of top features according to a chi-squared test. This is akin to yielding the features with the most predictive power._\n", " \n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "from pyspark.ml.feature import ChiSqSelector\n", "chisqSelector = ChiSqSelector(numTopFeatures=6, featuresCol=\"features\",\n", " outputCol=\"selectedFeatures\", labelCol=\"label\")" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "from pyspark.ml import Pipeline\n", "stages = []\n", "stages += indexers \n", "stages += [labelIndexer]\n", "stages += [vecAssembler]\n", "stages += [rf]\n", "stages += [chisqSelector]\n", "\n", "pipeline = Pipeline(stages=stages)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(3111, 1285)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "train_sdata = trainData.sample(False,0.1)\n", "test_sdata = testData.sample(False,0.1)\n", "train_sdata.count(),test_sdata.count()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 0 ns, sys: 0 ns, total: 0 ns\n", "Wall time: 8.82 µs\n" ] } ], "source": [ "%time\n", "model = pipeline.fit(train_sdata)\n", "predictions = model.transform(test_sdata)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Confusion Matrix\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "from sklearn.metrics import confusion_matrix\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sn\n", "\n", "outdataframe = predictions.select(\"label\",\"prediction\")\n", "pandadf = outdataframe.toPandas()\n", "npmat = pandadf.as_matrix()\n", "labels = npmat[:,0]\n", "predicted_label = npmat[:,1]\n", "\n", "cnf_matrix = confusion_matrix(labels, predicted_label)\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "def plot_confusion_matrix(cm,\n", " target_names,\n", " title='Confusion matrix',\n", " cmap=None,\n", " normalize=True):\n", "\n", " import matplotlib.pyplot as plt\n", " import numpy as np\n", " import itertools\n", "\n", " accuracy = np.trace(cm) / float(np.sum(cm))\n", " misclass = 1 - accuracy\n", "\n", " if cmap is None:\n", " cmap = plt.get_cmap('copper')\n", "\n", " plt.figure(figsize=(8, 6))\n", " plt.imshow(cm, interpolation='nearest', cmap=cmap)\n", " plt.title(title)\n", " plt.colorbar()\n", "\n", " if target_names is not None:\n", " tick_marks = np.arange(len(target_names))\n", " plt.xticks(tick_marks, target_names, rotation=45)\n", " plt.yticks(tick_marks, target_names)\n", "\n", " if normalize:\n", " cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n", "\n", " thresh = cm.max() / 1.5 if normalize else cm.max() / 2\n", " for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n", " if normalize:\n", " plt.text(j, i, \"{:0.4f}\".format(cm[i, j]),\n", " horizontalalignment=\"center\",\n", " color=\"white\" if cm[i, j] > thresh else \"black\")\n", " else:\n", " plt.text(j, i, \"{:,}\".format(cm[i, j]),\n", " horizontalalignment=\"center\",\n", " color=\"white\" if cm[i, j] > thresh else \"black\")\n", "\n", "\n", " plt.tight_layout()\n", " plt.ylabel('Call Disconnect Reason')\n", " plt.xlabel('Predicted \\naccuracy={:0.4f}; misclass={:0.4f}'.format(accuracy, misclass))\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plot_confusion_matrix(cnf_matrix,\n", " normalize = False,\n", " target_names = ['Positive', 'Negative'],\n", " title = \"Confusion Matrix\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Evaluate" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test Error = 0.375\n" ] } ], "source": [ "from pyspark.ml.evaluation import MulticlassClassificationEvaluator\n", "# Select (prediction, true label) and compute test error\n", "evaluator = MulticlassClassificationEvaluator(\n", " labelCol=\"label\", predictionCol=\"prediction\", metricName=\"accuracy\")\n", "accuracy = evaluator.evaluate(predictions)\n", "print(\"Test Error = %g\" % (1.0 - accuracy))" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Summary Stats\n", "Precision = 0.625\n", "Recall = 0.625\n", "F1 Score = 0.625\n" ] } ], "source": [ "from pyspark.mllib.evaluation import MulticlassMetrics\n", "\n", "predictionAndLabels = predictions.rdd.map(lambda lp: (float(lp.prediction), lp.label))\n", "# Instantiate metrics object\n", "metricsM = MulticlassMetrics(predictionAndLabels)\n", "# Overall statistics\n", "precision = metricsM.precision()\n", "recall = metricsM.recall()\n", "f1Score = metricsM.fMeasure()\n", "print(\"Summary Stats\")\n", "print(\"Precision = %s\" % precision)\n", "print(\"Recall = %s\" % recall)\n", "print(\"F1 Score = %s\" % f1Score)\n" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Class 0.0 precision = 0.6060606060606061\n", "Class 0.0 recall = 0.5882352941176471\n", "Class 0.0 F1 Measure = 0.5970149253731343\n", "Class 0.0 TP = 0.5882352941176471\n", "Class 0.0 FP = 0.34210526315789475\n", "Class 1.0 precision = 0.6410256410256411\n", "Class 1.0 recall = 0.6578947368421053\n", "Class 1.0 F1 Measure = 0.6493506493506495\n", "Class 1.0 TP = 0.6578947368421053\n", "Class 1.0 FP = 0.4117647058823529\n", "Class 1.0 WTP = 0.625\n", "Class 1.0 WFP = 0.3788699690402477\n", "Weighted recall = 0.625\n", "Weighted precision = 0.6245143745143745\n", "Weighted F(1) Score = 0.6246365574723785\n", "Weighted F(0.5) Score = 0.6245342193516333\n", "Weighted false positive rate = 0.3788699690402477\n" ] } ], "source": [ "# Statistics by class\n", "labels = predictions.rdd.map(lambda lp: lp.label).distinct().collect()\n", "for label in sorted(labels):\n", " print(\"Class %s precision = %s\" % (label, metricsM.precision(label)))\n", " print(\"Class %s recall = %s\" % (label, metricsM.recall(label)))\n", " print(\"Class %s F1 Measure = %s\" % (label, metricsM.fMeasure(label, beta=1.0)))\n", " print(\"Class %s TP = %s\" % (label, metricsM.truePositiveRate(label)))\n", " print(\"Class %s FP = %s\" % (label, metricsM.falsePositiveRate(label)))\n", "\n", " \n", "## Weighted stats\n", "print(\"Class %s WTP = %s\" % (label, metricsM.weightedTruePositiveRate))\n", "print(\"Class %s WFP = %s\" % (label, metricsM.weightedFalsePositiveRate))\n", "\n", "print(\"Weighted recall = %s\" % metricsM.weightedRecall)\n", "print(\"Weighted precision = %s\" % metricsM.weightedPrecision)\n", "print(\"Weighted F(1) Score = %s\" % metricsM.weightedFMeasure())\n", "print(\"Weighted F(0.5) Score = %s\" % metricsM.weightedFMeasure(beta=0.5))\n", "print(\"Weighted false positive rate = %s\" % metricsM.weightedFalsePositiveRate)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# AUC" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[(0.633300856681553, 0.0), (0.002530747832802999, 1.0), (0.009952834828875502, 0.0), (0.008724721938155024, 0.0), (0.0009897431824456993, 0.0), (0.006622611224542218, 0.0), (0.009458155741816207, 0.0), (0.001626392488176709, 1.0), (0.0014874596506423902, 1.0), (0.0014641637427679044, 0.0), (0.0021489209838883357, 0.0), (0.0032740238860963743, 0.0), (0.005820789413332423, 0.0), (0.6239075975785051, 1.0), (0.7038022944566017, 1.0), (0.6371033355465207, 1.0), (0.6489474744252897, 1.0), (0.5770809114899815, 0.0), (0.006737534727915526, 1.0), (0.6912896588061062, 1.0), (0.018546327424751704, 0.0), (0.6885728165051447, 1.0), (0.006698005743333521, 1.0), (0.6899651322782697, 0.0), (0.725939971107056, 0.0), (0.0015693552499344017, 1.0), (0.010166161549943636, 0.0), (0.007444049559074803, 0.0), (0.04435938160274833, 0.0), (0.0040559601007211215, 0.0), (0.6863589836021269, 1.0), (0.681237097262059, 0.0), (0.0015067600401474273, 0.0), (0.6096383373362803, 1.0), (0.0014311079344373713, 0.0), (0.6222521108870055, 1.0), (0.0025915200107054642, 1.0), (0.6018261109843259, 0.0), (0.0009352057986691817, 0.0), (0.6411468526035906, 0.0), (0.002761990574239745, 1.0), (0.6614872920048702, 1.0), (0.6881133862332026, 0.0), (0.0012322840060913325, 1.0), (0.0068143863603691915, 0.0), (0.6758233020714346, 0.0), (0.6801103804957505, 0.0), (0.5945615247032031, 1.0), (0.6645552491830066, 1.0), (0.5641736958402593, 1.0), (0.589502613833582, 1.0), (0.005901580313547702, 1.0), (0.6148232766084218, 0.0), (0.6105256777126076, 1.0), (0.00706426094350453, 0.0), (0.7737555088141026, 1.0), (0.0015400772525741926, 1.0), (0.002396298706519946, 0.0), (0.6061673301742473, 0.0), (0.6720202907157181, 0.0), (0.6355529025420721, 1.0), (0.02150947002031965, 0.0), (0.011452969485013243, 0.0), (0.002056754905085051, 1.0), (0.018731977753854455, 0.0), (0.0013817550496317238, 1.0), (0.010835807043141119, 1.0), (0.0014423635400579486, 0.0), (0.6486939105355909, 1.0), (0.6613894197294514, 1.0), (0.010787386302828121, 0.0), (0.5764040736430536, 1.0)]\n", "The ROC score is (@numTrees=200): 0.5619195046439629\n" ] } ], "source": [ "from pyspark.mllib.evaluation import BinaryClassificationMetrics\n", "# Instantiate metrics object\n", "\n", "results = predictions.select(['probability', 'label'])\n", "# prepare score-label set\n", "results_collect = results.collect()\n", "results_list = [(float(i[0][0]), 1.0-float(i[1])) for i in results_collect]\n", "print(results_list)\n", "scoreAndLabels = spark.sparkContext.parallelize(results_list)\n", "\n", "\n", "metrics = BinaryClassificationMetrics(scoreAndLabels)\n", "print(\"The ROC score is (@numTrees=200): \", metrics.areaUnderROC)\n" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Area under PR = 0.5475491895992292\n", "Area under ROC = 0.5619195046439629\n" ] } ], "source": [ "# Area under precision-recall curve\n", "print(\"Area under PR = %s\" % metrics.areaUnderPR)\n", "\n", "# Area under ROC curve\n", "print(\"Area under ROC = %s\" % metrics.areaUnderROC)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Plot Receiver Operating Characteristic (ROC)\n", "\n", "$$ FPR(T)=∫^∞_TP_0(T)dT \\\\\n", " TPR(T)=∫^∞_TP_1(T)dT $$\n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "from pyspark.sql.functions import udf\n", "from sklearn.metrics import roc_curve, auc\n", "import matplotlib.pyplot as plt\n", "import random\n", "\n", "'''\n", "To plot the ROC curve, we need the probability scores as predicted\n", "by our model. However our model outputs a probability vector for each\n", "record where it separately provides the probability for class 0 and also\n", "for class 1. We first write a user-defined function to extract the \n", "probability for the positive '1' class from the probability vector.\n", "'''\n", "y_test = [i[1] for i in results_list]\n", "y_score = [i[0] for i in results_list]\n", "\n", "labels = y_test\n", "probs = y_score\n", "\n", "\n", "'''\n", "We compute the false positive rate and true positive rate at various thresholds\n", "of the probability score and use that to recompute the auc and finally to \n", "plot the ROC curve.\n", "'''\n", "false_positive_rate, true_positive_rate, thresholds = roc_curve(labels, probs)\n", "roc_auc = auc(false_positive_rate, true_positive_rate)\n", "plt.title('Receiver Operating Characteristic')\n", "plt.plot(false_positive_rate, true_positive_rate, 'b', label='AUC = %0.2f'% roc_auc)\n", "plt.legend(loc='lower right')\n", "plt.plot([0,1],[0,1],'r--')\n", "plt.xlim([-0.1,1.2])\n", "plt.ylim([-0.1,1.2])\n", "plt.ylabel('True Positive Rate')\n", "plt.xlabel('False Positive Rate')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n", " warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n" ] }, { "data": { "image/png": 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IaYrI5bl4AHASMDNbibX7iOj+wKsi4skqgzEzs+5q9xHRh5wAzMwmnnZbAgslfRO4HPjzYKHvCZiZrdzaTQI75P8PbyjzPQEzs5Vcu08H7VZ1IGZm1n3tPh20R7NydweZma3c2u0Oavy28FrAVGAR7g4yM1upddQdJGlr4KhKIjIzs67paKL5iLgDeN0Yx2JmZl3WyT2BVUhPC3WUQMzMrHd0ck9gGfBb0kxjZma2EqvsEVFJ5wJ7Ao9ExDa5bDZpDKJH82qf8BNGZmbjp93uoD7gUOBtpC+JXQfMjYiBFpudB3wFOH9I+ekR0WymMjMz67J2+/W/QOr+uQqYn19/vtUGEXET8MQLis7MzCrVNzDQ6mI+kXQr8PqIWJbfrw7UI6LlE0KSJgPXDOkOmkmameznwNERsWSk/dfr9ZGDHMashbOals/ZeU6nVZqZrTRqtVpfq+Xt3hjuI3UDDRrIZaP1deCkvP1JwGnAB9vZsFardbA7YCH09/ePXX1jqF6v90Qcw+nl+Bxb53o5PsfWuWbx1ev1EbdrNwl8F7hW0nmkE/jMXDYqEfHw4GtJZwPXjLYOMzMbOy2TgKRVgTWBY0k3ht9NagEsAM4a7c4kbRIRD+W37wJuG20dZmY2dkZqCZwCRETMBb6R/yHpCOBzpOTQVJ5/YFdgI0n3AycAu0qaSmpNLAaad9ibmVlXjJQE9gCOa1L+FeBWWiSBiJjWpPic9kMzM7OqjfSI6IqIWD60MCJWACuqCcnMzLplpCSwhqQXDS2UtDbpXoGZma3ERkoClwLzJK0zWCBpXWAuab5hMzNbiY10T+BE0vAPD0j6TS6bQno6aHZ1YZmZWTe0TAL5G8IHStoC2I70eOiiiLirG8GZmVm12h1F9C7AJ34zswnGE8OYmRXMScDMrGBOAmZmBXMSMDMrmJOAmVnBnATMzArmJGBmVjAnATOzgjkJmJkVzEnAzKxgTgJmZgVzEjAzK5iTgJlZwZwEzMwK5iRgZlYwJwEzs4I5CZiZFcxJwMysYG1NL9kJSecCewKPRMQ2uWwD4FJgMrAY2D8illQVg5mZtVZlS+A84B1Dyo4Dro+IKcD1+b2ZmY2TypJARNwEPDGkeB9gXn49D9i3qv2bmdnI+gYGBiqrXNJk4JqG7qA/RMR6DcuXRMT6I9VTr9c7DnLWwllNy+fsPKfTKs3MVhq1Wq2v1fLK7gmMtVqt1tmGC6G/v3/s6htD9Xq9J+IYTi/H59g618vxObbONYuvXq+PuF23nw56WNImAPn/R7q8fzMza9DtJLAAmJFfzwDmd3n/ZmbWoMpHRL8J7ApsJOl+4ATgFOAySQcD9wLvqWr/ZmY2ssqSQERMG2bR7lXt08zMRsffGDYzK5iTgJlZwZwEzMwK5iRgZlYwJwEzs4I5CZiZFcxJwMysYE4CZmYFcxIwMyuYk4CZWcGcBMzMCuYkYGZWMCcBM7OCOQmYmRXMScDMrGBOAmZmBXMSMDMrmJOAmVnBnATMzArmJGBmVjAnATOzgjkJmJkVzEnAzKxgTgJmZgVbbTx2KmkxsBRYDiyLiO3HIw4zs0a7zdutafkNM27ociTdMy5JINstIh4bx/2bmRXP3UFmZgXrGxgY6PpOJd0DLAEGgDkRcVar9ev1esdBzlo4q2n5nJ3ndFqlmU1QE/F8UavV+lotH6/uoF0i4kFJGwPXSfpVRNzUaoNardbZnhZCf3//2NU3hur1ek/EMZxejs+xda6X4xvv2Ppve/65AtL5YrxjG0mz+Or1+ojbjUt3UEQ8mP9/BLgS2HE84jAzK13XWwKSXgysEhFL8+u3Ayd2Ow4zmzhKfKpnrIxHd9BLgCslDe7/4oj4j3GIw8yseF1PAhFxN7Btt/drZmbP50dEzcwK5iRgZlYwJwEzs4KN57ARZmZN+Wmf7nFLwMysYE4CZmYFcxIwMyuYk4CZWcGcBMzMClbs00F++sBsfDX7G1y6dOmwI3kOt81o92HP5ZaAmVnBnATMzArmJGBmVjAnATOzgjkJmJkVrNing8ysO/yETm9zS8DMrGBOAmZmBXMSMDMrmJOAmVnBnATMzArmp4OG8JhCZp2ZyE8B7TZvtxHHNWo02vNFq2NX9bnHLQEzs4I5CZiZFWxcuoMkvQP4MrAqMDciThmPOMzMStf1loCkVYGvAu8EtgamSdq623GYmdn4dAftCNwVEXdHxF+BS4B9xiEOM7Pi9Q0MDHR1h5L2A94RER/K76cDb4iIw4bbpl6vdzdIM7MJolar9bVaPh73BJoF1PIkP9KHMDOzzoxHd9D9wKSG95sBD45DHGZmxRuPlsDPgCmSXgE8ABwAvG8c4jAzK17XWwIRsQw4DPgucCdwWUTc3u04zMxsHG4Mm5lZ7/A3hs3MCuYkYGZWsAkziuhIQ1FIWhM4H6gBjwPvjYjFPRLbW4AzgNcBB0TEFd2Iq83YjgI+BCwDHgU+GBG/66H4Pgx8FFgOPAUcGhF39EJsDevtB1wO7BARP++F2CTNBL5IejgD4CsRMbcbsbUTX15nf2A26RHyX0ZEVx4gaePYnQ4MDvv5ImDjiFivG7G1Gd/mwDxgvbzOcRHxneHqmxAtgTaHojgYWBIRWwCnA5/vodjuBWYCF3cjplHG9gtg+4h4HXAF8IUei+/iiHhtREzNsX2ph2JDUj9wBPDTbsQ1mtiASyNiav7XzQQwYnySpgDHA7tExGuAj/VKbBFx5OBxA84Evt2N2NqND/gU6YGb7UhPX36tVZ0TIgnQ3lAU+5CyI6ST2e6SuvEltBFji4jFEXErsKIL8Yw2thsi4k/57c2k73X0UnxPNrx9MSN88bCbsWUnkZLTn7sU12hiGy/txHcI8NWIWAIQEY/0UGyNpgHf7EpkSTvxDQDr5NfrMsL3sCZKEtgUuK/h/f25rOk6+THVPwIb9khs42W0sR0MXFtpRM/VVnySPirpt6ST7RG9Epuk7YBJEXFNl2Ia1O7P9R8l3SrpCkmTmiyvSjvxbQlsKeknkm7OXSC9EhsAkl4OvAL4QRfiGtROfLOBAyXdD3wHOLxVhRMlCbQzFMWoh6sYI+O133a0HZukA4HtSf3I3dJWfBHx1Yh4FfBxUlO4G1rGJmkVUrfj0V2Kp1E7x+1qYHLu5vs+z7aSu6Gd+FYDpgC7kq6250rqRr/7aP5eDwCuiIjlFcYzVDvxTQPOi4jNgD2AC/LvY1MTJQm0MxTFM+tIWo3UTHqiR2IbL23FJultwCeBvSPiL12KDUZ/7C4B9q00omeNFFs/sA1wo6TFwE7AAknb90BsRMTjDT/Ls0kPTHRLu3+v8yPi6Yi4BwhSUuiF2AYdQHe7gqC9+A4GLgOIiIXAWsBGw1U4UZ4OamcoigXADGAhsB/wg4joxhV5Lw+TMWJsuUtjDmnk1271y44mvikR8Zv89h+A39AdLWOLiD/S8Icn6UbgmC49HdTOcdskIh7Kb/cmfXu/W9r5m7iKfEUraSNS99DdPRIbkgSsTzqfdFM78d0L7E46dluRksCjw1U4IVoCww1FIelESXvn1c4BNpR0F3AUcFyvxCZph9x/9x5gjqSuDKPR5nH7IrA2cLmkWyQt6EZso4jvMEm3S7qF9HOd0UOxjYs2YzsiH7dfku6jzOyx+L4LPC7pDuAG4F8i4vEeiQ1SgrqkSxeSo43vaOCQ/LP9JjCzVZweNsLMrGAToiVgZmadcRIwMyuYk4CZWcGcBMzMCuYkYGZWMCcBM7OCOQnYhCZptqRT8+sPSzpyhPUnSzr0BexvQNLanW4/TJ27SurKENRWnonyjWErhKTV8hdmRi0ivtHGapOBQ4GzOtmH2crGScB6gqQB4LPA20mju34iIr7VsOxY0rAQPwI+LelY0vAfq5G+Pn9IRPxe0rqkb4dvTfr6/KPAw7me2cDaEXFMfn886Sv3K4D/Ad5EGqv9FfkbyHdFxH55iIAzSMNArAGcERH/nut4N3AyaRyqYSfuyOu+KMf06oh4LJedBjwZEZ+VdBEgYE3gLtIEPkuG1LErcGpEbD/M+xnAP+Xj8kfgIxERkt4IfIXU+l8d+NeI6Pa4N9aD3B1kvWRFRLyRNJbNWZI2bli2SkTsGhGfziOabgHsFBGvJ518T8vrfYZ0Ut0aOBD4u2Y7yifLvUmTlmwL7BURK0izlN2RJw3ZLw82eDFwZETsQEoUx0l6dY7vbGCfHPdfW324PC/DfPJYL7nuaTw7guc/R8T2EfFa4HbSqKhtk/RmYH/gLRFRIw35cW5e/HHg9DwRyjZ0d0hw62FuCVgvOQcgX7kuIo+8mZc1DnW8N2lY60XpIv2Zq15I0/4dnut5TNJwsz7tCXx9cFKaFuPSbAlsBVyS9wXpSn0r0pSWiyIicvlZjDxj3XmkqQH/L2l2qDvj2WlOD5L0flJr48XAr0eoa6i9gG2Bn+ZY+0iDnEEaf+f4PAb+dRHRtZnOrLc5CViv6uO546Q/NWTZv0bEuTxfu7PFjWa9x/IV9HNIGvVsXRHxI0n9kl5LGrTtvFzXm4GPAG+MiEclvY90b2KoZTy3Bb/WkFjPjYjPNNnvGZKuBt4GnCnpexHRrbkXrIe5O8h6yQfgmfllpzL8vLwLgH+StH5ef01J2+Zl1zfUsyHwrmHquBr4iNIcwIPrAjxJmmtiUAB/kjR9sCB3Ba1DGkZ4uxwvwIfa/Jznk0Z6fAvwrVy2Hqk187ikNYEPDrPtPcArJa2vND3qtCGf6SBJm+U4V5VUy6+3jIjfRsQcUktkxzZjtQnOScB6yV8k/QS4Bpg13PwFEXEBcBHwQ0m3AnVgl7z4JGD9PATxBcD3htnX+aST5s15yN35efalW4GQdJukK/KTSHsBByhNxXg7aeLuNXJ8hwJXS/pP0lV6O+YB00mTpgzO33wt8FvgV/n1omE++wOk+x910oxgDzUsu4k0+c+C/Jlu49n5ZweHjv4Fqbvsk23GahOch5K2npCfAOqPiKdGXNnMxoxbAmZmBXNLwGyM5UdHm3VDfTsiTux2PGatOAmYmRXM3UFmZgVzEjAzK5iTgJlZwZwEzMwK9r/VS4SXpYWMmQAAAABJRU5ErkJggg==\n", 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M605JZmZWpI4O7kbENEkbARs1PyYibiiqMDMzK0ZHwS/pa8ChpMsnLsjNA4CD38xsKdPp6Zz7ABtFxPNFFmNmZsXr9HTOOQ59M7NlQ6c9/hmSLgIuA/7caPQYv5nZ0qfT4N8m//3ppjaP8ZuZLYU6Patn56ILMTOz7uj0rJ5dW7V7qMfMbOnT6VBP8692Vwa2BGbioR4zs6XOiIZ6JG0GHF1IRWZmVqgRXWw9Iu4D3jrKtZiZWReMZIx/OdJZPiP60DAzs94ayRj/fOAPpCtymZnZUqaw0zklnQd8CHgyIjbPbZNJc/48lVc7wWcGmZl1V6dDPX3AYcB7ST/cugmYGhEDQzxsGnAWcP6g9tMjotUVvczMrAs6Haf/Jmlo52rgmnz7G0M9ICJuA55dourMzGzU9Q0MDNVpTyTdDbwtIubn+ysA9YgY8sweSeOA6wcN9RxIuoLXb4BjImLucPuv1+vDF2lmtpSaNGNS22VTtp+yRNuu1Wp9g9s6PbjbRxriaRjIbYvrB8CX8+O/DJwGHNTJA2u12kL36/X6Im1lUebaoNz1ubaRK3N9Za4Nel9f/z39Qy4faW31er1le6fB/xPgRknTSKF9YG5bLBHxROO2pHOA6xd3G2ZmtmSGDH5JywMrAZ8jHdzdi9TTvxY4e3F3JmmdiJiT734YuGdxt2FmZktmuB7/KUBExFTgh/kPko4Evkr6QGgpz9+/E7CWpFnAicBOkrYkfWt4GGg/sGVmZoUYLvh3BY5r0X4WcDdDBH9ETGjRfG7npZmZWRGGO53zpYhYMLgxIl4CXiqmJDMzK9Jwwb+ipFUGN0palTT2b2ZmS5nhgv8SYLqk1RoNklYHppKuv2tmZkuZ4cb4TyZNvfC4pN/ntvGks3omF1eWmZkVZcjgz7/U3U/SxsBWpFM5Z0bEA90ozszMRl+ns3M+ADjszcyWAb6YiplZxTj4zcwqxsFvZlYxDn4zs4px8JuZVYyD38ysYhz8ZmYV4+A3M6sYB7+ZWcU4+M3MKsbBb2ZWMQ5+M7OKcfCbmVWMg9/MrGIc/GZmFePgNzOrGAe/mVnFOPjNzCqmo0svjoSk84APAU9GxOa5bU3gEmAc8DCwT0TMLaoGMzNbVJE9/mnABwa1HQfcHBHjgZvzfTMz66LCgj8ibgOeHdS8BzA9354O7FnU/s3MrLW+gYGBwjYuaRxwfdNQz3MRMbZp+dyIWGO47dTr9eKKNDPrsUkzJrVdNmX7KUu07Vqt1je4rbAx/tFWq9UWul+v1xdpK4sy1wblrs+1jVyZ6ytzbdD7+vrv6R9y+Uhrq9frLdu7fVbPE5LWAch/P9nl/ZuZVV63g/9aYGK+PRG4psv7NzOrvCJP57wI2AlYS9Is4ETgFOBSSQcDjwIfKWr/ZmbWWmHBHxET2izapah9mpnZ8PzLXTOzinHwm5lVjIPfzKxiHPxmZhXj4DczqxgHv5lZxTj4zcwqxsFvZlYxDn4zs4px8JuZVYyD38ysYhz8ZmYV4+A3M6sYB7+ZWcU4+M3MKsbBb2ZWMQ5+M7OKcfCbmVWMg9/MrGIc/GZmFePgNzOrGAe/mVnFOPjNzCrGwW9mVjFjerFTSQ8D84AFwPyI2LoXdRRt5+k7t2y/ZeItXa7EzOwVPQn+bOeIeLqH+zczqyQP9ZiZVUzfwMBA13cq6SFgLjAATImIs4dav16vd7/IUTBpxqSW7VO2n9LlSsyszNplBSx5XtRqtb7Bbb0a6tkxImZLWhu4SdL/RMRtQz2gVqstdL9ery/SVhaN2vrv6W+5vNd1Lw2vXRmVuTYod31lrg16X1+7rGgYaW31er1le0+GeiJidv77SeAqYNte1GFmVkVd7/FLejWwXETMy7ffB5zc7TqWJj47yMxGUy+Gel4HXCWpsf8LI+Lfe1CHmVkldT34I+JBYItu79fMzBKfzmlmVjEOfjOzinHwm5lVTC+nbLCC+CwgMxuKe/xmZhXj4DczqxgHv5lZxTj4zcwqxsFvZlYxPqunB9qddVP0dhrrz5s3b6HZAH22j1l3jNb//SXlHr+ZWcU4+M3MKsbBb2ZWMQ5+M7OKcfCbmVWMz+oxz+1jVjHu8ZuZVYyD38ysYhz8ZmYV4+A3M6sYB7+ZWcUs82f1jNYZK4szx8bguXCWVj7bx2xkyjInTzvu8ZuZVYyD38ysYnoy1CPpA8AZwPLA1Ig4pRd1mJlVUdd7/JKWB74HfBDYDJggabNu12FmVlW9GOrZFnggIh6MiL8CFwN79KAOM7NK6hsYGOjqDiXtDXwgIg7J9/cH3h4RR7R7TL1e726RZmbLiFqt1je4rRdj/IsUAQwZ7K0KNzOzkenFUM8sYIOm++sDs3tQh5lZJfWix38HMF7SG4HHgX2Bj/agDjOzSup6jz8i5gNHAD8B7gcujYh7u12HmVlVdf3grpmZ9ZZ/uWtmVjEOfjOziin97JzDTe8g6WjgEGA+8BRwUEQ8UpLaDgc+BSwAXgAOi4j7ylBb03p7A5cB20TEb7pRWyf1SToQ+BbpBACAsyJiahlqy+vsA0wmnYr824joygkKHbxupwONqSFXAdaOiLHdqK3D+t4ATAfG5nWOi4gbSlLbhsB5wGuBZ4H9ImJWl2o7D/gQ8GREbN5ieR+p9l2BF4EDI2LmSPdX6h5/h9M73AlsHRFvBS4Hvlmi2i6MiLdExJa5rm+XqDYk9QNHAr/uRl2LWx9wSURsmf90K/SHrU3SeOB4YMeIeDNwVFlqi4jPNF4z4Ezgym7U1ml9wBdJJ3RsRTqj7/slqu1U4PycJScDX+9Gbdk04ANDLP8gMD7/OQz4wZLsrNTBTwfTO0TELRHxYr57O+l3AWWp7fmmu69mmB+qdbO27MukD6Q/d6muhjJP29FJbYcC34uIuQAR8WSJams2AbioK5UlndQ3AKyWb69O937D00ltmwE359u3tFhemIi4jfQto509SB9KAxFxOzBW0joj3V/Zg3894LGm+7NyWzsHAzcWWtErOqpN0qck/YEUsEeWpTZJWwEbRMT1XaqpWafv6z9KulvS5ZI2aLG8CJ3UtgmwiaRfSbo9DyGUpTbg5WGLNwI/70JdDZ3UNxnYT9Is4Abg090praPafgv8Y779YaBf0mu6UFsnFjcLh1T24O94egdJ+wFbk8aFu6Gj2iLiexGxEfB50tfcbhiyNknLAacDx3SpnsE6ee2uA8blr90/I40Ld0MntY0hfeXeidSrniqpG+PoizPdyb7A5RGxoMB6BuukvgnAtIhYnzRefUH+91i0Tmo7Fni3pDuBd5OOL80vurAOLfZUN0Mpe/B3NL2DpPcCXwB2j4i/lKm2JhcDexZa0SuGq60f2By4VdLDwHbAtZK2Lkl9RMQzTe/lOUCtLLXlda6JiL9FxENAkD4IylBbw750d5gHOqvvYOBSgIiYAawMrFWG2iJidkTslY8/fCG3/akLtXViVKe6KftZPcNO75CHLKaQZvzs1lhrp7WNj4jf57v/APye7hiytvyP+eX/bJJuBY7t4lk9nbx260TEnHx3d9KvvEtRG3A1uecqaS3S0M+DJakNSQLWAGZ0oaZmndT3KLAL6bXblBT8T5WhtvxePhsRL5EO3p/Xhbo6dS1whKSLgbcDf2r6/7HYSt3jbze9g6STJe2eV/sWsCpwmaS7JF1botqOkHSvpLuAo4GJJaqtZzqs78j82v2WdGzkwBLV9hPgGUn3kQ4CfjYinilJbZA+lC6OiK7+LL/D+o4BDs3v60Wk0xILr7PD2nYCQtLvgNcBXy26rgZJF5E+qCVplqSDJR2eTwmHdDzkQeAB0jfgTy7J/jxlg5lZxZS6x29mZqPPwW9mVjEOfjOzinHwm5lVjIPfzKxiHPxmZhXj4LdlmqTJkk7Ntw+X9Jlh1h8n6bAl2N+ApFVH+vg229xJUtemzLZlX9l/uWu2EElj8o9xFltE/LCD1caRpr09eyT7MFsaOPitFCQNACcB7wNeA5wQEVc0LfscadqLXwBfkvQ5YG/Sv+HHgUMj4o+SVgfOJU2x+yhpOoAn8nYmA6tGxLH5/vGkn+2/BPwv8A7SnO1vzL+2fiAi9s5TIHyHNM3FisB3IuJf8zb2Ar5GmlJ3yAuKSFol1/R3EfF0bjsNeD4iTpL0Y0DASqRfaB7UmPq5aRs7AadGxNZt7k8k/apzDPAn4BMREZJ2AM4ifctfAfhKRHR7Lh8rCQ/1WJm8FBE7kObmOVvS2k3LlouInSLiS3km1o2B7SLibaTAPS2v9y+kIN0M2I80y+IickDuTrqYyhbAbnmOlk8B9+WLmewtaQxwIfCZiNiG9OFwnKS/y/WdA+yR6/7rUE8uXzfiGvIcMXnbE3hl5tF/joitI+ItwL2kGV07JumdwD7AuyKiRprOpDHfzOeB0/MFWjane9OXWwm5x29lci5A7qHOJM8ampc1T8u8O2kK7pmpM/5y7xbSZQc/nbfztKR2V6D6EPCDxsVyhphrZxNgU+DivC9IPfJNSZfUnBkRkdvPBr4xzHOcRrqE3ndJV1W6PyIezssOkPQx0reKVwO/G2Zbg+0GbAH8OtfaR5qsDdKcQsfnefpvioiuXnXNysXBb2XVx8Lzjb8waNlXIqLV7Imt5i1vt/1O13s695QXImmxr9AUEb+Q1C/pLaSJ56blbb0T+ASwQ0Q8JemjpGMNg81n4W/qKw+q9byI+JcW+/2OpOuA9wJnSvppRHTr+hBWMh7qsTL5OLx8TdstaX8t4GuBT0paI6+/kqQt8rKbm7bzGtKVlFq5DviE0nWHG+sCPE+6JGBDAC9K2r/RkId5ViPNprhVrhfgkA6f5/mkWSrfBVyR28aSvrU8I2kl4KA2j30IeJOkNZQuwD1h0HM6QNL6uc7lJdXy7U0i4g8RMYX0jWPbDmu1ZZCD38rkL5J+BVwPTGp3fYWIuAD4MfAfku4G6sCOefGXgTXylMkXAD9ts6/zSUF5e54i+Jp8Jai7SVPz3iPp8nwG0W7AvkqXgbyXdIHwFXN9hwHXSfpPOr9a03Rgf9LFXBrXi74R+APwP/n2zDbP/XHS8Yw66cpkc5qW3Ua6gMi1+TndwyvXjW1Mc30naSjsCx3WassgT8tspZDP3OmPiBeGXdnMloh7/GZmFeMev9koy6d5thpiujIiTu52PWaDOfjNzCrGQz1mZhXj4DczqxgHv5lZxTj4zcwq5v8BlX6LUiSd/PAAAAAASUVORK5CYII=\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "all_probs = predictions.select(\"probability\").collect()\n", "\n", "pos_probs = [i[0][0] for i in all_probs]\n", "neg_probs = [i[0][1] for i in all_probs]\n", " \n", "from matplotlib import pyplot as plt\n", "%matplotlib inline\n", " \n", "# pos\n", "plt.hist(pos_probs, 50, normed=1, facecolor='green', alpha=0.75)\n", "plt.xlabel('predicted_values')\n", "plt.ylabel('Counts')\n", "plt.title('Probabilities for positive cases')\n", "plt.grid(True)\n", "plt.show()\n", " \n", "# neg\n", "plt.hist(neg_probs, 50, normed=1, facecolor='green', alpha=0.75)\n", "plt.xlabel('predicted_values')\n", "plt.ylabel('Counts')\n", "plt.title('Probabilities for negative cases')\n", "plt.grid(True)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Plot Precision Recall\n" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Average precision-recall score: 0.56\n" ] }, { "data": { "image/png": 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RmJlZlhOFmZlljcn7UaxdC08+CTfemC+3cmV71+e74JnZWDQmE8W6dfCnP8Hmm7cuu9VW7Vmf74JnZmPVmEwUABtv3PrOde1cn++CZ2ZjlccozMwsy4nCzMyynCjMzCzLicLMzLKcKMzMLGvMnvXUaWWvtQBfb2Fmo8uQEoWkCfWviYiSl6yNfWWv3fD1FmY22pRKFJLeDHwZeHGxqAfoB8ZVFNeoVObaDV9v0T2WLYMHHihX1q1A25CVbVGcAbwduD4inim7ckkHAmeSEsp5EXFakzJvB+aREs+tEXFk2fWbrY8HHoCbb4ZJk/Ll3Aq0DV3ZRPFIRPxmKCuWNA44G3g9sBy4QdKCiFhcV2YX4CPAvhHxqKSth7IN27DkWgB9fZPp7x94XLYFMGlSd7YC3dqxblI2UfxA0v8Bvgesqi1sMUaxF7AkIu4AkHQJcBhQ/7M7Fjg7Ih4t1vfgEGK3DUyuBbB06eRn/x4LLQC3dqyblE0Uny7+P7tuWasxiqnAsrrHy4G9G8rsCiDpumJd8yLipyVjsi5XxVHxYC2Anp5VzJiR/r711vbOHlxGJ99rPY95WSeUShQRMZzrLXqaLOtveDwe2AXYH5gG/ErSbhHxl9yK+/r6shu+6650dLnJJk+VDDXv/vsnFdtdNWiZsttcunQS8BQ9Pe2Jrbe3ty3rqcLixZOJ2IQJExo/9udavbqHHXZYxeTJ+eGvVas24r77JtLT0/xzqH0vbr99MhE9vOAFrYfTtthiHX1967JlynxmnX6vrWLr5u9Fp7ku1l/p02MlvRDYh7Szvz4iHmnxkuVA/XHTNODeJmWuj4i1wFJJQUocN+RWPKN26DiI2pFii2KllVlP2W3296cys2evf1y9vb3MbseK6rTzyLg2ZtDqqLi3N12HMnlyvtykSanett32+c/19fU9+72ofRbtqpo1a2D77XnOGEijHXZI/3fivdZr9n2q4nsxWrkuBqxPwix7euzfA98BbikW7S5pbkT8LPOyG4BdJO0I3AMcATSe0fRDYA5wgaQppK6oO4YQv7XZSPWNt3ta+HYqe41MmXubQHe/V7NmhjJG8XcR0Qcg6WWkxDFoooiIdZKOB64ijT+cHxGLJJ0K3BgRC4rn3iBpMfA08KGIeHj4b8faoUzfeJlxgHaOAQxFq6P14fDO3TZkZRPFxrUkARARt0nauNWLImIhsLBh2Sfq/u4HTij+2SjS7qPsdurmHXoVScysamUTxQpJR0fEBQCSjgJWVBbVKLQh7gDadZTd7XXXzvi6OYmZDaZsongvcJGkr5IGs28B5lYW1SjU7TuAsoPUI9Fd1O111+3xmVWt7OmxtwP7SNoU6ImIx6sNy9qt7CA1jEx3kZl1r2yikLRjRCyVNLNhOQD103FY9yszSG1m1qhVi+LLwCHAT5o81w/s1PaIbFTo9nEFM2ufbKKIiEOK/3fsTDg2WrhlYrbhKHvB3a7A3RGxqrj4bg/g3NpkfmY2MprdWbFxJt0azzJrw1X2rKf5wKuKq6zPBa4GLgQOrSowK6ebz2ay6jW7nqV+Jt0azzJr66NsongmItZKeiNwTkScLumWlq+yyvlsJmu8nqV+Jt0azzJr66NsopgkaSqpBfHRYlmz2WFtBPhsJjOrUtnpw78ELAIej4gbJe0EPFZdWGZWxuTJPgPNqlf2gruvAV+rW3QXcEAlEZlZaW5JWie0uuBu34i4TtLBgxRZOMhyMzMbI1q1KI4GrgM+1OS5fpwozMzGvFYX3B1b/P/azoRjNWVOe+3rm/zsndXMzKpS9oK7dwFX1C6wk7QVcFBEXFRlcMM1Fgb3ypz2Wjtf3qe9mlmVyp4ee1JEfLv2ICIekXQS0JWJYqwM8LU67bXZ+fJm1nllL3xdsyb9P2FC67LddCV92UTRzLi2RWFmNoqVvfD1r39N/7e6M2TZK+nLJiiAnvW48q1sorhf0lsi4nIASW8FHhz+Zs3MRocyO+OVK8td+Fq7Qr5suVbKJqh162D33cuts5myieJfgR9JOr22XeCw4W/WzGxklT0af/JJiGg99llmrLBst3izyR6bKZugVq8ut93BlL3g7rbi5kUiTd1xW0Q8vX6bNrPRquxOtp397EPpZimz3aHMkzZ1Kmy7bbltt0OzyR4H04mTWYYyRrE/MCMizpK0taQtI+LPFcVlZiOgnUfZ7Z6xtuyO/amnQErlB5tyHcofjY+E2tQs3RJb2dNjPwwcDLwYOAuYAJwP7FddaGPTUJqUZp02lEHZxllrG5XtZx/KVPllduy9vQNH482mXK/XraeWd0uCqCnbopgD7An8HiAilksq0SiyRt3WpLQNQ7v7vHt72xdbFVPl15KYTyFvj7KJ4qnifhT1ywZp0FkrrY7EzNqt3QcoZS5qbXdyKmssXHDbbcomimWS9gP6JW1EuifFourCMrN2a+cBSpn1jFTr2Qdh7Vc2UXwA+BawG7AS+BXwzqqCMrP2GqmjbLeex4aWiaJoQWwdEW+QtAmwUUQ8UX1oZtYuI7GzdhfQ2NEyUUTEM5K+AbwqInwujpmV4pbE2FH2Vqh9kl5SZSBmZtadyo5RvAj4g6RfA892O0XE2yuJyszMukaZMYqtgJ8DVwKPVR6RmZl1lVb3zH4H8E3gcWAi8NaI+HknAjMzs+7QaoziY8D/johtgDcDJw9l5ZIOlBSSlhTTgAxW7nBJ/ZL2HMr6R6PaHC5mZqNFq0TxTETcAhARvwC2KLtiSeOAs4GDgJnAnGIG2sZymwH/Avyu7LpHs5kzfTaImY0urcYoJkiaQZpaHGBi/eOIyE37tRewJCLuAJB0CekeFo2v+XfgdOCkIcZuZmYd0CpRbAIsbFhWe9wP7JR57VRgWd3j5cDe9QUk7QFMj4grintwl9LX11e26JjnuhjguhjguhjguoA1a3qYNWv4r88mioh4yfBXTbM7tD47kWBxxfcXgaOHuuIZng4SSD8A10XiuhjguhjgukjSHe6GP+Vv2QvuhmM5UH/LkmnAvXWPNyPNHXWNpDuBfYAFG8KAtpnZaDKUO9wN1Q3ALpJ2BO4BjgCOrD0ZEY8BU2qPJV0DnBQRLSYlNjOzTqqsRRER64DjgauAPmB+RCySdKqkQ6varpmZtVeVLQoiYiENg+ER8YlByu5fZSxmZjY8VY5RmJnZGOBEYWZmWU4UZmaW5URhZmZZThRmZpblRGFmZllOFGZmluVEYWZmWU4UZmaW5URhZmZZThRmZpblRGFmZllOFGZmluVEYWZmWU4UZmaW5URhZmZZThRmZpblRGFmZllOFGZmluVEYWZmWU4UZmaW5URhZmZZThRmZpblRGFmZllOFGZmluVEYWZmWU4UZmaW5URhZmZZThRmZpblRGFmZllOFGZmluVEYWZmWeOrXLmkA4EzgXHAeRFxWsPzJwDvAdYBK4B3R8RdVcZkZmZDU1mLQtI44GzgIGAmMEfSzIZiNwN7RsTuwGXA6VXFY2Zmw1Nli2IvYElE3AEg6RLgMGBxrUBE/KKu/PXA3ArjMTOzYagyUUwFltU9Xg7snSl/DHBlmRX39fWtR1hji+tigOtigOtigOsC1qzpYdas4b++ykTR02RZf7OCkuYCewKvKbPiGTNmrEdYY0dfX5/rouC6GOC6GOC6SFavBugd9uurTBTLgel1j6cB9zYWknQA8DHgNRGxusJ4zMxsGKpMFDcAu0jaEbgHOAI4sr6ApD2Ac4EDI+LBCmMxM7Nhquysp4hYBxwPXAX0AfMjYpGkUyUdWhQ7A9gUuFTSLZIWVBWPmZkNT6XXUUTEQmBhw7JP1P19QJXbNzOz9ecrs83MLMuJwszMspwozMwsy4nCzMyynCjMzCzLicLMzLKcKMzMLMuJwszMspwozMwsy4nCzMyynCjMzCzLicLMzLKcKMzMLMuJwszMspwozMwsy4nCzMyynCjMzCzLicLMzLKcKMzMLMuJwszMspwozMwsy4nCzMyynCjMzCzLicLMzLKcKMzMLMuJwszMspwozMwsy4nCzMyynCjMzCzLicLMzLKcKMzMLMuJwszMspwozMwsa3yVK5d0IHAmMA44LyJOa3h+IvAtYDbwMPCOiLizypjMzGxoKmtRSBoHnA0cBMwE5kia2VDsGODRiNgZ+CLwuariMTOz4amyRbEXsCQi7gCQdAlwGLC4rsxhwLzi78uAsyT1RER/bsWrV7c/2NFozZoe10XBdTHAdTHAdZGsXQsTJw7/9VUmiqnAsrrHy4G9BysTEeskPQa8EHgot+KJE3vbGOboNWsWgOsCXBf1XBcDXBfJ+iQJqDZR9DRZ1thSKFPmOWbPnt3sNWZmVpEqz3paDkyvezwNuHewMpLGA1sAj1QYk5mZDVGVLYobgF0k7QjcAxwBHNlQZgFwFPBb4HDgv1qNT5iZWWdV1qKIiHXA8cBVQB8wPyIWSTpV0qFFsW8AL5S0BDgB+HBV8ZiZ2fD09Pf7AN7MzAbnK7PNzCzLicLMzLIqncJjfXj6jwEl6uIE4D3AOmAF8O6IuKvjgXZAq7qoK3c4cCnwqoi4sYMhdkyZupD0dtJFrf3ArRHReELJmFDiN7I9cCGwZVHmwxGxsOOBVkzS+cAhwIMRsVuT53tI9XQwsBI4OiJuarXermxRePqPASXr4mZgz4jYnXSF++mdjbIzStYFkjYD/gX4XWcj7JwydSFpF+AjwL4R8XLggx0PtANKfi8+TjqhZg/SGZjndDbKjrkAODDz/EHALsW/44CvlFlpVyYK6qb/iIg1QG36j3qHkY4QIO0cX1dky7GmZV1ExC8iYmXx8HrSNStjUZnvBcC/k5Llqk4G12Fl6uJY4OyIeBQgIh7scIydUqYu+oHNi7+34PnXdI0JEXEt+WvRDgO+FRH9EXE9sKWkF7dab7cmimbTf0wdrExxKm5t+o+xpkxd1DsGuLLSiEZOy7qQtAcwPSKu6GRgI6DM92JXYFdJ10m6vuieGYvK1MU8YK6k5cBC4AOdCa3rDHV/AnRvoqhk+o9RqvT7lDQX2BM4o9KIRk62LiRtROqGPLFjEY2cMt+L8aQuhv2BOcB5krasOK6RUKYu5gAXRMQ0Uv/8t4vvy4ZmWPvNbq0oT/8xoExdIOkA4GPAoRExVufLbFUXmwG7AddIuhPYB1ggac9OBdhBZX8jP4qItRGxFAhS4hhrytTFMcB8gIj4LTAJmNKR6LpLqf1Jo24968nTfwxoWRdFd8u5wIFjuB8aWtRFRDxG3Y9f0jXASWP0rKcyv5EfUhxJS5pC6oq6o6NRdkaZurgbeB2pLmaQEsWKjkbZHRYAxxe3fdgbeCwi7mv1oq5sUXj6jwEl6+IMYFPgUkm3SFowQuFWqmRdbBBK1sVVwMOSFgO/AD4UEQ+PTMTVKVkXJwLHSroVuJh0WuiYO7CUdDHp4FmSlks6RtL7JL2vKLKQdLCwBPg68P4y6/UUHmZmltWVLQozM+seThRmZpblRGFmZllOFGZmluVEYWZmWd16HYXZiCgu1FsFrAYmAJ+PiPMq2M4FwI0RcZakecCmEXFSu7dj1g5uUZg93+ERMQt4G3COpO1GOiCzkeQWhdkgIuJPkh4lTZp2r6R/I80CMJ50BfCxEXG/pAnAZ0jTOz8N3BERb5b0N6TprF9AuhL4axHxpZF4L2brwy0Ks0FI2hd4CLi1mHBxZ2CfiHgl6QrXzxdFPwLsBLyyaIkcWyy/EzigKL8XcFwxfYTZqOIWhdnzXVbc2+SlwJsjYk0xFcSewE2SIP12HivKHwKcWNwLgYh4qFi+CfAVSbOAZ4DtgFmkaSbMRg0nCrPnO7zodnob8B1Ju5KmZ/5URJzfpPxgN8z6DHA/aV6hdZKuJnVBmY0q7noyG0REXApcTZpwcgHwfkn/C9I924uWAsCPgQ8WYxUUM7VCuj/zsiJJ7Aa8uqNvwKxN3KIwy/sI0Eu6J/sU4JdF19NGpIHqW4HTgM8Ct0haQ5qZ83DgU6Qb5MwFbgeu7Xj0Zm3g2WPNzCzLXU9mZpblRGFmZllOFGZmluVEYWZmWU4UZmaW5URhZmZZThRmZpb1P/ZdN3pGA6F5AAAAAElFTkSuQmCC\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from sklearn.metrics import precision_recall_curve\n", "from sklearn.metrics import average_precision_score\n", "\n", "average_precision = average_precision_score(labels, probs)\n", "\n", "print('Average precision-recall score: {0:0.2f}'.format(\n", " average_precision))\n", "\n", "precision, recall, _ = precision_recall_curve(labels, probs)\n", "\n", "plt.step(recall, precision, color='b', alpha=0.2,\n", " where='post')\n", "plt.fill_between(recall, precision, step='post', alpha=0.2,\n", " color='b')\n", "\n", "plt.xlabel('Recall')\n", "plt.ylabel('Precision')\n", "plt.ylim([0.0, 1.05])\n", "plt.xlim([0.0, 1.0])\n", "plt.title('2-class Precision-Recall curve: AP={0:0.2f}'.format(\n", " average_precision))\n", "plt.show()\n" ] } ], "metadata": { "kernelspec": { "display_name": "conda_python3", "language": "python", "name": "conda_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": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }