Model5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import findspark\n",
    "findspark.init('/home/sdmohant/spark-3.0.0-bin-hadoop2.7')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyspark\n",
    "from pyspark import SparkContext,SparkConf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql import SparkSession"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sdmohant/spark-3.0.0-bin-hadoop2.7/python/pyspark/context.py:220: DeprecationWarning: Support for Python 2 and Python 3 prior to version 3.6 is deprecated as of Spark 3.0. See also the plan for dropping Python 2 support at https://spark.apache.org/news/plan-for-dropping-python-2-support.html.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "#spark = SparkSession.builder.appName(\"MyApp\").getOrCreate()\n",
    "#spark = SparkSession.builder.appName(\"MyName\").getOrCreate()\n",
    "conf = SparkConf().set(\"spark.cores.max\", \"16\") \\\n",
    "    .set(\"spark.driver.memory\", \"16g\") \\\n",
    "    .set(\"spark.executor.memory\", \"16g\") \\\n",
    "    .set(\"spark.executor.memory_overhead\", \"16g\") \\\n",
    "    .set(\"spark.driver.maxResultsSize\", \"0\") \\\n",
    "    .set(\"spark.sql.shuffle.partitions\",\"300\")\n",
    "\n",
    "sc = SparkContext(appName=\"newApp\", conf=conf)\n",
    "spark = SparkSession(sc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.sql.functions import isnan, count, col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = spark.read.options(header = True, inferSchema = True,delimiter=',').csv(\"AutPapJouCSCI.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- PaperID: long (nullable = true)\n",
      " |-- Paper_Rank: integer (nullable = true)\n",
      " |-- Paper_RefCnt: integer (nullable = true)\n",
      " |-- CitationCount: integer (nullable = true)\n",
      " |-- EstimatedCount: integer (nullable = true)\n",
      " |-- Journal_Rank: integer (nullable = true)\n",
      " |-- Journal_Pap_Cnt: integer (nullable = true)\n",
      " |-- Journal_Cit_Cnt: integer (nullable = true)\n",
      " |-- CSRank: integer (nullable = true)\n",
      " |-- CSPaperCount: integer (nullable = true)\n",
      " |-- CSCitation: integer (nullable = true)\n",
      " |-- CIRank: integer (nullable = true)\n",
      " |-- CIPaperCount: integer (nullable = true)\n",
      " |-- CIPaperCitationCnt: integer (nullable = true)\n",
      " |-- DocTypeIndex: double (nullable = true)\n",
      " |-- max_AuthorRank: integer (nullable = true)\n",
      " |-- min_AutPaperCount: integer (nullable = true)\n",
      " |-- max_AutPaperCount: integer (nullable = true)\n",
      " |-- min_AutCitationCount: integer (nullable = true)\n",
      " |-- max_AutCitationCount: integer (nullable = true)\n",
      " |-- min_AuthorRank: integer (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df1.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import matplotlib.pyplot as plt \n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1_reordered = df1.select(\"PaperID\",\"Paper_Rank\",\"Paper_RefCnt\",\"EstimatedCount\",\"Journal_Rank\",\"Journal_Pap_Cnt\",\"Journal_Cit_Cnt\",\"CSRank\",\"CSPaperCount\",\"CSCitation\",\"CIRank\",\"CIPaperCount\",\"CIPaperCitationCnt\",\"DocTypeIndex\",\"max_AuthorRank\",\"min_AutPaperCount\",\"max_AutPaperCount\",\"min_AutCitationCount\",\"max_AutCitationCount\",\"min_AuthorRank\",\"CitationCount\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "root\n",
      " |-- PaperID: long (nullable = true)\n",
      " |-- Paper_Rank: integer (nullable = true)\n",
      " |-- Paper_RefCnt: integer (nullable = true)\n",
      " |-- EstimatedCount: integer (nullable = true)\n",
      " |-- Journal_Rank: integer (nullable = true)\n",
      " |-- Journal_Pap_Cnt: integer (nullable = true)\n",
      " |-- Journal_Cit_Cnt: integer (nullable = true)\n",
      " |-- CSRank: integer (nullable = true)\n",
      " |-- CSPaperCount: integer (nullable = true)\n",
      " |-- CSCitation: integer (nullable = true)\n",
      " |-- CIRank: integer (nullable = true)\n",
      " |-- CIPaperCount: integer (nullable = true)\n",
      " |-- CIPaperCitationCnt: integer (nullable = true)\n",
      " |-- DocTypeIndex: double (nullable = true)\n",
      " |-- max_AuthorRank: integer (nullable = true)\n",
      " |-- min_AutPaperCount: integer (nullable = true)\n",
      " |-- max_AutPaperCount: integer (nullable = true)\n",
      " |-- min_AutCitationCount: integer (nullable = true)\n",
      " |-- max_AutCitationCount: integer (nullable = true)\n",
      " |-- min_AuthorRank: integer (nullable = true)\n",
      " |-- CitationCount: integer (nullable = true)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "df1_reordered.printSchema()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df1_reordered.withColumnRenamed(\"CitationCount\",\"label\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_cols = [\"PaperID\",\"EstimatedCount\"]\n",
    "df2 = df2.drop(*drop_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.feature import VectorAssembler, VectorIndexer\n",
    "feature_list = []\n",
    "for col in df2.columns:\n",
    "    if col == 'label':\n",
    "        continue\n",
    "    else:\n",
    "        feature_list.append(col)\n",
    "# Concatenates all feature columns into a single feature vector in a new column \"rawFeatures\"\n",
    "vectorAssembler = VectorAssembler(inputCols=feature_list, outputCol=\"features\")\n",
    "# Identifies categorical features and indexes them\n",
    "#vectorIndexer = VectorIndexer(inputCol=\"rawFeatures\", outputCol=\"features\", maxCategories=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.tuning import CrossValidator, ParamGridBuilder\n",
    "from pyspark.ml.evaluation import RegressionEvaluator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.regression import RandomForestRegressor\n",
    "\n",
    "rf = RandomForestRegressor(labelCol=\"label\", featuresCol=\"features\")\n",
    "#rf = RandomForestRegressor(featuresCol=\"features\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml import Pipeline\n",
    "pipeline = Pipeline(stages=[vectorAssembler, rf])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.tuning import ParamGridBuilder\n",
    "import numpy as np\n",
    "\n",
    "paramGrid = ParamGridBuilder() \\\n",
    "    .addGrid(rf.numTrees, [int(x) for x in np.linspace(start = 10, stop = 50, num = 3)]) \\\n",
    "    .addGrid(rf.maxDepth, [int(x) for x in np.linspace(start = 5, stop = 25, num = 3)]) \\\n",
    "    .build()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "crossval = CrossValidator(estimator=pipeline,\n",
    "                          estimatorParamMaps=paramGrid,\n",
    "                          evaluator=RegressionEvaluator(),\n",
    "                          numFolds=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "(train, test) = df2.randomSplit([0.8, 0.2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "cvModel = crossval.fit(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = cvModel.transform(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "evaluator = RegressionEvaluator(labelCol=\"label\", predictionCol=\"prediction\", metricName=\"rmse\")\n",
    "\n",
    "rmse = evaluator.evaluate(predictions)\n",
    "\n",
    "rfPred = cvModel.transform(df2)\n",
    "\n",
    "rfResult = rfPred.toPandas()\n",
    "\n",
    "plt.plot(rfResult.label, rfResult.prediction, 'bo')\n",
    "plt.xlabel('CitationCount')\n",
    "plt.ylabel('Prediction')\n",
    "plt.suptitle(\"Model Performance RMSE: %f\" % rmse)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Feature Importances')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bestPipeline = cvModel.bestModel\n",
    "bestModel = bestPipeline.stages[1]\n",
    "\n",
    "importances = bestModel.featureImportances\n",
    "\n",
    "x_values = list(range(len(importances)))\n",
    "\n",
    "plt.bar(x_values, importances, orientation = 'vertical')\n",
    "plt.xticks(x_values, feature_list, rotation=80)\n",
    "plt.ylabel('Importance')\n",
    "plt.xlabel('Feature')\n",
    "plt.title('Feature Importances')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.regression import GBTRegressor\n",
    "# Takes the \"features\" column and learns to predict \"cnt\"\n",
    "gbt = GBTRegressor(labelCol=\"label\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "paramGrid = ParamGridBuilder()\\\n",
    "  .addGrid(gbt.maxDepth, [2, 5])\\\n",
    "  .addGrid(gbt.maxIter, [10, 100])\\\n",
    "  .build()\n",
    "evaluator = RegressionEvaluator(metricName=\"r2\", labelCol=gbt.getLabelCol(), predictionCol=gbt.getPredictionCol())\n",
    "cv = CrossValidator(estimator=gbt, evaluator=evaluator, estimatorParamMaps=paramGrid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline = Pipeline(stages=[vectorAssembler, cv])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipelineModel = pipeline.fit(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrame[label: int, prediction: double, Paper_Rank: int, Paper_RefCnt: int, Journal_Rank: int, Journal_Pap_Cnt: int, Journal_Cit_Cnt: int, CSRank: int, CSPaperCount: int, CSCitation: int, CIRank: int, CIPaperCount: int, CIPaperCitationCnt: int, DocTypeIndex: double, max_AuthorRank: int, min_AutPaperCount: int, max_AutPaperCount: int, min_AutCitationCount: int, max_AutCitationCount: int, min_AuthorRank: int]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions = pipelineModel.transform(test)\n",
    "display(predictions.select(\"label\", \"prediction\", *feature_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2 on our test set: 0.578267\n"
     ]
    }
   ],
   "source": [
    "r2 = evaluator.evaluate(predictions)\n",
    "print(\"R2 on our test set: %g\" % r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "paramGrid = ParamGridBuilder()\\\n",
    "  .addGrid(gbt.maxDepth, [2, 7])\\\n",
    "  .addGrid(gbt.maxIter, [10, 100])\\\n",
    "  .build()\n",
    "evaluator = RegressionEvaluator(metricName=\"rmse\", labelCol=gbt.getLabelCol(), predictionCol=gbt.getPredictionCol())\n",
    "cv = CrossValidator(estimator=gbt, evaluator=evaluator, estimatorParamMaps=paramGrid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline = Pipeline(stages=[vectorAssembler, cv])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipelineModel = pipeline.fit(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrame[label: int, prediction: double, Paper_Rank: int, Paper_RefCnt: int, Journal_Rank: int, Journal_Pap_Cnt: int, Journal_Cit_Cnt: int, CSRank: int, CSPaperCount: int, CSCitation: int, CIRank: int, CIPaperCount: int, CIPaperCitationCnt: int, DocTypeIndex: double, max_AuthorRank: int, min_AutPaperCount: int, max_AutPaperCount: int, min_AutCitationCount: int, max_AutCitationCount: int, min_AuthorRank: int]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "predictions = pipelineModel.transform(test)\n",
    "display(predictions.select(\"label\", \"prediction\", *feature_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RMSE on our test set: 32.4973\n"
     ]
    }
   ],
   "source": [
    "rmse = evaluator.evaluate(predictions)\n",
    "print(\"RMSE on our test set: %g\" % rmse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df1_reordered.withColumnRenamed(\"EstimatedCount\",\"label\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_cols = [\"PaperID\",\"CitationCount\"]\n",
    "df2 = df2.drop(*drop_cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark.ml.feature import VectorAssembler, VectorIndexer\n",
    "feature_list = []\n",
    "for col in df2.columns:\n",
    "    if col == 'label':\n",
    "        continue\n",
    "    else:\n",
    "        feature_list.append(col)\n",
    "# Concatenates all feature columns into a single feature vector in a new column \"rawFeatures\"\n",
    "vectorAssembler = VectorAssembler(inputCols=feature_list, outputCol=\"features\")\n",
    "# Identifies categorical features and indexes them\n",
    "#vectorIndexer = VectorIndexer(inputCol=\"rawFeatures\", outputCol=\"features\", maxCategories=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestRegressor(labelCol=\"label\", featuresCol=\"features\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline = Pipeline(stages=[vectorAssembler, rf])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "paramGrid = ParamGridBuilder() \\\n",
    "    .addGrid(rf.numTrees, [int(x) for x in np.linspace(start = 10, stop = 50, num = 3)]) \\\n",
    "    .addGrid(rf.maxDepth, [int(x) for x in np.linspace(start = 5, stop = 25, num = 3)]) \\\n",
    "    .build()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "crossval = CrossValidator(estimator=pipeline,\n",
    "                          estimatorParamMaps=paramGrid,\n",
    "                          evaluator=RegressionEvaluator(),\n",
    "                          numFolds=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "(train, test) = df2.randomSplit([0.8, 0.2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "cvmodel= crossval.fit(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrame[Paper_Rank: int, Paper_RefCnt: int, label: int, Journal_Rank: int, Journal_Pap_Cnt: int, Journal_Cit_Cnt: int, CSRank: int, CSPaperCount: int, CSCitation: int, CIRank: int, CIPaperCount: int, CIPaperCitationCnt: int, DocTypeIndex: double, max_AuthorRank: int, min_AutPaperCount: int, max_AutPaperCount: int, min_AutCitationCount: int, max_AutCitationCount: int, min_AuthorRank: int, features: vector, prediction: double]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cvmodel.transform(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAEjCAYAAAAlhuZMAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAIABJREFUeJzt3XucXHV9//HXm4QAyy0JSflhILuIiFK1CCtg9Yd445KqQGsVGiSiNsVIwdYbmP4UbaMWL1RbDcZCDbKiVLRGi1KKotVWYINAuAhESCBpIDGRmxGE8Pn98f0OOZnM7M7ZndmZ2X0/H4/zmHO+5/aZyWY+8/1+z/keRQRmZmaN2qHdAZiZWXdx4jAzs1KcOMzMrBQnDjMzK8WJw8zMSnHiMDOzUpw4JhhJfZJC0uQGtn2rpJ+MUVy7SPqOpIcl/etYnNPMRsaJo4NJWiXpd5JmVJX/PH/597Unsm0S0GN5WiXpnFEc8o3A3sBeEfGnTQqzo0k6T9KT+fN7SNJ/S3ppYf3R+TP+VtV+f5DLry2UnSDpJkmPSPqVpB9I2r/GeSrTQw3GOEPSTyVtzDH+j6SXFdbPk7Q8n3eNpPMb/FFyWn4P7yiUTZW0VNL6PJ1XWDe7Kv7H8v7vyetfKWlFjnGjpG9JmlXY/1OS7pb0qKRfSDqtsO65kr4taYOkTZKuknRQI5/PROXE0fnuBU6pLEh6IdDTvnC2MzUidiPF+CFJx5U9gKRJQC9wV0Q8NYL9h/2i6mBfz5/fDOCHQHVtawPwUkl7FcrmAXdVFiQ9B7gEeA+wJ7A/8HlgS/V5CtPUBuN7DHgbMBOYBvw98J3CZ94DvDvHfwTwauC9Qx1Q0jTgg8BtVasuyMfrAw4H3iLpdICIuK8YP/BC4Gngirzv7cCx+X09C7gbWFw49m+A15M+n3nAZyX9YV43FVgGHET68XI98O3hPpiJzImj830FOK2wPI/0JfEMSXtKuiT/Ylot6W8k7ZDXTcq/tn4l6R7gj2rse5GkdZLWSvq7/EVeSkT8D+mL4AX5uM+TdHX+BXenpDcVzvllSYslXSnpN8CPgQ8Bb86/JN8uaYf8PlbnX5+XSNoz71+p7bxd0n3ADwplp0u6X9KvJZ0h6SWSbsm/RP+pEMMB+Vf5xvzZDEiaWli/StJ7874PS/q6pJ0L64u/8H9ZSZgj/TxzwhwAZkmaWVj1O+DfgJPz8ScBb87bVhwC3BsR10TyaERcERH3NfSPN3Rcj0fEnRHxNCBSMpoGTM/rF0fEf0XE7yJibY7rZfWPCMDHgc8Bv6oqfz1wfkRsjohVwEWkpFXLacCP83ZExIMR8b+F9VuA5xTex4cj4hcR8XREXAf8F/DSvO76iLgoIjZFxJOkBHZQVbK2AieOzvczYA9Jz89fGicDl1Zt84+kX1LPBl5B+k91el7358DrgBcD/aQmoaIvA0+R/pO9GDgGeAclKHkZ8PvAzyXtClwNfBX4vRzzFyQdXNjtz4BFwO6kX6kfY+uv4ouAt+bplfl97Qb8E9t6BfB84NhC2RHAgaQv138AFgKvybG9SdIrKmGTvsCelY+xH3Be1fHfBBxH+gX/ohwPkg4nJe/3kX6tHgWsyvt8mRF8npKmkP7dNgK/rlp9CVt/PBwL3AoUvyRvBJ4n6YLcZLPbcOerOvd3NUwzo6RbgMdJv8z/OSLW19n0KLavSRSPczjp7/DCeptUzb+gxjFE+jyWVpXPVmqC+y2p1nN+nRh2AV4yRJxHAQ9ExMZ672PCiwhPHTqRvoxeA/wN6UvuONIX8mQgSFX6SaRfpQcX9vsL4No8/wPgjMK6Y/K+k0nV8ieAXQrrTwF+mOffCvykTmx9+TgPkb7o7gDOyuveDPxX1fZfBD6c578MXFK1/jzg0sLyNcCCwvJBwJM57sq5n10jnlmFso3AmwvLVwDvrvN+TgR+XvXZn1pYPh+4sPBeLqhxjCE/zxrbn5f/7R4i/ULeCBxdWH80sCbP350/g68Bc0nJ6NrCtkcCl5Oath7Pn/FuNc5TmWrGNMzf4875/cyrs/5twBpgRp31k4BB4Mi8fC3wjsL6S4Fvkn5MPAf4JfBEjeP8X1IT2m51zjMd+EDlPDXWLwW+D6jGun2BtcAprfp/PR6mbm4bnki+QmrO2Z+qZipS2/KOwOpC2Wqg0jH4LOD+qnUVvXnfdelHHJBqocXthzMjtu+X6AWO0LYdsJPz+6gY7hzPYvv3VEl2Qx3jwcL8b2ss7wYgaW/gs6Qvod1J77v6l/4DhfnNOSZItZMra5x7JJ/n5RFxqtIFEFcAh5G+UKt9BTiTVAN7G6nG9oyI+BmphoSklwBfJ9W2zi2eZ4g4hhURjwOXSbpD0k0RcXNlnaQTST9uXhMR1U1QFQuAW3KstZxFqj3fTUqil1Ho3yuYB1wREY/ViXOTpKXAzZJmFf8+JX2SVIt5ZeRMUVg3E/gP4AsRcVmdGA2cOLpBRKyWdC8wB3h71epfkX6J95I6CAFmk341AawjfdFRWFdxP+kXcq0v/9G4H/hRRLx2iG2GG5b5f0nvqWI2qQnoQdKvwkaOMZSP5f1fmL9oTmT7prB67gcOqFM+os8zIn4laT4wKOmrEbGuapOvACtJNbXNhcRU61g3SPomNZp5mmRHUvPhzQC5f+dLwB9FxIoh9ns18ApJc/LydODFkg6JiDMjYhOpNkU+7sdIHdUUynYB/hQ4aZgYJ5OaSfcANuV9PwIcD7wiIh6pOu40UtJYFhGLhjn2hOc+ju7xduBVEfGbYmFEbCE1USyStLukXuCv2doPcjlwlqR983+Ocwr7riP9Z/m0pD1yh/QBhX6Akfou8FxJb5G0Y55eIun5JY5xGfBXkvbPbfaVPpBmJbjdSc0dDytdtvm+EvteBJwu6dX5M5sl6Xmj/Twj4k7gKuD9NdbdS+rTWVi9TtLLJf25pN/Ly88D3kDqHxsVSUfm409RutfmA6Ra33V5/atIHeJ/EhHXD3UsUtPn80md+YeQmq0+UnlP+bPaS+mCjuOB+cDfVR3jJFLN8IdVcf6xpIPyZz4T+Ayp6bGSNM4l1dJeE1V9F5L2IH3uP42I0VxSPmE4cXSJiPhlRAzWWf2XpMsN7wF+QuqUvjiv+xLpP8XNpE7Ub1btexowhVRb+TXwDWCfUcb6KKkv5WRSzeEB0mWcO5U4zMVsbaK7l9Ru/5ejiavKR4BDgYeBf2f7z6Wu/AV5Ounqm4eBH7G1djTaz/OTwPxKEqg6709i2yuHKh4iJYoVkh4jtd9/i207hytXrBWnSqL5nqQP1olnJ9KlvRtJtdg5pJpFJY7/R7ow48rCcb9X2bl47Ih4KCIeqEykfpdHIuLhvPlhwArgUVKz19yIqO7Angd8pbqZidQ0+/287wrSpbrFWsnHSLXWlYU4K+/5JFJn+elVn0+xdm4F2v7zNzMzq881DjMzK8WJw8zMSnHiMDOzUpw4zMysFCcOMzMrxYnDzMxKceIwM7NSnDjMzKwUJw4zMyvFicPMzEpx4jAzs1KcOMzMrBQnDjMzK8WJw8zMSnHiMDOzUpw4zMysFCcOMzMrZXK7A2iFGTNmRF9fX7vDMDPrKsuXL/9VRMwcbrtxmTj6+voYHKz3eG4zM6tF0upGtnNTlZmZleLEYWZmpThxmJlZKU4cZmZWihOHmZmV4sRhZjYODAxAXx/ssEN6HRho3bnG5eW4ZmYTycAAzJ8Pmzen5dWr0zLA3LnNP59rHGZmXW7hwq1Jo2Lz5lTeCk4cZmZd7r77ypWPlhOHmVmXmz27XPloOXGYmXW5RYugp2fbsp6eVN4KThxmZl1u7lxYsgR6e0FKr0uWtKZjHHxVlZnZuDB3busSRTXXOMzMrBQnDjMzK8WJw8zMSnHiMDOzUpw4zMysFCcOMzMrxYnDzMxKceIwM7NSWpY4JO0n6YeSbpd0m6Szc/l5ktZKuilPcwr7nCtppaQ7JR1bKD8ul62UdE6rYjYzs+G18s7xp4D3RMSNknYHlku6Oq+7ICI+VdxY0sHAycDvA88C/lPSc/PqzwOvBdYAN0haFhG3tzB2MzOro2WJIyLWAevy/KOS7gBmDbHLCcDXIuIJ4F5JK4HD87qVEXEPgKSv5W2dOMzM2mBM+jgk9QEvBq7LRWdKukXSxZKm5bJZwP2F3dbksnrl1eeYL2lQ0uCGDRua/A7MzKyi5YlD0m7AFcC7I+IRYDFwAHAIqUby6WacJyKWRER/RPTPnDmzGYc0M7MaWjo6rqQdSUljICK+CRARDxbWfwn4bl5cC+xX2H3fXMYQ5WZmNsZaeVWVgIuAOyLiM4XyfQqbnQTcmueXASdL2knS/sCBwPXADcCBkvaXNIXUgb6sVXGbmdnQWlnjeBnwFmCFpJty2QeBUyQdAgSwCvgLgIi4TdLlpE7vp4B3RcQWAElnAlcBk4CLI+K2FsZtZmZDUES0O4am6+/vj8HBwXaHYWbWVSQtj4j+4bbzneNmZlaKE4eZmZXixGFmZqU4cZiZWSlOHGZmVooTh5mZleLEYWZmpThxmJlZKU4cZmZWihOHmZmV4sRhZmalOHGYmVkpThxmZlaKE4eZmZXixGFmZqU4cZiZWSlOHGZmVooTh5mZleLEYWZmpThxmJlZKU4cZmZWihOHmZmV4sRhZmalOHGYmVkpThxmZlaKE4eZmZXixGFmZqW0LHFI2k/SDyXdLuk2SWfn8umSrpZ0d36dlssl6XOSVkq6RdKhhWPNy9vfLWleq2I2M7PhtbLG8RTwnog4GDgSeJekg4FzgGsi4kDgmrwMcDxwYJ7mA4shJRrgw8ARwOHAhyvJxszMxl7LEkdErIuIG/P8o8AdwCzgBGBp3mwpcGKePwG4JJKfAVMl7QMcC1wdEZsi4tfA1cBxrYrbbCQGBqCvD3bYIb0ODLQ7IrPWmTwWJ5HUB7wYuA7YOyLW5VUPAHvn+VnA/YXd1uSyeuXV55hPqqkwe/bs5gVvNoyBAZg/HzZvTsurV6dlgLlz2xeXWau0vHNc0m7AFcC7I+KR4rqICCCacZ6IWBIR/RHRP3PmzGYc0qwhCxduTRoVmzencrPxqKWJQ9KOpKQxEBHfzMUP5iYo8uv6XL4W2K+w+765rF65WUe4775y5WbdrpVXVQm4CLgjIj5TWLUMqFwZNQ/4dqH8tHx11ZHAw7lJ6yrgGEnTcqf4MbnMrCPUaxl1i6mNV62scbwMeAvwKkk35WkO8AngtZLuBl6TlwGuBO4BVgJfAhYARMQm4G+BG/L00Vxm1hEWLYKenm3LenpSudl4pNTNML709/fH4OBgu8OwCWRgIPVp3HdfqmksWuSOces+kpZHRP9w243JVVVm493cuU4UNnF4yBEzMyvFicPMzEpx4jCb4HzXu5XlPg6zCcx3vdtIuMZhNoH5rncbCScOswnMd73bSDhxmE1gvuvdRsKJw2wC813vNhJOHGYT2Ny5sGQJ9PaClF6XLHHHuA3NV1WZTXC+693Kco3DzMxKceIwM7NSnDjMzKwUJw4zMyvFicPMzEpx4jAzs1KcOMzMrBQnDjMzK6XhGwAlzQJ6i/tExI9bEZSZmXWuhhKHpL8H3gzcDmzJxQE4cZiZTTCN1jhOBA6KiCdaGYyZmXW+Rvs47gF2bGUgZmbWHRqtcWwGbpJ0DfBMrSMizmpJVGZm1rEaTRzL8mRmZhNcQ4kjIpZKmgI8NxfdGRFPti4sMzPrVI1eVXU0sBRYBQjYT9I8X45rZjbxNNo5/mngmIh4RUQcBRwLXDDUDpIulrRe0q2FsvMkrZV0U57mFNadK2mlpDslHVsoPy6XrZR0Trm3Z2ZmzdZo4tgxIu6sLETEXQx/ldWXgeNqlF8QEYfk6UoASQcDJwO/n/f5gqRJkiYBnweOBw4GTsnbmk0YAwPQ1wc77JBeBwbaHZFNdI12jg9K+mfg0rw8FxgcaoeI+LGkvgaPfwLwtXyfyL2SVgKH53UrI+IeAElfy9ve3uBxzbrawADMnw+bN6fl1avTMvhxr9Y+jdY43kn6sj4rT7fnspE4U9ItuSlrWi6bBdxf2GZNLqtXbjYhLFy4NWlUbN6cys3apaHEERFPRMRnIuKP83TBCO8iXwwcABwCrCP1nTSFpPmSBiUNbtiwoVmHNWur++4rV242FoZMHJIuz68rci1hm6nsySLiwYjYEhFPA19ia3PUWmC/wqb75rJ65bWOvSQi+iOif+bMmWVDM+tIs2eXKzcbC8PVOM7Or68DXl9jKkXSPoXFk4DKFVfLgJMl7SRpf+BA4HrgBuBASfvn+0hOxjci2hDGW0fyokXQ07NtWU9PKjdrlyE7xyNiXZ5dEBEfKK7LI+Z+YPu9nll/GXA0MEPSGuDDwNGSDiGNrLsK+It8ntty7eZ24CngXRGxJR/nTOAqYBJwcUTcVvI92gQxHjuSK3EvXJiap2bPTkmjW9+PjQ+KiOE3km6MiEOrym6JiBe1LLJR6O/vj8HBIS/6snGory8li2q9vbBq1VhHY9Z9JC2PiP7hthuyxiHpncAC4ICqPo3dgf8eXYhmzeWOZLOxMdx9HF8Fvgd8HCjetf1oRGxqWVRmIzB7du0ahzuSzZpryM7xiHg4IlYBnwU2RcTqiFgNPCXpiLEI0KxR7kg2GxuN3gC4GHissPxYLjPrGHPnwpIlqU9DSq9Llrgj2azZGh1yRFHoRY+IpyU1uq/ZmJk714nCrNUafnSspLMk7Zins0mPkzUzswmm0cRxBvCHpLu21wBHAPNbFZSZmXWuRp8AuJ5017aZmU1ww93H8f6IOF/SP5Lu9t5GRJzVssjMzKwjDVfjuCO/+jZsMzMDhh+r6jv5denYhGNmZp1uuKaq71CjiaoiIt7Q9IjMzKyjDddU9an8+sfA/2Hro2NPAR5sVVBmZta5hmuq+hGApE9XjZj4HUnu9zAzm4AavY9jV0nPrizkhy3t2pqQzMyskzU6bMhfAddKugcQ0Et+CJOZmU0sDdU4IuL7pMe5ng2cBRwUEVe1MjArZ7w9MtXMOldDiUNSD/A+4MyIuBmYLel1LY3MGlZ5ZOrq1RCx9ZGp3ZA8nPDMuk+jfRz/AvwOeGleXgv8XUsistIWLtz6nO2KzZtTeSfr5oRnNpE1mjgOiIjzgScBImIzqa/DOkC3PjK1WxOe2UTXaOL4naRdyDcDSjoAeKJlUVkp9R6N2umPTO3WhGc20TWaOD4MfB/YT9IAcA3w/pZFZaV06yNTuzXhmU10wyYOSQJ+Qbp7/K3AZUB/RFzb0sis4Y7jbn1karcmPLOJToUnwtbfSFoRES8cg3iaor+/PwYHu/vG9krHcbEPoKenOxJCGQMDqU/jvvtSTWPRovH1/sy6iaTlVaOE1N6uwcSxFPiniLihGcG12nhIHH196Sqjar29sGrVWEdjZhNBo4mj0TvHjwBOlbQK+A3piqqIiBeNPEQbijuOzaxTNZo4jm1pFLad2bNr1zjccWxm7TZk57iknSW9m3TX+HHA2ohYXZnGJMIJyh3HZtaphruqainQD6wAjgc+3eiBJV0sab2kWwtl0yVdLenu/Dotl0vS5yStlHSLpEML+8zL298taV6pd9fFuvVKKQ8hYjb+Ddk5XryaStJk4PqIOLTuDtvuexTwGHBJRLwgl50PbIqIT0g6B5gWER+QNAf4S2AOqT/lsxFxhKTppOed95NuPlwOHBYRvx7q3OOhc7wbTZQrwczGq0Y7x4ercTxZmYmIp8oEEBE/BjZVFZ9AqsWQX08slF8Syc+AqZL2IfWtXB0Rm3KyuJrUZGYdyEOImE0Mw3WO/4GkR/K8gF3ycuWqqj1Knm/viFiX5x8A9s7zs4D7C9utyWX1yrcjaT4wH2C2e5DbwleCmU0MQ9Y4ImJSROyRp90jYnJhvmzSqD52kMe+aoaIWBIR/RHRP3PmzGYd1krwECJmE0OjY1U1y4O5CYr8uj6XrwX2K2y3by6rV24dyFeCmU0MY504lgGVK6PmAd8ulJ+Wr646Eng4N2ldBRwjaVq+AuuYXGYdqFuvBDOzchq9AbA0SZcBRwMzJK0hjbD7CeBySW8HVgNvyptfSbqiaiWwGTgdICI2SfpboDLUyUcjorrD3TrI3LlOFGbjXUNjVXUbX47bXh640Kw7NetyXOswnX6DnR8Hazb+OXF0kW74Uva9HGbjnxNHF+mGL2Xfy2E2/jlxdJFu+FL2vRxm458TRxfphi9l38thNv45cXSRbvhS9r0cZuNfy+7jsOarfPl2+qWuvpfDbHxz4ugy/lI2s3ZzU5WZmZXixDFOFW8UnDEjTZ1606CZdRc3VY1D1U/i27hx67rKTYPgJi8zGxnXOMaZgQGYN2/7GwWLOu2mQTPrLk4c40ilprFly/DbdtJNg2bWXZw4xpFaQ5LU08hNg50+oKKZtYf7OMaRMrWIOXOGXl/dT+K+ETOrcI1jHCkz9MiVVw69vhsGVDSz9nDiGEdqDUlSz3C1k24YUNHM2sOJY4Q6sf2/1jhRe+1Ve9t6tZPK+6r3YMhOGlDRzNrDiWME2vVApUaS1dy5sGoVPP10ev3sZxsfGLH4vmrptAEVzaxNImLcTYcddli0Um9vREoZ2069va0756WXRvT0bHu+np5U3si+vb0RUnqtt0+991V5b42cy8y6FzAYDXzHKuq1SXSx/v7+GBwcbNnxd9ihdlOOlH7pt0JfX+2aQG9vqlk0Qzvel5l1DknLI6J/uO3cVDUC7Xig0lh0VnfDg6LMrP2cOEagHQ9UavRLfTSd9t3woCgzaz8njhFoxlPuyn7B1/tSnzMn7S/BpElw6qnbdtqffnrjI+P66X1m1gj3cbRB9V3ZkJLAcF/SAwPbPv1vzhxYurTxYUYaPY+ZTUzu4xgDI20WKnNXdvEcCxemmkflUtsrryyXNIY6j5lZozxW1QiNZiynRju6hzvHSDvGffe3mY1GW2ocklZJWiHpJkmDuWy6pKsl3Z1fp+VySfqcpJWSbpF0aDtirjaasZwa7ege7hwjvdrJV0mZ2Wi0s6nqlRFxSKE97Rzgmog4ELgmLwMcDxyYp/nA4rEOtFaT1Gguj120CKZM2bZsypTtr14a7hxlxqaqmDzZV0mZ2eh0Uh/HCcDSPL8UOLFQfkm+sfFnwFRJ+4xVUPWGF5k+vfb2tX7NVyeen/50+xvtal2jUK9mMH16Os5b3gK77LJ1PKpJk9LrUGNUPfVU7XIzs0a1K3EE8B+SlkvKrfbsHRHr8vwDwN55fhZwf2HfNblsTNRrLoLG7nmolXguvBCefHLb7Z58cvtmrlo1ih13hEcf3Xq8jRvht7+FSy9NSSEidZxv2jT0ezIzG6l2JY6XR8ShpGaod0k6qrgyj5lS6jphSfMlDUoa3LBhQ9MCrddctGlTY/c81Eo89a6Arj5X5b6KYu1hyxb43e+23W7z5vSc8WJT2lD9GO4cN7PRaEviiIi1+XU98C3gcODBShNUfl2fN18L7FfYfd9cVn3MJRHRHxH9M2fObFqsQ3VkV49EW+tqqjJf0sVzLViQ+iNOPTXVKirqjRm1Zcu2TWlDPeHPneNmNhpjnjgk7Spp98o8cAxwK7AMmJc3mwd8O88vA07LV1cdCTxcaNJqueGG4RjuXo56X9JS/WMuWACLF6dkMBKbN8Pll6fEU23HHd05bmaj044ax97ATyTdDFwP/HtEfB/4BPBaSXcDr8nLAFcC9wArgS8BC8Yy2KGG4WjkuRz1Es8ZZ2x/TEjJZ3ETrhvbuLF2R/gee/iucTMbpUbGXu+2qRXP46j1TItGn8vRyPMwaj1voxWT1PSPxszGCfw8juaNVVVvbKl6w32M5PkVu+8Ojz028hiLenrSZbrFvpGKZj6/w8zGF49V1URnn13/ktxaynY+L1gw+qQxadK2zV5lHhlrZlaGx6oaxsBA7V/u9ZT9ch4YaE6fxtNP167lFEfTXbTI/RtmNnqucQyj7M1y8+bV/3KuvgJrwYKtgxYOpbc3TUOpVctp5HLhMkbzkCgzGz+cOIYwMFD7Od9DWbw4NRlJ6QFKAwNpmjFj+4csXXjh8MOiV2owQ90PMhZNUI1cQWZmE4M7x+uo1SE+1nbdFb74xVRT6OurncQmTUoPc2p1E1S987uz3Wz8cOf4KNUaKmQsvfOdqcO8khDqjYQ7derYxDOa0YDNbHxx4qijXV+IPT1pwMIvfGHb8lrjVkHquB+LJqNGnyFiZuOfE0cdY/2FONRAiRVz58Juu21fPhaPgx1u6BUzmzicOOpYtGj78aRapbe38Suf2tVkNNTQK2Y2sThx1DF3bv3hz5vtOc9pfNt2Nhk1+/JeM+tOThxDGO7eiWb5wQ8a76Nwk5GZtZsTxxCGeqZFM0U03kfhJiMzazffxzGEGTPKDTcyGiMZGNHMrJl8H8colR2jarR8WauZdQsnjjrOPntsz+c+CjPrFk4cdYxlbWOvvdxHYWbdw4mjzXp60rMzzMy6hRNHHTvv3Ppz9PamYdgXLvRQ5WbWPZw4qlSGQH/88daeZ6+90uW+S5cOPVS5n4FhZp3Gl+MWjPVQ6lLtu9MrQ5XXe9a579sws1Zo9HJcJ46Ces+cGK16CWKo7Z9+2s/AMLOx5fs4RqAVAwX29MBXvlJuwMTKPR1+BoaZdSInjoLp05t3rOrhQOrd4FedUIrjTvkZGGbWiZw4CjZtat6xqkeQrTc44Rln1B93ygMamlknmtzuADrFggXNG0a91qi6lWSwcGFqapo9OyWAoTq5R7KPmVmruXM8a9ZDm3zVk5l1K3eOt8EOOzhpmNn41zWJQ9Jxku6UtFLSOe2Op9qUKXDJJU4aZjb+dUXikDQJ+DxwPHAwcIqkg9sb1Va9vXDxxU4aZjYxdEvn+OHAyoi4B0DS14ATgNvbGhVp6BDfjGdmE0lX1DiAWcD9heU1uewZkuZLGpQ0uGHDhjEJasoUj2xrZhNPtySOYUXEkojoj4j+mTNntvx8bp4ys4mqW5qq1gL7FZb3zWVjaq+9Ug3DycLMJrJuSRw3AAdK2p+UME4G/qyZJ4iofS/HOLzNxcxsVLoicUTEU5LOBK4CJgEXR8RtzT9Ps49oZjb+dEXiAIiIK4HPmR4wAAAH3UlEQVQr2x2HmdlEN246x83MbGw4cZiZWSlOHGZmVooTh5mZlTIuh1WXtAEYzdPDZwC/alI4Y8lxj61ujRu6N3bH3Vq9ETHsHdTjMnGMlqTBRsak7zSOe2x1a9zQvbE77s7gpiozMyvFicPMzEpx4qhtSbsDGCHHPba6NW7o3tgddwdwH4eZmZXiGoeZmZXixFHQac81l3SxpPWSbi2UTZd0taS78+u0XC5Jn8ux3yLp0MI+8/L2d0uaNwZx7yfph5Jul3SbpLO7KPadJV0v6eYc+0dy+f6Srssxfl3SlFy+U15emdf3FY51bi6/U9KxrY49n3OSpJ9L+m63xC1plaQVkm6SNJjLuuFvZaqkb0j6haQ7JL20G+JuiojwlJrrJgG/BJ4NTAFuBg5uc0xHAYcCtxbKzgfOyfPnAH+f5+cA3wMEHAlcl8unA/fk12l5flqL494HODTP7w7cRXpWfDfELmC3PL8jcF2O6XLg5Fx+IfDOPL8AuDDPnwx8Pc8fnP+GdgL2z39bk8bgb+avga8C383LHR83sAqYUVXWDX8rS4F35PkpwNRuiLsp773dAXTKBLwUuKqwfC5wbgfE1ce2ieNOYJ88vw9wZ57/InBK9XbAKcAXC+XbbDdG7+HbwGu7LXagB7gROIJ089bk6r8V0lD/L83zk/N2qv77KW7Xwnj3Ba4BXgV8N8fRDXGvYvvE0dF/K8CewL3kfuJuibtZk5uqthr2ueYdYu+IWJfnHwD2zvP14m/r+8pNIC8m/XLvithzc89NwHrgatKv7oci4qkacTwTY17/MLBXm2L/B+D9wNN5eS+6I+4A/kPScknzc1mn/63sD2wA/iU3Df6zpF27IO6mcOLoYpF+onTsZXGSdgOuAN4dEY8U13Vy7BGxJSIOIf2CPxx4XptDGpak1wHrI2J5u2MZgZdHxKHA8cC7JB1VXNmhfyuTSc3IiyPixcBvSE1Tz+jQuJvCiWOrjniueQMelLQPQH5dn8vrxd+W9yVpR1LSGIiIb+biroi9IiIeAn5IauKZKqny4LNiHM/EmNfvCWxk7GN/GfAGSauAr5Gaqz7bBXETEWvz63rgW6Rk3el/K2uANRFxXV7+BimRdHrcTeHEsdUzzzXPV56cDCxrc0y1LAMqV17MI/UfVMpPy1dvHAk8nKvMVwHHSJqWr/A4Jpe1jCQBFwF3RMRnuiz2mZKm5vldSH0zd5ASyBvrxF55T28EfpB/aS4DTs5XL+0PHAhc36q4I+LciNg3IvpIf7s/iIi5nR63pF0l7V6ZJ/0b30qH/61ExAPA/ZIOykWvBm7v9Libpt2dLJ00ka58uIvUpr2wA+K5DFgHPEn6hfN2Ujv0NcDdwH8C0/O2Aj6fY18B9BeO8zZgZZ5OH4O4X06qot8C3JSnOV0S+4uAn+fYbwU+lMufTfoCXQn8K7BTLt85L6/M659dONbC/J7uBI4fw7+bo9l6VVVHx53juzlPt1X+33XJ38ohwGD+W/k30lVRHR93MybfOW5mZqW4qcrMzEpx4jAzs1KcOMzMrBQnDjMzK8WJw8zMSnHisHFJ0pY82mplqjvasaQTJR1cWP6opNc0IYapkhaMYL/zJL23sPzePALrTZJukHTaaGNrRpw2cU0efhOzrvTbSMOGNOJE0qCAtwNExIeaFMNU0ii0XxjpASSdQboJ8fCIeETSHsBJTYqvYtRx2sTiGodNKJI+ofSckFskfUrSHwJvAD6Zf9EfIOnLkt6Yt18l6eN53aCkQyVdJemX+UsdSbtJukbSjUrPlTghn+4TwAF530/mbd+Xaw23KD/rI5cvlHSXpJ8ABxVC/iBpKPRHACLikYhYmvd5dR5gb4XSs1t2KsQ8I8/3S7o2z5+Xt7tW0j2SzqoXp9lQXOOw8WoXpRFuKz5OupP3JOB5ERGSpkbEQ5KWke60/gZAGjFlG/dFxCGSLgC+TBoXamfSneUXAo8DJ+UawQzgZ/mY5wAvqNR8JB1DGsLjcNKdxMuUBvT7DWmYkENI/ydvBJbn2sXuEXFPdUCSds6xvDoi7pJ0CfBO0gi5Q3ke8ErSc1LulLS4Ok6z4Thx2Hi1XVOV0mB+jwMXKT0h77sNHqsyZtkK0kOeHgUelfREHtfqN8DHchJ4mjQs9t41jnNMnn6el3cjJZLdgW9FxOYcZyNjpB0E3BsRd+XlpcC7GD5x/HtEPAE8IWl9nTjNhuSmKpswIj134nDSSKavA77f4K5P5NenC/OV5cnAXGAmcFhOVg+SaiTVBHw8Ig7J03Mi4qIh4n0EeEzSsxuMs+Iptv7fro6jGP8W/OPRRsCJwyYMpeeD7BkRVwJ/BfxBXvUo6Vf/SO1JehbGk5JeCfTWOe5VwNtyHEiaJen3gB8DJ0raJY8U+/rCPh8HPp+brSr9KaeRBiDsk/ScvN1bgB/l+VXAYXn+TxqIf7Tv3yYY/9qw8aq6j+P7pOdTfDv3D4j0fG5Iz6/4Uu4sfiPlDQDfkbSCNFrqLwAiYqOkn0q6FfheRLxP0vOB/8n9KI8Bp0bEjZK+Thohdj1piP+KxaQmrRskPUkaKfnTEfG4pNOBf81NcDeQ+lsAPkJqjvtb4Nrhgq8V5wg+A5tAPDqumZmV4qYqMzMrxYnDzMxKceIwM7NSnDjMzKwUJw4zMyvFicPMzEpx4jAzs1KcOMzMrJT/DwqlTswvuDYxAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "evaluator = RegressionEvaluator(labelCol=\"label\", predictionCol=\"prediction\", metricName=\"rmse\")\n",
    "\n",
    "rmse = evaluator.evaluate(predictions)\n",
    "\n",
    "rfPred = cvModel.transform(test)\n",
    "\n",
    "rfResult = rfPred.toPandas()\n",
    "\n",
    "plt.plot(rfResult.label, rfResult.prediction, 'bo')\n",
    "plt.xlabel('EstimatedCount')\n",
    "plt.ylabel('Prediction')\n",
    "plt.suptitle(\"Model Performance RMSE: %f\" % rmse)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Feature Importances')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bestPipeline = cvModel.bestModel\n",
    "bestModel = bestPipeline.stages[1]\n",
    "\n",
    "importances = bestModel.featureImportances\n",
    "\n",
    "x_values = list(range(len(importances)))\n",
    "\n",
    "plt.bar(x_values, importances, orientation = 'vertical')\n",
    "plt.xticks(x_values, feature_list, rotation=80)\n",
    "plt.ylabel('Importance')\n",
    "plt.xlabel('Feature')\n",
    "plt.title('Feature Importances')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "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": "2.7.15rc1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}