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takuti/hivemall-pyspark.ipynb Secret

Created October 21, 2019 06:41
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hivemall-pyspark.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "hivemall-pyspark.ipynb",
"provenance": [],
"collapsed_sections": [],
"toc_visible": true,
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/takuti/59bc761543786112a86528f61bbae67c/hivemall-pyspark.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WyH7JUqhF8sY",
"colab_type": "text"
},
"source": [
"# Hivemall with PySpark"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vcaAkopVGAYG",
"colab_type": "text"
},
"source": [
"## Installation\n",
"\n",
"Prepare Spark environment for Google Colabo:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Qc84kbLXx2kg",
"colab_type": "code",
"colab": {}
},
"source": [
"!apt-get install openjdk-8-jdk-headless -qq > /dev/null\n",
"!wget -q http://mirror.checkdomain.de/apache/spark/spark-2.3.4/spark-2.3.4-bin-hadoop2.7.tgz\n",
"!tar xf spark-2.3.4-bin-hadoop2.7.tgz\n",
"!pip install -q findspark"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "sG8vZYpp7JC7",
"colab_type": "code",
"colab": {}
},
"source": [
"import os\n",
"\n",
"os.environ[\"JAVA_HOME\"] = \"/usr/lib/jvm/java-8-openjdk-amd64\"\n",
"os.environ[\"SPARK_HOME\"] = \"/content/spark-2.3.4-bin-hadoop2.7\""
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "pIQZ4AcY7NwO",
"colab_type": "code",
"colab": {}
},
"source": [
"import findspark\n",
"findspark.init()"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "I-TF5acLIauO",
"colab_type": "text"
},
"source": [
"Download Hivemall JAR from the [releases](https://incubator.apache.org/clutch/hivemall.html):"
]
},
{
"cell_type": "code",
"metadata": {
"id": "CmL20W6k1zmB",
"colab_type": "code",
"colab": {}
},
"source": [
"!wget -q http://mirror.reverse.net/pub/apache/incubator/hivemall/0.5.2-incubating/hivemall-spark2.3-0.5.2-incubating-with-dependencies.jar"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "5s-BUNcNGECk",
"colab_type": "text"
},
"source": [
"## Basic Usage \n",
"\n",
"Create `SparkSession` with the Hivemall JAR:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "sptUhAvix4aP",
"colab_type": "code",
"colab": {}
},
"source": [
"from pyspark.sql import SparkSession\n",
"\n",
"spark = SparkSession \\\n",
" .builder \\\n",
" .master('local[*]') \\\n",
" .config('spark.jars', \\\n",
" 'hivemall-spark2.3-0.5.2-incubating-with-dependencies.jar') \\\n",
" .enableHiveSupport() \\\n",
" .getOrCreate()"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "s4_GFJYnTcWE",
"colab_type": "text"
},
"source": [
"`local[*]` runs Spark locally with as many worker threads as logical cores on this environment, and the number is:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "j8LwdUx6TwG4",
"colab_type": "code",
"outputId": "9aa11e90-8622-475c-ffce-90a9f6cd0d75",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"!grep -c ^processor /proc/cpuinfo"
],
"execution_count": 6,
"outputs": [
{
"output_type": "stream",
"text": [
"2\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "H8VpiD1BGIWy",
"colab_type": "text"
},
"source": [
"Register Hive(mall) UDFs to the session:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "DdkQpqHW0xJD",
"colab_type": "code",
"colab": {}
},
"source": [
"spark.sql(\"CREATE TEMPORARY FUNCTION hivemall_version AS 'hivemall.HivemallVersionUDF'\");"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "LxLfhUgYKCaj",
"colab_type": "text"
},
"source": [
"Use the functions in SQL context:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Nv4qpJVkKAT4",
"colab_type": "code",
"outputId": "f0cdf012-8f8c-4342-fc4e-fc6fc611f6b0",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 119
}
},
"source": [
"spark.sql(\"SELECT hivemall_version()\").show()"
],
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"text": [
"+------------------+\n",
"|hivemall_version()|\n",
"+------------------+\n",
"| 0.5.2-incubating|\n",
"+------------------+\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "64wPO26FGNMJ",
"colab_type": "text"
},
"source": [
"## Example: Binary Classification"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Nac0TWOzGUNf",
"colab_type": "text"
},
"source": [
"### Load data\n",
"\n",
"Use a [public dataset](https://aws.amazon.com/blogs/machine-learning/predicting-customer-churn-with-amazon-machine-learning/) that contains customers churn history from a telecom company:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "D6_zNAxK_IVf",
"colab_type": "code",
"outputId": "2ed48c56-8814-41b2-9d68-33732faa1d93",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 51
}
},
"source": [
"!wget -q http://dataminingconsultant.com/DKD2e_data_sets.zip\n",
"!unzip -j DKD2e_data_sets.zip \"**/churn.txt\""
],
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"text": [
"Archive: DKD2e_data_sets.zip\n",
" inflating: churn.txt \n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AImzvOJuLoxm",
"colab_type": "text"
},
"source": [
"Normalize column names, and load data as a Pandas DataFrame:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "SgvGtW8n_MEi",
"colab_type": "code",
"outputId": "d973d38d-9660-4228-dbf1-fc20f8f7c8c9",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 309
}
},
"source": [
"import re\n",
"import pandas as pd\n",
"\n",
"pddf = pd.read_csv('churn.txt') \\\n",
" .rename(lambda c: re.sub(r'[^a-zA-Z0-9 ]', '', str(c)).lower().replace(' ', '_'), axis='columns')\n",
"pddf.head()"
],
"execution_count": 10,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>state</th>\n",
" <th>account_length</th>\n",
" <th>area_code</th>\n",
" <th>phone</th>\n",
" <th>intl_plan</th>\n",
" <th>vmail_plan</th>\n",
" <th>vmail_message</th>\n",
" <th>day_mins</th>\n",
" <th>day_calls</th>\n",
" <th>day_charge</th>\n",
" <th>eve_mins</th>\n",
" <th>eve_calls</th>\n",
" <th>eve_charge</th>\n",
" <th>night_mins</th>\n",
" <th>night_calls</th>\n",
" <th>night_charge</th>\n",
" <th>intl_mins</th>\n",
" <th>intl_calls</th>\n",
" <th>intl_charge</th>\n",
" <th>custserv_calls</th>\n",
" <th>churn</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>KS</td>\n",
" <td>128</td>\n",
" <td>415</td>\n",
" <td>382-4657</td>\n",
" <td>no</td>\n",
" <td>yes</td>\n",
" <td>25</td>\n",
" <td>265.1</td>\n",
" <td>110</td>\n",
" <td>45.07</td>\n",
" <td>197.4</td>\n",
" <td>99</td>\n",
" <td>16.78</td>\n",
" <td>244.7</td>\n",
" <td>91</td>\n",
" <td>11.01</td>\n",
" <td>10.0</td>\n",
" <td>3</td>\n",
" <td>2.70</td>\n",
" <td>1</td>\n",
" <td>False.</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>OH</td>\n",
" <td>107</td>\n",
" <td>415</td>\n",
" <td>371-7191</td>\n",
" <td>no</td>\n",
" <td>yes</td>\n",
" <td>26</td>\n",
" <td>161.6</td>\n",
" <td>123</td>\n",
" <td>27.47</td>\n",
" <td>195.5</td>\n",
" <td>103</td>\n",
" <td>16.62</td>\n",
" <td>254.4</td>\n",
" <td>103</td>\n",
" <td>11.45</td>\n",
" <td>13.7</td>\n",
" <td>3</td>\n",
" <td>3.70</td>\n",
" <td>1</td>\n",
" <td>False.</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>NJ</td>\n",
" <td>137</td>\n",
" <td>415</td>\n",
" <td>358-1921</td>\n",
" <td>no</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" <td>243.4</td>\n",
" <td>114</td>\n",
" <td>41.38</td>\n",
" <td>121.2</td>\n",
" <td>110</td>\n",
" <td>10.30</td>\n",
" <td>162.6</td>\n",
" <td>104</td>\n",
" <td>7.32</td>\n",
" <td>12.2</td>\n",
" <td>5</td>\n",
" <td>3.29</td>\n",
" <td>0</td>\n",
" <td>False.</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>OH</td>\n",
" <td>84</td>\n",
" <td>408</td>\n",
" <td>375-9999</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" <td>299.4</td>\n",
" <td>71</td>\n",
" <td>50.90</td>\n",
" <td>61.9</td>\n",
" <td>88</td>\n",
" <td>5.26</td>\n",
" <td>196.9</td>\n",
" <td>89</td>\n",
" <td>8.86</td>\n",
" <td>6.6</td>\n",
" <td>7</td>\n",
" <td>1.78</td>\n",
" <td>2</td>\n",
" <td>False.</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>OK</td>\n",
" <td>75</td>\n",
" <td>415</td>\n",
" <td>330-6626</td>\n",
" <td>yes</td>\n",
" <td>no</td>\n",
" <td>0</td>\n",
" <td>166.7</td>\n",
" <td>113</td>\n",
" <td>28.34</td>\n",
" <td>148.3</td>\n",
" <td>122</td>\n",
" <td>12.61</td>\n",
" <td>186.9</td>\n",
" <td>121</td>\n",
" <td>8.41</td>\n",
" <td>10.1</td>\n",
" <td>3</td>\n",
" <td>2.73</td>\n",
" <td>3</td>\n",
" <td>False.</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" state account_length area_code ... intl_charge custserv_calls churn\n",
"0 KS 128 415 ... 2.70 1 False.\n",
"1 OH 107 415 ... 3.70 1 False.\n",
"2 NJ 137 415 ... 3.29 0 False.\n",
"3 OH 84 408 ... 1.78 2 False.\n",
"4 OK 75 415 ... 2.73 3 False.\n",
"\n",
"[5 rows x 21 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 10
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TeKfonPVMVSP",
"colab_type": "text"
},
"source": [
"Convert into a Spark DataFrame:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "p4Urhba2MddQ",
"colab_type": "code",
"outputId": "43a9b1e0-baf1-4ff8-ebdd-3c91c8c03aa3",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 408
}
},
"source": [
"df = spark.createDataFrame(pddf)\n",
"df.printSchema()"
],
"execution_count": 11,
"outputs": [
{
"output_type": "stream",
"text": [
"root\n",
" |-- state: string (nullable = true)\n",
" |-- account_length: long (nullable = true)\n",
" |-- area_code: long (nullable = true)\n",
" |-- phone: string (nullable = true)\n",
" |-- intl_plan: string (nullable = true)\n",
" |-- vmail_plan: string (nullable = true)\n",
" |-- vmail_message: long (nullable = true)\n",
" |-- day_mins: double (nullable = true)\n",
" |-- day_calls: long (nullable = true)\n",
" |-- day_charge: double (nullable = true)\n",
" |-- eve_mins: double (nullable = true)\n",
" |-- eve_calls: long (nullable = true)\n",
" |-- eve_charge: double (nullable = true)\n",
" |-- night_mins: double (nullable = true)\n",
" |-- night_calls: long (nullable = true)\n",
" |-- night_charge: double (nullable = true)\n",
" |-- intl_mins: double (nullable = true)\n",
" |-- intl_calls: long (nullable = true)\n",
" |-- intl_charge: double (nullable = true)\n",
" |-- custserv_calls: long (nullable = true)\n",
" |-- churn: string (nullable = true)\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "q8V-2bF1Qity",
"colab_type": "text"
},
"source": [
"### Preprocessing"
]
},
{
"cell_type": "code",
"metadata": {
"id": "iHM3tj5GVgg2",
"colab_type": "code",
"colab": {}
},
"source": [
"spark.sql(\"CREATE TEMPORARY FUNCTION array_concat AS 'hivemall.tools.array.ArrayConcatUDF'\")\n",
"spark.sql(\"CREATE TEMPORARY FUNCTION categorical_features AS 'hivemall.ftvec.trans.CategoricalFeaturesUDF'\")\n",
"spark.sql(\"CREATE TEMPORARY FUNCTION quantitative_features AS 'hivemall.ftvec.trans.QuantitativeFeaturesUDF'\")\n",
"spark.sql(\"CREATE TEMPORARY FUNCTION rescale AS 'hivemall.ftvec.scaling.RescaleUDF'\");"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "-rDg3uwSUHVU",
"colab_type": "text"
},
"source": [
"Create a view on the original DataFrame:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "qyhaKRS4UAS8",
"colab_type": "code",
"colab": {}
},
"source": [
"df.createOrReplaceTempView('churn')"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "AjS-mwtxU_Aa",
"colab_type": "text"
},
"source": [
"Query the view:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "zyD0a-lST_t_",
"colab_type": "code",
"outputId": "4e6e6852-df4c-4d63-b890-3eac343168be",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
}
},
"source": [
"df_preprocessed = spark.sql(\"\"\"\n",
"SELECT\n",
" phone,\n",
" array_concat(\n",
" categorical_features(\n",
" array('intl_plan', 'state', 'area_code', 'vmail_plan'),\n",
" intl_plan, state, area_code, vmail_plan\n",
" ),\n",
" quantitative_features(\n",
" array(\n",
" 'night_charge', 'day_charge', 'custserv_calls', \n",
" 'intl_charge', 'eve_charge', 'vmail_message'\n",
" ),\n",
" night_charge, day_charge, custserv_calls,\n",
" intl_charge, eve_charge, vmail_message\n",
" )\n",
" ) as features,\n",
" if(churn = 'True.', 1, 0) as label\n",
"FROM\n",
" churn\n",
"\"\"\")\n",
"df_preprocessed.toPandas().head()"
],
"execution_count": 14,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>phone</th>\n",
" <th>features</th>\n",
" <th>label</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>382-4657</td>\n",
" <td>[intl_plan#no, state#KS, area_code#415, vmail_...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>371-7191</td>\n",
" <td>[intl_plan#no, state#OH, area_code#415, vmail_...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>358-1921</td>\n",
" <td>[intl_plan#no, state#NJ, area_code#415, vmail_...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>375-9999</td>\n",
" <td>[intl_plan#yes, state#OH, area_code#408, vmail...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>330-6626</td>\n",
" <td>[intl_plan#yes, state#OK, area_code#415, vmail...</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" phone features label\n",
"0 382-4657 [intl_plan#no, state#KS, area_code#415, vmail_... 0\n",
"1 371-7191 [intl_plan#no, state#OH, area_code#415, vmail_... 0\n",
"2 358-1921 [intl_plan#no, state#NJ, area_code#415, vmail_... 0\n",
"3 375-9999 [intl_plan#yes, state#OH, area_code#408, vmail... 0\n",
"4 330-6626 [intl_plan#yes, state#OK, area_code#415, vmail... 0"
]
},
"metadata": {
"tags": []
},
"execution_count": 14
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "AfGRPa2ZWRoQ",
"colab_type": "text"
},
"source": [
"We can of course run Spark DataFrame operations:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "RrW1sluxV5YV",
"colab_type": "code",
"outputId": "c03425a9-9562-4070-df29-0c5aac0be539",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 136
}
},
"source": [
"df_preprocessed.groupby('label').count().show()"
],
"execution_count": 15,
"outputs": [
{
"output_type": "stream",
"text": [
"+-----+-----+\n",
"|label|count|\n",
"+-----+-----+\n",
"| 1| 483|\n",
"| 0| 2850|\n",
"+-----+-----+\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zNO7bKHTWZWW",
"colab_type": "text"
},
"source": [
"Split records into 80% train and 20% test set:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "SCap59YLBxr1",
"colab_type": "code",
"outputId": "406dfefe-a6ac-4f80-87d9-13000637f5de",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"df_train, df_test = df_preprocessed.randomSplit([0.8, 0.2], seed=31)\n",
"df_train.count(), df_test.count()"
],
"execution_count": 16,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(2658, 675)"
]
},
"metadata": {
"tags": []
},
"execution_count": 16
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Due2pk_GGYfu",
"colab_type": "text"
},
"source": [
"### Training"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "i9cDb0SaBVFn",
"colab_type": "text"
},
"source": [
"Use `train_classifier` UDF to build a liner classifier:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "hfBOFHHYWmgO",
"colab_type": "code",
"colab": {}
},
"source": [
"spark.sql(\"CREATE TEMPORARY FUNCTION train_classifier AS 'hivemall.classifier.GeneralClassifierUDTF'\");"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "8NpNjX1DFqB1",
"colab_type": "text"
},
"source": [
"Usage can be found by `-help` option:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "PePisTqXBkR0",
"colab_type": "code",
"outputId": "7b33d796-6f11-44d8-bf3b-969635521807",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
}
},
"source": [
"spark.sql(\"SELECT train_classifier(array(), 0, '-help')\")"
],
"execution_count": 18,
"outputs": [
{
"output_type": "error",
"ename": "AnalysisException",
"evalue": "ignored",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mPy4JJavaError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/content/spark-2.3.4-bin-hadoop2.7/python/pyspark/sql/utils.py\u001b[0m in \u001b[0;36mdeco\u001b[0;34m(*a, **kw)\u001b[0m\n\u001b[1;32m 62\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 63\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 64\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mpy4j\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprotocol\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPy4JJavaError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/content/spark-2.3.4-bin-hadoop2.7/python/lib/py4j-0.10.7-src.zip/py4j/protocol.py\u001b[0m in \u001b[0;36mget_return_value\u001b[0;34m(answer, gateway_client, target_id, name)\u001b[0m\n\u001b[1;32m 327\u001b[0m \u001b[0;34m\"An error occurred while calling {0}{1}{2}.\\n\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 328\u001b[0;31m format(target_id, \".\", name), value)\n\u001b[0m\u001b[1;32m 329\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mPy4JJavaError\u001b[0m: An error occurred while calling o28.sql.\n: org.apache.spark.sql.AnalysisException: No handler for UDF/UDAF/UDTF 'hivemall.classifier.GeneralClassifierUDTF': org.apache.hadoop.hive.ql.exec.UDFArgumentException: \nusage: train_classifier(list<string|int|bigint> features, int label [,\n const string options]) - Returns a relation consists of\n <string|int|bigint feature, float weight> [-cv_rate <arg>] [-dense]\n [-dims <arg>] [-disable_cv] [-disable_halffloat] [-eps <arg>] [-eta\n <arg>] [-eta0 <arg>] [-help] [-iter <arg>] [-iters <arg>]\n [-l1_ratio <arg>] [-lambda <arg>] [-loss <arg>] [-mini_batch <arg>]\n [-mix <arg>] [-mix_cancel] [-mix_session <arg>] [-mix_threshold\n <arg>] [-opt <arg>] [-power_t <arg>] [-reg <arg>] [-rho <arg>]\n [-scale <arg>] [-ssl] [-t <arg>]\n -cv_rate,--convergence_rate <arg> Threshold to determine\n convergence [default: 0.005]\n -dense,--densemodel Use dense model or not\n -dims,--feature_dimensions <arg> The dimension of model [default:\n 16777216 (2^24)]\n -disable_cv,--disable_cvtest Whether to disable convergence\n check [default: OFF]\n -disable_halffloat Toggle this option to disable the\n use of SpaceEfficientDenseModel\n -eps <arg> Denominator value of\n AdaDelta/AdaGrad [default 1e-6]\n -eta <arg> Learning rate scheme [default:\n inverse/inv, fixed, simple]\n -eta0 <arg> The initial learning rate\n [default 0.1]\n -help Show function help\n -iter,--iterations <arg> The maximum number of iterations\n [default: 10]\n -iters,--iterations <arg> The maximum number of iterations\n [default: 10]\n -l1_ratio <arg> Ratio of L1 regularizer as a part\n of Elastic Net regularization\n [default: 0.5]\n -lambda <arg> Regularization term [default\n 0.0001]\n -loss,--loss_function <arg> Loss function [HingeLoss\n (default), LogLoss,\n SquaredHingeLoss,\n ModifiedHuberLoss, or\n a regression loss: SquaredLoss,\n QuantileLoss,\n EpsilonInsensitiveLoss,\n SquaredEpsilonInsensitiveLoss,\n HuberLoss]\n -mini_batch,--mini_batch_size <arg> Mini batch size [default: 1].\n Expecting the value in range\n [1,100] or so.\n -mix,--mix_servers <arg> Comma separated list of MIX\n servers\n -mix_cancel,--enable_mix_canceling Enable mix cancel requests\n -mix_session,--mix_session_name <arg> Mix session name [default:\n ${mapred.job.id}]\n -mix_threshold <arg> Threshold to mix local updates in\n range (0,127] [default: 3]\n -opt,--optimizer <arg> Optimizer to update weights\n [default: adagrad, sgd, adadelta,\n adam]\n -power_t <arg> The exponent for inverse scaling\n learning rate [default 0.1]\n -reg,--regularization <arg> Regularization type [default:\n rda, l1, l2, elasticnet]\n -rho,--decay <arg> Decay rate of AdaDelta [default\n 0.95]\n -scale <arg> Scaling factor for cumulative\n weights [100.0]\n -ssl Use SSL for the communication\n with mix servers\n -t,--total_steps <arg> a total of n_samples * epochs\n time steps\n\nPlease make sure your function overrides `public StructObjectInspector initialize(ObjectInspector[] args)`.; line 1 pos 7\n\tat hivemall.UDTFWithOptions.parseOptions(UDTFWithOptions.java:117)\n\tat hivemall.LearnerBaseUDTF.processOptions(LearnerBaseUDTF.java:121)\n\tat hivemall.GeneralLearnerBaseUDTF.processOptions(GeneralLearnerBaseUDTF.java:186)\n\tat hivemall.GeneralLearnerBaseUDTF.initialize(GeneralLearnerBaseUDTF.java:151)\n\tat org.apache.spark.sql.hive.HiveGenericUDTF.outputInspector$lzycompute(hiveUDFs.scala:215)\n\tat org.apache.spark.sql.hive.HiveGenericUDTF.outputInspector(hiveUDFs.scala:215)\n\tat org.apache.spark.sql.hive.HiveGenericUDTF.elementSchema$lzycompute(hiveUDFs.scala:223)\n\tat org.apache.spark.sql.hive.HiveGenericUDTF.elementSchema(hiveUDFs.scala:223)\n\tat org.apache.spark.sql.hive.HiveSessionCatalog$$anonfun$makeFunctionExpression$2.apply(HiveSessionCatalog.scala:93)\n\tat org.apache.spark.sql.hive.HiveSessionCatalog$$anonfun$makeFunctionExpression$2.apply(HiveSessionCatalog.scala:69)\n\tat scala.util.Try.getOrElse(Try.scala:79)\n\tat org.apache.spark.sql.hive.HiveSessionCatalog.makeFunctionExpression(HiveSessionCatalog.scala:69)\n\tat org.apache.spark.sql.catalyst.catalog.SessionCatalog$$anonfun$org$apache$spark$sql$catalyst$catalog$SessionCatalog$$makeFunctionBuilder$1.apply(SessionCatalog.scala:1090)\n\tat org.apache.spark.sql.catalyst.catalog.SessionCatalog$$anonfun$org$apache$spark$sql$catalyst$catalog$SessionCatalog$$makeFunctionBuilder$1.apply(SessionCatalog.scala:1090)\n\tat org.apache.spark.sql.catalyst.analysis.SimpleFunctionRegistry.lookupFunction(FunctionRegistry.scala:115)\n\tat org.apache.spark.sql.catalyst.catalog.SessionCatalog.lookupFunction(SessionCatalog.scala:1221)\n\tat org.apache.spark.sql.hive.HiveSessionCatalog.org$apache$spark$sql$hive$HiveSessionCatalog$$super$lookupFunction(HiveSessionCatalog.scala:131)\n\tat org.apache.spark.sql.hive.HiveSessionCatalog$$anonfun$3.apply(HiveSessionCatalog.scala:131)\n\tat org.apache.spark.sql.hive.HiveSessionCatalog$$anonfun$3.apply(HiveSessionCatalog.scala:131)\n\tat scala.util.Try$.apply(Try.scala:192)\n\tat org.apache.spark.sql.hive.HiveSessionCatalog.lookupFunction0(HiveSessionCatalog.scala:131)\n\tat org.apache.spark.sql.hive.HiveSessionCatalog.lookupFunction(HiveSessionCatalog.scala:124)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveFunctions$$anonfun$apply$16$$anonfun$applyOrElse$6$$anonfun$applyOrElse$53.apply(Analyzer.scala:1242)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveFunctions$$anonfun$apply$16$$anonfun$applyOrElse$6$$anonfun$applyOrElse$53.apply(Analyzer.scala:1242)\n\tat org.apache.spark.sql.catalyst.analysis.package$.withPosition(package.scala:53)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveFunctions$$anonfun$apply$16$$anonfun$applyOrElse$6.applyOrElse(Analyzer.scala:1241)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveFunctions$$anonfun$apply$16$$anonfun$applyOrElse$6.applyOrElse(Analyzer.scala:1225)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$2.apply(TreeNode.scala:267)\n\tat org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:266)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformDown$1.apply(TreeNode.scala:272)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$4.apply(TreeNode.scala:306)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode.mapChildren(TreeNode.scala:304)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode.transformDown(TreeNode.scala:272)\n\tat org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$transformExpressionsDown$1.apply(QueryPlan.scala:85)\n\tat org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$transformExpressionsDown$1.apply(QueryPlan.scala:85)\n\tat org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$1.apply(QueryPlan.scala:107)\n\tat org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$1.apply(QueryPlan.scala:107)\n\tat org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)\n\tat org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpression$1(QueryPlan.scala:106)\n\tat org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$1(QueryPlan.scala:118)\n\tat org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$1$1.apply(QueryPlan.scala:122)\n\tat scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)\n\tat scala.collection.TraversableLike$$anonfun$map$1.apply(TraversableLike.scala:234)\n\tat scala.collection.immutable.List.foreach(List.scala:381)\n\tat scala.collection.TraversableLike$class.map(TraversableLike.scala:234)\n\tat scala.collection.immutable.List.map(List.scala:285)\n\tat org.apache.spark.sql.catalyst.plans.QueryPlan.org$apache$spark$sql$catalyst$plans$QueryPlan$$recursiveTransform$1(QueryPlan.scala:122)\n\tat org.apache.spark.sql.catalyst.plans.QueryPlan$$anonfun$2.apply(QueryPlan.scala:127)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode.mapProductIterator(TreeNode.scala:187)\n\tat org.apache.spark.sql.catalyst.plans.QueryPlan.mapExpressions(QueryPlan.scala:127)\n\tat org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressionsDown(QueryPlan.scala:85)\n\tat org.apache.spark.sql.catalyst.plans.QueryPlan.transformExpressions(QueryPlan.scala:76)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveFunctions$$anonfun$apply$16.applyOrElse(Analyzer.scala:1225)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveFunctions$$anonfun$apply$16.applyOrElse(Analyzer.scala:1223)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:289)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode$$anonfun$transformUp$1.apply(TreeNode.scala:289)\n\tat org.apache.spark.sql.catalyst.trees.CurrentOrigin$.withOrigin(TreeNode.scala:70)\n\tat org.apache.spark.sql.catalyst.trees.TreeNode.transformUp(TreeNode.scala:288)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveFunctions$.apply(Analyzer.scala:1223)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer$ResolveFunctions$.apply(Analyzer.scala:1222)\n\tat org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:87)\n\tat org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1$$anonfun$apply$1.apply(RuleExecutor.scala:84)\n\tat scala.collection.LinearSeqOptimized$class.foldLeft(LinearSeqOptimized.scala:124)\n\tat scala.collection.immutable.List.foldLeft(List.scala:84)\n\tat org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:84)\n\tat org.apache.spark.sql.catalyst.rules.RuleExecutor$$anonfun$execute$1.apply(RuleExecutor.scala:76)\n\tat scala.collection.immutable.List.foreach(List.scala:381)\n\tat org.apache.spark.sql.catalyst.rules.RuleExecutor.execute(RuleExecutor.scala:76)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer.org$apache$spark$sql$catalyst$analysis$Analyzer$$executeSameContext(Analyzer.scala:124)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer.execute(Analyzer.scala:118)\n\tat org.apache.spark.sql.catalyst.analysis.Analyzer.executeAndCheck(Analyzer.scala:103)\n\tat org.apache.spark.sql.execution.QueryExecution.analyzed$lzycompute(QueryExecution.scala:57)\n\tat org.apache.spark.sql.execution.QueryExecution.analyzed(QueryExecution.scala:55)\n\tat org.apache.spark.sql.execution.QueryExecution.assertAnalyzed(QueryExecution.scala:47)\n\tat org.apache.spark.sql.Dataset$.ofRows(Dataset.scala:74)\n\tat org.apache.spark.sql.SparkSession.sql(SparkSession.scala:642)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke0(Native Method)\n\tat sun.reflect.NativeMethodAccessorImpl.invoke(NativeMethodAccessorImpl.java:62)\n\tat sun.reflect.DelegatingMethodAccessorImpl.invoke(DelegatingMethodAccessorImpl.java:43)\n\tat java.lang.reflect.Method.invoke(Method.java:498)\n\tat py4j.reflection.MethodInvoker.invoke(MethodInvoker.java:244)\n\tat py4j.reflection.ReflectionEngine.invoke(ReflectionEngine.java:357)\n\tat py4j.Gateway.invoke(Gateway.java:282)\n\tat py4j.commands.AbstractCommand.invokeMethod(AbstractCommand.java:132)\n\tat py4j.commands.CallCommand.execute(CallCommand.java:79)\n\tat py4j.GatewayConnection.run(GatewayConnection.java:238)\n\tat java.lang.Thread.run(Thread.java:748)\n",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mAnalysisException\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-18-2244f8a1acb3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mspark\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msql\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"SELECT train_classifier(array(), 0, '-help')\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m/content/spark-2.3.4-bin-hadoop2.7/python/pyspark/sql/session.py\u001b[0m in \u001b[0;36msql\u001b[0;34m(self, sqlQuery)\u001b[0m\n\u001b[1;32m 708\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mRow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mu'row1'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mu'row2'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34mu'row3'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 709\u001b[0m \"\"\"\n\u001b[0;32m--> 710\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jsparkSession\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msql\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msqlQuery\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_wrapped\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 711\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 712\u001b[0m \u001b[0;34m@\u001b[0m\u001b[0msince\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2.0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/content/spark-2.3.4-bin-hadoop2.7/python/lib/py4j-0.10.7-src.zip/py4j/java_gateway.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m 1255\u001b[0m \u001b[0manswer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgateway_client\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend_command\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcommand\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1256\u001b[0m return_value = get_return_value(\n\u001b[0;32m-> 1257\u001b[0;31m answer, self.gateway_client, self.target_id, self.name)\n\u001b[0m\u001b[1;32m 1258\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1259\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mtemp_arg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtemp_args\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/content/spark-2.3.4-bin-hadoop2.7/python/pyspark/sql/utils.py\u001b[0m in \u001b[0;36mdeco\u001b[0;34m(*a, **kw)\u001b[0m\n\u001b[1;32m 67\u001b[0m e.java_exception.getStackTrace()))\n\u001b[1;32m 68\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'org.apache.spark.sql.AnalysisException: '\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 69\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mAnalysisException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m': '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstackTrace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 70\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'org.apache.spark.sql.catalyst.analysis'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 71\u001b[0m \u001b[0;32mraise\u001b[0m \u001b[0mAnalysisException\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m': '\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstackTrace\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mAnalysisException\u001b[0m: \"No handler for UDF/UDAF/UDTF 'hivemall.classifier.GeneralClassifierUDTF': org.apache.hadoop.hive.ql.exec.UDFArgumentException: \\nusage: train_classifier(list<string|int|bigint> features, int label [,\\n const string options]) - Returns a relation consists of\\n <string|int|bigint feature, float weight> [-cv_rate <arg>] [-dense]\\n [-dims <arg>] [-disable_cv] [-disable_halffloat] [-eps <arg>] [-eta\\n <arg>] [-eta0 <arg>] [-help] [-iter <arg>] [-iters <arg>]\\n [-l1_ratio <arg>] [-lambda <arg>] [-loss <arg>] [-mini_batch <arg>]\\n [-mix <arg>] [-mix_cancel] [-mix_session <arg>] [-mix_threshold\\n <arg>] [-opt <arg>] [-power_t <arg>] [-reg <arg>] [-rho <arg>]\\n [-scale <arg>] [-ssl] [-t <arg>]\\n -cv_rate,--convergence_rate <arg> Threshold to determine\\n convergence [default: 0.005]\\n -dense,--densemodel Use dense model or not\\n -dims,--feature_dimensions <arg> The dimension of model [default:\\n 16777216 (2^24)]\\n -disable_cv,--disable_cvtest Whether to disable convergence\\n check [default: OFF]\\n -disable_halffloat Toggle this option to disable the\\n use of SpaceEfficientDenseModel\\n -eps <arg> Denominator value of\\n ..."
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "K43cGiLpFvwo",
"colab_type": "text"
},
"source": [
"Train a model in the distributed manner by leveraging the available workers:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "DeqpkSBDCabD",
"colab_type": "code",
"outputId": "621c8bae-7737-4684-8a29-318e65fff50e",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 359
}
},
"source": [
"df_train.createOrReplaceTempView('train')\n",
"\n",
"df_model = spark.sql(\"\"\"\n",
"SELECT\n",
" feature,\n",
" avg(weight) as weight\n",
"FROM (\n",
" SELECT\n",
" train_classifier(\n",
" features, \n",
" label,\n",
" '-loss logloss -opt SGD -reg l1 -lambda 0.03 -eta0 0.01'\n",
" ) as (feature, weight)\n",
" FROM\n",
" train\n",
") t\n",
"GROUP BY 1\n",
"\"\"\")\n",
"df_model.toPandas().head(10)"
],
"execution_count": 19,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>feature</th>\n",
" <th>weight</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>state#TX</td>\n",
" <td>0.088104</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>state#MN</td>\n",
" <td>0.024230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>state#LA</td>\n",
" <td>-0.024339</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>area_code#408</td>\n",
" <td>-0.006044</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>night_charge</td>\n",
" <td>-0.194483</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>state#ND</td>\n",
" <td>-0.066669</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>state#VT</td>\n",
" <td>-0.063177</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>state#MA</td>\n",
" <td>0.000016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>state#OH</td>\n",
" <td>0.031865</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>state#MD</td>\n",
" <td>0.038399</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" feature weight\n",
"0 state#TX 0.088104\n",
"1 state#MN 0.024230\n",
"2 state#LA -0.024339\n",
"3 area_code#408 -0.006044\n",
"4 night_charge -0.194483\n",
"5 state#ND -0.066669\n",
"6 state#VT -0.063177\n",
"7 state#MA 0.000016\n",
"8 state#OH 0.031865\n",
"9 state#MD 0.038399"
]
},
"metadata": {
"tags": []
},
"execution_count": 19
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "HsQsS02eBxtd",
"colab_type": "text"
},
"source": [
"Model parameters can be easily visualized as:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "7EaqBaMIDTm7",
"colab_type": "code",
"outputId": "7fa82e34-ec07-4a25-cd21-bdca62766830",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 269
}
},
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import pyspark.sql.functions as F\n",
"\n",
"m = df_model.orderBy(F.abs(df_model.weight).desc()).limit(10).toPandas()\n",
"\n",
"x = range(m.shape[0])\n",
"plt.barh(x, m['weight'], align='center')\n",
"plt.yticks(x, m['feature'])\n",
"\n",
"plt.gca().invert_yaxis()\n",
"plt.gca().xaxis.grid(True)\n",
"\n",
"fig = plt.gcf()\n",
"fig.set_size_inches((8, 4))\n",
" \n",
"plt.show()"
],
"execution_count": 20,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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1MzOzXlLoZAUYDjyURjquBK4hG6WYLWleRCwlm/5ZBfyId07J5Ou1AhOAGZKW\nkSUbw3J1RwNLyJ4Euq9CHAuAobydrCwG9sTrVczMzHpd0aeB5gBzyoqbgam5OhPaOHdqWb25wKFt\n1C093XNhG8dvI5tKKu1vBLYpq3NV5V6YmZlZdxR9ZMXMzMzqnJMVMzMzKzQnK2ZmZlZohV6zYtZf\nrJlySq1DeEtTU1OtQzAz61EeWTEzM7NCc7JiZmZmheZkxczMzArNa1bMeljDpFk1vf708QNqen0z\ns57mkRUzMzMrNCcrZmZmVmhOVszMzKzQnKyYmZlZoW3WyYqkqyRdVus4zMzMrOs262SluyRtUesY\nzMzM6l2fJyuSzpX0kKQlkm6SdLGka3PHJ0j6Zht120weJI2XtFjSUkn35g4dIKlJ0mpJl+Tq/1zS\nI5JWSJqYK39Z0n9KWgocKelkSatS3Rsk/TLVGyDp+ym+RyWd1pPvk5mZmWX6NFmR9AHgTODoiBgJ\nvAG8DPxtrtqZwI/bqHtOG+0OAr4LfDQiDgY+ljs8DDgROAy4UtJWqfyCiDgEaAQukfSeVD4A+HVq\npxm4CTgp1R2Ua3cyMDciDgPGAddK2uQDLiRNlNQsqbm1tbWKd8nMzMzy+npk5UPAIcDDkpak/X2A\n1ZKOSAnDMGBBG3WHttHuEcD9EfE0QET8IXdsVkRsjIgXgOeB3VP5JWn0ZBHwXmC/VP4G8NO0PQxY\nXWoXmJFr9wRgUoqtCdgW2Ks8sIiYFhGNEdE4aNCg8sNmZmbWgb7+BFsBN0fE599RKF0AnAGsAu6I\niJBUsW4XbMxtvwFsKWkscDxwZES8IqmJLNkAeDUi3qiiXZGN5DzezfjMzMysHX09snIvcLqk3QAk\n7Sppb+AO4DTgbODHHdStZBFwrKR9SnU7iGNn4KWUqAwjG5mp5HFgqKSGtH9m7tgc4DMpqULSqA6u\naWZmZl3Qp8lKRKwErgB+JWkZcDcwOCJeAh4D9o6Ih9qr20a7rcBE4GdpaufWDkKZTTbC8hgwhSzZ\nqdTun4GLgNmSHgHWA+vS4f8AtgKWSVqR9s3MzKyH9fkXGUbErVRIJiLir6ut20a7dwF3lZVdVbZ/\nUG73pDba2aGsaF5EDEsjKN8iW3RbSmT+sZrYzMzMrOv8OSsd+4e0iHYF2fTRTTWOx8zMrK70+chK\nd0n6NbBNWfEnIqKlN64XEdcB1/VG22ZmZtaxfpesRMThtY7BrD1rppxS0+s3NTXV9PpmZj3N00Bm\nZmZWaE5WzMzMrNCcrJiZmVmh9bs1K2b9ScOkWX1+zenjN/mKKjOzfs0jK2ZmZlZoTlbMzMys0Jys\nmJmZWaH1q2RF0qWStu+permIlyerAAAJdElEQVT6J0q6On1Z4l0Vjv9c0qKysjmSluR+1qYPrDMz\nM7Me1K+SFeBSoJokpNp6JWOA+9PrA/kDkt4NHALsLGloqTwiToyIkRExEjga+BPZFy+amZlZDyrs\n00CSBgA/AfYEtgBuA4YA8yS9EBHjJN0IHApsB9weEVdKuqRCvROAq8k+pv83wPkR8bKkM4HPA0OB\n04DdgT9JOjwiTk2h/B3wC+A54CzgyxXCvR7434i4uxfeCjMzs7pW5JGV8cDaiDg4fVvyN4C1wLiI\nGJfqTI6IRmAEcJykERFxQ76epIFkIx7HR8Rosm9N/iy89a3Oo4DlETEcaAFG5RIVgLOBGenn7PIg\nJf0d0EiW9JiZmVkPK3Ky0gJ8WNJXJY2JiHUV6pwhaTHwKHAgcECFOkek8gXp25PPA/bOHd8fWJ22\nB0TE+tIBSbsD+wEPRMQTwGuSDsod34NsVOXjEbGxUickTZTULKm5tbW1up6bmZnZWwo7DRQRT0ga\nDZwMXCPp3vxxSfsAlwGHRsRLkqYD21ZoSsDdEVFpVKQZGAhsKWklMDglNJ+JiPnAGcAuwNOSAHYi\nG12ZrKzgZmBKRKxspx/TgGkAjY2N0Zn3wMzMzAo8siJpCPBKRNwCXAuMBtYDO6YqOwEbgHVpBOSk\n3On5eouAoyXtm9odIGl/gDSFNItsvcrXyKaVRqZEBbLEZHxENEREA9lC27PSscuAVyPiWz3bczMz\nM8sr7MgKMBy4VtKbwGvAp4EjgdmS1qb1KI8Cq4BngAW5c6eV1ZsAzJC0TTp+BfBE2h4NXAJcBPxX\nqQFJDWTTRW89shwRT0taJ+lw4Brgd2kkpuSl3HoaMzMz6wGFTVYiYg4wp6y4GZiaqzOhjXOnltWb\nS/bUUKW6R6bNC8vK1wB7VKg/Om1uU37MzMzMel5hp4HMzMzMwMmKmZmZFZyTFTMzMyu0wq5ZMdsc\nrJlySp9fs6mpqc+vaWbWmzyyYmZmZoXmZMXMzMwKzcmKmZmZFZqTFTMzMys0JytmZmZWaE5WzMzM\nrNCcrJiZmVmhOVkxMzOzQnOyYmZmZoWmiKh1DHVDUivw21rHUWYg8EKtg+gD9dJPqJ++1ks/oX76\nWi/9hPrp60BgQEQM6k4jTlbqnKTmiGisdRy9rV76CfXT13rpJ9RPX+uln1A/fe2pfnoayMzMzArN\nyYqZmZkVmpMVm1brAPpIvfQT6qev9dJPqJ++1ks/oX762iP99JoVMzMzKzSPrJiZmVmhOVmpA5J2\nlXS3pCfT6y5t1HtD0pL0MzNXvo+kX0t6StKtkrbuu+irV00/JY2UtFDSCknLJJ2ZOzZd0tO592Bk\n3/agY5LGS3o83YtJFY5vk+7RU+meNeSOfT6VPy7pxL6Mu7Oq6OdnJa1M9/BeSXvnjlX8PS6iKvo5\nQVJrrj9/nzt2Xvpdf1LSeX0beedV0dfrcv18QtIfc8f60z39vqTnJS1v47gk3ZDeh2WSRueO9Zt7\nWkU/z0n9a5H0oKSDc8fWpPIlkpqrumBE+Gcz/wG+BkxK25OAr7ZR7+U2yn8CnJW2vwN8utZ96mo/\ngf2B/dL2EOD3wLvT/nTg9Fr3o53+bQH8BhgKbA0sBQ4oq3MR8J20fRZwa9o+INXfBtgntbNFrfvU\njX6OA7ZP258u9TPtV/w9LtpPlf2cAHyzwrm7AqvT6y5pe5da96k7fS2r/xng+/3tnqZYjwVGA8vb\nOH4ycBcg4Ajg1/30nnbUz6NK8QMnlfqZ9tcAAztzPY+s1IfTgJvT9s3AR6o9UZKADwK3d+X8PtZh\nPyPiiYh4Mm2vBZ4HuvVhRX3oMOCpiFgdEX8BfkzW57z8e3A78KF0D08DfhwRGyPiaeCp1F4RddjP\niJgXEa+k3UXAnn0cY0+o5n625UTg7oj4Q0S8BNwNjO+lOHtCZ/t6NjCjTyLrYRFxP/CHdqqcBvx3\nZBYB75Y0mH52TzvqZ0Q8mPoBPfDfqJOV+rB7RPw+bf8fsHsb9baV1CxpkaTSH/r3AH+MiNfT/u+A\nPXox1u6otp8ASDqM7F95v8kVfykNXV4naZteirOr9gCeye1Xuhdv1Un3bB3ZPazm3KLobKwXkv1L\ntaTS73ERVdvPj6bfydslvbeT5xZF1fGmKb19gLm54v5yT6vR1nvR3+5pZ5T/NxrAryQ9ImliNQ1s\n2SthWZ+TdA/wVxUOTc7vRERIausRsL0j4llJQ4G5klrI/tgVRg/1k/Qvmf8BzouIN1Px58mSnK3J\nHre7HPhiT8RtvUPSuUAjcFyueJPf44j4TeUWCu8XwIyI2CjpH8lGzT5Y45h621nA7RHxRq5sc7qn\ndUXSOLJk5Zhc8THpfu4G3C1pVRqpaZOTlc1ERBzf1jFJz0kaHBG/T3+kn2+jjWfT62pJTcAo4Kdk\nw5Rbpn+p7wk82+MdqFJP9FPSTsAsYHIahi21XRqV2SjpB8BlPRh6T3gWeG9uv9K9KNX5naQtgZ2B\nF6s8tyiqilXS8WRJ6nERsbFU3sbvcRH/sHXYz4h4Mbf7PbJ1WaVzx5ad29TjEfaczvz+nQVcnC/o\nR/e0Gm29F/3tnnZI0giy39uT8r/Lufv5vKQ7yKYJ201WPA1UH2YCpZXl5wF3lleQtEtp2kPSQOBo\nYGVkq6HmAae3d35BVNPPrYE7yOaMby87Nji9imy9S8VV7jX0MLCfsqeztib7n3r5kxH59+B0YG66\nhzOBs5Q9LbQPsB/wUB/F3Vkd9lPSKOAm4NSIeD5XXvH3uM8i75xq+jk4t3sq8FjangOckPq7C3BC\nKiuqan53kTSMbHHpwlxZf7qn1ZgJfDI9FXQEsC79Q6m/3dN2SdoL+BnwiYh4Ilc+QNKOpW2yfnb8\n/9parib2T9/8kK1ZuBd4ErgH2DWVNwLfS9tHAS1kq/RbgAtz5w8l+8P2FHAbsE2t+9SNfp4LvAYs\nyf2MTMfmpr4vB24Bdqh1nyr08WTgCbJ/VU5OZV8k+6MNsG26R0+lezY0d+7kdN7jZP/SqXl/utHP\ne4DncvdwZke/x0X8qaKfXwFWpP7MA4blzr0g3eengPNr3Zfu9jXtXwVMKTuvv93TGWRPGb5Gtu7k\nQuBTwKfScQHfSu9DC9DYH+9pFf38HvBS7r/R5lQ+NN3Lpel3e3I11/Mn2JqZmVmheRrIzMzMCs3J\nipmZmRWakxUzMzMrNCcrZmZmVmhOVszMzKzQnKyYmZlZoTlZMTMzs0JzsmJmZmaF9v8BZVZOv/NT\nSeYAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 576x288 with 1 Axes>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "gXrgBjpUGbAG",
"colab_type": "text"
},
"source": [
"### Prediction"
]
},
{
"cell_type": "code",
"metadata": {
"id": "xUtbR1bB0_ZY",
"colab_type": "code",
"colab": {}
},
"source": [
"spark.sql(\"CREATE TEMPORARY FUNCTION sigmoid AS 'hivemall.tools.math.SigmoidGenericUDF'\")\n",
"spark.sql(\"CREATE TEMPORARY FUNCTION extract_feature AS 'hivemall.ftvec.ExtractFeatureUDFWrapper'\")\n",
"spark.sql(\"CREATE TEMPORARY FUNCTION extract_weight AS 'hivemall.ftvec.ExtractWeightUDFWrapper'\");"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "oPXKfK8rF4HZ",
"colab_type": "text"
},
"source": [
"Join over features, compute weighted sum of them, and take sigmoid:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "grOFSn13D_KU",
"colab_type": "code",
"outputId": "80b4bbaf-d541-44b3-d2f4-304285b3180f",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 359
}
},
"source": [
"df_test.createOrReplaceTempView('test')\n",
"df_model.createOrReplaceTempView('model')\n",
"\n",
"df_prediction = spark.sql(\"\"\"\n",
"SELECT\n",
" phone,\n",
" label as expected,\n",
" sigmoid(sum(weight * value)) as prob\n",
"FROM ( \n",
" SELECT\n",
" phone,\n",
" label,\n",
" extract_feature(fv) AS feature,\n",
" extract_weight(fv) AS value\n",
" FROM \n",
" test\n",
" LATERAL VIEW explode(features) t2 AS fv\n",
") t \n",
"LEFT OUTER JOIN model m \n",
" ON t.feature = m.feature\n",
"GROUP BY 1, 2\n",
"\"\"\")\n",
"df_prediction.toPandas().head(10)"
],
"execution_count": 22,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>phone</th>\n",
" <th>expected</th>\n",
" <th>prob</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>375-3003</td>\n",
" <td>0</td>\n",
" <td>0.043165</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>344-4022</td>\n",
" <td>0</td>\n",
" <td>0.032754</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>356-2992</td>\n",
" <td>0</td>\n",
" <td>0.000035</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>420-3028</td>\n",
" <td>0</td>\n",
" <td>0.009459</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>354-9062</td>\n",
" <td>0</td>\n",
" <td>0.128704</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>360-6024</td>\n",
" <td>0</td>\n",
" <td>0.000029</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>376-4705</td>\n",
" <td>1</td>\n",
" <td>0.019202</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>373-1448</td>\n",
" <td>1</td>\n",
" <td>0.007880</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>349-2157</td>\n",
" <td>0</td>\n",
" <td>0.009182</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>337-9569</td>\n",
" <td>0</td>\n",
" <td>0.052731</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" phone expected prob\n",
"0 375-3003 0 0.043165\n",
"1 344-4022 0 0.032754\n",
"2 356-2992 0 0.000035\n",
"3 420-3028 0 0.009459\n",
"4 354-9062 0 0.128704\n",
"5 360-6024 0 0.000029\n",
"6 376-4705 1 0.019202\n",
"7 373-1448 1 0.007880\n",
"8 349-2157 0 0.009182\n",
"9 337-9569 0 0.052731"
]
},
"metadata": {
"tags": []
},
"execution_count": 22
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "0vVGn7f4Gd-3",
"colab_type": "text"
},
"source": [
"### Evaluation"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "akplzrCbGFa3",
"colab_type": "text"
},
"source": [
"For binary classification, Hivemall offers [Log Loss](http://wiki.fast.ai/index.php/Log_Loss) and [AUC](http://gim.unmc.edu/dxtests/ROC3.htm) metric:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "uin0QSgo1dMk",
"colab_type": "code",
"colab": {}
},
"source": [
"spark.sql(\"CREATE TEMPORARY FUNCTION logloss AS 'hivemall.evaluation.LogarithmicLossUDAF'\")\n",
"spark.sql(\"CREATE TEMPORARY FUNCTION auc AS 'hivemall.evaluation.AUCUDAF'\");"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "kJ23r1fvEIUI",
"colab_type": "code",
"outputId": "693d0c11-2b60-495e-bc4f-741eabe46758",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 119
}
},
"source": [
"df_prediction.createOrReplaceTempView('prediction')\n",
"\n",
"spark.sql(\"\"\"\n",
"SELECT\n",
" auc(prob, expected) AS auc,\n",
" logloss(prob, expected) AS logloss\n",
"FROM (\n",
" SELECT prob, expected\n",
" FROM prediction\n",
" ORDER BY prob DESC\n",
") t\n",
"\"\"\").show()"
],
"execution_count": 24,
"outputs": [
{
"output_type": "stream",
"text": [
"+------------------+------------------+\n",
"| auc| logloss|\n",
"+------------------+------------------+\n",
"|0.6360049076499652|0.6456736373258979|\n",
"+------------------+------------------+\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "OEC49sOFo44O",
"colab_type": "text"
},
"source": [
"## Other Classifiers"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "3NbgswUPxTq4",
"colab_type": "text"
},
"source": [
"### Factorization Machines"
]
},
{
"cell_type": "code",
"metadata": {
"id": "TKhSEc3FpUww",
"colab_type": "code",
"colab": {}
},
"source": [
"spark.sql(\"CREATE TEMPORARY FUNCTION train_fm AS 'hivemall.fm.FactorizationMachineUDTF'\");"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "PVQb92jVo-fa",
"colab_type": "code",
"outputId": "cb5beffd-0ece-4da5-fa97-30a3d0ce4ecc",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 359
}
},
"source": [
"spark.sql(\"\"\"\n",
"SELECT\n",
" train_fm(\n",
" features, \n",
" label,\n",
" '-classification -factor 30 -eta 0.001'\n",
" ) as (feature, Wi, Vij)\n",
"FROM\n",
" train\n",
"\"\"\").toPandas().head(10)"
],
"execution_count": 26,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>feature</th>\n",
" <th>Wi</th>\n",
" <th>Vij</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>-0.125796</td>\n",
" <td>None</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>state#SC</td>\n",
" <td>-0.002943</td>\n",
" <td>[-0.0018758076475933194, 0.06696834415197372, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>area_code#408</td>\n",
" <td>-0.043842</td>\n",
" <td>[-0.022054938599467278, 0.007548300549387932, ...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>vmail_message</td>\n",
" <td>-0.070304</td>\n",
" <td>[0.013247097842395306, 0.005205452907830477, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>state#WV</td>\n",
" <td>-0.033708</td>\n",
" <td>[-0.07025326788425446, -0.14394021034240723, -...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>state#NC</td>\n",
" <td>0.002798</td>\n",
" <td>[0.06377897411584854, -0.09058145433664322, -0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>state#KY</td>\n",
" <td>0.002730</td>\n",
" <td>[0.06627880781888962, -0.14982153475284576, -0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>intl_charge</td>\n",
" <td>-0.042286</td>\n",
" <td>[-0.06048768013715744, 0.018183737993240356, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>state#CO</td>\n",
" <td>0.019807</td>\n",
" <td>[0.03619685024023056, 0.09739864617586136, -0....</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>state#VA</td>\n",
" <td>-0.019707</td>\n",
" <td>[-0.09031106531620026, 0.14337880909442902, -0...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" feature Wi Vij\n",
"0 0 -0.125796 None\n",
"1 state#SC -0.002943 [-0.0018758076475933194, 0.06696834415197372, ...\n",
"2 area_code#408 -0.043842 [-0.022054938599467278, 0.007548300549387932, ...\n",
"3 vmail_message -0.070304 [0.013247097842395306, 0.005205452907830477, 0...\n",
"4 state#WV -0.033708 [-0.07025326788425446, -0.14394021034240723, -...\n",
"5 state#NC 0.002798 [0.06377897411584854, -0.09058145433664322, -0...\n",
"6 state#KY 0.002730 [0.06627880781888962, -0.14982153475284576, -0...\n",
"7 intl_charge -0.042286 [-0.06048768013715744, 0.018183737993240356, 0...\n",
"8 state#CO 0.019807 [0.03619685024023056, 0.09739864617586136, -0....\n",
"9 state#VA -0.019707 [-0.09031106531620026, 0.14337880909442902, -0..."
]
},
"metadata": {
"tags": []
},
"execution_count": 26
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YpnpPcmKrVSG",
"colab_type": "text"
},
"source": [
"### RandomFores"
]
},
{
"cell_type": "code",
"metadata": {
"id": "FDm4vJj-rYJl",
"colab_type": "code",
"colab": {}
},
"source": [
"spark.sql(\"CREATE TEMPORARY FUNCTION feature_hashing AS 'hivemall.ftvec.hashing.FeatureHashingUDF'\")\n",
"spark.sql(\"CREATE TEMPORARY FUNCTION tree_predict AS 'hivemall.smile.tools.TreePredictUDF'\")\n",
"spark.sql(\"CREATE TEMPORARY FUNCTION rf_ensemble AS 'hivemall.smile.tools.RandomForestEnsembleUDAF'\")\n",
"spark.sql(\"CREATE TEMPORARY FUNCTION train_randomforest_classifier AS 'hivemall.smile.classification.RandomForestClassifierUDTF'\")\n",
"spark.sql(\"CREATE TEMPORARY FUNCTION tree_export AS 'hivemall.smile.tools.TreeExportUDF'\");"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "Zwkh8N9CxlcB",
"colab_type": "text"
},
"source": [
"Training:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Et3De7pwrX5w",
"colab_type": "code",
"outputId": "d74801ed-f71a-430a-f6fd-aeee1348baee",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 374
}
},
"source": [
"df_rf_model = spark.sql(\"\"\"\n",
"SELECT\n",
" train_randomforest_classifier(\n",
" feature_hashing(features),\n",
" label,\n",
" '-trees 50 -seed 71' -- hyperparameters\n",
" ) as (model_id, model_weight, model, var_importance, oob_errors, oob_tests)\n",
"FROM\n",
" train\n",
"\"\"\")\n",
"\n",
"df_rf_model.toPandas().head()"
],
"execution_count": 28,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>model_id</th>\n",
" <th>model_weight</th>\n",
" <th>model</th>\n",
" <th>var_importance</th>\n",
" <th>oob_errors</th>\n",
" <th>oob_tests</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>a3967714-914e-4267-b3f0-788fbef8225a</td>\n",
" <td>0.837838</td>\n",
" <td>I?XPI/%dsLd{LdDfaZTPM:)_@^i12w&lt;Ba)_j40icEw3jYE...</td>\n",
" <td>{15900165: 1.8062073885289325}</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>814a7805-f1b2-4799-a66a-722f96233bc5</td>\n",
" <td>0.816498</td>\n",
" <td>I?{Pt:IYsLd{3Ze;DYt:3%}g`]F:N}gYMLrRqViFPHu._X...</td>\n",
" <td>{15900165: 2.7499708167499106}</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>bea6eef6-d8d8-43ba-861e-217386e32be4</td>\n",
" <td>0.851724</td>\n",
" <td>I?{PI/%dsLd{2Yw+&lt;LzSF`agXnUPzzou^7Yl[Pws[y~yzD...</td>\n",
" <td>{15900165: 2.4154606004599684}</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>cfb19b11-4afb-4d5a-a2de-b3d1e940a098</td>\n",
" <td>0.807692</td>\n",
" <td>I?{P)`IYwL$|yz6!AGW.%p~UV=-B)WoH=;0c8uOOj+{BiM...</td>\n",
" <td>{15900165: 3.886217162213024}</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>efe91459-c0d5-4acf-953a-3c62c35f8f0d</td>\n",
" <td>0.824503</td>\n",
" <td>I?hRO|IYsL$|+Z&gt;ZwzRhE`&gt;&lt;L`+-vZ&gt;D]1UblfV48c=Cl4...</td>\n",
" <td>{15900165: 3.422917848419591}</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" model_id model_weight ... oob_errors oob_tests\n",
"0 a3967714-914e-4267-b3f0-788fbef8225a 0.837838 ... 0 0\n",
"1 814a7805-f1b2-4799-a66a-722f96233bc5 0.816498 ... 0 0\n",
"2 bea6eef6-d8d8-43ba-861e-217386e32be4 0.851724 ... 0 0\n",
"3 cfb19b11-4afb-4d5a-a2de-b3d1e940a098 0.807692 ... 0 0\n",
"4 efe91459-c0d5-4acf-953a-3c62c35f8f0d 0.824503 ... 0 0\n",
"\n",
"[5 rows x 6 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 28
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MXMqyYVVxoFS",
"colab_type": "text"
},
"source": [
"Prediction:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "z8klFXa9wJv8",
"colab_type": "code",
"outputId": "c23c354d-f18e-42d6-bc18-8f910259f5fb",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 359
}
},
"source": [
"df_rf_model.createOrReplaceTempView('rf_model')\n",
"\n",
"spark.sql(\"\"\"\n",
"SELECT\n",
" phone,\n",
" rf_ensemble(predicted.value, predicted.posteriori, model_weight) as predicted\n",
"FROM (\n",
" SELECT\n",
" t.phone,\n",
" m.model_weight,\n",
" tree_predict(m.model_id, m.model, feature_hashing(t.features), true) as predicted\n",
" FROM\n",
" test t\n",
" CROSS JOIN\n",
" rf_model m\n",
") t1\n",
"GROUP BY\n",
" 1\n",
"\"\"\").toPandas().head(10)"
],
"execution_count": 29,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>phone</th>\n",
" <th>predicted</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>366-3944</td>\n",
" <td>(0, 1.0, [1.0, 0.0])</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>378-4013</td>\n",
" <td>(0, 0.9572407298262462, [0.9572407298262462, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>385-9744</td>\n",
" <td>(0, 1.0, [1.0, 0.0])</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>408-4529</td>\n",
" <td>(0, 0.8680956719420105, [0.8680956719420105, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>355-6422</td>\n",
" <td>(0, 1.0, [1.0, 0.0])</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>373-2053</td>\n",
" <td>(0, 0.803639476792548, [0.803639476792548, 0.1...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>341-1916</td>\n",
" <td>(0, 0.9793229251609381, [0.9793229251609381, 0...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>368-8972</td>\n",
" <td>(0, 1.0, [1.0, 0.0])</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>383-4641</td>\n",
" <td>(0, 1.0, [1.0, 0.0])</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>386-3796</td>\n",
" <td>(0, 0.6022822895905967, [0.6022822895905967, 0...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" phone predicted\n",
"0 366-3944 (0, 1.0, [1.0, 0.0])\n",
"1 378-4013 (0, 0.9572407298262462, [0.9572407298262462, 0...\n",
"2 385-9744 (0, 1.0, [1.0, 0.0])\n",
"3 408-4529 (0, 0.8680956719420105, [0.8680956719420105, 0...\n",
"4 355-6422 (0, 1.0, [1.0, 0.0])\n",
"5 373-2053 (0, 0.803639476792548, [0.803639476792548, 0.1...\n",
"6 341-1916 (0, 0.9793229251609381, [0.9793229251609381, 0...\n",
"7 368-8972 (0, 1.0, [1.0, 0.0])\n",
"8 383-4641 (0, 1.0, [1.0, 0.0])\n",
"9 386-3796 (0, 0.6022822895905967, [0.6022822895905967, 0..."
]
},
"metadata": {
"tags": []
},
"execution_count": 29
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "LOOELmsox29k",
"colab_type": "text"
},
"source": [
"Visualization:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Etxef4Ryx36_",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 204
},
"outputId": "5e0d39e0-1197-40ec-978d-03b1003f5c1f"
},
"source": [
"df_rf_model_viz = spark.sql(\"\"\"\n",
"SELECT\n",
" tree_export(model, '-type javascript') as js,\n",
" tree_export(model, '-type graphviz') as dot\n",
"FROM\n",
" rf_model\n",
"\"\"\")\n",
"\n",
"df_rf_model_viz.toPandas().head()"
],
"execution_count": 30,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>js</th>\n",
" <th>dot</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>if( x[15900165] &lt;= 6.225 ) {\\n if( x[15900165...</td>\n",
" <td>digraph Tree {\\n node [shape=box, style=\"fille...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>if( x[15900165] &lt;= 5.755 ) {\\n if( x[15900165...</td>\n",
" <td>digraph Tree {\\n node [shape=box, style=\"fille...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>if( x[15900165] &lt;= 5.395 ) {\\n if( x[15900165...</td>\n",
" <td>digraph Tree {\\n node [shape=box, style=\"fille...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>if( x[15900165] &lt;= 3.74 ) {\\n if( x[15900165]...</td>\n",
" <td>digraph Tree {\\n node [shape=box, style=\"fille...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>if( x[15900165] &lt;= 6.015 ) {\\n if( x[15900165...</td>\n",
" <td>digraph Tree {\\n node [shape=box, style=\"fille...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" js dot\n",
"0 if( x[15900165] <= 6.225 ) {\\n if( x[15900165... digraph Tree {\\n node [shape=box, style=\"fille...\n",
"1 if( x[15900165] <= 5.755 ) {\\n if( x[15900165... digraph Tree {\\n node [shape=box, style=\"fille...\n",
"2 if( x[15900165] <= 5.395 ) {\\n if( x[15900165... digraph Tree {\\n node [shape=box, style=\"fille...\n",
"3 if( x[15900165] <= 3.74 ) {\\n if( x[15900165]... digraph Tree {\\n node [shape=box, style=\"fille...\n",
"4 if( x[15900165] <= 6.015 ) {\\n if( x[15900165... digraph Tree {\\n node [shape=box, style=\"fille..."
]
},
"metadata": {
"tags": []
},
"execution_count": 30
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "jQ54LDQlyHxN",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "f169373a-bab3-49a2-c37a-c9bd67932b24"
},
"source": [
"viz_sample_row = df_rf_model_viz.limit(1).collect()[0]\n",
"\n",
"print(viz_sample_row.js)"
],
"execution_count": 31,
"outputs": [
{
"output_type": "stream",
"text": [
"if( x[15900165] <= 6.225 ) {\n",
" if( x[15900165] <= 5.425000000000001 ) {\n",
" if( x[15900165] <= 4.85 ) {\n",
" if( x[15900165] <= 3.3499999999999996 ) {\n",
" 0;\n",
" } else {\n",
" 0;\n",
" }\n",
" } else {\n",
" 0;\n",
" }\n",
" } else {\n",
" if( x[15900165] <= 6.02 ) {\n",
" 0;\n",
" } else {\n",
" 0;\n",
" }\n",
" }\n",
"} else {\n",
" if( x[15900165] <= 6.25 ) {\n",
" 1;\n",
" } else {\n",
" if( x[15900165] <= 8.41 ) {\n",
" if( x[15900165] <= 8.395 ) {\n",
" if( x[15900165] <= 6.359999999999999 ) {\n",
" 0;\n",
" } else {\n",
" if( x[15900165] <= 6.445 ) {\n",
" 0;\n",
" } else {\n",
" if( x[15900165] <= 6.735 ) {\n",
" if( x[15900165] <= 6.615 ) {\n",
" 0;\n",
" } else {\n",
" 0;\n",
" }\n",
" } else {\n",
" if( x[15900165] <= 6.745 ) {\n",
" 1;\n",
" } else {\n",
" if( x[15900165] <= 6.825 ) {\n",
" 0;\n",
" } else {\n",
" if( x[15900165] <= 6.835 ) {\n",
" 1;\n",
" } else {\n",
" if( x[15900165] <= 7.545 ) {\n",
" if( x[15900165] <= 6.904999999999999 ) {\n",
" if( x[15900165] <= 6.85 ) {\n",
" 0;\n",
" } else {\n",
" 0;\n",
" }\n",
" } else {\n",
" if( x[15900165] <= 7.3100000000000005 ) {\n",
" if( x[15900165] <= 7.215 ) {\n",
" 0;\n",
" } else {\n",
" 0;\n",
" }\n",
" } else {\n",
" if( x[15900165] <= 7.345 ) {\n",
" 0;\n",
" } else {\n",
" if( x[15900165] <= 7.505 ) {\n",
" 0;\n",
" } else {\n",
" 0;\n",
" }\n",
" }\n",
" }\n",
" }\n",
" } else {\n",
" if( x[15900165] <= 7.555 ) {\n",
" 1;\n",
" } else {\n",
" if( x[15900165] <= 7.63 ) {\n",
" 0;\n",
" } else {\n",
" if( x[15900165] <= 7.655 ) {\n",
" 1;\n",
" } else {\n",
" if( x[15900165] <= 7.755 ) {\n",
" if( x[15900165] <= 7.7 ) {\n",
" if( x[15900165] <= 7.6850000000000005 ) {\n",
" 0;\n",
" } else {\n",
" 0;\n",
" }\n",
" } else {\n",
" if( x[15900165] <= 7.725 ) {\n",
" 0;\n",
" } else {\n",
" 0;\n",
" }\n",
" }\n",
" } else {\n",
" if( x[15900165] <= 7.955 ) {\n",
" if( x[15900165] <= 7.795 ) {\n",
" 0;\n",
" } else {\n",
" 0;\n",
" }\n",
" } else {\n",
" if( x[15900165] <= 8.245000000000001 ) {\n",
" if( x[15900165] <= 8.065000000000001 ) {\n",
" 0;\n",
" } else {\n",
" if( x[15900165] <= 8.16 ) {\n",
" 0;\n",
" } else {\n",
" 0;\n",
" }\n",
" }\n",
" } else {\n",
" if( x[15900165] <= 8.285 ) {\n",
" 0;\n",
" } else {\n",
" 0;\n",
" }\n",
" }\n",
" }\n",
" }\n",
" }\n",
" }\n",
" }\n",
" }\n",
" }\n",
" }\n",
" }\n",
" }\n",
" }\n",
" }\n",
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" 1;\n",
" }\n",
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" if( x[15900165] <= 8.635000000000002 ) {\n",
" if( x[15900165] <= 8.535 ) {\n",
" 0;\n",
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" 0;\n",
" }\n",
" } else {\n",
" if( x[15900165] <= 11.605 ) {\n",
" if( x[15900165] <= 11.465 ) {\n",
" if( x[15900165] <= 11.065000000000001 ) {\n",
" if( x[15900165] <= 10.085 ) {\n",
" if( x[15900165] <= 10.065000000000001 ) {\n",
" if( x[15900165] <= 9.745000000000001 ) {\n",
" if( x[15900165] <= 9.735 ) {\n",
" if( x[15900165] <= 9.195 ) {\n",
" if( x[15900165] <= 8.955 ) {\n",
" if( x[15900165] <= 8.875 ) {\n",
" if( x[15900165] <= 8.735 ) {\n",
" if( x[15900165] <= 8.684999999999999 ) {\n",
" if( x[15900165] <= 8.655000000000001 ) {\n",
" 0;\n",
" } else {\n",
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" }\n",
" } else {\n",
" if( x[15900165] <= 8.695 ) {\n",
" 1;\n",
" } else {\n",
" if( x[15900165] <= 8.705 ) {\n",
" 0;\n",
" } else {\n",
" if( x[15900165] <= 8.715 ) {\n",
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" 0;\n",
" }\n",
" }\n",
" }\n",
" }\n",
" } else {\n",
" if( x[15900165] <= 8.774999999999999 ) {\n",
" 0;\n",
" } else {\n",
" 0;\n",
" }\n",
" }\n",
" } else {\n",
" if( x[15900165] <= 8.94 ) {\n",
" 0;\n",
" } else {\n",
" 0;\n",
" }\n",
" }\n",
" } else {\n",
" 0;\n",
" }\n",
" } else {\n",
" if( x[15900165] <= 9.225000000000001 ) {\n",
" 0;\n",
" } else {\n",
" if( x[15900165] <= 9.385000000000002 ) {\n",
" if( x[15900165] <= 9.364999999999998 ) {\n",
" if( x[15900165] <= 9.24 ) {\n",
" 0;\n",
" } else {\n",
" 0;\n",
" }\n",
" } else {\n",
" 0;\n",
" }\n",
" } else {\n",
" if( x[15900165] <= 9.415 ) {\n",
" 1;\n",
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" if( x[15900165] <= 9.44 ) {\n",
" 0;\n",
" } else {\n",
" if( x[15900165] <= 9.71 ) {\n",
" if( x[15900165] <= 9.645 ) {\n",
" if( x[15900165] <= 9.455 ) {\n",
" 0;\n",
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" } else {\n",
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" 0;\n",
" } else {\n",
" if( x[15900165] <= 10.024999999999999 ) {\n",
" if( x[15900165] <= 9.945 ) {\n",
" 0;\n",
" } else {\n",
" 0;\n",
" }\n",
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" }\n",
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" }\n",
" } else {\n",
" if( x[15900165] <= 10.295 ) {\n",
" 0;\n",
" } else {\n",
" if( x[15900165] <= 10.305 ) {\n",
" 1;\n",
" } else {\n",
" if( x[15900165] <= 10.945 ) {\n",
" if( x[15900165] <= 10.93 ) {\n",
" if( x[15900165] <= 10.565000000000001 ) {\n",
" if( x[15900165] <= 10.375 ) {\n",
" 0;\n",
" } else {\n",
" if( x[15900165] <= 10.395 ) {\n",
" 1;\n",
" } else {\n",
" 0;\n",
" }\n",
" }\n",
" } else {\n",
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" 0;\n",
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" 0;\n",
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" }\n",
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" 1;\n",
" }\n",
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" }\n",
" }\n",
" }\n",
" }\n",
" } else {\n",
" if( x[15900165] <= 11.075 ) {\n",
" 1;\n",
" } else {\n",
" if( x[15900165] <= 11.1 ) {\n",
" 0;\n",
" } else {\n",
" if( x[15900165] <= 11.165 ) {\n",
" 0;\n",
" } else {\n",
" if( x[15900165] <= 11.285 ) {\n",
" 0;\n",
" } else {\n",
" 0;\n",
" }\n",
" }\n",
" }\n",
" }\n",
" }\n",
" } else {\n",
" if( x[15900165] <= 11.51 ) {\n",
" 1;\n",
" } else {\n",
" 0;\n",
" }\n",
" }\n",
" } else {\n",
" if( x[15900165] <= 12.274999999999999 ) {\n",
" if( x[15900165] <= 11.934999999999999 ) {\n",
" if( x[15900165] <= 11.925 ) {\n",
" 0;\n",
" } else {\n",
" 1;\n",
" }\n",
" } else {\n",
" 0;\n",
" }\n",
" } else {\n",
" if( x[15900165] <= 12.575 ) {\n",
" if( x[15900165] <= 12.485 ) {\n",
" if( x[15900165] <= 12.415 ) {\n",
" 0;\n",
" } else {\n",
" 0;\n",
" }\n",
" } else {\n",
" 1;\n",
" }\n",
" } else {\n",
" if( x[15900165] <= 12.780000000000001 ) {\n",
" 0;\n",
" } else {\n",
" if( x[15900165] <= 13.43 ) {\n",
" if( x[15900165] <= 13.17 ) {\n",
" 0;\n",
" } else {\n",
" if( x[15900165] <= 13.254999999999999 ) {\n",
" 1;\n",
" } else {\n",
" if( x[15900165] <= 13.295 ) {\n",
" 0;\n",
" } else {\n",
" 1;\n",
" }\n",
" }\n",
" }\n",
" } else {\n",
" if( x[15900165] <= 13.645 ) {\n",
" 0;\n",
" } else {\n",
" if( x[15900165] <= 14.11 ) {\n",
" 0;\n",
" } else {\n",
" 0;\n",
" }\n",
" }\n",
" }\n",
" }\n",
" }\n",
" }\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "sv3kat5YyjOi",
"colab_type": "code",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "c3844389-4e2e-429f-f6af-c5d1d489cbd6"
},
"source": [
"from graphviz import Source\n",
"\n",
"Source(viz_sample_row.dot)"
],
"execution_count": 32,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<graphviz.files.Source at 0x7f4f020fee48>"
],
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text-anchor=\"start\" x=\"600.1442\" y=\"-1166.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">predicted = 0</text>\n</g>\n<!-- 2&#45;&gt;6 -->\n<g id=\"edge6\" class=\"edge\">\n<title>2&#45;&gt;6</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M633.6723,-1223.8314C634.9557,-1216.131 636.4818,-1206.9743 637.9081,-1198.4166\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"641.3836,-1198.8526 639.5753,-1188.4133 634.4788,-1197.7018 641.3836,-1198.8526\"/>\n</g>\n<!-- 4 -->\n<g id=\"node5\" class=\"node\">\n<title>4</title>\n<ellipse fill=\"transparent\" stroke=\"#000000\" cx=\"336.6442\" cy=\"-1098\" rx=\"65.7887\" ry=\"18\"/>\n<text text-anchor=\"start\" x=\"294.1442\" y=\"-1094.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">predicted = 0</text>\n</g>\n<!-- 3&#45;&gt;4 -->\n<g id=\"edge4\" class=\"edge\">\n<title>3&#45;&gt;4</title>\n<path fill=\"none\" stroke=\"#000000\" 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fill=\"transparent\" stroke=\"#000000\" cx=\"815.6442\" cy=\"-1170\" rx=\"65.7887\" ry=\"18\"/>\n<text text-anchor=\"start\" x=\"773.1442\" y=\"-1166.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">predicted = 0</text>\n</g>\n<!-- 7&#45;&gt;8 -->\n<g id=\"edge8\" class=\"edge\">\n<title>7&#45;&gt;8</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M824.6161,-1223.8314C823.3327,-1216.131 821.8066,-1206.9743 820.3803,-1198.4166\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"823.8095,-1197.7018 818.7131,-1188.4133 816.9047,-1198.8526 823.8095,-1197.7018\"/>\n</g>\n<!-- 9 -->\n<g id=\"node10\" class=\"node\">\n<title>9</title>\n<ellipse fill=\"transparent\" stroke=\"#000000\" cx=\"964.6442\" cy=\"-1170\" rx=\"65.7887\" ry=\"18\"/>\n<text text-anchor=\"start\" x=\"922.1442\" y=\"-1166.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">predicted = 0</text>\n</g>\n<!-- 7&#45;&gt;9 -->\n<g id=\"edge9\" class=\"edge\">\n<title>7&#45;&gt;9</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M862.2151,-1223.8314C881.2387,-1213.8335 904.9332,-1201.3809 924.74,-1190.9715\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"926.6003,-1193.9479 933.824,-1186.1975 923.3437,-1187.7515 926.6003,-1193.9479\"/>\n</g>\n<!-- 11 -->\n<g id=\"node12\" class=\"node\">\n<title>11</title>\n<ellipse fill=\"transparent\" stroke=\"#000000\" cx=\"1020.6442\" cy=\"-1242\" rx=\"65.7887\" ry=\"18\"/>\n<text text-anchor=\"start\" x=\"978.1442\" y=\"-1238.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">predicted = 1</text>\n</g>\n<!-- 10&#45;&gt;11 -->\n<g id=\"edge11\" class=\"edge\">\n<title>10&#45;&gt;11</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1034.8497,-1295.8314C1032.7953,-1288.0463 1030.3481,-1278.7729 1028.0686,-1270.1347\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1031.439,-1269.1892 1025.5032,-1260.4133 1024.6707,-1270.9753 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rx=\"65.7887\" ry=\"18\"/>\n<text text-anchor=\"start\" x=\"670.1442\" y=\"-806.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">predicted = 1</text>\n</g>\n<!-- 22&#45;&gt;23 -->\n<g id=\"edge23\" class=\"edge\">\n<title>22&#45;&gt;23</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M800.8678,-863.8314C784.9684,-854.13 765.281,-842.1174 748.5406,-831.9029\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"749.9458,-828.6602 739.5863,-826.4393 746.2997,-834.6357 749.9458,-828.6602\"/>\n</g>\n<!-- 24 -->\n<g id=\"node25\" class=\"node\">\n<title>24</title>\n<path fill=\"transparent\" stroke=\"#000000\" d=\"M972.6442,-828C972.6442,-828 808.6442,-828 808.6442,-828 802.6442,-828 796.6442,-822 796.6442,-816 796.6442,-816 796.6442,-804 796.6442,-804 796.6442,-798 802.6442,-792 808.6442,-792 808.6442,-792 972.6442,-792 972.6442,-792 978.6442,-792 984.6442,-798 984.6442,-804 984.6442,-804 984.6442,-816 984.6442,-816 984.6442,-822 978.6442,-828 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d=\"M845.7273,-791.8314C819.3102,-781.1458 785.961,-767.6562 759.2553,-756.854\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"760.3249,-753.5112 749.7421,-753.0059 757.7,-760.0004 760.3249,-753.5112\"/>\n</g>\n<!-- 26 -->\n<g id=\"node27\" class=\"node\">\n<title>26</title>\n<path fill=\"transparent\" stroke=\"#000000\" d=\"M972.6442,-756C972.6442,-756 808.6442,-756 808.6442,-756 802.6442,-756 796.6442,-750 796.6442,-744 796.6442,-744 796.6442,-732 796.6442,-732 796.6442,-726 802.6442,-720 808.6442,-720 808.6442,-720 972.6442,-720 972.6442,-720 978.6442,-720 984.6442,-726 984.6442,-732 984.6442,-732 984.6442,-744 984.6442,-744 984.6442,-750 978.6442,-756 972.6442,-756\"/>\n<text text-anchor=\"start\" x=\"804.6442\" y=\"-734.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">feature#15900165 ≤ 6.835</text>\n</g>\n<!-- 24&#45;&gt;26 -->\n<g id=\"edge26\" class=\"edge\">\n<title>24&#45;&gt;26</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M890.6442,-791.8314C890.6442,-784.131 890.6442,-774.9743 890.6442,-766.4166\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"894.1443,-766.4132 890.6442,-756.4133 887.1443,-766.4133 894.1443,-766.4132\"/>\n</g>\n<!-- 27 -->\n<g id=\"node28\" class=\"node\">\n<title>27</title>\n<ellipse fill=\"transparent\" stroke=\"#000000\" cx=\"712.6442\" cy=\"-666\" rx=\"65.7887\" ry=\"18\"/>\n<text text-anchor=\"start\" x=\"670.1442\" y=\"-662.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">predicted = 1</text>\n</g>\n<!-- 26&#45;&gt;27 -->\n<g id=\"edge27\" class=\"edge\">\n<title>26&#45;&gt;27</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M845.7273,-719.8314C819.3102,-709.1458 785.961,-695.6562 759.2553,-684.854\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"760.3249,-681.5112 749.7421,-681.0059 757.7,-688.0004 760.3249,-681.5112\"/>\n</g>\n<!-- 28 -->\n<g id=\"node29\" class=\"node\">\n<title>28</title>\n<path fill=\"transparent\" 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id=\"edge32\" class=\"edge\">\n<title>30&#45;&gt;32</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M214.6442,-503.8314C214.6442,-496.131 214.6442,-486.9743 214.6442,-478.4166\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"218.1443,-478.4132 214.6442,-468.4133 211.1443,-478.4133 218.1443,-478.4132\"/>\n</g>\n<!-- 34 -->\n<g id=\"node35\" class=\"node\">\n<title>34</title>\n<path fill=\"transparent\" stroke=\"#000000\" d=\"M474.6442,-468C474.6442,-468 310.6442,-468 310.6442,-468 304.6442,-468 298.6442,-462 298.6442,-456 298.6442,-456 298.6442,-444 298.6442,-444 298.6442,-438 304.6442,-432 310.6442,-432 310.6442,-432 474.6442,-432 474.6442,-432 480.6442,-432 486.6442,-438 486.6442,-444 486.6442,-444 486.6442,-456 486.6442,-456 486.6442,-462 480.6442,-468 474.6442,-468\"/>\n<text text-anchor=\"start\" x=\"306.6442\" y=\"-446.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">feature#15900165 ≤ 7.215</text>\n</g>\n<!-- 33&#45;&gt;34 -->\n<g 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63&#45;&gt;65 -->\n<g id=\"edge65\" class=\"edge\">\n<title>63&#45;&gt;65</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1433.5698,-71.8314C1442.8373,-62.9346 1454.1297,-52.0939 1464.1453,-42.4789\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1466.7107,-44.8679 1471.5007,-35.4177 1461.863,-39.8182 1466.7107,-44.8679\"/>\n</g>\n<!-- 67 -->\n<g id=\"node68\" class=\"node\">\n<title>67</title>\n<ellipse fill=\"transparent\" stroke=\"#000000\" cx=\"1628.6442\" cy=\"-90\" rx=\"65.7887\" ry=\"18\"/>\n<text text-anchor=\"start\" x=\"1586.1442\" y=\"-86.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">predicted = 0</text>\n</g>\n<!-- 66&#45;&gt;67 -->\n<g id=\"edge67\" class=\"edge\">\n<title>66&#45;&gt;67</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M1644.345,-143.8314C1642.0744,-136.0463 1639.3696,-126.7729 1636.8501,-118.1347\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"1640.1748,-117.0332 1634.0147,-108.4133 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id=\"edge75\" class=\"edge\">\n<title>74&#45;&gt;75</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M3441.6442,-1079.8314C3441.6442,-1072.131 3441.6442,-1062.9743 3441.6442,-1054.4166\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"3445.1443,-1054.4132 3441.6442,-1044.4133 3438.1443,-1054.4133 3445.1443,-1054.4132\"/>\n</g>\n<!-- 162 -->\n<g id=\"node163\" class=\"node\">\n<title>162</title>\n<path fill=\"transparent\" stroke=\"#000000\" d=\"M4053.1442,-1044C4053.1442,-1044 3782.1442,-1044 3782.1442,-1044 3776.1442,-1044 3770.1442,-1038 3770.1442,-1032 3770.1442,-1032 3770.1442,-1020 3770.1442,-1020 3770.1442,-1014 3776.1442,-1008 3782.1442,-1008 3782.1442,-1008 4053.1442,-1008 4053.1442,-1008 4059.1442,-1008 4065.1442,-1014 4065.1442,-1020 4065.1442,-1020 4065.1442,-1032 4065.1442,-1032 4065.1442,-1038 4059.1442,-1044 4053.1442,-1044\"/>\n<text text-anchor=\"start\" x=\"3778.1442\" y=\"-1022.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">feature#15900165 ≤ 12.274999999999999</text>\n</g>\n<!-- 74&#45;&gt;162 -->\n<g id=\"edge162\" class=\"edge\">\n<title>74&#45;&gt;162</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M3539.6945,-1083.1689C3611.5976,-1072.2928 3709.932,-1057.4186 3788.2739,-1045.5686\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"3788.8486,-1049.0216 3798.2126,-1044.0653 3787.8016,-1042.1003 3788.8486,-1049.0216\"/>\n</g>\n<!-- 76 -->\n<g id=\"node77\" class=\"node\">\n<title>76</title>\n<path fill=\"transparent\" stroke=\"#000000\" d=\"M3447.1442,-972C3447.1442,-972 3176.1442,-972 3176.1442,-972 3170.1442,-972 3164.1442,-966 3164.1442,-960 3164.1442,-960 3164.1442,-948 3164.1442,-948 3164.1442,-942 3170.1442,-936 3176.1442,-936 3176.1442,-936 3447.1442,-936 3447.1442,-936 3453.1442,-936 3459.1442,-942 3459.1442,-948 3459.1442,-948 3459.1442,-960 3459.1442,-960 3459.1442,-966 3453.1442,-972 3447.1442,-972\"/>\n<text text-anchor=\"start\" x=\"3172.1442\" y=\"-950.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">feature#15900165 ≤ 11.065000000000001</text>\n</g>\n<!-- 75&#45;&gt;76 -->\n<g id=\"edge76\" class=\"edge\">\n<title>75&#45;&gt;76</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M3408.8397,-1007.8314C3392.0579,-998.5368 3371.4456,-987.1208 3353.5278,-977.1971\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"3354.8819,-973.9461 3344.4382,-972.1628 3351.4903,-980.0696 3354.8819,-973.9461\"/>\n</g>\n<!-- 159 -->\n<g id=\"node160\" class=\"node\">\n<title>159</title>\n<path fill=\"transparent\" stroke=\"#000000\" d=\"M3653.6442,-972C3653.6442,-972 3489.6442,-972 3489.6442,-972 3483.6442,-972 3477.6442,-966 3477.6442,-960 3477.6442,-960 3477.6442,-948 3477.6442,-948 3477.6442,-942 3483.6442,-936 3489.6442,-936 3489.6442,-936 3653.6442,-936 3653.6442,-936 3659.6442,-936 3665.6442,-942 3665.6442,-948 3665.6442,-948 3665.6442,-960 3665.6442,-960 3665.6442,-966 3659.6442,-972 3653.6442,-972\"/>\n<text text-anchor=\"start\" x=\"3485.6442\" y=\"-950.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">feature#15900165 ≤ 11.51</text>\n</g>\n<!-- 75&#45;&gt;159 -->\n<g id=\"edge159\" class=\"edge\">\n<title>75&#45;&gt;159</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M3474.4487,-1007.8314C3491.2304,-998.5368 3511.8427,-987.1208 3529.7605,-977.1971\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"3531.798,-980.0696 3538.8502,-972.1628 3528.4065,-973.9461 3531.798,-980.0696\"/>\n</g>\n<!-- 77 -->\n<g id=\"node78\" class=\"node\">\n<title>77</title>\n<path fill=\"transparent\" stroke=\"#000000\" d=\"M3226.6442,-900C3226.6442,-900 3054.6442,-900 3054.6442,-900 3048.6442,-900 3042.6442,-894 3042.6442,-888 3042.6442,-888 3042.6442,-876 3042.6442,-876 3042.6442,-870 3048.6442,-864 3054.6442,-864 3054.6442,-864 3226.6442,-864 3226.6442,-864 3232.6442,-864 3238.6442,-870 3238.6442,-876 3238.6442,-876 3238.6442,-888 3238.6442,-888 3238.6442,-894 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font-size=\"14.00\" fill=\"#000000\">predicted = 1</text>\n</g>\n<!-- 78&#45;&gt;130 -->\n<g id=\"edge130\" class=\"edge\">\n<title>78&#45;&gt;130</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M2928.112,-791.8314C2940.03,-782.6045 2954.6485,-771.287 2967.4009,-761.4142\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"2969.6997,-764.0608 2975.4644,-755.1715 2965.4145,-758.5257 2969.6997,-764.0608\"/>\n</g>\n<!-- 80 -->\n<g id=\"node81\" class=\"node\">\n<title>80</title>\n<path fill=\"transparent\" stroke=\"#000000\" d=\"M2799.6442,-684C2799.6442,-684 2635.6442,-684 2635.6442,-684 2629.6442,-684 2623.6442,-678 2623.6442,-672 2623.6442,-672 2623.6442,-660 2623.6442,-660 2623.6442,-654 2629.6442,-648 2635.6442,-648 2635.6442,-648 2799.6442,-648 2799.6442,-648 2805.6442,-648 2811.6442,-654 2811.6442,-660 2811.6442,-660 2811.6442,-672 2811.6442,-672 2811.6442,-678 2805.6442,-684 2799.6442,-684\"/>\n<text text-anchor=\"start\" x=\"2631.6442\" y=\"-662.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">feature#15900165 ≤ 9.735</text>\n</g>\n<!-- 79&#45;&gt;80 -->\n<g id=\"edge80\" class=\"edge\">\n<title>79&#45;&gt;80</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M2757.27,-719.8314C2751.2279,-711.6232 2743.9679,-701.7606 2737.3221,-692.7323\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"2739.9453,-690.3918 2731.1984,-684.4133 2734.3079,-694.5415 2739.9453,-690.3918\"/>\n</g>\n<!-- 123 -->\n<g id=\"node124\" class=\"node\">\n<title>123</title>\n<path fill=\"transparent\" stroke=\"#000000\" d=\"M3005.6442,-684C3005.6442,-684 2841.6442,-684 2841.6442,-684 2835.6442,-684 2829.6442,-678 2829.6442,-672 2829.6442,-672 2829.6442,-660 2829.6442,-660 2829.6442,-654 2835.6442,-648 2841.6442,-648 2841.6442,-648 3005.6442,-648 3005.6442,-648 3011.6442,-648 3017.6442,-654 3017.6442,-660 3017.6442,-660 3017.6442,-672 3017.6442,-672 3017.6442,-678 3011.6442,-684 3005.6442,-684\"/>\n<text text-anchor=\"start\" x=\"2837.6442\" y=\"-662.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">feature#15900165 ≤ 9.865</text>\n</g>\n<!-- 79&#45;&gt;123 -->\n<g id=\"edge123\" class=\"edge\">\n<title>79&#45;&gt;123</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M2809.2525,-719.8314C2829.5335,-710.2874 2854.5679,-698.5065 2876.0413,-688.4013\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"2877.7556,-691.4629 2885.3134,-684.038 2874.7749,-685.1291 2877.7556,-691.4629\"/>\n</g>\n<!-- 81 -->\n<g id=\"node82\" class=\"node\">\n<title>81</title>\n<path fill=\"transparent\" stroke=\"#000000\" d=\"M2636.6442,-612C2636.6442,-612 2472.6442,-612 2472.6442,-612 2466.6442,-612 2460.6442,-606 2460.6442,-600 2460.6442,-600 2460.6442,-588 2460.6442,-588 2460.6442,-582 2466.6442,-576 2472.6442,-576 2472.6442,-576 2636.6442,-576 2636.6442,-576 2642.6442,-576 2648.6442,-582 2648.6442,-588 2648.6442,-588 2648.6442,-600 2648.6442,-600 2648.6442,-606 2642.6442,-612 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stroke=\"#000000\" d=\"M2721.4293,-647.8314C2723.0336,-640.131 2724.9412,-630.9743 2726.724,-622.4166\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"2730.1949,-622.9169 2728.8081,-612.4133 2723.342,-621.4892 2730.1949,-622.9169\"/>\n</g>\n<!-- 82 -->\n<g id=\"node83\" class=\"node\">\n<title>82</title>\n<path fill=\"transparent\" stroke=\"#000000\" d=\"M2537.6442,-540C2537.6442,-540 2373.6442,-540 2373.6442,-540 2367.6442,-540 2361.6442,-534 2361.6442,-528 2361.6442,-528 2361.6442,-516 2361.6442,-516 2361.6442,-510 2367.6442,-504 2373.6442,-504 2373.6442,-504 2537.6442,-504 2537.6442,-504 2543.6442,-504 2549.6442,-510 2549.6442,-516 2549.6442,-516 2549.6442,-528 2549.6442,-528 2549.6442,-534 2543.6442,-540 2537.6442,-540\"/>\n<text text-anchor=\"start\" x=\"2369.6442\" y=\"-518.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">feature#15900165 ≤ 8.955</text>\n</g>\n<!-- 81&#45;&gt;82 -->\n<g id=\"edge82\" class=\"edge\">\n<title>81&#45;&gt;82</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M2529.6623,-575.8314C2517.3513,-566.8779 2502.3334,-555.9558 2489.0505,-546.2955\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"2490.7641,-543.214 2480.6181,-540.1628 2486.6468,-548.8752 2490.7641,-543.214\"/>\n</g>\n<!-- 103 -->\n<g id=\"node104\" class=\"node\">\n<title>103</title>\n<path fill=\"transparent\" stroke=\"#000000\" d=\"M2843.1442,-540C2843.1442,-540 2580.1442,-540 2580.1442,-540 2574.1442,-540 2568.1442,-534 2568.1442,-528 2568.1442,-528 2568.1442,-516 2568.1442,-516 2568.1442,-510 2574.1442,-504 2580.1442,-504 2580.1442,-504 2843.1442,-504 2843.1442,-504 2849.1442,-504 2855.1442,-510 2855.1442,-516 2855.1442,-516 2855.1442,-528 2855.1442,-528 2855.1442,-534 2849.1442,-540 2843.1442,-540\"/>\n<text text-anchor=\"start\" x=\"2576.1442\" y=\"-518.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">feature#15900165 ≤ 9.225000000000001</text>\n</g>\n<!-- 81&#45;&gt;103 -->\n<g id=\"edge103\" class=\"edge\">\n<title>81&#45;&gt;103</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M2594.2619,-575.8314C2615.1664,-566.2446 2640.9925,-554.4008 2663.093,-544.2655\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"2664.6806,-547.388 2672.3113,-540.038 2661.7626,-541.0252 2664.6806,-547.388\"/>\n</g>\n<!-- 83 -->\n<g id=\"node84\" class=\"node\">\n<title>83</title>\n<path fill=\"transparent\" stroke=\"#000000\" d=\"M2404.6442,-468C2404.6442,-468 2240.6442,-468 2240.6442,-468 2234.6442,-468 2228.6442,-462 2228.6442,-456 2228.6442,-456 2228.6442,-444 2228.6442,-444 2228.6442,-438 2234.6442,-432 2240.6442,-432 2240.6442,-432 2404.6442,-432 2404.6442,-432 2410.6442,-432 2416.6442,-438 2416.6442,-444 2416.6442,-444 2416.6442,-456 2416.6442,-456 2416.6442,-462 2410.6442,-468 2404.6442,-468\"/>\n<text text-anchor=\"start\" x=\"2236.6442\" y=\"-446.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">feature#15900165 ≤ 8.875</text>\n</g>\n<!-- 82&#45;&gt;83 -->\n<g id=\"edge83\" class=\"edge\">\n<title>82&#45;&gt;83</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M2422.0827,-503.8314C2404.7561,-494.4516 2383.4384,-482.9112 2364.9906,-472.9244\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"2366.6553,-469.8457 2356.195,-468.1628 2363.3228,-476.0015 2366.6553,-469.8457\"/>\n</g>\n<!-- 102 -->\n<g id=\"node103\" class=\"node\">\n<title>102</title>\n<ellipse fill=\"transparent\" stroke=\"#000000\" cx=\"2500.6442\" cy=\"-450\" rx=\"65.7887\" ry=\"18\"/>\n<text text-anchor=\"start\" x=\"2458.1442\" y=\"-446.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">predicted = 0</text>\n</g>\n<!-- 82&#45;&gt;102 -->\n<g id=\"edge102\" class=\"edge\">\n<title>82&#45;&gt;102</title>\n<path fill=\"none\" stroke=\"#000000\" d=\"M2466.9996,-503.8314C2472.1549,-495.5829 2478.3545,-485.6635 2484.0197,-476.5992\"/>\n<polygon 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d=\"M2374.8629,-431.9243C2403.4213,-422.0387 2438.9737,-409.7321 2468.901,-399.3726\"/>\n<polygon fill=\"#000000\" stroke=\"#000000\" points=\"2470.099,-402.6618 2478.404,-396.0831 2467.8092,-396.0469 2470.099,-402.6618\"/>\n</g>\n<!-- 85 -->\n<g id=\"node86\" class=\"node\">\n<title>85</title>\n<path fill=\"transparent\" stroke=\"#000000\" d=\"M2130.1442,-324C2130.1442,-324 1867.1442,-324 1867.1442,-324 1861.1442,-324 1855.1442,-318 1855.1442,-312 1855.1442,-312 1855.1442,-300 1855.1442,-300 1855.1442,-294 1861.1442,-288 1867.1442,-288 1867.1442,-288 2130.1442,-288 2130.1442,-288 2136.1442,-288 2142.1442,-294 2142.1442,-300 2142.1442,-300 2142.1442,-312 2142.1442,-312 2142.1442,-318 2136.1442,-324 2130.1442,-324\"/>\n<text text-anchor=\"start\" x=\"1863.1442\" y=\"-302.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\" fill=\"#000000\">feature#15900165 ≤ 8.684999999999999</text>\n</g>\n<!-- 84&#45;&gt;85 -->\n<g id=\"edge85\" 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