Setting up Fast Hyperparameter Search Framework with Pyspark.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setting up Fast Hyperparameter Search Framework with Pyspark"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we set up hyperparameter tuning framework in PySpark using machine learning libraries like scikit-learn/xgboost/lightgbm "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Manual tuning has to change a lot of parameters. Hyperopt works only one model at a time. So it was taking up a lot of time to train each model and I was pretty short on time.\n",
    "\n",
    "But can we parallelize my model hyperparameter search process?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can choose to load your data using Spark, but here I start by creating our own classification data to set up a minimal example which we can work with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#we use the findspark library to locate spark on our local machine\n",
    "import findspark\n",
    "findspark.init('C:/Users/bokhy/spark/spark-2.4.6-bin-hadoop2.7')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import datasets\n",
    "\n",
    "import random\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from datetime import date, timedelta, datetime\n",
    "import time\n",
    "from IPython.display import Image\n",
    "\n",
    "import pyspark # only run this after findspark.init()\n",
    "import pyspark.sql.functions as F\n",
    "from pyspark.sql.types import *\n",
    "from pyspark.sql import SparkSession, SQLContext\n",
    "from pyspark.context import SparkContext"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Spark Session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "spark = SparkSession.builder.appName('hyper-tuning').getOrCreate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <div>\n",
       "                <p><b>SparkSession - in-memory</b></p>\n",
       "                \n",
       "        <div>\n",
       "            <p><b>SparkContext</b></p>\n",
       "\n",
       "            <p><a href=\"http://DESKTOP-L9PS4IG.home:4040\">Spark UI</a></p>\n",
       "\n",
       "            <dl>\n",
       "              <dt>Version</dt>\n",
       "                <dd><code>v2.4.6</code></dd>\n",
       "              <dt>Master</dt>\n",
       "                <dd><code>local[*]</code></dd>\n",
       "              <dt>AppName</dt>\n",
       "                <dd><code>hyper-tuning</code></dd>\n",
       "            </dl>\n",
       "        </div>\n",
       "        \n",
       "            </div>\n",
       "        "
      ],
      "text/plain": [
       "<pyspark.sql.session.SparkSession at 0x22d6e112308>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "spark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "X,y = datasets.make_classification(n_samples=100, \n",
    "                                   n_features=4, \n",
    "                                   n_informative=2, \n",
    "                                   n_classes=2, \n",
    "                                   random_state=623,\n",
    "                                   shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.DataFrame(X)\n",
    "train['target'] = y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_sp = spark.createDataFrame(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_sp = train_sp.toDF(*['c0', 'c1', 'c2', 'c3', 'target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+--------------------+--------------------+--------------------+--------------------+------+\n",
      "|                  c0|                  c1|                  c2|                  c3|target|\n",
      "+--------------------+--------------------+--------------------+--------------------+------+\n",
      "| 0.32012361672251677| -0.4253715175371259|-0.19057549039497973|-0.14928231355839838|     0|\n",
      "| 0.33500090238265867| -0.5684299153776691|  0.7924806821551242| -1.2779690995202135|     0|\n",
      "| -0.7794082941605487|   0.864449299755419|  1.8414214193177343| -1.1942638608472032|     0|\n",
      "| -2.3459013184814097|  2.8848422500426705|  3.2657182909604896| -1.0198659288295877|     0|\n",
      "|-0.20483169280564287|  0.3952384975068274| -0.8681535296084092|  1.2152102540355667|     1|\n",
      "| 0.37712043827263486| -0.4000407378242594| -1.0376251786761053|  0.7436899038043132|     1|\n",
      "| -0.9923250730977524|  1.3755987312890325| 0.13196783924883856|  0.9815817381027226|     1|\n",
      "|  -1.700715223234123|  2.0611308363108085|   2.611348894738913|  -1.015077455733299|     0|\n",
      "|  0.9466470262511407|  -1.158303989021015| -1.3646721410643474|    0.46453042226772|     0|\n",
      "| -0.7745879905824107|  1.1662681338106065| -0.6412244333268021|  1.6078542769835462|     1|\n",
      "|-0.29169713230423777|  0.4879535931176029| -0.6337359342525181|  1.0490978348983324|     1|\n",
      "| -0.5591388847822621|  0.6163939812123045|  1.3512046412639416|  -0.890892591296086|     0|\n",
      "| 0.32037119453486984|-0.43914106256523655|-0.08258848378061842| -0.2716863866557099|     0|\n",
      "| 0.44499691164494004|-0.46046921877557356| -1.3174951377665165|   0.982843411702632|     1|\n",
      "|-0.34118281102286385|  0.6654475236209281| -1.5032557511918567|  2.0888256224625157|     1|\n",
      "|-0.10662095802684934|-0.09030322060686602|  1.9298247317786321|  -2.060926693681795|     0|\n",
      "| 0.38014135017684125| -0.4805768357019159| -0.4237756545723192| 0.04604814841103644|     0|\n",
      "|  -0.817425047894266|   1.296755371408712| -1.2075928418529067|   2.297173155641036|     1|\n",
      "| -0.6831996040348057|  0.7552741106463122|  1.6339854459735732| -1.0693121012065232|     0|\n",
      "| -0.7819093132740337|  1.1394915502330039| -0.3431699395267169|  1.2791291656295911|     1|\n",
      "+--------------------+--------------------+--------------------+--------------------+------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "train_sp.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So now we have got our training dataset in Spark. And we want to run multiple models on this DataFrame.\n",
    "\n",
    "We replicate our data n times and add a replication_id to our data so that each key has all the data.\n",
    "\n",
    "Ok, now we can send the whole data to multiple machines using groupby on replication_id. But how do we use pandas and scikit learn on that data?\n",
    "\n",
    "The answer is: we use pandas_udf. This functionality was introduced in the Spark version 2.3.1. And this allows you to utilise pandas functionality with Spark."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Replicate the Dataset n times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# replicate the spark dataframe into multiple copies\n",
    "replication_df = spark.createDataFrame(pd.DataFrame(list(range(1,100)),columns=['replication_id']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "replicated_train_df = train_sp.crossJoin(replication_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "+-------------------+-------------------+--------------------+--------------------+------+--------------+\n",
      "|                 c0|                 c1|                  c2|                  c3|target|replication_id|\n",
      "+-------------------+-------------------+--------------------+--------------------+------+--------------+\n",
      "|0.32012361672251677|-0.4253715175371259|-0.19057549039497973|-0.14928231355839838|     0|             1|\n",
      "|0.32012361672251677|-0.4253715175371259|-0.19057549039497973|-0.14928231355839838|     0|             2|\n",
      "|0.32012361672251677|-0.4253715175371259|-0.19057549039497973|-0.14928231355839838|     0|             3|\n",
      "|0.32012361672251677|-0.4253715175371259|-0.19057549039497973|-0.14928231355839838|     0|             4|\n",
      "|0.32012361672251677|-0.4253715175371259|-0.19057549039497973|-0.14928231355839838|     0|             5|\n",
      "|0.32012361672251677|-0.4253715175371259|-0.19057549039497973|-0.14928231355839838|     0|             6|\n",
      "|0.32012361672251677|-0.4253715175371259|-0.19057549039497973|-0.14928231355839838|     0|             7|\n",
      "|0.32012361672251677|-0.4253715175371259|-0.19057549039497973|-0.14928231355839838|     0|             8|\n",
      "|0.32012361672251677|-0.4253715175371259|-0.19057549039497973|-0.14928231355839838|     0|             9|\n",
      "|0.32012361672251677|-0.4253715175371259|-0.19057549039497973|-0.14928231355839838|     0|            10|\n",
      "|0.32012361672251677|-0.4253715175371259|-0.19057549039497973|-0.14928231355839838|     0|            11|\n",
      "|0.32012361672251677|-0.4253715175371259|-0.19057549039497973|-0.14928231355839838|     0|            12|\n",
      "|0.33500090238265867|-0.5684299153776691|  0.7924806821551242| -1.2779690995202135|     0|             1|\n",
      "|0.33500090238265867|-0.5684299153776691|  0.7924806821551242| -1.2779690995202135|     0|             2|\n",
      "|0.33500090238265867|-0.5684299153776691|  0.7924806821551242| -1.2779690995202135|     0|             3|\n",
      "|0.33500090238265867|-0.5684299153776691|  0.7924806821551242| -1.2779690995202135|     0|             4|\n",
      "|0.33500090238265867|-0.5684299153776691|  0.7924806821551242| -1.2779690995202135|     0|             5|\n",
      "|0.33500090238265867|-0.5684299153776691|  0.7924806821551242| -1.2779690995202135|     0|             6|\n",
      "|0.33500090238265867|-0.5684299153776691|  0.7924806821551242| -1.2779690995202135|     0|             7|\n",
      "|0.33500090238265867|-0.5684299153776691|  0.7924806821551242| -1.2779690995202135|     0|             8|\n",
      "+-------------------+-------------------+--------------------+--------------------+------+--------------+\n",
      "only showing top 20 rows\n",
      "\n"
     ]
    }
   ],
   "source": [
    "replicated_train_df.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also define a function that takes as input a pandas dataframe, gets random hyperparameters using the python random module, runs a model on data(Here I am training a scikit model, but you can replace it with any model like XGBoost or Lightgbm as well) and returns the result in the form of a Pandas Dataframe. Do take a look at the function and the comments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 0. Declare the schema for the output of our function\n",
    "outSchema = StructType([StructField('replication_id',IntegerType(),True),\n",
    "                        StructField('Accuracy',DoubleType(),True),\n",
    "                        StructField('num_trees',IntegerType(),True),\n",
    "                        StructField('depth',IntegerType(),True),\n",
    "                        StructField('criterion',StringType(),True)])\n",
    "\n",
    "# decorate our function with pandas_udf decorator\n",
    "@F.pandas_udf(outSchema, F.PandasUDFType.GROUPED_MAP)\n",
    "def run_model(pdf):\n",
    "    # 1. Get hyperparam values\n",
    "    num_trees =  random.choice(list(range(50,500)))\n",
    "    depth = random.choice(list(range(2,10)))\n",
    "    criterion = random.choice(['gini','entropy'])\n",
    "    replication_id = pdf.replication_id.values[0]\n",
    "    # 2. Train test split\n",
    "    X = pdf[['c0', 'c1', 'c2', 'c3']]\n",
    "    y = pdf['target']\n",
    "    #del X['target']\n",
    "    Xtrain,Xcv,ytrain,ycv = train_test_split(X, y, test_size=0.3, random_state=42)\n",
    "    # 3. Create model using the pandas dataframe\n",
    "    clf = RandomForestClassifier(n_estimators=num_trees, max_depth = depth, criterion =criterion)\n",
    "    clf.fit(Xtrain,ytrain)\n",
    "    # 4. Evaluate the model\n",
    "    accuracy = accuracy_score(clf.predict(Xcv),ycv)\n",
    "    # 5. return results as pandas DF\n",
    "    res =pd.DataFrame({'replication_id':replication_id,'Accuracy':accuracy, 'num_trees':num_trees,'depth':depth,'criterion':criterion}, index=[0])\n",
    "    return res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## We first replicate our train dataframe 100 times here by using cross_join with a data frame that contains a column with 1–100 replication_id."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "results = replicated_train_df.groupby(\"replication_id\").apply(run_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What the above code does is that it sends all the data with the same replication id to a single machine and applies the function run_model to the data. The above call happens lazily so you won’t be able to see the results till you run the below action call."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "results.sort(F.desc(\"Accuracy\")).show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_name1 = './img/hyper.png'\n",
    "Image(file_name1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For this toy example, the accuracy results may look pretty close to one another, but they will differ in the case of noisy real-world datasets. Since all of these 100 models run in parallel on different nodes, we can save a lot of time when doing random hyperparameter search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}