MVP- LR with just count .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import pyspark\n",
    "import pyspark.sql\n",
    "from pyspark.sql.functions import *\n",
    "from pyspark.sql import SparkSession\n",
    "from pyspark.sql.functions import *\n",
    "\n",
    "spark = SparkSession.builder.getOrCreate()\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "dftrain=pd.read_csv('dftrain.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfcount= dftrain[['Pitch_Type','ball_count', 'strike_count']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Pitch_Type</th>\n",
       "      <th>strike_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ball_count</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1171636</td>\n",
       "      <td>362823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>780656</td>\n",
       "      <td>408577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>417927</td>\n",
       "      <td>297488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>179815</td>\n",
       "      <td>163445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "            Pitch_Type  strike_count\n",
       "ball_count                          \n",
       "0              1171636        362823\n",
       "1               780656        408577\n",
       "2               417927        297488\n",
       "3               179815        163445\n",
       "4                    3             5"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfcount.groupby('ball_count').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pitch_Type      1.000000\n",
       "strike_count    0.078061\n",
       "ball_count     -0.054307\n",
       "Name: Pitch_Type, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix= dfcount.corr()\n",
    "#looking at each attribute's correlation\n",
    "corr_matrix['Pitch_Type'].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1400332 entries, 0 to 1400331\n",
      "Data columns (total 3 columns):\n",
      "Pitch_Type      1400332 non-null int64\n",
      "ball_count      1400332 non-null int64\n",
      "strike_count    1400332 non-null int64\n",
      "dtypes: int64(3)\n",
      "memory usage: 32.1 MB\n"
     ]
    }
   ],
   "source": [
    "dfcount.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ball_count</th>\n",
       "      <th>strike_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>780086</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>578325</th>\n",
       "      <td>0</td>\n",
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       "      <th>1047168</th>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1212563</th>\n",
       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "         ball_count  strike_count\n",
       "780086            3             2\n",
       "578325            0             1\n",
       "705263            2             1\n",
       "1047168           1             0\n",
       "1212563           1             1"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Split Data\n",
    "X= dfcount[['ball_count', 'strike_count']]\n",
    "y=dfcount['Pitch_Type']\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .30, random_state = 123)\n",
    "\n",
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train Model\n",
    "\n",
    "#Create the logistic regression object\n",
    "\n",
    "\n",
    "logit = LogisticRegression(C=1, random_state = 123, class_weight= 'balanced', multi_class='multinomial', solver='newton-cg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1, class_weight='balanced', dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='multinomial', n_jobs=None, penalty='l2',\n",
       "                   random_state=123, solver='newton-cg', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Fit the model to the training data\n",
    "\n",
    "logit.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficient: \n",
      " [[ 0.10721616 -0.19362715]\n",
      " [ 0.02473169  0.07908288]\n",
      " [-0.13194786  0.11454427]]\n",
      "Intercept: \n",
      " [ 0.0784202  -0.08929525  0.01087505]\n"
     ]
    }
   ],
   "source": [
    "#Print the coefficients and intercept of the model\n",
    "\n",
    "print('Coefficient: \\n', logit.coef_)\n",
    "print('Intercept: \\n', logit.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Estimate pitch \n",
    "\n",
    "y_pred = logit.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Estimate the probability each type of pitch\n",
    "\n",
    "y_pred_proba = logit.predict_proba(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of Logistic Regression classifier on training set: 0.43\n"
     ]
    }
   ],
   "source": [
    "#Evaluate Model\n",
    "\n",
    "#Compute the accuracy\n",
    "\n",
    "print('Accuracy of Logistic Regression classifier on training set: {:.2f}'\n",
    "     .format(logit.score(X_train, y_train)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[274370 112756 131887]\n",
      " [ 48340  32759  36405]\n",
      " [148753  79987 114975]]\n"
     ]
    }
   ],
   "source": [
    "#Create a confusion matrix\n",
    "\n",
    "print(confusion_matrix(y_train, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           1       0.58      0.53      0.55    519013\n",
      "           2       0.15      0.28      0.19    117504\n",
      "           3       0.41      0.33      0.37    343715\n",
      "\n",
      "    accuracy                           0.43    980232\n",
      "   macro avg       0.38      0.38      0.37    980232\n",
      "weighted avg       0.47      0.43      0.44    980232\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Compute Precision, Recall, F1-score, and Support\n",
    "\n",
    "print(classification_report(y_train, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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