2_DS_model_fit.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip3 install sklearn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>fixedAcidity</th>\n",
       "      <th>volatileAcidity</th>\n",
       "      <th>citricAcid</th>\n",
       "      <th>residualSugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>freeSulfurDioxide</th>\n",
       "      <th>totalSulfurDioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Quality</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>Excellent</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>Poor</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixedAcidity  volatileAcidity  citricAcid  residualSugar  chlorides  \\\n",
       "0           7.4             0.70        0.00            1.9      0.076   \n",
       "1           7.8             0.88        0.00            2.6      0.098   \n",
       "2           7.8             0.76        0.04            2.3      0.092   \n",
       "3          11.2             0.28        0.56            1.9      0.075   \n",
       "4           7.4             0.70        0.00            1.9      0.076   \n",
       "\n",
       "   freeSulfurDioxide  totalSulfurDioxide  density    pH  sulphates  Alcohol  \\\n",
       "0               11.0                34.0   0.9978  3.51       0.56      9.4   \n",
       "1               25.0                67.0   0.9968  3.20       0.68      9.8   \n",
       "2               15.0                54.0   0.9970  3.26       0.65      9.8   \n",
       "3               17.0                60.0   0.9980  3.16       0.58      9.8   \n",
       "4               11.0                34.0   0.9978  3.51       0.56      9.4   \n",
       "\n",
       "     Quality  \n",
       "0       Poor  \n",
       "1       Poor  \n",
       "2       Poor  \n",
       "3  Excellent  \n",
       "4       Poor  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "#If you created the Hive table in step 0 you can read the data from\n",
    "#there (as follows) instead of from local disk.\n",
    "\n",
    "#from pyspark.sql import SparkSession\n",
    "#from pyspark.sql.types import Row, StructField, StructType, StringType, IntegerType\n",
    "#\n",
    "#spark = SparkSession.builder    .appName(\"Import Wine Table\")    .config(\"spark.yarn.access.hadoopFileSystems\", \"s3a://ml-field/demo/wine/\")    .config(\"spark.hadoop.fs.s3a.s3guard.ddb.region\", \"us-west-2\")    .getOrCreate()\n",
    "#wine_df = spark.sql(\"SELECT * FROM `default`.`wine`\").toPandas()\n",
    "#spark.stop()\n",
    "\n",
    "#If you don't read from the Hive Metastore you need to provide the\n",
    "#schema and read from CSV.\n",
    "\n",
    "data_dir='/home/cdsw'\n",
    "csvpath = os.path.join(data_dir, 'raw', 'WineNewGBTDataSet.csv')\n",
    "\n",
    "col_Names=['fixedAcidity',\n",
    " 'volatileAcidity',\n",
    " 'citricAcid',\n",
    " 'residualSugar',\n",
    " 'chlorides',\n",
    " 'freeSulfurDioxide',\n",
    " 'totalSulfurDioxide',\n",
    " 'density',\n",
    " 'pH',\n",
    " 'sulphates',\n",
    " 'Alcohol',\n",
    " 'Quality']\n",
    "\n",
    "wine_df = pd.read_csv(csvpath, sep=\";\", header=None, names=col_Names, index_col=None)\n",
    "wine_df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Poor' 'Excellent' '1']\n"
     ]
    }
   ],
   "source": [
    "#check labels\n",
    "print(wine_df.Quality.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>fixedAcidity</th>\n",
       "      <th>volatileAcidity</th>\n",
       "      <th>citricAcid</th>\n",
       "      <th>residualSugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>freeSulfurDioxide</th>\n",
       "      <th>totalSulfurDioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>Alcohol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "      <td>1599.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>8.319637</td>\n",
       "      <td>0.527821</td>\n",
       "      <td>0.270976</td>\n",
       "      <td>2.538806</td>\n",
       "      <td>0.087467</td>\n",
       "      <td>15.874922</td>\n",
       "      <td>46.467792</td>\n",
       "      <td>0.996747</td>\n",
       "      <td>3.311113</td>\n",
       "      <td>0.658149</td>\n",
       "      <td>10.422983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>1.741096</td>\n",
       "      <td>0.179060</td>\n",
       "      <td>0.194801</td>\n",
       "      <td>1.409928</td>\n",
       "      <td>0.047065</td>\n",
       "      <td>10.460157</td>\n",
       "      <td>32.895324</td>\n",
       "      <td>0.001887</td>\n",
       "      <td>0.154386</td>\n",
       "      <td>0.169507</td>\n",
       "      <td>1.065668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>4.600000</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.012000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.990070</td>\n",
       "      <td>2.740000</td>\n",
       "      <td>0.330000</td>\n",
       "      <td>8.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>7.100000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>0.070000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.995600</td>\n",
       "      <td>3.210000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>9.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.260000</td>\n",
       "      <td>2.200000</td>\n",
       "      <td>0.079000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>0.996750</td>\n",
       "      <td>3.310000</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>10.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>9.200000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>0.997835</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>0.730000</td>\n",
       "      <td>11.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>15.900000</td>\n",
       "      <td>1.580000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>0.611000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>289.000000</td>\n",
       "      <td>1.003690</td>\n",
       "      <td>4.010000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>14.900000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       fixedAcidity  volatileAcidity   citricAcid  residualSugar    chlorides  \\\n",
       "count   1599.000000      1599.000000  1599.000000    1599.000000  1599.000000   \n",
       "mean       8.319637         0.527821     0.270976       2.538806     0.087467   \n",
       "std        1.741096         0.179060     0.194801       1.409928     0.047065   \n",
       "min        4.600000         0.120000     0.000000       0.900000     0.012000   \n",
       "25%        7.100000         0.390000     0.090000       1.900000     0.070000   \n",
       "50%        7.900000         0.520000     0.260000       2.200000     0.079000   \n",
       "75%        9.200000         0.640000     0.420000       2.600000     0.090000   \n",
       "max       15.900000         1.580000     1.000000      15.500000     0.611000   \n",
       "\n",
       "       freeSulfurDioxide  totalSulfurDioxide      density           pH  \\\n",
       "count        1599.000000         1599.000000  1599.000000  1599.000000   \n",
       "mean           15.874922           46.467792     0.996747     3.311113   \n",
       "std            10.460157           32.895324     0.001887     0.154386   \n",
       "min             1.000000            6.000000     0.990070     2.740000   \n",
       "25%             7.000000           22.000000     0.995600     3.210000   \n",
       "50%            14.000000           38.000000     0.996750     3.310000   \n",
       "75%            21.000000           62.000000     0.997835     3.400000   \n",
       "max            72.000000          289.000000     1.003690     4.010000   \n",
       "\n",
       "         sulphates      Alcohol  \n",
       "count  1599.000000  1599.000000  \n",
       "mean      0.658149    10.422983  \n",
       "std       0.169507     1.065668  \n",
       "min       0.330000     8.400000  \n",
       "25%       0.550000     9.500000  \n",
       "50%       0.620000    10.200000  \n",
       "75%       0.730000    11.100000  \n",
       "max       2.000000    14.900000  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#clean up and encode labels\n",
    "wine_df.Quality.replace('1',\"Excellent\",inplace=True)\n",
    "wine_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>fixedAcidity</th>\n",
       "      <th>volatileAcidity</th>\n",
       "      <th>citricAcid</th>\n",
       "      <th>residualSugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>freeSulfurDioxide</th>\n",
       "      <th>totalSulfurDioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Quality</th>\n",
       "      <th>Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>Poor</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.098</td>\n",
       "      <td>25.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>0.9968</td>\n",
       "      <td>3.20</td>\n",
       "      <td>0.68</td>\n",
       "      <td>9.8</td>\n",
       "      <td>Poor</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>7.8</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.04</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.092</td>\n",
       "      <td>15.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0.9970</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.65</td>\n",
       "      <td>9.8</td>\n",
       "      <td>Poor</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>11.2</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.075</td>\n",
       "      <td>17.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.9980</td>\n",
       "      <td>3.16</td>\n",
       "      <td>0.58</td>\n",
       "      <td>9.8</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>7.4</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.076</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.9978</td>\n",
       "      <td>3.51</td>\n",
       "      <td>0.56</td>\n",
       "      <td>9.4</td>\n",
       "      <td>Poor</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixedAcidity  volatileAcidity  citricAcid  residualSugar  chlorides  \\\n",
       "0           7.4             0.70        0.00            1.9      0.076   \n",
       "1           7.8             0.88        0.00            2.6      0.098   \n",
       "2           7.8             0.76        0.04            2.3      0.092   \n",
       "3          11.2             0.28        0.56            1.9      0.075   \n",
       "4           7.4             0.70        0.00            1.9      0.076   \n",
       "\n",
       "   freeSulfurDioxide  totalSulfurDioxide  density    pH  sulphates  Alcohol  \\\n",
       "0               11.0                34.0   0.9978  3.51       0.56      9.4   \n",
       "1               25.0                67.0   0.9968  3.20       0.68      9.8   \n",
       "2               15.0                54.0   0.9970  3.26       0.65      9.8   \n",
       "3               17.0                60.0   0.9980  3.16       0.58      9.8   \n",
       "4               11.0                34.0   0.9978  3.51       0.56      9.4   \n",
       "\n",
       "     Quality  Label  \n",
       "0       Poor      1  \n",
       "1       Poor      1  \n",
       "2       Poor      1  \n",
       "3  Excellent      0  \n",
       "4       Poor      1  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#encode labels \n",
    "wine_df.Quality = pd.Categorical(wine_df.Quality)\n",
    "wine_df['Label'] = wine_df.Quality.cat.codes\n",
    "wine_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 9 candidates, totalling 27 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'max_depth': 9, 'n_estimators': 30}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Done  27 out of  27 | elapsed:    5.7s finished\n"
     ]
    }
   ],
   "source": [
    "#random forest Classifier Grid Search\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(wine_df.iloc[:,:11],wine_df['Label'],test_size=0.2, random_state=30)\n",
    "\n",
    "#parameters for grid search\n",
    "rfc = RandomForestClassifier(random_state=10, n_jobs=-1)\n",
    "\n",
    "GS_params = { \n",
    "    'n_estimators': [25,30,35],\n",
    "    'max_depth' : [7,9,11]\n",
    "}\n",
    "\n",
    "#Cross Validation Grid Search\n",
    "CV_rfc = GridSearchCV(estimator=rfc, \n",
    "                      param_grid=GS_params, \n",
    "                      cv= 3, \n",
    "                      n_jobs=2,\n",
    "                      verbose=2)\n",
    "CV_rfc.fit(X_train, y_train)\n",
    "\n",
    "#Show Best Parameters \n",
    "print(CV_rfc.best_params_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=2)]: Done  30 out of  30 | elapsed:    0.1s finished\n",
      "[Parallel(n_jobs=2)]: Using backend ThreadingBackend with 2 concurrent workers.\n",
      "[Parallel(n_jobs=2)]: Done  30 out of  30 | elapsed:    0.0s finished\n"
     ]
    }
   ],
   "source": [
    "#final Model\n",
    "rfc_final= RandomForestClassifier(n_estimators=CV_rfc.best_params_['n_estimators'] , \n",
    "                                  max_depth=CV_rfc.best_params_['max_depth'], \n",
    "                                  random_state=10, \n",
    "                                  n_jobs=2,\n",
    "                                  verbose=1)\n",
    "rfc_final.fit(X_train, y_train)\n",
    "y_true, y_pred = y_test, rfc_final.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.85      0.82       170\n",
      "           1       0.81      0.76      0.79       150\n",
      "\n",
      "    accuracy                           0.81       320\n",
      "   macro avg       0.81      0.80      0.80       320\n",
      "weighted avg       0.81      0.81      0.81       320\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Evaluation metrics\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_true, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8035294117647058\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "print(roc_auc_score(y_true, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!jupyter nbconvert --to PYTHON 2_DS_model_fit.ipynb"
   ]
  }
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