Week_7/Day_2/ML_with_flask.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.datasets import load_wine\n",
    "import json\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.pipeline import Pipeline, FeatureUnion\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "\n",
    "import sklearn\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "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>alcohol</th>\n",
       "      <th>malic_acid</th>\n",
       "      <th>ash</th>\n",
       "      <th>alcalinity_of_ash</th>\n",
       "      <th>magnesium</th>\n",
       "      <th>total_phenols</th>\n",
       "      <th>flavanoids</th>\n",
       "      <th>nonflavanoid_phenols</th>\n",
       "      <th>proanthocyanins</th>\n",
       "      <th>color_intensity</th>\n",
       "      <th>hue</th>\n",
       "      <th>od280/od315_of_diluted_wines</th>\n",
       "      <th>proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113.0</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   alcohol  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
       "0    14.23        1.71  2.43               15.6      127.0           2.80   \n",
       "1    13.20        1.78  2.14               11.2      100.0           2.65   \n",
       "2    13.16        2.36  2.67               18.6      101.0           2.80   \n",
       "3    14.37        1.95  2.50               16.8      113.0           3.85   \n",
       "4    13.24        2.59  2.87               21.0      118.0           2.80   \n",
       "\n",
       "   flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
       "0        3.06                  0.28             2.29             5.64  1.04   \n",
       "1        2.76                  0.26             1.28             4.38  1.05   \n",
       "2        3.24                  0.30             2.81             5.68  1.03   \n",
       "3        3.49                  0.24             2.18             7.80  0.86   \n",
       "4        2.69                  0.39             1.82             4.32  1.04   \n",
       "\n",
       "   od280/od315_of_diluted_wines  proline  \n",
       "0                          3.92   1065.0  \n",
       "1                          3.40   1050.0  \n",
       "2                          3.17   1185.0  \n",
       "3                          3.45   1480.0  \n",
       "4                          2.93    735.0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = load_wine()\n",
    "df = pd.DataFrame(data['data'])\n",
    "df.columns = data['feature_names']\n",
    "y = data['target']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CLASS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RawFeats:\n",
    "    def __init__(self, feats):\n",
    "        self.feats = feats\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        pass\n",
    "\n",
    "\n",
    "    def transform(self, X, y=None):\n",
    "        return X[self.feats]\n",
    "\n",
    "    def fit_transform(self, X, y=None):\n",
    "        self.fit(X)\n",
    "        return self.transform(X)\n",
    "\n",
    "\n",
    "# features we want to keep for PCA\n",
    "feats = ['alcohol','malic_acid','ash','alcalinity_of_ash','magnesium',\n",
    "         'total_phenols','flavanoids','nonflavanoid_phenols']\n",
    "# creating class object with indexes we want to keep.\n",
    "raw_feats = RawFeats(feats)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc = StandardScaler()\n",
    "pca = PCA(n_components=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SELECTKBEST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "selection = SelectKBest(k=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "PCA_pipeline = Pipeline([\n",
    "    (\"rawFeats\", raw_feats),\n",
    "    (\"scaler\", sc),\n",
    "    (\"pca\", pca)\n",
    "])\n",
    "\n",
    "kbest_pipeline = Pipeline([(\"kBest\", selection)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_features = FeatureUnion([\n",
    "    (\"pcaPipeline\", PCA_pipeline), \n",
    "    (\"kBestPipeline\", kbest_pipeline)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "main_pipeline = Pipeline([\n",
    "    (\"features\", all_features),\n",
    "    (\"rf\", rf)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 27 candidates, totalling 135 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 6 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done   1 tasks      | elapsed:    0.0s\n",
      "[Parallel(n_jobs=-1)]: Batch computation too fast (0.0327s.) Setting batch_size=2.\n",
      "[Parallel(n_jobs=-1)]: Done   6 tasks      | elapsed:    0.1s\n",
      "[Parallel(n_jobs=-1)]: Done  14 tasks      | elapsed:    0.2s\n",
      "[Parallel(n_jobs=-1)]: Batch computation too fast (0.1037s.) Setting batch_size=4.\n",
      "[Parallel(n_jobs=-1)]: Done  28 tasks      | elapsed:    0.3s\n",
      "[Parallel(n_jobs=-1)]: Batch computation too fast (0.1391s.) Setting batch_size=8.\n",
      "[Parallel(n_jobs=-1)]: Done  56 tasks      | elapsed:    0.4s\n",
      "[Parallel(n_jobs=-1)]: Done 135 out of 135 | elapsed:    0.7s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=Pipeline(steps=[('features',\n",
       "                                        FeatureUnion(transformer_list=[('pcaPipeline',\n",
       "                                                                        Pipeline(steps=[('rawFeats',\n",
       "                                                                                         <__main__.RawFeats object at 0x7ff534d83a30>),\n",
       "                                                                                        ('scaler',\n",
       "                                                                                         StandardScaler()),\n",
       "                                                                                        ('pca',\n",
       "                                                                                         PCA(n_components=2))])),\n",
       "                                                                       ('kBestPipeline',\n",
       "                                                                        Pipeline(steps=[('kBest',\n",
       "                                                                                         SelectKBest(k=4))]))])),\n",
       "                                       ('rf', RandomForestClassifier())]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'features__kBestPipeline__kBest__k': [1, 2, 3],\n",
       "                         'features__pcaPipeline__pca__n_components': [1, 2, 3],\n",
       "                         'rf__n_estimators': [2, 5, 10]},\n",
       "             verbose=10)"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set up our parameters grid\n",
    "param_grid = {\"features__pcaPipeline__pca__n_components\": [1, 2, 3],\n",
    "                  \"features__kBestPipeline__kBest__k\": [1, 2, 3],\n",
    "                  \"rf__n_estimators\":[2, 5, 10]\n",
    "             }\n",
    "\n",
    "# create a Grid Search object\n",
    "grid_search = GridSearchCV(main_pipeline, param_grid, n_jobs = -1, verbose=10, refit=True)    \n",
    "\n",
    "# fit the model and tune parameters\n",
    "grid_search.fit(df, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "pickle.dump(grid_search, open( \"model.p\", \"wb\" ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(estimator=Pipeline(steps=[('features',\n",
       "                                        FeatureUnion(transformer_list=[('pcaPipeline',\n",
       "                                                                        Pipeline(steps=[('rawFeats',\n",
       "                                                                                         <__main__.RawFeats object at 0x7f5e9878bb20>),\n",
       "                                                                                        ('scaler',\n",
       "                                                                                         StandardScaler()),\n",
       "                                                                                        ('pca',\n",
       "                                                                                         PCA(n_components=2))])),\n",
       "                                                                       ('kBestPipeline',\n",
       "                                                                        Pipeline(steps=[('kBest',\n",
       "                                                                                         SelectKBest(k=4))]))])),\n",
       "                                       ('rf', RandomForestClassifier())]),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'features__kBestPipeline__kBest__k': [1, 2, 3],\n",
       "                         'features__pcaPipeline__pca__n_components': [1, 2, 3],\n",
       "                         'rf__n_estimators': [2, 5, 10]},\n",
       "             verbose=10)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pickle.load( open(\"model.p\", 'rb'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "dat = {\n",
    "        \"alcohol\": [50.13], \n",
    "        \"malic_acid\":[4.10], \n",
    "        \"ash\":[2.74], \n",
    "        \"alcalinity_of_ash\":[24.50], \n",
    "        \"magnesium\":[2.00], \n",
    "        \"total_phenols\":[2.05], \n",
    "        \"flavanoids\":[0.76], \n",
    "        \"nonflavanoid_phenols\": [0.56], \n",
    "        \"proanthocyanins\": [1.35], \n",
    "        \"color_intensity\": [10.20], \n",
    "        \"hue\": [0.61], \n",
    "        \"od280/od315_of_diluted_wines\":[1.60], \n",
    "        \"proline\": [560.00]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame.from_dict(dat)\n",
    "json_data = df.to_json(orient = 'records')[1:-1].replace('},{','} {')\n",
    "json_data =json.loads(json_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'alcohol': 50.13,\n",
       " 'malic_acid': 4.1,\n",
       " 'ash': 2.74,\n",
       " 'alcalinity_of_ash': 24.5,\n",
       " 'magnesium': 2.0,\n",
       " 'total_phenols': 2.05,\n",
       " 'flavanoids': 0.76,\n",
       " 'nonflavanoid_phenols': 0.56,\n",
       " 'proanthocyanins': 1.35,\n",
       " 'color_intensity': 10.2,\n",
       " 'hue': 0.61,\n",
       " 'od280/od315_of_diluted_wines': 1.6,\n",
       " 'proline': 560.0}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "json_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "URL = \"http://127.0.0.1:5000/scoring\"\n",
    "# sending get request and saving the response as response object \n",
    "r = requests.post(url = URL, json = json_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [200]>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0.0, 0.4, 0.6]]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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