Weight_prediction-ToDeliver.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import sklearn\n",
    "import scipy\n",
    "import seaborn as sns\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x1 = pd.read_csv(\"X1.csv \")\n",
    "y1 = pd.read_csv(\"Y1.csv \" , header=None , names=['weight'] ) #target\n",
    "df = x1\n",
    "df['weight'] = y1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Unnamed: 0</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>family_history_with_overweight</th>\n",
       "      <th>FAVC</th>\n",
       "      <th>FCVC</th>\n",
       "      <th>NCP</th>\n",
       "      <th>CAEC</th>\n",
       "      <th>SMOKE</th>\n",
       "      <th>CH2O</th>\n",
       "      <th>SCC</th>\n",
       "      <th>FAF</th>\n",
       "      <th>TUE</th>\n",
       "      <th>CALC</th>\n",
       "      <th>MTRANS</th>\n",
       "      <th>weight</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Unnamed: 0, Gender, Age, Height, family_history_with_overweight, FAVC, FCVC, NCP, CAEC, SMOKE, CH2O, SCC, FAF, TUE, CALC, MTRANS, weight]\n",
       "Index: []"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#verification of eventual dataset anomalies\n",
    "df.isna().any()\n",
    "#duplicates can have negative influence on the prediction\n",
    "df.duplicated()\n",
    "df[df.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_object=df.select_dtypes(include=['object'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cat_into_num(df):\n",
    "    l = []\n",
    "    df_object=df.select_dtypes(include=['object'])\n",
    "    for c in df_object:\n",
    "        cat = df.loc[:,c]\n",
    "        facc = pd.factorize(cat)\n",
    "        l.append(facc)\n",
    "        df[c] = facc[0]\n",
    "    return df\n",
    "\n",
    "df = cat_into_num(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x900 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#original distribution of variables\n",
    "fig=plt.figure(figsize=(10, 10), dpi=90)\n",
    "n_feats = len(df.columns)-1\n",
    "for i, feat in enumerate(df.columns[1:len(df.columns)-1]):\n",
    "    plt.subplot(n_feats//3+1,3,i+1)\n",
    "    plt.scatter(df[feat],df['weight'], s=10)\n",
    "    plt.title(feat)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.stats as stats\n",
    "z_scores = stats.zscore(df)\n",
    "\n",
    "abs_z_scores = np.abs(z_scores)\n",
    "#print(abs_z_scores) matrix\n",
    "filtered_entries = (abs_z_scores < 3).all(axis=1)\n",
    "df = df[filtered_entries]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x900 with 15 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=plt.figure(figsize=(10, 10), dpi=90)\n",
    "n_feats = len(df.columns)-1\n",
    "for i, feat in enumerate(df.columns[1:len(df.columns)-1]):\n",
    "    plt.subplot(n_feats//3+1,3,i+1)\n",
    "    plt.scatter(df[feat],df['weight'], s=10)\n",
    "    plt.title(feat)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>Age</th>\n",
       "      <th>Height</th>\n",
       "      <th>weight</th>\n",
       "      <th>family_history_with_overweight</th>\n",
       "      <th>FAVC</th>\n",
       "      <th>FCVC</th>\n",
       "      <th>NCP</th>\n",
       "      <th>CAEC</th>\n",
       "      <th>CH2O</th>\n",
       "      <th>SCC</th>\n",
       "      <th>FAF</th>\n",
       "      <th>TUE</th>\n",
       "      <th>CALC</th>\n",
       "      <th>MTRANS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21.0</td>\n",
       "      <td>1.88</td>\n",
       "      <td>84.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19.0</td>\n",
       "      <td>1.70</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21.0</td>\n",
       "      <td>1.61</td>\n",
       "      <td>54.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22.0</td>\n",
       "      <td>1.61</td>\n",
       "      <td>63.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>26.0</td>\n",
       "      <td>1.90</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>22.0</td>\n",
       "      <td>1.68</td>\n",
       "      <td>52.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>246</th>\n",
       "      <td>23.0</td>\n",
       "      <td>1.65</td>\n",
       "      <td>66.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>18.0</td>\n",
       "      <td>1.60</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>36.0</td>\n",
       "      <td>1.65</td>\n",
       "      <td>80.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>249</th>\n",
       "      <td>23.0</td>\n",
       "      <td>1.60</td>\n",
       "      <td>83.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>222 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Age  Height  weight  family_history_with_overweight  FAVC  FCVC  NCP  \\\n",
       "0    21.0    1.88    84.0                               0     0   3.0  3.0   \n",
       "1    19.0    1.70    60.0                               0     0   2.0  1.0   \n",
       "3    21.0    1.61    54.5                               0     0   3.0  3.0   \n",
       "4    22.0    1.61    63.0                               0     0   3.0  3.0   \n",
       "5    26.0    1.90    80.0                               0     0   2.0  3.0   \n",
       "..    ...     ...     ...                             ...   ...   ...  ...   \n",
       "245  22.0    1.68    52.0                               1     0   3.0  3.0   \n",
       "246  23.0    1.65    66.0                               1     1   3.0  3.0   \n",
       "247  18.0    1.60    60.0                               0     0   3.0  1.0   \n",
       "248  36.0    1.65    80.0                               0     0   2.0  3.0   \n",
       "249  23.0    1.60    83.0                               0     0   3.0  3.0   \n",
       "\n",
       "     CAEC  CH2O  SCC  FAF  TUE  CALC  MTRANS  \n",
       "0       0   3.0    0  2.0  1.0     0       0  \n",
       "1       0   2.0    0  2.0  0.0     0       1  \n",
       "3       0   3.0    0  0.0  1.0     0       0  \n",
       "4       1   3.0    0  1.0  0.0     1       1  \n",
       "5       1   1.0    1  1.0  0.0     0       2  \n",
       "..    ...   ...  ...  ...  ...   ...     ...  \n",
       "245     1   2.0    0  0.0  1.0     1       1  \n",
       "246     0   2.0    0  3.0  0.0     1       1  \n",
       "247     0   1.0    1  0.0  2.0     1       0  \n",
       "248     0   1.0    0  0.0  2.0     1       2  \n",
       "249     1   3.0    0  3.0  0.0     1       1  \n",
       "\n",
       "[222 rows x 14 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_numerical=df[['Age','Height', 'weight']] \n",
    "\n",
    "#Gender removed\n",
    "df_categorical = df[['family_history_with_overweight', 'FAVC', 'FCVC', 'NCP', 'CAEC',\n",
    "                     'CH2O', 'SCC', 'FAF', 'TUE', 'CALC', 'MTRANS']]\n",
    "df1 = pd.concat([df_numerical,df_categorical],axis =1)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "#SPLIT DATA INTO TRAIN AND TEST SET\n",
    "#A for loop on the Linear Regressor was done in order to test the split values that gives the best performances: \n",
    "#tested between [0.10, 0.30,0.05]\n",
    "def split_std(X,y):\n",
    "\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "                                                        test_size =0.3, #by default is 75%-25%\n",
    "                                                        #shuffle is set True by default,\n",
    "                                                        #useful to obtain proportional distribution in 0 and 1\n",
    "                                                        random_state= 123\n",
    "                                                       )\n",
    "    return X_train, X_test, y_train, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "# a simple for loop was done using the Linear Regressor in order to see which was the best number of features\n",
    "# tested between [2, X.columns]\n",
    "def select_features(X,y,n_feat):\n",
    "\n",
    "    x1 = X\n",
    "    x1['weight'] = y\n",
    "    plt.figure(figsize=(12,10))\n",
    "    cor = x1.corr()\n",
    "    #sns.heatmap(cor, annot=True, cmap=plt.cm.Reds)\n",
    "    plt.show()\n",
    "    trg_cor = (abs(cor['weight']).sort_values()) \n",
    "    if n_feat >= len(X.columns):\n",
    "        slc_feat = X.columns\n",
    "    \n",
    "    start = (len(X.columns)-n_feat-1)\n",
    "    stop = len(X.columns)-1\n",
    "    \n",
    "    slc_feat = trg_cor[start:stop].index\n",
    "    X_sel = X[slc_feat]\n",
    "    \n",
    "    return X_sel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_rmse(predict, target):\n",
    "\n",
    "    if len(target.shape) == 2:\n",
    "        target = target.squeeze()\n",
    "    if len(predict.shape) == 2:\n",
    "        predict = predict.squeeze()\n",
    "    diff = target-predict\n",
    "    if len(diff.shape) == 1:\n",
    "        diff = np.expand_dims(diff, axis=-1)\n",
    "    rmse = np.sqrt( 1/diff.shape[0] * diff.T@diff )\n",
    "    return rmse\n",
    "def scoreweightclass(bmi_pred , bmi_true , low , high):\n",
    "    tol = 1\n",
    "    v_pred = (bmi_pred>= (low-tol)) & ( bmi_pred<high+tol)\n",
    "    v_true = (bmi_true >= low) & (bmi_true < high)\n",
    "    if v_true.sum()==0:\n",
    "        print(\"no true samples here\")\n",
    "        return 0\n",
    "    \n",
    "    rmse = np.sqrt(((bmi_true[v_true]-bmi_pred[v_true])**2).mean())\n",
    "    rmse = rmse / ( high - low+tol ) # normalize rmse ininterval\n",
    "    acc = (v_pred&v_true).sum() / v_true.sum()\n",
    "    return rmse*(1-acc)\n",
    "\n",
    "def scoreregression (ytrue , ypred , height):\n",
    "    \n",
    "    bmi_pred = ypred / (height**2)\n",
    "    bmi_true = ytrue / (height**2)\n",
    "    scores = []\n",
    "    for bmi_low , bmi_high in  zip ( [0 ,18.5 ,25 ,30] , [18.5 ,25 ,30 ,100] ) :\n",
    "        scores.append( scoreweightclass( bmi_pred , bmi_true , low=bmi_low , high=bmi_high) )\n",
    "    return np.mean(scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weight prediction: non augmented dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.svm import SVR\n",
    "from sklearn.metrics import make_scorer, confusion_matrix\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "\n",
    "def perform_grid_search(model_, hyper_params_grid, score_function,X_train,y_train,X_test,y_test):\n",
    "    \n",
    "    search = GridSearchCV(model_, hyper_params_grid, scoring=score_function, n_jobs=-1, cv=5)\n",
    "    result = search.fit(X_train, y_train)\n",
    "    prediction = result.predict(X_test)\n",
    "    \n",
    "    print('Best Score: %s' % result.best_score_)\n",
    "    print('Best Hyperparameters: %s' % result.best_params_)\n",
    "    \n",
    "    from sklearn import metrics\n",
    "    print()\n",
    "    print(\"MAE  train %.3f    test %06.3f\" % (metrics.mean_absolute_error(y_train, result.predict(X_train)), metrics.mean_absolute_error(y_test, result.predict(X_test))) ) \n",
    "    print(\"MSE  train %.3f test %.3f\" % (metrics.mean_squared_error(y_train, result.predict(X_train)), metrics.mean_squared_error(y_test, result.predict(X_test))) ) \n",
    "    print(\"RMSE train %.3f   test %.3f\" % (np.sqrt(metrics.mean_squared_error(y_train, result.predict(X_train))), np.sqrt(metrics.mean_squared_error(y_test, result.predict(X_test)))) ) \n",
    "    print(\"r2   train %.3f     test %.3f\" % (metrics.r2_score(y_train, result.predict(X_train)), metrics.r2_score(y_test, result.predict(X_test))) ) \n",
    "\n",
    "    print(\" \")\n",
    "    \n",
    "    \n",
    "    \n",
    "    return prediction, result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    " \n",
    "def barplots(mlp, lr, knn, svm, rndf, group):\n",
    "    # set width of bars\n",
    "    barWidth = 0.20\n",
    "\n",
    "    # set heights of bars\n",
    "\n",
    "    # Set position of bar on X axis\n",
    "    ro1 = np.arange(len(mlp))\n",
    "    ro2 = [x + barWidth for x in ro1]\n",
    "    ro3 = [x + barWidth for x in ro2]\n",
    "    ro4 = [x + barWidth for x in ro3]\n",
    "    ro5 = [x + barWidth for x in ro4]\n",
    "    \n",
    "\n",
    "    # Make the plot\n",
    "    plt.bar(ro1, mlp, color='red', width=barWidth, edgecolor='white', label='mlp')\n",
    "    plt.bar(ro2, lr, color='yellow', width=barWidth, edgecolor='white', label='lr')\n",
    "    plt.bar(ro3, knn, color='lime', width=barWidth, edgecolor='white', label='knn')\n",
    "    plt.bar(ro4, svm, color='pink', width=barWidth, edgecolor='white', label='svm')\n",
    "    plt.bar(ro5, rndf, color='darkgreen', width=barWidth, edgecolor='white', label='rndf')\n",
    "    \n",
    "\n",
    "    # Add xticks on the middle of the group bars\n",
    "    plt.xlabel(group, fontweight='bold')\n",
    "    plt.xticks([r + barWidth for r in range(len(mlp))], ['MAE', 'MSE/10', 'RMSE', 'R2*10'])\n",
    "\n",
    "    # Create legend & Show graphic\n",
    "    plt.legend()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import make_scorer, confusion_matrix\n",
    "from sklearn import metrics\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "def train_predict(X_train,y_train,X_test,y_test,h):\n",
    "    \n",
    "    mlp = MLPRegressor()\n",
    "    lr = LinearRegression()\n",
    "    knn = KNeighborsRegressor()\n",
    "    svm = SVR()\n",
    "    rndf = RandomForestRegressor()\n",
    "    mlp_train = []\n",
    "    mlp_test = []\n",
    "    lr_train = []\n",
    "    lr_test = []\n",
    "    knn_train = []\n",
    "    knn_test = []\n",
    "    svm_train = []\n",
    "    svm_test = []\n",
    "    rndf_train = []\n",
    "    rndf_test = []\n",
    "\n",
    "\n",
    "    for i in [mlp, lr, knn, svm,rndf]:\n",
    "\n",
    "        \"\"\"        \n",
    "        if i==mlp:\n",
    "            model = mlp\n",
    "            print('MLPRegressor')\n",
    "            hyper_params_grid = {'hidden_layer_sizes':[(20,20),(40,40)],'activation':['logistic', 'tanh','relu'], 'solver':['lbfgs', 'adam'], 'alpha':[0.001,0.01,0.1],'learning_rate':['constant', 'invscaling', 'adaptive'],'max_iter':[400,600],'random_state':[43]}\n",
    "            grid_predictions, result = perform_grid_search(model,hyper_params_grid,'neg_root_mean_squared_error',X_train,y_train,X_test,y_test )\n",
    "            print(scoreregression(y_test, grid_predictions, h))\n",
    "            mlp_train.append(metrics.mean_absolute_error(y_train, result.predict(X_train)))\n",
    "            mlp_train.append(metrics.mean_squared_error(y_train, result.predict(X_train))/10)\n",
    "            mlp_train.append(np.sqrt(metrics.mean_squared_error(y_train, result.predict(X_train))))\n",
    "            mlp_train.append(abs(metrics.r2_score(y_train, result.predict(X_train))*10))\n",
    "            \n",
    "            mlp_test.append(metrics.mean_absolute_error(y_test, result.predict(X_test)))\n",
    "            mlp_test.append(metrics.mean_squared_error(y_test, result.predict(X_test))/10)\n",
    "            mlp_test.append(np.sqrt(metrics.mean_squared_error(y_test, result.predict(X_test))))\n",
    "            mlp_test.append(abs(metrics.r2_score(y_test, result.predict(X_test))*10))\n",
    "            \"\"\"\n",
    "\n",
    "        \n",
    "        if i==lr:\n",
    "            model = lr\n",
    "            print('LinearRegressor')\n",
    "            hyper_params_grid = {'n_jobs':[-1]}\n",
    "            grid_predictions, result_lr = perform_grid_search(model,hyper_params_grid,'neg_root_mean_squared_error',X_train,y_train,X_test,y_test)\n",
    "            sr_lr = scoreregression(y_test, grid_predictions, h)\n",
    "            print(sr_lr)\n",
    "            lr_train.append(metrics.mean_absolute_error(y_train, result_lr.predict(X_train)))\n",
    "            lr_train.append(metrics.mean_squared_error(y_train, result_lr.predict(X_train))/10)\n",
    "            lr_train.append(np.sqrt(metrics.mean_squared_error(y_train, result_lr.predict(X_train))))\n",
    "            lr_train.append(abs(metrics.r2_score(y_train, result_lr.predict(X_train))*10))\n",
    "            \n",
    "            lr_test.append(metrics.mean_absolute_error(y_test, result_lr.predict(X_test)))\n",
    "            lr_test.append(metrics.mean_squared_error(y_test, result_lr.predict(X_test))/10)\n",
    "            lr_test.append(np.sqrt(metrics.mean_squared_error(y_test, result_lr.predict(X_test))))\n",
    "            lr_test.append(abs(metrics.r2_score(y_test, result_lr.predict(X_test))*10))\n",
    "                \n",
    "        \"\"\"\n",
    "        if i==knn:\n",
    "            model = knn\n",
    "            print('KNN')\n",
    "            hyper_params_grid = {'n_neighbors':[5,10,20,30],'weights':['uniform', 'distance'],'algorithm':['auto', 'ball_tree', 'kd_tree', 'brute']}\n",
    "            grid_predictions, result = perform_grid_search(model,hyper_params_grid,'neg_root_mean_squared_error',X_train,y_train,X_test,y_test) \n",
    "            print(scoreregression(y_test, grid_predictions, h))\n",
    "            knn_train.append(metrics.mean_absolute_error(y_train, result.predict(X_train)))\n",
    "            knn_train.append(metrics.mean_squared_error(y_train, result.predict(X_train))/10)\n",
    "            knn_train.append(np.sqrt(metrics.mean_squared_error(y_train, result.predict(X_train))))\n",
    "            knn_train.append(abs(metrics.r2_score(y_train, result.predict(X_train))*10))\n",
    "            \n",
    "            knn_test.append(metrics.mean_absolute_error(y_test, result.predict(X_test)))\n",
    "            knn_test.append(metrics.mean_squared_error(y_test, result.predict(X_test))/10)\n",
    "            knn_test.append(np.sqrt(metrics.mean_squared_error(y_test, result.predict(X_test))))\n",
    "            knn_test.append(abs(metrics.r2_score(y_test, result.predict(X_test))*10))\n",
    "   \n",
    "        if i==svm:\n",
    "            model = svm\n",
    "            print('SVM')\n",
    "            hyper_params_grid = { 'kernel':['poly', 'rbf', 'sigmoid'], 'gamma': ['scale', 'auto'] }\n",
    "            grid_predictions, result = perform_grid_search(model,hyper_params_grid,'neg_root_mean_squared_error',X_train,y_train,X_test,y_test )\n",
    "            print(scoreregression(y_test, grid_predictions,h))\n",
    "            svm_train.append(metrics.mean_absolute_error(y_train, result.predict(X_train)))\n",
    "            svm_train.append(metrics.mean_squared_error(y_train, result.predict(X_train))/10)\n",
    "            svm_train.append(np.sqrt(metrics.mean_squared_error(y_train, result.predict(X_train))))\n",
    "            svm_train.append(abs(metrics.r2_score(y_train, result.predict(X_train))*10))\n",
    "            \n",
    "            svm_test.append(metrics.mean_absolute_error(y_test, result.predict(X_test)))\n",
    "            svm_test.append(metrics.mean_squared_error(y_test, result.predict(X_test))/10)\n",
    "            svm_test.append(np.sqrt(metrics.mean_squared_error(y_test, result.predict(X_test))))\n",
    "            svm_test.append(abs(metrics.r2_score(y_test, result.predict(X_test))*10))\n",
    "        if i == rndf:\n",
    "            model = rndf\n",
    "            print('RandomForestRegressor')\n",
    "            hyper_params_grid = {'n_estimators':[50,100,200],'criterion':['squared_error', 'absolute_error', 'poisson'], 'max_depth':[4,7],'random_state':[42]}\n",
    "            grid_predictions, result = perform_grid_search(model,hyper_params_grid,'neg_root_mean_squared_error',X_train,y_train,X_test,y_test) \n",
    "            print(scoreregression(y_test, grid_predictions, h))\n",
    "            rndf_train.append(metrics.mean_absolute_error(y_train, result.predict(X_train)))\n",
    "            rndf_train.append(metrics.mean_squared_error(y_train, result.predict(X_train))/10)\n",
    "            rndf_train.append(np.sqrt(metrics.mean_squared_error(y_train, result.predict(X_train))))\n",
    "            rndf_train.append(abs(metrics.r2_score(y_train, result.predict(X_train))*10))\n",
    "            \n",
    "            rndf_test.append(metrics.mean_absolute_error(y_test, result.predict(X_test)))\n",
    "            rndf_test.append(metrics.mean_squared_error(y_test, result.predict(X_test))/10)\n",
    "            rndf_test.append(np.sqrt(metrics.mean_squared_error(y_test, result.predict(X_test))))\n",
    "            rndf_test.append(abs(metrics.r2_score(y_test, result.predict(X_test))*10))\n",
    "     \n",
    "\n",
    "    barplots(mlp_train, lr_train, knn_train, svm_train, rndf_train, \"Train\")\n",
    "    barplots(mlp_test, lr_test, knn_test, svm_test, rndf_test, \"Test\")\n",
    "    \"\"\"\n",
    "    return result_lr, sr_lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 864x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>NCP</th>\n",
       "      <th>MTRANS</th>\n",
       "      <th>CAEC</th>\n",
       "      <th>Age</th>\n",
       "      <th>family_history_with_overweight</th>\n",
       "      <th>CH2O</th>\n",
       "      <th>Height</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>4.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>112</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>26.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>155 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     NCP  MTRANS  CAEC   Age  family_history_with_overweight  CH2O  Height\n",
       "141  4.0       0     0  20.0                               0   3.0    1.90\n",
       "41   4.0       1     2  22.0                               1   3.0    1.60\n",
       "151  3.0       1     0  20.0                               1   3.0    1.57\n",
       "91   3.0       1     0  21.0                               0   3.0    1.68\n",
       "112  1.0       1     0  23.0                               1   2.0    1.64\n",
       "..   ...     ...   ...   ...                             ...   ...     ...\n",
       "17   3.0       1     2  26.0                               1   2.0    1.83\n",
       "98   1.0       2     1  41.0                               0   3.0    1.80\n",
       "66   3.0       1     0  20.0                               1   2.0    1.65\n",
       "126  3.0       2     0  29.0                               1   2.0    1.62\n",
       "109  3.0       1     0  18.0                               1   2.0    1.74\n",
       "\n",
       "[155 rows x 7 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df1['weight']\n",
    "X = df1.drop([\"weight\"], 1)\n",
    "\n",
    "y = y.reset_index(drop=True)\n",
    "X = X.reset_index(drop=True)\n",
    "X_fake_train, X_fake_test, y_fake_train, y_fake_test = split_std(X,y)\n",
    "\n",
    "mm_scaler =  MinMaxScaler(copy=False,feature_range=(-1, 1))\n",
    "Xs= pd.DataFrame(mm_scaler.fit_transform(X))\n",
    "Xs.columns = X.columns\n",
    "\n",
    "\n",
    "df_sel = select_features(X,y,7)\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = split_std(df_sel,y)\n",
    "\n",
    "X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearRegressor\n",
      "Best Score: -11.391024359622751\n",
      "Best Hyperparameters: {'n_jobs': -1}\n",
      "\n",
      "MAE  train 8.081    test 07.566\n",
      "MSE  train 120.020 test 86.426\n",
      "RMSE train 10.955   test 9.297\n",
      "r2   train 0.430     test 0.481\n",
      " \n",
      "0.14701796724066774\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(GridSearchCV(cv=5, estimator=LinearRegression(), n_jobs=-1,\n",
       "              param_grid={'n_jobs': [-1]},\n",
       "              scoring='neg_root_mean_squared_error'),\n",
       " 0.14701796724066774)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_predict(X_train,y_train,X_test,y_test,X_fake_test['Height'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weight prediction: augmented dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pip install tabgan\n",
    "from tabgan.sampler import OriginalGenerator, GANGenerator\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df1.drop('weight',1)\n",
    "y = df1['weight']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 864x720 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\IreRi\\anaconda3\\lib\\site-packages\\lightgbm\\sklearn.py:726: UserWarning: 'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. Pass 'early_stopping()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "C:\\Users\\IreRi\\anaconda3\\lib\\site-packages\\lightgbm\\sklearn.py:736: UserWarning: 'verbose' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'verbose' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "C:\\Users\\IreRi\\anaconda3\\lib\\site-packages\\lightgbm\\sklearn.py:726: UserWarning: 'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. Pass 'early_stopping()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "C:\\Users\\IreRi\\anaconda3\\lib\\site-packages\\lightgbm\\sklearn.py:736: UserWarning: 'verbose' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'verbose' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "C:\\Users\\IreRi\\anaconda3\\lib\\site-packages\\lightgbm\\sklearn.py:726: UserWarning: 'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. Pass 'early_stopping()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "C:\\Users\\IreRi\\anaconda3\\lib\\site-packages\\lightgbm\\sklearn.py:736: UserWarning: 'verbose' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'verbose' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "C:\\Users\\IreRi\\anaconda3\\lib\\site-packages\\lightgbm\\sklearn.py:726: UserWarning: 'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. Pass 'early_stopping()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "C:\\Users\\IreRi\\anaconda3\\lib\\site-packages\\lightgbm\\sklearn.py:736: UserWarning: 'verbose' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'verbose' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "C:\\Users\\IreRi\\anaconda3\\lib\\site-packages\\lightgbm\\sklearn.py:726: UserWarning: 'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. Pass 'early_stopping()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'early_stopping_rounds' argument is deprecated and will be removed in a future release of LightGBM. \"\n",
      "C:\\Users\\IreRi\\anaconda3\\lib\\site-packages\\lightgbm\\sklearn.py:736: UserWarning: 'verbose' argument is deprecated and will be removed in a future release of LightGBM. Pass 'log_evaluation()' callback via 'callbacks' argument instead.\n",
      "  _log_warning(\"'verbose' argument is deprecated and will be removed in a future release of LightGBM. \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LinearRegressor\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\IreRi\\anaconda3\\lib\\site-packages\\joblib\\externals\\loky\\process_executor.py:688: UserWarning: A worker stopped while some jobs were given to the executor. This can be caused by a too short worker timeout or by a memory leak.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score: -11.075912697364306\n",
      "Best Hyperparameters: {'n_jobs': -1}\n",
      "\n",
      "MAE  train 7.261    test 07.458\n",
      "MSE  train 102.346 test 89.188\n",
      "RMSE train 10.117   test 9.444\n",
      "r2   train 0.494     test 0.464\n",
      " \n",
      "0.14356278160611946\n"
     ]
    }
   ],
   "source": [
    "y = df1['weight']\n",
    "X = df1.drop([\"weight\"], 1)\n",
    "\n",
    "y = y.reset_index(drop=True)\n",
    "X = X.reset_index(drop=True)\n",
    "X_fake_train, X_fake_test, y_fake_train, y_fake_test = split_std(X,y)\n",
    "\n",
    "mm_scaler =  MinMaxScaler(copy=False,feature_range=(-1, 1))\n",
    "Xs= pd.DataFrame(mm_scaler.fit_transform(X))\n",
    "Xs.columns = X.columns\n",
    "\n",
    "\n",
    "df_sel = select_features(X,y,7)\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = split_std(df_sel,y)\n",
    "\n",
    "new_train, new_target = OriginalGenerator().generate_data_pipe(X_train, pd.DataFrame(y_train), X_test, )\n",
    "\n",
    "result_lr, sr_lr = train_predict(new_train,new_target,X_test,y_test,X_fake_test['Height'])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "X2 = pd.read_csv(\"X2.csv \")\n",
    "\n",
    "X2.isna().any()\n",
    "X2.duplicated()\n",
    "X2[X2.duplicated()]\n",
    "\n",
    "X2 = cat_into_num(X2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "z_scores = stats.zscore(X2)\n",
    "abs_z_scores = np.abs(z_scores)\n",
    "filtered_entries = (abs_z_scores < 3).all(axis=1)\n",
    "X2 = X2[filtered_entries]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.        , -1.        , -0.74193548, ...,  1.        ,\n",
       "        -1.        , -1.        ],\n",
       "       [-0.99190283, -1.        , -0.67741935, ...,  1.        ,\n",
       "        -1.        , -1.        ],\n",
       "       [-0.98380567, -1.        , -0.29032258, ..., -1.        ,\n",
       "        -1.        , -1.        ],\n",
       "       ...,\n",
       "       [ 0.98380567,  1.        , -0.74193548, ..., -1.        ,\n",
       "         0.33333333, -1.        ],\n",
       "       [ 0.99190283,  1.        , -0.48387097, ..., -1.        ,\n",
       "        -1.        , -1.        ],\n",
       "       [ 1.        , -1.        , -0.61290323, ..., -1.        ,\n",
       "         0.33333333, -1.        ]])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mm_scaler =  MinMaxScaler(copy=False,feature_range=(-1, 1))\n",
    "mm_scaler.fit_transform(X2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "X2_sel = X2[X_train.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "y2_predict = result_lr.predict(X2_sel)\n",
    "y2_predict= np.append(y2_predict, sr_lr)\n",
    "predictions=pd.DataFrame()\n",
    "\n",
    "predictions['target']=y2_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions.to_csv('Y2.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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