Dry beans_honda.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import plotly.express as px\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=pd.read_csv(\"train.csv\")\n",
    "data.drop(columns='ID',inplace=True)\n",
    "data_test=pd.read_csv(\"test.csv\")\n",
    "id_col=data_test['ID']\n",
    "data_test.drop(columns='ID',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10834, 17)\n"
     ]
    }
   ],
   "source": [
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "class dry_beans :\n",
    "    def __init__(self,data_train,data_test):\n",
    "        self.data_train=data_train\n",
    "        self.data_test=data_test\n",
    "        \n",
    "        \n",
    "    def null_check(self):\n",
    "        return self.data_train.isnull().sum()\n",
    "    \n",
    "    def duplicates_check(self):\n",
    "        return self.data_train.duplicated().sum()\n",
    "    \n",
    "    def disp_info(self):\n",
    "        return self.data_train.info()\n",
    "    \n",
    "    def balance_data(self):\n",
    "        plt.hist(self.data_train['y'],bins=int(np.sqrt(len(self.data_train['y']))))\n",
    "        plt.show()\n",
    "        \n",
    "        class_count_0, class_count_1, class_count_2, class_count_3, class_count_4, class_count_5, class_count_6 = self.data_train['y'].value_counts()\n",
    "\n",
    "        # Separate class\n",
    "        class_0 = self.data_train[self.data_train['y'] == 'DERMASON']\n",
    "        class_1 = self.data_train[self.data_train['y'] == 'SIRA']\n",
    "        class_2 = self.data_train[self.data_train['y'] == 'SEKER']\n",
    "        class_3 = self.data_train[self.data_train['y'] == 'HOROZ']\n",
    "        class_4 = self.data_train[self.data_train['y'] == 'CALI']\n",
    "        class_5 = self.data_train[self.data_train['y'] == 'BARBUNYA']\n",
    "        class_6 = self.data_train[self.data_train['y'] == 'BOMBAY']\n",
    "        \n",
    "        class_0_over = class_0.sample(class_count_6, replace=True)\n",
    "        class_1_over = class_1.sample(class_count_6, replace=True)\n",
    "        class_2_over = class_2.sample(class_count_6, replace=True)\n",
    "        class_3_over = class_3.sample(class_count_6, replace=True)\n",
    "        class_4_over = class_4.sample(class_count_6, replace=True)\n",
    "        class_5_over = class_5.sample(class_count_6, replace=True)\n",
    "        class_6_over = class_6.sample(class_count_6, replace=True)\n",
    "\n",
    "        data = pd.concat([class_1_over,\n",
    "        class_2_over,\n",
    "        class_3_over,\n",
    "        class_4_over,\n",
    "        class_5_over,\n",
    "        class_6_over,\n",
    "        class_0_over], axis=0)\n",
    "        data['y'].value_counts().plot(kind='bar', title='count (target)')\n",
    "        return data\n",
    "\n",
    "  \n",
    "    def visualize_data(self,data):\n",
    "            data_visualize=data.copy()\n",
    "            target_visualize=data_visualize['y']\n",
    "            data_visualize.drop(['y'],axis=1,inplace=True)\n",
    "            for col in data_visualize.columns:\n",
    "                    sns.boxplot(target_visualize,data_visualize[col])\n",
    "                    plt.show()\n",
    "                    \n",
    "    \n",
    "\n",
    "    def Decision_tree_importance(self,data):\n",
    "            from sklearn.tree import DecisionTreeClassifier\n",
    "            data=data.copy()\n",
    "            y=data['y']\n",
    "            X=data.drop('y',axis=1)\n",
    "            clf=DecisionTreeClassifier()\n",
    "            clf.fit(X,y)\n",
    "            importances=clf.feature_importances_\n",
    "            forest_importances = pd.Series(importances, index=X.columns)\n",
    "            fig, ax = plt.subplots()\n",
    "            forest_importances.plot.bar(yerr=forest_importances, ax=ax)\n",
    "            ax.set_title(\"Feature importances using MDI\")\n",
    "            ax.set_ylabel(\"Mean decrease in impurity\")\n",
    "            fig.tight_layout()\n",
    "            \n",
    "            \n",
    "    def disp_skewness(self,data):\n",
    "\n",
    "        data.hist(bins=int(np.sqrt(len(self.data_train))),figsize=(15,15));\n",
    "        skew= data.select_dtypes(np.number).skew()\n",
    "        abs_skew = abs(skew)\n",
    "        kurt = data.select_dtypes(np.number).kurt()\n",
    "        skew_kurt_table = pd.concat([skew,abs_skew,kurt], axis = 1,\n",
    "                                    keys =['Skew','Absolute Skew','Kurtosis']).sort_values(\"Skew\",ascending = False)\n",
    "        skew_kurt_table['Skewed'] = skew_kurt_table['Absolute Skew'].apply(lambda x: True if x >= 0.5 else False)\n",
    "        return skew_kurt_table\n",
    "\n",
    "   \n",
    "\n",
    "    def encode_target(self,data):\n",
    "            data['y'].replace({'DERMASON':0,'SIRA':1,'SEKER':2,'HOROZ':3,'CALI':4,'BARBUNYA':5,'BOMBAY':6},inplace=True)\n",
    "            return data\n",
    "        \n",
    "    def decode_target(self,data):\n",
    "        data['y'].replace({0:'DERMASON',1:'SIRA',2:'SEKER',3:'HOROZ',4:'CALI',5:'BARBUNYA',6:'BOMBAY'},inplace=True)\n",
    "        return data\n",
    "    \n",
    "    def disp_corr(self,data):\n",
    "        correlation=data.corr()\n",
    "        return correlation.style.background_gradient(cmap='coolwarm')\n",
    "    \n",
    "    def remove_outliers(self,data):  ##?????\n",
    "        skew_check=self.disp_skewness(data)\n",
    "        skewed_columns=skew_check.index[skew_check['Skewed'] == True].tolist()\n",
    "        z_scores = stats. zscore(data[skewed_columns])\n",
    "        abs_z_scores = np. abs(z_scores)\n",
    "        filtered_entries = (abs_z_scores < 2.5). all(axis=1)\n",
    "        print(filtered_entries .shape)\n",
    "        new_df = data[filtered_entries]\n",
    "        return new_df\n",
    "    \n",
    "    def feature_engineering(self,data):\n",
    "        pass\n",
    "    \n",
    "    def make_PCA(self,data,n_components=7):\n",
    "        from sklearn.preprocessing import StandardScaler,RobustScaler\n",
    "        scale=RobustScaler()\n",
    "        from sklearn.decomposition import PCA\n",
    "        data_new_features=scale.fit_transform(data)\n",
    "        pca=PCA()\n",
    "        pca.fit(data_new_features)\n",
    "        exp_var=pca.explained_variance_ratio_\n",
    "        exp_cumsum=np.cumsum(exp_var)\n",
    "        plt.plot(exp_cumsum)\n",
    "        pca=PCA(n_components=n_components)\n",
    "        data_scaled=pca.fit_transform(data_new_features)\n",
    "        data_scaled=pd.DataFrame(data_scaled)\n",
    "        return data_scaled\n",
    "    \n",
    "    def data_prep (self,data,test=False):\n",
    "            if test==True:\n",
    "                data_scaled=scale.transform(data)\n",
    "                data_scaled=pca.transform(data_scaled)\n",
    "                data_scaled=pd.DataFrame(data_scaled)\n",
    "                return data_scaled\n",
    "            else :\n",
    "                new_df=self.remove_outliers(data)\n",
    "                labels=new_df['y']\n",
    "                new_df=new_df.drop('y',axis=1)\n",
    "                data_scaled=self.make_PCA(new_df)\n",
    "                return data_scaled,labels\n",
    "            \n",
    "    def try_model(self,data_prepared):\n",
    "        pass\n",
    "            \n",
    "    def train_on_all(self,data_train,data_val,y_train,y_val,bestmodel):\n",
    "            data_tot=pd.concat([data_train,data_val])\n",
    "            y=pd.concat([y_train,y_val])\n",
    "            bestmodel.fit(data_tot,y)\n",
    "            return bestmodel\n",
    "\n",
    "      \n",
    "\n",
    "    def generate_submission_file(data_test,predictions):\n",
    "            data_test_to_model['y']=predictions\n",
    "            s=decode_target(data_test_to_model)\n",
    "            f=pd.concat([id_col,s['y']],axis=1)\n",
    "            return f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "dry_beans_scenario_1=dry_beans(data,data_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(10834,)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_,O=dry_beans_scenario_1.data_prep(data,88)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
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       "             0         1         2         3         4         5         6\n",
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       "...        ...       ...       ...       ...       ...       ...       ...\n",
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       "\n",
       "[9316 rows x 7 columns]"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Area               0.895359\n",
       "Perimeter          0.623382\n",
       "MajorAxisLength    0.576835\n",
       "MinorAxisLength    0.607043\n",
       "AspectRation       0.624159\n",
       "Eccentricity      -0.738867\n",
       "ConvexArea         0.898496\n",
       "EquivDiameter      0.608384\n",
       "Extent            -0.514718\n",
       "Solidity          -0.998977\n",
       "roundness         -0.490783\n",
       "Compactness       -0.064453\n",
       "ShapeFactor1       0.057633\n",
       "ShapeFactor2       0.147655\n",
       "ShapeFactor3       0.115404\n",
       "ShapeFactor4      -1.310383\n",
       "y                  0.532493\n",
       "dtype: float64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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