ModelTraining_RPCA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 411,
   "metadata": {},
   "outputs": [],
   "source": [
    "%run custom_imports.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 412,
   "metadata": {},
   "outputs": [],
   "source": [
    "df= pd.read_csv('jm1.csv')\n",
    "\n",
    "df = df.loc[:, ~df.columns.str.contains('^Unnamed')]\n",
    "\n",
    "random_seed = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 413,
   "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>loc</th>\n",
       "      <th>v(g)</th>\n",
       "      <th>ev(g)</th>\n",
       "      <th>iv(g)</th>\n",
       "      <th>n</th>\n",
       "      <th>v</th>\n",
       "      <th>l</th>\n",
       "      <th>d</th>\n",
       "      <th>i</th>\n",
       "      <th>e</th>\n",
       "      <th>...</th>\n",
       "      <th>lOCode</th>\n",
       "      <th>lOComment</th>\n",
       "      <th>lOBlank</th>\n",
       "      <th>locCodeAndComment</th>\n",
       "      <th>uniq_Op</th>\n",
       "      <th>uniq_Opnd</th>\n",
       "      <th>total_Op</th>\n",
       "      <th>total_Opnd</th>\n",
       "      <th>branchCount</th>\n",
       "      <th>defects</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1.30</td>\n",
       "      <td>1.30</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1.4</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>72.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>1134.13</td>\n",
       "      <td>0.05</td>\n",
       "      <td>20.31</td>\n",
       "      <td>55.85</td>\n",
       "      <td>23029.10</td>\n",
       "      <td>...</td>\n",
       "      <td>51</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>36</td>\n",
       "      <td>112</td>\n",
       "      <td>86</td>\n",
       "      <td>13</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>190.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>4348.76</td>\n",
       "      <td>0.06</td>\n",
       "      <td>17.06</td>\n",
       "      <td>254.87</td>\n",
       "      <td>74202.67</td>\n",
       "      <td>...</td>\n",
       "      <td>129</td>\n",
       "      <td>29</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>17</td>\n",
       "      <td>135</td>\n",
       "      <td>329</td>\n",
       "      <td>271</td>\n",
       "      <td>5</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>37.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>126.0</td>\n",
       "      <td>599.12</td>\n",
       "      <td>0.06</td>\n",
       "      <td>17.19</td>\n",
       "      <td>34.86</td>\n",
       "      <td>10297.30</td>\n",
       "      <td>...</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "      <td>76</td>\n",
       "      <td>50</td>\n",
       "      <td>7</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     loc  v(g)  ev(g)  iv(g)      n        v     l      d       i         e  \\\n",
       "0    1.1   1.4    1.4    1.4    1.3     1.30  1.30   1.30    1.30      1.30   \n",
       "1    1.0   1.0    1.0    1.0    1.0     1.00  1.00   1.00    1.00      1.00   \n",
       "2   72.0   7.0    1.0    6.0  198.0  1134.13  0.05  20.31   55.85  23029.10   \n",
       "3  190.0   3.0    1.0    3.0  600.0  4348.76  0.06  17.06  254.87  74202.67   \n",
       "4   37.0   4.0    1.0    4.0  126.0   599.12  0.06  17.19   34.86  10297.30   \n",
       "\n",
       "   ...  lOCode  lOComment  lOBlank  locCodeAndComment  uniq_Op  uniq_Opnd  \\\n",
       "0  ...       2          2        2                  2      1.2        1.2   \n",
       "1  ...       1          1        1                  1        1          1   \n",
       "2  ...      51         10        8                  1       17         36   \n",
       "3  ...     129         29       28                  2       17        135   \n",
       "4  ...      28          1        6                  0       11         16   \n",
       "\n",
       "  total_Op total_Opnd branchCount defects  \n",
       "0      1.2        1.2         1.4   False  \n",
       "1        1          1           1    True  \n",
       "2      112         86          13    True  \n",
       "3      329        271           5    True  \n",
       "4       76         50           7    True  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 413,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 414,
   "metadata": {},
   "outputs": [],
   "source": [
    "def custom_preprocessing(data):\n",
    "\n",
    "    column_name = 'problems'\n",
    "\n",
    "    if column_name in data.columns:\n",
    "        data = data.rename(columns={'column_name': 'defects'})\n",
    "\n",
    "    data['defects'] = data['defects'].astype('str')\n",
    "    \n",
    "    data = data.replace(\"False\", '0', regex=True)\n",
    "    data = data.replace(\"True\", '1', regex=True)\n",
    "\n",
    "    data = data.replace(\"no\", '0', regex=True)\n",
    "    data = data.replace(\"yes\", '1', regex=True)\n",
    "\n",
    "    data['defects'] = data['defects'].astype('float64')\n",
    "\n",
    "    data = data.loc[:, ~data.columns.str.contains('^Unnamed')]\n",
    "\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 415,
   "metadata": {},
   "outputs": [],
   "source": [
    "def class_balance(df):\n",
    "    \n",
    "    list_class = df['defects'].value_counts()\n",
    "\n",
    "    total = sum(list_class)\n",
    "\n",
    "    a = list_class[0]/total *100\n",
    "\n",
    "    b = list_class[1]/total * 100\n",
    "\n",
    "    if a>b:\n",
    "        majority = a\n",
    "        minority = b\n",
    "    elif b>a:\n",
    "        majority = b\n",
    "        minority = a\n",
    "\n",
    "    specs = (majority,minority)\n",
    "\n",
    "    print('\\nMajority class is {:.3f} and Minority class is {:.3f}\\n'.format(majority, minority))\n",
    "\n",
    "    major, minor = specs\n",
    "\n",
    "    imbalance_group = 0\n",
    "\n",
    "    if minor<5:\n",
    "        imbalance_group = 3\n",
    "    elif minor>=5 and minor<=20:\n",
    "        imbalance_group = 2\n",
    "    elif minor>20 and minor<=40:\n",
    "        imbalance_group = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function to convert non-numeric values to zero\n",
    "def convert_to_numeric(value):\n",
    "    try:\n",
    "        return float(value)\n",
    "    except ValueError:\n",
    "        return 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 417,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = custom_preprocessing(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Majority class is 80.652 and Minority class is 19.348\n",
      "\n"
     ]
    }
   ],
   "source": [
    "class_balance(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10885 entries, 0 to 10884\n",
      "Data columns (total 22 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   loc                10885 non-null  float64\n",
      " 1   v(g)               10885 non-null  float64\n",
      " 2   ev(g)              10885 non-null  float64\n",
      " 3   iv(g)              10885 non-null  float64\n",
      " 4   n                  10885 non-null  float64\n",
      " 5   v                  10885 non-null  float64\n",
      " 6   l                  10885 non-null  float64\n",
      " 7   d                  10885 non-null  float64\n",
      " 8   i                  10885 non-null  float64\n",
      " 9   e                  10885 non-null  float64\n",
      " 10  b                  10885 non-null  float64\n",
      " 11  t                  10885 non-null  float64\n",
      " 12  lOCode             10885 non-null  int64  \n",
      " 13  lOComment          10885 non-null  int64  \n",
      " 14  lOBlank            10885 non-null  int64  \n",
      " 15  locCodeAndComment  10885 non-null  int64  \n",
      " 16  uniq_Op            10885 non-null  object \n",
      " 17  uniq_Opnd          10885 non-null  object \n",
      " 18  total_Op           10885 non-null  object \n",
      " 19  total_Opnd         10885 non-null  object \n",
      " 20  branchCount        10885 non-null  object \n",
      " 21  defects            10885 non-null  float64\n",
      "dtypes: float64(13), int64(4), object(5)\n",
      "memory usage: 1.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {},
   "outputs": [],
   "source": [
    "for column in df.columns:\n",
    "    if df[column].dtype == 'object':\n",
    "        df[column] = df[column].apply(convert_to_numeric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x1500 with 24 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create subplots\n",
    "fig, axes = plt.subplots(3, 8, figsize=(20, 15))\n",
    "\n",
    "# Flatten the axes array for easier indexing\n",
    "axes = axes.flatten()\n",
    "\n",
    "# Loop over the y_variables list and create boxplots\n",
    "for i, y_variable in enumerate(df.columns):\n",
    "    row = i // 10\n",
    "    col = i % 10\n",
    "    sns.boxplot(x=\"defects\",y=y_variable, data=df, ax=axes[i])\n",
    "    axes[i].set_title(y_variable)\n",
    "\n",
    "# Adjust layout and display the plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['defects'],axis=1)\n",
    "y = df['defects']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_holdout, y_train,y_holdout = train_test_split(X,y,test_size=0.3,random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 424,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(7619, 21)"
      ]
     },
     "execution_count": 424,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Principal Component Analysis - Robust"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 425,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_names = X_train.columns\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "X_scaled = scaler.fit_transform(X_train)\n",
    "\n",
    "robpca = rpca.RobustPCA(lmb=4e-3, max_iter=100)\n",
    "\n",
    "L, S = robpca.fit(X_scaled)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 426,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected Features: ['n', 'total_Opnd', 'total_Op', 'v', 'b', 'lOCode', 'loc', 'd', 'uniq_Opnd', 'branchCount', 'lOBlank', 'lOComment', 'v(g)']\n"
     ]
    }
   ],
   "source": [
    "feature_importance = np.linalg.norm(L, axis=0)\n",
    "\n",
    "# Rank features based on importance scores\n",
    "sorted_indices = np.argsort(feature_importance)[::-1]\n",
    "\n",
    "threshold = feature_importance.mean()\n",
    "\n",
    "# Select top features based on threshold\n",
    "selected_features = sorted_indices[feature_importance[sorted_indices] > threshold]\n",
    "\n",
    "selected_features = [feature_names[idx] for idx in selected_features]\n",
    "\n",
    "# Print selected feature indices or names\n",
    "print(\"Selected Features:\", selected_features)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 427,
   "metadata": {},
   "outputs": [],
   "source": [
    "reduced_df = X_train[selected_features]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 428,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Post Robust PCA, Reconstruction Error =  362447.8110496896\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Combined Feature Ranking across all Components:\n",
      "Index(['lOComment', 'd', 'lOBlank', 'uniq_Opnd', 'n', 'lOCode', 'v', 'loc',\n",
      "       'total_Op', 'b', 'branchCount', 'v(g)', 'total_Opnd'],\n",
      "      dtype='object')\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "\n",
    "X_scaled = scaler.fit_transform(reduced_df)\n",
    "\n",
    "pca = PCA(n_components='mle')\n",
    "\n",
    "pca.fit(X_scaled)\n",
    "\n",
    "X_transformed = pca.transform(X_scaled)\n",
    "\n",
    "X_recon = pca.inverse_transform(X_transformed)\n",
    "\n",
    "mse = mean_squared_error(reduced_df,X_recon)\n",
    "\n",
    "print(\"Post Robust PCA, Reconstruction Error = \",mse)\n",
    "\n",
    "# Calculate the cumulative explained variance\n",
    "cumulative_explained_variance = np.cumsum(pca.explained_variance_ratio_)\n",
    "\n",
    "# Plot the cumulative explained variance\n",
    "num_components = len(pca.explained_variance_ratio_)\n",
    "components = np.arange(1, num_components + 1)\n",
    "plt.plot(components, cumulative_explained_variance, marker='o', linestyle='-')\n",
    "plt.xlabel('Number of Components')\n",
    "plt.ylabel('Cumulative Explained Variance')\n",
    "plt.title('Elbow Method - Optimal Number of Components')\n",
    "plt.grid(True)\n",
    "\n",
    "diff = np.diff(cumulative_explained_variance)\n",
    "elbow_x = np.where(diff <= 0.005)[0][0]\n",
    "elbow_y = cumulative_explained_variance[elbow_x]\n",
    "\n",
    "plt.axhline(y=elbow_y,color='r',linestyle='--')\n",
    "plt.axvline(x=elbow_x,color='r',linestyle='--')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "n_features = reduced_df.shape[1]\n",
    "n_samples = reduced_df.shape[0]\n",
    "\n",
    "# Get principal component loadings (coefficients)\n",
    "pc_loadings = pca.components_\n",
    "\n",
    "# Create a DataFrame for the heatmap\n",
    "loadings_df = pd.DataFrame(pc_loadings, columns=reduced_df.columns)\n",
    "\n",
    "# Create a heatmap\n",
    "plt.figure(figsize=(15, 10))\n",
    "sns.heatmap(loadings_df, cmap='RdBu_r', center=0, annot=True, fmt=\".2f\")\n",
    "plt.title('Principal Component Loadings Heatmap')\n",
    "plt.xlabel('Original Features')\n",
    "plt.ylabel('Principal Components')\n",
    "plt.show()\n",
    "\n",
    "feature_ranking = loadings_df.abs().rank(ascending=True, axis=1)\n",
    "\n",
    "feature_names = reduced_df.columns\n",
    "\n",
    "# Map indices to feature names and print ranked features for each component\n",
    "for component_num in range(n_features):\n",
    "    feature_rankings = feature_ranking.iloc[:, component_num]\n",
    "    features_sorted_by_rank = feature_rankings.sort_values(ascending=True).index\n",
    "    ranked_features_with_names = [feature_names[i] for i in features_sorted_by_rank]\n",
    "\n",
    "\n",
    "# Calculate the average rank for each feature across all components\n",
    "average_ranks = feature_ranking.mean()\n",
    "\n",
    "# Sort features based on average ranks\n",
    "sorted_features = average_ranks.sort_values(ascending=True).index\n",
    "\n",
    "print(\"Combined Feature Ranking across all Components:\")\n",
    "print(sorted_features)\n",
    "print(\"\\n\")\n",
    "\n",
    "selected_loadings = loadings_df.iloc[:elbow_x, :]\n",
    "selected_features = selected_loadings.idxmax(axis=1)\n",
    "\n",
    "# Create the reduced dataset by selecting chosen principal components\n",
    "reduced_dataset = X_transformed[:, :elbow_x]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 429,
   "metadata": {},
   "outputs": [],
   "source": [
    "loadings_df.to_csv('RPCA_PCAL.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 430,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "selected features : \n",
      " 0            v\n",
      "1         v(g)\n",
      "2    lOComment\n",
      "3      lOBlank\n",
      "4    uniq_Opnd\n",
      "5    uniq_Opnd\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "print('selected features : \\n',selected_features)\n",
    "\n",
    "selected_features = list(set(selected_features))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 431,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('RobustPCA_features.pkl', 'wb') as file:\n",
    "    pickle.dump(selected_features, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 432,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['v(g)', 'uniq_Opnd', 'lOComment', 'lOBlank', 'v']"
      ]
     },
     "execution_count": 432,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "selected_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 433,
   "metadata": {},
   "outputs": [],
   "source": [
    "reduced_df = reduced_df[selected_features]\n",
    "\n",
    "reduced_df = reduced_df.loc[:, ~reduced_df.columns.str.contains('^Unnamed')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 434,
   "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>v(g)</th>\n",
       "      <th>uniq_Opnd</th>\n",
       "      <th>lOComment</th>\n",
       "      <th>lOBlank</th>\n",
       "      <th>v</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>613</th>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>48.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4030</th>\n",
       "      <td>4.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>421.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3582</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6237</th>\n",
       "      <td>12.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1430</th>\n",
       "      <td>4.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1192.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5734</th>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>83.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5191</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5390</th>\n",
       "      <td>12.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>684.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>860</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7270</th>\n",
       "      <td>37.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>37</td>\n",
       "      <td>33</td>\n",
       "      <td>3665.38</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7619 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      v(g)  uniq_Opnd  lOComment  lOBlank        v\n",
       "613    2.0        5.0          0        0    48.43\n",
       "4030   4.0       19.0          1        5   421.11\n",
       "3582   1.0        0.0          0        0     0.00\n",
       "6237  12.0        0.0          0        0     0.00\n",
       "1430   4.0       41.0          1        5  1192.08\n",
       "...    ...        ...        ...      ...      ...\n",
       "5734   2.0        5.0          0        0    83.76\n",
       "5191   1.0        1.0          0        0     8.00\n",
       "5390  12.0       29.0          0        2   684.99\n",
       "860    1.0        0.0          0        0     0.00\n",
       "7270  37.0       29.0         37       33  3665.38\n",
       "\n",
       "[7619 rows x 5 columns]"
      ]
     },
     "execution_count": 434,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reduced_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 435,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Decision Tree:\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean cross-validation accuracy is 0.753 with SD 0.014\n",
      "roc_auc 0.9915303324966611\n"
     ]
    }
   ],
   "source": [
    "print(\"Decision Tree:\")\n",
    "\n",
    "model_dt = tree.DecisionTreeClassifier(criterion='entropy', splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, class_weight=None, ccp_alpha=0.0)\n",
    "\n",
    "model_dt.fit(reduced_df, y_train)\n",
    "\n",
    "dump(model_dt,'model_dt.pkl')\n",
    "\n",
    "scores = cross_val_score(model_dt, reduced_df, y_train, cv=10, scoring='accuracy')\n",
    "print('Mean cross-validation accuracy is {:.3f} with SD {:.3f}'.format(np.mean(scores), np.std(scores)))\n",
    "\n",
    "# Make predictions on the training data\n",
    "y_train_pred_prob = model_dt.predict_proba(reduced_df)[:, 1]\n",
    "\n",
    "# Calculate ROC curve\n",
    "fpr, tpr, thresholds = roc_curve(y_train, y_train_pred_prob)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "print(\"roc_auc\",roc_auc)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "AdaBoost Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 436,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AdaBoost Classifier:\n",
      "Mean cross-validation accuracy is 0.756 with SD 0.016\n",
      "roc_auc 0.9915303324966611\n"
     ]
    }
   ],
   "source": [
    "print(\"AdaBoost Classifier:\")\n",
    "\n",
    "adaboosted_clf = AdaBoostClassifier(estimator=model_dt,\n",
    "                                    n_estimators=25,\n",
    "                                    learning_rate=0.001,\n",
    "                                    algorithm='SAMME.R',\n",
    "                                    random_state=random_seed)\n",
    "\n",
    "adaboosted_clf.fit(reduced_df,y_train)\n",
    "\n",
    "scores = cross_val_score(adaboosted_clf, reduced_df, y_train, cv=10, scoring='accuracy',n_jobs=-1)\n",
    "print('Mean cross-validation accuracy is {:.3f} with SD {:.3f}'.format(np.mean(scores), np.std(scores)))\n",
    "\n",
    "# Make predictions on the training data\n",
    "y_train_pred_prob = adaboosted_clf.predict_proba(reduced_df)[:, 1]\n",
    "\n",
    "# Calculate ROC curve\n",
    "fpr, tpr, thresholds = roc_curve(y_train, y_train_pred_prob)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "print(\"roc_auc\",roc_auc)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bagging Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 437,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bagging Classifier:\n",
      "Mean cross-validation accuracy is 0.810 with SD 0.002\n",
      "roc_auc 0.6915014152032309\n"
     ]
    }
   ],
   "source": [
    "print(\"Bagging Classifier:\")\n",
    "\n",
    "bagged_clf = BaggingClassifier(estimator=model_dt,\n",
    "                               n_estimators=50,\n",
    "                               random_state=random_seed,\n",
    "                               max_samples=0.001,\n",
    "                               max_features=0.8,\n",
    "                               bootstrap=True,\n",
    "                               oob_score=True,\n",
    "                               warm_start=False)\n",
    "\n",
    "bagged_clf.fit(reduced_df,y_train)\n",
    "\n",
    "scores = cross_val_score(bagged_clf, reduced_df, y_train, cv=10, scoring='accuracy',n_jobs=-1)\n",
    "print('Mean cross-validation accuracy is {:.3f} with SD {:.3f}'.format(np.mean(scores), np.std(scores)))\n",
    "\n",
    "# Make predictions on the training data\n",
    "y_train_pred_prob = bagged_clf.predict_proba(reduced_df)[:, 1]\n",
    "\n",
    "# Calculate ROC curve\n",
    "fpr, tpr, thresholds = roc_curve(y_train, y_train_pred_prob)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "print(\"roc_auc\",roc_auc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stacked Generalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 438,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stacked Generalization:\n",
      "Mean cross-validation accuracy is 0.810 with SD 0.006\n",
      "roc_auc 0.8676872186451776\n"
     ]
    }
   ],
   "source": [
    "print(\"Stacked Generalization:\")\n",
    "\n",
    "estimators = [(\"model_ada\", adaboosted_clf),\n",
    "              (\"model_bagged\",bagged_clf)]\n",
    "\n",
    "stacked_clf = StackingClassifier(estimators=estimators)\n",
    "\n",
    "stacked_clf.fit(reduced_df,y_train)\n",
    "\n",
    "with open('stacked_clf.pkl', 'wb') as f:\n",
    "  pickle.dump(stacked_clf, f)\n",
    "\n",
    "scores = cross_val_score(stacked_clf, reduced_df, y_train, cv=10, scoring='accuracy',n_jobs=-1)\n",
    "print('Mean cross-validation accuracy is {:.3f} with SD {:.3f}'.format(np.mean(scores), np.std(scores)))\n",
    "\n",
    "# Make predictions on the training data\n",
    "y_train_pred_prob = stacked_clf.predict_proba(reduced_df)[:, 1]\n",
    "\n",
    "# Calculate ROC curve\n",
    "fpr, tpr, thresholds = roc_curve(y_train, y_train_pred_prob)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "print(\"roc_auc\",roc_auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 439,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test = pd.read_csv('pc1.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 440,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "STACKED CLASSIFIER\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.83      0.98      0.90      8779\n",
      "         1.0       0.68      0.19      0.30      2106\n",
      "\n",
      "    accuracy                           0.83     10885\n",
      "   macro avg       0.76      0.59      0.60     10885\n",
      "weighted avg       0.80      0.83      0.78     10885\n",
      "\n",
      "Precision-Recall-AUC : 0.3213860784921878\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "with open('RobustPCA_features.pkl','rb') as file:\n",
    "    selected_features = pickle.load(file)\n",
    "\n",
    "\n",
    "X_test = df.drop(['defects'],axis=1)\n",
    "\n",
    "X_test = X_test[selected_features]\n",
    "\n",
    "y_test = df['defects']\n",
    "\n",
    "print(\"STACKED CLASSIFIER\")\n",
    "\n",
    "predictions = stacked_clf.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test,predictions))\n",
    "\n",
    "# Calculate false positive rate (fpr), true positive rate (tpr), and thresholds\n",
    "fpr1, tpr1, thresholds1 = roc_curve(y_test, predictions)\n",
    "# Calculate AUC (Area Under the ROC Curve)\n",
    "roc_auc1 = auc(fpr1, tpr1)\n",
    "#calculate precision, recall and threshold\n",
    "precision, recall, threshold_2 = precision_recall_curve(y_test, predictions)\n",
    "# Calculate AUC (Area Under the PR Curve)\n",
    "pr_auc = auc(precision, recall)\n",
    "\n",
    "print(\"Precision-Recall-AUC :\",pr_auc)\n",
    "\n",
    "# Plot ROC curve\n",
    "# plt.figure()\n",
    "plt.plot(fpr1, tpr1, color='green', label='ROC curve test (area = %0.2f)' % roc_auc1)\n",
    "plt.plot([0, 1], [0, 1], color='navy', linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()\n",
    "\n",
    "\n",
    "#confusion matrix\n",
    "cm = confusion_matrix(y_true=y_test, y_pred=predictions)\n",
    "plt.figure(figsize=(5,5))\n",
    "ax = sns.heatmap(cm,annot=True, xticklabels=['non-defective', 'defective'], cbar=False,\n",
    "yticklabels=['Bad', 'Good'], square=True,\n",
    "linewidths=8.0) # plots the confusion matrix\n",
    "ax.set_xlabel('Predicted Defect Status')\n",
    "ax.set_ylabel('Actual Defect Status')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 441,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.4, 1.2, 2. , 2. , 1.3]])"
      ]
     },
     "execution_count": 441,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test = np.array(X_test)\n",
    "\n",
    "X_test[0].reshape(1,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 442,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = stacked_clf.predict((X_test[102].reshape(1,-1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.])"
      ]
     },
     "execution_count": 443,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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