Classification/CatBoost/CatBoostClassifier.ipynb

{
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    "<img src=\"https://cdn.siberian.dev/siberian.png\" alt=\"Siberian Logo\">"
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CatBoost Classifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classification using CatBoostClassifier. CatBoost is an algorithm for gradient boosting on decision trees. CatBoost name comes from two words Category and Boosting. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Required Packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install catboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings \r\n",
    "import numpy as np \r\n",
    "import pandas as pd \r\n",
    "import matplotlib.pyplot as plt \r\n",
    "import seaborn as se \r\n",
    "from sklearn.preprocessing import LabelEncoder\r\n",
    "from sklearn.model_selection import train_test_split \r\n",
    "from sklearn.pipeline import make_pipeline\r\n",
    "from sklearn.metrics import classification_report,plot_confusion_matrix\r\n",
    "from catboost import CatBoostClassifier\r\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialization\n",
    "\n",
    "Filepath of CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#filepath\r\n",
    "file_path= ''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "List of features which are  required for model training ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#x_values\n",
    "features=[]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Target feature for prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#y_value\n",
    "target=''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Fetching\n",
    "\n",
    "Pandas is an open-source, BSD-licensed library providing high-performance, easy-to-use data manipulation and data analysis tools.\n",
    "\n",
    "We will use panda's library to read the CSV file using its storage path.And we use the head function to display the initial row or entry."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>Sex</th>\n",
       "      <th>BP</th>\n",
       "      <th>Cholesterol</th>\n",
       "      <th>Na_to_K</th>\n",
       "      <th>Drug</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23</td>\n",
       "      <td>F</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>25.355</td>\n",
       "      <td>DrugY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>47</td>\n",
       "      <td>M</td>\n",
       "      <td>LOW</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>13.093</td>\n",
       "      <td>drugC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47</td>\n",
       "      <td>M</td>\n",
       "      <td>LOW</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>10.114</td>\n",
       "      <td>drugC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28</td>\n",
       "      <td>F</td>\n",
       "      <td>NORMAL</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>7.798</td>\n",
       "      <td>drugX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>61</td>\n",
       "      <td>F</td>\n",
       "      <td>LOW</td>\n",
       "      <td>HIGH</td>\n",
       "      <td>18.043</td>\n",
       "      <td>DrugY</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age Sex      BP Cholesterol  Na_to_K   Drug\n",
       "0   23   F    HIGH        HIGH   25.355  DrugY\n",
       "1   47   M     LOW        HIGH   13.093  drugC\n",
       "2   47   M     LOW        HIGH   10.114  drugC\n",
       "3   28   F  NORMAL        HIGH    7.798  drugX\n",
       "4   61   F     LOW        HIGH   18.043  DrugY"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(file_path)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Selections\n",
    "\n",
    "It is the process of reducing the number of input variables when developing a predictive model. Used to reduce the number of input variables to both reduce the computational cost of modelling and, in some cases, to improve the performance of the model.\n",
    "\n",
    "We will assign all the required input features to X and target/outcome to Y."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df[features]\n",
    "Y = df[target]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preprocessing\n",
    "\n",
    "Since the majority of the machine learning models doesn't handle string category data and Null value, we have to explicitly remove or replace null values. The below snippet have functions, which removes the null value if any exists. And convert the string classes data in the datasets by encoding them to integer classes.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def NullClearner(df):\n",
    "    if(isinstance(df, pd.Series) and (df.dtype in [\"float64\",\"int64\"])):\n",
    "        df.fillna(df.mean(),inplace=True)\n",
    "        return df\n",
    "    elif(isinstance(df, pd.Series)):\n",
    "        df.fillna(df.mode()[0],inplace=True)\n",
    "        return df\n",
    "    else:return df\n",
    "def EncodeX(df):\n",
    "    return pd.get_dummies(df)\n",
    "def EncodeY(df):\n",
    "    if len(df.unique())<=2:\n",
    "        return df\n",
    "    else:\n",
    "        un_EncodedT=np.sort(pd.unique(df), axis=-1, kind='mergesort')\n",
    "        df=LabelEncoder().fit_transform(df)\n",
    "        EncodedT=[xi for xi in range(len(un_EncodedT))]\n",
    "        print(\"Encoded Target: {} to {}\".format(un_EncodedT,EncodedT))\n",
    "        return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Encoded Target: ['DrugY' 'drugA' 'drugB' 'drugC' 'drugX'] to [0, 1, 2, 3, 4]\n"
     ]
    },
    {
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       "\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>Na_to_K</th>\n",
       "      <th>Sex_F</th>\n",
       "      <th>Sex_M</th>\n",
       "      <th>BP_HIGH</th>\n",
       "      <th>BP_LOW</th>\n",
       "      <th>BP_NORMAL</th>\n",
       "      <th>Cholesterol_HIGH</th>\n",
       "      <th>Cholesterol_NORMAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>23</td>\n",
       "      <td>25.355</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>47</td>\n",
       "      <td>13.093</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>47</td>\n",
       "      <td>10.114</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>28</td>\n",
       "      <td>7.798</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>61</td>\n",
       "      <td>18.043</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Age  Na_to_K  Sex_F  Sex_M  BP_HIGH  BP_LOW  BP_NORMAL  Cholesterol_HIGH  \\\n",
       "0   23   25.355      1      0        1       0          0                 1   \n",
       "1   47   13.093      0      1        0       1          0                 1   \n",
       "2   47   10.114      0      1        0       1          0                 1   \n",
       "3   28    7.798      1      0        0       0          1                 1   \n",
       "4   61   18.043      1      0        0       1          0                 1   \n",
       "\n",
       "   Cholesterol_NORMAL  \n",
       "0                   0  \n",
       "1                   0  \n",
       "2                   0  \n",
       "3                   0  \n",
       "4                   0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=X.columns.to_list()\n",
    "for i in x:\n",
    "    X[i]=NullClearner(X[i])  \n",
    "X=EncodeX(X)\n",
    "Y=EncodeY(NullClearner(Y))\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Correlation Map\n",
    "\n",
    "In order to check the correlation between the features, we will plot a correlation matrix. It is effective in summarizing a large amount of data where the goal is to see patterns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1296x1296 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f,ax = plt.subplots(figsize=(18, 18))\n",
    "matrix = np.triu(X.corr())\n",
    "se.heatmap(X.corr(), annot=True, linewidths=.5, fmt= '.1f',ax=ax, mask=matrix)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Distribution Of Target Variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='count'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10,6))\n",
    "se.countplot(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Splitting\n",
    "\n",
    "The train-test split is a procedure for evaluating the performance of an algorithm. The procedure involves taking a dataset and dividing it into two subsets. The first subset is utilized to fit/train the model. The second subset is used for prediction. The main motive is to estimate the performance of the model on new data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(X,Y,test_size=0.2,random_state=123)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model\n",
    "\n",
    "CatBoost is an algorithm for gradient boosting on decision trees. Developed by Yandex researchers and engineers, it is the successor of the MatrixNet algorithm that is widely used within the company for ranking tasks, forecasting and making recommendations\n",
    "\n",
    "#### Tuning parameters\n",
    "\n",
    "1. **learning_rate**:, float, default = it is defined automatically for Logloss, MultiClass & RMSE loss functions depending on the number of iterations if none of these parameters is set\n",
    ">The learning rate. Used for reducing the gradient step.\n",
    "\n",
    "2. **l2_leaf_reg**: float, default = 3.0\n",
    ">Coefficient at the L2 regularization term of the cost function. Any positive value is allowed.\n",
    "\n",
    "3. **bootstrap_type**: string, default = depends on the selected mode and processing unit\n",
    ">Bootstrap type. Defines the method for sampling the weights of objects.\n",
    "        * Supported methods:\n",
    "        * Bayesian\n",
    "        * Bernoulli\n",
    "        * MVS\n",
    "        * Poisson (supported for GPU only)\n",
    "        * No\n",
    "4. **subsample**: float, default = depends on the dataset size and the bootstrap type\n",
    ">Sample rate for bagging. This parameter can be used if one of the following bootstrap types is selected:\n",
    "        * Poisson\n",
    "        * Bernoulli\n",
    "        * MVS\n",
    "\n",
    "For more information refer: [API](https://catboost.ai/docs/concepts/python-reference_parameters-list.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<catboost.core.CatBoostClassifier at 0x236c8474b20>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Build Model here\n",
    "model = CatBoostClassifier(verbose=False)\n",
    "model.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Model Accuracy\n",
    "\n",
    "score() method return the mean accuracy on the given test data and labels.\n",
    "\n",
    "In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score 100.00 %\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy score {:.2f} %\\n\".format(model.score(x_test,y_test)*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Confusion Matrix\n",
    "\n",
    "A confusion matrix is utilized to understand the performance of the classification model or algorithm in machine learning for a given test set where results are known."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x236c83762e0>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(model,x_test,y_test,cmap=plt.cm.Blues)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Classification Report\n",
    "A Classification report is used to measure the quality of predictions from a classification algorithm. How many predictions are True, how many are False.\n",
    "\n",
    "* **where**:\n",
    "    - Precision:- Accuracy of positive predictions.\n",
    "    - Recall:- Fraction of positives that were correctly identified.\n",
    "    - f1-score:-  percent of positive predictions were correct\n",
    "    - support:- Support is the number of actual occurrences of the class in the specified dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        23\n",
      "           1       1.00      1.00      1.00         2\n",
      "           2       1.00      1.00      1.00         3\n",
      "           3       1.00      1.00      1.00         3\n",
      "           4       1.00      1.00      1.00         9\n",
      "\n",
      "    accuracy                           1.00        40\n",
      "   macro avg       1.00      1.00      1.00        40\n",
      "weighted avg       1.00      1.00      1.00        40\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test,model.predict(x_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " #### Creator: Viraj Jayant, Github: [Profile](https://github.com/Viraj-Jayant/)"
   ]
  }
 ],
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