8 4 9 Light GBM.ipynb

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 "cells": [
  {
   "source": [
    "# LIGHT GBM"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import statsmodels.api as sm \n",
    "import seaborn as sns \n",
    "import matplotlib.pyplot as plt \n",
    "from sklearn.preprocessing import scale, StandardScaler\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, mean_squared_error, r2_score, roc_auc_score, roc_curve, classification_report "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from lightgbm import LGBMClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"verisetleri\\diabetes.csv\")\n",
    "y = df[\"Outcome\"]\n",
    "X = df.drop([\"Outcome\"], axis=1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install lightgbm\n",
    "# !conda install -c conda-forge lightgbm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgbm_model = LGBMClassifier().fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "text": [
      "\u001b[1;31mType:\u001b[0m           LGBMClassifier\n",
      "\u001b[1;31mString form:\u001b[0m    LGBMClassifier()\n",
      "\u001b[1;31mFile:\u001b[0m           d:\\btkakademi_turkcellgy\\.tgy_veribilimi\\lib\\site-packages\\lightgbm\\sklearn.py\n",
      "\u001b[1;31mDocstring:\u001b[0m      LightGBM classifier.\n",
      "\u001b[1;31mInit docstring:\u001b[0m\n",
      "Construct a gradient boosting model.\n",
      "\n",
      "Parameters\n",
      "----------\n",
      "boosting_type : string, optional (default='gbdt')\n",
      "    'gbdt', traditional Gradient Boosting Decision Tree.\n",
      "    'dart', Dropouts meet Multiple Additive Regression Trees.\n",
      "    'goss', Gradient-based One-Side Sampling.\n",
      "    'rf', Random Forest.\n",
      "num_leaves : int, optional (default=31)\n",
      "    Maximum tree leaves for base learners.\n",
      "max_depth : int, optional (default=-1)\n",
      "    Maximum tree depth for base learners, <=0 means no limit.\n",
      "learning_rate : float, optional (default=0.1)\n",
      "    Boosting learning rate.\n",
      "    You can use ``callbacks`` parameter of ``fit`` method to shrink/adapt learning rate\n",
      "    in training using ``reset_parameter`` callback.\n",
      "    Note, that this will ignore the ``learning_rate`` argument in training.\n",
      "n_estimators : int, optional (default=100)\n",
      "    Number of boosted trees to fit.\n",
      "subsample_for_bin : int, optional (default=200000)\n",
      "    Number of samples for constructing bins.\n",
      "objective : string, callable or None, optional (default=None)\n",
      "    Specify the learning task and the corresponding learning objective or\n",
      "    a custom objective function to be used (see note below).\n",
      "    Default: 'regression' for LGBMRegressor, 'binary' or 'multiclass' for LGBMClassifier, 'lambdarank' for LGBMRanker.\n",
      "class_weight : dict, 'balanced' or None, optional (default=None)\n",
      "    Weights associated with classes in the form ``{class_label: weight}``.\n",
      "    Use this parameter only for multi-class classification task;\n",
      "    for binary classification task you may use ``is_unbalance`` or ``scale_pos_weight`` parameters.\n",
      "    Note, that the usage of all these parameters will result in poor estimates of the individual class probabilities.\n",
      "    You may want to consider performing probability calibration\n",
      "    (https://scikit-learn.org/stable/modules/calibration.html) of your model.\n",
      "    The 'balanced' mode uses the values of y to automatically adjust weights\n",
      "    inversely proportional to class frequencies in the input data as ``n_samples / (n_classes * np.bincount(y))``.\n",
      "    If None, all classes are supposed to have weight one.\n",
      "    Note, that these weights will be multiplied with ``sample_weight`` (passed through the ``fit`` method)\n",
      "    if ``sample_weight`` is specified.\n",
      "min_split_gain : float, optional (default=0.)\n",
      "    Minimum loss reduction required to make a further partition on a leaf node of the tree.\n",
      "min_child_weight : float, optional (default=1e-3)\n",
      "    Minimum sum of instance weight (hessian) needed in a child (leaf).\n",
      "min_child_samples : int, optional (default=20)\n",
      "    Minimum number of data needed in a child (leaf).\n",
      "subsample : float, optional (default=1.)\n",
      "    Subsample ratio of the training instance.\n",
      "subsample_freq : int, optional (default=0)\n",
      "    Frequence of subsample, <=0 means no enable.\n",
      "colsample_bytree : float, optional (default=1.)\n",
      "    Subsample ratio of columns when constructing each tree.\n",
      "reg_alpha : float, optional (default=0.)\n",
      "    L1 regularization term on weights.\n",
      "reg_lambda : float, optional (default=0.)\n",
      "    L2 regularization term on weights.\n",
      "random_state : int, RandomState object or None, optional (default=None)\n",
      "    Random number seed.\n",
      "    If int, this number is used to seed the C++ code.\n",
      "    If RandomState object (numpy), a random integer is picked based on its state to seed the C++ code.\n",
      "    If None, default seeds in C++ code are used.\n",
      "n_jobs : int, optional (default=-1)\n",
      "    Number of parallel threads.\n",
      "silent : bool, optional (default=True)\n",
      "    Whether to print messages while running boosting.\n",
      "importance_type : string, optional (default='split')\n",
      "    The type of feature importance to be filled into ``feature_importances_``.\n",
      "    If 'split', result contains numbers of times the feature is used in a model.\n",
      "    If 'gain', result contains total gains of splits which use the feature.\n",
      "**kwargs\n",
      "    Other parameters for the model.\n",
      "    Check http://lightgbm.readthedocs.io/en/latest/Parameters.html for more parameters.\n",
      "\n",
      "    .. warning::\n",
      "\n",
      "        \\*\\*kwargs is not supported in sklearn, it may cause unexpected issues.\n",
      "\n",
      "Note\n",
      "----\n",
      "A custom objective function can be provided for the ``objective`` parameter.\n",
      "In this case, it should have the signature\n",
      "``objective(y_true, y_pred) -> grad, hess`` or\n",
      "``objective(y_true, y_pred, group) -> grad, hess``:\n",
      "\n",
      "    y_true : array-like of shape = [n_samples]\n",
      "        The target values.\n",
      "    y_pred : array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)\n",
      "        The predicted values.\n",
      "    group : array-like\n",
      "        Group/query data, used for ranking task.\n",
      "    grad : array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)\n",
      "        The value of the first order derivative (gradient) for each sample point.\n",
      "    hess : array-like of shape = [n_samples] or shape = [n_samples * n_classes] (for multi-class task)\n",
      "        The value of the second order derivative (Hessian) for each sample point.\n",
      "\n",
      "For binary task, the y_pred is margin.\n",
      "For multi-class task, the y_pred is group by class_id first, then group by row_id.\n",
      "If you want to get i-th row y_pred in j-th class, the access way is y_pred[j * num_data + i]\n",
      "and you should group grad and hess in this way as well.\n"
     ],
     "name": "stdout"
    }
   ],
   "source": [
    "?lgbm_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# class_wight : DENGESİZ SINIF PROBLEMİ AYARI\n",
    "# DENGESİZ SINIF PROBLEMİ : Örneğin sınıfların oranı %10, %90 olması gibi durumlardır.\n",
    "# min_split_gain : hatalarda meydana getirilmesi gereken minimum düşüşü ifade eder ki ancak bundan sonra ben seni bölerim dallanmalar devam eder, ayırmalar devam eder anlamına gelir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = lgbm_model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.7229437229437229"
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MODEL TUNNING"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgbm = LGBMClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgbm_params = {\"learning_rate\":[0.001, 0.01, 0.1], \"n_estimators\":[200, 500, 1000], \"max_depth\":[1,2,35,8]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Fitting 10 folds for each of 36 candidates, totalling 360 fits\n",
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    3.9s\n",
      "[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:    9.8s\n",
      "[Parallel(n_jobs=-1)]: Done 360 out of 360 | elapsed:   20.9s finished\n"
     ]
    }
   ],
   "source": [
    "lgbm_cv_model = GridSearchCV(lgbm, lgbm_params, cv=10, n_jobs=-1, verbose=2).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "{'learning_rate': 0.01, 'max_depth': 1, 'n_estimators': 500}"
      ]
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "source": [
    "lgbm_cv_model.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "lgbm_tuned = LGBMClassifier(learning_rate=0.01, max_depth=1, n_estimators=500).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "0.7575757575757576"
      ]
     },
     "metadata": {},
     "execution_count": 17
    }
   ],
   "source": [
    "y_pred = lgbm_tuned.predict(X_test)\n",
    "accuracy_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DEĞİŞKEN ÖNEM DÜZEYLERİ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "feature_imp = pd.Series(lgbm_tuned.feature_importances_, index=X_train.columns).sort_values(ascending=False)\n",
    "\n",
    "sns.barplot(x=feature_imp, y=feature_imp.index)\n",
    "plt.xlabel('Değişken Önem Skorları')\n",
    "plt.ylabel('Değişkenler')\n",
    "plt.title(\"Değişken Önem Düzeyleri\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}