examples/quick-starts/classification/XGBoostClassifier.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# `classification.XGBoostClassifier`"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "import slickml\n",
    "\n",
    "print(f\"Loaded SlickML Version = {slickml.__version__}\")"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Loaded SlickML Version = 0.2.0\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "source": [
    "from slickml.classification import XGBoostClassifier\n",
    "\n",
    "help(XGBoostClassifier)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Help on class XGBoostClassifier in module slickml.classification._xgboost:\n",
      "\n",
      "class XGBoostClassifier(slickml.base._estimator.BaseXGBoostEstimator, sklearn.base.ClassifierMixin)\n",
      " |  XGBoostClassifier(num_boost_round: Optional[int] = 200, sparse_matrix: Optional[bool] = False, scale_mean: Optional[bool] = False, scale_std: Optional[bool] = False, importance_type: Optional[str] = 'total_gain', params: Optional[Dict[str, Union[str, float, int]]] = None) -> None\n",
      " |  \n",
      " |  XGBoost Classifier.\n",
      " |  \n",
      " |  This is a wrapper using XGBoost classifier to train a XGBoost [xgboost-api]_ model using the number of\n",
      " |  boosting rounds from the inputs. This is also the base class for ``XGBoostCVClassifier``.\n",
      " |  \n",
      " |  Parameters\n",
      " |  ----------\n",
      " |  num_boost_round : int, optional\n",
      " |      Number of boosting rounds to fit a model, by default 200\n",
      " |  \n",
      " |  sparse_matrix : bool, optional\n",
      " |      Whether to convert the input features to sparse matrix with csr format or not. This would\n",
      " |      increase the speed of feature selection for relatively large/sparse datasets. Consequently,\n",
      " |      this would actually act like an un-optimize solution for dense feature matrix. Additionally,\n",
      " |      this parameter cannot be used along with ``scale_mean=True`` standardizing the feature matrix\n",
      " |      to have a mean value of zeros would turn the feature matrix into a dense matrix. Therefore,\n",
      " |      by default our API banned this feature, by default False\n",
      " |  \n",
      " |  scale_mean : bool, optional\n",
      " |      Whether to standarize the feauture matrix to have a mean value of zero per feature (center\n",
      " |      the features before scaling). As laid out in ``sparse_matrix``, ``scale_mean=False`` when\n",
      " |      using ``sparse_matrix=True``, since centering the feature matrix would decrease the sparsity\n",
      " |      and in practice it does not make any sense to use sparse matrix method and it would make\n",
      " |      it worse. The ``StandardScaler`` object can be accessed via ``cls.scaler_`` if ``scale_mean`` or\n",
      " |      ``scale_strd`` is used unless it is ``None``, by default False\n",
      " |  \n",
      " |  scale_std : bool, optional\n",
      " |      Whether to scale the feauture matrix to have unit variance (or equivalently, unit standard\n",
      " |      deviation) per feature. The ``StandardScaler`` object can be accessed via ``cls.scaler_``\n",
      " |      if ``scale_mean`` or ``scale_strd`` is used unless it is ``None``, by default False\n",
      " |  \n",
      " |  importance_type : str, optional\n",
      " |      Importance type of ``xgboost.train()`` with possible values ``\"weight\"``, ``\"gain\"``,\n",
      " |      ``\"total_gain\"``, ``\"cover\"``, ``\"total_cover\"``, by default \"total_gain\"\n",
      " |  \n",
      " |  params : Dict[str, Union[str, float, int]], optional\n",
      " |      Set of parameters required for fitting a Booster, by default {\"eval_metric\": \"auc\",\n",
      " |      \"tree_method\": \"hist\", \"objective\": \"binary:logistic\", \"learning_rate\": 0.05,\n",
      " |      \"max_depth\": 2, \"min_child_weight\": 1, \"gamma\": 0.0, \"reg_alpha\": 0.0, \"reg_lambda\": 1.0,\n",
      " |      \"subsample\": 0.9, \"max_delta_step\": 1, \"verbosity\": 0, \"nthread\": 4, \"scale_pos_weight\": 1}\n",
      " |  \n",
      " |  Methods\n",
      " |  -------\n",
      " |  fit(X_train, y_train)\n",
      " |      Fits a ``XGBoost.Booster`` to input training data. Proper ``dtrain_`` matrix based on chosen\n",
      " |      options i.e. ``sparse_matrix``, ``scale_mean``, ``scale_std`` is being created based on the\n",
      " |      passed ``X_train`` and ``y_train``\n",
      " |  \n",
      " |  predict_proba(X_test, y_test)\n",
      " |      Returns prediction probabilities for the positive class. ``predict_proba()`` only reports\n",
      " |      the probability of the positive class, while the sklearn API returns for both and slicing\n",
      " |      like ``pred_proba[:, 1]`` is needed for positive class predictions. Additionally, ``y_test``\n",
      " |      is optional while the targets might not be available in validiation (inference)\n",
      " |  \n",
      " |  predict(X_test, y_test, threshold=0.5)\n",
      " |      Returns prediction classes based on the threshold. The default ``threshold=0.5`` might not\n",
      " |      give you the best results while you can find the optimum thresholds based on different\n",
      " |      algorithms including Youden Index, maximizing the area under sensitivity-specificity curve,\n",
      " |      and maximizing the area under precision-recall curve by using ``BinaryClassificationMetrics``\n",
      " |  \n",
      " |  get_params()\n",
      " |      Returns final set of train parameters. The default set of parameters will be updated with\n",
      " |      the new ones that passed to ``params``\n",
      " |  \n",
      " |  get_default_params()\n",
      " |      Returns the default set of train parameters. The default set of parameters will be used when\n",
      " |      ``params=None``\n",
      " |  \n",
      " |  get_feature_importance()\n",
      " |      Returns the feature importance of the trained booster based on the given ``importance_type``\n",
      " |  \n",
      " |  get_shap_explainer()\n",
      " |      Returns the ``shap.TreeExplainer``\n",
      " |  \n",
      " |  plot_shap_summary()\n",
      " |      Visualizes Shapley values summary plot\n",
      " |  \n",
      " |  plot_shap_waterfall()\n",
      " |      Visualizes Shapley values waterfall plot\n",
      " |  \n",
      " |  Attributes\n",
      " |  ----------\n",
      " |  feature_importance_ : pd.DataFrame\n",
      " |      Features importance based on the given ``importance_type``\n",
      " |  \n",
      " |  scaler_ : StandardScaler, optional\n",
      " |      Standardization object when ``scale_mean=True`` or ``scale_std=True`` unless it is ``None``\n",
      " |  \n",
      " |  X_train_ : pd.DataFrame\n",
      " |      Fitted and Transformed features when ``scale_mean=True`` or ``scale_std=True``. In other case, it will\n",
      " |      be the same as the passed ``X_train`` features\n",
      " |  \n",
      " |  X_test_ : pd.DataFrame\n",
      " |      Transformed features when ``scale_mean=True`` or ``scale_std=True`` using `clf.scaler_` that\n",
      " |      has be fitted on ``X_train`` and ``y_train`` data. In other case, it will be the same as the\n",
      " |      passed ``X_train`` features\n",
      " |  \n",
      " |  dtrain_ : xgb.DMatrix\n",
      " |      Training data matrix via ``xgboost.DMatrix(clf.X_train_, clf.y_train)``\n",
      " |  \n",
      " |  dtest_ : xgb.DMatrix\n",
      " |      Testing data matrix via ``xgboost.DMatrix(clf.X_test_, clf.y_test)`` or\n",
      " |      ``xgboost.DMatrix(clf.X_test_, None)`` when ``y_test`` is not available in inference\n",
      " |  \n",
      " |  shap_values_train_ : np.ndarray\n",
      " |      Shapley values from ``TreeExplainer`` using ``X_train_``\n",
      " |  \n",
      " |  shap_values_test_ : np.ndarray\n",
      " |      Shapley values from ``TreeExplainer`` using ``X_test_``\n",
      " |  \n",
      " |  shap_explainer_ : shap.TreeExplainer\n",
      " |      Shap TreeExplainer object\n",
      " |  \n",
      " |  model_ : xgboost.Booster\n",
      " |      XGBoost Booster object\n",
      " |  \n",
      " |  References\n",
      " |  ----------\n",
      " |  .. [xgboost-api] https://xgboost.readthedocs.io/en/latest/python/python_api.html\n",
      " |  .. [markers-api] https://matplotlib.org/stable/api/markers_api.html\n",
      " |  .. [shap-api] https://shap-lrjball.readthedocs.io/en/latest/generated/shap.summary_plot.html\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      XGBoostClassifier\n",
      " |      slickml.base._estimator.BaseXGBoostEstimator\n",
      " |      abc.ABC\n",
      " |      sklearn.base.BaseEstimator\n",
      " |      sklearn.base.ClassifierMixin\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __eq__(self, other)\n",
      " |  \n",
      " |  __init__(self, num_boost_round: Optional[int] = 200, sparse_matrix: Optional[bool] = False, scale_mean: Optional[bool] = False, scale_std: Optional[bool] = False, importance_type: Optional[str] = 'total_gain', params: Optional[Dict[str, Union[str, float, int]]] = None) -> None\n",
      " |  \n",
      " |  __post_init__(self) -> None\n",
      " |      Post instantiation validations and assignments.\n",
      " |  \n",
      " |  __repr__(self)\n",
      " |  \n",
      " |  fit(self, X_train: Union[pandas.core.frame.DataFrame, numpy.ndarray], y_train: Union[List[float], numpy.ndarray, pandas.core.series.Series]) -> None\n",
      " |      Fits a ``XGBoost.Booster`` to input training data.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      Proper ``dtrain_`` matrix based on chosen options i.e. ``sparse_matrix``, ``scale_mean``,\n",
      " |      ``scale_std`` is being created based on the passed ``X_train`` and ``y_train``.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X_train : Union[pd.DataFrame, np.ndarray]\n",
      " |          Input data for training (features)\n",
      " |      \n",
      " |      y_train : Union[List[float], np.ndarray, pd.Series]\n",
      " |          Input ground truth for training (targets)\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      :meth:`xgboost.train()`\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      None\n",
      " |  \n",
      " |  get_default_params(self) -> Dict[str, Union[str, float, int]]\n",
      " |      Returns the default set of train parameters.\n",
      " |      \n",
      " |      The default set of parameters will be used when ``params=None``.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      :meth:`get_params()`\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      Dict[str, Union[str, float, int]]\n",
      " |  \n",
      " |  get_feature_importance(self) -> pandas.core.frame.DataFrame\n",
      " |      Returns the feature importance of the trained booster based on the given ``importance_type``.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      pd.DataFrame\n",
      " |  \n",
      " |  get_params(self) -> Optional[Dict[str, Union[str, float, int]]]\n",
      " |      Returns the final set of train parameters.\n",
      " |      \n",
      " |      The default set of parameters will be updated with the new ones that passed to ``params``.\n",
      " |      \n",
      " |      See Also\n",
      " |      --------\n",
      " |      :meth:`get_default_params()`\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      Dict[str, Union[str, float, int]]\n",
      " |  \n",
      " |  get_shap_explainer(self) -> shap.explainers._tree.Tree\n",
      " |      Returns the ``shap.TreeExplainer`` object.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      shap.TreeExplainer\n",
      " |  \n",
      " |  plot_feature_importance(self, figsize: Optional[Tuple[Union[int, float], Union[int, float]]] = (8, 5), color: Optional[str] = '#87CEEB', marker: Optional[str] = 'o', markersize: Union[int, float, NoneType] = 10, markeredgecolor: Optional[str] = '#1F77B4', markerfacecolor: Optional[str] = '#1F77B4', markeredgewidth: Union[int, float, NoneType] = 1, fontsize: Union[int, float, NoneType] = 12, save_path: Optional[str] = None, display_plot: Optional[bool] = True, return_fig: Optional[bool] = False) -> Optional[matplotlib.figure.Figure]\n",
      " |      Visualizes the XGBoost feature importance as bar chart.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      feature importance : pd.DataFrame\n",
      " |          Feature importance (``feature_importance_`` attribute)\n",
      " |      \n",
      " |      figsize : Tuple[Union[int, float], Union[int, float]], optional\n",
      " |          Figure size, by default (8, 5)\n",
      " |      \n",
      " |      color : str, optional\n",
      " |          Color of the horizontal lines of lollipops, by default \"#87CEEB\"\n",
      " |      \n",
      " |      marker : str, optional\n",
      " |          Marker style of the lollipops. More valid marker styles can be found at [markers-api]_, by default \"o\"\n",
      " |      \n",
      " |      markersize : Union[int, float], optional\n",
      " |          Markersize, by default 10\n",
      " |      \n",
      " |      markeredgecolor : str, optional\n",
      " |          Marker edge color, by default \"#1F77B4\"\n",
      " |      \n",
      " |      markerfacecolor : str, optional\n",
      " |          Marker face color, by defualt \"#1F77B4\"\n",
      " |      \n",
      " |      markeredgewidth : Union[int, float], optional\n",
      " |          Marker edge width, by default 1\n",
      " |      \n",
      " |      fontsize : Union[int, float], optional\n",
      " |          Fontsize for xlabel and ylabel, and ticks parameters, by default 12\n",
      " |      \n",
      " |      save_path : str, optional\n",
      " |          The full or relative path to save the plot including the image format such as\n",
      " |          \"myplot.png\" or \"../../myplot.pdf\", by default None\n",
      " |      \n",
      " |      display_plot : bool, optional\n",
      " |          Whether to show the plot, by default True\n",
      " |      \n",
      " |      return_fig : bool, optional\n",
      " |          Whether to return figure object, by default False\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      Figure, optional\n",
      " |  \n",
      " |  plot_shap_summary(self, validation: Optional[bool] = True, plot_type: Optional[str] = 'dot', figsize: Union[str, Tuple[float, float], NoneType] = 'auto', color: Optional[str] = None, cmap: Optional[matplotlib.colors.LinearSegmentedColormap] = None, max_display: Optional[int] = 20, feature_names: Optional[List[str]] = None, layered_violin_max_num_bins: Optional[int] = 10, title: Optional[str] = None, sort: Optional[bool] = True, color_bar: Optional[bool] = True, class_names: Optional[List[str]] = None, class_inds: Optional[List[int]] = None, color_bar_label: Optional[str] = 'Feature Value', save_path: Optional[str] = None, display_plot: Optional[bool] = True) -> None\n",
      " |      Visualizes shap beeswarm plot as summary of shapley values.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      This is a helper function to plot the ``shap`` summary plot based on all types of\n",
      " |      ``shap.Explainer`` including ``shap.LinearExplainer`` for linear models, ``shap.TreeExplainer``\n",
      " |      for tree-based models, and ``shap.DeepExplainer`` deep neural network models. More on details\n",
      " |      are available at [shap-api]_. Note that this function should be ran after the ``predict_proba()``\n",
      " |      to make sure the ``X_test`` is being instansiated or set ``validation=False``.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      validation : bool, optional\n",
      " |          Whether to calculate Shap values of using the validation data ``X_test`` or not. When\n",
      " |          ``validation=False``, Shap values are calculated using ``X_train``, be default True\n",
      " |      \n",
      " |      plot_type : str, optional\n",
      " |          The type of summary plot where possible options are \"bar\", \"dot\", \"violin\", \"layered_violin\",\n",
      " |          and \"compact_dot\". Recommendations are \"dot\" for single-output such as binary classifications,\n",
      " |          \"bar\" for multi-output problems, \"compact_dot\" for Shap interactions, by default \"dot\"\n",
      " |      \n",
      " |      figsize : tuple, optional\n",
      " |          Figure size where \"auto\" is auto-scaled figure size based on the number of features that are\n",
      " |          being displayed. Passing a single float will cause each row to be that many inches high.\n",
      " |          Passing a pair of floats will scale the plot by that number of inches. If None is passed\n",
      " |          then the size of the current figure will be left unchanged, by default \"auto\"\n",
      " |      \n",
      " |      color : str, optional\n",
      " |          Color of plots when ``plot_type=\"violin\"`` and ``plot_type=layered_violin\"`` are \"RdBl\"\n",
      " |          color-map while color of the horizontal lines when ``plot_type=\"bar\"`` is \"#D0AAF3\", by\n",
      " |          default None\n",
      " |      \n",
      " |      cmap : LinearSegmentedColormap, optional\n",
      " |          Color map when ``plot_type=\"violin\"`` and ``plot_type=layered_violin\"``, by default \"RdBl\"\n",
      " |      \n",
      " |      max_display : int, optional\n",
      " |          Limit to show the number of features in the plot, by default 20\n",
      " |      \n",
      " |      feature_names : List[str], optional\n",
      " |          List of feature names to pass. It should follow the order of features, by default None\n",
      " |      \n",
      " |      layered_violin_max_num_bins : int, optional\n",
      " |          The number of bins for calculating the violin plots ranges and outliers, by default 10\n",
      " |      \n",
      " |      title : str, optional\n",
      " |          Title of the plot, by default None\n",
      " |      \n",
      " |      sort : bool, optional\n",
      " |          Flag to plot sorted shap vlues in descending order, by default True\n",
      " |      \n",
      " |      color_bar : bool, optional\n",
      " |          Flag to show a color bar when ``plot_type=\"dot\"`` or ``plot_type=\"violin\"``\n",
      " |      \n",
      " |      class_names : List[str], optional\n",
      " |          List of class names for multi-output problems, by default None\n",
      " |      \n",
      " |      class_inds : List[int], optional\n",
      " |          List of class indices for multi-output problems, by default None\n",
      " |      \n",
      " |      color_bar_label : str, optional\n",
      " |          Label for color bar, by default \"Feature Value\"\n",
      " |      \n",
      " |      save_path : str, optional\n",
      " |          The full or relative path to save the plot including the image format such as\n",
      " |          \"myplot.png\" or \"../../myplot.pdf\", by default None\n",
      " |      \n",
      " |      display_plot : bool, optional\n",
      " |          Whether to show the plot, by default True\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      None\n",
      " |  \n",
      " |  plot_shap_waterfall(self, validation: Optional[bool] = True, figsize: Optional[Tuple[float, float]] = (8, 5), bar_color: Optional[str] = '#B3C3F3', bar_thickness: Union[int, float, NoneType] = 0.5, line_color: Optional[str] = 'purple', marker: Optional[str] = 'o', markersize: Union[int, float, NoneType] = 7, markeredgecolor: Optional[str] = 'purple', markerfacecolor: Optional[str] = 'purple', markeredgewidth: Union[int, float, NoneType] = 1, max_display: Optional[int] = 20, title: Optional[str] = None, fontsize: Union[int, float, NoneType] = 12, save_path: Optional[str] = None, display_plot: Optional[bool] = True, return_fig: Optional[bool] = False) -> Optional[matplotlib.figure.Figure]\n",
      " |      Visualizes the Shapley values as a waterfall plot.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      Waterfall is defined as the cumulitative/composite ratios of shap values per feature.\n",
      " |      Therefore, it can be easily seen with each feature how much explainability we can achieve.\n",
      " |      Note that this function should be ran after the ``predict_proba()`` to make sure the\n",
      " |      ``X_test`` is being instansiated or set ``validation=False``.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      validation : bool, optional\n",
      " |          Whether to calculate Shap values of using the validation data ``X_test`` or not. When\n",
      " |          ``validation=False``, Shap values are calculated using ``X_train``, be default True\n",
      " |      \n",
      " |      figsize : Tuple[float, float], optional\n",
      " |          Figure size, by default (8, 5)\n",
      " |      \n",
      " |      bar_color : str, optional\n",
      " |          Color of the horizontal bar lines, \"#B3C3F3\"\n",
      " |      \n",
      " |      bar_thickness : Union[float, int], optional\n",
      " |          Thickness (hight) of the horizontal bar lines, by default 0.5\n",
      " |      \n",
      " |      line_color : str, optional\n",
      " |          Color of the line plot, by default \"purple\"\n",
      " |      \n",
      " |      marker : str, optional\n",
      " |          Marker style of the lollipops. More valid marker styles can be found at [markers-api]_, by default \"o\"\n",
      " |      \n",
      " |      markersize : Union[int, float], optional\n",
      " |          Markersize, by default 7\n",
      " |      \n",
      " |      markeredgecolor : str, optional\n",
      " |          Marker edge color, by default \"purple\"\n",
      " |      \n",
      " |      markerfacecolor: str, optional\n",
      " |          Marker face color, by default \"purple\"\n",
      " |      \n",
      " |      markeredgewidth : Union[int, float], optional\n",
      " |          Marker edge width, by default 1\n",
      " |      \n",
      " |      max_display : int, optional\n",
      " |          Limit to show the number of features in the plot, by default 20\n",
      " |      \n",
      " |      title : str, optional\n",
      " |          Title of the plot, by default None\n",
      " |      \n",
      " |      fontsize : Union[int, float], optional\n",
      " |          Fontsize for xlabel and ylabel, and ticks parameters, by default 12\n",
      " |      \n",
      " |      save_path : str, optional\n",
      " |          The full or relative path to save the plot including the image format such as\n",
      " |          \"myplot.png\" or \"../../myplot.pdf\", by default None\n",
      " |      \n",
      " |      display_plot : bool, optional\n",
      " |          Whether to show the plot, by default True\n",
      " |      \n",
      " |      return_fig : bool, optional\n",
      " |          Whether to return figure object, by default False\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      Figure, optional\n",
      " |  \n",
      " |  predict(self, X_test: Union[pandas.core.frame.DataFrame, numpy.ndarray], y_test: Union[List[float], numpy.ndarray, pandas.core.series.Series, NoneType] = None, threshold: Optional[float] = 0.5) -> numpy.ndarray\n",
      " |      Returns the prediction classes based on the threshold.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      The default ``threshold=0.5`` might not give you the best results while you can find the\n",
      " |      optimum thresholds based on different algorithms including Youden Index, maximizing the area\n",
      " |      under sensitivity-specificity curve, and maximizing the area under precision-recall curve by\n",
      " |      using ``BinaryClassificationMetrics``.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X_test : Union[pd.DataFrame, np.ndarray]\n",
      " |          Input data for testing (features)\n",
      " |      \n",
      " |      y_test : Union[List[float], np.ndarray, pd.Series], optional\n",
      " |          Input ground truth for testing (targets)\n",
      " |      \n",
      " |      threshold : float, optional\n",
      " |          Inclusive threshold value to binarize ``y_pred_proba_`` to ``y_pred_`` where any value\n",
      " |          that satisfies ``y_pred_prob_ >= threshold`` will set to ``class=1 (positive class)``.\n",
      " |          Note that for ``\">=\"`` is used instead of ``\">\"``, by default 0.5\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      np.ndarray\n",
      " |  \n",
      " |  predict_proba(self, X_test: Union[pandas.core.frame.DataFrame, numpy.ndarray], y_test: Union[List[float], numpy.ndarray, pandas.core.series.Series, NoneType] = None) -> numpy.ndarray\n",
      " |      Returns the prediction probabilities for the positive class.\n",
      " |      \n",
      " |      Notes\n",
      " |      -----\n",
      " |      ``predict_proba()`` only reports the probability of the positive class, while the sklearn API\n",
      " |      returns for both and slicing like ``pred_proba[:, 1]`` is needed for positive class\n",
      " |      predictions. Additionally, ``y_test`` is optional while the targets might not be available\n",
      " |      in validiation (inference).\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X_test : Union[pd.DataFrame, np.ndarray]\n",
      " |          Input data for testing (features)\n",
      " |      \n",
      " |      y_test : Union[List[float], np.ndarray, pd.Series], optional\n",
      " |          Input ground truth for testing (targets)\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      np.ndarray\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes defined here:\n",
      " |  \n",
      " |  __abstractmethods__ = frozenset()\n",
      " |  \n",
      " |  __annotations__ = {'importance_type': typing.Optional[str], 'num_boost...\n",
      " |  \n",
      " |  __dataclass_fields__ = {'importance_type': Field(name='importance_type...\n",
      " |  \n",
      " |  __dataclass_params__ = _DataclassParams(init=True,repr=True,eq=True,or...\n",
      " |  \n",
      " |  __hash__ = None\n",
      " |  \n",
      " |  importance_type = 'total_gain'\n",
      " |  \n",
      " |  num_boost_round = 200\n",
      " |  \n",
      " |  params = None\n",
      " |  \n",
      " |  scale_mean = False\n",
      " |  \n",
      " |  scale_std = False\n",
      " |  \n",
      " |  sparse_matrix = False\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from slickml.base._estimator.BaseXGBoostEstimator:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.base.BaseEstimator:\n",
      " |  \n",
      " |  __getstate__(self)\n",
      " |  \n",
      " |  __setstate__(self, state)\n",
      " |  \n",
      " |  set_params(self, **params)\n",
      " |      Set the parameters of this estimator.\n",
      " |      \n",
      " |      The method works on simple estimators as well as on nested objects\n",
      " |      (such as :class:`~sklearn.pipeline.Pipeline`). The latter have\n",
      " |      parameters of the form ``<component>__<parameter>`` so that it's\n",
      " |      possible to update each component of a nested object.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      **params : dict\n",
      " |          Estimator parameters.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      self : estimator instance\n",
      " |          Estimator instance.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.base.ClassifierMixin:\n",
      " |  \n",
      " |  score(self, X, y, sample_weight=None)\n",
      " |      Return the mean accuracy on the given test data and labels.\n",
      " |      \n",
      " |      In multi-label classification, this is the subset accuracy\n",
      " |      which is a harsh metric since you require for each sample that\n",
      " |      each label set be correctly predicted.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like of shape (n_samples, n_features)\n",
      " |          Test samples.\n",
      " |      \n",
      " |      y : array-like of shape (n_samples,) or (n_samples, n_outputs)\n",
      " |          True labels for `X`.\n",
      " |      \n",
      " |      sample_weight : array-like of shape (n_samples,), default=None\n",
      " |          Sample weights.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      score : float\n",
      " |          Mean accuracy of ``self.predict(X)`` wrt. `y`.\n",
      "\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Example 1: `XGBoostClassifer` mode for `breast-cancer` data set"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "from sklearn.datasets import load_breast_cancer\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "data = load_breast_cancer()\n",
    "\n",
    "X, y = data.data, data.target\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X,\n",
    "    y,\n",
    "    test_size=0.3,\n",
    "    shuffle=True,\n",
    "    stratify=y,\n",
    ")"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "# custom params\n",
    "params = {\n",
    "    \"eval_metric\": \"logloss\",\n",
    "    \"learning_rate\": 0.05,\n",
    "    \"max_depth\": 2,\n",
    "    \"min_child_weight\": 1,\n",
    "    \"gamma\": 0.0,\n",
    "}\n",
    "clf = XGBoostClassifier(\n",
    "    num_boost_round=333,\n",
    "    params=params,\n",
    ")\n",
    "clf.fit(X_train, y_train)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "clf.plot_feature_importance(figsize=(10, 10))"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "# predic proba (or clf.y_pred_proba_)\n",
    "# please note that y_test is optional\n",
    "y_pred_proba = clf.predict_proba(X_test, y_test=None)\n",
    "y_pred_proba[:10]"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([8.5527293e-04, 9.9859780e-01, 5.2412052e-04, 9.9980670e-01,\n",
       "       4.1708272e-04, 9.9063778e-01, 9.9084270e-01, 1.0586309e-02,\n",
       "       1.3312799e-01, 9.9936873e-01], dtype=float32)"
      ]
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "# pred class (or clf.y_pred_)\n",
    "# threshold can be set\n",
    "y_pred = clf.predict(X_test, threshold=0.67)\n",
    "y_pred[:10]"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([0, 1, 0, 1, 0, 1, 1, 0, 0, 1])"
      ]
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "# shap summary plot violin on test data\n",
    "clf.plot_shap_summary(plot_type=\"violin\", validation=True)"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x950 with 2 Axes>"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "source": [
    "# shap summary plot layered violin plot on train data\n",
    "clf.plot_shap_summary(\n",
    "    plot_type=\"layered_violin\",\n",
    "    validation=False,\n",
    "    layered_violin_max_num_bins=5,\n",
    ")"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x950 with 2 Axes>"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {
    "scrolled": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "source": [
    "# shap waterfall plot\n",
    "clf.plot_shap_waterfall()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 2 Axes>"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "source": [
    "clf.shap_values_test_[:10]"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[-4.47408482e-03, -1.51780576e-01, -2.02141963e-02,\n",
       "        -1.85396850e-01, -4.01964068e-01,  1.21235818e-01,\n",
       "        -6.35339171e-02, -6.07047737e-01,  7.13285133e-02,\n",
       "         3.48655926e-03, -2.02936545e-01,  0.00000000e+00,\n",
       "        -5.29705882e-02, -1.29023957e+00, -3.05350237e-02,\n",
       "         2.78510630e-01,  6.12420663e-02, -1.89322114e-01,\n",
       "         1.88916102e-01,  1.84534088e-01, -9.11967903e-02,\n",
       "        -2.61333197e-01, -1.14804089e+00, -1.19664633e+00,\n",
       "        -7.37019897e-01, -1.75916106e-02, -5.26720941e-01,\n",
       "        -1.44604862e+00, -1.57236502e-01,  9.55877639e-03],\n",
       "       [-6.65673055e-03,  1.68346092e-01,  7.08013074e-03,\n",
       "         9.02095810e-02, -3.14889789e-01,  1.94482177e-01,\n",
       "         7.90395439e-02,  5.72497725e-01,  1.55236363e-01,\n",
       "         5.43389097e-03,  6.41452149e-02,  0.00000000e+00,\n",
       "         1.37650017e-02,  1.54911113e+00, -1.25565324e-02,\n",
       "        -2.77397752e-01,  1.23347407e-02,  2.43120119e-01,\n",
       "         1.48090884e-01, -1.99932918e-01,  1.80877075e-01,\n",
       "        -5.17946303e-01,  7.23497093e-01,  8.00994456e-01,\n",
       "        -1.77538663e-01,  2.30794195e-02,  1.39829803e+00,\n",
       "         6.92160070e-01,  1.11033246e-01,  4.21649627e-02],\n",
       "       [-4.47408482e-03, -1.20267354e-01, -2.02141963e-02,\n",
       "        -1.94295704e-01, -3.46460938e-01,  1.28622130e-01,\n",
       "        -5.64629510e-02, -5.94659388e-01, -5.35246767e-02,\n",
       "         3.48655926e-03, -2.93751121e-01,  0.00000000e+00,\n",
       "        -5.29705882e-02, -1.42149448e+00,  1.52465217e-02,\n",
       "         2.76029885e-01, -1.11604966e-02, -3.09343636e-01,\n",
       "        -1.27133027e-01,  3.97931691e-03, -9.11967903e-02,\n",
       "        -7.51444578e-01, -1.21565402e+00, -1.12665546e+00,\n",
       "        -7.82265142e-02,  1.49115268e-02, -6.03697717e-01,\n",
       "        -1.45644951e+00,  1.24236681e-01,  9.55877639e-03],\n",
       "       [-6.65673055e-03,  5.10582328e-01,  7.08013074e-03,\n",
       "         9.55385342e-02, -2.16492534e-01,  2.03525290e-01,\n",
       "         7.90395439e-02,  4.69495058e-01,  1.52318656e-01,\n",
       "         1.14298314e-02,  7.95408264e-02,  0.00000000e+00,\n",
       "         1.37650017e-02,  8.73001635e-01, -1.25565324e-02,\n",
       "         2.69482851e-01, -1.42693538e-02, -2.80027151e-01,\n",
       "         6.81243092e-02,  1.38984278e-01,  1.36017427e-01,\n",
       "         1.18257689e+00,  5.52899659e-01,  8.23707163e-01,\n",
       "         5.29604197e-01,  1.56662744e-02,  1.12580490e+00,\n",
       "         7.28368521e-01,  1.72225311e-01,  4.21649627e-02],\n",
       "       [-4.47408482e-03,  1.04561202e-01, -5.96616976e-03,\n",
       "        -1.46574125e-01, -4.02125180e-01,  1.18237600e-01,\n",
       "        -5.64629510e-02, -6.07047737e-01,  8.13089386e-02,\n",
       "         3.48655926e-03, -2.22528607e-01,  0.00000000e+00,\n",
       "        -5.29705882e-02, -1.20955300e+00,  1.52465217e-02,\n",
       "         2.55160153e-01, -1.11604966e-02,  1.44587472e-01,\n",
       "        -1.47735506e-01,  1.56902596e-02, -9.11967903e-02,\n",
       "        -6.78969979e-01, -1.15908778e+00, -1.18333769e+00,\n",
       "        -7.83664405e-01, -3.07070073e-02, -6.29486144e-01,\n",
       "        -1.50673234e+00, -3.87301981e-01, -3.20537016e-03],\n",
       "       [ 1.75677817e-02, -1.38140675e-02,  2.39984244e-02,\n",
       "         1.23311609e-01, -2.97279060e-01,  1.74399838e-01,\n",
       "        -4.06800024e-02, -6.56808317e-01,  1.42338216e-01,\n",
       "         1.14298314e-02,  9.32859406e-02,  0.00000000e+00,\n",
       "         4.83027240e-03,  9.84701931e-01, -1.25565324e-02,\n",
       "         3.37946147e-01, -1.42693538e-02, -2.46413246e-01,\n",
       "         1.44592330e-01,  1.76927835e-01, -2.95379106e-03,\n",
       "        -2.65795052e-01,  8.33929420e-01,  9.51444864e-01,\n",
       "         5.72888255e-01,  7.49838166e-03, -9.76023525e-02,\n",
       "         6.97154582e-01,  1.69227943e-01,  4.21649627e-02],\n",
       "       [-6.65673055e-03, -1.65447034e-03,  2.39984244e-02,\n",
       "         7.43161067e-02, -3.67864817e-01,  1.89599320e-01,\n",
       "         8.44710842e-02,  6.18638515e-01,  1.42338216e-01,\n",
       "         5.43389097e-03,  1.06343597e-01,  0.00000000e+00,\n",
       "         1.37650017e-02,  9.15325284e-02, -1.25565324e-02,\n",
       "        -1.46614552e-01,  3.55208106e-03,  2.41317749e-01,\n",
       "         7.26206303e-02,  9.59016681e-02,  1.80877075e-01,\n",
       "        -6.75264239e-01,  7.28753269e-01,  7.95879483e-01,\n",
       "        -9.64441061e-01,  1.49115268e-02,  1.50131536e+00,\n",
       "         8.82216096e-01,  1.53500766e-01,  3.75773497e-02],\n",
       "       [ 5.32831717e-03,  1.94025770e-01, -5.96616976e-03,\n",
       "        -2.26545885e-01, -3.31664801e-01,  1.32979721e-01,\n",
       "        -1.59650147e-02, -6.07047737e-01,  1.13306113e-01,\n",
       "         9.48249921e-03, -3.17381889e-01,  0.00000000e+00,\n",
       "        -4.92301732e-02, -1.41388214e+00, -3.05350237e-02,\n",
       "         3.11004370e-01, -5.24102338e-03,  2.10302472e-01,\n",
       "         2.72943042e-02,  9.23374444e-02, -8.68491605e-02,\n",
       "        -2.53162235e-01, -1.53193700e+00, -1.66121542e+00,\n",
       "         4.01883900e-01,  1.56662744e-02, -5.65258563e-01,\n",
       "         1.30354226e-01,  1.10603720e-01,  9.55877639e-03],\n",
       "       [-2.13527996e-02,  4.45938647e-01, -5.96616976e-03,\n",
       "        -2.08232179e-01,  4.85051304e-01,  1.34834036e-01,\n",
       "        -6.35339171e-02, -4.62645292e-01,  9.69411209e-02,\n",
       "        -6.27766363e-03, -2.47657746e-01,  0.00000000e+00,\n",
       "        -5.29705882e-02, -1.20601094e+00, -3.05350237e-02,\n",
       "         2.17415437e-01, -1.11604966e-02, -2.95454621e-01,\n",
       "         1.82015374e-01,  3.75350751e-02, -8.14794824e-02,\n",
       "         1.57414854e+00, -1.16326880e+00, -1.21845388e+00,\n",
       "         4.45894182e-01,  1.56662744e-02, -2.47262102e-02,\n",
       "        -1.48808300e+00,  1.37514710e-01,  1.41071871e-01],\n",
       "       [ 1.75677817e-02,  1.87875465e-01,  7.08013074e-03,\n",
       "         1.17354423e-01, -2.63467848e-01,  1.94482177e-01,\n",
       "         7.90395439e-02,  5.83636284e-01,  1.55236363e-01,\n",
       "         1.14298314e-02,  7.47339651e-02,  0.00000000e+00,\n",
       "         1.37650017e-02,  1.50599027e+00,  6.26963424e-03,\n",
       "        -2.04840451e-01,  1.23347407e-02,  2.02610075e-01,\n",
       "         5.60346395e-02,  2.12009810e-02,  2.93172803e-03,\n",
       "        -5.26516140e-01,  7.01211095e-01,  7.58094430e-01,\n",
       "         5.09637713e-01,  1.56662744e-02,  1.42298174e+00,\n",
       "         7.05831409e-01,  1.56498134e-01,  4.21649627e-02]], dtype=float32)"
      ]
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "source": [
    "clf.shap_values_train_[:10]"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[ 1.32322973e-02,  1.88023284e-01,  7.08013074e-03,\n",
       "         8.14742967e-02, -3.24441135e-01,  1.86154678e-01,\n",
       "         7.06284493e-02,  5.28463364e-01,  1.30848020e-01,\n",
       "         5.43389097e-03,  3.43064144e-02,  0.00000000e+00,\n",
       "         8.38498957e-03,  1.82729375e+00,  1.52465217e-02,\n",
       "        -2.71063477e-01,  7.41871074e-03,  1.93774268e-01,\n",
       "        -8.73778462e-02,  5.76712750e-02,  4.73742886e-03,\n",
       "        -2.46869728e-01,  6.81340635e-01,  7.12934375e-01,\n",
       "        -8.66347253e-01,  2.30794195e-02, -5.00262380e-01,\n",
       "         7.59737045e-02, -4.38845634e-01,  4.21649627e-02],\n",
       "       [ 5.32831717e-03,  1.06852047e-01, -5.96616976e-03,\n",
       "        -1.57496795e-01, -4.01964068e-01,  1.12164617e-01,\n",
       "        -6.35339171e-02, -6.07047737e-01,  8.13089386e-02,\n",
       "         3.48655926e-03, -1.60764493e-02,  0.00000000e+00,\n",
       "         1.08985929e-03, -1.18956172e+00, -3.05350237e-02,\n",
       "         2.74460107e-01,  6.82314672e-03, -1.89322114e-01,\n",
       "         1.88916102e-01,  1.84534088e-01, -9.11967903e-02,\n",
       "        -2.71923631e-01, -9.32651460e-01, -8.44749987e-01,\n",
       "        -8.22966754e-01, -3.07070073e-02, -5.96501946e-01,\n",
       "        -1.81345308e+00, -3.76930594e-01, -5.28060496e-02],\n",
       "       [-4.47408482e-03, -5.47934771e-01, -2.02141963e-02,\n",
       "        -1.52921408e-01, -3.82737458e-01,  1.23739272e-01,\n",
       "        -5.64629510e-02, -5.64066708e-01,  9.57168639e-02,\n",
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       "        -5.29705882e-02, -1.31316447e+00,  1.52465217e-02,\n",
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       "        -7.52639949e-01,  1.49115268e-02, -6.29486144e-01,\n",
       "        -1.34619927e+00,  1.36416659e-01,  9.55877639e-03],\n",
       "       [-6.65673055e-03,  6.80165440e-02,  1.72202140e-02,\n",
       "         9.77158919e-02,  5.26372075e-01,  1.71947807e-01,\n",
       "         1.27993710e-03, -6.40222132e-01,  1.43562466e-01,\n",
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       "         1.89072549e-01,  2.00675353e-01,  1.23549044e-01,\n",
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       "         5.15083313e-01,  7.49838166e-03,  1.21361077e+00,\n",
       "         9.36863959e-01,  9.49520022e-02,  2.30966397e-02],\n",
       "       [ 5.32831717e-03, -3.69058877e-01,  2.39984244e-02,\n",
       "         7.18805343e-02, -3.88673842e-01,  1.29632413e-01,\n",
       "        -6.35339171e-02, -6.69633567e-01,  1.34729385e-01,\n",
       "         5.43389097e-03, -1.97986081e-01,  0.00000000e+00,\n",
       "        -5.29705882e-02, -1.31925142e+00, -3.05350237e-02,\n",
       "         2.82449663e-01, -1.14127062e-02, -2.45944381e-01,\n",
       "         1.96786702e-01,  5.07522486e-02, -2.65588760e-02,\n",
       "        -9.54598248e-01, -6.24690235e-01,  1.10981680e-01,\n",
       "        -8.97672772e-01,  1.49115268e-02, -6.10912561e-01,\n",
       "        -1.59528577e+00,  7.17049465e-02, -4.74321879e-02],\n",
       "       [-6.65673055e-03,  5.74662983e-01,  7.08013074e-03,\n",
       "         9.55385342e-02, -1.55183583e-01, -6.50657535e-01,\n",
       "         6.23459332e-02,  3.64104778e-01,  1.55236363e-01,\n",
       "         1.14298314e-02,  4.76913601e-02,  0.00000000e+00,\n",
       "         1.37650017e-02,  1.37041438e+00,  6.26963424e-03,\n",
       "        -5.36629677e-01,  1.23347407e-02,  2.43027896e-01,\n",
       "         1.67740852e-01, -1.02932021e-01,  1.36017427e-01,\n",
       "         1.29702783e+00,  5.67052126e-01,  7.50934184e-01,\n",
       "         5.05309761e-01,  1.56662744e-02,  1.04711294e+00,\n",
       "         6.70533836e-01,  9.85330567e-02,  3.23332474e-02],\n",
       "       [ 5.32831717e-03, -4.34924871e-01, -2.02141963e-02,\n",
       "         8.17752853e-02, -3.60164344e-01,  1.12164617e-01,\n",
       "        -6.35339171e-02, -5.58270156e-01,  7.64598846e-02,\n",
       "         3.48655926e-03,  7.65047967e-02,  0.00000000e+00,\n",
       "         1.08985929e-03, -2.52878636e-01,  1.52465217e-02,\n",
       "         2.74460107e-01, -1.52897807e-02,  1.41429365e-01,\n",
       "         6.50685504e-02,  1.48871556e-01, -9.11967903e-02,\n",
       "        -7.58178651e-01, -1.13406992e+00, -8.47433865e-01,\n",
       "        -1.00162053e+00, -1.75916106e-02, -8.21011603e-01,\n",
       "        -1.60009527e+00, -4.73889232e-01, -7.08938763e-02],\n",
       "       [-4.47408482e-03, -1.69262767e-01, -2.02141963e-02,\n",
       "        -1.85396850e-01, -3.94587815e-01,  1.18237600e-01,\n",
       "        -6.35339171e-02, -6.07047737e-01,  7.13285133e-02,\n",
       "         3.48655926e-03, -2.19632342e-01,  0.00000000e+00,\n",
       "        -5.29705882e-02, -1.21277952e+00,  1.52465217e-02,\n",
       "         2.74460107e-01, -1.11604966e-02, -2.13212252e-01,\n",
       "         1.51378423e-01,  1.20110042e-01, -9.11967903e-02,\n",
       "        -6.85651302e-01, -1.14804089e+00, -1.20002270e+00,\n",
       "        -7.37019897e-01, -1.75916106e-02, -6.00750864e-01,\n",
       "        -1.48600519e+00, -3.69494528e-01,  9.55877639e-03],\n",
       "       [-6.65673055e-03,  4.86683190e-01,  7.08013074e-03,\n",
       "         9.12105069e-02, -1.83117062e-01, -6.50657535e-01,\n",
       "         5.39348349e-02,  4.43219662e-01,  1.55236363e-01,\n",
       "         1.14298314e-02,  6.73413202e-02,  0.00000000e+00,\n",
       "         1.37650017e-02,  1.48021543e+00, -1.25565324e-02,\n",
       "        -5.57559133e-01,  1.23347407e-02,  2.57039428e-01,\n",
       "        -1.24865025e-01, -1.83797598e-01,  1.52095452e-01,\n",
       "         7.99344599e-01,  6.94033563e-01,  7.88369894e-01,\n",
       "         4.93578166e-01,  1.56662744e-02,  1.04711294e+00,\n",
       "         6.70533836e-01,  1.72389463e-01,  3.23332474e-02],\n",
       "       [-6.65673055e-03,  4.20586526e-01,  7.08013074e-03,\n",
       "         9.02095810e-02, -2.01732919e-01,  1.95139110e-01,\n",
       "         7.90395439e-02,  5.44561327e-01,  1.52318656e-01,\n",
       "         5.43389097e-03,  6.58547506e-02,  0.00000000e+00,\n",
       "         1.37650017e-02,  6.16233826e-01, -1.25565324e-02,\n",
       "         2.47876883e-01,  3.55208106e-03, -2.79077977e-01,\n",
       "         1.77928820e-01,  1.76927835e-01,  1.80877075e-01,\n",
       "         8.19479942e-01,  7.45405853e-01,  8.79121125e-01,\n",
       "        -3.01142782e-01,  2.30794195e-02,  1.11212814e+00,\n",
       "         8.64077687e-01,  1.72225311e-01, -7.37405345e-02]], dtype=float32)"
      ]
     },
     "metadata": {},
     "execution_count": 13
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "source": [
    "# feature importance (or clf.feature_importance_)\n",
    "clf.get_feature_importance()"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "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>feature</th>\n",
       "      <th>total_gain</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>F_22</td>\n",
       "      <td>1805.329468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>F_27</td>\n",
       "      <td>1472.025879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F_23</td>\n",
       "      <td>746.812927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>F_7</td>\n",
       "      <td>429.703094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>F_13</td>\n",
       "      <td>270.629639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>F_26</td>\n",
       "      <td>240.595383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>F_21</td>\n",
       "      <td>236.443283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>F_24</td>\n",
       "      <td>117.907936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>F_1</td>\n",
       "      <td>106.006729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>F_4</td>\n",
       "      <td>47.317490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>F_28</td>\n",
       "      <td>32.696102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>F_10</td>\n",
       "      <td>32.342445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>F_6</td>\n",
       "      <td>30.542763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>F_15</td>\n",
       "      <td>26.914486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>F_20</td>\n",
       "      <td>20.304600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>F_19</td>\n",
       "      <td>15.789312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>F_5</td>\n",
       "      <td>14.336789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>F_8</td>\n",
       "      <td>13.525916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>F_17</td>\n",
       "      <td>12.255364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>F_3</td>\n",
       "      <td>10.770648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>F_18</td>\n",
       "      <td>9.195629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>F_16</td>\n",
       "      <td>7.799827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>F_29</td>\n",
       "      <td>6.217291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>F_12</td>\n",
       "      <td>4.946209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>F_9</td>\n",
       "      <td>2.851057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>F_14</td>\n",
       "      <td>2.234597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>F_25</td>\n",
       "      <td>1.495466</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>F_0</td>\n",
       "      <td>1.084855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>F_2</td>\n",
       "      <td>0.924129</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   feature   total_gain\n",
       "0     F_22  1805.329468\n",
       "1     F_27  1472.025879\n",
       "2     F_23   746.812927\n",
       "3      F_7   429.703094\n",
       "4     F_13   270.629639\n",
       "5     F_26   240.595383\n",
       "6     F_21   236.443283\n",
       "7     F_24   117.907936\n",
       "8      F_1   106.006729\n",
       "9      F_4    47.317490\n",
       "10    F_28    32.696102\n",
       "11    F_10    32.342445\n",
       "12     F_6    30.542763\n",
       "13    F_15    26.914486\n",
       "14    F_20    20.304600\n",
       "15    F_19    15.789312\n",
       "16     F_5    14.336789\n",
       "17     F_8    13.525916\n",
       "18    F_17    12.255364\n",
       "19     F_3    10.770648\n",
       "20    F_18     9.195629\n",
       "21    F_16     7.799827\n",
       "22    F_29     6.217291\n",
       "23    F_12     4.946209\n",
       "24     F_9     2.851057\n",
       "25    F_14     2.234597\n",
       "26    F_25     1.495466\n",
       "27     F_0     1.084855\n",
       "28     F_2     0.924129"
      ]
     },
     "metadata": {},
     "execution_count": 14
    }
   ],
   "metadata": {
    "scrolled": true
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "source": [
    "clf.model_"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<xgboost.core.Booster at 0x12f070fa0>"
      ]
     },
     "metadata": {},
     "execution_count": 15
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## You can use the `BinaryClassificationMetrics` class to evaluate your model"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "source": [
    "from slickml.metrics import BinaryClassificationMetrics\n",
    "\n",
    "metrics = BinaryClassificationMetrics(y_test, y_pred_proba)"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_64901 th {\n",
       "  font-size: 12px;\n",
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       "}\n",
       "#T_64901 td {\n",
       "  font-size: 12px;\n",
       "  text-align: center;\n",
       "}\n",
       "#T_64901_row0_col0, #T_64901_row0_col1, #T_64901_row0_col2, #T_64901_row0_col3, #T_64901_row0_col4, #T_64901_row0_col5, #T_64901_row0_col6, #T_64901_row0_col7, #T_64901_row0_col8, #T_64901_row0_col9, #T_64901_row0_col10, #T_64901_row0_col11, #T_64901_row0_col12, #T_64901_row0_col13, #T_64901_row0_col14 {\n",
       "  background-color: #f0f0f3;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_64901\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_64901_level0_col0\" class=\"col_heading level0 col0\" >Accuracy</th>\n",
       "      <th id=\"T_64901_level0_col1\" class=\"col_heading level0 col1\" >Balanced Accuracy</th>\n",
       "      <th id=\"T_64901_level0_col2\" class=\"col_heading level0 col2\" >ROC AUC</th>\n",
       "      <th id=\"T_64901_level0_col3\" class=\"col_heading level0 col3\" >PR AUC</th>\n",
       "      <th id=\"T_64901_level0_col4\" class=\"col_heading level0 col4\" >Precision</th>\n",
       "      <th id=\"T_64901_level0_col5\" class=\"col_heading level0 col5\" >Recall</th>\n",
       "      <th id=\"T_64901_level0_col6\" class=\"col_heading level0 col6\" >Average Precision</th>\n",
       "      <th id=\"T_64901_level0_col7\" class=\"col_heading level0 col7\" >F-1 Score</th>\n",
       "      <th id=\"T_64901_level0_col8\" class=\"col_heading level0 col8\" >F-2 Score</th>\n",
       "      <th id=\"T_64901_level0_col9\" class=\"col_heading level0 col9\" >F-0.50 Score</th>\n",
       "      <th id=\"T_64901_level0_col10\" class=\"col_heading level0 col10\" >Threat Score</th>\n",
       "      <th id=\"T_64901_level0_col11\" class=\"col_heading level0 col11\" >TP</th>\n",
       "      <th id=\"T_64901_level0_col12\" class=\"col_heading level0 col12\" >TN</th>\n",
       "      <th id=\"T_64901_level0_col13\" class=\"col_heading level0 col13\" >FP</th>\n",
       "      <th id=\"T_64901_level0_col14\" class=\"col_heading level0 col14\" >FN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_64901_level0_row0\" class=\"row_heading level0 row0\" >Threshold = 0.500 | Average =\n",
       "                Binary</th>\n",
       "      <td id=\"T_64901_row0_col0\" class=\"data row0 col0\" >0.977000</td>\n",
       "      <td id=\"T_64901_row0_col1\" class=\"data row0 col1\" >0.969000</td>\n",
       "      <td id=\"T_64901_row0_col2\" class=\"data row0 col2\" >0.996000</td>\n",
       "      <td id=\"T_64901_row0_col3\" class=\"data row0 col3\" >0.997000</td>\n",
       "      <td id=\"T_64901_row0_col4\" class=\"data row0 col4\" >0.964000</td>\n",
       "      <td id=\"T_64901_row0_col5\" class=\"data row0 col5\" >1.000000</td>\n",
       "      <td id=\"T_64901_row0_col6\" class=\"data row0 col6\" >0.997000</td>\n",
       "      <td id=\"T_64901_row0_col7\" class=\"data row0 col7\" >0.982000</td>\n",
       "      <td id=\"T_64901_row0_col8\" class=\"data row0 col8\" >0.993000</td>\n",
       "      <td id=\"T_64901_row0_col9\" class=\"data row0 col9\" >0.971000</td>\n",
       "      <td id=\"T_64901_row0_col10\" class=\"data row0 col10\" >0.964000</td>\n",
       "      <td id=\"T_64901_row0_col11\" class=\"data row0 col11\" >107</td>\n",
       "      <td id=\"T_64901_row0_col12\" class=\"data row0 col12\" >60</td>\n",
       "      <td id=\"T_64901_row0_col13\" class=\"data row0 col13\" >4</td>\n",
       "      <td id=\"T_64901_row0_col14\" class=\"data row0 col14\" >0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1069ed310>"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "source": [
    "metrics.plot()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x1200 with 4 Axes>"
      ]
     },
     "metadata": {}
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Feel free to add your favorite `Example` via a `pull-request`.\n",
    "### More details can be found in our [Contributing Document](https://github.com/slickml/slick-ml/blob/master/CONTRIBUTING.md)."
   ],
   "metadata": {}
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.9.12 64-bit ('.venv': poetry)"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  },
  "interpreter": {
   "hash": "07bad374b921cdabaf2ef6a1d4ae5a7996d892e7452f8d9d13efced363d002df"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}