"""
Getting started with categorical data
=====================================

Experimental support for categorical data.

In before, users need to run an encoder themselves before passing the data into XGBoost,
which creates a sparse matrix and potentially increase memory usage.  This demo
showcases the experimental categorical data support, more advanced features are planned.

  .. versionadded:: 1.5.0

See Also
--------
- :doc:`Tutorial </tutorials/categorical>`
- :ref:`sphx_glr_python_examples_cat_in_the_dat.py`
- :ref:`sphx_glr_python_examples_cat_pipeline.py`

"""
from typing import Tuple

import numpy as np
import pandas as pd

import xgboost as xgb


def make_categorical(
    n_samples: int, n_features: int, n_categories: int, onehot: bool
) -> Tuple[pd.DataFrame, pd.Series]:
    """Make some random data for demo."""
    rng = np.random.RandomState(1994)

    pd_dict = {}
    for i in range(n_features + 1):
        c = rng.randint(low=0, high=n_categories, size=n_samples)
        pd_dict[str(i)] = pd.Series(c, dtype=np.int64)

    df = pd.DataFrame(pd_dict)
    label = df.iloc[:, 0]
    df = df.iloc[:, 1:]
    for i in range(0, n_features):
        label += df.iloc[:, i]
    label += 1

    df = df.astype("category")
    categories = np.arange(0, n_categories)
    for col in df.columns:
        df[col] = df[col].cat.set_categories(categories)

    if onehot:
        return pd.get_dummies(df), label
    return df, label


def main() -> None:
    # Use builtin categorical data support

    # For scikit-learn interface, the input data should be pandas DataFrame or cudf
    # DataFrame with categorical features. If an numpy/cupy array is used instead, the
    # `feature_types` for `XGBRegressor` should be set accordingly.
    X, y = make_categorical(100, 10, 4, False)
    # Specify `enable_categorical` to True, also we use onehot-encoding-based split here
    # for demonstration. For details see the document of `max_cat_to_onehot`.
    reg = xgb.XGBRegressor(
        tree_method="hist", enable_categorical=True, max_cat_to_onehot=5, device="cuda"
    )
    reg.fit(X, y, eval_set=[(X, y)])

    # Pass in already encoded data
    X_enc, y_enc = make_categorical(100, 10, 4, True)
    reg_enc = xgb.XGBRegressor(tree_method="hist", device="cuda")
    reg_enc.fit(X_enc, y_enc, eval_set=[(X_enc, y_enc)])

    reg_results = np.array(reg.evals_result()["validation_0"]["rmse"])
    reg_enc_results = np.array(reg_enc.evals_result()["validation_0"]["rmse"])

    # Check that they have same results
    np.testing.assert_allclose(reg_results, reg_enc_results)

    # Convert to DMatrix for SHAP value
    booster: xgb.Booster = reg.get_booster()
    m = xgb.DMatrix(X, enable_categorical=True)  # specify categorical data support.
    SHAP = booster.predict(m, pred_contribs=True)
    margin = booster.predict(m, output_margin=True)
    np.testing.assert_allclose(
        np.sum(SHAP, axis=len(SHAP.shape) - 1), margin, rtol=1e-3
    )


if __name__ == "__main__":
    main()

"""
Getting started with categorical data
=====================================

Experimental support for categorical data.

In before, users need to run an encoder themselves before passing the data into XGBoost,
which creates a sparse matrix and potentially increase memory usage.  This demo
showcases the experimental categorical data support, more advanced features are planned.

Also, see :doc:`the tutorial </tutorials/categorical>` for using XGBoost with
categorical data.

    .. versionadded:: 1.5.0

"""
from typing import Tuple

import numpy as np
import pandas as pd

import xgboost as xgb


def make_categorical(
    n_samples: int, n_features: int, n_categories: int, onehot: bool
) -> Tuple[pd.DataFrame, pd.Series]:
    """Make some random data for demo."""
    rng = np.random.RandomState(1994)

    pd_dict = {}
    for i in range(n_features + 1):
        c = rng.randint(low=0, high=n_categories, size=n_samples)
        pd_dict[str(i)] = pd.Series(c, dtype=np.int64)

    df = pd.DataFrame(pd_dict)
    label = df.iloc[:, 0]
    df = df.iloc[:, 1:]
    for i in range(0, n_features):
        label += df.iloc[:, i]
    label += 1

    df = df.astype("category")
    categories = np.arange(0, n_categories)
    for col in df.columns:
        df[col] = df[col].cat.set_categories(categories)

    if onehot:
        return pd.get_dummies(df), label
    return df, label


def main() -> None:
    # Use builtin categorical data support
    # For scikit-learn interface, the input data must be pandas DataFrame or cudf
    # DataFrame with categorical features
    X, y = make_categorical(100, 10, 4, False)
    # Specify `enable_categorical` to True, also we use onehot encoding based split
    # here for demonstration. For details see the document of `max_cat_to_onehot`.
    reg = xgb.XGBRegressor(
        tree_method="hist", enable_categorical=True, max_cat_to_onehot=5, device="cuda"
    )
    reg.fit(X, y, eval_set=[(X, y)])

    # Pass in already encoded data
    X_enc, y_enc = make_categorical(100, 10, 4, True)
    reg_enc = xgb.XGBRegressor(tree_method="hist", device="cuda")
    reg_enc.fit(X_enc, y_enc, eval_set=[(X_enc, y_enc)])

    reg_results = np.array(reg.evals_result()["validation_0"]["rmse"])
    reg_enc_results = np.array(reg_enc.evals_result()["validation_0"]["rmse"])

    # Check that they have same results
    np.testing.assert_allclose(reg_results, reg_enc_results)

    # Convert to DMatrix for SHAP value
    booster: xgb.Booster = reg.get_booster()
    m = xgb.DMatrix(X, enable_categorical=True)  # specify categorical data support.
    SHAP = booster.predict(m, pred_contribs=True)
    margin = booster.predict(m, output_margin=True)
    np.testing.assert_allclose(
        np.sum(SHAP, axis=len(SHAP.shape) - 1), margin, rtol=1e-3
    )


if __name__ == "__main__":
    main()
