.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/impute/plot_impute_script.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_impute_plot_impute_script.py>`
        to download the full example code. or to run this example in your browser via JupyterLite or Binder

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_impute_plot_impute_script.py:


plot_impute with examples
==================================

Examples related to the :py:class:`~scikitplot.impute.AnnoyKNNImputer` class
with a scikit-learn regressor (e.g., :py:class:`~sklearn.linear_model.LinearRegression`) instance.

.. seealso::

    * https://scikit-learn.org/stable/auto_examples/impute/plot_missing_values.html#sphx-glr-auto-examples-impute-plot-missing-values-py

.. GENERATED FROM PYTHON SOURCE LINES 12-17

.. code-block:: Python
   :lineno-start: 13


    # Authors: The scikit-plots developers
    # SPDX-License-Identifier: BSD-3-Clause









.. GENERATED FROM PYTHON SOURCE LINES 18-132

.. code-block:: Python
   :lineno-start: 18

    import time
    import numpy as np; np.random.seed(0)  # reproducibility

    from sklearn.datasets import make_classification, make_regression
    from sklearn.datasets import (
        load_breast_cancer as data_2_classes,
        load_iris as data_3_classes,
        load_digits as data_10_classes,
    )
    from sklearn.datasets import fetch_california_housing, load_diabetes
    from sklearn.model_selection import train_test_split

    X_diabetes, y_diabetes = load_diabetes(return_X_y=True)
    X_california, y_california = fetch_california_housing(return_X_y=True)

    X_diabetes = X_diabetes[:300]
    y_diabetes = y_diabetes[:300]
    X_california = X_california[:300]
    y_california = y_california[:300]

    # Generate a sample dataset
    def make_realistic_regression(
        n_samples=3000,
        n_features=20,
        n_informative=15,
        noise=10.0,
        bias=10.0,
        corr_strength=0.15,
        nonlinear_strength=0.5,
        outlier_fraction=0.05,
        missing_fraction=0.0,
        categorical_features=6,
        random_state=None,
    ):
        """
        Generate a more realistic synthetic regression dataset.

        Parameters
        ----------
        n_samples : int
            Number of samples.
        n_features : int
            Number of numerical features.
        n_informative : int
            Number of informative (truly predictive) features.
        noise : float
            Standard deviation of Gaussian noise added to the target.
        bias : float
            Constant bias term in target generation.
        corr_strength : float
            Std. dev. of correlated noise added to create redundancy.
        nonlinear_strength : float
            Strength of nonlinear feature transformations (0 disables).
        outlier_fraction : float
            Fraction of samples with outlier targets.
        missing_fraction : float
            Fraction of missing entries (NaN) in X.
        categorical_features : int
            Number of discrete/categorical columns to append.
        random_state : int or None
            Random seed for reproducibility.
        """

        rng = np.random.default_rng(random_state)

        # Step 1: Linear base dataset
        X, y = make_regression(
            n_samples=n_samples,
            n_features=n_features,
            n_informative=n_informative,
            noise=noise,
            bias=bias,
            random_state=random_state,
        )

        # Step 2: Add nonlinear transformations
        if nonlinear_strength > 0:
            X_nl = X.copy()
            X_nl[:, 0] = np.exp(nonlinear_strength * 0.01 * X[:, 0])
            X_nl[:, 1] = np.sin(nonlinear_strength * X[:, 1])
            X_nl[:, 2] = (X[:, 2] ** 2) * nonlinear_strength
            X = np.hstack([X, X_nl])

        # Step 3: Add correlated (redundant) features
        if corr_strength > 0:
            corr_features = X[:, :min(5, X.shape[1])] + rng.normal(
                0, corr_strength, size=(n_samples, min(5, X.shape[1]))
            )
            X = np.hstack([X, corr_features])

        # Step 4: Add outliers in target
        if outlier_fraction > 0:
            n_outliers = int(n_samples * outlier_fraction)
            idx = rng.choice(n_samples, n_outliers, replace=False)
            y[idx] += rng.normal(0, noise * 10, size=n_outliers)

        # Step 5: Add missing values
        if missing_fraction > 0:
            missing_mask = rng.random(X.shape) < missing_fraction
            X[missing_mask] = np.nan

        # Step 6: Add categorical/discrete features
        if categorical_features > 0:
            cats = rng.integers(0, 3, size=(n_samples, categorical_features))
            X = np.hstack([X, cats])

        return X, y

    X, y = make_realistic_regression(
        random_state=0,
    )
    print(X.shape, np.isnan(X).mean(), y[:5])






.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    (3000, 51) 0.0 [ -83.88958588  -78.63531234 -255.02104686 -373.91061369   51.92810122]




.. GENERATED FROM PYTHON SOURCE LINES 133-165

.. code-block:: Python
   :lineno-start: 133

    X_train, X_val, y_train, y_val = train_test_split(
        X, y, test_size=0.2, random_state=0
    )

    def add_missing_values(X_full, y_full, rng):
        n_samples, n_features = X_full.shape

        # Add missing values in 75% of the lines
        missing_rate = 0.75
        n_missing_samples = int(n_samples * missing_rate)

        missing_samples = np.zeros(n_samples, dtype=bool)
        missing_samples[:n_missing_samples] = True

        rng.shuffle(missing_samples)
        missing_features = rng.randint(0, n_features, n_missing_samples)
        X_missing = X_full.copy()
        X_missing[missing_samples, missing_features] = np.nan
        y_missing = y_full.copy()

        return X_missing, y_missing

    rng = np.random.RandomState(42)
    X_miss_diabetes, y_miss_diabetes = add_missing_values(X_diabetes, y_diabetes, rng)
    X_miss_california, y_miss_california = add_missing_values(
        X_california, y_california, rng
    )
    X_miss_train, y_miss_train = add_missing_values(
        X_train, y_train, rng
    )









.. GENERATED FROM PYTHON SOURCE LINES 166-200

.. code-block:: Python
   :lineno-start: 166

    from sklearn.ensemble import RandomForestRegressor

    # To use the experimental IterativeImputer, we need to explicitly ask for it:
    from sklearn.experimental import enable_iterative_imputer  # noqa: F401
    from sklearn.impute import IterativeImputer, KNNImputer, SimpleImputer
    from sklearn.model_selection import cross_val_score
    from sklearn.pipeline import make_pipeline
    from sklearn.preprocessing import RobustScaler

    N_SPLITS = 4

    def get_score(X, y, imputer=None):
        regressor = RandomForestRegressor(random_state=0)
        if imputer is not None:
            estimator = make_pipeline(imputer, regressor)
        else:
            estimator = regressor
        scores = cross_val_score(
            estimator, X, y, scoring="neg_mean_squared_error", cv=N_SPLITS
        )
        return scores.mean(), scores.std()

    x_labels = []

    n_size = 7
    mses_diabetes = np.zeros(n_size)
    stds_diabetes = np.zeros(n_size)
    mses_california = np.zeros(n_size)
    stds_california = np.zeros(n_size)
    mses_train = np.zeros(n_size)
    stds_train = np.zeros(n_size)
    time_data = np.zeros(n_size)









.. GENERATED FROM PYTHON SOURCE LINES 201-211

.. code-block:: Python
   :lineno-start: 201

    t0 = time.time()
    mses_diabetes[0], stds_diabetes[0] = get_score(X_diabetes, y_diabetes)
    mses_california[0], stds_california[0] = get_score(X_california, y_california)
    mses_train[0], stds_train[0] = get_score(X_train, y_train)
    x_labels.append("Full Data")
    T = time.time() - t0
    print(T)
    time_data[0] = T






.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    25.60783314704895




.. GENERATED FROM PYTHON SOURCE LINES 212-229

.. code-block:: Python
   :lineno-start: 212

    t0 = time.time()
    imputer = SimpleImputer(strategy="constant", fill_value=0, add_indicator=True)
    mses_diabetes[1], stds_diabetes[1] = get_score(
        X_miss_diabetes, y_miss_diabetes, imputer
    )
    mses_california[1], stds_california[1] = get_score(
        X_miss_california, y_miss_california, imputer
    )
    mses_train[1], stds_train[1] = get_score(
        X_miss_train, y_miss_train, imputer
    )
    x_labels.append("Zero\nImputation\n(constant)")
    T = time.time() - t0
    print(T)
    time_data[1] = T






.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    27.146008253097534




.. GENERATED FROM PYTHON SOURCE LINES 230-247

.. code-block:: Python
   :lineno-start: 230

    t0 = time.time()
    imputer = SimpleImputer(strategy="mean", add_indicator=True)
    mses_diabetes[2], stds_diabetes[2] = get_score(
        X_miss_diabetes, y_miss_diabetes, imputer
    )
    mses_california[2], stds_california[2] = get_score(
        X_miss_california, y_miss_california, imputer
    )
    mses_train[2], stds_train[2] = get_score(
        X_miss_train, y_miss_train, imputer
    )
    x_labels.append("Mean\nImputation")
    T = time.time() - t0
    print(T)
    time_data[2] = T






.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    27.219724893569946




.. GENERATED FROM PYTHON SOURCE LINES 248-265

.. code-block:: Python
   :lineno-start: 248

    t0 = time.time()
    imputer = SimpleImputer(strategy="median", add_indicator=True)
    mses_diabetes[3], stds_diabetes[3] = get_score(
        X_miss_diabetes, y_miss_diabetes, imputer
    )
    mses_california[3], stds_california[3] = get_score(
        X_miss_california, y_miss_california, imputer
    )
    mses_train[3], stds_train[3] = get_score(
        X_miss_train, y_miss_train, imputer
    )
    x_labels.append("Median\nImputation")
    T = time.time() - t0
    print(T)
    time_data[3] = T






.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    27.022822380065918




.. GENERATED FROM PYTHON SOURCE LINES 266-283

.. code-block:: Python
   :lineno-start: 266

    t0 = time.time()
    imputer = KNNImputer(add_indicator=True)
    mses_diabetes[4], stds_diabetes[4] = get_score(
        X_miss_diabetes, y_miss_diabetes, imputer
    )
    mses_california[4], stds_california[4] = get_score(
        X_miss_california, y_miss_california, make_pipeline(RobustScaler(), imputer)
    )
    mses_train[4], stds_train[4] = get_score(
        X_miss_train, y_miss_train, make_pipeline(RobustScaler(), imputer)
    )
    x_labels.append("KNN\nImputation")
    T = time.time() - t0
    print(T)
    time_data[4] = T






.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    28.187212705612183




.. GENERATED FROM PYTHON SOURCE LINES 284-288

.. code-block:: Python
   :lineno-start: 284

    import scikitplot as sp
    sp.__version__






.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    '0.5.dev0+git.20251016.274425a'



.. GENERATED FROM PYTHON SOURCE LINES 289-293

.. code-block:: Python
   :lineno-start: 289

    from scikitplot.impute import AnnoyKNNImputer
    # print(AnnoyKNNImputer.__doc__)









.. GENERATED FROM PYTHON SOURCE LINES 294-317

.. code-block:: Python
   :lineno-start: 294

    t0 = time.time()
    imputer = AnnoyKNNImputer(
        add_indicator=True,
        n_trees=5,
        search_k=1000,
        n_neighbors=5,
        index_nan_strategy='mean',
    )
    mses_diabetes[5], stds_diabetes[5] = get_score(
        X_miss_diabetes, y_miss_diabetes, imputer
    )
    mses_california[5], stds_california[5] = get_score(
        X_miss_california, y_miss_california, make_pipeline(RobustScaler(), imputer)
    )
    mses_train[5], stds_train[5] = get_score(
        X_miss_train, y_miss_train, make_pipeline(RobustScaler(), imputer)
    )
    x_labels.append("AnnoyKNN\nImputation\n(Tree Based)")
    T = time.time() - t0
    print(T)
    time_data[5] = T






.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    28.35898518562317




.. GENERATED FROM PYTHON SOURCE LINES 318-336

.. code-block:: Python
   :lineno-start: 318

    t0 = time.time()
    imputer = IterativeImputer(add_indicator=True)

    mses_diabetes[6], stds_diabetes[6] = get_score(
        X_miss_diabetes, y_miss_diabetes, imputer
    )
    mses_california[6], stds_california[6] = get_score(
        X_miss_california, y_miss_california, make_pipeline(RobustScaler(), imputer)
    )
    mses_train[6], stds_train[6] = get_score(
        X_miss_train, y_miss_train, make_pipeline(RobustScaler(), imputer)
    )
    x_labels.append("Iterative\nImputation\n(BayesianRidge)")
    T = time.time() - t0
    print(T)
    time_data[6] = T






.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    31.3128182888031




.. GENERATED FROM PYTHON SOURCE LINES 337-429

.. code-block:: Python
   :lineno-start: 337

    import matplotlib.pyplot as plt

    mses_diabetes = np.abs(mses_diabetes)  # * -1
    mses_california = np.abs(mses_california)
    mses_train = np.abs(mses_train)

    n_bars = len(mses_diabetes)
    xval = np.arange(n_bars)

    colors = ["r", "g", "b", "m", "c", "orange", "olive", "gray"]

    # plot diabetes results
    plt.figure(figsize=(18, 6))
    ax1 = plt.subplot(131)
    bars1 = []
    for j, td in zip(xval, time_data):
        bar = ax1.barh(
            j,
            mses_diabetes[j],
            xerr=stds_diabetes[j],
            color=colors[j],
            alpha=0.6,
            align="center",
            label=str(np.round(td, 3))
        )
        bars1.append(bar)

    # Add bar value labels
    for bar in bars1:
        ax1.bar_label(bar, fmt="%.3f", label_type='center', padding=-20)

    ax1.set_title("Imputation Techniques with Diabetes Data")
    ax1.set_xlim(left=np.min(mses_diabetes) * 0.9, right=np.max(mses_diabetes) * 1.1)
    ax1.set_yticks(xval)
    ax1.set_xlabel("MSE")
    ax1.invert_yaxis()
    ax1.set_yticklabels(x_labels)

    # plot california dataset results
    ax2 = plt.subplot(132)
    bars2 = []
    for j, td in zip(xval, time_data):
        bar = ax2.barh(
            j,
            mses_california[j],
            xerr=stds_california[j],
            color=colors[j],
            alpha=0.6,
            align="center",
            label=str(np.round(td, 3))
        )
        bars2.append(bar)

    # Add bar value labels
    for bar in bars2:
        ax2.bar_label(bar, fmt="%.3f", label_type='center', padding=-20)

    ax2.set_title("Imputation Techniques with California Data")
    ax2.set_yticks(xval)
    ax2.set_xlabel("MSE")
    ax2.invert_yaxis()
    ax2.set_yticklabels([""] * n_bars)

    # plot train dataset results
    ax3 = plt.subplot(133)
    bars3 = []
    for j, td in zip(xval, time_data):
        bar = ax3.barh(
            j,
            mses_train[j],
            xerr=stds_train[j],
            color=colors[j],
            alpha=0.6,
            align="center",
            label=str(np.round(td, 3))
        )
        bars3.append(bar)

    # Add bar value labels
    for bar in bars3:
        ax3.bar_label(bar, fmt="%.3f", label_type='center', padding=-20)

    ax3.set_title("Imputation Techniques with Train Data")
    ax3.set_yticks(xval)
    ax3.set_xlabel("MSE")
    ax3.invert_yaxis()
    ax3.set_yticklabels([""] * n_bars)

    plt.legend(title='Time')
    plt.tight_layout()
    plt.show()




.. image-sg:: /auto_examples/impute/images/sphx_glr_plot_impute_script_001.png
   :alt: Imputation Techniques with Diabetes Data, Imputation Techniques with California Data, Imputation Techniques with Train Data
   :srcset: /auto_examples/impute/images/sphx_glr_plot_impute_script_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 430-437

.. tags::

   model-type: classification
   model-workflow: impute
   plot-type: bar
   level: beginner
   purpose: showcase


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (3 minutes 16.127 seconds)


.. _sphx_glr_download_auto_examples_impute_plot_impute_script.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/scikit-plots/scikit-plots/main?urlpath=lab/tree/notebooks/auto_examples/impute/plot_impute_script.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: lite-badge

      .. image:: images/jupyterlite_badge_logo.svg
        :target: ../../lite/lab/index.html?path=auto_examples/impute/plot_impute_script.ipynb
        :alt: Launch JupyterLite
        :width: 150 px

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_impute_script.ipynb <plot_impute_script.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_impute_script.py <plot_impute_script.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_impute_script.zip <plot_impute_script.zip>`


.. include:: plot_impute_script.recommendations


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_