.. 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:


annoy 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


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









.. GENERATED FROM PYTHON SOURCE LINES 18-31

.. code-block:: Python

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

    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








.. GENERATED FROM PYTHON SOURCE LINES 32-36

https://www.geeksforgeeks.org/machine-learning/ml-credit-card-fraud-detection/
https://media.geeksforgeeks.org/wp-content/uploads/20240904104950/creditcard.csv
df = pd.read_csv("https://media.geeksforgeeks.org/wp-content/uploads/20240904104950/creditcard.csv")
df.head()

.. GENERATED FROM PYTHON SOURCE LINES 38-50

.. code-block:: Python

    Xdi_train, Xdi_val, ydi_train, ydi_val = train_test_split(
        *load_diabetes(return_X_y=True), test_size=0.25, random_state=0
    )
    Xca_train, Xca_val, yca_train, yca_val = train_test_split(
        *fetch_california_housing(return_X_y=True), test_size=0.25, random_state=0
    )
    Xbc_train, Xbc_val, ybc_train, ybc_val = train_test_split(
        *data_2_classes(return_X_y=True), test_size=0.25, random_state=0,
        stratify=data_2_classes(return_X_y=True)[1]
    )









.. GENERATED FROM PYTHON SOURCE LINES 51-78

.. code-block:: Python

    def add_missing_values(X_full, y_full, rng=np.random.RandomState(0)):
        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

    Xdi_train_miss, ydi_train_miss = add_missing_values(Xdi_train, ydi_train)
    Xca_train_miss, yca_train_miss = add_missing_values(Xca_train, yca_train)
    Xbc_train_miss, ybc_train_miss = add_missing_values(Xbc_train, ybc_train)

    Xdi_val_miss, ydi_val_miss = add_missing_values(Xdi_val, ydi_val)
    Xca_val_miss, yca_val_miss = add_missing_values(Xca_val, yca_val)
    Xbc_val_miss, ybc_val_miss = add_missing_values(Xbc_val, ybc_val)









.. GENERATED FROM PYTHON SOURCE LINES 79-118

.. code-block:: Python

    from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier

    # 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 MaxAbsScaler, RobustScaler

    N_SPLITS = 4

    def get_score(Xt, Xv, yt, yv, imputer=None, regresion=True):
        if regresion:
            estimator = RandomForestRegressor(random_state=0)
            scoring="neg_mean_squared_error"
        else:
            estimator = RandomForestClassifier(random_state=0, max_depth=6, class_weight='balanced')
            scoring="neg_log_loss"
        if imputer is not None:
            Xt = imputer.fit_transform(Xt, yt)
            Xv = imputer.transform(Xv)
        estimator.fit(Xt, yt)
        scores = cross_val_score(
            estimator,
            Xv, yv, scoring=scoring, cv=N_SPLITS
        )
        return scores.mean(), scores.std()

    n_size = 7
    x_labels = np.zeros(n_size, dtype=object)
    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 119-129

.. code-block:: Python

    t0 = time.time()
    mses_diabetes[0], stds_diabetes[0] = get_score(Xdi_train, Xdi_val, ydi_train, ydi_val)
    mses_california[0], stds_california[0] = get_score(Xca_train, Xca_val, yca_train, yca_val)
    mses_train[0], stds_train[0] = get_score(Xbc_train, Xbc_val, ybc_train, ybc_val, regresion=False)
    x_labels[0] = "Full Data"
    T = time.time() - t0
    print(T)
    time_data[0] = T






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

 .. code-block:: none

    24.839556455612183




.. GENERATED FROM PYTHON SOURCE LINES 130-148

.. code-block:: Python

    t0 = time.time()
    imputer = SimpleImputer(strategy="constant", fill_value=0, add_indicator=True)
    mses_diabetes[1], stds_diabetes[1] = get_score(
        Xdi_train_miss, Xdi_val_miss, ydi_train_miss, ydi_val_miss, imputer
    )
    mses_california[1], stds_california[1] = get_score(
        Xca_train_miss, Xca_val_miss, yca_train_miss, yca_val_miss, imputer
    )
    mses_train[1], stds_train[1] = get_score(
        Xbc_train_miss, Xbc_val_miss, ybc_train_miss, ybc_val_miss, imputer,
        regresion=False
    )
    x_labels[1] = "Zero\nImputation\n(constant)"
    T = time.time() - t0
    print(T)
    time_data[1] = T






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

 .. code-block:: none

    25.968689680099487




.. GENERATED FROM PYTHON SOURCE LINES 149-167

.. code-block:: Python

    t0 = time.time()
    imputer = SimpleImputer(strategy="mean", add_indicator=True)
    mses_diabetes[2], stds_diabetes[2] = get_score(
        Xdi_train_miss, Xdi_val_miss, ydi_train_miss, ydi_val_miss, imputer
    )
    mses_california[2], stds_california[2] = get_score(
        Xca_train_miss, Xca_val_miss, yca_train_miss, yca_val_miss, imputer
    )
    mses_train[2], stds_train[2] = get_score(
        Xbc_train_miss, Xbc_val_miss, ybc_train_miss, ybc_val_miss, imputer,
        regresion=False
    )
    x_labels[2] = "Mean\nImputation"
    T = time.time() - t0
    print(T)
    time_data[2] = T






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

 .. code-block:: none

    28.599470853805542




.. GENERATED FROM PYTHON SOURCE LINES 168-186

.. code-block:: Python

    t0 = time.time()
    imputer = SimpleImputer(strategy="median", add_indicator=True)
    mses_diabetes[3], stds_diabetes[3] = get_score(
        Xdi_train_miss, Xdi_val_miss, ydi_train_miss, ydi_val_miss, imputer
    )
    mses_california[3], stds_california[3] = get_score(
        Xca_train_miss, Xca_val_miss, yca_train_miss, yca_val_miss, imputer
    )
    mses_train[3], stds_train[3] = get_score(
        Xbc_train_miss, Xbc_val_miss, ybc_train_miss, ybc_val_miss, imputer,
        regresion=False
    )
    x_labels[3] = "Median\nImputation"
    T = time.time() - t0
    print(T)
    time_data[3] = T






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

 .. code-block:: none

    28.06083917617798




.. GENERATED FROM PYTHON SOURCE LINES 187-208

.. code-block:: Python

    t0 = time.time()
    imputer = KNNImputer(add_indicator=True)
    mses_diabetes[4], stds_diabetes[4] = get_score(
        Xdi_train_miss, Xdi_val_miss, ydi_train_miss, ydi_val_miss,
        make_pipeline(RobustScaler(), MaxAbsScaler(), imputer)
    )
    mses_california[4], stds_california[4] = get_score(
        Xca_train_miss, Xca_val_miss, yca_train_miss, yca_val_miss,
        make_pipeline(RobustScaler(), MaxAbsScaler(), imputer)
    )
    mses_train[4], stds_train[4] = get_score(
        Xbc_train_miss, Xbc_val_miss, ybc_train_miss, ybc_val_miss,
        make_pipeline(RobustScaler(), MaxAbsScaler(), imputer),
        regresion=False
    )
    x_labels[4] = "KNN\nImputation"
    T = time.time() - t0
    print(T)
    time_data[4] = T






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

 .. code-block:: none

    72.31384253501892




.. GENERATED FROM PYTHON SOURCE LINES 209-231

.. code-block:: Python

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

    mses_diabetes[5], stds_diabetes[5] = get_score(
        Xdi_train_miss, Xdi_val_miss, ydi_train_miss, ydi_val_miss,
        make_pipeline(RobustScaler(), MaxAbsScaler(), imputer)
    )
    mses_california[5], stds_california[5] = get_score(
        Xca_train_miss, Xca_val_miss, yca_train_miss, yca_val_miss,
        make_pipeline(RobustScaler(), MaxAbsScaler(), imputer)
    )
    mses_train[5], stds_train[5] = get_score(
        Xbc_train_miss, Xbc_val_miss, ybc_train_miss, ybc_val_miss,
        make_pipeline(RobustScaler(), MaxAbsScaler(), imputer),
        regresion=False
    )
    x_labels[5] = "Iterative\nImputation\n(BayesianRidge)"
    T = time.time() - t0
    print(T)
    time_data[5] = T






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

 .. code-block:: none

    28.80577802658081




.. GENERATED FROM PYTHON SOURCE LINES 232-236

.. code-block:: Python

    import scikitplot as sp
    sp.__version__






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

 .. code-block:: none


    '0.4.0.post6'



.. GENERATED FROM PYTHON SOURCE LINES 237-242

.. code-block:: Python

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









.. GENERATED FROM PYTHON SOURCE LINES 243-268

.. code-block:: Python

    t0 = time.time()
    # 'angular', 'euclidean', 'manhattan', 'hamming', 'dot'
    imputer = AnnoyKNNImputer(add_indicator=True, random_state=0, weights="distance")
    mses_diabetes[6], stds_diabetes[6] = get_score(
        Xdi_train_miss, Xdi_val_miss, ydi_train_miss, ydi_val_miss,
        make_pipeline(RobustScaler(), MaxAbsScaler(), imputer),
    )
    # imputer = AnnoyKNNImputer(add_indicator=True, random_state=0, weights="distance", initial_strategy="median", metric='euclidean', n_neighbors=430, n_trees=-1)
    imputer = AnnoyKNNImputer(add_indicator=True, random_state=0, weights="distance", initial_strategy="median", metric='euclidean', n_neighbors=484)
    mses_california[6], stds_california[6] = get_score(
        Xca_train_miss, Xca_val_miss, yca_train_miss, yca_val_miss,
        make_pipeline(RobustScaler(), MaxAbsScaler(), imputer),
    )
    imputer = AnnoyKNNImputer(add_indicator=True, random_state=0, initial_strategy="median", metric='euclidean')
    mses_train[6], stds_train[6] = get_score(
        Xbc_train_miss, Xbc_val_miss, ybc_train_miss, ybc_val_miss,
        make_pipeline(RobustScaler(), MaxAbsScaler(), imputer),
        regresion=False
    )
    x_labels[6] = "AnnoyKNN\nImputation\n(Vector Based)"
    T = time.time() - t0
    print(T)
    time_data[6] = T






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

 .. code-block:: none

    45.16613411903381




.. GENERATED FROM PYTHON SOURCE LINES 269-361

.. code-block:: Python

    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] / 10,
            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=-10)

    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="%.5f", label_type='center', padding=-10)

    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="%.5f", label_type='center', padding=-10)

    ax3.set_title("Imputation Techniques with Breast Cancer Data")
    ax3.set_yticks(xval)
    ax3.set_xlabel("LogLoss")
    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 Breast Cancer Data
   :srcset: /auto_examples/impute/images/sphx_glr_plot_impute_script_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 362-366

AnnoyKNNImputer performance and accuracy are highly sensitive to both the
selected distance metric and the number of trees used to build the Annoy index.
An inappropriate metric or insufficient number of trees may lead to poor
neighbor retrieval and degraded imputation quality.

.. GENERATED FROM PYTHON SOURCE LINES 368-375

.. 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:** (4 minutes 15.120 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/maintenance/0.4.X?urlpath=lab/tree/notebooks/auto_examples/impute/plot_impute_script.ipynb
        :alt: Launch binder
        :width: 150 px

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        :alt: Launch JupyterLite
        :width: 150 px

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

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      :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>`_