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# Selecting dimensionality reduction with Pipeline and GridSearchCV

This example constructs a pipeline that does dimensionality reduction followed by prediction with a support vector classifier. It demonstrates the use of `GridSearchCV` and `Pipeline` to optimize over different classes of estimators in a single CV run – unsupervised `PCA` and `NMF` dimensionality reductions are compared to univariate feature selection during the grid search.

Additionally, `Pipeline` can be instantiated with the `memory` argument to memoize the transformers within the pipeline, avoiding to fit again the same transformers over and over.

Note that the use of `memory` to enable caching becomes interesting when the fitting of a transformer is costly.

    # Authors: Robert McGibbon
    #          Joel Nothman
    #          Guillaume Lemaitre

## Illustration of `Pipeline` and `GridSearchCV`

    import matplotlib.pyplot as plt
    import numpy as np

    from sklearn.datasets import load_digits
    from sklearn.decomposition import NMF, PCA
    from sklearn.feature_selection import SelectKBest, mutual_info_classif
    from sklearn.model_selection import GridSearchCV
    from sklearn.pipeline import Pipeline
    from sklearn.preprocessing import MinMaxScaler
    from sklearn.svm import LinearSVC

    X, y = load_digits(return_X_y=True)

    pipe = Pipeline(
        [
            ("scaling", MinMaxScaler()),
            # the reduce_dim stage is populated by the param_grid
            ("reduce_dim", "passthrough"),
            ("classify", LinearSVC(dual=False, max_iter=10000)),
        ]
    )

    N_FEATURES_OPTIONS = [2, 4, 8]
    C_OPTIONS = [1, 10, 100, 1000]
    param_grid = [
        {
            "reduce_dim": [PCA(iterated_power=7), NMF(max_iter=1_000)],
            "reduce_dim__n_components": N_FEATURES_OPTIONS,
            "classify__C": C_OPTIONS,
        },
        {
            "reduce_dim": [SelectKBest(mutual_info_classif)],
            "reduce_dim__k": N_FEATURES_OPTIONS,
            "classify__C": C_OPTIONS,
        },
    ]
    reducer_labels = ["PCA", "NMF", "KBest(mutual_info_classif)"]

    grid = GridSearchCV(pipe, n_jobs=1, param_grid=param_grid)
    grid.fit(X, y)

    GridSearchCV(estimator=Pipeline(steps=[('scaling', MinMaxScaler()),
                                           ('reduce_dim', 'passthrough'),
                                           ('classify',
                                            LinearSVC(dual=False,
                                                      max_iter=10000))]),
                 n_jobs=1,
                 param_grid=[{'classify__C': [1, 10, 100, 1000],
                              'reduce_dim': [PCA(iterated_power=7),
                                             NMF(max_iter=1000)],
                              'reduce_dim__n_components': [2, 4, 8]},
                             {'classify__C': [1, 10, 100, 1000],
                              'reduce_dim': [SelectKBest(score_func=<function mutual_info_classif at 0x7fca8a170900>)],
                              'reduce_dim__k': [2, 4, 8]}])

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GridSearchCV

    GridSearchCV(estimator=Pipeline(steps=[('scaling', MinMaxScaler()),
                                           ('reduce_dim', 'passthrough'),
                                           ('classify',
                                            LinearSVC(dual=False,
                                                      max_iter=10000))]),
                 n_jobs=1,
                 param_grid=[{'classify__C': [1, 10, 100, 1000],
                              'reduce_dim': [PCA(iterated_power=7),
                                             NMF(max_iter=1000)],
                              'reduce_dim__n_components': [2, 4, 8]},
                             {'classify__C': [1, 10, 100, 1000],
                              'reduce_dim': [SelectKBest(score_func=<function mutual_info_classif at 0x7fca8a170900>)],
                              'reduce_dim__k': [2, 4, 8]}])

estimator: Pipeline

    Pipeline(steps=[('scaling', MinMaxScaler()), ('reduce_dim', 'passthrough'),
                    ('classify', LinearSVC(dual=False, max_iter=10000))])

MinMaxScaler

    MinMaxScaler()

passthrough

    passthrough

LinearSVC

    LinearSVC(dual=False, max_iter=10000)

    import pandas as pd

    mean_scores = np.array(grid.cv_results_["mean_test_score"])
    # scores are in the order of param_grid iteration, which is alphabetical
    mean_scores = mean_scores.reshape(len(C_OPTIONS), -1, len(N_FEATURES_OPTIONS))
    # select score for best C
    mean_scores = mean_scores.max(axis=0)
    # create a dataframe to ease plotting
    mean_scores = pd.DataFrame(
        mean_scores.T, index=N_FEATURES_OPTIONS, columns=reducer_labels
    )

    ax = mean_scores.plot.bar()
    ax.set_title("Comparing feature reduction techniques")
    ax.set_xlabel("Reduced number of features")
    ax.set_ylabel("Digit classification accuracy")
    ax.set_ylim((0, 1))
    ax.legend(loc="upper left")

    plt.show()

## Caching transformers within a `Pipeline`

> It is sometimes worthwhile storing the state of a specific transformer since it could be used again. Using a pipeline in `GridSearchCV` triggers such situations. Therefore, we use the argument `memory` to enable caching.

> Warning

> Note that this example is, however, only an illustration since for this specific case fitting PCA is not necessarily slower than loading the cache. Hence, use the `memory` constructor parameter when the fitting of a transformer is costly.

    from shutil import rmtree

    from joblib import Memory

    # Create a temporary folder to store the transformers of the pipeline
    location = "cachedir"
    memory = Memory(location=location, verbose=10)
    cached_pipe = Pipeline(
        [("reduce_dim", PCA()), ("classify", LinearSVC(dual=False, max_iter=10000))],
        memory=memory,
    )

    # This time, a cached pipeline will be used within the grid search

    # Delete the temporary cache before exiting
    memory.clear(warn=False)
    rmtree(location)

The `PCA` fitting is only computed at the evaluation of the first configuration of the `C` parameter of the `LinearSVC` classifier. The other configurations of `C` will trigger the loading of the cached `PCA` estimator data, leading to save processing time. Therefore, the use of caching the pipeline using `memory` is highly beneficial when fitting a transformer is costly.

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