""" Visualizing cross-validation behavior in scikit-learn ===================================================== Choosing the right cross-validation object is a crucial part of fitting a model properly. There are many ways to split data into training and test sets in order to avoid model overfitting, to standardize the number of groups in test sets, etc. This example visualizes the behavior of several common scikit-learn objects for comparison. """ # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause import matplotlib.pyplot as plt import numpy as np from matplotlib.patches import Patch from sklearn.model_selection import ( GroupKFold, GroupShuffleSplit, KFold, ShuffleSplit, StratifiedGroupKFold, StratifiedKFold, StratifiedShuffleSplit, TimeSeriesSplit, ) rng = np.random.RandomState(1338) cmap_data = plt.cm.Paired cmap_cv = plt.cm.coolwarm n_splits = 4 # %% # Visualize our data # ------------------ # # First, we must understand the structure of our data. It has 100 randomly # generated input datapoints, 3 classes split unevenly across datapoints, # and 10 "groups" split evenly across datapoints. # # As we'll see, some cross-validation objects do specific things with # labeled data, others behave differently with grouped data, and others # do not use this information. # # To begin, we'll visualize our data. # Generate the class/group data n_points = 100 X = rng.randn(100, 10) percentiles_classes = [0.1, 0.3, 0.6] y = np.hstack([[ii] * int(100 * perc) for ii, perc in enumerate(percentiles_classes)]) # Generate uneven groups group_prior = rng.dirichlet([2] * 10) groups = np.repeat(np.arange(10), rng.multinomial(100, group_prior)) def visualize_groups(classes, groups, name): # Visualize dataset groups fig, ax = plt.subplots() ax.scatter( range(len(groups)), [0.5] * len(groups), c=groups, marker="_", lw=50, cmap=cmap_data, ) ax.scatter( range(len(groups)), [3.5] * len(groups), c=classes, marker="_", lw=50, cmap=cmap_data, ) ax.set( ylim=[-1, 5], yticks=[0.5, 3.5], yticklabels=["Data\ngroup", "Data\nclass"], xlabel="Sample index", ) visualize_groups(y, groups, "no groups") # %% # Define a function to visualize cross-validation behavior # -------------------------------------------------------- # # We'll define a function that lets us visualize the behavior of each # cross-validation object. We'll perform 4 splits of the data. On each # split, we'll visualize the indices chosen for the training set # (in blue) and the test set (in red). def plot_cv_indices(cv, X, y, group, ax, n_splits, lw=10): """Create a sample plot for indices of a cross-validation object.""" use_groups = "Group" in type(cv).__name__ groups = group if use_groups else None # Generate the training/testing visualizations for each CV split for ii, (tr, tt) in enumerate(cv.split(X=X, y=y, groups=groups)): # Fill in indices with the training/test groups indices = np.array([np.nan] * len(X)) indices[tt] = 1 indices[tr] = 0 # Visualize the results ax.scatter( range(len(indices)), [ii + 0.5] * len(indices), c=indices, marker="_", lw=lw, cmap=cmap_cv, vmin=-0.2, vmax=1.2, ) # Plot the data classes and groups at the end ax.scatter( range(len(X)), [ii + 1.5] * len(X), c=y, marker="_", lw=lw, cmap=cmap_data ) ax.scatter( range(len(X)), [ii + 2.5] * len(X), c=group, marker="_", lw=lw, cmap=cmap_data ) # Formatting yticklabels = list(range(n_splits)) + ["class", "group"] ax.set( yticks=np.arange(n_splits + 2) + 0.5, yticklabels=yticklabels, xlabel="Sample index", ylabel="CV iteration", ylim=[n_splits + 2.2, -0.2], xlim=[0, 100], ) ax.set_title("{}".format(type(cv).__name__), fontsize=15) return ax # %% # Let's see how it looks for the :class:`~sklearn.model_selection.KFold` # cross-validation object: fig, ax = plt.subplots() cv = KFold(n_splits) plot_cv_indices(cv, X, y, groups, ax, n_splits) # %% # As you can see, by default the KFold cross-validation iterator does not # take either datapoint class or group into consideration. We can change this # by using either: # # - ``StratifiedKFold`` to preserve the percentage of samples for each class. # - ``GroupKFold`` to ensure that the same group will not appear in two # different folds. # - ``StratifiedGroupKFold`` to keep the constraint of ``GroupKFold`` while # attempting to return stratified folds. cvs = [StratifiedKFold, GroupKFold, StratifiedGroupKFold] for cv in cvs: fig, ax = plt.subplots(figsize=(6, 3)) plot_cv_indices(cv(n_splits), X, y, groups, ax, n_splits) ax.legend( [Patch(color=cmap_cv(0.8)), Patch(color=cmap_cv(0.02))], ["Testing set", "Training set"], loc=(1.02, 0.8), ) # Make the legend fit plt.tight_layout() fig.subplots_adjust(right=0.7) # %% # Next we'll visualize this behavior for a number of CV iterators. # # Visualize cross-validation indices for many CV objects # ------------------------------------------------------ # # Let's visually compare the cross validation behavior for many # scikit-learn cross-validation objects. Below we will loop through several # common cross-validation objects, visualizing the behavior of each. # # Note how some use the group/class information while others do not. cvs = [ KFold, GroupKFold, ShuffleSplit, StratifiedKFold, StratifiedGroupKFold, GroupShuffleSplit, StratifiedShuffleSplit, TimeSeriesSplit, ] for cv in cvs: this_cv = cv(n_splits=n_splits) fig, ax = plt.subplots(figsize=(6, 3)) plot_cv_indices(this_cv, X, y, groups, ax, n_splits) ax.legend( [Patch(color=cmap_cv(0.8)), Patch(color=cmap_cv(0.02))], ["Testing set", "Training set"], loc=(1.02, 0.8), ) # Make the legend fit plt.tight_layout() fig.subplots_adjust(right=0.7) plt.show()