Введение в машинное обучение с помощью Python -- 2017 -- code.7z

import numpy as np import matplotlib.pyplot as plt def plot_label_kfold(): from sklearn.model_selection import GroupKFold groups = [0, 0, 0, 1, 1, 1, 1, 2, 2, 3, 3, 3] plt.figure(figsize=(10, 2)) plt.title("LabelKFold") axes = plt.gca() axes.set_frame_on(False) n_folds = 12 n_samples = 12 n_iter = 3 n_samples_per_fold = 1 cv = GroupKFold(n_splits=3) mask = np.zeros((n_iter, n_samples)) for i, (train, test) in enumerate(cv.split(range(12), groups=groups)): mask[i, train] = 1 mask[i, test] = 2 for i in range(n_folds): # test is grey colors = ["grey" if x == 2 else "white" for x in mask[:, i]] # not selected has no hatch boxes = axes.barh(bottom=range(n_iter), width=[1 - 0.1] * n_iter, left=i * n_samples_per_fold, height=.6, color=colors, hatch="//") for j in np.where(mask[:, i] == 0)[0]: boxes[j].set_hatch("") axes.barh(bottom=[n_iter] * n_folds, width=[1 - 0.1] * n_folds, left=np.arange(n_folds) * n_samples_per_fold, height=.6, color="w") for i in range(12): axes.text((i + .5) * n_samples_per_fold, 3.5, "%d" % labels[i], horizontalalignment="center") #ax.set_ylim(4, -0.1) axes.invert_yaxis() axes.set_xlim(0, n_samples + 1) axes.set_ylabel("CV iterations") axes.set_xlabel("Data points") axes.set_xticks(np.arange(n_samples) + .5) axes.set_xticklabels(np.arange(1, n_samples + 1)) axes.set_yticks(np.arange(n_iter + 1) + .3) axes.set_yticklabels(["Split %d" % x for x in range(1, n_iter + 1)] + ["Group"]); plt.legend([boxes[0], boxes[1]], ["Training set", "Test set"], loc=(1, .3)); plt.tight_layout() def plot_shuffle_split(): from sklearn.model_selection import ShuffleSplit plt.figure(figsize=(10, 2)) plt.title("ShuffleSplit with 10 points, train_size=5, test_size=2, n_splits=4") axes = plt.gca() axes.set_frame_on(False) n_folds = 10 n_samples = 10 n_iter = 4 n_samples_per_fold = 1 ss = ShuffleSplit(n_splits=4, train_size=5, test_size=2, random_state=43) mask = np.zeros((n_iter, n_samples)) for i, (train, test) in enumerate(ss.split(range(10))): mask[i, train] = 1 mask[i, test] = 2 for i in range(n_folds): # test is grey colors = ["grey" if x == 2 else "white" for x in mask[:, i]] # not selected has no hatch boxes = axes.barh(bottom=range(n_iter), width=[1 - 0.1] * n_iter, left=i * n_samples_per_fold, height=.6, color=colors, hatch="//") for j in np.where(mask[:, i] == 0)[0]: boxes[j].set_hatch("") axes.invert_yaxis() axes.set_xlim(0, n_samples + 1) axes.set_ylabel("CV iterations") axes.set_xlabel("Data points") axes.set_xticks(np.arange(n_samples) + .5) axes.set_xticklabels(np.arange(1, n_samples + 1)) axes.set_yticks(np.arange(n_iter) + .3) axes.set_yticklabels(["Split %d" % x for x in range(1, n_iter + 1)]); # legend hacked for this random state plt.legend([boxes[1], boxes[0], boxes[2]], ["Training set", "Test set", "Not selected"], loc=(1, .3)); plt.tight_layout() plt.savefig("images/06_shuffle_split.png") plt.close() def plot_stratified_cross_validation(): fig, both_axes = plt.subplots(2, 1, figsize=(12, 5)) #plt.title("cross_validation_not_stratified") axes = both_axes[0] axes.set_title("Standard cross-validation with sorted class labels") axes.set_frame_on(False) n_folds = 3 n_samples = 150 n_samples_per_fold = n_samples / float(n_folds) for i in range(n_folds): colors = ["w"] * n_folds colors[i] = "grey" axes.barh(bottom=range(n_folds), width=[n_samples_per_fold - 1] * n_folds, left=i * n_samples_per_fold, height=.6, color=colors, hatch="//") axes.barh(bottom=[n_folds] * n_folds, width=[n_samples_per_fold - 1] * n_folds, left=np.arange(3) * n_samples_per_fold, height=.6, color="w") axes.invert_yaxis() axes.set_xlim(0, n_samples + 1) axes.set_ylabel("CV iterations") axes.set_xlabel("Data points") axes.set_xticks(np.arange(n_samples_per_fold / 2., n_samples, n_samples_per_fold)) axes.set_xticklabels(["Fold %d" % x for x in range(1, n_folds + 1)]) axes.set_yticks(np.arange(n_folds + 1) + .3) axes.set_yticklabels(["Split %d" % x for x in range(1, n_folds + 1)] + ["Class label"]) for i in range(3): axes.text((i + .5) * n_samples_per_fold, 3.5, "Class %d" % i, horizontalalignment="center") ax = both_axes[1] ax.set_title("Stratified Cross-validation") ax.set_frame_on(False) ax.invert_yaxis() ax.set_xlim(0, n_samples + 1) ax.set_ylabel("CV iterations") ax.set_xlabel("Data points") ax.set_yticks(np.arange(n_folds + 1) + .3) ax.set_yticklabels(["Split %d" % x for x in range(1, n_folds + 1)] + ["Class label"]); n_subsplit = n_samples_per_fold / 3. for i in range(n_folds): test_bars = ax.barh(bottom=[i] * n_folds, width=[n_subsplit - 1] * n_folds, left=np.arange(n_folds) * n_samples_per_fold + i * n_subsplit, height=.6, color="grey", hatch="//") w = 2 * n_subsplit - 1 ax.barh(bottom=[0] * n_folds, width=[w] * n_folds, left=np.arange(n_folds) * n_samples_per_fold + (0 + 1) * n_subsplit, height=.6, color="w", hatch="//") ax.barh(bottom=[1] * (n_folds + 1), width=[w / 2., w, w, w / 2.], left=np.maximum(0, np.arange(n_folds + 1) * n_samples_per_fold - n_subsplit), height=.6, color="w", hatch="//") training_bars = ax.barh(bottom=[2] * n_folds, width=[w] * n_folds, left=np.arange(n_folds) * n_samples_per_fold , height=.6, color="w", hatch="//") ax.barh(bottom=[n_folds] * n_folds, width=[n_samples_per_fold - 1] * n_folds, left=np.arange(n_folds) * n_samples_per_fold, height=.6, color="w") for i in range(3): ax.text((i + .5) * n_samples_per_fold, 3.5, "Class %d" % i, horizontalalignment="center") ax.set_ylim(4, -0.1) plt.legend([training_bars[0], test_bars[0]], ['Training data', 'Test data'], loc=(1.05, 1), frameon=False); fig.tight_layout() def plot_cross_validation(): plt.figure(figsize=(12, 2)) plt.title("cross_validation") axes = plt.gca() axes.set_frame_on(False) n_folds = 5 n_samples = 25 n_samples_per_fold = n_samples / float(n_folds) for i in range(n_folds): colors = ["w"] * n_folds colors[i] = "grey" bars = plt.barh(bottom=range(n_folds), width=[n_samples_per_fold - 0.1] * n_folds, left=i * n_samples_per_fold, height=.6, color=colors, hatch="//") axes.invert_yaxis() axes.set_xlim(0, n_samples + 1) plt.ylabel("CV iterations") plt.xlabel("Data points") plt.xticks(np.arange(n_samples_per_fold / 2., n_samples, n_samples_per_fold), ["Fold %d" % x for x in range(1, n_folds + 1)]) plt.yticks(np.arange(n_folds) + .3, ["Split %d" % x for x in range(1, n_folds + 1)]) plt.legend([bars[0], bars[4]], ['Training data', 'Test data'], loc=(1.05, 0.4), frameon=False); def plot_threefold_split(): plt.figure(figsize=(15, 1)) axis = plt.gca() bars = axis.barh([0, 0, 0], [11.9, 2.9, 4.9], left=[0, 12, 15], color=['white', 'grey', 'grey'], hatch="//") bars[2].set_hatch(r"") axis.set_yticks(()) axis.set_frame_on(False) axis.set_ylim(-.1, .8) axis.set_xlim(-0.1, 20.1) axis.set_xticks([6, 13.3, 17.5]) axis.set_xticklabels(["training set", "validation set", "test set"], fontdict={'fontsize': 20}); axis.tick_params(length=0, labeltop=True, labelbottom=False) axis.text(6, -.3, "Model fitting", fontdict={'fontsize': 13}, horizontalalignment="center") axis.text(13.3, -.3, "Parameter selection", fontdict={'fontsize': 13}, horizontalalignment="center") axis.text(17.5, -.3, "Evaluation", fontdict={'fontsize': 13}, horizontalalignment="center")