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import numpy as np

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import matplotlib.pyplot as plt

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from sklearn.svm import SVC

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from sklearn.model_selection import GridSearchCV, train_test_split

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from sklearn.datasets import load_iris

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import pandas as pd

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def plot_cross_val_selection():

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    iris = load_iris()

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    X_trainval, X_test, y_trainval, y_test = train_test_split(iris.data,

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                                                              iris.target,

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                                                              random_state=0)

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    param_grid = {'C': [0.001, 0.01, 0.1, 1, 10, 100],

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                  'gamma': [0.001, 0.01, 0.1, 1, 10, 100]}

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    grid_search = GridSearchCV(SVC(), param_grid, cv=5,

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                               return_train_score=True)

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    grid_search.fit(X_trainval, y_trainval)

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    results = pd.DataFrame(grid_search.cv_results_)[15:]

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    best = np.argmax(results.mean_test_score.values)

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    plt.figure(figsize=(10, 3))

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    plt.xlim(-1, len(results))

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    plt.ylim(0, 1.1)

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    for i, (_, row) in enumerate(results.iterrows()):

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        scores = row[['split%d_test_score' % i for i in range(5)]]

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        marker_cv, = plt.plot([i] * 5, scores, '^', c='gray', markersize=5,

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                              alpha=.5)

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        marker_mean, = plt.plot(i, row.mean_test_score, 'v', c='none', alpha=1,

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                                markersize=10, markeredgecolor='k')

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        if i == best:

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            marker_best, = plt.plot(i, row.mean_test_score, 'o', c='red',

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                                    fillstyle="none", alpha=1, markersize=20,

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                                    markeredgewidth=3)

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    plt.xticks(range(len(results)), [str(x).strip("{}").replace("'", "") for x

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                                     in grid_search.cv_results_['params']],

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               rotation=90)

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    plt.ylabel("Validation accuracy")

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    plt.xlabel("Parameter settings")

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    plt.legend([marker_cv, marker_mean, marker_best],

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               ["cv accuracy", "mean accuracy", "best parameter setting"],

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               loc=(1.05, .4))

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def plot_grid_search_overview():

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    plt.figure(figsize=(10, 3), dpi=70)

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    axes = plt.gca()

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    axes.yaxis.set_visible(False)

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    axes.xaxis.set_visible(False)

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    axes.set_frame_on(False)

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    def draw(ax, text, start, target=None):

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        if target is not None:

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            patchB = target.get_bbox_patch()

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            end = target.get_position()

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        else:

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            end = start

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            patchB = None

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        annotation = ax.annotate(text, end, start, xycoords='axes pixels',

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                                 textcoords='axes pixels', size=20,

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                                 arrowprops=dict(

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                                     arrowstyle="-|>", fc="w", ec="k",

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                                     patchB=patchB,

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                                     connectionstyle="arc3,rad=0.0"),

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                                 bbox=dict(boxstyle="round", fc="w"),

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                                 horizontalalignment="center",

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                                 verticalalignment="center")

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        plt.draw()

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        return annotation

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    step = 100

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    grr = 400

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    final_evaluation = draw(axes, "final evaluation", (5 * step, grr - 3 *

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                                                       step))

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    retrained_model = draw(axes, "retrained model", (3 * step, grr - 3 * step),

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                           final_evaluation)

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    best_parameters = draw(axes, "best parameters", (.5 * step, grr - 3 *

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                                                     step), retrained_model)

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    cross_validation = draw(axes, "cross-validation", (.5 * step, grr - 2 *

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                                                       step), best_parameters)

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    draw(axes, "parameter grid", (0.0, grr - 0), cross_validation)

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    training_data = draw(axes, "training data", (2 * step, grr - step),

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                         cross_validation)

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    draw(axes, "training data", (2 * step, grr - step), retrained_model)

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    test_data = draw(axes, "test data", (5 * step, grr - step),

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                     final_evaluation)

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    draw(axes, "data set", (3.5 * step, grr - 0.0), training_data)

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    draw(axes, "data set", (3.5 * step, grr - 0.0), test_data)

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    plt.ylim(0, 1)

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    plt.xlim(0, 1.5)

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