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import numpy as np

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import matplotlib.pyplot as plt

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from .tools import plot_2d_separator, plot_2d_scores, cm, discrete_scatter

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from .plot_helpers import ReBl

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def plot_confusion_matrix_illustration():

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    plt.figure(figsize=(8, 8))

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    confusion = np.array([[401, 2], [8, 39]])

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    plt.text(0.40, .7, confusion[0, 0], size=70, horizontalalignment='right')

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    plt.text(0.40, .2, confusion[1, 0], size=70, horizontalalignment='right')

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    plt.text(.90, .7, confusion[0, 1], size=70, horizontalalignment='right')

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    plt.text(.90, 0.2, confusion[1, 1], size=70, horizontalalignment='right')

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    plt.xticks([.25, .75], ["predicted 'not nine'", "predicted 'nine'"], size=20)

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    plt.yticks([.25, .75], ["true 'nine'", "true 'not nine'"], size=20)

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    plt.plot([.5, .5], [0, 1], '--', c='k')

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    plt.plot([0, 1], [.5, .5], '--', c='k')

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    plt.xlim(0, 1)

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    plt.ylim(0, 1)

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def plot_binary_confusion_matrix():

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    plt.text(0.45, .6, "TN", size=100, horizontalalignment='right')

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    plt.text(0.45, .1, "FN", size=100, horizontalalignment='right')

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    plt.text(.95, .6, "FP", size=100, horizontalalignment='right')

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    plt.text(.95, 0.1, "TP", size=100, horizontalalignment='right')

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    plt.xticks([.25, .75], ["predicted negative", "predicted positive"], size=15)

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    plt.yticks([.25, .75], ["positive class", "negative class"], size=15)

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    plt.plot([.5, .5], [0, 1], '--', c='k')

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    plt.plot([0, 1], [.5, .5], '--', c='k')

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    plt.xlim(0, 1)

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    plt.ylim(0, 1)

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def plot_decision_threshold():

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    from sklearn.datasets import make_blobs

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    from sklearn.svm import SVC

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    from sklearn.model_selection import train_test_split

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    X, y = make_blobs(n_samples=(400, 50), cluster_std=[7.0, 2],

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                      random_state=22)

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    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

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    fig, axes = plt.subplots(2, 3, figsize=(15, 8), subplot_kw={'xticks': (), 'yticks': ()})

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    plt.suptitle("decision_threshold")

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    axes[0, 0].set_title("training data")

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    discrete_scatter(X_train[:, 0], X_train[:, 1], y_train, ax=axes[0, 0])

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    svc = SVC(gamma=.05).fit(X_train, y_train)

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    axes[0, 1].set_title("decision with threshold 0")

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    discrete_scatter(X_train[:, 0], X_train[:, 1], y_train, ax=axes[0, 1])

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    plot_2d_scores(svc, X_train, function="decision_function", alpha=.7,

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                   ax=axes[0, 1], cm=ReBl)

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    plot_2d_separator(svc, X_train, linewidth=3, ax=axes[0, 1])

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    axes[0, 2].set_title("decision with threshold -0.8")

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    discrete_scatter(X_train[:, 0], X_train[:, 1], y_train, ax=axes[0, 2])

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    plot_2d_separator(svc, X_train, linewidth=3, ax=axes[0, 2], threshold=-.8)

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    plot_2d_scores(svc, X_train, function="decision_function", alpha=.7,

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                   ax=axes[0, 2], cm=ReBl)

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    axes[1, 0].set_axis_off()

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    mask = np.abs(X_train[:, 1] - 7) < 5

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    bla = np.sum(mask)

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    line = np.linspace(X_train.min(), X_train.max(), 100)

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    axes[1, 1].set_title("Cross-section with threshold 0")

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    axes[1, 1].plot(line, svc.decision_function(np.c_[line, 10 * np.ones(100)]), c='k')

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    dec = svc.decision_function(np.c_[line, 10 * np.ones(100)])

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    contour = (dec > 0).reshape(1, -1).repeat(10, axis=0)

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    axes[1, 1].contourf(line, np.linspace(-1.5, 1.5, 10), contour, alpha=0.4, cmap=cm)

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    discrete_scatter(X_train[mask, 0], np.zeros(bla), y_train[mask], ax=axes[1, 1])

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    axes[1, 1].set_xlim(X_train.min(), X_train.max())

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    axes[1, 1].set_ylim(-1.5, 1.5)

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    axes[1, 1].set_xticks(())

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    axes[1, 1].set_ylabel("Decision value")

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    contour2 = (dec > -.8).reshape(1, -1).repeat(10, axis=0)

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    axes[1, 2].set_title("Cross-section with threshold -0.8")

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    axes[1, 2].contourf(line, np.linspace(-1.5, 1.5, 10), contour2, alpha=0.4, cmap=cm)

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    discrete_scatter(X_train[mask, 0], np.zeros(bla), y_train[mask], alpha=.1, ax=axes[1, 2])

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    axes[1, 2].plot(line, svc.decision_function(np.c_[line, 10 * np.ones(100)]), c='k')

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    axes[1, 2].set_xlim(X_train.min(), X_train.max())

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    axes[1, 2].set_ylim(-1.5, 1.5)

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    axes[1, 2].set_xticks(())

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    axes[1, 2].set_ylabel("Decision value")

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    axes[1, 0].legend(['negative class', 'positive class'])

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