%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt

from sklearn.datasets import make_multilabel_classification
from sklearn.multiclass import OneVsRestClassifier
from sklearn.svm import SVC
from sklearn.preprocessing import LabelBinarizer
from sklearn.decomposition import PCA
from sklearn.cross_decomposition import CCA

def plot_hyperplane(clf, min_x, max_x, linestyle, label):
    # get the separating hyperplane
    w = clf.coef_[0]
    a = -w[0] / w[1]
    xx = np.linspace(min_x - 5, max_x + 5)
    yy = a * xx - (clf.intercept_[0]) / w[1]
    plt.plot(xx, yy, linestyle, label=label)
    
def plot_subfigure(X, Y, subplot, title, transform):
    if transform == "pca":
        X = PCA(n_components=2).fit_transform(X)
    elif transform == "cca":
        X = CCA(n_components=2).fit(X, Y).transform(X)
    else:
        raise ValueError
        
    min_x = np.min(X[:, 0])
    max_x = np.max(X[:, 0])
    min_y = np.min(X[:, 1])
    max_y = np.max(X[:, 1])
    
    classif = OneVsRestClassifier(SVC(kernel='linear'))
    classif.fit(X, Y)
    
    plt.subplot(2, 2, subplot)
    plt.title(title)
    
    zero_class = np.where(Y[:, 0])
    one_class = np.where(Y[:, 1])
    plt.scatter(X[:, 0], X[:, 1], s=40, c='gray')
    plt.scatter(X[zero_class, 0], X[zero_class, 1], s=160, edgecolors='b',
               facecolors='none', linewidths=2, label='Class 1')
    plt.scatter(X[one_class, 0], X[one_class, 1], s=80, edgecolors='orange',
               facecolors='none', linewidths=2, label='Class 2')
    
    plot_hyperplane(classif.estimators_[0], min_x, max_x, 'k--',
                    'Boundary\nfor class 1')
    plot_hyperplane(classif.estimators_[1], min_x, max_x, 'k-.',
                    'Boundary\nfor class 2')
    
    plt.xticks(())
    plt.yticks(())
    
    plt.xlim(min_x - .5 * max_x, max_x + .5 * max_x)
    plt.ylim(min_y - .5 * max_y, max_y + .5 * max_y)
    if subplot == 2:
        plt.xlabel('First principal component')
        plt.ylabel('Second principal component')
        plt.legend(loc='upper left')

plt.figure(figsize=(8,6))

X, Y = make_multilabel_classification(n_classes=2, n_labels=1, 
                                      allow_unlabeled=True, random_state=1)

plot_subfigure(X, Y, 1, "With unlabeled samples + CCA", "cca")
plot_subfigure(X, Y, 2, "With unlabeled samples + PCA", "pca")

X, Y = make_multilabel_classification(n_classes=2, n_labels=1,
                                      allow_unlabeled=False, random_state=1)

plot_subfigure(X, Y, 3, "Without unlabeled samples + CCA", 'cca')
plot_subfigure(X, Y, 4, "Without unlabeled samples + PCA", 'pca')

plt.subplots_adjust(.04, .02, .97, .94, .09, .2)
plt.show()

"""
Explanation: Recreate multilabel example from scikit-learn.org
This example simulates a multi-label document classification problem. The dataset is generated randomly based on the following process:

pick the number of labels: n ~ Poisson(n_labels)
n times, choose a class c: c ~ Multinomial (theta)
pick the document length: k ~ Poisson(length)
k times, choose a word: w ~ Multinomial(theta_c)

In the above process, rejection sampling is used to make sure that n is more than 2, and that the document length is never zero. Likewise, we reject classes which have already been chosen. The documents that are assigned to both classes are plotted surrounded by two colored circles.
The classification is performed by profjecting to the first two principal components found by PCA and CCA for visualisation purposes, followed by using the sklearn.multiclass.OneVSRestClassifier metaclassifier using two SVCs with linear kernels to learn a discriminative model for each class. Note that PCA is used to perform an unsupervised dimensionality reduction, while CCA is used to perform a supervised one.
Note: in the plot, "unlabeled samples" does not mean that we don't know the labels (as in semi-supervised learning) but that the samples simply do not have a label.
End of explanation
"""


from sklearn.preprocessing import MultiLabelBinarizer

y_true = [[2,3,4], [2], [0,1,3], [0,1,2,3,4], [0,1,2]]

Y_true = MultiLabelBinarizer().fit_transform(y_true)

Y_true

y_pred = [[2,3], [2], [0,1,3], [0,1,3], [0,1,2]]
Y_pred = MultiLabelBinarizer(classes=[0,1,2,3,4]).fit_transform(y_pred)

from sklearn.metrics import f1_score

f1_score(y_pred=Y_pred, y_true=Y_true, average='macro')

Y_pred

"""
Explanation: Play with Multilabel classification format and f1-score
End of explanation
"""


