import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
from sklearn.tree import DecisionTreeClassifier
from sklearn.svm import SVC
from sklearn.metrics import confusion_matrix
from sklearn.metrics import f1_score
from sklearn.metrics import recall_score
from sklearn.metrics import precision_score
from sklearn.metrics import make_scorer
from sklearn.metrics import roc_curve
from sklearn.metrics import auc
from sklearn.metrics import roc_auc_score
from sklearn.metrics import accuracy_score
from scipy import interp

# for sklearn 0.18's alternative syntax
from distutils.version import LooseVersion as Version
from sklearn import __version__ as sklearn_version
if Version(sklearn_version) < '0.18':
    from sklearn.grid_search import train_test_split
    from sklearn.cross_validation import StratifiedKFold
    from sklearn.cross_validation import cross_val_score
    from sklearn.learning_curve import learning_curve
    from sklearn.learning_curve import validation_curve
    from sklearn.grid_search import GridSearchCV
else:
    from sklearn.model_selection import train_test_split
    from sklearn.model_selection import StratifiedKFold
    from sklearn.model_selection import cross_val_score
    from sklearn.model_selection import learning_curve
    from sklearn.model_selection import validation_curve
    from sklearn.model_selection import GridSearchCV

#############################################################################
df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases'
                 '/breast-cancer-wisconsin/wdbc.data', header=None)
X = df.loc[:, 2:].values
y = df.loc[:, 1].values
le = LabelEncoder()
y = le.fit_transform(y)
y_enc = le.transform(['M', 'B'])
X_train, X_test, y_train, y_test = \
    train_test_split(X, y, test_size=0.20, random_state=1)

pipe_lr = Pipeline([('scl', StandardScaler()),
                    ('clf', LogisticRegression(penalty='l2', random_state=0))])

#############################################################################
print(50 * '=')
print('Section: Addressing over- and underfitting with validation curves')
print(50 * '-')

param_range = [0.001, 0.01, 0.1, 1.0, 10.0, 100.0]
train_scores, test_scores = validation_curve(
    estimator=pipe_lr,
    X=X_train,
    y=y_train,
    param_name='clf__C',
    param_range=param_range,
    cv=10)

train_mean = np.mean(train_scores, axis=1)
train_std = np.std(train_scores, axis=1)
test_mean = np.mean(test_scores, axis=1)
test_std = np.std(test_scores, axis=1)

plt.plot(param_range, train_mean,
         color='blue', marker='o',
         markersize=5, label='training accuracy')

plt.fill_between(param_range, train_mean + train_std,
                 train_mean - train_std, alpha=0.15,
                 color='blue')

plt.plot(param_range, test_mean,
         color='green', linestyle='--',
         marker='s', markersize=5,
         label='validation accuracy')

plt.fill_between(param_range,
                 test_mean + test_std,
                 test_mean - test_std,
                 alpha=0.15, color='green')

plt.grid()
plt.xscale('log')
plt.legend(loc='lower right')
plt.xlabel('Parameter C')
plt.ylabel('Accuracy')
plt.ylim([0.8, 1.0])
# plt.tight_layout()
# plt.savefig('./figures/validation_curve.png', dpi=300)
plt.show()
