import numpy as np

from read_file import read_file

from eli5.sklearn import PermutationImportance

import matplotlib.pyplot as plt

from sklearn.metrics import roc_curve, auc

import pdb

def feature_importance(save_file, clf, feature_names, training_features, training_classes, 

                       random_state, clf_name, clf_params, coefs=None, perm=True):

    """ prints feature importance information for a trained estimator (clf)"""

    if coefs is None:

        coefs = compute_imp_score(clf, clf_name, training_features, training_classes, 

                                  random_state, perm)

    assert(len(coefs)==len(feature_names))

    out_text=''

    # algorithm seed    feature score

    for i,c in enumerate(coefs):

        out_text += '\t'.join([clf_name,

                               clf_params,

                               str(random_state),

                               feature_names[i],

                               str(c)])+'\n'

    ext = '.perm_score' if perm else '.imp_score'

    with open(save_file.split('.csv')[0] + ext,'a') as out:

        out.write(out_text)

def compute_imp_score(pipe, clf_name, training_features, training_classes, random_state, 

                      perm):

    # clf = pipe.named_steps[clf_name]  

    clf = pipe

    # pdb.set_trace()

    if hasattr(clf, 'coef_') :

        coefs = np.abs(clf.coef_.flatten())

        coefs = coefs/np.sum(coefs)

    elif clf_name == 'ScaleLR':

        coefs = np.abs(clf.named_steps['lr'].coef_.flatten())

        coefs = coefs/np.sum(coefs)

    else:

        coefs = getattr(clf, 'feature_importances_', None)

    # print('coefs:',coefs)

    if coefs is None or perm:

        perm = PermutationImportance(

                                    estimator=clf,

                                    n_iter=5,

                                    random_state=random_state,

                                    refit=False

                                    )

        perm.fit(training_features, training_classes)

        coefs = perm.feature_importances_

    #return (coefs-np.min(coefs))/(np.max(coefs)-np.min(coefs))

    # return coefs/np.sum(coefs)

    return coefs

# def plot_imp_score(save_file, coefs, feature_names, seed):

#     # plot bar charts for top 10 importanct features

#     num_bar = min(10, len(coefs))

#     indices = np.argsort(coefs)[-num_bar:]

#     h=plt.figure()

#     plt.title("Feature importances")

#     plt.barh(range(num_bar), coefs[indices], color="r", align="center")

#     plt.yticks(range(num_bar), feature_names[indices])

#     plt.ylim([-1, num_bar])

#     h.tight_layout()

#     plt.savefig(save_file.split('.')[0] + '_imp_score_' + str(seed) + '.pdf')

######################################################################################### ROC Curve

def roc(save_file, y_true, probabilities, random_state, clf_name, clf_params):

    """prints receiver operator chacteristic curve data"""

    # pdb.set_trace()

    fpr,tpr,_ = roc_curve(y_true, probabilities)

    AUC = auc(fpr,tpr)

    # print results

    out_text=''

    for f,t in zip(fpr,tpr):

        out_text += '\t'.join([clf_name,

                               clf_params,

                               str(random_state),

                               str(f),

                               str(t),

                               str(AUC)])+'\n'

    with open(save_file.split('.csv')[0] + '.roc','a') as out:

        out.write(out_text)