import numpy as np
from sklearn import metrics
from sklearn.metrics import average_precision_score
from sklearn.metrics import roc_auc_score, auc


class Evaluator(object):
    def __init__(self, edges_pos, edges_neg):
        self.edges_pos = edges_pos
        self.edges_neg = edges_neg

    def get_roc_score(self, emb, feas):
        # if emb is None:
        #     feed_dict.update({placeholders['dropout']: 0})
        #     emb = sess.run(model.z_mean, feed_dict=feed_dict)

        def sigmoid(x):
            return 1 / (1 + np.exp(-x))

        # Predict on test set of edges
        adj_rec = np.dot(emb, emb.T)
        preds = []
        pos = []
        for e in self.edges_pos:
            preds.append(sigmoid(adj_rec[e[0], e[1]]))
            pos.append(feas['adj_orig'][e[0], e[1]])

        preds_neg = []
        neg = []
        for e in self.edges_neg:
            preds_neg.append(sigmoid(adj_rec[e[0], e[1]]))
            neg.append(feas['adj_orig'][e[0], e[1]])

        preds_all = np.hstack([preds, preds_neg])
        labels_all = np.hstack([np.ones(len(preds)), np.zeros(len(preds))])
        roc_score = roc_auc_score(labels_all, preds_all)
        ap_score = average_precision_score(labels_all, preds_all)

        precision, recall, _thresholds = metrics.precision_recall_curve(labels_all, preds_all)
        aupr_score = auc(recall, precision)

        return roc_score, ap_score, emb, aupr_score