run.py

import numpy as np
from sklearn.metrics import roc_curve, precision_recall_curve
from scipy import interp
import pandas as pd
# from sklearn.ensemble import AdaBoostClassifier

from CUSBoost import CUSBoostClassifier
from rusboost import RusBoost
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import Normalizer
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
import math

from sklearn.metrics import roc_auc_score, average_precision_score

from sklearn.model_selection import StratifiedKFold

# datasets = ['gpcr_dataset_1282.txt' ]
dataset = 'pima.txt'

print("dataset : ", dataset)
df = pd.read_csv(dataset, header=None)
df['label'] = df[df.shape[1] - 1]
#
df.drop([df.shape[1] - 2], axis=1, inplace=True)
labelencoder = LabelEncoder()
df['label'] = labelencoder.fit_transform(df['label'])
#
X = np.array(df.drop(['label'], axis=1))
y = np.array(df['label'])

normalization_object = Normalizer()
X = normalization_object.fit_transform(X)
skf = StratifiedKFold(n_splits=5, shuffle=True)

top_auc = 0
mean_fpr = np.linspace(0, 1, 100)
number_of_clusters = 23
percentage_to_choose_from_each_cluster = 0.5

for depth in range(2, 20, 10):
    for estimators in range(20, 50, 10):

        current_param_auc = []
        current_param_aupr = []
        tprs = []

        for train_index, test_index in skf.split(X, y):
            X_train = X[train_index]
            X_test = X[test_index]

            y_train = y[train_index]
            y_test = y[test_index]



            classifier = CUSBoostClassifier(depth=depth, n_estimators=estimators)
            # classifier = RusBoost(depth=depth, n_estimators=estimators)

            classifier.fit(X, y)

            predictions = classifier.predict_proba_samme(X_test)

            auc = roc_auc_score(y_test, predictions[:, 1])

            aupr = average_precision_score(y_test, predictions[:, 1])

            current_param_auc.append(auc)

            current_param_aupr.append(aupr)

            fpr, tpr, thresholds = roc_curve(y_test, predictions[:, 1])
            tprs.append(interp(mean_fpr, fpr, tpr))
            tprs[-1][0] = 0.0

        current_mean_auc = np.mean(np.array(current_param_auc))
        current_mean_aupr = np.mean(np.array(current_param_aupr))

        if top_auc < current_mean_auc:
            top_auc = current_mean_auc

            best_depth = depth
            best_estimators = estimators

            best_auc = top_auc
            best_aupr = current_mean_aupr

            best_tpr = np.mean(tprs, axis=0)
            best_fpr = mean_fpr

            best_precision, best_recall, _ = precision_recall_curve(y_test, predictions[:, 1])
            best_fpr, best_tpr, thresholds = roc_curve(y_test, predictions[:, 1])


        print('ROC: ', top_auc, ' Aupr: ', best_aupr, ' for depth= ', best_depth, ' estimators = ', best_estimators)
print('ploting', dataset)
#    plt.clf()
plt.plot(best_recall, best_precision, lw=2, color='Blue',
         label='Precision-Recall Curve')
plt.plot(best_fpr, best_tpr, lw=2, color='red',
         label='ROC curve')

plt.xlabel('Recall')
plt.ylabel('Precision')
plt.ylim([0.0, 1.05])
plt.xlim([0.0, 1.0])
plt.legend(loc="upper right")
plt.show()

# plt.plot(fpr_c[1], tpr_c[1], lw=2, color='red',label='Roc curve: Clustered sampling')