import os import sys import pandas as pd from sklearn.externals import joblib sys.path.append("lib") from AllStateDataLoader import AllStateDataLoader l = AllStateDataLoader() data_train_all = l.get_data_all_train() data_train_all_np = l.get_X_without_scaler(data_train_all) def predict_AB(data, letter_1, letter_2): model_name = os.path.join("model_logistic", "model_logistic_data_all_%s%s_not_centered.pkl" % (letter_1, letter_2)) model = joblib.load(model_name) list_classes = model.best_estimator_.classes_ prediction = model.predict_proba(data) prediction_cumsum = np.cumsum(prediction, axis=1) prediction_classes = np.apply_along_axis( lambda x : np.searchsorted(x, np.random.uniform()), axis=1, arr=prediction_cumsum ) prediction_real_classes = list_classes[prediction_classes] return prediction_real_classes