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
