def main():
print("Reading the valid pairs")
valid = data_io.read_valid_pairs()
features = fe.feature_extractor()
print("Transforming features")
trans_valid = features.fit_transform(valid)
trans_valid = np.nan_to_num(trans_valid)
print("Saving Valid Features")
data_io.save_valid_features(trans_valid)
print("Loading the classifier")
#(both_classifier, A_classifier, B_classifier, none_classifier) = data_io.load_model()
classifier = data_io.load_model()
print("Making predictions")
valid_info = data_io.read_valid_info()
predictions = list()
curr_pred = None
"""
for i in range(len(trans_valid)):
if valid_info["A type"][i] == "Numerical" and valid_info["B type"][i] == "Numerical":
curr_pred = both_classifier.predict_proba(trans_valid[i, :])
elif valid_info["A type"][i] == "Numerical" and valid_info["B type"][i] != "Numerical":
curr_pred = A_classifier.predict_proba(trans_valid[i, :])
elif valid_info["A type"][i] != "Numerical" and valid_info["B type"][i] == "Numerical":
curr_pred = B_classifier.predict_proba(trans_valid[i, :])
else:
curr_pred = none_classifier.predict_proba(trans_valid[i, :])
predictions.append(curr_pred[0][2] - curr_pred[0][0])
"""
orig_predictions = classifier.predict_proba(trans_valid)
predictions = orig_predictions[:, 2] - orig_predictions[:, 0]
predictions = predictions.flatten()
print("Writing predictions to file")
data_io.write_submission(predictions)
def main():
print("Reading the valid pairs")
valid = data_io.read_valid_pairs()
features = fe.feature_extractor()
print("Transforming features")
trans_valid = features.fit_transform(valid)
trans_valid = np.nan_to_num(trans_valid)
print("Saving Valid Features")
data_io.save_valid_features(trans_valid)
print("Loading the classifier")
#(both_classifier, A_classifier, B_classifier, none_classifier) = data_io.load_model()
classifier = data_io.load_model()
print("Making predictions")
valid_info = data_io.read_valid_info()
predictions = list()
curr_pred = None
"""
for i in range(len(trans_valid)):
if valid_info["A type"][i] == "Numerical" and valid_info["B type"][i] == "Numerical":
curr_pred = both_classifier.predict_proba(trans_valid[i, :])
elif valid_info["A type"][i] == "Numerical" and valid_info["B type"][i] != "Numerical":
curr_pred = A_classifier.predict_proba(trans_valid[i, :])
elif valid_info["A type"][i] != "Numerical" and valid_info["B type"][i] == "Numerical":
curr_pred = B_classifier.predict_proba(trans_valid[i, :])
else:
curr_pred = none_classifier.predict_proba(trans_valid[i, :])
predictions.append(curr_pred[0][2] - curr_pred[0][0])
"""
orig_predictions = classifier.predict_proba(trans_valid)
predictions = orig_predictions[:, 2] - orig_predictions[:, 0]
predictions = predictions.flatten()
print("Writing predictions to file")
data_io.write_submission(predictions)
def main():
y = data_io.read_train_target()
X = data_io.load_train_features()
if(type(X) == type(None)):
print("No feature file found!")
exit(1)
X_old = data_io.load_features("./Models/old_csv/features_train_en_python.csv")
print X.shape
X = X_old.join(X)
print X.shape
#print X
data_io.save_train_features(X,y)
X = data_io.load_valid_features()
X_old = data_io.load_features("./Models/old_csv/features_valid_en_python.csv")
print X.shape
X = X_old.join(X)
print X.shape
data_io.save_valid_features(X)
def main():
y = data_io.read_train_target()
X = data_io.load_train_features()
if (type(X) == type(None)):
print("No feature file found!")
exit(1)
X_old = data_io.load_features(
"./Models/old_csv/features_train_en_python.csv")
print X.shape
X = X_old.join(X)
print X.shape
#print X
data_io.save_train_features(X, y)
X = data_io.load_valid_features()
X_old = data_io.load_features(
"./Models/old_csv/features_valid_en_python.csv")
print X.shape
X = X_old.join(X)
print X.shape
data_io.save_valid_features(X)
def extract_valid_features():
start = time.time()
features = feature_extractor()
header = []
for h in features.features:
header.append(h[0])
print("Reading the valid pairs")
X = data_io.read_valid_pairs()
print("Extracting features")
# well, no fit data, so y = None
extracted = features.fit_transform(X,y = None,type_map = data_io.read_valid_info())
elapsed = float(time.time() - start)
print("Features extracted in " + str(elapsed/60.0) + " Minutes")
print ("Saving features")
X = pd.DataFrame(extracted, index = X.index)
X.columns = header
data_io.save_valid_features(X)
def extract_valid_features():
start = time.time()
features = feature_extractor()
header = []
for h in features.features:
header.append(h[0])
print("Reading the valid pairs")
X = data_io.read_valid_pairs()
print("Extracting features")
# well, no fit data, so y = None
extracted = features.fit_transform(X,
y=None,
type_map=data_io.read_valid_info())
elapsed = float(time.time() - start)
print("Features extracted in " + str(elapsed / 60.0) + " Minutes")
print("Saving features")
X = pd.DataFrame(extracted, index=X.index)
X.columns = header
data_io.save_valid_features(X)
