def main():
# # Convert a Caffe model to a classifier in Core ML
# coreml_model = coremltools.converters.caffe.convert(
# ('bvlc_alexnet.caffemodel', 'deploy.prototxt'), predicted_feature_name='class_labels.txt'
# )
# # Now save the model
# coreml_model.save('BVLCObjectClassifier.mlmodel')
# Load data
data = pd.read_csv('split.csv')
le = preprocessing.LabelEncoder()
for i in range(4):
data[:,i] = le.fit_transform(data[:,i])
# Train a model
model = LinearRegression()
# model = svm.SVC()
# model.fit(data[["bedroom", "bath", "size"]], data["price"])
model.fit(data['raw_address'], data[['POI', 'street']])
# # Convert and save the scikit-learn model
# import coremltools
# coreml_model = coremltools.converters.sklearn.convert(model, ["bedroom", "bath", "size"], "price")
coreml_model = coremltools.converters.sklearn.convert(model, "raw_address", ["POI", "street"])
model.author = 'John Smith'
model.license = 'BSD'
# model.short_description = 'Predicts the price of a house in the Seattle area.'
# Set feature descriptions manually
# model.input_description['bedroom'] = 'Number of bedrooms'
# model.input_description['bathrooms'] = 'Number of bathrooms'
# model.input_description['size'] = 'Size (in square feet)'
# Set the output descriptions
# model.output_description['price'] = 'Price of the house'
# Save the model
model.save('HousePricer.mlmodel')
