def main():
# read data for train
train = pd.read_csv('data_for_model/new_with_price_per_sqm/training_data.csv')
LB = LabelBinarizer()
train['town'] = LB.fit_transform(train['town'])
train['flat_model'] = LB.fit_transform(train['flat_model'])
labels = train.iloc[:,20:].values
total_price = train.iloc[:,19:20].values
features = train.iloc[:,:19].values
floor_area = np.asarray(train['floor_area_sqm'].values).reshape(len(labels),1)
# preprocess training data
X_train = preprocessing_X(features)
scaler_y_train, y_train = preprocessing_Y(labels)
# read in test data
test = pd.read_csv('data_for_model/new_with_price_per_sqm/test_data.csv')
test['town'] = LB.fit_transform(test['town'])
test['flat_model'] = LB.fit_transform(test['flat_model'])
labels_test = test.iloc[:,20:].values
total_price_test = test.iloc[:,19:20].values
features_test = test.iloc[:,:19].values
floor_area_test = np.asarray(test['floor_area_sqm'].values).reshape(len(labels_test),1)
# preprocess test data
X_test = preprocessing_X(features_test)
scaler_y_test, y_test = preprocessing_Y(labels_test)
# fine_tune
# fine_tune(X_train, y_train, scaler_y, floor_area, total_price)
# train on all training data with best hyper-params
#model = build_model()
#result = model.fit(X_train, y_train, epochs=300, batch_size = int(len(X_train)/256), verbose=1, shuffle=False)
# train on all training data with best hyper-params with pdp
model = KerasRegressor(build_model, epochs=300, batch_size = int(len(X_train)/256), verbose=1, shuffle=False)
model._estimator_type = "regressor" # Cheap workaround for keras NN to work with plot_partial_dependence
model.dummy_ = "dummy" # Cheap workaround for keras NN to work with plot_partial_dependence
model.fit(X_train, y_train)
print('Computing partial dependence plots...')
tic = time()
pdp_features = [10] # remaining_lease, dist_nearest_mrt, dist_nearest_supermarkets, dist_nearest_sports_facilities
display = plot_partial_dependence(estimator=model, X=X_train, features=pdp_features,
kind='both', subsample=500, random_state=0, verbose=10)
print(f"done in {time() - tic:.3f}s")
display.figure_.suptitle(
'Plot'
)
display.figure_.subplots_adjust(hspace=0.3)
plt.show()
# get score for validation
#score = get_score(scaler_y_train.inverse_transform(model.predict(X_train)) * floor_area, total_price)
#print('score on validation = {}'.format(score))
# predict y values for test data
#val_res = scaler_y_test.inverse_transform(model.predict(X_test))
# get performance score on test data
#score = get_score(val_res * floor_area_test, total_price_test)
# score = get_score(val_res, total_price_test)
#print('score on test = {}'.format(score))
'''
