def predict(self, point, last_price):
"""Point debe ser un Data Frame de Pandas con las información
necesaria para realizar la predicción."""
# 1. Standardize point with training mean and standard deviation.
test_data = self.__standardize_features_for_test(
point, self.columns_to_standardize, self.column_means,
self.column_stds)
# 2. Add it to the data.
df = pd.concat([self.data, test_data])
# 3. Windowize.
fmt = DataFormatter()
X, Y = fmt.windowize_series(df.as_matrix(),
size=self.input_window_size,
column_indexes=self.columns_to_windowize)
# 4. Extract the last window.
last_window = fmt.get_last_window(
df.as_matrix(),
size=self.input_window_size,
column_indexes=self.columns_to_windowize)
# 5. Compute the error.
train_score = self.model.evaluate(X, Y, verbose=0)
train_score = np.array([
train_score[0],
np.sqrt(train_score[0]), train_score[1], train_score[2] * 100
])
# 6. Make the prediction.
prediction = self.model.predict(last_window)
# 7. Computing prediction intervals
pred_upper = prediction + 1.96 * train_score[1]
pred_lower = prediction - 1.96 * train_score[1]
# 8. Transform back the prediction.
prediction = last_price * np.exp(prediction)
pred_upper = last_price * np.exp(pred_upper)
pred_lower = last_price * np.exp(pred_lower)
return prediction, [pred_lower, pred_upper]
def predict(self, point=None):
"""Point debe ser un Data Frame de Pandas con las información
necesaria para realizar la predicción."""
# 1. Standardize point with training mean and standard deviation.
# 2. Add it to the data.
if point is None:
df = self.data
else:
test_data = self.__standardize_features_for_test(
point, self.columns_to_standardize, self.column_means,
self.column_stds)
df = pd.concat([self.data, test_data])
# 3. Windowize.
fmt = DataFormatter()
X, Y = fmt.windowize_series(df.as_matrix(),
size=self.input_window_size,
column_indexes=self.columns_to_windowize)
# 4. Extract the last window.
last_window = fmt.get_last_window(
df.as_matrix(),
size=self.input_window_size,
column_indexes=self.columns_to_windowize)
last_window = last_window[None, :]
# 5. Compute the error.
train_score = self.model.evaluate(X, Y, verbose=0)
train_score = np.array([
train_score[0],
np.sqrt(train_score[0]), train_score[1], train_score[2] * 100
])
# 6. Make the prediction.
prediction = np.squeeze(self.model.predict(last_window))
# 7. Computing prediction intervals
pred_upper = prediction + 1.96 * train_score[1]
pred_lower = prediction - 1.96 * train_score[1]
# Revert standardization
prediction = prediction * self.column_stds[
u'Close'] + self.column_means[u'Close']
pred_upper = pred_upper * self.column_stds[
u'Close'] + self.column_means[u'Close']
pred_lower = pred_lower * self.column_stds[
u'Close'] + self.column_means[u'Close']
return prediction, pred_lower, pred_upper