def run(self, train, predict):
if self.check_parameters() == False:
raise IndexError('Parameters is wrong, check out_class_type')
return
################################################################################
self.paras.save_folder = self.get_save_directory()
print('Save Directory: ', self.paras.save_folder)
self.paras.model_folder = self.get_model_directory()
print('Model Directory: ', self.paras.model_folder)
################################################################################
LabelColumnName = 'label'
for index in range(len(self.paras.window_len)):
data_feature = get_all_stocks_feature_data(
self.paras, self.paras.window_len[index], LabelColumnName)
model = None
if train:
model = self.train_data(data_feature, LabelColumnName, index)
if predict:
self.predict_data(model, data_feature, LabelColumnName, index)
def run(self, train, predict):
################################################################################
self.paras.save_folder = self.get_save_directory()
print(' Log Directory: ', self.paras.save_folder)
self.paras.model_folder = self.get_model_directory()
print('Model Directory: ', self.paras.model_folder)
################################################################################
LabelColumnName = 'label'
data_feature = get_all_stocks_feature_data(self.paras,
self.paras.window_len,
LabelColumnName)
model = None
if train: model = self.train_data(data_feature, LabelColumnName)
if predict: self.predict_data(model, data_feature, LabelColumnName)
def do_run(self, train, predict, window):
LabelColumnName = 'label'
data_file = "data_file_lstm_" + str(window) + ".pkl"
if os.path.exists(data_file):
input = open(data_file, 'rb')
data_feature = pickle.load(input)
input.close()
else:
data_feature = get_all_stocks_feature_data(self.paras, window, LabelColumnName)
output = open(data_file, 'wb')
pickle.dump(data_feature, output)
output.close()
model = None
if train: model = self.train_data(data_feature, window, LabelColumnName)
if predict: self.predict_data(model, data_feature, window, LabelColumnName)
def run(self, train, predict):
################################################################################
self.paras.save_folder = self.get_save_directory()
print('Save Directory: ', self.paras.save_folder)
self.paras.model_folder = self.get_model_directory()
print('Model Directory: ', self.paras.model_folder)
################################################################################
LabelColumnName = 'label'
for index in range(len(self.paras.window_len)):
data_feature = get_all_stocks_feature_data(
self.paras, self.paras.window_len[index], LabelColumnName)
model = None
if train:
model = self.train_data(data_feature, LabelColumnName, index)
if predict:
self.predict_data(model, data_feature, LabelColumnName, index)
# style.use("fivethirtyeight")
# pd.set_option('precision', 3)
# pd.set_option('display.width',1000)
#Read the csv file into a DataFrame
# df = pd.read_csv("Tesla_stocks.csv")
#Make two new columns which will be used for making predictions.
# df["HL_Perc"] = (df["High"]-df["Low"]) / df["Low"] * 100
# df["CO_Perc"] = (df["Close"] - df["Open"]) / df["Open"] * 100
#Make array of dates
#Last 30 dates will be used for forecasting.
# dates = np.array(df["Date"])
# dates_check = dates[-30:]
# dates = dates[:-30]
# df = df[["HL_Perc", "CO_Perc", "Adj Close", "Volume"]]
#Define the label column
#Make fetaure and label arrays
#Divide the data set into training data and testing data
# X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.3)
# #Define the prediction model
# model = RandomForestRegressor()
# #Fit the model using training data
# model.fit(X_train, y_train)
# #Calculate the confidence value by applying the model to testing data
# conf = model.score(X_test, y_test)
# print(conf)
# #Fit the model again using the whole data set
# model.fit(X,y)
# #Make predictions
# predictions = model.predict(X_Check)
#Make the final DataFrame containing Dates, ClosePrices, and Forecast values
# actual = pd.DataFrame(dates, columns = ["Date"])
# actual["ClosePrice"] = df["Adj Close"]
# actual["Forecast"] = np.nan
# actual.set_index("Date", inplace = True)
# forecast = pd.DataFrame(dates_check, columns=["Date"])
# forecast["Forecast"] = predictions
# forecast.set_index("Date", inplace = True)
# var = [actual, forecast]
# result = pd.concat(var) #This is the final DataFrame
# #Plot the results
# result.plot(figsize=(20,10), linewidth=1.5)
# plt.legend(loc=2, prop={'size':20})
# plt.xlabel('Date')
# plt.ylabel('Price')
