def main(): locations = [i for i in range(12,25)] stacked_errors = [] for location in locations: df1 = remove_trend(location, 27) df2 = remove_trend(location, 26) df3 = remove_trend(location, 28) X1, y1, removed_seasonality1, removed_std1 = generate_lags( df1, 20, 2 * 24) X2, y2, removed_seasonality2, removed_std2 = generate_lags( df2, 20, 2 * 24) X3, y3, removed_seasonality3, removed_std3 = generate_lags( df3, 20, 2 * 24) tf.reset_default_graph() merged_model = Merged_Model() errors = merged_model.run_epochs(X1, X2, X3, y1, y2, y3, removed_seasonality1, removed_seasonality2, removed_seasonality3, removed_std1, removed_std2, removed_std3, location) stacked_errors.append(errors) pd.DataFrame(stacked_errors, columns=["LGA", "JFK", "EWR"]).to_csv( 'data/multi_errors.csv', index=False, sep="\t")
def main(): hyper_config = HyperParam_Ranges() fn = Auxiliary_funcs(hyper_config) df = remove_trend(16, 27) X_train, X_validation = fn.split_data_into_training_validation( df['time series'].values) for i in range(50): internal_hidden_size, batch_size, learning_rate = fn.generate_random_hyperparams( ) # construct hyperparam string for each combination -> Tensorboard hparam = fn.make_hparam_string(internal_hidden_size, batch_size, learning_rate) # clear the default graph tf.reset_default_graph() config = Config(internal_hidden_size, batch_size, learning_rate) model = RNN_NeuralModel(config) model.run_epochs(X_train, X_validation, hparam)
def main(): loc_fixed = 23 df1 = remove_trend(loc_fixed, 24) df2 = remove_trend(loc_fixed, 21) df3 = remove_trend(loc_fixed, 22) X1, y1, removed_seasonality1, removed_std1 = generate_lags(df1, 20, 2 * 24) X2, y2, removed_seasonality2, removed_std2 = generate_lags(df2, 20, 2 * 24) X3, y3, removed_seasonality3, removed_std3 = generate_lags(df3, 20, 2 * 24) merged_model = Merged_Model() merged_model.run_epochs(X1, X2, X3, y1, y2, y3, removed_seasonality1, removed_seasonality2, removed_seasonality3, removed_std1, removed_std2, removed_std3)
def main(): df = remove_trend(16, 28) config = Config() model = RNN_NeuralModel(config) model.run_epochs(df['time series'].values, df['removed seasonality'].values, df['removed std'].values)
def main(): locations = [i for i in range(12,25)] stacked_errors = [] for location in locations: df = remove_trend(location, 27) tf.reset_default_graph() config = Config() model = RNN_NeuralModel(config) error = model.run_epochs(df['time series'].values, df['removed seasonality'].values, df['removed std'].values, location) stacked_errors.append(error) pd.DataFrame(stacked_errors).to_csv( 'data/LGA_single_errors.csv', index=False, sep="\t")
with tf.name_scope("Cost") as scope: mse = tf.reduce_mean(tf.pow(tf.subtract(pred, target_placeholder), 2.0)) with tf.name_scope("Optimize") as scope: optimizer = tf.train.RMSPropOptimizer(learning_rate) opt = optimizer.minimize(mse) """ Run graph """ with tf.Session() as sess: rmses = [] for dest in [26, 27, 28]: df = remove_trend(16,dest) X = df['time series'].values removed_seasonality = df['removed seasonality'].values removed_std = df['removed std'].values stacked_preds = [] stacked_ground_truth = [] for i in range(bootstrap_size, len(X) , n_test): print("Current window", i, i + n_test) sess.run(tf.global_variables_initializer()) X_train = X[:i]
def main(): df = remove_trend(16, 27) X, y, removed_seasonality, removed_std = generate_lags(df, 20, 2 * 24) run_epochs(X, y, removed_seasonality, removed_std)