def var_predict(df, n_forwards=(1, 3), n_lags=4, test_ratio=0.2):
n_sample, n_output = df.shape
n_test = int(round(n_sample * test_ratio))
n_train = n_sample - n_test
df_train, df_test = df[:n_train], df[n_train:]
scaler = StandardScaler(mean=df_train.values.mean(),
std=df_train.values.std())
data = scaler.transform(df_train.values)
var_model = VAR(data)
var_result = var_model.fit(n_lags)
max_n_forwards = np.max(n_forwards)
# Do forecasting.
result = np.zeros(shape=(len(n_forwards), n_test, n_output))
start = n_train - n_lags - max_n_forwards + 1
for input_ind in range(start, n_sample - n_lags):
prediction = var_result.forecast(
scaler.transform(df.values[input_ind:input_ind + n_lags]),
max_n_forwards)
for i, n_forward in enumerate(n_forwards):
result_ind = input_ind - n_train + n_lags + n_forward - 1
if 0 <= result_ind < n_test:
result[i, result_ind, :] = prediction[n_forward - 1, :]
df_predicts = []
for i, n_forward in enumerate(n_forwards):
df_predict = pd.DataFrame(scaler.inverse_transform(result[i]),
index=df_test.index,
columns=df_test.columns)
df_predicts.append(df_predict)
df_predict.to_csv("./df_predict.csv", sep=',', index=False)
df_test.to_csv("./df_test.csv", sep=',', index=False)
return df_predicts, df_test
def var_predict(df, n_forwards=(1, 3), n_lags=4, test_ratio=0.2):
"""
Multivariate time series forecasting using Vector Auto-Regressive Model.
:param df: pandas.DataFrame, index: time, columns: sensor id, content: data.
:param n_forwards: a tuple of horizons.
:param n_lags: the order of the VAR model.
:param test_ratio:
:return: [list of prediction in different horizon], dt_test
"""
n_sample, n_output = df.shape
n_test = int(round(n_sample * test_ratio))
n_train = n_sample - n_test
df_train, df_test = df[:n_train], df[n_train:]
scaler = StandardScaler(mean=df_train.values.mean(), std=df_train.values.std())
data = scaler.transform(df_train.values)
var_model = VAR(data)
var_result = var_model.fit(n_lags)
max_n_forwards = np.max(n_forwards)
# Do forecasting.
result = np.zeros(shape=(len(n_forwards), n_test, n_output))
start = n_train - n_lags - max_n_forwards + 1
for input_ind in range(start, n_sample - n_lags):
prediction = var_result.forecast(scaler.transform(df.values[input_ind: input_ind + n_lags]), max_n_forwards)
for i, n_forward in enumerate(n_forwards):
result_ind = input_ind - n_train + n_lags + n_forward - 1
if 0 <= result_ind < n_test:
result[i, result_ind, :] = prediction[n_forward - 1, :]
df_predicts = []
for i, n_forward in enumerate(n_forwards):
df_predict = pd.DataFrame(scaler.inverse_transform(result[i]), index=df_test.index, columns=df_test.columns)
df_predicts.append(df_predict)
return df_predicts, df_test
def evaluate(self, sess, **kwargs):
y_preds_all = []
half_length = int(len(self.clusters) / 2)
sclusters = self.clusters[0:32]
for cluster in sclusters:
node_count, adj_mx = self.cluster_data(cluster)
adj_mx = utils.calculate_random_walk_matrix(adj_mx).T
adj_mx = self._build_sparse_matrix(adj_mx)
global_step = sess.run(tf.train.get_or_create_global_step())
scaler_path = self._kwargs['data'].get(
'dataset_dir') + '/scaler.npy'
scaler_data_ = np.load(scaler_path)
mean, var = scaler_data_[0], scaler_data_[1]
scaler = StandardScaler(mean=mean, std=var)
# change val to test before run
test_data_path = self._kwargs['data'].get(
'dataset_dir') + '/test_' + str(cluster) + '.tfrecords'
test_dataset = tf.data.TFRecordDataset([test_data_path])
test_dataset = test_dataset.map(self._parse_record_fn)
test_dataset = test_dataset.make_one_shot_iterator()
test_next_element = test_dataset.get_next()
test_results = self.run_epoch_generator(sess,
self._test_model,
test_next_element,
adj_mx,
return_output=True,
training=False)
test_loss, y_preds = test_results['loss'], test_results['outputs']
utils.add_simple_summary(self._writer, ['loss/test_loss'],
[test_loss],
global_step=global_step)
y_preds = np.concatenate(y_preds, axis=0)
y_preds = scaler.inverse_transform(y_preds[:, self.horizon - 1, :,
0])
y_preds = y_preds[:, 0:node_count]
y_preds_all.append(y_preds)
y_preds_all = np.concatenate(y_preds_all, axis=1)
return y_preds_all
def test_reverse_transform_df(self):
df = pd.DataFrame([[0., -1.], [-1, -0.5], [1., 0.]])
expected_result = np.array([[35., 0.], [0., 17.5], [70., 35.]])
scaler = StandardScaler(mean=35., std=35.)
result = scaler.inverse_transform(df)
self.assertTrue(np.array_equal(expected_result, result.values))
def test_reverse_transform(self):
data = np.array([[0., -1.], [-1, -0.5], [1., 0.]])
expected_result = np.array([[35., 0.], [0., 17.5], [70., 35.]])
scaler = StandardScaler(mean=35., std=35.)
result = scaler.inverse_transform(data)
self.assertTrue(np.array_equal(expected_result, result))
def main(args):
with open(args.config_filename) as f:
supervisor_config = yaml.load(f)
graph_pkl_filename = supervisor_config['data'].get(
'graph_pkl_filename')
sensor_ids, sensor_id_to_ind, adj_mx = load_graph_data(
graph_pkl_filename)
supervisor_config['model']['num_nodes'] = num_nodes = len(sensor_ids)
# Data preprocessing
traffic_df_filename = supervisor_config['data']['hdf_filename']
df_data = pd.read_hdf(traffic_df_filename)
#df_data = df_data.iloc[int(df_data.shape[0]/3):,:]
validation_ratio = supervisor_config.get('data').get(
'validation_ratio')
test_ratio = supervisor_config.get('data').get('test_ratio')
df_train, df_val, df_test = train_val_test_split(
df_data, val_ratio=validation_ratio, test_ratio=test_ratio)
batch_size = supervisor_config.get('data').get('batch_size')
val_batch_size = supervisor_config.get('data').get('val_batch_size')
test_batch_size = supervisor_config.get('data').get('test_batch_size')
horizon = supervisor_config.get('model').get('horizon')
seq_len = supervisor_config.get('model').get('seq_len')
scaler = StandardScaler(mean=df_train.values.mean(),
std=df_train.values.std())
data_train = generate_seq2seq_data(df_train, batch_size, seq_len,
horizon, num_nodes, 'train', scaler)
data_val = generate_seq2seq_data(df_val, val_batch_size, seq_len,
horizon, num_nodes, 'val', scaler)
data_train.update(data_val)
#data_train['scaler'] = scaler
data_test = generate_seq2seq_data(df_test, test_batch_size, seq_len,
horizon, num_nodes, 'test', scaler)
#data_test['scaler'] = scaler
tf_config = tf.ConfigProto()
if args.use_cpu_only:
tf_config = tf.ConfigProto(device_count={'GPU': 0})
tf_config.gpu_options.allow_growth = True
with tf.Session(config=tf_config) as sess:
supervisor = DCRNNSupervisor(adj_mx, data_train, supervisor_config)
data_tag = supervisor_config.get('data').get('dataset_dir')
folder = data_tag + '/model/'
if not os.path.exists(folder):
os.makedirs(folder)
# Train
supervisor.train(sess=sess)
# Test
yaml_files = glob.glob('%s/model/*/*.yaml' % data_tag,
recursive=True)
yaml_files.sort(key=os.path.getmtime)
config_filename = yaml_files[-1] #'config_%d.yaml' % config_id
with open(config_filename) as f:
config = yaml.load(f)
# Load model and evaluate
supervisor.load(sess, config['train']['model_filename'])
y_preds = supervisor.evaluate(sess, data_test)
n_test_samples = data_test['y_test'].shape[0]
folder = data_tag + '/results/'
if not os.path.exists(folder):
os.makedirs(folder)
for horizon_i in range(data_test['y_test'].shape[1]):
y_pred = scaler.inverse_transform(y_preds[:, horizon_i, :, 0])
eval_dfs = df_test[seq_len + horizon_i:seq_len + horizon_i +
n_test_samples]
df = pd.DataFrame(y_pred,
index=eval_dfs.index,
columns=eval_dfs.columns)
#df = pd.DataFrame(y_pred, columns=df_test.columns)
filename = os.path.join(
'%s/results/' % data_tag,
'dcrnn_speed_prediction_%s.h5' % str(horizon_i + 1))
df.to_hdf(filename, 'results')
print(
'Predictions saved as %s/results/dcrnn_prediction_[1-12].h5...'
% data_tag)
