Using tf.data.experimental.make_csv_dataset for time series data

Question

How do I use tf.data.experimental.make_csv_dataset with CSV files containing time series data?

building_dataset = tf.data.experimental.make_csv_dataset(file_pattern=csv_file,
                                                        batch_size=5,num_epochs=1, shuffle=False,select_columns=feature_columns)

Answer 1

It is assumed that the CSV file is already sorted w.r.t. time. First, read the CSV file using:

building_dataset = tf.data.experimental.make_csv_dataset(file_pattern=csv_file,
                                                        batch_size=5,num_epochs=1, shuffle=False,select_columns=feature_columns)

Then define a pack_features_vector to convert to a features vector and unbatch using flat_map(). The tensors are also cast to float32.

def pack_features_vector(features):
    """Pack the features into a single array."""
    
    features = tf.stack([tf.cast(x,tf.float32) for x in list(features.values())], axis=1)
    return features

   
building_dataset = building_dataset.map(pack_features_vector)
building_dataset = building_dataset.flat_map(lambda x: tf.data.Dataset.from_tensor_slices(x))
for feature in building_dataset.take(1):
    print('Stacked tensor:',feature)

Then use the window and flat map method.

building_dataset = building_dataset.window(window_size, shift=1, drop_remainder=True)
building_dataset = building_dataset.flat_map(lambda window: window.batch(window_size))

Then use map method to separate features and labels.

building_dataset = building_dataset.map(lambda window: (window[:,:-1], window[-1:,-1]))
for feature, label in building_dataset.take(5):
    print(feature.shape)
    print('feature:',feature[:,0:4])
    print('label:',label)

Finally create batches using batch() and use as inputs to model training.

building_dataset = building_dataset.batch(32)