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|
Reads from a collection of CSV-formatted files.
tf.contrib.timeseries.CSVReader(
filenames, column_names=(feature_keys.TrainEvalFeatures.TIMES,
feature_keys.TrainEvalFeatures.VALUES), column_dtypes=None,
skip_header_lines=None, read_num_records_hint=4096
)
Args | |
|---|---|
filenames
|
A filename or list of filenames to read the time series from.
Each line must have columns corresponding to column_names.
|
column_names
|
A list indicating names for each feature.
TrainEvalFeatures.TIMES and TrainEvalFeatures.VALUES are required;
VALUES may be repeated to indicate a multivariate series.
|
column_dtypes
|
If provided, must be a list with the same length as
column_names, indicating dtypes for each column. Defaults to
tf.int64 for TrainEvalFeatures.TIMES and tf.float32 for everything
else.
|
skip_header_lines
|
Passed on to tf.compat.v1.TextLineReader; skips this
number of lines at the beginning of each file.
|
read_num_records_hint
|
When not reading a full dataset, indicates the number of records to parse/transfer in a single chunk (for efficiency). The actual number transferred at one time may be more or less. |
Raises | |
|---|---|
ValueError
|
If required column names are not specified, or if lengths do not match. |
Methods
check_dataset_size
check_dataset_size(
minimum_dataset_size
)
When possible, raises an error if the dataset is too small.
This method allows TimeSeriesReaders to raise informative error messages if the user has selected a window size in their TimeSeriesInputFn which is larger than the dataset size. However, many TimeSeriesReaders will not have access to a dataset size, in which case they do not need to override this method.
| Args | |
|---|---|
minimum_dataset_size
|
The minimum number of records which should be contained in the dataset. Readers should attempt to raise an error when possible if an epoch of data contains fewer records. |
read
read()
Reads a chunk of data from the tf.compat.v1.ReaderBase for later re-chunking.
read_full
read_full()
Reads a full epoch of data into memory.