tf.contrib.timeseries.CSVReader
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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
View source
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
View source
read()
Reads a chunk of data from the tf.compat.v1.ReaderBase for later re-chunking.
read_full
View source
read_full()
Reads a full epoch of data into memory.
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Last updated 2020-10-01 UTC.
[null,null,["Last updated 2020-10-01 UTC."],[],[],null,["# tf.contrib.timeseries.CSVReader\n\n\u003cbr /\u003e\n\n|------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| [View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/contrib/timeseries/python/timeseries/input_pipeline.py#L436-L513) |\n\nReads from a collection of CSV-formatted files. \n\n tf.contrib.timeseries.CSVReader(\n filenames, column_names=(feature_keys.TrainEvalFeatures.TIMES,\n feature_keys.TrainEvalFeatures.VALUES), column_dtypes=None,\n skip_header_lines=None, read_num_records_hint=4096\n )\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ---- ||\n|-------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `filenames` | A filename or list of filenames to read the time series from. Each line must have columns corresponding to `column_names`. |\n| `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. |\n| `column_dtypes` | If provided, must be a list with the same length as `column_names`, indicating dtypes for each column. Defaults to [`tf.int64`](../../../tf#int64) for `TrainEvalFeatures.TIMES` and [`tf.float32`](../../../tf#float32) for everything else. |\n| `skip_header_lines` | Passed on to [`tf.compat.v1.TextLineReader`](../../../tf/TextLineReader); skips this number of lines at the beginning of each file. |\n| `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. |\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Raises ------ ||\n|--------------|-------------------------------------------------------------------------|\n| `ValueError` | If required column names are not specified, or if lengths do not match. |\n\n\u003cbr /\u003e\n\nMethods\n-------\n\n### `check_dataset_size`\n\n[View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/contrib/timeseries/python/timeseries/input_pipeline.py#L169-L183) \n\n check_dataset_size(\n minimum_dataset_size\n )\n\nWhen possible, raises an error if the dataset is too small.\n\nThis method allows TimeSeriesReaders to raise informative error messages if\nthe user has selected a window size in their TimeSeriesInputFn which is\nlarger than the dataset size. However, many TimeSeriesReaders will not have\naccess to a dataset size, in which case they do not need to override this\nmethod.\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n\u003cbr /\u003e\n\n| Args ||\n|------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------|\n| `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. |\n\n\u003cbr /\u003e\n\n### `read`\n\n[View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/contrib/timeseries/python/timeseries/input_pipeline.py#L368-L383) \n\n read()\n\nReads a chunk of data from the [`tf.compat.v1.ReaderBase`](../../../tf/ReaderBase) for later re-chunking.\n\n### `read_full`\n\n[View source](https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/contrib/timeseries/python/timeseries/input_pipeline.py#L385-L433) \n\n read_full()\n\nReads a full epoch of data into memory."]]
View source on GitHub