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tfdv.validate_examples_in_csv

Validates examples in csv files.

Runs a Beam pipeline to detect anomalies on a per-example basis. If this function detects anomalous examples, it generates summary statistics regarding the set of examples that exhibit each anomaly.

This is a convenience function for users with data in CSV format. Users with data in unsupported file/data formats, or users who wish to create their own Beam pipelines need to use the 'IdentifyAnomalousExamples' PTransform API directly instead.

data_location The location of the input data files.
stats_options tfdv.StatsOptions for generating data statistics. This must contain a schema.
column_names A list of column names to be treated as the CSV header. Order must match the order in the input CSV files. If this argument is not specified, we assume the first line in the input CSV files as the header. Note that this option is valid only for 'csv' input file format.
delimiter A one-character string used to separate fields in a CSV file.
output_path The file path to output data statistics result to. If None, the function uses a temporary directory. The output will be a TFRecord file containing a single data statistics list proto, and can be read with the 'load_statistics' function. If you run this function on Google Cloud, you must specify an output_path. Specifying None may cause an error.
pipeline_options Optional beam pipeline options. This allows users to specify various beam pipeline execution parameters like pipeline runner (DirectRunner or DataflowRunner), cloud dataflow service project id, etc. See https://cloud.google.com/dataflow/pipelines/specifying-exec-params for more details.
num_sampled_examples If set, returns up to this many examples of each anomaly type as a map from anomaly reason string to pd.DataFrame.

If num_sampled_examples is zero, returns a single DatasetFeatureStatisticsList proto in which each dataset consists of the set of examples that exhibit a particular anomaly. If num_sampled_examples is nonzero, returns the same statistics proto as well as a mapping from anomaly to a pd.DataFrame of CSV rows exhibiting that anomaly.

ValueError If the specified stats_options does not include a schema.