tfma.analyze_raw_data

Runs TensorFlow model analysis on a pandas.DataFrame.

tfma.analyze_raw_data(
    data: pd.DataFrame,
    eval_config: Optional[tfma.EvalConfig] = None,
    output_path: Optional[str] = None,
    extractors: Optional[List[extractor.Extractor]] = None,
    evaluators: Optional[List[evaluator.Evaluator]] = None,
    writers: Optional[List[writer.Writer]] = None,
    add_metric_callbacks: Optional[List[types.AddMetricsCallbackType]] = None
) -> tfma.EvalResult

Used in the notebooks

Used in the tutorials

This function allows you to use TFMA with Pandas DataFrames. The dataframe must include a 'predicted' column for the predicted label and a 'label' column for the actual label.

In addition to a DataFrame, this function requires an eval_config, a tfma.EvalConfig object containing various configuration parameters (see config.proto for a comprehensive list)...

  • the metrics to compute
  • the slices to compute metrics on
  • the DataFrame's column names for example labels and predictions ('label' and 'prediction' by default)
  • confidence interval options

This function returns a tfma.EvalResult, which contains TFMA's computed metrics and can be used to generate plots with tfma.view.render_slicing_metrics.

Example usage:

model_specs = [
  tfma.ModelSpec(
      prediction_key='prediction',
      label_key='label')
]
metrics_specs = [
    tfma.MetricsSpec(metrics=[
      tfma.MetricConfig(class_name='Accuracy'),
      tfma.MetricConfig(class_name='ExampleCount')
    ])
]
slicing_specs = [
    tfma.SlicingSpec(),  # the empty slice represents overall dataset
    tfma.SlicingSpec(feature_keys=['language'])
]
eval_config = tfma.EvalConfig(
    model_specs=model_specs,
    metrics_specs=metrics_specs,
    slicing_specs=slicing_specs)
result = tfma.analyze_raw_data(df, eval_config)
tfma.view.render_slicing_metrics(result)

# Example with Fairness Indicators
from tensorflow_model_analysis.addons.fairness.post_export_metrics import
fairness_indicators
from tensorflow_model_analysis.addons.fairness.view import widget_view
add_metrics_callbacks = [
    tfma.post_export_metrics.fairness_indicators(thresholds=[0.25, 0.5, 0.75])
]
result = tfma.analyze_raw_data(
    data=df,
    metrics_specs=metrics_specs,
    slicing_specs=slicing_specs,
    add_metric_callbacks=add_metrics_callbacks
)
widget_view.render_fairness_indicator(result)

dataA pandas.DataFrame, where rows correspond to examples and columns correspond to features. One column must indicate a row's predicted label, and one column must indicate a row's actual label.
eval_configA tfma.EvalConfig, which contains various configuration parameters including metrics, slices, and label/prediction column names.
output_pathPath to write EvalResult to.
extractorsOptional list of Extractors to apply to Extracts. Typically these will be added by calling the default_extractors function. If no extractors are provided, default_extractors (non-materialized) will be used.
evaluatorsOptional list of Evaluators for evaluating Extracts. Typically these will be added by calling the default_evaluators function. If no evaluators are provided, default_evaluators will be used.
writersOptional list of Writers for writing Evaluation output. Typically these will be added by calling the default_writers function. If no writers are provided, default_writers with add_metric_callbacks will be used.
add_metric_callbacksOptional list of metric callbacks (if used).

A tfma.EvalResult to extract metrics or generate visualizations from.

KeyErrorIf the prediction or label columns are not found within the DataFrame.