Utilities¶

Utilities for the entire package.

coremltools.models.utils.convert_double_to_float_multiarray_type(spec)¶

Convert all double multiarrays feature descriptions (input, output, training input) to float multiarrays

Parameters:	spec: Model_pb The specification containing the multiarrays types to convert

Examples

# In-place convert multiarray type of spec
>>> spec = mlmodel.get_spec()
>>> coremltools.utils.convert_double_to_float_multiarray_type(spec)
>>> model = coremltools.models.MLModel(spec)

coremltools.models.utils.evaluate_classifier(model, data, target='target', verbose=False)¶

Evaluate a Core ML classifier model and compare against predictions from the original framework (for testing correctness of conversion). Use this evaluation for models that don’t deal with probabilities.

Parameters:	filename: list of str or list of MLModel File from where to load the model from (OR) a loaded version of the MLModel. data: list of str or list of Dataframe Test data on which to evaluate the models (dataframe, or path to a csv file). target: str Column to interpret as the target column verbose: bool Set to true for a more verbose output.

See also

evaluate_regressor, evaluate_classifier_with_probabilities

Examples

>>> metrics =  coremltools.utils.evaluate_classifier(spec, 'data_and_predictions.csv', 'target')
>>> print(metrics)
{"samples": 10, num_errors: 0}

coremltools.models.utils.evaluate_classifier_with_probabilities(model, data, probabilities='probabilities', verbose=False)¶

Evaluate a classifier specification for testing.

Parameters:	filename: [str \| Model] File from where to load the model from (OR) a loaded version of the MLModel. data: [str \| Dataframe] Test data on which to evaluate the models (dataframe, or path to a csv file). probabilities: str Column to interpret as the probabilities column verbose: bool Verbosity levels of the predictions.

coremltools.models.utils.evaluate_regressor(model, data, target='target', verbose=False)¶

Evaluate a CoreML regression model and compare against predictions from the original framework (for testing correctness of conversion)

Parameters:

filename: list of str or list of MLModel: File path from which to load the MLModel from (OR) a loaded version of MLModel.
data: list of str or list of Dataframe: Test data on which to evaluate the models (dataframe, or path to a .csv file).
target: str: Name of the column in the dataframe that must be interpreted as the target column.
verbose: bool: Set to true for a more verbose output.

See also

evaluate_classifier

Examples

>>> metrics = coremltools.utils.evaluate_regressor(spec, 'data_and_predictions.csv', 'target')
>>> print(metrics)
{"samples": 10, "rmse": 0.0, max_error: 0.0}

coremltools.models.utils.evaluate_transformer(model, input_data, reference_output, verbose=False)¶

Evaluate a transformer specification for testing.

Parameters:	spec: list of str or list of MLModel File from where to load the Model from (OR) a loaded version of MLModel. input_data: list of dict Test data on which to evaluate the models. reference_output: list of dict Expected results for the model. verbose: bool Verbosity levels of the predictions.

See also

evaluate_regressor, evaluate_classifier

Examples

>>> input_data = [{'input_1': 1, 'input_2': 2}, {'input_1': 3, 'input_2': 3}]
>>> expected_output = [{'input_1': 2.5, 'input_2': 2.0}, {'input_1': 1.3, 'input_2': 2.3}]
>>> metrics = coremltools.utils.evaluate_transformer(scaler_spec, input_data, expected_output)

coremltools.models.utils.has_custom_layer(spec)¶

Returns true if the given protobuf specification has a custom layer, and false otherwise.

Parameters:	spec: mlmodel spec
Returns:	True if the protobuf specification contains a neural network with a custom layer, False otherwise.

coremltools.models.utils.is_macos()¶: Returns True if current platform is MacOS, False otherwise.

coremltools.models.utils.load_spec(filename)¶

Load a protobuf model specification from file.

Parameters:	filename: str Location on disk (a valid file path) from which the file is loaded as a protobuf spec.
Returns:	model_spec: Model_pb Protobuf representation of the model

See also

save_spec

Examples

>>> spec = coremltools.utils.load_spec('HousePricer.mlmodel')

coremltools.models.utils.macos_version()¶: Returns macOS version as a tuple of integers, making it easy to do proper version comparisons. On non-Macs, it returns an empty tuple.

coremltools.models.utils.rename_feature(spec, current_name, new_name, rename_inputs=True, rename_outputs=True)¶

Rename a feature in the specification.

Parameters:

spec: Model_pb: The specification containing the feature to rename.
current_name: str: Current name of the feature. If this feature doesn’t exist, the rename is a no-op.
new_name: str: New name of the feature.
rename_inputs: bool: Search for current_name only in the input features (i.e ignore output features)
rename_outputs: bool: Search for current_name only in the output features (i.e ignore input features)

Examples

# In-place rename of spec
>>> coremltools.utils.rename_feature(spec, 'old_feature', 'new_feature_name')

coremltools.models.utils.save_spec(spec, filename, auto_set_specification_version=False)¶

Save a protobuf model specification to file.

Parameters:	spec: Model_pb Protobuf representation of the model filename: str File path where the spec gets saved. auto_set_specification_version: bool If true, will always try to set specification version automatically.

See also

load_spec

Examples

>>> coremltools.utils.save_spec(spec, 'HousePricer.mlmodel')