SKLearn

coremltools.converters.sklearn._converter.convert(sk_obj, input_features=None, output_feature_names=None)

Convert scikit-learn pipeline, classifier, or regressor to Core ML format.

Parameters:

sk_obj: model | [model] of scikit-learn format.

Scikit learn model(s) to convert to a Core ML format.

The input model may be a single scikit learn model, a scikit learn pipeline model, or a list of scikit learn models.

Currently supported scikit learn models are:

• Linear and Logistic Regression

• LinearSVC and LinearSVR

• Ridge Regression

• SVC and SVR

• NuSVC and NuSVR

• Gradient Boosting Classifier and Regressor

• Decision Tree Classifier and Regressor

• Random Forest Classifier and Regressor

• Normalizer

• Imputer

• Standard Scaler

• DictVectorizer

• One Hot Encoder

• KNeighborsClassifier

The input model, or the last model in a pipeline or list of models, determines whether this is exposed as a Transformer, Regressor, or Classifier.

Note that there may not be a one-to-one correspondence between scikit learn models and the Core ML models chosen to represent them. For example, many scikit learn models are embedded in a pipeline to handle processing of input features.

input_features: str | dict | list

Optional name(s) that can be given to the inputs of the scikit-learn model. Defaults to "input".

Input features can be specified in a number of forms.

• Single string: In this case, the input is assumed to be a single array, with the number of dimensions set using num_dimensions.

• List of strings: In this case, the overall input dimensions to the scikit-learn model are assumed to be the length of the list. If neighboring names are identical, they are assumed to be an input array of that length. For example:

  ["a", "b", "c"]

  resolves to:

  [("a", Double), ("b", Double), ("c", Double)].

  In addition:

  ["a", "a", "b"]

  resolves to:

  [("a", Array(2)), ("b", Double)].

• Dictionary: Where the keys are the names and the indices or ranges of feature indices.

  In this case, the Dictionary is presented as a mapping from keys to indices or ranges of contiguous indices. For example:

  {"a" : 0, "b" : [2,3], "c" : 1}

  resolves to:

  [("a", Double), ("c", Double), ("b", Array(2))].

  Note that the ordering is determined by the indices.

• List of tuples of the form (name, datatype), in which name is the name of the exposed feature, and datatype is an instance of String, Double, Int64, Array, or Dictionary.

output_feature_names: string or list of strings

Optional name(s) that can be given to the inputs of the scikit-learn model.

The output_feature_names is interpreted according to the model type:

• If the scikit-learn model is a transformer, it is the name of the array feature output by the final sequence of the transformer (defaults to "output").

• If it is a classifier, it should be a 2-tuple of names giving the top class prediction and the array of scores for each class (defaults to "classLabel" and "classScores").

• If it is a regressor, it should give the name of the prediction value (defaults to "prediction").

Returns:

model:MLModel

Returns an MLModel instance representing a Core ML model.

Examples

>>> from sklearn.linear_model import LinearRegression
>>> import pandas as pd

# Load data
>>> data = pd.read_csv('houses.csv')

# Train a model
>>> model = LinearRegression()
>>> model.fit(data[["bedroom", "bath", "size"]], data["price"])

 # Convert and save the scikit-learn model
>>> import coremltools
>>> coreml_model = coremltools.converters.sklearn.convert(model,
                                                         ["bedroom", "bath", "size"],
                                                         "price")
>>> coreml_model.save('HousePricer.mlmodel')