SKLearn — coremltools API Reference 8.3 documentation (original) (raw)

coremltools.converters.sklearn._converter.convert(sk_obj, input_features=None, output_feature_names=None)[source]

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 ofString, 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')