eland.ml.ImportedMLModel¶

class eland.ml.ImportedMLModel(es_client, model_id: str, model: Union[sklearn.tree._classes.DecisionTreeClassifier, sklearn.tree._classes.DecisionTreeRegressor, sklearn.ensemble._forest.RandomForestRegressor, sklearn.ensemble._forest.RandomForestClassifier, xgboost.sklearn.XGBClassifier, xgboost.sklearn.XGBRegressor], feature_names: List[str], classification_labels: List[str] = None, classification_weights: List[float] = None, overwrite=False)¶

Transform and serialize a trained 3rd party model into Elasticsearch. This model can then be used for inference in the Elastic Stack.

Parameters:

es_client: Elasticsearch client argument(s)

elasticsearch-py parameters or
elasticsearch-py instance or
eland.Client instance

model_id: str

The unique identifier of the trained inference model in Elasticsearch.

model: An instance of a supported python model. We support the following model types:

sklearn.tree.DecisionTreeClassifier
sklearn.tree.DecisionTreeRegressor
sklearn.ensemble.RandomForestRegressor
sklearn.ensemble.RandomForestClassifier
xgboost.XGBClassifier
xgboost.XGBRegressor

feature_names: List[str]

Names of the features (required)

classification_labels: List[str]

Labels of the classification targets

classification_weights: List[str]

Weights of the classification targets

overwrite: bool

Delete and overwrite existing model (if exists)

Examples

>>> from sklearn import datasets
>>> from sklearn.tree import DecisionTreeClassifier
>>> from eland.ml import ImportedMLModel

>>> # Train model
>>> training_data = datasets.make_classification(n_features=5, random_state=0)
>>> test_data = [[-50.1, 0.2, 0.3, -0.5, 1.0], [1.6, 2.1, -10, 50, -1.0]]
>>> classifier = DecisionTreeClassifier()
>>> classifier = classifier.fit(training_data[0], training_data[1])

>>> # Get some test results
>>> classifier.predict(test_data)
array([0, 1])

>>> # Serialise the model to Elasticsearch
>>> feature_names = ["f0", "f1", "f2", "f3", "f4"]
>>> model_id = "test_decision_tree_classifier"
>>> es_model = ImportedMLModel('localhost', model_id, classifier, feature_names, overwrite=True)

>>> # Get some test results from Elasticsearch model
>>> es_model.predict(test_data)
array([0, 1])

>>> # Delete model from Elasticsearch
>>> es_model.delete_model()