mirror of
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1a90e9232e
* Updating test matrix for 7.6 + removing oss for now. * Resolving 7.6.0 docs issues * Updating ML docs * Bumping version following doc fixes * Change ExternalMLModel to ImportedMLModel
239 lines
8.9 KiB
Python
239 lines
8.9 KiB
Python
# Copyright 2020 Elasticsearch BV
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from typing import Union, List
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import numpy as np
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from eland.ml._model_transformers import SKLearnDecisionTreeTransformer, SKLearnForestRegressorTransformer, \
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SKLearnForestClassifierTransformer, XGBoostRegressorTransformer, XGBoostClassifierTransformer
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from eland.ml._optional import import_optional_dependency
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from eland.ml.ml_model import MLModel
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sklearn = import_optional_dependency("sklearn")
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xgboost = import_optional_dependency("xgboost")
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from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
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from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
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from xgboost import XGBRegressor, XGBClassifier
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class ImportedMLModel(MLModel):
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"""
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Transform and serialize a trained 3rd party model into Elasticsearch.
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This model can then be used for inference in the Elastic Stack.
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Parameters
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----------
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es_client: Elasticsearch client argument(s)
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- elasticsearch-py parameters or
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- elasticsearch-py instance or
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- eland.Client instance
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model_id: str
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The unique identifier of the trained inference model in Elasticsearch.
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model: An instance of a supported python model. We support the following model types:
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- sklearn.tree.DecisionTreeClassifier
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- sklearn.tree.DecisionTreeRegressor
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- sklearn.ensemble.RandomForestRegressor
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- sklearn.ensemble.RandomForestClassifier
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- xgboost.XGBClassifier
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- xgboost.XGBRegressor
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feature_names: List[str]
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Names of the features (required)
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classification_labels: List[str]
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Labels of the classification targets
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classification_weights: List[str]
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Weights of the classification targets
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overwrite: bool
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Delete and overwrite existing model (if exists)
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Examples
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--------
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>>> from sklearn import datasets
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>>> from sklearn.tree import DecisionTreeClassifier
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>>> from eland.ml import ImportedMLModel
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>>> # Train model
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>>> training_data = datasets.make_classification(n_features=5, random_state=0)
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>>> test_data = [[-50.1, 0.2, 0.3, -0.5, 1.0], [1.6, 2.1, -10, 50, -1.0]]
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>>> classifier = DecisionTreeClassifier()
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>>> classifier = classifier.fit(training_data[0], training_data[1])
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>>> # Get some test results
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>>> classifier.predict(test_data)
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array([0, 1])
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>>> # Serialise the model to Elasticsearch
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>>> feature_names = ["f0", "f1", "f2", "f3", "f4"]
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>>> model_id = "test_decision_tree_classifier"
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>>> es_model = ImportedMLModel('localhost', model_id, classifier, feature_names, overwrite=True)
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>>> # Get some test results from Elasticsearch model
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>>> es_model.predict(test_data)
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array([0, 1])
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>>> # Delete model from Elasticsearch
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>>> es_model.delete_model()
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"""
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def __init__(self,
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es_client,
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model_id: str,
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model: Union[DecisionTreeClassifier,
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DecisionTreeRegressor,
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RandomForestRegressor,
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RandomForestClassifier,
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XGBClassifier,
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XGBRegressor],
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feature_names: List[str],
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classification_labels: List[str] = None,
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classification_weights: List[float] = None,
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overwrite=False):
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super().__init__(
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es_client,
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model_id
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)
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self._feature_names = feature_names
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self._model_type = None
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# Transform model
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if isinstance(model, DecisionTreeRegressor):
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serializer = SKLearnDecisionTreeTransformer(model, feature_names).transform()
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self._model_type = MLModel.TYPE_REGRESSION
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elif isinstance(model, DecisionTreeClassifier):
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serializer = SKLearnDecisionTreeTransformer(model, feature_names, classification_labels).transform()
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self._model_type = MLModel.TYPE_CLASSIFICATION
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elif isinstance(model, RandomForestRegressor):
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serializer = SKLearnForestRegressorTransformer(model, feature_names).transform()
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self._model_type = MLModel.TYPE_REGRESSION
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elif isinstance(model, RandomForestClassifier):
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serializer = SKLearnForestClassifierTransformer(model, feature_names, classification_labels).transform()
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self._model_type = MLModel.TYPE_CLASSIFICATION
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elif isinstance(model, XGBRegressor):
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serializer = XGBoostRegressorTransformer(model, feature_names).transform()
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self._model_type = MLModel.TYPE_REGRESSION
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elif isinstance(model, XGBClassifier):
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serializer = XGBoostClassifierTransformer(model, feature_names, classification_labels).transform()
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self._model_type = MLModel.TYPE_CLASSIFICATION
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else:
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raise NotImplementedError("ML model of type {}, not currently implemented".format(type(model)))
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if overwrite:
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self.delete_model()
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serialized_model = str(serializer.serialize_and_compress_model())[2:-1] # remove `b` and str quotes
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self._client.perform_request(
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"PUT", "/_ml/inference/" + self._model_id,
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body={
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"input": {
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"field_names": feature_names
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},
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"compressed_definition": serialized_model
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}
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)
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def predict(self, X):
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"""
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Make a prediction using a trained model stored in Elasticsearch.
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Parameters for this method are not yet fully compatible with standard sklearn.predict.
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Parameters
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----------
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X: list or list of lists of type float
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Input feature vector - TODO support DataFrame and other formats
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Returns
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-------
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y: np.ndarray of dtype float for regressors or int for classifiers
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Examples
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--------
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>>> from sklearn import datasets
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>>> from xgboost import XGBRegressor
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>>> from eland.ml import ImportedMLModel
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>>> # Train model
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>>> training_data = datasets.make_classification(n_features=6, random_state=0)
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>>> test_data = [[-1, -2, -3, -4, -5, -6], [10, 20, 30, 40, 50, 60]]
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>>> regressor = XGBRegressor(objective='reg:squarederror')
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>>> regressor = regressor.fit(training_data[0], training_data[1])
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>>> # Get some test results
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>>> regressor.predict(np.array(test_data))
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array([0.23733574, 1.1897984 ], dtype=float32)
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>>> # Serialise the model to Elasticsearch
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>>> feature_names = ["f0", "f1", "f2", "f3", "f4", "f5"]
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>>> model_id = "test_xgb_regressor"
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>>> es_model = ImportedMLModel('localhost', model_id, regressor, feature_names, overwrite=True)
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>>> # Get some test results from Elasticsearch model
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>>> es_model.predict(test_data)
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array([0.2373357, 1.1897984], dtype=float32)
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>>> # Delete model from Elasticsearch
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>>> es_model.delete_model()
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"""
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docs = []
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if isinstance(X, list):
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# Is it a list of lists?
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if all(isinstance(i, list) for i in X):
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for i in X:
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doc = dict()
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doc['_source'] = dict(zip(self._feature_names, i))
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docs.append(doc)
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else: # single feature vector1
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doc = dict()
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doc['_source'] = dict(zip(self._feature_names, i))
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docs.append(doc)
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else:
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raise NotImplementedError("Prediction for type {}, not supported".format(type(X)))
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results = self._client.perform_request(
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"POST",
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"/_ingest/pipeline/_simulate",
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body={
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"pipeline": {
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"processors": [
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{"inference": {
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"model_id": self._model_id,
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"inference_config": {self._model_type: {}},
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"field_mappings": {}
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}}
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]
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},
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"docs": docs
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})
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y = [
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doc['doc']['_source']['ml']['inference']['predicted_value'] for doc in results['docs']
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]
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# Return results as np.ndarray of float32 or int (consistent with sklearn/xgboost)
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if self._model_type == MLModel.TYPE_CLASSIFICATION:
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dt = np.int
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else:
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dt = np.float32
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return np.asarray(y, dtype=dt)
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