diff --git a/docs/source/examples/demo_notebook.ipynb b/docs/source/examples/demo_notebook.ipynb index 8417a81..69e94dc 100644 --- a/docs/source/examples/demo_notebook.ipynb +++ b/docs/source/examples/demo_notebook.ipynb @@ -753,7 +753,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 17, @@ -2704,15 +2704,15 @@ " \n", " \n", " 25%\n", - " 410.011039\n", + " 410.008918\n", " 2470.545974\n", " ...\n", - " 251.773003\n", + " 251.938710\n", " 1.000000\n", " \n", " \n", " 50%\n", - " 640.387285\n", + " 640.362667\n", " 7612.072403\n", " ...\n", " 503.148975\n", @@ -2720,11 +2720,11 @@ " \n", " \n", " 75%\n", - " 842.213490\n", - " 9734.960478\n", + " 840.617448\n", + " 9738.206675\n", " ...\n", - " 720.505705\n", - " 4.172535\n", + " 720.026320\n", + " 4.160448\n", " \n", " \n", " max\n", @@ -2745,9 +2745,9 @@ "mean 628.253689 7092.142457 ... 511.127842 2.835975\n", "std 266.386661 4578.263193 ... 334.741135 1.939365\n", "min 100.020531 0.000000 ... 0.000000 0.000000\n", - "25% 410.011039 2470.545974 ... 251.773003 1.000000\n", - "50% 640.387285 7612.072403 ... 503.148975 3.000000\n", - "75% 842.213490 9734.960478 ... 720.505705 4.172535\n", + "25% 410.008918 2470.545974 ... 251.938710 1.000000\n", + "50% 640.362667 7612.072403 ... 503.148975 3.000000\n", + "75% 840.617448 9738.206675 ... 720.026320 4.160448\n", "max 1199.729004 19881.482422 ... 1902.901978 6.000000\n", "\n", "[8 rows x 7 columns]" diff --git a/docs/source/examples/online_retail_analysis.ipynb b/docs/source/examples/online_retail_analysis.ipynb index 8733a52..171cd12 100644 --- a/docs/source/examples/online_retail_analysis.ipynb +++ b/docs/source/examples/online_retail_analysis.ipynb @@ -1023,21 +1023,21 @@ " \n", " \n", " 25%\n", - " 14217.474239\n", + " 14227.934845\n", " 1.000000\n", - " 1.250068\n", + " 1.250000\n", " \n", " \n", " 50%\n", - " 15662.024630\n", + " 15669.138235\n", " 2.000000\n", " 2.510000\n", " \n", " \n", " 75%\n", - " 17212.723881\n", - " 6.671951\n", - " 4.210000\n", + " 17212.690092\n", + " 6.610262\n", + " 4.211297\n", " \n", " \n", " max\n", @@ -1055,9 +1055,9 @@ "mean 15590.776680 7.464000 4.103233\n", "std 1764.025160 85.924387 20.104873\n", "min 12347.000000 -9360.000000 0.000000\n", - "25% 14217.474239 1.000000 1.250068\n", - "50% 15662.024630 2.000000 2.510000\n", - "75% 17212.723881 6.671951 4.210000\n", + "25% 14227.934845 1.000000 1.250000\n", + "50% 15669.138235 2.000000 2.510000\n", + "75% 17212.690092 6.610262 4.211297\n", "max 18239.000000 2880.000000 950.990000" ] }, diff --git a/docs/source/reference/api/eland.ml.ExternalMLModel.predict.rst b/docs/source/reference/api/eland.ml.ExternalMLModel.predict.rst deleted file mode 100644 index bd653a9..0000000 --- a/docs/source/reference/api/eland.ml.ExternalMLModel.predict.rst +++ /dev/null @@ -1,6 +0,0 @@ -eland.ml.ExternalMLModel.predict -================================ - -.. currentmodule:: eland.ml - -.. automethod:: ExternalMLModel.predict diff --git a/docs/source/reference/api/eland.ml.ExternalMLModel.rst b/docs/source/reference/api/eland.ml.ExternalMLModel.rst deleted file mode 100644 index bc84974..0000000 --- a/docs/source/reference/api/eland.ml.ExternalMLModel.rst +++ /dev/null @@ -1,6 +0,0 @@ -eland.ml.ExternalMLModel -======================== - -.. currentmodule:: eland.ml - -.. autoclass:: ExternalMLModel diff --git a/docs/source/reference/api/eland.ml.ImportedMLModel.predict.rst b/docs/source/reference/api/eland.ml.ImportedMLModel.predict.rst new file mode 100644 index 0000000..137da82 --- /dev/null +++ b/docs/source/reference/api/eland.ml.ImportedMLModel.predict.rst @@ -0,0 +1,6 @@ +eland.ml.ImportedMLModel.predict +================================ + +.. currentmodule:: eland.ml + +.. automethod:: ImportedMLModel.predict diff --git a/docs/source/reference/api/eland.ml.ImportedMLModel.rst b/docs/source/reference/api/eland.ml.ImportedMLModel.rst new file mode 100644 index 0000000..faf262b --- /dev/null +++ b/docs/source/reference/api/eland.ml.ImportedMLModel.rst @@ -0,0 +1,6 @@ +eland.ml.ImportedMLModel +======================== + +.. currentmodule:: eland.ml + +.. autoclass:: ImportedMLModel diff --git a/docs/source/reference/ml.rst b/docs/source/reference/ml.rst index bcb44bd..195f70c 100644 --- a/docs/source/reference/ml.rst +++ b/docs/source/reference/ml.rst @@ -17,7 +17,7 @@ The fastest way to get started with machine learning features is to start a free See https://www.elastic.co/guide/en/machine-learning/current/setup.html and other documentation for more detail. -ExternalMLModel +ImportedMLModel ~~~~~~~~~~~~~~~ .. currentmodule:: eland.ml @@ -26,12 +26,12 @@ Constructor .. autosummary:: :toctree: api/ - ExternalMLModel + ImportedMLModel Learning API ^^^^^^^^^^^^ .. autosummary:: :toctree: api/ - ExternalMLModel.predict + ImportedMLModel.predict diff --git a/eland/ml/__init__.py b/eland/ml/__init__.py index ea24b4e..8399942 100644 --- a/eland/ml/__init__.py +++ b/eland/ml/__init__.py @@ -13,4 +13,4 @@ # limitations under the License. from eland.ml.ml_model import * -from eland.ml.external_ml_model import * +from eland.ml.imported_ml_model import * diff --git a/eland/ml/external_ml_model.py b/eland/ml/imported_ml_model.py similarity index 97% rename from eland/ml/external_ml_model.py rename to eland/ml/imported_ml_model.py index b30a459..1c03649 100644 --- a/eland/ml/external_ml_model.py +++ b/eland/ml/imported_ml_model.py @@ -28,7 +28,7 @@ from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor from xgboost import XGBRegressor, XGBClassifier -class ExternalMLModel(MLModel): +class ImportedMLModel(MLModel): """ Transform and serialize a trained 3rd party model into Elasticsearch. This model can then be used for inference in the Elastic Stack. @@ -67,7 +67,7 @@ class ExternalMLModel(MLModel): -------- >>> from sklearn import datasets >>> from sklearn.tree import DecisionTreeClassifier - >>> from eland.ml import ExternalMLModel + >>> from eland.ml import ImportedMLModel >>> # Train model >>> training_data = datasets.make_classification(n_features=5, random_state=0) @@ -82,7 +82,7 @@ class ExternalMLModel(MLModel): >>> # Serialise the model to Elasticsearch >>> feature_names = ["f0", "f1", "f2", "f3", "f4"] >>> model_id = "test_decision_tree_classifier" - >>> es_model = ExternalMLModel('localhost', model_id, classifier, feature_names, overwrite=True) + >>> es_model = ImportedMLModel('localhost', model_id, classifier, feature_names, overwrite=True) >>> # Get some test results from Elasticsearch model >>> es_model.predict(test_data) @@ -169,7 +169,7 @@ class ExternalMLModel(MLModel): -------- >>> from sklearn import datasets >>> from xgboost import XGBRegressor - >>> from eland.ml import ExternalMLModel + >>> from eland.ml import ImportedMLModel >>> # Train model >>> training_data = datasets.make_classification(n_features=6, random_state=0) @@ -184,7 +184,7 @@ class ExternalMLModel(MLModel): >>> # Serialise the model to Elasticsearch >>> feature_names = ["f0", "f1", "f2", "f3", "f4", "f5"] >>> model_id = "test_xgb_regressor" - >>> es_model = ExternalMLModel('localhost', model_id, regressor, feature_names, overwrite=True) + >>> es_model = ImportedMLModel('localhost', model_id, regressor, feature_names, overwrite=True) >>> # Get some test results from Elasticsearch model >>> es_model.predict(test_data) diff --git a/eland/tests/ml/test_external_ml_model_pytest.py b/eland/tests/ml/test_imported_ml_model_pytest.py similarity index 92% rename from eland/tests/ml/test_external_ml_model_pytest.py rename to eland/tests/ml/test_imported_ml_model_pytest.py index 7fb983b..987214a 100644 --- a/eland/tests/ml/test_external_ml_model_pytest.py +++ b/eland/tests/ml/test_imported_ml_model_pytest.py @@ -18,11 +18,11 @@ from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor from xgboost import XGBRegressor, XGBClassifier -from eland.ml import ExternalMLModel +from eland.ml import ImportedMLModel from eland.tests import ES_TEST_CLIENT -class TestExternalMLModel: +class TestImportedMLModel: def test_decision_tree_classifier(self): # Train model @@ -38,7 +38,7 @@ class TestExternalMLModel: feature_names = ["f0", "f1", "f2", "f3", "f4"] model_id = "test_decision_tree_classifier" - es_model = ExternalMLModel(ES_TEST_CLIENT, model_id, classifier, feature_names, overwrite=True) + es_model = ImportedMLModel(ES_TEST_CLIENT, model_id, classifier, feature_names, overwrite=True) es_results = es_model.predict(test_data) np.testing.assert_almost_equal(test_results, es_results, decimal=4) @@ -60,7 +60,7 @@ class TestExternalMLModel: feature_names = ["f0", "f1", "f2", "f3", "f4"] model_id = "test_decision_tree_regressor" - es_model = ExternalMLModel(ES_TEST_CLIENT, model_id, regressor, feature_names, overwrite=True) + es_model = ImportedMLModel(ES_TEST_CLIENT, model_id, regressor, feature_names, overwrite=True) es_results = es_model.predict(test_data) np.testing.assert_almost_equal(test_results, es_results, decimal=4) @@ -82,7 +82,7 @@ class TestExternalMLModel: feature_names = ["f0", "f1", "f2", "f3", "f4"] model_id = "test_random_forest_classifier" - es_model = ExternalMLModel(ES_TEST_CLIENT, model_id, classifier, feature_names, overwrite=True) + es_model = ImportedMLModel(ES_TEST_CLIENT, model_id, classifier, feature_names, overwrite=True) es_results = es_model.predict(test_data) np.testing.assert_almost_equal(test_results, es_results, decimal=4) @@ -104,7 +104,7 @@ class TestExternalMLModel: feature_names = ["f0", "f1", "f2", "f3", "f4"] model_id = "test_random_forest_regressor" - es_model = ExternalMLModel(ES_TEST_CLIENT, model_id, regressor, feature_names, overwrite=True) + es_model = ImportedMLModel(ES_TEST_CLIENT, model_id, regressor, feature_names, overwrite=True) es_results = es_model.predict(test_data) np.testing.assert_almost_equal(test_results, es_results, decimal=4) @@ -126,7 +126,7 @@ class TestExternalMLModel: feature_names = ["f0", "f1", "f2", "f3", "f4"] model_id = "test_xgb_classifier" - es_model = ExternalMLModel(ES_TEST_CLIENT, model_id, classifier, feature_names, overwrite=True) + es_model = ImportedMLModel(ES_TEST_CLIENT, model_id, classifier, feature_names, overwrite=True) es_results = es_model.predict(test_data) np.testing.assert_almost_equal(test_results, es_results, decimal=4) @@ -148,7 +148,7 @@ class TestExternalMLModel: feature_names = ["f0", "f1", "f2", "f3", "f4"] model_id = "test_xgb_regressor" - es_model = ExternalMLModel(ES_TEST_CLIENT, model_id, regressor, feature_names, overwrite=True) + es_model = ImportedMLModel(ES_TEST_CLIENT, model_id, regressor, feature_names, overwrite=True) es_results = es_model.predict(test_data) np.testing.assert_almost_equal(test_results, es_results, decimal=4)