7.6.0a3 (#131)

* 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

docs/source/examples/demo_notebook.ipynb

@@ -753,7 +753,7 @@
     {
      "data": {
       "text/plain": [
-       "<eland.index.Index at 0x11631ffd0>"
+       "<eland.index.Index at 0x116b3efd0>"
       ]
      },
      "execution_count": 17,
@@ -2704,15 +2704,15 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
-       "      <td>410.011039</td>\n",
+       "      <td>410.008918</td>\n",
        "      <td>2470.545974</td>\n",
        "      <td>...</td>\n",
-       "      <td>251.773003</td>\n",
+       "      <td>251.938710</td>\n",
        "      <td>1.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
-       "      <td>640.387285</td>\n",
+       "      <td>640.362667</td>\n",
        "      <td>7612.072403</td>\n",
        "      <td>...</td>\n",
        "      <td>503.148975</td>\n",
@@ -2720,11 +2720,11 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
-       "      <td>842.213490</td>\n",
-       "      <td>9734.960478</td>\n",
+       "      <td>840.617448</td>\n",
+       "      <td>9738.206675</td>\n",
        "      <td>...</td>\n",
-       "      <td>720.505705</td>\n",
-       "      <td>4.172535</td>\n",
+       "      <td>720.026320</td>\n",
+       "      <td>4.160448</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\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]"

docs/source/examples/online_retail_analysis.ipynb

@@ -1023,21 +1023,21 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25%</th>\n",
-       "      <td>14217.474239</td>\n",
+       "      <td>14227.934845</td>\n",
        "      <td>1.000000</td>\n",
-       "      <td>1.250068</td>\n",
+       "      <td>1.250000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>50%</th>\n",
-       "      <td>15662.024630</td>\n",
+       "      <td>15669.138235</td>\n",
        "      <td>2.000000</td>\n",
        "      <td>2.510000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>75%</th>\n",
-       "      <td>17212.723881</td>\n",
-       "      <td>6.671951</td>\n",
-       "      <td>4.210000</td>\n",
+       "      <td>17212.690092</td>\n",
+       "      <td>6.610262</td>\n",
+       "      <td>4.211297</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>max</th>\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"
       ]
      },

docs/source/reference/api/eland.ml.ExternalMLModel.predict.rst

@@ -1,6 +0,0 @@
-eland.ml.ExternalMLModel.predict
-================================
-
-.. currentmodule:: eland.ml
-
-.. automethod:: ExternalMLModel.predict

docs/source/reference/api/eland.ml.ExternalMLModel.rst

@@ -1,6 +0,0 @@
-eland.ml.ExternalMLModel
-========================
-
-.. currentmodule:: eland.ml
-
-.. autoclass:: ExternalMLModel

docs/source/reference/api/eland.ml.ImportedMLModel.predict.rst

@@ -0,0 +1,6 @@
+eland.ml.ImportedMLModel.predict
+================================
+
+.. currentmodule:: eland.ml
+
+.. automethod:: ImportedMLModel.predict

docs/source/reference/api/eland.ml.ImportedMLModel.rst

@@ -0,0 +1,6 @@
+eland.ml.ImportedMLModel
+========================
+
+.. currentmodule:: eland.ml
+
+.. autoclass:: ImportedMLModel

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
 

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 *

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)

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)