Future warnings (#923)

Address sklearn future warnings Address yaml.load warning
equinor · Feb 6, 2020 · 1fd4e0b · 1fd4e0b
1 parent 0a49881
commit 1fd4e0b
Show file tree

Hide file tree

Showing 7 changed files with 22 additions and 22 deletions.
diff --git a/examples/Gordo-Workflow-High-Level.ipynb b/examples/Gordo-Workflow-High-Level.ipynb
@@ -67,9 +67,9 @@
     "        base_estimator:\n",
     "          sklearn.pipeline.Pipeline:\n",
     "            steps:\n",
-    "            - sklearn.decomposition.pca.PCA\n",
+    "            - sklearn.decomposition.PCA\n",
     "            - sklearn.multioutput.MultiOutputRegressor:\n",
-    "                estimator: sklearn.linear_model.base.LinearRegression\n",
+    "                estimator: sklearn.linear_model.LinearRegression\n",
     "    name: crazy-sweet-name\n",
     "\"\"\""
    ]
@@ -453,9 +453,9 @@
       "      base_estimator:\n",
       "        sklearn.pipeline.Pipeline:\n",
       "          steps:\n",
-      "          - sklearn.decomposition.pca.PCA\n",
+      "          - sklearn.decomposition.PCA\n",
       "          - sklearn.multioutput.MultiOutputRegressor:\n",
-      "              estimator: sklearn.linear_model.base.LinearRegression\n",
+      "              estimator: sklearn.linear_model.LinearRegression\n",
       "  model-creation-date: '2019-10-21 12:56:18.169609+02:00'\n",
       "  model-offset: 0\n",
       "  model-training-duration-sec: 0.005204916000366211\n",

diff --git a/examples/Gordo-Workflow-Semi-Low-Level.ipynb b/examples/Gordo-Workflow-Semi-Low-Level.ipynb
@@ -169,7 +169,7 @@
     "    \"\"\" \n",
     "    sklearn.pipeline.Pipeline:\n",
     "        steps:\n",
-    "          - sklearn.preprocessing.data.MinMaxScaler\n",
+    "          - sklearn.preprocessing.MinMaxScaler\n",
     "          - gordo.machine.model.models.KerasAutoEncoder:\n",
     "              kind: feedforward_hourglass\n",
     "    \"\"\"\n",

diff --git a/examples/Pipelines-with-Gordo.ipynb b/examples/Pipelines-with-Gordo.ipynb
@@ -69,23 +69,23 @@
       "sklearn.pipeline.Pipeline:\n",
       "  memory: null\n",
       "  steps:\n",
-      "  - sklearn.decomposition.pca.PCA: {copy: true, iterated_power: auto, n_components: 10,\n",
+      "  - sklearn.decomposition.PCA: {copy: true, iterated_power: auto, n_components: 10,\n",
       "      random_state: null, svd_solver: auto, tol: 0.0, whiten: false}\n",
       "  - sklearn.pipeline.FeatureUnion:\n",
       "      n_jobs: null\n",
       "      transformer_list:\n",
       "      - sklearn.pipeline.Pipeline:\n",
       "          memory: null\n",
       "          steps:\n",
-      "          - sklearn.preprocessing.data.MinMaxScaler:\n",
+      "          - sklearn.preprocessing.MinMaxScaler:\n",
       "              copy: true\n",
       "              feature_range: !!python/tuple [0, 1]\n",
-      "          - sklearn.decomposition.pca.PCA: {copy: true, iterated_power: auto, n_components: 2,\n",
+      "          - sklearn.decomposition.PCA: {copy: true, iterated_power: auto, n_components: 2,\n",
       "              random_state: null, svd_solver: auto, tol: 0.0, whiten: false}\n",
       "      - sklearn.pipeline.Pipeline:\n",
       "          memory: null\n",
       "          steps:\n",
-      "          - sklearn.preprocessing.data.QuantileTransformer: {copy: true, ignore_implicit_zeros: false,\n",
+      "          - sklearn.preprocessing.QuantileTransformer: {copy: true, ignore_implicit_zeros: false,\n",
       "              n_quantiles: 1000, output_distribution: uniform, random_state: null,\n",
       "              subsample: 100000}\n",
       "      transformer_weights: null\n",

diff --git a/examples/config.yaml b/examples/config.yaml
@@ -70,6 +70,6 @@ spec:
           base_estimator:
             sklearn.pipeline.Pipeline:
               steps:
-                - sklearn.preprocessing.data.MinMaxScaler
+                - sklearn.preprocessing.MinMaxScaler
                 - gordo.machine.model.models.KerasAutoEncoder:
                     kind: feedforward_hourglass
diff --git a/gordo/builder/build_model.py b/gordo/builder/build_model.py
@@ -54,7 +54,7 @@ def __init__(self, machine: Machine):
         >>> from gordo.machine import Machine
         >>> machine = Machine(
         ...     name="special-model-name",
-        ...     model={"sklearn.decomposition.pca.PCA": {"svd_solver": "auto"}},
+        ...     model={"sklearn.decomposition.PCA": {"svd_solver": "auto"}},
         ...     dataset={
         ...         "type": "RandomDataset",
         ...         "train_start_date": "2017-12-25 06:00:00Z",
@@ -535,7 +535,7 @@ def calculate_cache_key(machine: Machine) -> str:
                 >>> from gordo.machine.dataset.sensor_tag import SensorTag
                 >>> machine = Machine(
                 ...     name="special-model-name",
-                ...     model={"sklearn.decomposition.pca.PCA": {"svd_solver": "auto"}},
+                ...     model={"sklearn.decomposition.PCA": {"svd_solver": "auto"}},
                 ...     dataset={
                 ...         "type": "RandomDataset",
                 ...         "train_start_date": "2017-12-25 06:00:00Z",

diff --git a/gordo/builder/local_build.py b/gordo/builder/local_build.py
@@ -51,9 +51,9 @@ def local_build(
     ...             base_estimator:
     ...               sklearn.pipeline.Pipeline:
     ...                 steps:
-    ...                 - sklearn.decomposition.pca.PCA
+    ...                 - sklearn.decomposition.PCA
     ...                 - sklearn.multioutput.MultiOutputRegressor:
-    ...                     estimator: sklearn.linear_model.base.LinearRegression
+    ...                     estimator: sklearn.linear_model.LinearRegression
     ...         name: crazy-sweet-name
     ... '''
     >>> models_n_metadata = local_build(config)

diff --git a/gordo/serializer/from_definition.py b/gordo/serializer/from_definition.py
@@ -26,19 +26,19 @@ def from_definition(
     >>> raw_config = '''
     ... sklearn.pipeline.Pipeline:
     ...         steps:
-    ...             - sklearn.decomposition.pca.PCA:
+    ...             - sklearn.decomposition.PCA:
     ...                 n_components: 3
     ...             - sklearn.pipeline.FeatureUnion:
-    ...                 - sklearn.decomposition.pca.PCA:
+    ...                 - sklearn.decomposition.PCA:
     ...                     n_components: 3
     ...                 - sklearn.pipeline.Pipeline:
-    ...                     - sklearn.preprocessing.data.MinMaxScaler
-    ...                     - sklearn.decomposition.truncated_svd.TruncatedSVD:
+    ...                     - sklearn.preprocessing.MinMaxScaler
+    ...                     - sklearn.decomposition.TruncatedSVD:
     ...                         n_components: 2
-    ...             - sklearn.ensemble.forest.RandomForestClassifier:
+    ...             - sklearn.ensemble.RandomForestClassifier:
     ...                 max_depth: 3
     ... '''
-    >>> config = yaml.load(raw_config)
+    >>> config = yaml.safe_load(raw_config)
     >>> scikit_learn_pipeline = serializer.from_definition(config)
 
 
@@ -204,7 +204,7 @@ def _load_param_classes(params: dict):
 
     # Load an actual model, without any kwargs
     >>> from sklearn.ensemble import RandomForestRegressor
-    >>> params = {"base_estimator": "sklearn.ensemble.forest.RandomForestRegressor"}
+    >>> params = {"base_estimator": "sklearn.ensemble.RandomForestRegressor"}
     >>> print(_load_param_classes(params))
     {'base_estimator': RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',
                           max_depth=None, max_features='auto', max_leaf_nodes=None,
@@ -215,7 +215,7 @@ def _load_param_classes(params: dict):
                           random_state=None, verbose=0, warm_start=False)}
 
     # Load an actual model, with kwargs
-    >>> params = {"base_estimator": {"sklearn.ensemble.forest.RandomForestRegressor": {"n_estimators": 20}}}
+    >>> params = {"base_estimator": {"sklearn.ensemble.RandomForestRegressor": {"n_estimators": 20}}}
     >>> print(_load_param_classes(params))
     {'base_estimator': RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',
                           max_depth=None, max_features='auto', max_leaf_nodes=None,