Eddie Bergman: Clearup warnings (#1238)

development/_downloads/2991959d1e025c5f9f27e3b4d3265a81/example_multilabel_classification.ipynb

@@ -44,7 +44,7 @@
       },
       "outputs": [],
       "source": [
-        "# Using reuters multilabel dataset -- https://www.openml.org/d/40594\nX, y = sklearn.datasets.fetch_openml(data_id=40594, return_X_y=True, as_frame=False)\n\n# fetch openml downloads a numpy array with TRUE/FALSE strings. Re-map it to\n# integer dtype with ones and zeros\n# This is to comply with Scikit-learn requirement:\n# \"Positive classes are indicated with 1 and negative classes with 0 or -1.\"\n# More information on: https://scikit-learn.org/stable/modules/multiclass.html\ny[y == 'TRUE'] = 1\ny[y == 'FALSE'] = 0\ny = y.astype(np.int)\n\n# Using type of target is a good way to make sure your data\n# is properly formatted\nprint(f\"type_of_target={type_of_target(y)}\")\n\nX_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(\n    X, y, random_state=1\n)"
+        "# Using reuters multilabel dataset -- https://www.openml.org/d/40594\nX, y = sklearn.datasets.fetch_openml(data_id=40594, return_X_y=True, as_frame=False)\n\n# fetch openml downloads a numpy array with TRUE/FALSE strings. Re-map it to\n# integer dtype with ones and zeros\n# This is to comply with Scikit-learn requirement:\n# \"Positive classes are indicated with 1 and negative classes with 0 or -1.\"\n# More information on: https://scikit-learn.org/stable/modules/multiclass.html\ny[y == 'TRUE'] = 1\ny[y == 'FALSE'] = 0\ny = y.astype(int)\n\n# Using type of target is a good way to make sure your data\n# is properly formatted\nprint(f\"type_of_target={type_of_target(y)}\")\n\nX_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(\n    X, y, random_state=1\n)"
       ]
     },
     {

development/_downloads/89647a1665eba015b7197cfe70420e4d/example_multilabel_classification.py

@@ -30,7 +30,7 @@
 # More information on: https://scikit-learn.org/stable/modules/multiclass.html
 y[y == 'TRUE'] = 1
 y[y == 'FALSE'] = 0
-y = y.astype(np.int)
+y = y.astype(int)
 
 # Using type of target is a good way to make sure your data
 # is properly formatted

development/_modules/autosklearn/estimators.html

@@ -346,13 +346,13 @@ <h1>Source code for autosklearn.estimators</h1><div class="highlight"><pre>
 <span class="sd">        Attributes</span>
 <span class="sd">        ----------</span>
 
-<span class="sd">        cv_results\_ : dict of numpy (masked) ndarrays</span>
+<span class="sd">        cv_results_ : dict of numpy (masked) ndarrays</span>
 <span class="sd">            A dict with keys as column headers and values as columns, that can be</span>
 <span class="sd">            imported into a pandas ``DataFrame``.</span>
 
 <span class="sd">            Not all keys returned by scikit-learn are supported yet.</span>
 
-<span class="sd">        performance_over_time\_ : pandas.core.frame.DataFrame</span>
+<span class="sd">        performance_over_time_ : pandas.core.frame.DataFrame</span>
 <span class="sd">            A ``DataFrame`` containing the models performance over time data. Can be</span>
 <span class="sd">            used for plotting directly. Please refer to the example</span>
 <span class="sd">            :ref:`Train and Test Inputs &lt;sphx_glr_examples_40_advanced_example_pandas_train_test.py&gt;`.</span>

development/_modules/autosklearn/pipeline/components/base.html

@@ -259,13 +259,16 @@ <h1>Source code for autosklearn.pipeline.components.base</h1><div class="highlig
 
 
 <span class="k">class</span> <span class="nc">IterativeComponent</span><span class="p">(</span><span class="n">AutoSklearnComponent</span><span class="p">):</span>
+
     <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
         <span class="bp">self</span><span class="o">.</span><span class="n">iterative_fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">n_iter</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">refit</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
         <span class="n">iteration</span> <span class="o">=</span> <span class="mi">2</span>
         <span class="k">while</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">configuration_fully_fitted</span><span class="p">():</span>
             <span class="n">n_iter</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="mi">2</span> <span class="o">**</span> <span class="n">iteration</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span>
             <span class="bp">self</span><span class="o">.</span><span class="n">iterative_fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">n_iter</span><span class="o">=</span><span class="n">n_iter</span><span class="p">,</span> <span class="n">refit</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
             <span class="n">iteration</span> <span class="o">+=</span> <span class="mi">1</span>
+
         <span class="k">return</span> <span class="bp">self</span>
 
     <span class="nd">@staticmethod</span>
@@ -277,15 +280,16 @@ <h1>Source code for autosklearn.pipeline.components.base</h1><div class="highlig
 
 
 <span class="k">class</span> <span class="nc">IterativeComponentWithSampleWeight</span><span class="p">(</span><span class="n">AutoSklearnComponent</span><span class="p">):</span>
+
     <span class="k">def</span> <span class="nf">fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">iterative_fit</span><span class="p">(</span>
-            <span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">n_iter</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">refit</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="n">sample_weight</span>
-        <span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">iterative_fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">n_iter</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">refit</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="n">sample_weight</span><span class="p">)</span>
+
         <span class="n">iteration</span> <span class="o">=</span> <span class="mi">2</span>
         <span class="k">while</span> <span class="ow">not</span> <span class="bp">self</span><span class="o">.</span><span class="n">configuration_fully_fitted</span><span class="p">():</span>
             <span class="n">n_iter</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="mi">2</span> <span class="o">**</span> <span class="n">iteration</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">iterative_fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">n_iter</span><span class="o">=</span><span class="n">n_iter</span><span class="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="n">sample_weight</span><span class="p">)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">iterative_fit</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">n_iter</span><span class="o">=</span><span class="n">n_iter</span><span class="p">,</span> <span class="n">refit</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">sample_weight</span><span class="o">=</span><span class="n">sample_weight</span><span class="p">)</span>
             <span class="n">iteration</span> <span class="o">+=</span> <span class="mi">1</span>
+
         <span class="k">return</span> <span class="bp">self</span>
 
     <span class="nd">@staticmethod</span>

development/_sources/examples/20_basic/example_classification.rst.txt

@@ -120,25 +120,25 @@ View the models found by auto-sklearn
 
               rank  ensemble_weight                type      cost  duration
     model_id                                                               
-    34           1             0.16         extra_trees  0.014184  2.207980
-    7            2             0.10         extra_trees  0.014184  1.964377
-    29           3             0.06         extra_trees  0.021277  2.252809
-    16           4             0.04   gradient_boosting  0.021277  1.323334
-    26           5             0.02         extra_trees  0.028369  2.898711
-    22           6             0.04   gradient_boosting  0.028369  1.466107
-    2            7             0.04       random_forest  0.028369  2.222874
-    3            8             0.10                 mlp  0.028369  1.336115
-    14           9             0.02                 mlp  0.028369  2.660230
-    19          10             0.02         extra_trees  0.028369  3.563035
-    17          11             0.02   gradient_boosting  0.035461  2.186083
-    8           12             0.02       random_forest  0.035461  2.617924
-    5           13             0.02       random_forest  0.035461  2.600851
-    9           14             0.02         extra_trees  0.042553  2.392899
-    30          15             0.08       liblinear_svc  0.042553  1.263751
-    32          16             0.16         extra_trees  0.049645  2.269191
-    33          17             0.02       random_forest  0.056738  2.508028
-    28          18             0.04        bernoulli_nb  0.070922  1.163354
-    20          19             0.02  passive_aggressive  0.078014  0.921606
+    34           1             0.16         extra_trees  0.014184  2.158044
+    7            2             0.10         extra_trees  0.014184  1.967495
+    29           3             0.06         extra_trees  0.021277  2.250814
+    16           4             0.04   gradient_boosting  0.021277  1.271585
+    26           5             0.02         extra_trees  0.028369  2.775106
+    22           6             0.04   gradient_boosting  0.028369  1.468125
+    2            7             0.04       random_forest  0.028369  2.179592
+    3            8             0.10                 mlp  0.028369  1.314463
+    14           9             0.02                 mlp  0.028369  2.650562
+    19          10             0.02         extra_trees  0.028369  3.390082
+    17          11             0.02   gradient_boosting  0.035461  2.128715
+    8           12             0.02       random_forest  0.035461  2.570254
+    5           13             0.02       random_forest  0.035461  2.554585
+    9           14             0.02         extra_trees  0.042553  2.313817
+    30          15             0.08       liblinear_svc  0.042553  1.259079
+    32          16             0.16         extra_trees  0.049645  2.301594
+    33          17             0.02       random_forest  0.056738  2.475844
+    28          18             0.04        bernoulli_nb  0.070922  1.155173
+    20          19             0.02  passive_aggressive  0.078014  0.918509
 
 
 
@@ -353,7 +353,7 @@ Get the Score of the final ensemble
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 2 minutes  4.034 seconds)
+   **Total running time of the script:** ( 1 minutes  59.584 seconds)
 
 
 .. _sphx_glr_download_examples_20_basic_example_classification.py:

development/_sources/examples/20_basic/example_multilabel_classification.rst.txt

@@ -66,7 +66,7 @@ Data Loading
     # More information on: https://scikit-learn.org/stable/modules/multiclass.html
     y[y == 'TRUE'] = 1
     y[y == 'FALSE'] = 0
-    y = y.astype(np.int)
+    y = y.astype(int)
 
     # Using type of target is a good way to make sure your data
     # is properly formatted
@@ -86,9 +86,6 @@ Data Loading
 
  .. code-block:: none
 
-    /home/runner/work/auto-sklearn/auto-sklearn/examples/20_basic/example_multilabel_classification.py:33: DeprecationWarning: `np.int` is a deprecated alias for the builtin `int`. To silence this warning, use `int` by itself. Doing this will not modify any behavior and is safe. When replacing `np.int`, you may wish to use e.g. `np.int64` or `np.int32` to specify the precision. If you wish to review your current use, check the release note link for additional information.
-    Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
-      y = y.astype(np.int)
     type_of_target=multilabel-indicator
 
 
@@ -157,7 +154,7 @@ View the models found by auto-sklearn
 
               rank  ensemble_weight           type      cost  duration
     model_id                                                          
-    2            1              1.0  random_forest  0.447294  4.436343
+    2            1              1.0  random_forest  0.447294  4.533196
 
 
 
@@ -265,7 +262,7 @@ Get the Score of the final ensemble
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  16.045 seconds)
+   **Total running time of the script:** ( 0 minutes  16.172 seconds)
 
 
 .. _sphx_glr_download_examples_20_basic_example_multilabel_classification.py:

development/_sources/examples/20_basic/example_multioutput_regression.rst.txt

@@ -122,9 +122,9 @@ View the models found by auto-sklearn
 
  .. code-block:: none
 
-              rank  ensemble_weight              type      cost  duration
-    model_id                                                             
-    14           1              1.0  gaussian_process  0.000033  4.520127
+              rank  ensemble_weight              type          cost  duration
+    model_id                                                                 
+    19           1              1.0  gaussian_process  1.718088e-08  4.366592
 
 
 
@@ -151,7 +151,7 @@ Print the final ensemble constructed by auto-sklearn
 
  .. code-block:: none
 
-    [(1.000000, SimpleRegressionPipeline({'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'feature_agglomeration', 'regressor:__choice__': 'gaussian_process', 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'no_encoding', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:__choice__': 'no_coalescense', 'data_preprocessor:feature_type:numerical_transformer:imputation:strategy': 'median', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'power_transformer', 'feature_preprocessor:feature_agglomeration:affinity': 'manhattan', 'feature_preprocessor:feature_agglomeration:linkage': 'average', 'feature_preprocessor:feature_agglomeration:n_clusters': 365, 'feature_preprocessor:feature_agglomeration:pooling_func': 'mean', 'regressor:gaussian_process:alpha': 0.0001892420474677165, 'regressor:gaussian_process:thetaL': 1.7549152191973897e-09, 'regressor:gaussian_process:thetaU': 2227.384462976473},
+    [(1.000000, SimpleRegressionPipeline({'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'no_preprocessing', 'regressor:__choice__': 'gaussian_process', 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'one_hot_encoding', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:__choice__': 'minority_coalescer', 'data_preprocessor:feature_type:numerical_transformer:imputation:strategy': 'median', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'robust_scaler', 'regressor:gaussian_process:alpha': 7.521590906155149e-08, 'regressor:gaussian_process:thetaL': 4.052724541973572e-07, 'regressor:gaussian_process:thetaU': 17472.745774310217, 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.43055420836443836, 'data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max': 0.827399658564819, 'data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min': 0.13037866143850987},
     dataset_properties={
       'task': 5,
       'sparse': False,
@@ -186,7 +186,7 @@ Get the Score of the final ensemble
 
  .. code-block:: none
 
-    R2 score: 0.999962998298447
+    R2 score: 0.9999997983759025
 
 
 
@@ -409,7 +409,7 @@ Get the configuration space
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  56.999 seconds)
+   **Total running time of the script:** ( 1 minutes  56.909 seconds)
 
 
 .. _sphx_glr_download_examples_20_basic_example_multioutput_regression.py:

development/_sources/examples/20_basic/example_regression.rst.txt

@@ -121,11 +121,11 @@ View the models found by auto-sklearn
 
               rank  ensemble_weight               type      cost   duration
     model_id                                                               
-    25           1             0.46                sgd  0.436679   0.850374
-    6            2             0.32     ard_regression  0.455042   0.852782
-    27           3             0.14     ard_regression  0.462249   0.827338
-    11           4             0.02      random_forest  0.507400  11.182383
-    7            5             0.06  gradient_boosting  0.518673   1.586598
+    25           1             0.46                sgd  0.436679   0.802876
+    6            2             0.32     ard_regression  0.455042   0.828461
+    27           3             0.14     ard_regression  0.462249   0.795954
+    11           4             0.02      random_forest  0.507400  10.710532
+    7            5             0.06  gradient_boosting  0.518673   1.571794
 
 
 
@@ -267,7 +267,7 @@ the true value).
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  54.910 seconds)
+   **Total running time of the script:** ( 1 minutes  56.097 seconds)
 
 
 .. _sphx_glr_download_examples_20_basic_example_regression.py: