Eddie Bergman: Enable tests to be manually triggered (#1325)

automl · Dec 1, 2021 · bac8fda · bac8fda
1 parent b7a50d8
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diff --git a/...pment/_downloads/2991959d1e025c5f9f27e3b4d3265a81/example_multilabel_classification.ipynb b/...pment/_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)"
       ]
     },
     {

diff --git a/development/_downloads/89647a1665eba015b7197cfe70420e4d/example_multilabel_classification.py b/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

diff --git a/development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip b/development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip
diff --git a/development/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip b/development/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip
diff --git a/development/_images/sphx_glr_example_inspect_predictions_001.png b/development/_images/sphx_glr_example_inspect_predictions_001.png
diff --git a/development/_images/sphx_glr_example_inspect_predictions_002.png b/development/_images/sphx_glr_example_inspect_predictions_002.png
diff --git a/development/_images/sphx_glr_example_inspect_predictions_003.png b/development/_images/sphx_glr_example_inspect_predictions_003.png
diff --git a/development/_images/sphx_glr_example_inspect_predictions_thumb.png b/development/_images/sphx_glr_example_inspect_predictions_thumb.png
diff --git a/development/_images/sphx_glr_example_pandas_train_test_001.png b/development/_images/sphx_glr_example_pandas_train_test_001.png
diff --git a/development/_images/sphx_glr_example_pandas_train_test_thumb.png b/development/_images/sphx_glr_example_pandas_train_test_thumb.png
diff --git a/development/_images/sphx_glr_example_regression_001.png b/development/_images/sphx_glr_example_regression_001.png
diff --git a/development/_images/sphx_glr_example_regression_thumb.png b/development/_images/sphx_glr_example_regression_thumb.png
diff --git a/development/_modules/autosklearn/estimators.html b/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>

diff --git a/development/_modules/autosklearn/metrics.html b/development/_modules/autosklearn/metrics.html
diff --git a/development/_modules/autosklearn/pipeline/components/base.html b/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>