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Eddie Bergman: Clearup warnings (#1238)
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‎development/_downloads/2991959d1e025c5f9f27e3b4d3265a81/example_multilabel_classification.ipynb

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},
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"outputs": [],
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"source": [
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"# 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)"
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"# 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)"
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]
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},
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{

‎development/_downloads/89647a1665eba015b7197cfe70420e4d/example_multilabel_classification.py

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

‎development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip

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‎development/_images/sphx_glr_example_inspect_predictions_002.png

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‎development/_images/sphx_glr_example_pandas_train_test_001.png

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‎development/_modules/autosklearn/estimators.html

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

‎development/_modules/autosklearn/metrics.html

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

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@@ -259,13 +259,16 @@ <h1>Source code for autosklearn.pipeline.components.base</h1><div class="highlig
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<span class="k">class</span> <span class="nc">IterativeComponent</span><span class="p">(</span><span class="n">AutoSklearnComponent</span><span class="p">):</span>
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<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>
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<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>
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<span class="n">iteration</span> <span class="o">=</span> <span class="mi">2</span>
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<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>
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<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>
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<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>
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<span class="n">iteration</span> <span class="o">+=</span> <span class="mi">1</span>
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<span class="k">return</span> <span class="bp">self</span>
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<span class="nd">@staticmethod</span>
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<span class="k">class</span> <span class="nc">IterativeComponentWithSampleWeight</span><span class="p">(</span><span class="n">AutoSklearnComponent</span><span class="p">):</span>
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<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>
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<span class="bp">self</span><span class="o">.</span><span class="n">iterative_fit</span><span class="p">(</span>
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<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>
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<span class="p">)</span>
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<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>
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<span class="n">iteration</span> <span class="o">=</span> <span class="mi">2</span>
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<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>
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<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>
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<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>
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<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>
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<span class="n">iteration</span> <span class="o">+=</span> <span class="mi">1</span>
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<span class="k">return</span> <span class="bp">self</span>
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<span class="nd">@staticmethod</span>

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

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

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

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# More information on: https://scikit-learn.org/stable/modules/multiclass.html
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y[y == 'TRUE'] = 1
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y = y.astype(np.int)
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y = y.astype(int)
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# Using type of target is a good way to make sure your data
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# is properly formatted
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.. code-block:: none
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/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.
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Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
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y = y.astype(np.int)
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2 1 1.0 random_forest 0.447294 4.436343
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2 1 1.0 random_forest 0.447294 4.533196
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 0 minutes 16.045 seconds)
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**Total running time of the script:** ( 0 minutes 16.172 seconds)
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.. _sphx_glr_download_examples_20_basic_example_multilabel_classification.py:

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

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@@ -122,9 +122,9 @@ View the models found by auto-sklearn
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.. code-block:: none
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14 1 1.0 gaussian_process 0.000033 4.520127
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19 1 1.0 gaussian_process 1.718088e-08 4.366592
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.. code-block:: none
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[(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},
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[(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},
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'task': 5,
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.. code-block:: none
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R2 score: 0.999962998298447
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R2 score: 0.9999997983759025
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 1 minutes 56.999 seconds)
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**Total running time of the script:** ( 1 minutes 56.909 seconds)
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.. _sphx_glr_download_examples_20_basic_example_multioutput_regression.py:

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

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25 1 0.46 sgd 0.436679 0.850374
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6 2 0.32 ard_regression 0.455042 0.852782
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27 3 0.14 ard_regression 0.462249 0.827338
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11 4 0.02 random_forest 0.507400 11.182383
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7 5 0.06 gradient_boosting 0.518673 1.586598
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25 1 0.46 sgd 0.436679 0.802876
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6 2 0.32 ard_regression 0.455042 0.828461
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27 3 0.14 ard_regression 0.462249 0.795954
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11 4 0.02 random_forest 0.507400 10.710532
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7 5 0.06 gradient_boosting 0.518673 1.571794
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 1 minutes 54.910 seconds)
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**Total running time of the script:** ( 1 minutes 56.097 seconds)
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.. _sphx_glr_download_examples_20_basic_example_regression.py:

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