Eric Pedley: Changed deprecated DataFrame.append to pd.concat to fix warning (#1487)

Author: Github Actions

development/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip

@@ -93,10 +93,6 @@ Build and fit a classifier
 
  .. code-block:: none
 
-    /home/runner/work/auto-sklearn/auto-sklearn/autosklearn/metalearning/metalearning/meta_base.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
-      self.metafeatures = self.metafeatures.append(metafeatures)
-    /home/runner/work/auto-sklearn/auto-sklearn/autosklearn/metalearning/metalearning/meta_base.py:80: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
-      self.algorithm_runs[metric].append(runs)
 
     AutoSklearnClassifier(per_run_time_limit=30, time_left_for_this_task=120,
                           tmp_folder='/tmp/autosklearn_classification_example_tmp')
@@ -125,29 +121,29 @@ View the models found by auto-sklearn
 
  .. code-block:: none
 
-              rank  ensemble_weight                type      cost  duration
-    model_id                                                               
-    7            1             0.10         extra_trees  0.014184  1.508070
-    16           2             0.04   gradient_boosting  0.021277  1.069927
-    2            7             0.02       random_forest  0.028369  1.649826
-    3            9             0.08                 mlp  0.028369  0.929094
-    6            8             0.02                 mlp  0.028369  1.068695
-    11          10             0.04       random_forest  0.028369  2.018981
-    14           6             0.04                 mlp  0.028369  1.486160
-    19           4             0.12         extra_trees  0.028369  2.764316
-    22           3             0.06   gradient_boosting  0.028369  1.120712
-    31           5             0.04         extra_trees  0.028369  1.656933
-    8           12             0.06       random_forest  0.035461  1.894322
-    12          11             0.02   gradient_boosting  0.035461  1.260170
-    17          13             0.02   gradient_boosting  0.035461  1.564270
-    18          14             0.02       random_forest  0.035461  1.905224
-    9           15             0.06         extra_trees  0.042553  1.779394
-    30          16             0.12       liblinear_svc  0.042553  0.957406
-    15          17             0.02                 mlp  0.049645  3.210093
-    28          18             0.04                 mlp  0.056738  0.968477
-    29          19             0.04         extra_trees  0.056738  1.733633
-    20          20             0.02  passive_aggressive  0.078014  0.758144
-    32          21             0.02         extra_trees  0.106383  2.939105
+              rank  ensemble_weight                 type      cost  duration
+    model_id                                                                
+    7            1             0.10          extra_trees  0.014184  2.004601
+    31           2             0.04          extra_trees  0.014184  2.326990
+    16           4             0.04    gradient_boosting  0.021277  1.369266
+    21           3             0.02          extra_trees  0.021277  1.868113
+    2            5             0.04        random_forest  0.028369  2.128209
+    3            6             0.10                  mlp  0.028369  1.352958
+    6            8             0.02                  mlp  0.028369  1.477564
+    14          10             0.10                  mlp  0.028369  2.594337
+    19           9             0.06          extra_trees  0.028369  3.375372
+    26           7             0.02          extra_trees  0.028369  2.810159
+    5           13             0.02        random_forest  0.035461  2.558748
+    17          12             0.04    gradient_boosting  0.035461  2.110427
+    18          11             0.02        random_forest  0.035461  2.490679
+    32          14             0.10  k_nearest_neighbors  0.035461  1.093234
+    9           15             0.02          extra_trees  0.042553  2.325613
+    15          17             0.02                  mlp  0.049645  5.229511
+    28          16             0.06          extra_trees  0.049645  2.560032
+    4           18             0.04                  mlp  0.056738  2.125066
+    24          19             0.06        random_forest  0.070922  2.184559
+    29          20             0.06        decision_tree  0.070922  1.369401
+    20          21             0.02   passive_aggressive  0.078014  0.954702
 
 
 
@@ -203,15 +199,15 @@ Get the Score of the final ensemble
 
  .. code-block:: none
 
-    Accuracy score: 0.951048951048951
+    Accuracy score: 0.958041958041958
 
 
 
 
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 2 minutes  5.836 seconds)
+   **Total running time of the script:** ( 2 minutes  2.609 seconds)
 
 
 .. _sphx_glr_download_examples_20_basic_example_classification.py:

development/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip

@@ -155,7 +155,7 @@ View the models found by auto-sklearn
 
               rank  ensemble_weight           type      cost  duration
     model_id                                                          
-    2            1              1.0  random_forest  0.447294  3.379694
+    2            1              1.0  random_forest  0.447294  4.207705
 
 
 
@@ -257,7 +257,7 @@ Get the Score of the final ensemble
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  14.872 seconds)
+   **Total running time of the script:** ( 0 minutes  18.614 seconds)
 
 
 .. _sphx_glr_download_examples_20_basic_example_multilabel_classification.py:

development/_images/sphx_glr_example_inspect_predictions_001.png

@@ -123,10 +123,11 @@ View the models found by auto-sklearn
 
  .. code-block:: none
 
-              rank  ensemble_weight           type      cost  duration
-    model_id                                                          
-    2            1             0.84  random_forest  0.154940  2.282101
-    4            2             0.16    extra_trees  0.224544  1.123610
+              rank  ensemble_weight                 type      cost  duration
+    model_id                                                                
+    2            1             0.82        random_forest  0.135431  2.700041
+    17           2             0.14        decision_tree  0.290778  1.422128
+    10           3             0.04  k_nearest_neighbors  0.524106  0.694775
 
 
 
@@ -183,7 +184,7 @@ Get the Score of the final ensemble
 
  .. code-block:: none
 
-    R2 score: 0.8610416148903628
+    R2 score: 0.864934517601886
 
 
 
@@ -436,7 +437,7 @@ Get the configuration space
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 2 minutes  3.410 seconds)
+   **Total running time of the script:** ( 1 minutes  54.464 seconds)
 
 
 .. _sphx_glr_download_examples_20_basic_example_multioutput_regression.py:

development/_images/sphx_glr_example_inspect_predictions_002.png

@@ -94,10 +94,6 @@ Build and fit a regressor
 
  .. code-block:: none
 
-    /home/runner/work/auto-sklearn/auto-sklearn/autosklearn/metalearning/metalearning/meta_base.py:76: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
-      self.metafeatures = self.metafeatures.append(metafeatures)
-    /home/runner/work/auto-sklearn/auto-sklearn/autosklearn/metalearning/metalearning/meta_base.py:80: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.
-      self.algorithm_runs[metric].append(runs)
 
     AutoSklearnRegressor(per_run_time_limit=30, time_left_for_this_task=120,
                          tmp_folder='/tmp/autosklearn_regression_example_tmp')
@@ -126,12 +122,14 @@ View the models found by auto-sklearn
 
  .. code-block:: none
 
-              rank  ensemble_weight            type      cost  duration
-    model_id                                                           
-    31           1             0.48  ard_regression  0.428169  0.818530
-    25           2             0.26             sgd  0.436679  0.695193
-    33           3             0.20   liblinear_svr  0.472547  0.804050
-    29           4             0.06  ard_regression  0.493390  0.697902
+              rank  ensemble_weight               type      cost   duration
+    model_id                                                               
+    25           1             0.38                sgd  0.436679   0.908823
+    6            2             0.40     ard_regression  0.455042   0.921170
+    27           3             0.02     ard_regression  0.462249   0.921811
+    29           4             0.16     ard_regression  0.493390   0.878443
+    11           5             0.02      random_forest  0.507400  10.554423
+    7            6             0.02  gradient_boosting  0.518673   1.673570
 
 
 
@@ -194,8 +192,8 @@ predicting the data mean has an R2 score of 0.
 
  .. code-block:: none
 
-    Train R2 score: 0.5922043282790412
-    Test R2 score: 0.4073119977311549
+    Train R2 score: 0.5845958689841146
+    Test R2 score: 0.3969087782855073
 
 
 
@@ -240,7 +238,7 @@ the true value).
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 2 minutes  0.614 seconds)
+   **Total running time of the script:** ( 1 minutes  55.340 seconds)
 
 
 .. _sphx_glr_download_examples_20_basic_example_regression.py:

development/_images/sphx_glr_example_inspect_predictions_003.png

@@ -5,14 +5,14 @@
 
 Computation times
 =================
-**06:24.733** total execution time for **examples_20_basic** files:
+**06:11.027** total execution time for **examples_20_basic** files:
 
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basic_example_classification.py` (``example_classification.py``)                       | 02:05.836 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basic_example_classification.py` (``example_classification.py``)                       | 02:02.609 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basic_example_multioutput_regression.py` (``example_multioutput_regression.py``)       | 02:03.410 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basic_example_regression.py` (``example_regression.py``)                               | 01:55.340 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basic_example_regression.py` (``example_regression.py``)                               | 02:00.614 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basic_example_multioutput_regression.py` (``example_multioutput_regression.py``)       | 01:54.464 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basic_example_multilabel_classification.py` (``example_multilabel_classification.py``) | 00:14.872 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basic_example_multilabel_classification.py` (``example_multilabel_classification.py``) | 00:18.614 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+