Satyam Jha: added conda installation in docs (#1144)

Author: Github Actions

master/_downloads/bc82bea3a5dd7bdba60b65220891d9e5/examples_python.zip

@@ -157,7 +157,7 @@ View the models found by auto-sklearn
 
               rank  ensemble_weight           type     cost  duration
     model_id                                                         
-    2            1              1.0  random_forest  0.44715  3.882185
+    2            1              1.0  random_forest  0.44715  4.330315
 
 
 
@@ -265,7 +265,7 @@ Get the Score of the final ensemble
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 0 minutes  16.180 seconds)
+   **Total running time of the script:** ( 0 minutes  14.196 seconds)
 
 
 .. _sphx_glr_download_examples_20_basic_example_multilabel_classification.py:

master/_downloads/fb625db3c50d423b1b7881136ffdeec8/examples_jupyter.zip

@@ -122,9 +122,11 @@ View the models found by auto-sklearn
 
  .. code-block:: none
 
-              rank  ensemble_weight              type      cost  duration
-    model_id                                                             
-    20           1              1.0  gaussian_process  0.000004  2.687078
+              rank  ensemble_weight                 type      cost  duration
+    model_id                                                                
+    2            1             0.80        random_forest  0.163676  2.591488
+    11           2             0.08        random_forest  0.369114  2.524188
+    3            3             0.12  k_nearest_neighbors  0.445647  0.475347
 
 
 
@@ -151,7 +153,21 @@ Print the final ensemble constructed by auto-sklearn
 
  .. code-block:: none
 
-    [(1.000000, SimpleRegressionPipeline({'data_preprocessing:categorical_transformer:categorical_encoding:__choice__': 'encoding', 'data_preprocessing:categorical_transformer:category_coalescence:__choice__': 'minority_coalescer', 'data_preprocessing:numerical_transformer:imputation:strategy': 'median', 'data_preprocessing:numerical_transformer:rescaling:__choice__': 'standardize', 'feature_preprocessor:__choice__': 'fast_ica', 'regressor:__choice__': 'gaussian_process', 'data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.03525312698553994, 'feature_preprocessor:fast_ica:algorithm': 'deflation', 'feature_preprocessor:fast_ica:fun': 'cube', 'feature_preprocessor:fast_ica:whiten': 'True', 'regressor:gaussian_process:alpha': 1.6308437468964296e-06, 'regressor:gaussian_process:thetaL': 2.9247731111629564e-09, 'regressor:gaussian_process:thetaU': 551.0700100096957, 'feature_preprocessor:fast_ica:n_components': 100},
+    [(0.800000, SimpleRegressionPipeline({'data_preprocessing:categorical_transformer:categorical_encoding:__choice__': 'one_hot_encoding', 'data_preprocessing:categorical_transformer:category_coalescence:__choice__': 'minority_coalescer', 'data_preprocessing:numerical_transformer:imputation:strategy': 'mean', 'data_preprocessing:numerical_transformer:rescaling:__choice__': 'standardize', 'feature_preprocessor:__choice__': 'no_preprocessing', 'regressor:__choice__': 'random_forest', 'data_preprocessing:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.01, 'regressor:random_forest:bootstrap': 'True', 'regressor:random_forest:criterion': 'mse', 'regressor:random_forest:max_depth': 'None', 'regressor:random_forest:max_features': 1.0, 'regressor:random_forest:max_leaf_nodes': 'None', 'regressor:random_forest:min_impurity_decrease': 0.0, 'regressor:random_forest:min_samples_leaf': 1, 'regressor:random_forest:min_samples_split': 2, 'regressor:random_forest:min_weight_fraction_leaf': 0.0},
+    dataset_properties={
+      'task': 5,
+      'sparse': False,
+      'multioutput': True,
+      'target_type': 'regression',
+      'signed': False})),
+    (0.120000, SimpleRegressionPipeline({'data_preprocessing:categorical_transformer:categorical_encoding:__choice__': 'one_hot_encoding', 'data_preprocessing:categorical_transformer:category_coalescence:__choice__': 'no_coalescense', 'data_preprocessing:numerical_transformer:imputation:strategy': 'median', 'data_preprocessing:numerical_transformer:rescaling:__choice__': 'standardize', 'feature_preprocessor:__choice__': 'polynomial', 'regressor:__choice__': 'k_nearest_neighbors', 'feature_preprocessor:polynomial:degree': 3, 'feature_preprocessor:polynomial:include_bias': 'True', 'feature_preprocessor:polynomial:interaction_only': 'False', 'regressor:k_nearest_neighbors:n_neighbors': 1, 'regressor:k_nearest_neighbors:p': 2, 'regressor:k_nearest_neighbors:weights': 'distance'},
+    dataset_properties={
+      'task': 5,
+      'sparse': False,
+      'multioutput': True,
+      'target_type': 'regression',
+      'signed': False})),
+    (0.080000, SimpleRegressionPipeline({'data_preprocessing:categorical_transformer:categorical_encoding:__choice__': 'one_hot_encoding', 'data_preprocessing:categorical_transformer:category_coalescence:__choice__': 'no_coalescense', 'data_preprocessing:numerical_transformer:imputation:strategy': 'most_frequent', 'data_preprocessing:numerical_transformer:rescaling:__choice__': 'minmax', 'feature_preprocessor:__choice__': 'fast_ica', 'regressor:__choice__': 'random_forest', 'feature_preprocessor:fast_ica:algorithm': 'deflation', 'feature_preprocessor:fast_ica:fun': 'exp', 'feature_preprocessor:fast_ica:whiten': 'True', 'regressor:random_forest:bootstrap': 'False', 'regressor:random_forest:criterion': 'friedman_mse', 'regressor:random_forest:max_depth': 'None', 'regressor:random_forest:max_features': 0.9420125003886077, 'regressor:random_forest:max_leaf_nodes': 'None', 'regressor:random_forest:min_impurity_decrease': 0.0, 'regressor:random_forest:min_samples_leaf': 2, 'regressor:random_forest:min_samples_split': 9, 'regressor:random_forest:min_weight_fraction_leaf': 0.0, 'feature_preprocessor:fast_ica:n_components': 717},
     dataset_properties={
       'task': 5,
       'sparse': False,
@@ -186,7 +202,7 @@ Get the Score of the final ensemble
 
  .. code-block:: none
 
-    R2 score: 0.9999953767477678
+    R2 score: 0.8749114684180196
 
 
 
@@ -404,7 +420,7 @@ Get the configuration space
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 2 minutes  7.873 seconds)
+   **Total running time of the script:** ( 1 minutes  59.984 seconds)
 
 
 .. _sphx_glr_download_examples_20_basic_example_multioutput_regression.py:

master/_images/sphx_glr_example_inspect_predictions_002.png

@@ -121,14 +121,14 @@ View the models found by auto-sklearn
 
               rank  ensemble_weight               type      cost   duration
     model_id                                                               
-    6            1             0.30     ard_regression  0.455042   0.681650
-    5            2             0.12   gaussian_process  0.455084  22.354520
-    25           3             0.20                sgd  0.460012   0.634606
-    22           4             0.06         libsvm_svr  0.461373   0.668957
-    27           5             0.22     ard_regression  0.462249   0.631091
-    24           6             0.02      liblinear_svr  0.466041   0.636573
-    11           7             0.04      random_forest  0.510608  10.503567
-    7            8             0.04  gradient_boosting  0.518673   1.348920
+    6            1             0.30     ard_regression  0.455042   0.712780
+    5            2             0.12   gaussian_process  0.455084  23.455189
+    25           3             0.20                sgd  0.460012   0.700737
+    22           4             0.06         libsvm_svr  0.461373   0.723632
+    27           5             0.22     ard_regression  0.462249   0.700960
+    24           6             0.02      liblinear_svr  0.466041   0.675511
+    11           7             0.04      random_forest  0.510608  11.005815
+    7            8             0.04  gradient_boosting  0.518673   1.435113
 
 
 
@@ -290,7 +290,7 @@ the true value).
 
 .. rst-class:: sphx-glr-timing
 
-   **Total running time of the script:** ( 1 minutes  54.488 seconds)
+   **Total running time of the script:** ( 1 minutes  59.746 seconds)
 
 
 .. _sphx_glr_download_examples_20_basic_example_regression.py:

master/_images/sphx_glr_example_pandas_train_test_001.png

@@ -5,14 +5,14 @@
 
 Computation times
 =================
-**06:17.148** total execution time for **examples_20_basic** files:
+**06:13.026** total execution time for **examples_20_basic** files:
 
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basic_example_multioutput_regression.py` (``example_multioutput_regression.py``)       | 02:07.873 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basic_example_multioutput_regression.py` (``example_multioutput_regression.py``)       | 01:59.984 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basic_example_classification.py` (``example_classification.py``)                       | 01:58.607 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basic_example_regression.py` (``example_regression.py``)                               | 01:59.746 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basic_example_regression.py` (``example_regression.py``)                               | 01:54.488 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basic_example_classification.py` (``example_classification.py``)                       | 01:59.100 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basic_example_multilabel_classification.py` (``example_multilabel_classification.py``) | 00:16.180 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basic_example_multilabel_classification.py` (``example_multilabel_classification.py``) | 00:14.196 | 0.0 MB |
 +-------------------------------------------------------------------------------------------------------------------+-----------+--------+