Attributes
----------
- cv_results\_ : dict of numpy (masked) ndarrays
+ cv_results_ : dict of numpy (masked) ndarrays
A dict with keys as column headers and values as columns, that can be
imported into a pandas ``DataFrame``.
Not all keys returned by scikit-learn are supported yet.
- performance_over_time\_ : pandas.core.frame.DataFrame
+ performance_over_time_ : pandas.core.frame.DataFrame
A ``DataFrame`` containing the models performance over time data. Can be
used for plotting directly. Please refer to the example
:ref:`Train and Test Inputs <sphx_glr_examples_40_advanced_example_pandas_train_test.py>`.
diff --git a/development/_modules/autosklearn/metrics.html b/development/_modules/autosklearn/metrics.html
index cf0c38dac6..1b15cc179f 100644
--- a/development/_modules/autosklearn/metrics.html
+++ b/development/_modules/autosklearn/metrics.html
@@ -112,6 +112,7 @@
Source code for autosklearn.metrics
from abc import ABCMeta, abstractmethod
from functools import partial
+from itertools import product
from typing import Any, Callable, Dict, List, Optional, Union, cast
import numpy as np
@@ -390,16 +391,14 @@ Source code for autosklearn.metrics
optimum=0,
worst_possible_result=MAXINT,
greater_is_better=False)
-r2 = make_scorer('r2',
- sklearn.metrics.r2_score)
+
+r2 = make_scorer('r2', sklearn.metrics.r2_score)
# Standard Classification Scores
accuracy = make_scorer('accuracy',
sklearn.metrics.accuracy_score)
balanced_accuracy = make_scorer('balanced_accuracy',
sklearn.metrics.balanced_accuracy_score)
-f1 = make_scorer('f1',
- sklearn.metrics.f1_score)
# Score functions that need decision values
roc_auc = make_scorer('roc_auc',
@@ -409,10 +408,20 @@ Source code for autosklearn.metrics
average_precision = make_scorer('average_precision',
sklearn.metrics.average_precision_score,
needs_threshold=True)
-precision = make_scorer('precision',
- sklearn.metrics.precision_score)
-recall = make_scorer('recall',
- sklearn.metrics.recall_score)
+
+# NOTE: zero_division
+#
+# Specified as the explicit default, see sklearn docs:
+# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html#sklearn-metrics-precision-score
+precision = make_scorer(
+ 'precision', partial(sklearn.metrics.precision_score, zero_division=0)
+)
+recall = make_scorer(
+ 'recall', partial(sklearn.metrics.recall_score, zero_division=0)
+)
+f1 = make_scorer(
+ 'f1', partial(sklearn.metrics.f1_score, zero_division=0)
+)
# Score function for probabilistic classification
log_loss = make_scorer('log_loss',
@@ -424,29 +433,39 @@ Source code for autosklearn.metrics
# TODO what about mathews correlation coefficient etc?
-REGRESSION_METRICS = dict()
-for scorer in [mean_absolute_error, mean_squared_error, root_mean_squared_error,
- mean_squared_log_error, median_absolute_error, r2]:
- REGRESSION_METRICS[scorer.name] = scorer
-
-CLASSIFICATION_METRICS = dict()
-
-for scorer in [accuracy, balanced_accuracy, roc_auc, average_precision,
- log_loss]:
- CLASSIFICATION_METRICS[scorer.name] = scorer
-
-for name, metric in [('precision', sklearn.metrics.precision_score),
- ('recall', sklearn.metrics.recall_score),
- ('f1', sklearn.metrics.f1_score)]:
- globals()[name] = make_scorer(name, metric)
- CLASSIFICATION_METRICS[name] = globals()[name]
- for average in ['macro', 'micro', 'samples', 'weighted']:
- qualified_name = '{0}_{1}'.format(name, average)
- globals()[qualified_name] = make_scorer(qualified_name,
- partial(metric,
- pos_label=None,
- average=average))
- CLASSIFICATION_METRICS[qualified_name] = globals()[qualified_name]
+REGRESSION_METRICS = {
+ scorer.name: scorer
+ for scorer in [
+ mean_absolute_error, mean_squared_error, root_mean_squared_error,
+ mean_squared_log_error, median_absolute_error, r2
+ ]
+}
+
+CLASSIFICATION_METRICS = {
+ scorer.name: scorer
+ for scorer in [
+ accuracy, balanced_accuracy, roc_auc, average_precision, log_loss
+ ]
+}
+
+# NOTE: zero_division
+#
+# Specified as the explicit default, see sklearn docs:
+# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.precision_score.html#sklearn-metrics-precision-score
+for (base_name, sklearn_metric), average in product(
+ [
+ ('precision', sklearn.metrics.precision_score),
+ ('recall', sklearn.metrics.recall_score),
+ ('f1', sklearn.metrics.f1_score),
+ ],
+ ['macro', 'micro', 'samples', 'weighted']
+):
+ name = f'{base_name}_{average}'
+ scorer = make_scorer(
+ name, partial(sklearn_metric, pos_label=None, average=average, zero_division=0)
+ )
+ globals()[name] = scorer # Adds scorer to the module scope
+ CLASSIFICATION_METRICS[name] = scorer
def calculate_score(
diff --git a/development/_modules/autosklearn/pipeline/components/base.html b/development/_modules/autosklearn/pipeline/components/base.html
index 7a380d2026..a63293341e 100644
--- a/development/_modules/autosklearn/pipeline/components/base.html
+++ b/development/_modules/autosklearn/pipeline/components/base.html
@@ -259,13 +259,16 @@ Source code for autosklearn.pipeline.components.base
class IterativeComponent(AutoSklearnComponent):
+
def fit(self, X, y, sample_weight=None):
self.iterative_fit(X, y, n_iter=2, refit=True)
+
iteration = 2
while not self.configuration_fully_fitted():
n_iter = int(2 ** iteration / 2)
self.iterative_fit(X, y, n_iter=n_iter, refit=False)
iteration += 1
+
return self
@staticmethod
@@ -277,15 +280,16 @@ Source code for autosklearn.pipeline.components.base
class IterativeComponentWithSampleWeight(AutoSklearnComponent):
+
def fit(self, X, y, sample_weight=None):
- self.iterative_fit(
- X, y, n_iter=2, refit=True, sample_weight=sample_weight
- )
+ self.iterative_fit(X, y, n_iter=2, refit=True, sample_weight=sample_weight)
+
iteration = 2
while not self.configuration_fully_fitted():
n_iter = int(2 ** iteration / 2)
- self.iterative_fit(X, y, n_iter=n_iter, sample_weight=sample_weight)
+ self.iterative_fit(X, y, n_iter=n_iter, refit=False, sample_weight=sample_weight)
iteration += 1
+
return self
@staticmethod
diff --git a/development/_sources/examples/20_basic/example_classification.rst.txt b/development/_sources/examples/20_basic/example_classification.rst.txt
index 76b34adf1d..eba2897652 100644
--- a/development/_sources/examples/20_basic/example_classification.rst.txt
+++ b/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:
diff --git a/development/_sources/examples/20_basic/example_multilabel_classification.rst.txt b/development/_sources/examples/20_basic/example_multilabel_classification.rst.txt
index 5aa4c5bbfa..9b502e4ce1 100644
--- a/development/_sources/examples/20_basic/example_multilabel_classification.rst.txt
+++ b/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:
diff --git a/development/_sources/examples/20_basic/example_multioutput_regression.rst.txt b/development/_sources/examples/20_basic/example_multioutput_regression.rst.txt
index 3af8ecfd75..07d84ea8c1 100644
--- a/development/_sources/examples/20_basic/example_multioutput_regression.rst.txt
+++ b/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:
diff --git a/development/_sources/examples/20_basic/example_regression.rst.txt b/development/_sources/examples/20_basic/example_regression.rst.txt
index 73194cb7bd..4a723ca0e1 100644
--- a/development/_sources/examples/20_basic/example_regression.rst.txt
+++ b/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:
diff --git a/development/_sources/examples/20_basic/sg_execution_times.rst.txt b/development/_sources/examples/20_basic/sg_execution_times.rst.txt
index f612ef3af7..bc728bd08f 100644
--- a/development/_sources/examples/20_basic/sg_execution_times.rst.txt
+++ b/development/_sources/examples/20_basic/sg_execution_times.rst.txt
@@ -5,14 +5,14 @@
Computation times
=================
-**06:11.987** total execution time for **examples_20_basic** files:
+**06:08.762** total execution time for **examples_20_basic** files:
+-------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basic_example_classification.py` (``example_classification.py``) | 02:04.034 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basic_example_classification.py` (``example_classification.py``) | 01:59.584 | 0.0 MB |
+-------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basic_example_multioutput_regression.py` (``example_multioutput_regression.py``) | 01:56.999 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basic_example_multioutput_regression.py` (``example_multioutput_regression.py``) | 01:56.909 | 0.0 MB |
+-------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basic_example_regression.py` (``example_regression.py``) | 01:54.910 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basic_example_regression.py` (``example_regression.py``) | 01:56.097 | 0.0 MB |
+-------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_20_basic_example_multilabel_classification.py` (``example_multilabel_classification.py``) | 00:16.045 | 0.0 MB |
+| :ref:`sphx_glr_examples_20_basic_example_multilabel_classification.py` (``example_multilabel_classification.py``) | 00:16.172 | 0.0 MB |
+-------------------------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/development/_sources/examples/40_advanced/example_calc_multiple_metrics.rst.txt b/development/_sources/examples/40_advanced/example_calc_multiple_metrics.rst.txt
index 1fa82d5c7d..02b861f449 100644
--- a/development/_sources/examples/40_advanced/example_calc_multiple_metrics.rst.txt
+++ b/development/_sources/examples/40_advanced/example_calc_multiple_metrics.rst.txt
@@ -193,7 +193,7 @@ Get the Score of the final ensemble
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 59.039 seconds)
+ **Total running time of the script:** ( 2 minutes 12.705 seconds)
.. _sphx_glr_download_examples_40_advanced_example_calc_multiple_metrics.py:
diff --git a/development/_sources/examples/40_advanced/example_debug_logging.rst.txt b/development/_sources/examples/40_advanced/example_debug_logging.rst.txt
index 3f195987c9..510f617620 100644
--- a/development/_sources/examples/40_advanced/example_debug_logging.rst.txt
+++ b/development/_sources/examples/40_advanced/example_debug_logging.rst.txt
@@ -174,13 +174,13 @@ Build and fit a classifier
.. code-block:: none
- 2021-11-26 12:18:03,100 - Client-EnsembleBuilder - INFO - DummyFuture: ([{'Timestamp': Timestamp('2021-11-26 12:18:03.080832'), 'ensemble_optimization_score': 0.47853535353535354, 'ensemble_test_score': 0.47434292866082606}], 50, None, None, None)/SingleThreadedClient() Started Ensemble builder job at 2021.11.26-12.18.03 for iteration 0.
- 2021-11-26 12:18:09,786 - Client-EnsembleBuilder - INFO - DummyFuture: ([], 50, None, None, None)/SingleThreadedClient() Started Ensemble builder job at 2021.11.26-12.18.09 for iteration 1.
+ 2021-12-01 08:41:32,178 - Client-EnsembleBuilder - INFO - DummyFuture: ([{'Timestamp': Timestamp('2021-12-01 08:41:32.157030'), 'ensemble_optimization_score': 0.47853535353535354, 'ensemble_test_score': 0.47434292866082606}], 50, None, None, None)/SingleThreadedClient() Started Ensemble builder job at 2021.12.01-08.41.32 for iteration 0.
+ 2021-12-01 08:41:39,099 - Client-EnsembleBuilder - INFO - DummyFuture: ([], 50, None, None, None)/SingleThreadedClient() Started Ensemble builder job at 2021.12.01-08.41.39 for iteration 1.
tmp_folder/smac3-output
tmp_folder/.auto-sklearn
+ tmp_folder/AutoML(1):7915c761-5282-11ec-867a-b7e09c96a597.log
tmp_folder/space.json
tmp_folder/distributed.log
- tmp_folder/AutoML(1):df27e147-4eb2-11ec-86be-1de9961ebe90.log
@@ -188,7 +188,7 @@ Build and fit a classifier
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 28.048 seconds)
+ **Total running time of the script:** ( 0 minutes 18.790 seconds)
.. _sphx_glr_download_examples_40_advanced_example_debug_logging.py:
diff --git a/development/_sources/examples/40_advanced/example_feature_types.rst.txt b/development/_sources/examples/40_advanced/example_feature_types.rst.txt
index b29326c63f..6b6135b201 100644
--- a/development/_sources/examples/40_advanced/example_feature_types.rst.txt
+++ b/development/_sources/examples/40_advanced/example_feature_types.rst.txt
@@ -161,7 +161,7 @@ Get the Score of the final ensemble
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 11.524 seconds)
+ **Total running time of the script:** ( 0 minutes 11.247 seconds)
.. _sphx_glr_download_examples_40_advanced_example_feature_types.py:
diff --git a/development/_sources/examples/40_advanced/example_get_pipeline_components.rst.txt b/development/_sources/examples/40_advanced/example_get_pipeline_components.rst.txt
index 7325f92199..679b62fd68 100644
--- a/development/_sources/examples/40_advanced/example_get_pipeline_components.rst.txt
+++ b/development/_sources/examples/40_advanced/example_get_pipeline_components.rst.txt
@@ -302,7 +302,7 @@ runhistory.runhistory.html#smac.runhistory.runhistory.RunHistory>`_.
.. code-block:: none
-
+
@@ -359,31 +359,31 @@ Let's iterative over all entries
#########
RunKey(config_id=1, instance_id='{"task_id": "breast_cancer"}', seed=0, budget=0.0)
- RunValue(cost=0.07801418439716312, time=2.2674410343170166, status=, starttime=1637929603.0168607, endtime=1637929605.3085766, additional_info={'duration': 2.143280506134033, 'num_run': 2, 'train_loss': 0.0, 'configuration_origin': 'Initial design'})
+ RunValue(cost=0.07801418439716312, time=2.206578254699707, status=, starttime=1638348604.112344, endtime=1638348606.3461835, additional_info={'duration': 2.0872066020965576, 'num_run': 2, 'train_loss': 0.0, 'configuration_origin': 'Initial design'})
#########
RunKey(config_id=2, instance_id='{"task_id": "breast_cancer"}', seed=0, budget=0.0)
- RunValue(cost=0.07092198581560283, time=1.9829821586608887, status=, starttime=1637929605.3834343, endtime=1637929607.393531, additional_info={'duration': 1.865163803100586, 'num_run': 3, 'train_loss': 0.06315789473684208, 'configuration_origin': 'Initial design'})
+ RunValue(cost=0.07092198581560283, time=1.9409003257751465, status=, starttime=1638348606.4167924, endtime=1638348608.3840885, additional_info={'duration': 1.821763277053833, 'num_run': 3, 'train_loss': 0.06315789473684208, 'configuration_origin': 'Initial design'})
#########
RunKey(config_id=3, instance_id='{"task_id": "breast_cancer"}', seed=0, budget=0.0)
- RunValue(cost=0.028368794326241176, time=1.9624383449554443, status=, starttime=1637929607.5281405, endtime=1637929609.5176048, additional_info={'duration': 1.8751075267791748, 'num_run': 4, 'train_loss': 0.04210526315789476, 'configuration_origin': 'Initial design'})
+ RunValue(cost=0.028368794326241176, time=2.000692367553711, status=, starttime=1638348608.5113623, endtime=1638348610.53798, additional_info={'duration': 1.9083738327026367, 'num_run': 4, 'train_loss': 0.04210526315789476, 'configuration_origin': 'Initial design'})
#########
RunKey(config_id=4, instance_id='{"task_id": "breast_cancer"}', seed=0, budget=0.0)
- RunValue(cost=0.1063829787234043, time=0.9281930923461914, status=, starttime=1637929609.6777663, endtime=1637929610.6326327, additional_info={'duration': 0.8408629894256592, 'num_run': 5, 'train_loss': 0.0, 'configuration_origin': 'Initial design'})
+ RunValue(cost=0.1063829787234043, time=0.9042990207672119, status=, starttime=1638348610.6940732, endtime=1638348611.6238089, additional_info={'duration': 0.8163888454437256, 'num_run': 5, 'train_loss': 0.0, 'configuration_origin': 'Initial design'})
#########
RunKey(config_id=5, instance_id='{"task_id": "breast_cancer"}', seed=0, budget=0.0)
- RunValue(cost=0.11347517730496459, time=0.761063814163208, status=, starttime=1637929610.815513, endtime=1637929611.6076763, additional_info={'duration': 0.6701092720031738, 'num_run': 6, 'train_loss': 0.09122807017543855, 'configuration_origin': 'Initial design'})
+ RunValue(cost=0.11347517730496459, time=0.7549824714660645, status=, starttime=1638348611.808457, endtime=1638348612.591162, additional_info={'duration': 0.6663446426391602, 'num_run': 6, 'train_loss': 0.09122807017543855, 'configuration_origin': 'Initial design'})
#########
RunKey(config_id=6, instance_id='{"task_id": "breast_cancer"}', seed=0, budget=0.0)
- RunValue(cost=0.03546099290780147, time=0.995154857635498, status=, starttime=1637929614.425791, endtime=1637929615.4572074, additional_info={'duration': 0.9087774753570557, 'num_run': 7, 'train_loss': 0.04561403508771933, 'configuration_origin': 'Random Search (sorted)'})
+ RunValue(cost=0.03546099290780147, time=1.0110886096954346, status=, starttime=1638348615.476678, endtime=1638348616.5172002, additional_info={'duration': 0.922957181930542, 'num_run': 7, 'train_loss': 0.04561403508771933, 'configuration_origin': 'Random Search (sorted)'})
#########
RunKey(config_id=7, instance_id='{"task_id": "breast_cancer"}', seed=0, budget=0.0)
- RunValue(cost=0.05673758865248224, time=0.9270179271697998, status=, starttime=1637929618.4326363, endtime=1637929619.386097, additional_info={'duration': 0.8397572040557861, 'num_run': 8, 'train_loss': 0.06315789473684208, 'configuration_origin': 'Random Search (sorted)'})
+ RunValue(cost=0.05673758865248224, time=0.9271812438964844, status=, starttime=1638348619.5394175, endtime=1638348620.4911506, additional_info={'duration': 0.8355433940887451, 'num_run': 8, 'train_loss': 0.06315789473684208, 'configuration_origin': 'Random Search (sorted)'})
#########
RunKey(config_id=8, instance_id='{"task_id": "breast_cancer"}', seed=0, budget=0.0)
- RunValue(cost=0.028368794326241176, time=0.759753942489624, status=, starttime=1637929619.6515505, endtime=1637929620.4378793, additional_info={'duration': 0.6744487285614014, 'num_run': 9, 'train_loss': 0.007017543859649145, 'configuration_origin': 'Random Search'})
+ RunValue(cost=0.028368794326241176, time=0.7736327648162842, status=, starttime=1638348620.7530127, endtime=1638348621.5521383, additional_info={'duration': 0.6835799217224121, 'num_run': 9, 'train_loss': 0.007017543859649145, 'configuration_origin': 'Random Search'})
#########
RunKey(config_id=9, instance_id='{"task_id": "breast_cancer"}', seed=0, budget=0.0)
- RunValue(cost=1.0, time=0.0, status=, starttime=1637929624.30096, endtime=1637929624.3009603, additional_info={})
+ RunValue(cost=1.0, time=0.0, status=, starttime=1638348625.358134, endtime=1638348625.3581343, additional_info={})
@@ -522,11 +522,11 @@ The ``run_value`` contains all output from running the configuration:
.. code-block:: none
Cost: 0.07801418439716312
- Time: 2.2674410343170166
+ Time: 2.206578254699707
Status: StatusType.SUCCESS
- Additional information: {'duration': 2.143280506134033, 'num_run': 2, 'train_loss': 0.0, 'configuration_origin': 'Initial design'}
- Start time: 1637929603.0168607
- End time 1637929605.3085766
+ Additional information: {'duration': 2.0872066020965576, 'num_run': 2, 'train_loss': 0.0, 'configuration_origin': 'Initial design'}
+ Start time: 1638348604.112344
+ End time 1638348606.3461835
@@ -635,8 +635,8 @@ model_selection.GridSearchCV.html>`_.
.. code-block:: none
{'mean_test_score': array([0.92198582, 0.92907801, 0.97163121, 0.89361702, 0.88652482,
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- 0.99515486, 0.92701793, 0.75975394]), 'params': [{'balancing:strategy': 'none', 'classifier:__choice__': 'random_forest', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'pca', 'classifier:random_forest:bootstrap': 'True', 'classifier:random_forest:criterion': 'gini', 'classifier:random_forest:max_depth': 'None', 'classifier:random_forest:max_features': 0.5, 'classifier:random_forest:max_leaf_nodes': 'None', 'classifier:random_forest:min_impurity_decrease': 0.0, 'classifier:random_forest:min_samples_leaf': 1, 'classifier:random_forest:min_samples_split': 2, 'classifier:random_forest:min_weight_fraction_leaf': 0.0, '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': 'mean', 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'data_preprocessor:feature_type:numerical_transformer:imputation:strategy': 'mean', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'minmax', 'feature_preprocessor:pca:keep_variance': 0.9290439925152777, 'feature_preprocessor:pca:whiten': 'False', 'classifier:libsvm_svc:coef0': 0.08087614244138486, 'classifier:libsvm_svc:degree': 3, 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.010000000000000004}, {'balancing:strategy': 'weighting', 'classifier:__choice__': 'liblinear_svc', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'pca', 'classifier:liblinear_svc:C': 10.369811497206404, 'classifier:liblinear_svc:dual': 'False', 'classifier:liblinear_svc:fit_intercept': 'True', 'classifier:liblinear_svc:intercept_scaling': 1, 'classifier:liblinear_svc:loss': 'squared_hinge', 'classifier:liblinear_svc:multi_class': 'ovr', 'classifier:liblinear_svc:penalty': 'l2', 'classifier:liblinear_svc:tol': 0.0015130257264171173, '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': 'mean', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'power_transformer', 'feature_preprocessor:pca:keep_variance': 0.6661824659281315, 'feature_preprocessor:pca:whiten': 'False'}, {'balancing:strategy': 'weighting', 'classifier:__choice__': 'decision_tree', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'pca', 'classifier:decision_tree:criterion': 'gini', 'classifier:decision_tree:max_depth_factor': 0.14069368736662313, 'classifier:decision_tree:max_features': 1.0, 'classifier:decision_tree:max_leaf_nodes': 'None', 'classifier:decision_tree:min_impurity_decrease': 0.0, 'classifier:decision_tree:min_samples_leaf': 5, 'classifier:decision_tree:min_samples_split': 19, 'classifier:decision_tree:min_weight_fraction_leaf': 0.0, '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': 'mean', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'quantile_transformer', 'feature_preprocessor:pca:keep_variance': 0.7772788812704434, 'feature_preprocessor:pca:whiten': 'True', 'data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles': 526, 'data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution': 'uniform'}, {'balancing:strategy': 'none', 'classifier:__choice__': 'sgd', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'pca', 'classifier:sgd:alpha': 1.0333981795670597e-07, 'classifier:sgd:average': 'False', 'classifier:sgd:fit_intercept': 'True', 'classifier:sgd:learning_rate': 'constant', 'classifier:sgd:loss': 'modified_huber', 'classifier:sgd:penalty': 'l2', 'classifier:sgd:tol': 2.5846457091661748e-05, 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'encoding', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:__choice__': 'minority_coalescer', 'data_preprocessor:feature_type:numerical_transformer:imputation:strategy': 'most_frequent', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'minmax', 'feature_preprocessor:pca:keep_variance': 0.9755563101648113, 'feature_preprocessor:pca:whiten': 'True', 'classifier:sgd:epsilon': 0.03457277874230573, 'classifier:sgd:eta0': 0.004635634934629576, 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.00015788887334393744}], 'rank_test_scores': array([6, 5, 1, 7, 8, 3, 4, 1]), 'status': ['Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success', 'Success'], 'budgets': [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], 'param_balancing:strategy': masked_array(data=['none', 'none', 'weighting', 'weighting', 'none',
'weighting', 'weighting', 'none'],
mask=[False, False, False, False, False, False, False, False],
fill_value='N/A',
@@ -1019,7 +1019,7 @@ The explained variance ratio per stage
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 27.787 seconds)
+ **Total running time of the script:** ( 0 minutes 27.855 seconds)
.. _sphx_glr_download_examples_40_advanced_example_get_pipeline_components.py:
diff --git a/development/_sources/examples/40_advanced/example_inspect_predictions.rst.txt b/development/_sources/examples/40_advanced/example_inspect_predictions.rst.txt
index 1580bec708..b4559b8b29 100644
--- a/development/_sources/examples/40_advanced/example_inspect_predictions.rst.txt
+++ b/development/_sources/examples/40_advanced/example_inspect_predictions.rst.txt
@@ -233,7 +233,7 @@ these features. Again, we'll look at acceleration_y and acceleration_z.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 3 minutes 57.255 seconds)
+ **Total running time of the script:** ( 3 minutes 53.448 seconds)
.. _sphx_glr_download_examples_40_advanced_example_inspect_predictions.py:
diff --git a/development/_sources/examples/40_advanced/example_interpretable_models.rst.txt b/development/_sources/examples/40_advanced/example_interpretable_models.rst.txt
index 7c2cdab356..cd0696d63a 100644
--- a/development/_sources/examples/40_advanced/example_interpretable_models.rst.txt
+++ b/development/_sources/examples/40_advanced/example_interpretable_models.rst.txt
@@ -274,7 +274,7 @@ Get the Score of the final ensemble
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 2 minutes 1.398 seconds)
+ **Total running time of the script:** ( 1 minutes 56.956 seconds)
.. _sphx_glr_download_examples_40_advanced_example_interpretable_models.py:
diff --git a/development/_sources/examples/40_advanced/example_metrics.rst.txt b/development/_sources/examples/40_advanced/example_metrics.rst.txt
index 1e2a51ed83..ce87a71136 100644
--- a/development/_sources/examples/40_advanced/example_metrics.rst.txt
+++ b/development/_sources/examples/40_advanced/example_metrics.rst.txt
@@ -141,17 +141,14 @@ Print a list of available metrics
*roc_auc
*average_precision
*log_loss
- *precision
*precision_macro
*precision_micro
*precision_samples
*precision_weighted
- *recall
*recall_macro
*recall_micro
*recall_samples
*recall_weighted
- *f1
*f1_macro
*f1_micro
*f1_samples
@@ -304,16 +301,15 @@ Third example: Use own error metric
################################################################################
Use self defined error metric
- [WARNING] [2021-11-26 12:29:18,730:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
- [WARNING] [2021-11-26 12:29:19,980:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
- [WARNING] [2021-11-26 12:29:24,208:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
- [WARNING] [2021-11-26 12:29:25,339:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
- [WARNING] [2021-11-26 12:29:26,689:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
- [WARNING] [2021-11-26 12:29:31,934:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
- [WARNING] [2021-11-26 12:29:34,378:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
- [WARNING] [2021-11-26 12:29:39,579:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
- [WARNING] [2021-11-26 12:29:45,037:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
- [WARNING] [2021-11-26 12:29:51,106:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
+ [WARNING] [2021-12-01 08:52:49,155:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
+ [WARNING] [2021-12-01 08:52:50,426:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
+ [WARNING] [2021-12-01 08:52:54,693:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
+ [WARNING] [2021-12-01 08:52:55,833:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
+ [WARNING] [2021-12-01 08:52:57,181:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
+ [WARNING] [2021-12-01 08:53:02,573:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
+ [WARNING] [2021-12-01 08:53:05,081:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
+ [WARNING] [2021-12-01 08:53:10,555:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
+ [WARNING] [2021-12-01 08:53:17,027:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
Error score -0.056 using error
@@ -415,17 +411,17 @@ Fifth example: Use own accuracy metric with additional argument
################################################################################
Use self defined error with additional argument
- [WARNING] [2021-11-26 12:31:07,220:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
- [WARNING] [2021-11-26 12:31:11,973:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
- [WARNING] [2021-11-26 12:31:20,106:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
- [WARNING] [2021-11-26 12:31:21,357:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
- [WARNING] [2021-11-26 12:31:25,824:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
- [WARNING] [2021-11-26 12:31:27,119:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
- [WARNING] [2021-11-26 12:31:28,586:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
- [WARNING] [2021-11-26 12:31:35,528:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
- [WARNING] [2021-11-26 12:31:40,869:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
- [WARNING] [2021-11-26 12:31:46,732:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
- [WARNING] [2021-11-26 12:31:49,762:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
+ [WARNING] [2021-12-01 08:54:30,416:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
+ [WARNING] [2021-12-01 08:54:35,391:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
+ [WARNING] [2021-12-01 08:54:48,563:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
+ [WARNING] [2021-12-01 08:54:49,811:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
+ [WARNING] [2021-12-01 08:54:54,149:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
+ [WARNING] [2021-12-01 08:54:55,243:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
+ [WARNING] [2021-12-01 08:54:56,477:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
+ [WARNING] [2021-12-01 08:55:03,431:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
+ [WARNING] [2021-12-01 08:55:09,055:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
+ [WARNING] [2021-12-01 08:55:15,039:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
+ [WARNING] [2021-12-01 08:55:17,160:smac.runhistory.runhistory2epm.RunHistory2EPM4LogCost] Got cost of smaller/equal to 0. Replace by 0.000010 since we use log cost.
Error score 0.615 using error_add
@@ -434,7 +430,7 @@ Fifth example: Use own accuracy metric with additional argument
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 4 minutes 49.441 seconds)
+ **Total running time of the script:** ( 4 minutes 55.579 seconds)
.. _sphx_glr_download_examples_40_advanced_example_metrics.py:
diff --git a/development/_sources/examples/40_advanced/example_pandas_train_test.rst.txt b/development/_sources/examples/40_advanced/example_pandas_train_test.rst.txt
index 3641a4edad..334277d2b0 100644
--- a/development/_sources/examples/40_advanced/example_pandas_train_test.rst.txt
+++ b/development/_sources/examples/40_advanced/example_pandas_train_test.rst.txt
@@ -239,7 +239,7 @@ be directly used for plotting
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 57.369 seconds)
+ **Total running time of the script:** ( 2 minutes 1.825 seconds)
.. _sphx_glr_download_examples_40_advanced_example_pandas_train_test.py:
diff --git a/development/_sources/examples/40_advanced/example_resampling.rst.txt b/development/_sources/examples/40_advanced/example_resampling.rst.txt
index 819fe08634..c2d0023d09 100644
--- a/development/_sources/examples/40_advanced/example_resampling.rst.txt
+++ b/development/_sources/examples/40_advanced/example_resampling.rst.txt
@@ -329,7 +329,7 @@ splitting it on the first feature.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 6 minutes 17.614 seconds)
+ **Total running time of the script:** ( 6 minutes 7.974 seconds)
.. _sphx_glr_download_examples_40_advanced_example_resampling.py:
diff --git a/development/_sources/examples/40_advanced/example_single_configuration.rst.txt b/development/_sources/examples/40_advanced/example_single_configuration.rst.txt
index 6d70c95e48..a6c7949276 100644
--- a/development/_sources/examples/40_advanced/example_single_configuration.rst.txt
+++ b/development/_sources/examples/40_advanced/example_single_configuration.rst.txt
@@ -161,51 +161,57 @@ Fit an user provided configuration
.. code-block:: none
- {'data_preprocessor': , 'balancing': Balancing(random_state=1, strategy='weighting'), 'feature_preprocessor': , 'classifier': }
+ {'data_preprocessor': , 'balancing': Balancing(random_state=1), 'feature_preprocessor': , 'classifier': }
RunInfo(config=Configuration:
- balancing:strategy, Value: 'weighting'
+ balancing:strategy, Value: 'none'
classifier:__choice__, Value: 'random_forest'
- classifier:random_forest:bootstrap, Value: 'False'
+ classifier:random_forest:bootstrap, Value: 'True'
classifier:random_forest:criterion, Value: 'gini'
classifier:random_forest:max_depth, Constant: 'None'
- classifier:random_forest:max_features, Value: 0.21677283489586374
+ classifier:random_forest:max_features, Value: 0.5732565623244906
classifier:random_forest:max_leaf_nodes, Constant: 'None'
classifier:random_forest:min_impurity_decrease, Constant: 0.0
- classifier:random_forest:min_samples_leaf, Value: 13
+ classifier:random_forest:min_samples_leaf, Value: 18
classifier:random_forest:min_samples_split, Value: 11
classifier:random_forest:min_weight_fraction_leaf, Constant: 0.0
data_preprocessor:__choice__, Value: 'feature_type'
data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__, Value: 'one_hot_encoding'
data_preprocessor:feature_type:categorical_transformer:category_coalescence:__choice__, Value: 'no_coalescense'
data_preprocessor:feature_type:numerical_transformer:imputation:strategy, Value: 'median'
- data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__, Value: 'standardize'
- feature_preprocessor:__choice__, Value: 'pca'
- feature_preprocessor:pca:keep_variance, Value: 0.7089309936936103
- feature_preprocessor:pca:whiten, Value: 'True'
+ data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__, Value: 'robust_scaler'
+ data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max, Value: 0.9778526754210007
+ data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min, Value: 0.052046634033680775
+ feature_preprocessor:__choice__, Value: 'select_rates_classification'
+ feature_preprocessor:select_rates_classification:alpha, Value: 0.08612700739849519
+ feature_preprocessor:select_rates_classification:mode, Value: 'fdr'
+ feature_preprocessor:select_rates_classification:score_func, Value: 'f_classif'
, instance=None, instance_specific=None, seed=1, cutoff=60, capped=False, budget=0.0, source_id=0)
- RunValue(cost=0.09289099526066347, time=3.9110684394836426, status=, starttime=1637929104.2086856, endtime=1637929108.1486168, additional_info={'duration': 3.787130355834961, 'num_run': 2, 'train_loss': 0.03222793087342368, 'configuration_origin': None})
+ RunValue(cost=0.03696682464454981, time=2.9550387859344482, status=, starttime=1638348110.1792455, endtime=1638348113.1629763, additional_info={'duration': 2.84236216545105, 'num_run': 2, 'train_loss': 0.04670714619336758, 'configuration_origin': None})
Passed Configuration: Configuration:
- balancing:strategy, Value: 'weighting'
+ balancing:strategy, Value: 'none'
classifier:__choice__, Value: 'random_forest'
- classifier:random_forest:bootstrap, Value: 'False'
+ classifier:random_forest:bootstrap, Value: 'True'
classifier:random_forest:criterion, Value: 'gini'
classifier:random_forest:max_depth, Constant: 'None'
- classifier:random_forest:max_features, Value: 0.21677283489586374
+ classifier:random_forest:max_features, Value: 0.5732565623244906
classifier:random_forest:max_leaf_nodes, Constant: 'None'
classifier:random_forest:min_impurity_decrease, Constant: 0.0
- classifier:random_forest:min_samples_leaf, Value: 13
+ classifier:random_forest:min_samples_leaf, Value: 18
classifier:random_forest:min_samples_split, Value: 11
classifier:random_forest:min_weight_fraction_leaf, Constant: 0.0
data_preprocessor:__choice__, Value: 'feature_type'
data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__, Value: 'one_hot_encoding'
data_preprocessor:feature_type:categorical_transformer:category_coalescence:__choice__, Value: 'no_coalescense'
data_preprocessor:feature_type:numerical_transformer:imputation:strategy, Value: 'median'
- data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__, Value: 'standardize'
- feature_preprocessor:__choice__, Value: 'pca'
- feature_preprocessor:pca:keep_variance, Value: 0.7089309936936103
- feature_preprocessor:pca:whiten, Value: 'True'
-
- Random Forest: RandomForestClassifier(bootstrap=False, max_features=1, min_samples_leaf=13,
+ data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__, Value: 'robust_scaler'
+ data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max, Value: 0.9778526754210007
+ data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min, Value: 0.052046634033680775
+ feature_preprocessor:__choice__, Value: 'select_rates_classification'
+ feature_preprocessor:select_rates_classification:alpha, Value: 0.08612700739849519
+ feature_preprocessor:select_rates_classification:mode, Value: 'fdr'
+ feature_preprocessor:select_rates_classification:score_func, Value: 'f_classif'
+
+ Random Forest: RandomForestClassifier(max_features=9, min_samples_leaf=18,
min_samples_split=11, n_estimators=512, n_jobs=1,
random_state=1, warm_start=True)
@@ -215,7 +221,7 @@ Fit an user provided configuration
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 2 minutes 8.947 seconds)
+ **Total running time of the script:** ( 2 minutes 10.295 seconds)
.. _sphx_glr_download_examples_40_advanced_example_single_configuration.py:
diff --git a/development/_sources/examples/40_advanced/sg_execution_times.rst.txt b/development/_sources/examples/40_advanced/sg_execution_times.rst.txt
index d3e2c93bf8..b7f65ca536 100644
--- a/development/_sources/examples/40_advanced/sg_execution_times.rst.txt
+++ b/development/_sources/examples/40_advanced/sg_execution_times.rst.txt
@@ -5,26 +5,26 @@
Computation times
=================
-**24:18.421** total execution time for **examples_40_advanced** files:
+**24:16.674** total execution time for **examples_40_advanced** files:
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_40_advanced_example_resampling.py` (``example_resampling.py``) | 06:17.614 | 0.0 MB |
+| :ref:`sphx_glr_examples_40_advanced_example_resampling.py` (``example_resampling.py``) | 06:07.974 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_40_advanced_example_metrics.py` (``example_metrics.py``) | 04:49.441 | 0.0 MB |
+| :ref:`sphx_glr_examples_40_advanced_example_metrics.py` (``example_metrics.py``) | 04:55.579 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_40_advanced_example_inspect_predictions.py` (``example_inspect_predictions.py``) | 03:57.255 | 0.0 MB |
+| :ref:`sphx_glr_examples_40_advanced_example_inspect_predictions.py` (``example_inspect_predictions.py``) | 03:53.448 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_40_advanced_example_single_configuration.py` (``example_single_configuration.py``) | 02:08.947 | 0.0 MB |
+| :ref:`sphx_glr_examples_40_advanced_example_calc_multiple_metrics.py` (``example_calc_multiple_metrics.py``) | 02:12.705 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_40_advanced_example_interpretable_models.py` (``example_interpretable_models.py``) | 02:01.398 | 0.0 MB |
+| :ref:`sphx_glr_examples_40_advanced_example_single_configuration.py` (``example_single_configuration.py``) | 02:10.295 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_40_advanced_example_calc_multiple_metrics.py` (``example_calc_multiple_metrics.py``) | 01:59.039 | 0.0 MB |
+| :ref:`sphx_glr_examples_40_advanced_example_pandas_train_test.py` (``example_pandas_train_test.py``) | 02:01.825 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_40_advanced_example_pandas_train_test.py` (``example_pandas_train_test.py``) | 01:57.369 | 0.0 MB |
+| :ref:`sphx_glr_examples_40_advanced_example_interpretable_models.py` (``example_interpretable_models.py``) | 01:56.956 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_40_advanced_example_debug_logging.py` (``example_debug_logging.py``) | 00:28.048 | 0.0 MB |
+| :ref:`sphx_glr_examples_40_advanced_example_get_pipeline_components.py` (``example_get_pipeline_components.py``) | 00:27.855 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_40_advanced_example_get_pipeline_components.py` (``example_get_pipeline_components.py``) | 00:27.787 | 0.0 MB |
+| :ref:`sphx_glr_examples_40_advanced_example_debug_logging.py` (``example_debug_logging.py``) | 00:18.790 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_40_advanced_example_feature_types.py` (``example_feature_types.py``) | 00:11.524 | 0.0 MB |
+| :ref:`sphx_glr_examples_40_advanced_example_feature_types.py` (``example_feature_types.py``) | 00:11.247 | 0.0 MB |
+------------------------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/development/_sources/examples/60_search/example_parallel_manual_spawning_cli.rst.txt b/development/_sources/examples/60_search/example_parallel_manual_spawning_cli.rst.txt
index c4b8fee270..d70350ffd5 100644
--- a/development/_sources/examples/60_search/example_parallel_manual_spawning_cli.rst.txt
+++ b/development/_sources/examples/60_search/example_parallel_manual_spawning_cli.rst.txt
@@ -301,13 +301,13 @@ Start Auto-sklearn
.. code-block:: none
auto-sklearn results:
- Dataset name: 69f82a38-4eb5-11ec-86be-1de9961ebe90
+ Dataset name: fb4e6e28-5284-11ec-867a-b7e09c96a597
Metric: accuracy
Best validation score: 0.992908
- Number of target algorithm runs: 10
+ Number of target algorithm runs: 9
Number of successful target algorithm runs: 9
Number of crashed target algorithm runs: 0
- Number of target algorithms that exceeded the time limit: 1
+ Number of target algorithms that exceeded the time limit: 0
Number of target algorithms that exceeded the memory limit: 0
Accuracy score 0.958041958041958
@@ -342,7 +342,7 @@ line.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 38.787 seconds)
+ **Total running time of the script:** ( 0 minutes 38.352 seconds)
.. _sphx_glr_download_examples_60_search_example_parallel_manual_spawning_cli.py:
diff --git a/development/_sources/examples/60_search/example_parallel_manual_spawning_python.rst.txt b/development/_sources/examples/60_search/example_parallel_manual_spawning_python.rst.txt
index f920fd968d..5423a29027 100644
--- a/development/_sources/examples/60_search/example_parallel_manual_spawning_python.rst.txt
+++ b/development/_sources/examples/60_search/example_parallel_manual_spawning_python.rst.txt
@@ -209,11 +209,11 @@ which means that it is automatically stopped once all computation is done.
.. code-block:: none
- [ERROR] [2021-11-26 12:36:48,306:asyncio] _GatheringFuture exception was never retrieved
+ [ERROR] [2021-12-01 09:00:11,009:asyncio] _GatheringFuture exception was never retrieved
future: <_GatheringFuture finished exception=CancelledError()>
asyncio.exceptions.CancelledError
auto-sklearn results:
- Dataset name: 8098c347-4eb5-11ec-86be-1de9961ebe90
+ Dataset name: 11ad30fd-5285-11ec-867a-b7e09c96a597
Metric: accuracy
Best validation score: 0.992908
Number of target algorithm runs: 11
@@ -230,7 +230,7 @@ which means that it is automatically stopped once all computation is done.
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 36.081 seconds)
+ **Total running time of the script:** ( 0 minutes 36.069 seconds)
.. _sphx_glr_download_examples_60_search_example_parallel_manual_spawning_python.py:
diff --git a/development/_sources/examples/60_search/example_parallel_n_jobs.rst.txt b/development/_sources/examples/60_search/example_parallel_n_jobs.rst.txt
index 0d80f67fa5..5445204b1b 100644
--- a/development/_sources/examples/60_search/example_parallel_n_jobs.rst.txt
+++ b/development/_sources/examples/60_search/example_parallel_n_jobs.rst.txt
@@ -113,10 +113,10 @@ To use ``n_jobs_`` we must guard the code
Dataset name: breast_cancer
Metric: accuracy
Best validation score: 0.985816
- Number of target algorithm runs: 47
- Number of successful target algorithm runs: 46
+ Number of target algorithm runs: 45
+ Number of successful target algorithm runs: 43
Number of crashed target algorithm runs: 0
- Number of target algorithms that exceeded the time limit: 1
+ Number of target algorithms that exceeded the time limit: 2
Number of target algorithms that exceeded the memory limit: 0
@@ -126,7 +126,7 @@ To use ``n_jobs_`` we must guard the code
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 2 minutes 2.135 seconds)
+ **Total running time of the script:** ( 1 minutes 57.626 seconds)
.. _sphx_glr_download_examples_60_search_example_parallel_n_jobs.py:
diff --git a/development/_sources/examples/60_search/example_random_search.rst.txt b/development/_sources/examples/60_search/example_random_search.rst.txt
index 93cdac7252..bd5bd77157 100644
--- a/development/_sources/examples/60_search/example_random_search.rst.txt
+++ b/development/_sources/examples/60_search/example_random_search.rst.txt
@@ -325,10 +325,10 @@ Fit a classifier using ROAR
Dataset name: breast_cancer
Metric: accuracy
Best validation score: 0.992908
- Number of target algorithm runs: 28
+ Number of target algorithm runs: 27
Number of successful target algorithm runs: 26
Number of crashed target algorithm runs: 1
- Number of target algorithms that exceeded the time limit: 1
+ Number of target algorithms that exceeded the time limit: 0
Number of target algorithms that exceeded the memory limit: 0
Accuracy score 0.951048951048951
@@ -589,10 +589,10 @@ Fit a classifier using Random Search
Dataset name: breast_cancer
Metric: accuracy
Best validation score: 0.992908
- Number of target algorithm runs: 28
+ Number of target algorithm runs: 27
Number of successful target algorithm runs: 26
Number of crashed target algorithm runs: 1
- Number of target algorithms that exceeded the time limit: 1
+ Number of target algorithms that exceeded the time limit: 0
Number of target algorithms that exceeded the memory limit: 0
Accuracy score 0.951048951048951
@@ -603,7 +603,7 @@ Fit a classifier using Random Search
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 54.709 seconds)
+ **Total running time of the script:** ( 1 minutes 49.377 seconds)
.. _sphx_glr_download_examples_60_search_example_random_search.py:
diff --git a/development/_sources/examples/60_search/example_sequential.rst.txt b/development/_sources/examples/60_search/example_sequential.rst.txt
index f78b982610..f33ef24c2f 100644
--- a/development/_sources/examples/60_search/example_sequential.rst.txt
+++ b/development/_sources/examples/60_search/example_sequential.rst.txt
@@ -132,7 +132,7 @@ Print the final ensemble constructed by auto-sklearn
.. code-block:: none
- [(0.180000, SimpleClassificationPipeline({'balancing:strategy': 'none', 'classifier:__choice__': 'extra_trees', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'polynomial', 'classifier:extra_trees:bootstrap': 'False', 'classifier:extra_trees:criterion': 'gini', 'classifier:extra_trees:max_depth': 'None', 'classifier:extra_trees:max_features': 0.4791448484072812, 'classifier:extra_trees:max_leaf_nodes': 'None', 'classifier:extra_trees:min_impurity_decrease': 0.0, 'classifier:extra_trees:min_samples_leaf': 1, 'classifier:extra_trees:min_samples_split': 6, 'classifier:extra_trees:min_weight_fraction_leaf': 0.0, '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': 'mean', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'none', 'feature_preprocessor:polynomial:degree': 3, 'feature_preprocessor:polynomial:include_bias': 'True', 'feature_preprocessor:polynomial:interaction_only': 'True', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.17184142431776436},
+ [(0.140000, SimpleClassificationPipeline({'balancing:strategy': 'none', 'classifier:__choice__': 'extra_trees', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'feature_agglomeration', 'classifier:extra_trees:bootstrap': 'True', 'classifier:extra_trees:criterion': 'entropy', 'classifier:extra_trees:max_depth': 'None', 'classifier:extra_trees:max_features': 0.3871420537981852, 'classifier:extra_trees:max_leaf_nodes': 'None', 'classifier:extra_trees:min_impurity_decrease': 0.0, 'classifier:extra_trees:min_samples_leaf': 5, 'classifier:extra_trees:min_samples_split': 14, 'classifier:extra_trees:min_weight_fraction_leaf': 0.0, 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'one_hot_encoding', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:__choice__': 'no_coalescense', 'data_preprocessor:feature_type:numerical_transformer:imputation:strategy': 'most_frequent', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'robust_scaler', 'feature_preprocessor:feature_agglomeration:affinity': 'manhattan', 'feature_preprocessor:feature_agglomeration:linkage': 'complete', 'feature_preprocessor:feature_agglomeration:n_clusters': 355, 'feature_preprocessor:feature_agglomeration:pooling_func': 'mean', 'data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max': 0.726182961545702, 'data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min': 0.02260942170772907},
dataset_properties={
'task': 1,
'sparse': False,
@@ -140,7 +140,7 @@ Print the final ensemble constructed by auto-sklearn
'multiclass': False,
'target_type': 'classification',
'signed': False})),
- (0.140000, SimpleClassificationPipeline({'balancing:strategy': 'none', 'classifier:__choice__': 'extra_trees', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'polynomial', 'classifier:extra_trees:bootstrap': 'False', 'classifier:extra_trees:criterion': 'gini', 'classifier:extra_trees:max_depth': 'None', 'classifier:extra_trees:max_features': 0.5707983257382487, 'classifier:extra_trees:max_leaf_nodes': 'None', 'classifier:extra_trees:min_impurity_decrease': 0.0, 'classifier:extra_trees:min_samples_leaf': 3, 'classifier:extra_trees:min_samples_split': 11, 'classifier:extra_trees:min_weight_fraction_leaf': 0.0, 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'one_hot_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__': 'none', 'feature_preprocessor:polynomial:degree': 2, 'feature_preprocessor:polynomial:include_bias': 'False', 'feature_preprocessor:polynomial:interaction_only': 'False'},
+ (0.120000, SimpleClassificationPipeline({'balancing:strategy': 'none', 'classifier:__choice__': 'extra_trees', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'polynomial', 'classifier:extra_trees:bootstrap': 'False', 'classifier:extra_trees:criterion': 'gini', 'classifier:extra_trees:max_depth': 'None', 'classifier:extra_trees:max_features': 0.4791448484072812, 'classifier:extra_trees:max_leaf_nodes': 'None', 'classifier:extra_trees:min_impurity_decrease': 0.0, 'classifier:extra_trees:min_samples_leaf': 1, 'classifier:extra_trees:min_samples_split': 6, 'classifier:extra_trees:min_weight_fraction_leaf': 0.0, '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': 'mean', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'none', 'feature_preprocessor:polynomial:degree': 3, 'feature_preprocessor:polynomial:include_bias': 'True', 'feature_preprocessor:polynomial:interaction_only': 'True', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.17184142431776436},
dataset_properties={
'task': 1,
'sparse': False,
@@ -156,7 +156,7 @@ Print the final ensemble constructed by auto-sklearn
'multiclass': False,
'target_type': 'classification',
'signed': False})),
- (0.080000, SimpleClassificationPipeline({'balancing:strategy': 'weighting', 'classifier:__choice__': 'extra_trees', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'polynomial', 'classifier:extra_trees:bootstrap': 'True', 'classifier:extra_trees:criterion': 'entropy', 'classifier:extra_trees:max_depth': 'None', 'classifier:extra_trees:max_features': 0.3793256905143867, 'classifier:extra_trees:max_leaf_nodes': 'None', 'classifier:extra_trees:min_impurity_decrease': 0.0, 'classifier:extra_trees:min_samples_leaf': 20, 'classifier:extra_trees:min_samples_split': 5, 'classifier:extra_trees:min_weight_fraction_leaf': 0.0, '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': 'mean', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'none', 'feature_preprocessor:polynomial:degree': 3, 'feature_preprocessor:polynomial:include_bias': 'False', 'feature_preprocessor:polynomial:interaction_only': 'True', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.00010083678209087315},
+ (0.080000, SimpleClassificationPipeline({'balancing:strategy': 'none', 'classifier:__choice__': 'extra_trees', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'polynomial', 'classifier:extra_trees:bootstrap': 'False', 'classifier:extra_trees:criterion': 'gini', 'classifier:extra_trees:max_depth': 'None', 'classifier:extra_trees:max_features': 0.5707983257382487, 'classifier:extra_trees:max_leaf_nodes': 'None', 'classifier:extra_trees:min_impurity_decrease': 0.0, 'classifier:extra_trees:min_samples_leaf': 3, 'classifier:extra_trees:min_samples_split': 11, 'classifier:extra_trees:min_weight_fraction_leaf': 0.0, 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'one_hot_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__': 'none', 'feature_preprocessor:polynomial:degree': 2, 'feature_preprocessor:polynomial:include_bias': 'False', 'feature_preprocessor:polynomial:interaction_only': 'False'},
dataset_properties={
'task': 1,
'sparse': False,
@@ -164,7 +164,7 @@ Print the final ensemble constructed by auto-sklearn
'multiclass': False,
'target_type': 'classification',
'signed': False})),
- (0.060000, SimpleClassificationPipeline({'balancing:strategy': 'none', 'classifier:__choice__': 'random_forest', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'no_preprocessing', 'classifier:random_forest:bootstrap': 'True', 'classifier:random_forest:criterion': 'gini', 'classifier:random_forest:max_depth': 'None', 'classifier:random_forest:max_features': 0.5, 'classifier:random_forest:max_leaf_nodes': 'None', 'classifier:random_forest:min_impurity_decrease': 0.0, 'classifier:random_forest:min_samples_leaf': 1, 'classifier:random_forest:min_samples_split': 2, 'classifier:random_forest:min_weight_fraction_leaf': 0.0, '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': 'mean', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'standardize', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.01},
+ (0.080000, SimpleClassificationPipeline({'balancing:strategy': 'weighting', 'classifier:__choice__': 'liblinear_svc', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'select_percentile_classification', 'classifier:liblinear_svc:C': 140.23434217313954, 'classifier:liblinear_svc:dual': 'False', 'classifier:liblinear_svc:fit_intercept': 'True', 'classifier:liblinear_svc:intercept_scaling': 1, 'classifier:liblinear_svc:loss': 'squared_hinge', 'classifier:liblinear_svc:multi_class': 'ovr', 'classifier:liblinear_svc:penalty': 'l2', 'classifier:liblinear_svc:tol': 0.0002944604673080368, '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': 'most_frequent', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'quantile_transformer', 'feature_preprocessor:select_percentile_classification:percentile': 29.807816607300627, 'feature_preprocessor:select_percentile_classification:score_func': 'chi2', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.034330895903523305, 'data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles': 1450, 'data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution': 'uniform'},
dataset_properties={
'task': 1,
'sparse': False,
@@ -172,7 +172,7 @@ Print the final ensemble constructed by auto-sklearn
'multiclass': False,
'target_type': 'classification',
'signed': False})),
- (0.060000, SimpleClassificationPipeline({'balancing:strategy': 'weighting', 'classifier:__choice__': 'liblinear_svc', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'select_percentile_classification', 'classifier:liblinear_svc:C': 140.23434217313954, 'classifier:liblinear_svc:dual': 'False', 'classifier:liblinear_svc:fit_intercept': 'True', 'classifier:liblinear_svc:intercept_scaling': 1, 'classifier:liblinear_svc:loss': 'squared_hinge', 'classifier:liblinear_svc:multi_class': 'ovr', 'classifier:liblinear_svc:penalty': 'l2', 'classifier:liblinear_svc:tol': 0.0002944604673080368, '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': 'most_frequent', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'quantile_transformer', 'feature_preprocessor:select_percentile_classification:percentile': 29.807816607300627, 'feature_preprocessor:select_percentile_classification:score_func': 'chi2', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.034330895903523305, 'data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles': 1450, 'data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution': 'uniform'},
+ (0.060000, SimpleClassificationPipeline({'balancing:strategy': 'weighting', 'classifier:__choice__': 'gradient_boosting', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'select_percentile_classification', 'classifier:gradient_boosting:early_stop': 'train', 'classifier:gradient_boosting:l2_regularization': 3.387912939529945e-10, 'classifier:gradient_boosting:learning_rate': 0.30755227194768237, 'classifier:gradient_boosting:loss': 'auto', 'classifier:gradient_boosting:max_bins': 255, 'classifier:gradient_boosting:max_depth': 'None', 'classifier:gradient_boosting:max_leaf_nodes': 60, 'classifier:gradient_boosting:min_samples_leaf': 39, 'classifier:gradient_boosting:scoring': 'loss', 'classifier:gradient_boosting:tol': 1e-07, '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': 'most_frequent', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'none', 'feature_preprocessor:select_percentile_classification:percentile': 93.39844669585806, 'feature_preprocessor:select_percentile_classification:score_func': 'f_classif', 'classifier:gradient_boosting:n_iter_no_change': 18, 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.010000000000000004},
dataset_properties={
'task': 1,
'sparse': False,
@@ -180,7 +180,7 @@ Print the final ensemble constructed by auto-sklearn
'multiclass': False,
'target_type': 'classification',
'signed': False})),
- (0.060000, SimpleClassificationPipeline({'balancing:strategy': 'none', 'classifier:__choice__': 'extra_trees', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'feature_agglomeration', 'classifier:extra_trees:bootstrap': 'True', 'classifier:extra_trees:criterion': 'entropy', 'classifier:extra_trees:max_depth': 'None', 'classifier:extra_trees:max_features': 0.3871420537981852, 'classifier:extra_trees:max_leaf_nodes': 'None', 'classifier:extra_trees:min_impurity_decrease': 0.0, 'classifier:extra_trees:min_samples_leaf': 5, 'classifier:extra_trees:min_samples_split': 14, 'classifier:extra_trees:min_weight_fraction_leaf': 0.0, 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'one_hot_encoding', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:__choice__': 'no_coalescense', 'data_preprocessor:feature_type:numerical_transformer:imputation:strategy': 'most_frequent', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'robust_scaler', 'feature_preprocessor:feature_agglomeration:affinity': 'manhattan', 'feature_preprocessor:feature_agglomeration:linkage': 'complete', 'feature_preprocessor:feature_agglomeration:n_clusters': 355, 'feature_preprocessor:feature_agglomeration:pooling_func': 'mean', 'data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max': 0.726182961545702, 'data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min': 0.02260942170772907},
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dataset_properties={
'task': 1,
'sparse': False,
@@ -188,7 +188,7 @@ Print the final ensemble constructed by auto-sklearn
'multiclass': False,
'target_type': 'classification',
'signed': False})),
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dataset_properties={
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@@ -196,7 +196,7 @@ Print the final ensemble constructed by auto-sklearn
'multiclass': False,
'target_type': 'classification',
'signed': False})),
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dataset_properties={
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'sparse': False,
@@ -204,7 +204,7 @@ Print the final ensemble constructed by auto-sklearn
'multiclass': False,
'target_type': 'classification',
'signed': False})),
- (0.040000, SimpleClassificationPipeline({'balancing:strategy': 'none', 'classifier:__choice__': 'gradient_boosting', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'extra_trees_preproc_for_classification', 'classifier:gradient_boosting:early_stop': 'valid', 'classifier:gradient_boosting:l2_regularization': 8.057778875694463e-05, 'classifier:gradient_boosting:learning_rate': 0.09179220974965213, 'classifier:gradient_boosting:loss': 'auto', 'classifier:gradient_boosting:max_bins': 255, 'classifier:gradient_boosting:max_depth': 'None', 'classifier:gradient_boosting:max_leaf_nodes': 200, 'classifier:gradient_boosting:min_samples_leaf': 20, 'classifier:gradient_boosting:scoring': 'loss', 'classifier:gradient_boosting:tol': 1e-07, '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': 'most_frequent', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'quantile_transformer', 'feature_preprocessor:extra_trees_preproc_for_classification:bootstrap': 'False', 'feature_preprocessor:extra_trees_preproc_for_classification:criterion': 'gini', 'feature_preprocessor:extra_trees_preproc_for_classification:max_depth': 'None', 'feature_preprocessor:extra_trees_preproc_for_classification:max_features': 0.9984367650965825, 'feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes': 'None', 'feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease': 0.0, 'feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf': 13, 'feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split': 18, 'feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf': 0.0, 'feature_preprocessor:extra_trees_preproc_for_classification:n_estimators': 100, 'classifier:gradient_boosting:n_iter_no_change': 18, 'classifier:gradient_boosting:validation_fraction': 0.14295295806077554, 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.008542188583124829, 'data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:n_quantiles': 1102, 'data_preprocessor:feature_type:numerical_transformer:rescaling:quantile_transformer:output_distribution': 'uniform'},
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dataset_properties={
'task': 1,
'sparse': False,
@@ -212,7 +212,7 @@ Print the final ensemble constructed by auto-sklearn
'multiclass': False,
'target_type': 'classification',
'signed': False})),
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dataset_properties={
'task': 1,
'sparse': False,
@@ -220,7 +220,7 @@ Print the final ensemble constructed by auto-sklearn
'multiclass': False,
'target_type': 'classification',
'signed': False})),
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dataset_properties={
'task': 1,
'sparse': False,
@@ -228,7 +228,7 @@ Print the final ensemble constructed by auto-sklearn
'multiclass': False,
'target_type': 'classification',
'signed': False})),
- (0.020000, SimpleClassificationPipeline({'balancing:strategy': 'weighting', 'classifier:__choice__': 'extra_trees', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'no_preprocessing', 'classifier:extra_trees:bootstrap': 'False', 'classifier:extra_trees:criterion': 'gini', 'classifier:extra_trees:max_depth': 'None', 'classifier:extra_trees:max_features': 0.661377361743419, 'classifier:extra_trees:max_leaf_nodes': 'None', 'classifier:extra_trees:min_impurity_decrease': 0.0, 'classifier:extra_trees:min_samples_leaf': 2, 'classifier:extra_trees:min_samples_split': 13, 'classifier:extra_trees:min_weight_fraction_leaf': 0.0, 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'encoding', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:__choice__': 'no_coalescense', 'data_preprocessor:feature_type:numerical_transformer:imputation:strategy': 'mean', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'none'},
+ (0.020000, SimpleClassificationPipeline({'balancing:strategy': 'none', 'classifier:__choice__': 'mlp', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'extra_trees_preproc_for_classification', 'classifier:mlp:activation': 'tanh', 'classifier:mlp:alpha': 2.5550223982458062e-06, 'classifier:mlp:batch_size': 'auto', 'classifier:mlp:beta_1': 0.9, 'classifier:mlp:beta_2': 0.999, 'classifier:mlp:early_stopping': 'train', 'classifier:mlp:epsilon': 1e-08, 'classifier:mlp:hidden_layer_depth': 3, 'classifier:mlp:learning_rate_init': 0.00027271287919467994, 'classifier:mlp:n_iter_no_change': 32, 'classifier:mlp:num_nodes_per_layer': 54, 'classifier:mlp:shuffle': 'True', 'classifier:mlp:solver': 'adam', 'classifier:mlp:tol': 0.0001, 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'no_encoding', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:__choice__': 'minority_coalescer', 'data_preprocessor:feature_type:numerical_transformer:imputation:strategy': 'mean', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'standardize', 'feature_preprocessor:extra_trees_preproc_for_classification:bootstrap': 'False', 'feature_preprocessor:extra_trees_preproc_for_classification:criterion': 'entropy', 'feature_preprocessor:extra_trees_preproc_for_classification:max_depth': 'None', 'feature_preprocessor:extra_trees_preproc_for_classification:max_features': 0.7171678618990129, 'feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes': 'None', 'feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease': 0.0, 'feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf': 4, 'feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split': 17, 'feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf': 0.0, 'feature_preprocessor:extra_trees_preproc_for_classification:n_estimators': 100, 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.017116661677715188},
dataset_properties={
'task': 1,
'sparse': False,
@@ -236,7 +236,15 @@ Print the final ensemble constructed by auto-sklearn
'multiclass': False,
'target_type': 'classification',
'signed': False})),
- (0.020000, SimpleClassificationPipeline({'balancing:strategy': 'none', 'classifier:__choice__': 'mlp', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'extra_trees_preproc_for_classification', 'classifier:mlp:activation': 'tanh', 'classifier:mlp:alpha': 2.5550223982458062e-06, 'classifier:mlp:batch_size': 'auto', 'classifier:mlp:beta_1': 0.9, 'classifier:mlp:beta_2': 0.999, 'classifier:mlp:early_stopping': 'train', 'classifier:mlp:epsilon': 1e-08, 'classifier:mlp:hidden_layer_depth': 3, 'classifier:mlp:learning_rate_init': 0.00027271287919467994, 'classifier:mlp:n_iter_no_change': 32, 'classifier:mlp:num_nodes_per_layer': 54, 'classifier:mlp:shuffle': 'True', 'classifier:mlp:solver': 'adam', 'classifier:mlp:tol': 0.0001, 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'no_encoding', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:__choice__': 'minority_coalescer', 'data_preprocessor:feature_type:numerical_transformer:imputation:strategy': 'mean', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'standardize', 'feature_preprocessor:extra_trees_preproc_for_classification:bootstrap': 'False', 'feature_preprocessor:extra_trees_preproc_for_classification:criterion': 'entropy', 'feature_preprocessor:extra_trees_preproc_for_classification:max_depth': 'None', 'feature_preprocessor:extra_trees_preproc_for_classification:max_features': 0.7171678618990129, 'feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes': 'None', 'feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease': 0.0, 'feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf': 4, 'feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split': 17, 'feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf': 0.0, 'feature_preprocessor:extra_trees_preproc_for_classification:n_estimators': 100, 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.017116661677715188},
+ (0.020000, SimpleClassificationPipeline({'balancing:strategy': 'weighting', 'classifier:__choice__': 'extra_trees', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'polynomial', 'classifier:extra_trees:bootstrap': 'False', 'classifier:extra_trees:criterion': 'entropy', 'classifier:extra_trees:max_depth': 'None', 'classifier:extra_trees:max_features': 0.993803313878608, 'classifier:extra_trees:max_leaf_nodes': 'None', 'classifier:extra_trees:min_impurity_decrease': 0.0, 'classifier:extra_trees:min_samples_leaf': 2, 'classifier:extra_trees:min_samples_split': 20, 'classifier:extra_trees:min_weight_fraction_leaf': 0.0, 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'no_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', 'feature_preprocessor:polynomial:degree': 2, 'feature_preprocessor:polynomial:include_bias': 'True', 'feature_preprocessor:polynomial:interaction_only': 'True', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.41826215858914706, 'data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max': 0.7305615609807856, 'data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min': 0.25595970768123566},
+ dataset_properties={
+ 'task': 1,
+ 'sparse': False,
+ 'multilabel': False,
+ 'multiclass': False,
+ 'target_type': 'classification',
+ 'signed': False})),
+ (0.020000, SimpleClassificationPipeline({'balancing:strategy': 'none', 'classifier:__choice__': 'extra_trees', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'polynomial', 'classifier:extra_trees:bootstrap': 'False', 'classifier:extra_trees:criterion': 'entropy', 'classifier:extra_trees:max_depth': 'None', 'classifier:extra_trees:max_features': 0.971348315581384, 'classifier:extra_trees:max_leaf_nodes': 'None', 'classifier:extra_trees:min_impurity_decrease': 0.0, 'classifier:extra_trees:min_samples_leaf': 2, 'classifier:extra_trees:min_samples_split': 19, 'classifier:extra_trees:min_weight_fraction_leaf': 0.0, 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'one_hot_encoding', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:__choice__': 'no_coalescense', 'data_preprocessor:feature_type:numerical_transformer:imputation:strategy': 'most_frequent', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'normalize', 'feature_preprocessor:polynomial:degree': 2, 'feature_preprocessor:polynomial:include_bias': 'False', 'feature_preprocessor:polynomial:interaction_only': 'False'},
dataset_properties={
'task': 1,
'sparse': False,
@@ -252,7 +260,15 @@ Print the final ensemble constructed by auto-sklearn
'multiclass': False,
'target_type': 'classification',
'signed': False})),
- (0.020000, SimpleClassificationPipeline({'balancing:strategy': 'weighting', 'classifier:__choice__': 'random_forest', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'extra_trees_preproc_for_classification', 'classifier:random_forest:bootstrap': 'True', 'classifier:random_forest:criterion': 'entropy', 'classifier:random_forest:max_depth': 'None', 'classifier:random_forest:max_features': 0.5805882119281688, 'classifier:random_forest:max_leaf_nodes': 'None', 'classifier:random_forest:min_impurity_decrease': 0.0, 'classifier:random_forest:min_samples_leaf': 1, 'classifier:random_forest:min_samples_split': 2, 'classifier:random_forest:min_weight_fraction_leaf': 0.0, '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__': 'minmax', 'feature_preprocessor:extra_trees_preproc_for_classification:bootstrap': 'False', 'feature_preprocessor:extra_trees_preproc_for_classification:criterion': 'gini', 'feature_preprocessor:extra_trees_preproc_for_classification:max_depth': 'None', 'feature_preprocessor:extra_trees_preproc_for_classification:max_features': 0.8490063449951434, 'feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes': 'None', 'feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease': 0.0, 'feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf': 9, 'feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split': 2, 'feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf': 0.0, 'feature_preprocessor:extra_trees_preproc_for_classification:n_estimators': 100, 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.11533421526707399},
+ (0.020000, SimpleClassificationPipeline({'balancing:strategy': 'weighting', 'classifier:__choice__': 'random_forest', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'extra_trees_preproc_for_classification', 'classifier:random_forest:bootstrap': 'True', 'classifier:random_forest:criterion': 'gini', 'classifier:random_forest:max_depth': 'None', 'classifier:random_forest:max_features': 0.43999367631975456, 'classifier:random_forest:max_leaf_nodes': 'None', 'classifier:random_forest:min_impurity_decrease': 0.0, 'classifier:random_forest:min_samples_leaf': 2, 'classifier:random_forest:min_samples_split': 2, 'classifier:random_forest:min_weight_fraction_leaf': 0.0, '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': 'most_frequent', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'standardize', 'feature_preprocessor:extra_trees_preproc_for_classification:bootstrap': 'True', 'feature_preprocessor:extra_trees_preproc_for_classification:criterion': 'entropy', 'feature_preprocessor:extra_trees_preproc_for_classification:max_depth': 'None', 'feature_preprocessor:extra_trees_preproc_for_classification:max_features': 0.8134515743047006, 'feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes': 'None', 'feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease': 0.0, 'feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf': 9, 'feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split': 20, 'feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf': 0.0, 'feature_preprocessor:extra_trees_preproc_for_classification:n_estimators': 100},
+ dataset_properties={
+ 'task': 1,
+ 'sparse': False,
+ 'multilabel': False,
+ 'multiclass': False,
+ 'target_type': 'classification',
+ 'signed': False})),
+ (0.020000, SimpleClassificationPipeline({'balancing:strategy': 'none', 'classifier:__choice__': 'gradient_boosting', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'liblinear_svc_preprocessor', 'classifier:gradient_boosting:early_stop': 'train', 'classifier:gradient_boosting:l2_regularization': 0.005326508887463406, 'classifier:gradient_boosting:learning_rate': 0.060800813211425456, 'classifier:gradient_boosting:loss': 'auto', 'classifier:gradient_boosting:max_bins': 255, 'classifier:gradient_boosting:max_depth': 'None', 'classifier:gradient_boosting:max_leaf_nodes': 6, 'classifier:gradient_boosting:min_samples_leaf': 5, 'classifier:gradient_boosting:scoring': 'loss', 'classifier:gradient_boosting:tol': 1e-07, '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__': 'minmax', 'feature_preprocessor:liblinear_svc_preprocessor:C': 13.550960330919455, 'feature_preprocessor:liblinear_svc_preprocessor:dual': 'False', 'feature_preprocessor:liblinear_svc_preprocessor:fit_intercept': 'True', 'feature_preprocessor:liblinear_svc_preprocessor:intercept_scaling': 1, 'feature_preprocessor:liblinear_svc_preprocessor:loss': 'squared_hinge', 'feature_preprocessor:liblinear_svc_preprocessor:multi_class': 'ovr', 'feature_preprocessor:liblinear_svc_preprocessor:penalty': 'l1', 'feature_preprocessor:liblinear_svc_preprocessor:tol': 1.2958033930435781e-05, 'classifier:gradient_boosting:n_iter_no_change': 5},
dataset_properties={
'task': 1,
'sparse': False,
@@ -261,6 +277,14 @@ Print the final ensemble constructed by auto-sklearn
'target_type': 'classification',
'signed': False})),
(0.020000, SimpleClassificationPipeline({'balancing:strategy': 'weighting', 'classifier:__choice__': 'extra_trees', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'extra_trees_preproc_for_classification', 'classifier:extra_trees:bootstrap': 'False', 'classifier:extra_trees:criterion': 'gini', 'classifier:extra_trees:max_depth': 'None', 'classifier:extra_trees:max_features': 0.9292309396985746, 'classifier:extra_trees:max_leaf_nodes': 'None', 'classifier:extra_trees:min_impurity_decrease': 0.0, 'classifier:extra_trees:min_samples_leaf': 1, 'classifier:extra_trees:min_samples_split': 10, 'classifier:extra_trees:min_weight_fraction_leaf': 0.0, 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'no_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__': 'normalize', 'feature_preprocessor:extra_trees_preproc_for_classification:bootstrap': 'False', 'feature_preprocessor:extra_trees_preproc_for_classification:criterion': 'entropy', 'feature_preprocessor:extra_trees_preproc_for_classification:max_depth': 'None', 'feature_preprocessor:extra_trees_preproc_for_classification:max_features': 0.9929881254946676, 'feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes': 'None', 'feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease': 0.0, 'feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf': 1, 'feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split': 2, 'feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf': 0.0, 'feature_preprocessor:extra_trees_preproc_for_classification:n_estimators': 100, 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.032719158639429445},
+ dataset_properties={
+ 'task': 1,
+ 'sparse': False,
+ 'multilabel': False,
+ 'multiclass': False,
+ 'target_type': 'classification',
+ 'signed': False})),
+ (0.020000, SimpleClassificationPipeline({'balancing:strategy': 'weighting', 'classifier:__choice__': 'extra_trees', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'polynomial', 'classifier:extra_trees:bootstrap': 'True', 'classifier:extra_trees:criterion': 'entropy', 'classifier:extra_trees:max_depth': 'None', 'classifier:extra_trees:max_features': 0.3793256905143867, 'classifier:extra_trees:max_leaf_nodes': 'None', 'classifier:extra_trees:min_impurity_decrease': 0.0, 'classifier:extra_trees:min_samples_leaf': 20, 'classifier:extra_trees:min_samples_split': 5, 'classifier:extra_trees:min_weight_fraction_leaf': 0.0, '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': 'mean', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'none', 'feature_preprocessor:polynomial:degree': 3, 'feature_preprocessor:polynomial:include_bias': 'False', 'feature_preprocessor:polynomial:interaction_only': 'True', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.00010083678209087315},
dataset_properties={
'task': 1,
'sparse': False,
@@ -308,13 +332,13 @@ Get the Score of the final ensemble
Dataset name: breast_cancer
Metric: accuracy
Best validation score: 0.985816
- Number of target algorithm runs: 35
- Number of successful target algorithm runs: 35
+ Number of target algorithm runs: 34
+ Number of successful target algorithm runs: 34
Number of crashed target algorithm runs: 0
Number of target algorithms that exceeded the time limit: 0
Number of target algorithms that exceeded the memory limit: 0
- Accuracy score 0.958041958041958
+ Accuracy score 0.9440559440559441
@@ -322,7 +346,7 @@ Get the Score of the final ensemble
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 1 minutes 57.396 seconds)
+ **Total running time of the script:** ( 1 minutes 57.887 seconds)
.. _sphx_glr_download_examples_60_search_example_sequential.py:
diff --git a/development/_sources/examples/60_search/example_successive_halving.rst.txt b/development/_sources/examples/60_search/example_successive_halving.rst.txt
index 2efbc4c886..9103c6653b 100644
--- a/development/_sources/examples/60_search/example_successive_halving.rst.txt
+++ b/development/_sources/examples/60_search/example_successive_halving.rst.txt
@@ -190,7 +190,7 @@ Build and fit a classifier
/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/smac/intensification/parallel_scheduling.py:152: UserWarning: SuccessiveHalving is intended to be used with more than 1 worker but num_workers=1
warnings.warn("{} is intended to be used "
- [(0.120000, SimpleClassificationPipeline({'balancing:strategy': 'weighting', 'classifier:__choice__': 'random_forest', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'no_preprocessing', 'classifier:random_forest:bootstrap': 'False', 'classifier:random_forest:criterion': 'entropy', 'classifier:random_forest:max_depth': 'None', 'classifier:random_forest:max_features': 0.41808321658160696, 'classifier:random_forest:max_leaf_nodes': 'None', 'classifier:random_forest:min_impurity_decrease': 0.0, 'classifier:random_forest:min_samples_leaf': 1, 'classifier:random_forest:min_samples_split': 4, 'classifier:random_forest:min_weight_fraction_leaf': 0.0, 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'no_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__': 'standardize', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.011283688651384545},
+ [(0.180000, SimpleClassificationPipeline({'balancing:strategy': 'none', 'classifier:__choice__': 'passive_aggressive', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'no_preprocessing', 'classifier:passive_aggressive:C': 0.14268277711454813, 'classifier:passive_aggressive:average': 'False', 'classifier:passive_aggressive:fit_intercept': 'True', 'classifier:passive_aggressive:loss': 'hinge', 'classifier:passive_aggressive:tol': 0.0002600768160857831, 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'no_encoding', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:__choice__': 'minority_coalescer', 'data_preprocessor:feature_type:numerical_transformer:imputation:strategy': 'most_frequent', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'none', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.0070580904199417415},
dataset_properties={
'task': 1,
'sparse': False,
@@ -198,7 +198,7 @@ Build and fit a classifier
'multiclass': False,
'target_type': 'classification',
'signed': False})),
- (0.120000, SimpleClassificationPipeline({'balancing:strategy': 'none', 'classifier:__choice__': 'gradient_boosting', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'no_preprocessing', 'classifier:gradient_boosting:early_stop': 'off', 'classifier:gradient_boosting:l2_regularization': 1e-10, 'classifier:gradient_boosting:learning_rate': 0.16262682406125173, 'classifier:gradient_boosting:loss': 'auto', 'classifier:gradient_boosting:max_bins': 255, 'classifier:gradient_boosting:max_depth': 'None', 'classifier:gradient_boosting:max_leaf_nodes': 66, 'classifier:gradient_boosting:min_samples_leaf': 20, 'classifier:gradient_boosting:scoring': 'loss', 'classifier:gradient_boosting:tol': 1e-07, 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'no_encoding', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:__choice__': 'minority_coalescer', 'data_preprocessor:feature_type:numerical_transformer:imputation:strategy': 'most_frequent', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'robust_scaler', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.005428587241449129, 'data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max': 0.75, 'data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min': 0.23746960178084334},
+ (0.120000, SimpleClassificationPipeline({'balancing:strategy': 'weighting', 'classifier:__choice__': 'random_forest', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'no_preprocessing', 'classifier:random_forest:bootstrap': 'False', 'classifier:random_forest:criterion': 'entropy', 'classifier:random_forest:max_depth': 'None', 'classifier:random_forest:max_features': 0.41808321658160696, 'classifier:random_forest:max_leaf_nodes': 'None', 'classifier:random_forest:min_impurity_decrease': 0.0, 'classifier:random_forest:min_samples_leaf': 1, 'classifier:random_forest:min_samples_split': 4, 'classifier:random_forest:min_weight_fraction_leaf': 0.0, 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'no_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__': 'standardize', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.011283688651384545},
dataset_properties={
'task': 1,
'sparse': False,
@@ -206,7 +206,7 @@ Build and fit a classifier
'multiclass': False,
'target_type': 'classification',
'signed': False})),
- (0.100000, SimpleClassificationPipeline({'balancing:strategy': 'none', 'classifier:__choice__': 'passive_aggressive', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'no_preprocessing', 'classifier:passive_aggressive:C': 0.14268277711454813, 'classifier:passive_aggressive:average': 'False', 'classifier:passive_aggressive:fit_intercept': 'True', 'classifier:passive_aggressive:loss': 'hinge', 'classifier:passive_aggressive:tol': 0.0002600768160857831, 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'no_encoding', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:__choice__': 'minority_coalescer', 'data_preprocessor:feature_type:numerical_transformer:imputation:strategy': 'most_frequent', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'none', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.0070580904199417415},
+ (0.080000, SimpleClassificationPipeline({'balancing:strategy': 'weighting', 'classifier:__choice__': 'random_forest', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'no_preprocessing', 'classifier:random_forest:bootstrap': 'False', 'classifier:random_forest:criterion': 'entropy', 'classifier:random_forest:max_depth': 'None', 'classifier:random_forest:max_features': 0.41808321658160696, 'classifier:random_forest:max_leaf_nodes': 'None', 'classifier:random_forest:min_impurity_decrease': 0.0, 'classifier:random_forest:min_samples_leaf': 1, 'classifier:random_forest:min_samples_split': 4, 'classifier:random_forest:min_weight_fraction_leaf': 0.0, 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'no_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__': 'standardize', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.011283688651384545},
dataset_properties={
'task': 1,
'sparse': False,
@@ -214,7 +214,7 @@ Build and fit a classifier
'multiclass': False,
'target_type': 'classification',
'signed': False})),
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@@ -318,7 +318,7 @@ Build and fit a classifier
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'multiclass': False,
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@@ -339,10 +339,10 @@ Build and fit a classifier
Dataset name: breast_cancer
Metric: accuracy
Best validation score: 0.985816
- Number of target algorithm runs: 21
+ Number of target algorithm runs: 20
Number of successful target algorithm runs: 20
Number of crashed target algorithm runs: 0
- Number of target algorithms that exceeded the time limit: 1
+ Number of target algorithms that exceeded the time limit: 0
Number of target algorithms that exceeded the memory limit: 0
Accuracy score 0.951048951048951
@@ -479,10 +479,10 @@ We can also use cross-validation with successive halving
Dataset name: breast_cancer
Metric: accuracy
Best validation score: 0.971831
- Number of target algorithm runs: 10
+ Number of target algorithm runs: 9
Number of successful target algorithm runs: 9
Number of crashed target algorithm runs: 0
- Number of target algorithms that exceeded the time limit: 1
+ Number of target algorithms that exceeded the time limit: 0
Number of target algorithms that exceeded the memory limit: 0
Accuracy score 0.958041958041958
@@ -542,7 +542,7 @@ Use an iterative fit cross-validation with successive halving
/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/smac/intensification/parallel_scheduling.py:152: UserWarning: SuccessiveHalving is intended to be used with more than 1 worker but num_workers=1
warnings.warn("{} is intended to be used "
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@@ -550,7 +550,7 @@ Use an iterative fit cross-validation with successive halving
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@@ -558,7 +558,7 @@ Use an iterative fit cross-validation with successive halving
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@@ -566,7 +566,7 @@ Use an iterative fit cross-validation with successive halving
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'target_type': 'classification',
'signed': False})),
- (0.060000, SimpleClassificationPipeline({'balancing:strategy': 'none', 'classifier:__choice__': 'gradient_boosting', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'no_preprocessing', 'classifier:gradient_boosting:early_stop': 'off', 'classifier:gradient_boosting:l2_regularization': 1e-10, 'classifier:gradient_boosting:learning_rate': 0.16262682406125173, 'classifier:gradient_boosting:loss': 'auto', 'classifier:gradient_boosting:max_bins': 255, 'classifier:gradient_boosting:max_depth': 'None', 'classifier:gradient_boosting:max_leaf_nodes': 66, 'classifier:gradient_boosting:min_samples_leaf': 20, 'classifier:gradient_boosting:scoring': 'loss', 'classifier:gradient_boosting:tol': 1e-07, 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'no_encoding', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:__choice__': 'minority_coalescer', 'data_preprocessor:feature_type:numerical_transformer:imputation:strategy': 'most_frequent', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'robust_scaler', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.005428587241449129, 'data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max': 0.75, 'data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min': 0.23746960178084334},
+ (0.100000, SimpleClassificationPipeline({'balancing:strategy': 'none', 'classifier:__choice__': 'gradient_boosting', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'no_preprocessing', 'classifier:gradient_boosting:early_stop': 'train', 'classifier:gradient_boosting:l2_regularization': 3.609412172481434e-10, 'classifier:gradient_boosting:learning_rate': 0.05972079854295879, 'classifier:gradient_boosting:loss': 'auto', 'classifier:gradient_boosting:max_bins': 255, 'classifier:gradient_boosting:max_depth': 'None', 'classifier:gradient_boosting:max_leaf_nodes': 4, 'classifier:gradient_boosting:min_samples_leaf': 2, 'classifier:gradient_boosting:scoring': 'loss', 'classifier:gradient_boosting:tol': 1e-07, 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'one_hot_encoding', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:__choice__': 'no_coalescense', 'data_preprocessor:feature_type:numerical_transformer:imputation:strategy': 'mean', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'minmax', 'classifier:gradient_boosting:n_iter_no_change': 14},
dataset_properties={
'task': 1,
'sparse': False,
@@ -574,7 +574,7 @@ Use an iterative fit cross-validation with successive halving
'multiclass': False,
'target_type': 'classification',
'signed': False})),
- (0.060000, SimpleClassificationPipeline({'balancing:strategy': 'none', 'classifier:__choice__': 'gradient_boosting', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'no_preprocessing', 'classifier:gradient_boosting:early_stop': 'train', 'classifier:gradient_boosting:l2_regularization': 3.609412172481434e-10, 'classifier:gradient_boosting:learning_rate': 0.05972079854295879, 'classifier:gradient_boosting:loss': 'auto', 'classifier:gradient_boosting:max_bins': 255, 'classifier:gradient_boosting:max_depth': 'None', 'classifier:gradient_boosting:max_leaf_nodes': 4, 'classifier:gradient_boosting:min_samples_leaf': 2, 'classifier:gradient_boosting:scoring': 'loss', 'classifier:gradient_boosting:tol': 1e-07, 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'one_hot_encoding', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:__choice__': 'no_coalescense', 'data_preprocessor:feature_type:numerical_transformer:imputation:strategy': 'mean', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'minmax', 'classifier:gradient_boosting:n_iter_no_change': 14},
+ (0.080000, SimpleClassificationPipeline({'balancing:strategy': 'none', 'classifier:__choice__': 'gradient_boosting', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'no_preprocessing', 'classifier:gradient_boosting:early_stop': 'off', 'classifier:gradient_boosting:l2_regularization': 1e-10, 'classifier:gradient_boosting:learning_rate': 0.16262682406125173, 'classifier:gradient_boosting:loss': 'auto', 'classifier:gradient_boosting:max_bins': 255, 'classifier:gradient_boosting:max_depth': 'None', 'classifier:gradient_boosting:max_leaf_nodes': 66, 'classifier:gradient_boosting:min_samples_leaf': 20, 'classifier:gradient_boosting:scoring': 'loss', 'classifier:gradient_boosting:tol': 1e-07, 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'no_encoding', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:__choice__': 'minority_coalescer', 'data_preprocessor:feature_type:numerical_transformer:imputation:strategy': 'most_frequent', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'robust_scaler', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.005428587241449129, 'data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max': 0.75, 'data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min': 0.23746960178084334},
dataset_properties={
'task': 1,
'sparse': False,
@@ -582,7 +582,7 @@ Use an iterative fit cross-validation with successive halving
'multiclass': False,
'target_type': 'classification',
'signed': False})),
- (0.040000, SimpleClassificationPipeline({'balancing:strategy': 'none', 'classifier:__choice__': 'passive_aggressive', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'no_preprocessing', 'classifier:passive_aggressive:C': 0.14268277711454813, 'classifier:passive_aggressive:average': 'False', 'classifier:passive_aggressive:fit_intercept': 'True', 'classifier:passive_aggressive:loss': 'hinge', 'classifier:passive_aggressive:tol': 0.0002600768160857831, 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'no_encoding', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:__choice__': 'minority_coalescer', 'data_preprocessor:feature_type:numerical_transformer:imputation:strategy': 'most_frequent', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'none', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.0070580904199417415},
+ (0.060000, SimpleClassificationPipeline({'balancing:strategy': 'none', 'classifier:__choice__': 'random_forest', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'no_preprocessing', 'classifier:random_forest:bootstrap': 'True', 'classifier:random_forest:criterion': 'entropy', 'classifier:random_forest:max_depth': 'None', 'classifier:random_forest:max_features': 0.926283631486858, 'classifier:random_forest:max_leaf_nodes': 'None', 'classifier:random_forest:min_impurity_decrease': 0.0, 'classifier:random_forest:min_samples_leaf': 7, 'classifier:random_forest:min_samples_split': 2, 'classifier:random_forest:min_weight_fraction_leaf': 0.0, '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': 'most_frequent', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'standardize', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.16265262021972576},
dataset_properties={
'task': 1,
'sparse': False,
@@ -594,14 +594,14 @@ Use an iterative fit cross-validation with successive halving
auto-sklearn results:
Dataset name: breast_cancer
Metric: accuracy
- Best validation score: 0.967136
+ Best validation score: 0.971831
Number of target algorithm runs: 6
Number of successful target algorithm runs: 6
Number of crashed target algorithm runs: 0
Number of target algorithms that exceeded the time limit: 0
Number of target algorithms that exceeded the memory limit: 0
- Accuracy score 0.972027972027972
+ Accuracy score 0.958041958041958
@@ -654,7 +654,7 @@ Next, we see the use of subsampling as a budget in Auto-sklearn
/opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages/smac/intensification/parallel_scheduling.py:152: UserWarning: SuccessiveHalving is intended to be used with more than 1 worker but num_workers=1
warnings.warn("{} is intended to be used "
- [(0.320000, SimpleClassificationPipeline({'balancing:strategy': 'weighting', 'classifier:__choice__': 'mlp', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'fast_ica', 'classifier:mlp:activation': 'relu', 'classifier:mlp:alpha': 0.0017940473175767063, 'classifier:mlp:batch_size': 'auto', 'classifier:mlp:beta_1': 0.9, 'classifier:mlp:beta_2': 0.999, 'classifier:mlp:early_stopping': 'valid', 'classifier:mlp:epsilon': 1e-08, 'classifier:mlp:hidden_layer_depth': 2, 'classifier:mlp:learning_rate_init': 0.0004684917334431039, 'classifier:mlp:n_iter_no_change': 32, 'classifier:mlp:num_nodes_per_layer': 101, 'classifier:mlp:shuffle': 'True', 'classifier:mlp:solver': 'adam', 'classifier:mlp:tol': 0.0001, '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': 'most_frequent', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'standardize', 'feature_preprocessor:fast_ica:algorithm': 'parallel', 'feature_preprocessor:fast_ica:fun': 'exp', 'feature_preprocessor:fast_ica:whiten': 'False', 'classifier:mlp:validation_fraction': 0.1},
+ [(0.280000, SimpleClassificationPipeline({'balancing:strategy': 'none', 'classifier:__choice__': 'extra_trees', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'polynomial', 'classifier:extra_trees:bootstrap': 'False', 'classifier:extra_trees:criterion': 'gini', 'classifier:extra_trees:max_depth': 'None', 'classifier:extra_trees:max_features': 0.5707983257382487, 'classifier:extra_trees:max_leaf_nodes': 'None', 'classifier:extra_trees:min_impurity_decrease': 0.0, 'classifier:extra_trees:min_samples_leaf': 3, 'classifier:extra_trees:min_samples_split': 11, 'classifier:extra_trees:min_weight_fraction_leaf': 0.0, 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'one_hot_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__': 'none', 'feature_preprocessor:polynomial:degree': 2, 'feature_preprocessor:polynomial:include_bias': 'False', 'feature_preprocessor:polynomial:interaction_only': 'False'},
dataset_properties={
'task': 1,
'sparse': False,
@@ -662,7 +662,7 @@ Next, we see the use of subsampling as a budget in Auto-sklearn
'multiclass': False,
'target_type': 'classification',
'signed': False})),
- (0.260000, SimpleClassificationPipeline({'balancing:strategy': 'none', 'classifier:__choice__': 'mlp', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'feature_agglomeration', 'classifier:mlp:activation': 'tanh', 'classifier:mlp:alpha': 0.0001363185819149026, 'classifier:mlp:batch_size': 'auto', 'classifier:mlp:beta_1': 0.9, 'classifier:mlp:beta_2': 0.999, 'classifier:mlp:early_stopping': 'valid', 'classifier:mlp:epsilon': 1e-08, 'classifier:mlp:hidden_layer_depth': 3, 'classifier:mlp:learning_rate_init': 0.00018009776276177523, 'classifier:mlp:n_iter_no_change': 32, 'classifier:mlp:num_nodes_per_layer': 115, 'classifier:mlp:shuffle': 'True', 'classifier:mlp:solver': 'adam', 'classifier:mlp:tol': 0.0001, '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__': 'standardize', 'feature_preprocessor:feature_agglomeration:affinity': 'euclidean', 'feature_preprocessor:feature_agglomeration:linkage': 'ward', 'feature_preprocessor:feature_agglomeration:n_clusters': 182, 'feature_preprocessor:feature_agglomeration:pooling_func': 'mean', 'classifier:mlp:validation_fraction': 0.1},
+ (0.240000, SimpleClassificationPipeline({'balancing:strategy': 'none', 'classifier:__choice__': 'mlp', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'feature_agglomeration', 'classifier:mlp:activation': 'tanh', 'classifier:mlp:alpha': 0.0001363185819149026, 'classifier:mlp:batch_size': 'auto', 'classifier:mlp:beta_1': 0.9, 'classifier:mlp:beta_2': 0.999, 'classifier:mlp:early_stopping': 'valid', 'classifier:mlp:epsilon': 1e-08, 'classifier:mlp:hidden_layer_depth': 3, 'classifier:mlp:learning_rate_init': 0.00018009776276177523, 'classifier:mlp:n_iter_no_change': 32, 'classifier:mlp:num_nodes_per_layer': 115, 'classifier:mlp:shuffle': 'True', 'classifier:mlp:solver': 'adam', 'classifier:mlp:tol': 0.0001, '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__': 'standardize', 'feature_preprocessor:feature_agglomeration:affinity': 'euclidean', 'feature_preprocessor:feature_agglomeration:linkage': 'ward', 'feature_preprocessor:feature_agglomeration:n_clusters': 182, 'feature_preprocessor:feature_agglomeration:pooling_func': 'mean', 'classifier:mlp:validation_fraction': 0.1},
dataset_properties={
'task': 1,
'sparse': False,
@@ -670,7 +670,7 @@ Next, we see the use of subsampling as a budget in Auto-sklearn
'multiclass': False,
'target_type': 'classification',
'signed': False})),
- (0.140000, SimpleClassificationPipeline({'balancing:strategy': 'weighting', 'classifier:__choice__': 'random_forest', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'extra_trees_preproc_for_classification', 'classifier:random_forest:bootstrap': 'True', 'classifier:random_forest:criterion': 'gini', 'classifier:random_forest:max_depth': 'None', 'classifier:random_forest:max_features': 0.43999367631975456, 'classifier:random_forest:max_leaf_nodes': 'None', 'classifier:random_forest:min_impurity_decrease': 0.0, 'classifier:random_forest:min_samples_leaf': 2, 'classifier:random_forest:min_samples_split': 2, 'classifier:random_forest:min_weight_fraction_leaf': 0.0, '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': 'most_frequent', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'standardize', 'feature_preprocessor:extra_trees_preproc_for_classification:bootstrap': 'True', 'feature_preprocessor:extra_trees_preproc_for_classification:criterion': 'entropy', 'feature_preprocessor:extra_trees_preproc_for_classification:max_depth': 'None', 'feature_preprocessor:extra_trees_preproc_for_classification:max_features': 0.8134515743047006, 'feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes': 'None', 'feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease': 0.0, 'feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf': 9, 'feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split': 20, 'feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf': 0.0, 'feature_preprocessor:extra_trees_preproc_for_classification:n_estimators': 100},
+ (0.180000, SimpleClassificationPipeline({'balancing:strategy': 'weighting', 'classifier:__choice__': 'mlp', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'fast_ica', 'classifier:mlp:activation': 'relu', 'classifier:mlp:alpha': 0.0017940473175767063, 'classifier:mlp:batch_size': 'auto', 'classifier:mlp:beta_1': 0.9, 'classifier:mlp:beta_2': 0.999, 'classifier:mlp:early_stopping': 'valid', 'classifier:mlp:epsilon': 1e-08, 'classifier:mlp:hidden_layer_depth': 2, 'classifier:mlp:learning_rate_init': 0.0004684917334431039, 'classifier:mlp:n_iter_no_change': 32, 'classifier:mlp:num_nodes_per_layer': 101, 'classifier:mlp:shuffle': 'True', 'classifier:mlp:solver': 'adam', 'classifier:mlp:tol': 0.0001, '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': 'most_frequent', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'standardize', 'feature_preprocessor:fast_ica:algorithm': 'parallel', 'feature_preprocessor:fast_ica:fun': 'exp', 'feature_preprocessor:fast_ica:whiten': 'False', 'classifier:mlp:validation_fraction': 0.1},
dataset_properties={
'task': 1,
'sparse': False,
@@ -678,7 +678,7 @@ Next, we see the use of subsampling as a budget in Auto-sklearn
'multiclass': False,
'target_type': 'classification',
'signed': False})),
- (0.080000, SimpleClassificationPipeline({'balancing:strategy': 'weighting', 'classifier:__choice__': 'random_forest', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'extra_trees_preproc_for_classification', 'classifier:random_forest:bootstrap': 'True', 'classifier:random_forest:criterion': 'gini', 'classifier:random_forest:max_depth': 'None', 'classifier:random_forest:max_features': 0.43999367631975456, 'classifier:random_forest:max_leaf_nodes': 'None', 'classifier:random_forest:min_impurity_decrease': 0.0, 'classifier:random_forest:min_samples_leaf': 2, 'classifier:random_forest:min_samples_split': 2, 'classifier:random_forest:min_weight_fraction_leaf': 0.0, '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': 'most_frequent', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'standardize', 'feature_preprocessor:extra_trees_preproc_for_classification:bootstrap': 'True', 'feature_preprocessor:extra_trees_preproc_for_classification:criterion': 'entropy', 'feature_preprocessor:extra_trees_preproc_for_classification:max_depth': 'None', 'feature_preprocessor:extra_trees_preproc_for_classification:max_features': 0.8134515743047006, 'feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes': 'None', 'feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease': 0.0, 'feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf': 9, 'feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split': 20, 'feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf': 0.0, 'feature_preprocessor:extra_trees_preproc_for_classification:n_estimators': 100},
+ (0.100000, SimpleClassificationPipeline({'balancing:strategy': 'none', 'classifier:__choice__': 'extra_trees', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'polynomial', 'classifier:extra_trees:bootstrap': 'False', 'classifier:extra_trees:criterion': 'gini', 'classifier:extra_trees:max_depth': 'None', 'classifier:extra_trees:max_features': 0.5707983257382487, 'classifier:extra_trees:max_leaf_nodes': 'None', 'classifier:extra_trees:min_impurity_decrease': 0.0, 'classifier:extra_trees:min_samples_leaf': 3, 'classifier:extra_trees:min_samples_split': 11, 'classifier:extra_trees:min_weight_fraction_leaf': 0.0, 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'one_hot_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__': 'none', 'feature_preprocessor:polynomial:degree': 2, 'feature_preprocessor:polynomial:include_bias': 'False', 'feature_preprocessor:polynomial:interaction_only': 'False'},
dataset_properties={
'task': 1,
'sparse': False,
@@ -686,7 +686,7 @@ Next, we see the use of subsampling as a budget in Auto-sklearn
'multiclass': False,
'target_type': 'classification',
'signed': False})),
- (0.060000, SimpleClassificationPipeline({'balancing:strategy': 'weighting', 'classifier:__choice__': 'random_forest', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'extra_trees_preproc_for_classification', 'classifier:random_forest:bootstrap': 'True', 'classifier:random_forest:criterion': 'entropy', 'classifier:random_forest:max_depth': 'None', 'classifier:random_forest:max_features': 0.4775492074518431, 'classifier:random_forest:max_leaf_nodes': 'None', 'classifier:random_forest:min_impurity_decrease': 0.0, 'classifier:random_forest:min_samples_leaf': 2, 'classifier:random_forest:min_samples_split': 2, 'classifier:random_forest:min_weight_fraction_leaf': 0.0, 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'no_encoding', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:__choice__': 'minority_coalescer', 'data_preprocessor:feature_type:numerical_transformer:imputation:strategy': 'most_frequent', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'robust_scaler', 'feature_preprocessor:extra_trees_preproc_for_classification:bootstrap': 'False', 'feature_preprocessor:extra_trees_preproc_for_classification:criterion': 'entropy', 'feature_preprocessor:extra_trees_preproc_for_classification:max_depth': 'None', 'feature_preprocessor:extra_trees_preproc_for_classification:max_features': 0.5662900693317384, 'feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes': 'None', 'feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease': 0.0, 'feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf': 1, 'feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split': 7, 'feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf': 0.0, 'feature_preprocessor:extra_trees_preproc_for_classification:n_estimators': 100, 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.015996368052062886, 'data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max': 0.7845396961078424, 'data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min': 0.25545052141264185},
+ (0.040000, SimpleClassificationPipeline({'balancing:strategy': 'none', 'classifier:__choice__': 'extra_trees', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'polynomial', 'classifier:extra_trees:bootstrap': 'False', 'classifier:extra_trees:criterion': 'gini', 'classifier:extra_trees:max_depth': 'None', 'classifier:extra_trees:max_features': 0.5707983257382487, 'classifier:extra_trees:max_leaf_nodes': 'None', 'classifier:extra_trees:min_impurity_decrease': 0.0, 'classifier:extra_trees:min_samples_leaf': 3, 'classifier:extra_trees:min_samples_split': 11, 'classifier:extra_trees:min_weight_fraction_leaf': 0.0, 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'one_hot_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__': 'none', 'feature_preprocessor:polynomial:degree': 2, 'feature_preprocessor:polynomial:include_bias': 'False', 'feature_preprocessor:polynomial:interaction_only': 'False'},
dataset_properties={
'task': 1,
'sparse': False,
@@ -694,7 +694,7 @@ Next, we see the use of subsampling as a budget in Auto-sklearn
'multiclass': False,
'target_type': 'classification',
'signed': False})),
- (0.040000, SimpleClassificationPipeline({'balancing:strategy': 'none', 'classifier:__choice__': 'random_forest', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'no_preprocessing', 'classifier:random_forest:bootstrap': 'True', 'classifier:random_forest:criterion': 'gini', 'classifier:random_forest:max_depth': 'None', 'classifier:random_forest:max_features': 0.5, 'classifier:random_forest:max_leaf_nodes': 'None', 'classifier:random_forest:min_impurity_decrease': 0.0, 'classifier:random_forest:min_samples_leaf': 1, 'classifier:random_forest:min_samples_split': 2, 'classifier:random_forest:min_weight_fraction_leaf': 0.0, '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': 'mean', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'standardize', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.01},
+ (0.040000, SimpleClassificationPipeline({'balancing:strategy': 'none', 'classifier:__choice__': 'extra_trees', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'polynomial', 'classifier:extra_trees:bootstrap': 'False', 'classifier:extra_trees:criterion': 'gini', 'classifier:extra_trees:max_depth': 'None', 'classifier:extra_trees:max_features': 0.5707983257382487, 'classifier:extra_trees:max_leaf_nodes': 'None', 'classifier:extra_trees:min_impurity_decrease': 0.0, 'classifier:extra_trees:min_samples_leaf': 3, 'classifier:extra_trees:min_samples_split': 11, 'classifier:extra_trees:min_weight_fraction_leaf': 0.0, 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'one_hot_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__': 'none', 'feature_preprocessor:polynomial:degree': 2, 'feature_preprocessor:polynomial:include_bias': 'False', 'feature_preprocessor:polynomial:interaction_only': 'False'},
dataset_properties={
'task': 1,
'sparse': False,
@@ -702,7 +702,7 @@ Next, we see the use of subsampling as a budget in Auto-sklearn
'multiclass': False,
'target_type': 'classification',
'signed': False})),
- (0.040000, SimpleClassificationPipeline({'balancing:strategy': 'weighting', 'classifier:__choice__': 'random_forest', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'extra_trees_preproc_for_classification', 'classifier:random_forest:bootstrap': 'True', 'classifier:random_forest:criterion': 'gini', 'classifier:random_forest:max_depth': 'None', 'classifier:random_forest:max_features': 0.43999367631975456, 'classifier:random_forest:max_leaf_nodes': 'None', 'classifier:random_forest:min_impurity_decrease': 0.0, 'classifier:random_forest:min_samples_leaf': 2, 'classifier:random_forest:min_samples_split': 2, 'classifier:random_forest:min_weight_fraction_leaf': 0.0, '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': 'most_frequent', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'standardize', 'feature_preprocessor:extra_trees_preproc_for_classification:bootstrap': 'True', 'feature_preprocessor:extra_trees_preproc_for_classification:criterion': 'entropy', 'feature_preprocessor:extra_trees_preproc_for_classification:max_depth': 'None', 'feature_preprocessor:extra_trees_preproc_for_classification:max_features': 0.8134515743047006, 'feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes': 'None', 'feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease': 0.0, 'feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf': 9, 'feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split': 20, 'feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf': 0.0, 'feature_preprocessor:extra_trees_preproc_for_classification:n_estimators': 100},
+ (0.040000, SimpleClassificationPipeline({'balancing:strategy': 'none', 'classifier:__choice__': 'random_forest', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'no_preprocessing', 'classifier:random_forest:bootstrap': 'True', 'classifier:random_forest:criterion': 'gini', 'classifier:random_forest:max_depth': 'None', 'classifier:random_forest:max_features': 0.5, 'classifier:random_forest:max_leaf_nodes': 'None', 'classifier:random_forest:min_impurity_decrease': 0.0, 'classifier:random_forest:min_samples_leaf': 1, 'classifier:random_forest:min_samples_split': 2, 'classifier:random_forest:min_weight_fraction_leaf': 0.0, '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': 'mean', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'standardize', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.01},
dataset_properties={
'task': 1,
'sparse': False,
@@ -710,7 +710,7 @@ Next, we see the use of subsampling as a budget in Auto-sklearn
'multiclass': False,
'target_type': 'classification',
'signed': False})),
- (0.020000, SimpleClassificationPipeline({'balancing:strategy': 'weighting', 'classifier:__choice__': 'mlp', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'feature_agglomeration', 'classifier:mlp:activation': 'tanh', 'classifier:mlp:alpha': 0.00021148999718383549, 'classifier:mlp:batch_size': 'auto', 'classifier:mlp:beta_1': 0.9, 'classifier:mlp:beta_2': 0.999, 'classifier:mlp:early_stopping': 'train', 'classifier:mlp:epsilon': 1e-08, 'classifier:mlp:hidden_layer_depth': 3, 'classifier:mlp:learning_rate_init': 0.0007452270241186694, 'classifier:mlp:n_iter_no_change': 32, 'classifier:mlp:num_nodes_per_layer': 113, 'classifier:mlp:shuffle': 'True', 'classifier:mlp:solver': 'adam', 'classifier:mlp:tol': 0.0001, 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'one_hot_encoding', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:__choice__': 'no_coalescense', 'data_preprocessor:feature_type:numerical_transformer:imputation:strategy': 'most_frequent', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'standardize', 'feature_preprocessor:feature_agglomeration:affinity': 'euclidean', 'feature_preprocessor:feature_agglomeration:linkage': 'complete', 'feature_preprocessor:feature_agglomeration:n_clusters': 247, 'feature_preprocessor:feature_agglomeration:pooling_func': 'max'},
+ (0.040000, SimpleClassificationPipeline({'balancing:strategy': 'weighting', 'classifier:__choice__': 'random_forest', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'extra_trees_preproc_for_classification', 'classifier:random_forest:bootstrap': 'True', 'classifier:random_forest:criterion': 'gini', 'classifier:random_forest:max_depth': 'None', 'classifier:random_forest:max_features': 0.43999367631975456, 'classifier:random_forest:max_leaf_nodes': 'None', 'classifier:random_forest:min_impurity_decrease': 0.0, 'classifier:random_forest:min_samples_leaf': 2, 'classifier:random_forest:min_samples_split': 2, 'classifier:random_forest:min_weight_fraction_leaf': 0.0, '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': 'most_frequent', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'standardize', 'feature_preprocessor:extra_trees_preproc_for_classification:bootstrap': 'True', 'feature_preprocessor:extra_trees_preproc_for_classification:criterion': 'entropy', 'feature_preprocessor:extra_trees_preproc_for_classification:max_depth': 'None', 'feature_preprocessor:extra_trees_preproc_for_classification:max_features': 0.8134515743047006, 'feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes': 'None', 'feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease': 0.0, 'feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf': 9, 'feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split': 20, 'feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf': 0.0, 'feature_preprocessor:extra_trees_preproc_for_classification:n_estimators': 100},
dataset_properties={
'task': 1,
'sparse': False,
@@ -718,7 +718,7 @@ Next, we see the use of subsampling as a budget in Auto-sklearn
'multiclass': False,
'target_type': 'classification',
'signed': False})),
- (0.020000, SimpleClassificationPipeline({'balancing:strategy': 'none', 'classifier:__choice__': 'extra_trees', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'polynomial', 'classifier:extra_trees:bootstrap': 'False', 'classifier:extra_trees:criterion': 'gini', 'classifier:extra_trees:max_depth': 'None', 'classifier:extra_trees:max_features': 0.5707983257382487, 'classifier:extra_trees:max_leaf_nodes': 'None', 'classifier:extra_trees:min_impurity_decrease': 0.0, 'classifier:extra_trees:min_samples_leaf': 3, 'classifier:extra_trees:min_samples_split': 11, 'classifier:extra_trees:min_weight_fraction_leaf': 0.0, 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'one_hot_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__': 'none', 'feature_preprocessor:polynomial:degree': 2, 'feature_preprocessor:polynomial:include_bias': 'False', 'feature_preprocessor:polynomial:interaction_only': 'False'},
+ (0.020000, SimpleClassificationPipeline({'balancing:strategy': 'weighting', 'classifier:__choice__': 'random_forest', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'extra_trees_preproc_for_classification', 'classifier:random_forest:bootstrap': 'True', 'classifier:random_forest:criterion': 'entropy', 'classifier:random_forest:max_depth': 'None', 'classifier:random_forest:max_features': 0.4775492074518431, 'classifier:random_forest:max_leaf_nodes': 'None', 'classifier:random_forest:min_impurity_decrease': 0.0, 'classifier:random_forest:min_samples_leaf': 2, 'classifier:random_forest:min_samples_split': 2, 'classifier:random_forest:min_weight_fraction_leaf': 0.0, 'data_preprocessor:feature_type:categorical_transformer:categorical_encoding:__choice__': 'no_encoding', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:__choice__': 'minority_coalescer', 'data_preprocessor:feature_type:numerical_transformer:imputation:strategy': 'most_frequent', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'robust_scaler', 'feature_preprocessor:extra_trees_preproc_for_classification:bootstrap': 'False', 'feature_preprocessor:extra_trees_preproc_for_classification:criterion': 'entropy', 'feature_preprocessor:extra_trees_preproc_for_classification:max_depth': 'None', 'feature_preprocessor:extra_trees_preproc_for_classification:max_features': 0.5662900693317384, 'feature_preprocessor:extra_trees_preproc_for_classification:max_leaf_nodes': 'None', 'feature_preprocessor:extra_trees_preproc_for_classification:min_impurity_decrease': 0.0, 'feature_preprocessor:extra_trees_preproc_for_classification:min_samples_leaf': 1, 'feature_preprocessor:extra_trees_preproc_for_classification:min_samples_split': 7, 'feature_preprocessor:extra_trees_preproc_for_classification:min_weight_fraction_leaf': 0.0, 'feature_preprocessor:extra_trees_preproc_for_classification:n_estimators': 100, 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.015996368052062886, 'data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max': 0.7845396961078424, 'data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min': 0.25545052141264185},
dataset_properties={
'task': 1,
'sparse': False,
@@ -738,14 +738,14 @@ Next, we see the use of subsampling as a budget in Auto-sklearn
auto-sklearn results:
Dataset name: breast_cancer
Metric: accuracy
- Best validation score: 0.978723
+ Best validation score: 0.985816
Number of target algorithm runs: 12
Number of successful target algorithm runs: 11
Number of crashed target algorithm runs: 0
Number of target algorithms that exceeded the time limit: 1
Number of target algorithms that exceeded the memory limit: 0
- Accuracy score 0.951048951048951
+ Accuracy score 0.958041958041958
@@ -911,7 +911,7 @@ subsamples otherwise
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 3 minutes 11.615 seconds)
+ **Total running time of the script:** ( 2 minutes 59.360 seconds)
.. _sphx_glr_download_examples_60_search_example_successive_halving.py:
diff --git a/development/_sources/examples/60_search/sg_execution_times.rst.txt b/development/_sources/examples/60_search/sg_execution_times.rst.txt
index afcc7c9559..f630ea1801 100644
--- a/development/_sources/examples/60_search/sg_execution_times.rst.txt
+++ b/development/_sources/examples/60_search/sg_execution_times.rst.txt
@@ -5,18 +5,18 @@
Computation times
=================
-**10:20.722** total execution time for **examples_60_search** files:
+**09:58.672** total execution time for **examples_60_search** files:
+--------------------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_60_search_example_successive_halving.py` (``example_successive_halving.py``) | 03:11.615 | 0.0 MB |
+| :ref:`sphx_glr_examples_60_search_example_successive_halving.py` (``example_successive_halving.py``) | 02:59.360 | 0.0 MB |
+--------------------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_60_search_example_parallel_n_jobs.py` (``example_parallel_n_jobs.py``) | 02:02.135 | 0.0 MB |
+| :ref:`sphx_glr_examples_60_search_example_sequential.py` (``example_sequential.py``) | 01:57.887 | 0.0 MB |
+--------------------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_60_search_example_sequential.py` (``example_sequential.py``) | 01:57.396 | 0.0 MB |
+| :ref:`sphx_glr_examples_60_search_example_parallel_n_jobs.py` (``example_parallel_n_jobs.py``) | 01:57.626 | 0.0 MB |
+--------------------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_60_search_example_random_search.py` (``example_random_search.py``) | 01:54.709 | 0.0 MB |
+| :ref:`sphx_glr_examples_60_search_example_random_search.py` (``example_random_search.py``) | 01:49.377 | 0.0 MB |
+--------------------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_60_search_example_parallel_manual_spawning_cli.py` (``example_parallel_manual_spawning_cli.py``) | 00:38.787 | 0.0 MB |
+| :ref:`sphx_glr_examples_60_search_example_parallel_manual_spawning_cli.py` (``example_parallel_manual_spawning_cli.py``) | 00:38.352 | 0.0 MB |
+--------------------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_60_search_example_parallel_manual_spawning_python.py` (``example_parallel_manual_spawning_python.py``) | 00:36.081 | 0.0 MB |
+| :ref:`sphx_glr_examples_60_search_example_parallel_manual_spawning_python.py` (``example_parallel_manual_spawning_python.py``) | 00:36.069 | 0.0 MB |
+--------------------------------------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/development/_sources/examples/80_extending/example_extending_classification.rst.txt b/development/_sources/examples/80_extending/example_extending_classification.rst.txt
index 2d3b11a2c0..258a52db51 100644
--- a/development/_sources/examples/80_extending/example_extending_classification.rst.txt
+++ b/development/_sources/examples/80_extending/example_extending_classification.rst.txt
@@ -262,8 +262,8 @@ Print test accuracy and statistics
.. code-block:: none
- accuracy: 0.9790209790209791
- [(0.900000, SimpleClassificationPipeline({'balancing:strategy': 'none', 'classifier:__choice__': 'MLPClassifier', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'no_preprocessing', 'classifier:MLPClassifier:activation': 'relu', 'classifier:MLPClassifier:alpha': 0.0001, 'classifier:MLPClassifier:hidden_layer_depth': 1, 'classifier:MLPClassifier:num_nodes_per_layer': 32, 'classifier:MLPClassifier:solver': 'adam', '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': 'mean', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'standardize', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.01},
+ accuracy: 0.993006993006993
+ [(0.740000, SimpleClassificationPipeline({'balancing:strategy': 'none', 'classifier:__choice__': 'MLPClassifier', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'no_preprocessing', 'classifier:MLPClassifier:activation': 'relu', 'classifier:MLPClassifier:alpha': 0.0001, 'classifier:MLPClassifier:hidden_layer_depth': 1, 'classifier:MLPClassifier:num_nodes_per_layer': 32, 'classifier:MLPClassifier:solver': 'adam', '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': 'mean', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'standardize', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.01},
dataset_properties={
'task': 1,
'sparse': False,
@@ -271,7 +271,7 @@ Print test accuracy and statistics
'multiclass': False,
'target_type': 'classification',
'signed': False})),
- (0.100000, SimpleClassificationPipeline({'balancing:strategy': 'weighting', 'classifier:__choice__': 'MLPClassifier', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'select_rates_classification', 'classifier:MLPClassifier:activation': 'relu', 'classifier:MLPClassifier:alpha': 0.054648061621061846, 'classifier:MLPClassifier:hidden_layer_depth': 1, 'classifier:MLPClassifier:num_nodes_per_layer': 187, 'classifier:MLPClassifier:solver': 'adam', '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': 'mean', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'robust_scaler', 'feature_preprocessor:select_rates_classification:alpha': 0.17677801820619737, 'feature_preprocessor:select_rates_classification:score_func': 'mutual_info_classif', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.06846183238648647, 'data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max': 0.8465036896999549, 'data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min': 0.14555017892884228},
+ (0.260000, SimpleClassificationPipeline({'balancing:strategy': 'weighting', 'classifier:__choice__': 'MLPClassifier', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'select_rates_classification', 'classifier:MLPClassifier:activation': 'relu', 'classifier:MLPClassifier:alpha': 0.054648061621061846, 'classifier:MLPClassifier:hidden_layer_depth': 1, 'classifier:MLPClassifier:num_nodes_per_layer': 187, 'classifier:MLPClassifier:solver': 'adam', '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': 'mean', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'robust_scaler', 'feature_preprocessor:select_rates_classification:alpha': 0.17677801820619737, 'feature_preprocessor:select_rates_classification:score_func': 'mutual_info_classif', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.06846183238648647, 'data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_max': 0.8465036896999549, 'data_preprocessor:feature_type:numerical_transformer:rescaling:robust_scaler:q_min': 0.14555017892884228},
dataset_properties={
'task': 1,
'sparse': False,
@@ -287,7 +287,7 @@ Print test accuracy and statistics
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 15.914 seconds)
+ **Total running time of the script:** ( 0 minutes 16.128 seconds)
.. _sphx_glr_download_examples_80_extending_example_extending_classification.py:
diff --git a/development/_sources/examples/80_extending/example_extending_data_preprocessor.rst.txt b/development/_sources/examples/80_extending/example_extending_data_preprocessor.rst.txt
index 33efc56d64..2d15cd1be3 100644
--- a/development/_sources/examples/80_extending/example_extending_data_preprocessor.rst.txt
+++ b/development/_sources/examples/80_extending/example_extending_data_preprocessor.rst.txt
@@ -156,9 +156,9 @@ Fit the model without performing data preprocessing
.. code-block:: none
auto-sklearn results:
- Dataset name: 4c8da221-4eb6-11ec-86be-1de9961ebe90
+ Dataset name: d31cfe3e-5285-11ec-867a-b7e09c96a597
Metric: accuracy
- Best validation score: 0.957447
+ Best validation score: 0.936170
Number of target algorithm runs: 5
Number of successful target algorithm runs: 5
Number of crashed target algorithm runs: 0
@@ -209,15 +209,7 @@ Print prediction score and statistics
'multiclass': False,
'target_type': 'classification',
'signed': False})),
- (0.020000, SimpleClassificationPipeline({'balancing:strategy': 'none', 'classifier:__choice__': 'lda', 'data_preprocessor:__choice__': 'NoPreprocessing', 'feature_preprocessor:__choice__': 'select_rates_classification', 'classifier:lda:shrinkage': 'auto', 'classifier:lda:tol': 0.00010495766342828794, 'feature_preprocessor:select_rates_classification:alpha': 0.4886183647550176, 'feature_preprocessor:select_rates_classification:score_func': 'mutual_info_classif'},
- dataset_properties={
- 'task': 1,
- 'sparse': False,
- 'multilabel': False,
- 'multiclass': False,
- 'target_type': 'classification',
- 'signed': False})),
- (0.020000, SimpleClassificationPipeline({'balancing:strategy': 'none', 'classifier:__choice__': 'libsvm_svc', 'data_preprocessor:__choice__': 'NoPreprocessing', 'feature_preprocessor:__choice__': 'polynomial', 'classifier:libsvm_svc:C': 5.6217653025966605, 'classifier:libsvm_svc:gamma': 0.00016239358614064282, 'classifier:libsvm_svc:kernel': 'poly', 'classifier:libsvm_svc:max_iter': -1, 'classifier:libsvm_svc:shrinking': 'True', 'classifier:libsvm_svc:tol': 0.0001346237047084913, 'feature_preprocessor:polynomial:degree': 2, 'feature_preprocessor:polynomial:include_bias': 'True', 'feature_preprocessor:polynomial:interaction_only': 'False', 'classifier:libsvm_svc:coef0': -0.011192631375838147, 'classifier:libsvm_svc:degree': 5},
+ (0.040000, SimpleClassificationPipeline({'balancing:strategy': 'none', 'classifier:__choice__': 'adaboost', 'data_preprocessor:__choice__': 'NoPreprocessing', 'feature_preprocessor:__choice__': 'liblinear_svc_preprocessor', 'classifier:adaboost:algorithm': 'SAMME.R', 'classifier:adaboost:learning_rate': 1.077525751047041, 'classifier:adaboost:max_depth': 9, 'classifier:adaboost:n_estimators': 208, 'feature_preprocessor:liblinear_svc_preprocessor:C': 1202.4376014010597, 'feature_preprocessor:liblinear_svc_preprocessor:dual': 'False', 'feature_preprocessor:liblinear_svc_preprocessor:fit_intercept': 'True', 'feature_preprocessor:liblinear_svc_preprocessor:intercept_scaling': 1, 'feature_preprocessor:liblinear_svc_preprocessor:loss': 'squared_hinge', 'feature_preprocessor:liblinear_svc_preprocessor:multi_class': 'ovr', 'feature_preprocessor:liblinear_svc_preprocessor:penalty': 'l1', 'feature_preprocessor:liblinear_svc_preprocessor:tol': 8.931650858806469e-05},
dataset_properties={
'task': 1,
'sparse': False,
@@ -233,7 +225,7 @@ Print prediction score and statistics
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 21.197 seconds)
+ **Total running time of the script:** ( 0 minutes 21.556 seconds)
.. _sphx_glr_download_examples_80_extending_example_extending_data_preprocessor.py:
diff --git a/development/_sources/examples/80_extending/example_extending_preprocessor.rst.txt b/development/_sources/examples/80_extending/example_extending_preprocessor.rst.txt
index 847c725f48..8618f85c83 100644
--- a/development/_sources/examples/80_extending/example_extending_preprocessor.rst.txt
+++ b/development/_sources/examples/80_extending/example_extending_preprocessor.rst.txt
@@ -256,7 +256,7 @@ Print prediction score and statistics
.. code-block:: none
- accuracy: 0.951048951048951
+ accuracy: 0.972027972027972
[(1.000000, SimpleClassificationPipeline({'balancing:strategy': 'none', 'classifier:__choice__': 'random_forest', 'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'LDA', 'classifier:random_forest:bootstrap': 'True', 'classifier:random_forest:criterion': 'gini', 'classifier:random_forest:max_depth': 'None', 'classifier:random_forest:max_features': 0.5, 'classifier:random_forest:max_leaf_nodes': 'None', 'classifier:random_forest:min_impurity_decrease': 0.0, 'classifier:random_forest:min_samples_leaf': 1, 'classifier:random_forest:min_samples_split': 2, 'classifier:random_forest:min_weight_fraction_leaf': 0.0, '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': 'mean', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'standardize', 'feature_preprocessor:LDA:solver': 'svd', 'feature_preprocessor:LDA:tol': 0.0001, 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.01},
dataset_properties={
'task': 1,
@@ -273,7 +273,7 @@ Print prediction score and statistics
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 20.297 seconds)
+ **Total running time of the script:** ( 0 minutes 20.630 seconds)
.. _sphx_glr_download_examples_80_extending_example_extending_preprocessor.py:
diff --git a/development/_sources/examples/80_extending/example_extending_regression.rst.txt b/development/_sources/examples/80_extending/example_extending_regression.rst.txt
index c2717da926..1893230519 100644
--- a/development/_sources/examples/80_extending/example_extending_regression.rst.txt
+++ b/development/_sources/examples/80_extending/example_extending_regression.rst.txt
@@ -253,7 +253,7 @@ Print prediction score and statistics
.. code-block:: none
- r2 score: 0.2114377367426139
+ r2 score: 0.1004827505593453
[(1.000000, SimpleRegressionPipeline({'data_preprocessor:__choice__': 'feature_type', 'feature_preprocessor:__choice__': 'no_preprocessing', 'regressor:__choice__': 'KernelRidgeRegression', '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': 'mean', 'data_preprocessor:feature_type:numerical_transformer:rescaling:__choice__': 'standardize', 'regressor:KernelRidgeRegression:alpha': 1.0, 'regressor:KernelRidgeRegression:gamma': 0.1, 'regressor:KernelRidgeRegression:kernel': 'polynomial', 'data_preprocessor:feature_type:categorical_transformer:category_coalescence:minority_coalescer:minimum_fraction': 0.01, 'regressor:KernelRidgeRegression:coef0': 1.0, 'regressor:KernelRidgeRegression:degree': 3},
dataset_properties={
'task': 4,
@@ -269,7 +269,7 @@ Print prediction score and statistics
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 13.179 seconds)
+ **Total running time of the script:** ( 0 minutes 13.992 seconds)
.. _sphx_glr_download_examples_80_extending_example_extending_regression.py:
diff --git a/development/_sources/examples/80_extending/example_restrict_number_of_hyperparameters.rst.txt b/development/_sources/examples/80_extending/example_restrict_number_of_hyperparameters.rst.txt
index bb384c8bb0..2287e343ef 100644
--- a/development/_sources/examples/80_extending/example_restrict_number_of_hyperparameters.rst.txt
+++ b/development/_sources/examples/80_extending/example_restrict_number_of_hyperparameters.rst.txt
@@ -628,7 +628,7 @@ Print the configuration space
.. rst-class:: sphx-glr-timing
- **Total running time of the script:** ( 0 minutes 7.659 seconds)
+ **Total running time of the script:** ( 0 minutes 7.413 seconds)
.. _sphx_glr_download_examples_80_extending_example_restrict_number_of_hyperparameters.py:
diff --git a/development/_sources/examples/80_extending/sg_execution_times.rst.txt b/development/_sources/examples/80_extending/sg_execution_times.rst.txt
index e818b56e1a..f7c963003b 100644
--- a/development/_sources/examples/80_extending/sg_execution_times.rst.txt
+++ b/development/_sources/examples/80_extending/sg_execution_times.rst.txt
@@ -5,16 +5,16 @@
Computation times
=================
-**01:18.245** total execution time for **examples_80_extending** files:
+**01:19.719** total execution time for **examples_80_extending** files:
+-----------------------------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_80_extending_example_extending_data_preprocessor.py` (``example_extending_data_preprocessor.py``) | 00:21.197 | 0.0 MB |
+| :ref:`sphx_glr_examples_80_extending_example_extending_data_preprocessor.py` (``example_extending_data_preprocessor.py``) | 00:21.556 | 0.0 MB |
+-----------------------------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_80_extending_example_extending_preprocessor.py` (``example_extending_preprocessor.py``) | 00:20.297 | 0.0 MB |
+| :ref:`sphx_glr_examples_80_extending_example_extending_preprocessor.py` (``example_extending_preprocessor.py``) | 00:20.630 | 0.0 MB |
+-----------------------------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_80_extending_example_extending_classification.py` (``example_extending_classification.py``) | 00:15.914 | 0.0 MB |
+| :ref:`sphx_glr_examples_80_extending_example_extending_classification.py` (``example_extending_classification.py``) | 00:16.128 | 0.0 MB |
+-----------------------------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_80_extending_example_extending_regression.py` (``example_extending_regression.py``) | 00:13.179 | 0.0 MB |
+| :ref:`sphx_glr_examples_80_extending_example_extending_regression.py` (``example_extending_regression.py``) | 00:13.992 | 0.0 MB |
+-----------------------------------------------------------------------------------------------------------------------------------------+-----------+--------+
-| :ref:`sphx_glr_examples_80_extending_example_restrict_number_of_hyperparameters.py` (``example_restrict_number_of_hyperparameters.py``) | 00:07.659 | 0.0 MB |
+| :ref:`sphx_glr_examples_80_extending_example_restrict_number_of_hyperparameters.py` (``example_restrict_number_of_hyperparameters.py``) | 00:07.413 | 0.0 MB |
+-----------------------------------------------------------------------------------------------------------------------------------------+-----------+--------+
diff --git a/development/examples/20_basic/example_classification.html b/development/examples/20_basic/example_classification.html
index c245299351..5bbe9935f8 100644
--- a/development/examples/20_basic/example_classification.html
+++ b/development/examples/20_basic/example_classification.html
@@ -166,25 +166,25 @@ View the models found by auto-sklearnOut:
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
@@ -360,7 +360,7 @@ Get the Score of the final ensembleAccuracy score: 0.951048951048951
-Total running time of the script: ( 2 minutes 4.034 seconds)
+Total running time of the script: ( 1 minutes 59.584 seconds)
diff --git a/development/examples/20_basic/example_multilabel_classification.html b/development/examples/20_basic/example_multilabel_classification.html
index f7803a198d..c50bb17aa5 100644
--- a/development/examples/20_basic/example_multilabel_classification.html
+++ b/development/examples/20_basic/example_multilabel_classification.html
@@ -151,7 +151,7 @@ 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
@@ -163,10 +163,7 @@ Data LoadingOut:
-/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
+type_of_target=multilabel-indicator
@@ -199,7 +196,7 @@ View the models found by auto-sklearnOut:
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
@@ -251,7 +248,7 @@ Get the Score of the final ensembleAccuracy score 0.604
-Total running time of the script: ( 0 minutes 16.045 seconds)
+Total running time of the script: ( 0 minutes 16.172 seconds)
diff --git a/development/examples/20_basic/example_multioutput_regression.html b/development/examples/20_basic/example_multioutput_regression.html
index 55a629e395..9ebd85bebb 100644
--- a/development/examples/20_basic/example_multioutput_regression.html
+++ b/development/examples/20_basic/example_multioutput_regression.html
@@ -168,9 +168,9 @@ View the models found by auto-sklearnOut:
-
Out:
-[(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,
@@ -198,7 +198,7 @@ Get the Score of the final ensembleOut:
-R2 score: 0.999962998298447
+R2 score: 0.9999997983759025
@@ -399,7 +399,7 @@ Get the configuration spaceTotal running time of the script: ( 1 minutes 56.999 seconds)
+Total running time of the script: ( 1 minutes 56.909 seconds)
diff --git a/development/examples/20_basic/example_regression.html b/development/examples/20_basic/example_regression.html
index d9c1c8e100..e769d82735 100644
--- a/development/examples/20_basic/example_regression.html
+++ b/development/examples/20_basic/example_regression.html
@@ -169,11 +169,11 @@ View the models found by auto-sklearnOut:
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
@@ -259,7 +259,7 @@ Plot the predictionsplt.show()
-
Total running time of the script: ( 1 minutes 54.910 seconds)
+Total running time of the script: ( 1 minutes 56.097 seconds)
diff --git a/development/examples/20_basic/sg_execution_times.html b/development/examples/20_basic/sg_execution_times.html
index 4f57981312..705b648482 100644
--- a/development/examples/20_basic/sg_execution_times.html
+++ b/development/examples/20_basic/sg_execution_times.html
@@ -114,7 +114,7 @@
Computation times¶
-06:11.987 total execution time for examples_20_basic files:
+06:08.762 total execution time for examples_20_basic files:
