Conformal Predictions Example¶
In [1]:
Copied!
import photonai_conformal
from sklearn.datasets import load_diabetes
from sklearn.model_selection import KFold
from sklearn.gaussian_process import GaussianProcessRegressor
from photonai.base import Hyperpipe, PipelineElement
import photonai_conformal
from sklearn.datasets import load_diabetes
from sklearn.model_selection import KFold
from sklearn.gaussian_process import GaussianProcessRegressor
from photonai.base import Hyperpipe, PipelineElement
Define Hyperpipe¶
In [2]:
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my_pipe = Hyperpipe('conformal_pipe',
optimizer='random_search',
metrics=['mean_squared_error', 'mean_absolute_error', 'explained_variance'],
best_config_metric='mean_squared_error',
outer_cv=KFold(n_splits=3, shuffle=True),
inner_cv=KFold(n_splits=3, shuffle=True),
verbosity=1,
project_folder='./tmp/')
my_pipe += PipelineElement("ConformalRegressor", estimator=GaussianProcessRegressor(), alpha=[.05, .5, .95])
my_pipe = Hyperpipe('conformal_pipe',
optimizer='random_search',
metrics=['mean_squared_error', 'mean_absolute_error', 'explained_variance'],
best_config_metric='mean_squared_error',
outer_cv=KFold(n_splits=3, shuffle=True),
inner_cv=KFold(n_splits=3, shuffle=True),
verbosity=1,
project_folder='./tmp/')
my_pipe += PipelineElement("ConformalRegressor", estimator=GaussianProcessRegressor(), alpha=[.05, .5, .95])
Load Data and fit Hyperpipe¶
In [3]:
Copied!
# load data and train
X, y = load_diabetes(return_X_y=True)
my_pipe.fit(X, y)
# load data and train
X, y = load_diabetes(return_X_y=True)
my_pipe.fit(X, y)
08/12/2022-10:09:45 | Output Folder: ./tmp/conformal_pipe_results_2022-12-08_10-09-45
=====================================================================================================
PHOTONAI ANALYSIS: conformal_pipe
=====================================================================================================
08/12/2022-10:09:45 | Preparing data and PHOTONAI objects for analysis...
08/12/2022-10:09:45 | Checking input data...
08/12/2022-10:09:45 | Running analysis with 442 samples.
*****************************************************************************************************
Outer Cross validation Fold 1
*****************************************************************************************************
08/12/2022-10:09:45 | Preparing data for outer fold 1...
08/12/2022-10:09:45 | Preparing Hyperparameter Optimization...
08/12/2022-10:09:45 | Running Dummy Estimator...
+---------------------+-----------+
| PERFORMANCE DUMMY | |
+---------------------+-----------+
| mean_squared_error | 5665.3336 |
| mean_absolute_error | 63.2711 |
| explained_variance | 0.0000 |
+---------------------+-----------+
-----------------------------------------------------------------------------------------------------
{}
{}
{}
08/12/2022-10:09:45 | Computed configuration 1/10 in 0:00:00.593783
08/12/2022-10:09:45 | Performance: mean_squared_error - Train: 1.1064, Validation: 64398.6893
08/12/2022-10:09:45 | Best Performance So Far: mean_squared_error - Train: 1.1064, Validation: 64398.6893
-----------------------------------------------------------------------------------------------------
{}
{}
{}
08/12/2022-10:09:46 | Computed configuration 2/10 in 0:00:00.579337
08/12/2022-10:09:46 | Performance: mean_squared_error - Train: 1.1064, Validation: 64398.6893
08/12/2022-10:09:46 | Best Performance So Far: mean_squared_error - Train: 1.1064, Validation: 64398.6893
-----------------------------------------------------------------------------------------------------
{}
{}
{}
08/12/2022-10:09:47 | Computed configuration 3/10 in 0:00:00.656006
08/12/2022-10:09:47 | Performance: mean_squared_error - Train: 1.1064, Validation: 64398.6893
08/12/2022-10:09:47 | Best Performance So Far: mean_squared_error - Train: 1.1064, Validation: 64398.6893
-----------------------------------------------------------------------------------------------------
{}
{}
{}
08/12/2022-10:09:47 | Computed configuration 4/10 in 0:00:00.595077
08/12/2022-10:09:47 | Performance: mean_squared_error - Train: 1.1064, Validation: 64398.6893
08/12/2022-10:09:47 | Best Performance So Far: mean_squared_error - Train: 1.1064, Validation: 64398.6893
-----------------------------------------------------------------------------------------------------
{}
{}
{}
08/12/2022-10:09:48 | Computed configuration 5/10 in 0:00:00.626041
08/12/2022-10:09:48 | Performance: mean_squared_error - Train: 1.1064, Validation: 64398.6893
08/12/2022-10:09:48 | Best Performance So Far: mean_squared_error - Train: 1.1064, Validation: 64398.6893
-----------------------------------------------------------------------------------------------------
{}
{}
{}
08/12/2022-10:09:48 | Computed configuration 6/10 in 0:00:00.625105
08/12/2022-10:09:48 | Performance: mean_squared_error - Train: 1.1064, Validation: 64398.6893
08/12/2022-10:09:48 | Best Performance So Far: mean_squared_error - Train: 1.1064, Validation: 64398.6893
-----------------------------------------------------------------------------------------------------
{}
{}
{}
08/12/2022-10:09:49 | Computed configuration 7/10 in 0:00:00.789633
08/12/2022-10:09:49 | Performance: mean_squared_error - Train: 1.1064, Validation: 64398.6893
08/12/2022-10:09:49 | Best Performance So Far: mean_squared_error - Train: 1.1064, Validation: 64398.6893
-----------------------------------------------------------------------------------------------------
{}
{}
{}
08/12/2022-10:09:50 | Computed configuration 8/10 in 0:00:00.542478
08/12/2022-10:09:50 | Performance: mean_squared_error - Train: 1.1064, Validation: 64398.6893
08/12/2022-10:09:50 | Best Performance So Far: mean_squared_error - Train: 1.1064, Validation: 64398.6893
-----------------------------------------------------------------------------------------------------
{}
{}
{}
08/12/2022-10:09:51 | Computed configuration 9/10 in 0:00:00.732999
08/12/2022-10:09:51 | Performance: mean_squared_error - Train: 1.1064, Validation: 64398.6893
08/12/2022-10:09:51 | Best Performance So Far: mean_squared_error - Train: 1.1064, Validation: 64398.6893
-----------------------------------------------------------------------------------------------------
{}
{}
{}
08/12/2022-10:09:51 | Computed configuration 10/10 in 0:00:00.575098
08/12/2022-10:09:51 | Performance: mean_squared_error - Train: 1.1064, Validation: 64398.6893
08/12/2022-10:09:51 | Best Performance So Far: mean_squared_error - Train: 1.1064, Validation: 64398.6893
-----------------------------------------------------------------------------------------------------
08/12/2022-10:09:51 | Hyperparameter Optimization finished. Now finding best configuration ....
08/12/2022-10:09:51 | 10
-----------------------------------------------------------------------------------------------------
BEST_CONFIG
-----------------------------------------------------------------------------------------------------
{}
-----------------------------------------------------------------------------------------------------
VALIDATION PERFORMANCE
-----------------------------------------------------------------------------------------------------
+---------------------+-------------------+------------------+
| METRIC | PERFORMANCE TRAIN | PERFORMANCE TEST |
+---------------------+-------------------+------------------+
| mean_squared_error | 1.1064 | 64398.6893 |
| mean_absolute_error | 0.5583 | 178.7847 |
| explained_variance | 0.9998 | -9.6507 |
+---------------------+-------------------+------------------+
08/12/2022-10:09:51 | Calculating best model performance on test set...
-----------------------------------------------------------------------------------------------------
TEST PERFORMANCE
-----------------------------------------------------------------------------------------------------
+---------------------+-------------------+------------------+
| METRIC | PERFORMANCE TRAIN | PERFORMANCE TEST |
+---------------------+-------------------+------------------+
| mean_squared_error | 31.5952 | 78518.8824 |
| mean_absolute_error | 2.8278 | 179.6178 |
| explained_variance | 0.9948 | -12.8099 |
+---------------------+-------------------+------------------+
08/12/2022-10:09:51 | Computations in outer fold 1 took 0.11271133333333333 minutes.
08/12/2022-10:09:51 | Writing results to project folder...
*****************************************************************************************************
Outer Cross validation Fold 2
*****************************************************************************************************
08/12/2022-10:09:52 | Preparing data for outer fold 2...
08/12/2022-10:09:52 | Preparing Hyperparameter Optimization...
08/12/2022-10:09:52 | Running Dummy Estimator...
+---------------------+-----------+
| PERFORMANCE DUMMY | |
+---------------------+-----------+
| mean_squared_error | 5589.0128 |
| mean_absolute_error | 65.0693 |
| explained_variance | 0.0000 |
+---------------------+-----------+
-----------------------------------------------------------------------------------------------------
{}
{}
{}
08/12/2022-10:09:52 | Computed configuration 1/10 in 0:00:00.487182
08/12/2022-10:09:52 | Performance: mean_squared_error - Train: 2.9381, Validation: 158668.6659
08/12/2022-10:09:52 | Best Performance So Far: mean_squared_error - Train: 2.9381, Validation: 158668.6659
-----------------------------------------------------------------------------------------------------
{}
{}
{}
08/12/2022-10:09:53 | Computed configuration 2/10 in 0:00:00.695313
08/12/2022-10:09:53 | Performance: mean_squared_error - Train: 2.9381, Validation: 158668.6659
08/12/2022-10:09:53 | Best Performance So Far: mean_squared_error - Train: 2.9381, Validation: 158668.6659
-----------------------------------------------------------------------------------------------------
{}
{}
{}
08/12/2022-10:09:53 | Computed configuration 3/10 in 0:00:00.613144
08/12/2022-10:09:53 | Performance: mean_squared_error - Train: 2.9381, Validation: 158668.6659
08/12/2022-10:09:53 | Best Performance So Far: mean_squared_error - Train: 2.9381, Validation: 158668.6659
-----------------------------------------------------------------------------------------------------
{}
{}
{}
08/12/2022-10:09:54 | Computed configuration 4/10 in 0:00:00.685237
08/12/2022-10:09:54 | Performance: mean_squared_error - Train: 2.9381, Validation: 158668.6659
08/12/2022-10:09:54 | Best Performance So Far: mean_squared_error - Train: 2.9381, Validation: 158668.6659
-----------------------------------------------------------------------------------------------------
{}
{}
{}
08/12/2022-10:09:55 | Computed configuration 5/10 in 0:00:00.561149
08/12/2022-10:09:55 | Performance: mean_squared_error - Train: 2.9381, Validation: 158668.6659
08/12/2022-10:09:55 | Best Performance So Far: mean_squared_error - Train: 2.9381, Validation: 158668.6659
-----------------------------------------------------------------------------------------------------
{}
{}
{}
08/12/2022-10:09:55 | Computed configuration 6/10 in 0:00:00.768976
08/12/2022-10:09:55 | Performance: mean_squared_error - Train: 2.9381, Validation: 158668.6659
08/12/2022-10:09:55 | Best Performance So Far: mean_squared_error - Train: 2.9381, Validation: 158668.6659
-----------------------------------------------------------------------------------------------------
{}
{}
{}
08/12/2022-10:09:56 | Computed configuration 7/10 in 0:00:00.543172
08/12/2022-10:09:56 | Performance: mean_squared_error - Train: 2.9381, Validation: 158668.6659
08/12/2022-10:09:56 | Best Performance So Far: mean_squared_error - Train: 2.9381, Validation: 158668.6659
-----------------------------------------------------------------------------------------------------
{}
{}
{}
08/12/2022-10:09:57 | Computed configuration 8/10 in 0:00:00.571397
08/12/2022-10:09:57 | Performance: mean_squared_error - Train: 2.9381, Validation: 158668.6659
08/12/2022-10:09:57 | Best Performance So Far: mean_squared_error - Train: 2.9381, Validation: 158668.6659
-----------------------------------------------------------------------------------------------------
{}
{}
{}
08/12/2022-10:09:57 | Computed configuration 9/10 in 0:00:00.601174
08/12/2022-10:09:57 | Performance: mean_squared_error - Train: 2.9381, Validation: 158668.6659
08/12/2022-10:09:57 | Best Performance So Far: mean_squared_error - Train: 2.9381, Validation: 158668.6659
-----------------------------------------------------------------------------------------------------
{}
{}
{}
08/12/2022-10:09:58 | Computed configuration 10/10 in 0:00:00.767903
08/12/2022-10:09:58 | Performance: mean_squared_error - Train: 2.9381, Validation: 158668.6659
08/12/2022-10:09:58 | Best Performance So Far: mean_squared_error - Train: 2.9381, Validation: 158668.6659
-----------------------------------------------------------------------------------------------------
08/12/2022-10:09:58 | Hyperparameter Optimization finished. Now finding best configuration ....
08/12/2022-10:09:58 | 10
-----------------------------------------------------------------------------------------------------
BEST_CONFIG
-----------------------------------------------------------------------------------------------------
{}
-----------------------------------------------------------------------------------------------------
VALIDATION PERFORMANCE
-----------------------------------------------------------------------------------------------------
+---------------------+-------------------+------------------+
| METRIC | PERFORMANCE TRAIN | PERFORMANCE TEST |
+---------------------+-------------------+------------------+
| mean_squared_error | 2.9381 | 158668.6659 |
| mean_absolute_error | 0.8032 | 254.6984 |
| explained_variance | 0.9995 | -25.0422 |
+---------------------+-------------------+------------------+
08/12/2022-10:09:58 | Calculating best model performance on test set...
-----------------------------------------------------------------------------------------------------
TEST PERFORMANCE
-----------------------------------------------------------------------------------------------------
+---------------------+-------------------+------------------+
| METRIC | PERFORMANCE TRAIN | PERFORMANCE TEST |
+---------------------+-------------------+------------------+
| mean_squared_error | 46.8030 | 61504.4041 |
| mean_absolute_error | 3.8900 | 161.8759 |
| explained_variance | 0.9924 | -10.8666 |
+---------------------+-------------------+------------------+
08/12/2022-10:09:58 | Computations in outer fold 2 took 0.11137263333333333 minutes.
08/12/2022-10:09:58 | Writing results to project folder...
*****************************************************************************************************
Outer Cross validation Fold 3
*****************************************************************************************************
08/12/2022-10:09:58 | Preparing data for outer fold 3...
08/12/2022-10:09:58 | Preparing Hyperparameter Optimization...
08/12/2022-10:09:58 | Running Dummy Estimator...
+---------------------+-----------+
| PERFORMANCE DUMMY | |
+---------------------+-----------+
| mean_squared_error | 6922.8665 |
| mean_absolute_error | 70.1794 |
| explained_variance | 0.0000 |
+---------------------+-----------+
-----------------------------------------------------------------------------------------------------
{}
{}
{}
08/12/2022-10:09:59 | Computed configuration 1/10 in 0:00:00.690743
08/12/2022-10:09:59 | Performance: mean_squared_error - Train: 2.3669, Validation: 234026.1518
08/12/2022-10:09:59 | Best Performance So Far: mean_squared_error - Train: 2.3669, Validation: 234026.1518
-----------------------------------------------------------------------------------------------------
{}
{}
{}
08/12/2022-10:10:00 | Computed configuration 2/10 in 0:00:00.655810
08/12/2022-10:10:00 | Performance: mean_squared_error - Train: 2.3669, Validation: 234026.1518
08/12/2022-10:10:00 | Best Performance So Far: mean_squared_error - Train: 2.3669, Validation: 234026.1518
-----------------------------------------------------------------------------------------------------
{}
{}
{}
08/12/2022-10:10:01 | Computed configuration 3/10 in 0:00:00.710578
08/12/2022-10:10:01 | Performance: mean_squared_error - Train: 2.3669, Validation: 234026.1518
08/12/2022-10:10:01 | Best Performance So Far: mean_squared_error - Train: 2.3669, Validation: 234026.1518
-----------------------------------------------------------------------------------------------------
{}
{}
{}
08/12/2022-10:10:01 | Computed configuration 4/10 in 0:00:00.571774
08/12/2022-10:10:01 | Performance: mean_squared_error - Train: 2.3669, Validation: 234026.1518
08/12/2022-10:10:01 | Best Performance So Far: mean_squared_error - Train: 2.3669, Validation: 234026.1518
-----------------------------------------------------------------------------------------------------
{}
{}
{}
08/12/2022-10:10:02 | Computed configuration 5/10 in 0:00:00.573869
08/12/2022-10:10:02 | Performance: mean_squared_error - Train: 2.3669, Validation: 234026.1518
08/12/2022-10:10:02 | Best Performance So Far: mean_squared_error - Train: 2.3669, Validation: 234026.1518
-----------------------------------------------------------------------------------------------------
{}
{}
{}
08/12/2022-10:10:02 | Computed configuration 6/10 in 0:00:00.621695
08/12/2022-10:10:02 | Performance: mean_squared_error - Train: 2.3669, Validation: 234026.1518
08/12/2022-10:10:02 | Best Performance So Far: mean_squared_error - Train: 2.3669, Validation: 234026.1518
-----------------------------------------------------------------------------------------------------
{}
{}
{}
08/12/2022-10:10:03 | Computed configuration 7/10 in 0:00:00.630495
08/12/2022-10:10:03 | Performance: mean_squared_error - Train: 2.3669, Validation: 234026.1518
08/12/2022-10:10:03 | Best Performance So Far: mean_squared_error - Train: 2.3669, Validation: 234026.1518
-----------------------------------------------------------------------------------------------------
{}
{}
{}
08/12/2022-10:10:04 | Computed configuration 8/10 in 0:00:00.724085
08/12/2022-10:10:04 | Performance: mean_squared_error - Train: 2.3669, Validation: 234026.1518
08/12/2022-10:10:04 | Best Performance So Far: mean_squared_error - Train: 2.3669, Validation: 234026.1518
-----------------------------------------------------------------------------------------------------
{}
{}
{}
08/12/2022-10:10:04 | Computed configuration 9/10 in 0:00:00.594450
08/12/2022-10:10:04 | Performance: mean_squared_error - Train: 2.3669, Validation: 234026.1518
08/12/2022-10:10:04 | Best Performance So Far: mean_squared_error - Train: 2.3669, Validation: 234026.1518
-----------------------------------------------------------------------------------------------------
{}
{}
{}
08/12/2022-10:10:05 | Computed configuration 10/10 in 0:00:00.686244
08/12/2022-10:10:05 | Performance: mean_squared_error - Train: 2.3669, Validation: 234026.1518
08/12/2022-10:10:05 | Best Performance So Far: mean_squared_error - Train: 2.3669, Validation: 234026.1518
-----------------------------------------------------------------------------------------------------
08/12/2022-10:10:05 | Hyperparameter Optimization finished. Now finding best configuration ....
08/12/2022-10:10:05 | 10
-----------------------------------------------------------------------------------------------------
BEST_CONFIG
-----------------------------------------------------------------------------------------------------
{}
-----------------------------------------------------------------------------------------------------
VALIDATION PERFORMANCE
-----------------------------------------------------------------------------------------------------
+---------------------+-------------------+------------------+
| METRIC | PERFORMANCE TRAIN | PERFORMANCE TEST |
+---------------------+-------------------+------------------+
| mean_squared_error | 2.3669 | 234026.1518 |
| mean_absolute_error | 0.7924 | 260.0707 |
| explained_variance | 0.9996 | -42.4732 |
+---------------------+-------------------+------------------+
08/12/2022-10:10:05 | Calculating best model performance on test set...
-----------------------------------------------------------------------------------------------------
TEST PERFORMANCE
-----------------------------------------------------------------------------------------------------
+---------------------+-------------------+------------------+
| METRIC | PERFORMANCE TRAIN | PERFORMANCE TEST |
+---------------------+-------------------+------------------+
| mean_squared_error | 47.3563 | 77965.6949 |
| mean_absolute_error | 3.8732 | 169.7198 |
| explained_variance | 0.9914 | -10.2653 |
+---------------------+-------------------+------------------+
08/12/2022-10:10:05 | Computations in outer fold 3 took 0.11340325 minutes.
08/12/2022-10:10:05 | Writing results to project folder...
*****************************************************************************************************
Finished all outer fold computations.
08/12/2022-10:10:06 | Now analysing the final results...
08/12/2022-10:10:06 | Computing dummy metrics...
08/12/2022-10:10:06 | Computing mean and std for all outer fold metrics...
08/12/2022-10:10:06 | Find best config across outer folds...
08/12/2022-10:10:06 | Save final results...
08/12/2022-10:10:06 | Writing results to project folder...
08/12/2022-10:10:06 | Prepare Hyperpipe.optimum pipe with best config..
08/12/2022-10:10:06 | Fitting best model...
08/12/2022-10:10:06 | Saved best model to file.
08/12/2022-10:10:06 | Mapping back feature importances...
08/12/2022-10:10:06 | No feature importances available for ConformalRegressor!
08/12/2022-10:10:06 | Summarizing results...
08/12/2022-10:10:06 | Write predictions to files...
08/12/2022-10:10:06 | Write summary...
*****************************************************************************************************
ANALYSIS INFORMATION ================================================================================
Project Folder: ./tmp/conformal_pipe_results_2022-12-08_10-09-45,
Computation Time: 2022-12-08 10:09:45.177943 - 2022-12-08 10:10:06.009422
Duration: 0:00:20.831479
Optimized for: mean_squared_error
Hyperparameter Optimizer: random_search
DUMMY RESULTS =======================================================================================
+---------------------+-----------+
| PERFORMANCE DUMMY | |
+---------------------+-----------+
| mean_squared_error | 6059.0709 |
| mean_absolute_error | 66.1733 |
| explained_variance | 0.0000 |
+---------------------+-----------+
AVERAGE PERFORMANCE ACROSS OUTER FOLDS ==============================================================
+---------------------+---------------+--------------+--------------+-------------+
| Metric Name | Training Mean | Training Std | Test Mean | Test Std |
+---------------------+---------------+--------------+--------------+-------------+
| mean_squared_error | 41.918178 | 7.302952 | 72662.993791 | 7893.545766 |
| mean_absolute_error | 3.53034 | 0.496815 | 170.404479 | 7.259271 |
| explained_variance | 0.992854 | 0.001436 | -11.313916 | 1.08594 |
+---------------------+---------------+--------------+--------------+-------------+
BEST HYPERPARAMETER CONFIGURATION ===================================================================
{}
+--------+--------------------+---------------------+--------------------+----------------------------+
| fold # | mean_squared_error | mean_absolute_error | explained_variance | Best Hyperparameter Config |
+--------+--------------------+---------------------+--------------------+----------------------------+
| 1 | 78518.8824 | 179.6178 | -12.8099 | {} |
| 2* | 61504.4041 | 161.8759 | -10.8666 | {} |
| 3 | 77965.6949 | 169.7198 | -10.2653 | {} |
+--------+--------------------+---------------------+--------------------+----------------------------+
PHOTONAI 2.2.1 ======================================================================================
Your results are stored in ./tmp/conformal_pipe_results_2022-12-08_10-09-45
Go to https://explorer.photon-ai.com and upload your photon_result_file.json for convenient result visualization!
For more info and documentation visit https://www.photon-ai.com
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) File /opt/hostedtoolcache/Python/3.10.8/x64/lib/python3.10/site-packages/IPython/core/formatters.py:972, in MimeBundleFormatter.__call__(self, obj, include, exclude) 969 method = get_real_method(obj, self.print_method) 971 if method is not None: --> 972 return method(include=include, exclude=exclude) 973 return None 974 else: File /opt/hostedtoolcache/Python/3.10.8/x64/lib/python3.10/site-packages/sklearn/base.py:631, in BaseEstimator._repr_mimebundle_(self, **kwargs) 629 output = {"text/plain": repr(self)} 630 if get_config()["display"] == "diagram": --> 631 output["text/html"] = estimator_html_repr(self) 632 return output File /opt/hostedtoolcache/Python/3.10.8/x64/lib/python3.10/site-packages/sklearn/utils/_estimator_html_repr.py:417, in estimator_html_repr(estimator) 403 fallback_msg = ( 404 "In a Jupyter environment, please rerun this cell to show the HTML" 405 " representation or trust the notebook. <br />On GitHub, the" 406 " HTML representation is unable to render, please try loading this page" 407 " with nbviewer.org." 408 ) 409 out.write( 410 f"<style>{style_with_id}</style>" 411 f'<div id="{container_id}" class="sk-top-container">' (...) 415 '<div class="sk-container" hidden>' 416 ) --> 417 _write_estimator_html( 418 out, 419 estimator, 420 estimator.__class__.__name__, 421 estimator_str, 422 first_call=True, 423 ) 424 out.write("</div></div>") 426 html_output = out.getvalue() File /opt/hostedtoolcache/Python/3.10.8/x64/lib/python3.10/site-packages/sklearn/utils/_estimator_html_repr.py:151, in _write_estimator_html(out, estimator, estimator_label, estimator_label_details, first_call) 149 """Write estimator to html in serial, parallel, or by itself (single).""" 150 if first_call: --> 151 est_block = _get_visual_block(estimator) 152 else: 153 with config_context(print_changed_only=True): File /opt/hostedtoolcache/Python/3.10.8/x64/lib/python3.10/site-packages/sklearn/utils/_estimator_html_repr.py:127, in _get_visual_block(estimator) 123 # check if estimator looks like a meta estimator wraps estimators 124 if hasattr(estimator, "get_params"): 125 estimators = [ 126 (key, est) --> 127 for key, est in estimator.get_params(deep=False).items() 128 if hasattr(est, "get_params") and hasattr(est, "fit") 129 ] 130 if estimators: 131 return _VisualBlock( 132 "parallel", 133 [est for _, est in estimators], 134 names=[f"{key}: {est.__class__.__name__}" for key, est in estimators], 135 name_details=[str(est) for _, est in estimators], 136 ) File /opt/hostedtoolcache/Python/3.10.8/x64/lib/python3.10/site-packages/sklearn/base.py:211, in BaseEstimator.get_params(self, deep) 209 out = dict() 210 for key in self._get_param_names(): --> 211 value = getattr(self, key) 212 if deep and hasattr(value, "get_params") and not isinstance(value, type): 213 deep_items = value.get_params().items() AttributeError: 'Hyperpipe' object has no attribute 'best_config_metric'
--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) File /opt/hostedtoolcache/Python/3.10.8/x64/lib/python3.10/site-packages/IPython/core/formatters.py:342, in BaseFormatter.__call__(self, obj) 340 method = get_real_method(obj, self.print_method) 341 if method is not None: --> 342 return method() 343 return None 344 else: File /opt/hostedtoolcache/Python/3.10.8/x64/lib/python3.10/site-packages/sklearn/base.py:625, in BaseEstimator._repr_html_inner(self) 620 def _repr_html_inner(self): 621 """This function is returned by the @property `_repr_html_` to make 622 `hasattr(estimator, "_repr_html_") return `True` or `False` depending 623 on `get_config()["display"]`. 624 """ --> 625 return estimator_html_repr(self) File /opt/hostedtoolcache/Python/3.10.8/x64/lib/python3.10/site-packages/sklearn/utils/_estimator_html_repr.py:417, in estimator_html_repr(estimator) 403 fallback_msg = ( 404 "In a Jupyter environment, please rerun this cell to show the HTML" 405 " representation or trust the notebook. <br />On GitHub, the" 406 " HTML representation is unable to render, please try loading this page" 407 " with nbviewer.org." 408 ) 409 out.write( 410 f"<style>{style_with_id}</style>" 411 f'<div id="{container_id}" class="sk-top-container">' (...) 415 '<div class="sk-container" hidden>' 416 ) --> 417 _write_estimator_html( 418 out, 419 estimator, 420 estimator.__class__.__name__, 421 estimator_str, 422 first_call=True, 423 ) 424 out.write("</div></div>") 426 html_output = out.getvalue() File /opt/hostedtoolcache/Python/3.10.8/x64/lib/python3.10/site-packages/sklearn/utils/_estimator_html_repr.py:151, in _write_estimator_html(out, estimator, estimator_label, estimator_label_details, first_call) 149 """Write estimator to html in serial, parallel, or by itself (single).""" 150 if first_call: --> 151 est_block = _get_visual_block(estimator) 152 else: 153 with config_context(print_changed_only=True): File /opt/hostedtoolcache/Python/3.10.8/x64/lib/python3.10/site-packages/sklearn/utils/_estimator_html_repr.py:127, in _get_visual_block(estimator) 123 # check if estimator looks like a meta estimator wraps estimators 124 if hasattr(estimator, "get_params"): 125 estimators = [ 126 (key, est) --> 127 for key, est in estimator.get_params(deep=False).items() 128 if hasattr(est, "get_params") and hasattr(est, "fit") 129 ] 130 if estimators: 131 return _VisualBlock( 132 "parallel", 133 [est for _, est in estimators], 134 names=[f"{key}: {est.__class__.__name__}" for key, est in estimators], 135 name_details=[str(est) for _, est in estimators], 136 ) File /opt/hostedtoolcache/Python/3.10.8/x64/lib/python3.10/site-packages/sklearn/base.py:211, in BaseEstimator.get_params(self, deep) 209 out = dict() 210 for key in self._get_param_names(): --> 211 value = getattr(self, key) 212 if deep and hasattr(value, "get_params") and not isinstance(value, type): 213 deep_items = value.get_params().items() AttributeError: 'Hyperpipe' object has no attribute 'best_config_metric'
Out[3]:
Hyperpipe(name='conformal_pipe')