Graph Embedding LLE Example¶
This example will show you how to use LLE graph embedding with a PHOTONAI Graph Pipeline
At first we have to import the requirements
In [1]:
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from photonai.base import Hyperpipe, PipelineElement
from photonai_graph.GraphUtilities import get_random_connectivity_data, get_random_labels
from sklearn.model_selection import KFold
from photonai.base import Hyperpipe, PipelineElement
from photonai_graph.GraphUtilities import get_random_connectivity_data, get_random_labels
from sklearn.model_selection import KFold
Now we generate test data
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X = get_random_connectivity_data(number_of_nodes=50, number_of_individuals=100)
y = get_random_labels(l_type="classification", number_of_labels=100)
X = get_random_connectivity_data(number_of_nodes=50, number_of_individuals=100)
y = get_random_labels(l_type="classification", number_of_labels=100)
After we have generated some test data, we can finally define our PHOTONAI Pipeline
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my_pipe = Hyperpipe('basic_gembedding_pipe',
inner_cv=KFold(n_splits=5),
outer_cv=KFold(n_splits=5),
optimizer='sk_opt',
optimizer_params={'n_configurations': 25},
metrics=['accuracy', 'balanced_accuracy', 'recall', 'precision'],
best_config_metric='accuracy')
my_pipe.add(PipelineElement('GraphConstructorThreshold', threshold=0.95))
my_pipe.add(PipelineElement('GraphEmbeddingLocallyLinearEmbedding'))
my_pipe.add(PipelineElement('SVC'))
my_pipe = Hyperpipe('basic_gembedding_pipe',
inner_cv=KFold(n_splits=5),
outer_cv=KFold(n_splits=5),
optimizer='sk_opt',
optimizer_params={'n_configurations': 25},
metrics=['accuracy', 'balanced_accuracy', 'recall', 'precision'],
best_config_metric='accuracy')
my_pipe.add(PipelineElement('GraphConstructorThreshold', threshold=0.95))
my_pipe.add(PipelineElement('GraphEmbeddingLocallyLinearEmbedding'))
my_pipe.add(PipelineElement('SVC'))
Finally we have to fit our Pipeline to the data
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my_pipe.fit(X, y)
my_pipe.fit(X, y)
===================================================================================================== PHOTONAI ANALYSIS: basic_gembedding_pipe ===================================================================================================== Found 2 target classes: [0. 1.] ***************************************************************************************************** Outer Cross validation Fold 1 ***************************************************************************************************** Did not find any hyperparameter to convert into skopt space.
/opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/photonai/optimization/scikit_optimize/sk_opt.py:169: UserWarning: Did not find any hyperparameter to convert into skopt space. warnings.warn(msg)
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BEST_CONFIG
-----------------------------------------------------------------------------------------------------
{}
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VALIDATION PERFORMANCE
-----------------------------------------------------------------------------------------------------
+-------------------+-------------------+------------------+
| METRIC | PERFORMANCE TRAIN | PERFORMANCE TEST |
+-------------------+-------------------+------------------+
| accuracy | 0.8688 | 0.4875 |
| balanced_accuracy | 0.8646 | 0.5121 |
| recall | 0.9397 | 0.7214 |
| precision | 0.8313 | 0.5356 |
+-------------------+-------------------+------------------+
-----------------------------------------------------------------------------------------------------
TEST PERFORMANCE
-----------------------------------------------------------------------------------------------------
+-------------------+-------------------+------------------+
| METRIC | PERFORMANCE TRAIN | PERFORMANCE TEST |
+-------------------+-------------------+------------------+
| accuracy | 0.8875 | 0.5500 |
| balanced_accuracy | 0.8853 | 0.5909 |
| recall | 0.9286 | 1.0000 |
| precision | 0.8667 | 0.5000 |
+-------------------+-------------------+------------------+
*****************************************************************************************************
Outer Cross validation Fold 2
*****************************************************************************************************
Did not find any hyperparameter to convert into skopt space.
/opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/photonai/optimization/scikit_optimize/sk_opt.py:169: UserWarning: Did not find any hyperparameter to convert into skopt space. warnings.warn(msg)
-----------------------------------------------------------------------------------------------------
BEST_CONFIG
-----------------------------------------------------------------------------------------------------
{}
-----------------------------------------------------------------------------------------------------
VALIDATION PERFORMANCE
-----------------------------------------------------------------------------------------------------
+-------------------+-------------------+------------------+
| METRIC | PERFORMANCE TRAIN | PERFORMANCE TEST |
+-------------------+-------------------+------------------+
| accuracy | 0.8469 | 0.5250 |
| balanced_accuracy | 0.8458 | 0.5383 |
| recall | 0.8391 | 0.3571 |
| precision | 0.8489 | 0.4617 |
+-------------------+-------------------+------------------+
-----------------------------------------------------------------------------------------------------
TEST PERFORMANCE
-----------------------------------------------------------------------------------------------------
+-------------------+-------------------+------------------+
| METRIC | PERFORMANCE TRAIN | PERFORMANCE TEST |
+-------------------+-------------------+------------------+
| accuracy | 0.8625 | 0.6000 |
| balanced_accuracy | 0.8627 | 0.6458 |
| recall | 0.8718 | 0.4167 |
| precision | 0.8500 | 0.8333 |
+-------------------+-------------------+------------------+
*****************************************************************************************************
Outer Cross validation Fold 3
*****************************************************************************************************
Did not find any hyperparameter to convert into skopt space.
/opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/photonai/optimization/scikit_optimize/sk_opt.py:169: UserWarning: Did not find any hyperparameter to convert into skopt space. warnings.warn(msg)
-----------------------------------------------------------------------------------------------------
BEST_CONFIG
-----------------------------------------------------------------------------------------------------
{}
-----------------------------------------------------------------------------------------------------
VALIDATION PERFORMANCE
-----------------------------------------------------------------------------------------------------
+-------------------+-------------------+------------------+
| METRIC | PERFORMANCE TRAIN | PERFORMANCE TEST |
+-------------------+-------------------+------------------+
| accuracy | 0.8719 | 0.4250 |
| balanced_accuracy | 0.8688 | 0.4891 |
| recall | 0.8395 | 0.3902 |
| precision | 0.9010 | 0.6040 |
+-------------------+-------------------+------------------+
-----------------------------------------------------------------------------------------------------
TEST PERFORMANCE
-----------------------------------------------------------------------------------------------------
+-------------------+-------------------+------------------+
| METRIC | PERFORMANCE TRAIN | PERFORMANCE TEST |
+-------------------+-------------------+------------------+
| accuracy | 0.8500 | 0.4000 |
| balanced_accuracy | 0.8493 | 0.4167 |
| recall | 0.8205 | 0.3333 |
| precision | 0.8649 | 0.5000 |
+-------------------+-------------------+------------------+
*****************************************************************************************************
Outer Cross validation Fold 4
*****************************************************************************************************
Did not find any hyperparameter to convert into skopt space.
/opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/photonai/optimization/scikit_optimize/sk_opt.py:169: UserWarning: Did not find any hyperparameter to convert into skopt space. warnings.warn(msg)
-----------------------------------------------------------------------------------------------------
BEST_CONFIG
-----------------------------------------------------------------------------------------------------
{}
-----------------------------------------------------------------------------------------------------
VALIDATION PERFORMANCE
-----------------------------------------------------------------------------------------------------
+-------------------+-------------------+------------------+
| METRIC | PERFORMANCE TRAIN | PERFORMANCE TEST |
+-------------------+-------------------+------------------+
| accuracy | 0.8750 | 0.4375 |
| balanced_accuracy | 0.8612 | 0.5172 |
| recall | 0.9194 | 0.7944 |
| precision | 0.8659 | 0.5885 |
+-------------------+-------------------+------------------+
-----------------------------------------------------------------------------------------------------
TEST PERFORMANCE
-----------------------------------------------------------------------------------------------------
+-------------------+-------------------+------------------+
| METRIC | PERFORMANCE TRAIN | PERFORMANCE TEST |
+-------------------+-------------------+------------------+
| accuracy | 0.8375 | 0.4000 |
| balanced_accuracy | 0.8270 | 0.5055 |
| recall | 0.9318 | 0.8571 |
| precision | 0.8039 | 0.3529 |
+-------------------+-------------------+------------------+
*****************************************************************************************************
Outer Cross validation Fold 5
*****************************************************************************************************
Did not find any hyperparameter to convert into skopt space.
/opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/photonai/optimization/scikit_optimize/sk_opt.py:169: UserWarning: Did not find any hyperparameter to convert into skopt space. warnings.warn(msg)
-----------------------------------------------------------------------------------------------------
BEST_CONFIG
-----------------------------------------------------------------------------------------------------
{}
-----------------------------------------------------------------------------------------------------
VALIDATION PERFORMANCE
-----------------------------------------------------------------------------------------------------
+-------------------+-------------------+------------------+
| METRIC | PERFORMANCE TRAIN | PERFORMANCE TEST |
+-------------------+-------------------+------------------+
| accuracy | 0.8969 | 0.3375 |
| balanced_accuracy | 0.8886 | 0.4548 |
| recall | 0.8803 | 0.4797 |
| precision | 0.9209 | 0.4238 |
+-------------------+-------------------+------------------+
-----------------------------------------------------------------------------------------------------
TEST PERFORMANCE
-----------------------------------------------------------------------------------------------------
+-------------------+-------------------+------------------+
| METRIC | PERFORMANCE TRAIN | PERFORMANCE TEST |
+-------------------+-------------------+------------------+
| accuracy | 0.9125 | 0.5000 |
| balanced_accuracy | 0.9125 | 0.4747 |
| recall | 0.9500 | 0.7273 |
| precision | 0.8837 | 0.5333 |
+-------------------+-------------------+------------------+
*****************************************************************************************************
Finished all outer fold computations.
*****************************************************************************************************
ANALYSIS INFORMATION ================================================================================
Project Folder: /home/runner/work/photonai_graph/photonai_graph/documentation/docs/examples/basic_gembedding_pipe_results_2023-02-17_15-46-54,
Computation Time: 2023-02-17 15:46:54.135831 - 2023-02-17 15:48:07.411344
Duration: 0:01:13.275513
Optimized for: accuracy
Hyperparameter Optimizer: sk_opt
DUMMY RESULTS =======================================================================================
+-------------------+--+
| PERFORMANCE DUMMY | |
+-------------------+--+
+-------------------+--+
AVERAGE PERFORMANCE ACROSS OUTER FOLDS ==============================================================
+-------------------+---------------+--------------+-----------+----------+
| Metric Name | Training Mean | Training Std | Test Mean | Test Std |
+-------------------+---------------+--------------+-----------+----------+
| accuracy | 0.87 | 0.026926 | 0.49 | 0.08 |
| balanced_accuracy | 0.867373 | 0.02945 | 0.52673 | 0.081941 |
| recall | 0.900539 | 0.047835 | 0.666883 | 0.254824 |
| precision | 0.853835 | 0.02715 | 0.543922 | 0.157609 |
+-------------------+---------------+--------------+-----------+----------+
BEST HYPERPARAMETER CONFIGURATION ===================================================================
{}
+--------+----------+-------------------+--------+-----------+----------------------------+
| fold # | accuracy | balanced_accuracy | recall | precision | Best Hyperparameter Config |
+--------+----------+-------------------+--------+-----------+----------------------------+
| 1 | 0.5500 | 0.5909 | 1.0000 | 0.5000 | {} |
| 2* | 0.6000 | 0.6458 | 0.4167 | 0.8333 | {} |
| 3 | 0.4000 | 0.4167 | 0.3333 | 0.5000 | {} |
| 4 | 0.4000 | 0.5055 | 0.8571 | 0.3529 | {} |
| 5 | 0.5000 | 0.4747 | 0.7273 | 0.5333 | {} |
+--------+----------+-------------------+--------+-----------+----------------------------+
PHOTONAI 2.3.0 ======================================================================================
Your results are stored in /home/runner/work/photonai_graph/photonai_graph/documentation/docs/examples/basic_gembedding_pipe_results_2023-02-17_15-46-54
Go to https://explorer.photon-ai.com and upload your photonai_results.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/3.9.16/x64/lib/python3.9/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/3.9.16/x64/lib/python3.9/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/3.9.16/x64/lib/python3.9/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/3.9.16/x64/lib/python3.9/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/3.9.16/x64/lib/python3.9/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/3.9.16/x64/lib/python3.9/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/3.9.16/x64/lib/python3.9/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/3.9.16/x64/lib/python3.9/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/3.9.16/x64/lib/python3.9/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/3.9.16/x64/lib/python3.9/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/3.9.16/x64/lib/python3.9/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/3.9.16/x64/lib/python3.9/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[4]:
Hyperpipe(name='basic_gembedding_pipe')