Graph Measure Pipeline example¶

This example will show you how you can use graph measures directly in your hyperpipe.

In [1]:

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

Generate random matrices to simulate connectivity matrices

In [2]:

X = get_random_connectivity_data(number_of_nodes=50, number_of_individuals=200)
y = get_random_labels(l_type="regression", number_of_labels=200)

X = get_random_connectivity_data(number_of_nodes=50, number_of_individuals=200) y = get_random_labels(l_type="regression", number_of_labels=200)

Design your Pipeline

In [3]:

my_pipe = Hyperpipe('basic_measure_pipe',
                    inner_cv=KFold(n_splits=5),
                    outer_cv=KFold(n_splits=5),
                    optimizer='grid_search',
                    metrics=['mean_absolute_error'],
                    best_config_metric='mean_absolute_error')

my_pipe.add(PipelineElement('GraphConstructorThreshold', threshold=0.95))

my_pipe.add(PipelineElement('GraphMeasureTransform', graph_functions={"global_efficiency": {},
                                                                      "local_efficiency": {},
                                                                      "average_clustering": {},
                                                                      "degree_centrality": {},
                                                                      "betweenness_centrality": {},
                                                                      "overall_reciprocity": {}}))

my_pipe.add(PipelineElement("SVR"))

my_pipe = Hyperpipe('basic_measure_pipe', inner_cv=KFold(n_splits=5), outer_cv=KFold(n_splits=5), optimizer='grid_search', metrics=['mean_absolute_error'], best_config_metric='mean_absolute_error') my_pipe.add(PipelineElement('GraphConstructorThreshold', threshold=0.95)) my_pipe.add(PipelineElement('GraphMeasureTransform', graph_functions={"global_efficiency": {}, "local_efficiency": {}, "average_clustering": {}, "degree_centrality": {}, "betweenness_centrality": {}, "overall_reciprocity": {}})) my_pipe.add(PipelineElement("SVR"))

Element not supported right now:GraphMeasureTransform

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
Cell In[3], line 10
      1 my_pipe = Hyperpipe('basic_measure_pipe',
      2                     inner_cv=KFold(n_splits=5),
      3                     outer_cv=KFold(n_splits=5),
      4                     optimizer='grid_search',
      5                     metrics=['mean_absolute_error'],
      6                     best_config_metric='mean_absolute_error')
      8 my_pipe.add(PipelineElement('GraphConstructorThreshold', threshold=0.95))
---> 10 my_pipe.add(PipelineElement('GraphMeasureTransform', graph_functions={"global_efficiency": {},
     11                                                                       "local_efficiency": {},
     12                                                                       "average_clustering": {},
     13                                                                       "degree_centrality": {},
     14                                                                       "betweenness_centrality": {},
     15                                                                       "overall_reciprocity": {}}))
     17 my_pipe.add(PipelineElement("SVR"))

File /opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/photonai/base/photon_elements.py:109, in PipelineElement.__init__(self, name, hyperparameters, test_disabled, disabled, base_element, batch_size, **kwargs)
    106     else:
    107         # if even after reload the element does not appear, it is not supported
    108         logger.error('Element not supported right now:' + name)
--> 109         raise NameError('Element not supported right now:', name)
    110 else:
    111     self.base_element = base_element

NameError: ('Element not supported right now:', 'GraphMeasureTransform')

Finally fit the pipeline to your data

In [4]:

my_pipe.fit(X, y)

my_pipe.fit(X, y)

=====================================================================================================
PHOTONAI ANALYSIS: basic_measure_pipe
=====================================================================================================
Last element in Hyperpipe should be an estimator.
Traceback (most recent call last):
  File "/opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/photonai/base/hyperpipe.py", line 1041, in fit
    self.check_for_imbalanced_data()
  File "/opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/photonai/base/hyperpipe.py", line 836, in check_for_imbalanced_data
    if self.estimation_type == 'classifier':
  File "/opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/photonai/base/hyperpipe.py", line 642, in estimation_type
    raise NotImplementedError("Last element in Hyperpipe should be an estimator.")
NotImplementedError: Last element in Hyperpipe should be an estimator.

Traceback (most recent call last):
  File "/opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/photonai/base/hyperpipe.py", line 1041, in fit
    self.check_for_imbalanced_data()
  File "/opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/photonai/base/hyperpipe.py", line 836, in check_for_imbalanced_data
    if self.estimation_type == 'classifier':
  File "/opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/photonai/base/hyperpipe.py", line 642, in estimation_type
    raise NotImplementedError("Last element in Hyperpipe should be an estimator.")
NotImplementedError: Last element in Hyperpipe should be an estimator.

---------------------------------------------------------------------------
NotImplementedError                       Traceback (most recent call last)
Cell In[4], line 1
----> 1 my_pipe.fit(X, y)

File /opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/photonai/base/hyperpipe.py:1119, in Hyperpipe.fit(self, data, targets, **kwargs)
   1117     logger.error(traceback.format_exc())
   1118     traceback.print_exc()
-> 1119     raise e
   1120 finally:
   1121     if self.nr_of_processes > 1:

File /opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/photonai/base/hyperpipe.py:1041, in Hyperpipe.fit(self, data, targets, **kwargs)
   1039 # sanity check data with setup
   1040 if not self.ignore_sanity_checks:
-> 1041     self.check_for_imbalanced_data()
   1043 # create photon pipeline
   1044 self._prepare_pipeline()

File /opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/photonai/base/hyperpipe.py:836, in Hyperpipe.check_for_imbalanced_data(self)
    835 def check_for_imbalanced_data(self):
--> 836     if self.estimation_type == 'classifier':
    837         targets = np.unique(self.data.y)
    838         num_classes = len(targets)

File /opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages/photonai/base/hyperpipe.py:642, in Hyperpipe.estimation_type(self)
    640 estimation_type = getattr(self.elements[-1], '_estimator_type')
    641 if estimation_type is None:
--> 642     raise NotImplementedError("Last element in Hyperpipe should be an estimator.")
    643 else:
    644     return estimation_type

NotImplementedError: Last element in Hyperpipe should be an estimator.