2.5.3.6. Kernel Method for Persistence Diagrams

In this section, we provide classification example that uses the precomputed kernels for persistence diagrams. The detailed information about this approach is provided in Kernel Method. Transfer learning and parameter tuning is not available for this approach.

>>> from teaspoon.ML.PD_Classification import getPercentScore
>>> from teaspoon.ML import feature_functions as fF
>>> from teaspoon.ML.Base import ParameterBucket
>>> from teaspoon.MakeData.PointCloud import testSetManifolds
>>> from sklearn.preprocessing import LabelEncoder
>>> from sklearn.svm import SVC

>>> # generate persistence diagrams
>>> DgmsDF = testSetManifolds(numDgms=5, numPts=100)
>>> labels_col='trainingLabel'
>>> dgm_col=['Dgm1']

>>> # convert categorical labels into integers
>>> label_encoder = LabelEncoder()
>>> x = DgmsDF[labels_col]
>>> y = label_encoder.fit_transform(x)
>>> DgmsDF[labels_col] = y

>>> # set classification parameters
>>> params = ParameterBucket()
>>> params.feature_function = fF.KernelMethod
>>> params.k_fold_cv=5
>>> params.sigma = 0.25
>>> params.clf_model = SVC

>>> # perform classification
>>> c_report_train,c_report_test=getPercentScore(DgmsDF,
>>>                                             labels_col='trainingLabel',
>>>                                             dgm_col='Dgm1',
>>>                                             params=params,
>>>                                             precomputed = False,
>>>                                             saving = False,
>>>                                             saving_path = None)

Beginning experiments

Run Number: 1
Test set acc.: 0.167
Training set acc.: 0.542
------------------------------
Run Number: 2
Test set acc.: 0.500
Training set acc.: 0.667
------------------------------
Run Number: 3
Test set acc.: 0.667
Training set acc.: 0.750
------------------------------
Run Number: 4
Test set acc.: 0.167
Training set acc.: 0.792
------------------------------
Run Number: 5
Test set acc.: 0.667
Training set acc.: 0.792
------------------------------

Finished with training/testing experiments

Test Set
---------
Average accuracy: 0.433
Standard deviation: 0.226

Training Set
---------
Average accuracy: 0.708
Standard deviation: 0.095

For more metrics, see the outputs.