2.5.3.3. Carlsson Coordinates
In this section, we provide examples for classification of persistence diagrams using Carlsson Coordinates provided in Carlsson Coordinates. In below example, user provide a set of persistence diagrams in a Pandas dataframe inluding the labels of each persistence diagram. Then, classification parameters are selected and persistence diagrams are classified. In addition, user can choose the number of coordinates to be used in feature matrix generation.
>>> 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=20, 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.F_CCoordinates
>>> params.k_fold_cv=5
>>> params.FN =3
>>> params.clf_model = SVC
>>> 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.: 0.625
Training set acc.: 0.698
------------------------------
Run Number: 2
Test set acc.: 0.583
Training set acc.: 0.677
------------------------------
Run Number: 3
Test set acc.: 0.542
Training set acc.: 0.656
------------------------------
Run Number: 4
Test set acc.: 0.667
Training set acc.: 0.667
------------------------------
Run Number: 5
Test set acc.: 0.583
Training set acc.: 0.688
------------------------------
Finished with training/testing experiments
Test Set
---------
Average accuracy: 0.600
Standard deviation: 0.042
Training Set
---------
Average accuracy: 0.677
Standard deviation: 0.015
For more metrics, see the outputs.
2.5.3.3.1. Transfer learning between two sets of persistence diagrams
Machine learning module of teaspoon also provides user with transfer learning option. When it is enabled, user can train and test a classifier on two different sets of persistence diagrams. In this example, we first generate two sets of persistence diagrams. Categorical labels of the diagrams are converted into the integers. In the last step, we set classification parameters and perform the classification using SVM.
>>> 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
>>> import numpy as np
>>> # generate persistence diagrams
>>> DgmsDF_train = testSetManifolds(numDgms=20, numPts=100)
>>> DgmsDF_test = testSetManifolds(numDgms=20, numPts=100)
>>> labels_col='trainingLabel'
>>> dgm_col='Dgm1'
>>> # convert categorical labels into integers
>>> label_encoder = LabelEncoder()
>>> x_train,x_test = DgmsDF_train[labels_col],DgmsDF_test[labels_col]
>>> y_train = label_encoder.fit_transform(x_train)
>>> y_test = label_encoder.fit_transform(x_test)
>>> DgmsDF_train[labels_col],DgmsDF_test[labels_col] = y_train,y_test
>>> # set classification parameters
>>> params = ParameterBucket()
>>> params.feature_function = fF.F_CCoordinates
>>> params.k_fold_cv=5
>>> params.TF_Learning=True
>>> params.FN = 5
>>> params.clf_model = SVC
>>> c_report_train,c_report_test=getPercentScore(DgmsDF_train,
>>> labels_col='trainingLabel',
>>> dgm_col='Dgm1',
>>> params=params,
>>> precomputed = False,
>>> saving = False,
>>> saving_path = None,
>>> DgmsDF_test = DgmsDF_test)
Beginning experiments
Run Number: 1
Test set acc.: 0.688
Training set acc.: 0.708
------------------------------
Run Number: 2
Test set acc.: 0.708
Training set acc.: 0.719
------------------------------
Run Number: 3
Test set acc.: 0.656
Training set acc.: 0.708
------------------------------
Run Number: 4
Test set acc.: 0.771
Training set acc.: 0.708
------------------------------
Run Number: 5
Test set acc.: 0.667
Training set acc.: 0.729
------------------------------
Finished with training/testing experiments
Test Set
---------
Average accuracy: 0.698
Standard deviation: 0.041
Training Set
---------
Average accuracy: 0.715
Standard deviation: 0.008
For more metrics, see the outputs.
2.5.3.3.2. Hyperparameter tuning
Our package also provides user with hyperparameter tuning. When it is enabled, user is expected to provide the parameters and their range in a dictionary to tune parameters. Algorithm implements GridSearchCV.
>>> import numpy as np
>>> 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=20, 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.F_CCoordinates
>>> params.k_fold_cv=5
>>> params.FN =3
>>> params.clf_model = SVC
>>> params.param_tuning = True
>>> # parameters to tune and their range
>>> gamma_range = np.logspace(-3, 3, num=5)
>>> lambda_range = np.logspace(-3, 3, num=5)
>>> params.parToTune = [] # the list that contains the parameters to tune for each classifier
>>> params.parToTune.append({'C': lambda_range,'gamma':gamma_range}) # SVM paramters
>>> #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.: 0.750
Training set acc.: 0.844
------------------------------
Run Number: 2
Test set acc.: 0.750
Training set acc.: 0.844
------------------------------
Run Number: 3
Test set acc.: 0.750
Training set acc.: 0.854
------------------------------
Run Number: 4
Test set acc.: 0.875
Training set acc.: 0.823
------------------------------
Run Number: 5
Test set acc.: 0.792
Training set acc.: 0.844
------------------------------
Finished with training/testing experiments
Test Set
---------
Average accuracy: 0.783
Standard deviation: 0.049
Training Set
---------
Average accuracy: 0.842
Standard deviation: 0.010
For more metrics, see the outputs.