2.5.3.4. Template Functions
In this section, we provide classification examples using Template Functions approach. For more details, please refer to Ref. [8]. We perform classification using SVM. User can select different classifier models as well. Multiple dimensions of persistence diagrams can be selected to generate features matrices. This option is only valid for template function.
>>> 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=['Dgm0','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.interp_polynomial
>>> params.k_fold_cv=10
>>> params.d = 20
>>> params.jacobi_poly = 'cheb1' # choose the interpolating polynomial
>>> params.useAdaptivePart = False
>>> params.clf_model = SVC
>>> params.TF_Learning = False
>>> # perform classification
>>> c_report_train,c_report_test=getPercentScore(DgmsDF,
>>> labels_col='trainingLabel',
>>> dgm_col=dgm_col,
>>> params=params,
>>> precomputed = False,
>>> saving = False,
>>> saving_path = None)
Beginning experiments
Run Number: 1
Test set acc.: 1.000
Training set acc.: 1.000
------------------------------
Run Number: 2
Test set acc.: 0.958
Training set acc.: 0.979
------------------------------
Run Number: 3
Test set acc.: 0.917
Training set acc.: 1.000
------------------------------
Run Number: 4
Test set acc.: 1.000
Training set acc.: 1.000
------------------------------
Run Number: 5
Test set acc.: 1.000
Training set acc.: 1.000
------------------------------
Finished with training/testing experiments
Test Set
---------
Average accuracy: 0.975
Standard deviation: 0.033
Training Set
---------
Average accuracy: 0.996
Standard deviation: 0.008
For more metrics, see the outputs.
2.5.3.4.1. 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
f>> rom 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=['Dgm0']
>>> # 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.interp_polynomial
>>> params.k_fold_cv=5
>>> params.d = 20
>>> params.jacobi_poly = 'cheb1' # choose the interpolating polynomial
>>> params.useAdaptivePart = False
>>> params.clf_model = SVC
>>> params.TF_Learning = True
>>> # perform classification
>>> c_report_train,c_report_test=getPercentScore(DgmsDF_train,
>>> labels_col='trainingLabel',
>>> dgm_col=dgm_col,
>>> params=params,
>>> precomputed = False,
>>> saving = False,
>>> saving_path = None,
>>> DgmsDF_test = DgmsDF_test)
Beginning experiments
Run Number: 1
Test set acc.: 0.823
Training set acc.: 0.865
------------------------------
Run Number: 2
Test set acc.: 0.823
Training set acc.: 0.885
------------------------------
Run Number: 3
Test set acc.: 0.844
Training set acc.: 0.844
------------------------------
Run Number: 4
Test set acc.: 0.854
Training set acc.: 0.854
------------------------------
Run Number: 5
Test set acc.: 0.854
Training set acc.: 0.865
------------------------------
Finished with training/testing experiments
Test Set
---------
Average accuracy: 0.840
Standard deviation: 0.014
Training Set
---------
Average accuracy: 0.863
Standard deviation: 0.014
For more metrics, see the outputs.
2.5.3.4.2. 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.interp_polynomial
>>> params.k_fold_cv=5
>>> params.d = 20
>>> params.jacobi_poly = 'cheb1' # choose the interpolating polynomial
>>> params.useAdaptivePart = False
>>> params.clf_model = SVC
>>> params.TF_Learning = False
>>> params.param_tuning = True
>>> # parameters to tune and their ranges
>>> gamma_range = np.logspace(-3, 3, num=5)
>>> lambda_range = np.logspace(-3, 3, num=5)
>>> params.parToTune = [] # the list that contains the paramters 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.: 1.000
Training set acc.: 1.000
------------------------------
Run Number: 2
Test set acc.: 0.958
Training set acc.: 1.000
------------------------------
Run Number: 3
Test set acc.: 0.917
Training set acc.: 1.000
------------------------------
Run Number: 4
Test set acc.: 1.000
Training set acc.: 1.000
------------------------------
Run Number: 5
Test set acc.: 1.000
Training set acc.: 1.000
------------------------------
Finished with training/testing experiments
Test Set
---------
Average accuracy: 0.975
Standard deviation: 0.033
Training Set
---------
Average accuracy: 1.000
Standard deviation: 0.000
For more metrics, see the outputs.