Carlsson Coordinates -------------------- In this section, we provide examples for classification of persistence diagrams using Carlsson Coordinates provided in :ref:`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. 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. 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.