From 3094e1fe51acc49e4ea7e4f38648bb25d96784a4 Mon Sep 17 00:00:00 2001 From: Vincent Rouvreau Date: Fri, 5 Nov 2021 10:27:46 +0100 Subject: code review: factorize sample range computation --- src/python/gudhi/representations/vector_methods.py | 46 ++++++++++++---------- 1 file changed, 26 insertions(+), 20 deletions(-) (limited to 'src/python/gudhi') diff --git a/src/python/gudhi/representations/vector_methods.py b/src/python/gudhi/representations/vector_methods.py index e7ee57a4..140162af 100644 --- a/src/python/gudhi/representations/vector_methods.py +++ b/src/python/gudhi/representations/vector_methods.py @@ -6,6 +6,7 @@ # # Modification(s): # - 2020/06 Martin: ATOL integration +# - 2021/11 Vincent Rouvreau: factorize _automatic_sample_range import numpy as np from sklearn.base import BaseEstimator, TransformerMixin @@ -98,6 +99,23 @@ class PersistenceImage(BaseEstimator, TransformerMixin): """ return self.fit_transform([diag])[0,:] +def _automatic_sample_range(sample_range, X, y): + """ + Compute and returns sample range from the persistence diagrams if one of the sample_range values is numpy.nan. + + Parameters: + sample_range (a numpy array of 2 float): minimum and maximum of all piecewise-linear function domains, of + the form [x_min, x_max]. + X (list of n x 2 numpy arrays): input persistence diagrams. + y (n x 1 array): persistence diagram labels (unused). + """ + nan_in_range = np.isnan(sample_range) + if nan_in_range.any(): + pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler()), ([1], MinMaxScaler())]).fit(X,y) + [mx,my],[Mx,My] = [pre.scalers[0][1].data_min_[0], pre.scalers[1][1].data_min_[0]], [pre.scalers[0][1].data_max_[0], pre.scalers[1][1].data_max_[0]] + return np.where(nan_in_range, np.array([mx, My]), sample_range) + return sample_range + class Landscape(BaseEstimator, TransformerMixin): """ This is a class for computing persistence landscapes from a list of persistence diagrams. A persistence landscape is a collection of 1D piecewise-linear functions computed from the rank function associated to the persistence diagram. These piecewise-linear functions are then sampled evenly on a given range and the corresponding vectors of samples are concatenated and returned. See http://jmlr.org/papers/v16/bubenik15a.html for more details. @@ -123,14 +141,11 @@ class Landscape(BaseEstimator, TransformerMixin): X (list of n x 2 numpy arrays): input persistence diagrams. y (n x 1 array): persistence diagram labels (unused). """ - if self.nan_in_range.any(): - try: - pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler()), ([1], MinMaxScaler())]).fit(X,y) - [mx,my],[Mx,My] = [pre.scalers[0][1].data_min_[0], pre.scalers[1][1].data_min_[0]], [pre.scalers[0][1].data_max_[0], pre.scalers[1][1].data_max_[0]] - self.sample_range = np.where(self.nan_in_range, np.array([mx, My]), np.array(self.sample_range)) - except ValueError: - # Empty persistence diagram case - https://github.com/GUDHI/gudhi-devel/issues/507 - pass + try: + self.sample_range = _automatic_sample_range(np.array(self.sample_range), X, y) + except ValueError: + # Empty persistence diagram case - https://github.com/GUDHI/gudhi-devel/issues/507 + pass return self def transform(self, X): @@ -227,10 +242,7 @@ class Silhouette(BaseEstimator, TransformerMixin): y (n x 1 array): persistence diagram labels (unused). """ try: - if np.isnan(np.array(self.sample_range)).any(): - pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler()), ([1], MinMaxScaler())]).fit(X,y) - [mx,my],[Mx,My] = [pre.scalers[0][1].data_min_[0], pre.scalers[1][1].data_min_[0]], [pre.scalers[0][1].data_max_[0], pre.scalers[1][1].data_max_[0]] - self.sample_range = np.where(np.isnan(np.array(self.sample_range)), np.array([mx, My]), np.array(self.sample_range)) + self.sample_range = _automatic_sample_range(np.array(self.sample_range), X, y) except ValueError: # Empty persistence diagram case - https://github.com/GUDHI/gudhi-devel/issues/507 pass @@ -320,10 +332,7 @@ class BettiCurve(BaseEstimator, TransformerMixin): y (n x 1 array): persistence diagram labels (unused). """ try: - if np.isnan(np.array(self.sample_range)).any(): - pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler()), ([1], MinMaxScaler())]).fit(X,y) - [mx,my],[Mx,My] = [pre.scalers[0][1].data_min_[0], pre.scalers[1][1].data_min_[0]], [pre.scalers[0][1].data_max_[0], pre.scalers[1][1].data_max_[0]] - self.sample_range = np.where(np.isnan(np.array(self.sample_range)), np.array([mx, My]), np.array(self.sample_range)) + self.sample_range = _automatic_sample_range(np.array(self.sample_range), X, y) except ValueError: # Empty persistence diagram case - https://github.com/GUDHI/gudhi-devel/issues/507 pass @@ -391,10 +400,7 @@ class Entropy(BaseEstimator, TransformerMixin): y (n x 1 array): persistence diagram labels (unused). """ try: - if np.isnan(np.array(self.sample_range)).any(): - pre = DiagramScaler(use=True, scalers=[([0], MinMaxScaler()), ([1], MinMaxScaler())]).fit(X,y) - [mx,my],[Mx,My] = [pre.scalers[0][1].data_min_[0], pre.scalers[1][1].data_min_[0]], [pre.scalers[0][1].data_max_[0], pre.scalers[1][1].data_max_[0]] - self.sample_range = np.where(np.isnan(np.array(self.sample_range)), np.array([mx, My]), np.array(self.sample_range)) + self.sample_range = _automatic_sample_range(np.array(self.sample_range), X, y) except ValueError: # Empty persistence diagram case - https://github.com/GUDHI/gudhi-devel/issues/507 pass -- cgit v1.2.3