Commit ⋅ scikit-tda/kepler-mapper

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# A small helper class to house functions needed by KeplerMapper.visualize
import numpy as np
import scipy.sparse
from sklearn import preprocessing
import json
from collections import defaultdict

@@ -266,6 +267,12 @@

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        if X_names.shape[0] == 0:
            X_names = np.array(["f_%s" % (i) for i in range(X.shape[1])])

        # be explicit about the allowed sparse formats
        if scipy.sparse.issparse(X):
            if X.format not in ["csr", "csc"]:
                raise ValueError(
                    f"sparse matrix format must be csr or csc but found {X.format}")

        # wrap cluster_X_mean, X_mean, and X_std in np.array(---).squeeze()
        # to get the same treatment for dense and sparse arrays
        cluster_X_mean = np.array(

Files		•	•	•	Coverage
kmapper	726	571	28	127	78.65%
Project Totals (10 files)	726	571	28	127	78.65%

3.6=.6
TRAVIS_OS_NAME=linux

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3.7=.7
TRAVIS_OS_NAME=linux

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266	267		if X_names.shape[0] == 0:
267	268		X_names = np.array(["f_%s" % (i) for i in range(X.shape[1])])
268	269
	270	+	# be explicit about the allowed sparse formats
	271	+	if scipy.sparse.issparse(X):
	272	+	if X.format not in ["csr", "csc"]:
	273	+	raise ValueError(
	274	+	f"sparse matrix format must be csr or csc but found {X.format}")
	275	+
269	276		# wrap cluster_X_mean, X_mean, and X_std in np.array(---).squeeze()
270	277		# to get the same treatment for dense and sparse arrays
271	278		cluster_X_mean = np.array(

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1	1		# A small helper class to house functions needed by KeplerMapper.visualize
2	2		import numpy as np
	3	+	import scipy.sparse
3	4		from sklearn import preprocessing
4	5		import json
5	6		from collections import defaultdict