Commit ⋅ scikit-tda/kepler-mapper

Showing 1 of 2 files from the diff.

Other files ignored by Codecov

@@ -1,5 +1,6 @@

Loading

# 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

@@ -58,7 +59,7 @@

Loading



def _colors_to_rgb(colorscale):
    """ Ensure that the color scale is formatted in rgb strings. 
    """ Ensure that the color scale is formatted in rgb strings.
        If the colorscale is a hex string, then convert to rgb.
    """
    if colorscale[0][1][0] == "#":

@@ -266,9 +267,25 @@

Loading

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

        cluster_X_mean = np.mean(X[member_ids], axis=0)
        X_mean = np.mean(X, axis=0)
        X_std = np.std(X, axis=0)
        # be explicit about the allowed sparse formats
        if scipy.sparse.issparse(X):
            if X.format not in ["csr", "csc"]:
                raise ValueError(
                    "sparse matrix format must be csr or csc but found {}".format(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(
            np.mean(X[member_ids], axis=0)
        ).squeeze()
        X_mean = np.array(
            np.mean(X, axis=0)
        ).squeeze()
        X_std = np.array(
            # use StandardScaler as a way to get std for dense or sparse array
            np.sqrt(preprocessing.StandardScaler(with_mean=False).fit(X).var_)
        ).squeeze()

        above_mean = cluster_X_mean > X_mean
        std_m = np.sqrt((cluster_X_mean - X_mean) ** 2) / X_std


@@ -276,10 +293,10 @@

Loading

            zip(
                std_m,
                X_names,
                np.mean(X, axis=0),
                X_mean,
                cluster_X_mean,
                above_mean,
                np.std(X, axis=0),
                X_std,
            )
        )
        stats = sorted(stat_zip, reverse=True)

Files		•	•	•	Coverage
kmapper	726	575	27	124	79.20%
Project Totals (10 files)	726	575	27	124	79.20%

3.6=.6
TRAVIS_OS_NAME=linux

View CI Build Download

TRAVIS_OS_NAME=linux
2.7=.7

View CI Build Download

3.5=.5
TRAVIS_OS_NAME=linux

View CI Build Download

3.7=.7
TRAVIS_OS_NAME=linux

View CI Build Download

3.4=.4
TRAVIS_OS_NAME=linux

View CI Build Download

No yaml found.

Create your codecov.yml to customize your Codecov experience

Read the docs

Interact now

The inner-most circle is the entire project, moving away from the center are folders then, finally, a single file. The size and color of each slice is representing the number of statements and the coverage, respectively.

SVG

The top section represents the entire project. Proceeding with folders and finally individual files. The size and color of each slice is representing the number of statements and the coverage, respectively.

SVG

Each block represents a single file in the project. The size and color of each block is represented by the number of statements and the coverage, respectively.

SVG

Learn more about the coverage sunburst graph

58	59
59	60
60	61		def _colors_to_rgb(colorscale):
61		-	""" Ensure that the color scale is formatted in rgb strings.
	62	+	""" Ensure that the color scale is formatted in rgb strings.
62	63		If the colorscale is a hex string, then convert to rgb.
63	64		"""
64	65		if colorscale[0][1][0] == "#":

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
269		-	cluster_X_mean = np.mean(X[member_ids], axis=0)
270		-	X_mean = np.mean(X, axis=0)
271		-	X_std = np.std(X, axis=0)
	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	+	"sparse matrix format must be csr or csc but found {}".format(X.format))
	275	+
	276	+	# wrap cluster_X_mean, X_mean, and X_std in np.array(---).squeeze()
	277	+	# to get the same treatment for dense and sparse arrays
	278	+	cluster_X_mean = np.array(
	279	+	np.mean(X[member_ids], axis=0)
	280	+	).squeeze()
	281	+	X_mean = np.array(
	282	+	np.mean(X, axis=0)
	283	+	).squeeze()
	284	+	X_std = np.array(
	285	+	# use StandardScaler as a way to get std for dense or sparse array
	286	+	np.sqrt(preprocessing.StandardScaler(with_mean=False).fit(X).var_)
	287	+	).squeeze()
	288	+
272	289		above_mean = cluster_X_mean > X_mean
273	290		std_m = np.sqrt((cluster_X_mean - X_mean) ** 2) / X_std
274	291

276	293		zip(
277	294		std_m,
278	295		X_names,
279		-	np.mean(X, axis=0),
	296	+	X_mean,
280	297		cluster_X_mean,
281	298		above_mean,
282		-	np.std(X, axis=0),
	299	+	X_std,
283	300		)
284	301		)
285	302		stats = sorted(stat_zip, reverse=True)

Navigation	Overlay
`t` Navigate files	`h` Toggle hits
`y` Change url to tip of branch	`m` Toggle misses
`b / v` Jump to prev/next hit line	`p` Toggle partial
`z / x` Jump to prev/next missed or partial line	`1..9` Toggle flags
`shift + o` Open current page in GitHub	`a` Toggle all on
`/ or ?` Show keyboard shortcuts dialog	`c` Toggle context lines or commits

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