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@@ -4,13 +4,14 @@
 4 4 5 5 `__all__ = ["pagerank", "pagerank_numpy", "pagerank_scipy", "google_matrix"]` 6 6 7 + 7 8 `class PageRankResult(dict):` 8 - ` def __init__(self, pagerank_score, analytics_info) -> None:` 9 - ` super().__init__(pagerank_score)` 10 - ` self.pagerank_iterations = analytics_info['x']` 11 - ` self.convergence = analytics_info['err']` 12 - ` self.iterations = analytics_info['iterations']` 13 - ` self.return_message=analytics_info['return_message']` 9 + ` def __init__(self, pagerank_score, analytics_info) -> None:` 10 + ` super().__init__(pagerank_score)` 11 + ` self.pagerank_iterations = analytics_info["x"]` 12 + ` self.convergence = analytics_info["err"]` 13 + ` self.iterations = analytics_info["iterations"]` 14 + ` self.return_message = analytics_info["return_message"]` 14 15 15 16 16 17 `@not_implemented_for("multigraph")`
@@ -23,7 +24,7 @@
 23 24 ` nstart=None,` 24 25 ` weight="weight",` 25 26 ` dangling=None,` 26 - ` analytics=False` 27 + ` analytics=False,` 27 28 `):` 28 29 ` """Returns the PageRank of the nodes in the graph.` 29 30
@@ -153,11 +154,7 @@
 153 154 ` dangling_nodes = [n for n in W if W.out_degree(n, weight=weight) == 0.0]` 154 155 155 156 ` # power iteration: make up to max_iter iterations` 156 - ` analytics_info=dict(` 157 - ` x=[],` 158 - ` err=[],` 159 - ` iterations=0` 160 - ` )` 157 + ` analytics_info = dict(x=[], err=[], iterations=0)` 161 158 ` for _ in range(max_iter):` 162 159 ` xlast = x` 163 160 ` x = dict.fromkeys(xlast.keys(), 0)`
@@ -171,17 +168,22 @@
 171 168 ` # check convergence, l1 norm` 172 169 ` err = sum([abs(x[n] - xlast[n]) for n in x])` 173 170 ` if analytics:` 174 - ` analytics_info['x'].append(x)` 175 - ` analytics_info['err'].append(err)` 176 - ` analytics_info['iterations']+=1` 171 + ` analytics_info["x"].append(x)` 172 + ` analytics_info["err"].append(err)` 173 + ` analytics_info["iterations"] += 1` 177 174 ` if err < N * tol:` 178 - ` analytics_info['return_message'] = f"iteration converged within {analytics_info['iterations']} iterations"` 175 + ` analytics_info[` 176 + ` "return_message"` 177 + ` ] = f"iteration converged within {analytics_info['iterations']} iterations"` 179 178 ` return PageRankResult(x, analytics_info)` 180 179 ` if not analytics:` 181 180 ` raise nx.PowerIterationFailedConvergence(max_iter)` 182 - ` analytics_info['return_message']=f"power iteration failed to converge within {max_iter} iterations"` 181 + ` analytics_info[` 182 + ` "return_message"` 183 + ` ] = f"power iteration failed to converge within {max_iter} iterations"` 183 184 ` return PageRankResult(dict(), analytics_info)` 184 185 186 + 185 187 `def google_matrix(` 186 188 ` G, alpha=0.85, personalization=None, nodelist=None, weight="weight", dangling=None` 187 189 `):`

@@ -4,16 +4,18 @@
 4 4 5 5 `__all__ = ["hits", "hits_numpy", "hits_scipy", "authority_matrix", "hub_matrix"]` 6 6 7 + 7 8 `class HitsResult(tuple):` 8 - ` def __new__ (cls, hub_score, authority_score, analytics_info) -> tuple:` 9 + ` def __new__(cls, hub_score, authority_score, analytics_info) -> tuple:` 9 10 ` return super().__new__(cls, (hub_score, authority_score))` 10 - ` ` 11 + 11 12 ` def __init__(self, hub_score, authority_score, analytics_info) -> None:` 12 - ` self.hub_iterations=analytics_info['h']` 13 - ` self.authority_iterations=analytics_info['a']` 14 - ` self.convergence = analytics_info['err']` 15 - ` self.iterations = analytics_info['iterations']` 16 - ` self.return_message=analytics_info['return_message']` 13 + ` self.hub_iterations = analytics_info["h"]` 14 + ` self.authority_iterations = analytics_info["a"]` 15 + ` self.convergence = analytics_info["err"]` 16 + ` self.iterations = analytics_info["iterations"]` 17 + ` self.return_message = analytics_info["return_message"]` 18 + 17 19 18 20 `def hits(G, max_iter=100, tol=1.0e-8, nstart=None, normalized=True, analytics=False):` 19 21 ` """Returns HITS hubs and authorities values for nodes.`
@@ -38,7 +40,7 @@
 38 40 39 41 ` normalized : bool (default=True)` 40 42 ` Normalize results by the sum of all of the values.` 41 - ` ` 43 + 42 44 ` analytics : bool (default=False)` 43 45 ` Store the authority and hub scores and error delta of each Iteration.` 44 46 ` Iteration values are not normalized.`
@@ -96,12 +98,7 @@
 96 98 ` s = 1.0 / sum(h.values())` 97 99 ` for k in h:` 98 100 ` h[k] *= s` 99 - ` analytics_info=dict(` 100 - ` h=[],` 101 - ` a=[],` 102 - ` err=[],` 103 - ` iterations=0` 104 - ` )` 101 + ` analytics_info = dict(h=[], a=[], err=[], iterations=0)` 105 102 ` for _ in range(max_iter): # power iteration: make up to max_iter iterations` 106 103 ` hlast = h` 107 104 ` h = dict.fromkeys(hlast.keys(), 0)`
@@ -122,22 +119,26 @@
 122 119 ` # normalize vector` 123 120 ` s = 1.0 / max(a.values())` 124 121 ` for n in a:` 125 - ` a[n] *= s ` 122 + ` a[n] *= s` 126 123 ` # check convergence, l1 norm` 127 124 ` err = sum([abs(h[n] - hlast[n]) for n in h])` 128 125 ` if analytics:` 129 - ` analytics_info['a'].append(a)` 130 - ` analytics_info['h'].append(h)` 131 - ` analytics_info['err'].append(err)` 132 - ` analytics_info['iterations']+=1` 126 + ` analytics_info["a"].append(a)` 127 + ` analytics_info["h"].append(h)` 128 + ` analytics_info["err"].append(err)` 129 + ` analytics_info["iterations"] += 1` 133 130 ` if err < tol:` 134 - ` analytics_info['return_message']=f"iteration converged within {analytics_info['iterations']} iterations"` 131 + ` analytics_info[` 132 + ` "return_message"` 133 + ` ] = f"iteration converged within {analytics_info['iterations']} iterations"` 135 134 ` break` 136 135 ` else:` 137 136 ` if not analytics:` 138 137 ` raise nx.PowerIterationFailedConvergence(max_iter)` 139 - ` analytics_info['return_message']=f"power iteration failed to converge within {max_iter} iterations"` 140 - ` return HitsResult(dict(),dict(),analytics_info)` 138 + ` analytics_info[` 139 + ` "return_message"` 140 + ` ] = f"power iteration failed to converge within {max_iter} iterations"` 141 + ` return HitsResult(dict(), dict(), analytics_info)` 141 142 ` if normalized:` 142 143 ` s = 1.0 / sum(a.values())` 143 144 ` for n in a:`
@@ -145,7 +146,7 @@
 145 146 ` s = 1.0 / sum(h.values())` 146 147 ` for n in h:` 147 148 ` h[n] *= s` 148 - ` return HitsResult(h,a,analytics_info)` 149 + ` return HitsResult(h, a, analytics_info)` 149 150 150 151 151 152 `def authority_matrix(G, nodelist=None):`
Files Coverage
networkx 93.90%
Project Totals (275 files) 93.90%
7085.1
```TRAVIS_PYTHON_VERSION=3.6
TRAVIS_OS_NAME=linux
```
 1 ```# Allow coverage to decrease by 0.05%. ``` 2 ```coverage: ``` 3 ``` status: ``` 4 ``` project: ``` 5 ``` default: ``` 6 ``` threshold: 0.05% ``` 7 8 ```# Don't post a comment on pull requests. ``` 9 ```comment: off ```
Sunburst
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.
Icicle
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.
Grid
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.