rename color_function (#195)

* rename `color_function` rename it to `color_values`; raise deprecation warning if old used. * resolve confusion comment
scikit-tda · Aug 28, 2020 · 956b8c9 · 956b8c9
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diff --git a/.gitignore b/.gitignore
@@ -61,3 +61,5 @@ _modules
 .tox/
 .python-version
 
+# Kepler-Mapper specific things
+mapper_visualization_output.html
diff --git a/docs/notebooks/Cancer-demo.ipynb b/docs/notebooks/Cancer-demo.ipynb
@@ -117,10 +117,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "color_function = lens [:,0] - lens[:,0].min()\n",
+    "color_values = lens [:,0] - lens[:,0].min()\n",
     "my_colorscale = pl_brewer\n",
     "kmgraph,  mapper_summary, colorf_distribution = get_mapper_graph(scomplex, \n",
-    "                                                                 color_function,  \n",
+    "                                                                 color_values,  \n",
     "                                                                 color_function_name='Distance to x-min', \n",
     "                                                                 colorscale=my_colorscale)\n",
     "\n",
@@ -471,11 +471,11 @@
    ],
    "source": [
     "# visualise scomplex_isoForest\n",
-    "color_function = lens1[:,0] - lens1[:,0].min()\n",
+    "color_values = lens1[:,0] - lens1[:,0].min()\n",
     "my_colorscale = pl_brewer\n",
     "\n",
     "kmgraph,  mapper_summary, colorf_distribution = get_mapper_graph(scomplex_isoForest, \n",
-    "                                                                 color_function,  \n",
+    "                                                                 color_values,  \n",
     "                                                                 color_function_name='Distance to x-min', \n",
     "                                                                 colorscale=my_colorscale)\n",
     "\n",
@@ -537,11 +537,11 @@
    ],
    "source": [
     "# visualise scomplex_isoForest_pca\n",
-    "color_function = isoForest_pca[:,0] - isoForest_pca[:,0].min()\n",
+    "color_values = isoForest_pca[:,0] - isoForest_pca[:,0].min()\n",
     "my_colorscale = pl_brewer\n",
     "\n",
     "kmgraph,  mapper_summary, colorf_distribution = get_mapper_graph(scomplex_isoForest_pca, \n",
-    "                                                                 color_function,  \n",
+    "                                                                 color_values,  \n",
     "                                                                 color_function_name='Distance to x-min', \n",
     "                                                                 colorscale=my_colorscale)\n",
     "\n",
@@ -683,11 +683,11 @@
    ],
    "source": [
     "# visualise scomplex_radius\n",
-    "color_function = lens4[:,0] - lens4[:,0].min()\n",
+    "color_values = lens4[:,0] - lens4[:,0].min()\n",
     "my_colorscale = pl_brewer\n",
     "\n",
     "kmgraph,  mapper_summary, colorf_distribution = get_mapper_graph(scomplex_texture, \n",
-    "                                                                 color_function,  \n",
+    "                                                                 color_values,  \n",
     "                                                                 color_function_name='Distance to x-min', \n",
     "                                                                 colorscale=my_colorscale)\n",
     "\n",
@@ -749,11 +749,11 @@
    ],
    "source": [
     "# visualise scomplex_radius_l2norm\n",
-    "color_function = texture_l2norm[:,0] - texture_l2norm[:,0].min()\n",
+    "color_values = texture_l2norm[:,0] - texture_l2norm[:,0].min()\n",
     "my_colorscale = pl_brewer\n",
     "\n",
     "kmgraph,  mapper_summary, colorf_distribution = get_mapper_graph(scomplex_texture_l2norm, \n",
-    "                                                                 color_function,  \n",
+    "                                                                 color_values,  \n",
     "                                                                 color_function_name='Distance to x-min', \n",
     "                                                                 colorscale=my_colorscale)\n",
     "\n",

diff --git a/docs/notebooks/Confidence-Graphs.ipynb b/docs/notebooks/Confidence-Graphs.ipynb
@@ -912,7 +912,7 @@
     "                     lens=X_projected_test,\n",
     "                     lens_names=[\"Uncertainty\", \"Error\"],\n",
     "                     custom_tooltips=tooltip_s,\n",
-    "                     color_function=color_function_output.values,\n",
+    "                     color_values=color_function_output.values,\n",
     "                     title=\"Confidence Graph for a MLP trained on MNIST\",\n",
     "                     path_html=\"confidence_graph_output.html\")"
    ]

diff --git a/docs/notebooks/KeplerMapper-Newsgroup20-Pipeline.ipynb b/docs/notebooks/KeplerMapper-Newsgroup20-Pipeline.ipynb
@@ -594,7 +594,7 @@
     "                        projected_X_names=[\"ISOMAP1\", \"ISOMAP2\"],\n",
     "                        title=\"Newsgroups20: Latent Semantic Char-gram Analysis with Isometric Embedding\",\n",
     "                        custom_tooltips=np.array([target_names[ys] for ys in y]),\n",
-    "                        color_function=y)\n",
+    "                        color_values=y)\n",
     "# jupyter.display(\"newsgroups20.html\")"
    ]
   },

diff --git a/docs/notebooks/TOR-XGB-TDA.ipynb b/docs/notebooks/TOR-XGB-TDA.ipynb
@@ -766,7 +766,7 @@
     "_ = mapper.visualize(\n",
     "    G,\n",
     "    custom_tooltips=y,\n",
-    "    color_function=y,\n",
+    "    color_values=y,\n",
     "    path_html=\"tor-tda.html\",\n",
     "    inverse_X=X,\n",
     "    inverse_X_names=list(df[features].columns),\n",

diff --git a/docs/reference/stubs/kmapper.adapter.rst b/docs/reference/stubs/kmapper.adapter.rst
@@ -1,10 +1,14 @@
-kmapper.adapter
+kmapper.adapter
 ===============
 
 .. automodule:: kmapper.adapter
 
 
 
+
+
+
+
    .. rubric:: Functions
 
    .. autosummary::
@@ -20,4 +24,7 @@ kmapper.adapter
 
 
 
-
+
+
+
+
diff --git a/docs/reference/stubs/kmapper.jupyter.rst b/docs/reference/stubs/kmapper.jupyter.rst
@@ -1,10 +1,14 @@
-kmapper.jupyter
+kmapper.jupyter
 ===============
 
 .. automodule:: kmapper.jupyter
 
 
 
+
+
+
+
    .. rubric:: Functions
 
    .. autosummary::
@@ -19,4 +23,7 @@ kmapper.jupyter
 
 
 
-
+
+
+
+
diff --git a/examples/breast-cancer/breast-cancer.ipynb b/examples/breast-cancer/breast-cancer.ipynb
@@ -116,10 +116,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "color_function = lens [:,0] - lens[:,0].min()\n",
+    "color_values = lens [:,0] - lens[:,0].min()\n",
     "my_colorscale = pl_brewer\n",
     "kmgraph,  mapper_summary, colorf_distribution = get_mapper_graph(scomplex, \n",
-    "                                                                 color_function,  \n",
+    "                                                                 color_values,  \n",
     "                                                                 color_function_name='Distance to x-min', \n",
     "                                                                 colorscale=my_colorscale)\n",
     "\n",

diff --git a/examples/digits/digits.ipynb b/examples/digits/digits.ipynb
@@ -80,7 +80,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The mapper graph nodes associated to this data set are colored according to the `color_function` values with Viridis colorscale,\n",
+    "The mapper graph nodes associated to this data set are colored according to the `color_values` values with Viridis colorscale,\n",
     "which is the default color scale, defined in `plotlyviz`."
    ]
   },
@@ -90,10 +90,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "color_function = projected_data[:, 1]-projected_data[:, 1].min()\n",
+    "color_values = projected_data[:, 1]-projected_data[:, 1].min()\n",
     "plotlyviz(scomplex, \n",
     "          title='Mapper graph of digits dataset',\n",
-    "          color_function=color_function, \n",
+    "          color_values=color_values, \n",
     "          color_function_name='Distance to y-min', \n",
     "          node_linecolor='rgb(100,100,100)',\n",
     "          bgcolor='rgb(240,240,240)',\n",
@@ -111,10 +111,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "color_function = projected_data[:, 1]-projected_data[:, 1].min()\n",
+    "color_values = projected_data[:, 1]-projected_data[:, 1].min()\n",
     "plotlyviz(scomplex, \n",
     "          title='Mapper graph of digits dataset',\n",
-    "          color_function=color_function, \n",
+    "          color_values=color_values, \n",
     "          color_function_name='Distance to y-min', \n",
     "          node_linecolor='rgb(100,100,100)',\n",
     "          bgcolor='rgb(240,240,240)',\n",

diff --git a/examples/makecircles/make_circles_distmean.py b/examples/makecircles/make_circles_distmean.py
@@ -20,4 +20,4 @@
 mapper.visualize(simplicial_complex, path_html="keplermapper-makecircles-distmean.html",
                  custom_meta={"Data:": "datasets.make_circles(n_samples=5000, noise=0.05, factor=0.3)"},
                  custom_tooltips=labels,
-                 color_function=labels)
+                 color_values=labels)
diff --git a/examples/output/breast-cancer.html b/examples/output/breast-cancer.html
diff --git a/examples/output/cat.html b/examples/output/cat.html
@@ -396,9 +396,9 @@ <h3>n_cubes </h3> <p> 15</p>
 
         <h3>perc_overlap </h3> <p> 0.2</p>
 
-        <h3>clusterer </h3> <p> DBSCAN(algorithm='auto', eps=0.1, leaf_size=30, metric='euclidean',<br>    metric_params=None, min_samples=5, n_jobs=None, p=None)</p>
+        <h3>clusterer </h3> <p> DBSCAN(eps=0.1)</p>
 
-        <h3>scaler </h3> <p> MinMaxScaler(copy=True, feature_range=(0, 1))</p>
+        <h3>scaler </h3> <p> MinMaxScaler()</p>
 
 
 

diff --git a/examples/output/horse.html b/examples/output/horse.html
@@ -396,9 +396,9 @@ <h3>n_cubes </h3> <p> 30</p>
 
         <h3>perc_overlap </h3> <p> 0.2</p>
 
-        <h3>clusterer </h3> <p> DBSCAN(algorithm='auto', eps=0.1, leaf_size=30, metric='euclidean',<br>    metric_params=None, min_samples=5, n_jobs=None, p=None)</p>
+        <h3>clusterer </h3> <p> DBSCAN(eps=0.1)</p>
 
-        <h3>scaler </h3> <p> MinMaxScaler(copy=True, feature_range=(0, 1))</p>
+        <h3>scaler </h3> <p> MinMaxScaler()</p>
 
 
 

diff --git a/examples/plot_digits.py b/examples/plot_digits.py
@@ -3,7 +3,7 @@
 Digits Dataset
 ================
 
-This digits example shows two ways of customizing the tooltips options in the HTML visualization. It generates the visualization with tooltips set as the y-label, or number of the image. The second generated result uses the actual image in the tooltips. 
+This digits example shows two ways of customizing the tooltips options in the HTML visualization. It generates the visualization with tooltips set as the y-label, or number of the image. The second generated result uses the actual image in the tooltips.
 
 `Visualization with y-label tooltip <../../_static/digits_ylabel_tooltips.html>`_
 
@@ -63,7 +63,7 @@
 mapper.visualize(graph,
                  title="Handwritten digits Mapper",
                  path_html="output/digits_custom_tooltips.html",
-                 color_function=labels,
+                 color_values=labels,
                  custom_tooltips=tooltip_s)
 # Tooltips with the target y-labels for every cluster member
 mapper.visualize(graph,
@@ -73,4 +73,4 @@
 
 # Matplotlib examples
 km.draw_matplotlib(graph, layout="spring")
-plt.show()
+plt.show()
diff --git a/kmapper/drawing.py b/kmapper/drawing.py
@@ -19,15 +19,15 @@ def draw_matplotlib(g, ax=None, fig=None, layout="kk"):
 
     ax: matplotlib Axes object
         A matplotlib axes object to plot graph on. If none, then use ``plt.gca()``
-    
+
     fig: matplotlib Figure object
         A matplotlib Figure object to plot graph on. If none, then use ``plt.figure()``
 
     layout: string
-        Key for which of NetworkX's layout functions. 
-        Key options implemented are: 
+        Key for which of NetworkX's layout functions.
+        Key options implemented are:
         ::
-        
+
             >>> "kk": nx.kamada_kawai_layout,
             >>> "spring": nx.spring_layout,
             >>> "bi": nx.bipartite_layout,
@@ -41,6 +41,13 @@ def draw_matplotlib(g, ax=None, fig=None, layout="kk"):
 
     """
     import networkx as nx
+    import os
+    # https://stackoverflow.com/a/50089385/5917194
+    import matplotlib as mpl
+    if os.environ.get('DISPLAY','') == '':
+        print('no display found. Using non-interactive Agg backend')
+        mpl.use('Agg')
+
     import matplotlib.pyplot as plt
 
     fig = fig if fig else plt.figure()

diff --git a/kmapper/kmapper.py b/kmapper/kmapper.py
@@ -18,13 +18,14 @@
 from .cover import Cover
 from .nerve import GraphNerve
 from .visuals import (
-    init_color_function,
+    init_color_values,
     format_meta,
     format_mapper_data,
     build_histogram,
     graph_data_distribution,
     colorscale_default,
 )
+from .utils import deprecated_alias
 
 __all__ = [
     "KeplerMapper",
@@ -601,17 +602,18 @@ def _remove_duplicate_nodes(self, nodes):
         return deduped_nodes
 
     def _summary(self, graph, time):
-        # TODO: this summary is dependant on the type of Nerve being built.
+        # TODO: this summary is dependent on the type of Nerve being built.
         links = graph["links"]
         nodes = graph["nodes"]
         nr_links = sum(len(v) for k, v in links.items())
 
         print("\nCreated %s edges and %s nodes in %s." % (nr_links, len(nodes), time))
 
+    @deprecated_alias(color_function='color_values')
     def visualize(
         self,
         graph,
-        color_function=None,
+        color_values=None,
         colorscale=None,
         custom_tooltips=None,
         custom_meta=None,
@@ -633,6 +635,10 @@ def visualize(
             Simplicial complex output from the `map` method.
 
         color_function : list or 1d array
+            .. deprecated:: 1.4.1
+               Use `color_values` instead.
+
+        color_values : list or 1d array
             A 1d vector with length equal to number of data points used to build Mapper. Each value should correspond to a value for each data point and color of node is computed as the average value for members in a node.
 
         colorscale : list
@@ -693,17 +699,17 @@ def visualize(
         >>>                  "Cluster": "HBSCAN()"}
         >>> )
 
-        >>> # Custom coloring function based on your 1d lens
+        >>> # Custom coloring data based on your 1d lens
         >>> html = mapper.visualize(
         >>>     graph,
-        >>>     color_function=lens
+        >>>     color_values=lens
         >>> )
 
-        >>> # Custom coloring function based on the first variable
+        >>> # Custom coloring data based on the first variable
         >>> cf = mapper.project(X, projection=[0])
         >>> html = mapper.visualize(
         >>>     graph,
-        >>>     color_function=cf
+        >>>     color_values=cf
         >>> )
 
         >>> # Customizing the tooltips with binary target variables
@@ -741,8 +747,7 @@ def visualize(
         # Find the module absolute path and locate templates
         module_root = os.path.join(os.path.dirname(__file__), "templates")
         env = Environment(loader=FileSystemLoader(module_root))
-        # Color function is a vector of colors?
-        color_function = init_color_function(graph, color_function)
+        color_values = init_color_values(graph, color_values)
 
         if X_names is None:
             X_names = []
@@ -752,7 +757,7 @@ def visualize(
 
         mapper_data = format_mapper_data(
             graph,
-            color_function,
+            color_values,
             X,
             X_names,
             lens,
@@ -763,7 +768,7 @@ def visualize(
             colorscale=colorscale,
         )
 
-        histogram = graph_data_distribution(graph, color_function, colorscale)
+        histogram = graph_data_distribution(graph, color_values, colorscale)
 
         mapper_summary = format_meta(graph, custom_meta)