Venturellac dev ff upset plot

CHANGELOG.md

@@ -3,6 +3,8 @@ All notable changes to this project will be documented in this file.
 This project adheres to [Semantic Versioning](http://semver.org/).
 
 ## UNRELEASED
+  - added the upset plot type in figure factory for showing the intersection between non-mutually exclusive characteristics in a population (my forum post can be found here which details more of the functionality: https://community.plotly.com/t/plotly-upset-plot/63858)
+
 
 ### Updated
   - Updated Plotly.js to from version 2.12.1 to version 2.13.3. See the [plotly.js CHANGELOG](https://github.com/plotly/plotly.js/blob/master/CHANGELOG.md#2133----2022-07-25) for more information. Notable changes include:

packages/python/plotly/plotly/figure_factory/__init__.py

@@ -26,6 +26,7 @@
 from plotly.figure_factory._table import create_table
 from plotly.figure_factory._trisurf import create_trisurf
 from plotly.figure_factory._violin import create_violin
+from plotly.figure_factory._upset import create_upset
 
 if optional_imports.get_module("pandas") is not None:
     from plotly.figure_factory._county_choropleth import create_choropleth
@@ -66,4 +67,5 @@ def create_ternary_contour(*args, **kwargs):
     "create_ternary_contour",
     "create_trisurf",
     "create_violin",
+    "create_upset"
 ]

packages/python/plotly/plotly/figure_factory/_upset.py

@@ -0,0 +1,81 @@
+from __future__ import absolute_import
+
+from numbers import Number
+import itertools
+from plotly import exceptions, optional_imports
+import plotly.colors as clrs
+from plotly.graph_objs import graph_objs
+from plotly.subplots import make_subplots
+
+pd = optional_imports.get_module("pandas")
+np = optional_imports.get_module("numpy")
+
+
+def create_upset(df, include_empty_set = False, max_width = 50):
+    # an array of dimensions d x d*2^d possible subsets where d is the number of columns
+    subsets = []
+    # the sizes of each subset (2^d array)
+    subset_sizes = [ ]
+    d = len(df.columns)
+    for i in range(1, d + 1):
+        subsets = subsets + [list(x) for x in list(itertools.combinations(df.columns, i))]
+    if include_empty_set: subsets = subsets + [[]]
+
+    for s in subsets:
+        curr_bool = [1]*len(df)
+        for col in df.columns:
+            if col in s: curr_bool = [x and y for x, y in zip(curr_bool, list(df.loc[:, col].copy()))]
+            else: curr_bool = [x and not y for x, y in zip(curr_bool, list(df.loc[:, col].copy()))]
+        subset_sizes.append(sum(curr_bool))
+
+
+    plot_df = pd.DataFrame({'Intersection': subsets, 'Size':subset_sizes})
+    plot_df = plot_df.sort_values(by = 'Size', ascending = False)
+    max_y = max(plot_df['Size'])+0.1*max(plot_df['Size'])
+
+    if not max_width is None and len(plot_df) > max_width: plot_df = plot_df.iloc[0:max_width,:]
+
+    subsets = list(plot_df['Intersection'])
+    scatter_x = []
+    scatter_y = []
+    for i, s in enumerate(subsets):
+        for j in range(d):
+            scatter_x.append(i)
+            scatter_y.append(-j*max_y/d-0.1*max_y)
+
+    fig = graph_objs.Figure()
+#     fig.add_trace(graph_objs.Scatter(x=[-1.2,len(subsets)],y= [max_y+0.1*max_y,max_y+0.1*max_y],fill='tozeroy'))
+    template =  ['' for x in scatter_x]
+    fig.add_trace(graph_objs.Scatter(x = scatter_x, y = scatter_y, mode = 'markers', showlegend=False, marker=dict(size=16,color='#C9C9C9'), hovertemplate = template))
+    fig.update_layout(xaxis=dict(showgrid=False, zeroline=False),
+                  yaxis=dict(showgrid=True, zeroline=False),
+                   plot_bgcolor = "#FFFFFF", margin=dict(t=40, l=150)) 
+    for i, s in enumerate(subsets):
+        scatter_x_has = []
+        scatter_y_has = []
+        for j in range(d):
+            if df.columns[j] in s:
+                scatter_x_has.append(i)
+                scatter_y_has.append(-j*max_y/d-0.1*max_y)
+                fig.add_trace(graph_objs.Scatter(x = scatter_x_has, y = scatter_y_has, mode = 'markers+lines', showlegend=False, marker=dict(size=16,color='#000000',showscale=False), hovertemplate = template))
+    fig.update_xaxes(showticklabels=False) # Hide x axis ticks 
+    fig.update_yaxes(showticklabels=False) # Hide y axis ticks
+    fig.update_traces(hoverinfo=None)
+
+    plot_df['Intersection'] = ['+'.join(x) for x in plot_df['Intersection']]
+    template =  [f'<extra><br><b>{lab}</b><br><b>N-Count</b>: {n}</extra>' for  lab, n in zip(plot_df['Intersection'], plot_df['Size'])]
+    bar = graph_objs.Bar(x = list(range(len(subsets))), y = plot_df['Size'], marker = dict(color='#000000'),  text = plot_df['Size'], hovertemplate = template, textposition='outside', hoverinfo='none')
+    fig.add_trace(bar)
+
+    template =  ['' for x in range(d)]
+    max_string_len = max([len(x) for x in df.columns])
+    print(max_string_len**2)
+
+    ### the adjusment of the x range to accomodate labels probably needs work
+    fig_lab = graph_objs.Scatter(x = [-0.02*max_string_len]*d, y = scatter_y, text = df.columns, mode = 'text', textposition='middle left',showlegend=False, hovertemplate = template)
+    fig_lab = graph_objs.Scatter(x = [-0.02*max_string_len]*d, y = scatter_y, text = df.columns, mode = 'text', textposition='middle left',showlegend=False, hovertemplate = template)
+    fig.add_trace(fig_lab)
+    fig.update_layout(title = '<b>Intersections<b>', yaxis_range=[-max_y-0.1*max_y-1, max_y+0.1*max_y], xaxis_range = [-0.015*max_string_len**1.3, len(subsets)], showlegend = False, title_x=0.5)
+
+    return fig
+

packages/python/plotly/plotly/tests/test_optional/test_figure_factory/test_figure_factory.py

@@ -4516,3 +4516,34 @@ def test_build_dataframe(self):
         assert len(fig6.frames) == n_frames
         assert len(fig7.frames) == n_frames
         assert fig6.data[0].geojson == fig1.data[0].geojson
+
+class TestUpsetPlot():
+
+    def test_defaults(self):
+        df = pd.DataFrame()
+        df['Attr 1'] = list(np.random.binomial(1, 0.3, size=1000))
+        df['Attr 2'] = list(np.random.binomial(1, 0.3, size=1000))
+        df['Attr 3'] = list(np.random.binomial(1, 0.8, size=1000))
+        df['Attr 4'] = list(np.random.binomial(1, 0.05, size=1000))
+        df['Attr 5'] = list(np.random.binomial(1, 0.1, size=1000))
+        fig  = ff.create_upset(df)
+
+
+    def test_max_width(self):
+        df = pd.DataFrame()
+        df['Attr 1'] = list(np.random.binomial(1, 0.3, size=1000))
+        df['Attr 2'] = list(np.random.binomial(1, 0.3, size=1000))
+        df['Attr 3'] = list(np.random.binomial(1, 0.8, size=1000))
+        df['Attr 4'] = list(np.random.binomial(1, 0.05, size=1000))
+        df['Attr 5'] = list(np.random.binomial(1, 0.1, size=1000))
+        fig  = ff.create_upset(df, max_width = 10)
+
+    def test_empty_set_true(self):
+        df = pd.DataFrame()
+        df['Attr 1'] = list(np.random.binomial(1, 0.3, size=1000))
+        df['Attr 2'] = list(np.random.binomial(1, 0.3, size=1000))
+        df['Attr 3'] = list(np.random.binomial(1, 0.8, size=1000))
+        df['Attr 4'] = list(np.random.binomial(1, 0.05, size=1000))
+        df['Attr 5'] = list(np.random.binomial(1, 0.1, size=1000))
+        fig  = ff.create_upset(df, include_empty_set = True, max_width = 10)
+