done
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from collections import OrderedDict
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from plotly import exceptions, optional_imports
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from plotly.graph_objs import graph_objs
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# Optional imports, may be None for users that only use our core functionality.
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np = optional_imports.get_module("numpy")
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scp = optional_imports.get_module("scipy")
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sch = optional_imports.get_module("scipy.cluster.hierarchy")
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scs = optional_imports.get_module("scipy.spatial")
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def create_dendrogram(
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X,
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orientation="bottom",
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labels=None,
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colorscale=None,
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distfun=None,
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linkagefun=lambda x: sch.linkage(x, "complete"),
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hovertext=None,
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color_threshold=None,
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):
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"""
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Function that returns a dendrogram Plotly figure object. This is a thin
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wrapper around scipy.cluster.hierarchy.dendrogram.
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See also https://dash.plot.ly/dash-bio/clustergram.
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:param (ndarray) X: Matrix of observations as array of arrays
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:param (str) orientation: 'top', 'right', 'bottom', or 'left'
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:param (list) labels: List of axis category labels(observation labels)
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:param (list) colorscale: Optional colorscale for the dendrogram tree.
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Requires 8 colors to be specified, the 7th of
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which is ignored. With scipy>=1.5.0, the 2nd, 3rd
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and 6th are used twice as often as the others.
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Given a shorter list, the missing values are
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replaced with defaults and with a longer list the
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extra values are ignored.
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:param (function) distfun: Function to compute the pairwise distance from
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the observations
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:param (function) linkagefun: Function to compute the linkage matrix from
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the pairwise distances
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:param (list[list]) hovertext: List of hovertext for constituent traces of dendrogram
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clusters
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:param (double) color_threshold: Value at which the separation of clusters will be made
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Example 1: Simple bottom oriented dendrogram
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>>> from plotly.figure_factory import create_dendrogram
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>>> import numpy as np
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>>> X = np.random.rand(10,10)
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>>> fig = create_dendrogram(X)
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>>> fig.show()
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Example 2: Dendrogram to put on the left of the heatmap
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>>> from plotly.figure_factory import create_dendrogram
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>>> import numpy as np
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>>> X = np.random.rand(5,5)
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>>> names = ['Jack', 'Oxana', 'John', 'Chelsea', 'Mark']
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>>> dendro = create_dendrogram(X, orientation='right', labels=names)
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>>> dendro.update_layout({'width':700, 'height':500}) # doctest: +SKIP
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>>> dendro.show()
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Example 3: Dendrogram with Pandas
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>>> from plotly.figure_factory import create_dendrogram
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>>> import numpy as np
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>>> import pandas as pd
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>>> Index= ['A','B','C','D','E','F','G','H','I','J']
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>>> df = pd.DataFrame(abs(np.random.randn(10, 10)), index=Index)
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>>> fig = create_dendrogram(df, labels=Index)
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>>> fig.show()
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"""
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if not scp or not scs or not sch:
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raise ImportError(
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"FigureFactory.create_dendrogram requires scipy, \
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scipy.spatial and scipy.hierarchy"
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)
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s = X.shape
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if len(s) != 2:
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exceptions.PlotlyError("X should be 2-dimensional array.")
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if distfun is None:
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distfun = scs.distance.pdist
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dendrogram = _Dendrogram(
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X,
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orientation,
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labels,
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colorscale,
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distfun=distfun,
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linkagefun=linkagefun,
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hovertext=hovertext,
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color_threshold=color_threshold,
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)
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return graph_objs.Figure(data=dendrogram.data, layout=dendrogram.layout)
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class _Dendrogram(object):
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"""Refer to FigureFactory.create_dendrogram() for docstring."""
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def __init__(
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self,
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X,
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orientation="bottom",
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labels=None,
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colorscale=None,
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width=np.inf,
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height=np.inf,
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xaxis="xaxis",
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yaxis="yaxis",
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distfun=None,
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linkagefun=lambda x: sch.linkage(x, "complete"),
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hovertext=None,
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color_threshold=None,
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):
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self.orientation = orientation
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self.labels = labels
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self.xaxis = xaxis
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self.yaxis = yaxis
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self.data = []
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self.leaves = []
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self.sign = {self.xaxis: 1, self.yaxis: 1}
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self.layout = {self.xaxis: {}, self.yaxis: {}}
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if self.orientation in ["left", "bottom"]:
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self.sign[self.xaxis] = 1
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else:
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self.sign[self.xaxis] = -1
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if self.orientation in ["right", "bottom"]:
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self.sign[self.yaxis] = 1
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else:
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self.sign[self.yaxis] = -1
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if distfun is None:
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distfun = scs.distance.pdist
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(dd_traces, xvals, yvals, ordered_labels, leaves) = self.get_dendrogram_traces(
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X, colorscale, distfun, linkagefun, hovertext, color_threshold
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)
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self.labels = ordered_labels
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self.leaves = leaves
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yvals_flat = yvals.flatten()
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xvals_flat = xvals.flatten()
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self.zero_vals = []
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for i in range(len(yvals_flat)):
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if yvals_flat[i] == 0.0 and xvals_flat[i] not in self.zero_vals:
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self.zero_vals.append(xvals_flat[i])
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if len(self.zero_vals) > len(yvals) + 1:
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# If the length of zero_vals is larger than the length of yvals,
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# it means that there are wrong vals because of the identicial samples.
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# Three and more identicial samples will make the yvals of spliting
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# center into 0 and it will accidentally take it as leaves.
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l_border = int(min(self.zero_vals))
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r_border = int(max(self.zero_vals))
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correct_leaves_pos = range(
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l_border, r_border + 1, int((r_border - l_border) / len(yvals))
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)
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# Regenerating the leaves pos from the self.zero_vals with equally intervals.
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self.zero_vals = [v for v in correct_leaves_pos]
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self.zero_vals.sort()
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self.layout = self.set_figure_layout(width, height)
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self.data = dd_traces
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def get_color_dict(self, colorscale):
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"""
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Returns colorscale used for dendrogram tree clusters.
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:param (list) colorscale: Colors to use for the plot in rgb format.
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:rtype (dict): A dict of default colors mapped to the user colorscale.
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"""
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# These are the color codes returned for dendrograms
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# We're replacing them with nicer colors
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# This list is the colors that can be used by dendrogram, which were
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# determined as the combination of the default above_threshold_color and
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# the default color palette (see scipy/cluster/hierarchy.py)
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d = {
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"r": "red",
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"g": "green",
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"b": "blue",
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"c": "cyan",
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"m": "magenta",
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"y": "yellow",
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"k": "black",
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# TODO: 'w' doesn't seem to be in the default color
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# palette in scipy/cluster/hierarchy.py
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"w": "white",
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}
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default_colors = OrderedDict(sorted(d.items(), key=lambda t: t[0]))
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if colorscale is None:
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rgb_colorscale = [
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"rgb(0,116,217)", # blue
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"rgb(35,205,205)", # cyan
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"rgb(61,153,112)", # green
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"rgb(40,35,35)", # black
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"rgb(133,20,75)", # magenta
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"rgb(255,65,54)", # red
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"rgb(255,255,255)", # white
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"rgb(255,220,0)", # yellow
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]
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else:
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rgb_colorscale = colorscale
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for i in range(len(default_colors.keys())):
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k = list(default_colors.keys())[i] # PY3 won't index keys
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if i < len(rgb_colorscale):
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default_colors[k] = rgb_colorscale[i]
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# add support for cyclic format colors as introduced in scipy===1.5.0
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# before this, the colors were named 'r', 'b', 'y' etc., now they are
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# named 'C0', 'C1', etc. To keep the colors consistent regardless of the
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# scipy version, we try as much as possible to map the new colors to the
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# old colors
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# this mapping was found by inpecting scipy/cluster/hierarchy.py (see
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# comment above).
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new_old_color_map = [
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("C0", "b"),
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("C1", "g"),
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("C2", "r"),
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("C3", "c"),
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("C4", "m"),
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("C5", "y"),
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("C6", "k"),
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("C7", "g"),
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("C8", "r"),
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("C9", "c"),
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]
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for nc, oc in new_old_color_map:
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try:
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default_colors[nc] = default_colors[oc]
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except KeyError:
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# it could happen that the old color isn't found (if a custom
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# colorscale was specified), in this case we set it to an
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# arbitrary default.
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default_colors[nc] = "rgb(0,116,217)"
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return default_colors
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def set_axis_layout(self, axis_key):
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"""
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Sets and returns default axis object for dendrogram figure.
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:param (str) axis_key: E.g., 'xaxis', 'xaxis1', 'yaxis', yaxis1', etc.
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:rtype (dict): An axis_key dictionary with set parameters.
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"""
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axis_defaults = {
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"type": "linear",
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"ticks": "outside",
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"mirror": "allticks",
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"rangemode": "tozero",
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"showticklabels": True,
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"zeroline": False,
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"showgrid": False,
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"showline": True,
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}
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if len(self.labels) != 0:
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axis_key_labels = self.xaxis
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if self.orientation in ["left", "right"]:
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axis_key_labels = self.yaxis
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if axis_key_labels not in self.layout:
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self.layout[axis_key_labels] = {}
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self.layout[axis_key_labels]["tickvals"] = [
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zv * self.sign[axis_key] for zv in self.zero_vals
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]
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self.layout[axis_key_labels]["ticktext"] = self.labels
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self.layout[axis_key_labels]["tickmode"] = "array"
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self.layout[axis_key].update(axis_defaults)
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return self.layout[axis_key]
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def set_figure_layout(self, width, height):
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"""
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Sets and returns default layout object for dendrogram figure.
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"""
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self.layout.update(
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{
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"showlegend": False,
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"autosize": False,
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"hovermode": "closest",
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"width": width,
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"height": height,
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}
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)
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self.set_axis_layout(self.xaxis)
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self.set_axis_layout(self.yaxis)
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return self.layout
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def get_dendrogram_traces(
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self, X, colorscale, distfun, linkagefun, hovertext, color_threshold
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):
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"""
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Calculates all the elements needed for plotting a dendrogram.
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:param (ndarray) X: Matrix of observations as array of arrays
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:param (list) colorscale: Color scale for dendrogram tree clusters
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:param (function) distfun: Function to compute the pairwise distance
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from the observations
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:param (function) linkagefun: Function to compute the linkage matrix
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from the pairwise distances
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:param (list) hovertext: List of hovertext for constituent traces of dendrogram
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:rtype (tuple): Contains all the traces in the following order:
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(a) trace_list: List of Plotly trace objects for dendrogram tree
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(b) icoord: All X points of the dendrogram tree as array of arrays
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with length 4
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(c) dcoord: All Y points of the dendrogram tree as array of arrays
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with length 4
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(d) ordered_labels: leaf labels in the order they are going to
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appear on the plot
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(e) P['leaves']: left-to-right traversal of the leaves
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"""
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d = distfun(X)
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Z = linkagefun(d)
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P = sch.dendrogram(
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Z,
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orientation=self.orientation,
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labels=self.labels,
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no_plot=True,
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color_threshold=color_threshold,
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)
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icoord = np.array(P["icoord"])
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dcoord = np.array(P["dcoord"])
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ordered_labels = np.array(P["ivl"])
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color_list = np.array(P["color_list"])
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colors = self.get_color_dict(colorscale)
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trace_list = []
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for i in range(len(icoord)):
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# xs and ys are arrays of 4 points that make up the '∩' shapes
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# of the dendrogram tree
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if self.orientation in ["top", "bottom"]:
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xs = icoord[i]
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else:
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xs = dcoord[i]
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if self.orientation in ["top", "bottom"]:
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ys = dcoord[i]
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else:
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ys = icoord[i]
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color_key = color_list[i]
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hovertext_label = None
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if hovertext:
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hovertext_label = hovertext[i]
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trace = dict(
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type="scatter",
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x=np.multiply(self.sign[self.xaxis], xs),
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y=np.multiply(self.sign[self.yaxis], ys),
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mode="lines",
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marker=dict(color=colors[color_key]),
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text=hovertext_label,
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hoverinfo="text",
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)
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try:
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x_index = int(self.xaxis[-1])
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except ValueError:
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x_index = ""
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try:
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y_index = int(self.yaxis[-1])
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except ValueError:
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y_index = ""
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trace["xaxis"] = f"x{x_index}"
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trace["yaxis"] = f"y{y_index}"
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trace_list.append(trace)
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return trace_list, icoord, dcoord, ordered_labels, P["leaves"]
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