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@clintval
Last active February 23, 2021 14:36
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k-means silhouette analysis using sklearn and matplotlib on Iris data.
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{
"cells": [
{
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"cell_type": "code",
"outputExpanded": false,
"source": [
"%matplotlib inline\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Data\n",
"--"
],
"outputExpanded": false
},
{
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"cell_type": "code",
"outputExpanded": false,
"source": [
"import numpy as np\n",
"X = np.array([[1.1, 0.5], [1.1, 0.6], [1.2, 0.5], [1.2, 0.6],\n",
" [0.8, 1.4], [0.8, 1.5], [0.9, 1.4], [0.9, 1.5]])"
]
},
{
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"cell_type": "code",
"outputExpanded": false,
"source": [
"from sklearn.datasets import load_iris\n",
"iris = load_iris()\n",
"X = iris['data'][:, 1:3]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Unsupervised KMeans Clustering\n",
"---"
],
"outputExpanded": false
},
{
"execution_count": 212,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"For n_clusters = 2 The average silhouette_score is: 0.74\n",
"For n_clusters = 3 The average silhouette_score is: 0.59\n",
"For n_clusters = 4 The average silhouette_score is: 0.56\n",
"For n_clusters = 5 The average silhouette_score is: 0.44\n",
"For n_clusters = 6 The average silhouette_score is: 0.39\n",
"For n_clusters = 7 The average silhouette_score is: 0.38\n"
]
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9247fae588>"
]
},
"metadata": {},
"output_type": "display_data",
"text": []
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x7f924c96e278>"
]
},
"metadata": {},
"output_type": "display_data",
"text": []
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9247dc29b0>"
]
},
"metadata": {},
"output_type": "display_data",
"text": []
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x7f9247dc2128>"
]
},
"metadata": {},
"output_type": "display_data",
"text": []
},
{
"data": {
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x7f92580da5c0>"
]
},
"metadata": {},
"output_type": "display_data",
"text": []
},
{
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],
"text/plain": [
"<matplotlib.figure.Figure at 0x7f924532cd68>"
]
},
"metadata": {},
"output_type": "display_data",
"text": []
}
],
"cell_type": "code",
"outputExpanded": true,
"source": [
"import matplotlib.pyplot as plt\n",
"from sklearn.cluster import KMeans\n",
"from sklearn.metrics import silhouette_score\n",
"from kmeansplots import kmeans_plot, silhouette_plot\n",
"\n",
"for n_clusters in range(2, 8):\n",
" clusterer = KMeans(n_clusters=n_clusters, random_state=10)\n",
" y = clusterer.fit_predict(X)\n",
"\n",
" fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(11,4), dpi=500)\n",
" ax1 = silhouette_plot(X, y, n_clusters=n_clusters, ax=ax1)\n",
" ax2 = kmeans_plot(X, y, clusterer.cluster_centers_, ax=ax2)\n",
"\n\n",
" fig.subplots_adjust(top=0.825)\n",
" fig.suptitle((\"Silhouette Analysis for KMeans Clustering on Iris Data \"\n",
" \"\\n$clusters = {}$\".format(n_clusters)),\n",
" fontsize=14, fontweight='bold')\n",
" message = \"For n_clusters = {} The average silhouette_score is: {}\"\n",
" print(message.format(n_clusters, round(silhouette_score(X, y), 2)))\n"
]
}
],
"metadata": {
"kernelspec": {
"name": "python3",
"language": "python",
"display_name": "Python 3"
},
"language_info": {
"codemirror_mode": {
"version": 3,
"name": "ipython"
},
"version": "3.5.2",
"name": "python",
"file_extension": ".py",
"mimetype": "text/x-python",
"pygments_lexer": "ipython3",
"nbconvert_exporter": "python"
},
"kernel_info": {
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
import numpy as np
import matplotlib.cm as cm
import matplotlib.pyplot as plt
from matplotlib.colors import colorConverter
__license__ = 'MIT'
__author__ = 'clintval'
def darken_rgb(rgb, p):
"""
Will darken an rgb value by p percent
"""
assert 0 <= p <= 1, "Proportion must be [0, 1]"
return [int(x * (1 - p)) for x in rgb]
def lighten_rgb(rgb, p):
"""
Will lighten an rgb value by p percent
"""
assert 0 <= p <= 1, "Proportion must be [0, 1]"
return [int((255 - x) * p + x) for x in rgb]
def is_luminous(rgb):
new_color = []
for c in rgb:
if c <= 0.03928:
new_color.append(c / 12.92)
else:
new_color.append(((c + 0.055) / 1.055) ** 2.4)
L = sum([x * y for x, y in zip([0.2126, 0.7152, 0.0722], new_color)])
return True if L < 0.179 else False
def kmeans_plot(X, y, cluster_centers, ax=None):
import matplotlib.patheffects as path_effects
from sklearn.metrics.pairwise import pairwise_distances_argmin_min
if ax is None:
ax = plt.gca()
colors = cm.spectral(y.astype(float) / len(cluster_centers))
ax.scatter(*list(zip(*X)), lw=0, c=colors, s=30)
offset = max(list(zip(*cluster_centers))[0]) * 0.2
for i, cluster in enumerate(cluster_centers):
index, _ = pairwise_distances_argmin_min(cluster.reshape(1, -1), Y=X)
cluster_color = colorConverter.to_rgb(colors[index[0]])
if is_luminous(cluster_color) is False:
cluster_color = darken_rgb(cluster_color, 0.35)
label = ax.text(x=cluster[0] + offset,
y=cluster[1],
s='{:d}'.format(i + 1),
color=cluster_color)
label.set_path_effects([path_effects.Stroke(lw=2, foreground='white'),
path_effects.Normal()])
limit = max(*ax.get_xlim(), *ax.get_xlim())
ax.set_xlim(0, limit)
ax.set_ylim(0, limit)
ax.set_xlabel("Feature space for the 1st feature")
ax.set_ylabel("Feature space for the 2nd feature")
return ax
def silhouette_plot(X, y, n_clusters, ax=None):
from sklearn.metrics import silhouette_samples, silhouette_score
if ax is None:
ax = plt.gca()
# Compute the silhouette scores for each sample
silhouette_avg = silhouette_score(X, y)
sample_silhouette_values = silhouette_samples(X, y)
y_lower = padding = 2
for i in range(n_clusters):
# Aggregate the silhouette scores for samples belonging to
ith_cluster_silhouette_values = sample_silhouette_values[y == i]
ith_cluster_silhouette_values.sort()
size_cluster_i = ith_cluster_silhouette_values.shape[0]
y_upper = y_lower + size_cluster_i
color = cm.spectral(float(i) / n_clusters)
ax.fill_betweenx(np.arange(y_lower, y_upper),
0,
ith_cluster_silhouette_values,
facecolor=color,
edgecolor=color,
alpha=0.7)
# Label the silhouette plots with their cluster numbers at the middle
ax.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i + 1))
# Compute the new y_lower for next plot
y_lower = y_upper + padding
ax.set_xlabel("The silhouette coefficient values")
ax.set_ylabel("Cluster label")
# The vertical line for average silhoutte score of all the values
ax.axvline(x=silhouette_avg, c='r', alpha=0.8, lw=0.8, ls='-')
ax.annotate('Average',
xytext=(silhouette_avg, y_lower * 1.025),
xy=(0, 0),
ha='center',
alpha=0.8,
c='r')
ax.set_yticks([]) # Clear the yaxis labels / ticks
ax.set_xticks([0, 0.2, 0.4, 0.6, 0.8, 1])
ax.set_ylim(0, y_upper + 1)
ax.set_xlim(-0.075, 1.0)
return ax
@shvthr
Copy link

shvthr commented Apr 26, 2019

Hi,
I continuously get error running this code:

`~\AppData\Roaming\Python\Python36\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs)
     48                 renderer.start_filter()
     49 
---> 50             return draw(artist, renderer, *args, **kwargs)
     51         finally:
     52             if artist.get_agg_filter() is not None:

~\AppData\Roaming\Python\Python36\site-packages\matplotlib\axes\_base.py in draw(self, renderer, inframe)
   2626             renderer.stop_rasterizing()
   2627 
-> 2628         mimage._draw_list_compositing_images(renderer, self, artists)
   2629 
   2630         renderer.close_group('axes')

~\AppData\Roaming\Python\Python36\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite)
    136     if not_composite or not has_images:
    137         for a in artists:
--> 138             a.draw(renderer)
    139     else:
    140         # Composite any adjacent images together

~\AppData\Roaming\Python\Python36\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs)
     48                 renderer.start_filter()
     49 
---> 50             return draw(artist, renderer, *args, **kwargs)
     51         finally:
     52             if artist.get_agg_filter() is not None:

~\AppData\Roaming\Python\Python36\site-packages\matplotlib\text.py in draw(self, renderer)
    756                     textrenderer.draw_text(gc, x, y, clean_line,
    757                                            textobj._fontproperties, angle,
--> 758                                            ismath=ismath, mtext=mtext)
    759 
    760         gc.restore()

~\AppData\Roaming\Python\Python36\site-packages\matplotlib\backend_bases.py in draw_text(self, gc, x, y, s, prop, angle, ismath, mtext)
    546         """
    547 
--> 548         self._draw_text_as_path(gc, x, y, s, prop, angle, ismath)
    549 
    550     def _get_text_path_transform(self, x, y, s, prop, angle, ismath):

~\AppData\Roaming\Python\Python36\site-packages\matplotlib\patheffects.py in _draw_text_as_path(self, gc, x, y, s, prop, angle, ismath)
    152         color = gc.get_rgb()
    153         gc.set_linewidth(0.0)
--> 154         self.draw_path(gc, path, transform, rgbFace=color)
    155 
    156     def __getattribute__(self, name):

~\AppData\Roaming\Python\Python36\site-packages\matplotlib\patheffects.py in draw_path(self, gc, tpath, affine, rgbFace)
    103         for path_effect in self._path_effects:
    104             path_effect.draw_path(self._renderer, gc, tpath, affine,
--> 105                                   rgbFace)
    106 
    107     def draw_markers(self, gc, marker_path, marker_trans, path, *args,

~\AppData\Roaming\Python\Python36\site-packages\matplotlib\patheffects.py in draw_path(self, renderer, gc, tpath, affine, rgbFace)
    191         gc0.copy_properties(gc)
    192 
--> 193         gc0 = self._update_gc(gc0, self._gc)
    194         trans = self._offset_transform(renderer, affine)
    195         renderer.draw_path(gc0, tpath, trans, rgbFace)

~\AppData\Roaming\Python\Python36\site-packages\matplotlib\patheffects.py in _update_gc(self, gc, new_gc_dict)
     52             set_method = getattr(gc, 'set_' + k, None)
     53             if not callable(set_method):
---> 54                 raise AttributeError('Unknown property {0}'.format(k))
     55             set_method(v)
     56         return gc

AttributeError: Unknown property lw

<Figure size 5500x2000 with 2 Axes>
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
~\AppData\Local\Continuum\anaconda3\envs\py36\lib\site-packages\IPython\core\formatters.py in __call__(self, obj)
    339                 pass
    340             else:
--> 341                 return printer(obj)
    342             # Finally look for special method names
    343             method = get_real_method(obj, self.print_method)

~\AppData\Local\Continuum\anaconda3\envs\py36\lib\site-packages\IPython\core\pylabtools.py in <lambda>(fig)
    242 
    243     if 'png' in formats:
--> 244         png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs))
    245     if 'retina' in formats or 'png2x' in formats:
    246         png_formatter.for_type(Figure, lambda fig: retina_figure(fig, **kwargs))

~\AppData\Local\Continuum\anaconda3\envs\py36\lib\site-packages\IPython\core\pylabtools.py in print_figure(fig, fmt, bbox_inches, **kwargs)
    126 
    127     bytes_io = BytesIO()
--> 128     fig.canvas.print_figure(bytes_io, **kw)
    129     data = bytes_io.getvalue()
    130     if fmt == 'svg':

~\AppData\Roaming\Python\Python36\site-packages\matplotlib\backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, bbox_inches, **kwargs)
   2047                         orientation=orientation,
   2048                         dryrun=True,
-> 2049                         **kwargs)
   2050                     renderer = self.figure._cachedRenderer
   2051                     bbox_artists = kwargs.pop("bbox_extra_artists", None)

~\AppData\Roaming\Python\Python36\site-packages\matplotlib\backends\backend_agg.py in print_png(self, filename_or_obj, *args, **kwargs)
    508 
    509         """
--> 510         FigureCanvasAgg.draw(self)
    511         renderer = self.get_renderer()
    512 

~\AppData\Roaming\Python\Python36\site-packages\matplotlib\backends\backend_agg.py in draw(self)
    400         toolbar = self.toolbar
    401         try:
--> 402             self.figure.draw(self.renderer)
    403             # A GUI class may be need to update a window using this draw, so
    404             # don't forget to call the superclass.

~\AppData\Roaming\Python\Python36\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs)
     48                 renderer.start_filter()
     49 
---> 50             return draw(artist, renderer, *args, **kwargs)
     51         finally:
     52             if artist.get_agg_filter() is not None:

~\AppData\Roaming\Python\Python36\site-packages\matplotlib\figure.py in draw(self, renderer)
   1647 
   1648             mimage._draw_list_compositing_images(
-> 1649                 renderer, self, artists, self.suppressComposite)
   1650 
   1651             renderer.close_group('figure')

~\AppData\Roaming\Python\Python36\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite)
    136     if not_composite or not has_images:
    137         for a in artists:
--> 138             a.draw(renderer)
    139     else:
    140         # Composite any adjacent images together

~\AppData\Roaming\Python\Python36\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs)
     48                 renderer.start_filter()
     49 
---> 50             return draw(artist, renderer, *args, **kwargs)
     51         finally:
     52             if artist.get_agg_filter() is not None:

~\AppData\Roaming\Python\Python36\site-packages\matplotlib\axes\_base.py in draw(self, renderer, inframe)
   2626             renderer.stop_rasterizing()
   2627 
-> 2628         mimage._draw_list_compositing_images(renderer, self, artists)
   2629 
   2630         renderer.close_group('axes')

~\AppData\Roaming\Python\Python36\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite)
    136     if not_composite or not has_images:
    137         for a in artists:
--> 138             a.draw(renderer)
    139     else:
    140         # Composite any adjacent images together

~\AppData\Roaming\Python\Python36\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs)
     48                 renderer.start_filter()
     49 
---> 50             return draw(artist, renderer, *args, **kwargs)
     51         finally:
     52             if artist.get_agg_filter() is not None:

~\AppData\Roaming\Python\Python36\site-packages\matplotlib\text.py in draw(self, renderer)
    756                     textrenderer.draw_text(gc, x, y, clean_line,
    757                                            textobj._fontproperties, angle,
--> 758                                            ismath=ismath, mtext=mtext)
    759 
    760         gc.restore()

~\AppData\Roaming\Python\Python36\site-packages\matplotlib\backend_bases.py in draw_text(self, gc, x, y, s, prop, angle, ismath, mtext)
    546         """
    547 
--> 548         self._draw_text_as_path(gc, x, y, s, prop, angle, ismath)
    549 
    550     def _get_text_path_transform(self, x, y, s, prop, angle, ismath):

~\AppData\Roaming\Python\Python36\site-packages\matplotlib\patheffects.py in _draw_text_as_path(self, gc, x, y, s, prop, angle, ismath)
    152         color = gc.get_rgb()
    153         gc.set_linewidth(0.0)
--> 154         self.draw_path(gc, path, transform, rgbFace=color)
    155 
    156     def __getattribute__(self, name):

~\AppData\Roaming\Python\Python36\site-packages\matplotlib\patheffects.py in draw_path(self, gc, tpath, affine, rgbFace)
    103         for path_effect in self._path_effects:
    104             path_effect.draw_path(self._renderer, gc, tpath, affine,
--> 105                                   rgbFace)
    106 
    107     def draw_markers(self, gc, marker_path, marker_trans, path, *args,

~\AppData\Roaming\Python\Python36\site-packages\matplotlib\patheffects.py in draw_path(self, renderer, gc, tpath, affine, rgbFace)
    191         gc0.copy_properties(gc)
    192 
--> 193         gc0 = self._update_gc(gc0, self._gc)
    194         trans = self._offset_transform(renderer, affine)
    195         renderer.draw_path(gc0, tpath, trans, rgbFace)

~\AppData\Roaming\Python\Python36\site-packages\matplotlib\patheffects.py in _update_gc(self, gc, new_gc_dict)
     52             set_method = getattr(gc, 'set_' + k, None)
     53             if not callable(set_method):
---> 54                 raise AttributeError('Unknown property {0}'.format(k))
     55             set_method(v)
     56         return gc

AttributeError: Unknown property lw

<Figure size 5500x2000 with 2 Axes>`

I'm running it on Windows 10,

asn1crypto==0.24.0
astropy==3.1.2
atomicwrites==1.3.0
attrs==19.1.0
backcall==0.1.0
bak==0.2.0
bleach==3.1.0
bokeh==1.1.0
Bottleneck==1.2.1
brewer2mpl==1.4.1
certifi==2019.3.9
cffi==1.12.3
chardet==3.0.4
Click==7.0
cloudpickle==0.8.1
colorama==0.4.1
cryptography==2.6.1
cycler==0.10.0
cytoolz==0.9.0.1
dask==1.2.0
decorator==4.4.0
defusedxml==0.6.0
dill==0.2.9
distributed==1.27.0
entrypoints==0.3
ez-setup==0.9
fast-histogram==0.7
feather-format==0.4.0
Flask==1.0.2
ggplot==0.11.5
glue-core==0.14.2
glue-vispy-viewers==0.11
h5py==2.9.0
heapdict==1.0.0
idna==2.8
imageio==2.5.0
ipykernel==5.1.0
ipython==7.4.0
ipython-genutils==0.2.0
ipywidgets==7.4.2
itsdangerous==1.1.0
jedi==0.13.3
Jinja2==2.10.1
jsonschema==3.0.1
jupyter==1.0.0
jupyter-client==5.2.4
jupyter-console==6.0.0
jupyter-contrib-core==0.3.3
jupyter-contrib-nbextensions==0.5.1
jupyter-core==4.4.0
jupyter-highlight-selected-word==0.2.0
jupyter-latex-envs==1.4.6
jupyter-nbextensions-configurator==0.4.1
jupyterlab==0.35.4
jupyterlab-server==0.2.0
kiwisolver==1.0.1
locket==0.2.0
lxml==4.3.0
MarkupSafe==1.1.1
matplotlib==3.0.3
mistune==0.8.4
mkl-fft==1.0.10
mkl-random==1.0.2
moderngl==5.5.0
more-itertools==7.0.0
mpl-scatter-density==0.5
msgpack==0.6.1
nbconvert==5.4.1
nbformat==4.4.0
networkx==2.3
notebook==5.7.8
numpy==1.16.3
olefile==0.46
packaging==19.0
pandas==0.24.2
pandocfilters==1.4.2
parso==0.4.0
partd==0.3.10
patsy==0.5.1
pickleshare==0.7.5
Pillow==6.0.0
plotly==3.8.1
pluggy==0.9.0
prometheus-client==0.6.0
prompt-toolkit==2.0.9
psutil==5.6.1
py==1.8.0
pyarrow==0.12.0
Pycluster==1.54
pyclustering==0.8.2
pycparser==2.19
Pygments==2.3.1
pyodbc==4.0.26
PyOpenGL==3.1.1a1
pyOpenSSL==19.0.0
pyparsing==2.4.0
pypi==2.1
pyreadline==2.1
pyrsistent==0.14.11
PySocks==1.6.8
pytest==4.4.1
pytest-arraydiff==0.3
pytest-astropy==0.5.0
pytest-doctestplus==0.3.0
pytest-openfiles==0.3.2
pytest-remotedata==0.3.1
python-dateutil==2.8.0
pytz==2019.1
PyWavelets==1.0.3
pywinpty==0.5.5
PyYAML==5.1
pyzmq==18.0.0
qtconsole==4.4.3
QtPy==1.7.0
regex==2019.2.18
requests==2.21.0
retrying==1.3.3
rstudio-spark-install==0.8.0
scikit-image==0.15.0
scikit-learn==0.20.2
scipy==1.2.1
seaborn==0.9.0
Send2Trash==1.5.0
six==1.12.0
sortedcontainers==2.1.0
statsmodels==0.9.0
tblib==1.3.2
terminado==0.8.2
testpath==0.4.2
toolz==0.9.0
tornado==6.0.2
traitlets==4.3.2
unicodecsv==0.14.1
URF==0.0.5
urllib3==1.24.1
wcwidth==0.1.7
webencodings==0.5.1
Werkzeug==0.14.1
widgetsnbextension==3.4.2
win-inet-pton==1.1.0
wincertstore==0.2
xlrd==1.2.0
zict==0.1.4

@cest0
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cest0 commented Sep 13, 2019

same issue here.

@clintval
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Author

Ha, I've not seen these comments until now. Have you tried removing the lw=0 call to ax.scatter? Right here: https://gist.github.com/clintval/e9afc246e77f6488cda79f86e4d37148#file-kmeansplots-py-L47

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