ensure that there are enough colors to match the score index in visua… #3560
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Hey @thelinuxkid , are you able to attach a screenshot of what the visualization looks like prior to this change and what it looks like after this change? |
| if num_metrics <= 10: | ||
| colors = plt.get_cmap("tab10").colors | ||
| else: | ||
| colors = plt.get_cmap("tab20").colors |
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Hmm, I want to see if we can work together to find a better solution. The problem I see here is, what if we have 30 categories? This would break once again and we'd have to add another conditional block.
Can you give something like this a try?
import colorsys
def generate_color_palette(num_colors):
base_colors = [(0.0, 0.5, 0.7), # Blue
(0.3, 0.6, 0.2), # Green
(0.9, 0.4, 0.1)] # Orange
palette = []
for base_color in base_colors:
for i in range(num_colors):
hue = (base_color[0] + i * 0.1) % 1.0
saturation = base_color[1]
lightness = base_color[2]
rgb = colorsys.hls_to_rgb(hue, lightness, saturation)
palette.append(rgb)
return palette
This should create good interpolation between colors and create something perceptually distinctive, but need to actually give it a try.
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I ran this locally and this is what I see:
>>> plt.get_cmap("tab10").colors
((0.12156862745098039, 0.4666666666666667, 0.7058823529411765), (1.0, 0.4980392156862745, 0.054901960784313725), (0.17254901960784313, 0.6274509803921569, 0.17254901960784313), (0.8392156862745098, 0.15294117647058825, 0.1568627450980392), (0.5803921568627451, 0.403921568627451, 0.7411764705882353), (0.5490196078431373, 0.33725490196078434, 0.29411764705882354), (0.8901960784313725, 0.4666666666666667, 0.7607843137254902), (0.4980392156862745, 0.4980392156862745, 0.4980392156862745), (0.7372549019607844, 0.7411764705882353, 0.13333333333333333), (0.09019607843137255, 0.7450980392156863, 0.8117647058823529))
>>> generate_color_palette(10)
[(0.85, 0.5499999999999999, 0.5499999999999999), (0.85, 0.73, 0.5499999999999999), (0.7899999999999999, 0.85, 0.5499999999999999), (0.6099999999999999, 0.85, 0.5499999999999999), (0.5499999999999999, 0.85, 0.67), (0.5499999999999999, 0.8499999999999999, 0.85), (0.5499999999999999, 0.6699999999999997, 0.85), (0.6100000000000001, 0.5499999999999999, 0.85), (0.7899999999999999, 0.5499999999999999, 0.85), (0.85, 0.5499999999999999, 0.73), (0.12799999999999997, 0.32000000000000006, 0.07999999999999996), (0.07999999999999996, 0.32000000000000006, 0.17600000000000007), (0.07999999999999996, 0.32, 0.32000000000000006), (0.07999999999999996, 0.17599999999999982, 0.32000000000000006), (0.1279999999999998, 0.07999999999999996, 0.32000000000000006), (0.272, 0.07999999999999996, 0.32000000000000006), (0.32000000000000006, 0.07999999999999996, 0.22399999999999967), (0.32000000000000006, 0.07999999999999996, 0.07999999999999996), (0.32000000000000006, 0.22400000000000014, 0.07999999999999996), (0.2720000000000002, 0.32000000000000006, 0.07999999999999996), (0.13999999999999999, 0.060000000000000026, 0.108), (0.13999999999999999, 0.060000000000000026, 0.060000000000000026), (0.13999999999999999, 0.10800000000000004, 0.060000000000000026), (0.12399999999999993, 0.13999999999999999, 0.060000000000000026), (0.07600000000000001, 0.13999999999999999, 0.060000000000000026), (0.060000000000000026, 0.13999999999999999, 0.09199999999999997), (0.060000000000000026, 0.13999999999999996, 0.13999999999999999), (0.060000000000000026, 0.09199999999999994, 0.13999999999999999), (0.07600000000000007, 0.060000000000000026, 0.13999999999999999), (0.12399999999999999, 0.060000000000000026, 0.13999999999999999)]
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This is a great suggestion. Are there any concerns about potentially having inconsistent viz colors between different models? (same question for tab10/tab20 switching)
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Yeah, I considered the same thing @arnavgarg1 but thought from index not found to 20 was improvement. It is not a full solution however. If we are going to look for a proper solution instead of a hotfix then perhaps using the built-in normalization from matplotlib would be best? Something like this https://matplotlib.org/stable/gallery/images_contours_and_fields/colormap_normalizations.html#sphx-glr-gallery-images-contours-and-fields-colormap-normalizations-py
I'm fine with either one of the changes. Just let me know.
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@thelinuxkid Thanks for the prompt response - I guess the only challenge with this is that normalization may not ensure enough perceptual difference.
For now, if it helps to unblock you, I'm happy to merge your change in but would love to follow-up with another fix for this!
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@justinxzhao any thoughts on this?
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Going with what @thelinuxkid SGTM! Let's merge it.
Unit Test Results 6 files ± 0 6 suites ±0 46m 10s ⏱️ - 24m 55s Results for commit a3f0d87. ± Comparison against base commit 25f3d62. This pull request skips 2 tests. |
Description
Only one of the visualization functions matches ensures that there are enough colors to match if the scores length is over 10. Add this to the other functions to avoid the index not found error.
@arnavgarg1 @jeffkinnison