metriculous-ml / metriculous Goto Github PK

Measure and visualize machine learning model performance without the usual boilerplate.

License: MIT License

Makefile 0.86% Python 99.00% Shell 0.14%

classification confusion-matrix data-science deep-learning machine-learning model-comparsion model-evaluation model-selection precision-recall-curve python regression residual-plot roc-curve statistics visual-analysis

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ficstamas pplonski magnusja jgreene mksarker fw1121 ericxsun jtmedley21 bikashshaw marijacausevska finyellow

metriculous's Issues

Warning with most frequent classifier

Hi,

Great library! Thank you!

Question: I am getting the following warning:

D:\Anaconda3\envs\fml\lib\site-packages\metriculous\evaluators\_classification_figures_bokeh.py:146: RuntimeWarning: invalid value encountered in true_divide
  cm_normalized_by_pred = cm.astype("float") / cm.sum(axis=0, keepdims=True)

I have created a dummy model which predicts the most frequent class (ie always 1). Here is the confusion matrix "cm" in the code:

Is there a way to prevent the warning from appearing in this corner case?

Thank you.

could it be show different dataset predictions with the same model?

for example, with the trained model, I would like to display all metrics of the model on different dataset: train-set, valid-set, test-set?

How to do it?

Thanks a lot

Error running compare_classifiers

I am running compare_classifiers in a ipynb and I am getting this error

/metriculous/_comparison.py:333, in _html_quantity_comparison_table.<locals>.stylish_table_html(df, highlight_fn)
    331 df_styled = df_styled.apply(highlight_primary_metric, axis=1)
    332 if highlight_fn is None:
--> 333     return df_styled.render()
    334 else:
    335     return df_styled.apply(highlight_fn, axis=1, subset=df.columns[1:]).render()

AttributeError: 'Styler' object has no attribute 'render'

my code is:

metriculous.compare_classifiers(
       ground_truth=labels,
       model_predictions=[outputs],
       model_names=["..."],
       class_names=["Negative", "Positive"],
       one_vs_all_figures=True, 
   ).save_html(path)

just wondering if someone has the same problem or knows how to solve it

Documentation automation rate analysis

Could you provide some documentation or at least a link about the automation rate analysis plot in classification reports?

Thanks!

Show numbers on confusion matrix

First, thanks for the library, it's really cool.

I only obtain the plots until the "scatter confusion matrix", I get the following error:

python3.8/site-packages/metriculous/evaluators/_classification_figures_bokeh.py:161: RuntimeWarning: invalid value encountered in true_divide
  cm_normalized_by_pred = cm.astype("float") / cm.sum(axis=0, keepdims=True)

This happens every time I have a model which NEVER predicts one of the classes. E.g.: a naive model always predicting the majority class.

Also, there would be nice to have an option that allows to show the numbers directly on the confusion matrix, without the need of hovering over it.

Metriculous+Bokeh Out of range float values are not JSON compliant

I believe this is a bug with the Metriculous Bokeh integration but I'm not sure what is triggering it.

Here is a a code snippet to reproduce it:

import metriculous
import numpy as np

predictions = [np.array([[0.02651198, 0.31270504, 0.04171027, 0.08619863, 0.16541226,
        0.11558858, 0.12004455, 0.13182868],
       [0.0636719 , 0.18429258, 0.08450789, 0.1157857 , 0.21399513,
        0.08028985, 0.09965403, 0.15780292],
       [0.03115291, 0.30915564, 0.04554546, 0.11180063, 0.11298609,
        0.12809892, 0.12540455, 0.1358558 ],
       [0.07111797, 0.12305891, 0.07020132, 0.15796672, 0.24655697,
        0.0836428 , 0.07912376, 0.16833155],
       [0.07959884, 0.12210452, 0.06945297, 0.15947734, 0.2427768 ,
        0.08263489, 0.07852125, 0.16543338],
       [0.01766902, 0.33444592, 0.0312464 , 0.304933  , 0.07606831,
        0.11432294, 0.0662983 , 0.05501606],
       [0.07516593, 0.09484643, 0.14125083, 0.06031764, 0.25438443,
        0.09057547, 0.12231521, 0.16114406],
       [0.01725348, 0.2626249 , 0.03184433, 0.09360438, 0.28739157,
        0.14218643, 0.07242204, 0.09267287],
       [0.06459036, 0.16860159, 0.06819151, 0.10878273, 0.24783328,
        0.08361257, 0.08607707, 0.17231087],
       [0.02426506, 0.35321227, 0.0365901 , 0.10876535, 0.19372101,
        0.11075294, 0.07755516, 0.09513812],
       [0.01334525, 0.40144277, 0.02168825, 0.2738081 , 0.07262825,
        0.11011887, 0.06275296, 0.04421557],
       [0.05111578, 0.18965946, 0.06345861, 0.10171634, 0.29270712,
        0.06656697, 0.06664741, 0.16812828],
       [0.03433737, 0.35056743, 0.04554536, 0.1223052 , 0.14757192,
        0.10423103, 0.08725566, 0.10818605],
       [0.02045722, 0.36372438, 0.06161968, 0.20598333, 0.07393774,
        0.13290711, 0.06163201, 0.07973854],
       [0.06024894, 0.17526025, 0.0700492 , 0.11108048, 0.20588736,
        0.11613052, 0.08757272, 0.17377052],
       [0.06320383, 0.11961269, 0.07309992, 0.13411018, 0.21522452,
        0.12376933, 0.09002887, 0.18095064],
       [0.06320383, 0.11961269, 0.07309992, 0.13411018, 0.21522452,
        0.12376933, 0.09002887, 0.18095064],
       [0.02111111, 0.37781438, 0.03542091, 0.15676846, 0.07582452,
        0.11120252, 0.07607654, 0.14578155],
       [0.01435957, 0.56506133, 0.02622501, 0.10991825, 0.05670529,
        0.09522142, 0.05140135, 0.08110777],
       [0.01447272, 0.56697184, 0.02637914, 0.11005513, 0.05682856,
        0.09225151, 0.05152839, 0.08151273],
       [0.04570318, 0.11008076, 0.06587777, 0.09217358, 0.25748935,
        0.1264705 , 0.11295175, 0.1892531 ],
       [0.01105192, 0.43763763, 0.02243476, 0.25057724, 0.07013009,
        0.1070659 , 0.06017836, 0.04092411],
       [0.01105192, 0.43763763, 0.02243476, 0.25057724, 0.07013009,
        0.1070659 , 0.06017836, 0.04092411],
       [0.06399985, 0.12088028, 0.09812263, 0.13254756, 0.2368868 ,
        0.06306084, 0.07799452, 0.20650752],
       [0.02580354, 0.2912477 , 0.0452958 , 0.10479757, 0.19409011,
        0.11631037, 0.07928232, 0.14317259],
       [0.02394   , 0.28100577, 0.06761745, 0.10079238, 0.18286197,
        0.11120233, 0.07584769, 0.15673243],
       [0.02859701, 0.12345824, 0.02452153, 0.12446079, 0.10724279,
        0.30250114, 0.14545403, 0.14376447],
       [0.01763562, 0.26390415, 0.02797072, 0.09410465, 0.33519107,
        0.09990271, 0.07291644, 0.08837461],
       [0.05719835, 0.11961728, 0.06110419, 0.09842541, 0.23005427,
        0.12413418, 0.10744236, 0.20202397],
       [0.01512977, 0.44863623, 0.02352207, 0.09241457, 0.1371182 ,
        0.1376256 , 0.06627141, 0.07928215],
       [0.13617955, 0.11844175, 0.06990904, 0.09725405, 0.2089029 ,
        0.14193644, 0.07938706, 0.14798921],
       [0.05659495, 0.12756449, 0.09210333, 0.12443028, 0.23832083,
        0.0680717 , 0.0928731 , 0.20004131],
       [0.04699743, 0.17098074, 0.06562918, 0.06444676, 0.25861254,
        0.0523265 , 0.10781392, 0.23319292],
       [0.06176807, 0.17528789, 0.07962973, 0.06739685, 0.2270808 ,
        0.06453936, 0.11641537, 0.20788196],
       [0.05633127, 0.11881155, 0.07823401, 0.1879416 , 0.19890387,
        0.10915504, 0.08803149, 0.16259117],
       [0.03467053, 0.3494804 , 0.06560414, 0.08256355, 0.11014081,
        0.13175112, 0.10785011, 0.11793937],
       [0.02571611, 0.4676369 , 0.0442673 , 0.14291236, 0.07799932,
        0.12573668, 0.0587831 , 0.05694821],
       [0.02525828, 0.40146384, 0.05145144, 0.11157996, 0.12324058,
        0.10363442, 0.07611745, 0.10725403],
       [0.04221298, 0.05616246, 0.05433249, 0.09642576, 0.26045817,
        0.10190966, 0.18601942, 0.20247905],
       [0.04178631, 0.1104403 , 0.0894035 , 0.09241916, 0.2568906 ,
        0.10427111, 0.11317873, 0.19161028],
       [0.04844856, 0.16488735, 0.082458  , 0.10612834, 0.23859234,
        0.09024726, 0.09954155, 0.1696966 ],
       [0.12209805, 0.05936319, 0.22111115, 0.08510352, 0.09670409,
        0.0603577 , 0.21275316, 0.14250913]], dtype='float32')]
ground_truth = [3, 4, 3, 5, 7, 1, 7, 1, 2, 5, 3, 6, 0, 6, 4, 6, 5, 4, 1, 6, 0, 4, 7, 2, 6, 6, 5, 7, 1, 3, 4, 5, 1, 3, 7, 3, 1, 1, 4, 0, 5, 7]

reporter = metriculous.compare_classifiers(
    ground_truth=ground_truth,
    model_predictions=predictions
)

reporter.save_html('./metriculous_out.html')

I was able to reproduce this on the latest version and the develop branch.

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