Profiles

Profiles: tracking a metric over time

Available profiles

The following metrics are implemented:

Dimension	Histogram Type	Metric
Any	Any	Count
Any	Any	Entropy
1D	Any	Filled
1D	Any	Distinct
1D	Any	Underflow, Overflow
1D	Any	NaN
1D	Any	Mode
1D	Numeric	Mean
1D	Numeric	1%, 5%, 16%, 50% (median), 84%, 95%, 99% percentiles
1D	Numeric	Standard deviation
1D	Numeric	Min, Max
1D	Categorical	Fraction of True
2D	Any	PhiK Correlation

The comparisons registry can be consulted for available comparisons:

from popmon.analysis import Profiles

print(Profiles.get_keys())

Profile extensions

How to enable profile extensions:

Install the required package(s). This can be achieved via popmon’s extras: pip install popmon[extension_name]
To show the profile values in your report:
- Include the relevant values to the show_stats list: settings.report.show_stats.append("[value_prefix]*") or
- Show all statistics: settings.report.extended_report = True

Available profile extensions:

diptest: Hartigan & Hartigan’s dip test for unimodality. Available for 1D numerical histograms. Uses the diptest package by Ralph Urlus.

Custom profiles

Tracking custom metrics over time is easy. The following code snippet registers a new metric to popmon.

import numpy as np

from popmon.analysis.profiling.profiles import Profiles


@Profiles.register(key="name_of_profile", description="<description_for_report>", dim=2)
def your_profile_function_name(hist) -> float:
    """Write your function to profile the histogram."""
    return np.sum(p)

Variations:

A profile function may return multiple values for efficiency (e.g. quantiles do not need to be computed)

@Profiles.register(
    key=["key1", "key2"], description=["Statistic 1", "Statistic 2"], dim=None
)
def your_profile_function_name(hist) -> float:
    result1, result2 = your_logic(hist)
    return result1, result2

A profile may work on the histogram, or on the value counts/labels (also for efficiency). This occurs when the htype parameter is passed (1D only)

@Profiles.register(
    key="name_of_profile", description="<description_for_report>", dim=1, htype="all"
)
def your_profile_function_name(bin_labels, bin_counts) -> float:
    return bin_counts.sum()

Profiles may depend on variable type (possible values for htype: num, cat, all).

@Profiles.register(
    key="name_of_profile", description="<description_for_report>", dim=1, htype="num"
)
def your_profile_function_name(bin_labels, bin_counts) -> float:
    return bin_counts.sum()

If you developed a custom profiles that could be generically used, then please considering contributing it to the package.