Residuals#

Objectives: what you will take away#

  • How-To Retrieve global and local residuals.

Prerequisites: before you begin#

Data#

Our example dataset for this recipe is the well known Adult dataset. It is accessible via the pmlb package installed earlier. We use the fetch_data() function to retrieve the dataset in Step 1 below.

Concepts & Terminology#

How-To Guide#

Setup#

The user guide assumes you have created and setup a Trainee as demonstrated in basic workflow. The created Trainee will be referenced as trainee in the sections below.

[1]:

import pandas as pd
from pmlb import fetch_data

from howso.engine import Trainee
from howso.utilities import infer_feature_attributes

df = fetch_data('adult').sample(1_000)
features = infer_feature_attributes(df)

trainee = Trainee(features=features)
trainee.train(df)
trainee.analyze()

features.to_dataframe()

/home/docs/checkouts/readthedocs.org/user_builds/diveplane-howso-docs/envs/latest/lib/python3.11/site-packages/howso/utilities/feature_attributes/pandas.py:145: UserWarning: You have one or more suggestions to consider for your feature attributes configuration. Please view them by printing the `suggestions` property of your returned feature attributes object (`your_attributes_object.suggestions`).
  warnings.warn(suggestion_warning, UserWarning)

[1]:
type decimal_places bounds data_type original_type
min max allow_null observed_min observed_max data_type size
age continuous 0 0.0 137.0 True 17.0 90.0 number numeric 8
workclass nominal 0 NaN NaN False NaN NaN number integer 8
fnlwgt continuous 0 0.0 1138747.0 True 19513.0 698363.0 number numeric 8
education nominal 0 NaN NaN False NaN NaN number integer 8
education-num continuous 0 0.0 25.0 True 2.0 16.0 number numeric 8
marital-status nominal 0 NaN NaN False NaN NaN number integer 8
occupation nominal 0 NaN NaN False NaN NaN number integer 8
relationship nominal 0 NaN NaN False NaN NaN number integer 8
race nominal 0 NaN NaN False NaN NaN number integer 8
sex nominal 0 NaN NaN False NaN NaN number integer 8
capital-gain continuous 0 0.0 164870.0 True 0.0 99999.0 number numeric 8
capital-loss continuous 0 0.0 7182.0 True 0.0 4356.0 number numeric 8
hours-per-week continuous 0 0.0 163.0 True 1.0 99.0 number numeric 8
native-country nominal 0 NaN NaN False NaN NaN number integer 8
target nominal 0 NaN NaN False NaN NaN number integer 8

Local Residuals#

Local metrics are retrieved through using Trainee.react(). Both Robust and non-robust (full) versions are available, although full is recommended for residuals.

[2]:

# Get local full residuals
details = {'feature_full_residuals_for_case': True}
results = trainee.react(
    df.iloc[[-1]],
    context_features=features.get_names(without=["target"]),
    action_features=["target"],
    details=details
)

residuals = results['details']['feature_full_residuals_for_case']
residuals

[2]:
target native-country occupation hours-per-week capital-gain education fnlwgt capital-loss relationship race sex workclass education-num age marital-status
0 [0, 0] [0.03057671020897401, 0] [0.6330436283275698, 0] [0.5686706826669123, 0] [6332.263937304816, 0] [0, 0] [9169.826011628058, 0] [0, 0] [0, 0] [0, 0] [0, 0] [0.9163968318881919, 0] [1.7763568394002505e-15, 0] [5.701897709782905, 0] [0, 0]

Global Residuals#

Howso has the ability to retrieve both local vs global metrics. Global metrics are retrieved through using Trainee.react_aggregate(). Both Robust and non-robust (full) versions are also available.

[3]:

# Get global full residuals
residuals = trainee.react_aggregate(
    details={'feature_full_residuals': True},
).to_dataframe()
residuals

[3]:
target occupation native-country hours-per-week capital-gain education fnlwgt capital-loss relationship race sex workclass education-num age marital-status
feature_full_residuals [0.2185190200928327, 0] [0.7778581397400318, 0] [0.16639153869479642, 0] [8.116940311767786, 0] [1554.791175757134, 0] [2.3412910377107466e-10, 0] [73677.85580708538, 0] [144.7348814799382, 0] [0.31324225860455274, 0] [0.21868947876617037, 0] [0.2630674127082105, 0] [0.33438174325373593, 0] [0.09921981315421642, 0] [8.603370467159685, 0] [0.2248582799032369, 0]

API References#