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()

[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 1590234.0 True 19896.0 972354.0 number numeric 8
education nominal 0 NaN NaN False NaN NaN number integer 8
education-num continuous 0 0.0 26.0 True 1.0 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 6216.0 True 0.0 3770.0 number numeric 8
hours-per-week continuous 0 0.0 162.0 True 2.0 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]:
fnlwgt marital-status target race capital-loss age education occupation sex hours-per-week workclass education-num relationship capital-gain native-country
0 [152775.59743629294, 0] [0, 0] [0.291982313854123, 0] [0.9307332621982874, 0] [78.93948758118927, 0] [13.794169342356668, 0] [0, 0] [0.8456927249679234, 0] [0, 0] [0.014684201390615215, 0] [0.06518279565913454, 0] [1.7763568394002505e-15, 0] [0, 0] [239.1724376805735, 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]:
fnlwgt marital-status target race capital-loss age education occupation sex workclass hours-per-week education-num relationship capital-gain native-country
feature_full_residuals [80702.38340740163, 0] [0.24972513297818139, 0] [0.22142206109226897, 0] [0.20782668520488648, 0] [149.8266306601822, 0] [8.43773094129456, 0] [0, 0] [0.7729749032231602, 0] [0.2441306514878831, 0] [0.37222984205566645, 0] [7.878912166286146, 0] [0.49593079559383846, 0] [0.36449702912475407, 0] [1277.1246434946027, 0] [0.1734581778544932, 0]

API References#