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 0.0 127.0 True 17.0 84.0 number numeric 8
workclass nominal 0 NaN NaN False NaN NaN number integer 8
fnlwgt continuous 0 0.0 1843230.0 True 19410.0 1125613.0 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 4029.0 True 0.0 2444.0 number numeric 8
hours-per-week continuous 0 0.0 158.0 True 1.0 96.0 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 education-num relationship native-country race capital-loss age education marital-status sex workclass occupation capital-gain hours-per-week target
0 [71505.99099960318, 0] [0, 0] [0.7766678833881989, 0] [0.229691071499588, 0] [0.6249774572936355, 0] [0, 0] [1.413087910768514, 0] [0, 0] [0.2246086750673828, 0] [0, 0] [0.05553129080460051, 0] [0.6894580592244579, 0] [0, 0] [5.947642909015428, 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 education-num relationship native-country race capital-loss age education marital-status sex workclass occupation capital-gain hours-per-week target
feature_full_residuals [86296.70277816901, 0] [0.14952072573404296, 0] [0.3379589012544307, 0] [0.15163282804638373, 0] [0.2183281606234621, 0] [158.09932913448975, 0] [8.470617443342636, 0] [1.5513130224853456e-11, 0] [0.23201469074088232, 0] [0.23360759579097068, 0] [0.3633867029178122, 0] [0.7741236403184258, 0] [1647.593204297032, 0] [8.328392646025712, 0] [0.2092208249680922, 0]

API References#