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 1776872.0 True 19793.0 1085515.0 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 4656.0 True 0.0 2824.0 number numeric 8
hours-per-week continuous 0 0.0 162.0 True 2.0 99.0 number numeric 8
native-country continuous 0 0.0 66.0 False 0.0 40.0 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]:
age sex hours-per-week education occupation capital-gain workclass native-country education-num relationship race target capital-loss fnlwgt marital-status
0 [3.779600261088323, 0] [0.25933988779753003, 0] [2.074711518377981, 0] [0, 0] [0.7489676990448575, 0] [0, 0] [0.11134517605497984, 0] [2.2114556119394564, 0] [3.552713678800501e-15, 0] [0.7465451125928213, 0] [0.08999984668533723, 0] [0, 0] [0, 0] [34752.118028217985, 0] [0.061433947228705876, 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]:
age sex hours-per-week education occupation capital-gain workclass native-country education-num relationship race target capital-loss fnlwgt marital-status
feature_full_residuals [8.782611121727863, 0] [0.27659463962971004, 0] [8.377644830199735, 0] [0.00100000793767787, 0] [0.7723175547241362, 0] [1635.1554619274127, 0] [0.36785555080084437, 0] [3.7541066428252003, 0] [0.17963813786401645, 0] [0.35498012952713637, 0] [0.21462193322021422, 0] [0.21630571142437827, 0] [127.80279153176161, 0] [81303.60445139007, 0] [0.23640258900312858, 0]

API References#