Residuals#

Objectives: what you will take away#

How-To Retrieve global and local residuals.

Prerequisites: before you begin#

You’ve successfully installed Howso Engine
You have an understanding of Howso’s basic workflow.

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	1205220.0	True	19847.0	738812.0	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	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]:

	marital-status	education-num	race	workclass	capital-loss	occupation	age	fnlwgt	sex	hours-per-week	relationship	education	capital-gain	native-country	target
0	0.211634	0	0	0.220598	0	0.897823	12	55931	0.443595	1	0.403238	0	705	0.180793	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]:

	marital-status	workclass	race	education-num	capital-loss	occupation	age	fnlwgt	sex	hours-per-week	relationship	education	capital-gain	native-country	target
feature_full_residuals	0.238535	0.368851	0.17494	0.038573	133.517704	0.787336	8.791346	81162.255661	0.261883	7.73046	0.350777	0.002	1367.786305	0.167058	0.219467

API References#