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 0.0 121.0 True 17.0 80.0 number numeric 8
workclass nominal 0 NaN NaN False NaN NaN number integer 8
fnlwgt continuous 0 0.0 1098084.0 True 19678.0 673764.0 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 4029.0 True 0.0 2444.0 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]:

[{'hours-per-week': 28,
  'sex': 0,
  'occupation': 0.7335757605466264,
  'age': 3,
  'native-country': 0,
  'fnlwgt': 51534,
  'education-num': 0,
  'capital-gain': 114,
  'workclass': 0.8115459571184314,
  'relationship': 0.06439061700901239,
  'target': 0.2700843254972193,
  'capital-loss': 0,
  'education': 0,
  'marital-status': 0,
  'race': 0.10703621806027186}]

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]:
hours-per-week sex occupation age native-country fnlwgt education-num workclass capital-gain relationship target capital-loss education marital-status race
feature_full_residuals 8.293756 0.256471 0.785627 8.216137 0.178461 74784.750172 0.011779 0.385171 2316.958528 0.366044 0.266758 148.943909 0.0 0.269275 0.187492

API References#