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 1902182.0 True 19395.0 1161363.0 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 4953.0 True 0.0 3004.0 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 68.0 False 0.0 41.0 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]:

[{'sex': 0,
  'workclass': 0.5331587655580869,
  'race': 0,
  'occupation': 0.7978098338827468,
  'fnlwgt': 135036,
  'capital-loss': 133,
  'capital-gain': 185,
  'target': 0.466527866685404,
  'hours-per-week': 6,
  'education': 0,
  'relationship': 0.05012456999954262,
  'age': 12,
  'native-country': 26,
  'education-num': 0,
  'marital-status': 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},
)
residuals

[3]:

{'feature_full_residuals': {'sex': 0.2570762289270188,
  'workclass': 0.36810121214714053,
  'race': 0.20137945439242821,
  'occupation': 0.7711453769837245,
  'target': 0.21644047023383686,
  'fnlwgt': 78093.13751516423,
  'capital-gain': 938.679181774177,
  'capital-loss': 133.79947992722927,
  'hours-per-week': 8.237219438250245,
  'education': 9.896150565680273e-14,
  'relationship': 0.33661721607414674,
  'marital-status': 0.22055382814740387,
  'native-country': 2.88085842471993,
  'education-num': 0.0009155718366581049,
  'age': 8.238250587342119}}

API References#