Estimating local and global feature importance scores using DiCE
Summaries of counterfactual examples can be used to estimate importance of features. Intuitively, a feature that is changed more often to generate a proximal counterfactual is an important feature. We use this intuition to build a feature importance score.
This score can be interpreted as a measure of the necessity of a feature to cause a particular model output. That is, if the feature’s value changes, then it is likely that the model’s output class will also change (or the model’s output will significantly change in case of regression model).
Below we show how counterfactuals can be used to provide local feature importance scores for any input, and how those scores can be combined to yield a global importance score for each feature.
[1]:
from sklearn.compose import ColumnTransformer
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, OneHotEncoder
from sklearn.ensemble import RandomForestClassifier
import dice_ml
from dice_ml import Dice
from dice_ml.utils import helpers # helper functions
[2]:
%load_ext autoreload
%autoreload 2
Preliminaries: Loading the data and ML model
[3]:
dataset = helpers.load_adult_income_dataset().sample(5000) # downsampling to reduce ML model fitting time
helpers.get_adult_data_info()
[3]:
{'age': 'age',
'workclass': 'type of industry (Government, Other/Unknown, Private, Self-Employed)',
'education': 'education level (Assoc, Bachelors, Doctorate, HS-grad, Masters, Prof-school, School, Some-college)',
'marital_status': 'marital status (Divorced, Married, Separated, Single, Widowed)',
'occupation': 'occupation (Blue-Collar, Other/Unknown, Professional, Sales, Service, White-Collar)',
'race': 'white or other race?',
'gender': 'male or female?',
'hours_per_week': 'total work hours per week',
'income': '0 (<=50K) vs 1 (>50K)'}
[4]:
target = dataset["income"]
# Split data into train and test
datasetX = dataset.drop("income", axis=1)
x_train, x_test, y_train, y_test = train_test_split(datasetX,
target,
test_size=0.2,
random_state=0,
stratify=target)
numerical = ["age", "hours_per_week"]
categorical = x_train.columns.difference(numerical)
# We create the preprocessing pipelines for both numeric and categorical data.
numeric_transformer = Pipeline(steps=[
('scaler', StandardScaler())])
categorical_transformer = Pipeline(steps=[
('onehot', OneHotEncoder(handle_unknown='ignore'))])
transformations = ColumnTransformer(
transformers=[
('num', numeric_transformer, numerical),
('cat', categorical_transformer, categorical)])
# Append classifier to preprocessing pipeline.
# Now we have a full prediction pipeline.
clf = Pipeline(steps=[('preprocessor', transformations),
('classifier', RandomForestClassifier())])
model = clf.fit(x_train, y_train)
[5]:
d = dice_ml.Data(dataframe=dataset, continuous_features=['age', 'hours_per_week'], outcome_name='income')
m = dice_ml.Model(model=model, backend="sklearn")
Local feature importance
We first generate counterfactuals for a given input point.
[6]:
exp = Dice(d, m, method="random")
query_instance = x_train[1:2]
e1 = exp.generate_counterfactuals(query_instance, total_CFs=10, desired_range=None,
desired_class="opposite",
permitted_range=None, features_to_vary="all")
e1.visualize_as_dataframe(show_only_changes=True)
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Query instance (original outcome : 1)
| age | workclass | education | marital_status | occupation | race | gender | hours_per_week | income | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 37 | Private | Some-college | Married | Sales | White | Male | 44 | 1 |
Diverse Counterfactual set (new outcome: 0)
| age | workclass | education | marital_status | occupation | race | gender | hours_per_week | income | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 30 | - | - | Separated | - | - | - | - | 0 |
| 1 | - | - | Doctorate | Single | - | - | - | - | 0 |
| 2 | 77 | - | Assoc | - | - | - | - | - | 0 |
| 3 | - | - | School | Divorced | - | - | - | - | 0 |
| 4 | - | - | - | Divorced | - | - | - | - | 0 |
| 5 | 24 | - | - | - | - | - | - | - | 0 |
| 6 | - | - | - | - | - | Other | - | 4 | 0 |
| 7 | - | Self-Employed | School | - | - | - | - | - | 0 |
| 8 | - | - | - | - | - | - | Female | 4 | 0 |
| 9 | - | - | - | - | Service | - | - | 65 | 0 |
These can now be used to calculate the feature importance scores.
[7]:
imp = exp.local_feature_importance(query_instance, cf_examples_list=e1.cf_examples_list)
print(imp.local_importance)
[{'education': 0.4, 'marital_status': 0.4, 'age': 0.3, 'hours_per_week': 0.3, 'workclass': 0.1, 'occupation': 0.1, 'race': 0.1, 'gender': 0.1}]
Feature importance can also be estimated directly, by leaving the cf_examples_list argument blank.
[8]:
imp = exp.local_feature_importance(query_instance, posthoc_sparsity_param=None)
print(imp.local_importance)
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[{'age': 0.5, 'hours_per_week': 0.5, 'education': 0.3, 'marital_status': 0.2, 'workclass': 0.1, 'occupation': 0.1, 'race': 0.1, 'gender': 0.1}]
Global importance
For global importance, we need to generate counterfactuals for a representative sample of the dataset.
[9]:
cobj = exp.global_feature_importance(x_train[0:10], total_CFs=10, posthoc_sparsity_param=None)
print(cobj.summary_importance)
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{'age': 0.53, 'marital_status': 0.46, 'hours_per_week': 0.45, 'occupation': 0.21, 'gender': 0.15, 'education': 0.13, 'workclass': 0.11, 'race': 0.07}
Convert the counterfactual output to json
[10]:
json_str = cobj.to_json()
print(json_str)
{"test_data": [[[48, "Government", "Bachelors", "Divorced", "Professional", "White", "Female", 60, 0]], [[37, "Private", "Some-college", "Married", "Sales", "White", "Male", 44, 1]], [[36, "Private", "Some-college", "Married", "White-Collar", "White", "Female", 35, 1]], [[30, "Private", "HS-grad", "Single", "Blue-Collar", "White", "Male", 40, 0]], [[32, "Private", "Assoc", "Married", "White-Collar", "White", "Female", 40, 1]], [[17, "Private", "School", "Single", "Service", "White", "Male", 20, 0]], [[28, "Private", "Bachelors", "Married", "Sales", "White", "Male", 45, 1]], [[41, "Private", "Some-college", "Married", "Sales", "White", "Male", 65, 1]], [[23, "Government", "Bachelors", "Single", "Professional", "White", "Male", 40, 0]], [[26, "Private", "Masters", "Single", "White-Collar", "White", "Female", 48, 0]]], "cfs_list": [[[48, "Government", "Prof-school", "Divorced", "Professional", "White", "Male", 60, 1], [48, "Government", "Bachelors", "Married", "Professional", "White", "Female", 41, 1], [65, "Government", "Doctorate", "Divorced", "Professional", "White", "Female", 60, 1], [48, "Government", "Bachelors", "Married", "Professional", "White", "Female", 91, 1], [48, "Government", "Bachelors", "Married", "Professional", "White", "Female", 32, 1], [48, "Private", "Bachelors", "Married", "Professional", "White", "Female", 60, 1], [48, "Government", "Bachelors", "Married", "Professional", "White", "Female", 46, 1], [48, "Government", "Bachelors", "Married", "Professional", "White", "Female", 30, 1], [48, "Government", "Bachelors", "Married", "Professional", "White", "Female", 80, 1], [41, "Government", "Bachelors", "Married", "Professional", "White", "Female", 60, 1]], [[37, "Private", "Some-college", "Widowed", "Sales", "White", "Male", 81, 0], [37, "Other/Unknown", "Some-college", "Divorced", "Sales", "White", "Male", 44, 0], [37, "Private", "Some-college", "Married", "Sales", "White", "Male", 17, 0], [37, "Self-Employed", "Some-college", "Married", "Sales", "White", "Male", 98, 0], [83, "Private", "Some-college", "Married", "Sales", "Other", "Male", 44, 0], [47, "Private", "Some-college", "Married", "Sales", "White", "Male", 22, 0], [37, "Private", "Some-college", "Married", "Sales", "White", "Male", 7, 0], [59, "Private", "Some-college", "Married", "Sales", "White", "Male", 89, 0], [37, "Private", "Some-college", "Single", "Sales", "Other", "Male", 44, 0], [32, "Private", "Some-college", "Married", "White-Collar", "White", "Male", 44, 0]], [[63, "Government", "Some-college", "Married", "White-Collar", "White", "Female", 35, 0], [73, "Private", "Some-college", "Separated", "White-Collar", "White", "Female", 35, 0], [36, "Private", "Some-college", "Married", "White-Collar", "White", "Female", 7, 0], [36, "Private", "Some-college", "Divorced", "White-Collar", "White", "Female", 5, 0], [36, "Private", "Some-college", "Separated", "White-Collar", "White", "Female", 24, 0], [36, "Private", "Some-college", "Married", "White-Collar", "Other", "Female", 83, 0], [36, "Private", "Some-college", "Widowed", "White-Collar", "White", "Female", 89, 0], [36, "Private", "Some-college", "Married", "White-Collar", "White", "Male", 47, 0], [36, "Private", "Some-college", "Single", "Service", "White", "Female", 35, 0], [54, "Private", "Some-college", "Married", "White-Collar", "White", "Female", 61, 0]], [[77, "Private", "HS-grad", "Single", "White-Collar", "White", "Male", 59, 1], [67, "Private", "HS-grad", "Married", "Blue-Collar", "White", "Female", 40, 1], [30, "Private", "HS-grad", "Married", "Blue-Collar", "White", "Male", 83, 1], [30, "Self-Employed", "HS-grad", "Married", "Blue-Collar", "White", "Male", 47, 1], [30, "Private", "HS-grad", "Married", "White-Collar", "White", "Male", 40, 1], [34, "Private", "Prof-school", "Single", "Sales", "White", "Male", 40, 1], [64, "Private", "HS-grad", "Married", "Professional", "White", "Male", 40, 1], [30, "Private", "Assoc", "Single", "Professional", "White", "Female", 40, 1], [30, "Private", "HS-grad", "Married", "Blue-Collar", "White", "Male", 94, 1], [68, "Private", "HS-grad", "Married", "Blue-Collar", "White", "Male", 89, 1]], [[43, "Other/Unknown", "Assoc", "Married", "White-Collar", "White", "Female", 40, 0], [32, "Private", "Assoc", "Divorced", "White-Collar", "White", "Female", 21, 0], [32, "Private", "Assoc", "Single", "White-Collar", "White", "Female", 40, 0], [32, "Private", "Assoc", "Married", "Other/Unknown", "Other", "Female", 40, 0], [76, "Private", "Assoc", "Single", "White-Collar", "White", "Female", 40, 0], [32, "Private", "Assoc", "Married", "White-Collar", "White", "Female", 16, 0], [68, "Private", "Assoc", "Married", "White-Collar", "Other", "Female", 40, 0], [32, "Private", "Assoc", "Single", "White-Collar", "White", "Female", 75, 0], [49, "Self-Employed", "Assoc", "Married", "White-Collar", "White", "Female", 40, 0], [68, "Private", "Assoc", "Married", "White-Collar", "White", "Female", 40, 0]], [[39, "Private", "School", "Single", "Professional", "White", "Male", 99, 1], [49, "Private", "School", "Single", "White-Collar", "White", "Male", 59, 1], [42, "Private", "Masters", "Single", "Service", "White", "Male", 76, 1], [81, "Private", "Doctorate", "Married", "Service", "White", "Male", 20, 1], [49, "Private", "Doctorate", "Single", "Service", "White", "Male", 92, 1], [66, "Other/Unknown", "Some-college", "Married", "Service", "White", "Male", 20, 1], [52, "Private", "Masters", "Married", "Service", "White", "Male", 20, 1], [81, "Private", "Doctorate", "Married", "Service", "White", "Male", 38, 1], [55, "Private", "School", "Single", "Professional", "White", "Male", 64, 0], [49, "Self-Employed", "Doctorate", "Single", "Service", "White", "Male", 92, 1]], [[28, "Private", "Bachelors", "Single", "Sales", "White", "Female", 45, 0], [28, "Private", "Bachelors", "Married", "Other/Unknown", "White", "Male", 14, 0], [27, "Private", "Bachelors", "Married", "Sales", "White", "Male", 79, 0], [28, "Private", "Bachelors", "Married", "Professional", "Other", "Male", 45, 0], [28, "Private", "Assoc", "Married", "Sales", "White", "Male", 45, 0], [28, "Private", "Bachelors", "Separated", "White-Collar", "White", "Male", 45, 0], [28, "Private", "Bachelors", "Separated", "Sales", "White", "Male", 77, 0], [46, "Private", "Bachelors", "Married", "Sales", "White", "Female", 45, 0], [28, "Other/Unknown", "Bachelors", "Married", "Professional", "White", "Male", 45, 0], [28, "Private", "HS-grad", "Married", "Sales", "White", "Male", 22, 0]], [[41, "Private", "Some-college", "Married", "White-Collar", "White", "Female", 65, 0], [72, "Private", "Some-college", "Married", "Service", "White", "Male", 65, 0], [41, "Private", "Some-college", "Separated", "Sales", "White", "Male", 25, 0], [28, "Private", "Some-college", "Married", "Blue-Collar", "White", "Male", 65, 0], [26, "Private", "Some-college", "Married", "Sales", "White", "Male", 65, 0], [41, "Private", "Some-college", "Married", "Professional", "White", "Male", 5, 0], [81, "Private", "Some-college", "Divorced", "Sales", "White", "Male", 65, 0], [30, "Private", "Some-college", "Married", "Sales", "White", "Male", 65, 0], [41, "Government", "Some-college", "Divorced", "Sales", "White", "Male", 65, 0], [41, "Private", "Some-college", "Single", "Professional", "White", "Male", 65, 0]], [[23, "Government", "Bachelors", "Married", "Professional", "Other", "Male", 40, 1], [55, "Government", "Bachelors", "Married", "Professional", "White", "Male", 40, 1], [37, "Government", "Bachelors", "Married", "Professional", "White", "Male", 40, 1], [82, "Government", "Bachelors", "Single", "Professional", "White", "Female", 40, 1], [88, "Government", "Bachelors", "Single", "Professional", "White", "Female", 40, 1], [23, "Government", "Bachelors", "Married", "Service", "White", "Male", 40, 1], [75, "Government", "Bachelors", "Single", "Professional", "White", "Female", 40, 1], [73, "Government", "Bachelors", "Married", "Professional", "White", "Male", 40, 1], [23, "Government", "Bachelors", "Married", "Professional", "White", "Male", 44, 1], [88, "Government", "Bachelors", "Married", "Professional", "White", "Male", 40, 1]], [[76, "Private", "Masters", "Single", "White-Collar", "White", "Female", 71, 1], [35, "Private", "Masters", "Single", "White-Collar", "White", "Female", 81, 1], [42, "Private", "Masters", "Single", "White-Collar", "White", "Male", 48, 1], [51, "Private", "Masters", "Single", "White-Collar", "White", "Female", 68, 1], [58, "Private", "Masters", "Single", "White-Collar", "White", "Male", 48, 1], [34, "Private", "Masters", "Single", "White-Collar", "White", "Male", 48, 1], [88, "Private", "Masters", "Single", "White-Collar", "White", "Male", 48, 1], [48, "Private", "Masters", "Married", "White-Collar", "White", "Female", 48, 1], [52, "Private", "Masters", "Single", "White-Collar", "White", "Male", 48, 1], [79, "Private", "Masters", "Single", "White-Collar", "White", "Female", 60, 1]]], "local_importance": [[0.2, 0.1, 0.2, 0.8, 0.0, 0.0, 0.1, 0.6], [0.4, 0.2, 0.0, 0.3, 0.1, 0.2, 0.0, 0.6], [0.3, 0.1, 0.0, 0.5, 0.1, 0.1, 0.1, 0.7], [0.5, 0.1, 0.2, 0.7, 0.5, 0.0, 0.2, 0.5], [0.5, 0.2, 0.0, 0.4, 0.1, 0.2, 0.0, 0.3], [1.0, 0.2, 0.7, 0.4, 0.3, 0.0, 0.0, 0.7], [0.2, 0.1, 0.2, 0.3, 0.4, 0.1, 0.2, 0.4], [0.5, 0.1, 0.0, 0.4, 0.5, 0.0, 0.1, 0.2], [0.7, 0.0, 0.0, 0.7, 0.1, 0.1, 0.3, 0.1], [1.0, 0.0, 0.0, 0.1, 0.0, 0.0, 0.5, 0.4]], "summary_importance": [0.53, 0.11, 0.13, 0.46, 0.21, 0.07, 0.15, 0.45], "data_interface": {"outcome_name": "income", "data_df": "dummy_data"}, "feature_names": ["age", "workclass", "education", "marital_status", "occupation", "race", "gender", "hours_per_week"], "feature_names_including_target": ["age", "workclass", "education", "marital_status", "occupation", "race", "gender", "hours_per_week", "income"], "model_type": "classifier", "desired_class": 1, "desired_range": null, "metadata": {"version": "2.0"}}
Convert the json output to a counterfactual object
[11]:
imp_r = imp.from_json(json_str)
print([o.visualize_as_dataframe(show_only_changes=True) for o in imp_r.cf_examples_list])
print(imp_r.local_importance)
print(imp_r.summary_importance)
Query instance (original outcome : 0)
| age | workclass | education | marital_status | occupation | race | gender | hours_per_week | income | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 48 | Government | Bachelors | Divorced | Professional | White | Female | 60 | 0 |
Counterfactual set (new outcome: 1)
| age | workclass | education | marital_status | occupation | race | gender | hours_per_week | income | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | - | - | Prof-school | - | - | - | Male | - | 1 |
| 1 | - | - | - | Married | - | - | - | 41 | 1 |
| 2 | 65 | - | Doctorate | - | - | - | - | - | 1 |
| 3 | - | - | - | Married | - | - | - | 91 | 1 |
| 4 | - | - | - | Married | - | - | - | 32 | 1 |
| 5 | - | Private | - | Married | - | - | - | - | 1 |
| 6 | - | - | - | Married | - | - | - | 46 | 1 |
| 7 | - | - | - | Married | - | - | - | 30 | 1 |
| 8 | - | - | - | Married | - | - | - | 80 | 1 |
| 9 | 41 | - | - | Married | - | - | - | - | 1 |
Query instance (original outcome : 1)
| age | workclass | education | marital_status | occupation | race | gender | hours_per_week | income | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 37 | Private | Some-college | Married | Sales | White | Male | 44 | 1 |
Counterfactual set (new outcome: 1)
| age | workclass | education | marital_status | occupation | race | gender | hours_per_week | income | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | - | - | - | Widowed | - | - | - | 81 | 0 |
| 1 | - | Other/Unknown | - | Divorced | - | - | - | - | 0 |
| 2 | - | - | - | - | - | - | - | 17 | 0 |
| 3 | - | Self-Employed | - | - | - | - | - | 98 | 0 |
| 4 | 83 | - | - | - | - | Other | - | - | 0 |
| 5 | 47 | - | - | - | - | - | - | 22 | 0 |
| 6 | - | - | - | - | - | - | - | 7 | 0 |
| 7 | 59 | - | - | - | - | - | - | 89 | 0 |
| 8 | - | - | - | Single | - | Other | - | - | 0 |
| 9 | 32 | - | - | - | White-Collar | - | - | - | 0 |
Query instance (original outcome : 1)
| age | workclass | education | marital_status | occupation | race | gender | hours_per_week | income | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 36 | Private | Some-college | Married | White-Collar | White | Female | 35 | 1 |
Counterfactual set (new outcome: 1)
| age | workclass | education | marital_status | occupation | race | gender | hours_per_week | income | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 63 | Government | - | - | - | - | - | - | 0 |
| 1 | 73 | - | - | Separated | - | - | - | - | 0 |
| 2 | - | - | - | - | - | - | - | 7 | 0 |
| 3 | - | - | - | Divorced | - | - | - | 5 | 0 |
| 4 | - | - | - | Separated | - | - | - | 24 | 0 |
| 5 | - | - | - | - | - | Other | - | 83 | 0 |
| 6 | - | - | - | Widowed | - | - | - | 89 | 0 |
| 7 | - | - | - | - | - | - | Male | 47 | 0 |
| 8 | - | - | - | Single | Service | - | - | - | 0 |
| 9 | 54 | - | - | - | - | - | - | 61 | 0 |
Query instance (original outcome : 0)
| age | workclass | education | marital_status | occupation | race | gender | hours_per_week | income | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 30 | Private | HS-grad | Single | Blue-Collar | White | Male | 40 | 0 |
Counterfactual set (new outcome: 1)
| age | workclass | education | marital_status | occupation | race | gender | hours_per_week | income | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 77 | - | - | - | White-Collar | - | - | 59 | 1 |
| 1 | 67 | - | - | Married | - | - | Female | - | 1 |
| 2 | - | - | - | Married | - | - | - | 83 | 1 |
| 3 | - | Self-Employed | - | Married | - | - | - | 47 | 1 |
| 4 | - | - | - | Married | White-Collar | - | - | - | 1 |
| 5 | 34 | - | Prof-school | - | Sales | - | - | - | 1 |
| 6 | 64 | - | - | Married | Professional | - | - | - | 1 |
| 7 | - | - | Assoc | - | Professional | - | Female | - | 1 |
| 8 | - | - | - | Married | - | - | - | 94 | 1 |
| 9 | 68 | - | - | Married | - | - | - | 89 | 1 |
Query instance (original outcome : 1)
| age | workclass | education | marital_status | occupation | race | gender | hours_per_week | income | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 32 | Private | Assoc | Married | White-Collar | White | Female | 40 | 1 |
Counterfactual set (new outcome: 1)
| age | workclass | education | marital_status | occupation | race | gender | hours_per_week | income | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 43 | Other/Unknown | - | - | - | - | - | - | 0 |
| 1 | - | - | - | Divorced | - | - | - | 21 | 0 |
| 2 | - | - | - | Single | - | - | - | - | 0 |
| 3 | - | - | - | - | Other/Unknown | Other | - | - | 0 |
| 4 | 76 | - | - | Single | - | - | - | - | 0 |
| 5 | - | - | - | - | - | - | - | 16 | 0 |
| 6 | 68 | - | - | - | - | Other | - | - | 0 |
| 7 | - | - | - | Single | - | - | - | 75 | 0 |
| 8 | 49 | Self-Employed | - | - | - | - | - | - | 0 |
| 9 | 68 | - | - | - | - | - | - | - | 0 |
Query instance (original outcome : 0)
| age | workclass | education | marital_status | occupation | race | gender | hours_per_week | income | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 17 | Private | School | Single | Service | White | Male | 20 | 0 |
Counterfactual set (new outcome: 1)
| age | workclass | education | marital_status | occupation | race | gender | hours_per_week | income | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 39 | - | - | - | Professional | - | - | 99 | 1 |
| 1 | 49 | - | - | - | White-Collar | - | - | 59 | 1 |
| 2 | 42 | - | Masters | - | - | - | - | 76 | 1 |
| 3 | 81 | - | Doctorate | Married | - | - | - | - | 1 |
| 4 | 49 | - | Doctorate | - | - | - | - | 92 | 1 |
| 5 | 66 | Other/Unknown | Some-college | Married | - | - | - | - | 1 |
| 6 | 52 | - | Masters | Married | - | - | - | - | 1 |
| 7 | 81 | - | Doctorate | Married | - | - | - | 38 | 1 |
| 8 | 55 | - | - | - | Professional | - | - | 64 | - |
| 9 | 49 | Self-Employed | Doctorate | - | - | - | - | 92 | 1 |
Query instance (original outcome : 1)
| age | workclass | education | marital_status | occupation | race | gender | hours_per_week | income | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 28 | Private | Bachelors | Married | Sales | White | Male | 45 | 1 |
Counterfactual set (new outcome: 1)
| age | workclass | education | marital_status | occupation | race | gender | hours_per_week | income | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | - | - | - | Single | - | - | Female | - | 0 |
| 1 | - | - | - | - | Other/Unknown | - | - | 14 | 0 |
| 2 | 27 | - | - | - | - | - | - | 79 | 0 |
| 3 | - | - | - | - | Professional | Other | - | - | 0 |
| 4 | - | - | Assoc | - | - | - | - | - | 0 |
| 5 | - | - | - | Separated | White-Collar | - | - | - | 0 |
| 6 | - | - | - | Separated | - | - | - | 77 | 0 |
| 7 | 46 | - | - | - | - | - | Female | - | 0 |
| 8 | - | Other/Unknown | - | - | Professional | - | - | - | 0 |
| 9 | - | - | HS-grad | - | - | - | - | 22 | 0 |
Query instance (original outcome : 1)
| age | workclass | education | marital_status | occupation | race | gender | hours_per_week | income | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 41 | Private | Some-college | Married | Sales | White | Male | 65 | 1 |
Counterfactual set (new outcome: 1)
| age | workclass | education | marital_status | occupation | race | gender | hours_per_week | income | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | - | - | - | - | White-Collar | - | Female | - | 0 |
| 1 | 72 | - | - | - | Service | - | - | - | 0 |
| 2 | - | - | - | Separated | - | - | - | 25 | 0 |
| 3 | 28 | - | - | - | Blue-Collar | - | - | - | 0 |
| 4 | 26 | - | - | - | - | - | - | - | 0 |
| 5 | - | - | - | - | Professional | - | - | 5 | 0 |
| 6 | 81 | - | - | Divorced | - | - | - | - | 0 |
| 7 | 30 | - | - | - | - | - | - | - | 0 |
| 8 | - | Government | - | Divorced | - | - | - | - | 0 |
| 9 | - | - | - | Single | Professional | - | - | - | 0 |
Query instance (original outcome : 0)
| age | workclass | education | marital_status | occupation | race | gender | hours_per_week | income | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 23 | Government | Bachelors | Single | Professional | White | Male | 40 | 0 |
Counterfactual set (new outcome: 1)
| age | workclass | education | marital_status | occupation | race | gender | hours_per_week | income | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | - | - | - | Married | - | Other | - | - | 1 |
| 1 | 55 | - | - | Married | - | - | - | - | 1 |
| 2 | 37 | - | - | Married | - | - | - | - | 1 |
| 3 | 82 | - | - | - | - | - | Female | - | 1 |
| 4 | 88 | - | - | - | - | - | Female | - | 1 |
| 5 | - | - | - | Married | Service | - | - | - | 1 |
| 6 | 75 | - | - | - | - | - | Female | - | 1 |
| 7 | 73 | - | - | Married | - | - | - | - | 1 |
| 8 | - | - | - | Married | - | - | - | 44 | 1 |
| 9 | 88 | - | - | Married | - | - | - | - | 1 |
Query instance (original outcome : 0)
| age | workclass | education | marital_status | occupation | race | gender | hours_per_week | income | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 26 | Private | Masters | Single | White-Collar | White | Female | 48 | 0 |
Counterfactual set (new outcome: 1)
| age | workclass | education | marital_status | occupation | race | gender | hours_per_week | income | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 76 | - | - | - | - | - | - | 71 | 1 |
| 1 | 35 | - | - | - | - | - | - | 81 | 1 |
| 2 | 42 | - | - | - | - | - | Male | - | 1 |
| 3 | 51 | - | - | - | - | - | - | 68 | 1 |
| 4 | 58 | - | - | - | - | - | Male | - | 1 |
| 5 | 34 | - | - | - | - | - | Male | - | 1 |
| 6 | 88 | - | - | - | - | - | Male | - | 1 |
| 7 | 48 | - | - | Married | - | - | - | - | 1 |
| 8 | 52 | - | - | - | - | - | Male | - | 1 |
| 9 | 79 | - | - | - | - | - | - | 60 | 1 |
[None, None, None, None, None, None, None, None, None, None]
[{'marital_status': 0.8, 'hours_per_week': 0.6, 'age': 0.2, 'education': 0.2, 'workclass': 0.1, 'gender': 0.1, 'occupation': 0.0, 'race': 0.0}, {'hours_per_week': 0.6, 'age': 0.4, 'marital_status': 0.3, 'workclass': 0.2, 'race': 0.2, 'occupation': 0.1, 'education': 0.0, 'gender': 0.0}, {'hours_per_week': 0.7, 'marital_status': 0.5, 'age': 0.3, 'workclass': 0.1, 'occupation': 0.1, 'race': 0.1, 'gender': 0.1, 'education': 0.0}, {'marital_status': 0.7, 'age': 0.5, 'occupation': 0.5, 'hours_per_week': 0.5, 'education': 0.2, 'gender': 0.2, 'workclass': 0.1, 'race': 0.0}, {'age': 0.5, 'marital_status': 0.4, 'hours_per_week': 0.3, 'workclass': 0.2, 'race': 0.2, 'occupation': 0.1, 'education': 0.0, 'gender': 0.0}, {'age': 1.0, 'education': 0.7, 'hours_per_week': 0.7, 'marital_status': 0.4, 'occupation': 0.3, 'workclass': 0.2, 'race': 0.0, 'gender': 0.0}, {'occupation': 0.4, 'hours_per_week': 0.4, 'marital_status': 0.3, 'age': 0.2, 'education': 0.2, 'gender': 0.2, 'workclass': 0.1, 'race': 0.1}, {'age': 0.5, 'occupation': 0.5, 'marital_status': 0.4, 'hours_per_week': 0.2, 'workclass': 0.1, 'gender': 0.1, 'education': 0.0, 'race': 0.0}, {'age': 0.7, 'marital_status': 0.7, 'gender': 0.3, 'occupation': 0.1, 'race': 0.1, 'hours_per_week': 0.1, 'workclass': 0.0, 'education': 0.0}, {'age': 1.0, 'gender': 0.5, 'hours_per_week': 0.4, 'marital_status': 0.1, 'workclass': 0.0, 'education': 0.0, 'occupation': 0.0, 'race': 0.0}]
{'age': 0.53, 'marital_status': 0.46, 'hours_per_week': 0.45, 'occupation': 0.21, 'gender': 0.15, 'education': 0.13, 'workclass': 0.11, 'race': 0.07}