Anchor (#3)

* add tabular anchors

* add type annotations

* add adult anchor example

* expose anchor at explainer level and fix setup file

* fix type annotation

* add tests for anchor

* update style and annotations

alibi/datasets.py

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+import numpy as np
+import pandas as pd
+from sklearn.preprocessing import LabelEncoder
+from typing import Tuple
+
+
+def adult(features_drop: list = ["fnlwgt", "Education-Num"]) -> Tuple[np.ndarray, np.ndarray, list, dict]:
+    """
+    Downloads and pre-processes 'adult' dataset.
+    More info: http://mlr.cs.umass.edu/ml/machine-learning-databases/adult/
+
+    Parameters
+    ----------
+    features_drop
+        List of features to be dropped from dataset
+
+    Returns
+    -------
+    Dataset, labels, a list of features and a dictionary containing a list with the potential categories
+    for each categorical feature where the key refers to the feature column.
+    """
+    # download data
+    dataset_url = 'http://mlr.cs.umass.edu/ml/machine-learning-databases/adult/adult.data'
+    raw_features = ['Age', 'Workclass', 'fnlwgt', 'Education', 'Education-Num', 'Marital Status',
+                    'Occupation', 'Relationship', 'Race', 'Sex', 'Capital Gain', 'Capital Loss',
+                    'Hours per week', 'Country', 'Target']
+    raw_data = pd.read_csv(dataset_url, names=raw_features, delimiter=', ').fillna('?')
+
+    # get labels, features and drop unnecessary features
+    labels = (raw_data['Target'] == '>50K').astype(int).values
+    features_drop += ['Target']
+    data = raw_data.drop(features_drop, axis=1)
+    features = list(data.columns)
+
+    # map categorical features
+    education_map = {
+        '10th': 'Dropout', '11th': 'Dropout', '12th': 'Dropout', '1st-4th':
+            'Dropout', '5th-6th': 'Dropout', '7th-8th': 'Dropout', '9th':
+            'Dropout', 'Preschool': 'Dropout', 'HS-grad': 'High School grad',
+        'Some-college': 'High School grad', 'Masters': 'Masters',
+        'Prof-school': 'Prof-School', 'Assoc-acdm': 'Associates',
+        'Assoc-voc': 'Associates'
+    }
+    occupation_map = {
+        "Adm-clerical": "Admin", "Armed-Forces": "Military",
+        "Craft-repair": "Blue-Collar", "Exec-managerial": "White-Collar",
+        "Farming-fishing": "Blue-Collar", "Handlers-cleaners":
+            "Blue-Collar", "Machine-op-inspct": "Blue-Collar", "Other-service":
+            "Service", "Priv-house-serv": "Service", "Prof-specialty":
+            "Professional", "Protective-serv": "Other", "Sales":
+            "Sales", "Tech-support": "Other", "Transport-moving":
+            "Blue-Collar"
+    }
+    country_map = {
+        'Cambodia': 'SE-Asia', 'Canada': 'British-Commonwealth', 'China':
+            'China', 'Columbia': 'South-America', 'Cuba': 'Other',
+        'Dominican-Republic': 'Latin-America', 'Ecuador': 'South-America',
+        'El-Salvador': 'South-America', 'England': 'British-Commonwealth',
+        'France': 'Euro_1', 'Germany': 'Euro_1', 'Greece': 'Euro_2',
+        'Guatemala': 'Latin-America', 'Haiti': 'Latin-America',
+        'Holand-Netherlands': 'Euro_1', 'Honduras': 'Latin-America',
+        'Hong': 'China', 'Hungary': 'Euro_2', 'India':
+            'British-Commonwealth', 'Iran': 'Other', 'Ireland':
+            'British-Commonwealth', 'Italy': 'Euro_1', 'Jamaica':
+            'Latin-America', 'Japan': 'Other', 'Laos': 'SE-Asia', 'Mexico':
+            'Latin-America', 'Nicaragua': 'Latin-America',
+        'Outlying-US(Guam-USVI-etc)': 'Latin-America', 'Peru':
+            'South-America', 'Philippines': 'SE-Asia', 'Poland': 'Euro_2',
+        'Portugal': 'Euro_2', 'Puerto-Rico': 'Latin-America', 'Scotland':
+            'British-Commonwealth', 'South': 'Euro_2', 'Taiwan': 'China',
+        'Thailand': 'SE-Asia', 'Trinadad&Tobago': 'Latin-America',
+        'United-States': 'United-States', 'Vietnam': 'SE-Asia'
+    }
+    married_map = {
+        'Never-married': 'Never-Married', 'Married-AF-spouse': 'Married',
+        'Married-civ-spouse': 'Married', 'Married-spouse-absent':
+            'Separated', 'Separated': 'Separated', 'Divorced':
+            'Separated', 'Widowed': 'Widowed'
+    }
+    mapping = {'Education': education_map, 'Occupation': occupation_map, 'Country': country_map,
+               'Marital Status': married_map}
+
+    data_copy = data.copy()
+    for f, f_map in mapping.items():
+        data_tmp = data_copy[f].values
+        for key, value in f_map.items():
+            data_tmp[data_tmp == key] = value
+        data[f] = data_tmp
+
+    # get categorical features and apply labelencoding
+    categorical_features = [f for f in features if data[f].dtype == 'O']
+    category_map = {}
+    for f in categorical_features:
+        le = LabelEncoder()
+        data_tmp = le.fit_transform(data[f].values)
+        data[f] = data_tmp
+        category_map[features.index(f)] = list(le.classes_)
+
+    # only return data values
+    data = data.values
+
+    return data, labels, features, category_map

alibi/explainers/__init__.py

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+"""
+The 'alibi.explainers' module includes feature importance, counterfactual and anchor-based explainers.
+"""
+
+from .anchor.anchor_tabular import AnchorTabular
+
+__all__ = ["AnchorTabular"]