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https://github.com/IBM/ai-privacy-toolkit.git
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13a0567183
* Update requirements * Update incompatible scipy version * Reduce runtime of dataset assessment tests * ncp is now a class that contains 3 values: fit_score, transform_score and generalizations_score so that it doesn't matter in what order the different methods are called, all calculated ncp scores are stored. Generalizations can now be applied either from tree cells or from global generalizations struct depending on the value of generalize_using_transform. Representative values can also be computed from global generalizations. Removing a feature from the generalization can also be applied in either mode. * Compute generalizations with test data when possible (for computing better representatives). * Externalize common test code to methods.
1281 lines
66 KiB
Python
1281 lines
66 KiB
Python
"""
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This module implements all classes needed to perform data minimization
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"""
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from typing import Union, Optional
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from dataclasses import dataclass
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from collections import Counter
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import pandas as pd
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import numpy as np
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import copy
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import sys
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from scipy.spatial import distance
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from sklearn.base import BaseEstimator, TransformerMixin, MetaEstimatorMixin
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from sklearn.compose import ColumnTransformer
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from sklearn.impute import SimpleImputer
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from sklearn.pipeline import Pipeline
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from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder
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from sklearn.utils.validation import check_is_fitted
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from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
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from sklearn.model_selection import train_test_split
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from apt.utils.datasets import ArrayDataset, DATA_PANDAS_NUMPY_TYPE
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from apt.utils.models import Model, SklearnRegressor, ModelOutputType, SklearnClassifier
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@dataclass
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class NCPScores:
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fit_score: float = None
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transform_score: float = None
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generalizations_score: float = None
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class GeneralizeToRepresentative(BaseEstimator, MetaEstimatorMixin, TransformerMixin):
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"""
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A transformer that generalizes data to representative points.
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Learns data generalizations based on an original model's predictions
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and a target accuracy. Once the generalizations are learned, can
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receive one or more data records and transform them to representative
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points based on the learned generalization.
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An alternative way to use the transformer is to supply ``cells`` in
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init or set_params and those will be used to transform
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data to representatives. In this case, fit must still be called but
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there is no need to supply it with ``X`` and ``y``, and there is no
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need to supply an existing ``estimator`` to init.
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In summary, either ``estimator`` and ``target_accuracy`` should be
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supplied or ``cells`` should be supplied.
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:param estimator: The original model for which generalization is being performed. Should be pre-fitted.
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:type estimator: sklearn `BaseEstimator` or `Model`
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:param target_accuracy: The required relative accuracy when applying the base model to the generalized data.
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Accuracy is measured relative to the original accuracy of the model.
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:type target_accuracy: float, optional
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:param cells: The cells used to generalize records. Each cell must define a range or subset of categories for
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each feature, as well as a representative value for each feature. This parameter should be used
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when instantiating a transformer object without first fitting it.
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:type cells: list of objects, optional
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:param categorical_features: The list of categorical features (if supplied, these featurtes will be one-hot
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encoded before using them to train the decision tree model).
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:param encoder: Optional encoder for encoding data before feeding it into the estimator (e.g., for categorical
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features)
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:type encoder: sklearn OrdinalEncoder or OneHotEncoder
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:type categorical_features: list of strings, optional
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:param features_to_minimize: The features to be minimized.
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:type features_to_minimize: list of strings or int, optional
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:param train_only_features_to_minimize: Whether to train the tree just on the ``features_to_minimize`` or on all
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features. Default is only on ``features_to_minimize``.
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:type train_only_features_to_minimize: boolean, optional
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:param is_regression: Whether the model is a regression model or not (if False, assumes a classification model).
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Default is False.
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:type is_regression: boolean, optional
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:param generalize_using_transform: Indicates how to calculate NCP and accuracy during the generalization
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process. True means that the `transform` method is used to transform original
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data into generalized data that is used for accuracy and NCP calculation.
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False indicates that the `generalizations` structure should be used.
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Default is True.
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:type generalize_using_transform: boolean, optional
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"""
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def __init__(self, estimator: Union[BaseEstimator, Model] = None,
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target_accuracy: Optional[float] = 0.998,
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cells: Optional[list] = None,
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categorical_features: Optional[Union[np.ndarray, list]] = None,
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encoder: Optional[Union[OrdinalEncoder, OneHotEncoder]] = None,
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features_to_minimize: Optional[Union[np.ndarray, list]] = None,
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train_only_features_to_minimize: Optional[bool] = True,
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is_regression: Optional[bool] = False,
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generalize_using_transform: bool = True):
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self.estimator = estimator
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if estimator is not None and not issubclass(estimator.__class__, Model):
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if is_regression:
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self.estimator = SklearnRegressor(estimator)
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else:
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self.estimator = SklearnClassifier(estimator, ModelOutputType.CLASSIFIER_PROBABILITIES)
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self.target_accuracy = target_accuracy
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self.cells = cells
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if cells:
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self._calculate_generalizations()
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self.categorical_features = []
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if categorical_features:
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self.categorical_features = categorical_features
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self.features_to_minimize = features_to_minimize
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self.train_only_features_to_minimize = train_only_features_to_minimize
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self.is_regression = is_regression
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self.encoder = encoder
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self.generalize_using_transform = generalize_using_transform
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self._ncp_scores = NCPScores()
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self._feature_data = None
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self._categorical_values = {}
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self._dt = None
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self._features = None
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self._level = 0
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def get_params(self, deep=True):
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"""
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Get parameters
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:param deep: If True, will return the parameters for this estimator and contained
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sub-objects that are estimators.
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:type deep: boolean, optional
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:return: Parameter names mapped to their values
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"""
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ret = {}
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ret['target_accuracy'] = self.target_accuracy
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ret['categorical_features'] = self.categorical_features
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ret['features_to_minimize'] = self.features_to_minimize
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ret['train_only_features_to_minimize'] = self.train_only_features_to_minimize
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ret['is_regression'] = self.is_regression
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ret['estimator'] = self.estimator
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ret['encoder'] = self.encoder
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if deep:
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ret['cells'] = copy.deepcopy(self.cells)
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else:
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ret['cells'] = copy.copy(self.cells)
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return ret
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def set_params(self, **params):
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"""
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Set parameters
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:param target_accuracy: The required relative accuracy when applying the base model to the generalized data.
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Accuracy is measured relative to the original accuracy of the model.
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:type target_accuracy: float, optional
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:param cells: The cells used to generalize records. Each cell must define a range or subset of categories for
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each feature, as well as a representative value for each feature. This parameter should be used
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when instantiating a transformer object without first fitting it.
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:type cells: list of objects, optional
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:return: self
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"""
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if 'target_accuracy' in params:
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self.target_accuracy = params['target_accuracy']
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if 'categorical_features' in params:
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self.categorical_features = params['categorical_features']
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if 'features_to_minimize' in params:
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self.features_to_minimize = params['features_to_minimize']
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if 'train_only_features_to_minimize' in params:
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self.train_only_features_to_minimize = params['train_only_features_to_minimize']
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if 'is_regression' in params:
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self.is_regression = params['is_regression']
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if 'cells' in params:
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self.cells = params['cells']
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if 'estimator' in params:
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self.estimator = params['estimator']
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if 'encoder' in params:
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self.encoder = params['encoder']
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return self
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@property
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def generalizations(self):
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"""
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Return the generalizations derived from the model and test data.
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:return: generalizations object. Contains 3 sections: 'ranges' that contains ranges for numerical features,
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'categories' that contains sub-groups of categories for categorical features, and
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'untouched' that contains the features that could not be generalized.
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"""
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return self._generalizations
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@property
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def ncp(self):
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"""
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Return the last calculated NCP scores. NCP score is calculated upon calling `fit` (on the training data),
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`transform' (on the test data) or when explicitly calling `calculate_ncp` and providing it a dataset.
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:return: NCPScores object, that contains a score corresponding to the last fit call, one for the last
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transform call, and a score based on global generalizations.
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"""
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return self._ncp_scores
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def fit_transform(self, X: Optional[DATA_PANDAS_NUMPY_TYPE] = None, y: Optional[DATA_PANDAS_NUMPY_TYPE] = None,
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features_names: Optional[list] = None, dataset: Optional[ArrayDataset] = None):
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"""
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Learns the generalizations based on training data, and applies them to the data. Also sets the fit_score,
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transform_score and generalizations_score in self.ncp.
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:param X: The training input samples.
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:type X: {array-like, sparse matrix}, shape (n_samples, n_features), optional
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:param y: The target values. This should contain the predictions of the original model on ``X``.
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:type y: array-like, shape (n_samples,), optional
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:param features_names: The feature names, in the order that they appear in the data. Can be provided when
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passing the data as ``X`` and ``y``
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:type features_names: list of strings, optional
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:param dataset: Data wrapper containing the training input samples and the predictions of the original model
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on the training data. Either ``X``, ``y`` OR ``dataset`` need to be provided, not both.
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:type dataset: `ArrayDataset`, optional
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:return: Array containing the representative values to which each record in ``X`` is mapped, as numpy array or
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pandas DataFrame (depending on the type of ``X``), shape (n_samples, n_features)
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"""
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if not self.generalize_using_transform:
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raise ValueError('fit_transform method called even though generalize_using_transform parameter was False. '
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'This can lead to inconsistent results.')
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self.fit(X, y, features_names, dataset=dataset)
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return self.transform(X, features_names, dataset=dataset)
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def fit(self, X: Optional[DATA_PANDAS_NUMPY_TYPE] = None, y: Optional[DATA_PANDAS_NUMPY_TYPE] = None,
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features_names: Optional = None, dataset: ArrayDataset = None):
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"""Learns the generalizations based on training data. Also sets the fit_score and generalizations_score in
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self.ncp.
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:param X: The training input samples.
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:type X: {array-like, sparse matrix}, shape (n_samples, n_features), optional
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:param y: The target values. This should contain the predictions of the original model on ``X``.
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:type y: array-like, shape (n_samples,), optional
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:param features_names: The feature names, in the order that they appear in the data. Should be provided when
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passing the data as ``X`` as a numpy array
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:type features_names: list of strings, optional
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:param dataset: Data wrapper containing the training input samples and the predictions of the original model
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on the training data. Either ``X``, ``y`` OR ``dataset`` need to be provided, not both.
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:type dataset: `ArrayDataset`, optional
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:return: self
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"""
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# take into account that estimator, X, y, cells, features may be None
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if X is not None and y is not None:
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if dataset is not None:
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raise ValueError('Either X,y OR dataset need to be provided, not both')
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else:
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dataset = ArrayDataset(X, y, features_names)
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if dataset and dataset.get_samples() is not None and dataset.get_labels() is not None:
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self._n_features = dataset.get_samples().shape[1]
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elif dataset and dataset.features_names:
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self._n_features = len(dataset.features_names)
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else:
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self._n_features = 0
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if dataset and dataset.features_names:
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self._features = dataset.features_names
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# if features is None, use numbers instead of names
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elif self._n_features != 0:
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self._features = [str(i) for i in range(self._n_features)]
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else:
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self._features = None
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# Going to fit
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# (currently not dealing with option to fit with only X and y and no estimator)
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if self.estimator and dataset and dataset.get_samples() is not None and dataset.get_labels() is not None:
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x = pd.DataFrame(dataset.get_samples(), columns=self._features)
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if not self.features_to_minimize:
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self.features_to_minimize = self._features
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self.features_to_minimize = [str(i) for i in self.features_to_minimize]
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if not all(elem in self._features for elem in self.features_to_minimize):
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raise ValueError('features to minimize should be a subset of features names')
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x_qi = x.loc[:, self.features_to_minimize]
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# divide dataset into train and test
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used_data = x
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if self.train_only_features_to_minimize:
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used_data = x_qi
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if self.is_regression:
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x_train, x_test, y_train, y_test = train_test_split(x, dataset.get_labels(), test_size=0.4,
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random_state=14)
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else:
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try:
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x_train, x_test, y_train, y_test = train_test_split(x, dataset.get_labels(),
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stratify=dataset.get_labels(), test_size=0.4,
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random_state=18)
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except ValueError:
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print('Could not stratify split due to uncommon class value, doing unstratified split instead')
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x_train, x_test, y_train, y_test = train_test_split(x, dataset.get_labels(), test_size=0.4,
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random_state=18)
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x_train_qi = x_train.loc[:, self.features_to_minimize]
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x_test_qi = x_test.loc[:, self.features_to_minimize]
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used_x_train = x_train
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used_x_test = x_test
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if self.train_only_features_to_minimize:
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used_x_train = x_train_qi
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used_x_test = x_test_qi
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# collect feature data (such as min, max)
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self._feature_data = self._get_feature_data(x)
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# default encoder in case none provided
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if self.encoder is None:
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numeric_features = [f for f in self._features if f not in self.categorical_features]
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numeric_transformer = Pipeline(
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steps=[('imputer', SimpleImputer(strategy='constant', fill_value=0))]
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)
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categorical_transformer = OneHotEncoder(handle_unknown="ignore", sparse=False)
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self.encoder = ColumnTransformer(
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transformers=[
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("num", numeric_transformer, numeric_features),
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("cat", categorical_transformer, self.categorical_features),
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]
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)
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self.encoder.fit(x)
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self.cells = []
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self._categorical_values = {}
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if self.is_regression:
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self._dt = DecisionTreeRegressor(random_state=10, min_samples_split=2, min_samples_leaf=1)
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else:
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self._dt = DecisionTreeClassifier(random_state=0, min_samples_split=2,
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min_samples_leaf=1)
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# prepare data for DT
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self._encode_categorical_features(used_data, save_mapping=True)
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x_prepared = self._encode_categorical_features(used_x_train)
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self._dt.fit(x_prepared, y_train)
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x_prepared_test = self._encode_categorical_features(used_x_test)
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self._calculate_cells()
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self._modify_cells()
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# features that are not from QI should not be part of generalizations
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for feature in self._features:
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if feature not in self.features_to_minimize:
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self._remove_feature_from_cells(self.cells, self._cells_by_id, feature)
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nodes = self._get_nodes_level(0)
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self._attach_cells_representatives(x_prepared, used_x_train, y_train, nodes)
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# self._cells currently holds the generalization created from the tree leaves
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self._calculate_generalizations(x_test)
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if self.generalize_using_transform:
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generalized = self._generalize_from_tree(x_test, x_prepared_test, nodes, self.cells, self._cells_by_id)
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else:
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generalized = self._generalize_from_generalizations(x_test, self.generalizations)
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# check accuracy
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accuracy = self.estimator.score(ArrayDataset(self.encoder.transform(generalized), y_test))
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print('Initial accuracy of model on generalized data, relative to original model predictions '
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'(base generalization derived from tree, before improvements): %f' % accuracy)
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# if accuracy above threshold, improve generalization
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if accuracy > self.target_accuracy:
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print('Improving generalizations')
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self._level = 1
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while accuracy > self.target_accuracy:
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cells_previous_iter = self.cells
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generalization_prev_iter = self._generalizations
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cells_by_id_prev = self._cells_by_id
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nodes = self._get_nodes_level(self._level)
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try:
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self._calculate_level_cells(self._level)
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except TypeError as e:
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print(e)
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self._level -= 1
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break
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self._attach_cells_representatives(x_prepared, used_x_train, y_train, nodes)
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self._calculate_generalizations(x_test)
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if self.generalize_using_transform:
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generalized = self._generalize_from_tree(x_test, x_prepared_test, nodes, self.cells,
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self._cells_by_id)
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else:
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generalized = self._generalize_from_generalizations(x_test, self.generalizations)
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accuracy = self.estimator.score(ArrayDataset(self.encoder.transform(generalized), y_test))
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# if accuracy passed threshold roll back to previous iteration generalizations
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if accuracy < self.target_accuracy:
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self.cells = cells_previous_iter
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self._generalizations = generalization_prev_iter
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self._cells_by_id = cells_by_id_prev
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self._level -= 1
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break
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else:
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print('Pruned tree to level: %d, new relative accuracy: %f' % (self._level, accuracy))
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self._level += 1
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# if accuracy below threshold, improve accuracy by removing features from generalization
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elif accuracy < self.target_accuracy:
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print('Improving accuracy')
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while accuracy < self.target_accuracy:
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removed_feature = self._remove_feature_from_generalization(x_test, x_prepared_test,
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nodes, y_test,
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self._feature_data, accuracy,
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self.generalize_using_transform)
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if removed_feature is None:
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break
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self._calculate_generalizations(x_test)
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if self.generalize_using_transform:
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generalized = self._generalize_from_tree(x_test, x_prepared_test, nodes, self.cells,
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self._cells_by_id)
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else:
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generalized = self._generalize_from_generalizations(x_test, self.generalizations)
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||
accuracy = self.estimator.score(ArrayDataset(self.encoder.transform(generalized), y_test))
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print('Removed feature: %s, new relative accuracy: %f' % (removed_feature, accuracy))
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||
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||
# self._cells currently holds the chosen generalization based on target accuracy
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||
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||
# calculate iLoss
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||
x_test_dataset = ArrayDataset(x_test, features_names=self._features)
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||
self._ncp_scores.fit_score = self.calculate_ncp(x_test_dataset)
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||
self._ncp_scores.generalizations_score = self.calculate_ncp(x_test_dataset)
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||
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||
# Return the transformer
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||
return self
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||
|
||
def transform(self, X: Optional[DATA_PANDAS_NUMPY_TYPE] = None, features_names: Optional[list] = None,
|
||
dataset: Optional[ArrayDataset] = None):
|
||
""" Transforms data records to representative points. Also sets the transform_score in self.ncp.
|
||
|
||
:param X: The training input samples.
|
||
:type X: {array-like, sparse matrix}, shape (n_samples, n_features), optional
|
||
:param features_names: The feature names, in the order that they appear in the data. Should be provided when
|
||
passing the data as ``X`` as a numpy array
|
||
:type features_names: list of strings, optional
|
||
:param dataset: Data wrapper containing the training input samples and the predictions of the original model
|
||
on the training data. Either ``X`` OR ``dataset`` need to be provided, not both.
|
||
:type dataset: `ArrayDataset`, optional
|
||
:return: Array containing the representative values to which each record in ``X`` is mapped, as numpy array or
|
||
pandas DataFrame (depending on the type of ``X``), shape (n_samples, n_features)
|
||
"""
|
||
if not self.generalize_using_transform:
|
||
raise ValueError('transform method called even though generalize_using_transform parameter was False. This '
|
||
'can lead to inconsistent results.')
|
||
transformed = self._inner_transform(X, features_names, dataset)
|
||
transformed_dataset = ArrayDataset(transformed, features_names=self._features)
|
||
self._ncp_scores.transform_score = self.calculate_ncp(transformed_dataset)
|
||
return transformed
|
||
|
||
def calculate_ncp(self, samples: ArrayDataset):
|
||
"""
|
||
Compute the NCP score of the generalization. Calculation is based on the value of the
|
||
generalize_using_transform param. If samples are provided, updates stored ncp value to the one computed on the
|
||
provided data. If samples not provided, returns the last NCP score computed by the `fit` or `transform` method.
|
||
|
||
Based on the NCP score presented in: Ghinita, G., Karras, P., Kalnis, P., Mamoulis, N.: Fast data anonymization
|
||
with low information loss (https://www.vldb.org/conf/2007/papers/research/p758-ghinita.pdf)
|
||
|
||
:param samples: The input samples to compute the NCP score on.
|
||
:type samples: ArrayDataset, optional. feature_names should be set.
|
||
:return: NCP score as float.
|
||
"""
|
||
if not samples.features_names:
|
||
raise ValueError('features_names should be set in input ArrayDataset.')
|
||
samples_pd = pd.DataFrame(samples.get_samples(), columns=samples.features_names)
|
||
if self._features is None:
|
||
self._features = samples.features_names
|
||
if self._feature_data is None:
|
||
self._feature_data = self._get_feature_data(samples_pd)
|
||
total_samples = samples_pd.shape[0]
|
||
|
||
if self.generalize_using_transform:
|
||
generalizations = self._calculate_cell_generalizations()
|
||
# count how many records are mapped to each cell
|
||
counted = np.zeros(samples_pd.shape[0]) # to mark records we already counted
|
||
ncp = 0
|
||
for cell in self.cells:
|
||
count = self._get_record_count_for_cell(samples_pd, cell, counted)
|
||
range_counts = {}
|
||
category_counts = {}
|
||
for feature in cell['ranges']:
|
||
range_counts[feature] = [count]
|
||
for feature in cell['categories']:
|
||
category_counts[feature] = [count]
|
||
ncp += self._calc_ncp_for_generalization(generalizations[cell['id']], range_counts, category_counts,
|
||
total_samples)
|
||
else: # use generalizations
|
||
generalizations = self.generalizations
|
||
range_counts = self._find_range_counts(samples_pd, generalizations['ranges'])
|
||
category_counts = self._find_category_counts(samples_pd, generalizations['categories'])
|
||
ncp = self._calc_ncp_for_generalization(generalizations, range_counts, category_counts, total_samples)
|
||
|
||
return ncp
|
||
|
||
def _inner_transform(self, x: Optional[DATA_PANDAS_NUMPY_TYPE] = None, features_names: Optional[list] = None,
|
||
dataset: Optional[ArrayDataset] = None):
|
||
# Check if fit has been called
|
||
msg = 'This %(name)s instance is not initialized yet. ' \
|
||
'Call ‘fit’ or ‘set_params’ with ' \
|
||
'appropriate arguments before using this method.'
|
||
check_is_fitted(self, ['cells'], msg=msg)
|
||
|
||
if x is not None:
|
||
if dataset is not None:
|
||
raise ValueError('Either x OR dataset need to be provided, not both')
|
||
else:
|
||
dataset = ArrayDataset(x, features_names=features_names)
|
||
elif dataset is None:
|
||
raise ValueError('Either x OR dataset need to be provided, not both')
|
||
if dataset and dataset.features_names:
|
||
if self._features is None:
|
||
self._features = dataset.features_names
|
||
if dataset and dataset.get_samples() is not None:
|
||
x_pd = pd.DataFrame(dataset.get_samples(), columns=self._features)
|
||
|
||
if x_pd.shape[1] != self._n_features and self._n_features != 0:
|
||
raise ValueError('Shape of input is different from what was seen'
|
||
'in `fit`')
|
||
|
||
if not self._features:
|
||
self._features = [i for i in range(x_pd.shape[1])]
|
||
|
||
if self._dt: # only works if fit was called previously (but much more efficient)
|
||
nodes = self._get_nodes_level(self._level)
|
||
QI = x_pd.loc[:, self.features_to_minimize]
|
||
used_x = x_pd
|
||
if self.train_only_features_to_minimize:
|
||
used_x = QI
|
||
prepared = self._encode_categorical_features(used_x)
|
||
generalized = self._generalize_from_tree(x_pd, prepared, nodes, self.cells, self._cells_by_id)
|
||
else:
|
||
mapped = np.zeros(x_pd.shape[0]) # to mark records we already mapped
|
||
all_indexes = []
|
||
for cell in self.cells:
|
||
indexes = self._get_record_indexes_for_cell(x_pd, cell, mapped)
|
||
all_indexes.append(indexes)
|
||
generalized = self._generalize_indexes(x_pd, self.cells, all_indexes)
|
||
|
||
if dataset and dataset.is_pandas:
|
||
return generalized
|
||
elif isinstance(x, pd.DataFrame):
|
||
return generalized
|
||
return generalized.to_numpy()
|
||
|
||
def _calc_ncp_for_generalization(self, generalization, range_counts, category_counts, total_count):
|
||
total_ncp = 0
|
||
total_features = len(generalization['untouched'])
|
||
ranges = generalization['ranges']
|
||
categories = generalization['categories']
|
||
|
||
# suppressed features are already taken care of within _calc_ncp_numeric
|
||
for feature in ranges.keys():
|
||
feature_ncp = self._calc_ncp_numeric(ranges[feature], range_counts[feature],
|
||
self._feature_data[feature], total_count)
|
||
total_ncp = total_ncp + feature_ncp
|
||
total_features += 1
|
||
for feature in categories.keys():
|
||
feature_ncp = self._calc_ncp_categorical(categories[feature], category_counts[feature],
|
||
self._feature_data[feature],
|
||
total_count)
|
||
total_ncp = total_ncp + feature_ncp
|
||
total_features += 1
|
||
if total_features == 0:
|
||
return 0
|
||
return total_ncp / total_features
|
||
|
||
@staticmethod
|
||
def _calc_ncp_categorical(categories, category_count, feature_data, total):
|
||
category_sizes = [len(g) if len(g) > 1 else 0 for g in categories]
|
||
normalized_category_sizes = [s * n / total for s, n in zip(category_sizes, category_count)]
|
||
average_group_size = sum(normalized_category_sizes) / len(normalized_category_sizes)
|
||
return average_group_size / feature_data['range'] # number of values in category
|
||
|
||
@staticmethod
|
||
def _calc_ncp_numeric(range, range_count, feature_data, total):
|
||
# if there are no ranges, feature is suppressed and iLoss is 1
|
||
if not range:
|
||
return 1
|
||
# range only contains the split values, need to add min and max value of feature
|
||
# to enable computing sizes of all ranges
|
||
new_range = [feature_data['min']] + range + [feature_data['max']]
|
||
range_sizes = [b - a for a, b in zip(new_range[::1], new_range[1::1])]
|
||
normalized_range_sizes = [s * n / total for s, n in zip(range_sizes, range_count)]
|
||
average_range_size = sum(normalized_range_sizes) / len(normalized_range_sizes)
|
||
return average_range_size / (feature_data['max'] - feature_data['min'])
|
||
|
||
def _get_feature_data(self, x):
|
||
feature_data = {}
|
||
for feature in self._features:
|
||
if feature not in feature_data.keys():
|
||
fd = {}
|
||
values = list(x.loc[:, feature])
|
||
if feature not in self.categorical_features:
|
||
fd['min'] = min(values)
|
||
fd['max'] = max(values)
|
||
fd['range'] = max(values) - min(values)
|
||
else:
|
||
fd['range'] = len(np.unique(values))
|
||
feature_data[feature] = fd
|
||
return feature_data
|
||
|
||
def _get_record_indexes_for_cell(self, x, cell, mapped):
|
||
indexes = []
|
||
for index, row in x.iterrows():
|
||
if not mapped.item(index) and self._cell_contains(cell, row, index, mapped):
|
||
indexes.append(index)
|
||
return indexes
|
||
|
||
def _get_record_count_for_cell(self, x, cell, mapped):
|
||
count = 0
|
||
for index, (_, row) in enumerate(x.iterrows()):
|
||
if not mapped.item(index) and self._cell_contains(cell, row, index, mapped):
|
||
count += 1
|
||
return count
|
||
|
||
def _cell_contains(self, cell, row, index, mapped):
|
||
for i, feature in enumerate(self._features):
|
||
if feature in cell['ranges']:
|
||
if not self._cell_contains_numeric(i, cell['ranges'][feature], row):
|
||
return False
|
||
elif feature in cell['categories']:
|
||
if not self._cell_contains_categorical(i, cell['categories'][feature], row):
|
||
return False
|
||
elif feature in cell['untouched']:
|
||
continue
|
||
else:
|
||
raise TypeError("feature " + feature + "not found in cell" + cell['id'])
|
||
# Mark as mapped
|
||
mapped.itemset(index, 1)
|
||
return True
|
||
|
||
def _encode_categorical_features(self, x, save_mapping=False):
|
||
if save_mapping:
|
||
self._categorical_values = {}
|
||
self._one_hot_vector_features_to_features = {}
|
||
features_to_remove = []
|
||
used_features = self._features
|
||
if self.train_only_features_to_minimize:
|
||
used_features = self.features_to_minimize
|
||
for feature in self.categorical_features:
|
||
if feature in used_features:
|
||
try:
|
||
all_values = x.loc[:, feature]
|
||
values = list(all_values.unique())
|
||
if save_mapping:
|
||
self._categorical_values[feature] = values
|
||
x[feature] = pd.Categorical(x.loc[:, feature], categories=self._categorical_values[feature],
|
||
ordered=False)
|
||
ohe = pd.get_dummies(x[feature], prefix=feature)
|
||
if save_mapping:
|
||
for one_hot_vector_feature in ohe.columns:
|
||
self._one_hot_vector_features_to_features[one_hot_vector_feature] = feature
|
||
x = pd.concat([x, ohe], axis=1)
|
||
features_to_remove.append(feature)
|
||
except KeyError:
|
||
print("feature " + feature + "not found in training data")
|
||
|
||
new_data = x.drop(features_to_remove, axis=1)
|
||
if save_mapping:
|
||
self._encoded_features = new_data.columns
|
||
return new_data
|
||
|
||
@staticmethod
|
||
def _cell_contains_numeric(index, range, row):
|
||
# convert row to ndarray to allow indexing
|
||
a = np.array(row)
|
||
value = a.item(index)
|
||
if range['start']:
|
||
if value <= range['start']:
|
||
return False
|
||
if range['end']:
|
||
if value > range['end']:
|
||
return False
|
||
return True
|
||
|
||
@staticmethod
|
||
def _cell_contains_categorical(index, range, row):
|
||
# convert row to ndarray to allow indexing
|
||
a = np.array(row)
|
||
value = a.item(index)
|
||
if value in range:
|
||
return True
|
||
return False
|
||
|
||
def _calculate_cells(self):
|
||
self._cells_by_id = {}
|
||
self.cells = self._calculate_cells_recursive(0)
|
||
|
||
def _calculate_cells_recursive(self, node):
|
||
feature_index = self._dt.tree_.feature[node]
|
||
if feature_index == -2:
|
||
# this is a leaf
|
||
# if it is a regression problem we do not use label
|
||
label = self._calculate_cell_label(node) if not self.is_regression else 1
|
||
hist = [int(i) for i in self._dt.tree_.value[node][0]] if not self.is_regression else []
|
||
cell = {'label': label, 'hist': hist, 'ranges': {}, 'id': int(node)}
|
||
return [cell]
|
||
|
||
cells = []
|
||
feature = self._encoded_features[feature_index]
|
||
threshold = self._dt.tree_.threshold[node]
|
||
left_child = self._dt.tree_.children_left[node]
|
||
right_child = self._dt.tree_.children_right[node]
|
||
|
||
left_child_cells = self._calculate_cells_recursive(left_child)
|
||
for cell in left_child_cells:
|
||
if feature not in cell['ranges'].keys():
|
||
cell['ranges'][feature] = {'start': None, 'end': None}
|
||
if cell['ranges'][feature]['end'] is None:
|
||
cell['ranges'][feature]['end'] = threshold
|
||
cells.append(cell)
|
||
self._cells_by_id[cell['id']] = cell
|
||
|
||
right_child_cells = self._calculate_cells_recursive(right_child)
|
||
for cell in right_child_cells:
|
||
if feature not in cell['ranges'].keys():
|
||
cell['ranges'][feature] = {'start': None, 'end': None}
|
||
if cell['ranges'][feature]['start'] is None:
|
||
cell['ranges'][feature]['start'] = threshold
|
||
cells.append(cell)
|
||
self._cells_by_id[cell['id']] = cell
|
||
|
||
return cells
|
||
|
||
def _calculate_cell_label(self, node):
|
||
label_hist = self._dt.tree_.value[node][0]
|
||
return int(self._dt.classes_[np.argmax(label_hist)])
|
||
|
||
def _modify_cells(self):
|
||
cells = []
|
||
features = self._encoded_features
|
||
for cell in self.cells:
|
||
new_cell = {'id': cell['id'], 'label': cell['label'], 'ranges': {}, 'categories': {}, 'hist': cell['hist'],
|
||
'untouched': [], 'representative': None}
|
||
for feature in features:
|
||
if feature in self._one_hot_vector_features_to_features.keys():
|
||
# feature is categorical and should be mapped
|
||
categorical_feature = self._one_hot_vector_features_to_features[feature]
|
||
if categorical_feature not in new_cell['categories'].keys():
|
||
new_cell['categories'][categorical_feature] = self._categorical_values[
|
||
categorical_feature].copy()
|
||
if feature in cell['ranges'].keys():
|
||
categorical_value = feature[len(categorical_feature) + 1:]
|
||
if cell['ranges'][feature]['start'] is not None:
|
||
# categorical feature must have this value
|
||
new_cell['categories'][categorical_feature] = [categorical_value]
|
||
else:
|
||
# categorical feature can not have this value
|
||
if categorical_value in new_cell['categories'][categorical_feature]:
|
||
new_cell['categories'][categorical_feature].remove(categorical_value)
|
||
else:
|
||
if feature in cell['ranges'].keys():
|
||
new_cell['ranges'][feature] = cell['ranges'][feature]
|
||
else:
|
||
new_cell['ranges'][feature] = {'start': None, 'end': None}
|
||
cells.append(new_cell)
|
||
self._cells_by_id[new_cell['id']] = new_cell
|
||
self.cells = cells
|
||
|
||
def _calculate_level_cells(self, level):
|
||
if level < 0 or level > self._dt.get_depth():
|
||
raise TypeError("Illegal level %d' % level", level)
|
||
|
||
if level > 0:
|
||
new_cells = []
|
||
new_cells_by_id = {}
|
||
nodes = self._get_nodes_level(level)
|
||
if nodes:
|
||
for node in nodes:
|
||
if self._dt.tree_.feature[node] == -2: # leaf node
|
||
new_cell = self._cells_by_id[node]
|
||
else:
|
||
left_child = self._dt.tree_.children_left[node]
|
||
right_child = self._dt.tree_.children_right[node]
|
||
left_cell = self._cells_by_id[left_child]
|
||
right_cell = self._cells_by_id[right_child]
|
||
new_cell = {'id': int(node), 'ranges': {}, 'categories': {}, 'untouched': [],
|
||
'label': None, 'representative': None}
|
||
for feature in left_cell['ranges'].keys():
|
||
new_cell['ranges'][feature] = {}
|
||
new_cell['ranges'][feature]['start'] = left_cell['ranges'][feature]['start']
|
||
new_cell['ranges'][feature]['end'] = right_cell['ranges'][feature]['start']
|
||
for feature in left_cell['categories'].keys():
|
||
new_cell['categories'][feature] = \
|
||
list(set(left_cell['categories'][feature])
|
||
| set(right_cell['categories'][feature]))
|
||
for feature in left_cell['untouched']:
|
||
if feature in right_cell['untouched']:
|
||
new_cell['untouched'].append(feature)
|
||
self._calculate_level_cell_label(left_cell, right_cell, new_cell)
|
||
new_cells.append(new_cell)
|
||
new_cells_by_id[new_cell['id']] = new_cell
|
||
self.cells = new_cells
|
||
self._cells_by_id = new_cells_by_id
|
||
# else: nothing to do, stay with previous cells
|
||
|
||
def _calculate_level_cell_label(self, left_cell, right_cell, new_cell):
|
||
new_cell['hist'] = [x + y for x, y in
|
||
zip(left_cell['hist'], right_cell['hist'])] if not self.is_regression else []
|
||
new_cell['label'] = int(self._dt.classes_[np.argmax(new_cell['hist'])]) if not self.is_regression else 1
|
||
|
||
def _get_nodes_level(self, level):
|
||
# level = distance from lowest leaf
|
||
node_depth = np.zeros(shape=self._dt.tree_.node_count, dtype=np.int64)
|
||
is_leaves = np.zeros(shape=self._dt.tree_.node_count, dtype=bool)
|
||
stack = [(0, -1)] # seed is the root node id and its parent depth
|
||
while len(stack) > 0:
|
||
node_id, parent_depth = stack.pop()
|
||
# depth = distance from root
|
||
node_depth[node_id] = parent_depth + 1
|
||
|
||
if self._dt.tree_.children_left[node_id] != self._dt.tree_.children_right[node_id]:
|
||
stack.append((self._dt.tree_.children_left[node_id], parent_depth + 1))
|
||
stack.append((self._dt.tree_.children_right[node_id], parent_depth + 1))
|
||
else:
|
||
is_leaves[node_id] = True
|
||
|
||
# depth of entire tree
|
||
max_depth = max(node_depth)
|
||
# depth of current level
|
||
depth = max_depth - level
|
||
# level is higher than root
|
||
if depth < 0:
|
||
return None
|
||
# return all nodes with depth == level or leaves higher than level
|
||
return [i for i, x in enumerate(node_depth) if x == depth or (x < depth and is_leaves[i])]
|
||
|
||
def _attach_cells_representatives(self, prepared_data, originalTrainFeatures, labelFeature, level_nodes):
|
||
# prepared data include one hot encoded categorical data,
|
||
# if there is no categorical data prepared data is original data
|
||
nodeIds = self._find_sample_nodes(prepared_data, level_nodes)
|
||
labels_df = pd.DataFrame(labelFeature, columns=['label'])
|
||
for cell in self.cells:
|
||
cell['representative'] = {}
|
||
# get all rows in cell
|
||
indexes = [i for i, x in enumerate(nodeIds) if x == cell['id']]
|
||
original_rows = originalTrainFeatures.iloc[indexes]
|
||
sample_rows = prepared_data.iloc[indexes]
|
||
sample_labels = labels_df.iloc[indexes]['label'].values.tolist()
|
||
# get rows with matching label
|
||
if self.is_regression:
|
||
match_samples = sample_rows
|
||
match_rows = original_rows
|
||
else:
|
||
indexes = [i for i, label in enumerate(sample_labels) if label == cell['label']]
|
||
match_samples = sample_rows.iloc[indexes]
|
||
match_rows = original_rows.iloc[indexes]
|
||
# find the "middle" of the cluster
|
||
array = match_samples.values
|
||
# Only works with numpy 1.9.0 and higher!!!
|
||
median = np.median(array, axis=0)
|
||
i = 0
|
||
min = len(array)
|
||
min_dist = float("inf")
|
||
for row in array:
|
||
dist = distance.euclidean(row, median)
|
||
if dist < min_dist:
|
||
min_dist = dist
|
||
min = i
|
||
i = i + 1
|
||
row = match_rows.iloc[min]
|
||
for feature in cell['ranges'].keys():
|
||
cell['representative'][feature] = row[feature]
|
||
for feature in cell['categories'].keys():
|
||
cell['representative'][feature] = row[feature]
|
||
|
||
def _find_sample_nodes(self, samples, nodes):
|
||
paths = self._dt.decision_path(samples).toarray()
|
||
nodeSet = set(nodes)
|
||
return [(list(set([i for i, v in enumerate(p) if v == 1]) & nodeSet))[0] for p in paths]
|
||
|
||
# method for applying generalizations (for global generalization-based acuuracy) without dt
|
||
def _generalize_from_generalizations(self, original_data, generalizations):
|
||
sample_indexes = self._map_to_ranges_categories(original_data,
|
||
generalizations['ranges'],
|
||
generalizations['categories'])
|
||
original_data_generalized = pd.DataFrame(original_data, columns=self._features, copy=True)
|
||
for feature in self._generalizations['categories']:
|
||
if 'untouched' not in generalizations or feature not in generalizations['untouched']:
|
||
for g_index, group in enumerate(generalizations['categories'][feature]):
|
||
indexes = [i for i, s in enumerate(sample_indexes) if s[feature] == g_index]
|
||
if indexes:
|
||
rows = original_data_generalized.iloc[indexes]
|
||
rows[feature] = generalizations['category_representatives'][feature][g_index]
|
||
for feature in self._generalizations['ranges']:
|
||
if 'untouched' not in generalizations or feature not in generalizations['untouched']:
|
||
for r_index, range in enumerate(generalizations['ranges'][feature]):
|
||
indexes = [i for i, s in enumerate(sample_indexes) if s[feature] == r_index]
|
||
if indexes:
|
||
rows = original_data_generalized.iloc[indexes]
|
||
rows[feature] = generalizations['range_representatives'][feature][r_index]
|
||
return original_data_generalized
|
||
|
||
def _generalize_from_tree(self, original_data, prepared_data, level_nodes, cells, cells_by_id):
|
||
mapping_to_cells = self._map_to_cells(prepared_data, level_nodes, cells_by_id)
|
||
all_indexes = []
|
||
for i in range(len(cells)):
|
||
# get the indexes of all records that map to this cell
|
||
indexes = [j for j in mapping_to_cells if mapping_to_cells[j]['id'] == cells[i]['id']]
|
||
all_indexes.append(indexes)
|
||
return self._generalize_indexes(original_data, cells, all_indexes)
|
||
|
||
def _generalize_indexes(self, original_data, cells, all_indexes):
|
||
# prepared data include one hot encoded categorical data + QI
|
||
representatives = pd.DataFrame(columns=self._features) # empty except for columns
|
||
original_data_generalized = pd.DataFrame(original_data, columns=self._features, copy=True)
|
||
|
||
# iterate over cells (leaves in decision tree)
|
||
for i in range(len(cells)):
|
||
# This code just copies the representatives from the cells into another data structure
|
||
# iterate over features
|
||
for feature in self._features:
|
||
# if feature has a representative value in the cell and should not be left untouched,
|
||
# take the representative value
|
||
if feature in cells[i]['representative'] \
|
||
and ('untouched' not in cells[i] or feature not in cells[i]['untouched']):
|
||
representatives.loc[i, feature] = cells[i]['representative'][feature]
|
||
# else, drop the feature (removes from representatives columns that do not have a
|
||
# representative value or should remain untouched)
|
||
elif feature in representatives.columns.tolist():
|
||
representatives = representatives.drop(feature, axis=1)
|
||
|
||
indexes = all_indexes[i]
|
||
# replaces the values in the representative columns with the representative values
|
||
# (leaves others untouched)
|
||
if indexes and not representatives.columns.empty:
|
||
if len(indexes) > 1:
|
||
replace = pd.concat([representatives.loc[i].to_frame().T] * len(indexes)).reset_index(drop=True)
|
||
else:
|
||
replace = representatives.loc[i].to_frame().T.reset_index(drop=True)
|
||
replace.index = indexes
|
||
replace = pd.DataFrame(replace, indexes, columns=self._features)
|
||
original_data_generalized.loc[indexes, representatives.columns.tolist()] = replace
|
||
|
||
return original_data_generalized
|
||
|
||
@staticmethod
|
||
def _map_to_ranges_categories(samples, ranges, categories):
|
||
all_sample_indexes = []
|
||
for _, row in samples.iterrows():
|
||
sample_indexes = {}
|
||
for feature in ranges:
|
||
if not ranges[feature]:
|
||
# no values means whole range
|
||
sample_indexes[feature] = 0
|
||
else:
|
||
for index, value in enumerate(ranges[feature]):
|
||
if row[feature] <= value:
|
||
sample_indexes[feature] = index
|
||
break
|
||
sample_indexes[feature] = index + 1
|
||
for feature in categories:
|
||
for g_index, group in enumerate(categories[feature]):
|
||
if row[feature] in group:
|
||
sample_indexes[feature] = g_index
|
||
break
|
||
all_sample_indexes.append(sample_indexes)
|
||
return all_sample_indexes
|
||
|
||
def _map_to_cells(self, samples, nodes, cells_by_id):
|
||
mapping_to_cells = {}
|
||
for index, row in samples.iterrows():
|
||
cell = self._find_sample_cells([row], nodes, cells_by_id)[0]
|
||
mapping_to_cells[index] = cell
|
||
return mapping_to_cells
|
||
|
||
def _find_sample_cells(self, samples, nodes, cells_by_id):
|
||
node_ids = self._find_sample_nodes(samples, nodes)
|
||
return [cells_by_id[nodeId] for nodeId in node_ids]
|
||
|
||
def _remove_feature_from_generalization(self, original_data, prepared_data, nodes, labels, feature_data,
|
||
current_accuracy, generalize_using_transform):
|
||
# prepared data include one hot encoded categorical data,
|
||
# if there is no categorical data prepared data is original data
|
||
feature = self._get_feature_to_remove(original_data, prepared_data, nodes, labels, feature_data,
|
||
current_accuracy, generalize_using_transform)
|
||
if feature is None:
|
||
return None
|
||
self._remove_feature_from_cells(self.cells, self._cells_by_id, feature)
|
||
return feature
|
||
|
||
def _get_feature_to_remove(self, original_data, prepared_data, nodes, labels, feature_data, current_accuracy,
|
||
generalize_using_transform):
|
||
# prepared data include one hot encoded categorical data,
|
||
# if there is no categorical data prepared data is original data
|
||
# We want to remove features with low iLoss (NCP) and high accuracy gain
|
||
# (after removing them)
|
||
ranges = self._generalizations['ranges']
|
||
range_counts = self._find_range_counts(original_data, ranges)
|
||
total = prepared_data.size
|
||
range_min = sys.float_info.max
|
||
remove_feature = None
|
||
categories = self.generalizations['categories']
|
||
category_counts = self._find_category_counts(original_data, categories)
|
||
|
||
for feature in ranges.keys():
|
||
if feature not in self._generalizations['untouched']:
|
||
if generalize_using_transform:
|
||
feature_ncp = self._calculate_ncp_for_feature_from_cells(feature, feature_data, original_data)
|
||
else:
|
||
feature_ncp = self._calc_ncp_numeric(ranges[feature],
|
||
range_counts[feature],
|
||
feature_data[feature],
|
||
total)
|
||
if feature_ncp > 0:
|
||
# divide by accuracy gain
|
||
new_cells = copy.deepcopy(self.cells)
|
||
cells_by_id = copy.deepcopy(self._cells_by_id)
|
||
GeneralizeToRepresentative._remove_feature_from_cells(new_cells, cells_by_id, feature)
|
||
generalized = self._generalize_from_tree(original_data, prepared_data, nodes, new_cells,
|
||
cells_by_id)
|
||
accuracy_gain = self.estimator.score(ArrayDataset(self.encoder.transform(generalized),
|
||
labels)) - current_accuracy
|
||
if accuracy_gain < 0:
|
||
accuracy_gain = 0
|
||
if accuracy_gain != 0:
|
||
feature_ncp = feature_ncp / accuracy_gain
|
||
|
||
if feature_ncp < range_min:
|
||
range_min = feature_ncp
|
||
remove_feature = feature
|
||
|
||
for feature in categories.keys():
|
||
if feature not in self.generalizations['untouched']:
|
||
if generalize_using_transform:
|
||
feature_ncp = self._calculate_ncp_for_feature_from_cells(feature, feature_data, original_data)
|
||
else:
|
||
feature_ncp = self._calc_ncp_categorical(categories[feature],
|
||
category_counts[feature],
|
||
feature_data[feature],
|
||
total)
|
||
if feature_ncp > 0:
|
||
# divide by accuracy loss
|
||
new_cells = copy.deepcopy(self.cells)
|
||
cells_by_id = copy.deepcopy(self._cells_by_id)
|
||
GeneralizeToRepresentative._remove_feature_from_cells(new_cells, cells_by_id, feature)
|
||
generalized = self._generalize_from_tree(original_data, prepared_data, nodes, new_cells,
|
||
cells_by_id)
|
||
accuracy_gain = self.estimator.score(ArrayDataset(self.encoder.transform(generalized),
|
||
labels)) - current_accuracy
|
||
|
||
if accuracy_gain < 0:
|
||
accuracy_gain = 0
|
||
if accuracy_gain != 0:
|
||
feature_ncp = feature_ncp / accuracy_gain
|
||
if feature_ncp < range_min:
|
||
range_min = feature_ncp
|
||
remove_feature = feature
|
||
|
||
print('feature to remove: ' + (str(remove_feature) if remove_feature is not None else 'none'))
|
||
return remove_feature
|
||
|
||
def _calculate_ncp_for_feature_from_cells(self, feature, feature_data, samples_pd):
|
||
# count how many records are mapped to each cell
|
||
counted = np.zeros(samples_pd.shape[0]) # to mark records we already counted
|
||
total = samples_pd.shape[0]
|
||
feature_ncp = 0
|
||
for cell in self.cells:
|
||
count = self._get_record_count_for_cell(samples_pd, cell, counted)
|
||
generalizations = self._calculate_generalizations_for_cell(cell)
|
||
cell_ncp = 0
|
||
if feature in cell['ranges']:
|
||
cell_ncp = self._calc_ncp_numeric(generalizations['ranges'][feature],
|
||
[count],
|
||
feature_data[feature],
|
||
total)
|
||
elif feature in cell['categories']:
|
||
cell_ncp = self._calc_ncp_categorical(generalizations['categories'][feature],
|
||
[count],
|
||
feature_data[feature],
|
||
total)
|
||
feature_ncp += cell_ncp
|
||
return feature_ncp
|
||
|
||
def _calculate_generalizations(self, samples: Optional[pd.DataFrame] = None):
|
||
ranges, range_representatives = self._calculate_ranges(self.cells)
|
||
categories, category_representatives = self._calculate_categories(self.cells)
|
||
self._generalizations = {'ranges': ranges,
|
||
'categories': categories,
|
||
'untouched': self._calculate_untouched(self.cells)}
|
||
self._remove_categorical_untouched(self._generalizations)
|
||
# compute representative value for each feature (based on data)
|
||
if samples is not None:
|
||
sample_indexes = self._map_to_ranges_categories(samples,
|
||
self._generalizations['ranges'],
|
||
self._generalizations['categories'])
|
||
# categorical - use most common value
|
||
old_category_representatives = category_representatives
|
||
category_representatives = {}
|
||
for feature in self._generalizations['categories']:
|
||
category_representatives[feature] = []
|
||
for g_index, group in enumerate(self._generalizations['categories'][feature]):
|
||
indexes = [i for i, s in enumerate(sample_indexes) if s[feature] == g_index]
|
||
if indexes:
|
||
rows = samples.iloc[indexes]
|
||
values = rows[feature]
|
||
category = Counter(values).most_common(1)[0][0]
|
||
category_representatives[feature].append(category)
|
||
else:
|
||
category_representatives[feature].append(old_category_representatives[feature][g_index])
|
||
|
||
# numerical - use actual value closest to mean
|
||
old_range_representatives = range_representatives
|
||
range_representatives = {}
|
||
for feature in self._generalizations['ranges']:
|
||
range_representatives[feature] = []
|
||
# find the mean value (per feature)
|
||
for index in range(len(self._generalizations['ranges'][feature])):
|
||
indexes = [i for i, s in enumerate(sample_indexes) if s[feature] == index]
|
||
if indexes:
|
||
rows = samples.iloc[indexes]
|
||
values = rows[feature]
|
||
median = np.median(values)
|
||
min_value = max(values)
|
||
min_dist = float("inf")
|
||
for value in values:
|
||
# euclidean distance between two floating point values
|
||
dist = abs(value - median)
|
||
if dist < min_dist:
|
||
min_dist = dist
|
||
min_value = value
|
||
range_representatives[feature].append(min_value)
|
||
else:
|
||
range_representatives[feature].append(old_range_representatives[feature][index])
|
||
self._generalizations['category_representatives'] = category_representatives
|
||
self._generalizations['range_representatives'] = range_representatives
|
||
|
||
def _calculate_generalizations_for_cell(self, cell):
|
||
ranges, range_representatives = self._calculate_ranges([cell])
|
||
categories, category_representatives = self._calculate_categories([cell])
|
||
generalizations = {'ranges': ranges,
|
||
'categories': categories,
|
||
'untouched': self._calculate_untouched([cell]),
|
||
'range_representatives': range_representatives,
|
||
'category_representatives': category_representatives}
|
||
self._remove_categorical_untouched(generalizations)
|
||
return generalizations
|
||
|
||
def _calculate_cell_generalizations(self):
|
||
# calculate generalizations separately per cell
|
||
cell_generalizations = {}
|
||
for cell in self.cells:
|
||
cell_generalizations[cell['id']] = self._calculate_generalizations_for_cell(cell)
|
||
return cell_generalizations
|
||
|
||
@staticmethod
|
||
def _find_range_counts(samples, ranges):
|
||
range_counts = {}
|
||
last_value = None
|
||
for r in ranges.keys():
|
||
range_counts[r] = []
|
||
# if empty list, all samples should be counted
|
||
if not ranges[r]:
|
||
range_counts[r].append(samples.shape[0])
|
||
else:
|
||
for value in ranges[r]:
|
||
counter = [item for item in samples[r] if int(item) <= value]
|
||
range_counts[r].append(len(counter))
|
||
last_value = value
|
||
counter = [item for item in samples[r] if int(item) > last_value]
|
||
range_counts[r].append(len(counter))
|
||
return range_counts
|
||
|
||
@staticmethod
|
||
def _find_category_counts(samples, categories):
|
||
category_counts = {}
|
||
for c in categories.keys():
|
||
category_counts[c] = []
|
||
for value in categories[c]:
|
||
category_counts[c].append(len(samples.loc[samples[c].isin(value)]))
|
||
return category_counts
|
||
|
||
@staticmethod
|
||
def _calculate_ranges(cells):
|
||
ranges = {}
|
||
range_representatives = {}
|
||
for cell in cells:
|
||
for feature in [key for key in cell['ranges'].keys() if
|
||
'untouched' not in cell or key not in cell['untouched']]:
|
||
if feature not in ranges.keys():
|
||
ranges[feature] = []
|
||
if cell['ranges'][feature]['start'] is not None:
|
||
ranges[feature].append(cell['ranges'][feature]['start'])
|
||
if cell['ranges'][feature]['end'] is not None:
|
||
ranges[feature].append(cell['ranges'][feature]['end'])
|
||
# default representative values (computed with no data)
|
||
for feature in ranges.keys():
|
||
range_representatives[feature] = []
|
||
if not ranges[feature]:
|
||
# no values means the complete range. Without data we cannot know what to put here.
|
||
# Using 0 as a placeholder.
|
||
range_representatives[feature].append(0)
|
||
else:
|
||
ranges[feature] = list(set(ranges[feature]))
|
||
ranges[feature].sort()
|
||
prev_value = 0
|
||
for index, value in enumerate(ranges[feature]):
|
||
if index == 0:
|
||
# for first range, use min value
|
||
range_representatives[feature].append(value)
|
||
else:
|
||
# use middle of range (this will be a float)
|
||
range_representatives[feature].append((value - prev_value) / 2)
|
||
prev_value = value
|
||
# for last range use max value + 1
|
||
range_representatives[feature].append(prev_value + 1)
|
||
return ranges, range_representatives
|
||
|
||
@staticmethod
|
||
def _calculate_categories(cells):
|
||
categories = {}
|
||
category_representatives = {}
|
||
categorical_features_values = GeneralizeToRepresentative._calculate_categorical_features_values(cells)
|
||
for feature in categorical_features_values.keys():
|
||
partitions = []
|
||
category_representatives[feature] = []
|
||
values = categorical_features_values[feature]
|
||
assigned = []
|
||
for i in range(len(values)):
|
||
value1 = values[i]
|
||
if value1 in assigned:
|
||
continue
|
||
partition = [value1]
|
||
assigned.append(value1)
|
||
for j in range(len(values)):
|
||
if j <= i:
|
||
continue
|
||
value2 = values[j]
|
||
if GeneralizeToRepresentative._are_inseparable(cells, feature, value1, value2):
|
||
partition.append(value2)
|
||
assigned.append(value2)
|
||
partitions.append(partition)
|
||
# default representative values (computed with no data)
|
||
category_representatives[feature].append(partition[0]) # random
|
||
categories[feature] = partitions
|
||
return categories, category_representatives
|
||
|
||
@staticmethod
|
||
def _calculate_categorical_features_values(cells):
|
||
categorical_features_values = {}
|
||
for cell in cells:
|
||
for feature in [key for key in cell['categories'].keys() if
|
||
'untouched' not in cell or key not in cell['untouched']]:
|
||
if feature not in categorical_features_values.keys():
|
||
categorical_features_values[feature] = []
|
||
for value in cell['categories'][feature]:
|
||
if value not in categorical_features_values[feature]:
|
||
categorical_features_values[feature].append(value)
|
||
return categorical_features_values
|
||
|
||
@staticmethod
|
||
def _are_inseparable(cells, feature, value1, value2):
|
||
for cell in cells:
|
||
if feature not in cell['categories'].keys():
|
||
continue
|
||
value1_in = value1 in cell['categories'][feature]
|
||
value2_in = value2 in cell['categories'][feature]
|
||
if value1_in != value2_in:
|
||
return False
|
||
return True
|
||
|
||
@staticmethod
|
||
def _calculate_untouched(cells):
|
||
untouched_lists = [cell['untouched'] if 'untouched' in cell else [] for cell in cells]
|
||
untouched = set(untouched_lists[0])
|
||
untouched = untouched.intersection(*untouched_lists)
|
||
return list(untouched)
|
||
|
||
@staticmethod
|
||
def _remove_feature_from_cells(cells, cells_by_id, feature):
|
||
for cell in cells:
|
||
if 'untouched' not in cell:
|
||
cell['untouched'] = []
|
||
if feature in cell['ranges'].keys():
|
||
del cell['ranges'][feature]
|
||
elif feature in cell['categories'].keys():
|
||
del cell['categories'][feature]
|
||
cell['untouched'].append(feature)
|
||
cells_by_id[cell['id']] = cell.copy()
|
||
|
||
@staticmethod
|
||
def _remove_categorical_untouched(generalizations):
|
||
to_remove = []
|
||
for feature in generalizations['categories'].keys():
|
||
category_sizes = [len(g) if len(g) > 1 else 0 for g in generalizations['categories'][feature]]
|
||
if sum(category_sizes) == 0:
|
||
if 'untouched' not in generalizations:
|
||
generalizations['untouched'] = []
|
||
generalizations['untouched'].append(feature)
|
||
to_remove.append(feature)
|
||
|
||
for feature in to_remove:
|
||
del generalizations['categories'][feature]
|