Source code for alibi.utils.discretizer

import numpy as np

from alibi.tests.utils import issorted
from functools import partial
from typing import Dict, Callable, List, Sequence, Union

[docs] class Discretizer(object):
[docs] def __init__(self, data: np.ndarray, numerical_features: List[int], feature_names: List[str], percentiles: Sequence[Union[int, float]] = (25, 50, 75)) -> None: """ Initialize the discretizer. Parameters ---------- data Data to discretize. numerical_features List of indices corresponding to the continuous feature columns. Only these features will be discretized. feature_names List with feature names. percentiles Percentiles used for discretization. """ self.to_discretize = numerical_features self.percentiles = percentiles bins = self.bins(data) bins = [np.unique(x) for x in bins] self.feature_intervals: Dict[int, list] = {} self.lambdas: Dict[int, Callable] = {} for feature, qts in zip(self.to_discretize, bins): # get nb of borders (nb of bins - 1) and the feature name n_bins = qts.shape[0] name = feature_names[feature] # create names for bins of discretized features self.feature_intervals[feature] = ['%s <= %.2f' % (name, qts[0])] for i in range(n_bins - 1): self.feature_intervals[feature].append('%.2f < %s <= %.2f' % (qts[i], name, qts[i + 1])) self.feature_intervals[feature].append('%s > %.2f' % (name, qts[n_bins - 1])) self.lambdas[feature] = partial(self.get_percentiles, qts=qts)
[docs] @staticmethod def get_percentiles(x: np.ndarray, qts: np.ndarray) -> np.ndarray: """ Discretizes the the data in `x` using the quantiles in `qts`. This is achieved by searching for the index of each value in `x` into `qts`, which is assumed to be a 1-D sorted array. Parameters ---------- x A `numpy` array of data to be discretized qts: A `numpy` array of percentiles. This should be a 1-D array sorted in ascending order. Returns ------- A discretized data `numpy` array. """ if len(qts.shape) != 1: raise ValueError("Expected 1D quantiles array!") if not issorted(qts): raise ValueError("Quantiles array should be sorted!") return np.searchsorted(qts, x)
[docs] def bins(self, data: np.ndarray) -> List[np.ndarray]: """ Parameters ---------- data Data to discretize. Returns ------- List with bin values for each feature that is discretized. """ bins = [] for feature in self.to_discretize: qts = np.array(np.percentile(data[:, feature], self.percentiles)) bins.append(qts) return bins
[docs] def discretize(self, data: np.ndarray) -> np.ndarray: """ Parameters ---------- data Data to discretize. Returns ------- Discretized version of data with the same dimension. """ data_disc = data.copy() for feature in self.lambdas: if len(data.shape) == 1: data_disc[feature] = int(self.lambdas[feature](data_disc[feature])) else: data_disc[:, feature] = self.lambdas[feature](data_disc[:, feature]).astype(int) return data_disc