hugobuddel / orange3

Orange/preprocess/fss.py

import random
import Orange
import numpy as np

The import numpy could not be resolved.

from itertools import takewhile
from operator import itemgetter

from Orange.preprocess.preprocess import Preprocess
from Orange.preprocess.score import ANOVA, GainRatio, UnivariateLinearRegression
from Orange.data import Domain

Unused Domain imported from Orange.data

__all__ = ["SelectBestFeatures", "RemoveNaNColumns", "SelectRandomFeatures"]


class SelectBestFeatures:
    """
    A feature selector that builds a new data set consisting of either the top
    `k` features or all those that exceed a given `threshold`. Features are
    scored using the provided feature scoring `method`. By default it is
    assumed that feature importance diminishes with decreasing scores.

    If both `k` and `threshold` are set, only features satisfying both
    conditions will be selected.

    If `method` is not set, it is automatically selected when presented with
    the data set. Data sets with both continuous and discrete features are
    scored using a method suitable for the majority of features.

    Parameters
    ----------
    method : Orange.preprocess.score.ClassificationScorer, Orange.preprocess.score.SklScorer
        Univariate feature scoring method.

    k : int
        The number of top features to select.

    threshold : float
        A threshold that a feature should meet according to the provided method.

    decreasing : boolean
        The order of feature importance when sorted from the most to the least
        important feature.
    """

    def __init__(self, method=None, k=None, threshold=None, decreasing=True):
        self.method = method
        self.k = k
        self.threshold = threshold
        self.decreasing = decreasing

    def __call__(self, data):
        method = self.method
        # select default method according to the provided data
        if method is None:
            autoMethod = True

The variable autoMethod seems to be unused.

            discr_ratio = (sum(a.is_discrete
                               for a in data.domain.attributes)
                           / len(data.domain.attributes))
            if data.domain.has_discrete_class:
                if discr_ratio >= 0.5:
                    method = GainRatio()
                else:
                    method = ANOVA()
            else:
                method = UnivariateLinearRegression()

        if not isinstance(data.domain.class_var, method.class_type):
            raise ValueError(("Scoring method {} requires a class variable " +
                              "of type {}.").format(
                (method if type(method) == type else type(method)).__name__,
                method.class_type.__name__)
            )
        features = data.domain.attributes
        try:
            scores = method(data)
        except ValueError:
            scores = self.score_only_nice_features(data, method)
        best = sorted(zip(scores, features), key=itemgetter(0),
                      reverse=self.decreasing)
        if self.k:
            best = best[:self.k]
        if self.threshold:
            pred = ((lambda x: x[0] >= self.threshold) if self.decreasing else
                    (lambda x: x[0] <= self.threshold))
            best = takewhile(pred, best)

        domain = Orange.data.Domain([f for s, f in best],
                                    data.domain.class_vars, data.domain.metas)
        return data.from_table(domain, data)

    def score_only_nice_features(self, data, method):
        mask = np.array([isinstance(a, method.feature_type)
                         for a in data.domain.attributes])
        features = [f for f in data.domain.attributes
                    if isinstance(f, method.feature_type)]
        scores = [method(data, f) for f in features]
        bad = float('-inf') if self.decreasing else float('inf')
        all_scores = np.array([bad] * len(data.domain.attributes))
        all_scores[mask] = scores
        return all_scores


class SelectRandomFeatures:
    """
    A feature selector that selects random `k` features from an input
    data set and returns a data set with selected features. Parameter
    `k` is either an integer (number of feature) or float (from 0.0 to
    1.0, proportion of retained features).

    Parameters
    ----------

    k : int or float (default = 0.1)
        The number or proportion of features to retain.
    """

    def __init__(self, k=0.1):
        self.k = k

    def __call__(self, data):
        if type(self.k) == float:
            self.k = int(len(data.domain.attributes) * self.k)
        domain = Orange.data.Domain(
            random.sample(data.domain.attributes, self.k),
            data.domain.class_vars, data.domain.metas)
        return data.from_table(domain, data)


class RemoveNaNColumns(Preprocess):
    """
    Removes data columns that contain only unknown values. Returns the
    resulting data set. Does not check optional class attribute(s).

    data : data table
        an input data table
    """
    def __call__(self, data):
        nan_col = np.all(np.isnan(data.X), axis=0)
        att = [a for a, nan in zip(data.domain.attributes, nan_col) if not nan]
        domain = Orange.data.Domain(att, data.domain.class_vars,
                                    data.domain.metas)
        return Orange.data.Table(domain, data)