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score.py
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score.py
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import re
from collections import defaultdict
from itertools import chain
import numpy as np
from sklearn import feature_selection as skl_fss
from Orange.data import Domain, Variable, DiscreteVariable, ContinuousVariable
from Orange.data.filter import HasClass
from Orange.misc.wrapper_meta import WrapperMeta
from Orange.preprocess.preprocess import Discretize, SklImpute, RemoveNaNColumns
from Orange.statistics import contingency, distribution
from Orange.util import Reprable
__all__ = ["Chi2",
"ANOVA",
"UnivariateLinearRegression",
"InfoGain",
"GainRatio",
"Gini",
"ReliefF",
"RReliefF",
"FCBF"]
class Scorer(Reprable):
feature_type = None
class_type = None
supports_sparse_data = None
preprocessors = [HasClass()]
@property
def friendly_name(self):
"""Return type name with camel-case separated into words.
Derived classes can provide a better property or a class attribute.
"""
return re.sub("([a-z])([A-Z])",
lambda mo: mo.group(1) + " " + mo.group(2).lower(),
type(self).__name__)
@staticmethod
def _friendly_vartype_name(vartype):
if vartype == DiscreteVariable:
return "categorical"
if vartype == ContinuousVariable:
return "numeric"
# Fallbacks
name = vartype.__name__
if name.endswith("Variable"):
return name.lower()[:-8]
return name
def __call__(self, data, feature=None):
if not data.domain.class_var:
raise ValueError(
"{} requires data with a target variable."
.format(self.friendly_name))
if not isinstance(data.domain.class_var, self.class_type):
raise ValueError(
"{} requires a {} target variable."
.format(self.friendly_name,
self._friendly_vartype_name(self.class_type)))
if feature is not None:
f = data.domain[feature]
data = data.transform(Domain([f], data.domain.class_vars))
orig_domain = data.domain
for pp in self.preprocessors:
data = pp(data)
for var in data.domain.attributes:
if not isinstance(var, self.feature_type):
raise ValueError(
"{} cannot score {} variables."
.format(self.friendly_name,
self._friendly_vartype_name(type(var))))
if feature is not None:
return self.score_data(data, feature)
scores = np.full(len(orig_domain.attributes), np.nan)
names = [a.name for a in data.domain.attributes]
mask = np.array([a.name in names for a in orig_domain.attributes])
if len(mask):
scores[mask] = self.score_data(data, feature)
return scores
def score_data(self, data, feature):
raise NotImplementedError
class SklScorer(Scorer, metaclass=WrapperMeta):
supports_sparse_data = True
preprocessors = Scorer.preprocessors + [
SklImpute()
]
def score_data(self, data, feature):
score = self.score(data.X, data.Y)
if feature is not None:
return score[0]
return score
class Chi2(SklScorer):
"""
A wrapper for `${sklname}`. The following is the documentation
from `scikit-learn <http://scikit-learn.org>`_.
${skldoc}
"""
__wraps__ = skl_fss.chi2
feature_type = DiscreteVariable
class_type = DiscreteVariable
preprocessors = SklScorer.preprocessors + [
Discretize(remove_const=False)
]
def score(self, X, y):
f, _ = skl_fss.chi2(X, y)
return f
class ANOVA(SklScorer):
"""
A wrapper for `${sklname}`. The following is the documentation
from `scikit-learn <http://scikit-learn.org>`_.
${skldoc}
"""
__wraps__ = skl_fss.f_classif
feature_type = ContinuousVariable
class_type = DiscreteVariable
def score(self, X, y):
f, _ = skl_fss.f_classif(X, y)
return f
class UnivariateLinearRegression(SklScorer):
"""
A wrapper for `${sklname}`. The following is the documentation
from `scikit-learn <http://scikit-learn.org>`_.
${skldoc}
"""
__wraps__ = skl_fss.f_regression
feature_type = ContinuousVariable
class_type = ContinuousVariable
def score(self, X, y):
f, p = skl_fss.f_regression(X, y)
return f
class LearnerScorer(Scorer):
def score(self, data):
raise NotImplementedError
def score_data(self, data, feature=None):
def join_derived_features(scores):
""" Obtain scores for original attributes.
If a learner preprocessed the data before building a model, current scores do not
directly correspond to the domain. For example, continuization creates multiple
features from a discrete feature. """
scores_grouped = defaultdict(list)
for attr, score in zip(model_attributes, scores):
# Go up the chain of preprocessors to obtain the original variable, but no further
# than the data.domain, because the data is perhaphs already preprocessed.
while not (attr in data.domain) and attr.compute_value is not None:
if hasattr(attr.compute_value, 'variable'):
attr = getattr(attr.compute_value, 'variable')
else:
# The attributes's parent can not be identified. Thus, the attributes
# score will be ignored.
break
scores_grouped[attr].append(score)
return [max(scores_grouped[attr])
if attr in scores_grouped else 0
for attr in data.domain.attributes]
scores, model_attributes = self.score(data)
scores = np.atleast_2d(scores)
if data.domain.attributes != model_attributes:
scores = np.array([join_derived_features(row) for row in scores])
return scores[:, data.domain.attributes.index(feature)] \
if feature else scores
class ClassificationScorer(Scorer):
"""
Base class for feature scores in a class-labeled dataset.
Parameters
----------
feature : int, string, Orange.data.Variable
Feature id
data : Orange.data.Table
Dataset
Attributes
----------
feature_type : Orange.data.Variable
Required type of features.
class_type : Orange.data.Variable
Required type of class variable.
"""
feature_type = DiscreteVariable
class_type = DiscreteVariable
supports_sparse_data = True
preprocessors = Scorer.preprocessors + [
Discretize(remove_const=False)
]
def score_data(self, data, feature):
instances_with_class = \
np.sum(distribution.Discrete(data, data.domain.class_var))
def score_from_contingency(f):
cont = contingency.Discrete(data, f)
return self.from_contingency(
cont, 1. - np.sum(cont.unknowns)/instances_with_class)
scores = [score_from_contingency(f) for f in data.domain.attributes]
if feature is not None:
return scores[0]
return scores
def _entropy(dist):
"""Entropy of class-distribution matrix"""
p = dist / np.sum(dist, axis=0)
pc = np.clip(p, 1e-15, 1)
return np.sum(np.sum(- p * np.log2(pc), axis=0) *
np.sum(dist, axis=0) / np.sum(dist))
def _gini(dist):
"""Gini index of class-distribution matrix"""
p = np.asarray(dist / np.sum(dist, axis=0))
return np.sum((1 - np.sum(p ** 2, axis=0)) *
np.sum(dist, axis=0) / np.sum(dist))
def _symmetrical_uncertainty(data, attr1, attr2):
"""Symmetrical uncertainty, Press et al., 1988."""
cont = np.asarray(contingency.Discrete(data, attr1, attr2), dtype=float)
ig = InfoGain().from_contingency(cont, 1)
return 2 * ig / (_entropy(cont) + _entropy(cont.T))
class FCBF(ClassificationScorer):
"""
Fast Correlation-Based Filter. Described in:
Yu, L., Liu, H.,
Feature selection for high-dimensional data: A fast correlation-based filter solution.
2003. http://www.aaai.org/Papers/ICML/2003/ICML03-111.pdf
"""
def score_data(self, data, feature=None):
attributes = data.domain.attributes
s = []
for i, attr in enumerate(attributes):
s.append((_symmetrical_uncertainty(data, attr, data.domain.class_var), i))
s.sort()
worst = []
p = 1
while True:
try:
_, Fp = s[-p]
except IndexError:
break
q = p + 1
while True:
try:
suqc, Fq = s[-q]
except IndexError:
break
if _symmetrical_uncertainty(data, attributes[Fp], attributes[Fq]) >= suqc:
del s[-q]
worst.append((1e-4*suqc, Fq))
else:
q += 1
p += 1
best = s
scores = [i[0] for i in sorted(chain(best, worst), key=lambda i: i[1])]
return np.array(scores) if not feature else scores[0]
class InfoGain(ClassificationScorer):
"""
Information gain is the expected decrease of entropy. See `Wikipedia entry on information gain
<http://en.wikipedia.org/wiki/Information_gain_ratio>`_.
"""
def from_contingency(self, cont, nan_adjustment):
h_class = _entropy(np.sum(cont, axis=1))
h_residual = _entropy(np.compress(np.sum(cont, axis=0), cont, axis=1))
return nan_adjustment * (h_class - h_residual)
class GainRatio(ClassificationScorer):
"""
Information gain ratio is the ratio between information gain and
the entropy of the feature's
value distribution. The score was introduced in [Quinlan1986]_
to alleviate overestimation for multi-valued features. See `Wikipedia entry on gain ratio
<http://en.wikipedia.org/wiki/Information_gain_ratio>`_.
.. [Quinlan1986] J R Quinlan: Induction of Decision Trees, Machine Learning, 1986.
"""
def from_contingency(self, cont, nan_adjustment):
h_class = _entropy(np.sum(cont, axis=1))
h_residual = _entropy(np.compress(np.sum(cont, axis=0), cont, axis=1))
h_attribute = _entropy(np.sum(cont, axis=0))
if h_attribute == 0:
h_attribute = 1
return nan_adjustment * (h_class - h_residual) / h_attribute
class Gini(ClassificationScorer):
"""
Gini impurity is the probability that two randomly chosen instances will have different
classes. See `Wikipedia entry on Gini impurity
<https://en.wikipedia.org/wiki/Decision_tree_learning#Gini_impurity>`_.
"""
def from_contingency(self, cont, nan_adjustment):
return (_gini(np.sum(cont, axis=1)) - _gini(cont)) * nan_adjustment
class ReliefF(Scorer):
"""
ReliefF algorithm. Contrary to most other scorers, Relief family of
algorithms is not as myoptic but tends to give unreliable results with
datasets with lots (hundreds) of features.
Robnik-艩ikonja, M., Kononenko, I.
Theoretical and empirical analysis of ReliefF and RReliefF.
2003. http://lkm.fri.uni-lj.si/rmarko/papers/robnik03-mlj.pdf
"""
feature_type = Variable
class_type = DiscreteVariable
supports_sparse_data = False
friendly_name = "ReliefF"
preprocessors = Scorer.preprocessors + [RemoveNaNColumns()]
def __init__(self, n_iterations=50, k_nearest=10, random_state=None):
self.n_iterations = n_iterations
self.k_nearest = k_nearest
self.random_state = random_state
def score_data(self, data, feature):
if len(data.domain.class_vars) != 1:
raise ValueError('ReliefF requires one single class')
if not data.domain.class_var.is_discrete:
raise ValueError('ReliefF supports classification; use RReliefF '
'for regression')
if len(data.domain.class_var.values) == 1: # Single-class value non-problem
return 0 if feature else np.zeros(data.X.shape[1])
if isinstance(self.random_state, np.random.RandomState):
rstate = self.random_state
else:
rstate = np.random.RandomState(self.random_state)
from Orange.preprocess._relieff import relieff
weights = np.asarray(relieff(data.X, data.Y,
self.n_iterations, self.k_nearest,
np.array([a.is_discrete for a in data.domain.attributes], dtype=bool),
rstate))
if feature:
return weights[0]
return weights
class RReliefF(Scorer):
feature_type = Variable
class_type = ContinuousVariable
supports_sparse_data = False
friendly_name = "RReliefF"
preprocessors = Scorer.preprocessors + [RemoveNaNColumns()]
def __init__(self, n_iterations=50, k_nearest=50, random_state=None):
self.n_iterations = n_iterations
self.k_nearest = k_nearest
self.random_state = random_state
def score_data(self, data, feature):
if len(data.domain.class_vars) != 1:
raise ValueError('RReliefF requires one single class')
if not data.domain.class_var.is_continuous:
raise ValueError('RReliefF supports regression; use ReliefF '
'for classification')
if isinstance(self.random_state, np.random.RandomState):
rstate = self.random_state
else:
rstate = np.random.RandomState(self.random_state)
from Orange.preprocess._relieff import rrelieff
weights = np.asarray(rrelieff(data.X, data.Y,
self.n_iterations, self.k_nearest,
np.array([a.is_discrete for a in data.domain.attributes]),
rstate))
if feature:
return weights[0]
return weights
if __name__ == '__main__':
from Orange.data import Table
X = np.random.random((500, 20))
X[np.random.random(X.shape) > .95] = np.nan
y_cls = np.zeros(X.shape[0])
y_cls[(X[:, 0] > .5) ^ (X[:, 1] > .6)] = 1
y_reg = np.nansum(X[:, 0:3], 1)
for relief, y in ((ReliefF(), y_cls),
(RReliefF(), y_reg)):
data = Table.from_numpy(None, X, y)
weights = relief.score_data(data, False)
print(relief.__class__.__name__)
print('Best =', weights.argsort()[::-1])
print('Weights =', weights[weights.argsort()[::-1]])
X *= 10
data = Table.from_numpy(None, X, y_cls)
weights = FCBF().score_data(data, False)
print('FCBF')
print('Best =', weights.argsort()[::-1])
print('Weights =', weights[weights.argsort()[::-1]])