/
utils_fuji.py
386 lines (357 loc) · 15.6 KB
/
utils_fuji.py
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from typing import List, Union, Dict, Tuple
from tqdm import trange
import numpy as np
import scipy.stats.stats as stats
ALLOWED_MEASURES = [
"fuzzy_jaccard", "jaccard", "hamming", "pog", "npog", "kuncheva", "wald",
"lustgarten", "krizek", "cwrel", "pearson", "correlation", "fuzzy_gamma"
]
STEP_1 = 1
STEP_SQUARED = "squared"
STEP_EXP = "exp"
ALLOWED_STEPS = [STEP_1, STEP_SQUARED, STEP_EXP]
IMPORTANCE_HANDLER_RAISE = "raise"
IMPORTANCE_HANDLER_CORRECT = "correct"
ALLOWED_IMPORTANCE_HANDLERS = [
IMPORTANCE_HANDLER_RAISE, IMPORTANCE_HANDLER_CORRECT
]
class Fimp:
def __init__(self, feature_dictionary: Dict[str, Tuple[int, List[int],
List[float]]]):
"""
Creates a feature importance structure.
:param feature_dictionary: A dictionary of the form
{feature name: (feature index, feature ranks, feature importances)}. The lengths of the last two items in each
value triplet are the same, and they correspond to the number of feature rankings stored in the Fimp structure.
Taking i-th components of these two lists, gives us the ranks and importances from the i-th feature ranking,
for all features.
"""
self.table = [] # [[dataset index, name, ranks, relevances], ...]
self.features = {} # {name: [dataset index, ranks, relevances], ...}
self.features = feature_dictionary
for attr in feature_dictionary:
row = [
feature_dictionary[attr][0], attr, feature_dictionary[attr][1],
feature_dictionary[attr][2]
]
self.table.append(row)
def sort_by_feature_index(self):
self.table.sort(key=lambda row: row[0])
def sort_by_relevance(self, ranking_index=0):
self.table.sort(key=lambda row: row[2][ranking_index])
def get_feature_names(self):
return [row[1] for row in self.table]
def get_relevances(self, ranking_index=None):
return [
row[-1] if ranking_index is None else row[-1][ranking_index]
for row in self.table
]
def get_relevance(self, feature_name, ranking_index=None):
i = 0 if ranking_index is None else ranking_index
return self.features[feature_name][-1][i]
def set_relevances(self, ranking_index, feature_relevances):
"""Overwrites the current feature relevances. Does not recompute the ranks."""
assert len(feature_relevances) == len(self.table)
for i, (_, name, _, _) in enumerate(self.table):
self.table[i][-1][ranking_index] = feature_relevances[i]
self.features[name][-1][ranking_index] = feature_relevances[i]
def get_rank(self, feature_name, ranking_index=None):
i = 0 if ranking_index is None else ranking_index
return self.features[feature_name][-2][i]
@staticmethod
def create_fimp_from_relevances(feature_relavance_scores,
feature_names: Union[List[str],
None] = None,
feature_indices: Union[List[int],
None] = None):
n = len(feature_relavance_scores)
if feature_indices is None:
feature_indices = [i + 1 for i in range(n)]
if feature_names is None:
assert isinstance(feature_indices, list)
feature_names = ["a{}".format(i) for i in feature_indices]
# compute ranks
ranks = [-1 for _ in range(n)]
relevances_positions = list(zip(feature_relavance_scores, range(n)))
relevances_positions.sort(reverse=True)
rank = 0
for i, relevance_position in enumerate(relevances_positions):
relevance, position = relevance_position
if i == 0 or abs(relevance -
relevances_positions[i - 1][0]) > 10**-12:
rank = i + 1
ranks[position] = rank
d = {
a: (i, [rank], [relevance])
for a, i, rank, relevance in zip(feature_names, feature_indices,
ranks, feature_relavance_scores)
}
return Fimp(feature_dictionary=d)
def compute_similarity_helper(fimp1: Fimp, fimp2: Fimp,
similarity_measure: str, eps: float,
step: Union[str, int], use_tqdm: bool,
negative_importances_handler: str):
def fuzzy_jaccard(f1: Fimp, f2: Fimp):
def relative_score(absolute_score, normalisation_factor):
if normalisation_factor == 0.0:
return 1.0
else:
return absolute_score / normalisation_factor
for f in [f1, f2]:
f.sort_by_relevance()
attributes = [f1.get_feature_names(), f2.get_feature_names()]
n = len(attributes[0])
for f in [f1, f2]:
feature_relevances = f.get_relevances(0)
if min(feature_relevances) < 0:
if negative_importances_handler == "raise":
raise ValueError(
"Feature importances must not be negative")
elif negative_importances_handler == "correct":
non_negative = [max(0, r) for r in feature_relevances]
f.set_relevances(0, non_negative)
else:
raise ValueError(
"Wrong negative importances handling: {}".format(
negative_importances_handler))
if isinstance(step, str):
feature_subset_sizes = []
if step == "exp":
i = 1
while i <= n:
feature_subset_sizes.append(i - 1)
i *= 2
elif step == "squared":
i = 1
while i**2 <= n:
feature_subset_sizes.append(i**2 - 1)
i += 1
else:
raise ValueError("Wrong step specification: {}".format(step))
else:
feature_subset_sizes = list(range(0, n, step))
if feature_subset_sizes[-1] != n - 1:
feature_subset_sizes.append(n - 1)
n_evaluated_subsets = len(feature_subset_sizes)
i_subset = 0
results = [-1.0] * n_evaluated_subsets
min_scores = [float("inf")] * 2
union_set = set() # more exactly, union - intersection
intersection_set = set()
iterator = trange(n) if use_tqdm else range(n)
for i in iterator:
for j, (attributes_ranking,
f) in enumerate(zip(attributes, [f1, f2])):
feature = attributes_ranking[i]
s = f.get_relevance(feature, 0)
min_scores[j] = min(min_scores[j], s)
if max(min_scores) <= eps:
for i1 in range(i_subset, n_evaluated_subsets):
results[i1] = 1.0
return results
if feature in union_set:
union_set.remove(feature)
intersection_set.add(feature)
else:
union_set.add(feature)
if i != feature_subset_sizes[i_subset]:
continue
fuzzy_intersection = len(intersection_set)
for feature in union_set:
s1 = min(
1.0,
relative_score(f1.get_relevance(feature, 0),
min_scores[0]))
s2 = min(
1.0,
relative_score(f2.get_relevance(feature, 0),
min_scores[1]))
fuzzy_intersection += min(s1, s2)
fuzzy_union = len(intersection_set) + len(union_set)
results[i_subset] = fuzzy_intersection / fuzzy_union
i_subset += 1
return results
def correlation(f1: Fimp, f2: Fimp):
for f in [f1, f2]:
f.sort_by_feature_index()
scores = f.get_relevances(0)
n = len(scores)
finite = [s for s in scores if -float("inf") < s < float("inf")]
min_max = [min(finite), max(finite)]
for i, s in enumerate(scores):
if -float("inf") < s < float("inf"):
continue
elif -float("inf") == s:
scores[i] = min_max[0]
elif float("inf") == s:
scores[i] = min_max[1]
else:
print("Very special value:", s, "at the position", i)
scores[i] = 0 # NaN
f.set_relevances(0, scores)
sort_fimps(f1, f2)
results = [1.0] * n
attributes = [f1.get_feature_names(), f2.get_feature_names()]
part1 = []
part2 = []
union = set()
for i in range(n):
a1, a2 = attributes[0][i], attributes[1][i]
for a in [a1, a2]:
if a not in union:
union.add(a)
for part, f in zip([part1, part2], [f1, f2]):
s = f.get_relevance(a, 0)
part.append(s)
coefficient, _ = stats.pearsonr(part1, part2)
if np.isnan(coefficient):
coefficient = 1.0
results[i] = coefficient
return results
def jaccard_hamming_pog_npog_kuncheva_lustgarten_wald_krizek_cwrel_pearson(
f1: Fimp, f2: Fimp, measure):
sort_fimps(f1, f2)
attributes = [f1.get_feature_names(), f2.get_feature_names()]
n = len(attributes[0])
results = [-1.0] * n
intersection = set()
union = set()
for i in range(n):
a1, a2 = attributes[0][i], attributes[1][i]
if a1 == a2:
intersection.add(a1)
union.add(a1)
else:
if a1 in union:
# a1 has been added as part of the attributes2 before
intersection.add(a1)
if a2 in union:
# symmetric case
intersection.add(a2)
union.add(a1)
union.add(a2)
k = i + 1
if measure == "jaccard":
results[i] = len(intersection) / len(union)
elif measure == "hamming":
results[i] = 1.0 - ((len(union) - len(intersection)) / n)
elif measure == "pog":
results[i] = len(intersection) / k
elif measure in ["npog", "kuncheva", "wald", "pearson"]:
if k < n:
results[i] = (len(intersection) - k**2 / n) / (k -
k**2 / n)
else:
results[i] = 1.0
elif measure == "lustgarten":
if k < n:
results[i] = (len(intersection) -
k**2 / n) / (k - max(0, 2 * k - n))
else:
results[i] = 1.0
elif measure == "krizek":
# this is what krizek boils down to when we compare two feature subsets.
results[i] = float(len(intersection) == len(union))
elif measure == "cwrel":
# this is what cwrel boils down to when we compare two feature subsets.
y = n # keep the notation from the paper to avoid mistakes
n_capital = len(union) + len(
intersection) # sum of feature subset sizes
d = n_capital % y
h = n_capital % 2 # 2: number of feature subsets
numerator = y * (n_capital - d +
2 * len(intersection)) - n_capital**2 + d**2
nominator = y * (h**2 + 2 *
(n_capital - h) - d) - n_capital**2 + d**2
if k < n:
results[i] = numerator / nominator
else:
results[i] = 1.0
else:
raise ValueError("Wrong measure: {}".format(measure))
return results
def fuzzy_gamma(f1: Fimp, f2: Fimp):
# S. Henzgen, E. Hüllermeier.
# Weighted Rank Correlation: A Flexible Approach based on Fuzzy Order Realtions. ECML/PKDD 2015.
def distance(rank1, rank2):
if rank1 == rank2:
return 0.0
else:
return 1.0 # a.k.a max(ws[min(rank1, rank2): max(rank1, rank2)])
# def t_function(a, b):
# return a * b
def r_function(rank1, rank2):
return 0.0 if rank1 >= rank2 else distance(rank1, rank2)
def c_d_function(feature1, feature2):
rank11 = f1.get_rank(feature1, 0)
rank12 = f1.get_rank(feature2, 0)
rank21 = f2.get_rank(feature1, 0)
rank22 = f2.get_rank(feature2, 0)
r11_12 = r_function(rank11, rank12)
r12_11 = r_function(rank12, rank11)
r21_22 = r_function(rank21, rank22)
r22_21 = r_function(rank22, rank21)
c = r11_12 * r21_22 + r12_11 * r22_21
d = r11_12 * r22_21 + r12_11 * r21_22
return c, d
f1.sort_by_relevance(0)
f2.sort_by_relevance(0)
attributes = [f1.get_feature_names(), f2.get_feature_names()]
n = len(attributes[0])
# ws = [1.0] * n # ws[i]: distance between rank i and i + 1, i >= 0
results = [0.0] * n
union = set()
c_total = 0.0
d_total = 0.0
iterator = trange(n) if (
use_tqdm or use_tqdm is None and n > 1000) else range(n)
for i in iterator:
a1, a2 = attributes[0][i], attributes[1][i]
new_features = set()
for a in [a1, a2]:
if a not in union:
new_features.add(a)
# previous and one of the new
for a2 in new_features:
for a1 in union:
c_part, d_part = c_d_function(a1, a2)
c_total += c_part
d_total += d_part
union |= new_features
if len(new_features) == 2:
c_part, d_part = c_d_function(attributes[0][i],
attributes[1][i])
c_total += c_part
d_total += d_part
numerator = c_total - d_total
nominator = c_total + d_total
results[i] = numerator / nominator if nominator > 0 else 1.0
return results
def sort_fimps(f1: Fimp, f2: Fimp):
for f in [f1, f2]:
f.sort_by_relevance(0)
# sanity check
for fimp in [fimp1, fimp2]:
fimp.sort_by_feature_index()
if fimp1.get_feature_names() != fimp2.get_feature_names():
raise ValueError("Names of the attributes are not the same")
if similarity_measure == "fuzzy_jaccard":
return fuzzy_jaccard(fimp1, fimp2)
elif similarity_measure == "correlation":
return correlation(fimp1, fimp2)
elif similarity_measure in [
"jaccard", "hamming", "pog", "npog", "kuncheva", "wald",
"lustgarten", "krizek", "cwrel", "pearson"
]:
return jaccard_hamming_pog_npog_kuncheva_lustgarten_wald_krizek_cwrel_pearson(
fimp1, fimp2, similarity_measure)
elif similarity_measure == "fuzzy_gamma":
return fuzzy_gamma(fimp1, fimp2)
else:
raise ValueError("Wrong Error measure: {}".format(similarity_measure))
def area_under_the_curve(points):
n = len(points) - 1
a = 0.0
for i in range(n):
a += (points[i] + points[i + 1]) / 2
return a / n