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QuaPy/Transduction/prueba.py

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import itertools
from typing import Iterable
from densratio import densratio
from scipy.sparse import issparse, vstack
from scipy.stats import multivariate_normal
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV
import quapy as qp
from Transduction_office.grid_naive_quantif import GridQuantifier, binned_indexer, Indexer, GridQuantifier2, \
classifier_indexer
from method.non_aggregative import MLPE
from quapy.protocol import AbstractStochasticSeededProtocol, OnLabelledCollectionProtocol, UPP
from quapy.data import LabelledCollection
from quapy.method.aggregative import *
import quapy.functional as F
from time import time
from scipy.spatial.distance import cdist
from Transduction.pykliep import DensityRatioEstimator
from quapy.protocol import AbstractStochasticSeededProtocol, OnLabelledCollectionProtocol
from quapy.method.aggregative import *
import quapy.functional as F
plottting = False
def gaussian(mean, cov=0.1, label=0, size=100, random_state=0):
"""
Creates a label collection in which the instances are distributed according to a Gaussian with specified
parameters and labels all data points with a specific label.
:param mean: ndarray of shape (n_dimensions) with the center
:param cov: ndarray of shape (n_dimensions, n_dimensions) with the covariance matrix, or a number for np.eye
:param label: the class label for the collection
:param size: number of instances
:param random_state: allows for replicating experiments
:return: an instance of LabelledCollection
"""
mean = np.asarray(mean)
assert mean.ndim==1, 'wrong shape for mean'
n_features = mean.shape[0]
if isinstance(cov, (int, float)):
cov = np.eye(n_features) * cov
instances = multivariate_normal.rvs(mean, cov, size, random_state=random_state)
return LabelledCollection(instances, labels=[label]*size)
def _internal_plot(train, val, test):
if plottting:
xmin = min(train.X[:, 0].min(), val.X[:, 0].min(), test[:, 0].min())
xmax = max(train.X[:, 0].max(), val.X[:, 0].max(), test[:, 0].max())
ymin = min(train.X[:, 1].min(), val.X[:, 1].min(), test[:, 1].min())
ymax = max(train.X[:, 1].max(), val.X[:, 1].max(), test[:, 1].max())
plot(train, 'sel_train.png', xlim=(xmin, xmax), ylim=(ymin, ymax))
plot(val, 'sel_val.png', xlim=(xmin, xmax), ylim=(ymin, ymax))
plot(test, 'test.png', xlim=(xmin, xmax), ylim=(ymin, ymax))
def plot(data: LabelledCollection, path, xlim=None, ylim=None):
import matplotlib.pyplot as plt
plt.clf()
if isinstance(data, LabelledCollection):
if data.instances.shape[1] != 2:
return
negative, positive = data.separate()
plt.scatter(negative.X[:,0], negative.X[:,1], label='neg', alpha=0.5)
plt.scatter(positive.X[:, 0], positive.X[:, 1], label='pos', alpha=0.5)
else:
if data.shape[1] != 2:
return
plt.scatter(data[:, 0], data[:, 1], label='test', alpha=0.5)
if xlim is not None:
plt.xlim(*xlim)
plt.ylim(*ylim)
plt.legend()
plt.savefig(path)
# ------------------------------------------------------------------------------------
# Protocol for generating prior probability shift + covariate shift by mixing "domains"
# ------------------------------------------------------------------------------------
class CovPriorShift(AbstractStochasticSeededProtocol):
def __init__(self, domains: Iterable[LabelledCollection], sample_size=None, repeats=100, min_support=0, random_state=0,
return_type='sample_prev'):
super(CovPriorShift, self).__init__(random_state)
self.domains = list(itertools.chain.from_iterable(lc.separate() for lc in domains))
self.sample_size = qp._get_sample_size(sample_size)
self.repeats = repeats
self.min_support = min_support
self.collator = OnLabelledCollectionProtocol.get_collator(return_type)
def samples_parameters(self):
"""
Return all the necessary parameters to replicate the samples as according to the UPP protocol.
:return: a list of indexes that realize the UPP sampling
"""
indexes = []
tentatives = 0
while len(indexes) < self.repeats:
alpha = F.uniform_simplex_sampling(n_classes=len(self.domains))
sizes = (alpha * self.sample_size).astype(int)
if all(sizes > self.min_support):
indexes_i = [lc.sampling_index(size) for lc, size in zip(self.domains, sizes)]
indexes.append(indexes_i)
tentatives = 0
else:
tentatives += 1
if tentatives > 100:
raise ValueError('the support is too strict, and it is difficult '
'or impossible to generate valid samples')
return indexes
def sample(self, params):
indexes = params
lcs = [lc.sampling_from_index(index) for index, lc in zip(indexes, self.domains)]
return LabelledCollection.join(*lcs)
def total(self):
"""
Returns the number of samples that will be generated
:return: int
"""
return self.repeats
# ---------------------------------------------------------------------------------------
# Methods of "importance weight", e.g., by ratio density estimation (KLIEP, SILF, LogReg)
# ---------------------------------------------------------------------------------------
class ImportanceWeight:
@abstractmethod
def weights(self, Xtr, ytr, Xte):
pass
class KLIEP(ImportanceWeight):
def __init__(self):
pass
def weights(self, Xtr, ytr, Xte):
kliep = DensityRatioEstimator()
kliep.fit(Xtr, Xte)
return kliep.predict(Xtr)
class USILF(ImportanceWeight):
def __init__(self, alpha=0.):
self.alpha = alpha
def weights(self, Xtr, ytr, Xte):
dense_ratio_obj = densratio(Xtr, Xte, alpha=self.alpha, verbose=False)
return dense_ratio_obj.compute_density_ratio(Xtr)
class LogReg(ImportanceWeight):
def __init__(self):
pass
def weights(self, Xtr, ytr, Xte):
# check "Direct Density Ratio Estimation for
# Large-scale Covariate Shift Adaptation", Eq.28
if issparse(Xtr):
X = vstack([Xtr, Xte])
else:
X = np.concatenate([Xtr, Xte])
y = [0]*len(Xtr) + [1]*len(Xte)
logreg = GridSearchCV(
LogisticRegression(),
param_grid={'C':np.logspace(-3,3,7), 'class_weight': ['balanced', None]},
n_jobs=-1
)
logreg.fit(X, y)
prob_train = logreg.predict_proba(Xtr)[:,0]
prob_test = logreg.predict_proba(Xtr)[:,1]
prior_train = len(Xtr)
prior_test = len(Xte)
w = (prior_train/prior_test)*(prob_test/prob_train)
return w
class MostTest(ImportanceWeight):
def __init__(self):
pass
def weights(self, Xtr, ytr, Xte):
# check "Direct Density Ratio Estimation for
# Large-scale Covariate Shift Adaptation", Eq.28
if issparse(Xtr):
X = vstack([Xtr, Xte])
else:
X = np.concatenate([Xtr, Xte])
y = [0]*len(Xtr) + [1]*len(Xte)
logreg = GridSearchCV(
LogisticRegression(),
param_grid={'C':np.logspace(-3,3,7), 'class_weight': ['balanced', None]},
n_jobs=-1
)
# logreg = LogisticRegression()
# logreg.fit(X, y)
# prob_test = logreg.predict_proba(Xtr)[:,1]
prob_test = cross_val_predict(logreg, X, y, n_jobs=-1, method="predict_proba")[:len(Xtr),1]
return prob_test
class Random(ImportanceWeight):
def __init__(self):
pass
def weights(self, Xtr, ytr, Xte):
return np.random.rand(len(Xtr))
class MostSimilarK(ImportanceWeight):
# retains the training documents that are most similar in average to the k closest test points
def __init__(self, k):
self.k = k
def weights(self, Xtr, ytr, Xte):
distances = cdist(Xtr, Xte)
min_dist = np.min(distances)
max_dist = np.max(distances)
distances = (distances-min_dist)/(max_dist-min_dist)
similarities = 1 / (1+distances)
top_k_sim = np.sort(similarities, axis=1)[:,-self.k:]
ave_sim = np.mean(top_k_sim, axis=1)
return ave_sim
class MostSimilarTest(ImportanceWeight):
# retains the training documents that are the most similar to one test document
# i.e., for each test point, selects the K most similar train instances
def __init__(self, k=1):
self.k = k
def weights(self, Xtr, ytr, Xte):
distances = cdist(Xtr, Xte)
most_similar_idx = np.argsort(distances, axis=0)[:self.k, :].flatten()
weights = np.zeros(shape=Xtr.shape[0])
weights[most_similar_idx] = 1
return weights
# --------------------------------------------------------------------------------------------
# Quantification Methods that rely on Importance Weight for reweighting the training instances
# --------------------------------------------------------------------------------------------
class TransductiveQuantifier(BaseQuantifier):
def fit(self, data: LabelledCollection):
self.training_ = data
return self
@property
def training(self):
return self.training_
class ReweightingAggregative(TransductiveQuantifier):
def __init__(self, classifier, weighter: ImportanceWeight, quantif_method=CC):
self.classifier = classifier
self.weighter = weighter
self.quantif_method = quantif_method
def quantify(self, instances):
# time_weight = 2.95340 time_train = 0.00619
w = self.weighter.weights(*self.training.Xy, instances)
self.classifier.fit(*self.training.Xy, sample_weight=w)
quantifier = self.quantif_method(self.classifier).fit(self.training, fit_classifier=False)
return quantifier.quantify(instances)
# --------------------------------------------------------------------------------------------
# Quantification Methods that rely on Importance Weight for selecting a validation partition
# --------------------------------------------------------------------------------------------
class SelectorQuantifiersTrainVal(TransductiveQuantifier):
def __init__(self, classifier, weighter: ImportanceWeight, quantif_method=ACC, val_split=0.4, only_positives=False):
self.classifier = classifier
self.weighter = weighter
self.quantif_method = quantif_method
self.val_split = val_split
self.only_positives = only_positives
def quantify(self, instances):
w = self.weighter.weights(*self.training.Xy, instances)
train, val = self.select_from_weights(w, self.training, self.val_split, self.only_positives)
_internal_plot(train, val, instances)
# print('\ttraining size', len(train), '\tval size', len(val))
quantifier = self.quantif_method(self.classifier).fit(train, val_split=val)
return quantifier.quantify(instances)
def select_from_weights(self, w, data: LabelledCollection, val_prop=0.4, only_positives=False):
order = np.argsort(w)
if only_positives:
val_prop = np.mean(w > 0)
split_point = int(len(w) * val_prop)
different_idx, similar_idx = order[:-split_point], order[-split_point:]
different, similar = data.sampling_from_index(different_idx), data.sampling_from_index(similar_idx)
# return different, similar
train, val = similar.split_stratified(0.6)
return train, val
class SelectorQuantifiersTrain(TransductiveQuantifier):
def __init__(self, classifier, weighter: ImportanceWeight, quantif_method=ACC, only_positives=False):
self.classifier = classifier
self.weighter = weighter
self.quantif_method = quantif_method
self.only_positives = only_positives
def quantify(self, instances):
w = self.weighter.weights(*self.training.Xy, instances)
train = self.select_from_weights(w, self.training, select_prop=None, only_positives=self.only_positives)
# _internal_plot(train, None, instances)
# print('\ttraining size', len(train))
quantifier = self.quantif_method(self.classifier).fit(train)
return quantifier.quantify(instances)
def select_from_weights(self, w, data: LabelledCollection, select_prop=0.5, only_positives=False):
order = np.argsort(w)
if only_positives:
select_prop = np.mean(w > 0)
split_point = int(len(w) * select_prop)
different_idx, similar_idx = order[:-split_point], order[-split_point:]
different, similar = data.sampling_from_index(different_idx), data.sampling_from_index(similar_idx)
return similar
if __name__ == '__main__':
qp.environ['SAMPLE_SIZE'] = 500
dA_l0 = gaussian(mean=[0,0], label=0, size=5000)
dA_l1 = gaussian(mean=[1,0], label=1, size=5000)
dB_l0 = gaussian(mean=[0,1], label=0, size=5000)
dB_l1 = gaussian(mean=[1,1], label=1, size=5000)
dA = LabelledCollection.join(dA_l0, dA_l1)
dB = LabelledCollection.join(dB_l0, dB_l1)
dA_train, dA_test = dA.split_stratified(0.5, random_state=0)
dB_train, dB_test = dB.split_stratified(0.5, random_state=0)
train = LabelledCollection.join(dA_train, dB_train)
plot(train, 'train.png')
def lr():
return LogisticRegression()
# EMQ.MAX_ITER*=10
# val_split = 0.5
k_sim = 10
Q=ACC
methods = [
('MLPE', MLPE()),
('CC', CC(lr())),
('PCC', PCC(lr())),
('ACC', ACC(lr())),
('PACC', PACC(lr())),
('HDy', HDy(lr())),
('EMQ', EMQ(lr())),
('GridQ', GridQuantifier2(classifier=lr())),
# ('GridQ', GridQuantifier(Indexer(binned_indexer(train.X, nbins_by_dim=2)), cell_quantifier=Q(lr()))),
# ('GridQ', GridQuantifier(Indexer(binned_indexer(train.X, nbins_by_dim=4)), cell_quantifier=Q(lr()))),
# ('GridQ', GridQuantifier(Indexer(binned_indexer(train.X, nbins_by_dim=6)), cell_quantifier=Q(lr()))),
# ('GridQ', GridQuantifier(Indexer(binned_indexer(train.X, nbins_by_dim=8)), cell_quantifier=Q(lr()))),
# ('GridQ', GridQuantifier(Indexer(binned_indexer(train.X, nbins_by_dim=10)), cell_quantifier=Q(lr()))),
# ('GridQ', GridQuantifier(Indexer(binned_indexer(train.X, nbins_by_dim=20)), cell_quantifier=Q(lr()))),
# ('kSim-ACC', SelectorQuantifiers(lr(), MostSimilar(k_sim), ACC, val_split=val_split)),
# ('kSim-PACC', SelectorQuantifiers(lr(), MostSimilar(k_sim), PACC, val_split=val_split)),
# ('kSim-HDy', SelectorQuantifiers(lr(), MostSimilar(k_sim), HDy, val_split=val_split)),
# ('Sel-CC', SelectorQuantifiersTrain(lr(), MostSimilarTest(k=k_sim), CC, only_positives=True)),
# ('Sel-PCC', SelectorQuantifiersTrain(lr(), MostSimilarTest(k=k_sim), PCC, only_positives=True)),
# ('Sel-ACC', SelectorQuantifiersTrainVal(lr(), MostSimilarTest(k=k_sim), ACC, only_positives=True)),
# ('Sel-PACC', SelectorQuantifiersTrainVal(lr(), MostSimilarTest(k=k_sim), PACC, only_positives=True)),
# ('Sel-HDy', SelectorQuantifiersTrainVal(lr(), MostSimilarTest(k=k_sim), HDy, only_positives=True)),
# ('Sel-EMQ', SelectorQuantifiersTrain(lr(), MostSimilarTest(k=k_sim), EMQ, only_positives=True)),
# ('Sel-EMQ', SelectorQuantifiersTrainVal(lr(), USILF(), PACC, only_positives=False)),
# ('Sel-PACC', SelectorQuantifiers(lr(), MostTest(), PACC)),
# ('Sel-HDy', SelectorQuantifiers(lr(), MostTest(), HDy)),
# ('LogReg-CC', ReweightingAggregative(lr(), LogReg(), CC)),
# ('LogReg-PCC', ReweightingAggregative(lr(), LogReg(), PCC)),
# ('LogReg-EMQ', ReweightingAggregative(lr(), LogReg(), EMQ)),
# ('KLIEP-CC', ReweightingAggregative(lr(), KLIEP(), CC)),
# ('KLIEP-PCC', ReweightingAggregative(lr(), KLIEP(), PCC)),
# ('KLIEP-EMQ', ReweightingAggregative(lr(), KLIEP(), EMQ)),
# ('SILF-CC', ReweightingAggregative(lr(), USILF(), CC)),
# ('SILF-PCC', ReweightingAggregative(lr(), USILF(), PCC)),
# ('SILF-EMQ', ReweightingAggregative(lr(), USILF(), EMQ))
]
for name, model in methods:
with qp.util.temp_seed(5):
# print('original training size', len(train))
model.fit(train)
prot = CovPriorShift([dA_test, dB_test], repeats=1 if plottting else 150)
# prot = UPP(dA_test+dB_test, repeats=1 if plottting else 150)
mae = qp.evaluation.evaluate(model, protocol=prot, error_metric='mae')
print(f'{name}: {mae = :.4f}')
# mrae = qp.evaluation.evaluate(model, protocol=prot, error_metric='mrae')
# print(f'{name}: {mrae = :.4f}')
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