def test_iwe_kernel_mean_matching():
"""Test for estimating through kernel mean matching."""
X = rnd.randn(10, 2)
Z = rnd.randn(10, 2) + 1
clf = ImportanceWeightedClassifier()
iw = clf.iwe_kernel_mean_matching(X, Z)
assert np.all(iw >= 0)
def test_iwe_kernel_densities():
"""Test for estimating through kernel density estimation."""
X = rnd.randn(10, 2)
Z = rnd.randn(10, 2) + 1
clf = ImportanceWeightedClassifier()
iw = clf.iwe_kernel_densities(X, Z)
assert np.all(iw >= 0)
def test_iwe_nearest_neighbours():
"""Test for estimating through nearest neighbours."""
X = rnd.randn(10, 2)
Z = rnd.randn(10, 2) + 1
clf = ImportanceWeightedClassifier()
iw = clf.iwe_nearest_neighbours(X, Z)
assert np.all(iw >= 0)
def test_iwe_logistic_discrimination():
"""Test for estimating through logistic classifier."""
X = rnd.randn(10, 2)
Z = rnd.randn(10, 2) + 1
clf = ImportanceWeightedClassifier()
iw = clf.iwe_logistic_discrimination(X, Z)
assert np.all(iw >= 0)
def test_iwe_ratio_Gaussians():
"""Test for estimating ratio of Gaussians."""
X = rnd.randn(10, 2)
Z = rnd.randn(10, 2) + 1
clf = ImportanceWeightedClassifier()
iw = clf.iwe_ratio_gaussians(X, Z)
assert np.all(iw >= 0)
def test_fit():
"""Test for fitting the model."""
X = rnd.randn(10, 2)
y = np.hstack((-np.ones((5,)), np.ones((5,))))
Z = rnd.randn(10, 2) + 1
clf = ImportanceWeightedClassifier()
clf.fit(X, y, Z)
assert clf.is_trained
def test_predict():
"""Test for making predictions."""
X = rnd.randn(10, 2)
y = np.hstack((-np.ones((5,)), np.ones((5,))))
Z = rnd.randn(10, 2) + 1
clf = ImportanceWeightedClassifier()
clf.fit(X, y, Z)
u_pred = clf.predict(Z)
labels = np.unique(y)
assert len(np.setdiff1d(np.unique(u_pred), labels)) == 0
def test_regularization():
"""Test for fitting the model."""
X = rnd.randn(10, 2)
y = np.hstack((-np.ones((5, )), np.ones((5, ))))
Z = rnd.randn(10, 2) + 1
clf = ImportanceWeightedClassifier(loss_function='lr',
l2_regularization=None)
assert isinstance(clf.clf, LogisticRegressionCV)
clf = ImportanceWeightedClassifier(loss_function='lr',
l2_regularization=1.0)
assert isinstance(clf.clf, LogisticRegression)
def run_6():
X = np.random.randn(10, 2)
y = np.vstack((-np.ones((5, )), np.ones((5, ))))
Z = np.random.randn(10, 2)
from libtlda.iw import ImportanceWeightedClassifier
clf = ImportanceWeightedClassifier(loss='quadratic', iwe='kmm')
clf.fit(X, y, Z)
u_pred = clf.predict(Z)
print(u_pred)
def main(source, target, model, target_train_ratio, random_state):
params = {
'source': source,
'target': target,
'target_train_ratio': target_train_ratio,
'max_features': 5000,
'random_state': random_state
}
params['partition'] = 'tr'
tr_X, tr_y = get_data(AmazonDatasetCombined(**params))
params['partition'] = 'te'
te_X, te_y = get_data(AmazonDatasetCombined(**params))
tr_y = tr_y.reshape(-1)
te_y = te_y.reshape(-1)
if model == 'lr':
C = 0.2
clf = LogisticRegression(solver='lbfgs', max_iter=1000, C=C)
clf.fit(tr_X, tr_y)
elif model == 'svm':
C = 0.2
clf = LinearSVC(C=C)
clf.fit(tr_X, tr_y)
elif model == 'kmm':
clf = ImportanceWeightedClassifier(iwe='kmm')
clf.fit(tr_X, tr_y, te_X)
elif model == 'suba-lr':
clf = SubspaceAlignedClassifier(loss='logistic')
clf.fit(tr_X, tr_y, te_X)
elif model == 'suba-hi':
clf = SubspaceAlignedClassifier(loss='hinge')
clf.fit(tr_X, tr_y, te_X)
elif model == 'tca-lr':
clf = TransferComponentClassifier(loss='logistic')
clf.fit(tr_X, tr_y, te_X)
elif model == 'tca-hi':
clf = TransferComponentClassifier(loss='hinge')
clf.fit(tr_X, tr_y, te_X)
else:
raise Exception('Unknown model called..')
tr_score = accuracy_score(tr_y, clf.predict(tr_X))
te_score = accuracy_score(te_y, clf.predict(te_X))
return tr_score, te_score
def InitTransferClassifier(index,l,iw):
if index==1:
clf = ImportanceWeightedClassifier(loss=l,iwe=iw)
if index==2:
clf = TransferComponentClassifier()
if index==3:
clf=SubspaceAlignedClassifier()
if index==4:
clf=StructuralCorrespondenceClassifier()
if index==5:
clf=RobustBiasAwareClassifier()
if index==6:
clf=FeatureLevelDomainAdaptiveClassifier()
if index==7:
clf=TargetContrastivePessimisticClassifier()
return clf
def model_build(classifier, trian_features, train_labels, test_features):
if classifier == "IW":
# pipe = make_pipeline(StandardScaler(), ImportanceWeightedClassifier(iwe='nn'))
# param_grid = [{}]
# model = GridSearchCV(pipe, param_grid, cv=3)
# model.fit(trian_features, train_labels, test_features)
# print(model.best_params_)
model = ImportanceWeightedClassifier(iwe='kde')
model.fit(preprocessing.scale(trian_features), train_labels,
preprocessing.scale(test_features))
else:
if classifier == "LR":
pipe = make_pipeline(StandardScaler(), LogisticRegression())
param_grid = [{'logisticregression__C': [1, 10, 100]}]
elif classifier == "SVM":
# pipe = make_pipeline(StandardScaler(), LinearSVC(random_state=0, tol=1e-5)) SVC(kernel='linear',probability=True)
pipe = make_pipeline(StandardScaler(),
SVC(kernel='linear', probability=True))
param_grid = [{'svc__C': [0.01, 0.1, 1]}]
elif classifier == "RF":
pipe = make_pipeline(StandardScaler(),
RandomForestClassifier(max_features='sqrt'))
param_grid = {
'randomforestclassifier__n_estimators': range(230, 300, 10),
'randomforestclassifier__max_depth': range(8, 12, 1),
'randomforestclassifier__min_samples_leaf': range(1, 5, 1),
'randomforestclassifier__max_features': range(1, 20, 1)
# 'learning_rate': np.linspace(0.01, 2, 20),
# 'subsample': np.linspace(0.7, 0.9, 20),
# 'colsample_bytree': np.linspace(0.5, 0.98, 10),
# 'min_child_weight': range(1, 9, 1)
}
model = GridSearchCV(pipe, param_grid, cv=3)
model.fit(trian_features, train_labels)
print(model.best_params_)
# save the model
model_file_name = classifier + time.strftime("%m%d-%H%M%S") + ".model"
joblib.dump(filename=model_file_name, value=model)
return model
def apply_ENSEMBLE(trainX, trainY, testX, testY, window, source_pos, target_pos):
classifier_SA_DT = SubspaceAlignedClassifier(loss="dtree")
classifier_SA_LR = SubspaceAlignedClassifier(loss="logistic")
classifier_SA_NB = SubspaceAlignedClassifier(loss="berno")
classifier_TCA_DT = TransferComponentClassifier(loss="dtree")
classifier_TCA_LR = TransferComponentClassifier(loss="logistic")
classifier_TCA_NB = TransferComponentClassifier(loss="berno")
classifier_NN_DT = ImportanceWeightedClassifier(iwe='nn', loss="dtree")
classifier_NN_LR = ImportanceWeightedClassifier(iwe='nn', loss="logistic")
classifier_NN_NB = ImportanceWeightedClassifier(iwe='nn', loss="berno")
classifier_KMM_DT = ImportanceWeightedClassifier(iwe='kmm', loss="dtree")
classifier_KMM_LR = ImportanceWeightedClassifier(iwe='kmm', loss="logistic")
classifier_KMM_NB = ImportanceWeightedClassifier(iwe='kmm', loss="berno")
#
eclf = EnsembleClassifier(clfs=[
#classifier_SA_DT,
#classifier_SA_LR,
#classifier_SA_NB,
#classifier_TCA_DT,
#classifier_TCA_LR,
classifier_TCA_NB,
classifier_NN_DT,
#classifier_NN_LR,
#classifier_NN_NB,
classifier_KMM_DT,
classifier_KMM_LR,
#classifier_KMM_NB
])
eclf.fit(trainX, trainY, testX)
pred = eclf.predict(testX)
acc_ENSEMBLE, acc_ENSEMBLE_INFO = check_accuracy(testY, pred)
#
return pd.DataFrame(
[{
'window': window,
'source_position': source_pos,
'target_position': target_pos,
'acc_ENSEMBLE': acc_ENSEMBLE,
'acc_ENSEMBLE_INFO': acc_ENSEMBLE_INFO,
}]
)
#i = 0
#for pred_label in pred_LR:
# print(pred_label, np.max(prob[i]))
# i+=1
#.predict(testX)
#acc_LR_SA = checkAccuracy(testY, pred)
#print("ACC:", acc_LR_SA);
#
classifier_SA_DT = SubspaceAlignedClassifier(loss="dtree")
classifier_SA_LR = SubspaceAlignedClassifier(loss="logistic")
classifier_SA_NB = SubspaceAlignedClassifier(loss="berno")
classifier_TCA_DT = TransferComponentClassifier(loss="dtree")
classifier_TCA_LR = TransferComponentClassifier(loss="logistic")
classifier_TCA_NB = TransferComponentClassifier(loss="berno")
classifier_NN_DT = ImportanceWeightedClassifier(iwe='nn', loss="dtree")
classifier_NN_LR = ImportanceWeightedClassifier(iwe='nn', loss="logistic")
classifier_NN_NB = ImportanceWeightedClassifier(iwe='nn', loss="berno")
classifier_KMM_DT = ImportanceWeightedClassifier(iwe='kmm', loss="dtree")
classifier_KMM_LR = ImportanceWeightedClassifier(iwe='kmm', loss="logistic")
classifier_KMM_NB = ImportanceWeightedClassifier(iwe='kmm', loss="berno")
#
eclf = EnsembleClassifier(clfs=[
classifier_SA_DT, classifier_SA_LR, classifier_SA_NB, classifier_TCA_LR,
classifier_TCA_DT, classifier_TCA_NB, classifier_NN_DT, classifier_NN_LR,
classifier_NN_NB, classifier_KMM_DT, classifier_KMM_LR, classifier_KMM_NB
],
weights=[1, 1])
eclf.fit(trainX, trainY, testX)
Z = np.concatenate((Z0, Z1), axis=0)
u = np.concatenate((labels[0] * np.ones(
(M0, ), dtype='int'), labels[1] * np.ones((M1, ), dtype='int')),
axis=0)
"""Classifiers"""
# Train a naive logistic regressor
lr = LogisticRegression().fit(X, y)
# Make predictions
pred_naive = lr.predict(Z)
# Select adaptive classifier
if classifier == 'iw':
# Call an importance-weighted classifier
clf = ImportanceWeightedClassifier(iwe='lr', loss='logistic')
elif classifier == 'tca':
# Classifier based on transfer component analysis
clf = TransferComponentClassifier(loss='logistic', mu=1.)
elif classifier == 'suba':
# Classifier based on subspace alignment
clf = SubspaceAlignedClassifier(loss='logistic')
elif classifier == 'scl':
# Classifier based on subspace alignment
clf = StructuralCorrespondenceClassifier(num_pivots=2, num_components=1)
elif classifier == 'rba':
# Robust bias-aware classifier
def test_init():
"""Test for object type."""
clf = ImportanceWeightedClassifier()
assert type(clf) == ImportanceWeightedClassifier
assert not clf.is_trained
import numpy as np from libtlda.iw import ImportanceWeightedClassifier X = np.random.randn(10, 2) # y = np.random.randint(10,) y = np.array([1, 0, 1, 0, 1, 1, 0, 1, 0, 1]) Z = np.random.randn(10, 2) ImportanceWeightedClassifier() clf = ImportanceWeightedClassifier() clf.fit(X, y, Z) print(clf.predict_proba(Z))
clf.fit(X,y,Y)
probas_ = clf.predict(X)
accuracy_org = sum((probas_>0.5)==y)/(1.0*nsamples)
target_probas_ = clf.predict(Y)
#print(classes_test.shape,Y.shape,n_test_samples)
accuracy_targ = sum((target_probas_>0.5)==classes_test)/(1.0*n_test_samples)
print (subject,target_subj,index,l,iw,accuracy_org,accuracy_targ)
elif mode==3: # Evaluation of classifiers using the full training set as test set
y = classes_train
nsamples = y.shape[0]
n_test_samples = classes_test.shape[0]
for index in [1,2,6,7,8,9,10,11,12,13]: #range(1,n_classifiers):
for iw in [0,1,2,3]:
iwe = weighting_functions[iw]
w_clf = ImportanceWeightedClassifier(iwe=iwe)
X = np.asarray(MEG_data_train)
Y = np.asarray(MEG_data_test)
w_clf.fit(X,y,Y)
if iwe == 'lr':
w = w_clf.iwe_logistic_discrimination(X, Y)
elif iwe == 'rg':
w = w_clf.iwe_ratio_gaussians(X, Y)
elif iwe == 'nn':
w = w_clf.iwe_nearest_neighbours(X, Y)
elif iwe == 'kde':
w = w_clf.iwe_kernel_densities(X, Y)
elif iwe == 'kmm':
w = w_clf.iwe_kernel_mean_matching(X, Y)
else:
raise NotImplementedError('Estimator not implemented.')
def build_models(trainX, trainY, testX, testY, source_pos, target_pos, window):
#######################
### SEMI-SUPERVISED ###
########################
# Label Propagation
label_prop_model = LabelPropagation(kernel='knn')
label_prop_model.fit(trainX, trainY)
Y_Pred = label_prop_model.predict(testX)
acc_ss_propagation, acc_ss_propagation_INFO = checkAccuracy(testY, Y_Pred)
# Label Spreading
label_prop_models_spr = LabelSpreading(kernel='knn')
label_prop_models_spr.fit(trainX, trainY)
Y_Pred = label_prop_models_spr.predict(testX)
acc_ss_spreading, acc_ss_spreading_INFO = checkAccuracy(testY, Y_Pred)
########################
#### WITHOUT TL ########
########################
# LogisticRegression
modelLR = LogisticRegression()
modelLR.fit(trainX, trainY)
predLR = modelLR.predict(testX)
accLR, acc_LR_INFO = checkAccuracy(testY, predLR)
# DecisionTreeClassifier
modelDT = tree.DecisionTreeClassifier()
modelDT.fit(trainX, trainY)
predDT = modelDT.predict(testX)
accDT, acc_DT_INFO = checkAccuracy(testY, predDT)
# BernoulliNB
modelNB = BernoulliNB()
modelNB.fit(trainX, trainY)
predND = modelNB.predict(testX)
accNB, acc_NB_INFO = checkAccuracy(testY, predND)
#
print("WITHOUT TL ACC_LR:", accLR, " ACC_DT:", accDT, " ACC_NB:", accNB)
########################
#### WITH TL ########
########################
####################################################
### Kernel Mean Matching (Huang et al., 2006)
###
# Decision Tree
print("\n Kernel Mean Matching (Huang et al., 2006) ")
classifier = ImportanceWeightedClassifier(iwe='kmm', loss="dtree")
classifier.fit(trainX, trainY, testX)
pred_naive = classifier.predict(testX)
acc_DT_KMM, acc_DT_KMM_INFO = checkAccuracy(testY, pred_naive)
print("ACC:", acc_DT_KMM)
# Logistic Regression
classifier = ImportanceWeightedClassifier(iwe='kmm', loss="logistic")
classifier.fit(trainX, trainY, testX)
pred_naive = classifier.predict(testX)
acc_LR_KMM, acc_LR_KMM_INFO = checkAccuracy(testY, pred_naive)
print("ACC:", acc_LR_KMM)
# Naive Bayes Bernoulli
classifier = ImportanceWeightedClassifier(iwe='kmm', loss="berno")
classifier.fit(trainX, trainY, testX)
pred_naive = classifier.predict(testX)
acc_NB_KMM, acc_NB_KMM_INFO = checkAccuracy(testY, pred_naive)
print("ACC:", acc_NB_KMM)
####################################################
### Nearest-neighbour-based weighting (Loog, 2015)
###
# Decision Tree
print("\n Nearest-neighbour-based weighting (Loog, 2015) ")
classifier = ImportanceWeightedClassifier(iwe='nn', loss="dtree")
classifier.fit(trainX, trainY, testX)
pred_naive = classifier.predict(testX)
acc_DT_NN, acc_DT_NN_INFO = checkAccuracy(testY, pred_naive)
print("ACC:", acc_DT_NN)
# Logistic Regression
print("\n Nearest-neighbour-based weighting (Loog, 2015) ")
classifier = ImportanceWeightedClassifier(iwe='nn', loss="logistic")
classifier.fit(trainX, trainY, testX)
pred_naive = classifier.predict(testX)
acc_LR_NN, acc_LR_NN_INFO = checkAccuracy(testY, pred_naive)
print("ACC:", acc_LR_NN)
# Naive Bayes Bernoulli
print("\n Nearest-neighbour-based weighting (Loog, 2015) ")
classifier = ImportanceWeightedClassifier(iwe='nn', loss="berno")
classifier.fit(trainX, trainY, testX)
pred_naive = classifier.predict(testX)
acc_NB_NN, acc_NB_NN_INFO = checkAccuracy(testY, pred_naive)
print("ACC:", acc_NB_NN)
####################################################
### Transfer Component Analysis (Pan et al, 2009)
###
# Decision Tree
print("\n Transfer Component Analysis (Pan et al, 2009)")
classifier = TransferComponentClassifier(loss="dtree", num_components=6)
classifier.fit(trainX, trainY, testX)
pred_naive = classifier.predict(testX)
acc_DT_TCA, acc_DT_TCA_INFO = checkAccuracy(testY, pred_naive)
print("ACC:", acc_DT_TCA)
# Logistic Regression
classifier = TransferComponentClassifier(loss="logistic", num_components=6)
classifier.fit(trainX, trainY, testX)
pred_naive = classifier.predict(testX)
acc_LR_TCA, acc_LR_TCA_INFO = checkAccuracy(testY, pred_naive)
print("ACC:", acc_LR_TCA)
# Naive Bayes Bernoulli
classifier = TransferComponentClassifier(loss="berno", num_components=6)
classifier.fit(trainX, trainY, testX)
pred_naive = classifier.predict(testX)
acc_NB_TCA, acc_NB_TCA_INFO = checkAccuracy(testY, pred_naive)
print("ACC:", acc_NB_TCA)
####################################################
### Subspace Alignment (Fernando et al., 2013)
###
# Decision Tree
print("\n Subspace Alignment (Fernando et al., 2013) ")
classifier = SubspaceAlignedClassifier(loss="dtree")
classifier.fit(trainX, trainY, testX)
pred_naive = classifier.predict(testX)
acc_DT_SA, acc_DT_SA_INFO = checkAccuracy(testY, pred_naive)
print("ACC:", acc_DT_SA)
# Logistic Regression
print("\n Subspace Alignment (Fernando et al., 2013) ")
classifier = SubspaceAlignedClassifier(loss="logistic")
classifier.fit(trainX, trainY, testX)
pred_naive = classifier.predict(testX)
acc_LR_SA, acc_LR_SA_INFO = checkAccuracy(testY, pred_naive)
print("ACC:", acc_LR_SA)
# Naive Bayes Bernoulli
print("\n Subspace Alignment (Fernando et al., 2013) ")
classifier = SubspaceAlignedClassifier(loss="berno")
classifier.fit(trainX, trainY, testX)
pred_naive = classifier.predict(testX)
acc_NB_SA, acc_NB_SA_INFO = checkAccuracy(testY, pred_naive)
print("ACC:", acc_NB_SA)
#################################
############# ENSEMBLE ##########
#################################
classifier_SA_DT = SubspaceAlignedClassifier(loss="dtree")
classifier_SA_LR = SubspaceAlignedClassifier(loss="logistic")
classifier_SA_NB = SubspaceAlignedClassifier(loss="berno")
classifier_TCA_DT = TransferComponentClassifier(loss="dtree")
classifier_TCA_LR = TransferComponentClassifier(loss="logistic")
classifier_TCA_NB = TransferComponentClassifier(loss="berno")
classifier_NN_DT = ImportanceWeightedClassifier(iwe='nn', loss="dtree")
classifier_NN_LR = ImportanceWeightedClassifier(iwe='nn', loss="logistic")
classifier_NN_NB = ImportanceWeightedClassifier(iwe='nn', loss="berno")
classifier_KMM_DT = ImportanceWeightedClassifier(iwe='kmm', loss="dtree")
classifier_KMM_LR = ImportanceWeightedClassifier(iwe='kmm',
loss="logistic")
classifier_KMM_NB = ImportanceWeightedClassifier(iwe='kmm', loss="berno")
#
eclf = EnsembleClassifier(
clfs=[classifier_TCA_DT, classifier_NN_DT, classifier_KMM_DT])
eclf.fit(trainX, trainY, testX)
pred = eclf.predict_v2(testX)
acc_ENSEMBLE, acc_ENSEMBLE_INFO = checkAccuracy(testY, pred)
########################
#### RETURN ########
########################
return pd.DataFrame([{
'window': window,
'source_position': source_pos,
'target_position': target_pos,
'acc_SS_propagation': acc_ss_propagation,
'acc_SS_propagation_INFO': acc_ss_propagation_INFO,
'acc_SS_spreading': acc_ss_spreading,
'acc_SS_spreading_INFO': acc_ss_spreading_INFO,
'acc_ENSEMBLE': acc_ENSEMBLE,
'acc_LR': accLR,
'acc_LR_INFO': str(acc_LR_INFO),
'acc_DT': accDT,
'acc_DT_INFO': str(acc_DT_INFO),
'acc_NB': accNB,
'acc_NB_INFO': str(acc_NB_INFO),
'acc_LR_KMM': acc_LR_KMM,
'acc_LR_KMM_INFO': str(acc_LR_KMM_INFO),
'acc_LR_NN': acc_LR_NN,
'acc_LR_NN_INFO': str(acc_LR_NN_INFO),
'acc_LR_TCA': acc_LR_TCA,
'acc_LR_TCA_INFO': str(acc_LR_TCA_INFO),
'acc_LR_SA': acc_LR_SA,
'acc_LR_SA_INFO': str(acc_LR_SA_INFO),
'acc_DT_KMM': acc_DT_KMM,
'acc_DT_KMM_INFO': str(acc_DT_KMM_INFO),
'acc_DT_NN': acc_DT_NN,
'acc_DT_NN_INFO': str(acc_DT_NN_INFO),
'acc_DT_TCA': acc_DT_TCA,
'acc_DT_TCA_INFO': str(acc_DT_TCA_INFO),
'acc_DT_SA': acc_DT_SA,
'acc_DT_SA_INFO': str(acc_DT_SA_INFO),
'acc_NB_KMM': acc_NB_KMM,
'acc_NB_KMM_INFO': str(acc_NB_KMM_INFO),
'acc_NB_NN': acc_NB_NN,
'acc_NB_NN_INFO': str(acc_NB_NN_INFO),
'acc_NB_TCA': acc_NB_TCA,
'acc_NB_TCA_INFO': str(acc_NB_TCA_INFO),
'acc_NB_SA': acc_NB_SA,
'acc_NB_SA_INFO': str(acc_NB_SA_INFO)
}])
def apply_KMM(trainX, trainY, testX, testY, window, source_pos, target_pos):
# Decision Tree
print("\n Kernel Mean Matching (Huang et al., 2006) ")
classifier = ImportanceWeightedClassifier(iwe='kmm', loss="dtree")
classifier.fit(trainX, trainY, testX)
pred_naive = classifier.predict(testX)
acc_DT_KMM, acc_DT_KMM_INFO = check_accuracy(testY, pred_naive)
# Logistic Regression
classifier = ImportanceWeightedClassifier(iwe='kmm', loss="logistic")
classifier.fit(trainX, trainY, testX)
pred_naive = classifier.predict(testX)
acc_LR_KMM, acc_LR_KMM_INFO = check_accuracy(testY, pred_naive)
# Naive Bayes Bernoulli
classifier = ImportanceWeightedClassifier(iwe='kmm', loss="berno")
classifier.fit(trainX, trainY, testX)
pred_naive = classifier.predict(testX)
acc_NB_KMM, acc_NB_KMM_INFO = check_accuracy(testY, pred_naive)
#
return pd.DataFrame(
[{
'window': window,
'source_position': source_pos,
'target_position': target_pos,
'acc_LR_KMM': acc_LR_KMM,
'acc_LR_KMM_INFO': str(acc_LR_KMM_INFO),
'acc_DT_KMM': acc_DT_KMM,
'acc_DT_KMM_INFO': str(acc_DT_KMM_INFO),
'acc_NB_KMM': acc_NB_KMM,
'acc_NB_KMM_INFO': str(acc_NB_KMM_INFO),
}]
)
def run_5(n_samples=100):
from libtlda.iw import ImportanceWeightedClassifier
clf = ImportanceWeightedClassifier(loss='quadratic', iwe='kmm')
# X ~ N(0.5, 0.5²)
# Z ~ N(0.0, 0.3²)
x = np.random.normal(0.5, 0.5**2, (n_samples, 1))
z = np.random.normal(0, 0.3**2, (n_samples, 1))
x_noise = np.random.normal(0, 0.07, (n_samples, 1))
z_noise = np.random.normal(0, 0.03, (n_samples, 1))
def data_func(var):
return var**3 - var
y = data_func(x)
y = np.array(y)
y = y.ravel()
# + bruit
X = x + x_noise
Z = z + z_noise
# distribution différente à approximer avec une distrib initiale
y_bis = data_func(z)
y_bis = np.array(y_bis)
y_bis = y_bis.ravel()
print(X.shape)
print(y.shape)
print(Z.shape)
print(y_bis.shape)
clf.fit(X, y, Z)
preds = clf.predict(Z)
print(np.linalg.norm(preds - Z))
from sklearn.linear_model import LinearRegression
clf_linear = LinearRegression()
clf_linear.fit(Z, y_bis)
true_coefs = clf_linear.coef_
# print(clf.get_weights())
print(preds)
# plot facilities
x_range = np.linspace(-0.4, 1.2, 100)
kmm_line = x_range * preds
true_line = x_range * true_coefs
plt.axis([-0.4, 1.2, -0.5, 1])
plt.scatter(X, y, label='X points', color='blue', marker='o')
plt.plot(x_range, data_func(x_range), label='X distribution', color='blue')
plt.scatter(Z, y_bis, label='Z points', color='red', marker='+')
plt.plot(x_range, kmm_line, label='Z kmm regression line', color='red')
plt.plot(x_range, true_line, label='Z OLS line', color='black')
plt.legend()
plt.show()
"""
trn_index = tweets_r['created_date'].isin(days[past]).values.tolist()
# Find all tweets from today
tst_index = (tweets_r['created_date'] == days[d]).values.tolist()
# Split out training data
trn_X = X[trn_index, :]
trn_Y = Y[trn_index]
# Split out test data
tst_X = X[tst_index, :]
tst_Y = Y[tst_index]
# Define classifiers
clf_n = linear_model.LogisticRegression(C=0.1)
clf_a = ImportanceWeightedClassifier(loss='logistic', l2=0.1)
# Train classifier on data from current and previous days
clf_n.fit(trn_X, trn_Y)
clf_a.fit(trn_X, trn_Y, tst_X)
# Make predictions
preds_n = clf_n.predict(tst_X)
preds_a = clf_a.predict(tst_X)
# Test on data from current day and store
perf_n.append(np.mean(preds_n != tst_Y))
perf_a.append(np.mean(preds_a != tst_Y))
# Store day and rumour
days_array.append(days[d])
def apply_NN(trainX, trainY, testX, testY, window, source_pos, target_pos):
# Decision Tree
print("\n Nearest-neighbour-based weighting (Loog, 2015) ")
classifier = ImportanceWeightedClassifier(iwe='nn', loss="dtree")
classifier.fit(trainX, trainY, testX)
pred_naive = classifier.predict(testX)
acc_DT_NN, acc_DT_NN_INFO = check_accuracy(testY, pred_naive)
# Logistic Regression
print("\n Nearest-neighbour-based weighting (Loog, 2015) ")
classifier = ImportanceWeightedClassifier(iwe='nn', loss="logistic")
classifier.fit(trainX, trainY, testX)
pred_naive = classifier.predict(testX)
acc_LR_NN, acc_LR_NN_INFO = check_accuracy(testY, pred_naive)
# Naive Bayes Bernoulli
print("\n Nearest-neighbour-based weighting (Loog, 2015) ")
classifier = ImportanceWeightedClassifier(iwe='nn', loss="berno")
classifier.fit(trainX, trainY, testX)
pred_naive = classifier.predict(testX)
acc_NB_NN, acc_NB_NN_INFO = check_accuracy(testY, pred_naive)
#
return pd.DataFrame(
[{
'window': window,
'source_position': source_pos,
'target_position': target_pos,
'acc_LR_NN': acc_LR_NN,
'acc_LR_NN_INFO': str(acc_LR_NN_INFO),
'acc_DT_NN': acc_DT_NN,
'acc_DT_NN_INFO': str(acc_DT_NN_INFO),
'acc_NB_NN': acc_NB_NN,
'acc_NB_NN_INFO': str(acc_NB_NN_INFO),
}]
)
