def GridSearchCV_base(classif, dataset_train_x, dataset_train_y):
rangefloat = [round(x * 0.1, 1) for x in range(1, 11)]
parameters = [
{
'base_classifier': [GaussianNB()],
#'labelset_size':
},
{
'base_classifier': [MultinomialNB()],
'base_classifier__alpha':
rangefloat, #for smoothing {Additive smoothing parameter NB}
},
{
'base_classifier': [SVC()],
'base_classifier__kernel': ['rbf', 'linear', 'sigmoid'],
},
]
classifier = GridSearchCV(RakelD(),
parameters,
scoring=make_scorer(metrics.hamming_loss,
greater_is_better=False),
n_jobs=3)
classifier.fit(dataset_train_x, dataset_train_y)
return classifier.best_params_
def rakeld_ensemble(vec, label):
problem_transform_classifier = LabelPowerset(classifier=LinearSVC(),
require_dense=[False, True])
classifier = RakelD(classifier=problem_transform_classifier,
labelset_size=5)
classifier.fit(vec, label)
return classifier
def RAkELd(dataset_train_x, dataset_train_y, dataset_test_x, dataset_test_y,
base_clasif, num_labels):
classifier = RakelD(base_classifier=base_clasif, labelset_size=num_labels)
classifier.fit(dataset_train_x, dataset_train_y)
predictions = classifier.predict(dataset_test_x)
Metrics_Accuracy("RAkELd", predictions, dataset_test_y)
def build_Rake(X_train, y_train, X_test, y_test):
classifier = RakelD(base_classifier=GaussianNB(),
base_classifier_require_dense=[True, True],
labelset_size=4)
classifier.fit(X_train, y_train)
prediction = classifier.predict(X_test)
print('Test accuracy is {}'.format(accuracy_score(y_test, prediction)))
def RAkELd(dataset_train_x, dataset_train_y, dataset_test_x, dataset_test_y,
base_clasif, num_labels):
classifier = RakelD(base_classifier=base_clasif, labelset_size=num_labels)
start_time = time.time()
classifier.fit(dataset_train_x, dataset_train_y)
stop_time = time.time()
time_lapsed = stop_time - start_time
predictions = classifier.predict(dataset_test_x)
Metrics_Accuracy("RAkELd", predictions, dataset_test_y)
print("Execution time: {}s".format(time_lapsed))
def run(classifier, train_test_set):
X_train, X_test, y_train, y_test = train_test_set
# init model and fit to train data
rakel = RakelD(base_classifier=classifier)
rakel.fit(X_train, y_train)
# make predictions
y_pred = rakel.predict(X_test)
print('\n--------Rakel with {:}'.format(rakel))
return y_test, y_pred
def rakel_model(X_train, X_test, y_train, y_test, labels, seed):
rakel = Pipeline([
('count_vectorizer', CountVectorizer()),
('tf-idf_log', TfidfTransformer(sublinear_tf=True)),
('rakel',
RakelD(base_classifier=LinearSVC(C=1,
class_weight='balanced',
random_state=seed),
base_classifier_require_dense=[True, True],
labelset_size=3))
])
# train and predict model
start_time = time.time()
rakel.fit(X_train, y_train)
prediction = rakel.predict(X_test)
stop_time = time.time()
# calculate scores
f1 = f1_score(y_test, prediction, average=None)
accuracy = jaccard_score(y_test, prediction, average=None)
return f1, accuracy, stop_time - start_time
def RAkEL_fit(clfs, steps, X_train, y_train, X_test, y_test):
metrics = {}
for key, clf in zip(clfs.keys(), clfs.values()):
acc = []
prec_micro = []
prec_macro = []
hamm_loss = []
f1_micro = []
f1_macro = []
print('Fitting RAkEL with Base Classifier: %s' % key)
for k in steps:
classifier = RakelD(base_classifier=clf, labelset_size=k)
classifier.fit(X_train, y_train)
prediction = classifier.predict(X_test)
acc.append(accuracy_score(y_test, prediction))
prec_micro.append(precision_score(y_test, prediction, average='micro'))
prec_macro.append(precision_score(y_test, prediction, average='macro'))
hamm_loss.append(hamming_loss(y_test, prediction))
f1_micro.append(f1_score(y_test, prediction, average='micro'))
f1_macro.append(f1_score(y_test, prediction, average='macro'))
metrics[key] = [acc, hamm_loss, f1_micro, f1_macro, prec_micro, prec_macro]
return metrics
ft_OT.rename(mapper=lambda x: x + "_OT", axis=1, inplace=True)
X = np.concatenate((ft_FP, ft_OT), axis=1)
scoring_funcs = {
"hamming loss": hamming_func,
"aiming": aiming_func,
"coverage": coverage_func,
"accuracy": accuracy_func,
"absolute true": absolute_true_func,
} # Keep recorded
parameters = {'labelset_size': [2, 3, 4, 5, 6, 7, 8, 9, 10]}
rakeld = GridSearchCV(RakelD(
base_classifier=RandomForestClassifier(),
baseclassifier_require_dense=[True, True],
),
param_grid=parameters,
n_jobs=-1,
cv=loocv,
scoring=scoring_funcs,
verbose=3,
refit="absolute true")
rakeld.fit(X, Y.values)
print(rakeld.best_score_)
mytuple = (rakeld, )
to_save = dump(mytuple, filename="rakeld-rf.joblib")
def test_if_works_with_cross_validation(self):
classifier = RakelD(classifier=self.get_labelpowerset_with_nb(),
labelset_size=3)
self.assertClassifierWorksWithCV(classifier)
def test_if_dense_classification_works_on_dense_base_classifier(self):
classifier = RakelD(classifier=self.get_labelpowerset_with_nb(),
labelset_size=3)
self.assertClassifierWorksWithSparsity(classifier, 'dense')
def class_multi_label(x, Y, model, wekamodelname, value):
# detect is the data classification is a multi-label problem.
num_of_labels = Y.ndim
print("\n\n-----------------------------------------------------------\n")
if (num_of_labels == 1):
print("This is not a multi-label problem!!!!!!")
return model
javapath = "C:\\" "Program Files" "\\Java\\jre1.8.0_251\\bin\\javaw.exe"
myclasspath = download_meka()
print(myclasspath)
try:
while 1:
if (value < 1) or (value > 9):
print("This is a Multi label problem")
print("Please select:")
print("1. For binary relevance")
print("2. For pairwise comparison")
print("3. Calibrated label ranking")
print("4. Chain classifier ")
print("5. PowerSet no pruning ")
print("6. PowerSet with pruning ")
print("7. Random-k Labelsets ")
print("8. Pairwise comparison ")
print("9. Multi Label knn ")
value = input("Please enter a choice:\n")
if value == 1:
print("Applying binary relevance")
#clf=BinaryRelevance(classifier=model,require_dense=[False, True])
if wekamodelname == "nothing":
print("WEKA does not support this classifier")
clf = 0
break
clf = Meka(
meka_classifier="meka.classifiers.multilabel.BR",
weka_classifier=wekamodelname,
meka_classpath=myclasspath,
java_command=javapath # path to java executable
)
break
elif value == 2:
print("Fourclass Pairwise")
if wekamodelname == "nothing":
print("WEKA does not support this classifier")
clf = 0
break
clf = Meka(
meka_classifier="meka.classifiers.multilabel.FW",
weka_classifier=wekamodelname,
meka_classpath=myclasspath,
java_command=javapath # path to java executable
)
break
elif value == 3:
print("Applying calibrated label ranking")
if wekamodelname == "nothing":
print("WEKA does not support this classifier")
clf = 0
break
clf = Meka(
meka_classifier="meka.classifiers.multilabel.MULAN",
weka_classifier=wekamodelname + " -S CLR",
meka_classpath=myclasspath,
java_command=javapath # path to java executable
)
break
elif value == 4:
print("Applying Chain Classifier")
##clf = ClassifierChain(classifier=model,require_dense=[False, True])
if wekamodelname == "nothing":
print("WEKA does not support this classifier")
clf = 0
break
clf = Meka(
meka_classifier="meka.classifiers.multilabel.CC",
weka_classifier=wekamodelname,
meka_classpath=myclasspath,
java_command=javapath # path to java executable
)
break
elif value == 5:
print("Applying powerset NO pruning")
clf = LabelPowerset(classifier=model,
require_dense=[False, True])
break
elif value == 6:
print("Applying powerset with pruning")
if wekamodelname == "nothing":
print("WEKA does not support this classifier")
clf = 0
break
clf = Meka(
meka_classifier="meka.classifiers.multilabel.PS",
weka_classifier=wekamodelname,
meka_classpath=myclasspath,
java_command=javapath # path to java executable
)
break
elif value == 7:
print("Applying Random-k Labelsets")
try:
clf = RakelD(base_classifier=model,
base_classifier_require_dense=[False, True],
labelset_size=4)
except:
print("RakelD exception")
break
elif value == 8:
print("Monte-Carlo Classifier Chains")
if wekamodelname == "nothing":
print("WEKA does not support this classifier")
clf = 0
break
clf = Meka(
meka_classifier="meka.classifiers.multilabel.MCC",
weka_classifier=wekamodelname,
meka_classpath=myclasspath,
java_command=javapath # path to java executable
)
break
elif value == 9:
print("Applying Multilabel k Nearest Neighbours")
try:
clf = MLkNN(k=3)
except:
print("Multilabel k Nearest Neighbours exception")
break
else:
print("Try again!!!!")
except:
print("\nSomething went wrong, but continue\n")
return clf
def Util_ClassifierMethods(dataset_train_x, dataset_train_y, dataset_test_x,
dataset_test_y):
#BR
Util_Title("Binary Relevance")
base_classif = GaussianNB()
BinaryRelevance(dataset_train_x, dataset_train_y, dataset_test_x,
dataset_test_y, base_classif, "GaussianNB")
dict_res = FindBestSVCParams(skpt.BinaryRelevance(), dataset_train_x,
dataset_train_y)
base_classif = SVC(kernel=dict_res['classifier__kernel'],
degree=dict_res['classifier__degree'])
BinaryRelevance(dataset_train_x, dataset_train_y, dataset_test_x,
dataset_test_y, base_classif, "SVC tuned")
dict_res = FindBestMNBParams(skpt.BinaryRelevance(), dataset_train_x,
dataset_train_y)
base_classif = MultinomialNB(alpha=dict_res['classifier__alpha'])
BinaryRelevance(dataset_train_x, dataset_train_y, dataset_test_x,
dataset_test_y, base_classif, "MNB tuned")
#CC
Util_Title("Classifier Chain")
base_classif = GaussianNB()
ClassifierChain(dataset_train_x, dataset_train_y, dataset_test_x,
dataset_test_y, base_classif, "GaussianNB")
dict_res = FindBestSVCParams(skpt.ClassifierChain(), dataset_train_x,
dataset_train_y)
base_classif = SVC(kernel=dict_res['classifier__kernel'],
degree=dict_res['classifier__degree'])
ClassifierChain(dataset_train_x, dataset_train_y, dataset_test_x,
dataset_test_y, base_classif, "SVC tuned")
dict_res = FindBestMNBParams(skpt.ClassifierChain(), dataset_train_x,
dataset_train_y)
base_classif = MultinomialNB(alpha=dict_res['classifier__alpha'])
ClassifierChain(dataset_train_x, dataset_train_y, dataset_test_x,
dataset_test_y, base_classif, "MNB tuned")
#LP
Util_Title("Label Powerset")
base_classif = GaussianNB()
LabelPowerset(dataset_train_x, dataset_train_y, dataset_test_x,
dataset_test_y, base_classif, "GaussianNB")
dict_res = FindBestSVCParams(skpt.LabelPowerset(), dataset_train_x,
dataset_train_y)
base_classif = SVC(kernel=dict_res['classifier__kernel'],
degree=dict_res['classifier__degree'])
LabelPowerset(dataset_train_x, dataset_train_y, dataset_test_x,
dataset_test_y, base_classif, "SVC tuned")
dict_res = FindBestMNBParams(skpt.LabelPowerset(), dataset_train_x,
dataset_train_y)
base_classif = MultinomialNB(alpha=dict_res['classifier__alpha'])
LabelPowerset(dataset_train_x, dataset_train_y, dataset_test_x,
dataset_test_y, base_classif, "MNB tuned")
#MLkNN
Util_Title("MLkNN")
dict_res = FindBestK(skadapt.MLkNN(), dataset_train_x, dataset_train_y)
MLkNN(dataset_train_x, dataset_train_y, dataset_test_x, dataset_test_y,
dict_res['k'], dict_res['s'])
#MLARAM
Util_Title("MLARAM")
dict_res = FindBestVT(dataset_train_x, dataset_train_y)
MLARAM(dataset_train_x, dataset_train_y, dataset_test_x, dataset_test_y,
dict_res['vigilance'], dict_res['threshold'])
#BRkNNa
Util_Title("BRkNNa")
dict_res = FindBestK(skadapt.BRkNNaClassifier(), dataset_train_x,
dataset_train_y)
BRkNNa(dataset_train_x, dataset_train_y, dataset_test_x, dataset_test_y,
dict_res['k'])
#BRkNNb
Util_Title("BRkNNb")
dict_res = FindBestK(skadapt.BRkNNbClassifier(), dataset_train_x,
dataset_train_y)
BRkNNb(dataset_train_x, dataset_train_y, dataset_test_x, dataset_test_y,
dict_res['k'])
#RAkELD
Util_Title("RAkELd")
dict_res = GridSearchCV_baseRakel(RakelD(), dataset_train_x,
dataset_train_y)
RAkELd(dataset_train_x, dataset_train_y, dataset_test_x, dataset_test_y,
dict_res['base_classifier'], dict_res['labelset_size'])
#RAkELo
Util_Title("RAkELo")
dict_res = GridSearchCV_baseRakel(RakelO(), dataset_train_x,
dataset_train_y)
RAkELO(dataset_train_x, dataset_train_y, dataset_test_x, dataset_test_y,
dict_res['base_classifier'], dict_res['labelset_size'],
dict_res['model_count'])
#MLTSVM
Util_Title("MLTSVM")
dict_res = FindCKParam(dataset_train_x, dataset_train_y, dataset_test_x,
dataset_test_y)
TwinMLSVM(dataset_train_x, dataset_train_y, dataset_test_x, dataset_test_y,
dict_res['c_k'], dict_res['sor_omega'])
def get_rakeld_with_nb(self):
return RakelD(base_classifier=GaussianNB(),
base_classifier_require_dense=[True, True],
labelset_size=TEST_LABELSET_SIZE)
classifier = LabelSpacePartitioningClassifier(
problem_transform_classifier,
clusterer) # setup the ensemble metaclassifier
classifier.fit(X_train, t_train)
predictions = classifier.predict(X_test) # all zero using SVC
probabilities = classifier.predict_proba(X_test)
accuracy_score(t_test,
predictions) # 0.029049295774647887, the result is reasonable
mean_squared_error(t_test.toarray(), probabilities.toarray())
# by trying different cluster methods, the walktrap initially is 0.043, greedy 0.029, infomap 0.051
# with a naive test of boosting with some combinations of parameters, forest is better than boosting
# Rakel
base_classifier = RandomForestClassifier()
problem_transform_classifier = LabelPowerset(classifier=base_classifier)
classifier = RakelD(problem_transform_classifier,
labelset_size=3) # setup the ensemble meta-classifier
classifier.fit(X_train, t_train)
predictions = classifier.predict(X_test)
probabilities = classifier.predict_proba(X_test)
accuracy_score(
t_test,
predictions) # 0.0079225352112676055, random partition is not good here
mean_squared_error(t_test.toarray(), probabilities.toarray())
# parameter tuning of space partitioning with clusterer
parameters = {
'classifier':
[LabelPowerset()], # BinaryRelevance performs pretty bad here
'clusterer': [
IGraphLabelCooccurenceClusterer('infomap',
weighted=True,
#Methode 3 : Chaineclassifieur
clf = clf = ClassifierChain(classifier=RandomForestClassifier(max_depth=200),
require_dense=[False, True])
anova_clf = Pipeline([('anova', vare), ('chaine', clf)])
anova_clf.fit(Xtrain, Ytrain)
pred = anova_clf.predict(Xtest)
matrix = multilabel_confusion_matrix(Ytest, pred)
accuracy = accuracy_score(Ytest, pred)
print(accuracy)
#Methode 4 : onevsrest
clf = OneVsRestClassifier(RandomForestClassifier(n_estimators=100))
anova_clf = Pipeline([('anova', vare), ('oneVSrest', clf)])
anova_clf.fit(Xtrain, Ytrain)
pred = anova_clf.predict(Xtest)
matrix = multilabel_confusion_matrix(Ytest, pred)
accuracy = accuracy_score(Ytest, pred)
print(accuracy)
## Methode 5 : Rakel
clf = clf = RakelD(labelset_size=2, base_classifier=RandomForestClassifier())
anova_clf = Pipeline([('anova', vare), ('Rekel', clf)])
anova_clf.fit(Xtrain, Ytrain)
pred = anova_clf.predict(Xtest)
matrix = multilabel_confusion_matrix(Ytest, pred)
accuracy = accuracy_score(Ytest, pred)
print(accuracy)
ft_OT.rename(mapper=lambda x: x + "_OT", axis=1, inplace=True)
X = np.concatenate((ft_FP, ft_OT), axis=1)
scoring_funcs = {
"hamming loss": hamming_func,
"aiming": aiming_func,
"coverage": coverage_func,
"accuracy": accuracy_func,
"absolute true": absolute_true_func,
} # Keep recorded
parameters = {'labelset_size': [2, 3, 4, 5, 6, 7, 8, 9, 10]}
rakeld = GridSearchCV(RakelD(
base_classifier=GaussianNB(),
baseclassifier_require_dense=[True, True],
),
param_grid=parameters,
n_jobs=-1,
cv=loocv,
scoring=scoring_funcs,
verbose=3,
refit="absolute true")
rakeld.fit(X, Y.values)
print(rakeld.best_score_)
mytuple = (rakeld, )
to_save = dump(mytuple, filename="rakeld.joblib")
def pipeline(method, X_train, y_train, scoring, params=None, search_r=True, best=None):
if search_r:
# Random search params
r = np.random.uniform(-2, 2, size=5)
C = np.array(10 ** r)
alpha = np.random.uniform(0, 1, size=5)
params_tree = {'__max_depth': sp.randint(1, 30),
'__max_features': sp.randint(1, X_train.shape[1]),
'__min_samples_split': sp.randint(2, X_train.shape[0] / 3),
'__criterion': ['gini', 'entropy']}
params_lgr = {'__C': C}
params_nb = {'__alpha': alpha}
tree_k, tree_v = list(params_tree.keys()), list(params_tree.values())
lgr_k, lgr_v = list(params_lgr.keys()), list(params_lgr.values())
nb_k, nb_v = list(params_nb.keys()), list(params_nb.values())
else:
params_cc, params_rk, params_bn = params[0], params[1], params[2]
if method == 'CC':
base_str = 'base_estimator'
if search_r:
params_tree, params_lgr, params_nb = redefine(base_str, tree_k, tree_v), \
redefine(base_str, lgr_k, lgr_v), \
redefine(base_str, nb_k, nb_v)
params = [params_lgr, params_tree, params_nb]
else:
params = params_cc
tree_k, tree_v = list(params[1].keys()), list(params[1].values())
lgr_k, lgr_v = list(params[0].keys()), list(params[0].values())
nb_k, nb_v = list(params[2].keys()), list(params[2].values())
params_tree, params_lgr, params_nb = redefine(base_str, tree_k, tree_v), \
redefine(base_str, lgr_k, lgr_v), \
redefine(base_str, nb_k, nb_v)
params = [params_lgr, params_tree, params_nb]
print(colored('Fitting Classifiers Chain pipeline...', 'green'))
classifiers = {
"Logistic Regression": ClassifierChain(LogisticRegression(random_state=0, solver='lbfgs', n_jobs=-1)),
"Decision Tree Classifier": ClassifierChain(DecisionTreeClassifier()),
"MultinomialNB": ClassifierChain(MultinomialNB())}
elif method == 'RAkEL':
base_str = 'base_classifier'
if search_r:
params_tree, params_lgr, params_nb = redefine(base_str, tree_k, tree_v), \
redefine(base_str, lgr_k, lgr_v), \
redefine(base_str, nb_k, nb_v)
params = [params_lgr, params_tree, params_nb]
else:
params = params_rk
tree_k, tree_v = list(params[1].keys()), list(params[1].values())
lgr_k, lgr_v = list(params[0].keys()), list(params[0].values())
nb_k, nb_v = list(params[2].keys()), list(params[2].values())
params_tree, params_lgr, params_nb = redefine(base_str, tree_k, tree_v), \
redefine(base_str, lgr_k, lgr_v), \
redefine(base_str, nb_k, nb_v)
params = [params_lgr, params_tree, params_nb]
print(colored('Fitting RAkEL pipeline...', 'green'))
classifiers = {"Logistic Regression": RakelD(LogisticRegression(random_state=0, solver='lbfgs', n_jobs=-1)),
"Decision Tree Classifier": RakelD(DecisionTreeClassifier(),
labelset_size=5),
"MultinomialNB": RakelD(MultinomialNB(),
labelset_size=5)}
elif method == 'BinaryRelevance':
base_str = 'classifier'
if search_r:
params_tree, params_lgr, params_nb = redefine(base_str, tree_k, tree_v), \
redefine(base_str, lgr_k, lgr_v), \
redefine(base_str, nb_k, nb_v)
params = [params_lgr, params_tree, params_nb]
else:
params = params_bn
tree_k, tree_v = list(params[1].keys()), list(params[1].values())
lgr_k, lgr_v = list(params[0].keys()), list(params[0].values())
nb_k, nb_v = list(params[2].keys()), list(params[2].values())
params_tree, params_lgr, params_nb = redefine(base_str, tree_k, tree_v), \
redefine(base_str, lgr_k, lgr_v), \
redefine(base_str, nb_k, nb_v)
params = [params_lgr, params_tree, params_nb]
print(colored('Fitting BinaryRelevance pipeline...', 'green'))
classifiers = {
"Logistic Regression": BinaryRelevance(LogisticRegression(random_state=0, solver='lbfgs', n_jobs=-1)),
"Decision Tree Classifier": BinaryRelevance(DecisionTreeClassifier()),
"MultinomialNB": BinaryRelevance(MultinomialNB())}
else:
raise ValueError('Invalid method passed. Expected one of: "CC", "RAkEL", "BinaryRelevance", got {} instead'
.format(method))
res = {}
for keys, classifier, par in zip(classifiers.keys(), classifiers.values(), params):
res[keys] = hyperparameters_search(classifier, par, X_train, y_train, best, scoring, keys,
candidates=30, random_search=search_r)
# initialize classifier chains multi-label classifier
from sklearn.metrics import hamming_loss
from sklearn.metrics import f1_score
from sklearn.metrics import jaccard_similarity_score
from sklearn.metrics import precision_score
from sklearn.metrics import recall_score
from sklearn.naive_bayes import GaussianNB
from skmultilearn.ensemble import RakelD
from datetime import timedelta
import time
start = time.time()
classifier = RakelD(base_classifier=GaussianNB(),
base_classifier_require_dense=[True, True],
labelset_size=4)
classifier.fit(x_train, y_train)
predictions = classifier.predict(x_test)
# accuracy
print("Accuracy = ", accuracy_score(y_test, predictions))
print("\n")
print("F1 = ", f1_score(y_test, predictions, average='micro'))
print("\n")
print("Jaccard = ", jaccard_similarity_score(y_test, predictions))
print("\n")
print("Precision = ", precision_score(y_test, predictions, average='micro'))
def get_rakeld_with_svc(self):
return RakelD(base_classifier=SVC(probability=True),
base_classifier_require_dense=[False, True],
labelset_size=TEST_LABELSET_SIZE)