from skmultiflow.meta import MultiOutputLearner
from skmultiflow.trees import LabelCombinationHoeffdingTreeClassifier, HoeffdingTreeClassifier
from skmultiflow.metrics import hamming_score
# Setting up a data stream
stream = MultilabelGenerator(random_state=1, n_samples=200,
n_targets=5, n_features=10)
# Setup Label Combination Hoeffding Tree classifier
lc_ht = LabelCombinationHoeffdingTreeClassifier(n_labels=stream.n_targets)
# Setup variables to control loop and track performance
n_samples = 0
max_samples = 200
true_labels = []
predicts = []
# Train the estimator with the samples provided by the data stream
while n_samples < max_samples and stream.has_more_samples():
X, y = stream.next_sample()
y_pred = lc_ht.predict(X)
lc_ht.partial_fit(X, y, classes=stream.target_values)
predicts.extend(y_pred)
true_labels.extend(y)
n_samples += 1
# Display results
perf = hamming_score(true_labels, predicts)
print('{} samples analyzed.'.format(n_samples))
print('Label Combination Hoeffding Tree Hamming score: ' + str(perf))
from skmultiflow.meta.multi_output_learner import MultiOutputLearner
from skmultiflow.trees import HoeffdingTreeClassifier
from skmultiflow.data.file_stream import FileStream
from sklearn.linear_model import Perceptron
from skmultiflow.metrics import hamming_score
# Setup the file stream
stream = MultilabelGenerator(random_state=1, n_samples=200,
n_targets=5, n_features=10)
ht = HoeffdingTreeClassifier()
br = MultiOutputLearner(ht)
# Setup the pipeline
# Pre training the classifier with 150 samples
X, y = stream.next_sample(150)
br.partial_fit(X, y, classes=stream.target_values)
# Keeping track of sample count, true labels and predictions to later
# compute the classifier's hamming score
count = 0
true_labels = []
predicts = []
while stream.has_more_samples():
X, y = stream.next_sample()
p = br.predict(X)
br.partial_fit(X, y)
predicts.extend(p)
true_labels.extend(y)
count += 1
perf = hamming_score(true_labels, predicts)
print('Total samples analyzed: ' + str(count))
print("The classifier's static Hamming score : " + str(perf))