def train(self, training_data, config, **kwargs):
# type: (TrainingData, RasaNLUConfig, **Any) -> None
"""Train the intent classifier on a data set.
:param num_threads: number of threads used during training time"""
from sklearn.model_selection import GridSearchCV
from sklearn.svm import SVC
import numpy as np
labels = [e.get("intent") for e in training_data.intent_examples]
if len(set(labels)) < 2:
logger.warn("Can not train an intent classifier. Need at least 2 different classes. " +
"Skipping training of intent classifier.")
else:
y = self.transform_labels_str2num(labels)
X = np.stack([example.get("text_features") for example in training_data.intent_examples])
sklearn_config = config.get("intent_classifier_sklearn")
C = sklearn_config.get("C", [1, 2, 5, 10, 20, 100])
kernel = sklearn_config.get("kernel", "linear")
# dirty str fix because sklearn is expecting str not instance of basestr...
tuned_parameters = [{"C": C, "kernel": [str(kernel)]}]
cv_splits = max(2, min(MAX_CV_FOLDS, np.min(np.bincount(y)) // 5)) # aim for 5 examples in each fold
self.clf = GridSearchCV(SVC(C=1, probability=True, class_weight='balanced'),
param_grid=tuned_parameters, n_jobs=config["num_threads"],
cv=cv_splits, scoring='f1_weighted', verbose=1)
self.clf.fit(X, y)
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