def get_confusion_matrix():
# db = DoubleBayes()
# predicted, actual = db.generate_predictions(weighted=True)
nb = Naive_Bayes()
predicted, actual = nb.generate_predictions()
cnf_matrix = confusion_matrix(actual, predicted)
return cnf_matrix
def post():
if request.is_json:
data = request.get_json()
else:
return jsonify({'request': request.get_data()})
assert (len(data) == 3)
try:
age = int(data['age'])
condition_list = list(data['conditions'])
state = str(data['state'])
except KeyError:
return jsonify(dict())
nb = Naive_Bayes(age, state, condition_list)
return jsonify({'probability': nb.get_probability()})
hmm.fit(data_train)
print(f"Duration of training: {time.time() - start_time}")
# evaluation hmm
# -------------------------------------------------------------------------
# plot confusion matrix, calculate precision, recall, f1-score
hmm.evaluate(data_test)
# show misclassifications
features_test, labels_test = separate_labels_from_features(data_test)
predictions = hmm.predict(features_test)
show_misclassifications(data_test, predictions)
elif model_type == "NB":
# fit naive bayes model
# -------------------------------------------------------------------------
nb = Naive_Bayes()
data_train_featurized = feature_maker.get_pos_features_nltk(
data_train
) if not load_entities else feature_maker.get_ner_features_nltk(data_train)
data_train_featurized = flatten(data_train_featurized)
start_time = time.time()
nb.fit_nltk(data_train_featurized)
print(f"Duration of training: {time.time() - start_time}")
# evaluation naive bayes
# -------------------------------------------------------------------------
data_test_featurized = feature_maker.get_pos_features_nltk(
data_test
) if not load_entities else feature_maker.get_ner_features_nltk(data_test)
def train(instances, algorithm, high_idx, learn_rate, iterate, peg_lambda,
k_val, T, clus_lambda, K, clus_iter):
if (algorithm == "perceptron"):
classifier = Perceptron(instances, high_idx, learn_rate)
#iterate the training
for i in range(iterate):
classifier.train(instances)
return classifier
elif (algorithm == "averaged_perceptron"):
classifier2 = AveragePerceptron(instances, high_idx, learn_rate)
for i in range(iterate):
classifier2.train(instances)
return classifier2
elif (algorithm == "pegasos"):
classifier3 = Pegasos(instances, high_idx, peg_lambda)
for i in range(iterate):
classifier3.train(instances)
return classifier3
elif (algorithm == "margin_perceptron"):
classifier4 = PerceptronMargin(instances, high_idx, learn_rate,
iterate)
for i in range(iterate):
classifier4.train(instances)
return classifier4
elif (algorithm == "knn"):
classifier5 = KNN(instances, k_val, high_idx)
for i in range(iterate):
classifier5.train(instances)
return classifier5
elif (algorithm == "distance_knn"):
classifier6 = Distance_KNN(instances, k_val, high_idx)
for i in range(iterate):
classifier6.train(instances)
return classifier6
elif (algorithm == "adaboost"):
classifier7 = Adaboost(instances, T, high_idx)
for i in range(iterate):
classifier7.train(instances)
return classifier7
elif (algorithm == "lambda_means"):
classifier8 = Lambda_Means2(instances, high_idx, clus_lambda,
clus_iter)
for i in range(iterate):
#print "##################Training", i+1, "out of", iterate,"###############"
classifier8.train(instances)
return classifier8
elif (algorithm == "nb_clustering"):
classifier9 = Naive_Bayes(instances, high_idx, K)
for i in range(iterate):
#print "##################Training", i+1, "out of", iterate,"###############"
classifier9.train(instances)
return classifier9
elif (algorithm == "mc_perceptron"):
classifier10 = MC_Perceptron(instances, high_idx)
for i in range(iterate):
classifier10.train(instances)
return classifier10
else:
return None
