def main(dataset, model):
Sentenca = dataset.iloc[:, 1]
Intencoes = dataset.iloc[:, 0]
cleaner = Cleaner()
Sentenca_cleaned = [cleaner.clean_text(x) for x in Sentenca]
vectorizer = CountVectorizer()
Sentenca_counts = vectorizer.fit_transform(Sentenca_cleaned)
model.fit(Sentenca_counts, Intencoes)
print("Digite um comando:")
nova_sentenca = input()
nova_sentenca_clean = cleaner.clean_text(nova_sentenca)
#nova_sentenca = "liga a luz"
counts_da_nova_sentenca = vectorizer.transform(
[cleaner.clean_text(nova_sentenca_clean)])
interpretacao_sentenca(counts_da_nova_sentenca, nova_sentenca)
class DomesticViolenceClassifier:
def __init__(self):
self.cleaner = Cleaner()
self.maxlen = 900
self.tokenizer = None
self.parent_path = Path(__file__).parent.parent
with open(
self.parent_path /
'data/neural_network_config/tokenizer.pickle', 'rb') as handle:
self.tokenizer = pickle.load(handle)
# load json and create model
json_model_keras = open(
self.parent_path / 'data/neural_network_config/model.json', 'r')
loaded_model_json = json_model_keras.read()
json_model_keras.close()
self.loaded_model = model_from_json(loaded_model_json)
# load weights into new model
self.loaded_model.load_weights(self.parent_path /
"data/neural_network_config/model.h5")
# evaluate loaded model on test data
self.loaded_model.compile(optimizer='adam',
loss='mean_squared_error',
metrics=['mae', 'accuracy'])
#returns the sentiment of a text string
def domestic_violence_subject_probability(self, text: str):
x = self.tokenizer.texts_to_sequences([self.cleaner.clean_text(text)])
x = pad_sequences(x, padding='post', maxlen=self.maxlen)
y_new = self.loaded_model.predict(x)
return y_new[0][0]
def performance_modelo(dataset, model):
Sentenca = dataset.iloc[:, 1]
Intencoes = dataset.iloc[:, 0]
cleaner = Cleaner()
Sentenca_cleaned = [cleaner.clean_text(x) for x in Sentenca]
Sentenca_counts = CountVectorizer().fit_transform(Sentenca_cleaned)
X_train, X_test, y_train, y_test = train_test_split(Sentenca_counts,
Intencoes,
test_size=0.15,
random_state=1)
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
accuracy_score_model = accuracy_score(y_test, y_pred)
return print(f"A acurácia do model é de {accuracy_score_model*100:.1f}%")
