def test_train_model_untrained_multi(self):
multi_text = pd.read_csv("../../data/sentences_multilingual.csv")
multi_text_train, multi_text_test = train_test_split(multi_text,
test_size=0.2)
multi_text.groupby('lang').count()
multi_text_train.groupby('lang').count()
_c2i, _i2c = vocab.build_vocab(multi_text.sentence.values)
_l2i, _i2l = vocab.build_label_vocab(multi_text.lang.values)
_untrained_multi_class = lang_id.LangID(input_vocab_n=len(_c2i),
embedding_dims=10,
hidden_dims=20,
lstm_layers=1,
output_class_n=5)
acc_untrained_multi, y_hat_untrained_multi = lang_id.eval_acc(
_untrained_multi_class, multi_text_test, _c2i, _i2c, _l2i, _i2l)
print(f"Untrained Multi Accuracy: {acc_untrained_multi}")
from sklearn.metrics import classification_report, confusion_matrix
y_multi = multi_text_test.lang.values
print(classification_report(y_multi, y_hat_untrained_multi))
cm = confusion_matrix(y_multi, y_hat_untrained_multi)
cm
lang_id.pretty_conf_matrix(cm, ['deu', 'eng', 'fra', 'ita', 'spa'])
assert_greater(acc_untrained_multi, 0.4)
assert_less(acc_untrained_multi, 0.6)
def test_train_model_multi_embed20_hidden40(self):
multi_text = pd.read_csv("../../data/sentences_multilingual.csv")
multi_text_train, multi_text_test = train_test_split(multi_text,
test_size=0.2)
multi_text.groupby('lang').count()
multi_text_train.groupby('lang').count()
_c2i, _i2c = vocab.build_vocab(multi_text.sentence.values)
_l2i, _i2l = vocab.build_label_vocab(multi_text.lang.values)
multi_class = lang_id.LangID(input_vocab_n=len(_c2i),
embedding_dims=20,
hidden_dims=40,
lstm_layers=1,
output_class_n=5)
lang_id.train_model(model=multi_class,
n_epochs=1,
training_data=multi_text_train,
c2i=_c2i,
i2c=_i2c,
l2i=_l2i,
i2l=_i2l)
print("done")
acc_multi, y_hat_multi = lang_id.eval_acc(multi_class, multi_text_test,
_c2i, _i2c, _l2i, _i2l)
# Jupyter reported Accuracy: 0.6954
# Run 1: Accuracy: 0.6954
print(f"Accuracy: {acc_multi}")
from sklearn.metrics import classification_report, confusion_matrix
y_multi = multi_text_test.lang.values
print(classification_report(y_multi, y_hat_multi))
cm = confusion_matrix(y_multi, y_hat_multi)
cm
#reload(lang_id);
lang_id.pretty_conf_matrix(cm, ['deu', 'eng', 'fra', 'ita', 'spa'])
assert_greater(acc_multi, 0.60)