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_d1_1_char_vocab():
global tiny_corpus
c2i, i2c = vocab.build_vocab(tiny_corpus)
# are BOS_SYM and EOS_SYM in there?
assert_in(vocab.BOS_SYM, c2i)
assert_in(vocab.EOS_SYM, c2i)
# does it map things correctly?
t_idx = c2i["T"]
eq_(i2c[t_idx], "T")
# are they the same length?
eq_(len(c2i), len(i2c))
# is it exhaustive?
all_seen = defaultdict(int)
for doc in tiny_corpus:
for c in list(doc):
all_seen[c] = 1
char_count = len(all_seen)
# account for BOS_SYM and EOS_SYM
char_count += 2
eq_(char_count, len(c2i))
def setUp(self):
global bi_text, bi_text_train, bi_text_test, bt_c2i, bt_i2c, bt_l2i, bt_i2l
bi_text = pd.read_csv("../../data/sentences_bilingual.csv")
bt_c2i, bt_i2c = vocab.build_vocab(bi_text.sentence.values)
bt_l2i, bt_i2l = vocab.build_label_vocab(bi_text.lang.values)
bi_text_train, bi_text_test = train_test_split(bi_text, test_size=0.2)
def test_untrained_model(self):
# Adding prefix _ to local variables
# Noticed some variation in the scores every time I run it.
# Don't want to pollute my variables with globals when running all tests
_bi_text = pd.read_csv("../../data/sentences_bilingual.csv")
_bi_text_train, _bi_text_test = train_test_split(_bi_text,
test_size=0.2)
_c2i, _i2c = vocab.build_vocab(_bi_text.sentence.values)
_l2i, _i2l = vocab.build_label_vocab(_bi_text.lang.values)
_untrained = lang_id.LangID(input_vocab_n=len(_c2i),
embedding_dims=10,
hidden_dims=20,
lstm_layers=1,
output_class_n=2)
_acc_untrained, _y_hat_untrained = lang_id.eval_acc(
_untrained, _bi_text_test, _c2i, _i2c, _l2i, _i2l)
print(f"Untrained Accuracy: {_acc_untrained}")
pred_label_list = np.asarray(_y_hat_untrained)
pd.options.mode.chained_assignment = None
_bi_text_test["predicted"] = pred_label_list
_bi_text_test = _bi_text_test[["sentence", "lang", "predicted"]]
_bi_text_test.to_csv("../../data/deliverable_2.4_untrained.csv",
index=False)
assert_greater(_acc_untrained, 0.4)
assert_less(_acc_untrained, 0.6)
def test_d1_3_sentence_tensor():
global tiny_corpus
c2i, i2c = vocab.build_vocab(tiny_corpus)
s = "This is a sentence."
s_tens = vocab.sentence_to_tensor(s, c2i)
eq_(s_tens.shape[0], 1)
eq_(s_tens.shape[1], len(s))
eq_(len(s_tens.shape), 2)
# does BOS/EOS padding work?
with_padding = vocab.sentence_to_tensor(s, c2i, True)
eq_(with_padding.shape[1], len(s) + 2)
eq_(with_padding[0, 0].item(), c2i[vocab.BOS_SYM])
def test_d1_2_sentence_vector():
global tiny_corpus
c2i, i2c = vocab.build_vocab(tiny_corpus)
test_sentence = "Here is a sentence."
sent_vec = vocab.sentence_to_vector(test_sentence, c2i)
eq_(len(sent_vec), len(test_sentence))
back_to_chars = [i2c[c] for c in sent_vec]
eq_(test_sentence, ''.join(back_to_chars))
# does the BOS/EOS padding work?
with_padding = vocab.sentence_to_vector(test_sentence, c2i, True)
eq_(len(with_padding), len(test_sentence) + 2)
eq_(with_padding[0], c2i[vocab.BOS_SYM])
eq_(with_padding[-1], c2i[vocab.EOS_SYM])
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)
def test_train_model(self):
# Adding prefix _ to local variables
# Noticed some variation in the scores every time I run it.
# Don't want to pollute my variables with globals when running all tests
_bi_text = pd.read_csv("../../data/sentences_bilingual.csv")
_bi_text_train, _bi_text_test = train_test_split(_bi_text,
test_size=0.2)
_c2i, _i2c = vocab.build_vocab(bi_text.sentence.values)
_l2i, _i2l = vocab.build_label_vocab(bi_text.lang.values)
_li = lang_id.LangID(input_vocab_n=len(_c2i),
embedding_dims=10,
hidden_dims=20,
lstm_layers=1,
output_class_n=2)
_trained_model = lang_id.train_model(model=_li,
n_epochs=1,
training_data=_bi_text_train,
c2i=_c2i,
i2c=_i2c,
l2i=_l2i,
i2l=_i2l)
_trained_model(vocab.sentence_to_tensor("this is a sentence", _c2i))
_trained_model(vocab.sentence_to_tensor("quien estas", _c2i))
_acc, _y_hat = lang_id.eval_acc(_trained_model, _bi_text_test, _c2i,
_i2c, _l2i, _i2l)
print(f"Trained Accuracy: {_acc}")
_untrained = lang_id.LangID(input_vocab_n=len(_c2i),
embedding_dims=10,
hidden_dims=20,
lstm_layers=1,
output_class_n=2)
_acc_untrained, _y_hat_untrained = lang_id.eval_acc(
_untrained, _bi_text_test, _c2i, _i2c, _l2i, _i2l)
print(f"Untrained Accuracy: {_acc_untrained}")
assert_greater(_acc, 0.89)
def test_train_model_embed2_hidden2(self):
# Adding prefix _ to local variables
# Noticed some variation in the scores every time I run it.
# Don't want to pollute my variables with globals when running all tests
_bi_text = pd.read_csv("../../data/sentences_bilingual.csv")
_bi_text_train, _bi_text_test = train_test_split(_bi_text,
test_size=0.2)
_c2i, _i2c = vocab.build_vocab(bi_text.sentence.values)
_l2i, _i2l = vocab.build_label_vocab(bi_text.lang.values)
_li = lang_id.LangID(input_vocab_n=len(_c2i),
embedding_dims=2,
hidden_dims=2,
lstm_layers=1,
output_class_n=2)
_trained_model = lang_id.train_model(model=_li,
n_epochs=1,
training_data=_bi_text_train,
c2i=_c2i,
i2c=_i2c,
l2i=_l2i,
i2l=_i2l)
_trained_model(vocab.sentence_to_tensor("this is a sentence", _c2i))
_trained_model(vocab.sentence_to_tensor("quien estas", _c2i))
_acc, _y_hat = lang_id.eval_acc(_trained_model, _bi_text_test, _c2i,
_i2c, _l2i, _i2l)
print(f"Trained Accuracy: {_acc}")
pred_label_list = np.asarray(_y_hat)
pd.options.mode.chained_assignment = None
pred_label_list = np.asarray(_y_hat)
pd.options.mode.chained_assignment = None
_bi_text_test["predicted"] = pred_label_list
_bi_text_test = _bi_text_test[["sentence", "lang", "predicted"]]
_bi_text_test.to_csv("../../data/deliverable_2.4.csv", index=False)
assert_greater(_acc, 0.89)
def setup_module():
global corpus, c2i, i2c
corpus = [l.strip() for l in open('data/towns_clean.txt')]
c2i, i2c = vocab.build_vocab(corpus)
def setUp(self):
global corpus, c2i, i2c
corpus = [l.strip() for l in open('../../data/towns_clean.txt')]
c2i, i2c = vocab.build_vocab(corpus)
