Kaggle competition: Google Landmark Recognition Challenge
<SOS> metric: GoogleGlobalAP Kaggle data source: landmark-recognition-challenge<load_from_csv>
%matplotlib inline
PATH = '.. /input/google-quest-challenge/'
BERT_PATH = '.. /input/bert-base-from-tfhub/bert_en_uncased_L-12_H-768_A-12'
tokenizer = FullTokenizer(BERT_PATH+'/assets/vocab.txt', True)
 MAX_SEQUENCE_LENGTH = 512
df_train = pd.read_csv(PATH+'train.csv')
 df_test = pd.read_csv(PATH+'test.csv')
 df_sub = pd.read_csv(PATH+'sample_submission.csv')
 print('train shape =', df_train.shape)
 print('test shape =', df_test.shape)
 output_categories = list(df_train.columns[11:])
 input_categories = list(df_train.columns[[1,2,5]])
 print('
output categories:
\t', output_categories)
 print('
input categories:
\t', input_categories )<string_transform>
train_d = pd.read_csv(".. /input/train.csv")
 test_d = pd.read_csv(".. /input/test.csv")
 submission = pd.read_csv(".. /input/sample_submission.csv" )
def _get_masks(tokens, max_seq_length):

 if len(tokens)>max_seq_length:
raise IndexError("Token length more than max seq length!")
 return [1]*len(tokens)+ [0] *(max_seq_length - len(tokens))
def _get_segments(tokens, max_seq_length):

 if len(tokens)>max_seq_length:
raise IndexError("Token length more than max seq length!")
 segments = []
first_sep = True
current_segment_id = 0
for token in tokens:
segments.append(current_segment_id)
 if token == "[SEP]":
if first_sep:
first_sep = False
else:
current_segment_id = 1
return segments + [0] *(max_seq_length - len(tokens))
def _get_ids(tokens, tokenizer, max_seq_length):

 token_ids = tokenizer.convert_tokens_to_ids(tokens)
 input_ids = token_ids + [0] *(max_seq_length-len(token_ids))
return input_ids
def _trim_input(title, question, answer, max_sequence_length,
t_max_len=30, q_max_len=239, a_max_len=239):
t = tokenizer.tokenize(title)
 q = tokenizer.tokenize(question)
 a = tokenizer.tokenize(answer)
 t_len = len(t)
 q_len = len(q)
 a_len = len(a)
 if(t_len+q_len+a_len+4)> max_sequence_length:
if t_max_len > t_len:
t_new_len = t_len
a_max_len = a_max_len + floor(( t_max_len - t_len)/2)
 q_max_len = q_max_len + ceil(( t_max_len - t_len)/2)
 else:
t_new_len = t_max_len
if a_max_len > a_len:
a_new_len = a_len
q_new_len = q_max_len +(a_max_len - a_len)
 elif q_max_len > q_len:
a_new_len = a_max_len +(q_max_len - q_len)
 q_new_len = q_len
else:
a_new_len = a_max_len
q_new_len = q_max_len
if t_new_len+a_new_len+q_new_len+4 != max_sequence_length:
raise ValueError("New sequence length should be %d, but is %d"
%(max_sequence_length,(t_new_len+a_new_len+q_new_len+4)))
 t = t[:t_new_len]
q = q[:q_new_len]
a = a[:a_new_len]
return t, q, a
def _convert_to_bert_inputs(title, question, answer, tokenizer, max_sequence_length):

 stoken = ["[CLS]"] + title + ["[SEP]"] + question + ["[SEP]"] + answer + ["[SEP]"]
input_ids = _get_ids(stoken, tokenizer, max_sequence_length)
 input_masks = _get_masks(stoken, max_sequence_length)
 input_segments = _get_segments(stoken, max_sequence_length)
 return [input_ids, input_masks, input_segments]
def compute_input_arays(df, columns, tokenizer, max_sequence_length):
input_ids, input_masks, input_segments = [], [], []
for _, instance in tqdm(df[columns].iterrows()):
t, q, a = instance.question_title, instance.question_body, instance.answer
t, q, a = _trim_input(t, q, a, max_sequence_length)
 ids, masks, segments = _convert_to_bert_inputs(t, q, a, tokenizer, max_sequence_length)
 input_ids.append(ids)
 input_masks.append(masks)
 input_segments.append(segments)
 return [np.asarray(input_ids, dtype=np.int32),
np.asarray(input_masks, dtype=np.int32),
np.asarray(input_segments, dtype=np.int32)]
def compute_output_arrays(df, columns):
return np.asarray(df[columns] )<compute_test_metric>
print("Train data size - rows:",train_d.shape[0]," columns:", train_d.shape[1])
 print("Test data size - rows:",test_d.shape[0]," columns:", test_d.shape[1])
 print("Submission data size - rows:",submission.shape[0]," columns:", submission.shape[1] )
def compute_spearmanr(trues, preds):
rhos = []
for col_trues, col_pred in zip(trues.T, preds.T):
rhos.append(
 spearmanr(col_trues, col_pred + np.random.normal(0, 1e-7, col_pred.shape[0])).correlation)
 return np.nanmean(rhos)
 class CustomCallback(tf.keras.callbacks.Callback):
def __init__(self, valid_data, test_data, batch_size=16, fold=None):
self.valid_inputs = valid_data[0]
self.valid_outputs = valid_data[1]
self.test_inputs = test_data
self.batch_size = batch_size
self.fold = fold
def on_train_begin(self, logs={}):
self.valid_predictions = []
self.test_predictions = []
def on_epoch_end(self, epoch, logs={}):
self.valid_predictions.append(
 self.model.predict(self.valid_inputs, batch_size=self.batch_size))
rho_val = compute_spearmanr(
 self.valid_outputs, np.average(self.valid_predictions, axis=0))
print("
validation rho: %.4f" % rho_val)
 if self.fold is not None:
self.model.save_weights(f'bert-base-{fold}-{epoch}.h5py')
 self.test_predictions.append(
 self.model.predict(self.test_inputs, batch_size=self.batch_size)
)
 def bert_model() :
input_word_ids = tf.keras.layers.Input(
(MAX_SEQUENCE_LENGTH,), dtype=tf.int32, name='input_word_ids')
 input_masks = tf.keras.layers.Input(
(MAX_SEQUENCE_LENGTH,), dtype=tf.int32, name='input_masks')
 input_segments = tf.keras.layers.Input(
(MAX_SEQUENCE_LENGTH,), dtype=tf.int32, name='input_segments')
 bert_layer = hub.KerasLayer(BERT_PATH, trainable=True)
 _, sequence_output = bert_layer([input_word_ids, input_masks, input_segments])
 x = tf.keras.layers.GlobalAveragePooling1D()(sequence_output)
 x = tf.keras.layers.Dropout(0.2 )(x)
 out = tf.keras.layers.Dense(30, activation="sigmoid", name="dense_output" )(x)
 model = tf.keras.models.Model(
 inputs=[input_word_ids, input_masks, input_segments], outputs=out)
 return model
def train_and_predict(model, train_data, valid_data, test_data,
learning_rate, epochs, batch_size, loss_function, fold):
custom_callback = CustomCallback(
 valid_data=(valid_data[0], valid_data[1]),
test_data=test_data,
batch_size=batch_size,
fold=None)
 optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)
 model.compile(loss=loss_function, optimizer=optimizer)
 model.fit(train_data[0], train_data[1], epochs=epochs,
batch_size=batch_size, callbacks=[custom_callback])
 return custom_callback<split>
missingt1 = train_d.isnull().sum()
all_val = train_d.count()
missing_train_d = pd.concat([missingt1, all_val], axis=1, keys=['Missing', 'All'])
 missing_train_d
gkf = GroupKFold(n_splits=10 ).split(X=df_train.question_body, groups=df_train.question_body)
 test_inputs = compute_input_arays(df_test, input_categories, tokenizer, MAX_SEQUENCE_LENGTH )<load_pretrained>
missingt2 = test_d.isnull().sum()
all_val = test_d.count()
missing_test_d = pd.concat([missingt2, all_val], axis=1, keys=['Missing', 'All'])
 missing_test_d
models = []
for i in range(5):
model_path = f'.. /input/bertuned-f{i}/bertuned_f{i}.h5'
model = bert_model()
model.load_weights(model_path)
 models.append(model)
<load_pretrained>
train_d.nunique()
model_path = f'.. /input/bertf1e15/Full-0.h5'
model = bert_model()
model.load_weights(model_path)
 models.append(model )<load_pretrained>
test_d.nunique()
model_path = f'.. /input/bertmodelpretrained/bert-0.h5'
model = bert_model()
model.load_weights(model_path)
 models.append(model )<load_pretrained>
concatenated = pd.concat([train_d, test_d])
 concatenated.shape
model_path = f'.. /input/bertmodelpretrained/bert-1.h5'
model = bert_model()
model.load_weights(model_path)
 models.append(model )<load_pretrained>
concatenated.nunique()
for i in range(7):
model_path = f'.. /input/pretrained-bert/bert-{i}.h5'
model = bert_model()
model.load_weights(model_path)
 models.append(model )<define_variables>
warnings.filterwarnings("ignore" )
test_predictions = []<predict_on_test>
temp = pd.DataFrame(train_d.landmark_id.value_counts().head(25))
temp.reset_index(inplace=True)
 temp.columns = ['landmark_id','count']
temp
for model in models:
test_predictions.append(model.predict(test_inputs, batch_size=8))<prepare_output>
temp1 = pd.DataFrame(train_d.landmark_id.value_counts().tail(25))
temp1.reset_index(inplace=True)
 temp1.columns = ['landmark_id','count']
temp1
final_predictions = np.mean(test_predictions, axis=0 )<save_to_csv>
freq_label = train_d['landmark_id'].value_counts() /train_d['landmark_id'].value_counts().sum()
df_sub.iloc[:, 1:] = final_predictions
df_sub.to_csv('submission.csv', index=False )<import_modules>
submission['landmarks'] = '%d %2.2f' %(freq_label.index[0], freq_label.values[0])
 submission.to_csv('submission.csv', index=False )
pyLDAvis.enable_notebook()
warnings.filterwarnings('ignore' )<load_from_csv>
np.random.seed(2018)
 r_idx = lambda : np.random.choice(freq_label.index, p = freq_label.values )
sample = pd.read_csv('/kaggle/input/google-quest-challenge/sample_submission.csv')
 sample.head(3 )<load_from_csv>
r_score = lambda idx: '%d %2.4f' %(freq_label.index[idx], freq_label.values[idx])
 submission['landmarks'] = submission.id.map(lambda _: r_score(r_idx()))
submission.to_csv('rand_submission.csv', index=False )
train = pd.read_csv('/kaggle/input/google-quest-challenge/train.csv')
 train.head(3 )<load_from_csv>
temp_l1 = list()
for path in train_d['url']:
temp_l1.append(( path.split('//', 1)[1] ).split('/', 1)[0])
 train_d['site_name'] = temp_l1
temp_l1 = list()
for path in test_d['url']:
temp_l1.append(( path.split('//', 1)[1] ).split('/', 1)[0])
 test_d['site_name'] = temp_l1
print("Training data size",train_d.shape)
 print("test data size",test_d.shape )
test = pd.read_csv('/kaggle/input/google-quest-challenge/test.csv')
 test.head(3 )<define_variables>
train_d1 = train_d
train_d1.head()
targets = [
'question_asker_intent_understanding',
'question_body_critical',
'question_conversational',
'question_expect_short_answer',
'question_fact_seeking',
'question_has_commonly_accepted_answer',
'question_interestingness_others',
'question_interestingness_self',
'question_multi_intent',
'question_not_really_a_question',
'question_opinion_seeking',
'question_type_choice',
'question_type_compare',
'question_type_consequence',
'question_type_definition',
'question_type_entity',
'question_type_instructions',
'question_type_procedure',
'question_type_reason_explanation',
'question_type_spelling',
'question_well_written',
'answer_helpful',
'answer_level_of_information',
'answer_plausible',
'answer_relevance',
'answer_satisfaction',
'answer_type_instructions',
'answer_type_procedure',
'answer_type_reason_explanation',
'answer_well_written'
] 
<feature_engineering>
train_d1 = train_d1.drop('url',1)
 train_d1.head()
stopwords=stopwords.words('english')
 train['que_stopwords']=train['question_body'].apply(lambda x : [x for x in x.split() if x in stopwords])
 train['ans_stopwords']=train['answer'].apply(lambda x: [x for x in x.split() if x in stopwords] )<count_unique_values>
temp = pd.DataFrame(train_d1.site_name.value_counts())
 temp.reset_index(inplace=True)
 temp.columns = ['site_name','count']
temp
def common_ngrams(col,common=10):
corpus=[]
for question in train[col].values:
words=[str(x[0]+' '+x[1])for x in ngrams(question.split() ,2)]
corpus.append(words)
 flatten=[x for one in corpus for x in one]
counter=Counter(flatten)
 most_common=counter.most_common(common)
 string,value=zip(*(most_common))
return string,value 
<set_options>
test_d1 = test_d
test_d1.head()
np.set_printoptions(suppress=True )<load_from_csv>
test_d1 = test_d1.drop('url',1)
 test_d1.head()
PATH = '.. /input/google-quest-challenge/'
BERT_PATH = '.. /input/bert-base-from-tfhub/bert_en_uncased_L-12_H-768_A-12'
tokenizer = FullTokenizer(BERT_PATH+'/assets/vocab.txt', True)
 MAX_SEQUENCE_LENGTH = 512
df_train = pd.read_csv(PATH+'train.csv')
 df_test = pd.read_csv(PATH+'test.csv')
 df_sub = pd.read_csv(PATH+'sample_submission.csv')
 print('train shape =', df_train.shape)
 print('test shape =', df_test.shape)
 output_categories = list(df_train.columns[11:])
 input_categories = list(df_train.columns[[1,2,5]])
 print('
output categories:
\t', output_categories)
 print('
input categories:
\t', input_categories )<string_transform>
temp = pd.DataFrame(test_d.site_name.value_counts())
 temp.reset_index(inplace=True)
 temp.columns = ['site_name','count']
temp
def _get_masks(tokens, max_seq_length):

 if len(tokens)>max_seq_length:
raise IndexError("Token length more than max seq length!")
 return [1]*len(tokens)+ [0] *(max_seq_length - len(tokens))
def _get_segments(tokens, max_seq_length):

 if len(tokens)>max_seq_length:
raise IndexError("Token length more than max seq length!")
 segments = []
first_sep = True
current_segment_id = 0
for token in tokens:
segments.append(current_segment_id)
 if token == "[SEP]":
if first_sep:
first_sep = False
else:
current_segment_id = 1
return segments + [0] *(max_seq_length - len(tokens))
def _get_ids(tokens, tokenizer, max_seq_length):

 token_ids = tokenizer.convert_tokens_to_ids(tokens)
 input_ids = token_ids + [0] *(max_seq_length-len(token_ids))
return input_ids
def _trim_input(title, question, answer, max_sequence_length,
t_max_len=30, q_max_len=239, a_max_len=239):
t = tokenizer.tokenize(title)
 q = tokenizer.tokenize(question)
 a = tokenizer.tokenize(answer)
 t_len = len(t)
 q_len = len(q)
 a_len = len(a)
 if(t_len+q_len+a_len+4)> max_sequence_length:
if t_max_len > t_len:
t_new_len = t_len
a_max_len = a_max_len + floor(( t_max_len - t_len)/2)
 q_max_len = q_max_len + ceil(( t_max_len - t_len)/2)
 else:
t_new_len = t_max_len
if a_max_len > a_len:
a_new_len = a_len
q_new_len = q_max_len +(a_max_len - a_len)
 elif q_max_len > q_len:
a_new_len = a_max_len +(q_max_len - q_len)
 q_new_len = q_len
else:
a_new_len = a_max_len
q_new_len = q_max_len
if t_new_len+a_new_len+q_new_len+4 != max_sequence_length:
raise ValueError("New sequence length should be %d, but is %d"
%(max_sequence_length,(t_new_len+a_new_len+q_new_len+4)))
 t = t[:t_new_len]
q = q[:q_new_len]
a = a[:a_new_len]
return t, q, a
def _convert_to_bert_inputs(title, question, answer, tokenizer, max_sequence_length):

 stoken = ["[CLS]"] + title + ["[SEP]"] + question + ["[SEP]"] + answer + ["[SEP]"]
input_ids = _get_ids(stoken, tokenizer, max_sequence_length)
 input_masks = _get_masks(stoken, max_sequence_length)
 input_segments = _get_segments(stoken, max_sequence_length)
 return [input_ids, input_masks, input_segments]
def compute_input_arays(df, columns, tokenizer, max_sequence_length):
input_ids, input_masks, input_segments = [], [], []
for _, instance in tqdm(df[columns].iterrows()):
t, q, a = instance.question_title, instance.question_body, instance.answer
t, q, a = _trim_input(t, q, a, max_sequence_length)
 ids, masks, segments = _convert_to_bert_inputs(t, q, a, tokenizer, max_sequence_length)
 input_ids.append(ids)
 input_masks.append(masks)
 input_segments.append(segments)
 return [np.asarray(input_ids, dtype=np.int32),
np.asarray(input_masks, dtype=np.int32),
np.asarray(input_segments, dtype=np.int32)]
def compute_output_arrays(df, columns):
return np.asarray(df[columns] )<compute_test_metric>
train_d2 = train_d
test_d2 = test_d
train_d2.head()
def compute_spearmanr(trues, preds):
rhos = []
for col_trues, col_pred in zip(trues.T, preds.T):
rhos.append(
 spearmanr(col_trues, col_pred + np.random.normal(0, 1e-7, col_pred.shape[0])).correlation)
 return np.nanmean(rhos)
 class CustomCallback(tf.keras.callbacks.Callback):
def __init__(self, valid_data, test_data, batch_size=16, fold=None):
self.valid_inputs = valid_data[0]
self.valid_outputs = valid_data[1]
self.test_inputs = test_data
self.batch_size = batch_size
self.fold = fold
def on_train_begin(self, logs={}):
self.valid_predictions = []
self.test_predictions = []
def on_epoch_end(self, epoch, logs={}):
self.valid_predictions.append(
 self.model.predict(self.valid_inputs, batch_size=self.batch_size))
rho_val = compute_spearmanr(
 self.valid_outputs, np.average(self.valid_predictions, axis=0))
print("
validation rho: %.4f" % rho_val)
 if self.fold is not None:
self.model.save_weights(f'bert-base-{fold}-{epoch}.h5py')
 self.test_predictions.append(
 self.model.predict(self.test_inputs, batch_size=self.batch_size)
)
 def bert_model() :
input_word_ids = tf.keras.layers.Input(
(MAX_SEQUENCE_LENGTH,), dtype=tf.int32, name='input_word_ids')
 input_masks = tf.keras.layers.Input(
(MAX_SEQUENCE_LENGTH,), dtype=tf.int32, name='input_masks')
 input_segments = tf.keras.layers.Input(
(MAX_SEQUENCE_LENGTH,), dtype=tf.int32, name='input_segments')
 bert_layer = hub.KerasLayer(BERT_PATH, trainable=True)
 _, sequence_output = bert_layer([input_word_ids, input_masks, input_segments])
 x = tf.keras.layers.GlobalAveragePooling1D()(sequence_output)
 x = tf.keras.layers.Dropout(0.2 )(x)
 out = tf.keras.layers.Dense(30, activation="sigmoid", name="dense_output" )(x)
 model = tf.keras.models.Model(
 inputs=[input_word_ids, input_masks, input_segments], outputs=out)
 return model
def train_and_predict(model, train_data, valid_data, test_data,
learning_rate, epochs, batch_size, loss_function, fold):
custom_callback = CustomCallback(
 valid_data=(valid_data[0], valid_data[1]),
test_data=test_data,
batch_size=batch_size,
fold=None)
 optimizer = tf.keras.optimizers.Adam(learning_rate=learning_rate)
 model.compile(loss=loss_function, optimizer=optimizer)
 model.fit(train_data[0], train_data[1], epochs=epochs,
batch_size=batch_size, callbacks=[custom_callback])
 return custom_callback<split>
train_d2 = train_d2.drop('site_name',1)
 train_d2.head()
gkf = GroupKFold(n_splits=10 ).split(X=df_train.question_body, groups=df_train.question_body)
 test_inputs = compute_input_arays(df_test, input_categories, tokenizer, MAX_SEQUENCE_LENGTH )<load_pretrained>
test_d2 = test_d2.drop('site_name',1)
 test_d2.head()
models = []
for i in range(5):
model_path = f'.. /input/bertuned-f{i}/bertuned_f{i}.h5'
model = bert_model()
model.load_weights(model_path)
 models.append(model)
<load_pretrained>
pl.seed = 0
N = 1500
probs = train_d.landmark_id.value_counts() / train_d.shape[0]
probs = probs.iloc[:N]
probs = pd.DataFrame({'landmark_id': probs.index,
'probability': probs.values}, index=pl.arange(N))
T = pd.merge(train_d, probs, on='landmark_id', how='outer')
 inx = pl.randint(0, T.shape[0], submission.shape[0])
 submission['landmark_id'] = T.landmark_id.iloc[inx].values
submission['prob'] = T.probability.iloc[inx].values
submission['landmarks'] = submission.landmark_id.astype(str)+ ' ' + submission.prob.astype(str)
 submission[['id','landmarks']].head()
submission[['id','landmarks']].to_csv('submission_inner.csv', index=False )
<load_pretrained><EOS> 
pl.seed = 0
N = 1500
probs = train_d.landmark_id.value_counts() / train_d.shape[0]
probs = probs.iloc[:N]
probs = pd.DataFrame({'landmark_id': probs.index,
'probability': probs.values}, index=pl.arange(N))
T = pd.merge(train_d, probs, on='landmark_id', how='inner')
 inx = pl.randint(0, T.shape[0], submission.shape[0])
 submission['landmark_id'] = T.landmark_id.iloc[inx].values
submission['prob'] = T.probability.iloc[inx].values
submission['landmarks'] = submission.landmark_id.astype(str)+ ' ' + submission.prob.astype(str)
 submission[['id','landmarks']].head()
submission[['id','landmarks']].to_csv('submission_outer.csv', index=False )