Kaggle competition: Tweet Sentiment Extraction
<SOS> metric: Jaccard Kaggle data source: tweet-sentiment-extraction<predict_on_test>
print('TF version',tf.__version__ )
gaussian = GaussianNB()
gaussian.fit(x_train, y_train)
 y_pred = gaussian.predict(x_val)
 acc_gaussian = round(accuracy_score(y_pred, y_val)* 100, 2 )<compute_test_metric>
def read_train() :
train=pd.read_csv('.. /input/tweet-sentiment-extraction/train.csv')
 train['text']=train['text'].astype(str)
 train['selected_text']=train['selected_text'].astype(str)
 return train
def read_test() :
test=pd.read_csv('.. /input/tweet-sentiment-extraction/test.csv')
 test['text']=test['text'].astype(str)
 return test
def read_submission() :
test=pd.read_csv('.. /input/tweet-sentiment-extraction/sample_submission.csv')
 return test
train_df = read_train()
test_df = read_test()
submission_df = read_submission()
print('gaussian accuracy score is:',acc_gaussian )<compute_train_metric>
def jaccard_improve(str1, str2):
str1=str1.lower()
str2=str2.lower()
index=str1.find(str2)
 text1=str1[:index]
text2=str1[index:].replace(str2,'')
 words1=text1.split()
words2=text2.split()
if len(words1)>len(words2):
words1=words1[-3:]
mod_text=" ".join(words1)+" "+ str2
else:
words2=words2[0:2]
mod_text=str2+" "+" ".join(words2)
 return mod_text
logreg = LogisticRegression()
logreg.fit(x_train, y_train)
 y_pred = logreg.predict(x_val)
 acc_logreg = round(accuracy_score(y_pred, y_val)* 100, 2)
 print('logreg accuracy score is:',acc_logreg )<compute_train_metric>
def jaccard(str1, str2):
a = set(str(str1 ).lower().split())
 b = set(str(str2 ).lower().split())
 c = a.intersection(b)
 return float(len(c)) /(len(a)+ len(b)- len(c))
svc = SVC()
svc.fit(x_train, y_train)
 y_pred = svc.predict(x_val)
 acc_svc = round(accuracy_score(y_pred, y_val)* 100, 2)
 print('svc accuracy score is:',acc_svc )<compute_train_metric>
train_df['selected_text_mod']=train_df['selected_text']
train_df['mod']=0
linear_svc = LinearSVC()
linear_svc.fit(x_train, y_train)
 y_pred = linear_svc.predict(x_val)
 acc_linear_svc = round(accuracy_score(y_pred, y_val)* 100, 2)
 print('linear svc accuracy score is:',acc_linear_svc )<compute_train_metric>
MAX_LEN = 96
PATH = '.. /input/tf-roberta/'
tokenizer = tokenizers.ByteLevelBPETokenizer(
 vocab_file=PATH+'vocab-roberta-base.json',
merges_file=PATH+'merges-roberta-base.txt',
lowercase=True,
add_prefix_space=True 
)
 sentiment_id = {'positive': 1313, 'negative': 2430, 'neutral': 7974}
perceptron = Perceptron()
perceptron.fit(x_train, y_train)
 y_pred = perceptron.predict(x_val)
 acc_perceptron = round(accuracy_score(y_pred, y_val)* 100, 2)
 print('perceptron accuracy score is:',acc_perceptron )<compute_train_metric>
ct = train_df.shape[0]
input_ids = np.ones(( ct,MAX_LEN),dtype='int32')
 attention_mask = np.zeros(( ct,MAX_LEN),dtype='int32')
 token_type_ids = np.zeros(( ct,MAX_LEN),dtype='int32')
 start_tokens = np.zeros(( ct,MAX_LEN),dtype='int32')
 end_tokens = np.zeros(( ct,MAX_LEN),dtype='int32' )
decisiontree = DecisionTreeClassifier()
decisiontree.fit(x_train, y_train)
 y_pred = decisiontree.predict(x_val)
 acc_decisiontree = round(accuracy_score(y_pred, y_val)* 100, 2)
 print('decision tree accuracy score is:',acc_decisiontree )<train_model>
randomforest = RandomForestClassifier()
randomforest.fit(x_train, y_train)
 y_pred = randomforest.predict(x_val)
 acc_randomforest = round(accuracy_score(y_pred, y_val)* 100, 2)
 print('random forest accuracy score is:',acc_randomforest )<train_model>
ct = train_df.shape[0]
input_ids = np.ones(( ct,MAX_LEN),dtype='int32')
 attention_mask = np.zeros(( ct,MAX_LEN),dtype='int32')
 token_type_ids = np.zeros(( ct,MAX_LEN),dtype='int32')
 start_tokens = np.zeros(( ct,MAX_LEN),dtype='int32')
 end_tokens = np.zeros(( ct,MAX_LEN),dtype='int32')
 print(train_df)
 for k in range(train_df.shape[0]):
text1 = " "+" ".join(train_df.loc[k,'text'].split())
 text2 = " ".join(train_df.loc[k,'selected_text'].split())
 idx = text1.find(text2)
 chars = np.zeros(( len(text1)))
 chars[idx:idx+len(text2)]=1
if text1[idx-1]==' ': chars[idx-1] = 1
enc = tokenizer.encode(text1)
 print(tokenizer.decode([2]))
offsets = []; idx=0
for t in enc.ids:
w = tokenizer.decode([t])
 offsets.append(( idx,idx+len(w)))
 idx += len(w)
 toks = []
for i,(a,b)in enumerate(offsets):
sm = np.sum(chars[a:b])
 if sm>0: toks.append(i)
 s_tok = sentiment_id[train_df.loc[k,'sentiment']]
input_ids[k,:len(enc.ids)+5] = [0] + enc.ids + [2,2] + [s_tok] + [2]
print(input_ids[k])
 attention_mask[k,:len(enc.ids)+5] = 1
if len(toks)>0:
start_tokens[k,toks[0]+1] = 1
end_tokens[k,toks[-1]+1] = 1
knn = KNeighborsClassifier()
knn.fit(x_train, y_train)
 y_pred = knn.predict(x_val)
 acc_knn = round(accuracy_score(y_pred, y_val)* 100, 2)
 print('knn accuracy score is:',acc_knn )<compute_train_metric>
ct = test_df.shape[0]
input_ids_t = np.ones(( ct,MAX_LEN),dtype='int32')
 attention_mask_t = np.zeros(( ct,MAX_LEN),dtype='int32')
 token_type_ids_t = np.zeros(( ct,MAX_LEN),dtype='int32')
 for k in range(test_df.shape[0]):
text1 = " "+" ".join(test_df.loc[k,'text'].split())
 enc = tokenizer.encode(text1)
 s_tok = sentiment_id[test_df.loc[k,'sentiment']]
input_ids_t[k,:len(enc.ids)+5] = [0] + enc.ids + [2,2] + [s_tok] + [2]
attention_mask_t[k,:len(enc.ids)+5] = 1
sgd = SGDClassifier()
sgd.fit(x_train, y_train)
 y_pred = sgd.predict(x_val)
 acc_sgd = round(accuracy_score(y_pred, y_val)* 100, 2)
 print('stochastic gradient descent accuracy score is:',acc_sgd )<train_model>
def build_model() :
ids = tf.keras.layers.Input(( MAX_LEN,), dtype=tf.int32)
 att = tf.keras.layers.Input(( MAX_LEN,), dtype=tf.int32)
 tok = tf.keras.layers.Input(( MAX_LEN,), dtype=tf.int32)
 config = RobertaConfig.from_pretrained(PATH+'config-roberta-base.json')
 bert_model = TFRobertaModel.from_pretrained(PATH+'pretrained-roberta-base.h5',config=config)
 x = bert_model(ids,attention_mask=att,token_type_ids=tok)
 x1 = tf.keras.layers.Dropout(0.1 )(x[0])
 x1 = tf.keras.layers.Conv1D(128, 2,padding='same' )(x1)
 x1 = tf.keras.layers.LeakyReLU()(x1)
 x1 = tf.keras.layers.Conv1D(64, 2,padding='same' )(x1)
 x1 = tf.keras.layers.Dense(1 )(x1)
 x1 = tf.keras.layers.Flatten()(x1)
 x1 = tf.keras.layers.Activation('softmax' )(x1)
 x2 = tf.keras.layers.Dropout(0.1 )(x[0])
 x2 = tf.keras.layers.Conv1D(128, 2, padding='same' )(x2)
 x2 = tf.keras.layers.LeakyReLU()(x2)
 x2 = tf.keras.layers.Conv1D(64, 2, padding='same' )(x2)
 x2 = tf.keras.layers.Dense(1 )(x2)
 x2 = tf.keras.layers.Flatten()(x2)
 x2 = tf.keras.layers.Activation('softmax' )(x2)
 model = tf.keras.models.Model(inputs=[ids, att, tok], outputs=[x1,x2])
 optimizer = tf.keras.optimizers.Adam(learning_rate=3e-5)
 model.compile(loss='binary_crossentropy', optimizer=optimizer)
 return model
gbk = GradientBoostingClassifier()
gbk.fit(x_train, y_train)
 y_pred = gbk.predict(x_val)
 acc_gbk = round(accuracy_score(y_pred, y_val)* 100, 2)
 print('gradient boosting accuracy score is:',acc_gbk )<create_dataframe>
n_splits = 5
models = pd.DataFrame({
'Model': ['Support Vector Machines', 'KNN', 'Logistic Regression',
'Random Forest', 'Naive Bayes', 'Perceptron', 'Linear SVC',
'Decision Tree', 'Stochastic Gradient Descent', 'Gradient Boosting Classifier'],
'Score': [acc_svc, acc_knn, acc_logreg,
acc_randomforest, acc_gaussian, acc_perceptron,acc_linear_svc, acc_decisiontree,
acc_sgd, acc_gbk]})
 models.sort_values(by='Score', ascending=False )<save_to_csv>
preds_start = np.zeros(( input_ids_t.shape[0],MAX_LEN))
preds_end = np.zeros(( input_ids_t.shape[0],MAX_LEN))
DISPLAY=1
for i in range(5):
print('
print('
print('
K.clear_session()
model = build_model()
model.load_weights('.. /input/model8/v8-roberta-%i.h5'%i)
 print('Predicting Test...')
 preds = model.predict([input_ids_t,attention_mask_t,token_type_ids_t],verbose=DISPLAY)
 preds_start += preds[0]/n_splits
preds_end += preds[1]/n_splits
ids = test['PassengerId']
predictions = gbk.predict(test.drop('PassengerId', axis=1))
output = pd.DataFrame({ 'PassengerId' : ids, 'Survived': predictions })
 output.to_csv('submission.csv', index=False )<import_modules>
all = []
for k in range(input_ids_t.shape[0]):
a = np.argmax(preds_start[k,])
 b = np.argmax(preds_end[k,])
 if a>b:
st = test_df.loc[k,'text']
else:
text1 = " "+" ".join(test_df.loc[k,'text'].split())
 enc = tokenizer.encode(text1)
 st = tokenizer.decode(enc.ids[a-1:b])
 st1=st.strip()
if st1=='****' or st1=='****!' or st1=='****!' or st1=='****!' or st1=='****,' or st1=='****,' or st1=='****.' or st1=='****.':
st=text1
elif st1=='(good':
st='good'
elif st1=='__joy':
st='joy'
all.append(st )
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
import tensorflow as tf
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Embedding, Dense, GRU, Dropout, Bidirectional, SpatialDropout1D
from tensorflow.keras.utils import to_categorical<load_from_csv>
df_train = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t', usecols=['Phrase', 'Sentiment'])
 df_submission = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t', usecols=['Phrase'] )<split>
test_df['selected_text'] = all
test_df[['textID','selected_text']].to_csv('submission.csv',index=False)
X_train, X_test, y_train, y_test = train_test_split(df_train['Phrase'].values, df_train['Sentiment'].values, test_size=0.1 )<string_transform>
<categorify><EOS>