kernel_id
int64 24.2k
23.3M
| prompt
stringlengths 8
1.85M
| completetion
stringlengths 1
182k
| comp_name
stringlengths 5
57
|
|---|---|---|---|
8,872,016 | def bert_encode(texts, bert_layer, max_len=128):
vocab_file = bert_layer.resolved_object.vocab_file.asset_path.numpy()
do_lower_case = bert_layer.resolved_object.do_lower_case.numpy()
tokenizer = tokenization.FullTokenizer(vocab_file, do_lower_case)
all_tokens = []
all_masks = []
all_segments = []
for text in texts:
text = tokenizer.tokenize(text)
text = text[:max_len - 2]
input_sequence = ["[CLS]"] + text + ["[SEP]"]
pad_len = max_len - len(input_sequence)
tokens = tokenizer.convert_tokens_to_ids(input_sequence)
input_ids = tokens + [0]* pad_len
all_tokens.append(input_ids)
masks = [1]*len(input_sequence)+ [0]* pad_len
all_masks.append(masks)
segments = [0]* max_len
all_segments.append(segments)
return np.array(all_tokens), np.array(all_masks), np.array(all_segments)
def build_model(bert_layer, max_len = 128, lr = 1e-5):
input_word_ids = tf.keras.layers.Input(shape=(max_len,), dtype=tf.int32,name="input_word_ids")
input_mask = tf.keras.layers.Input(shape=(max_len,), dtype=tf.int32,name="input_mask")
segment_ids = tf.keras.layers.Input(shape=(max_len,), dtype=tf.int32,name="segment_ids")
pooled_output, sequence_output = bert_layer([input_word_ids, input_mask, segment_ids])
dense_out = Dense(1,activation="relu" )(pooled_output)
drop_out = tf.keras.layers.Dropout(0.8 )(dense_out)
out = Dense(1,activation="sigmoid" )(pooled_output)
model = Model(inputs=[input_word_ids, input_mask, segment_ids], outputs=out)
adam = tf.keras.optimizers.Adam(lr)
model.compile(optimizer=adam, loss='binary_crossentropy', metrics=['accuracy'])
return model
def plot_curve(history):
plt.plot(history.history['accuracy'], label='accuracy')
plt.plot(history.history['val_accuracy'], label='val_accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.ylim([0.5,1])
plt.legend()
plt.show()<choose_model_class> | datagen = ImageDataGenerator(
rotation_range=10,
width_shift_range=0.1,
height_shift_range=0.1,
zoom_range=0.1)
history = datagen.fit(X_train ) | Digit Recognizer |
8,872,016 | %%time
module_url = "https://tfhub.dev/tensorflow/bert_en_uncased_L-24_H-1024_A-16/1"
bert_layer = hub.KerasLayer(module_url, trainable=True )<load_from_csv> | history = model.fit_generator(datagen.flow(X_train, Y_train, batch_size=100), steps_per_epoch=len(X_train)/100,
epochs=20, validation_data=(X_test, Y_test), callbacks=[reduce_lr] ) | Digit Recognizer |
8,872,016 | train = pd.read_csv("/kaggle/input/nlp-getting-started/train.csv")
train_input = bert_encode(train.text.values, bert_layer, max_len=128)
train_labels = np.array(train.target )<load_from_csv> | Digit Recognizer |
|
8,872,016 | test = pd.read_csv("/kaggle/input/nlp-getting-started/test.csv")
test_input = bert_encode(test.text.values, bert_layer, max_len=128)
model.load_weights('model.h5')
test_pred = model.predict(test_input )<save_to_csv> | score = model.evaluate(X_test, Y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1] ) | Digit Recognizer |
8,872,016 | submission = pd.read_csv("/kaggle/input/nlp-getting-started/sample_submission.csv")
submission['target'] = np.round(test_pred ).astype('int')
submission.to_csv('submission.csv', index=False)
submission.groupby('target' ).count()<load_from_csv> | test_data = pd.read_csv('/kaggle/input/digit-recognizer/test.csv' ) | Digit Recognizer |
8,872,016 | for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
data = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
data.sample(10 )<count_duplicates> | test_data = test_data.values
test_data = test_data.reshape(28000, 28, 28,1)
test_data = test_data.astype('float32')
test_data /= 255
print("Test data matrix shape", test_data.shape ) | Digit Recognizer |
8,872,016 | text = data.text
duplicates = data[text.isin(text[text.duplicated() ])].sort_values(by='text')
conflicting_check = pd.DataFrame(duplicates.groupby(['text'] ).target.mean())
conflicting_check.sample(10 )<filter> | y_pred = model.predict_classes(test_data, verbose=0)
print(y_pred ) | Digit Recognizer |
8,872,016 | conflicting = conflicting_check.loc[(conflicting_check.target != 1)&(conflicting_check.target != 0)].index
data = data.drop(data[text.isin(conflicting)].index)
print('Conflicting samples count:', conflicting.shape[0] )<set_options> | i = 9713
predicted_value = np.argmax(model.predict(X_test[i].reshape(1,28, 28,1)))
print('predicted value:',predicted_value)
plt.imshow(X_test[i].reshape([28, 28]), cmap='Greys_r' ) | Digit Recognizer |
8,872,016 | if torch.cuda.is_available() :
device = torch.device("cuda")
print('There are %d GPU(s)available.' % torch.cuda.device_count())
print('We will use the GPU:', torch.cuda.get_device_name(0))
else:
print('No GPU available, using the CPU instead.')
device = torch.device("cpu" )<install_modules> | submissions=pd.DataFrame({"ImageId": list(range(1,len(y_pred)+1)) ,
"Label": y_pred})
submissions.to_csv("LeNet_CNN.csv", index=False ) | Digit Recognizer |
8,872,016 | !pip install transformers<define_variables> | !pip install emnist | Digit Recognizer |
8,872,016 | sentences = data.text.values
labels =data.target.values<load_pretrained> | import matplotlib.pyplot as plt,seaborn as sns,pandas as pd,numpy as np
from keras.models import Sequential, load_model
from keras.layers.core import Dense, Dropout, Activation
from keras.layers import Conv2D, MaxPooling2D,MaxPool2D,Flatten,BatchNormalization
from keras.utils import np_utils
from keras.preprocessing.image import ImageDataGenerator
from keras.callbacks import ReduceLROnPlateau
from emnist import extract_training_samples
from emnist import extract_test_samples
from keras.optimizers import Adam | Digit Recognizer |
8,872,016 | tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True )<categorify> | x_train, y_train = extract_training_samples('digits')
x_test, y_test = extract_test_samples('digits' ) | Digit Recognizer |
8,872,016 | print(' Original: ', sentences[0])
print('Tokenized: ', tokenizer.tokenize(sentences[0]))
print('Token IDs: ', tokenizer.convert_tokens_to_ids(tokenizer.tokenize(sentences[0])) )<define_variables> | in_train_data = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
ex_y_train = in_train_data["label"]
ex_x_train = in_train_data.drop(labels = ["label"],axis = 1 ) | Digit Recognizer |
8,872,016 | max_len = 0
for sent in sentences:
input_ids = tokenizer.encode(sent, add_special_tokens=True)
max_len = max(max_len, len(input_ids))
print('Max tweet length: ', max_len )<categorify> | X_train = x_train.reshape(240000, 28, 28,1)
X_test = x_test.reshape(40000, 28, 28,1)
ex_x_train = ex_x_train.values.reshape(42000,28,28,1)
X_train = np.vstack(( X_train, ex_x_train))
print(X_train.shape ) | Digit Recognizer |
8,872,016 | input_ids = []
attention_masks = []
for sent in sentences:
encoded_dict = tokenizer.encode_plus(
sent,
add_special_tokens = True,
max_length = 64,
pad_to_max_length = True,
return_attention_mask = True,
return_tensors = 'pt',
)
input_ids.append(encoded_dict['input_ids'])
attention_masks.append(encoded_dict['attention_mask'])
input_ids = torch.cat(input_ids, dim=0)
attention_masks = torch.cat(attention_masks, dim=0)
labels = torch.tensor(labels)
print('Original: ', sentences[0])
print('Token IDs:', input_ids[0] )<split> | X_train = X_train.astype('float32')
X_test = X_test.astype('float32' ) | Digit Recognizer |
8,872,016 | SPLIT = 0.999
dataset = TensorDataset(input_ids, attention_masks, labels)
train_size = int(SPLIT * len(dataset))
val_size = len(dataset)- train_size
train_dataset, val_dataset = random_split(dataset, [train_size, val_size])
print('{:>5,} training samples'.format(train_size))
print('{:>5,} validation samples'.format(val_size))<load_pretrained> | X_train /= 255
X_test /= 255 | Digit Recognizer |
8,872,016 | batch_size = 32
train_dataloader = DataLoader(
train_dataset,
sampler = RandomSampler(train_dataset),
batch_size = batch_size
)
validation_dataloader = DataLoader(
val_dataset,
sampler = SequentialSampler(val_dataset),
batch_size = batch_size
)<load_pretrained> | y_train = np.concatenate([y_train,ex_y_train.values])
print(y_train.shape ) | Digit Recognizer |
8,872,016 | model = BertForSequenceClassification.from_pretrained(
"bert-base-uncased",
num_labels = 2,
output_attentions = False,
output_hidden_states = False,
)
model.cuda()<choose_model_class> | n_classes = 10
print("Shape before one-hot encoding: ", y_train.shape)
Y_train = np_utils.to_categorical(y_train, n_classes)
Y_test = np_utils.to_categorical(y_test, n_classes)
print("Shape after one-hot encoding: ", Y_train.shape ) | Digit Recognizer |
8,872,016 | optimizer = AdamW(model.parameters() ,
lr = 2e-5,
eps = 1e-8
)<init_hyperparams> | model = Sequential()
model.add(Conv2D(filters=32, kernel_size=(5,5), padding='same', activation='relu', input_shape=(28, 28, 1)))
model.add(MaxPool2D(pool_size = 2,strides=2))
model.add(Conv2D(filters=48, kernel_size=(5,5), padding='valid', activation='relu'))
model.add(MaxPool2D(pool_size = 2,strides=2))
model.add(Flatten())
model.add(Dense(120, activation='relu'))
model.add(Dense(84, activation='relu'))
model.add(Dense(10, activation='softmax'))
adam = Adam(lr=5e-4)
model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer=adam ) | Digit Recognizer |
8,872,016 | epochs = 2
total_steps = len(train_dataloader)* epochs
scheduler = get_linear_schedule_with_warmup(optimizer,
num_warmup_steps = 0,
num_training_steps = total_steps )<compute_test_metric> | reduce_lr = ReduceLROnPlateau(monitor='val_acc',
patience=3,
verbose=1,
factor=0.2,
min_lr=1e-6 ) | Digit Recognizer |
8,872,016 | def flat_accuracy(preds, labels):
pred_flat = np.argmax(preds, axis=1 ).flatten()
labels_flat = labels.flatten()
return np.sum(pred_flat == labels_flat)/ len(labels_flat )<define_variables> | datagen = ImageDataGenerator(
rotation_range=10,
width_shift_range=0.1,
height_shift_range=0.1,
zoom_range=0.1)
history = datagen.fit(X_train ) | Digit Recognizer |
8,872,016 | seed_val = 42
random.seed(seed_val)
np.random.seed(seed_val)
torch.manual_seed(seed_val)
torch.cuda.manual_seed_all(seed_val)
training_stats = []
total_t0 = time.time()
for epoch_i in range(0, epochs):
print("")
print('======== Epoch {:} / {:} ========'.format(epoch_i + 1, epochs))
print('Training...')
t0 = time.time()
total_train_loss = 0
model.train()
for step, batch in enumerate(train_dataloader):
if step % 40 == 0 and not step == 0:
elapsed = format_time(time.time() - t0)
print(' Batch {:>5,} of {:>5,}.Elapsed: {:}.'.format(step, len(train_dataloader), elapsed))
b_input_ids = batch[0].to(device)
b_input_mask = batch[1].to(device)
b_labels = batch[2].to(device)
model.zero_grad()
outputs = model(b_input_ids,
token_type_ids=None,
attention_mask=b_input_mask,
labels=b_labels)
loss = outputs[0]
logits = outputs[1]
total_train_loss += loss.item()
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters() , 1.0)
optimizer.step()
scheduler.step()
avg_train_loss = total_train_loss / len(train_dataloader)
training_time = format_time(time.time() - t0)
print("")
print(" Average training loss: {0:.2f}".format(avg_train_loss))
print(" Training epcoh took: {:}".format(training_time))
print("")
print("Running Validation...")
t0 = time.time()
model.eval()
total_eval_accuracy = 0
total_eval_loss = 0
nb_eval_steps = 0
for batch in validation_dataloader:
b_input_ids = batch[0].to(device)
b_input_mask = batch[1].to(device)
b_labels = batch[2].to(device)
with torch.no_grad() :
output = model(b_input_ids,
token_type_ids=None,
attention_mask=b_input_mask,
labels=b_labels)
loss = output[0]
logits = output[1]
total_eval_loss += loss.item()
logits = logits.detach().cpu().numpy()
label_ids = b_labels.to('cpu' ).numpy()
total_eval_accuracy += flat_accuracy(logits, label_ids)
avg_val_accuracy = total_eval_accuracy / len(validation_dataloader)
print(" Accuracy: {0:.2f}".format(avg_val_accuracy))
avg_val_loss = total_eval_loss / len(validation_dataloader)
validation_time = format_time(time.time() - t0)
print(" Validation Loss: {0:.2f}".format(avg_val_loss))
print(" Validation took: {:}".format(validation_time))
training_stats.append(
{
'epoch': epoch_i + 1,
'Training Loss': avg_train_loss,
'Valid.Loss': avg_val_loss,
'Valid.Accur.': avg_val_accuracy,
'Training Time': training_time,
'Validation Time': validation_time
}
)
print("")
print("Training complete!")
print("Total training took {:}(h:mm:ss)".format(format_time(time.time() -total_t0)) )<create_dataframe> | history = model.fit_generator(datagen.flow(X_train, Y_train, batch_size=100), steps_per_epoch=len(X_train)/100,
epochs=20, validation_data=(X_test, Y_test), callbacks=[reduce_lr] ) | Digit Recognizer |
8,872,016 | pd.set_option('precision', 2)
df_stats = pd.DataFrame(data=training_stats)
df_stats = df_stats.set_index('epoch')
df_stats<load_from_csv> | Digit Recognizer |
|
8,872,016 | test_data = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
print('Number of test sentences: {:,}
'.format(test_data.shape[0]))
sentences = test_data.text.values
input_ids = []
attention_masks = []
for sent in sentences:
encoded_dict = tokenizer.encode_plus(
sent,
add_special_tokens = True,
max_length = 64,
pad_to_max_length = True,
return_attention_mask = True,
return_tensors = 'pt',
)
input_ids.append(encoded_dict['input_ids'])
attention_masks.append(encoded_dict['attention_mask'])
input_ids = torch.cat(input_ids, dim=0)
attention_masks = torch.cat(attention_masks, dim=0)
batch_size = 32
prediction_data = TensorDataset(input_ids, attention_masks,)
prediction_sampler = SequentialSampler(prediction_data)
prediction_dataloader = DataLoader(prediction_data, sampler=prediction_sampler, batch_size=batch_size )<predict_on_test> | score = model.evaluate(X_test, Y_test, verbose=0)
print('Test loss:', score[0])
print('Test accuracy:', score[1] ) | Digit Recognizer |
8,872,016 | print('Predicting labels for {:,} test sentences...'.format(len(input_ids)))
model.eval()
predictions = []
for batch in prediction_dataloader:
batch = tuple(t.to(device)for t in batch)
b_input_ids, b_input_mask = batch
with torch.no_grad() :
outputs = model(b_input_ids, token_type_ids=None,
attention_mask=b_input_mask)
logits = outputs[0]
logits = logits.detach().cpu().numpy()
label_ids = b_labels.to('cpu' ).numpy()
predictions.append(logits)
print(' DONE.' )<define_variables> | test_data = pd.read_csv('/kaggle/input/digit-recognizer/test.csv' ) | Digit Recognizer |
8,872,016 | flat_predictions = np.concatenate(predictions, axis=0)
flat_predictions = np.argmax(flat_predictions, axis=1 ).flatten()<save_to_csv> | test_data = test_data.values
test_data = test_data.reshape(28000, 28, 28,1)
test_data = test_data.astype('float32')
test_data /= 255
print("Test data matrix shape", test_data.shape ) | Digit Recognizer |
8,872,016 | submission = pd.read_csv('/kaggle/input/nlp-getting-started/sample_submission.csv')
submission.target = flat_predictions
submission.to_csv('submission.csv', index=False )<set_options> | y_pred = model.predict_classes(test_data, verbose=0)
print(y_pred ) | Digit Recognizer |
8,872,016 | pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
warnings.filterwarnings("ignore")
eng_stopwords = set(stopwords.words("english"))<load_from_csv> | i = 9713
predicted_value = np.argmax(model.predict(X_test[i].reshape(1,28, 28,1)))
print('predicted value:',predicted_value)
plt.imshow(X_test[i].reshape([28, 28]), cmap='Greys_r' ) | Digit Recognizer |
8,872,016 | train_df = pd.read_csv(".. /input/nlp-getting-started/train.csv")
test_df = pd.read_csv(".. /input/nlp-getting-started/test.csv")
submission = pd.read_csv(".. /input/nlp-getting-started/sample_submission.csv")
print("Training Shape rows = {}, columns = {}".format(train_df.shape[0],train_df.shape[1]))
print("Testing Shape rows = {}, columns = {}".format(test_df.shape[0],test_df.shape[1]))<count_missing_values> | submissions=pd.DataFrame({"ImageId": list(range(1,len(y_pred)+1)) ,
"Label": y_pred})
submissions.to_csv("LeNet_CNN.csv", index=False ) | Digit Recognizer |
7,764,469 | train_df.isnull().sum()<count_missing_values> | random_seed = 2020
np.random.seed(random_seed)
| Digit Recognizer |
7,764,469 | test_df.isnull().sum()<groupby> | train = pd.read_csv('.. /input/digit-recognizer/train.csv')
test = pd.read_csv('.. /input/digit-recognizer/test.csv')
Y = train['label']
X = train.drop(labels="label", axis=1)
X = X.values.reshape(-1, 28, 28, 1)/ 255
test = test.values.reshape(-1, 28, 28, 1)/ 255
print(X.shape, test.shape ) | Digit Recognizer |
7,764,469 | keyword_dist = train_df.groupby("keyword")['target'].value_counts().unstack(fill_value=0)
keyword_dist = keyword_dist.add_prefix(keyword_dist.columns.name ).rename_axis(columns=None ).reset_index()<sort_values> | learning_rate_reduction = ReduceLROnPlateau(monitor = 'val_acc',
patience = 3,
verbose = 1,
factor = 0.5,
min_lr = 0.0001)
es = EarlyStopping(monitor='val_loss',
mode='min',
verbose=1,
patience=15,
restore_best_weights=True)
def new_model(hidden=512, learning_rate=0.00128):
INPUT = Input(( 28, 28, 1))
inputs = Conv2D(64,(5, 5), activation='relu', padding='same' )(INPUT)
inputs = MaxPool2D(pool_size=(3,3), strides=(1,1))(inputs)
inputs = BatchNormalization()(inputs)
inputs = Activation('relu' )(inputs)
inputs = Dropout(0.25 )(inputs)
tower_1 = Conv2D(64,(1, 1), activation='relu', padding='same' )(inputs)
tower_1 = Conv2D(128,(2, 2), activation='relu', padding='same' )(tower_1)
tower_1 = Dropout(0.5 )(tower_1)
tower_1 = Conv2D(256,(3, 3), activation='relu', padding='same' )(tower_1)
tower_1 = MaxPool2D(pool_size=(3,3), strides=(2,2))(tower_1)
tower_1 = BatchNormalization()(tower_1)
tower_2 = Conv2D(64,(2, 2), activation='relu', padding='same' )(inputs)
tower_2 = Conv2D(128,(3, 3), activation='relu', padding='same' )(tower_2)
tower_2 = Dropout(0.5 )(tower_2)
tower_2 = Conv2D(256,(5, 5), activation='relu', padding='same' )(tower_2)
tower_2 = MaxPool2D(pool_size=(3,3), strides=(2,2))(tower_2)
tower_2 = BatchNormalization()(tower_2)
tower_3 = Conv2D(64,(1, 1), activation='relu', padding='same' )(inputs)
tower_3 = Conv2D(128,(3, 3), activation='relu', padding='same' )(tower_3)
tower_3 = Dropout(0.5 )(tower_3)
tower_3 = Conv2D(256,(5, 5), activation='relu', padding='same' )(tower_3)
tower_3 = MaxPool2D(pool_size=(3,3), strides=(2,2))(tower_3)
tower_3 = BatchNormalization()(tower_3)
x = Add()([tower_1, tower_2, tower_3])
x = Activation('relu' )(x)
x = Conv2D(256,(5, 5), activation='relu', padding='same' )(x)
x = MaxPool2D(pool_size=(5,5), strides=(4,4))(x)
x = BatchNormalization()(x)
x = Activation('relu' )(x)
x = Flatten()(x)
x = Dense(hidden, activation='relu' )(x)
x = Dropout(0.5 )(x)
x = Dense(hidden//4, activation='relu' )(x)
x = Dropout(0.5 )(x)
preds = Dense(10, activation='softmax', name='preds' )(x)
model = Model(inputs=INPUT, outputs=preds)
optimizer = Adam(lr=learning_rate)
model.compile(optimizer=optimizer, loss='sparse_categorical_crossentropy', metrics=['acc'])
return model
model = new_model() | Digit Recognizer |
7,764,469 | keyword_dist.sort_values('target1',ascending = False ).head(10 )<sort_values> | datagen = ImageDataGenerator(featurewise_center=False,
samplewise_center=False,
featurewise_std_normalization=False,
samplewise_std_normalization=False,
zca_whitening=False,
rotation_range=10,
zoom_range=0.1,
shear_range=0.02,
width_shift_range=0.1,
height_shift_range=0.1,
horizontal_flip=False,
vertical_flip=False ) | Digit Recognizer |
7,764,469 | keyword_dist.sort_values('target0',ascending = False ).head(10 )<feature_engineering> | epochs = 200
batch_size = 128
print("Learning Properties: Epoch:%i \t Batch Size:%i" %(epochs, batch_size))
predict_accumulator = np.zeros(model.predict(test ).shape)
accumulated_history = []
for i in range(1, 6):
X_train, X_val, Y_train, Y_val = train_test_split(X, Y, test_size=0.20, shuffle=True, random_state=random_seed*i)
model = new_model(512, 0.01)
datagen.fit(X_train)
history = model.fit_generator(datagen.flow(X_train, Y_train, batch_size=batch_size),
epochs=epochs, validation_data=(X_val, Y_val), verbose=1,
steps_per_epoch=X_train.shape[0]//batch_size,
callbacks=[learning_rate_reduction, es],
workers=4)
loss, acc = model.evaluate(X, Y)
if acc > 0.99:
predict_accumulator += model.predict(test)*acc
accumulated_history.append(history)
print("Current Predictions on fold number %i" %i)
print(*np.argmax(predict_accumulator, axis=1), sep='\t' ) | Digit Recognizer |
7,764,469 | train_df['word_count'] = train_df['text'].apply(lambda x : len(str(x ).split()))
test_df['word_count'] = test_df['text'].apply(lambda x : len(str(x ).split()))
train_df['unique_word_count'] = train_df['text'].apply(lambda x : len(set(str(x ).split())))
test_df['unique_word_count'] = test_df['text'].apply(lambda x : len(set(str(x ).split())))
train_df['count_letters'] = train_df['text'].apply(lambda x : len(str(x)))
test_df['count_letters'] = test_df['text'].apply(lambda x : len(str(x)))
train_df['count_punctuations'] = train_df['text'].apply(lambda x: len([c for c in str(x)if c in string.punctuation]))
test_df['count_punctuations'] = test_df['text'].apply(lambda x: len([c for c in str(x)if c in string.punctuation]))
train_df['stop_word_count'] = train_df['text'].apply(lambda x: len([w for w in str(x ).lower().split() if w in eng_stopwords]))
test_df['stop_word_count'] = test_df['text'].apply(lambda x: len([w for w in str(x ).lower().split() if w in eng_stopwords]))
train_df['hashtag_count'] = train_df['text'].apply(lambda x : len([c for c in str(x)if c == '
test_df['hashtag_count'] = test_df['text'].apply(lambda x : len([c for c in str(x)if c == '
train_df['mention_count'] = train_df['text'].apply(lambda x : len([c for c in str(x)if c=='@']))
test_df['mention_count'] = test_df['text'].apply(lambda x : len([c for c in str(x)if c=='@']))<categorify> | print("Completed Training.")
results = np.argmax(predict_accumulator, axis=1)
results = pd.Series(results, name="Label")
print("Saving prediction to output...")
submission = pd.concat([pd.Series(range(1, 1+test.shape[0]), name="ImageId"), results], axis=1)
submission.to_csv('submission.csv', index=False ) | Digit Recognizer |
7,764,469 | <categorify><EOS> | end_time = time.time()
total_time = int(end_time - start_time)
print("Total time spent: %i hours, %i minutes, %i seconds" \
%(( total_time//3600),(total_time%3600)//60,(total_time%60)) ) | Digit Recognizer |
5,786,490 | <SOS> metric: categorizationaccuracy Kaggle data source: digit-recognizer<categorify> | for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
| Digit Recognizer |
5,786,490 | def clean(tweet):
tweet = re.sub(r"\x89Û_", "", tweet)
tweet = re.sub(r"\x89ÛÒ", "", tweet)
tweet = re.sub(r"\x89ÛÓ", "", tweet)
tweet = re.sub(r"\x89ÛÏWhen", "When", tweet)
tweet = re.sub(r"\x89ÛÏ", "", tweet)
tweet = re.sub(r"China\x89Ûªs", "China's", tweet)
tweet = re.sub(r"let\x89Ûªs", "let's", tweet)
tweet = re.sub(r"\x89Û÷", "", tweet)
tweet = re.sub(r"\x89Ûª", "", tweet)
tweet = re.sub(r"\x89Û\x9d", "", tweet)
tweet = re.sub(r"å_", "", tweet)
tweet = re.sub(r"\x89Û¢", "", tweet)
tweet = re.sub(r"\x89Û¢åÊ", "", tweet)
tweet = re.sub(r"fromåÊwounds", "from wounds", tweet)
tweet = re.sub(r"åÊ", "", tweet)
tweet = re.sub(r"åÈ", "", tweet)
tweet = re.sub(r"JapÌ_n", "Japan", tweet)
tweet = re.sub(r"Ì©", "e", tweet)
tweet = re.sub(r"å¨", "", tweet)
tweet = re.sub(r"Surṳ", "Suruc", tweet)
tweet = re.sub(r"åÇ", "", tweet)
tweet = re.sub(r"å£3million", "3 million", tweet)
tweet = re.sub(r"åÀ", "", tweet)
tweet = re.sub(r"he's", "he is", tweet)
tweet = re.sub(r"there's", "there is", tweet)
tweet = re.sub(r"We're", "We are", tweet)
tweet = re.sub(r"That's", "That is", tweet)
tweet = re.sub(r"won't", "will not", tweet)
tweet = re.sub(r"they're", "they are", tweet)
tweet = re.sub(r"Can't", "Cannot", tweet)
tweet = re.sub(r"wasn't", "was not", tweet)
tweet = re.sub(r"don\x89Ûªt", "do not", tweet)
tweet = re.sub(r"aren't", "are not", tweet)
tweet = re.sub(r"isn't", "is not", tweet)
tweet = re.sub(r"What's", "What is", tweet)
tweet = re.sub(r"haven't", "have not", tweet)
tweet = re.sub(r"hasn't", "has not", tweet)
tweet = re.sub(r"There's", "There is", tweet)
tweet = re.sub(r"He's", "He is", tweet)
tweet = re.sub(r"It's", "It is", tweet)
tweet = re.sub(r"You're", "You are", tweet)
tweet = re.sub(r"I'M", "I am", tweet)
tweet = re.sub(r"shouldn't", "should not", tweet)
tweet = re.sub(r"wouldn't", "would not", tweet)
tweet = re.sub(r"i'm", "I am", tweet)
tweet = re.sub(r"I\x89Ûªm", "I am", tweet)
tweet = re.sub(r"I'm", "I am", tweet)
tweet = re.sub(r"Isn't", "is not", tweet)
tweet = re.sub(r"Here's", "Here is", tweet)
tweet = re.sub(r"you've", "you have", tweet)
tweet = re.sub(r"you\x89Ûªve", "you have", tweet)
tweet = re.sub(r"we're", "we are", tweet)
tweet = re.sub(r"what's", "what is", tweet)
tweet = re.sub(r"couldn't", "could not", tweet)
tweet = re.sub(r"we've", "we have", tweet)
tweet = re.sub(r"it\x89Ûªs", "it is", tweet)
tweet = re.sub(r"doesn\x89Ûªt", "does not", tweet)
tweet = re.sub(r"It\x89Ûªs", "It is", tweet)
tweet = re.sub(r"Here\x89Ûªs", "Here is", tweet)
tweet = re.sub(r"who's", "who is", tweet)
tweet = re.sub(r"I\x89Ûªve", "I have", tweet)
tweet = re.sub(r"y'all", "you all", tweet)
tweet = re.sub(r"can\x89Ûªt", "cannot", tweet)
tweet = re.sub(r"would've", "would have", tweet)
tweet = re.sub(r"it'll", "it will", tweet)
tweet = re.sub(r"we'll", "we will", tweet)
tweet = re.sub(r"wouldn\x89Ûªt", "would not", tweet)
tweet = re.sub(r"We've", "We have", tweet)
tweet = re.sub(r"he'll", "he will", tweet)
tweet = re.sub(r"Y'all", "You all", tweet)
tweet = re.sub(r"Weren't", "Were not", tweet)
tweet = re.sub(r"Didn't", "Did not", tweet)
tweet = re.sub(r"they'll", "they will", tweet)
tweet = re.sub(r"they'd", "they would", tweet)
tweet = re.sub(r"DON'T", "DO NOT", tweet)
tweet = re.sub(r"That\x89Ûªs", "That is", tweet)
tweet = re.sub(r"they've", "they have", tweet)
tweet = re.sub(r"i'd", "I would", tweet)
tweet = re.sub(r"should've", "should have", tweet)
tweet = re.sub(r"You\x89Ûªre", "You are", tweet)
tweet = re.sub(r"where's", "where is", tweet)
tweet = re.sub(r"Don\x89Ûªt", "Do not", tweet)
tweet = re.sub(r"we'd", "we would", tweet)
tweet = re.sub(r"i'll", "I will", tweet)
tweet = re.sub(r"weren't", "were not", tweet)
tweet = re.sub(r"They're", "They are", tweet)
tweet = re.sub(r"Can\x89Ûªt", "Cannot", tweet)
tweet = re.sub(r"you\x89Ûªll", "you will", tweet)
tweet = re.sub(r"I\x89Ûªd", "I would", tweet)
tweet = re.sub(r"let's", "let us", tweet)
tweet = re.sub(r"it's", "it is", tweet)
tweet = re.sub(r"can't", "cannot", tweet)
tweet = re.sub(r"don't", "do not", tweet)
tweet = re.sub(r"you're", "you are", tweet)
tweet = re.sub(r"i've", "I have", tweet)
tweet = re.sub(r"that's", "that is", tweet)
tweet = re.sub(r"i'll", "I will", tweet)
tweet = re.sub(r"doesn't", "does not", tweet)
tweet = re.sub(r"i'd", "I would", tweet)
tweet = re.sub(r"didn't", "did not", tweet)
tweet = re.sub(r"ain't", "am not", tweet)
tweet = re.sub(r"you'll", "you will", tweet)
tweet = re.sub(r"I've", "I have", tweet)
tweet = re.sub(r"Don't", "do not", tweet)
tweet = re.sub(r"I'll", "I will", tweet)
tweet = re.sub(r"I'd", "I would", tweet)
tweet = re.sub(r"Let's", "Let us", tweet)
tweet = re.sub(r"you'd", "You would", tweet)
tweet = re.sub(r"It's", "It is", tweet)
tweet = re.sub(r"Ain't", "am not", tweet)
tweet = re.sub(r"Haven't", "Have not", tweet)
tweet = re.sub(r"Could've", "Could have", tweet)
tweet = re.sub(r"youve", "you have", tweet)
tweet = re.sub(r"donå«t", "do not", tweet)
tweet = re.sub(r">", ">", tweet)
tweet = re.sub(r"<", "<", tweet)
tweet = re.sub(r"&", "&", tweet)
tweet = re.sub(r"w/e", "whatever", tweet)
tweet = re.sub(r"w/", "with", tweet)
tweet = re.sub(r"USAgov", "USA government", tweet)
tweet = re.sub(r"recentlu", "recently", tweet)
tweet = re.sub(r"Ph0tos", "Photos", tweet)
tweet = re.sub(r"amirite", "am I right", tweet)
tweet = re.sub(r"exp0sed", "exposed", tweet)
tweet = re.sub(r"<3", "love", tweet)
tweet = re.sub(r"amageddon", "armageddon", tweet)
tweet = re.sub(r"Trfc", "Traffic", tweet)
tweet = re.sub(r"8/5/2015", "2015-08-05", tweet)
tweet = re.sub(r"WindStorm", "Wind Storm", tweet)
tweet = re.sub(r"8/6/2015", "2015-08-06", tweet)
tweet = re.sub(r"10:38PM", "10:38 PM", tweet)
tweet = re.sub(r"10:30pm", "10:30 PM", tweet)
tweet = re.sub(r"16yr", "16 year", tweet)
tweet = re.sub(r"lmao", "laughing my ass off", tweet)
tweet = re.sub(r"TRAUMATISED", "traumatized", tweet)
tweet = re.sub(r"IranDeal", "Iran Deal", tweet)
tweet = re.sub(r"ArianaGrande", "Ariana Grande", tweet)
tweet = re.sub(r"camilacabello97", "camila cabello", tweet)
tweet = re.sub(r"RondaRousey", "Ronda Rousey", tweet)
tweet = re.sub(r"MTVHottest", "MTV Hottest", tweet)
tweet = re.sub(r"TrapMusic", "Trap Music", tweet)
tweet = re.sub(r"ProphetMuhammad", "Prophet Muhammad", tweet)
tweet = re.sub(r"PantherAttack", "Panther Attack", tweet)
tweet = re.sub(r"StrategicPatience", "Strategic Patience", tweet)
tweet = re.sub(r"socialnews", "social news", tweet)
tweet = re.sub(r"NASAHurricane", "NASA Hurricane", tweet)
tweet = re.sub(r"onlinecommunities", "online communities", tweet)
tweet = re.sub(r"humanconsumption", "human consumption", tweet)
tweet = re.sub(r"Typhoon-Devastated", "Typhoon Devastated", tweet)
tweet = re.sub(r"Meat-Loving", "Meat Loving", tweet)
tweet = re.sub(r"facialabuse", "facial abuse", tweet)
tweet = re.sub(r"LakeCounty", "Lake County", tweet)
tweet = re.sub(r"BeingAuthor", "Being Author", tweet)
tweet = re.sub(r"withheavenly", "with heavenly", tweet)
tweet = re.sub(r"thankU", "thank you", tweet)
tweet = re.sub(r"iTunesMusic", "iTunes Music", tweet)
tweet = re.sub(r"OffensiveContent", "Offensive Content", tweet)
tweet = re.sub(r"WorstSummerJob", "Worst Summer Job", tweet)
tweet = re.sub(r"HarryBeCareful", "Harry Be Careful", tweet)
tweet = re.sub(r"NASASolarSystem", "NASA Solar System", tweet)
tweet = re.sub(r"animalrescue", "animal rescue", tweet)
tweet = re.sub(r"KurtSchlichter", "Kurt Schlichter", tweet)
tweet = re.sub(r"aRmageddon", "armageddon", tweet)
tweet = re.sub(r"Throwingknifes", "Throwing knives", tweet)
tweet = re.sub(r"GodsLove", "God's Love", tweet)
tweet = re.sub(r"bookboost", "book boost", tweet)
tweet = re.sub(r"ibooklove", "I book love", tweet)
tweet = re.sub(r"NestleIndia", "Nestle India", tweet)
tweet = re.sub(r"realDonaldTrump", "Donald Trump", tweet)
tweet = re.sub(r"DavidVonderhaar", "David Vonderhaar", tweet)
tweet = re.sub(r"CecilTheLion", "Cecil The Lion", tweet)
tweet = re.sub(r"weathernetwork", "weather network", tweet)
tweet = re.sub(r"withBioterrorism&use", "with Bioterrorism & use", tweet)
tweet = re.sub(r"Hostage&2", "Hostage & 2", tweet)
tweet = re.sub(r"GOPDebate", "GOP Debate", tweet)
tweet = re.sub(r"RickPerry", "Rick Perry", tweet)
tweet = re.sub(r"frontpage", "front page", tweet)
tweet = re.sub(r"NewsInTweets", "News In Tweets", tweet)
tweet = re.sub(r"ViralSpell", "Viral Spell", tweet)
tweet = re.sub(r"til_now", "until now", tweet)
tweet = re.sub(r"volcanoinRussia", "volcano in Russia", tweet)
tweet = re.sub(r"ZippedNews", "Zipped News", tweet)
tweet = re.sub(r"MicheleBachman", "Michele Bachman", tweet)
tweet = re.sub(r"53inch", "53 inch", tweet)
tweet = re.sub(r"KerrickTrial", "Kerrick Trial", tweet)
tweet = re.sub(r"abstorm", "Alberta Storm", tweet)
tweet = re.sub(r"Beyhive", "Beyonce hive", tweet)
tweet = re.sub(r"IDFire", "Idaho Fire", tweet)
tweet = re.sub(r"DETECTADO", "Detected", tweet)
tweet = re.sub(r"RockyFire", "Rocky Fire", tweet)
tweet = re.sub(r"Listen/Buy", "Listen / Buy", tweet)
tweet = re.sub(r"NickCannon", "Nick Cannon", tweet)
tweet = re.sub(r"FaroeIslands", "Faroe Islands", tweet)
tweet = re.sub(r"yycstorm", "Calgary Storm", tweet)
tweet = re.sub(r"IDPs:", "Internally Displaced People :", tweet)
tweet = re.sub(r"ArtistsUnited", "Artists United", tweet)
tweet = re.sub(r"ClaytonBryant", "Clayton Bryant", tweet)
tweet = re.sub(r"jimmyfallon", "jimmy fallon", tweet)
tweet = re.sub(r"justinbieber", "justin bieber", tweet)
tweet = re.sub(r"UTC2015", "UTC 2015", tweet)
tweet = re.sub(r"Time2015", "Time 2015", tweet)
tweet = re.sub(r"djicemoon", "dj icemoon", tweet)
tweet = re.sub(r"LivingSafely", "Living Safely", tweet)
tweet = re.sub(r"FIFA16", "Fifa 2016", tweet)
tweet = re.sub(r"thisiswhywecanthavenicethings", "this is why we cannot have nice things", tweet)
tweet = re.sub(r"bbcnews", "bbc news", tweet)
tweet = re.sub(r"UndergroundRailraod", "Underground Railraod", tweet)
tweet = re.sub(r"c4news", "c4 news", tweet)
tweet = re.sub(r"OBLITERATION", "obliteration", tweet)
tweet = re.sub(r"MUDSLIDE", "mudslide", tweet)
tweet = re.sub(r"NoSurrender", "No Surrender", tweet)
tweet = re.sub(r"NotExplained", "Not Explained", tweet)
tweet = re.sub(r"greatbritishbakeoff", "great british bake off", tweet)
tweet = re.sub(r"LondonFire", "London Fire", tweet)
tweet = re.sub(r"KOTAWeather", "KOTA Weather", tweet)
tweet = re.sub(r"LuchaUnderground", "Lucha Underground", tweet)
tweet = re.sub(r"KOIN6News", "KOIN 6 News", tweet)
tweet = re.sub(r"LiveOnK2", "Live On K2", tweet)
tweet = re.sub(r"9NewsGoldCoast", "9 News Gold Coast", tweet)
tweet = re.sub(r"nikeplus", "nike plus", tweet)
tweet = re.sub(r"david_cameron", "David Cameron", tweet)
tweet = re.sub(r"peterjukes", "Peter Jukes", tweet)
tweet = re.sub(r"JamesMelville", "James Melville", tweet)
tweet = re.sub(r"megynkelly", "Megyn Kelly", tweet)
tweet = re.sub(r"cnewslive", "C News Live", tweet)
tweet = re.sub(r"JamaicaObserver", "Jamaica Observer", tweet)
tweet = re.sub(r"TweetLikeItsSeptember11th2001", "Tweet like it is september 11th 2001", tweet)
tweet = re.sub(r"cbplawyers", "cbp lawyers", tweet)
tweet = re.sub(r"fewmoretweets", "few more tweets", tweet)
tweet = re.sub(r"BlackLivesMatter", "Black Lives Matter", tweet)
tweet = re.sub(r"cjoyner", "Chris Joyner", tweet)
tweet = re.sub(r"ENGvAUS", "England vs Australia", tweet)
tweet = re.sub(r"ScottWalker", "Scott Walker", tweet)
tweet = re.sub(r"MikeParrActor", "Michael Parr", tweet)
tweet = re.sub(r"4PlayThursdays", "Foreplay Thursdays", tweet)
tweet = re.sub(r"TGF2015", "Tontitown Grape Festival", tweet)
tweet = re.sub(r"realmandyrain", "Mandy Rain", tweet)
tweet = re.sub(r"GraysonDolan", "Grayson Dolan", tweet)
tweet = re.sub(r"ApolloBrown", "Apollo Brown", tweet)
tweet = re.sub(r"saddlebrooke", "Saddlebrooke", tweet)
tweet = re.sub(r"TontitownGrape", "Tontitown Grape", tweet)
tweet = re.sub(r"AbbsWinston", "Abbs Winston", tweet)
tweet = re.sub(r"ShaunKing", "Shaun King", tweet)
tweet = re.sub(r"MeekMill", "Meek Mill", tweet)
tweet = re.sub(r"TornadoGiveaway", "Tornado Giveaway", tweet)
tweet = re.sub(r"GRupdates", "GR updates", tweet)
tweet = re.sub(r"SouthDowns", "South Downs", tweet)
tweet = re.sub(r"braininjury", "brain injury", tweet)
tweet = re.sub(r"auspol", "Australian politics", tweet)
tweet = re.sub(r"PlannedParenthood", "Planned Parenthood", tweet)
tweet = re.sub(r"calgaryweather", "Calgary Weather", tweet)
tweet = re.sub(r"weallheartonedirection", "we all heart one direction", tweet)
tweet = re.sub(r"edsheeran", "Ed Sheeran", tweet)
tweet = re.sub(r"TrueHeroes", "True Heroes", tweet)
tweet = re.sub(r"S3XLEAK", "sex leak", tweet)
tweet = re.sub(r"ComplexMag", "Complex Magazine", tweet)
tweet = re.sub(r"TheAdvocateMag", "The Advocate Magazine", tweet)
tweet = re.sub(r"CityofCalgary", "City of Calgary", tweet)
tweet = re.sub(r"EbolaOutbreak", "Ebola Outbreak", tweet)
tweet = re.sub(r"SummerFate", "Summer Fate", tweet)
tweet = re.sub(r"RAmag", "Royal Academy Magazine", tweet)
tweet = re.sub(r"offers2go", "offers to go", tweet)
tweet = re.sub(r"foodscare", "food scare", tweet)
tweet = re.sub(r"MNPDNashville", "Metropolitan Nashville Police Department", tweet)
tweet = re.sub(r"TfLBusAlerts", "TfL Bus Alerts", tweet)
tweet = re.sub(r"GamerGate", "Gamer Gate", tweet)
tweet = re.sub(r"IHHen", "Humanitarian Relief", tweet)
tweet = re.sub(r"spinningbot", "spinning bot", tweet)
tweet = re.sub(r"ModiMinistry", "Modi Ministry", tweet)
tweet = re.sub(r"TAXIWAYS", "taxi ways", tweet)
tweet = re.sub(r"Calum5SOS", "Calum Hood", tweet)
tweet = re.sub(r"po_st", "po.st", tweet)
tweet = re.sub(r"scoopit", "scoop.it", tweet)
tweet = re.sub(r"UltimaLucha", "Ultima Lucha", tweet)
tweet = re.sub(r"JonathanFerrell", "Jonathan Ferrell", tweet)
tweet = re.sub(r"aria_ahrary", "Aria Ahrary", tweet)
tweet = re.sub(r"rapidcity", "Rapid City", tweet)
tweet = re.sub(r"OutBid", "outbid", tweet)
tweet = re.sub(r"lavenderpoetrycafe", "lavender poetry cafe", tweet)
tweet = re.sub(r"EudryLantiqua", "Eudry Lantiqua", tweet)
tweet = re.sub(r"15PM", "15 PM", tweet)
tweet = re.sub(r"OriginalFunko", "Funko", tweet)
tweet = re.sub(r"rightwaystan", "Richard Tan", tweet)
tweet = re.sub(r"CindyNoonan", "Cindy Noonan", tweet)
tweet = re.sub(r"RT_America", "RT America", tweet)
tweet = re.sub(r"narendramodi", "Narendra Modi", tweet)
tweet = re.sub(r"BakeOffFriends", "Bake Off Friends", tweet)
tweet = re.sub(r"TeamHendrick", "Hendrick Motorsports", tweet)
tweet = re.sub(r"alexbelloli", "Alex Belloli", tweet)
tweet = re.sub(r"itsjustinstuart", "Justin Stuart", tweet)
tweet = re.sub(r"gunsense", "gun sense", tweet)
tweet = re.sub(r"DebateQuestionsWeWantToHear", "debate questions we want to hear", tweet)
tweet = re.sub(r"RoyalCarribean", "Royal Carribean", tweet)
tweet = re.sub(r"samanthaturne19", "Samantha Turner", tweet)
tweet = re.sub(r"JonVoyage", "Jon Stewart", tweet)
tweet = re.sub(r"renew911health", "renew 911 health", tweet)
tweet = re.sub(r"SuryaRay", "Surya Ray", tweet)
tweet = re.sub(r"pattonoswalt", "Patton Oswalt", tweet)
tweet = re.sub(r"minhazmerchant", "Minhaz Merchant", tweet)
tweet = re.sub(r"TLVFaces", "Israel Diaspora Coalition", tweet)
tweet = re.sub(r"pmarca", "Marc Andreessen", tweet)
tweet = re.sub(r"pdx911", "Portland Police", tweet)
tweet = re.sub(r"jamaicaplain", "Jamaica Plain", tweet)
tweet = re.sub(r"Japton", "Arkansas", tweet)
tweet = re.sub(r"RouteComplex", "Route Complex", tweet)
tweet = re.sub(r"INSubcontinent", "Indian Subcontinent", tweet)
tweet = re.sub(r"NJTurnpike", "New Jersey Turnpike", tweet)
tweet = re.sub(r"Politifiact", "PolitiFact", tweet)
tweet = re.sub(r"Hiroshima70", "Hiroshima", tweet)
tweet = re.sub(r"GMMBC", "Greater Mt Moriah Baptist Church", tweet)
tweet = re.sub(r"versethe", "verse the", tweet)
tweet = re.sub(r"TubeStrike", "Tube Strike", tweet)
tweet = re.sub(r"MissionHills", "Mission Hills", tweet)
tweet = re.sub(r"ProtectDenaliWolves", "Protect Denali Wolves", tweet)
tweet = re.sub(r"NANKANA", "Nankana", tweet)
tweet = re.sub(r"SAHIB", "Sahib", tweet)
tweet = re.sub(r"PAKPATTAN", "Pakpattan", tweet)
tweet = re.sub(r"Newz_Sacramento", "News Sacramento", tweet)
tweet = re.sub(r"gofundme", "go fund me", tweet)
tweet = re.sub(r"pmharper", "Stephen Harper", tweet)
tweet = re.sub(r"IvanBerroa", "Ivan Berroa", tweet)
tweet = re.sub(r"LosDelSonido", "Los Del Sonido", tweet)
tweet = re.sub(r"bancodeseries", "banco de series", tweet)
tweet = re.sub(r"timkaine", "Tim Kaine", tweet)
tweet = re.sub(r"IdentityTheft", "Identity Theft", tweet)
tweet = re.sub(r"AllLivesMatter", "All Lives Matter", tweet)
tweet = re.sub(r"mishacollins", "Misha Collins", tweet)
tweet = re.sub(r"BillNeelyNBC", "Bill Neely", tweet)
tweet = re.sub(r"BeClearOnCancer", "be clear on cancer", tweet)
tweet = re.sub(r"Kowing", "Knowing", tweet)
tweet = re.sub(r"ScreamQueens", "Scream Queens", tweet)
tweet = re.sub(r"AskCharley", "Ask Charley", tweet)
tweet = re.sub(r"BlizzHeroes", "Heroes of the Storm", tweet)
tweet = re.sub(r"BradleyBrad47", "Bradley Brad", tweet)
tweet = re.sub(r"HannaPH", "Typhoon Hanna", tweet)
tweet = re.sub(r"meinlcymbals", "MEINL Cymbals", tweet)
tweet = re.sub(r"Ptbo", "Peterborough", tweet)
tweet = re.sub(r"cnnbrk", "CNN Breaking News", tweet)
tweet = re.sub(r"IndianNews", "Indian News", tweet)
tweet = re.sub(r"savebees", "save bees", tweet)
tweet = re.sub(r"GreenHarvard", "Green Harvard", tweet)
tweet = re.sub(r"StandwithPP", "Stand with planned parenthood", tweet)
tweet = re.sub(r"hermancranston", "Herman Cranston", tweet)
tweet = re.sub(r"WMUR9", "WMUR-TV", tweet)
tweet = re.sub(r"RockBottomRadFM", "Rock Bottom Radio", tweet)
tweet = re.sub(r"ameenshaikh3", "Ameen Shaikh", tweet)
tweet = re.sub(r"ProSyn", "Project Syndicate", tweet)
tweet = re.sub(r"Daesh", "ISIS", tweet)
tweet = re.sub(r"s2g", "swear to god", tweet)
tweet = re.sub(r"listenlive", "listen live", tweet)
tweet = re.sub(r"CDCgov", "Centers for Disease Control and Prevention", tweet)
tweet = re.sub(r"FoxNew", "Fox News", tweet)
tweet = re.sub(r"CBSBigBrother", "Big Brother", tweet)
tweet = re.sub(r"JulieDiCaro", "Julie DiCaro", tweet)
tweet = re.sub(r"theadvocatemag", "The Advocate Magazine", tweet)
tweet = re.sub(r"RohnertParkDPS", "Rohnert Park Police Department", tweet)
tweet = re.sub(r"THISIZBWRIGHT", "Bonnie Wright", tweet)
tweet = re.sub(r"Popularmmos", "Popular MMOs", tweet)
tweet = re.sub(r"WildHorses", "Wild Horses", tweet)
tweet = re.sub(r"FantasticFour", "Fantastic Four", tweet)
tweet = re.sub(r"HORNDALE", "Horndale", tweet)
tweet = re.sub(r"PINER", "Piner", tweet)
tweet = re.sub(r"BathAndNorthEastSomerset", "Bath and North East Somerset", tweet)
tweet = re.sub(r"thatswhatfriendsarefor", "that is what friends are for", tweet)
tweet = re.sub(r"residualincome", "residual income", tweet)
tweet = re.sub(r"YahooNewsDigest", "Yahoo News Digest", tweet)
tweet = re.sub(r"MalaysiaAirlines", "Malaysia Airlines", tweet)
tweet = re.sub(r"AmazonDeals", "Amazon Deals", tweet)
tweet = re.sub(r"MissCharleyWebb", "Charley Webb", tweet)
tweet = re.sub(r"shoalstraffic", "shoals traffic", tweet)
tweet = re.sub(r"GeorgeFoster72", "George Foster", tweet)
tweet = re.sub(r"pop2015", "pop 2015", tweet)
tweet = re.sub(r"_PokemonCards_", "Pokemon Cards", tweet)
tweet = re.sub(r"DianneG", "Dianne Gallagher", tweet)
tweet = re.sub(r"KashmirConflict", "Kashmir Conflict", tweet)
tweet = re.sub(r"BritishBakeOff", "British Bake Off", tweet)
tweet = re.sub(r"FreeKashmir", "Free Kashmir", tweet)
tweet = re.sub(r"mattmosley", "Matt Mosley", tweet)
tweet = re.sub(r"BishopFred", "Bishop Fred", tweet)
tweet = re.sub(r"EndConflict", "End Conflict", tweet)
tweet = re.sub(r"EndOccupation", "End Occupation", tweet)
tweet = re.sub(r"UNHEALED", "unhealed", tweet)
tweet = re.sub(r"CharlesDagnall", "Charles Dagnall", tweet)
tweet = re.sub(r"Latestnews", "Latest news", tweet)
tweet = re.sub(r"KindleCountdown", "Kindle Countdown", tweet)
tweet = re.sub(r"NoMoreHandouts", "No More Handouts", tweet)
tweet = re.sub(r"datingtips", "dating tips", tweet)
tweet = re.sub(r"charlesadler", "Charles Adler", tweet)
tweet = re.sub(r"twia", "Texas Windstorm Insurance Association", tweet)
tweet = re.sub(r"txlege", "Texas Legislature", tweet)
tweet = re.sub(r"WindstormInsurer", "Windstorm Insurer", tweet)
tweet = re.sub(r"Newss", "News", tweet)
tweet = re.sub(r"hempoil", "hemp oil", tweet)
tweet = re.sub(r"CommoditiesAre", "Commodities are", tweet)
tweet = re.sub(r"tubestrike", "tube strike", tweet)
tweet = re.sub(r"JoeNBC", "Joe Scarborough", tweet)
tweet = re.sub(r"LiteraryCakes", "Literary Cakes", tweet)
tweet = re.sub(r"TI5", "The International 5", tweet)
tweet = re.sub(r"thehill", "the hill", tweet)
tweet = re.sub(r"3others", "3 others", tweet)
tweet = re.sub(r"stighefootball", "Sam Tighe", tweet)
tweet = re.sub(r"whatstheimportantvideo", "what is the important video", tweet)
tweet = re.sub(r"ClaudioMeloni", "Claudio Meloni", tweet)
tweet = re.sub(r"DukeSkywalker", "Duke Skywalker", tweet)
tweet = re.sub(r"carsonmwr", "Fort Carson", tweet)
tweet = re.sub(r"offdishduty", "off dish duty", tweet)
tweet = re.sub(r"andword", "and word", tweet)
tweet = re.sub(r"rhodeisland", "Rhode Island", tweet)
tweet = re.sub(r"easternoregon", "Eastern Oregon", tweet)
tweet = re.sub(r"WAwildfire", "Washington Wildfire", tweet)
tweet = re.sub(r"fingerrockfire", "Finger Rock Fire", tweet)
tweet = re.sub(r"57am", "57 am", tweet)
tweet = re.sub(r"fingerrockfire", "Finger Rock Fire", tweet)
tweet = re.sub(r"JacobHoggard", "Jacob Hoggard", tweet)
tweet = re.sub(r"newnewnew", "new new new", tweet)
tweet = re.sub(r"under50", "under 50", tweet)
tweet = re.sub(r"getitbeforeitsgone", "get it before it is gone", tweet)
tweet = re.sub(r"freshoutofthebox", "fresh out of the box", tweet)
tweet = re.sub(r"amwriting", "am writing", tweet)
tweet = re.sub(r"Bokoharm", "Boko Haram", tweet)
tweet = re.sub(r"Nowlike", "Now like", tweet)
tweet = re.sub(r"seasonfrom", "season from", tweet)
tweet = re.sub(r"epicente", "epicenter", tweet)
tweet = re.sub(r"epicenterr", "epicenter", tweet)
tweet = re.sub(r"sicklife", "sick life", tweet)
tweet = re.sub(r"yycweather", "Calgary Weather", tweet)
tweet = re.sub(r"calgarysun", "Calgary Sun", tweet)
tweet = re.sub(r"approachng", "approaching", tweet)
tweet = re.sub(r"evng", "evening", tweet)
tweet = re.sub(r"Sumthng", "something", tweet)
tweet = re.sub(r"EllenPompeo", "Ellen Pompeo", tweet)
tweet = re.sub(r"shondarhimes", "Shonda Rhimes", tweet)
tweet = re.sub(r"ABCNetwork", "ABC Network", tweet)
tweet = re.sub(r"SushmaSwaraj", "Sushma Swaraj", tweet)
tweet = re.sub(r"pray4japan", "Pray for Japan", tweet)
tweet = re.sub(r"hope4japan", "Hope for Japan", tweet)
tweet = re.sub(r"Illusionimagess", "Illusion images", tweet)
tweet = re.sub(r"SummerUnderTheStars", "Summer Under The Stars", tweet)
tweet = re.sub(r"ShallWeDance", "Shall We Dance", tweet)
tweet = re.sub(r"TCMParty", "TCM Party", tweet)
tweet = re.sub(r"marijuananews", "marijuana news", tweet)
tweet = re.sub(r"onbeingwithKristaTippett", "on being with Krista Tippett", tweet)
tweet = re.sub(r"Beingtweets", "Being tweets", tweet)
tweet = re.sub(r"newauthors", "new authors", tweet)
tweet = re.sub(r"remedyyyy", "remedy", tweet)
tweet = re.sub(r"44PM", "44 PM", tweet)
tweet = re.sub(r"HeadlinesApp", "Headlines App", tweet)
tweet = re.sub(r"40PM", "40 PM", tweet)
tweet = re.sub(r"myswc", "Severe Weather Center", tweet)
tweet = re.sub(r"ithats", "that is", tweet)
tweet = re.sub(r"icouldsitinthismomentforever", "I could sit in this moment forever", tweet)
tweet = re.sub(r"FatLoss", "Fat Loss", tweet)
tweet = re.sub(r"02PM", "02 PM", tweet)
tweet = re.sub(r"MetroFmTalk", "Metro Fm Talk", tweet)
tweet = re.sub(r"Bstrd", "bastard", tweet)
tweet = re.sub(r"bldy", "bloody", tweet)
tweet = re.sub(r"MetrofmTalk", "Metro Fm Talk", tweet)
tweet = re.sub(r"terrorismturn", "terrorism turn", tweet)
tweet = re.sub(r"BBCNewsAsia", "BBC News Asia", tweet)
tweet = re.sub(r"BehindTheScenes", "Behind The Scenes", tweet)
tweet = re.sub(r"GeorgeTakei", "George Takei", tweet)
tweet = re.sub(r"WomensWeeklyMag", "Womens Weekly Magazine", tweet)
tweet = re.sub(r"SurvivorsGuidetoEarth", "Survivors Guide to Earth", tweet)
tweet = re.sub(r"incubusband", "incubus band", tweet)
tweet = re.sub(r"Babypicturethis", "Baby picture this", tweet)
tweet = re.sub(r"BombEffects", "Bomb Effects", tweet)
tweet = re.sub(r"win10", "Windows 10", tweet)
tweet = re.sub(r"idkidk", "I do not know I do not know", tweet)
tweet = re.sub(r"TheWalkingDead", "The Walking Dead", tweet)
tweet = re.sub(r"amyschumer", "Amy Schumer", tweet)
tweet = re.sub(r"crewlist", "crew list", tweet)
tweet = re.sub(r"Erdogans", "Erdogan", tweet)
tweet = re.sub(r"BBCLive", "BBC Live", tweet)
tweet = re.sub(r"TonyAbbottMHR", "Tony Abbott", tweet)
tweet = re.sub(r"paulmyerscough", "Paul Myerscough", tweet)
tweet = re.sub(r"georgegallagher", "George Gallagher", tweet)
tweet = re.sub(r"JimmieJohnson", "Jimmie Johnson", tweet)
tweet = re.sub(r"pctool", "pc tool", tweet)
tweet = re.sub(r"DoingHashtagsRight", "Doing Hashtags Right", tweet)
tweet = re.sub(r"ThrowbackThursday", "Throwback Thursday", tweet)
tweet = re.sub(r"SnowBackSunday", "Snowback Sunday", tweet)
tweet = re.sub(r"LakeEffect", "Lake Effect", tweet)
tweet = re.sub(r"RTphotographyUK", "Richard Thomas Photography UK", tweet)
tweet = re.sub(r"BigBang_CBS", "Big Bang CBS", tweet)
tweet = re.sub(r"writerslife", "writers life", tweet)
tweet = re.sub(r"NaturalBirth", "Natural Birth", tweet)
tweet = re.sub(r"UnusualWords", "Unusual Words", tweet)
tweet = re.sub(r"wizkhalifa", "Wiz Khalifa", tweet)
tweet = re.sub(r"acreativedc", "a creative DC", tweet)
tweet = re.sub(r"vscodc", "vsco DC", tweet)
tweet = re.sub(r"VSCOcam", "vsco camera", tweet)
tweet = re.sub(r"TheBEACHDC", "The beach DC", tweet)
tweet = re.sub(r"buildingmuseum", "building museum", tweet)
tweet = re.sub(r"WorldOil", "World Oil", tweet)
tweet = re.sub(r"redwedding", "red wedding", tweet)
tweet = re.sub(r"AmazingRaceCanada", "Amazing Race Canada", tweet)
tweet = re.sub(r"WakeUpAmerica", "Wake Up America", tweet)
tweet = re.sub(r"\\Allahuakbar\", "Allahu Akbar", tweet)
tweet = re.sub(r"bleased", "blessed", tweet)
tweet = re.sub(r"nigeriantribune", "Nigerian Tribune", tweet)
tweet = re.sub(r"HIDEO_KOJIMA_EN", "Hideo Kojima", tweet)
tweet = re.sub(r"FusionFestival", "Fusion Festival", tweet)
tweet = re.sub(r"50Mixed", "50 Mixed", tweet)
tweet = re.sub(r"NoAgenda", "No Agenda", tweet)
tweet = re.sub(r"WhiteGenocide", "White Genocide", tweet)
tweet = re.sub(r"dirtylying", "dirty lying", tweet)
tweet = re.sub(r"SyrianRefugees", "Syrian Refugees", tweet)
tweet = re.sub(r"changetheworld", "change the world", tweet)
tweet = re.sub(r"Ebolacase", "Ebola case", tweet)
tweet = re.sub(r"mcgtech", "mcg technologies", tweet)
tweet = re.sub(r"withweapons", "with weapons", tweet)
tweet = re.sub(r"advancedwarfare", "advanced warfare", tweet)
tweet = re.sub(r"letsFootball", "let us Football", tweet)
tweet = re.sub(r"LateNiteMix", "late night mix", tweet)
tweet = re.sub(r"PhilCollinsFeed", "Phil Collins", tweet)
tweet = re.sub(r"RudyHavenstein", "Rudy Havenstein", tweet)
tweet = re.sub(r"22PM", "22 PM", tweet)
tweet = re.sub(r"54am", "54 AM", tweet)
tweet = re.sub(r"38am", "38 AM", tweet)
tweet = re.sub(r"OldFolkExplainStuff", "Old Folk Explain Stuff", tweet)
tweet = re.sub(r"BlacklivesMatter", "Black Lives Matter", tweet)
tweet = re.sub(r"InsaneLimits", "Insane Limits", tweet)
tweet = re.sub(r"youcantsitwithus", "you cannot sit with us", tweet)
tweet = re.sub(r"2k15", "2015", tweet)
tweet = re.sub(r"TheIran", "Iran", tweet)
tweet = re.sub(r"JimmyFallon", "Jimmy Fallon", tweet)
tweet = re.sub(r"AlbertBrooks", "Albert Brooks", tweet)
tweet = re.sub(r"defense_news", "defense news", tweet)
tweet = re.sub(r"nuclearrcSA", "Nuclear Risk Control Self Assessment", tweet)
tweet = re.sub(r"Auspol", "Australia Politics", tweet)
tweet = re.sub(r"NuclearPower", "Nuclear Power", tweet)
tweet = re.sub(r"WhiteTerrorism", "White Terrorism", tweet)
tweet = re.sub(r"truthfrequencyradio", "Truth Frequency Radio", tweet)
tweet = re.sub(r"ErasureIsNotEquality", "Erasure is not equality", tweet)
tweet = re.sub(r"ProBonoNews", "Pro Bono News", tweet)
tweet = re.sub(r"JakartaPost", "Jakarta Post", tweet)
tweet = re.sub(r"toopainful", "too painful", tweet)
tweet = re.sub(r"melindahaunton", "Melinda Haunton", tweet)
tweet = re.sub(r"NoNukes", "No Nukes", tweet)
tweet = re.sub(r"curryspcworld", "Currys PC World", tweet)
tweet = re.sub(r"ineedcake", "I need cake", tweet)
tweet = re.sub(r"blackforestgateau", "black forest gateau", tweet)
tweet = re.sub(r"BBCOne", "BBC One", tweet)
tweet = re.sub(r"AlexxPage", "Alex Page", tweet)
tweet = re.sub(r"jonathanserrie", "Jonathan Serrie", tweet)
tweet = re.sub(r"SocialJerkBlog", "Social Jerk Blog", tweet)
tweet = re.sub(r"ChelseaVPeretti", "Chelsea Peretti", tweet)
tweet = re.sub(r"irongiant", "iron giant", tweet)
tweet = re.sub(r"RonFunches", "Ron Funches", tweet)
tweet = re.sub(r"TimCook", "Tim Cook", tweet)
tweet = re.sub(r"sebastianstanisaliveandwell", "Sebastian Stan is alive and well", tweet)
tweet = re.sub(r"Madsummer", "Mad summer", tweet)
tweet = re.sub(r"NowYouKnow", "Now you know", tweet)
tweet = re.sub(r"concertphotography", "concert photography", tweet)
tweet = re.sub(r"TomLandry", "Tom Landry", tweet)
tweet = re.sub(r"showgirldayoff", "show girl day off", tweet)
tweet = re.sub(r"Yougslavia", "Yugoslavia", tweet)
tweet = re.sub(r"QuantumDataInformatics", "Quantum Data Informatics", tweet)
tweet = re.sub(r"FromTheDesk", "From The Desk", tweet)
tweet = re.sub(r"TheaterTrial", "Theater Trial", tweet)
tweet = re.sub(r"CatoInstitute", "Cato Institute", tweet)
tweet = re.sub(r"EmekaGift", "Emeka Gift", tweet)
tweet = re.sub(r"LetsBe_Rational", "Let us be rational", tweet)
tweet = re.sub(r"Cynicalreality", "Cynical reality", tweet)
tweet = re.sub(r"FredOlsenCruise", "Fred Olsen Cruise", tweet)
tweet = re.sub(r"NotSorry", "not sorry", tweet)
tweet = re.sub(r"UseYourWords", "use your words", tweet)
tweet = re.sub(r"WordoftheDay", "word of the day", tweet)
tweet = re.sub(r"Dictionarycom", "Dictionary.com", tweet)
tweet = re.sub(r"TheBrooklynLife", "The Brooklyn Life", tweet)
tweet = re.sub(r"jokethey", "joke they", tweet)
tweet = re.sub(r"nflweek1picks", "NFL week 1 picks", tweet)
tweet = re.sub(r"uiseful", "useful", tweet)
tweet = re.sub(r"JusticeDotOrg", "The American Association for Justice", tweet)
tweet = re.sub(r"autoaccidents", "auto accidents", tweet)
tweet = re.sub(r"SteveGursten", "Steve Gursten", tweet)
tweet = re.sub(r"MichiganAutoLaw", "Michigan Auto Law", tweet)
tweet = re.sub(r"birdgang", "bird gang", tweet)
tweet = re.sub(r"nflnetwork", "NFL Network", tweet)
tweet = re.sub(r"NYDNSports", "NY Daily News Sports", tweet)
tweet = re.sub(r"RVacchianoNYDN", "Ralph Vacchiano NY Daily News", tweet)
tweet = re.sub(r"EdmontonEsks", "Edmonton Eskimos", tweet)
tweet = re.sub(r"david_brelsford", "David Brelsford", tweet)
tweet = re.sub(r"TOI_India", "The Times of India", tweet)
tweet = re.sub(r"hegot", "he got", tweet)
tweet = re.sub(r"SkinsOn9", "Skins on 9", tweet)
tweet = re.sub(r"sothathappened", "so that happened", tweet)
tweet = re.sub(r"LCOutOfDoors", "LC Out Of Doors", tweet)
tweet = re.sub(r"NationFirst", "Nation First", tweet)
tweet = re.sub(r"IndiaToday", "India Today", tweet)
tweet = re.sub(r"HLPS", "helps", tweet)
tweet = re.sub(r"HOSTAGESTHROSW", "hostages throw", tweet)
tweet = re.sub(r"SNCTIONS", "sanctions", tweet)
tweet = re.sub(r"BidTime", "Bid Time", tweet)
tweet = re.sub(r"crunchysensible", "crunchy sensible", tweet)
tweet = re.sub(r"RandomActsOfRomance", "Random acts of romance", tweet)
tweet = re.sub(r"MomentsAtHill", "Moments at hill", tweet)
tweet = re.sub(r"eatshit", "eat shit", tweet)
tweet = re.sub(r"liveleakfun", "live leak fun", tweet)
tweet = re.sub(r"SahelNews", "Sahel News", tweet)
tweet = re.sub(r"abc7newsbayarea", "ABC 7 News Bay Area", tweet)
tweet = re.sub(r"facilitiesmanagement", "facilities management", tweet)
tweet = re.sub(r"facilitydude", "facility dude", tweet)
tweet = re.sub(r"CampLogistics", "Camp logistics", tweet)
tweet = re.sub(r"alaskapublic", "Alaska public", tweet)
tweet = re.sub(r"MarketResearch", "Market Research", tweet)
tweet = re.sub(r"AccuracyEsports", "Accuracy Esports", tweet)
tweet = re.sub(r"TheBodyShopAust", "The Body Shop Australia", tweet)
tweet = re.sub(r"yychail", "Calgary hail", tweet)
tweet = re.sub(r"yyctraffic", "Calgary traffic", tweet)
tweet = re.sub(r"eliotschool", "eliot school", tweet)
tweet = re.sub(r"TheBrokenCity", "The Broken City", tweet)
tweet = re.sub(r"OldsFireDept", "Olds Fire Department", tweet)
tweet = re.sub(r"RiverComplex", "River Complex", tweet)
tweet = re.sub(r"fieldworksmells", "field work smells", tweet)
tweet = re.sub(r"IranElection", "Iran Election", tweet)
tweet = re.sub(r"glowng", "glowing", tweet)
tweet = re.sub(r"kindlng", "kindling", tweet)
tweet = re.sub(r"riggd", "rigged", tweet)
tweet = re.sub(r"slownewsday", "slow news day", tweet)
tweet = re.sub(r"MyanmarFlood", "Myanmar Flood", tweet)
tweet = re.sub(r"abc7chicago", "ABC 7 Chicago", tweet)
tweet = re.sub(r"copolitics", "Colorado Politics", tweet)
tweet = re.sub(r"AdilGhumro", "Adil Ghumro", tweet)
tweet = re.sub(r"netbots", "net bots", tweet)
tweet = re.sub(r"byebyeroad", "bye bye road", tweet)
tweet = re.sub(r"massiveflooding", "massive flooding", tweet)
tweet = re.sub(r"EndofUS", "End of United States", tweet)
tweet = re.sub(r"35PM", "35 PM", tweet)
tweet = re.sub(r"greektheatrela", "Greek Theatre Los Angeles", tweet)
tweet = re.sub(r"76mins", "76 minutes", tweet)
tweet = re.sub(r"publicsafetyfirst", "public safety first", tweet)
tweet = re.sub(r"livesmatter", "lives matter", tweet)
tweet = re.sub(r"myhometown", "my hometown", tweet)
tweet = re.sub(r"tankerfire", "tanker fire", tweet)
tweet = re.sub(r"MEMORIALDAY", "memorial day", tweet)
tweet = re.sub(r"MEMORIAL_DAY", "memorial day", tweet)
tweet = re.sub(r"instaxbooty", "instagram booty", tweet)
tweet = re.sub(r"Jerusalem_Post", "Jerusalem Post", tweet)
tweet = re.sub(r"WayneRooney_INA", "Wayne Rooney", tweet)
tweet = re.sub(r"VirtualReality", "Virtual Reality", tweet)
tweet = re.sub(r"OculusRift", "Oculus Rift", tweet)
tweet = re.sub(r"OwenJones84", "Owen Jones", tweet)
tweet = re.sub(r"jeremycorbyn", "Jeremy Corbyn", tweet)
tweet = re.sub(r"paulrogers002", "Paul Rogers", tweet)
tweet = re.sub(r"mortalkombatx", "Mortal Kombat X", tweet)
tweet = re.sub(r"mortalkombat", "Mortal Kombat", tweet)
tweet = re.sub(r"FilipeCoelho92", "Filipe Coelho", tweet)
tweet = re.sub(r"OnlyQuakeNews", "Only Quake News", tweet)
tweet = re.sub(r"kostumes", "costumes", tweet)
tweet = re.sub(r"YEEESSSS", "yes", tweet)
tweet = re.sub(r"ToshikazuKatayama", "Toshikazu Katayama", tweet)
tweet = re.sub(r"IntlDevelopment", "Intl Development", tweet)
tweet = re.sub(r"ExtremeWeather", "Extreme Weather", tweet)
tweet = re.sub(r"WereNotGruberVoters", "We are not gruber voters", tweet)
tweet = re.sub(r"NewsThousands", "News Thousands", tweet)
tweet = re.sub(r"EdmundAdamus", "Edmund Adamus", tweet)
tweet = re.sub(r"EyewitnessWV", "Eye witness WV", tweet)
tweet = re.sub(r"PhiladelphiaMuseu", "Philadelphia Museum", tweet)
tweet = re.sub(r"DublinComicCon", "Dublin Comic Con", tweet)
tweet = re.sub(r"NicholasBrendon", "Nicholas Brendon", tweet)
tweet = re.sub(r"Alltheway80s", "All the way 80s", tweet)
tweet = re.sub(r"FromTheField", "From the field", tweet)
tweet = re.sub(r"NorthIowa", "North Iowa", tweet)
tweet = re.sub(r"WillowFire", "Willow Fire", tweet)
tweet = re.sub(r"MadRiverComplex", "Mad River Complex", tweet)
tweet = re.sub(r"feelingmanly", "feeling manly", tweet)
tweet = re.sub(r"stillnotoverit", "still not over it", tweet)
tweet = re.sub(r"FortitudeValley", "Fortitude Valley", tweet)
tweet = re.sub(r"CoastpowerlineTramTr", "Coast powerline", tweet)
tweet = re.sub(r"ServicesGold", "Services Gold", tweet)
tweet = re.sub(r"NewsbrokenEmergency", "News broken emergency", tweet)
tweet = re.sub(r"Evaucation", "evacuation", tweet)
tweet = re.sub(r"leaveevacuateexitbe", "leave evacuate exit be", tweet)
tweet = re.sub(r"P_EOPLE", "PEOPLE", tweet)
tweet = re.sub(r"Tubestrike", "tube strike", tweet)
tweet = re.sub(r"CLASS_SICK", "CLASS SICK", tweet)
tweet = re.sub(r"localplumber", "local plumber", tweet)
tweet = re.sub(r"awesomejobsiri", "awesome job siri", tweet)
tweet = re.sub(r"PayForItHow", "Pay for it how", tweet)
tweet = re.sub(r"ThisIsAfrica", "This is Africa", tweet)
tweet = re.sub(r"crimeairnetwork", "crime air network", tweet)
tweet = re.sub(r"KimAcheson", "Kim Acheson", tweet)
tweet = re.sub(r"cityofcalgary", "City of Calgary", tweet)
tweet = re.sub(r"prosyndicate", "pro syndicate", tweet)
tweet = re.sub(r"660NEWS", "660 NEWS", tweet)
tweet = re.sub(r"BusInsMagazine", "Business Insurance Magazine", tweet)
tweet = re.sub(r"wfocus", "focus", tweet)
tweet = re.sub(r"ShastaDam", "Shasta Dam", tweet)
tweet = re.sub(r"go2MarkFranco", "Mark Franco", tweet)
tweet = re.sub(r"StephGHinojosa", "Steph Hinojosa", tweet)
tweet = re.sub(r"Nashgrier", "Nash Grier", tweet)
tweet = re.sub(r"NashNewVideo", "Nash new video", tweet)
tweet = re.sub(r"IWouldntGetElectedBecause", "I would not get elected because", tweet)
tweet = re.sub(r"SHGames", "Sledgehammer Games", tweet)
tweet = re.sub(r"bedhair", "bed hair", tweet)
tweet = re.sub(r"JoelHeyman", "Joel Heyman", tweet)
tweet = re.sub(r"viaYouTube", "via YouTube", tweet)
tweet = re.sub(r"https?:\/\/t.co\/[A-Za-z0-9]+", "", tweet)
punctuations = '@
for p in punctuations:
tweet = tweet.replace(p, f' {p} ')
tweet = tweet.replace('...', '...')
if '...' not in tweet:
tweet = tweet.replace('.. ', '...')
tweet = re.sub(r"MH370", "Malaysia Airlines Flight 370", tweet)
tweet = re.sub(r"m̼sica", "music", tweet)
tweet = re.sub(r"okwx", "Oklahoma City Weather", tweet)
tweet = re.sub(r"arwx", "Arkansas Weather", tweet)
tweet = re.sub(r"gawx", "Georgia Weather", tweet)
tweet = re.sub(r"scwx", "South Carolina Weather", tweet)
tweet = re.sub(r"cawx", "California Weather", tweet)
tweet = re.sub(r"tnwx", "Tennessee Weather", tweet)
tweet = re.sub(r"azwx", "Arizona Weather", tweet)
tweet = re.sub(r"alwx", "Alabama Weather", tweet)
tweet = re.sub(r"wordpressdotcom", "wordpress", tweet)
tweet = re.sub(r"usNWSgov", "United States National Weather Service", tweet)
tweet = re.sub(r"Suruc", "Sanliurfa", tweet)
tweet = re.sub(r"Bestnaijamade", "bestnaijamade", tweet)
tweet = re.sub(r"SOUDELOR", "Soudelor", tweet)
tweet = re.sub(u"\U0001F600-\U0001F64F","", tweet)
tweet = re.sub(u"\U0001F300-\U0001F5FF","", tweet)
tweet = re.sub(u"\U0001F680-\U0001F6FF","", tweet)
tweet = re.sub(u"\U0001F1E0-\U0001F1FF","", tweet)
tweet = re.sub(u"\U00002702-\U000027B0","", tweet)
tweet = re.sub(u"\U000024C2-\U0001F251","", tweet)
return tweet
train_df['text_cleaned'] = train_df['text'].apply(lambda s : clean(s))
test_df['text_cleaned'] = test_df['text'].apply(lambda s : clean(s))<categorify> | train = pd.read_csv('/kaggle/input/digit-recognizer/train.csv')
test = pd.read_csv("/kaggle/input/digit-recognizer/test.csv" ) | Digit Recognizer |
5,786,490 | def encode(texts, tokenizer, max_len=512):
all_tokens = []
all_masks = []
all_segments = []
for text in texts:
text = tokenizer.tokenize(text)
text = text[:max_len-2]
input_sequence = ["[CLS]"] + text + ["[SEP]"]
pad_len = max_len - len(input_sequence)
tokens = tokenizer.convert_tokens_to_ids(input_sequence)
tokens += [0] * pad_len
pad_masks = [1] * len(input_sequence)+ [0] * pad_len
segment_ids = [0] * max_len
all_tokens.append(tokens)
all_masks.append(pad_masks)
all_segments.append(segment_ids)
return np.array(all_tokens), np.array(all_masks), np.array(all_segments )<choose_model_class> | X=train.iloc[:,1:].values
Y=train.iloc[:,0].values | Digit Recognizer |
5,786,490 | def build_model(bert_layer, max_len=512):
input_word_ids = Input(shape=(max_len,), dtype=tf.int32, name="input_word_ids")
input_mask = Input(shape=(max_len,), dtype=tf.int32, name="input_mask")
segment_ids = Input(shape=(max_len,), dtype=tf.int32, name="segment_ids")
_, sequence_output = bert_layer([input_word_ids, input_mask, segment_ids])
clf_output = sequence_output[:, 0, :]
out = Dense(1, activation='sigmoid' )(clf_output)
model = Model(inputs=[input_word_ids, input_mask, segment_ids], outputs=out)
model.compile(Adam(lr=1e-5), loss='binary_crossentropy', metrics=['accuracy'])
return model<choose_model_class> | X = X.reshape(X.shape[0], 28, 28,1)
print(X.shape)
Y = keras.utils.to_categorical(Y, 10)
print(Y.shape ) | Digit Recognizer |
5,786,490 | %%time
bert_layer = hub.KerasLayer('https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/1', trainable=True )<feature_engineering> | X_train, X_valid, Y_train, Y_valid = train_test_split(X, Y, test_size = 0.15, random_state=42 ) | Digit Recognizer |
5,786,490 | vocab_file = bert_layer.resolved_object.vocab_file.asset_path.numpy()
do_lower_case = bert_layer.resolved_object.do_lower_case.numpy()
tokenizer = tokenization.FullTokenizer(vocab_file, do_lower_case )<categorify> | train_datagen = ImageDataGenerator(rescale = 1./255.,
rotation_range = 10,
width_shift_range = 0.15,
height_shift_range = 0.15,
shear_range = 0.1,
zoom_range = 0.2,
horizontal_flip = False ) | Digit Recognizer |
5,786,490 | train_input = encode(train_df.text_cleaned.values, tokenizer, max_len=160)
test_input = encode(test_df.text_cleaned.values, tokenizer, max_len=160)
train_labels = train_df.target.values<train_model> | valid_datagen = ImageDataGenerator(rescale=1./255 ) | Digit Recognizer |
5,786,490 | checkpoint = ModelCheckpoint('model.h5', monitor='val_loss', save_best_only=True)
train_history = model.fit(
train_input, train_labels,
validation_split=0.2,
epochs=3,
callbacks=[checkpoint],
batch_size=32
)<predict_on_test> | model = tf.keras.models.Sequential([
tf.keras.layers.Conv2D(64,(3,3), padding='same', input_shape=(28, 28, 1)) ,
tf.keras.layers.LeakyReLU(alpha=0.1),
tf.keras.layers.Conv2D(64,(3,3), padding='same'),
tf.keras.layers.LeakyReLU(alpha=0.1),
tf.keras.layers.MaxPooling2D(2, 2),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Conv2D(64,(3,3), padding='same'),
tf.keras.layers.LeakyReLU(alpha=0.1),
tf.keras.layers.Conv2D(128,(3,3), padding='same'),
tf.keras.layers.LeakyReLU(alpha=0.1),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Conv2D(128,(3,3), padding='same'),
tf.keras.layers.LeakyReLU(alpha=0.1),
tf.keras.layers.Conv2D(256,(3,3), padding='same'),
tf.keras.layers.LeakyReLU(alpha=0.1),
tf.keras.layers.MaxPooling2D(2,2),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Flatten() ,
tf.keras.layers.Dense(256),
tf.keras.layers.LeakyReLU(alpha=0.1),
tf.keras.layers.BatchNormalization() ,
tf.keras.layers.Dense(10, activation='softmax')
] ) | Digit Recognizer |
5,786,490 | model.load_weights('model.h5')
test_pred_BERT = model.predict(test_input)
test_pred_BERT_int = test_pred_BERT.round().astype('int' )<save_to_csv> | initial_learningrate=1e-3
batch_size = 128
epochs = 40
input_shape =(28, 28, 1 ) | Digit Recognizer |
5,786,490 | submission['target'] = test_pred_BERT_int
submission.to_csv("submission_BERT.csv", index=False, header=True )<import_modules> | def lr_decay(epoch):
return initial_learningrate * 0.9 ** epoch | Digit Recognizer |
5,786,490 | import pandas as pd
from tqdm import tqdm<load_from_csv> | model.compile(loss="categorical_crossentropy",
optimizer=RMSprop(lr=initial_learningrate),
metrics=['accuracy'] ) | Digit Recognizer |
5,786,490 | train = pd.read_csv('.. /input/ames-housing-dataset/AmesHousing.csv')
train.drop(['PID'], axis=1, inplace=True)
origin = pd.read_csv('.. /input/house-prices-advanced-regression-techniques/train.csv')
train.columns = origin.columns
test = pd.read_csv('.. /input/house-prices-advanced-regression-techniques/test.csv')
submission = pd.read_csv('.. /input/house-prices-advanced-regression-techniques/sample_submission.csv')
print('Train:{} Test:{}'.format(train.shape,test.shape))<drop_column> | history = model.fit_generator(
train_datagen.flow(X_train,Y_train, batch_size=batch_size),
steps_per_epoch=100,
epochs=epochs,
callbacks=[LearningRateScheduler(lr_decay)
],
validation_data=valid_datagen.flow(X_valid,Y_valid),
validation_steps=50,
verbose=2 ) | Digit Recognizer |
5,786,490 | missing = test.isnull().sum()
missing = missing[missing>0]
train.drop(missing.index, axis=1, inplace=True)
train.drop(['Electrical'], axis=1, inplace=True)
test.dropna(axis=1, inplace=True)
test.drop(['Electrical'], axis=1, inplace=True )<feature_engineering> | predictions = model.predict_classes(x_test/255.) | Digit Recognizer |
5,786,490 | l_test = tqdm(range(0, len(test)) , desc='Matching')
for i in l_test:
for j in range(0, len(train)) :
for k in range(1, len(test.columns)) :
if test.iloc[i,k] == train.iloc[j,k]:
continue
else:
break
else:
submission.iloc[i, 1] = train.iloc[j, -1]
break
l_test.close()<save_to_csv> | final=pd.DataFrame({"ImageId": list(range(1,len(predictions)+1)) ,
"Label": predictions} ) | Digit Recognizer |
5,786,490 | <import_modules><EOS> | final.to_csv("cnn_submission.csv",index=False)
| Digit Recognizer |
2,539,513 | <SOS> metric: categorizationaccuracy Kaggle data source: digit-recognizer<load_from_csv> | %matplotlib inline | Digit Recognizer |
2,539,513 | def load_data() :
data_dir = Path(".. /input/house-prices-advanced-regression-techniques/")
df_train = pd.read_csv(data_dir / "train.csv", index_col="Id")
df_test = pd.read_csv(data_dir / "test.csv", index_col="Id")
df = pd.concat([df_train, df_test])
df = clean(df)
df = encode(df)
df = impute_plus(df)
df_train = df.loc[df_train.index, :]
df_test = df.loc[df_test.index, :]
return df_train, df_test<load_from_csv> | train = pd.read_csv(".. /input/train.csv")
test = pd.read_csv(".. /input/test.csv" ) | Digit Recognizer |
2,539,513 | data_dir = Path(".. /input/house-prices-advanced-regression-techniques/")
df = pd.read_csv(data_dir / "train.csv", index_col="Id")
df.Exterior2nd.unique()<feature_engineering> | train = pd.read_csv(".. /input/train.csv")
test = pd.read_csv(".. /input/test.csv" ) | Digit Recognizer |
2,539,513 | def clean(df):
df['Exterior2nd'] = df['Exterior2nd'].replace({'Brk Cmn': 'BrkComm'})
df['GarageYrBlt'] = df['GarageYrBlt'].where(df.GarageYrBlt <= 2010, df.YearBuilt)
df.rename(columns={
'1stFlrSF': 'FirstFlrSF',
'2ndFlrSF': 'SecondFlrSF',
'3SsnPorch': 'Threeseasonporch'
}, inplace=True)
return df<define_variables> | X_train = train.drop(labels = ["label"],axis = 1)
Y_train = train["label"]
len(Y_train ) | Digit Recognizer |
2,539,513 | features_nom = ["MSSubClass", "MSZoning", "Street", "Alley", "LandContour", "LotConfig",
"Neighborhood", "Condition1", "Condition2", "BldgType", "HouseStyle",
"RoofStyle", "RoofMatl", "Exterior1st", "Exterior2nd", "MasVnrType",
"Foundation", "Heating", "CentralAir", "GarageType", "MiscFeature",
"SaleType", "SaleCondition"]
five_levels = ["Po", "Fa", "TA", "Gd", "Ex"]
ten_levels = list(range(10))
ordered_levels = {
"OverallQual": ten_levels,
"OverallCond": ten_levels,
"ExterQual": five_levels,
"ExterCond": five_levels,
"BsmtQual": five_levels,
"BsmtCond": five_levels,
"HeatingQC": five_levels,
"KitchenQual": five_levels,
"FireplaceQu": five_levels,
"GarageQual": five_levels,
"GarageCond": five_levels,
"PoolQC": five_levels,
"LotShape": ["Reg", "IR1", "IR2", "IR3"],
"LandSlope": ["Sev", "Mod", "Gtl"],
"BsmtExposure": ["No", "Mn", "Av", "Gd"],
"BsmtFinType1": ["Unf", "LwQ", "Rec", "BLQ", "ALQ", "GLQ"],
"BsmtFinType2": ["Unf", "LwQ", "Rec", "BLQ", "ALQ", "GLQ"],
"Functional": ["Sal", "Sev", "Maj1", "Maj2", "Mod", "Min2", "Min1", "Typ"],
"GarageFinish": ["Unf", "RFn", "Fin"],
"PavedDrive": ["N", "P", "Y"],
"Utilities": ["NoSeWa", "NoSewr", "AllPub"],
"CentralAir": ["N", "Y"],
"Electrical": ["Mix", "FuseP", "FuseF", "FuseA", "SBrkr"],
"Fence": ["MnWw", "GdWo", "MnPrv", "GdPrv"],
}
ordered_levels = {key: ["None"] + value for key, value in
ordered_levels.items() }
def encode(df):
for name in features_nom:
df[name] = df[name].astype("category")
if "None" not in df[name].cat.categories:
df[name].cat.add_categories("None", inplace=True)
for name, levels in ordered_levels.items() :
df[name] = df[name].astype(CategoricalDtype(levels,
ordered=True))
return df<data_type_conversions> | X_train = X_train / 255.0
test = test / 255.0 | Digit Recognizer |
2,539,513 | def impute_plus(df):
cols_with_missing = [col for col in df.columns if col != 'SalePrice' and df[col].isnull().any() ]
for col in cols_with_missing:
df[col + '_was_missing'] = df[col].isnull()
df[col + '_was_missing'] =(df[col + '_was_missing'])* 1
for name in df.select_dtypes("number"):
df[name] = df[name].fillna(0)
for name in df.select_dtypes("category"):
df[name] = df[name].fillna("None")
return df<split> | img_width = 28
img_height = 28
n_channels = 1
X_train = X_train.values.reshape(-1,img_height,img_width,n_channels)
test = test.values.reshape(-1,img_height,img_width,n_channels ) | Digit Recognizer |
2,539,513 | df_train, df_test = load_data()<init_hyperparams> | Y_train = to_categorical(Y_train, num_classes = 10 ) | Digit Recognizer |
2,539,513 | xgb_params = dict(
max_depth=3,
learning_rate=0.1,
n_estimators=100,
min_child_weight=1,
colsample_bytree=1,
subsample=1,
reg_alpha=0,
reg_lambda=1,
num_parallel_tree=1,
)<compute_train_metric> | X_train, X_val, Y_train, Y_val = train_test_split(X_train, Y_train, test_size = 0.1, random_state=2 ) | Digit Recognizer |
2,539,513 | def score_dataset(X, y, model=XGBRegressor(**xgb_params)) :
for colname in X.select_dtypes(["category"]):
X[colname] = X[colname].cat.codes
log_y = np.log(y)
score = cross_val_score(
model, X, log_y, cv=5, scoring='neg_mean_squared_error'
)
score = -1 * score.mean()
score = np.sqrt(score)
return score<compute_test_metric> | print("Total Images:",len(Y_train)+len(Y_val))
print("Training Images:",len(Y_train))
print("Validation Images:",len(Y_val)) | Digit Recognizer |
2,539,513 | X = df_train.copy()
y = X.pop("SalePrice")
baseline_score = score_dataset(X, y)
print(f"Baseline score: {baseline_score:.5f} RMSE" )<normalization> | model = Sequential()
model.add(Convolution2D(filters = 32, kernel_size =(5,5),padding = 'Same', activation ='relu', input_shape = input_shape))
model.add(Convolution2D(filters = 32, kernel_size =(5,5),padding = 'Same', activation ='relu'))
model.add(MaxPool2D(pool_size=(2,2)))
model.add(Dropout(0.25))
model.add(Convolution2D(filters = 64, kernel_size =(3,3),padding = 'Same', activation ='relu'))
model.add(Convolution2D(filters = 64, kernel_size =(3,3),padding = 'Same', activation ='relu'))
model.add(MaxPool2D(pool_size=(2,2), strides=(2,2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(256, activation = "relu"))
model.add(Dropout(0.5))
model.add(Dense(10, activation = "softmax")) | Digit Recognizer |
2,539,513 | mi_scores = make_mi_scores(X, y)
<drop_column> | optimizer = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)
model.compile(optimizer = optimizer , loss = "categorical_crossentropy", metrics=["accuracy"] ) | Digit Recognizer |
2,539,513 | def drop_uninformative(df, mi_scores, threshold=0.0):
return df.loc[:, mi_scores > threshold]<drop_column> | datagen = ImageDataGenerator(
featurewise_center=False, samplewise_center=False, featurewise_std_normalization=False,
samplewise_std_normalization=False, zca_whitening=False, rotation_range=10,
zoom_range = 0.1, width_shift_range=0.1, height_shift_range=0.1,
horizontal_flip=False, vertical_flip=False)
datagen.fit(X_train ) | Digit Recognizer |
2,539,513 | drop_uninformative(X, mi_scores )<prepare_x_and_y> | Model = model.fit_generator(datagen.flow(X_train, Y_train,batch_size=200),epochs=30,verbose=1,validation_data=(X_val, Y_val)) | Digit Recognizer |
2,539,513 | X = df_train.copy()
y = X.pop("SalePrice")
mi_scores = make_mi_scores(X, y)
X["AllPub"] = X["Utilities"] == "AllPub"
mi_scores = make_mi_scores(X, y)
X = drop_uninformative(X, mi_scores)
X.head()
score_dataset(X, y )<categorify> | model.save("cnn_digit_recognizer.h5" ) | Digit Recognizer |
2,539,513 | def label_encode(df):
X = df.copy()
for colname in X.select_dtypes(['category']):
X[colname] = X[colname].cat.codes
return X<feature_engineering> | score = model.evaluate(X_train, Y_train, verbose=1)
print('Train Loss:', score[0])
print('Train Accuracy:', score[1] ) | Digit Recognizer |
2,539,513 | def mathematical_transforms(df):
X = pd.DataFrame()
X['LivLotRatio'] = df.GrLivArea / df.LotArea
X['Spaciousness'] =(df.FirstFlrSF + df.SecondFlrSF)/ df.TotRmsAbvGrd
X['AgeAtTOS'] = df.YrSold - df.YearBuilt
return X<categorify> | score = model.X_valuate(X_val, Y_val, verbose=1)
print('Validation Loss:', score[0])
print('Validation Accuracy:', score[1] ) | Digit Recognizer |
2,539,513 | def interactions(df):
X_inter_1 = pd.get_dummies(df.BldgType, prefix='Bldg')
X_inter_1 = X_inter_1.mul(df.GrLivArea, axis=0)
X_inter_2 = pd.get_dummies(df.BsmtCond, prefix='BsmtCond')
X_inter_2 = X_inter_2.mul(df.TotalBsmtSF, axis=0)
X_inter_3 = pd.get_dummies(df.GarageQual, prefix='GarageQual')
X_inter_3 = X_inter_3.mul(df.GarageArea, axis=0)
X = X_inter_1.join(X_inter_2)
return X<prepare_x_and_y> | Y_pred = model.predict(X_val)
Y_pred_classes = np.argmax(Y_pred,axis = 1)
Y_true = np.argmax(Y_val,axis = 1)
confusion_Matrix = confusion_matrix(Y_true, Y_pred_classes)
print(confusion_Matrix ) | Digit Recognizer |
2,539,513 | def counts(df):
X = pd.DataFrame()
X['PorchTypes'] = df[['WoodDeckSF',
'OpenPorchSF',
'EnclosedPorch',
'Threeseasonporch',
'ScreenPorch'
]].gt(0.0 ).sum(axis=1)
X['TotalHalfBath'] = df.BsmtFullBath + df.BsmtHalfBath
X['TotalRoom'] = df.TotRmsAbvGrd + df.FullBath + df.HalfBath
return X<create_dataframe> | results = model.predict(test)
results = np.argmax(results,axis = 1)
results = pd.Series(results,name="Label" ) | Digit Recognizer |
2,539,513 | def group_transforms(df):
X = pd.DataFrame()
X['MedNhbdArea'] = df.groupby('Neighborhood')['GrLivArea'].transform('median')
X['MeanAgeAtTOS'] = df.groupby('Neighborhood')['AgeAtTOS'].transform('mean')
return X<define_variables> | final_Result = pd.concat([pd.Series(range(1,28001),name = "ImageId"),results],axis = 1)
final_Result.to_csv("cnn_mnist_datagen.csv",index=False ) | Digit Recognizer |
7,324,632 | cluster_features = [
"LotArea",
"TotalBsmtSF",
"FirstFlrSF",
"SecondFlrSF",
"GrLivArea",
]<find_best_model_class> | train = pd.read_csv('/kaggle/input/digit-recognizer/train.csv');
test = pd.read_csv('/kaggle/input/digit-recognizer/test.csv'); | Digit Recognizer |
7,324,632 | def cluster_labels(df, features, n_clusters=20):
X = df.copy()
X_scaled = X.loc[:, features]
X_scaled =(X_scaled - X_scaled.mean(axis=0)) / X_scaled.std(axis=0)
kmeans = KMeans(n_clusters=n_clusters, n_init=50, random_state=0)
X_new = pd.DataFrame()
X_new["Cluster"] = kmeans.fit_predict(X_scaled)
return X_new<normalization> | rows = 28
cols = 28
tot_rows = train.shape[0]
X_train = train.values[:,1:]
y_train = keras.utils.to_categorical(train.label, 10)
X_train = X_train.reshape(tot_rows, rows, cols, 1)/255.0
X_test = test.values[:]
test_num_img = test.shape[0]
X_test = X_test.reshape(test_num_img, rows, cols, 1)/255.0 | Digit Recognizer |
7,324,632 | def cluster_distance(df, features, n_clusters=20):
X = df.copy()
X_scaled = X.loc[:, features]
X_scaled =(X_scaled - X_scaled.mean(axis=0)) / X_scaled.std(axis=0)
kmeans = KMeans(n_clusters=20, n_init=50, random_state=0)
X_cd = kmeans.fit_transform(X_scaled)
X_cd = pd.DataFrame(
X_cd, columns=[f"Centroid_{i}" for i in range(X_cd.shape[1])]
)
return X_cd<create_dataframe> | classifier = Sequential()
classifier.add(Conv2D(32,(5,5),input_shape=(28,28,1),activation = 'relu',padding='same'))
classifier.add(BatchNormalization())
classifier.add(Conv2D(32,(3,3),activation = 'relu',padding='same'))
classifier.add(BatchNormalization())
classifier.add(MaxPooling2D(pool_size=(2,2), strides=None))
classifier.add(BatchNormalization())
classifier.add(Dropout(0.25))
classifier.add(Conv2D(64,(5,5),activation = 'relu',padding='same'))
classifier.add(BatchNormalization())
classifier.add(Conv2D(64,(3,3),activation = 'relu',padding='same'))
classifier.add(BatchNormalization())
classifier.add(Conv2D(64,(3,3),strides=(2,2),activation = 'relu',padding='same'))
classifier.add(BatchNormalization())
classifier.add(Dropout(0.25))
classifier.add(Flatten())
classifier.add(Dense(units=128,activation='relu'))
classifier.add(Dropout(0.4))
classifier.add(Dense(units=10,activation='softmax')) | Digit Recognizer |
7,324,632 | def apply_pca(X, standardize=True):
if standardize:
X =(X - X.mean(axis=0)) / X.std(axis=0)
pca = PCA()
X_pca = pca.fit_transform(X)
component_names = [f"PC{i+1}" for i in range(X_pca.shape[1])]
X_pca = pd.DataFrame(X_pca, columns=component_names)
loadings = pd.DataFrame(
pca.components_.T,
columns=component_names,
index=X.columns,
)
return pca, X_pca, loadings
def plot_variance(pca, width=8, dpi=100):
fig, axs = plt.subplots(1, 2)
n = pca.n_components_
grid = np.arange(1, n + 1)
evr = pca.explained_variance_ratio_
axs[0].bar(grid, evr)
axs[0].set(
xlabel="Component", title="% Explained Variance", ylim=(0.0, 1.0)
)
cv = np.cumsum(evr)
axs[1].plot(np.r_[0, grid], np.r_[0, cv], "o-")
axs[1].set(
xlabel="Component", title="% Cumulative Variance", ylim=(0.0, 1.0)
)
fig.set(figwidth=8, dpi=100)
return axs<define_variables> | classifier.compile(optimizer='adam',loss = 'binary_crossentropy',metrics=['accuracy'])
classifier.fit(X_train,y_train,epochs=100,batch_size=64,validation_split=0.1,shuffle=True ) | Digit Recognizer |
7,324,632 | pca_features = [
"GarageArea",
"YearRemodAdd",
"TotalBsmtSF",
"GrLivArea",
]<load_pretrained> | result = classifier.predict_classes(X_test ) | Digit Recognizer |
7,324,632 | <feature_engineering><EOS> | out = pd.DataFrame({"ImageId": i+1 , "Label": result[i]} for i in range(0, test_num_img))
out.to_csv('submission.csv', index=False ) | Digit Recognizer |
3,811,526 | <SOS> metric: categorizationaccuracy Kaggle data source: digit-recognizer<sort_values> | import PIL
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import keras
from matplotlib import pyplot
from sklearn import preprocessing | Digit Recognizer |
3,811,526 | component = "PC1"
idx = X_pca[component].sort_values(ascending=False ).index
df_train[["SalePrice", "Neighborhood", "SaleCondition"] + pca_features].iloc[idx]<create_dataframe> | run_model1 = False
run_model2 = False
run_model3 = False
run_model_adv = True | Digit Recognizer |
3,811,526 | def indicate_outliers(df):
X_new = pd.DataFrame()
X_new["Outlier"] =(df.Neighborhood == "Edwards")&(df.SaleCondition == "Partial")
return X_new<categorify> | train = pd.read_csv('.. /input/train.csv', delimiter=',')
test = pd.read_csv('.. /input/test.csv', delimiter=',' ) | Digit Recognizer |
3,811,526 | class CrossFoldEncoder:
def __init__(self, encoder, **kwargs):
self.encoder_ = encoder
self.kwargs_ = kwargs
self.cv_ = KFold(n_splits=5)
def fit_transform(self, X, y, cols):
self.fitted_encoders_ = []
self.cols_ = cols
X_encoded = []
for idx_encode, idx_train in self.cv_.split(X):
fitted_encoder = self.encoder_(cols=cols, **self.kwargs_)
fitted_encoder.fit(
X.iloc[idx_encode, :], y.iloc[idx_encode],
)
X_encoded.append(fitted_encoder.transform(X.iloc[idx_train, :])[cols])
self.fitted_encoders_.append(fitted_encoder)
X_encoded = pd.concat(X_encoded)
X_encoded.columns = [name + "_encoded" for name in X_encoded.columns]
return X_encoded
def transform(self, X):
X_encoded_list = []
for fitted_encoder in self.fitted_encoders_:
X_encoded = fitted_encoder.transform(X)
X_encoded_list.append(X_encoded[self.cols_])
X_encoded = reduce(
lambda x, y: x.add(y, fill_value=0), X_encoded_list
)/ len(X_encoded_list)
X_encoded.columns = [name + "_encoded" for name in X_encoded.columns]
return X_encoded<drop_column> | train_size = train.shape[0]
test_size = test.shape[0]
X_train = train.iloc[:, 1:].values.astype('uint8')
Y_train = train.iloc[:, 0]
X_test = test.iloc[:, :].values.astype('uint8')
img_dimension = np.int32(np.sqrt(X_train.shape[1]))
img_rows, img_cols = img_dimension, img_dimension
nb_of_color_channels = 1
if(keras.backend.image_dim_ordering() =="th"):
X_train = X_train.reshape(train.shape[0], nb_of_color_channels, img_rows, img_cols)
X_test = X_test.reshape(test.shape[0], nb_of_color_channels, img_rows, img_cols)
in_shape =(nb_of_color_channels, img_rows, img_cols)
else:
X_train = X_train.reshape(train.shape[0], img_rows, img_cols, nb_of_color_channels)
X_test = X_test.reshape(test.shape[0], img_rows, img_cols, nb_of_color_channels)
in_shape =(img_rows, img_cols, nb_of_color_channels)
print('Data Information
')
print('Training set size: {}
Testing set size: {}'.format(train_size, test_size))
print('Image dimension: {0}*{0}'.format(img_dimension))
| Digit Recognizer |
3,811,526 | def create_features(df, df_test=None):
X = df.copy()
y = X.pop('SalePrice')
mi_scores = make_mi_scores(X, y)
if df_test is not None:
X_test = df_test.copy()
y_test = X_test.pop("SalePrice")
X = pd.concat([X, X_test])
X = X.join(mathematical_transforms(X))
X = X.join(counts(X))
X = X.join(group_transforms(X))
X = X.join(pca_inspired(X))
X = label_encode(X)
if df_test is not None:
mi_scores = make_mi_scores(X, pd.concat([y, y_test]))
else:
mi_scores = make_mi_scores(X, y)
X = drop_uninformative(X, mi_scores, 0.02)
if df_test is not None:
X_test = X.loc[df_test.index, :]
X.drop(df_test.index, inplace=True)
encoder = CrossFoldEncoder(MEstimateEncoder, m=1)
X = X.join(encoder.fit_transform(X, y, cols=["MSSubClass"]))
if df_test is not None:
X_test = X_test.join(encoder.transform(X_test))
if df_test is not None:
return X, X_test
else:
return X<prepare_x_and_y> | X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train_nor = X_train / 255
X_test_nor= X_test / 255 | Digit Recognizer |
3,811,526 | df_train, df_test = load_data()
X_train = create_features(df_train)
y_train = df_train.loc[:, 'SalePrice']
score_dataset(X_train, y_train )<prepare_x_and_y> | oh_encoder = preprocessing.OneHotEncoder(categories='auto')
oh_encoder.fit(Y_train.values.reshape(-1,1))
Y_train_oh = oh_encoder.transform(Y_train.values.reshape(-1,1)).toarray() | Digit Recognizer |
3,811,526 | X_train = create_features(df_train)
y_train = df_train.loc[:, "SalePrice"]
xgb_params = dict(
max_depth=4,
learning_rate=0.0058603076512435655,
n_estimators=5045,
min_child_weight=2,
colsample_bytree=0.22556099175248345,
subsample=0.5632348136091383,
reg_alpha=0.09888625622197889,
reg_lambda=0.00890758697724437,
num_parallel_tree=1,
)
xgb = XGBRegressor(**xgb_params)
score_dataset(X_train, y_train, xgb )<init_hyperparams> | print('One-hot:')
print(Y_train_oh[:5])
print('
Label:')
print(Y_train[:5] ) | Digit Recognizer |
3,811,526 |
<predict_on_test> | to_categorical(Y_train, Y_train.unique().shape[0])[:5]
| Digit Recognizer |
3,811,526 | X_train, X_test = create_features(df_train, df_test)
y_train = df_train.loc[:, "SalePrice"]
xgb = XGBRegressor(**xgb_params)
xgb.fit(X_train, np.log(y))
predictions = np.exp(xgb.predict(X_test))
output = pd.DataFrame({'Id': X_test.index, 'SalePrice': predictions} )<save_to_csv> | from keras.layers import Activation,Dropout,Dense,Conv2D,AveragePooling2D,Flatten,ZeroPadding2D,MaxPooling2D
from keras.models import Sequential
from keras import optimizers
from keras.callbacks import ReduceLROnPlateau | Digit Recognizer |
3,811,526 | output.to_csv('submission.csv', index=False)
print("Your predictions are successfully saved!" )<save_to_csv> | def build_lenet5(model, input_shape=X_train.shape[1:], dropout=0):
S = [1,2,1,2,1]
N_input = [28,28,14,10,5]
P = [2,0,0,0,0]
N = [28,14,10,5,1]
F = [i[0] + 2*i[1] - i[3]*(i[2] - 1)for i in zip(N_input, P, N, S)]
model.add(Conv2D(filters=6, kernel_size=(F[0],F[0]), padding='same', strides=S[0],
activation='relu', input_shape=input_shape))
model.add(MaxPooling2D(pool_size=F[1], strides=S[1]))
model.add(Conv2D(filters=16, kernel_size=(F[2],F[2]), padding='valid', strides=S[2],
activation='relu'))
model.add(MaxPooling2D(pool_size=F[3], strides=S[3]))
model.add(Conv2D(filters=120, kernel_size=(F[4],F[4]), padding='valid', strides=S[4],
activation='relu'))
model.add(Dropout(dropout))
model.add(Flatten())
model.add(Dense(84, activation='relu'))
model.add(Dense(10, activation='softmax'))
if __name__ == '__main__' and run_model1:
model = Sequential()
build_lenet5(model, input_shape=X_train.shape[1:], dropout=0)
model.summary()
| Digit Recognizer |
3,811,526 | filename = 'ames_house_xgb_model.pkl'
pickle.dump(xgb, open(filename, 'wb'))
X_test.to_csv('df_test_processed.csv', index=False )<predict_on_test> | hist_dict = {}
if __name__ == '__main__' and run_model1:
adam = optimizers.Adam()
model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer=adam)
hist_dict['run_model1'] = model.fit(X_train, Y_train_oh, batch_size=64, epochs=20,
shuffle=True, validation_split=0.2, verbose=2)
| Digit Recognizer |
3,811,526 | row_to_show = 42
data_for_prediction = X_test.iloc[[row_to_show]]
y_sample = np.exp(xgb.predict(data_for_prediction))
explainer = shap.TreeExplainer(xgb)
shap_values = explainer.shap_values(data_for_prediction )<predict_on_test> | def model_predict(model):
print("Generating test predictions...")
predictions = model.predict_classes(X_test, verbose=1)
print("OK.")
return predictions
def model_predict_val(model, set_check):
print("Generating set predictions...")
predictions = model.predict_classes(set_check, verbose=1)
print("OK.")
return predictions
def write_preds(preds, filename):
pd.DataFrame({"ImageId": list(range(1,len(preds)+1)) , "Label": preds} ).to_csv(filename, index=False, header=True)
if __name__ == '__main__' and run_model1:
predictions = model_predict(model)
print(predictions[:5])
write_preds(predictions, "keras-lenet5-basic.csv")
| Digit Recognizer |
3,811,526 | data_for_prediction = X_test
y_sample = np.exp(xgb.predict(data_for_prediction))
explainer = shap.TreeExplainer(xgb)
shap_values = explainer.shap_values(data_for_prediction )<define_variables> | if __name__ == '__main__' and run_model2:
model = Sequential()
build_lenet5(model, input_shape=X_train.shape[1:], dropout=0.3)
model.summary()
adam = optimizers.Adam()
model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer=adam)
hist_dict['run_model2'] = model.fit(X_train, Y_train_oh, batch_size=64, epochs=20, shuffle=True, validation_split=0.2, verbose=2 ) | Digit Recognizer |
3,811,526 | BATCH_SIZE = 128
EPOCHS = 15<load_from_csv> | if __name__ == '__main__' and run_model2:
predictions = model_predict(model)
print(predictions[:5])
write_preds(predictions, "keras-lenet5-basic-droupout.csv" ) | Digit Recognizer |
3,811,526 | train = pd.read_csv("/kaggle/input/house-prices-advanced-regression-techniques/train.csv")
test = pd.read_csv("/kaggle/input/house-prices-advanced-regression-techniques/test.csv" )<set_options> |
datagen = ImageDataGenerator(
featurewise_center=False,
samplewise_center=False,
featurewise_std_normalization=False,
samplewise_std_normalization=False,
zca_whitening=False,
rotation_range=15,
zoom_range = 0.1,
width_shift_range=0.1,
height_shift_range=0.1,
horizontal_flip=False,
vertical_flip=False)
datagen.fit(X_train ) | Digit Recognizer |
3,811,526 | sns.set_theme(rc = {'grid.linewidth': 0.5,
'axes.linewidth': 0.75, 'axes.facecolor': '
'figure.facecolor': '
'xtick.labelcolor': '<prepare_x_and_y> | for x_batch, y_batch in datagen.flow(X_train, Y_train_oh, batch_size=9, shuffle = False):
print(x_batch.shape)
print(y_batch.shape)
break | Digit Recognizer |
3,811,526 | ntrain = train.shape[0]
ntest = test.shape[0]
y_train = train.SalePrice.values
all_data = pd.concat(( train, test)).reset_index(drop=True)
all_data.drop(['SalePrice', 'GarageArea', 'TotRmsAbvGrd'], axis=1, inplace=True)
print("all_data size is : {}".format(all_data.shape))<create_dataframe> | if __name__ == '__main__' and run_model3:
X_train_s, X_val, Y_train_s, Y_val = train_test_split(X_train, Y_train_oh, test_size=0.13, random_state=42)
model = Sequential()
build_lenet5(model, input_shape=X_train_s.shape[1:], dropout=0.15)
model.summary()
adam = optimizers.Adam()
model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer=adam)
epochs = 45
batch_size = 72
Train_gen_batch = datagen.flow(X_train_s, Y_train_s, batch_size=batch_size)
datagen_no_aug = ImageDataGenerator()
Val_gen_batch = datagen_no_aug.flow(X_val, Y_val, batch_size=batch_size)
hist_dict['run_model3'] = model.fit_generator(Train_gen_batch, epochs = epochs, verbose = 2,
steps_per_epoch = X_train.shape[0] // batch_size,
validation_data = Val_gen_batch,
validation_steps = X_val.shape[0] // batch_size,
callbacks=[learning_rate_reduction])
| Digit Recognizer |
3,811,526 | all_data_na =(all_data.isnull().sum() / len(all_data)) * 100
all_data_na = all_data_na.drop(all_data_na[all_data_na == 0].index ).sort_values(ascending=False)[:30]
missing_data = pd.DataFrame({'Missing Ratio' :all_data_na})
missing_data.head(20 )<data_type_conversions> | if __name__ == '__main__' and run_model3:
predictions = model_predict(model)
print(predictions[:5])
write_preds(predictions, "keras-lenet5-aug.csv")
| Digit Recognizer |
3,811,526 | all_data["PoolQC"] = all_data["PoolQC"].fillna("None" )<data_type_conversions> | Digit Recognizer |
|
3,811,526 | all_data["MiscFeature"] = all_data["MiscFeature"].fillna("None" )<data_type_conversions> | def build_net_advanced(model, input_shape=X_train.shape[1:], dropout=0.25):
model.add(Conv2D(filters=32, kernel_size=(5,5), padding='same', strides=1,
activation='relu', input_shape=input_shape))
model.add(Conv2D(filters=32, kernel_size=(5,5), padding='valid', strides=2,
activation='relu'))
model.add(MaxPooling2D(pool_size=(3,3), strides=1))
model.add(Dropout(dropout))
model.add(Conv2D(filters=64, kernel_size=(3,3), padding='same', strides=1,
activation='relu'))
model.add(Conv2D(filters=64, kernel_size=(3,3), padding='valid', strides=2,
activation='relu'))
model.add(MaxPooling2D(pool_size=(2,2), strides=1))
model.add(Dropout(dropout))
model.add(Flatten())
model.add(Dense(256, activation='relu'))
model.add(Dropout(dropout))
model.add(Dense(128, activation='relu'))
model.add(Dropout(dropout))
model.add(Dense(10, activation='softmax')) | Digit Recognizer |
3,811,526 | all_data["Alley"] = all_data["Alley"].fillna("None" )<data_type_conversions> | if __name__ == '__main__' and run_model_adv:
X_train_s, X_val, Y_train_s, Y_val = train_test_split(X_train, Y_train_oh, test_size=0.15, random_state=42)
model = Sequential()
build_net_advanced(model, input_shape=X_train_s.shape[1:], dropout=0.3)
model.summary()
adam = optimizers.Adam()
model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer=adam)
epochs = 35
batch_size = 84
Train_gen_batch = datagen.flow(X_train_s, Y_train_s, batch_size=batch_size)
datagen_no_aug = ImageDataGenerator()
Val_gen_batch = datagen_no_aug.flow(X_val, Y_val, batch_size=batch_size)
hist_dict['run_model_adv'] = model.fit_generator(Train_gen_batch, epochs = epochs, verbose = 2,
steps_per_epoch = X_train.shape[0] // batch_size,
validation_data = Val_gen_batch,
validation_steps = X_val.shape[0] // batch_size,
callbacks=[learning_rate_reduction] ) | Digit Recognizer |
3,811,526 | all_data["Fence"] = all_data["Fence"].fillna("None" )<data_type_conversions> | if __name__ == '__main__' and run_model_adv:
predictions = model_predict(model)
print(predictions[:5])
write_preds(predictions, "keras-adv-net.csv")
| Digit Recognizer |
3,811,526 | all_data["FireplaceQu"] = all_data["FireplaceQu"].fillna("None" )<categorify> | _, X_val_check, _, Y_val_check = train_test_split(X_train, Y_train, test_size=0.1, random_state=1)
Ypred_val_check = model_predict_val(model, set_check=X_val_check)
| Digit Recognizer |
3,811,526 | all_data["LotFrontage"] = all_data.groupby("Neighborhood")["LotFrontage"].transform(
lambda x: x.fillna(x.median()))<data_type_conversions> | cm = confusion_matrix(Y_val_check.values, Ypred_val_check)
cm | Digit Recognizer |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.