kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
12,165,368 | best_th = 0.38
preds_t,y_t = learn.TTA(is_test=True,n_aug=8)
preds_t = np.stack(preds_t, axis=-1)
preds_t = np.exp(preds_t)
preds_t = preds_t.mean(axis=-1)
preds_t = np.concatenate([np.zeros(( preds_t.shape[0],1)) +best_th, preds_t],axis=1)
np.save('preds_dn201.npy', preds_t )<save_to_csv> | augmenter = WordAugmentation(device='cuda' ) | Natural Language Processing with Disaster Tweets |
12,165,368 | sample_df = pd.read_csv(SAMPLE_SUB)
sample_list = list(sample_df.Image)
labels_list = ["new_whale"]+labels_list
pred_list = [[labels_list[i] for i in p.argsort() [-5:][::-1]] for p in preds_t]
pred_dic = dict(( key, value)for(key, value)in zip(learn.data.test_ds.fnames,pred_list))
pred_list_cor = [' '.join(pred_dic[i... | tqdm.tqdm.pandas()
new_sentences_replace = train_set.progress_apply(lambda x: augmenter.apply(x["text"], n_word=max(int(len(x["text"])*0.05), 1), action='replace'), axis=1 ) | Natural Language Processing with Disaster Tweets |
12,165,368 | !pip install lap
Lambda, MaxPooling2D, Reshape
<load_from_csv> | new_train_set_replace = pd.DataFrame({"text": new_sentences_replace.to_numpy() , "target": train_set.target.to_numpy() } ) | Natural Language Processing with Disaster Tweets |
12,165,368 | TRAIN_DF = '.. /input/humpback-whale-identification/train.csv'
SUB_Df = '.. /input/humpback-whale-identification/sample_submission.csv'
TRAIN = '.. /input/humpback-whale-identification/train/'
TEST = '.. /input/humpback-whale-identification/test/'
P2H = '.. /input/metadata/p2h.pickle'
P2SIZE = '.. /input/metadata/p2siz... | augmented_train_set = pd.concat([train_set[["text", "target"]], new_train_set_replace], ignore_index=True ) | Natural Language Processing with Disaster Tweets |
12,165,368 | if isfile(P2SIZE):
print("P2SIZE exists.")
with open(P2SIZE, 'rb')as f:
p2size = pickle.load(f)
else:
p2size = {}
for p in tqdm(join):
size = pil_image.open(expand_path(p)).size
p2size[p] = size<compute_test_metric> | augmented_train_set.drop_duplicates(subset="text", inplace=True ) | Natural Language Processing with Disaster Tweets |
12,165,368 | def match(h1, h2):
for p1 in h2ps[h1]:
for p2 in h2ps[h2]:
i1 = pil_image.open(expand_path(p1))
i2 = pil_image.open(expand_path(p2))
if i1.mode != i2.mode or i1.size != i2.size: return False
a1 = np.array(i1)
a1 = a1 - a1.mean()
a1 = a1 / sqrt(( a1 ** 2 ).mean())
a2 = np.array(i2)
a2 = a2 - a2.mean()
a2 = a2 / sqrt(... | !pip install fastai==2.0.16 | Natural Language Processing with Disaster Tweets |
12,165,368 | def prefer(ps):
if len(ps)== 1: return ps[0]
best_p = ps[0]
best_s = p2size[best_p]
for i in range(1, len(ps)) :
p = ps[i]
s = p2size[p]
if s[0] * s[1] > best_s[0] * best_s[1]:
best_p = p
best_s = s
return best_p
h2p = {}
for h, ps in h2ps.items() :
h2p[h] = prefer(ps)
len(h2p), list(h2p.items())[:5]<set_options> | accuracy, Perplexity, F1Score | Natural Language Processing with Disaster Tweets |
12,165,368 | p2bb = pd.read_csv(BB_DF ).set_index("Image")
old_stderr = sys.stderr
sys.stderr = open('/dev/null' if platform.system() != 'Windows' else 'nul', 'w')
sys.stderr = old_stderr
img_shape =(384, 384, 1)
anisotropy = 2.15
crop_margin = 0.05<normalization> | train_target_0, validate_target_0 = np.split(augmented_train_set[augmented_train_set.target == 0].sample(frac=1,
random_state=1),
[int (.85 * len(augmented_train_set[augmented_train_set.target == 0])) ])
train_target_1, validate_target_1 = np.split(augmented_train_set[augmented_train_set.target == 1].sample(frac=1,
ra... | Natural Language Processing with Disaster Tweets |
12,165,368 | def build_transform(rotation, shear, height_zoom, width_zoom, height_shift, width_shift):
rotation = np.deg2rad(rotation)
shear = np.deg2rad(shear)
rotation_matrix = np.array(
[[np.cos(rotation), np.sin(rotation), 0], [-np.sin(rotation), np.cos(rotation), 0], [0, 0, 1]])
shift_matrix = np.array([[1, 0, height_shi... | test_set["target"] = test_set.text.apply(lambda x : learn.predict(x)[0])
test_set[["id", "target"]].to_csv("submission.csv", index=False ) | Natural Language Processing with Disaster Tweets |
12,108,082 | def read_cropped_image(p, augment):
if p in h2p:
p = h2p[p]
size_x, size_y = p2size[p]
row = p2bb.loc[p]
x0, y0, x1, y1 = row['x0'], row['y0'], row['x1'], row['y1']
dx = x1 - x0
dy = y1 - y0
x0 -= dx * crop_margin
x1 += dx * crop_margin + 1
y0 -= dy * crop_margin
y1 += dy * crop_margin + 1
if x0 < 0:
x0 = 0
if x1 > s... | !wget --quiet https://raw.githubusercontent.com/tensorflow/models/master/official/nlp/bert/tokenization.py | Natural Language Processing with Disaster Tweets |
12,108,082 | def subblock(x, filter, **kwargs):
x = BatchNormalization()(x)
y = x
y = Conv2D(filter,(1, 1), activation='relu', **kwargs )(y)
y = BatchNormalization()(y)
y = Conv2D(filter,(3, 3), activation='relu', **kwargs )(y)
y = BatchNormalization()(y)
y = Conv2D(K.int_shape(x)[-1],(1, 1), **kwargs )(y)
y = Add()([x, y])
... | import tokenization
import matplotlib.pyplot as plt
import seaborn as sns
import re
import nltk
import spacy
import sys
import random
import fuzzywuzzy
from fuzzywuzzy import process
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from spacy.util import minibatch
from ten... | Natural Language Processing with Disaster Tweets |
12,108,082 | h2ws = {}
new_whale = 'new_whale'
for p, w in tagged.items() :
if w != new_whale:
h = p2h[p]
if h not in h2ws: h2ws[h] = []
if w not in h2ws[h]: h2ws[h].append(w)
for h, ws in h2ws.items() :
if len(ws)> 1:
h2ws[h] = sorted(ws)
w2hs = {}
for h, ws in h2ws.items() :
if len(ws)== 1:
w = ws[0]
if w not in w2hs: w2hs[w] =... | random_seed = 0
data = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')
print(data.shape)
data.head() | Natural Language Processing with Disaster Tweets |
12,108,082 | train = []
for hs in w2hs.values() :
if len(hs)> 1:
train += hs
random.shuffle(train)
train_set = set(train)
w2ts = {}
for w, hs in w2hs.items() :
for h in hs:
if h in train_set:
if w not in w2ts:
w2ts[w] = []
if h not in w2ts[w]:
w2ts[w].append(h)
for w, ts in w2ts.items() :
w2ts[w] = np.array(ts)
t2i = {}
for i, ... | print(data.isnull().sum())
display(data.nunique() ) | Natural Language Processing with Disaster Tweets |
12,108,082 | class TrainingData(Sequence):
def __init__(self, score, steps=1000, batch_size=32):
super(TrainingData, self ).__init__()
self.score = -score
self.steps = steps
self.batch_size = batch_size
for ts in w2ts.values() :
idxs = [t2i[t] for t in ts]
for i in idxs:
for j in idxs:
self.score[
i, j] = 10000.0
self.on_epoch_en... | def utils_preprocess_text(text, flg_stemm=False, flg_lemm=True, lst_stopwords=None):
text = re.sub(r"https?://\S+|www\.\S+", "", text)
html = re.compile(r"<.*?>|&([a-z0-9]+|
text = re.sub(html, "", text)
l = len(text)
t = text
text= re.sub(r'[^\x00-\x7f]',r'', text)
text = re.sub(r'[^\w\s]', '', str(text ).lower().... | Natural Language Processing with Disaster Tweets |
12,108,082 | def set_lr(model, lr):
K.set_value(model.optimizer.lr, float(lr))
def get_lr(model):
return K.get_value(model.optimizer.lr)
def score_reshape(score, x, y=None):
if y is None:
m = np.zeros(( x.shape[0], x.shape[0]), dtype=K.floatx())
m[np.triu_indices(x.shape[0], 1)] = score.squeeze()
m += m.transpose()
else:
m = np... | lst_stopwords = nltk.corpus.stopwords.words("english")
data["text_clean"] = data["text"].apply(lambda x: utils_preprocess_text(x, flg_stemm=False, flg_lemm=True, lst_stopwords=lst_stopwords))
data.head() | Natural Language Processing with Disaster Tweets |
12,108,082 | def prepare_submission(threshold, filename):
vtop = 0
vhigh = 0
pos = [0, 0, 0, 0, 0, 0]
with open(filename, 'wt', newline='
')as f:
f.write('Image,Id
')
for i, p in enumerate(tqdm(submit)) :
t = []
s = set()
a = score[i, :]
for j in list(reversed(np.argsort(a))):
h = known[j]
if a[j] < threshold and new_whale not i... | def clean_location(data):
data['location'] = data['location'].str.lower()
data['location'] = data['location'].str.strip()
data['location'] = data['location'].apply(lambda x: re.sub(r',(?s ).*$', r'', x)if str(x)!= str(np.nan)else np.nan)
return data | Natural Language Processing with Disaster Tweets |
12,108,082 | histories = []
steps = 0
if isfile('.. /input/piotte/mpiotte-standard.model'):
tmp = keras.models.load_model('.. /input/piotte/mpiotte-standard.model')
model.set_weights(tmp.get_weights())
tic = time.time()
h2ws = {}
for p, w in tagged.items() :
if w != new_whale:
h = p2h[p]
if h not in h2ws: h2ws[h] = []
if w not in... | data = clean_location(data ) | Natural Language Processing with Disaster Tweets |
12,108,082 | score = 0.45*score1 + 0.55*score2<train_model> | def replace_matches_in_column(df, column, string_to_match, min_ratio = 90):
strings = df[column].unique()
matches = fuzzywuzzy.process.extract(string_to_match, strings, limit=10, scorer=fuzzywuzzy.fuzz.token_sort_ratio)
close_matches = [matches[0] for matches in matches if matches[1] >= min_ratio]
rows_with_matches = ... | Natural Language Processing with Disaster Tweets |
12,108,082 | prepare_submission(0.92, 'submission_0.45_standard_0.55_boostrap-400.csv')
toc = time.time()
print("Submission time: ",(toc - tic)/ 60.)<import_modules> | locations = data.groupby('location' ).location.count().sort_values(ascending=False)
locations = locations[locations.values > 3]
locations = locations.index
for loc in locations:
replace_matches_in_column(df=data, column='location', string_to_match=loc ) | Natural Language Processing with Disaster Tweets |
12,108,082 | !pip install lapjv==1.3.1
Lambda, MaxPooling2D, Reshape
<load_from_csv> | def rename_location(data):
data.loc[(data.location == 'united states'), 'location'] = 'usa'
data.loc[(data.location == 'united states of america'), 'location'] = 'usa'
data.loc[(data.location == 'us'), 'location'] = 'usa'
data.loc[(data.location == 'u.s.'), 'location'] = 'usa'
data.loc[(data.location == 'u.s.a'), 'loca... | Natural Language Processing with Disaster Tweets |
12,108,082 | TRAIN_DF = '.. /input/humpback-whale-identification/train.csv'
SUB_Df = '.. /input/humpback-whale-identification/sample_submission.csv'
TRAIN = '.. /input/humpback-whale-identification/train/'
TEST = '.. /input/humpback-whale-identification/test/'
P2H = '.. /input/metadata/p2h.pickle'
P2SIZE = '.. /input/metadata/p2siz... | locations = data.groupby('location' ).location.count().sort_values(ascending=False)
locations = locations[locations.values <= 3]
locations = locations.index
for loc in locations:
data.loc[(data.location == loc), 'location'] = np.nan
data.groupby('location' ).location.count().sort_values(ascending=False ) | Natural Language Processing with Disaster Tweets |
12,108,082 | if isfile(P2SIZE):
print("P2SIZE exists.")
with open(P2SIZE, 'rb')as f:
p2size = pickle.load(f)
else:
p2size = {}
for p in tqdm(join):
size = pil_image.open(expand_path(p)).size
p2size[p] = size<compute_test_metric> | def concatenate(data):
data['sequence'] = data['text_clean'].map(str)+ ' XXLOC ' + data['location'].map(str)\
+ ' XXKEY ' + data['keyword'].map(str)+ ' TWTXX'
return data
data = concatenate(data)
print(data.sequence[0])
print(data.sequence[98])
print(data.sequence[100] ) | Natural Language Processing with Disaster Tweets |
12,108,082 | def match(h1, h2):
for p1 in h2ps[h1]:
for p2 in h2ps[h2]:
i1 = pil_image.open(expand_path(p1))
i2 = pil_image.open(expand_path(p2))
if i1.mode != i2.mode or i1.size != i2.size: return False
a1 = np.array(i1)
a1 = a1 - a1.mean()
a1 = a1 / sqrt(( a1 ** 2 ).mean())
a2 = np.array(i2)
a2 = a2 - a2.mean()
a2 = a2 / sqrt(... | test = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')
test["text_clean"] = test["text"].apply(lambda x: utils_preprocess_text(x, flg_stemm=False, flg_lemm=True, lst_stopwords=lst_stopwords))
test = clean_location(test)
locations = test.groupby('location' ).location.count().sort_values(ascending=False)
loc... | Natural Language Processing with Disaster Tweets |
12,108,082 | def prefer(ps):
if len(ps)== 1: return ps[0]
best_p = ps[0]
best_s = p2size[best_p]
for i in range(1, len(ps)) :
p = ps[i]
s = p2size[p]
if s[0] * s[1] > best_s[0] * best_s[1]:
best_p = p
best_s = s
return best_p
h2p = {}
for h, ps in h2ps.items() :
h2p[h] = prefer(ps)
len(h2p), list(h2p.items())[:5]
<set_options> | def bert_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)
to... | Natural Language Processing with Disaster Tweets |
12,108,082 | p2bb = pd.read_csv(BB_DF ).set_index("Image")
old_stderr = sys.stderr
sys.stderr = open('/dev/null' if platform.system() != 'Windows' else 'nul', 'w')
sys.stderr = old_stderr
img_shape =(384, 384, 1)
anisotropy = 2.15
crop_margin = 0.05<normalization> | 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, ... | Natural Language Processing with Disaster Tweets |
12,108,082 | def build_transform(rotation, shear, height_zoom, width_zoom, height_shift, width_shift):
rotation = np.deg2rad(rotation)
shear = np.deg2rad(shear)
rotation_matrix = np.array(
[[np.cos(rotation), np.sin(rotation), 0], [-np.sin(rotation), np.cos(rotation), 0], [0, 0, 1]])
shift_matrix = np.array([[1, 0, height_shi... | module_url = "https://tfhub.dev/tensorflow/bert_en_uncased_L-24_H-1024_A-16/1"
bert_layer = hub.KerasLayer(module_url, trainable=True ) | Natural Language Processing with Disaster Tweets |
12,108,082 | def read_cropped_image(p, augment):
if p in h2p:
p = h2p[p]
size_x, size_y = p2size[p]
row = p2bb.loc[p]
x0, y0, x1, y1 = row['x0'], row['y0'], row['x1'], row['y1']
dx = x1 - x0
dy = y1 - y0
x0 -= dx * crop_margin
x1 += dx * crop_margin + 1
y0 -= dy * crop_margin
y1 += dy * crop_margin + 1
if x0 < 0:
x0 = 0
if x1 > s... | 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 ) | Natural Language Processing with Disaster Tweets |
12,108,082 | def subblock(x, filter, **kwargs):
x = BatchNormalization()(x)
y = x
y = Conv2D(filter,(1, 1), activation='relu', **kwargs )(y)
y = BatchNormalization()(y)
y = Conv2D(filter,(3, 3), activation='relu', **kwargs )(y)
y = BatchNormalization()(y)
y = Conv2D(K.int_shape(x)[-1],(1, 1), **kwargs )(y)
y = Add()([x, y])
... | train_input = bert_encode(data.sequence.values, tokenizer, max_len=65)
test_input = bert_encode(test.sequence.values, tokenizer, max_len=65)
train_labels = data.target.values | Natural Language Processing with Disaster Tweets |
12,108,082 | h2ws = {}
new_whale = 'new_whale'
for p, w in tagged.items() :
if w != new_whale:
h = p2h[p]
if h not in h2ws: h2ws[h] = []
if w not in h2ws[h]: h2ws[h].append(w)
for h, ws in h2ws.items() :
if len(ws)> 1:
h2ws[h] = sorted(ws)
w2hs = {}
for h, ws in h2ws.items() :
if len(ws)== 1:
w = ws[0]
if w not in w2hs: w2hs[w] =... | checkpoint = ModelCheckpoint('model.h5', monitor='val_loss', save_best_only=True)
train_history = model.fit(
train_input, train_labels,
validation_split=0.15,
epochs=3,
callbacks=[checkpoint],
batch_size=8
) | Natural Language Processing with Disaster Tweets |
12,108,082 | train = []
for hs in w2hs.values() :
if len(hs)> 1:
train += hs
random.shuffle(train)
train_set = set(train)
w2ts = {}
for w, hs in w2hs.items() :
for h in hs:
if h in train_set:
if w not in w2ts:
w2ts[w] = []
if h not in w2ts[w]:
w2ts[w].append(h)
for w, ts in w2ts.items() :
w2ts[w] = np.array(ts)
t2i = {}
for i, ... | model.load_weights('model.h5')
test_pred = model.predict(test_input ) | Natural Language Processing with Disaster Tweets |
12,108,082 | class TrainingData(Sequence):
def __init__(self, score, steps=1000, batch_size=32):
super(TrainingData, self ).__init__()
self.score = -score
self.steps = steps
self.batch_size = batch_size
for ts in w2ts.values() :
idxs = [t2i[t] for t in ts]
for i in idxs:
for j in idxs:
self.score[
i, j] = 10000.0
self.on_epoch_en... | submission = pd.read_csv("/kaggle/input/nlp-getting-started/sample_submission.csv")
submission['target'] = test_pred.round().astype(int)
submission.to_csv('submission.csv', index=False ) | Natural Language Processing with Disaster Tweets |
12,023,430 | def set_lr(model, lr):
K.set_value(model.optimizer.lr, float(lr))
def get_lr(model):
return K.get_value(model.optimizer.lr)
def score_reshape(score, x, y=None):
if y is None:
m = np.zeros(( x.shape[0], x.shape[0]), dtype=K.floatx())
m[np.triu_indices(x.shape[0], 1)] = score.squeeze()
m += m.transpose()
else:
m = np... | path_data_test='/kaggle/input/nlp-getting-started/test.csv'
test_data=pd.read_csv(path_data_test)
path_data_train='/kaggle/input/nlp-getting-started/train.csv'
train_data=pd.read_csv(path_data_train ) | Natural Language Processing with Disaster Tweets |
12,023,430 | histories = []
steps = 0
tmp = keras.models.load_model('.. /input/reset-v3-100/siamese_v3_100')
model.set_weights(tmp.get_weights())
set_lr(model, 4e-5)
make_steps(5, 0.25)
set_lr(model, 4e-5)
make_steps(5, 0.25)
model.save('siamese_v3_110' )<save_model> | def clean(text):
text = re.sub(r"
","",text)
text = text.lower()
text = re.sub(r"\d","",text)
text = re.sub(r'[^\x00-\x7f]',r' ',text)
text = re.sub(r'[^\w\s]','',text)
text = re.sub(r'http\S+|www.\S+', '', text)
return text
| Natural Language Processing with Disaster Tweets |
12,023,430 | set_lr(model, 4e-5)
make_steps(5, 0.25)
set_lr(model, 4e-5)
make_steps(5, 0.25)
model.save('siamese_v3_120' )<load_pretrained> | train_data['cleaned'] = train_data['text'].apply(lambda x : clean(x))
test_data['cleaned']= test_data['text'].apply(lambda x : clean(x))
| Natural Language Processing with Disaster Tweets |
12,023,430 | <feature_engineering><EOS> | tweets_pipeline = Pipeline([('CVec', CountVectorizer(stop_words='english')) ,
('Tfidf', TfidfTransformer())])
X=train_data['cleaned'].to_numpy()
Y=train_data['target'].to_numpy()
X_train_tranformed = tweets_pipeline.fit_transform(X)
X_test=test_data['cleaned']
X_test_tranformed = tweets_pipeline.transform(X_test)
t... | Natural Language Processing with Disaster Tweets |
7,207,018 | <SOS> metric: meanfscore Kaggle data source: natural-language-processing-with-disaster-tweets<import_modules> | !pip install tweet-preprocessor | Natural Language Processing with Disaster Tweets |
7,207,018 | !pip install lap
Lambda, MaxPooling2D, Reshape
<load_from_csv> | import os
from google.cloud import storage, automl_v1beta1 as automl
import numpy as np
import pandas as pd
from sklearn import feature_extraction, linear_model, model_selection, preprocessing
from sklearn.ensemble import RandomForestClassifier
from sklearn.tree import DecisionTreeClassifier
import scipy as sp
from skl... | Natural Language Processing with Disaster Tweets |
7,207,018 | TRAIN_DF = '.. /input/humpback-whale-identification/train.csv'
SUB_Df = '.. /input/humpback-whale-identification/sample_submission.csv'
TRAIN = '.. /input/humpback-whale-identification/train/'
TEST = '.. /input/humpback-whale-identification/test/'
P2H = '.. /input/metadata/p2h.pickle'
P2SIZE = '.. /input/metadata/p2siz... | from sklearn import metrics | Natural Language Processing with Disaster Tweets |
7,207,018 | if isfile(P2SIZE):
print("P2SIZE exists.")
with open(P2SIZE, 'rb')as f:
p2size = pickle.load(f)
else:
p2size = {}
for p in tqdm(join):
size = pil_image.open(expand_path(p)).size
p2size[p] = size<compute_test_metric> | stop = set(STOPWORDS ).union(set(['FAV' , 'RT']))
lemma = WordNetLemmatizer()
preprocessor.set_options(preprocessor.OPT.URL, preprocessor.OPT.MENTION, preprocessor.OPT.NUMBER, preprocessor.OPT.RESERVED)
def clean(text):
text = preprocessor.clean(text)
text = re.sub(r'[^\w\s]','',text)
stop_free = " ".join([i for i i... | Natural Language Processing with Disaster Tweets |
7,207,018 | def match(h1, h2):
for p1 in h2ps[h1]:
for p2 in h2ps[h2]:
i1 = pil_image.open(expand_path(p1))
i2 = pil_image.open(expand_path(p2))
if i1.mode != i2.mode or i1.size != i2.size: return False
a1 = np.array(i1)
a1 = a1 - a1.mean()
a1 = a1 / sqrt(( a1 ** 2 ).mean())
a2 = np.array(i2)
a2 = a2 - a2.mean()
a2 = a2 / sqrt(... | train_df = pd.read_csv("/kaggle/input/nlp-getting-started/train.csv")
test_df = pd.read_csv("/kaggle/input/nlp-getting-started/test.csv" ) | Natural Language Processing with Disaster Tweets |
7,207,018 | def prefer(ps):
if len(ps)== 1: return ps[0]
best_p = ps[0]
best_s = p2size[best_p]
for i in range(1, len(ps)) :
p = ps[i]
s = p2size[p]
if s[0] * s[1] > best_s[0] * best_s[1]:
best_p = p
best_s = s
return best_p
h2p = {}
for h, ps in h2ps.items() :
h2p[h] = prefer(ps)
len(h2p), list(h2p.items())[:5]<set_options> | train_df.text = train_df.text.apply(clean)
test_df.text = test_df.text.apply(clean ) | Natural Language Processing with Disaster Tweets |
7,207,018 | p2bb = pd.read_csv(BB_DF ).set_index("Image")
old_stderr = sys.stderr
sys.stderr = open('/dev/null' if platform.system() != 'Windows' else 'nul', 'w')
sys.stderr = old_stderr
img_shape =(384, 384, 1)
anisotropy = 2.15
crop_margin = 0.05<normalization> | PROJECT_ID = 'automl-kaggle-263107' | Natural Language Processing with Disaster Tweets |
7,207,018 | def build_transform(rotation, shear, height_zoom, width_zoom, height_shift, width_shift):
rotation = np.deg2rad(rotation)
shear = np.deg2rad(shear)
rotation_matrix = np.array(
[[np.cos(rotation), np.sin(rotation), 0], [-np.sin(rotation), np.cos(rotation), 0], [0, 0, 1]])
shift_matrix = np.array([[1, 0, height_shi... | BUCKET_NAME = 'automl-disaster-tweet-cleaned'
BUCKET_REGION = 'us-central1' | Natural Language Processing with Disaster Tweets |
7,207,018 | def read_cropped_image(p, augment):
if p in h2p:
p = h2p[p]
size_x, size_y = p2size[p]
row = p2bb.loc[p]
x0, y0, x1, y1 = row['x0'], row['y0'], row['x1'], row['y1']
dx = x1 - x0
dy = y1 - y0
x0 -= dx * crop_margin
x1 += dx * crop_margin + 1
y0 -= dy * crop_margin
y1 += dy * crop_margin + 1
if x0 < 0:
x0 = 0
if x1 > s... | storage_client = storage.Client(project=PROJECT_ID)
tables_gcs_client = automl.GcsClient(client=storage_client, bucket_name=BUCKET_NAME)
automl_client = automl.AutoMlClient()
prediction_client = automl.PredictionServiceClient()
tables_client = automl.TablesClient(project=PROJECT_ID, region=BUCKET_REGION, client=autom... | Natural Language Processing with Disaster Tweets |
7,207,018 | def subblock(x, filter, **kwargs):
x = BatchNormalization()(x)
y = x
y = Conv2D(filter,(1, 1), activation='relu', **kwargs )(y)
y = BatchNormalization()(y)
y = Conv2D(filter,(3, 3), activation='relu', **kwargs )(y)
y = BatchNormalization()(y)
y = Conv2D(K.int_shape(x)[-1],(1, 1), **kwargs )(y)
y = Add()([x, y])
... | bucket = storage.Bucket(storage_client, name=BUCKET_NAME)
if not bucket.exists() :
bucket.create(location=BUCKET_REGION ) | Natural Language Processing with Disaster Tweets |
7,207,018 | h2ws = {}
new_whale = 'new_whale'
for p, w in tagged.items() :
if w != new_whale:
h = p2h[p]
if h not in h2ws: h2ws[h] = []
if w not in h2ws[h]: h2ws[h].append(w)
for h, ws in h2ws.items() :
if len(ws)> 1:
h2ws[h] = sorted(ws)
w2hs = {}
for h, ws in h2ws.items() :
if len(ws)== 1:
w = ws[0]
if w not in w2hs: w2hs[w] =... | def upload_blob(bucket_name, source_file_name, destination_blob_name):
bucket = storage_client.get_bucket(bucket_name)
blob = bucket.blob(destination_blob_name)
blob.upload_from_filename(source_file_name)
print('File {} uploaded to {}.'.format(
source_file_name,
destination_blob_name))
def download_to_kaggle(buck... | Natural Language Processing with Disaster Tweets |
7,207,018 | train = []
for hs in w2hs.values() :
if len(hs)> 1:
train += hs
random.shuffle(train)
train_set = set(train)
w2ts = {}
for w, hs in w2hs.items() :
for h in hs:
if h in train_set:
if w not in w2ts:
w2ts[w] = []
if h not in w2ts[w]:
w2ts[w].append(h)
for w, ts in w2ts.items() :
w2ts[w] = np.array(ts)
t2i = {}
for i, ... | train_df[['id','text','target']].to_csv('/kaggle/working/train.csv', index=False)
test_df[['id','text']].to_csv('/kaggle/working/test.csv', index=False ) | Natural Language Processing with Disaster Tweets |
7,207,018 | class TrainingData(Sequence):
def __init__(self, score, steps=1000, batch_size=32):
super(TrainingData, self ).__init__()
self.score = -score
self.steps = steps
self.batch_size = batch_size
for ts in w2ts.values() :
idxs = [t2i[t] for t in ts]
for i in idxs:
for j in idxs:
self.score[
i, j] = 10000.0
self.on_epoch_en... | upload_blob(BUCKET_NAME, '/kaggle/working/train.csv', 'train.csv')
upload_blob(BUCKET_NAME, '/kaggle/working/test.csv', 'test.csv' ) | Natural Language Processing with Disaster Tweets |
7,207,018 | def set_lr(model, lr):
K.set_value(model.optimizer.lr, float(lr))
def get_lr(model):
return K.get_value(model.optimizer.lr)
def score_reshape(score, x, y=None):
if y is None:
m = np.zeros(( x.shape[0], x.shape[0]), dtype=K.floatx())
m[np.triu_indices(x.shape[0], 1)] = score.squeeze()
m += m.transpose()
else:
m = np... | dataset_display_name = 'tweet_disaster_cleaned'
new_dataset = False
try:
dataset = tables_client.get_dataset(dataset_display_name=dataset_display_name)
except:
new_dataset = True
dataset = tables_client.create_dataset(dataset_display_name ) | Natural Language Processing with Disaster Tweets |
7,207,018 | def prepare_submission(threshold, filename):
vtop = 0
vhigh = 0
pos = [0, 0, 0, 0, 0, 0]
with open(filename, 'wt', newline='
')as f:
f.write('Image,Id
')
for i, p in enumerate(tqdm(submit)) :
t = []
s = set()
a = score[i, :]
for j in list(reversed(np.argsort(a))):
h = known[j]
if a[j] < threshold and new_whale not i... | if new_dataset:
gcs_input_uris = ['gs://' + BUCKET_NAME + '/train.csv']
import_data_operation = tables_client.import_data(
dataset=dataset,
gcs_input_uris=gcs_input_uris
)
print('Dataset
import_data_operation.result() | Natural Language Processing with Disaster Tweets |
7,207,018 | histories = []
steps = 0
if isfile('.. /input/piotte/mpiotte-standard.model'):
tmp = keras.models.load_model('.. /input/piotte/mpiotte-standard.model')
model.set_weights(tmp.get_weights())
tic = time.time()
h2ws = {}
for p, w in tagged.items() :
if w != new_whale:
h = p2h[p]
if h not in h2ws: h2ws[h] = []
if w not in... | ID_COLUMN = 'id' | Natural Language Processing with Disaster Tweets |
7,207,018 | score = 0.45*score1 + 0.55*score2<train_model> | TRAIN_BUDGET = 1000
model = None
model_display_name = 'tweet_disaster_model_clean'
try:
model = tables_client.get_model(model_display_name=model_display_name)
except:
response = tables_client.create_model(
model_display_name,
dataset=dataset,
train_budget_milli_node_hours=TRAIN_BUDGET,
exclude_column_spec_names=[TARG... | Natural Language Processing with Disaster Tweets |
7,207,018 | prepare_submission(0.92, 'submission_0.45_standard_0.55_boostrap.csv')
toc = time.time()
print("Submission time: ",(toc - tic)/ 60.)<install_modules> | gcs_input_uris = 'gs://' + BUCKET_NAME + '/test.csv'
gcs_output_uri_prefix = 'gs://' + BUCKET_NAME + '/predictions'
batch_predict_response = tables_client.batch_predict(
model=model,
gcs_input_uris=gcs_input_uris,
gcs_output_uri_prefix=gcs_output_uri_prefix,
)
print('Batch prediction operation: {}'.format(batch_pred... | Natural Language Processing with Disaster Tweets |
7,207,018 | !pip -q install aiohttp faiss-prebuilt pyxtools pymltools
!apt -qq install -y libopenblas-base libomp-dev
tensorflow.__version__<import_modules> | gcs_output_folder = batch_predict_response.metadata.batch_predict_details.output_info.gcs_output_directory.replace('gs://' + BUCKET_NAME + '/','')
download_to_kaggle(BUCKET_NAME,'/kaggle/working','submissions.csv', prefix=gcs_output_folder ) | Natural Language Processing with Disaster Tweets |
7,207,018 | MaxPooling2D
show_embedding, keras_convert_model_to_estimator_ckpt, InitFromPretrainedCheckpointHook, \
AbstractEstimator, estimator_iter_process, colab_save_file_func, OptimizerType, tf_model_fn, \
get_wsl_path, map_per_set, LossStepHookForTrain, ProcessMode, load_data_from_h5file, \
store_data_in_h5file, get_triplet_... | preds_df = pd.read_csv("/kaggle/working/submissions.csv")
preds_df = preds_df.sort_values(by=['id'])
preds_df['target'] =(preds_df['target_1_score'] >= 0.5 ).astype(int ) | Natural Language Processing with Disaster Tweets |
7,207,018 | def combine_csv(file_weight: dict, out_file: str):
sub_files = []
sub_weight = []
for csv_file, weight in file_weight.items() :
sub_files.append(csv_file)
sub_weight.append(weight)
place_weights = {}
for i in range(5):
place_weights[i] = 10 - i * 2
h_label = 'Image'
h_target = 'Id'
sub = [None] * len(sub_files)
for ... | preds_df[['id','target']].to_csv("submission.csv", index=False ) | Natural Language Processing with Disaster Tweets |
7,207,018 | class WhaleRankingUtils(object):
def __init__(self, data_utils: WhaleDataUtils, top_k: int = 5):
self.logger = logging.getLogger(self.__class__.__name__)
self.top_k = top_k
self.data_utils = data_utils
def simple_rank(self, result_list: list, distance_cutoff: float = None, only_distance_fit: bool = False)-> list:
_c... | tfidf_vectorizer = feature_extraction.text.TfidfVectorizer(ngram_range =(1,2), stop_words='english',strip_accents='unicode' ) | Natural Language Processing with Disaster Tweets |
7,207,018 | class TripletLossModelCNN(AbstractEstimator):
def __init__(self, train_ckpt_dir, data_utils: WhaleDataUtils, timeout: int = int(3600 * 5),
pretrained_ckpt_file: str = None):
super(TripletLossModelCNN, self ).__init__(
model_name="TripletLoss",
train_ckpt_dir=train_ckpt_dir,
pretrained_ckpt_file=pretrained_ckpt_file
)... | train_vectors = tfidf_vectorizer.fit_transform(train_df["text"])
test_vectors = tfidf_vectorizer.transform(test_df["text"] ) | Natural Language Processing with Disaster Tweets |
7,207,018 | !mkdir -p./keras
!cp.. /input/piotte/mpiotte-standard.model./keras/
!cp.. /input/whale-triplet-pretrained-model/tripletk/tripletK triplet -R
init_logger()
path_manager = PathManager("kaggle")
data_utils = WhaleDataUtils(path_manager=path_manager, gen_data_setting={
"x_train_num": 4,
"ignore_blank_prob": 0.9,
"ignore_s... | feature_cols = ['keyword', 'location']
X = train_df[feature_cols]
y = train_df.target
one_hot_encoded_training_predictors = pd.get_dummies(X)
clf = RandomForestClassifier(n_estimators = 100)
scores = model_selection.cross_val_score(clf, one_hot_encoded_training_predictors, y, cv=5, scoring="f1")
scores | Natural Language Processing with Disaster Tweets |
7,207,018 | estimator.show_predict_result(count=10, top_k=5)
estimator.show_predict_result(count=20, top_k=3)
<install_modules> | clf.fit(one_hot_encoded_training_predictors, y ) | Natural Language Processing with Disaster Tweets |
7,207,018 | !rm *.pkl
!rm./keras/ -R
!pip uninstall aiohttp faiss-prebuilt pyxtools pymltools -y
!apt remove -y libopenblas-base libomp-dev
<import_modules> | clf = linear_model.RidgeClassifier()
| Natural Language Processing with Disaster Tweets |
7,207,018 | from keras.layers import Dense, Flatten, Dropout, Lambda, Input, Concatenate, concatenate
from keras.models import Model
from keras.applications import *
from keras.preprocessing.image import ImageDataGenerator, load_img, img_to_array
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
... | scores = model_selection.cross_val_score(clf, train_vectors, train_df["target"], cv=10, scoring="f1")
scores | Natural Language Processing with Disaster Tweets |
7,207,018 | filenames = os.listdir(".. /input/train/train")
labels = []
for file in filenames:
category = file.split('.')[0]
if category == 'cat':
labels.append('cat')
else:
labels.append('dog' )<split> | clf.fit(train_vectors, train_df["target"] ) | Natural Language Processing with Disaster Tweets |
7,207,018 | df = pd.DataFrame({
'filename': filenames,
'label': labels
})
train_df, validation_df = train_test_split(df, test_size=0.1, random_state = 42)
train_df = train_df.reset_index(drop=True)
validation_df = validation_df.reset_index(drop=True)
<define_variables> | parameters = {
'gamma': [0.7, 1, 'auto', 'scale']
}
clf = GridSearchCV(SVC(kernel='rbf'), parameters, cv=5, n_jobs=-1, scoring="f1" ).fit(train_vectors, train_df["target"] ) | Natural Language Processing with Disaster Tweets |
7,207,018 | batch_size = 64
train_num = len(train_df)
validation_num = len(validation_df )<create_dataframe> | clf.best_estimator_ | Natural Language Processing with Disaster Tweets |
7,207,018 | def two_image_generator(generator, df, directory, batch_size,
x_col = 'filename', y_col = None, model = None, shuffle = False,
img_size1 =(224, 224), img_size2 =(299,299)) :
gen1 = generator.flow_from_dataframe(
df,
directory,
x_col = x_col,
y_col = y_col,
target_size = img_size1,
class_mode = model,
batch_size = batc... | clf.best_score_ | Natural Language Processing with Disaster Tweets |
7,207,018 |
<load_pretrained> | sample_submission = pd.read_csv("/kaggle/input/nlp-getting-started/sample_submission.csv")
sample_submission["target"] = clf.predict(test_vectors)
df = pd.DataFrame({'text' : test_df['text'], 'prediction' : sample_submission["target"]} ) | Natural Language Processing with Disaster Tweets |
7,207,018 | train_aug_datagen = ImageDataGenerator(
rotation_range = 20,
shear_range = 0.1,
zoom_range = 0.2,
width_shift_range = 0.1,
height_shift_range = 0.1,
horizontal_flip = True
)
train_generator = two_image_generator(train_aug_datagen, train_df, '.. /input/train/train/',
batch_size = batch_size, y_col = 'label',
model = ... | sample_submission.to_csv("submission1.csv", index=False ) | Natural Language Processing with Disaster Tweets |
7,207,018 | validation_datagen = ImageDataGenerator()
validation_generator = two_image_generator(validation_datagen, validation_df,
'.. /input/train/train/', batch_size = batch_size,
y_col = 'label',model = 'binary', shuffle = True )<choose_model_class> | print(tf.__version__)
| Natural Language Processing with Disaster Tweets |
7,207,018 | def create_base_model(MODEL, img_size, lambda_fun = None):
inp = Input(shape =(img_size[0], img_size[1], 3))
x = inp
if lambda_fun:
x = Lambda(lambda_fun )(x)
base_model = MODEL(input_tensor = x, weights = 'imagenet', include_top = False, pooling = 'avg')
model = Model(inp, base_model.output)
return model<choose_mod... | X = train_df["text"]
y = train_df["target"]
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.3, random_state=42 ) | Natural Language Processing with Disaster Tweets |
7,207,018 | model1 = create_base_model(vgg16.VGG16,(224, 224), vgg16.preprocess_input)
model2 = create_base_model(resnet50.ResNet50,(224, 224), resnet50.preprocess_input)
model3 = create_base_model(inception_v3.InceptionV3,(299, 299), inception_v3.preprocess_input)
model1.trainable = False
model2.trainable = False
model3.traina... | vocab_size = 10000
embedding_dim = 16
max_length = 30
trunc_type='post'
padding_type='post'
oov_tok = "<OOV>" | Natural Language Processing with Disaster Tweets |
7,207,018 | checkpointer = ModelCheckpoint(filepath='dogcat.weights.best.hdf5', verbose=1,
save_best_only=True, save_weights_only=True )<train_model> | tokenizer = Tokenizer(num_words = vocab_size, oov_token=oov_tok)
tokenizer.fit_on_texts(X_train)
word_index = tokenizer.word_index
sequences = tokenizer.texts_to_sequences(X_train)
padded = pad_sequences(sequences,maxlen=max_length, padding=padding_type, truncating=trunc_type)
testing_sequences = tokenizer.texts_to... | Natural Language Processing with Disaster Tweets |
7,207,018 | multiple_pretained_model.fit_generator(
train_generator,
epochs = 5,
steps_per_epoch = train_num // batch_size,
validation_data = validation_generator,
validation_steps = validation_num // batch_size,
verbose = 1,
callbacks = [checkpointer]
)<load_pretrained> | reverse_word_index = dict([(value, key)for(key, value)in word_index.items() ])
def decode_sentence(text):
return ' '.join([reverse_word_index.get(i, '?')for i in text])
print(decode_sentence(padded[0])) | Natural Language Processing with Disaster Tweets |
7,207,018 | multiple_pretained_model.load_weights('dogcat.weights.best.hdf5' )<define_variables> | !wget --no-check-certificate \
https://storage.googleapis.com/laurencemoroney-blog.appspot.com/glove.6B.100d.txt \
-O /tmp/glove.6B.100d.txt
embeddings_index = {};
vocab_size=len(word_index)
embedding_dim = 100
with open('/tmp/glove.6B.100d.txt')as f:
for line in f:
values = line.split() ;
word = values[0];
coefs = np... | Natural Language Processing with Disaster Tweets |
7,207,018 | test_filenames = os.listdir(".. /input/test/test")
test_df = pd.DataFrame({
'filename': test_filenames
})
num_test = len(test_df)
test_datagen = ImageDataGenerator()
test_generator = two_image_generator(test_datagen, test_df, '.. /input/test/test/', batch_size = batch_size )<predict_on_test> | model = tf.keras.Sequential([
tf.keras.layers.Embedding(vocab_size, embedding_dim, input_length=max_length),
tf.keras.layers.GlobalAveragePooling1D() ,
tf.keras.layers.Dense(24, activation='relu'),
tf.keras.layers.Dense(1, activation='sigmoid')
])
model.compile(loss='binary_crossentropy',optimizer='adam',metrics=['ac... | Natural Language Processing with Disaster Tweets |
7,207,018 | prediction = multiple_pretained_model.predict_generator(test_generator,
steps=np.ceil(num_test/batch_size))
prediction = prediction.clip(min = 0.005, max = 0.995 )<save_to_csv> | num_epochs = 3
history = model.fit(padded, y_train, epochs=num_epochs, validation_data=(testing_padded, y_val)) | Natural Language Processing with Disaster Tweets |
7,207,018 | submission_df = pd.read_csv('.. /input/sample_submission.csv')
for i, fname in enumerate(test_filenames):
index = int(fname[fname.rfind('/')+1:fname.rfind('.')])
submission_df.at[index-1, 'label'] = prediction[i]
submission_df.to_csv('submission.csv', index=False )<import_modules> | model_loss = pd.DataFrame(model.history.history)
model_loss.head() | Natural Language Processing with Disaster Tweets |
7,207,018 | import numpy as np
import pandas as pd
import os
from fastai.vision import *<define_variables> | testing_sequences2 = tokenizer.texts_to_sequences(test_df.text)
testing_padded2 = pad_sequences(testing_sequences2, maxlen=max_length, padding=padding_type, truncating=trunc_type ) | Natural Language Processing with Disaster Tweets |
7,207,018 | path = Path('.. /input' )<set_options> | probabilities = model.predict(testing_padded2 ) | Natural Language Processing with Disaster Tweets |
7,207,018 | path.ls()<define_variables> | predictions =(probabilities > 0.5 ).astype(int)
predictions = np.ndarray.flatten(predictions)
pd.value_counts(predictions ) | Natural Language Processing with Disaster Tweets |
7,207,018 | path_img = path/'train'<features_selection> | original_test_df = pd.read_csv("/kaggle/input/nlp-getting-started/test.csv")
df = pd.DataFrame({'text' : original_test_df['text'],'cleaned_text' : test_df['text'], 'prediction' : predictions,'probabilities' : np.ndarray.flatten(probabilities)})
df.to_csv("test_df.csv", index=False ) | Natural Language Processing with Disaster Tweets |
7,207,018 | get_image_files(path_img)[:5]<define_variables> | sample_submission = pd.read_csv("/kaggle/input/nlp-getting-started/sample_submission.csv")
sample_submission["target"] = predictions
sample_submission.to_csv("submission.csv", index=False ) | Natural Language Processing with Disaster Tweets |
7,207,018 | np.random.seed(42)
size = 224
bs = 64
num_workers = 0
pat = r'/([^/.]+ ).\d+.jpg$'<categorify> | import tensorflow_hub as hub
import lightgbm as lgb
from lightgbm import LGBMClassifier | Natural Language Processing with Disaster Tweets |
7,207,018 | tfms = get_transforms()
data =(ImageItemList.from_folder(path_img)
.random_split_by_pct()
.label_from_re(pat)
.add_test_folder('.. /test')
.transform(tfms, size=size)
.databunch(bs=bs, num_workers=num_workers)
.normalize(imagenet_stats))<define_variables> | module_url = "https://tfhub.dev/google/nnlm-en-dim128/2"
embed = hub.KerasLayer(module_url)
embeddings = embed(["A long sentence.", "single-word",
"http://example.com"])
print(embeddings.shape ) | Natural Language Processing with Disaster Tweets |
7,207,018 | data.show_batch(rows=3, figsize=(7,6))<choose_model_class> | embed = hub.load("https://tfhub.dev/google/universal-sentence-encoder/3" ) | Natural Language Processing with Disaster Tweets |
7,207,018 | learn = create_cnn(data, models.resnet50, metrics=accuracy, model_dir='/tmp/models' )<train_model> | X_train_embeddings = embed(train_df.text.values)
X_test_embeddings = embed(test_df.text.values ) | Natural Language Processing with Disaster Tweets |
7,207,018 | learn.fit_one_cycle(4 )<save_model> | params = {
'learning_rate': 0.04,
'n_estimators': 1500,
'colsample_bytree': 0.4,
'metric':'auc'
} | Natural Language Processing with Disaster Tweets |
7,207,018 | learn.save('stage-1' )<train_model> | text_clf = LGBMClassifier(**params ) | Natural Language Processing with Disaster Tweets |
7,207,018 | learn.fit_one_cycle(2, max_lr=slice(1e-6,1e-4))<save_model> | text_clf.fit(X_train_embeddings['outputs'][:5000,:], train_df.target.values[:5000],
eval_set=[(X_train_embeddings['outputs'][:5000,:], train_df.target.values[:5000]),
(X_train_embeddings['outputs'][5000:,:], train_df.target.values[5000:])],
verbose=200, early_stopping_rounds=20,
)
| Natural Language Processing with Disaster Tweets |
7,207,018 | learn.save('stage-2' )<find_best_params> | text_clf.fit(X_train_embeddings['outputs'][:5000,:], train_df.target.values[:5000])
Y_pred = text_clf.predict(X_train_embeddings['outputs'][5000:] ) | Natural Language Processing with Disaster Tweets |
7,207,018 | interp = ClassificationInterpretation.from_learner(learn)
losses,idxs = interp.top_losses()
len(data.valid_ds)==len(losses)==len(idxs )<predict_on_test> | print(metrics.classification_report(train_df.target[5000:], Y_pred, digits=3),)
print(metrics.confusion_matrix(train_df.target[5000:], Y_pred)) | Natural Language Processing with Disaster Tweets |
7,207,018 | preds, y = learn.get_preds(ds_type=DatasetType.Test )<prepare_output> | text_clf.fit(X_train_embeddings['outputs'], train_df.target.values)
pred_test = text_clf.predict(X_test_embeddings['outputs'] ) | Natural Language Processing with Disaster Tweets |
7,207,018 | dog_preds = preds[:,1]<create_dataframe> | df = pd.DataFrame({'cleaned_text' : test_df['text'], 'prediction' : pred_test})
df.head(20 ) | Natural Language Processing with Disaster Tweets |
7,207,018 | <feature_engineering><EOS> | sample_submission = pd.read_csv("/kaggle/input/nlp-getting-started/sample_submission.csv")
sample_submission["target"] = pred_test
sample_submission.to_csv("submission.csv", index=False ) | Natural Language Processing with Disaster Tweets |
7,384,080 | <SOS> metric: meanfscore Kaggle data source: natural-language-processing-with-disaster-tweets<data_type_conversions> | !wget --quiet https://raw.githubusercontent.com/tensorflow/models/master/official/nlp/bert/tokenization.py | Natural Language Processing with Disaster Tweets |
7,384,080 | submission['id'] = submission['id'].astype(int )<sort_values> | BASE_PATH = "/kaggle/input/nlp-getting-started/" | Natural Language Processing with Disaster Tweets |
7,384,080 | submission = submission.sort_values('id' )<save_to_csv> | train =pd.read_csv(BASE_PATH + "train.csv")
train.head() | Natural Language Processing with Disaster Tweets |
7,384,080 | submission.to_csv('submission.csv', index=False )<save_to_csv> | test =pd.read_csv(BASE_PATH + "test.csv")
test.head() | Natural Language Processing with Disaster Tweets |
7,384,080 | submission.to_csv('submission.csv', index=False )<define_variables> | %%time
module_url = "https://tfhub.dev/tensorflow/bert_en_uncased_L-12_H-768_A-12/1"
bert_layer = hub.KerasLayer(module_url, trainable=True ) | Natural Language Processing with Disaster Tweets |
7,384,080 | labels=['dog','cat']<define_variables> | 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 ) | Natural Language Processing with Disaster Tweets |
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