Kaggle competition: APTOS 2019 Blindness Detection
<SOS> metric: QuadraticWeightedKappa Kaggle data source: aptos-2019-blindness-detection<find_best_params>
package_dir = ".. /input/pretrained-models/pretrained-models/pretrained-models.pytorch-master/"
sys.path.insert(0, package_dir)
 device = torch.device("cuda:0")
 ImageFile.LOAD_TRUNCATED_IMAGES = True
curve = f1_curve(hold_y, np.mean(ensemble_hold, axis=0))
thresh = curve.idxmax()
curve.max() , thresh<save_to_csv>
class RetinopathyDatasetTest(Dataset):
def __init__(self, csv_file, transform):
self.data = pd.read_csv(csv_file)
 self.transform = transform
def __len__(self):
return len(self.data)
 def __getitem__(self, idx):
img_name = os.path.join('.. /input/aptos2019-blindness-detection/test_images', self.data.loc[idx, 'id_code'] + '.png')
 image = Image.open(img_name)
 image = self.transform(image)
 return {'image': image}
submission_df = pd.read_csv('.. /input/sample_submission.csv')
 submission_df['prediction'] =(ensemble_test_s.loc[submission_df.qid] >= thresh ).astype(int ).values
submission_df.to_csv('submission.csv', index=False)
 submission_df.prediction.mean()<import_modules>
model = pretrainedmodels.__dict__['resnet101'](pretrained=None)
 model.avg_pool = nn.AdaptiveAvgPool2d(1)
 model.last_linear = nn.Sequential(
 nn.BatchNorm1d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),
nn.Dropout(p=0.25),
nn.Linear(in_features=2048, out_features=2048, bias=True),
nn.ReLU() ,
nn.BatchNorm1d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True),
nn.Dropout(p=0.5),
nn.Linear(in_features=2048, out_features=1, bias=True), 
)
 model.load_state_dict(torch.load(".. /input/mmmodel/model.bin"))
model = model.to(device )
YouTubeVideo('dEFd_2_b6X8' )<define_variables>
for param in model.parameters() :
param.requires_grad = False
model.eval()
idx = ['003069ba70645b15c3ba',
'0039457910b8a802121b',
'0061c39bba71f03ac780',
'007e65e4441890f4416b',
'00888dc5bfb277276e36',
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'ffd60158ae1a32735bda',
'ffe3e5dac8b60c486704',
'ffe7a676812dfe4926c3',
'fffed08be2626f74b139']<save_to_csv>
test_transform = transforms.Compose([
transforms.Resize(( 224, 224)) ,
transforms.RandomHorizontalFlip() ,
transforms.ToTensor() ,
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
 ])
 test_dataset = RetinopathyDatasetTest(csv_file='.. /input/aptos2019-blindness-detection/sample_submission.csv',
transform=test_transform )
test = pd.read_csv('.. /input/test.csv')
 sub = pd.DataFrame({'qid': test.qid})
 sub['prediction'] = sub['qid'].map(dict(zip(idx,np.ones(len(idx)))) ).fillna(0 ).astype(int)
 sub.to_csv('submission.csv', index=False )<define_variables>
test_data_loader = torch.utils.data.DataLoader(test_dataset, batch_size=32, shuffle=False, num_workers=4)
 test_preds1 = np.zeros(( len(test_dataset), 1))
tk0 = tqdm(test_data_loader)
 for i, x_batch in enumerate(tk0):
x_batch = x_batch["image"]
pred = model(x_batch.to(device))
test_preds1[i * 32:(i + 1)* 32] = pred.detach().cpu().squeeze().numpy().ravel().reshape(-1, 1 )
num_words=50000
max_len=64
tqdm.pandas()
%matplotlib inline
print(os.listdir(".. /input")) 
<load_from_csv>
test_data_loader = torch.utils.data.DataLoader(test_dataset, batch_size=32, shuffle=False, num_workers=4)
 test_preds2 = np.zeros(( len(test_dataset), 1))
tk0 = tqdm(test_data_loader)
 for i, x_batch in enumerate(tk0):
x_batch = x_batch["image"]
pred = model(x_batch.to(device))
test_preds2[i * 32:(i + 1)* 32] = pred.detach().cpu().squeeze().numpy().ravel().reshape(-1, 1 )
train = pd.read_csv(".. /input/train.csv")
 test = pd.read_csv(".. /input/test.csv")
 print("Train shape : ",train.shape)
 print("Test shape : ",test.shape )<feature_engineering>
test_data_loader = torch.utils.data.DataLoader(test_dataset, batch_size=32, shuffle=False, num_workers=4)
 test_preds3 = np.zeros(( len(test_dataset), 1))
tk0 = tqdm(test_data_loader)
 for i, x_batch in enumerate(tk0):
x_batch = x_batch["image"]
pred = model(x_batch.to(device))
test_preds3[i * 32:(i + 1)* 32] = pred.detach().cpu().squeeze().numpy().ravel().reshape(-1, 1 )
def build_vocab(sentences, verbose = True):

 vocab = {}
for sentence in tqdm(sentences, disable =(not verbose)) :
for word in sentence:
try:
vocab[word] += 1
except KeyError:
vocab[word] = 1
return vocab
def clean_text(x):
x = str(x)
 for punct in "/-'":
x = x.replace(punct, ' ')
 for punct in '&':
x = x.replace(punct, f' {punct} ')
 for punct in '?!.,"
x = x.replace(punct, '')
 return x
def clean_numbers(x):
x = re.sub('[0-9]{5,}', ' huge number ', x)
 x = re.sub('[0-9]{4}', ' year ', x)
 x = re.sub('[0-9]{3}', ' number ', x)
 x = re.sub('[0-9]{2}', ' number ', x)
 return x
def clean_more(x):
x=re.sub('\s+', ' ', x ).strip()
regex = re.compile('[^a-zA-Z] ')
 return regex.sub('', x)
 def _get_mispell(mispell_dict):
mispell_re = re.compile('(%s)' % '|'.join(mispell_dict.keys()))
return mispell_dict, mispell_re
mispell_dict = {'colour':'color',
'centre':'center',
'didnt':'did not',
'doesnt':'does not',
'isnt':'is not',
'shouldnt':'should not',
'favourite':'favorite',
'travelling':'traveling',
'counselling':'counseling',
'theatre':'theater',
'cancelled':'canceled',
'labour':'labor',
'organisation':'organization',
'citicise':'criticize',
}
mispellings, mispellings_re = _get_mispell(mispell_dict)
 def replace_typical_misspell(text):
def replace(match):
return mispellings[match.group(0)]
return mispellings_re.sub(replace, text)
 train["question_text"] = train["question_text"].progress_apply(lambda x: clean_text(x))
train["question_text"] = train["question_text"].progress_apply(lambda x: clean_numbers(x))
train["question_text"] = train["question_text"].progress_apply(lambda x: clean_more(x))
train["question_text"] = train["question_text"].progress_apply(lambda x: x.lower())
 train["question_text"] = train["question_text"].progress_apply(lambda x: replace_typical_misspell(x))
sentences = train["question_text"].apply(lambda x: x.split())
 test["question_text"] = test["question_text"].progress_apply(lambda x: clean_text(x))
test["question_text"] = test["question_text"].progress_apply(lambda x: clean_numbers(x))
test["question_text"] = test["question_text"].progress_apply(lambda x: clean_more(x))
test["question_text"] = test["question_text"].progress_apply(lambda x: x.lower())
 test["question_text"] = test["question_text"].progress_apply(lambda x: replace_typical_misspell(x))
tsentences = test["question_text"].apply(lambda x: x.split())
 vocab = build_vocab(list(sentences)+list(tsentences))
def wordindex(vocab,n):
word_index={}
sorted_v = sorted(vocab.items() , key=operator.itemgetter(1)) [::-1]
for i in range(n-3):
word_index[sorted_v[i][0]]=i+3
return(word_index)
 word_index=wordindex(vocab,num_words)
 def zif(word):
ans=2
if(word in word_index):
ans=word_index[word]
return ans
x_train = [[zif(word)for word in sentence] for sentence in tqdm(sentences)]
x_train=pad_sequences(x_train, maxlen=max_len)
 x_test = [[zif(word)for word in sentence] for sentence in tqdm(tsentences)]
x_test=pad_sequences(x_test, maxlen=max_len)
 print({k: vocab[k] for k in list(vocab)[:5]} )<prepare_x_and_y>
test_data_loader = torch.utils.data.DataLoader(test_dataset, batch_size=32, shuffle=False, num_workers=4)
 test_preds4 = np.zeros(( len(test_dataset), 1))
tk0 = tqdm(test_data_loader)
 for i, x_batch in enumerate(tk0):
x_batch = x_batch["image"]
pred = model(x_batch.to(device))
test_preds4[i * 32:(i + 1)* 32] = pred.detach().cpu().squeeze().numpy().ravel().reshape(-1, 1 )
def wordindex(vocab,n):
word_index={}
sorted_v = sorted(vocab.items() , key=operator.itemgetter(1)) [::-1]
for i in range(n-3):
word_index[sorted_v[i][0]]=i+3
return(word_index)
 word_index=wordindex(vocab,num_words)
 def zif(word):
ans=2
if(word in word_index):
ans=word_index[word]
return ans
x_train = [[zif(word)for word in sentence] for sentence in tqdm(sentences)]
x_train=pad_sequences(x_train, maxlen=max_len)
 x_test = [[zif(word)for word in sentence] for sentence in tqdm(tsentences)]
x_test=pad_sequences(x_test, maxlen=max_len)
 y_train=train['target']<drop_column>
test_data_loader = torch.utils.data.DataLoader(test_dataset, batch_size=32, shuffle=False, num_workers=4)
 test_preds5 = np.zeros(( len(test_dataset), 1))
tk0 = tqdm(test_data_loader)
 for i, x_batch in enumerate(tk0):
x_batch = x_batch["image"]
pred = model(x_batch.to(device))
test_preds5[i * 32:(i + 1)* 32] = pred.detach().cpu().squeeze().numpy().ravel().reshape(-1, 1 )
del train
del sentences
del tsentences<string_transform>
test_data_loader = torch.utils.data.DataLoader(test_dataset, batch_size=32, shuffle=False, num_workers=4)
 test_preds6 = np.zeros(( len(test_dataset), 1))
tk0 = tqdm(test_data_loader)
 for i, x_batch in enumerate(tk0):
x_batch = x_batch["image"]
pred = model(x_batch.to(device))
test_preds6[i * 32:(i + 1)* 32] = pred.detach().cpu().squeeze().numpy().ravel().reshape(-1, 1 )
embedding_matrix_Glove = np.zeros(( num_words, 300))
with open('.. /input/embeddings/glove.840B.300d/glove.840B.300d.txt', 'r')as f:
for line in tqdm(f):
values = line.split()
word = values[0]
if(word in word_index):
try:
word_vector = np.asarray(values[1:], dtype='float32')
 except ValueError:
pass
else:
embedding_matrix_Glove[word_index[word]] = word_vector<string_transform>
test_data_loader = torch.utils.data.DataLoader(test_dataset, batch_size=32, shuffle=False, num_workers=4)
 test_preds7 = np.zeros(( len(test_dataset), 1))
tk0 = tqdm(test_data_loader)
 for i, x_batch in enumerate(tk0):
x_batch = x_batch["image"]
pred = model(x_batch.to(device))
test_preds7[i * 32:(i + 1)* 32] = pred.detach().cpu().squeeze().numpy().ravel().reshape(-1, 1 )
embedding_matrix_Wiki = np.zeros(( num_words, 300))
with open('.. /input/embeddings/wiki-news-300d-1M/wiki-news-300d-1M.vec', 'r')as f:
for line in tqdm(f):
values = line.split()
word = values[0]
if(word in word_index):
try:
word_vector = np.asarray(values[1:], dtype='float32')
 except ValueError:
pass
else:
embedding_matrix_Wiki[word_index[word]] = word_vector<compute_test_metric>
test_data_loader = torch.utils.data.DataLoader(test_dataset, batch_size=32, shuffle=False, num_workers=4)
 test_preds8 = np.zeros(( len(test_dataset), 1))
tk0 = tqdm(test_data_loader)
 for i, x_batch in enumerate(tk0):
x_batch = x_batch["image"]
pred = model(x_batch.to(device))
test_preds8[i * 32:(i + 1)* 32] = pred.detach().cpu().squeeze().numpy().ravel().reshape(-1, 1 )
def f1(y_true, y_pred):
y_pred = K.round(y_pred+0.15)
 tp = K.sum(K.cast(y_true*y_pred, 'float'), axis=0)
 tn = K.sum(K.cast(( 1-y_true)*(1-y_pred), 'float'), axis=0)
 fp = K.sum(K.cast(( 1-y_true)*y_pred, 'float'), axis=0)
 fn = K.sum(K.cast(y_true*(1-y_pred), 'float'), axis=0)
 p = tp /(tp + fp + K.epsilon())
 r = tp /(tp + fn + K.epsilon())
 f1 = 2*p*r /(p+r+K.epsilon())
 f1 = tf.where(tf.is_nan(f1), tf.zeros_like(f1), f1)
 return K.mean(f1 )<categorify>
test_data_loader = torch.utils.data.DataLoader(test_dataset, batch_size=32, shuffle=False, num_workers=4)
 test_preds9 = np.zeros(( len(test_dataset), 1))
tk0 = tqdm(test_data_loader)
 for i, x_batch in enumerate(tk0):
x_batch = x_batch["image"]
pred = model(x_batch.to(device))
test_preds9[i * 32:(i + 1)* 32] = pred.detach().cpu().squeeze().numpy().ravel().reshape(-1, 1 )
class Netn:
def __init__(self,em):
tweet_input = Input(shape=(max_len,), dtype='int32')
 tweet_encoder = Embedding(num_words, 300, input_length=max_len,
weights=[em], trainable=False )(tweet_input)
 X = SpatialDropout1D(0.1 )(tweet_encoder)
 X = Bidirectional(CuDNNGRU(64, return_sequences=True))(X)
 activations = CuDNNGRU(64, return_sequences=True )(X)
 attention = Dense(1, activation='tanh' )(activations)
 attention = Flatten()(attention)
 attention = Activation('softmax' )(attention)
 attention = RepeatVector(64 )(attention)
 attention = Permute([2, 1] )(attention)
 attention = Dropout(0.5 )(attention)
 X = concatenate([activations, attention])
 x=CuDNNGRU(64, return_sequences=True )(X)
 a=GlobalMaxPooling1D()(x)
 b=GlobalAveragePooling1D()(x)
 x=concatenate([a,b])
 x=Dense(64, activation='relu' )(x)
 x=Dropout(0.4 )(x)
 output = Dense(1, activation='sigmoid' )(x)
 self.model = Model(inputs=[tweet_input], outputs=[output])
 self.model.summary()
def unfreeze(self):
self.model.layers[1].trainable = True
def fit(self,b1,b2,epp,bs,**data):
self.model.compile(**data)
 x_t=x_train[b1:b2]
y_t=y_train[b1:b2]
if(epp>0):
filepath='tmp.hd5'
cp=ModelCheckpoint(filepath, monitor="val_f1",verbose=1, save_best_only=True,mode='max')
 history=self.model.fit(x_t,
y_t,
epochs=epp,
batch_size=bs,
callbacks=[cp],
validation_split=0.1)
 self.model.load_weights(filepath)
 plt.plot(history.history['f1'], label='f1 train')
 plt.plot(history.history['val_f1'], label='f1 val')
 plt.xlabel('epoche')
 plt.ylabel('f1')
 plt.legend()
plt.show()
ansz=history.history['val_f1']
mx=max(ansz)
 print('val f1 is maximal {} on a step {}'.format(mx,ansz.index(mx)+1))
def predvec(self,x_test):
return(self.model.predict(x_test)) 
<choose_model_class>
test_data_loader = torch.utils.data.DataLoader(test_dataset, batch_size=32, shuffle=False, num_workers=4)
 test_preds10 = np.zeros(( len(test_dataset), 1))
tk0 = tqdm(test_data_loader)
 for i, x_batch in enumerate(tk0):
x_batch = x_batch["image"]
pred = model(x_batch.to(device))
test_preds10[i * 32:(i + 1)* 32] = pred.detach().cpu().squeeze().numpy().ravel().reshape(-1, 1 )
m1=Netn(embedding_matrix_Wiki)
<train_model>
test_preds =(test_preds1 + test_preds2 + test_preds3 + test_preds4 + test_preds5
+ test_preds6 + test_preds7 + test_preds8 + test_preds9 + test_preds10)/ 10.0
<choose_model_class><EOS> 
coef = [0.5, 1.5, 2.5, 3.5]
for i, pred in enumerate(test_preds):
if pred < coef[0]:
test_preds[i] = 0
elif pred >= coef[0] and pred < coef[1]:
test_preds[i] = 1
elif pred >= coef[1] and pred < coef[2]:
test_preds[i] = 2
elif pred >= coef[2] and pred < coef[3]:
test_preds[i] = 3
else:
test_preds[i] = 4
sample = pd.read_csv(".. /input/aptos2019-blindness-detection/sample_submission.csv")
 sample.diagnosis = test_preds.astype(int)
 sample.to_csv("submission.csv", index=False )