Kaggle competition: OSIC Pulmonary Fibrosis Progression
<SOS> metric: LaplaceLogLikelihood Kaggle data source: osic-pulmonary-fibrosis-progression<set_options>
Dropout_model = 0.65
FVC_weight = 0.12
Confidence_weight = 0.25
def seed_everything(seed=SEED):
random.seed(seed)
 os.environ['PYTHONHASHSEED'] = str(seed)
 np.random.seed(seed)
 torch.manual_seed(seed)
 torch.cuda.manual_seed(seed)
 torch.backends.cudnn.deterministic = True
seed_everything() 
<feature_engineering>
!pip install.. /input/kerasapplications/keras-team-keras-applications-3b180cb -f./ --no-index
!pip install.. /input/efficientnet/efficientnet-1.1.0/ -f./ --no-index
os.environ['OMP_NUM_THREADS'] = '4'<load_from_csv>
def seed_everything(seed=2020):
random.seed(seed)
 os.environ['PYTHONHASHSEED'] = str(seed)
 np.random.seed(seed)
 tf.random.set_seed(seed)
 seed_everything(42 )
df_train = pd.read_csv(".. /input/train.csv")
 df_test = pd.read_csv(".. /input/test.csv" )<randomize_order>
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.compat.v1.Session(config=config )
np.random.seed(SEED)
 trn_idx = np.random.permutation(len(df_train))
df_train = df_train.iloc[trn_idx]
df = pd.concat([df_train ,df_test],sort=True )<define_variables>
train = pd.read_csv('.. /input/osic-pulmonary-fibrosis-progression/train.csv' )
puncts = [',', '.', '"', ':', ')', '(', '-', '!', '?', '|', ';', "'", '$', '&', '/', '[', ']', '>', '%', '=', '
'·', '_', '{', '}', '©', '^', '®', '`', '<', '→', '°', '€', '™', '›', '♥', '←', '×', '§', '″', '′', 'Â', '█', '½', 'à', '…',
'“', '★', '”', '–', '●', 'â', '►', '−', '¢', '²', '¬', '░', '¶', '↑', '±', '¿', '▾', '═', '¦', '║', '―', '¥', '▓', '—', '‹', '─',
'▒', '：', '¼', '⊕', '▼', '▪', '†', '■', '’', '▀', '¨', '▄', '♫', '☆', 'é', '¯', '♦', '¤', '▲', 'è', '¸', '¾', 'Ã', '⋅', '‘', '∞',
'∙', '）', '↓', '、', '│', '（', '»', '，', '♪', '╩', '╚', '³', '・', '╦', '╣', '╔', '╗', '▬', '❤', 'ï', 'Ø', '¹', '≤', '‡', '√', ]
def clean_text(x):
x = str(x)
 for punct in puncts:
x = x.replace(punct, f' {punct} ')
 return x
df_train["question_text"] = df_train["question_text"].progress_apply(lambda x: clean_text(x))
df_test["question_text"] = df_test["question_text"].apply(lambda x: clean_text(x))<categorify>
def get_tab(df):
vector = [(df.Age.values[0] - 30)/ 30]
if df.Sex.values[0] == 'male':
vector.append(0)
 else:
vector.append(1)
 if df.SmokingStatus.values[0] == 'Never smoked':
vector.extend([0,0])
 elif df.SmokingStatus.values[0] == 'Ex-smoker':
vector.extend([1,1])
 elif df.SmokingStatus.values[0] == 'Currently smokes':
vector.extend([0,1])
 else:
vector.extend([1,0])
 return np.array(vector )
def clean_numbers(x):
x = re.sub('[0-9]{5,}', '
x = re.sub('[0-9]{4}', '
x = re.sub('[0-9]{3}', '
x = re.sub('[0-9]{2}', '
return x
df_train["question_text"] = df_train["question_text"].progress_apply(lambda x: clean_numbers(x))
df_test["question_text"] = df_test["question_text"].apply(lambda x: clean_numbers(x))<feature_engineering>
A = {}
TAB = {}
P = []
for i, p in tqdm(enumerate(train.Patient.unique())) :
sub = train.loc[train.Patient == p, :]
fvc = sub.FVC.values
weeks = sub.Weeks.values
c = np.vstack([weeks, np.ones(len(weeks)) ] ).T
a, b = np.linalg.lstsq(c, fvc)[0]
A[p] = a
TAB[p] = get_tab(sub)
 P.append(p )
specials = {'\u200b': ' ', '…': '...', '\ufeff': '', 'करना': '', 'है': ''}
def clean_special_chars(text):
for s in specials:
text = text.replace(s, specials[s])
 return text
df_train["question_text"] = df_train["question_text"].progress_apply(lambda x: clean_special_chars(x))
df_test["question_text"] = df_test["question_text"].apply(lambda x: clean_special_chars(x))<feature_engineering>
class IGenerator(Sequence):
BAD_ID = ['ID00011637202177653955184', 'ID00052637202186188008618']
def __init__(self, keys, a, tab, batch_size=32):
self.keys = [k for k in keys if k not in self.BAD_ID]
self.a = a
self.tab = tab
self.batch_size = batch_size
self.train_data = {}
for p in train.Patient.values:
self.train_data[p] = os.listdir(f'.. /input/osic-pulmonary-fibrosis-progression/train/{p}/')
 def __len__(self):
return 1000
def __getitem__(self, idx):
x = []
a, tab = [], []
keys = np.random.choice(self.keys, size = self.batch_size)
 for k in keys:
try:
i = np.random.choice(self.train_data[k], size=1)[0]
img = get_img(f'.. /input/osic-pulmonary-fibrosis-progression/train/{k}/{i}')
 x.append(img)
 a.append(self.a[k])
 tab.append(self.tab[k])
 except:
print(k, i)
 x,a,tab = np.array(x), np.array(a), np.array(tab)
 x = np.expand_dims(x, axis=-1)
 return [x, tab] , a
df_train["question_text"] = df_train["question_text"].apply(lambda x: x.lower())
 df_test["question_text"] = df_test["question_text"].apply(lambda x: x.lower() )<string_transform>
Dense, Dropout, Activation, Flatten, Input, BatchNormalization, GlobalAveragePooling2D, Add, Conv2D, AveragePooling2D,
LeakyReLU, Concatenate 
)
 def get_efficientnet(model, shape):
models_dict = {
'b0': efn.EfficientNetB0(input_shape=shape,weights=None,include_top=False),
'b1': efn.EfficientNetB1(input_shape=shape,weights=None,include_top=False),
'b2': efn.EfficientNetB2(input_shape=shape,weights=None,include_top=False),
'b3': efn.EfficientNetB3(input_shape=shape,weights=None,include_top=False),
'b4': efn.EfficientNetB4(input_shape=shape,weights=None,include_top=False),
'b5': efn.EfficientNetB5(input_shape=shape,weights=None,include_top=False),
'b6': efn.EfficientNetB6(input_shape=shape,weights=None,include_top=False),
'b7': efn.EfficientNetB7(input_shape=shape,weights=None,include_top=False)
 }
return models_dict[model]
def build_model(shape=(512, 512, 1), model_class=None):
inp = Input(shape=shape)
 base = get_efficientnet(model_class, shape)
 x = base(inp)
 x = GlobalAveragePooling2D()(x)
 inp2 = Input(shape=(4,))
x2 = tf.keras.layers.GaussianNoise(0.2 )(inp2)
 x = Concatenate()([x, x2])
 x = Dropout(Dropout_model )(x)
 x = Dense(1 )(x)
 model = Model([inp, inp2] , x)
 weights = [w for w in os.listdir('.. /input/osic-model-weights')if model_class in w][0]
model.load_weights('.. /input/osic-model-weights/' + weights)
 return model
model_classes = ['b5']
models = [build_model(shape=(512, 512, 1), model_class=m)for m in model_classes]
print('Number of models: ' + str(len(models)) )
list_sentences_train = df_train['question_text']
list_sentences_test = df_test['question_text']
list_sentences_combined = list_sentences_train.append(list_sentences_test, ignore_index=True)
 tokenizer = Tokenizer(num_words=max_features, filters='\t
')
 tokenizer.fit_on_texts(list(list_sentences_combined))
list_tokenized_train = tokenizer.texts_to_sequences(list_sentences_train)
 list_tokenized_test = tokenizer.texts_to_sequences(list_sentences_test )<prepare_x_and_y>
tr_p, vl_p = train_test_split(P, shuffle=True, train_size = 0.8 )
train_x = pad_sequences(list_tokenized_train, maxlen=maxlen)
 test_x = pad_sequences(list_tokenized_test, maxlen=maxlen)
 train_y = df_train['target'].values<compute_test_metric>
def score(fvc_true, fvc_pred, sigma):
sigma_clip = np.maximum(sigma, 70)
 delta = np.abs(fvc_true - fvc_pred)
 delta = np.minimum(delta, 1000)
 sq2 = np.sqrt(2)
 metric =(delta / sigma_clip)*sq2 + np.log(sigma_clip* sq2)
 return np.mean(metric )
start = time.time()
def get_coefs(word, *arr): return word, np.asarray(arr, dtype='float32')
 embeddings_index = dict(get_coefs(*o.rstrip().rsplit(' ')) for o in open(EMBEDDING_FILE_GLOVE))
end = time.time()
print(end-start )<compute_train_metric>
subs = []
for model in models:
metric = []
for q in tqdm(range(1, 10)) :
m = []
for p in vl_p:
x = []
tab = []
if p in ['ID00011637202177653955184', 'ID00052637202186188008618']:
continue
ldir = os.listdir(f'.. /input/osic-pulmonary-fibrosis-progression/train/{p}/')
 for i in ldir:
if int(i[:-4])/ len(ldir)< 0.8 and int(i[:-4])/ len(ldir)> 0.15:
x.append(get_img(f'.. /input/osic-pulmonary-fibrosis-progression/train/{p}/{i}'))
tab.append(get_tab(train.loc[train.Patient == p, :]))
if len(x)< 1:
continue
tab = np.array(tab)
 x = np.expand_dims(x, axis=-1)
 _a = model.predict([x, tab])
 a = np.quantile(_a, q / 10)
 percent_true = train.Percent.values[train.Patient == p]
fvc_true = train.FVC.values[train.Patient == p]
weeks_true = train.Weeks.values[train.Patient == p]
fvc = a *(weeks_true - weeks_true[0])+ fvc_true[0]
percent = percent_true[0] - a * abs(weeks_true - weeks_true[0])
 m.append(score(fvc_true, fvc, percent))
print(np.mean(m))
metric.append(np.mean(m))
q =(np.argmin(metric)+ 1)/ 10
sub = pd.read_csv('.. /input/osic-pulmonary-fibrosis-progression/sample_submission.csv')
 test = pd.read_csv('.. /input/osic-pulmonary-fibrosis-progression/test.csv')
 A_test, B_test, P_test,W, FVC= {}, {}, {},{},{}
STD, WEEK = {}, {}
for p in test.Patient.unique() :
x = []
tab = []
ldir = os.listdir(f'.. /input/osic-pulmonary-fibrosis-progression/test/{p}/')
 for i in ldir:
if int(i[:-4])/ len(ldir)< 0.8 and int(i[:-4])/ len(ldir)> 0.15:
x.append(get_img(f'.. /input/osic-pulmonary-fibrosis-progression/test/{p}/{i}'))
tab.append(get_tab(test.loc[test.Patient == p, :]))
if len(x)<= 1:
continue
tab = np.array(tab)
 x = np.expand_dims(x, axis=-1)
 _a = model.predict([x, tab])
 a = np.quantile(_a, q)
 A_test[p] = a
B_test[p] = test.FVC.values[test.Patient == p] - a*test.Weeks.values[test.Patient == p]
P_test[p] = test.Percent.values[test.Patient == p]
WEEK[p] = test.Weeks.values[test.Patient == p]
for k in sub.Patient_Week.values:
p, w = k.split('_')
 w = int(w)
 fvc = A_test[p] * w + B_test[p]
sub.loc[sub.Patient_Week == k, 'FVC'] = fvc
sub.loc[sub.Patient_Week == k, 'Confidence'] =(
 P_test[p] - A_test[p] * abs(WEEK[p] - w)
)
 _sub = sub[["Patient_Week","FVC","Confidence"]].copy()
subs.append(_sub )
all_embs = np.stack(embeddings_index.values())
 emb_mean,emb_std = all_embs.mean() , all_embs.std()
emb_mean,emb_std<feature_engineering>
N = len(subs)
 sub = subs[0].copy()
sub["FVC"] = 0
sub["Confidence"] = 0
for i in range(N):
sub["FVC"] += subs[0]["FVC"] *(1/N)
 sub["Confidence"] += subs[0]["Confidence"] *(1/N )
word_index = tokenizer.word_index
nb_words = min(max_features, len(word_index))
embedding_matrix = np.random.normal(emb_mean, emb_std,(nb_words, embed_size))
for word, i in word_index.items() :
if i >= max_features: continue
embedding_vector = embeddings_index.get(word)
 if embedding_vector is not None: embedding_matrix[i] = embedding_vector<choose_model_class>
sub[["Patient_Week","FVC","Confidence"]].to_csv("submission_img.csv", index=False )
seed_everything()
inp = Input(shape=(maxlen,))
x = Embedding(max_features, embed_size, weights=[embedding_matrix] )(inp)
 x = Bidirectional(LSTM(50, return_sequences=True, dropout=0.1, recurrent_dropout=0.1))(x)
 x = GlobalMaxPool1D()(x)
 x = Dense(50, activation="relu" )(x)
 x = Dropout(0.1 )(x)
 x = Dense(1, activation="sigmoid" )(x)
 model = Model(inputs=inp, outputs=x)
 model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'] )<split>
img_sub = sub[["Patient_Week","FVC","Confidence"]].copy()
def train_validate_test_split(df, df_y, train_percent=.6, validate_percent=.2, random_state=10):
np.random.seed(random_state)
 perm = np.random.permutation(len(df))
m = len(df)
 train_end = int(train_percent * m)
 validate_end = int(validate_percent * m)+ train_end
train = df[perm[:train_end]]
validate = df[perm[train_end:validate_end]]
test = df[perm[validate_end:]]
train_y = df_y[perm[:train_end]]
validate_y = df_y[perm[train_end:validate_end]]
test_y = df_y[perm[validate_end:]]
return train, validate, test, train_y, validate_y, test_y, perm<split>
ROOT = ".. /input/osic-pulmonary-fibrosis-progression"
BATCH_SIZE=128
tr = pd.read_csv(f"{ROOT}/train.csv")
 tr.drop_duplicates(keep=False, inplace=True, subset=['Patient','Weeks'])
 chunk = pd.read_csv(f"{ROOT}/test.csv")
 print("add infos")
 sub = pd.read_csv(f"{ROOT}/sample_submission.csv")
 sub['Patient'] = sub['Patient_Week'].apply(lambda x:x.split('_')[0])
 sub['Weeks'] = sub['Patient_Week'].apply(lambda x: int(x.split('_')[-1]))
sub = sub[['Patient','Weeks','Confidence','Patient_Week']]
sub = sub.merge(chunk.drop('Weeks', axis=1), on="Patient" )
seed_everything()
X_tra, X_val, X_test, y_tra, y_val, y_test, permutation = train_validate_test_split(train_x, train_y,
train_percent=0.95, validate_percent=0.04, random_state=SEED+2 )<train_model>
tr['WHERE'] = 'train'
chunk['WHERE'] = 'val'
sub['WHERE'] = 'test'
data = tr.append([chunk, sub] )
seed_everything()
model.fit(X_tra, y_tra, batch_size=batch_size, epochs=n_epochs, validation_data=(X_val, y_val)) ;<predict_on_test>
print(tr.shape, chunk.shape, sub.shape, data.shape)
 print(tr.Patient.nunique() , chunk.Patient.nunique() , sub.Patient.nunique() ,
data.Patient.nunique() )
train_preds = model.predict(X_tra, batch_size=1024)
 print(len(train_preds))<compute_test_metric>
data['min_week'] = data['Weeks']
data.loc[data.WHERE=='test','min_week'] = np.nan
data['min_week'] = data.groupby('Patient')['min_week'].transform('min' )
def bestThresshold(train_y,train_preds):
tmp = [0,0,0]
delta = 0
for tmp[0] in tqdm(np.arange(0.1, 0.501, 0.01)) :
tmp[1] = f1_score(train_y, np.array(train_preds)>tmp[0])
 if tmp[1] > tmp[2]:
delta = tmp[0]
tmp[2] = tmp[1]
print('best threshold is {:.4f} with F1 score: {:.4f}'.format(delta, tmp[2]))
return delta
delta = bestThresshold(y_tra,train_preds )<predict_on_test>
base = data.loc[data.Weeks == data.min_week]
base = base[['Patient','FVC']].copy()
base.columns = ['Patient','min_FVC']
base['nb'] = 1
base['nb'] = base.groupby('Patient')['nb'].transform('cumsum')
 base = base[base.nb==1]
base.drop('nb', axis=1, inplace=True )
val_preds = model.predict(X_val, batch_size=1024)
 print(len(val_preds))
print(f1_score(y_val, np.array(val_preds)>delta))<predict_on_test>
data = data.merge(base, on='Patient', how='left')
 data['base_week'] = data['Weeks'] - data['min_week']
del base
test_preds = model.predict(X_test, batch_size=1024)
 print(len(test_preds))
print(f1_score(y_test, np.array(test_preds)>delta))<predict_on_test>
COLS = ['Sex','SmokingStatus']
FE = []
for col in COLS:
for mod in data[col].unique() :
FE.append(mod)
 data[mod] =(data[col] == mod ).astype(int )
final_preds = model.predict(test_x, batch_size=1024 )<save_to_csv>
data['age'] =(data['Age'] - data['Age'].min())/(data['Age'].max() - data['Age'].min())
 data['BASE'] =(data['min_FVC'] - data['min_FVC'].min())/(data['min_FVC'].max() - data['min_FVC'].min())
 data['week'] =(data['base_week'] - data['base_week'].min())/(data['base_week'].max() - data['base_week'].min())
 data['percent'] =(data['Percent'] - data['Percent'].min())/(data['Percent'].max() - data['Percent'].min())
 FE += ['age','percent','week','BASE']
submission = df_test[['qid']].copy()
submission['prediction'] =(final_preds > delta ).astype(int)
 submission.to_csv('submission.csv', index=False )<save_to_csv>
tr = data.loc[data.WHERE=='train']
chunk = data.loc[data.WHERE=='val']
sub = data.loc[data.WHERE=='test']
del data
predicted = pd.DataFrame(train_preds)
 predicted.columns = ['predicted']
predicted.to_csv('train_preds.csv', index=False )<save_to_csv>
C1, C2 = tf.constant(70, dtype='float32'), tf.constant(1000, dtype="float32")
 def score(y_true, y_pred):
tf.dtypes.cast(y_true, tf.float32)
 tf.dtypes.cast(y_pred, tf.float32)
 sigma = y_pred[:, 2] - y_pred[:, 0]
fvc_pred = y_pred[:, 1]
sigma_clip = tf.maximum(sigma, C1)
 delta = tf.abs(y_true[:, 0] - fvc_pred)
 delta = tf.minimum(delta, C2)
 sq2 = tf.sqrt(tf.dtypes.cast(2, dtype=tf.float32))
metric =(delta / sigma_clip)*sq2 + tf.math.log(sigma_clip* sq2)
 return K.mean(metric)
 def qloss(y_true, y_pred):
qs = [0.2, 0.60, 0.85]
q = tf.constant(np.array([qs]), dtype=tf.float32)
 e = y_true - y_pred
v = tf.maximum(q*e,(q-1)*e)
 return K.mean(v)
 def mloss(_lambda):
def loss(y_true, y_pred):
return _lambda * qloss(y_true, y_pred)+(1 - _lambda)*score(y_true, y_pred)
 return loss
def make_model(nh):
z = L.Input(( nh,), name="Patient")
 x = L.Dense(100, activation="relu", name="d1" )(z)
 x = L.Dense(100, activation="relu", name="d2" )(x)
 p1 = L.Dense(3, activation="linear", name="p1" )(x)
 p2 = L.Dense(3, activation="relu", name="p2" )(x)
 preds = L.Lambda(lambda x: x[0] + tf.cumsum(x[1], axis=1),
name="preds" )([p1, p2])
 model = M.Model(z, preds, name="CNN")
 model.compile(loss=mloss(0.65), optimizer=tf.keras.optimizers.Adam(lr=0.1, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0.01, amsgrad=False), metrics=[score])
 return model
df_train_preproc = df_train
df_train_preproc.to_csv('df_train_preprocessed.csv' )<load_from_csv>
y = tr['FVC'].values
z = tr[FE].values
ze = sub[FE].values
nh = z.shape[1]
pe = np.zeros(( ze.shape[0], 3))
pred = np.zeros(( z.shape[0], 3))
df_train = pd.read_csv(".. /input/train.csv")
 df_train_preproc = pd.read_csv("df_train_preprocessed.csv")
 train_preds = pd.read_csv('train_preds.csv' )<split>
NFOLD = 5
kf = KFold(n_splits=NFOLD )
y_train = y_tra; y_train[0:10]<concatenate>
%%time
cnt = 0
EPOCHS = 800
for tr_idx, val_idx in kf.split(z):
cnt += 1
print(f"FOLD {cnt}")
 net = make_model(nh)
 net.fit(z[tr_idx], y[tr_idx], batch_size=BATCH_SIZE, epochs=EPOCHS,
validation_data=(z[val_idx], y[val_idx]), verbose=0)
 print("train", net.evaluate(z[tr_idx], y[tr_idx], verbose=0, batch_size=BATCH_SIZE))
print("val", net.evaluate(z[val_idx], y[val_idx], verbose=0, batch_size=BATCH_SIZE))
print("predict val...")
 pred[val_idx] = net.predict(z[val_idx], batch_size=BATCH_SIZE, verbose=0)
 print("predict test...")
 pe += net.predict(ze, batch_size=BATCH_SIZE, verbose=0)/ NFOLD
combined = pd.concat([df_train, train_preds], axis=1, sort=False)
 combined.head()<compute_test_metric>
sigma_opt = mean_absolute_error(y, pred[:, 1])
 unc = pred[:,2] - pred[:, 0]
sigma_mean = np.mean(unc)
 print(sigma_opt, sigma_mean )
combined['error'] = abs(combined['target'] - combined['predicted'])
 combined.head()<create_dataframe>
sub['FVC1'] = 1.*pe[:, 1]
sub['Confidence1'] = pe[:, 2] - pe[:, 0]
subm = sub[['Patient_Week','FVC','Confidence','FVC1','Confidence1']].copy()
subm.loc[~subm.FVC1.isnull() ].head(10 )
pd.set_option('display.max_colwidth', -1 )<sort_values>
subm.loc[~subm.FVC1.isnull() ,'FVC'] = subm.loc[~subm.FVC1.isnull() ,'FVC1']
if sigma_mean<70:
subm['Confidence'] = sigma_opt
else:
subm.loc[~subm.FVC1.isnull() ,'Confidence'] = subm.loc[~subm.FVC1.isnull() ,'Confidence1']
sorted = combined.sort_values(by=['error'], ascending=False )<set_options>
otest = pd.read_csv('.. /input/osic-pulmonary-fibrosis-progression/test.csv')
 for i in range(len(otest)) :
subm.loc[subm['Patient_Week']==otest.Patient[i]+'_'+str(otest.Weeks[i]), 'FVC'] = otest.FVC[i]
subm.loc[subm['Patient_Week']==otest.Patient[i]+'_'+str(otest.Weeks[i]), 'Confidence'] = 0.1
pd.options.display.float_format = "{:.8f}".format<sort_values>
subm[["Patient_Week","FVC","Confidence"]].to_csv("submission_regression.csv", index=False )
insincere = sorted[sorted['target']==1]
insincere[0:14]<sort_values>
reg_sub = subm[["Patient_Week","FVC","Confidence"]].copy()
sorted_increasing = combined.sort_values(by=['error'], ascending=True)
 sorted_increasing[0:10]<sort_values>
df1 = img_sub.sort_values(by=['Patient_Week'], ascending=True ).reset_index(drop=True)
 df2 = reg_sub.sort_values(by=['Patient_Week'], ascending=True ).reset_index(drop=True )
combined['question_length']=combined['question_text'].apply(lambda x: len(x))
sorted_len = combined.sort_values(by=['question_length'], ascending=True)
 sorted_len[0:20]<sort_values>
df = df1[['Patient_Week']].copy()
df['FVC'] = FVC_weight*df1['FVC'] +(1-FVC_weight)*df2['FVC']
df['Confidence'] = Confidence_weight*df1['Confidence'] +(1-Confidence_weight)*df2['Confidence']
df.head()
<filter><EOS> 
df.to_csv('submission.csv', index=False )