Kaggle competition: COVID19 Global Forecasting (Week 4)
<SOS> metric: MCRMSLE Kaggle data source: covid19-global-forecasting-(week-4)<define_variables>
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
sys.path = [
'.. /input/efficientnet-pytorch/EfficientNet-PyTorch/EfficientNet-PyTorch-master',
] + sys.path<import_modules>
root = '/kaggle/input/covid19-global-forecasting-week-4'
def get_cpmp_sub(save_oof=False, save_public_test=False):
train = pd.read_csv(f'{root}/train.csv')
 train['Province_State'].fillna('', inplace=True)
 train['Date'] = pd.to_datetime(train['Date'])
 train['day'] = train.Date.dt.dayofyear
train['geo'] = ['_'.join(x)for x in zip(train['Country_Region'], train['Province_State'])]
train
test = pd.read_csv(f'{root}/test.csv')
 test['Province_State'].fillna('', inplace=True)
 test['Date'] = pd.to_datetime(test['Date'])
 test['day'] = test.Date.dt.dayofyear
test['geo'] = ['_'.join(x)for x in zip(test['Country_Region'], test['Province_State'])]
test
day_min = train['day'].min()
train['day'] -= day_min
test['day'] -= day_min
min_test_val_day = test.day.min()
max_test_val_day = train.day.max()
max_test_day = test.day.max()
num_days = max_test_day + 1
min_test_val_day, max_test_val_day, num_days
train['ForecastId'] = -1
test['Id'] = -1
test['ConfirmedCases'] = 0
test['Fatalities'] = 0
debug = False
data = pd.concat([train,
test[test.day > max_test_val_day][train.columns]
] ).reset_index(drop=True)
 if debug:
data = data[data['geo'] >= 'France_'].reset_index(drop=True)
 gc.collect()
dates = data[data['geo'] == 'France_'].Date.values
if 0:
gr = data.groupby('geo')
 data['ConfirmedCases'] = gr.ConfirmedCases.transform('cummax')
 data['Fatalities'] = gr.Fatalities.transform('cummax')
 geo_data = data.pivot(index='geo', columns='day', values='ForecastId')
 num_geo = geo_data.shape[0]
geo_data
geo_id = {}
for i,g in enumerate(geo_data.index):
geo_id[g] = i
ConfirmedCases = data.pivot(index='geo', columns='day', values='ConfirmedCases')
 Fatalities = data.pivot(index='geo', columns='day', values='Fatalities')
 if debug:
cases = ConfirmedCases.values
deaths = Fatalities.values
else:
cases = np.log1p(ConfirmedCases.values)
 deaths = np.log1p(Fatalities.values)
 def get_dataset(start_pred, num_train, lag_period):
days = np.arange(start_pred - num_train + 1, start_pred + 1)
 lag_cases = np.vstack([cases[:, d - lag_period : d] for d in days])
 lag_deaths = np.vstack([deaths[:, d - lag_period : d] for d in days])
 target_cases = np.vstack([cases[:, d : d + 1] for d in days])
 target_deaths = np.vstack([deaths[:, d : d + 1] for d in days])
 geo_ids = np.vstack([geo_ids_base for d in days])
 country_ids = np.vstack([country_ids_base for d in days])
 return lag_cases, lag_deaths, target_cases, target_deaths, geo_ids, country_ids, days
def update_valid_dataset(data, pred_death, pred_case):
lag_cases, lag_deaths, target_cases, target_deaths, geo_ids, country_ids, days = data
day = days[-1] + 1
new_lag_cases = np.hstack([lag_cases[:, 1:], pred_case])
 new_lag_deaths = np.hstack([lag_deaths[:, 1:], pred_death])
 new_target_cases = cases[:, day:day+1]
new_target_deaths = deaths[:, day:day+1]
new_geo_ids = geo_ids
new_country_ids = country_ids
new_days = 1 + days
return new_lag_cases, new_lag_deaths, new_target_cases, new_target_deaths, new_geo_ids, new_country_ids, new_days
def fit_eval(lr_death, lr_case, data, start_lag_death, end_lag_death, num_lag_case, fit, score):
lag_cases, lag_deaths, target_cases, target_deaths, geo_ids, country_ids, days = data
X_death = np.hstack([lag_cases[:, -start_lag_death:-end_lag_death], country_ids])
 X_death = np.hstack([lag_deaths[:, -num_lag_case:], country_ids])
 X_death = np.hstack([lag_cases[:, -start_lag_death:-end_lag_death], lag_deaths[:, -num_lag_case:], country_ids])
 y_death = target_deaths
y_death_prev = lag_deaths[:, -1:]
if fit:
if 0:
keep =(y_death > 0 ).ravel()
X_death = X_death[keep]
y_death = y_death[keep]
y_death_prev = y_death_prev[keep]
lr_death.fit(X_death, y_death)
 y_pred_death = lr_death.predict(X_death)
 y_pred_death = np.maximum(y_pred_death, y_death_prev)
 X_case = np.hstack([lag_cases[:, -num_lag_case:], geo_ids])
 X_case = lag_cases[:, -num_lag_case:]
y_case = target_cases
y_case_prev = lag_cases[:, -1:]
if fit:
lr_case.fit(X_case, y_case)
 y_pred_case = lr_case.predict(X_case)
 y_pred_case = np.maximum(y_pred_case, y_case_prev)
 if score:
death_score = val_score(y_death, y_pred_death)
 case_score = val_score(y_case, y_pred_case)
 else:
death_score = 0
case_score = 0
return death_score, case_score, y_pred_death, y_pred_case
def train_model(train, valid, start_lag_death, end_lag_death, num_lag_case, num_val, score=True):
alpha = 3
lr_death = Ridge(alpha=alpha, fit_intercept=False)
 lr_case = Ridge(alpha=alpha, fit_intercept=True)
(train_death_score, train_case_score, train_pred_death, train_pred_case, 
)= fit_eval(lr_death, lr_case, train, start_lag_death, end_lag_death, num_lag_case, fit=True, score=score)
 death_scores = []
case_scores = []
death_pred = []
case_pred = []
for i in range(num_val): 
(valid_death_score, valid_case_score, valid_pred_death, valid_pred_case, 
)= fit_eval(lr_death, lr_case, valid, start_lag_death, end_lag_death, num_lag_case, fit=False, score=score)
 death_scores.append(valid_death_score)
 case_scores.append(valid_case_score)
 death_pred.append(valid_pred_death)
 case_pred.append(valid_pred_case)
 if 0:
print('val death: %0.3f' % valid_death_score,
'val case: %0.3f' % valid_case_score,
'val : %0.3f' % np.mean([valid_death_score, valid_case_score]),
flush=True)
 valid = update_valid_dataset(valid, valid_pred_death, valid_pred_case)
 if score:
death_scores = np.sqrt(np.mean([s**2 for s in death_scores]))
case_scores = np.sqrt(np.mean([s**2 for s in case_scores]))
if 0:
print('train death: %0.3f' % train_death_score,
'train case: %0.3f' % train_case_score,
'val death: %0.3f' % death_scores,
'val case: %0.3f' % case_scores,
'val : %0.3f' %(( death_scores + case_scores)/ 2),
flush=True)
 else:
print('%0.4f' % case_scores,
', %0.4f' % death_scores,
'= %0.4f' %(( death_scores + case_scores)/ 2),
flush=True)
 death_pred = np.hstack(death_pred)
 case_pred = np.hstack(case_pred)
 return death_scores, case_scores, death_pred, case_pred
countries = [g.split('_')[0] for g in geo_data.index]
countries = pd.factorize(countries)[0]
country_ids_base = countries.reshape(( -1, 1))
ohe = OneHotEncoder(sparse=False)
 country_ids_base = 0.2 * ohe.fit_transform(country_ids_base)
 country_ids_base.shape
geo_ids_base = np.arange(num_geo ).reshape(( -1, 1))
ohe = OneHotEncoder(sparse=False)
 geo_ids_base = 0.1 * ohe.fit_transform(geo_ids_base)
 geo_ids_base.shape
def val_score(true, pred):
pred = np.log1p(np.round(np.expm1(pred)- 0.2))
return np.sqrt(mean_squared_error(true.ravel() , pred.ravel()))
def val_score(true, pred):
return np.sqrt(mean_squared_error(true.ravel() , pred.ravel()))
start_lag_death, end_lag_death = 14, 6,
num_train = 5
num_lag_case = 14
lag_period = max(start_lag_death, num_lag_case)
 def get_oof(start_val_delta=0):
start_val = min_test_val_day + start_val_delta
last_train = start_val - 1
num_val = max_test_val_day - start_val + 1
print(dates[start_val], start_val, num_val)
 train_data = get_dataset(last_train, num_train, lag_period)
 valid_data = get_dataset(start_val, 1, lag_period)
 _, _, val_death_preds, val_case_preds = train_model(train_data, valid_data,
start_lag_death, end_lag_death, num_lag_case, num_val)
 pred_deaths = Fatalities.iloc[:, start_val:start_val+num_val].copy()
pred_deaths.iloc[:, :] = np.expm1(val_death_preds)
 pred_deaths = pred_deaths.stack().reset_index()
pred_deaths.columns = ['geo', 'day', 'Fatalities']
pred_deaths
pred_cases = ConfirmedCases.iloc[:, start_val:start_val+num_val].copy()
pred_cases.iloc[:, :] = np.expm1(val_case_preds)
 pred_cases = pred_cases.stack().reset_index()
pred_cases.columns = ['geo', 'day', 'ConfirmedCases']
pred_cases
sub = train[['Date', 'Id', 'geo', 'day']]
sub = sub.merge(pred_cases, how='left', on=['geo', 'day'])
 sub = sub.merge(pred_deaths, how='left', on=['geo', 'day'])
 sub = sub[sub.day >= start_val]
sub = sub[['Id', 'ConfirmedCases', 'Fatalities']].copy()
return sub
if save_oof:
for start_val_delta, date in zip(range(3, -8, -3),
['2020-03-25', '2020-03-22', '2020-03-19', '2020-03-16']):
print(date, end=' ')
 oof = get_oof(start_val_delta)
 oof.to_csv('.. /submissions/cpmp-%s.csv' % date, index=None)
 def get_sub(start_val_delta=0):
start_val = min_test_val_day + start_val_delta
last_train = start_val - 1
num_val = max_test_val_day - start_val + 1
print(dates[last_train], start_val, num_val)
 num_lag_case = 14
train_data = get_dataset(last_train, num_train, lag_period)
 valid_data = get_dataset(start_val, 1, lag_period)
 _, _, val_death_preds, val_case_preds = train_model(train_data, valid_data,
start_lag_death, end_lag_death, num_lag_case, num_val)
 pred_deaths = Fatalities.iloc[:, start_val:start_val+num_val].copy()
pred_deaths.iloc[:, :] = np.expm1(val_death_preds)
 pred_deaths = pred_deaths.stack().reset_index()
pred_deaths.columns = ['geo', 'day', 'Fatalities']
pred_deaths
pred_cases = ConfirmedCases.iloc[:, start_val:start_val+num_val].copy()
pred_cases.iloc[:, :] = np.expm1(val_case_preds)
 pred_cases = pred_cases.stack().reset_index()
pred_cases.columns = ['geo', 'day', 'ConfirmedCases']
pred_cases
sub = test[['Date', 'ForecastId', 'geo', 'day']]
sub = sub.merge(pred_cases, how='left', on=['geo', 'day'])
 sub = sub.merge(pred_deaths, how='left', on=['geo', 'day'])
 sub = sub.fillna(0)
 sub = sub[['ForecastId', 'ConfirmedCases', 'Fatalities']]
return sub
return sub
known_test = train[['geo', 'day', 'ConfirmedCases', 'Fatalities']
].merge(test[['geo', 'day', 'ForecastId']], how='left', on=['geo', 'day'])
 known_test = known_test[['ForecastId', 'ConfirmedCases', 'Fatalities']][known_test.ForecastId.notnull() ].copy()
known_test
unknow_test = test[test.day > max_test_val_day]
unknow_test
def get_final_sub() :
start_val = max_test_val_day + 1
last_train = start_val - 1
num_val = max_test_day - start_val + 1
print(dates[last_train], start_val, num_val)
 num_lag_case = num_val + 3
train_data = get_dataset(last_train, num_train, lag_period)
 valid_data = get_dataset(start_val, 1, lag_period)
(_, _, val_death_preds, val_case_preds 
)= train_model(train_data, valid_data, start_lag_death, end_lag_death, num_lag_case, num_val, score=False)
 pred_deaths = Fatalities.iloc[:, start_val:start_val+num_val].copy()
pred_deaths.iloc[:, :] = np.expm1(val_death_preds)
 pred_deaths = pred_deaths.stack().reset_index()
pred_deaths.columns = ['geo', 'day', 'Fatalities']
pred_deaths
pred_cases = ConfirmedCases.iloc[:, start_val:start_val+num_val].copy()
pred_cases.iloc[:, :] = np.expm1(val_case_preds)
 pred_cases = pred_cases.stack().reset_index()
pred_cases.columns = ['geo', 'day', 'ConfirmedCases']
pred_cases
print(unknow_test.shape, pred_deaths.shape, pred_cases.shape)
 sub = unknow_test[['Date', 'ForecastId', 'geo', 'day']]
sub = sub.merge(pred_cases, how='left', on=['geo', 'day'])
 sub = sub.merge(pred_deaths, how='left', on=['geo', 'day'])
 sub = sub[['ForecastId', 'ConfirmedCases', 'Fatalities']]
sub = pd.concat([known_test, sub])
 return sub
if save_public_test:
sub = get_sub()
else:
sub = get_final_sub()
return sub
import skimage.io
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, Dataset
from efficientnet_pytorch import model as enet
import matplotlib.pyplot as plt
from tqdm import tqdm_notebook as tqdm 
<load_from_csv>
def get_nn_sub() :
df = pd.read_csv(f'{root}/train.csv')
 sub_df = pd.read_csv(f'{root}/test.csv')
 coo_df = pd.read_csv(".. /input/covid19week1/train.csv" ).rename(columns={"Country/Region": "Country_Region"})
 coo_df = coo_df.groupby("Country_Region")[["Lat", "Long"]].mean().reset_index()
coo_df = coo_df[coo_df["Country_Region"].notnull() ]
loc_group = ["Province_State", "Country_Region"]
def preprocess(df):
df["Date"] = df["Date"].astype("datetime64[ms]")
 df["days"] =(df["Date"] - pd.to_datetime("2020-01-01")).dt.days
df["weekend"] = df["Date"].dt.dayofweek//5
df = df.merge(coo_df, how="left", on="Country_Region")
 df["Lat"] =(df["Lat"] // 30 ).astype(np.float32 ).fillna(0)
 df["Long"] =(df["Long"] // 60 ).astype(np.float32 ).fillna(0)
 for col in loc_group:
df[col].fillna("none", inplace=True)
 return df
df = preprocess(df)
 sub_df = preprocess(sub_df)
 print(df.shape)
 TARGETS = ["ConfirmedCases", "Fatalities"]
for col in TARGETS:
df[col] = np.log1p(df[col])
 NUM_SHIFT = 5
features = ["Lat", "Long"]
for s in range(1, NUM_SHIFT+1):
for col in TARGETS:
df["prev_{}_{}".format(col, s)] = df.groupby(loc_group)[col].shift(s)
 features.append("prev_{}_{}".format(col, s))
df = df[df["Date"] >= df["Date"].min() + timedelta(days=NUM_SHIFT)].copy()
TEST_FIRST = sub_df["Date"].min()
TEST_DAYS =(df["Date"].max() - TEST_FIRST ).days + 1
dev_df, test_df = df[df["Date"] < TEST_FIRST].copy() , df[df["Date"] >= TEST_FIRST].copy()
def nn_block(input_layer, size, dropout_rate, activation):
out_layer = KL.Dense(size, activation=None )(input_layer)
 out_layer = KL.Activation(activation )(out_layer)
 out_layer = KL.Dropout(dropout_rate )(out_layer)
 return out_layer
def get_model() :
inp = KL.Input(shape=(len(features),))
hidden_layer = nn_block(inp, 128, 0.0, "relu")
 gate_layer = nn_block(hidden_layer, 64, 0.0, "sigmoid")
 hidden_layer = nn_block(hidden_layer, 64, 0.0, "relu")
 hidden_layer = KL.multiply([hidden_layer, gate_layer])
 out = KL.Dense(len(TARGETS), activation="linear" )(hidden_layer)
 model = tf.keras.models.Model(inputs=[inp], outputs=out)
 return model
get_model().summary()
def get_input(df):
return [df[features]]
NUM_MODELS = 10
def train_models(df, save=False):
models = []
for i in range(NUM_MODELS):
model = get_model()
model.compile(loss="mean_squared_error", optimizer=Nadam(lr=1e-4))
hist = model.fit(get_input(df), df[TARGETS],
batch_size=2048, epochs=2500, verbose=0, shuffle=True)
 if save:
model.save_weights("model{}.h5".format(i))
models.append(model)
 return models
models = train_models(dev_df)
 prev_targets = ['prev_ConfirmedCases_1', 'prev_Fatalities_1']
def predict_one(df, models):
pred = np.zeros(( df.shape[0], 2))
for model in models:
pred += model.predict(get_input(df)) /len(models)
 pred = np.maximum(pred, df[prev_targets].values)
 pred[:, 0] = np.log1p(np.expm1(pred[:, 0])+ 0.1)
 pred[:, 1] = np.log1p(np.expm1(pred[:, 1])+ 0.01)
 return np.clip(pred, None, 15)
 print([mean_squared_error(dev_df[TARGETS[i]], predict_one(dev_df, models)[:, i])for i in range(len(TARGETS)) ])
 def rmse(y_true, y_pred):
return np.sqrt(mean_squared_error(y_true, y_pred))
def evaluate(df):
error = 0
for col in TARGETS:
error += rmse(df[col].values, df["pred_{}".format(col)].values)
 return np.round(error/len(TARGETS), 5)
 def predict(test_df, first_day, num_days, models, val=False):
temp_df = test_df.loc[test_df["Date"] == first_day].copy()
y_pred = predict_one(temp_df, models)
 for i, col in enumerate(TARGETS):
test_df["pred_{}".format(col)] = 0
test_df.loc[test_df["Date"] == first_day, "pred_{}".format(col)] = y_pred[:, i]
print(first_day, np.isnan(y_pred ).sum() , y_pred.min() , y_pred.max())
 if val:
print(evaluate(test_df[test_df["Date"] == first_day]))
y_prevs = [None]*NUM_SHIFT
for i in range(1, NUM_SHIFT):
y_prevs[i] = temp_df[['prev_ConfirmedCases_{}'.format(i), 'prev_Fatalities_{}'.format(i)]].values
for d in range(1, num_days):
date = first_day + timedelta(days=d)
 print(date, np.isnan(y_pred ).sum() , y_pred.min() , y_pred.max())
 temp_df = test_df.loc[test_df["Date"] == date].copy()
temp_df[prev_targets] = y_pred
for i in range(2, NUM_SHIFT+1):
temp_df[['prev_ConfirmedCases_{}'.format(i), 'prev_Fatalities_{}'.format(i)]] = y_prevs[i-1]
y_pred, y_prevs = predict_one(temp_df, models), [None, y_pred] + y_prevs[1:-1]
for i, col in enumerate(TARGETS):
test_df.loc[test_df["Date"] == date, "pred_{}".format(col)] = y_pred[:, i]
if val:
print(evaluate(test_df[test_df["Date"] == date]))
return test_df
test_df = predict(test_df, TEST_FIRST, TEST_DAYS, models, val=True)
 print(evaluate(test_df))
for col in TARGETS:
test_df[col] = np.expm1(test_df[col])
 test_df["pred_{}".format(col)] = np.expm1(test_df["pred_{}".format(col)])
 models = train_models(df, save=True)
 sub_df_public = sub_df[sub_df["Date"] <= df["Date"].max() ].copy()
sub_df_private = sub_df[sub_df["Date"] > df["Date"].max() ].copy()
pred_cols = ["pred_{}".format(col)for col in TARGETS]
sub_df_public = sub_df_public.merge(test_df[["Date"] + loc_group + TARGETS], how="left", on=["Date"] + loc_group)
 SUB_FIRST = sub_df_private["Date"].min()
SUB_DAYS =(sub_df_private["Date"].max() - sub_df_private["Date"].min() ).days + 1
sub_df_private = df.append(sub_df_private, sort=False)
 for s in range(1, NUM_SHIFT+1):
for col in TARGETS:
sub_df_private["prev_{}_{}".format(col, s)] = sub_df_private.groupby(loc_group)[col].shift(s)
 sub_df_private = sub_df_private[sub_df_private["Date"] >= SUB_FIRST].copy()
sub_df_private = predict(sub_df_private, SUB_FIRST, SUB_DAYS, models)
 for col in TARGETS:
sub_df_private[col] = np.expm1(sub_df_private["pred_{}".format(col)])
 sub_df = sub_df_public.append(sub_df_private, sort=False)
 sub_df["ForecastId"] = sub_df["ForecastId"].astype(np.int16)
 return sub_df[["ForecastId"] + TARGETS]
data_dir = '.. /input/prostate-cancer-grade-assessment'
df_train = pd.read_csv(os.path.join(data_dir, 'train.csv'))
df_test = pd.read_csv(os.path.join(data_dir, 'test.csv'))
df_sub = pd.read_csv(os.path.join(data_dir, 'sample_submission.csv'))
model_dir = '.. /input/panda-public-models'
image_folder = os.path.join(data_dir, 'test_images')
 is_test = os.path.exists(image_folder)
 image_folder = image_folder if is_test else os.path.join(data_dir, 'train_images')
 df = df_test if is_test else df_train.loc[:100]
tile_size = 256
image_size = 256
n_tiles = 36
batch_size = 8
num_workers = 4
device = torch.device('cuda')
 print(image_folder )<define_search_model>
sub1 = get_cpmp_sub()
sub1['ForecastId'] = sub1['ForecastId'].astype('int' )
class enetv2(nn.Module):
def __init__(self, backbone, out_dim):
super(enetv2, self ).__init__()
self.enet = enet.EfficientNet.from_name(backbone)
 self.myfc = nn.Linear(self.enet._fc.in_features, out_dim)
 self.enet._fc = nn.Identity()
def extract(self, x):
return self.enet(x)
 def forward(self, x):
x = self.extract(x)
 x = self.myfc(x)
 return x
def load_models(model_files):
models = []
for model_f in model_files:
model_f = os.path.join(model_dir, model_f)
 backbone = 'efficientnet-b3'
model = enetv2(backbone, out_dim=5)
 model.load_state_dict(torch.load(model_f, map_location=lambda storage, loc: storage), strict=True)
 model.eval()
model.to(device)
 models.append(model)
 print(f'{model_f} loaded!')
 return models
model_files = [
'cls_effnet_b3_Rand36r36tiles256_big_bce_lr0.3_augx2_30epo_model_fold0.pth'
]
models = load_models(model_files )<categorify>
sub1.sort_values("ForecastId", inplace=True)
 sub2.sort_values("ForecastId", inplace=True )
def get_tiles(img, mode=0):
result = []
h, w, c = img.shape
pad_h =(tile_size - h % tile_size)% tile_size +(( tile_size * mode)// 2)
 pad_w =(tile_size - w % tile_size)% tile_size +(( tile_size * mode)// 2)
 img2 = np.pad(img,[[pad_h // 2, pad_h - pad_h // 2], [pad_w // 2,pad_w - pad_w//2], [0,0]], constant_values=255)
 img3 = img2.reshape(
 img2.shape[0] // tile_size,
tile_size,
img2.shape[1] // tile_size,
tile_size,
3 
)
 img3 = img3.transpose(0,2,1,3,4 ).reshape(-1, tile_size, tile_size,3)
 n_tiles_with_info =(img3.reshape(img3.shape[0],-1 ).sum(1)< tile_size ** 2 * 3 * 255 ).sum()
if len(img)< n_tiles:
img3 = np.pad(img3,[[0,N-len(img3)],[0,0],[0,0],[0,0]], constant_values=255)
 idxs = np.argsort(img3.reshape(img3.shape[0],-1 ).sum(-1)) [:n_tiles]
img3 = img3[idxs]
for i in range(len(img3)) :
result.append({'img':img3[i], 'idx':i})
 return result, n_tiles_with_info >= n_tiles
class PANDADataset(Dataset):
def __init__(self,
df,
image_size,
n_tiles=n_tiles,
tile_mode=0,
rand=False,
sub_imgs=False 
):
self.df = df.reset_index(drop=True)
 self.image_size = image_size
self.n_tiles = n_tiles
self.tile_mode = tile_mode
self.rand = rand
self.sub_imgs = sub_imgs
def __len__(self):
return self.df.shape[0]
def __getitem__(self, index):
row = self.df.iloc[index]
img_id = row.image_id
tiff_file = os.path.join(image_folder, f'{img_id}.tiff')
 image = skimage.io.MultiImage(tiff_file)[1]
tiles, OK = get_tiles(image, self.tile_mode)
 if self.rand:
idxes = np.random.choice(list(range(self.n_tiles)) , self.n_tiles, replace=False)
 else:
idxes = list(range(self.n_tiles))
idxes = np.asarray(idxes)+ self.n_tiles if self.sub_imgs else idxes
n_row_tiles = int(np.sqrt(self.n_tiles))
images = np.zeros(( image_size * n_row_tiles, image_size * n_row_tiles, 3))
for h in range(n_row_tiles):
for w in range(n_row_tiles):
i = h * n_row_tiles + w
if len(tiles)> idxes[i]:
this_img = tiles[idxes[i]]['img']
else:
this_img = np.ones(( self.image_size, self.image_size, 3)).astype(np.uint8)* 255
this_img = 255 - this_img
h1 = h * image_size
w1 = w * image_size
images[h1:h1+image_size, w1:w1+image_size] = this_img
images = images.astype(np.float32)
 images /= 255
images = images.transpose(2, 0, 1)
 return torch.tensor(images)
<load_pretrained>
TARGETS = ["ConfirmedCases", "Fatalities"]
[np.sqrt(mean_squared_error(np.log1p(sub1[t].values), np.log1p(sub2[t].values)))for t in TARGETS]
<save_to_csv><EOS> 
sub_df = sub1.copy()
for t in TARGETS:
sub_df[t] = np.expm1(np.log1p(sub1[t].values)*0.5 + np.log1p(sub2[t].values)*0.5)
 sub_df.to_csv("submission.csv", index=False )