Kaggle competition: COVID19 Global Forecasting (Week 4)
def process(i,df_sample_submission,project_id):
id_code = df_sample_submission.index[i]
if id_code in df_solution.index:
return None
exp_len = id_code.find('_')
 experiment = id_code[0:exp_len]
plate = id_code[(exp_len+1):(exp_len+2)]
well = id_code[exp_len+3:]
pred_dict = {}
res = []
for site in range(1,3):
file_path = '.. /input/recursion_rgb_512/testrgb512/testRGB512/{}_{}_{}_s{}.png'.format(experiment,plate,well,site)
 try:
prediction_request = get_prediction(file_path, project_id, model_id)
 except Exception as e:
print('Something got wrong: ', e)
 return None
for prediction in prediction_request.payload:
label = make_int(prediction.display_name)
 if label <= 1108:
pred_dict[label] = float(prediction.classification.score)
 sirna_prediction = max(pred_dict.items() , key=operator.itemgetter(1)) [0]
confidence = pred_dict[sirna_prediction]
res.append({
'id_code': id_code,
'site': site,
'sirna_prediction': sirna_prediction,
'confidence': confidence
})
 return res 
<load_from_csv>
def rmsle(y, y_pred):
assert len(y)== len(y_pred)
 terms_to_sum = [(math.log(y_pred[i] + 1)- math.log(y[i] + 1)) ** 2.0 for i,pred in enumerate(y_pred)]
return(sum(terms_to_sum)*(1.0/len(y)))** 0.5
def fix_target(frame, key, target, new_target_name="target"):
corrections = 0
group_keys = frame[ key].values.tolist()
target = frame[target].values.tolist()
for i in range(1, len(group_keys)- 1):
previous_group = group_keys[i - 1]
current_group = group_keys[i]
previous_value = target[i - 1]
current_value = target[i]
if current_group == previous_group:
if current_value<previous_value:
current_value=previous_value
target[i] =current_value
target[i] =max(0,target[i])
 frame[new_target_name] = np.array(target)
 def rate(frame, key, target, new_target_name="rate"):
corrections = 0
group_keys = frame[ key].values.tolist()
target = frame[target].values.tolist()
rate=[1.0 for k in range(len(target)) ]
for i in range(1, len(group_keys)) :
previous_group = group_keys[i - 1]
current_group = group_keys[i]
previous_value = target[i - 1]
current_value = target[i]
if current_group == previous_group:
if previous_value!=0.0:
rate[i]=current_value/previous_value
rate[i] =max(1,rate[i])
 frame[new_target_name] = np.array(rate)
 def difference(frame, key, target, new_target_name="diff"):
corrections = 0
group_keys = frame[ key].values.tolist()
target = frame[target].values.tolist()
rate=[0 for k in range(len(target)) ]
for i in range(1, len(group_keys)) :
previous_group = group_keys[i - 1]
current_group = group_keys[i]
previous_value = target[i - 1]
current_value = target[i]
if current_group == previous_group:
rate[i]=np.log1p(max(0,current_value-previous_value))
rate[i] =max(0,rate[i])
 frame[new_target_name] = np.array(rate)
 def get_data_by_key(dataframe, key, key_value, fields=None):
mini_frame=dataframe[dataframe[key]==key_value]
if not fields is None:
mini_frame=mini_frame[fields].values
return mini_frame
directory="/kaggle/input/covid19-global-forecasting-week-4/"
model_directory="model"
us_county="/kaggle/input/covid19-us-county-jhu-data-demographics/covid_us_county"
geo_dir=None
extra_stable_columns=None
group_by_columns=None
if not group_by_columns is None and "continent" in group_by_columns:
assert not geo_dir is None
train=pd.read_csv(directory + "train.csv", parse_dates=["Date"] , engine="python")
 test=pd.read_csv(directory + "test.csv", parse_dates=["Date"], engine="python")
 train["key"]=train[["Province_State","Country_Region"]].apply(lambda row: str(row[0])+ "_" + str(row[1]),axis=1)
 test["key"]=test[["Province_State","Country_Region"]].apply(lambda row: str(row[0])+ "_" + str(row[1]),axis=1)
 if not geo_dir is None:
region_metadata=pd.read_csv(geo_dir+"region_metadata.csv")
 km=KMeans(n_clusters=30, init='k-means++', n_init=10, max_iter=300, tol=0.0001,
precompute_distances='auto', verbose=0, random_state=1234, copy_x=True, n_jobs=None, algorithm='auto')
 region_metadata["clusters"]=km.fit_predict(region_metadata[["lat","lon"]].values)
 train=pd.merge(train,region_metadata, how="left", left_on=["Province_State","Country_Region"], right_on=["Province_State","Country_Region"])
 test=pd.merge(test,region_metadata, how="left", left_on=["Province_State","Country_Region"], right_on=["Province_State","Country_Region"])
 group_names=None
if not group_by_columns is None and len(group_by_columns)>0:
group_names=[]
for group in group_by_columns:
groupss=train[["Date", group,target1,target2]]
grp=groupss.groupby(["Date", group], as_index=False ).sum()
grp.columns=["Date", group,group +"_" + target1,group +"_" + target2 ]
train=pd.merge(train,grp, how="left", left_on=["Date", group,group], right_on=["Date", group,group])
 for gr in [group +"_" + target1,group +"_" + target2]:
rate(train, key, gr, new_target_name="rate_" +gr)
 group_names+=["rate_" +gr]
train.to_csv(directory + "train_plus_groups.csv", index=False)
 max_train_date=train["Date"].max()
max_test_date=test["Date"].max()
horizon=(max_test_date-max_train_date ).days
print("horizon", int(horizon))
target1="ConfirmedCases"
target2="Fatalities"
key="key"
train_supplamanteary2=None
new_data_file2=[us_county]
if not new_data_file2 is None:
trains=pd.read_csv(directory + "train.csv", parse_dates=["Date"] , engine="python")
 tests=pd.read_csv(directory + "test.csv", parse_dates=["Date"], engine="python")
 trains["key"]=train[["Province_State","Country_Region"]].apply(lambda row: str(row[0])+ "_" + str(row[1]),axis=1)
 tests["key"]=test[["Province_State","Country_Region"]].apply(lambda row: str(row[0])+ "_" + str(row[1]),axis=1)
 train_supplamanteary2=None
for new_data_files in new_data_file2:
sumplementary_data=pd.read_csv(new_data_files +".csv" , parse_dates=["date"] , engine="python")
 sumplementary_data.columns=["fips","Province_State","Country_Region","lat","long","Date",
"ConfirmedCases","state_code","Fatalities"]
sumplementary_data["key"]=sumplementary_data[["Province_State","Country_Region"]].apply(lambda row: str(row[0])+ "_" + str(row[1]),axis=1)
 sumplementary_data=sumplementary_data[["Date","key","ConfirmedCases","Fatalities","Province_State","Country_Region"]]
sumplementary_data["Id"]=[k for k in range(999999999,sumplementary_data.shape[0]+999999999)]
sumplementary_data=sumplementary_data[sumplementary_data.Date>= '2020-02-20']
if train_supplamanteary2 is None:
train_supplamanteary2=pd.concat([trains.reset_index(drop=True), sumplementary_data.reset_index(drop=True)], axis=0 ).reset_index(drop=True)
 else :
train_supplamanteary2=pd.concat([train_supplamanteary2.reset_index(drop=True), sumplementary_data.reset_index(drop=True)], axis=0 ).reset_index(drop=True)
 print(train_supplamanteary2.head(5))
train_supplamanteary2 = train_supplamanteary2.sort_values(["Country_Region","key","Id"], ascending =(True, True, True))
fix_target(train_supplamanteary2, key, target1, new_target_name=target1)
 fix_target(train_supplamanteary2, key, target2, new_target_name=target2)
 rate(train_supplamanteary2, key, target1, new_target_name="rate_" +target1)
 rate(train_supplamanteary2, key, target2, new_target_name="rate_" +target2)
 difference(train_supplamanteary2, key, target1, new_target_name="diff_" +target1)
 difference(train_supplamanteary2, key, target2, new_target_name="diff_" +target2)
 unique_keys_sup2=train_supplamanteary2[key].unique()
print(len(unique_keys_sup2))
if not geo_dir is None:
region_metadata=pd.read_csv(geo_dir+"region_metadata.csv")
 km=KMeans(n_clusters=30, init='k-means++', n_init=10, max_iter=300, tol=0.0001,
precompute_distances='auto', verbose=0, random_state=1234, copy_x=True, n_jobs=None, algorithm='auto')
 region_metadata["clusters"]=km.fit_predict(region_metadata[["lat","lon"]].values)
 train_supplamanteary2=pd.merge(train_supplamanteary2,region_metadata, how="left", left_on=["Province_State","Country_Region"], right_on=["Province_State","Country_Region"])
 if not group_by_columns is None and len(group_by_columns)>0:
group_names=[]
for group in group_by_columns:
groupss=train_supplamanteary2[["Date", group,target1,target2]]
grp=groupss.groupby(["Date", group], as_index=False ).sum()
grp.columns=["Date", group,group +"_" + target1,group +"_" + target2 ]
train_supplamanteary2=pd.merge(train_supplamanteary2,grp, how="left", left_on=["Date", group,group], right_on=["Date", group,group])
 for gr in [group +"_" + target1,group +"_" + target2]:
rate(train_supplamanteary2, key, gr, new_target_name="rate_" +gr)
 group_names+=["rate_" +gr]
def generated_predictions_with_pool_executor(max_workers,gcp_project_id):
results = []
df_sample_submission = pd.read_csv('.. /input/recursion-cellular-image-classification/sample_submission.csv',index_col=[0])
 with PoolExecutor(max_workers=max_workers)as executor:
futures_list = [executor.submit(process, i,df_sample_submission,gcp_project_id)for i in range(len(df_sample_submission)) ]
for f in tqdm(as_completed(futures_list), total=len(futures_list)) :
results.append(f.result())
 nb_escaped = 0
for r in results:
if r is None:
nb_escaped += 1
continue
for site in r:
df_solution.loc[site['id_code'], ['site{}_sirna'.format(site['site']),'site{}_confidence'.format(site['site'])]] = [site['sirna_prediction'], site['confidence']] 
<load_from_csv>
def get_lags(rate_array, current_index, size=20):
lag_confirmed_rate=[-1 for k in range(size)]
for j in range(0, size):
if current_index-j>=0:
lag_confirmed_rate[j]=rate_array[current_index-j]
else :
break
return lag_confirmed_rate
def days_ago_thresold_hit(full_array, indx, thresold):
days_ago_confirmed_count_10=-1
if full_array[indx]>thresold:
for j in range(indx,-1,-1):
entered=False
if full_array[j]<=thresold:
days_ago_confirmed_count_10=abs(j-indx)
 entered=True
break
if entered==False:
days_ago_confirmed_count_10=100
return days_ago_confirmed_count_10
def ewma_vectorized(data, alpha):
sums=sum([(alpha**(k+1)) *data[k] for k in range(len(data)) ])
 counts=sum([(alpha**(k+1)) for k in range(len(data)) ])
 return sums/counts
def generate_ma_std_window(rate_array, current_index, size=20, window=3):
ma_rate_confirmed=[-1 for k in range(size)]
std_rate_confirmed=[-1 for k in range(size)]
for j in range(0, size):
if current_index-j>=0:
ma_rate_confirmed[j]=np.mean(rate_array[max(0,current_index-j-window+1):current_index-j+1])
 std_rate_confirmed[j]=np.std(rate_array[max(0,current_index-j-window+1):current_index-j+1])
 else :
break
return ma_rate_confirmed, std_rate_confirmed
def generate_ewma_window(rate_array, current_index, size=20, window=3, alpha=0.05):
ewma_rate_confirmed=[-1 for k in range(size)]
for j in range(0, size):
if current_index-j>=0:
ewma_rate_confirmed[j]=ewma_vectorized(rate_array[max(0,current_index-j-window+1):current_index-j+1, ], alpha)
 else :
break
return ewma_rate_confirmed
def get_target(rate_col, indx, horizon=33, average=3, use_hard_rule=False):
target_values=[-1 for k in range(horizon)]
cou=0
for j in range(indx+1, indx+1+horizon):
if j<len(rate_col):
if average==1:
target_values[cou]=rate_col[j]
else :
if use_hard_rule and j +average <=len(rate_col):
target_values[cou]=np.mean(rate_col[j:j +average])
 else :
target_values[cou]=np.mean(rate_col[j:min(len(rate_col),j +average)])
 cou+=1
else :
break
return target_values
def get_target_count(rate_col, indx, horizon=33, average=3, use_hard_rule=False):
target_values=[-1 for k in range(horizon)]
cou=0
for j in range(indx+1, indx+1+horizon):
if j<len(rate_col):
if average==1:
target_values[cou]=rate_col[j]
else :
if use_hard_rule and j +average <=len(rate_col):
target_values[cou]=np.mean(rate_col[j:j +average])
 else :
target_values[cou]=np.mean(rate_col[j:min(len(rate_col),j +average)])
 cou+=1
else :
break
return target_values
def dereive_features(frame, confirmed, fatalities, rate_confirmed, rate_fatalities, count_confirmed, count_fatalities ,
horizon ,size=20, windows=[3,7], days_back_confimed=[1,10,100], days_back_fatalities=[1,2,10],
extra_data=None, groups_data=None, windows_group=[3,7], size_group=20,
days_back_confimed_group=[1,10,100]):
targets=[]
if not extra_data is None:
assert len(extra_stable_columns)==extra_data.shape[1]
if not groups_data is None:
assert len(group_names)==groups_data.shape[1]
names=[]
for day in days_back_confimed:
names+=["days_ago_confirmed_count_" + str(day)]
for window in windows:
names+=["ma" + str(window)+ "_rate_confirmed" + str(k+1)for k in range(size)]
names+=["ma" + str(window)+ "_count_confirmed" + str(k+1)for k in range(size)]
for day in days_back_fatalities:
names+=["days_ago_fatalitiescount_" + str(day)]
for window in windows:
names+=["ma" + str(window)+ "_rate_fatalities" + str(k+1)for k in range(size)]
names+=["ma" + str(window)+ "_count_fatalities" + str(k+1)for k in range(size)]
names+=["confirmed_level"]
names+=["fatalities_level"]
if not extra_data is None:
names+=[k for k in extra_stable_columns]
if not groups_data is None:
for gg in range(groups_data.shape[1]):
for day in days_back_confimed_group:
names+=["days_ago_grooupcount_" + str(gg+1)+ "_" + str(day)]
for window in windows_group:
names+=["ma_group_" + str(gg+1)+ "_" + str(window)+ "_rate_" + str(k+1)for k in range(size_group)]
names+=["confirmed_plus" + str(k+1)for k in range(horizon)]
names+=["fatalities_plus" + str(k+1)for k in range(horizon)]
names+=["confirmed_count_plus" + str(k+1)for k in range(horizon)]
names+=["fatalities_count_plus" + str(k+1)for k in range(horizon)]
features=[]
for i in range(len(confirmed)) :
row_features=[]
lag_confirmed_rate=get_lags(rate_confirmed, i, size=size)
 for day in days_back_confimed:
days_ago_confirmed_count_10=days_ago_thresold_hit(confirmed, i, day)
 row_features+=[days_ago_confirmed_count_10]
for window in windows:
ma3_rate_confirmed,std3_rate_confirmed= generate_ma_std_window(rate_confirmed, i, size=size, window=window)
 row_features+= ma3_rate_confirmed
ma3_count_confirmed,std3_count_confirmed= generate_ma_std_window(count_confirmed, i, size=size, window=window)
 row_features+= ma3_count_confirmed
lag_fatalities_rate=get_lags(rate_fatalities, i, size=size)
 for day in days_back_fatalities:
days_ago_fatalitiescount_2=days_ago_thresold_hit(fatalities, i, day)
 row_features+=[days_ago_fatalitiescount_2]
for window in windows:
ma3_rate_fatalities,std3_rate_fatalities= generate_ma_std_window(rate_fatalities, i, size=size, window=window)
 row_features+= ma3_rate_fatalities
ma3_count_fatalities,std3_count_fatalities= generate_ma_std_window(count_fatalities, i, size=size, window=window)
 row_features+= ma3_count_fatalities
confirmed_level=0

 confirmed_level= confirmed[i]
row_features+=[confirmed_level]
fatalities_is_level=0

 fatalities_is_level= fatalities[i]
row_features+=[fatalities_is_level]
if not extra_data is None:
row_features+=extra_data[i].tolist()
if not groups_data is None:
for gg in range(groups_data.shape[1]):
this_group=groups_data[:,gg].tolist()
lag_group_rate=get_lags(this_group, i, size=size_group)
 for day in days_back_confimed_group:
days_ago_groupcount_2=days_ago_thresold_hit(this_group, i, day)
 row_features+=[days_ago_groupcount_2]
for window in windows_group:
ma3_rate_group,std3_rate_group= generate_ma_std_window(this_group, i, size=size_group, window=window)
 row_features+= ma3_rate_group
confirmed_plus=get_target(rate_confirmed, i, horizon=horizon)
 row_features+= confirmed_plus
fatalities_plus=get_target(rate_fatalities, i, horizon=horizon)
 row_features+= fatalities_plus
confirmed_plus=get_target(count_confirmed, i, horizon=horizon)
 row_features+= confirmed_plus
fatalities_plus=get_target(count_fatalities, i, horizon=horizon)
 row_features+= fatalities_plus
features.append(row_features)
 new_frame=pd.DataFrame(data=features, columns=names ).reset_index(drop=True)
 frame=frame.reset_index(drop=True)
 frame=pd.concat([frame, new_frame], axis=1)
 return frame
def feature_engineering_for_single_key(frame, group, key, horizon=33, size=20, windows=[3,7],
days_back_confimed=[1,10,100], days_back_fatalities=[1,2,10],
extra_stable_=None, group_nams=None,windows_group=[3,7],
size_group=20, days_back_confimed_group=[1,10,100]):
mini_frame=get_data_by_key(frame, group, key, fields=None)
 mini_frame_with_features=dereive_features(mini_frame, mini_frame["ConfirmedCases"].values,
mini_frame["Fatalities"].values, mini_frame["rate_ConfirmedCases"].values,
mini_frame["rate_Fatalities"].values, mini_frame["diff_ConfirmedCases"].values,
mini_frame["diff_Fatalities"].values,
horizon ,size=size, windows=windows,
days_back_confimed=days_back_confimed, days_back_fatalities=days_back_fatalities,
extra_data=mini_frame[extra_stable_].values if not extra_stable_ is None else None,
groups_data=mini_frame[group_nams].values if not group_nams is None else None,
windows_group=windows_group, size_group=size_group,
days_back_confimed_group=days_back_confimed_group)
 return mini_frame_with_features
solution_file_path = ',/submissions/submission_{}.csv'.format(model_id)
 if os.path.exists(solution_file_path):
df_solution = pd.read_csv(solution_file_path,index_col=[0])
 else:
df_solution = pd.DataFrame(columns=['site1_sirna','site1_confidence','site2_sirna','site2_confidence'])
 df_solution.index.name = 'id_code'
prediction_client = automl_v1beta1.PredictionServiceClient.from_service_account_json(gcp_service_account_json)
 generated_predictions_with_pool_executor(20,gcp_project_id)
 generated_predictions_with_pool_executor(5,gcp_project_id)
 generated_predictions_with_pool_executor(5,gcp_project_id)
<save_to_csv>
train_frame=[]
size=10
windows=[3]
days_back_confimed=[1,5,10,20,50,100,250,500,1000]
days_back_fatalities=[1,2,5,10,20,50]
size_group=10
windows_group=[3]
days_back_confimed_group=[1,10,100]
for unique_k in tqdm(unique_keys):
mini_frame=feature_engineering_for_single_key(train, key, unique_k, horizon=horizon, size=size,
windows=windows, days_back_confimed=days_back_confimed,
days_back_fatalities=days_back_fatalities,
extra_stable_=extra_stable_columns if extra_stable_columns is not None and len(extra_stable_columns)>0 else None,
group_nams=group_names,windows_group=windows_group,
size_group=size_group, days_back_confimed_group=days_back_confimed_group
).reset_index(drop=True)
 train_frame.append(mini_frame)
 train_frame = pd.concat(train_frame, axis=0 ).reset_index(drop=True)
 new_unique_keys=train_frame['key'].unique()
for kee in new_unique_keys:
if kee not in unique_keys:
print(kee , " is not there ")
 print("==================================================")
 train_frame_supplamanteary2=None
if not train_supplamanteary2 is None:
train_frame_supplamanteary2=[]
for unique_k in tqdm(unique_keys_sup2):
mini_frame_sup=feature_engineering_for_single_key(train_supplamanteary2, key, unique_k, horizon=horizon, size=size,
windows=windows, days_back_confimed=days_back_confimed,
days_back_fatalities=days_back_fatalities,
extra_stable_=extra_stable_columns if extra_stable_columns is not None and len(extra_stable_columns)>0 else None,
group_nams=group_names,windows_group=windows_group,
size_group=size_group, days_back_confimed_group=days_back_confimed_group ).reset_index(drop=True)
 train_frame_supplamanteary2.append(mini_frame_sup)
 train_frame_supplamanteary2 = pd.concat(train_frame_supplamanteary2, axis=0 ).reset_index(drop=True)
 new_unique_keys_sup2=train_frame_supplamanteary2['key'].unique()
site1_wins = df_solution['site1_confidence'] >= df_solution['site2_confidence']
site2_wins = df_solution['site2_confidence'] > df_solution['site1_confidence']
df_solution.loc[site1_wins,'sirna'] =(df_solution[site1_wins])['site1_sirna']
df_solution.loc[site2_wins,'sirna'] =(df_solution[site2_wins])['site2_sirna']
df_solution['sirna'] = df_solution['sirna'].astype(int)
 df_solution['sirna'].sort_index().to_csv('submit_{}.csv'.format(model_id),header=True)
<set_options>
def bagged_set_train(X_ts,y_cs,wts, seed, estimators,xtest, xt=None,yt=None, output_name=None):
baggedpred=np.array([ 0.0 for d in range(0, xtest.shape[0])])
 for n in range(0, estimators):
params = {'objective': 'rmse',
'metric': 'rmse',
'boosting': 'gbdt',
'learning_rate': 0.005,
'drop_rate':0.01,
'skip_drop':0.6,
'uniform_drop':True,
'verbose': -1,
'num_leaves': 40,
'bagging_fraction': 0.9,
'bagging_freq': 1,
'bagging_seed': seed + n,
'feature_fraction': 0.8,
'feature_fraction_seed': seed + n,
'min_data_in_leaf': 10,
'max_bin': 100,
'max_depth':20,
'reg_alpha':1,
'lambda_l2': 10,
'num_threads':6
}
d_train = lgb.Dataset(X_ts,y_cs, weight=wts, free_raw_data=False)
 if not type(yt)is type(None):
d_cv = lgb.Dataset(xt,yt, free_raw_data=False, reference=d_train)
 model = lgb.train(params,d_train,num_boost_round=500,
valid_sets=d_cv,
verbose_eval=50)
 else :
model = lgb.train(params,d_train,num_boost_round=500)
 preds=model.predict(xtest)
 baggedpred+=preds
if not output_name is None:
joblib.dump(( model), output_name)

baggedpred/= estimators
return baggedpred
WORKERS = 2
CHANNEL = 3
warnings.filterwarnings("ignore")
 SIZE = 224
NUM_CLASSES = 1108<load_from_csv>
def predict(xtest,input_name=None):
baggedpred=np.array([ 0.0 for d in range(0, xtest.shape[0])])
 model= joblib.load(input_name)
 preds=model.predict(xtest)
 baggedpred+=preds
return baggedpred
df_train = pd.read_csv('.. /input/recursion-complete/new_train.csv')
 df_test = pd.read_csv('.. /input/recursion-complete/new_test.csv')
 x = df_train['filename']
y = df_train['sirna']
x, y = shuffle(x, y, random_state=10)
 y.hist() 
<compute_train_metric>
names=[]
for day in days_back_confimed:
names+=["days_ago_confirmed_count_" + str(day)]
for window in windows:
names+=["ma" + str(window)+ "_rate_confirmed" + str(k+1)for k in range(size)]
for day in days_back_fatalities:
names+=["days_ago_fatalitiescount_" + str(day)]
for window in windows:
names+=["ma" + str(window)+ "_rate_fatalities" + str(k+1)for k in range(size)]
names+=["confirmed_level"]
names+=["fatalities_level"]
if not extra_stable_columns is None and len(extra_stable_columns)>0:
names+=[k for k in extra_stable_columns]
if not group_names is None:
for gg in range(len(group_names)) :
for day in days_back_confimed_group:
names+=["days_ago_grooupcount_" + str(gg+1)+ "_" + str(day)]
for window in windows_group:
names+=["ma_group_" + str(gg+1)+ "_" + str(window)+ "_rate_" + str(k+1)for k in range(size_group)]
y = to_categorical(y, num_classes=NUM_CLASSES )<split>
tr_frame=train_frame
target_confirmed=["confirmed_plus" + str(k+1)for k in range(horizon)]
target_fatalities=["fatalities_plus" + str(k+1)for k in range(horizon)]
seed=1412
target_confirmed_train=tr_frame[target_confirmed].values
print(" original shape of train is {} ".format(target_confirmed_train.shape))
target_fatalities_train=tr_frame[target_fatalities].values
features_train=tr_frame[names].values
standard_confirmed_train=tr_frame["ConfirmedCases"].values
standard_fatalities_train=tr_frame["Fatalities"].values
current_confirmed_train=tr_frame["ConfirmedCases"].values
features_cv=[]
name_cv=[]
standard_confirmed_cv=[]
standard_fatalities_cv=[]
names_=tr_frame["key"].values
training_horizon=int(features_train.shape[0]/len(unique_keys))
print("training horizon = ",training_horizon)
 for dd in range(training_horizon-1,features_train.shape[0],training_horizon):
features_cv.append(features_train[dd])
 name_cv.append(names_[dd])
 standard_confirmed_cv.append(standard_confirmed_train[dd])
 standard_fatalities_cv.append(standard_fatalities_train[dd])
 print(name_cv[-1], standard_confirmed_cv[-1], standard_fatalities_cv[-1])
 current_confirmed_train=[k for k in range(len(current_confirmed_train)) if current_confirmed_train[k]>0]
target_confirmed_train=target_confirmed_train[current_confirmed_train]
target_fatalities_train=target_fatalities_train[current_confirmed_train]
features_train=features_train[current_confirmed_train]
standard_confirmed_train=standard_confirmed_train[current_confirmed_train]
standard_fatalities_train=standard_fatalities_train[current_confirmed_train]
features_cv=np.array(features_cv)
 preds_confirmed_cv=np.zeros(( features_cv.shape[0],horizon))
preds_confirmed_standard_cv=np.zeros(( features_cv.shape[0],horizon))
preds_fatalities_cv=np.zeros(( features_cv.shape[0],horizon))
preds_fatalities_standard_cv=np.zeros(( features_cv.shape[0],horizon))
overal_rmsle_metric_confirmed=0.0
for j in range(preds_confirmed_cv.shape[1]):
this_target=target_confirmed_train[:,j]
index_positive=[k for k in range(len(this_target)) if this_target[k]!=-1]
this_features=features_train[index_positive]
this_target=this_target[index_positive]
this_weight=np.log(standard_confirmed_train[index_positive]+3.)
this_features_cv=features_cv
preds=bagged_set_train(this_features,this_target,this_weight, seed, 1,features_cv, xt=None,yt=None, output_name=model_directory +"confirmed"+ str(j))
preds_confirmed_cv[:,j]=preds
print(" modelling confirmed, case %d, original train %d, and after %d, original cv %d and after %d "%(
 j,target_confirmed_train.shape[0],this_target.shape[0],this_features_cv.shape[0],this_features_cv.shape[0]))
predictions=[]
for ii in range(preds_confirmed_cv.shape[0]):
current_prediction=standard_confirmed_cv[ii]
if current_prediction==0 :
current_prediction=0.1
for j in range(preds_confirmed_cv.shape[1]):
current_prediction*=max(1,preds_confirmed_cv[ii][j])
 preds_confirmed_standard_cv[ii][j]=current_prediction
for j in range(preds_confirmed_cv.shape[1]):
this_target=target_fatalities_train[:,j]
index_positive=[k for k in range(len(this_target)) if this_target[k]!=-1]
this_features=features_train[index_positive]
this_target=this_target[index_positive]
this_weight=np.log(standard_confirmed_train[index_positive]+2.)
this_features_cv=features_cv
preds=bagged_set_train(this_features,this_target,this_weight, seed, 1,features_cv, xt=None,yt=None, output_name=model_directory +"fatal"+ str(j))
preds_fatalities_cv[:,j]=preds
print(" modelling fatalities, case %d, original train %d, and after %d, original cv %d and after %d "%(
 j,target_confirmed_train.shape[0],this_target.shape[0],this_features_cv.shape[0],this_features_cv.shape[0]))
predictions=[]
for ii in range(preds_fatalities_cv.shape[0]):
current_prediction=standard_fatalities_cv[ii]
if current_prediction==0 and standard_confirmed_cv[ii]>400:
current_prediction=0.1
for j in range(preds_fatalities_cv.shape[1]):
if current_prediction==0 and preds_confirmed_standard_cv[ii][j]>400:
current_prediction=1.
 current_prediction*=max(1,preds_fatalities_cv[ii][j])
 preds_fatalities_standard_cv[ii][j]=current_prediction
train_x, valid_x, train_y, valid_y = train_test_split(x, y, test_size=0.15,
stratify=y, random_state=10)
 print(train_x.shape)
 print(train_y.shape)
 print(valid_x.shape)
 print(valid_y.shape )<define_variables>
def decay_4_first_10_then_1_f(array):
arr=[1.0 for k in range(len(array)) ]
for j in range(len(array)) :
if j<10:
arr[j]=1.+(max(1,array[j])-1.) /4.
 else :
arr[j]=1.
 return arr
	
def decay_16_first_10_then_1_f(array):
arr=[1.0 for k in range(len(array)) ]
for j in range(len(array)) :
if j<10:
arr[j]=1.+(max(1,array[j])-1.) /16.
 else :
arr[j]=1.
 return arr	
def decay_2_f(array):
arr=[1.0 for k in range(len(array)) ]
for j in range(len(array)) :
arr[j]=1.+(max(1,array[j])-1.) /2.
 return arr
def decay_4_f(array):
arr=[1.0 for k in range(len(array)) ]
for j in range(len(array)) :
arr[j]=1.+(max(1,array[j])-1.) /4.
 return arr 	
	
def acceleratorx2_f(array):
arr=[1.0 for k in range(len(array)) ]
for j in range(len(array)) :
arr[j]=1.+(max(1,array[j])-1.) *2.
 return arr
def decay_1_5_f(array):
arr=[1.0 for k in range(len(array)) ]
for j in range(len(array)) :
arr[j]=1.+(max(1,array[j])-1.) /1.5
return arr
def decay_1_2_f(array):
arr=[1.0 for k in range(len(array)) ]
for j in range(len(array)) :
arr[j]=1.+(max(1,array[j])-1.) /1.2
return arr
def decay_1_1_f(array):
arr=[1.0 for k in range(len(array)) ]
for j in range(len(array)) :
arr[j]=1.+(max(1,array[j])-1.) /1.1
return arr
def stay_same_f(array):
arr=[1.0 for k in range(len(array)) ]
for j in range(len(array)) :
arr[j]=1.
 return arr
def decay_2_last_12_linear_inter_f(array):
arr=[1.0 for k in range(len(array)) ]
for j in range(len(array)) :
arr[j]=1.+(max(1,array[j])-1.) /2.
 arr12=(max(1,arr[-12])-1.) /12.
 for j in range(0, 12):
arr[len(arr)-12 +j]= max(1, 1 +(( arr12*12)-(j+1)*arr12))
return arr
def decay_1_2_last_12_linear_inter_f(array):
arr=[1.0 for k in range(len(array)) ]
for j in range(len(array)) :
arr[j]=1.+(max(1,array[j])-1.) /1.2
arr12=(max(1,arr[-12])-1.) /12.
 for j in range(0, 12):
arr[len(arr)-12 +j]= max(1, 1 +(( arr12*12)-(j+1)*arr12))
return arr
def decay_1_5_last_12_linear_inter_f(array):
arr=[1.0 for k in range(len(array)) ]
for j in range(len(array)) :
arr[j]=1.+(max(1,array[j])-1.) /1.5
arr12=(max(1,arr[-12])-1.) /12.
 for j in range(0, 12):
arr[len(arr)-12 +j]= max(1, 1 +(( arr12*12)-(j+1)*arr12))
return arr
def decay_4_last_12_linear_inter_f(array):
arr=[1.0 for k in range(len(array)) ]
for j in range(len(array)) :
arr[j]=1.+(max(1,array[j])-1.) /4.
 arr12=(max(1,arr[-12])-1.) /12.
 for j in range(0, 12):
arr[len(arr)-12 +j]= max(1, 1 +(( arr12*12)-(j+1)*arr12))
return arr
def decay_8_last_12_linear_inter_f(array):
arr=[1.0 for k in range(len(array)) ]
for j in range(len(array)) :
arr[j]=1.+(max(1,array[j])-1.) /8.
 arr12=(max(1,arr[-12])-1.) /12.
 for j in range(0, 12):
arr[len(arr)-12 +j]= max(1, 1 +(( arr12*12)-(j+1)*arr12))
return arr
def linear_last_12_f(array):
arr=[1.0 for k in range(len(array)) ]
for j in range(len(array)) :
arr[j]=max(1,array[j])
 arr12=(max(1,arr[-12])-1.) /12.
 for j in range(0, 12):
arr[len(arr)-12 +j]= max(1, 1 +(( arr12*12)-(j+1)*arr12))
return arr
decay_4_first_10_then_1 =[ "Jilin_China","Tianjin_China"]
decay_4_first_10_then_1_fatality=[]
decay_16_first_10_then_1 =["Beijing_China","Fujian_China","Jiangsu_China"]
decay_16_first_10_then_1_fatality=[]
decay_4=["nan_Central African Republic","Shanghai_China","nan_Korea, South","nan_Zambia"]
decay_4_fatality=["Heilongjiang_China","Inner Mongolia_China"]
decay_2 =["nan_Burundi","Faroe Islands_Denmark","nan_Eritrea","nan_Italy","nan_Brunei""Heilongjiang_China","Inner Mongolia_China",
"Shanxi_China","nan_Congo(Kinshasa)","nan_Cyprus","Reunion_France","Sint Maarten_Netherlands","nan_Angola"]
decay_2_fatality=["nan_Iran"]
stay_same=["nan_Diamond Princess"]
stay_same_fatality=["Beijing_China","Fujian_China","Qinghai_China","Jiangsu_China","Jilin_China","Macau_China","Ningxia_China","Tianjin_China"
,"Faroe Islands_Denmark","Shanxi_China"]
normal=[]
normal_fatality=["nan_Andorra","New South Wales_Australia","nan_Korea, South","New York_US","nan_Austria","Alberta_Canada","Nova Scotia_Canada",
"Saskatchewan_Canada","nan_Costa Rica","nan_Croatia","nan_Finland","Mayotte_France","nan_Hungary","nan_Iceland","nan_Latvia","nan_Czechia"
,"nan_Luxembourg","nan_Malta","nan_Montenegro","nan_New Zealand","nan_Norway","nan_South Africa","Hawaii_US","Ohio_US","Pennsylvania_US","nan_Uruguay","nan_Venezuela",
"nan_West Bank and Gaza"]
decay_4_last_12_linear_inter =[ "nan_Dominica","New Caledonia_France",
"Saint Barthelemy_France","nan_Gambia","nan_Grenada","nan_Holy See",
"nan_Mauritania","nan_Nicaragua","nan_Saint Lucia","nan_Saint Vincent and the Grenadines",
"nan_Seychelles","nan_Suriname","nan_Mongolia","nan_Cabo Verde",
"Montserrat_United Kingdom","Turks and Caicos Islands_United Kingdom", "Hong Kong_China","Northern Territory_Australia","Northwest Territories_Canada","Yukon_Canada",
"Guangdong_China","nan_MS Zaandam","nan_Maldives","nan_Saint Kitts and Nevis","nan_Sao Tome and Principe","nan_South Sudan","nan_Western Sahara"]
decay_4_last_12_linear_inter_fatality=["nan_Somalia"]
decay_2_last_12_linear_inter =[ "nan_Chad","French Polynesia_France","nan_Laos","nan_Nepal","nan_Sudan","Bermuda_United Kingdom","Cayman Islands_United Kingdom"
,"nan_Eswatini","nan_Guyana","nan_Liberia","nan_Malawi","nan_Mozambique","nan_Timor-Leste",
"Falkland Islands(Malvinas)_United Kingdom","nan_Equatorial Guinea"]
decay_2_last_12_linear_inter_fatality=[]
decay_1_5 =["nan_Cambodia","nan_Croatia","nan_Denmark",
"nan_Bahamas","nan_Bahrain","nan_Barbados" ,"Manitoba_Canada","New Brunswick_Canada",
"Saskatchewan_Canada","nan_France","nan_Libya","nan_Malta",
	"Aruba_Netherlands","nan_Niger","nan_Spain","Virgin Islands_US",
	"Channel Islands_United Kingdom","Gibraltar_United Kingdom","nan_United Kingdom",'nan_Burma',"nan_Congo(Brazzaville)",
	"French Guiana_France","Martinique_France","Mayotte_France","nan_Georgia","nan_Iran","nan_New Zealand",
	"nan_West Bank and Gaza"]
decay_1_5_fatality=["nan_Cuba","nan_Morocco","Ohio_US","Pennsylvania_US","Puerto Rico_US" ,"nan_Uzbekistan",
"nan_Cyprus","nan_France","nan_Niger","South Dakota_US","nan_Gabon"]
acceleratorx2=[]
acceleratorx2_fatality=["Newfoundland and Labrador_Canada"]
decay_1_5_last_12_linear_inter =["nan_Antigua and Barbuda","nan_Botswana","nan_Haiti","nan_Madagascar","nan_Somalia",
"nan_Zimbabwe"]
decay_1_5_last_12_linear_inter_fatality =["nan_Burma","nan_Cabo Verde","nan_Djibouti"]
decay_1_2 =["nan_Albania","nan_Andorra","New South Wales_Australia","nan_Austria","nan_Azerbaijan","nan_Bangladesh","nan_Belize","Alberta_Canada","nan_Mauritius","nan_Monaco","nan_Montenegro",
"Newfoundland and Labrador_Canada","nan_Costa Rica","nan_Czechia","nan_Dominican Republic","nan_Estonia","Guadeloupe_France","nan_Iceland","nan_Latvia","nan_Liechtenstein","nan_Luxembourg","nan_Norway",
"nan_Poland","nan_Rwanda","nan_South Africa","nan_Trinidad and Tobago","Arizona_US","California_US","Colorado_US","Connecticut_US","Georgia_US","Hawaii_US","Maine_US","New York_US","Oklahoma_US",
"Oregon_US","Pennsylvania_US","Tennessee_US","Washington_US","nan_Uruguay","nan_Venezuela","nan_Vietnam",
"nan_Gabon","nan_Syria","nan_Djibouti"]
decay_1_2_fatality=["nan_Afghanistan","nan_Belarus","nan_Mali","nan_Mexico","nan_Peru","nan_Serbia","Kentucky_US","Puerto Rico_US","Texas_US","nan_Ukraine"]
decay_1_1 =["nan_Algeria","nan_Argentina","Tasmania_Australia","Nova Scotia_Canada","nan_Finland","nan_Hungary","nan_Iraq",
"nan_Jordan","nan_Kyrgyzstan","nan_Malaysia","Alabama_US","Iowa_US","Michigan_US","Missouri_US","Nebraska_US","North Dakota_US ","Ohio_US","South Carolina_US"]
decay_1_1_fatality=["nan_Ecuador","nan_Kenya","nan_Korea, South","nan_Kosovo","Indiana_US","New Mexico_US","Oregon_US","Rhode Island_US","Tennessee_US","nan_United Arab Emirates"]
decay_1_2_last_12_linear_inter =[]
decay_1_2_last_12_linear_inter_fatality=["nan_Angola" ]
linear_last_12=["nan_Afghanistan","nan_Ireland","nan_Benin","nan_Tanzania"]
linear_last_12_fatality=["nan_Guatemala","nan_Ireland"]
decay_8_last_12_linear_inter =[ "nan_Bhutan","Prince Edward Island_Canada","Greenland_Denmark","nan_Fiji","Saint Pierre and Miquelon_France","St Martin_France","nan_Namibia",
"Bonaire, Sint Eustatius and Saba_Netherlands","Curacao_Netherlands","nan_Papua New Guinea","Anguilla_United Kingdom","British Virgin Islands_United Kingdom",]
decay_8_last_12_linear_inter_fatality=[]
tr_frame=train_frame
features_train=tr_frame[names].values
standard_confirmed_train=tr_frame["ConfirmedCases"].values
standard_fatalities_train=tr_frame["Fatalities"].values
current_confirmed_train=tr_frame["ConfirmedCases"].values
features_cv=[]
name_cv=[]
standard_confirmed_cv=[]
standard_fatalities_cv=[]
names_=tr_frame["key"].values
training_horizon=int(features_train.shape[0]/len(unique_keys))
print("training horizon = ",training_horizon)
 for dd in range(training_horizon-1,features_train.shape[0],training_horizon):
features_cv.append(features_train[dd])
 name_cv.append(names_[dd])
 standard_confirmed_cv.append(standard_confirmed_train[dd])
 standard_fatalities_cv.append(standard_fatalities_train[dd])
 print(name_cv[-1], standard_confirmed_cv[-1], standard_fatalities_cv[-1])
 features_cv=np.array(features_cv)
 preds_confirmed_cv=np.zeros(( features_cv.shape[0],horizon))
preds_confirmed_standard_cv=np.zeros(( features_cv.shape[0],horizon))
preds_fatalities_cv=np.zeros(( features_cv.shape[0],horizon))
preds_fatalities_standard_cv=np.zeros(( features_cv.shape[0],horizon))
overal_rmsle_metric_confirmed=0.0
for j in range(preds_confirmed_cv.shape[1]):
this_features_cv=features_cv
preds=predict(features_cv, input_name=model_directory +"confirmed"+ str(j))
preds_confirmed_cv[:,j]=preds
print(" modelling confirmed, case %d, , original cv %d and after %d "%(j,this_features_cv.shape[0],this_features_cv.shape[0]))
predictions=[]
for ii in range(preds_confirmed_cv.shape[0]):
current_prediction=standard_confirmed_cv[ii]
if current_prediction==0 :
current_prediction=0.1
this_preds=preds_confirmed_cv[ii].tolist()
name=name_cv[ii]
reserve=this_preds[0]
if name in normal:
this_preds=this_preds	
	
elif name in decay_4_first_10_then_1:
this_preds=decay_4_first_10_then_1_f(this_preds)
 		
elif name in decay_16_first_10_then_1:
this_preds=decay_16_first_10_then_1_f(this_preds)		
		
elif name in decay_4_last_12_linear_inter:
this_preds=decay_4_last_12_linear_inter_f(this_preds)		
elif name in decay_8_last_12_linear_inter:
this_preds=decay_8_last_12_linear_inter_f(this_preds)		
elif name in decay_4:
this_preds=decay_4_f(this_preds)
 elif name in decay_2:
this_preds=decay_2_f(this_preds)
 elif name in decay_2_last_12_linear_inter:
this_preds=decay_2_last_12_linear_inter_f(this_preds)
 elif name in decay_1_5:
this_preds=decay_1_5_f(this_preds)
 elif name in decay_1_5_last_12_linear_inter:
this_preds=decay_1_5_last_12_linear_inter_f(this_preds)
 elif name in decay_1_2:
this_preds=decay_1_2_f(this_preds)
 elif name in decay_1_2_last_12_linear_inter:
this_preds=decay_1_2_last_12_linear_inter_f(this_preds)
 elif name in decay_1_1:
this_preds=decay_1_1_f(this_preds)
 elif name in linear_last_12:
this_preds=linear_last_12_f(this_preds)
 elif name in acceleratorx2:
this_preds=acceleratorx2_f(this_preds)
 elif name in stay_same or "China" in name:
this_preds=stay_same_f(this_preds)
 for j in range(preds_confirmed_cv.shape[1]):
if j==0 and "nan_Congo(Brazzaville)" in name:
current_prediction=117.
 if j==0 and "nan_Cabo Verde" in name:
current_prediction=56.
 current_prediction*=max(1,this_preds[j])
 preds_confirmed_standard_cv[ii][j]=current_prediction
for j in range(preds_confirmed_cv.shape[1]):
this_features_cv=features_cv
preds=predict(features_cv, input_name=model_directory +"fatal"+ str(j))
preds_fatalities_cv[:,j]=preds
print(" modelling fatalities, case %d, original cv %d and after %d "%(j,this_features_cv.shape[0],this_features_cv.shape[0]))
predictions=[]
for ii in range(preds_fatalities_cv.shape[0]):
current_prediction=standard_fatalities_cv[ii]
this_preds=preds_fatalities_cv[ii].tolist()
name=name_cv[ii]
reserve=this_preds[0]
if name in normal_fatality:
this_preds=this_preds	 	
	
elif name in decay_4_first_10_then_1_fatality:
this_preds=decay_4_first_10_then_1_f(this_preds)
 		
elif name in decay_16_first_10_then_1_fatality:
this_preds=decay_16_first_10_then_1_f(this_preds)
 elif name in decay_4_last_12_linear_inter_fatality:
this_preds=decay_4_last_12_linear_inter_f(this_preds)		
elif name in decay_8_last_12_linear_inter_fatality:
this_preds=decay_8_last_12_linear_inter_f(this_preds)		
elif name in decay_4_fatality:
this_preds=decay_4_f(this_preds)		
		
elif name in decay_2_fatality:
this_preds=decay_2_f(this_preds)
 elif name in decay_2_last_12_linear_inter_fatality:
this_preds=decay_2_last_12_linear_inter_f(this_preds)
 elif name in decay_1_5_fatality:
this_preds=decay_1_5_f(this_preds)	
elif name in decay_1_5_last_12_linear_inter_fatality:
this_preds=decay_1_5_last_12_linear_inter_f(this_preds)
 elif name in decay_1_2_fatality:
this_preds=decay_1_2_f(this_preds)
 elif name in decay_1_2_last_12_linear_inter_fatality:
this_preds=decay_1_2_last_12_linear_inter_f(this_preds)
 elif name in decay_1_1_fatality:
this_preds=decay_1_1_f(this_preds)
 elif name in linear_last_12_fatality:
this_preds=linear_last_12_f(this_preds)
 elif name in acceleratorx2_fatality:
this_preds=acceleratorx2_f(this_preds)
 elif name in stay_same_fatality:
this_preds=stay_same_f(this_preds)
 elif name in normal:
this_preds=this_preds	
	
elif name in decay_4_first_10_then_1:
this_preds=decay_4_first_10_then_1_f(this_preds)
 		
elif name in decay_16_first_10_then_1:
this_preds=decay_16_first_10_then_1_f(this_preds)		
elif name in decay_4_last_12_linear_inter:
this_preds=decay_4_last_12_linear_inter_f(this_preds)		
elif name in decay_8_last_12_linear_inter:
this_preds=decay_8_last_12_linear_inter_f(this_preds)		
elif name in decay_4:
this_preds=decay_4_f(this_preds)
 		
elif name in decay_2:
this_preds=decay_2_f(this_preds)
 elif name in decay_2_last_12_linear_inter:
this_preds=decay_2_last_12_linear_inter_f(this_preds)
 elif name in decay_1_5:
this_preds=decay_1_5_f(this_preds)
 elif name in decay_1_5_last_12_linear_inter:
this_preds=decay_1_5_last_12_linear_inter_f(this_preds)
 elif name in decay_1_2:
this_preds=decay_1_2_f(this_preds)
 elif name in decay_1_2_last_12_linear_inter:
this_preds=decay_1_2_last_12_linear_inter_f(this_preds)
 elif name in decay_1_1:
this_preds=decay_1_1_f(this_preds)
 elif name in linear_last_12:
this_preds=linear_last_12_f(this_preds)
 elif name in acceleratorx2:
this_preds=acceleratorx2_f(this_preds)
 elif name in stay_same or "China" in name:
this_preds=stay_same_f(this_preds)
 for j in range(preds_fatalities_cv.shape[1]):
if current_prediction==0 and preds_confirmed_standard_cv[ii][j]>400 and(name not in stay_same and name not in stay_same_fatality and "China" not in name):
current_prediction=1.
 if j==0 and "Aruba_Netherlands" in name:
current_prediction=1.
 if j==0 and "nan_Monaco" in name:
current_prediction=3.
 if j==0 and 'nan_Costa Rica' in name or "Isle of Man_United Kingdom" in name:
current_prediction=4.
 if j==0 and "nan_Somalia" in name:
current_prediction=5
if j==0 and "Martinique_France" in name:
current_prediction=8.
 current_prediction*=max(1,this_preds[j])
 preds_fatalities_standard_cv[ii][j]=current_prediction
class My_Generator(Sequence):
def __init__(self, image_filenames, labels,
batch_size, is_train=True,
mix=False, augment=False):
self.image_filenames, self.labels = image_filenames, labels
self.batch_size = batch_size
self.is_train = is_train
self.is_augment = augment
if(self.is_train):
self.on_epoch_end()
self.is_mix = mix
def __len__(self):
return int(np.ceil(len(self.image_filenames)/ float(self.batch_size)))
 def __getitem__(self, idx):
batch_x = self.image_filenames[idx * self.batch_size:(idx + 1)* self.batch_size]
batch_y = self.labels[idx * self.batch_size:(idx + 1)* self.batch_size]
if(self.is_train):
return self.train_generate(batch_x, batch_y)
 return self.valid_generate(batch_x, batch_y)
 def on_epoch_end(self):
if(self.is_train):
self.image_filenames, self.labels = shuffle(self.image_filenames, self.labels)
 else:
pass
def mix_up(self, x, y):
lam = np.random.beta(0.2, 0.4)
 ori_index = np.arange(int(len(x)))
 index_array = np.arange(int(len(x)))
 np.random.shuffle(index_array)
 mixed_x = lam * x[ori_index] +(1 - lam)* x[index_array]
mixed_y = lam * y[ori_index] +(1 - lam)* y[index_array]
return mixed_x, mixed_y
def train_generate(self, batch_x, batch_y):
batch_images = []
for(sample, label)in zip(batch_x, batch_y):
img = cv2.imread('.. /input/recursion-complete/train/train/'+sample)
 if(self.is_augment):
img = seq.augment_image(img)
 batch_images.append(img)
 batch_images = np.array(batch_images, np.float32)/255
batch_y = np.array(batch_y, np.float32)
 if(self.is_mix):
batch_images, batch_y = self.mix_up(batch_images, batch_y)
 return batch_images, batch_y
def valid_generate(self, batch_x, batch_y):
batch_images = []
for(sample, label)in zip(batch_x, batch_y):
img = cv2.imread('.. /input/recursion-complete/train/train/'+sample)
 batch_images.append(img)
 batch_images = np.array(batch_images, np.float32)/255
batch_y = np.array(batch_y, np.float32)
 return batch_images, batch_y<import_modules>
key_to_confirmed_rate={}
key_to_fatality_rate={}
key_to_confirmed={}
key_to_fatality={}
print(len(features_cv), len(name_cv),len(standard_confirmed_cv),len(standard_fatalities_cv))
print(preds_confirmed_cv.shape,preds_confirmed_standard_cv.shape,preds_fatalities_cv.shape,preds_fatalities_standard_cv.shape)
 for j in range(len(name_cv)) :
key_to_confirmed_rate[name_cv[j]]=preds_confirmed_cv[j,:].tolist()
key_to_fatality_rate[name_cv[j]]=preds_fatalities_cv[j,:].tolist()
key_to_confirmed[name_cv[j]] =preds_confirmed_standard_cv[j,:].tolist()
key_to_fatality[name_cv[j]]=preds_fatalities_standard_cv[j,:].tolist()
BatchNormalization, Input, Conv2D, GlobalAveragePooling2D)
<choose_model_class>
train_new=train[["Date","ConfirmedCases","Fatalities","key","rate_ConfirmedCases","rate_Fatalities"]]
test_new=pd.merge(test,train_new, how="left", left_on=["key","Date"], right_on=["key","Date"] ).reset_index(drop=True)
 test_new
def create_model(input_shape,n_out):
input_tensor = Input(shape=input_shape)
 base_model = DenseNet121(include_top=False,
weights=None,
input_tensor=input_tensor)
 base_model.load_weights('.. /input/densenet-keras/DenseNet-BC-121-32-no-top.h5')
 x = GlobalAveragePooling2D()(base_model.output)
 x = Dense(1024, activation='relu' )(x)
 final_output = Dense(n_out, activation='softmax', name='final_output' )(x)
 model = Model(input_tensor, final_output)
 return model<choose_model_class>
def fillin_columns(frame,key_column, original_name, training_horizon, test_horizon, unique_values, key_to_values):
keys=frame[key_column].values
original_values=frame[original_name].values.tolist()
print(len(keys), len(original_values), training_horizon ,test_horizon,len(key_to_values))
for j in range(unique_values):
current_index=(j *(training_horizon +test_horizon)) +training_horizon
current_key=keys[current_index]
values=key_to_values[current_key]
co=0
for g in range(current_index, current_index + test_horizon):
original_values[g]=values[co]
co+=1
frame[original_name]=original_values
all_days=int(test_new.shape[0]/len(unique_keys))
tr_horizon=all_days-horizon
print(all_days,tr_horizon, horizon)
 fillin_columns(test_new,"key", 'ConfirmedCases', tr_horizon, horizon, len(unique_keys), key_to_confirmed)
 fillin_columns(test_new,"key", 'Fatalities', tr_horizon, horizon, len(unique_keys), key_to_fatality)
 fillin_columns(test_new,"key", 'rate_ConfirmedCases', tr_horizon, horizon, len(unique_keys), key_to_confirmed_rate)
 fillin_columns(test_new,"key", 'rate_Fatalities', tr_horizon, horizon, len(unique_keys), key_to_fatality_rate )
EarlyStopping, ReduceLROnPlateau,CSVLogger)
 epochs = 10; batch_size = 8
checkpoint = ModelCheckpoint('.. /working/Densenet121.h5', monitor='val_loss', verbose=1,
save_best_only=True, mode='min', save_weights_only = True)
 reduceLROnPlat = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=4,
verbose=1, mode='auto', epsilon=0.0001)
 early = EarlyStopping(monitor="val_loss",
mode="min",
patience=15)
 csv_logger = CSVLogger(filename='.. /working/training_log.csv',
separator=',',
append=True)
 train_generator = My_Generator(train_x, train_y, 128, is_train=True)
 train_mixup = My_Generator(train_x, train_y, batch_size, is_train=True, mix=False, augment=True)
 valid_generator = My_Generator(valid_x, valid_y, batch_size, is_train=False)
 model = create_model(input_shape=(SIZE,SIZE,3),n_out=NUM_CLASSES)
<compute_train_metric>
def kappa_loss(y_true, y_pred, y_pow=2, eps=1e-12, N=1108, bsize=8, name='kappa'):

 with tf.name_scope(name):
y_true = tf.to_float(y_true)
 repeat_op = tf.to_float(tf.tile(tf.reshape(tf.range(0, N), [N, 1]), [1, N]))
repeat_op_sq = tf.square(( repeat_op - tf.transpose(repeat_op)))
 weights = repeat_op_sq / tf.to_float(( N - 1)** 2)
 pred_ = y_pred ** y_pow
try:
pred_norm = pred_ /(eps + tf.reshape(tf.reduce_sum(pred_, 1), [-1, 1]))
except Exception:
pred_norm = pred_ /(eps + tf.reshape(tf.reduce_sum(pred_, 1), [bsize, 1]))
hist_rater_a = tf.reduce_sum(pred_norm, 0)
 hist_rater_b = tf.reduce_sum(y_true, 0)
 conf_mat = tf.matmul(tf.transpose(pred_norm), y_true)
 nom = tf.reduce_sum(weights * conf_mat)
 denom = tf.reduce_sum(weights * tf.matmul(
 tf.reshape(hist_rater_a, [N, 1]), tf.reshape(hist_rater_b, [1, N])) /
tf.to_float(bsize))
return nom*0.5 /(denom + eps)+ categorical_crossentropy(y_true, y_pred)*0.5<train_model>
def bagged_set_trainc(X_ts,y_cs,wts, seed, estimators,xtest, xt=None,yt=None, output_name=None):
baggedpred=np.array([ 0.0 for d in range(0, xtest.shape[0])])
 for n in range(0, estimators):
params = {'objective': 'rmse',
'metric': 'rmse',
'boosting': 'gbdt',
'learning_rate': 0.005,
'drop_rate':0.01,
'skip_drop':0.6,
'uniform_drop':True,
'verbose': -1,
'num_leaves': 30,
'bagging_fraction': 0.9,
'bagging_freq': 1,
'bagging_seed': seed + n,
'feature_fraction': 0.8,
'feature_fraction_seed': seed + n,
'min_data_in_leaf': 10,
'max_bin': 100,
'max_depth':20,
'reg_alpha':1,
'lambda_l2': 10,
'num_threads':6
}
d_train = lgb.Dataset(X_ts,y_cs, weight=wts, free_raw_data=False)
 if not type(yt)is type(None):
d_cv = lgb.Dataset(xt,yt, free_raw_data=False, reference=d_train)
 model = lgb.train(params,d_train,num_boost_round=500,
valid_sets=d_cv,
verbose_eval=50)
 else :
model = lgb.train(params,d_train,num_boost_round=500)
 preds=model.predict(xtest)
 baggedpred+=preds
if not output_name is None:
joblib.dump(( model), output_name)

baggedpred/= estimators
return baggedpred
for layer in model.layers:
layer.trainable = False
for i in range(-3,0):
model.layers[i].trainable = True
model.compile(
 loss='categorical_crossentropy',
optimizer=Nadam(1e-3))
model.fit_generator(
 train_generator,
steps_per_epoch=np.ceil(float(len(train_y)) / float(128)) ,
epochs=30,
workers=WORKERS, use_multiprocessing=True,
verbose=1 )<train_model>
names=[]
for day in days_back_confimed:
names+=["days_ago_confirmed_count_" + str(day)]
for window in windows:
names+=["ma" + str(window)+ "_rate_confirmed" + str(k+1)for k in range(size)]
names+=["ma" + str(window)+ "_count_confirmed" + str(k+1)for k in range(size)]
for day in days_back_fatalities:
names+=["days_ago_fatalitiescount_" + str(day)]
for window in windows:
names+=["ma" + str(window)+ "_rate_fatalities" + str(k+1)for k in range(size)]
names+=["ma" + str(window)+ "_count_fatalities" + str(k+1)for k in range(size)]
names+=["confirmed_level"]
names+=["fatalities_level"]
if not extra_stable_columns is None and len(extra_stable_columns)>0:
names+=[k for k in extra_stable_columns]
if not group_names is None:
for gg in range(len(group_names)) :
for day in days_back_confimed_group:
names+=["days_ago_grooupcount_" + str(gg+1)+ "_" + str(day)]
for window in windows_group:
names+=["ma_group_" + str(gg+1)+ "_" + str(window)+ "_rate_" + str(k+1)for k in range(size_group)]
for layer in model.layers:
layer.trainable = True
callbacks_list = [csv_logger, reduceLROnPlat]
model.compile(optimizer=Nadam(1e-4),
loss='categorical_crossentropy',
metrics=['accuracy'])
 model.fit_generator(
 train_mixup,
steps_per_epoch=np.ceil(float(len(train_x)) / float(batch_size)) ,
validation_data=valid_generator,
validation_steps=np.ceil(float(len(valid_x)) / float(batch_size)) ,
epochs=epochs,
verbose=1,
workers=2, use_multiprocessing=True,
callbacks=callbacks_list )<load_pretrained>
tr_frame=train_frame
if not train_frame_supplamanteary2 is None:
tr_frame=train_frame_supplamanteary2
target_confirmed=["confirmed_count_plus" + str(k+1)for k in range(horizon)]
target_fatalities=["fatalities_count_plus" + str(k+1)for k in range(horizon)]
seed=1412
target_confirmed_train=tr_frame[target_confirmed].values
print(" original shape of train is {} ".format(target_confirmed_train.shape))
target_fatalities_train=tr_frame[target_fatalities].values
features_train=tr_frame[names].values
standard_confirmed_train=tr_frame["ConfirmedCases"].values
standard_fatalities_train=tr_frame["Fatalities"].values
current_confirmed_train=tr_frame["ConfirmedCases"].values
features_cv=[]
name_cv=[]
standard_confirmed_cv=[]
standard_fatalities_cv=[]
names_=tr_frame["key"].values
training_horizon=int(features_train.shape[0]/len(unique_keys))
print("training horizon = ",training_horizon)
 for dd in range(training_horizon-1,features_train.shape[0],training_horizon):
features_cv.append(features_train[dd])
 name_cv.append(names_[dd])
 standard_confirmed_cv.append(standard_confirmed_train[dd])
 standard_fatalities_cv.append(standard_fatalities_train[dd])
 print(name_cv[-1], standard_confirmed_cv[-1], standard_fatalities_cv[-1])
 current_confirmed_train=[k for k in range(len(current_confirmed_train)) if current_confirmed_train[k]>0]
target_confirmed_train=target_confirmed_train[current_confirmed_train]
target_fatalities_train=target_fatalities_train[current_confirmed_train]
features_train=features_train[current_confirmed_train]
standard_confirmed_train=standard_confirmed_train[current_confirmed_train]
standard_fatalities_train=standard_fatalities_train[current_confirmed_train]
features_cv=np.array(features_cv)
 preds_confirmed_cv=np.zeros(( features_cv.shape[0],horizon))
preds_confirmed_standard_cv=np.zeros(( features_cv.shape[0],horizon))
preds_fatalities_cv=np.zeros(( features_cv.shape[0],horizon))
preds_fatalities_standard_cv=np.zeros(( features_cv.shape[0],horizon))
overal_rmsle_metric_confirmed=0.0
for j in range(preds_confirmed_cv.shape[1]):
this_target=target_confirmed_train[:,j]
index_positive=[k for k in range(len(this_target)) if this_target[k]!=-1]
this_features=features_train[index_positive]
this_target=this_target[index_positive]
this_weight=np.log(standard_confirmed_train[index_positive]+3.)
this_weight=[1.for k in range(len(this_weight)) ]
this_features_cv=features_cv
preds=bagged_set_trainc(this_features,this_target,this_weight, seed, 1,features_cv, xt=None,yt=None, output_name=model_directory +"confirmedc"+ str(j))
preds_confirmed_cv[:,j]=preds
print(" modelling count confirmed, case %d, original train %d, and after %d, original cv %d and after %d "%(
 j,target_confirmed_train.shape[0],this_target.shape[0],this_features_cv.shape[0],this_features_cv.shape[0]))
predictions=[]
for ii in range(preds_confirmed_cv.shape[0]):
current_prediction=standard_confirmed_cv[ii]
for j in range(preds_confirmed_cv.shape[1]):
current_prediction+=np.expm1(max(0,preds_confirmed_cv[ii][j]))
preds_confirmed_standard_cv[ii][j]=current_prediction
for j in range(preds_confirmed_cv.shape[1]):
this_target=target_fatalities_train[:,j]
index_positive=[k for k in range(len(this_target)) if this_target[k]!=-1]
this_features=features_train[index_positive]
this_target=this_target[index_positive]
this_weight=np.log(standard_confirmed_train[index_positive]+2.)
this_weight=[1.for k in range(len(this_weight)) ]
this_features_cv=features_cv
preds=bagged_set_trainc(this_features,this_target,this_weight, seed, 1,features_cv, xt=None,yt=None, output_name=model_directory +"fatalc"+ str(j))
preds_fatalities_cv[:,j]=preds
print(" modelling count fatalities, case %d, original train %d, and after %d, original cv %d and after %d "%(
 j,target_confirmed_train.shape[0],this_target.shape[0],this_features_cv.shape[0],this_features_cv.shape[0]))
predictions=[]
for ii in range(preds_fatalities_cv.shape[0]):
current_prediction=standard_fatalities_cv[ii]
for j in range(preds_fatalities_cv.shape[1]):
current_prediction+=np.expm1(max(0,preds_fatalities_cv[ii][j]))
preds_fatalities_standard_cv[ii][j]=current_prediction
model.save_weights(".. /working/Densenet121.h5" )<load_from_csv>
def decay_4_first_10_then_1_f(array):
arr=[k for k in array]
for j in range(len(array)) :
if j<10:
arr[j]*=1./4.
 else :
arr[j]=0
return arr
	
def decay_16_first_10_then_1_f(array):
arr=[k for k in array]
for j in range(len(array)) :
if j<10:
arr[j]*=1./16.
 else :
arr[j]=0
return arr
def decay_2_f(array):
arr=[k for k in array]
for j in range(len(array)) :
arr[j]*=1./2.
 return arr
def decay_4_f(array):
arr=[k for k in array]
for j in range(len(array)) :
arr[j]*=1./4.
 return arr
	
def acceleratorx2_f(array):
arr=[k for k in array]
for j in range(len(array)) :
arr[j]*=2.
 return arr
def decay_1_5_f(array):
arr=[k for k in array]
for j in range(len(array)) :
arr[j]*=1./1.5
return arr
def decay_1_2_f(array):
arr=[k for k in array]
for j in range(len(array)) :
arr[j]*=1./1.2
return arr
def decay_1_1_f(array):
arr=[k for k in array]
for j in range(len(array)) :
arr[j]*=1./1.1
return arr
def stay_same_f(array):
arr=[0.0 for k in array]
return arr
def decay_2_last_12_linear_inter_f(array):
arr=[k for k in array]
for j in range(len(array)) :
arr[j]*=1/2.
 arr12= arr[-12]
for j in range(0, 12):
arr[len(arr)-12 +j]= arr12/(j+1.)
return arr
def decay_1_5_last_12_linear_inter_f(array):
arr=[k for k in array]
for j in range(len(array)) :
arr[j]*=1/(1.5)
 arr12= arr[-12]
for j in range(0, 12):
arr[len(arr)-12 +j]= arr12/(j+1.)
return arr
def decay_1_2_last_12_linear_inter_f(array):
arr=[k for k in array]
for j in range(len(array)) :
arr[j]*=1./(1.2)
 arr12= arr[-12]
for j in range(0, 12):
arr[len(arr)-12 +j]= arr12/(j+1.)
return arr
def decay_4_last_12_linear_inter_f(array):
arr=[k for k in array]
for j in range(len(array)) :
arr[j]*=1/4.
 arr12= arr[-12]
for j in range(0, 12):
arr[len(arr)-12 +j]= arr12/(j+1.)
return arr
def decay_8_last_12_linear_inter_f(array):
arr=[k for k in array]
for j in range(len(array)) :
arr[j]*=1/8.
 arr12= arr[-12]
for j in range(0, 12):
arr[len(arr)-12 +j]= arr12/(j+1.)
return arr
def linear_last_12_f(array):
arr=[k for k in array]
for j in range(len(array)) :
arr[j]=array[j]
arr12= arr[-12]
for j in range(0, 12):
arr[len(arr)-12 +j]= arr12/(j+1.)
return arr
decay_4_first_10_then_1 =[ "Jilin_China","Tianjin_China"]
decay_4_first_10_then_1_fatality=[]
decay_16_first_10_then_1 =["Beijing_China","Fujian_China","Jiangsu_China"]
decay_16_first_10_then_1_fatality=[]
decay_4=["nan_Central African Republic","Shanghai_China","nan_Equatorial Guinea","nan_Korea, South","nan_Zambia"]
decay_4_fatality=["Heilongjiang_China","Inner Mongolia_China"]
decay_2 =["nan_Burundi","Faroe Islands_Denmark","nan_Eritrea","nan_Gabon","nan_Italy","nan_Brunei""Heilongjiang_China","Inner Mongolia_China",
"Shanxi_China","nan_Congo(Kinshasa)","nan_Cyprus","Reunion_France","Sint Maarten_Netherlands","nan_Angola"]
decay_2_fatality=["nan_Iran"]
stay_same=["nan_Diamond Princess"]
stay_same_fatality=["Beijing_China","Fujian_China","Qinghai_China","Jiangsu_China","Jilin_China","Macau_China","Ningxia_China","Tianjin_China"
,"Faroe Islands_Denmark","Shanxi_China"]
normal=[]
normal_fatality=["nan_Andorra","New South Wales_Australia","nan_Korea, South","New York_US","nan_Austria","Alberta_Canada","Nova Scotia_Canada",
"Saskatchewan_Canada","nan_Costa Rica","nan_Croatia","nan_Finland","Martinique_France","Mayotte_France","nan_Hungary","nan_Iceland","nan_Latvia","nan_Czechia"
,"nan_Luxembourg","nan_Malta","nan_Montenegro","nan_New Zealand","nan_Norway","nan_South Africa","Hawaii_US","Ohio_US","Pennsylvania_US","nan_Uruguay","nan_Venezuela",
"nan_West Bank and Gaza"]
decay_4_last_12_linear_inter =[ "nan_Dominica","New Caledonia_France",
"Saint Barthelemy_France","nan_Gambia","nan_Grenada","nan_Holy See",
"nan_Mauritania","nan_Nicaragua","nan_Saint Lucia","nan_Saint Vincent and the Grenadines",
"nan_Seychelles","nan_Suriname","nan_Mongolia",
"Montserrat_United Kingdom","Turks and Caicos Islands_United Kingdom", "Hong Kong_China","Northern Territory_Australia","Northwest Territories_Canada","Yukon_Canada"
,"Guangdong_China","nan_MS Zaandam","nan_Maldives","nan_Saint Kitts and Nevis","nan_Sao Tome and Principe","nan_South Sudan","nan_Western Sahara"]
decay_4_last_12_linear_inter_fatality=["nan_Somalia"]
decay_2_last_12_linear_inter =[ "nan_Chad","French Polynesia_France","nan_Laos","nan_Nepal","nan_Sudan","nan_Tanzania","Bermuda_United Kingdom","Cayman Islands_United Kingdom"
,"nan_Cabo Verde","nan_Eswatini","nan_Guyana","nan_Liberia","nan_Malawi","nan_Mozambique","nan_Somalia","nan_Timor-Leste","Falkland Islands(Malvinas)_United Kingdom",
"nan_Zimbabwe"]
decay_2_last_12_linear_inter_fatality=[]
decay_1_5 =["nan_Cambodia","nan_Croatia","nan_Denmark",
"nan_Bahamas","nan_Bahrain","nan_Barbados" ,"Manitoba_Canada","New Brunswick_Canada",
"Saskatchewan_Canada","nan_France","nan_Libya","nan_Malta",
	"Aruba_Netherlands","nan_Niger","nan_Spain","Virgin Islands_US",
	"Channel Islands_United Kingdom","Gibraltar_United Kingdom","nan_United Kingdom",'nan_Burma',"nan_Congo(Brazzaville)",
	"French Guiana_France","Martinique_France","Mayotte_France","nan_Georgia","nan_Iran","nan_New Zealand","nan_Syria",
	"nan_West Bank and Gaza"]
decay_1_5_fatality=["nan_Cuba","nan_Morocco","Ohio_US","Pennsylvania_US","Puerto Rico_US" ,"nan_Uzbekistan","nan_Cyprus","nan_France","nan_Niger","South Dakota_US"]
acceleratorx2=[]
acceleratorx2_fatality=["Newfoundland and Labrador_Canada"]
decay_1_5_last_12_linear_inter =["nan_Antigua and Barbuda","nan_Botswana","nan_Haiti","nan_Madagascar"]
decay_1_5_last_12_linear_inter_fatality =[]
decay_1_2 =["nan_Albania","nan_Andorra","New South Wales_Australia","nan_Austria","nan_Azerbaijan","nan_Bangladesh","nan_Belize","Alberta_Canada","nan_Mauritius","nan_Monaco","nan_Montenegro",
"Newfoundland and Labrador_Canada","nan_Costa Rica","nan_Czechia","nan_Dominican Republic","nan_Estonia","Guadeloupe_France","nan_Iceland","nan_Latvia","nan_Liechtenstein","nan_Luxembourg","nan_Norway",
"nan_Poland","nan_Rwanda","nan_South Africa","nan_Trinidad and Tobago","Arizona_US","California_US","Colorado_US","Connecticut_US","Georgia_US","Hawaii_US","Maine_US","New York_US","Oklahoma_US",
"Oregon_US","Pennsylvania_US","Tennessee_US","Washington_US","nan_Uruguay","nan_Venezuela","nan_Vietnam"]
decay_1_2_fatality=["nan_Afghanistan","nan_Belarus","nan_Djibouti","nan_Mali","nan_Mexico","nan_Peru","nan_Serbia","Kentucky_US","Puerto Rico_US","Texas_US","nan_Ukraine"]
decay_1_1 =["nan_Algeria","nan_Argentina","Tasmania_Australia","nan_Bolivia","Nova Scotia_Canada","nan_Finland","nan_Hungary","nan_Iraq",
"nan_Jordan","nan_Kyrgyzstan","nan_Malaysia","Alabama_US","Iowa_US","Michigan_US","Missouri_US","Nebraska_US","North Dakota_US ","Ohio_US","South Carolina_US"]
decay_1_1_fatality=["nan_Afghanistan","nan_Ecuador","nan_Kenya","nan_Korea, South","nan_Kosovo","Indiana_US","New Mexico_US","Oregon_US","Rhode Island_US","Tennessee_US","nan_United Arab Emirates"]
decay_1_2_last_12_linear_inter =[]
decay_1_2_last_12_linear_inter_fatality=["nan_Angola" ]
linear_last_12=["nan_Afghanistan","nan_Ireland","nan_Benin"]
linear_last_12_fatality=["nan_Guatemala","nan_Ireland"]
decay_8_last_12_linear_inter =[ "nan_Bhutan","Prince Edward Island_Canada","Greenland_Denmark","nan_Fiji","Saint Pierre and Miquelon_France","St Martin_France","nan_Namibia",
"Bonaire, Sint Eustatius and Saba_Netherlands","Curacao_Netherlands","nan_Papua New Guinea","Anguilla_United Kingdom","British Virgin Islands_United Kingdom",]
decay_8_last_12_linear_inter_fatality=[]
tr_frame=train_frame
features_train=tr_frame[names].values
standard_confirmed_train=tr_frame["ConfirmedCases"].values
standard_fatalities_train=tr_frame["Fatalities"].values
current_confirmed_train=tr_frame["ConfirmedCases"].values
features_cv=[]
name_cv=[]
standard_confirmed_cv=[]
standard_fatalities_cv=[]
names_=tr_frame["key"].values
training_horizon=int(features_train.shape[0]/len(unique_keys))
print("training horizon = ",training_horizon)
 for dd in range(training_horizon-1,features_train.shape[0],training_horizon):
features_cv.append(features_train[dd])
 name_cv.append(names_[dd])
 standard_confirmed_cv.append(standard_confirmed_train[dd])
 standard_fatalities_cv.append(standard_fatalities_train[dd])
 print(name_cv[-1], standard_confirmed_cv[-1], standard_fatalities_cv[-1])
 features_cv=np.array(features_cv)
 preds_confirmed_cv=np.zeros(( features_cv.shape[0],horizon))
preds_confirmed_standard_cv=np.zeros(( features_cv.shape[0],horizon))
preds_fatalities_cv=np.zeros(( features_cv.shape[0],horizon))
preds_fatalities_standard_cv=np.zeros(( features_cv.shape[0],horizon))
overal_rmsle_metric_confirmed=0.0
for j in range(preds_confirmed_cv.shape[1]):
this_features_cv=features_cv
preds=predict(features_cv, input_name=model_directory +"confirmedc"+ str(j))
preds_confirmed_cv[:,j]=preds
print(" modelling confirmed, case %d, , original cv %d and after %d "%(j,this_features_cv.shape[0],this_features_cv.shape[0]))
predictions=[]
for ii in range(preds_confirmed_cv.shape[0]):
current_prediction=standard_confirmed_cv[ii]
if current_prediction==0 :
current_prediction=0.1
this_preds=preds_confirmed_cv[ii].tolist()
name=name_cv[ii]
reserve=this_preds[0]
	
if name in normal:
this_preds=this_preds	
	
elif name in decay_4_first_10_then_1:
this_preds=decay_4_first_10_then_1_f(this_preds)
 		
elif name in decay_16_first_10_then_1:
this_preds=decay_16_first_10_then_1_f(this_preds)		
		
elif name in decay_4_last_12_linear_inter:
this_preds=decay_4_last_12_linear_inter_f(this_preds)		
elif name in decay_8_last_12_linear_inter:
this_preds=decay_8_last_12_linear_inter_f(this_preds)		
elif name in decay_4:
this_preds=decay_4_f(this_preds)
 elif name in decay_2:
this_preds=decay_2_f(this_preds)
 elif name in decay_2_last_12_linear_inter:
this_preds=decay_2_last_12_linear_inter_f(this_preds)
 elif name in decay_1_5:
this_preds=decay_1_5_f(this_preds)
 elif name in decay_1_5_last_12_linear_inter:
this_preds=decay_1_5_last_12_linear_inter_f(this_preds)
 elif name in decay_1_2:
this_preds=decay_1_2_f(this_preds)
 elif name in decay_1_2_last_12_linear_inter:
this_preds=decay_1_2_last_12_linear_inter_f(this_preds)
 elif name in decay_1_1:
this_preds=decay_1_1_f(this_preds)
 elif name in linear_last_12:
this_preds=linear_last_12_f(this_preds)
 elif name in acceleratorx2:
this_preds=acceleratorx2_f(this_preds)
 elif name in stay_same or "China" in name:
this_preds=stay_same_f(this_preds)
 for j in range(preds_confirmed_cv.shape[1]):
current_prediction+=np.expm1(max(0,this_preds[j]))
preds_confirmed_standard_cv[ii][j]=current_prediction
for j in range(preds_confirmed_cv.shape[1]):
this_features_cv=features_cv
preds=predict(features_cv, input_name=model_directory +"fatalc"+ str(j))
preds_fatalities_cv[:,j]=preds
print(" modelling fatalities, case %d, original cv %d and after %d "%(j,this_features_cv.shape[0],this_features_cv.shape[0]))
predictions=[]
for ii in range(preds_fatalities_cv.shape[0]):
current_prediction=standard_fatalities_cv[ii]
this_preds=preds_fatalities_cv[ii].tolist()
name=name_cv[ii]
reserve=this_preds[0]
	
if name in normal_fatality:
this_preds=this_preds	 	
	
elif name in decay_4_first_10_then_1_fatality:
this_preds=decay_4_first_10_then_1_f(this_preds)
 		
elif name in decay_16_first_10_then_1_fatality:
this_preds=decay_16_first_10_then_1_f(this_preds)
 elif name in decay_4_last_12_linear_inter_fatality:
this_preds=decay_4_last_12_linear_inter_f(this_preds)		
elif name in decay_8_last_12_linear_inter_fatality:
this_preds=decay_8_last_12_linear_inter_f(this_preds)		
elif name in decay_4_fatality:
this_preds=decay_4_f(this_preds)		
		
elif name in decay_2_fatality:
this_preds=decay_2_f(this_preds)
 elif name in decay_2_last_12_linear_inter_fatality:
this_preds=decay_2_last_12_linear_inter_f(this_preds)
 elif name in decay_1_5_fatality:
this_preds=decay_1_5_f(this_preds)	
elif name in decay_1_5_last_12_linear_inter_fatality:
this_preds=decay_1_5_last_12_linear_inter_f(this_preds)
 elif name in decay_1_2_fatality:
this_preds=decay_1_2_f(this_preds)
 elif name in decay_1_2_last_12_linear_inter_fatality:
this_preds=decay_1_2_last_12_linear_inter_f(this_preds)
 elif name in decay_1_1_fatality:
this_preds=decay_1_1_f(this_preds)
 elif name in linear_last_12_fatality:
this_preds=linear_last_12_f(this_preds)
 elif name in acceleratorx2_fatality:
this_preds=acceleratorx2_f(this_preds)
 elif name in stay_same_fatality:
this_preds=stay_same_f(this_preds)
 elif name in normal:
this_preds=this_preds	
	
elif name in decay_4_first_10_then_1:
this_preds=decay_4_first_10_then_1_f(this_preds)
 		
elif name in decay_16_first_10_then_1:
this_preds=decay_16_first_10_then_1_f(this_preds)		
elif name in decay_4_last_12_linear_inter:
this_preds=decay_4_last_12_linear_inter_f(this_preds)		
elif name in decay_8_last_12_linear_inter:
this_preds=decay_8_last_12_linear_inter_f(this_preds)		
elif name in decay_4:
this_preds=decay_4_f(this_preds)
 		
elif name in decay_2:
this_preds=decay_2_f(this_preds)
 elif name in decay_2_last_12_linear_inter:
this_preds=decay_2_last_12_linear_inter_f(this_preds)
 elif name in decay_1_5:
this_preds=decay_1_5_f(this_preds)
 elif name in decay_1_5_last_12_linear_inter:
this_preds=decay_1_5_last_12_linear_inter_f(this_preds)
 elif name in decay_1_2:
this_preds=decay_1_2_f(this_preds)
 elif name in decay_1_2_last_12_linear_inter:
this_preds=decay_1_2_last_12_linear_inter_f(this_preds)
 elif name in decay_1_1:
this_preds=decay_1_1_f(this_preds)
 elif name in linear_last_12:
this_preds=linear_last_12_f(this_preds)
 elif name in acceleratorx2:
this_preds=acceleratorx2_f(this_preds)
 elif name in stay_same or "China" in name:
this_preds=stay_same_f(this_preds)
 for j in range(preds_fatalities_cv.shape[1]):
current_prediction+=np.expm1(max(0,this_preds[j]))
preds_fatalities_standard_cv[ii][j]=current_prediction
submit = pd.read_csv('.. /input/recursion-cellular-image-classification/sample_submission.csv')
 model.load_weights('.. /working/Densenet121.h5')
 predicted = []<predict_on_test>
key_to_confirmed_rate={}
key_to_fatality_rate={}
key_to_confirmed={}
key_to_fatality={}
print(len(features_cv), len(name_cv),len(standard_confirmed_cv),len(standard_fatalities_cv))
print(preds_confirmed_cv.shape,preds_confirmed_standard_cv.shape,preds_fatalities_cv.shape,preds_fatalities_standard_cv.shape)
 for j in range(len(name_cv)) :
key_to_confirmed_rate[name_cv[j]]=preds_confirmed_cv[j,:].tolist()
key_to_fatality_rate[name_cv[j]]=preds_fatalities_cv[j,:].tolist()
key_to_confirmed[name_cv[j]] =preds_confirmed_standard_cv[j,:].tolist()
key_to_fatality[name_cv[j]]=preds_fatalities_standard_cv[j,:].tolist()
for i, name in tqdm(enumerate(submit['id_code'])) :
path = os.path.join('.. /input/recursion-complete/test/test/', name+'_s1.jpeg')
 image = cv2.imread(path)
 score_predict = model.predict(( image[np.newaxis])/255)
 label_predict = np.argmax(score_predict)
 predicted.append(str(label_predict)) 
<save_to_csv>
train_new=train[["Date","ConfirmedCases","Fatalities","key","rate_ConfirmedCases","rate_Fatalities"]]
test_new_count=pd.merge(test,train_new, how="left", left_on=["key","Date"], right_on=["key","Date"] ).reset_index(drop=True)
 test_new_count
submit['sirna'] = predicted
submit.to_csv('submission.csv', index=False)
 submit.head() 
<install_modules>
def fillin_columns(frame,key_column, original_name, training_horizon, test_horizon, unique_values, key_to_values):
keys=frame[key_column].values
original_values=frame[original_name].values.tolist()
print(len(keys), len(original_values), training_horizon ,test_horizon,len(key_to_values))
for j in range(unique_values):
current_index=(j *(training_horizon +test_horizon)) +training_horizon
current_key=keys[current_index]
values=key_to_values[current_key]
co=0
for g in range(current_index, current_index + test_horizon):
original_values[g]=values[co]
co+=1
frame[original_name]=original_values
all_days=int(test_new.shape[0]/len(unique_keys))
tr_horizon=all_days-horizon
print(all_days,tr_horizon, horizon)
 fillin_columns(test_new_count,"key", 'ConfirmedCases', tr_horizon, horizon, len(unique_keys), key_to_confirmed)
 fillin_columns(test_new_count,"key", 'Fatalities', tr_horizon, horizon, len(unique_keys), key_to_fatality)
 fillin_columns(test_new_count,"key", 'rate_ConfirmedCases', tr_horizon, horizon, len(unique_keys), key_to_confirmed_rate)
 fillin_columns(test_new_count,"key", 'rate_Fatalities', tr_horizon, horizon, len(unique_keys), key_to_fatality_rate)
 test_dat=test_new['Fatalities'].values.tolist()
test_new_countfat=test_new_count['Fatalities'].values.tolist()
for j in range(len(test_dat)) :
if test_dat[j]<1:
test_dat[j]=test_new_countfat[j]
test_new2= test_new.copy()
test_new2['Fatalities']=test_dat
test_new2
!pip install efficientnet 
<define_variables>
submission=test_new2[["ForecastId","ConfirmedCases","Fatalities"]]
submission.to_csv("submission.csv", index=False)