Kaggle competition: COVID19 Global Forecasting (Week 4)
<SOS> metric: MCRMSLE Kaggle data source: covid19-global-forecasting-(week-4)<data_type_conversions>
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)- 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 previous_value!=0.0:
rate[i]=current_value/previous_value
rate[i] =max(1,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="/kaggle/input/modelv2-dir/model"
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('/kaggle/input/datatestadd2/test_new.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")
 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"])
 train.to_csv(directory + "train_plus_geo.csv", index=False)
 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))
print("max_train_date",(max_train_date))
print("max_test_date",(max_test_date))
target1="ConfirmedCases"
target2="Fatalities"
key="key"
df['revenue'] = df['sold']*df['sell_price'].astype(np.float32 )<feature_engineering>
fix_target(train, key, target1, new_target_name=target1)
 fix_target(train, key, target2, new_target_name=target2)
 rate(train, key, target1, new_target_name="rate_" +target1)
 rate(train, key, target2, new_target_name="rate_" +target2)
 unique_keys=train[key].unique()
print(len(unique_keys))
train
cal_data = group.copy()
cal_data = cal_data[cal_data.date <= '22-05-2016']
cal_data['week'] = cal_data.date.dt.weekofyear
cal_data['day_name'] = cal_data.date.dt.day_name()<define_variables>
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 dereive_features(frame, confirmed, fatalities, rate_confirmed, rate_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=["lag_confirmed_rate" + str(k+1)for k in range(size)]
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+=["std" + str(window)+ "_rate_confirmed" + str(k+1)for k in range(size)]
names+=["ewma" + str(window)+ "_rate_confirmed" + str(k+1)for k in range(size)]
names+=["lag_fatalities_rate" + 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+=["std" + str(window)+ "_rate_fatalities" + str(k+1)for k in range(size)]
names+=["ewma" + str(window)+ "_rate_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]):
names+=["lag_rate_group_"+ str(gg+1)+ "_" + str(k+1)for k in range(size_group)]
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+=["std_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)]
features=[]
for i in range(len(confirmed)) :
row_features=[]
lag_confirmed_rate=get_lags(rate_confirmed, i, size=size)
 row_features+=lag_confirmed_rate
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
row_features+= std3_rate_confirmed
ewma3_rate_confirmed=generate_ewma_window(rate_confirmed, i, size=size, window=window, alpha=0.05)
 row_features+= ewma3_rate_confirmed
lag_fatalities_rate=get_lags(rate_fatalities, i, size=size)
 row_features+=lag_fatalities_rate
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
row_features+= std3_rate_fatalities
ewma3_rate_fatalities=generate_ewma_window(rate_fatalities, i, size=size, window=window, alpha=0.05)
 row_features+= ewma3_rate_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)
 row_features+=lag_group_rate
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
row_features+= std3_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
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, 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
d_id = dict(zip(df.id.cat.codes, df.id))
d_item_id = dict(zip(df.item_id.cat.codes, df.item_id))
d_dept_id = dict(zip(df.dept_id.cat.codes, df.dept_id))
d_cat_id = dict(zip(df.cat_id.cat.codes, df.cat_id))
d_store_id = dict(zip(df.store_id.cat.codes, df.store_id))
d_state_id = dict(zip(df.state_id.cat.codes, df.state_id))<data_type_conversions>
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
del group, group_price_cat, group_price_store, group_state, group_state_store, cal_data
gc.collect() ;
df.d = df['d'].apply(lambda x: x.split('_')[1] ).astype(np.int16)
 cols = df.dtypes.index.tolist()
types = df.dtypes.values.tolist()
for i,type in enumerate(types):
if type.name == 'category':
df[cols[i]] = df[cols[i]].cat.codes
df.drop('date',axis=1,inplace=True )<categorify>
names=["lag_confirmed_rate" + str(k+1)for k in range(size)]
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+=["std" + str(window)+ "_rate_confirmed" + str(k+1)for k in range(size)]
names+=["ewma" + str(window)+ "_rate_confirmed" + str(k+1)for k in range(size)]
names+=["lag_fatalities_rate" + 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+=["std" + str(window)+ "_rate_fatalities" + str(k+1)for k in range(size)]
names+=["ewma" + 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)) :
names+=["lag_rate_group_"+ str(gg+1)+ "_" + str(k+1)for k in range(size_group)]
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+=["std_group_" + str(gg+1)+ "_" + str(window)+ "_rate_" + str(k+1)for k in range(size_group)]
lags = [1,2,3,6,12,24,36]
for lag in lags:
df['sold_lag_'+str(lag)] = df.groupby(['id', 'item_id', 'dept_id', 'cat_id', 'store_id', 'state_id'],as_index=False)['sold'].shift(lag ).astype(np.float16 )<data_type_conversions>
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 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_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 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 =[ "Heilongjiang_China","Liaoning_China","Shanghai_China"]
decay_4_first_10_then_1_fatality=[]
decay_16_first_10_then_1 =["Beijing_China","Fujian_China","Guangdong_China","Shandong_China","Sichuan_China","Zhejiang_China"]
decay_16_first_10_then_1_fatality=[]
decay_4=["nan_Bhutan","nan_Burundi","nan_Cabo Verde","Prince Edward Island_Canada",
"nan_Central African Republic","Inner Mongolia_China","nan_Maldives"]
decay_4_fatality=["nan_Congo(Kinshasa)"]
decay_2 =["nan_Congo(Kinshasa)","Faroe Islands_Denmark","nan_Eritrea","French Guiana_France","nan_Korea, South","nan_MS Zaandam"]
decay_2_fatality=[]
stay_same=["nan_Diamond Princess","nan_Timor-Leste"]
		
stay_same_fatality=["Beijing_China","Fujian_China","Guangdong_China","Shandong_China",
"Sichuan_China","Zhejiang_China", "Heilongjiang_China","Liaoning_China","Shanghai_China"]
normal=[]
normal_fatality=["nan_Korea, South","New York_US"]
decay_4_last_12_linear_inter =[ "Greenland_Denmark","nan_Dominica","nan_Equatorial Guinea","nan_Eswatini","New Caledonia_France",
"Saint Barthelemy_France","St Martin_France","nan_Gambia","nan_Grenada","nan_Holy See","nan_Mauritania","nan_Namibia","nan_Nicaragua"
,"nan_Papua New Guinea","nan_Saint Lucia","nan_Saint Vincent and the Grenadines","nan_Seychelles","nan_Sierra Leone","nan_Somalia","nan_Suriname",
"Anguilla_United Kingdom","British Virgin Islands_United Kingdom","Montserrat_United Kingdom","Turks and Caicos Islands_United Kingdom","nan_Zimbabwe"
, "Hong Kong_China","Curacao_Netherlands"]
decay_4_last_12_linear_inter_fatality=[]
decay_2_last_12_linear_inter =[ "nan_Chad",
"nan_Congo(Brazzaville)","nan_Fiji","French Polynesia_France","nan_Gabon",
"nan_Guyana","nan_Laos","nan_Nepal","Sint Maarten_Netherlands",
"nan_Saint Kitts and Nevis","nan_Sudan","nan_Syria","nan_Tanzania",
"Bermuda_United Kingdom","Cayman Islands_United Kingdom","nan_Zambia","Northwest Territories_Canada","Yukon_Canada"
,"nan_Mongolia","nan_Uganda"]
decay_2_last_12_linear_inter_fatality=[]
acceleratorx2=[]
acceleratorx2_fatality=[]
warm_st=['nan_Angola','nan_Antigua and Barbuda','Northern Territory_Australia','nan_Bahamas',
'nan_Bangladesh','nan_Belize','nan_Benin','nan_Botswana','nan_Burundi','nan_Cabo Verde','nan_Cameroon',
'nan_Central African Republic','nan_Chad','Hong Kong_China',"nan_Cote d'Ivoire",'nan_Cuba','Greenland_Denmark',
'nan_Dominica','nan_Equatorial Guinea','nan_Eritrea','nan_Eswatini','nan_Fiji','French Polynesia_France','New Caledonia_France',
'Saint Barthelemy_France','St Martin_France','nan_Gabon','nan_Gambia','nan_Grenada','nan_Guyana','nan_Haiti','nan_Holy See',
'nan_Honduras','nan_Ireland','nan_Korea, South','nan_Laos','nan_Liberia','nan_Libya','nan_Maldives','nan_Mali',
'nan_Mauritania','nan_Mauritius','nan_Mongolia','nan_Mozambique','nan_Namibia','nan_Nepal','Aruba_Netherlands',
'nan_Nicaragua','nan_Niger','nan_Papua New Guinea','nan_Saint Kitts and Nevis','nan_Saint Lucia',
'nan_Saint Vincent and the Grenadines','nan_Seychelles','nan_Sierra Leone','nan_Somalia',
'nan_Spain','nan_Sudan','nan_Suriname','nan_Syria','nan_Tanzania','nan_Togo','nan_Uganda','Anguilla_United Kingdom',
'Bermuda_United Kingdom','British Virgin Islands_United Kingdom','Channel Islands_United Kingdom',
'Gibraltar_United Kingdom','Isle of Man_United Kingdom','Montserrat_United Kingdom','nan_United Kingdom',
'Turks and Caicos Islands_United Kingdom','nan_Uzbekistan','nan_Zimbabwe',
]
decay_1_5 =["nan_Angola" ,"nan_Antigua and Barbuda","Montana_US","Nebraska_US","nan_Bangladesh","Illinois_US"
,"Northern Territory_Australia","nan_Bahamas","nan_Bahrain","nan_Barbados" ,"nan_Belize","nan_Benin",
	"nan_Botswana","nan_Brunei","Manitoba_Canada","New Brunswick_Canada","Saskatchewan_Canada",
	"nan_Cote d'Ivoire","nan_France","nan_Guinea-Bissau","nan_Haiti","nan_Italy","nan_Libya","nan_Malta","nan_Mauritius",
	"Aruba_Netherlands","nan_Niger","nan_Spain","nan_Togo","Guam_US","Iowa_US","Idaho_US","Connecticut_US","California_US","New York_US","Virgin Islands_US",
	"Channel Islands_United Kingdom","Gibraltar_United Kingdom","Isle of Man_United Kingdom","nan_United Kingdom",'nan_Burma']
decay_1_5_fatality=["nan_Cameroon","nan_Mali","nan_Cuba","Delaware_US","District of Columbia_US",
"Kansas_US","Louisiana_US","Michigan_US","New Mexico_US","Ohio_US","Oklahoma_US","Pennsylvania_US","Puerto Rico_US","Rhode Island_US",
"South Dakota_US" ,"Tennessee_US","Texas_US","Vermont_US","Virginia_US","West Virginia_US","nan_Uzbekistan"]
linear_last_12=["nan_Honduras","nan_Ireland","Colorado_US","nan_Liberia","nan_Mozambique"]
linear_last_12_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%30))
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_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 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)
 if name in warm_st:
this_preds[0]=reserve
for j in range(preds_confirmed_cv.shape[1]):
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%30))
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]
if current_prediction==0 and standard_confirmed_cv[ii]>400:
current_prediction=0.1
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_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 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_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 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)
 if name in warm_st:
this_preds[0]=reserve
for j in range(preds_fatalities_cv.shape[1]):
if current_prediction==0 and(preds_confirmed_standard_cv[ii][j]>400 or "Malta" in name or "Somalia" in name):
current_prediction=1.
 if j==0 and "nan_Antigua and Barbuda" in name:
current_prediction=2.
 if j==0 and 'nan_Burma' in name:
current_prediction=3.
 current_prediction*=max(1,this_preds[j])
 preds_fatalities_standard_cv[ii][j]=current_prediction
df['iteam_sold_avg'] = df.groupby('item_id')['sold'].transform('mean' ).astype(np.float16)
 df['state_sold_avg'] = df.groupby('state_id')['sold'].transform('mean' ).astype(np.float16)
 df['store_sold_avg'] = df.groupby('store_id')['sold'].transform('mean' ).astype(np.float16)
 df['cat_sold_avg'] = df.groupby('cat_id')['sold'].transform('mean' ).astype(np.float16)
 df['dept_sold_avg'] = df.groupby('dept_id')['sold'].transform('mean' ).astype(np.float16)
 df['cat_dept_sold_avg'] = df.groupby(['cat_id','dept_id'])['sold'].transform('mean' ).astype(np.float16)
 df['store_item_sold_avg'] = df.groupby(['store_id','item_id'])['sold'].transform('mean' ).astype(np.float16)
 df['cat_item_sold_avg'] = df.groupby(['cat_id','item_id'])['sold'].transform('mean' ).astype(np.float16)
 df['dept_item_sold_avg'] = df.groupby(['dept_id','item_id'])['sold'].transform('mean' ).astype(np.float16)
 df['state_store_sold_avg'] = df.groupby(['state_id','store_id'])['sold'].transform('mean' ).astype(np.float16)
 df['state_store_cat_sold_avg'] = df.groupby(['state_id','store_id','cat_id'])['sold'].transform('mean' ).astype(np.float16)
 df['store_cat_dept_sold_avg'] = df.groupby(['store_id','cat_id','dept_id'])['sold'].transform('mean' ).astype(np.float16 )<data_type_conversions>
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()
df['rolling_sold_mean'] = df.groupby(['id', 'item_id', 'dept_id', 'cat_id', 'store_id', 'state_id'])['sold'].transform(lambda x: x.rolling(window=7 ).mean() ).astype(np.float16 )<data_type_conversions>
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
df['expanding_sold_mean'] = df.groupby(['id', 'item_id', 'dept_id', 'cat_id', 'store_id', 'state_id'])['sold'].transform(lambda x: x.expanding(2 ).mean() ).astype(np.float16 )<data_type_conversions>
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)
 submission=test_new[["ForecastId","ConfirmedCases","Fatalities"]]
submission
df['daily_avg_sold'] = df.groupby(['id', 'item_id', 'dept_id', 'cat_id', 'store_id', 'state_id','d'])['sold'].transform('mean' ).astype(np.float16)
 df['avg_sold'] = df.groupby(['id', 'item_id', 'dept_id', 'cat_id', 'store_id', 'state_id'])['sold'].transform('mean' ).astype(np.float16)
 df['selling_trend'] =(df['daily_avg_sold'] - df['avg_sold'] ).astype(np.float16)
 df.drop(['daily_avg_sold','avg_sold'],axis=1,inplace=True )<feature_engineering>
train[train['Country_Region']=='Iran']
df = df[df['d']>=36]<load_pretrained>
test[test['Country_Region']=='Iran']
df.to_pickle('data.pkl')
 del df
gc.collect() ;<categorify>
df = pd.concat([test, submission], axis=0)
 df.tail()
data = pd.read_pickle('data.pkl')
 valid = data[(data['d']>=1914)&(data['d']<1942)][['id','d','sold']]
test = data[data['d']>=1942][['id','d','sold']]
eval_preds = test['sold']
valid_preds = valid['sold']<prepare_x_and_y>
df[df.Country_Region == 'Russia']
stores = sales.store_id.cat.codes.unique().tolist()
for store in stores:
df = data[data['store_id']==store]
X_train, y_train = df[df['d']<1914].drop('sold',axis=1), df[df['d']<1914]['sold']
X_valid, y_valid = df[(df['d']>=1914)&(df['d']<1942)].drop('sold',axis=1), df[(df['d']>=1914)&(df['d']<1942)]['sold']
X_test = df[df['d']>=1942].drop('sold',axis=1)
 model = LGBMRegressor(
 n_estimators=1000,
learning_rate=0.3,
subsample=0.8,
colsample_bytree=0.8,
max_depth=8,
num_leaves=50,
min_child_weight=300 
)
 print('*****Prediction for Store: {}*****'.format(d_store_id[store]))
model.fit(X_train, y_train, eval_set=[(X_train,y_train),(X_valid,y_valid)],
eval_metric='rmse', verbose=20, early_stopping_rounds=20)
 valid_preds[X_valid.index] = model.predict(X_valid)
 eval_preds[X_test.index] = model.predict(X_test)
 filename = 'model'+str(d_store_id[store])+'.pkl'
joblib.dump(model, filename)
 del model, X_train, y_train, X_valid, y_valid
gc.collect()<create_dataframe>
submission.to_csv("submission.csv", index=False )