Kaggle competition: COVID19 Global Forecasting (Week 4)
print('START Fit')
 print(datetime.now() , 'StackingCVRegressor')
 stack_gen_model = stack_gen.fit(np.array(X), np.array(y))
print(datetime.now() , 'elasticnet')
 elastic_model_full_data = elasticnet.fit(X, y)
 print(datetime.now() , 'lasso')
 lasso_model_full_data = lasso.fit(X, y)
 print(datetime.now() , 'ridge')
 ridge_model_full_data = ridge.fit(X, y)
 print(datetime.now() , 'svr')
 svr_model_full_data = svr.fit(X, y)
 print(datetime.now() , 'GradientBoosting')
 gbr_model_full_data = gbr.fit(X, y)
 print(datetime.now() , 'xgboost')
 xgb_model_full_data = xgboost.fit(X, y)
 print(datetime.now() , 'lightgbm')
 lgb_model_full_data = lightgbm.fit(X, y )<predict_on_test>
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="/kaggle/input/model-dirv5/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 blend_models_predict(X):
return(( 0.1 * elastic_model_full_data.predict(X)) + \ 
(0.1 * lasso_model_full_data.predict(X)) + \ 
(0.1 * ridge_model_full_data.predict(X)) + \ 
(0.1 * svr_model_full_data.predict(X)) + \ 
(0.1 * gbr_model_full_data.predict(X)) + \ 
(0.15 * xgb_model_full_data.predict(X)) + \ 
(0.1 * lgb_model_full_data.predict(X)) + \ 
(0.25 * stack_gen_model.predict(np.array(X))))
print('RMSLE score on train data:')
 print(rmsle(y, blend_models_predict(X)) )<feature_engineering>
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
submission.iloc[:,1] = np.floor(np.expm1(blend_models_predict(X_sub)) )<feature_engineering>
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()
q1 = submission['SalePrice'].quantile(0.0045)
 q2 = submission['SalePrice'].quantile(0.99)
 submission['SalePrice'] = submission['SalePrice'].apply(lambda x: x if x > q1 else x*0.77)
 submission['SalePrice'] = submission['SalePrice'].apply(lambda x: x if x < q2 else x*1.1 )<save_to_csv>
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
submission.to_csv("new_submission.csv", index=False)
 print("Submission was successfully saved!" )<compute_test_metric>
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
def root_mean_squared_log_error(y_valid, y_preds):

 if len(y_preds)!=len(y_valid): return 'error_mismatch'
y_preds_new = [math.log(x)for x in y_preds]
y_valid_new = [math.log(x)for x in y_valid]
return mean_squared_error(y_valid_new, y_preds_new, squared=False )<load_from_csv>
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)]
house_data = pd.read_csv('.. /input/house-prices-advanced-regression-techniques/train.csv')
 pd.set_option('display.max_columns', None)
 house_data.head()<data_type_conversions>
print(house_data.columns[house_data.isna().any() ].tolist())
 len(house_data.columns[house_data.isna().any() ].tolist() )<prepare_x_and_y>
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
features = [x for x in house_data.columns if x not in ['SalePrice']]
X = house_data[features]
y = house_data['SalePrice']<categorify>
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()
<train_on_grid>
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
<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 )
<data_type_conversions>
X.columns.to_list()<import_modules>
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
from lightgbm import LGBMRegressor<feature_engineering>
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)]
X_feat_eng = X.copy()
X_feat_eng['years_since_update'] = X_feat_eng['YearRemodAdd'] - X_feat_eng['YearBuilt']
X_feat_eng['geometry'] = X_feat_eng['LotArea'] / X_feat_eng['LotFrontage']
X_feat_eng['land_topology'] = X_feat_eng['LandSlope'] + '_' + X_feat_eng['LandContour']
X_feat_eng['value_proposition'] = X_feat_eng['YearBuilt'] * X_feat_eng['OverallQual']
X_feat_eng['finished_basement'] = X_feat_eng['BsmtFinSF1'] > 0
X_feat_eng['garage_value'] = X_feat_eng['YearBuilt'] * X_feat_eng['GarageCars']
X_feat_eng['misc_value'] = X_feat_eng['Fireplaces'] + X_feat_eng['OverallQual']
X_feat_eng = X_feat_eng.drop(columns=['GarageCars'])
 feature_numerical_cols = [cname for cname in X_feat_eng.columns if
X_feat_eng[cname].dtype in ['int64', 'float64']]
feature_categorical_cols = [cname for cname in X_feat_eng.columns if X_feat_eng[cname].nunique() < 100
and X_feat_eng[cname].dtype in ['object', 'bool']]
feature_numerical_transformer = SimpleImputer(strategy='constant')
 feature_categorical_transformer = Pipeline(steps=[ 
('imputer', SimpleImputer(strategy='most_frequent')) , 
('onehot', OneHotEncoder(handle_unknown='ignore'))
])
 feature_preprocessor = ColumnTransformer(
 transformers=[ 
('num', feature_numerical_transformer, feature_numerical_cols), 
('cat', feature_categorical_transformer, feature_categorical_cols)
 ])
 feature_model = XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,
colsample_bynode=1, colsample_bytree=0.6, gamma=0.0, gpu_id=-1,
importance_type='gain', interaction_constraints='',
learning_rate=0.02, max_delta_step=0, max_depth=4,
min_child_weight=0.0, monotone_constraints='() ',
n_estimators=1250, n_jobs=0, num_parallel_tree=1, random_state=0,
reg_alpha=0, reg_lambda=1, scale_pos_weight=1, subsample=0.8,
tree_method='exact', validate_parameters=1, verbosity=None)
 feature_clf = Pipeline(steps=[('feature_preprocessor', feature_preprocessor), 
('feature_model', feature_model)
 ])
 feature_X_train, feature_X_valid, feature_y_train, feature_y_valid = train_test_split(X_feat_eng, y, random_state=0)
 feature_clf.fit(feature_X_train, feature_y_train, feature_model__verbose=False)
 feature_preds = feature_clf.predict(feature_X_valid)
 print('RMSLE:', root_mean_squared_log_error(feature_y_valid, feature_preds)) 
<load_from_csv>
X_test = pd.read_csv('.. /input/house-prices-advanced-regression-techniques/test.csv' )<train_model>
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
X_test['years_since_update'] = X_test['YearRemodAdd'] - X_test['YearBuilt']
X_test['geometry'] = X_test['LotArea'] / X_test['LotFrontage']
X_test['land_topology'] = X_test['LandSlope'] + '_' + X_test['LandContour']
X_test['value_proposition'] = X_test['YearBuilt'] * X_test['OverallQual']
X_test['finished_basement'] = X_test['BsmtFinSF1'] > 0
X_test['garage_value'] = X_test['YearBuilt'] * X_test['GarageCars']
X_test['misc_value'] = X_test['Fireplaces'] + X_test['OverallQual']
feature_clf.fit(X_feat_eng, y, feature_model__verbose=False )<save_to_csv>
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()
preds = feature_clf.predict(X_test)
 output = pd.DataFrame({'Id': X_test.Id,
'SalePrice': preds})
 output.to_csv('submission.csv', index=False )<load_from_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
df_train = pd.read_csv(".. /input/house-prices-advanced-regression-techniques/train.csv")
 df_test = pd.read_csv(".. /input/house-prices-advanced-regression-techniques/test.csv")
<count_values>
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
df_train["YrSold"].value_counts()<drop_column>
submission=test_new2[["ForecastId","ConfirmedCases","Fatalities"]]
submission.to_csv("submission.csv", index=False)