Kaggle competition: Costa Rican Household Poverty Level Prediction
<SOS> metric: MacroFScore Kaggle data source: costa-rican-household-poverty-level-prediction<choose_model_class>
%matplotlib inline
warnings.filterwarnings("ignore" )
if cfg.strategy == "KFOLD":
oof_preds_all = []
oof_targets_all = []
scores_all = []
scores_auc_all= []
preds_test = []
idx_all = []
for seed in range(cfg.num_ensembling):
print("
mskf = MultilabelStratifiedKFold(n_splits=cfg.SPLITS, random_state=cfg.seed+seed, shuffle=True)
 oof_preds = []
oof_targets = []
scores = []
scores_auc = []
p = []
for j,(train_idx, val_idx)in enumerate(mskf.split(np.zeros(len(cat_tr)) , targets_tr)) :
print("FOLDS : ", j)
 idx_all.append(val_idx)
 X_train, y_train = torch.as_tensor(np.concatenate([cat_tr[train_idx], numerical_tr[train_idx] ], axis=1)) , torch.as_tensor(targets_tr[train_idx])
 X_val, y_val = torch.as_tensor(np.concatenate([cat_tr[val_idx], numerical_tr[val_idx] ], axis=1)) , torch.as_tensor(targets_tr[val_idx])
 model = TabNetRegressor(n_d=24, n_a=64, n_steps=1, gamma=1.3, lambda_sparse=0, cat_dims=cfg.cat_dims, cat_emb_dim=cfg.cat_emb_dim, cat_idxs=cfg.cats_idx, optimizer_fn=torch.optim.Adam,
optimizer_params=dict(lr=2e-2), mask_type='entmax', device_name=cfg.device, scheduler_params=dict(milestones=[ 50,100,150], gamma=0.9), scheduler_fn=torch.optim.lr_scheduler.MultiStepLR)
 name = cfg.save_name + f"_fold{j}_{seed}"
model.load_model(name)
 preds = model.predict(X_val)
 preds = torch.sigmoid(torch.as_tensor(preds)).detach().cpu().numpy()
score = log_loss_multi(y_val, preds)
 temp = model.predict(X_test)
 p.append(torch.sigmoid(torch.as_tensor(temp)).detach().cpu().numpy())
 oof_preds.append(preds)
 oof_targets.append(y_val)
 scores.append(score)
 scores_auc.append(auc_multi(y_val,preds))
print(f"validation fold {j} : {score}")
 p = np.stack(p)
 preds_test.append(p)
 oof_preds_all.append(np.concatenate(oof_preds))
oof_targets_all.append(np.concatenate(oof_targets))
scores_all.append(np.array(scores))
scores_auc_all.append(np.array(scores_auc))
preds_test = np.stack(preds_test )<create_dataframe>
def encode_data(df):

 df['idhogar'] = LabelEncoder().fit_transform(df['idhogar'])
 def feature_importance(forest, X_train):
ranked_list = []
importances = forest.feature_importances_
indices = np.argsort(importances)[::-1]
print("Feature ranking:")
 for f in range(X_train.shape[1]):
print("%d.feature %d(%f)" %(f + 1, indices[f], importances[indices[f]])+ " - " + X_train.columns[indices[f]])
 ranked_list.append(X_train.columns[indices[f]])
 return ranked_list
idx_list = []
for idx_ in idx_all:
for ix in idx_:
idx_list.append(ix)
 oof_preds_df = pd.DataFrame(oof_preds_all[0])
 oof_preds_df['idx'] = idx_list<load_from_csv>
def do_features(df):
feats_div = [('children_fraction', 'r4t1', 'r4t3'), 
('working_man_fraction', 'r4h2', 'r4t3'), 
('all_man_fraction', 'r4h3', 'r4t3'), 
('human_density', 'tamviv', 'rooms'), 
('human_bed_density', 'tamviv', 'bedrooms'), 
('rent_per_person', 'v2a1', 'r4t3'), 
('rent_per_room', 'v2a1', 'rooms'), 
('mobile_density', 'qmobilephone', 'r4t3'), 
('tablet_density', 'v18q1', 'r4t3'), 
('mobile_adult_density', 'qmobilephone', 'r4t2'), 
('tablet_adult_density', 'v18q1', 'r4t2'),
]
feats_sub = [('people_not_living', 'tamhog', 'tamviv'), 
('people_weird_stat', 'tamhog', 'r4t3')]
for f_new, f1, f2 in feats_div:
df['fe_' + f_new] =(df[f1] / df[f2] ).astype(np.float32)
 for f_new, f1, f2 in feats_sub:
df['fe_' + f_new] =(df[f1] - df[f2] ).astype(np.float32)
 aggs_num = {'age': ['min', 'max', 'mean'],
'escolari': ['min', 'max', 'mean']
}
aggs_cat = {'dis': ['mean']}
for s_ in ['estadocivil', 'parentesco', 'instlevel']:
for f_ in [f_ for f_ in df.columns if f_.startswith(s_)]:
aggs_cat[f_] = ['mean', 'count']
for name_, df_ in [('18', df.query('age >= 18')) ]:
df_agg = df_.groupby('idhogar' ).agg({**aggs_num, **aggs_cat} ).astype(np.float32)
 df_agg.columns = pd.Index(['agg' + name_ + '_' + e[0] + "_" + e[1].upper() for e in df_agg.columns.tolist() ])
 df = df.join(df_agg, how='left', on='idhogar')
 del df_agg
df.drop(['Id'], axis=1, inplace=True)
 return df
train_org = pd.read_csv('.. /input/lish-moa/train_features.csv')
 target_org = pd.read_csv('.. /input/lish-moa/train_targets_scored.csv')
 train_org = train_org.loc[train_org['cp_type']=='trt_cp'].reset_index(drop=True)
 train_org['idx'] = train_org.index
oof_final = train_org.merge(oof_preds_df, how='left', on='idx')[list(range(206)) + ['sig_id']]
oof_final = target_org.merge(oof_final, how='left', on='sig_id')[list(range(206)) ].fillna(0.0 )<train_model>
def convert_OHE2LE(df):
tmp_df = df.copy(deep=True)
 for s_ in ['pared', 'piso', 'techo', 'abastagua', 'sanitario', 'energcocinar', 'elimbasu',
'epared', 'etecho', 'eviv', 'estadocivil', 'parentesco',
'instlevel', 'lugar', 'tipovivi',
'manual_elec']:
if 'manual_' not in s_:
cols_s_ = [f_ for f_ in df.columns if f_.startswith(s_)]
elif 'elec' in s_:
cols_s_ = ['public', 'planpri', 'noelec', 'coopele']
sum_ohe = tmp_df[cols_s_].sum(axis=1 ).unique()
if 0 in sum_ohe:
print('The OHE in {} is incomplete.A new column will be added before label encoding' 
.format(s_))
col_dummy = s_+'_dummy'
tmp_df[col_dummy] =(tmp_df[cols_s_].sum(axis=1)== 0 ).astype(np.int8)
 cols_s_.append(col_dummy)
 sum_ohe = tmp_df[cols_s_].sum(axis=1 ).unique()
if 0 in sum_ohe:
print("The category completion did not work")
 tmp_cat = tmp_df[cols_s_].idxmax(axis=1)
 tmp_df[s_ + '_LE'] = LabelEncoder().fit_transform(tmp_cat ).astype(np.int16)
 if 'parentesco1' in cols_s_:
cols_s_.remove('parentesco1')
 tmp_df.drop(cols_s_, axis=1, inplace=True)
 return tmp_df
targets = target_org[[col for col in target_org.columns.to_list() if col != 'sig_id']]
log_loss_multi(targets.to_numpy() , oof_final.to_numpy() )<compute_test_metric>
train = pd.read_csv('.. /input/train.csv')
 test = pd.read_csv('.. /input/test.csv')
 test_ids = test.Id
if cfg.strategy == "KFOLD":
for i in range(cfg.num_ensembling):
print("CV score fold : ", log_loss_multi(oof_targets_all[i], oof_preds_all[i]))
print("auc mean : ", sum(scores_auc_all[i])/len(scores_auc_all[i]))<feature_engineering>
def process_df(df_):
encode_data(df_)
 return do_features(df_)
 train = process_df(train)
 test = process_df(test )
submission[columns] = preds_test.mean(1 ).mean(0)
 submission.loc[test_features['cp_type']=='ctl_vehicle', submission.columns[1:]] = 0<save_to_csv>
train['dependency'] = np.sqrt(train['SQBdependency'])
 test['dependency'] = np.sqrt(test['SQBdependency'])
 train.loc[train['edjefa'] == "no", "edjefa"] = 0
train.loc[train['edjefa'] == "yes", "edjefa"] = 1
train['edjefa'] = train['edjefa'].astype("int")
 train.loc[train['edjefe'] == "no", "edjefe"] = 0
train.loc[train['edjefe'] == "yes", "edjefe"] = 1
train['edjefe'] = train['edjefe'].astype("int")
 test.loc[test['edjefa'] == "no", "edjefa"] = 0
test.loc[test['edjefa'] == "yes", "edjefa"] = 4
test['edjefa'] = test['edjefa'].astype("int")
 test.loc[test['edjefe'] == "no", "edjefe"] = 0
test.loc[test['edjefe'] == "yes", "edjefe"] = 4
test['edjefe'] = test['edjefe'].astype("int")
 train['edjef'] = np.max(train[['edjefa','edjefe']], axis=1)
 test['edjef'] = np.max(test[['edjefa','edjefe']], axis=1)
 train['v2a1']=train['v2a1'].fillna(0)
 test['v2a1']=test['v2a1'].fillna(0)
 test['v18q1']=test['v18q1'].fillna(0)
 train['v18q1']=train['v18q1'].fillna(0)
 train['rez_esc']=train['rez_esc'].fillna(0)
 test['rez_esc']=test['rez_esc'].fillna(0)
 train.loc[train.meaneduc.isnull() , "meaneduc"] = 0
train.loc[train.SQBmeaned.isnull() , "SQBmeaned"] = 0
test.loc[test.meaneduc.isnull() , "meaneduc"] = 0
test.loc[test.SQBmeaned.isnull() , "SQBmeaned"] = 0
train.loc[(train.v14a == 1)&(train.sanitario1 == 1)&(train.abastaguano == 0), "v14a"] = 0
train.loc[(train.v14a == 1)&(train.sanitario1 == 1)&(train.abastaguano == 0), "sanitario1"] = 0
test.loc[(test.v14a == 1)&(test.sanitario1 == 1)&(test.abastaguano == 0), "v14a"] = 0
test.loc[(test.v14a == 1)&(test.sanitario1 == 1)&(test.abastaguano == 0), "sanitario1"] = 0
submission.to_csv("submission.csv", index=False )<save_to_csv>
def train_test_apply_func(train_, test_, func_):
test_['Target'] = 0
xx = pd.concat([train_, test_])
 xx_func = func_(xx)
 train_ = xx_func.iloc[:train_.shape[0], :]
test_ = xx_func.iloc[train_.shape[0]:, :].drop('Target', axis=1)
 del xx, xx_func
return train_, test_
submission.to_csv("submission.csv", index=False )<define_variables>
train, test = train_test_apply_func(train, test, convert_OHE2LE )
file_data: Dict = {'src/preprocess.py': 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lYWi9O+7rmJ2a8/2e12r2W5eadwn50+rOWQPteZeEReyQtqFCHUsNaQv82QsysZu2IFPM+FyWM2Se+gsNWhRZBWGAtRtO2yJTKXA8deMiUPZY6b9mMFYQB5YpG0xYskcO7h8pDMzqo+6Xv/HvsqtoBJthxclMNXQ+pM7bwqDtkfpXUcaSGlQBzLvbef59ZAuK/wCGVFVe3TkAAA==', 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nY5bIayd0ItKMsm1Zlpm/j8q1Wpq5eJnmCLdIR/H3221215ppCaG6tA8QEX3WqzM83TXrv7FoYoSf6/7Iw82IkVljnGN3uCPt6s5kWktHgEnbBxX70KR66Yp5R+gwXGbcfG8DlSnq3uOyKeS7vBcG5kSjOGJA5wEGloxFB2eoyfFEam9H5lr5w5xU2xtMaFU2640BIYAILj8DHwszQ7g0VK1XO9NBwaEuz7DSACesQC48V2M681CEvT0/vEnZrfcULod2rby7gEEna22pa1ujcyfjrIQ95jveBB8syJOITPUDqCxiLVLfA8o0iPhFRSjl4EWCFhTWuMl/AQF1pS9ZTQAA', 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', 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'src/utils/splitter.py': 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', 'src/utils/transformers.py': 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'src/utils/cache.py': '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', 'src/utils/environment.py': 'H4sIALjOw18C/41Pu27DMAzc9RWEF8eLP8BAhg4NkK1Dl06GItMxAUVyKCpN/j7yI7BQdAi3I+/BE340CtLQZfQscLG99b/zph10aBcMe/jmiArvBkeB48z9ZPbc/EM9aBtQKdVhD4zXSIyv665a03pwXnLdsp+GNQXMM3blj4/gEDsQD+SCaGvXT+GEvWeEGMidQQYK8PF1rMsq5cufbuLZDNu/M3yr2YuZFzujtB3eyGDWaWNndVAiuyW8XhWFiZ0uJsWynmBNodU3TVafbPIETFlQmDEW1ew14c21TIdSPQG0FUw+vgEAAA=='}
for path, encoded in file_data.items() :
print(path)
 path = Path(path)
 path.parent.mkdir(exist_ok=True)
 path.write_bytes(gzip.decompress(base64.b64decode(encoded)))
 def run(command):
os.system('echo "
os.system('export PYTHONPATH=${PYTHONPATH}:/kaggle/working && ' + command)
 run('python setup.py develop --install-dir /kaggle/working')
 print('b4e58086057ee83c7234b744eb37e39aee01efc3' )<import_modules>
cols_2_ohe = ['eviv_LE', 'etecho_LE', 'epared_LE', 'elimbasu_LE',
'energcocinar_LE', 'sanitario_LE', 'manual_elec_LE',
'pared_LE']
cols_nums = ['age', 'meaneduc', 'dependency',
'hogar_nin', 'hogar_adul', 'hogar_mayor', 'hogar_total',
'bedrooms', 'overcrowding']
def convert_geo2aggs(df_):
tmp_df = pd.concat([df_[(['lugar_LE', 'idhogar']+cols_nums)],
pd.get_dummies(df_[cols_2_ohe],
columns=cols_2_ohe)],axis=1)
 geo_agg = tmp_df.groupby(['lugar_LE','idhogar'] ).mean().groupby('lugar_LE' ).mean().astype(np.float32)
 geo_agg.columns = pd.Index(['geo_' + e for e in geo_agg.columns.tolist() ])
 del tmp_df
return df_.join(geo_agg, how='left', on='lugar_LE')
 train, test = train_test_apply_func(train, test, convert_geo2aggs )
logger = LoggerFactory('notebook' ).getLogger('notebook')
 seed_everything()<split>
train['num_over_18'] = 0
train['num_over_18'] = train[train.age >= 18].groupby('idhogar' ).transform("count")
 train['num_over_18'] = train.groupby("idhogar")["num_over_18"].transform("max")
 train['num_over_18'] = train['num_over_18'].fillna(0)
 test['num_over_18'] = 0
test['num_over_18'] = test[test.age >= 18].groupby('idhogar' ).transform("count")
 test['num_over_18'] = test.groupby("idhogar")["num_over_18"].transform("max")
 test['num_over_18'] = test['num_over_18'].fillna(0)
 def extract_features(df):
df['bedrooms_to_rooms'] = df['bedrooms']/df['rooms']
df['rent_to_rooms'] = df['v2a1']/df['rooms']
df['tamhog_to_rooms'] = df['tamhog']/df['rooms']
df['r4t3_to_tamhog'] = df['r4t3']/df['tamhog']
df['r4t3_to_rooms'] = df['r4t3']/df['rooms']
df['v2a1_to_r4t3'] = df['v2a1']/df['r4t3']
df['v2a1_to_r4t3'] = df['v2a1']/(df['r4t3'] - df['r4t1'])
 df['hhsize_to_rooms'] = df['hhsize']/df['rooms']
df['rent_to_hhsize'] = df['v2a1']/df['hhsize']
df['rent_to_over_18'] = df['v2a1']/df['num_over_18']
df.loc[df.num_over_18 == 0, "rent_to_over_18"] = df[df.num_over_18 == 0].v2a1
df['dependency_yes'] = df['dependency'].apply(lambda x: 1 if x == 'yes' else 0)
 df['dependency_no'] = df['dependency'].apply(lambda x: 1 if x == 'no' else 0)
 extract_features(train)
 extract_features(test )
(
train_features_df,
stage_1_1_target_cols,
stage_1_2_target_cols,
stage_1_train_features, 
)= preprocess.preprocess_train()
test_features_df, sample_submission_df = preprocess.preprocess_test(train_features_df=train_features_df )<feature_engineering>
needless_cols = ['r4t3', 'tamhog', 'tamviv', 'hhsize', 'v18q', 'v14a', 'agesq',
'mobilephone', 'female', ]
instlevel_cols = [s for s in train.columns.tolist() if 'instlevel' in s]
needless_cols.extend(instlevel_cols)
 train = train.drop(needless_cols, axis=1)
 test = test.drop(needless_cols, axis=1 )
def get_feature(df):
features_g = list([x for x in df.columns if x.startswith("g-")])
 features_c = list([x for x in df.columns if x.startswith("c-")])
 df["g_sum"] = df[features_g].sum(axis=1)
 df["g_mean"] = df[features_g].mean(axis=1)
 df["g_median"] = df[features_g].median(axis=1)
 df["g_std"] = df[features_g].std(axis=1)
 df["g_kurt"] = df[features_g].kurtosis(axis=1)
 df["g_skew"] = df[features_g].skew(axis=1)
 df["c_sum"] = df[features_c].sum(axis=1)
 df["c_mean"] = df[features_c].mean(axis=1)
 df["c_std"] = df[features_c].std(axis=1)
 df["c_median"] = df[features_c].median(axis=1)
 df["c_kurt"] = df[features_c].kurtosis(axis=1)
 df["c_skew"] = df[features_c].skew(axis=1)
 df["gc_sum"] = df[features_g + features_c].sum(axis=1)
 df["gc_mean"] = df[features_g + features_c].mean(axis=1)
 df["gc_std"] = df[features_g + features_c].std(axis=1)
 df["gc_kurt"] = df[features_g + features_c].kurtosis(axis=1)
 df["gc_skew"] = df[features_g + features_c].skew(axis=1)
 df["gc_median"] = df[features_g + features_c].median(axis=1)
 return df<categorify>
def split_data(train, target, households, test_percentage=0.20, seed=None):
y = target
np.random.seed(seed=seed)
 train2 = train.copy()
cv_hhs = np.random.choice(households, size=int(len(households)* test_percentage), replace=False)
 cv_idx = np.isin(households, cv_hhs)
 X_test = train2[cv_idx]
y_test = y[cv_idx]
X_train = train2[~cv_idx]
y_train = y[~cv_idx]
return X_train, y_train, X_test, y_test
n_comp_CELLS = 60
train_features_df = get_feature(train_features_df)
 features_c = list([x for x in train_features_df.columns if x.startswith("c-")])
 pca = PCA(n_components=n_comp_CELLS, random_state=42)
 pca_feat = [f'pca_C-{i}' for i in range(n_comp_CELLS)]
train_features_df[pca_feat] = pca.fit_transform(train_features_df[features_c])
 transformer = normalizer(transformer='quantile', params=dict(n_quantiles=100, random_state=42, output_distribution="normal"), df=train_features_df.loc[:, stage_1_train_features])
 train_features_df[stage_1_train_features] = transformer.transform(train_features_df.loc[:, stage_1_train_features] )<categorify>
X = train.query('parentesco1==1')
 y = X['Target'] - 1
X = X.drop(['Target'], axis=1)
 np.random.seed(seed=1977)
 train2 = X.copy()
train_hhs = train2.idhogar
households = train2.idhogar.unique()
cv_hhs = np.random.choice(households, size=int(len(households)* 0.1), replace=False)
 cv_idx = np.isin(train2.idhogar, cv_hhs)
 X_test = train2[cv_idx]
y_test = y[cv_idx]
X_train = train2[~cv_idx]
y_train = y[~cv_idx]
X_train = train2
y_train = y
train_households = X_train.idhogar
test_features_df = get_feature(test_features_df)
 test_features_df[pca_feat] = pca.transform(test_features_df[features_c])
 test_features_df[stage_1_train_features] = transformer.transform(test_features_df.loc[:, stage_1_train_features] )<define_variables>
extra_drop_features = ['geo_manual_elec_LE_2',
'agg18_estadocivil5_COUNT',
'agg18_estadocivil7_COUNT',
'geo_pared_LE_5',
'agg18_estadocivil6_COUNT',
'geo_pared_LE_4',
'agg18_parentesco10_COUNT',
'num_over_18',
'agg18_estadocivil1_MEAN',
'agg18_estadocivil4_COUNT',
'r4t3_to_tamhog',
'agg18_estadocivil3_COUNT',
'geo_pared_LE_3',
'geo_pared_LE_6',
'geo_pared_LE_0',
'geo_pared_LE_7',
'agg18_parentesco7_COUNT',
'agg18_parentesco10_MEAN',
'agg18_parentesco8_MEAN',
'parentesco1',
'rez_esc',
'dependency_yes',
'hacdor',
'hacapo',
'fe_people_weird_stat',
'geo_hogar_mayor',
'geo_eviv_LE_0',
'geo_epared_LE_0',
'geo_epared_LE_2',
'geo_elimbasu_LE_4',
'geo_energcocinar_LE_0',
'agg18_parentesco9_COUNT',
'agg18_parentesco8_COUNT',
'agg18_parentesco11_COUNT',
'parentesco_LE',
'agg18_parentesco6_MEAN',
'agg18_parentesco6_COUNT',
'agg18_parentesco5_COUNT',
'agg18_parentesco4_MEAN',
'agg18_parentesco4_COUNT',
'geo_energcocinar_LE_1',
'agg18_parentesco3_COUNT',
'geo_energcocinar_LE_2',
'agg18_parentesco2_COUNT',
'agg18_parentesco1_COUNT',
'agg18_parentesco12_MEAN',
'agg18_parentesco12_COUNT',
'dependency_no'] + ['geo_hogar_nin', 'geo_manual_elec_LE_3']
stage_1_train_features += pca_feat<import_modules>
lgb_drop_cols = extra_drop_features + ["idhogar"]
try:
X_train.drop(lgb_drop_cols, axis=1, inplace=True)
 X_test.drop(lgb_drop_cols, axis=1, inplace=True)
 except:
print("Error dropping columns" )
from typing import List
import pandas as pd
from src.utils.cache import Cache
from src.utils.splitter import splitter_for_moa
from src.metrics import logloss_for_multilabel<train_model>
opt_parameters = {'colsample_bytree': 0.93, 'min_child_samples': 56, 'num_leaves': 19, 'subsample': 0.84}
def evaluate_macroF1_lgb(truth, predictions):
pred_labels = predictions.reshape(len(np.unique(truth)) ,-1 ).argmax(axis=0)
 f1 = f1_score(truth, pred_labels, average='macro')
 return('macroF1', f1, True)
 fit_params={"early_stopping_rounds":300,
"eval_metric" : evaluate_macroF1_lgb,
"eval_set" : [(X_train,y_train),(X_test,y_test)],
'eval_names': ['train', 'valid'],
'verbose': False,
'categorical_feature': 'auto'}
def learning_rate_power_0997(current_iter):
base_learning_rate = 0.1
min_learning_rate = 0.02
lr = base_learning_rate * np.power (.995, current_iter)
 return max(lr, min_learning_rate)
 fit_params['verbose'] = 200
fit_params['callbacks'] = [lgb.reset_parameter(learning_rate=learning_rate_power_0997)]
@Cache('./cache')
 def build_trainer(trainer: MoaBase, df: pd.DataFrame, predictors: List[str], target_cols: List[str], num_seed_blends: int, n_splits: int, params: dict):
folds = splitter_for_moa(df, target_cols, n_splits=n_splits)
 model = trainer(
 target_cols = target_cols,
categorical_cols=[],
ignore_cols=[],
num_seed_blends=num_seed_blends,
metric=logloss_for_multilabel,
params=params,
exp=None 
)
 model.train(df[predictors], df[target_cols], folds)
 return model
stage1_1_tabnet_trainer = build_trainer(
 trainer=Tabnet,
df=train_features_df,
target_cols=stage_1_1_target_cols,
predictors=stage_1_train_features,
num_seed_blends=5,
n_splits=7,
params={}, 
)
 stage1_2_tabnet_trainer = build_trainer(
 trainer=Tabnet,
df=train_features_df,
target_cols=stage_1_2_target_cols,
predictors=stage_1_train_features,
num_seed_blends=5,
n_splits=7,
params={},
)<choose_model_class>
def _parallel_fit_estimator(estimator, X, y, sample_weight=None, **fit_params):
X_train, y_train, X_test, y_test = split_data(X, y, households=train_households, seed=None)
 fit_params["eval_set"] = [(X_train,y_train),(X_test,y_test)]

 if sample_weight is not None:
estimator.fit(X_train, y_train, sample_weight=sample_weight, **fit_params)
 else:
estimator.fit(X_train, y_train, **fit_params)
 return estimator
class VotingClassifierLGBM(VotingClassifier):

 def fit(self, X, y, sample_weight=None, **fit_params):
if isinstance(y, np.ndarray)and len(y.shape)> 1 and y.shape[1] > 1:
raise NotImplementedError('Multilabel and multi-output'
' classification is not supported.')
 if self.voting not in('soft', 'hard'):
raise ValueError("Voting must be 'soft' or 'hard'; got(voting=%r)"
% self.voting)
 if self.estimators is None or len(self.estimators)== 0:
raise AttributeError('Invalid `estimators` attribute, `estimators`'
' should be a list of(string, estimator)'
' tuples')
 if(self.weights is not None and
len(self.weights)!= len(self.estimators)) :
raise ValueError('Number of classifiers and weights must be equal'
'; got %d weights, %d estimators'
%(len(self.weights), len(self.estimators)))
 if sample_weight is not None:
for name, step in self.estimators:
if not has_fit_parameter(step, 'sample_weight'):
raise ValueError('Underlying estimator '%s' does not'
' support sample weights.' % name)
 names, clfs = zip(*self.estimators)
 self._validate_names(names)
 n_isnone = np.sum([clf is None for _, clf in self.estimators])
 if n_isnone == len(self.estimators):
raise ValueError('All estimators are None.At least one is '
'required to be a classifier!')
 self.le_ = LabelEncoder().fit(y)
 self.classes_ = self.le_.classes_
self.estimators_ = []
transformed_y = self.le_.transform(y)
 self.estimators_ = Parallel(n_jobs=self.n_jobs )(
 delayed(_parallel_fit_estimator )(clone(clf), X, transformed_y,
sample_weight=sample_weight, **fit_params)
 for clf in clfs if clf is not None)
 return self
stage1_2_xgb_trainer = build_trainer(
 trainer=XGBTrainer,
df=train_features_df,
target_cols=stage_1_2_target_cols,
predictors=stage_1_train_features,
num_seed_blends=2,
n_splits=5,
params={},
)<find_best_model_class>
clfs = []
for i in range(10):
clf = lgb.LGBMClassifier(max_depth=-1, learning_rate=0.1, objective='multiclass',
random_state=314+i, silent=True, metric='None',
n_jobs=4, n_estimators=5000, class_weight='balanced')
 clf.set_params(**opt_parameters)
 clfs.append(( 'lgbm{}'.format(i), clf))
vc = VotingClassifierLGBM(clfs, voting='soft')
 del clfs
_ = vc.fit(X_train, y_train, **fit_params)
 clf_final = vc.estimators_[0]
folds = splitter_for_moa(train_features_df, stage_1_2_target_cols, n_splits=4)
 stage1_2_svm_trainer = SVMTrainer(
 target_cols = stage_1_2_target_cols,
categorical_cols=[],
ignore_cols=[],
num_seed_blends=1,
metric=logloss_for_multilabel,
params={
},
exp=None 
)
 stage1_2_svm_trainer.train_predict(
 X_train=train_features_df[stage_1_train_features],
y_train=train_features_df[stage_1_2_target_cols],
X_test=test_features_df[stage_1_train_features],
folds=folds
)<predict_on_test>
global_score = f1_score(y_test, clf_final.predict(X_test), average='macro')
 vc.voting = 'soft'
global_score_soft = f1_score(y_test, vc.predict(X_test), average='macro')
 vc.voting = 'hard'
global_score_hard = f1_score(y_test, vc.predict(X_test), average='macro')
 print('Validation score of a single LGBM Classifier: {:.4f}'.format(global_score))
print('Validation score of a VotingClassifier on 3 LGBMs with soft voting strategy: {:.4f}'.format(global_score_soft))
print('Validation score of a VotingClassifier on 3 LGBMs with hard voting strategy: {:.4f}'.format(global_score_hard))
stage1_1_nn_pred = stage1_1_nn_trainer.predict(test_features_df)
 stage1_2_nn_pred = stage1_2_nn_trainer.predict(test_features_df)
 stage1_1_tabnet_pred = stage1_1_tabnet_trainer.predict(test_features_df)
 stage1_2_tabnet_pred = stage1_2_tabnet_trainer.predict(test_features_df)
 stage1_2_svm_pred = stage1_2_svm_trainer.result.preds
stage1_2_xgb_pred = stage1_2_xgb_trainer.predict(test_features_df )<concatenate>
global_score = f1_score(y_test, clf_final.predict(X_test), average='macro')
 vc.voting = 'soft'
global_score_soft = f1_score(y_test, vc.predict(X_test), average='macro')
 vc.voting = 'hard'
global_score_hard = f1_score(y_test, vc.predict(X_test), average='macro')
 print('Validation score of a single LGBM Classifier: {:.4f}'.format(global_score))
print('Validation score of a VotingClassifier on 3 LGBMs with soft voting strategy: {:.4f}'.format(global_score_soft))
print('Validation score of a VotingClassifier on 3 LGBMs with hard voting strategy: {:.4f}'.format(global_score_hard))
stack_train_df = pd.concat(
 [
pd.DataFrame(stage1_1_nn_trainer.result.oof_preds ).add_prefix("stage1_1_nn_"),
pd.DataFrame(stage1_2_nn_trainer.result.oof_preds ).add_prefix("stage1_2_nn_"),
train_features_df[stage_1_2_target_cols + ["drug_id", "sig_id"]],
],
axis=1, 
)
 stack_test_df = pd.concat(
 [
pd.DataFrame(stage1_1_nn_pred ).add_prefix("stage1_1_nn_"),
pd.DataFrame(stage1_2_nn_pred ).add_prefix("stage1_2_nn_"),
],
axis=1, 
)
 stack_train_predictors = [x for x in stack_train_df.columns if x not in stage_1_2_target_cols + ["drug_id", "sig_id"]]
stack_nn_trainer = train_nn_model(
 df=stack_train_df,
target_cols=stage_1_2_target_cols,
predictors=stack_train_predictors,
model_class=TabularMLP_2,
num_seed_blends=5,
n_splits=7 
)
 stacking_nn_pred = stack_nn_trainer.predict(stack_test_df )<concatenate>
ranked_features = feature_importance(clf_final, X_train )
stack_train_df = pd.concat(
 [
pd.DataFrame(stage1_1_tabnet_trainer.result.oof_preds ).add_prefix('stage1_1_tabnet_'),
pd.DataFrame(stage1_2_tabnet_trainer.result.oof_preds ).add_prefix('stage1_2_tabnet_'),
train_features_df[stage_1_2_target_cols + ["drug_id", "sig_id"]],
],
axis=1, 
)
 stack_test_df = pd.concat(
 [
pd.DataFrame(stage1_1_tabnet_pred ).add_prefix("stage1_1_tabnet_"),
pd.DataFrame(stage1_2_tabnet_pred ).add_prefix("stage1_2_tabnet_"),
],
axis=1, 
)
 stack_train_predictors = [x for x in stack_train_df.columns if x not in stage_1_2_target_cols + ["drug_id", "sig_id"]]
stack_tabnet_trainer = build_trainer(
 trainer=Tabnet,
df=stack_train_df,
target_cols=stage_1_2_target_cols,
predictors=stack_train_predictors,
num_seed_blends=5,
n_splits=7,
params={}, 
)
 stacking_tabnet_pred = stack_tabnet_trainer.predict(stack_test_df )<load_pretrained>
y_subm = pd.DataFrame()
y_subm['Id'] = test_ids
stage1_2_trainers = {
'nn': stage1_2_nn_trainer,
'tabnet': stage1_2_tabnet_trainer,
'svm': stage1_2_svm_trainer,
'stack_nn': stack_nn_trainer,
'stack_tabnet': stack_tabnet_trainer,
'xgb': stage1_2_xgb_trainer,
}
for name, trainer in stage1_2_trainers.items() :
print(name, logloss_for_multilabel(train_features_df[stage_1_2_target_cols].values, trainer.result.oof_preds))<define_variables>
y_subm['Target'] = clf_final.predict(test[X_train.columns])+ 1
vc.voting = 'soft'
y_subm_soft = y_subm.copy(deep=True)
 y_subm_soft['Target'] = vc.predict(test[X_train.columns])+ 1
vc.voting = 'hard'
y_subm_hard = y_subm.copy(deep=True)
 y_subm_hard['Target'] = vc.predict(test[X_train.columns])+ 1
<import_modules><EOS> 
now = datetime.now()
sub_file = 'submission_LGB_{:.4f}_{}.csv'.format(global_score, str(now.strftime('%Y-%m-%d-%H-%M')))
 sub_file_soft = 'submission_soft_LGB_{:.4f}_{}.csv'.format(global_score_soft, str(now.strftime('%Y-%m-%d-%H-%M')))
 sub_file_hard = 'submission_hard_LGB_{:.4f}_{}.csv'.format(global_score_hard, str(now.strftime('%Y-%m-%d-%H-%M')))
 y_subm_soft.to_csv(sub_file_soft, index=False)
 y_subm_hard.to_csv(sub_file_hard, index=False )