Kaggle competition: Santander Customer Transaction Prediction
results_q2_final = train.loc[train.content_type_id == False, ['question_id','part']].groupby(['question_id'] ).agg(['count'])
 results_q2_final.columns = ['count']<merge>
random_state = 42
np.random.seed(random_state)
 df_train = pd.read_csv('.. /input/train.csv')
 df_test = pd.read_csv('.. /input/test.csv' )
question2 = pd.merge(questions_df, results_q_final, left_on = 'question_id', right_on = 'question_id', how = 'left' )<merge>
print("Total values in the dataset : {}".format(df_train['target'].count()))
Ones = df_train.groupby('target')['target'].count()
print("% of 1s in total {}".format(Ones[1]*100.0/200000))
question2 = pd.merge(question2, results_q2_final, left_on = 'question_id', right_on = 'question_id', how = 'left' )<feature_engineering>
y = df_train['target']
y.head()
x = df_train[y > 0].copy()
x.head()
question2.quest_pct = round(question2.quest_pct,5 )<drop_column>
def augment(x,y,t=2):
xs,xn = [],[]
for i in range(t):
mask = y>0
x1 = x[mask].copy()
ids = np.arange(x1.shape[0])
 for c in range(x1.shape[1]):
np.random.shuffle(ids)
 x1[:,c] = x1[ids][:,c]
xs.append(x1)
 for i in range(t//2):
mask = y==0
x1 = x[mask].copy()
ids = np.arange(x1.shape[0])
 for c in range(x1.shape[1]):
np.random.shuffle(ids)
 x1[:,c] = x1[ids][:,c]
xn.append(x1)
 xs = np.vstack(xs)
 xn = np.vstack(xn)
 ys = np.ones(xs.shape[0])
 yn = np.zeros(xn.shape[0])
 x = np.vstack([x,xs,xn])
 y = np.concatenate([y,ys,yn])
 return x,y
train.drop(['timestamp', 'content_type_id', 'question_id', 'part'], axis=1, inplace=True )<filter>
lgb_params = {
"objective" : "binary",
"metric" : "auc",
"boosting": 'gbdt',
"max_depth" : 4,
"num_leaves" : 13,
"learning_rate" : 0.01,
"bagging_freq": 5,
"bagging_fraction" : 0.4,
"feature_fraction" : 0.05,
"min_data_in_leaf": 80,
"tree_learner": "serial",
"boost_from_average": "false",
"bagging_seed" : random_state,
"verbosity" : 1,
"seed": random_state
}
validation = train.groupby('user_id' ).tail(5)
 train = train[~train.index.isin(validation.index)]
len(train)+ len(validation )<groupby>
skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=random_state)
 oof = df_train[['ID_code', 'target']]
oof['predict'] = 0
predictions = df_test[['ID_code']]
val_aucs = []
feature_importance_df = pd.DataFrame()
results_u_val = train[['user_id','answered_correctly']].groupby(['user_id'] ).agg(['mean'])
 results_u_val.columns = ['answered_correctly_user']
results_u2_val = train[['user_id','prior_question_had_explanation']].groupby(['user_id'] ).agg(['mean'])
 results_u2_val.columns = ['explanation_mean_user']<groupby>
features = [col for col in df_train.columns if col not in ['target', 'ID_code']]
X_test = df_test[features].values
X = train.groupby('user_id' ).tail(18)
 train = train[~train.index.isin(X.index)]
len(X)+ len(train)+ len(validation )<groupby>
for fold,(trn_idx, val_idx)in enumerate(skf.split(df_train, df_train['target'])) :
X_train, y_train = df_train.iloc[trn_idx][features], df_train.iloc[trn_idx]['target']
X_valid, y_valid = df_train.iloc[val_idx][features], df_train.iloc[val_idx]['target']
N = 5
p_valid,yp = 0,0
for i in range(N):
X_t, y_t = augment(X_train.values, y_train.values)
 X_t = pd.DataFrame(X_t)
 X_t = X_t.add_prefix('var_')
 trn_data = lgb.Dataset(X_t, label=y_t)
 val_data = lgb.Dataset(X_valid, label=y_valid)
 evals_result = {}
lgb_clf = lgb.train(lgb_params,
trn_data,
100000,
valid_sets = [trn_data, val_data],
early_stopping_rounds=3000,
verbose_eval = 1000,
evals_result=evals_result 
)
 p_valid += lgb_clf.predict(X_valid)
 yp += lgb_clf.predict(X_test)
 fold_importance_df = pd.DataFrame()
fold_importance_df["feature"] = features
fold_importance_df["importance"] = lgb_clf.feature_importance()
fold_importance_df["fold"] = fold + 1
feature_importance_df = pd.concat([feature_importance_df, fold_importance_df], axis=0)
 oof['predict'][val_idx] = p_valid/N
val_score = roc_auc_score(y_valid, p_valid)
 val_aucs.append(val_score)
 predictions['fold{}'.format(fold+1)] = yp/N
results_u_X = train[['user_id','answered_correctly']].groupby(['user_id'] ).agg(['mean'])
 results_u_X.columns = ['answered_correctly_user']
results_u2_X = train[['user_id','prior_question_had_explanation']].groupby(['user_id'] ).agg(['mean'])
 results_u2_X.columns = ['explanation_mean_user']<drop_column>
print("Distribution of 1s in original data : {} / {} ".format(np.sum(y_train), len(y_train)))
 print("Percentage of 1s in original data : {}".format(np.sum(y_train)*100.0/len(y_train)))
 print("Percentage of 1s in augmented data : {}".format(np.sum(y_t)*100.0/len(y_t)))
 print("Distribution of 1s in augmented data : {} / {} ".format(np.sum(y_t), len(y_t)))
del(train )<merge>
mean_auc = np.mean(val_aucs)
 std_auc = np.std(val_aucs)
 all_auc = roc_auc_score(oof['target'], oof['predict'])
 print("Mean auc: %.9f, std: %.9f.All auc: %.9f." %(mean_auc, std_auc, all_auc))
<merge><EOS> 
predictions['target'] = np.mean(predictions[[col for col in predictions.columns if col not in ['ID_code', 'target']]].values, axis=1)
 predictions.to_csv('lgb_all_predictions.csv', index=None)
 sub_df = pd.DataFrame({"ID_code":df_test["ID_code"].values})
 sub_df["target"] = predictions['target']
sub_df.to_csv("lgb_submission.csv", index=False)
 oof.to_csv('lgb_oof.csv', index=False )