import pandas as pd
import numpy as np
import os
import lightgbm as lgb
from sklearn.model_selection import train_test_split

os.listdir('../input')

submission = pd.read_csv("../input/sample_submission.csv")

pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)

submission.head(3)

def to_category_columns(df) :
    string_columns = df.columns[df.dtypes == 'object']
    for c in string_columns :
        df[c] = df[c].astype('category')
    return df

raw_transaction = pd.read_csv("../input/train_transaction.csv")
raw_identity = pd.read_csv("../input/train_identity.csv")

COMPETITION_raw_transaction = pd.read_csv("../input/test_transaction.csv")
COMPETITION_raw_identity = pd.read_csv("../input/test_identity.csv")

# 카테고리 컬럼으로 변환
raw_transaction = to_category_columns(raw_transaction)
raw_identity = to_category_columns(raw_identity)

COMPETITION_raw_transaction = to_category_columns(COMPETITION_raw_transaction)
COMPETITION_raw_identity = to_category_columns(COMPETITION_raw_identity)

raw_transaction.head(2)

raw_identity.head(2)

raw_all = raw_transaction.merge(raw_identity, left_on = 'TransactionID', right_on='TransactionID')
COMPETITION_raw_all = COMPETITION_raw_transaction.merge(COMPETITION_raw_identity, on='TransactionID', how='left')

pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

feature_list = ['TransactionDT', 'TransactionAmt', 'ProductCD', 
                'card1', 'card2', 'card3', 'card4', 'card5', 'card6', 
                'addr1', 'addr2', 'dist1', 'dist2', 
                'P_emaildomain', 'R_emaildomain', 
                'C1', 'C2', 'C3', 'C4', 'C5', 'C6', 'C7', 'C8', 'C9', 'C10', 'C11', 'C12', 'C13', 'C14', 
                'D1', 'D2', 'D3', 'D4', 'D5', 'D6', 'D7', 'D8', 'D9', 'D10', 'D11', 'D12', 'D13', 'D14', 'D15', 
                'M1', 'M2', 'M3', 'M4', 'M5', 'M6', 'M7', 'M8', 'M9', 
                'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10', 'V11', 'V12', 'V13', 'V14', 'V15', 
                'V16', 'V17', 'V18', 'V19', 'V20', 'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'V29', 
                'V30', 'V31', 'V32', 'V33', 'V34', 'V35', 'V36', 'V37', 'V38', 'V39', 'V40', 'V41', 'V42', 'V43', 
                'V44', 'V45', 'V46', 'V47', 'V48', 'V49', 'V50', 'V51', 'V52', 'V53', 'V54', 'V55', 'V56', 'V57', 
                'V58', 'V59', 'V60', 'V61', 'V62', 'V63', 'V64', 'V65', 'V66', 'V67', 'V68', 'V69', 'V70', 'V71', 
                'V72', 'V73', 'V74', 'V75', 'V76', 'V77', 'V78', 'V79', 'V80', 'V81', 'V82', 'V83', 'V84', 'V85', 
                'V86', 'V87', 'V88', 'V89', 'V90', 'V91', 'V92', 'V93', 'V94', 'V95', 'V96', 'V97', 'V98', 'V99',
                'V100', 'V101', 'V102', 'V103', 'V104', 'V105', 'V106', 'V107', 'V108', 'V109', 'V110', 'V111', 
                'V112', 'V113', 'V114', 'V115', 'V116', 'V117', 'V118', 'V119', 'V120', 'V121', 'V122', 'V123', 
                'V124', 'V125', 'V126', 'V127', 'V128', 'V129', 'V130', 'V131', 'V132', 'V133', 'V134', 'V135', 
                'V136', 'V137', 'V138', 'V139', 'V140', 'V141', 'V142', 'V143', 'V144', 'V145', 'V146', 'V147', 
                'V148', 'V149', 'V150', 'V151', 'V152', 'V153', 'V154', 'V155', 'V156', 'V157', 'V158', 'V159', 
                'V160', 'V161', 'V162', 'V163', 'V164', 'V165', 'V166', 'V167', 'V168', 'V169', 'V170', 'V171', 
                'V172', 'V173', 'V174', 'V175', 'V176', 'V177', 'V178', 'V179', 'V180', 'V181', 'V182', 'V183', 
                'V184', 'V185', 'V186', 'V187', 'V188', 'V189', 'V190', 'V191', 'V192', 'V193', 'V194', 'V195', 
                'V196', 'V197', 'V198', 'V199', 'V200', 'V201', 'V202', 'V203', 'V204', 'V205', 'V206', 'V207', 
                'V208', 'V209', 'V210', 'V211', 'V212', 'V213', 'V214', 'V215', 'V216', 'V217', 'V218', 'V219', 
                'V220', 'V221', 'V222', 'V223', 'V224', 'V225', 'V226', 'V227', 'V228', 'V229', 'V230', 'V231', 
                'V232', 'V233', 'V234', 'V235', 'V236', 'V237', 'V238', 'V239', 'V240', 'V241', 'V242', 'V243', 
                'V244', 'V245', 'V246', 'V247', 'V248', 'V249', 'V250', 'V251', 'V252', 'V253', 'V254', 'V255', 
                'V256', 'V257', 'V258', 'V259', 'V260', 'V261', 'V262', 'V263', 'V264', 'V265', 'V266', 'V267', 
                'V268', 'V269', 'V270', 'V271', 'V272', 'V273', 'V274', 'V275', 'V276', 'V277', 'V278', 'V279', 
                'V280', 'V281', 'V282', 'V283', 'V284', 'V285', 'V286', 'V287', 'V288', 'V289', 'V290', 'V291', 
                'V292', 'V293', 'V294', 'V295', 'V296', 'V297', 'V298', 'V299', 'V300', 'V301', 'V302', 'V303', 
                'V304', 'V305', 'V306', 'V307', 'V308', 'V309', 'V310', 'V311', 'V312', 'V313', 'V314', 'V315', 
                'V316', 'V317', 'V318', 'V319', 'V320', 'V321', 'V322', 'V323', 'V324', 'V325', 'V326', 'V327', 
                'V328', 'V329', 'V330', 'V331', 'V332', 'V333', 'V334', 'V335', 'V336', 'V337', 'V338', 'V339', 
                'id_01', 'id_02', 'id_03', 'id_04', 'id_05', 'id_06', 'id_07', 'id_08', 'id_09', 'id_10', 'id_11', 
                'id_12', 'id_13', 'id_14', 'id_15', 'id_16', 'id_17', 'id_18', 'id_19', 'id_20', 'id_21', 'id_22', 
                'id_23', 'id_24', 'id_25', 'id_26', 'id_27', 'id_28', 'id_29', 'id_30', 'id_31', 'id_32', 'id_33', 
                'id_34', 'id_35', 'id_36', 'id_37', 'id_38', 'DeviceType', 'DeviceInfo']

train_raw_X = raw_all[feature_list]
train_raw_y = raw_all[['isFraud']]

COMPETITION_X = COMPETITION_raw_all[feature_list]

train_X, valid_X, train_y, valid_y = train_test_split(train_raw_X, train_raw_y, test_size=0.2, random_state=1493)

params = {'learning_rate': 0.1, 
          'max_depth': 16, 
          'boosting': 'gbdt', 
          'objective': 'binary', 
          'metric': 'auc', 
          'is_training_metric': True, 
          'num_leaves': 144, 
          'feature_fraction': 0.9, 
          'bagging_fraction': 0.7, 
          'bagging_freq': 5, 
          'seed':2018}

train_ds = lgb.Dataset(train_X, label = train_y) 
valid_ds = lgb.Dataset(valid_X, label = valid_y) 

model = lgb.train(params, train_ds, 1000, valid_ds, verbose_eval=100, early_stopping_rounds=100)

predict = model.predict(COMPETITION_X)

submission['isFraud'] = predict

submission.to_csv("./submission/submission_fraud_0829_all.csv", index=False)

from sklearn.ensemble import RandomForestClassifier
category_list = list(train_X.columns[train_X.dtypes == 'category']) + ['V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10', 'V11']
train_wo_dummy_X = train_X.drop(category_list, axis = 1).fillna(0)
valid_wo_dummy_X = valid_X.drop(category_list, axis = 1).fillna(0)

train_wo_dummy_X.describe()

importance_model = RandomForestClassifier(random_state=0).fit(train_wo_dummy_X, train_y)

import eli5
from eli5.sklearn import PermutationImportance

perm = PermutationImportance(importance_model, random_state=1).fit(valid_wo_dummy_X, valid_y)
eli5.show_weights(perm, feature_names = valid_wo_dummy_X.columns.tolist())

eli5.explain_weights(perm, top=400)