ELI5를 이용한 Permutation Importance 예제

EDA baseline-full_permutation
In [1]:
import pandas as pd
import numpy as np
import os
import lightgbm as lgb
from sklearn.model_selection import train_test_split
In [2]:
os.listdir('../input')
Out[2]:
['sample_submission.csv',
 'test_identity.csv',
 'test_transaction.csv',
 'train_identity.csv',
 'train_transaction.csv']
In [3]:
submission = pd.read_csv("../input/sample_submission.csv")
In [4]:
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
In [5]:
submission.head(3)
Out[5]:
TransactionID isFraud
0 3663549 0.5
1 3663550 0.5
2 3663551 0.5
In [6]:
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
In [7]:
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")
In [8]:
# 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)
In [9]:
raw_transaction.head(2)
Out[9]:
TransactionID isFraud 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
0 2987000 0 86400 68.5 W 13926 NaN 150.0 discover 142.0 credit 315.0 87.0 19.0 NaN NaN NaN 1.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 2.0 0.0 1.0 1.0 14.0 NaN 13.0 NaN NaN NaN NaN NaN NaN 13.0 13.0 NaN NaN NaN 0.0 T T T M2 F T NaN NaN NaN 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 117.0 0.0 0.0 0.0 0.0 0.0 117.0 0.0 0.0 0.0 0.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 117.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 117.0 0.0 0.0 0.0 0.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1 2987001 0 86401 29.0 W 2755 404.0 150.0 mastercard 102.0 credit 325.0 87.0 NaN NaN gmail.com NaN 1.0 1.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 0.0 1.0 1.0 0.0 NaN NaN 0.0 NaN NaN NaN NaN NaN 0.0 NaN NaN NaN NaN 0.0 NaN NaN NaN M0 T T NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 1.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 1.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 1.0 1.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 0.0 0.0 0.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 1.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
In [10]:
raw_identity.head(2)
Out[10]:
TransactionID 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
0 2987004 0.0 70787.0 NaN NaN NaN NaN NaN NaN NaN NaN 100.0 NotFound NaN -480.0 New NotFound 166.0 NaN 542.0 144.0 NaN NaN NaN NaN NaN NaN NaN New NotFound Android 7.0 samsung browser 6.2 32.0 2220×1080 match_status:2 T F T T mobile SAMSUNG SM-G892A Build/NRD90M
1 2987008 -5.0 98945.0 NaN NaN 0.0 -5.0 NaN NaN NaN NaN 100.0 NotFound 49.0 -300.0 New NotFound 166.0 NaN 621.0 500.0 NaN NaN NaN NaN NaN NaN NaN New NotFound iOS 11.1.2 mobile safari 11.0 32.0 1334×750 match_status:1 T F F T mobile iOS Device
In [23]:
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')
In [24]:
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
In [25]:
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']
In [26]:
train_raw_X = raw_all[feature_list]
train_raw_y = raw_all[['isFraud']]

COMPETITION_X = COMPETITION_raw_all[feature_list]
In [15]:
train_X, valid_X, train_y, valid_y = train_test_split(train_raw_X, train_raw_y, test_size=0.2, random_state=1493)
In [37]:
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}
In [38]:
train_ds = lgb.Dataset(train_X, label = train_y) 
valid_ds = lgb.Dataset(valid_X, label = valid_y) 
In [39]:
model = lgb.train(params, train_ds, 1000, valid_ds, verbose_eval=100, early_stopping_rounds=100)
C:\Anaconda3\lib\site-packages\lightgbm\basic.py:762: UserWarning: categorical_feature in param dict is overridden.
  warnings.warn('categorical_feature in param dict is overridden.')
Training until validation scores don't improve for 100 rounds.
[100]	valid_0's auc: 0.980277
[200]	valid_0's auc: 0.981711
[300]	valid_0's auc: 0.981681
Early stopping, best iteration is:
[272]	valid_0's auc: 0.982139
In [40]:
predict = model.predict(COMPETITION_X)
In [41]:
submission['isFraud'] = predict
In [42]:
submission.to_csv("./submission/submission_fraud_0829_all.csv", index=False)

Permutation Importance

  • Predict시 하나의 Feature를 엉망진창으로 만든다음에 예측력이 어떻게 변하는지 확인
In [56]:
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)
In [54]:
train_wo_dummy_X.describe()
Out[54]:
TransactionDT TransactionAmt card1 card2 card3 card5 addr1 addr2 dist1 dist2 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 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_13 id_14 id_17 id_18 id_19 id_20 id_21 id_22 id_24 id_25 id_26 id_32
count 1.153860e+05 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.0 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.0 115386.000000 115386.000000 115386.000000 115386.0 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.0 115386.000000 115386.000000 115386.000000 115386.000000 115386.0 115386.0 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.0 115386.0 115386.000000 115386.000000 115386.000000 115386.000000 115386.0 115386.0 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.0 115386.0 115386.000000 115386.000000 115386.000000 115386.0 115386.0 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.0 115386.0 115386.000000 115386.000000 115386.000000 115386.000000 115386.0 115386.0 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.0 115386.0 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 115386.000000 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mean 6.168889e+06 83.507936 9874.665315 388.922694 161.493084 189.878616 172.478316 50.095861 0.0 60.594032 28.094483 34.705112 0.022949 15.762580 0.0 15.741710 10.585166 19.566351 0.0 19.968107 20.569740 14.927964 20.733755 10.608393 29.459137 26.741996 6.965559 27.457733 10.621453 33.167975 10.323783 75.931604 0.292479 11.219455 0.0 21.939100 7.699175 24.977484 25.942315 0.0 0.0 0.477770 0.396131 0.398896 0.476479 0.481124 0.436734 0.439828 0.461165 0.470534 0.515738 0.520098 0.464328 0.467873 0.002903 0.003137 0.0 0.0 0.462240 0.466963 0.396131 0.398861 0.0 0.0 0.663625 0.746416 0.476548 0.507852 0.446510 0.448313 0.485059 0.580807 0.615023 0.471400 0.489756 0.0 0.0 0.441145 0.429212 0.446718 0.0 0.0 0.606157 0.690768 0.414513 0.428605 0.470690 0.499229 0.438476 0.452854 0.458245 0.497166 0.467986 0.464840 0.481653 0.002037 0.0 0.0 0.458297 0.473619 0.420190 0.436639 0.0 0.0 0.671615 0.766375 0.432340 0.495502 0.525584 0.460472 0.473212 0.472310 0.515860 0.583017 0.609831 0.470568 0.003215 0.0 0.0 0.479590 0.494826 0.406921 3.525124 7.381753 5.205736 0.031876 0.450427 0.158936 3.300790 6.189754 4.606443 0.187579 0.735167 0.434801 0.999558 1.007800 1.008251 1.007947 1.009074 1.009360 1.009230 1.014742 1.016631 1.015530 1.001777 1.001915 1.001855 1.000815 1.001153 1.000962 1.059669 1.093668 1.079516 401.776520 771.533151 569.061776 5.510615 34.010612 15.965294 364.799591 651.065360 495.037021 29.970758 83.574306 55.705049 0.019881 0.606460 0.635510 0.021034 0.027170 4.723025 2.098521 12.518824 0.089023 0.096138 0.432669 0.438580 157.090973 3.657697 5.348422 0.426170 0.428579 0.433987 0.439767 0.462162 0.471244 1537.238823 26890.691299 2.657573 3.650898 3.048877 492.798761 1265.469140 202.005243 3.768395 5.665878 0.164318 1.392916 1.641274 0.128473 0.053733 0.124885 0.206672 1.334651 3.379717 6.411731 4.701047 0.894771 0.245723 0.828575 0.471175 0.128872 0.169050 1.112804 1.780814 0.982546 1.005226 1.174337 1.025896 1.197199 1.114303 0.916732 0.924653 1.050665 0.919704 0.931291 1.229837 1.084066 1.122424 424.467690 1039.871266 663.012137 17.814547 6.323119 70.892849 8.668791 33.755783 13.945417 368.084546 738.090710 517.087101 36.148096 127.732332 67.973191 0.958366 1.561957 1.240367 0.167533 1.253896 1.335925 0.084300 0.353483 0.171581 0.222791 0.148744 1.220919 1.473316 1.320628 0.696592 0.914201 0.826573 1.988837 0.166771 0.278595 0.230115 0.126150 0.134713 0.903810 0.903134 1.004975 1.061463 1.009637 0.861326 1.067729 0.925754 0.964112 0.940929 0.772269 0.777685 0.931473 1.042891 0.968003 0.791413 0.799482 1.127719 1.208838 0.950687 0.871900 0.999194 0.915241 104.962798 179.527843 136.581873 8.527182 32.620792 16.824959 5.639887 7.555432 9.273321 8.299797 66.894739 96.077602 79.809308 27.456282 45.564335 36.915860 3.672335 5.437939 0.120283 1.195734 1.305826 0.082913 0.599197 0.047163 0.244016 0.123958 0.140095 1.194911 1.548073 1.301354 3.388548 6.511726 4.753930 0.737906 0.190257 0.718380 0.427418 0.084577 0.087940 1.025384 1.142487 1.073362 0.999991 409.193198 791.126184 579.503936 7.501585 37.906779 4.820404 18.586758 7.989806 16.356228 8.772873 370.193875 670.356693 503.831401 29.601777 79.890230 54.262577 3.509923 7.426837 5.210034 0.033210 0.485154 0.168591 0.192536 0.748722 0.443191 406.037173 778.373055 574.128566 5.766349 33.954261 16.450058 30.799268 85.541892 56.821558 -10.191930 170463.321755 0.028478 -0.027187 1.518850 -6.366925 0.468766 -1.370487 0.048784 -0.153259 97.499820 42.402259 -191.145286 183.011423 4.448789 340.985345 390.026000 13.176729 0.576058 0.421854 11.768880 5.344834 14.274314
std 4.812374e+06 99.571840 5051.312255 162.775094 20.177633 47.294224 164.828145 42.745039 0.0 290.181117 250.624813 292.049031 0.293378 132.328064 0.0 132.911829 117.136641 181.424952 0.0 182.058329 177.229337 164.402871 166.092147 87.481930 95.616678 92.075660 41.212396 97.448790 54.887019 105.309414 53.004538 182.112890 0.361711 68.102769 0.0 83.571363 45.148951 94.135698 99.259342 0.0 0.0 0.499508 0.497719 0.513517 0.555602 0.569428 0.505982 0.522001 0.505467 0.548393 0.630685 0.646442 0.508298 0.522289 0.055235 0.060823 0.0 0.0 0.516084 0.536880 0.496219 0.509189 0.0 0.0 1.294798 1.549764 0.658786 0.746661 0.497133 0.538103 0.620494 1.183443 1.307171 0.556876 0.609215 0.0 0.0 0.503408 0.549151 0.599338 0.0 0.0 0.890855 1.317557 0.523100 0.568872 0.585013 0.656991 0.526934 0.572332 0.540442 0.633534 0.498976 0.541791 0.576148 0.046596 0.0 0.0 0.534350 0.582644 0.536306 0.571524 0.0 0.0 1.136032 1.532520 0.573303 0.633265 0.710533 0.530332 0.578475 0.540447 0.644855 0.912059 1.022860 0.499135 0.060462 0.0 0.0 0.538909 0.591569 0.491262 42.149673 80.411069 55.536508 0.226179 2.930663 1.018043 41.251922 71.954590 51.524315 1.191882 6.627205 3.582567 0.021019 0.115414 0.117320 0.116041 0.130116 0.131191 0.130704 0.150972 0.157357 0.153879 0.051886 0.053201 0.052630 0.042450 0.046253 0.044148 0.361301 0.463380 0.410900 4677.735487 8427.088848 6003.625745 208.912256 386.263770 237.619166 4528.938282 7573.806525 5543.962826 540.640188 850.683456 648.624263 0.305209 1.125642 1.227248 0.161082 0.235547 41.590002 8.090266 49.647993 0.518527 0.570866 0.576823 0.607925 638.868549 11.896626 16.891030 0.548292 0.558164 0.589986 0.620013 0.650834 0.687523 6419.487580 109496.737434 42.450968 50.691539 46.209395 4548.709730 6268.907299 887.016199 41.277305 53.073725 0.896375 1.735216 2.426707 0.915882 0.262138 0.375720 0.841849 1.822599 40.403220 68.170838 49.577900 6.004512 1.229909 5.687866 2.906652 0.536065 0.695229 1.230422 9.679993 0.679674 0.794987 1.474167 0.697569 2.596965 1.711186 0.342229 0.406249 1.114130 0.366149 0.503274 1.656561 1.236425 1.402048 4568.108808 8939.436375 5935.385334 276.232812 209.130812 923.801350 57.724303 243.661747 84.050381 4441.359084 7359.556854 5371.980258 441.432901 961.489517 574.184065 9.158402 13.263291 11.525525 0.996177 2.814989 2.996290 0.381995 3.169526 1.182066 2.485717 2.165442 1.388272 3.990228 2.013513 8.514386 10.017184 9.722313 11.027960 0.861375 2.050995 1.641686 0.570677 0.611499 0.300184 0.297005 0.705552 1.328238 0.738795 1.905293 1.033820 0.402024 0.867004 0.573311 0.497695 0.510353 0.474940 3.311509 1.267559 0.978814 1.010306 1.264928 1.888422 2.185641 0.459831 1.271829 0.654934 1171.565500 2108.614152 1464.968630 215.006945 603.542094 298.285794 214.351426 63.826745 73.089779 68.346827 896.993337 1194.107497 1020.205168 454.678767 589.317177 509.797559 42.114237 55.342612 0.678262 1.071134 1.503093 0.332672 2.989988 0.235096 1.031999 0.364333 0.450220 0.904881 11.369370 2.558249 41.259109 72.883534 51.575367 6.194574 1.121861 6.138211 3.337412 0.430291 0.443063 0.380787 0.718921 0.503022 0.002944 4663.439167 8412.161238 5977.793742 207.196634 364.089725 204.683938 230.084800 42.875767 89.748046 47.154161 4530.396024 7623.043865 5547.501981 520.773847 809.147724 627.933947 42.162314 80.499098 55.555905 0.231967 3.001756 1.035762 1.206598 6.633856 3.589735 4670.231426 8417.667931 5994.766375 201.080924 307.169049 222.860262 430.623747 786.326832 558.524878 14.413061 159880.093140 0.404705 0.479142 5.100343 16.164043 3.256050 8.630918 0.702569 1.944754 14.839538 19.012923 184.817989 45.461489 6.658530 152.748589 166.805997 78.049705 3.263052 2.327507 63.924462 28.370703 13.497414
min 8.650600e+04 0.251000 1000.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.0 0.000000 0.000000 0.000000 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 -83.000000 0.000000 -83.000000 0.000000 0.000000 0.000000 0.000000 0.0 -83.000000 0.000000 -193.000000 -83.000000 0.0 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.0 0.0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 -100.000000 0.000000 -13.000000 -28.000000 -72.000000 -100.000000 -46.000000 -100.000000 -36.000000 -100.000000 0.000000 0.000000 -660.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
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In [55]:
importance_model = RandomForestClassifier(random_state=0).fit(train_wo_dummy_X, train_y)
C:\Anaconda3\lib\site-packages\sklearn\ensemble\forest.py:246: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.
  "10 in version 0.20 to 100 in 0.22.", FutureWarning)
C:\Anaconda3\lib\site-packages\ipykernel_launcher.py:1: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  """Entry point for launching an IPython kernel.
In [60]:
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())
Out[60]:
Weight Feature
0.0073 ± 0.0011 C1
0.0016 ± 0.0004 C2
0.0015 ± 0.0004 C11
0.0014 ± 0.0005 C14
0.0013 ± 0.0001 card2
0.0013 ± 0.0004 C13
0.0011 ± 0.0003 TransactionDT
0.0011 ± 0.0003 TransactionAmt
0.0008 ± 0.0004 card1
0.0008 ± 0.0008 id_17
0.0007 ± 0.0001 addr2
0.0007 ± 0.0002 id_01
0.0006 ± 0.0001 V258
0.0006 ± 0.0002 V45
0.0005 ± 0.0003 V212
0.0005 ± 0.0001 V244
0.0005 ± 0.0002 D8
0.0005 ± 0.0001 V187
0.0005 ± 0.0001 V87
0.0004 ± 0.0002 C12
… 370 more …
In [61]:
eli5.explain_weights(perm, top=400)
Out[61]:
Weight Feature
0.0073 ± 0.0011 x10
0.0016 ± 0.0004 x11
0.0015 ± 0.0004 x20
0.0014 ± 0.0005 x23
0.0013 ± 0.0001 x3
0.0013 ± 0.0004 x22
0.0011 ± 0.0003 x0
0.0011 ± 0.0003 x1
0.0008 ± 0.0004 x2
0.0008 ± 0.0008 x380
0.0007 ± 0.0001 x7
0.0007 ± 0.0002 x367
0.0006 ± 0.0001 x285
0.0006 ± 0.0002 x72
0.0005 ± 0.0003 x239
0.0005 ± 0.0001 x271
0.0005 ± 0.0002 x31
0.0005 ± 0.0001 x214
0.0005 ± 0.0001 x114
0.0004 ± 0.0002 x21
0.0004 ± 0.0002 x19
0.0004 ± 0.0004 x368
0.0004 ± 0.0002 x13
0.0004 ± 0.0002 x4
0.0004 ± 0.0001 x216
0.0004 ± 0.0001 x295
0.0004 ± 0.0003 x302
0.0004 ± 0.0002 x17
0.0003 ± 0.0002 x288
0.0003 ± 0.0002 x301
0.0003 ± 0.0003 x25
0.0003 ± 0.0003 x383
0.0003 ± 0.0002 x292
0.0003 ± 0.0001 x215
0.0003 ± 0.0002 x6
0.0003 ± 0.0002 x382
0.0003 ± 0.0002 x71
0.0003 ± 0.0001 x112
0.0003 ± 0.0003 x101
0.0002 ± 0.0001 x389
0.0002 ± 0.0001 x273
0.0002 ± 0.0002 x5
0.0002 ± 0.0001 x232
0.0002 ± 0.0001 x228
0.0002 ± 0.0001 x49
0.0002 ± 0.0001 x74
0.0002 ± 0.0001 x67
0.0002 ± 0.0001 x183
0.0002 ± 0.0002 x29
0.0002 ± 0.0002 x15
0.0002 ± 0.0001 x286
0.0002 ± 0.0001 x303
0.0002 ± 0.0001 x284
0.0002 ± 0.0001 x65
0.0002 ± 0.0001 x305
0.0002 ± 0.0001 x251
0.0002 ± 0.0004 x121
0.0002 ± 0.0003 x32
0.0002 ± 0.0002 x371
0.0002 ± 0.0001 x194
0.0002 ± 0.0001 x388
0.0002 ± 0.0001 x241
0.0002 ± 0.0001 x304
0.0002 ± 0.0001 x246
0.0002 ± 0.0001 x210
0.0002 ± 0.0001 x84
0.0002 ± 0.0001 x51
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0.0001 ± 0.0001 x249
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0.0001 ± 0.0003 x372
0.0001 ± 0.0000 x169
0.0001 ± 0.0000 x263
0.0001 ± 0.0002 x9
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0.0001 ± 0.0002 x334
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0.0001 ± 0.0001 x272
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0.0001 ± 0.0001 x289
0.0001 ± 0.0001 x321
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0.0001 ± 0.0001 x128
0.0001 ± 0.0001 x342
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0.0001 ± 0.0000 x313
0.0001 ± 0.0001 x161
0.0001 ± 0.0000 x168
0.0001 ± 0.0001 x276
0.0001 ± 0.0002 x106
0.0001 ± 0.0001 x299
0.0001 ± 0.0000 x370
0.0001 ± 0.0001 x254
0.0001 ± 0.0001 x175
0.0001 ± 0.0000 x364
0.0001 ± 0.0001 x341
0.0001 ± 0.0001 x278
0.0001 ± 0.0001 x344
0.0001 ± 0.0001 x69
0.0001 ± 0.0002 x293
0.0001 ± 0.0002 x335
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0.0001 ± 0.0001 x207
0.0001 ± 0.0001 x37
0.0001 ± 0.0000 x243
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0.0001 ± 0.0000 x353
0.0001 ± 0.0001 x202
0.0001 ± 0.0001 x197
0.0001 ± 0.0000 x223
0.0001 ± 0.0001 x227
0.0001 ± 0.0001 x104
0.0001 ± 0.0001 x350
0.0001 ± 0.0000 x199
0.0001 ± 0.0001 x170
0.0001 ± 0.0000 x377
0.0001 ± 0.0002 x314
0.0001 ± 0.0001 x322
0.0001 ± 0.0001 x257
0.0000 ± 0.0001 x351
0.0000 ± 0.0002 x378
0.0000 ± 0.0001 x245
0.0000 ± 0.0001 x261
0.0000 ± 0.0001 x264
0.0000 ± 0.0001 x248
0.0000 ± 0.0001 x343
0.0000 ± 0.0001 x88
0.0000 ± 0.0000 x193
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0.0000 ± 0.0000 x190
0.0000 ± 0.0001 x200
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0.0000 ± 0.0000 x363
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0.0000 ± 0.0000 x336
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0.0000 ± 0.0000 x165
0.0000 ± 0.0000 x386
0.0000 ± 0.0000 x374
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0.0000 ± 0.0001 x291
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0.0000 ± 0.0000 x189
0.0000 ± 0.0000 x132
0.0000 ± 0.0001 x111
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0.0000 ± 0.0000 x186
0.0000 ± 0.0001 x129
0.0000 ± 0.0001 x99
0.0000 ± 0.0001 x258
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0.0000 ± 0.0001 x53
0.0000 ± 0.0001 x325
0.0000 ± 0.0001 x123
0.0000 ± 0.0002 x297
0.0000 ± 0.0001 x274
0.0000 ± 0.0001 x345
0.0000 ± 0.0002 x230
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0.0000 ± 0.0001 x157
0.0000 ± 0.0001 x119
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0.0000 ± 0.0001 x316
0.0000 ± 0.0002 x92
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0.0000 ± 0.0000 x250
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0.0000 ± 0.0001 x317
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0.0000 ± 0.0001 x58
0.0000 ± 0.0001 x339
0.0000 ± 0.0001 x36
0.0000 ± 0.0001 x178
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0.0000 ± 0.0000 x149
0.0000 ± 0.0000 x338
0.0000 ± 0.0000 x311
0.0000 ± 0.0000 x337
0.0000 ± 0.0000 x140
0.0000 ± 0.0000 x162
0.0000 ± 0.0000 x324
0.0000 ± 0.0001 x60
0.0000 ± 0.0001 x320
0.0000 ± 0.0001 x42
0.0000 ± 0.0001 x369
0.0000 ± 0.0001 x191
0.0000 ± 0.0001 x139
0.0000 ± 0.0001 x109
0.0000 ± 0.0002 x120
0.0000 ± 0.0003 x256
0.0000 ± 0.0001 x98
0 ± 0.0000 x361
0 ± 0.0000 x34
0 ± 0.0000 x55
0 ± 0.0000 x116
0 ± 0.0000 x63
0 ± 0.0000 x62
0 ± 0.0000 x117
0 ± 0.0000 x118
0 ± 0.0000 x103
0 ± 0.0000 x54
0 ± 0.0000 x39
0 ± 0.0000 x102
0 ± 0.0000 x40
0 ± 0.0000 x365
0 ± 0.0000 x296
0 ± 0.0000 x97
0 ± 0.0000 x352
0 ± 0.0000 x96
0 ± 0.0000 x75
0 ± 0.0000 x76
0 ± 0.0000 x80
0 ± 0.0000 x95
0 ± 0.0000 x81
0 ± 0.0000 x354
0 ± 0.0000 x135
0 ± 0.0000 x57
0 ± 0.0000 x220
0 ± 0.0000 x171
0 ± 0.0000 x332
0 ± 0.0000 x18
0 ± 0.0000 x8
0 ± 0.0000 x156
0 ± 0.0000 x328
0 ± 0.0000 x376
0 ± 0.0000 x148
0 ± 0.0000 x147
0 ± 0.0000 x146
0 ± 0.0000 x145
0 ± 0.0000 x144
0 ± 0.0000 x143
0 ± 0.0000 x142
0 ± 0.0000 x56
0 ± 0.0000 x12
0 ± 0.0000 x326
0 ± 0.0000 x247
0 ± 0.0000 x357
0 ± 0.0000 x134
0 ± 0.0000 x137
0 ± 0.0000 x14
0 ± 0.0000 x138
0 ± 0.0000 x385
-0.0000 ± 0.0000 x163
-0.0000 ± 0.0000 x47
-0.0000 ± 0.0002 x307
-0.0000 ± 0.0001 x87
-0.0000 ± 0.0001 x312
-0.0000 ± 0.0001 x287
-0.0000 ± 0.0001 x360
-0.0000 ± 0.0001 x110
-0.0000 ± 0.0001 x59
-0.0000 ± 0.0001 x108
-0.0000 ± 0.0000 x218
-0.0000 ± 0.0003 x61
-0.0000 ± 0.0001 x366
-0.0000 ± 0.0001 x318
-0.0000 ± 0.0001 x164
-0.0000 ± 0.0001 x26
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-0.0000 ± 0.0001 x177
-0.0000 ± 0.0001 x315
-0.0000 ± 0.0000 x122
-0.0000 ± 0.0001 x231
-0.0000 ± 0.0000 x131
-0.0000 ± 0.0000 x33
-0.0000 ± 0.0001 x233
-0.0000 ± 0.0001 x234
-0.0000 ± 0.0002 x255
-0.0000 ± 0.0001 x340
-0.0000 ± 0.0001 x203
-0.0000 ± 0.0001 x107
-0.0000 ± 0.0001 x348
-0.0000 ± 0.0001 x153
-0.0000 ± 0.0000 x358
-0.0000 ± 0.0001 x180
-0.0000 ± 0.0001 x176
-0.0000 ± 0.0001 x105
-0.0000 ± 0.0003 x86
-0.0000 ± 0.0001 x306
-0.0000 ± 0.0001 x209
-0.0000 ± 0.0000 x349
-0.0000 ± 0.0000 x151
-0.0000 ± 0.0000 x212
-0.0000 ± 0.0001 x275
-0.0000 ± 0.0001 x167
-0.0000 ± 0.0001 x219
-0.0000 ± 0.0000 x133
-0.0000 ± 0.0001 x269
-0.0000 ± 0.0000 x130
-0.0000 ± 0.0000 x356
-0.0000 ± 0.0000 x158
-0.0000 ± 0.0000 x373
-0.0000 ± 0.0000 x127
-0.0000 ± 0.0002 x333
-0.0000 ± 0.0001 x204
-0.0000 ± 0.0001 x41
-0.0000 ± 0.0001 x173
-0.0000 ± 0.0001 x46
-0.0000 ± 0.0001 x126
-0.0000 ± 0.0001 x222
-0.0000 ± 0.0000 x265
-0.0000 ± 0.0000 x331
-0.0000 ± 0.0001 x35
-0.0000 ± 0.0001 x50
-0.0000 ± 0.0001 x44
-0.0000 ± 0.0001 x185
-0.0000 ± 0.0000 x355
-0.0001 ± 0.0001 x28
-0.0001 ± 0.0001 x310
-0.0001 ± 0.0001 x277
-0.0001 ± 0.0000 x362
-0.0001 ± 0.0001 x323
-0.0001 ± 0.0002 x201
-0.0001 ± 0.0002 x91
-0.0001 ± 0.0001 x30
-0.0001 ± 0.0001 x262
-0.0001 ± 0.0000 x43
-0.0001 ± 0.0000 x159
-0.0001 ± 0.0002 x93
-0.0001 ± 0.0001 x375
-0.0001 ± 0.0001 x70
-0.0001 ± 0.0001 x90
-0.0001 ± 0.0001 x238
-0.0001 ± 0.0000 x192
-0.0001 ± 0.0001 x282
-0.0001 ± 0.0001 x281
-0.0001 ± 0.0002 x79
-0.0001 ± 0.0001 x152
-0.0001 ± 0.0001 x208
-0.0001 ± 0.0001 x240
-0.0001 ± 0.0001 x198
-0.0001 ± 0.0002 x229
-0.0001 ± 0.0002 x94
-0.0001 ± 0.0001 x89
-0.0002 ± 0.0001 x224
-0.0002 ± 0.0001 x166
-0.0002 ± 0.0003 x83
In [ ]:
 

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