tanu/ML_AI_training
1
0
Fork 0
Code Issues Pull requests Releases Wiki Activity

git add UQ_imbalance.py

Browse source
This commit is contained in:
Tanushree Tunstall 2022-05-27 06:05:34 +01:00
parent 42c8c47e2d
commit 1da87ba177
4 changed files with 134 additions and 56 deletions
Download patch file Download diff file Expand all files Collapse all files

53
MultClassPipe3_CALL.py
View file

@ -21,6 +21,7 @@ mm_skf_scores_df_all = pd.DataFrame(mm_skf_scoresD)
mm_skf_scores_df_all
mm_skf_scores_df_test = mm_skf_scores_df_all.filter(like='test_', axis=0)
mm_skf_scores_df_train = mm_skf_scores_df_all.filter(like='train_', axis=0) # helps to see if you trust the results
baseline_BT = mm_skf_scores_df_all.filter(like='bts_', axis=0)
#%%
mm_skf_scoresD2 = MultClassPipeSKFCV(input_df = X_sm
@ -29,28 +30,70 @@ mm_skf_scoresD2 = MultClassPipeSKFCV(input_df = X_sm
, skf_cv = skf_cv)
sm_all = pd.DataFrame(mm_skf_scoresD2)
sm_df_CT = sm_all.filter(like='test_', axis=0)
sm_df_BT = sm_all.filter(like='bts_', axis=0)
#%%
mm_skf_scoresD3 = MultClassPipeSKFCV(input_df = X_ros
, target = y_ros
, var_type = 'mixed'
, skf_cv = skf_cv)
, skf_cv = rskf_cv
, blind_test_input_df = X_bts
, blind_test_target = y_bts)
ros_all = pd.DataFrame(mm_skf_scoresD3)
ros_CT = ros_all.filter(like='test_', axis=0)
ros_BT = ros_all.filter(like='bts_', axis=0)
#--------- combined
mm_skf_scoresD3v2 = MultClassPipeSKFCV(input_df = X_rouC
, target = y_rouC
, var_type = 'mixed'
, skf_cv = rskf_cv
, blind_test_input_df = X_bts
, blind_test_target = y_bts)
rouC_all = pd.DataFrame(mm_skf_scoresD3v2)
rouC_CT = ros_all.filter(like='test_', axis=0)
rouC_BT = ros_all.filter(like='bts_', axis=0)
#%%
mm_skf_scoresD4 = MultClassPipeSKFCV(input_df = X_rus
, target = y_rus
, var_type = 'mixed'
, skf_cv = skf_cv)
, var_type = 'numerical'
, skf_cv = rskf_cv
, blind_test_input_df = X_bts
, blind_test_target = y_bts)
rus_all = pd.DataFrame(mm_skf_scoresD4)
rus_CT = rus_all.filter(like='test_', axis=0)
rus_BT = rus_all.filter(like='bts_' , axis=0)
#%%
mm_skf_scoresD5 = MultClassPipeSKFCV(input_df = X_enn
, target = y_enn
, var_type = 'mixed'
, skf_cv = skf_cv)
, var_type = 'numerical'
, skf_cv = rskf_cv
, blind_test_input_df = X_bts
, blind_test_target = y_bts)
enn_all = pd.DataFrame(mm_skf_scoresD5)
enn_CT = enn_all.filter(like='test_', axis=0)
enn_BT = enn_all.filter(like='bts_', axis=0)
#%%
mm_skf_scoresD6 = MultClassPipeSKFCV(input_df = X_renn
, target = y_renn
, var_type = 'numerical'
, skf_cv = rskf_cv
, blind_test_input_df = X_bts
, blind_test_target = y_bts)
renn_all = pd.DataFrame(mm_skf_scoresD6)
renn_CT = renn_all.filter(like='test_', axis=0)
renn_BT = renn_all.filter(like='bts_', axis=0)
#%%: with categorical values + oversampling
mm_skf_scoresD7 = MultClassPipeSKFCV(input_df = X_smnc
, target = y_smnc
, var_type = 'mixed'
, skf_cv = rskf_cv
, blind_test_input_df = X_bts
, blind_test_target = y_bts)
smnc_all = pd.DataFrame(mm_skf_scoresD7)
smnc_CT = smnc_all.filter(like='test_', axis=0)
smnc_BT = smnc_all.filter(like='bts_', axis=0)