updated ml_iterator function args
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1 changed files with 20 additions and 5 deletions
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@ -24,6 +24,14 @@ from MultClfs import *
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from GetMLData import *
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from GetMLData import *
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from SplitTTS import *
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from SplitTTS import *
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skf_cv = StratifiedKFold(n_splits = 10
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#, shuffle = False, random_state= None)
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, shuffle = True,**rs)
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#rskf_cv = RepeatedStratifiedKFold(n_splits = 10
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# , n_repeats = 3
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# , **rs)
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# param dict for getmldata()
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# param dict for getmldata()
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gene_model_paramD = {'data_combined_model' : False
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gene_model_paramD = {'data_combined_model' : False
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, 'use_or' : False
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, 'use_or' : False
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@ -89,14 +97,21 @@ for gene, drug in ml_gene_drugD.items():
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mmDD = {}
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mmDD = {}
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for k, v in paramD.items():
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for k, v in paramD.items():
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scoresD = MultModelsCl(**paramD[k]
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scoresD = MultModelsCl(**paramD[k]
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, tts_split_type = split_type
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, sel_cv = skf_cv
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, sel_cv = skf_cv
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, blind_test_df = tempD['X_bts']
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, tts_split_type = split_type
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, blind_test_target = tempD['y_bts']
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, scale_numeric = ['min_max']
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, add_cm = True
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, add_cm = True
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, add_yn = True
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, add_yn = True
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, return_formatted_output = True)
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, scale_numeric = ['min_max']
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, run_blind_test = True
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, blind_test_df = tempD['X_bts']
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, blind_test_target = tempD['y_bts']
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, return_formatted_output = True
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, random_state = 42
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, n_jobs = os.cpu_count()
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)
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mmDD[k] = scoresD
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mmDD[k] = scoresD
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# Extracting the dfs from within the dict and concatenating to output as one df
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# Extracting the dfs from within the dict and concatenating to output as one df
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