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Tanushree Tunstall 2022-07-08 13:53:17 +01:00
parent 5577f5b195
commit 23799275a0
3 changed files with 49 additions and 31 deletions
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22
scripts/count_vars_ML.R
View file

@ -4,7 +4,7 @@
#source("~/git/LSHTM_analysis/config/embb.R")
#source("~/git/LSHTM_analysis/config/gid.R")
#source("~/git/LSHTM_analysis/config/katg.R")
#source("~/git/LSHTM_analysis/config/pnca.R")
source("~/git/LSHTM_analysis/config/pnca.R")
source("~/git/LSHTM_analysis/config/rpob.R")
#############################
@ -55,7 +55,7 @@ if (check12) {
cat('\nPASS: dst mode labels verified. merged_df3 CAN be trusted! ')
}else{
stop('FAIL: Something is wrong with the dst_mode column. Quitting!')
``}
}
#==========================
# CHECK: active site labels
@ -189,6 +189,24 @@ if ( all( check12 && aa_check1 && aa_check2 && a1 && b1 && a2 && b2 && l1 && l2
#quit()
}
#%%###################################################################
# check merged_df3
check_mdf3 = merged_df3[, cols_sel]
check_mdf3T = table(check_mdf3$mutationinformation, check_mdf3$dst_mode)
ft_mdf3 = as.data.frame.matrix(check_mdf3T)
#==================
# CHECK: dst mode
#===================
dst_check_mdf3 = all((ft_mdf3[,1]==0)==(ft_mdf3[,2]!=0)); dst_check_mdf3
sel = c("mutationinformation", "dst", "dst_mode")
a = merged_df3[, sel]
str(a)
# write file
# outfile_merged_df3 = paste0(outdir, '/', tolower(gene), '_merged_df3.csv')
# outfile_merged_df3

53
scripts/ml/ml_functions/MultClfs.py
View file

@ -146,8 +146,8 @@ scoreBT_mapD = {'bts_mcc' : 'MCC'
def MultModelsCl(input_df, target
#, skf_cv
, sel_cv
, blind_test_df
, blind_test_target
#, blind_test_df
#, blind_test_target
, tts_split_type
, resampling_type = 'none' # default
@ -231,35 +231,36 @@ def MultModelsCl(input_df, target
# Specify multiple Classification Models
#======================================================
models = [('AdaBoost Classifier' , AdaBoostClassifier(**rs) )
, ('Bagging Classifier' , BaggingClassifier(**rs, **njobs, bootstrap = True, oob_score = True, verbose = 3, n_estimators = 100) )
, ('Decision Tree' , DecisionTreeClassifier(**rs) )
, ('Extra Tree' , ExtraTreeClassifier(**rs) )
, ('Extra Trees' , ExtraTreesClassifier(**rs) )
, ('Gradient Boosting' , GradientBoostingClassifier(**rs) )
, ('Gaussian NB' , GaussianNB() )
, ('Gaussian Process' , GaussianProcessClassifier(**rs) )
, ('K-Nearest Neighbors' , KNeighborsClassifier() )
, ('LDA' , LinearDiscriminantAnalysis() )
, ('Logistic Regression' , LogisticRegression(**rs) )
, ('Logistic RegressionCV' , LogisticRegressionCV(cv = 3, **rs))
, ('MLP' , MLPClassifier(max_iter = 500, **rs) )
# , ('Bagging Classifier' , BaggingClassifier(**rs, **njobs, bootstrap = True, oob_score = True, verbose = 3, n_estimators = 100) )
# , ('Decision Tree' , DecisionTreeClassifier(**rs) )
# , ('Extra Tree' , ExtraTreeClassifier(**rs) )
# , ('Extra Trees' , ExtraTreesClassifier(**rs) )
# , ('Gradient Boosting' , GradientBoostingClassifier(**rs) )
# , ('Gaussian NB' , GaussianNB() )
# , ('Gaussian Process' , GaussianProcessClassifier(**rs) )
# , ('K-Nearest Neighbors' , KNeighborsClassifier() )
, ('LDA' , LinearDiscriminantAnalysis() )
# , ('Logistic Regression' , LogisticRegression(**rs) )
# , ('Logistic RegressionCV' , LogisticRegressionCV(cv = 3, **rs))
# , ('MLP' , MLPClassifier(max_iter = 500, **rs) )
#, ('Multinomial' , MultinomialNB() )
, ('Naive Bayes' , BernoulliNB() )
, ('Passive Aggresive' , PassiveAggressiveClassifier(**rs, **njobs) )
, ('QDA' , QuadraticDiscriminantAnalysis() )
, ('Random Forest' , RandomForestClassifier(**rs, n_estimators = 1000, **njobs ) )
# , ('Random Forest2' , RandomForestClassifier(min_samples_leaf = 5
# , ('Naive Bayes' , BernoulliNB() )
# , ('Passive Aggresive' , PassiveAggressiveClassifier(**rs, **njobs) )
# , ('QDA' , QuadraticDiscriminantAnalysis() )
# , ('Random Forest' , RandomForestClassifier(**rs, n_estimators = 1000, **njobs ) )
# # , ('Random Forest2' , RandomForestClassifier(min_samples_leaf = 5
# , n_estimators = 1000
# , bootstrap = True
# , oob_score = True
# , **njobs
# , **rs
# , max_features = 'auto') )
, ('Ridge Classifier' , RidgeClassifier(**rs) )
, ('Ridge ClassifierCV' , RidgeClassifierCV(cv = 3) )
, ('SVC' , SVC(**rs) )
, ('Stochastic GDescent' , SGDClassifier(**rs, **njobs) )
, ('XGBoost' , XGBClassifier(**rs, verbosity = 0, use_label_encoder =False, **njobs) )
# , ('Ridge Classifier' , RidgeClassifier(**rs) )
# , ('Ridge ClassifierCV' , RidgeClassifierCV(cv = 3) )
# , ('SVC' , SVC(**rs) )
# , ('Stochastic GDescent' , SGDClassifier(**rs, **njobs) )
# , ('XGBoost' , XGBClassifier(**rs, verbosity = 0, use_label_encoder =False, **njobs) )
#
]
mm_skf_scoresD = {}
@ -308,7 +309,7 @@ def MultModelsCl(input_df, target
# ADD more info: meta data related to input df
mm_skf_scoresD[model_name]['resampling'] = resampling_type
mm_skf_scoresD[model_name]['n_training_size'] = len(input_df)
mm_skf_scoresD[model_name]['n_trainingY_ratio'] = round(Counter(target)[0]/Counter(target)[1], 2)
#mm_skf_scoresD[model_name]['n_trainingY_ratio'] = round(Counter(target)[0]/Counter(target)[1], 2)
mm_skf_scoresD[model_name]['n_features'] = len(input_df.columns)
mm_skf_scoresD[model_name]['tts_split'] = tts_split_type
@ -357,7 +358,7 @@ def MultModelsCl(input_df, target
# Build bts numbers dict
btD = {'n_blindY_neg' : Counter(blind_test_target)[0]
, 'n_blindY_pos' : Counter(blind_test_target)[1]
, 'n_testY_ratio' : round(Counter(blind_test_target)[0]/Counter(blind_test_target)[1], 2)
#, 'n_testY_ratio' : round(Counter(blind_test_target)[0]/Counter(blind_test_target)[1], 2)
, 'n_test_size' : len(blind_test_df) }
# Update cmD+tnD dicts with btD

5
scripts/ml/ml_functions/test_func_singlegene.py
View file

@ -58,8 +58,8 @@ all(df.columns.isin(['gene_name'])) # should be False
spl_type = '70_30'
spl_type = '80_20'
spl_type = 'sl'
#spl_type = '80_20'
#spl_type = 'sl'
df2 = split_tts(df
, data_type = 'actual'
@ -84,7 +84,6 @@ fooD = MultModelsCl(input_df = df2['X']
, var_type = ['mixed']
, scale_numeric = ['min_max']
, return_formatted_output = False
)
for k, v in fooD.items():