added FS to MultClfs.py and modified data for different splits for consistency
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edb7aebd6a
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12 changed files with 1585 additions and 994 deletions
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@ -98,7 +98,7 @@ mcc_score_fn = {'mcc': make_scorer(matthews_corrcoef)}
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jacc_score_fn = {'jcc': make_scorer(jaccard_score)}
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###############################################################################
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def fsgs(input_df
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def fsgs_rfecv(input_df
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, target
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, param_gridLd = [{'fs__min_features_to_select' : [1]}]
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, blind_test_df = pd.DataFrame()
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@ -502,10 +502,11 @@ def ProcessMultModelsCl(inputD = {}):
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# Combine WF+Metadata: Final output
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#-------------------------------------
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# checking indices for the dfs to combine:
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c1 = list(set(combined_baseline_wf.index))
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c2 = list(metaDF.index)
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c1L = list(set(combined_baseline_wf.index))
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c2L = list(metaDF.index)
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if c1 == c2:
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#if set(c1L) == set(c2L):
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if set(c1L) == set(c2L) and all(x in c2L for x in c1L) and all(x in c1L for x in c2L):
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print('\nPASS: proceeding to merge metadata with CV and BT dfs')
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combDF = pd.merge(combined_baseline_wf, metaDF, left_index = True, right_index = True)
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else:
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@ -531,5 +532,302 @@ def ProcessMultModelsCl(inputD = {}):
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return combDF
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###############################################################################
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#%% Feature selection function ################################################
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############################
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# fsgs_rfecv()
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############################
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# Run FS using some classifier models
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#
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def fsgs_rfecv(input_df
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, target
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, param_gridLd = [{'fs__min_features_to_select' : [1]}]
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, blind_test_df = pd.DataFrame()
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, blind_test_target = pd.Series(dtype = 'int64')
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, estimator = LogisticRegression(**rs) # placeholder
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, use_fs = False # uses estimator as the RFECV parameter for fs. Set to TRUE if you want to supply custom_fs as shown below
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, custom_fs = RFECV(DecisionTreeClassifier(**rs) , cv = skf_cv, scoring = 'matthews_corrcoef')
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, cv_method = skf_cv
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, var_type = ['numerical', 'categorical' , 'mixed']
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, verbose = 3
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):
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'''
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returns
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Dict containing results from FS and hyperparam tuning for a given estiamtor
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>>> ADD MORE <<<
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optimised/selected based on mcc
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'''
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###########################################################################
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#================================================
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# Determine categorical and numerical features
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#================================================
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numerical_ix = input_df.select_dtypes(include=['int64', 'float64']).columns
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numerical_ix
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categorical_ix = input_df.select_dtypes(include=['object', 'bool']).columns
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categorical_ix
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#================================================
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# Determine preprocessing steps ~ var_type
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#================================================
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if var_type == 'numerical':
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t = [('num', MinMaxScaler(), numerical_ix)]
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if var_type == 'categorical':
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t = [('cat', OneHotEncoder(), categorical_ix)]
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if var_type == 'mixed':
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t = [('cat', OneHotEncoder(), categorical_ix)
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, ('num', MinMaxScaler(), numerical_ix)]
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col_transform = ColumnTransformer(transformers = t
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, remainder='passthrough')
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###########################################################################
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#==================================================
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# Create var_type ~ column names
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# using one hot encoder with RFECV means
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# the names internally are lost. Hence
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# fit col_transformeer to my input_df and get
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# all the column names out and stored in a var
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# to allow the 'selected features' to be subsetted
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# from the numpy boolean array
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#=================================================
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col_transform.fit(input_df)
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col_transform.get_feature_names_out()
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var_type_colnames = col_transform.get_feature_names_out()
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var_type_colnames = pd.Index(var_type_colnames)
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if var_type == 'mixed':
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print('\nVariable type is:', var_type
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, '\nNo. of columns in input_df:', len(input_df.columns)
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, '\nNo. of columns post one hot encoder:', len(var_type_colnames))
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else:
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print('\nNo. of columns in input_df:', len(input_df.columns))
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#==================================
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# Build FS with supplied estimator
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#==================================
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if use_fs:
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fs = custom_fs
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else:
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fs = RFECV(estimator, cv = skf_cv, scoring = 'matthews_corrcoef')
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#==================================
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# Build basic param grid
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#==================================
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# param_gridD = [
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# {'fs__min_features_to_select' : [1]
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# }]
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############################################################################
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# Create Pipeline object
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pipe = Pipeline([
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('pre', col_transform),
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('fs', fs),
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('clf', estimator)])
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############################################################################
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# Define GridSearchCV
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gscv_fs = GridSearchCV(pipe
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#, param_gridLd = param_gridD
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, param_gridLd
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, cv = cv_method
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, scoring = scoring_fn
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, refit = 'mcc'
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, verbose = 3
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, return_train_score = True
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, **njobs)
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gscv_fs.fit(input_df, target)
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###########################################################################
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# Get best param and scores out
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gscv_fs.best_params_
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gscv_fs.best_score_
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# Training best score corresponds to the max of the mean_test<score>
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train_bscore = round(gscv_fs.best_score_, 2); train_bscore
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print('\nTraining best score (MCC):', train_bscore)
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gscv_fs.cv_results_['mean_test_mcc']
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round(gscv_fs.cv_results_['mean_test_mcc'].max(),2)
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round(np.nanmax(gscv_fs.cv_results_['mean_test_mcc']),2)
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check_train_score = [round(gscv_fs.cv_results_['mean_test_mcc'].max(),2)
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, round(np.nanmax(gscv_fs.cv_results_['mean_test_mcc']),2)]
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check_train_score = np.nanmax(check_train_score)
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# Training results
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gscv_tr_resD = gscv_fs.cv_results_
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mod_refit_param = gscv_fs.refit
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# sanity check
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if train_bscore == check_train_score:
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print('\nVerified training score (MCC):', train_bscore )
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else:
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sys.exit('\nTraining score could not be internatlly verified. Please check training results dict')
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#-------------------------
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# Dict of CV results
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#-------------------------
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cv_allD = gscv_fs.cv_results_
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cvdf0 = pd.DataFrame(cv_allD)
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cvdf = cvdf0.filter(regex='mean_test', axis = 1)
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cvdfT = cvdf.T
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cvdfT.columns = ['cv_score']
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cvdfTr = cvdfT.loc[:,'cv_score'].round(decimals = 2) # round values
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cvD = cvdfTr.to_dict()
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print('\n CV results dict generated for:', len(scoring_fn), 'scores'
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, '\nThese are:', scoring_fn.keys())
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#-------------------------
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# Blind test: REAL check!
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#-------------------------
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#tp = gscv_fs.predict(X_bts)
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tp = gscv_fs.predict(blind_test_df)
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print('\nMCC on Blind test:' , round(matthews_corrcoef(blind_test_target, tp),2))
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print('\nAccuracy on Blind test:', round(accuracy_score(blind_test_target, tp),2))
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#=================
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# info extraction
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#=================
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# gives input vals??
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gscv_fs._check_n_features
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# gives gscv params used
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gscv_fs._get_param_names()
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# gives ??
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gscv_fs.best_estimator_
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gscv_fs.best_params_ # gives best estimator params as a dict
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gscv_fs.best_estimator_._final_estimator # similar to above, doesn't contain max_iter
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gscv_fs.best_estimator_.named_steps['fs'].get_support()
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gscv_fs.best_estimator_.named_steps['fs'].ranking_ # array of ranks for the features
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gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.mean()
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gscv_fs.best_estimator_.named_steps['fs'].grid_scores_.max()
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#gscv_fs.best_estimator_.named_steps['fs'].grid_scores_
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estimator_mask = gscv_fs.best_estimator_.named_steps['fs'].get_support()
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############################################################################
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#============
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# FS results
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#============
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# Now get the features out
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#--------------
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# All features
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#--------------
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all_features = gscv_fs.feature_names_in_
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n_all_features = gscv_fs.n_features_in_
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#all_features = gsfit.feature_names_in_
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#--------------
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# Selected features by the classifier
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# Important to have var_type_colnames here
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#----------------
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#sel_features = X.columns[gscv_fs.best_estimator_.named_steps['fs'].get_support()] 3 only for numerical df
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sel_features = var_type_colnames[gscv_fs.best_estimator_.named_steps['fs'].get_support()]
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n_sf = gscv_fs.best_estimator_.named_steps['fs'].n_features_
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#--------------
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# Get model name
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#--------------
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model_name = gscv_fs.best_estimator_.named_steps['clf']
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b_model_params = gscv_fs.best_params_
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print('\n========================================'
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, '\nRunning model:'
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, '\nModel name:', model_name
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, '\n==============================================='
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, '\nRunning feature selection with RFECV for model'
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, '\nTotal no. of features in model:', len(all_features)
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, '\nThese are:\n', all_features, '\n\n'
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, '\nNo of features for best model: ', n_sf
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, '\nThese are:', sel_features, '\n\n'
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, '\nBest Model hyperparams:', b_model_params
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)
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###########################################################################
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############################## OUTPUT #####################################
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###########################################################################
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#=========================
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# Blind test: BTS results
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#=========================
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# Build the final results with all scores for a feature selected model
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#bts_predict = gscv_fs.predict(X_bts)
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bts_predict = gscv_fs.predict(blind_test_df)
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print('\nMCC on Blind test:' , round(matthews_corrcoef(blind_test_target, bts_predict),2))
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print('\nAccuracy on Blind test:', round(accuracy_score(blind_test_target, bts_predict),2))
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bts_mcc_score = round(matthews_corrcoef(blind_test_target, bts_predict),2)
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# Diff b/w train and bts test scores
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train_test_diff = train_bscore - bts_mcc_score
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print('\nDiff b/w train and blind test score (MCC):', train_test_diff)
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lr_btsD ={}
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#lr_btsD['bts_mcc'] = bts_mcc_score
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lr_btsD['bts_fscore'] = round(f1_score(blind_test_target, bts_predict),2)
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lr_btsD['bts_precision'] = round(precision_score(blind_test_target, bts_predict),2)
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lr_btsD['bts_recall'] = round(recall_score(blind_test_target, bts_predict),2)
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lr_btsD['bts_accuracy'] = round(accuracy_score(blind_test_target, bts_predict),2)
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lr_btsD['bts_roc_auc'] = round(roc_auc_score(blind_test_target, bts_predict),2)
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lr_btsD['bts_jcc'] = round(jaccard_score(blind_test_target, bts_predict),2)
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lr_btsD
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#===========================
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# Add FS related model info
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#===========================
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model_namef = str(model_name)
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# FIXME: doesn't tell you which it has chosen
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fs_methodf = str(gscv_fs.best_estimator_.named_steps['fs'])
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all_featuresL = list(all_features)
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fs_res_arrayf = str(list( gscv_fs.best_estimator_.named_steps['fs'].get_support()))
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fs_res_array_rankf = str(list( gscv_fs.best_estimator_.named_steps['fs'].ranking_))
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sel_featuresf = list(sel_features)
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n_sf = int(n_sf)
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output_modelD = {'model_name': model_namef
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, 'model_refit_param': mod_refit_param
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, 'Best_model_params': b_model_params
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, 'n_all_features': n_all_features
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, 'fs_method': fs_methodf
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, 'fs_res_array': fs_res_arrayf
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, 'fs_res_array_rank': fs_res_array_rankf
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, 'all_feature_names': all_featuresL
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, 'n_sel_features': n_sf
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, 'sel_features_names': sel_featuresf}
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#output_modelD
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#========================================
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# Update output_modelD with bts_results
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#========================================
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output_modelD.update(lr_btsD)
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output_modelD
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output_modelD['train_score (MCC)'] = train_bscore
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output_modelD['bts_mcc'] = bts_mcc_score
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output_modelD['train_bts_diff'] = round(train_test_diff,2)
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print(output_modelD)
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nlen = len(output_modelD)
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#========================================
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# Update output_modelD with cv_results
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#========================================
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output_modelD.update(cvD)
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if (len(output_modelD) == nlen + len(cvD)):
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print('\nFS run complete for model:', estimator
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, '\nFS using:', fs
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, '\nOutput dict size:', len(output_modelD))
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return(output_modelD)
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else:
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sys.exit('\nFAIL:numbers mismatch output dict length not as expected. Please check')
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@ -37,7 +37,7 @@ def setvars(gene,drug):
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import argparse
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import re
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#%% GLOBALS
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tts_split = "70/30"
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tts_split = "70_30"
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rs = {'random_state': 42}
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njobs = {'n_jobs': 10}
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@ -727,7 +727,7 @@ def setvars(gene,drug):
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#------------------------------
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oversample = RandomOverSampler(sampling_strategy='minority')
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X_ros, y_ros = oversample.fit_resample(X, y)
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print('Simple Random OverSampling\n', Counter(y_ros))
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print('\nSimple Random OverSampling\n', Counter(y_ros))
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print(X_ros.shape)
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#------------------------------
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@ -736,7 +736,7 @@ def setvars(gene,drug):
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#------------------------------
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undersample = RandomUnderSampler(sampling_strategy='majority')
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X_rus, y_rus = undersample.fit_resample(X, y)
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print('Simple Random UnderSampling\n', Counter(y_rus))
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print('\nSimple Random UnderSampling\n', Counter(y_rus))
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print(X_rus.shape)
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#------------------------------
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@ -747,7 +747,7 @@ def setvars(gene,drug):
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X_ros, y_ros = oversample.fit_resample(X, y)
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undersample = RandomUnderSampler(sampling_strategy='majority')
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X_rouC, y_rouC = undersample.fit_resample(X_ros, y_ros)
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print('Simple Combined Over and UnderSampling\n', Counter(y_rouC))
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print('\nSimple Combined Over and UnderSampling\n', Counter(y_rouC))
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print(X_rouC.shape)
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#------------------------------
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@ -767,7 +767,7 @@ def setvars(gene,drug):
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categorical_colind = X.columns.get_indexer(list(categorical_ix))
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categorical_colind
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k_sm = 5 # 5 is deafult
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k_sm = 5 # 5 is default
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sm_nc = SMOTENC(categorical_features=categorical_colind, k_neighbors = k_sm, **rs, **njobs)
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X_smnc, y_smnc = sm_nc.fit_resample(X, y)
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print('\nSMOTE_NC OverSampling\n', Counter(y_smnc))
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@ -797,5 +797,10 @@ def setvars(gene,drug):
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# print(X_enn.shape)
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# print('\nSMOTE Over+Under Sampling combined\n', Counter(y_enn))
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###############################################################################
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###########################################################################
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# TODO: Find over and undersampling JUST for categorical data
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###########################################################################
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print('\n#################################################################'
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, '\nDim of X for gene:', gene.lower(), '\n', X.shape
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, '\n###############################################################')
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@ -34,7 +34,11 @@ def setvars(gene,drug):
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from sklearn.model_selection import StratifiedKFold,RepeatedStratifiedKFold, RepeatedKFold
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from sklearn.pipeline import Pipeline, make_pipeline
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import argparse
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import re
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#%% GLOBALS
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tts_split = "80_20"
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rs = {'random_state': 42}
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njobs = {'n_jobs': 10}
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@ -57,11 +61,9 @@ def setvars(gene,drug):
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mcc_score_fn = {'mcc': make_scorer(matthews_corrcoef)}
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jacc_score_fn = {'jcc': make_scorer(jaccard_score)}
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#%% FOR LATER: Combine ED logo data
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###########################################################################
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rs = {'random_state': 42}
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njobs = {'n_jobs': 10}
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homedir = os.path.expanduser("~")
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geneL_basic = ['pnca']
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@ -422,118 +424,31 @@ def setvars(gene,drug):
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#==========================
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my_df_ml = my_df.copy()
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#%% Build X: input for ML
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common_cols_stabiltyN = ['ligand_distance'
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, 'ligand_affinity_change'
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, 'duet_stability_change'
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, 'ddg_foldx'
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, 'deepddg'
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, 'ddg_dynamut2'
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, 'mmcsm_lig'
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, 'contacts']
|
||||
|
||||
# Build stability columns ~ gene
|
||||
# Build column names to mask for affinity chanhes
|
||||
if gene.lower() in geneL_basic:
|
||||
X_stabilityN = common_cols_stabiltyN
|
||||
#X_stabilityN = common_cols_stabiltyN
|
||||
gene_affinity_colnames = []# not needed as its the common ones
|
||||
cols_to_mask = ['ligand_affinity_change']
|
||||
|
||||
if gene.lower() in geneL_ppi2:
|
||||
# X_stabilityN = common_cols_stabiltyN + ['mcsm_ppi2_affinity' , 'interface_dist']
|
||||
geneL_ppi2_st_cols = ['mcsm_ppi2_affinity', 'interface_dist']
|
||||
X_stabilityN = common_cols_stabiltyN + geneL_ppi2_st_cols
|
||||
gene_affinity_colnames = ['mcsm_ppi2_affinity', 'interface_dist']
|
||||
#X_stabilityN = common_cols_stabiltyN + geneL_ppi2_st_cols
|
||||
cols_to_mask = ['ligand_affinity_change', 'mcsm_ppi2_affinity']
|
||||
|
||||
if gene.lower() in geneL_na:
|
||||
# X_stabilityN = common_cols_stabiltyN + ['mcsm_na_affinity']
|
||||
geneL_na_st_cols = ['mcsm_na_affinity']
|
||||
X_stabilityN = common_cols_stabiltyN + geneL_na_st_cols
|
||||
gene_affinity_colnames = ['mcsm_na_affinity']
|
||||
#X_stabilityN = common_cols_stabiltyN + geneL_na_st_cols
|
||||
cols_to_mask = ['ligand_affinity_change', 'mcsm_na_affinity']
|
||||
|
||||
if gene.lower() in geneL_na_ppi2:
|
||||
# X_stabilityN = common_cols_stabiltyN + ['mcsm_na_affinity'] + ['mcsm_ppi2_affinity', 'interface_dist']
|
||||
geneL_na_ppi2_st_cols = ['mcsm_na_affinity'] + ['mcsm_ppi2_affinity', 'interface_dist']
|
||||
X_stabilityN = common_cols_stabiltyN + geneL_na_ppi2_st_cols
|
||||
gene_affinity_colnames = ['mcsm_na_affinity'] + ['mcsm_ppi2_affinity', 'interface_dist']
|
||||
#X_stabilityN = common_cols_stabiltyN + geneL_na_ppi2_st_cols
|
||||
cols_to_mask = ['ligand_affinity_change', 'mcsm_na_affinity', 'mcsm_ppi2_affinity']
|
||||
|
||||
|
||||
X_foldX_cols = [ 'electro_rr', 'electro_mm', 'electro_sm', 'electro_ss'
|
||||
, 'disulfide_rr', 'disulfide_mm', 'disulfide_sm', 'disulfide_ss'
|
||||
, 'hbonds_rr', 'hbonds_mm', 'hbonds_sm', 'hbonds_ss'
|
||||
, 'partcov_rr', 'partcov_mm', 'partcov_sm', 'partcov_ss'
|
||||
, 'vdwclashes_rr', 'vdwclashes_mm', 'vdwclashes_sm', 'vdwclashes_ss'
|
||||
, 'volumetric_rr', 'volumetric_mm', 'volumetric_ss'
|
||||
]
|
||||
|
||||
X_str = ['rsa'
|
||||
#, 'asa'
|
||||
, 'kd_values'
|
||||
, 'rd_values']
|
||||
|
||||
X_ssFN = X_stabilityN + X_str + X_foldX_cols
|
||||
|
||||
X_evolFN = ['consurf_score'
|
||||
, 'snap2_score'
|
||||
, 'provean_score']
|
||||
|
||||
X_genomic_mafor = ['maf'
|
||||
, 'logorI'
|
||||
# , 'or_rawI'
|
||||
# , 'or_mychisq'
|
||||
# , 'or_logistic'
|
||||
# , 'or_fisher'
|
||||
# , 'pval_fisher'
|
||||
]
|
||||
|
||||
X_genomic_linegae = ['lineage_proportion'
|
||||
, 'dist_lineage_proportion'
|
||||
#, 'lineage' # could be included as a category but it has L2;L4 formatting
|
||||
, 'lineage_count_all'
|
||||
, 'lineage_count_unique'
|
||||
]
|
||||
|
||||
X_genomicFN = X_genomic_mafor + X_genomic_linegae
|
||||
|
||||
X_aaindexFN = list(aa_df_cols)
|
||||
|
||||
print('\nTotal no. of features for aaindex:', len(X_aaindexFN))
|
||||
|
||||
# numerical feature names
|
||||
numerical_FN = X_ssFN + X_evolFN + X_genomicFN + X_aaindexFN
|
||||
|
||||
# categorical feature names
|
||||
categorical_FN = ['ss_class'
|
||||
# , 'wt_prop_water'
|
||||
# , 'mut_prop_water'
|
||||
# , 'wt_prop_polarity'
|
||||
# , 'mut_prop_polarity'
|
||||
# , 'wt_calcprop'
|
||||
# , 'mut_calcprop'
|
||||
, 'aa_prop_change'
|
||||
, 'electrostatics_change'
|
||||
, 'polarity_change'
|
||||
, 'water_change'
|
||||
, 'drtype_mode_labels' # beware then you can't use it to predict [USED it for uq_v1, not v2]
|
||||
, 'active_site' #[didn't use it for uq_v1]
|
||||
#, 'gene_name' # will be required for the combined stuff
|
||||
]
|
||||
|
||||
#----------------------------------------------
|
||||
# count numerical and categorical features
|
||||
#----------------------------------------------
|
||||
|
||||
print('\nNo. of numerical features:', len(numerical_FN)
|
||||
, '\nNo. of categorical features:', len(categorical_FN))
|
||||
|
||||
#=======================
|
||||
# Masking columns:
|
||||
# (mCSM-lig, mCSM-NA, mCSM-ppi2) values for lig_dist >10
|
||||
#=======================
|
||||
# my_df_ml['mutationinformation'][my_df['ligand_distance']>10].value_counts()
|
||||
# my_df_ml.groupby('mutationinformation')['ligand_distance'].apply(lambda x: (x>10)).value_counts()
|
||||
|
||||
# my_df_ml.loc[(my_df_ml['ligand_distance'] > 10), 'ligand_affinity_change'] = 0
|
||||
# (my_df_ml['ligand_affinity_change'] == 0).sum()
|
||||
|
||||
my_df_ml['mutationinformation'][my_df_ml['ligand_distance']>10].value_counts()
|
||||
my_df_ml.groupby('mutationinformation')['ligand_distance'].apply(lambda x: (x>10)).value_counts()
|
||||
my_df_ml.loc[(my_df_ml['ligand_distance'] > 10), cols_to_mask].value_counts()
|
||||
|
|
@ -544,23 +459,150 @@ def setvars(gene,drug):
|
|||
|
||||
mask_check = my_df_ml[['mutationinformation', 'ligand_distance'] + cols_to_mask]
|
||||
|
||||
#===================================================
|
||||
# write file for check
|
||||
mask_check.sort_values(by = ['ligand_distance'], ascending = True, inplace = True)
|
||||
mask_check.to_csv(outdir + 'ml/' + gene.lower() + '_mask_check.csv')
|
||||
#===================================================
|
||||
###############################################################################
|
||||
#%% Feature groups (FG): Build X for Input ML
|
||||
############################################################################
|
||||
#===========================
|
||||
# FG1: Evolutionary features
|
||||
#===========================
|
||||
X_evolFN = ['consurf_score'
|
||||
, 'snap2_score'
|
||||
, 'provean_score']
|
||||
|
||||
#####################################################################
|
||||
###############################################################################
|
||||
#========================
|
||||
# FG2: Stability features
|
||||
#========================
|
||||
#--------
|
||||
# common
|
||||
#--------
|
||||
X_common_stability_Fnum = [
|
||||
'duet_stability_change'
|
||||
, 'ddg_foldx'
|
||||
, 'deepddg'
|
||||
, 'ddg_dynamut2'
|
||||
, 'contacts']
|
||||
#--------
|
||||
# FoldX
|
||||
#--------
|
||||
X_foldX_Fnum = [ 'electro_rr', 'electro_mm', 'electro_sm', 'electro_ss'
|
||||
, 'disulfide_rr', 'disulfide_mm', 'disulfide_sm', 'disulfide_ss'
|
||||
, 'hbonds_rr', 'hbonds_mm', 'hbonds_sm', 'hbonds_ss'
|
||||
, 'partcov_rr', 'partcov_mm', 'partcov_sm', 'partcov_ss'
|
||||
, 'vdwclashes_rr', 'vdwclashes_mm', 'vdwclashes_sm', 'vdwclashes_ss'
|
||||
, 'volumetric_rr', 'volumetric_mm', 'volumetric_ss']
|
||||
|
||||
X_stability_FN = X_common_stability_Fnum + X_foldX_Fnum
|
||||
|
||||
###############################################################################
|
||||
#===================
|
||||
# FG3: Affinity features
|
||||
#===================
|
||||
common_affinity_Fnum = ['ligand_distance'
|
||||
, 'ligand_affinity_change'
|
||||
, 'mmcsm_lig']
|
||||
|
||||
# if gene.lower() in geneL_basic:
|
||||
# X_affinityFN = common_affinity_Fnum
|
||||
# else:
|
||||
# X_affinityFN = common_affinity_Fnum + gene_affinity_colnames
|
||||
|
||||
X_affinityFN = common_affinity_Fnum + gene_affinity_colnames
|
||||
|
||||
###############################################################################
|
||||
#============================
|
||||
# FG4: Residue level features
|
||||
#============================
|
||||
#-----------
|
||||
# AA index
|
||||
#-----------
|
||||
X_aaindex_Fnum = list(aa_df_cols)
|
||||
print('\nTotal no. of features for aaindex:', len(X_aaindex_Fnum))
|
||||
|
||||
#-----------------
|
||||
# surface area
|
||||
# depth
|
||||
# hydrophobicity
|
||||
#-----------------
|
||||
X_str_Fnum = ['rsa'
|
||||
#, 'asa'
|
||||
, 'kd_values'
|
||||
, 'rd_values']
|
||||
|
||||
#---------------------------
|
||||
# Other aa properties
|
||||
# active site indication
|
||||
#---------------------------
|
||||
X_aap_Fcat = ['ss_class'
|
||||
# , 'wt_prop_water'
|
||||
# , 'mut_prop_water'
|
||||
# , 'wt_prop_polarity'
|
||||
# , 'mut_prop_polarity'
|
||||
# , 'wt_calcprop'
|
||||
# , 'mut_calcprop'
|
||||
, 'aa_prop_change'
|
||||
, 'electrostatics_change'
|
||||
, 'polarity_change'
|
||||
, 'water_change'
|
||||
, 'active_site']
|
||||
|
||||
X_resprop_FN = X_aaindex_Fnum + X_str_Fnum + X_aap_Fcat
|
||||
###############################################################################
|
||||
#========================
|
||||
# FG5: Genomic features
|
||||
#========================
|
||||
X_gn_mafor_Fnum = ['maf'
|
||||
#, 'logorI'
|
||||
# , 'or_rawI'
|
||||
# , 'or_mychisq'
|
||||
# , 'or_logistic'
|
||||
# , 'or_fisher'
|
||||
# , 'pval_fisher'
|
||||
]
|
||||
|
||||
X_gn_linegae_Fnum = ['lineage_proportion'
|
||||
, 'dist_lineage_proportion'
|
||||
#, 'lineage' # could be included as a category but it has L2;L4 formatting
|
||||
, 'lineage_count_all'
|
||||
, 'lineage_count_unique'
|
||||
]
|
||||
|
||||
# X_gn_Fcat = ['drtype_mode_labels' # beware then you can't use it to predict [USED it for uq_v1, not v2]
|
||||
# #, 'gene_name' # will be required for the combined stuff
|
||||
# ]
|
||||
X_gn_Fcat = []
|
||||
|
||||
X_genomicFN = X_gn_mafor_Fnum + X_gn_linegae_Fnum + X_gn_Fcat
|
||||
###############################################################################
|
||||
#========================
|
||||
# FG6 collapsed: Structural : Atability + Affinity + ResidueProp
|
||||
#========================
|
||||
X_structural_FN = X_stability_FN + X_affinityFN + X_resprop_FN
|
||||
|
||||
###############################################################################
|
||||
#========================
|
||||
# BUILDING all features
|
||||
#========================
|
||||
all_featuresN = X_evolFN + X_structural_FN + X_genomicFN
|
||||
|
||||
###############################################################################
|
||||
#%% Define training and test data
|
||||
#================================================================
|
||||
# Training and BLIND test set: 80/20
|
||||
|
||||
# Throw away previous blind_test_df, and call the 30% data as blind_test
|
||||
# as these were imputed values and initial analysis shows that this
|
||||
# is not very representative
|
||||
# dst with actual values : training set
|
||||
# dst with imputed values : THROW AWAY [unrepresentative]
|
||||
#================================================================
|
||||
my_df_ml[drug].isna().sum()
|
||||
|
||||
# blind_test_df = my_df_ml[my_df_ml[drug].isna()]
|
||||
# blind_test_df.shape
|
||||
|
||||
training_df = my_df_ml[my_df_ml[drug].notna()]
|
||||
training_df = my_df_ml[my_df_ml[drug].notna()]
|
||||
training_df.shape
|
||||
|
||||
# Target 1: dst_mode
|
||||
|
|
@ -568,68 +610,14 @@ def setvars(gene,drug):
|
|||
training_df['dst_mode'].value_counts()
|
||||
|
||||
####################################################################
|
||||
|
||||
###############################################################################
|
||||
###############################################################################
|
||||
# #%% extracting dfs based on numerical, categorical column names
|
||||
# #----------------------------------
|
||||
# # WITHOUT the target var included
|
||||
# #----------------------------------
|
||||
# num_df = training_df[numerical_FN]
|
||||
# num_df.shape
|
||||
|
||||
# cat_df = training_df[categorical_FN]
|
||||
# cat_df.shape
|
||||
|
||||
# all_df = training_df[numerical_FN + categorical_FN]
|
||||
# all_df.shape
|
||||
|
||||
# #------------------------------
|
||||
# # WITH the target var included:
|
||||
# #'wtgt': with target
|
||||
# #------------------------------
|
||||
# # drug and dst_mode should be the same thing
|
||||
# num_df_wtgt = training_df[numerical_FN + ['dst_mode']]
|
||||
# num_df_wtgt.shape
|
||||
|
||||
# cat_df_wtgt = training_df[categorical_FN + ['dst_mode']]
|
||||
# cat_df_wtgt.shape
|
||||
|
||||
# all_df_wtgt = training_df[numerical_FN + categorical_FN + ['dst_mode']]
|
||||
# all_df_wtgt.shape
|
||||
|
||||
#%%########################################################################
|
||||
# #============
|
||||
# # ML data: OLD
|
||||
# #============
|
||||
# #------
|
||||
# # X: Training and Blind test (BTS)
|
||||
# #------
|
||||
# X = all_df_wtgt[numerical_FN + categorical_FN] # training data ALL
|
||||
# X_bts = blind_test_df[numerical_FN + categorical_FN] # blind test data ALL
|
||||
# #X = all_df_wtgt[numerical_FN] # training numerical only
|
||||
# #X_bts = blind_test_df[numerical_FN] # blind test data numerical
|
||||
|
||||
# #------
|
||||
# # y
|
||||
# #------
|
||||
# y = all_df_wtgt['dst_mode'] # training data y
|
||||
# y_bts = blind_test_df['dst_mode'] # blind data test y
|
||||
|
||||
# #X_bts_wt = blind_test_df[numerical_FN + ['dst_mode']]
|
||||
|
||||
###############################################################################
|
||||
###############################################################################
|
||||
#======================================
|
||||
#====================================
|
||||
# ML data: Train test split: 80/20
|
||||
# with stratification
|
||||
# 80% : training_data for CV
|
||||
# 20% : blind test
|
||||
#======================================
|
||||
|
||||
# features: all_df or
|
||||
x_features = training_df[numerical_FN + categorical_FN]
|
||||
y_target = training_df['dst_mode']
|
||||
#=====================================
|
||||
x_features = training_df[all_featuresN]
|
||||
y_target = training_df['dst_mode']
|
||||
|
||||
# sanity check
|
||||
if not 'dst_mode' in x_features.columns:
|
||||
|
|
@ -640,7 +628,9 @@ def setvars(gene,drug):
|
|||
#https://towardsdatascience.com/finally-why-we-use-an-80-20-split-for-training-and-test-data-plus-an-alternative-method-oh-yes-edc77e96295d
|
||||
else:
|
||||
sys.exit('\nFAIL: x_features has target variable included. FIX it and rerun!')
|
||||
|
||||
#-------------------
|
||||
# train-test split
|
||||
#-------------------
|
||||
#x_train, x_test, y_train, y_test # traditional var_names
|
||||
# so my downstream code doesn't need to change
|
||||
X, X_bts, y, y_bts = train_test_split(x_features, y_target
|
||||
|
|
@ -653,15 +643,64 @@ def setvars(gene,drug):
|
|||
yc2 = Counter(y_bts)
|
||||
yc2_ratio = yc2[0]/yc2[1]
|
||||
|
||||
###############################################################################
|
||||
#======================================================
|
||||
# Determine categorical and numerical features
|
||||
#======================================================
|
||||
numerical_cols = X.select_dtypes(include=['int64', 'float64']).columns
|
||||
numerical_cols
|
||||
categorical_cols = X.select_dtypes(include=['object', 'bool']).columns
|
||||
categorical_cols
|
||||
|
||||
################################################################################
|
||||
# IMPORTANT sanity checks
|
||||
if len(X.columns) == len(X_evolFN) + len(X_stability_FN) + len(X_affinityFN) + len(X_resprop_FN) + len(X_genomicFN):
|
||||
print('\nPASS: ML data with input features, training and test generated...'
|
||||
, '\n\nTotal no. of input features:' , len(X.columns)
|
||||
, '\n--------No. of numerical features:' , len(numerical_cols)
|
||||
, '\n--------No. of categorical features:' , len(categorical_cols)
|
||||
|
||||
, '\n\nTotal no. of evolutionary features:' , len(X_evolFN)
|
||||
|
||||
, '\n\nTotal no. of stability features:' , len(X_stability_FN)
|
||||
, '\n--------Common stabilty cols:' , len(X_common_stability_Fnum)
|
||||
, '\n--------Foldx cols:' , len(X_foldX_Fnum)
|
||||
|
||||
, '\n\nTotal no. of affinity features:' , len(X_affinityFN)
|
||||
, '\n--------Common affinity cols:' , len(common_affinity_Fnum)
|
||||
, '\n--------Gene specific affinity cols:' , len(gene_affinity_colnames)
|
||||
|
||||
, '\n\nTotal no. of residue level features:', len(X_resprop_FN)
|
||||
, '\n--------AA index cols:' , len(X_aaindex_Fnum)
|
||||
, '\n--------Residue Prop cols:' , len(X_str_Fnum)
|
||||
, '\n--------AA change Prop cols:' , len(X_aap_Fcat)
|
||||
|
||||
, '\n\nTotal no. of genomic features:' , len(X_genomicFN)
|
||||
, '\n--------MAF+OR cols:' , len(X_gn_mafor_Fnum)
|
||||
, '\n--------Lineage cols:' , len(X_gn_linegae_Fnum)
|
||||
, '\n--------Other cols:' , len(X_gn_Fcat)
|
||||
)
|
||||
else:
|
||||
print('\nFAIL: numbers mismatch'
|
||||
, '\nExpected:',len(X_evolFN) + len(X_stability_FN) + len(X_affinityFN) + len(X_resprop_FN) + len(X_genomicFN)
|
||||
, '\nGot:', len(X.columns))
|
||||
sys.exit()
|
||||
###############################################################################
|
||||
print('\n-------------------------------------------------------------'
|
||||
, '\nSuccessfully split data with stratification: 80/20 '
|
||||
, '\nInput features data size:', x_features.shape
|
||||
, '\nTrain data size:', X.shape
|
||||
, '\nTest data size:', X_bts.shape
|
||||
, '\nSuccessfully split data: ALL features'
|
||||
, '\nactual values: training set'
|
||||
, '\nSplit:', tts_split
|
||||
#, '\nimputed values: blind test set'
|
||||
|
||||
, '\n\nTotal data size:', len(X) + len(X_bts)
|
||||
|
||||
, '\n\nTrain data size:', X.shape
|
||||
, '\ny_train numbers:', yc1
|
||||
, '\ny_train ratio:',yc1_ratio
|
||||
, '\n'
|
||||
|
||||
, '\n\nTest data size:', X_bts.shape
|
||||
, '\ny_test_numbers:', yc2
|
||||
|
||||
, '\n\ny_train ratio:',yc1_ratio
|
||||
, '\ny_test ratio:', yc2_ratio
|
||||
, '\n-------------------------------------------------------------'
|
||||
)
|
||||
|
|
@ -760,3 +799,8 @@ def setvars(gene,drug):
|
|||
|
||||
###############################################################################
|
||||
# TODO: Find over and undersampling JUST for categorical data
|
||||
###########################################################################
|
||||
|
||||
print('\n#################################################################'
|
||||
, '\nDim of X for gene:', gene.lower(), '\n', X.shape
|
||||
, '\n###############################################################')
|
||||
|
|
|
|||
|
|
@ -34,7 +34,11 @@ def setvars(gene,drug):
|
|||
from sklearn.model_selection import StratifiedKFold,RepeatedStratifiedKFold, RepeatedKFold
|
||||
|
||||
from sklearn.pipeline import Pipeline, make_pipeline
|
||||
import argparse
|
||||
import re
|
||||
#%% GLOBALS
|
||||
tts_split = "70_30"
|
||||
|
||||
rs = {'random_state': 42}
|
||||
njobs = {'n_jobs': 10}
|
||||
|
||||
|
|
@ -57,11 +61,9 @@ def setvars(gene,drug):
|
|||
|
||||
mcc_score_fn = {'mcc': make_scorer(matthews_corrcoef)}
|
||||
jacc_score_fn = {'jcc': make_scorer(jaccard_score)}
|
||||
|
||||
#%% FOR LATER: Combine ED logo data
|
||||
###########################################################################
|
||||
rs = {'random_state': 42}
|
||||
njobs = {'n_jobs': 10}
|
||||
|
||||
homedir = os.path.expanduser("~")
|
||||
|
||||
geneL_basic = ['pnca']
|
||||
|
|
@ -422,118 +424,31 @@ def setvars(gene,drug):
|
|||
#==========================
|
||||
my_df_ml = my_df.copy()
|
||||
|
||||
#%% Build X: input for ML
|
||||
common_cols_stabiltyN = ['ligand_distance'
|
||||
, 'ligand_affinity_change'
|
||||
, 'duet_stability_change'
|
||||
, 'ddg_foldx'
|
||||
, 'deepddg'
|
||||
, 'ddg_dynamut2'
|
||||
, 'mmcsm_lig'
|
||||
, 'contacts']
|
||||
|
||||
# Build stability columns ~ gene
|
||||
# Build column names to mask for affinity chanhes
|
||||
if gene.lower() in geneL_basic:
|
||||
X_stabilityN = common_cols_stabiltyN
|
||||
#X_stabilityN = common_cols_stabiltyN
|
||||
gene_affinity_colnames = []# not needed as its the common ones
|
||||
cols_to_mask = ['ligand_affinity_change']
|
||||
|
||||
if gene.lower() in geneL_ppi2:
|
||||
# X_stabilityN = common_cols_stabiltyN + ['mcsm_ppi2_affinity' , 'interface_dist']
|
||||
geneL_ppi2_st_cols = ['mcsm_ppi2_affinity', 'interface_dist']
|
||||
X_stabilityN = common_cols_stabiltyN + geneL_ppi2_st_cols
|
||||
gene_affinity_colnames = ['mcsm_ppi2_affinity', 'interface_dist']
|
||||
#X_stabilityN = common_cols_stabiltyN + geneL_ppi2_st_cols
|
||||
cols_to_mask = ['ligand_affinity_change', 'mcsm_ppi2_affinity']
|
||||
|
||||
if gene.lower() in geneL_na:
|
||||
# X_stabilityN = common_cols_stabiltyN + ['mcsm_na_affinity']
|
||||
geneL_na_st_cols = ['mcsm_na_affinity']
|
||||
X_stabilityN = common_cols_stabiltyN + geneL_na_st_cols
|
||||
gene_affinity_colnames = ['mcsm_na_affinity']
|
||||
#X_stabilityN = common_cols_stabiltyN + geneL_na_st_cols
|
||||
cols_to_mask = ['ligand_affinity_change', 'mcsm_na_affinity']
|
||||
|
||||
if gene.lower() in geneL_na_ppi2:
|
||||
# X_stabilityN = common_cols_stabiltyN + ['mcsm_na_affinity'] + ['mcsm_ppi2_affinity', 'interface_dist']
|
||||
geneL_na_ppi2_st_cols = ['mcsm_na_affinity'] + ['mcsm_ppi2_affinity', 'interface_dist']
|
||||
X_stabilityN = common_cols_stabiltyN + geneL_na_ppi2_st_cols
|
||||
gene_affinity_colnames = ['mcsm_na_affinity'] + ['mcsm_ppi2_affinity', 'interface_dist']
|
||||
#X_stabilityN = common_cols_stabiltyN + geneL_na_ppi2_st_cols
|
||||
cols_to_mask = ['ligand_affinity_change', 'mcsm_na_affinity', 'mcsm_ppi2_affinity']
|
||||
|
||||
|
||||
X_foldX_cols = [ 'electro_rr', 'electro_mm', 'electro_sm', 'electro_ss'
|
||||
, 'disulfide_rr', 'disulfide_mm', 'disulfide_sm', 'disulfide_ss'
|
||||
, 'hbonds_rr', 'hbonds_mm', 'hbonds_sm', 'hbonds_ss'
|
||||
, 'partcov_rr', 'partcov_mm', 'partcov_sm', 'partcov_ss'
|
||||
, 'vdwclashes_rr', 'vdwclashes_mm', 'vdwclashes_sm', 'vdwclashes_ss'
|
||||
, 'volumetric_rr', 'volumetric_mm', 'volumetric_ss'
|
||||
]
|
||||
|
||||
X_str = ['rsa'
|
||||
#, 'asa'
|
||||
, 'kd_values'
|
||||
, 'rd_values']
|
||||
|
||||
X_ssFN = X_stabilityN + X_str + X_foldX_cols
|
||||
|
||||
X_evolFN = ['consurf_score'
|
||||
, 'snap2_score'
|
||||
, 'provean_score']
|
||||
|
||||
X_genomic_mafor = ['maf'
|
||||
, 'logorI'
|
||||
# , 'or_rawI'
|
||||
# , 'or_mychisq'
|
||||
# , 'or_logistic'
|
||||
# , 'or_fisher'
|
||||
# , 'pval_fisher'
|
||||
]
|
||||
|
||||
X_genomic_linegae = ['lineage_proportion'
|
||||
, 'dist_lineage_proportion'
|
||||
#, 'lineage' # could be included as a category but it has L2;L4 formatting
|
||||
, 'lineage_count_all'
|
||||
, 'lineage_count_unique'
|
||||
]
|
||||
|
||||
X_genomicFN = X_genomic_mafor + X_genomic_linegae
|
||||
|
||||
X_aaindexFN = list(aa_df_cols)
|
||||
|
||||
print('\nTotal no. of features for aaindex:', len(X_aaindexFN))
|
||||
|
||||
# numerical feature names
|
||||
numerical_FN = X_ssFN + X_evolFN + X_genomicFN + X_aaindexFN
|
||||
|
||||
# categorical feature names
|
||||
categorical_FN = ['ss_class'
|
||||
# , 'wt_prop_water'
|
||||
# , 'mut_prop_water'
|
||||
# , 'wt_prop_polarity'
|
||||
# , 'mut_prop_polarity'
|
||||
# , 'wt_calcprop'
|
||||
# , 'mut_calcprop'
|
||||
, 'aa_prop_change'
|
||||
, 'electrostatics_change'
|
||||
, 'polarity_change'
|
||||
, 'water_change'
|
||||
, 'drtype_mode_labels' # beware then you can't use it to predict [USED it for uq_v1, not v2]
|
||||
, 'active_site' #[didn't use it for uq_v1]
|
||||
#, 'gene_name' # will be required for the combined stuff
|
||||
]
|
||||
|
||||
#----------------------------------------------
|
||||
# count numerical and categorical features
|
||||
#----------------------------------------------
|
||||
|
||||
print('\nNo. of numerical features:', len(numerical_FN)
|
||||
, '\nNo. of categorical features:', len(categorical_FN))
|
||||
|
||||
#=======================
|
||||
# Masking columns:
|
||||
# (mCSM-lig, mCSM-NA, mCSM-ppi2) values for lig_dist >10
|
||||
#=======================
|
||||
# my_df_ml['mutationinformation'][my_df['ligand_distance']>10].value_counts()
|
||||
# my_df_ml.groupby('mutationinformation')['ligand_distance'].apply(lambda x: (x>10)).value_counts()
|
||||
|
||||
# my_df_ml.loc[(my_df_ml['ligand_distance'] > 10), 'ligand_affinity_change'] = 0
|
||||
# (my_df_ml['ligand_affinity_change'] == 0).sum()
|
||||
|
||||
my_df_ml['mutationinformation'][my_df_ml['ligand_distance']>10].value_counts()
|
||||
my_df_ml.groupby('mutationinformation')['ligand_distance'].apply(lambda x: (x>10)).value_counts()
|
||||
my_df_ml.loc[(my_df_ml['ligand_distance'] > 10), cols_to_mask].value_counts()
|
||||
|
|
@ -544,21 +459,150 @@ def setvars(gene,drug):
|
|||
|
||||
mask_check = my_df_ml[['mutationinformation', 'ligand_distance'] + cols_to_mask]
|
||||
|
||||
#===================================================
|
||||
# write file for check
|
||||
mask_check.sort_values(by = ['ligand_distance'], ascending = True, inplace = True)
|
||||
mask_check.to_csv(outdir + 'ml/' + gene.lower() + '_mask_check.csv')
|
||||
#===================================================
|
||||
###############################################################################
|
||||
#%% Feature groups (FG): Build X for Input ML
|
||||
############################################################################
|
||||
#===========================
|
||||
# FG1: Evolutionary features
|
||||
#===========================
|
||||
X_evolFN = ['consurf_score'
|
||||
, 'snap2_score'
|
||||
, 'provean_score']
|
||||
|
||||
#####################################################################
|
||||
###############################################################################
|
||||
#========================
|
||||
# FG2: Stability features
|
||||
#========================
|
||||
#--------
|
||||
# common
|
||||
#--------
|
||||
X_common_stability_Fnum = [
|
||||
'duet_stability_change'
|
||||
, 'ddg_foldx'
|
||||
, 'deepddg'
|
||||
, 'ddg_dynamut2'
|
||||
, 'contacts']
|
||||
#--------
|
||||
# FoldX
|
||||
#--------
|
||||
X_foldX_Fnum = [ 'electro_rr', 'electro_mm', 'electro_sm', 'electro_ss'
|
||||
, 'disulfide_rr', 'disulfide_mm', 'disulfide_sm', 'disulfide_ss'
|
||||
, 'hbonds_rr', 'hbonds_mm', 'hbonds_sm', 'hbonds_ss'
|
||||
, 'partcov_rr', 'partcov_mm', 'partcov_sm', 'partcov_ss'
|
||||
, 'vdwclashes_rr', 'vdwclashes_mm', 'vdwclashes_sm', 'vdwclashes_ss'
|
||||
, 'volumetric_rr', 'volumetric_mm', 'volumetric_ss']
|
||||
|
||||
X_stability_FN = X_common_stability_Fnum + X_foldX_Fnum
|
||||
|
||||
###############################################################################
|
||||
#===================
|
||||
# FG3: Affinity features
|
||||
#===================
|
||||
common_affinity_Fnum = ['ligand_distance'
|
||||
, 'ligand_affinity_change'
|
||||
, 'mmcsm_lig']
|
||||
|
||||
# if gene.lower() in geneL_basic:
|
||||
# X_affinityFN = common_affinity_Fnum
|
||||
# else:
|
||||
# X_affinityFN = common_affinity_Fnum + gene_affinity_colnames
|
||||
|
||||
X_affinityFN = common_affinity_Fnum + gene_affinity_colnames
|
||||
|
||||
###############################################################################
|
||||
#============================
|
||||
# FG4: Residue level features
|
||||
#============================
|
||||
#-----------
|
||||
# AA index
|
||||
#-----------
|
||||
X_aaindex_Fnum = list(aa_df_cols)
|
||||
print('\nTotal no. of features for aaindex:', len(X_aaindex_Fnum))
|
||||
|
||||
#-----------------
|
||||
# surface area
|
||||
# depth
|
||||
# hydrophobicity
|
||||
#-----------------
|
||||
X_str_Fnum = ['rsa'
|
||||
#, 'asa'
|
||||
, 'kd_values'
|
||||
, 'rd_values']
|
||||
|
||||
#---------------------------
|
||||
# Other aa properties
|
||||
# active site indication
|
||||
#---------------------------
|
||||
X_aap_Fcat = ['ss_class'
|
||||
# , 'wt_prop_water'
|
||||
# , 'mut_prop_water'
|
||||
# , 'wt_prop_polarity'
|
||||
# , 'mut_prop_polarity'
|
||||
# , 'wt_calcprop'
|
||||
# , 'mut_calcprop'
|
||||
, 'aa_prop_change'
|
||||
, 'electrostatics_change'
|
||||
, 'polarity_change'
|
||||
, 'water_change'
|
||||
, 'active_site']
|
||||
|
||||
X_resprop_FN = X_aaindex_Fnum + X_str_Fnum + X_aap_Fcat
|
||||
###############################################################################
|
||||
#========================
|
||||
# FG5: Genomic features
|
||||
#========================
|
||||
X_gn_mafor_Fnum = ['maf'
|
||||
#, 'logorI'
|
||||
# , 'or_rawI'
|
||||
# , 'or_mychisq'
|
||||
# , 'or_logistic'
|
||||
# , 'or_fisher'
|
||||
# , 'pval_fisher'
|
||||
]
|
||||
|
||||
X_gn_linegae_Fnum = ['lineage_proportion'
|
||||
, 'dist_lineage_proportion'
|
||||
#, 'lineage' # could be included as a category but it has L2;L4 formatting
|
||||
, 'lineage_count_all'
|
||||
, 'lineage_count_unique'
|
||||
]
|
||||
|
||||
# X_gn_Fcat = ['drtype_mode_labels' # beware then you can't use it to predict [USED it for uq_v1, not v2]
|
||||
# #, 'gene_name' # will be required for the combined stuff
|
||||
# ]
|
||||
X_gn_Fcat = []
|
||||
|
||||
X_genomicFN = X_gn_mafor_Fnum + X_gn_linegae_Fnum + X_gn_Fcat
|
||||
###############################################################################
|
||||
#========================
|
||||
# FG6 collapsed: Structural : Atability + Affinity + ResidueProp
|
||||
#========================
|
||||
X_structural_FN = X_stability_FN + X_affinityFN + X_resprop_FN
|
||||
|
||||
###############################################################################
|
||||
#========================
|
||||
# BUILDING all features
|
||||
#========================
|
||||
all_featuresN = X_evolFN + X_structural_FN + X_genomicFN
|
||||
|
||||
###############################################################################
|
||||
#%% Define training and test data
|
||||
#================================================================
|
||||
# Training and Blind test [COMPLETE data]: 70/30
|
||||
|
||||
# Use complete data, call the 30% as blind test
|
||||
# Training and BLIND test set: 70/30
|
||||
# dst with actual values : training set
|
||||
# dst with imputed values : THROW AWAY [unrepresentative]
|
||||
#================================================================
|
||||
my_df_ml[drug].isna().sum()
|
||||
|
||||
# blind_test_df = my_df_ml[my_df_ml[drug].isna()]
|
||||
# blind_test_df.shape
|
||||
|
||||
#training_df = my_df_ml[my_df_ml[drug].notna()]
|
||||
#training_df = my_df_ml[my_df_ml[drug].notna()]
|
||||
#training_df.shape
|
||||
|
||||
training_df = my_df_ml.copy()
|
||||
|
|
@ -568,80 +612,14 @@ def setvars(gene,drug):
|
|||
training_df['dst_mode'].value_counts()
|
||||
|
||||
####################################################################
|
||||
|
||||
###############################################################################
|
||||
###############################################################################
|
||||
# #%% extracting dfs based on numerical, categorical column names
|
||||
# #----------------------------------
|
||||
# # WITHOUT the target var included
|
||||
# #----------------------------------
|
||||
# num_df = training_df[numerical_FN]
|
||||
# num_df.shape
|
||||
|
||||
# cat_df = training_df[categorical_FN]
|
||||
# cat_df.shape
|
||||
|
||||
# all_df = training_df[numerical_FN + categorical_FN]
|
||||
# all_df.shape
|
||||
|
||||
# #------------------------------
|
||||
# # WITH the target var included:
|
||||
# #'wtgt': with target
|
||||
# #------------------------------
|
||||
# # drug and dst_mode should be the same thing
|
||||
# num_df_wtgt = training_df[numerical_FN + ['dst_mode']]
|
||||
# num_df_wtgt.shape
|
||||
|
||||
# cat_df_wtgt = training_df[categorical_FN + ['dst_mode']]
|
||||
# cat_df_wtgt.shape
|
||||
|
||||
# all_df_wtgt = training_df[numerical_FN + categorical_FN + ['dst_mode']]
|
||||
# all_df_wtgt.shape
|
||||
|
||||
#%%########################################################################
|
||||
# #============
|
||||
# # ML data: OLD
|
||||
# #============
|
||||
# #------
|
||||
# # X: Training and Blind test (BTS)
|
||||
# #------
|
||||
# X = all_df_wtgt[numerical_FN + categorical_FN] # training data ALL
|
||||
# X_bts = blind_test_df[numerical_FN + categorical_FN] # blind test data ALL
|
||||
# #X = all_df_wtgt[numerical_FN] # training numerical only
|
||||
# #X_bts = blind_test_df[numerical_FN] # blind test data numerical
|
||||
|
||||
# #------
|
||||
# # y
|
||||
# #------
|
||||
# y = all_df_wtgt['dst_mode'] # training data y
|
||||
# y_bts = blind_test_df['dst_mode'] # blind data test y
|
||||
|
||||
# #X_bts_wt = blind_test_df[numerical_FN + ['dst_mode']]
|
||||
|
||||
# # Quick check
|
||||
# #(X['ligand_affinity_change']==0).sum() == (X['ligand_distance']>10).sum()
|
||||
# for i in range(len(cols_to_mask)):
|
||||
# ind = i+1
|
||||
# print('\nindex:', i, '\nind:', ind)
|
||||
# print('\nMask count check:'
|
||||
# , (my_df_ml[cols_to_mask[i]]==0).sum() == (my_df_ml['ligand_distance']>10).sum()
|
||||
# )
|
||||
|
||||
# print('Original Data\n', Counter(y)
|
||||
# , 'Data dim:', X.shape)
|
||||
|
||||
###############################################################################
|
||||
###############################################################################
|
||||
#====================================
|
||||
# ML data: Train test split [COMPLETE data]: 70/30
|
||||
# ML data: Train test split: 70/30
|
||||
# with stratification
|
||||
# 70% : training_data for CV
|
||||
# 30% : blind test
|
||||
#=====================================
|
||||
|
||||
# features: all_df or
|
||||
x_features = training_df[numerical_FN + categorical_FN]
|
||||
y_target = training_df['dst_mode']
|
||||
x_features = training_df[all_featuresN]
|
||||
y_target = training_df['dst_mode']
|
||||
|
||||
# sanity check
|
||||
if not 'dst_mode' in x_features.columns:
|
||||
|
|
@ -652,7 +630,9 @@ def setvars(gene,drug):
|
|||
#https://towardsdatascience.com/finally-why-we-use-an-80-20-split-for-training-and-test-data-plus-an-alternative-method-oh-yes-edc77e96295d
|
||||
else:
|
||||
sys.exit('\nFAIL: x_features has target variable included. FIX it and rerun!')
|
||||
|
||||
#-------------------
|
||||
# train-test split
|
||||
#-------------------
|
||||
#x_train, x_test, y_train, y_test # traditional var_names
|
||||
# so my downstream code doesn't need to change
|
||||
X, X_bts, y, y_bts = train_test_split(x_features, y_target
|
||||
|
|
@ -665,15 +645,64 @@ def setvars(gene,drug):
|
|||
yc2 = Counter(y_bts)
|
||||
yc2_ratio = yc2[0]/yc2[1]
|
||||
|
||||
###############################################################################
|
||||
#======================================================
|
||||
# Determine categorical and numerical features
|
||||
#======================================================
|
||||
numerical_cols = X.select_dtypes(include=['int64', 'float64']).columns
|
||||
numerical_cols
|
||||
categorical_cols = X.select_dtypes(include=['object', 'bool']).columns
|
||||
categorical_cols
|
||||
|
||||
################################################################################
|
||||
# IMPORTANT sanity checks
|
||||
if len(X.columns) == len(X_evolFN) + len(X_stability_FN) + len(X_affinityFN) + len(X_resprop_FN) + len(X_genomicFN):
|
||||
print('\nPASS: ML data with input features, training and test generated...'
|
||||
, '\n\nTotal no. of input features:' , len(X.columns)
|
||||
, '\n--------No. of numerical features:' , len(numerical_cols)
|
||||
, '\n--------No. of categorical features:' , len(categorical_cols)
|
||||
|
||||
, '\n\nTotal no. of evolutionary features:' , len(X_evolFN)
|
||||
|
||||
, '\n\nTotal no. of stability features:' , len(X_stability_FN)
|
||||
, '\n--------Common stabilty cols:' , len(X_common_stability_Fnum)
|
||||
, '\n--------Foldx cols:' , len(X_foldX_Fnum)
|
||||
|
||||
, '\n\nTotal no. of affinity features:' , len(X_affinityFN)
|
||||
, '\n--------Common affinity cols:' , len(common_affinity_Fnum)
|
||||
, '\n--------Gene specific affinity cols:' , len(gene_affinity_colnames)
|
||||
|
||||
, '\n\nTotal no. of residue level features:', len(X_resprop_FN)
|
||||
, '\n--------AA index cols:' , len(X_aaindex_Fnum)
|
||||
, '\n--------Residue Prop cols:' , len(X_str_Fnum)
|
||||
, '\n--------AA change Prop cols:' , len(X_aap_Fcat)
|
||||
|
||||
, '\n\nTotal no. of genomic features:' , len(X_genomicFN)
|
||||
, '\n--------MAF+OR cols:' , len(X_gn_mafor_Fnum)
|
||||
, '\n--------Lineage cols:' , len(X_gn_linegae_Fnum)
|
||||
, '\n--------Other cols:' , len(X_gn_Fcat)
|
||||
)
|
||||
else:
|
||||
print('\nFAIL: numbers mismatch'
|
||||
, '\nExpected:',len(X_evolFN) + len(X_stability_FN) + len(X_affinityFN) + len(X_resprop_FN) + len(X_genomicFN)
|
||||
, '\nGot:', len(X.columns))
|
||||
sys.exit()
|
||||
###############################################################################
|
||||
print('\n-------------------------------------------------------------'
|
||||
, '\nSuccessfully split data with stratification [COMPLETE data]: 70/30'
|
||||
, '\nInput features data size:', x_features.shape
|
||||
, '\nTrain data size:', X.shape
|
||||
, '\nTest data size:', X_bts.shape
|
||||
, '\nSuccessfully split data: ALL features'
|
||||
, '\nactual values: training set'
|
||||
, '\nSplit:', tts_split
|
||||
#, '\nimputed values: blind test set'
|
||||
|
||||
, '\n\nTotal data size:', len(X) + len(X_bts)
|
||||
|
||||
, '\n\nTrain data size:', X.shape
|
||||
, '\ny_train numbers:', yc1
|
||||
, '\ny_train ratio:',yc1_ratio
|
||||
, '\n'
|
||||
|
||||
, '\n\nTest data size:', X_bts.shape
|
||||
, '\ny_test_numbers:', yc2
|
||||
|
||||
, '\n\ny_train ratio:',yc1_ratio
|
||||
, '\ny_test ratio:', yc2_ratio
|
||||
, '\n-------------------------------------------------------------'
|
||||
)
|
||||
|
|
@ -772,3 +801,8 @@ def setvars(gene,drug):
|
|||
|
||||
###############################################################################
|
||||
# TODO: Find over and undersampling JUST for categorical data
|
||||
###########################################################################
|
||||
|
||||
print('\n#################################################################'
|
||||
, '\nDim of X for gene:', gene.lower(), '\n', X.shape
|
||||
, '\n###############################################################')
|
||||
|
|
@ -34,7 +34,11 @@ def setvars(gene,drug):
|
|||
from sklearn.model_selection import StratifiedKFold,RepeatedStratifiedKFold, RepeatedKFold
|
||||
|
||||
from sklearn.pipeline import Pipeline, make_pipeline
|
||||
import argparse
|
||||
import re
|
||||
#%% GLOBALS
|
||||
tts_split = "80_20"
|
||||
|
||||
rs = {'random_state': 42}
|
||||
njobs = {'n_jobs': 10}
|
||||
|
||||
|
|
@ -57,11 +61,9 @@ def setvars(gene,drug):
|
|||
|
||||
mcc_score_fn = {'mcc': make_scorer(matthews_corrcoef)}
|
||||
jacc_score_fn = {'jcc': make_scorer(jaccard_score)}
|
||||
|
||||
#%% FOR LATER: Combine ED logo data
|
||||
###########################################################################
|
||||
rs = {'random_state': 42}
|
||||
njobs = {'n_jobs': 10}
|
||||
|
||||
homedir = os.path.expanduser("~")
|
||||
|
||||
geneL_basic = ['pnca']
|
||||
|
|
@ -422,118 +424,31 @@ def setvars(gene,drug):
|
|||
#==========================
|
||||
my_df_ml = my_df.copy()
|
||||
|
||||
#%% Build X: input for ML
|
||||
common_cols_stabiltyN = ['ligand_distance'
|
||||
, 'ligand_affinity_change'
|
||||
, 'duet_stability_change'
|
||||
, 'ddg_foldx'
|
||||
, 'deepddg'
|
||||
, 'ddg_dynamut2'
|
||||
, 'mmcsm_lig'
|
||||
, 'contacts']
|
||||
|
||||
# Build stability columns ~ gene
|
||||
# Build column names to mask for affinity chanhes
|
||||
if gene.lower() in geneL_basic:
|
||||
X_stabilityN = common_cols_stabiltyN
|
||||
#X_stabilityN = common_cols_stabiltyN
|
||||
gene_affinity_colnames = []# not needed as its the common ones
|
||||
cols_to_mask = ['ligand_affinity_change']
|
||||
|
||||
if gene.lower() in geneL_ppi2:
|
||||
# X_stabilityN = common_cols_stabiltyN + ['mcsm_ppi2_affinity' , 'interface_dist']
|
||||
geneL_ppi2_st_cols = ['mcsm_ppi2_affinity', 'interface_dist']
|
||||
X_stabilityN = common_cols_stabiltyN + geneL_ppi2_st_cols
|
||||
gene_affinity_colnames = ['mcsm_ppi2_affinity', 'interface_dist']
|
||||
#X_stabilityN = common_cols_stabiltyN + geneL_ppi2_st_cols
|
||||
cols_to_mask = ['ligand_affinity_change', 'mcsm_ppi2_affinity']
|
||||
|
||||
if gene.lower() in geneL_na:
|
||||
# X_stabilityN = common_cols_stabiltyN + ['mcsm_na_affinity']
|
||||
geneL_na_st_cols = ['mcsm_na_affinity']
|
||||
X_stabilityN = common_cols_stabiltyN + geneL_na_st_cols
|
||||
gene_affinity_colnames = ['mcsm_na_affinity']
|
||||
#X_stabilityN = common_cols_stabiltyN + geneL_na_st_cols
|
||||
cols_to_mask = ['ligand_affinity_change', 'mcsm_na_affinity']
|
||||
|
||||
if gene.lower() in geneL_na_ppi2:
|
||||
# X_stabilityN = common_cols_stabiltyN + ['mcsm_na_affinity'] + ['mcsm_ppi2_affinity', 'interface_dist']
|
||||
geneL_na_ppi2_st_cols = ['mcsm_na_affinity'] + ['mcsm_ppi2_affinity', 'interface_dist']
|
||||
X_stabilityN = common_cols_stabiltyN + geneL_na_ppi2_st_cols
|
||||
gene_affinity_colnames = ['mcsm_na_affinity'] + ['mcsm_ppi2_affinity', 'interface_dist']
|
||||
#X_stabilityN = common_cols_stabiltyN + geneL_na_ppi2_st_cols
|
||||
cols_to_mask = ['ligand_affinity_change', 'mcsm_na_affinity', 'mcsm_ppi2_affinity']
|
||||
|
||||
|
||||
X_foldX_cols = [ 'electro_rr', 'electro_mm', 'electro_sm', 'electro_ss'
|
||||
, 'disulfide_rr', 'disulfide_mm', 'disulfide_sm', 'disulfide_ss'
|
||||
, 'hbonds_rr', 'hbonds_mm', 'hbonds_sm', 'hbonds_ss'
|
||||
, 'partcov_rr', 'partcov_mm', 'partcov_sm', 'partcov_ss'
|
||||
, 'vdwclashes_rr', 'vdwclashes_mm', 'vdwclashes_sm', 'vdwclashes_ss'
|
||||
, 'volumetric_rr', 'volumetric_mm', 'volumetric_ss'
|
||||
]
|
||||
|
||||
X_str = ['rsa'
|
||||
#, 'asa'
|
||||
, 'kd_values'
|
||||
, 'rd_values']
|
||||
|
||||
X_ssFN = X_stabilityN + X_str + X_foldX_cols
|
||||
|
||||
X_evolFN = ['consurf_score'
|
||||
, 'snap2_score'
|
||||
, 'provean_score']
|
||||
|
||||
X_genomic_mafor = ['maf'
|
||||
, 'logorI'
|
||||
# , 'or_rawI'
|
||||
# , 'or_mychisq'
|
||||
# , 'or_logistic'
|
||||
# , 'or_fisher'
|
||||
# , 'pval_fisher'
|
||||
]
|
||||
|
||||
X_genomic_linegae = ['lineage_proportion'
|
||||
, 'dist_lineage_proportion'
|
||||
#, 'lineage' # could be included as a category but it has L2;L4 formatting
|
||||
, 'lineage_count_all'
|
||||
, 'lineage_count_unique'
|
||||
]
|
||||
|
||||
X_genomicFN = X_genomic_mafor + X_genomic_linegae
|
||||
|
||||
X_aaindexFN = list(aa_df_cols)
|
||||
|
||||
print('\nTotal no. of features for aaindex:', len(X_aaindexFN))
|
||||
|
||||
# numerical feature names
|
||||
numerical_FN = X_ssFN + X_evolFN + X_genomicFN + X_aaindexFN
|
||||
|
||||
# categorical feature names
|
||||
categorical_FN = ['ss_class'
|
||||
# , 'wt_prop_water'
|
||||
# , 'mut_prop_water'
|
||||
# , 'wt_prop_polarity'
|
||||
# , 'mut_prop_polarity'
|
||||
# , 'wt_calcprop'
|
||||
# , 'mut_calcprop'
|
||||
, 'aa_prop_change'
|
||||
, 'electrostatics_change'
|
||||
, 'polarity_change'
|
||||
, 'water_change'
|
||||
, 'drtype_mode_labels' # beware then you can't use it to predict [USED it for uq_v1, not v2]
|
||||
, 'active_site' #[didn't use it for uq_v1]
|
||||
#, 'gene_name' # will be required for the combined stuff
|
||||
]
|
||||
|
||||
#----------------------------------------------
|
||||
# count numerical and categorical features
|
||||
#----------------------------------------------
|
||||
|
||||
print('\nNo. of numerical features:', len(numerical_FN)
|
||||
, '\nNo. of categorical features:', len(categorical_FN))
|
||||
|
||||
#=======================
|
||||
# Masking columns:
|
||||
# (mCSM-lig, mCSM-NA, mCSM-ppi2) values for lig_dist >10
|
||||
#=======================
|
||||
# my_df_ml['mutationinformation'][my_df['ligand_distance']>10].value_counts()
|
||||
# my_df_ml.groupby('mutationinformation')['ligand_distance'].apply(lambda x: (x>10)).value_counts()
|
||||
|
||||
# my_df_ml.loc[(my_df_ml['ligand_distance'] > 10), 'ligand_affinity_change'] = 0
|
||||
# (my_df_ml['ligand_affinity_change'] == 0).sum()
|
||||
|
||||
my_df_ml['mutationinformation'][my_df_ml['ligand_distance']>10].value_counts()
|
||||
my_df_ml.groupby('mutationinformation')['ligand_distance'].apply(lambda x: (x>10)).value_counts()
|
||||
my_df_ml.loc[(my_df_ml['ligand_distance'] > 10), cols_to_mask].value_counts()
|
||||
|
|
@ -544,21 +459,150 @@ def setvars(gene,drug):
|
|||
|
||||
mask_check = my_df_ml[['mutationinformation', 'ligand_distance'] + cols_to_mask]
|
||||
|
||||
#===================================================
|
||||
# write file for check
|
||||
mask_check.sort_values(by = ['ligand_distance'], ascending = True, inplace = True)
|
||||
mask_check.to_csv(outdir + 'ml/' + gene.lower() + '_mask_check.csv')
|
||||
#===================================================
|
||||
###############################################################################
|
||||
#%% Feature groups (FG): Build X for Input ML
|
||||
############################################################################
|
||||
#===========================
|
||||
# FG1: Evolutionary features
|
||||
#===========================
|
||||
X_evolFN = ['consurf_score'
|
||||
, 'snap2_score'
|
||||
, 'provean_score']
|
||||
|
||||
#####################################################################
|
||||
###############################################################################
|
||||
#========================
|
||||
# FG2: Stability features
|
||||
#========================
|
||||
#--------
|
||||
# common
|
||||
#--------
|
||||
X_common_stability_Fnum = [
|
||||
'duet_stability_change'
|
||||
, 'ddg_foldx'
|
||||
, 'deepddg'
|
||||
, 'ddg_dynamut2'
|
||||
, 'contacts']
|
||||
#--------
|
||||
# FoldX
|
||||
#--------
|
||||
X_foldX_Fnum = [ 'electro_rr', 'electro_mm', 'electro_sm', 'electro_ss'
|
||||
, 'disulfide_rr', 'disulfide_mm', 'disulfide_sm', 'disulfide_ss'
|
||||
, 'hbonds_rr', 'hbonds_mm', 'hbonds_sm', 'hbonds_ss'
|
||||
, 'partcov_rr', 'partcov_mm', 'partcov_sm', 'partcov_ss'
|
||||
, 'vdwclashes_rr', 'vdwclashes_mm', 'vdwclashes_sm', 'vdwclashes_ss'
|
||||
, 'volumetric_rr', 'volumetric_mm', 'volumetric_ss']
|
||||
|
||||
X_stability_FN = X_common_stability_Fnum + X_foldX_Fnum
|
||||
|
||||
###############################################################################
|
||||
#===================
|
||||
# FG3: Affinity features
|
||||
#===================
|
||||
common_affinity_Fnum = ['ligand_distance'
|
||||
, 'ligand_affinity_change'
|
||||
, 'mmcsm_lig']
|
||||
|
||||
# if gene.lower() in geneL_basic:
|
||||
# X_affinityFN = common_affinity_Fnum
|
||||
# else:
|
||||
# X_affinityFN = common_affinity_Fnum + gene_affinity_colnames
|
||||
|
||||
X_affinityFN = common_affinity_Fnum + gene_affinity_colnames
|
||||
|
||||
###############################################################################
|
||||
#============================
|
||||
# FG4: Residue level features
|
||||
#============================
|
||||
#-----------
|
||||
# AA index
|
||||
#-----------
|
||||
X_aaindex_Fnum = list(aa_df_cols)
|
||||
print('\nTotal no. of features for aaindex:', len(X_aaindex_Fnum))
|
||||
|
||||
#-----------------
|
||||
# surface area
|
||||
# depth
|
||||
# hydrophobicity
|
||||
#-----------------
|
||||
X_str_Fnum = ['rsa'
|
||||
#, 'asa'
|
||||
, 'kd_values'
|
||||
, 'rd_values']
|
||||
|
||||
#---------------------------
|
||||
# Other aa properties
|
||||
# active site indication
|
||||
#---------------------------
|
||||
X_aap_Fcat = ['ss_class'
|
||||
# , 'wt_prop_water'
|
||||
# , 'mut_prop_water'
|
||||
# , 'wt_prop_polarity'
|
||||
# , 'mut_prop_polarity'
|
||||
# , 'wt_calcprop'
|
||||
# , 'mut_calcprop'
|
||||
, 'aa_prop_change'
|
||||
, 'electrostatics_change'
|
||||
, 'polarity_change'
|
||||
, 'water_change'
|
||||
, 'active_site']
|
||||
|
||||
X_resprop_FN = X_aaindex_Fnum + X_str_Fnum + X_aap_Fcat
|
||||
###############################################################################
|
||||
#========================
|
||||
# FG5: Genomic features
|
||||
#========================
|
||||
X_gn_mafor_Fnum = ['maf'
|
||||
#, 'logorI'
|
||||
# , 'or_rawI'
|
||||
# , 'or_mychisq'
|
||||
# , 'or_logistic'
|
||||
# , 'or_fisher'
|
||||
# , 'pval_fisher'
|
||||
]
|
||||
|
||||
X_gn_linegae_Fnum = ['lineage_proportion'
|
||||
, 'dist_lineage_proportion'
|
||||
#, 'lineage' # could be included as a category but it has L2;L4 formatting
|
||||
, 'lineage_count_all'
|
||||
, 'lineage_count_unique'
|
||||
]
|
||||
|
||||
# X_gn_Fcat = ['drtype_mode_labels' # beware then you can't use it to predict [USED it for uq_v1, not v2]
|
||||
# #, 'gene_name' # will be required for the combined stuff
|
||||
# ]
|
||||
X_gn_Fcat = []
|
||||
|
||||
X_genomicFN = X_gn_mafor_Fnum + X_gn_linegae_Fnum + X_gn_Fcat
|
||||
###############################################################################
|
||||
#========================
|
||||
# FG6 collapsed: Structural : Atability + Affinity + ResidueProp
|
||||
#========================
|
||||
X_structural_FN = X_stability_FN + X_affinityFN + X_resprop_FN
|
||||
|
||||
###############################################################################
|
||||
#========================
|
||||
# BUILDING all features
|
||||
#========================
|
||||
all_featuresN = X_evolFN + X_structural_FN + X_genomicFN
|
||||
|
||||
###############################################################################
|
||||
#%% Define training and test data
|
||||
#================================================================
|
||||
# Training and Blind test [COMPLETE data]: 80/20
|
||||
|
||||
# Use complete data, call the 20% as blind test
|
||||
# Training and BLIND test set: 80/20
|
||||
# dst with actual values : training set
|
||||
# dst with imputed values : THROW AWAY [unrepresentative]
|
||||
#================================================================
|
||||
my_df_ml[drug].isna().sum()
|
||||
|
||||
# blind_test_df = my_df_ml[my_df_ml[drug].isna()]
|
||||
# blind_test_df.shape
|
||||
|
||||
#training_df = my_df_ml[my_df_ml[drug].notna()]
|
||||
#training_df = my_df_ml[my_df_ml[drug].notna()]
|
||||
#training_df.shape
|
||||
|
||||
training_df = my_df_ml.copy()
|
||||
|
|
@ -568,80 +612,14 @@ def setvars(gene,drug):
|
|||
training_df['dst_mode'].value_counts()
|
||||
|
||||
####################################################################
|
||||
|
||||
###############################################################################
|
||||
###############################################################################
|
||||
# #%% extracting dfs based on numerical, categorical column names
|
||||
# #----------------------------------
|
||||
# # WITHOUT the target var included
|
||||
# #----------------------------------
|
||||
# num_df = training_df[numerical_FN]
|
||||
# num_df.shape
|
||||
|
||||
# cat_df = training_df[categorical_FN]
|
||||
# cat_df.shape
|
||||
|
||||
# all_df = training_df[numerical_FN + categorical_FN]
|
||||
# all_df.shape
|
||||
|
||||
# #------------------------------
|
||||
# # WITH the target var included:
|
||||
# #'wtgt': with target
|
||||
# #------------------------------
|
||||
# # drug and dst_mode should be the same thing
|
||||
# num_df_wtgt = training_df[numerical_FN + ['dst_mode']]
|
||||
# num_df_wtgt.shape
|
||||
|
||||
# cat_df_wtgt = training_df[categorical_FN + ['dst_mode']]
|
||||
# cat_df_wtgt.shape
|
||||
|
||||
# all_df_wtgt = training_df[numerical_FN + categorical_FN + ['dst_mode']]
|
||||
# all_df_wtgt.shape
|
||||
|
||||
#%%########################################################################
|
||||
# #============
|
||||
# # ML data: OLD
|
||||
# #============
|
||||
# #------
|
||||
# # X: Training and Blind test (BTS)
|
||||
# #------
|
||||
# X = all_df_wtgt[numerical_FN + categorical_FN] # training data ALL
|
||||
# X_bts = blind_test_df[numerical_FN + categorical_FN] # blind test data ALL
|
||||
# #X = all_df_wtgt[numerical_FN] # training numerical only
|
||||
# #X_bts = blind_test_df[numerical_FN] # blind test data numerical
|
||||
|
||||
# #------
|
||||
# # y
|
||||
# #------
|
||||
# y = all_df_wtgt['dst_mode'] # training data y
|
||||
# y_bts = blind_test_df['dst_mode'] # blind data test y
|
||||
|
||||
# #X_bts_wt = blind_test_df[numerical_FN + ['dst_mode']]
|
||||
|
||||
# # Quick check
|
||||
# #(X['ligand_affinity_change']==0).sum() == (X['ligand_distance']>10).sum()
|
||||
# for i in range(len(cols_to_mask)):
|
||||
# ind = i+1
|
||||
# print('\nindex:', i, '\nind:', ind)
|
||||
# print('\nMask count check:'
|
||||
# , (my_df_ml[cols_to_mask[i]]==0).sum() == (my_df_ml['ligand_distance']>10).sum()
|
||||
# )
|
||||
|
||||
# print('Original Data\n', Counter(y)
|
||||
# , 'Data dim:', X.shape)
|
||||
|
||||
###############################################################################
|
||||
###############################################################################
|
||||
#====================================
|
||||
# ML data: Train test split [COMPLETE data]: 80/20
|
||||
# ML data: Train test split: 80/20
|
||||
# with stratification
|
||||
# 80% : training_data for CV
|
||||
# 20% : blind test
|
||||
#=====================================
|
||||
|
||||
# features: all_df or
|
||||
x_features = training_df[numerical_FN + categorical_FN]
|
||||
y_target = training_df['dst_mode']
|
||||
x_features = training_df[all_featuresN]
|
||||
y_target = training_df['dst_mode']
|
||||
|
||||
# sanity check
|
||||
if not 'dst_mode' in x_features.columns:
|
||||
|
|
@ -652,7 +630,9 @@ def setvars(gene,drug):
|
|||
#https://towardsdatascience.com/finally-why-we-use-an-80-20-split-for-training-and-test-data-plus-an-alternative-method-oh-yes-edc77e96295d
|
||||
else:
|
||||
sys.exit('\nFAIL: x_features has target variable included. FIX it and rerun!')
|
||||
|
||||
#-------------------
|
||||
# train-test split
|
||||
#-------------------
|
||||
#x_train, x_test, y_train, y_test # traditional var_names
|
||||
# so my downstream code doesn't need to change
|
||||
X, X_bts, y, y_bts = train_test_split(x_features, y_target
|
||||
|
|
@ -665,15 +645,64 @@ def setvars(gene,drug):
|
|||
yc2 = Counter(y_bts)
|
||||
yc2_ratio = yc2[0]/yc2[1]
|
||||
|
||||
###############################################################################
|
||||
#======================================================
|
||||
# Determine categorical and numerical features
|
||||
#======================================================
|
||||
numerical_cols = X.select_dtypes(include=['int64', 'float64']).columns
|
||||
numerical_cols
|
||||
categorical_cols = X.select_dtypes(include=['object', 'bool']).columns
|
||||
categorical_cols
|
||||
|
||||
################################################################################
|
||||
# IMPORTANT sanity checks
|
||||
if len(X.columns) == len(X_evolFN) + len(X_stability_FN) + len(X_affinityFN) + len(X_resprop_FN) + len(X_genomicFN):
|
||||
print('\nPASS: ML data with input features, training and test generated...'
|
||||
, '\n\nTotal no. of input features:' , len(X.columns)
|
||||
, '\n--------No. of numerical features:' , len(numerical_cols)
|
||||
, '\n--------No. of categorical features:' , len(categorical_cols)
|
||||
|
||||
, '\n\nTotal no. of evolutionary features:' , len(X_evolFN)
|
||||
|
||||
, '\n\nTotal no. of stability features:' , len(X_stability_FN)
|
||||
, '\n--------Common stabilty cols:' , len(X_common_stability_Fnum)
|
||||
, '\n--------Foldx cols:' , len(X_foldX_Fnum)
|
||||
|
||||
, '\n\nTotal no. of affinity features:' , len(X_affinityFN)
|
||||
, '\n--------Common affinity cols:' , len(common_affinity_Fnum)
|
||||
, '\n--------Gene specific affinity cols:' , len(gene_affinity_colnames)
|
||||
|
||||
, '\n\nTotal no. of residue level features:', len(X_resprop_FN)
|
||||
, '\n--------AA index cols:' , len(X_aaindex_Fnum)
|
||||
, '\n--------Residue Prop cols:' , len(X_str_Fnum)
|
||||
, '\n--------AA change Prop cols:' , len(X_aap_Fcat)
|
||||
|
||||
, '\n\nTotal no. of genomic features:' , len(X_genomicFN)
|
||||
, '\n--------MAF+OR cols:' , len(X_gn_mafor_Fnum)
|
||||
, '\n--------Lineage cols:' , len(X_gn_linegae_Fnum)
|
||||
, '\n--------Other cols:' , len(X_gn_Fcat)
|
||||
)
|
||||
else:
|
||||
print('\nFAIL: numbers mismatch'
|
||||
, '\nExpected:',len(X_evolFN) + len(X_stability_FN) + len(X_affinityFN) + len(X_resprop_FN) + len(X_genomicFN)
|
||||
, '\nGot:', len(X.columns))
|
||||
sys.exit()
|
||||
###############################################################################
|
||||
print('\n-------------------------------------------------------------'
|
||||
, '\nSuccessfully split data with stratification [COMPLETE data]: 80/20'
|
||||
, '\nInput features data size:', x_features.shape
|
||||
, '\nTrain data size:', X.shape
|
||||
, '\nTest data size:', X_bts.shape
|
||||
, '\nSuccessfully split data: ALL features'
|
||||
, '\nactual values: training set'
|
||||
, '\nSplit:', tts_split
|
||||
#, '\nimputed values: blind test set'
|
||||
|
||||
, '\n\nTotal data size:', len(X) + len(X_bts)
|
||||
|
||||
, '\n\nTrain data size:', X.shape
|
||||
, '\ny_train numbers:', yc1
|
||||
, '\ny_train ratio:',yc1_ratio
|
||||
, '\n'
|
||||
|
||||
, '\n\nTest data size:', X_bts.shape
|
||||
, '\ny_test_numbers:', yc2
|
||||
|
||||
, '\n\ny_train ratio:',yc1_ratio
|
||||
, '\ny_test ratio:', yc2_ratio
|
||||
, '\n-------------------------------------------------------------'
|
||||
)
|
||||
|
|
@ -772,3 +801,8 @@ def setvars(gene,drug):
|
|||
|
||||
###############################################################################
|
||||
# TODO: Find over and undersampling JUST for categorical data
|
||||
###########################################################################
|
||||
|
||||
print('\n#################################################################'
|
||||
, '\nDim of X for gene:', gene.lower(), '\n', X.shape
|
||||
, '\n###############################################################')
|
||||
|
|
|
|||
|
|
@ -34,7 +34,11 @@ def setvars(gene,drug):
|
|||
from sklearn.model_selection import StratifiedKFold,RepeatedStratifiedKFold, RepeatedKFold
|
||||
|
||||
from sklearn.pipeline import Pipeline, make_pipeline
|
||||
import argparse
|
||||
import re
|
||||
#%% GLOBALS
|
||||
tts_split = "sl"
|
||||
|
||||
rs = {'random_state': 42}
|
||||
njobs = {'n_jobs': 10}
|
||||
|
||||
|
|
@ -57,11 +61,9 @@ def setvars(gene,drug):
|
|||
|
||||
mcc_score_fn = {'mcc': make_scorer(matthews_corrcoef)}
|
||||
jacc_score_fn = {'jcc': make_scorer(jaccard_score)}
|
||||
|
||||
#%% FOR LATER: Combine ED logo data
|
||||
###########################################################################
|
||||
rs = {'random_state': 42}
|
||||
njobs = {'n_jobs': 10}
|
||||
|
||||
homedir = os.path.expanduser("~")
|
||||
|
||||
geneL_basic = ['pnca']
|
||||
|
|
@ -422,118 +424,31 @@ def setvars(gene,drug):
|
|||
#==========================
|
||||
my_df_ml = my_df.copy()
|
||||
|
||||
#%% Build X: input for ML
|
||||
common_cols_stabiltyN = ['ligand_distance'
|
||||
, 'ligand_affinity_change'
|
||||
, 'duet_stability_change'
|
||||
, 'ddg_foldx'
|
||||
, 'deepddg'
|
||||
, 'ddg_dynamut2'
|
||||
, 'mmcsm_lig'
|
||||
, 'contacts']
|
||||
|
||||
# Build stability columns ~ gene
|
||||
# Build column names to mask for affinity chanhes
|
||||
if gene.lower() in geneL_basic:
|
||||
X_stabilityN = common_cols_stabiltyN
|
||||
#X_stabilityN = common_cols_stabiltyN
|
||||
gene_affinity_colnames = []# not needed as its the common ones
|
||||
cols_to_mask = ['ligand_affinity_change']
|
||||
|
||||
if gene.lower() in geneL_ppi2:
|
||||
# X_stabilityN = common_cols_stabiltyN + ['mcsm_ppi2_affinity' , 'interface_dist']
|
||||
geneL_ppi2_st_cols = ['mcsm_ppi2_affinity', 'interface_dist']
|
||||
X_stabilityN = common_cols_stabiltyN + geneL_ppi2_st_cols
|
||||
gene_affinity_colnames = ['mcsm_ppi2_affinity', 'interface_dist']
|
||||
#X_stabilityN = common_cols_stabiltyN + geneL_ppi2_st_cols
|
||||
cols_to_mask = ['ligand_affinity_change', 'mcsm_ppi2_affinity']
|
||||
|
||||
if gene.lower() in geneL_na:
|
||||
# X_stabilityN = common_cols_stabiltyN + ['mcsm_na_affinity']
|
||||
geneL_na_st_cols = ['mcsm_na_affinity']
|
||||
X_stabilityN = common_cols_stabiltyN + geneL_na_st_cols
|
||||
gene_affinity_colnames = ['mcsm_na_affinity']
|
||||
#X_stabilityN = common_cols_stabiltyN + geneL_na_st_cols
|
||||
cols_to_mask = ['ligand_affinity_change', 'mcsm_na_affinity']
|
||||
|
||||
if gene.lower() in geneL_na_ppi2:
|
||||
# X_stabilityN = common_cols_stabiltyN + ['mcsm_na_affinity'] + ['mcsm_ppi2_affinity', 'interface_dist']
|
||||
geneL_na_ppi2_st_cols = ['mcsm_na_affinity'] + ['mcsm_ppi2_affinity', 'interface_dist']
|
||||
X_stabilityN = common_cols_stabiltyN + geneL_na_ppi2_st_cols
|
||||
gene_affinity_colnames = ['mcsm_na_affinity'] + ['mcsm_ppi2_affinity', 'interface_dist']
|
||||
#X_stabilityN = common_cols_stabiltyN + geneL_na_ppi2_st_cols
|
||||
cols_to_mask = ['ligand_affinity_change', 'mcsm_na_affinity', 'mcsm_ppi2_affinity']
|
||||
|
||||
|
||||
X_foldX_cols = [ 'electro_rr', 'electro_mm', 'electro_sm', 'electro_ss'
|
||||
, 'disulfide_rr', 'disulfide_mm', 'disulfide_sm', 'disulfide_ss'
|
||||
, 'hbonds_rr', 'hbonds_mm', 'hbonds_sm', 'hbonds_ss'
|
||||
, 'partcov_rr', 'partcov_mm', 'partcov_sm', 'partcov_ss'
|
||||
, 'vdwclashes_rr', 'vdwclashes_mm', 'vdwclashes_sm', 'vdwclashes_ss'
|
||||
, 'volumetric_rr', 'volumetric_mm', 'volumetric_ss'
|
||||
]
|
||||
|
||||
X_str = ['rsa'
|
||||
#, 'asa'
|
||||
, 'kd_values'
|
||||
, 'rd_values']
|
||||
|
||||
X_ssFN = X_stabilityN + X_str + X_foldX_cols
|
||||
|
||||
X_evolFN = ['consurf_score'
|
||||
, 'snap2_score'
|
||||
, 'provean_score']
|
||||
|
||||
X_genomic_mafor = ['maf'
|
||||
, 'logorI'
|
||||
# , 'or_rawI'
|
||||
# , 'or_mychisq'
|
||||
# , 'or_logistic'
|
||||
# , 'or_fisher'
|
||||
# , 'pval_fisher'
|
||||
]
|
||||
|
||||
X_genomic_linegae = ['lineage_proportion'
|
||||
, 'dist_lineage_proportion'
|
||||
#, 'lineage' # could be included as a category but it has L2;L4 formatting
|
||||
, 'lineage_count_all'
|
||||
, 'lineage_count_unique'
|
||||
]
|
||||
|
||||
X_genomicFN = X_genomic_mafor + X_genomic_linegae
|
||||
|
||||
X_aaindexFN = list(aa_df_cols)
|
||||
|
||||
print('\nTotal no. of features for aaindex:', len(X_aaindexFN))
|
||||
|
||||
# numerical feature names
|
||||
numerical_FN = X_ssFN + X_evolFN + X_genomicFN + X_aaindexFN
|
||||
|
||||
# categorical feature names
|
||||
categorical_FN = ['ss_class'
|
||||
# , 'wt_prop_water'
|
||||
# , 'mut_prop_water'
|
||||
# , 'wt_prop_polarity'
|
||||
# , 'mut_prop_polarity'
|
||||
# , 'wt_calcprop'
|
||||
# , 'mut_calcprop'
|
||||
, 'aa_prop_change'
|
||||
, 'electrostatics_change'
|
||||
, 'polarity_change'
|
||||
, 'water_change'
|
||||
, 'drtype_mode_labels' # beware then you can't use it to predict [USED it for uq_v1, not v2]
|
||||
, 'active_site' #[didn't use it for uq_v1]
|
||||
#, 'gene_name' # will be required for the combined stuff
|
||||
]
|
||||
|
||||
#----------------------------------------------
|
||||
# count numerical and categorical features
|
||||
#----------------------------------------------
|
||||
|
||||
print('\nNo. of numerical features:', len(numerical_FN)
|
||||
, '\nNo. of categorical features:', len(categorical_FN))
|
||||
|
||||
#=======================
|
||||
# Masking columns:
|
||||
# (mCSM-lig, mCSM-NA, mCSM-ppi2) values for lig_dist >10
|
||||
#=======================
|
||||
# my_df_ml['mutationinformation'][my_df['ligand_distance']>10].value_counts()
|
||||
# my_df_ml.groupby('mutationinformation')['ligand_distance'].apply(lambda x: (x>10)).value_counts()
|
||||
|
||||
# my_df_ml.loc[(my_df_ml['ligand_distance'] > 10), 'ligand_affinity_change'] = 0
|
||||
# (my_df_ml['ligand_affinity_change'] == 0).sum()
|
||||
|
||||
my_df_ml['mutationinformation'][my_df_ml['ligand_distance']>10].value_counts()
|
||||
my_df_ml.groupby('mutationinformation')['ligand_distance'].apply(lambda x: (x>10)).value_counts()
|
||||
my_df_ml.loc[(my_df_ml['ligand_distance'] > 10), cols_to_mask].value_counts()
|
||||
|
|
@ -544,22 +459,152 @@ def setvars(gene,drug):
|
|||
|
||||
mask_check = my_df_ml[['mutationinformation', 'ligand_distance'] + cols_to_mask]
|
||||
|
||||
#===================================================
|
||||
# write file for check
|
||||
mask_check.sort_values(by = ['ligand_distance'], ascending = True, inplace = True)
|
||||
mask_check.to_csv(outdir + 'ml/' + gene.lower() + '_mask_check.csv')
|
||||
#===================================================
|
||||
###############################################################################
|
||||
#%% Feature groups (FG): Build X for Input ML
|
||||
############################################################################
|
||||
#===========================
|
||||
# FG1: Evolutionary features
|
||||
#===========================
|
||||
X_evolFN = ['consurf_score'
|
||||
, 'snap2_score'
|
||||
, 'provean_score']
|
||||
|
||||
#####################################################################
|
||||
###############################################################################
|
||||
#========================
|
||||
# FG2: Stability features
|
||||
#========================
|
||||
#--------
|
||||
# common
|
||||
#--------
|
||||
X_common_stability_Fnum = [
|
||||
'duet_stability_change'
|
||||
, 'ddg_foldx'
|
||||
, 'deepddg'
|
||||
, 'ddg_dynamut2'
|
||||
, 'contacts']
|
||||
#--------
|
||||
# FoldX
|
||||
#--------
|
||||
X_foldX_Fnum = [ 'electro_rr', 'electro_mm', 'electro_sm', 'electro_ss'
|
||||
, 'disulfide_rr', 'disulfide_mm', 'disulfide_sm', 'disulfide_ss'
|
||||
, 'hbonds_rr', 'hbonds_mm', 'hbonds_sm', 'hbonds_ss'
|
||||
, 'partcov_rr', 'partcov_mm', 'partcov_sm', 'partcov_ss'
|
||||
, 'vdwclashes_rr', 'vdwclashes_mm', 'vdwclashes_sm', 'vdwclashes_ss'
|
||||
, 'volumetric_rr', 'volumetric_mm', 'volumetric_ss']
|
||||
|
||||
X_stability_FN = X_common_stability_Fnum + X_foldX_Fnum
|
||||
|
||||
###############################################################################
|
||||
#===================
|
||||
# FG3: Affinity features
|
||||
#===================
|
||||
common_affinity_Fnum = ['ligand_distance'
|
||||
, 'ligand_affinity_change'
|
||||
, 'mmcsm_lig']
|
||||
|
||||
# if gene.lower() in geneL_basic:
|
||||
# X_affinityFN = common_affinity_Fnum
|
||||
# else:
|
||||
# X_affinityFN = common_affinity_Fnum + gene_affinity_colnames
|
||||
|
||||
X_affinityFN = common_affinity_Fnum + gene_affinity_colnames
|
||||
|
||||
###############################################################################
|
||||
#============================
|
||||
# FG4: Residue level features
|
||||
#============================
|
||||
#-----------
|
||||
# AA index
|
||||
#-----------
|
||||
X_aaindex_Fnum = list(aa_df_cols)
|
||||
print('\nTotal no. of features for aaindex:', len(X_aaindex_Fnum))
|
||||
|
||||
#-----------------
|
||||
# surface area
|
||||
# depth
|
||||
# hydrophobicity
|
||||
#-----------------
|
||||
X_str_Fnum = ['rsa'
|
||||
#, 'asa'
|
||||
, 'kd_values'
|
||||
, 'rd_values']
|
||||
|
||||
#---------------------------
|
||||
# Other aa properties
|
||||
# active site indication
|
||||
#---------------------------
|
||||
X_aap_Fcat = ['ss_class'
|
||||
# , 'wt_prop_water'
|
||||
# , 'mut_prop_water'
|
||||
# , 'wt_prop_polarity'
|
||||
# , 'mut_prop_polarity'
|
||||
# , 'wt_calcprop'
|
||||
# , 'mut_calcprop'
|
||||
, 'aa_prop_change'
|
||||
, 'electrostatics_change'
|
||||
, 'polarity_change'
|
||||
, 'water_change'
|
||||
, 'active_site']
|
||||
|
||||
X_resprop_FN = X_aaindex_Fnum + X_str_Fnum + X_aap_Fcat
|
||||
###############################################################################
|
||||
#========================
|
||||
# FG5: Genomic features
|
||||
#========================
|
||||
X_gn_mafor_Fnum = ['maf'
|
||||
#, 'logorI'
|
||||
# , 'or_rawI'
|
||||
# , 'or_mychisq'
|
||||
# , 'or_logistic'
|
||||
# , 'or_fisher'
|
||||
# , 'pval_fisher'
|
||||
]
|
||||
|
||||
X_gn_linegae_Fnum = ['lineage_proportion'
|
||||
, 'dist_lineage_proportion'
|
||||
#, 'lineage' # could be included as a category but it has L2;L4 formatting
|
||||
, 'lineage_count_all'
|
||||
, 'lineage_count_unique'
|
||||
]
|
||||
|
||||
# X_gn_Fcat = ['drtype_mode_labels' # beware then you can't use it to predict [USED it for uq_v1, not v2]
|
||||
# #, 'gene_name' # will be required for the combined stuff
|
||||
# ]
|
||||
X_gn_Fcat = []
|
||||
|
||||
X_genomicFN = X_gn_mafor_Fnum + X_gn_linegae_Fnum + X_gn_Fcat
|
||||
###############################################################################
|
||||
#========================
|
||||
# FG6 collapsed: Structural : Atability + Affinity + ResidueProp
|
||||
#========================
|
||||
X_structural_FN = X_stability_FN + X_affinityFN + X_resprop_FN
|
||||
|
||||
###############################################################################
|
||||
#========================
|
||||
# BUILDING all features
|
||||
#========================
|
||||
all_featuresN = X_evolFN + X_structural_FN + X_genomicFN
|
||||
|
||||
###############################################################################
|
||||
#%% Define training and test data
|
||||
#================================================================
|
||||
# Training and Blind test [COMPLETE data]: scaling law split
|
||||
# https://towardsdatascience.com/finally-why-we-use-an-80-20-split-for-training-and-test-data-plus-an-alternative-method-oh-yes-edc77e96295d
|
||||
|
||||
# Training and BLIND test set: scaling law split
|
||||
#https://towardsdatascience.com/finally-why-we-use-an-80-20-split-for-training-and-test-data-plus-an-alternative-method-oh-yes-edc77e96295d
|
||||
# dst with actual values : training set
|
||||
# dst with imputed values : THROW AWAY [unrepresentative]
|
||||
# test data size ~ 1/sqrt(features NOT including target variable)
|
||||
#================================================================
|
||||
my_df_ml[drug].isna().sum()
|
||||
|
||||
# blind_test_df = my_df_ml[my_df_ml[drug].isna()]
|
||||
# blind_test_df.shape
|
||||
|
||||
#training_df = my_df_ml[my_df_ml[drug].notna()]
|
||||
#training_df = my_df_ml[my_df_ml[drug].notna()]
|
||||
#training_df.shape
|
||||
|
||||
training_df = my_df_ml.copy()
|
||||
|
|
@ -569,80 +614,14 @@ def setvars(gene,drug):
|
|||
training_df['dst_mode'].value_counts()
|
||||
|
||||
####################################################################
|
||||
|
||||
###############################################################################
|
||||
###############################################################################
|
||||
# #%% extracting dfs based on numerical, categorical column names
|
||||
# #----------------------------------
|
||||
# # WITHOUT the target var included
|
||||
# #----------------------------------
|
||||
# num_df = training_df[numerical_FN]
|
||||
# num_df.shape
|
||||
|
||||
# cat_df = training_df[categorical_FN]
|
||||
# cat_df.shape
|
||||
|
||||
# all_df = training_df[numerical_FN + categorical_FN]
|
||||
# all_df.shape
|
||||
|
||||
# #------------------------------
|
||||
# # WITH the target var included:
|
||||
# #'wtgt': with target
|
||||
# #------------------------------
|
||||
# # drug and dst_mode should be the same thing
|
||||
# num_df_wtgt = training_df[numerical_FN + ['dst_mode']]
|
||||
# num_df_wtgt.shape
|
||||
|
||||
# cat_df_wtgt = training_df[categorical_FN + ['dst_mode']]
|
||||
# cat_df_wtgt.shape
|
||||
|
||||
# all_df_wtgt = training_df[numerical_FN + categorical_FN + ['dst_mode']]
|
||||
# all_df_wtgt.shape
|
||||
|
||||
#%%########################################################################
|
||||
# #============
|
||||
# # ML data: OLD
|
||||
# #============
|
||||
# #------
|
||||
# # X: Training and Blind test (BTS)
|
||||
# #------
|
||||
# X = all_df_wtgt[numerical_FN + categorical_FN] # training data ALL
|
||||
# X_bts = blind_test_df[numerical_FN + categorical_FN] # blind test data ALL
|
||||
# #X = all_df_wtgt[numerical_FN] # training numerical only
|
||||
# #X_bts = blind_test_df[numerical_FN] # blind test data numerical
|
||||
|
||||
# #------
|
||||
# # y
|
||||
# #------
|
||||
# y = all_df_wtgt['dst_mode'] # training data y
|
||||
# y_bts = blind_test_df['dst_mode'] # blind data test y
|
||||
|
||||
# #X_bts_wt = blind_test_df[numerical_FN + ['dst_mode']]
|
||||
|
||||
# # Quick check
|
||||
# #(X['ligand_affinity_change']==0).sum() == (X['ligand_distance']>10).sum()
|
||||
# for i in range(len(cols_to_mask)):
|
||||
# ind = i+1
|
||||
# print('\nindex:', i, '\nind:', ind)
|
||||
# print('\nMask count check:'
|
||||
# , (my_df_ml[cols_to_mask[i]]==0).sum() == (my_df_ml['ligand_distance']>10).sum()
|
||||
# )
|
||||
|
||||
# print('Original Data\n', Counter(y)
|
||||
# , 'Data dim:', X.shape)
|
||||
|
||||
###############################################################################
|
||||
###############################################################################
|
||||
#====================================
|
||||
# ML data: Train test split [COMPLETE data]: scaling law
|
||||
# ML data: Train test split: SL
|
||||
# with stratification
|
||||
# 1-blind test : training_data for CV
|
||||
# 1/sqrt(columns) : blind test
|
||||
#=====================================
|
||||
|
||||
# features: all_df or
|
||||
x_features = training_df[numerical_FN + categorical_FN]
|
||||
y_target = training_df['dst_mode']
|
||||
#===========================================
|
||||
x_features = training_df[all_featuresN]
|
||||
y_target = training_df['dst_mode']
|
||||
|
||||
# sanity check
|
||||
if not 'dst_mode' in x_features.columns:
|
||||
|
|
@ -650,9 +629,12 @@ def setvars(gene,drug):
|
|||
x_ncols = len(x_features.columns)
|
||||
print('\nNo. of columns for x_features:', x_ncols)
|
||||
# NEED It for scaling law split
|
||||
#https://towardsdatascience.com/finally-why-we-use-an-80-20-split-for-training-and-test-data-plus-an-alternative-method-oh-yes-edc77e96295d
|
||||
else:
|
||||
sys.exit('\nFAIL: x_features has target variable included. FIX it and rerun!')
|
||||
|
||||
#-------------------
|
||||
# train-test split
|
||||
#-------------------
|
||||
sl_test_size = 1/np.sqrt(x_ncols)
|
||||
train = 1 - sl_test_size
|
||||
|
||||
|
|
@ -668,15 +650,64 @@ def setvars(gene,drug):
|
|||
yc2 = Counter(y_bts)
|
||||
yc2_ratio = yc2[0]/yc2[1]
|
||||
|
||||
###############################################################################
|
||||
#======================================================
|
||||
# Determine categorical and numerical features
|
||||
#======================================================
|
||||
numerical_cols = X.select_dtypes(include=['int64', 'float64']).columns
|
||||
numerical_cols
|
||||
categorical_cols = X.select_dtypes(include=['object', 'bool']).columns
|
||||
categorical_cols
|
||||
|
||||
################################################################################
|
||||
# IMPORTANT sanity checks
|
||||
if len(X.columns) == len(X_evolFN) + len(X_stability_FN) + len(X_affinityFN) + len(X_resprop_FN) + len(X_genomicFN):
|
||||
print('\nPASS: ML data with input features, training and test generated...'
|
||||
, '\n\nTotal no. of input features:' , len(X.columns)
|
||||
, '\n--------No. of numerical features:' , len(numerical_cols)
|
||||
, '\n--------No. of categorical features:' , len(categorical_cols)
|
||||
|
||||
, '\n\nTotal no. of evolutionary features:' , len(X_evolFN)
|
||||
|
||||
, '\n\nTotal no. of stability features:' , len(X_stability_FN)
|
||||
, '\n--------Common stabilty cols:' , len(X_common_stability_Fnum)
|
||||
, '\n--------Foldx cols:' , len(X_foldX_Fnum)
|
||||
|
||||
, '\n\nTotal no. of affinity features:' , len(X_affinityFN)
|
||||
, '\n--------Common affinity cols:' , len(common_affinity_Fnum)
|
||||
, '\n--------Gene specific affinity cols:' , len(gene_affinity_colnames)
|
||||
|
||||
, '\n\nTotal no. of residue level features:', len(X_resprop_FN)
|
||||
, '\n--------AA index cols:' , len(X_aaindex_Fnum)
|
||||
, '\n--------Residue Prop cols:' , len(X_str_Fnum)
|
||||
, '\n--------AA change Prop cols:' , len(X_aap_Fcat)
|
||||
|
||||
, '\n\nTotal no. of genomic features:' , len(X_genomicFN)
|
||||
, '\n--------MAF+OR cols:' , len(X_gn_mafor_Fnum)
|
||||
, '\n--------Lineage cols:' , len(X_gn_linegae_Fnum)
|
||||
, '\n--------Other cols:' , len(X_gn_Fcat)
|
||||
)
|
||||
else:
|
||||
print('\nFAIL: numbers mismatch'
|
||||
, '\nExpected:',len(X_evolFN) + len(X_stability_FN) + len(X_affinityFN) + len(X_resprop_FN) + len(X_genomicFN)
|
||||
, '\nGot:', len(X.columns))
|
||||
sys.exit()
|
||||
###############################################################################
|
||||
print('\n-------------------------------------------------------------'
|
||||
, '\nSuccessfully split data with stratification according to scaling law [COMPLETE data]: 1/sqrt(x_ncols)'
|
||||
, '\nInput features data size:', x_features.shape
|
||||
, '\nTrain data size:', X.shape
|
||||
, '\nTest data size:', X_bts.shape
|
||||
, '\nSuccessfully split data: ALL features'
|
||||
, '\nactual values: training set'
|
||||
, '\nSplit:', tts_split
|
||||
#, '\nimputed values: blind test set'
|
||||
|
||||
, '\n\nTotal data size:', len(X) + len(X_bts)
|
||||
|
||||
, '\n\nTrain data size:', X.shape
|
||||
, '\ny_train numbers:', yc1
|
||||
, '\ny_train ratio:',yc1_ratio
|
||||
, '\n'
|
||||
|
||||
, '\n\nTest data size:', X_bts.shape
|
||||
, '\ny_test_numbers:', yc2
|
||||
|
||||
, '\n\ny_train ratio:',yc1_ratio
|
||||
, '\ny_test ratio:', yc2_ratio
|
||||
, '\n-------------------------------------------------------------'
|
||||
)
|
||||
|
|
@ -775,3 +806,8 @@ def setvars(gene,drug):
|
|||
|
||||
###############################################################################
|
||||
# TODO: Find over and undersampling JUST for categorical data
|
||||
###########################################################################
|
||||
|
||||
print('\n#################################################################'
|
||||
, '\nDim of X for gene:', gene.lower(), '\n', X.shape
|
||||
, '\n###############################################################')
|
||||
|
|
|
|||
|
|
@ -34,7 +34,11 @@ def setvars(gene,drug):
|
|||
from sklearn.model_selection import StratifiedKFold,RepeatedStratifiedKFold, RepeatedKFold
|
||||
|
||||
from sklearn.pipeline import Pipeline, make_pipeline
|
||||
import argparse
|
||||
import re
|
||||
#%% GLOBALS
|
||||
tts_split = "sl"
|
||||
|
||||
rs = {'random_state': 42}
|
||||
njobs = {'n_jobs': 10}
|
||||
|
||||
|
|
@ -57,11 +61,9 @@ def setvars(gene,drug):
|
|||
|
||||
mcc_score_fn = {'mcc': make_scorer(matthews_corrcoef)}
|
||||
jacc_score_fn = {'jcc': make_scorer(jaccard_score)}
|
||||
|
||||
#%% FOR LATER: Combine ED logo data
|
||||
###########################################################################
|
||||
rs = {'random_state': 42}
|
||||
njobs = {'n_jobs': 10}
|
||||
|
||||
homedir = os.path.expanduser("~")
|
||||
|
||||
geneL_basic = ['pnca']
|
||||
|
|
@ -422,118 +424,31 @@ def setvars(gene,drug):
|
|||
#==========================
|
||||
my_df_ml = my_df.copy()
|
||||
|
||||
#%% Build X: input for ML
|
||||
common_cols_stabiltyN = ['ligand_distance'
|
||||
, 'ligand_affinity_change'
|
||||
, 'duet_stability_change'
|
||||
, 'ddg_foldx'
|
||||
, 'deepddg'
|
||||
, 'ddg_dynamut2'
|
||||
, 'mmcsm_lig'
|
||||
, 'contacts']
|
||||
|
||||
# Build stability columns ~ gene
|
||||
# Build column names to mask for affinity chanhes
|
||||
if gene.lower() in geneL_basic:
|
||||
X_stabilityN = common_cols_stabiltyN
|
||||
#X_stabilityN = common_cols_stabiltyN
|
||||
gene_affinity_colnames = []# not needed as its the common ones
|
||||
cols_to_mask = ['ligand_affinity_change']
|
||||
|
||||
if gene.lower() in geneL_ppi2:
|
||||
# X_stabilityN = common_cols_stabiltyN + ['mcsm_ppi2_affinity' , 'interface_dist']
|
||||
geneL_ppi2_st_cols = ['mcsm_ppi2_affinity', 'interface_dist']
|
||||
X_stabilityN = common_cols_stabiltyN + geneL_ppi2_st_cols
|
||||
gene_affinity_colnames = ['mcsm_ppi2_affinity', 'interface_dist']
|
||||
#X_stabilityN = common_cols_stabiltyN + geneL_ppi2_st_cols
|
||||
cols_to_mask = ['ligand_affinity_change', 'mcsm_ppi2_affinity']
|
||||
|
||||
if gene.lower() in geneL_na:
|
||||
# X_stabilityN = common_cols_stabiltyN + ['mcsm_na_affinity']
|
||||
geneL_na_st_cols = ['mcsm_na_affinity']
|
||||
X_stabilityN = common_cols_stabiltyN + geneL_na_st_cols
|
||||
gene_affinity_colnames = ['mcsm_na_affinity']
|
||||
#X_stabilityN = common_cols_stabiltyN + geneL_na_st_cols
|
||||
cols_to_mask = ['ligand_affinity_change', 'mcsm_na_affinity']
|
||||
|
||||
if gene.lower() in geneL_na_ppi2:
|
||||
# X_stabilityN = common_cols_stabiltyN + ['mcsm_na_affinity'] + ['mcsm_ppi2_affinity', 'interface_dist']
|
||||
geneL_na_ppi2_st_cols = ['mcsm_na_affinity'] + ['mcsm_ppi2_affinity', 'interface_dist']
|
||||
X_stabilityN = common_cols_stabiltyN + geneL_na_ppi2_st_cols
|
||||
gene_affinity_colnames = ['mcsm_na_affinity'] + ['mcsm_ppi2_affinity', 'interface_dist']
|
||||
#X_stabilityN = common_cols_stabiltyN + geneL_na_ppi2_st_cols
|
||||
cols_to_mask = ['ligand_affinity_change', 'mcsm_na_affinity', 'mcsm_ppi2_affinity']
|
||||
|
||||
|
||||
X_foldX_cols = [ 'electro_rr', 'electro_mm', 'electro_sm', 'electro_ss'
|
||||
, 'disulfide_rr', 'disulfide_mm', 'disulfide_sm', 'disulfide_ss'
|
||||
, 'hbonds_rr', 'hbonds_mm', 'hbonds_sm', 'hbonds_ss'
|
||||
, 'partcov_rr', 'partcov_mm', 'partcov_sm', 'partcov_ss'
|
||||
, 'vdwclashes_rr', 'vdwclashes_mm', 'vdwclashes_sm', 'vdwclashes_ss'
|
||||
, 'volumetric_rr', 'volumetric_mm', 'volumetric_ss'
|
||||
]
|
||||
|
||||
X_str = ['rsa'
|
||||
#, 'asa'
|
||||
, 'kd_values'
|
||||
, 'rd_values']
|
||||
|
||||
X_ssFN = X_stabilityN + X_str + X_foldX_cols
|
||||
|
||||
X_evolFN = ['consurf_score'
|
||||
, 'snap2_score'
|
||||
, 'provean_score']
|
||||
|
||||
X_genomic_mafor = ['maf'
|
||||
, 'logorI'
|
||||
# , 'or_rawI'
|
||||
# , 'or_mychisq'
|
||||
# , 'or_logistic'
|
||||
# , 'or_fisher'
|
||||
# , 'pval_fisher'
|
||||
]
|
||||
|
||||
X_genomic_linegae = ['lineage_proportion'
|
||||
, 'dist_lineage_proportion'
|
||||
#, 'lineage' # could be included as a category but it has L2;L4 formatting
|
||||
, 'lineage_count_all'
|
||||
, 'lineage_count_unique'
|
||||
]
|
||||
|
||||
X_genomicFN = X_genomic_mafor + X_genomic_linegae
|
||||
|
||||
X_aaindexFN = list(aa_df_cols)
|
||||
|
||||
print('\nTotal no. of features for aaindex:', len(X_aaindexFN))
|
||||
|
||||
# numerical feature names
|
||||
numerical_FN = X_ssFN + X_evolFN + X_genomicFN + X_aaindexFN
|
||||
|
||||
# categorical feature names
|
||||
categorical_FN = ['ss_class'
|
||||
# , 'wt_prop_water'
|
||||
# , 'mut_prop_water'
|
||||
# , 'wt_prop_polarity'
|
||||
# , 'mut_prop_polarity'
|
||||
# , 'wt_calcprop'
|
||||
# , 'mut_calcprop'
|
||||
, 'aa_prop_change'
|
||||
, 'electrostatics_change'
|
||||
, 'polarity_change'
|
||||
, 'water_change'
|
||||
, 'drtype_mode_labels' # beware then you can't use it to predict [USED it for uq_v1, not v2]
|
||||
, 'active_site' #[didn't use it for uq_v1]
|
||||
#, 'gene_name' # will be required for the combined stuff
|
||||
]
|
||||
|
||||
#----------------------------------------------
|
||||
# count numerical and categorical features
|
||||
#----------------------------------------------
|
||||
|
||||
print('\nNo. of numerical features:', len(numerical_FN)
|
||||
, '\nNo. of categorical features:', len(categorical_FN))
|
||||
|
||||
#=======================
|
||||
# Masking columns:
|
||||
# (mCSM-lig, mCSM-NA, mCSM-ppi2) values for lig_dist >10
|
||||
#=======================
|
||||
# my_df_ml['mutationinformation'][my_df['ligand_distance']>10].value_counts()
|
||||
# my_df_ml.groupby('mutationinformation')['ligand_distance'].apply(lambda x: (x>10)).value_counts()
|
||||
|
||||
# my_df_ml.loc[(my_df_ml['ligand_distance'] > 10), 'ligand_affinity_change'] = 0
|
||||
# (my_df_ml['ligand_affinity_change'] == 0).sum()
|
||||
|
||||
my_df_ml['mutationinformation'][my_df_ml['ligand_distance']>10].value_counts()
|
||||
my_df_ml.groupby('mutationinformation')['ligand_distance'].apply(lambda x: (x>10)).value_counts()
|
||||
my_df_ml.loc[(my_df_ml['ligand_distance'] > 10), cols_to_mask].value_counts()
|
||||
|
|
@ -544,22 +459,152 @@ def setvars(gene,drug):
|
|||
|
||||
mask_check = my_df_ml[['mutationinformation', 'ligand_distance'] + cols_to_mask]
|
||||
|
||||
#===================================================
|
||||
# write file for check
|
||||
mask_check.sort_values(by = ['ligand_distance'], ascending = True, inplace = True)
|
||||
mask_check.to_csv(outdir + 'ml/' + gene.lower() + '_mask_check.csv')
|
||||
#===================================================
|
||||
###############################################################################
|
||||
#%% Feature groups (FG): Build X for Input ML
|
||||
############################################################################
|
||||
#===========================
|
||||
# FG1: Evolutionary features
|
||||
#===========================
|
||||
X_evolFN = ['consurf_score'
|
||||
, 'snap2_score'
|
||||
, 'provean_score']
|
||||
|
||||
#####################################################################
|
||||
###############################################################################
|
||||
#========================
|
||||
# FG2: Stability features
|
||||
#========================
|
||||
#--------
|
||||
# common
|
||||
#--------
|
||||
X_common_stability_Fnum = [
|
||||
'duet_stability_change'
|
||||
, 'ddg_foldx'
|
||||
, 'deepddg'
|
||||
, 'ddg_dynamut2'
|
||||
, 'contacts']
|
||||
#--------
|
||||
# FoldX
|
||||
#--------
|
||||
X_foldX_Fnum = [ 'electro_rr', 'electro_mm', 'electro_sm', 'electro_ss'
|
||||
, 'disulfide_rr', 'disulfide_mm', 'disulfide_sm', 'disulfide_ss'
|
||||
, 'hbonds_rr', 'hbonds_mm', 'hbonds_sm', 'hbonds_ss'
|
||||
, 'partcov_rr', 'partcov_mm', 'partcov_sm', 'partcov_ss'
|
||||
, 'vdwclashes_rr', 'vdwclashes_mm', 'vdwclashes_sm', 'vdwclashes_ss'
|
||||
, 'volumetric_rr', 'volumetric_mm', 'volumetric_ss']
|
||||
|
||||
X_stability_FN = X_common_stability_Fnum + X_foldX_Fnum
|
||||
|
||||
###############################################################################
|
||||
#===================
|
||||
# FG3: Affinity features
|
||||
#===================
|
||||
common_affinity_Fnum = ['ligand_distance'
|
||||
, 'ligand_affinity_change'
|
||||
, 'mmcsm_lig']
|
||||
|
||||
# if gene.lower() in geneL_basic:
|
||||
# X_affinityFN = common_affinity_Fnum
|
||||
# else:
|
||||
# X_affinityFN = common_affinity_Fnum + gene_affinity_colnames
|
||||
|
||||
X_affinityFN = common_affinity_Fnum + gene_affinity_colnames
|
||||
|
||||
###############################################################################
|
||||
#============================
|
||||
# FG4: Residue level features
|
||||
#============================
|
||||
#-----------
|
||||
# AA index
|
||||
#-----------
|
||||
X_aaindex_Fnum = list(aa_df_cols)
|
||||
print('\nTotal no. of features for aaindex:', len(X_aaindex_Fnum))
|
||||
|
||||
#-----------------
|
||||
# surface area
|
||||
# depth
|
||||
# hydrophobicity
|
||||
#-----------------
|
||||
X_str_Fnum = ['rsa'
|
||||
#, 'asa'
|
||||
, 'kd_values'
|
||||
, 'rd_values']
|
||||
|
||||
#---------------------------
|
||||
# Other aa properties
|
||||
# active site indication
|
||||
#---------------------------
|
||||
X_aap_Fcat = ['ss_class'
|
||||
# , 'wt_prop_water'
|
||||
# , 'mut_prop_water'
|
||||
# , 'wt_prop_polarity'
|
||||
# , 'mut_prop_polarity'
|
||||
# , 'wt_calcprop'
|
||||
# , 'mut_calcprop'
|
||||
, 'aa_prop_change'
|
||||
, 'electrostatics_change'
|
||||
, 'polarity_change'
|
||||
, 'water_change'
|
||||
, 'active_site']
|
||||
|
||||
X_resprop_FN = X_aaindex_Fnum + X_str_Fnum + X_aap_Fcat
|
||||
###############################################################################
|
||||
#========================
|
||||
# FG5: Genomic features
|
||||
#========================
|
||||
X_gn_mafor_Fnum = ['maf'
|
||||
#, 'logorI'
|
||||
# , 'or_rawI'
|
||||
# , 'or_mychisq'
|
||||
# , 'or_logistic'
|
||||
# , 'or_fisher'
|
||||
# , 'pval_fisher'
|
||||
]
|
||||
|
||||
X_gn_linegae_Fnum = ['lineage_proportion'
|
||||
, 'dist_lineage_proportion'
|
||||
#, 'lineage' # could be included as a category but it has L2;L4 formatting
|
||||
, 'lineage_count_all'
|
||||
, 'lineage_count_unique'
|
||||
]
|
||||
|
||||
# X_gn_Fcat = ['drtype_mode_labels' # beware then you can't use it to predict [USED it for uq_v1, not v2]
|
||||
# #, 'gene_name' # will be required for the combined stuff
|
||||
# ]
|
||||
X_gn_Fcat = []
|
||||
|
||||
X_genomicFN = X_gn_mafor_Fnum + X_gn_linegae_Fnum + X_gn_Fcat
|
||||
###############################################################################
|
||||
#========================
|
||||
# FG6 collapsed: Structural : Atability + Affinity + ResidueProp
|
||||
#========================
|
||||
X_structural_FN = X_stability_FN + X_affinityFN + X_resprop_FN
|
||||
|
||||
###############################################################################
|
||||
#========================
|
||||
# BUILDING all features
|
||||
#========================
|
||||
all_featuresN = X_evolFN + X_structural_FN + X_genomicFN
|
||||
|
||||
###############################################################################
|
||||
#%% Define training and test data
|
||||
#================================================================
|
||||
# Training and BLIND test set: scaling law split
|
||||
# https://towardsdatascience.com/finally-why-we-use-an-80-20-split-for-training-and-test-data-plus-an-alternative-method-oh-yes-edc77e96295d
|
||||
|
||||
#https://towardsdatascience.com/finally-why-we-use-an-80-20-split-for-training-and-test-data-plus-an-alternative-method-oh-yes-edc77e96295d
|
||||
# dst with actual values : training set
|
||||
# dst with imputed values : THROW AWAY [unrepresentative]
|
||||
# test data size ~ 1/sqrt(features NOT including target variable)
|
||||
#================================================================
|
||||
my_df_ml[drug].isna().sum()
|
||||
|
||||
# blind_test_df = my_df_ml[my_df_ml[drug].isna()]
|
||||
# blind_test_df.shape
|
||||
|
||||
training_df = my_df_ml[my_df_ml[drug].notna()]
|
||||
training_df = my_df_ml[my_df_ml[drug].notna()]
|
||||
training_df.shape
|
||||
|
||||
# Target 1: dst_mode
|
||||
|
|
@ -567,80 +612,14 @@ def setvars(gene,drug):
|
|||
training_df['dst_mode'].value_counts()
|
||||
|
||||
####################################################################
|
||||
|
||||
###############################################################################
|
||||
###############################################################################
|
||||
# #%% extracting dfs based on numerical, categorical column names
|
||||
# #----------------------------------
|
||||
# # WITHOUT the target var included
|
||||
# #----------------------------------
|
||||
# num_df = training_df[numerical_FN]
|
||||
# num_df.shape
|
||||
|
||||
# cat_df = training_df[categorical_FN]
|
||||
# cat_df.shape
|
||||
|
||||
# all_df = training_df[numerical_FN + categorical_FN]
|
||||
# all_df.shape
|
||||
|
||||
# #------------------------------
|
||||
# # WITH the target var included:
|
||||
# #'wtgt': with target
|
||||
# #------------------------------
|
||||
# # drug and dst_mode should be the same thing
|
||||
# num_df_wtgt = training_df[numerical_FN + ['dst_mode']]
|
||||
# num_df_wtgt.shape
|
||||
|
||||
# cat_df_wtgt = training_df[categorical_FN + ['dst_mode']]
|
||||
# cat_df_wtgt.shape
|
||||
|
||||
# all_df_wtgt = training_df[numerical_FN + categorical_FN + ['dst_mode']]
|
||||
# all_df_wtgt.shape
|
||||
|
||||
#%%########################################################################
|
||||
# #============
|
||||
# # ML data: OLD
|
||||
# #============
|
||||
# #------
|
||||
# # X: Training and Blind test (BTS)
|
||||
# #------
|
||||
# X = all_df_wtgt[numerical_FN + categorical_FN] # training data ALL
|
||||
# X_bts = blind_test_df[numerical_FN + categorical_FN] # blind test data ALL
|
||||
# #X = all_df_wtgt[numerical_FN] # training numerical only
|
||||
# #X_bts = blind_test_df[numerical_FN] # blind test data numerical
|
||||
|
||||
# #------
|
||||
# # y
|
||||
# #------
|
||||
# y = all_df_wtgt['dst_mode'] # training data y
|
||||
# y_bts = blind_test_df['dst_mode'] # blind data test y
|
||||
|
||||
# #X_bts_wt = blind_test_df[numerical_FN + ['dst_mode']]
|
||||
|
||||
# # Quick check
|
||||
# #(X['ligand_affinity_change']==0).sum() == (X['ligand_distance']>10).sum()
|
||||
# for i in range(len(cols_to_mask)):
|
||||
# ind = i+1
|
||||
# print('\nindex:', i, '\nind:', ind)
|
||||
# print('\nMask count check:'
|
||||
# , (my_df_ml[cols_to_mask[i]]==0).sum() == (my_df_ml['ligand_distance']>10).sum()
|
||||
# )
|
||||
|
||||
# print('Original Data\n', Counter(y)
|
||||
# , 'Data dim:', X.shape)
|
||||
|
||||
###############################################################################
|
||||
###############################################################################
|
||||
#===========================================
|
||||
# ML data: Train test split:scaling law
|
||||
#====================================
|
||||
# ML data: Train test split: SL
|
||||
# with stratification
|
||||
# 1-blind test : training_data for CV
|
||||
# 1/sqrt(columns) : blind test
|
||||
#===========================================
|
||||
|
||||
# features: all_df or
|
||||
x_features = training_df[numerical_FN + categorical_FN]
|
||||
y_target = training_df['dst_mode']
|
||||
x_features = training_df[all_featuresN]
|
||||
y_target = training_df['dst_mode']
|
||||
|
||||
# sanity check
|
||||
if not 'dst_mode' in x_features.columns:
|
||||
|
|
@ -648,9 +627,12 @@ def setvars(gene,drug):
|
|||
x_ncols = len(x_features.columns)
|
||||
print('\nNo. of columns for x_features:', x_ncols)
|
||||
# NEED It for scaling law split
|
||||
#https://towardsdatascience.com/finally-why-we-use-an-80-20-split-for-training-and-test-data-plus-an-alternative-method-oh-yes-edc77e96295d
|
||||
else:
|
||||
sys.exit('\nFAIL: x_features has target variable included. FIX it and rerun!')
|
||||
|
||||
#-------------------
|
||||
# train-test split
|
||||
#-------------------
|
||||
sl_test_size = 1/np.sqrt(x_ncols)
|
||||
train = 1 - sl_test_size
|
||||
|
||||
|
|
@ -666,15 +648,64 @@ def setvars(gene,drug):
|
|||
yc2 = Counter(y_bts)
|
||||
yc2_ratio = yc2[0]/yc2[1]
|
||||
|
||||
###############################################################################
|
||||
#======================================================
|
||||
# Determine categorical and numerical features
|
||||
#======================================================
|
||||
numerical_cols = X.select_dtypes(include=['int64', 'float64']).columns
|
||||
numerical_cols
|
||||
categorical_cols = X.select_dtypes(include=['object', 'bool']).columns
|
||||
categorical_cols
|
||||
|
||||
################################################################################
|
||||
# IMPORTANT sanity checks
|
||||
if len(X.columns) == len(X_evolFN) + len(X_stability_FN) + len(X_affinityFN) + len(X_resprop_FN) + len(X_genomicFN):
|
||||
print('\nPASS: ML data with input features, training and test generated...'
|
||||
, '\n\nTotal no. of input features:' , len(X.columns)
|
||||
, '\n--------No. of numerical features:' , len(numerical_cols)
|
||||
, '\n--------No. of categorical features:' , len(categorical_cols)
|
||||
|
||||
, '\n\nTotal no. of evolutionary features:' , len(X_evolFN)
|
||||
|
||||
, '\n\nTotal no. of stability features:' , len(X_stability_FN)
|
||||
, '\n--------Common stabilty cols:' , len(X_common_stability_Fnum)
|
||||
, '\n--------Foldx cols:' , len(X_foldX_Fnum)
|
||||
|
||||
, '\n\nTotal no. of affinity features:' , len(X_affinityFN)
|
||||
, '\n--------Common affinity cols:' , len(common_affinity_Fnum)
|
||||
, '\n--------Gene specific affinity cols:' , len(gene_affinity_colnames)
|
||||
|
||||
, '\n\nTotal no. of residue level features:', len(X_resprop_FN)
|
||||
, '\n--------AA index cols:' , len(X_aaindex_Fnum)
|
||||
, '\n--------Residue Prop cols:' , len(X_str_Fnum)
|
||||
, '\n--------AA change Prop cols:' , len(X_aap_Fcat)
|
||||
|
||||
, '\n\nTotal no. of genomic features:' , len(X_genomicFN)
|
||||
, '\n--------MAF+OR cols:' , len(X_gn_mafor_Fnum)
|
||||
, '\n--------Lineage cols:' , len(X_gn_linegae_Fnum)
|
||||
, '\n--------Other cols:' , len(X_gn_Fcat)
|
||||
)
|
||||
else:
|
||||
print('\nFAIL: numbers mismatch'
|
||||
, '\nExpected:',len(X_evolFN) + len(X_stability_FN) + len(X_affinityFN) + len(X_resprop_FN) + len(X_genomicFN)
|
||||
, '\nGot:', len(X.columns))
|
||||
sys.exit()
|
||||
###############################################################################
|
||||
print('\n-------------------------------------------------------------'
|
||||
, '\nSuccessfully split data with stratification according to scaling law: 1/sqrt(x_ncols)'
|
||||
, '\nInput features data size:', x_features.shape
|
||||
, '\nTrain data size:', X.shape
|
||||
, '\nTest data size:', sl_test_size, ' ', X_bts.shape
|
||||
, '\nSuccessfully split data: ALL features'
|
||||
, '\nactual values: training set'
|
||||
, '\nSplit:', tts_split
|
||||
#, '\nimputed values: blind test set'
|
||||
|
||||
, '\n\nTotal data size:', len(X) + len(X_bts)
|
||||
|
||||
, '\n\nTrain data size:', X.shape
|
||||
, '\ny_train numbers:', yc1
|
||||
, '\ny_train ratio:',yc1_ratio
|
||||
, '\n'
|
||||
|
||||
, '\n\nTest data size:', X_bts.shape
|
||||
, '\ny_test_numbers:', yc2
|
||||
|
||||
, '\n\ny_train ratio:',yc1_ratio
|
||||
, '\ny_test ratio:', yc2_ratio
|
||||
, '\n-------------------------------------------------------------'
|
||||
)
|
||||
|
|
@ -773,3 +804,8 @@ def setvars(gene,drug):
|
|||
|
||||
###############################################################################
|
||||
# TODO: Find over and undersampling JUST for categorical data
|
||||
###########################################################################
|
||||
|
||||
print('\n#################################################################'
|
||||
, '\nDim of X for gene:', gene.lower(), '\n', X.shape
|
||||
, '\n###############################################################')
|
||||
|
|
|
|||
|
|
@ -55,9 +55,8 @@ OutFile_suffix = '7030'
|
|||
outdir_ml = outdir + 'ml/tts_7030/'
|
||||
print('\nOutput directory:', outdir_ml)
|
||||
|
||||
outFile_wf = outdir_ml + gene.lower() + '_baselineC_' + OutFile_suffix + '.csv'
|
||||
#outFile_lf = outdir_ml + gene.lower() + '_baselineC_ext_' + OutFile_suffix + '.csv'
|
||||
|
||||
#outFile_wf = outdir_ml + gene.lower() + '_baselineC_' + OutFile_suffix + '.csv'
|
||||
outFile_wf = outdir_ml + gene.lower() + '_baselineC_noOR' + OutFile_suffix + '.csv'
|
||||
#%% Running models ############################################################
|
||||
print('\n#####################################################################\n'
|
||||
, '\nStarting--> Running ML analysis: Baseline modes (No FS)'
|
||||
|
|
@ -92,10 +91,24 @@ paramD = {
|
|||
, 'resampling_type' : 'rouC'}
|
||||
}
|
||||
|
||||
# Initial run to get the dict containing CV, BT and metadata DFs
|
||||
mmD = {}
|
||||
##==============================================================================
|
||||
## Dict with no CV BT formatted df
|
||||
## mmD = {}
|
||||
## for k, v in paramD.items():
|
||||
## # print(mmD[k])
|
||||
## scores_7030D = MultModelsCl(**paramD[k]
|
||||
## , tts_split_type = tts_split_7030
|
||||
## , skf_cv = skf_cv
|
||||
## , blind_test_df = X_bts
|
||||
## , blind_test_target = y_bts
|
||||
## , add_cm = True
|
||||
## , add_yn = True
|
||||
## , return_formatted_output = False)
|
||||
## mmD[k] = scores_7030D
|
||||
##==============================================================================
|
||||
## Initial run to get the dict of dicts for each sampling type containing CV, BT and metadata DFs
|
||||
mmDD = {}
|
||||
for k, v in paramD.items():
|
||||
# print(mmD[k])
|
||||
scores_7030D = MultModelsCl(**paramD[k]
|
||||
, tts_split_type = tts_split_7030
|
||||
, skf_cv = skf_cv
|
||||
|
|
@ -104,23 +117,25 @@ for k, v in paramD.items():
|
|||
, add_cm = True
|
||||
, add_yn = True
|
||||
, return_formatted_output = True)
|
||||
mmD[k] = scores_7030D
|
||||
mmDD[k] = scores_7030D
|
||||
|
||||
# Extracting the dfs from within the dict and concatenating to output as one df
|
||||
for k, v in mmD.items():
|
||||
out_wf_7030 = pd.concat(mmD, ignore_index = True)
|
||||
for k, v in mmDD.items():
|
||||
out_wf_7030 = pd.concat(mmDD, ignore_index = True)
|
||||
|
||||
out_wf_7030f = out_wf_7030.sort_values(by = ['resampling', 'source_data', 'MCC'], ascending = [True, True, False], inplace = False)
|
||||
|
||||
print('\n######################################################################'
|
||||
, '\nEnd--> Successfully generated output DF for Multiple classifiers (baseline models)'
|
||||
, '\nGene:', gene.lower()
|
||||
, '\nDrug:', drug
|
||||
, '\noutput file:', outFile_wf
|
||||
, '\nDim of output:', out_wf_7030.shape
|
||||
, '\nDim of output:', out_wf_7030f.shape
|
||||
, '\n######################################################################')
|
||||
###############################################################################
|
||||
#====================
|
||||
# Write output file
|
||||
#====================
|
||||
#out_wf_7030.to_csv(outFile_wf, index = False)
|
||||
out_wf_7030f.to_csv(outFile_wf, index = False)
|
||||
print('\nFile successfully written:', outFile_wf)
|
||||
###############################################################################
|
||||
|
|
@ -92,7 +92,7 @@ gene = args.gene
|
|||
#==================
|
||||
# other vars
|
||||
#==================
|
||||
tts_split = '70/30'
|
||||
tts_split = '70_30'
|
||||
OutFile_suffix = '7030_FS'
|
||||
###############################################################################
|
||||
#==================
|
||||
|
|
@ -116,7 +116,8 @@ from FS import fsgs
|
|||
#==================
|
||||
outdir_ml = outdir + 'ml/tts_7030/fs/'
|
||||
print('\nOutput directory:', outdir_ml)
|
||||
OutFileFS = outdir_ml + gene.lower() + '_FS_' + OutFile_suffix + '.json'
|
||||
#OutFileFS = outdir_ml + gene.lower() + '_FS' + OutFile_suffix + '.json'
|
||||
OutFileFS = outdir_ml + gene.lower() + '_FS_noOR' + OutFile_suffix + '.json'
|
||||
|
||||
############################################################################
|
||||
|
||||
|
|
@ -153,17 +154,17 @@ models = [('AdaBoost Classifier' , AdaBoostClassifier(**rs) )
|
|||
, ('Extra Tree' , ExtraTreeClassifier(**rs) )
|
||||
, ('Extra Trees' , ExtraTreesClassifier(**rs) )
|
||||
, ('Gradient Boosting' , GradientBoostingClassifier(**rs) )
|
||||
#, ('Gaussian NB' , GaussianNB() )
|
||||
#, ('Gaussian Process' , GaussianProcessClassifier(**rs) )
|
||||
#, ('K-Nearest Neighbors' , KNeighborsClassifier() )
|
||||
##, ('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() )
|
||||
##, ('MLP' , MLPClassifier(max_iter = 500, **rs) )
|
||||
##, ('Multinomial' , MultinomialNB() )
|
||||
##, ('Naive Bayes' , BernoulliNB() )
|
||||
, ('Passive Aggresive' , PassiveAggressiveClassifier(**rs, **njobs) )
|
||||
#, ('QDA' , QuadraticDiscriminantAnalysis() )
|
||||
##, ('QDA' , QuadraticDiscriminantAnalysis() )
|
||||
, ('Random Forest' , RandomForestClassifier(**rs, n_estimators = 1000 ) )
|
||||
, ('Random Forest2' , RandomForestClassifier(min_samples_leaf = 5
|
||||
, n_estimators = 1000
|
||||
|
|
@ -174,10 +175,10 @@ models = [('AdaBoost Classifier' , AdaBoostClassifier(**rs) )
|
|||
, max_features = 'auto') )
|
||||
, ('Ridge Classifier' , RidgeClassifier(**rs) )
|
||||
, ('Ridge ClassifierCV' , RidgeClassifierCV(cv = 3) )
|
||||
#, ('SVC' , SVC(**rs) )
|
||||
##, ('SVC' , SVC(**rs) )
|
||||
, ('Stochastic GDescent' , SGDClassifier(**rs, **njobs) )
|
||||
# , ('XGBoost' , XGBClassifier(**rs, **njobs, verbosity = 3
|
||||
# , use_label_encoder = False) )
|
||||
## , ('XGBoost' , XGBClassifier(**rs, **njobs, verbosity = 3
|
||||
## , use_label_encoder = False) )
|
||||
]
|
||||
|
||||
print('\n#####################################################################'
|
||||
|
|
|
|||
|
|
@ -1,11 +1,13 @@
|
|||
=================================
|
||||
# Split: 70/30
|
||||
########################################################################
|
||||
|
||||
#70/30
|
||||
|
||||
########################################################################
|
||||
=-----------------------------------=
|
||||
# All features including AA index
|
||||
# Date: 22/06/2022
|
||||
# captures error: 2>$1
|
||||
# omitted drtype_labels
|
||||
=================================
|
||||
time ./run_7030.py -g pncA -d pyrazinamide 2>&1 | tee log_pnca_7030.txt
|
||||
# [WITH OR]
|
||||
=-----------------------------------=
|
||||
time ./run_7030.py -g pncA -d pyrazinamide 2>&1 | tee log_pnca_7030.txt #d
|
||||
time ./run_7030.py -g embB -d ethambutol 2>&1 | tee log_embb_7030.txt
|
||||
time ./run_7030.py -g katG -d isoniazid 2>&1 | tee log_katg_7030.txt
|
||||
time ./run_7030.py -g rpoB -d rifampicin 2>&1 | tee log_rpob_7030.txt
|
||||
|
|
@ -14,71 +16,155 @@ time ./run_7030.py -g alr -d cycloserine 2>&1 | tee log_alr_7030.txt
|
|||
|
||||
# alr: # ERROR, as expected, too few values!
|
||||
# gid: problems
|
||||
########################################################################
|
||||
|
||||
=================================
|
||||
# Split: 80/20
|
||||
=-----------------------------------=
|
||||
# All features including AA index
|
||||
# Date: 17/05/2022, 18:48
|
||||
# captures error: 2>$1
|
||||
=================================
|
||||
# [WITHOUT OR] **DONE
|
||||
#------------------------------------=
|
||||
time ./run_7030.py -g pncA -d pyrazinamide 2>&1 | tee log_pnca_7030_noOR.txt
|
||||
time ./run_7030.py -g embB -d ethambutol 2>&1 | tee log_embb_7030_noOR.txt
|
||||
time ./run_7030.py -g katG -d isoniazid 2>&1 | tee log_katg_7030_noOR.txt
|
||||
time ./run_7030.py -g rpoB -d rifampicin 2>&1 | tee log_rpob_7030_noOR.txt
|
||||
time ./run_7030.py -g gid -d streptomycin 2>&1 | tee log_gid_7030_noOR.txt
|
||||
time ./run_7030.py -g alr -d cycloserine 2>&1 | tee log_alr_7030_noOR.txt
|
||||
########################################################################
|
||||
|
||||
# 80/20
|
||||
|
||||
########################################################################
|
||||
|
||||
=================================
|
||||
# Split: scaling law
|
||||
=-----------------------------------=
|
||||
# All features including AA index
|
||||
# Date: 17/05/2022, 18:48
|
||||
# captures error: 2>$1
|
||||
=================================
|
||||
# [WITH OR]
|
||||
=-----------------------------------=
|
||||
time ./run_8020.py -g pncA -d pyrazinamide 2>&1 | tee log_pnca_8020.txt
|
||||
time ./run_8020.py -g embB -d ethambutol 2>&1 | tee log_embb_8020.txt
|
||||
time ./run_8020.py -g katG -d isoniazid 2>&1 | tee log_katg_8020.txt
|
||||
time ./run_8020.py -g rpoB -d rifampicin 2>&1 | tee log_rpob_8020.txt
|
||||
time ./run_8020.py -g gid -d streptomycin 2>&1 | tee log_gid_8020.txt
|
||||
time ./run_8020.py -g alr -d cycloserine 2>&1 | tee log_alr_8020.txt
|
||||
|
||||
|
||||
########################################################################
|
||||
=================================
|
||||
# Split: REVERSE training
|
||||
# imputed values: training set
|
||||
# actual values: blind set
|
||||
=-----------------------------------=
|
||||
# All features including AA index
|
||||
# Date: 18/05/2022
|
||||
# captures error: 2>$1
|
||||
=================================
|
||||
# [WITHOUT OR] **DONE
|
||||
real 0m1.099s
|
||||
user 0m1.308s
|
||||
sys 0m1.474s
|
||||
=-----------------------------------=
|
||||
time ./run_8020.py -g pncA -d pyrazinamide 2>&1 | tee log_pnca_8020_noOR.txt
|
||||
time ./run_8020.py -g embB -d ethambutol 2>&1 | tee log_embb_8020_noOR.txt
|
||||
time ./run_8020.py -g katG -d isoniazid 2>&1 | tee log_katg_8020_noOR.txt
|
||||
time ./run_8020.py -g rpoB -d rifampicin 2>&1 | tee log_rpob_8020_noOR.txt
|
||||
time ./run_8020.py -g gid -d streptomycin 2>&1 | tee log_gid_8020_noOR.txt
|
||||
time ./run_8020.py -g alr -d cycloserine 2>&1 | tee log_alr_8020_noOR.txt
|
||||
|
||||
########################################################################
|
||||
# COMPLETE Data: actual + na i.e imputed
|
||||
|
||||
# SL
|
||||
|
||||
########################################################################
|
||||
|
||||
=================================
|
||||
# Split: 70/30 [COMPLETE DATA]
|
||||
=-----------------------------------=
|
||||
# All features including AA index
|
||||
# Date: 18/05/2022
|
||||
# captures error: 2>$1
|
||||
=================================
|
||||
=-----------------------------------=
|
||||
|
||||
|
||||
########################################################################
|
||||
=================================
|
||||
# Split: 80/20 [COMPLETE DATA]
|
||||
|
||||
|
||||
|
||||
|
||||
=-----------------------------------=
|
||||
# All features including AA index
|
||||
# Date: 18/05/2022
|
||||
# captures error: 2>$1
|
||||
=================================
|
||||
# [WITHOUT OR]
|
||||
=-----------------------------------=
|
||||
time ./run_sl.py -g pncA -d pyrazinamide 2>&1 | tee log_pnca_sl_noOR.txt
|
||||
time ./run_sl.py -g embB -d ethambutol 2>&1 | tee log_embb_sl_noOR.txt
|
||||
time ./run_sl.py -g katG -d isoniazid 2>&1 | tee log_katg_sl_noOR.txt
|
||||
time ./run_sl.py -g rpoB -d rifampicin 2>&1 | tee log_rpob_sl_noOR.txt
|
||||
time ./run_sl.py -g gid -d streptomycin 2>&1 | tee log_gid_sl_noOR.txt
|
||||
time ./run_sl.py -g alr -d cycloserine 2>&1 | tee log_alr_sl_noOR.txt
|
||||
|
||||
=================================
|
||||
# Split: scaling law [COMPLETE DATA]
|
||||
########################################################################
|
||||
|
||||
|
||||
########################################################################
|
||||
########################################################################
|
||||
###################### COMPLETE DATA ##############################
|
||||
########################################################################
|
||||
########################################################################
|
||||
|
||||
|
||||
########################################################################
|
||||
|
||||
#70/30
|
||||
|
||||
########################################################################
|
||||
|
||||
|
||||
=-----------------------------------=
|
||||
# All features including AA index
|
||||
# Date: 18/05/2022
|
||||
# captures error: 2>$1
|
||||
=================================
|
||||
# [WITHOUT OR]
|
||||
#------------------------------------=
|
||||
time ./run_cd_7030.py -g pncA -d pyrazinamide 2>&1 | tee log_pnca_cd_7030_noOR.txt
|
||||
time ./run_cd_7030.py -g embB -d ethambutol 2>&1 | tee log_embb_cd_7030_noOR.txt
|
||||
time ./run_cd_7030.py -g katG -d isoniazid 2>&1 | tee log_katg_cd_7030_noOR.txt
|
||||
time ./run_cd_7030.py -g rpoB -d rifampicin 2>&1 | tee log_rpob_cd_7030_noOR.txt
|
||||
time ./run_cd_7030.py -g gid -d streptomycin 2>&1 | tee log_gid_cd_7030_noOR.txt
|
||||
time ./run_cd_7030.py -g alr -d cycloserine 2>&1 | tee log_alr_cd_7030_noOR.txt
|
||||
|
||||
|
||||
########################################################################
|
||||
|
||||
# 80/20
|
||||
|
||||
########################################################################
|
||||
|
||||
|
||||
|
||||
=-----------------------------------=
|
||||
# All features including AA index
|
||||
# [WITHOUT OR]
|
||||
#------------------------------------=
|
||||
time ./run_cd_8020.py -g pncA -d pyrazinamide 2>&1 | tee log_pnca_cd_8020_noOR.txt
|
||||
time ./run_cd_8020.py -g embB -d ethambutol 2>&1 | tee log_embb_cd_8020_noOR.txt
|
||||
time ./run_cd_8020.py -g katG -d isoniazid 2>&1 | tee log_katg_cd_8020_noOR.txt
|
||||
time ./run_cd_8020.py -g rpoB -d rifampicin 2>&1 | tee log_rpob_cd_8020_noOR.txt
|
||||
time ./run_cd_8020.py -g gid -d streptomycin 2>&1 | tee log_gid_cd_8020_noOR.txt
|
||||
time ./run_cd_8020.py -g alr -d cycloserine 2>&1 | tee log_alr_cd_8020_noOR.txt
|
||||
|
||||
########################################################################
|
||||
|
||||
# SL
|
||||
|
||||
########################################################################
|
||||
|
||||
|
||||
|
||||
=-----------------------------------=
|
||||
# All features including AA index
|
||||
# [WITHOUT OR]
|
||||
#------------------------------------=
|
||||
time ./run_cd_sl.py -g pncA -d pyrazinamide 2>&1 | tee log_pnca_cd_sl_noOR.txt
|
||||
time ./run_cd_sl.py -g embB -d ethambutol 2>&1 | tee log_embb_cd_sl_noOR.txt
|
||||
time ./run_cd_sl.py -g katG -d isoniazid 2>&1 | tee log_katg_cd_sl_noOR.txt
|
||||
time ./run_cd_sl.py -g rpoB -d rifampicin 2>&1 | tee log_rpob_cd_sl_noOR.txt
|
||||
time ./run_cd_sl.py -g gid -d streptomycin 2>&1 | tee log_gid_cd_sl_noOR.txt
|
||||
time ./run_cd_sl.py -g alr -d cycloserine 2>&1 | tee log_alr_cd_sl_noOR.txt
|
||||
|
||||
|
||||
|
||||
|
||||
########################################################################
|
||||
|
||||
########################################################################
|
||||
########################################################################
|
||||
###################### Feature Selection ##########################
|
||||
########################################################################
|
||||
########################################################################
|
||||
|
||||
# running feature selection
|
||||
# Split:70/30
|
||||
# 7030
|
||||
time ./run_FS.py -g pncA -d pyrazinamide 2>&1 | tee log_FS_pnca_7030.txt
|
||||
|
||||
real 338m26.705s
|
||||
user 1946m12.173s
|
||||
sys 189m40.122s
|
||||
|
||||
time ./run_FS_7030.py -g pncA -d pyrazinamide 2>&1 | tee log_FS_pnca_7030_noOR.txt
|
||||
|
|
|
|||
|
|
@ -100,3 +100,5 @@ mmDF3 = MultModelsCl(input_df = X_smnc
|
|||
#=================
|
||||
# output from function call
|
||||
ProcessMultModelsCl(mmD)
|
||||
ProcessMultModelsCl(testD)
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue