added FS to MultClfs.py and modified data for different splits for consistency
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12 changed files with 1585 additions and 994 deletions
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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 = "sl"
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rs = {'random_state': 42}
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njobs = {'n_jobs': 10}
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@ -56,12 +60,10 @@ def setvars(gene,drug):
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, **rs)
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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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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']
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# Build stability columns ~ gene
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# Build column names to mask for affinity chanhes
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if gene.lower() in geneL_basic:
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X_stabilityN = common_cols_stabiltyN
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#X_stabilityN = common_cols_stabiltyN
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gene_affinity_colnames = []# not needed as its the common ones
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cols_to_mask = ['ligand_affinity_change']
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if gene.lower() in geneL_ppi2:
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# X_stabilityN = common_cols_stabiltyN + ['mcsm_ppi2_affinity' , 'interface_dist']
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geneL_ppi2_st_cols = ['mcsm_ppi2_affinity', 'interface_dist']
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X_stabilityN = common_cols_stabiltyN + geneL_ppi2_st_cols
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gene_affinity_colnames = ['mcsm_ppi2_affinity', 'interface_dist']
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#X_stabilityN = common_cols_stabiltyN + geneL_ppi2_st_cols
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cols_to_mask = ['ligand_affinity_change', 'mcsm_ppi2_affinity']
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if gene.lower() in geneL_na:
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# X_stabilityN = common_cols_stabiltyN + ['mcsm_na_affinity']
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geneL_na_st_cols = ['mcsm_na_affinity']
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X_stabilityN = common_cols_stabiltyN + geneL_na_st_cols
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gene_affinity_colnames = ['mcsm_na_affinity']
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#X_stabilityN = common_cols_stabiltyN + geneL_na_st_cols
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cols_to_mask = ['ligand_affinity_change', 'mcsm_na_affinity']
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if gene.lower() in geneL_na_ppi2:
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# X_stabilityN = common_cols_stabiltyN + ['mcsm_na_affinity'] + ['mcsm_ppi2_affinity', 'interface_dist']
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geneL_na_ppi2_st_cols = ['mcsm_na_affinity'] + ['mcsm_ppi2_affinity', 'interface_dist']
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X_stabilityN = common_cols_stabiltyN + geneL_na_ppi2_st_cols
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gene_affinity_colnames = ['mcsm_na_affinity'] + ['mcsm_ppi2_affinity', 'interface_dist']
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#X_stabilityN = common_cols_stabiltyN + geneL_na_ppi2_st_cols
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cols_to_mask = ['ligand_affinity_change', 'mcsm_na_affinity', 'mcsm_ppi2_affinity']
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X_foldX_cols = [ 'electro_rr', 'electro_mm', 'electro_sm', 'electro_ss'
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, 'disulfide_rr', 'disulfide_mm', 'disulfide_sm', 'disulfide_ss'
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, 'hbonds_rr', 'hbonds_mm', 'hbonds_sm', 'hbonds_ss'
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, 'partcov_rr', 'partcov_mm', 'partcov_sm', 'partcov_ss'
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, 'vdwclashes_rr', 'vdwclashes_mm', 'vdwclashes_sm', 'vdwclashes_ss'
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, 'volumetric_rr', 'volumetric_mm', 'volumetric_ss'
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]
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X_str = ['rsa'
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#, 'asa'
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, 'kd_values'
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, 'rd_values']
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X_ssFN = X_stabilityN + X_str + X_foldX_cols
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X_evolFN = ['consurf_score'
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, 'snap2_score'
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, 'provean_score']
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X_genomic_mafor = ['maf'
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, 'logorI'
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# , 'or_rawI'
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# , 'or_mychisq'
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# , 'or_logistic'
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# , 'or_fisher'
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# , 'pval_fisher'
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]
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X_genomic_linegae = ['lineage_proportion'
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, 'dist_lineage_proportion'
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#, 'lineage' # could be included as a category but it has L2;L4 formatting
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, 'lineage_count_all'
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, 'lineage_count_unique'
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]
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X_genomicFN = X_genomic_mafor + X_genomic_linegae
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X_aaindexFN = list(aa_df_cols)
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print('\nTotal no. of features for aaindex:', len(X_aaindexFN))
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# numerical feature names
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numerical_FN = X_ssFN + X_evolFN + X_genomicFN + X_aaindexFN
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# categorical feature names
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categorical_FN = ['ss_class'
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# , 'wt_prop_water'
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# , 'mut_prop_water'
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# , 'wt_prop_polarity'
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# , 'mut_prop_polarity'
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# , 'wt_calcprop'
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# , 'mut_calcprop'
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, 'aa_prop_change'
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, 'electrostatics_change'
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, 'polarity_change'
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, 'water_change'
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, 'drtype_mode_labels' # beware then you can't use it to predict [USED it for uq_v1, not v2]
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, 'active_site' #[didn't use it for uq_v1]
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#, 'gene_name' # will be required for the combined stuff
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]
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#----------------------------------------------
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# count numerical and categorical features
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#----------------------------------------------
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print('\nNo. of numerical features:', len(numerical_FN)
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, '\nNo. of categorical features:', len(categorical_FN))
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#=======================
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# Masking columns:
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# (mCSM-lig, mCSM-NA, mCSM-ppi2) values for lig_dist >10
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#=======================
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# my_df_ml['mutationinformation'][my_df['ligand_distance']>10].value_counts()
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# my_df_ml.groupby('mutationinformation')['ligand_distance'].apply(lambda x: (x>10)).value_counts()
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# my_df_ml.loc[(my_df_ml['ligand_distance'] > 10), 'ligand_affinity_change'] = 0
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# (my_df_ml['ligand_affinity_change'] == 0).sum()
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my_df_ml['mutationinformation'][my_df_ml['ligand_distance']>10].value_counts()
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my_df_ml.groupby('mutationinformation')['ligand_distance'].apply(lambda x: (x>10)).value_counts()
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my_df_ml.loc[(my_df_ml['ligand_distance'] > 10), cols_to_mask].value_counts()
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@ -544,24 +459,154 @@ def setvars(gene,drug):
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mask_check = my_df_ml[['mutationinformation', 'ligand_distance'] + cols_to_mask]
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#===================================================
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# write file for check
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mask_check.sort_values(by = ['ligand_distance'], ascending = True, inplace = True)
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mask_check.to_csv(outdir + 'ml/' + gene.lower() + '_mask_check.csv')
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#####################################################################
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#================================================================
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# Training and Blind test [COMPLETE data]: scaling law split
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# https://towardsdatascience.com/finally-why-we-use-an-80-20-split-for-training-and-test-data-plus-an-alternative-method-oh-yes-edc77e96295d
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mask_check.to_csv(outdir + 'ml/' + gene.lower() + '_mask_check.csv')
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#===================================================
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###############################################################################
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#%% Feature groups (FG): Build X for Input ML
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############################################################################
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#===========================
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# FG1: Evolutionary features
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#===========================
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X_evolFN = ['consurf_score'
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, 'snap2_score'
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, 'provean_score']
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###############################################################################
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#========================
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# FG2: Stability features
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#========================
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#--------
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# common
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#--------
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X_common_stability_Fnum = [
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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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, 'contacts']
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#--------
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# FoldX
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#--------
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X_foldX_Fnum = [ 'electro_rr', 'electro_mm', 'electro_sm', 'electro_ss'
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, 'disulfide_rr', 'disulfide_mm', 'disulfide_sm', 'disulfide_ss'
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, 'hbonds_rr', 'hbonds_mm', 'hbonds_sm', 'hbonds_ss'
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, 'partcov_rr', 'partcov_mm', 'partcov_sm', 'partcov_ss'
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, 'vdwclashes_rr', 'vdwclashes_mm', 'vdwclashes_sm', 'vdwclashes_ss'
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, 'volumetric_rr', 'volumetric_mm', 'volumetric_ss']
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X_stability_FN = X_common_stability_Fnum + X_foldX_Fnum
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###############################################################################
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#===================
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# FG3: Affinity features
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#===================
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common_affinity_Fnum = ['ligand_distance'
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, 'ligand_affinity_change'
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, 'mmcsm_lig']
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# if gene.lower() in geneL_basic:
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# X_affinityFN = common_affinity_Fnum
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# else:
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# X_affinityFN = common_affinity_Fnum + gene_affinity_colnames
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X_affinityFN = common_affinity_Fnum + gene_affinity_colnames
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###############################################################################
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#============================
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# FG4: Residue level features
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#============================
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#-----------
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# AA index
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#-----------
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X_aaindex_Fnum = list(aa_df_cols)
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print('\nTotal no. of features for aaindex:', len(X_aaindex_Fnum))
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#-----------------
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# surface area
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# depth
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# hydrophobicity
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#-----------------
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X_str_Fnum = ['rsa'
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#, 'asa'
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, 'kd_values'
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, 'rd_values']
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#---------------------------
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# Other aa properties
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# active site indication
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#---------------------------
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X_aap_Fcat = ['ss_class'
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# , 'wt_prop_water'
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# , 'mut_prop_water'
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# , 'wt_prop_polarity'
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# , 'mut_prop_polarity'
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# , 'wt_calcprop'
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# , 'mut_calcprop'
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, 'aa_prop_change'
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, 'electrostatics_change'
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, 'polarity_change'
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, 'water_change'
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, 'active_site']
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X_resprop_FN = X_aaindex_Fnum + X_str_Fnum + X_aap_Fcat
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###############################################################################
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#========================
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# FG5: Genomic features
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#========================
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X_gn_mafor_Fnum = ['maf'
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#, 'logorI'
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# , 'or_rawI'
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# , 'or_mychisq'
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# , 'or_logistic'
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# , 'or_fisher'
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# , 'pval_fisher'
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]
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X_gn_linegae_Fnum = ['lineage_proportion'
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, 'dist_lineage_proportion'
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#, 'lineage' # could be included as a category but it has L2;L4 formatting
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, 'lineage_count_all'
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, 'lineage_count_unique'
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]
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# X_gn_Fcat = ['drtype_mode_labels' # beware then you can't use it to predict [USED it for uq_v1, not v2]
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# #, 'gene_name' # will be required for the combined stuff
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# ]
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X_gn_Fcat = []
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X_genomicFN = X_gn_mafor_Fnum + X_gn_linegae_Fnum + X_gn_Fcat
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###############################################################################
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#========================
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# FG6 collapsed: Structural : Atability + Affinity + ResidueProp
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#========================
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X_structural_FN = X_stability_FN + X_affinityFN + X_resprop_FN
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###############################################################################
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#========================
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# BUILDING all features
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#========================
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all_featuresN = X_evolFN + X_structural_FN + X_genomicFN
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###############################################################################
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#%% Define training and test data
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#================================================================
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# Training and BLIND test set: scaling law split
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#https://towardsdatascience.com/finally-why-we-use-an-80-20-split-for-training-and-test-data-plus-an-alternative-method-oh-yes-edc77e96295d
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# dst with actual values : training set
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# dst with imputed values : THROW AWAY [unrepresentative]
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# test data size ~ 1/sqrt(features NOT including target variable)
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#================================================================
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my_df_ml[drug].isna().sum()
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# blind_test_df = my_df_ml[my_df_ml[drug].isna()]
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# blind_test_df.shape
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#training_df = my_df_ml[my_df_ml[drug].notna()]
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#training_df = my_df_ml[my_df_ml[drug].notna()]
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#training_df.shape
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training_df = my_df_ml.copy()
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# Target 1: dst_mode
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@ -569,80 +614,14 @@ def setvars(gene,drug):
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training_df['dst_mode'].value_counts()
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####################################################################
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###############################################################################
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###############################################################################
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# #%% extracting dfs based on numerical, categorical column names
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# #----------------------------------
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# # WITHOUT the target var included
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# #----------------------------------
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# num_df = training_df[numerical_FN]
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# num_df.shape
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# cat_df = training_df[categorical_FN]
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# cat_df.shape
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# all_df = training_df[numerical_FN + categorical_FN]
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# all_df.shape
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# #------------------------------
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# # WITH the target var included:
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# #'wtgt': with target
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# #------------------------------
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# # drug and dst_mode should be the same thing
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# num_df_wtgt = training_df[numerical_FN + ['dst_mode']]
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# num_df_wtgt.shape
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# cat_df_wtgt = training_df[categorical_FN + ['dst_mode']]
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# cat_df_wtgt.shape
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# all_df_wtgt = training_df[numerical_FN + categorical_FN + ['dst_mode']]
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# all_df_wtgt.shape
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#%%########################################################################
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# #============
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# # ML data: OLD
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# #============
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# #------
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# # X: Training and Blind test (BTS)
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# #------
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# X = all_df_wtgt[numerical_FN + categorical_FN] # training data ALL
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# X_bts = blind_test_df[numerical_FN + categorical_FN] # blind test data ALL
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# #X = all_df_wtgt[numerical_FN] # training numerical only
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# #X_bts = blind_test_df[numerical_FN] # blind test data numerical
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# #------
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# # y
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# #------
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# y = all_df_wtgt['dst_mode'] # training data y
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# y_bts = blind_test_df['dst_mode'] # blind data test y
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# #X_bts_wt = blind_test_df[numerical_FN + ['dst_mode']]
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# # Quick check
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# #(X['ligand_affinity_change']==0).sum() == (X['ligand_distance']>10).sum()
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# for i in range(len(cols_to_mask)):
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# ind = i+1
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# print('\nindex:', i, '\nind:', ind)
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# print('\nMask count check:'
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# , (my_df_ml[cols_to_mask[i]]==0).sum() == (my_df_ml['ligand_distance']>10).sum()
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# )
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# print('Original Data\n', Counter(y)
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# , 'Data dim:', X.shape)
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###############################################################################
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###############################################################################
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#====================================
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# ML data: Train test split [COMPLETE data]: scaling law
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# ML data: Train test split: SL
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# with stratification
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# 1-blind test : training_data for CV
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# 1/sqrt(columns) : blind test
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#=====================================
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# features: all_df or
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x_features = training_df[numerical_FN + categorical_FN]
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y_target = training_df['dst_mode']
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#===========================================
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x_features = training_df[all_featuresN]
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y_target = training_df['dst_mode']
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# sanity check
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if not 'dst_mode' in x_features.columns:
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@ -650,12 +629,15 @@ def setvars(gene,drug):
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x_ncols = len(x_features.columns)
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print('\nNo. of columns for x_features:', x_ncols)
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# NEED It for scaling law split
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#https://towardsdatascience.com/finally-why-we-use-an-80-20-split-for-training-and-test-data-plus-an-alternative-method-oh-yes-edc77e96295d
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else:
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sys.exit('\nFAIL: x_features has target variable included. FIX it and rerun!')
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#-------------------
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# train-test split
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#-------------------
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sl_test_size = 1/np.sqrt(x_ncols)
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train = 1 - sl_test_size
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#x_train, x_test, y_train, y_test # traditional var_names
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# so my downstream code doesn't need to change
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X, X_bts, y, y_bts = train_test_split(x_features, y_target
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@ -667,16 +649,65 @@ def setvars(gene,drug):
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yc2 = Counter(y_bts)
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yc2_ratio = yc2[0]/yc2[1]
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###############################################################################
|
||||
#======================================================
|
||||
# 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###############################################################')
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue