added lambda func to normalise duet and aff values

mcsm/format_results.py

@@ -37,7 +37,7 @@ gene_match = gene + '_p.'
 # data dir
 #==========
 datadir = homedir + '/' + 'git/Data'
-#5am0chod
+
 #=======
 # input:
 #=======
@@ -53,22 +53,25 @@ print('Input filename:', in_filename
 # output 
 #=======
 outdir =   datadir + '/' + drug + '/' + 'output'
-#out_filename = gene.lower() + '.csv'
+out_filename = gene.lower() + '_complex_mcsm_norm.csv'
-#outfile =  outdir + '/' + out_filename
+outfile =  outdir + '/' + out_filename
-#print('Output filename:', out_filename
+print('Output filename:', out_filename
-#      , '\nOutput path:', outdir
+      , '\nOutput path:', outdir
-#      , '\n=============================================================')
+      , '\n=============================================================')
 
 #=======================================================================
-
+print('Reading input file')
 mcsm_data  = pd.read_csv(infile, sep = ',')
 
-
 mcsm_data.columns
 # PredAffLog = affinity_change_log
 # "DUETStability_Kcalpermol = DUET_change_kcalpermol
+dforig_shape = mcsm_data.shape
+print('dim of infile:', dforig_shape) 
 
 # change colnames to reflect units and no spaces, and replace '-' with '-'
+print('Assigning meaningful colnames i.e without spaces and hyphen and reflecting units'
+      , '\n===================================================================')
 my_colnames_dict = {'Predicted Affinity Change': 'PredAffLog'
                , 'Mutation information': 'Mutationinformation'
                , 'Wild-type': 'Wild_type'
@@ -81,44 +84,33 @@ my_colnames_dict = {'Predicted Affinity Change': 'PredAffLog'
 
 mcsm_data.rename(columns = my_colnames_dict, inplace = True)
 mcsm_data.columns
-#%%=========================================================================== 
-# Extract only the numeric part from col: Dis_lig_Ang
-# number: '-?\d+\.?\d*'
-mcsm_data['Dis_lig_Ang']
-print('extracting numeric part of col: Dis_lig_Ang')
-mcsm_data['Dis_lig_Ang'] = mcsm_data['Dis_lig_Ang'].str.extract('(\d+\.?\d*)')
-mcsm_data['Dis_lig_Ang']
-
-# changing dtype to numeric
-#if is_numeric_dtype(mcsm_data['Dis_lig_Ang']):
-#    print('Data type is already numeric, doing nothing')
-#else:
-#    print('Changing dtype in col: Dis_lig_Ang to numeric since Distance should be numeric')
-## FIXME: either do it here, or in the end for all the required cols at once    
-
 #%%===========================================================================
 # populate mutationinformation column:mcsm style  muts {WT}<POS>{MUT}
 print('populating column : Mutationinformation which is currently empty\n', mcsm_data['Mutationinformation'])
 mcsm_data['Mutationinformation'] = mcsm_data['Wild_type'] +  mcsm_data['Position'].astype(str) + mcsm_data['Mutant_type']
-print('checking after populating:\n', mcsm_data['Mutationinformation'])
+print('checking after populating:\n', mcsm_data['Mutationinformation']
+      , '\n===================================================================')
 
 # Remove spaces b/w pasted columns
 print('removing white space within column: \Mutationinformation')
 mcsm_data['Mutationinformation'] = mcsm_data['Mutationinformation'].str.replace(' ', '')
-print('Correctly formatted column: Mutationinformation\n', mcsm_data['Mutationinformation'])
+print('Correctly formatted column: Mutationinformation\n', mcsm_data['Mutationinformation']
+      , '\n===================================================================')
 #%%===========================================================================
 # very important
 print('Sanity check:'
       , '\nChecking duplicate mutations')
 if mcsm_data['Mutationinformation'].duplicated().sum() == 0:
     print('PASS: No duplicate mutations detected (as expected)'
-          , '\nDim of data:', mcsm_data.shape)
+          , '\nDim of data:', mcsm_data.shape
+          , '\n===============================================================')
 else:
     print('FAIL (but not fatal): Duplicate mutations detected'
           , '\nDim of df with duplicates:', mcsm_data.shape
           , 'Removing duplicate entries')
     mcsm_data = mcsm_data.drop_duplicates(['Mutationinformation'])
-    print('Dim of data after removing duplicate muts:', mcsm_data.shape)
+    print('Dim of data after removing duplicate muts:', mcsm_data.shape
+          , '\n===============================================================')
 #%%=========================================================================== 
 # create DUET_outcome column: classification based on DUET stability values
 print('Assigning col: DUET_outcome based on DUET stability values')
@@ -135,25 +127,41 @@ if DUET_pos == mcsm_data['DUET_outcome'].value_counts()['Stabilising']:
 else:
     print('FAIL: DUET outcome assigned incorrectly'
           , '\nExpected no. of stabilising mutations:', DUET_pos
-          , '\nGot no. of stabilising mutations', mcsm_data['DUET_outcome'].value_counts()['Stabilising'])
+          , '\nGot no. of stabilising mutations', mcsm_data['DUET_outcome'].value_counts()['Stabilising']
+          , '\n===============================================================')
+#%%=========================================================================== 
+# Extract only the numeric part from col: Dis_lig_Ang
+# number: '-?\d+\.?\d*'
+mcsm_data['Dis_lig_Ang']
+print('extracting numeric part of col: Dis_lig_Ang')
+mcsm_data['Dis_lig_Ang'] = mcsm_data['Dis_lig_Ang'].str.extract('(\d+\.?\d*)')
+mcsm_data['Dis_lig_Ang']
 
+# changing dtype to numeric
+#if is_numeric_dtype(mcsm_data['Dis_lig_Ang']):
+#    print('Data type is already numeric, doing nothing')
+#else:
+#    print('Changing dtype in col: Dis_lig_Ang to numeric since Distance should be numeric')
+## FIXME: either do it here, or in the end for all the required cols at once    
 #%%=========================================================================== 
 # create Lig_outcome column: classification based on affinity change values
 # the numerical and categorical parts need to be extracted from column: PredAffLog
 # regex used 
 # number: '-?\d+\.?\d*'
 # category: '\b(\w+ing)\b'
-print('Creating affinity change columns...')
 print('Extracting numerical and categorical parts from the col: PredAffLog')
-#aff_regex = re.compile(r'\b(\w+ing)\b')
+print('to create two columns: affinity_change_log and Lig_outcome'
+      , '\n===================================================================')
+
+# Extracting the predicted affinity change (numerical part)
 mcsm_data['affinity_change_log'] = mcsm_data['PredAffLog'].str.extract('(-?\d+\.?\d*)', expand = True)
 print(mcsm_data['affinity_change_log'])
-
+# Extracting the categorical part (Destabillizing and Stabilizing) using word boundary ('ing')
+#aff_regex = re.compile(r'\b(\w+ing)\b')
 mcsm_data['Lig_outcome']= mcsm_data['PredAffLog'].str.extract(r'(\b\w+ing\b)', expand = True)
 print(mcsm_data['Lig_outcome'])
 print(mcsm_data['Lig_outcome'].value_counts())
 american_spl = mcsm_data['Lig_outcome'].value_counts()
-
 print('Changing to Bristish spellings for col: Lig_outcome')
 mcsm_data['Lig_outcome'].replace({'Destabilizing': 'Destabilising', 'Stabilizing': 'Stabilising'}, inplace = True)
 print(mcsm_data['Lig_outcome'].value_counts())
@@ -161,34 +169,100 @@ british_spl = mcsm_data['Lig_outcome'].value_counts()
 
 # since series object will have different names on account of our spelling change
 # use .equals
-if american_spl.equals(british_spl):
+check = american_spl.values == british_spl.values
-    print('PASS: spelling change successfull')
+if check.all():
+    print('PASS: spelling change successfull'
+          , '\nNo. of predicted affinity changes:\n', british_spl
+          , '\n===============================================================')
 else:
     print('FAIL: spelling change unsucessfull'
           , '\nExpected:\n', american_spl
-          , '\nGot:\n', british_spl)
+          , '\nGot:\n', british_spl
+          , '\n===============================================================')
 #%%===========================================================================
 # check dtype in cols
-print(mcsm_data.dtypes)
+print('Checking dtypes in all columns:\n', mcsm_data.dtypes
+      , '\n===================================================================')
+print('Converting the following cols to numeric:'
+      , '\nDis_lig_Ang'
+      , '\nDUET_change_kcalpermol'
+      , '\naffinity_change_log'
+      , '\n===================================================================')
 # using apply method  to change stabilty and affinity values to numeric
-mcsm_data[['affinity_change_log'
+numeric_cols = ['DUET_change_kcalpermol', 'affinity_change_log', 'Dis_lig_Ang']
-           , 'DUET_change_kcalpermol'
+mcsm_data[numeric_cols] = mcsm_data[numeric_cols].apply(pd.to_numeric)
-           , 'Dis_lig_Ang']] = mcsm_data[['affinity_change_log'
-                                          , 'DUET_change_kcalpermol'
-                                          , 'Dis_lig_Ang']].apply(pd.to_numeric)
 # check dtype in cols
+print('checking dtype after conversion')
+cols_check = mcsm_data.select_dtypes(include='float64').columns.isin(numeric_cols)
+if cols_check.all():
+    print('PASS: dtypes for selected cols:', numeric_cols
+          , '\nchanged to numeric'
+          , '\n===============================================================')
+else:
+    print('FAIL:dtype change to numeric for selected cols unsuccessful'
+          , '\n===============================================================')
+#mcsm_data['Dis_lig_Ang', 'affinity_change_log'].apply(is_numeric_dtype(mcsm_data['Dis_lig_Ang', 'affinity_change_log']))
 print(mcsm_data.dtypes)
+
 #%%===========================================================================
+
+#%%===========================================================================
+# Normalise the DUET and affinity change cols
+#converter = lambda x : x*2 if x < 10 else (x*3 if x < 20 else x)
+duet_min = mcsm_data['DUET_change_kcalpermol'].min() 
+duet_max = mcsm_data['DUET_change_kcalpermol'].max() 
+
+converter = lambda x : x/abs(duet_min) if x < 0 else (x/duet_max if x >= 0 else 'failed')
+
+mcsm_data['DUET_change_kcalpermol']
+mcsm_data['ratioDUET'] = mcsm_data['DUET_change_kcalpermol'].apply(converter)
+mcsm_data['ratioDUET']
+#%%===========================================================================
+# Normalise the affinity change cols
+aff_min = mcsm_data['affinity_change_log'].min() 
+aff_max = mcsm_data['affinity_change_log'].max() 
+
+converter = lambda x : x/abs(aff_min) if x < 0 else (x/aff_max if x >= 0 else 'failed')
+#converter(mcsm_data['affinity_change_log'])
+
+mcsm_data['affinity_change_log']
+mcsm_data['ratioPredAff'] = mcsm_data['affinity_change_log'].apply(converter)
+mcsm_data['ratioPredAff']
+#=============================================================================
 # Removing PredAff log column as it is not needed?
 print('Removing col: PredAffLog since relevant info has been extracted from it')
 mcsm_dataf = mcsm_data.drop(columns = ['PredAffLog'])
 #%%===========================================================================
+expected_cols_toadd = 4
+dforig_len = dforig_shape[1]
+expected_cols = dforig_len + expected_cols_toadd
+if len(mcsm_dataf.columns) == expected_cols:
+    print('PASS: formatting successful'
+          , '\nformatted df has expected no. of cols:', expected_cols
+          , '\n---------------------------------------------------------------'
+          , '\ncolnames:', mcsm_dataf.columns
+          , '\n----------------------------------------------------------------'
+          , '\ndtypes in cols:', mcsm_dataf.dtypes
+          , '\n----------------------------------------------------------------'
+          , '\norig data shape:', dforig_shape
+          , '\nformatted df shape:', mcsm_dataf.shape
+          , '\n===============================================================')
+else: 
+    print('FAIL: something went wrong in formatting df'
+          , '\nExpected no. of cols:', expected_cols
+          , '\nGot no. of cols:', len(mcsm_dataf.columns)
+          , '\nCheck formatting'
+          , '\n===============================================================')
+#%%============================================================================
+# writing file
+print('Writing formatted df to csv')
+mcsm_dataf.to_csv(outfile, index = False)
 
-
+print('Finished writing file:'
-
+      , '\nFilename:', out_filename
-#%%===========================================================================
+      , '\nPath:', outdir
-# Normalise the DUET and affinity change cols:
+      , '\nExpected no. of rows:', len(mcsm_dataf)
-
+      , '\nExpected no. of cols:', len(mcsm_dataf.columns)
-
+      , '\n=============================================================')
-
+#%%
-
+#End of script