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finalised categorical and lineage col classifications

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Tanushree Tunstall 2022-05-29 05:22:01 +01:00
parent c37780350e
commit 084c280f16
2 changed files with 94 additions and 61 deletions
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120
UQ_TODO_categorical_classification_columns.py
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@ -6,64 +6,104 @@ Created on Wed May 25 02:01:19 2022
@author: tanu
"""
# TODO
categorical_cols = ['ss_class', 'wt_prop_water', 'mut_prop_water', 'wt_prop_polarity',
'mut_prop_polarity', 'wt_calcprop', 'mut_calcprop']
# categorical_cols = ['ss_class'
# , 'wt_prop_water'
# , 'mut_prop_water'
# , 'wt_prop_polarity'
# , 'mut_prop_polarity'
# , 'wt_calcprop'
# , 'mut_calcprop']
foo['water_prop_change'] = foo['wt_prop_water'] + str('_to_') + foo['mut_prop_water']
foo['water_prop_change'].value_counts()
my_df['water_change'] = my_df['wt_prop_water'] + str('_to_') + my_df['mut_prop_water']
my_df['water_change'].value_counts()
water_prop_changeD = {
'hydrophobic_to_neutral' : ''
'hydrophobic_to_neutral' : 'change'
, 'hydrophobic_to_hydrophobic' : 'no_change'
, 'neutral_to_neutral' : 'no_change'
, 'neutral_to_hydrophobic' : ''
, 'hydrophobic_to_hydrophilic' : ''
, 'neutral_to_hydrophilic' : ''
, 'hydrophilic_to_neutral' : ''
, 'hydrophilic_to_hydrophobic' : ''
, 'neutral_to_hydrophobic' : 'change'
, 'hydrophobic_to_hydrophilic' : 'change'
, 'neutral_to_hydrophilic' : 'change'
, 'hydrophilic_to_neutral' : 'change'
, 'hydrophilic_to_hydrophobic' : 'change'
, 'hydrophilic_to_hydrophilic' : 'no_change'
}
foo['polarity_prop_change'] = foo['wt_prop_polarity'] + str('_to_') + foo['mut_prop_polarity']
foo['polarity_prop_change'].value_counts()
my_df['water_change'] = my_df['water_change'].map(water_prop_changeD)
my_df['water_change'].value_counts()
#%%
my_df['polarity_change'] = my_df['wt_prop_polarity'] + str('_to_') + my_df['mut_prop_polarity']
my_df['polarity_change'].value_counts()
# add a no change category
polarity_prop_changeD = {
'non-polar_to_non-polar' : 'no_change'
, 'non-polar_to_neutral' : ''
, 'neutral_to_non-polar' : ''
, 'neutral_to_neutral' : ''
, 'non-polar_to_basic' : ''
, 'acidic_to_neutral' : ''
, 'basic_to_neutral' : ''
, 'non-polar_to_acidic' : ''
, 'neutral_to_basic' : ''
, 'acidic_to_non-polar' : ''
, 'basic_to_non-polar' : ''
, 'neutral_to_acidic' : ''
, 'non-polar_to_neutral' : 'change'
, 'neutral_to_non-polar' : 'change'
, 'neutral_to_neutral' : 'no_change'
, 'non-polar_to_basic' : 'change'
, 'acidic_to_neutral' : 'change'
, 'basic_to_neutral' : 'change'
, 'non-polar_to_acidic' : 'change'
, 'neutral_to_basic' : 'change'
, 'acidic_to_non-polar' : 'change'
, 'basic_to_non-polar' : 'change'
, 'neutral_to_acidic' : 'change'
, 'acidic_to_acidic' : 'no_change'
, 'basic_to_acidic' : ''
, 'basic_to_acidic' : 'change'
, 'basic_to_basic' : 'no_change'
, 'acidic_to_basic' : ''}
, 'acidic_to_basic' : 'change'}
my_df['polarity_change'] = my_df['polarity_change'].map(polarity_prop_changeD)
my_df['polarity_change'].value_counts()
foo['calc_prop_change'] = foo['wt_calcprop'] + str('_to_') + foo['mut_calcprop']
foo['calc_prop_change'].value_counts()
#%%
my_df['electrostatics_change'] = my_df['wt_calcprop'] + str('_to_') + my_df['mut_calcprop']
my_df['electrostatics_change'].value_counts()
calc_prop_changeD = {
'non-polar_to_non-polar' : 'no_change'
, 'non-polar_to_polar' : ''
, 'polar_to_non-polar' : ''
, 'non-polar_to_pos' : ''
, 'neg_to_non-polar' : ''
, 'non-polar_to_neg' : ''
, 'pos_to_polar' : ''
, 'pos_to_non-polar' : ''
, 'non-polar_to_polar' : 'change'
, 'polar_to_non-polar' : 'change'
, 'non-polar_to_pos' : 'change'
, 'neg_to_non-polar' : 'change'
, 'non-polar_to_neg' : 'change'
, 'pos_to_polar' : 'change'
, 'pos_to_non-polar' : 'change'
, 'polar_to_polar' : 'no_change'
, 'neg_to_neg' : 'no_change'
, 'polar_to_neg' : ''
, 'pos_to_neg' : ''
, 'pos_to_pos' : ''
, 'polar_to_pos' : ''
, 'neg_to_polar' : ''
, 'neg_to_pos' : ''
, 'polar_to_neg' : 'change'
, 'pos_to_neg' : 'change'
, 'pos_to_pos' : 'no_change'
, 'polar_to_pos' : 'change'
, 'neg_to_polar' : 'change'
, 'neg_to_pos' : 'change'
}
my_df['electrostatics_change'] = my_df['electrostatics_change'].map(calc_prop_changeD)
my_df['electrostatics_change'].value_counts()
#%%
#https://stackoverflow.com/questions/47181187/finding-string-over-multiple-columns-in-pandas
detect_change = 'change'
# if detect_change in my_df['water_change'] | my_df['polarity_change'] | my_df['electrostatics_change']:
# print('\nChange detected')
check = ['mutationinformation', 'wild_type', 'water_change', 'polarity_change', 'electrostatics_change']
check_prop_cols = ['water_change', 'polarity_change', 'electrostatics_change']
foo = my_df[check]
foo['new'] = (foo.values == detect_change).any(1).astype(int)
#foo['new2'] = foo[check_prop_cols].applymap(lambda x: detect_change in x).any(1).astype(int) # lose match so alwasys 1
foo['new3'] = (foo[check_prop_cols].values == detect_change).any(1).astype(int)
all(foo['new'] == foo['new3'])
#%%lineage
lineage_colnames = ['lineage', 'lineage_list_all', 'lineage_count_all', 'lineage_count_unique', 'lineage_list_unique', 'lineage_multimode']
bar = my_df[lineage_colnames]
tot_lineage_u = 8
bar['lineage_proportion'] = bar['lineage_count_unique']/bar['lineage_count_all']
bar['dist_lineage_proportion'] = bar['lineage_count_unique']/tot_lineage_u