115 lines
3.8 KiB
Python
115 lines
3.8 KiB
Python
import pandas as pd
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import os, sys
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import numpy as np
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from sklearn.datasets import load_boston
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from sklearn.ensemble import RandomForestRegressor
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from sklearn.model_selection import train_test_split
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from sklearn.feature_selection import RFECV
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import matplotlib.pyplot as plt
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###############################################################################
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homedir = os.path.expanduser("~")
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sys.path.append(homedir + '/git/LSHTM_analysis/scripts/ml/ml_functions')
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sys.path
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# import
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from GetMLData import *
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from SplitTTS import *
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#from MultClfs_fi import *
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from MultClfs import *
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#%%
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# X,y = load_boston(return_X_y=True)
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# features = load_boston()['feature_names']
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# X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.33, random_state=42)
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# rf = RandomForestRegressor(random_state=0)
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# rf.fit(X_train,y_train)
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# f_i = list(zip(features,rf.feature_importances_))
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# f_i.sort(key = lambda x : x[1])
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# plt.barh([x[0] for x in f_i],[x[1] for x in f_i])
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# plt.show()
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#%%
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skf_cv = StratifiedKFold(n_splits = 10
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, shuffle = True,**rs)
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#sel_cv = logo
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# sel_cv = RepeatedStratifiedKFold(n_splits = 5
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# , n_repeats = 3
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# , **rs)
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# param dict for getmldata()
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gene_model_paramD = {'data_combined_model' : False
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, 'use_or' : False
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, 'omit_all_genomic_features': False
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, 'write_maskfile' : False
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, 'write_outfile' : False }
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#df = getmldata(gene, drug, **gene_model_paramD)
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df = getmldata('pncA', 'pyrazinamide', **gene_model_paramD)
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df = getmldata('embB', 'ethambutol' , **gene_model_paramD)
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df = getmldata('katG', 'isoniazid' , **gene_model_paramD)
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df = getmldata('rpoB', 'rifampicin' , **gene_model_paramD)
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df = getmldata('gid' , 'streptomycin' , **gene_model_paramD)
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#df = getmldata('alr' , 'cycloserine' , **combined_model_paramD)
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all(df.columns.isin(['gene_name'])) # should be False
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spl_type = '70_30'
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spl_type = '80_20'
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spl_type = 'sl'
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df2 = split_tts(df
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, data_type = 'actual'
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, split_type = spl_type
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, oversampling = False
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, dst_colname = 'dst'
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, target_colname = 'dst_mode'
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, include_gene_name = True
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, random_state = 42 # default
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)
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all(df2['X'].columns.isin(['gene_name'])) # should be False
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fooD = MultModelsCl(input_df = df2['X']
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, target = df2['y']
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, sel_cv = skf_cv
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, run_blind_test = True
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, blind_test_df = df2['X_bts']
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, blind_test_target = df2['y_bts']
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, tts_split_type = spl_type
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, resampling_type = 'none' # default
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, var_type = ['mixed']
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, scale_numeric = ['min_max']
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, return_formatted_output = False
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)
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for k, v in fooD.items():
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print('\nModel:', k
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, '\nTRAIN MCC:', fooD[k]['test_mcc']
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, '\nBTS MCC:' , fooD[k]['bts_mcc']
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, '\nDIFF:',fooD[k]['bts_mcc'] - fooD[k]['test_mcc'] )
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#%% CHECK SCALING
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embb_df = getmldata('embB', 'ethambutol' , **combined_model_paramD)
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all(embb_df.columns.isin(['gene_name'])) # should be False
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scaler = MinMaxScaler(feature_range=(-1, 1))
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bar = embb_df[['vdwclashes_rr', 'electro_rr']]
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bar_df1 = scaler.fit_transform(bar)
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bar_df1 = pd.DataFrame(bar_df1)
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bar_df1.rename(columns = {0:'vdw_scaled', 1: 'ele_scaled'}, inplace = True)
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bar2 = pd.concat([bar, bar_df1], axis = 1)
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scaler2 = StandardScaler()
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baz = embb_df[['vdwclashes_rr', 'electro_rr']]
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baz_df1 = scaler2.fit_transform(baz)
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baz_df1 = pd.DataFrame(baz_df1)
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baz_df1.rename(columns = {0:'vdw_scaled', 1: 'ele_scaled'}, inplace = True)
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baz2 = pd.concat([baz, baz_df1], axis = 1)
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a = pd.concat([bar2, baz2], axis = 1)
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