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renamed hyperparams to gscv

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Tanushree Tunstall 2022-03-22 11:08:20 +00:00
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Mar 4 14:54:30 2022
@author: tanu
"""
import os, sys
import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import BernoulliNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.pipeline import Pipeline
from xgboost import XGBClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score, roc_auc_score, roc_curve, f1_score
#%%
homedir = os.path.expanduser("~")
os.chdir(homedir + "/git/ML_AI_training/")
# my function
from MultClassPipe import MultClassPipeline
gene = 'pncA'
drug = 'pyrazinamide'
#==============
# directories
#==============
datadir = homedir + '/git/Data/'
indir = datadir + drug + '/input/'
outdir = datadir + drug + '/output/'
#=======
# input
#=======
infile_ml1 = outdir + gene.lower() + '_merged_df3.csv'
#infile_ml2 = outdir + gene.lower() + '_merged_df2.csv'
my_df = pd.read_csv(infile_ml1)
my_df.dtypes
my_df_cols = my_df.columns
geneL_basic = ['pnca']
geneL_na = ['gid']
geneL_na_ppi2 = ['rpob']
geneL_ppi2 = ['alr', 'embb', 'katg']
#%% get cols
mycols = my_df.columns
#%%============================================================================
# GET Y
# Target1: mutation_info_labels
dm_om_map = {'DM': 1, 'OM': 0}
target1 = my_df['mutation_info_labels'].map(dm_om_map)
# Target2: drug
drug_labels = drug + '_labels'
drug_labels
my_df[drug_labels] = my_df[drug].map({1: 'resistant', 0: 'sensitive'})
my_df[drug_labels].value_counts()
my_df[drug_labels] = my_df[drug_labels].fillna('unknown')
my_df[drug_labels].value_counts()
target2 = my_df[drug_labels]
# Target3: drtype
drtype_labels = 'drtype_labels'
my_df[drtype_labels] = my_df['drtype'].map({'Sensitive' : 0
, 'Other' : 0
, 'Pre-MDR' : 1
, 'MDR' : 1
, 'Pre-XDR' : 1
, 'XDR' : 1})
# target3 = my_df['drtype']
target3 = my_df[drtype_labels]
# target4
drtype_labels2 = 'drtype_labels2'
my_df[drtype_labels2] = my_df['drtype'].map({'Sensitive' : 0
, 'Other' : 0
, 'Pre-MDR' : 1
, 'MDR' : 1
, 'Pre-XDR' : 2
, 'XDR' : 2})
target4 = my_df[drtype_labels2]
# sanity checks
target1.value_counts()
my_df['mutation_info_labels'].value_counts()
target2.value_counts()
my_df[drug_labels].value_counts()
target3.value_counts()
my_df['drtype'].value_counts()
target4.value_counts()
my_df['drtype'].value_counts()
#%%
# GET X
common_cols_stabilty = ['ligand_distance'
, 'ligand_affinity_change'
, 'duet_stability_change'
, 'ddg_foldx'
, 'deepddg'
, 'ddg_dynamut2']
# Build stability columns ~ gene
if gene.lower() in geneL_basic:
x_stability_cols = common_cols_stabilty
if gene.lower() in geneL_ppi2:
x_stability_cols = common_cols_stabilty + ['mcsm_ppi2_affinity'
, 'interface_dist']
if gene.lower() in geneL_na:
x_stability_cols = common_cols_stabilty + ['mcsm_na_affinity']
if gene.lower() in geneL_na_ppi2:
x_stability_cols = common_cols_stabilty + ['mcsm_na_affinity'] + ['mcsm_ppi2_affinity', 'interface_dist']
#D1148 get rid of
na_index = my_df['mutationinformation'].index[my_df['mcsm_na_affinity'].apply(np.isnan)]
my_df = my_df.drop(index=na_index)
X_strF = ['asa'
, 'rsa'
, 'kd_values'
, 'rd_values']
X_evolF = ['consurf_score'
, 'snap2_score'
, 'snap2_accuracy_pc']
# TODO: ADD ED values
# Problematic due to NA
# X_genomicF = ['af'
# , 'or_mychisq'
# , 'or_logistic'
# , 'or_fisher'
# , 'pval_fisher']
#%% try combinations
X_vars1 = my_df[x_stability_cols]
X_vars2 = my_df[X_strF]
X_vars3 = my_df[X_evolF]
#X_vars4 = my_df[X_genomicF]
#X_vars4 = X_vars4.fillna('unknown') # need one hot encoder!
X_vars5 = my_df[x_stability_cols + X_strF]
X_vars6 = my_df[x_stability_cols + X_evolF]
#X_vars7 = my_df[x_stability_cols + X_genomicF]
X_vars8 = my_df[X_strF + X_evolF]
#X_vars9 = my_df[X_strF + X_genomicF]
#X_vars10 = my_df[X_evolF + X_genomicF]
X_vars11 = my_df[x_stability_cols + X_strF + X_evolF]
#X_vars12 = my_df[x_stability_cols + X_strF + X_evolF + X_genomicF]
numerical_features_names = x_stability_cols + X_strF + X_evolF
# separate ones for foldx?
categorical_features_names = ['ss_class'
, 'wt_prop_water'
# , 'lineage_labels' # misleading if using merged_df3
, 'mut_prop_water'
, 'wt_prop_polarity'
, 'mut_prop_polarity'
, 'wt_calcprop'
, 'mut_calcprop'
, 'active_aa_pos']
numerical_features_df = my_df[numerical_features_names]
numerical_features_df.shape
categorical_features_df = my_df[categorical_features_names]
categorical_features_df.shape
all_features_df = my_df[numerical_features_names + categorical_features_names]
all_features_df.shape
#%%
X_vars1.shape[1]
X_vars5.shape[1]
# TODO: stratified cross validate
# Train-test Split
# TARGET1
X_train, X_test, y_train, y_test = train_test_split(X_vars1,
target1,
test_size = 0.33,
random_state = 42)
t1_res = MultClassPipeline(X_train, X_test, y_train, y_test)
t1_res
# TARGET3
X_train3, X_test3, y_train3, y_test3 = train_test_split(X_vars5,
target3,
test_size = 0.33,
random_state = 42)
t3_res = MultClassPipeline(X_train3, X_test3, y_train3, y_test3)
t3_res
#%%