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trying one_hot encoder for categ vars, which was sucessful but not rfecv

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Tanushree Tunstall 2022-03-06 14:49:51 +00:00
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Mar 6 13:41:54 2022
@author: tanu
"""
import os, sys
import pandas as pd
import numpy as np
from sklearn import linear_model
from sklearn.linear_model import LogisticRegression, LinearRegression
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 xgboost import XGBClassifier
from sklearn.preprocessing import StandardScaler, MinMaxScaler, OneHotEncoder
from sklearn.compose import ColumnTransformer
from sklearn.compose import make_column_transformer
from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score, roc_auc_score, roc_curve, f1_score
from sklearn.metrics import make_scorer
from sklearn.metrics import classification_report
from sklearn.model_selection import cross_validate
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline
from sklearn.pipeline import make_pipeline
from sklearn.feature_selection import RFE
from sklearn.feature_selection import RFECV
import itertools
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
print(np.__version__)
print(pd.__version__)
from statistics import mean, stdev
#%%
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 [Binary]
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 = 'drtype' [Multinomial]
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_stabiltyN = ['ligand_distance'
, 'ligand_affinity_change'
, 'duet_stability_change'
, 'ddg_foldx'
, 'deepddg'
, 'ddg_dynamut2']
# Build stability columns ~ gene
if gene.lower() in geneL_basic:
x_stabilityN = common_cols_stabiltyN
if gene.lower() in geneL_ppi2:
x_stabilityN = common_cols_stabiltyN + ['mcsm_ppi2_affinity'
, 'interface_dist']
if gene.lower() in geneL_na:
x_stabilityN = common_cols_stabiltyN + ['mcsm_na_affinity']
if gene.lower() in geneL_na_ppi2:
x_stabilityN = common_cols_stabiltyN + ['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_strFN = ['asa'
, 'rsa'
, 'kd_values'
, 'rd_values']
X_evolFN = ['consurf_score'
, 'snap2_score'
, 'snap2_accuracy_pc']
# TODO: ADD ED values
# Problematic due to NA: filling NA with unknown or string will make it categorical
# OPTIONS
# 1. Imputing: KNN or MICE or from distribution
# 2. Fill na with median or mode
# 3. Separate datset without including genomic features AT ALL for ML, then using this as a 'blind test set'
# this means the size of the training data gets reduced!
# 4. Remove genomic features from ML COMPLETELEY!
# X_genomicFN = ['af'
# , 'or_mychisq'
# , 'or_logistic'
# , 'or_fisher'
# , 'pval_fisher']
#%% try combinations
X_vars1 = my_df[x_stabilityN]
X_vars2 = my_df[X_strFN]
X_vars3 = my_df[X_evolFN]
X_vars5 = my_df[x_stabilityN + X_strFN]
X_vars6 = my_df[x_stabilityN + X_evolFN]
#X_vars7 = my_df[x_stabilityN + X_genomicFN]
X_vars8 = my_df[X_strFN + X_evolFN]
#X_vars9 = my_df[X_strFN + X_genomicFN]
#X_vars10 = my_df[X_evolFN + X_genomicFN]
X_vars11 = my_df[x_stabilityN + X_strFN + X_evolFN]
#X_vars12 = my_df[x_stabilityN + X_strFN + X_evolFN + X_genomicFN]
numerical_features_names = x_stabilityN + X_strFN + X_evolFN
# 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

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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Mar 5 12:57:32 2022
@author: tanu
"""
#%%
# data, etc for now comes from my_data6.py and/or my_data5.py
#%% try combinations
#import sys, os
#os.system("imports.py")
#%%
seed = 42
features_to_encode = list(X_train.select_dtypes(include = ['object']).columns)
col_trans = make_column_transformer(
(OneHotEncoder(),features_to_encode),
remainder = "passthrough"
)
rf_classifier = RandomForestClassifier(
min_samples_leaf=50,
n_estimators=150,
bootstrap=True,
oob_score=True,
n_jobs=-1,
random_state=seed,
max_features='auto')
pipe = make_pipeline(col_trans, rf_classifier)
pipe.fit(X_train, y_train)
y_pred = pipe.predict(X_test)
#%%
all_features_df.shape
X_train, X_test, y_train, y_test = train_test_split(all_features_df,
target1,
test_size = 0.33,
random_state = 42)
preprocessor = ColumnTransformer(
transformers=[
('num', MinMaxScaler() , numerical_features_df)
,('cat', OneHotEncoder(), categorical_features_df)])
seed = 42
rf_classifier = RandomForestClassifier(
min_samples_leaf=50,
n_estimators=150,
bootstrap=True,
oob_score=True,
n_jobs=-1,
random_state=seed,
max_features='auto')
preprocessor.fit(all_features_df)
preprocessor.transform(all_features_df)
model = Pipeline(steps = [
('preprocess', preprocessor)
,('regression',linear_model.LogisticRegression())
])
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
y_pred
def precision(y_true,y_pred):
return precision_score(y_true,y_pred,pos_label = 1)
def recall(y_true,y_pred):
return recall_score(y_true, y_pred, pos_label = 1)
def f1(y_true,y_pred):
return f1_score(y_true, y_pred, pos_label = 1)
acc = make_scorer(accuracy_score)
prec = make_scorer(precision)
rec = make_scorer(recall)
f1 = make_scorer(f1)
output = cross_validate(model, X_train, y_train
, scoring = {'acc' : acc
,'prec': prec
,'rec' : rec
,'f1' : f1}
, cv = 10
, return_train_score = False)
pd.DataFrame(output).mean()
#%% with feature selection
preprocessor.fit(numerical_features_df)
preprocessor.transform(numerical_features_df)
model = Pipeline(steps = [
('preprocess', preprocessor)
,('regression',linear_model.LogisticRegression())
])
selector_logistic = RFECV(estimator = model
, cv = 10
, step = 1)
X_trainN, X_testN, y_trainN, y_testN = train_test_split(numerical_features_df
, target1
, test_size = 0.33
, random_state = 42)
selector_logistic_xtrain = selector_logistic.fit_transform(X_trainN, y_trainN)
print(sel_rfe_logistic.get_support())
X_trainN.columns
print(sel_rfe_logistic.ranking_)