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ML_AI_training/my_datap4.py

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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Feb 24 10:48:10 2022
@author: tanu
"""
###############################################################################
# questions:
# which data to use: merged_df3 or merged_df2
# which is the target? or_mychisq or drtype col
# scaling: can it be from -1 to 1?
# how to include the mutation information?
# 'wild_type', 'mutant', 'postion'
# whether to log transform the af and or cols
# to allow mean mode values to be imputed for validation set
# whether to calculate mean, median accounting for NA or removing them?
# strategy:
# available data = X_train
# available data but NAN = validation_test
# test data: mut generated not in mcsm
###############################################################################
import os, sys
import re
from sklearn.datasets import load_boston
from sklearn import datasets
from sklearn import linear_model
from sklearn import preprocessing
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
print(np.__version__)
print(pd.__version__)
from statistics import mean, stdev
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_validate
from sklearn.metrics import make_scorer
from sklearn.ensemble import RandomForestClassifier
from sklearn.pipeline import Pipeline
from sklearn.pipeline import make_pipeline
from sklearn.datasets import load_digits
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import make_column_transformer
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix, precision_score, recall_score, roc_auc_score, roc_curve, f1_score
from sklearn.metrics import plot_precision_recall_curve
import itertools
#%% read data
homedir = os.path.expanduser("~")
os.chdir(homedir + "/git/ML_AI_training/test_data")
# this needs to be merged_df2 or merged_df3?
#gene 'pncA'
drug = 'pyrazinamide'
my_df = pd.read_csv("pnca_merged_df3.csv")
my_df.dtypes
my_df_cols = my_df.columns
#%%
# GET Y
# Y = my_df.loc[:,drug] #has NA
dm_om_map = {'DM': 1, 'OM': 0}
my_df['resistance'] = my_df['mutation_info_labels'].map(dm_om_map)
# sanity check
my_df['resistance'].value_counts()
my_df['mutation_info_labels'].value_counts()
Y = my_df['resistance']
#%%
# GET X
cols = my_df.columns
X = my_df[['ligand_distance'
, 'ligand_affinity_change'
, 'duet_stability_change', 'ddg_foldx', 'deepddg', 'ddg_dynamut2', 'consurf_score'
, 'snap2_score'
#, 'snap2_accuracy_pc'
, 'asa'
, 'rsa']]
#%%
####################################
# SIMPLEST case of train_test split
# Random forest
# one hot encoder
# MinMaxScaler
# https://towardsdatascience.com/my-random-forest-classifier-cheat-sheet-in-python-fedb84f8cf4f
####################################
seed = 50
X_train, X_test, y_train, y_test = train_test_split(X,Y
, test_size = 0.333
, random_state = seed)
features_to_encode = list(X_train.select_dtypes(include = ['object']).columns)
col_trans = make_column_transformer(
(OneHotEncoder(),features_to_encode),
remainder = "passthrough"
)
MinMaxS = preprocessing.MinMaxScaler()
standardS = preprocessing.StandardScaler()
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
#, MinMaxS
#, standardS
, rf_classifier)
pipe.fit(X_train, y_train)
y_pred = pipe.predict(X_test)
accuracy_score(y_test, y_pred)
print("\nModel evaluation:\n")
print(f"Accuracy: {round(accuracy_score(y_test,y_pred),3)*100} %")
print(f"Recall: {round(recall_score(y_test,y_pred),3)*100} %")
print(f"Precision: {round(precision_score(y_test,y_pred),3)*100} %")
print(f"F1-score: {round(f1_score(y_test,y_pred),3)*100} %")
recall_score(y_test, y_pred)
precision_score(y_test, y_pred)
f1_score(y_test, y_pred)
roc_auc_score (y_test, y_pred) # not sure!
roc_curve(y_test, y_pred) # not sure!
disp = plot_precision_recall_curve(pipe, X_test, y_test)
train_probs = pipe.predict_proba(X_train)[:,1]
probs = pipe.predict_proba(X_test)[:, 1]
train_predictions = pipe.predict(X_train)
print(f'Train ROC AUC Score: {roc_auc_score(y_train, train_probs)}')
print(f'Test ROC AUC Score: {roc_auc_score(y_test, probs)}')
def evaluate_model(y_pred, probs,train_predictions, train_probs):
baseline = {}
baseline['recall']=recall_score(y_test,
[1 for _ in range(len(y_test))])
baseline['precision'] = precision_score(y_test,
[1 for _ in range(len(y_test))])
baseline['roc'] = 0.5
results = {}
results['recall'] = recall_score(y_test, y_pred)
results['precision'] = precision_score(y_test, y_pred)
results['roc'] = roc_auc_score(y_test, probs)
train_results = {}
train_results['recall'] = recall_score(y_train,
train_predictions)
train_results['precision'] = precision_score(y_train, train_predictions)
train_results['roc'] = roc_auc_score(y_train, train_probs)
# for metric in ['recall', 'precision', 'roc']:
# print(f"Baseline: {round(baseline[metric], 2)}Test: {round(results[metric], 2)} Train: {round(train_results[metric], 2)}")
# Calculate false positive rates and true positive rates
base_fpr, base_tpr, _ = roc_curve(y_test, [1 for _ in range(len(y_test))])
model_fpr, model_tpr, _ = roc_curve(y_test, probs)
plt.figure(figsize = (8, 6))
plt.rcParams['font.size'] = 16
# Plot both curves
plt.plot(base_fpr, base_tpr, 'b', label = 'baseline')
plt.plot(model_fpr, model_tpr, 'r', label = 'model')
plt.legend(); plt.xlabel('False Positive Rate');
plt.ylabel('True Positive Rate'); plt.title('ROC Curves');
plt.show()
# Recall Baseline: 1.0 Test: 0.92 Train: 0.93
# Precision Baseline: 0.48 Test: 0.9 Train: 0.91
# Roc Baseline: 0.5 Test: 0.97 Train: 0.97
evaluate_model(y_pred,probs,train_predictions,train_probs)
def plot_confusion_matrix(cm, classes, normalize = False,
title='Confusion matrix',
cmap=plt.cm.Greens): # can change color
plt.figure(figsize = (10, 10))
plt.imshow(cm, interpolation='nearest', cmap=cmap)
plt.title(title, size = 24)
plt.colorbar(aspect=4)
tick_marks = np.arange(len(classes))
plt.xticks(tick_marks, classes, rotation=45, size = 14)
plt.yticks(tick_marks, classes, size = 14)
fmt = '.2f' if normalize else 'd'
thresh = cm.max() / 2.
# Label the plot
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])): plt.text(j, i, format(cm[i, j], fmt),
fontsize = 20,
horizontalalignment="center",
color="white" if cm[i, j] > thresh else "black")
plt.grid(None)
plt.tight_layout()
plt.ylabel('True label', size = 18)
plt.xlabel('Predicted label', size = 18)
# Let's plot it out
cm = confusion_matrix(y_test, y_pred)
plot_confusion_matrix(cm, classes = ['0 - Susceptible', '1 - Resistant'],
title = 'R/S Confusion Matrix')
print(rf_classifier.feature_importances_)
print(f" There are {len(rf_classifier.feature_importances_)} features in total")
#%%
####################################
# Model 2: case of stratified K-fold
# Logistic regression
# MinMaxScaler
# https://towardsdatascience.com/stratified-k-fold-what-it-is-how-to-use-it-cf3d107d3ea2 [ Didn't work!]
# https://www.geeksforgeeks.org/stratified-k-fold-cross-validation/
####################################
print('Class Ratio:',
sum(Y)/len(Y))
print('Class Ratio:',
sum(my_df['resistance'])/len(my_df['resistance']))
seed_skf = 50
skf = StratifiedKFold(n_splits = 10
, shuffle = True
, random_state = seed_skf)
lst_accu_stratified = []
scaler = preprocessing.MinMaxScaler()
X_scaled = scaler.fit_transform(X)
#X_scaled = X_scaled[:,[1,2,3]]
#lr = linear_model.LogisticRegression(class_weight = 'unbalanced')
lr = linear_model.LogisticRegression()
for train_index, test_index in skf.split(X, Y):
#print(train_index)
#print(test_index)
x_train_fold, x_test_fold = X_scaled[train_index], X_scaled[test_index]
y_train_fold, y_test_fold = Y[train_index], Y[test_index]
lr.fit(x_train_fold, y_train_fold)
lst_accu_stratified.append(lr.score(x_test_fold, y_test_fold))
# print output
print('List of possible accuracy', lst_accu_stratified)
print('Max accuracy:', max(lst_accu_stratified)*100, "%")
print('Min accuracy:', min(lst_accu_stratified)*100, "%")
print('Mean accuracy:', mean(lst_accu_stratified)*100,"%")
print('St Dev:', stdev(lst_accu_stratified)*100,"%")
#%%
#--------------------------------------
# Model2.1: same one but with pipeline
# slightly different results when using
# transformed or untransformed values!
#--------------------------------------
model_logisP = Pipeline(steps = [('preprocess', preprocessing.MinMaxScaler())
, ('logis', LogisticRegression(class_weight = 'unbalanced')) ]) # changes stdev
seed_skf = 50
skf = StratifiedKFold(n_splits = 10
, shuffle = True
, random_state = seed_skf)
X_array = np.array(X)
lst_accu_stratified = []
for train_index, test_index in skf.split(X_array, Y):
x_train_fold, x_test_fold = X_array[train_index], X_array[test_index]
y_train_fold, y_test_fold = Y[train_index], Y[test_index]
model_logisP.fit(x_train_fold, y_train_fold)
lst_accu_stratified.append(model_logisP.score(x_test_fold, y_test_fold))
# print output
print('List of possible accuracy', lst_accu_stratified)
print('Max accuracy:', max(lst_accu_stratified)*100, "%")
print('Min accuracy:', min(lst_accu_stratified)*100, "%")
print('Mean accuracy:', mean(lst_accu_stratified)*100,"%")
print('St Dev:', stdev(lst_accu_stratified)*100,"%")
####################################
# Model 3: stratified K-fold
# Random forest
# MinMaxScaler
# X: needs to be an array for str Kfold
####################################
model_rf = Pipeline(steps = [('preprocess', preprocessing.MinMaxScaler())
, ('rf' , RandomForestClassifier(n_estimators=100, random_state=42))])
seed_skf = 50
skf = StratifiedKFold(n_splits = 10
, shuffle = True
, random_state = seed_skf)
X_array = np.array(X)
lst_accu_stratified_rf = []
for train_index, test_index in skf.split(X_array, Y):
x_train_fold, x_test_fold = X_array[train_index], X_array[test_index]
y_train_fold, y_test_fold = Y[train_index], Y[test_index]
model_rf.fit(x_train_fold, y_train_fold)
lst_accu_stratified_rf.append(model_rf.score(x_test_fold, y_test_fold))
# print output
print('List of possible accuracy', lst_accu_stratified_rf)
print('Max accuracy:', max(lst_accu_stratified_rf)*100, "%")
print('Min accuracy:', min(lst_accu_stratified_rf)*100, "%")
print('Mean accuracy:', mean(lst_accu_stratified_rf)*100,"%")
print('St Dev:', stdev(lst_accu_stratified_rf)*100,"%")
####################################
# Model 4: Cross validate K-fold
# Random forest
# MinMaxScaler
# X: needs to be an array for Kfold
# FIXME: DOESNT WORK BECAUSE MSE is for LR, not Logistic or random?
####################################
from sklearn.metrics import mean_squared_error, make_scorer
from sklearn.model_selection import cross_validate
score_fn = make_scorer(mean_squared_error)
scores = cross_validate(model_rf, X_train, y_train
, scoring = score_fn
, cv = 10)
from itertools import combinations
def train(X):
return cross_validate(model_rf, X, y_train
, scoring = score_fn
, cv = 10
, return_estimator = True)['test_score']
scores = [train(X_train.loc[:,vars]) for vars in combinations(X_train.columns,11)]
means = [score.mean() for score in scores]
#%%
# https://stackoverflow.com/questions/52316237/finding-logistic-regression-weights-from-k-fold-cv
from sklearn.linear_model import LogisticRegressionCV
from sklearn.model_selection import KFold
kf = KFold(n_splits=10, shuffle=True, random_state=42)
logistic = LogisticRegressionCV(Cs=2, fit_intercept=True, cv=kf, verbose =1, random_state=42)
logistic.fit(X_train, y_train)
print("Train Coefficient:" , logistic.coef_) #weights of each feature
print("Train Intercept:" , logistic.intercept_) #value of intercept
#%%
# https://machinelearningmastery.com/repeated-k-fold-cross-validation-with-python/
from sklearn.model_selection import cross_val_score
from numpy import std
cv = KFold(n_splits=10, random_state=1, shuffle=True)
scores = cross_val_score(model_rf, X,Y, scoring='accuracy', cv=cv, n_jobs=-1)
scores2 = cross_val_score(model_logisP, X, Y, scoring='accuracy', cv=cv, n_jobs=-1)
# report performance
print('Accuracy: %.3f (%.3f)' % (mean(scores), std(scores)))
print('Accuracy: %.3f (%.3f)' % (mean(scores2), stdev(scores2)))
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