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from sklearn.neighbors import KNeighborsClassifier
import streamlit as st
from rdkit.Chem import MACCSkeys
from rdkit import Chem
import numpy as np
import pandas as pd
import xgboost as xgb
from sklearn.svm import SVC
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.metrics import classification_report, confusion_matrix, average_precision_score, roc_auc_score
import pickle
global header
model_path = 'model/'
def load_tpr_fpr(ml, enzyme):
tpr_file = 'AUC/' + ml + '_' + enzyme + '_tpr.pickle'
fpr_file = 'AUC/' + ml + '_' + enzyme + '_fpr.pickle'
with open(tpr_file, 'rb') as f:
tpr = pickle.load(f)
with open(fpr_file, 'rb') as f:
fpr = pickle.load(f)
return tpr, fpr
def smile_list_to_MACCS(smi_list):
MACCS_list = []
for smi in smi_list:
mol = Chem.MolFromSmiles(smi)
maccs = list(MACCSkeys.GenMACCSKeys(mol).ToBitString())
MACCS_list.append(maccs)
return MACCS_list
st.write("""
# Area Under the Curve Ploting
""")
st.sidebar.header('User Input Parameters')
def user_input_features():
# name = st.text_input('compound name', 'Fedratinib')
# if name == None:
# name = 'test'
# smi = st.text_input('compound SMILES', 'CC1=CN=C(N=C1NC2=CC(=CC=C2)S(=O)(=O)NC(C)(C)C)NC3=CC=C(C=C3)OCCN4CCCC4')
# if name == None and smi == None:
# name ='Fedratinib'
# smi = 'CC1=CN=C(N=C1NC2=CC(=CC=C2)S(=O)(=O)NC(C)(C)C)NC3=CC=C(C=C3)OCCN4CCCC4'
# enzyme = st.multiselect(
# 'Choose JAK: ',
# ['JAK1', 'JAK2', 'JAK3', 'TYK2'])
# if enzyme == None:
# enzyme = 'JAK1'
st.write('Select JAK kinase: ')
JAK1 = st.checkbox('JAK1')
JAK2 = st.checkbox('JAK2')
JAK3 = st.checkbox('JAK3')
TYK2 = st.checkbox('TYK2')
all_enzyme = st.checkbox('Select all JAKs')
enzyme = []
if JAK1 == True:
enzyme.append('JAK1')
if JAK2 == True:
enzyme.append('JAK2')
if JAK3 == True:
enzyme.append('JAK3')
if TYK2 == True:
enzyme.append('TYK2')
if all_enzyme == True:
enzyme = ['JAK1', 'JAK2', 'JAK3', 'TYK2']
# model = st.multiselect(
# 'Choose model: ',
# ['knn','SVM_linear', 'SVM_poly', 'SVM_rbf', 'SVM_sigmoid', 'XGBoost'])
model = []
st.write('Select model: ')
knn = st.checkbox('KNN')
SVM_linear = st.checkbox('SVM_linear')
SVM_poly = st.checkbox('SVM_poly')
SVM_rbf = st.checkbox('SVM_rbf')
SVM_sigmoid = st.checkbox('SVM_sigmoid')
RF = st.checkbox('RF')
XGBoost = st.checkbox('XGBoost')
CNN = st.checkbox('CNN')
GVAE = st.checkbox('GraphVAE')
chemBERTa = st.checkbox('chembert')
all_model = st.checkbox('Select all models')
if knn == True:
model.append('knn')
if SVM_linear == True:
model.append('SVM_linear')
if SVM_poly == True:
model.append('SVM_poly')
if SVM_rbf == True:
model.append('SVM_rbf')
if SVM_sigmoid == True:
model.append('SVM_sigmoid')
if RF == True:
model.append('RF')
if XGBoost == True:
model.append('XGBoost')
if CNN == True:
model.append('CNN')
if GVAE == True:
model.append('GVAE')
if chemBERTa == True:
model.append('chembert')
if all_model == True:
model = ['knn', 'SVM_linear', 'SVM_poly', 'SVM_rbf', 'SVM_sigmoid', 'RF', 'XGBoost', 'CNN', 'GVAE', 'chembert']
return enzyme, model
with st.sidebar:
enzymes, model_chosen = user_input_features()
st.subheader('User Input parameters:')
# st.write('Current compound: ', name)
# st.write('Current compound SMILE: ', smi)
st.write('Selected JAK:', enzymes)
st.write('Selected model: ', model_chosen)
if st.button('Start Plot AUC'):
if model_chosen==[]:
st.write('Did not choose model!')
if enzymes==[]:
st.write('Did not choose JAK kinase!')
elif model_chosen != [] and enzymes != []:
for enzyme in enzymes:
title = enzyme + ' Receiver Operating Characteristic Curve'
models = model_chosen
fig, ax = plt.subplots(figsize=(10,10))
for ml in models:
tpr, fpr = load_tpr_fpr(ml, enzyme)
ax.plot(fpr, tpr, label=ml)
ax.plot(np.linspace(0, 1, 100),
np.linspace(0, 1, 100),
label='baseline',
linestyle='--')
plt.title(title, fontsize=18)
plt.ylabel('TPR', fontsize=16)
plt.xlabel('FPR', fontsize=16)
plt.legend(fontsize=12)
# plt.savefig('figures/'+enzyme+'.png')
st.pyplot(fig)
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