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# -*- coding: utf-8 -*-
"""
Created on Mon Jun 8 09:32:26 2020
@author: Muammer
"""
import os
script_dir = os.path.dirname(os.path.abspath(__file__))
import numpy as np
from sklearn.model_selection import cross_validate
from sklearn.model_selection import cross_val_predict
from sklearn.metrics import matthews_corrcoef
from sklearn.metrics import classification_report
from sklearn.multiclass import OneVsRestClassifier
from sklearn import linear_model
from sklearn.metrics import f1_score
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
import pandas as pd
from numpy import save
from sklearn.metrics import precision_recall_fscore_support
from tqdm import tqdm
from sklearn.metrics import accuracy_score
import math
representation_name = ""
representation_path = ""
dataset = "nc"
detailed_output = False
def convert_dataframe_to_multi_col(representation_dataframe):
entry = pd.DataFrame(representation_dataframe['Entry'])
vector = pd.DataFrame(list(representation_dataframe['Vector']))
multi_col_representation_vector = pd.merge(left=entry,right=vector,left_index=True, right_index=True)
return multi_col_representation_vector
def class_based_scores(c_report, c_matrix):
c_report = pd.DataFrame(c_report).transpose()
#print(c_report)
c_report = c_report.drop(['precision', 'recall'], axis=1)
c_report = c_report.drop(labels=['accuracy', 'macro avg', 'weighted avg'], axis=0)
cm = c_matrix.astype('float') / c_matrix.sum(axis=1)[:, np.newaxis]
#print(c_report)
accuracy = cm.diagonal()
#print(accuracy)
#if len(accuracy) == 6:
# accuracy = np.delete(accuracy, 5)
accuracy = pd.Series(accuracy, index=c_report.index)
c_report['accuracy'] = accuracy
total = c_report['support'].sum()
#print(total)
num_classes = np.shape(c_matrix)[0]
mcc = np.zeros(shape=(num_classes,), dtype='float32')
weights = np.sum(c_matrix, axis=0)/np.sum(c_matrix)
total_tp = 0
total_fp = 0
total_fn = 0
total_tn = 0
for j in range(num_classes):
tp = np.sum(c_matrix[j, j])
fp = np.sum(c_matrix[j, np.concatenate((np.arange(0, j), np.arange(j+1, num_classes)))])
fn = np.sum(c_matrix[np.concatenate((np.arange(0, j), np.arange(j+1, num_classes))), j])
tn = int(total - tp - fp - fn)
total_tp = total_tp + tp
total_fp = total_fp + fp
total_fn = total_fn + fn
total_tn = total_tn + tn
#print(tp,fp,fn,tn)
mcc[j] = ((tp*tn)-(fp*fn))/math.sqrt((tp+fp)*(tp+fn)*(tn+fp)*(tn+fn))
#print(mcc)
#if len(mcc) == 6:
# mcc = np.delete(mcc, 5)
mcc = pd.Series(mcc, index=c_report.index)
c_report['mcc'] = mcc
#c_report.to_excel('../results/resultss_class_based_'+dataset+'.xlsx')
#print(c_report)
return c_report, total_tp, total_fp, total_fn, total_tn
def score_protein_rep(dataset):
#def score_protein_rep(pkl_data_path):
vecsize = 0
#protein_list = pd.read_csv('../data/auxilary_input/entry_class.csv')
protein_list = pd.read_csv(os.path.join(script_dir, '../data/preprocess/entry_class_nn.csv'))
dataframe = pd.read_csv(representation_path)
#dataframe = convert_dataframe_to_multi_col(dataframe)
#dataframe = pd.read_pickle(pkl_data_path)
vecsize = dataframe.shape[1]-1
x = np.empty([0, vecsize])
xemp = np.zeros((1, vecsize), dtype=float)
y = []
ne = []
print("\n\nPreprocessing data for drug-target protein family prediction...\n ")
for index, row in tqdm(protein_list.iterrows(), total=len(protein_list)):
pdrow = dataframe.loc[dataframe['Entry'] == row['Entry']]
if len(pdrow) != 0:
a = pdrow.loc[ : , pdrow.columns != 'Entry']
a = np.array(a)
a.shape = (1,vecsize)
x = np.append(x, a, axis=0)
y.append(row['Class'])
else:
ne.append(index)
x = np.append(x, xemp, axis=0,)
y.append(0.0)
#print(index)
x = x.astype(np.float64)
y = np.array(y)
y = y.astype(np.float64)
#print(len(y))
scoring = ['precision_weighted', 'recall_weighted', 'f1_weighted', 'accuracy']
target_names = ['Enzyme', 'Membrane receptor', 'Transcription factor', 'Ion channel', 'Other']
labels = [1.0, 11.0, 12.0, 1005.0, 2000.0]
f1 = []
accuracy = []
mcc = []
f1_perclass = []
ac_perclass = []
mcc_perclass = []
sup_perclass = []
report_list = []
train_index = pd.read_csv(os.path.join(script_dir, '../data/preprocess/indexes/'+dataset+'_trainindex.csv'))
test_index = pd.read_csv(os.path.join(script_dir, '../data/preprocess/indexes/testindex_family.csv'))
train_index = train_index.dropna(axis=1)
test_index = test_index.dropna(axis=1)
#print(train_index)
#for index in ne:
conf = pd.DataFrame()
print('Producing protein family predictions...\n')
for i in tqdm(range(10)):
clf = linear_model.SGDClassifier(class_weight="balanced", loss="log", penalty="elasticnet", max_iter=1000, tol=1e-3,random_state=i,n_jobs=-1)
clf2 = OneVsRestClassifier(clf,n_jobs=-1)
#print(test_index)
train_indexx = train_index.iloc[i].astype(int)
test_indexx = test_index.iloc[i].astype(int)
#print(train_indexx)
#train_indexx.drop(labels=ne)
#print(type(train_indexx))
for index in ne:
train_indexx = train_indexx[train_indexx!=index]
test_indexx = test_indexx[test_indexx!=index]
train_X, test_X = x[train_indexx], x[test_indexx]
train_y, test_y = y[train_indexx], y[test_indexx]
clf2.fit(train_X, train_y)
#print(train_X)
y_pred = clf2.predict(test_X)
#y_pred = cross_val_predict(clf2, x, y, cv=10, n_jobs=-1)
#mcc.append(matthews_corrcoef(test_y, y_pred, sample_weight = test_y))
f1_ = f1_score(test_y, y_pred, average='weighted')
f1.append(f1_)
ac = accuracy_score(test_y, y_pred)
accuracy.append(ac)
c_report = classification_report(test_y, y_pred, target_names=target_names, output_dict=True)
c_matrix = confusion_matrix(test_y, y_pred, labels=labels)
conf = conf.append(pd.DataFrame(c_matrix, columns=['Enzymes', 'Membrane receptor', 'Transcription factor', 'Ion channel', 'Other']), ignore_index=True)
class_report, tp, fp, fn, tn = class_based_scores(c_report, c_matrix)
#print(total_tp)
mcc.append(((tp*tn)-(fp*fn))/math.sqrt((tp+fp)*(tp+fn)*(tn+fp)*(tn+fn)))
f1_perclass.append(class_report['f1-score'])
ac_perclass.append(class_report['accuracy'])
mcc_perclass.append(class_report['mcc'])
sup_perclass.append(class_report['support'])
report_list.append(class_report)
if detailed_output:
conf.to_csv(os.path.join(script_dir, '../results/Drug_target_protein_family_classification_confusion_'+dataset+'_'+representation_name+'.csv'), index=None)
f1_perclass = pd.concat(f1_perclass, axis=1)
ac_perclass = pd.concat(ac_perclass, axis=1)
mcc_perclass = pd.concat(mcc_perclass, axis=1)
sup_perclass = pd.concat(sup_perclass, axis=1)
report_list = pd.concat(report_list, axis=1)
report_list.to_csv(os,path,join(script_dir, '../results/Drug_target_protein_family_classification_class_based_results_'+dataset+'_'+representation_name+'.csv'))
report = pd.DataFrame()
f1mean = np.mean(f1, axis=0)
#print(f1mean)
f1mean = f1mean.round(decimals=5)
f1std = np.std(f1).round(decimals=5)
acmean = np.mean(accuracy, axis=0).round(decimals=5)
acstd = np.std(accuracy).round(decimals=5)
mccmean = np.mean(mcc, axis=0).round(decimals=5)
mccstd = np.std(mcc).round(decimals=5)
labels = ['Average Score', 'Standard Deviation']
report['Protein Family'] = labels
report['F1_score'] = [f1mean, f1std]
report['Accuracy'] = [acmean, acstd]
report['MCC'] = [mccmean, mccstd]
report.to_csv(os.path.join(script_dir, '../results/Drug_target_protein_family_classification_mean_results_'+dataset+'_'+representation_name+'.csv',index=False))
#report.to_csv('scores_general.csv')
#print(report)
if detailed_output:
save('../results/Drug_target_protein_family_classification_f1_'+dataset+'_'+representation_name+'.npy', f1)
save('../results/Drug_target_protein_family_classification_accuracy_'+dataset+'_'+representation_name+'.npy', accuracy)
save('../results/Drug_target_protein_family_classification_mcc_'+dataset+'_'+representation_name+'.npy', mcc)
save('../results/Drug_target_protein_family_classification_class_based_f1_'+dataset+'_'+representation_name+'.npy', f1_perclass)
save('../results/Drug_target_protein_family_classification_class_based_accuracy_'+dataset+'_'+representation_name+'.npy', ac_perclass)
save('../results/Drug_target_protein_family_classification_class_based_mcc_'+dataset+'_'+representation_name+'.npy', mcc_perclass)
save('../results/Drug_target_protein_family_classification_class_based_support_'+dataset+'_'+representation_name+'.npy', sup_perclass)
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