def main():
args = parse_arguments()
unp_id_list = [row[0] for row in read_csv(args.target_ids)]
category_list = [row[1] for row in read_csv(args.target_ids)]
plt.figure(figsize=(5, 5))
for unp_id, category in list(zip(unp_id_list, category_list)):
print(unp_id)
sub_dir_list = next(os.walk(os.path.join(args.result_dir)))[1]
tprs = []
mean_fpr = np.linspace(0, 1, 100)
for sub_dir in sub_dir_list:
scores_dir = os.path.join(args.result_dir, sub_dir,
'{}'.format(unp_id))
print(scores_dir)
if os.path.isdir(scores_dir):
os.chdir(scores_dir)
if os.path.isdir(scores_dir):
for filename in os.listdir(scores_dir):
if filename.endswith('.csv'):
print(filename)
rows = read_csv(filename)
scores = []
for row in rows:
scores.append([row[0], int(row[1])])
fpr, tpr = Scoring.CalcROC(scores, 1)
tpr = np.array(tpr)
tprs.append(interp(mean_fpr, fpr, tpr))
if tprs:
mean_tpr = np.mean(tprs, axis=0)
mean_tpr[-1] = 1.0
if (category) == 'easy':
plot_curve(fpr=mean_fpr, tpr=mean_tpr, color='blue')
elif (category) == 'moderate':
plot_curve(fpr=mean_fpr, tpr=mean_tpr, color='orange')
elif (category) == 'hard':
plot_curve(fpr=mean_fpr, tpr=mean_tpr, color='green')
elif (category) == 'unfeasible':
plot_curve(fpr=mean_fpr, tpr=mean_tpr, color='magenta')
else:
print("no info for target {}".format(unp_id))
plt.savefig(os.path.join(args.output_dir, 'avg_roc.png'))
def main():
args = parse_arguments()
unp_id_list = [row[0] for row in read_csv(args.target_ids)]
category_list = [row[1] for row in read_csv(args.target_ids)]
plt.figure(figsize=(5,5))
for unp_id, category in list(zip(unp_id_list, category_list)):
scores_dir = os.path.join(args.result_dir, '{}'.format(unp_id))
if os.path.isdir(scores_dir):
os.chdir(scores_dir)
if os.path.isdir(scores_dir):
for filename in os.listdir(scores_dir):
if filename.endswith('.csv'):
print(filename)
rows = read_csv(filename)
scores = []
for row in rows:
scores.append([row[0], int(row[1])])
print(scores)
fpr, tpr = Scoring.CalcROC(scores, 1)
tpr = np.array(tpr)
print(unp_id)
if (category) == 'easy':
plot_curve(fpr=fpr, tpr=tpr, color='blue')
elif (category) =='moderate':
plot_curve(fpr=fpr, tpr=tpr, color='orange')
elif (category) == 'hard':
plot_curve(fpr=fpr, tpr=tpr, color='green')
elif (category) =='unfeasible':
plot_curve(fpr=fpr, tpr=tpr, color='magenta')
plt.text(0.57, 0.05, args.label)
plt.savefig(os.path.join(args.output_dir, 'mlt_roc.png'))
def main():
rows = read_csv('/home/amukhopadhyay/ligand_screener_testing/screening_scores.csv')
scores = []
for row in rows:
scores.append([row[0], int(row[1])])
#print(scores) rdkit methods
#fractions = [0.01, 0.05, 0.1]
#print(Scoring.CalcAUC(scores, 1))
#print(Scoring.CalcBEDROC(scores, 1, 20))
#print(Scoring.CalcEnrichment(scores, 1, fractions))
#print(Scoring.CalcRIE(scores, 1, 20))
#print((Scoring.CalcAUC(scores, 1)))
#print((Scoring.CalcROC(scores, 1)))
rank_stats = StatisticalDescriptors.RankStatistics(scores, activity_column=operator.itemgetter(1))
print(round(rank_stats.EF(0.01), 1))
print(round(rank_stats.EF(0.02), 1))
print(round(rank_stats.EF(0.05), 1))
print(round(rank_stats.EF(0.1), 1))
print(round(rank_stats.AUC(), 1))
print(round(rank_stats.BEDROC(alpha=20), 1))
print(round(rank_stats.RIE(alpha=20), 1))
fpr, tpr = Scoring.CalcROC(scores, 1)
roc_auc = metrics.auc(fpr, tpr)
plt.title('Receiver Operating Characteristic')
plt.plot(fpr, tpr, 'b', label = 'AUC = %0.2f' % roc_auc)
plt.legend(loc = 'lower right')
plt.plot([0, 1], [0, 1],'r--')
plt.xlim([0, 1])
plt.ylim([0, 1])
plt.ylabel('True Positive Rate')
plt.xlabel('False Positive Rate')
plt.savefig('test_roc.png')