| import argparse |
| import os |
|
|
| import numpy as np |
| import pandas as pd |
| from scipy.stats import pearsonr |
| from sklearn import linear_model |
| from sklearn.metrics import mean_squared_error |
|
|
| from _common import add_model_args, formatter, score_preprocessed |
|
|
|
|
| def parse_args(): |
| parser = argparse.ArgumentParser( |
| description="Run GenScore CASF-2016 scoring/ranking benchmark.", |
| formatter_class=formatter(), |
| ) |
| add_model_args(parser) |
| parser.add_argument("--data-dir", required=True, help="Directory containing preprocessed CASF tensors.") |
| parser.add_argument("--test-prefix", default="v2020_casf") |
| parser.add_argument("--coreset-file", required=True, help="Path to CASF-2016 CoreSet.dat.") |
| parser.add_argument("--outdir", required=True, help="Output directory for CASF ranking .dat file.") |
| return parser.parse_args() |
|
|
|
|
| def obtain_metrics(df): |
| regr = linear_model.LinearRegression() |
| regr.fit(df.score.values.reshape(-1, 1), df.logKa.values.reshape(-1, 1)) |
| preds = regr.predict(df.score.values.reshape(-1, 1)) |
| rp = pearsonr(df.logKa, df.score)[0] |
| mse = mean_squared_error(df.logKa, preds) |
| num = df.shape[0] |
| sd = np.sqrt((mse * num) / (num - 1)) |
| print("The regression equation: logKa = %.2f + %.2f * Score" % (float(regr.coef_), float(regr.intercept_))) |
| print("Number of favorable sample (N): %d" % num) |
| print("Pearson correlation coefficient (R): %.3f" % rp) |
| print("Standard deviation in fitting (SD): %.2f" % sd) |
|
|
|
|
| def main(): |
| args = parse_args() |
| prots = os.path.join(args.data_dir, f"{args.test_prefix}_prot.pt") |
| ligs = os.path.join(args.data_dir, f"{args.test_prefix}_lig.pt") |
| ids = os.path.join(args.data_dir, f"{args.test_prefix}_ids.npy") |
|
|
| _, preds = score_preprocessed(ids, prots, ligs, args) |
|
|
| core = pd.read_csv(args.coreset_file, sep=r"[,,\t, ]+", header=0, engine="python") |
| df_score = pd.DataFrame(zip(np.load(ids, allow_pickle=True)[0], preds), columns=["#code", "score"]) |
| testdf = pd.merge(core, df_score, on="#code") |
|
|
| os.makedirs(args.outdir, exist_ok=True) |
| testdf[["#code", "score"]].to_csv( |
| os.path.join(args.outdir, f"{args.outprefix}.dat"), |
| index=False, |
| sep="\t", |
| ) |
| obtain_metrics(testdf) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
