def compare(self):
parser = argparse.ArgumentParser(
prog='DeepCCS compare',
description=
"Compare the CCS values in a file with the value used to create " +
"train this predictive model. No predictions involved in the " +
"process, only comparaison.")
parser.add_argument("-i", help="Input file name", required=True)
parser.add_argument(
"-o",
help=
"Prefix of output file name (ex: MyFile_). If not specified, stdout will be "
+ "used. If 'none', onlyt the stats will be shown.",
default="")
parser.add_argument(
"-d",
help=
"List of datasets to compare to separated by coma (dtA,dtB,dtC)",
default=None)
if len(argv) <= 2:
parser.print_help()
exit()
args = parser.parse_args(argv[2:])
from DeepCCS.utils import read_dataset, read_reference_table, output_results, output_global_stats
logging.debug("Starting comparaison tool with the following args:" +
str(args))
if not path.isfile(args.i):
raise IOError("Reference file cannot be found")
# Output prefix, if none : output to stdout
out_file_name_prefix = None
if args.o != "":
out_file_name_prefix = args.o
# Data used to create algorithm
if args.d is not None:
dt_list = args.d.split(", ")
else:
dt_list = [
"MetCCS_train_pos", "MetCCS_train_neg", "MetCCS_test_pos",
"MetCCS_test_neg", "Astarita_pos", "Astarita_neg", "Baker",
"McLean", "CBM"
]
# Get a pandas dataframe for each dataset asked for comparaison
# output another table with all the original values + the ccs given by user in an extra column
# print general stats on the compaison
logging.debug("Starting iterating on the dataset list of comparaison")
for i in dt_list:
df_dt = read_dataset("../DATASETS.h5", i)
smiles = df_dt["SMILES"]
adducts = df_dt["Adducts"]
ccs = df_dt["CCS"]
out_file_name = None
if out_file_name_prefix is not None:
out_file_name = out_file_name_prefix + i + ".txt"
if out_file_name is None or out_file_name_prefix.lower() != "none":
output_results(args.i, smiles, adducts, ccs, out_file_name)
smiles_u, adducts_u, ccs_u = read_reference_table(args.i)
df_user = pd.DataFrame({
"SMILES": smiles_u,
"Adducts": adducts_u,
"CCS": ccs_u
})
df_ref = pd.DataFrame({
"SMILES": smiles,
"Adducts": adducts,
"CCS_DeepCCS": ccs
})
merged_df = pd.merge(left=df_user,
right=df_ref,
on=["SMILES", "Adducts"],
how='inner')
if len(merged_df["CCS"]) == 0:
print(i)
print("No corresponding molecule, moving to next dataset.")
continue
else:
print("{} dataset :".format(i))
print("=> {} molecules used for comparaison".format(
len(merged_df["CCS"])))
print("--------Comparaison stats--------")
output_global_stats(merged_df["CCS_DeepCCS"], merged_df["CCS"])
def predict(self):
parser = argparse.ArgumentParser(
prog='DeepCCS predict',
description=
"Predict CCS for some SMILES and adducts using a pre-trained " +
"model.")
parser.add_argument("-mp",
help="Path to model directory",
default="../saved_models/default/")
parser.add_argument("-ap",
help="Path to adducts_encoder directory",
default="../saved_models/default/")
parser.add_argument("-sp",
help="Path to smiles_encoder directory",
default="../saved_models/default/")
parser.add_argument(
"-i",
help="Input file name with SMILES and adduct columns",
required=True)
parser.add_argument(
"-o",
help=
"Output file name (ex: MyFile.csv). If not specified, stdout will be used",
default="")
if len(argv) <= 2:
parser.print_help()
exit()
args = parser.parse_args(argv[2:])
from DeepCCS.utils import read_input_table, output_results
from DeepCCS.model import DeepCCS
print("Starting prediction tool with the following args:" + str(args))
if not path.isdir(args.mp):
raise IOError("Model directory cannot be found")
if not path.isdir(args.ap):
raise IOError("adducts_encoder directory cannot be found")
if not path.isdir(args.sp):
raise IOError("smiles_encoder directory cannot be found")
if not path.isfile(path.join(args.mp, "model.h5")):
raise IOError("Model file is missing from model directory")
if not path.isfile(path.join(args.ap, "adducts_encoder.json")):
raise IOError(
"adduct_encoder.json is missing from the adducts_encoder directory directory"
)
if not path.isfile(path.join(args.sp, "smiles_encoder.json")):
raise IOError(
"smiles_encoder.json is missing from the smiles_encoder directory directory"
)
model = DeepCCS.DeepCCSModel()
model.load_model_from_file(
filename=path.join(args.mp, "model.h5"),
adduct_encoder_file=path.join(args.ap, "adducts_encoder.json"),
smiles_encoder_file=path.join(args.sp, "smiles_encoder.json"))
X_smiles, X_adducts = read_input_table(args.i)
ccs_pred = model.predict(X_smiles, X_adducts)
ccs_pred = ccs_pred.flatten()
out_file_name = None
if args.o != "":
out_file_name = args.o
output_results(args.i, X_smiles, X_adducts, ccs_pred, out_file_name)
def evaluate(self):
parser = argparse.ArgumentParser(
prog='DeepCCS evaluate',
description=
"Evaluate the model performances using SMILES and adducts for " +
"which the CCS was measured.")
parser.add_argument("-mp",
help="Path to model directory",
default="../saved_models/default/")
parser.add_argument("-ap",
help="Path to adducts_encoder directory",
default="../saved_models/default/")
parser.add_argument("-sp",
help="Path to smiles_encoder directory",
default="../saved_models/default/")
parser.add_argument(
"-i",
help=
"Input file name. Must contain columns SMILES, adducts and CCS",
required=True)
parser.add_argument(
"-o",
help=
"Output file name (ex: MyFile.csv). If not specified, stdout will be used."
+ " If 'none', only global stats will be shown.",
default="")
if len(argv) <= 2:
parser.print_help()
exit()
args = parser.parse_args(argv[2:])
from DeepCCS.utils import read_reference_table, output_results, output_global_stats
from DeepCCS.model import DeepCCS
print("Starting evaluation tool with the following args:" + str(args))
if not path.isdir(args.mp):
raise IOError("Model directory cannot be found")
if not path.isdir(args.ap):
raise IOError("adducts_encoder directory cannot be found")
if not path.isdir(args.sp):
raise IOError("smiles_encoder directory cannot be found")
if not path.isfile(args.i):
raise IOError("Input file cannot be found")
if not path.isfile(path.join(args.mp, "model.h5")):
raise IOError("Model file is missing from model directory")
if not path.isfile(path.join(args.ap, "adducts_encoder.json")):
raise IOError(
"adducts_encoder.json is missing from the adducts_encoder directory directory"
)
if not path.isfile(path.join(args.sp, "smiles_encoder.json")):
raise IOError(
"smiles_encoder.json is missing from the smiles_encoder directory directory"
)
model = DeepCCS.DeepCCSModel()
model.load_model_from_file(
filename=path.join(args.mp, "model.h5"),
adduct_encoder_file=path.join(args.ap, "adducts_encoder.json"),
smiles_encoder_file=path.join(args.sp, "smiles_encoder.json"))
X_smiles, X_adducts, X_ccs = read_reference_table(args.i)
ccs_pred = model.predict(X_smiles, X_adducts)
ccs_pred = ccs_pred.flatten()
out_file_name = None
if args.o != "":
out_file_name = args.o
if out_file_name is None or out_file_name.lower() != "none":
output_results(args.i, X_smiles, X_adducts, ccs_pred,
out_file_name)
print("-----------Model stats-----------")
output_global_stats(X_ccs, ccs_pred)