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Command line tool datasets names updated
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Signed-off-by: ElinaFF <[email protected]>
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@ElinaFF
ElinaFF committed Dec 20, 2018
1 parent b0bc643 commit 64a5802
Showing 1 changed file with 27 additions and 33 deletions.
60 changes: 27 additions & 33 deletions interface/command_line_tool.py
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Original file line number Diff line number Diff line change
Expand Up @@ -126,26 +126,24 @@ def predict(self):
output_results(args.i, X_smiles, X_adducts, ccs_pred, out_file_name)

def train(self):
parser = argparse.ArgumentParser(prog='DeepCCS train',
description="Train a new model.")
parser = argparse.ArgumentParser(prog='DeepCCS train', description="Train a new model.")
parser.add_argument("-ap", help="path to adducts_encoder directory", default=None)
parser.add_argument("-sp", help="path to smiles_encoder directory", default=None)

parser.add_argument("-mtrain", help="Use MetCCS train datasets to create the model", default=False,
action="store_true", dest="mtrain")
parser.add_argument("-mtestA", help="Use MetCCS Agilent test datasets to create the model", default=False,
parser.add_argument("-mtest", help="Use MetCCS test datasets to create the model", default=False,
action="store_true", dest="mtestA")
parser.add_argument("-mtestW", help="MetCCS Waters test datasets to create the model", default=False,
parser.add_argument("-ast", help="Astarita test datasets to create the model", default=False,
action="store_true", dest="mtestW")

parser.add_argument("-pnnl", help="PNNL dataset to create the model", default=False, action="store_true",
parser.add_argument("-baker", help="Baker dataset to create the model", default=False, action="store_true",
dest="pnnl")
parser.add_argument("-cbm", help="CBM2018 dataset to create the model", default=False, action="store_true",
dest="cbm")
parser.add_argument("-mclean", help="McLean dataset to create the model", default=False, action="store_true",
dest="mclean")
parser.add_argument("-f", help="h5 file containing all source datasets", required=True)


parser.add_argument("-nd", help="New Data to create the model, list of template file (file1.csv,file2.csv,...)",
default=None)
parser.add_argument("-nepochs", help="Number of epochs", default=150)
Expand All @@ -164,8 +162,8 @@ def train(self):
if 0 >= args.test >= 1:
raise ValueError("Proportion in test set must be between 0 and 1. Recommended: 0.2")

logging.debug("\nCondition is: {}".format(not(args.mtrain or args.mtestA or args.mtestW or args.pnnl or args.cbm or args.mclean)))
if not (args.mtrain or args.mtestA or args.mtestW or args.pnnl or args.cbm or args.mclean or
logging.debug("\nCondition is: {}".format(not(args.mtrain or args.mtest or args.ast or args.baker or args.cbm or args.mclean)))
if not (args.mtrain or args.mtest or args.ast or args.baker or args.cbm or args.mclean or
args.nd is not None):
raise ValueError("At least one datafile must be used to train a model.")

Expand All @@ -176,8 +174,6 @@ def train(self):
logging.debug("Starting prediction tool with the following args:" + str(args))
if not path.isdir(args.o):
raise IOError("Directory for output model cannot be found")
if not path.isfile(args.f):
raise IOError("h5 file of source datasets cannot be found")

# Initialize lists
training_datasets = []
Expand All @@ -195,40 +191,41 @@ def train(self):
# MetCCS datasets are the only possible exception to the 80-20 rule

# Load source datasets according to args
source_data = "../DATASETS.h5"
if args.mtrain:
for d in ["MetCCS_pos", "MetCCS_neg"]:
df_dt = read_dataset(args.f, d)
for d in ["MetCCS_train_pos", "MetCCS_train_neg"]:
df_dt = read_dataset(source_data, d)
smiles = df_dt["SMILES"]
adducts = df_dt["Adducts"]
ccs = df_dt["CCS"]
training_datasets.append([smiles, adducts, ccs])

if args.mtestA:
dt_list.extend(["Agilent_pos", "Agilent_neg"])
name_test_dataset.extend(["Agilent_pos", "Agilent_neg"])
if args.mtest:
dt_list.extend(["MetCCS_test_pos", "MetCCS_test_neg"])
name_test_dataset.extend(["MetCCS_test_pos", "MetCCS_test_neg"])
else:
for d in ["Agilent_pos", "Agilent_neg"]:
df_dt = read_dataset(args.f, d)
for d in ["MetCCS_test_pos", "MetCCS_test_neg"]:
df_dt = read_dataset(source_data, d)
smiles = df_dt["SMILES"]
adducts = df_dt["Adducts"]
ccs = df_dt["CCS"]
testing_datasets.append([smiles, adducts, ccs])
name_test_dataset.extend(["Agilent_pos", "Agilent_neg"])
name_test_dataset.extend(["MetCCS_test_pos", "MetCCS_test_neg"])

if args.mtestW:
dt_list.extend(["Waters_pos", "Waters_neg"])
name_test_dataset.extend(["Waters_pos", "Waters_neg"])
if args.ast:
dt_list.extend(["Astarita_pos", "Astarita_neg"])
name_test_dataset.extend(["Astarita_pos", "Astarita_neg"])
else:
for d in ["Waters_pos", "Waters_neg"]:
df_dt = read_dataset(args.f, d)
for d in ["Astarita_pos", "Astarita_neg"]:
df_dt = read_dataset(source_data, d)
smiles = df_dt["SMILES"]
adducts = df_dt["Adducts"]
ccs = df_dt["CCS"]
testing_datasets.append([smiles, adducts, ccs])
name_test_dataset.extend(["Waters_pos", "Waters_neg"])
name_test_dataset.extend(["Astarita_pos", "Astarita_neg"])

if args.pnnl:
dt_list.append("PNL")
if args.baker:
dt_list.append("Baker")

if args.mclean:
dt_list.append("McLean")
Expand All @@ -244,7 +241,7 @@ def train(self):
train_fraction = 1 - args.test
for d in dt_list:
name_test_dataset.append(d)
data = read_dataset(args.f, d)
data = read_dataset(source_data, d)
train = data.sample(frac=train_fraction)
test = data.drop(train.index)

Expand Down Expand Up @@ -470,7 +467,6 @@ def compare(self):
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("-f", help="hdf5 file containing the datasets used to create this algoritm", required=True)
parser.add_argument("-d", help="List of datasets to compare to separated by coma (dtA,dtB,dtC)", default=None)

if len(argv) <= 2:
Expand All @@ -484,8 +480,6 @@ def compare(self):
logging.debug("Starting comparaison tool with the following args:" + str(args))
if not path.isfile(args.i):
raise IOError("Reference file cannot be found")
if not path.isfile(args.f):
raise IOError("h5 file cannot be found")

# Output prefix, if none : output to stdout
out_file_name_prefix = None
Expand All @@ -496,15 +490,15 @@ def compare(self):
if args.d is not None:
dt_list = args.d.split(", ")
else:
dt_list = ["MetCCS_pos", "MetCCS_neg", "Agilent_pos", "Agilent_neg", "Waters_pos", "Waters_neg", "PNL",
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(args.f, i)
df_dt = read_dataset("../DATASETS.h5", i)
smiles = df_dt["SMILES"]
adducts = df_dt["Adducts"]
ccs = df_dt["CCS"]
Expand Down

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