def train(df, tar, save=False):
set1 = 'train' if len(sys.argv) < 2 else sys.argv[1]
# set2 = [] if len(sys.argv) < 3 else sys.argv[2:]
train_filter = None
model = MODEL(**MODEL_PARAMS)
print("Reading in training data " + set1)
train = df
print("Extracting features")
train = model.extract(train)
if save:
print("Saving train features")
data_io.write_data(set1, train)
# target = data_io.read_target(set1)
# Data selection
train, target = util.random_permutation(train, tar)
train_filter = None
if train_filter is not None:
train = train[train_filter]
target = target[train_filter]
print("Training model with optimal weights")
X = pd.concat([train])
y = np.concatenate((tar))
model.fit(X, y)
if save:
model_path = "model.pkl"
print("Saving model", model_path)
data_io.save_model(model, model_path)
return model
def main():
set1 = 'train' if len(sys.argv) < 2 else sys.argv[1]
set2 = [] if len(sys.argv) < 3 else sys.argv[2:]
train_filter = None
train_filter2 = None
model = MODEL(**MODEL_PARAMS)
print("Reading in training data " + set1)
train = data_io.read_data(set1)
print("Extracting features")
train = model.extract(train)
print("Saving train features")
data_io.write_data(set1, train)
target = data_io.read_target(set1)
train2 = None
target2 = None
for s in set2:
print "Reading in training data", s
tr = data_io.read_data(s)
print "Extracting features"
tr = model.extract(tr)
print "Saving train features"
data_io.write_data(s, tr)
tg = data_io.read_target(s)
train2 = tr if train2 is None else pd.concat(
(train2, tr), ignore_index=True)
target2 = tg if target2 is None else pd.concat(
(target2, tg), ignore_index=True)
train2, target2 = util.random_permutation(train2, target2)
train_filter2 = (train2['A type'] == 'Numerical') & (train2['B type']
== 'Numerical')
# Data selection
train, target = util.random_permutation(train, target)
train_filter = ((train['A type'] == 'Numerical') &
(train['B type'] == 'Numerical'))
if train_filter is not None:
train = train[train_filter]
target = target[train_filter]
if train_filter2 is not None:
train2 = train2[train_filter2]
target2 = target2[train_filter2]
print("Training model with optimal weights")
X = pd.concat([train, train2]) if train2 is not None else train
y = np.concatenate((target.Target.values, target2.Target.values
)) if target2 is not None else target.Target.values
model.fit(X, y)
model_path = "nnmodel.pkl"
print "Saving model", model_path
data_io.save_model(model, model_path)
def main():
set1 = 'train' if len(sys.argv) < 2 else sys.argv[1]
set2 = [] if len(sys.argv) < 3 else sys.argv[2:]
train_filter = None
train_filter2 = None
model = MODEL(**MODEL_PARAMS)
print("Reading in training data " + set1)
train = data_io.read_data(set1)
print("Extracting features")
train = model.extract(train)
print("Saving train features")
data_io.write_data(set1, train)
target = data_io.read_target(set1)
train2 = None
target2 = None
for s in set2:
print "Reading in training data", s
tr = data_io.read_data(s)
print "Extracting features"
tr = model.extract(tr)
print "Saving train features"
data_io.write_data(s, tr)
tg = data_io.read_target(s)
train2 = tr if train2 is None else pd.concat((train2, tr), ignore_index=True)
target2 = tg if target2 is None else pd.concat((target2, tg), ignore_index=True)
train2, target2 = util.random_permutation(train2, target2)
train_filter2 = ((train2['A type'] != 'Numerical') & (train2['B type'] == 'Numerical'))
#train_filter2 |= ((train2['A type'] == 'Numerical') & (train2['B type'] != 'Numerical'))
# Data selection
train, target = util.random_permutation(train, target)
train_filter = ((train['A type'] != 'Numerical') & (train['B type'] == 'Numerical'))
#train_filter |= ((train['A type'] == 'Numerical') & (train['B type'] != 'Numerical'))
if train_filter is not None:
train = train[train_filter]
target = target[train_filter]
if train_filter2 is not None:
train2 = train2[train_filter2]
target2 = target2[train_filter2]
print("Training model with optimal weights")
X = pd.concat([train, train2]) if train2 is not None else train
y = np.concatenate((target.Target.values, target2.Target.values)) if target2 is not None else target.Target.values
model.fit(X, y)
model_path = "cnmodel.pkl"
print "Saving model", model_path
data_io.save_model(model, model_path)
def main():
if data.IS_DEBUG:
import time
t1 = time.time()
data_io.read_data()
simulations.start()
# analysis.start()
# data_io.write_data()
# theory.start()
if data.IS_DEBUG:
import time
t2 = time.time()
print(f'Time = {t2 - t1}')
else:
data_io.write_data()