def final_model(df):
#final model we decided on through selection
model = RandomForestClassifier(bootstrap=True,
class_weight=None,
criterion='gini',
max_depth=None,
max_features='log2',
max_leaf_nodes=None,
min_impurity_decrease=0.0,
min_impurity_split=None,
min_samples_leaf=1,
min_samples_split=2,
min_weight_fraction_leaf=0.0,
n_estimators=400,
n_jobs=1,
oob_score=False,
random_state=None,
verbose=0,
warm_start=False)
#get the data in the form we want according to transform function
X, y = transform_train(df)
#fit the model
model.fit(X, y)
with open('website/model.pkl', 'wb') as f:
# Write the model to a file.
pickle.dump(model, f)
if args.src == 'visda':
src = visda_train
tgt = visda_test
visda = True
return src, tgt, office, visda, noe
src, tgt, office, visda, noe = get_datasetname(args)
batch_size = {"train": 36, "val": 36, "test": 4}
for i in range(10):
batch_size["val" + str(i)] = 4
if visda == False:
data_transforms = {
'train': tran.transform_train(resize_size=28, crop_size=28),
'val': tran.transform_train(resize_size=28, crop_size=28),
}
data_transforms = tran.transform_test(data_transforms=data_transforms,
resize_size=28,
crop_size=28)
dsets = {
"train":
ImageList(open(src).readlines(), transform=data_transforms["train"]),
"val":
ImageList(open(tgt).readlines(), transform=data_transforms["val"]),
"test":
ImageList(open(tgt).readlines(), transform=data_transforms["val"])
}
dset_loaders = {
x: torch.utils.data.DataLoader(dsets[x],
if args.src == 'visda':
src = visda_train
tgt = visda_test
visda = True
return src, tgt, office, visda, noe
src, tgt, office, visda, noe = get_datasetname(args)
batch_size = {"train": 36, "val": 36, "test": 4}
for i in range(10):
batch_size["val" + str(i)] = 4
if visda == False:
data_transforms = {
'train': tran.transform_train(resize_size=256, crop_size=224),
'val': tran.transform_train(resize_size=256, crop_size=224),
}
data_transforms = tran.transform_test(data_transforms=data_transforms,
resize_size=256,
crop_size=224)
dsets = {
"train":
ImageList(open(src).readlines(), transform=data_transforms["train"]),
"val":
ImageList(open(tgt).readlines(), transform=data_transforms["val"]),
"test":
ImageList(open(tgt).readlines(), transform=data_transforms["val"])
}
dset_loaders = {
x: torch.utils.data.DataLoader(dsets[x],
