def test_fastai_model_file(tmp_dir, data_loader):
dvclive.init("dvc_logs")
learn = tabular_learner(data_loader, metrics=accuracy)
learn.model_dir = os.path.abspath("./")
learn.fit_one_cycle(2, cbs=[DvcLiveCallback("model")])
assert (tmp_dir / "model.pth").is_file()
def fastai_model():
iris = datasets.load_iris()
X = pd.DataFrame(iris.data[:, :2], columns=iris.feature_names[:2])
y = pd.Series(iris.target, name="label")
dl = TabularDataLoaders.from_df(df=pd.concat([X, y], axis=1),
cont_names=list(X.columns),
y_names="label")
model = tabular_learner(dl, metrics=accuracy, layers=[3])
model.fit(1)
return ModelWithData(model=model, inference_dataframe=X)
def test_fastai_callback(tmp_dir, data_loader):
learn = tabular_learner(data_loader, metrics=accuracy)
learn.model_dir = os.path.abspath("./")
learn.fit_one_cycle(2, cbs=[DvcLiveCallback("model")])
assert os.path.exists("dvclive")
train_path = tmp_dir / "dvclive" / Scalar.subfolder / "train"
valid_path = tmp_dir / "dvclive" / Scalar.subfolder / "valid"
assert train_path.is_dir()
assert valid_path.is_dir()
assert (tmp_dir / "dvclive" / Scalar.subfolder / "accuracy.tsv").exists()
def tabular_predict_from_nn(tab_fn, weights_fn, xs=None):
#learner = load_learner(model_fn)
to_nn = fasttab.load_pickle(tab_fn)
dls = to_nn.dataloaders(1024)
learn = fasttab.tabular_learner(
dls, metrics=fasttab.accuracy
) #BrierScore doesn't seem to work with lr_find()
learn.load(weights_fn)
if not isinstance(xs, pd.DataFrame):
return learn
dl = learn.dls.test_dl(xs, bs=64) # apply transforms
preds, _ = learn.get_preds(dl=dl) # get prediction
return preds
