def test_pick_indices():
N = 100
classes = ["foo", "bar"]
filepaths = [str(x) for x in range(N)]
df = prep_label_dataframe(filepaths, classes)
df["foo"].iloc[:int(N/2)] = 1
pred_df = df.copy().drop(["exclude", "filepath", "validation"], 1)
pred_df["foo"] = np.linspace(0,1,N)
pred_df["bar"] = np.ones(N)
assert len(pick_indices(df, pred_df, 10, "random", "unlabeled")) == 10
assert len(pick_indices(df, pred_df, 10, "max entropy", "unlabeled")) == 10
assert len(pick_indices(df, pred_df, 10, "maxent: foo", "unlabeled")) == 10
assert len(pick_indices(df, pred_df, 10,
"maxent: bar", "partially labeled")) == 10
assert len(pick_indices(df, pred_df, 100, "maxent: foo", "unlabeled")) == 50
def test_prep_label_dataframe():
filepaths = ["foo.png", "bar.png", "foobar.png"]
classes = ["shoe", "biscotti", "punctuality"]
df = prep_label_dataframe(filepaths, classes)
assert len(df) == 3
assert "exclude" in df.columns
assert "shoe" in df.columns
assert (df["exclude"] == False).sum() == 3
assert (pd.isnull(df["punctuality"])).sum() == 3
def test_buttonpanel():
def _mock_load_func(x):
"""
randomly generate array instead of loading an image file
from disk to an array
"""
return np.random.uniform(0, 1, (256,256, 3))
classes = ["foo", "bar"]
filepaths = ["foo.jpg", "bar.jpg"]
df = prep_label_dataframe(filepaths, classes)
bp = ButtonPanel(classes, df, _mock_load_func)
bp.load(np.array([0]))
bp.record_values()
def test_pick_indices():
N = 100
classes = ["foo", "bar"]
filepaths = [str(x) for x in range(N)]
df = prep_label_dataframe(filepaths, classes)
foo = np.concatenate([np.ones(int(N / 2)), np.nan * np.zeros(int(N / 2))])
df = df.assign(foo=foo)
pred_df = df.copy().drop(["exclude", "filepath", "validation"], 1)
pred_df = pred_df.assign(foo=np.linspace(0, 1, N))
pred_df = pred_df.assign(bar=np.ones(N))
assert len(pick_indices(df, pred_df, 10, "random", "unlabeled")) == 10
assert len(pick_indices(df, pred_df, 10, "max entropy", "unlabeled")) == 10
assert len(pick_indices(df, pred_df, 10, "maxent: foo", "unlabeled")) == 10
assert len(
pick_indices(df, pred_df, 10, "maxent: bar",
"partially labeled")) == 10
assert len(pick_indices(df, pred_df, 100, "maxent: foo",
"unlabeled")) == 50