def setup_test_df():
"""Create a dataframe populated with some test columns."""
df = learn.DataFrame()
df["a"] = learn.TransformedSeries([mocks.MockSeries("foobar", [])],
mocks.MockTwoOutputTransform(
"iue", "eui", "snt"), "out1")
df["b"] = learn.TransformedSeries([mocks.MockSeries("foobar", [])],
mocks.MockTwoOutputTransform(
"iue", "eui", "snt"), "out2")
df["c"] = learn.TransformedSeries([mocks.MockSeries("foobar", [])],
mocks.MockTwoOutputTransform(
"iue", "eui", "snt"), "out1")
return df
def test_build_multiple_output(self):
col = learn.TransformedSeries([mocks.MockSeries("foobar", [])],
mocks.MockTwoOutputTransform(
"thb", "nth", "snt"), "out2")
result = col.build()
expected = mocks.MockTensor("Mock Tensor 2", dtypes.int32)
self.assertEqual(expected, result)
def test_build_single_output(self):
col = learn.TransformedSeries([mocks.MockSeries("foobar", [])],
mocks.MockOneOutputTransform(
"thb", "nth"), "out1")
result = col.build()
expected = "Fake Tensor 1"
self.assertEqual(expected, result)
def test_repr(self):
col = learn.TransformedSeries([mocks.MockSeries("foobar", [])],
mocks.MockTwoOutputTransform(
"thb", "nth", "snt"), "qux")
# note params are sorted by name
expected = ("MockTransform({'param_one': 'thb', 'param_three': 'snt', "
"'param_two': 'nth'})"
"(foobar)[qux]")
self.assertEqual(expected, repr(col))
def test_to_feature_columns_and_input_fn(self):
df = setup_test_df_3layer()
feature_columns, input_fn = (
estimator_utils.to_feature_columns_and_input_fn(
df,
base_input_keys_with_defaults={
"a": 1,
"b": 2,
"c": 3,
"d": 4
},
label_keys=["g"],
feature_keys=["a", "b", "f"]))
expected_feature_column_a = feature_column.DataFrameColumn(
"a",
learn.PredefinedSeries(
"a",
parsing_ops.FixedLenFeature(tensor_shape.unknown_shape(),
dtypes.int32, 1)))
expected_feature_column_b = feature_column.DataFrameColumn(
"b",
learn.PredefinedSeries("b",
parsing_ops.VarLenFeature(dtypes.int32)))
expected_feature_column_f = feature_column.DataFrameColumn(
"f",
learn.TransformedSeries([
learn.PredefinedSeries(
"c",
parsing_ops.FixedLenFeature(tensor_shape.unknown_shape(),
dtypes.int32, 3)),
learn.PredefinedSeries("d",
parsing_ops.VarLenFeature(dtypes.int32))
], mocks.Mock2x2Transform("iue", "eui", "snt"), "out2"))
expected_feature_columns = [
expected_feature_column_a, expected_feature_column_b,
expected_feature_column_f
]
self.assertEqual(sorted(expected_feature_columns),
sorted(feature_columns))
base_features, labels = input_fn()
expected_base_features = {
"a": mocks.MockTensor("Tensor a", dtypes.int32),
"b": mocks.MockSparseTensor("SparseTensor b", dtypes.int32),
"c": mocks.MockTensor("Tensor c", dtypes.int32),
"d": mocks.MockSparseTensor("SparseTensor d", dtypes.int32)
}
self.assertEqual(expected_base_features, base_features)
expected_labels = mocks.MockTensor("Out iue", dtypes.int32)
self.assertEqual(expected_labels, labels)
self.assertEqual(3, len(feature_columns))
def setup_test_df_3layer():
"""Create a dataframe populated with some test columns."""
df = learn.DataFrame()
df["a"] = mocks.MockSeries("a_series", mocks.MockTensor("Tensor a", tf.int32))
df["b"] = mocks.MockSeries("b_series",
mocks.MockSparseTensor("SparseTensor b", tf.int32))
df["c"] = mocks.MockSeries("c_series", mocks.MockTensor("Tensor c", tf.int32))
df["d"] = mocks.MockSeries("d_series",
mocks.MockSparseTensor("SparseTensor d", tf.int32))
df["e"] = learn.TransformedSeries([df["a"], df["b"]],
mocks.Mock2x2Transform("iue", "eui", "snt"),
"out1")
df["f"] = learn.TransformedSeries([df["c"], df["d"]],
mocks.Mock2x2Transform("iue", "eui", "snt"),
"out2")
df["g"] = learn.TransformedSeries([df["e"], df["f"]],
mocks.Mock2x2Transform("iue", "eui", "snt"),
"out1")
return df
def create_no_output_series():
return learn.TransformedSeries([mocks.MockSeries("foobar", [])],
mocks.MockZeroOutputTransform(
"thb", "nth"), None)
