def get_not_disjoint():
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},
target_keys=["f"],
feature_keys=["a", "b", "f"],
)
def test_to_feature_columns_and_input_fn_no_labels(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
},
feature_keys=["a", "b", "f"]))
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 = {}
self.assertEqual(expected_labels, labels)
self.assertEqual(3, len(feature_columns))
def get_not_disjoint():
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},
target_keys=["f"],
feature_keys=["a", "b", "f"]))
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 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 test_to_feature_columns_and_input_fn_no_targets(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}, feature_keys=["a", "b", "f"]
)
base_features, targets = input_fn()
expected_base_features = {
"a": mocks.MockTensor("Tensor a", tf.int32),
"b": mocks.MockSparseTensor("SparseTensor b", tf.int32),
"c": mocks.MockTensor("Tensor c", tf.int32),
"d": mocks.MockSparseTensor("SparseTensor d", tf.int32),
}
self.assertEqual(expected_base_features, base_features)
expected_targets = {}
self.assertEqual(expected_targets, targets)
self.assertEqual(3, len(feature_columns))
