def test_prep_data_tf_keras_fn_without_sparse_col(self):
has_sparse_col = False
feature_columns = ['col1', 'col2']
label_columns = ['label1', 'label2']
sample_weight_col = 'sample_weight'
col1 = tf.constant([3.])
col2 = tf.constant([float(i) for i in range(10)])
label1 = tf.constant([1., 2., 3., 4.])
label2 = tf.constant([1., 2., 3., 4.])
sw1 = tf.constant([.06])
input_shapes = [[-1, 1], [-1, 2, 5]]
output_shapes = [[-1, 4], [-1, 2, 2]]
output_names = ['label1', 'label2']
prep_data_tf_keras = \
TFKerasUtil._prep_data_fn(has_sparse_col, sample_weight_col,
feature_columns, label_columns, input_shapes,
output_shapes, output_names)
Row = collections.namedtuple(
'row', ['col1', 'col2', sample_weight_col, 'label1', 'label2'])
row = Row(col1=col1,
col2=col2,
label1=label1,
label2=label2,
sample_weight=sw1)
prepped_row = prep_data_tf_keras(row)
prepped_row_vals = self.evaluate(prepped_row)
assert np.array_equal(prepped_row_vals[0][0], np.array([[3.]]))
assert np.array_equal(
prepped_row_vals[0][1],
np.array([[[0., 1., 2., 3., 4.], [5., 6., 7., 8., 9.]]]))
assert np.array_equal(prepped_row_vals[1][0],
np.array([[1., 2., 3., 4.]]))
assert np.array_equal(prepped_row_vals[1][1],
np.array([[[1., 2.], [3., 4.]]]))
assert np.allclose(prepped_row_vals[2]['label1'], np.array([0.06]))
assert np.allclose(prepped_row_vals[2]['label2'], np.array([0.06]))
def test_prep_data_tf_keras_fn_with_sparse_col(self):
has_sparse_col = True
feature_columns = ['col1', 'col2']
label_columns = ['label1', 'label2']
sample_weight_col = 'sample_weight'
col1 = tf.constant([3.])
col2 = tf.constant([3., 1., 3., 6., 10., 30., 60., 0, 0, 0])
label1 = tf.constant([1., 2., 3., 4.])
label2 = tf.constant([1., 2., 3., 4.])
sw1 = tf.constant([.06])
input_shapes = [[-1, 1], [-1, 2, 5]]
output_shapes = [[-1, 4], [-1, 2, 2]]
output_names = ['label1', 'label2']
prep_data_tf_keras = \
TFKerasUtil._prep_data_fn(has_sparse_col, sample_weight_col,
feature_columns, label_columns, input_shapes,
output_shapes, output_names)
row = {
'col1': col1,
'col2': col2,
'label1': label1,
'label2': label2,
sample_weight_col: sw1
}
prepped_row = prep_data_tf_keras(row)
prepped_row_vals = self.evaluate(prepped_row)
assert np.array_equal(prepped_row_vals[0][0], np.array([[3.]]))
assert np.array_equal(
prepped_row_vals[0][1],
np.array([[[3., 1., 3., 6., 10.], [30., 60., 0., 0., 0.]]]))
assert np.array_equal(prepped_row_vals[1][0],
np.array([[1., 2., 3., 4.]]))
assert np.array_equal(prepped_row_vals[1][1],
np.array([[[1., 2.], [3., 4.]]]))
assert np.allclose(prepped_row_vals[2]['label1'], np.array([0.06]))
assert np.allclose(prepped_row_vals[2]['label2'], np.array([0.06]))