def testBucketizedFeatures(self):
"""Tests SVM classifier with bucketized features."""
def input_fn():
return {
'example_id': constant_op.constant(['1', '2', '3']),
'price': constant_op.constant([[600.0], [800.0], [400.0]]),
'sq_footage': constant_op.constant([[1000.0], [800.0],
[500.0]]),
'weights': constant_op.constant([[1.0], [1.0], [1.0]])
}, constant_op.constant([[1], [0], [1]])
price_bucket = feature_column.bucketized_column(
feature_column.real_valued_column('price'),
boundaries=[500.0, 700.0])
sq_footage_bucket = feature_column.bucketized_column(
feature_column.real_valued_column('sq_footage'),
boundaries=[650.0])
svm_classifier = svm.SVM(
feature_columns=[price_bucket, sq_footage_bucket],
example_id_column='example_id',
l1_regularization=0.1,
l2_regularization=1.0)
svm_classifier.fit(input_fn=input_fn, steps=30)
accuracy = svm_classifier.evaluate(input_fn=input_fn,
steps=1)['accuracy']
self.assertAlmostEqual(accuracy, 1.0, places=3)
def testBucketizedFeatures(self):
"""Tests SDCALogisticClassifier with bucketized features."""
def input_fn():
return {
'example_id': constant_op.constant(['1', '2', '3']),
'price': constant_op.constant([600.0, 1000.0, 400.0]),
'sq_footage': constant_op.constant([[1000.0], [600.0],
[700.0]]),
'weights': constant_op.constant([[1.0], [1.0], [1.0]])
}, constant_op.constant([[1], [0], [1]])
with self._single_threaded_test_session():
price_bucket = feature_column_lib.bucketized_column(
feature_column_lib.real_valued_column('price'),
boundaries=[500.0, 700.0])
sq_footage_bucket = feature_column_lib.bucketized_column(
feature_column_lib.real_valued_column('sq_footage'),
boundaries=[650.0])
classifier = sdca_estimator.SDCALogisticClassifier(
example_id_column='example_id',
feature_columns=[price_bucket, sq_footage_bucket],
weight_column_name='weights',
l2_regularization=1.0)
classifier.fit(input_fn=input_fn, steps=50)
metrics = classifier.evaluate(input_fn=input_fn, steps=1)
self.assertGreater(metrics['accuracy'], 0.9)
def testBucketizedFeatures(self):
"""Tests SDCALogisticClassifier with bucketized features."""
def input_fn():
return {
'example_id': constant_op.constant(['1', '2', '3']),
'price': constant_op.constant([600.0, 1000.0, 400.0]),
'sq_footage': constant_op.constant([[1000.0], [600.0], [700.0]]),
'weights': constant_op.constant([[1.0], [1.0], [1.0]])
}, constant_op.constant([[1], [0], [1]])
with self._single_threaded_test_session():
price_bucket = feature_column_lib.bucketized_column(
feature_column_lib.real_valued_column('price'),
boundaries=[500.0, 700.0])
sq_footage_bucket = feature_column_lib.bucketized_column(
feature_column_lib.real_valued_column('sq_footage'),
boundaries=[650.0])
classifier = sdca_estimator.SDCALogisticClassifier(
example_id_column='example_id',
feature_columns=[price_bucket, sq_footage_bucket],
weight_column_name='weights',
l2_regularization=1.0)
classifier.fit(input_fn=input_fn, steps=50)
metrics = classifier.evaluate(input_fn=input_fn, steps=1)
self.assertGreater(metrics['accuracy'], 0.9)
def testBucketizedColumnRequiresRealValuedColumn(self):
with self.assertRaisesRegexp(
TypeError, "source_column must be an instance of _RealValuedColumn"):
fc.bucketized_column("bbb", [0])
with self.assertRaisesRegexp(
TypeError, "source_column must be an instance of _RealValuedColumn"):
fc.bucketized_column(
fc.sparse_column_with_integerized_feature(
column_name="bbb", bucket_size=10), [0])
def testBucketizedColumnRequiresRealValuedColumn(self):
with self.assertRaisesRegexp(
TypeError, "source_column must be an instance of _RealValuedColumn"):
fc.bucketized_column("bbb", [0])
with self.assertRaisesRegexp(
TypeError, "source_column must be an instance of _RealValuedColumn"):
fc.bucketized_column(
fc.sparse_column_with_integerized_feature(
column_name="bbb", bucket_size=10), [0])
def testMakePlaceHolderTensorsForBaseFeatures(self):
sparse_col = fc.sparse_column_with_hash_bucket(
"sparse_column", hash_bucket_size=100)
real_valued_col = fc.real_valued_column("real_valued_column", 5)
vlen_real_valued_col = fc.real_valued_column(
"vlen_real_valued_column", dimension=None)
bucketized_col = fc.bucketized_column(
fc.real_valued_column("real_valued_column_for_bucketization"), [0, 4])
feature_columns = set(
[sparse_col, real_valued_col, vlen_real_valued_col, bucketized_col])
placeholders = (
fc.make_place_holder_tensors_for_base_features(feature_columns))
self.assertEqual(4, len(placeholders))
self.assertTrue(
isinstance(placeholders["sparse_column"],
sparse_tensor_lib.SparseTensor))
self.assertTrue(
isinstance(placeholders["vlen_real_valued_column"],
sparse_tensor_lib.SparseTensor))
placeholder = placeholders["real_valued_column"]
self.assertGreaterEqual(
placeholder.name.find(u"Placeholder_real_valued_column"), 0)
self.assertEqual(dtypes.float32, placeholder.dtype)
self.assertEqual([None, 5], placeholder.get_shape().as_list())
placeholder = placeholders["real_valued_column_for_bucketization"]
self.assertGreaterEqual(
placeholder.name.find(
u"Placeholder_real_valued_column_for_bucketization"), 0)
self.assertEqual(dtypes.float32, placeholder.dtype)
self.assertEqual([None, 1], placeholder.get_shape().as_list())
def testMixedFeatures(self):
"""Tests SDCALogisticClassifier with a mix of features."""
def input_fn():
return {
'example_id':
constant_op.constant(['1', '2', '3']),
'price':
constant_op.constant([[0.6], [0.8], [0.3]]),
'sq_footage':
constant_op.constant([[900.0], [700.0], [600.0]]),
'country':
sparse_tensor.SparseTensor(
values=['IT', 'US', 'GB'],
indices=[[0, 0], [1, 3], [2, 1]],
dense_shape=[3, 5]),
'weights':
constant_op.constant([[3.0], [1.0], [1.0]])
}, constant_op.constant([[1], [0], [1]])
price = feature_column_lib.real_valued_column('price')
sq_footage_bucket = feature_column_lib.bucketized_column(
feature_column_lib.real_valued_column('sq_footage'),
boundaries=[650.0, 800.0])
country = feature_column_lib.sparse_column_with_hash_bucket(
'country', hash_bucket_size=5)
sq_footage_country = feature_column_lib.crossed_column(
[sq_footage_bucket, country], hash_bucket_size=10)
classifier = sdca_estimator.SDCALogisticClassifier(
example_id_column='example_id',
feature_columns=[price, sq_footage_bucket, country, sq_footage_country],
weight_column_name='weights')
classifier.fit(input_fn=input_fn, steps=50)
metrics = classifier.evaluate(input_fn=input_fn, steps=1)
self.assertGreater(metrics['accuracy'], 0.9)
def testMakePlaceHolderTensorsForBaseFeatures(self):
sparse_col = fc.sparse_column_with_hash_bucket("sparse_column",
hash_bucket_size=100)
real_valued_col = fc.real_valued_column("real_valued_column", 5)
vlen_real_valued_col = fc.real_valued_column("vlen_real_valued_column",
dimension=None)
bucketized_col = fc.bucketized_column(
fc.real_valued_column("real_valued_column_for_bucketization"),
[0, 4])
feature_columns = set([
sparse_col, real_valued_col, vlen_real_valued_col, bucketized_col
])
placeholders = (
fc.make_place_holder_tensors_for_base_features(feature_columns))
self.assertEqual(4, len(placeholders))
self.assertTrue(
isinstance(placeholders["sparse_column"],
sparse_tensor_lib.SparseTensor))
self.assertTrue(
isinstance(placeholders["vlen_real_valued_column"],
sparse_tensor_lib.SparseTensor))
placeholder = placeholders["real_valued_column"]
self.assertGreaterEqual(
placeholder.name.find(u"Placeholder_real_valued_column"), 0)
self.assertEqual(dtypes.float32, placeholder.dtype)
self.assertEqual([None, 5], placeholder.get_shape().as_list())
placeholder = placeholders["real_valued_column_for_bucketization"]
self.assertGreaterEqual(
placeholder.name.find(
u"Placeholder_real_valued_column_for_bucketization"), 0)
self.assertEqual(dtypes.float32, placeholder.dtype)
self.assertEqual([None, 1], placeholder.get_shape().as_list())
def testMixedFeatures(self):
"""Tests SDCALogisticClassifier with a mix of features."""
def input_fn():
return {
'example_id':
constant_op.constant(['1', '2', '3']),
'price':
constant_op.constant([[0.6], [0.8], [0.3]]),
'sq_footage':
constant_op.constant([[900.0], [700.0], [600.0]]),
'country':
sparse_tensor.SparseTensor(values=['IT', 'US', 'GB'],
indices=[[0, 0], [1, 3], [2, 1]],
dense_shape=[3, 5]),
'weights':
constant_op.constant([[3.0], [1.0], [1.0]])
}, constant_op.constant([[1], [0], [1]])
price = feature_column_lib.real_valued_column('price')
sq_footage_bucket = feature_column_lib.bucketized_column(
feature_column_lib.real_valued_column('sq_footage'),
boundaries=[650.0, 800.0])
country = feature_column_lib.sparse_column_with_hash_bucket(
'country', hash_bucket_size=5)
sq_footage_country = feature_column_lib.crossed_column(
[sq_footage_bucket, country], hash_bucket_size=10)
classifier = SDCALogisticClassifier(example_id_column='example_id',
feature_columns=[
price, sq_footage_bucket,
country, sq_footage_country
],
weight_column_name='weights')
classifier.fit(input_fn=input_fn, steps=50)
metrics = classifier.evaluate(input_fn=input_fn, steps=1)
self.assertGreater(metrics['accuracy'], 0.9)
def testCrossedColumnNameCreatesSortedNames(self):
a = fc.sparse_column_with_hash_bucket("aaa", hash_bucket_size=100)
b = fc.sparse_column_with_hash_bucket("bbb", hash_bucket_size=100)
bucket = fc.bucketized_column(fc.real_valued_column("cost"), [0, 4])
crossed = fc.crossed_column(set([b, bucket, a]), hash_bucket_size=10000)
self.assertEqual("aaa_X_bbb_X_cost_bucketized", crossed.name,
"name should be generated by sorted column names")
self.assertEqual("aaa", crossed.columns[0].name)
self.assertEqual("bbb", crossed.columns[1].name)
self.assertEqual("cost_bucketized", crossed.columns[2].name)
def testCrossedColumnDeepCopy(self):
a = fc.sparse_column_with_hash_bucket("aaa", hash_bucket_size=100)
b = fc.sparse_column_with_hash_bucket("bbb", hash_bucket_size=100)
bucket = fc.bucketized_column(fc.real_valued_column("cost"), [0, 4])
crossed = fc.crossed_column(set([b, bucket, a]), hash_bucket_size=10000)
crossed_copy = copy.deepcopy(crossed)
self.assertEqual("aaa_X_bbb_X_cost_bucketized", crossed_copy.name,
"name should be generated by sorted column names")
self.assertEqual("aaa", crossed_copy.columns[0].name)
self.assertEqual("bbb", crossed_copy.columns[1].name)
self.assertEqual("cost_bucketized", crossed_copy.columns[2].name)
def testBucketizedColumnDeepCopy(self):
"""Tests that we can do a deepcopy of a bucketized column.
This test requires that the bucketized column also accept boundaries
as tuples.
"""
bucketized = fc.bucketized_column(fc.real_valued_column("a"),
[1., 2., 2., 3., 3.])
self.assertEqual(bucketized.name, "a_bucketized")
self.assertTupleEqual(bucketized.boundaries, (1., 2., 3.))
bucketized_copy = copy.deepcopy(bucketized)
self.assertEqual(bucketized_copy.name, "a_bucketized")
self.assertTupleEqual(bucketized_copy.boundaries, (1., 2., 3.))
def testBucketizedColumnDeepCopy(self):
"""Tests that we can do a deepcopy of a bucketized column.
This test requires that the bucketized column also accept boundaries
as tuples.
"""
bucketized = fc.bucketized_column(
fc.real_valued_column("a"), [1., 2., 2., 3., 3.])
self.assertEqual(bucketized.name, "a_bucketized")
self.assertTupleEqual(bucketized.boundaries, (1., 2., 3.))
bucketized_copy = copy.deepcopy(bucketized)
self.assertEqual(bucketized_copy.name, "a_bucketized")
self.assertTupleEqual(bucketized_copy.boundaries, (1., 2., 3.))
def testBucketizedFeatures(self):
"""Tests SVM classifier with bucketized features."""
def input_fn():
return {
'example_id': constant_op.constant(['1', '2', '3']),
'price': constant_op.constant([[600.0], [800.0], [400.0]]),
'sq_footage': constant_op.constant([[1000.0], [800.0], [500.0]]),
'weights': constant_op.constant([[1.0], [1.0], [1.0]])
}, constant_op.constant([[1], [0], [1]])
price_bucket = feature_column.bucketized_column(
feature_column.real_valued_column('price'), boundaries=[500.0, 700.0])
sq_footage_bucket = feature_column.bucketized_column(
feature_column.real_valued_column('sq_footage'), boundaries=[650.0])
svm_classifier = svm.SVM(feature_columns=[price_bucket, sq_footage_bucket],
example_id_column='example_id',
l1_regularization=0.1,
l2_regularization=1.0)
svm_classifier.fit(input_fn=input_fn, steps=30)
accuracy = svm_classifier.evaluate(input_fn=input_fn, steps=1)['accuracy']
self.assertAlmostEqual(accuracy, 1.0, places=3)
def benchmarkTensorData(self):
def _input_fn():
iris = test_data.prepare_iris_data_for_logistic_regression()
features = {}
for i in range(4):
# The following shows how to provide the Tensor data for
# RealValuedColumns.
features.update({
str(i):
array_ops.reshape(
constant_op.constant(
iris.data[:, i], dtype=dtypes.float32), (-1, 1))
})
# The following shows how to provide the SparseTensor data for
# a SparseColumn.
features['dummy_sparse_column'] = sparse_tensor.SparseTensor(
values=('en', 'fr', 'zh'),
indices=((0, 0), (0, 1), (60, 0)),
dense_shape=(len(iris.target), 2))
labels = array_ops.reshape(
constant_op.constant(
iris.target, dtype=dtypes.int32), (-1, 1))
return features, labels
iris = test_data.prepare_iris_data_for_logistic_regression()
cont_features = [
feature_column.real_valued_column(str(i)) for i in range(4)
]
linear_features = [
feature_column.bucketized_column(
cont_features[i],
test_data.get_quantile_based_buckets(iris.data[:, i], 10))
for i in range(4)
]
linear_features.append(
feature_column.sparse_column_with_hash_bucket(
'dummy_sparse_column', hash_bucket_size=100))
classifier = dnn_linear_combined.DNNLinearCombinedClassifier(
model_dir=tempfile.mkdtemp(),
linear_feature_columns=linear_features,
dnn_feature_columns=cont_features,
dnn_hidden_units=(3, 3))
metrics = classifier.fit(input_fn=_input_fn, steps=_ITERS).evaluate(
input_fn=_input_fn, steps=100)
self._assertSingleClassMetrics(metrics)
def benchmarkMatrixData(self):
iris = test_data.prepare_iris_data_for_logistic_regression()
cont_feature = feature_column.real_valued_column('feature', dimension=4)
bucketized_feature = feature_column.bucketized_column(
cont_feature, test_data.get_quantile_based_buckets(iris.data, 10))
classifier = dnn_linear_combined.DNNLinearCombinedClassifier(
model_dir=tempfile.mkdtemp(),
linear_feature_columns=(bucketized_feature,),
dnn_feature_columns=(cont_feature,),
dnn_hidden_units=(3, 3))
input_fn = test_data.iris_input_logistic_fn
metrics = classifier.fit(input_fn=input_fn, steps=_ITERS).evaluate(
input_fn=input_fn, steps=100)
self._assertSingleClassMetrics(metrics)
def benchmarkMultiClass(self):
iris = base.load_iris()
cont_feature = feature_column.real_valued_column('feature', dimension=4)
bucketized_feature = feature_column.bucketized_column(
cont_feature, test_data.get_quantile_based_buckets(iris.data, 10))
classifier = dnn_linear_combined.DNNLinearCombinedClassifier(
n_classes=3,
linear_feature_columns=(bucketized_feature,),
dnn_feature_columns=(cont_feature,),
dnn_hidden_units=(3, 3))
input_fn = test_data.iris_input_multiclass_fn
metrics = classifier.fit(input_fn=input_fn, steps=_ITERS).evaluate(
input_fn=input_fn, steps=100)
self._assertCommonMetrics(metrics)
def benchmarkTensorData(self):
def _input_fn():
iris = test_data.prepare_iris_data_for_logistic_regression()
features = {}
for i in range(4):
# The following shows how to provide the Tensor data for
# RealValuedColumns.
features.update({
str(i):
array_ops.reshape(
constant_op.constant(iris.data[:, i],
dtype=dtypes.float32), (-1, 1))
})
# The following shows how to provide the SparseTensor data for
# a SparseColumn.
features['dummy_sparse_column'] = sparse_tensor.SparseTensor(
values=('en', 'fr', 'zh'),
indices=((0, 0), (0, 1), (60, 0)),
dense_shape=(len(iris.target), 2))
labels = array_ops.reshape(
constant_op.constant(iris.target, dtype=dtypes.int32), (-1, 1))
return features, labels
iris = test_data.prepare_iris_data_for_logistic_regression()
cont_features = [
feature_column.real_valued_column(str(i)) for i in range(4)
]
linear_features = [
feature_column.bucketized_column(
cont_features[i],
test_data.get_quantile_based_buckets(iris.data[:, i], 10))
for i in range(4)
]
linear_features.append(
feature_column.sparse_column_with_hash_bucket(
'dummy_sparse_column', hash_bucket_size=100))
classifier = dnn_linear_combined.DNNLinearCombinedClassifier(
model_dir=tempfile.mkdtemp(),
linear_feature_columns=linear_features,
dnn_feature_columns=cont_features,
dnn_hidden_units=(3, 3))
metrics = classifier.fit(input_fn=_input_fn,
steps=_ITERS).evaluate(input_fn=_input_fn,
steps=100)
self._assertSingleClassMetrics(metrics)
def benchmarkMultiClass(self):
iris = base.load_iris()
cont_feature = feature_column.real_valued_column('feature',
dimension=4)
bucketized_feature = feature_column.bucketized_column(
cont_feature, test_data.get_quantile_based_buckets(iris.data, 10))
classifier = dnn_linear_combined.DNNLinearCombinedClassifier(
n_classes=3,
linear_feature_columns=(bucketized_feature, ),
dnn_feature_columns=(cont_feature, ),
dnn_hidden_units=(3, 3))
input_fn = test_data.iris_input_multiclass_fn
metrics = classifier.fit(input_fn=input_fn,
steps=_ITERS).evaluate(input_fn=input_fn,
steps=100)
self._assertCommonMetrics(metrics)
def benchmarkMatrixData(self):
iris = test_data.prepare_iris_data_for_logistic_regression()
cont_feature = feature_column.real_valued_column('feature',
dimension=4)
bucketized_feature = feature_column.bucketized_column(
cont_feature, test_data.get_quantile_based_buckets(iris.data, 10))
classifier = dnn_linear_combined.DNNLinearCombinedClassifier(
model_dir=tempfile.mkdtemp(),
linear_feature_columns=(bucketized_feature, ),
dnn_feature_columns=(cont_feature, ),
dnn_hidden_units=(3, 3))
input_fn = test_data.iris_input_logistic_fn
metrics = classifier.fit(input_fn=input_fn,
steps=_ITERS).evaluate(input_fn=input_fn,
steps=100)
self._assertSingleClassMetrics(metrics)
def test_make_parsing_export_strategy(self):
"""Only tests that an ExportStrategy instance is created."""
sparse_col = fc.sparse_column_with_hash_bucket(
"sparse_column", hash_bucket_size=100)
embedding_col = fc.embedding_column(
fc.sparse_column_with_hash_bucket(
"sparse_column_for_embedding", hash_bucket_size=10),
dimension=4)
real_valued_col1 = fc.real_valued_column("real_valued_column1")
bucketized_col1 = fc.bucketized_column(
fc.real_valued_column("real_valued_column_for_bucketization1"), [0, 4])
feature_columns = [sparse_col, embedding_col, real_valued_col1,
bucketized_col1]
export_strategy = saved_model_export_utils.make_parsing_export_strategy(
feature_columns=feature_columns)
self.assertTrue(
isinstance(export_strategy, export_strategy_lib.ExportStrategy))
def test_make_parsing_export_strategy(self):
"""Only tests that an ExportStrategy instance is created."""
sparse_col = fc.sparse_column_with_hash_bucket(
"sparse_column", hash_bucket_size=100)
embedding_col = fc.embedding_column(
fc.sparse_column_with_hash_bucket(
"sparse_column_for_embedding", hash_bucket_size=10),
dimension=4)
real_valued_col1 = fc.real_valued_column("real_valued_column1")
bucketized_col1 = fc.bucketized_column(
fc.real_valued_column("real_valued_column_for_bucketization1"), [0, 4])
feature_columns = [sparse_col, embedding_col, real_valued_col1,
bucketized_col1]
export_strategy = saved_model_export_utils.make_parsing_export_strategy(
feature_columns=feature_columns)
self.assertTrue(
isinstance(export_strategy, export_strategy_lib.ExportStrategy))
def testMixedFeaturesArbitraryWeightsPartitioned(self):
"""Tests SDCALinearRegressor works with a mix of features (partitioned)."""
def input_fn():
return {
'example_id':
constant_op.constant(['1', '2', '3']),
'price':
constant_op.constant([[0.6], [0.8], [0.3]]),
'sq_footage':
constant_op.constant([[900.0], [700.0], [600.0]]),
'country':
sparse_tensor.SparseTensor(
values=['IT', 'US', 'GB'],
indices=[[0, 0], [1, 3], [2, 1]],
dense_shape=[3, 5]),
'weights':
constant_op.constant([[3.0], [5.0], [7.0]])
}, constant_op.constant([[1.55], [-1.25], [-3.0]])
with self._single_threaded_test_session():
price = feature_column_lib.real_valued_column('price')
sq_footage_bucket = feature_column_lib.bucketized_column(
feature_column_lib.real_valued_column('sq_footage'),
boundaries=[650.0, 800.0])
country = feature_column_lib.sparse_column_with_hash_bucket(
'country', hash_bucket_size=5)
sq_footage_country = feature_column_lib.crossed_column(
[sq_footage_bucket, country], hash_bucket_size=10)
regressor = sdca_estimator.SDCALinearRegressor(
example_id_column='example_id',
feature_columns=[
price, sq_footage_bucket, country, sq_footage_country
],
l2_regularization=1.0,
weight_column_name='weights',
partitioner=partitioned_variables.fixed_size_partitioner(
num_shards=2, axis=0))
regressor.fit(input_fn=input_fn, steps=20)
loss = regressor.evaluate(input_fn=input_fn, steps=1)['loss']
self.assertLess(loss, 0.05)
def testMixedFeaturesArbitraryWeightsPartitioned(self):
"""Tests SDCALinearRegressor works with a mix of features (partitioned)."""
def input_fn():
return {
'example_id':
constant_op.constant(['1', '2', '3']),
'price':
constant_op.constant([[0.6], [0.8], [0.3]]),
'sq_footage':
constant_op.constant([[900.0], [700.0], [600.0]]),
'country':
sparse_tensor.SparseTensor(
values=['IT', 'US', 'GB'],
indices=[[0, 0], [1, 3], [2, 1]],
dense_shape=[3, 5]),
'weights':
constant_op.constant([[3.0], [5.0], [7.0]])
}, constant_op.constant([[1.55], [-1.25], [-3.0]])
with self._single_threaded_test_session():
price = feature_column_lib.real_valued_column('price')
sq_footage_bucket = feature_column_lib.bucketized_column(
feature_column_lib.real_valued_column('sq_footage'),
boundaries=[650.0, 800.0])
country = feature_column_lib.sparse_column_with_hash_bucket(
'country', hash_bucket_size=5)
sq_footage_country = feature_column_lib.crossed_column(
[sq_footage_bucket, country], hash_bucket_size=10)
regressor = sdca_estimator.SDCALinearRegressor(
example_id_column='example_id',
feature_columns=[
price, sq_footage_bucket, country, sq_footage_country
],
l2_regularization=1.0,
weight_column_name='weights',
partitioner=partitioned_variables.fixed_size_partitioner(
num_shards=2, axis=0))
regressor.fit(input_fn=input_fn, steps=20)
loss = regressor.evaluate(input_fn=input_fn, steps=1)['loss']
self.assertLess(loss, 0.05)
def testMixedFeatures(self):
"""Tests SVM classifier with a mix of features."""
def input_fn():
return {
'example_id':
constant_op.constant(['1', '2', '3']),
'price':
constant_op.constant([[0.6], [0.8], [0.3]]),
'sq_footage':
constant_op.constant([[900.0], [700.0], [600.0]]),
'country':
sparse_tensor.SparseTensor(values=['IT', 'US', 'GB'],
indices=[[0, 0], [1, 3], [2, 1]],
dense_shape=[3, 5]),
'weights':
constant_op.constant([[3.0], [1.0], [1.0]])
}, constant_op.constant([[1], [0], [1]])
price = feature_column.real_valued_column('price')
sq_footage_bucket = feature_column.bucketized_column(
feature_column.real_valued_column('sq_footage'),
boundaries=[650.0, 800.0])
country = feature_column.sparse_column_with_hash_bucket(
'country', hash_bucket_size=5)
sq_footage_country = feature_column.crossed_column(
[sq_footage_bucket, country], hash_bucket_size=10)
svm_classifier = svm.SVM(feature_columns=[
price, sq_footage_bucket, country, sq_footage_country
],
example_id_column='example_id',
weight_column_name='weights',
l1_regularization=0.1,
l2_regularization=1.0)
svm_classifier.fit(input_fn=input_fn, steps=30)
accuracy = svm_classifier.evaluate(input_fn=input_fn,
steps=1)['accuracy']
self.assertAlmostEqual(accuracy, 1.0, places=3)
def testMixedFeatures(self):
"""Tests SVM classifier with a mix of features."""
def input_fn():
return {
'example_id':
constant_op.constant(['1', '2', '3']),
'price':
constant_op.constant([0.6, 0.8, 0.3]),
'sq_footage':
constant_op.constant([[900.0], [700.0], [600.0]]),
'country':
sparse_tensor.SparseTensor(
values=['IT', 'US', 'GB'],
indices=[[0, 0], [1, 3], [2, 1]],
dense_shape=[3, 5]),
'weights':
constant_op.constant([[3.0], [1.0], [1.0]])
}, constant_op.constant([[1], [0], [1]])
price = feature_column.real_valued_column('price')
sq_footage_bucket = feature_column.bucketized_column(
feature_column.real_valued_column('sq_footage'),
boundaries=[650.0, 800.0])
country = feature_column.sparse_column_with_hash_bucket(
'country', hash_bucket_size=5)
sq_footage_country = feature_column.crossed_column(
[sq_footage_bucket, country], hash_bucket_size=10)
svm_classifier = svm.SVM(
feature_columns=[price, sq_footage_bucket, country, sq_footage_country],
example_id_column='example_id',
weight_column_name='weights',
l1_regularization=0.1,
l2_regularization=1.0)
svm_classifier.fit(input_fn=input_fn, steps=30)
accuracy = svm_classifier.evaluate(input_fn=input_fn, steps=1)['accuracy']
self.assertAlmostEqual(accuracy, 1.0, places=3)
def testBucketizedColumnWithSameBucketBoundaries(self):
a_bucketized = fc.bucketized_column(fc.real_valued_column("a"),
[1., 2., 2., 3., 3.])
self.assertEqual(a_bucketized.name, "a_bucketized")
self.assertTupleEqual(a_bucketized.boundaries, (1., 2., 3.))
def testBucketizedColumnRequiresSortedBuckets(self):
with self.assertRaisesRegexp(ValueError,
"boundaries must be a sorted list"):
fc.bucketized_column(fc.real_valued_column("ccc"), [5, 0, 4])
def testBucketizedColumnRequiresRealValuedColumnDimension(self):
with self.assertRaisesRegexp(
ValueError, "source_column must have a defined dimension"):
fc.bucketized_column(fc.real_valued_column("bbb", dimension=None),
[0])
def testBucketizedColumnNameEndsWithUnderscoreBucketized(self):
a = fc.bucketized_column(fc.real_valued_column("aaa"), [0, 4])
self.assertEqual(a.name, "aaa_bucketized")
def testCreateFeatureSpec(self):
sparse_col = fc.sparse_column_with_hash_bucket(
"sparse_column", hash_bucket_size=100)
embedding_col = fc.embedding_column(
fc.sparse_column_with_hash_bucket(
"sparse_column_for_embedding", hash_bucket_size=10),
dimension=4)
sparse_id_col = fc.sparse_column_with_keys("id_column",
["marlo", "omar", "stringer"])
weighted_id_col = fc.weighted_sparse_column(sparse_id_col,
"id_weights_column")
real_valued_col1 = fc.real_valued_column("real_valued_column1")
real_valued_col2 = fc.real_valued_column("real_valued_column2", 5)
real_valued_col3 = fc.real_valued_column(
"real_valued_column3", dimension=None)
bucketized_col1 = fc.bucketized_column(
fc.real_valued_column("real_valued_column_for_bucketization1"), [0, 4])
bucketized_col2 = fc.bucketized_column(
fc.real_valued_column("real_valued_column_for_bucketization2", 4),
[0, 4])
a = fc.sparse_column_with_hash_bucket("cross_aaa", hash_bucket_size=100)
b = fc.sparse_column_with_hash_bucket("cross_bbb", hash_bucket_size=100)
cross_col = fc.crossed_column(set([a, b]), hash_bucket_size=10000)
feature_columns = set([
sparse_col, embedding_col, weighted_id_col, real_valued_col1,
real_valued_col2, real_valued_col3, bucketized_col1, bucketized_col2,
cross_col
])
expected_config = {
"sparse_column":
parsing_ops.VarLenFeature(dtypes.string),
"sparse_column_for_embedding":
parsing_ops.VarLenFeature(dtypes.string),
"id_column":
parsing_ops.VarLenFeature(dtypes.string),
"id_weights_column":
parsing_ops.VarLenFeature(dtypes.float32),
"real_valued_column1":
parsing_ops.FixedLenFeature(
[1], dtype=dtypes.float32),
"real_valued_column2":
parsing_ops.FixedLenFeature(
[5], dtype=dtypes.float32),
"real_valued_column3":
parsing_ops.VarLenFeature(dtype=dtypes.float32),
"real_valued_column_for_bucketization1":
parsing_ops.FixedLenFeature(
[1], dtype=dtypes.float32),
"real_valued_column_for_bucketization2":
parsing_ops.FixedLenFeature(
[4], dtype=dtypes.float32),
"cross_aaa":
parsing_ops.VarLenFeature(dtypes.string),
"cross_bbb":
parsing_ops.VarLenFeature(dtypes.string)
}
config = fc.create_feature_spec_for_parsing(feature_columns)
self.assertDictEqual(expected_config, config)
# Test that the same config is parsed out if we pass a dictionary.
feature_columns_dict = {
str(i): val
for i, val in enumerate(feature_columns)
}
config = fc.create_feature_spec_for_parsing(feature_columns_dict)
self.assertDictEqual(expected_config, config)
def testBucketizedColumnRequiresRealValuedColumnDimension(self):
with self.assertRaisesRegexp(
TypeError, "source_column must be an instance of _RealValuedColumn.*"):
fc.bucketized_column(fc._real_valued_var_len_column("bbb",
is_sparse=True),
[0])
def testBucketizedColumnWithSameBucketBoundaries(self):
a_bucketized = fc.bucketized_column(
fc.real_valued_column("a"), [1., 2., 2., 3., 3.])
self.assertEqual(a_bucketized.name, "a_bucketized")
self.assertTupleEqual(a_bucketized.boundaries, (1., 2., 3.))
def testBucketizedColumnRequiresSortedBuckets(self):
with self.assertRaisesRegexp(ValueError,
"boundaries must be a sorted list"):
fc.bucketized_column(fc.real_valued_column("ccc"), [5, 0, 4])
def testBucketizedColumnRequiresRealValuedColumnDimension(self):
with self.assertRaisesRegexp(ValueError,
"source_column must have a defined dimension"):
fc.bucketized_column(fc.real_valued_column("bbb", dimension=None), [0])
def testBucketizedColumnRequiresRealValuedColumnDimension(self):
with self.assertRaisesRegexp(
TypeError, "source_column must be an instance of _RealValuedColumn.*"):
fc.bucketized_column(fc._real_valued_var_len_column("bbb",
is_sparse=True),
[0])
def testBucketizedColumnNameEndsWithUnderscoreBucketized(self):
a = fc.bucketized_column(fc.real_valued_column("aaa"), [0, 4])
self.assertEqual(a.name, "aaa_bucketized")
def testCreateFeatureSpec(self):
sparse_col = fc.sparse_column_with_hash_bucket(
"sparse_column", hash_bucket_size=100)
embedding_col = fc.embedding_column(
fc.sparse_column_with_hash_bucket(
"sparse_column_for_embedding", hash_bucket_size=10),
dimension=4)
str_sparse_id_col = fc.sparse_column_with_keys(
"str_id_column", ["marlo", "omar", "stringer"])
int32_sparse_id_col = fc.sparse_column_with_keys(
"int32_id_column", [42, 1, -1000], dtype=dtypes.int32)
int64_sparse_id_col = fc.sparse_column_with_keys(
"int64_id_column", [42, 1, -1000], dtype=dtypes.int64)
weighted_id_col = fc.weighted_sparse_column(str_sparse_id_col,
"str_id_weights_column")
real_valued_col1 = fc.real_valued_column("real_valued_column1")
real_valued_col2 = fc.real_valued_column("real_valued_column2", 5)
real_valued_col3 = fc._real_valued_var_len_column(
"real_valued_column3", is_sparse=True)
real_valued_col4 = fc._real_valued_var_len_column(
"real_valued_column4", dtype=dtypes.int64, default_value=0,
is_sparse=False)
bucketized_col1 = fc.bucketized_column(
fc.real_valued_column("real_valued_column_for_bucketization1"), [0, 4])
bucketized_col2 = fc.bucketized_column(
fc.real_valued_column("real_valued_column_for_bucketization2", 4),
[0, 4])
a = fc.sparse_column_with_hash_bucket("cross_aaa", hash_bucket_size=100)
b = fc.sparse_column_with_hash_bucket("cross_bbb", hash_bucket_size=100)
cross_col = fc.crossed_column(set([a, b]), hash_bucket_size=10000)
one_hot_col = fc.one_hot_column(fc.sparse_column_with_hash_bucket(
"sparse_column_for_one_hot", hash_bucket_size=100))
scattered_embedding_col = fc.scattered_embedding_column(
"scattered_embedding_column", size=100, dimension=10, hash_key=1)
feature_columns = set([
sparse_col, embedding_col, weighted_id_col, int32_sparse_id_col,
int64_sparse_id_col, real_valued_col1, real_valued_col2,
real_valued_col3, real_valued_col4, bucketized_col1, bucketized_col2,
cross_col, one_hot_col, scattered_embedding_col
])
expected_config = {
"sparse_column":
parsing_ops.VarLenFeature(dtypes.string),
"sparse_column_for_embedding":
parsing_ops.VarLenFeature(dtypes.string),
"str_id_column":
parsing_ops.VarLenFeature(dtypes.string),
"int32_id_column":
parsing_ops.VarLenFeature(dtypes.int32),
"int64_id_column":
parsing_ops.VarLenFeature(dtypes.int64),
"str_id_weights_column":
parsing_ops.VarLenFeature(dtypes.float32),
"real_valued_column1":
parsing_ops.FixedLenFeature(
[1], dtype=dtypes.float32),
"real_valued_column2":
parsing_ops.FixedLenFeature(
[5], dtype=dtypes.float32),
"real_valued_column3":
parsing_ops.VarLenFeature(dtype=dtypes.float32),
"real_valued_column4":
parsing_ops.FixedLenSequenceFeature(
[], dtype=dtypes.int64, allow_missing=True, default_value=0),
"real_valued_column_for_bucketization1":
parsing_ops.FixedLenFeature(
[1], dtype=dtypes.float32),
"real_valued_column_for_bucketization2":
parsing_ops.FixedLenFeature(
[4], dtype=dtypes.float32),
"cross_aaa":
parsing_ops.VarLenFeature(dtypes.string),
"cross_bbb":
parsing_ops.VarLenFeature(dtypes.string),
"sparse_column_for_one_hot":
parsing_ops.VarLenFeature(dtypes.string),
"scattered_embedding_column":
parsing_ops.VarLenFeature(dtypes.string),
}
config = fc.create_feature_spec_for_parsing(feature_columns)
self.assertDictEqual(expected_config, config)
# Test that the same config is parsed out if we pass a dictionary.
feature_columns_dict = {
str(i): val
for i, val in enumerate(feature_columns)
}
config = fc.create_feature_spec_for_parsing(feature_columns_dict)
self.assertDictEqual(expected_config, config)
def testCreateFeatureSpec(self):
sparse_col = fc.sparse_column_with_hash_bucket("sparse_column",
hash_bucket_size=100)
embedding_col = fc.embedding_column(fc.sparse_column_with_hash_bucket(
"sparse_column_for_embedding", hash_bucket_size=10),
dimension=4)
sparse_id_col = fc.sparse_column_with_keys(
"id_column", ["marlo", "omar", "stringer"])
weighted_id_col = fc.weighted_sparse_column(sparse_id_col,
"id_weights_column")
real_valued_col1 = fc.real_valued_column("real_valued_column1")
real_valued_col2 = fc.real_valued_column("real_valued_column2", 5)
real_valued_col3 = fc.real_valued_column("real_valued_column3",
dimension=None)
bucketized_col1 = fc.bucketized_column(
fc.real_valued_column("real_valued_column_for_bucketization1"),
[0, 4])
bucketized_col2 = fc.bucketized_column(
fc.real_valued_column("real_valued_column_for_bucketization2", 4),
[0, 4])
a = fc.sparse_column_with_hash_bucket("cross_aaa",
hash_bucket_size=100)
b = fc.sparse_column_with_hash_bucket("cross_bbb",
hash_bucket_size=100)
cross_col = fc.crossed_column(set([a, b]), hash_bucket_size=10000)
feature_columns = set([
sparse_col, embedding_col, weighted_id_col, real_valued_col1,
real_valued_col2, real_valued_col3, bucketized_col1,
bucketized_col2, cross_col
])
expected_config = {
"sparse_column":
parsing_ops.VarLenFeature(dtypes.string),
"sparse_column_for_embedding":
parsing_ops.VarLenFeature(dtypes.string),
"id_column":
parsing_ops.VarLenFeature(dtypes.string),
"id_weights_column":
parsing_ops.VarLenFeature(dtypes.float32),
"real_valued_column1":
parsing_ops.FixedLenFeature([1], dtype=dtypes.float32),
"real_valued_column2":
parsing_ops.FixedLenFeature([5], dtype=dtypes.float32),
"real_valued_column3":
parsing_ops.VarLenFeature(dtype=dtypes.float32),
"real_valued_column_for_bucketization1":
parsing_ops.FixedLenFeature([1], dtype=dtypes.float32),
"real_valued_column_for_bucketization2":
parsing_ops.FixedLenFeature([4], dtype=dtypes.float32),
"cross_aaa":
parsing_ops.VarLenFeature(dtypes.string),
"cross_bbb":
parsing_ops.VarLenFeature(dtypes.string)
}
config = fc.create_feature_spec_for_parsing(feature_columns)
self.assertDictEqual(expected_config, config)
# Test that the same config is parsed out if we pass a dictionary.
feature_columns_dict = {
str(i): val
for i, val in enumerate(feature_columns)
}
config = fc.create_feature_spec_for_parsing(feature_columns_dict)
self.assertDictEqual(expected_config, config)
def testCreateFeatureSpec(self):
sparse_col = fc.sparse_column_with_hash_bucket(
"sparse_column", hash_bucket_size=100)
embedding_col = fc.embedding_column(
fc.sparse_column_with_hash_bucket(
"sparse_column_for_embedding", hash_bucket_size=10),
dimension=4)
str_sparse_id_col = fc.sparse_column_with_keys(
"str_id_column", ["marlo", "omar", "stringer"])
int32_sparse_id_col = fc.sparse_column_with_keys(
"int32_id_column", [42, 1, -1000], dtype=dtypes.int32)
int64_sparse_id_col = fc.sparse_column_with_keys(
"int64_id_column", [42, 1, -1000], dtype=dtypes.int64)
weighted_id_col = fc.weighted_sparse_column(str_sparse_id_col,
"str_id_weights_column")
real_valued_col1 = fc.real_valued_column("real_valued_column1")
real_valued_col2 = fc.real_valued_column("real_valued_column2", 5)
bucketized_col1 = fc.bucketized_column(
fc.real_valued_column("real_valued_column_for_bucketization1"), [0, 4])
bucketized_col2 = fc.bucketized_column(
fc.real_valued_column("real_valued_column_for_bucketization2", 4),
[0, 4])
a = fc.sparse_column_with_hash_bucket("cross_aaa", hash_bucket_size=100)
b = fc.sparse_column_with_hash_bucket("cross_bbb", hash_bucket_size=100)
cross_col = fc.crossed_column(set([a, b]), hash_bucket_size=10000)
one_hot_col = fc.one_hot_column(fc.sparse_column_with_hash_bucket(
"sparse_column_for_one_hot", hash_bucket_size=100))
scattered_embedding_col = fc.scattered_embedding_column(
"scattered_embedding_column", size=100, dimension=10, hash_key=1)
feature_columns = set([
sparse_col, embedding_col, weighted_id_col, int32_sparse_id_col,
int64_sparse_id_col, real_valued_col1, real_valued_col2,
bucketized_col1, bucketized_col2, cross_col, one_hot_col,
scattered_embedding_col
])
expected_config = {
"sparse_column":
parsing_ops.VarLenFeature(dtypes.string),
"sparse_column_for_embedding":
parsing_ops.VarLenFeature(dtypes.string),
"str_id_column":
parsing_ops.VarLenFeature(dtypes.string),
"int32_id_column":
parsing_ops.VarLenFeature(dtypes.int32),
"int64_id_column":
parsing_ops.VarLenFeature(dtypes.int64),
"str_id_weights_column":
parsing_ops.VarLenFeature(dtypes.float32),
"real_valued_column1":
parsing_ops.FixedLenFeature(
[1], dtype=dtypes.float32),
"real_valued_column2":
parsing_ops.FixedLenFeature(
[5], dtype=dtypes.float32),
"real_valued_column_for_bucketization1":
parsing_ops.FixedLenFeature(
[1], dtype=dtypes.float32),
"real_valued_column_for_bucketization2":
parsing_ops.FixedLenFeature(
[4], dtype=dtypes.float32),
"cross_aaa":
parsing_ops.VarLenFeature(dtypes.string),
"cross_bbb":
parsing_ops.VarLenFeature(dtypes.string),
"sparse_column_for_one_hot":
parsing_ops.VarLenFeature(dtypes.string),
"scattered_embedding_column":
parsing_ops.VarLenFeature(dtypes.string),
}
config = fc.create_feature_spec_for_parsing(feature_columns)
self.assertDictEqual(expected_config, config)
# Tests that contrib feature columns work with core library:
config_core = fc_core.make_parse_example_spec(feature_columns)
self.assertDictEqual(expected_config, config_core)
# Test that the same config is parsed out if we pass a dictionary.
feature_columns_dict = {
str(i): val
for i, val in enumerate(feature_columns)
}
config = fc.create_feature_spec_for_parsing(feature_columns_dict)
self.assertDictEqual(expected_config, config)
def testCreateFeatureSpec(self):
sparse_col = fc.sparse_column_with_hash_bucket(
"sparse_column", hash_bucket_size=100)
embedding_col = fc.embedding_column(
fc.sparse_column_with_hash_bucket(
"sparse_column_for_embedding", hash_bucket_size=10),
dimension=4)
str_sparse_id_col = fc.sparse_column_with_keys(
"str_id_column", ["marlo", "omar", "stringer"])
int32_sparse_id_col = fc.sparse_column_with_keys(
"int32_id_column", [42, 1, -1000], dtype=dtypes.int32)
int64_sparse_id_col = fc.sparse_column_with_keys(
"int64_id_column", [42, 1, -1000], dtype=dtypes.int64)
weighted_id_col = fc.weighted_sparse_column(str_sparse_id_col,
"str_id_weights_column")
real_valued_col1 = fc.real_valued_column("real_valued_column1")
real_valued_col2 = fc.real_valued_column("real_valued_column2", 5)
bucketized_col1 = fc.bucketized_column(
fc.real_valued_column("real_valued_column_for_bucketization1"), [0, 4])
bucketized_col2 = fc.bucketized_column(
fc.real_valued_column("real_valued_column_for_bucketization2", 4),
[0, 4])
a = fc.sparse_column_with_hash_bucket("cross_aaa", hash_bucket_size=100)
b = fc.sparse_column_with_hash_bucket("cross_bbb", hash_bucket_size=100)
cross_col = fc.crossed_column(set([a, b]), hash_bucket_size=10000)
one_hot_col = fc.one_hot_column(fc.sparse_column_with_hash_bucket(
"sparse_column_for_one_hot", hash_bucket_size=100))
scattered_embedding_col = fc.scattered_embedding_column(
"scattered_embedding_column", size=100, dimension=10, hash_key=1)
feature_columns = set([
sparse_col, embedding_col, weighted_id_col, int32_sparse_id_col,
int64_sparse_id_col, real_valued_col1, real_valued_col2,
bucketized_col1, bucketized_col2, cross_col, one_hot_col,
scattered_embedding_col
])
expected_config = {
"sparse_column":
parsing_ops.VarLenFeature(dtypes.string),
"sparse_column_for_embedding":
parsing_ops.VarLenFeature(dtypes.string),
"str_id_column":
parsing_ops.VarLenFeature(dtypes.string),
"int32_id_column":
parsing_ops.VarLenFeature(dtypes.int32),
"int64_id_column":
parsing_ops.VarLenFeature(dtypes.int64),
"str_id_weights_column":
parsing_ops.VarLenFeature(dtypes.float32),
"real_valued_column1":
parsing_ops.FixedLenFeature(
[1], dtype=dtypes.float32),
"real_valued_column2":
parsing_ops.FixedLenFeature(
[5], dtype=dtypes.float32),
"real_valued_column_for_bucketization1":
parsing_ops.FixedLenFeature(
[1], dtype=dtypes.float32),
"real_valued_column_for_bucketization2":
parsing_ops.FixedLenFeature(
[4], dtype=dtypes.float32),
"cross_aaa":
parsing_ops.VarLenFeature(dtypes.string),
"cross_bbb":
parsing_ops.VarLenFeature(dtypes.string),
"sparse_column_for_one_hot":
parsing_ops.VarLenFeature(dtypes.string),
"scattered_embedding_column":
parsing_ops.VarLenFeature(dtypes.string),
}
config = fc.create_feature_spec_for_parsing(feature_columns)
self.assertDictEqual(expected_config, config)
# Tests that contrib feature columns work with core library:
config_core = fc_core.make_parse_example_spec(feature_columns)
self.assertDictEqual(expected_config, config_core)
# Test that the same config is parsed out if we pass a dictionary.
feature_columns_dict = {
str(i): val
for i, val in enumerate(feature_columns)
}
config = fc.create_feature_spec_for_parsing(feature_columns_dict)
self.assertDictEqual(expected_config, config)
def testCreateFeatureSpec(self):
sparse_col = fc.sparse_column_with_hash_bucket(
"sparse_column", hash_bucket_size=100)
embedding_col = fc.embedding_column(
fc.sparse_column_with_hash_bucket(
"sparse_column_for_embedding", hash_bucket_size=10),
dimension=4)
str_sparse_id_col = fc.sparse_column_with_keys(
"str_id_column", ["marlo", "omar", "stringer"])
int32_sparse_id_col = fc.sparse_column_with_keys(
"int32_id_column", [42, 1, -1000], dtype=dtypes.int32)
int64_sparse_id_col = fc.sparse_column_with_keys(
"int64_id_column", [42, 1, -1000], dtype=dtypes.int64)
weighted_id_col = fc.weighted_sparse_column(str_sparse_id_col,
"str_id_weights_column")
real_valued_col1 = fc.real_valued_column("real_valued_column1")
real_valued_col2 = fc.real_valued_column("real_valued_column2", 5)
real_valued_col3 = fc._real_valued_var_len_column(
"real_valued_column3", is_sparse=True)
real_valued_col4 = fc._real_valued_var_len_column(
"real_valued_column4", dtype=dtypes.int64, default_value=0,
is_sparse=False)
bucketized_col1 = fc.bucketized_column(
fc.real_valued_column("real_valued_column_for_bucketization1"), [0, 4])
bucketized_col2 = fc.bucketized_column(
fc.real_valued_column("real_valued_column_for_bucketization2", 4),
[0, 4])
a = fc.sparse_column_with_hash_bucket("cross_aaa", hash_bucket_size=100)
b = fc.sparse_column_with_hash_bucket("cross_bbb", hash_bucket_size=100)
cross_col = fc.crossed_column(set([a, b]), hash_bucket_size=10000)
one_hot_col = fc.one_hot_column(fc.sparse_column_with_hash_bucket(
"sparse_column_for_one_hot", hash_bucket_size=100))
scattered_embedding_col = fc.scattered_embedding_column(
"scattered_embedding_column", size=100, dimension=10, hash_key=1)
feature_columns = set([
sparse_col, embedding_col, weighted_id_col, int32_sparse_id_col,
int64_sparse_id_col, real_valued_col1, real_valued_col2,
real_valued_col3, real_valued_col4, bucketized_col1, bucketized_col2,
cross_col, one_hot_col, scattered_embedding_col
])
expected_config = {
"sparse_column":
parsing_ops.VarLenFeature(dtypes.string),
"sparse_column_for_embedding":
parsing_ops.VarLenFeature(dtypes.string),
"str_id_column":
parsing_ops.VarLenFeature(dtypes.string),
"int32_id_column":
parsing_ops.VarLenFeature(dtypes.int32),
"int64_id_column":
parsing_ops.VarLenFeature(dtypes.int64),
"str_id_weights_column":
parsing_ops.VarLenFeature(dtypes.float32),
"real_valued_column1":
parsing_ops.FixedLenFeature(
[1], dtype=dtypes.float32),
"real_valued_column2":
parsing_ops.FixedLenFeature(
[5], dtype=dtypes.float32),
"real_valued_column3":
parsing_ops.VarLenFeature(dtype=dtypes.float32),
"real_valued_column4":
parsing_ops.FixedLenSequenceFeature(
[], dtype=dtypes.int64, allow_missing=True, default_value=0),
"real_valued_column_for_bucketization1":
parsing_ops.FixedLenFeature(
[1], dtype=dtypes.float32),
"real_valued_column_for_bucketization2":
parsing_ops.FixedLenFeature(
[4], dtype=dtypes.float32),
"cross_aaa":
parsing_ops.VarLenFeature(dtypes.string),
"cross_bbb":
parsing_ops.VarLenFeature(dtypes.string),
"sparse_column_for_one_hot":
parsing_ops.VarLenFeature(dtypes.string),
"scattered_embedding_column":
parsing_ops.VarLenFeature(dtypes.string),
}
config = fc.create_feature_spec_for_parsing(feature_columns)
self.assertDictEqual(expected_config, config)
# Test that the same config is parsed out if we pass a dictionary.
feature_columns_dict = {
str(i): val
for i, val in enumerate(feature_columns)
}
config = fc.create_feature_spec_for_parsing(feature_columns_dict)
self.assertDictEqual(expected_config, config)
