Add DP-SGD version of v1 LinearClassifier.

PiperOrigin-RevId: 551350685

tensorflow_privacy/privacy/estimators/v1/BUILD

@@ -26,6 +26,15 @@ py_library(
     deps = [":head"],
 )
 
+py_library(
+    name = "linear",
+    srcs = [
+        "linear.py",
+    ],
+    srcs_version = "PY3",
+    deps = [":head"],
+)
+
 py_test(
     name = "head_test",
     timeout = "long",
@@ -51,3 +60,16 @@ py_test(
         "//tensorflow_privacy/privacy/optimizers:dp_optimizer",
     ],
 )
+
+py_test(
+    name = "linear_test",
+    timeout = "long",
+    srcs = ["linear_test.py"],
+    python_version = "PY3",
+    srcs_version = "PY3",
+    deps = [
+        ":linear",
+        "//tensorflow_privacy/privacy/estimators:test_utils",
+        "//tensorflow_privacy/privacy/optimizers:dp_optimizer",
+    ],
+)

tensorflow_privacy/privacy/estimators/v1/dnn.py

@@ -38,7 +38,7 @@ class DNNClassifier(estimator.Estimator):
       input_layer_partitioner=None,
       config=None,
       warm_start_from=None,
-      loss_reduction=tf.compat.v1.losses.Reduction.SUM,
+      loss_reduction=tf.compat.v1.losses.Reduction.SUM,  # For scalar summary.
       batch_norm=False,
   ):
     """See `tf.compat.v1.estimator.DNNClassifier`."""

tensorflow_privacy/privacy/estimators/v1/linear.py

@@ -0,0 +1,72 @@
+# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""DP version of LinearClassifier v1."""
+
+import tensorflow as tf
+from tensorflow_privacy.privacy.estimators.v1 import head as head_lib
+from tensorflow_estimator.python.estimator import estimator  # pylint: disable=g-deprecated-tf-checker
+from tensorflow_estimator.python.estimator.canned import linear  # pylint: disable=g-deprecated-tf-checker
+
+
+class LinearClassifier(estimator.Estimator):
+  """DP version of `tf.compat.v1.estimator.LinearClassifier`."""
+
+  def __init__(
+      self,
+      feature_columns,
+      model_dir=None,
+      n_classes=2,
+      weight_column=None,
+      label_vocabulary=None,
+      optimizer='Ftrl',
+      config=None,
+      partitioner=None,
+      warm_start_from=None,
+      loss_reduction=tf.compat.v1.losses.Reduction.SUM,  # For scalar summary.
+      sparse_combiner='sum',
+  ):
+    """See `tf.compat.v1.estimator.LinearClassifier`."""
+    linear._validate_linear_sdca_optimizer_for_linear_classifier(  # pylint: disable=protected-access
+        feature_columns=feature_columns,
+        n_classes=n_classes,
+        optimizer=optimizer,
+        sparse_combiner=sparse_combiner,
+    )
+    estimator._canned_estimator_api_gauge.get_cell('Classifier').set('Linear')  # pylint: disable=protected-access
+
+    head = head_lib._binary_logistic_or_multi_class_head(  # pylint: disable=protected-access
+        n_classes, weight_column, label_vocabulary, loss_reduction
+    )
+
+    def _model_fn(features, labels, mode, config):
+      """Call the defined shared _linear_model_fn."""
+      return linear._linear_model_fn(  # pylint: disable=protected-access
+          features=features,
+          labels=labels,
+          mode=mode,
+          head=head,
+          feature_columns=tuple(feature_columns or []),
+          optimizer=optimizer,
+          partitioner=partitioner,
+          config=config,
+          sparse_combiner=sparse_combiner,
+      )
+
+    super(LinearClassifier, self).__init__(
+        model_fn=_model_fn,
+        model_dir=model_dir,
+        config=config,
+        warm_start_from=warm_start_from,
+    )

tensorflow_privacy/privacy/estimators/v1/linear_test.py

@@ -0,0 +1,76 @@
+# Copyright 2023, The TensorFlow Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tests for LinearClassifier."""
+
+import functools
+
+from absl.testing import parameterized
+import tensorflow as tf
+from tensorflow_privacy.privacy.estimators import test_utils
+from tensorflow_privacy.privacy.estimators.v1 import linear
+from tensorflow_privacy.privacy.optimizers.dp_optimizer import DPGradientDescentGaussianOptimizer
+
+
+class DPLinearClassifierClassifierTest(
+    tf.test.TestCase, parameterized.TestCase
+):
+  """Tests for DP-enabled LinearClassifier."""
+
+  @parameterized.named_parameters(
+      ('BinaryClassLinear', 2),
+      ('MultiClassLinear 3', 3),
+      ('MultiClassLinear 4', 4),
+  )
+  def testLinearClassifier(self, n_classes):
+    train_features, train_labels = test_utils.make_input_data(256, n_classes)
+    feature_columns = []
+    for key in train_features:
+      feature_columns.append(tf.feature_column.numeric_column(key=key))  # pylint: disable=g-deprecated-tf-checker
+
+    optimizer = functools.partial(
+        DPGradientDescentGaussianOptimizer,
+        learning_rate=0.5,
+        l2_norm_clip=1.0,
+        noise_multiplier=0.0,
+        num_microbatches=1,
+    )
+
+    classifier = linear.LinearClassifier(
+        feature_columns=feature_columns,
+        n_classes=n_classes,
+        optimizer=optimizer,
+        loss_reduction=tf.compat.v1.losses.Reduction.SUM,
+    )
+
+    classifier.train(
+        input_fn=test_utils.make_input_fn(
+            train_features, train_labels, True, 16
+        )
+    )
+
+    test_features, test_labels = test_utils.make_input_data(64, n_classes)
+    classifier.evaluate(
+        input_fn=test_utils.make_input_fn(test_features, test_labels, False, 16)
+    )
+
+    predict_features, predict_labels = test_utils.make_input_data(64, n_classes)
+    classifier.predict(
+        input_fn=test_utils.make_input_fn(
+            predict_features, predict_labels, False
+        )
+    )
+
+
+if __name__ == '__main__':
+  tf.test.main()