Create Text2RealProblem class for regression-based problems

tensor2tensor/data_generators/text_problems.py

@@ -17,6 +17,7 @@
 
 * Text2TextProblem: input=text, target=text.
 * Text2ClassProblem: input=text, target=class.
+* Text2RealProblem: input=text, target=float.
 * Text2SelfProblem (for language modeling): target=text
 * QuestionAndContext2TextProblem: input=text, context=text, target=text.
 
@@ -605,6 +606,94 @@ def generate_encoded_samples(self, data_dir, tmp_dir, dataset_split):
       yield {"inputs": inputs, "targets": [label]}
 
 
+class Text2RealProblem(Text2TextProblem):
+  """Base class for text regression problems with one or more tasks.
+    Suitable for text-based problems where targets are continuous, real values.
+    When ntasks = 1, each text example is mapped to a single scalar value. When
+    ntasks > 1, each text example is mapped to a 1-d vector of length ntasks.
+  """
+
+  @property
+  def ntasks(self):
+    """Set to n > 1 for multitask regression."""
+    return 1
+
+  def generate_samples(self, data_dir, tmp_dir, dataset_split):
+    """Generate samples of text and real-valued target pairs.
+    Each yielded dict will be a single example. The inputs should be raw text.
+    The target should be a list containing ntasks floats.
+    Args:
+      data_dir: final data directory. Typically only used in this method to copy
+        over user-supplied vocab files (for example, if vocab_type ==
+        VocabType.TOKEN).
+      tmp_dir: temporary directory that you can use for downloading and scratch.
+      dataset_split: problem.DatasetSplit, which data split to generate samples
+        for (for example, training and evaluation).
+    Yields:
+      {"inputs": text, "targets": [x1, x2, ..., xN]} where N is ntasks
+    """
+    raise NotImplementedError()
+
+  def generate_text_for_vocab(self, data_dir, tmp_dir):
+    for i, sample in enumerate(
+        self.generate_samples(data_dir, tmp_dir, problem.DatasetSplit.TRAIN)):
+      yield sample["inputs"]
+      if self.max_samples_for_vocab and (i + 1) >= self.max_samples_for_vocab:
+        break
+
+  def generate_encoded_samples(self, data_dir, tmp_dir, dataset_split):
+    generator = self.generate_samples(data_dir, tmp_dir, dataset_split)
+    encoder = self.get_or_create_vocab(data_dir, tmp_dir)
+    for sample in generator:
+      inputs = encoder.encode(sample["inputs"])
+      inputs.append(text_encoder.EOS_ID)
+      yield {"inputs": inputs, "targets": sample["targets"]}
+
+  def feature_encoders(self, data_dir):
+    encoder = self.get_or_create_vocab(data_dir, None, force_get=True)
+
+    return {
+        "inputs": encoder,
+        "targets": text_encoder.RealEncoder(),
+    }
+
+  def hparams(self, defaults, unused_model_hparams):
+    p = defaults
+    p.modality = {
+        "inputs": modalities.ModalityType.SYMBOL,
+        "targets": modalities.ModalityType.REAL_L2_LOSS,
+    }
+    p.vocab_size = {
+        "inputs": self._encoders["inputs"].vocab_size,
+        "targets": self.ntasks
+    }
+    p.target_space_id = problem.SpaceID.REAL
+    p.add_hparam("regression_targets", True)
+
+  def max_length(self, model_hparams):
+    return model_hparams.batch_size * self.ntasks
+
+  def preprocess_example(self, example, unused_mode, unused_hparams):
+    example = problem.preprocess_example_common(example, unused_mode,
+                                                unused_hparams)
+    example["targets"] = tf.reshape(example["targets"], [1, 1, self.ntasks])
+    return example
+
+  def example_reading_spec(self):
+    data_fields = {
+        "inputs": tf.VarLenFeature(tf.int64),
+        "targets": tf.FixedLenFeature([self.ntasks], tf.float32),
+    }
+    data_items_to_decoders = None
+    return (data_fields, data_items_to_decoders)
+
+  def eval_metrics(self):
+    metrics_list = [metrics.Metrics.RMSE]
+    if self.ntasks == 1:
+      metrics_list.append(metrics.Metrics.PEARSON)
+    return metrics_list
+
+
 def txt_line_iterator(txt_path):
   """Iterate through lines of file."""
   with tf.gfile.Open(txt_path) as f:
@@ -692,6 +781,21 @@ def text2class_txt_iterator(source_txt_path, label_txt_path, class_strs=None):
     yield {"inputs": inputs, "label": label}
 
 
+def text2real_txt_iterator(source_txt_path, target_txt_path):
+  """Yield dicts for Text2RealProblem.generate_samples from lines of files.
+  Args:
+    source_txt_path: txt file with record per line.
+    target_txt_path: txt file with float (or space-separated float list for
+      multitask) per line.
+  Yields:
+    {"inputs": inputs, "targets": targets}
+  """
+  for inputs, targets in zip(
+      txt_line_iterator(source_txt_path), txt_line_iterator(target_txt_path)):
+    targets = [float(x) for x in targets.split(" ")]
+    yield {"inputs": inputs, "targets": targets}
+
+
 def text2text_txt_tab_iterator(txt_path):
   """Yield dicts for Text2TextProblem.generate_samples from lines of txt_path.
 

tensor2tensor/data_generators/text_problems_test.py

@@ -94,6 +94,13 @@ def setUpClass(cls):
     tf.gfile.Copy(cls.targets_file, os.path.join(cls.tmp_dir,
                                                  "targets.eval.txt"))
 
+    cls.targets_regr = [[1.23, 2.34], [4.56, 5.67]]
+    cls.targets_regr_file = os.path.join(cls.tmp_dir, "targets_regr.train.txt")
+    with tf.gfile.Open(cls.targets_regr_file, "w") as f:
+      for targets in cls.targets_regr:
+        f.write(" ".join([str(x) for x in targets]) + "\n")
+
+
   def testTxtLineIterator(self):
     lines = [line for line in text_problems.txt_line_iterator(self.inputs_file)]
     self.assertEqual(lines, self.inputs)
@@ -136,6 +143,16 @@ def testText2ClassTxtIteratorWithStrs(self):
     self.assertEqual(inputs, self.inputs)
     self.assertEqual(labels, self.labels)
 
+  def testText2RealTxtIterator(self):
+    inputs = []
+    targets = []
+    for entry in text_problems.text2real_txt_iterator(self.inputs_file,
+                                                      self.targets_regr_file):
+      inputs.append(entry["inputs"])
+      targets.append(entry["targets"])
+    self.assertEqual(inputs, self.inputs)
+    self.assertEqual(targets, self.targets_regr)
+
   def testText2TextTxtTabIterator(self):
     inputs = []
     targets = []

tensor2tensor/models/transformer.py

@@ -462,7 +462,8 @@ def _fast_decode_tpu(self,
 
     if self.has_input:
       inputs_shape = common_layers.shape_list(features["inputs"])
-      if target_modality == modalities.ModalityType.CLASS_LABEL:
+      if (target_modality == modalities.ModalityType.CLASS_LABEL or
+          self._problem_hparams.get("regression_targets")):
         decode_length = 1
       else:
         decode_length = (
@@ -704,7 +705,8 @@ def _fast_decode(self,
           " of the dataset when decoding.")
     if self.has_input:
       inputs_shape = common_layers.shape_list(features["inputs"])
-      if target_modality == modalities.ModalityType.CLASS_LABEL:
+      if (target_modality == modalities.ModalityType.CLASS_LABEL or
+          self._problem_hparams.get("regression_targets")):
         decode_length = 1
       else:
         decode_length = (

tensor2tensor/utils/t2t_model.py

@@ -806,8 +806,10 @@ def infer(self,
 
       if self._problem_hparams:
         target_modality = self._problem_hparams.modality["targets"]
-        if target_modality == modalities.ModalityType.CLASS_LABEL:
-          beam_size = 1  # No use to run beam-search for a single class.
+      if (target_modality == modalities.ModalityType.CLASS_LABEL or
+          self._problem_hparams.get("regression_targets")):
+        # No use to run beam-search for classification or regression.
+        beam_size = 1
       if beam_size == 1:
         log_info("Greedy Decoding")
         results = self._greedy_infer(features, decode_length, use_tpu)
@@ -1064,7 +1066,8 @@ def infer_step(i, recent_output, recent_logits, unused_loss):
     initial_output = tf.slice(initial_output, [0, 0, 0, 0],
                               common_layers.shape_list(initial_output))
     target_modality = self._problem_hparams.modality["targets"]
-    if target_modality == modalities.ModalityType.CLASS_LABEL:
+    if (target_modality == modalities.ModalityType.CLASS_LABEL or
+        self._problem_hparams.get("regression_targets")):
       decode_length = 1
     else:
       if "partial_targets" in features:
@@ -1243,7 +1246,8 @@ def infer_step(recent_output, recent_logits, unused_loss):
     initial_output = tf.slice(initial_output, [0, 0, 0, 0],
                               common_layers.shape_list(initial_output))
     target_modality = self._problem_hparams.modality["targets"]
-    if target_modality == modalities.ModalityType.CLASS_LABEL:
+    if (target_modality == modalities.ModalityType.CLASS_LABEL or
+        self._problem_hparams.get("regression_targets")):
       decode_length = 1
     else:
       if "partial_targets" in features: