Merge pull request #5546 from cshallue/master

Improvements to AstroNet and add AstroWaveNet

research/astronet/README.md

@@ -40,6 +40,10 @@ Full text available at [*The Astronomical Journal*](http://iopscience.iop.org/ar
   * Training and evaluating a new model.
   * Using a trained model to generate new predictions.
 
+[astrowavenet/](astrowavenet/)
+
+* A generative model for light curves.
+
 [light_curve_util/](light_curve_util)
 
 * Utilities for operating on light curves. These include:
@@ -63,11 +67,11 @@ First, ensure that you have installed the following required packages:
 * **TensorFlow** ([instructions](https://www.tensorflow.org/install/))
 * **Pandas** ([instructions](http://pandas.pydata.org/pandas-docs/stable/install.html))
 * **NumPy** ([instructions](https://docs.scipy.org/doc/numpy/user/install.html))
+* **SciPy** ([instructions](https://scipy.org/install.html))
 * **AstroPy** ([instructions](http://www.astropy.org/))
 * **PyDl** ([instructions](https://pypi.python.org/pypi/pydl))
 * **Bazel** ([instructions](https://docs.bazel.build/versions/master/install.html))
 * **Abseil Python Common Libraries** ([instructions](https://github.com/abseil/abseil-py))
-    * Optional: only required for unit tests.
 
 ### Optional: Run Unit Tests
 
@@ -207,7 +211,7 @@ the second deepest transits).
 
 To train a model to identify exoplanets, you will need to provide TensorFlow
 with training data in
-[TFRecord](https://www.tensorflow.org/guide/datasets) format. The
+[TFRecord](https://www.tensorflow.org/programmers_guide/datasets) format. The
 TFRecord format consists of a set of sharded files containing serialized
 `tf.Example` [protocol buffers](https://developers.google.com/protocol-buffers/).
 
@@ -343,7 +347,7 @@ bazel-bin/astronet/train \
   --model_dir=${MODEL_DIR}
 ```
 
-Optionally, you can also run a [TensorBoard](https://www.tensorflow.org/guide/summaries_and_tensorboard)
+Optionally, you can also run a [TensorBoard](https://www.tensorflow.org/programmers_guide/summaries_and_tensorboard)
 server in a separate process for real-time
 monitoring of training progress and evaluation metrics.
 

research/astronet/astronet/BUILD

@@ -25,6 +25,7 @@ py_binary(
         ":models",
         "//astronet/util:config_util",
         "//astronet/util:configdict",
+        "//astronet/util:estimator_runner",
         "//astronet/util:estimator_util",
     ],
 )
@@ -37,6 +38,7 @@ py_binary(
         ":models",
         "//astronet/util:config_util",
         "//astronet/util:configdict",
+        "//astronet/util:estimator_runner",
         "//astronet/util:estimator_util",
     ],
 )

research/astronet/astronet/astro_cnn_model/astro_cnn_model.py

@@ -54,24 +54,6 @@ from astronet.astro_model import astro_model
 class AstroCNNModel(astro_model.AstroModel):
   """A model for classifying light curves using a convolutional neural net."""
 
-  def __init__(self, features, labels, hparams, mode):
-    """Basic setup. The actual TensorFlow graph is constructed in build().
-
-    Args:
-      features: A dictionary containing "time_series_features" and
-          "aux_features", each of which is a dictionary of named input Tensors.
-          All features have dtype float32 and shape [batch_size, length].
-      labels: An int64 Tensor with shape [batch_size]. May be None if mode is
-          tf.estimator.ModeKeys.PREDICT.
-      hparams: A ConfigDict of hyperparameters for building the model.
-      mode: A tf.estimator.ModeKeys to specify whether the graph should be built
-          for training, evaluation or prediction.
-
-    Raises:
-      ValueError: If mode is invalid.
-    """
-    super(AstroCNNModel, self).__init__(features, labels, hparams, mode)
-
   def _build_cnn_layers(self, inputs, hparams, scope="cnn"):
     """Builds convolutional layers.
 
@@ -95,7 +77,7 @@ class AstroCNNModel(astro_model.AstroModel):
       for i in range(hparams.cnn_num_blocks):
         num_filters = int(hparams.cnn_initial_num_filters *
                           hparams.cnn_block_filter_factor**i)
-        with tf.variable_scope("block_%d" % (i + 1)):
+        with tf.variable_scope("block_{}".format(i + 1)):
           for j in range(hparams.cnn_block_size):
             net = tf.layers.conv1d(
                 inputs=net,
@@ -103,7 +85,7 @@ class AstroCNNModel(astro_model.AstroModel):
                 kernel_size=int(hparams.cnn_kernel_size),
                 padding=hparams.convolution_padding,
                 activation=tf.nn.relu,
-                name="conv_%d" % (j + 1))
+                name="conv_{}".format(j + 1))
 
           if hparams.pool_size > 1:  # pool_size 0 or 1 denotes no pooling
             net = tf.layers.max_pooling1d(

research/astronet/astronet/astro_cnn_model/astro_cnn_model_test.py

@@ -35,8 +35,7 @@ class AstroCNNModelTest(tf.test.TestCase):
 
     Args:
       shape: Numpy array or anything that can be converted to one.
-      tensor_or_array: tf.Tensor, tf.Variable, Numpy array or anything that can
-          be converted to one.
+      tensor_or_array: tf.Tensor, tf.Variable, or Numpy array.
     """
     if isinstance(tensor_or_array, (np.ndarray, np.generic)):
       self.assertAllEqual(shape, tensor_or_array.shape)

research/astronet/astronet/astro_fc_model/astro_fc_model.py

@@ -58,24 +58,6 @@ from astronet.astro_model import astro_model
 class AstroFCModel(astro_model.AstroModel):
   """A model for classifying light curves using fully connected layers."""
 
-  def __init__(self, features, labels, hparams, mode):
-    """Basic setup. The actual TensorFlow graph is constructed in build().
-
-    Args:
-      features: A dictionary containing "time_series_features" and
-          "aux_features", each of which is a dictionary of named input Tensors.
-          All features have dtype float32 and shape [batch_size, length].
-      labels: An int64 Tensor with shape [batch_size]. May be None if mode is
-          tf.estimator.ModeKeys.PREDICT.
-      hparams: A ConfigDict of hyperparameters for building the model.
-      mode: A tf.estimator.ModeKeys to specify whether the graph should be built
-          for training, evaluation or prediction.
-
-    Raises:
-      ValueError: If mode is invalid.
-    """
-    super(AstroFCModel, self).__init__(features, labels, hparams, mode)
-
   def _build_local_fc_layers(self, inputs, hparams, scope):
     """Builds locally fully connected layers.
 
@@ -120,8 +102,8 @@ class AstroFCModel(astro_model.AstroModel):
         elif hparams.pooling_type == "avg":
           net = tf.reduce_mean(net, axis=1, name="avg_pool")
         else:
-          raise ValueError(
-              "Unrecognized pooling_type: %s" % hparams.pooling_type)
+          raise ValueError("Unrecognized pooling_type: {}".format(
+              hparams.pooling_type))
 
         remaining_layers = hparams.num_local_layers - 1
       else:
@@ -133,7 +115,7 @@ class AstroFCModel(astro_model.AstroModel):
             inputs=net,
             num_outputs=hparams.local_layer_size,
             activation_fn=tf.nn.relu,
-            scope="fully_connected_%d" % (i + 1))
+            scope="fully_connected_{}".format(i + 1))
 
         if hparams.dropout_rate > 0:
           net = tf.layers.dropout(

research/astronet/astronet/astro_fc_model/astro_fc_model_test.py

@@ -35,8 +35,7 @@ class AstroFCModelTest(tf.test.TestCase):
 
     Args:
       shape: Numpy array or anything that can be converted to one.
-      tensor_or_array: tf.Tensor, tf.Variable, Numpy array or anything that can
-          be converted to one.
+      tensor_or_array: tf.Tensor, tf.Variable, or Numpy array.
     """
     if isinstance(tensor_or_array, (np.ndarray, np.generic)):
       self.assertAllEqual(shape, tensor_or_array.shape)

research/astronet/astronet/astro_model/astro_model.py

@@ -73,7 +73,9 @@ class AstroModel(object):
   """A TensorFlow model for classifying astrophysical light curves."""
 
   def __init__(self, features, labels, hparams, mode):
-    """Basic setup. The actual TensorFlow graph is constructed in build().
+    """Basic setup.
+
+    The actual TensorFlow graph is constructed in build().
 
     Args:
       features: A dictionary containing "time_series_features" and
@@ -93,7 +95,7 @@ class AstroModel(object):
         tf.estimator.ModeKeys.PREDICT
     ]
     if mode not in valid_modes:
-      raise ValueError("Expected mode in %s. Got: %s" % (valid_modes, mode))
+      raise ValueError("Expected mode in {}. Got: {}".format(valid_modes, mode))
 
     self.hparams = hparams
     self.mode = mode
@@ -201,8 +203,7 @@ class AstroModel(object):
     if len(hidden_layers) == 1:
       pre_logits_concat = hidden_layers[0][1]
     else:
-      pre_logits_concat = tf.concat(
-          [layer[1] for layer in hidden_layers],
+      pre_logits_concat = tf.concat([layer[1] for layer in hidden_layers],
                                     axis=1,
                                     name="pre_logits_concat")
 
@@ -213,7 +214,7 @@ class AstroModel(object):
             inputs=net,
             units=self.hparams.pre_logits_hidden_layer_size,
             activation=tf.nn.relu,
-            name="fully_connected_%s" % (i + 1))
+            name="fully_connected_{}".format(i + 1))
 
         if self.hparams.pre_logits_dropout_rate > 0:
           net = tf.layers.dropout(

research/astronet/astronet/astro_model/astro_model_test.py

@@ -35,8 +35,7 @@ class AstroModelTest(tf.test.TestCase):
 
     Args:
       shape: Numpy array or anything that can be converted to one.
-      tensor_or_array: tf.Tensor, tf.Variable, Numpy array or anything that can
-          be converted to one.
+      tensor_or_array: tf.Tensor, tf.Variable, or Numpy array.
     """
     if isinstance(tensor_or_array, (np.ndarray, np.generic)):
       self.assertAllEqual(shape, tensor_or_array.shape)

research/astronet/astronet/astro_model/configurations.py

@@ -45,6 +45,9 @@ def base():
               "PC": 1,  # Planet Candidate.
               "AFP": 0,  # Astrophysical False Positive.
               "NTP": 0,  # Non-Transiting Phenomenon.
+              "SCR1": 0,  # TCE from scrambled light curve with SCR1 order.
+              "INV": 0,  # TCE from inverted light curve.
+              "INJ1": 1,  # Injected Planet.
           },
       },
       # Hyperparameters for building and training the model.
@@ -60,10 +63,10 @@ def base():
           "pre_logits_dropout_rate": 0.0,
 
           # Number of examples per training batch.
-          "batch_size": 64,
+          "batch_size": 256,
 
           # Learning rate parameters.
-          "learning_rate": 1e-5,
+          "learning_rate": 2e-4,
           "learning_rate_decay_steps": 0,
           "learning_rate_decay_factor": 0,
           "learning_rate_decay_staircase": True,

research/astronet/astronet/data/generate_input_records.py

@@ -88,7 +88,6 @@ import tensorflow as tf
 
 from astronet.data import preprocess
 
-
 parser = argparse.ArgumentParser()
 
 _DR24_TCE_URL = ("https://exoplanetarchive.ipac.caltech.edu/cgi-bin/TblView/"
@@ -100,7 +99,7 @@ parser.add_argument(
     required=True,
     help="CSV file containing the Q1-Q17 DR24 Kepler TCE table. Must contain "
     "columns: rowid, kepid, tce_plnt_num, tce_period, tce_duration, "
-    "tce_time0bk. Download from: %s" % _DR24_TCE_URL)
+    "tce_time0bk. Download from: {}".format(_DR24_TCE_URL))
 
 parser.add_argument(
     "--kepler_data_dir",
@@ -219,14 +218,16 @@ def main(argv):
   for i in range(FLAGS.num_train_shards):
     start = boundaries[i]
     end = boundaries[i + 1]
-    file_shards.append((train_tces[start:end], os.path.join(
-        FLAGS.output_dir, "train-%.5d-of-%.5d" % (i, FLAGS.num_train_shards))))
+    filename = os.path.join(
+        FLAGS.output_dir, "train-{:05d}-of-{:05d}".format(
+            i, FLAGS.num_train_shards))
+    file_shards.append((train_tces[start:end], filename))
 
   # Validation and test sets each have a single shard.
-  file_shards.append((val_tces, os.path.join(FLAGS.output_dir,
-                                             "val-00000-of-00001")))
-  file_shards.append((test_tces, os.path.join(FLAGS.output_dir,
-                                              "test-00000-of-00001")))
+  file_shards.append((val_tces,
+                      os.path.join(FLAGS.output_dir, "val-00000-of-00001")))
+  file_shards.append((test_tces,
+                      os.path.join(FLAGS.output_dir, "test-00000-of-00001")))
   num_file_shards = len(file_shards)
 
   # Launch subprocesses for the file shards.

research/astronet/astronet/data/preprocess.py

@@ -47,8 +47,8 @@ def read_light_curve(kepid, kepler_data_dir):
   # Read the Kepler light curve.
   file_names = kepler_io.kepler_filenames(kepler_data_dir, kepid)
   if not file_names:
-    raise IOError("Failed to find .fits files in %s for Kepler ID %s" %
-                  (kepler_data_dir, kepid))
+    raise IOError("Failed to find .fits files in {} for Kepler ID {}".format(
+        kepler_data_dir, kepid))
 
   return kepler_io.read_kepler_light_curve(file_names)
 

research/astronet/astronet/evaluate.py

@@ -26,6 +26,7 @@ import tensorflow as tf
 from astronet import models
 from astronet.util import config_util
 from astronet.util import configdict
+from astronet.util import estimator_runner
 from astronet.util import estimator_util
 
 parser = argparse.ArgumentParser()
@@ -86,7 +87,9 @@ def main(_):
 
   # Run evaluation. This will log the result to stderr and also write a summary
   # file in the model_dir.
-  estimator_util.evaluate(estimator, input_fn, eval_name=FLAGS.eval_name)
+  eval_steps = None  # Evaluate over all examples in the file.
+  eval_args = {FLAGS.eval_name: (input_fn, eval_steps)}
+  estimator_runner.evaluate(estimator, eval_args)
 
 
 if __name__ == "__main__":

research/astronet/astronet/models.py

@@ -46,7 +46,7 @@ def get_model_class(model_name):
     ValueError: If model_name is unrecognized.
   """
   if model_name not in _MODELS:
-    raise ValueError("Unrecognized model name: %s" % model_name)
+    raise ValueError("Unrecognized model name: {}".format(model_name))
 
   return _MODELS[model_name][0]
 
@@ -67,11 +67,12 @@ def get_model_config(model_name, config_name):
     ValueError: If model_name or config_name is unrecognized.
   """
   if model_name not in _MODELS:
-    raise ValueError("Unrecognized model name: %s" % model_name)
+    raise ValueError("Unrecognized model name: {}".format(model_name))
 
   config_module = _MODELS[model_name][1]
   try:
     return getattr(config_module, config_name)()
   except AttributeError:
-    raise ValueError("Config name '%s' not found in configuration module: %s" %
-                     (config_name, config_module.__name__))
+    raise ValueError(
+        "Config name '{}' not found in configuration module: {}".format(
+            config_name, config_module.__name__))

research/astronet/astronet/ops/dataset_ops.py

@@ -69,7 +69,7 @@ def _recursive_pad_to_batch_size(tensor_or_collection, batch_size):
         for t in tensor_or_collection
     ]
 
-  raise ValueError("Unknown input type: %s" % tensor_or_collection)
+  raise ValueError("Unknown input type: {}".format(tensor_or_collection))
 
 
 def pad_dataset_to_batch_size(dataset, batch_size):
@@ -119,7 +119,7 @@ def _recursive_set_batch_size(tensor_or_collection, batch_size):
     for t in tensor_or_collection:
       _recursive_set_batch_size(t, batch_size)
   else:
-    raise ValueError("Unknown input type: %s" % tensor_or_collection)
+    raise ValueError("Unknown input type: {}".format(tensor_or_collection))
 
   return tensor_or_collection
 
@@ -170,7 +170,7 @@ def build_dataset(file_pattern,
   for p in file_patterns:
     matches = tf.gfile.Glob(p)
     if not matches:
-      raise ValueError("Found no input files matching %s" % p)
+      raise ValueError("Found no input files matching {}".format(p))
     filenames.extend(matches)
   tf.logging.info("Building input pipeline from %d files matching patterns: %s",
                   len(filenames), file_patterns)
@@ -180,8 +180,8 @@ def build_dataset(file_pattern,
     label_ids = set(input_config.label_map.values())
     if label_ids != set(range(len(label_ids))):
       raise ValueError(
-          "Label IDs must be contiguous integers starting at 0. Got: %s" %
-          label_ids)
+          "Label IDs must be contiguous integers starting at 0. Got: {}".format(
+              label_ids))
 
     # Create a HashTable mapping label strings to integer ids.
     table_initializer = tf.contrib.lookup.KeyValueTensorInitializer(

research/astronet/astronet/ops/dataset_ops_test.py

@@ -48,11 +48,6 @@ class DatasetOpsTest(tf.test.TestCase):
       self.assertEqual([5], tensor_1d.shape)
       self.assertAllEqual([0, 1, 2, 3, 4], tensor_1d.eval())
 
-      # Invalid to pad Tensor with batch size 5 to batch size 3.
-      tensor_1d_pad3 = dataset_ops.pad_tensor_to_batch_size(tensor_1d, 3)
-      with self.assertRaises(tf.errors.InvalidArgumentError):
-        tensor_1d_pad3.eval()
-
       tensor_1d_pad5 = dataset_ops.pad_tensor_to_batch_size(tensor_1d, 5)
       self.assertEqual([5], tensor_1d_pad5.shape)
       self.assertAllEqual([0, 1, 2, 3, 4], tensor_1d_pad5.eval())
@@ -66,11 +61,6 @@ class DatasetOpsTest(tf.test.TestCase):
       self.assertEqual([3, 3], tensor_2d.shape)
       self.assertAllEqual([[0, 1, 2], [3, 4, 5], [6, 7, 8]], tensor_2d.eval())
 
-      tensor_2d_pad2 = dataset_ops.pad_tensor_to_batch_size(tensor_2d, 2)
-      # Invalid to pad Tensor with batch size 2 to batch size 2.
-      with self.assertRaises(tf.errors.InvalidArgumentError):
-        tensor_2d_pad2.eval()
-
       tensor_2d_pad3 = dataset_ops.pad_tensor_to_batch_size(tensor_2d, 3)
       self.assertEqual([3, 3], tensor_2d_pad3.shape)
       self.assertAllEqual([[0, 1, 2], [3, 4, 5], [6, 7, 8]],

research/astronet/astronet/ops/input_ops.py

@@ -27,8 +27,7 @@ def prepare_feed_dict(model, features, labels=None, is_training=None):
   Args:
     model: An instance of AstroModel.
     features: Dictionary containing "time_series_features" and "aux_features".
-        Each is a dictionary of named numpy arrays of shape
-        [batch_size, length].
+      Each is a dictionary of named numpy arrays of shape [batch_size, length].
     labels: (Optional). Numpy array of shape [batch_size].
     is_training: (Optional). Python boolean to feed to the model.is_training
       Tensor (if None, no value is fed).

research/astronet/astronet/ops/metrics.py

@@ -30,7 +30,7 @@ def _metric_variable(name, shape, dtype):
       collections=[tf.GraphKeys.LOCAL_VARIABLES, tf.GraphKeys.METRIC_VARIABLES])
 
 
-def _build_metrics(labels, predictions, weights, batch_losses):
+def _build_metrics(labels, predictions, weights, batch_losses, output_dim=1):
   """Builds TensorFlow operations to compute model evaluation metrics.
 
   Args:
@@ -38,14 +38,16 @@ def _build_metrics(labels, predictions, weights, batch_losses):
     predictions: Tensor with shape [batch_size, output_dim].
     weights: Tensor with shape [batch_size].
     batch_losses: Tensor with shape [batch_size].
+    output_dim: Dimension of model output
 
   Returns:
     A dictionary {metric_name: (metric_value, update_op).
   """
   # Compute the predicted labels.
   assert len(predictions.shape) == 2
-  binary_classification = (predictions.shape[1] == 1)
+  binary_classification = output_dim == 1
   if binary_classification:
+    assert predictions.shape[1] == 1
     predictions = tf.squeeze(predictions, axis=[1])
     predicted_labels = tf.to_int32(
         tf.greater(predictions, 0.5), name="predicted_labels")
@@ -73,15 +75,6 @@ def _build_metrics(labels, predictions, weights, batch_losses):
     metrics["losses/weighted_cross_entropy"] = tf.metrics.mean(
         batch_losses, weights=weights, name="cross_entropy_loss")
 
-    # Possibly create additional metrics for binary classification.
-    if binary_classification:
-      labels = tf.cast(labels, dtype=tf.bool)
-      predicted_labels = tf.cast(predicted_labels, dtype=tf.bool)
-
-      # AUC.
-      metrics["auc"] = tf.metrics.auc(
-          labels, predictions, weights=weights, num_thresholds=1000)
-
     def _count_condition(name, labels_value, predicted_value):
       """Creates a counter for given values of predictions and labels."""
       count = _metric_variable(name, [], tf.float32)
@@ -93,14 +86,19 @@ def _build_metrics(labels, predictions, weights, batch_losses):
       return count.read_value(), update_op
 
     # Confusion matrix metrics.
-      metrics["confusion_matrix/true_positives"] = _count_condition(
-          "true_positives", labels_value=True, predicted_value=True)
-      metrics["confusion_matrix/false_positives"] = _count_condition(
-          "false_positives", labels_value=False, predicted_value=True)
-      metrics["confusion_matrix/true_negatives"] = _count_condition(
-          "true_negatives", labels_value=False, predicted_value=False)
-      metrics["confusion_matrix/false_negatives"] = _count_condition(
-          "false_negatives", labels_value=True, predicted_value=False)
+    num_labels = 2 if binary_classification else output_dim
+    for gold_label in range(num_labels):
+      for pred_label in range(num_labels):
+        metric_name = "confusion_matrix/label_{}_pred_{}".format(
+            gold_label, pred_label)
+        metrics[metric_name] = _count_condition(
+            metric_name, labels_value=gold_label, predicted_value=pred_label)
+
+    # Possibly create AUC metric for binary classification.
+    if binary_classification:
+      labels = tf.cast(labels, dtype=tf.bool)
+      metrics["auc"] = tf.metrics.auc(
+          labels, predictions, weights=weights, num_thresholds=1000)
 
   return metrics
 
@@ -130,7 +128,12 @@ def create_metric_fn(model):
   }
 
   def metric_fn(labels, predictions, weights, batch_losses):
-    return _build_metrics(labels, predictions, weights, batch_losses)
+    return _build_metrics(
+        labels,
+        predictions,
+        weights,
+        batch_losses,
+        output_dim=model.hparams.output_dim)
 
   return metric_fn, metric_fn_inputs
 

research/astronet/astronet/ops/metrics_test.py

@@ -30,15 +30,23 @@ def _unpack_metric_map(names_to_tuples):
   return dict(zip(metric_names, value_ops)), dict(zip(metric_names, update_ops))
 
 
+class _MockHparams(object):
+  """Mock Hparams class to support accessing with dot notation."""
+
+  pass
+
+
 class _MockModel(object):
   """Mock model for testing."""
 
-  def __init__(self, labels, predictions, weights, batch_losses):
+  def __init__(self, labels, predictions, weights, batch_losses, output_dim):
     self.labels = tf.constant(labels, dtype=tf.int32)
     self.predictions = tf.constant(predictions, dtype=tf.float32)
     self.weights = None if weights is None else tf.constant(
         weights, dtype=tf.float32)
     self.batch_losses = tf.constant(batch_losses, dtype=tf.float32)
+    self.hparams = _MockHparams()
+    self.hparams.output_dim = output_dim
 
 
 class MetricsTest(tf.test.TestCase):
@@ -48,13 +56,13 @@ class MetricsTest(tf.test.TestCase):
     predictions = [
         [0.7, 0.2, 0.1, 0.0],  # Predicted label = 0
         [0.2, 0.4, 0.2, 0.2],  # Predicted label = 1
-        [0.0, 0.0, 0.0, 1.0],  # Predicted label = 4
-        [0.1, 0.1, 0.7, 0.1],  # Predicted label = 3
+        [0.0, 0.0, 0.0, 1.0],  # Predicted label = 3
+        [0.1, 0.1, 0.7, 0.1],  # Predicted label = 2
     ]
     weights = None
     batch_losses = [0, 0, 4, 2]
 
-    model = _MockModel(labels, predictions, weights, batch_losses)
+    model = _MockModel(labels, predictions, weights, batch_losses, output_dim=4)
     metric_map = metrics.create_metrics(model)
     value_ops, update_ops = _unpack_metric_map(metric_map)
     initializer = tf.local_variables_initializer()
@@ -68,6 +76,22 @@ class MetricsTest(tf.test.TestCase):
           "accuracy/num_correct": 2,
           "accuracy/accuracy": 0.5,
           "losses/weighted_cross_entropy": 1.5,
+          "confusion_matrix/label_0_pred_0": 1,
+          "confusion_matrix/label_0_pred_1": 0,
+          "confusion_matrix/label_0_pred_2": 0,
+          "confusion_matrix/label_0_pred_3": 0,
+          "confusion_matrix/label_1_pred_0": 0,
+          "confusion_matrix/label_1_pred_1": 1,
+          "confusion_matrix/label_1_pred_2": 0,
+          "confusion_matrix/label_1_pred_3": 0,
+          "confusion_matrix/label_2_pred_0": 0,
+          "confusion_matrix/label_2_pred_1": 0,
+          "confusion_matrix/label_2_pred_2": 0,
+          "confusion_matrix/label_2_pred_3": 1,
+          "confusion_matrix/label_3_pred_0": 0,
+          "confusion_matrix/label_3_pred_1": 0,
+          "confusion_matrix/label_3_pred_2": 1,
+          "confusion_matrix/label_3_pred_3": 0
       }, sess.run(value_ops))
 
       sess.run(update_ops)
@@ -76,6 +100,22 @@ class MetricsTest(tf.test.TestCase):
           "accuracy/num_correct": 4,
           "accuracy/accuracy": 0.5,
           "losses/weighted_cross_entropy": 1.5,
+          "confusion_matrix/label_0_pred_0": 2,
+          "confusion_matrix/label_0_pred_1": 0,
+          "confusion_matrix/label_0_pred_2": 0,
+          "confusion_matrix/label_0_pred_3": 0,
+          "confusion_matrix/label_1_pred_0": 0,
+          "confusion_matrix/label_1_pred_1": 2,
+          "confusion_matrix/label_1_pred_2": 0,
+          "confusion_matrix/label_1_pred_3": 0,
+          "confusion_matrix/label_2_pred_0": 0,
+          "confusion_matrix/label_2_pred_1": 0,
+          "confusion_matrix/label_2_pred_2": 0,
+          "confusion_matrix/label_2_pred_3": 2,
+          "confusion_matrix/label_3_pred_0": 0,
+          "confusion_matrix/label_3_pred_1": 0,
+          "confusion_matrix/label_3_pred_2": 2,
+          "confusion_matrix/label_3_pred_3": 0
       }, sess.run(value_ops))
 
   def testMultiClassificationWithWeights(self):
@@ -83,13 +123,13 @@ class MetricsTest(tf.test.TestCase):
     predictions = [
         [0.7, 0.2, 0.1, 0.0],  # Predicted label = 0
         [0.2, 0.4, 0.2, 0.2],  # Predicted label = 1
-        [0.0, 0.0, 0.0, 1.0],  # Predicted label = 4
-        [0.1, 0.1, 0.7, 0.1],  # Predicted label = 3
+        [0.0, 0.0, 0.0, 1.0],  # Predicted label = 3
+        [0.1, 0.1, 0.7, 0.1],  # Predicted label = 2
     ]
     weights = [0, 1, 0, 1]
     batch_losses = [0, 0, 4, 2]
 
-    model = _MockModel(labels, predictions, weights, batch_losses)
+    model = _MockModel(labels, predictions, weights, batch_losses, output_dim=4)
     metric_map = metrics.create_metrics(model)
     value_ops, update_ops = _unpack_metric_map(metric_map)
     initializer = tf.local_variables_initializer()
@@ -103,6 +143,22 @@ class MetricsTest(tf.test.TestCase):
           "accuracy/num_correct": 1,
           "accuracy/accuracy": 0.5,
           "losses/weighted_cross_entropy": 1,
+          "confusion_matrix/label_0_pred_0": 0,
+          "confusion_matrix/label_0_pred_1": 0,
+          "confusion_matrix/label_0_pred_2": 0,
+          "confusion_matrix/label_0_pred_3": 0,
+          "confusion_matrix/label_1_pred_0": 0,
+          "confusion_matrix/label_1_pred_1": 1,
+          "confusion_matrix/label_1_pred_2": 0,
+          "confusion_matrix/label_1_pred_3": 0,
+          "confusion_matrix/label_2_pred_0": 0,
+          "confusion_matrix/label_2_pred_1": 0,
+          "confusion_matrix/label_2_pred_2": 0,
+          "confusion_matrix/label_2_pred_3": 0,
+          "confusion_matrix/label_3_pred_0": 0,
+          "confusion_matrix/label_3_pred_1": 0,
+          "confusion_matrix/label_3_pred_2": 1,
+          "confusion_matrix/label_3_pred_3": 0
       }, sess.run(value_ops))
 
       sess.run(update_ops)
@@ -111,6 +167,22 @@ class MetricsTest(tf.test.TestCase):
           "accuracy/num_correct": 2,
           "accuracy/accuracy": 0.5,
           "losses/weighted_cross_entropy": 1,
+          "confusion_matrix/label_0_pred_0": 0,
+          "confusion_matrix/label_0_pred_1": 0,
+          "confusion_matrix/label_0_pred_2": 0,
+          "confusion_matrix/label_0_pred_3": 0,
+          "confusion_matrix/label_1_pred_0": 0,
+          "confusion_matrix/label_1_pred_1": 2,
+          "confusion_matrix/label_1_pred_2": 0,
+          "confusion_matrix/label_1_pred_3": 0,
+          "confusion_matrix/label_2_pred_0": 0,
+          "confusion_matrix/label_2_pred_1": 0,
+          "confusion_matrix/label_2_pred_2": 0,
+          "confusion_matrix/label_2_pred_3": 0,
+          "confusion_matrix/label_3_pred_0": 0,
+          "confusion_matrix/label_3_pred_1": 0,
+          "confusion_matrix/label_3_pred_2": 2,
+          "confusion_matrix/label_3_pred_3": 0
       }, sess.run(value_ops))
 
   def testBinaryClassificationWithoutWeights(self):
@@ -124,7 +196,7 @@ class MetricsTest(tf.test.TestCase):
     weights = None
     batch_losses = [0, 0, 4, 2]
 
-    model = _MockModel(labels, predictions, weights, batch_losses)
+    model = _MockModel(labels, predictions, weights, batch_losses, output_dim=1)
     metric_map = metrics.create_metrics(model)
     value_ops, update_ops = _unpack_metric_map(metric_map)
     initializer = tf.local_variables_initializer()
@@ -139,10 +211,10 @@ class MetricsTest(tf.test.TestCase):
           "accuracy/accuracy": 0.5,
           "losses/weighted_cross_entropy": 1.5,
           "auc": 0.25,
-          "confusion_matrix/true_positives": 1,
-          "confusion_matrix/true_negatives": 1,
-          "confusion_matrix/false_positives": 1,
-          "confusion_matrix/false_negatives": 1,
+          "confusion_matrix/label_0_pred_0": 1,
+          "confusion_matrix/label_0_pred_1": 1,
+          "confusion_matrix/label_1_pred_0": 1,
+          "confusion_matrix/label_1_pred_1": 1,
       }, sess.run(value_ops))
 
       sess.run(update_ops)
@@ -152,10 +224,10 @@ class MetricsTest(tf.test.TestCase):
           "accuracy/accuracy": 0.5,
           "losses/weighted_cross_entropy": 1.5,
           "auc": 0.25,
-          "confusion_matrix/true_positives": 2,
-          "confusion_matrix/true_negatives": 2,
-          "confusion_matrix/false_positives": 2,
-          "confusion_matrix/false_negatives": 2,
+          "confusion_matrix/label_0_pred_0": 2,
+          "confusion_matrix/label_0_pred_1": 2,
+          "confusion_matrix/label_1_pred_0": 2,
+          "confusion_matrix/label_1_pred_1": 2,
       }, sess.run(value_ops))
 
   def testBinaryClassificationWithWeights(self):
@@ -169,7 +241,7 @@ class MetricsTest(tf.test.TestCase):
     weights = [0, 1, 0, 1]
     batch_losses = [0, 0, 4, 2]
 
-    model = _MockModel(labels, predictions, weights, batch_losses)
+    model = _MockModel(labels, predictions, weights, batch_losses, output_dim=1)
     metric_map = metrics.create_metrics(model)
     value_ops, update_ops = _unpack_metric_map(metric_map)
     initializer = tf.local_variables_initializer()
@@ -184,10 +256,10 @@ class MetricsTest(tf.test.TestCase):
           "accuracy/accuracy": 0.5,
           "losses/weighted_cross_entropy": 1,
           "auc": 0,
-          "confusion_matrix/true_positives": 1,
-          "confusion_matrix/true_negatives": 0,
-          "confusion_matrix/false_positives": 1,
-          "confusion_matrix/false_negatives": 0,
+          "confusion_matrix/label_0_pred_0": 0,
+          "confusion_matrix/label_0_pred_1": 1,
+          "confusion_matrix/label_1_pred_0": 0,
+          "confusion_matrix/label_1_pred_1": 1,
       }, sess.run(value_ops))
 
       sess.run(update_ops)
@@ -197,10 +269,10 @@ class MetricsTest(tf.test.TestCase):
           "accuracy/accuracy": 0.5,
           "losses/weighted_cross_entropy": 1,
           "auc": 0,
-          "confusion_matrix/true_positives": 2,
-          "confusion_matrix/true_negatives": 0,
-          "confusion_matrix/false_positives": 2,
-          "confusion_matrix/false_negatives": 0,
+          "confusion_matrix/label_0_pred_0": 0,
+          "confusion_matrix/label_0_pred_1": 2,
+          "confusion_matrix/label_1_pred_0": 0,
+          "confusion_matrix/label_1_pred_1": 2,
       }, sess.run(value_ops))
 
 

research/astronet/astronet/ops/testing.py

@@ -47,15 +47,10 @@ def fake_features(feature_spec, batch_size):
     Dictionary containing "time_series_features" and "aux_features". Each is a
         dictionary of named numpy arrays of shape [batch_size, length].
   """
-  features = {}
-  features["time_series_features"] = {
-      name: np.random.random([batch_size, spec["length"]])
-      for name, spec in feature_spec.items() if spec["is_time_series"]
-  }
-  features["aux_features"] = {
-      name: np.random.random([batch_size, spec["length"]])
-      for name, spec in feature_spec.items() if not spec["is_time_series"]
-  }
+  features = {"time_series_features": {}, "aux_features": {}}
+  for name, spec in feature_spec.items():
+    ftype = "time_series_features" if spec["is_time_series"] else "aux_features"
+    features[ftype][name] = np.random.random([batch_size, spec["length"]])
   return features