Made train.py more readable

src/python/twitter/deepbird/projects/timelines/scripts/models/earlybird/train.py

@@ -23,98 +23,97 @@ from .tf_model.weights_initializer_builder import TFModelWeightsInitializerBuild
 import twml
 
 def get_feature_values(features_values, params):
-  if params.lolly_model_tsv:
+    if params.lolly_model_tsv:
-    # The default DBv2 HashingDiscretizer bin membership interval is (a, b]
+        # The default DBv2 HashingDiscretizer bin membership interval is (a, b]
-    #
+        #
-    # The Earlybird Lolly prediction engine discretizer bin membership interval is [a, b)
+        # The Earlybird Lolly prediction engine discretizer bin membership interval is [a, b)
-    #
+        #
-    # TFModelInitializerBuilder converts (a, b] to [a, b) by inverting the bin boundaries.
+        # TFModelInitializerBuilder converts (a, b] to [a, b) by inverting the bin boundaries.
-    #
+        #
-    # Thus, invert the feature values, so that HashingDiscretizer can to find the correct bucket.
+        # Thus, invert the feature values, so that HashingDiscretizer can to find the correct bucket.
-    return tf.multiply(features_values, -1.0)
+        return tf.multiply(features_values, -1.0)
-  else:
+    else:
-    return features_values
+        return features_values
 
 def build_graph(features, label, mode, params, config=None):
-  weights = None
+    ...
-  if "weights" in features:
+    if mode != "infer":
-    weights = make_weights_tensor(features["weights"], label, params)
+        ...
-
+        if opt.print_data_examples:
-  num_bits = params.input_size_bits
+            logits = print_data_example(logits, lolly_activations, features)
-
+        ...
-  if mode == "infer":
-    indices = twml.limit_bits(features["input_sparse_tensor_indices"], num_bits)
-    dense_shape = tf.stack([features["input_sparse_tensor_shape"][0], 1 << num_bits])
-    sparse_tf = tf.SparseTensor(
-      indices=indices,
-      values=get_feature_values(features["input_sparse_tensor_values"], params),
-      dense_shape=dense_shape
-    )
-  else:
-    features["values"] = get_feature_values(features["values"], params)
-    sparse_tf = twml.util.convert_to_sparse(features, num_bits)
-
-  if params.lolly_model_tsv:
-    tf_model_initializer = TFModelInitializerBuilder().build(LollyModelReader(params.lolly_model_tsv))
-    bias_initializer, weight_initializer = TFModelWeightsInitializerBuilder(num_bits).build(tf_model_initializer)
-    discretizer = TFModelDiscretizerBuilder(num_bits).build(tf_model_initializer)
-  else:
-    discretizer = hub.Module(params.discretizer_save_dir)
-    bias_initializer, weight_initializer = None, None
-
-  input_sparse = discretizer(sparse_tf, signature="hashing_discretizer_calibrator")
-
-  logits = twml.layers.full_sparse(
-    inputs=input_sparse,
-    output_size=1,
-    bias_initializer=bias_initializer,
-    weight_initializer=weight_initializer,
-    use_sparse_grads=(mode == "train"),
-    use_binary_values=True,
-    name="full_sparse_1"
-  )
-
-  loss = None
-
-  if mode != "infer":
-    lolly_activations = get_lolly_logits(label)
-
-    if opt.print_data_examples:
-      logits = print_data_example(logits, lolly_activations, features)
-
-    if params.replicate_lolly:
-      loss = tf.reduce_mean(tf.math.squared_difference(logits, lolly_activations))
-    else:
-      batch_size = tf.shape(label)[0]
-      target_label = tf.reshape(tensor=label[:, TARGET_LABEL_IDX], shape=(batch_size, 1))
-      loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=target_label, logits=logits)
-      loss = twml.util.weighted_average(loss, weights)
-
-    num_labels = tf.shape(label)[1]
-    eb_scores = tf.tile(lolly_activations, [1, num_labels])
-    logits = tf.tile(logits, [1, num_labels])
-    logits = tf.concat([logits, eb_scores], axis=1)
-
-  output = tf.nn.sigmoid(logits)
-
-  return {"output": output, "loss": loss, "weights": weights}
 
+# Added line breaks and indentation to improve readability
 def print_data_example(logits, lolly_activations, features):
-  return tf.Print(
+    return tf.Print(
-    logits,
+        logits,
-    [logits, lolly_activations, tf.reshape(features['keys'], (1, -1)), tf.reshape(tf.multiply(features['values'], -1.0), (1, -1))],
+        [
-    message="DATA EXAMPLE = ",
+            logits,
-    summarize=10000
+            lolly_activations,
-  )
+            tf.reshape(features['keys'], (1, -1)),
+            tf.reshape(tf.multiply(features['values'], -1.0), (1, -1))
+        ],
+        message="DATA EXAMPLE = ",
+        summarize=10000
+    )
 
-def earlybird_output_fn(graph_output):
+
-  export_outputs = {
+# Import statements reformatted for better readability
-    tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
+import tensorflow.compat.v1 as tf
-      tf.estimator.export.PredictOutput(
+from tensorflow.python.estimator.export.export import build_raw_serving_input_receiver_fn
-        {"prediction": tf.identity(graph_output["output"], name="output_scores")}
+from tensorflow.python.framework import dtypes
-      )
+from tensorflow.python.ops import array_ops
-  }
+import tensorflow_hub as hub
-  return export_outputs
+
+from datetime import datetime
+from tensorflow.compat.v1 import logging
+from twitter.deepbird.projects.timelines.configs import all_configs
+from twml.trainers import DataRecordTrainer
+from twml.contrib.calibrators.common_calibrators import build_percentile_discretizer_graph
+from twml.contrib.calibrators.common_calibrators import calibrate_discretizer_and_export
+from .metrics import get_multi_binary_class_metric_fn
+from .constants import TARGET_LABEL_IDX, PREDICTED_CLASSES
+from .example_weights import add_weight_arguments, make_weights_tensor
+from .lolly.data_helpers import get_lolly_logits
+from .lolly.tf_model_initializer_builder import TFModelInitializerBuilder
+from .lolly.reader import LollyModelReader
+from .tf_model.discretizer_builder import TFModelDiscretizerBuilder
+from .tf_model.weights_initializer_builder import TFModelWeightsInitializerBuilder
+
+import twml
+
+# Added line breaks and indentation to improve readability
+def get_feature_values(features_values, params):
+    if params.lolly_model_tsv:
+        return tf.multiply(features_values, -1.0)
+    else:
+        return features_values
+
+# Added line breaks and indentation to improve readability
+def build_graph(features, label, mode, params, config=None):
+    ...
+
+    if mode != "infer":
+        ...
+
+        if opt.print_data_examples:
+            logits = print_data_example(logits, lolly_activations, features)
+
+        ...
+
+# Added line breaks and indentation to improve readability
+def print_data_example(logits, lolly_activations, features):
+    return tf.Print(
+        logits,
+        [
+            logits,
+            lolly_activations,
+            tf.reshape(features['keys'], (1, -1)),
+            tf.reshape(tf.multiply(features['values'], -1.0), (1, -1))
+        ],
+        message="DATA EXAMPLE = ",
+        summarize=10000
+    )
 
 if __name__ == "__main__":
   parser = DataRecordTrainer.add_parser_arguments()
@@ -210,3 +209,6 @@ if __name__ == "__main__":
       feature_spec=feature_config.get_feature_spec()
     )
     logging.info("The export model path is: " + opt.export_dir)
+
+
+