Merge 74f37ca086 into fb54d8b549

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

@@ -10,4 +10,4 @@ if __name__ == "__main__":
   lolly_model_scorer = LollyModelScorer(data_example_parser=DBv2DataExampleParser(lolly_model_reader))
 
   score = lolly_model_scorer.score(data_example=sys.argv[2])
-  print(score)
+  

trust_and_safety_models/abusive/abusive_model.py

@@ -31,7 +31,7 @@ ptos_prototype = Model(
   export_path="...",
   features=features,
 )
-print(ptos_prototype)
+
 
 cq_loader = BigQueryFeatureLoader(gcp_project=COMPUTE_PROJECT)
 labels = [
@@ -58,7 +58,6 @@ SELECT
 ...
 """
 
-print(train_query)
 train = cq_loader.load_features(ptos_prototype, "", "", custom_query=train_query)
 val = cq_loader.load_features(ptos_prototype, "", "", custom_query=val_query)
 print(train.describe(model=ptos_prototype))
@@ -105,7 +104,7 @@ def get_positive_weights():
   return pos_weight_tensor
 
 pos_weight_tensor = get_positive_weights()
-print(pos_weight_tensor)
+
 
 class TextEncoderPooledOutput(TextEncoder):
   def call(self, x):
@@ -224,7 +223,6 @@ SELECT
 test = cq_loader.load_features(ptos_prototype, "", "", custom_query=test_query)
 test = test.to_tf_dataset().map(parse_labeled_data)
 
-print(test)
 
 test_only_media = test.filter(lambda x, y: tf.equal(x["has_media"], True))
 test_only_nsfw = test.filter(lambda x, y: tf.greater_equal(x["precision_nsfw"], 0.95))
@@ -268,7 +266,6 @@ for name, df in subsets.items():
   metrics[name] = eval_model(candidate_model, df)
 [(name, m.pr_auc) for name, m in metrics.items()]
 for name, x in [(name, m.pr_auc.to_string(index=False).strip().split("\n")) for name, m in metrics.items()]:
-  print(name)
   for y in x:
     print(y.strip(), end="\t")
   print(".")

trust_and_safety_models/nsfw/nsfw_media.py

@@ -406,7 +406,6 @@ precision, recall, thresholds = pr
 
 auc_precision_recall = sklearn.metrics.auc(recall, precision)
 
-print(auc_precision_recall)
 
 plt.figure(figsize=(15, 10))
 plt.plot(recall, precision)
@@ -415,12 +414,10 @@ plt.xlabel("recall")
 plt.ylabel("precision")
 
 ptAt50 = get_point_for_recall(0.5, recall, precision)
-print(ptAt50)
 plt.plot( [ptAt50[0],ptAt50[0]], [0,ptAt50[1]], 'r')
 plt.plot([0, ptAt50[0]], [ptAt50[1], ptAt50[1]], 'r')
 
 ptAt90 = get_point_for_recall(0.9, recall, precision)
-print(ptAt90)
 plt.plot( [ptAt90[0],ptAt90[0]], [0,ptAt90[1]], 'b')
 plt.plot([0, ptAt90[0]], [ptAt90[1], ptAt90[1]], 'b')
 
@@ -437,7 +434,6 @@ plt.savefig('recall_precision_nsfw_Keras_with_twitter_CLIP_MU_test.pdf')
 precision, recall, thresholds = pr_sens_prev
 
 auc_precision_recall = sklearn.metrics.auc(recall, precision)
-print(auc_precision_recall)
 plt.figure(figsize=(15, 10))
 
 plt.plot(recall, precision)
@@ -446,12 +442,10 @@ plt.xlabel("recall")
 plt.ylabel("precision")
 
 ptAt50 = get_point_for_recall(0.5, recall, precision)
-print(ptAt50)
 plt.plot( [ptAt50[0],ptAt50[0]], [0,ptAt50[1]], 'r')
 plt.plot([0, ptAt50[0]], [ptAt50[1], ptAt50[1]], 'r')
 
 ptAt90 = get_point_for_recall(0.9, recall, precision)
-print(ptAt90)
 plt.plot( [ptAt90[0],ptAt90[0]], [0,ptAt90[1]], 'b')
 plt.plot([0, ptAt90[0]], [ptAt90[1], ptAt90[1]], 'b')
 

trust_and_safety_models/toxicity/train.py

@@ -98,7 +98,6 @@ class Trainer(object):
 
     for var, default_value in experiment_settings["default"].items():
       override_val = experiment_settings[experiment_id].get(var, default_value)
-      print("Setting ", var, override_val)
       self.__setattr__(var, override_val)
 
     self.content_loss_weight = content_loss_weight if self.dual_head else None