Twitter Recommendation Algorithm

Please note we have force-pushed a new initial commit in order to remove some publicly-available Twitter user information. Note that this process may be required in the future.

trust_and_safety_models/toxicity/utils/helpers.py

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+import bisect
+import os
+import random as python_random
+import subprocess
+
+from toxicity_ml_pipeline.settings.default_settings_tox import LOCAL_DIR
+
+import numpy as np
+from sklearn.metrics import precision_recall_curve
+
+
+try:
+  import tensorflow as tf
+except ModuleNotFoundError:
+  pass
+
+
+def upload_model(full_gcs_model_path):
+  folder_name = full_gcs_model_path
+  if folder_name[:5] != "gs://":
+    folder_name = "gs://" + folder_name
+
+  dirname = os.path.dirname(folder_name)
+  epoch = os.path.basename(folder_name)
+
+  model_dir = os.path.join(LOCAL_DIR, "models")
+  cmd = f"mkdir {model_dir}"
+  try:
+    execute_command(cmd)
+  except subprocess.CalledProcessError:
+    pass
+  model_dir = os.path.join(model_dir, os.path.basename(dirname))
+  cmd = f"mkdir {model_dir}"
+  try:
+    execute_command(cmd)
+  except subprocess.CalledProcessError:
+    pass
+
+  try:
+    _ = int(epoch)
+  except ValueError:
+    cmd = f"gsutil rsync -r '{folder_name}' {model_dir}"
+    weights_dir = model_dir
+
+  else:
+    cmd = f"gsutil cp '{dirname}/checkpoint' {model_dir}/"
+    execute_command(cmd)
+    cmd = f"gsutil cp '{os.path.join(dirname, epoch)}*' {model_dir}/"
+    weights_dir = f"{model_dir}/{epoch}"
+
+  execute_command(cmd)
+  return weights_dir
+
+def compute_precision_fixed_recall(labels, preds, fixed_recall):
+  precision_values, recall_values, thresholds = precision_recall_curve(y_true=labels, probas_pred=preds)
+  index_recall = bisect.bisect_left(-recall_values, -1 * fixed_recall)
+  result = precision_values[index_recall - 1]
+  print(f"Precision at {recall_values[index_recall-1]} recall: {result}")
+
+  return result, thresholds[index_recall - 1]
+
+def load_inference_func(model_folder):
+  model = tf.saved_model.load(model_folder, ["serve"])
+  inference_func = model.signatures["serving_default"]
+  return inference_func
+
+
+def execute_query(client, query):
+  job = client.query(query)
+  df = job.result().to_dataframe()
+  return df
+
+
+def execute_command(cmd, print_=True):
+  s = subprocess.run(cmd, shell=True, capture_output=print_, check=True)
+  if print_:
+    print(s.stderr.decode("utf-8"))
+    print(s.stdout.decode("utf-8"))
+
+
+def check_gpu():
+  try:
+    execute_command("nvidia-smi")
+  except subprocess.CalledProcessError:
+    print("There is no GPU when there should be one.")
+    raise AttributeError
+
+  l = tf.config.list_physical_devices("GPU")
+  if len(l) == 0:
+    raise ModuleNotFoundError("Tensorflow has not found the GPU. Check your installation")
+  print(l)
+
+
+def set_seeds(seed):
+  np.random.seed(seed)
+
+  python_random.seed(seed)
+
+  tf.random.set_seed(seed)