replace tensorflow io API with python standard API or API from popular third party library

model_zoo/official/recommend/ncf/src/movielens.py

@@ -22,6 +22,7 @@ from __future__ import division
 from __future__ import print_function
 
 import os
+import shutil
 import tempfile
 import zipfile
 import argparse
@@ -32,7 +33,6 @@ from six.moves import urllib
 import numpy as np
 import pandas as pd
 from absl import logging
-import tensorflow as tf
 
 ML_1M = "ml-1m"
 ML_20M = "ml-20m"
@@ -100,9 +100,9 @@ def _download_and_clean(dataset, data_dir):
 
     expected_files = ["{}.zip".format(dataset), RATINGS_FILE, MOVIES_FILE]
 
-    tf.io.gfile.makedirs(data_subdir)
+    os.makedirs(data_subdir, exist_ok=True)
     if set(expected_files).intersection(
-            tf.io.gfile.listdir(data_subdir)) == set(expected_files):
+            os.listdir(data_subdir)) == set(expected_files):
         logging.info("Dataset {} has already been downloaded".format(dataset))
         return
 
@@ -127,16 +127,16 @@ def _download_and_clean(dataset, data_dir):
         else:
             _regularize_20m_dataset(temp_dir)
 
-        for fname in tf.io.gfile.listdir(temp_dir):
-            if not tf.io.gfile.exists(os.path.join(data_subdir, fname)):
-                tf.io.gfile.copy(os.path.join(temp_dir, fname),
-                                 os.path.join(data_subdir, fname))
+        for fname in os.listdir(temp_dir):
+            if not os.path.exists(os.path.join(data_subdir, fname)):
+                shutil.copy(os.path.join(temp_dir, fname),
+                            os.path.join(data_subdir, fname))
             else:
                 logging.info("Skipping copy of {}, as it already exists in the "
                              "destination folder.".format(fname))
 
     finally:
-        tf.io.gfile.rmtree(temp_dir)
+        shutil.rmtree(temp_dir)
 
 
 def _transform_csv(input_path, output_path, names, skip_first, separator=","):
@@ -152,8 +152,8 @@ def _transform_csv(input_path, output_path, names, skip_first, separator=","):
     if six.PY2:
         names = [six.ensure_text(n, "utf-8") for n in names]
 
-    with tf.io.gfile.GFile(output_path, "wb") as f_out, \
-            tf.io.gfile.GFile(input_path, "rb") as f_in:
+    with open(output_path, "wb") as f_out, \
+            open(input_path, "rb") as f_in:
 
         # Write column names to the csv.
         f_out.write(",".join(names).encode("utf-8"))
@@ -199,7 +199,7 @@ def _regularize_1m_dataset(temp_dir):
         output_path=os.path.join(temp_dir, MOVIES_FILE),
         names=MOVIE_COLUMNS, skip_first=False, separator="::")
 
-    tf.io.gfile.rmtree(working_dir)
+    shutil.rmtree(working_dir)
 
 
 def _regularize_20m_dataset(temp_dir):
@@ -233,7 +233,7 @@ def _regularize_20m_dataset(temp_dir):
         output_path=os.path.join(temp_dir, MOVIES_FILE),
         names=MOVIE_COLUMNS, skip_first=True, separator=",")
 
-    tf.io.gfile.rmtree(working_dir)
+    shutil.rmtree(working_dir)
 
 
 def download(dataset, data_dir):
@@ -244,14 +244,14 @@ def download(dataset, data_dir):
 
 
 def ratings_csv_to_dataframe(data_dir, dataset):
-    with tf.io.gfile.GFile(os.path.join(data_dir, dataset, RATINGS_FILE)) as f:
+    with open(os.path.join(data_dir, dataset, RATINGS_FILE)) as f:
         return pd.read_csv(f, encoding="utf-8")
 
 
 def csv_to_joint_dataframe(data_dir, dataset):
     ratings = ratings_csv_to_dataframe(data_dir, dataset)
 
-    with tf.io.gfile.GFile(os.path.join(data_dir, dataset, MOVIES_FILE)) as f:
+    with open(os.path.join(data_dir, dataset, MOVIES_FILE)) as f:
         movies = pd.read_csv(f, encoding="utf-8")
 
     df = ratings.merge(movies, on=ITEM_COLUMN)