NVIDIA · okuchaiev · Oct 2, 2017 · Oct 2, 2017 · Oct 2, 2017 · Oct 2, 2017
diff --git a/.gitignore b/.gitignore
@@ -1,3 +1,9 @@
+# Data files and folders
+download/
+training_set/
+nf_prize_dataset.tar.gz
+Netflix/
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

diff --git a/README.md b/README.md
@@ -22,29 +22,16 @@ $ python -m unittest test/test_model.py
 
 ### Get the data
 
+**Note: Run all these commands within your `DeepRecommender` folder**
+
 [Netflix prize](http://netflixprize.com/)
 
-* ```$ mkdir -p ~/Recommendations``` you can use any other folder name
-* Download from [here](http://academictorrents.com/details/9b13183dc4d60676b773c9e2cd6de5e5542cee9a) to ```~/Recommendations```
-* ```$ cd ~/Recommendations```
-* ```$ tar -xvf nf_prize_dataset.tar.gz```
-* ```$ tar -xf download/training_set.tar ```
-* Create necessary folders
+* Download from [here](http://academictorrents.com/details/9b13183dc4d60676b773c9e2cd6de5e5542cee9a) into your ```DeepRecommender``` folder
 ```
-mkdir -p Netflix/N3M_TRAIN
-mkdir -p Netflix/N3M_VALID
-mkdir -p Netflix/N3M_TEST
-mkdir -p Netflix/N6M_TRAIN
-mkdir -p Netflix/N6M_VALID
-mkdir -p Netflix/N6M_TEST
-mkdir -p Netflix/N1Y_TRAIN
-mkdir -p Netflix/N1Y_VALID
-mkdir -p Netflix/N1Y_TEST
-mkdir -p Netflix/NF_TRAIN
-mkdir -p Netflix/NF_VALID
-mkdir -p Netflix/NF_TEST
+$ tar -xvf nf_prize_dataset.tar.gz
+$ tar -xf download/training_set.tar
+$ python ./data_utils/netflix_data_convert.py training_set Netflix
 ```
-* ```$ python ~/repos/DeepRecoEncoders/data_utils/netflix_data_convert.py training_set Netflix```. Here ```~/repos/DeepRecoEncoders''' is a path to this repo.
 
 #### Data stats
 | Dataset  | Netflix 3 months | Netflix 6 months | Netflix 1 year | Netflix full |
@@ -62,7 +49,7 @@ mkdir -p Netflix/NF_TEST
 ### Train the model
 In this example, the model will be trained for 12 epochs. In paper we train for 102.
 ```
-python ~/repos/DeepRecoEncoders/run.py --gpu_ids 0 \
+python run.py --gpu_ids 0 \
 --path_to_train_data Netflix/NF_TRAIN \
 --path_to_eval_data Netflix/NF_VALID \
 --hidden_layers 512,512,1024 \
@@ -86,7 +73,7 @@ $ tensorboard --logdir=model_save
 
 ### Run inference on the Test set
 ```
-python ~/repos/DeepRecoEncoders/infer.py \
+python infer.py \
 --path_to_train_data Netflix/NF_TRAIN \
 --path_to_eval_data Netflix/NF_TEST \
 --hidden_layers 512,512,1024 \
@@ -98,7 +85,7 @@ python ~/repos/DeepRecoEncoders/infer.py \
 
 ### Compute Test RMSE
 ```
-python ~/repos/DeepRecoEncoders/compute_RMSE.py --path_to_predictions=preds.txt
+python compute_RMSE.py --path_to_predictions=preds.txt
 ```
 After 12 epochs you should get RMSE around 0.927. Train longer to get below 0.92
 

diff --git a/data_utils/netflix_data_convert.py b/data_utils/netflix_data_convert.py
@@ -1,5 +1,5 @@
 # Copyright (c) 2017 NVIDIA Corporation
-from os import listdir, path
+from os import listdir, path, makedirs
 import random
 import sys
 import time
@@ -75,6 +75,13 @@ def create_NETFLIX_data_timesplit(all_data,
 
 
 def main(args):
+  # create necessary folders:
+  for output_dir in [
+    "Netflix/N3M_TRAIN", "Netflix/N3M_VALID", "Netflix/N3M_TEST", "Netflix/N6M_TRAIN",
+    "Netflix/N6M_VALID", "Netflix/N6M_TEST", "Netflix/N1Y_TRAIN", "Netflix/N1Y_VALID",
+    "Netflix/N1Y_TEST", "Netflix/NF_TRAIN", "Netflix/NF_VALID", "Netflix/NF_TEST"]:
+    makedirs(output_dir, exist_ok=True)
+
   user2id_map = dict()
   item2id_map = dict()
   userId = 0

diff --git a/infer.py b/infer.py
@@ -3,13 +3,9 @@
 import argparse
 from reco_encoder.data import input_layer
 from reco_encoder.model import model
-import torch.optim as optim
-import torch.nn as nn
 from torch.autograd import Variable
 import copy
-import time
 from pathlib import Path
-import numpy as np
 
 parser = argparse.ArgumentParser(description='RecoEncoder')
 
@@ -92,4 +88,3 @@ def main():
 if __name__ == '__main__':
   main()
 
-
diff --git a/reco_encoder/__init__.py b/reco_encoder/__init__.py
@@ -1,3 +1 @@
 # Copyright (c) 2017 NVIDIA Corporation
-from . import data
-from . import model
diff --git a/reco_encoder/data/__init__.py b/reco_encoder/data/__init__.py
@@ -1,2 +1 @@
 # Copyright (c) 2017 NVIDIA Corporation
-from . import input_layer
diff --git a/reco_encoder/model/__init__.py b/reco_encoder/model/__init__.py
@@ -1,3 +1 @@
 # Copyright (c) 2017 NVIDIA Corporation
-from .model import AutoEncoder
-from .model import MSEloss
diff --git a/test/context.py b/test/context.py
@@ -2,5 +2,3 @@
 import os
 import sys
 sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
-
-import reco_encoder
diff --git a/test/data_layer_tests.py b/test/data_layer_tests.py
@@ -1,6 +1,5 @@
 # Copyright (c) 2017 NVIDIA Corporation
 import unittest
-import sys
 from .context import reco_encoder
 
 class UserItemRecDataProviderTest(unittest.TestCase):

diff --git a/test/test_model.py b/test/test_model.py
@@ -3,10 +3,8 @@
 import sys
 sys.path.append('data')
 sys.path.append('model')
-import torch
 from .context import reco_encoder
 import torch.optim as optim
-import torch.nn as nn
 from torch.autograd import Variable
 
 class iRecAutoEncoderTest(unittest.TestCase):
Original file line number	Diff line number	Diff line change
		@@ -1,2 +1 @@
		# Copyright (c) 2017 NVIDIA Corporation
		from . import input_layer