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feat: Extend 'sample_listwise' to support more movielens Dataset features #735

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feat: Extend 'sample_listwise' to support more movielens Dataset features #735

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0eaac99
feat: Extend to support more Dataset features
keivanipchihagh Aug 30, 2024
14f6644
fix: Add user_id to the features
keivanipchihagh Sep 2, 2024
57cfc11
fix: Remove reshaping user embeddings
keivanipchihagh Sep 2, 2024
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37 changes: 15 additions & 22 deletions docs/examples/listwise_ranking.ipynb
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -39,20 +39,20 @@
"source": [
"# Listwise ranking\n",
"\n",
"\u003ctable class=\"tfo-notebook-buttons\" align=\"left\"\u003e\n",
" \u003ctd\u003e\n",
" \u003ca target=\"_blank\" href=\"https://www.tensorflow.org/recommenders/examples/listwise_ranking\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" /\u003eView on TensorFlow.org\u003c/a\u003e\n",
" \u003c/td\u003e\n",
" \u003ctd\u003e\n",
" \u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/recommenders/blob/main/docs/examples/listwise_ranking.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /\u003eRun in Google Colab\u003c/a\u003e\n",
" \u003c/td\u003e\n",
" \u003ctd\u003e\n",
" \u003ca target=\"_blank\" href=\"https://github.com/tensorflow/recommenders/blob/main/docs/examples/listwise_ranking.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /\u003eView source on GitHub\u003c/a\u003e\n",
" \u003c/td\u003e\n",
" \u003ctd\u003e\n",
" \u003ca href=\"https://storage.googleapis.com/tensorflow_docs/recommenders/docs/examples/listwise_ranking.ipynb\"\u003e\u003cimg src=\"https://www.tensorflow.org/images/download_logo_32px.png\" /\u003eDownload notebook\u003c/a\u003e\n",
" \u003c/td\u003e\n",
"\u003c/table\u003e"
"<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://www.tensorflow.org/recommenders/examples/listwise_ranking\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/recommenders/blob/main/docs/examples/listwise_ranking.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
" </td>\n",
" <td>\n",
" <a target=\"_blank\" href=\"https://github.com/tensorflow/recommenders/blob/main/docs/examples/listwise_ranking.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
" </td>\n",
" <td>\n",
" <a href=\"https://storage.googleapis.com/tensorflow_docs/recommenders/docs/examples/listwise_ranking.ipynb\"><img src=\"https://www.tensorflow.org/images/download_logo_32px.png\" />Download notebook</a>\n",
" </td>\n",
"</table>"
]
},
{
Expand Down Expand Up @@ -309,18 +309,11 @@
" # Movie embeddings are a [batch_size, num_movies_in_list, embedding_dim]\n",
" # tensor.\n",
" movie_embeddings = self.movie_embeddings(features[\"movie_title\"])\n",
" \n",
" # We want to concatenate user embeddings with movie emebeddings to pass\n",
" # them into the ranking model. To do so, we need to reshape the user\n",
" # embeddings to match the shape of movie embeddings.\n",
" list_length = features[\"movie_title\"].shape[1]\n",
" user_embedding_repeated = tf.repeat(\n",
" tf.expand_dims(user_embeddings, 1), [list_length], axis=1)\n",
"\n",
" # Once reshaped, we concatenate and pass into the dense layers to generate\n",
" # predictions.\n",
" concatenated_embeddings = tf.concat(\n",
" [user_embedding_repeated, movie_embeddings], 2)\n",
" [user_embeddings, movie_embeddings], 2)\n",
" \n",
" return self.score_model(concatenated_embeddings)\n",
"\n",
Expand Down
74 changes: 34 additions & 40 deletions tensorflow_recommenders/examples/movielens.py
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Original file line number Diff line number Diff line change
Expand Up @@ -93,9 +93,9 @@ def evaluate(user_model: tf.keras.Model,
}


def _create_feature_dict() -> Dict[Text, List[tf.Tensor]]:
def _create_feature_dict(features: List[Text]) -> Dict[Text, List[tf.Tensor]]:
"""Helper function for creating an empty feature dict for defaultdict."""
return {"movie_title": [], "user_rating": []}
return {key: [] for key in features}


def _sample_list(
Expand All @@ -108,22 +108,20 @@ def _sample_list(
random_state = np.random.RandomState()

sampled_indices = random_state.choice(
range(len(feature_lists["movie_title"])),
range(len(feature_lists["user_rating"])),
size=num_examples_per_list,
replace=False,
)
sampled_movie_titles = [
feature_lists["movie_title"][idx] for idx in sampled_indices
]
sampled_ratings = [
feature_lists["user_rating"][idx]
for idx in sampled_indices
]

return (
tf.stack(sampled_movie_titles, 0),
tf.stack(sampled_ratings, 0),
)
sampled_features = {}
for name, values in feature_lists.items():
sampled_features[name] = [
values[idx] for idx in sampled_indices
]

return {
name: tf.stack(values, 0)
for name, values in sampled_features.items()
}


def sample_listwise(
Expand All @@ -136,8 +134,8 @@ def sample_listwise(

Args:
rating_dataset:
The MovieLens ratings dataset loaded from TFDS with features
"movie_title", "user_id", and "user_rating".
The MovieLens ratings dataset loaded from TFDS. Feature must be provided
in the dataset. The dataset must contain the "user_rating" feature.
num_list_per_user:
An integer representing the number of lists that should be sampled for
each user in the training dataset.
Expand All @@ -150,28 +148,24 @@ def sample_listwise(
Returns:
A tf.data.Dataset containing list examples.

Each example contains three keys: "user_id", "movie_title", and
"user_rating". "user_id" maps to a string tensor that represents the user
id for the example. "movie_title" maps to a tensor of shape
[sum(num_example_per_list)] with dtype tf.string. It represents the list
of candidate movie ids. "user_rating" maps to a tensor of shape
[sum(num_example_per_list)] with dtype tf.float32. It represents the
rating of each movie in the candidate list.
Each example contains multiple keys. "user_id" maps to a string
tensor that represents the user id for the example. "movie_title" maps
to a tensor of shape [sum(num_example_per_list)] with dtype tf.string.
It represents the list of candidate movie ids. "user_rating" maps to
a tensor of shape [sum(num_example_per_list)] with dtype tf.float32.
It represents the rating of each movie in the candidate list.
"""
random_state = np.random.RandomState(seed)

example_lists_by_user = collections.defaultdict(_create_feature_dict)
features = rating_dataset.take(1).get_single_element().keys()
example_lists_by_user = collections.defaultdict(lambda: _create_feature_dict(features))

movie_title_vocab = set()
for example in rating_dataset:
user_id = example["user_id"].numpy()
example_lists_by_user[user_id]["movie_title"].append(
example["movie_title"])
example_lists_by_user[user_id]["user_rating"].append(
example["user_rating"])
movie_title_vocab.add(example["movie_title"].numpy())
user_id = example.get('user_id').numpy()
for key, value in example.items():
example_lists_by_user[user_id][key].append(value.numpy())

tensor_slices = {"user_id": [], "movie_title": [], "user_rating": []}
tensor_slices = {key: [] for key in features}

for user_id, feature_lists in example_lists_by_user.items():
for _ in range(num_list_per_user):
Expand All @@ -180,13 +174,13 @@ def sample_listwise(
if len(feature_lists["movie_title"]) < num_examples_per_list:
continue

sampled_movie_titles, sampled_ratings = _sample_list(
feature_lists,
num_examples_per_list,
random_state=random_state,
sampled_features = _sample_list(
feature_lists,
num_examples_per_list,
random_state=random_state,
)
tensor_slices["user_id"].append(user_id)
tensor_slices["movie_title"].append(sampled_movie_titles)
tensor_slices["user_rating"].append(sampled_ratings)

for feature, samples in sampled_features.items():
tensor_slices[feature].append(samples)

return tf.data.Dataset.from_tensor_slices(tensor_slices)