automatic import of python-tensorflow-recommenders

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+/tensorflow-recommenders-0.7.3.tar.gz
diff --git a/python-tensorflow-recommenders.spec b/python-tensorflow-recommenders.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-tensorflow-recommenders
+Version:	0.7.3
+Release:	1
+Summary:	Tensorflow Recommenders, a TensorFlow library for recommender systems.
+License:	Apache 2.0
+URL:		https://github.com/tensorflow/recommenders
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/c0/d0/50dbb1a2b9d775a580441c45269b71b60aea3b0358f8131ae3f5c8e4ccec/tensorflow-recommenders-0.7.3.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-absl-py
+Requires:	python3-tensorflow
+Requires:	python3-tensorflow-macos
+Requires:	python3-annoy
+Requires:	python3-fire
+Requires:	python3-scann
+Requires:	python3-tensorflow-ranking
+
+%description
+# TensorFlow Recommenders
+
+![TensorFlow Recommenders logo](assets/full_logo.png)
+
+![TensorFlow Recommenders build badge](https://github.com/tensorflow/recommenders/actions/workflows/test.yaml/badge.svg)
+[![PyPI badge](https://img.shields.io/pypi/v/tensorflow-recommenders.svg)](https://pypi.python.org/pypi/tensorflow-recommenders/)
+
+TensorFlow Recommenders is a library for building recommender system models
+using [TensorFlow](https://www.tensorflow.org).
+
+It helps with the full workflow of building a recommender system: data
+preparation, model formulation, training, evaluation, and deployment.
+
+It's built on Keras and aims to have a gentle learning curve while still giving
+you the flexibility to build complex models.
+
+## Installation
+
+Make sure you have TensorFlow 2.x installed, and install from `pip`:
+
+```shell
+pip install tensorflow-recommenders
+```
+
+## Documentation
+
+Have a look at our
+[tutorials](https://tensorflow.org/recommenders/examples/quickstart) and
+[API reference](https://www.tensorflow.org/recommenders/api_docs/python/tfrs/).
+
+## Quick start
+
+Building a factorization model for the Movielens 100K dataset is very simple
+([Colab](https://tensorflow.org/recommenders/examples/quickstart)):
+
+```python
+from typing import Dict, Text
+
+import tensorflow as tf
+import tensorflow_datasets as tfds
+import tensorflow_recommenders as tfrs
+
+# Ratings data.
+ratings = tfds.load('movielens/100k-ratings', split="train")
+# Features of all the available movies.
+movies = tfds.load('movielens/100k-movies', split="train")
+
+# Select the basic features.
+ratings = ratings.map(lambda x: {
+    "movie_id": tf.strings.to_number(x["movie_id"]),
+    "user_id": tf.strings.to_number(x["user_id"])
+})
+movies = movies.map(lambda x: tf.strings.to_number(x["movie_id"]))
+
+# Build a model.
+class Model(tfrs.Model):
+
+  def __init__(self):
+    super().__init__()
+
+    # Set up user representation.
+    self.user_model = tf.keras.layers.Embedding(
+        input_dim=2000, output_dim=64)
+    # Set up movie representation.
+    self.item_model = tf.keras.layers.Embedding(
+        input_dim=2000, output_dim=64)
+    # Set up a retrieval task and evaluation metrics over the
+    # entire dataset of candidates.
+    self.task = tfrs.tasks.Retrieval(
+        metrics=tfrs.metrics.FactorizedTopK(
+            candidates=movies.batch(128).map(self.item_model)
+        )
+    )
+
+  def compute_loss(self, features: Dict[Text, tf.Tensor], training=False) -> tf.Tensor:
+
+    user_embeddings = self.user_model(features["user_id"])
+    movie_embeddings = self.item_model(features["movie_id"])
+
+    return self.task(user_embeddings, movie_embeddings)
+
+
+model = Model()
+model.compile(optimizer=tf.keras.optimizers.Adagrad(0.5))
+
+# Randomly shuffle data and split between train and test.
+tf.random.set_seed(42)
+shuffled = ratings.shuffle(100_000, seed=42, reshuffle_each_iteration=False)
+
+train = shuffled.take(80_000)
+test = shuffled.skip(80_000).take(20_000)
+
+# Train.
+model.fit(train.batch(4096), epochs=5)
+
+# Evaluate.
+model.evaluate(test.batch(4096), return_dict=True)
+```
+
+
+
+
+%package -n python3-tensorflow-recommenders
+Summary:	Tensorflow Recommenders, a TensorFlow library for recommender systems.
+Provides:	python-tensorflow-recommenders
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-tensorflow-recommenders
+# TensorFlow Recommenders
+
+![TensorFlow Recommenders logo](assets/full_logo.png)
+
+![TensorFlow Recommenders build badge](https://github.com/tensorflow/recommenders/actions/workflows/test.yaml/badge.svg)
+[![PyPI badge](https://img.shields.io/pypi/v/tensorflow-recommenders.svg)](https://pypi.python.org/pypi/tensorflow-recommenders/)
+
+TensorFlow Recommenders is a library for building recommender system models
+using [TensorFlow](https://www.tensorflow.org).
+
+It helps with the full workflow of building a recommender system: data
+preparation, model formulation, training, evaluation, and deployment.
+
+It's built on Keras and aims to have a gentle learning curve while still giving
+you the flexibility to build complex models.
+
+## Installation
+
+Make sure you have TensorFlow 2.x installed, and install from `pip`:
+
+```shell
+pip install tensorflow-recommenders
+```
+
+## Documentation
+
+Have a look at our
+[tutorials](https://tensorflow.org/recommenders/examples/quickstart) and
+[API reference](https://www.tensorflow.org/recommenders/api_docs/python/tfrs/).
+
+## Quick start
+
+Building a factorization model for the Movielens 100K dataset is very simple
+([Colab](https://tensorflow.org/recommenders/examples/quickstart)):
+
+```python
+from typing import Dict, Text
+
+import tensorflow as tf
+import tensorflow_datasets as tfds
+import tensorflow_recommenders as tfrs
+
+# Ratings data.
+ratings = tfds.load('movielens/100k-ratings', split="train")
+# Features of all the available movies.
+movies = tfds.load('movielens/100k-movies', split="train")
+
+# Select the basic features.
+ratings = ratings.map(lambda x: {
+    "movie_id": tf.strings.to_number(x["movie_id"]),
+    "user_id": tf.strings.to_number(x["user_id"])
+})
+movies = movies.map(lambda x: tf.strings.to_number(x["movie_id"]))
+
+# Build a model.
+class Model(tfrs.Model):
+
+  def __init__(self):
+    super().__init__()
+
+    # Set up user representation.
+    self.user_model = tf.keras.layers.Embedding(
+        input_dim=2000, output_dim=64)
+    # Set up movie representation.
+    self.item_model = tf.keras.layers.Embedding(
+        input_dim=2000, output_dim=64)
+    # Set up a retrieval task and evaluation metrics over the
+    # entire dataset of candidates.
+    self.task = tfrs.tasks.Retrieval(
+        metrics=tfrs.metrics.FactorizedTopK(
+            candidates=movies.batch(128).map(self.item_model)
+        )
+    )
+
+  def compute_loss(self, features: Dict[Text, tf.Tensor], training=False) -> tf.Tensor:
+
+    user_embeddings = self.user_model(features["user_id"])
+    movie_embeddings = self.item_model(features["movie_id"])
+
+    return self.task(user_embeddings, movie_embeddings)
+
+
+model = Model()
+model.compile(optimizer=tf.keras.optimizers.Adagrad(0.5))
+
+# Randomly shuffle data and split between train and test.
+tf.random.set_seed(42)
+shuffled = ratings.shuffle(100_000, seed=42, reshuffle_each_iteration=False)
+
+train = shuffled.take(80_000)
+test = shuffled.skip(80_000).take(20_000)
+
+# Train.
+model.fit(train.batch(4096), epochs=5)
+
+# Evaluate.
+model.evaluate(test.batch(4096), return_dict=True)
+```
+
+
+
+
+%package help
+Summary:	Development documents and examples for tensorflow-recommenders
+Provides:	python3-tensorflow-recommenders-doc
+%description help
+# TensorFlow Recommenders
+
+![TensorFlow Recommenders logo](assets/full_logo.png)
+
+![TensorFlow Recommenders build badge](https://github.com/tensorflow/recommenders/actions/workflows/test.yaml/badge.svg)
+[![PyPI badge](https://img.shields.io/pypi/v/tensorflow-recommenders.svg)](https://pypi.python.org/pypi/tensorflow-recommenders/)
+
+TensorFlow Recommenders is a library for building recommender system models
+using [TensorFlow](https://www.tensorflow.org).
+
+It helps with the full workflow of building a recommender system: data
+preparation, model formulation, training, evaluation, and deployment.
+
+It's built on Keras and aims to have a gentle learning curve while still giving
+you the flexibility to build complex models.
+
+## Installation
+
+Make sure you have TensorFlow 2.x installed, and install from `pip`:
+
+```shell
+pip install tensorflow-recommenders
+```
+
+## Documentation
+
+Have a look at our
+[tutorials](https://tensorflow.org/recommenders/examples/quickstart) and
+[API reference](https://www.tensorflow.org/recommenders/api_docs/python/tfrs/).
+
+## Quick start
+
+Building a factorization model for the Movielens 100K dataset is very simple
+([Colab](https://tensorflow.org/recommenders/examples/quickstart)):
+
+```python
+from typing import Dict, Text
+
+import tensorflow as tf
+import tensorflow_datasets as tfds
+import tensorflow_recommenders as tfrs
+
+# Ratings data.
+ratings = tfds.load('movielens/100k-ratings', split="train")
+# Features of all the available movies.
+movies = tfds.load('movielens/100k-movies', split="train")
+
+# Select the basic features.
+ratings = ratings.map(lambda x: {
+    "movie_id": tf.strings.to_number(x["movie_id"]),
+    "user_id": tf.strings.to_number(x["user_id"])
+})
+movies = movies.map(lambda x: tf.strings.to_number(x["movie_id"]))
+
+# Build a model.
+class Model(tfrs.Model):
+
+  def __init__(self):
+    super().__init__()
+
+    # Set up user representation.
+    self.user_model = tf.keras.layers.Embedding(
+        input_dim=2000, output_dim=64)
+    # Set up movie representation.
+    self.item_model = tf.keras.layers.Embedding(
+        input_dim=2000, output_dim=64)
+    # Set up a retrieval task and evaluation metrics over the
+    # entire dataset of candidates.
+    self.task = tfrs.tasks.Retrieval(
+        metrics=tfrs.metrics.FactorizedTopK(
+            candidates=movies.batch(128).map(self.item_model)
+        )
+    )
+
+  def compute_loss(self, features: Dict[Text, tf.Tensor], training=False) -> tf.Tensor:
+
+    user_embeddings = self.user_model(features["user_id"])
+    movie_embeddings = self.item_model(features["movie_id"])
+
+    return self.task(user_embeddings, movie_embeddings)
+
+
+model = Model()
+model.compile(optimizer=tf.keras.optimizers.Adagrad(0.5))
+
+# Randomly shuffle data and split between train and test.
+tf.random.set_seed(42)
+shuffled = ratings.shuffle(100_000, seed=42, reshuffle_each_iteration=False)
+
+train = shuffled.take(80_000)
+test = shuffled.skip(80_000).take(20_000)
+
+# Train.
+model.fit(train.batch(4096), epochs=5)
+
+# Evaluate.
+model.evaluate(test.batch(4096), return_dict=True)
+```
+
+
+
+
+%prep
+%autosetup -n tensorflow-recommenders-0.7.3
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-tensorflow-recommenders -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon Apr 10 2023 Python_Bot <Python_Bot@openeuler.org> - 0.7.3-1
+- Package Spec generated
diff --git a/sources b/sources
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+1b4f0e1c3fd3e40347fbbec2b694605f  tensorflow-recommenders-0.7.3.tar.gz