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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Copyright 2020 The TensorFlow Authors.
Step1:  Recommending movies
Step2:  Preparing the dataset
We're going to use the same data as the retrieval tutorial. This time, we're also going to keep the ratings
Step3:  As before, we'll split the data by putting 80% of the ratings in the train set, and 20% in the test set.
Step4:  Let's also figure out unique user ids and movie titles present in the data. 
This is important because we need to be able to map the raw values of our categorical features to embedding vectors in our models. To do that, we need a vocabulary that maps a raw feature value to an integer in a contiguous range
Step5:  Implementing a model
Architecture
Ranking models do not face the same efficiency constraints as retrieval models do, and so we have a little bit more freedom in our choice of architectures.
A model composed of multiple stacked dense layers is a relatively common architecture for ranking tasks. We can implement it as follows
Step6:  This model takes user ids and movie titles, and outputs a predicted rating
Step7:  Loss and metrics
The next component is the loss used to train our model. TFRS has several loss layers and tasks to make this easy.
In this instance, we'll make use of the Ranking task object
Step8:  The task itself is a Keras layer that takes true and predicted as arguments, and returns the computed loss. We'll use that to implement the model's training loop.
The full model
We can now put it all together into a model. TFRS exposes a base model class (tfrs.models.Model) which streamlines bulding models
Step9:  Fitting and evaluating
After defining the model, we can use standard Keras fitting and evaluation routines to fit and evaluate the model.
Let's first instantiate the model.
Step10:  Then shuffle, batch, and cache the training and evaluation data.
Step11:  Then train the  model
Step12:  As the model trains, the loss is falling and the RMSE metric is improving.
Finally, we can evaluate our model on the test set
Step13:  The lower the RMSE metric, the more accurate our model is at predicting ratings.
Testing the ranking model
Now we can test the ranking model by computing predictions for a set of movies and then rank these movies based on the predictions
Step14:  Exporting for serving
The model can be easily exported for serving
Step15:  We can now load it back and perform predictions
Step16:  Convert the model to TensorFLow Lite
Although TensorFlow Recommenders is primarily designed to perform server-side recommendations, you can still convert the trained ranking model to TensorFLow Lite and run it on-device (for better user privacy privacy and lower latency).
Step17:  Once the model is converted, you can run it like regular TensorFlow Lite models. Please check out TensorFlow Lite documentation to learn more.
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Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
Explanation: Copyright 2020 The TensorFlow Authors.
End of explanation
!pip install -q tensorflow-recommenders
!pip install -q --upgrade tensorflow-datasets
import os
import pprint
import tempfile
from typing import Dict, Text
import numpy as np
import tensorflow as tf
import tensorflow_datasets as tfds
import tensorflow_recommenders as tfrs
Explanation: Recommending movies: ranking
<table class="tfo-notebook-buttons" align="left">
  <td>
    <a target="_blank" href="https://www.tensorflow.org/recommenders/examples/basic_ranking"><img src="https://www.tensorflow.org/images/tf_logo_32px.png" />View on TensorFlow.org</a>
  </td>
  <td>
    <a target="_blank" href="https://colab.research.google.com/github/tensorflow/recommenders/blob/main/docs/examples/basic_ranking.ipynb"><img src="https://www.tensorflow.org/images/colab_logo_32px.png" />Run in Google Colab</a>
  </td>
  <td>
    <a target="_blank" href="https://github.com/tensorflow/recommenders/blob/main/docs/examples/basic_ranking.ipynb"><img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" />View source on GitHub</a>
  </td>
  <td>
    <a href="https://storage.googleapis.com/tensorflow_docs/recommenders/docs/examples/basic_ranking.ipynb"><img src="https://www.tensorflow.org/images/download_logo_32px.png" />Download notebook</a>
  </td>
</table>
Real-world recommender systems are often composed of two stages:
The retrieval stage is responsible for selecting an initial set of hundreds of candidates from all possible candidates. The main objective of this model is to efficiently weed out all candidates that the user is not interested in. Because the retrieval model may be dealing with millions of candidates, it has to be computationally efficient.
The ranking stage takes the outputs of the retrieval model and fine-tunes them to select the best possible handful of recommendations. Its task is to narrow down the set of items the user may be interested in to a shortlist of likely candidates.
We're going to focus on the second stage, ranking. If you are interested in the retrieval stage, have a look at our retrieval tutorial.
In this tutorial, we're going to:
Get our data and split it into a training and test set.
Implement a ranking model.
Fit and evaluate it.
Imports
Let's first get our imports out of the way.
End of explanation
ratings = tfds.load("movielens/100k-ratings", split="train")
ratings = ratings.map(lambda x: {
    "movie_title": x["movie_title"],
    "user_id": x["user_id"],
    "user_rating": x["user_rating"]
})
Explanation: Preparing the dataset
We're going to use the same data as the retrieval tutorial. This time, we're also going to keep the ratings: these are the objectives we are trying to predict.
End of explanation
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)
Explanation: As before, we'll split the data by putting 80% of the ratings in the train set, and 20% in the test set.
End of explanation
movie_titles = ratings.batch(1_000_000).map(lambda x: x["movie_title"])
user_ids = ratings.batch(1_000_000).map(lambda x: x["user_id"])
unique_movie_titles = np.unique(np.concatenate(list(movie_titles)))
unique_user_ids = np.unique(np.concatenate(list(user_ids)))
Explanation: Let's also figure out unique user ids and movie titles present in the data. 
This is important because we need to be able to map the raw values of our categorical features to embedding vectors in our models. To do that, we need a vocabulary that maps a raw feature value to an integer in a contiguous range: this allows us to look up the corresponding embeddings in our embedding tables.
End of explanation
class RankingModel(tf.keras.Model):
  def __init__(self):
    super().__init__()
    embedding_dimension = 32
    # Compute embeddings for users.
    self.user_embeddings = tf.keras.Sequential([
      tf.keras.layers.StringLookup(
        vocabulary=unique_user_ids, mask_token=None),
      tf.keras.layers.Embedding(len(unique_user_ids) + 1, embedding_dimension)
    ])
    # Compute embeddings for movies.
    self.movie_embeddings = tf.keras.Sequential([
      tf.keras.layers.StringLookup(
        vocabulary=unique_movie_titles, mask_token=None),
      tf.keras.layers.Embedding(len(unique_movie_titles) + 1, embedding_dimension)
    ])
    # Compute predictions.
    self.ratings = tf.keras.Sequential([
      # Learn multiple dense layers.
      tf.keras.layers.Dense(256, activation="relu"),
      tf.keras.layers.Dense(64, activation="relu"),
      # Make rating predictions in the final layer.
      tf.keras.layers.Dense(1)
  ])
    
  def call(self, inputs):
    user_id, movie_title = inputs
    user_embedding = self.user_embeddings(user_id)
    movie_embedding = self.movie_embeddings(movie_title)
    return self.ratings(tf.concat([user_embedding, movie_embedding], axis=1))
Explanation: Implementing a model
Architecture
Ranking models do not face the same efficiency constraints as retrieval models do, and so we have a little bit more freedom in our choice of architectures.
A model composed of multiple stacked dense layers is a relatively common architecture for ranking tasks. We can implement it as follows:
End of explanation
RankingModel()((["42"], ["One Flew Over the Cuckoo's Nest (1975)"]))
Explanation: This model takes user ids and movie titles, and outputs a predicted rating:
End of explanation
task = tfrs.tasks.Ranking(
  loss = tf.keras.losses.MeanSquaredError(),
  metrics=[tf.keras.metrics.RootMeanSquaredError()]
)
Explanation: Loss and metrics
The next component is the loss used to train our model. TFRS has several loss layers and tasks to make this easy.
In this instance, we'll make use of the Ranking task object: a convenience wrapper that bundles together the loss function and metric computation. 
We'll use it together with the MeanSquaredError Keras loss in order to predict the ratings.
End of explanation
class MovielensModel(tfrs.models.Model):
  def __init__(self):
    super().__init__()
    self.ranking_model: tf.keras.Model = RankingModel()
    self.task: tf.keras.layers.Layer = tfrs.tasks.Ranking(
      loss = tf.keras.losses.MeanSquaredError(),
      metrics=[tf.keras.metrics.RootMeanSquaredError()]
    )
  def call(self, features: Dict[str, tf.Tensor]) -> tf.Tensor:
    return self.ranking_model(
        (features["user_id"], features["movie_title"]))
  def compute_loss(self, features: Dict[Text, tf.Tensor], training=False) -> tf.Tensor:
    labels = features.pop("user_rating")
    
    rating_predictions = self(features)
    # The task computes the loss and the metrics.
    return self.task(labels=labels, predictions=rating_predictions)
Explanation: The task itself is a Keras layer that takes true and predicted as arguments, and returns the computed loss. We'll use that to implement the model's training loop.
The full model
We can now put it all together into a model. TFRS exposes a base model class (tfrs.models.Model) which streamlines bulding models: all we need to do is to set up the components in the __init__ method, and implement the compute_loss method, taking in the raw features and returning a loss value.
The base model will then take care of creating the appropriate training loop to fit our model.
End of explanation
model = MovielensModel()
model.compile(optimizer=tf.keras.optimizers.Adagrad(learning_rate=0.1))
Explanation: Fitting and evaluating
After defining the model, we can use standard Keras fitting and evaluation routines to fit and evaluate the model.
Let's first instantiate the model.
End of explanation
cached_train = train.shuffle(100_000).batch(8192).cache()
cached_test = test.batch(4096).cache()
Explanation: Then shuffle, batch, and cache the training and evaluation data.
End of explanation
model.fit(cached_train, epochs=3)
Explanation: Then train the  model:
End of explanation
model.evaluate(cached_test, return_dict=True)
Explanation: As the model trains, the loss is falling and the RMSE metric is improving.
Finally, we can evaluate our model on the test set:
End of explanation
test_ratings = {}
test_movie_titles = ["M*A*S*H (1970)", "Dances with Wolves (1990)", "Speed (1994)"]
for movie_title in test_movie_titles:
  test_ratings[movie_title] = model({
      "user_id": np.array(["42"]),
      "movie_title": np.array([movie_title])
  })
print("Ratings:")
for title, score in sorted(test_ratings.items(), key=lambda x: x[1], reverse=True):
  print(f"{title}: {score}")
Explanation: The lower the RMSE metric, the more accurate our model is at predicting ratings.
Testing the ranking model
Now we can test the ranking model by computing predictions for a set of movies and then rank these movies based on the predictions:
End of explanation
tf.saved_model.save(model, "export")
Explanation: Exporting for serving
The model can be easily exported for serving:
End of explanation
loaded = tf.saved_model.load("export")
loaded({"user_id": np.array(["42"]), "movie_title": ["Speed (1994)"]}).numpy()
Explanation: We can now load it back and perform predictions:
End of explanation
converter = tf.lite.TFLiteConverter.from_saved_model("export")
tflite_model = converter.convert()
open("converted_model.tflite", "wb").write(tflite_model)
Explanation: Convert the model to TensorFLow Lite
Although TensorFlow Recommenders is primarily designed to perform server-side recommendations, you can still convert the trained ranking model to TensorFLow Lite and run it on-device (for better user privacy privacy and lower latency).
End of explanation
interpreter = tf.lite.Interpreter(model_path="converted_model.tflite")
interpreter.allocate_tensors()
# Get input and output tensors.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
# Test the model.
if input_details[0]["name"] == "serving_default_movie_title:0":
  interpreter.set_tensor(input_details[0]["index"], np.array(["Speed (1994)"]))
  interpreter.set_tensor(input_details[1]["index"], np.array(["42"]))
else:
  interpreter.set_tensor(input_details[0]["index"], np.array(["42"]))
  interpreter.set_tensor(input_details[1]["index"], np.array(["Speed (1994)"]))
interpreter.invoke()
rating = interpreter.get_tensor(output_details[0]['index'])
print(rating)
Explanation: Once the model is converted, you can run it like regular TensorFlow Lite models. Please check out TensorFlow Lite documentation to learn more.
End of explanation
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