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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:  TF Lattice Custom Estimators
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    <a target="_blank" href="https
Step2:  Importing required packages
Step3:  Downloading the UCI Statlog (Heart) dataset
Step4:  Setting the default values used for training in this guide
Step5:  Feature Columns
As for any other TF estimator, data needs to be passed to the estimator, which is typically via an input_fn and parsed using FeatureColumns.
Step6:  Note that categorical features do not need to be wrapped by a dense feature column, since tfl.laysers.CategoricalCalibration layer can directly consume category indices.
Creating input_fn
As for any other estimator, you can use input_fn to feed data to the model for training and evaluation.
Step8:  Creating model_fn
There are several ways to create a custom estimator. Here we will construct a model_fn that calls a Keras model on the parsed input tensors. To parse the input features, you can use tf.feature_column.input_layer, tf.keras.layers.DenseFeatures, or tfl.estimators.transform_features. If you use the latter, you will not need to wrap categorical features with dense feature columns, and the resulting tensors will not be concatenated, which makes it easier to use the features in the calibration layers.
To construct a model, you can mix and match TFL layers or any other Keras layers. Here we create a calibrated lattice Keras model out of TFL layers and impose several monotonicity constraints. We then use the Keras model to create the custom estimator.
Step9:  Training and Estimator
Using the model_fn we can create and train the estimator.
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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
#@test {"skip": true}
!pip install tensorflow-lattice
Explanation: TF Lattice Custom Estimators
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    <a target="_blank" href="https://www.tensorflow.org/lattice/tutorials/custom_estimators"><img src="https://www.tensorflow.org/images/tf_logo_32px.png" />View on TensorFlow.org</a>
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    <a target="_blank" href="https://colab.research.google.com/github/tensorflow/lattice/blob/master/docs/tutorials/custom_estimators.ipynb"><img src="https://www.tensorflow.org/images/colab_logo_32px.png" />Run in Google Colab</a>
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    <a target="_blank" href="https://github.com/tensorflow/lattice/blob/master/docs/tutorials/custom_estimators.ipynb"><img src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" />View source on GitHub</a>
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    <a href="https://storage.googleapis.com/tensorflow_docs/lattice/docs/tutorials/custom_estimators.ipynb"><img src="https://www.tensorflow.org/images/download_logo_32px.png" />Download notebook</a>
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Warning: Estimators are not recommended for new code.  Estimators run v1. Session-style code which is more difficult to write correctly, and can behave unexpectedly, especially when combined with TF 2 code. Estimators do fall under our [compatibility guarantees] (https://tensorflow.org/guide/versions), but they will not receive any additional features, and there will be no fixes other than to security vulnerabilities. See the migration guide for details.
Overview
You can use custom estimators to create arbitrarily monotonic models using TFL layers. This guide outlines the steps needed to create such estimators.
Setup
Installing TF Lattice package:
End of explanation
import tensorflow as tf
import logging
import numpy as np
import pandas as pd
import sys
import tensorflow_lattice as tfl
from tensorflow import feature_column as fc
from tensorflow_estimator.python.estimator.canned import optimizers
from tensorflow_estimator.python.estimator.head import binary_class_head
logging.disable(sys.maxsize)
Explanation: Importing required packages:
End of explanation
csv_file = tf.keras.utils.get_file(
    'heart.csv', 'http://storage.googleapis.com/download.tensorflow.org/data/heart.csv')
df = pd.read_csv(csv_file)
target = df.pop('target')
train_size = int(len(df) * 0.8)
train_x = df[:train_size]
train_y = target[:train_size]
test_x = df[train_size:]
test_y = target[train_size:]
df.head()
Explanation: Downloading the UCI Statlog (Heart) dataset:
End of explanation
LEARNING_RATE = 0.1
BATCH_SIZE = 128
NUM_EPOCHS = 1000
Explanation: Setting the default values used for training in this guide:
End of explanation
# Feature columns.
# - age
# - sex
# - ca        number of major vessels (0-3) colored by flourosopy
# - thal      3 = normal; 6 = fixed defect; 7 = reversable defect
feature_columns = [
    fc.numeric_column('age', default_value=-1),
    fc.categorical_column_with_vocabulary_list('sex', [0, 1]),
    fc.numeric_column('ca'),
    fc.categorical_column_with_vocabulary_list(
        'thal', ['normal', 'fixed', 'reversible']),
]
Explanation: Feature Columns
As for any other TF estimator, data needs to be passed to the estimator, which is typically via an input_fn and parsed using FeatureColumns.
End of explanation
train_input_fn = tf.compat.v1.estimator.inputs.pandas_input_fn(
    x=train_x,
    y=train_y,
    shuffle=True,
    batch_size=BATCH_SIZE,
    num_epochs=NUM_EPOCHS,
    num_threads=1)
test_input_fn = tf.compat.v1.estimator.inputs.pandas_input_fn(
    x=test_x,
    y=test_y,
    shuffle=False,
    batch_size=BATCH_SIZE,
    num_epochs=1,
    num_threads=1)
Explanation: Note that categorical features do not need to be wrapped by a dense feature column, since tfl.laysers.CategoricalCalibration layer can directly consume category indices.
Creating input_fn
As for any other estimator, you can use input_fn to feed data to the model for training and evaluation.
End of explanation
def model_fn(features, labels, mode, config):
  model_fn for the custom estimator.
  del config
  input_tensors = tfl.estimators.transform_features(features, feature_columns)
  inputs = {
      key: tf.keras.layers.Input(shape=(1,), name=key) for key in input_tensors
  }
  lattice_sizes = [3, 2, 2, 2]
  lattice_monotonicities = ['increasing', 'none', 'increasing', 'increasing']
  lattice_input = tf.keras.layers.Concatenate(axis=1)([
      tfl.layers.PWLCalibration(
          input_keypoints=np.linspace(10, 100, num=8, dtype=np.float32),
          # The output range of the calibrator should be the input range of
          # the following lattice dimension.
          output_min=0.0,
          output_max=lattice_sizes[0] - 1.0,
          monotonicity='increasing',
      )(inputs['age']),
      tfl.layers.CategoricalCalibration(
          # Number of categories including any missing/default category.
          num_buckets=2,
          output_min=0.0,
          output_max=lattice_sizes[1] - 1.0,
      )(inputs['sex']),
      tfl.layers.PWLCalibration(
          input_keypoints=[0.0, 1.0, 2.0, 3.0],
          output_min=0.0,
          output_max=lattice_sizes[0] - 1.0,
          # You can specify TFL regularizers as tuple
          # ('regularizer name', l1, l2).
          kernel_regularizer=('hessian', 0.0, 1e-4),
          monotonicity='increasing',
      )(inputs['ca']),
      tfl.layers.CategoricalCalibration(
          num_buckets=3,
          output_min=0.0,
          output_max=lattice_sizes[1] - 1.0,
          # Categorical monotonicity can be partial order.
          # (i, j) indicates that we must have output(i) <= output(j).
          # Make sure to set the lattice monotonicity to 'increasing' for this
          # dimension.
          monotonicities=[(0, 1), (0, 2)],
      )(inputs['thal']),
  ])
  output = tfl.layers.Lattice(
      lattice_sizes=lattice_sizes, monotonicities=lattice_monotonicities)(
          lattice_input)
  training = (mode == tf.estimator.ModeKeys.TRAIN)
  model = tf.keras.Model(inputs=inputs, outputs=output)
  logits = model(input_tensors, training=training)
  if training:
    optimizer = optimizers.get_optimizer_instance_v2('Adagrad', LEARNING_RATE)
  else:
    optimizer = None
  head = binary_class_head.BinaryClassHead()
  return head.create_estimator_spec(
      features=features,
      mode=mode,
      labels=labels,
      optimizer=optimizer,
      logits=logits,
      trainable_variables=model.trainable_variables,
      update_ops=model.updates)
Explanation: Creating model_fn
There are several ways to create a custom estimator. Here we will construct a model_fn that calls a Keras model on the parsed input tensors. To parse the input features, you can use tf.feature_column.input_layer, tf.keras.layers.DenseFeatures, or tfl.estimators.transform_features. If you use the latter, you will not need to wrap categorical features with dense feature columns, and the resulting tensors will not be concatenated, which makes it easier to use the features in the calibration layers.
To construct a model, you can mix and match TFL layers or any other Keras layers. Here we create a calibrated lattice Keras model out of TFL layers and impose several monotonicity constraints. We then use the Keras model to create the custom estimator.
End of explanation
estimator = tf.estimator.Estimator(model_fn=model_fn)
estimator.train(input_fn=train_input_fn)
results = estimator.evaluate(input_fn=test_input_fn)
print('AUC: {}'.format(results['auc']))
Explanation: Training and Estimator
Using the model_fn we can create and train the estimator.
End of explanation
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