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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:
 Classification with Neural Decision Forests
Author
Step1:  Prepare the data
Step2:  Remove the first record (because it is not a valid data example) and a trailing
'dot' in the class labels.
Step3:  We store the training and test data splits locally as CSV files.
Step4:  Define dataset metadata
Here, we define the metadata of the dataset that will be useful for reading and parsing
and encoding input features.
Step5:  Create tf.data.Dataset objects for training and validation
We create an input function to read and parse the file, and convert features and labels
into a tf.data.Dataset
for training and validation. We also preprocess the input by mapping the target label
to an index.
Step6:  Create model inputs
Step7:  Encode input features
Step8:  Deep Neural Decision Tree
A neural decision tree model has two sets of weights to learn. The first set is pi,
which represents the probability distribution of the classes in the tree leaves.
The second set is the weights of the routing layer decision_fn, which represents the probability
of going to each leave. The forward pass of the model works as follows
Step9:  Deep Neural Decision Forest
The neural decision forest model consists of a set of neural decision trees that are
trained simultaneously. The output of the forest model is the average outputs of its trees.
Step10:  Finally, let's set up the code that will train and evaluate the model.
Step11:  Experiment 1
Step12:  Experiment 2
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Python Code:
import tensorflow as tf
import numpy as np
import pandas as pd
from tensorflow import keras
from tensorflow.keras import layers
import math
Explanation: Classification with Neural Decision Forests
Author: Khalid Salama<br>
Date created: 2021/01/15<br>
Last modified: 2021/01/15<br>
Description: How to train differentiable decision trees for end-to-end learning in deep neural networks.
Introduction
This example provides an implementation of the
Deep Neural Decision Forest
model introduced by P. Kontschieder et al. for structured data classification.
It demonstrates how to build a stochastic and differentiable decision tree model,
train it end-to-end, and unify decision trees with deep representation learning.
The dataset
This example uses the
United States Census Income Dataset
provided by the
UC Irvine Machine Learning Repository.
The task is binary classification
to predict whether a person is likely to be making over USD 50,000 a year.
The dataset includes 48,842 instances with 14 input features (such as age, work class, education, occupation, and so on): 5 numerical features
and 9 categorical features.
Setup
End of explanation
CSV_HEADER = [
    "age",
    "workclass",
    "fnlwgt",
    "education",
    "education_num",
    "marital_status",
    "occupation",
    "relationship",
    "race",
    "gender",
    "capital_gain",
    "capital_loss",
    "hours_per_week",
    "native_country",
    "income_bracket",
]
train_data_url = (
    "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data"
)
train_data = pd.read_csv(train_data_url, header=None, names=CSV_HEADER)
test_data_url = (
    "https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test"
)
test_data = pd.read_csv(test_data_url, header=None, names=CSV_HEADER)
print(f"Train dataset shape: {train_data.shape}")
print(f"Test dataset shape: {test_data.shape}")
Explanation: Prepare the data
End of explanation
test_data = test_data[1:]
test_data.income_bracket = test_data.income_bracket.apply(
    lambda value: value.replace(".", "")
)
Explanation: Remove the first record (because it is not a valid data example) and a trailing
'dot' in the class labels.
End of explanation
train_data_file = "train_data.csv"
test_data_file = "test_data.csv"
train_data.to_csv(train_data_file, index=False, header=False)
test_data.to_csv(test_data_file, index=False, header=False)
Explanation: We store the training and test data splits locally as CSV files.
End of explanation
# A list of the numerical feature names.
NUMERIC_FEATURE_NAMES = [
    "age",
    "education_num",
    "capital_gain",
    "capital_loss",
    "hours_per_week",
]
# A dictionary of the categorical features and their vocabulary.
CATEGORICAL_FEATURES_WITH_VOCABULARY = {
    "workclass": sorted(list(train_data["workclass"].unique())),
    "education": sorted(list(train_data["education"].unique())),
    "marital_status": sorted(list(train_data["marital_status"].unique())),
    "occupation": sorted(list(train_data["occupation"].unique())),
    "relationship": sorted(list(train_data["relationship"].unique())),
    "race": sorted(list(train_data["race"].unique())),
    "gender": sorted(list(train_data["gender"].unique())),
    "native_country": sorted(list(train_data["native_country"].unique())),
}
# A list of the columns to ignore from the dataset.
IGNORE_COLUMN_NAMES = ["fnlwgt"]
# A list of the categorical feature names.
CATEGORICAL_FEATURE_NAMES = list(CATEGORICAL_FEATURES_WITH_VOCABULARY.keys())
# A list of all the input features.
FEATURE_NAMES = NUMERIC_FEATURE_NAMES + CATEGORICAL_FEATURE_NAMES
# A list of column default values for each feature.
COLUMN_DEFAULTS = [
    [0.0] if feature_name in NUMERIC_FEATURE_NAMES + IGNORE_COLUMN_NAMES else ["NA"]
    for feature_name in CSV_HEADER
]
# The name of the target feature.
TARGET_FEATURE_NAME = "income_bracket"
# A list of the labels of the target features.
TARGET_LABELS = [" <=50K", " >50K"]
Explanation: Define dataset metadata
Here, we define the metadata of the dataset that will be useful for reading and parsing
and encoding input features.
End of explanation
from tensorflow.keras.layers import StringLookup
target_label_lookup = StringLookup(
    vocabulary=TARGET_LABELS, mask_token=None, num_oov_indices=0
)
def get_dataset_from_csv(csv_file_path, shuffle=False, batch_size=128):
    dataset = tf.data.experimental.make_csv_dataset(
        csv_file_path,
        batch_size=batch_size,
        column_names=CSV_HEADER,
        column_defaults=COLUMN_DEFAULTS,
        label_name=TARGET_FEATURE_NAME,
        num_epochs=1,
        header=False,
        na_value="?",
        shuffle=shuffle,
    ).map(lambda features, target: (features, target_label_lookup(target)))
    return dataset.cache()
Explanation: Create tf.data.Dataset objects for training and validation
We create an input function to read and parse the file, and convert features and labels
into a tf.data.Dataset
for training and validation. We also preprocess the input by mapping the target label
to an index.
End of explanation
def create_model_inputs():
    inputs = {}
    for feature_name in FEATURE_NAMES:
        if feature_name in NUMERIC_FEATURE_NAMES:
            inputs[feature_name] = layers.Input(
                name=feature_name, shape=(), dtype=tf.float32
            )
        else:
            inputs[feature_name] = layers.Input(
                name=feature_name, shape=(), dtype=tf.string
            )
    return inputs
Explanation: Create model inputs
End of explanation
def encode_inputs(inputs):
    encoded_features = []
    for feature_name in inputs:
        if feature_name in CATEGORICAL_FEATURE_NAMES:
            vocabulary = CATEGORICAL_FEATURES_WITH_VOCABULARY[feature_name]
            # Create a lookup to convert a string values to an integer indices.
            # Since we are not using a mask token, nor expecting any out of vocabulary
            # (oov) token, we set mask_token to None and num_oov_indices to 0.
            lookup = StringLookup(
                vocabulary=vocabulary, mask_token=None, num_oov_indices=0
            )
            # Convert the string input values into integer indices.
            value_index = lookup(inputs[feature_name])
            embedding_dims = int(math.sqrt(lookup.vocabulary_size()))
            # Create an embedding layer with the specified dimensions.
            embedding = layers.Embedding(
                input_dim=lookup.vocabulary_size(), output_dim=embedding_dims
            )
            # Convert the index values to embedding representations.
            encoded_feature = embedding(value_index)
        else:
            # Use the numerical features as-is.
            encoded_feature = inputs[feature_name]
            if inputs[feature_name].shape[-1] is None:
                encoded_feature = tf.expand_dims(encoded_feature, -1)
        encoded_features.append(encoded_feature)
    encoded_features = layers.concatenate(encoded_features)
    return encoded_features
Explanation: Encode input features
End of explanation
class NeuralDecisionTree(keras.Model):
    def __init__(self, depth, num_features, used_features_rate, num_classes):
        super(NeuralDecisionTree, self).__init__()
        self.depth = depth
        self.num_leaves = 2 ** depth
        self.num_classes = num_classes
        # Create a mask for the randomly selected features.
        num_used_features = int(num_features * used_features_rate)
        one_hot = np.eye(num_features)
        sampled_feature_indicies = np.random.choice(
            np.arange(num_features), num_used_features, replace=False
        )
        self.used_features_mask = one_hot[sampled_feature_indicies]
        # Initialize the weights of the classes in leaves.
        self.pi = tf.Variable(
            initial_value=tf.random_normal_initializer()(
                shape=[self.num_leaves, self.num_classes]
            ),
            dtype="float32",
            trainable=True,
        )
        # Initialize the stochastic routing layer.
        self.decision_fn = layers.Dense(
            units=self.num_leaves, activation="sigmoid", name="decision"
        )
    def call(self, features):
        batch_size = tf.shape(features)[0]
        # Apply the feature mask to the input features.
        features = tf.matmul(
            features, self.used_features_mask, transpose_b=True
        )  # [batch_size, num_used_features]
        # Compute the routing probabilities.
        decisions = tf.expand_dims(
            self.decision_fn(features), axis=2
        )  # [batch_size, num_leaves, 1]
        # Concatenate the routing probabilities with their complements.
        decisions = layers.concatenate(
            [decisions, 1 - decisions], axis=2
        )  # [batch_size, num_leaves, 2]
        mu = tf.ones([batch_size, 1, 1])
        begin_idx = 1
        end_idx = 2
        # Traverse the tree in breadth-first order.
        for level in range(self.depth):
            mu = tf.reshape(mu, [batch_size, -1, 1])  # [batch_size, 2 ** level, 1]
            mu = tf.tile(mu, (1, 1, 2))  # [batch_size, 2 ** level, 2]
            level_decisions = decisions[
                :, begin_idx:end_idx, :
            ]  # [batch_size, 2 ** level, 2]
            mu = mu * level_decisions  # [batch_size, 2**level, 2]
            begin_idx = end_idx
            end_idx = begin_idx + 2 ** (level + 1)
        mu = tf.reshape(mu, [batch_size, self.num_leaves])  # [batch_size, num_leaves]
        probabilities = keras.activations.softmax(self.pi)  # [num_leaves, num_classes]
        outputs = tf.matmul(mu, probabilities)  # [batch_size, num_classes]
        return outputs
Explanation: Deep Neural Decision Tree
A neural decision tree model has two sets of weights to learn. The first set is pi,
which represents the probability distribution of the classes in the tree leaves.
The second set is the weights of the routing layer decision_fn, which represents the probability
of going to each leave. The forward pass of the model works as follows:
The model expects input features as a single vector encoding all the features of an instance
in the batch. This vector can be generated from a Convolution Neural Network (CNN) applied to images
or dense transformations applied to structured data features.
The model first applies a used_features_mask to randomly select a subset of input features to use.
Then, the model computes the probabilities (mu) for the input instances to reach the tree leaves
by iteratively performing a stochastic routing throughout the tree levels.
Finally, the probabilities of reaching the leaves are combined by the class probabilities at the
leaves to produce the final outputs.
End of explanation
class NeuralDecisionForest(keras.Model):
    def __init__(self, num_trees, depth, num_features, used_features_rate, num_classes):
        super(NeuralDecisionForest, self).__init__()
        self.ensemble = []
        # Initialize the ensemble by adding NeuralDecisionTree instances.
        # Each tree will have its own randomly selected input features to use.
        for _ in range(num_trees):
            self.ensemble.append(
                NeuralDecisionTree(depth, num_features, used_features_rate, num_classes)
            )
    def call(self, inputs):
        # Initialize the outputs: a [batch_size, num_classes] matrix of zeros.
        batch_size = tf.shape(inputs)[0]
        outputs = tf.zeros([batch_size, num_classes])
        # Aggregate the outputs of trees in the ensemble.
        for tree in self.ensemble:
            outputs += tree(inputs)
        # Divide the outputs by the ensemble size to get the average.
        outputs /= len(self.ensemble)
        return outputs
Explanation: Deep Neural Decision Forest
The neural decision forest model consists of a set of neural decision trees that are
trained simultaneously. The output of the forest model is the average outputs of its trees.
End of explanation
learning_rate = 0.01
batch_size = 265
num_epochs = 10
hidden_units = [64, 64]
def run_experiment(model):
    model.compile(
        optimizer=keras.optimizers.Adam(learning_rate=learning_rate),
        loss=keras.losses.SparseCategoricalCrossentropy(),
        metrics=[keras.metrics.SparseCategoricalAccuracy()],
    )
    print("Start training the model...")
    train_dataset = get_dataset_from_csv(
        train_data_file, shuffle=True, batch_size=batch_size
    )
    model.fit(train_dataset, epochs=num_epochs)
    print("Model training finished")
    print("Evaluating the model on the test data...")
    test_dataset = get_dataset_from_csv(test_data_file, batch_size=batch_size)
    _, accuracy = model.evaluate(test_dataset)
    print(f"Test accuracy: {round(accuracy * 100, 2)}%")
Explanation: Finally, let's set up the code that will train and evaluate the model.
End of explanation
num_trees = 10
depth = 10
used_features_rate = 1.0
num_classes = len(TARGET_LABELS)
def create_tree_model():
    inputs = create_model_inputs()
    features = encode_inputs(inputs)
    features = layers.BatchNormalization()(features)
    num_features = features.shape[1]
    tree = NeuralDecisionTree(depth, num_features, used_features_rate, num_classes)
    outputs = tree(features)
    model = keras.Model(inputs=inputs, outputs=outputs)
    return model
tree_model = create_tree_model()
run_experiment(tree_model)
Explanation: Experiment 1: train a decision tree model
In this experiment, we train a single neural decision tree model
where we use all input features.
End of explanation
num_trees = 25
depth = 5
used_features_rate = 0.5
def create_forest_model():
    inputs = create_model_inputs()
    features = encode_inputs(inputs)
    features = layers.BatchNormalization()(features)
    num_features = features.shape[1]
    forest_model = NeuralDecisionForest(
        num_trees, depth, num_features, used_features_rate, num_classes
    )
    outputs = forest_model(features)
    model = keras.Model(inputs=inputs, outputs=outputs)
    return model
forest_model = create_forest_model()
run_experiment(forest_model)
Explanation: Experiment 2: train a forest model
In this experiment, we train a neural decision forest with num_trees trees
where each tree uses randomly selected 50% of the input features. You can control the number
of features to be used in each tree by setting the used_features_rate variable.
In addition, we set the depth to 5 instead of 10 compared to the previous experiment.
End of explanation
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