structured_data_classification_with_feature_space.py

"""
Title: Structured data classification with FeatureSpace
Author: [fchollet](https://twitter.com/fchollet)
Date created: 2022/11/09
Last modified: 2022/11/09
Description: Classify tabular data in a few lines of code.
Accelerator: GPU
"""

"""
## Introduction

This example demonstrates how to do structured data classification
(also known as tabular data classification), starting from a raw
CSV file. Our data includes numerical features,
and integer categorical features, and string categorical features.
We will use the utility `keras.utils.FeatureSpace` to index,
preprocess, and encode our features.

The code is adapted from the example
[Structured data classification from scratch](https://keras.io/examples/structured_data/structured_data_classification_from_scratch/).
While the previous example managed its own low-level feature preprocessing and
encoding with Keras preprocessing layers, in this example we
delegate everything to `FeatureSpace`, making the workflow
extremely quick and easy.

### The dataset

[Our dataset](https://archive.ics.uci.edu/ml/datasets/heart+Disease) is provided by the
Cleveland Clinic Foundation for Heart Disease.
It's a CSV file with 303 rows. Each row contains information about a patient (a
**sample**), and each column describes an attribute of the patient (a **feature**). We
use the features to predict whether a patient has a heart disease
(**binary classification**).

Here's the description of each feature:

Column| Description| Feature Type
------------|--------------------|----------------------
Age | Age in years | Numerical
Sex | (1 = male; 0 = female) | Categorical
CP | Chest pain type (0, 1, 2, 3, 4) | Categorical
Trestbpd | Resting blood pressure (in mm Hg on admission) | Numerical
Chol | Serum cholesterol in mg/dl | Numerical
FBS | fasting blood sugar in 120 mg/dl (1 = true; 0 = false) | Categorical
RestECG | Resting electrocardiogram results (0, 1, 2) | Categorical
Thalach | Maximum heart rate achieved | Numerical
Exang | Exercise induced angina (1 = yes; 0 = no) | Categorical
Oldpeak | ST depression induced by exercise relative to rest | Numerical
Slope | Slope of the peak exercise ST segment | Numerical
CA | Number of major vessels (0-3) colored by fluoroscopy | Both numerical & categorical
Thal | 3 = normal; 6 = fixed defect; 7 = reversible defect | Categorical
Target | Diagnosis of heart disease (1 = true; 0 = false) | Target
"""

"""
## Setup
"""

import os

os.environ["KERAS_BACKEND"] = "tensorflow"

import tensorflow as tf
import pandas as pd
import keras
from keras.utils import FeatureSpace

"""
## Preparing the data

Let's download the data and load it into a Pandas dataframe:
"""

file_url = "http://storage.googleapis.com/download.tensorflow.org/data/heart.csv"
dataframe = pd.read_csv(file_url)

"""
The dataset includes 303 samples with 14 columns per sample
(13 features, plus the target label):
"""

print(dataframe.shape)

"""
Here's a preview of a few samples:
"""

dataframe.head()

"""
The last column, "target", indicates whether the patient
has a heart disease (1) or not (0).

Let's split the data into a training and validation set:
"""

val_dataframe = dataframe.sample(frac=0.2, random_state=1337)
train_dataframe = dataframe.drop(val_dataframe.index)

print(
    "Using %d samples for training and %d for validation"
    % (len(train_dataframe), len(val_dataframe))
)

"""
Let's generate `tf.data.Dataset` objects for each dataframe:
"""


def dataframe_to_dataset(dataframe):
    dataframe = dataframe.copy()
    labels = dataframe.pop("target")
    ds = tf.data.Dataset.from_tensor_slices((dict(dataframe), labels))
    ds = ds.shuffle(buffer_size=len(dataframe))
    return ds


train_ds = dataframe_to_dataset(train_dataframe)
val_ds = dataframe_to_dataset(val_dataframe)

"""
Each `Dataset` yields a tuple `(input, target)` where `input` is a dictionary of features
and `target` is the value `0` or `1`:
"""

for x, y in train_ds.take(1):
    print("Input:", x)
    print("Target:", y)

"""
Let's batch the datasets:
"""

train_ds = train_ds.batch(32)
val_ds = val_ds.batch(32)

"""
## Configuring a `FeatureSpace`

To configure how each feature should be preprocessed,
we instantiate a `keras.utils.FeatureSpace`, and we
pass to it a dictionary that maps the name of our features
to a string that describes the feature type.

We have a few "integer categorical" features such as `"FBS"`,
one "string categorical" feature (`"thal"`),
and a few numerical features, which we'd like to normalize
-- except `"age"`, which we'd like to discretize into
a number of bins.

We also use the `crosses` argument
to capture *feature interactions* for some categorical
features, that is to say, create additional features
that represent value co-occurrences for these categorical features.
You can compute feature crosses like this for arbitrary sets of
categorical features -- not just tuples of two features.
Because the resulting co-occurences are hashed
into a fixed-sized vector, you don't need to worry about whether
the co-occurence space is too large.
"""

feature_space = FeatureSpace(
    features={
        # Categorical features encoded as integers
        "sex": "integer_categorical",
        "cp": "integer_categorical",
        "fbs": "integer_categorical",
        "restecg": "integer_categorical",
        "exang": "integer_categorical",
        "ca": "integer_categorical",
        # Categorical feature encoded as string
        "thal": "string_categorical",
        # Numerical features to discretize
        "age": "float_discretized",
        # Numerical features to normalize
        "trestbps": "float_normalized",
        "chol": "float_normalized",
        "thalach": "float_normalized",
        "oldpeak": "float_normalized",
        "slope": "float_normalized",
    },
    # We create additional features by hashing
    # value co-occurrences for the
    # following groups of categorical features.
    crosses=[("sex", "age"), ("thal", "ca")],
    # The hashing space for these co-occurrences
    # wil be 32-dimensional.
    crossing_dim=32,
    # Our utility will one-hot encode all categorical
    # features and concat all features into a single
    # vector (one vector per sample).
    output_mode="concat",
)

"""
## Further customizing a `FeatureSpace`

Specifying the feature type via a string name is quick and easy,
but sometimes you may want to further configure the preprocessing
of each feature. For instance, in our case, our categorical
features don't have a large set of possible values -- it's only
a handful of values per feature (e.g. `1` and `0` for the feature `"FBS"`),
and all possible values are represented in the training set.
As a result, we don't need to reserve an index to represent "out of vocabulary" values
for these features -- which would have been the default behavior.
Below, we just specify `num_oov_indices=0` in each of these features
to tell the feature preprocessor to skip "out of vocabulary" indexing.

Other customizations you have access to include specifying the number of
bins for discretizing features of type `"float_discretized"`,
or the dimensionality of the hashing space for feature crossing.
"""

feature_space = FeatureSpace(
    features={
        # Categorical features encoded as integers
        "sex": FeatureSpace.integer_categorical(num_oov_indices=0),
        "cp": FeatureSpace.integer_categorical(num_oov_indices=0),
        "fbs": FeatureSpace.integer_categorical(num_oov_indices=0),
        "restecg": FeatureSpace.integer_categorical(num_oov_indices=0),
        "exang": FeatureSpace.integer_categorical(num_oov_indices=0),
        "ca": FeatureSpace.integer_categorical(num_oov_indices=0),
        # Categorical feature encoded as string
        "thal": FeatureSpace.string_categorical(num_oov_indices=0),
        # Numerical features to discretize
        "age": FeatureSpace.float_discretized(num_bins=30),
        # Numerical features to normalize
        "trestbps": FeatureSpace.float_normalized(),
        "chol": FeatureSpace.float_normalized(),
        "thalach": FeatureSpace.float_normalized(),
        "oldpeak": FeatureSpace.float_normalized(),
        "slope": FeatureSpace.float_normalized(),
    },
    # Specify feature cross with a custom crossing dim.
    crosses=[
        FeatureSpace.cross(feature_names=("sex", "age"), crossing_dim=64),
        FeatureSpace.cross(
            feature_names=("thal", "ca"),
            crossing_dim=16,
        ),
    ],
    output_mode="concat",
)

"""
## Adapt the `FeatureSpace` to the training data

Before we start using the `FeatureSpace` to build a model, we have
to adapt it to the training data. During `adapt()`, the `FeatureSpace` will:

- Index the set of possible values for categorical features.
- Compute the mean and variance for numerical features to normalize.
- Compute the value boundaries for the different bins for numerical features to discretize.

Note that `adapt()` should be called on a `tf.data.Dataset` which yields dicts
of feature values -- no labels.
"""

train_ds_with_no_labels = train_ds.map(lambda x, _: x)
feature_space.adapt(train_ds_with_no_labels)

"""
At this point, the `FeatureSpace` can be called on a dict of raw feature values, and will return a
single concatenate vector for each sample, combining encoded features and feature crosses.
"""

for x, _ in train_ds.take(1):
    preprocessed_x = feature_space(x)
    print("preprocessed_x.shape:", preprocessed_x.shape)
    print("preprocessed_x.dtype:", preprocessed_x.dtype)

"""
## Two ways to manage preprocessing: as part of the `tf.data` pipeline, or in the model itself

There are two ways in which you can leverage your `FeatureSpace`:

### Asynchronous preprocessing in `tf.data`

You can make it part of your data pipeline, before the model. This enables asynchronous parallel
preprocessing of the data on CPU before it hits the model. Do this if you're training on GPU or TPU,
or if you want to speed up preprocessing. Usually, this is always the right thing to do during training.

### Synchronous preprocessing in the model

You can make it part of your model. This means that the model will expect dicts of raw feature
values, and the preprocessing batch will be done synchronously (in a blocking manner) before the
rest of the forward pass. Do this if you want to have an end-to-end model that can process
raw feature values -- but keep in mind that your model will only be able to run on CPU,
since most types of feature preprocessing (e.g. string preprocessing) are not GPU or TPU compatible.

Do not do this on GPU / TPU or in performance-sensitive settings. In general, you want to do in-model
preprocessing when you do inference on CPU.

In our case, we will apply the `FeatureSpace` in the tf.data pipeline during training, but we will
do inference with an end-to-end model that includes the `FeatureSpace`.
"""

"""
Let's create a training and validation dataset of preprocessed batches:
"""

preprocessed_train_ds = train_ds.map(
    lambda x, y: (feature_space(x), y), num_parallel_calls=tf.data.AUTOTUNE
)
preprocessed_train_ds = preprocessed_train_ds.prefetch(tf.data.AUTOTUNE)

preprocessed_val_ds = val_ds.map(
    lambda x, y: (feature_space(x), y), num_parallel_calls=tf.data.AUTOTUNE
)
preprocessed_val_ds = preprocessed_val_ds.prefetch(tf.data.AUTOTUNE)

"""
## Build a model

Time to build a model -- or rather two models:

- A training model that expects preprocessed features (one sample = one vector)
- An inference model that expects raw features (one sample = dict of raw feature values)
"""

dict_inputs = feature_space.get_inputs()
encoded_features = feature_space.get_encoded_features()

x = keras.layers.Dense(32, activation="relu")(encoded_features)
x = keras.layers.Dropout(0.5)(x)
predictions = keras.layers.Dense(1, activation="sigmoid")(x)

training_model = keras.Model(inputs=encoded_features, outputs=predictions)
training_model.compile(
    optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"]
)

inference_model = keras.Model(inputs=dict_inputs, outputs=predictions)

"""
## Train the model

Let's train our model for 50 epochs. Note that feature preprocessing is happening
as part of the tf.data pipeline, not as part of the model.
"""

training_model.fit(
    preprocessed_train_ds,
    epochs=20,
    validation_data=preprocessed_val_ds,
    verbose=2,
)

"""
We quickly get to 80% validation accuracy.
"""

"""
## Inference on new data with the end-to-end model

Now, we can use our inference model (which includes the `FeatureSpace`)
to make predictions based on dicts of raw features values, as follows:
"""

sample = {
    "age": 60,
    "sex": 1,
    "cp": 1,
    "trestbps": 145,
    "chol": 233,
    "fbs": 1,
    "restecg": 2,
    "thalach": 150,
    "exang": 0,
    "oldpeak": 2.3,
    "slope": 3,
    "ca": 0,
    "thal": "fixed",
}

input_dict = {name: tf.convert_to_tensor([value]) for name, value in sample.items()}
predictions = inference_model.predict(input_dict)

print(
    f"This particular patient had a {100 * predictions[0][0]:.2f}% probability "
    "of having a heart disease, as evaluated by our model."
)