structured_data_classification_from_scratch.ipynb

Kernel: Python 3

Structured data classification from scratch

Author: fchollet
Date created: 2020/06/09
Last modified: 2020/06/09
Description: Binary classification of structured data including numerical and categorical features.

Introduction

This example demonstrates how to do structured data classification, starting from a raw CSV file. Our data includes both numerical and categorical features. We will use Keras preprocessing layers to normalize the numerical features and vectorize the categorical ones.

Note that this example should be run with TensorFlow 2.5 or higher.

The dataset

Our dataset 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"] = "torch"  # or torch, or tensorflow

import pandas as pd
import keras
from keras import layers

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):

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(
    f"Using {len(train_dataframe)} samples for training "
    f"and {len(val_dataframe)} for validation"
)

Define dataset metadata

Here, we define the metadata of the dataset that will be useful for reading and parsing the data into input features, and encoding the input features with respect to their types.

COLUMN_NAMES = [
    "age",
    "sex",
    "cp",
    "trestbps",
    "chol",
    "fbs",
    "restecg",
    "thalach",
    "exang",
    "oldpeak",
    "slope",
    "ca",
    "thal",
    "target",
]
# Target feature name.
TARGET_FEATURE_NAME = "target"
# Numeric feature names.
NUMERIC_FEATURE_NAMES = ["age", "trestbps", "thalach", "oldpeak", "slope", "chol"]
# Categorical features and their vocabulary lists.
# Note that we add 'v=' as a prefix to all categorical feature values to make
# sure that they are treated as strings.

CATEGORICAL_FEATURES_WITH_VOCABULARY = {
    feature_name: sorted(
        [
            # Integer categorcal must be int and string must be str
            value if dataframe[feature_name].dtype == "int64" else str(value)
            for value in list(dataframe[feature_name].unique())
        ]
    )
    for feature_name in COLUMN_NAMES
    if feature_name not in list(NUMERIC_FEATURE_NAMES + [TARGET_FEATURE_NAME])
}
# All features names.
FEATURE_NAMES = NUMERIC_FEATURE_NAMES + list(
    CATEGORICAL_FEATURES_WITH_VOCABULARY.keys()
)

Feature preprocessing with Keras layers

The following features are categorical features encoded as integers:

• sex

• cp

• fbs

• restecg

• exang

• ca

We will encode these features using one-hot encoding. We have two options here:

• Use CategoryEncoding(), which requires knowing the range of input values and will error on input outside the range.

• Use IntegerLookup() which will build a lookup table for inputs and reserve an output index for unkown input values.

For this example, we want a simple solution that will handle out of range inputs at inference, so we will use IntegerLookup().

We also have a categorical feature encoded as a string: thal. We will create an index of all possible features and encode output using the StringLookup() layer.

Finally, the following feature are continuous numerical features:

• age

• trestbps

• chol

• thalach

• oldpeak

• slope

For each of these features, we will use a Normalization() layer to make sure the mean of each feature is 0 and its standard deviation is 1.

Below, we define 2 utility functions to do the operations:

• encode_numerical_feature to apply featurewise normalization to numerical features.

• process to one-hot encode string or integer categorical features.

# Tensorflow required for tf.data.Dataset
import tensorflow as tf


# We process our datasets elements here (categorical) and convert them to indices to avoid this step
# during model training since only tensorflow support strings.
def encode_categorical(features, target):
    for feature_name in features:
        if feature_name in CATEGORICAL_FEATURES_WITH_VOCABULARY:
            lookup_class = (
                layers.StringLookup
                if features[feature_name].dtype == "string"
                else layers.IntegerLookup
            )
            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.
            index = lookup_class(
                vocabulary=vocabulary,
                mask_token=None,
                num_oov_indices=0,
                output_mode="binary",
            )
            # Convert the string input values into integer indices.
            value_index = index(features[feature_name])
            features[feature_name] = value_index

        else:
            pass

    # Change features from OrderedDict to Dict to match Inputs as they are Dict.
    return dict(features), target


def encode_numerical_feature(feature, name, dataset):
    # Create a Normalization layer for our feature
    normalizer = layers.Normalization()
    # Prepare a Dataset that only yields our feature
    feature_ds = dataset.map(lambda x, y: x[name])
    feature_ds = feature_ds.map(lambda x: tf.expand_dims(x, -1))
    # Learn the statistics of the data
    normalizer.adapt(feature_ds)
    # Normalize the input feature
    encoded_feature = normalizer(feature)
    return encoded_feature

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)).map(
        encode_categorical
    )
    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)

Build a model

With this done, we can create our end-to-end model:

# Categorical features have different shapes after the encoding, dependent on the
# vocabulary or unique values of each feature. We create them accordinly to match the
# input data elements generated by tf.data.Dataset after pre-processing them
def create_model_inputs():
    inputs = {}

    # This a helper function for creating categorical features
    def create_input_helper(feature_name):
        num_categories = len(CATEGORICAL_FEATURES_WITH_VOCABULARY[feature_name])
        inputs[feature_name] = layers.Input(
            name=feature_name, shape=(num_categories,), dtype="int64"
        )
        return inputs

    for feature_name in FEATURE_NAMES:
        if feature_name in CATEGORICAL_FEATURES_WITH_VOCABULARY:
            # Categorical features
            create_input_helper(feature_name)
        else:
            # Make them float32, they are Real numbers
            feature_input = layers.Input(name=feature_name, shape=(1,), dtype="float32")
            # Process the Inputs here
            inputs[feature_name] = encode_numerical_feature(
                feature_input, feature_name, train_ds
            )
    return inputs


# This Layer defines the logic of the Model to perform the classification
class Classifier(keras.layers.Layer):

    def __init__(self, **kwargs):
        super().__init__(**kwargs)
        self.dense_1 = layers.Dense(32, activation="relu")
        self.dropout = layers.Dropout(0.5)
        self.dense_2 = layers.Dense(1, activation="sigmoid")

    def call(self, inputs):
        all_features = layers.concatenate(list(inputs.values()))
        x = self.dense_1(all_features)
        x = self.dropout(x)
        output = self.dense_2(x)
        return output

    # Surpress build warnings
    def build(self, input_shape):
        self.built = True


# Create the Classifier model
def create_model():
    all_inputs = create_model_inputs()
    output = Classifier()(all_inputs)
    model = keras.Model(all_inputs, output)
    return model


model = create_model()
model.compile("adam", "binary_crossentropy", metrics=["accuracy"])

Let's visualize our connectivity graph:

# `rankdir='LR'` is to make the graph horizontal.
keras.utils.plot_model(model, show_shapes=True, rankdir="LR")

Train the model

model.fit(train_ds, epochs=50, validation_data=val_ds)

We quickly get to 80% validation accuracy.

Inference on new data

To get a prediction for a new sample, you can simply call model.predict(). There are just two things you need to do:

1. wrap scalars into a list so as to have a batch dimension (models only process batches of data, not single samples)

2. Call convert_to_tensor on each feature

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",
}


# Given the category (in the sample above - key) and the category value (in the sample above - value),
# we return its one-hot encoding
def get_cat_encoding(cat, cat_value):
    # Create a list of zeros with the same length as categories
    encoding = [0] * len(cat)
    # Find the index of category_value in categories and set the corresponding position to 1
    if cat_value in cat:
        encoding[cat.index(cat_value)] = 1
    return encoding


for name, value in sample.items():
    if name in CATEGORICAL_FEATURES_WITH_VOCABULARY:
        sample.update(
            {
                name: get_cat_encoding(
                    CATEGORICAL_FEATURES_WITH_VOCABULARY[name], sample[name]
                )
            }
        )
# Convert inputs to tensors
input_dict = {name: tf.convert_to_tensor([value]) for name, value in sample.items()}
predictions = model.predict(input_dict)

print(
    f"This particular patient had a {100 * predictions[0][0]:.1f} "
    "percent probability of having a heart disease, "
    "as evaluated by our model."
)

Conclusions

• The orignal model (the one that runs only on tensorflow) converges quickly to around 80% and remains there for extended periods and at times hits 85%

• The updated model (the backed-agnostic) model may fluctuate between 78% and 83% and at times hitting 86% validation accuracy and converges around 80% also.