Preprocessing Data¶

This page describes how to perform data preprocessing in Ray AIR.

Data preprocessing is a common technique for transforming raw data into features that will be input to a machine learning model. In general, you may want to apply the same preprocessing logic to your offline training data and online inference data. Ray AIR provides several common preprocessors out of the box as well as interfaces that enable you to define your own custom logic.

Overview¶

Ray AIR exposes a Preprocessor class for preprocessing. The Preprocessor has four methods that make up its core interface.

fit(): Compute state information about a Dataset (e.g. the mean or standard deviation of a column) and save it to the Preprocessor. This information should then be used to perform transform(). This is typically called on the training dataset.
transform(): Apply a transformation to a Dataset. If the Preprocessor is stateful, then fit() must be called first. This is typically called on the training, validation, test datasets.
transform_batch(): Apply a transformation to a single batch of data. This is typically called on online or offline inference data.
fit_transform(): Syntactic sugar for calling both fit() and transform() on a Dataset.

To show these in action, let’s walk through a basic example. First we’ll set up two simple Ray Datasets.

import pandas as pd
import ray
from ray.data.preprocessors import MinMaxScaler

# Generate two simple datasets.
dataset = ray.data.range_table(8)
dataset1, dataset2 = dataset.split(2)

print(dataset1.take())
# [{'value': 0}, {'value': 1}, {'value': 2}, {'value': 3}]

print(dataset2.take())
# [{'value': 4}, {'value': 5}, {'value': 6}, {'value': 7}]

Next, fit the Preprocessor on one Dataset, and transform both Datasets with this fitted information.

# Fit the preprocessor on dataset1, and transform both dataset1 and dataset2.
preprocessor = MinMaxScaler(["value"])

dataset1_transformed = preprocessor.fit_transform(dataset1)
print(dataset1_transformed.take())
# [{'value': 0.0}, {'value': 0.3333333333333333}, {'value': 0.6666666666666666}, {'value': 1.0}]

dataset2_transformed = preprocessor.transform(dataset2)
print(dataset2_transformed.take())
# [{'value': 1.3333333333333333}, {'value': 1.6666666666666667}, {'value': 2.0}, {'value': 2.3333333333333335}]

Finally, call transform_batch on a single batch of data.

batch = pd.DataFrame({"value": list(range(8, 12))})
batch_transformed = preprocessor.transform_batch(batch)
print(batch_transformed)
#       value
# 0  2.666667
# 1  3.000000
# 2  3.333333
# 3  3.666667

Life of an AIR Preprocessor¶

Now that we’ve gone over the basics, let’s dive into how Preprocessors fit into an end-to-end application built with AIR. The diagram below depicts an overview of the main steps of a Preprocessor:

Passed into a Trainer to fit and transform input Datasets.
Saved as a Checkpoint.
Reconstructed in a Predictor to fit_batch on batches of data.

Throughout this section we’ll go through this workflow in more detail, with code examples using XGBoost. The same logic is applicable to other integrations as well.

Trainer¶

The journey of the Preprocessor starts with the Trainer. If the Trainer is instantiated with a Preprocessor, then the following logic will be executed when Trainer.fit() is called:

If a "train" Dataset is passed in, then the Preprocessor will call fit() on it.
The Preprocessor will then call transform() on all Datasets, including the "train" Dataset.
The Trainer will then perform training on the preprocessed Datasets.

import ray

from ray.data.preprocessors import MinMaxScaler
from ray.train.xgboost import XGBoostTrainer

train_dataset = ray.data.from_items([{"x": x, "y": 2 * x} for x in range(0, 32, 3)])
valid_dataset = ray.data.from_items([{"x": x, "y": 2 * x} for x in range(1, 32, 3)])

preprocessor = MinMaxScaler(["x"])

trainer = XGBoostTrainer(
    label_column="y",
    params={"objective": "reg:squarederror"},
    scaling_config={"num_workers": 2},
    datasets={"train": train_dataset, "valid": valid_dataset},
    preprocessor=preprocessor,
)
result = trainer.fit()

Note

If you’re passing a Preprocessor that is already fitted, it will be refitted on the "train" Dataset. Adding the functionality to support passing in a fitted Preprocessor is being tracked here.

Tune¶

If you’re using Ray Tune for hyperparameter optimization, be aware that each Trial will instantiate its own copy of the Preprocessor and the fitting and transformation logic will occur once per Trial.

Checkpoint¶

Trainer.fit() returns a Results object which contains a Checkpoint. If a Preprocessor was passed into the Trainer, then it will be saved in the Checkpoint along with any fitted state.

As a sanity check, let’s confirm the Preprocessor is available in the Checkpoint. In practice you should not need to do this.

import os
import ray.cloudpickle as cpickle
from ray.air.constants import PREPROCESSOR_KEY

checkpoint = result.checkpoint
with checkpoint.as_directory() as checkpoint_path:
    path = os.path.join(checkpoint_path, PREPROCESSOR_KEY)
    with open(path, "rb") as f:
        preprocessor = cpickle.load(f)
    print(preprocessor)
# MixMaxScaler(columns=['x'], stats={'min(x)': 0, 'max(x)': 30})

Predictor¶

A Predictor can be constructed from a saved Checkpoint. If the Checkpoint contains a Preprocessor, then the Preprocessor will be used to call transform_batch on input batches prior to performing inference.

In the following example, we show the Batch Predictor flow. The same logic applies to the Online Inference flow.

from ray.train.batch_predictor import BatchPredictor
from ray.train.xgboost import XGBoostPredictor

test_dataset = ray.data.from_items([{"x": x} for x in range(2, 32, 3)])

batch_predictor = BatchPredictor.from_checkpoint(checkpoint, XGBoostPredictor)
predicted_probabilities = batch_predictor.predict(test_dataset)
predicted_probabilities.show()
# {'predictions': 0.09843720495700836}
# {'predictions': 5.604666709899902}
# {'predictions': 11.405311584472656}
# {'predictions': 15.684700012207031}
# {'predictions': 23.990947723388672}
# {'predictions': 29.900211334228516}
# {'predictions': 34.59944152832031}
# {'predictions': 40.6968994140625}
# {'predictions': 45.68107604980469}

Types of Preprocessors¶

Basic Preprocessors¶

Ray AIR provides a handful of Preprocessors that you can use out of the box, and more will be added over time. Contributions are welcome!

Common APIs

Tabular

Text

Image

Coming soon!

Utilities

train_test_split

Chaining Preprocessors¶

More often than not, your preprocessing logic will contain multiple logical steps or apply different transformations to each column. A simple Chain Preprocessor is provided which can be used to apply individual Preprocessor operations sequentially.

import ray
from ray.data.preprocessors import Chain, MinMaxScaler, SimpleImputer

# Generate one simple dataset.
dataset = ray.data.from_items(
    [{"value": 0}, {"value": 1}, {"value": 2}, {"value": 3}, {"value": None}]
)
print(dataset.take())
# [{'value': 0}, {'value': 1}, {'value': 2}, {'value': 3}, {'value': None}]

preprocessor = Chain(SimpleImputer(["value"]), MinMaxScaler(["value"]))

dataset_transformed = preprocessor.fit_transform(dataset)
print(dataset_transformed.take())
# [{'value': 0.0}, {'value': 0.3333333333333333}, {'value': 0.6666666666666666}, {'value': 1.0}, {'value': 0.5}]

Tip

Keep in mind that the operations are sequential. For example, if you define a Preprocessor Chain([preprocessorA, preprocessorB]), then preprocessorB.transform() will be applied to the result of preprocessorA.transform().

Custom Preprocessors¶

Stateless Preprocessors: Stateless preprocessors can be implemented with the BatchMapper.

import ray
from ray.data.preprocessors import BatchMapper

# Generate a simple dataset.
dataset = ray.data.range_table(4)
print(dataset.take())
# [{'value': 0}, {'value': 1}, {'value': 2}, {'value': 3}]

# Create a stateless preprocess that multiplies values by 2.
preprocessor = BatchMapper(lambda df: df * 2)
dataset_transformed = preprocessor.transform(dataset)
print(dataset_transformed.take())
# [{'value': 0}, {'value': 2}, {'value': 4}, {'value': 6}]

Stateful Preprocessors: Stateful preprocessors can be implemented with the CustomStatefulPreprocessor.

from typing import Dict
import ray
from pandas import DataFrame
from ray.data.preprocessors import CustomStatefulPreprocessor
from ray.data import Dataset
from ray.data.aggregate import Max


def get_max(ds: Dataset):
    return ds.aggregate(Max("value"))


def scale_by_max(df: DataFrame, stats: Dict):
    return df * stats["max(value)"]


# Generate a simple dataset.
dataset = ray.data.range_table(4)
print(dataset.take())
# [{'value': 0}, {'value': 1}, {'value': 2}, {'value': 3}]

# Create a stateful preprocessor that finds the max value and scales each value by it.
preprocessor = CustomStatefulPreprocessor(get_max, scale_by_max)
dataset_transformed = preprocessor.fit_transform(dataset)
print(dataset_transformed.take())
# [{'value': 0}, {'value': 3}, {'value': 6}, {'value': 9}]

Ray 3.0.0.dev0