[Datasets] Last-mile preprocessing docs. (#20712)

Datasets docs for last-mile preprocessing, particularly geared towards ML ingest. This gives groupby, aggregations, and random shuffling examples in the overview page (not present previously), adds some concreteness to our last-mile preprocessing positioning, and provides some preprocessing recipes for a few common transformations.

Author: clarkzinzow

doc/source/data/dataset-ml-preprocessing.rst

@@ -0,0 +1,173 @@
+.. _datasets-ml-preprocessing:
+
+ML Preprocessing
+=======================
+
+Datasets supports data preprocessing transformations commonly performed just before model training and model inference, which we refer to as **last-mile preprocessing**. These transformations are carried out via a few key operations: mapping, groupbys + aggregations, and random shuffling.
+
+Mapping
+-------
+
+Many common preprocessing transformations, such as:
+
+- adding new columns
+- transforming existing columns
+- dropping columns
+- dropping nulls
+- one-hot encoding
+
+can be efficiently applied to a ``Dataset`` using Pandas DataFrame UDFs and ``.map_batches()``; this will execute these transformations in parallel over the ``Dataset`` blocks, and allows you to apply vectorized Pandas operations to the block columns within the UDF.
+
+.. code-block:: python
+
+    # A Pandas DataFrame UDF for transforming the underlying blocks of a Dataset in parallel.
+    def transform_batch(df: pd.DataFrame):
+        # Drop nulls.
+        df = df.dropna(subset=["feature_1"])
+        # Add new column.
+        df["new_col"] = df["feature_1"] - 2 * df["feature_2"] + df["feature_3"] / 3
+        # Transform existing column.
+        df["feature_1"] = 2 * df["feature_1"] + 1
+        # Drop column.
+        df.drop(columns="feature_2", inplace=True)
+        # One-hot encoding.
+        categories = ["cat_1", "cat_2", "cat_3"]
+        for category in categories:
+            df[f"category_{category}"] = df["category"].map(
+                collections.defaultdict(int, **{category: 1}))
+        return df
+
+    # batch_format="pandas" tells Datasets to provide the transformer with blocks
+    # represented as Pandas DataFrames.
+    ds = ds.map_batches(transform_batch, batch_format="pandas")
+
+Groupbys and aggregations
+-------------------------
+
+Other preprocessing operations require global operations, such as groupbys and grouped/global aggregations. Just like other transformations, grouped/global aggregations are executed *eagerly* and block until the aggregation has been computed.
+
+.. code-block:: python
+
+    ds: ray.data.Dataset = ray.data.from_items([
+        {"A": x % 3, "B": 2 * x, "C": 3 * x}
+        for x in range(10)])
+
+    # Group by the A column and calculate the per-group mean for B and C columns.
+    agg_ds: ray.data.Dataset = ds.groupby("A").mean(["B", "C"])
+    # -> Sort Sample: 100%|███████████████████████████████████████| 10/10 [00:01<00:00,  9.04it/s]
+    # -> GroupBy Map: 100%|███████████████████████████████████████| 10/10 [00:00<00:00, 23.66it/s]
+    # -> GroupBy Reduce: 100%|████████████████████████████████████| 10/10 [00:00<00:00, 937.21it/s]
+    # -> Dataset(num_blocks=10, num_rows=3, schema={})
+    agg_ds.to_pandas()
+    # ->
+    #    A  mean(B)  mean(C)
+    # 0  0      9.0     13.5
+    # 1  1      8.0     12.0
+    # 2  2     10.0     15.0
+
+    # Global mean on B column.
+    ds.mean("B")
+    # -> GroupBy Map: 100%|███████████████████████████████████████| 10/10 [00:00<00:00, 2851.91it/s]
+    # -> GroupBy Reduce: 100%|████████████████████████████████████| 1/1 [00:00<00:00, 319.69it/s]
+    # -> 9.0
+
+    # Global mean on multiple columns.
+    ds.mean(["B", "C"])
+    # -> GroupBy Map: 100%|███████████████████████████████████████| 10/10 [00:00<00:00, 1730.32it/s]
+    # -> GroupBy Reduce: 100%|████████████████████████████████████| 1/1 [00:00<00:00, 231.41it/s]
+    # -> {'mean(B)': 9.0, 'mean(C)': 13.5} 
+
+    # Multiple global aggregations on multiple columns.
+    from ray.data.aggregate import Mean, Std
+    ds.aggregate(Mean("B"), Std("B", ddof=0), Mean("C"), Std("C", ddof=0))
+    # -> GroupBy Map: 100%|███████████████████████████████████████| 10/10 [00:00<00:00, 1568.73it/s]
+    # -> GroupBy Reduce: 100%|████████████████████████████████████| 1/1 [00:00<00:00, 133.51it/s]
+    # -> {'mean(A)': 0.9, 'std(A)': 0.8306623862918076, 'mean(B)': 9.0, 'std(B)': 5.744562646538029}
+
+These aggregations can be combined with batch mapping to transform a dataset using computed statistics. For example, you can efficiently standardize feature columns and impute missing values with calculated column means.
+
+.. code-block:: python
+
+    # Impute missing values with the column mean.
+    b_mean = ds.mean("B")
+    # -> GroupBy Map: 100%|███████████████████████████████████████| 10/10 [00:00<00:00, 4054.03it/s]
+    # -> GroupBy Reduce: 100%|████████████████████████████████████| 1/1 [00:00<00:00, 359.22it/s]
+    # -> 9.0
+
+    def impute_b(df: pd.DataFrame):
+        df["B"].fillna(b_mean)
+        return df
+
+    ds = ds.map_batches(impute_b, batch_format="pandas")
+    # -> Map Progress: 100%|██████████████████████████████████████| 10/10 [00:00<00:00, 132.66it/s]
+    # -> Dataset(num_blocks=10, num_rows=10, schema={A: int64, B: int64, C: int64})
+
+    # Standard scaling of all feature columns.
+    stats = ds.aggregate(Mean("B"), Std("B"), Mean("C"), Std("C"))
+    # -> GroupBy Map: 100%|███████████████████████████████████████| 10/10 [00:00<00:00, 1260.99it/s]
+    # -> GroupBy Reduce: 100%|████████████████████████████████████| 1/1 [00:00<00:00, 128.77it/s]
+    # -> {'mean(B)': 9.0, 'std(B)': 6.0553007081949835, 'mean(C)': 13.5, 'std(C)': 9.082951062292475}
+
+    def batch_standard_scaler(df: pd.DataFrame):
+        def column_standard_scaler(s: pd.Series):
+            s_mean = stats[f"mean({s.name})"]
+            s_std = stats[f"std({s.name})"]
+            return (s - s_mean) / s_std
+
+        cols = df.columns.difference(["A"])
+        df.loc[:, cols] = df.loc[:, cols].transform(column_standard_scaler)
+        return df
+
+    ds = ds.map_batches(batch_standard_scaler, batch_format="pandas")
+    # -> Map Progress: 100%|██████████████████████████████████████| 10/10 [00:00<00:00, 144.79it/s]
+    # -> Dataset(num_blocks=10, num_rows=10, schema={A: int64, B: double, C: double})
+
+Random shuffle
+--------------
+
+Randomly shuffling data is an important part of training machine learning models: it decorrelates samples, preventing overfitting and improving generalization. For many models, even between-epoch shuffling can drastically improve the precision gain per step/epoch. Datasets has a hyper-scalable distributed random shuffle that allows you to realize the model accuracy benefits of per-epoch shuffling without sacrificing training throughput, even at large data scales and even when doing distributed data-parallel training across multiple GPUs/nodes.
+
+.. code-block:: python
+
+    ds = ray.data.range(10)
+    # -> [0, 1, ..., 9]
+
+    # Global random shuffle.
+    ds = ds.random_shuffle()
+    # -> Shuffle Map: 100%|███████████████████████████████████████| 10/10 [00:00<00:00, 12.35it/s]
+    # -> Shuffle Reduce: 100%|████████████████████████████████████| 10/10 [00:00<00:00, 45.54it/s]
+    # -> [7, 1, ..., 3]
+
+    # Scales to terabytes of data with the same simple API.
+    ds = ray.data.read_parquet("s3://ursa-labs-taxi-data")  # open, tabular, NYC taxi dataset
+    # -> Dataset(num_blocks=125, num_rows=1547741381, schema={
+    #        vendor_id: string, pickup_at: timestamp[us], dropoff_at: timestamp[us],
+    #        passenger_count: int8, trip_distance: float, ...})
+
+    # Don't run this next one on your laptop; it will probably crash since it will
+    # try to read and shuffle ~99 GB of data!
+    ds = ds.random_shuffle()
+    # -> Shuffle Map: 100%|███████████████████████████████████████| 125/125 [00:00<00:00, 5021.94it/s]
+    # -> Shuffle Reduce: 100%|████████████████████████████████████| 125/125 [00:00<00:00, 4034.33it/s]
+    # -> Dataset(num_blocks=125, num_rows=1547741381, schema={
+    #        vendor_id: string, pickup_at: timestamp[us], dropoff_at: timestamp[us],
+    #        passenger_count: int8, trip_distance: float, ...})
+
+    # Per-epoch shuffling is as simple as changing where we invoke the shuffle:
+    #   - Before repeating => dataset is shuffled once.
+    #   - After repeating  => dataset is shuffled on every epoch.
+    num_epochs = 20
+
+    # Shuffle once, then repeat this once-shuffled dataset for num_epochs epochs.
+    ds.random_shuffle().repeat(num_epochs)
+    # -> Shuffle Map: 100%|███████████████████████████████████████| 10/10 [00:00<00:00, 13.43it/s]
+    # -> Shuffle Reduce: 100%|████████████████████████████████████| 10/10 [00:00<00:00, 42.70it/s]
+    # -> DatasetPipeline(num_windows=10, num_stages=1)
+
+    # Shuffle repeatedly, where the original dataset is shuffled into a different
+    # order at the beginning of each epoch.
+    ds.repeat(num_epochs).random_shuffle_each_window()
+    # -> DatasetPipeline(num_windows=10, num_stages=2)
+
+See the `large-scale ML ingest example <examples/big_data_ingestion.html>`__ for an end-to-end example of per-epoch shuffled data loading for distributed training.
+

doc/source/data/dataset.rst

@@ -34,7 +34,7 @@ Ray-integrated DataFrame libraries can also be seamlessly used with Datasets, to
    :width: 650px
    :align: center
 
-See :ref:`the Talks section <data-talks>` for more Dataset ML use cases and benchmarks.
+See the :ref:`ML preprocessing docs <datasets-ml-preprocessing>` for information on how to use Datasets as the last-mile bridge to model training and inference, and see :ref:`the Talks section <data-talks>` for more Datasets ML use cases and benchmarks.
 
 General Parallel Compute
 ------------------------

doc/source/index.rst

@@ -281,7 +281,7 @@ Papers
 
    data/dataset.rst
    data/dataset-pipeline.rst
-   data/examples/big_data_ingestion
+   data/dataset-ml-preprocessing.rst
    data/dataset-tensor-support.rst
    data/package-ref.rst
    data/dask-on-ray.rst