keras_spark_rossmann_run.py

# Copyright 2017 onwards, fast.ai, Inc.
# Modifications copyright (C) 2018 Uber Technologies, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

import argparse
import datetime
import h5py
import io
import os
import pyarrow as pa
from pyspark import SparkConf, Row
from pyspark.sql import SparkSession
import pyspark.sql.types as T
import pyspark.sql.functions as F

parser = argparse.ArgumentParser(description='Keras Spark Rossmann Run Example',
                                 formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--processing-master',
                    help='spark cluster to use for light processing (data preparation & prediction).'
                         'If set to None, uses current default cluster. Cluster should be set up to provide'
                         'one task per CPU core. Example: spark://hostname:7077')
parser.add_argument('--training-master',
                    help='spark cluster to use for training. If set to None, uses current default cluster. Cluster'
                         'should be set up to provide a Spark task per multiple CPU cores, or per GPU, e.g. by'
                         'supplying `-c <NUM_GPUS>` in Spark Standalone mode. Example: spark://hostname:7077')
parser.add_argument('--num-proc', type=int, default=4,
                    help='number of worker processes for training, default: `spark.default.parallelism`')
parser.add_argument('--learning-rate', type=float, default=0.0001,
                    help='initial learning rate')
parser.add_argument('--batch-size', type=int, default=100,
                    help='batch size')
parser.add_argument('--epochs', type=int, default=100,
                    help='number of epochs to train')
parser.add_argument('--sample-rate', type=float,
                    help='desired sampling rate. Useful to set to low number (e.g. 0.01) to make sure that '
                         'end-to-end process works')
parser.add_argument('--data-dir', default='file://' + os.getcwd(),
                    help='location of data on local filesystem (prefixed with file://) or on HDFS')
parser.add_argument('--local-submission-csv', default='submission.csv',
                    help='output submission predictions CSV on local filesystem (without file:// prefix)')
parser.add_argument('--local-checkpoint-file', default='checkpoint.h5',
                    help='model checkpoint on local filesystem (without file:// prefix)')

if __name__ == '__main__':
    args = parser.parse_args()

    # HDFS driver to use with Petastorm.
    PETASTORM_HDFS_DRIVER = 'libhdfs'

    # ================ #
    # DATA PREPARATION #
    # ================ #

    print('================')
    print('Data preparation')
    print('================')

    # Create Spark session for data preparation.
    conf = SparkConf().setAppName('data_prep').set('spark.sql.shuffle.partitions', '16')
    if args.processing_master:
        conf.setMaster(args.processing_master)
    spark = SparkSession.builder.config(conf=conf).getOrCreate()

    train_csv = spark.read.csv('%s/train.csv' % args.data_dir, header=True)
    test_csv = spark.read.csv('%s/test.csv' % args.data_dir, header=True)

    store_csv = spark.read.csv('%s/store.csv' % args.data_dir, header=True)
    store_states_csv = spark.read.csv('%s/store_states.csv' % args.data_dir, header=True)
    state_names_csv = spark.read.csv('%s/state_names.csv' % args.data_dir, header=True)
    google_trend_csv = spark.read.csv('%s/googletrend.csv' % args.data_dir, header=True)
    weather_csv = spark.read.csv('%s/weather.csv' % args.data_dir, header=True)


    def expand_date(df):
        df = df.withColumn('Date', df.Date.cast(T.DateType()))
        return df \
            .withColumn('Year', F.year(df.Date)) \
            .withColumn('Month', F.month(df.Date)) \
            .withColumn('Week', F.weekofyear(df.Date)) \
            .withColumn('Day', F.dayofmonth(df.Date))


    def prepare_google_trend():
        # Extract week start date and state.
        google_trend_all = google_trend_csv \
            .withColumn('Date', F.regexp_extract(google_trend_csv.week, '(.*?) -', 1)) \
            .withColumn('State', F.regexp_extract(google_trend_csv.file, 'Rossmann_DE_(.*)', 1))

        # Map state NI -> HB,NI to align with other data sources.
        google_trend_all = google_trend_all \
            .withColumn('State', F.when(google_trend_all.State == 'NI', 'HB,NI').otherwise(google_trend_all.State))

        # Expand dates.
        return expand_date(google_trend_all)


    def add_elapsed(df, cols):
        def add_elapsed_column(col, asc):
            def fn(rows):
                last_store, last_date = None, None
                for r in rows:
                    if last_store != r.Store:
                        last_store = r.Store
                        last_date = r.Date
                    if r[col]:
                        last_date = r.Date
                    fields = r.asDict().copy()
                    fields[('After' if asc else 'Before') + col] = (r.Date - last_date).days
                    yield Row(**fields)
            return fn

        df = df.repartition(df.Store)
        for asc in [False, True]:
            sort_col = df.Date.asc() if asc else df.Date.desc()
            rdd = df.sortWithinPartitions(df.Store.asc(), sort_col).rdd
            for col in cols:
                rdd = rdd.mapPartitions(add_elapsed_column(col, asc))
            df = rdd.toDF()
        return df


    def prepare_df(df):
        num_rows = df.count()

        # Expand dates.
        df = expand_date(df)

        df = df \
            .withColumn('Open', df.Open != '0') \
            .withColumn('Promo', df.Promo != '0') \
            .withColumn('StateHoliday', df.StateHoliday != '0') \
            .withColumn('SchoolHoliday', df.SchoolHoliday != '0')

        # Merge in store information.
        store = store_csv.join(store_states_csv, 'Store')
        df = df.join(store, 'Store')

        # Merge in Google Trend information.
        google_trend_all = prepare_google_trend()
        df = df.join(google_trend_all, ['State', 'Year', 'Week']).select(df['*'], google_trend_all.trend)

        # Merge in Google Trend for whole Germany.
        google_trend_de = google_trend_all[google_trend_all.file == 'Rossmann_DE']
        df = df.join(google_trend_de, ['Year', 'Week']).select(df['*'], google_trend_all.trend.alias('trend_de'))

        # Merge in weather.
        weather = weather_csv.join(state_names_csv, weather_csv.file == state_names_csv.StateName)
        df = df.join(weather, ['State', 'Date'])

        # Fix null values.
        df = df \
            .withColumn('CompetitionOpenSinceYear', F.coalesce(df.CompetitionOpenSinceYear, F.lit(1900))) \
            .withColumn('CompetitionOpenSinceMonth', F.coalesce(df.CompetitionOpenSinceMonth, F.lit(1))) \
            .withColumn('Promo2SinceYear', F.coalesce(df.Promo2SinceYear, F.lit(1900))) \
            .withColumn('Promo2SinceWeek', F.coalesce(df.Promo2SinceWeek, F.lit(1)))

        # Days & months competition was open, cap to 2 years.
        df = df.withColumn('CompetitionOpenSince',
                           F.to_date(F.format_string('%s-%s-15', df.CompetitionOpenSinceYear,
                                                     df.CompetitionOpenSinceMonth)))
        df = df.withColumn('CompetitionDaysOpen',
                           F.when(df.CompetitionOpenSinceYear > 1900,
                                  F.greatest(F.lit(0), F.least(F.lit(360 * 2), F.datediff(df.Date, df.CompetitionOpenSince))))
                           .otherwise(0))
        df = df.withColumn('CompetitionMonthsOpen', (df.CompetitionDaysOpen / 30).cast(T.IntegerType()))

        # Days & weeks of promotion, cap to 25 weeks.
        df = df.withColumn('Promo2Since',
                           F.expr('date_add(format_string("%s-01-01", Promo2SinceYear), (Promo2SinceWeek - 1) * 7)'))
        df = df.withColumn('Promo2Days',
                           F.when(df.Promo2SinceYear > 1900,
                                  F.greatest(F.lit(0), F.least(F.lit(25 * 7), F.datediff(df.Date, df.Promo2Since))))
                           .otherwise(0))
        df = df.withColumn('Promo2Weeks', (df.Promo2Days / 7).cast(T.IntegerType()))

        # Check that we did not lose any rows through inner joins.
        assert num_rows == df.count(), 'lost rows in joins'
        return df


    def build_vocabulary(df, cols):
        vocab = {}
        for col in cols:
            values = [r[0] for r in df.select(col).distinct().collect()]
            col_type = type([x for x in values if x is not None][0])
            default_value = col_type()
            vocab[col] = sorted(values, key=lambda x: x or default_value)
        return vocab


    def cast_columns(df, cols):
        for col in cols:
            df = df.withColumn(col, F.coalesce(df[col].cast(T.FloatType()), F.lit(0.0)))
        return df


    def lookup_columns(df, vocab):
        def lookup(mapping):
            def fn(v):
                return mapping.index(v)
            return F.udf(fn, returnType=T.IntegerType())

        for col, mapping in vocab.items():
            df = df.withColumn(col, lookup(mapping)(df[col]))
        return df


    if args.sample_rate:
        train_csv = train_csv.sample(withReplacement=False, fraction=args.sample_rate)
        test_csv = test_csv.sample(withReplacement=False, fraction=args.sample_rate)

    # Prepare data frames from CSV files.
    train_df = prepare_df(train_csv).cache()
    test_df = prepare_df(test_csv).cache()

    # Add elapsed times from holidays & promos, the data spanning training & test datasets.
    elapsed_cols = ['Promo', 'StateHoliday', 'SchoolHoliday']
    elapsed = add_elapsed(train_df.select('Date', 'Store', *elapsed_cols)
                                  .unionAll(test_df.select('Date', 'Store', *elapsed_cols)),
                          elapsed_cols)

    # Join with elapsed times.
    train_df = train_df \
        .join(elapsed, ['Date', 'Store']) \
        .select(train_df['*'], *[prefix + col for prefix in ['Before', 'After'] for col in elapsed_cols])
    test_df = test_df \
        .join(elapsed, ['Date', 'Store']) \
        .select(test_df['*'], *[prefix + col for prefix in ['Before', 'After'] for col in elapsed_cols])

    # Filter out zero sales.
    train_df = train_df.filter(train_df.Sales > 0)

    print('===================')
    print('Prepared data frame')
    print('===================')
    train_df.show()

    categorical_cols = [
        'Store', 'State', 'DayOfWeek', 'Year', 'Month', 'Day', 'Week', 'CompetitionMonthsOpen', 'Promo2Weeks', 'StoreType',
        'Assortment', 'PromoInterval', 'CompetitionOpenSinceYear', 'Promo2SinceYear', 'Events', 'Promo',
        'StateHoliday', 'SchoolHoliday'
    ]

    continuous_cols = [
        'CompetitionDistance', 'Max_TemperatureC', 'Mean_TemperatureC', 'Min_TemperatureC', 'Max_Humidity',
        'Mean_Humidity', 'Min_Humidity', 'Max_Wind_SpeedKm_h', 'Mean_Wind_SpeedKm_h', 'CloudCover', 'trend', 'trend_DE',
        'BeforePromo', 'AfterPromo', 'AfterStateHoliday', 'BeforeStateHoliday', 'BeforeSchoolHoliday', 'AfterSchoolHoliday'
    ]

    all_cols = categorical_cols + continuous_cols

    # Select features.
    train_df = train_df.select(*(all_cols + ['Sales', 'Date'])).cache()
    test_df = test_df.select(*(all_cols + ['Id', 'Date'])).cache()

    # Build vocabulary of categorical columns.
    vocab = build_vocabulary(train_df.select(*categorical_cols)
                                     .unionAll(test_df.select(*categorical_cols)).cache(),
                             categorical_cols)

    # Cast continuous columns to float & lookup categorical columns.
    train_df = cast_columns(train_df, continuous_cols + ['Sales'])
    train_df = lookup_columns(train_df, vocab)
    test_df = cast_columns(test_df, continuous_cols)
    test_df = lookup_columns(test_df, vocab)

    # Split into training & validation.
    # Test set is in 2015, use the same period in 2014 from the training set as a validation set.
    test_min_date = test_df.agg(F.min(test_df.Date)).collect()[0][0]
    test_max_date = test_df.agg(F.max(test_df.Date)).collect()[0][0]
    a_year = datetime.timedelta(365)
    val_df = train_df.filter((test_min_date - a_year <= train_df.Date) & (train_df.Date < test_max_date - a_year))
    train_df = train_df.filter((train_df.Date < test_min_date - a_year) | (train_df.Date >= test_max_date - a_year))

    # Determine max Sales number.
    max_sales = train_df.agg(F.max(train_df.Sales)).collect()[0][0]

    print('===================================')
    print('Data frame with transformed columns')
    print('===================================')
    train_df.show()

    print('================')
    print('Data frame sizes')
    print('================')
    train_rows, val_rows, test_rows = train_df.count(), val_df.count(), test_df.count()
    print('Training: %d' % train_rows)
    print('Validation: %d' % val_rows)
    print('Test: %d' % test_rows)

    # Save data frames as Parquet files.
    train_df.write.parquet('%s/train_df.parquet' % args.data_dir, mode='overwrite')
    val_df.write.parquet('%s/val_df.parquet' % args.data_dir, mode='overwrite')
    test_df.write.parquet('%s/test_df.parquet' % args.data_dir, mode='overwrite')

    spark.stop()

    # ============== #
    # MODEL TRAINING #
    # ============== #

    print('==============')
    print('Model training')
    print('==============')

    import tensorflow as tf
    from tensorflow.keras.layers import Input, Embedding, Concatenate, Dense, Flatten, Reshape, BatchNormalization, Dropout
    import tensorflow.keras.backend as K
    import horovod.spark
    import horovod.tensorflow.keras as hvd


    def exp_rmspe(y_true, y_pred):
        """Competition evaluation metric, expects logarithic inputs."""
        pct = tf.square((tf.exp(y_true) - tf.exp(y_pred)) / tf.exp(y_true))
        # Compute mean excluding stores with zero denominator.
        x = tf.reduce_sum(tf.where(y_true > 0.001, pct, tf.zeros_like(pct)))
        y = tf.reduce_sum(tf.where(y_true > 0.001, tf.ones_like(pct), tf.zeros_like(pct)))
        return tf.sqrt(x / y)


    def act_sigmoid_scaled(x):
        """Sigmoid scaled to logarithm of maximum sales scaled by 20%."""
        return tf.nn.sigmoid(x) * tf.log(max_sales) * 1.2


    CUSTOM_OBJECTS = {'exp_rmspe': exp_rmspe,
                      'act_sigmoid_scaled': act_sigmoid_scaled}


    def serialize_model(model):
        """Serialize model into byte array."""
        bio = io.BytesIO()
        with h5py.File(bio) as f:
            model.save(f)
        return bio.getvalue()


    def deserialize_model(model_bytes, load_model_fn):
        """Deserialize model from byte array."""
        bio = io.BytesIO(model_bytes)
        with h5py.File(bio) as f:
            return load_model_fn(f, custom_objects=CUSTOM_OBJECTS)


    # Do not use GPU for the session creation.
    config = tf.ConfigProto(device_count={'GPU': 0})
    K.set_session(tf.Session(config=config))

    # Build the model.
    inputs = {col: Input(shape=(1,), name=col) for col in all_cols}
    embeddings = [Embedding(len(vocab[col]), 10, input_length=1, name='emb_' + col)(inputs[col])
                  for col in categorical_cols]
    continuous_bn = Concatenate()([Reshape((1, 1), name='reshape_' + col)(inputs[col])
                                   for col in continuous_cols])
    continuous_bn = BatchNormalization()(continuous_bn)
    x = Concatenate()(embeddings + [continuous_bn])
    x = Flatten()(x)
    x = Dense(1000, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.00005))(x)
    x = Dense(1000, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.00005))(x)
    x = Dense(1000, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.00005))(x)
    x = Dense(500, activation='relu', kernel_regularizer=tf.keras.regularizers.l2(0.00005))(x)
    x = Dropout(0.5)(x)
    output = Dense(1, activation=act_sigmoid_scaled)(x)
    model = tf.keras.Model([inputs[f] for f in all_cols], output)
    model.summary()

    # Horovod: add Distributed Optimizer.
    opt = tf.keras.optimizers.Adam(lr=args.learning_rate, epsilon=1e-3)
    opt = hvd.DistributedOptimizer(opt)
    model.compile(opt, 'mae', metrics=[exp_rmspe])
    model_bytes = serialize_model(model)


    def train_fn(model_bytes):
        # Make sure pyarrow is referenced before anything else to avoid segfault due to conflict
        # with TensorFlow libraries.  Use `pa` package reference to ensure it's loaded before
        # functions like `deserialize_model` which are implemented at the top level.
        # See https://jira.apache.org/jira/browse/ARROW-3346
        pa

        import atexit
        import horovod.tensorflow.keras as hvd
        import os
        from petastorm import make_batch_reader
        from petastorm.tf_utils import make_petastorm_dataset
        import tempfile
        import tensorflow as tf
        import tensorflow.keras.backend as K
        import shutil

        # Horovod: initialize Horovod inside the trainer.
        hvd.init()

        # Horovod: pin GPU to be used to process local rank (one GPU per process), if GPUs are available.
        config = tf.ConfigProto()
        config.gpu_options.allow_growth = True
        config.gpu_options.visible_device_list = str(hvd.local_rank())
        K.set_session(tf.Session(config=config))

        # Horovod: restore from checkpoint, use hvd.load_model under the hood.
        model = deserialize_model(model_bytes, hvd.load_model)

        # Horovod: adjust learning rate based on number of processes.
        scaled_lr = K.get_value(model.optimizer.lr) * hvd.size()
        K.set_value(model.optimizer.lr, scaled_lr)

        # Horovod: print summary logs on the first worker.
        verbose = 2 if hvd.rank() == 0 else 0

        callbacks = [
            # Horovod: broadcast initial variable states from rank 0 to all other processes.
            # This is necessary to ensure consistent initialization of all workers when
            # training is started with random weights or restored from a checkpoint.
            hvd.callbacks.BroadcastGlobalVariablesCallback(root_rank=0),

            # Horovod: average metrics among workers at the end of every epoch.
            #
            # Note: This callback must be in the list before the ReduceLROnPlateau,
            # TensorBoard, or other metrics-based callbacks.
            hvd.callbacks.MetricAverageCallback(),

            # Horovod: using `lr = 1.0 * hvd.size()` from the very beginning leads to worse final
            # accuracy. Scale the learning rate `lr = 1.0` ---> `lr = 1.0 * hvd.size()` during
            # the first five epochs. See https://arxiv.org/abs/1706.02677 for details.
            hvd.callbacks.LearningRateWarmupCallback(initial_lr=scaled_lr, warmup_epochs=5, verbose=verbose),

            # Reduce LR if the metric is not improved for 10 epochs, and stop training
            # if it has not improved for 20 epochs.
            tf.keras.callbacks.ReduceLROnPlateau(monitor='val_exp_rmspe', patience=10, verbose=verbose),
            tf.keras.callbacks.EarlyStopping(monitor='val_exp_rmspe', mode='min', patience=20, verbose=verbose),
            tf.keras.callbacks.TerminateOnNaN()
        ]

        # Model checkpoint location.
        ckpt_dir = tempfile.mkdtemp()
        ckpt_file = os.path.join(ckpt_dir, 'checkpoint.h5')
        atexit.register(lambda: shutil.rmtree(ckpt_dir))

        # Horovod: save checkpoints only on the first worker to prevent other workers from corrupting them.
        if hvd.rank() == 0:
            callbacks.append(tf.keras.callbacks.ModelCheckpoint(ckpt_file, monitor='val_exp_rmspe', mode='min',
                                                                save_best_only=True))

        # Make Petastorm readers.
        with make_batch_reader('%s/train_df.parquet' % args.data_dir, num_epochs=None,
                               cur_shard=hvd.rank(), shard_count=hvd.size(),
                               hdfs_driver=PETASTORM_HDFS_DRIVER) as train_reader:
            with make_batch_reader('%s/val_df.parquet' % args.data_dir, num_epochs=None,
                                   cur_shard=hvd.rank(), shard_count=hvd.size(),
                                   hdfs_driver=PETASTORM_HDFS_DRIVER) as val_reader:
                # Convert readers to tf.data.Dataset.
                train_ds = make_petastorm_dataset(train_reader) \
                    .apply(tf.data.experimental.unbatch()) \
                    .shuffle(int(train_rows / hvd.size())) \
                    .batch(args.batch_size) \
                    .map(lambda x: (tuple(getattr(x, col) for col in all_cols), tf.log(x.Sales)))

                val_ds = make_petastorm_dataset(val_reader) \
                    .apply(tf.data.experimental.unbatch()) \
                    .batch(args.batch_size) \
                    .map(lambda x: (tuple(getattr(x, col) for col in all_cols), tf.log(x.Sales)))

                history = model.fit(train_ds,
                                    validation_data=val_ds,
                                    steps_per_epoch=int(train_rows / args.batch_size / hvd.size()),
                                    validation_steps=int(val_rows / args.batch_size / hvd.size()),
                                    callbacks=callbacks,
                                    verbose=verbose,
                                    epochs=args.epochs)

        # Dataset API usage currently displays a wall of errors upon termination.
        # This global model registration ensures clean termination.
        # Tracked in https://github.com/tensorflow/tensorflow/issues/24570
        globals()['_DATASET_FINALIZATION_HACK'] = model

        if hvd.rank() == 0:
            with open(ckpt_file, 'rb') as f:
                return history.history, f.read()


    # Create Spark session for training.
    conf = SparkConf().setAppName('training')
    if args.training_master:
        conf.setMaster(args.training_master)
    spark = SparkSession.builder.config(conf=conf).getOrCreate()

    # Horovod: run training.
    history, best_model_bytes = \
        horovod.spark.run(train_fn, args=(model_bytes,), num_proc=args.num_proc, verbose=2)[0]

    best_val_rmspe = min(history['val_exp_rmspe'])
    print('Best RMSPE: %f' % best_val_rmspe)

    # Write checkpoint.
    with open(args.local_checkpoint_file, 'wb') as f:
        f.write(best_model_bytes)
    print('Written checkpoint to %s' % args.local_checkpoint_file)

    spark.stop()

    # ================ #
    # FINAL PREDICTION #
    # ================ #

    print('================')
    print('Final prediction')
    print('================')

    # Create Spark session for prediction.
    conf = SparkConf().setAppName('prediction') \
        .setExecutorEnv('LD_LIBRARY_PATH', os.environ.get('LD_LIBRARY_PATH')) \
        .setExecutorEnv('PATH', os.environ.get('PATH'))
    if args.processing_master:
        conf.setMaster(args.processing_master)
    spark = SparkSession.builder.config(conf=conf).getOrCreate()


    def predict_fn(model_bytes):
        def fn(rows):
            import math
            import tensorflow as tf
            import tensorflow.keras.backend as K

            # Do not use GPUs for prediction, use single CPU core per task.
            config = tf.ConfigProto(device_count={'GPU': 0})
            config.inter_op_parallelism_threads = 1
            config.intra_op_parallelism_threads = 1
            K.set_session(tf.Session(config=config))

            # Restore from checkpoint.
            model = deserialize_model(model_bytes, tf.keras.models.load_model)

            # Perform predictions.
            for row in rows:
                fields = row.asDict().copy()
                # Convert from log domain to real Sales numbers.
                log_sales = model.predict_on_batch([[row[col]] for col in all_cols])[0]
                # Add 'Sales' column with prediction results.
                fields['Sales'] = math.exp(log_sales)
                yield Row(**fields)

        return fn


    # Submit a Spark job to do inference. Horovod framework is not involved here.
    pred_df = spark.read.parquet('%s/test_df.parquet' % args.data_dir) \
        .rdd.mapPartitions(predict_fn(best_model_bytes)).toDF()
    submission_df = pred_df.select(pred_df.Id.cast(T.IntegerType()), pred_df.Sales).toPandas()
    submission_df.sort_values(by=['Id']).to_csv(args.local_submission_csv, index=False)
    print('Saved predictions to %s' % args.local_submission_csv)

    spark.stop()