# Copyright 2017 onwards, fast.ai, Inc. # Modifications copyright (C) 2019 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 os from distutils.version import LooseVersion import pyspark.sql.types as T import pyspark.sql.functions as F from pyspark import SparkConf, Row from pyspark.sql import SparkSession import tensorflow as tf import tensorflow.keras.backend as K from tensorflow.keras.layers import Input, Embedding, Concatenate, Dense, Flatten, Reshape, BatchNormalization, Dropout import horovod.spark.keras as hvd from horovod.spark.common.store import Store from horovod.tensorflow.keras.callbacks import BestModelCheckpoint parser = argparse.ArgumentParser(description='Keras Spark Rossmann Estimator Example', formatter_class=argparse.ArgumentDefaultsHelpFormatter) parser.add_argument('--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 ` in Spark Standalone mode') parser.add_argument('--num-proc', type=int, 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') parser.add_argument('--local-checkpoint-file', default='checkpoint', help='model checkpoint') parser.add_argument('--work-dir', default='/tmp', help='temporary working directory to write intermediate files (prefix with hdfs:// to use HDFS)') if __name__ == '__main__': args = parser.parse_args() # ================ # # DATA PREPARATION # # ================ # print('================') print('Data preparation') print('================') # Create Spark session for data preparation. conf = SparkConf().setAppName('Keras Spark Rossmann Estimator Example').set('spark.sql.shuffle.partitions', '16') if args.master: conf.setMaster(args.master) elif args.num_proc: conf.setMaster('local[{}]'.format(args.num_proc)) 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'] google_trend_de = google_trend_de.withColumnRenamed('trend', 'trend_de') df = df.join(google_trend_de, ['Year', 'Week']).select(df['*'], google_trend_de.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), (cast(Promo2SinceWeek as int) - 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] one_year = datetime.timedelta(365) train_df = train_df.withColumn('Validation', (train_df.Date > test_min_date - one_year) & (train_df.Date <= test_max_date - one_year)) # Determine max Sales number. max_sales = train_df.agg(F.max(train_df.Sales)).collect()[0][0] # Convert Sales to log domain train_df = train_df.withColumn('Sales', F.log(train_df.Sales)) print('===================================') print('Data frame with transformed columns') print('===================================') train_df.show() print('================') print('Data frame sizes') print('================') train_rows = train_df.filter(~train_df.Validation).count() val_rows = train_df.filter(train_df.Validation).count() test_rows = test_df.count() print('Training: %d' % train_rows) print('Validation: %d' % val_rows) print('Test: %d' % test_rows) # ============== # # MODEL TRAINING # # ============== # print('==============') print('Model training') print('==============') 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.math.log(max_sales) * 1.2 CUSTOM_OBJECTS = {'exp_rmspe': exp_rmspe, 'act_sigmoid_scaled': act_sigmoid_scaled} # Disable GPUs when building the model to prevent memory leaks if LooseVersion(tf.__version__) >= LooseVersion('2.0.0'): # See https://github.com/tensorflow/tensorflow/issues/33168 os.environ['CUDA_VISIBLE_DEVICES'] = '-1' else: K.set_session(tf.Session(config=tf.ConfigProto(device_count={'GPU': 0}))) # 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() opt = tf.keras.optimizers.Adam(lr=args.learning_rate, epsilon=1e-3) # Checkpoint callback to specify options for the returned Keras model ckpt_callback = BestModelCheckpoint(monitor='val_loss', mode='auto', save_freq='epoch') # Horovod: run training. store = Store.create(args.work_dir) keras_estimator = hvd.KerasEstimator(num_proc=args.num_proc, store=store, model=model, optimizer=opt, loss='mae', metrics=[exp_rmspe], custom_objects=CUSTOM_OBJECTS, feature_cols=all_cols, label_cols=['Sales'], validation='Validation', batch_size=args.batch_size, epochs=args.epochs, verbose=2, checkpoint_callback=ckpt_callback) keras_model = keras_estimator.fit(train_df).setOutputCols(['Sales']) history = keras_model.getHistory() best_val_rmspe = min(history['val_exp_rmspe']) print('Best RMSPE: %f' % best_val_rmspe) # Save the trained model. keras_model.save(args.local_checkpoint_file) print('Written checkpoint to %s' % args.local_checkpoint_file) # ================ # # FINAL PREDICTION # # ================ # print('================') print('Final prediction') print('================') pred_df = keras_model.transform(test_df) # Convert from log domain to real Sales numbers pred_df = pred_df.withColumn('Sales', F.exp(pred_df.Sales)) 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()