Local pyspark example (aws#3421)

* adding local pyspark example

* updated notebook per review comments

* fixing typo in local_pyspark_example.ipynb

Co-authored-by: Sean Morgan <[email protected]>

* adding import sagemaker

* fix formatting

* fix formatting and grammar

Co-authored-by: durgasury <[email protected]>
Co-authored-by: Sean Morgan <[email protected]>
Co-authored-by: Sean Morgan <[email protected]>

sagemaker_processing/index.rst

@@ -15,4 +15,5 @@ Processing
    fairness_and_explainability/fairness_and_explainability_spark
    fairness_and_explainability/text_explainability/text_explainability
    fairness_and_explainability/text_explainability_sagemaker_algorithm/byo_blazingtext_model_hosting
-   computer_vision/explainability_image_classification
+   computer_vision/explainability_image_classification
+   local_pyspark/local_pyspark_example

sagemaker_processing/local_pyspark/code/preprocess.py

@@ -0,0 +1,107 @@
+import argparse
+import csv
+import os
+import shutil
+import sys
+import time
+
+import pyspark
+from pyspark.sql import SparkSession
+from pyspark.ml import Pipeline
+from pyspark.ml.feature import (
+    OneHotEncoder,
+    StringIndexer,
+    VectorAssembler,
+    VectorIndexer,
+)
+from pyspark.sql.functions import *
+from pyspark.sql.types import (
+    DoubleType,
+    StringType,
+    StructField,
+    StructType,
+)
+from pyspark.ml.functions import vector_to_array
+
+
+def main():
+    parser = argparse.ArgumentParser(description="app inputs and outputs")
+    parser.add_argument("--s3_input_bucket", type=str, help="s3 input bucket")
+    parser.add_argument("--s3_input_key_prefix", type=str, help="s3 input key prefix")
+    parser.add_argument("--s3_output_bucket", type=str, help="s3 output bucket")
+    parser.add_argument("--s3_output_key_prefix", type=str, help="s3 output key prefix")
+    args = parser.parse_args()
+
+    spark = SparkSession.builder.appName("PySparkApp").getOrCreate()
+
+    # Defining the schema corresponding to the input data. The input data does not contain headers
+    schema = StructType(
+        [
+            StructField("sex", StringType(), True),
+            StructField("length", DoubleType(), True),
+            StructField("diameter", DoubleType(), True),
+            StructField("height", DoubleType(), True),
+            StructField("whole_weight", DoubleType(), True),
+            StructField("shucked_weight", DoubleType(), True),
+            StructField("viscera_weight", DoubleType(), True),
+            StructField("shell_weight", DoubleType(), True),
+            StructField("rings", DoubleType(), True),
+        ]
+    )
+
+    # Downloading the data from S3 into a Dataframe
+    total_df = spark.read.csv(
+        ("s3://" + os.path.join(args.s3_input_bucket, args.s3_input_key_prefix, "abalone.csv")),
+        header=False,
+        schema=schema,
+    )
+
+    # StringIndexer on the sex column which has categorical value
+    sex_indexer = StringIndexer(inputCol="sex", outputCol="indexed_sex")
+
+    # one-hot-encoding is being performed on the string-indexed sex column (indexed_sex)
+    sex_encoder = OneHotEncoder(inputCol="indexed_sex", outputCol="sex_vec")
+
+    # vector-assembler will bring all the features to a 1D vector for us to save easily into CSV format
+    assembler = VectorAssembler(
+        inputCols=[
+            "sex_vec",
+            "length",
+            "diameter",
+            "height",
+            "whole_weight",
+            "shucked_weight",
+            "viscera_weight",
+            "shell_weight",
+        ],
+        outputCol="features",
+    )
+
+    # add the above steps to a pipeline
+    pipeline = Pipeline(stages=[sex_indexer, sex_encoder, assembler])
+
+    # train the feature transformers
+    model = pipeline.fit(total_df)
+
+    # transform the dataset with information obtained from the previous fit
+    transformed_total_df = model.transform(total_df)
+
+    # split the overall dataset into 80-20 training and validation
+    (train_df, validation_df) = transformed_total_df.randomSplit([0.8, 0.2])
+
+    # extract only rings and features columns to write to csv
+    train_df_final = train_df.withColumn("feature", vector_to_array("features")).select(
+        ["rings"] + [col("feature")[i] for i in range(9)]
+    )
+
+    val_df_final = validation_df.withColumn("feature", vector_to_array("features")).select(
+        ["rings"] + [col("feature")[i] for i in range(9)]
+    )
+
+    # write to csv files in S3
+    train_df_final.write.csv(f"s3://{args.s3_output_bucket}/{args.s3_output_key_prefix}/train")
+    val_df_final.write.csv(f"s3://{args.s3_output_bucket}/{args.s3_output_key_prefix}/validation")
+
+
+if __name__ == "__main__":
+    main()