package org.apache.flink.ml.examples.recommendation;

import org.apache.flink.ml.recommendation.swing.Swing;
import org.apache.flink.streaming.api.datastream.DataStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.table.api.Table;
import org.apache.flink.table.api.bridge.java.StreamTableEnvironment;
import org.apache.flink.types.Row;
import org.apache.flink.util.CloseableIterator;

/**
 * Simple program that creates a Swing instance and uses it to generate recommendations for items.
 */
public class SwingExample {
    public static void main(String[] args) {
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        StreamTableEnvironment tEnv = StreamTableEnvironment.create(env);

        // Generates input data.
        DataStream<Row> inputStream =
                env.fromElements(
                        Row.of(0L, 10L),
                        Row.of(0L, 11L),
                        Row.of(0L, 12L),
                        Row.of(1L, 13L),
                        Row.of(1L, 12L),
                        Row.of(2L, 10L),
                        Row.of(2L, 11L),
                        Row.of(2L, 12L),
                        Row.of(3L, 13L),
                        Row.of(3L, 12L));

        Table inputTable = tEnv.fromDataStream(inputStream).as("user", "item");

        // Creates a Swing object and initializes its parameters.
        Swing swing = new Swing().setUserCol("user").setItemCol("item").setMinUserBehavior(1);

        // Transforms the data.
        Table[] outputTable = swing.transform(inputTable);

        // Extracts and displays the result of swing algorithm.
        for (CloseableIterator<Row> it = outputTable[0].execute().collect(); it.hasNext(); ) {
            Row row = it.next();

            long mainItem = row.getFieldAs(0);
            String itemRankScore = row.getFieldAs(1);

            System.out.printf("item: %d, top-k similar items: %s\n", mainItem, itemRankScore);
        }
    }
}

# Simple program that creates a Swing instance and gives recommendations for items.

from pyflink.common import Types
from pyflink.datastream import StreamExecutionEnvironment
from pyflink.table import StreamTableEnvironment

from pyflink.ml.recommendation.swing import Swing

# Creates a new StreamExecutionEnvironment.
env = StreamExecutionEnvironment.get_execution_environment()

# Creates a StreamTableEnvironment.
t_env = StreamTableEnvironment.create(env)

# Generates input data.
input_table = t_env.from_data_stream(
    env.from_collection([
        (0, 10),
        (0, 11),
        (0, 12),
        (1, 13),
        (1, 12),
        (2, 10),
        (2, 11),
        (2, 12),
        (3, 13),
        (3, 12)
    ],
        type_info=Types.ROW_NAMED(
            ['user', 'item'],
            [Types.LONG(), Types.LONG()])
    ))

# Creates a swing object and initialize its parameters.
swing = Swing()
    .set_item_col('item')
    .set_user_col("user")
    .set_min_user_behavior(1)

# Transforms the data to Swing algorithm result.
output_table = swing.transform(input_table)

# Extracts and display the results.
field_names = output_table[0].get_schema().get_field_names()

results = t_env.to_data_stream(
    output_table[0]).execute_and_collect()

for result in results:
    main_item = result[field_names.index(swing.get_item_col())]
    item_rank_score = result[1]
    print(f'item: {main_item}, top-k similar items: {item_rank_score}')