import org.apache.flink.api.common.typeinfo.TypeInformation;
import org.apache.flink.api.common.typeinfo.Types;
import org.apache.flink.ml.feature.lsh.MinHashLSH;
import org.apache.flink.ml.feature.lsh.MinHashLSHModel;
import org.apache.flink.ml.linalg.DenseVector;
import org.apache.flink.ml.linalg.SparseVector;
import org.apache.flink.ml.linalg.Vector;
import org.apache.flink.ml.linalg.Vectors;
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.commons.collections.IteratorUtils;

import java.util.Arrays;
import java.util.List;

import static org.apache.flink.table.api.Expressions.$;

/**
 * Simple program that trains a MinHashLSH model and uses it for approximate nearest neighbors and
 * similarity join.
 */
public class MinHashLSHExample {
    public static void main(String[] args) throws Exception {

        // Creates a new StreamExecutionEnvironment.
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();

        // Creates a StreamTableEnvironment.
        StreamTableEnvironment tEnv = StreamTableEnvironment.create(env);

        // Generates two datasets.
        Table dataA =
                tEnv.fromDataStream(
                        env.fromCollection(
                                Arrays.asList(
                                        Row.of(
                                                0,
                                                Vectors.sparse(
                                                        6,
                                                        new int[] {0, 1, 2},
                                                        new double[] {1., 1., 1.})),
                                        Row.of(
                                                1,
                                                Vectors.sparse(
                                                        6,
                                                        new int[] {2, 3, 4},
                                                        new double[] {1., 1., 1.})),
                                        Row.of(
                                                2,
                                                Vectors.sparse(
                                                        6,
                                                        new int[] {0, 2, 4},
                                                        new double[] {1., 1., 1.}))),
                                Types.ROW_NAMED(
                                        new String[] {"id", "vec"},
                                        Types.INT,
                                        TypeInformation.of(SparseVector.class))));

        Table dataB =
                tEnv.fromDataStream(
                        env.fromCollection(
                                Arrays.asList(
                                        Row.of(
                                                3,
                                                Vectors.sparse(
                                                        6,
                                                        new int[] {1, 3, 5},
                                                        new double[] {1., 1., 1.})),
                                        Row.of(
                                                4,
                                                Vectors.sparse(
                                                        6,
                                                        new int[] {2, 3, 5},
                                                        new double[] {1., 1., 1.})),
                                        Row.of(
                                                5,
                                                Vectors.sparse(
                                                        6,
                                                        new int[] {1, 2, 4},
                                                        new double[] {1., 1., 1.}))),
                                Types.ROW_NAMED(
                                        new String[] {"id", "vec"},
                                        Types.INT,
                                        TypeInformation.of(SparseVector.class))));

        // Creates a MinHashLSH estimator object and initializes its parameters.
        MinHashLSH lsh =
                new MinHashLSH()
                        .setInputCol("vec")
                        .setOutputCol("hashes")
                        .setSeed(2022)
                        .setNumHashTables(5);

        // Trains the MinHashLSH model.
        MinHashLSHModel model = lsh.fit(dataA);

        // Uses the MinHashLSH model for transformation.
        Table output = model.transform(dataA)[0];

        // Extracts and displays the results.
        List<String> fieldNames = output.getResolvedSchema().getColumnNames();
        for (Row result :
                (List<Row>) IteratorUtils.toList(tEnv.toDataStream(output).executeAndCollect())) {
            Vector inputValue = result.getFieldAs(fieldNames.indexOf(lsh.getInputCol()));
            DenseVector[] outputValue = result.getFieldAs(fieldNames.indexOf(lsh.getOutputCol()));
            System.out.printf(
                    "Vector: %s \tHash values: %s\n", inputValue, Arrays.toString(outputValue));
        }

        // Finds approximate nearest neighbors of the key.
        Vector key = Vectors.sparse(6, new int[] {1, 3}, new double[] {1., 1.});
        output = model.approxNearestNeighbors(dataA, key, 2).select($("id"), $("distCol"));
        for (Row result :
                (List<Row>) IteratorUtils.toList(tEnv.toDataStream(output).executeAndCollect())) {
            int idValue = result.getFieldAs(fieldNames.indexOf("id"));
            double distValue = result.getFieldAs(result.getArity() - 1);
            System.out.printf("ID: %d \tDistance: %f\n", idValue, distValue);
        }

        // Approximately finds pairs from two datasets with distances smaller than the threshold.
        output = model.approxSimilarityJoin(dataA, dataB, .6, "id");
        for (Row result :
                (List<Row>) IteratorUtils.toList(tEnv.toDataStream(output).executeAndCollect())) {
            int idAValue = result.getFieldAs(0);
            int idBValue = result.getFieldAs(1);
            double distValue = result.getFieldAs(2);
            System.out.printf(
                    "ID from left: %d \tID from right: %d \t Distance: %f\n",
                    idAValue, idAValue, distValue);
        }
    }
}

# Simple program that trains a MinHashLSH model and uses it for approximate nearest neighbors 
# and similarity join.


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

from pyflink.ml.linalg import Vectors, SparseVectorTypeInfo
from pyflink.ml.feature.lsh import MinHashLSH

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

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

# Generates two datasets.
data_a = t_env.from_data_stream(
    env.from_collection([
        (0, Vectors.sparse(6, [0, 1, 2], [1., 1., 1.])),
        (1, Vectors.sparse(6, [2, 3, 4], [1., 1., 1.])),
        (2, Vectors.sparse(6, [0, 2, 4], [1., 1., 1.])),
    ], type_info=Types.ROW_NAMED(['id', 'vec'], [Types.INT(), SparseVectorTypeInfo()])))

data_b = t_env.from_data_stream(
    env.from_collection([
        (3, Vectors.sparse(6, [1, 3, 5], [1., 1., 1.])),
        (4, Vectors.sparse(6, [2, 3, 5], [1., 1., 1.])),
        (5, Vectors.sparse(6, [1, 2, 4], [1., 1., 1.])),
    ], type_info=Types.ROW_NAMED(['id', 'vec'], [Types.INT(), SparseVectorTypeInfo()])))

# Creates a MinHashLSH estimator object and initializes its parameters.
lsh = MinHashLSH() \
    .set_input_col('vec') \
    .set_output_col('hashes') \
    .set_seed(2022) \
    .set_num_hash_tables(5)

# Trains the MinHashLSH model.
model = lsh.fit(data_a)

# Uses the MinHashLSH model for transformation.
output = model.transform(data_a)[0]

# Extracts and displays the results.
field_names = output.get_schema().get_field_names()
for result in t_env.to_data_stream(output).execute_and_collect():
    input_value = result[field_names.index(lsh.get_input_col())]
    output_value = result[field_names.index(lsh.get_output_col())]
    print(f'Vector: {input_value} \tHash Values: {output_value}')

# Finds approximate nearest neighbors of the key.
key = Vectors.sparse(6, [1, 3], [1., 1.])
output = model.approx_nearest_neighbors(data_a, key, 2).select("id, distCol")
for result in t_env.to_data_stream(output).execute_and_collect():
    id_value = result[field_names.index("id")]
    dist_value = result[-1]
    print(f'ID: {id_value} \tDistance: {dist_value}')

# Approximately finds pairs from two datasets with distances smaller than the threshold.
output = model.approx_similarity_join(data_a, data_b, .6, "id")
for result in t_env.to_data_stream(output).execute_and_collect():
    id_a_value, id_b_value, dist_value = result
    print(f'ID from left: {id_a_value} \tID from right: {id_b_value} \t Distance: {dist_value}')