# SPDX-License-Identifier: Apache-2.0

import sys
import unittest
import pandas
import numpy
from pyspark.ml.feature import BucketedRandomProjectionLSH
from pyspark.ml.linalg import Vectors
from onnx.defs import onnx_opset_version
from onnxconverter_common.onnx_ex import DEFAULT_OPSET_NUMBER
from onnxmltools import convert_sparkml
from onnxmltools.convert.common.data_types import FloatTensorType
from tests.sparkml.sparkml_test_utils import save_data_models, run_onnx_model, compare_results
from tests.sparkml import SparkMlTestCase


TARGET_OPSET = min(DEFAULT_OPSET_NUMBER, onnx_opset_version())


class TestBucketedRandomProjectionLSH(SparkMlTestCase):

    @unittest.skipIf(sys.platform == 'win32',
                     reason="UnsatisfiedLinkError")
    @unittest.skipIf(sys.version_info < (3, 8),
                     reason="pickle fails on python 3.7")
    def test_bucketed_random_projection_lsh(self):
        data = self.spark.createDataFrame([
            (0, Vectors.dense([-1.0, -1.0 ]),),
            (1, Vectors.dense([-1.0, 1.0 ]),),
            (2, Vectors.dense([1.0, -1.0 ]),),
            (3, Vectors.dense([1.0, 1.0]),)
        ], ["id", "features"])
        mh = BucketedRandomProjectionLSH(inputCol="features", outputCol="hashes", seed=12345, bucketLength=1.0)
        model = mh.fit(data)

        feature_count = data.first()[1].size
        model_onnx = convert_sparkml(model, 'Sparkml BucketedRandomProjectionLSH', [
            ('features', FloatTensorType([None, feature_count]))
        ], spark_session=self.spark, target_opset=TARGET_OPSET)
        self.assertTrue(model_onnx is not None)

        # run the model
        predicted = model.transform(data)
        data_np = data.toPandas().features.apply(
            lambda x: pandas.Series(x.toArray())).values.astype(numpy.float32)
        expected = [
            predicted.toPandas().hashes.apply(lambda x: pandas.Series(x)
                                              .map(lambda y: y.values[0])).values.astype(numpy.float32),
        ]
        paths = save_data_models(data_np, expected, model, model_onnx,
                                    basename="SparkmlBucketedRandomProjectionLSH")
        onnx_model_path = paths[-1]
        output, output_shapes = run_onnx_model(['hashes'], data_np, onnx_model_path)
        compare_results(expected, output, decimal=5)


if __name__ == "__main__":
    unittest.main()
