# SPDX-License-Identifier: Apache-2.0

import sys
import unittest
import inspect
import os
import numpy
import pandas
from pyspark.ml.classification import MultilayerPerceptronClassifier, MultilayerPerceptronClassificationModel
from pyspark.ml.linalg import VectorUDT, SparseVector
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 TestSparkmlMLPClassifier(SparkMlTestCase):
    @unittest.skipIf(sys.version_info < (3, 8), reason="pickle fails on python 3.7")
    def test_model_mlp_classifier_binary_class(self):
        this_script_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
        input_path = os.path.join(this_script_dir, "data", "sample_libsvm_data.txt")
        original_data = self.spark.read.format("libsvm").load(input_path)
        #
        # truncate the features
        #
        self.spark.udf.register(
            "truncateFeatures", lambda x: SparseVector(100, range(0, 100), x.toArray()[30:130]), VectorUDT()
        )

        data = original_data.selectExpr("label", "truncateFeatures(features) as features")

        mlp = MultilayerPerceptronClassifier(maxIter=100, tol=0.0001, seed=137, layers=[100, 20, 5, 2],)
        model: MultilayerPerceptronClassificationModel = mlp.fit(data)

        # the name of the input for Logistic Regression is 'features'
        C = model.numFeatures
        model_onnx = convert_sparkml(
            model,
            "sparkml multilayer perceptron classifier",
            [("features", FloatTensorType([None, C]))],
            target_opset=TARGET_OPSET,
        )

        self.assertTrue(model_onnx is not None)

        # run the model
        predicted = model.transform(data)
        # predicted.select("prediction", "probability", "label").show(100, truncate=False)

        data_np = data.toPandas().features.apply(lambda x: pandas.Series(x.toArray())).values.astype(numpy.float32)
        expected = [
            predicted.toPandas().prediction.values.astype(numpy.float32),
            predicted.toPandas().probability.apply(lambda x: pandas.Series(x.toArray())).values.astype(numpy.float32),
        ]

        paths = save_data_models(data_np, expected, model, model_onnx, basename="SparkmlMLPClassifier")
        onnx_model_path = paths[-1]
        output, output_shapes = run_onnx_model(["prediction", "probability"], data_np, onnx_model_path)
        compare_results(expected, output, decimal=5)


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