# Copyright 2021 Huawei Technologies Co., Ltd # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== import numpy as np import pytest from mindspore import log import mindspore.dataset as ds import mindspore.dataset.text as text import mindspore.dataset.text.transforms as T DATASET_ROOT_PATH = "../data/dataset/testVectors/" def _count_unequal_element(data_expected, data_me, rtol, atol): assert data_expected.shape == data_me.shape total_count = len(data_expected.flatten()) error = np.abs(data_expected - data_me) greater = np.greater(error, atol + np.abs(data_expected)*rtol) loss_count = np.count_nonzero(greater) assert (loss_count/total_count) < rtol,\ "\ndata_expected_std:{0}\ndata_me_error:{1}\nloss:{2}".\ format(data_expected[greater], data_me[greater], error[greater]) def allclose_nparray(data_expected, data_me, rtol, atol, equal_nan=True): if np.any(np.isnan(data_expected)): assert np.allclose(data_me, data_expected, rtol, atol, equal_nan=equal_nan) elif not np.allclose(data_me, data_expected, rtol, atol, equal_nan=equal_nan): _count_unequal_element(data_expected, data_me, rtol, atol) else: assert True def test_char_n_gram_all_to_vectors_params_eager(): """ Feature: CharNGram Description: test with all parameters which include `unk_init` and `lower_case_backup` in function ToVectors in eager mode Expectation: output is equal to the expected value """ char_n_gram = text.CharNGram.from_file(DATASET_ROOT_PATH + "char_n_gram_20.txt", max_vectors=18) unk_init = (-np.ones(5)).tolist() to_vectors = T.ToVectors(char_n_gram, unk_init=unk_init, lower_case_backup=True) result1 = to_vectors("THE") result2 = to_vectors(".") result3 = to_vectors("To") res = [[-1.34121733e+00, 4.42693333e-02, -4.86969667e-01, 6.62939000e-01, -3.67669000e-01], [-1.00000000e+00, -1.00000000e+00, -1.00000000e+00, -1.00000000e+00, -1.00000000e+00], [-9.68530000e-01, -7.89463000e-01, 5.15762000e-01, 2.02107000e+00, -1.64635000e+00]] res_array = np.array(res, dtype=np.float32) allclose_nparray(res_array[0], result1, 0.0001, 0.0001) allclose_nparray(res_array[1], result2, 0.0001, 0.0001) allclose_nparray(res_array[2], result3, 0.0001, 0.0001) def test_char_n_gram_build_from_file(): """ Feature: CharNGram Description: test with only default parameter Expectation: output is equal to the expected value """ char_n_gram = text.CharNGram.from_file(DATASET_ROOT_PATH + "char_n_gram_20.txt") to_vectors = text.ToVectors(char_n_gram) data = ds.TextFileDataset(DATASET_ROOT_PATH + "words.txt", shuffle=False) data = data.map(operations=to_vectors, input_columns=["text"]) ind = 0 res = [[0., 0., 0., 0., 0.], [0., 0., 0., 0., 0.], [0.117336, 0.362446, -0.983326, 0.939264, -0.05648], [0.657201, 2.11761, -1.59276, 0.432072, 1.21395], [0., 0., 0., 0., 0.], [-2.26956, 0.288491, -0.740001, 0.661703, 0.147355], [0., 0., 0., 0., 0.]] for d in data.create_dict_iterator(num_epochs=1, output_numpy=True): res_array = np.array(res[ind], dtype=np.float32) allclose_nparray(res_array, d["text"], 0.0001, 0.0001) ind += 1 def test_char_n_gram_all_build_from_file_params(): """ Feature: CharNGram Description: test with all parameters which include `path` and `max_vector` in function BuildFromFile Expectation: output is equal to the expected value """ char_n_gram = text.CharNGram.from_file(DATASET_ROOT_PATH + "char_n_gram_20.txt", max_vectors=100) to_vectors = text.ToVectors(char_n_gram) data = ds.TextFileDataset(DATASET_ROOT_PATH + "words.txt", shuffle=False) data = data.map(operations=to_vectors, input_columns=["text"]) ind = 0 res = [[0., 0., 0., 0., 0.], [0., 0., 0., 0., 0.], [0.117336, 0.362446, -0.983326, 0.939264, -0.05648], [0.657201, 2.11761, -1.59276, 0.432072, 1.21395], [0., 0., 0., 0., 0.], [-2.26956, 0.288491, -0.740001, 0.661703, 0.147355], [0., 0., 0., 0., 0.]] for d in data.create_dict_iterator(num_epochs=1, output_numpy=True): res_array = np.array(res[ind], dtype=np.float32) allclose_nparray(res_array, d["text"], 0.0001, 0.0001) ind += 1 def test_char_n_gram_all_build_from_file_params_eager(): """ Feature: CharNGram Description: test with all parameters which include `path` and `max_vector` in function BuildFromFile in eager mode Expectation: output is equal to the expected value """ char_n_gram = text.CharNGram.from_file(DATASET_ROOT_PATH + "char_n_gram_20.txt", max_vectors=18) to_vectors = T.ToVectors(char_n_gram) result1 = to_vectors("the") result2 = to_vectors(".") result3 = to_vectors("to") res = [[-1.34121733e+00, 4.42693333e-02, -4.86969667e-01, 6.62939000e-01, -3.67669000e-01], [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], [-9.68530000e-01, -7.89463000e-01, 5.15762000e-01, 2.02107000e+00, -1.64635000e+00]] res_array = np.array(res, dtype=np.float32) allclose_nparray(res_array[0], result1, 0.0001, 0.0001) allclose_nparray(res_array[1], result2, 0.0001, 0.0001) allclose_nparray(res_array[2], result3, 0.0001, 0.0001) def test_char_n_gram_build_from_file_eager(): """ Feature: CharNGram Description: test with only default parameter in eager mode Expectation: output is equal to the expected value """ char_n_gram = text.CharNGram.from_file(DATASET_ROOT_PATH + "char_n_gram_20.txt") to_vectors = T.ToVectors(char_n_gram) result1 = to_vectors("the") result2 = to_vectors(".") result3 = to_vectors("to") res = [[-8.40079000e-01, -2.70002500e-02, -8.33472250e-01, 5.88367000e-01, -2.10011750e-01], [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00], [-9.68530000e-01, -7.89463000e-01, 5.15762000e-01, 2.02107000e+00, -1.64635000e+00]] res_array = np.array(res, dtype=np.float32) allclose_nparray(res_array[0], result1, 0.0001, 0.0001) allclose_nparray(res_array[1], result2, 0.0001, 0.0001) allclose_nparray(res_array[2], result3, 0.0001, 0.0001) def test_char_n_gram_invalid_input(): """ Feature: CharNGram Description: test the validate function with invalid parameters. Expectation: Verification of correct error message for invalid input. """ def test_invalid_input(test_name, file_path, error, error_msg, max_vectors=None, unk_init=None, lower_case_backup=False, token="ok"): log.info("Test CharNGram with wrong input: {0}".format(test_name)) with pytest.raises(error) as error_info: char_n_gram = text.CharNGram.from_file(file_path, max_vectors=max_vectors) to_vectors = T.ToVectors(char_n_gram, unk_init=unk_init, lower_case_backup=lower_case_backup) to_vectors(token) assert error_msg in str(error_info.value) test_invalid_input("Not all vectors have the same number of dimensions", DATASET_ROOT_PATH + "char_n_gram_20_dim_different.txt", error=RuntimeError, error_msg="all vectors must have the same number of dimensions, " + "but got dim 4 while expecting 5") test_invalid_input("the file is empty.", DATASET_ROOT_PATH + "vectors_empty.txt", error=RuntimeError, error_msg="invalid file, file is empty.") test_invalid_input("the count of `unknown_init`'s element is different with word vector.", DATASET_ROOT_PATH + "char_n_gram_20.txt", error=RuntimeError, error_msg="unk_init must be the same length as vectors, " + "but got unk_init: 6 and vectors: 5", unk_init=np.ones(6).tolist()) test_invalid_input("The file not exist", DATASET_ROOT_PATH + "not_exist.txt", RuntimeError, error_msg="get real path failed") test_invalid_input("max_vectors parameter must be greater than 0", DATASET_ROOT_PATH + "char_n_gram_20.txt", error=ValueError, error_msg="Input max_vectors is not within the required interval", max_vectors=-1) test_invalid_input("invalid max_vectors parameter type as a float", DATASET_ROOT_PATH + "char_n_gram_20.txt", error=TypeError, error_msg="Argument max_vectors with value 1.0 is not of type []," " but got .", max_vectors=1.0) test_invalid_input("invalid max_vectors parameter type as a string", DATASET_ROOT_PATH + "char_n_gram_20.txt", error=TypeError, error_msg="Argument max_vectors with value 1 is not of type []," " but got .", max_vectors="1") test_invalid_input("invalid token parameter type as a float", DATASET_ROOT_PATH + "char_n_gram_20.txt", error=RuntimeError, error_msg="input tensor type should be string.", token=1.0) test_invalid_input("invalid lower_case_backup parameter type as a string", DATASET_ROOT_PATH + "char_n_gram_20.txt", error=TypeError, error_msg="Argument lower_case_backup with " + "value True is not of type []," " but got .", lower_case_backup="True") test_invalid_input("invalid lower_case_backup parameter type as a string", DATASET_ROOT_PATH + "char_n_gram_20.txt", error=TypeError, error_msg="Argument lower_case_backup with " + "value True is not of type []," " but got .", lower_case_backup="True") if __name__ == '__main__': test_char_n_gram_all_to_vectors_params_eager() test_char_n_gram_build_from_file() test_char_n_gram_all_build_from_file_params() test_char_n_gram_all_build_from_file_params_eager() test_char_n_gram_build_from_file_eager() test_char_n_gram_invalid_input()