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68 lines
2.7 KiB
Python
68 lines
2.7 KiB
Python
# Copyright (c) 2023 Predibase, Inc., 2020 Uber Technologies, Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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import pytest
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from tests.integration_tests.utils import (
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category_feature,
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generate_data,
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generate_output_features_with_dependencies,
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number_feature,
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run_experiment,
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sequence_feature,
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set_feature,
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text_feature,
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)
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@pytest.mark.parametrize(
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"output_features",
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[
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# baseline test case
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[
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category_feature(decoder={"vocab_size": 2}, reduce_input="sum"),
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sequence_feature(decoder={"vocab_size": 10, "max_len": 5}),
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number_feature(),
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],
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# use generator as decoder
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[
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category_feature(decoder={"vocab_size": 2}, reduce_input="sum"),
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sequence_feature(decoder={"vocab_size": 10, "max_len": 5, "type": "generator"}),
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number_feature(),
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],
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# Generator decoder and reduce_input = None
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[
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category_feature(decoder={"vocab_size": 2}, reduce_input="sum"),
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sequence_feature(decoder={"max_len": 5, "type": "generator"}, reduce_input=None),
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number_feature(normalization="minmax"),
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],
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# output features with dependencies single dependency
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generate_output_features_with_dependencies("number_feature", ["category_feature"]),
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# output features with dependencies multiple dependencies
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generate_output_features_with_dependencies("number_feature", ["category_feature", "sequence_feature"]),
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],
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)
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def test_experiment_multiple_seq_seq(csv_filename, output_features):
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input_features = [
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text_feature(encoder={"vocab_size": 100, "min_len": 1, "type": "stacked_cnn"}),
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number_feature(normalization="zscore"),
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category_feature(encoder={"vocab_size": 10, "embedding_size": 5}),
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set_feature(),
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sequence_feature(encoder={"vocab_size": 10, "max_len": 10, "type": "embed"}),
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]
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output_features = output_features
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rel_path = generate_data(input_features, output_features, csv_filename)
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run_experiment(input_features, output_features, dataset=rel_path)
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