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111 lines
4.1 KiB
Python
111 lines
4.1 KiB
Python
# Copyright (c) 2023 Predibase, Inc., 2019 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 logging
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import os
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import pandas as pd
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import pytest
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from ludwig.constants import NAME
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from tests.integration_tests.utils import (
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bag_feature,
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binary_feature,
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category_feature,
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generate_data,
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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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vector_feature,
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)
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logger = logging.getLogger(__name__)
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logger.setLevel(logging.INFO)
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logging.getLogger("ludwig").setLevel(logging.INFO)
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@pytest.mark.parametrize(
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"input_test_feature, output_test_feature, output_loss_parameter",
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[
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# number features
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(number_feature(), number_feature(), None),
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(number_feature(normalization="minmax"), number_feature(), {"loss": {"type": "mean_squared_error"}}),
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(number_feature(normalization="zscore"), number_feature(), {"loss": {"type": "mean_absolute_error"}}),
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# binary feature
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(binary_feature(), binary_feature(), None),
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# Categorical feature
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(category_feature(), category_feature(output_feature=True), None),
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(category_feature(), category_feature(output_feature=True), {"loss": {"type": "softmax_cross_entropy"}}),
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],
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)
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def test_feature(input_test_feature, output_test_feature, output_loss_parameter, csv_filename):
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input_features = [input_test_feature]
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of_test_feature = output_test_feature
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if output_loss_parameter is not None:
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of_test_feature.update(output_loss_parameter)
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output_features = [of_test_feature]
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# Generate test data
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rel_path = generate_data(input_features, output_features, csv_filename, 100)
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run_experiment(input_features, output_features, dataset=rel_path)
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@pytest.mark.parametrize(
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"input_test_feature, output_test_feature",
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[
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([category_feature()], [binary_feature(), binary_feature()]),
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(
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[category_feature()],
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[category_feature(decoder={"vocab_size": 5}), category_feature(decoder={"vocab_size": 7})],
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),
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([category_feature()], [number_feature(), number_feature()]),
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(
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[category_feature()],
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[sequence_feature(decoder={"vocab_size": 5}), sequence_feature(decoder={"vocab_size": 7})],
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),
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(
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[set_feature(encoder={"vocab_size": 5})],
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[set_feature(decoder={"vocab_size": 5}), set_feature(decoder={"vocab_size": 7})],
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),
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([category_feature()], [text_feature(decoder={"vocab_size": 5}), text_feature(decoder={"vocab_size": 7})]),
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([category_feature()], [vector_feature(), vector_feature()]),
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([vector_feature()], [vector_feature(), vector_feature()]),
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([bag_feature()], [vector_feature(), vector_feature()]),
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],
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)
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def test_feature_multiple_outputs(input_test_feature, output_test_feature, csv_filename):
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# Generate test data
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rel_path = generate_data(input_test_feature, output_test_feature, csv_filename, 100)
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run_experiment(input_test_feature, output_test_feature, dataset=rel_path)
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def test_category_int_dtype(tmpdir):
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feature = category_feature()
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input_features = [feature]
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output_features = [binary_feature()]
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csv_fname = generate_data(input_features, output_features, os.path.join(tmpdir, "dataset.csv"))
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df = pd.read_csv(csv_fname)
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distinct_values = df[feature[NAME]].drop_duplicates().values
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value_map = {v: idx for idx, v in enumerate(distinct_values)}
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df[feature[NAME]] = df[feature[NAME]].map(lambda x: value_map[x])
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run_experiment(input_features, output_features, dataset=df)
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