66 lines
2.1 KiB
Python
66 lines
2.1 KiB
Python
import os
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import pandas as pd
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import pytest
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from src.modelimpl.dataset import prepare_input, load_df, compute_labels, Labelling, SplitData, df_validation_split, \
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Datasets
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@pytest.fixture
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def sample_dataframe():
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return pd.DataFrame({
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'id': [1, 2],
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'title': ['Title1', 'Title2'],
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'body': ['Body1', 'Body2'],
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'title_body': ['Title1\nBody1', 'Title2\nBody2'],
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'assignee': ['A', 'B']
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})
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def test_prepare_input(sample_dataframe):
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result_df = prepare_input(sample_dataframe)
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assert list(result_df.columns) == ['title_body', 'assignee']
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expected_title_body = ['Title1\nBody1', 'Title2\nBody2']
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assert result_df['title_body'].tolist() == expected_title_body
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assert result_df['assignee'].tolist() == ['A', 'B']
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def test_load_df(sample_dataframe, tmpdir):
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# Save sample DataFrame to a CSV file
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csv_filename = os.path.join(tmpdir, 'sample_issues.csv')
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sample_dataframe.to_csv(csv_filename, index=False)
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result_df = load_df(csv_filename)
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assert list(result_df.columns) == ['title_body', 'assignee']
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assert len(result_df) == len(sample_dataframe)
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def test_compute_labels():
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sample_frames = [pd.DataFrame({'assignee': ['A', 'B', 'C']}), pd.DataFrame({'assignee': ['B', 'C', 'D']})]
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labels_dict, num_bounds = compute_labels(sample_frames)
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assert labels_dict == {'A': 0, 'B': 1, 'C': 2, 'D': 3}
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assert num_bounds == [0, 3, 4]
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def test_labelling_methods(tmpdir):
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labels = {'A': 0, 'B': 1, 'C': 2}
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labelling = Labelling(labels)
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filename = os.path.join(tmpdir, 'test_labels.csv')
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labelling.save(filename)
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loaded_labelling = Labelling.load(filename)
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assert labelling.labels == loaded_labelling.labels
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def test_split_data_methods(sample_dataframe):
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labels = Labelling({'A': 0, 'B': 1})
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split_data = SplitData.from_df(sample_dataframe, labels, 1)
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assert len(split_data) == len(sample_dataframe)
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def test_df_validation_split(sample_dataframe):
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df_train, df_val = df_validation_split(sample_dataframe)
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assert len(df_train) > 0
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assert len(df_val) > 0
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assert len(df_train) + len(df_val) == len(sample_dataframe)
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