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Update the insights workflows and examples. (#67)
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.github/workflows/ci-workflow.yml

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@@ -5,8 +5,8 @@ jobs:
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name: lint-python
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runs-on: ubuntu-latest
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steps:
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- uses: actions/checkout@v3
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- uses: actions/setup-python@v4
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- uses: actions/checkout@v4
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- uses: actions/setup-python@v5
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with:
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python-version: '3.x'
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- name: Install ruff
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runs-on: ubuntu-latest
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strategy:
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matrix:
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asr_versions: ['1.0', '1.1', '1.2']
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asr_versions: ['1.0', '1.2', '1.4', '1.6']
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steps:
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- uses: actions/checkout@v3
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- uses: actions/setup-python@v4
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- uses: actions/checkout@v4
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- uses: actions/setup-python@v5
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with:
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python-version: '3.x'
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- name: Install packages and run tests
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run: |
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pip install pytest
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pip install asreview[all]==${{ matrix.asr_versions }}
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pip install asreview==${{ matrix.asr_versions }}
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pip install .
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pytest tests

docs/example_absolute_axes.py

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from asreviewcontrib.insights.plot import plot_recall
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with open_state("tests/asreview_files/sim_van_de_schoot_2017_1.asreview") as s:
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with open_state("tests/asreview_files/sim_van_de_schoot_2017_logistic.asreview") as s:
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fig, ax = plt.subplots()
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plot_recall(ax, s, x_absolute=True, y_absolute=True)
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docs/example_custom_title.py

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from asreviewcontrib.insights.plot import plot_wss
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with open_state("tests/asreview_files/sim_van_de_schoot_2017_1.asreview") as s:
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with open_state("tests/asreview_files/sim_van_de_schoot_2017_logistic.asreview") as s:
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fig, ax = plt.subplots()
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plot_wss(ax, s)

docs/example_multiple_lines.py

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fig, ax = plt.subplots()
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with open_state("tests/asreview_files/sim_van_de_schoot_2017_1.asreview") as s1:
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with open_state(
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"tests/asreview_files/sim_van_de_schoot_2017_stop_if_min.asreview"
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) as s1:
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plot_recall(ax, s1)
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with open_state(
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"tests/asreview_files/" "sim_van_de_schoot_2017_logistic.asreview"
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) as s2:
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plot_recall(ax, s2)
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# Set the labels for the legend. Both plots add the recall line and the random
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# line. Hence the recall lines are the 0th and 2nd line.
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# Set the labels for the legend. Both plots add the recall line, the random
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# curve, and the perfect curve. Hence the recall lines are the 0th and 3nd line.
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ax.lines[0].set_label("Naive Bayes")
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ax.lines[2].set_label("Logistic")
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ax.lines[3].set_label("Logistic")
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ax.legend()
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fig.savefig("docs/example_multiple_lines.png")

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