| import matplotlib |
| import pandas as pd |
| import pytest |
|
|
| matplotlib.use("Agg") |
|
|
| from shinka.plots import ( |
| plot_generation_runtime_timeline, |
| plot_normalized_occupancy_over_time, |
| ) |
| from shinka.plots.plot_throughput import ( |
| _compute_occupancy_series, |
| _prepare_pool_runtime_data, |
| ) |
|
|
|
|
| def _runtime_df() -> pd.DataFrame: |
| return pd.DataFrame( |
| [ |
| { |
| "id": "job-a-main", |
| "source_job_id": "job-a", |
| "is_island_copy": False, |
| "correct": True, |
| "combined_score": 0.9, |
| "timestamp": 10, |
| "generation": 1, |
| "patch_name": "patch-a", |
| "model_name": "model-a", |
| "timeline_lane_mode": "pool_slots", |
| "pipeline_started_at": 0, |
| "sampling_started_at": 0, |
| "sampling_finished_at": 2, |
| "evaluation_started_at": 2, |
| "evaluation_finished_at": 6, |
| "postprocess_started_at": 6, |
| "postprocess_finished_at": 8, |
| "sampling_worker_id": 1, |
| "evaluation_worker_id": 1, |
| "postprocess_worker_id": 1, |
| "sampling_worker_capacity": 2, |
| "evaluation_worker_capacity": 2, |
| "postprocess_worker_capacity": 1, |
| }, |
| { |
| "id": "job-a-copy", |
| "source_job_id": "job-a", |
| "is_island_copy": True, |
| "correct": False, |
| "combined_score": 0.1, |
| "timestamp": 11, |
| "generation": 1, |
| "patch_name": "patch-a-copy", |
| "model_name": "model-a", |
| "timeline_lane_mode": "pool_slots", |
| "pipeline_started_at": 0, |
| "sampling_started_at": 0, |
| "sampling_finished_at": 2, |
| "evaluation_started_at": 2, |
| "evaluation_finished_at": 6, |
| "postprocess_started_at": 6, |
| "postprocess_finished_at": 8, |
| "sampling_worker_id": 1, |
| "evaluation_worker_id": 1, |
| "postprocess_worker_id": 1, |
| "sampling_worker_capacity": 2, |
| "evaluation_worker_capacity": 2, |
| "postprocess_worker_capacity": 1, |
| }, |
| { |
| "id": "job-b", |
| "source_job_id": "job-b", |
| "is_island_copy": False, |
| "correct": True, |
| "combined_score": 0.8, |
| "timestamp": 20, |
| "generation": 2, |
| "patch_name": "patch-b", |
| "model_name": "model-b", |
| "timeline_lane_mode": "pool_slots", |
| "pipeline_started_at": 1, |
| "sampling_started_at": 1, |
| "sampling_finished_at": 3, |
| "evaluation_started_at": 4, |
| "evaluation_finished_at": 8, |
| "postprocess_started_at": 8, |
| "postprocess_finished_at": 10, |
| "sampling_worker_id": 2, |
| "evaluation_worker_id": 2, |
| "postprocess_worker_id": 1, |
| "sampling_worker_capacity": 2, |
| "evaluation_worker_capacity": 2, |
| "postprocess_worker_capacity": 1, |
| }, |
| { |
| "id": "job-missing", |
| "source_job_id": "job-missing", |
| "is_island_copy": False, |
| "correct": True, |
| "combined_score": 0.7, |
| "timestamp": 30, |
| "generation": 3, |
| "patch_name": "patch-missing", |
| "model_name": "model-c", |
| "timeline_lane_mode": "pool_slots", |
| "pipeline_started_at": 2, |
| "sampling_started_at": 2, |
| "sampling_finished_at": 4, |
| "evaluation_started_at": 5, |
| "evaluation_finished_at": 9, |
| "postprocess_started_at": 9, |
| "postprocess_finished_at": None, |
| "sampling_worker_id": 1, |
| "evaluation_worker_id": 1, |
| "postprocess_worker_id": 1, |
| "sampling_worker_capacity": 2, |
| "evaluation_worker_capacity": 2, |
| "postprocess_worker_capacity": 1, |
| }, |
| ] |
| ) |
|
|
|
|
| def test_prepare_pool_runtime_data_dedupes_rows_and_computes_capacities(): |
| prepared = _prepare_pool_runtime_data(_runtime_df()) |
|
|
| assert prepared is not None |
| assert prepared.capacities == {"sampling": 2, "evaluation": 2, "postprocess": 1} |
| assert prepared.lane_labels == [ |
| "Sampling W1", |
| "Evaluation W1", |
| "Postprocess W1", |
| "Sampling W2", |
| "Evaluation W2", |
| ] |
| assert list(prepared.rows["id"]) == ["job-a-main", "job-b"] |
| assert prepared.peaks == {"sampling": 2, "evaluation": 2, "postprocess": 1} |
|
|
|
|
| def test_prepare_pool_runtime_data_handles_missing_optional_columns(): |
| runtime_df = _runtime_df().drop( |
| columns=["source_job_id", "is_island_copy", "patch_name", "model_name"] |
| ) |
|
|
| prepared = _prepare_pool_runtime_data(runtime_df) |
|
|
| assert prepared is not None |
| assert list(prepared.rows["id"]) == ["job-a-main", "job-a-copy", "job-b"] |
| assert list(prepared.rows["source_job_id"]) == ["job-a-main", "job-a-copy", "job-b"] |
| assert list(prepared.rows["patch_name"]) == ["unnamed", "unnamed", "unnamed"] |
| assert list(prepared.rows["model_name"]) == ["N/A", "N/A", "N/A"] |
| assert prepared.capacities == {"sampling": 2, "evaluation": 2, "postprocess": 1} |
|
|
|
|
| def test_compute_occupancy_series_matches_expected_utilization_stats(): |
| prepared = _prepare_pool_runtime_data(_runtime_df()) |
| assert prepared is not None |
|
|
| series = _compute_occupancy_series( |
| prepared.rows, |
| start_key="evaluation_started_at", |
| end_key="evaluation_finished_at", |
| capacity=prepared.capacities["evaluation"], |
| ) |
|
|
| assert series is not None |
| assert series.total_duration == pytest.approx(6.0) |
| assert series.avg_occupied == pytest.approx(8.0 / 6.0) |
| assert series.utilization_pct == pytest.approx((8.0 / 12.0) * 100.0) |
| assert series.full_occupancy_pct == pytest.approx((2.0 / 6.0) * 100.0) |
| assert series.idle_pct == pytest.approx(0.0) |
|
|
|
|
| def test_plot_generation_runtime_timeline_uses_deduped_pool_rows(): |
| fig, ax = plot_generation_runtime_timeline(_runtime_df(), title="Runtime Timeline") |
|
|
| assert fig is not None |
| assert ax is not None |
| assert [tick.get_text() for tick in ax.get_yticklabels()] == [ |
| "Sampling W1", |
| "Evaluation W1", |
| "Postprocess W1", |
| "Sampling W2", |
| "Evaluation W2", |
| ] |
| assert len(ax.patches) == 6 |
| assert {text.get_text() for text in ax.get_legend().get_texts()} == { |
| "Sampling", |
| "Evaluation", |
| "Postprocess", |
| } |
| assert ax.get_legend()._ncols == 3 |
| assert ax.get_legend()._loc == 9 |
| assert {text.get_fontsize() for text in ax.get_legend().get_texts()} == {10.0} |
| assert ax.get_legend().get_bbox_to_anchor()._bbox.y0 < 0 |
|
|
|
|
| def test_plot_normalized_occupancy_over_time_adds_reference_line(): |
| fig, ax = plot_normalized_occupancy_over_time( |
| _runtime_df(), title="Normalized Occupancy" |
| ) |
|
|
| assert fig is not None |
| assert ax is not None |
| labels = [line.get_label() for line in ax.lines] |
| assert labels == [ |
| "Sampling Occupancy", |
| "Evaluation Occupancy", |
| "Postprocess Occupancy", |
| "100% Capacity", |
| ] |
| assert ax.get_ylim()[1] >= 100 |
| assert list(ax.lines[-1].get_ydata()) == [100, 100] |
| assert ax.get_legend()._ncols == 2 |
| assert ax.get_legend()._loc == 9 |
| assert {text.get_fontsize() for text in ax.get_legend().get_texts()} == {10.0} |
| assert ax.get_legend().get_bbox_to_anchor()._bbox.y0 < 0 |
|
|
