ci: enforce Ruff lint checks
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ENGIN CI / Build, test and smoke (push) Successful in 31s
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@ -27,6 +27,18 @@ jobs:
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- name: Build production image
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run: docker build --tag engin-console:ci .
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- name: Run Ruff
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run: |
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docker run --rm \
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--entrypoint sh \
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engin-console:ci \
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-c "python -m pip install --quiet \
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--disable-pip-version-check \
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--root-user-action=ignore \
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ruff==0.16.4 &&
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ruff check --no-cache \
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app.py engin tests final_pipeline.py ml_polish_benchmark.py"
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- name: Check installed dependencies
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run: |
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docker run --rm \
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5
app.py
5
app.py
@ -13,12 +13,12 @@ import streamlit as st
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from engin.charts import cylinder_spectrum, deviation_chart, engine_heatmap
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from engin.config import (
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AppConfig,
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LABEL_COLORS,
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LABEL_DISPLAY,
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LABEL_ICONS,
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NOT_APPLICABLE,
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SEVERITY_DISPLAY,
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AppConfig,
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)
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from engin.errors import UserFacingError
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from engin.explainability import (
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@ -32,7 +32,6 @@ from engin.model import PrecomputedPredictionModel, SklearnPredictionModel
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from engin.service import DiagnosisResult, DiagnosticService
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from engin.validation import SpectrumFrameValidator
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LOGGER = logging.getLogger("engin.app")
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BASE_DIR = Path(__file__).resolve().parent
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@ -62,7 +61,7 @@ def build_dependencies() -> AppDependencies:
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service=DiagnosticService(reader=reader, validator=validator, model=model),
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demo_payload=demo_payload,
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)
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except Exception as exc:
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except Exception:
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LOGGER.exception("Live model initialization failed; enabling demo fallback")
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submission = pd.read_csv(BASE_DIR / "predictions.csv")
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diagnostics = pd.read_csv(BASE_DIR / "prediction_diagnostics.csv")
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@ -1,5 +1,5 @@
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"""Core application package for the ENGIN diagnostic console."""
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from .service import DiagnosticService, DiagnosisResult
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from .service import DiagnosisResult, DiagnosticService
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__all__ = ["DiagnosticService", "DiagnosisResult"]
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@ -8,7 +8,6 @@ import plotly.graph_objects as go
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from .config import FREQ_COLS, LABEL_COLORS
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from .explainability import EngineAnalysis
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PLOT_BG = "#101820"
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GRID = "rgba(148, 163, 184, 0.14)"
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TEXT = "#dce8ee"
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@ -4,8 +4,13 @@ from __future__ import annotations
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from dataclasses import dataclass
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from benchmark_grouped import FAULT_LABELS, FREQ_COLS, LABELS, NOT_APPLICABLE, SEVERITIES
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from benchmark_grouped import (
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FAULT_LABELS,
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FREQ_COLS,
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LABELS,
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NOT_APPLICABLE,
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SEVERITIES,
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)
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ALLOWED_ENGINE_SIZES = (8, 12, 16)
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MAX_UPLOAD_BYTES = 10 * 1024 * 1024
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@ -7,7 +7,12 @@ from typing import Protocol
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import pandas as pd
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from final_pipeline import DiagnosticModels, predict_test, train_models, validate_submission
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from final_pipeline import (
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DiagnosticModels,
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predict_test,
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train_models,
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validate_submission,
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)
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from .errors import InferenceError
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@ -8,7 +8,7 @@ from typing import Protocol
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import numpy as np
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import pandas as pd
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from .config import AppConfig, FREQ_COLS
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from .config import FREQ_COLS, AppConfig
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from .errors import InputDataError
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@ -28,7 +28,6 @@ from benchmark_grouped import (
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LABELS,
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NOT_APPLICABLE,
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SEVERITIES,
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make_pipeline,
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validate_data,
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)
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from severity_benchmark import (
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@ -39,7 +38,6 @@ from severity_benchmark import (
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prepare_labeled_frame,
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)
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LABEL_MODEL_NAME = "deviation_logistic_c10"
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LABEL_FEATURE_SET = "deviation"
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SEVERITY_CANDIDATE_ID = "deviation_extra_trees_mf03"
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@ -19,8 +19,8 @@ from benchmark_grouped import (
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FAULT_LABELS,
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LABELS,
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NOT_APPLICABLE,
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make_splits,
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macro_f1,
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make_splits,
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ml_points,
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raw_score,
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validate_data,
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@ -38,7 +38,6 @@ from severity_benchmark import (
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prepare_labeled_frame,
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)
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DEFAULT_SEEDS = [7, 21, 42, 77, 123]
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@ -166,7 +165,7 @@ def main() -> None:
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"ood_overrides": ood_count[scenario],
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}
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row.update(
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{f"f1_{label}": float(value) for label, value in zip(LABELS, per_class)}
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{f"f1_{label}": float(value) for label, value in zip(LABELS, per_class, strict=True)}
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)
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run_rows.append(row)
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5
ruff.toml
Normal file
5
ruff.toml
Normal file
@ -0,0 +1,5 @@
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target-version = "py312"
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extend-exclude = ["archive/legacy_experiments"]
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[lint]
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select = ["E4", "E7", "E9", "F", "I", "B"]
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@ -5,7 +5,6 @@ from pathlib import Path
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from streamlit.testing.v1 import AppTest
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ROOT = Path(__file__).resolve().parents[1]
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@ -14,7 +14,6 @@ from engin.explainability import (
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)
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from engin.service import DiagnosisResult
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ROOT = Path(__file__).resolve().parents[1]
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@ -4,7 +4,7 @@ import unittest
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import pandas as pd
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from engin.model import PredictionBundle, PrecomputedPredictionModel
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from engin.model import PrecomputedPredictionModel, PredictionBundle
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from engin.service import DiagnosticService
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from engin.validation import ValidationResult
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@ -10,7 +10,6 @@ from engin.config import FREQ_COLS
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from engin.errors import InputDataError
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from engin.validation import SpectrumFrameValidator
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ROOT = Path(__file__).resolve().parents[1]
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