79 lines
2.6 KiB
Python
79 lines
2.6 KiB
Python
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"""绘图。
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`plot` 是一个通用入口，按 `kind` 分派；三个专用图有真实的额外逻辑
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（显著性星标、残差诊断、收敛曲线），所以单独保留。
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"""
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from __future__ import annotations
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from typing import Any
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__all__ = ["plot", "plot_convergence", "plot_correlation_matrix", "plot_residuals"]
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def plot(
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x: Any = None,
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y: Any = None,
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kind: str = "line",
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ax: Any = None,
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labels: Any = None,
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title: str | None = None,
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xlabel: str | None = None,
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ylabel: str | None = None,
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**kwargs: Any,
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) -> Any:
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"""通用绘图入口。
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Parameters
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----------
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kind:
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`"line"` / `"scatter"` / `"hist"` / `"box"` / `"bar"` / `"heatmap"` / `"pie"`。
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ax:
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传入已有 axes 就在上面画（多子图场景）；`None` 时新建 figure。
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Returns
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-------
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符合"创建—绘制—保存"三段的 `(fig, ax)`，交给 :func:`save_figure`。
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Examples
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--------
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>>> fig, ax = plot([1, 2, 3], [1, 4, 9], kind="line") # doctest: +SKIP
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"""
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# TODO(L1)
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raise NotImplementedError
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def plot_correlation_matrix(data: Any, method: str = "pearson", annot: bool = True, significance: bool = True) -> Any:
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"""相关矩阵热图。
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`significance=True` 时在不显著的格子（`p > 0.05`）上打叉。
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这比只画颜色有意义得多——**不显著的相关性画出来是误导**。
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"""
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# TODO(L1)
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raise NotImplementedError
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def plot_residuals(y_true: Any, y_pred: Any, n_features: int | None = None) -> Any:
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"""残差诊断四联图：残差-拟合值、Q-Q 图、直方图、残差-序。
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拟合完模型**第一件事就是看这个**：
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* 残差-拟合值有漏斗形 → 异方差，考虑加权最小二乘或对数变换；
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* Q-Q 图偏离直线 → 正态性假设不成立，区间估计要改用 bootstrap；
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* 残差有周期性 → 漏了时间/结构项。
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这三个诊断结论是论文"模型检验"一节的素材，比只报一个 R² 有说服力。
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"""
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# TODO(L1)
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raise NotImplementedError
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def plot_convergence(history: Any, ax: Any = None, labels: Any = None, log: bool = False, reference: float | None = None) -> Any:
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"""收敛曲线。
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`history` 可以是迭代轨迹数组、也接受 `Result.extras["history"]`
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（`simulated_annealing` 与 `monte_carlo` 都会填这个字段）。
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`reference` 传入解析解时会把真值画成虚线，
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用于"MC 积分误差随 n 收敛"这类图——这是证明 `n` 选得合理的标准做法。
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"""
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# TODO(L1)
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raise NotImplementedError
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