move plotting utilities into core Plotter class
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trainlib/diagnostic.py
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trainlib/diagnostic.py
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283
trainlib/plotter.py
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283
trainlib/plotter.py
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from typing import Self
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from functools import partial
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from collections.abc import Callable, Generator
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import numpy as np
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import matplotlib.pyplot as plt
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from torch import Tensor
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from numpy.typing import NDArray
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from torch.utils.data import DataLoader
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from trainlib.trainer import Trainer
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from trainlib.estimator import EstimatorKwargs
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from trainlib.utils.type import AxesArray, SubplotsKwargs
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class Plotter[B, K: EstimatorKwargs]:
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def __init__(
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self,
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trainer: Trainer[..., K],
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dataloaders: list[DataLoader],
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batch_estimator_map: Callable[[B, Trainer], ...],
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estimator_to_output_map: Callable[[K], ...],
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dataloader_labels: list[str] | None = None,
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) -> None:
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self.trainer = trainer
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self.dataloaders = dataloaders
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self.dataloader_labels = (
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dataloader_labels or list(map(str, range(1, len(dataloaders)+1)))
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)
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self.batch_estimator_map = batch_estimator_map
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self.estimator_to_output_map = estimator_to_output_map
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self._outputs: list[list[Tensor]] | None = None
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self._metrics: list[list[dict[str, float]]] | None = None
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self._batch_outputs_fn = partial(
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self.trainer.get_batch_outputs,
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batch_estimator_map=self.batch_estimator_map
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)
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self._batch_metrics_fn = partial(
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self.trainer.get_batch_metrics,
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batch_estimator_map=self.batch_estimator_map
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)
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self._data_tuples = None
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@property
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def outputs(self) -> list[list[Tensor]]:
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if self._outputs is None:
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self._outputs = [
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list(map(self._batch_outputs_fn, loader))
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for loader in self.dataloaders
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]
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return self._outputs
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@property
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def metrics(self) -> list[list[dict[str, float]]]:
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if self._metrics is None:
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self._metrics = [
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list(map(self._batch_metrics_fn, loader))
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for loader in self.dataloaders
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]
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return self._metrics
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@property
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def data_tuples(self) -> list[tuple[Tensor, Tensor, str]]:
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"""
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Produce data items; to be cached. Zip later with axes
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"""
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if self._data_tuples is not None:
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return self._data_tuples
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data_tuples = []
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for i, loader in enumerate(self.dataloaders):
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label = self.dataloader_labels[i]
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batch = next(iter(loader))
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est_kwargs = self.batch_estimator_map(batch, self.trainer)
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actual = self.estimator_to_output_map(est_kwargs)
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output = self._batch_outputs_fn(batch)
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data_tuples.append((actual, output, label))
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self._data_tuples = data_tuples
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return self._data_tuples
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def _default_figure_kwargs(self, rows: int, cols: int) -> dict:
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return {
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"sharex": True,
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"figsize": (4*cols, 2*rows),
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"constrained_layout": True,
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}
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def _default_subplot_kwargs(self) -> dict:
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return {}
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def _create_subplots(
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self,
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**figure_kwargs: SubplotsKwargs,
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) -> tuple[plt.Figure, AxesArray]:
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"""
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"""
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rows, cols = len(self.dataloaders), 1
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figure_kwargs = {
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**self._default_figure_kwargs(rows, cols),
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**figure_kwargs,
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}
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fig, axes = plt.subplots(
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rows, cols,
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squeeze=False,
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**figure_kwargs
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)
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return fig, axes
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def plot_actual_output_dim(
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self,
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figure_kwargs: dict | None = None,
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subplot_kwargs: dict | None = None,
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) -> tuple[plt.Figure, AxesArray]:
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"""
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Wrapper like this works fine, but it's smelly: we *don't* want @wraps,
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do this method doesn't actually have this signature at runtime (it has
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the dec wrapper's sig). I think the cleaner thing is to just have
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internal methods (_func) like the one below, and then the main method
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entry just pass that internal method through to the skeleton
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"""
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figure_kwargs = figure_kwargs or {}
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subplot_kwargs = {
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**self._default_subplot_kwargs(),
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**(subplot_kwargs or {}),
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}
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fig, axes = self._create_subplots(**figure_kwargs)
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for ax, data_tuple in zip(axes, self.data_tuples, strict=True):
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actual, output, label = data_tuple
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ax.plot(
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[0, 1], [0, 1],
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transform=ax.transAxes,
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c="black",
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alpha=0.2
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)
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for dim in range(actual.size(-1)):
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ax.scatter(
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actual[:, dim],
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output[:, dim],
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label=f"$d_{dim}$",
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**subplot_kwargs
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)
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ax.set_title(f"[{label}] True labels vs Predictions (dim-wise)")
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ax.set_xlabel("actual")
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ax.set_ylabel("output")
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ax.legend()
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return fig, axes
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def plot_actual_output_residual_dim(
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self,
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figure_kwargs: dict | None = None,
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subplot_kwargs: dict | None = None,
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) -> tuple[plt.Figure, AxesArray]:
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"""
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"""
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figure_kwargs = figure_kwargs or {}
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subplot_kwargs = {
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**self._default_subplot_kwargs(),
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**(subplot_kwargs or {}),
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}
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fig, axes = self._create_subplots(**figure_kwargs)
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for ax, data_tuple in zip(axes, self.data_tuples, strict=True):
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actual, output, label = data_tuple
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# compare residuals to y=0
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ax.axhline(y=0, c="black", alpha=0.2)
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for dim in range(actual.size(-1)):
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ax.scatter(
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actual[:, dim],
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actual[:, dim] - output[:, dim],
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label=f"$d_{dim}$",
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**subplot_kwargs
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)
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ax.set_title(f"[{label}] Residuals (dim-wise)")
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ax.set_xlabel("actual")
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ax.set_ylabel("residual")
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ax.legend()
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return fig, axes
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def plot_actual_output_ordered_residual_dim(
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self,
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figure_kwargs: dict | None = None,
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subplot_kwargs: dict | None = None,
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) -> tuple[plt.Figure, AxesArray]:
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"""
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"""
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figure_kwargs = figure_kwargs or {}
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subplot_kwargs = {
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**self._default_subplot_kwargs(),
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**(subplot_kwargs or {}),
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}
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fig, axes = self._create_subplots(**figure_kwargs)
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for ax, data_tuple in zip(axes, self.data_tuples, strict=True):
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actual, output, label = data_tuple
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# compare residuals to y=0
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ax.axhline(y=0, c="black", alpha=0.2)
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for dim in range(actual.size(-1)):
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ax.scatter(
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actual[:, dim],
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actual[:, dim] - output[:, dim],
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label=f"$d_{dim}$",
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**subplot_kwargs
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)
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ax.set_title(f"[{label}] Residuals (dim-wise)")
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ax.set_xlabel("actual")
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ax.set_ylabel("residual")
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ax.legend()
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return fig, axes
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def plot_actual_output_residual_dist(
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self,
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figure_kwargs: dict | None = None,
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subplot_kwargs: dict | None = None,
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) -> tuple[plt.Figure, AxesArray]:
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"""
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"""
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figure_kwargs = figure_kwargs or {}
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subplot_kwargs = {
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**self._default_subplot_kwargs(),
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**(subplot_kwargs or {}),
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}
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fig, axes = self._create_subplots(**figure_kwargs)
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for ax, data_tuple in zip(axes, self.data_tuples, strict=True):
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actual, output, label = data_tuple
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N = actual.size(0)
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for dim in range(actual.size(-1)):
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residuals = actual[:, dim] - output[:, dim]
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_, _, patches = ax.hist(
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residuals.abs(),
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bins=int(np.sqrt(N)),
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density=True,
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alpha=0.2,
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label=f"$d_{dim}$",
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**subplot_kwargs
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)
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# grab color used for hist and mirror in the v-line
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color = patches[0].get_facecolor()
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mu = residuals.abs().mean().item()
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ax.axvline(mu, linestyle=":", c=color, label=f"$\mu_{dim}$")
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ax.set_title(f"[{label}] Residual distribution (dim-wise)")
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ax.set_xlabel("actual")
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ax.set_ylabel("residual")
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# transpose legend layout for more natural view
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handles, labels = ax.get_legend_handles_labels()
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handles = handles[::2] + handles[1::2]
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labels = labels[::2] + labels[1::2]
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ax.legend(handles, labels, ncols=2)
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return fig, axes
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@@ -282,7 +282,8 @@ class Trainer[I, K: EstimatorKwargs]:
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"""
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TODO: consider making the dataloader ``collate_fn`` an explicit
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parameter with a type signature that reflects ``B``, connecting the
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``batch_estimator_map`` somewhere
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``batch_estimator_map`` somewhere. Might also re-type a ``DataLoader``
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in-house to allow a generic around ``B``
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Note: this method attempts to implement a general scheme for passing
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needed items to the estimator's loss function from the dataloader. The
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@@ -545,6 +546,31 @@ class Trainer[I, K: EstimatorKwargs]:
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def _add_summary_item(self, name: str, value: float) -> None:
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self._summary[name].append((value, self._step))
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def get_batch_outputs[B](
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self,
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batch: B,
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batch_estimator_map: Callable[[B, Self], K],
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) -> Tensor:
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self.estimator.eval()
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est_kwargs = batch_estimator_map(batch, self)
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output = self.estimator(**est_kwargs)[0]
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output = output.detach().cpu()
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return output
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def get_batch_metrics[B](
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self,
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batch: B,
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batch_estimator_map: Callable[[B, Self], K],
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) -> dict[str, float]:
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self.estimator.eval()
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est_kwargs = batch_estimator_map(batch, self)
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metrics = self.estimator.metrics(**est_kwargs)
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return metrics
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def save_model(
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self,
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epoch: int,
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@@ -1,11 +1,14 @@
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from typing import Any, TypedDict
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from collections.abc import Callable, Iterable
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import numpy as np
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from torch import Tensor
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from torch.utils.data.sampler import Sampler
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from trainlib.dataset import BatchedDataset
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# need b/c matplotlib axes are insanely stupid
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type AxesArray = np.ndarray[tuple[int, int], np.dtype[np.object_]]
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class LoaderKwargs(TypedDict, total=False):
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batch_size: int
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@@ -50,3 +53,24 @@ class OptimizerKwargs(TypedDict, total=False):
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capturable: bool
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differentiable: bool
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fused: bool | None
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class SubplotsKwargs(TypedDict, total=False):
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sharex: bool | str
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sharey: bool | str
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squeeze: bool
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width_ratios: list[float]
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height_ratios: list[float]
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subplot_kw: dict
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gridspec_kw: dict
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figsize: tuple[float, float]
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dpi: float
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layout: str
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sharex: bool | Literal["none", "all", "row", "col"] = False,
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sharey: bool | Literal["none", "all", "row", "col"] = False,
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squeeze: bool = True,
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width_ratios: Sequence[float] | None = None,
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height_ratios: Sequence[float] | None = None,
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subplot_kw: dict[str, Any] | None = None,
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gridspec_kw: dict[str, Any] | None = None,
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