refactor Plotter to use a common plot base

trainlib/plotter.py

@@ -1,19 +1,134 @@
-from typing import Self
+"""
+    def plot_actual_output_residual(
+        self,
+        order_residuals: bool = False,
+        row_size: int | float = 2,
+        col_size: int | float = 4,
+        norm_samples: bool = False,
+        combine_dims: bool = True,
+        figure_kwargs: SubplotsKwargs | None = None,
+        subplot_kwargs: dict | None = None,
+        gof_kwargs: dict | None = None,
+    ) -> tuple[plt.Figure, AxesArray]:
+        Note: transform samples in dataloader definitions beforehand if you
+        want to change data
+
+        Parameters:
+            row_size:
+            col_size:
+            figure_kwargs:
+            subplot_kwargs:
+            gof_kwargs:
+        
+        ndims = self.data_tuples[0][0].size(-1)
+        colors = plt.rcParams["axes.prop_cycle"].by_key()["color"]
+        fig, axes = self._create_subplots(
+            rows=len(self.dataloaders),
+            cols=1 if (norm_samples or combine_dims) else ndims,
+            row_size=row_size,
+            col_size=col_size,
+            figure_kwargs=figure_kwargs,
+        )
+        subplot_kwargs = self._prepare_subplot_kwargs(subplot_kwargs)
+        gof_kwargs = self._prepare_gof_kwargs(gof_kwargs)
+
+        for axes_row, data_tuple in zip(axes, self.data_tuples, strict=True):
+            actual, output, loader_label = data_tuple
+
+            if norm_samples:
+                actual = actual.norm(dim=1, keepdim=True)
+                output = output.norm(dim=1, keepdim=True)
+
+            for dim in range(ndims):
+                ax = axes_row[0 if combine_dims else dim]
+                dim_color = colors[dim % len(colors)]
+                group_name = "norm" if norm_samples else f"$d_{dim}$"
+
+                dim_actual = actual[:, dim]
+                dim_output = output[:, dim]
+                residuals = dim_actual - dim_output
+
+                X, Y = dim_actual, residuals
+                if order_residuals:
+                    X = range(1, residuals.size(0)+1)
+                    Y = residuals[residuals.argsort()]
+
+                ax.scatter(
+                    X, Y,
+                    color=dim_color,
+                    label=group_name,
+                    **subplot_kwargs,
+                )
+
+                # plot goodness of fit line
+                m, b = self._lstsq_dim(dim_actual, dim_output)
+                ax.plot(
+                    dim_actual,
+                    m * dim_actual + b,
+                    color=dim_color,
+                    label=f"GoF {group_name}",
+                    **gof_kwargs,
+                )
+
+                add_plot_context = (
+                    norm_samples  # dim=0 implicit b/c we break
+                    or not combine_dims  # add to every subplot across grid
+                    or combine_dims and dim == ndims-1  # wait for last dim
+                )
+
+                # always exec plot logic if not combining, o/w just once
+                if add_plot_context:
+                    # compare residuals to y=0
+                    ax.axhline(y=0, c="black", alpha=0.2)
+
+                    ax.set_title(f"[{loader_label}] Prediction residuals")
+                    ax.set_xlabel("actual")
+                    ax.set_ylabel("residual")
+
+                    # transpose legend layout for more natural view
+                    if norm_samples or not combine_dims:
+                        ax.legend()
+                    else:
+                        handles, labels = self.get_transposed_handles_labels(ax)
+                        ax.legend(handles, labels, ncols=2)
+
+                # break dimension loop if collapsed by norm
+                if norm_samples:
+                    break
+
+        return fig, axes
+"""
+
 from functools import partial
-from collections.abc import Callable, Generator
+from collections.abc import Callable
 
 import numpy as np
+import torch
 import matplotlib.pyplot as plt
 from torch import Tensor
-from numpy.typing import NDArray
 from torch.utils.data import DataLoader
 
 from trainlib.trainer import Trainer
 from trainlib.estimator import EstimatorKwargs
 from trainlib.utils.type import AxesArray, SubplotsKwargs
 
+type SubplotFn = Callable[[plt.Axes, int, Tensor, Tensor], None]
+type ContextFn = Callable[[plt.Axes, str], None]
+
 
 class Plotter[B, K: EstimatorKwargs]:
+    """
+    TODOs:
+
+    - best fit lines for plots and residuals (compare to ideal lines in each
+      case)
+    - show val options across columns; preview how val is changing across
+      natural training, and what the best will look (so plot like uniform
+      intervals broken over the training epochs at 0, 50, 100, 150, ... and
+      highlight the best one, even if that's not actually the single best
+      epoch)
+    """
+
     def __init__(
         self,
         trainer: Trainer[..., K],
@@ -35,11 +150,11 @@ class Plotter[B, K: EstimatorKwargs]:
 
         self._batch_outputs_fn = partial(
             self.trainer.get_batch_outputs,
-            batch_estimator_map=self.batch_estimator_map
+            batch_estimator_map=batch_estimator_map
         )
         self._batch_metrics_fn = partial(
             self.trainer.get_batch_metrics,
-            batch_estimator_map=self.batch_estimator_map
+            batch_estimator_map=batch_estimator_map
         )
 
         self._data_tuples = None
@@ -83,201 +198,369 @@ class Plotter[B, K: EstimatorKwargs]:
             data_tuples.append((actual, output, label))
 
         self._data_tuples = data_tuples
+
         return self._data_tuples
 
-    def _default_figure_kwargs(self, rows: int, cols: int) -> dict:
+    def get_transposed_handles_labels(
-        return {
-            "sharex": True,
-            "figsize": (4*cols, 2*rows),
-            "constrained_layout": True,
-        }
-
-    def _default_subplot_kwargs(self) -> dict:
-        return {}
-
-    def _create_subplots(
         self,
-        **figure_kwargs: SubplotsKwargs,
+        ax: plt.Axes
-    ) -> tuple[plt.Figure, AxesArray]:
+    ) -> tuple[list, list]:
-        """
+        # transpose legend layout for more natural view
-        """
+        handles, labels = ax.get_legend_handles_labels()
+        handles = handles[::2] + handles[1::2]
+        labels = labels[::2] + labels[1::2]
 
-        rows, cols = len(self.dataloaders), 1
+        return handles, labels
-        figure_kwargs = {
+
-            **self._default_figure_kwargs(rows, cols),
+    def _lstsq_dim(
-            **figure_kwargs,
+        self,
-        }
+        dim_actual: Tensor,
-        fig, axes = plt.subplots(
+        dim_output: Tensor
-            rows, cols,
+    ) -> tuple[Tensor, Tensor]:
-            squeeze=False,
+        A = torch.stack(
-            **figure_kwargs
+            [dim_actual, torch.ones_like(dim_actual)],
+            dim=1
         )
+        m, b = torch.linalg.lstsq(A, dim_output).solution
 
-        return fig, axes
+        return m, b
 
-    def plot_actual_output_dim(
+    def _prepare_figure_kwargs(
         self,
-        figure_kwargs: dict | None = None,
+        rows: int,
-        subplot_kwargs: dict | None = None,
+        cols: int,
-    ) -> tuple[plt.Figure, AxesArray]:
+        row_size: int | float = 2,
+        col_size: int | float = 4,
+        figure_kwargs: SubplotsKwargs | None = None,
+    ) -> SubplotsKwargs:
         """
-        Wrapper like this works fine, but it's smelly: we *don't* want @wraps,
-        do this method doesn't actually have this signature at runtime (it has
-        the dec wrapper's sig). I think the cleaner thing is to just have
-        internal methods (_func) like the one below, and then the main method
-        entry just pass that internal method through to the skeleton
         """
 
-        figure_kwargs = figure_kwargs or {}
+        default_figure_kwargs = {
+            "sharex": True,
+            "figsize": (col_size*cols, row_size*rows),
+        }
+        figure_kwargs = {
+            **default_figure_kwargs,
+            **(figure_kwargs or {}),
+        }
+
+        return figure_kwargs
+
+    def _prepare_subplot_kwargs(
+        self,
+        subplot_kwargs: dict | None = None,
+    ) -> dict:
+        """
+        """
+
+        default_subplot_kwargs = {}
         subplot_kwargs = {
-            **self._default_subplot_kwargs(),
+            **default_subplot_kwargs,
             **(subplot_kwargs or {}),
         }
 
-        fig, axes = self._create_subplots(**figure_kwargs)
+        return subplot_kwargs
-        for ax, data_tuple in zip(axes, self.data_tuples, strict=True):
-            actual, output, label = data_tuple
 
+    def _prepare_gof_kwargs(
+        self,
+        gof_kwargs: dict | None = None,
+    ) -> dict:
+        """
+        """
+
+        default_gof_kwargs = {
+            "alpha": 0.5
+        }
+        gof_kwargs = {
+            **default_gof_kwargs,
+            **(gof_kwargs or {}),
+        }
+
+        return gof_kwargs
+
+    def _create_subplots(
+        self,
+        rows: int,
+        cols: int,
+        row_size: int | float = 2,
+        col_size: int | float = 4,
+        figure_kwargs: SubplotsKwargs | None = None,
+    ) -> tuple[plt.Figure, AxesArray]:
+        """
+        """
+
+        figure_kwargs: SubplotsKwargs = self._prepare_figure_kwargs(
+            rows,
+            cols,
+            row_size=row_size,
+            col_size=col_size,
+            figure_kwargs=figure_kwargs,
+        )
+        fig, axes = plt.subplots(
+            rows,
+            cols,
+            squeeze=False,
+            **figure_kwargs,
+        )  # ty:ignore[no-matching-overload]
+
+        return fig, axes
+
+    def _plot_base(
+        self,
+        subplot_fn: SubplotFn,
+        context_fn: ContextFn,
+        row_size: int | float = 2,
+        col_size: int | float = 4,
+        norm_samples: bool = False,
+        combine_dims: bool = True,
+        figure_kwargs: SubplotsKwargs | None = None,
+    ) -> tuple[plt.Figure, AxesArray]:
+        """
+        Note: transform samples in dataloader definitions beforehand if you
+        want to change data
+
+        Parameters:
+            row_size:
+            col_size:
+            figure_kwargs:
+            subplot_kwargs:
+            gof_kwargs:
+        """
+        
+        ndims = self.data_tuples[0][0].size(-1)
+        fig, axes = self._create_subplots(
+            rows=len(self.dataloaders),
+            cols=1 if (norm_samples or combine_dims) else ndims,
+            row_size=row_size,
+            col_size=col_size,
+            figure_kwargs=figure_kwargs,
+        )
+
+        for axes_row, data_tuple in zip(axes, self.data_tuples, strict=True):
+            actual, output, loader_label = data_tuple
+
+            if norm_samples:
+                actual = actual.norm(dim=1, keepdim=True)
+                output = output.norm(dim=1, keepdim=True)
+
+            for dim in range(ndims):
+                ax = axes_row[0 if combine_dims else dim]
+
+                subplot_fn(ax, dim, actual, output)
+
+                add_plot_context = (
+                    norm_samples  # dim=0 implicit b/c we break
+                    or not combine_dims  # add to every subplot across grid
+                    or combine_dims and dim == ndims-1  # wait for last dim
+                )
+
+                # always exec plot logic if not combining, o/w exec just once
+                if add_plot_context:
+                    context_fn(ax, loader_label)
+
+                    # transpose legend layout for more natural view
+                    if norm_samples or not combine_dims:
+                        ax.legend()
+                    else:
+                        handles, labels = self.get_transposed_handles_labels(ax)
+                        ax.legend(handles, labels, ncols=2)
+
+                # break dimension loop if collapsed by norm
+                if norm_samples:
+                    break
+
+        return fig, axes
+
+    def plot_actual_output(
+        self,
+        row_size: int | float = 2,
+        col_size: int | float = 4,
+        norm_samples: bool = False,
+        combine_dims: bool = True,
+        figure_kwargs: SubplotsKwargs | None = None,
+        subplot_kwargs: dict | None = None,
+        gof_kwargs: dict | None = None,
+    ) -> tuple[plt.Figure, AxesArray]:
+        """
+        Plot residual distribution.
+        """
+
+        subplot_kwargs = self._prepare_subplot_kwargs(subplot_kwargs)
+        gof_kwargs = self._prepare_gof_kwargs(gof_kwargs)
+        colors = plt.rcParams["axes.prop_cycle"].by_key()["color"]
+
+        def subplot_fn(
+            ax: plt.Axes, dim: int, actual: Tensor, output: Tensor
+        ) -> None:
+            dim_color = colors[dim % len(colors)]
+            group_name = "norm" if norm_samples else f"$d_{dim}$"
+
+            dim_actual = actual[:, dim]
+            dim_output = output[:, dim]
+
+            ax.scatter(
+                dim_actual, dim_output,
+                color=dim_color,
+                label=group_name,
+                **subplot_kwargs,
+            )
+
+            # plot goodness of fit line
+            m, b = self._lstsq_dim(dim_actual, dim_output)
+            ax.plot(
+                dim_actual, m * dim_actual + b,
+                color=dim_color,
+                label=f"GoF {group_name}",
+                **gof_kwargs,
+            )
+
+        def context_fn(ax: plt.Axes, loader_label: str) -> None:
+            # plot perfect prediction reference line, y=x
             ax.plot(
                 [0, 1], [0, 1],
                 transform=ax.transAxes,
                 c="black",
-                alpha=0.2
+                alpha=0.2,
             )
 
-            for dim in range(actual.size(-1)):
+            ax.set_title(
-                ax.scatter(
+                f"[{loader_label}] True labels vs Predictions"
-                    actual[:, dim],
+            )
-                    output[:, dim],
-                    label=f"$d_{dim}$",
-                    **subplot_kwargs
-                )
-
-            ax.set_title(f"[{label}] True labels vs Predictions (dim-wise)")
             ax.set_xlabel("actual")
             ax.set_ylabel("output")
-            ax.legend()
 
-        return fig, axes
+        return self._plot_base(
+            subplot_fn, context_fn,
+            row_size=row_size,
+            col_size=col_size,
+            norm_samples=norm_samples,
+            combine_dims=combine_dims,
+            figure_kwargs=figure_kwargs,
+        )
 
-    def plot_actual_output_residual_dim(
+    def plot_actual_output_residual(
         self,
-        figure_kwargs: dict | None = None,
+        row_size: int | float = 2,
+        col_size: int | float = 4,
+        order_residuals: bool = False,
+        norm_samples: bool = False,
+        combine_dims: bool = True,
+        figure_kwargs: SubplotsKwargs | None = None,
         subplot_kwargs: dict | None = None,
+        gof_kwargs: dict | None = None,
     ) -> tuple[plt.Figure, AxesArray]:
         """
+        Plot prediction residuals.
         """
 
-        figure_kwargs = figure_kwargs or {}
+        subplot_kwargs = self._prepare_subplot_kwargs(subplot_kwargs)
-        subplot_kwargs = {
+        gof_kwargs = self._prepare_gof_kwargs(gof_kwargs)
-            **self._default_subplot_kwargs(),
+        colors = plt.rcParams["axes.prop_cycle"].by_key()["color"]
-            **(subplot_kwargs or {}),
-        }
 
-        fig, axes = self._create_subplots(**figure_kwargs)
+        def subplot_fn(
-        for ax, data_tuple in zip(axes, self.data_tuples, strict=True):
+            ax: plt.Axes, dim: int, actual: Tensor, output: Tensor
-            actual, output, label = data_tuple
+        ) -> None:
+            dim_color = colors[dim % len(colors)]
+            group_name = "norm" if norm_samples else f"$d_{dim}$"
 
+            dim_actual = actual[:, dim]
+            dim_output = output[:, dim]
+
+            residuals = dim_actual - dim_output
+            X, Y = dim_actual, residuals
+
+            if order_residuals:
+                X = range(1, residuals.size(0)+1)
+                Y = residuals[residuals.argsort()]
+
+            ax.scatter(
+                X, Y,
+                color=dim_color,
+                label=group_name,
+                **subplot_kwargs,
+            )
+
+            # plot goodness of fit line
+            if not order_residuals:
+                m, b = self._lstsq_dim(dim_actual, residuals)
+                ax.plot(
+                    dim_actual, m * dim_actual + b,
+                    color=dim_color,
+                    label=f"GoF {group_name}",
+                    **gof_kwargs,
+                )
+
+        def context_fn(ax: plt.Axes, loader_label: str) -> None:
             # compare residuals to y=0
             ax.axhline(y=0, c="black", alpha=0.2)
 
-            for dim in range(actual.size(-1)):
+            ax.set_title(f"[{loader_label}] Prediction residuals")
-                ax.scatter(
-                    actual[:, dim],
-                    actual[:, dim] - output[:, dim],
-                    label=f"$d_{dim}$",
-                    **subplot_kwargs
-                )
-
-            ax.set_title(f"[{label}] Residuals (dim-wise)")
             ax.set_xlabel("actual")
             ax.set_ylabel("residual")
-            ax.legend()
-
-        return fig, axes
-
-    def plot_actual_output_ordered_residual_dim(
-        self,
-        figure_kwargs: dict | None = None,
-        subplot_kwargs: dict | None = None,
-    ) -> tuple[plt.Figure, AxesArray]:
-        """
-        """
-
-        figure_kwargs = figure_kwargs or {}
-        subplot_kwargs = {
-            **self._default_subplot_kwargs(),
-            **(subplot_kwargs or {}),
-        }
-
-        fig, axes = self._create_subplots(**figure_kwargs)
-        for ax, data_tuple in zip(axes, self.data_tuples, strict=True):
-            actual, output, label = data_tuple
-
-            # compare residuals to y=0
-            ax.axhline(y=0, c="black", alpha=0.2)
-
-            for dim in range(actual.size(-1)):
-                ax.scatter(
-                    actual[:, dim],
-                    actual[:, dim] - output[:, dim],
-                    label=f"$d_{dim}$",
-                    **subplot_kwargs
-                )
-
-            ax.set_title(f"[{label}] Residuals (dim-wise)")
-            ax.set_xlabel("actual")
-            ax.set_ylabel("residual")
-            ax.legend()
-
-        return fig, axes
 
+        return self._plot_base(
+            subplot_fn, context_fn,
+            row_size=row_size,
+            col_size=col_size,
+            norm_samples=norm_samples,
+            combine_dims=combine_dims,
+            figure_kwargs=figure_kwargs,
+        )
+ 
     def plot_actual_output_residual_dist(
         self,
-        figure_kwargs: dict | None = None,
+        row_size: int | float = 2,
+        col_size: int | float = 4,
+        norm_samples: bool = False,
+        combine_dims: bool = True,
+        figure_kwargs: SubplotsKwargs | None = None,
         subplot_kwargs: dict | None = None,
+        gof_kwargs: dict | None = None,
     ) -> tuple[plt.Figure, AxesArray]:
         """
+        Plot residual distribution.
         """
 
-        figure_kwargs = figure_kwargs or {}
+        subplot_kwargs = self._prepare_subplot_kwargs(subplot_kwargs)
-        subplot_kwargs = {
+        gof_kwargs = self._prepare_gof_kwargs(gof_kwargs)
-            **self._default_subplot_kwargs(),
+        colors = plt.rcParams["axes.prop_cycle"].by_key()["color"]
-            **(subplot_kwargs or {}),
-        }
 
-        fig, axes = self._create_subplots(**figure_kwargs)
+        def subplot_fn(
-        for ax, data_tuple in zip(axes, self.data_tuples, strict=True):
+            ax: plt.Axes, dim: int, actual: Tensor, output: Tensor
-            actual, output, label = data_tuple
+        ) -> None:
+            dim_color = colors[dim % len(colors)]
+            group_name = "norm" if norm_samples else f"$d_{dim}$"
 
-            N = actual.size(0)
+            dim_actual = actual[:, dim]
-            for dim in range(actual.size(-1)):
+            dim_output = output[:, dim]
-                residuals = actual[:, dim] - output[:, dim]
 
-                _, _, patches = ax.hist(
+            N = dim_actual.size(0)
-                    residuals.abs(),
+            residuals = dim_actual - dim_output
-                    bins=int(np.sqrt(N)),
-                    density=True,
-                    alpha=0.2,
-                    label=f"$d_{dim}$",
-                    **subplot_kwargs
-                )
 
-                # grab color used for hist and mirror in the v-line
+            _, _, patches = ax.hist(
-                color = patches[0].get_facecolor()
+                residuals.abs(),
+                bins=int(np.sqrt(N)),
+                density=True,
+                alpha=0.3,
+                color=dim_color,
+                label=group_name,
+                **subplot_kwargs
+            )
 
-                mu = residuals.abs().mean().item()
+            mu = residuals.abs().mean().item()
-                ax.axvline(mu, linestyle=":", c=color, label=f"$\mu_{dim}$")
+            ax.axvline(mu, linestyle=":", c=dim_color, label=f"$\\mu_{dim}$")
 
-            ax.set_title(f"[{label}] Residual distribution (dim-wise)")
+        def context_fn(ax: plt.Axes, loader_label: str) -> None:
+            ax.set_title(f"[{loader_label}] Residual distribution")
             ax.set_xlabel("actual")
-            ax.set_ylabel("residual")
+            ax.set_ylabel("residual (density)")
 
-            # transpose legend layout for more natural view
+        return self._plot_base(
-            handles, labels = ax.get_legend_handles_labels()
+            subplot_fn, context_fn,
-            handles = handles[::2] + handles[1::2]
+            row_size=row_size,
-            labels = labels[::2] + labels[1::2]
+            col_size=col_size,
-
+            norm_samples=norm_samples,
-            ax.legend(handles, labels, ncols=2)
+            combine_dims=combine_dims,
-
+            figure_kwargs=figure_kwargs,
-        return fig, axes
+        )

trainlib/utils/custom.mplstyle

@@ -1,46 +1,49 @@
-text.usetex          : False
+text.usetex                   : False
-mathtext.default     : regular
+mathtext.default              : regular
 
-font.family          : sans-serif
+# testing to prevent component overlap/clipping
-font.sans-serif      : DejaVu Sans
+figure.constrained_layout.use : True
-font.serif           : DejaVu Serif
-font.cursive         : DejaVu Sans
-mathtext.fontset     : dejavuserif
-font.size            : 9
-figure.titlesize     : 9
-legend.fontsize      : 9
-axes.titlesize       : 9
-axes.labelsize       : 9
-xtick.labelsize      : 9
-ytick.labelsize      : 9
 
-#axes.prop_cycle : cycler('color', ['4f7dd5', 'af7031', '55905e', 'd84739', '888348', 'b75e8b', '2f8f99', '9862cb'])
+font.family                   : sans-serif
-axes.prop_cycle : cycler('color', ['5e8de4', 'c38141', '67a771', 'e15344', '9e9858', '41a6b0', 'a46fd7', 'c86d9a'])
+font.sans-serif               : DejaVu Sans
+font.serif                    : DejaVu Serif
+font.cursive                  : DejaVu Sans
+mathtext.fontset              : dejavuserif
+font.size                     : 9
+figure.titlesize              : 9
+legend.fontsize               : 9
+axes.titlesize                : 9
+axes.labelsize                : 9
+xtick.labelsize               : 9
+ytick.labelsize               : 9
 
-image.interpolation   : nearest
+# monobiome -d 0.45 -l 22
-image.resample        : False
+axes.prop_cycle               : cycler('color', ['5e8de4', 'c38141', '67a771', 'e15344', '9e9858', '41a6b0', 'a46fd7', 'c86d9a'])
-image.composite_image : True
 
-axes.spines.left     : True
+image.interpolation           : nearest
-axes.spines.bottom   : True
+image.resample                : False
-axes.spines.top      : False
+image.composite_image         : True
-axes.spines.right    : False
 
-axes.linewidth       : 1
+axes.spines.left              : True
-xtick.major.width    : 1
+axes.spines.bottom            : True
-xtick.minor.width    : 1
+axes.spines.top               : False
-ytick.major.width    : 1
+axes.spines.right             : False
-ytick.minor.width    : 1
 
-lines.linewidth      : 1
+axes.linewidth                : 1
-lines.markersize     : 1
+xtick.major.width             : 1
+xtick.minor.width             : 1
+ytick.major.width             : 1
+ytick.minor.width             : 1
 
-savefig.dpi          : 300
+lines.linewidth               : 1
-savefig.format       : svg
+lines.markersize              : 1
-savefig.bbox         : tight
-savefig.pad_inches   : 0.1
 
-svg.image_inline     : True
+savefig.dpi                   : 300
-svg.fonttype         : none
+savefig.format                : svg
+savefig.bbox                  : tight
+savefig.pad_inches            : 0.1
 
-legend.frameon       : False
+svg.image_inline              : True
+svg.fonttype                  : none
+
+legend.frameon                : False

trainlib/utils/type.py

@@ -1,5 +1,5 @@
 from typing import Any, TypedDict
-from collections.abc import Callable, Iterable
+from collections.abc import Callable, Iterable, Sequence
 
 import numpy as np
 from torch import Tensor
@@ -59,18 +59,11 @@ class SubplotsKwargs(TypedDict, total=False):
     sharex: bool | str
     sharey: bool | str
     squeeze: bool
-    width_ratios: list[float]
+    width_ratios: Sequence[float]
-    height_ratios: list[float]
+    height_ratios: Sequence[float]
-    subplot_kw: dict
+    subplot_kw: dict[str, ...]
-    gridspec_kw: dict
+    gridspec_kw: dict[str, ...]
     figsize: tuple[float, float]
     dpi: float
     layout: str
-
+    constrained_layout: bool
-    sharex: bool | Literal["none", "all", "row", "col"] = False,
-    sharey: bool | Literal["none", "all", "row", "col"] = False,
-    squeeze: bool = True,
-    width_ratios: Sequence[float] | None = None,
-    height_ratios: Sequence[float] | None = None,
-    subplot_kw: dict[str, Any] | None = None,
-    gridspec_kw: dict[str, Any] | None = None,