Merge pull request #3079 from aftersomemath:sysid-pr
PiperOrigin-RevId: 868229512 Change-Id: I790bc08fc8b0745583a2f92d9ee2c5a19ba558ea
This commit is contained in:
Copybara-Service
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# Copyright 2026 DeepMind Technologies Limited
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Parameter table report section."""
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from typing import Any
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from mujoco.sysid._src import parameter
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from mujoco.sysid.report.sections.base import ReportSection
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import numpy as np
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class ParametersTable(ReportSection):
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"""Displays a table of identified, initial, and nominal parameters."""
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def __init__(
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self,
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title: str,
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opt_params: parameter.ParameterDict,
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initial_params: parameter.ParameterDict,
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anchor: str = "",
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collapsible: bool = True,
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):
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super().__init__(collapsible=collapsible)
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self._title = title
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self._anchor = anchor
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self._opt_params = opt_params
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self._initial_params = initial_params
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self._nominal_params = initial_params.copy()
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self._nominal_params.reset()
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self._x_nominal = self._nominal_params.as_vector()
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self._x_hat = self._opt_params.as_vector()
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self._x_initial = self._initial_params.as_vector()
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@property
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def title(self) -> str:
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return self._title
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@property
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def anchor(self) -> str:
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return self._anchor
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@property
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def template_filename(self) -> str:
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"""Tells the builder to look for 'parameters_table.html'."""
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return "parameters_table.html"
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def header_includes(self) -> set[str]:
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return {""}
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def get_context(self) -> dict[str, Any]:
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# Get the vector of non-frozen parameters
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non_frozen_vector = self._opt_params.as_vector()
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if len(self._x_hat) != non_frozen_vector.size:
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raise ValueError(
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"Parameter vector lengths don't match. Expected"
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f" {non_frozen_vector.size}, got {len(self._x_hat)}"
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)
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if self._x_nominal is not None:
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if len(self._x_nominal) != non_frozen_vector.size:
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raise ValueError(
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"Parameter vector lengths don't match. Expected"
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f" {non_frozen_vector.size}, got {len(self._x_nominal)}"
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)
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if self._x_initial is not None:
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if len(self._x_initial) != non_frozen_vector.size:
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raise ValueError(
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"Parameter vector lengths don't match. Expected"
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f" {non_frozen_vector.size}, got {len(self._x_initial)}"
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)
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# Compute error metrics if nominal exists
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if self._x_nominal is not None:
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if self._x_hat.size > 0:
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rel_errors = np.abs(self._x_hat - self._x_nominal) / (
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np.abs(self._x_nominal) + 1e-8
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)
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overall_rmse = np.sqrt(np.mean((self._x_hat - self._x_nominal) ** 2))
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abs_errors = np.abs(self._x_hat - self._x_nominal)
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else:
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rel_errors = np.array([])
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overall_rmse = 0.0
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abs_errors = np.array([])
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else:
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rel_errors = None
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overall_rmse = None
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abs_errors = None
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def create_table_row(param_name, val, lb, ub, idx=None, is_frozen=False):
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"""Create a dict for a table row."""
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error_class = ""
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# Bounds check
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if not is_frozen:
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# Check if on boundary
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if (abs(val - lb) < 1e-8 + 1e-3 * abs(lb)) or (
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abs(val - ub) < 1e-8 + 1e-3 * abs(ub)
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):
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error_class = "pt_on_boundary"
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# If not on boundary, check errors if nominal exists
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elif rel_errors is not None and idx is not None:
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rel_error = rel_errors[idx]
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if rel_error < 0.02:
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error_class = "pt_small_error"
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elif rel_error < 0.1:
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error_class = "pt_medium_error"
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else:
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error_class = "pt_large_error"
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else:
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error_class = "pt_frozen"
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row = {
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"name": param_name + ("*" if is_frozen else ""),
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"pred": val,
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"lower_bound": lb,
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"upper_bound": ub,
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"error_class": error_class,
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"is_frozen": is_frozen,
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}
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if self._x_initial is not None:
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if is_frozen:
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# Initial for frozen is assumed same as val
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row["initial"] = val
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elif idx is not None:
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row["initial"] = self._x_initial[idx]
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if self._x_nominal is not None:
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if is_frozen:
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# Nominal for frozen is assumed same as val
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row.update({
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"nominal": val,
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"abs_err": 0.0,
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"rel_err": 0.0,
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})
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elif (
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idx is not None
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and abs_errors is not None
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and rel_errors is not None
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):
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row.update({
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"nominal": self._x_nominal[idx],
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"abs_err": abs_errors[idx],
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"rel_err": rel_errors[idx],
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})
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return row
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# Build table data.
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table_data = []
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non_frozen_idx = 0 # Index for non-frozen parameters in the arrays
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for param_name, param in self._opt_params.parameters.items():
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# Get bounds for this specific parameter
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p_min, p_max = param.get_bounds()
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if param.size == 1:
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flat_value = (
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param.value if np.isscalar(param.value) else param.value.item()
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)
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lb = p_min.item()
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ub = p_max.item()
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if param.frozen:
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table_data.append(
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create_table_row(param_name, flat_value, lb, ub, is_frozen=True)
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)
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else:
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val = self._x_hat[non_frozen_idx]
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table_data.append(
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create_table_row(param_name, val, lb, ub, idx=non_frozen_idx)
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)
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non_frozen_idx += 1
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else:
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for i in range(param.size):
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if param.shape == (param.size,):
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element_name = f"{param_name}[{i}]"
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val_frozen = param.value[i]
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lb = p_min[i]
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ub = p_max[i]
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else:
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multi_idx = np.unravel_index(i, param.shape)
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idx_str = ",".join(str(x) for x in multi_idx)
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element_name = f"{param_name}[{idx_str}]"
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val_frozen = param.value[multi_idx]
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lb = p_min[multi_idx]
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ub = p_max[multi_idx]
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if param.frozen:
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table_data.append(
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create_table_row(
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element_name, val_frozen, lb, ub, is_frozen=True
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)
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)
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else:
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val = self._x_hat[non_frozen_idx]
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table_data.append(
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create_table_row(element_name, val, lb, ub, idx=non_frozen_idx)
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)
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non_frozen_idx += 1
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headers = ["Parameter"]
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if self._x_initial is not None:
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headers.append("Initial")
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if self._x_nominal is not None:
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headers.append("Nominal")
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headers.append("Identified")
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headers.extend(["Lower Bound", "Upper Bound"])
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if self._x_nominal is not None:
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headers.extend(["Absolute Change", "Relative Change"])
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return {
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"title": self._title,
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"headers": headers,
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"table_data": table_data,
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"overall_rmse": overall_rmse,
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"has_initial": self._x_initial is not None,
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"has_nominal": self._x_nominal is not None,
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}
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