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Migrate np.polyfit to np.polynomial.polynomial.polyfit #2984

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Migrate np.polyfit to np.polynomial.polynomial.polyfit #2984

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981fcf5
Migrate np.polyfit to np.polynomial.polynomial.polyfit
danstam Apr 1, 2026
1806fa3
Fix mypy type error in mitiq_polyfit residuals unpacking
danstam Apr 1, 2026
3e506d6
Preserve descending coefficient order in mitiq_polyfit return value
danstam Apr 2, 2026
89faf47
Reverse params_cov ordering and update docstring
danstam Apr 4, 2026
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32 changes: 22 additions & 10 deletions mitiq/zne/inference.py
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Original file line number Diff line number Diff line change
Expand Up @@ -145,7 +145,7 @@ def mitiq_polyfit(
weights: npt.ArrayLike | None = None,
) -> tuple[list[float], npt.NDArray[np.float64] | None]:
"""Fits the ansatz to the (scale factor, expectation value) data using
``numpy.polyfit``, returning the optimal parameters and covariance matrix
polynomial fitting, returning the optimal parameters and covariance matrix
of the parameters.

Args:
Expand All @@ -167,13 +167,23 @@ def mitiq_polyfit(
w = None if weights is None else np.asarray(weights, dtype=float)

with warnings.catch_warnings(record=True) as warn_list:
try:
opt_params, params_cov = np.polyfit(
scale_factors, exp_values, deg, w=w, cov=True
)
except (ValueError, np.linalg.LinAlgError):
opt_params = np.polyfit(scale_factors, exp_values, deg, w=w)
params_cov = None
opt_params, [_res, rank, *_] = np.polynomial.polynomial.polyfit(
scale_factors, exp_values, deg, w=w, full=True
)
residuals = cast(npt.NDArray[np.float64], _res)
if rank < deg + 1:
warnings.warn(_EXTR_WARN, ExtrapolationWarning)

params_cov = None
if len(residuals) > 0:
n = len(scale_factors)
fac = residuals[0] / (n - deg - 1)
V = np.vander(
np.asarray(scale_factors, dtype=float), N=deg + 1, increasing=True
)
if w is not None:
V = np.diag(w) @ V
params_cov = fac * np.linalg.inv(V.T @ V)

for warn in warn_list:
# replace RankWarning with ExtrapolationWarning
Expand All @@ -184,7 +194,9 @@ def mitiq_polyfit(
warnings.warn_explicit(
warn.message, warn.category, warn.filename, warn.lineno
)
return list(opt_params), params_cov
return list(opt_params)[::-1], (
params_cov[::-1, ::-1] if params_cov is not None else None
)


class Factory(ABC):
Expand Down Expand Up @@ -1443,7 +1455,7 @@ def zne_curve(scale_factor: float) -> float:
zstack = list(np.log(shifted_y)) # type: ignore

# Get coefficients {z_j} of z(x)= z_0 + z_1*x + z_2*x**2...
# Note: coefficients are ordered from high powers to powers of x
# Note: coefficients are ordered from high powers to low powers of x
# Weights "w" are used to compensate for error propagation
# after the log transformation y --> z
z_coefficients, params_cov = mitiq_polyfit(
Expand Down
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