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Sep 2, 2025
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MNT speedup narx grad #157

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217 changes: 50 additions & 167 deletions fastcan/narx/_base.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,6 +13,7 @@
from sklearn.base import BaseEstimator, MultiOutputMixin, RegressorMixin
from sklearn.linear_model import LinearRegression
from sklearn.utils import check_array, check_consistent_length, column_or_1d
from sklearn.utils._openmp_helpers import _openmp_effective_n_threads
from sklearn.utils._param_validation import Interval, StrOptions, validate_params
from sklearn.utils.validation import (
_check_sample_weight,
Expand All @@ -29,9 +30,8 @@
make_time_shift_features,
)
from ._narx_fast import ( # type: ignore[attr-defined]
_predict_step,
_update_cfd,
_update_terms,
_predict,
_update_dydx,
)


Expand Down Expand Up @@ -341,7 +341,7 @@ def fit(self, X, y, sample_weight=None, coef_init=None, **params):
f"({(n_coef_intercept,)}), but got {coef_init.shape}."
)

grad_yyd_ids, grad_coef_ids, grad_feat_ids, grad_delay_ids = NARX._get_cfd_ids(
grad_yyd_ids, grad_delay_ids, grad_coef_ids, grad_feat_ids = NARX._get_dcf_ids(
self.feat_ids_, self.delay_ids_, self.output_ids_, X.shape[1]
)
sample_weight_sqrt = np.sqrt(sample_weight).reshape(-1, 1)
Expand All @@ -350,7 +350,6 @@ def fit(self, X, y, sample_weight=None, coef_init=None, **params):
x0=coef_init,
jac=NARX._grad,
args=(
_predict_step,
X,
y,
self.feat_ids_,
Expand All @@ -359,9 +358,9 @@ def fit(self, X, y, sample_weight=None, coef_init=None, **params):
self.fit_intercept,
sample_weight_sqrt,
grad_yyd_ids,
grad_delay_ids,
grad_coef_ids,
grad_feat_ids,
grad_delay_ids,
),
**params,
)
Expand All @@ -374,64 +373,17 @@ def fit(self, X, y, sample_weight=None, coef_init=None, **params):
return self

@staticmethod
def _predict(
expression,
X,
y_ref,
coef,
intercept,
feat_ids,
delay_ids,
output_ids,
):
n_samples = X.shape[0]
n_ref, n_outputs = y_ref.shape
max_delay = np.max(delay_ids)
y_hat = np.zeros((n_samples, n_outputs), dtype=float)
at_init = True
init_k = 0
for k in range(n_samples):
if not np.all(np.isfinite(X[k])):
at_init = True
init_k = k + 1
y_hat[k] = np.nan
continue
if k - init_k == max_delay:
at_init = False

if at_init:
y_hat[k] = y_ref[k % n_ref]
if not np.all(np.isfinite(y_hat[k])):
at_init = True
init_k = k + 1
else:
y_hat[k] = intercept
expression(
X,
y_hat,
y_hat[k],
coef,
feat_ids,
delay_ids,
output_ids,
k,
)
if np.any(y_hat[k] > 1e20):
y_hat[k:] = 1e20
return y_hat
return y_hat

@staticmethod
def _get_cfd_ids(feat_ids, delay_ids, output_ids, n_features_in):
def _get_dcf_ids(feat_ids, delay_ids, output_ids, n_features_in):
"""
Get ids of CFD (Coef, Feature, and Delay) matrix to update dyn(k)/dx.
Get ids of DCF (Delay, Coef, and Feature) matrix to update dyn(k)/dx.
Maps coefficients to their corresponding features and delays.
"""

# Initialize cfd_ids as a list of lists n_outputs * n_outputs * max_delay
# axis-0 (i): [dy0(k)/dx, dy1(k)/dx, ..., dyn(k)/dx]
# axis-1 (j): [dy0(k-d)/dx, dy1(k-d)/dx, ..., dyn(k-d)/dx]
# axis-2 (d): [dyj(k-1)/dx, dyj(k-2)/dx, ..., dyj(k-max_delay)/dx]
# Initialize dcf_ids as a list of lists max_delay * n_outputs * n_outputs
# axis-0 (d): [dyj(k-1)/dx, dyj(k-2)/dx, ..., dyj(k-max_delay)/dx]
# axis-1 (i): [dy0(k)/dx, dy1(k)/dx, ..., dyn(k)/dx]
# axis-2 (j): [dy0(k-d)/dx, dy1(k-d)/dx, ..., dyn(k-d)/dx]

grad_yyd_ids = []
grad_coef_ids = []
grad_feat_ids = []
Expand All @@ -447,106 +399,20 @@ def _get_cfd_ids(feat_ids, delay_ids, output_ids, n_features_in):
if feat_id >= n_features_in and delay_id > 0:
col_y_id = feat_id - n_features_in # y(k-1, id)
grad_yyd_ids.append([row_y_id, col_y_id, delay_id - 1])
grad_delay_ids.append(np.delete(term_delay_ids, var_id))
grad_coef_ids.append(coef_id)
grad_feat_ids.append(np.delete(term_feat_ids, var_id))
grad_delay_ids.append(np.delete(term_delay_ids, var_id))

return (
np.array(grad_yyd_ids, dtype=np.int32),
np.array(grad_yyd_ids, dtype=np.int32, ndmin=2),
np.array(grad_delay_ids, dtype=np.int32, ndmin=2),
np.array(grad_coef_ids, dtype=np.int32),
np.array(grad_feat_ids, dtype=np.int32),
np.array(grad_delay_ids, dtype=np.int32),
np.array(grad_feat_ids, dtype=np.int32, ndmin=2),
)

@staticmethod
def _update_dydx(
X,
y_hat,
coef,
feat_ids,
delay_ids,
output_ids,
fit_intercept,
grad_yyd_ids,
grad_coef_ids,
grad_feat_ids,
grad_delay_ids,
):
"""
Computation of the Jacobian matrix dydx.

Returns
-------
dydx : ndarray of shape (n_samples, n_outputs, n_x)
Jacobian matrix of the outputs with respect to coefficients and intercepts.
"""
n_samples, n_y = y_hat.shape
max_delay = np.max(delay_ids)
n_coefs = feat_ids.shape[0]
if fit_intercept:
n_x = n_coefs + n_y # Total number of coefficients and intercepts
y_ids = np.r_[output_ids, np.arange(n_y)]
else:
n_x = n_coefs
y_ids = output_ids

x_ids = np.arange(n_x)

dydx = np.zeros((n_samples, n_y, n_x), dtype=float)
at_init = True
init_k = 0
for k in range(n_samples):
if not (np.all(np.isfinite(X[k])) and np.all(np.isfinite(y_hat[k]))):
at_init = True
init_k = k + 1
continue
if k - init_k == max_delay:
at_init = False

if at_init:
continue
# Compute terms for time step k
terms = np.ones(n_x, dtype=float)
_update_terms(
X,
y_hat,
terms,
feat_ids,
delay_ids,
k,
)

# Update constant terms of Jacobian
dydx[k, y_ids, x_ids] = terms

# Update dynamic terms of Jacobian
if max_delay > 0 and grad_yyd_ids.size > 0:
cfd = np.zeros((n_y, n_y, max_delay), dtype=float)
_update_cfd(
X,
y_hat,
cfd,
coef,
grad_yyd_ids,
grad_coef_ids,
grad_feat_ids,
grad_delay_ids,
k,
)
for d in range(max_delay):
dydx[k] += cfd[:, :, d] @ dydx[k - d - 1]

# Handle divergence
if np.any(dydx[k] > 1e20):
dydx[k:] = 1e20
break

return dydx

@staticmethod
def _loss(
coef_intercept,
expression,
X,
y,
feat_ids,
Expand All @@ -557,23 +423,24 @@ def _loss(
*args,
):
# Sum of squared errors
n_outputs = y.shape[1]
n_samples, n_y = y.shape
if fit_intercept:
coef = coef_intercept[:-n_outputs]
intercept = coef_intercept[-n_outputs:]
coef = coef_intercept[:-n_y]
intercept = coef_intercept[-n_y:]
else:
coef = coef_intercept
intercept = np.zeros(n_outputs, dtype=float)
intercept = np.zeros(n_y, dtype=float)

y_hat = NARX._predict(
expression,
y_hat = np.zeros((n_samples, n_y), dtype=float)
_predict(
X,
y,
coef,
intercept,
feat_ids,
delay_ids,
output_ids,
y_hat,
)

y_masked, y_hat_masked, sample_weight_sqrt_masked = mask_missing_values(
Expand All @@ -585,7 +452,6 @@ def _loss(
@staticmethod
def _grad(
coef_intercept,
expression,
X,
y,
feat_ids,
Expand All @@ -594,41 +460,57 @@ def _grad(
fit_intercept,
sample_weight_sqrt,
grad_yyd_ids,
grad_delay_ids,
grad_coef_ids,
grad_feat_ids,
grad_delay_ids,
):
# Sum of squared errors
n_outputs = y.shape[1]
n_samples, n_outputs = y.shape
if fit_intercept:
coef = coef_intercept[:-n_outputs]
intercept = coef_intercept[-n_outputs:]
else:
coef = coef_intercept
intercept = np.zeros(n_outputs, dtype=float)

y_hat = NARX._predict(
expression,
y_hat = np.zeros((n_samples, n_outputs), dtype=float)
_predict(
X,
y,
coef,
intercept,
feat_ids,
delay_ids,
output_ids,
y_hat,
)
dydx = NARX._update_dydx(

max_delay = np.max(delay_ids)
n_coefs = feat_ids.shape[0]
if fit_intercept:
n_x = n_coefs + n_outputs # Total number of coefficients and intercepts
y_ids = np.r_[output_ids, np.arange(n_outputs, dtype=np.int32)]
else:
n_x = n_coefs
y_ids = output_ids
dydx = np.zeros((n_samples, n_outputs, n_x), dtype=float)
dcf = np.zeros((max_delay, n_outputs, n_outputs), dtype=float)
n_threads = _openmp_effective_n_threads()

_update_dydx(
X,
y_hat,
coef,
feat_ids,
delay_ids,
output_ids,
fit_intercept,
y_ids,
grad_yyd_ids,
grad_delay_ids,
grad_coef_ids,
grad_feat_ids,
grad_delay_ids,
dydx,
dcf,
n_threads,
)

mask_valid = mask_missing_values(y, y_hat, sample_weight_sqrt, return_mask=True)
Expand Down Expand Up @@ -702,15 +584,16 @@ def predict(self, X, y_init=None):
f"`y_init` should have {self.n_outputs_} outputs "
f"but got {y_init.shape[1]}."
)
y_hat = NARX._predict(
_predict_step,
y_hat = np.zeros((X.shape[0], self.n_outputs_), dtype=float)
_predict(
X,
y_init,
self.coef_,
self.intercept_,
self.feat_ids_,
self.delay_ids_,
self.output_ids_,
y_hat,
)
if self.n_outputs_ == 1:
y_hat = y_hat.flatten()
Expand Down
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