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FIX Move logistic regression parameter validation to fit() #6109

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FIX Move logistic regression parameter validation to fit() #6109

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9b0acde
Move parameter validation to fit()
betatim Oct 14, 2024
8bc76b6
Move parameter validation to separate method
betatim Oct 24, 2024
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86 changes: 44 additions & 42 deletions python/cuml/cuml/linear_model/logistic_regression.pyx
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Original file line number Diff line number Diff line change
Expand Up @@ -211,68 +211,54 @@ class LogisticRegression(UniversalBase,
verbose=verbose,
output_type=output_type)

if penalty not in supported_penalties:
raise ValueError("`penalty` " + str(penalty) + " not supported.")

if solver not in supported_solvers:
raise ValueError("Only quasi-newton `qn` solver is "
" supported, not %s" % solver)
self.solver = solver

self.C = C

if penalty == "none":
warnings.warn(
"The 'none' option was deprecated in version 24.06, and will "
"be removed in 25.08. Use None instead.",
FutureWarning
)
penalty = None
self.penalty = penalty

self.tol = tol
self.fit_intercept = fit_intercept
self.max_iter = max_iter
self.linesearch_max_iter = linesearch_max_iter
self.l1_ratio = None
if self.penalty == "elasticnet":
if l1_ratio is None:
raise ValueError(
"l1_ratio has to be specified for" "loss='elasticnet'"
)
if l1_ratio < 0.0 or l1_ratio > 1.0:
msg = "l1_ratio value has to be between 0.0 and 1.0"
raise ValueError(msg.format(l1_ratio))
self.l1_ratio = l1_ratio

l1_strength, l2_strength = self._get_qn_params()

loss = "sigmoid"
self.l1_ratio = l1_ratio

if class_weight is not None:
self._build_class_weights(class_weight)
else:
self.class_weight = None

self.solver_model = QN(
loss=loss,
fit_intercept=self.fit_intercept,
l1_strength=l1_strength,
l2_strength=l2_strength,
max_iter=self.max_iter,
linesearch_max_iter=self.linesearch_max_iter,
tol=self.tol,
verbose=self.verbose,
handle=self.handle,
)

if logger.should_log_for(logger.level_debug):
self.verb_prefix = "CY::"
logger.debug(self.verb_prefix + "Estimator parameters:")
logger.debug(pprint.pformat(self.__dict__))
else:
self.verb_prefix = ""

def _validate_params(self):
if self.penalty not in supported_penalties:
raise ValueError("`penalty` " + str(self.penalty) + " not supported.")

if self.solver not in supported_solvers:
raise ValueError("Only quasi-newton `qn` solver is "
" supported, not %s" % self.solver)

if self.penalty == "none":
warnings.warn(
"The 'none' option was deprecated in version 24.06, and will "
"be removed in 25.08. Use None instead.",
FutureWarning
)
penalty = None
else:
penalty = self.penalty

if penalty == "elasticnet":
if self.l1_ratio is None:
raise ValueError(
"l1_ratio has to be specified for" "loss='elasticnet'"
)
if self.l1_ratio < 0.0 or self.l1_ratio > 1.0:
msg = "l1_ratio value has to be between 0.0 and 1.0"
raise ValueError(msg.format(self.l1_ratio))
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@generate_docstring(X='dense_sparse')
@cuml.internals.api_base_return_any(set_output_dtype=True)
@enable_device_interop
Expand All @@ -282,6 +268,22 @@ class LogisticRegression(UniversalBase,
Fit the model with X and y.

"""
self._validate_params()

l1_strength, l2_strength = self._get_qn_params()
self.solver_model = QN(
loss="sigmoid",
fit_intercept=self.fit_intercept,
l1_strength=l1_strength,
l2_strength=l2_strength,
max_iter=self.max_iter,
linesearch_max_iter=self.linesearch_max_iter,
tol=self.tol,
verbose=self.verbose,
handle=self.handle,
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Suggested change
handle=self.handle,
handle=self.handle,
output_type=self.output_type

If I'm not mistaken, that's what 99% of the pytest failures are complaining about

output_type=self.output_type,
)

self.n_features_in_ = X.shape[1] if X.ndim == 2 else 1
if hasattr(X, 'index'):
self.feature_names_in_ = X.index
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