Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

feat!: rename ml model params #491

Merged
merged 5 commits into from
Mar 23, 2024
Merged

feat!: rename ml model params #491

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
5 commits
Select commit Hold shift + click to select a range
d06a40d
feat: rename ml model params
GarrettWu Mar 21, 2024
7ebba72
merge with main
GarrettWu Mar 22, 2024
57a8b65
fix tests
GarrettWu Mar 22, 2024
532beb3
fix tests
GarrettWu Mar 22, 2024
d3e6523
Merge branch 'main' into garrettwu-breaking
GarrettWu Mar 23, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
1 change: 0 additions & 1 deletion bigframes/ml/cluster.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -33,7 +33,6 @@
"init_col": "kmeansInitializationColumn",
"distance_type": "distanceType",
"max_iter": "maxIterations",
"early_stop": "earlyStop",
"tol": "minRelativeProgress",
}

Expand Down
29 changes: 23 additions & 6 deletions bigframes/ml/decomposition.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -37,7 +37,7 @@ class PCA(

def __init__(
self,
n_components: int = 3,
n_components: Optional[Union[int, float]] = None,
*,
svd_solver: Literal["full", "randomized", "auto"] = "auto",
):
Expand All @@ -56,13 +56,31 @@ def _from_bq(cls, session: bigframes.Session, model: bigquery.Model) -> PCA:
last_fitting = model.training_runs[-1]["trainingOptions"]
if "numPrincipalComponents" in last_fitting:
kwargs["n_components"] = int(last_fitting["numPrincipalComponents"])
if "pcaExplainedVarianceRatio" in last_fitting:
kwargs["n_components"] = float(last_fitting["pcaExplainedVarianceRatio"])
if "pcaSolver" in last_fitting:
kwargs["svd_solver"] = str(last_fitting["pcaSolver"])

new_pca = cls(**kwargs)
new_pca._bqml_model = core.BqmlModel(session, model)
return new_pca

@property
def _bqml_options(self) -> dict:
"""The model options as they will be set for BQML"""
options: dict = {
"model_type": "PCA",
"pca_solver": self.svd_solver,
}

assert self.n_components is not None
if 0 < self.n_components < 1:
options["pca_explained_variance_ratio"] = float(self.n_components)
elif self.n_components >= 1:
options["num_principal_components"] = int(self.n_components)

return options

def _fit(
self,
X: Union[bpd.DataFrame, bpd.Series],
Expand All @@ -71,14 +89,13 @@ def _fit(
) -> PCA:
(X,) = utils.convert_to_dataframe(X)

# To mimic sklearn's behavior
if self.n_components is None:
self.n_components = min(X.shape)
self._bqml_model = self._bqml_model_factory.create_model(
X_train=X,
transforms=transforms,
options={
"model_type": "PCA",
"num_principal_components": self.n_components,
"pca_solver": self.svd_solver,
},
options=self._bqml_options,
)
return self

Expand Down
69 changes: 30 additions & 39 deletions bigframes/ml/ensemble.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -31,7 +31,6 @@
_BQML_PARAMS_MAPPING = {
"booster": "boosterType",
"tree_method": "treeMethod",
"early_stop": "earlyStop",
"colsample_bytree": "colsampleBylevel",
"colsample_bylevel": "colsampleBytree",
"colsample_bynode": "colsampleBynode",
Expand All @@ -40,8 +39,8 @@
"reg_alpha": "l1Regularization",
"reg_lambda": "l2Regularization",
"learning_rate": "learnRate",
"min_rel_progress": "minRelativeProgress",
"num_parallel_tree": "numParallelTree",
"tol": "minRelativeProgress",
"n_estimators": "numParallelTree",
"min_tree_child_weight": "minTreeChildWeight",
"max_depth": "maxTreeDepth",
"max_iterations": "maxIterations",
Expand All @@ -57,7 +56,7 @@ class XGBRegressor(

def __init__(
self,
num_parallel_tree: int = 1,
n_estimators: int = 1,
*,
booster: Literal["gbtree", "dart"] = "gbtree",
dart_normalized_type: Literal["tree", "forest"] = "tree",
Expand All @@ -71,14 +70,13 @@ def __init__(
subsample: float = 1.0,
reg_alpha: float = 0.0,
reg_lambda: float = 1.0,
early_stop: float = True,
learning_rate: float = 0.3,
max_iterations: int = 20,
min_rel_progress: float = 0.01,
tol: float = 0.01,
enable_global_explain: bool = False,
xgboost_version: Literal["0.9", "1.1"] = "0.9",
):
self.num_parallel_tree = num_parallel_tree
self.n_estimators = n_estimators
self.booster = booster
self.dart_normalized_type = dart_normalized_type
self.tree_method = tree_method
Expand All @@ -91,10 +89,9 @@ def __init__(
self.subsample = subsample
self.reg_alpha = reg_alpha
self.reg_lambda = reg_lambda
self.early_stop = early_stop
self.learning_rate = learning_rate
self.max_iterations = max_iterations
self.min_rel_progress = min_rel_progress
self.tol = tol
self.enable_global_explain = enable_global_explain
self.xgboost_version = xgboost_version
self._bqml_model: Optional[core.BqmlModel] = None
Expand Down Expand Up @@ -127,7 +124,8 @@ def _bqml_options(self) -> Dict[str, str | int | bool | float | List[str]]:
return {
"model_type": "BOOSTED_TREE_REGRESSOR",
"data_split_method": "NO_SPLIT",
"num_parallel_tree": self.num_parallel_tree,
"early_stop": True,
"num_parallel_tree": self.n_estimators,
"booster_type": self.booster,
"tree_method": self.tree_method,
"min_tree_child_weight": self.min_tree_child_weight,
Expand All @@ -139,10 +137,9 @@ def _bqml_options(self) -> Dict[str, str | int | bool | float | List[str]]:
"subsample": self.subsample,
"l1_reg": self.reg_alpha,
"l2_reg": self.reg_lambda,
"early_stop": self.early_stop,
"learn_rate": self.learning_rate,
"max_iterations": self.max_iterations,
"min_rel_progress": self.min_rel_progress,
"min_rel_progress": self.tol,
"enable_global_explain": self.enable_global_explain,
"xgboost_version": self.xgboost_version,
}
Expand Down Expand Up @@ -215,7 +212,7 @@ class XGBClassifier(

def __init__(
self,
num_parallel_tree: int = 1,
n_estimators: int = 1,
*,
booster: Literal["gbtree", "dart"] = "gbtree",
dart_normalized_type: Literal["tree", "forest"] = "tree",
Expand All @@ -229,14 +226,13 @@ def __init__(
subsample: float = 1.0,
reg_alpha: float = 0.0,
reg_lambda: float = 1.0,
early_stop: bool = True,
learning_rate: float = 0.3,
max_iterations: int = 20,
min_rel_progress: float = 0.01,
tol: float = 0.01,
enable_global_explain: bool = False,
xgboost_version: Literal["0.9", "1.1"] = "0.9",
):
self.num_parallel_tree = num_parallel_tree
self.n_estimators = n_estimators
self.booster = booster
self.dart_normalized_type = dart_normalized_type
self.tree_method = tree_method
Expand All @@ -249,10 +245,9 @@ def __init__(
self.subsample = subsample
self.reg_alpha = reg_alpha
self.reg_lambda = reg_lambda
self.early_stop = early_stop
self.learning_rate = learning_rate
self.max_iterations = max_iterations
self.min_rel_progress = min_rel_progress
self.tol = tol
self.enable_global_explain = enable_global_explain
self.xgboost_version = xgboost_version
self._bqml_model: Optional[core.BqmlModel] = None
Expand Down Expand Up @@ -285,7 +280,8 @@ def _bqml_options(self) -> Dict[str, str | int | bool | float | List[str]]:
return {
"model_type": "BOOSTED_TREE_CLASSIFIER",
"data_split_method": "NO_SPLIT",
"num_parallel_tree": self.num_parallel_tree,
"early_stop": True,
"num_parallel_tree": self.n_estimators,
"booster_type": self.booster,
"tree_method": self.tree_method,
"min_tree_child_weight": self.min_tree_child_weight,
Expand All @@ -297,10 +293,9 @@ def _bqml_options(self) -> Dict[str, str | int | bool | float | List[str]]:
"subsample": self.subsample,
"l1_reg": self.reg_alpha,
"l2_reg": self.reg_lambda,
"early_stop": self.early_stop,
"learn_rate": self.learning_rate,
"max_iterations": self.max_iterations,
"min_rel_progress": self.min_rel_progress,
"min_rel_progress": self.tol,
"enable_global_explain": self.enable_global_explain,
"xgboost_version": self.xgboost_version,
}
Expand Down Expand Up @@ -371,7 +366,7 @@ class RandomForestRegressor(

def __init__(
self,
num_parallel_tree: int = 100,
n_estimators: int = 100,
*,
tree_method: Literal["auto", "exact", "approx", "hist"] = "auto",
min_tree_child_weight: int = 1,
Expand All @@ -383,12 +378,11 @@ def __init__(
subsample=0.8,
reg_alpha=0.0,
reg_lambda=1.0,
early_stop=True,
min_rel_progress=0.01,
tol=0.01,
enable_global_explain=False,
xgboost_version: Literal["0.9", "1.1"] = "0.9",
):
self.num_parallel_tree = num_parallel_tree
self.n_estimators = n_estimators
self.tree_method = tree_method
self.min_tree_child_weight = min_tree_child_weight
self.colsample_bytree = colsample_bytree
Expand All @@ -399,8 +393,7 @@ def __init__(
self.subsample = subsample
self.reg_alpha = reg_alpha
self.reg_lambda = reg_lambda
self.early_stop = early_stop
self.min_rel_progress = min_rel_progress
self.tol = tol
self.enable_global_explain = enable_global_explain
self.xgboost_version = xgboost_version
self._bqml_model: Optional[core.BqmlModel] = None
Expand Down Expand Up @@ -432,7 +425,8 @@ def _bqml_options(self) -> Dict[str, str | int | bool | float | List[str]]:
"""The model options as they will be set for BQML"""
return {
"model_type": "RANDOM_FOREST_REGRESSOR",
"num_parallel_tree": self.num_parallel_tree,
"early_stop": True,
"num_parallel_tree": self.n_estimators,
"tree_method": self.tree_method,
"min_tree_child_weight": self.min_tree_child_weight,
"colsample_bytree": self.colsample_bytree,
Expand All @@ -443,8 +437,7 @@ def _bqml_options(self) -> Dict[str, str | int | bool | float | List[str]]:
"subsample": self.subsample,
"l1_reg": self.reg_alpha,
"l2_reg": self.reg_lambda,
"early_stop": self.early_stop,
"min_rel_progress": self.min_rel_progress,
"min_rel_progress": self.tol,
"data_split_method": "NO_SPLIT",
"enable_global_explain": self.enable_global_explain,
"xgboost_version": self.xgboost_version,
Expand Down Expand Up @@ -536,7 +529,7 @@ class RandomForestClassifier(

def __init__(
self,
num_parallel_tree: int = 100,
n_estimators: int = 100,
*,
tree_method: Literal["auto", "exact", "approx", "hist"] = "auto",
min_tree_child_weight: int = 1,
Expand All @@ -548,12 +541,11 @@ def __init__(
subsample: float = 0.8,
reg_alpha: float = 0.0,
reg_lambda: float = 1.0,
early_stop=True,
min_rel_progress: float = 0.01,
tol: float = 0.01,
enable_global_explain=False,
xgboost_version: Literal["0.9", "1.1"] = "0.9",
):
self.num_parallel_tree = num_parallel_tree
self.n_estimators = n_estimators
self.tree_method = tree_method
self.min_tree_child_weight = min_tree_child_weight
self.colsample_bytree = colsample_bytree
Expand All @@ -564,8 +556,7 @@ def __init__(
self.subsample = subsample
self.reg_alpha = reg_alpha
self.reg_lambda = reg_lambda
self.early_stop = early_stop
self.min_rel_progress = min_rel_progress
self.tol = tol
self.enable_global_explain = enable_global_explain
self.xgboost_version = xgboost_version
self._bqml_model: Optional[core.BqmlModel] = None
Expand Down Expand Up @@ -597,7 +588,8 @@ def _bqml_options(self) -> Dict[str, str | int | bool | float | List[str]]:
"""The model options as they will be set for BQML"""
return {
"model_type": "RANDOM_FOREST_CLASSIFIER",
"num_parallel_tree": self.num_parallel_tree,
"early_stop": True,
"num_parallel_tree": self.n_estimators,
"tree_method": self.tree_method,
"min_tree_child_weight": self.min_tree_child_weight,
"colsample_bytree": self.colsample_bytree,
Expand All @@ -608,8 +600,7 @@ def _bqml_options(self) -> Dict[str, str | int | bool | float | List[str]]:
"subsample": self.subsample,
"l1_reg": self.reg_alpha,
"l2_reg": self.reg_lambda,
"early_stop": self.early_stop,
"min_rel_progress": self.min_rel_progress,
"min_rel_progress": self.tol,
"data_split_method": "NO_SPLIT",
"enable_global_explain": self.enable_global_explain,
"xgboost_version": self.xgboost_version,
Expand Down
Loading