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zeekay
e037e8f7ea
The distribution called itself torus-ml while shipping a package directory named concrete, so the name a user installs and the name they import disagreed — and most of the tree already said torus.ml, which could not resolve against src/concrete. Moved src/concrete to src/torus and pointed the include at it. The dependency was Zama's concrete-python from their index. It is torus-fhe from ours, which is what luxfhe/torus already ships: that frontend declares torus-fhe and ships a torus/fhe module, so every 'from torus.fhe' in this tree was already naming our compiler and only the dependency lagged. Two distributions share one namespace, which is the shape the upstream this forked from used and the reason the split works: torus-fhe gives torus.fhe, torus-ml gives torus.ml. Swept every other spelling — identifiers, enum members, workflow variables, filenames, notebooks. LICENSE and NOTICE keep the upstream attribution a fork owes; the figures carry the old name in JPEG metadata a text sweep cannot reach.
301 lines
12 KiB
Python
301 lines
12 KiB
Python
"""Implement RandomForest models."""
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from typing import Any, Dict, Union
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import numpy
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import sklearn.ensemble
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from ..sklearn.tree_to_numpy import tree_to_numpy
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from .base import BaseTreeClassifierMixin, BaseTreeEstimatorMixin, BaseTreeRegressorMixin
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# pylint: disable=too-many-instance-attributes
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class RandomForestClassifier(BaseTreeClassifierMixin):
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"""Implements the RandomForest classifier."""
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sklearn_model_class = sklearn.ensemble.RandomForestClassifier
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framework = "sklearn"
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_is_a_public_cml_model = True
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# pylint: disable-next=too-many-arguments
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def __init__(
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self,
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n_bits: Union[int, Dict[str, int]] = 6,
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n_estimators=20,
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criterion="gini",
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max_depth=4,
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min_samples_split=2,
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min_samples_leaf=1,
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min_weight_fraction_leaf=0.0,
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max_features="sqrt",
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max_leaf_nodes=None,
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min_impurity_decrease=0.0,
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bootstrap=True,
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oob_score=False,
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n_jobs=None,
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random_state=None,
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verbose=0,
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warm_start=False,
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class_weight=None,
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ccp_alpha=0.0,
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max_samples=None,
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monotonic_cst=None,
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):
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"""Initialize the RandomForestClassifier.
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# noqa: DAR101
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"""
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# Call BaseClassifier's __init__ method
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super().__init__(n_bits=n_bits)
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self.n_estimators = n_estimators
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self.bootstrap = bootstrap
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self.oob_score = oob_score
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self.n_jobs = n_jobs
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self.random_state = random_state
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self.verbose = verbose
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self.warm_start = warm_start
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self.class_weight = class_weight
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self.max_samples = max_samples
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self.criterion = criterion
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self.max_depth = max_depth
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self.min_samples_split = min_samples_split
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self.min_samples_leaf = min_samples_leaf
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self.min_weight_fraction_leaf = min_weight_fraction_leaf
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self.max_features = max_features
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self.max_leaf_nodes = max_leaf_nodes
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self.min_impurity_decrease = min_impurity_decrease
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self.ccp_alpha = ccp_alpha
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self.monotonic_cst = monotonic_cst
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def post_processing(self, y_preds: numpy.ndarray) -> numpy.ndarray:
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# Here, we want to use BaseTreeEstimatorMixin's `post-processing` method as
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# RandomForestClassifier models directly computes probabilities and therefore don't require
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# to apply a sigmoid or softmax in post-processing
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return BaseTreeEstimatorMixin.post_processing(self, y_preds)
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def dump_dict(self) -> Dict[str, Any]:
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metadata: Dict[str, Any] = {}
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# Torus ML
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metadata["n_bits"] = self.n_bits
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metadata["sklearn_model"] = self.sklearn_model
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metadata["_is_fitted"] = self._is_fitted
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metadata["_is_compiled"] = self._is_compiled
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metadata["input_quantizers"] = self.input_quantizers
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metadata["output_quantizers"] = self.output_quantizers
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metadata["onnx_model_"] = self.onnx_model_
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metadata["framework"] = self.framework
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metadata["post_processing_params"] = self.post_processing_params
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metadata["_fhe_ensembling"] = self._fhe_ensembling
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# Scikit-Learn
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metadata["n_estimators"] = self.n_estimators
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metadata["bootstrap"] = self.bootstrap
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metadata["oob_score"] = self.oob_score
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metadata["n_jobs"] = self.n_jobs
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metadata["random_state"] = self.random_state
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metadata["verbose"] = self.verbose
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metadata["warm_start"] = self.warm_start
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metadata["class_weight"] = self.class_weight
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metadata["max_samples"] = self.max_samples
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metadata["criterion"] = self.criterion
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metadata["max_depth"] = self.max_depth
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metadata["min_samples_split"] = self.min_samples_split
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metadata["min_samples_leaf"] = self.min_samples_leaf
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metadata["min_weight_fraction_leaf"] = self.min_weight_fraction_leaf
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metadata["max_features"] = self.max_features
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metadata["max_leaf_nodes"] = self.max_leaf_nodes
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metadata["min_impurity_decrease"] = self.min_impurity_decrease
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metadata["ccp_alpha"] = self.ccp_alpha
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metadata["monotonic_cst"] = self.monotonic_cst
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return metadata
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@classmethod
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def load_dict(cls, metadata: Dict):
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# Instantiate the model
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obj = RandomForestClassifier(n_bits=metadata["n_bits"])
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# Torus ML
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obj.sklearn_model = metadata["sklearn_model"]
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obj._is_fitted = metadata["_is_fitted"]
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obj._is_compiled = metadata["_is_compiled"]
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obj.input_quantizers = metadata["input_quantizers"]
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obj.framework = metadata["framework"]
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obj.onnx_model_ = metadata["onnx_model_"]
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obj.output_quantizers = metadata["output_quantizers"]
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obj._fhe_ensembling = metadata["_fhe_ensembling"]
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obj._tree_inference = tree_to_numpy(
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obj.sklearn_model,
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numpy.zeros((len(obj.input_quantizers),))[None, ...],
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framework=obj.framework,
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output_n_bits=obj.n_bits["op_leaves"] if isinstance(obj.n_bits, Dict) else obj.n_bits,
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fhe_ensembling=obj._fhe_ensembling,
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)[0]
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obj.post_processing_params = metadata["post_processing_params"]
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# Scikit-Learn
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obj.n_estimators = metadata["n_estimators"]
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obj.bootstrap = metadata["bootstrap"]
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obj.oob_score = metadata["oob_score"]
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obj.n_jobs = metadata["n_jobs"]
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obj.random_state = metadata["random_state"]
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obj.verbose = metadata["verbose"]
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obj.warm_start = metadata["warm_start"]
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obj.class_weight = metadata["class_weight"]
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obj.max_samples = metadata["max_samples"]
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obj.criterion = metadata["criterion"]
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obj.max_depth = metadata["max_depth"]
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obj.min_samples_split = metadata["min_samples_split"]
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obj.min_samples_leaf = metadata["min_samples_leaf"]
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obj.min_weight_fraction_leaf = metadata["min_weight_fraction_leaf"]
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obj.max_features = metadata["max_features"]
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obj.max_leaf_nodes = metadata["max_leaf_nodes"]
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obj.min_impurity_decrease = metadata["min_impurity_decrease"]
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obj.ccp_alpha = metadata["ccp_alpha"]
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obj.monotonic_cst = metadata["monotonic_cst"]
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return obj
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# pylint: disable=too-many-instance-attributes
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class RandomForestRegressor(BaseTreeRegressorMixin):
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"""Implements the RandomForest regressor."""
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sklearn_model_class = sklearn.ensemble.RandomForestRegressor
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framework = "sklearn"
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_is_a_public_cml_model = True
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# pylint: disable-next=too-many-arguments
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def __init__(
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self,
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n_bits: Union[int, Dict[str, int]] = 6,
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n_estimators=20,
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criterion="squared_error",
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max_depth=4,
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min_samples_split=2,
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min_samples_leaf=1,
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min_weight_fraction_leaf=0.0,
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max_features=1.0,
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max_leaf_nodes=None,
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min_impurity_decrease=0.0,
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bootstrap=True,
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oob_score=False,
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n_jobs=None,
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random_state=None,
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verbose=0,
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warm_start=False,
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ccp_alpha=0.0,
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max_samples=None,
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monotonic_cst=None,
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):
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"""Initialize the RandomForestRegressor.
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# noqa: DAR101
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"""
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# Call BaseTreeEstimatorMixin's __init__ method
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super().__init__(n_bits=n_bits)
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self.n_estimators = n_estimators
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self.bootstrap = bootstrap
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self.oob_score = oob_score
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self.n_jobs = n_jobs
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self.random_state = random_state
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self.verbose = verbose
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self.warm_start = warm_start
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self.max_samples = max_samples
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self.criterion = criterion
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self.max_depth = max_depth
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self.min_samples_split = min_samples_split
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self.min_samples_leaf = min_samples_leaf
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self.min_weight_fraction_leaf = min_weight_fraction_leaf
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self.max_features = max_features
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self.max_leaf_nodes = max_leaf_nodes
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self.min_impurity_decrease = min_impurity_decrease
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self.ccp_alpha = ccp_alpha
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self.monotonic_cst = monotonic_cst
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def dump_dict(self) -> Dict[str, Any]:
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metadata: Dict[str, Any] = {}
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# Torus ML
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metadata["n_bits"] = self.n_bits
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metadata["sklearn_model"] = self.sklearn_model
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metadata["_is_fitted"] = self._is_fitted
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metadata["_is_compiled"] = self._is_compiled
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metadata["input_quantizers"] = self.input_quantizers
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metadata["output_quantizers"] = self.output_quantizers
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metadata["onnx_model_"] = self.onnx_model_
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metadata["framework"] = self.framework
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metadata["post_processing_params"] = self.post_processing_params
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metadata["_fhe_ensembling"] = self._fhe_ensembling
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# Scikit-Learn
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metadata["n_estimators"] = self.n_estimators
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metadata["bootstrap"] = self.bootstrap
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metadata["oob_score"] = self.oob_score
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metadata["n_jobs"] = self.n_jobs
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metadata["random_state"] = self.random_state
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metadata["verbose"] = self.verbose
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metadata["warm_start"] = self.warm_start
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metadata["max_samples"] = self.max_samples
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metadata["criterion"] = self.criterion
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metadata["max_depth"] = self.max_depth
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metadata["min_samples_split"] = self.min_samples_split
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metadata["min_samples_leaf"] = self.min_samples_leaf
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metadata["min_weight_fraction_leaf"] = self.min_weight_fraction_leaf
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metadata["max_features"] = self.max_features
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metadata["max_leaf_nodes"] = self.max_leaf_nodes
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metadata["min_impurity_decrease"] = self.min_impurity_decrease
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metadata["ccp_alpha"] = self.ccp_alpha
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metadata["monotonic_cst"] = self.monotonic_cst
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return metadata
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@classmethod
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def load_dict(cls, metadata: Dict):
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# Instantiate the model
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obj = RandomForestRegressor(n_bits=metadata["n_bits"])
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# Torus ML
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obj.sklearn_model = metadata["sklearn_model"]
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obj._is_fitted = metadata["_is_fitted"]
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obj._is_compiled = metadata["_is_compiled"]
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obj.input_quantizers = metadata["input_quantizers"]
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obj.framework = metadata["framework"]
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obj.onnx_model_ = metadata["onnx_model_"]
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obj.output_quantizers = metadata["output_quantizers"]
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obj._fhe_ensembling = metadata["_fhe_ensembling"]
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obj._tree_inference = tree_to_numpy(
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obj.sklearn_model,
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numpy.zeros((len(obj.input_quantizers),))[None, ...],
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framework=obj.framework,
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output_n_bits=obj.n_bits["op_leaves"] if isinstance(obj.n_bits, Dict) else obj.n_bits,
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fhe_ensembling=obj._fhe_ensembling,
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)[0]
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obj.post_processing_params = metadata["post_processing_params"]
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# Scikit-Learn
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obj.n_estimators = metadata["n_estimators"]
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obj.bootstrap = metadata["bootstrap"]
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obj.oob_score = metadata["oob_score"]
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obj.n_jobs = metadata["n_jobs"]
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obj.random_state = metadata["random_state"]
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obj.verbose = metadata["verbose"]
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obj.warm_start = metadata["warm_start"]
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obj.max_samples = metadata["max_samples"]
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obj.criterion = metadata["criterion"]
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obj.max_depth = metadata["max_depth"]
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obj.min_samples_split = metadata["min_samples_split"]
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obj.min_samples_leaf = metadata["min_samples_leaf"]
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obj.min_weight_fraction_leaf = metadata["min_weight_fraction_leaf"]
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obj.max_features = metadata["max_features"]
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obj.max_leaf_nodes = metadata["max_leaf_nodes"]
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obj.min_impurity_decrease = metadata["min_impurity_decrease"]
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obj.ccp_alpha = metadata["ccp_alpha"]
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obj.monotonic_cst = metadata["monotonic_cst"]
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return obj
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