/
linear_model.py
593 lines (504 loc) · 21.3 KB
/
linear_model.py
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"""Implement sklearn linear model."""
from typing import Any, Dict
import sklearn.linear_model
from .base import SklearnLinearClassifierMixin, SklearnLinearRegressorMixin
# pylint: disable=invalid-name,too-many-instance-attributes
class LinearRegression(SklearnLinearRegressorMixin):
"""A linear regression model with FHE.
Parameters:
n_bits (int, Dict[str, int]): Number of bits to quantize the model. If an int is passed
for n_bits, the value will be used for quantizing inputs and weights. If a dict is
passed, then it should contain "op_inputs" and "op_weights" as keys with
corresponding number of quantization bits so that:
- op_inputs : number of bits to quantize the input values
- op_weights: number of bits to quantize the learned parameters
Default to 8.
For more details on LinearRegression please refer to the scikit-learn documentation:
https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html
"""
sklearn_model_class = sklearn.linear_model.LinearRegression
_is_a_public_cml_model = True
def __init__(
self,
n_bits=8,
fit_intercept=True,
normalize="deprecated",
copy_X=True,
n_jobs=None,
positive=False,
):
# Call SklearnLinearModelMixin's __init__ method
super().__init__(n_bits=n_bits)
self.fit_intercept = fit_intercept
self.normalize = normalize
self.copy_X = copy_X
self.n_jobs = n_jobs
self.positive = positive
def dump_dict(self) -> Dict[str, Any]:
assert self._weight_quantizer is not None, self._is_not_fitted_error_message()
metadata: Dict[str, Any] = {}
# Concrete-ML
metadata["n_bits"] = self.n_bits
metadata["sklearn_model"] = self.sklearn_model
metadata["_is_fitted"] = self._is_fitted
metadata["_is_compiled"] = self._is_compiled
metadata["input_quantizers"] = self.input_quantizers
metadata["_weight_quantizer"] = self._weight_quantizer
metadata["output_quantizers"] = self.output_quantizers
metadata["onnx_model_"] = self.onnx_model_
metadata["_q_weights"] = self._q_weights
metadata["_q_bias"] = self._q_bias
metadata["post_processing_params"] = self.post_processing_params
# Scikit-Learn
metadata["fit_intercept"] = self.fit_intercept
metadata["normalize"] = self.normalize
metadata["copy_X"] = self.copy_X
metadata["n_jobs"] = self.n_jobs
metadata["positive"] = self.positive
return metadata
@classmethod
def load_dict(cls, metadata: Dict):
# Instantiate the model
obj = LinearRegression()
# Concrete-ML
obj.n_bits = metadata["n_bits"]
obj.sklearn_model = metadata["sklearn_model"]
obj._is_fitted = metadata["_is_fitted"]
obj._is_compiled = metadata["_is_compiled"]
obj.input_quantizers = metadata["input_quantizers"]
obj.output_quantizers = metadata["output_quantizers"]
obj._weight_quantizer = metadata["_weight_quantizer"]
obj.onnx_model_ = metadata["onnx_model_"]
obj._q_weights = metadata["_q_weights"]
obj._q_bias = metadata["_q_bias"]
obj.post_processing_params = metadata["post_processing_params"]
# Scikit-Learn
obj.fit_intercept = metadata["fit_intercept"]
obj.normalize = metadata["normalize"]
obj.copy_X = metadata["copy_X"]
obj.n_jobs = metadata["n_jobs"]
obj.positive = metadata["positive"]
return obj
class ElasticNet(SklearnLinearRegressorMixin):
"""An ElasticNet regression model with FHE.
Parameters:
n_bits (int, Dict[str, int]): Number of bits to quantize the model. If an int is passed
for n_bits, the value will be used for quantizing inputs and weights. If a dict is
passed, then it should contain "op_inputs" and "op_weights" as keys with
corresponding number of quantization bits so that:
- op_inputs : number of bits to quantize the input values
- op_weights: number of bits to quantize the learned parameters
Default to 8.
For more details on ElasticNet please refer to the scikit-learn documentation:
https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNet.html
"""
sklearn_model_class = sklearn.linear_model.ElasticNet
_is_a_public_cml_model = True
# pylint: disable-next=too-many-arguments
def __init__(
self,
n_bits=8,
alpha=1.0,
l1_ratio=0.5,
fit_intercept=True,
normalize="deprecated",
precompute=False,
max_iter=1000,
copy_X=True,
tol=0.0001,
warm_start=False,
positive=False,
random_state=None,
selection="cyclic",
):
# Call SklearnLinearModelMixin's __init__ method
super().__init__(n_bits=n_bits)
self.alpha = alpha
self.l1_ratio = l1_ratio
self.fit_intercept = fit_intercept
self.normalize = normalize
self.copy_X = copy_X
self.positive = positive
self.precompute = precompute
self.max_iter = max_iter
self.tol = tol
self.warm_start = warm_start
self.random_state = random_state
self.selection = selection
def dump_dict(self) -> Dict[str, Any]:
assert self._weight_quantizer is not None, self._is_not_fitted_error_message()
metadata: Dict[str, Any] = {}
# Concrete-ML
metadata["n_bits"] = self.n_bits
metadata["sklearn_model"] = self.sklearn_model
metadata["_is_fitted"] = self._is_fitted
metadata["_is_compiled"] = self._is_compiled
metadata["input_quantizers"] = self.input_quantizers
metadata["_weight_quantizer"] = self._weight_quantizer
metadata["output_quantizers"] = self.output_quantizers
metadata["onnx_model_"] = self.onnx_model_
metadata["_q_weights"] = self._q_weights
metadata["_q_bias"] = self._q_bias
metadata["post_processing_params"] = self.post_processing_params
# Scikit-Learn
metadata["alpha"] = self.alpha
metadata["l1_ratio"] = self.l1_ratio
metadata["fit_intercept"] = self.fit_intercept
metadata["normalize"] = self.normalize
metadata["copy_X"] = self.copy_X
metadata["positive"] = self.positive
metadata["precompute"] = self.precompute
metadata["max_iter"] = self.max_iter
metadata["tol"] = self.tol
metadata["warm_start"] = self.warm_start
metadata["random_state"] = self.random_state
metadata["selection"] = self.selection
return metadata
@classmethod
def load_dict(cls, metadata: Dict):
# Instantiate the model
obj = ElasticNet()
# Concrete-ML
obj.n_bits = metadata["n_bits"]
obj.sklearn_model = metadata["sklearn_model"]
obj._is_fitted = metadata["_is_fitted"]
obj._is_compiled = metadata["_is_compiled"]
obj.input_quantizers = metadata["input_quantizers"]
obj.output_quantizers = metadata["output_quantizers"]
obj._weight_quantizer = metadata["_weight_quantizer"]
obj.onnx_model_ = metadata["onnx_model_"]
obj._q_weights = metadata["_q_weights"]
obj._q_bias = metadata["_q_bias"]
obj.post_processing_params = metadata["post_processing_params"]
# Scikit-Learn
obj.alpha = metadata["alpha"]
obj.l1_ratio = metadata["l1_ratio"]
obj.fit_intercept = metadata["fit_intercept"]
obj.normalize = metadata["normalize"]
obj.copy_X = metadata["copy_X"]
obj.positive = metadata["positive"]
obj.precompute = metadata["precompute"]
obj.max_iter = metadata["max_iter"]
obj.tol = metadata["tol"]
obj.warm_start = metadata["warm_start"]
obj.random_state = metadata["random_state"]
obj.selection = metadata["selection"]
return obj
class Lasso(SklearnLinearRegressorMixin):
"""A Lasso regression model with FHE.
Parameters:
n_bits (int, Dict[str, int]): Number of bits to quantize the model. If an int is passed
for n_bits, the value will be used for quantizing inputs and weights. If a dict is
passed, then it should contain "op_inputs" and "op_weights" as keys with
corresponding number of quantization bits so that:
- op_inputs : number of bits to quantize the input values
- op_weights: number of bits to quantize the learned parameters
Default to 8.
For more details on Lasso please refer to the scikit-learn documentation:
https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html
"""
sklearn_model_class = sklearn.linear_model.Lasso
_is_a_public_cml_model = True
# pylint: disable-next=too-many-arguments
def __init__(
self,
n_bits=8,
alpha: float = 1.0,
fit_intercept=True,
normalize="deprecated",
precompute=False,
copy_X=True,
max_iter=1000,
tol=0.0001,
warm_start=False,
positive=False,
random_state=None,
selection="cyclic",
):
# Call SklearnLinearModelMixin's __init__ method
super().__init__(n_bits=n_bits)
self.alpha = alpha
self.fit_intercept = fit_intercept
self.normalize = normalize
self.copy_X = copy_X
self.positive = positive
self.max_iter = max_iter
self.warm_start = warm_start
self.selection = selection
self.tol = tol
self.precompute = precompute
self.random_state = random_state
def dump_dict(self) -> Dict[str, Any]:
assert self._weight_quantizer is not None, self._is_not_fitted_error_message()
metadata: Dict[str, Any] = {}
# Concrete-ML
metadata["n_bits"] = self.n_bits
metadata["sklearn_model"] = self.sklearn_model
metadata["_is_fitted"] = self._is_fitted
metadata["_is_compiled"] = self._is_compiled
metadata["input_quantizers"] = self.input_quantizers
metadata["_weight_quantizer"] = self._weight_quantizer
metadata["output_quantizers"] = self.output_quantizers
metadata["onnx_model_"] = self.onnx_model_
metadata["_q_weights"] = self._q_weights
metadata["_q_bias"] = self._q_bias
metadata["post_processing_params"] = self.post_processing_params
# Scikit-Learn
metadata["alpha"] = self.alpha
metadata["fit_intercept"] = self.fit_intercept
metadata["normalize"] = self.normalize
metadata["copy_X"] = self.copy_X
metadata["positive"] = self.positive
metadata["max_iter"] = self.max_iter
metadata["warm_start"] = self.warm_start
metadata["selection"] = self.selection
metadata["tol"] = self.tol
metadata["precompute"] = self.precompute
metadata["random_state"] = self.random_state
return metadata
@classmethod
def load_dict(cls, metadata: Dict):
# Instantiate the model
obj = Lasso()
# Concrete-ML
obj.n_bits = metadata["n_bits"]
obj.sklearn_model = metadata["sklearn_model"]
obj._is_fitted = metadata["_is_fitted"]
obj._is_compiled = metadata["_is_compiled"]
obj.input_quantizers = metadata["input_quantizers"]
obj.output_quantizers = metadata["output_quantizers"]
obj._weight_quantizer = metadata["_weight_quantizer"]
obj.onnx_model_ = metadata["onnx_model_"]
obj._q_weights = metadata["_q_weights"]
obj._q_bias = metadata["_q_bias"]
obj.post_processing_params = metadata["post_processing_params"]
# Scikit-Learn
obj.alpha = metadata["alpha"]
obj.fit_intercept = metadata["fit_intercept"]
obj.normalize = metadata["normalize"]
obj.copy_X = metadata["copy_X"]
obj.positive = metadata["positive"]
obj.max_iter = metadata["max_iter"]
obj.warm_start = metadata["warm_start"]
obj.selection = metadata["selection"]
obj.tol = metadata["tol"]
obj.precompute = metadata["precompute"]
obj.random_state = metadata["random_state"]
return obj
class Ridge(SklearnLinearRegressorMixin):
"""A Ridge regression model with FHE.
Parameters:
n_bits (int, Dict[str, int]): Number of bits to quantize the model. If an int is passed
for n_bits, the value will be used for quantizing inputs and weights. If a dict is
passed, then it should contain "op_inputs" and "op_weights" as keys with
corresponding number of quantization bits so that:
- op_inputs : number of bits to quantize the input values
- op_weights: number of bits to quantize the learned parameters
Default to 8.
For more details on Ridge please refer to the scikit-learn documentation:
https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html
"""
sklearn_model_class = sklearn.linear_model.Ridge
_is_a_public_cml_model = True
# pylint: disable-next=too-many-arguments
def __init__(
self,
n_bits=8,
alpha: float = 1.0,
fit_intercept=True,
normalize="deprecated",
copy_X=True,
max_iter=None,
tol=0.001,
solver="auto",
positive=False,
random_state=None,
):
# Call SklearnLinearModelMixin's __init__ method
super().__init__(n_bits=n_bits)
self.alpha = alpha
self.fit_intercept = fit_intercept
self.normalize = normalize
self.copy_X = copy_X
self.positive = positive
self.max_iter = max_iter
self.tol = tol
self.solver = solver
self.random_state = random_state
def dump_dict(self) -> Dict[str, Any]:
assert self._weight_quantizer is not None, self._is_not_fitted_error_message()
metadata: Dict[str, Any] = {}
# Concrete-ML
metadata["n_bits"] = self.n_bits
metadata["sklearn_model"] = self.sklearn_model
metadata["_is_fitted"] = self._is_fitted
metadata["_is_compiled"] = self._is_compiled
metadata["input_quantizers"] = self.input_quantizers
metadata["_weight_quantizer"] = self._weight_quantizer
metadata["output_quantizers"] = self.output_quantizers
metadata["onnx_model_"] = self.onnx_model_
metadata["_q_weights"] = self._q_weights
metadata["_q_bias"] = self._q_bias
metadata["post_processing_params"] = self.post_processing_params
# Scikit-Learn
metadata["alpha"] = self.alpha
metadata["fit_intercept"] = self.fit_intercept
metadata["normalize"] = self.normalize
metadata["copy_X"] = self.copy_X
metadata["positive"] = self.positive
metadata["max_iter"] = self.max_iter
metadata["tol"] = self.tol
metadata["solver"] = self.solver
metadata["random_state"] = self.random_state
return metadata
@classmethod
def load_dict(cls, metadata: Dict):
# Instantiate the model
obj = Ridge()
# Concrete-ML
obj.n_bits = metadata["n_bits"]
obj.sklearn_model = metadata["sklearn_model"]
obj._is_fitted = metadata["_is_fitted"]
obj._is_compiled = metadata["_is_compiled"]
obj.input_quantizers = metadata["input_quantizers"]
obj.output_quantizers = metadata["output_quantizers"]
obj._weight_quantizer = metadata["_weight_quantizer"]
obj.onnx_model_ = metadata["onnx_model_"]
obj._q_weights = metadata["_q_weights"]
obj._q_bias = metadata["_q_bias"]
obj.post_processing_params = metadata["post_processing_params"]
# Scikit-Learn
obj.alpha = metadata["alpha"]
obj.fit_intercept = metadata["fit_intercept"]
obj.normalize = metadata["normalize"]
obj.copy_X = metadata["copy_X"]
obj.positive = metadata["positive"]
obj.max_iter = metadata["max_iter"]
obj.tol = metadata["tol"]
obj.solver = metadata["solver"]
obj.random_state = metadata["random_state"]
return obj
class LogisticRegression(SklearnLinearClassifierMixin):
"""A logistic regression model with FHE.
Parameters:
n_bits (int, Dict[str, int]): Number of bits to quantize the model. If an int is passed
for n_bits, the value will be used for quantizing inputs and weights. If a dict is
passed, then it should contain "op_inputs" and "op_weights" as keys with
corresponding number of quantization bits so that:
- op_inputs : number of bits to quantize the input values
- op_weights: number of bits to quantize the learned parameters
Default to 8.
For more details on LogisticRegression please refer to the scikit-learn documentation:
https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html
"""
sklearn_model_class = sklearn.linear_model.LogisticRegression
_is_a_public_cml_model = True
# pylint: disable-next=too-many-arguments
def __init__(
self,
n_bits=8,
penalty="l2",
dual=False,
tol=1e-4,
C=1.0,
fit_intercept=True,
intercept_scaling=1,
class_weight=None,
random_state=None,
solver="lbfgs",
max_iter=100,
multi_class="auto",
verbose=0,
warm_start=False,
n_jobs=None,
l1_ratio=None,
):
# Call BaseClassifier's __init__ method
super().__init__(n_bits=n_bits)
self.penalty = penalty
self.dual = dual
self.tol = tol
self.C = C
self.fit_intercept = fit_intercept
self.intercept_scaling = intercept_scaling
self.class_weight = class_weight
self.random_state = random_state
self.solver = solver
self.max_iter = max_iter
self.multi_class = multi_class
self.verbose = verbose
self.warm_start = warm_start
self.n_jobs = n_jobs
self.l1_ratio = l1_ratio
def dump_dict(self) -> Dict[str, Any]:
assert self._weight_quantizer is not None, self._is_not_fitted_error_message()
metadata: Dict[str, Any] = {}
# Concrete-ML
metadata["n_bits"] = self.n_bits
metadata["sklearn_model"] = self.sklearn_model
metadata["_is_fitted"] = self._is_fitted
metadata["_is_compiled"] = self._is_compiled
metadata["input_quantizers"] = self.input_quantizers
metadata["_weight_quantizer"] = self._weight_quantizer
metadata["output_quantizers"] = self.output_quantizers
metadata["onnx_model_"] = self.onnx_model_
metadata["_q_weights"] = self._q_weights
metadata["_q_bias"] = self._q_bias
metadata["post_processing_params"] = self.post_processing_params
# Classifier
metadata["target_classes_"] = self.target_classes_
metadata["n_classes_"] = self.n_classes_
# Scikit-Learn
metadata["penalty"] = self.penalty
metadata["dual"] = self.dual
metadata["tol"] = self.tol
metadata["C"] = self.C
metadata["fit_intercept"] = self.fit_intercept
metadata["intercept_scaling"] = self.intercept_scaling
metadata["class_weight"] = self.class_weight
metadata["random_state"] = self.random_state
metadata["solver"] = self.solver
metadata["max_iter"] = self.max_iter
metadata["multi_class"] = self.multi_class
metadata["verbose"] = self.verbose
metadata["warm_start"] = self.warm_start
metadata["n_jobs"] = self.n_jobs
metadata["l1_ratio"] = self.l1_ratio
return metadata
@classmethod
def load_dict(cls, metadata: Dict):
# Instantiate the model
obj = LogisticRegression()
# Concrete-ML
obj.n_bits = metadata["n_bits"]
obj.sklearn_model = metadata["sklearn_model"]
obj._is_fitted = metadata["_is_fitted"]
obj._is_compiled = metadata["_is_compiled"]
obj.input_quantizers = metadata["input_quantizers"]
obj.output_quantizers = metadata["output_quantizers"]
obj._weight_quantizer = metadata["_weight_quantizer"]
obj.onnx_model_ = metadata["onnx_model_"]
obj._q_weights = metadata["_q_weights"]
obj._q_bias = metadata["_q_bias"]
obj.post_processing_params = metadata["post_processing_params"]
# Classifier
obj.target_classes_ = metadata["target_classes_"]
obj.n_classes_ = metadata["n_classes_"]
# Scikit-Learn
obj.penalty = metadata["penalty"]
obj.dual = metadata["dual"]
obj.tol = metadata["tol"]
obj.C = metadata["C"]
obj.fit_intercept = metadata["fit_intercept"]
obj.intercept_scaling = metadata["intercept_scaling"]
obj.class_weight = metadata["class_weight"]
obj.random_state = metadata["random_state"]
obj.solver = metadata["solver"]
obj.max_iter = metadata["max_iter"]
obj.multi_class = metadata["multi_class"]
obj.verbose = metadata["verbose"]
obj.warm_start = metadata["warm_start"]
obj.n_jobs = metadata["n_jobs"]
obj.l1_ratio = metadata["l1_ratio"]
return obj
# pylint: enable=too-many-instance-attributes,invalid-name