concrete.ml.sklearn.base
Last updated
Last updated
concrete.ml.sklearn.base
Module that contains base classes for our libraries estimators.
DEFAULT_P_ERROR_PBS
OPSET_VERSION_FOR_ONNX_EXPORT
QuantizedTorchEstimatorMixin
Mixin that provides quantization for a torch module and follows the Estimator API.
This class should be mixed in with another that provides the full Estimator API. This class only provides modifiers for .fit() (with quantization) and .predict() (optionally in FHE)
__init__
property base_estimator_type
Get the sklearn estimator that should be trained by the child class.
property base_module_to_compile
Get the Torch module that should be compiled to FHE.
property fhe_circuit
Get the FHE circuit.
Returns:
Circuit
: the FHE circuit
property input_quantizers
Get the input quantizers.
Returns:
List[Quantizer]
: the input quantizers
property n_bits_quant
Get the number of quantization bits.
property onnx_model
Get the ONNX model.
.. # noqa: DAR201
Returns:
_onnx_model_
(onnx.ModelProto): the ONNX model
property output_quantizers
Get the input quantizers.
Returns:
List[QuantizedArray]
: the input quantizers
property quantize_input
Get the input quantization function.
Returns:
Callable
: function that quantizes the input
compile
Compile the model.
Args:
X
(numpy.ndarray): the dequantized dataset
configuration
(Optional[Configuration]): the options for compilation
compilation_artifacts
(Optional[DebugArtifacts]): artifacts object to fill during compilation
show_mlir
(bool): whether or not to show MLIR during the compilation
use_virtual_lib
(bool): whether to compile using the virtual library that allows higher bitwidths
p_error
(Optional[float]): probability of error of a PBS
Returns:
Circuit
: the compiled Circuit.
Raises:
ValueError
: if called before the model is trained
fit
Initialize and fit the module.
If the module was already initialized, by calling fit, the module will be re-initialized (unless warm_start
is True). In addition to the torch training step, this method performs quantization of the trained torch model.
Args:
X
: training data By default, you should be able to pass: * numpy arrays * torch tensors * pandas DataFrame or Series
y
(numpy.ndarray): labels associated with training data
**fit_params
: additional parameters that can be used during training, these are passed to the torch training interface
Returns:
self
: the trained quantized estimator
fit_benchmark
Fit the quantized estimator and return reference estimator.
This function returns both the quantized estimator (itself), but also a wrapper around the non-quantized trained NN. This is useful in order to compare performance between the quantized and fp32 versions of the classifier
Args:
X
: training data By default, you should be able to pass: * numpy arrays * torch tensors * pandas DataFrame or Series
y
(numpy.ndarray): labels associated with training data
*args
: The arguments to pass to the sklearn linear model.
**kwargs
: The keyword arguments to pass to the sklearn linear model.
Returns:
self
: the trained quantized estimator
fp32_model
: trained raw (fp32) wrapped NN estimator
get_params_for_benchmark
Get the parameters to instantiate the sklearn estimator trained by the child class.
Returns:
params
(dict): dictionary with parameters that will initialize a new Estimator
post_processing
Post-processing the output.
Args:
y_preds
(numpy.ndarray): the output to post-process
Raises:
ValueError
: if unknown post-processing function
Returns:
numpy.ndarray
: the post-processed output
predict
Predict on user provided data.
Predicts using the quantized clear or FHE classifier
Args:
X
: input data, a numpy array of raw values (non quantized)
execute_in_fhe
: whether to execute the inference in FHE or in the clear
Returns:
y_pred
: numpy ndarray with predictions
predict_proba
Predict on user provided data, returning probabilities.
Predicts using the quantized clear or FHE classifier
Args:
X
: input data, a numpy array of raw values (non quantized)
execute_in_fhe
: whether to execute the inference in FHE or in the clear
Returns:
y_pred
: numpy ndarray with probabilities (if applicable)
Raises:
ValueError
: if the estimator was not yet trained or compiled
BaseTreeEstimatorMixin
Mixin class for tree-based estimators.
A place to share methods that are used on all tree-based estimators.
__init__
Initialize the TreeBasedEstimatorMixin.
Args:
n_bits
(int): number of bits used for quantization
property onnx_model
Get the ONNX model.
.. # noqa: DAR201
Returns:
onnx.ModelProto
: the ONNX model
compile
Compile the model.
Args:
X
(numpy.ndarray): the dequantized dataset
configuration
(Optional[Configuration]): the options for compilation
compilation_artifacts
(Optional[DebugArtifacts]): artifacts object to fill during compilation
show_mlir
(bool): whether or not to show MLIR during the compilation
use_virtual_lib
(bool): set to True to use the so called virtual lib simulating FHE computation. Defaults to False
p_error
(Optional[float]): probability of error of a PBS
Returns:
Circuit
: the compiled Circuit.
dequantize_output
Dequantize the integer predictions.
Args:
y_preds
(numpy.ndarray): the predictions
Returns: the dequantized predictions
fit_benchmark
Fit the sklearn tree-based model and the FHE tree-based model.
Args:
X
(numpy.ndarray): The input data.
y
(numpy.ndarray): The target data. random_state (Optional[Union[int, numpy.random.RandomState, None]]): The random state. Defaults to None.
*args
: args for super().fit
**kwargs
: kwargs for super().fit
Returns: Tuple[ConcreteEstimators, SklearnEstimators]: The FHE and sklearn tree-based models.
quantize_input
Quantize the input.
Args:
X
(numpy.ndarray): the input
Returns: the quantized input
BaseTreeRegressorMixin
Mixin class for tree-based regressors.
A place to share methods that are used on all tree-based regressors.
__init__
Initialize the TreeBasedEstimatorMixin.
Args:
n_bits
(int): number of bits used for quantization
property onnx_model
Get the ONNX model.
.. # noqa: DAR201
Returns:
onnx.ModelProto
: the ONNX model
compile
Compile the model.
Args:
X
(numpy.ndarray): the dequantized dataset
configuration
(Optional[Configuration]): the options for compilation
compilation_artifacts
(Optional[DebugArtifacts]): artifacts object to fill during compilation
show_mlir
(bool): whether or not to show MLIR during the compilation
use_virtual_lib
(bool): set to True to use the so called virtual lib simulating FHE computation. Defaults to False
p_error
(Optional[float]): probability of error of a PBS
Returns:
Circuit
: the compiled Circuit.
dequantize_output
Dequantize the integer predictions.
Args:
y_preds
(numpy.ndarray): the predictions
Returns: the dequantized predictions
fit
Fit the tree-based estimator.
Args:
X
: training data By default, you should be able to pass: * numpy arrays * torch tensors * pandas DataFrame or Series
y
(numpy.ndarray): The target data.
**kwargs
: args for super().fit
Returns:
Any
: The fitted model.
fit_benchmark
Fit the sklearn tree-based model and the FHE tree-based model.
Args:
X
(numpy.ndarray): The input data.
y
(numpy.ndarray): The target data. random_state (Optional[Union[int, numpy.random.RandomState, None]]): The random state. Defaults to None.
*args
: args for super().fit
**kwargs
: kwargs for super().fit
Returns: Tuple[ConcreteEstimators, SklearnEstimators]: The FHE and sklearn tree-based models.
post_processing
Apply post-processing to the predictions.
Args:
y_preds
(numpy.ndarray): The predictions.
Returns:
numpy.ndarray
: The post-processed predictions.
predict
Predict the probability.
Args:
X
(numpy.ndarray): The input data.
execute_in_fhe
(bool): Whether to execute in FHE. Defaults to False.
Returns:
numpy.ndarray
: The predicted probabilities.
quantize_input
Quantize the input.
Args:
X
(numpy.ndarray): the input
Returns: the quantized input
BaseTreeClassifierMixin
Mixin class for tree-based classifiers.
A place to share methods that are used on all tree-based classifiers.
__init__
Initialize the TreeBasedEstimatorMixin.
Args:
n_bits
(int): number of bits used for quantization
property onnx_model
Get the ONNX model.
.. # noqa: DAR201
Returns:
onnx.ModelProto
: the ONNX model
compile
Compile the model.
Args:
X
(numpy.ndarray): the dequantized dataset
configuration
(Optional[Configuration]): the options for compilation
compilation_artifacts
(Optional[DebugArtifacts]): artifacts object to fill during compilation
show_mlir
(bool): whether or not to show MLIR during the compilation
use_virtual_lib
(bool): set to True to use the so called virtual lib simulating FHE computation. Defaults to False
p_error
(Optional[float]): probability of error of a PBS
Returns:
Circuit
: the compiled Circuit.
dequantize_output
Dequantize the integer predictions.
Args:
y_preds
(numpy.ndarray): the predictions
Returns: the dequantized predictions
fit
Fit the tree-based estimator.
Args:
X
: training data By default, you should be able to pass: * numpy arrays * torch tensors * pandas DataFrame or Series
y
(numpy.ndarray): The target data.
**kwargs
: args for super().fit
Returns:
Any
: The fitted model.
fit_benchmark
Fit the sklearn tree-based model and the FHE tree-based model.
Args:
X
(numpy.ndarray): The input data.
y
(numpy.ndarray): The target data. random_state (Optional[Union[int, numpy.random.RandomState, None]]): The random state. Defaults to None.
*args
: args for super().fit
**kwargs
: kwargs for super().fit
Returns: Tuple[ConcreteEstimators, SklearnEstimators]: The FHE and sklearn tree-based models.
post_processing
Apply post-processing to the predictions.
Args:
y_preds
(numpy.ndarray): The predictions.
Returns:
numpy.ndarray
: The post-processed predictions.
predict
Predict the class with highest probability.
Args:
X
(numpy.ndarray): The input data.
execute_in_fhe
(bool): Whether to execute in FHE. Defaults to False.
Returns:
numpy.ndarray
: The predicted target values.
predict_proba
Predict the probability.
Args:
X
(numpy.ndarray): The input data.
execute_in_fhe
(bool): Whether to execute in FHE. Defaults to False.
Returns:
numpy.ndarray
: The predicted probabilities.
quantize_input
Quantize the input.
Args:
X
(numpy.ndarray): the input
Returns: the quantized input
SklearnLinearModelMixin
A Mixin class for sklearn linear models with FHE.
__init__
Initialize the FHE linear model.
Args:
n_bits
(int, Dict): Number of bits to quantize the model. If an int is passed for n_bits, the value will be used for activation, inputs and weights. If a dict is passed, then it should contain "model_inputs", "op_inputs", "op_weights" and "model_outputs" keys with corresponding number of quantization bits for: - model_inputs : number of bits for model input - op_inputs : number of bits to quantize layer input values - op_weights: learned parameters or constants in the network - model_outputs: final model output quantization bits Default to 2.
*args
: The arguments to pass to the sklearn linear model.
**kwargs
: The keyword arguments to pass to the sklearn linear model.
property fhe_circuit
Get the FHE circuit.
Returns:
Circuit
: the FHE circuit
property input_quantizers
Get the input quantizers.
Returns:
List[QuantizedArray]
: the input quantizers
property onnx_model
Get the ONNX model.
.. # noqa: DAR201
Returns:
onnx.ModelProto
: the ONNX model
property output_quantizers
Get the input quantizers.
Returns:
List[QuantizedArray]
: the input quantizers
property quantize_input
Get the input quantization function.
Returns:
Callable
: function that quantizes the input
clean_graph
Clean the graph of the onnx model.
This will remove the Cast node in the model's onnx.graph since they have no use in quantized or FHE models.
compile
Compile the FHE linear model.
Args:
X
(numpy.ndarray): The input data.
configuration
(Optional[Configuration]): Configuration object to use during compilation
compilation_artifacts
(Optional[DebugArtifacts]): Artifacts object to fill during compilation
show_mlir
(bool): if set, the MLIR produced by the converter and which is going to be sent to the compiler backend is shown on the screen, e.g., for debugging or demo. Defaults to False.
use_virtual_lib
(bool): whether to compile using the virtual library that allows higher bitwidths with simulated FHE computation. Defaults to False
p_error
(Optional[float]): probability of error of a PBS
Returns:
Circuit
: the compiled Circuit.
fit
Fit the FHE linear model.
Args:
X
: training data By default, you should be able to pass: * numpy arrays * torch tensors * pandas DataFrame or Series
y
(numpy.ndarray): The target data.
*args
: The arguments to pass to the sklearn linear model.
**kwargs
: The keyword arguments to pass to the sklearn linear model.
Returns: Any
fit_benchmark
Fit the sklearn linear model and the FHE linear model.
Args:
X
(numpy.ndarray): The input data.
y
(numpy.ndarray): The target data. random_state (Optional[Union[int, numpy.random.RandomState, None]]): The random state. Defaults to None.
*args
: The arguments to pass to the sklearn linear model. or not (False). Default to False.
*args
: args for super().fit
**kwargs
: kwargs for super().fit
Returns: Tuple[SklearnLinearModelMixin, sklearn.linear_model.LinearRegression]: The FHE and sklearn LinearRegression.
post_processing
Post-processing the output.
Args:
y_preds
(numpy.ndarray): the output to post-process
Returns:
numpy.ndarray
: the post-processed output
predict
Predict on user data.
Predict on user data using either the quantized clear model, implemented with tensors, or, if execute_in_fhe is set, using the compiled FHE circuit
Args:
X
(numpy.ndarray): the input data
execute_in_fhe
(bool): whether to execute the inference in FHE
Returns:
numpy.ndarray
: the prediction as ordinals
SklearnLinearClassifierMixin
A Mixin class for sklearn linear classifiers with FHE.
__init__
Initialize the FHE linear model.
Args:
n_bits
(int, Dict): Number of bits to quantize the model. If an int is passed for n_bits, the value will be used for activation, inputs and weights. If a dict is passed, then it should contain "model_inputs", "op_inputs", "op_weights" and "model_outputs" keys with corresponding number of quantization bits for: - model_inputs : number of bits for model input - op_inputs : number of bits to quantize layer input values - op_weights: learned parameters or constants in the network - model_outputs: final model output quantization bits Default to 2.
*args
: The arguments to pass to the sklearn linear model.
**kwargs
: The keyword arguments to pass to the sklearn linear model.
property fhe_circuit
Get the FHE circuit.
Returns:
Circuit
: the FHE circuit
property input_quantizers
Get the input quantizers.
Returns:
List[QuantizedArray]
: the input quantizers
property onnx_model
Get the ONNX model.
.. # noqa: DAR201
Returns:
onnx.ModelProto
: the ONNX model
property output_quantizers
Get the input quantizers.
Returns:
List[QuantizedArray]
: the input quantizers
property quantize_input
Get the input quantization function.
Returns:
Callable
: function that quantizes the input
clean_graph
Clean the graph of the onnx model.
Any operators following gemm, including the sigmoid, softmax and argmax operators, are removed from the graph. They will be executed in clear in the post-processing method.
compile
Compile the FHE linear model.
Args:
X
(numpy.ndarray): The input data.
configuration
(Optional[Configuration]): Configuration object to use during compilation
compilation_artifacts
(Optional[DebugArtifacts]): Artifacts object to fill during compilation
show_mlir
(bool): if set, the MLIR produced by the converter and which is going to be sent to the compiler backend is shown on the screen, e.g., for debugging or demo. Defaults to False.
use_virtual_lib
(bool): whether to compile using the virtual library that allows higher bitwidths with simulated FHE computation. Defaults to False