concrete.ml.sklearn.glm.md
Last updated
Last updated
concrete.ml.sklearn.glm
Implement sklearn's Generalized Linear Models (GLM).
PoissonRegressor
A Poisson 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 PoissonRegressor please refer to the scikit-learn documentation: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.PoissonRegressor.html
__init__
property fhe_circuit
Get the FHE circuit.
The FHE circuit combines computational graph, mlir, client and server into a single object. More information available in Concrete documentation: https://docs.zama.ai/concrete/developer/terminology_and_structure#terminology Is None if the model is not fitted.
Returns:
Circuit
: The FHE circuit.
property is_compiled
Indicate if the model is compiled.
Returns:
bool
: If the model is compiled.
property is_fitted
Indicate if the model is fitted.
Returns:
bool
: If the model is fitted.
property onnx_model
Get the ONNX model.
Is None if the model is not fitted.
Returns:
onnx.ModelProto
: The ONNX model.
dump_dict
load_dict
post_processing
predict
GammaRegressor
A Gamma 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 GammaRegressor please refer to the scikit-learn documentation: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.GammaRegressor.html
__init__
property fhe_circuit
Get the FHE circuit.
The FHE circuit combines computational graph, mlir, client and server into a single object. More information available in Concrete documentation: https://docs.zama.ai/concrete/developer/terminology_and_structure#terminology Is None if the model is not fitted.
Returns:
Circuit
: The FHE circuit.
property is_compiled
Indicate if the model is compiled.
Returns:
bool
: If the model is compiled.
property is_fitted
Indicate if the model is fitted.
Returns:
bool
: If the model is fitted.
property onnx_model
Get the ONNX model.
Is None if the model is not fitted.
Returns:
onnx.ModelProto
: The ONNX model.
dump_dict
load_dict
post_processing
predict
TweedieRegressor
A Tweedie 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 TweedieRegressor please refer to the scikit-learn documentation: https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.TweedieRegressor.html
__init__
property fhe_circuit
Get the FHE circuit.
The FHE circuit combines computational graph, mlir, client and server into a single object. More information available in Concrete documentation: https://docs.zama.ai/concrete/developer/terminology_and_structure#terminology Is None if the model is not fitted.
Returns:
Circuit
: The FHE circuit.
property is_compiled
Indicate if the model is compiled.
Returns:
bool
: If the model is compiled.
property is_fitted
Indicate if the model is fitted.
Returns:
bool
: If the model is fitted.
property onnx_model
Get the ONNX model.
Is None if the model is not fitted.
Returns:
onnx.ModelProto
: The ONNX model.
dump_dict
load_dict
post_processing
predict