lale.lib.snapml.snap_logistic_regression module¶
- class lale.lib.snapml.snap_logistic_regression.SnapLogisticRegression(*, max_iter=100, regularizer=1.0, use_gpu=False, device_ids=None, class_weight=None, dual=True, verbose=False, n_jobs=1, penalty='l2', tol=0.001, generate_training_history=None, privacy=False, eta=0.3, batch_size=100, privacy_epsilon=10.0, grad_clip=1.0, fit_intercept=True, intercept_scaling=1.0, normalize=True, kernel='linear', gamma=1.0, n_components=100, random_state=None)¶
Bases:
PlannedIndividualOp
Logistic Regression from Snap ML.
This documentation is auto-generated from JSON schemas.
- Parameters
max_iter (integer, >=1, >=10 for optimizer, <=1000 for optimizer, optional, default 100) – Maximum number of iterations used by the solver to converge.
regularizer (float, >0.0, >=1.0 for optimizer, <=100.0 for optimizer, uniform distribution, optional, default 1.0) – Larger regularization values imply stronger regularization.
use_gpu (boolean, optional, not for optimizer, default False) – Use GPU Acceleration.
device_ids (union type, optional, not for optimizer, default None) –
Device IDs of the GPUs which will be used when GPU acceleration is enabled.
None
Use [0].
or array of items : integer
class_weight (‘balanced’ or None, optional, not for optimizer, default None) – If set to ‘balanced’ samples weights will be applied to account for class imbalance, otherwise no sample weights will be used.
dual (boolean, optional, not for optimizer, default True) –
Use dual formulation (rather than primal).
See also constraint-1, constraint-2.
verbose (boolean, optional, not for optimizer, default False) – If True, it prints the training cost, one per iteration. Warning: this will increase the training time. For performance evaluation, use verbose=False.
n_jobs (integer, >=1, optional, not for optimizer, default 1) – The number of threads used for running the training. The value of this parameter should be a multiple of 32 if the training is performed on GPU (use_gpu=True).
penalty (‘l1’ or ‘l2’, optional, not for optimizer, default ‘l2’) –
The regularization / penalty type. Possible values are ‘l2’ for L2 regularization (LogisticRegression) or ‘l1’ for L1 regularization (SparseLogisticRegression). L1 regularization is possible only for the primal optimization problem (dual=False).
See also constraint-1, constraint-3.
tol (float, >0.0, optional, not for optimizer, default 0.001) – The tolerance parameter. Training will finish when maximum change in model coefficients is less than tol.
generate_training_history (‘summary’, ‘full’, or None, optional, not for optimizer, default None) – Determines the level of summary statistics that are generated during training.
privacy (boolean, optional, not for optimizer, default False) –
Train the model using a differentially private algorithm.
See also constraint-2, constraint-3, constraint-4.
eta (float, >0.0, optional, not for optimizer, default 0.3) – Learning rate for the differentially private training algorithm.
batch_size (integer, >=1, optional, not for optimizer, default 100) – Mini-batch size for the differentially private training algorithm.
privacy_epsilon (float, >0.0, optional, not for optimizer, default 10.0) – Target privacy gaurantee. Learned model will be (privacy_epsilon, 0.01)-private.
grad_clip (float, >=0.0, optional, not for optimizer, default 1.0) – Gradient clipping parameter for the differentially private training algorithm.
fit_intercept (boolean, optional, always print, default True) –
Add bias term – note, may affect speed of convergence, especially for sparse datasets.
See also constraint-4.
intercept_scaling (float, >0.0, optional, not for optimizer, default 1.0) – Scaling of bias term. The inclusion of a bias term is implemented by appending an additional feature to the dataset. This feature has a constant value, that can be set using this parameter.
normalize (boolean, optional, not for optimizer, always print, default True) – Normalize rows of dataset (recommended for fast convergence).
kernel (‘rbf’ or ‘linear’, optional, not for optimizer, default ‘linear’) – Approximate feature map of a specified kernel function.
gamma (float, >0.0, optional, not for optimizer, default 1.0) – Parameter of RBF kernel: exp(-gamma * x^2).
n_components (integer, >=1, optional, not for optimizer, default 100) – Dimensionality of the feature space when approximating a kernel function.
random_state (union type, optional, not for optimizer, default None) –
Seed of pseudo-random number generator.
None
RandomState used by np.random
or integer
Explicit seed.
Notes
constraint-1 : union type
L1 regularization is supported only for primal optimization problems.
penalty : ‘l2’
or dual : False
constraint-2 : union type
Privacy only supported for primal objective functions.
privacy : False
or dual : False
constraint-3 : union type
Privacy only supported for L2-regularized objective functions.
privacy : False
or penalty : ‘l2’
constraint-4 : union type
Privacy not supported with fit_intercept=True.
privacy : False
or fit_intercept : False
- fit(X, y=None, **fit_params)¶
Train the operator.
Note: The fit method is not available until this operator is trainable.
Once this method is available, it will have the following signature:
- Parameters
X (array) –
The outer array is over samples aka rows.
items : array of items : float
The inner array is over features aka columns.
y (union type) –
The classes.
array of items : float
or array of items : string
or array of items : boolean
- predict(X, **predict_params)¶
Make predictions.
Note: The predict method is not available until this operator is trained.
Once this method is available, it will have the following signature:
- Parameters
X (array) –
The outer array is over samples aka rows.
items : array of items : float
The inner array is over features aka columns.
n_jobs (integer, >=0, optional, default 0) – Number of threads used to run inference. By default inference runs with maximum number of available threads.
- Returns
result – The predicted classes.
array of items : float
or array of items : string
or array of items : boolean
- Return type
union type
- predict_proba(X)¶
Probability estimates for all classes.
Note: The predict_proba method is not available until this operator is trained.
Once this method is available, it will have the following signature:
- Parameters
X (array, optional) –
The outer array is over samples aka rows.
items : array of items : float
The inner array is over features aka columns.
n_jobs (integer, >=0, optional, default 0) – Number of threads used to run inference. By default inference runs with maximum number of available threads.
- Returns
result – The outer array is over samples aka rows.
items : array of items : float
The inner array contains probabilities corresponding to each class.
- Return type
array