Objets des apprenants MicrosoftML
Description
Une instance des objets suivants est retournée par chaque fonction d’apprentissage. Ils héritent tous de la classe BaseLearner et implémentent des méthodes courantes.
get_algo_args
retourne les paramètres d’apprentissage,coef_
récupère les coefficients,summary_
retourne les informations d’apprentissage.
Le contenu change en fonction de l’apprenant formé.
BaseLearner de classe
microsoftml.modules.base_learner.BaseLearner(**kwargs)
Classe de base pour tous les apprenants.
coef_
Obtient les coefficients de modèle.
fit(formula: str, data: [revoscalepy.datasource.RxDataSource.RxDataSource,
pandas.core.frame.DataFrame], ml_transforms: list = None,
ml_transform_vars: list = None, row_selection: str = None,
transforms: dict = None, transform_objects: dict = None,
transform_function: str = None,
transform_variables: list = None,
transform_packages: list = None,
transform_environment: dict = None, blocks_per_read: int = None,
report_progress: int = None, verbose: int = 1,
compute_context: revoscalepy.computecontext.RxComputeContext.RxComputeContext = None,
**kargs)
Ajuste le modèle.
get_algo_args()
Obtient les arguments de l’algorithme.
predict(*args, **kwargs)
Appelle microsoftml.rx_predict()
.
summary_
Obtenir le résumé du modèle.
Apprenants spécifiques
microsoftml.FastTrees(method: ['binary', 'regression'] = 'binary',
num_trees: int = 100, num_leaves: int = 20,
learning_rate: float = 0.2, min_split: int = 10,
example_fraction: float = 0.7, feature_fraction: float = 1,
split_fraction: float = 1, num_bins: int = 255,
first_use_penalty: float = 0, gain_conf_level: float = 0,
unbalanced_sets: bool = False, train_threads: int = 8,
random_seed: int = None,
ensemble: microsoftml.modules.ensemble.EnsembleControl = None,
**kargs)
Modèle binaire ou de régression FastTree
get_train_node(**all_args)
Obtient le nœud de formation
microsoftml.OneClassSvm(cache_size: float = 100,
kernel: [<function linear_kernel at 0x0000007156EAC8C8>,
<function polynomial_kernel at 0x0000007156EAC950>,
<function rbf_kernel at 0x0000007156EAC7B8>,
<function sigmoid_kernel at 0x0000007156EACA60>] = {'Name': 'RbfKernel',
'Settings': {}}, epsilon: float = 0.001, nu: float = 0.1,
shrink: bool = True, normalize: ['No', 'Warn', 'Auto',
'Yes'] = 'Auto',
ensemble: microsoftml.modules.ensemble.EnsembleControl = None,
**kargs)
one-class svm
get_train_node(**all_args)
Obtient le nœud de formation
microsoftml.FastForest(method: ['binary', 'regression'] = 'binary',
num_trees: int = 100, num_leaves: int = 20,
min_split: int = 10, example_fraction: float = 0.7,
feature_fraction: float = 0.7, split_fraction: float = 0.7,
num_bins: int = 255, first_use_penalty: float = 0,
gain_conf_level: float = 0, train_threads: int = 8,
random_seed: int = None,
ensemble: microsoftml.modules.ensemble.EnsembleControl = None,
**kargs)
Modèle binaire ou de régression FastForest
get_train_node(**all_args)
Obtient le nœud de formation
microsoftml.FastLinear(method: ['binary', 'regression'] = 'binary',
loss_function: {'binary': [<function hinge_loss at 0x0000007156E8EA60>,
<function log_loss at 0x0000007156E8E6A8>,
<function smoothed_hinge_loss at 0x0000007156E8EAE8>],
'regression': [<function squared_loss at 0x0000007156E8E950>]} = None,
l2_weight: float = None, l1_weight: float = None,
train_threads: int = None, convergence_tolerance: float = 0.1,
max_iterations: int = None, shuffle: bool = True,
check_frequency: int = None, normalize: ['No', 'Warn', 'Auto',
'Yes'] = 'Auto',
ensemble: microsoftml.modules.ensemble.EnsembleControl = None,
**kargs)
Modèle binaire ou de régression SDCA
get_train_node(**all_args)
Obtient le nœud de formation
microsoftml.LogisticRegression(method: ['binary',
'multiClass'] = 'binary', l2_weight: float = 1,
l1_weight: float = 1, opt_tol: float = 1e-07,
memory_size: int = 20, init_wts_diameter: float = 0,
max_iterations: int = 2147483647,
show_training_stats: bool = False, sgd_init_tol: float = 0,
train_threads: int = None, dense_optimizer: bool = False,
normalize: ['No', 'Warn', 'Auto', 'Yes'] = 'Auto',
ensemble: microsoftml.modules.ensemble.EnsembleControl = None,
**kargs)
Régression logique
aic(k=2)
obtient l’AIC de modèle
coef_
obtient les coefficients de modèle
deviance_
obtient la déviance résiduelle
get_algo_args()
obtient les arguments de l’algorithme
get_train_node(**all_args)
Obtient le nœud de formation
microsoftml.NeuralNetwork(method: ['binary', 'multiClass',
'regression'] = 'binary', num_hidden_nodes: int = 100,
num_iterations: int = 100, optimizer: ['adadelta_optimizer',
'sgd_optimizer'] = {'Name': 'SgdOptimizer', 'Settings': {}},
net_definition: str = None, init_wts_diameter: float = 0.1,
max_norm: float = 0, acceleration: ['avx_math', 'clr_math',
'gpu_math', 'mkl_math', 'sse_math'] = {'Name': 'AvxMath',
'Settings': {}}, mini_batch_size: int = 1, normalize: ['No',
'Warn', 'Auto', 'Yes'] = 'Auto',
ensemble: microsoftml.modules.ensemble.EnsembleControl = None,
**kargs)
réseau neuronal
get_train_node(**all_args)
Obtient le nœud de formation
microsoftml.OneClassSvm(cache_size: float = 100,
kernel: [<function linear_kernel at 0x0000007156EAC8C8>,
<function polynomial_kernel at 0x0000007156EAC950>,
<function rbf_kernel at 0x0000007156EAC7B8>,
<function sigmoid_kernel at 0x0000007156EACA60>] = {'Name': 'RbfKernel',
'Settings': {}}, epsilon: float = 0.001, nu: float = 0.1,
shrink: bool = True, normalize: ['No', 'Warn', 'Auto',
'Yes'] = 'Auto',
ensemble: microsoftml.modules.ensemble.EnsembleControl = None,
**kargs)
one-class svm
get_train_node(**all_args)
Obtient le nœud de formation
Voir aussi
rx_fast_forest
, rx_fast_trees
, rx_fast_linear
, rx_logistic_regression
, rx_neural_network
, rx_oneclass_svm
, rx_predict
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