Model Dictionary Templates

This page includes templates for defining models supported by the pyMAISE.Tuner and pyMAISE.PostProcessor. The parameters of the dictionaries are set to their defaults.

Classical Model Templates

Regression

Linear Regressor

"Linear": {
     "fit_intercept" = True,
     "copy_X" = True,
     "n_jobs" = None,
     "positive" = False,
}

Lasso Regressor

"Lasso": {
     "alpha" = 1.0,
     "fit_intercept" = True,
     "precompute" = False,
     "copy_X" = True,
     "max_iter" = 1000,
     "tol" = 1e-4,
     "warm_start" = False,
     "positive" = False,
     "selection" = "cyclic",
}

Ridge Regressor

"RD": {
    "alpha" = 1.0,
    "fit_intercept" = True,
    "copy_X" = True,
    "max_iter" = None,
    "tol" = 1e-3,
    "solver" = "auto",
    "positive" = False,
}

ElasticNet Regressor

"EN": {
    "alpha" = 1.0,
    "l1_ratio" = 0.5,
    "fit_intercept" = True,
    "precompute" = False,
    "max_iter" = 1000,
    "copy_X" = True,
    "tol" = 1e-3,
    "warm_start" = False,
    "positive" = False,
    "selection" = "cyclic",
}

Support Vector Machine Regressor

"SVM": {
     "kernel" = "rbf",
     "degree" = 3,
     "gamma" = "scale",
     "coef0" = 0.0,
     "tol" = 1e-3,
     "C" = 1.0,
     "epsilon" = 0.1,
     "shrinking" = True,
     "cache_size" = 200,
     "max_iter" = -1,
}

Decision Tree Regressor

"DT": {
     "criterion" = "squared_error",
     "splitter" = "best",
     "max_depth" = None,
     "min_samples_split" = 2,
     "min_samples_leaf" = 1,
     "min_weight_fraction_leaf" = 0.0,
     "max_features" = None,
     "max_leaf_nodes" = None,
     "min_impurity_decrease" = 0.0,
     "ccp_alpha" = 0.0,
}

Random Forest Regressor

"RF": {
     "n_estimators" = 100,
     "criterion" = "squared_error",
     "max_depth" = None,
     "min_samples_split" = 2,
     "min_samples_leaf" = 1,
     "min_weight_fraction_leaf" = 0.0,
     "max_features" = None,
     "max_leaf_nodes" = None,
     "min_impurity_decrease" = 0.0,
     "bootstrap" = True,
     "oob_score" = False,
     "n_jobs" = None,
     "warm_start" = False,
     "ccp_alpha" = 0.0,
     "max_samples" = None,
}

ExtraTrees Regressor

"ET": {
    "n_estimators" = 100,
    "criterion" = "squared_error",
    "max_depth" = None,
    "min_samples_split" = 2,
    "min_samples_leaf" = 1,
    "min_weight_fraction_leaf" = 0.0,
    "max_features" = 1.0,
    "max_leaf_nodes" = None,
    "min_impurity_decrease" = 0.0,
    "bootstrap" = False,
    "oob_score" = False,
    "n_jobs" = None,
    "verbose" = 0,
    "warm_start" = False,
    "ccp_alpha" = 0.0,
    "max_samples" = None,
}

AdaBoost Regressor

"AB": {
    "estimator" = None,
    "n_estimators" = 50,
    "learning_rate" = 1.0,
    "loss" = "linear",
    "multi_output" = False,
}

Gradient Boosting Regressor

"GB": {
    "loss" = "squared_error",
    "learning_rate" = 0.1,
    "n_estimators" = 100,
    "subsample" = 1.0,
    "criterion" = "friedman_mse",
    "min_samples_split" = 2,
    "min_samples_leaf" = 1,
    "min_weight_fraction_leaf" = 0.0,
    "max_depth" = 3,
    "min_impurity_decrease" = 0.0,
    "init" = None,
    "max_features" = None,
    "alpha" = 0.9,
    "verbose" = 0,
    "max_leaf_nodes" = None,
    "warm_start" = False,
    "validation_fraction" = 0.1,
    "n_iter_no_change" = None,
    "tol" = 1e-3,
    "multi_output" = False,
}

K-Nearest Neighbors Regressor

"KN": {
     "n_neighbors" = 5,
     "weights" = "uniform",
     "algorithm" = "auto",
     "leaf_size" = 30,
     "p" = 2,
     "metric" = "minkowski",
     "metric_params" = None,
     "n_jobs" = None,
}

GaussianProcess Regressor

"GP": {
    "kernel" = None,
    "alpha" = 1e-10,
    "optimizer" = "fmin_l_bfgs_b",
    "n_restarts_optimizer" = 0,
    "normalize_y" = False,
    "copy_X_train" = True,
    "n_targets" = None,
}

Multi Output Regressor

"MultiOutput": {
    "estimators" = None,
    "n_jobs" = None,
}

Stacking Regressor

"Stacking": {
    "estimators" = None,
    "final_estimator" = RidgeRegression,
    "cv": 5,
    "n_jobs": 5,
    "passthrough": False,
    "verbose": 0,
    "multi_output": False,
}

Classification

Logistic Regression

"Logistic": {
     "penalty": "l2",
     "dual": False,
     "tol": 1e-4,
     "C": 1.0,
     "fit_intercept": True,
     "intercept_scaling": 1,
     "class_weight": None,
     "solver": "lbfgs",
     "max_iter": 100,
     "multi_class": "auto",
     "verbose": 0,
     "warm_start": False,
     "n_jobs": None,
     "l1_ratio": None,
}

Support Vector Machine Classifier

"SVM": {
     "C": 1.0,
     "kernel": "rbf",
     "degree": 3,
     "gamma": "scale",
     "coef0": 0.0,
     "shrinking": True,
     "probability": False,
     "tol": 1e-3,
     "cache_size": 200,
     "class_weight": None,
     "verbose": False,
     "max_iter": -1,
     "decision_function_shape": "ovr",
     "break_ties": False,
}

Decision Tree Classifier

"DT": {
     "criterion": "gini",
     "spitter": "best",
     "max_depth": None,
     "min_samples_split": 2,
     "min_samples_leaf": 1,
     "min_weight_fraction_leaf": 0.0,
     "max_features": None,
     "max_leaf_nodes": None,
     "min_impurity_decrease": 0.0,
     "class_weight": None,
     "ccp_alpha": 0.0,
}

Random Forest Classifier

"RF": {
     "n_estimators": 100,
     "criterion": "gini",
     "max_depth": None,
     "min_samples_split": 2,
     "min_samples_leaf": 1,
     "min_weight_fraction_leaf": 0.0,
     "max_features": "sqrt",
     "max_leaf_nodes": None,
     "min_impurity_decrease": 0.0,
     "bootstrap": True,
     "oob_score": False,
     "n_jobs": False,
     "warm_start": False,
     "class_weight": None,
     "ccp_alpha": 0.0,
     "max_samples": None,
}

ExtraTrees Classifier

"ExtraTreesClassifier": {
    "n_estimators" = 100,
    "criterion" = "gini",
    "max_depth" = None,
    "min_samples_split" = 2,
    "min_samples_leaf" = 1,
    "min_weight_fraction_leaf" = 0.0,
    "max_features" = 1.0,
    "max_leaf_nodes" = None,
    "min_impurity_decrease" = 0.0,
    "bootstrap" = False,
    "oob_score" = False,
    "n_jobs" = None,
    "verbose" = 0,
    "warm_start" = False,
    "ccp_alpha" = 0.0,
    "max_samples" = None,
    "class_weight" = None,
}

AdaBoost Classifer

"AB": {
    "estimator" = None,
    "n_estimators" = 50,
    "learning_rate" = 1.0,
    "algorithm" = "SAMME.R",
    "multi_output" = False,
}

GradientBoosting Classifier

"GB": {
    "loss" = "log_loss",
    "learning_rate" = 0.1,
    "n_estimators" = 100,
    "subsample" = 1.0,
    "criterion" = "friedman_mse",
    "min_samples_split" = 2,
    "min_samples_leaf" = 1,
    "min_weight_fraction_leaf" = 0.0,
    "max_depth" = 3,
    "min_impurity_decrease" = 0.0,
    "init" = None,
    "max_features" = None,
    "verbose" = 0,
    "max_leaf_nodes" = None,
    "warm_start" = False,
    "validation_fraction" = 0.1,
    "n_iter_no_change" = None,
    "tol" = 1e-3,
    "multi_output" = False,
}

K-Nearest Neighbors Classifier

"KN": {
     "n_neighbors": 5,
     "weights": "uniform",
     "algorithm": "auto",
     "leaf_size": 30,
     "p": 2,
     "metric": "minkowski",
     "metric_params": None,
     "n_jobs": None,
}

GaussianProcess Classifier

"GP": {
    "kernel" = None,
    "optimizer" = "fmin_l_bfgs_b",
    "n_restarts_optimizer" = 0,
    "copy_X_train" = True,
    "random_state" = settings.values.random_state,
    "max_iter_predict" = 100,
    "warm_start" = False,
    "multi_class" = "one_vs_rest",
    "n_jobs" = None,
}

Multi Output Classifer

"MultiOutput": {
    "estimators" = None,
    "n_jobs" = None,
}

Stacking Classifer

"Stacking": {
    "estimators" = None,
    "final_estimator" = LogisticRegression,
    "cv": 5,
    "n_jobs": 5,
    "passthrough": False,
    "verbose": 0,
    "multi_output": False,
}

Neural Network Templates

Layers

Dense

"Dense": {
    "units": ,
    "activation": None,
    "use_bias": True,
    "kernel_initializer": "glorot_uniform",
    "bias_initializer": "zeros",
    "kernel_regularizer": None,
    "bias_regularizer": None,
    "activity_regularizer": None,
    "kernel_constraint": None,
    "bias_constraint": None,
}

Dropout

"Dropout": {
    "rate": ,
    "noise_shape": None,
}

LSTM

"LSTM": {
    "units": ,
    "activation": "tanh",
    "recurrent_activation": "sigmoid",
    "use_bias": True,
    "kernel_initializer": "glorot_uniform",
    "recurrent_initializer": "orthogonal",
    "bias_initializer": "zeros",
    "unit_forget_bias": True,
    "kernel_regularizer": None,
    "recurrent_regularizer": None,
    "bias_regularizer": None,
    "activity_regularizer": None,
    "kernel_constraint": None,
    "recurrent_constraint": None,
    "bias_constraint": None,
    "dropout": 0.0,
    "recurrent_dropout": 0.0,
    "return_sequences": False,
    "return_state": False,
    "go_backwards": False,
    "stateful": False,
    "time_major": False,
    "unroll": False,
}

GRU

"GRU": {
    "units": ,
    "activation": "tanh",
    "recurrent_activation": "sigmoid",
    "use_bias": True,
    "kernel_initializer": "glorot_uniform",
    "recurrent_initializer": "orthogonal",
    "bias_initializer": "zeros",
    "kernel_regularizer": None,
    "recurrent_regularizer": None,
    "bias_regularizer": None,
    "activity_regularizer": None,
    "kernel_constraint": None,
    "recurrent_constraint": None,
    "bias_constraint": None,
    "dropout": 0.0,
    "recurrent_dropout": 0.0,
    "return_sequences": False,
    "return_state": False,
    "go_backwards": False,
    "stateful": False,
    "time_major": False,
    "unroll": False,
    "reset_after": True,
}

Conv1D

"Conv1D": {
    "filters": ,
    "kernel_size": ,
    "strides": 1,
    "padding": "valid",
    "data_format": "channels_last",
    "dilation_rate": 1,
    "groups": 1,
    "activation": "None",
    "use_bias": True,
    "kernel_initializer": "glorot_uniform",
    "bias_initializer": "zeros",
    "kernel_regularizer": None,
    "bias_regularizer": None,
    "activity_regularizer": None,
    "kernel_constraint": None,
    "bias_constraint": None,
}

Conv2D

"Conv2D": {
    "filters": ,
    "kernel_size": ,
    "strides": (1, 1),
    "padding": "valid",
    "data_format": None,
    "dilation_rate": (1, 1),
    "groups": 1,
    "activation": "None",
    "use_bias": True,
    "kernel_initializer": "glorot_uniform",
    "bias_initializer": "zeros",
    "kernel_regularizer": None,
    "bias_regularizer": None,
    "activity_regularizer": None,
    "kernel_constraint": None,
    "bias_constraint": None,
    "input_shape": None,
}

Conv3D

"Conv3D": {
    "filters": ,
    "kernel_size": ,
    "strides": (1, 1, 1),
    "padding": "valid",
    "data_format": None,
    "dilation_rate": (1, 1, 1),
    "groups": 1,
    "activation": "None",
    "use_bias": True,
    "kernel_initializer": "glorot_uniform",
    "bias_initializer": "zeros",
    "kernel_regularizer": None,
    "bias_regularizer": None,
    "activity_regularizer": None,
    "kernel_constraint": None,
    "bias_constraint": None,
}

MaxPooling1D

"MaxPooling1D": {
    "pool_size": 2,
    "strides": None,
    "padding": "valid",
    "data_format": "channels_last",
}

MaxPooling2D

"MaxPooling2D": {
    "pool_size": (2, 2),
    "strides": None,
    "padding": "valid",
    "data_format": None,
}

MaxPooling3D

"MaxPooling3D": {
    "pool_size": (2, 2, 2),
    "strides": None,
    "padding": "valid",
    "data_format": None,
}

Flatten

"Flatten": {
    "data_format": None,
}

Reshape

"Reshape": {
    "target_shape": None,
}

Optimizers

SGD

"SGD": {
    "learning_rate": 0.01,
    "momentum": 0.0,
    "nesterov": False,
    "weight_decay": None,
    "clipnorm": None,
    "clipvalue": None,
    "global_clipnorm": None,
    "use_ema": False,
    "ema_momentum": 0.99,
    "ema_overwrite_frequency": None,
}

RMSprop

"RMSprop": {
    "learning_rate": 0.001,
    "rho": 0.9,
    "momentum": 0.0,
    "epsilon": 1e-07,
    "centered": False,
    "weight_decay": None,
    "clipnorm": None,
    "clipvalue": None,
    "global_clipnorm": None,
    "use_ema": False,
    "ema_momentum": 0.99,
    "ema_overwrite_frequency": 100,
}

Adam

"Adam": {
    "learning_rate": 0.001,
    "beta_1": 0.9,
    "beta_2": 0.999,
    "epsilon": 1e-07,
    "amsgrad": False,
    "weight_decay": None,
    "clipnorm": None,
    "clipvalue": None,
    "global_clipnorm": None,
    "use_ema": False,
    "ema_momentum": 0.99,
    "ema_overwrite_frequency": None,
}

AdamW

"AdamW": {
    "learning_rate": 0.001,
    "weight_decay": 0.004,
    "beta_1": 0.9,
    "beta_2": 0.999,
    "epsilon": 1e-07,
    "amsgrad": False,
    "clipnorm": None,
    "clipvalue": None,
    "global_clipnorm": None,
    "use_ema": False,
    "ema_momentum": 0.99,
    "ema_overwrite_frequency": None,
}

Adadelta

"Adadelta": {
    "learning_rate": 0.001,
    "rho": 0.95,
    "epsilon": 1e-7,
    "weight_decay": None,
    "clipnorm": None,
    "clipvalue": None,
    "global_clipnorm": None,
    "use_ema": False,
    "ema_momentum": 0.99,
    "ema_overwrite_frequency": None,
}

Adagrad

"Adagrad": {
    "learning_rate": 0.001,
    "initial_accumulator_value": 0.1,
    "epsilon": 1e-07,
    "weight_decay": None,
    "clipnorm": None,
    "clipvalue": None,
    "global_clipnorm": None,
    "use_ema": False,
    "ema_momentum": 0.99,
    "ema_overwrite_frequency": None,
}

Adamax

"Adamax": {
    "learning_rate": 0.001,
    "beta_1": 0.9,
    "beta_2": 0.999,
    "epsilon": 1e-07,
    "weight_decay": None,
    "clipnorm": None,
    "clipvalue": None,
    "global_clipnorm": None,
    "use_ema": False,
    "ema_momentum": 0.99,
    "ema_overwrite_frequency": None,
}

Adafactor

"Adafactor": {
    "learning_rate": 0.001,
    "beta_2_decay": -0.8,
    "epsilon_1": 1e-30,
    "epsilon_2": 0.001,
    "clip_threshold": 1.0,
    "relative_step": True,
    "weight_decay": None,
    "clipnorm": None,
    "clipvalue": None,
    "global_clipnorm": None,
    "use_ema": False,
    "ema_momentum": 0.99,
    "ema_overwrite_frequency": None,
}

FTRL

"Ftrl": {
    "learning_rate": 0.001,
    "learning_rate_power": -0.5,
    "initial_accumulator_value": 0.1,
    "l1_regularization_strength": 0.0,
    "l2_regularization_strength": 0.0,
    "l2_shrinkage_regularization_strength": 0.0,
    "beta": 0.0,
    "weight_decay": None,
    "clipnorm": None,
    "clipvalue": None,
    "global_clipnorm": None,
    "use_ema": False,
    "ema_momentum": 0.99,
    "ema_overwrite_frequency": None,
}