Python AlgorithmsRegistry примеры использования

Пример #1

Файл: lightgbm.py Проект: shiji203/mljar-supervised

    "one_step": 10,
    "train_cant_improve_limit": 5,
    "max_steps": 500,
    "max_rows_limit": None,
    "max_cols_limit": None,
}
required_preprocessing = [
    "missing_values_inputation",
    "convert_categorical",
    "target_preprocessing",
]

lgbm_multi_params = copy.deepcopy(lgbm_bin_params)
lgbm_multi_params["objective"] = ["multiclass"]
lgbm_multi_params["metric"] = ["multi_logloss", "multi_error"]
"""
AlgorithmsRegistry.add(
    BINARY_CLASSIFICATION,
    LightgbmAlgorithm,
    lgbm_bin_params,
    required_preprocessing,
    additional,
)

AlgorithmsRegistry.add(
    MULTICLASS_CLASSIFICATION,
    LightgbmAlgorithm,
    lgbm_multi_params,
    required_preprocessing,
    additional,
)

Пример #2

    "early_stopping_rounds": 50,
    "max_rows_limit": None,
    "max_cols_limit": None,
}
required_preprocessing = [
    "missing_values_inputation",
    "convert_categorical",
    "datetime_transform",
    "text_transform",
    "target_as_integer",
]

AlgorithmsRegistry.add(
    BINARY_CLASSIFICATION,
    RandomForestAlgorithm,
    rf_params,
    required_preprocessing,
    additional,
    classification_default_params,
)

AlgorithmsRegistry.add(
    MULTICLASS_CLASSIFICATION,
    RandomForestAlgorithm,
    rf_params,
    required_preprocessing,
    additional,
    classification_default_params,
)

#
# REGRESSION

Пример #3

Файл: extra_trees.py Проект: zaxebo1/mljar-supervised

    "trees_in_step": 100,
    "max_steps": 50,
    "early_stopping_rounds": 50,
    "max_rows_limit": None,
    "max_cols_limit": None,
}
required_preprocessing = [
    "missing_values_inputation",
    "convert_categorical",
    "target_as_integer",
]

AlgorithmsRegistry.add(
    BINARY_CLASSIFICATION,
    ExtraTreesAlgorithm,
    et_params,
    required_preprocessing,
    additional,
    classification_default_params,
)

AlgorithmsRegistry.add(
    MULTICLASS_CLASSIFICATION,
    ExtraTreesAlgorithm,
    et_params,
    required_preprocessing,
    additional,
    classification_default_params,
)

#
# REGRESSION

Пример #4

    "max_rounds": 10000,
    "early_stopping_rounds": 50,
    "max_rows_limit": None,
    "max_cols_limit": None,
}
required_preprocessing = [
    "missing_values_inputation",
    "datetime_transform",
    "text_transform",
    "target_as_integer",
]

AlgorithmsRegistry.add(
    BINARY_CLASSIFICATION,
    CatBoostAlgorithm,
    classification_params,
    required_preprocessing,
    additional,
    classification_default_params,
)

AlgorithmsRegistry.add(
    MULTICLASS_CLASSIFICATION,
    CatBoostAlgorithm,
    classification_params,
    required_preprocessing,
    additional,
    classification_default_params,
)

regression_params = copy.deepcopy(classification_params)

Пример #5

classification_multi_default_params = {
    "objective": "multiclass",
    "num_leaves": 63,
    "learning_rate": 0.05,
    "feature_fraction": 0.9,
    "bagging_fraction": 0.9,
    "min_data_in_leaf": 10,
}

lgbr_params = copy.deepcopy(lgbm_bin_params)
lgbr_params["objective"] = ["regression"]

AlgorithmsRegistry.add(
    BINARY_CLASSIFICATION,
    LightgbmAlgorithm,
    lgbm_bin_params,
    required_preprocessing,
    additional,
    classification_bin_default_params,
)

AlgorithmsRegistry.add(
    MULTICLASS_CLASSIFICATION,
    LightgbmAlgorithm,
    lgbm_multi_params,
    required_preprocessing,
    additional,
    classification_multi_default_params,
)

regression_required_preprocessing = [
    "missing_values_inputation",

Пример #6

    "max_steps": 1,
    "max_rows_limit": None,
    "max_cols_limit": None,
}
required_preprocessing = [
    "missing_values_inputation",
    "convert_categorical",
    "datetime_transform",
    "text_transform",
    "target_as_integer",
]

AlgorithmsRegistry.add(
    BINARY_CLASSIFICATION,
    DecisionTreeAlgorithm,
    dt_params,
    required_preprocessing,
    additional,
    classification_default_params,
)

AlgorithmsRegistry.add(
    MULTICLASS_CLASSIFICATION,
    DecisionTreeAlgorithm,
    dt_params,
    required_preprocessing,
    additional,
    classification_default_params,
)

dt_regression_params = {
    "criterion": [

Пример #7

Файл: xgboost.py Проект: MTrawinska/mljar-supervised

    "train_cant_improve_limit": 5,
    "min_steps": 5,
    "max_steps": 500,
    "max_rows_limit": None,
    "max_cols_limit": None,
}
required_preprocessing = [
    "missing_values_inputation",
    "convert_categorical",
    "target_as_integer",
]

AlgorithmsRegistry.add(
    BINARY_CLASSIFICATION,
    XgbAlgorithm,
    xgb_bin_class_params,
    required_preprocessing,
    additional,
    classification_bin_default_params,
)

AlgorithmsRegistry.add(
    MULTICLASS_CLASSIFICATION,
    XgbAlgorithm,
    xgb_multi_class_params,
    required_preprocessing,
    additional,
    classification_multi_default_params,
)

regression_required_preprocessing = [
    "missing_values_inputation",

Пример #8

Файл: baseline.py Проект: zhweiliu/mljar-supervised

    def file_extension(self):
        return "baseline"

    def is_fitted(self):
        return (hasattr(self.model, "n_outputs_")
                and self.model.n_outputs_ is not None
                and self.model.n_outputs_ > 0)


additional = {"max_steps": 1, "max_rows_limit": None, "max_cols_limit": None}
required_preprocessing = ["target_as_integer"]

AlgorithmsRegistry.add(
    BINARY_CLASSIFICATION,
    BaselineClassifierAlgorithm,
    {},
    required_preprocessing,
    additional,
    {},
)

AlgorithmsRegistry.add(
    MULTICLASS_CLASSIFICATION,
    BaselineClassifierAlgorithm,
    {},
    required_preprocessing,
    additional,
    {},
)

AlgorithmsRegistry.add(REGRESSION, BaselineRegressorAlgorithm, {}, {},
                       additional, {})

Пример #9

Файл: nn.py Проект: johnjdailey/mljar-supervised

    "max_cols_limit": None,
}

required_preprocessing = [
    "missing_values_inputation",
    "convert_categorical",
    "datetime_transform",
    "text_transform",
    "scale",
    "target_as_integer",
]

AlgorithmsRegistry.add(
    BINARY_CLASSIFICATION,
    NeuralNetworkAlgorithm,
    nn_params,
    required_preprocessing,
    additional,
    default_nn_params,
)

required_preprocessing = [
    "missing_values_inputation",
    "convert_categorical",
    "datetime_transform",
    "text_transform",
    "scale",
    "target_as_one_hot",
]
AlgorithmsRegistry.add(
    MULTICLASS_CLASSIFICATION,
    NeuralNetworkAlgorithm,

Пример #10

Файл: linear.py Проект: peter-WeiZhang/mljar-supervised

        )
        df.to_csv(
            os.path.join(model_file_path, f"{learner_name}_coefs.csv"), index=False
        )


additional = {"max_steps": 1, "max_rows_limit": None, "max_cols_limit": None}
required_preprocessing = [
    "missing_values_inputation",
    "convert_categorical",
    "scale",
    "target_as_integer",
]

AlgorithmsRegistry.add(
    BINARY_CLASSIFICATION, LinearAlgorithm, {}, required_preprocessing, additional, {}
)
AlgorithmsRegistry.add(
    MULTICLASS_CLASSIFICATION,
    LinearAlgorithm,
    {},
    required_preprocessing,
    additional,
    {},
)

regression_required_preprocessing = [
    "missing_values_inputation",
    "convert_categorical",
    "scale",
    "target_scale",

Пример #11

default_params = {"n_neighbors": 5, "weights": "uniform"}

additional = {"max_rows_limit": 100000, "max_cols_limit": 100}

required_preprocessing = [
    "missing_values_inputation",
    "convert_categorical",
    "scale",
    "target_as_integer",
]

AlgorithmsRegistry.add(
    BINARY_CLASSIFICATION,
    KNeighborsAlgorithm,
    knn_params,
    required_preprocessing,
    additional,
    default_params,
)
AlgorithmsRegistry.add(
    MULTICLASS_CLASSIFICATION,
    KNeighborsAlgorithm,
    knn_params,
    required_preprocessing,
    additional,
    default_params,
)

AlgorithmsRegistry.add(
    REGRESSION,
    KNeighborsRegressorAlgorithm,