snowflake.ml.modeling.ensemble.AdaBoostClassifier¶

class snowflake.ml.modeling.ensemble.AdaBoostClassifier(*, estimator=None, n_estimators=50, learning_rate=1.0, algorithm='SAMME.R', random_state=None, base_estimator='deprecated', input_cols: Optional[Union[str, Iterable[str]]] = None, output_cols: Optional[Union[str, Iterable[str]]] = None, label_cols: Optional[Union[str, Iterable[str]]] = None, passthrough_cols: Optional[Union[str, Iterable[str]]] = None, drop_input_cols: Optional[bool] = False, sample_weight_col: Optional[str] = None)¶

Bases: BaseTransformer

An AdaBoost classifier For more details on this class, see sklearn.ensemble.AdaBoostClassifier

Parameters:

input_cols (Optional[Union[str, List[str]]]) – A string or list of strings representing column names that contain features. If this parameter is not specified, all columns in the input DataFrame except the columns specified by label_cols, sample_weight_col, and passthrough_cols parameters are considered input columns. Input columns can also be set after initialization with the set_input_cols method.
label_cols (Optional[Union[str, List[str]]]) – A string or list of strings representing column names that contain labels. Label columns must be specified with this parameter during initialization or with the set_label_cols method before fitting.
output_cols (Optional[Union[str, List[str]]]) – A string or list of strings representing column names that will store the output of predict and transform operations. The length of output_cols must match the expected number of output columns from the specific predictor or transformer class used. If you omit this parameter, output column names are derived by adding an OUTPUT_ prefix to the label column names for supervised estimators, or OUTPUT_<IDX>for unsupervised estimators. These inferred output column names work for predictors, but output_cols must be set explicitly for transformers. In general, explicitly specifying output column names is clearer, especially if you don’t specify the input column names. To transform in place, pass the same names for input_cols and output_cols. be set explicitly for transformers. Output columns can also be set after initialization with the set_output_cols method.
sample_weight_col (Optional[str]) – A string representing the column name containing the sample weights. This argument is only required when working with weighted datasets. Sample weight column can also be set after initialization with the set_sample_weight_col method.
passthrough_cols (Optional[Union[str, List[str]]]) – A string or a list of strings indicating column names to be excluded from any operations (such as train, transform, or inference). These specified column(s) will remain untouched throughout the process. This option is helpful in scenarios requiring automatic input_cols inference, but need to avoid using specific columns, like index columns, during training or inference. Passthrough columns can also be set after initialization with the set_passthrough_cols method.
drop_input_cols (Optional[bool], default=False) – If set, the response of predict(), transform() methods will not contain input columns.
estimator (object, default=None) – The base estimator from which the boosted ensemble is built. Support for sample weighting is required, as well as proper classes_ and n_classes_ attributes. If None, then the base estimator is DecisionTreeClassifier initialized with max_depth=1.
n_estimators (int, default=50) – The maximum number of estimators at which boosting is terminated. In case of perfect fit, the learning procedure is stopped early. Values must be in the range [1, inf).
learning_rate (float, default=1.0) – Weight applied to each classifier at each boosting iteration. A higher learning rate increases the contribution of each classifier. There is a trade-off between the learning_rate and n_estimators parameters. Values must be in the range (0.0, inf).
algorithm ({'SAMME', 'SAMME.R'}, default='SAMME.R') – If ‘SAMME.R’ then use the SAMME.R real boosting algorithm. estimator must support calculation of class probabilities. If ‘SAMME’ then use the SAMME discrete boosting algorithm. The SAMME.R algorithm typically converges faster than SAMME, achieving a lower test error with fewer boosting iterations.
random_state (int, RandomState instance or None, default=None) – Controls the random seed given at each estimator at each boosting iteration. Thus, it is only used when estimator exposes a random_state. Pass an int for reproducible output across multiple function calls. See Glossary.
base_estimator (object, default=None) – The base estimator from which the boosted ensemble is built. Support for sample weighting is required, as well as proper classes_ and n_classes_ attributes. If None, then the base estimator is DecisionTreeClassifier initialized with max_depth=1.

Base class for all transformers.

Methods

decision_function(dataset: Union[DataFrame, DataFrame], output_cols_prefix: str = 'decision_function_') → Union[DataFrame, DataFrame]¶

Compute the decision function of X For more details on this function, see sklearn.ensemble.AdaBoostClassifier.decision_function

Raises:

TypeError – Supported dataset types: snowpark.DataFrame, pandas.DataFrame.

Parameters:

dataset – Union[snowflake.snowpark.DataFrame, pandas.DataFrame] Snowpark or Pandas DataFrame.
output_cols_prefix – str Prefix for the response columns

Returns:

Output dataset with results of the decision function for the samples in input dataset.

fit(dataset: Union[DataFrame, DataFrame]) → BaseEstimator¶: Runs universal logics for all fit implementations.

fit_transform(dataset: Union[DataFrame, DataFrame], output_cols_prefix: str = 'fit_transform_') → Union[DataFrame, DataFrame]¶

Method not supported for this class.

Raises:: TypeError – Supported dataset types: snowpark.DataFrame, pandas.DataFrame.
Parameters:: dataset – Union[snowflake.snowpark.DataFrame, pandas.DataFrame] Snowpark or Pandas DataFrame.

output_cols_prefix: Prefix for the response columns :returns: Transformed dataset.

get_input_cols() → List[str]¶

Input columns getter.

Returns:: Input columns.

get_label_cols() → List[str]¶

Label column getter.

Returns:: Label column(s).

get_output_cols() → List[str]¶

Output columns getter.

Returns:: Output columns.

get_params(deep: bool = True) → Dict[str, Any]¶

Get parameters for this transformer.

Parameters:: deep – If True, will return the parameters for this transformer and contained subobjects that are transformers.
Returns:: Parameter names mapped to their values.

get_passthrough_cols() → List[str]¶

Passthrough columns getter.

Returns:: Passthrough column(s).

get_sample_weight_col() → Optional[str]¶

Sample weight column getter.

Returns:: Sample weight column.

get_sklearn_args(default_sklearn_obj: Optional[object] = None, sklearn_initial_keywords: Optional[Union[str, Iterable[str]]] = None, sklearn_unused_keywords: Optional[Union[str, Iterable[str]]] = None, snowml_only_keywords: Optional[Union[str, Iterable[str]]] = None, sklearn_added_keyword_to_version_dict: Optional[Dict[str, str]] = None, sklearn_added_kwarg_value_to_version_dict: Optional[Dict[str, Dict[str, str]]] = None, sklearn_deprecated_keyword_to_version_dict: Optional[Dict[str, str]] = None, sklearn_removed_keyword_to_version_dict: Optional[Dict[str, str]] = None) → Dict[str, Any]¶

Get sklearn keyword arguments.

This method enables modifying object parameters for special cases.

Parameters:

default_sklearn_obj – Sklearn object used to get default parameter values. Necessary when sklearn_added_keyword_to_version_dict is provided.
sklearn_initial_keywords – Initial keywords in sklearn.
sklearn_unused_keywords – Sklearn keywords that are unused in snowml.
snowml_only_keywords – snowml only keywords not present in sklearn.
sklearn_added_keyword_to_version_dict – Added keywords mapped to the sklearn versions in which they were added.
sklearn_added_kwarg_value_to_version_dict – Added keyword argument values mapped to the sklearn versions in which they were added.
sklearn_deprecated_keyword_to_version_dict – Deprecated keywords mapped to the sklearn versions in which they were deprecated.
sklearn_removed_keyword_to_version_dict – Removed keywords mapped to the sklearn versions in which they were removed.

Returns:

Sklearn parameter names mapped to their values.

predict(dataset: Union[DataFrame, DataFrame]) → Union[DataFrame, DataFrame]¶

Predict classes for X For more details on this function, see sklearn.ensemble.AdaBoostClassifier.predict

Raises:: TypeError – Supported dataset types: snowpark.DataFrame, pandas.DataFrame.
Parameters:: dataset – Union[snowflake.snowpark.DataFrame, pandas.DataFrame] Snowpark or Pandas DataFrame.
Returns:: Transformed dataset.

predict_log_proba(dataset: Union[DataFrame, DataFrame], output_cols_prefix: str = 'predict_log_proba_') → Union[DataFrame, DataFrame]¶

Predict class probabilities for X For more details on this function, see sklearn.ensemble.AdaBoostClassifier.predict_proba

Raises:

TypeError – Supported dataset types: snowpark.DataFrame, pandas.DataFrame.

Parameters:

dataset – Union[snowflake.snowpark.DataFrame, pandas.DataFrame] Snowpark or Pandas DataFrame.
output_cols_prefix – str Prefix for the response columns

Returns:

Output dataset with log probability of the sample for each class in the model.

predict_proba(dataset: Union[DataFrame, DataFrame], output_cols_prefix: str = 'predict_proba_') → Union[DataFrame, DataFrame]¶

Predict class probabilities for X For more details on this function, see sklearn.ensemble.AdaBoostClassifier.predict_proba

Raises:

TypeError – Supported dataset types: snowpark.DataFrame, pandas.DataFrame.

Parameters:

dataset – Union[snowflake.snowpark.DataFrame, pandas.DataFrame] Snowpark or Pandas DataFrame.
output_cols_prefix – Prefix for the response columns

Returns:

Output dataset with probability of the sample for each class in the model.

score(dataset: Union[DataFrame, DataFrame]) → float¶

Return the mean accuracy on the given test data and labels For more details on this function, see sklearn.ensemble.AdaBoostClassifier.score

Raises:: TypeError – Supported dataset types: snowpark.DataFrame, pandas.DataFrame.
Parameters:: dataset – Union[snowflake.snowpark.DataFrame, pandas.DataFrame] Snowpark or Pandas DataFrame.
Returns:: Score.

score_samples(dataset: Union[DataFrame, DataFrame], output_cols_prefix: str = 'score_samples_') → Union[DataFrame, DataFrame]¶

Method not supported for this class.

Raises:

TypeError – Supported dataset types: snowpark.DataFrame, pandas.DataFrame.

Parameters:

dataset – Union[snowflake.snowpark.DataFrame, pandas.DataFrame] Snowpark or Pandas DataFrame.
output_cols_prefix – Prefix for the response columns

Returns:

Output dataset with probability of the sample for each class in the model.

set_drop_input_cols(drop_input_cols: Optional[bool] = False) → None¶

set_input_cols(input_cols: Optional[Union[str, Iterable[str]]]) → AdaBoostClassifier¶

Input columns setter.

Parameters:: input_cols – A single input column or multiple input columns.
Returns:: self

set_label_cols(label_cols: Optional[Union[str, Iterable[str]]]) → Base¶

Label column setter.

Parameters:: label_cols – A single label column or multiple label columns if multi task learning.
Returns:: self

set_output_cols(output_cols: Optional[Union[str, Iterable[str]]]) → Base¶

Output columns setter.

Parameters:: output_cols – A single output column or multiple output columns.
Returns:: self

set_params(**params: Dict[str, Any]) → None¶

Set the parameters of this transformer.

The method works on simple transformers as well as on nested objects. The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:: **params – Transformer parameter names mapped to their values.
Raises:: SnowflakeMLException – Invalid parameter keys.

set_passthrough_cols(passthrough_cols: Optional[Union[str, Iterable[str]]]) → Base¶

Passthrough columns setter.

Parameters:: passthrough_cols – Column(s) that should not be used or modified by the estimator/transformer. Estimator/Transformer just passthrough these columns without any modifications.
Returns:: self

set_sample_weight_col(sample_weight_col: Optional[str]) → Base¶

Sample weight column setter.

Parameters:: sample_weight_col – A single column that represents sample weight.
Returns:: self

to_sklearn() → Any¶: Get sklearn.ensemble.AdaBoostClassifier object.

Attributes

model_signatures¶

Returns model signature of current class.

Raises:: SnowflakeMLException – If estimator is not fitted, then model signature cannot be inferred
Returns:: Dict with each method and its input output signature