snowflake.ml.modeling.ensemble.IsolationForest¶

class snowflake.ml.modeling.ensemble.IsolationForest(*, n_estimators=100, max_samples='auto', contamination='auto', max_features=1.0, bootstrap=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, 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

Isolation Forest Algorithm For more details on this class, see sklearn.ensemble.IsolationForest

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.
n_estimators (int, default=100) – The number of base estimators in the ensemble.
max_samples ("auto", int or float, default="auto") –
The number of samples to draw from X to train each base estimator.
- If int, then draw max_samples samples.
- If float, then draw max_samples * X.shape[0] samples.
- If “auto”, then max_samples=min(256, n_samples).
If max_samples is larger than the number of samples provided, all samples will be used for all trees (no sampling).
contamination ('auto' or float, default='auto') –
The amount of contamination of the data set, i.e. the proportion of outliers in the data set. Used when fitting to define the threshold on the scores of the samples.
- If ‘auto’, the threshold is determined as in the original paper.
- If float, the contamination should be in the range (0, 0.5].
max_features (int or float, default=1.0) –
The number of features to draw from X to train each base estimator.
- If int, then draw max_features features.
- If float, then draw max(1, int(max_features * n_features_in_)) features.
Note: using a float number less than 1.0 or integer less than number of features will enable feature subsampling and leads to a longer runtime.
bootstrap (bool, default=False) – If True, individual trees are fit on random subsets of the training data sampled with replacement. If False, sampling without replacement is performed.
n_jobs (int, default=None) – The number of jobs to run in parallel for both fit() and predict(). None means 1 unless in a joblib.parallel_backend context. -1 means using all processors. See Glossary for more details.
random_state (int, RandomState instance or None, default=None) –
Controls the pseudo-randomness of the selection of the feature and split values for each branching step and each tree in the forest.

Pass an int for reproducible results across multiple function calls. See Glossary.
verbose (int, default=0) – Controls the verbosity of the tree building process.
warm_start (bool, default=False) – When set to True, reuse the solution of the previous call to fit and add more estimators to the ensemble, otherwise, just fit a whole new forest. See the Glossary.

Base class for all transformers.

Methods

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

Average anomaly score of X of the base classifiers For more details on this function, see sklearn.ensemble.IsolationForest.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_predict(dataset: Union[DataFrame, DataFrame], output_cols_prefix: str = 'fit_predict_') → Union[DataFrame, DataFrame]¶

Perform fit on X and returns labels for X For more details on this function, see sklearn.ensemble.IsolationForest.fit_predict

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: Predicted dataset.

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 the snowflake-ml 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 if a particular sample is an outlier or not For more details on this function, see sklearn.ensemble.IsolationForest.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.

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

Opposite of the anomaly score defined in the original paper For more details on this function, see sklearn.ensemble.IsolationForest.score_samples

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]]]) → IsolationForest¶

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: Any) → None¶

Set the parameters of this transformer.

The method works on simple transformers as well as on sklearn compatible pipelines with nested objects, once the transformer has been fit. Nested objects 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.IsolationForest 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