snowflake.ml.modeling.ensemble.RandomForestClassifier¶
- class snowflake.ml.modeling.ensemble.RandomForestClassifier(*, 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=None, random_state=None, verbose=0, warm_start=False, class_weight=None, ccp_alpha=0.0, max_samples=None, 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
A random forest classifier For more details on this class, see sklearn.ensemble.RandomForestClassifier
- 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 trees in the forest.
- criterion: {“gini”, “entropy”, “log_loss”}, default=”gini”
The function to measure the quality of a split. Supported criteria are “gini” for the Gini impurity and “log_loss” and “entropy” both for the Shannon information gain, see tree_mathematical_formulation. Note: This parameter is tree-specific.
- max_depth: int, default=None
The maximum depth of the tree. If None, then nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples.
- min_samples_split: int or float, default=2
The minimum number of samples required to split an internal node:
If int, then consider min_samples_split as the minimum number.
If float, then min_samples_split is a fraction and ceil(min_samples_split * n_samples) are the minimum number of samples for each split.
- min_samples_leaf: int or float, default=1
The minimum number of samples required to be at a leaf node. A split point at any depth will only be considered if it leaves at least
min_samples_leaf
training samples in each of the left and right branches. This may have the effect of smoothing the model, especially in regression.If int, then consider min_samples_leaf as the minimum number.
If float, then min_samples_leaf is a fraction and ceil(min_samples_leaf * n_samples) are the minimum number of samples for each node.
- min_weight_fraction_leaf: float, default=0.0
The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Samples have equal weight when sample_weight is not provided.
- max_features: {“sqrt”, “log2”, None}, int or float, default=”sqrt”
The number of features to consider when looking for the best split:
If int, then consider max_features features at each split.
If float, then max_features is a fraction and max(1, int(max_features * n_features_in_)) features are considered at each split.
If “sqrt”, then max_features=sqrt(n_features).
If “log2”, then max_features=log2(n_features).
If None, then max_features=n_features.
Note: the search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than
max_features
features.- max_leaf_nodes: int, default=None
Grow trees with
max_leaf_nodes
in best-first fashion. Best nodes are defined as relative reduction in impurity. If None then unlimited number of leaf nodes.- min_impurity_decrease: float, default=0.0
A node will be split if this split induces a decrease of the impurity greater than or equal to this value.
The weighted impurity decrease equation is the following:
N_t / N * (impurity - N_t_R / N_t * right_impurity - N_t_L / N_t * left_impurity)
where
N
is the total number of samples,N_t
is the number of samples at the current node,N_t_L
is the number of samples in the left child, andN_t_R
is the number of samples in the right child.N
,N_t
,N_t_R
andN_t_L
all refer to the weighted sum, ifsample_weight
is passed.- bootstrap: bool, default=True
Whether bootstrap samples are used when building trees. If False, the whole dataset is used to build each tree.
- oob_score: bool or callable, default=False
Whether to use out-of-bag samples to estimate the generalization score. By default,
accuracy_score()
is used. Provide a callable with signature metric(y_true, y_pred) to use a custom metric. Only available if bootstrap=True.- n_jobs: int, default=None
The number of jobs to run in parallel.
fit()
,predict()
,decision_path()
andapply()
are all parallelized over the trees.None
means 1 unless in ajoblib.parallel_backend
context.-1
means using all processors. See Glossary for more details.- random_state: int, RandomState instance or None, default=None
Controls both the randomness of the bootstrapping of the samples used when building trees (if
bootstrap=True
) and the sampling of the features to consider when looking for the best split at each node (ifmax_features < n_features
). See Glossary for details.- verbose: int, default=0
Controls the verbosity when fitting and predicting.
- 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 Glossary and gradient_boosting_warm_start for details.- class_weight: {“balanced”, “balanced_subsample”}, dict or list of dicts, default=None
Weights associated with classes in the form
{class_label: weight}
. If not given, all classes are supposed to have weight one. For multi-output problems, a list of dicts can be provided in the same order as the columns of y.Note that for multioutput (including multilabel) weights should be defined for each class of every column in its own dict. For example, for four-class multilabel classification weights should be [{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of [{1:1}, {2:5}, {3:1}, {4:1}].
The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as
n_samples / (n_classes * np.bincount(y))
The “balanced_subsample” mode is the same as “balanced” except that weights are computed based on the bootstrap sample for every tree grown.
For multi-output, the weights of each column of y will be multiplied.
Note that these weights will be multiplied with sample_weight (passed through the fit method) if sample_weight is specified.
- ccp_alpha: non-negative float, default=0.0
Complexity parameter used for Minimal Cost-Complexity Pruning. The subtree with the largest cost complexity that is smaller than
ccp_alpha
will be chosen. By default, no pruning is performed. See minimal_cost_complexity_pruning for details.- max_samples: int or float, default=None
If bootstrap is True, the number of samples to draw from X to train each base estimator.
If None (default), then draw X.shape[0] samples.
If int, then draw max_samples samples.
If float, then draw max(round(n_samples * max_samples), 1) samples. Thus, max_samples should be in the interval (0.0, 1.0].
Base class for all transformers.
Methods
- fit(dataset: Union[DataFrame, DataFrame]) RandomForestClassifier ¶
Build a forest of trees from the training set (X, y) For more details on this function, see sklearn.ensemble.RandomForestClassifier.fit
- Raises:
TypeError: Supported dataset types: snowpark.DataFrame, pandas.DataFrame.
- Args:
- dataset: Union[snowflake.snowpark.DataFrame, pandas.DataFrame]
Snowpark or Pandas DataFrame.
- Returns:
self
- fit_transform(dataset: Union[DataFrame, DataFrame]) Union[Any, ndarray[Any, dtype[Any]]] ¶
- 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.
- Args:
- 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.
- Args:
- 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 class for X For more details on this function, see sklearn.ensemble.RandomForestClassifier.predict
- Raises:
TypeError: Supported dataset types: snowpark.DataFrame, pandas.DataFrame.
- Args:
- 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.RandomForestClassifier.predict_proba
- Raises:
TypeError: Supported dataset types: snowpark.DataFrame, pandas.DataFrame.
- Args:
- 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.RandomForestClassifier.predict_proba
- Raises:
TypeError: Supported dataset types: snowpark.DataFrame, pandas.DataFrame.
- Args:
- 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.RandomForestClassifier.score
- Raises:
TypeError: Supported dataset types: snowpark.DataFrame, pandas.DataFrame.
- Args:
- dataset: Union[snowflake.snowpark.DataFrame, pandas.DataFrame]
Snowpark or Pandas DataFrame.
- Returns:
Score.
- set_drop_input_cols(drop_input_cols: Optional[bool] = False) None ¶
- set_input_cols(input_cols: Optional[Union[str, Iterable[str]]]) RandomForestClassifier ¶
Input columns setter.
- Args:
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.
- Args:
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.
- Args:
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.- Args:
**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.
- Args:
- 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.
- Args:
sample_weight_col: A single column that represents sample weight.
- Returns:
self
- to_sklearn() Any ¶
Get sklearn.ensemble.RandomForestClassifier object.
Attributes
- model_signatures¶
Returns model signature of current class.
- Raises:
exceptions.SnowflakeMLException: If estimator is not fitted, then model signature cannot be inferred
- Returns:
Dict[str, ModelSignature]: each method and its input output signature