snowflake.ml.modeling.discriminant_analysis.LinearDiscriminantAnalysis¶
- class snowflake.ml.modeling.discriminant_analysis.LinearDiscriminantAnalysis(*, solver='svd', shrinkage=None, priors=None, n_components=None, store_covariance=False, tol=0.0001, covariance_estimator=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
Linear Discriminant Analysis For more details on this class, see sklearn.discriminant_analysis.LinearDiscriminantAnalysis
- 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.
- solver: {‘svd’, ‘lsqr’, ‘eigen’}, default=’svd’
- Solver to use, possible values:
‘svd’: Singular value decomposition (default). Does not compute the covariance matrix, therefore this solver is recommended for data with a large number of features.
‘lsqr’: Least squares solution. Can be combined with shrinkage or custom covariance estimator.
‘eigen’: Eigenvalue decomposition. Can be combined with shrinkage or custom covariance estimator.
- shrinkage: ‘auto’ or float, default=None
- Shrinkage parameter, possible values:
None: no shrinkage (default).
‘auto’: automatic shrinkage using the Ledoit-Wolf lemma.
float between 0 and 1: fixed shrinkage parameter.
This should be left to None if covariance_estimator is used. Note that shrinkage works only with ‘lsqr’ and ‘eigen’ solvers.
- priors: array-like of shape (n_classes,), default=None
The class prior probabilities. By default, the class proportions are inferred from the training data.
- n_components: int, default=None
Number of components (<= min(n_classes - 1, n_features)) for dimensionality reduction. If None, will be set to min(n_classes - 1, n_features). This parameter only affects the transform method.
- store_covariance: bool, default=False
If True, explicitly compute the weighted within-class covariance matrix when solver is ‘svd’. The matrix is always computed and stored for the other solvers.
- tol: float, default=1.0e-4
Absolute threshold for a singular value of X to be considered significant, used to estimate the rank of X. Dimensions whose singular values are non-significant are discarded. Only used if solver is ‘svd’.
- covariance_estimator: covariance estimator, default=None
If not None, covariance_estimator is used to estimate the covariance matrices instead of relying on the empirical covariance estimator (with potential shrinkage). The object should have a fit method and a
covariance_
attribute like the estimators insklearn.covariance
. if None the shrinkage parameter drives the estimate.This should be left to None if shrinkage is used. Note that covariance_estimator works only with ‘lsqr’ and ‘eigen’ solvers.
Base class for all transformers.
Methods
- decision_function(dataset: Union[DataFrame, DataFrame], output_cols_prefix: str = 'decision_function_') Union[DataFrame, DataFrame] ¶
Apply decision function to an array of samples For more details on this function, see sklearn.discriminant_analysis.LinearDiscriminantAnalysis.decision_function
- 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 results of the decision function for the samples in input dataset.
- fit(dataset: Union[DataFrame, DataFrame]) LinearDiscriminantAnalysis ¶
Fit the Linear Discriminant Analysis model For more details on this function, see sklearn.discriminant_analysis.LinearDiscriminantAnalysis.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 labels for samples in X For more details on this function, see sklearn.discriminant_analysis.LinearDiscriminantAnalysis.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] ¶
Estimate probability For more details on this function, see sklearn.discriminant_analysis.LinearDiscriminantAnalysis.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] ¶
Estimate probability For more details on this function, see sklearn.discriminant_analysis.LinearDiscriminantAnalysis.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.discriminant_analysis.LinearDiscriminantAnalysis.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]]]) LinearDiscriminantAnalysis ¶
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.discriminant_analysis.LinearDiscriminantAnalysis object.
- transform(dataset: Union[DataFrame, DataFrame]) Union[DataFrame, DataFrame] ¶
Project data to maximize class separation For more details on this function, see sklearn.discriminant_analysis.LinearDiscriminantAnalysis.transform
- Raises:
TypeError: Supported dataset types: snowpark.DataFrame, pandas.DataFrame.
- Args:
- dataset: Union[snowflake.snowpark.DataFrame, pandas.DataFrame]
Snowpark or Pandas DataFrame.
- Returns:
Transformed dataset.
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