snowflake.ml.modeling.linear_model.OrthogonalMatchingPursuit¶
- class snowflake.ml.modeling.linear_model.OrthogonalMatchingPursuit(*, n_nonzero_coefs=None, tol=None, fit_intercept=True, normalize='deprecated', precompute='auto', input_cols: Optional[Union[str, Iterable[str]]] = None, output_cols: Optional[Union[str, Iterable[str]]] = None, label_cols: Optional[Union[str, Iterable[str]]] = None, drop_input_cols: Optional[bool] = False, sample_weight_col: Optional[str] = None)¶
Bases:
BaseTransformerOrthogonal Matching Pursuit model (OMP) For more details on this class, see sklearn.linear_model.OrthogonalMatchingPursuit
- n_nonzero_coefs: int, default=None
Desired number of non-zero entries in the solution. If None (by default) this value is set to 10% of n_features.
- tol: float, default=None
Maximum norm of the residual. If not None, overrides n_nonzero_coefs.
- fit_intercept: bool, default=True
Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (i.e. data is expected to be centered).
- normalize: bool, default=False
This parameter is ignored when
fit_interceptis set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please useStandardScalerbefore callingfiton an estimator withnormalize=False.- precompute: ‘auto’ or bool, default=’auto’
Whether to use a precomputed Gram and Xy matrix to speed up calculations. Improves performance when n_targets or n_samples is very large. Note that if you already have such matrices, you can pass them directly to the fit method.
- 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 and sample-weight_col parameters are considered input columns.
- label_cols: Optional[Union[str, List[str]]]
A string or list of strings representing column names that contain labels. This is a required param for estimators, as there is no way to infer these columns. If this parameter is not specified, then object is fitted without labels(Like a transformer).
- 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 mus match the expected number of output columns from the specific estimator or transformer class used. If this parameter is not specified, output column names are derived by adding an OUTPUT_ prefix to the label column names. These inferred output column names work for estimator’s predict() method, but output_cols must be set explicitly for transformers.
- sample_weight_col: Optional[str]
A string representing the column name containing the examples’ weights. This argument is only required when working with weighted datasets.
- drop_input_cols: Optional[bool], default=False
If set, the response of predict(), transform() methods will not contain input columns.
Methods
fit(dataset)Fit the model using X, y as training data For more details on this function, see sklearn.linear_model.OrthogonalMatchingPursuit.fit
predict(dataset)Predict using the linear model For more details on this function, see sklearn.linear_model.OrthogonalMatchingPursuit.predict
score(dataset)Return the coefficient of determination of the prediction For more details on this function, see sklearn.linear_model.OrthogonalMatchingPursuit.score
set_input_cols(input_cols)Input columns setter.
to_sklearn()Get sklearn.linear_model.OrthogonalMatchingPursuit object.
Attributes
model_signaturesReturns model signature of current class.