An alias for getOrDefault().

Param for the alpha parameter in the implicit preference formulation (nonnegative). Default: 1.0

Param for block size for stacking input data in matrices. Data is stacked within partitions. If block size is more than remaining data in a partition then it is adjusted to the size of this data..

Attempts to safely cast a user/item id to an Int. Throws an exception if the value is out of integer range or contains a fractional part.

Param for set checkpoint interval (>= 1) or disable checkpoint (-1). E.g. 10 means that the cache will get checkpointed every 10 iterations. Note: this setting will be ignored if the checkpoint directory is not set in the SparkContext.

Clears the user-supplied value for the input param.

Param for strategy for dealing with unknown or new users/items at prediction time. This may be useful in cross-validation or production scenarios, for handling user/item ids the model has not seen in the training data. Supported values: - "nan": predicted value for unknown ids will be NaN. - "drop": rows in the input DataFrame containing unknown ids will be dropped from the output DataFrame containing predictions. Default: "nan".

Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See defaultCopy().

Copies param values from this instance to another instance for params shared by them.

This handles default Params and explicitly set Params separately. Default Params are copied from and to defaultParamMap, and explicitly set Params are copied from and to paramMap. Warning: This implicitly assumes that this Params instance and the target instance share the same set of default Params.

Default implementation of copy with extra params. It tries to create a new instance with the same UID. Then it copies the embedded and extra parameters over and returns the new instance.

Explains a param.

Explains all params of this instance. See explainParam().

extractParamMap with no extra values.

Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.

Param for StorageLevel for ALS model factors. Pass in a string representation of StorageLevel. Default: "MEMORY_AND_DISK".

Fits a model to the input data.

Fits multiple models to the input data with multiple sets of parameters. The default implementation uses a for loop on each parameter map. Subclasses could override this to optimize multi-model training.

Fits a single model to the input data with provided parameter map.

Fits a single model to the input data with optional parameters.

Optionally returns the user-supplied value of a param.

Gets the default value of a parameter.

Gets the value of a param in the embedded param map or its default value. Throws an exception if neither is set.

Gets a param by its name.

Tests whether the input param has a default value set.

Tests whether this instance contains a param with a given name.

Param to decide whether to use implicit preference. Default: false

Param for StorageLevel for intermediate datasets. Pass in a string representation of StorageLevel. Cannot be "NONE". Default: "MEMORY_AND_DISK".

Checks whether a param is explicitly set or has a default value.

Checks whether a param is explicitly set.

Param for the column name for item ids. Ids must be integers. Other numeric types are supported for this column, but will be cast to integers as long as they fall within the integer value range. Default: "item"

Param for maximum number of iterations (>= 0).

Param for whether to apply nonnegativity constraints. Default: false

Param for number of item blocks (positive). Default: 10

Param for number of user blocks (positive). Default: 10

Returns all params sorted by their names. The default implementation uses Java reflection to list all public methods that have no arguments and return Param.

Param for prediction column name.

Param for rank of the matrix factorization (positive). Default: 10

Param for the column name for ratings. Default: "rating"

Param for regularization parameter (>= 0).

Saves this ML instance to the input path, a shortcut of write.save(path).

Param for random seed.

Sets a parameter in the embedded param map.

Sets a parameter (by name) in the embedded param map.

Sets a parameter in the embedded param map.

Set block size for stacking input data in matrices. Default is 4096.

Sets default values for a list of params.

Note: Java developers should use the single-parameter setDefault. Annotating this with varargs can cause compilation failures due to a Scala compiler bug. See SPARK-9268.

Sets a default value for a param.

Sets both numUserBlocks and numItemBlocks to the specific value.

Check transform validity and derive the output schema from the input schema.

We check validity for interactions between parameters during transformSchema and raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled by Param.validate().

Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.

:: DeveloperApi ::

Derives the output schema from the input schema and parameters, optionally with logging.

This should be optimistic. If it is unclear whether the schema will be valid, then it should be assumed valid until proven otherwise.

An immutable unique ID for the object and its derivatives.

Param for the column name for user ids. Ids must be integers. Other numeric types are supported for this column, but will be cast to integers as long as they fall within the integer value range. Default: "user"

Validates and transforms the input schema.

Returns an MLWriter instance for this ML instance.