ALSModel¶

class pyspark.ml.recommendation.ALSModel(java_model: Optional[JavaObject] = None)¶

Model fitted by ALS.

Methods

`clear`(param)	Clears a param from the param map if it has been explicitly set.
`copy`([extra])	Creates a copy of this instance with the same uid and some extra params.
`explainParam`(param)	Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.
`explainParams`()	Returns the documentation of all params with their optionally default values and user-supplied values.
`extractParamMap`([extra])	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 < user-supplied values < extra.
`getBlockSize`()	Gets the value of blockSize or its default value.
`getColdStartStrategy`()	Gets the value of coldStartStrategy or its default value.
`getItemCol`()	Gets the value of itemCol or its default value.
`getOrDefault`(param)	Gets the value of a param in the user-supplied param map or its default value.
`getParam`(paramName)	Gets a param by its name.
`getPredictionCol`()	Gets the value of predictionCol or its default value.
`getUserCol`()	Gets the value of userCol or its default value.
`hasDefault`(param)	Checks whether a param has a default value.
`hasParam`(paramName)	Tests whether this instance contains a param with a given (string) name.
`isDefined`(param)	Checks whether a param is explicitly set by user or has a default value.
`isSet`(param)	Checks whether a param is explicitly set by user.
`load`(path)	Reads an ML instance from the input path, a shortcut of read().load(path).
`read`()	Returns an MLReader instance for this class.
`recommendForAllItems`(numUsers)	Returns top numUsers users recommended for each item, for all items.
`recommendForAllUsers`(numItems)	Returns top numItems items recommended for each user, for all users.
`recommendForItemSubset`(dataset, numUsers)	Returns top numUsers users recommended for each item id in the input data set.
`recommendForUserSubset`(dataset, numItems)	Returns top numItems items recommended for each user id in the input data set.
`save`(path)	Save this ML instance to the given path, a shortcut of ‘write().save(path)’.
`set`(param, value)	Sets a parameter in the embedded param map.
`setBlockSize`(value)	Sets the value of `blockSize`.
`setColdStartStrategy`(value)	Sets the value of `coldStartStrategy`.
`setItemCol`(value)	Sets the value of `itemCol`.
`setPredictionCol`(value)	Sets the value of `predictionCol`.
`setUserCol`(value)	Sets the value of `userCol`.
`transform`(dataset[, params])	Transforms the input dataset with optional parameters.
`write`()	Returns an MLWriter instance for this ML instance.

Attributes

`blockSize`
`coldStartStrategy`
`itemCol`
`itemFactors`	a DataFrame that stores item factors in two columns: id and features
`params`	Returns all params ordered by name.
`predictionCol`
`rank`	rank of the matrix factorization model
`userCol`
`userFactors`	a DataFrame that stores user factors in two columns: id and features

Methods Documentation

clear(param: pyspark.ml.param.Param) → None¶: Clears a param from the param map if it has been explicitly set.

copy(extra: Optional[ParamMap] = None) → JP¶

Creates a copy of this instance with the same uid and some extra params. This implementation first calls Params.copy and then make a copy of the companion Java pipeline component with extra params. So both the Python wrapper and the Java pipeline component get copied.

Parameters

extradict, optional: Extra parameters to copy to the new instance

Returns

JavaParams: Copy of this instance

explainParam(param: Union[str, pyspark.ml.param.Param]) → str¶: Explains a single param and returns its name, doc, and optional default value and user-supplied value in a string.

explainParams() → str¶: Returns the documentation of all params with their optionally default values and user-supplied values.

extractParamMap(extra: Optional[ParamMap] = None) → ParamMap¶

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 < user-supplied values < extra.

Parameters

extradict, optional: extra param values

Returns

dict: merged param map

getBlockSize() → int¶: Gets the value of blockSize or its default value.

getColdStartStrategy() → str¶: Gets the value of coldStartStrategy or its default value.

getItemCol() → str¶: Gets the value of itemCol or its default value.

getOrDefault(param: Union[str, pyspark.ml.param.Param[T]]) → Union[Any, T]¶: Gets the value of a param in the user-supplied param map or its default value. Raises an error if neither is set.

getParam(paramName: str) → pyspark.ml.param.Param ¶: Gets a param by its name.

getPredictionCol() → str¶: Gets the value of predictionCol or its default value.

getUserCol() → str¶: Gets the value of userCol or its default value.

hasDefault(param: Union[str, pyspark.ml.param.Param[Any]]) → bool¶: Checks whether a param has a default value.

hasParam(paramName: str) → bool¶: Tests whether this instance contains a param with a given (string) name.

isDefined(param: Union[str, pyspark.ml.param.Param[Any]]) → bool¶: Checks whether a param is explicitly set by user or has a default value.

isSet(param: Union[str, pyspark.ml.param.Param[Any]]) → bool¶: Checks whether a param is explicitly set by user.

classmethod load(path: str) → RL¶: Reads an ML instance from the input path, a shortcut of read().load(path).

classmethod read() → pyspark.ml.util.JavaMLReader[RL]¶: Returns an MLReader instance for this class.

recommendForAllItems(numUsers: int) → pyspark.sql.dataframe.DataFrame¶

Returns top numUsers users recommended for each item, for all items.

Parameters

numUsersint: max number of recommendations for each item

Returns

pyspark.sql.DataFrame: a DataFrame of (itemCol, recommendations), where recommendations are stored as an array of (userCol, rating) Rows.

recommendForAllUsers(numItems: int) → pyspark.sql.dataframe.DataFrame¶

Returns top numItems items recommended for each user, for all users.

Parameters

numItemsint: max number of recommendations for each user

Returns

pyspark.sql.DataFrame: a DataFrame of (userCol, recommendations), where recommendations are stored as an array of (itemCol, rating) Rows.

recommendForItemSubset(dataset: pyspark.sql.dataframe.DataFrame, numUsers: int) → pyspark.sql.dataframe.DataFrame¶

Returns top numUsers users recommended for each item id in the input data set. Note that if there are duplicate ids in the input dataset, only one set of recommendations per unique id will be returned.

Parameters

datasetpyspark.sql.DataFrame: a DataFrame containing a column of item ids. The column name must match itemCol.
numUsersint: max number of recommendations for each item

Returns

pyspark.sql.DataFrame: a DataFrame of (itemCol, recommendations), where recommendations are stored as an array of (userCol, rating) Rows.

recommendForUserSubset(dataset: pyspark.sql.dataframe.DataFrame, numItems: int) → pyspark.sql.dataframe.DataFrame¶

Returns top numItems items recommended for each user id in the input data set. Note that if there are duplicate ids in the input dataset, only one set of recommendations per unique id will be returned.

Parameters

datasetpyspark.sql.DataFrame: a DataFrame containing a column of user ids. The column name must match userCol.
numItemsint: max number of recommendations for each user

Returns

pyspark.sql.DataFrame: a DataFrame of (userCol, recommendations), where recommendations are stored as an array of (itemCol, rating) Rows.

save(path: str) → None¶: Save this ML instance to the given path, a shortcut of ‘write().save(path)’.

set(param: pyspark.ml.param.Param, value: Any) → None¶: Sets a parameter in the embedded param map.

setBlockSize(value: int) → pyspark.ml.recommendation.ALSModel ¶: Sets the value of blockSize.

setColdStartStrategy(value: str) → pyspark.ml.recommendation.ALSModel ¶: Sets the value of coldStartStrategy.

setItemCol(value: str) → pyspark.ml.recommendation.ALSModel ¶: Sets the value of itemCol.

setPredictionCol(value: str) → pyspark.ml.recommendation.ALSModel ¶: Sets the value of predictionCol.

setUserCol(value: str) → pyspark.ml.recommendation.ALSModel ¶: Sets the value of userCol.

transform(dataset: pyspark.sql.dataframe.DataFrame, params: Optional[ParamMap] = None) → pyspark.sql.dataframe.DataFrame¶

Transforms the input dataset with optional parameters.

Parameters

datasetpyspark.sql.DataFrame: input dataset
paramsdict, optional: an optional param map that overrides embedded params.

Returns

pyspark.sql.DataFrame: transformed dataset

write() → pyspark.ml.util.JavaMLWriter¶: Returns an MLWriter instance for this ML instance.

Attributes Documentation

blockSize = Param(parent='undefined', name='blockSize', doc='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.')¶

coldStartStrategy: pyspark.ml.param.Param[str] = Param(parent='undefined', name='coldStartStrategy', doc="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', 'drop'.")¶

itemCol: pyspark.ml.param.Param[str] = Param(parent='undefined', name='itemCol', doc='column name for item ids. Ids must be within the integer value range.')¶

itemFactors¶: a DataFrame that stores item factors in two columns: id and features

params¶: Returns all params ordered by name. The default implementation uses dir() to get all attributes of type Param.

predictionCol = Param(parent='undefined', name='predictionCol', doc='prediction column name.')¶

rank¶: rank of the matrix factorization model

userCol: pyspark.ml.param.Param[str] = Param(parent='undefined', name='userCol', doc='column name for user ids. Ids must be within the integer value range.')¶

userFactors¶: a DataFrame that stores user factors in two columns: id and features

ALS

AFTSurvivalRegression