public class ALSModel extends Model<ALSModel> implements ALSModelParams, MLWritable
param: rank rank of the matrix factorization model
param: userFactors a DataFrame that stores user factors in two columns: id
and features
param: itemFactors a DataFrame that stores item factors in two columns: id
and features
Modifier and Type | Method and Description |
---|---|
IntParam |
blockSize()
Param for block size for stacking input data in matrices.
|
Param<String> |
coldStartStrategy()
Param for strategy for dealing with unknown or new users/items at prediction time.
|
ALSModel |
copy(ParamMap extra)
Creates a copy of this instance with the same UID and some extra params.
|
Param<String> |
itemCol()
Param for the column name for item ids.
|
Dataset<Row> |
itemFactors() |
static ALSModel |
load(String path) |
Param<String> |
predictionCol()
Param for prediction column name.
|
int |
rank() |
static MLReader<ALSModel> |
read() |
Dataset<Row> |
recommendForAllItems(int numUsers)
Returns top
numUsers users recommended for each item, for all items. |
Dataset<Row> |
recommendForAllUsers(int numItems)
Returns top
numItems items recommended for each user, for all users. |
Dataset<Row> |
recommendForItemSubset(Dataset<?> dataset,
int numUsers)
Returns top
numUsers users recommended for each item id in the input data set. |
Dataset<Row> |
recommendForUserSubset(Dataset<?> dataset,
int numItems)
Returns top
numItems items recommended for each user id in the input data set. |
ALSModel |
setBlockSize(int value)
Set block size for stacking input data in matrices.
|
ALSModel |
setColdStartStrategy(String value) |
ALSModel |
setItemCol(String value) |
ALSModel |
setPredictionCol(String value) |
ALSModel |
setUserCol(String value) |
String |
toString() |
Dataset<Row> |
transform(Dataset<?> dataset)
Transforms the input dataset.
|
StructType |
transformSchema(StructType schema)
Check transform validity and derive the output schema from the input schema.
|
String |
uid()
An immutable unique ID for the object and its derivatives.
|
Param<String> |
userCol()
Param for the column name for user ids.
|
Dataset<Row> |
userFactors() |
MLWriter |
write()
Returns an
MLWriter instance for this ML instance. |
transform, transform, transform
params
checkIntegers, getColdStartStrategy, getItemCol, getUserCol
getPredictionCol
getBlockSize
clear, copyValues, defaultCopy, defaultParamMap, explainParam, explainParams, extractParamMap, extractParamMap, get, getDefault, getOrDefault, getParam, hasDefault, hasParam, isDefined, isSet, onParamChange, paramMap, params, set, set, set, setDefault, setDefault, shouldOwn
save
$init$, initializeForcefully, initializeLogIfNecessary, initializeLogIfNecessary, initializeLogIfNecessary$default$2, initLock, isTraceEnabled, log, logDebug, logDebug, logError, logError, logInfo, logInfo, logName, logTrace, logTrace, logWarning, logWarning, org$apache$spark$internal$Logging$$log__$eq, org$apache$spark$internal$Logging$$log_, uninitialize
public static ALSModel load(String path)
public Param<String> userCol()
ALSModelParams
userCol
in interface ALSModelParams
public Param<String> itemCol()
ALSModelParams
itemCol
in interface ALSModelParams
public Param<String> coldStartStrategy()
ALSModelParams
coldStartStrategy
in interface ALSModelParams
public final IntParam blockSize()
HasBlockSize
blockSize
in interface HasBlockSize
public final Param<String> predictionCol()
HasPredictionCol
predictionCol
in interface HasPredictionCol
public String uid()
Identifiable
uid
in interface Identifiable
public int rank()
public ALSModel setUserCol(String value)
public ALSModel setItemCol(String value)
public ALSModel setPredictionCol(String value)
public ALSModel setColdStartStrategy(String value)
public ALSModel setBlockSize(int value)
value
- (undocumented)public Dataset<Row> transform(Dataset<?> dataset)
Transformer
transform
in class Transformer
dataset
- (undocumented)public StructType transformSchema(StructType schema)
PipelineStage
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.
transformSchema
in class PipelineStage
schema
- (undocumented)public ALSModel copy(ParamMap extra)
Params
defaultCopy()
.public MLWriter write()
MLWritable
MLWriter
instance for this ML instance.write
in interface MLWritable
public String toString()
toString
in interface Identifiable
toString
in class Object
public Dataset<Row> recommendForAllUsers(int numItems)
numItems
items recommended for each user, for all users.numItems
- max number of recommendations for each userpublic Dataset<Row> recommendForUserSubset(Dataset<?> dataset, int numItems)
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.dataset
- a Dataset containing a column of user ids. The column name must match userCol
.numItems
- max number of recommendations for each user.public Dataset<Row> recommendForAllItems(int numUsers)
numUsers
users recommended for each item, for all items.numUsers
- max number of recommendations for each itempublic Dataset<Row> recommendForItemSubset(Dataset<?> dataset, int numUsers)
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.dataset
- a Dataset containing a column of item ids. The column name must match itemCol
.numUsers
- max number of recommendations for each item.