org.apache.spark.ml.classification
LogisticRegression
Related Docs:
object LogisticRegression
| package classification
org.apache.spark.ml.classification
An alias for getOrDefault().
An alias for getOrDefault().
Param for suggested depth for treeAggregate (>= 2).
Param for suggested depth for treeAggregate (>= 2).
If threshold and thresholds are both set, ensures they are consistent.
If threshold and thresholds are both set, ensures they are consistent.
IllegalArgumentException if threshold and thresholds are not equivalent
Clears the user-supplied value for the input param.
Clears the user-supplied value for the input param.
Creates a copy of this instance with the same UID and some extra params.
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.
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.
the target instance, which should work with the same set of default Params as this source instance
extra params to be copied to the target's paramMap
the target instance with param values copied
Default implementation of copy with extra 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.
Param for the ElasticNet mixing parameter, in range [0, 1].
Param for the ElasticNet mixing parameter, in range [0, 1]. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.
Explains a param.
Explains a param.
input param, must belong to this instance.
a string that contains the input param name, doc, and optionally its default value and the user-supplied value
Explains all params of this instance.
Explains all params of this instance. See explainParam().
Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.
Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.
DataFrame with columns for labels (org.apache.spark.sql.types.NumericType)
and features (Vector).
Number of classes label can take. Labels must be integers in the range [0, numClasses).
Throws SparkException if any label is a non-integer or is negative
Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.
Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.
extractParamMap with no extra values.
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.
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 the name of family which is a description of the label distribution to be used in the model.
Param for the name of family which is a description of the label distribution to be used in the model. Supported options:
Param for features column name.
Param for features column name.
Fits a model to the input data.
Fits multiple models to the input data with multiple sets of parameters.
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.
input dataset
An array of parameter maps. These values override any specified in this Estimator's embedded ParamMap.
fitted models, matching the input parameter maps
Fits a single model to the input data with provided parameter map.
Fits a single model to the input data with provided parameter map.
input dataset
Parameter map. These values override any specified in this Estimator's embedded ParamMap.
fitted model
Fits a single model to the input data with optional parameters.
Fits a single model to the input data with optional parameters.
input dataset
the first param pair, overrides embedded params
other param pairs. These values override any specified in this Estimator's embedded ParamMap.
fitted model
Param for whether to fit an intercept term.
Param for whether to fit an intercept term.
Optionally returns the user-supplied value of a param.
Optionally returns the user-supplied value of a param.
Gets the default value of a parameter.
Gets the default value of a parameter.
Get the number of classes.
Get the number of classes. This looks in column metadata first, and if that is missing, then this assumes classes are indexed 0,1,...,numClasses-1 and computes numClasses by finding the maximum label value.
Label validation (ensuring all labels are integers >= 0) needs to be handled elsewhere,
such as in extractLabeledPoints().
Dataset which contains a column labelCol
Maximum number of classes allowed when inferred from data. If numClasses is specified in the metadata, then maxNumClasses is ignored.
number of classes
IllegalArgumentException if metadata does not specify numClasses, and the
actual numClasses exceeds maxNumClasses
Gets the value of a param in the embedded param map or its default value.
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.
Gets a param by its name.
Get threshold for binary classification.
Get threshold for binary classification.
If thresholds is set with length 2 (i.e., binary classification),
this returns the equivalent threshold:
1 / (1 + thresholds(0) / thresholds(1))
.
Otherwise, returns threshold if set, or its default value if unset.
1 / (1 + thresholds(0) / thresholds(1))
}}}
Otherwise, returns threshold if set, or its default value if unset.
IllegalArgumentException if thresholds is set to an array of length other than 2.
Get thresholds for binary or multiclass classification.
Get thresholds for binary or multiclass classification.
If thresholds is set, return its value.
Otherwise, if threshold is set, return the equivalent thresholds for binary
classification: (1-threshold, threshold).
If neither are set, throw an exception.
Tests whether the input param has a default value set.
Tests whether the input param has a default value set.
Tests whether this instance contains a param with a given name.
Tests whether this instance contains a param with a given name.
Checks whether a param is explicitly set or has a default value.
Checks whether a param is explicitly set or has a default value.
Checks whether a param is explicitly set.
Checks whether a param is explicitly set.
Param for label column name.
Param for label column name.
The lower bounds on coefficients if fitting under bound constrained optimization.
The lower bounds on coefficients if fitting under bound constrained optimization. The bound matrix must be compatible with the shape (1, number of features) for binomial regression, or (number of classes, number of features) for multinomial regression. Otherwise, it throws exception. Default is none.
The lower bounds on intercepts if fitting under bound constrained optimization.
The lower bounds on intercepts if fitting under bound constrained optimization. The bounds vector size must be equal to 1 for binomial regression, or the number of classes for multinomial regression. Otherwise, it throws exception. Default is none.
Param for maximum number of iterations (>= 0).
Param for maximum number of iterations (>= 0).
Returns all params sorted by their names.
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.
Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.
Param for prediction column name.
Param for prediction column name.
Param for Column name for predicted class conditional probabilities.
Param for Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.
Param for raw prediction (a.k.a.
Param for raw prediction (a.k.a. confidence) column name.
Param for regularization parameter (>= 0).
Param for regularization parameter (>= 0).
Saves this ML instance to the input path, a shortcut of write.save(path).
Saves this ML instance to the input path, a shortcut of write.save(path).
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.
Sets a parameter (by name) in the embedded param map.
Sets a parameter (by name) in the embedded param map.
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.
Suggested depth for treeAggregate (greater than or equal to 2).
Suggested depth for treeAggregate (greater than or equal to 2). If the dimensions of features or the number of partitions are large, this param could be adjusted to a larger size. Default is 2.
Sets default values for a list of params.
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.
a list of param pairs that specify params and their default values to set respectively. Make sure that the params are initialized before this method gets called.
Sets a default value for a param.
Sets a default value for a param.
param to set the default value. Make sure that this param is initialized before this method gets called.
the default value
Set the ElasticNet mixing parameter.
Set the ElasticNet mixing parameter. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty. For alpha in (0,1), the penalty is a combination of L1 and L2. Default is 0.0 which is an L2 penalty.
Note: Fitting under bound constrained optimization only supports L2 regularization, so throws exception if this param is non-zero value.
Sets the value of param family.
Sets the value of param family. Default is "auto".
Whether to fit an intercept term.
Whether to fit an intercept term. Default is true.
Set the lower bounds on coefficients if fitting under bound constrained optimization.
Set the lower bounds on coefficients if fitting under bound constrained optimization.
Set the lower bounds on intercepts if fitting under bound constrained optimization.
Set the lower bounds on intercepts if fitting under bound constrained optimization.
Set the maximum number of iterations.
Set the maximum number of iterations. Default is 100.
Set the regularization parameter.
Set the regularization parameter. Default is 0.0.
Whether to standardize the training features before fitting the model.
Whether to standardize the training features before fitting the model. The coefficients of models will be always returned on the original scale, so it will be transparent for users. Note that with/without standardization, the models should be always converged to the same solution when no regularization is applied. In R's GLMNET package, the default behavior is true as well. Default is true.
Set threshold in binary classification, in range [0, 1].
Set threshold in binary classification, in range [0, 1].
If the estimated probability of class label 1 is greater than threshold, then predict 1, else 0. A high threshold encourages the model to predict 0 more often; a low threshold encourages the model to predict 1 more often.
Note: Calling this with threshold p is equivalent to calling setThresholds(Array(1-p, p)).
When setThreshold() is called, any user-set value for thresholds will be cleared.
If both threshold and thresholds are set in a ParamMap, then they must be
equivalent.
Default is 0.5.
Set thresholds in multiclass (or binary) classification to adjust the probability of predicting each class.
Set thresholds in multiclass (or binary) classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values greater than 0, excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold.
Note: When setThresholds() is called, any user-set value for threshold will be cleared.
If both threshold and thresholds are set in a ParamMap, then they must be
equivalent.
Set the convergence tolerance of iterations.
Set the convergence tolerance of iterations. Smaller value will lead to higher accuracy at the cost of more iterations. Default is 1E-6.
Set the upper bounds on coefficients if fitting under bound constrained optimization.
Set the upper bounds on coefficients if fitting under bound constrained optimization.
Set the upper bounds on intercepts if fitting under bound constrained optimization.
Set the upper bounds on intercepts if fitting under bound constrained optimization.
Sets the value of param weightCol.
Sets the value of param weightCol. If this is not set or empty, we treat all instance weights as 1.0. Default is not set, so all instances have weight one.
Param for whether to standardize the training features before fitting the model.
Param for whether to standardize the training features before fitting the model.
Param for threshold in binary classification prediction, in range [0, 1].
Param for threshold in binary classification prediction, in range [0, 1].
Param for Thresholds in multi-class classification to adjust the probability of predicting each class.
Param for Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0 excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold.
Param for the convergence tolerance for iterative algorithms (>= 0).
Param for the convergence tolerance for iterative algorithms (>= 0).
Train a model using the given dataset and parameters.
Train a model using the given dataset and parameters.
Developers can implement this instead of fit() to avoid dealing with schema validation
and copying parameters into the model.
Training dataset
Fitted model
:: DeveloperApi ::
:: DeveloperApi ::
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 ::
:: 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.
An immutable unique ID for the object and its derivatives.
The upper bounds on coefficients if fitting under bound constrained optimization.
The upper bounds on coefficients if fitting under bound constrained optimization. The bound matrix must be compatible with the shape (1, number of features) for binomial regression, or (number of classes, number of features) for multinomial regression. Otherwise, it throws exception. Default is none.
The upper bounds on intercepts if fitting under bound constrained optimization.
The upper bounds on intercepts if fitting under bound constrained optimization. The bound vector size must be equal to 1 for binomial regression, or the number of classes for multinomial regression. Otherwise, it throws exception. Default is none.
Validates and transforms the input schema with the provided param map.
Validates and transforms the input schema with the provided param map.
input schema
whether this is in fitting
SQL DataType for FeaturesType.
E.g., VectorUDT for vector features.
output schema
Param for weight column name.
Param for weight column name. If this is not set or empty, we treat all instance weights as 1.0.
Returns an MLWriter instance for this ML instance.
Returns an MLWriter instance for this ML instance.
A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.
A list of advanced, expert-only (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.
Logistic regression. Supports:
This class supports fitting traditional logistic regression model by LBFGS/OWLQN and bound (box) constrained logistic regression model by LBFGSB.