def train(cls, sc, data, iterations=100, step=1.0,
mini_batch_fraction=1.0, initial_weights=None):
"""Train a logistic regression model on the given data."""
return _regression_train_wrapper(sc, lambda d, i:
sc._jvm.PythonMLLibAPI().trainLogisticRegressionModelWithSGD(d._jrdd,
iterations, step, mini_batch_fraction, i),
LogisticRegressionModel, data, initial_weights)
def train(cls, sc, data, iterations=100, step=1.0, reg_param=1.0,
mini_batch_fraction=1.0, initial_weights=None):
"""Train a support vector machine on the given data."""
return _regression_train_wrapper(sc, lambda d, i:
sc._jvm.PythonMLLibAPI().trainSVMModelWithSGD(d._jrdd,
iterations, step, reg_param, mini_batch_fraction, i),
SVMModel, data, initial_weights)
def train(cls, data, iterations=100, step=1.0, regParam=1.0,
miniBatchFraction=1.0, initialWeights=None):
"""Train a ridge regression model on the given data."""
sc = data.context
train_func = lambda d, i: sc._jvm.PythonMLLibAPI().trainRidgeModelWithSGD(
d._jrdd, iterations, step, regParam, miniBatchFraction, i)
return _regression_train_wrapper(sc, train_func, RidgeRegressionModel, data, initialWeights)
def train(cls, data, iterations=100, step=1.0, miniBatchFraction=1.0,
initialWeights=None, regParam=1.0, regType=None, intercept=False):
"""
Train a linear regression model on the given data.
@param data: The training data.
@param iterations: The number of iterations (default: 100).
@param step: The step parameter used in SGD
(default: 1.0).
@param miniBatchFraction: Fraction of data to be used for each SGD
iteration.
@param initialWeights: The initial weights (default: None).
@param regParam: The regularizer parameter (default: 1.0).
@param regType: The type of regularizer used for training
our model.
Allowed values: "l1" for using L1Updater,
"l2" for using
SquaredL2Updater,
"none" for no regularizer.
(default: "none")
@param intercept: Boolean parameter which indicates the use
or not of the augmented representation for
training data (i.e. whether bias features
are activated or not).
"""
sc = data.context
if regType is None:
regType = "none"
train_f = lambda d, i: sc._jvm.PythonMLLibAPI().trainLinearRegressionModelWithSGD(
d._jrdd, iterations, step, miniBatchFraction, i, regParam, regType, intercept)
return _regression_train_wrapper(sc, train_f, LinearRegressionModel, data, initialWeights)
def train(cls, data, iterations=100, step=1.0,
miniBatchFraction=1.0, initialWeights=None):
"""Train a linear regression model on the given data."""
sc = data.context
return _regression_train_wrapper(sc, lambda d, i:
sc._jvm.PythonMLLibAPI().trainLinearRegressionModelWithSGD(
d._jrdd, iterations, step, miniBatchFraction, i),
LinearRegressionModel, data, initialWeights)
def train(cls, data, iterations=100, step=1.0,
miniBatchFraction=1.0, initialWeights=None):
"""Train a linear regression model on the given data."""
sc = data.context
return _regression_train_wrapper(sc, lambda d, i:
sc._jvm.PythonMLLibAPI().trainLinearRegressionModelWithSGD(
d._jrdd, iterations, step, miniBatchFraction, i),
LinearRegressionModel, data, initialWeights)
def train(cls, data, iterations=100, step=1.0, regParam=1.0,
miniBatchFraction=1.0, initialWeights=None):
"""Train a support vector machine on the given data."""
sc = data.context
return _regression_train_wrapper(sc, lambda d, i:
sc._jvm.PythonMLLibAPI().trainSVMModelWithSGD(d._jrdd,
iterations, step, regParam, miniBatchFraction, i),
SVMModel, data, initialWeights)
def train(cls,
sc,
data,
iterations=100,
step=1.0,
mini_batch_fraction=1.0,
initial_weights=None):
"""Train a logistic regression model on the given data."""
return _regression_train_wrapper(
sc, lambda d, i: sc._jvm.
PythonMLLibAPI().trainLogisticRegressionModelWithSGD(
d._jrdd, iterations, step, mini_batch_fraction, i),
LogisticRegressionModel, data, initial_weights)
