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, 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)