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meteos
Code Issues Proposed changes

Merge "Add RidgeRegression Model"

This commit is contained in:
Jenkins 2017-02-21 06:58:21 +00:00 committed by Gerrit Code Review
parent da756774af b72ac81d9c
commit 3bf27d30da
1 changed files with 47 additions and 14 deletions
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61
meteos/cluster/binary/meteos-script-1.6.0.py
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@ -62,6 +62,8 @@ from pyspark.mllib.recommendation import Rating
from pyspark.mllib.regression import LabeledPoint
from pyspark.mllib.regression import LinearRegressionModel
from pyspark.mllib.regression import LinearRegressionWithSGD
from pyspark.mllib.regression import RidgeRegressionModel
from pyspark.mllib.regression import RidgeRegressionWithSGD
from pyspark.mllib.tree import DecisionTree
from pyspark.mllib.tree import DecisionTreeModel
from pyspark.mllib.util import MLUtils
@ -185,29 +187,40 @@ class RecommendationController(ModelController):
return model.predict(parsedData[0], parsedData[1])
class LinearRegressionModelController(ModelController):
class RegressionModelController(ModelController):
def __init__(self):
super(LinearRegressionModelController, self).__init__()
def __init__(self, train_name, model_name):
super(RegressionModelController, self).__init__()
self.train_class = eval(train_name)
self.model_class = eval(model_name)
self.model_params = {}
def _parse_model_params(self, params):
p = {}
p['iterations'] = int(params.get('numIterations', 100))
p['step'] = float(params.get('step', 0.00000001))
p['miniBatchFraction'] = float(params.get('miniBatchFraction', 1.0))
p['convergenceTol'] = float(params.get('convergenceTol', 0.001))
if self.__class__.__name__ == 'LinearRegressionModelController':
p['regParam'] = float(params.get('regParam', 0.0))
elif self.__class__.__name__ == 'RidgeRegressionModelController':
p['regParam'] = float(params.get('regParam', 0.01))
self.model_params = p
def create_model(self, data, params):
iterations = int(params.get('numIterations', 10))
step = float(params.get('step', 0.00000001))
self._parse_model_params(params)
points = data.map(self.parsePoint)
return LinearRegressionWithSGD.train(points,
iterations=iterations,
step=step)
return getattr(self.train_class, 'train')(points, **self.model_params)
def create_model_libsvm(self, data, params):
iterations = int(params.get('numIterations', 10))
step = float(params.get('step', 0.00000001))
self._parse_model_params(params)
return LinearRegressionWithSGD.train(data,
iterations=iterations,
step=step)
return getattr(self.train_class, 'train')(data, **self.model_params)
def evaluate_model(self, context, model, data):
@ -224,7 +237,7 @@ class LinearRegressionModelController(ModelController):
return result
def load_model(self, context, path):
return LinearRegressionModel.load(context, path)
return getattr(self.model_class, 'load')(context, path)
def predict(self, model, params):
return model.predict(params.split(','))
@ -233,6 +246,24 @@ class LinearRegressionModelController(ModelController):
return self.predict(model, params)
class LinearRegressionModelController(RegressionModelController):
def __init__(self):
train_name = 'LinearRegressionWithSGD'
model_name = 'LinearRegressionModel'
super(LinearRegressionModelController, self).__init__(train_name,
model_name)
class RidgeRegressionModelController(RegressionModelController):
def __init__(self):
train_name = 'RidgeRegressionWithSGD'
model_name = 'RidgeRegressionModel'
super(RidgeRegressionModelController, self).__init__(train_name,
model_name)
class LogisticRegressionModelController(ModelController):
def __init__(self):
@ -456,6 +487,8 @@ class MeteosSparkController(object):
self.controller = LogisticRegressionModelController()
elif model_type == 'LinearRegression':
self.controller = LinearRegressionModelController()
elif model_type == 'RidgeRegression':
self.controller = RidgeRegressionModelController()
elif model_type == 'DecisionTreeRegression':
self.controller = DecisionTreeModelController()
elif model_type == 'Word2Vec':