Integrate with dashboard
- accept string format in model parameters for meteos-ui - modify value list which are returned by meteos-api Change-Id: I10b270d3f33aaf470466f6daa96f1756739d723e
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@ -41,6 +41,7 @@ class ViewBuilder(common.ViewBuilder):
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'description': dataset.get('display_description'),
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'status': dataset.get('status'),
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'created_at': dataset.get('created_at'),
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'head': dataset.get('head'),
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'links': self._get_links(request, dataset['id'])
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}
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}
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@ -41,6 +41,7 @@ class ViewBuilder(common.ViewBuilder):
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'description': model.get('display_description'),
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'type': model.get('model_type'),
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'status': model.get('status'),
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'stdout': model.get('stdout'),
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'created_at': model.get('created_at'),
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'links': self._get_links(request, model['id'])
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}
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@ -113,9 +113,9 @@ class KMeansModelController(ModelController):
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def create_model(self, data, params):
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numClasses = params.get('numClasses', 2)
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numIterations = params.get('numIterations', 10)
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runs = params.get('runs', 10)
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numClasses = int(params.get('numClasses', 2))
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numIterations = int(params.get('numIterations', 10))
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runs = int(params.get('runs', 10))
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mode = params.get('mode', 'random')
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parsedData = data.map(
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@ -147,8 +147,8 @@ class RecommendationController(ModelController):
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def create_model(self, data, params):
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# Build the recommendation model using Alternating Least Squares
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rank = params.get('rank', 10)
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numIterations = params.get('numIterations', 10)
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rank = int(params.get('rank', 10))
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numIterations = int(params.get('numIterations', 10))
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ratings = self._create_ratings(data)
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@ -190,8 +190,8 @@ class LinearRegressionModelController(ModelController):
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def create_model(self, data, params):
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iterations = params.get('numIterations', 10)
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step = params.get('step', 0.00000001)
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iterations = int(params.get('numIterations', 10))
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step = float(params.get('step', 0.00000001))
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points = data.map(self.parsePoint)
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return LinearRegressionWithSGD.train(points,
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@ -200,8 +200,8 @@ class LinearRegressionModelController(ModelController):
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def create_model_libsvm(self, data, params):
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iterations = params.get('numIterations', 10)
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step = params.get('step', 0.00000001)
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iterations = int(params.get('numIterations', 10))
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step = float(params.get('step', 0.00000001))
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return LinearRegressionWithSGD.train(data,
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iterations=iterations,
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@ -238,14 +238,14 @@ class LogisticRegressionModelController(ModelController):
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def create_model(self, data, params):
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numIterations = params.get('numIterations', 10)
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numIterations = int(params.get('numIterations', 10))
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points = data.map(self.parsePoint)
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return LogisticRegressionWithSGD.train(points, numIterations)
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def create_model_libsvm(self, data, params):
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numIterations = params.get('numIterations', 10)
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numIterations = int(params.get('numIterations', 10))
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return LogisticRegressionWithSGD.train(data, numIterations)
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@ -298,8 +298,8 @@ class DecisionTreeModelController(ModelController):
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def create_model_libsvm(self, data, params):
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impurity = params.get('impurity', 'variance')
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maxDepth = params.get('maxDepth', 5)
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maxBins = params.get('maxBins', 32)
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maxDepth = int(params.get('maxDepth', 5))
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maxBins = int(params.get('maxBins', 32))
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return DecisionTree.trainRegressor(data,
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categoricalFeaturesInfo={},
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@ -337,9 +337,9 @@ class Word2VecModelController(ModelController):
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def create_model(self, data, params):
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learningRate = params.get('learningRate', 0.025)
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numIterations = params.get('numIterations', 10)
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minCount = params.get('minCount', 5)
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learningRate = float(params.get('learningRate', 0.025))
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numIterations = int(params.get('numIterations', 10))
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minCount = int(params.get('minCount', 5))
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word2vec = Word2Vec()
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word2vec.setLearningRate(learningRate)
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@ -376,9 +376,9 @@ class FPGrowthModelController(ModelController):
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def create_model(self, data, params):
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minSupport = params.get('minSupport', 0.2)
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numPartitions = params.get('numPartitions', 10)
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limits = params.get('limits', 10)
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minSupport = float(params.get('minSupport', 0.2))
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numPartitions = int(params.get('numPartitions', 10))
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limits = int(params.get('limits', 10))
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transactions = data.map(lambda line: line.strip().split(' '))
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