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sparktk.models.regression.linear_regression_test_metrics module

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# vim: set encoding=utf-8

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#
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#       http://www.apache.org/licenses/LICENSE-2.0
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#  distributed under the License is distributed on an "AS IS" BASIS,
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from sparktk.propobj import PropertiesObject

class LinearRegressionTestMetrics(PropertiesObject):
    """
    RegressionMetrics class used to hold the data returned from linear regression test
    """
    def __init__(self, scala_result):
        self._explained_variance = scala_result.explainedVariance()
        self._mean_absolute_error = scala_result.meanAbsoluteError()
        self._mean_squared_error = scala_result.meanSquaredError()
        self._r2 = scala_result.r2()
        self._root_mean_squared_error = scala_result.rootMeanSquaredError()

    @property
    def explained_variance(self):
        """The explained variance regression score"""
        return self._explained_variance

    @property
    def mean_absolute_error(self):
        """The risk function corresponding to the expected value of the absolute error loss or l1-norm loss"""
        return self._mean_absolute_error

    @property
    def mean_squared_error(self):
        """The risk function corresponding to the expected value of the squared error loss or quadratic loss"""
        return self._mean_squared_error

    @property
    def r2(self):
        """The coefficient of determination"""
        return self._r2

    @property
    def root_mean_squared_error(self):
        """The square root of the mean squared error"""
        return self._root_mean_squared_error

Classes

class LinearRegressionTestMetrics

RegressionMetrics class used to hold the data returned from linear regression test

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class LinearRegressionTestMetrics(PropertiesObject):
    """
    RegressionMetrics class used to hold the data returned from linear regression test
    """
    def __init__(self, scala_result):
        self._explained_variance = scala_result.explainedVariance()
        self._mean_absolute_error = scala_result.meanAbsoluteError()
        self._mean_squared_error = scala_result.meanSquaredError()
        self._r2 = scala_result.r2()
        self._root_mean_squared_error = scala_result.rootMeanSquaredError()

    @property
    def explained_variance(self):
        """The explained variance regression score"""
        return self._explained_variance

    @property
    def mean_absolute_error(self):
        """The risk function corresponding to the expected value of the absolute error loss or l1-norm loss"""
        return self._mean_absolute_error

    @property
    def mean_squared_error(self):
        """The risk function corresponding to the expected value of the squared error loss or quadratic loss"""
        return self._mean_squared_error

    @property
    def r2(self):
        """The coefficient of determination"""
        return self._r2

    @property
    def root_mean_squared_error(self):
        """The square root of the mean squared error"""
        return self._root_mean_squared_error

Ancestors (in MRO)

Instance variables

var explained_variance

The explained variance regression score

var mean_absolute_error

The risk function corresponding to the expected value of the absolute error loss or l1-norm loss

var mean_squared_error

The risk function corresponding to the expected value of the squared error loss or quadratic loss

var r2

The coefficient of determination

var root_mean_squared_error

The square root of the mean squared error

Methods

def __init__(

self, scala_result)

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def __init__(self, scala_result):
    self._explained_variance = scala_result.explainedVariance()
    self._mean_absolute_error = scala_result.meanAbsoluteError()
    self._mean_squared_error = scala_result.meanSquaredError()
    self._r2 = scala_result.r2()
    self._root_mean_squared_error = scala_result.rootMeanSquaredError()

def to_dict(

self)

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def to_dict(self):
    d = self._properties()
    d.update(self._attributes())
    return d

def to_json(

self)

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def to_json(self):
    return json.dumps(self.to_dict())