Orange.classification.SGDClassifier.__init__() - Code Metrics - hugobuddel/orange3 - Measure and Improve Code Quality continuously with Scrutinizer

Orange.classification.SGDClassifier.init() A
last analyzed 2015-12-08 12:38 UTC

↳ Parent: Orange.classification.SGDClassifier

Complexity

Conditions

Size

Total Lines

Duplication

Lines	0
Ratio	0 %

Metric	Value
cc	1
dl	0
loc	2
rs	10

import numpy
# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3
from sklearn import linear_model
# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3
from sklearn.preprocessing import Imputer
# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3
import Orange.data
import Orange.classification
#from Orange.data.continuizer import DomainContinuizer

class SGDLearner(Orange.classification.SklLearner):

    def __init__(self, all_classes, means=None, stds=None):
class SomeParent:
    def __init__(self):
        self.x = 1

class SomeChild(SomeParent):
    def __init__(self):
        # Initialize the super class
        SomeParent.__init__(self)
        self.all_classes = all_classes
        # The SGD Learner works significantly better with normalized data.
        # However, we cannot get the normalization from the data because we
        # do partial fitting. So it has to be provided.
        self.means = means
        self.stds = stds
        self.clf = linear_model.SGDClassifier(loss='log')
        self.clf.means = self.means
        self.clf.stds = self.stds

        #self.reset()

    def partial_fit(self, X, Y, W, normalize=True):

        X = X.copy()
        if normalize:
            if self.means is not None:
                X -= self.means
            if self.stds is not None:
                X /= self.stds

        # Sometimes this exception occurs, but it is unclear when:
        """
        Exception in thread Thread-3:
        Traceback (most recent call last):
          File "/home/evis/anaconda3/lib/python3.4/threading.py", line 920, in _bootstra
        p_inner
            self.run()
          File "/home/evis/anaconda3/lib/python3.4/threading.py", line 868, in run
            self._target(*self._args, **self._kwargs)
          File "/home/evis/orange3/Orange/widgets/classify/owsgd.py", line 239, in train
        ing_thread
            self.train()
          File "/home/evis/orange3/Orange/widgets/classify/owsgd.py", line 261, in train
            classifier = self.learner.partial_fit(new_instances.X, new_instances.Y, None)
          File "/home/evis/orange3/Orange/classification/sgd.py", line 31, in partial_fit
            self.clf = self.clf.partial_fit(X, Y.reshape(-1), self.all_classes)
          File "/home/evis/anaconda3/lib/python3.4/site-packages/sklearn/linear_model/stochastic_gradient.py", line 526, in partial_fit
            coef_init=None, intercept_init=None)
          File "/home/evis/anaconda3/lib/python3.4/site-packages/sklearn/linear_model/stochastic_gradient.py", line 358, in _partial_fit
            X, y = check_X_y(X, y, 'csr', dtype=np.float64, order="C")
          File "/home/evis/anaconda3/lib/python3.4/site-packages/sklearn/utils/validation.py", line 450, in check_X_y
            _assert_all_finite(y)
          File "/home/evis/anaconda3/lib/python3.4/site-packages/sklearn/utils/validation.py", line 52, in _assert_all_finite
            " or a value too large for %r." % X.dtype)
        ValueError: Input contains NaN, infinity or a value too large for dtype('float64').
        """

        #print("SGD training to %s" % Y.reshape(-1))

        self.clf = self.clf.partial_fit(X, Y.reshape(-1), self.all_classes)
        

        return SGDClassifier(self.clf)
    

    # TODO: Remove fit completely?

    fit = partial_fit

    # TODO: Is this reset function necessary?

    def reset(self):
        # 'log' or 'modified_huber' required to predict probabilities.
        self.clf = linear_model.SGDClassifier(loss='log')
        self.clf.means = self.means
        self.clf.stds = self.stds
    

    def decision_function(self, X):
        if self.means is not None and self.stds is not None:
            Xa = (numpy.array(X)-numpy.array(self.means))/numpy.array(self.stds)
        else:
            Xa = X
        

        value = self.clf.decision_function(Xa)
        return value

    def predict(self, X):
        if self.means is not None and self.stds is not None:
            Xa = (numpy.array(X)-numpy.array(self.means))/numpy.array(self.stds)
        else:
            1/0
class SomeClass:
    def __init__(self):
        self._x = 5

    def squared(self):
        return self._x * self._x

some_class = SomeClass()
some_class.squared()        # Flagged, as the return value is not used
print(some_class.squared()) # Ok
            Xa = X
        

        value = self.clf.predict(Xa)
        return value
        


class SGDClassifier(Orange.classification.SklModel):

    def __init__(self, clf):
class SomeParent:
    def __init__(self):
        self.x = 1

class SomeChild(SomeParent):
    def __init__(self):
        # Initialize the super class
        SomeParent.__init__(self)
        self.clf = clf

    def predict(self, X):
        if self.clf.means is not None and self.clf.stds is not None:
            Xa = (numpy.array(X)-numpy.array(self.clf.means))/numpy.array(self.clf.stds)
        else:
            Xa = X
        

        value = self.clf.predict(Xa)
        return value
        

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Orange.classification.SGDClassifier.init() A
last analyzed 2015-12-08 12:38 UTC

Complexity

Size

Duplication

1. Missing Dependencies

2. Missing init.py files

1. Missing Dependencies

2. Missing init.py files

1. Missing Dependencies

2. Missing init.py files

1			import numpy
			0 ignored issues – show Configuration introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report The import `numpy` could not be resolved. This can be caused by one of the following: 1. Missing Dependencies This error could indicate a configuration issue of Pylint. Make sure that your libraries are available by adding the necessary commands. # .scrutinizer.yml before_commands: - sudo pip install abc # Python2 - sudo pip3 install abc # Python3 Tip: We are currently not using virtualenv to run pylint, when installing your modules make sure to use the command for the correct version. 2. Missing __init__.py files This error could also result from missing `__init__.py` files in your module folders. Make sure that you place one file in each sub-folder. Loading history...
2			from sklearn import linear_model
			0 ignored issues – show Configuration introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report The import `sklearn` could not be resolved. This can be caused by one of the following: 1. Missing Dependencies This error could indicate a configuration issue of Pylint. Make sure that your libraries are available by adding the necessary commands. # .scrutinizer.yml before_commands: - sudo pip install abc # Python2 - sudo pip3 install abc # Python3 Tip: We are currently not using virtualenv to run pylint, when installing your modules make sure to use the command for the correct version. 2. Missing __init__.py files This error could also result from missing `__init__.py` files in your module folders. Make sure that you place one file in each sub-folder. Loading history...
3			from sklearn.preprocessing import Imputer
			0 ignored issues – show Configuration introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report The import `sklearn.preprocessing` could not be resolved. This can be caused by one of the following: 1. Missing Dependencies This error could indicate a configuration issue of Pylint. Make sure that your libraries are available by adding the necessary commands. # .scrutinizer.yml before_commands: - sudo pip install abc # Python2 - sudo pip3 install abc # Python3 Tip: We are currently not using virtualenv to run pylint, when installing your modules make sure to use the command for the correct version. 2. Missing __init__.py files This error could also result from missing `__init__.py` files in your module folders. Make sure that you place one file in each sub-folder. Loading history... Unused Code introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report Unused Imputer imported from sklearn.preprocessing Loading history...
4			import Orange.data
5			import Orange.classification
6			#from Orange.data.continuizer import DomainContinuizer
7
8			class SGDLearner(Orange.classification.SklLearner):
9
10			def __init__(self, all_classes, means=None, stds=None):
			0 ignored issues – show Bug introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report The `__init__` method of the super-class `SklLearnerClassification` is not called. It is generally advisable to initialize the super-class by calling its `__init__` method: class SomeParent: def __init__(self): self.x = 1 class SomeChild(SomeParent): def __init__(self): # Initialize the super class SomeParent.__init__(self) Loading history...
11			self.all_classes = all_classes
12			# The SGD Learner works significantly better with normalized data.
13			# However, we cannot get the normalization from the data because we
14			# do partial fitting. So it has to be provided.
15			self.means = means
16			self.stds = stds
17			self.clf = linear_model.SGDClassifier(loss='log')
18			self.clf.means = self.means
19			self.clf.stds = self.stds
20
21			#self.reset()
22
23			def partial_fit(self, X, Y, W, normalize=True):
			0 ignored issues – show Unused Code introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report The argument `W` seems to be unused. Loading history...
24			X = X.copy()
25			if normalize:
26			if self.means is not None:
27			X -= self.means
28			if self.stds is not None:
29			X /= self.stds
30
31			# Sometimes this exception occurs, but it is unclear when:
32			"""
33			Exception in thread Thread-3:
34			Traceback (most recent call last):
35			File "/home/evis/anaconda3/lib/python3.4/threading.py", line 920, in _bootstra
36			p_inner
37			self.run()
38			File "/home/evis/anaconda3/lib/python3.4/threading.py", line 868, in run
39			self._target(self._args, *self._kwargs)
40			File "/home/evis/orange3/Orange/widgets/classify/owsgd.py", line 239, in train
41			ing_thread
42			self.train()
43			File "/home/evis/orange3/Orange/widgets/classify/owsgd.py", line 261, in train
44			classifier = self.learner.partial_fit(new_instances.X, new_instances.Y, None)
45			File "/home/evis/orange3/Orange/classification/sgd.py", line 31, in partial_fit
46			self.clf = self.clf.partial_fit(X, Y.reshape(-1), self.all_classes)
47			File "/home/evis/anaconda3/lib/python3.4/site-packages/sklearn/linear_model/stochastic_gradient.py", line 526, in partial_fit
48			coef_init=None, intercept_init=None)
49			File "/home/evis/anaconda3/lib/python3.4/site-packages/sklearn/linear_model/stochastic_gradient.py", line 358, in _partial_fit
50			X, y = check_X_y(X, y, 'csr', dtype=np.float64, order="C")
51			File "/home/evis/anaconda3/lib/python3.4/site-packages/sklearn/utils/validation.py", line 450, in check_X_y
52			_assert_all_finite(y)
53			File "/home/evis/anaconda3/lib/python3.4/site-packages/sklearn/utils/validation.py", line 52, in _assert_all_finite
54			" or a value too large for %r." % X.dtype)
55			ValueError: Input contains NaN, infinity or a value too large for dtype('float64').
56			"""
			0 ignored issues – show Unused Code introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report This string statement has no effect and could be removed. Loading history...
57			#print("SGD training to %s" % Y.reshape(-1))
58
59			self.clf = self.clf.partial_fit(X, Y.reshape(-1), self.all_classes)
60
			0 ignored issues – show Coding Style introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report Trailing whitespace Loading history...
61			return SGDClassifier(self.clf)
62
			0 ignored issues – show Coding Style introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report Trailing whitespace Loading history...
63			# TODO: Remove fit completely?
			0 ignored issues – show Coding Style introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report `TODO` and `FIXME` comments should generally be avoided. Loading history...
64			fit = partial_fit
65
66			# TODO: Is this reset function necessary?
			0 ignored issues – show Coding Style introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report `TODO` and `FIXME` comments should generally be avoided. Loading history...
67			def reset(self):
68			# 'log' or 'modified_huber' required to predict probabilities.
69			self.clf = linear_model.SGDClassifier(loss='log')
70			self.clf.means = self.means
71			self.clf.stds = self.stds
72
			0 ignored issues – show Coding Style introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report Trailing whitespace Loading history...
73			def decision_function(self, X):
74			if self.means is not None and self.stds is not None:
75			Xa = (numpy.array(X)-numpy.array(self.means))/numpy.array(self.stds)
76			else:
77			Xa = X
78
			0 ignored issues – show Coding Style introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report Trailing whitespace Loading history...
79			value = self.clf.decision_function(Xa)
80			return value
81
82			def predict(self, X):
83			if self.means is not None and self.stds is not None:
84			Xa = (numpy.array(X)-numpy.array(self.means))/numpy.array(self.stds)
85			else:
86			1/0
			0 ignored issues – show Unused Code introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report This statement seems to have no effect and could be removed. This issue is typically triggered when a function that does not have side-effects is called and the return value is discarded: class SomeClass: def __init__(self): self._x = 5 def squared(self): return self._x * self._x some_class = SomeClass() some_class.squared() # Flagged, as the return value is not used print(some_class.squared()) # Ok Loading history...
87			Xa = X
88
			0 ignored issues – show Coding Style introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report Trailing whitespace Loading history...
89			value = self.clf.predict(Xa)
90			return value
91
			0 ignored issues – show Coding Style introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report Trailing whitespace Loading history...
92
93			class SGDClassifier(Orange.classification.SklModel):
94
95			def __init__(self, clf):
			0 ignored issues – show Bug introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report The `__init__` method of the super-class `SklModelClassification` is not called. It is generally advisable to initialize the super-class by calling its `__init__` method: class SomeParent: def __init__(self): self.x = 1 class SomeChild(SomeParent): def __init__(self): # Initialize the super class SomeParent.__init__(self) Loading history...
96			self.clf = clf
97
98			def predict(self, X):
99			if self.clf.means is not None and self.clf.stds is not None:
100			Xa = (numpy.array(X)-numpy.array(self.clf.means))/numpy.array(self.clf.stds)
101			else:
102			Xa = X
103
			0 ignored issues – show Coding Style introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report Trailing whitespace Loading history...
104			value = self.clf.predict(Xa)
105			return value
106

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Orange.classification.SGDClassifier.__init__() A last analyzed 2015-12-08 12:38 UTC

Complexity

Size

Duplication

1. Missing Dependencies

2. Missing __init__.py files

1. Missing Dependencies

2. Missing __init__.py files

1. Missing Dependencies

2. Missing __init__.py files

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Orange.classification.SGDClassifier.init() A
last analyzed 2015-12-08 12:38 UTC

2. Missing init.py files

2. Missing init.py files

2. Missing init.py files