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

Orange.clustering.DBSCANModel.init() A
last analyzed 2015-12-08 12:38 UTC

↳ Parent: Orange.clustering.DBSCANModel

Complexity

Conditions

Size

Total Lines

Duplication

Lines	0
Ratio	0 %

Metric	Value
cc	1
dl	0
loc	2
rs	10

import sklearn.cluster as skl_cluster
# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3
from Orange.data import Table, DiscreteVariable, Domain, Instance
from Orange.projection import SklProjector, Projection
from numpy import atleast_2d, ndarray, where
# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3


__all__ = ["DBSCAN"]

class DBSCAN(SklProjector):
    __wraps__ = skl_cluster.DBSCAN

    def __init__(self, eps=0.5, min_samples=5, metric='euclidean',

                 algorithm='auto', leaf_size=30, p=None,

                 preprocessors=None):
        super().__init__(preprocessors=preprocessors)
        self.params = vars()

    def fit(self, X, Y=None):
        proj = skl_cluster.DBSCAN(**self.params)
        self.X = X
class Foo:
    def __init__(self, x=None):
        self.x = x
        if isinstance(X, Table):
            proj = proj.fit(X.X,)
        else:
            proj = proj.fit(X, )
        return DBSCANModel(proj)


class DBSCANModel(Projection):
    def __init__(self, proj):
        super().__init__(proj=proj)

    def __call__(self, data):
        if isinstance(data, ndarray):
            return self.proj.fit_predict(data).reshape((len(data), 1))

        if isinstance(data, Table):
            if data.domain is not self.pre_domain:
                data = Table(self.pre_domain, data)
            y = self.proj.fit_predict(data.X)
            vals = [-1] + list(self.proj.core_sample_indices_)
            c = DiscreteVariable(name='Core sample index', values=vals)
            domain = Domain([c])
            return Table(domain, y.reshape(len(y), 1))

        elif isinstance(data, Instance):
            if data.domain is not self.pre_domain:
                data = Instance(self.pre_domain, data)
            # Instances-by-Instance classification is not defined;
            raise Exception("Core sample assignment is not supported "
                            "for single instances.")


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Orange.clustering.DBSCANModel.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			import sklearn.cluster as skl_cluster
			0 ignored issues – show Configuration introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report The import `sklearn.cluster` 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 Orange.data import Table, DiscreteVariable, Domain, Instance
3			from Orange.projection import SklProjector, Projection
4			from numpy import atleast_2d, ndarray, where
			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... Unused Code introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report Unused where imported from numpy Loading history... Unused Code introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report Unused atleast_2d imported from numpy Loading history...
5
6
7			__all__ = ["DBSCAN"]
8
9			class DBSCAN(SklProjector):
10			__wraps__ = skl_cluster.DBSCAN
11
12			def __init__(self, eps=0.5, min_samples=5, metric='euclidean',
			0 ignored issues – show Unused Code introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report The argument `min_samples` seems to be unused. Loading history... Unused Code introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report The argument `metric` seems to be unused. Loading history... Unused Code introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report The argument `eps` seems to be unused. Loading history...
13			algorithm='auto', leaf_size=30, p=None,
			0 ignored issues – show Unused Code introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report The argument `leaf_size` seems to be unused. Loading history... Unused Code introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report The argument `p` seems to be unused. Loading history... Unused Code introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report The argument `algorithm` seems to be unused. Loading history...
14			preprocessors=None):
15			super().__init__(preprocessors=preprocessors)
16			self.params = vars()
17
18			def fit(self, X, Y=None):
19			proj = skl_cluster.DBSCAN(**self.params)
20			self.X = X
			0 ignored issues – show Coding Style introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report The attribute `X` was defined outside `__init__`. It is generally a good practice to initialize all attributes to default values in the `__init__` method: class Foo: def __init__(self, x=None): self.x = x Loading history...
21			if isinstance(X, Table):
22			proj = proj.fit(X.X,)
23			else:
24			proj = proj.fit(X, )
25			return DBSCANModel(proj)
26
27
28			class DBSCANModel(Projection):
29			def __init__(self, proj):
30			super().__init__(proj=proj)
31
32			def __call__(self, data):
33			if isinstance(data, ndarray):
34			return self.proj.fit_predict(data).reshape((len(data), 1))
35
36			if isinstance(data, Table):
37			if data.domain is not self.pre_domain:
38			data = Table(self.pre_domain, data)
39			y = self.proj.fit_predict(data.X)
40			vals = [-1] + list(self.proj.core_sample_indices_)
41			c = DiscreteVariable(name='Core sample index', values=vals)
42			domain = Domain([c])
43			return Table(domain, y.reshape(len(y), 1))
44
45			elif isinstance(data, Instance):
46			if data.domain is not self.pre_domain:
47			data = Instance(self.pre_domain, data)
48			# Instances-by-Instance classification is not defined;
49			raise Exception("Core sample assignment is not supported "
50			"for single instances.")
51

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Orange.clustering.DBSCANModel.__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

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Orange.clustering.DBSCANModel.init() A
last analyzed 2015-12-08 12:38 UTC

2. Missing init.py files

2. Missing init.py files