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

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

↳ Parent: Orange.clustering.KMeans

Complexity

Conditions

Size

Total Lines

Duplication

Lines	0
Ratio	0 %

Metric	Value
cc	1
dl	0
loc	4
rs	10

import numpy as np
# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3
import sklearn.cluster as skl_cluster
# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3
from sklearn.metrics import silhouette_score
# .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 Orange.distance import Euclidean


__all__ = ["KMeans"]


class KMeans(SklProjector):
    __wraps__ = skl_cluster.KMeans

    def __init__(self, n_clusters=8, init='k-means++', n_init=10, max_iter=300,

                 tol=0.0001, random_state=None, preprocessors=None):

        super().__init__(preprocessors=preprocessors)
        self.params = vars()

    def fit(self, X, Y=None):
        proj = skl_cluster.KMeans(**self.params)
        if isinstance(X, Table):
            proj = proj.fit(X.X, Y)
            proj.silhouette = silhouette_score(X.X, proj.labels_)
        else:
            proj = proj.fit(X, Y)
            proj.silhouette = silhouette_score(X, proj.labels_)
        proj.inertia = proj.inertia_ / len(X)
        cluster_dist = Euclidean(proj.cluster_centers_)
        proj.inter_cluster = np.mean(cluster_dist[np.triu_indices_from(cluster_dist, 1)])
        return KMeansModel(proj, self.preprocessors)


class KMeansModel(Projection):
    def __init__(self, proj, preprocessors=None):

        super().__init__(proj=proj)
        self.k = self.proj.get_params()["n_clusters"]
        self.centroids = self.proj.cluster_centers_

    def __call__(self, data):
        if isinstance(data, Table):
            if data.domain is not self.pre_domain:
                data = Table(self.pre_domain, data)
            c = DiscreteVariable(name='Cluster id', values=range(self.k))
            domain = Domain([c])
            return Table(
                domain,
                self.proj.predict(data.X).astype(int).reshape((len(data), 1)))
        elif isinstance(data, Instance):
            if data.domain is not self.pre_domain:
                data = Instance(self.pre_domain, data)
            c = DiscreteVariable(name='Cluster id', values=range(self.k))
            domain = Domain([c])
            return Table(
                domain,
                np.atleast_2d(self.proj.predict(data._x)).astype(int))
class MyParent:
    def __init__(self):
        self._x = 1;
        self.y = 2;

class MyChild(MyParent):
    def some_method(self):
        return self._x    # Ok, since accessed from a child class

class AnotherClass:
    def some_method(self, instance_of_my_child):
        return instance_of_my_child._x   # Would be flagged as AnotherClass is not
                                         # a child class of MyParent
        else:
            return self.proj.predict(data).reshape((len(data), 1))


GitHub Access Token became invalid

Orange.clustering.KMeans.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 as np
			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			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...
3			from sklearn.metrics import silhouette_score
			0 ignored issues – show Configuration introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report The import `sklearn.metrics` 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...
4
5			from Orange.data import Table, DiscreteVariable, Domain, Instance
6			from Orange.projection import SklProjector, Projection
7			from Orange.distance import Euclidean
8
9
10			__all__ = ["KMeans"]
11
12
13			class KMeans(SklProjector):
14			__wraps__ = skl_cluster.KMeans
15
16			def __init__(self, n_clusters=8, init='k-means++', n_init=10, max_iter=300,
			0 ignored issues – show Unused Code introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report The argument `init` seems to be unused. Loading history... Unused Code introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report The argument `n_init` seems to be unused. Loading history... Unused Code introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report The argument `max_iter` seems to be unused. Loading history... Unused Code introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report The argument `n_clusters` seems to be unused. Loading history...
17			tol=0.0001, random_state=None, preprocessors=None):
			0 ignored issues – show Unused Code introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report The argument `random_state` seems to be unused. Loading history... Unused Code introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report The argument `tol` seems to be unused. Loading history...
18			super().__init__(preprocessors=preprocessors)
19			self.params = vars()
20
21			def fit(self, X, Y=None):
22			proj = skl_cluster.KMeans(**self.params)
23			if isinstance(X, Table):
24			proj = proj.fit(X.X, Y)
25			proj.silhouette = silhouette_score(X.X, proj.labels_)
26			else:
27			proj = proj.fit(X, Y)
28			proj.silhouette = silhouette_score(X, proj.labels_)
29			proj.inertia = proj.inertia_ / len(X)
30			cluster_dist = Euclidean(proj.cluster_centers_)
31			proj.inter_cluster = np.mean(cluster_dist[np.triu_indices_from(cluster_dist, 1)])
32			return KMeansModel(proj, self.preprocessors)
33
34
35			class KMeansModel(Projection):
36			def __init__(self, proj, preprocessors=None):
			0 ignored issues – show Unused Code introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report The argument `preprocessors` seems to be unused. Loading history...
37			super().__init__(proj=proj)
38			self.k = self.proj.get_params()["n_clusters"]
39			self.centroids = self.proj.cluster_centers_
40
41			def __call__(self, data):
42			if isinstance(data, Table):
43			if data.domain is not self.pre_domain:
44			data = Table(self.pre_domain, data)
45			c = DiscreteVariable(name='Cluster id', values=range(self.k))
46			domain = Domain([c])
47			return Table(
48			domain,
49			self.proj.predict(data.X).astype(int).reshape((len(data), 1)))
50			elif isinstance(data, Instance):
51			if data.domain is not self.pre_domain:
52			data = Instance(self.pre_domain, data)
53			c = DiscreteVariable(name='Cluster id', values=range(self.k))
54			domain = Domain([c])
55			return Table(
56			domain,
57			np.atleast_2d(self.proj.predict(data._x)).astype(int))
			0 ignored issues – show Coding Style Best Practice introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report It seems like `_x` was declared protected and should not be accessed from this context. Prefixing a member variable `_` is usually regarded as the equivalent of declaring it with protected visibility that exists in other languages. Consequentially, such a member should only be accessed from the same class or a child class: class MyParent: def __init__(self): self._x = 1; self.y = 2; class MyChild(MyParent): def some_method(self): return self._x # Ok, since accessed from a child class class AnotherClass: def some_method(self, instance_of_my_child): return instance_of_my_child._x # Would be flagged as AnotherClass is not # a child class of MyParent Loading history...
58			else:
59			return self.proj.predict(data).reshape((len(data), 1))
60

hugobuddel / orange3

GitHub Access Token became invalid

Orange.clustering.KMeans.__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

Duplication Side-by-Side

Filter issues like

Orange.clustering.KMeans.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