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

Orange.classification.MLPLearner.fit() B
last analyzed 2015-12-08 12:38 UTC

↳ Parent: Orange.classification.MLPLearner

Complexity

Conditions

Size

Total Lines

Duplication

Lines	0
Ratio	0 %

Metric	Value
cc	5
dl	0
loc	23
rs	8.2508

import numpy as np
# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3
from scipy.optimize import fmin_l_bfgs_b
# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3

from Orange.classification import Learner, Model

__all__ = ["MLPLearner"]


def sigmoid(x):
    return 1.0 / (1.0 + np.exp(-x))


class MLPLearner(Learner):
    """Multilayer perceptron (feedforward neural network)

    This model uses stochastic gradient descent and the
    backpropagation algorithm to train the weights of a feedforward
    neural network.  The network uses the sigmoid activation
    functions, except for the last layer which computes the softmax
    activation function. The network can be used for binary and
    multiclass classification. Stochastic gradient descent minimizes
    the L2 regularize categorical crossentropy cost function. The
    topology of the network can be customized by setting the layers
    attribute. When using this model you should:

    - Choose a suitable:
        * topology (layers)
        * regularization parameter (lambda\_)
        * dropout (values of 0.2 for the input layer and 0.5 for the hidden
          layers usually work well)
        * The number of epochs of stochastic gradient descent (num_epochs)
        * The learning rate of stochastic gradient descent (learning_rate)
    - Continuize all discrete attributes
    - Transform the data set so that the columns are on a similar scale

    layers : list
        The topology of the network. A network with
        layer=[10, 100, 100, 3] has two hidden layers with 100 neurons each,
        10 features and a class value with 3 distinct values.

    lambda\_ : float, optional (default = 1.0)
        The regularization parameter. Higher values of lambda\_
        force the coefficients to be small.

    dropout : list, optional (default = None)
        The dropout rate for each, but the last,
        layer. The list should have one element less then the parameter layers.
        Values of 0.2 for the input layer and 0.5 for the hidden layers usually
        work well.

    num_epochs : int, optional
        The number of epochs of stochastic gradient descent

    learning_rate : float, optional
        The learning rate of stochastic gradient descent

    batch_size : int, optional
        The batch size of stochastic gradient descent
    """

    name = 'mlp'

    def __init__(self, layers, lambda_=1.0, dropout=None, preprocessors=None,
param = 5

class Foo:
    def __init__(self, param):   # "param" would be flagged here
        self.param = param
                 **opt_args):
        super().__init__(preprocessors=preprocessors)
        if dropout is None:
            dropout = [0] * (len(layers) - 1)
        assert len(dropout) == len(layers) - 1

        self.layers = layers
        self.lambda_ = lambda_
        self.dropout = dropout
        self.opt_args = opt_args

    def unfold_params(self, params):
        T, b = [], []
        acc = 0
        for l1, l2 in zip(self.layers, self.layers[1:]):
            b.append(params[acc:acc + l2])
            acc += l2
            T.append(params[acc:acc + l1 * l2].reshape((l2, l1)))
            acc += l1 * l2
        return T, b

    def cost_grad(self, params, X, Y):
        T, b = self.unfold_params(params)

        # forward pass
        a, z = [], []

        dropout_mask = []
        for i in range(len(T)):
            if self.dropout is None or self.dropout[0] < 1e-7:
                dropout_mask.append(1)
            else:
                dropout_mask.append(
                    np.random.binomial(1, 1 - self.dropout[0],
                                       (X.shape[0], self.layers[i])))

        a.append(X * dropout_mask[0])
        for i in range(len(self.layers) - 2):
            z.append(a[i].dot(T[i].T) + b[i])
            a.append(sigmoid(z[i]) * dropout_mask[i + 1])

        # softmax last layer
        z.append(a[-1].dot(T[-1].T) + b[-1])
        P = np.exp(z[-1] - np.max(z[-1], axis=1)[:, None])
        P /= np.sum(P, axis=1)[:, None]
        a.append(P)

        # cost
        cost = -np.sum(np.log(a[-1] + 1e-15) * Y)
        for theta in T:
            cost += self.lambda_ * np.dot(theta.flat, theta.flat) / 2.0
        cost /= X.shape[0]

        # gradient
        params = []
        for i in range(len(self.layers) - 1):
            if i == 0:
                d = a[-1] - Y
param = 5

class Foo:
    def __init__(self, param):   # "param" would be flagged here
        self.param = param
            else:
                d = d.dot(T[-i]) * a[-i - 1] * (1 - a[-i - 1])
            dT = (a[-i - 2] * dropout_mask[-i - 1]).T.dot(d).T + self.lambda_\
                * T[-i - 1]
            db = np.sum(d, axis=0)

            params.extend([dT.flat, db.flat])
        grad = np.concatenate(params[::-1]) / X.shape[0]

        return cost, grad

    def fit_bfgs(self, params, X, Y):
        params, j, ret = fmin_l_bfgs_b(self.cost_grad, params,

                                       args=(X, Y), **self.opt_args)
        return params

    def fit_sgd(self, params, X, Y, num_epochs=1000, batch_size=100,
                learning_rate=0.1):
        # shuffle examples
        inds = np.random.permutation(X.shape[0])
        X = X[inds]
        Y = Y[inds]

        # split training and validation set
        num_tr = int(X.shape[0] * 0.8)

        X_tr, Y_tr = X[:num_tr], Y[:num_tr]
        X_va, Y_va = X[num_tr:], Y[num_tr:]

        early_stop = 100

        best_params = None

        best_cost = np.inf

        for epoch in range(num_epochs):
            for i in range(0, num_tr, batch_size):

                cost, grad = self.cost_grad(params, X_tr, Y_tr)
                params -= learning_rate * grad

            # test on validation set
            T, b = self.unfold_params(params)
            P_va = MLPModel(T, b, self.dropout).predict(X_va)
            cost = -np.sum(np.log(P_va + 1e-15) * Y_va)

            if cost < best_cost:
                best_cost = cost
                best_params = np.copy(params)
                early_stop *= 2

            if epoch > early_stop:
                break

        return params

    def fit(self, X, Y, W):

        if np.isnan(np.sum(X)) or np.isnan(np.sum(Y)):
            raise ValueError('MLP does not support unknown values')

        if Y.shape[1] == 1:
            num_classes = np.unique(Y).size
            Y = np.eye(num_classes)[Y.ravel().astype(int)]

        params = []
        num_params = 0

        for l1, l2 in zip(self.layers, self.layers[1:]):
            num_params += l1 * l2 + l2
            i = 4.0 * np.sqrt(6.0 / (l1 + l2))
            params.append(np.random.uniform(-i, i, l1 * l2))
            params.append(np.zeros(l2))

        params = np.concatenate(params)

        #params = self.fit_bfgs(params, X, Y)
        params = self.fit_sgd(params, X, Y, **self.opt_args)

        T, b = self.unfold_params(params)
        return MLPModel(T, b, self.dropout)


class MLPModel(Model):
    def __init__(self, T, b, dropout):
class SomeParent:
    def __init__(self):
        self.x = 1

class SomeChild(SomeParent):
    def __init__(self):
        # Initialize the super class
        SomeParent.__init__(self)
        self.T = T
        self.b = b
        self.dropout = dropout

    def predict(self, X):
        a = X
        for i in range(len(self.T) - 1):
            d = 1 - self.dropout[i]
param = 5

class Foo:
    def __init__(self, param):   # "param" would be flagged here
        self.param = param
            a = sigmoid(a.dot(self.T[i].T * d) + self.b[i])
        z = a.dot(self.T[-1].T) + self.b[-1]
        P = np.exp(z - np.max(z, axis=1)[:, None])
        P /= np.sum(P, axis=1)[:, None]
        return P

if __name__ == '__main__':
    import Orange.data
    import sklearn.cross_validation as skl_cross_validation
# .scrutinizer.yml
before_commands:
    - sudo pip install abc # Python2
    - sudo pip3 install abc # Python3

    np.random.seed(42)

    def numerical_grad(f, params, e=1e-4):

        grad = np.zeros_like(params)
        perturb = np.zeros_like(params)
        for i in range(params.size):
            perturb[i] = e
            j1 = f(params - perturb)
            j2 = f(params + perturb)
            grad[i] = (j2 - j1) / (2.0 * e)
            perturb[i] = 0
        return grad

    d = Orange.data.Table('iris')

#    # gradient check
#    m = MLPLearner([4, 20, 20, 3], dropout=[0.0, 0.5, 0.0], lambda_=0.0)
#    params = np.random.randn(5 * 20 + 21 * 20 + 21 * 3) * 0.1
#    Y = np.eye(3)[d.Y.ravel().astype(int)]
#
#    ga = m.cost_grad(params, d.X, Y)[1]
#    gm = numerical_grad(lambda t: m.cost_grad(t, d.X, Y)[0], params)
#
#    print(np.sum((ga - gm)**2))

#    m = MLPLearner([4, 20, 3], dropout=[0.0, 0.0], lambda_=1.0)
#    m(d)

    for lambda_ in [0.03, 0.1, 0.3, 1, 3]:
        m = MLPLearner([4, 20, 20, 3], lambda_=lambda_, num_epochs=1000,
                       learning_rate=0.1)
        scores = []
        for tr_ind, te_ind in skl_cross_validation.StratifiedKFold(d.Y.ravel()):
            s = np.mean(m(d[tr_ind])(d[te_ind]) == d[te_ind].Y.ravel())
            scores.append(s)
        print(np.mean(scores), lambda_)


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Orange.classification.MLPLearner.fit() B
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			from scipy.optimize import fmin_l_bfgs_b
			0 ignored issues – show Configuration introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report The import `scipy.optimize` 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
4			from Orange.classification import Learner, Model
5
6			__all__ = ["MLPLearner"]
7
8
9			def sigmoid(x):
10			return 1.0 / (1.0 + np.exp(-x))
11
12
13			class MLPLearner(Learner):
14			"""Multilayer perceptron (feedforward neural network)
15
16			This model uses stochastic gradient descent and the
17			backpropagation algorithm to train the weights of a feedforward
18			neural network. The network uses the sigmoid activation
19			functions, except for the last layer which computes the softmax
20			activation function. The network can be used for binary and
21			multiclass classification. Stochastic gradient descent minimizes
22			the L2 regularize categorical crossentropy cost function. The
23			topology of the network can be customized by setting the layers
24			attribute. When using this model you should:
25
26			- Choose a suitable:
27			* topology (layers)
28			* regularization parameter (lambda\_)
29			* dropout (values of 0.2 for the input layer and 0.5 for the hidden
30			layers usually work well)
31			* The number of epochs of stochastic gradient descent (num_epochs)
32			* The learning rate of stochastic gradient descent (learning_rate)
33			- Continuize all discrete attributes
34			- Transform the data set so that the columns are on a similar scale
35
36			layers : list
37			The topology of the network. A network with
38			layer=[10, 100, 100, 3] has two hidden layers with 100 neurons each,
39			10 features and a class value with 3 distinct values.
40
41			lambda\_ : float, optional (default = 1.0)
42			The regularization parameter. Higher values of lambda\_
43			force the coefficients to be small.
44
45			dropout : list, optional (default = None)
46			The dropout rate for each, but the last,
47			layer. The list should have one element less then the parameter layers.
48			Values of 0.2 for the input layer and 0.5 for the hidden layers usually
49			work well.
50
51			num_epochs : int, optional
52			The number of epochs of stochastic gradient descent
53
54			learning_rate : float, optional
55			The learning rate of stochastic gradient descent
56
57			batch_size : int, optional
58			The batch size of stochastic gradient descent
59			"""
60
61			name = 'mlp'
62
63			def __init__(self, layers, lambda_=1.0, dropout=None, preprocessors=None,
			0 ignored issues – show Comprehensibility Bug introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report `lambda_` is re-defining a name which is already available in the outer-scope (previously defined on line `249`). It is generally a bad practice to shadow variables from the outer-scope. In most cases, this is done unintentionally and might lead to unexpected behavior: param = 5 class Foo: def __init__(self, param): # "param" would be flagged here self.param = param Loading history...
64			**opt_args):
65			super().__init__(preprocessors=preprocessors)
66			if dropout is None:
67			dropout = [0] * (len(layers) - 1)
68			assert len(dropout) == len(layers) - 1
69
70			self.layers = layers
71			self.lambda_ = lambda_
72			self.dropout = dropout
73			self.opt_args = opt_args
74
75			def unfold_params(self, params):
76			T, b = [], []
77			acc = 0
78			for l1, l2 in zip(self.layers, self.layers[1:]):
79			b.append(params[acc:acc + l2])
80			acc += l2
81			T.append(params[acc:acc + l1 * l2].reshape((l2, l1)))
82			acc += l1 * l2
83			return T, b
84
85			def cost_grad(self, params, X, Y):
86			T, b = self.unfold_params(params)
87
88			# forward pass
89			a, z = [], []
90
91			dropout_mask = []
92			for i in range(len(T)):
93			if self.dropout is None or self.dropout[0] < 1e-7:
94			dropout_mask.append(1)
95			else:
96			dropout_mask.append(
97			np.random.binomial(1, 1 - self.dropout[0],
98			(X.shape[0], self.layers[i])))
99
100			a.append(X * dropout_mask[0])
101			for i in range(len(self.layers) - 2):
102			z.append(a[i].dot(T[i].T) + b[i])
103			a.append(sigmoid(z[i]) * dropout_mask[i + 1])
104
105			# softmax last layer
106			z.append(a[-1].dot(T[-1].T) + b[-1])
107			P = np.exp(z[-1] - np.max(z[-1], axis=1)[:, None])
108			P /= np.sum(P, axis=1)[:, None]
109			a.append(P)
110
111			# cost
112			cost = -np.sum(np.log(a[-1] + 1e-15) * Y)
113			for theta in T:
114			cost += self.lambda_ * np.dot(theta.flat, theta.flat) / 2.0
115			cost /= X.shape[0]
116
117			# gradient
118			params = []
119			for i in range(len(self.layers) - 1):
120			if i == 0:
121			d = a[-1] - Y
			0 ignored issues – show Comprehensibility Bug introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report `d` is re-defining a name which is already available in the outer-scope (previously defined on line `234`). It is generally a bad practice to shadow variables from the outer-scope. In most cases, this is done unintentionally and might lead to unexpected behavior: param = 5 class Foo: def __init__(self, param): # "param" would be flagged here self.param = param Loading history...
122			else:
123			d = d.dot(T[-i]) * a[-i - 1] * (1 - a[-i - 1])
124			dT = (a[-i - 2] * dropout_mask[-i - 1]).T.dot(d).T + self.lambda_\
125			* T[-i - 1]
126			db = np.sum(d, axis=0)
127
128			params.extend([dT.flat, db.flat])
129			grad = np.concatenate(params[::-1]) / X.shape[0]
130
131			return cost, grad
132
133			def fit_bfgs(self, params, X, Y):
134			params, j, ret = fmin_l_bfgs_b(self.cost_grad, params,
			0 ignored issues – show Unused Code introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report The variable `ret` seems to be unused. Loading history... Unused Code introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report The variable `j` seems to be unused. Loading history...
135			args=(X, Y), **self.opt_args)
136			return params
137
138			def fit_sgd(self, params, X, Y, num_epochs=1000, batch_size=100,
139			learning_rate=0.1):
140			# shuffle examples
141			inds = np.random.permutation(X.shape[0])
142			X = X[inds]
143			Y = Y[inds]
144
145			# split training and validation set
146			num_tr = int(X.shape[0] * 0.8)
147
148			X_tr, Y_tr = X[:num_tr], Y[:num_tr]
149			X_va, Y_va = X[num_tr:], Y[num_tr:]
150
151			early_stop = 100
152
153			best_params = None
			0 ignored issues – show Unused Code introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report The variable `best_params` seems to be unused. Loading history...
154			best_cost = np.inf
155
156			for epoch in range(num_epochs):
157			for i in range(0, num_tr, batch_size):
			0 ignored issues – show Unused Code introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report The variable `i` seems to be unused. Loading history...
158			cost, grad = self.cost_grad(params, X_tr, Y_tr)
159			params -= learning_rate * grad
160
161			# test on validation set
162			T, b = self.unfold_params(params)
163			P_va = MLPModel(T, b, self.dropout).predict(X_va)
164			cost = -np.sum(np.log(P_va + 1e-15) * Y_va)
165
166			if cost < best_cost:
167			best_cost = cost
168			best_params = np.copy(params)
169			early_stop *= 2
170
171			if epoch > early_stop:
172			break
173
174			return params
175
176			def fit(self, X, Y, W):
			0 ignored issues – show Bug Best Practice introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report Signature differs from overridden 'fit' method It is generally a good practice to use signatures that are compatible with the Liskov substitution principle. This allows to pass instances of the child class anywhere where the instances of the super-class/interface would be acceptable. Loading history...
177			if np.isnan(np.sum(X)) or np.isnan(np.sum(Y)):
178			raise ValueError('MLP does not support unknown values')
179
180			if Y.shape[1] == 1:
181			num_classes = np.unique(Y).size
182			Y = np.eye(num_classes)[Y.ravel().astype(int)]
183
184			params = []
185			num_params = 0
			0 ignored issues – show Unused Code introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report The variable `num_params` seems to be unused. Loading history...
186			for l1, l2 in zip(self.layers, self.layers[1:]):
187			num_params += l1 * l2 + l2
188			i = 4.0 * np.sqrt(6.0 / (l1 + l2))
189			params.append(np.random.uniform(-i, i, l1 * l2))
190			params.append(np.zeros(l2))
191
192			params = np.concatenate(params)
193
194			#params = self.fit_bfgs(params, X, Y)
195			params = self.fit_sgd(params, X, Y, **self.opt_args)
196
197			T, b = self.unfold_params(params)
198			return MLPModel(T, b, self.dropout)
199
200
201			class MLPModel(Model):
202			def __init__(self, T, b, dropout):
			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 `ModelClassification` 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...
203			self.T = T
204			self.b = b
205			self.dropout = dropout
206
207			def predict(self, X):
208			a = X
209			for i in range(len(self.T) - 1):
210			d = 1 - self.dropout[i]
			0 ignored issues – show Comprehensibility Bug introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report `d` is re-defining a name which is already available in the outer-scope (previously defined on line `234`). It is generally a bad practice to shadow variables from the outer-scope. In most cases, this is done unintentionally and might lead to unexpected behavior: param = 5 class Foo: def __init__(self, param): # "param" would be flagged here self.param = param Loading history...
211			a = sigmoid(a.dot(self.T[i].T * d) + self.b[i])
212			z = a.dot(self.T[-1].T) + self.b[-1]
213			P = np.exp(z - np.max(z, axis=1)[:, None])
214			P /= np.sum(P, axis=1)[:, None]
215			return P
216
217			if __name__ == '__main__':
218			import Orange.data
219			import sklearn.cross_validation as skl_cross_validation
			0 ignored issues – show Configuration introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report The import `sklearn.cross_validation` 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...
220
221			np.random.seed(42)
222
223			def numerical_grad(f, params, e=1e-4):
			0 ignored issues – show Duplication introduced 2015-12-02 09:15 UTC by Report Bug Copy Issue Report This code seems to be duplicated in your project. Duplicated code is one of the most pungent code smells. If you need to duplicate the same code in three or more different places, we strongly encourage you to look into extracting the code into a single class or operation. You can also find more detailed suggestions in the “Code” section of your repository. Loading history...
224			grad = np.zeros_like(params)
225			perturb = np.zeros_like(params)
226			for i in range(params.size):
227			perturb[i] = e
228			j1 = f(params - perturb)
229			j2 = f(params + perturb)
230			grad[i] = (j2 - j1) / (2.0 * e)
231			perturb[i] = 0
232			return grad
233
234			d = Orange.data.Table('iris')
235
236			# # gradient check
237			# m = MLPLearner([4, 20, 20, 3], dropout=[0.0, 0.5, 0.0], lambda_=0.0)
238			# params = np.random.randn(5 * 20 + 21 * 20 + 21 * 3) * 0.1
239			# Y = np.eye(3)[d.Y.ravel().astype(int)]
240			#
241			# ga = m.cost_grad(params, d.X, Y)[1]
242			# gm = numerical_grad(lambda t: m.cost_grad(t, d.X, Y)[0], params)
243			#
244			# print(np.sum((ga - gm)**2))
245
246			# m = MLPLearner([4, 20, 3], dropout=[0.0, 0.0], lambda_=1.0)
247			# m(d)
248
249			for lambda_ in [0.03, 0.1, 0.3, 1, 3]:
250			m = MLPLearner([4, 20, 20, 3], lambda_=lambda_, num_epochs=1000,
251			learning_rate=0.1)
252			scores = []
253			for tr_ind, te_ind in skl_cross_validation.StratifiedKFold(d.Y.ravel()):
254			s = np.mean(m(d[tr_ind])(d[te_ind]) == d[te_ind].Y.ravel())
255			scores.append(s)
256			print(np.mean(scores), lambda_)
257

hugobuddel / orange3

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Orange.classification.MLPLearner.fit() B last analyzed 2015-12-08 12:38 UTC

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Orange.classification.MLPLearner.fit() B
last analyzed 2015-12-08 12:38 UTC

2. Missing init.py files

2. Missing init.py files

2. Missing init.py files