Mercurial > repos > rnateam > rnacommender

comparison model.py @ 0:d04fa5201f51 draft

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planemo upload for repository https://github.com/bgruening/galaxytools/tree/rna_commander/tools/rna_tools/rna_commender commit 7ad344d108076116e702e1c1e91cea73d8fcadc4
author rnateam
date Thu, 28 Jul 2016 05:56:54 -0400
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-1:000000000000 0:d04fa5201f51
1 """Recommender model."""
2 from __future__ import print_function
3
4 import sys
5
6 import numpy as np
7
8 from theano import function, shared
9 import theano.tensor as T
10
11 __author__ = "Gianluca Corrado"
12 __copyright__ = "Copyright 2016, Gianluca Corrado"
13 __license__ = "MIT"
14 __maintainer__ = "Gianluca Corrado"
15 __email__ = "gianluca.corrado@unitn.it"
16 __status__ = "Production"
17
18
19 class Model():
20 """Factorization model."""
21
22 def __init__(self, sp, sr, kp, kr, irange=0.01, learning_rate=0.01,
23 lambda_reg=0.01, verbose=True, seed=1234):
24 """
25 Constructor.
26
27 Parameters
28 ----------
29 sp : int
30 Number of protein features.
31
32 sr : int
33 Number of RNA features.
34
35 kp : int
36 Size of the protein latent space.
37
38 kr : int
39 Size of the RNA latent space.
40
41 irange : float (default : 0.01)
42 Initialization range for the model weights.
43
44 learning_rate : float (default : 0.01)
45 Learning rate for the weights update.
46
47 lambda_reg : (default : 0.01)
48 Lambda parameter for the regularization.
49
50 verbose : bool (default : True)
51 Print information at STDOUT.
52
53 seed : int (default : 1234)
54 Seed for random number generator.
55 """
56 if verbose:
57 print("Compiling model...", end=' ')
58 sys.stdout.flush()
59
60 self.learning_rate = learning_rate
61 self.lambda_reg = lambda_reg
62 np.random.seed(seed)
63 # explictit features for proteins
64 fp = T.matrix("Fp", dtype='float32')
65 # explictit features for RNAs
66 fr = T.matrix("Fr", dtype='float32')
67 # Correct label
68 y = T.vector("y")
69
70 # projection matrix for proteins
71 self.Ap = shared(((.5 - np.random.rand(kp, sp)) *
72 irange).astype('float32'), name="Ap")
73 self.bp = shared(((.5 - np.random.rand(kp)) *
74 irange).astype('float32'), name="bp")
75 # projection matrix for RNAs
76 self.Ar = shared(((.5 - np.random.rand(kr, sr)) *
77 irange).astype('float32'), name="Ar")
78 self.br = shared(((.5 - np.random.rand(kr)) *
79 irange).astype('float32'), name="br")
80 # generalization matrix
81 self.B = shared(((.5 - np.random.rand(kp, kr)) *
82 irange).astype('float32'), name="B")
83
84 # Latent space for proteins
85 p = T.nnet.sigmoid(T.dot(fp, self.Ap.T) + self.bp)
86 # Latent space for RNAs
87 r = T.nnet.sigmoid(T.dot(fr, self.Ar.T) + self.br)
88 # Predicted output
89 y_hat = T.nnet.sigmoid(T.sum(T.dot(p, self.B) * r, axis=1))
90
91 def _regularization():
92 """Normalized Frobenius norm."""
93 norm_proteins = self.Ap.norm(2) + self.bp.norm(2)
94 norm_rnas = self.Ar.norm(2) + self.br.norm(2)
95 norm_b = self.B.norm(2)
96
97 num_proteins = self.Ap.flatten().shape[0] + self.bp.shape[0]
98 num_rnas = self.Ar.flatten().shape[0] + self.br.shape[0]
99 num_b = self.B.flatten().shape[0]
100
101 return (norm_proteins / num_proteins + norm_rnas / num_rnas +
102 norm_b / num_b) / 3
103
104 # mean squared error
105 cost_ = (T.sqr(y - y_hat)).mean()
106 reg = lambda_reg * _regularization()
107 cost = cost_ + reg
108
109 # compute sgd updates
110 g_Ap, g_bp, g_Ar, g_br, g_B = T.grad(
111 cost, [self.Ap, self.bp, self.Ar, self.br, self.B])
112 updates = ((self.Ap, self.Ap - learning_rate * g_Ap),
113 (self.bp, self.bp - learning_rate * g_bp),
114 (self.Ar, self.Ar - learning_rate * g_Ar),
115 (self.br, self.br - learning_rate * g_br),
116 (self.B, self.B - learning_rate * g_B))
117
118 # training step
119 self.train = function(
120 inputs=[fp, fr, y],
121 outputs=[y_hat, cost],
122 updates=updates)
123 # test
124 self.test = function(
125 inputs=[fp, fr, y],
126 outputs=[y_hat, cost])
127
128 # predict
129 self.predict = function(
130 inputs=[fp, fr],
131 outputs=y_hat)
132
133 if verbose:
134 print("Done.")
135 sys.stdout.flush()
136
137 def get_params(self):
138 """Return the parameters of the model."""
139 return {'Ap': self.Ap.get_value(), 'bp': self.bp.get_value(),
140 'Ar': self.Ar.get_value(), 'br': self.br.get_value(),
141 'B': self.B.get_value()}