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basicgrad.py
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basicgrad.py
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from __future__ import division
# -*- coding: utf-8 -*-
"""
contains most of the important learning and evaluating functions
"""
import numpy as np
#import matplotlib.pyplot as mpl
import scipy.special as s
from const import *
def trainConsts():
"""If you want to experiment with training constants, change them here"""
l=5e-3 #regularisation constant
alpha=1e-7 #learning constant
return(l,alpha)
class q(object):
"""Holds question with all its answers and T/F values as well as counted probabilities"""
q=[]
a=[]
y=[]
t=[]
tcount=[]
clues=[]
qtext=[]
atext=[]
counts=[]
idf=[]
def __init__(self,q,a1,a0,qtext,atext1,atext0,clues1=0,clues0=0):
self.q=np.transpose(np.array(q,ndmin=2)) # question emb. (column)
a1=np.array(a1,ndmin=2) # correct ans. emb. (answers in rows)
a0=np.array(a0,ndmin=2) # incorrect
self.a=np.hstack((np.transpose(a1),np.transpose(a0))) # answer matrix (answer per column, correct come first)
self.y=np.hstack((np.ones(len(a1)),np.zeros(len(a0)))) # answer labels
self.qtext=qtext
self.atext=atext1
self.atext.extend((atext0))
self.setCounts()
self.setClues(clues1,clues0)
def sett(self,M,b):
""" compute answer labels based on model M,b """
self.t=s.expit(z(self.q,M,self.a,b)[0]) # answer labels as estimated by the model
def settcount(self,results):
self.tcount=results
def setClues(self,clues1,clues0):
self.clues=np.hstack((clues1,clues0))
def setCounts(self):
""" compute counts of common words in question and each answer """
N=len(self.y)
self.counts=np.zeros(len(self.y))
self.idf=np.zeros(len(self.y))
for i in range(0,len(self.counts)):
for word in self.qtext:
wc=self.atext[i].tolist().count(word)
self.counts[i]+=wc/len(self.atext[i])
if wc>0:
d=0
for sentence in self.atext:
if word in sentence:
d+=1
continue
self.idf[i]+=wc*np.log(N/d)
def ttlist(qa,a1a,a0a,ans1,ans0,sentences,c1=False,c0=False):
"""Returns list of qs"""
clues1=np.zeros((2,sum(ans1)))
clues0=np.zeros((2,sum(ans0)))
if(c1):
i=0
with open(c1,'r') as f:
for line in f:
s=line.split(" ")
clues1[0,i]=float(s[0])
# clues1[1,i]=float(s[1])
i+=1
i=0
with open(c0,'r') as f:
for line in f:
s=line.split(" ")
clues0[0,i]=float(s[0])
# clues0[1,i]=float(s[1])
i+=1
(questions,answers1,answers0)=sentences
li=[]
ones=0
zeros=0
for i in range(0,len(ans1)):
li.append(q(qa[i],a1a[ones:ones+ans1[i]],a0a[zeros:zeros+ans0[i]],questions[i],
answers1[ones:ones+ans1[i]],answers0[zeros:zeros+ans0[i]],clues1[:,ones:ones+ans1[i]],clues0[:,zeros:zeros+ans0[i]]))
ones+=ans1[i]
zeros+=ans0[i]
return li
def testGrad(M,b,li,idx):
"""Updates weights using basic gradient descent"""
l,alpha=trainConsts()
bestmrr=0.0
n_iter = 200
plot = np.zeros(int(n_iter / 5))
for i in range(0, n_iter):
ggM=0.0
ggb=0.0
if i%5==0:
plot[int(i/5)]=lossAll(li,M,b)
print '[%d/%d] loss function: %.1f (bestMRR %.3f) Thread number %d' % (i, n_iter, plot[int(i/5)], bestmrr, idx)
for q in li:
labels=q.y
# np.transpose(np.array(q.a[:,j],ndmin=2))
(gM,gb)=grad(labels,q.q,M,q.a,b)
ggM+=gM
ggb+=gb
M=M-alpha*ggM
b=b-alpha*ggb
curmrr=mrr(M,b,li)
if bestmrr<curmrr:
bestmrr=curmrr
bestM=M
bestb=b
# mpl.plot(plot)
return(bestM,bestb)
def loss(labels,q,M,a,b):
"""#Loss cross-entropy function with regularization
inputs: labels-row array of {0.1};q-column vector;M-matrix;a-row of columns;b-scalar
"""
l,alpha=trainConsts()
x=-(labels*np.log(s.expit(z(q,M,a,b)))+(1-labels)*np.log(1-s.expit(z(q,M,a,b))))
return np.sum(x)+l/2*(np.sum(M**2)+b**2)
#qTMa+b
def z(q,M,a,b):
return np.dot(np.dot(np.transpose(q),M),a)+b
#Grad of loss over weights, 1 question 1 answer input
def grad(labels,q,M,anss,b):
l,alpha=trainConsts()
d=np.reshape(s.expit(z(q,M,anss,b)),(len(labels),))-labels
gM=0
# gb=0
for i in range(0,len(d)):
gM+=np.transpose(np.dot(np.reshape(anss[:,i],(GLOVELEN,1)),q.reshape((1,GLOVELEN))))*d[i]+l*M
return (gM,sum(d))
class yt(object):
y=0
t=0
def __init__(self,y,t):
self.y=y
self.t=t
#Sorts probabilities and returns first True
def firstTrue(y,t):
li=[]
for i in range(0,len(y)):
li.append(yt(y[i],t[i]))
li.reverse()
li.sort(key=lambda x: x.t,reverse=True)
i=0
for item in li:
i+=1
if item.y==1:
return i
return i+1
#Sum of losses for multiple qs
def lossAll(li,M,b):
los=0
for q in li:
los+=loss(q.y,q.q,M,q.a,b)
return los
#Returns MRR (used in uni)
def mrr(M,b,li):
mrr=0.0
for q in li:
q.sett(M,b)
mrr+=1/firstTrue(q.y,q.t)
return mrr/len(li)
def setRes(li,ans1,ans0,res):
p=0
for i in range(0,len(li)):
li[i].settcount(res[p:p+ans1[i]+ans0[i]])
p+=ans1[i]+ans0[i]
return
#Returns MRR (used in uni+count)
def mrrcount(li,ans1,ans0):
mrr=0.0
for q in li:
mrr+=1/firstTrue(q.y,q.tcount)
return mrr/len(ans1)
#Returns number of questions with correct answers in the first 3 sentences
def strictPercentage(li,ans1,ans0):
p=0.0
for q in li:
x=firstTrue(q.y,q.tcount)
if x<=3:
p+=1
return p/len(ans1)
def getInputs(li,ans1,ans0):
y=np.zeros(sum(ans1)+sum(ans0))
x=np.zeros((len(y),3))
poz=0
for q in li:
for i in range(0,len(q.y)):
x[poz+i]=[q.t[i],q.counts[i],q.idf[i]]
y[poz+i]=q.y[i]
poz+=len(q.y)
return (x,y)
def getInputsClues(li,ans1,ans0):
y=np.zeros(sum(ans1)+sum(ans0))
x=np.zeros((len(y),2))
poz=0
for q in li:
for i in range(0,len(q.y)):
x[poz+i]=[q.t[i],q.clues[0,i]]
# x[poz+i]=[q.clues[0,i],q.clues[1,i]]
# x[poz+i]=[q.t[i]]
y[poz+i]=q.y[i]
poz+=len(q.y)
return (x,y)