cython_Wsabie_kernel.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import random\n",
    "import numpy as np\n",
    "import sys\n",
    "from random import randint\n",
    "from data_load import *\n",
    "from cwsabie_inner import *\n",
    "from transE_label import *\n",
    "from gensim.corpora import Dictionary\n",
    "from collections import Counter\n",
    "from gensim.models import Word2Vec\n",
    "%load_ext cython"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# Wsabie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%%cython\n",
    "import numpy as np\n",
    "\n",
    "cimport numpy as np\n",
    "from random import randint\n",
    "import sys\n",
    "import cython\n",
    "cdef extern from \"math.h\":\n",
    "    double sqrt(double m)\n",
    "import math\n",
    "from libc.stdlib cimport malloc, free\n",
    "\n",
    "from libc.math cimport exp\n",
    "from libc.math cimport log\n",
    "from gensim.matutils import argsort\n",
    "\n",
    "from libc.string cimport memset\n",
    "import random\n",
    "# scipy <= 0.15\n",
    "\n",
    "import scipy.linalg.blas as fblas\n",
    "ctypedef np.float32_t REAL_t\n",
    "cdef int ONE = 1\n",
    "\n",
    "\n",
    "REAL = np.float32\n",
    "cdef extern from \"/Users/mayk/working/figer/baseline/PLE/Model/warp/voidptr.h\":\n",
    "    void* PyCObject_AsVoidPtr(object obj)\n",
    "DEF MAX_SENTENCE_LEN = 10000\n",
    "ctypedef void (*scopy_ptr) (const int *N, const float *X, const int *incX, float *Y, const int *incY) nogil\n",
    "ctypedef void (*saxpy_ptr) (const int *N, const float *alpha, const float *X, const int *incX, float *Y, const int *incY) nogil\n",
    "ctypedef float (*sdot_ptr) (const int *N, const float *X, const int *incX, const float *Y, const int *incY) nogil\n",
    "ctypedef double (*dsdot_ptr) (const int *N, const float *X, const int *incX, const float *Y, const int *incY) nogil\n",
    "ctypedef double (*snrm2_ptr) (const int *N, const float *X, const int *incX) nogil\n",
    "ctypedef void (*sscal_ptr) (const int *N, const float *alpha, const float *X, const int *incX) nogil\n",
    "\n",
    "\n",
    "cdef scopy_ptr scopy = <scopy_ptr>PyCObject_AsVoidPtr(fblas.scopy._cpointer)  # y = x\n",
    "cdef saxpy_ptr saxpy=<saxpy_ptr>PyCObject_AsVoidPtr(fblas.saxpy._cpointer)  # y += alpha * x\n",
    "cdef sdot_ptr sdot=<sdot_ptr>PyCObject_AsVoidPtr(fblas.sdot._cpointer)  # float = dot(x, y)\n",
    "cdef dsdot_ptr dsdot=<dsdot_ptr>PyCObject_AsVoidPtr(fblas.sdot._cpointer)  # double = dot(x, y)\n",
    "cdef snrm2_ptr snrm2=<snrm2_ptr>PyCObject_AsVoidPtr(fblas.snrm2._cpointer)  # sqrt(x^2)\n",
    "cdef sscal_ptr sscal=<sscal_ptr>PyCObject_AsVoidPtr(fblas.sscal._cpointer) # x = alpha * x\n",
    "DEF EXP_TABLE_SIZE = 10000\n",
    "DEF MAX_EXP = 50\n",
    "\n",
    "cdef REAL_t[EXP_TABLE_SIZE] EXP_TABLE\n",
    "cdef REAL_t[EXP_TABLE_SIZE] LOG_TABLE\n",
    "\n",
    "cdef REAL_t ONEF = <REAL_t>1.0\n",
    "\n",
    "# for when fblas.sdot returns a double\n",
    "cdef REAL_t our_dot_double(const int *N, const float *X, const int *incX, const float *Y, const int *incY) nogil:\n",
    "    return <REAL_t>dsdot(N, X, incX, Y, incY)\n",
    "\n",
    "# for when fblas.sdot returns a float\n",
    "cdef REAL_t our_dot_float(const int *N, const float *X, const int *incX, const float *Y, const int *incY) nogil:\n",
    "    return <REAL_t>sdot(N, X, incX, Y, incY)\n",
    "\n",
    "# for when no blas availablese\n",
    "cdef REAL_t our_dot_noblas(const int *N, const float *X, const int *incX, const float *Y, const int *incY) nogil:\n",
    "    # not a true full dot()-implementation: just enough for our cases\n",
    "    cdef int i\n",
    "    cdef REAL_t a\n",
    "    a = <REAL_t>0.0\n",
    "    for i from 0 <= i < 50 by 1:\n",
    "        a += X[i] * Y[i]\n",
    "    return a\n",
    "\n",
    "# for when no blas available\n",
    "cdef void our_saxpy_noblas(const int *N, const float *alpha, const float *X, const int *incX, float *Y, const int *incY) nogil:\n",
    "    cdef int i\n",
    "    for i from 0 <= i < N[0] by 1:\n",
    "        Y[i * (incY[0])] = (alpha[0]) * X[i * (incX[0])] + Y[i * (incY[0])]\n",
    "cdef REAL_t cvdot(vec1,vec2,size):\n",
    "    cdef int csize = size\n",
    "    f= dsdot(&csize,<REAL_t *>(np.PyArray_DATA(vec1)),&ONE,<REAL_t *>(np.PyArray_DATA(vec2)),&ONE)\n",
    "    return f\n",
    "def csaxpy(vec1,vec2,alpha,size):\n",
    "    cdef int csize = size\n",
    "    cdef float calpha = alpha\n",
    "    f= our_saxpy_noblas(&csize,&calpha,<REAL_t *>(np.PyArray_DATA(vec1)),&ONE,<REAL_t *>(np.PyArray_DATA(vec2)),&ONE)\n",
    "    return f\n",
    "cdef REAL_t crank(int k):\n",
    "    cdef REAL_t loss = 0.\n",
    "    cdef int i = 1\n",
    "    for i in range(1,k+1):\n",
    "        loss += ONEF/i\n",
    "    return loss\n",
    "cdef REAL_t vsum(REAL_t *vec,int *size):\n",
    "    cdef int i\n",
    "    cdef REAL_t product\n",
    "    product = <REAL_t>0.0\n",
    "    for i from 0 <= i < size[0] by 1:\n",
    "        product += vec[i] **2\n",
    "    return sqrt(product)\n",
    "def cnorm(vec):\n",
    "    cdef int size\n",
    "    size  = len(vec)\n",
    "    return vsum(<REAL_t *>(np.PyArray_DATA(vec)),&size)\n",
    "def init():\n",
    "    for i in range(EXP_TABLE_SIZE):\n",
    "        EXP_TABLE[i] = <REAL_t>exp((i / <REAL_t>EXP_TABLE_SIZE * 2 - 1) * MAX_EXP)\n",
    "        EXP_TABLE[i] = <REAL_t>(EXP_TABLE[i] / (EXP_TABLE[i] + 1))\n",
    "#init()\n",
    "\n",
    "\n",
    "def ctrain(A,B,C,insts,size,lr,gradient,it,Verbose=False):\n",
    "    cdef float error\n",
    "    next_random = 1\n",
    "    error = 0.\n",
    "   # cdef lam = 0.0\n",
    "    for i,inst in enumerate(insts):\n",
    "        err,next_random =gradient(A,B,C,inst,size,next_random,lr=lr)\n",
    "      \n",
    "        error += err\n",
    "        if i % 1000 ==0 and Verbose:\n",
    "            sys.stdout.write(\"\\rIteration %d \" % (it)+ \"trained {0:.0f}%\".format(float(i)*100/len(insts))+\" Loss:{0:.2f}\".format(error))\n",
    "            sys.stdout.flush()\n",
    "    if Verbose:\n",
    "        sys.stdout.write(\"\\n\")\n",
    "    return error\n",
    "\n",
    "cdef void divide(REAL_t *vec, const float *alpha, const int *size):\n",
    "    cdef int i\n",
    "    for i from 0 <= i < size[0] by 1:\n",
    "        vec[i] = vec[i]/alpha[0]\n",
    "def cdivide(vec,alpha):\n",
    "    cdef int size\n",
    "    size  = len(vec)\n",
    "    cdef float r = alpha\n",
    "    divide(<REAL_t *>(np.PyArray_DATA(vec)),&r,&size)\n",
    "\n",
    "    \n",
    "\n",
    "\n",
    "def warp_gradient(A,B,C,inst,size,next_random,lr=0.01):\n",
    "    #print B\n",
    "    #print B[0]-B[9]\n",
    "    cdef unsigned long long  c_next_random = next_random\n",
    "    dA = dict()#np.zeros(A.shape,dtype=REAL)\n",
    "\n",
    "   # x = np.sum(A[inst.features],axis=0)\n",
    "    cdef REAL_t error = 0.\n",
    "    cdef REAL_t clr = lr\n",
    "    cdef int N,n_sample \n",
    "    cdef int neg_num = len(inst.negative_labels)\n",
    "    cdef REAL_t norm\n",
    "    cdef int cSize = size\n",
    "    cdef REAL_t float\n",
    "#     scores = [ for l in isnt.spar]\n",
    "#     ranks = argsort(scores,reverse=True)\n",
    "    M = len(inst.sparse_labels)\n",
    "    for i,l in enumerate(inst.sparse_labels):\n",
    "        f_pos =[f  for f in inst.features if C[f][l] ==1. ]\n",
    "        x = np.sum(A[f_pos],axis=0)\n",
    "        s1= cvdot(x,B[l],cSize)\n",
    "        N=1\n",
    "        n_sample  = -1\n",
    "        for k in range(neg_num):\n",
    "            c_next_random = random_int32(&c_next_random)\n",
    "            nl = inst.negative_labels[c_next_random%neg_num]#randint(0,neg_num-1)]\n",
    "#             if filtered == []:\n",
    "#                 s2 = 0.\n",
    "#             else:\n",
    "            f_neg = [f  for f in inst.features if C[f][nl] ==1. ]\n",
    "            x = np.sum(A[f_neg],axis=0)\n",
    "\n",
    "            s2 = cvdot(x,B[nl],cSize)\n",
    "            if s1 - s2<1:\n",
    "                n_sample = nl\n",
    "                N = k+1\n",
    "                break\n",
    "        if n_sample!=-1:\n",
    "            L = crank(len(inst.negative_labels)/N)#*(crank(M/(ranks[i]+1)))\n",
    "            negL = -L\n",
    "            error += (1+s2-s1)*L\n",
    "            for f in f_pos:\n",
    "                if f not in dA:dA[f] = np.zeros(size,dtype=REAL)\n",
    "                    \n",
    "                csaxpy(B[l],dA[f],L,cSize)\n",
    "            for f in f_neg:\n",
    "                if f not in dA:dA[f] = np.zeros(size,dtype=REAL)\n",
    "                csaxpy(B[n_sample],dA[f],negL,cSize) \n",
    "\n",
    "#             csaxpy(x,dB[l],L,cSize)\n",
    "#             csaxpy(x,dB[n_sample],-L,cSize)\n",
    "    for f in dA:#inst.features:\n",
    "        csaxpy(dA[f],A[f],clr,cSize)\n",
    "        norm = cnorm(A[f])\n",
    "        if norm >1:\n",
    "            cdivide(A[f],norm)\n",
    "#     for i in range(len(B)):\n",
    "#         csaxpy(dB[i],B[i],clr,cSize)\n",
    "#         #B[i] += lr*dB[i]\n",
    "#         norm =  cnorm(B[i])\n",
    "#         if norm >1:\n",
    "#             cdivide(B[i],norm)\n",
    "#             B[i] /=norm\n",
    "    \n",
    "    \n",
    "    return error,c_next_random\n",
    "def save_to_text(matrix,output):\n",
    "    shape = matrix.shape\n",
    "    with open(output,'wb') as out:\n",
    "        out.write(\"%d %d\\n\" % (shape))\n",
    "        for row in matrix:\n",
    "            x = \" \".join(map(lambda x:\"{0:.5}\".format(x),row))\n",
    "            out.write(x+\"\\n\")\n",
    "\n",
    "cdef inline unsigned long long random_int32(unsigned long long *next_random) nogil:\n",
    "    next_random[0] = (next_random[0] * <unsigned long long>25214903917ULL + 11) & 281474976710655ULL\n",
    "    return next_random[0]\n",
    "def crand(sed):\n",
    "    cdef unsigned long long csed = sed\n",
    "    return random_int32(&csed)\n",
    "def save2bin(mat,dct,fn):\n",
    "    n,d  = mat.shape\n",
    "    with open(fn,'w') as out:\n",
    "        out.write(\"%d %d\\n\" % (n,d))\n",
    "        for i in range(n):\n",
    "            text = \" \".join(map(str,mat[i]))\n",
    "            out.write(\"%s %s\\n\" %(dct[i],text))\n",
    "def normalize(mat):\n",
    "    for v in mat:\n",
    "        norm = np.linalg.norm(v)\n",
    "        if norm >=1:\n",
    "            v /= norm\n",
    "def select(A,B,k=10):\n",
    "    C=np.zeros([A.shape[0],B.shape[0]])\n",
    "    D = np.dot(A,B.T)\n",
    "    for i in range(D.shape[1]):\n",
    "        X = argsort(D[:,i],reverse=True)[:k]\n",
    "        C[X,i] = 1.0\n",
    "    return C\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "in_dir= \"/Users/mayk/working/figer/baseline/PLE/Intermediate/BBN\"\n",
    "a=MentionData('/Users/mayk/working/figer/baseline/PLE/Intermediate/BBN/train_x_new.txt',\n",
    "              \"/Users/mayk/working/figer/baseline/PLE/Intermediate/BBN/train_y.txt\",\n",
    "             in_dir+\"/feature.txt\",in_dir+\"/type.txt\")\n",
    "label_bin  = Word2Vec.load_word2vec_format('/Users/mayk/working/figer/baseline/PLE/Intermediate/BBN/labels.bin')\n",
    "label_bin_hier  = Word2Vec.load_word2vec_format('/Users/mayk/working/figer/baseline/PLE/Intermediate/BBN/labels_hier.bin')\n",
    "#label_bin  = Word2Vec.load_word2vec_format('/Users/mayk/working/figer/baseline/PLE/Model/warp/embedding/BBN/inter_l2v_300.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 0 trained 100% Loss:258041.17\n",
      "Iteration 1 trained 100% Loss:246416.84\n",
      "Iteration 2 trained 100% Loss:213142.94\n",
      "Iteration 3 trained 94% Loss:198841.16"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
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      "\u001b[0;32m_cython_magic_ae9ad03114c6d013b4b48cbf5d89eeff.pyx\u001b[0m in \u001b[0;36m_cython_magic_ae9ad03114c6d013b4b48cbf5d89eeff.ctrain (/Users/mayk/.ipython/cython/_cython_magic_ae9ad03114c6d013b4b48cbf5d89eeff.c:2922)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m_cython_magic_ae9ad03114c6d013b4b48cbf5d89eeff.pyx\u001b[0m in \u001b[0;36m_cython_magic_ae9ad03114c6d013b4b48cbf5d89eeff.warp_gradient (/Users/mayk/.ipython/cython/_cython_magic_ae9ad03114c6d013b4b48cbf5d89eeff.c:4082)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m/Users/mayk/working/figer/baseline/PLE/Model/warp/venv/lib/python2.7/site-packages/numpy/core/fromnumeric.pyc\u001b[0m in \u001b[0;36msum\u001b[0;34m(a, axis, dtype, out, keepdims)\u001b[0m\n\u001b[1;32m   1741\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1742\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1743\u001b[0;31m \u001b[0;32mdef\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1744\u001b[0m     \"\"\"\n\u001b[1;32m   1745\u001b[0m     \u001b[0mSum\u001b[0m \u001b[0mof\u001b[0m \u001b[0marray\u001b[0m \u001b[0melements\u001b[0m \u001b[0mover\u001b[0m \u001b[0ma\u001b[0m \u001b[0mgiven\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "np.random.seed(3)\n",
    "size= 300\n",
    "A= np.random.uniform(-6/np.sqrt(size),6/np.sqrt(size), [len(a.feature2id),size]).astype(np.float32)#np.random.rand(len(a.feature2id),size).astype(np.float32)\n",
    "B = np.asarray([label_bin[a.id2label[i]] for i in range(len(label_bin.vocab))],dtype=np.float32)#np.random.normal(0,1.0, [len(a.label2id),size]).astype(np.float32)\n",
    "\n",
    "next_random = 1\n",
    "normalize(A)\n",
    "normalize(B)\n",
    "C=np.ones(([A.shape[0],B.shape[0]]),dtype=np.float32)\n",
    "for i in range(15): \n",
    "    reg_err =0.\n",
    "    ctrain(A,B,C,a.data,size,0.001,warp_gradient,it=i,Verbose=True)\n",
    "    C = select(A,B,k=1000)\n",
    "save2bin(A,a.id2feature,'/Users/mayk/working/figer/baseline/PLE/Results/BBN/warp_A.bin')\n",
    "save2bin(B,a.id2label,'/Users/mayk/working/figer/baseline/PLE/Results/BBN/warp_B.bin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 0 trained 100% Loss:602607.88\n",
      "Iteration 1 trained 100% Loss:619231.38\n",
      "Iteration 2 trained 46% Loss:290194.72"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-14-dc27241cd0b1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m15\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mreg_err\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0;36m0.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mctrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mB\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mC\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0.001\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mwarp_gradient\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mVerbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0mC\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mselect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mB\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m200\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m_cython_magic_b6be3abdc6d7f23c7dd5667d2b65d0d5.pyx\u001b[0m in \u001b[0;36m_cython_magic_b6be3abdc6d7f23c7dd5667d2b65d0d5.ctrain (/Users/mayk/.ipython/cython/_cython_magic_b6be3abdc6d7f23c7dd5667d2b65d0d5.c:2918)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m_cython_magic_b6be3abdc6d7f23c7dd5667d2b65d0d5.pyx\u001b[0m in \u001b[0;36m_cython_magic_b6be3abdc6d7f23c7dd5667d2b65d0d5.warp_gradient (/Users/mayk/.ipython/cython/_cython_magic_b6be3abdc6d7f23c7dd5667d2b65d0d5.c:4063)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m/Users/mayk/working/figer/baseline/PLE/Model/warp/venv/lib/python2.7/site-packages/numpy/core/fromnumeric.pyc\u001b[0m in \u001b[0;36msum\u001b[0;34m(a, axis, dtype, out, keepdims)\u001b[0m\n\u001b[1;32m   1741\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1742\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1743\u001b[0;31m \u001b[0;32mdef\u001b[0m \u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdims\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1744\u001b[0m     \"\"\"\n\u001b[1;32m   1745\u001b[0m     \u001b[0mSum\u001b[0m \u001b[0mof\u001b[0m \u001b[0marray\u001b[0m \u001b[0melements\u001b[0m \u001b[0mover\u001b[0m \u001b[0ma\u001b[0m \u001b[0mgiven\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "\n",
    "for i in range(15): \n",
    "    reg_err =0.\n",
    "    ctrain(A,B,C,a.data,size,0.001,warp_gradient,it=i,Verbose=True)\n",
    "    C = select(A,B,k=200)\n",
    "\n",
    "save2bin(A,a.id2feature,'/Users/mayk/working/figer/baseline/PLE/Results/BBN/warp_A.bin')\n",
    "save2bin(B,a.id2label,'/Users/mayk/working/figer/baseline/PLE/Results/BBN/warp_B.bin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.21361181953443675"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(B[a.data[0].labels[0]],np.dot(C[a.data[0].features,a.data[0].labels[0]].T,A[a.data[0].features]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "D = np.dot(A,B.T)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C[argsort(D[:,1],reverse=True)[:10],1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  0., ...,  0.,  0.,  0.])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
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