Collaborative-Deep-Learning-for-Recommender-Systems/mf_auto_mono_v2.py at master · xiaoouzhang/Collaborative-Deep-Learning-for-Recommender-Systems · GitHub

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import h5py
import numpy as np
import random
#import matplotlib.pyplot as plt
#import matplotlib
#matplotlib.style.use('ggplot')
#import pandas as pd
import auto_fun as auto
import time

INPUT_LAYER = 314

LEARNING_RATE = 0.001/50*2
EPOCH_NUM = 50
#randomSeed = np.random.RandomState(42)
mu, sigma = 0, 0.1
l=40
HIDDEN_UNIT1 = l
alpha=40
l2_u=100.0
l2_v=100.0
batch=500
ratio_l=400.0
ratio_u=0.5
l1=0.01

def main(denoise = True):
    #allMatrix, xtrain, xval, xtest,lenList,accList = getData()
    diction = [('ind_empleado', 5), ('pais_residencia', 24), ('sexo', 3), ('ind_nuevo', 2), ('indrel', 2), ('indrel_1mes', 4), ('tiprel_1mes', 4), ('indresi', 2), ('indext', 2), ('conyuemp', 3), ('canal_entrada', 158), ('indfall', 2), ('cod_prov', 53), ('ind_actividad_cliente', 2), ('segmento', 4), ('antiguedad_binned', 10), ('age_binned', 24), ('renta_binned', 10)]
    lenList = []
    for tuppl in diction:
        val = tuppl[1]
        lenList.append(val)
    accList = []
    for i in range(len(lenList)):
        if i ==0:
            accList.append(lenList[i])
        else:
            accList.append(accList[i-1]+lenList[i])
    #read user infor
    with h5py.File('user_infor.h5', 'r') as hf:
        xtrain = hf['infor'][:]
    #read rating matrix
    with h5py.File('rating_tr_numpy.h5', 'r') as hf:
        rating_mat = hf['rating'][:]

    W1,b1,c1 = auto.initialization(INPUT_LAYER,[HIDDEN_UNIT1],mu,sigma)
    #define user and item matrices
    u=np.random.rand(rating_mat.shape[0],l)
    v=np.random.rand(rating_mat.shape[1],l)
    '''
    with h5py.File('u_40_mono_40+100_try_auto.h5', 'r') as hf:
        u = hf['u'][:]
    with h5py.File('v_40_mono_40+100_try_auto.h5', 'r') as hf:
        v = hf['v'][:]
    with h5py.File('W1_40_mono_40+100_try.h5', 'r') as hf:
        W1 = hf['W1'][:]
    with h5py.File('b1_40_mono_40+100_try.h5', 'r') as hf:
        b1 = hf['b1'][:]
    with h5py.File('c1_40_mono_40+100_try.h5', 'r') as hf:
        c1 = hf['c1'][:]
    '''
    #define preference and confidence matrices
    p=np.zeros(rating_mat.shape)
    p[rating_mat>0]=1
    c=np.zeros(rating_mat.shape)
    c=1+alpha*rating_mat

    iteration=100

    print('start')
    for iterate in range(iteration):
        #update u

        start=time.time()
        v2=v.T.dot(v)
        for i in range(rating_mat.shape[0]):

            nonzero_list=np.nonzero(p[i,:])[0]
            v_nonzero=v[nonzero_list,:]
            c_diag_nonzero=np.diag(c[i,nonzero_list]-1)

            temp_u=np.dot(c[i,nonzero_list],v_nonzero)
            u[i,:]=np.dot(temp_u,np.linalg.pinv(l2_u*np.identity(l)+np.dot(np.dot(v_nonzero.T,c_diag_nonzero),v_nonzero)+v2))
        print('u complete')
        end=time.time()
        print(end-start)

        #update v
        start=time.time()

        u2=u.T.dot(u)
        for j in range(rating_mat.shape[1]):
            #print(j)
            nonzero_list=np.nonzero(p[:,j])[0]
            c_diag_nonzero=np.diag(c[nonzero_list,j]-1)
            u_nonzero=u[nonzero_list,:]

            temp_v=np.dot(c[nonzero_list,j],u_nonzero)
            v[j,:]=np.dot(temp_v,np.linalg.pinv(l2_v*np.identity(l)+np.dot(np.dot(u_nonzero.T,c_diag_nonzero),u_nonzero)+u2))
        print('v complete')

        end=time.time()
        print(end-start)

        print(np.linalg.norm(p-np.dot(u,v.T)))


        W1,b1,c1 = auto.autoEncoder_mono(ratio_l,ratio_u,batch,W1,xtrain,u,b1,c1,accList,EPOCH_NUM,LEARNING_RATE,l1,denoise = True)
        hidden = auto.getoutPut_mono(W1,b1,xtrain,accList)
        u=hidden
        print(np.linalg.norm(p-np.dot(u,v.T)))

    with h5py.File('u_40_mono_40+100_try_auto.h5', 'w') as hf:
        hf.create_dataset("u",  data=u)
    with h5py.File('v_40_mono_40+100_try_auto.h5', 'w') as hf:
        hf.create_dataset("v",  data=v)
    with h5py.File('W1_40_mono_40+100_try.h5', 'w') as hf:
        hf.create_dataset("W1",  data=W1)
    with h5py.File('b1_40_mono_40+100_try.h5', 'w') as hf:
        hf.create_dataset("b1",  data=b1)
    with h5py.File('c1_40_mono_40+100_try.h5', 'w') as hf:
        hf.create_dataset("c1",  data=c1)
    with h5py.File('h_40_mono_40+100_auto_try.h5', 'w') as hf:
        hf.create_dataset("hidden",  data=hidden)

    #making_graph(trainLoss, valiLoss, testLoss)

    #
    # hidden = pd.DataFrame(hidden.T)
    # prediction = pd.DataFrame(prediction.T)
    # hidden.to_csv('hidden.csv')
    # prediction.to_csv('prediction.csv')

    return hidden

main(denoise = True)