Killian / decodopr

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LDA/04e-mm_vae.py 6.4 KB
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  # coding: utf-8
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  import gensim
  from scipy import sparse
  import itertools
  from sklearn import preprocessing
  from keras.models import Sequential
  from keras.optimizers import SGD,Adam
  from mlp import *
  from vae import *
  import sklearn.metrics
  import shelve
  import pickle
  from utils import *
  import sys
  import os
  import json
  # In[4]:
  
  infer_model=shelve.open("{}".format(sys.argv[2]))
  in_dir = sys.argv[1]
  #['ASR', 'TRS', 'LABEL']
  # In[6]:
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  if len(sys.argv) > 4 :
      features_key = sys.argv[4]
  else :
      features_key = "LDA"
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  save_projection = True









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  json_conf =json.load(open(sys.argv[3]))
  vae_conf = json_conf["vae"]
  
  hidden_size= vae_conf["hidden_size"]
  input_activation=vae_conf["input_activation"]
  output_activation=vae_conf["output_activation"]
  epochs=vae_conf["epochs"]
  batch=vae_conf["batch"]
  patience=vae_conf["patience"]
  latent_dim = vae_conf["latent"]
  try:
      k = vae_conf["sgd"]
      if vae_conf["sgd"]["name"] == "adam":
          sgd = Adam(lr=vae_conf["sgd"]["lr"])#SGD(lr=0.00001,nesterov=False) #'rmsprop'# Adam(lr=0.00001)#SGD(lr=0.001, momentum=0.9, nesterov=True)
      elif vae_conf["sgd"]["name"] == "sgd":
          sgd = SGD(lr=vae_conf["sgd"]["lr"])
  except: 
      sgd = vae_conf["sgd"]
  
  mlp_conf = json_conf["mlp"]
  mlp_h = mlp_conf["hidden_size"]
  mlp_loss = mlp_conf["loss"]
  mlp_dropouts = mlp_conf["do"]
  mlp_epochs = mlp_conf["epochs"]
  mlp_batch_size = mlp_conf["batch"]
  mlp_input_activation=mlp_conf["input_activation"]
  mlp_output_activation=mlp_conf["output_activation"]
  
  
  try:
      k = mlp_conf["sgd"]
      if mlp_conf["sgd"]["name"] == "adam":
          mlp_sgd = Adam(lr=mlp_conf["sgd"]["lr"])#SGD(lr=0.00001,nesterov=False) #'rmsprop'# Adam(lr=0.00001)#SGD(lr=0.001, momentum=0.9, nesterov=True)
      elif mlp_conf["sgd"]["name"] == "sgd":
          mlp_sgd = SGD(lr=mlp_conf["sgd"]["lr"])
  except: 
      mlp_sgd = mlp_conf["sgd"]
  
  
  name = json_conf["name"]









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  try :
      print "make folder "









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      os.mkdir("{}/{}".format(in_dir,name))









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  except:









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      print "folder not maked"









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      pass









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  db = shelve.open("{}/{}/ae_model.shelve".format(in_dir,name),writeback=True)









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  db["LABEL"]=infer_model["LABEL"]
  #









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  keys = infer_model[features_key].keys()









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  db["VAE"] = {}









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  db[features_key] = {}









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  for mod in keys : 









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      #print mod









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      db[features_key][mod] = train_mlp(infer_model[features_key][mod]["TRAIN"],infer_model["LABEL"][mod]["TRAIN"],
                              infer_model[features_key][mod]["DEV"],infer_model["LABEL"][mod]["DEV"],
                              infer_model[features_key][mod]["TEST"],infer_model["LABEL"][mod]["TEST"],









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                              mlp_h ,sgd=mlp_sgd,
                              epochs=mlp_epochs,
                              batch_size=mlp_batch_size,
                              input_activation=input_activation,
                              output_activation=mlp_output_activation,
                              dropouts=mlp_dropouts,
                              fit_verbose=0)









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      res=train_vae(infer_model[features_key][mod]["TRAIN"],infer_model[features_key][mod]["DEV"],infer_model[features_key][mod]["TEST"],









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                   hidden_size=hidden_size[0],
                   latent_dim=latent_dim,sgd=sgd,
                   input_activation=input_activation,output_activation=output_activation,
                   nb_epochs=epochs,batch_size=batch)
      mlp_res_list=[]









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      for nb,layer in enumerate(res) :
          if save_projection:
              pd = pandas.DataFrame(layer[0])
              col_count = (pd.sum(axis=0) != 0)
              pd = pd.loc[:,cyyol_count]
              pd.to_hdf("{}/{}/VAE_{}_{}_df.hdf".format(in_dir,name,nb,mod),"TRAIN")
              pd = pandas.DataFrame(layer[1])
              pd = pd.loc[:,col_count]
              pd.to_hdf("{}/{}/VAE_{}_{}_df.hdf".format(in_dir,name,nb,mod),"DEV")
              pd = pandas.DataFrame(layer[2])
              pd = pd.loc[:,col_count]
              pd.to_hdf("{}/{}/VAE_{}_{}_df.hdf".format(in_dir,name,nb,mod),"TEST")
              del pd









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          mlp_res_list.append(train_mlp(layer[0],infer_model['LABEL'][mod]["TRAIN"],
                                        layer[1],infer_model["LABEL"][mod]["DEV"],
                                        layer[2],infer_model["LABEL"][mod]["TEST"],
                                        mlp_h,loss=mlp_loss,dropouts=mlp_dropouts,sgd=mlp_sgd,epochs=mlp_epochs,
                                        output_activation=mlp_output_activation,
                                        input_activation=input_activation,
                                        batch_size=mlp_batch_size,fit_verbose=0))
      db["VAE"][mod]=mlp_res_list









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  if "ASR" in keys and "TRS" in keys :
      mod = "ASR"
      mod2= "TRS"
      mlp_res_list=[]
  
      res = train_vae(infer_model[features_key][mod]["TRAIN"],
                      infer_model[features_key][mod]["DEV"],
                      infer_model[features_key][mod]["TEST"],
                      hidden_size=hidden_size[0],
                      sgd=sgd,input_activation=input_activation,output_activation=output_activation,
                      latent_dim=latent_dim,
                      nb_epochs=epochs,
                      batch_size=batch,
                      y_train=infer_model[features_key][mod2]["TRAIN"],
                      y_dev=infer_model[features_key][mod2]["DEV"],
                      y_test=infer_model[features_key][mod2]["TEST"])
  
      for nb,layer in enumerate(res) :
          if save_projection:
              pd = pandas.DataFrame(layer[0])
              col_count = (pd.sum(axis=0) != 0)
              pd = pd.loc[:,col_count]
              pd.to_hdf("{}/{}/VAE_{}_{}_df.hdf".format(in_dir,name,nb,"SPE"),"TRAIN")
              pd = pandas.DataFrame(layer[1])
              pd = pd.loc[:,col_count]
              pd.to_hdf("{}/{}/VAE_{}_{}_df.hdf".format(in_dir,name,nb,"SPE"),"DEV")
              pd = pandas.DataFrame(layer[2])
              pd = pd.loc[:,col_count]
              pd.to_hdf("{}/{}/VAE_{}_{}_df.hdf".format(in_dir,name,nb,"SPE"),"TEST")
              del pd
  
          mlp_res_list.append(train_mlp(layer[0],infer_model["LABEL"][mod]["TRAIN"],
                                        layer[1],infer_model["LABEL"][mod]["DEV"],
                                        layer[2],infer_model["LABEL"][mod]["TEST"],
                                        mlp_h,loss=mlp_loss,dropouts=mlp_dropouts,sgd=mlp_sgd,epochs=mlp_epochs,
                                        output_activation=mlp_output_activation,
                                        input_activation=input_activation,
                                        batch_size=mlp_batch_size,fit_verbose=0))
  
      db["VAE"]["SPE"] = mlp_res_list









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  db.sync()
  db.close()