02c-tsne_mlproj.py
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# coding: utf-8
# In[2]:
# Import
import gensim
from scipy import sparse
import itertools
from sklearn import preprocessing
from keras.models import Sequential
from keras.optimizers import SGD,Adam
from keras.layers.advanced_activations import ELU,PReLU
from keras.callbacks import ModelCheckpoint
from mlp import *
import pandas as pd
import sklearn.metrics
from sklearn.preprocessing import LabelBinarizer
from sklearn.manifold import TSNE
import shelve
import pickle
from utils import *
import sys
import os
import json
# In[4]:
in_dir = sys.argv[1]
#['ASR', 'TRS', 'LABEL']
# In[6]:
json_conf =json.load(open(sys.argv[2]))
mlp_conf = json_conf["transfert"]
hidden_size = mlp_conf["hidden_size"]
loss = mlp_conf["loss"]
patience = mlp_conf["patience"]
dropouts = mlp_conf["do"]
epochs = mlp_conf["epochs"]
batch_size = mlp_conf["batch"]
input_activation=mlp_conf["input_activation"]
output_activation=mlp_conf["output_activation"]
try:
k = mlp_conf["sgd"]
if mlp_conf["sgd"]["name"] == "adam":
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":
sgd = SGD(lr=mlp_conf["sgd"]["lr"])
except:
sgd = mlp_conf["sgd"]
name = json_conf["name"]
#
print " MLP"
proj_hdf = pandas.HDFStore("{}/{}/MLP_proj_df.hdf".format(in_dir,name))
hdf_keys = proj_hdf.keys()
proj_hdf.close()
hdf_mods = set([ x.split("/")[1] for x in hdf_keys ])
hdf_lvl = set( [ x.split("/")[2] for x in hdf_keys ])
hdf_crossval = set([ x.split("/")[3] for x in hdf_keys ])
print hdf_mods
print hdf_lvl
print hdf_crossval
hdf_proj_path = "{}/{}/MLP_proj_df.hdf".format(in_dir,name)
tsne_proj_path = "{}/{}/tsne_proj_df.hdf".format(in_dir,name)
for mod in hdf_mods:
for lvl in hdf_lvl :
x_train = pandas.read_hdf(hdf_proj_path,key="/{}/{}/{}".format(mod,lvl,"TRAIN"))
x_dev = pandas.read_hdf(hdf_proj_path,key="/{}/{}/{}".format(mod,lvl,"DEV"))
x_test = pandas.read_hdf(hdf_proj_path,key="/{}/{}/{}".format(mod,lvl,"TEST"))
if x_train.shape[1] <= 8 :
continue
tsne= TSNE()
tsne_train=tsne.fit_transform(x_train.values)
pd.DataFrame(tsne_train).to_hdf(tsne_proj_path,key="MLP/{}/{}/{}".format(mod,lvl,"TRAIN"))
tsne= TSNE()
tsne_dev=tsne.fit_transform(x_dev.values)
pd.DataFrame(tsne_dev).to_hdf(tsne_proj_path,key="MLP/{}/{}/{}".format(mod,lvl,"DEV"))
tsne= TSNE()
tsne_test=tsne.fit_transform(x_test.values)
pd.DataFrame(tsne_test).to_hdf(tsne_proj_path,key="MLP/{}/{}/{}".format(mod,lvl,"TEST"))
tsne = TSNE()
tsne_all = tsne.fit_transform(pd.concat([x_train,x_dev,x_test]).values)
pd.DataFrame(tsne_all).to_hdf(tsne_proj_path,key="MLP/{}/{}/{}".format(mod,lvl,"CONCAT"))
print " TRANSFERT"
hdf_proj_path = "{}/{}/transfert_proj_df.hdf".format(in_dir,name)
proj_hdf = pandas.HDFStore(hdf_proj_path)
print proj_hdf
hdf_keys = proj_hdf.keys()
proj_hdf.close()
print hdf_keys
hdf_lvl = set([ x.split("/")[1] for x in hdf_keys ])
hdf_layer = set( [ x.split("/")[2] for x in hdf_keys ])
hdf_crossval = set([ x.split("/")[3] for x in hdf_keys ])
print hdf_lvl
print hdf_layer
print hdf_crossval
tsne_proj_path = "{}/{}/tsne_proj_df.hdf".format(in_dir,name)
for lvl in hdf_lvl :
for layer in hdf_layer:
x_train = pandas.read_hdf(hdf_proj_path,key="/{}/{}/{}".format(lvl,layer,"TRAIN"))
x_dev = pandas.read_hdf(hdf_proj_path,key="/{}/{}/{}".format(lvl,layer,"DEV"))
x_test = pandas.read_hdf(hdf_proj_path,key="/{}/{}/{}".format(lvl,layer,"TEST"))
if x_train.shape[1] <= 8 :
continue
tsne= TSNE()
tsne_train=tsne.fit_transform(x_train.values)
pd.DataFrame(tsne_train).to_hdf(tsne_proj_path,key="transfert/{}/{}/{}".format(mod,lvl,"TRAIN"))
tsne= TSNE()
tsne_dev=tsne.fit_transform(x_dev.values)
pd.DataFrame(tsne_dev).to_hdf(tsne_proj_path,key="transfert/{}/{}/{}".format(mod,lvl,"DEV"))
tsne= TSNE()
tsne_test=tsne.fit_transform(x_test.values)
pd.DataFrame(tsne_test).to_hdf(tsne_proj_path,key="transfert/{}/{}/{}".format(mod,lvl,"TEST"))
tsne = TSNE()
tsne_all = tsne.fit_transform(pd.concat([x_train,x_dev,x_test]).values)
pd.DataFrame(tsne_all).to_hdf(tsne_proj_path,key="transfert/{}/{}/{}".format(mod,lvl,"CONCAT"))