print __doc__

# Author: Olivier Grisel <olivier.grisel@ensta.org>

# Peter Prettenhofer <peter.prettenhofer@gmail.com>

# Mathieu Blondel <mathieu@mblondel.org>

# License: Simplified BSD

from pprint import pprint

from time import time

import logging

import pickle

from sklearn.datasets import fetch_20newsgroups

from sklearn.feature_extraction.text import CountVectorizer

from sklearn.feature_extraction.text import TfidfTransformer

from sklearn.linear_model import SGDClassifier

from sklearn.ensemble import RandomForestClassifier

from sklearn.grid_search import GridSearchCV

from sklearn.pipeline import Pipeline

import featureGeneration as fg

import utils

from sklearn.decomposition import FastICA

import numpy as np

import matplotlib.pyplot as plt

# Display progress logs on stdout

logging.basicConfig(level=logging.INFO,

format='%(asctime)s %(levelname)s %(message)s')

###############################################################################

#data

path = '/local/attale00/AFLW_ALL/'

path_ea = '/local/attale00/AFLW_cropped/cropped3/'

#path_ea = '/local/attale00/AFLW_ALL/color128/'

#

fileNames = utils.getAllFiles(path_ea);

labs=utils.parseLabelFiles(path+'/labels/labels','mouth_opening',fileNames,cutoffSeq='.png',suffix='_face0.labels')

testSet = fg.dataContainer(labs)

roi=(0,37,0,115)

roi=None

#roi=(50,74,96,160)

X=fg.getAllImagesFlat(path_ea,testSet.fileNames,(40,120),roi=roi)

#

# perform ICA

ica = FastICA(n_components=100,whiten=True)

ica.fit(X)

meanI=np.mean(X,axis=0)

X1=X-meanI

data=ica.transform(X1)

filters=ica.components_

for i in range(len(fileNames)):

testSet.data[i].extend(data[i,:])

#fg.getHogFeature(testSet,roi,path=path_ea,ending='.png',extraMask = None,orientations = 5, cells_per_block=(3,3),pixels_per_cell=(24,8),maskFromAlpha=False)

#

#fg.getColorHistogram(testSet,(0,40,40,80),path=path_ea,ending='.png',colorspace='lab',bins=20)

#fg.getImagePatchStat(testSet,path=path_ea,patchSize=(4,12))

#fg.getImagePatchStat(testSet,path='/local/attale00/AFLW_cropped/mouth_img_error/',patchSize=(4,12))

DATA=np.zeros((len(testSet.data),len(testSet.data[0])))

for i in range(len(testSet.data)):

DATA[i,:]=np.array(testSet.data[i])

testSet.targetNum=map(utils.mapMouthLabels2Two,testSet.target)

###############################################################################

# define a pipeline combining a text feature extractor with a simple

# classifier

clf = RandomForestClassifier()

parameters = {'n_estimators': range(90, 260,20),

'min_samples_leaf':range(1,30,5)}

#if __name__ == "__main__":

# multiprocessing requires the fork to happen in a __main__ protected

# block

# find the best parameters for both the feature extraction and the

# classifier

grid_search = GridSearchCV(clf, parameters, n_jobs=-1, verbose=1)

print "Performing grid search..."

print "parameters:"

pprint(parameters)

t0 = time()

grid_search.fit(DATA, np.array(testSet.targetNum))

print "done in %0.3fs" % (time() - t0)

print

print "Best score: %0.3f" % grid_search.best_score_

try:

print grid_search.best_estimator_

except Exception:

print 'printing did not work'

#d=[]

#for i in range(len(grid_search.grid_scores_)):

# d.append(grid_search.grid_scores_[i][1])

#

#plt.plot(range(len(d)),d,'*')

#plt.show()

root = '/local/attale00/gridsearches/'

pickle.dump(grid_search.param_grid,open(root+'patchesPara','w'))

pickle.dump(grid_search.grid_scores_,open(root+'patchesScore','w'))

pickle.dump(grid_search.best_score_,open(root+'patchesBestScore','w'))