def loadData(params, withhold, ffs, trainfile="train.xml", testfile="testcases.xml"):
"""
loads the movie data
arguments:
params : dict with several keys:
load : loading mode; either: 'extract' to load from
`params['extractFile']`, 'split' to load from
`params['splitFile']`, or None to extract features and
save to `params['extractFile']` and/or
`params['splitFile']`.
extractFile : file to load/save extracted features to/from,
depending on loading mode
splitFile : file to load/save split data to/from,
depending on loading mode
withhold : number of data points to withhold for cross-validation
ffs : list of feature functions
trainfile : path to training file (train.xml)
testfile : path to test cases file
returns:
"""
# load data from `params['splitFile']`
if params['load']=='split':
X_train, y_train, train_ids,X_test,y_test,test_ids = unpickle(params['splitFile'])
print "loaded %d fds" % len(train_ids)
print "withholding %d of %d fds" % (len(test_ids), len(train_ids))
else:
# load data from scratch
if params['load']==None:
fds, targets, train_ids = regress.extract_feats_helper(ffs, trainfile)
print "loaded %d fds" % len(fds)
if params['extractFile'] != None:
pickle((fds,targets,train_ids),params['extractFile'])
# load data from `params['extractFile']`, but split it anew
elif params['load']=='extract':
fds,targets,train_ids=unpickle(params['extractFile'])
# load the test data from the testcases file
if withhold==0:
X_train,feat_dict = regress.make_design_mat(fds)
y_train=np.array(targets)
X_test,_,y_test,test_ids = regress.extract_feats(ffs, testfile, global_feat_dict=feat_dict)
train_ids = []
# withhold some of the training data into test data
else:
fds, targets, train_ids, fdsTest, targetsTest, test_ids = splitData(fds, targets, train_ids, withhold, params['splitMethod'])
X_train,feat_dict = regress.make_design_mat(fds)
X_test,_ = regress.make_design_mat(fdsTest, feat_dict)
y_train=np.array(targets)
y_test=np.array(targetsTest)
if params['splitFile'] != None:
pickle((X_train, y_train, train_ids,X_test,y_test,test_ids), params['splitFile'])
return X_train,y_train,train_ids, X_test,y_test,test_ids
def postproc(feat_dict):
return rs.make_design_mat(feat_dict)
assert False
else:
curr_inst = [line]
in_instance = True
elif end_tag in line:
curr_inst.append(line)
movies.append(util.MovieData(ET.fromstring("".join(curr_inst))))
curr_inst = []
in_instance = False
elif in_instance:
curr_inst.append(line)
return movies
'''movies = get_movies('train.xml')
print regression_starter.metadata_feats(movies[0])
print '-------'
print regression_starter.unigram_feats(movies[0])'''
fds = [{'hi':1,'bye':0,'foo':3},{'hi':1,'hello':1,'foo':2,'bar':0}]
'''movies = get_movies('train.xml')
fd1 = regression_starter.metadata_feats(movies[0])
fd2 = regression_starter.metadata_feats(movies[1])
print fd1
print fd2
print '------'
fds = [fd1,fd2]'''
X, dict = regression_starter.make_design_mat(fds)
print X
print '------'
print dict
