"""

Classifies the CEFR level of 'sent'.

2016 june - based on check_readability() in sent_match.py

@ sent:

@ stats: SentStatistics instance

@ params: parameters for SentMatch (HitEx)

@ match: SentMatch instance

# TO DO: add argument for choosing bw WEKA and sklearn

adapt to both sents and texts

in- vs cross-domain setups

"""

sent_feats = SentFeatures(sent, stats, params)

fs = sent_feats.features

feature_names = fs.keys()

# set the order of training attributes for values

with codecs.open("auxiliaries/feature_names.txt") as f:

train_fn = [l.strip("\n") for l in f.readlines()]

f_list = [fs[tfn] for tfn in train_fn]

# create Instance, attributes and a dummy dataset (required for prediction)

inst = Instance.create_instance(f_list)

attributes = []

for feat_n in train_fn:

attributes.append(Attribute.create_numeric(feat_n))

attributes.append(Attribute.create_nominal("level", ["A1", "A2", "B1", "B2", "C1"]))

dataset = Instances.create_instances("readability", attributes, 0)

dataset.add_instance(inst)

dataset.class_is_last()

# make prediction

cefr_mapping = {"A1":1.0, "A2":2.0, "B1":3.0, "B2":4.0, "C1":5.0}

trg_cefr_fl = cefr_mapping[params["target_cefr"]]

for instance in dataset:

pred = classifier.classify_instance(instance)

pred_cefr = pred+1

#if pred_cefr < 1 or pred_cefr > 5:

level_diff = pred_cefr - trg_cefr_fl # negative value = easier than target

nominal_level = [k for k,v in cefr_mapping.items() if v == pred_cefr][0]

return (level_diff, nominal_level, fs) #return also fs -> for detailed info in webservice