# SQL Database session
from database import db_session, init_db
from database import Base
metadata=Base.metadata
from sklearn.preprocessing import LabelEncoder
from collections import namedtuple
# Two main objects: Mail & MailThread
from mail_model import Mail
from thread_model import MailThread
def str_bool(s):
    return s in ["1", "true", "True", "t","T"]

# Lade Trainingsdaten fuer einen angegebenen key (Label/Eigenschaft) 
def get_training_threads(key="answered", filters=[]):
    #------------------------------------
    
    db_fields= {"answered": lambda t: t.is_answered(),
                "lang": lambda t: t.lang,
                "maintopic": lambda t: t.maintopic}
    
    if not db_fields.has_key(key):
        raise ValueError("Key "+str(key)+" unknown")
        
    q=db_session.query(MailThread)
    q=q.filter(MailThread.istrained.is_(True))

    if "de" in filters:
        q=q.filter(MailThread.lang=="de")
    elif "en" in filters:
        q=q.filter(MailThread.lang=="en")

    # load and extract thread fields
    threads=q.all()
    labels = map(db_fields[key], threads)

    # encode using LabelEncoder
    le=LabelEncoder()
    labels=le.fit_transform(labels)
    
    TrainingThreads=namedtuple("TrainingThreads", ["MailThreads","EncodedLabels","LabelEncoder"])
    
    return TrainingThreads(threads,labels,le)