import numpy as np

from clf_base import predict

def acc(y_hat,y):
    return (y_hat == y).mean()

def write_predictions(y_hat,filename):
    with open(filename,'w') as fout:
        for y_hat_i in y_hat:
            print >>fout, y_hat_i

def read_predictions(filename):
    with open(filename,'r') as fin:
        return [line.rstrip() for line in fin.readlines()]
            
## these are just for fun

def f1(y_hat,y,label):
    tp = sum((y_hat==label) & (y==label))
    fp = sum((y_hat==label) & (y!=label))
    fn = sum((y_hat!=label) & (y==label))
    #print tp,fp,fn
    r = tp/float(tp + fn + 1e-10)
    p = tp/float(tp + fp + 1e-10)
    f = 2 * r * p / (r + p + 1e-10)
    return f

def macro_f1(y_hat,y):
    all_labels = set(y)
    y_hat = np.array(y_hat)
    f1s = {label:f1(y_hat,y,label) for label in all_labels}
    return sum(f1s.values())/len(all_labels),f1s