'''
Created on Jul 10, 2017

@author: David
'''

from train_classifier import train_classifier
from evaluate2 import evaluate

svalex_file  = "siwoco-svalex-d-topic-bigram-compact-nh.csv"
swellex_file = "siwoco-swellex-topic-bigram-compact-nh.csv"

files = [svalex_file, swellex_file]
layer_sizes = [(1000,700,200), (1000,500,200), (1000,700,500,200), (1000,700,200,50), (1000,500,200,50), (1000,500,200,100), (1000,500,200,100,50), (1000, 700, 500, 200, 100, 50)]
learning_rates = [0.001, 0.01, 0.1]

best_svalex  = None
best_swellex = None
best_svalex_f1  = 0
best_swellex_f1 = 0

for f in files:
    for lr in learning_rates:
        for layer_size in layer_sizes:
            pname = train_classifier(f, layer_size, lr)
            avg_f1 = evaluate(pname, f)
            if avg_f1 > best_svalex_f1:
                best_svalex_f1 = avg_f1
                best_svalex = pname
            print("Best classifier: {}".format(pname))

print("Done with Grid Search!")