main_modified.py

import argparse
from random import seed
from sklearn import datasets
from modifications import test_dynamic, ozaboost_dynamic
import numpy as np
import data_loader
from ensembles import osboost, ocpboost, expboost, ozaboost, smoothboost
from sklearn.model_selection import train_test_split
from learners import nb_gaussian,sk_nb, nb, perceptron, random_stump, sk_decisiontree, decision_trees
from tqdm import *

if __name__ == "__main__":
    seed(0)
    parser = argparse.ArgumentParser()

    parser.add_argument('dataset', help='dataset filename')
    parser.add_argument('algorithm', help = 'Boosting algorithm')
    parser.add_argument('weak_learner', help='chosen weak learner')
    parser.add_argument('M', metavar='# weak_learners', help='number of weak learners', type=int)
    parser.add_argument('trials', help='number of trials (each with different shuffling of the data); defaults to 1', type=int, default=1, nargs='?')
    parser.add_argument('--record', action='store_const',const=True, default = False, help = 'export the results in file')
    args = parser.parse_args()


    X,y = data_loader.load_data(args.dataset)

    algorithms = {
    "ozaboost":ozaboost_dynamic.OzaBoostClassifier}

    weak_learners ={
    "sk_nb":sk_nb.NaiveBayes,
    "gaussian_nb":nb_gaussian.NaiveBayes,
    "nb": nb.NaiveBayes,
    "sk_decisiontree": sk_decisiontree.DecisionTree,
    "random_stump":random_stump.RandomStump,
    "decisiontree":decision_trees.DecisionTree,
    "perceptron":perceptron.Perceptron}

    X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.20, random_state = 1)

    results = []
    T = 10

    for t in range(T):
        model = test_dynamic.Test(algorithms[args.algorithm], weak_learners[args.weak_learner],X_train,y_train,args.M)
        train_accuracy, baseline_train_accuracy = model.test(weak_learners[args.weak_learner], X_train,y_train,X_test, y_test, args.M,trials=args.trials)
        test_accuracy, baseline_test_accuracy = model.final_test(X_test,y_test,args.M)
        results.append(test_accuracy)
        print test_accuracy
    f = open('greedyresults_datasets.txt','a')
    f.write("Dataset: "+str(args.dataset)+"\n")
    f.write("Algorithm: "+str(args.algorithm)+"\n")
    f.write("Weak learner: "+str(args.weak_learner)+"\n")
    f.write("baseline acc: "+str(baseline_test_accuracy)+"\n")
    f.write(str(results))
    f.write("Final Accuracy: "+str(float(sum(results))/T)+"\n\n\n")


    #print "Running for "+str(args.M)+" weak learners.."
    #print "Shape of Train Data: ",X_train.shape,y_train.shape
    #print "Shape of Test Data: ",X_test.shape, y_test.shape
    #test_accuracy, baseline_test_accuracy = model.final_test(X_test,y_test,args.M)
    #print "Accuracy on Training Set/ Baseline :"
    #print train_accuracy[-1], baseline_train_accuracy[-1]
    #print "Test Accuracy/ Baseline: "
    #print test_accuracy, baseline_test_accuracy


    if args.record:
        results = {
            'm': args.M,
            'Training Accuracy':train_accuracy,
            'Baseline Training Accuracy': baseline_train_accuracy,
            'Testing Accuracy': test_accuracy,
            'Baseline Testing Accuracy': baseline_test_accuracy,
            'trials': args.trials,
            'seed': 0
        }
        filename = open('adaboost_results_'+str(args.dataset)+'_'+str(args.M)+'.txt','a')
        filename.write("BEGIN\n")
        for k,v in results.items():
            filename.write(str(k)+"~~ "+str(v)+"\n")
        filename.write("END\n\n")
        filename.close()