#/usr/bin/env python

'''
Digit recognition adjustment.
Grid search is used to find the best parameters for SVM and KNearest classifiers.
SVM adjustment follows the guidelines given in
http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf

Threading or cloud computing (with http://www.picloud.com/)) may be used
to speedup the computation.

Usage:
  digits_adjust.py [--model {svm|knearest}] [--cloud] [--env <PiCloud environment>]

  --model {svm|knearest}   - select the classifier (SVM is the default)
  --cloud                  - use PiCloud computing platform
  --env                    - cloud environment name

'''
# TODO cloud env setup tutorial

import numpy as np
import cv2
from multiprocessing.pool import ThreadPool

from digits import *

try:
    import cloud
    have_cloud = True
except ImportError:
    have_cloud = False



def cross_validate(model_class, params, samples, labels, kfold = 3, pool = None):
    n = len(samples)
    folds = np.array_split(np.arange(n), kfold)
    def f(i):
        model = model_class(**params)
        test_idx = folds[i]
        train_idx = list(folds)
        train_idx.pop(i)
        train_idx = np.hstack(train_idx)
        train_samples, train_labels = samples[train_idx], labels[train_idx]
        test_samples, test_labels = samples[test_idx], labels[test_idx]
        model.train(train_samples, train_labels)
        resp = model.predict(test_samples)
        score = (resp != test_labels).mean()
        print ".",
        return score
    if pool is None:
        scores = map(f, xrange(kfold))
    else:
        scores = pool.map(f, xrange(kfold))
    return np.mean(scores)


class App(object):
    def __init__(self, usecloud=False, cloud_env=''):
        if usecloud and not have_cloud:
            print 'warning: cloud module is not installed, running locally'
            usecloud = False
        self.usecloud = usecloud
        self.cloud_env = cloud_env

        if self.usecloud:
            print 'uploading dataset to cloud...'
            cloud.files.put(DIGITS_FN)
            self.preprocess_job = cloud.call(self.preprocess, _env=self.cloud_env)
        else:
            self._samples, self._labels = self.preprocess()

    def preprocess(self):
        if self.usecloud:
            cloud.files.get(DIGITS_FN)
        digits, labels = load_digits(DIGITS_FN)
        shuffle = np.random.permutation(len(digits))
        digits, labels = digits[shuffle], labels[shuffle]
        digits2 = map(deskew, digits)
        samples = preprocess_hog(digits2)
        return samples, labels

    def get_dataset(self):
        if self.usecloud:
            return cloud.result(self.preprocess_job)
        else:
            return self._samples, self._labels

    def run_jobs(self, f, jobs):
        if self.usecloud:
            jids = cloud.map(f, jobs, _env=self.cloud_env, _profile=True, _depends_on=self.preprocess_job)
            ires = cloud.iresult(jids)
        else:
            pool = ThreadPool(processes=cv2.getNumberOfCPUs())
            ires = pool.imap_unordered(f, jobs)
        return ires

    def adjust_SVM(self):
        Cs = np.logspace(0, 10, 15, base=2)
        gammas = np.logspace(-7, 4, 15, base=2)
        scores = np.zeros((len(Cs), len(gammas)))
        scores[:] = np.nan

        print 'adjusting SVM (may take a long time) ...'
        def f(job):
            i, j = job
            samples, labels = self.get_dataset()
            params = dict(C = Cs[i], gamma=gammas[j])
            score = cross_validate(SVM, params, samples, labels)
            return i, j, score

        ires = self.run_jobs(f, np.ndindex(*scores.shape))
        for count, (i, j, score) in enumerate(ires):
            scores[i, j] = score
            print '%d / %d (best error: %.2f %%, last: %.2f %%)' % (count+1, scores.size, np.nanmin(scores)*100, score*100)
        print scores

        print 'writing score table to "svm_scores.npz"'
        np.savez('svm_scores.npz', scores=scores, Cs=Cs, gammas=gammas)

        i, j = np.unravel_index(scores.argmin(), scores.shape)
        best_params = dict(C = Cs[i], gamma=gammas[j])
        print 'best params:', best_params
        print 'best error: %.2f %%' % (scores.min()*100)
        return best_params

    def adjust_KNearest(self):
        print 'adjusting KNearest ...'
        def f(k):
            samples, labels = self.get_dataset()
            err = cross_validate(KNearest, dict(k=k), samples, labels)
            return k, err
        best_err, best_k = np.inf, -1
        for k, err in self.run_jobs(f, xrange(1, 9)):
            if err < best_err:
                best_err, best_k = err, k
            print 'k = %d, error: %.2f %%' % (k, err*100)
        best_params = dict(k=best_k)
        print 'best params:', best_params, 'err: %.2f' % (best_err*100)
        return best_params


if __name__ == '__main__':
    import getopt
    import sys

    print __doc__

    args, _ = getopt.getopt(sys.argv[1:], '', ['model=', 'cloud', 'env='])
    args = dict(args)
    args.setdefault('--model', 'svm')
    args.setdefault('--env', '')
    if args['--model'] not in ['svm', 'knearest']:
        print 'unknown model "%s"' % args['--model']
        sys.exit(1)

    t = clock()
    app = App(usecloud='--cloud' in args, cloud_env = args['--env'])
    if args['--model'] == 'knearest':
        app.adjust_KNearest()
    else:
        app.adjust_SVM()
    print 'work time: %f s' % (clock() - t)