source: trunk/bin/train @ 24

#!/usr/bin/env python3
#-*- mode: Python;-*-

import sys
import os
import time
import random
import statistics
import functools
import argparse
import pprint
import json


VERSION = "{DEVELOPMENT}"
if VERSION == "{DEVELOPMENT}":
    script_dir = '.'
    try:
        script_dir = os.path.dirname(os.path.realpath(__file__))
    except:
        try:
            script_dir = os.path.dirname(os.path.abspath(sys.argv[0]))
        except:
            pass
    sys.path.append("%s/../lib" % script_dir)


from nanownlib import *
from nanownlib.stats import *
from nanownlib.train import *
from nanownlib.parallel import WorkerThreads
import nanownlib.storage



parser = argparse.ArgumentParser(
    description="")
#parser.add_argument('-c', dest='cases', type=str, default='{"short":10000,"long":1010000}',
#                    help='JSON representation of echo timing cases. Default: {"short":10000,"long":1010000}')
parser.add_argument('--unusual-case', action='store', default=None, help='Specify the unusual case and whether it is greater than the other cases.  Format: {case name},{1 or 0}')
parser.add_argument('--retrain', action='append', default=[], help='Force a classifier to be retrained (and retested).  May be specified multiple times.')
parser.add_argument('--retest', action='append', default=[], help='Force a classifier to be retested.  May be specified multiple times.')
parser.add_argument('session_data', default=None,
                    help='Database file storing session information')
options = parser.parse_args()
db = nanownlib.storage.db(options.session_data)



def trainClassifier(db, unusual_case, greater, classifier, retrain=False):
    if retrain:
        print("Dropping stored training results...")
        db.deleteClassifierResults(classifier, 'train')
    
    trainer = classifiers[classifier]['train']
    threshold = 5.0 # in percent
    num_obs = 7
    max_obs = int(db.populationSize('train')/5)
    result = None
    while num_obs < max_obs:
        num_obs = min(int(num_obs*1.5), max_obs)
        result = db.fetchClassifierResult(classifier, 'train', num_obs)
        if result != None:
            train_time = "(stored)"
        else:
            start = time.time()
            result = trainer(db,unusual_case,greater,num_obs)
            result['classifier'] = classifier
            train_time = "%8.2f" % (time.time()-start)
            
        error = statistics.mean([result['false_positives'],result['false_negatives']])
        print("num. observations: %5d | error: %6.2f | fp: %6.2f | fn: %6.2f | train time: %s | params: %s"
              % (num_obs, error, result['false_positives'],result['false_negatives'], train_time, result['params']))
        db.addClassifierResult(result)
        classifiers[classifier]['train_results'].append(result)

        if error < threshold and num_obs > 100:
            break

    return result



def testClassifier(db, unusual_case, greater, classifier, retest=False):
    target_error = 5.0 # in percent
    num_trials = 1000
    max_obs = int(db.populationSize('test')/5)

    tester = classifiers[classifier]['test']
    
    def testAux(params, num_trials, num_observations):
        estimator = functools.partial(tester, params, greater)
        estimates = bootstrap3(estimator, db, 'test', unusual_case, num_observations, num_trials)
        null_estimates = bootstrap3(estimator, db, 'train_null', unusual_case, num_observations, num_trials)

        bad_estimates = len([e for e in estimates if e != 1])
        bad_null_estimates = len([e for e in null_estimates if e != 0])
        
        false_negatives = 100.0*bad_estimates/num_trials
        false_positives = 100.0*bad_null_estimates/num_trials
        return false_positives,false_negatives


    def getResult(classifier, params, num_obs, num_trials):
        jparams = json.dumps(params, sort_keys=True)
        result = db.fetchClassifierResult(classifier, 'test', num_obs, jparams)
        if result:
            test_time = '(stored)'
            fp = result['false_positives']
            fn = result['false_negatives']
        else:
            start = time.time()
            fp,fn = testAux(params, num_trials, num_obs)
            result = {'classifier':classifier,
                      'trial_type':"test",
                      'num_observations':num_obs,
                      'num_trials':num_trials,
                      'params':jparams,
                      'false_positives':fp,
                      'false_negatives':fn}
            db.addClassifierResult(result)
            test_time = '%8.2f' % (time.time()-start)
            
        print("num. observations: %5d | error: %6.2f | fp: %6.2f | fn: %6.2f | test time: %s"
              % (num_obs,(fp+fn)/2.0,fp,fn,test_time))
        return ((fp+fn)/2.0,result)
    
    if retest:
        print("Dropping stored test results...")
        db.deleteClassifierResults(classifier, 'test')


    lte = math.log(target_error/100.0)
    for tr in classifiers[classifier]['train_results']:
        db.resetOffsets()
        params = json.loads(tr['params'])
        num_obs = tr['num_observations']
    
        print("parameters:", params)
        error,result = getResult(classifier,params,num_obs,num_trials)
        #print("walking up")
        while (error > target_error) and (num_obs < max_obs):
            increase_factor = 1.5 * lte/math.log(error/100.0) # don't ask how I came up with this
            #print("increase_factor:", increase_factor)
            num_obs = min(int(increase_factor*num_obs), max_obs)
            error,result = getResult(classifier,params,num_obs,num_trials)

        #print("walking down")
        while (num_obs > 0):
            num_obs = int(0.95*num_obs)
            error,result = getResult(classifier,params,num_obs,num_trials)
            if error > target_error:
                break
    

if options.unusual_case != None:
    unusual_case,greater = options.unusual_case.split(',')
    greater = bool(int(greater))
    db.setUnusualCase(unusual_case,greater)
else:
    ucg = db.getUnusualCase()
    if ucg != None:
        unusual_case,greater = ucg
        print("Using cached unusual_case:", unusual_case)
    else:
        unusual_case,delta = findUnusualTestCase(db)
        greater = (delta > 0)
        print("Auto-detected unusual_case '%s' with delta: %d" %  (unusual_case,delta))
        db.setUnusualCase(unusual_case,greater)


for c in sorted(classifiers.keys()):
    if classifiers[c]['train'] == None:
        continue
    start = time.time()
    print("Training %s..." % c)
    result = trainClassifier(db, unusual_case, greater, c, c in options.retrain)
    #print("%s result:" % c)
    #pprint.pprint(result)
    print("completed in: %8.2f\n"% (time.time()-start))

db.clearCache()

for c in sorted(classifiers.keys()):
    start = time.time()
    print("Testing %s..." % c)
    testClassifier(db, unusual_case, greater, c, c in (options.retest+options.retrain))
    print("completed in: %8.2f\n"% (time.time()-start))


best_obs,best_error = evaluateTestResults(db)
best_obs =   sorted(best_obs,   key=lambda x: x['num_observations'])
best_error = sorted(best_error, key=lambda x: x['error'])
winner = None
for bo in best_obs:
    sys.stdout.write("%(num_observations)5d obs   | %(classifier)12s | %(params)s" % bo)
    if winner == None:
        sys.stdout.write(" (winner)")
        winner = bo
    print()
        
for be in best_error:
    sys.stdout.write("%(error)3.2f%% error | %(classifier)12s | %(params)s" % be)
    if winner == None:
        sys.stdout.write(" (winner)")
        winner = be
    print()