def estimate(master, input, center, k, iterations, map_reader=reader): """ Optimize k-clustering for `iterations` iterations with cluster center definitions as given in `center`. """ job = master.new_job(name='k-clustering_init', input=input, map_reader=map_reader, map_init=map_init, map=random_init_map, combiner=estimate_combiner, reduce=estimate_reduce, params=Params(k=k, seed=None, **center), nr_reduces=k) centers = [(i, c) for i, c in result_iterator(job.wait())] job.purge() for j in range(iterations): job = master.new_job(name='k-clustering_iteration_%s' % (j, ), input=input, map_reader=map_reader, map=estimate_map, combiner=estimate_combiner, reduce=estimate_reduce, params=Params(centers=centers, **center), nr_reduces=k) centers = [(i, c) for i, c in result_iterator(job.wait())] job.purge() return centers
def _assert_csv_reader(self, fields, values, expected): stream = StringIO.StringIO(values) params = Params() params.csv_fields = fields params.csv_dialect = csv.excel_tab actual = csv_reader(stream, None, None, params) ok_(isinstance(actual, types.GeneratorType)) eq_(list(actual), expected)
def __init__(self, rule, settings, urls=None): self.job_options = JobOptions(rule, settings) self.rule = rule self.settings = settings rule_params = dict(rule.params.__dict__) self.disco, self.ddfs = get_disco_handle(rule_params.get('server', settings.get('server'))) rule_params.update(settings) self.params = Params(**rule_params) self.urls = urls try: # attempt to allow for overriden worker class from settings file or rule if rule.worker: worker = rule.worker else: worker_mod, dot, worker_class = settings.get('worker').rpartition('.') mod = __import__(worker_mod, {}, {}, worker_mod) worker = getattr(mod, worker_class)() self.job = Job(name=rule.name, master=self.disco.master, worker=worker) except Exception as e: log.warn("Error instantiating worker: %s %s - loading default worker" % (settings.get('worker'), e)) self.job = Job(name=rule.name, master=self.disco.master) self.full_job_id = None self.jobinfo = None self._notify(JOB_START)
def __init__(self, config, map, reduce): self.config = DiscoJob.DEFAULT_CONFIG.copy() self.config.update(config) self.map = map self.reduce = reduce self.job = Job() self.params = Params(**self.config)
def _assert_reduce(self, data, expected, **kwargs): # turn disco_debug on for more code coverage if kwargs is None: kwargs = dict() kwargs['disco_debug'] = True params = Params(**kwargs) actual = keyset_reduce(data, params) ok_(isinstance(actual, types.GeneratorType)) eq_(list(actual), expected)
class PartialJob(TestJob): map_init = partial(init, extra='d') map = partial(map, extra='a') combiner = partial(combiner, extra='b') reduce_init = partial(init, extra='e') reduce = partial(reduce, extra='c') map_reader = partial(reader, extra='f') reduce_reader = partial(reader, extra='h') params = Params(foo=partial(foo, extra='z'))
def run(task, zinput, payload=None): import pagerank zclass = getattr(pagerank, task) job = zclass(); job.params = Params(payload=payload) job.run(input=zinput) result = job.wait(show = False) return result
def predict(input, loglikelihoods, ys, splitter=' ', map_reader=chain_reader): ys = dict([(id, 1) for id in ys]) job = Job(name='naive_bayes_predict') job.run(input=input, map_reader=map_reader, map=predict_map, params=Params(loglikelihoods=loglikelihoods, ys=ys, splitter=splitter), clean=False) return job.wait()
def test_keyset_multiplier(self): params = Params() params.keysets = { 'last_name_keyset': dict( key_parts=['_keyset', 'last_name'], value_parts=['count'], ), 'first_name_keyset': dict( key_parts=['_keyset', 'first_name'], value_parts=['count'], ) } data = [{ 'first_name': 'Willow', 'last_name': 'Harvey' }, { 'first_name': 'Noam', 'last_name': 'Clarke' }] expected = [{ 'first_name': 'Willow', 'last_name': 'Harvey', '_keyset': 'first_name_keyset' }, { 'first_name': 'Willow', 'last_name': 'Harvey', '_keyset': 'last_name_keyset' }, { 'first_name': 'Noam', 'last_name': 'Clarke', '_keyset': 'first_name_keyset' }, { 'first_name': 'Noam', 'last_name': 'Clarke', '_keyset': 'last_name_keyset' }] actual = keyset_multiplier(data, None, None, params) ok_(isinstance(actual, types.GeneratorType)) eq_(list(actual), expected)
def predict(master, input, center, centers, map_reader=reader): """ Predict the closest clusters for the datapoints in input. """ job = master.new_job(name='kcluster_predict', input=input, map_reader=map_reader, map=predict_map, params=Params(centers=centers, **center), nr_reduces=0) return job.wait()
def setUp(self): sys.stdout = self.capture_stdout = cStringIO.StringIO() self.params = Params() self.params.keysets = { 'last_name_keyset': dict( key_parts=['_keyset', 'last_name'], value_parts=['count'], ), 'first_name_keyset': dict( key_parts=['_keyset', 'first_name'], value_parts=['count'], )}
class ParamsJob(TestJob): params = Params(x=5, f1=fun1, f2=fun2, now=datetime.now()) sort = False @staticmethod def map(e, params): yield e, params.f1(int(e), params.x) @staticmethod def reduce(iter, params): for k, v in iter: yield k, params.f2(int(v))
def test_http(self): url = 'http://google.com/' source = datasources.source_for(url) assert isinstance(source, HTTPSource) urls = source.segment_between(datetime(2011, 5, 31), datetime(2011, 6, 1)) eq_(len(urls), 1) params = Params() input_stream = datasources.input_stream_for(None, None, urls[0], params)
def test_keyset_multiplier(self): params = Params() params.keysets = { 'last_name_keyset': dict( key_parts=['_keyset', 'last_name'], value_parts=['count'], ), 'first_name_keyset': dict( key_parts=['_keyset', 'first_name'], value_parts=['count'], )} data = [ {'first_name': 'Willow', 'last_name': 'Harvey'}, {'first_name': 'Noam', 'last_name': 'Clarke'}] expected = [ { 'first_name': 'Willow', 'last_name': 'Harvey', '_keyset': 'first_name_keyset' }, { 'first_name': 'Willow', 'last_name': 'Harvey', '_keyset': 'last_name_keyset' }, { 'first_name': 'Noam', 'last_name': 'Clarke', '_keyset': 'first_name_keyset' }, { 'first_name': 'Noam', 'last_name': 'Clarke', '_keyset': 'last_name_keyset' }] actual = keyset_multiplier(data, None, None, params) ok_(isinstance(actual, types.GeneratorType)) eq_(list(actual), expected)
neighbors = v score = 1 - d + d * sum_v yield node_id, str(node_id) + " " + str(score) + " " + neighbors if __name__ == '__main__': parser = OptionParser(usage='%prog [options] inputs') parser.add_option('--iterations', default=10, help='Numbers of iteration') parser.add_option( '--damping-factor', default=0.85, help='probability a web surfer will continue clicking on links') (options, input) = parser.parse_args() results = input params = Params(damping_factor=float(options.damping_factor)) for j in range(int(options.iterations)): job = Job().run(input=results, map=send_score, map_reader=chain_reader, reduce=receive_score, params=params) results = job.wait() for _, node in result_iterator(results): fields = node.split() print fields[0], ":", fields[1]
shortest_length = cost shortest_path = tour yield (None, (shortest_length, shortest_path)) @staticmethod def reduce(iter, params): from disco.util import kvgroup for _, winners in kvgroup(sorted(iter)): yield min(winners) if __name__ == '__main__': line = sys.stdin.readline() sales_trip = json.loads(line) m = numpy.matrix(sales_trip['graph']) num_nodes = m.shape[0] num_tours = factorial(num_nodes - 1) #Here we break down the full range of possible tours into smaller #pieces. Each piece is passed along as a key along with the trip #description. step_size = int(100 if num_tours < 100**2 else num_tours / 100) steps = range(0, num_tours, step_size) + [num_tours] ranges = zip(steps[0:-1], steps[1:]) input = map(lambda x: 'raw://' + str(x[0]) + "-" + str(x[1]), ranges) from travelling_salesman import TSPJob job = TSPJob().run(input=input, params=Params(trip=sales_trip)) for k, v in result_iterator(job.wait()): print k, v
def setUp(self): self.settings = InfernoSettings() self._make_temp_pid_dir() self.job = InfernoJob(InfernoRule(name='some_rule_name'), {}, Params()) self.pid_dir = pid.pid_dir(self.settings)
def __init__(self, # name, on/off name='_unnamed_', run=True, # throttle min_blobs=1, max_blobs=sys.maxint, partitions=200, partition_function=crc_partition, scheduler=None, worker=None, time_delta=None, newest_first=True, # archive archive=False, archive_tag_prefix='processed', archive_lookback=0, # nuke nuke=False, # map map_init_function=lambda x, y: x, map_function=keyset_map, map_input_stream=chunk_csv_stream, map_output_stream=(map_output_stream, disco_output_stream), #combine combiner_function=None, # reduce reduce_function=keyset_reduce, reduce_output_stream=(reduce_output_stream, disco_output_stream), # result # result_iterator_override --> # see inferno.lib.disco_ext.sorted_iterator for signature result_iterator_override=None, result_processor=keyset_result, result_tag=None, result_tag_suffix=True, save=False, sort=True, sort_buffer_size='10%', sorted_results=True, # keysets keysets=None, key_parts=None, value_parts=None, column_mappings=None, table=None, keyset_parts_preprocess=None, parts_postprocess=None, # input day_range=0, day_offset=0, day_start=None, source_tags=None, source_urls=None, # other rule_init_function=None, rule_cleanup=None, parts_preprocess=None, field_transforms=None, required_files=None, required_modules=None, retry=False, retry_limit=2, retry_delay=1, # notifications --> notify_addresses must be list of addresses notify_on_fail=False, notify_on_success=False, notify_addresses=None, notify_pagerduty=False, notify_pagerduty_key=None, **kwargs): self.qualified_name = name if kwargs: self.params = Params(**kwargs) else: self.params = Params() if not scheduler: scheduler = {'force_local': False, 'max_cores': 200} # name, on/off self.run = run self.name = name # throttle self.min_blobs = min_blobs self.max_blobs = max_blobs self.partitions = partitions self.partition_function = partition_function self.scheduler = scheduler self.time_delta = time_delta if self.time_delta is None: self.time_delta = {'minutes': 5} self.newest_first = newest_first self.worker = worker # archive self.archive = archive self.archive_tag_prefix = archive_tag_prefix # nuke self.nuke = nuke # map self.map_init_function = map_init_function self.map_function = map_function self.map_input_stream = map_input_stream self.map_output_stream = map_output_stream self.combiner_function = combiner_function # reduce self.reduce_function = reduce_function self.reduce_output_stream = reduce_output_stream # result self.result_processor = result_processor self.result_tag = result_tag self.result_tag_suffix = result_tag_suffix self.save = save self.sort = sort self.sort_buffer_size = sort_buffer_size if result_iterator_override: self.result_iterator = result_iterator_override elif self.sort and sorted_results: self.result_iterator = sorted_iterator else: self.result_iterator = result_iterator # input if isinstance(source_tags, basestring): source_tags = [source_tags] if archive_lookback: source_tags = get_date_lookback(source_tags, archive_lookback) self.day_range = day_range self.day_offset = day_offset self.day_start = day_start self.source_tags = source_tags or [] # keysets keyset_dict = {} if keysets: for keyset_name, keyset_obj in keysets.items(): keyset_dict[keyset_name] = keyset_obj.as_dict() else: keyset_dict['_default'] = Keyset( key_parts, value_parts, column_mappings, table, keyset_parts_preprocess, parts_postprocess).as_dict() self.params.keysets = keyset_dict self.params.parts_preprocess = parts_preprocess or [] self.params.field_transforms = field_transforms or dict() # other self.rule_init_function = rule_init_function self.rule_cleanup = rule_cleanup self.retry = retry self.retry_limit = retry_limit self.retry_delay = retry_delay self.required_modules = required_modules or [] self.required_files = required_files or [] self.notify_on_fail = notify_on_fail self.notify_on_success = notify_on_success self.notify_addresses = notify_addresses or [] self.notify_pagerduty = notify_pagerduty self.notify_pagerduty_key = notify_pagerduty_key self.source_urls = source_urls
def estimate(input, ys, splitter=' ', map_reader=chain_reader): ys = dict([(id, 1) for id in ys]) job = Job(name='naive_bayes_estimate') job.run(input=input, map_reader=map_reader, map=estimate_map, combiner=estimate_combiner, reduce=estimate_reduce, params=Params(ys=ys, splitter=splitter), clean=False) results = job.wait() total = 0 # will include the items for which we'll be classifying, # for example if the dataset includes males and females, # this dict will include the keys male and female and the # number of times these have been observed in the train set items = {} # the number of times the classes have been observed. For # example, if the feature is something like tall or short, then the dict # will contain the total number of times we have seen tall and short. classes = {} # the number of times we have seen a class with a feature. pairs = {} for key, value in result_iterator(results): l = key.split(splitter) value = int(value) if len(l) == 1: if l[0] == '': total = value elif ys.has_key(l[0]): classes[l[0]] = value else: items[l[0]] = value else: pairs[key] = value #counts[key] = [[c,i], [not c, i], [c, not i], [not c, not i]] counts = {} for i in items: for y in ys: key = y + splitter + i counts[key] = [0, 0, 0, 0] if pairs.has_key(key): counts[key][0] = pairs[key] counts[key][1] = items[i] - counts[key][0] if not classes.has_key(y): counts[key][2] = 0 else: counts[key][2] = classes[y] - counts[key][0] counts[key][3] = total - sum(counts[key][:3]) # add pseudocounts counts[key] = map(lambda x: x + 1, counts[key]) total += 4 import math loglikelihoods = {} for key, value in counts.iteritems(): l = key.split(splitter) if not loglikelihoods.has_key(l[0]): loglikelihoods[l[0]] = 0.0 loglikelihoods[l[0]] += math.log(value[0] + value[2]) - math.log(value[1] + value[3]) loglikelihoods[key] = math.log(value[0]) - math.log(value[1]) return loglikelihoods
def __test_warc_mime_type(self): params = Params() input_stream = datasources.input_stream_for(None, None, segment[0], params)