def measure(test_data,
predictions,
measure="ca",
save_results=True,
show=False):
from disco.worker.pipeline.worker import Worker, Stage
from disco.core import Job, result_iterator
from disco.worker.task_io import task_input_stream, chain_reader
if measure not in ["ca", "mse"]:
raise Exception("measure should be ca or mse.")
if test_data.params["id_index"] == -1:
raise Exception("ID index should be defined.")
if predictions == []:
return "No predictions", None
# define a job and set save of results to ddfs
job = Job(worker=Worker(save_results=save_results))
job = Job(worker=Worker(save_results=save_results))
job.pipeline = [("split",
Stage("map",
input_chain=test_data.params["input_chain"],
init=simple_init,
process=map_test_data))]
job.params = test_data.params
job.run(name="ma_parse_testdata", input=test_data.params["data_tag"])
parsed_testdata = job.wait(show=show)
reduce_proces = reduce_ca if measure == "ca" else reduce_mse
job = Job(worker=Worker(save_results=save_results))
job.pipeline = [("split",
Stage("map",
init=simple_init,
input_chain=[task_input_stream, chain_reader],
process=map_predictions)),
('group_all',
Stage("reduce",
init=simple_init,
process=reduce_proces,
sort=True,
combine=True))]
job.run(name="ma_measure_accuracy", input=parsed_testdata + predictions)
measure, acc = [
(measure, acc) for measure, acc in result_iterator(job.wait(show=show))
][0]
return measure, acc
def get(program, key, jobname):
"""Usage: key jobname
Print the oob value for the given key and jobname.
"""
from disco.core import Job
print Job(program.disco, jobname).oob_get(key)
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 oob(program, jobname):
"""Usage: jobname
Print the oob keys for the named job.
"""
from disco.core import Job
for key in Job(program.disco, jobname).oob_list():
print key
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 fit(dataset, nu=0.1, save_results=True, show=False):
"""
Function starts a job for calculation of model parameters
Parameters
----------
input - dataset object with input urls and other parameters
nu - parameter to adjust the classifier
save_results - save results to ddfs
show - show info about job execution
Returns
-------
Urls of fit model results on ddfs
"""
from disco.worker.pipeline.worker import Worker, Stage
from disco.core import Job
if dataset.params["y_map"] == []:
raise Exception(
"Linear proximal SVM requires a target label mapping parameter.")
try:
nu = float(nu)
if nu <= 0:
raise Exception("Parameter nu should be greater than 0")
except ValueError:
raise Exception("Parameter should be numerical.")
job = Job(worker=Worker(save_results=save_results))
# job parallelizes mappers and joins them with one reducer
job.pipeline = [("split",
Stage("map",
input_chain=dataset.params["input_chain"],
init=simple_init,
process=map_fit)),
('group_all',
Stage("reduce",
init=simple_init,
process=reduce_fit,
combine=True))]
job.params = dataset.params
job.params["nu"] = nu
job.run(name="linearsvm_fit", input=dataset.params["data_tag"])
fitmodel_url = job.wait(show=show)
return {"linsvm_fitmodel": fitmodel_url} # return results url
def process_featurization_with_disco(input_list, params, partitions=4):
'''
Called from within featurize_in_parallel.
Returns disco.core.result_iterator
Arguments:
input_list: path to file listing filename,class_name for each individual time series data file.
params: dictionary of parameters to be passed to each map & reduce function.
partitions: Number of nodes/partitions in system.
'''
from disco.core import Job, result_iterator
job = Job().run(input=input_list,
map=map,
partitions=partitions,
reduce=featurize_reduce,
params=params)
result = result_iterator(job.wait(show=True))
return result
def predict(dataset, fitmodel_url, save_results=True, show=False):
from disco.worker.pipeline.worker import Worker, Stage
from disco.core import Job, result_iterator
if "linreg_fitmodel" not in fitmodel_url:
raise Exception("Incorrect fit model.")
job = Job(worker=Worker(save_results=save_results))
job.pipeline = [("split",
Stage("map",
input_chain=dataset.params["input_chain"],
init=simple_init,
process=map_predict))]
job.params = dataset.params
job.params["thetas"] = [
v for _, v in result_iterator(fitmodel_url["linreg_fitmodel"])
][0]
job.run(name="linreg_predict", input=dataset.params["data_tag"])
return job.wait(show=show)
def predict(dataset, fitmodel_url, save_results=True, show=False):
"""
Function starts a job that makes predictions to input data with a given model
Parameters
----------
input - dataset object with input urls and other parameters
fitmodel_url - model created in fit phase
save_results - save results to ddfs
show - show info about job execution
Returns
-------
Urls with predictions on ddfs
"""
from disco.worker.pipeline.worker import Worker, Stage
from disco.core import Job, result_iterator
if dataset.params["y_map"] == []:
raise Exception(
"Logistic regression requires a target label mapping parameter.")
if "logreg_fitmodel" not in fitmodel_url:
raise Exception("Incorrect fit model.")
job = Job(worker=Worker(save_results=save_results))
# job parallelizes execution of mappers
job.pipeline = [("split",
Stage("map",
input_chain=dataset.params["input_chain"],
init=simple_init,
process=map_predict))]
job.params = dataset.params # job parameters (dataset object)
job.params["thetas"] = [
v for k, v in result_iterator(fitmodel_url["logreg_fitmodel"])
if k == "thetas"
][0] # thetas are loaded from ddfs
job.run(name="logreg_predict", input=dataset.params["data_tag"])
results = job.wait(show=show)
return results
def fit(dataset, save_results=True, show=False):
from disco.worker.pipeline.worker import Worker, Stage
from disco.core import Job
job = Job(worker=Worker(save_results=save_results))
job.pipeline = [("split",
Stage("map",
input_chain=dataset.params["input_chain"],
init=simple_init,
process=map_fit)),
('group_all',
Stage("reduce",
init=simple_init,
process=reduce_fit,
combine=True))]
job.params = dataset.params
job.run(name="linreg_fit", input=dataset.params["data_tag"])
fitmodel_url = job.wait(show=show)
return {"linreg_fitmodel": fitmodel_url} # return results url
def predict(dataset, fitmodel_url, coeff=0.5, save_results=True, show=False):
from disco.worker.pipeline.worker import Worker, Stage
from disco.core import Job, result_iterator
import discomll
path = "/".join(
discomll.__file__.split("/")[:-1] + ["ensemble", "core", ""])
job = Job(worker=Worker(save_results=save_results))
job.pipeline = [("split",
Stage("map",
input_chain=dataset.params["input_chain"],
init=simple_init,
process=map_predict))]
if "dwf_fitmodel" not in fitmodel_url:
raise Exception("Incorrect fit model.")
try:
coeff = float(coeff)
if coeff < 0:
raise Exception("Parameter coeff should be greater than 0.")
except ValueError:
raise Exception("Parameter coeff should be numerical.")
job.params = dataset.params
job.params["coeff"] = coeff
for k, v in result_iterator(fitmodel_url["dwf_fitmodel"]):
job.params[k] = v
if len(job.params["forest"]) == 0:
print "Warning: There is no decision trees in forest"
return []
job.run(name="distributed_weighted_forest_predict",
input=dataset.params["data_tag"],
required_files=[path + "decision_tree.py"])
return job.wait(show=show)
def fit(dataset, save_results=True, show=False):
"""
Function builds a model for Naive Bayes. It executes multiple map functions and one reduce function which aggregates intermediate results and returns a model.
Parameters
----------
input - dataset object with input urls and other parameters
save_results - save results to ddfs
show - show info about job execution
Returns
-------
Urls of fit model results on ddfs
"""
from disco.worker.pipeline.worker import Worker, Stage
from disco.core import Job
# define a job and set save of results to ddfs
job = Job(worker=Worker(save_results=save_results))
# job parallelizes mappers, sorts intermediate pairs and joins them with one reducer
job.pipeline = [("split",
Stage("map",
input_chain=dataset.params["input_chain"],
init=simple_init,
process=map_fit)),
('group_all',
Stage("reduce",
init=simple_init,
process=reduce_fit,
sort=True,
combine=True))]
job.params = dataset.params # job parameters (dataset object)
# define name of a job and input data urls
job.run(name="naivebayes_fit", input=dataset.params["data_tag"])
fitmodel_url = job.wait(show=show)
return {"naivebayes_fitmodel": fitmodel_url} # return results url
def _fit_predict(fit_data, samples, tau, save_results, show):
from disco.worker.pipeline.worker import Worker, Stage
from disco.core import Job
job = Job(worker=Worker(save_results=save_results))
job.pipeline = [("split",
Stage("map",
input_chain=fit_data.params["input_chain"],
init=simple_init,
process=map_fit)),
('group_all',
Stage("reduce",
init=simple_init,
process=reduce_fit,
sort=True,
combine=True))]
job.params = fit_data.params
job.params["tau"] = tau
job.params["samples"] = samples
job.run(name="lwlr_fit_predict", input=fit_data.params["data_tag"])
return job.wait(show=show)
def predict(dataset,
fitmodel_url,
voting=False,
save_results=True,
show=False):
from disco.worker.pipeline.worker import Worker, Stage
from disco.core import Job, result_iterator
import discomll
path = "/".join(
discomll.__file__.split("/")[:-1] + ["ensemble", "core", ""])
if "drf_fitmodel" not in fitmodel_url:
raise Exception("Incorrect fit model.")
job = Job(worker=Worker(save_results=save_results))
job.pipeline = [
("split",
Stage("map",
input_chain=dataset.params["input_chain"],
init=simple_init,
process=map_predict_voting if voting else map_predict_dist))
]
job.params = dataset.params
for k, v in result_iterator(fitmodel_url["drf_fitmodel"]):
job.params[k] = v
if len(job.params["forest"]) == 0:
print "Warning: There is no decision trees in forest"
return []
job.run(name="distributed_random_forest_predict",
input=dataset.params["data_tag"],
required_files=[path + "decision_tree.py"])
return job.wait(show=show)
yield strippedWord, 1
def reduce(iter, params):
from disco.util import kvgroup
for word, counts in kvgroup(sorted(iter)):
yield word, sum(counts)
if __name__ == '__main__':
job = Job().run(input="There are known knowns.\
These are things we know that we know.\
There are known unknowns. \
That is to say,\
there are things that \
we know we do not know.\
But there are also unknown unknowns.\
There are things \
we do not know we don't know",
map=map,
reduce=reduce)
sort_in_numerical_order =\
open('SortNumerical.txt', 'w')
sort_in_alpbabetically_order = \
open('SortAlphabetical.txt', 'w')
wordCount = []
for word, count in \
result_iterator(job.wait(show=True)):
tweeter, tweet = count_tweet_words.get_username_tweet(line)
# return each word in the tweet (to count frequency of each term)
for word in tweet.split():
yield word, 1
def reduce(iter, params):
from disco.util import kvgroup
for word, counts in kvgroup(sorted(iter)):
yield word, sum(counts)
if __name__ == '__main__':
input_filename = "./tweet_data/tweets_357.json"
#input_filename = "./tweet_data/tweets_859157.json"
#input_filename = "/media/3TBStorage/tweets_all.json"
# we need a fully qualified file name for the server
fully_qualified_path = os.path.realpath(input_filename)
input = [fully_qualified_path]
# import this module so pickle knows what to send to workers
import count_tweet_words
job = Job().run(input=input, map=map, reduce=reduce)
out = open(OUTPUT_FILENAME, 'w')
for word, count in result_iterator(job.wait(show=True)):
#print(word, count)
out.write(json.dumps([word, count]) + '\n')
if __name__ == "__main__":
pattern = re.compile("^logs-")
current_directory = os.path.realpath(
os.path.dirname(os.path.realpath(__file__))) + "/"
input_directory = os.path.normpath(current_directory +
"../../generate/target/")
input_files = [
input_directory + "/" + file for file in os.listdir(input_directory)
if pattern.match(file)
and os.path.isfile(os.path.join(input_directory, file))
]
job = Job().run(required_files=[
os.path.normpath(current_directory + "../model/log.py")
],
input=input_files,
map=map,
reduce=reduce)
data = []
timestamp = int(time.time())
for word, count in result_iterator(job.wait(show=True)):
datapoint = {
"metric": "provider.channel.bps",
"timestamp": timestamp,
"value": count,
"tags": {
"provider": word[0],
"channel": word[1]
}
}
def fit(dataset,
trees_per_chunk=1,
bootstrap=True,
max_tree_nodes=50,
min_samples_leaf=10,
min_samples_split=5,
class_majority=1,
separate_max=True,
measure="info_gain",
accuracy=1,
random_state=None,
save_results=True,
show=False):
from disco.worker.pipeline.worker import Worker, Stage
from disco.core import Job
import discomll
path = "/".join(
discomll.__file__.split("/")[:-1] + ["ensemble", "core", ""])
try:
trees_per_chunk = int(trees_per_chunk)
max_tree_nodes = int(
max_tree_nodes) if max_tree_nodes != None else max_tree_nodes
min_samples_leaf = int(min_samples_leaf)
min_samples_split = int(min_samples_split)
class_majority = float(class_majority)
accuracy = int(accuracy)
separate_max = separate_max
if trees_per_chunk > 1 and bootstrap == False:
raise Exception(
"Parameter trees_per_chunk (or Trees per subset) should be 1 to disable bootstrap."
)
if trees_per_chunk <= 0 or min_samples_leaf <= 0 or class_majority <= 0 or min_samples_split <= 0 and accuracy < 0 or type(
bootstrap) != bool:
raise Exception("Parameters should be greater than 0.")
except ValueError:
raise Exception("Parameters should be numerical.")
if measure not in ["info_gain", "mdl"]:
raise Exception("measure should be set to info_gain or mdl.")
job = Job(worker=Worker(save_results=save_results))
job.pipeline = [
("split",
Stage("map",
input_chain=dataset.params["input_chain"],
init=map_init,
process=map_fit_bootstrap if bootstrap else map_fit)),
('group_all',
Stage("reduce", init=simple_init, process=reduce_fit, combine=True))
]
job.params = dataset.params
job.params["trees_per_chunk"] = trees_per_chunk
job.params["max_tree_nodes"] = max_tree_nodes
job.params["min_samples_leaf"] = min_samples_leaf
job.params["min_samples_split"] = min_samples_split
job.params["class_majority"] = class_majority
job.params["measure"] = measure
job.params["bootstrap"] = bootstrap
job.params["accuracy"] = accuracy
job.params["separate_max"] = separate_max
job.params['seed'] = random_state
job.run(name="forest_distributed_decision_trees_fit",
input=dataset.params["data_tag"],
required_files=[path + "decision_tree.py", path + "measures.py"])
fitmodel_url = job.wait(show=show)
return {"fddt_fitmodel": fitmodel_url} # return results url
def runTest(self):
threading.Thread(target=startServer).start()
input = 'http:' + self.disco.master.split(':')[1] + ":" + str(PORT)
self.job = Job().run(input=[input], map=map, reduce=reduce)
self.assertEqual(sorted(self.results(self.job)), [(b'Hello', 1), (b'World', 1)])
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]
from disco.core import Job, result_iterator
def map(line, params):
for word in line.split():
yield word, 1
def reduce(iter, params):
from disco.util import kvgroup
for word, counts in kvgroup(sorted(iter)):
yield word, sum(counts)
if __name__ == '__main__':
job = Job().run(input=["erl://erl_inputs:test/dummy"],
map=map,
reduce=reduce)
for word, count in result_iterator(job.wait(show=True)):
print word, count
def map(line, params):
for char in line.lower():
if char >= 'a' and char <= 'z':
yield char, 1
def reduce(iter, params):
from disco.util import kvgroup
for char, counts in kvgroup(sorted(iter)):
yield char, sum(counts)
# run the disco job
from disco.core import Job, result_iterator
job = Job().run(input=["http://en.wikipedia.org/wiki/MapReduce"], map=map, reduce=reduce)
# plot the results with matplotlib
#%matplotlib inline
xs, ys = zip(*result_iterator(job.wait()))
import scipy
from matplotlib import pylab
x = scipy.arange(len(xs))
y = scipy.array(ys)
f = pylab.figure()
ax = f.add_axes([0, 0, 3, 1])
ax.bar(x, y, align='center')
ax.set_xticks(x)
ax.set_xticklabels(xs)
f.show()
# This program estimates the value of pi (3.14...)
# Usage:
# python estimate_pi.py
from disco.core import Job, result_iterator
def map(line, params):
from random import random
x, y = random(), random()
yield 0, 1 if x * x + y * y < 1 else 0
if __name__ == '__main__':
COUNT = 5000
job = Job().run(input=["raw://0"] * COUNT, map=map)
tot = 0
for k, v in result_iterator(job.wait()):
tot += v
print(4.0 * tot) / COUNT
from disco.util import kvgroup
for word, counts in kvgroup(sorted(iter)):
yield word, sum(counts)
if __name__ == '__main__':
#input_filename = "./tweet_data/tweets_357.json"
#input_filename = "./tweet_data/tweets_859157.json"
#input_filename = "/media/3TBStorage/tweets_all.json"
# we need a fully qualified file name for the server
#fully_qualified_path = os.path.realpath(input_filename)
#input = [fully_qualified_path]
#input = ["tag://data:tweets357"]
input = ["tag://data:tweets859157xa"]
# import this module so pickle knows what to send to workers
import count_tweet_words
job = Job().run(input=input,
map=map,
map_reader=chain_reader,
reduce=reduce,
combiner=sum_combiner)
out = open(OUTPUT_FILENAME, 'w')
for word, count in result_iterator(job.wait(show=True)):
#print(word, count)
out.write(json.dumps([word, count]) + '\n')
item = q.get()
if item == 0:
return
yield item
q.task_done()
def map(line, params):
import __builtin__
unwanted = u",!.#()][{}-><=|/\"'*:?"
words = line.translate(
__builtin__.dict.fromkeys([ord(x) for x in unwanted], u" ")).lower()
for word in words.split():
yield word, 1
def reduce(iter, params):
from disco.util import kvgroup
for word, counts in kvgroup(sorted(iter)):
yield word, sum(counts)
if __name__ == '__main__':
job = Job().run(input=["tag://" + DDFS_TAG],
map=map,
reduce=reduce,
map_reader=chain_reader)
for line, count in result_iterator(job.wait(show=True)):
print(line, count)
from disco.core import Job, result_iterator
def map(line, params):
for word in line.split():
yield word,1
def reduce(iter, params):
from disco.util import kvgroup
for word, counts in kvgroup(sorted(iter)):
yield word, sum(counts)
'''
def mongodb_output(stream,partition,url,params):
return mongoDisco_output.MongoDBoutput(stream,params)
'''
if __name__ == '__main__':
job = Job().run(input=["r"],
map=map,
reduce=reduce,
map_input_stream = mongodb_input_stream
reduce_output_stream=mongodb_output_stream)
job.wait(show=True)
from mongoDisco_output import MongoDBoutput
from disco.worker.classic.func import task_output_stream
import logging
def map(record, params):
logging.info("%s" % record.get('_id'))
yield record.get('name', "NoName"), 1
def reduce(iter, params):
from disco.util import kvgroup
for word, counts in kvgroup(sorted(iter)):
yield word, sum(counts)
def mongodb_output(stream, partition, url, params):
return mongoDisco_output.MongoDBoutput(stream, params)
if __name__ == '__main__':
mongodb_stream = tuple([mongodb_output])
job = Job().run(input=["mongodb://localhost/test.modforty"],
map=map,
reduce=reduce,
reduce_output_stream=mongodb_stream)
job.wait(show=True)
# for word, count in result_iterator(job.wait(show=True)):
# print word, count
from disco.core import Job, result_iterator
def map(line, params):
for word in line.split():
yield word, 1
def reduce(iter, params):
from disco.util import kvgroup
for word, counts in kvgroup(sorted(iter)):
yield word, sum(counts)
if __name__ == '__main__':
print "runnning job"
job = Job().run(input=["http://discoproject.org/media/text/chekhov.txt"],
map=map,
reduce=reduce)
for word, count in result_iterator(job.wait(show=True)):
print(word, count)
def map(line, params):
import github_crawler
n = int(line)
users = github_crawler.get_users_with_n_followers(n)
print str(len(users)) + " users"
for user in users:
repos = github_crawler.get_user_parent_repos(user)
print str(len(repos)) + " repos"
for owner, repo, branch in repos:
print owner + "/" + repo + "#" + branch
directory = github_crawler.clone_repo(owner, repo, branch)
for item in github_crawler.analyze_repo(directory):
yield item
def reduce(iter, params):
from disco.util import kvgroup
for extension, ratios in kvgroup(sorted(iter)):
l_ratios = [r for r in ratios]
yield extension, sum(l_ratios) / len(l_ratios)
if __name__ == "__main__":
input = ["raw://" + str(i) for i in range(0, 1000, 10)]
job = Job().run(input=input,
map=map,
reduce=reduce,
required_files=["github_crawler.py"])
for extension, avg in result_iterator(job.wait(show=True)):
print extension, ": ", avg
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
