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 "dwfr_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["dwfr_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_rand_predict", input=dataset.params["data_tag"],
required_files=[path + "decision_tree.py"])
return job.wait(show=show)
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 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)
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 "linsvm_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.params["fit_params"] = [v for _, v in result_iterator(fitmodel_url["linsvm_fitmodel"])][0]
job.run(name="linsvm_predict", input=dataset.params["data_tag"])
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(training_data, fitting_data, tau=1, samples_per_job=0, save_results=True, show=False):
from disco.worker.pipeline.worker import Worker, Stage
from disco.core import Job, result_iterator
from disco.core import Disco
"""
training_data - training samples
fitting_data - dataset to be fitted to training data.
tau - controls how quickly the weight of a training sample falls off with distance of its x(i) from the query point x.
samples_per_job - define a number of samples that will be processed in single mapreduce job. If 0, algorithm will calculate number of samples per job.
"""
try:
tau = float(tau)
if tau <= 0:
raise Exception("Parameter tau should be >= 0.")
except ValueError:
raise Exception("Parameter tau should be numerical.")
if fitting_data.params["id_index"] == -1:
raise Exception("Predict data should have id_index set.")
job = Job(worker=Worker(save_results=save_results))
job.pipeline = [
("split", Stage("map", input_chain=fitting_data.params["input_chain"], init=simple_init, process=map_predict))
]
job.params = fitting_data.params
job.run(name="lwlr_read_data", input=fitting_data.params["data_tag"])
samples = {}
results = []
tau = float(2 * tau ** 2) # calculate tau once
counter = 0
for test_id, x in result_iterator(job.wait(show=show)):
if samples_per_job == 0:
# calculate number of samples per job
if len(x) <= 100: # if there is less than 100 attributes
samples_per_job = 100 # 100 samples is max per on job
else:
# there is more than 100 attributes
samples_per_job = len(x) * -25 / 900.0 + 53 # linear function
samples[test_id] = x
if counter == samples_per_job:
results.append(_fit_predict(training_data, samples, tau, save_results, show))
counter = 0
samples = {}
counter += 1
if len(samples) > 0: # if there is some samples left in the the dictionary
results.append(_fit_predict(training_data, samples, tau, save_results, show))
# merge results of every iteration into a single tag
ddfs = Disco().ddfs
ddfs.tag(job.name, [[list(ddfs.blobs(tag))[0][0]] for tag in results])
return ["tag://" + job.name]
def predict(dataset, fitmodel_url, m=1, 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
m - m estimate is used with discrete features
save_results - save results to ddfs
show - show info about job execution
Returns
-------
Urls of predictions on ddfs
"""
from disco.worker.pipeline.worker import Worker, Stage
from disco.core import Job, result_iterator
import numpy as np
try:
m = float(m)
except ValueError:
raise Exception("Parameter m should be numerical.")
if "naivebayes_fitmodel" in fitmodel_url:
# fit model is loaded from ddfs
fit_model = dict((k, v) for k, v in result_iterator(fitmodel_url["naivebayes_fitmodel"]))
if len(fit_model["y_labels"]) < 2:
print "There is only one class in training data."
return []
else:
raise Exception("Incorrect fit model.")
if dataset.params["X_meta"].count("d") > 0: # if there are discrete features in the model
# code calculates logarithms to optimize predict phase as opposed to calculation by every mapped.
np.seterr(divide='ignore')
for iv in fit_model["iv"]:
dist = [fit_model.pop((y,) + iv, 0) for y in fit_model["y_labels"]]
fit_model[iv] = np.nan_to_num(
np.log(np.true_divide(np.array(dist) + m * fit_model["prior"], np.sum(dist) + m))) - fit_model[
"prior_log"]
del (fit_model["iv"])
# define a job and set save of results to ddfs
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["fit_model"] = fit_model
# define name of a job and input data urls
job.run(name="naivebayes_predict", input=dataset.params["data_tag"])
results = job.wait(show=show)
return results
def fit(dataset, alpha=1e-8, max_iterations=10, save_results=True, show=False):
"""
Function starts a job for calculation of theta parameters
Parameters
----------
input - dataset object with input urls and other parameters
alpha - convergence value
max_iterations - define maximum number of iterations
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, result_iterator
import numpy as np
if dataset.params["y_map"] == []:
raise Exception("Logistic regression requires a target label mapping parameter.")
try:
alpha = float(alpha)
max_iterations = int(max_iterations)
if max_iterations < 1:
raise Exception("Parameter max_iterations should be greater than 0.")
except ValueError:
raise Exception("Parameters should be numerical.")
# initialize thetas to 0 and add intercept term
thetas = np.zeros(len(dataset.params["X_indices"]) + 1)
J = [0] # J cost function values for every iteration
for i in range(max_iterations):
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 parameters (dataset object)
job.params["thetas"] = thetas # every iteration set new thetas
job.run(name="logreg_fit_iter_%d" % (i + 1), input=dataset.params["data_tag"])
fitmodel_url = job.wait(show=show)
for k, v in result_iterator(fitmodel_url):
if k == "J": #
J.append(v) # save value of J cost function
else:
thetas = v # save new thetas
if np.abs(J[-2] - J[-1]) < alpha: # check for convergence
if show:
print("Converged at iteration %d" % (i + 1))
break
return {"logreg_fitmodel": fitmodel_url} # return results url
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 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()
class DiscoJob():
def __init__(self,config,map,reduce):
import config_util
self.config = config_util.config
#if the user doesn't specify output, print to stdout
if not config.get('output_uri') and not config.get('print_to_stdout'):
config['print_to_stdout'] = True
for item in config:
self.config[item] = config[item]
self.map = map
self.reduce = reduce
self.job = Job()
self.params = Params()
for key in self.config:
self.params.__dict__[key] = self.config[key]
def run(self):
if self.config['print_to_stdout']:
self.job.run(input = do_split(self.config),
map = self.map,
reduce = self.reduce,
params = self.params,
map_input_stream = mongodb_input_stream,
required_modules= ['mongodb_io',
'mongodb_input',
'config_util',
'mongo_util',
'mongodb_output'])
for key, value in result_iterator(self.job.wait(show=True)):
print key, value
else:
self.job.run(input = do_split(self.config),
map = self.map,
reduce = self.reduce,
params = self.params,
map_input_stream = mongodb_input_stream,
reduce_output_stream = mongodb_output_stream,
required_modules= ['mongodb_io',
'mongodb_input',
'config_util',
'mongo_util',
'mongodb_output'])
if self.config.get("job_wait",False):
self.job.wait(show=True)
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 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 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 measure(input, save_results=True, show=False):
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.pipeline = [
("split", Stage("map", input_chain=input.params["input_chain"], init=simple_init, process=map_fit)),
('group_all', Stage("reduce", init=simple_init, process=reduce_fit, combine=True))]
job.params = input.params # job parameters (dataset object)
job.run(name="Distribution", input=input.params["data_tag"])
return job.wait(show=show) # return results url
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 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 _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, 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 start(self):
"""Starts the entire process of querying twitter and classification.
This method is responsible for running MapReduce for feature
extraction.
"""
def range_reader(stream, size, url):
page_num = stream.getvalue()
# Map readers should return a list of values, so page_num is
# explicitly converted to an integer and then wrapped into a
# list. By doing this each mapper instance will get exactly
# one page number
# If we don't do this, the mapper API just reads the numbers
# character by character and we end up fetching the same 10
# pages: digits 0, 9 all through since each character of a number
# should be one of these 10 digits.
return [int(page_num)]
job = Job()
inputs = [('raw://%d' % (i)) for i in range(1, self.num_pages)]
job.run(input=inputs, map=mapper, reduce=reducer,
map_reader=range_reader, params=Params(
query=self.query,
trained_vectorizer=self.vectorizer
),
required_modules=[
('vectorizer', os.path.join(datasettings.PROJECT_ROOT,
'analyzer',
'vectorizer.py'),),
('models', os.path.join(datasettings.PROJECT_ROOT,
'webui', 'fatninja',
'models.py'),),
])
self.feature_vector, self.row_num_to_tweet_id_map = \
self.vectorizer.build_feature_matrix(job)
self.classify()
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 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 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_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)
class DiscoJob():
DEFAULT_CONFIG = {
"job_output_key" : "_id",
"job_output_value" : "value",
"input_uri" : "mongodb://localhost/test.in",
"output_uri" : "mongodb://localhost/test.out",
"print_to_stdout": False,
"job_wait":True,
"split_size" : 8,
"split_key" : {"_id" : 1},
"create_input_splits" : True,
"use_shards" : False,
"use_chunks" : True,
"slave_ok" : False,
"limit" : 0,
"skip" : 0,
"input_key" : None,
"sort" : None,
"timeout" : False,
"fields" : None,
"query" : {}
}
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 run(self):
if self.config['print_to_stdout']:
self.job.run(input = do_split(self.config),
map = self.map,
reduce = self.reduce,
params = self.params,
map_input_stream = mongodb_input_stream,
required_modules= ['mongodisco.mongodb_io',
'mongodisco.mongodb_input',
'mongodisco.mongo_util',
'mongodisco.mongodb_output'])
for key, value in result_iterator(self.job.wait(show=True)):
print key, value
else:
self.job.run(input = do_split(self.config),
map = self.map,
reduce = self.reduce,
params = self.params,
map_input_stream = mongodb_input_stream,
reduce_output_stream = mongodb_output_stream,
required_modules= ['mongodisco.mongodb_io',
'mongodisco.mongodb_input',
'mongodisco.mongo_util',
'mongodisco.mongodb_output'])
if self.config.get("job_wait",False):
self.job.wait(show=True)
def fit_predict(training_data,
fitting_data,
tau=1,
samples_per_job=0,
save_results=True,
show=False):
from disco.worker.pipeline.worker import Worker, Stage
from disco.core import Job, result_iterator
from disco.core import Disco
"""
training_data - training samples
fitting_data - dataset to be fitted to training data.
tau - controls how quickly the weight of a training sample falls off with distance of its x(i) from the query point x.
samples_per_job - define a number of samples that will be processed in single mapreduce job. If 0, algorithm will calculate number of samples per job.
"""
try:
tau = float(tau)
if tau <= 0:
raise Exception("Parameter tau should be >= 0.")
except ValueError:
raise Exception("Parameter tau should be numerical.")
if fitting_data.params["id_index"] == -1:
raise Exception("Predict data should have id_index set.")
job = Job(worker=Worker(save_results=save_results))
job.pipeline = [("split",
Stage("map",
input_chain=fitting_data.params["input_chain"],
init=simple_init,
process=map_predict))]
job.params = fitting_data.params
job.run(name="lwlr_read_data", input=fitting_data.params["data_tag"])
samples = {}
results = []
tau = float(2 * tau**2) # calculate tau once
counter = 0
for test_id, x in result_iterator(job.wait(show=show)):
if samples_per_job == 0:
# calculate number of samples per job
if len(x) <= 100: # if there is less than 100 attributes
samples_per_job = 100 # 100 samples is max per on job
else:
# there is more than 100 attributes
samples_per_job = len(x) * -25 / 900. + 53 # linear function
samples[test_id] = x
if counter == samples_per_job:
results.append(
_fit_predict(training_data, samples, tau, save_results, show))
counter = 0
samples = {}
counter += 1
if len(samples) > 0: # if there is some samples left in the the dictionary
results.append(
_fit_predict(training_data, samples, tau, save_results, show))
# merge results of every iteration into a single tag
ddfs = Disco().ddfs
ddfs.tag(job.name, [[list(ddfs.blobs(tag))[0][0]] for tag in results])
return ["tag://" + job.name]
def fit(
dataset,
trees_per_chunk=3,
max_tree_nodes=50,
min_samples_leaf=10,
min_samples_split=5,
class_majority=1,
measure="info_gain",
k="sqrt",
accuracy=1,
random_state=None,
separate_max=True,
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)
separate_max = separate_max
accuracy = int(accuracy)
if (
trees_per_chunk <= 0
or min_samples_leaf <= 0
or min_samples_split <= 0
or class_majority <= 0
or accuracy < 0
):
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)),
("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["accuracy"] = accuracy
job.params["k"] = k
job.params["seed"] = random_state
job.params["separate_max"] = separate_max
job.run(
name="distributed_weighted_forest_fit",
input=dataset.params["data_tag"],
required_files=[path + "decision_tree.py", path + "measures.py", path + "k_medoids.py"],
)
fitmodel_url = job.wait(show=show)
return {"dwf_fitmodel": fitmodel_url} # return results url
rest = [(x[:-3],here+x) for x in src_files]
for x in rest:
print "import ",x[0]
print "Making Job"
print input_files
worker = Worker(required_modules=rest,
master='http://disco1:8989',
input=input_files,
map=mapper,
map_init=mapper_init,
sort=False,
reduce=disco.worker.classic.func.nop_reduce)
job = Job(name="GreenSpace",worker=worker)
print "Running Job"
job.run()
try:
print "Job run"
r = job.wait(show=True)
print "Done Job"
finally:
httpd.shutdown()
for status, reason in result_iterator(r):
print status, reason
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
class DiscoJob():
DEFAULT_CONFIG = {
"job_output_key": "_id",
"job_output_value": "value",
"input_uri": "mongodb://localhost/test.in",
"output_uri": "mongodb://localhost/test.out",
"print_to_stdout": False,
"job_wait": True,
"split_size": 8,
"split_key": {
"_id": 1
},
"create_input_splits": True,
"use_shards": False,
"use_chunks": True,
"slave_ok": False,
"limit": 0,
"skip": 0,
"input_key": None,
"sort": None,
"timeout": False,
"fields": None,
"query": {}
}
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 run(self):
if self.config['print_to_stdout']:
self.job.run(input=do_split(self.config),
map=self.map,
reduce=self.reduce,
params=self.params,
map_input_stream=mongodb_input_stream,
required_modules=[
'mongodisco.mongodb_io',
'mongodisco.mongodb_input',
'mongodisco.mongo_util',
'mongodisco.mongodb_output'
])
for key, value in result_iterator(self.job.wait(show=True)):
print key, value
else:
self.job.run(input=do_split(self.config),
map=self.map,
reduce=self.reduce,
params=self.params,
map_input_stream=mongodb_input_stream,
reduce_output_stream=mongodb_output_stream,
required_modules=[
'mongodisco.mongodb_io',
'mongodisco.mongodb_input',
'mongodisco.mongo_util',
'mongodisco.mongodb_output'
])
if self.config.get("job_wait", False):
self.job.wait(show=True)
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 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 predict(dataset, fitmodel_url, m=1, 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
m - m estimate is used with discrete features
save_results - save results to ddfs
show - show info about job execution
Returns
-------
Urls of predictions on ddfs
"""
from disco.worker.pipeline.worker import Worker, Stage
from disco.core import Job, result_iterator
import numpy as np
try:
m = float(m)
except ValueError:
raise Exception("Parameter m should be numerical.")
if "naivebayes_fitmodel" in fitmodel_url:
# fit model is loaded from ddfs
fit_model = dict(
(k, v)
for k, v in result_iterator(fitmodel_url["naivebayes_fitmodel"]))
if len(fit_model["y_labels"]) < 2:
print "There is only one class in training data."
return []
else:
raise Exception("Incorrect fit model.")
if dataset.params["X_meta"].count(
"d") > 0: # if there are discrete features in the model
# code calculates logarithms to optimize predict phase as opposed to calculation by every mapped.
np.seterr(divide='ignore')
for iv in fit_model["iv"]:
dist = [
fit_model.pop((y, ) + iv, 0) for y in fit_model["y_labels"]
]
fit_model[iv] = np.nan_to_num(
np.log(
np.true_divide(
np.array(dist) + m * fit_model["prior"],
np.sum(dist) + m))) - fit_model["prior_log"]
del (fit_model["iv"])
# define a job and set save of results to ddfs
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["fit_model"] = fit_model
# define name of a job and input data urls
job.run(name="naivebayes_predict", input=dataset.params["data_tag"])
results = job.wait(show=show)
return results
def fit(dataset, alpha=1e-8, max_iterations=10, save_results=True, show=False):
"""
Function starts a job for calculation of theta parameters
Parameters
----------
input - dataset object with input urls and other parameters
alpha - convergence value
max_iterations - define maximum number of iterations
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, result_iterator
import numpy as np
if dataset.params["y_map"] == []:
raise Exception(
"Logistic regression requires a target label mapping parameter.")
try:
alpha = float(alpha)
max_iterations = int(max_iterations)
if max_iterations < 1:
raise Exception(
"Parameter max_iterations should be greater than 0.")
except ValueError:
raise Exception("Parameters should be numerical.")
# initialize thetas to 0 and add intercept term
thetas = np.zeros(len(dataset.params["X_indices"]) + 1)
J = [0] # J cost function values for every iteration
for i in range(max_iterations):
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 parameters (dataset object)
job.params["thetas"] = thetas # every iteration set new thetas
job.run(name="logreg_fit_iter_%d" % (i + 1),
input=dataset.params["data_tag"])
fitmodel_url = job.wait(show=show)
for k, v in result_iterator(fitmodel_url):
if k == "J": #
J.append(v) # save value of J cost function
else:
thetas = v # save new thetas
if np.abs(J[-2] - J[-1]) < alpha: # check for convergence
if show:
print("Converged at iteration %d" % (i + 1))
break
return {"logreg_fitmodel": fitmodel_url} # return results url
