def main(args):
"""
Trains Apriori on the specified dataset (uses vote UCI dataset if no dataset specified).
:param args: the commandline arguments
:type args: list
"""
# load a dataset
if len(args) <= 1:
data_file = helper.get_data_dir() + os.sep + "vote.arff"
else:
data_file = args[1]
helper.print_info("Loading dataset: " + data_file)
loader = Loader("weka.core.converters.ArffLoader")
data = loader.load_file(data_file)
data.class_is_last()
# build Apriori, using last attribute as class attribute
apriori = Associator(classname="weka.associations.Apriori", options=["-c", "-1"])
apriori.build_associations(data)
print(str(apriori))
# iterate association rules (low-level)
helper.print_info("Rules (low-level)")
# make the underlying rules list object iterable in Python
rules = javabridge.iterate_collection(apriori.jwrapper.getAssociationRules().getRules().o)
for i, r in enumerate(rules):
# wrap the Java object to make its methods accessible
rule = JWrapper(r)
print(str(i+1) + ". " + str(rule))
# output some details on rule
print(" - consequence support: " + str(rule.getConsequenceSupport()))
print(" - premise support: " + str(rule.getPremiseSupport()))
print(" - total support: " + str(rule.getTotalSupport()))
print(" - total transactions: " + str(rule.getTotalTransactions()))
# iterate association rules (high-level)
helper.print_info("Rules (high-level)")
print("can produce rules? " + str(apriori.can_produce_rules()))
print("rule metric names: " + str(apriori.rule_metric_names))
rules = apriori.association_rules()
if rules is not None:
print("producer: " + rules.producer)
print("# rules: " + str(len(rules)))
for i, rule in enumerate(rules):
print(str(i+1) + ". " + str(rule))
# output some details on rule
print(" - consequence support: " + str(rule.consequence_support))
print(" - consequence: " + str(rule.consequence))
print(" - premise support: " + str(rule.premise_support))
print(" - premise: " + str(rule.premise))
print(" - total support: " + str(rule.total_support))
print(" - total transactions: " + str(rule.total_transactions))
print(" - metric names: " + str(rule.metric_names))
print(" - metric values: " + str(rule.metric_values))
print(" - metric value 'Confidence': " + str(rule.metric_value('Confidence')))
print(" - primary metric name: " + str(rule.primary_metric_name))
print(" - primary metric value: " + str(rule.primary_metric_value))
def main():
"""
Just runs some example code.
"""
# load a dataset
vote_file = helper.get_data_dir() + os.sep + "vote.arff"
helper.print_info("Loading dataset: " + vote_file)
loader = Loader("weka.core.converters.ArffLoader")
vote_data = loader.load_file(vote_file)
vote_data.class_is_last()
# train and output associator
associator = Associator(classname="weka.associations.Apriori", options=["-N", "9", "-I"])
associator.build_associations(vote_data)
print(associator)
def main():
"""
Just runs some example code.
"""
# load a dataset
vote_file = helper.get_data_dir() + os.sep + "vote.arff"
helper.print_info("Loading dataset: " + vote_file)
loader = Loader("weka.core.converters.ArffLoader")
vote_data = loader.load_file(vote_file)
vote_data.class_is_last()
# train and output associator
associator = Associator(classname="weka.associations.Apriori",
options=["-N", "9", "-I"])
associator.build_associations(vote_data)
print(associator)
def weka_algorithm(algorithm, type, minsup, minconf):
# Weka worker start
jvm.start()
loader = Loader(classname="weka.core.converters.ArffLoader")
data = loader.load_file(arr_file)
associator = Associator(classname=f"weka.associations.{algorithm}",
options=["-M", minsup, "-C", minconf, "-N", "100"])
associator.build_associations(data)
with open(f"results/{type}_weka_{algorithm}.output", "w") as fw:
fw.write(associator.__str__())
fw.close()
# Weka worker end
jvm.stop()
def associateRule(request):
jvm.start()
data_dir = os.path.dirname(os.path.abspath(__file__))
data = converters.load_any_file(data_dir +
"/templates/upload_files/export.csv")
data.class_is_last()
associator = Associator(classname="weka.associations.Apriori",
options=["-C", "-1", "-I"])
# associator = Associator(classname="weka.associations.Apriori", options=["-N", "9", "-I"])
associator.build_associations(data)
rules = str(associator)
jvm.stop()
return HttpResponse(rules)
def __build_associations(algorithm_name, data, result_dest=None):
"""
Function for building association algorithms. Based on name we select
proper algorithm.
:param algorithm_name: string
:param data: weka arff data
:param result_dest: results destination
:return: None
"""
args, _sufix = parsers_dict[algorithm_name]()
associator = Associator(classname=algorithms_path_dict[algorithm_name],
options=args_to_weka_options(args, _sufix))
associator.build_associations(data)
if result_dest:
with open(result_dest, 'a') as file:
file.write(
__print_algorithm_header(associator.to_commandline(),
__get_header_of_data(data),
algorithm_name))
file.write(str(associator))
else:
print(
__print_algorithm_header(associator.to_commandline(),
__get_header_of_data(data),
algorithm_name))
print(str(associator))
def main(args):
"""
Trains Apriori on the specified dataset (uses vote UCI dataset if no dataset specified).
:param args: the commandline arguments
:type args: list
"""
# load a dataset
if len(args) <= 1:
data_file = helper.get_data_dir() + os.sep + "vote.arff"
else:
data_file = args[1]
helper.print_info("Loading dataset: " + data_file)
loader = Loader("weka.core.converters.ArffLoader")
data = loader.load_file(data_file)
data.class_is_last()
# build Apriori, using last attribute as class attribute
apriori = Associator(classname="weka.associations.Apriori",
options=["-c", "-1"])
apriori.build_associations(data)
print(str(apriori))
# More Data Mining with Weka - Class 3.3
# Copyright (C) 2014 Fracpete (fracpete at gmail dot com)
# Use the WEKAMOOC_DATA environment variable to set the location
# for the datasets
import os
data_dir = os.environ.get("WEKAMOOC_DATA")
if data_dir is None:
data_dir = "." + os.sep + "data"
import os
import weka.core.jvm as jvm
from weka.core.converters import Loader
from weka.associations import Associator
jvm.start()
# load weather.nominal
fname = data_dir + os.sep + "weather.nominal.arff"
print("\nLoading dataset: " + fname + "\n")
loader = Loader(classname="weka.core.converters.ArffLoader")
data = loader.load_file(fname)
data.set_class_index(data.num_attributes() - 1)
# build Apriori
assoc = Associator(classname="weka.associations.Apriori")
assoc.build_associations(data)
print(assoc)
jvm.stop()
def main(args):
"""
Trains Apriori on the specified dataset (uses vote UCI dataset if no dataset specified).
:param args: the commandline arguments
:type args: list
"""
# load a dataset
if len(args) <= 1:
data_file = helper.get_data_dir() + os.sep + "vote.arff"
else:
data_file = args[1]
helper.print_info("Loading dataset: " + data_file)
loader = Loader("weka.core.converters.ArffLoader")
data = loader.load_file(data_file)
data.class_is_last()
# build Apriori, using last attribute as class attribute
apriori = Associator(classname="weka.associations.Apriori",
options=["-c", "-1"])
apriori.build_associations(data)
print(str(apriori))
# iterate association rules (low-level)
helper.print_info("Rules (low-level)")
# make the underlying rules list object iterable in Python
rules = javabridge.iterate_collection(
apriori.jwrapper.getAssociationRules().getRules().o)
for i, r in enumerate(rules):
# wrap the Java object to make its methods accessible
rule = JWrapper(r)
print(str(i + 1) + ". " + str(rule))
# output some details on rule
print(" - consequence support: " + str(rule.getConsequenceSupport()))
print(" - premise support: " + str(rule.getPremiseSupport()))
print(" - total support: " + str(rule.getTotalSupport()))
print(" - total transactions: " + str(rule.getTotalTransactions()))
# iterate association rules (high-level)
helper.print_info("Rules (high-level)")
print("can produce rules? " + str(apriori.can_produce_rules()))
print("rule metric names: " + str(apriori.rule_metric_names))
rules = apriori.association_rules()
if rules is not None:
print("producer: " + rules.producer)
print("# rules: " + str(len(rules)))
for i, rule in enumerate(rules):
print(str(i + 1) + ". " + str(rule))
# output some details on rule
print(" - consequence support: " + str(rule.consequence_support))
print(" - consequence: " + str(rule.consequence))
print(" - premise support: " + str(rule.premise_support))
print(" - premise: " + str(rule.premise))
print(" - total support: " + str(rule.total_support))
print(" - total transactions: " + str(rule.total_transactions))
print(" - metric names: " + str(rule.metric_names))
print(" - metric values: " + str(rule.metric_values))
print(" - metric value 'Confidence': " +
str(rule.metric_value('Confidence')))
print(" - primary metric name: " + str(rule.primary_metric_name))
print(" - primary metric value: " +
str(rule.primary_metric_value))
# More Data Mining with Weka - Class 3.3
# Copyright (C) 2014 Fracpete (fracpete at gmail dot com)
# Use the WEKAMOOC_DATA environment variable to set the location
# for the datasets
import os
data_dir = os.environ.get("WEKAMOOC_DATA")
if data_dir is None:
data_dir = "." + os.sep + "data"
import weka.core.jvm as jvm
from weka.core.converters import Loader
from weka.associations import Associator
jvm.start()
# load weather.nominal
fname = data_dir + os.sep + "weather.nominal.arff"
print("\nLoading dataset: " + fname + "\n")
loader = Loader(classname="weka.core.converters.ArffLoader")
data = loader.load_file(fname)
data.class_is_last()
# build Apriori
assoc = Associator(classname="weka.associations.Apriori")
assoc.build_associations(data)
print(assoc)
jvm.stop()
import weka.core.jvm as jvm
from weka.core.converters import Loader
from weka.associations import Associator
jvm.start()
# load weather.nominal
fname = data_dir + os.sep + "weather.nominal.arff"
print("\nLoading dataset: " + fname + "\n")
loader = Loader(classname="weka.core.converters.ArffLoader")
data = loader.load_file(fname)
data.class_is_last()
# build Apriori (output as many rules as possible that min support of 0.01)
print("\n--> Output many weather rules\n")
assoc = Associator(classname="weka.associations.Apriori",
options=["-M", "0.01", "-N", "10000"])
assoc.build_associations(data)
print(assoc)
# build Apriori (default options, outputting itemsets)
print("\n--> Output weather itemsets\n")
assoc = Associator(classname="weka.associations.Apriori", options=["-I"])
assoc.build_associations(data)
print(assoc)
# load supermarket
fname = data_dir + os.sep + "supermarket.arff"
print("\nLoading dataset: " + fname + "\n")
loader = Loader(classname="weka.core.converters.ArffLoader")
data = loader.load_file(fname)
data.class_is_last()