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 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 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)
# 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()