def remove_package(pkg):
if packages.is_installed(pkg):
print("Removing %s..." % pkg)
packages.uninstall_package(pkg)
print("Removed %s, please re-run script!" % pkg)
jvm.stop()
sys.exit(0)
print('No such package is installed')
def uninstall_unofficial_packages(path):
with open(path, "r") as f:
weka_packages = json.load(f)
for package_name, package_path in weka_packages.items():
if package_name != package_path and packages.is_installed(
package_name):
v.app.logger.info("Uninstalling %s", package_name)
packages.uninstall_package(package_name)
def install_package(pkg):
# install weka package if necessary
if not packages.is_installed(pkg):
print("Installing %s..." % pkg)
packages.install_package(pkg)
print("Installed %s, please re-run script!" % pkg)
jvm.stop()
sys.exit(0)
print('Package already installed.')
def main():
if not is_installed("CLOPE"):
print("CLOPE is not installed, installing now")
install_package("CLOPE")
print("please restart")
return
cls = Clusterer(classname="weka.clusterers.CLOPE")
print("CLOPE is installed:", cls.to_commandline())
def install_packages(path):
"""Install weka packages
Arguments:
path {str} -- path to install weka packages json
"""
with open(path, "r") as f:
weka_packages = json.load(f)
for package_name, package_path in weka_packages.items():
if not packages.is_installed(package_name):
v.app.logger.info("Installing: %s %s", package_name,
package_path)
packages.install_package(package_path)
import weka.core.jvm as jvm
import weka.core.packages as packages
from weka.core.classes import complete_classname
jvm.start(packages=True)
pkg = "SMOTE"
# install package if necessary
if not packages.is_installed(pkg):
print("Installing %s..." % pkg)
packages.install_package(pkg)
print("Installed %s, please re-run script!" % pkg)
jvm.stop()
# testing classname completion
print(complete_classname(".SMOTE"))
jvm.stop()
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.core.classes import Random
import weka.core.packages as packages
from weka.classifiers import Classifier, Evaluation
jvm.start(packages=True)
# install stackingC if necessary
if not packages.is_installed("stackingC"):
print("Installing stackingC...")
packages.install_package("stackingC")
jvm.stop()
print("Installed package, please restart")
exit()
# load glass
loader = Loader(classname="weka.core.converters.ArffLoader")
fname = data_dir + os.sep + "glass.arff"
print("\nLoading dataset: " + fname + "\n")
data = loader.load_file(fname)
data.class_is_last()
# compare several meta-classifiers with J48
for classifier in [("weka.classifiers.trees.J48", []),
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.core.classes import Random
import weka.core.packages as packages
from weka.classifiers import Classifier, Evaluation
jvm.start(packages=True)
# install stackingC if necessary
if not packages.is_installed("stackingC"):
print("Installing stackingC...")
packages.install_package("stackingC")
jvm.stop()
print("Please restart")
exit()
# load glass
loader = Loader(classname="weka.core.converters.ArffLoader")
fname = data_dir + os.sep + "glass.arff"
print("\nLoading dataset: " + fname + "\n")
data = loader.load_file(fname)
data.set_class_index(data.num_attributes() - 1)
# compare several meta-classifiers with J48
for classifier in [("weka.classifiers.trees.J48", []), ("weka.classifiers.meta.Bagging", []),
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
import weka.core.packages as packages
from weka.core.converters import Loader
from weka.core.classes import Random
from weka.classifiers import Classifier, Evaluation
jvm.start(packages=True)
pkg = "simpleEducationalLearningSchemes"
if not packages.is_installed(pkg):
packages.install_package(pkg):
jvm.stop()
print("Please restart")
exit()
# 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)
# cross-validate classifiers
classifiers = [
"weka.classifiers.trees.J48",
import weka.core.packages as packages
from weka.classifiers import Classifier, Evaluation
import os
from weka.core.converters import Loader
from weka.attribute_selection import ASSearch, ASEvaluation, AttributeSelection
from weka.filters import Filter
########SetUp########################
os.environ["WEKA_HOME"] = os.path.abspath(
"./weka-3-8-4") #point it to your weka instalation folder
jvm.start(packages=True, max_heap_size='6g')
#####################################
########Install######################
packages.refresh_cache()
if not packages.is_installed('discriminantAnalysis'):
print("Installing discriminantAnalysis")
packages.install_package('discriminantAnalysis')
if not packages.is_installed('PBC4cip'):
print("Installing PBC4cip")
packages.install_package(os.path.abspath('./weka_packages/PBC4cip.zip'))
######################################
#############Data#####################
loader = Loader(classname="weka.core.converters.ArffLoader")
data = loader.load_file(os.path.abspath('./universities.arff'))
data.class_is_last()
######################################
#######rub1########################
print("rub1")