def main():
"""
Just runs some example code.
"""
# load a dataset
iris_file = helper.get_data_dir() + os.sep + "iris.arff"
helper.print_info("Loading dataset: " + iris_file)
loader = Loader("weka.core.converters.ArffLoader")
iris_data = loader.load_file(iris_file)
iris_data.class_is_last()
# train classifier
classifier = Classifier("weka.classifiers.trees.J48")
classifier.build_classifier(iris_data)
# save and read object
helper.print_title("I/O: single object")
outfile = tempfile.gettempdir() + os.sep + "j48.model"
serialization.write(outfile, classifier)
model = Classifier(jobject=serialization.read(outfile))
print(model)
# save classifier and dataset header (multiple objects)
helper.print_title("I/O: single object")
serialization.write_all(outfile, [classifier, Instances.template_instances(iris_data)])
objects = serialization.read_all(outfile)
for i, obj in enumerate(objects):
helper.print_info("Object #" + str(i+1) + ":")
if javabridge.get_env().is_instance_of(obj, javabridge.get_env().find_class("weka/core/Instances")):
obj = Instances(jobject=obj)
elif javabridge.get_env().is_instance_of(obj, javabridge.get_env().find_class("weka/classifiers/Classifier")):
obj = Classifier(jobject=obj)
print(obj)
def train_internal(self):
best_weights_arr = []
#create an empty F with source as template
F = Instances.template_instances(self.source[0])
withF = False
print("Find weight for each source data set")
for source in self.source:
bestWeight, bestError = self.process_source(source, F, withF)
best_weights_arr.append(bestWeight)
#sort the data based on the weights
self.source = [
source for _, source in sorted(zip(best_weights_arr, self.source),
reverse=True,
key=operator.itemgetter(0))
]
print("Train for final stage")
withF = True
while len(self.source) > 0: #self.max_source_dataset):
weight, _ = self.process_source(self.source[0], F, withF)
for inst in self.source[0]:
inst.weight = weight
F = Instances.append_instances(F, self.source[0])
F.class_is_last()
self.source.pop(0)
return F
def save_model(model, data, filename):
"""Save the model to the target caching file.
The caches should be defined in the config file. See README and
config.sample for reference.
Args:
model(obj): The model to be saved. Should be a
weka.classifier.Classifier object.
data(obj): The training set to be cached.
target(str): The target option in '[cached]' section in the config
file.
filename(str): The target file to save.
Returns:
True if the target caching is saved, otherwise False.
"""
folder = os.path.join('caches', 'model')
path = os.path.join(folder, filename + '.cache')
build_if_not_exist(folder)
serialization.write_all(path, [model, Instances.template_instances(data)])
localizer_log.msg(
"Saved cache of {target_name}.".format(target_name='model'))
return True
def main():
"""
Just runs some example code.
"""
# load a dataset
iris_file = helper.get_data_dir() + os.sep + "iris.arff"
helper.print_info("Loading dataset: " + iris_file)
loader = Loader("weka.core.converters.ArffLoader")
iris_data = loader.load_file(iris_file)
iris_data.class_is_last()
# train classifier
classifier = Classifier("weka.classifiers.trees.J48")
classifier.build_classifier(iris_data)
# save and read object
helper.print_title("I/O: model (using serialization module)")
outfile = tempfile.gettempdir() + os.sep + "j48.model"
serialization.write(outfile, classifier)
model = Classifier(jobject=serialization.read(outfile))
print(model)
# save classifier and dataset header (multiple objects)
helper.print_title("I/O: model and header (using serialization module)")
serialization.write_all(
outfile,
[classifier, Instances.template_instances(iris_data)])
objects = serialization.read_all(outfile)
for i, obj in enumerate(objects):
helper.print_info("Object #" + str(i + 1) + ":")
if javabridge.get_env().is_instance_of(
obj,
javabridge.get_env().find_class("weka/core/Instances")):
obj = Instances(jobject=obj)
elif javabridge.get_env().is_instance_of(
obj,
javabridge.get_env().find_class(
"weka/classifiers/Classifier")):
obj = Classifier(jobject=obj)
print(obj)
# save and read object
helper.print_title("I/O: just model (using Classifier class)")
outfile = tempfile.gettempdir() + os.sep + "j48.model"
classifier.serialize(outfile)
model, _ = Classifier.deserialize(outfile)
print(model)
# save classifier and dataset header (multiple objects)
helper.print_title("I/O: model and header (using Classifier class)")
classifier.serialize(outfile, header=iris_data)
model, header = Classifier.deserialize(outfile)
print(model)
if header is not None:
print(header)
def __init__(self, model=None, header=None):
"""
Initializes the container.
:param model: the model to store (eg Classifier or Clusterer)
:type model: object
:param header: the header instances
:type header: Instances
"""
super(ModelContainer, self).__init__()
self.set("Model", model)
if header is not None:
header = Instances.template_instances(header)
self.set("Header", header)
self._allowed = ["Model", "Header"]
def __init__(self, model=None, header=None):
"""
Initializes the container.
:param model: the model to store (eg Classifier or Clusterer)
:type model: object
:param header: the header instances
:type header: Instances
"""
super(ModelContainer, self).__init__()
self.set("Model", model)
if header is not None:
header = Instances.template_instances(header)
self.set("Header", header)
self._allowed = ["Model", "Header"]
def serialize(self, ser_file, header=None):
"""
Serializes the clusterer to the specified file.
:param ser_file: the file to save the model to
:type ser_file: str
:param header: the (optional) dataset header to store alongside; recommended
:type header: Instances
"""
if (header is not None) and header.num_instances > 0:
header = Instances.template_instances(header)
if header is not None:
serialization_write_all(ser_file, [self, header])
else:
serialization_write(ser_file, self)
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
result = None
data = self.input.payload
if isinstance(self._input.payload, Instance):
inst = self.input.payload
data = inst.dataset
elif isinstance(self.input.payload, Instances):
data = self.input.payload
inst = None
append = True
if self._header is None or (self._header.equal_headers(data)
is not None):
self._header = Instances.template_instances(data, 0)
outstr = str(data)
append = False
elif inst is not None:
outstr = str(inst)
else:
outstr = str(data)
f = None
try:
if append:
f = open(str(self.resolve_option("output")), "a")
else:
f = open(str(self.resolve_option("output")), "w")
f.write(outstr)
f.write("\n")
except Exception as e:
result = self.full_name + "\n" + traceback.format_exc()
finally:
if f is not None:
f.close()
return result
def do_execute(self):
"""
The actual execution of the actor.
:return: None if successful, otherwise error message
:rtype: str
"""
result = None
data = self.input.payload
if isinstance(self._input.payload, Instance):
inst = self.input.payload
data = inst.dataset
elif isinstance(self.input.payload, Instances):
data = self.input.payload
inst = None
append = True
if self._header is None or (self._header.equal_headers(data) is not None):
self._header = Instances.template_instances(data, 0)
outstr = str(data)
append = False
elif inst is not None:
outstr = str(inst)
else:
outstr = str(data)
f = None
try:
if append:
f = open(str(self.resolve_option("output")), "a")
else:
f = open(str(self.resolve_option("output")), "w")
f.write(outstr)
f.write("\n")
except Exception as e:
result = self.full_name + "\n" + traceback.format_exc()
finally:
if f is not None:
f.close()
return result
def fit(self, data, targets):
"""
Trains the estimator.
:param data: the input variables as matrix, array-like of shape (n_samples, n_features)
:type data: ndarray
:param targets: the optional class attribute column, array-like of shape (n_samples,)
:type targets: ndarray
:return: itself
:rtype: WekaTransformer
"""
if targets is None:
check_array(data)
else:
check_X_y(data, targets)
d = to_instances(data,
y=targets,
num_nominal_labels=self._num_nominal_input_labels,
num_class_labels=self._num_nominal_output_labels)
self.header_ = Instances.template_instances(d)
self._filter.inputformat(d)
self._filter.filter(d)
return self
fltr.inputformat(data)
filtered = fltr.filter(data)
print(filtered)
# PCA
print("Principal components analysis")
fltr = Filter(
classname="weka.filters.unsupervised.attribute.PrincipalComponents")
fltr.inputformat(data)
filtered = fltr.filter(data)
print(filtered)
# load anneal
loader = Loader(classname="weka.core.converters.ArffLoader")
fname = data_dir + os.sep + "anneal.arff"
print("\nLoading dataset: " + fname + "\n")
data = loader.load_file(fname)
data.class_is_last()
# RemoveUseless
print("RemoveUseless")
fltr = Filter(classname="weka.filters.unsupervised.attribute.RemoveUseless")
fltr.inputformat(data)
filtered = fltr.filter(data)
print("Original header (#att=" + str(data.num_attributes) + "):\n" +
str(Instances.template_instances(data)))
print("Filtered header (#att=" + str(filtered.num_attributes) + "):\n" +
str(Instances.template_instances(filtered)))
jvm.stop()
def main():
"""
Just runs some example code.
"""
# load a dataset
iris_file = helper.get_data_dir() + os.sep + "iris.arff"
helper.print_info("Loading dataset: " + iris_file)
loader = Loader("weka.core.converters.ArffLoader")
iris_data = loader.load_file(iris_file)
iris_data.class_is_last()
helper.print_title("Iris dataset")
print(iris_data)
helper.print_title("Iris dataset (incrementally output)")
for i in iris_data:
print(i)
helper.print_title("Iris summary")
print(Instances.summary(iris_data))
helper.print_title("Iris attributes")
for a in iris_data.attributes():
print(a)
helper.print_title("Instance at #0")
print(iris_data.get_instance(0))
print(iris_data.get_instance(0).values)
print("Attribute stats (first):\n" + str(iris_data.attribute_stats(0)))
print("total count (first attribute):\n" +
str(iris_data.attribute_stats(0).total_count))
print("numeric stats (first attribute):\n" +
str(iris_data.attribute_stats(0).numeric_stats))
print("nominal counts (last attribute):\n" + str(
iris_data.attribute_stats(iris_data.num_attributes -
1).nominal_counts))
helper.print_title("Instance values at #0")
for v in iris_data.get_instance(0):
print(v)
# append datasets
helper.print_title("append datasets")
data1 = Instances.copy_instances(iris_data, 0, 2)
data2 = Instances.copy_instances(iris_data, 2, 2)
print("Dataset #1:\n" + str(data1))
print("Dataset #2:\n" + str(data2))
msg = data1.equal_headers(data2)
print("#1 == #2 ? " + "yes" if msg is None else msg)
combined = Instances.append_instances(data1, data2)
print("Combined:\n" + str(combined))
# merge datasets
helper.print_title("merge datasets")
data1 = Instances.copy_instances(iris_data, 0, 2)
data1.class_index = -1
data1.delete_attribute(1)
data1.delete_first_attribute()
data2 = Instances.copy_instances(iris_data, 0, 2)
data2.class_index = -1
data2.delete_attribute(4)
data2.delete_attribute(3)
data2.delete_attribute(2)
print("Dataset #1:\n" + str(data1))
print("Dataset #2:\n" + str(data2))
msg = data1.equal_headers(data2)
print("#1 == #2 ? " + ("yes" if msg is None else msg))
combined = Instances.merge_instances(data2, data1)
print("Combined:\n" + str(combined))
# load dataset incrementally
iris_file = helper.get_data_dir() + os.sep + "iris.arff"
helper.print_info("Loading dataset incrementally: " + iris_file)
loader = Loader("weka.core.converters.ArffLoader")
iris_data = loader.load_file(iris_file, incremental=True)
iris_data.class_is_last()
helper.print_title("Iris dataset")
print(iris_data)
for inst in loader:
print(inst)
# create attributes
helper.print_title("Creating attributes")
num_att = Attribute.create_numeric("num")
print("numeric: " + str(num_att))
date_att = Attribute.create_date("dat", "yyyy-MM-dd")
print("date: " + str(date_att))
nom_att = Attribute.create_nominal("nom", ["label1", "label2"])
print("nominal: " + str(nom_att))
# create dataset
helper.print_title("Create dataset")
dataset = Instances.create_instances("helloworld",
[num_att, date_att, nom_att], 0)
print(str(dataset))
# create an instance
helper.print_title("Create and add instance")
values = [3.1415926, date_att.parse_date("2014-04-10"), 1.0]
inst = Instance.create_instance(values)
print("Instance #1:\n" + str(inst))
dataset.add_instance(inst)
values = [
2.71828,
date_att.parse_date("2014-08-09"),
Instance.missing_value()
]
inst = Instance.create_instance(values)
dataset.add_instance(inst)
print("Instance #2:\n" + str(inst))
inst.set_value(0, 4.0)
print("Instance #2 (updated):\n" + str(inst))
print("Dataset:\n" + str(dataset))
dataset.delete_with_missing(2)
print("Dataset (after delete of missing):\n" + str(dataset))
values = [(1, date_att.parse_date("2014-07-11"))]
inst = Instance.create_sparse_instance(
values, 3, classname="weka.core.SparseInstance")
print("sparse Instance:\n" + str(inst))
dataset.add_instance(inst)
print("dataset with mixed dense/sparse instance objects:\n" + str(dataset))
# create dataset (lists)
helper.print_title("Create dataset from lists")
x = [[randint(1, 10) for _ in range(5)] for _ in range(10)]
y = [randint(0, 1) for _ in range(10)]
dataset2 = ds.create_instances_from_lists(x, y, "generated from lists")
print(dataset2)
x = [[randint(1, 10) for _ in range(5)] for _ in range(10)]
dataset2 = ds.create_instances_from_lists(
x, name="generated from lists (no y)")
print(dataset2)
# create dataset (matrices)
helper.print_title("Create dataset from matrices")
x = np.random.randn(10, 5)
y = np.random.randn(10)
dataset3 = ds.create_instances_from_matrices(x, y,
"generated from matrices")
print(dataset3)
x = np.random.randn(10, 5)
dataset3 = ds.create_instances_from_matrices(
x, name="generated from matrices (no y)")
print(dataset3)
# create more sparse instances
diabetes_file = helper.get_data_dir() + os.sep + "diabetes.arff"
helper.print_info("Loading dataset: " + diabetes_file)
loader = Loader("weka.core.converters.ArffLoader")
diabetes_data = loader.load_file(diabetes_file)
diabetes_data.class_is_last()
helper.print_title("Create sparse instances using template dataset")
sparse_data = Instances.template_instances(diabetes_data)
for i in xrange(diabetes_data.num_attributes - 1):
inst = Instance.create_sparse_instance(
[(i, float(i + 1) / 10.0)],
sparse_data.num_attributes,
classname="weka.core.SparseInstance")
sparse_data.add_instance(inst)
print("sparse dataset:\n" + str(sparse_data))
# simple scatterplot of iris dataset: petalwidth x petallength
iris_data = loader.load_file(iris_file)
iris_data.class_is_last()
pld.scatter_plot(iris_data,
iris_data.attribute_by_name("petalwidth").index,
iris_data.attribute_by_name("petallength").index,
percent=50,
wait=False)
# line plot of iris dataset (without class attribute)
iris_data = loader.load_file(iris_file)
iris_data.class_is_last()
pld.line_plot(iris_data,
atts=xrange(iris_data.num_attributes - 1),
percent=50,
title="Line plot iris",
wait=False)
# matrix plot of iris dataset
iris_data = loader.load_file(iris_file)
iris_data.class_is_last()
pld.matrix_plot(iris_data, percent=50, title="Matrix plot iris", wait=True)
import weka.core.jvm as jvm
from weka.core.converters import Loader, Saver
from weka.core.dataset import Instances
from weka.filters import Filter
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)
# output header
print(Instances.template_instances(data))
# remove attribute no 3
print("\nRemove attribute no 3")
fltr = Filter(classname="weka.filters.unsupervised.attribute.Remove", options=["-R", "3"])
fltr.inputformat(data)
filtered = fltr.filter(data)
# output header
print(Instances.template_instances(filtered))
# save modified dataset
saver = Saver(classname="weka.core.converters.ArffSaver")
saver.save_file(filtered, data_dir + os.sep + "weather.nominal-filtered.arff")
jvm.stop()
def createTrainedModel():
from weka.core.converters import Loader
folderList = os.listdir(outputModel)
i = 0
classi = ""
loader = Loader(classname="weka.core.converters.ArffLoader")
data = loader.load_file(os.path.join(outputModel, "genderTrain.arff"))
data.class_is_last()
from weka.classifiers import Classifier
classi = "weka.classifiers.bayes.NaiveBayes"
cls = Classifier(classname=classi)
from weka.attribute_selection import ASSearch, ASEvaluation, AttributeSelection
search = ASSearch(classname="weka.attributeSelection.Ranker",
options=["-1.7976931348623157E308", "-1"])
#evaluator = ASEvaluation(classname="weka.attributeSelection.ChiSquaredAttributeEval")
#attsel = AttributeSelection()
#attsel.search(search)
#attsel.evaluator(evaluator)
#attsel.select_attributes(data)
cls.build_classifier(data)
import weka.core.serialization as serialization
from weka.core.dataset import Instances
serialization.write_all(
os.path.join(outputModel, "GenderModel" + ".model"),
[cls, Instances.template_instances(data)])
from weka.classifiers import Evaluation
from weka.core.classes import Random
evl = Evaluation(data)
evl.crossvalidate_model(cls, data, 10, Random(1))
print "Gender model predictions"
print cls
#print(evl.percent_correct)
print(evl.summary())
print(evl.class_details())
data = loader.load_file(os.path.join(outputModel, "ageTrain.arff"))
data.class_is_last()
classi = "weka.classifiers.bayes.NaiveBayes"
cls = Classifier(classname=classi)
from weka.attribute_selection import ASSearch, ASEvaluation, AttributeSelection
search = ASSearch(classname="weka.attributeSelection.Ranker",
options=["-1.7976931348623157E308", "-1"])
#evaluator = ASEvaluation(classname="weka.attributeSelection.ChiSquaredAttributeEval")
#attsel = AttributeSelection()
#attsel.search(search)
#attsel.evaluator(evaluator)
#attsel.select_attributes(data)
#classi = "weka.classifiers.trees.J48"
#classi = "weka.classifiers.functions.Logistic"
#classi = "weka.classifiers.trees.RandomForest"
#classi = "weka.classifiers.bayes.NaiveBayes"
#classi = "weka.classifiers.functions.SMOreg"
cls.build_classifier(data)
print "Age model predictions"
print cls
import weka.core.serialization as serialization
from weka.core.dataset import Instances
serialization.write_all(os.path.join(outputModel, "AgeModel" + ".model"),
[cls, Instances.template_instances(data)])
evl = Evaluation(data)
evl.crossvalidate_model(cls, data, 10, Random(1))
#print(evl.percent_correct)
print(evl.summary())
print(evl.class_details())
os._exit(0)
def classification(data, train, test, num_clases):
baseClassifiers_list = [
"weka.classifiers.bayes.NaiveBayes",
"weka.classifiers.functions.MultilayerPerceptron",
"weka.classifiers.functions.SMO", "weka.classifiers.lazy.IBk",
"weka.classifiers.lazy.KStar", "weka.classifiers.meta.AdaBoostM1",
"weka.classifiers.meta.Bagging", "weka.classifiers.meta.LogitBoost",
"weka.classifiers.trees.J48", "weka.classifiers.trees.DecisionStump",
"weka.classifiers.trees.LMT", "weka.classifiers.trees.RandomForest",
"weka.classifiers.trees.REPTree", "weka.classifiers.rules.PART",
"weka.classifiers.rules.JRip", "weka.classifiers.functions.Logistic",
"weka.classifiers.meta.ClassificationViaRegression",
"weka.classifiers.bayes.BayesNet"
]
results_train = pd.DataFrame()
results_test = pd.DataFrame()
cost_matrix_list = [
"[]", "[0]", "[0.0 1.0; 1.0 0.0]",
"[0.0 1.0 2.0; 1.0 0.0 1.0; 2.0 1.0 0.0]",
"[0.0 1.0 2.0 3.0; 1.0 0.0 1.0 2.0; 2.0 1.0 0.0 1.0; 3.0 2.0 1.0 0.0]",
"[0.0 1.0 2.0 3.0 4.0; 1.0 0.0 1.0 2.0 3.0; 2.0 1.0 0.0 1.0 2.0; 3.0 2.0 1.0 0.0 1.0; 4.0 3.0 2.0 1.0 0.0]",
"[0.0 1.0 2.0 3.0 4.0 5.0; 1.0 0.0 1.0 2.0 3.0 4.0; 2.0 1.0 0.0 1.0 2.0 3.0; 3.0 2.0 1.0 0.0 1.0 2.0; 4.0 3.0 2.0 1.0 0.0 1.0; 5.0 4.0 3.0 2.0 1.0 0.0]",
"[0.0 1.0 2.0 3.0 4.0 5.0 6.0; 1.0 0.0 1.0 2.0 3.0 4.0 5.0; 2.0 1.0 0.0 1.0 2.0 3.0 4.0; 3.0 2.0 1.0 0.0 1.0 2.0 3.0; 4.0 3.0 2.0 1.0 0.0 1.0 2.0; 5.0 4.0 3.0 2.0 1.0 0.0 1.0; 6.0 5.0 4.0 3.0 2.0 1.0 0.0]",
"[0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0; 1.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0; 2.0 1.0 0.0 1.0 2.0 3.0 4.0 5.0; 3.0 2.0 1.0 0.0 1.0 2.0 3.0 4.0; 4.0 3.0 2.0 1.0 0.0 1.0 2.0 3.0; 5.0 4.0 3.0 2.0 1.0 0.0 1.0 2.0; 6.0 5.0 4.0 3.0 2.0 1.0 0.0 1.0; 7.0 6.0 5.0 4.0 3.0 2.0 1.0 0.0]",
"[0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0; 1.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0; 2.0 1.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0; 3.0 2.0 1.0 0.0 1.0 2.0 3.0 4.0 5.0; 4.0 3.0 2.0 1.0 0.0 1.0 2.0 3.0 4.0; 5.0 4.0 3.0 2.0 1.0 0.0 1.0 2.0 3.0; 6.0 5.0 4.0 3.0 2.0 1.0 0.0 1.0 2.0; 7.0 6.0 5.0 4.0 3.0 2.0 1.0 0.0 1.0; 8.0 7.0 6.0 5.0 4.0 3.0 2.0 1.0 0.0]",
"[0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0; 1.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0; 2.0 1.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0; 3.0 2.0 1.0 0.0 1.0 2.0 3.0 4.0 5.0 6.0; 4.0 3.0 2.0 1.0 0.0 1.0 2.0 3.0 4.0 5.0; 5.0 4.0 3.0 2.0 1.0 0.0 1.0 2.0 3.0 4.0; 6.0 5.0 4.0 3.0 2.0 1.0 0.0 1.0 2.0 3.0; 7.0 6.0 5.0 4.0 3.0 2.0 1.0 0.0 1.0 2.0; 8.0 7.0 6.0 5.0 4.0 3.0 2.0 1.0 0.0 1.0; 9.0 8.0 7.0 6.0 5.0 4.0 3.0 2.0 1.0 0.0]"
]
real_train = [] # the real label of the dataset
for i in range(train.num_instances):
real_train.append(
train.get_instance(i).values[(train.num_attributes - 1)])
results_train['real'] = real_train
real_test = [] # the real label of the dataset
for i in range(test.num_instances):
real_test.append(
test.get_instance(i).values[(test.num_attributes - 1)])
results_test['real'] = real_test
num = 0
for clas in baseClassifiers_list:
column = "p" + np.str(num)
#classifier
classifier = SingleClassifierEnhancer(
classname="weka.classifiers.meta.CostSensitiveClassifier",
options=[
"-cost-matrix", cost_matrix_list[num_clases], "-M", "-S", "1"
])
base = Classifier(classname=clas)
classifier.classifier = base
predicted_data_train = None
predicted_data_test = None
evaluation = Evaluation(data)
classifier.build_classifier(train)
#evaluation.test_model(classifier, train)
# add predictions
addcls = Filter(
classname="weka.filters.supervised.attribute.AddClassification",
options=["-classification"])
addcls.set_property("classifier", Classifier.make_copy(classifier))
addcls.inputformat(train)
#addcls.filter(train) # trains the classifier
pred_train = addcls.filter(train)
pred_test = addcls.filter(test)
if predicted_data_train is None:
predicted_data_train = Instances.template_instances(pred_train, 0)
for n in range(pred_train.num_instances):
predicted_data_train.add_instance(pred_train.get_instance(n))
if predicted_data_test is None:
predicted_data_test = Instances.template_instances(pred_test, 0)
for n in range(pred_test.num_instances):
predicted_data_test.add_instance(pred_test.get_instance(n))
preds_train = [
] #labels predicted for the classifer trained in the iteration
preds_test = []
for i in range(predicted_data_train.num_instances):
preds_train.append(
predicted_data_train.get_instance(i).values[(
predicted_data_train.num_attributes - 1)])
for i in range(predicted_data_test.num_instances):
preds_test.append(
predicted_data_test.get_instance(i).values[(
predicted_data_test.num_attributes - 1)])
results_train[column] = preds_train
results_test[column] = preds_test
num = num + 1
return results_train, results_test
def saveClassifier(self, filename, path='/home/sbiastoch/Schreibtisch/classifiers/'): serialization.write_all(path+filename, [self.classifier, Instances.template_instances(self.data)])
def main():
"""
Just runs some example code.
"""
# load a dataset
iris_file = helper.get_data_dir() + os.sep + "iris.arff"
helper.print_info("Loading dataset: " + iris_file)
loader = Loader("weka.core.converters.ArffLoader")
iris_data = loader.load_file(iris_file)
iris_data.class_is_last()
helper.print_title("Iris dataset")
print(iris_data)
helper.print_title("Iris dataset (incrementally output)")
for i in iris_data:
print(i)
helper.print_title("Iris summary")
print(Instances.summary(iris_data))
helper.print_title("Iris attributes")
for a in iris_data.attributes():
print(a)
helper.print_title("Instance at #0")
print(iris_data.get_instance(0))
print(iris_data.get_instance(0).values)
print("Attribute stats (first):\n" + str(iris_data.attribute_stats(0)))
print("total count (first attribute):\n" + str(iris_data.attribute_stats(0).total_count))
print("numeric stats (first attribute):\n" + str(iris_data.attribute_stats(0).numeric_stats))
print("nominal counts (last attribute):\n"
+ str(iris_data.attribute_stats(iris_data.num_attributes - 1).nominal_counts))
helper.print_title("Instance values at #0")
for v in iris_data.get_instance(0):
print(v)
# append datasets
helper.print_title("append datasets")
data1 = Instances.copy_instances(iris_data, 0, 2)
data2 = Instances.copy_instances(iris_data, 2, 2)
print("Dataset #1:\n" + str(data1))
print("Dataset #2:\n" + str(data2))
msg = data1.equal_headers(data2)
print("#1 == #2 ? " + "yes" if msg is None else msg)
combined = Instances.append_instances(data1, data2)
print("Combined:\n" + str(combined))
# merge datasets
helper.print_title("merge datasets")
data1 = Instances.copy_instances(iris_data, 0, 2)
data1.class_index = -1
data1.delete_attribute(1)
data1.delete_first_attribute()
data2 = Instances.copy_instances(iris_data, 0, 2)
data2.class_index = -1
data2.delete_attribute(4)
data2.delete_attribute(3)
data2.delete_attribute(2)
print("Dataset #1:\n" + str(data1))
print("Dataset #2:\n" + str(data2))
msg = data1.equal_headers(data2)
print("#1 == #2 ? " + ("yes" if msg is None else msg))
combined = Instances.merge_instances(data2, data1)
print("Combined:\n" + str(combined))
# load dataset incrementally
iris_file = helper.get_data_dir() + os.sep + "iris.arff"
helper.print_info("Loading dataset incrementally: " + iris_file)
loader = Loader("weka.core.converters.ArffLoader")
iris_data = loader.load_file(iris_file, incremental=True)
iris_data.class_is_last()
helper.print_title("Iris dataset")
print(iris_data)
for inst in loader:
print(inst)
# create attributes
helper.print_title("Creating attributes")
num_att = Attribute.create_numeric("num")
print("numeric: " + str(num_att))
date_att = Attribute.create_date("dat", "yyyy-MM-dd")
print("date: " + str(date_att))
nom_att = Attribute.create_nominal("nom", ["label1", "label2"])
print("nominal: " + str(nom_att))
# create dataset
helper.print_title("Create dataset")
dataset = Instances.create_instances("helloworld", [num_att, date_att, nom_att], 0)
print(str(dataset))
# create an instance
helper.print_title("Create and add instance")
values = [3.1415926, date_att.parse_date("2014-04-10"), 1.0]
inst = Instance.create_instance(values)
print("Instance #1:\n" + str(inst))
dataset.add_instance(inst)
values = [2.71828, date_att.parse_date("2014-08-09"), Instance.missing_value()]
inst = Instance.create_instance(values)
dataset.add_instance(inst)
print("Instance #2:\n" + str(inst))
inst.set_value(0, 4.0)
print("Instance #2 (updated):\n" + str(inst))
print("Dataset:\n" + str(dataset))
dataset.delete_with_missing(2)
print("Dataset (after delete of missing):\n" + str(dataset))
values = [(1, date_att.parse_date("2014-07-11"))]
inst = Instance.create_sparse_instance(values, 3, classname="weka.core.SparseInstance")
print("sparse Instance:\n" + str(inst))
dataset.add_instance(inst)
print("dataset with mixed dense/sparse instance objects:\n" + str(dataset))
# create dataset (lists)
helper.print_title("Create dataset from lists")
x = [[randint(1, 10) for _ in range(5)] for _ in range(10)]
y = [randint(0, 1) for _ in range(10)]
dataset2 = ds.create_instances_from_lists(x, y, "generated from lists")
print(dataset2)
x = [[randint(1, 10) for _ in range(5)] for _ in range(10)]
dataset2 = ds.create_instances_from_lists(x, name="generated from lists (no y)")
print(dataset2)
# create dataset (matrices)
helper.print_title("Create dataset from matrices")
x = np.random.randn(10, 5)
y = np.random.randn(10)
dataset3 = ds.create_instances_from_matrices(x, y, "generated from matrices")
print(dataset3)
x = np.random.randn(10, 5)
dataset3 = ds.create_instances_from_matrices(x, name="generated from matrices (no y)")
print(dataset3)
# create more sparse instances
diabetes_file = helper.get_data_dir() + os.sep + "diabetes.arff"
helper.print_info("Loading dataset: " + diabetes_file)
loader = Loader("weka.core.converters.ArffLoader")
diabetes_data = loader.load_file(diabetes_file)
diabetes_data.class_is_last()
helper.print_title("Create sparse instances using template dataset")
sparse_data = Instances.template_instances(diabetes_data)
for i in range(diabetes_data.num_attributes - 1):
inst = Instance.create_sparse_instance(
[(i, float(i+1) / 10.0)], sparse_data.num_attributes, classname="weka.core.SparseInstance")
sparse_data.add_instance(inst)
print("sparse dataset:\n" + str(sparse_data))
# simple scatterplot of iris dataset: petalwidth x petallength
iris_data = loader.load_file(iris_file)
iris_data.class_is_last()
pld.scatter_plot(
iris_data, iris_data.attribute_by_name("petalwidth").index,
iris_data.attribute_by_name("petallength").index,
percent=50,
wait=False)
# line plot of iris dataset (without class attribute)
iris_data = loader.load_file(iris_file)
iris_data.class_is_last()
pld.line_plot(iris_data, atts=range(iris_data.num_attributes - 1), percent=50, title="Line plot iris", wait=False)
# matrix plot of iris dataset
iris_data = loader.load_file(iris_file)
iris_data.class_is_last()
pld.matrix_plot(iris_data, percent=50, title="Matrix plot iris", wait=True)
test = rand_data.test_cv(folds, i)
# build and evaluate classifier
cls = Classifier.make_copy(classifier)
cls.build_classifier(train)
evaluation.test_model(cls, test)
# add predictions
addcls = Filter(
classname="weka.filters.supervised.attribute.AddClassification",
options=["-classification", "-distribution", "-error"])
# setting the java object directory avoids issues with correct quoting in option array
addcls.set_property("classifier", Classifier.make_copy(classifier))
addcls.inputformat(train)
addcls.filter(train) # trains the classifier
pred = addcls.filter(test)
if predicted_data is None:
predicted_data = Instances.template_instances(pred, 0)
for n in xrange(pred.num_instances):
predicted_data.add_instance(pred.get_instance(n))
print("")
print("=== Setup ===")
print("Classifier: " + classifier.to_commandline())
print("Dataset: " + data.relationname)
print("Folds: " + str(folds))
print("Seed: " + str(seed))
print("")
print(evaluation.summary("=== " + str(folds) + " -fold Cross-Validation ==="))
print("")
print(predicted_data)
# Discretize
print("Discretize numeric attributes (supervised)")
fltr = Filter(classname="weka.filters.supervised.attribute.Discretize")
fltr.inputformat(data)
filtered = fltr.filter(data)
print(filtered)
# PCA
print("Principal components analysis")
fltr = Filter(classname="weka.filters.unsupervised.attribute.PrincipalComponents")
fltr.inputformat(data)
filtered = fltr.filter(data)
print(filtered)
# load anneal
loader = Loader(classname="weka.core.converters.ArffLoader")
fname = data_dir + os.sep + "anneal.arff"
print("\nLoading dataset: " + fname + "\n")
data = loader.load_file(fname)
data.class_is_last()
# RemoveUseless
print("RemoveUseless")
fltr = Filter(classname="weka.filters.unsupervised.attribute.RemoveUseless")
fltr.inputformat(data)
filtered = fltr.filter(data)
print("Original header (#att=" + str(data.num_attributes) + "):\n" + str(Instances.template_instances(data)))
print("Filtered header (#att=" + str(filtered.num_attributes) + "):\n" + str(Instances.template_instances(filtered)))
jvm.stop()
import os
import weka.core.jvm as jvm
from weka.core.converters import Loader, Saver
from weka.core.dataset import Instances
from weka.filters import Filter
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)
# output header
print(Instances.template_instances(data))
# remove attribute no 3
print("\nRemove attribute no 3")
fltr = Filter(classname="weka.filters.unsupervised.attribute.Remove", options=["-R", "3"])
fltr.set_inputformat(data)
filtered = fltr.filter(data)
# output header
print(Instances.template_instances(filtered))
# save modified dataset
saver = Saver(classname="weka.core.converters.ArffSaver")
saver.save_file(filtered, data_dir + os.sep + "weather.nominal-filtered.arff")
jvm.stop()
def main():
"""
Just runs some example code.
"""
# load a dataset
data_file = helper.get_data_dir() + os.sep + "vote.arff"
helper.print_info("Loading dataset: " + data_file)
loader = Loader("weka.core.converters.ArffLoader")
data = loader.load_file(data_file)
data.class_is_last()
# classifier
classifier = Classifier(classname="weka.classifiers.trees.J48")
# randomize data
folds = 10
seed = 1
rnd = Random(seed)
rand_data = Instances.copy_instances(data)
rand_data.randomize(rnd)
if rand_data.class_attribute.is_nominal:
rand_data.stratify(folds)
# perform cross-validation and add predictions
predicted_data = None
evaluation = Evaluation(rand_data)
for i in xrange(folds):
train = rand_data.train_cv(folds, i)
# the above code is used by the StratifiedRemoveFolds filter,
# the following code is used by the Explorer/Experimenter
# train = rand_data.train_cv(folds, i, rnd)
test = rand_data.test_cv(folds, i)
# build and evaluate classifier
cls = Classifier.make_copy(classifier)
cls.build_classifier(train)
evaluation.test_model(cls, test)
# add predictions
addcls = Filter(
classname="weka.filters.supervised.attribute.AddClassification",
options=["-classification", "-distribution", "-error"])
# setting the java object directory avoids issues with correct quoting in option array
addcls.set_property("classifier", Classifier.make_copy(classifier))
addcls.inputformat(train)
addcls.filter(train) # trains the classifier
pred = addcls.filter(test)
if predicted_data is None:
predicted_data = Instances.template_instances(pred, 0)
for n in xrange(pred.num_instances):
predicted_data.add_instance(pred.get_instance(n))
print("")
print("=== Setup ===")
print("Classifier: " + classifier.to_commandline())
print("Dataset: " + data.relationname)
print("Folds: " + str(folds))
print("Seed: " + str(seed))
print("")
print(evaluation.summary("=== " + str(folds) + " -fold Cross-Validation ==="))
print("")
print(predicted_data)