def main():
"""
Shows how to use the CostSensitiveClassifier.
"""
# load a dataset
data_file = helper.get_data_dir() + os.sep + "diabetes.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 = SingleClassifierEnhancer(
classname="weka.classifiers.meta.CostSensitiveClassifier",
options=["-cost-matrix", "[0 1; 2 0]", "-S", "2"])
base = Classifier(classname="weka.classifiers.trees.J48", options=["-C", "0.3"])
classifier.classifier = base
folds = 10
evaluation = Evaluation(data)
evaluation.crossvalidate_model(classifier, data, folds, Random(1))
print("")
print("=== Setup ===")
print("Classifier: " + classifier.to_commandline())
print("Dataset: " + data.relationname)
print("")
print(evaluation.summary("=== " + str(folds) + " -fold Cross-Validation ==="))
def naiveBayes(data):
classifier = Classifier(classname="weka.classifiers.bayes.NaiveBayes", options=["-D"])
nfolds=13
rnd = Random(0)
evaluation = Evaluation(data)
evaluation.crossvalidate_model(classifier, data,
nfolds, rnd)
print(" Naive Bayes Cross-validation information")
print(evaluation.summary())
print("precision: " + str(evaluation.precision(1)))
print("recall: " + str(evaluation.recall(1)))
print("F-measure: " + str(evaluation.f_measure(1)))
print("==confusion matrix==")
print(" a b")
print(evaluation.confusion_matrix)
print
#write to file
f = open("naiveeval.txt", "w")
f.write(evaluation.summary())
f.write("\n")
f.write("==confusion matrix==\n")
f.write(" a b\n")
for item in evaluation.confusion_matrix:
f.write("%s\n" % item)
f.close()
#plot roc graph
plcls.plot_roc(evaluation, title="Naive Bayes ROC", outfile="NBROC", wait=True)
return evaluation.percent_correct
def vote_classifier_train(dicrectory, nameOfDataSet, flag):
loader = Loader(classname="weka.core.converters.CSVLoader")
data = loader.load_file(dicrectory)
data.class_is_last()
meta = MultipleClassifiersCombiner(
classname="weka.classifiers.meta.Vote",
options=[
'-S', '1', '-B', 'weka.classifiers.trees.J48 -C 0.25 -M 2', '-B',
'weka.classifiers.trees.RandomTree -K 6 -M 1.0 -V 0.001 -S 1',
'-B',
'weka.classifiers.meta.Bagging -P 100 -S 1 -num-slots 1 -I 10 -W weka.classifiers.trees.REPTree -- '
'-M 2 -V 0.001 -N 3 -S 1 -L -1 -I 0.0', '-B',
'weka.classifiers.meta.AdaBoostM1 -P 100 -S 1 -I 10 -W weka.classifiers.trees.DecisionStump',
'-B',
'weka.classifiers.meta.Bagging -P 100 -S 1 -num-slots 1 -I 10 -W weka.classifiers.trees.REPTree -- '
'-M 2 -V 0.001 -N 3 -S 1 -L -1 -I 0.0', '-B',
'weka.classifiers.bayes.NaiveBayes ', '-R', 'AVG'
])
eval = Evaluation(data)
pout = PredictionOutput(
classname="weka.classifiers.evaluation.output.prediction.PlainText")
if flag:
eval.crossvalidate_model(meta, data, 10, Random(1), pout)
else:
eval.evaluate_train_test_split(meta, data, 80.0, Random(1), pout)
gc.collect()
print_and_save('Proposed model', flag, nameOfDataSet, eval)
def main():
"""
Shows how to use the CostSensitiveClassifier.
"""
# load a dataset
data_file = helper.get_data_dir() + os.sep + "diabetes.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 = SingleClassifierEnhancer(
classname="weka.classifiers.meta.CostSensitiveClassifier",
options=["-cost-matrix", "[0 1; 2 0]", "-S", "2"])
base = Classifier(classname="weka.classifiers.trees.J48",
options=["-C", "0.3"])
classifier.classifier = base
folds = 10
evaluation = Evaluation(data)
evaluation.crossvalidate_model(classifier, data, folds, Random(1))
print("")
print("=== Setup ===")
print("Classifier: " + classifier.to_commandline())
print("Dataset: " + data.relationname)
print("")
print(
evaluation.summary("=== " + str(folds) +
" -fold Cross-Validation ==="))
def evaluation(self, classifier, trainingData, testingData = None):
trainingData.set_class_index(trainingData.num_attributes() - 1)
if testingData == None:
evaluation = Evaluation(trainingData)
# initialize with priors
evaluation.crossvalidate_model(classifier, trainingData, 10, Random(42)) # 10-fold CV
return evaluation
else:
print "testing data exists"
if testingData.num_attributes() == trainingData.num_attributes():
testingData.set_class_index(testingData.num_attributes() - 1)
evaluation = Evaluation(trainingData)
classifier.build_classifier(trainingData)
evaluation.test_model(classifier, testingData)
#for attribute in trainingData.attributes():
# print "train:" + str(attribute)
#for attribute in testingData.attributes():
# print "test:" + str(attribute)
return evaluation
else:
print "testing Data doesn't have same attribute with training data"
for attribute in trainingData.attributes():
print "train:" + str(attribute)
for attribute in testingData.attributes():
print "test:" + str(attribute)
def experiment_file_random(path_features, path_folder_save_results, options,
classifier, fold, random, name):
print("start weka")
cls = Classifier(classname=classifier,
options=weka.core.classes.split_options(options))
d_results = {
'percent_correct': [],
'percent_incorrect': [],
'confusion_matrix': []
}
data = converters.load_any_file(path_features)
data.class_is_last()
pout = PredictionOutput(
classname="weka.classifiers.evaluation.output.prediction.CSV")
evl = Evaluation(data)
evl.crossvalidate_model(cls, data, fold, Random(random), pout)
d_results['percent_correct'].append(evl.percent_correct)
d_results['percent_incorrect'].append(evl.percent_incorrect)
d_results['confusion_matrix'].append(
evl.matrix()) # Generates the confusion matrix.
d_results = pd.DataFrame(data=d_results)
d_results.to_csv(path_folder_save_results + '/' + str(name) + '.csv',
index=False)
save = pout.buffer_content()
with open(
path_folder_save_results + '/' + 'prediction/' + str(name) +
'.csv', 'w') as f:
f.write(save)
def evaluation(self, classifier, trainingData, testingData=None):
trainingData.set_class_index(trainingData.num_attributes() - 1)
if testingData == None:
evaluation = Evaluation(trainingData)
# initialize with priors
evaluation.crossvalidate_model(classifier, trainingData, 10,
Random(42)) # 10-fold CV
return evaluation
else:
print "testing data exists"
if testingData.num_attributes() == trainingData.num_attributes():
testingData.set_class_index(testingData.num_attributes() - 1)
evaluation = Evaluation(trainingData)
classifier.build_classifier(trainingData)
evaluation.test_model(classifier, testingData)
#for attribute in trainingData.attributes():
# print "train:" + str(attribute)
#for attribute in testingData.attributes():
# print "test:" + str(attribute)
return evaluation
else:
print "testing Data doesn't have same attribute with training data"
for attribute in trainingData.attributes():
print "train:" + str(attribute)
for attribute in testingData.attributes():
print "test:" + str(attribute)
def naivebay_classifier_weka(data):
classifier = Classifier("weka.classifiers.bayes.NaiveBayes")
evaluation = Evaluation(data)
evaluation.crossvalidate_model(classifier, data, 10, Random(42))
print(evaluation.summary())
print(evaluation.confusion_matrix)
return classifier
def Boost_J48(data, rnm):
data.class_is_last()
fc1 = FilteredClassifier()
fc1.classifier = Classifier(classname="weka.classifiers.trees.J48", options=["-C", "0.25", "-M", "2"])
fc1.filter = Filter(classname="weka.filters.unsupervised.attribute.Remove", options=["-R", "first"])
fc2 = SingleClassifierEnhancer(classname="weka.classifiers.meta.AdaBoostM1", options=["-P", "100", "-S", "1", "-I", "10"])
fc2.classifier = fc1
pred_output = PredictionOutput(classname="weka.classifiers.evaluation.output.prediction.CSV", options=["-p", "1"])
folds = 10
fc2.build_classifier(data)
evaluation = Evaluation(data)
evaluation.crossvalidate_model(fc2, data, folds, Random(1), pred_output)
f0 = open(rnm + '_Boost_J48_Tree.txt', 'w')
print >> f0, "Filename: ", rnm
print >> f0, '\n\n'
print >> f0, str(fc2)
f0.close()
f1 = open(rnm + '_Boost_J48_Prediction.txt', 'w')
print >> f1, 'Filename:', rnm
print >> f1, 'Prediction Summary:', (pred_output.buffer_content())
f1.close()
f2 = open(rnm + '_Boost_j48_Evaluation.txt', 'w')
print >> f2, 'Filename:', rnm
print >> f2, 'Evaluation Summary:', (evaluation.summary())
print >> f2, '\n\n\n'
print >> f2, (evaluation.class_details())
f2.close()
plot_roc(evaluation, class_index=[0,1], title=rnm, key_loc='best', outfile=rnm + '_Boost_J48_ROC.png', wait=False)
value_Boost_J48 = str(evaluation.percent_correct)
return value_Boost_J48
def run():
jvm.start()
load_csv = Loader("weka.core.converters.CSVLoader")
data_csv = load_csv.load_file(
"/Users/imeiliasantoso/web_graduate_project4/predict_page/predict_data.csv"
)
saver = Saver("weka.core.converters.ArffSaver")
saver.save_file(
data_csv,
"/Users/imeiliasantoso/web_graduate_project4/predict_page/predict_data.arff"
)
load_arff = Loader("weka.core.converters.ArffLoader")
data_arff = load_arff.load_file(
"/Users/imeiliasantoso/web_graduate_project4/predict_page/predict_data.arff"
)
data_arff.class_is_last()
global j48
J48_class = Classifier(classname="weka.classifiers.trees.J48",
options=["-C", "0.25", "-M", "2"])
J48_class.build_classifier(data_arff)
evaluationj48 = Evaluation(data_arff)
evaluationj48.crossvalidate_model(J48_class, data_arff, 10, Random(100))
j48 = str(evaluationj48.percent_correct)
jvm.stop()
return j48
def test_classifier(dataset: Instances, classifier: Classifier, params: dict):
vars = params.keys()
vals = params.values()
results = defaultdict(list)
for val_combo in itertools.product(*vals):
results["numInstances"].append(dataset.num_instances)
results["numAttributes"].append(dataset.num_attributes)
opts = dict(zip(vars, val_combo))
for opt in opts:
results[opt].append(opts[opt])
classifier.set_property(
opt, opts[opt] if not isinstance(opts[opt], float) else
typeconv.double_to_float(opts[opt]))
evl = Evaluation(dataset)
classifier.build_classifier(dataset)
evl.test_model(classifier, dataset)
results["Training_Accuracy"].append(evl.percent_correct)
results["size"].append(
int(javabridge.call(classifier.jobject, "measureTreeSize", "()D")))
evl.crossvalidate_model(classifier, dataset, 10, Random(1))
results["CV_Accuracy"].append(evl.percent_correct)
return results
def RandomTree(data, rnm):
data.class_is_last()
fc = FilteredClassifier()
fc.classifier = Classifier(classname="weka.classifiers.trees.RandomTree", options=["-K", "0", "-M", "1.0", "-V", "0.001", "-S", "1"])
fc.filter = Filter(classname="weka.filters.unsupervised.attribute.Remove", options=["-R", "first"])
pred_output = PredictionOutput(classname="weka.classifiers.evaluation.output.prediction.CSV", options=["-p", "1"])
folds = 10
evl = Evaluation(data)
evl.crossvalidate_model(fc, data, folds, Random(1), pred_output)
fc.build_classifier(data)
f0 = open(rnm + '_RT_Tree.txt', 'w')
print >> f0, "Filename: ", rnm
print >> f0, '\n\n'
print >> f0, str(fc)
f0.close()
f1 = open(rnm + '_RT_Prediction.txt', 'w')
print >> f1, 'Filename:', rnm
print >> f1, 'Prediction Summary:', (pred_output.buffer_content())
f1.close()
f2 = open(rnm + '_RT_Evaluation.txt', 'w')
print >> f2, 'Filename:', rnm
print >> f2, 'Evaluation Summary:', (evl.summary())
print >> f2, '\n\n\n'
print >> f2, (evl.class_details())
f2.close()
plot_roc(evl, class_index=[0,1], title=rnm, key_loc='best', outfile=rnm+'_RT_ROC.png', wait=False)
value_RT = str(evl.percent_correct)
return value_RT
def runSMO(file, bound):
loader = Loader(classname="weka.core.converters.CSVLoader")
data = loader.load_file(file)
data.class_is_first()
remove = Filter(classname="weka.filters.unsupervised.attribute.Remove",
options=["-R", bound])
cls = KernelClassifier(
classname="weka.classifiers.functions.SMO",
options=["-C", "1.0", "-L", "0.001", "-P", "1.0E-12", "-N", "0"])
kernel = Kernel(
classname="weka.classifiers.functions.supportVector.PolyKernel",
options=["-C", "250007", "-E", "1.0"])
cls.kernel = kernel
pout = PredictionOutput(
classname="weka.classifiers.evaluation.output.prediction.PlainText")
remove.inputformat(data)
filtered = remove.filter(data)
evl = Evaluation(filtered)
evl.crossvalidate_model(cls, filtered, 10, Random(1), pout)
#print(pout.buffer_content())
print(evl.percent_correct)
#print(evl.summary())
result = evl.class_details()
print(result)
return result
def create_model(input_file, output_file):
# Load data
data = converters.load_any_file(input_file)
data.class_is_last() # set class attribute
# filter data
print_title("Filtering Data")
discretize = Filter(
classname="weka.filters.unsupervised.attribute.Discretize",
options=["-B", "10", "-M", "-1.0", "-R", "first-last"])
discretize.inputformat(
data) # let the filter know about the type of data to filter
filtered_data = discretize.filter(data)
print("Done! (believe it or not)")
print_title("Build Classifier")
classifier = Classifier(classname="weka.classifiers.trees.RandomForest",
options=["-I", "100", "-K", "0", "-S", "1"])
classifier.build_classifier(filtered_data)
print("Done! (believe it or not)")
serialization.write_all(output_file, [classifier, discretize])
print("Model and filter saved to ", output_file)
evaluation = Evaluation(data) # initialize with priors
evaluation.crossvalidate_model(classifier, filtered_data, 10,
Random(42)) # 10-fold CV
print(evaluation.summary())
print("pctCorrect: " + str(evaluation.percent_correct))
print("incorrect: " + str(evaluation.incorrect))
def run_naive_bayes_crossval(self, output_directory):
# build classifier
print("\nBuilding Classifier on training data.")
buildTimeStart = time.time()
cls = Classifier(classname="weka.classifiers.bayes.NaiveBayes")
cls.build_classifier(self.training_data)
resultsString = ""
resultsString = self.print_both(str(cls), resultsString)
buildTimeString = "NB Cross Eval Classifier Built in " + str(
time.time() - buildTimeStart) + " secs.\n"
resultsString = self.print_both(buildTimeString, resultsString)
#Evaluate Classifier
resultsString = self.print_both("\nCross Evaluating on test data.",
resultsString)
buildTimeStart = time.time()
evl = Evaluation(self.training_data)
evl.crossvalidate_model(cls, self.training_data, 10, Random(1))
resultsString = self.print_both(str(evl.summary()), resultsString)
resultsString = self.print_both(str(evl.class_details()),
resultsString)
resultsString = self.print_both(str(evl.confusion_matrix),
resultsString)
buildTimeString = "\nNB Cross Eval Classifier Evaluated in " + str(
time.time() - buildTimeStart) + " secs.\n"
resultsString = self.print_both(buildTimeString, resultsString)
#Save Results and Cleanup
self.save_results("Naive_Bayes_Crossval", resultsString,
output_directory)
def proses(): #diluar def index = 0
import math
from weka.classifiers import Kernel, KernelClassifier
from weka.classifiers import PredictionOutput
import numpy as np
klasifi = KernelClassifier(classname="weka.classifiers.functions.SMOreg",
options=["-N", "0"])
vm = Kernel(classname="weka.classifiers.functions.supportVector.RBFKernel",
options=["-G", "0.1"])
klasifi.vm = vm
output_x = PredictionOutput(
classname="weka.classifiers.evaluation.output.prediction.PlainText")
kelola = Evaluation(anomali)
kelola.crossvalidate_model(klasifi,
anomali,
10,
Random(0),
output=output_x)
process = 0
for x in anomali.values(anomali.class_index):
data_inst.append(x)
for x in kelola.predictions:
i = str(x)
index = i.split()
data_pred.append(float(index[2]))
data_std.insert(idx, math.ceil(np.std(data_inst)) * 0.1)
print('\n DONE PROCESSING DATASET ATTRIBUTE ',
anomali.attribute(anomali.class_index), '...')
def run(dataset_path):
start = time.time()
### load a dataset ###
train_data = model.load_dataset_weka(dataset_path) #
to_nomial_class_filter = Filter(
classname="weka.filters.unsupervised.attribute.NumericToNominal",
options=["-R", "last"])
to_nomial_class_filter.inputformat(train_data)
### Naive Bayes ### Choose what you want
classifier = Classifier("weka.classifiers.bayes.NaiveBayesMultinomial")
# classifier = Classifier("weka.classifiers.bayes.NaiveBayes")
# classifier.build_classifer(train_data)
evaluation = Evaluation(to_nomial_class_filter.filter(train_data))
evaluation.crossvalidate_model(classifier,
to_nomial_class_filter.filter(train_data),
10, Random(42))
# print(evaluation.summary())
# print(evaluation.class_details())
# print(evaluation.matrix())
# ### Naive Bayes ###
# mlp = Classifier("weka.classifiers.bayes.Naive Bayes")
# mlp.build_classifer(train_file_5EMO)
print(time.time() - start)
def use_classifier(data, cli, args):
cli = cli.format(cli, **args)
cls = from_commandline(cli, classname="weka.classifiers.Classifier")
cls.build_classifier(data)
evaluation = Evaluation(data)
evaluation.crossvalidate_model(cls, data, 10, Random(1))
return cls, evaluation
def runCV(this, arffFile, classifier, folds):
loader = Loader(classname="weka.core.converters.ArffLoader")
data = loader.load_file(arffFile)
data.class_is_last()
classes = [str(code) for code in data.class_attribute.values]
header = ["Accuracy"]
for name in classes:
header += [name + " TP", name + " FP", name + " AUC ROC"]
values = []
cls = Classifier(classname=classifier)
evl = Evaluation(data)
evl.crossvalidate_model(cls, data, folds, Random(1))
values.append(evl.percent_correct)
for name in classes:
index = classes.index(name)
values += [
evl.true_positive_rate(index) * 100,
evl.false_positive_rate(index) * 100,
evl.area_under_roc(index)
]
this.values = values
this.header = header
def fitness(toeval : Individual):
cls = Classifier(classname="weka.classifiers.functions.MultilayerPerceptron", options=toeval.settings())
fc = FilteredClassifier()
fc.filter = remove
fc.classifier = cls
evl = Evaluation(data)
evl.crossvalidate_model(fc, data, 10, Random(1))
return evl.percent_correct
def f_smote():
jvm.start()
train_data, test_data = b_i_impute_data()
train_data = train_data[:10000]
y_train = train_data["class"]
x_train = train_data.drop("class", axis=1)
sm = SMOTE(ratio="minority")
x_train_sm, y_train_sm = sm.fit_sample(x_train, y_train)
x_train_sm_df = pd.DataFrame(x_train_sm, columns=x_train.columns)
y_train_sm_df = pd.DataFrame(y_train_sm, columns=["class"])
train_data_sm_df = pd.concat([y_train_sm_df, x_train_sm_df], axis=1)
print_f("smote train data shape", train_data_sm_df.shape)
train_data_sm_df.to_csv("./train_data_sm.csv", index=False)
train_data_sm = converters.load_any_file("train_data_sm.csv")
train_data_sm.class_is_first()
test_data = converters.load_any_file("test_data.csv")
test_data.class_is_first()
print_f("1")
cls = Classifier(classname="weka.classifiers.trees.LMT")
print_f("bulding classifier")
cls.build_classifier(train_data_sm)
print_f("Evaluating")
evl = Evaluation(train_data_sm)
evl.crossvalidate_model(cls, train_data_sm, 5, Random(1))
print_f("Train Accuracy:", evl.percent_correct)
print_f("Train summary")
print_f(evl.summary())
print_f("Train class details")
print_f(evl.class_details())
print_f("Train confusion matrix")
print_f(evl.confusion_matrix)
plcls.plot_roc(evl,
class_index=[0, 1],
wait=True,
outfile="./plots/2_f_smote_10k.png")
plt.suptitle("Train ROC Curve", fontsize=20, y=0.95)
evl = Evaluation(test_data)
print_f("testing model")
evl.test_model(cls, test_data)
print_f("Test Accuracy:", evl.percent_correct)
print_f("Test summary")
print_f(evl.summary())
print_f(" Testclass details")
print_f(evl.class_details())
print_f("Testconfusion matrix")
print_f(evl.confusion_matrix)
plcls.plot_roc(evl, class_index=[0, 1], wait=True)
plt.suptitle("Test ROC Curve", fontsize=20, y=0.95)
savefig("./plots/f_test_roc_curve.png")
def use_classifier(data_filename, cli):
loader = Loader(classname="weka.core.converters.ArffLoader")
data = loader.load_file(data_filename)
data.class_is_last()
cls = from_commandline(cli, classname="weka.classifiers.Classifier")
cls.build_classifier(data)
evaluation = Evaluation(data)
evaluation.crossvalidate_model(cls, data, 10, Random(1))
return cls, evaluation
def run_bayesNet(file):
# Get filename from Pathlib object
filename = file.parts[-1]
dir = file.parents[0]
print("Running BayesNet on %s" % filename)
if not filename.endswith(".arff"):
print("%s not ARFF file." % filename)
return
# Removes '.arff' from filename
filename_base = filename[:-5]
# Load data with class as first attr
data = load_Arff_file(file)
data.class_is_first()
# Use BayesNet and set options
cls = Classifier(classname="weka.classifiers.bayes.BayesNet",
options=[
"-D", "-Q",
"weka.classifiers.bayes.net.search.local.TAN", "--",
"-P", "1", "-S", "BAYES", "-E",
"weka.classifiers.bayes.net.estimate.SimpleEstimator",
"--", "-A", "0.5"
])
# Predictions stored in pout
pout = PredictionOutput(
classname="weka.classifiers.evaluation.output.prediction.PlainText")
# Evaluate data
evaluation = Evaluation(data)
evaluation.crossvalidate_model(cls, data, 10, Random(1), output=pout)
print(evaluation.summary())
print(evaluation.class_details())
print(evaluation.confusion_matrix)
# Generate grid for ROC
# plcls.plot_roc(evaluation, class_index=[0,1], wait=True)
# mk dirs for output
dir = dir / "bayesNet_results"
dir.mkdir(parents=True, exist_ok=True)
# Save summary, class details and confusion matrix to file
result_output = filename_base + "_bayesNet_eval_results_TAN.txt"
output_eval(evaluation, dir / result_output)
# Save the predicited results to file
prediction_output = filename_base + "_bayesNet_pred_results_TAN.txt"
output_pred(pout, dir / prediction_output)
print("BayesNet complete")
def experiment_more_file(path_files, path_folder_save_results, fold, options,
classifier, random, name):
cls = Classifier(classname=classifier,
options=weka.core.classes.split_options(options))
file_list = os.listdir(path_files)
for file in file_list:
if ".csv" not in file:
file_list.remove(file)
d_results = {
'name_file': [],
'percent_correct': [],
'percent_incorrect': [],
'confusion_matrix': []
}
print(file_list)
for file in file_list:
print(str(file))
data = converters.load_any_file(path_files + "/" + file)
data.class_is_last()
pout = PredictionOutput(
classname="weka.classifiers.evaluation.output.prediction.CSV")
evl = Evaluation(data)
evl.crossvalidate_model(cls, data, fold, Random(random), pout)
d_results['name_file'].append(str(file))
d_results['percent_correct'].append(evl.percent_correct)
d_results['percent_incorrect'].append(evl.percent_incorrect)
d_results['confusion_matrix'].append(
evl.matrix()) # Generates the confusion matrix.
save = pout.buffer_content()
with open(
path_folder_save_results + '/' + 'prediction/' + str(name) +
str(file)[:-4] + 'pred_data.csv', 'w') as f:
f.write(save)
d_results = pd.DataFrame(data=d_results)
d_results.to_csv(path_folder_save_results + '/' + str(name) + ".csv",
index=False)
def naive_bayse(dicrectory, nameOfDataSet, flag):
loader = Loader(classname="weka.core.converters.CSVLoader")
data = loader.load_file(dicrectory)
data.class_is_last()
cls = Classifier(classname='weka.classifiers.bayes.NaiveBayes')
eval = Evaluation(data)
pout = PredictionOutput(
classname="weka.classifiers.evaluation.output.prediction.PlainText")
if flag:
eval.crossvalidate_model(cls, data, 10, Random(1), pout)
else:
eval.evaluate_train_test_split(cls, data, 80.0, Random(1), pout)
print_and_save('Naive Bayes model', flag, nameOfDataSet, eval)
gc.collect()
def CV5x2(dataset, algo, num_datasets):
loader = Loader(classname="weka.core.converters.ArffLoader")
data = loader.load_file(dataset)
data.class_is_last()
cls = Classifier(classname=algo)
evl = Evaluation(data)
evl.crossvalidate_model(cls, data, 2, Random(5))
print(evl.summary("=== " +str(algo)+ " on" + str(dataset) + " ===",False))
print(evl.matrix("=== on click prediction(confusion matrix) ==="))
print("For Algo"+ str(algo)+"areaUnderROC/1: for CV5x2 " + str(evl.area_under_roc(1)))
return evl.area_under_roc(1)
def evaluate_classifier(cls, data, crossvalidate=False, n_folds=10):
"""
Evaluation
:param cls: trained classifier
:param data: data to test the model on
:param crossvalidate: True to use crossvalidation
:param n_folds: number of folds to cross validate for
:return: evaluation object
"""
evl = Evaluation(data)
if crossvalidate:
evl.crossvalidate_model(cls, data, n_folds, Random(5))
else:
evl.test_model(cls, data)
return evl
def obtainSVM(file):
data = converters.load_any_file(folderPathOfArffFiles + file + ".arff")
remove = Filter(classname="weka.filters.unsupervised.attribute.Remove",
options=["-R", "1-2"])
remove.inputformat(data)
data = remove.filter(data)
data.class_is_last()
classifier = Classifier(classname="weka.classifiers.functions.LibSVM")
evaluation = Evaluation(data)
evaluation.crossvalidate_model(classifier, data, kFold, Random(42))
info = evaluation.class_details()
roc_area = float(info[406:411])
return roc_area
def execute(self,featureInclusion, kFold, classIndex):
deletedFeatures = 0
for i in range(0,len(featureInclusion)):
if featureInclusion[i] == False:
self.instances.deleteAttributeAt( i - deletedFeatures)
deletedFeatures += 1
self.instances.setClassIndex(classIndex)
cvParameterSelection = javabridge.make_instance("Lweka/classifiers/meta/CVParameterSelection","()V")
javabridge.call(cvParameterSelection, "setNumFolds", "(I)V", kFold)
javabridge.call(cvParameterSelection,"buildClassifier(Lweka/core/Instances)V",self.instances)
eval = Evaluation(self.instances)
eval.crossvalidate_model(cvParameterSelection, self.instances, kFold, Random(1))
return eval.percent_correct()
def crossValidate(self, arrfFile = None, classname="weka.classifiers.trees.J48", options=["-C", "0.3"]):
if arrfFile is not None:
self.initData( arrfFile )
if self.data is None:
return
print 'Classificador ' + str(classname) + ' ' + ' '.join(options)
cls = Classifier(classname=classname, options=options)
evl = Evaluation(self.data)
evl.crossvalidate_model(cls, self.data, 10, Random(1))
print(evl.percent_correct)
print(evl.summary())
print(evl.class_details())
def SimpleLogistic():
# load a dataset
loader = Loader(classname="weka.core.converters.ArffLoader")
data = loader.load_file("First_trial_classification.arff")
data.class_is_last() # set class attribute
cls = Classifier(classname="weka.classifiers.functions.SimpleLogistic")
pout = PredictionOutput(
classname="weka.classifiers.evaluation.output.prediction.PlainText")
evl = Evaluation(data)
evl.crossvalidate_model(cls, data, 10, Random(486), pout)
print(evl.summary())
print(pout.buffer_content())
# save model
serialization.write_all("SimpleLogistic2.model", cls)
def cross_validate(self, detail = True):
"""Perform cross validation using trained data.
Parameters
----------
detail : boolean, optional, default = True
If true return a detailed information of cross validation.
Returns
-------
info : string
Info with results of cross validation.
"""
#print 'cross_validation'
start_time = TimeUtils.get_time()
info = "Scheme:\t%s %s\n" % (str(self.classifier.classname) , " ".join([str(option) for option in self.classifier.options]))
if detail == True:
info += "Relation:\t%s\n" % (self.data.relationname)
info += "Instances:\t%d\n" % (self.data.num_instances)
info += "Attributes:\t%d\n\n" % (self.data.num_attributes)
evl = WEvaluation(self.data)
evl.crossvalidate_model(self.classifier, self.data, 10, WRandom(1))
if detail == False:
info += "Correctly Classified Instances: %0.4f%%\n" % (evl.percent_correct)
info += "Time taken to build model: %0.5f seconds\n\n" % (TimeUtils.get_time() - start_time)
#info += str(evl.percent_correct) + "\n\n"
if detail == True:
info += "=== Stratified cross-validation ===\n"
info += evl.summary() + "\n\n"
info += str(evl.class_details()) + "\n\n"
classes = [str(self.data.class_attribute.value(i)) for i in range(0, self.data.class_attribute.num_values)]
cm = evl.confusion_matrix
info += Classifier.confusion_matrix(classes, cm)
return info
def run_crossval(self, output_directory, classifier_name,
classifier_weka_spec, options_list):
# build classifier
print("\nBuilding " + classifier_name +
" Classifier on training data.")
buildTimeStart = time.time()
cls = Classifier(classname=classifier_weka_spec, options=options_list)
cls.build_classifier(self.training_data)
resultsString = ""
resultsString = self.print_both(str(cls), resultsString)
buildTimeString = classifier_name + " Cross Eval Classifier Built in " + str(
time.time() - buildTimeStart) + " secs.\n"
resultsString = self.print_both(buildTimeString, resultsString)
#Evaluate Classifier
resultsString = self.print_both("\nCross Evaluating on test data.",
resultsString)
buildTimeStart = time.time()
evl = Evaluation(self.training_data)
evl.crossvalidate_model(cls, self.training_data, 10, Random(1))
resultsString = self.print_both(str(evl.summary()), resultsString)
resultsString += "\n"
resultsString = self.print_both(str(evl.class_details()),
resultsString)
resultsString += "\n"
resultsString = self.print_both(str(evl.confusion_matrix),
resultsString)
buildTimeString = "\n\n" + classifier_name + " Cross Eval Classifier Evaluated in " + str(
time.time() - buildTimeStart) + " secs.\n"
resultsString = self.print_both(buildTimeString, resultsString)
options_string = ""
for option in options_list:
options_string = options_string + str(option)
options_string = options_string.replace(".", "-")
options_string = options_string.replace("-", "_")
#Save Results and Cleanup
self.save_results(classifier_name + options_string + "_Crossval",
resultsString, output_directory)
def use_classifier(data):
"""
Uses the meta-classifier AttributeSelectedClassifier for attribute selection.
:param data: the dataset to use
:type data: Instances
"""
print("\n1. Meta-classifier")
classifier = Classifier(classname="weka.classifiers.meta.AttributeSelectedClassifier")
aseval = ASEvaluation(classname="weka.attributeSelection.CfsSubsetEval")
assearch = ASSearch(classname="weka.attributeSelection.GreedyStepwise", options=["-B"])
base = Classifier(classname="weka.classifiers.trees.J48")
# setting nested options is always a bit tricky, getting all the escaped double quotes right
# simply using the bean property for setting Java objects is often easier and less error prone
classifier.set_property("classifier", base.jobject)
classifier.set_property("evaluator", aseval.jobject)
classifier.set_property("search", assearch.jobject)
evaluation = Evaluation(data)
evaluation.crossvalidate_model(classifier, data, 10, Random(1))
print(evaluation.summary())
def use_classifier(data):
"""
Uses the meta-classifier AttributeSelectedClassifier for attribute selection.
:param data: the dataset to use
:type data: Instances
"""
print("\n1. Meta-classifier")
classifier = Classifier(classname="weka.classifiers.meta.AttributeSelectedClassifier")
aseval = ASEvaluation(classname="weka.attributeSelection.CfsSubsetEval")
assearch = ASSearch(classname="weka.attributeSelection.GreedyStepwise", options=["-B"])
base = Classifier(classname="weka.classifiers.trees.J48")
# setting nested options is always a bit tricky, getting all the escaped double quotes right
# simply using the bean property for setting Java objects is often easier and less error prone
classifier.set_property("classifier", base.jobject)
classifier.set_property("evaluator", aseval.jobject)
classifier.set_property("search", assearch.jobject)
evaluation = Evaluation(data)
evaluation.crossvalidate_model(classifier, data, 10, Random(1))
print(evaluation.summary())
def run():
jvm.start()
load_csv = Loader("weka.core.converters.CSVLoader")
data_csv = load_csv.load_file(
"/Users/imeiliasantoso/web_graduate_project5/register_page/bank-full_input.csv"
)
saver = Saver("weka.core.converters.ArffSaver")
saver.save_file(
data_csv,
"/Users/imeiliasantoso/web_graduate_project5/register_page/bank-full_input.arff"
)
load_arff = Loader("weka.core.converters.ArffLoader")
data_arff = load_arff.load_file(
"/Users/imeiliasantoso/web_graduate_project5/register_page/bank-full_input.arff"
)
data_arff.class_is_last()
cls = Classifier(classname="weka.classifiers.trees.J48",
options=["-C", "0.5"])
cls.build_classifier(data_arff)
for index, inst in enumerate(data_arff):
pred = cls.classify_instance(inst)
dist = cls.distribution_for_instance(inst)
# save tree prune in txt file
saveFile = open(
"/Users/imeiliasantoso/web_graduate_project5/register_page/bank-full_input.txt",
"w")
saveFile.write(str(cls))
# print(cls)
saveFile.close()
global j48
J48_class = Classifier(classname="weka.classifiers.trees.J48",
options=["-C", "0.25", "-M", "2"])
J48_class.build_classifier(data_arff)
evaluationj48 = Evaluation(data_arff)
evaluationj48.crossvalidate_model(J48_class, data_arff, 10, Random(100))
j48 = str(evaluationj48.percent_correct)
jvm.stop()
return j48
print("Baseline accuracy (ZeroR): %0.1f%%" % evl.percent_correct())
print("\nHoldout 10%...")
# use seed 1-10 and perform random split with 90%
perc = []
for i in xrange(1, 11):
evl = Evaluation(data)
evl.evaluate_train_test_split(
Classifier(classname="weka.classifiers.trees.J48"), data, 90.0, Random(i))
perc.append(round(evl.percent_correct(), 1))
print("Accuracy with seed %i: %0.1f%%" % (i, evl.percent_correct()))
# calculate mean and standard deviation
nperc = numpy.array(perc)
print("mean=%0.2f stdev=%0.2f" % (numpy.mean(nperc), numpy.std(nperc)))
print("\n10-fold Cross-validation...")
# use seed 1-10 and perform 10-fold CV
perc = []
for i in xrange(1, 11):
evl = Evaluation(data)
evl.crossvalidate_model(Classifier(classname="weka.classifiers.trees.J48"), data, 10, Random(i))
perc.append(round(evl.percent_correct(), 1))
print("Accuracy with seed %i: %0.1f%%" % (i, evl.percent_correct()))
# calculate mean and standard deviation
nperc = numpy.array(perc)
print("mean=%0.2f stdev=%0.2f" % (numpy.mean(nperc), numpy.std(nperc)))
jvm.stop()
# load weather.nominal
loader = Loader(classname="weka.core.converters.ArffLoader")
fname = data_dir + os.sep + "weather.nominal.arff"
print("\nLoading dataset: " + fname + "\n")
data = loader.load_file(fname)
data.class_is_last()
# define classifiers
classifiers = ["weka.classifiers.rules.OneR", "weka.classifiers.trees.J48"]
# cross-validate original dataset
for classifier in classifiers:
cls = Classifier(classname=classifier)
evl = Evaluation(data)
evl.crossvalidate_model(cls, data, 10, Random(1))
print("%s (original): %0.0f%%" % (classifier, evl.percent_correct))
# replace 'outlook' in first 4 'no' instances with 'missing'
modified = Instances.copy_instances(data)
count = 0
for i in xrange(modified.num_instances):
if modified.get_instance(i).get_string_value(modified.class_index) == "no":
count += 1
modified.get_instance(i).set_missing(0)
if count == 4:
break
# cross-validate modified dataset
for classifier in classifiers:
cls = Classifier(classname=classifier)
def test(self, folds = 10):
evaluation = Evaluation(self.data) # initialize with priors
evaluation.crossvalidate_model(self.classifier, self.data, folds, Random(42)) # 10-fold CV
print('Total number of instances: '+str(evaluation.num_instances)+'.')
print(str(round(evaluation.percent_correct,2))+'% / '+str(round(evaluation.correct, 2))+' correct.')
print(str(round(evaluation.percent_incorrect,2))+'% / '+str(round(evaluation.incorrect, 2))+' incorrect.')
wfilter = Filter(classname="weka.filters.unsupervised.attribute.StringToNominal", options=["-R", "last"])
wfilter.set_inputformat(data)
data = wfilter.filter(data)
# convert content to string
wfilter = Filter(classname="weka.filters.unsupervised.attribute.NominalToString", options=["-C", "first"])
wfilter.set_inputformat(data)
data = wfilter.filter(data)
# set class attribute
data.set_class_index(data.num_attributes() - 1)
# generate baseline
zeror = Classifier(classname="weka.classifiers.rules.ZeroR")
evaluation = Evaluation(data)
evaluation.crossvalidate_model(zeror, data, 10, Random(1))
print("\nBaseline:\n" + evaluation.to_summary())
# perform text mining
j48 = Classifier(classname="weka.classifiers.trees.J48")
stwv = Filter(
classname="weka.filters.unsupervised.attribute.StringToWordVector",
options=["-R", "1", "-P", "att-"])
stwv.set_inputformat(data)
data = stwv.filter(data)
evaluation = Evaluation(data)
evaluation.crossvalidate_model(j48, data, 10, Random(1))
print("\nJ48:\n" + evaluation.to_summary())
# stop JVM
jvm.stop()
def process_classifier(runType, cls, occ, devList, fewCats, label, subtract):
global devCount
global save_orig
global save_subtract
conf_matrix = {}
if occ:
table = 'temp_dat_occ_vector_occ'
else:
table = 'temp_dat_occ_vector_2'
writeStr = '=========================================================================================\n' + \
'Running ' + runType + ' classifier for \'' + label + '\''
sys.stdout.write(writeStr + '\r')
total_conf.write(writeStr + '\n')
sys.stdout.flush()
if runType == 'unseen':
i = 0
indiv_results = {}
for dev in devList:
devCount += 1
remaining = chop_microseconds(((datetime.utcnow() - item_start)*totalDevs/devCount)-(datetime.utcnow() - item_start))
sys.stdout.write('Running ' + runType + ' classifier for \'' + label + '\' - ' + \
str(round(100*float(devCount)/totalDevs,2)) + ' pct complete (' + str(remaining) + ' remaining) \r')
sys.stdout.flush()
if fewCats:
aws_c.execute('select * from ' + table + ' ' \
'where duty!=0 and deviceMAC not in (select * from vector_reject) ' \
'and deviceMAC in (select * from id_fewcats_mac) '
'and deviceMAC!=\'' + dev + '\';')
else:
aws_c.execute('select * from ' + table + ' ' \
'where duty!=0 and deviceMAC not in (select * from vector_reject) ' \
'and deviceMAC!=\'' + dev + '\';')
results = aws_c.fetchall()
# Generate type list
total_types = ['{']
for data in results:
if(data[-1] not in total_types):
total_types.append('\"')
total_types.append(data[-1])
total_types.append('\"')
total_types.append(',')
total_types[-1] = '}'
typeStr = ''.join(total_types)
arff_train = label + '_' + dev + '_train'
arff_test = label + '_' + dev + '_test'
gen_arff(arff_train, typeStr, results, occ, arff_idcol)
if fewCats:
aws_c.execute('select * from ' + table + ' ' \
'where duty!=0 and deviceMAC not in (select * from vector_reject) ' \
'and deviceMAC in (select * from id_fewcats_mac) '
'and deviceMAC=\'' + dev + '\';')
else:
aws_c.execute('select * from ' + table + ' ' \
'where duty!=0 and deviceMAC not in (select * from vector_reject) ' \
'and deviceMAC=\'' + dev + '\';')
gen_arff(arff_test, typeStr, aws_c.fetchall(), occ, arff_idcol)
train = loader.load_file(arff_train + '.arff')
train.class_is_last()
mv(arff_train + '.arff', master_saveDir)
test = loader.load_file(arff_test + '.arff')
test.class_is_last()
mv(arff_test + '.arff', master_saveDir)
cls.build_classifier(train)
# output predictions
testName = ''
predictions = []
for index, inst in enumerate(test):
if testName != '':
if testName != inst.get_string_value(inst.class_index):
print(str(testName) + ' ' + str(inst.get_string_value(inst.class_index)))
exit()
else:
testName = inst.get_string_value(inst.class_index)
else:
testName = inst.get_string_value(inst.class_index)
if testName not in conf_matrix:
conf_matrix[testName] = {}
pred = cls.classify_instance(inst)
# dist = cls.distribution_for_instance(inst)
# if(pred == inst.get_value(inst.class_index)):
predName = inst.class_attribute.value(int(pred))
if predName not in conf_matrix[testName]:
conf_matrix[testName][predName] = 0
conf_matrix[testName][predName] += 1
predictions.append(predName)
total = 0
if testName != '':
for predName in conf_matrix[testName]:
if predName == testName:
correct = conf_matrix[testName][predName]
total += correct
else:
total += conf_matrix[testName][predName]
# while (len(predictions) * 2) <= 100:
# predictions += pyrandom.sample(predictions, len(predictions))
# if len(predictions) < 100:
# predictions += pyrandom.sample(predictions, 100 - len(predictions))
lots_predictions = []
while len(lots_predictions) < 10000:
lots_predictions += pyrandom.sample(predictions, 1)
#indiv_results[dev] = [testName, pyrandom.sample(predictions, 100)]
indiv_results[dev] = [testName, lots_predictions]
# while len(predictions) < 100:
# predictions += pyrandom.sample(predictions, 1)
# indiv_results[dev] = [testName, predictions]
# indiv_results[dev] = [testName, predictions]
# Prep to print the how-many-days graph
# days_output.write('\n\n\"' + dev + '\"\n')
#print(str(testName) + ' ' + str(correct) + ' ' + str(total) + ' ' + str(float(correct)/total))
# i += 1
# if i == 10:
# break
correct, total = print_conf_matrix(conf_matrix, sys.stdout, False, False, False)
correct, total = print_conf_matrix(conf_matrix, total_conf, False, False, False)
if subtract == 'orig':
save_orig = copy.deepcopy(conf_matrix)
elif subtract == 'subtract':
save_subtract = copy.deepcopy(conf_matrix)
final_result = round(100*float(correct)/total,2)
writeStr = '\nCorrectly Classified Instances\t\t' + str(correct) + '\t\t' + str(final_result) + '\n' + \
'Incorrectly Classified Instances\t' + str(total-correct) + '\t\t' + str(round(100*float(total-correct)/total,2)) + '\n' + \
'Total Number of Instances\t\t' + str(total) + '\n'
print(writeStr)
total_conf.write(writeStr + '\n')
conf_interval = 10
total_instances = float(sum([sum([conf_matrix[test][pred] for pred in conf_matrix[test]]) for test in conf_matrix]))
p_d = {}
p_e = {}
p_e_given_d = {}
for testName in conf_matrix:
count_d = float(sum([conf_matrix[testName][label] for label in conf_matrix[testName]]))
p_d[testName] = count_d / total_instances
p_e[testName] = float(sum([conf_matrix[label][testName] for label in conf_matrix if testName in conf_matrix[label]]) / total_instances)
p_e_given_d[testName] = {}
for predName in conf_matrix:
if predName in conf_matrix[testName]:
p_e_given_d[testName][predName] = conf_matrix[testName][predName] / count_d
else:
p_e_given_d[testName][predName] = 0
confidence = open('confidence.dat', 'w')
for testName in conf_matrix:
confidence.write('\n\n\"' + testName + '\"\n')
print(testName)
for classEvents in range(1, (conf_interval+1)):
numerator = math.pow(p_e_given_d[testName][testName], classEvents) * p_d[testName]
demoninator = 0
for otherName in conf_matrix:
demoninator += math.pow(p_e_given_d[otherName][testName], classEvents) * p_d[otherName]
confidence.write(str(classEvents) + '\t' + str(numerator/demoninator) + '\n')
print(str(classEvents) + '\t' + str(numerator/demoninator))
print('')
for predName in p_e_given_d['Router/Modem']:
print('P( ' + predName + ' | Router/Modem ):\t' + str(p_e_given_d['Router/Modem'][predName]))
for predName in p_e_given_d['Cable Box']:
print('P( ' + predName + ' | Cable Box ):\t' + str(p_e_given_d['Cable Box'][predName]))
#router = open('router', 'w')
print('Router Stuff:')
routerDev = 'Router/Modem'
lampDev = 'Lamp'
cableDev = 'Cable Box'
origClassList = ['Router/Modem', 'Cable Box', 'Lamp', 'Router/Modem', 'Router/Modem', 'Cable Box', 'Router/Modem', 'Lamp', 'Router/Modem']
classListList = [['Router/Modem'] + list(listItem) for listItem in set(itertools.permutations(origClassList))]
classListList = [
['Router/Modem', 'Router/Modem', 'Lamp', 'Lamp', 'Router/Modem', 'Router/Modem', 'Router/Modem', 'Router/Modem', 'Cable Box', 'Router/Modem'],
['Router/Modem', 'Cable Box', 'Lamp', 'Lamp', 'Router/Modem', 'Router/Modem', 'Router/Modem', 'Router/Modem', 'Cable Box', 'Router/Modem'],
['Router/Modem', 'Router/Modem', 'Lamp', 'Lamp', 'Cable Box', 'Router/Modem', 'Router/Modem', 'Router/Modem', 'Cable Box', 'Router/Modem'],
['Router/Modem', 'Cable Box', 'Lamp', 'Lamp', 'Cable Box', 'Router/Modem', 'Router/Modem', 'Router/Modem', 'Cable Box', 'Router/Modem'],
['Router/Modem', 'Cable Box', 'Router/Modem', 'Lamp', 'Cable Box', 'Router/Modem', 'Router/Modem', 'Lamp', 'Router/Modem', 'Router/Modem'],
['Router/Modem', 'Router/Modem', 'Router/Modem', 'Lamp', 'Router/Modem', 'Router/Modem', 'Router/Modem', 'Lamp', 'Router/Modem', 'Router/Modem'],
['Router/Modem', 'Router/Modem', 'Router/Modem', 'Router/Modem', 'Router/Modem', 'Router/Modem', 'Router/Modem', 'Router/Modem', 'Router/Modem', 'Lamp']
]
for idClass, classList in enumerate(classListList):
print(idClass)
for classEvents in range(1, (conf_interval+1)):
numerator_router = p_d[routerDev]
numerator_lamp = p_d[lampDev]
numerator_cable = p_d[cableDev]
for idx, classInst in enumerate(classList):
if idx < classEvents:
numerator_router *= p_e_given_d[routerDev][classInst]
numerator_lamp *= p_e_given_d[lampDev][classInst]
numerator_cable *= p_e_given_d[cableDev][classInst]
demoninator = 0
for otherName in conf_matrix:
obsValue = p_d[otherName]
for idx, classInst in enumerate(classList):
if idx < classEvents:
obsValue *= p_e_given_d[otherName][classInst]
demoninator += obsValue
print(str(classEvents) + '\t' + str(numerator_router/demoninator) + '\t' + str(numerator_lamp/demoninator) + '\t' + str(numerator_cable/demoninator) + '\t\"' + classList[classEvents-1]) + '\"'
print('')
numberDevList(indiv_results)
eachDev = open('indiv_results.dat', 'w')
newIDStream = open('new_id.dat', 'w')
for devItem in indiv_results:
print_obsResults(conf_matrix, conf_interval, p_d, p_e, p_e_given_d, indiv_results[devItem], eachDev, devItem, newIDStream)
print('')
print('total devices: ' + str(len(indiv_results)))
# print('total devices: ' + str(total_devices))
# print('total correct: ' + str(total_correct))
# print(' pct correct: ' + str(round(100*float(total_correct)/total_devices,2)) + '\n')
print('initial confidence: ' + str(round(100*float(sum(initial_confidence))/len(initial_confidence),2)))
print('initial accuracy: ' + str(round(100*float(sum(initial_accuracy))/len(initial_accuracy),2)) + '\n')
# print('final confidence (correct): ' + str(round(100*float(sum(final_confidence_correct))/len(final_confidence_correct),2)))
# print('final confidence (correct): ' + str(round(100*float(sum(final_confidence_incorrect))/len(final_confidence_incorrect),2)))
# print('final accuracy: ' + str(round(100*float(total_correct)/total_devices,2)))
for devType in final_accuracy:
print('final accuracy ' + devType + ' : ' + str(round(float(sum(final_accuracy[devType]))/len(final_accuracy[devType]),6)))
print('final confidence (correct) ' + devType + ' : ' + str(round(float(sum(final_confidence_correct[devType]))/len(final_confidence_correct[devType]),6)))
if len(final_confidence_incorrect[devType]) > 0:
print('final confidence (incorrect) ' + devType + ' : ' + str(round(float(sum(final_confidence_incorrect[devType]))/len(final_confidence_incorrect[devType]),6)))
else:
print('final confidence (incorrect) ' + devType + ' : ' + str(0))
print('final confidence ' + devType + ' : ' + str(round(float(sum(final_confidence_correct[devType])+sum(final_confidence_incorrect[devType]))/(len(final_confidence_correct[devType])+len(final_confidence_incorrect[devType])),2)))
print_conf_matrix(new_conf_matrix, sys.stdout, False, False, False)
for topType in actual_confidence_matrix:
for botType in actual_confidence_matrix[topType]:
storeArray = actual_confidence_matrix[topType][botType]
if len(storeArray) > 0:
actual_confidence_matrix[topType][botType] = round(sum(storeArray)/len(storeArray),2)
else:
actual_confidence_matrix[topType][botType] = 0
print_conf_matrix(conf_matrix, sys.stdout, False, False, False)
print_conf_matrix(actual_confidence_matrix, sys.stdout, False, False, False)
print_conf_matrix(actual_confidence_matrix, sys.stdout, True, False, True)
for devType in acc_over_time_dev:
printOverTime(devType, acc_over_time_dev[devType], conf_over_time_dev[devType])
printOverTime('total', acc_over_time, conf_over_time)
elif runType == 'seen':
if fewCats:
aws_c.execute('select * from ' + table + ' ' \
'where duty!=0 and deviceMAC not in (select * from vector_reject) ' \
'and deviceMAC in (select * from id_fewcats_mac);')
else:
aws_c.execute('select * from ' + table + ' ' \
'where duty!=0 and deviceMAC not in (select * from vector_reject);')
results = aws_c.fetchall()
devCount += 1
remaining = chop_microseconds(((datetime.utcnow() - item_start)*totalDevs/devCount)-(datetime.utcnow() - item_start))
sys.stdout.write('Running ' + runType + ' classifier for \'' + label + '\' - ' + \
str(round(100*float(devCount)/totalDevs,2)) + ' pct complete (' + str(remaining) + ' remaining) \r')
sys.stdout.flush()
# Generate type list
total_types = ['{']
for data in results:
if(data[-1] not in total_types):
total_types.append('\"')
total_types.append(data[-1])
total_types.append('\"')
total_types.append(',')
total_types[-1] = '}'
typeStr = ''.join(total_types)
arff_file = label + '_train'
gen_arff(arff_file, typeStr, results, occ, arff_idcol)
train = loader.load_file(arff_file + '.arff')
train.class_is_last()
mv(arff_file + '.arff', master_saveDir)
cls.build_classifier(train)
evl = Evaluation(train)
evl.crossvalidate_model(cls, train, 10, Random(1))
print('\n')
#print(evl.percent_correct)
#print(evl.class_details())
print(evl.matrix())
total_conf.write('\n' + evl.matrix())
print(evl.summary())
total_conf.write(evl.summary() + '\n')
final_result = round(evl.percent_correct, 2)
else:
success = []
for startDev in devList:
for changeToDev in devList:
if startDev != changeToDev:
devCount += 1
remaining = chop_microseconds(((datetime.utcnow() - item_start)*totalDevs/devCount)-(datetime.utcnow() - item_start))
sys.stdout.write('Running ' + runType + ' classifier for \'' + label + '\' - ' + \
str(round(100*float(devCount)/totalDevs,2)) + ' pct complete (' + str(remaining) + ' remaining) \r')
sys.stdout.flush()
aws_c.execute('select * from temp_dat_occ_vector_2 ' \
'where duty!=0 and deviceMAC in (\'' + startDev + '\',\'' + changeToDev + '\');')
results = [x[:-1] + (x[1],) for x in aws_c.fetchall()] # Class label is just the deviceMAC
if len(results) > 10:
# Generate type list
typeStr = '{' + startDev + ',' + changeToDev + '}'
arff_file = label + '_' + startDev + '_' + changeToDev + '_train'
gen_arff(arff_file, typeStr, results, occ, arff_idcol)
train = loader.load_file(arff_file + '.arff')
train.class_is_last()
mv(arff_file + '.arff', master_saveDir)
cls.build_classifier(train)
evl = Evaluation(train)
evl.crossvalidate_model(cls, train, 10, Random(1))
print('\n')
#print(evl.percent_correct)
#print(evl.class_details())
print(evl.matrix())
total_conf.write('\n' + evl.matrix())
print(evl.summary())
total_conf.write(evl.summary() + '\n')
success.append(evl.percent_correct)
if len(success) > 0:
final_result = [sum(success)/len(success), percentile(success, 5), percentile(success, 10), percentile(success, 95)]
else:
final_result = False
if label in total_results:
print('Warning label ' + label + ' exists twice, overwriting...')
if final_result != False:
total_results[label] = final_result
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)
for equal in ["", "-F"]:
print("\nEqual frequency binning? " + str(equal == "-F") + "\n")
for bins in [0, 40, 10, 5, 2]:
if bins > 0:
fltr = Filter(classname="weka.filters.unsupervised.attribute.Discretize", options=["-B", str(bins), equal])
fltr.set_inputformat(data)
filtered = fltr.filter(data)
else:
filtered = data
cls = Classifier(classname="weka.classifiers.trees.J48")
# cross-validate
evl = Evaluation(filtered)
evl.crossvalidate_model(cls, filtered, 10, Random(1))
# build classifier on full dataset
cls.build_classifier(filtered)
# get size of tree from model strings
lines = str(cls).split("\n")
nodes = "N/A"
for line in lines:
if line.find("Size of the tree :") > -1:
nodes = line.replace("Size of the tree :", "").strip()
# output stats
print("bins=%i accuracy=%0.1f nodes=%s" % (bins, evl.percent_correct(), nodes))
jvm.stop()
import weka.plot as plot
if plot.matplotlib_available:
import matplotlib.pyplot as plt
jvm.start()
# load glass
fname = data_dir + os.sep + "glass.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)
# compute baseline
evl = Evaluation(data)
evl.crossvalidate_model(Classifier("weka.classifiers.rules.ZeroR"), data, 10, Random(1))
baseline = evl.percent_correct()
# generate learning curves
percentages = [1, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
repetitions = [1, 10, 100]
curves = {}
for repetition in repetitions:
# progress info
sys.stdout.write("Repetitions=" + str(repetition))
# initialize curve
curve = {}
for percentage in percentages:
curve[percentage] = 0
curves[repetition] = curve
# run and add up percentage correct from repetition
# load diabetes
loader = Loader(classname="weka.core.converters.ArffLoader")
fname = data_dir + os.sep + "diabetes.arff"
print("\nLoading dataset: " + fname + "\n")
data = loader.load_file(fname)
data.set_class_index(data.num_attributes() - 1)
for classifier in ["weka.classifiers.bayes.NaiveBayes", "weka.classifiers.rules.ZeroR", "weka.classifiers.trees.J48"]:
# train/test split 90% using classifier
cls = Classifier(classname=classifier)
evl = Evaluation(data)
evl.evaluate_train_test_split(cls, data, 90.0, Random(1))
print("\n" + classifier + " train/test split (90%):\n" + evl.to_summary())
cls.build_classifier(data)
print(classifier + " model:\n\n" + str(cls))
# calculate mean/stdev over 10 cross-validations
for classifier in [
"weka.classifiers.meta.ClassificationViaRegression", "weka.classifiers.bayes.NaiveBayes",
"weka.classifiers.rules.ZeroR", "weka.classifiers.trees.J48", "weka.classifiers.functions.Logistic"]:
accuracy = []
for i in xrange(1,11):
cls = Classifier(classname=classifier)
evl = Evaluation(data)
evl.crossvalidate_model(cls, data, 10, Random(i))
accuracy.append(evl.percent_correct())
nacc = numpy.array(accuracy)
print("%s: %0.2f +/-%0.2f" % (classifier, numpy.mean(nacc), numpy.std(nacc)))
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()
# classifier help
helper.print_title("Creating help string")
classifier = Classifier(classname="weka.classifiers.trees.J48")
print(classifier.to_help())
# partial classname
helper.print_title("Creating classifier from partial classname")
clsname = ".J48"
classifier = Classifier(classname=clsname)
print(clsname + " --> " + classifier.classname)
# classifier from commandline
helper.print_title("Creating SMO from command-line string")
cmdline = 'weka.classifiers.functions.SMO -K "weka.classifiers.functions.supportVector.NormalizedPolyKernel -E 3.0"'
classifier = from_commandline(cmdline, classname="weka.classifiers.Classifier")
classifier.build_classifier(iris_data)
print("input: " + cmdline)
print("output: " + classifier.to_commandline())
print("model:\n" + str(classifier))
# kernel classifier
helper.print_title("Creating SMO as KernelClassifier")
kernel = Kernel(classname="weka.classifiers.functions.supportVector.RBFKernel", options=["-G", "0.001"])
classifier = KernelClassifier(classname="weka.classifiers.functions.SMO", options=["-M"])
classifier.kernel = kernel
classifier.build_classifier(iris_data)
print("classifier: " + classifier.to_commandline())
print("model:\n" + str(classifier))
# build a classifier and output model
helper.print_title("Training J48 classifier on iris")
classifier = Classifier(classname="weka.classifiers.trees.J48")
# Instead of using 'options=["-C", "0.3"]' in the constructor, we can also set the "confidenceFactor"
# property of the J48 classifier itself. However, being of type float rather than double, we need
# to convert it to the correct type first using the double_to_float function:
classifier.set_property("confidenceFactor", typeconv.double_to_float(0.3))
classifier.build_classifier(iris_data)
print(classifier)
print(classifier.graph)
print(classifier.to_source("MyJ48"))
plot_graph.plot_dot_graph(classifier.graph)
# evaluate model on test set
helper.print_title("Evaluating J48 classifier on iris")
evaluation = Evaluation(iris_data)
evl = evaluation.test_model(classifier, iris_data)
print(evl)
print(evaluation.summary())
# evaluate model on train/test split
helper.print_title("Evaluating J48 classifier on iris (random split 66%)")
classifier = Classifier(classname="weka.classifiers.trees.J48", options=["-C", "0.3"])
evaluation = Evaluation(iris_data)
evaluation.evaluate_train_test_split(classifier, iris_data, 66.0, Random(1))
print(evaluation.summary())
# load a dataset incrementally and build classifier incrementally
helper.print_title("Build classifier incrementally on iris")
helper.print_info("Loading dataset: " + iris_file)
loader = Loader("weka.core.converters.ArffLoader")
iris_inc = loader.load_file(iris_file, incremental=True)
iris_inc.class_is_last()
classifier = Classifier(classname="weka.classifiers.bayes.NaiveBayesUpdateable")
classifier.build_classifier(iris_inc)
for inst in loader:
classifier.update_classifier(inst)
print(classifier)
# construct meta-classifiers
helper.print_title("Meta classifiers")
# generic FilteredClassifier instantiation
print("generic FilteredClassifier instantiation")
meta = SingleClassifierEnhancer(classname="weka.classifiers.meta.FilteredClassifier")
meta.classifier = Classifier(classname="weka.classifiers.functions.LinearRegression")
flter = Filter("weka.filters.unsupervised.attribute.Remove")
flter.options = ["-R", "first"]
meta.set_property("filter", flter.jobject)
print(meta.to_commandline())
# direct FilteredClassifier instantiation
print("direct FilteredClassifier instantiation")
meta = FilteredClassifier()
meta.classifier = Classifier(classname="weka.classifiers.functions.LinearRegression")
flter = Filter("weka.filters.unsupervised.attribute.Remove")
flter.options = ["-R", "first"]
meta.filter = flter
print(meta.to_commandline())
# generic Vote
print("generic Vote instantiation")
meta = MultipleClassifiersCombiner(classname="weka.classifiers.meta.Vote")
classifiers = [
Classifier(classname="weka.classifiers.functions.SMO"),
Classifier(classname="weka.classifiers.trees.J48")
]
meta.classifiers = classifiers
print(meta.to_commandline())
# cross-validate nominal classifier
helper.print_title("Cross-validating NaiveBayes on diabetes")
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()
classifier = Classifier(classname="weka.classifiers.bayes.NaiveBayes")
pred_output = PredictionOutput(
classname="weka.classifiers.evaluation.output.prediction.PlainText", options=["-distribution"])
evaluation = Evaluation(diabetes_data)
evaluation.crossvalidate_model(classifier, diabetes_data, 10, Random(42), output=pred_output)
print(evaluation.summary())
print(evaluation.class_details())
print(evaluation.matrix())
print("areaUnderPRC/0: " + str(evaluation.area_under_prc(0)))
print("weightedAreaUnderPRC: " + str(evaluation.weighted_area_under_prc))
print("areaUnderROC/1: " + str(evaluation.area_under_roc(1)))
print("weightedAreaUnderROC: " + str(evaluation.weighted_area_under_roc))
print("avgCost: " + str(evaluation.avg_cost))
print("totalCost: " + str(evaluation.total_cost))
print("confusionMatrix: " + str(evaluation.confusion_matrix))
print("correct: " + str(evaluation.correct))
print("pctCorrect: " + str(evaluation.percent_correct))
print("incorrect: " + str(evaluation.incorrect))
print("pctIncorrect: " + str(evaluation.percent_incorrect))
print("unclassified: " + str(evaluation.unclassified))
print("pctUnclassified: " + str(evaluation.percent_unclassified))
print("coverageOfTestCasesByPredictedRegions: " + str(evaluation.coverage_of_test_cases_by_predicted_regions))
print("sizeOfPredictedRegions: " + str(evaluation.size_of_predicted_regions))
print("falseNegativeRate: " + str(evaluation.false_negative_rate(1)))
print("weightedFalseNegativeRate: " + str(evaluation.weighted_false_negative_rate))
print("numFalseNegatives: " + str(evaluation.num_false_negatives(1)))
print("trueNegativeRate: " + str(evaluation.true_negative_rate(1)))
print("weightedTrueNegativeRate: " + str(evaluation.weighted_true_negative_rate))
print("numTrueNegatives: " + str(evaluation.num_true_negatives(1)))
print("falsePositiveRate: " + str(evaluation.false_positive_rate(1)))
print("weightedFalsePositiveRate: " + str(evaluation.weighted_false_positive_rate))
print("numFalsePositives: " + str(evaluation.num_false_positives(1)))
print("truePositiveRate: " + str(evaluation.true_positive_rate(1)))
print("weightedTruePositiveRate: " + str(evaluation.weighted_true_positive_rate))
print("numTruePositives: " + str(evaluation.num_true_positives(1)))
print("fMeasure: " + str(evaluation.f_measure(1)))
print("weightedFMeasure: " + str(evaluation.weighted_f_measure))
print("unweightedMacroFmeasure: " + str(evaluation.unweighted_macro_f_measure))
print("unweightedMicroFmeasure: " + str(evaluation.unweighted_micro_f_measure))
print("precision: " + str(evaluation.precision(1)))
print("weightedPrecision: " + str(evaluation.weighted_precision))
print("recall: " + str(evaluation.recall(1)))
print("weightedRecall: " + str(evaluation.weighted_recall))
print("kappa: " + str(evaluation.kappa))
print("KBInformation: " + str(evaluation.kb_information))
print("KBMeanInformation: " + str(evaluation.kb_mean_information))
print("KBRelativeInformation: " + str(evaluation.kb_relative_information))
print("SFEntropyGain: " + str(evaluation.sf_entropy_gain))
print("SFMeanEntropyGain: " + str(evaluation.sf_mean_entropy_gain))
print("SFMeanPriorEntropy: " + str(evaluation.sf_mean_prior_entropy))
print("SFMeanSchemeEntropy: " + str(evaluation.sf_mean_scheme_entropy))
print("matthewsCorrelationCoefficient: " + str(evaluation.matthews_correlation_coefficient(1)))
print("weightedMatthewsCorrelation: " + str(evaluation.weighted_matthews_correlation))
print("class priors: " + str(evaluation.class_priors))
print("numInstances: " + str(evaluation.num_instances))
print("meanAbsoluteError: " + str(evaluation.mean_absolute_error))
print("meanPriorAbsoluteError: " + str(evaluation.mean_prior_absolute_error))
print("relativeAbsoluteError: " + str(evaluation.relative_absolute_error))
print("rootMeanSquaredError: " + str(evaluation.root_mean_squared_error))
print("rootMeanPriorSquaredError: " + str(evaluation.root_mean_prior_squared_error))
print("rootRelativeSquaredError: " + str(evaluation.root_relative_squared_error))
print("prediction output:\n" + str(pred_output))
plot_cls.plot_roc(
evaluation, title="ROC diabetes",
class_index=range(0, diabetes_data.class_attribute.num_values), wait=False)
plot_cls.plot_prc(
evaluation, title="PRC diabetes",
class_index=range(0, diabetes_data.class_attribute.num_values), wait=False)
# load a numeric dataset
bolts_file = helper.get_data_dir() + os.sep + "bolts.arff"
helper.print_info("Loading dataset: " + bolts_file)
loader = Loader("weka.core.converters.ArffLoader")
bolts_data = loader.load_file(bolts_file)
bolts_data.class_is_last()
# build a classifier and output model
helper.print_title("Training LinearRegression on bolts")
classifier = Classifier(classname="weka.classifiers.functions.LinearRegression", options=["-S", "1", "-C"])
classifier.build_classifier(bolts_data)
print(classifier)
# cross-validate numeric classifier
helper.print_title("Cross-validating LinearRegression on bolts")
classifier = Classifier(classname="weka.classifiers.functions.LinearRegression", options=["-S", "1", "-C"])
evaluation = Evaluation(bolts_data)
evaluation.crossvalidate_model(classifier, bolts_data, 10, Random(42))
print(evaluation.summary())
print("correlationCoefficient: " + str(evaluation.correlation_coefficient))
print("errorRate: " + str(evaluation.error_rate))
helper.print_title("Header - bolts")
print(str(evaluation.header))
helper.print_title("Predictions on bolts")
for index, pred in enumerate(evaluation.predictions):
print(str(index+1) + ": " + str(pred) + " -> error=" + str(pred.error))
plot_cls.plot_classifier_errors(evaluation.predictions, wait=False)
# learning curve
cls = [
Classifier(classname="weka.classifiers.trees.J48"),
Classifier(classname="weka.classifiers.bayes.NaiveBayesUpdateable")]
plot_cls.plot_learning_curve(
cls, diabetes_data, increments=0.05, label_template="[#] !", metric="percent_correct", wait=True)
# access classifier's Java API
labor_file = helper.get_data_dir() + os.sep + "labor.arff"
helper.print_info("Loading dataset: " + labor_file)
loader = Loader("weka.core.converters.ArffLoader")
labor_data = loader.load_file(labor_file)
labor_data.class_is_last()
helper.print_title("Using JRip's Java API to access rules")
jrip = Classifier(classname="weka.classifiers.rules.JRip")
jrip.build_classifier(labor_data)
rset = jrip.jwrapper.getRuleset()
for i in range(rset.size()):
r = rset.get(i)
print(str(r.toString(labor_data.class_attribute.jobject)))
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.classifiers import Classifier, Evaluation, PredictionOutput
from weka.core.classes import Random
import weka.plot.classifiers as plc
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()
# cross-validate NaiveBayes
cls = Classifier(classname="weka.classifiers.bayes.NaiveBayes")
pout = PredictionOutput(classname="weka.classifiers.evaluation.output.prediction.PlainText", options=["-distribution"])
evl = Evaluation(data)
evl.crossvalidate_model(cls, data, 10, Random(1), pout)
print(evl.summary())
print(evl.matrix())
print(pout)
plc.plot_roc(evl, wait=True)
jvm.stop()
fname = data_dir + os.sep + "diabetes.arff"
print("\nLoading dataset: " + fname + "\n")
data = loader.load_file(fname)
# we'll set the class attribute after filtering
# apply NominalToBinary filter and set class attribute
fltr = Filter("weka.filters.unsupervised.attribute.NominalToBinary")
fltr.inputformat(data)
filtered = fltr.filter(data)
filtered.class_is_last()
# cross-validate LinearRegression on filtered data, display model
cls = Classifier(classname="weka.classifiers.functions.LinearRegression")
pout = PredictionOutput(classname="weka.classifiers.evaluation.output.prediction.PlainText")
evl = Evaluation(filtered)
evl.crossvalidate_model(cls, filtered, 10, Random(1), pout)
print("10-fold cross-validation:\n" + evl.summary())
print("Predictions:\n\n" + str(pout))
cls.build_classifier(filtered)
print("Model:\n\n" + str(cls))
# use AddClassification filter with LinearRegression on filtered data
print("Applying AddClassification to filtered data:\n")
fltr = Filter(
classname="weka.filters.supervised.attribute.AddClassification",
options=["-W", "weka.classifiers.functions.LinearRegression", "-classification"])
fltr.inputformat(filtered)
classified = fltr.filter(filtered)
print(classified)
# convert class back to nominal
from weka.classifiers import Classifier
cls = Classifier(classname= "weka.classifiers.bayes.NaiveBayes" )
# No options of interest to adjust
# Build classifier on training data
cls.build_classifier(train)
# print(cls)
#import weka.plot.graph as graph
#graph.plot_dot_graph(cls.graph)
from weka.classifiers import Evaluation
from weka.core.classes import Random
evl = Evaluation(train)
evl.crossvalidate_model(cls, train, 10, Random(1))
print ("Kappa Score")
print (evl.kappa) # 0.50 - Not bad
print ("Evaluation Summary")
print (evl.summary()) # Accuracy: 83%
## Test model on new data ##
evl = Evaluation(test)
from weka.classifiers import PredictionOutput
pred_output = PredictionOutput(
classname="weka.classifiers.evaluation.output.prediction.PlainText", options=["-distribution"])
evl.crossvalidate_model(cls, test, 10, Random(1), pred_output)
from weka.filters import Filter
# convert csv into arff format (weka compatable)
# use convertcsvtoarff.py file
# load arff file
loader = Loader("weka.core.converters.ArffLoader")
iris_data = loader.load_file("reviewsinformation_task2.arff")
iris_data.class_is_last()
loader = Loader("weka.core.converters.ArffLoader")
iris_data = loader.load_file(iris_file)
iris_data.class_is_last()
# kernel classifier
helper.print_title("Creating SMO as KernelClassifier")
kernel = Kernel(classname="weka.classifiers.functions.supportVector.RBFKernel", options=["-G", "0.001"])
classifier = KernelClassifier(classname="weka.classifiers.functions.SMO", options=["-M"])
classifier.kernel = kernel
classifier.build_classifier(iris_data)
print("classifier: " + classifier.to_commandline())
print("model:\n" + str(classifier))
#print("model:\n" + str(classifier))
evaluation = Evaluation('test_data.arff')
evaluation.crossvalidate_model(classifier, diabetes_data, 10, Random(42), output=pred_output)
print(evaluation.summary())
print(evaluation.class_details())
print(evaluation.matrix())
loader = Loader(classname="weka.core.converters.ArffLoader")
data = loader.load_file(fname)
data.class_is_last()
classifiers = [
"weka.classifiers.bayes.NaiveBayes",
"weka.classifiers.lazy.IBk",
"weka.classifiers.trees.J48"
]
# cross-validate classifiers
for classifier in classifiers:
# classifier itself
cls = Classifier(classname=classifier)
evl = Evaluation(data)
evl.crossvalidate_model(cls, data, 10, Random(1))
print("%s: %0.0f%%" % (classifier, evl.percent_correct))
# meta with cfssubseteval
meta = SingleClassifierEnhancer(classname="weka.classifiers.meta.AttributeSelectedClassifier")
meta.options = \
["-E", "weka.attributeSelection.CfsSubsetEval",
"-S", "weka.attributeSelection.BestFirst",
"-W", classifier]
evl = Evaluation(data)
evl.crossvalidate_model(meta, data, 10, Random(1))
print("%s (cfs): %0.0f%%" % (classifier, evl.percent_correct))
# meta with wrapper
meta = SingleClassifierEnhancer(classname="weka.classifiers.meta.AttributeSelectedClassifier")
meta.options = \
["-E", "weka.attributeSelection.WrapperSubsetEval -B " + classifier,
"-S", "weka.attributeSelection.BestFirst",
# load a dataset
iris_file = "HairEyeColor.csv"
print("Loading dataset: " + iris_file)
loader = Loader(classname="weka.core.converters.CSVLoader")
iris_data = loader.load_file(iris_file)
print (iris_data.num_attributes)
iris_data.set_class_index(iris_data.num_attributes() - 1)
# build a classifier and output model
print ("Training J48 classifier on iris")
classifier = Classifier(classname="weka.test.Regression")
#classifier = Classifier(classname="weka.classifiers.trees.J48", options=["-C", "0.5"])
# Instead of using 'options=["-C", "0.3"]' in the constructor, we can also set the "confidenceFactor"
# property of the J48 classifier itself. However, being of type float rather than double, we need
# to convert it to the correct type first using the double_to_float function:
#classifier.set_property("confidenceFactor", types.double_to_float(0.3))
classifier.build_classifier(iris_data)
print(classifier)
print(classifier.graph())
#plot_graph.plot_dot_graph(classifier.graph())
evaluation = Evaluation(iris_data) # initialize with priors
evaluation.crossvalidate_model(classifier, iris_data, 10, Random(42)) # 10-fold CV
print(evaluation.to_summary())
print("pctCorrect: " + str(evaluation.percent_correct()))
print("incorrect: " + str(evaluation.incorrect()))
jvm.stop()
