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 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 TrainingModel(arff, modelOutput, clsfier):
# 启动java虚拟机
jvm.start()
# 导入训练集
loader = Loader(classname="weka.core.converters.ArffLoader")
train = loader.load_file(arff)
train.class_is_first()
# 使用RandomForest算法进行训练，因为在GUI版本weka中使用多种方式训练后发现此方式TPR与TNR较高
cls_name = "weka.classifiers." + clsfier
clsf = Classifier(classname=cls_name)
clsf.build_classifier(train)
print(clsf)
# 建立模型
fc = FilteredClassifier()
fc.classifier = clsf
evl = Evaluation(train)
evl.crossvalidate_model(fc, train, 10, Random(1))
print(evl.percent_correct)
print(evl.summary())
print(evl.class_details())
print(evl.matrix())
# 结果统计
matrixResults = evl.confusion_matrix
TN = float(matrixResults[0][0])
FP = float(matrixResults[0][1])
FN = float(matrixResults[1][0])
TP = float(matrixResults[1][1])
TPR = TP / (TP + FN)
TNR = TN / (FP + TN)
PPV = TP / (TP + FP)
NPV = TN / (TN + FN)
print("算法： " + clsfier)
print("敏感度 TPR: " + str(TPR))
print("特异度 TNR: " + str(TNR))
print("PPV: " + str(PPV))
print("NPV: " + str(NPV))
# 保存模型
clsf.serialize(modelOutput, header=train)
# 退出虚拟机
jvm.stop()
print("分析模型建立完成")
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
fname = data_dir + os.sep + "ReutersGrain-test.arff"
print("\nLoading dataset: " + fname + "\n")
loader = Loader(classname="weka.core.converters.ArffLoader")
test = loader.load_file(fname)
test.set_class_index(test.num_attributes() - 1)
setups = (
("weka.classifiers.trees.J48", []),
("weka.classifiers.bayes.NaiveBayes", []),
("weka.classifiers.bayes.NaiveBayesMultinomial", []),
("weka.classifiers.bayes.NaiveBayesMultinomial", ["-C"]),
("weka.classifiers.bayes.NaiveBayesMultinomial", ["-C", "-L", "-S"])
)
# cross-validate classifiers
for setup in setups:
classifier, opt = setup
print("\n--> %s (filter options: %s)\n" % (classifier, " ".join(opt)))
cls = FilteredClassifier()
cls.set_classifier(Classifier(classname=classifier))
cls.set_filter(Filter(classname="weka.filters.unsupervised.attribute.StringToWordVector", options=opt))
cls.build_classifier(data)
evl = Evaluation(test)
evl.test_model(cls, test)
print("Accuracy: %0.0f%%" % evl.percent_correct())
tcdata = plc.generate_thresholdcurve_data(evl, 0)
print("AUC: %0.3f" % plc.get_auc(tcdata))
print(evl.to_matrix("Matrix:"))
jvm.stop()
data.class_is_last()
# 1. cheating with default filter
fltr = Filter(classname="weka.filters.supervised.attribute.Discretize",
options=[])
fltr.inputformat(data)
filtered = fltr.filter(data)
cls = Classifier(classname="weka.classifiers.trees.J48")
evl = Evaluation(filtered)
evl.crossvalidate_model(cls, filtered, 10, Random(1))
cls.build_classifier(filtered)
print("cheating (default): accuracy=%0.1f nodes=%s" %
(evl.percent_correct, get_nodes(str(cls))))
# 2. using FilteredClassifier with default filter
cls = FilteredClassifier()
cls.classifier = Classifier(classname="weka.classifiers.trees.J48")
cls.filter = Filter(classname="weka.filters.supervised.attribute.Discretize",
options=[])
evl = Evaluation(data)
evl.crossvalidate_model(cls, data, 10, Random(1))
cls.build_classifier(data)
print("FilteredClassifier (default): accuracy=%0.1f nodes=%s" %
(evl.percent_correct, get_nodes(str(cls))))
# 3. using FilteredClassifier (make binary)
cls = FilteredClassifier()
cls.classifier = Classifier(classname="weka.classifiers.trees.J48")
cls.filter = Filter(classname="weka.filters.supervised.attribute.Discretize",
options=["-D"])
evl = Evaluation(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()
# 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", types.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)
# train 2nd classifier on diabetes dataset
classifier2 = Classifier(classname="weka.classifiers.trees.RandomForest")
evaluation2 = Evaluation(diabetes_data)
evaluation2.crossvalidate_model(classifier2, diabetes_data, 10, Random(42))
plot_cls.plot_rocs({
"NB": evaluation,
"RF": evaluation2
},
title="ROC diabetes",
class_index=0,
wait=False)
plot_cls.plot_prcs({
"NB": evaluation,
"RF": evaluation2
},
title="PRC diabetes",
class_index=0,
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)
# train 2nd classifier and show errors in same plot
classifier2 = Classifier(classname="weka.classifiers.functions.SMOreg")
evaluation2 = Evaluation(bolts_data)
evaluation2.crossvalidate_model(classifier2, bolts_data, 10, Random(42))
plot_cls.plot_classifier_errors(
{
"LR": evaluation.predictions,
"SMOreg": evaluation2.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 xrange(rset.size()):
r = rset.get(i)
print(str(r.toString(labor_data.class_attribute.jobject)))
# 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
for seed in xrange(repetition):
seed += 1
sys.stdout.write(".")
for percentage in percentages:
cls = Classifier(classname="weka.classifiers.trees.J48")
flt = Filter(classname="weka.filters.unsupervised.instance.Resample",
options=["-Z", str(percentage), "-no-replacement"])
fc = FilteredClassifier()
fc.set_classifier(cls)
fc.set_filter(flt)
evl = Evaluation(data)
evl.crossvalidate_model(fc, data, 10, Random(seed))
curve[percentage] += (evl.percent_correct() / repetition)
# progress info
sys.stdout.write("\n")
# output the results
if not plot.matplotlib_available:
print("ZeroR: " + str(baseline))
for repetition in repetitions:
y = []
for percentage in percentages:
y.append(curves[repetition][percentage])
def main():
"""
Just runs some example code.
"""
#case=["selected_chi100","selected_chi150","selected_chi200","selected_chi250","selected_chi300","selected_chi350","selected_fe100","selected_fe150","selected_fe200","selected_fe250","selected_fe300","selected_fe350","selected_sfm100","selected_sfm150","selected_sfm200","selected_sfm250","selected_sfm300","selected_sfm350","selected_sfmt100","selected_sfmt150","selected_sfmt200","selected_sfmt250","selected_sfmt300","selected_sfmt350","selected_cs100","selected_cs150","selected_cs200","selected_cs250","selected_cs300","selected_cs350"]
#case=["feature_vector100","feature_vector110","feature_vector120","feature_vector130","feature_vector140","feature_vector150","feature_vector90","feature_vector80","feature_vector70","feature_vector60","feature_vector50","feature_vector40","feature_vector30","feature_vector20","feature_vector10","feature_vector5"]
case = [
"selected_chi100", "selected_chi150", "selected_chi200",
"selected_chi250", "selected_chi300", "selected_chi350",
"selected_fe100", "selected_fe150", "selected_fe200", "selected_fe250",
"selected_fe300", "selected_fe350", "selected_sfm100",
"selected_sfm150", "selected_sfm200", "selected_sfm250",
"selected_sfm300", "selected_sfm350", "selected_sfmt100",
"selected_sfmt150", "selected_sfmt200", "selected_sfmt250",
"selected_sfmt300", "selected_sfmt350"
]
#case=["selected_chi100","selected_chi150","selected_chi200","selected_fe100","selected_fe150","selected_fe200","selected_sfm100","selected_sfm150","selected_sfm200","selected_sfmt100","selected_sfmt150","selected_sfmt200","selected_cs100","selected_cs150","selected_cs200",]
for nomefile in case:
print(nomefile)
feature_vector = "./selected_vectors30/" + nomefile + ".arff" # legge il file in formato arff
loader = Loader("weka.core.converters.ArffLoader")
data = loader.load_file(feature_vector)
data.class_is_last()
#print(data)
f = open("./selected_vectors30/risultati/" + nomefile + ".txt",
"w+") # file risultati in txt
#intestazione excel
intest = [
"Correlation coefficient", "Mean absolute error",
"Root mean squared error", "Relative absolute error",
"Root relative squared error", "Total Number of Instances"
]
workbook = xlsxwriter.Workbook("./selected_vectors30/risultati/" +
nomefile + ".xlsx") # file excel
worksheet = workbook.add_worksheet()
for col_num, dati in enumerate(intest):
worksheet.write(0, col_num + 1, dati)
riga = 1
#lista degli algoritmi da eseguire
#alg=["meta.Bagging","meta.RandomSubSpace","rules.M5Rules","trees.M5P","trees.RandomForest"]
alg = [
"bayes.NaiveBayes", "bayes.NaiveBayesUpdateable",
"functions.Logistic", "functions.SGD", "functions.SimpleLogistic",
"functions.SMO", "functions.VotedPerceptron", "meta.AdaBoostM1",
"meta.AttributeSelectedClassifier", "meta.Bagging",
"meta.ClassificationViaRegression",
"meta.IterativeClassifierOptimizer", "meta.LogitBoost",
"meta.RandomCommittee", "meta.RandomSubSpace",
"rules.DecisionTable", "rules.JRip", "rules.OneR",
"trees.DecisionStump", "trees.J48", "trees.RandomForest",
"trees.REPTree"
]
for row_num, dati in enumerate(alg):
worksheet.write(row_num + 1, 0, dati)
for i in alg:
remove = Filter(
classname="weka.filters.unsupervised.attribute.Remove")
cls = Classifier(classname="weka.classifiers." + i)
fc = FilteredClassifier()
fc.filter = remove
fc.classifier = cls
evl = Evaluation(data)
evl.crossvalidate_model(fc, data, 10,
Random(1)) # 10 fold cross validation
#evl.evaluate_train_test_split(fc,data,50,None,None) # 50% split cross validation
k = evl.summary()
#scrittura sui file
f.write(i + "\n")
f.write(k + "\n")
my_list = k.split('\n')
for col_num, dati in enumerate(my_list):
worksheet.write(riga, col_num, dati[-10:])
print(i)
riga += 1
f.close()
workbook.close()
train = converters.load_any_file("imbalanced_train.arff")
test = converters.load_any_file("imbalanced_test.arff")
train.class_is_last()
test.class_is_last()
# Minority Class is getting Sampled 5x
smote = Filter(classname="weka.filters.supervised.instance.SMOTE",
options=["-P", "500.0"])
# Base Classifier
cls = Classifier(classname="weka.classifiers.trees.LMT",
options=["-B", "-I", "10"])
# Filtered Classifier
fc = FilteredClassifier()
fc.filter = smote
fc.classifier = cls
# 5 Fold K cross validation
evl = Evaluation(train)
evl.crossvalidate_model(fc, train, 5, Random(1))
# Prints Out Confusion Matrix along with other summary statistics
print("LMT (SMOTE balanced classes) CV = 5 Error: %.2f%%" %
(evl.percent_incorrect))
print(evl.matrix()) #Confusion Matrix
# Plots ROC
plcls.plot_roc(evl, class_index=[0, 1], wait=True)
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
for seed in xrange(repetition):
seed += 1
sys.stdout.write(".")
for percentage in percentages:
cls = Classifier(classname="weka.classifiers.trees.J48")
flt = Filter(
classname="weka.filters.unsupervised.instance.Resample",
options=["-Z", str(percentage), "-no-replacement"])
fc = FilteredClassifier()
fc.classifier = cls
fc.filter = flt
evl = Evaluation(data)
evl.crossvalidate_model(fc, data, 10, Random(seed))
curve[percentage] += (evl.percent_correct / repetition)
# progress info
sys.stdout.write("\n")
# output the results
if not plot.matplotlib_available:
print("ZeroR: " + str(baseline))
for repetition in repetitions:
y = []
for percentage in percentages:
y.append(curves[repetition][percentage])
db = client["news-scraper"]
articles_collection = db.articles
article = articles_collection.find_one({"_id": article_id})
jvm.start(system_cp=True, packages=True, max_heap_size="512m")
# Train classifier
loader = Loader(classname="weka.core.converters.ArffLoader", options=["-charset", "UTF-8"])
train_data = loader.load_file(os.path.dirname(os.path.realpath(__file__)) + "/datasets/train.arff")
train_data.class_is_last()
string_to_word_vector_filter = Filter(classname="weka.filters.unsupervised.attribute.StringToWordVector")
cls = Classifier(classname="weka.classifiers.bayes.NaiveBayesMultinomial")
fc = FilteredClassifier()
fc.filter = string_to_word_vector_filter
fc.classifier = cls
fc.build_classifier(train_data)
# Create test data
class_att = Attribute.create_nominal("class", ["good", "neutral", "bad"])
str_att = Attribute.create_string("title")
test_dataset = Instances.create_instances(
name="test_news_set",
atts=[str_att, class_att],
capacity=1
)
fname = data_dir + os.sep + "ReutersGrain-test.arff"
print("\nLoading dataset: " + fname + "\n")
loader = Loader(classname="weka.core.converters.ArffLoader")
test = loader.load_file(fname)
test.class_is_last()
setups = (
("weka.classifiers.trees.J48", []),
("weka.classifiers.bayes.NaiveBayes", []),
("weka.classifiers.bayes.NaiveBayesMultinomial", []),
("weka.classifiers.bayes.NaiveBayesMultinomial", ["-C"]),
("weka.classifiers.bayes.NaiveBayesMultinomial", ["-C", "-L", "-stopwords-handler", "weka.core.stopwords.Rainbow"])
)
# cross-validate classifiers
for setup in setups:
classifier, opt = setup
print("\n--> %s (filter options: %s)\n" % (classifier, " ".join(opt)))
cls = FilteredClassifier()
cls.classifier = Classifier(classname=classifier)
cls.filter = Filter(classname="weka.filters.unsupervised.attribute.StringToWordVector", options=opt)
cls.build_classifier(data)
evl = Evaluation(test)
evl.test_model(cls, test)
print("Accuracy: %0.0f%%" % evl.percent_correct)
tcdata = plc.generate_thresholdcurve_data(evl, 0)
print("AUC: %0.3f" % plc.get_auc(tcdata))
print(evl.matrix("Matrix:"))
jvm.stop()
import matplotlib as plt
jvm.start()
loader = Loader(classname="weka.core.converters.CSVLoader")
data = loader.load_file("C:/Arpit/aps.failure_training_set.csv")
data_test = loader.load_file("C:/Arpit/aps.failure_test_set.csv")
# print(str(data))data = loader.load_file( + "aps.failure_training_set.csv")
data.class_is_last()
data_test.class_is_last()
# remove = Filter(classname="weka.filters.unsupervised.attribute.Remove", options=["-R", "1-3"])
cls = Classifier(classname="weka.classifiers.trees.LMT")
fc = FilteredClassifier()
# fc.filter = remove
fc.classifier = cls
evl = Evaluation(data)
evl.crossvalidate_model(fc, data, 10, Random(1))
preds = evl.test_model(cls, data_test)
conf = evl.confusion_matrix
print(evl.percent_incorrect)
# print(evl.summary())
# print(evl.class_details())
sns.heatmap(conf, cmap="YlGnBu", annot=True, linewidths=.5, fmt='d')
print("AUC", evl.area_under_prc)
print("Train/test/predict...")
groups = ["DataSet1", "DataSet2"]
# groups = ["DataSet2"]
for group in groups:
print(group)
train = data_dir + os.sep + group + "_Cal.arff"
test = data_dir + os.sep + group + "_Test.arff"
pred = data_dir + os.sep + group + "_Val.arff"
loader = Loader(classname="weka.core.converters.ArffLoader")
print(train)
train_data = loader.load_file(train)
train_data.class_index = train_data.attribute_by_name("reference value").index
print(test)
test_data = loader.load_file(test)
test_data.class_index = test_data.attribute_by_name("reference value").index
print(pred)
pred_data = loader.load_file(pred)
pred_data.class_index = pred_data.attribute_by_name("reference value").index
cls = FilteredClassifier()
cls.classifier = Classifier(classname="weka.classifiers.functions.LinearRegression", options=["-S", "1", "-C"])
cls.filter = Filter(classname="weka.filters.unsupervised.attribute.Remove", options=["-R", "first"])
cls.build_classifier(train_data)
evl = Evaluation(train_data)
evl.test_model(cls, test_data)
print(evl.summary())
jvm.stop()
print(group)
train = data_dir + os.sep + group + "_Cal.arff"
test = data_dir + os.sep + group + "_Test.arff"
pred = data_dir + os.sep + group + "_Val.arff"
loader = Loader(classname="weka.core.converters.ArffLoader")
print(train)
train_data = loader.load_file(train)
train_data.class_index = train_data.attribute_by_name(
"reference value").index
print(test)
test_data = loader.load_file(test)
test_data.class_index = test_data.attribute_by_name(
"reference value").index
print(pred)
pred_data = loader.load_file(pred)
pred_data.class_index = pred_data.attribute_by_name(
"reference value").index
cls = FilteredClassifier()
cls.classifier = Classifier(
classname="weka.classifiers.functions.LinearRegression",
options=["-S", "1", "-C"])
cls.filter = Filter(classname="weka.filters.unsupervised.attribute.Remove",
options=["-R", "first"])
cls.build_classifier(train_data)
evl = Evaluation(train_data)
evl.test_model(cls, test_data)
print(evl.summary())
jvm.stop()
print("Error is",tevlmt.error_rate)
tcm2e = tevlmt.confusion_matrix
tcm2E = pd.DataFrame(tcm2e, index = ["neg","pos"],columns = ["neg","pos"])
plt.figure(figsize = (7,7))
axis = sns.heatmap(tcm2E, annot=True, cbar=False, cmap="Reds")
plcls.plot_roc(tevlmt,class_index=[1])
packages.install_package("SMOTE")
smote = Filter(classname="weka.filters.supervised.instance.SMOTE",options=["-P", "4800"])
smt = Classifier(classname="weka.classifiers.trees.LMT")
fc = FilteredClassifier()
fc.filter = smote
fc.classifier = smt
fc.build_classifier(Wtrain)
evsmt = Evaluation(Wtrain)
evsmt.crossvalidate_model(fc, Wtrain, 5, Random(1))
print("Error is",evsmt.error_rate)
cm2f = evsmt.confusion_matrix
cm2F = pd.DataFrame(cm2f, index = ["neg","pos"],columns = ["neg","pos"])
plt.figure(figsize = (7,7))
axis = sns.heatmap(cm2F, annot=True, cbar=False, cmap="Reds")
plcls.plot_roc(evsmt,class_index=[1])
