def main() -> int:
    """Train each classifier using all the training data and run on test data."""
    y_train, x_train = libsvm_commonutil.svm_read_problem(TRAIN_DATA_PATH)
    y_test, x_test = libsvm_commonutil.svm_read_problem(TEST_DATA_PATH)

    trained_linear_kernel = libsvm_svmutil.svm_train(y_train, x_train, "-t 0")
    _, (train_linear_accuracy, _,
        _), _ = libsvm_svmutil.svm_predict(y_train, x_train,
                                           trained_linear_kernel)
    _, (test_linear_accuracy, _,
        _), _ = libsvm_svmutil.svm_predict(y_test, x_test,
                                           trained_linear_kernel)

    trained_polynomial_kernel = libsvm_svmutil.svm_train(
        y_train, x_train, "-t 1 -d 5")
    _, (train_polynomial_accuracy, _,
        _), _ = libsvm_svmutil.svm_predict(y_train, x_train,
                                           trained_polynomial_kernel)
    _, (test_polynomial_accuracy, _,
        _), _ = libsvm_svmutil.svm_predict(y_test, x_test,
                                           trained_polynomial_kernel)

    print(f"Linear Training Accuracy: {train_linear_accuracy}%")
    print(f"Linear Test Accuracy: {test_linear_accuracy}%")
    print(f"Polynomial Training Accuracy: {train_polynomial_accuracy}%")
    print(f"Polynomial Test Accuracy: {test_polynomial_accuracy}%")

    return EXIT_CODE_SUCCESS
Exemple #2
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def TrainSvmLinear(Y, X, sweep_c=range(-2,8)):
    num_positives = float(Y.count(1))
    num_negatives = float(Y.count(-1))

    best_c = -1
    best_acc = -1
    for c_pow in sweep_c:
        current_c = np.power(2.0,c_pow)
        prob = svm.svm_problem(Y,X)
        param = svm.svm_parameter('-v 5 -t 0 -c %f -w-1 %f -w1 %f -q' % (current_c,
                                                                         100/num_negatives,
                                                                         100/num_positives))
        current_acc = svm.svm_train(prob, param)
        print '%f, %f' % (current_c, current_acc)
        if best_acc < current_acc:
            best_acc = current_acc
            best_c = current_c

        # recompute accuracy
        param = svm.svm_parameter('-t 0 -c %f -w-1 %f -w1 %f -q' % (best_c,
                                                                    100/num_negatives,
                                                                    100/num_positives))
        svm_model = svm.svm_train(prob, param)
        p_labs, p_acc, p_vals = svm.svm_predict(Y, X, svm_model, '-q')


    prob = svm.svm_problem(Y,X)
    param = svm.svm_parameter('-t 0 -c %f -w-1 %f -w1 %f -q' % (best_c,
                                                                100/num_negatives,
                                                                100/num_positives))
    svm_model = svm.svm_train(prob, param)
    p_labs, p_acc, p_vals = svm.svm_predict(Y, X, svm_model, '-q')
    pdb.set_trace()
    return svm_model
def part_d():
    print("\n--- Part D ---\n")

    print("Reading Data")
    train_y, train_x = read_data("train")
    test_y, test_x = read_data("test")

    print("Normalizing")
    train_x = normalize(train_x)
    test_x = normalize(test_x)

    problem = svm_problem(train_y, train_x)
    params = "-q -s 0 -t 2 -g 0.05"

    results = []
    for c in [10**-5, 10**-3, 1, 5, 10]:

        c = " -c %f " % c
        print("10-fold CV using" + c)
        cv_acc = svm_train(problem, params + c + "-v 10")

        print("On test data using" + c)
        model = svm_train(problem, params + c)
        _, test_acc, _ = svm_predict(test_y, test_x, model)
        print("C, Accuracy: ", c, cv_acc, test_acc)

        results.append((c, cv_acc, test_acc[0]))
def part_c():
    print("\n--- Part C ---\n")

    print("Reading Data")
    train_y, train_x = read_data("train")
    test_y, test_x = read_data("test")

    print("Normalizing")
    train_x = normalize(train_x).tolist()
    test_x = normalize(test_x).tolist()

    problem = svm_problem(train_y, train_x)
    params = svm_parameter("-q -s 0 -c 1")

    # Timing calculations
    print("Training SVM (linear kernel)")
    params.parse_options("-t 0")
    model = svm_train(problem, params)

    _, p_acc, _ = svm_predict(test_y, test_x, model)
    print("Accuracy: ", p_acc)

    print("Training SVM (gaussian kernel)")
    params.parse_options("-t 2 -g 0.05")
    model = svm_train(problem, params)

    _, p_acc, _ = svm_predict(test_y, test_x, model)
    print("Accuracy: ", p_acc)
Exemple #5
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def calculate_race():
    correct = 0
    answers = []
    input = []
    count = 0
    for d in data:
        answers.append(question2b_race_truth.truth[count])
        input.append(d)
        if count == 49:
            break
        count += 1

    prob = svmutil.svm_problem(answers, input)
    param = svmutil.svm_parameter('-t 2 -c 4')
    param.cross_validation = 1
    param.nr_fold = 10
    cv = svmutil.svm_train(prob, param)

    param = svmutil.svm_parameter('-t 2 -c 4')
    m = svmutil.svm_train(prob, param)
    count = 0
    for d in data:
        if count < 50:
            count += 1
            continue
        else:
            x0, max_idx = gen_svm_nodearray(d)
            p = libsvm.svm_predict(m, x0)
            if p == question2b_race_truth.truth[count]:
                correct += 1
            count += 1
    return cv, correct / float(50) * 100
def main() -> int:
    """Use 10-fold cross-validation to predict test accuracy for each kernel."""
    y, x = libsvm_commonutil.svm_read_problem(TRAIN_DATA_PATH)
    ten_fold_cross_validator = KFold(n_splits=10, shuffle=True)

    linear_accuracies = []
    polynomial_accuracies = []

    for train_index, test_index in ten_fold_cross_validator.split(x):
        x_test = np.array(x)[test_index]
        x_train = np.array(x)[train_index]
        y_test = np.array(y)[test_index]
        y_train = np.array(y)[train_index]

        # For reference on options used in training, refer to: https://www.csie.ntu.edu.tw/~cjlin/libsvm/

        trained_linear_kernel = libsvm_svmutil.svm_train(y_train, x_train, "-t 0")
        _, (linear_accuracy, _, _), _ = libsvm_svmutil.svm_predict(y_test, x_test, trained_linear_kernel)
        linear_accuracies.append(linear_accuracy)

        trained_polynomial_kernel = libsvm_svmutil.svm_train(y_train, x_train, "-t 1 -d 5")
        _, (polynomial_accuracy, _, _), _ = libsvm_svmutil.svm_predict(y_test, x_test, trained_polynomial_kernel)
        polynomial_accuracies.append(polynomial_accuracy)

    print(f"Linear Accuracies: {linear_accuracies}")
    print(f"Avg. Linear Accuracy: {sum(linear_accuracies) / len(linear_accuracies)}")

    print(f"Polynomial Accuracies: {polynomial_accuracies}")
    print(f"Avg. Polynomial Accuracy: {sum(polynomial_accuracies) / len(polynomial_accuracies)}")

    return EXIT_CODE_SUCCESS
Exemple #7
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def TrainSvmRbf(Y, X, sweep_c=range(-5,5), sweep_g=range(-5,5)):
    num_negatives = float(Y.count(-1))
    num_positives = float(Y.count(1))

    best_c = -1
    best_g = -1
    best_acc = -1
    for c_pow in sweep_c:
        for g_pow in sweep_g:
            current_c = np.power(2.0,c_pow)
            current_g = np.power(2.0,g_pow)
            prob = svm.svm_problem(Y,X)
            param = svm.svm_parameter('-v 5 -t 2 -c %f -g %f -w-1 %f -w1 %f -q' % (current_c,
                                                                                   current_g,
                                                                                   100/num_negatives,
                                                                                   100/num_positives))
            current_acc = svm.svm_train(prob, param)
            print 'c = %f, g = %f, cv acc = %f' % (current_c, current_g, current_acc)
            if best_acc < current_acc:
                best_acc = current_acc
                best_c = current_c
                best_g = current_g

    prob = svm.svm_problem(Y,X)
    param = svm.svm_parameter('-t 2 -c %f -g %f -w-1 %f -w1 %f -q' % (best_c, best_g,
                                                                      100/num_negatives,
                                                                      100/num_positives))
    svm_model = svm.svm_train(prob, param)
    p_labs, p_acc, p_vals = svm.svm_predict(Y, X, svm_model, '-q')
    pdb.set_trace()
    return svm_model
Exemple #8
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def main():
    """Build representation from files."""
    parser = argparse.ArgumentParser()
    parser.add_argument("-r",
                        "--regen",
                        help="increase output verbosity",
                        action="store_true")
    args = parser.parse_args()

    if args.regen:
        train_class, train_frames = read_dir('dataset/train/*.txt', ' ')
        test_class, test_frames = read_dir('dataset/test/*.txt', ' ')

        rad_d1 = [get_rad(action) for action in train_frames]
        rad_d1_t = [get_rad(action) for action in test_frames]

        cust_d1 = [get_custom(action) for action in train_frames]
        cust_d1_t = [get_custom(action) for action in test_frames]

        with open('rad_d1', 'w') as f:
            f.writelines([' '.join(line) + '\n' for line in rad_d1])
        with open('rad_d1.t', 'w') as f:
            f.writelines([' '.join(line) + '\n' for line in rad_d1_t])

        with open('cust_d1', 'w') as f:
            f.writelines([' '.join(line) + '\n' for line in cust_d1])
        with open('cust_d1.t', 'w') as f:
            f.writelines([' '.join(line) + '\n' for line in cust_d1_t])

        rad_d2 = reformat(rad_d1, train_class)
        rad_d2_t = reformat(rad_d1_t, test_class)

        cust_d2 = reformat(cust_d1, train_class)
        cust_d2_t = reformat(cust_d1_t, test_class)

        with open('rad_d2', 'w') as f:
            f.writelines([' '.join(line) + '\n' for line in rad_d2])
        with open('rad_d2.t', 'w') as f:
            f.writelines([' '.join(line) + '\n' for line in rad_d2_t])

        with open('cust_d2', 'w') as f:
            f.writelines([' '.join(line) + '\n' for line in cust_d2])
        with open('cust_d2.t', 'w') as f:
            f.writelines([' '.join(line) + '\n' for line in cust_d2_t])

    # Train the models and test with them
    y, x = svm.svm_read_problem('rad_d2')
    y_t, x_t = svm.svm_read_problem('rad_d2.t')
    rad_model = svm.svm_train(y, x, '-s 0 -t 2 -c 2 -g 0.0005')
    rad_labels, (rad_acc, *_), _ = svm.svm_predict(y_t, x_t, rad_model)

    y, x = svm.svm_read_problem('cust_d2')
    y_t, x_t = svm.svm_read_problem('cust_d2.t')
    cust_model = svm.svm_train(y, x, '-s 0 -t 2 -c 8 -g 0.0005')
    cust_labels, (cust_acc, *_), _ = svm.svm_predict(y_t, x_t, cust_model)

    print(f'RAD accuracy: {rad_acc}')
    print(f'Custom accuracy: {cust_acc}')
Exemple #9
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 def train(self):
     for i in range(4):
         self.convert()
         #rbf
         param1 = svmutil.svm_parameter("-t 2 -b 1 -c 1 -g 0.001")
         param2 = svmutil.svm_parameter("-t 2 -b 1 -c 0.1 -g 0.001")
         self.mr.append(svmutil.svm_train(self.problem[0], param1))#hist
         self.mr.append(svmutil.svm_train(self.problem[1], param2))#vector
         #linear
         param3 = svmutil.svm_parameter("-t 0 -b 1 -c 0.1")
         param4 = svmutil.svm_parameter("-t 0 -b 1 -c 0.01")
         self.ml.append(svmutil.svm_train(self.problem[0], param3))#hist
         self.ml.append(svmutil.svm_train(self.problem[1], param4))#vector
         self.images = self.images[1:]+self.images[:1]
Exemple #10
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	def valid(self,datasets,opt,opp,method = fold,part_ids = None,seed = None,test_data = None):
		if seed is None:
			# If seed is not set. UNIX time is used as seed.
			seed = time.time()
		saving_seed = "%s/log/%s.log.seed" % (self._dir,self._name)
		with open(saving_seed,"w") as fp:
			# Save used seed value.
			fp.write("seed:%f\n" % seed)
		
		if part_ids is None:
			part_ids = datasets.pids
		groups = [(test,train) for test,train in method(part_ids,seed = seed)]
		
		for cnt,pdtsts in enumerate(groups):
			# cnt is number of cluster.
			if test_data is None:
				test = False
				ltest,dtest,itest = test2svm_prob(datasets.mkTest(pdtsts[0]))
			else:
				test = True
				ltest,dtest,itest = test2svm_prob(test_data.mkTest(test_data.pids))

			print "start %s validation" % (cnt)
			ptrn,itrain = train2svm_prob(datasets.mkTrain(pdtsts[1]))
			#opt = svm.svm_parameter(opt)
			model = svmutil.svm_train(ptrn,opt)
			
			plbl,pacc,pval = svmutil.svm_predict(ltest,dtest,model,opp)

			# create saving direcotry
			#self._mkdir(cnt)
			# create log files
			self._save_log(itest,plbl,pval,cnt,test)
			model_name = "%s/model/%s.model.%s" % (self._dir,self._name,cnt)
Exemple #11
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def problem14(X_train, y_train):
    label = list(2 * (np.array(y_train) == 0) - 1)
    train = np.array(pd.DataFrame(X_train))
    for t in range(-3, 2):
        c = 10**t
        model = svmutil.svm_train(label, X_train, '-g 80 -c %s' % c)
        sv_indices = model.get_sv_indices()
Exemple #12
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 def train_list(self):
     print('Start to train.')
     paras = '-c 4 -t 0 -h 0 -m 1024'
     self.y, self.x = self.load_data_list(train=True)
     self.model = svmutil.svm_train(self.y, self.x, paras)
     svmutil.svm_save_model('./news/svmmodel', self.model)
     print('Train finished.')
Exemple #13
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    def test_convert_svmc_linear_raw_multi(self):
        iris = load_iris()

        X = iris.data[:, :2]
        y = iris.target
        y[-5:] = 3

        prob = svmutil.svm_problem(y, X.tolist())

        param = svmutil.svm_parameter()
        param.svm_type = SVC
        param.kernel_type = svmutil.LINEAR
        param.eps = 1
        param.probability = 0
        if noprint:
            param.print_func = noprint

        libsvm_model = svmutil.svm_train(prob, param)

        node = convert(libsvm_model, "LibSvmNuSvmcMultiRaw",
                       [('input', FloatTensorType(shape=['None', 2]))])
        self.assertTrue(node is not None)
        X2 = numpy.vstack([X[:2], X[60:62], X[110:112],
                           X[147:149]])  # 5x0, 5x1
        dump_data_and_model(
            X2.astype(numpy.float32),
            SkAPICl(libsvm_model),
            node,
            basename="LibSvmSvmcRaw-Dec3",
            verbose=False,
            allow_failure=
            "StrictVersion(onnxruntime.__version__) <= StrictVersion('0.1.3')")
Exemple #14
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    def test_convert_nusvmc(self):
        iris = load_iris()

        X = iris.data[:, :2]
        y = iris.target
        y[y == 2] = 1

        prob = svmutil.svm_problem(y, X.tolist())

        param = svmutil.svm_parameter()
        param.svm_type = NuSVC
        param.kernel_type = svmutil.RBF
        param.eps = 1
        param.probability = 1
        if noprint:
            param.print_func = noprint

        libsvm_model = svmutil.svm_train(prob, param)

        node = convert(libsvm_model, "LibSvmNuSvmc",
                       [('input', FloatTensorType(shape=['None', 'None']))])
        self.assertTrue(node is not None)
        dump_data_and_model(
            X[:5].astype(numpy.float32),
            SkAPIClProba2(libsvm_model),
            node,
            basename="LibSvmNuSvmc-Dec2",
            allow_failure=
            "StrictVersion(onnxruntime.__version__) <= StrictVersion('0.1.3')")
Exemple #15
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def kfold(data, labels, k):
	try:
		import svmutil
	except:
		return 0
	prabs = []

	for xxx in range(0, 10):
		picks = np.random.choice(len(data), len(data) / k, replace=False)
		testLabel = labels[picks]
		testPoint = data[picks]
		trainPoint = data[np.setdiff1d(range(0, len(data)), picks)]
		trainLabel = labels[np.setdiff1d(range(0, len(data)), picks)]

		trainLabel = trainLabel.tolist()
		trainPoint = trainPoint.tolist()

		prob = svmutil.svm_problem(trainLabel, trainPoint)
		param = svmutil.svm_parameter('-t 3 -c 4 -b 1 -q')
		testLabel = testLabel.tolist()
		testPoint = testPoint.tolist()

		m = svmutil.svm_train(prob, param)
		svmutil.svm_save_model('n.model', m)

		p_label, p_acc, p_val = svmutil.svm_predict(testLabel, testPoint, m, '-b 1')

		prabs.append(p_acc[0])

	print sum(prabs) / float(len(prabs))
	print 'std' + str(np.std(prabs))
	return sum(prabs) / float(len(prabs))
Exemple #16
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def svm_predizer(X, Y, X_teste, Y_teste, C, G, K):
    """
    Retorna o melhor valor para os parâmetros custo e gamma do SVM radial.
    
    Parâmetros
    ----------
    X : matriz com os dados de treinamento
    
    y : vetor com classes de cada dados de treinamento
    
    X_teste : amostra
    
    Y_teste : classe da amostra
    
    C : lista com valores para custo
    
    G : lista com valores para gamma
    
    K : inteiro indicando o kernel a ser usado
    
    Retorno
    -------
    resultado : classificação do SVM.
    
    """
    model = svm_train(Y, X, '-c %f -t %d -g %f -q' % (C, K, G))
    resultado = svm_predict(Y_teste, X_teste, model)
    return resultado
Exemple #17
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def TrainSvmLinear2(Y, X, sweep_c=range(-2,18)):
    num_positives = float(Y.count(1))
    num_negatives = float(Y.count(-1))

    best_c = -1
    best_acc = -1
    for c_pow in sweep_c:
        current_c = np.power(2.0,c_pow)
        param = svm.svm_parameter('-t 0 -c %f -w-1 %f -w1 %f -q' % (current_c,
                                                                    100/num_negatives,
                                                                    100/num_positives))
        current_pos_acc, current_neg_acc = CrossValidate(Y, X, param)
        current_acc = current_pos_acc
        print '%f, %f, %f' % (current_c, current_acc, current_neg_acc)
        if best_acc < current_acc:
            best_acc = current_acc
            best_c = current_c

    prob = svm.svm_problem(Y,X)
    param = svm.svm_parameter('-t 0 -c %f -w-1 %f -w1 %f -q' % (best_c,
                                                                100/num_negatives,
                                                                100/num_positives))
    svm_model = svm.svm_train(prob, param)
    p_labs, p_acc, p_vals = svm.svm_predict(Y, X, svm_model, '-q')
    return svm_model
Exemple #18
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    def test_default_names(self):
        df = pd.DataFrame({'input': self.x})

        # Test with probabilities
        spec = libsvm.convert(self.libsvm_model).get_spec()
        (_, _, probability_lists) = svm_predict(self.y, self.x,
                                                self.libsvm_model, '-b 1 -q')
        probability_dicts = [
            dict(zip([1, 2], cur_vals)) for cur_vals in probability_lists
        ]
        df['classProbability'] = probability_dicts
        metrics = evaluate_classifier_with_probabilities(
            spec, df, verbose=False, probabilities='classProbability')
        self.assertLess(metrics['max_probability_error'], 0.00001)

        # Test model without probabilities
        no_probability_model = svmutil.svm_train(self.prob,
                                                 svmutil.svm_parameter())
        spec = libsvm.convert(no_probability_model).get_spec()
        self.assertEqual(len(spec.description.output), 1)
        self.assertEqual(spec.description.output[0].name, u'target')
        (df['prediction'], _, _) = svm_predict(self.y, self.x,
                                               no_probability_model, ' -q')
        metrics = evaluate_classifier(spec, df, verbose=False)
        self.assertEquals(metrics['num_errors'], 0)
Exemple #19
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    def _evaluation_test_helper_with_probability(self, labels, allow_slow):
        import copy
        df = pd.DataFrame(self.x, columns=self.column_names)
        y = copy.copy(self.y)
        for i, val in enumerate(labels):
            y[i] = val
        probability_param = '-b 1'

        for param1 in self.non_kernel_parameters:
            for param2 in self.kernel_parameters:
                param_str = ' '.join([self.base_param, param1, param2, probability_param])
                # print("PARAMS: ", param_str)
                param = svm_parameter(param_str)

                model = svm_train(self.prob, param)

                # Get predictions with probabilities as dictionaries
                (df['prediction'], _, probability_lists) = svm_predict(y, self.x, model, probability_param + ' -q')
                probability_dicts = [dict(zip([1, 2], cur_vals)) for cur_vals in probability_lists]
                df['probabilities'] = probability_dicts

                spec = libsvm.convert(model, self.column_names, 'target', 'probabilities')

                if macos_version() >= (10, 13):
                    metrics = evaluate_classifier_with_probabilities(spec, df, verbose=False)
                    self.assertEquals(metrics['num_key_mismatch'], 0)
                    self.assertLess(metrics['max_probability_error'], 0.00001)

                if not allow_slow:
                    break

            if not allow_slow:
                break
def printSvmValidationAccuracy(input, output):
	
	prob = svmutil.svm_problem(output, input)
	param = getSvmParam(True)
	
	accuracy = svmutil.svm_train(prob, param)
	return accuracy
Exemple #21
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 def train(self,x,y):
     """
     training using y=list,x=dict
     parameter = string of parameters
     """
     prob=su.svm_problem(y,x)
     para=""
     para+= "-s %d -t %d -d %d -g %f -r %f -c %f -n %f -p %f -e %f -b %d" %\
         (
             self.type,
             self.kernel,
             self.degree,
             self.gamma,
             self.coef0,
             self.c,
             self.nu,
             self.p,
             self.eps,
             self.prob
         )
     if(self.v!=0):
         para+=" -v %d" % self.v
     if(self.q!=0):
         para+= " -q"
     print para
     para1=su.svm_parameter(para)
     self.model=su.svm_train(prob,para1)
     return True
Exemple #22
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    def test_multi_class_without_probability(self):
        # Generate some random data.
        # This unit test should not rely on scikit learn for test data.
        x, y = [], []
        for _ in range(50):
            x.append([
                random.gauss(200, 30),
                random.gauss(-100, 22),
                random.gauss(100, 42)
            ])
            y.append(random.choice([1, 2, 10, 12]))
        y[0], y[1], y[2], y[3] = 1, 2, 10, 12
        column_names = ['x1', 'x2', 'x3']
        prob = svmutil.svm_problem(y, x)

        df = pd.DataFrame(x, columns=column_names)

        for param1 in self.non_kernel_parameters:
            for param2 in self.kernel_parameters:
                param_str = ' '.join([self.base_param, param1, param2])
                param = svm_parameter(param_str)

                model = svm_train(prob, param)

                # Get predictions with probabilities as dictionaries
                (df['prediction'], _, _) = svm_predict(y, x, model, ' -q')

                spec = libsvm.convert(model, column_names, 'target')

                metrics = evaluate_classifier(spec, df, verbose=False)
                self.assertEquals(metrics['num_errors'], 0)
def train_test_model(train_datafile, test_datafile):
    from svmutil import svm_read_problem, svm_train, svm_predict
    y,x = svm_read_problem(train_datafile)
    m = svm_train(y,x,'-t 0 -e .01 -m 1000 -h 0')
    y_test,x_test = svm_read_problem(test_datafile)
    p_labs,p_acc,p_vals = svm_predict(y_test,x_test,m)
    return p_labs, p_acc, p_vals
Exemple #24
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    def setUpClass(self):
        """
        Set up the unit test by loading the dataset and training a model.
        """
        if not HAS_SKLEARN:
            return
        if not HAS_LIBSVM:
            return

        scikit_data = load_boston()
        prob = svmutil.svm_problem(
            scikit_data['target'] > scikit_data['target'].mean(),
            scikit_data['data'].tolist())
        param = svmutil.svm_parameter()
        param.svm_type = svmutil.C_SVC
        param.kernel_type = svmutil.LINEAR
        param.eps = 1

        libsvm_model = svmutil.svm_train(prob, param)
        libsvm_spec = libsvm_converter.convert(libsvm_model,
                                               scikit_data.feature_names,
                                               'target').get_spec()

        # Save the data and the model
        self.scikit_data = scikit_data
        self.libsvm_spec = libsvm_spec
Exemple #25
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def iqr_model_train(matrix_kernel_train, labels_train, idx2clipid,
                    svm_para = '-w1 50 -t 4 -b 1 -c 1'):
    """
    Light-weighted SVM learning module for online IQR

    @param matrix_kernel_train: n-by-n square numpy array with kernel values
        between training data
    @param labels_train: row-wise labels of training data (1 or True indicates
        positive, 0 or False otherwise
    @param idx2clipid: idx2clipid(row_idx) returns the clipid for the 0-base row
        in matrix
    @param svm_para: (optional) SVM learning parameter

    @rtype: dictionary with 'clipids_SV': list of clipids for support vectors
    @return: output as a dictionary with 'clipids_SV'

    """
    log = logging.getLogger('iqr_model_train')

    # set training inputs
    matrix_kernel_train = np.vstack((np.arange(1, len(matrix_kernel_train)+1),
                                     matrix_kernel_train)).T
    log.debug("Done matrix_kernel_train")

    problem = svm.svm_problem(labels_train.tolist(), matrix_kernel_train.tolist(), isKernel=True)
    log.debug("Done problem")
    svm_param = svm.svm_parameter(svm_para)
    log.debug("Done svm_param")

    # train model
    model = svmutil.svm_train(problem, svm_param)
    log.debug("Done train model")

    # release memory
    del problem
    del svm_param
    log.debug("Done release memory")

    # check learning failure
    if model.l == 0:
        raise Exception('svm model learning failure')
    log.debug("Done checking learning failure (no failure)")

    n_SVs = model.l
    clipids_SVs = []
    idxs_train_SVs = svmtools.get_SV_idxs_nonlinear_svm(model)
    for i in range(n_SVs):
        _idx_1base = idxs_train_SVs[i]
        _idx_0base = _idx_1base - 1
        clipids_SVs.append(idx2clipid[_idx_0base])
        model.SV[i][0].value = i+1 # within SVM model, index needs to be 1-base
    log.debug("Done collecting support vector IDs")

    #svmutil.svm_save_model(filepath_model, model)

    output = dict()
    output['model'] = model
    output['clipids_SVs'] = clipids_SVs

    return output
def classify(train_inputs, train_outputs, test_inputs, test_outputs):
    kernel_train, kernel_test = \
        compute_kernel_matrices(train_inputs, test_inputs)
       
    counter = 0
    average_ap = 0
    average_acc = 0
    for label in range(train_outputs.shape[1]):
        n_tot = train_inputs.shape[0]
        n_pos = train_outputs[:, label].sum()
        n_neg = n_tot - n_pos
        w_pos = np.float32(n_tot)/(2*n_pos)
        w_neg = np.float32(n_tot)/(2*n_neg)
        option_string = '-t 4 -q -s 0 -b 1 -c %f -w1 %f -w0 %f' % (100, w_pos, w_neg)

        model = svmutil.svm_train(
            train_outputs[:, label].tolist(),
            kernel_train.tolist(),
            option_string)

        _, accuracy, prob_estimates = svmutil.svm_predict(
            test_outputs[:, label].tolist(),
            kernel_test.tolist(), model, '-b  1')
        ap = compute_aps(np.array(prob_estimates)[:,np.where(np.asarray(model.get_labels())==1)], 
            test_outputs[:,label])
        average_ap += ap
        average_acc += accuracy[0]
        counter += 1

        print 'label = %d, ap = %f, w_neg = %f, w_pos = %f\n' % (
            label, ap, w_neg, w_pos)

    mean_ap = np.float32(average_ap) / train_outputs.shape[1]
    mean_acc = np.float32(average_acc) / train_outputs.shape[1]
    print 'mean_ap = %f, mean_acc = %f\n' % (mean_ap, mean_acc)
Exemple #27
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def main(path, k):
	
	prabs = []
	lns = []
	for kk in range(0,k-1):
		testLabel = []
		trainPoint = []
		trainLabel = []
		testPoint = []
		wcCount = 0
		for u in os.listdir(path): 
			if u[-2:] == 'WC':r
				wcCount += 1
				filePath = path+u
				WC = pickle.load(open(filePath, 'rb'))
				if wcCount % k == 0 + kk:
					testLabel.append(int(u[1]))
					testPoint.append(WC)
					
				else:
					trainLabel.append(int(u[1]))
					trainPoint.append(WC)

		lns.append(len(testLabel))
		prob = svmutil.svm_problem(trainLabel, trainPoint)
		param = svmutil.svm_parameter('-t 0 -c 4 -b 1 -q')


		m = svmutil.svm_train(prob, param)
		svmutil.svm_save_model('n.model', m)
		p_label, p_acc, p_val = svmutil.svm_predict(testLabel, testPoint, m, '-b 1')
		prabs.append(p_acc[0])
Exemple #28
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def CrossValidate(Y, X, param, k_folds=5):
    rand_idx = range(len(Y))
    random.shuffle(rand_idx)
    idx_groups = SplitIntoK(k_folds, rand_idx)
    pos_acc = 0
    neg_acc = 0
    for i in range(k_folds):
        test_idx = idx_groups[i]
        exclude_test = [idx_groups[j] for j in range(len(idx_groups)) if i != j]
        train_idx = list(chain(*exclude_test))

        Y_test = [Y[test_i] for test_i in test_idx]
        X_test = [X[test_i] for test_i in test_idx]        

        Y_train = [Y[train_i] for train_i in train_idx]
        X_train = [X[train_i] for train_i in train_idx]        

        # recompute accuracy
        prob = svm.svm_problem(Y_train,X_train)
        svm_model = svm.svm_train(prob, param)

        p_labs, p_acc, p_vals = svm.svm_predict(Y_test, X_test, svm_model, '-q')

        tps = sum([1 for j in range(len(p_labs)) if (p_labs[j]==1 and Y_test[j]==1)])
        fns = sum([1 for j in range(len(p_labs)) if (p_labs[j]==-1 and Y_test[j]==1)])

        tns = sum([1 for j in range(len(p_labs)) if (p_labs[j]==-1 and Y_test[j]==-1)])
        fps = sum([1 for j in range(len(p_labs)) if (p_labs[j]==1 and Y_test[j]==-1)])

        pos_acc += tps / float(tps + fns)
        neg_acc += tns / float(tns + fps)

    pos_acc = pos_acc / k_folds
    neg_acc = neg_acc / k_folds
    return (pos_acc, neg_acc)
Exemple #29
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    def setUpClass(self):
        """
        Set up the unit test by loading the dataset and training a model.
        """
        if not HAS_LIBSVM:
            # setUpClass is still called even if class is skipped.
            return

        # Generate some random data.
        # This unit test should not rely on scikit learn for test data.
        self.x, self.y = [], []
        random.seed(42)
        for _ in range(50):
            self.x.append([random.gauss(200, 30), random.gauss(-100, 22)])
            self.y.append(random.choice([1, 2]))
        self.y[0] = 1  # Make sure 1 is always the first label it sees
        self.y[1] = 2
        self.column_names = ['x1', 'x2']
        self.prob = svmutil.svm_problem(self.y, self.x)

        param = svmutil.svm_parameter()
        param.svm_type = svmutil.C_SVC
        param.kernel_type = svmutil.LINEAR
        param.eps = 1
        param.probability = 1

        self.libsvm_model = svmutil.svm_train(self.prob, param)
Exemple #30
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    def test_convert_svmc(self):
        iris = load_iris()

        X = iris.data[:, :2]
        y = iris.target
        y[y == 2] = 1

        prob = svmutil.svm_problem(y, X.tolist())

        param = svmutil.svm_parameter()
        param.svm_type = SVC
        param.kernel_type = svmutil.RBF
        param.eps = 1
        param.probability = 1
        if noprint:
            param.print_func = noprint

        libsvm_model = svmutil.svm_train(prob, param)

        node = convert(libsvm_model, "LibSvmSvmc",
                       [('input', FloatTensorType())])
        self.assertTrue(node is not None)
        dump_data_and_model(X[:5].astype(numpy.float32),
                            SkAPIClProba2(libsvm_model),
                            node,
                            basename="LibSvmSvmc-Dec2")
Exemple #31
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def run_cross_validation(dTrain,dTest):
    'Work with Polynomal kernel with cross validation'
    print '--run_cross_validation--'

    print '-- 1 versus 5 with Q = 2 and Cross Validation--'

    Cs = [0.0001,0.001,0.01,0.1,1]
    Ecvs = [[],[],[],[],[]]
    
    print '-- Train and Test --'

    dTrain_shuffle = dTrain
    # Try 100 runs with different partitions
    for j in range(100):
        # roll those dices
        shuffle(dTrain_shuffle)
        # Get data and formated vectors
        dTrain_1vs5 = getDataOneVsOne(dTrain_shuffle,1,5)
        X_train_1vs5,Y_train_1vs5 = get_svm_vector_format(dTrain_1vs5)
        # Try all Cs with cross validation
        for i in range(len(Cs)):
            # Type = Polynomial. Degree = 2. Gamma 1.
            # Coef = 1. C = Cs[i].Cross Validation at 10. be quiet
            options = '-t 1 -d 2 -g 1 -r 1 -c '+str(Cs[i])+ ' -v 10 -q'
            m = svm_train(Y_train_1vs5,X_train_1vs5,options)
            Ecvs[i].append(100 - m)
    # display
    print
    for i in range(len(Ecvs)):
        print 'Ecv = %s \tfor C = %s'%(sum(Ecvs[i])/100.,Cs[i])
    print
Exemple #32
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def train_test():
	train_subdir = "data/train/"
	test_subdir = "data/test/"
	img_kinds = ["happy", "anger", "neutral", "surprise"]
	models = {}
	params = "-t 0 -c 3"
	svm_params = {	"happy": params,
					"anger": params,
					"neutral": params,
					"surprise": params}

	#train the models
	print 'BUILDING TRAIN MODELS'
	for img_kind in img_kinds:
		print "\t" + img_kind
		problem = build_problem(img_kind, train_subdir)
		param = svm.svm_parameter(svm_params[img_kind])
		models[img_kind] = svmutil.svm_train(problem, param)
	print '================================'

	#for each image in test set let's see what is the answe
	total_count = 0
	correct_count = 0
	wrong_count = 0

	print 'TESTING MODELS'
	for img_kind in img_kinds:
		images = glob.glob(test_subdir + "f_" + img_kind + "*.jpg")
		for image in images:
			print "\t" + image
			image_data = cv.LoadImage(image)
			
			# Let's see what are the results from the models
			results = {}
			for kind in img_kinds:
				test_data = get_image_features(image_data, True, kind)
				predict_input_data = []
				predict_input_data.append(test_data)

				# do svm query
				(val, val_2, label) = svmutil.svm_predict([1] ,predict_input_data, models[kind])
				results[kind] = label[0][0]
			
			sorted_results = sorted(results.iteritems(), key=operator.itemgetter(1))
			result = sorted_results[len(sorted_results)-1][0]

			total_count += 1
			if result == img_kind:
				print 'YES :' + result
				correct_count += 1
			else:
				print 'NO  :' + result
				print sorted_results
				wrong_count += 1
			print '-----------------------'
	print '================================'
	print "Total Pictures: " + str(total_count)
	print "Correct: " + str(correct_count)
	print "Wrong: " + str(wrong_count)
	print "Accuracy: " + str(correct_count/float(total_count) * 100)
    def train(cls, featuresets, params="-t 0 -q"):
        """Train a classifier using the given featuresets.

        Args:
            featuresets: List of featuresets.
            params: Parameter string to pass to svmutil.svm_parameter.

        Returns:
            SvmClassifier object.
        """
        all_features = set()
        all_labels = set()
        for featuredict, label in featuresets:
            all_features.update(set(featuredict.keys()))
            all_labels.add(label)
        all_labels = sorted(all_labels)
        all_features = sorted(all_features)
        featureindex = dict(zip(all_features, range(1, len(all_features) + 1)))
        labelindex = dict(zip(all_labels, range(1, len(all_labels) + 1)))
        vectors, labels = cls.featuresets_to_svm(featureindex, labelindex,
                                                 featuresets)
        prob = svmutil.svm_problem(labels, vectors)
        param = svmutil.svm_parameter(params)
        model = svmutil.svm_train(prob, param)
        return cls(featureindex, labelindex, model)
Exemple #34
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 def __init__(self,train_feature_file = TRAIN_FEATURE_FILE):
     if os.path.exists(SAVED_MODEL):
         self.model = svmutil.svm_load_model(SAVED_MODEL)
     else:
         y, x = svmutil.svm_read_problem(train_feature_file)
         self.model = svmutil.svm_train(y, x, '-c 4')
         svmutil.svm_save_model(SAVED_MODEL,self.model)
def run(history, tweek, pref, selections):
    logstr = ''
    #
    w, x, y = history.grab(nsample, ntracks, tweek + 1, pref, 180, 90, 80000)
    x = xfilter(x)
    timer = timer_c()
    lsvm = svmutil.svm_train(y, x, options)
    #svmutil.svm_save_model('benchmark.svm', lsvm)
    #lsvm = svmutil.svm_load_model('benchmark.svm')
    print 'svm trained successfully in %s sec with %d samples.' % (str(
        float('{0:.3f}'.format(timer.lag()))), len(w))
    #
    timer = timer_c()
    w, x, y = history.grab(1, ntracks, tweek, pref, 180, 90, 80000)
    x = xfilter(x)
    p_labels, p_acc, p_vals = svmutil.svm_predict(y, x, lsvm, '')
    print 'svm predicted successfully in %s sec with %d samples.' % (str(
        float('{0:.3f}'.format(timer.lag()))), len(w))
    foo = []
    for i in xrange(len(y)):
        foo.append((p_labels[i], y[i], w[i]))
    foo.sort(key=lambda tup: (-tup[0]))
    selections['code'].append(foo[0][2])
    selections['tweek'].append(tweek)
    return [row[1] for row in foo]
Exemple #36
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def prob20():
    import random
    gamma = [1, 10, 100, 1000, 10000]
    chosen = {1:0, 10:0, 100:0, 1000:0, 10000:0}
    for _ in range(100):
        Eout = []
        for g in gamma:
            trainX, testX, trainy, testy = readdat()
            mul_label_2_bin(trainy, testy, 0)

            trainX = zip(trainX, trainy)
            random.shuffle(trainX)
            trainX, trainy = zip(*trainX)
            valX = trainX[:1000]
            valy = trainy[:1000]
            trainX = trainX[1000:]
            trainy = trainy[1000:]

            m = svmutil.svm_train(trainy, trainX, '-s 0 -t 2 -c 0.1 -g %f -h 0'%(g))
            p_label, p_acc, p_val = svmutil.svm_predict(valy, valX, m)

            Eout.append(100.0 - p_acc[0])
        chosen[gamma[Eout.index(min(Eout))]] += 1
    print "prob20: ",
    for k in chosen.keys():
        print "gamma=%d:%d, " % (k, chosen[k]),
    print ""
Exemple #37
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def prob2_to_4():
    x = np.array([[1, 0], [0, 1], [0, -1], [-1, 0], [0, 2], [0, -2], [-2, 0]])
    y = np.array([-1, -1, -1, 1, 1, 1, 1])

    print "===prob 2==="
    xf = np.fliplr(x.copy())
    print xf * xf - 2 * x + np.array([[3, -3]])

    print "===prob 3==="
    prob = SVM.svm_problem(y.tolist(), x.tolist())
    param = SVM.svm_parameter(
        '-t 1 -c 100 -d 2 -r 1 -g 1')  # very large C for hard margin
    m = SVM.svm_train(prob, param)
    sumA = 0
    poly = [0] * 6  # xx, xy, yy, x, y, 1
    for i in xrange(m.l):
        idx = m.sv_indices[i]
        alphay = m.sv_coef[0][i]
        alpha = abs(m.sv_coef[0][i])
        print "{:d} {:+1.2f}".format(idx, alpha)
        sumA += alpha
        v = x[idx - 1]
        poly[0] += alphay * v[0] * v[0]
        poly[1] += alphay * v[1] * v[0] * 2
        poly[2] += alphay * v[1] * v[1]
        poly[3] += alphay * v[0] * 2
        poly[4] += alphay * v[1] * 2
        # poly[5] += alphay*1 # no need because Sum(alphay) = 0
    poly[5] -= m.rho[0]
    print "Sum of alpha is {:1.3f}\nb = {}".format(sumA, m.rho[0])
    print "{:+2.2f}xx {:+2.2f}xy {:+2.2f}yy {:+2.2f}x {:+2.2f}y {:+2.2f}".format(
        *poly)
Exemple #38
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    def test_convert_svmc_raw(self):
        iris = load_iris()

        X = iris.data[:, :2]
        y = iris.target
        y[y == 2] = 1

        prob = svmutil.svm_problem(y, X.tolist())

        param = svmutil.svm_parameter()
        param.svm_type = SVC
        param.kernel_type = svmutil.RBF
        param.eps = 1
        param.probability = 0
        if noprint:
            param.print_func = noprint

        libsvm_model = svmutil.svm_train(prob, param)

        # known svm runtime dimension error in ONNX Runtime
        node = convert(libsvm_model, "LibSvmSvmcRaw",
                       [('input', FloatTensorType(shape=['None', 'None']))])
        self.assertTrue(node is not None)
        dump_data_and_model(
            X[:5].astype(numpy.float32),
            SkAPICl(libsvm_model),
            node,
            basename="LibSvmSvmcRaw",
            allow_failure=
            "StrictVersion(onnxruntime.__version__) < StrictVersion('0.5.0')")
Exemple #39
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def n_gram_svm(class_size, take_size):
    cost = np.array([2.0, 2.0, 2.0, 2.0, 2.0])
    gamma = np.array([0.0078125, 0.0078125, 0.0078125, 0.0078125, 0.0078125])

    tst = time()

    preset = np.load(get_feature_file('small'))
    vocab_size = int(np.max(preset[:, :-class_size])) + 1
    np.random.shuffle(preset)

    train_set = preset[take_size:]
    train_txt = train_set[:, :-class_size].astype(np.int64)
    train_cls = train_set[:, -class_size:].astype(np.float32)

    train_dict = [{
        gram: 1 if gram in txt else 0
        for gram in np.arange(1, vocab_size)
    } for txt in train_txt]
    train_major: List[Set[int]] = [set() for _ in np.arange(class_size)]

    for i, cls in enumerate(train_cls):
        for k in np.nonzero(np.abs(cls - np.max(cls)) < 1e-4)[0]:
            train_major[k].add(i)

    models = []

    for k in np.arange(class_size):
        problem = svm_problem([
            1 if i in train_major[k] else -1 for i in np.arange(len(train_cls))
        ], train_dict)
        param = svm_parameter('-t 0 -c %f -g %f -b 1 -q' % (cost[k], gamma[k]))
        models.append(svm_train(problem, param))

    train_time = time() - tst
    tst = time()

    test_set = preset[:take_size]
    test_txt = test_set[:, :-5].astype(np.int64)
    test_cls = test_set[:, -5:].astype(np.float32)

    res = np.array([])
    test_dict = [{
        gram: 1 if gram in txt else 0
        for gram in np.arange(1, vocab_size)
    } for txt in test_txt]

    for dic, cls in zip(test_dict, test_cls):
        prob = np.zeros(class_size)

        for k in np.arange(class_size):
            _, _, p = svm_predict([], [dic], models[k], '-b 1 -q')
            prob[k] = p[0][0]

        prob /= np.sum(prob)
        res = np.append(
            res, cls @ prob / (np.linalg.norm(cls) * np.linalg.norm(prob)))

    test_time = time() - tst
    test_acc = np.mean(res)
    return train_time, test_time, test_acc
Exemple #40
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    def test_default_names(self):
        df = pd.DataFrame({"input": self.x})
        df["input"] = df["input"].apply(np.array)

        # Test with probabilities
        spec = libsvm.convert(self.libsvm_model).get_spec()
        if _is_macos() and _macos_version() >= (10, 13):
            (_, _, probability_lists) = svm_predict(self.y, self.x,
                                                    self.libsvm_model,
                                                    "-b 1 -q")
            probability_dicts = [
                dict(zip([1, 2], cur_vals)) for cur_vals in probability_lists
            ]
            df["classProbability"] = probability_dicts
            metrics = evaluate_classifier_with_probabilities(
                spec, df, verbose=False, probabilities="classProbability")
            self.assertLess(metrics["max_probability_error"], 0.00001)

        # Test model without probabilities
        no_probability_model = svmutil.svm_train(self.prob,
                                                 svmutil.svm_parameter())
        spec = libsvm.convert(no_probability_model).get_spec()
        self.assertEqual(len(spec.description.output), 1)
        self.assertEqual(spec.description.output[0].name, u"target")
        if _is_macos() and _macos_version() >= (10, 13):
            (df["prediction"], _, _) = svm_predict(self.y, self.x,
                                                   no_probability_model, " -q")
            metrics = evaluate_classifier(spec, df, verbose=False)
            self.assertEquals(metrics["num_errors"], 0)
Exemple #41
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    def _test_prob_model(self, param1, param2):
        probability_param = '-b 1'
        df = self.df

        param_str = ' '.join(
            [self.base_param, param1, param2, probability_param])
        param = svm_parameter(param_str)

        model = svm_train(self.prob, param)

        # Get predictions with probabilities as dictionaries
        (df['prediction'], _,
         probability_lists) = svm_predict(self.y, self.x, model,
                                          probability_param + ' -q')
        probability_dicts = [
            dict(zip([1, 2], cur_vals)) for cur_vals in probability_lists
        ]
        df['probabilities'] = probability_dicts

        spec = libsvm.convert(model, self.column_names, 'target',
                              'probabilities')

        if macos_version() >= (10, 13):
            metrics = evaluate_classifier_with_probabilities(spec,
                                                             df,
                                                             verbose=False)
            self.assertEquals(metrics['num_key_mismatch'], 0)
            self.assertLess(metrics['max_probability_error'], 0.00001)
Exemple #42
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def Train_SVM_model(PathToFeatureFile):#生成训练模型文件,model.txt
    #print(PathToFeatureFile)
    y,x=svmutil.svm_read_problem(PathToFeatureFile)
    model=svmutil.svm_train(y,x)
    modelFilePath=os.path.join(os.path.split(PathToFeatureFile)[0],f"model_{PathToFeatureFile.split('_',1)[1]}")
    svmutil.svm_save_model(modelFilePath,model)
    print(modelFilePath)
Exemple #43
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def svm_training_function(vltr, vftr, parameters):
    assert isinstance(vltr, list)
    assert isinstance(vftr, list)
    assert isinstance(parameters, str)
    all_parameters = ' -q -s 0 -t 2 ' + parameters
    yellow_err('svm_training_function(): parameters = {0}'.format(all_parameters))
    return svmutil.svm_train(vltr, vftr, all_parameters)
def _lib_train_libsvm(user_tfidf, num_pos, num_neg, ignore):
    sparse_user_tfidf, num_pos, num_neg = _convert_to_sparse_matrix(user_tfidf, num_pos, num_neg, ignore)
    labels = ([1] * num_pos) + ([-1] * num_neg)

    param = svm_parameter("-t %d" % KERNEL_NUMBER)
    prob = svm_problem(labels, sparse_user_tfidf)
    modellog = svm_train(prob, param)
    return modellog
Exemple #45
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def trainSVM(kernel, labels):
    #need to add an id number as the first column of the list
    svmKernel = column_stack((arange(1, len(kernel.tolist()) + 1), kernel))
    prob = svm_problem(labels.tolist(), svmKernel.tolist(), isKernel=True)
    param = svm_parameter('-t 4')   

    model = svm_train(prob, param)
    return model
Exemple #46
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def simulate_skin_with_svm(data_size=None, train_params='-s 0 -t 0'):
    """Simulate learning skin data set with libsvm."""
    convert_skin_to_svm(data_size)

    train_y, train_x = svm.svm_read_problem('skin_train.svm')
    model = svm.svm_train(train_y, train_x, train_params)

    test_y, test_x = svm.svm_read_problem('skin_test.svm')
    p_label, p_acc, p_val = svm.svm_predict(test_y, test_x, model)
def get_cross_val(x, y, x_val, y_val, gamma_c):
    prob  = svmutil.svm_problem(y, x)
    param = svmutil.svm_parameter('-t 2 -q -c {0} -g {1}'.format(gamma_c.C, gamma_c.gamma))
    m = svmutil.svm_train(prob, param)

    svmutil.svm_save_model("model", m)

    p_label_validation, p_acc_validation, p_val_validation = svmutil.svm_predict(y_val, x_val, m)

    return p_acc_validation[0]
Exemple #48
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def trainSVM(trainMatrix, trainCategory):
    svm.svm_model.predict = lambda self, x: svm.svm_predict([0], [x], self)[0][0]

    prob = svm.svm_problem(trainCategory, trainMatrix)
    param = svm.svm_parameter()
    param.kernel_type = svm.LINEAR
    param.C = 10

    model = svm.svm_train(prob, param)
    return model
Exemple #49
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def leave_one_out(y, x, param, n='DUMMY'):
    results = []
    for i, test in enumerate(zip(y, x)):
        training_y = y[:i] + y[i+1:]
        training_x = x[:i] + x[i+1:]
        problem = svm.svm_problem(training_y, training_x)
        model = svmutil.svm_train(problem, param, '-q')
        result = svmutil.svm_predict(y[i:i+1], x[i:i+1], model, '-b 1')
        results.append(result + (test[0], make_d.decode(x[i], make_d.decode_dic)))
    return results
Exemple #50
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def train_and_get(c, lr):
    c.reset_weight()
    c.compile_lr(lr)
    print '..building the cnn model: %r with lr: %r' % (c.nkerns, lr)
    c.fit_lr(n_epochs = 200, slient = True)

    features = test_features.tran2libsvm(c.get_feature(x))
    m = svm_train(y[:l], features[:l], '-q')
    p_label, p_acc, p_val = svm_predict(y[l:], features[l:], m)
    ans = p_acc[0]
    return ans
def getSVMAccuracy(trainingData):
	numOutputs = len(trainingData[0][1])
	for outputIndex in range(numOutputs):
		inputs = [input for (input, output) in trainingData]
		outputs = [output[outputIndex] for (input, output) in trainingData]
		
		prob = svmutil.svm_problem(outputs, inputs)
		param = svmAccuracy.getSvmParam(cross_validation_only = True)
		
		model = svmutil.svm_train(prob, param)
		print 'output index: %d - %s\n' % (outputIndex, {0 : "Index", 1: "Middle"}[outputIndex])
    def _stop_training(self):
        super(LibSVMClassifier, self)._stop_training()
        self.normalizer = _LabelNormalizer(self.labels)
                
        labels = self.normalizer.normalize(self.labels.tolist())
        features = self.data

        # Call svm training method.
        prob = libsvmutil.svm_problem(labels, features.tolist())
        # Train
        self.model = libsvmutil.svm_train(prob, self.parameter)
Exemple #53
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def hiksvm_train(labels, features, beta):
    # calculate class prior
    np = len([1 for lab in labels if  1 == lab])
    nn = len([1 for lab in labels if -1 == lab])
    wp = float(beta)/np
    wn = (1.0-beta)/nn
    wp *= (np+nn)
    wn *= (np+nn)
    parameters = "-s 0 -c 1 -t %d -w-1 %g -w1 %g" % (KERNEL_TYPE.index("HI"), wn, wp)
    model = svm_train(labels, features, parameters)
    return model
def train_test_model(train_datafile, test_datafile):
    """
    :param train_datafile: relative path
    :param test_datafile: relative path
    :return: trains a libsvm model using the training data in train_datafile and test in on the data in test_datafile
    """
    from svmutil import svm_read_problem, svm_predict, svm_train
    y_test, x_test = svm_read_problem(test_datafile)
    y_train, x_train = svm_read_problem(train_datafile)
    model = svm_train(y_train, x_train, '-t 0 -e .01 -m 1000 -h 0')
    p_labs, p_acc, p_vals = svm_predict(y_test, x_test, model)
    return p_labs, p_acc, p_vals
Exemple #55
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def multiclass_train(valid_labels, labels, data, svm_parameters=None):
    if svm_parameters == None:
        # make default empty parameters 
        svm_parameters = []
        for i in valid_labels:
            svm_parameters.append(svmutil.svm_parameter())
    models = []
    for i in valid_labels:
        oaa_labels = relabel_one_against_all(labels, i)
        prob = svmutil.svm_problem(oaa_labels, data)
        model = svmutil.svm_train(prob, svm_parameters[i])
        models.append(model)
    return models