コード例 #1
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ファイル: HW2.py プロジェクト: parrottsquawk/School
def decisionTree(trainx, trainy,testx,testy):
    treeLearn = mlpy.ClassTree(minsize=2)
    treeLearn.learn(trainx, trainy)
    treeVals = treeLearn.pred(testx)
    treeVals = correctVals(treeVals)

    print("The decision tree accuracy on the test set is: " + str(mlpy.accuracy(treeVals, testy)))
    trainVals = treeLearn.pred(trainx)
    treeVals = correctVals(trainVals)
    print("The decision tree accuracy on the training set is: " + str(mlpy.accuracy(trainVals, trainy)))  
コード例 #2
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ファイル: explore.py プロジェクト: zarch/segments-ml
def test_gamma_C(x_arr, y_arr, itraining, K_chk, y_chk,
                 get_ml, get_kernel=None):
    shp = (len(y_arr), len(x_arr))
    pnl = pd.Panel(data=[np.zeros(shp), np.zeros(shp)],
                   items=['error', 'accuracy'],
                   major_axis=y_arr, minor_axis=x_arr)
    for g in x_arr:
        for c in y_arr:
            print "computing g=%r, c=%r" % (g, c)
            ml = get_ml(g, c, get_kernel)
            y_all = cls(ml, itraining, K_chk, y_chk)
            pnl['error'][g][c] = mlpy.error(y_chk, y_all[y_chk.index])
            pnl['accuracy'][g][c] = mlpy.accuracy(y_chk, y_all[y_chk.index])
    return pnl
コード例 #3
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#ACCURACY measure CLASSIFICATION
import mlpy
t = [3,2,3,3,3,1,1,1]
p = [3,2,1,3,3,2,1,1]
mlpy.error(t, p)
mlpy.accuracy(t, p)


#Sensitivity, Specitivity, AUC
import mlpy
t = [1, 1, 1,-1, 1,-1,-1,-1]
p = [1,-1, 1, 1, 1,-1, 1,-1]
mlpy.error_p(t, p)
mlpy.error_n(t, p)
mlpy.sensitivity(t, p)
mlpy.specificity(t, p)
mlpy.ppv(t, p)
mlpy.npv(t, p)
mlpy.mcc(t, p)
p = [2.3,-0.4, 1.6, 0.6, 3.2,-4.9, 1.3,-0.3]
mlpy.auc_wmw(t, p)
p = [2.3,0.4, 1.6, -0.6, 3.2,-4.9, -1.3,-0.3]
mlpy.auc_wmw(t, p)


#Mean Squared Error REGRESSION
import mlpy
t = [2.4,0.4,1.2,-0.2,3.3,-4.9,-1.1,-0.1]
p = [2.3,0.4,1.6,-0.6,3.2,-4.9,-1.3,-0.3]
mlpy.mse(t, p)
コード例 #4
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        test_y = train_y[:split_index]
        train_X = train_X[split_index:]
        train_y = train_y[split_index:]
        print strftime("%Y-%m-%d %H:%M:%S", gmtime()), ": Parsing complete!\n"
    else:
        print strftime("%Y-%m-%d %H:%M:%S", gmtime()), ": Parsing test data..."
        # Parsing test input data
        data_reader = csv.reader(open(input_test_file, 'rb'), delimiter = ",")
        data_reader.next() # Skip the first line, since it contains the labels
        test_X = []
        for row in data_reader:
            line_x = [1]
            for i in range(len(row)):
                line_x.append(float(row[i]))
            test_X.append(line_x)
        print strftime("%Y-%m-%d %H:%M:%S", gmtime()), ": Finished parsing test data!"
    
    predicted_labels = Predict_svm(train_X, train_y, test_X)
    print strftime("%Y-%m-%d %H:%M:%S", gmtime()), ": Finished predicting!"

    if crossval == True:
        # Only useful for cross-validation, otherwise test_y is unknown and we get error=1/accuracy=0
        print "Error: ", mlpy.error(test_y, predicted_labels)
        print "Accuracy: ", mlpy.accuracy(test_y, predicted_labels)
    else:
        print strftime("%Y-%m-%d %H:%M:%S", gmtime()), ": Writing predicted labels to file..."    
        prediction_filename = "test_" + strftime("%Y-%m-%d_%H-%M-%S", gmtime()) + ".csv"
        prediction_writer = csv.writer(open(prediction_filename, 'wb'))
        for row in predicted_labels:
            list_row = [row]
            prediction_writer.writerow(list_row)
コード例 #5
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ファイル: HW2.py プロジェクト: parrottsquawk/School
def mlpyKNN(k,trainx,trainy,testx,testy,typist):
    mlknn = mlpy.KNN(k)
    mlknn.learn(trainx, trainy)
    knnVals = mlknn.pred(testx)
    print("The mlpy KNN accuracy on the " + typist + " set for k = "+ str(k) +" is: " + str(mlpy.accuracy(knnVals, testy)))
コード例 #6
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ファイル: HW3.py プロジェクト: parrottsquawk/School
    testy = []
    indx = i.pop()
    
    for c in indx[0]:
        trainx.append(x[c].split(","))
        trainy.append(y[c])
        
    for c in indx[1]:
        testx.append(x[c].split(","))
        testy.append(y[c])
    
    
    mlknn = mlpy.KNN(5)
    mlknn.learn(trainx, trainy)
    knnVals = mlknn.pred(testx)
    print("The mlpy KNN accuracy on fold " + str(m) + " set for k = 5 is: " + str(mlpy.accuracy(knnVals, testy)))
    KNNRESULTS.append(mlpy.accuracy(knnVals, testy))
    
    treeLearn = mlpy.ClassTree(minsize=2)
    treeLearn.learn(trainx, trainy)
    treeVals = treeLearn.pred(testx)
    treeVals = correctVals(treeVals)

    print("The Decision Tree accuracy on on fold " + str(m) + " is: "+ str(mlpy.accuracy(treeVals, testy)))
    DTRESULTS.append(mlpy.accuracy(treeVals, testy))
    
    svm = mlpy.LibLinear(solver_type="l2r_lr")
    svm.learn(trainx,trainy)
    svmVals = svm.pred(testx)
    print("The mlpy Logistic Regression accuracy on fold " + str(m) + " is: " + str(mlpy.accuracy(svmVals, testy)))
    LRRESULTS.append(mlpy.accuracy(svmVals, testy))
コード例 #7
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ファイル: ORFpred_GUI.py プロジェクト: vivekmpl/ORFpred
 def make_model(self):
     if self.model_type not in [
             "Combined", "comb", "Comb", "overall", "Overall", "o", "O"
     ]:
         training_fet = np.array([
             select_fet2(i[0], i[1], self.model_type)
             for i in self.datasets.training()
         ])
         testing_fet = np.array([
             select_fet2(i[0], i[1], self.model_type)
             for i in self.datasets.testing()
         ])
         self.logit1.learn(training_fet[:, :-1], training_fet[:, -1])
         test_pred = [self.logit1.pred(i[:-1]) for i in testing_fet]
         test_vals = [i[-1] for i in testing_fet]
         cds_test_pred = [
             self.logit1.pred(i[:-1]) for i in testing_fet if i[-1] == '1'
         ]
         cds_test_vals = [i[-1] for i in testing_fet if i[-1] == '1']
         ncds_test_pred = [
             self.logit1.pred(i[:-1]) for i in testing_fet if i[-1] == '0'
         ]
         ncds_test_vals = [i[-1] for i in testing_fet if i[-1] == '0']
         print "=-" * 5 + "Accuracies" + "=-" * 5
         print "Overall accuracy of predictor : %s" % mlpy.accuracy(
             test_vals, test_pred)
         print "Accuracy of predicting CDS correctly : %s" % mlpy.accuracy(
             cds_test_vals, cds_test_pred)
         print "Accuracy of predicting NCDS correctly : %s" % mlpy.accuracy(
             ncds_test_vals, ncds_test_pred)
     else:
         training_fet_1 = np.array([
             select_fet2(i[0], i[1], "u") for i in self.datasets.training()
         ])
         testing_fet_1 = np.array([
             select_fet2(i[0], i[1], "u") for i in self.datasets.testing()
         ])
         self.logit1.learn(training_fet_1[:, :-1], training_fet_1[:, -1])
         training_fet_2 = np.array([
             select_fet2(i[0], i[1], "c") for i in self.datasets.training()
         ])
         testing_fet_2 = np.array([
             select_fet2(i[0], i[1], "c") for i in self.datasets.testing()
         ])
         self.logit2.learn(training_fet_2[:, :-1], training_fet_2[:, -1])
         k = len(training_fet_1)
         j = len(testing_fet_1)
         comb_training = [[
             self.logit1.pred_probability(training_fet_1[i][:-1])[1],
             self.logit2.pred_probability(training_fet_2[i][:-1])[1],
             training_fet_1[i][-1]
         ] for i in range(k)]  # training file
         comb_training = np.array(comb_training, dtype=float)
         self.logit3.learn(comb_training[:, :-1], comb_training[:, -1])
         comb_testing = [[
             self.logit1.pred_probability(testing_fet_1[i][:-1])[1],
             self.logit2.pred_probability(testing_fet_2[i][:-1])[1],
             testing_fet_1[i][-1]
         ] for i in range(j)]  # testing file
         comb_testing = np.array(comb_testing, dtype=float)
         test_pred = [self.logit3.pred(i[:-1]) for i in comb_testing]
         test_vals = [i[-1] for i in comb_testing]
         cds_test_pred = [
             self.logit3.pred(i[:-1]) for i in comb_testing if i[-1] == 1
         ]
         cds_test_vals = [i[-1] for i in comb_testing if i[-1] == 1]
         ncds_test_pred = [
             self.logit3.pred(i[:-1]) for i in comb_testing if i[-1] == 0
         ]
         ncds_test_vals = [i[-1] for i in comb_testing if i[-1] == 0]
         print "=-" * 5 + "Accuracies" + "=-" * 5
         print "Overall accuracy of predictor : %s" % mlpy.accuracy(
             test_vals, test_pred)
         print "Accuracy of predicting CDS correctly : %s" % mlpy.accuracy(
             cds_test_vals, cds_test_pred)
         print "Accuracy of predicting NCDS correctly : %s" % mlpy.accuracy(
             ncds_test_vals, ncds_test_pred)