def main():
f = open("me.stdout", "r").read()
print f
(confusionMatrix, labels, ytrue, ypred, trueCount) = readConfusionMatrix.readText(f)
for row in confusionMatrix:
print row
precisionMicro = np.float(metrics.precision_score(ytrue, ypred, average="micro"))
recallMicro = np.float(metrics.recall_score(ytrue, ypred, average="micro"))
f1Micro = np.float(metrics.f1_score(ytrue, ypred, average="micro"))
f1Macro = np.float(metrics.f1_score(ytrue, ypred, pos_label=1, average="macro"))
precisionMacro = np.float(metrics.precision_score(ytrue, ypred, average="macro"))
recallMacro = np.float(metrics.recall_score(ytrue, ypred, average="macro"))
mConf = metrics.confusion_matrix(ytrue, ypred)
print mConf
print labels
print len(ytrue)
print len(ypred)
print trueCount
print metrics.accuracy_score(ytrue, ypred)
print precisionMicro
print recallMicro
print f1Micro
print f1Macro
print precisionMacro
print recallMacro
def readAndUploadClassifierOutput(featureSet, diseaseFolder, disease, connection, source): text = open(diseaseFolder + "/me.stdout", "r").read() (confusionMatrix, confusionLabelDict, y_truth, y_pred, trueCount) = readConfusionMatrix.readText(text) precisionMicro = np.float(metrics.precision_score(y_truth, y_pred, average="micro")) recallMicro = np.float(metrics.recall_score(y_truth, y_pred, average="micro")) f1Micro = np.float(metrics.f1_score(y_truth, y_pred, average="micro")) f1Macro = np.float(metrics.f1_score(y_truth, y_pred, average="macro")) precisionMacro = np.float(metrics.precision_score(y_truth, y_pred, average="macro")) recallMacro = np.float(metrics.recall_score(y_truth, y_pred, average="macro")) accuracy = np.float(metrics.accuracy_score(y_truth, y_pred)) insertResult.insertClassifierResults(connection, (featureSet, "maxent", trueCount, disease, precisionMicro, recallMicro, f1Micro, recallMacro, precisionMacro, f1Macro, source, accuracy))
def readAndUploadClassifierOutput(featureSet, diseaseFolder, disease,
connection, source):
text = open(diseaseFolder + "/me.stdout", "r").read()
(confusionMatrix, confusionLabelDict, y_truth, y_pred,
trueCount) = readConfusionMatrix.readText(text)
precisionMicro = np.float(
metrics.precision_score(y_truth, y_pred, average="micro"))
recallMicro = np.float(
metrics.recall_score(y_truth, y_pred, average="micro"))
f1Micro = np.float(metrics.f1_score(y_truth, y_pred, average="micro"))
f1Macro = np.float(metrics.f1_score(y_truth, y_pred, average="macro"))
precisionMacro = np.float(
metrics.precision_score(y_truth, y_pred, average="macro"))
recallMacro = np.float(
metrics.recall_score(y_truth, y_pred, average="macro"))
accuracy = np.float(metrics.accuracy_score(y_truth, y_pred))
insertResult.insertClassifierResults(
connection,
(featureSet, "maxent", trueCount, disease, precisionMicro, recallMicro,
f1Micro, recallMacro, precisionMacro, f1Macro, source, accuracy))
def main():
f = open("me.stdout", "r").read()
print f
(confusionMatrix, labels, ytrue, ypred,
trueCount) = readConfusionMatrix.readText(f)
for row in confusionMatrix:
print row
precisionMicro = np.float(
metrics.precision_score(ytrue, ypred, average="micro"))
recallMicro = np.float(metrics.recall_score(ytrue, ypred, average="micro"))
f1Micro = np.float(metrics.f1_score(ytrue, ypred, average="micro"))
f1Macro = np.float(
metrics.f1_score(ytrue, ypred, pos_label=1, average="macro"))
precisionMacro = np.float(
metrics.precision_score(ytrue, ypred, average="macro"))
recallMacro = np.float(metrics.recall_score(ytrue, ypred, average="macro"))
mConf = metrics.confusion_matrix(ytrue, ypred)
print mConf
print labels
print len(ytrue)
print len(ypred)
print trueCount
print metrics.accuracy_score(ytrue, ypred)
print precisionMicro
print recallMicro
print f1Micro
print f1Macro
print precisionMacro
print recallMacro