Example #1
0
    def btnConvert_click(self):
        msgBox = QMessageBox()
        tStart = time.time()
        Activation = ui.cbActivation.currentData()
        LossNorm = ui.cbLossNorm.currentData()
        LossType = ui.cbType.currentData()
        try:
            Layers = strRange(ui.txtLayers.text(), Unique=False)
            if Layers is None:
                raise Exception('')

        except:
            msgBox.setText("Layers is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            Alpha = np.float32(ui.txtAlpha.text())
            if Alpha < 0:
                raise Exception
        except:
            msgBox.setText("Alpha is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            Iter = np.int32(ui.txtIter.text())
        except:
            msgBox.setText("Number of iteration is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            BatchSize = np.int32(ui.txtBatch.text())
        except:
            msgBox.setText("Number of batch is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            ReportStep = np.int32(ui.txtReportStep.text())
        except:
            msgBox.setText("Number of Report Step is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            LearningRate = np.float32(ui.txtRate.text())
        except:
            msgBox.setText("Number of Report Step is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        if not ui.cbCov.isChecked() and not ui.cbCorr.isChecked():
            msgBox.setText("At least, you must select one metric!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Filter
        try:
            Filter = ui.txtFilter.text()
            if not len(Filter):
                Filter = None
            else:
                Filter = Filter.replace("\'", " ").replace(",", " ").replace(
                    "[", "").replace("]", "").split()
                Filter = np.int32(Filter)
        except:
            print("Filter is wrong!")
            return

        # OutFile
        OutFile = ui.txtOutFile.text()
        if not len(OutFile):
            msgBox.setText("Please enter out file!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        OutData = dict()

        # InFile
        InFile = ui.txtInFile.text()
        if not len(InFile):
            msgBox.setText("Please enter input file!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        if not os.path.isfile(InFile):
            msgBox.setText("Input file not found!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        print("Loading ...")
        InData = io.loadmat(InFile)

        # Data
        if not len(ui.txtData.currentText()):
            msgBox.setText("Please enter Input Data variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Label
        if not len(ui.txtLabel.currentText()):
            msgBox.setText("Please enter Train Label variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Design
        if not len(ui.txtDesign.currentText()):
            msgBox.setText("Please enter Input Design variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            Design = InData[ui.txtDesign.currentText()]
        except:
            msgBox.setText("Design value is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            X = InData[ui.txtData.currentText()]
            L = InData[ui.txtLabel.currentText()][0]
        except:
            print("Cannot load data or label")
            return

        # Task
        if not len(ui.txtTask.currentText()):
            msgBox.setText("Please enter Task variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        # Task Val
        if not len(ui.txtTaskVal.currentText()):
            msgBox.setText("Please enter Task value!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            TaskIDTitle = ui.txtTaskVal.currentText()
        except:
            msgBox.setText("Task value is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            TaskTitle = InData[ui.txtTask.currentText()][0]
        except:
            msgBox.setText("Task variable name is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        TaskTitleUnique = np.unique(TaskTitle)
        Task = np.zeros(np.shape(TaskTitle))

        for ttinx, tt in enumerate(TaskTitle):
            for ttlinx, ttl in enumerate(TaskTitleUnique):
                if tt[0] == ttl:
                    Task[ttinx] = ttlinx + 1
                    break

        for ttlinx, ttl in enumerate(TaskTitleUnique):
            if TaskIDTitle == ttl:
                TaskID = ttlinx + 1
                break

        OutData["Task"] = TaskIDTitle

        # Subject
        if not len(ui.txtSubject.currentText()):
            msgBox.setText("Please enter Subject variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        # Subject Val
        if not len(ui.txtSubjectVal.currentText()):
            msgBox.setText("Please enter Subject value!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            SubID = np.int32(ui.txtSubjectVal.currentText())
        except:
            msgBox.setText("Subject value is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            Sub = InData[ui.txtSubject.currentText()][0]
        except:
            msgBox.setText("Subject variable name is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        OutData["SubjectID"] = SubID

        # Run
        if not len(ui.txtRun.currentText()):
            msgBox.setText("Please enter Run variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        # Run Val
        if not len(ui.txtRunVal.currentText()):
            msgBox.setText("Please enter Run value!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            RunID = np.int32(ui.txtRunVal.currentText())
        except:
            msgBox.setText("Run value is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            Run = InData[ui.txtRun.currentText()][0]
        except:
            msgBox.setText("Run variable name is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        OutData["RunID"] = RunID

        # Counter
        if not len(ui.txtCounter.currentText()):
            msgBox.setText("Please enter Counter variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        # Counter Val
        if not len(ui.txtCounterVal.currentText()):
            msgBox.setText("Please enter Counter value!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            ConID = np.int32(ui.txtCounterVal.currentText())
        except:
            msgBox.setText("Counter value is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            Con = InData[ui.txtCounter.currentText()][0]
        except:
            msgBox.setText("Counter variable name is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        OutData["CounterID"] = ConID

        if Filter is not None:
            for fil in Filter:
                # Remove Training Set
                labelIndx = np.where(L == fil)[0]
                Design = np.delete(Design, labelIndx, axis=0)
                X = np.delete(X, labelIndx, axis=0)
                L = np.delete(L, labelIndx, axis=0)
                Task = np.delete(Task, labelIndx, axis=0)
                Sub = np.delete(Sub, labelIndx, axis=0)
                Run = np.delete(Run, labelIndx, axis=0)
                Con = np.delete(Con, labelIndx, axis=0)
                print("Class ID = " + str(fil) + " is removed from data.")

        # Select Task
        TaskIndex = np.where(Task == TaskID)
        Design = Design[TaskIndex, :][0]
        X = X[TaskIndex, :][0]
        L = L[TaskIndex]
        Sub = Sub[TaskIndex]
        Run = Run[TaskIndex]
        Con = Con[TaskIndex]
        # Select Subject
        SubIndex = np.where(Sub == SubID)
        Design = Design[SubIndex, :][0]
        X = X[SubIndex, :][0]
        L = L[SubIndex]
        Run = Run[SubIndex]
        Con = Con[SubIndex]
        # Select Counter
        ConIndex = np.where(Con == ConID)
        Design = Design[ConIndex, :][0]
        X = X[ConIndex, :][0]
        L = L[ConIndex]
        Run = Run[ConIndex]
        # Select Run
        RunIndex = np.where(Run == RunID)
        Design = Design[RunIndex, :][0]
        X = X[RunIndex, :][0]
        L = L[RunIndex]  # This will only use in supervised methods
        LUnique = np.unique(L)
        LNum = np.shape(LUnique)[0]
        OutData["Label"] = LUnique
        OutData["ModelAnalysis"] = "Tensorflow.Session.Deep.RSA"

        if np.shape(X)[0] == 0:
            msgBox.setText("The selected data is empty!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        if ui.cbScale.isChecked():
            X = preprocessing.scale(X)
            print("Data is scaled to N(0,1).")
        print("Running Deep RSA ...")
        # RSA Method
        OutData['Method'] = dict()
        OutData['Method']['Layers'] = ui.txtLayers.text()
        OutData['Method']['Alpha'] = Alpha
        OutData['Method']['Activation'] = Activation
        OutData['Method']['LossNorm'] = LossNorm
        OutData['Method']['LearningRate'] = LearningRate
        OutData['Method']['NumIter'] = Iter
        OutData['Method']['BatchSize'] = BatchSize
        OutData['Method']['ReportStep'] = ReportStep
        OutData['Method']['Verbose'] = ui.cbVerbose.isChecked()

        rsa = DeepRSA(layers=Layers, n_iter=Iter, learning_rate=LearningRate,loss_norm=LossNorm,activation=Activation,\
                      batch_size=BatchSize,report_step=ReportStep,verbose=ui.cbVerbose.isChecked(),\
                      CPU=ui.cbDevice.currentData(), alpha=Alpha, loss_type=LossType)
        Betas, Weights, Biases, loss_vec, MSE, Performance = rsa.fit(
            data_vals=X, design_vals=Design)

        OutData["LossVec"] = loss_vec
        OutData["MSE"] = MSE
        OutData["Performance"] = Performance

        print("MSE: %f" % (MSE))

        if ui.cbBeta.isChecked():
            OutData["Betas"] = Betas
            OutData["Weights"] = Weights
            OutData["Biases"] = Biases

        # Calculate Results
        if ui.cbCorr.isChecked():
            print("Calculating Correlation ...")
            Corr = np.corrcoef(Betas)
            corClass = SimilarityMatrixBetweenClass(Corr)
            OutData["Correlation"] = Corr
            OutData["Correlation_min"] = corClass.min()
            OutData["Correlation_max"] = corClass.max()
            OutData["Correlation_std"] = corClass.std()
            OutData["Correlation_mean"] = corClass.mean()
            print("Correlation: min: {:3.10f}, max: {:3.10f}, mean: {:3.10f}, std: {:3.10f}".format(corClass.min(), \
                    corClass.max(), corClass.mean(), corClass.std()))

        if ui.cbCov.isChecked():
            print("Calculating Covariance ...")
            Cov = np.cov(Betas)
            covClass = SimilarityMatrixBetweenClass(Cov)
            OutData["Covariance"] = Cov
            OutData["Covariance_min"] = covClass.min()
            OutData["Covariance_max"] = covClass.max()
            OutData["Covariance_std"] = covClass.std()
            OutData["Covariance_mean"] = covClass.mean()
            print("Covariance: min: {:3.10f}, max: {:3.10f}, mean: {:3.10f}, std: {:3.10f}".format(covClass.min(), \
                    covClass.max(), covClass.mean(), covClass.std()))

        OutData["RunTime"] = time.time() - tStart
        print("Runtime (s): %f" % (OutData["RunTime"]))
        print("Saving results ...")
        io.savemat(OutFile, mdict=OutData, do_compression=True)
        print("Output is saved.")

        if ui.cbDiagram.isChecked():
            if ui.cbCorr.isChecked():
                fig1 = plt.figure(num=None, figsize=(5, 5), dpi=100)
                plt.pcolor(Corr, vmin=-0.1, vmax=1)
                plt.xlim([0, LNum])
                plt.ylim([0, LNum])
                plt.colorbar()
                ax = plt.gca()
                ax.set_aspect(1)
                plt.title(
                    'DeepRSA: Correlation\nTask: %s\nSub: %d, Counter: %d, Run: %d'
                    % (TaskIDTitle, SubID, ConID, RunID))
                plt.show()

            if ui.cbCov.isChecked():
                fig2 = plt.figure(num=None, figsize=(5, 5), dpi=100)
                plt.pcolor(Cov)
                plt.xlim([0, LNum])
                plt.ylim([0, LNum])
                plt.colorbar()
                ax = plt.gca()
                ax.set_aspect(1)
                plt.title(
                    'DeepRSA: Covariance\nTask: %s\nSub: %d, Counter: %d, Run: %d'
                    % (TaskIDTitle, SubID, ConID, RunID))
                plt.show()
        print("DONE.")
        msgBox.setText(
            "Gradient Representational Similarity Analysis (RSA) is done.")
        msgBox.setIcon(QMessageBox.Information)
        msgBox.setStandardButtons(QMessageBox.Ok)
        msgBox.exec_()
Example #2
0
    def btnConvert_click(self):
        msgBox = QMessageBox()
        tStart = time.time()
        if not ui.cbCov.isChecked() and not ui.cbCorr.isChecked():
            msgBox.setText("At least, you must select one metric!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Filter
        try:
            Filter = ui.txtFilter.text()
            if not len(Filter):
                Filter = None
            else:
                Filter = Filter.replace("\'", " ").replace(",", " ").replace(
                    "[", "").replace("]", "").split()
                Filter = np.int32(Filter)
        except:
            print("Filter is wrong!")
            return

        # OutFile
        OutFile = ui.txtOutFile.text()
        if not len(OutFile):
            msgBox.setText("Please enter out file!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        OutData = dict()
        OutData["ModelAnalysis"] = "RSA"

        # InFile
        InFile = ui.txtInFile.text()
        if not len(InFile):
            msgBox.setText("Please enter input file!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        if not os.path.isfile(InFile):
            msgBox.setText("Input file not found!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        print("Loading ...")
        InData = io.loadmat(InFile)

        # Data
        if not len(ui.txtData.currentText()):
            msgBox.setText("Please enter Input Data variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Label
        if not len(ui.txtLabel.currentText()):
            msgBox.setText("Please enter Train Label variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Design
        if not len(ui.txtDesign.currentText()):
            msgBox.setText("Please enter Input Design variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            Design = InData[ui.txtDesign.currentText()]
        except:
            msgBox.setText("Design value is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            X = InData[ui.txtData.currentText()]
            L = InData[ui.txtLabel.currentText()][0]
            if ui.cbScale.isChecked() and not ui.rbScale.isChecked():
                X = preprocessing.scale(X)
                print("Whole of data is scaled X~N(0,1).")

        except:
            print("Cannot load data or label")
            return

        # Task
        if not len(ui.txtTask.currentText()):
            msgBox.setText("Please enter Task variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            TaskTitle = InData[ui.txtTask.currentText()][0]
        except:
            msgBox.setText("Task variable name is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        TaskTitleUnique = np.unique(TaskTitle)
        Task = np.zeros(np.shape(TaskTitle))

        for ttinx, tt in enumerate(TaskTitle):
            for ttlinx, ttl in enumerate(TaskTitleUnique):
                if tt[0] == ttl:
                    Task[ttinx] = ttlinx + 1
                    break

        # Subject
        if not len(ui.txtSubject.currentText()):
            msgBox.setText("Please enter Subject variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            Sub = InData[ui.txtSubject.currentText()][0]
        except:
            msgBox.setText("Subject variable name is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Run
        if not len(ui.txtRun.currentText()):
            msgBox.setText("Please enter Run variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            Run = InData[ui.txtRun.currentText()][0]
        except:
            msgBox.setText("Run variable name is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Counter
        if not len(ui.txtCounter.currentText()):
            msgBox.setText("Please enter Counter variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            Con = InData[ui.txtCounter.currentText()][0]
        except:
            msgBox.setText("Counter variable name is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        if Filter is not None:
            for fil in Filter:
                # Remove Training Set
                labelIndx = np.where(L == fil)[0]
                Design = np.delete(Design, labelIndx, axis=0)
                X = np.delete(X, labelIndx, axis=0)
                L = np.delete(L, labelIndx, axis=0)
                Task = np.delete(Task, labelIndx, axis=0)
                Sub = np.delete(Sub, labelIndx, axis=0)
                Run = np.delete(Run, labelIndx, axis=0)
                Con = np.delete(Con, labelIndx, axis=0)
                print("Class ID = " + str(fil) + " is removed from data.")

        try:
            Unit = np.int32(ui.txtUnit.text())
        except:
            msgBox.setText("Unit for the test set must be a number!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        if Unit < 1:
            msgBox.setText("Unit for the test set must be greater than zero!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        print("Calculating Levels ...")
        GroupFold = None
        FoldStr = ""
        if ui.cbFSubject.isChecked():
            if not ui.rbFRun.isChecked():
                GroupFold = [Sub]
                FoldStr = "Subject"
            else:
                GroupFold = np.concatenate(([Sub], [Run]))
                FoldStr = "Subject+Run"

        if ui.cbFTask.isChecked():
            GroupFold = np.concatenate(
                (GroupFold, [Task])) if GroupFold is not None else [Task]
            FoldStr = FoldStr + "+Task"

        if ui.cbFCounter.isChecked():
            GroupFold = np.concatenate(
                (GroupFold, [Con])) if GroupFold is not None else [Con]
            FoldStr = FoldStr + "+Counter"

        if FoldStr == "":
            FoldStr = "Whole-Data"
            GUFold = [1]
            ListFold = [1]
            UniqFold = [1]
            GroupFold = [1]
            UnitFold = np.ones((1, np.shape(X)[0]))
        else:
            GroupFold = np.transpose(GroupFold)
            UniqFold = np.array(list(set(tuple(i)
                                         for i in GroupFold.tolist())))
            FoldIDs = np.arange(len(UniqFold)) + 1

            if len(UniqFold) <= Unit:
                msgBox.setText(
                    "Unit must be smaller than all possible levels! Number of all levels is: "
                    + str(len(UniqFold)))
                msgBox.setIcon(QMessageBox.Critical)
                msgBox.setStandardButtons(QMessageBox.Ok)
                msgBox.exec_()
                return False

            if np.mod(len(UniqFold), Unit):
                msgBox.setText(
                    "Unit must be divorceable to all possible levels! Number of all levels is: "
                    + str(len(UniqFold)))
                msgBox.setIcon(QMessageBox.Critical)
                msgBox.setStandardButtons(QMessageBox.Ok)
                msgBox.exec_()
                return False
            ListFold = list()
            for gfold in GroupFold:
                for ufoldindx, ufold in enumerate(UniqFold):
                    if (ufold == gfold).all():
                        currentID = FoldIDs[ufoldindx]
                        break
                ListFold.append(currentID)

            ListFold = np.int32(ListFold)
            if Unit == 1:
                UnitFold = np.int32(ListFold)
            else:
                UnitFold = np.int32((ListFold - 0.1) / Unit) + 1

            GUFold = np.unique(UnitFold)

        FoldInfo = dict()
        FoldInfo["Unit"] = Unit
        FoldInfo["Group"] = GroupFold
        FoldInfo["Order"] = FoldStr
        FoldInfo["List"] = ListFold
        FoldInfo["Unique"] = UniqFold
        FoldInfo["Folds"] = UnitFold

        OutData = dict()
        OutData["FoldInfo"] = FoldInfo
        OutData["ModelAnalysis"] = "Numpy.Group.RSA"

        print("Number of all levels is: " + str(len(UniqFold)))

        Cov = None
        Corr = None
        AMSE = list()

        for foldID, fold in enumerate(GUFold):
            print("Analyzing level " + str(foldID + 1), " of ",
                  str(len(UniqFold)), " ...")
            Index = np.where(UnitFold == fold)
            # Whole-Data
            if FoldStr == "Whole-Data" and np.shape(Index)[0]:
                Index = [Index[1]]
            XLi = X[Index]
            if ui.cbScale.isChecked() and ui.rbScale.isChecked():
                XLi = preprocessing.scale(XLi)
                print("Whole of data is scaled X%d~N(0,1)." % (foldID + 1))
            RegLi = np.insert(Design[Index], 0, 1, axis=1)
            BetaLi = np.linalg.lstsq(RegLi, XLi)[0][1:, :]
            print("Calculating MSE for level %d ..." % (foldID + 1))
            MSE = mean_squared_error(XLi, np.matmul(Design[Index], BetaLi))
            print("MSE%d: %f" % (foldID + 1, MSE))
            OutData["MSE" + str(foldID)] = MSE
            AMSE.append(MSE)
            if ui.cbBeta.isChecked():
                OutData["BetaL" + str(foldID + 1)] = BetaLi
            # Calculate Results
            if ui.cbCorr.isChecked():
                print("Calculating Correlation for level %d ..." %
                      (foldID + 1))
                CorrLi = np.corrcoef(BetaLi)
                OutData["Corr" + str(foldID + 1)] = CorrLi
                if Corr is None:
                    Corr = CorrLi.copy()
                else:
                    if ui.rbAvg.isChecked():
                        Corr = np.add(Corr, CorrLi)
                    elif ui.rbMin.isChecked():
                        Corr = np.minimum(Corr, CorrLi)
                    else:
                        Corr = np.maximum(Corr, CorrLi)
            if ui.cbCov.isChecked():
                print("Calculating Covariance for level %d ..." % (foldID + 1))
                CovLi = np.cov(BetaLi)
                OutData["Cov" + str(foldID + 1)] = CovLi
                if Cov is None:
                    Cov = CovLi.copy()
                else:
                    if ui.rbAvg.isChecked():
                        Cov = np.add(Cov, CovLi)
                    elif ui.rbMin.isChecked():
                        Cov = np.minimum(Cov, CovLi)
                    else:
                        Cov = np.maximum(Cov, CovLi)

        CoEff = len(UniqFold) - 1 if len(UniqFold) > 2 else 1
        if ui.cbCov.isChecked():
            if ui.rbAvg.isChecked():
                Cov = Cov / CoEff
            covClass = SimilarityMatrixBetweenClass(Cov)
            OutData["Covariance"] = Cov
            OutData["Covariance_min"] = covClass.min()
            OutData["Covariance_max"] = covClass.max()
            OutData["Covariance_std"] = covClass.std()
            OutData["Covariance_mean"] = covClass.mean()

        if ui.cbCorr.isChecked():
            if ui.rbAvg.isChecked():
                Corr = Corr / CoEff
            corClass = SimilarityMatrixBetweenClass(Corr)
            OutData["Correlation"] = Corr
            OutData["Correlation_min"] = corClass.min()
            OutData["Correlation_max"] = corClass.max()
            OutData["Correlation_std"] = corClass.std()
            OutData["Correlation_mean"] = corClass.mean()

        OutData["MSE"] = np.mean(AMSE)
        OutData["MSE_std"] = np.std(AMSE)
        print("Average MSE: %f" % (OutData["MSE"]))
        OutData["RunTime"] = time.time() - tStart
        print("Runtime (s): %f" % (OutData["RunTime"]))
        print("Saving results ...")
        io.savemat(OutFile, mdict=OutData, do_compression=True)
        print("Output is saved.")

        if ui.cbDiagram.isChecked():
            if ui.cbCorr.isChecked():
                fig1 = plt.figure(num=None, figsize=(5, 5), dpi=100)
                plt.pcolor(Corr, vmin=-0.1, vmax=1)
                plt.xlim([0, np.shape(Corr)[0]])
                plt.ylim([0, np.shape(Corr)[0]])
                plt.colorbar()
                ax = plt.gca()
                ax.set_aspect(1)
                plt.title('Correlation of Categories\nLevel: ' + FoldStr)
                plt.show()

            if ui.cbCov.isChecked():
                fig2 = plt.figure(num=None, figsize=(5, 5), dpi=100)
                plt.pcolor(Cov)
                plt.xlim([0, np.shape(Cov)[0]])
                plt.ylim([0, np.shape(Cov)[0]])
                plt.colorbar()
                ax = plt.gca()
                ax.set_aspect(1)
                plt.title('Covariance of Categories\nLevel: ' + FoldStr)
                plt.show()
        print("DONE.")
        msgBox.setText(
            "Group Representational Similarity Analysis (RSA) is done.")
        msgBox.setIcon(QMessageBox.Information)
        msgBox.setStandardButtons(QMessageBox.Ok)
        msgBox.exec_()
Example #3
0
    def btnConvert_click(self):
        msgBox = QMessageBox()
        tStart = time.time()
        if not ui.cbCov.isChecked() and not ui.cbCorr.isChecked():
            msgBox.setText("At least, you must select one metric!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Method
        method = ui.cbMethod.currentData()

        # Solver
        solver = ui.cbSolver.currentText()

        # Selection
        selection = ui.cbSelection.currentText()

        # Fit
        fit = ui.cbFit.isChecked()

        # normalize
        normalize = ui.cbNormalize.isChecked()

        try:
            alpha = np.float(ui.txtAlpha.text())
        except:
            msgBox.setText("Alpha is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            iter = np.int(ui.txtMaxIter.text())
        except:
            msgBox.setText("Max Iteration is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            tol = np.float(ui.txtTole.text())
        except:
            msgBox.setText("Tolerance is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            l1 = np.float(ui.txtL1.text())
        except:
            msgBox.setText("L1 is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            njob = np.float(ui.txtJobs.text())
        except:
            msgBox.setText("Number of jobs is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Filter
        try:
            Filter = ui.txtFilter.text()
            if not len(Filter):
                Filter = None
            else:
                Filter = Filter.replace("\'", " ").replace(",", " ").replace(
                    "[", "").replace("]", "").split()
                Filter = np.int32(Filter)
        except:
            print("Filter is wrong!")
            return

        # OutFile
        OutFile = ui.txtOutFile.text()
        if not len(OutFile):
            msgBox.setText("Please enter out file!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        OutData = dict()

        # InFile
        InFile = ui.txtInFile.text()
        if not len(InFile):
            msgBox.setText("Please enter input file!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        if not os.path.isfile(InFile):
            msgBox.setText("Input file not found!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        print("Loading ...")
        InData = mainIO_load(InFile)

        # Data
        if not len(ui.txtData.currentText()):
            msgBox.setText("Please enter Input Data variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Label
        if not len(ui.txtLabel.currentText()):
            msgBox.setText("Please enter Train Label variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Design
        if not len(ui.txtDesign.currentText()):
            msgBox.setText("Please enter Input Design variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            Design = InData[ui.txtDesign.currentText()]
        except:
            msgBox.setText("Design value is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Condition
        if not len(ui.txtCond.currentText()):
            msgBox.setText("Please enter Condition variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            Cond = InData[ui.txtCond.currentText()]
            OutData[ui.txtCond.currentText()] = Cond
            labels = list()
            for con in Cond:
                labels.append(reshape_condition_cell(con[1]))

        except:
            msgBox.setText("Condition value is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        FontSize = ui.txtFontSize.value()

        try:
            X = InData[ui.txtData.currentText()]
            L = InData[ui.txtLabel.currentText()][0]
            if ui.cbScale.isChecked() and not ui.rbScale.isChecked():
                X = preprocessing.scale(X)
                print("Whole of data is scaled X~N(0,1).")

        except:
            print("Cannot load data or label")
            return

        # Task
        if not len(ui.txtTask.currentText()):
            msgBox.setText("Please enter Task variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            TaskTitle = np.array(InData[ui.txtTask.currentText()][0])
        except:
            msgBox.setText("Task variable name is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        TaskTitleUnique = np.unique(TaskTitle)
        Task = np.zeros(np.shape(TaskTitle))

        for ttinx, tt in enumerate(TaskTitle):
            for ttlinx, ttl in enumerate(TaskTitleUnique):
                if tt[0] == ttl:
                    Task[ttinx] = ttlinx + 1
                    break

        # Subject
        if not len(ui.txtSubject.currentText()):
            msgBox.setText("Please enter Subject variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            Sub = InData[ui.txtSubject.currentText()][0]
        except:
            msgBox.setText("Subject variable name is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Run
        if not len(ui.txtRun.currentText()):
            msgBox.setText("Please enter Run variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            Run = InData[ui.txtRun.currentText()][0]
        except:
            msgBox.setText("Run variable name is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Counter
        if not len(ui.txtCounter.currentText()):
            msgBox.setText("Please enter Counter variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            Con = InData[ui.txtCounter.currentText()][0]
        except:
            msgBox.setText("Counter variable name is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        if Filter is not None:
            for fil in Filter:
                # Remove Training Set
                labelIndx = np.where(L == fil)[0]
                Design = np.delete(Design, labelIndx, axis=0)
                X = np.delete(X, labelIndx, axis=0)
                L = np.delete(L, labelIndx, axis=0)
                Task = np.delete(Task, labelIndx, axis=0)
                Sub = np.delete(Sub, labelIndx, axis=0)
                Run = np.delete(Run, labelIndx, axis=0)
                Con = np.delete(Con, labelIndx, axis=0)
                print("Class ID = " + str(fil) + " is removed from data.")

        try:
            Unit = np.int32(ui.txtUnit.text())
        except:
            msgBox.setText("Unit for the test set must be a number!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        if Unit < 1:
            msgBox.setText("Unit for the test set must be greater than zero!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        print("Calculating Levels ...")
        GroupFold = None
        FoldStr = ""
        if ui.cbFSubject.isChecked():
            if not ui.rbFRun.isChecked():
                GroupFold = [Sub]
                FoldStr = "Subject"
            else:
                GroupFold = np.concatenate(([Sub], [Run]))
                FoldStr = "Subject+Run"

        if ui.cbFTask.isChecked():
            GroupFold = np.concatenate(
                (GroupFold, [Task])) if GroupFold is not None else [Task]
            FoldStr = FoldStr + "+Task"

        if ui.cbFCounter.isChecked():
            GroupFold = np.concatenate(
                (GroupFold, [Con])) if GroupFold is not None else [Con]
            FoldStr = FoldStr + "+Counter"

        if FoldStr == "":
            FoldStr = "Whole-Data"
            GUFold = [1]
            ListFold = [1]
            UniqFold = [1]
            GroupFold = [1]
            UnitFold = np.ones((1, np.shape(X)[0]))
        else:
            GroupFold = np.transpose(GroupFold)
            UniqFold = np.array(list(set(tuple(i)
                                         for i in GroupFold.tolist())))
            FoldIDs = np.arange(len(UniqFold)) + 1

            if len(UniqFold) <= Unit:
                msgBox.setText(
                    "Unit must be smaller than all possible levels! Number of all levels is: "
                    + str(len(UniqFold)))
                msgBox.setIcon(QMessageBox.Critical)
                msgBox.setStandardButtons(QMessageBox.Ok)
                msgBox.exec_()
                return False

            if np.mod(len(UniqFold), Unit):
                msgBox.setText(
                    "Unit must be divorceable to all possible levels! Number of all levels is: "
                    + str(len(UniqFold)))
                msgBox.setIcon(QMessageBox.Critical)
                msgBox.setStandardButtons(QMessageBox.Ok)
                msgBox.exec_()
                return False
            ListFold = list()
            for gfold in GroupFold:
                for ufoldindx, ufold in enumerate(UniqFold):
                    if (ufold == gfold).all():
                        currentID = FoldIDs[ufoldindx]
                        break
                ListFold.append(currentID)

            ListFold = np.int32(ListFold)
            if Unit == 1:
                UnitFold = np.int32(ListFold)
            else:
                UnitFold = np.int32((ListFold - 0.1) / Unit) + 1

            GUFold = np.unique(UnitFold)

        FoldInfo = dict()
        FoldInfo["Unit"] = Unit
        FoldInfo["Group"] = GroupFold
        FoldInfo["Order"] = FoldStr
        FoldInfo["List"] = ListFold
        FoldInfo["Unique"] = UniqFold
        FoldInfo["Folds"] = UnitFold

        OutData["FoldInfo"] = FoldInfo
        OutData["ModelAnalysis"] = "SK.Group.RSA." + ui.cbMethod.currentText()

        print("Number of all levels is: " + str(len(UniqFold)))

        Cov = None
        Corr = None
        AMSE = list()
        Beta = None

        for foldID, fold in enumerate(GUFold):
            print("Analyzing level " + str(foldID + 1), " of ",
                  str(len(UniqFold)), " ...")
            Index = np.where(UnitFold == fold)
            # Whole-Data
            if FoldStr == "Whole-Data" and np.shape(Index)[0]:
                Index = [Index[1]]
            XLi = X[Index]
            if ui.cbScale.isChecked() and ui.rbScale.isChecked():
                XLi = preprocessing.scale(XLi)
                print("Whole of data is scaled X%d~N(0,1)." % (foldID + 1))
            RegLi = np.insert(Design[Index], 0, 1, axis=1)

            if method == "ols":
                model = linmdl.LinearRegression(fit_intercept=fit,
                                                normalize=normalize,
                                                n_jobs=njob)
            elif method == "ridge":
                model = linmdl.Ridge(alpha=alpha,
                                     fit_intercept=fit,
                                     normalize=normalize,
                                     max_iter=iter,
                                     tol=tol,
                                     solver=solver)
            elif method == "lasso":
                model = linmdl.Lasso(alpha=alpha,
                                     fit_intercept=fit,
                                     normalize=normalize,
                                     max_iter=iter,
                                     tol=tol,
                                     selection=selection)
            elif method == "elast":
                model = linmdl.ElasticNet(alpha=alpha, l1_ratio=l1, fit_intercept=fit, normalize=normalize, \
                                          max_iter=iter, tol=tol, selection=selection)
            model.fit(RegLi, XLi)
            BetaLi = np.transpose(model.coef_)[1:, :]
            Beta = BetaLi if Beta is None else Beta + BetaLi

            print("Calculating MSE for level %d ..." % (foldID + 1))
            MSE = mean_squared_error(XLi, np.matmul(Design[Index], BetaLi))
            print("MSE%d: %f" % (foldID + 1, MSE))
            OutData["MSE" + str(foldID)] = MSE
            AMSE.append(MSE)
            if ui.cbBeta.isChecked():
                OutData["BetaL" + str(foldID + 1)] = BetaLi
            # Calculate Results
            if ui.cbCorr.isChecked():
                print("Calculating Correlation for level %d ..." %
                      (foldID + 1))
                CorrLi = np.corrcoef(BetaLi)
                OutData["Corr" + str(foldID + 1)] = CorrLi
                if Corr is None:
                    Corr = CorrLi.copy()
                else:
                    if ui.rbAvg.isChecked():
                        Corr = np.add(Corr, CorrLi)
                    elif ui.rbMin.isChecked():
                        Corr = np.minimum(Corr, CorrLi)
                    else:
                        Corr = np.maximum(Corr, CorrLi)
            if ui.cbCov.isChecked():
                print("Calculating Covariance for level %d ..." % (foldID + 1))
                CovLi = np.cov(BetaLi)
                OutData["Cov" + str(foldID + 1)] = CovLi
                if Cov is None:
                    Cov = CovLi.copy()
                else:
                    if ui.rbAvg.isChecked():
                        Cov = np.add(Cov, CovLi)
                    elif ui.rbMin.isChecked():
                        Cov = np.minimum(Cov, CovLi)
                    else:
                        Cov = np.maximum(Cov, CovLi)

        CoEff = len(UniqFold) - 1 if len(UniqFold) > 2 else 1
        if ui.cbCov.isChecked():
            if ui.rbAvg.isChecked():
                Cov = Cov / CoEff
            covClass = SimilarityMatrixBetweenClass(Cov)
            OutData["Covariance"] = Cov
            OutData["Covariance_min"] = covClass.min()
            OutData["Covariance_max"] = covClass.max()
            OutData["Covariance_std"] = covClass.std()
            OutData["Covariance_mean"] = covClass.mean()
        if ui.cbCorr.isChecked():
            if ui.rbAvg.isChecked():
                Corr = Corr / CoEff
            corClass = SimilarityMatrixBetweenClass(Corr)
            OutData["Correlation"] = Corr
            OutData["Correlation_min"] = corClass.min()
            OutData["Correlation_max"] = corClass.max()
            OutData["Correlation_std"] = corClass.std()
            OutData["Correlation_mean"] = corClass.mean()

        # Calculating Distance Matrix
        dis = np.zeros((np.shape(Beta)[0], np.shape(Beta)[0]))

        for i in range(np.shape(Beta)[0]):
            for j in range(i + 1, np.shape(Beta)[0]):
                dis[i, j] = 1 - np.dot(Beta[i, :], Beta[j, :].T)
                dis[j, i] = dis[i, j]
        OutData["DistanceMatrix"] = dis
        Z = linkage(dis)
        OutData["Linkage"] = Z

        OutData["MSE"] = np.mean(AMSE)
        print("Average MSE: %f" % (OutData["MSE"]))
        OutData["RunTime"] = time.time() - tStart
        print("Runtime (s): %f" % (OutData["RunTime"]))
        print("Saving results ...")
        mainIO_save(OutData, OutFile)
        print("Output is saved.")

        if ui.cbDiagram.isChecked():
            if ui.cbCorr.isChecked():
                NumData = np.shape(Corr)[0]
                fig1 = plt.figure(num=None,
                                  figsize=(NumData, NumData),
                                  dpi=100)
                plt.pcolor(Corr, vmin=np.min(Corr), vmax=np.max(Corr))
                plt.xlim([0, NumData])
                plt.ylim([0, NumData])
                cbar = plt.colorbar()
                cbar.ax.tick_params(labelsize=FontSize)
                ax = plt.gca()
                ax.invert_yaxis()
                ax.set_aspect(1)

                ax.set_yticks(np.arange(NumData) + 0.5, minor=False)
                ax.set_xticks(np.arange(NumData) + 0.5, minor=False)
                ax.set_xticklabels(labels,
                                   minor=False,
                                   fontsize=FontSize,
                                   rotation=ui.txtXRotation.value())
                ax.set_yticklabels(labels,
                                   minor=False,
                                   fontsize=FontSize,
                                   rotation=ui.txtYRotation.value())
                ax.grid(False)
                ax.set_aspect(1)
                ax.set_frame_on(False)
                for t in ax.xaxis.get_major_ticks():
                    t.tick1On = False
                    t.tick2On = False
                for t in ax.yaxis.get_major_ticks():
                    t.tick1On = False
                    t.tick2On = False

                if len(ui.txtTitleCorr.text()):
                    plt.title(ui.txtTitleCorr.text())
                else:
                    plt.title('Group RSA: Correlation\nLevel: ' + FoldStr)
                plt.show()

            if ui.cbCov.isChecked():
                NumData = np.shape(Cov)[0]
                fig2 = plt.figure(num=None,
                                  figsize=(NumData, NumData),
                                  dpi=100)
                plt.pcolor(Cov, vmin=np.min(Cov), vmax=np.max(Cov))
                plt.xlim([0, NumData])
                plt.ylim([0, NumData])
                cbar = plt.colorbar()
                cbar.ax.tick_params(labelsize=FontSize)
                ax = plt.gca()
                ax.invert_yaxis()
                ax.set_aspect(1)

                ax.set_yticks(np.arange(NumData) + 0.5, minor=False)
                ax.set_xticks(np.arange(NumData) + 0.5, minor=False)
                ax.set_xticklabels(labels,
                                   minor=False,
                                   fontsize=FontSize,
                                   rotation=ui.txtXRotation.value())
                ax.set_yticklabels(labels,
                                   minor=False,
                                   fontsize=FontSize,
                                   rotation=ui.txtYRotation.value())
                ax.grid(False)
                ax.set_aspect(1)
                ax.set_frame_on(False)
                for t in ax.xaxis.get_major_ticks():
                    t.tick1On = False
                    t.tick2On = False
                for t in ax.yaxis.get_major_ticks():
                    t.tick1On = False
                    t.tick2On = False
                if len(ui.txtTitleCov.text()):
                    plt.title(ui.txtTitleCov.text())
                else:
                    plt.title('Group RSA: Covariance\nLevel: ' + FoldStr)
                plt.show()

            fig3 = plt.figure(figsize=(25, 10), )
            if len(ui.txtTitleDen.text()):
                plt.title(ui.txtTitleDen.text())
            else:
                plt.title(
                    'Group MP Gradient RSA: Similarity Analysis\nLevel: ' +
                    FoldStr)

            dn = dendrogram(Z,
                            labels=labels,
                            leaf_font_size=FontSize,
                            color_threshold=1,
                            leaf_rotation=ui.txtXRotation.value())
            plt.show()

        print("DONE.")
        msgBox.setText(
            "Group Representational Similarity Analysis (RSA) is done.")
        msgBox.setIcon(QMessageBox.Information)
        msgBox.setStandardButtons(QMessageBox.Ok)
        msgBox.exec_()
Example #4
0
    def btnConvert_click(self):
        msgBox = QMessageBox()
        tStart = time.time()
        if not ui.cbCov.isChecked() and not ui.cbCorr.isChecked():
            msgBox.setText("At least, you must select one metric!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Filter
        try:
            Filter = ui.txtFilter.text()
            if not len(Filter):
                Filter = None
            else:
                Filter = Filter.replace("\'", " ").replace(",", " ").replace(
                    "[", "").replace("]", "").split()
                Filter = np.int32(Filter)
        except:
            print("Filter is wrong!")
            return

        # OutFile
        OutFile = ui.txtOutFile.text()
        if not len(OutFile):
            msgBox.setText("Please enter out file!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        OutData = dict()

        # InFile
        InFile = ui.txtInFile.text()
        if not len(InFile):
            msgBox.setText("Please enter input file!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        if not os.path.isfile(InFile):
            msgBox.setText("Input file not found!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        print("Loading ...")
        InData = io.loadmat(InFile)

        # Data
        if not len(ui.txtData.currentText()):
            msgBox.setText("Please enter Input Data variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Label
        if not len(ui.txtLabel.currentText()):
            msgBox.setText("Please enter Train Label variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Design
        if not len(ui.txtDesign.currentText()):
            msgBox.setText("Please enter Input Design variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            Design = InData[ui.txtDesign.currentText()]
        except:
            msgBox.setText("Design value is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            X = InData[ui.txtData.currentText()]
            L = InData[ui.txtLabel.currentText()][0]
        except:
            print("Cannot load data or label")
            return

        # Task
        if not len(ui.txtTask.currentText()):
            msgBox.setText("Please enter Task variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        # Task Val
        if not len(ui.txtTaskVal.currentText()):
            msgBox.setText("Please enter Task value!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            TaskIDTitle = ui.txtTaskVal.currentText()
        except:
            msgBox.setText("Task value is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            TaskTitle = InData[ui.txtTask.currentText()][0]
        except:
            msgBox.setText("Task variable name is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        TaskTitleUnique = np.unique(TaskTitle)
        Task = np.zeros(np.shape(TaskTitle))

        for ttinx, tt in enumerate(TaskTitle):
            for ttlinx, ttl in enumerate(TaskTitleUnique):
                if tt[0] == ttl:
                    Task[ttinx] = ttlinx + 1
                    break

        for ttlinx, ttl in enumerate(TaskTitleUnique):
            if TaskIDTitle == ttl:
                TaskID = ttlinx + 1
                break

        OutData["Task"] = TaskIDTitle

        # Subject
        if not len(ui.txtSubject.currentText()):
            msgBox.setText("Please enter Subject variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        # Subject Val
        if not len(ui.txtSubjectVal.currentText()):
            msgBox.setText("Please enter Subject value!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            SubID = np.int32(ui.txtSubjectVal.currentText())
        except:
            msgBox.setText("Subject value is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            Sub = InData[ui.txtSubject.currentText()][0]
        except:
            msgBox.setText("Subject variable name is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        OutData["SubjectID"] = SubID

        # Run
        if not len(ui.txtRun.currentText()):
            msgBox.setText("Please enter Run variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        # Run Val
        if not len(ui.txtRunVal.currentText()):
            msgBox.setText("Please enter Run value!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            RunID = np.int32(ui.txtRunVal.currentText())
        except:
            msgBox.setText("Run value is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            Run = InData[ui.txtRun.currentText()][0]
        except:
            msgBox.setText("Run variable name is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        OutData["RunID"] = RunID

        # Counter
        if not len(ui.txtCounter.currentText()):
            msgBox.setText("Please enter Counter variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        # Counter Val
        if not len(ui.txtCounterVal.currentText()):
            msgBox.setText("Please enter Counter value!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            ConID = np.int32(ui.txtCounterVal.currentText())
        except:
            msgBox.setText("Counter value is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            Con = InData[ui.txtCounter.currentText()][0]
        except:
            msgBox.setText("Counter variable name is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        OutData["CounterID"] = ConID

        if Filter is not None:
            for fil in Filter:
                # Remove Training Set
                labelIndx = np.where(L == fil)[0]
                Design = np.delete(Design, labelIndx, axis=0)
                X = np.delete(X, labelIndx, axis=0)
                L = np.delete(L, labelIndx, axis=0)
                Task = np.delete(Task, labelIndx, axis=0)
                Sub = np.delete(Sub, labelIndx, axis=0)
                Run = np.delete(Run, labelIndx, axis=0)
                Con = np.delete(Con, labelIndx, axis=0)
                print("Class ID = " + str(fil) + " is removed from data.")

        # Select Task
        TaskIndex = np.where(Task == TaskID)
        Design = Design[TaskIndex, :][0]
        X = X[TaskIndex, :][0]
        L = L[TaskIndex]
        Sub = Sub[TaskIndex]
        Run = Run[TaskIndex]
        Con = Con[TaskIndex]
        # Select Subject
        SubIndex = np.where(Sub == SubID)
        Design = Design[SubIndex, :][0]
        X = X[SubIndex, :][0]
        L = L[SubIndex]
        Run = Run[SubIndex]
        Con = Con[SubIndex]
        # Select Counter
        ConIndex = np.where(Con == ConID)
        Design = Design[ConIndex, :][0]
        X = X[ConIndex, :][0]
        L = L[ConIndex]
        Run = Run[ConIndex]
        # Select Run
        RunIndex = np.where(Run == RunID)
        Design = Design[RunIndex, :][0]
        X = X[RunIndex, :][0]
        L = L[RunIndex]  # This will only use in supervised methods
        LUnique = np.unique(L)
        LNum = np.shape(LUnique)[0]
        OutData["Label"] = LUnique
        OutData["ModelAnalysis"] = "Numpy.Session.RSA"

        if np.shape(X)[0] == 0:
            msgBox.setText("The selected data is empty!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        if ui.cbScale.isChecked():
            X = preprocessing.scale(X)
            print("Data is scaled to N(0,1).")
        print("Running RSA ...")
        # RSA Method
        Reg = np.insert(Design, 0, 1, axis=1)
        Betas = np.linalg.lstsq(Reg, X)[0][1:, :]
        print("Calculating MSE ...")
        MSE = mean_squared_error(X, np.matmul(Design, Betas))
        print("MSE: %f" % (MSE))
        OutData["MSE"] = MSE

        if ui.cbBeta.isChecked():
            OutData["Betas"] = Betas
        # Calculate Results
        if ui.cbCorr.isChecked():
            print("Calculating Correlation ...")
            Corr = np.corrcoef(Betas)
            corClass = SimilarityMatrixBetweenClass(Corr)
            OutData["Correlation"] = Corr
            OutData["Correlation_min"] = corClass.min()
            OutData["Correlation_max"] = corClass.max()
            OutData["Correlation_std"] = corClass.std()
            OutData["Correlation_mean"] = corClass.mean()

        if ui.cbCov.isChecked():
            print("Calculating Covariance ...")
            Cov = np.cov(Betas)
            covClass = SimilarityMatrixBetweenClass(Cov)
            OutData["Covariance"] = Cov
            OutData["Covariance_min"] = covClass.min()
            OutData["Covariance_max"] = covClass.max()
            OutData["Covariance_std"] = covClass.std()
            OutData["Covariance_mean"] = covClass.mean()

        OutData["RunTime"] = time.time() - tStart
        print("Runtime (s): %f" % (OutData["RunTime"]))
        print("Saving results ...")
        io.savemat(OutFile, mdict=OutData, do_compression=True)
        print("Output is saved.")

        if ui.cbDiagram.isChecked():
            if ui.cbCorr.isChecked():
                fig1 = plt.figure(num=None, figsize=(5, 5), dpi=100)
                plt.pcolor(Corr, vmin=-0.1, vmax=1)
                plt.xlim([0, LNum])
                plt.ylim([0, LNum])
                plt.colorbar()
                ax = plt.gca()
                ax.set_aspect(1)
                plt.title(
                    'Correlation of Categories\nTask: %s\nSub: %d, Counter: %d, Run: %d'
                    % (TaskIDTitle, SubID, ConID, RunID))
                plt.show()

            if ui.cbCov.isChecked():
                fig2 = plt.figure(num=None, figsize=(5, 5), dpi=100)
                plt.pcolor(Cov)
                plt.xlim([0, LNum])
                plt.ylim([0, LNum])
                plt.colorbar()
                ax = plt.gca()
                ax.set_aspect(1)
                plt.title(
                    'Covariance of Categories\nTask: %s\nSub: %d, Counter: %d, Run: %d'
                    % (TaskIDTitle, SubID, ConID, RunID))
                plt.show()
        print("DONE.")
        msgBox.setText("Representational Similarity Analysis (RSA) is done.")
        msgBox.setIcon(QMessageBox.Information)
        msgBox.setStandardButtons(QMessageBox.Ok)
        msgBox.exec_()
Example #5
0
    def btnConvert_click(self):
        msgBox = QMessageBox()
        tStart = time.time()
        Method = ui.cbMethod.currentData()
        LossType = ui.cbLossType.currentData()
        Optim = ui.cbOptim.currentData()

        try:
            Epoch = np.int32(ui.txtIter.text())
        except:
            msgBox.setText("Number of iteration is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            BatchSize = np.int32(ui.txtBatch.text())
        except:
            msgBox.setText("Number of batch is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            ReportStep = np.int32(ui.txtReportStep.text())
        except:
            msgBox.setText("Number of Report Step is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            LearningRate = np.float32(ui.txtRate.text())
        except:
            msgBox.setText("Learning rate is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            LassoAlpha = np.float32(ui.txtLParam.text())
        except:
            msgBox.setText("Number of Lasso Parameter is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            ElasticLambda1 = np.float32(ui.txtEL1.text())
        except:
            msgBox.setText("Number of Elastic Lambda 1 is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            ElasticAlpha = np.float32(ui.txtEL2.text())
        except:
            msgBox.setText("Number of Elastic Lambda 2 is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            RidgeReg = np.float32(ui.txtRRP.text())
        except:
            msgBox.setText("Number of Ridge Regression Parameter is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        if not ui.cbCov.isChecked() and not ui.cbCorr.isChecked():
            msgBox.setText("At least, you must select one metric!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Filter
        try:
            Filter = ui.txtFilter.text()
            if not len(Filter):
                Filter = None
            else:
                Filter = Filter.replace("\'", " ").replace(",", " ").replace(
                    "[", "").replace("]", "").split()
                Filter = np.int32(Filter)
        except:
            print("Filter is wrong!")
            return

        # OutFile
        OutFile = ui.txtOutFile.text()
        if not len(OutFile):
            msgBox.setText("Please enter out file!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        OutData = dict()

        # InFile
        InFile = ui.txtInFile.text()
        if not len(InFile):
            msgBox.setText("Please enter input file!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        if not os.path.isfile(InFile):
            msgBox.setText("Input file not found!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        print("Loading ...")
        InData = io.loadmat(InFile)

        # Data
        if not len(ui.txtData.currentText()):
            msgBox.setText("Please enter Input Data variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Label
        if not len(ui.txtLabel.currentText()):
            msgBox.setText("Please enter Train Label variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Design
        if not len(ui.txtDesign.currentText()):
            msgBox.setText("Please enter Input Design variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            Design = InData[ui.txtDesign.currentText()]
        except:
            msgBox.setText("Design value is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Condition
        if not len(ui.txtCond.currentText()):
            msgBox.setText("Please enter Condition variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            Cond = InData[ui.txtCond.currentText()]
            OutData[ui.txtCond.currentText()] = Cond
            labels = list()
            for con in Cond:
                labels.append(con[1][0])
            labels = np.array(labels)

        except:
            msgBox.setText("Condition value is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        FontSize = ui.txtFontSize.value()

        try:
            X = InData[ui.txtData.currentText()]
            L = InData[ui.txtLabel.currentText()][0]
            if ui.cbScale.isChecked() and not ui.rbScale.isChecked():
                X = preprocessing.scale(X)
                print("Whole of data is scaled X~N(0,1).")

        except:
            print("Cannot load data or label")
            return

        # Task
        if not len(ui.txtTask.currentText()):
            msgBox.setText("Please enter Task variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            TaskTitle = InData[ui.txtTask.currentText()][0]
        except:
            msgBox.setText("Task variable name is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        TaskTitleUnique = np.unique(TaskTitle)
        Task = np.zeros(np.shape(TaskTitle))

        for ttinx, tt in enumerate(TaskTitle):
            for ttlinx, ttl in enumerate(TaskTitleUnique):
                if tt[0] == ttl:
                    Task[ttinx] = ttlinx + 1
                    break

        # Subject
        if not len(ui.txtSubject.currentText()):
            msgBox.setText("Please enter Subject variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            Sub = InData[ui.txtSubject.currentText()][0]
        except:
            msgBox.setText("Subject variable name is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Run
        if not len(ui.txtRun.currentText()):
            msgBox.setText("Please enter Run variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            Run = InData[ui.txtRun.currentText()][0]
        except:
            msgBox.setText("Run variable name is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Counter
        if not len(ui.txtCounter.currentText()):
            msgBox.setText("Please enter Counter variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            Con = InData[ui.txtCounter.currentText()][0]
        except:
            msgBox.setText("Counter variable name is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        if Filter is not None:
            for fil in Filter:
                # Remove Training Set
                labelIndx = np.where(L == fil)[0]
                Design = np.delete(Design, labelIndx, axis=0)
                X = np.delete(X, labelIndx, axis=0)
                L = np.delete(L, labelIndx, axis=0)
                Task = np.delete(Task, labelIndx, axis=0)
                Sub = np.delete(Sub, labelIndx, axis=0)
                Run = np.delete(Run, labelIndx, axis=0)
                Con = np.delete(Con, labelIndx, axis=0)
                print("Class ID = " + str(fil) + " is removed from data.")

        try:
            Unit = np.int32(ui.txtUnit.text())
        except:
            msgBox.setText("Unit for the test set must be a number!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        if Unit < 1:
            msgBox.setText("Unit for the test set must be greater than zero!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        print("Calculating Levels ...")
        GroupFold = None
        FoldStr = ""
        if ui.cbFSubject.isChecked():
            if not ui.rbFRun.isChecked():
                GroupFold = [Sub]
                FoldStr = "Subject"
            else:
                GroupFold = np.concatenate(([Sub], [Run]))
                FoldStr = "Subject+Run"

        if ui.cbFTask.isChecked():
            GroupFold = np.concatenate(
                (GroupFold, [Task])) if GroupFold is not None else [Task]
            FoldStr = FoldStr + "+Task"

        if ui.cbFCounter.isChecked():
            GroupFold = np.concatenate(
                (GroupFold, [Con])) if GroupFold is not None else [Con]
            FoldStr = FoldStr + "+Counter"

        if FoldStr == "":
            FoldStr = "Whole-Data"
            GUFold = [1]
            ListFold = [1]
            UniqFold = [1]
            GroupFold = [1]
            UnitFold = np.ones((1, np.shape(X)[0]))
        else:
            GroupFold = np.transpose(GroupFold)
            UniqFold = np.array(list(set(tuple(i)
                                         for i in GroupFold.tolist())))
            FoldIDs = np.arange(len(UniqFold)) + 1

            if len(UniqFold) <= Unit:
                msgBox.setText(
                    "Unit must be smaller than all possible levels! Number of all levels is: "
                    + str(len(UniqFold)))
                msgBox.setIcon(QMessageBox.Critical)
                msgBox.setStandardButtons(QMessageBox.Ok)
                msgBox.exec_()
                return False

            if np.mod(len(UniqFold), Unit):
                msgBox.setText(
                    "Unit must be divorceable to all possible levels! Number of all levels is: "
                    + str(len(UniqFold)))
                msgBox.setIcon(QMessageBox.Critical)
                msgBox.setStandardButtons(QMessageBox.Ok)
                msgBox.exec_()
                return False
            ListFold = list()
            for gfold in GroupFold:
                for ufoldindx, ufold in enumerate(UniqFold):
                    if (ufold == gfold).all():
                        currentID = FoldIDs[ufoldindx]
                        break
                ListFold.append(currentID)

            ListFold = np.int32(ListFold)
            if Unit == 1:
                UnitFold = np.int32(ListFold)
            else:
                UnitFold = np.int32((ListFold - 0.1) / Unit) + 1

            GUFold = np.unique(UnitFold)

        FoldInfo = dict()
        FoldInfo["Unit"] = Unit
        FoldInfo["Group"] = GroupFold
        FoldInfo["Order"] = FoldStr
        FoldInfo["List"] = ListFold
        FoldInfo["Unique"] = UniqFold
        FoldInfo["Folds"] = UnitFold

        OutData["FoldInfo"] = FoldInfo
        OutData[
            "ModelAnalysis"] = "PyTorch.Group.Gradient.RSA." + ui.cbMethod.currentText(
            )

        print("Number of all levels is: " + str(len(UniqFold)))

        Cov = None
        Corr = None
        AMSE = list()
        APer = list()

        # RSA Method
        OutData['Method'] = dict()
        OutData['Method']['Method'] = Method
        OutData['Method']['LossType'] = LossType
        OutData['Method']['Optimization'] = Optim
        OutData['Method']['LearningRate'] = LearningRate
        OutData['Method']['Epoch'] = Epoch
        OutData['Method']['BatchSize'] = BatchSize
        OutData['Method']['ReportStep'] = ReportStep
        OutData['Method']['RidgeAlpha'] = RidgeReg
        OutData['Method']['ElaticLambda1'] = ElasticLambda1
        OutData['Method']['ElaticAlpha'] = ElasticAlpha
        OutData['Method']['LassoAlpha'] = LassoAlpha
        OutData['Method']['Verbose'] = ui.cbVerbose.isChecked()

        Beta = None

        for foldID, fold in enumerate(GUFold):
            print("Analyzing level " + str(foldID + 1), " of ",
                  str(len(UniqFold)), " ...")
            Index = np.where(UnitFold == fold)
            # Whole-Data
            if FoldStr == "Whole-Data" and np.shape(Index)[0]:
                Index = [Index[1]]
            XLi = X[Index]
            RegLi = Design[Index]
            if ui.cbScale.isChecked() and ui.rbScale.isChecked():
                XLi = preprocessing.scale(XLi)
                print("Whole of data is scaled X%d~N(0,1)." % (foldID + 1))

            print("Running Gradient RSA ...")
            rsa = GrRSA(method=Method, loss_type=LossType, optim=Optim, learning_rate=LearningRate, epoch=Epoch, \
                        batch_size=BatchSize, report_step=ReportStep, ridge_param=RidgeReg,
                        elstnet_l1_ratio=ElasticLambda1, \
                        elstnet_alpha=ElasticAlpha, lasso_alpha=LassoAlpha, verbose=ui.cbVerbose.isChecked(), \
                        gpu_enable=ui.cbDevice.currentData(), normalization=False)

            BetaLi, EpsLi, loss_vec, MSE, Performance, _ = rsa.fit(
                data_vals=XLi, design_vals=RegLi)
            OutData["LossVec"] = loss_vec
            print("Calculating MSE for level %d ..." % (foldID + 1))
            print("MSE%d: %f" % (foldID + 1, MSE))
            print("Perfromance%d: %f" % (foldID + 1, Performance))

            OutData["MSE" + str(foldID)] = MSE
            OutData["Performance" + str(foldID)] = MSE
            AMSE.append(MSE)
            APer.append(Performance)

            Beta = BetaLi if Beta is None else Beta + BetaLi

            if ui.cbBeta.isChecked():
                OutData["BetaL" + str(foldID + 1)] = BetaLi
                OutData["EpsL" + str(foldID + 1)] = EpsLi
            # Calculate Results
            if ui.cbCorr.isChecked():
                print("Calculating Correlation for level %d ..." %
                      (foldID + 1))
                CorrLi = np.corrcoef(BetaLi)
                OutData["Corr" + str(foldID + 1)] = CorrLi
                if Corr is None:
                    Corr = CorrLi.copy()
                else:
                    if ui.rbAvg.isChecked():
                        Corr = np.add(Corr, CorrLi)
                    elif ui.rbMin.isChecked():
                        Corr = np.minimum(Corr, CorrLi)
                    else:
                        Corr = np.maximum(Corr, CorrLi)
            if ui.cbCov.isChecked():
                print("Calculating Covariance for level %d ..." % (foldID + 1))
                CovLi = np.cov(BetaLi)
                OutData["Cov" + str(foldID + 1)] = CovLi
                if Cov is None:
                    Cov = CovLi.copy()
                else:
                    if ui.rbAvg.isChecked():
                        Cov = np.add(Cov, CovLi)
                    elif ui.rbMin.isChecked():
                        Cov = np.minimum(Cov, CovLi)
                    else:
                        Cov = np.maximum(Cov, CovLi)

        if ui.cbCov.isChecked():
            if ui.rbAvg.isChecked():
                Cov = Cov / len(UniqFold)
            covClass = SimilarityMatrixBetweenClass(Cov)
            OutData["Covariance"] = Cov
            OutData["Covariance_min"] = covClass.min()
            OutData["Covariance_max"] = covClass.max()
            OutData["Covariance_std"] = covClass.std()
            OutData["Covariance_mean"] = covClass.mean()

        if ui.cbCorr.isChecked():
            if ui.rbAvg.isChecked():
                Corr = Corr / len(UniqFold)
            corClass = SimilarityMatrixBetweenClass(Corr)
            OutData["Correlation"] = Corr
            OutData["Correlation_min"] = corClass.min()
            OutData["Correlation_max"] = corClass.max()
            OutData["Correlation_std"] = corClass.std()
            OutData["Correlation_mean"] = corClass.mean()

        OutData["MSE"] = np.mean(AMSE)
        OutData["MSE_std"] = np.std(AMSE)
        OutData["Performance"] = np.mean(APer)
        OutData["Performance_std"] = np.std(APer)

        # Calculating Distance Matrix
        dis = np.zeros((np.shape(Beta)[0], np.shape(Beta)[0]))

        for i in range(np.shape(Beta)[0]):
            for j in range(i + 1, np.shape(Beta)[0]):
                dis[i, j] = 1 - np.dot(Beta[i, :], Beta[j, :].T)
                dis[j, i] = dis[i, j]
        OutData["DistanceMatrix"] = dis
        Z = linkage(dis)
        OutData["Linkage"] = Z

        print("Average MSE: %f" % (OutData["MSE"]))
        OutData["RunTime"] = time.time() - tStart
        print("Runtime (s): %f" % (OutData["RunTime"]))
        print("Saving results ...")
        io.savemat(OutFile, mdict=OutData, do_compression=True)
        print("Output is saved.")
        if ui.cbDiagram.isChecked():
            if ui.cbCorr.isChecked():
                NumData = np.shape(Corr)[0]
                fig1 = plt.figure(num=None,
                                  figsize=(NumData, NumData),
                                  dpi=100)
                plt.pcolor(Corr, vmin=np.min(Corr), vmax=np.max(Corr))
                plt.xlim([0, NumData])
                plt.ylim([0, NumData])
                cbar = plt.colorbar()
                cbar.ax.tick_params(labelsize=FontSize)
                ax = plt.gca()
                ax.invert_yaxis()
                ax.set_aspect(1)

                ax.set_yticks(np.arange(NumData) + 0.5, minor=False)
                ax.set_xticks(np.arange(NumData) + 0.5, minor=False)
                ax.set_xticklabels(labels,
                                   minor=False,
                                   fontsize=FontSize,
                                   rotation=45)
                ax.set_yticklabels(labels, minor=False, fontsize=FontSize)
                ax.grid(False)
                ax.set_aspect(1)
                ax.set_frame_on(False)
                for t in ax.xaxis.get_major_ticks():
                    t.tick1On = False
                    t.tick2On = False
                for t in ax.yaxis.get_major_ticks():
                    t.tick1On = False
                    t.tick2On = False

                if len(ui.txtTitleCorr.text()):
                    plt.title(ui.txtTitleCorr.text())
                else:
                    plt.title('Group MP Gradient RSA: Correlation\nLevel: ' +
                              FoldStr)
                plt.show()

            if ui.cbCov.isChecked():
                NumData = np.shape(Cov)[0]
                fig2 = plt.figure(num=None,
                                  figsize=(NumData, NumData),
                                  dpi=100)
                plt.pcolor(Cov, vmin=np.min(Cov), vmax=np.max(Cov))
                plt.xlim([0, NumData])
                plt.ylim([0, NumData])
                cbar = plt.colorbar()
                cbar.ax.tick_params(labelsize=FontSize)
                ax = plt.gca()
                ax.invert_yaxis()
                ax.set_aspect(1)

                ax.set_yticks(np.arange(NumData) + 0.5, minor=False)
                ax.set_xticks(np.arange(NumData) + 0.5, minor=False)
                ax.set_xticklabels(labels,
                                   minor=False,
                                   fontsize=FontSize,
                                   rotation=45)
                ax.set_yticklabels(labels, minor=False, fontsize=FontSize)
                ax.grid(False)
                ax.set_aspect(1)
                ax.set_frame_on(False)
                for t in ax.xaxis.get_major_ticks():
                    t.tick1On = False
                    t.tick2On = False
                for t in ax.yaxis.get_major_ticks():
                    t.tick1On = False
                    t.tick2On = False
                if len(ui.txtTitleCov.text()):
                    plt.title(ui.txtTitleCov.text())
                else:
                    plt.title('Group MP Gradient RSA: Covariance\nLevel: ' +
                              FoldStr)
                plt.show()

            fig3 = plt.figure(figsize=(25, 10), )
            if len(ui.txtTitleDen.text()):
                plt.title(ui.txtTitleDen.text())
            else:
                plt.title(
                    'Group MP Gradient RSA: Similarity Analysis\nLevel: ' +
                    FoldStr)

            dn = dendrogram(Z,
                            labels=labels,
                            leaf_font_size=FontSize,
                            color_threshold=1)
            plt.show()

        print("DONE.")
        msgBox.setText(
            "Group Representational Similarity Analysis (RSA) is done.")
        msgBox.setIcon(QMessageBox.Information)
        msgBox.setStandardButtons(QMessageBox.Ok)
        msgBox.exec_()
Example #6
0
    def btnConvert_click(self):
        msgBox      = QMessageBox()
        tStart      = time.time()
        Method      = ui.cbMethod.currentData()
        LossType    = ui.cbLossType.currentData()
        Optim       = ui.cbOptim.currentData()

        try:
            Epoch = np.int32(ui.txtIter.text())
        except:
            msgBox.setText("Number of iteration is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            BatchSize = np.int32(ui.txtBatch.text())
        except:
            msgBox.setText("Number of batch is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            ReportStep = np.int32(ui.txtReportStep.text())
        except:
            msgBox.setText("Number of Report Step is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            LearningRate = np.float32(ui.txtRate.text())
        except:
            msgBox.setText("Number of Report Step is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            LassoAlpha = np.float32(ui.txtLParam.text())
        except:
            msgBox.setText("Number of Lasso Parameter is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False


        try:
            ElasticLambda1 = np.float32(ui.txtEL1.text())
        except:
            msgBox.setText("Number of Elastic Lambda 1 is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            ElasticAlpha = np.float32(ui.txtEL2.text())
        except:
            msgBox.setText("Number of Elastic Lambda 2 is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            RidgeReg = np.float32(ui.txtRRP.text())
        except:
            msgBox.setText("Number of Ridge Regression Parameter is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False


        if not ui.cbCov.isChecked() and not ui.cbCorr.isChecked():
            msgBox.setText("At least, you must select one metric!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Filter
        try:
            Filter = ui.txtFilter.text()
            if not len(Filter):
                Filter = None
            else:
                Filter = Filter.replace("\'", " ").replace(",", " ").replace("[", "").replace("]","").split()
                Filter = np.int32(Filter)
        except:
            print("Filter is wrong!")
            return

        # OutFile
        OutFile = ui.txtOutFile.text()
        if not len(OutFile):
            msgBox.setText("Please enter out file!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        OutData = dict()

        # InFile
        InFile = ui.txtInFile.text()
        if not len(InFile):
            msgBox.setText("Please enter input file!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        if not os.path.isfile(InFile):
            msgBox.setText("Input file not found!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        print("Loading ...")
        InData = mainIO_load(InFile)

        # Data
        if not len(ui.txtData.currentText()):
            msgBox.setText("Please enter Input Data variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Label
        if not len(ui.txtLabel.currentText()):
                msgBox.setText("Please enter Train Label variable name!")
                msgBox.setIcon(QMessageBox.Critical)
                msgBox.setStandardButtons(QMessageBox.Ok)
                msgBox.exec_()
                return False

        # Design
        if not len(ui.txtDesign.currentText()):
            msgBox.setText("Please enter Input Design variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            Design = InData[ui.txtDesign.currentText()]
        except:
            msgBox.setText("Design value is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Condition
        if not len(ui.txtCond.currentText()):
            msgBox.setText("Please enter Condition variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            Cond = InData[ui.txtCond.currentText()]
            OutData[ui.txtCond.currentText()] = Cond
            labels = list()
            for con in Cond:
                labels.append(reshape_condition_cell(con[1]))
        except:
            msgBox.setText("Condition value is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        FontSize = ui.txtFontSize.value()


        try:
            X = InData[ui.txtData.currentText()]
            L = InData[ui.txtLabel.currentText()][0]
        except:
            print("Cannot load data or label")
            return

        # Task
        if not len(ui.txtTask.currentText()):
                msgBox.setText("Please enter Task variable name!")
                msgBox.setIcon(QMessageBox.Critical)
                msgBox.setStandardButtons(QMessageBox.Ok)
                msgBox.exec_()
                return False
        # Task Val
        if not len(ui.txtTaskVal.currentText()):
                msgBox.setText("Please enter Task value!")
                msgBox.setIcon(QMessageBox.Critical)
                msgBox.setStandardButtons(QMessageBox.Ok)
                msgBox.exec_()
                return False

        try:
            TaskIDTitle = ui.txtTaskVal.currentText()
        except:
            msgBox.setText("Task value is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            TaskTitle = np.array(InData[ui.txtTask.currentText()][0])
        except:
            msgBox.setText("Task variable name is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        TaskTitleUnique = np.unique(TaskTitle)
        Task = np.zeros(np.shape(TaskTitle))

        for ttinx, tt in enumerate(TaskTitle):
            for ttlinx, ttl in enumerate(TaskTitleUnique):
                if tt[0] == ttl:
                    Task[ttinx] = ttlinx + 1
                    break

        for ttlinx, ttl in enumerate(TaskTitleUnique):
            if TaskIDTitle == ttl:
                TaskID = ttlinx + 1
                break

        OutData["Task"] = TaskIDTitle

        # Subject
        if not len(ui.txtSubject.currentText()):
            msgBox.setText("Please enter Subject variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        # Subject Val
        if not len(ui.txtSubjectVal.currentText()):
            msgBox.setText("Please enter Subject value!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            SubID = np.int32(ui.txtSubjectVal.currentText())
        except:
            msgBox.setText("Subject value is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            Sub = InData[ui.txtSubject.currentText()][0]
        except:
            msgBox.setText("Subject variable name is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        OutData["SubjectID"] = SubID

        # Run
        if not len(ui.txtRun.currentText()):
            msgBox.setText("Please enter Run variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        # Run Val
        if not len(ui.txtRunVal.currentText()):
            msgBox.setText("Please enter Run value!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            RunID = np.int32(ui.txtRunVal.currentText())
        except:
            msgBox.setText("Run value is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            Run = InData[ui.txtRun.currentText()][0]
        except:
            msgBox.setText("Run variable name is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        OutData["RunID"] = RunID

        # Counter
        if not len(ui.txtCounter.currentText()):
            msgBox.setText("Please enter Counter variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        # Counter Val
        if not len(ui.txtCounterVal.currentText()):
            msgBox.setText("Please enter Counter value!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            ConID = np.int32(ui.txtCounterVal.currentText())
        except:
            msgBox.setText("Counter value is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            Con = InData[ui.txtCounter.currentText()][0]
        except:
            msgBox.setText("Counter variable name is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        OutData["CounterID"] = ConID


        if Filter is not None:
            for fil in Filter:
                # Remove Training Set
                labelIndx = np.where(L == fil)[0]
                Design = np.delete(Design, labelIndx, axis=0)
                X = np.delete(X, labelIndx, axis=0)
                L = np.delete(L, labelIndx, axis=0)
                Task = np.delete(Task, labelIndx, axis=0)
                Sub = np.delete(Sub, labelIndx, axis=0)
                Run = np.delete(Run, labelIndx, axis=0)
                Con = np.delete(Con, labelIndx, axis=0)
                print("Class ID = " + str(fil) + " is removed from data.")

        # Select Task
        TaskIndex = np.where(Task == TaskID)
        Design  = Design[TaskIndex,:][0]
        X       = X[TaskIndex,:][0]
        L       = L[TaskIndex]
        Sub     = Sub[TaskIndex]
        Run     = Run[TaskIndex]
        Con     = Con[TaskIndex]
        # Select Subject
        SubIndex = np.where(Sub == SubID)
        Design  = Design[SubIndex,:][0]
        X       = X[SubIndex,:][0]
        L       = L[SubIndex]
        Run     = Run[SubIndex]
        Con     = Con[SubIndex]
        # Select Counter
        ConIndex = np.where(Con == ConID)
        Design  = Design[ConIndex,:][0]
        X       = X[ConIndex,:][0]
        L       = L[ConIndex]
        Run     = Run[ConIndex]
        # Select Run
        RunIndex = np.where(Run == RunID)
        Design  = Design[RunIndex,:][0]
        X       = X[RunIndex,:][0]
        L       = L[RunIndex]           # This will only use in supervised methods
        LUnique = np.unique(L)
        LNum    = np.shape(LUnique)[0]
        OutData["Label"] = LUnique
        OutData["ModelAnalysis"] = "PyTorch.Session.Gradient.RSA." + ui.cbMethod.currentText()


        if np.shape(X)[0] == 0:
            msgBox.setText("The selected data is empty!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        if ui.cbScale.isChecked():
            X = preprocessing.scale(X)
            print("Data is scaled to N(0,1).")
        print("Running Gradient RSA ...")
        # RSA Method
        OutData['Method'] = dict()
        OutData['Method']['Method']         = Method
        OutData['Method']['LossType']       = LossType
        OutData['Method']['Optimization']   = Optim
        OutData['Method']['LearningRate']   = LearningRate
        OutData['Method']['Epoch']          = Epoch
        OutData['Method']['BatchSize']      = BatchSize
        OutData['Method']['ReportStep']     = ReportStep
        OutData['Method']['RidgeAlpha']     = RidgeReg
        OutData['Method']['ElaticLambda1']  = ElasticLambda1
        OutData['Method']['ElaticAlpha']    = ElasticAlpha
        OutData['Method']['LassoAlpha']     = LassoAlpha
        OutData['Method']['Verbose']        = ui.cbVerbose.isChecked()

        rsa = GrRSA(method=Method,loss_type=LossType, optim=Optim, learning_rate=LearningRate, epoch=Epoch, \
                    batch_size=BatchSize, report_step=ReportStep, ridge_param=RidgeReg, elstnet_l1_ratio=ElasticLambda1,\
                    elstnet_alpha=ElasticAlpha, lasso_alpha=LassoAlpha, verbose=ui.cbVerbose.isChecked(),\
                    gpu_enable=ui.cbDevice.currentData(),normalization=False)



        Betas, Eps, loss_vec, MSE, Performacne, _ = rsa.fit(data_vals=X, design_vals=Design)
        OutData["LossVec"] = loss_vec
        OutData["MSE"] = MSE
        OutData["Performance"] = Performacne
        print("MSE: ", OutData["MSE"])

        if ui.cbBeta.isChecked():
            OutData["Betas"]  = Betas
            OutData["Eps"]    = Eps
        # Calculate Results
        if ui.cbCorr.isChecked():
            print("Calculating Correlation ...")
            Corr = np.corrcoef(Betas)
            corClass = SimilarityMatrixBetweenClass(Corr)
            OutData["Correlation"]      = Corr
            OutData["Correlation_min"]  = corClass.min()
            OutData["Correlation_max"]  = corClass.max()
            OutData["Correlation_std"]  = corClass.std()
            OutData["Correlation_mean"] = corClass.mean()

        if ui.cbCov.isChecked():
            print("Calculating Covariance ...")
            Cov = np.cov(Betas)
            covClass = SimilarityMatrixBetweenClass(Cov)
            OutData["Covariance"]       = Cov
            OutData["Covariance_min"]   = covClass.min()
            OutData["Covariance_max"]   = covClass.max()
            OutData["Covariance_std"]   = covClass.std()
            OutData["Covariance_mean"]  = covClass.mean()


        # Calculating Distance Matrix
        dis = np.zeros((np.shape(Betas)[0], np.shape(Betas)[0]))

        for i in range(np.shape(Betas)[0]):
            for j in range(i + 1, np.shape(Betas)[0]):
                dis[i, j] = 1 - np.dot(Betas[i, :], Betas[j, :].T)
                dis[j, i] = dis[i, j]
        OutData["DistanceMatrix"] = dis
        Z = linkage(dis)
        OutData["Linkage"] = Z


        OutData["RunTime"] = time.time() - tStart
        print("Runtime (s): %f" % (OutData["RunTime"]))
        print("Saving results ...")
        mainIO_save(OutData, OutFile)
        print("Output is saved.")

        if ui.cbDiagram.isChecked():
            if ui.cbCorr.isChecked():
                NumData = np.shape(Corr)[0]
                fig1 = plt.figure(num=None, figsize=(NumData, NumData), dpi=100)
                plt.pcolor(Corr, vmin=np.min(Corr), vmax=np.max(Corr))
                plt.xlim([0, NumData])
                plt.ylim([0, NumData])
                cbar = plt.colorbar()
                cbar.ax.tick_params(labelsize=FontSize)
                ax = plt.gca()
                ax.invert_yaxis()
                ax.set_aspect(1)

                ax.set_yticks(np.arange(NumData) + 0.5, minor=False)
                ax.set_xticks(np.arange(NumData) + 0.5, minor=False)
                ax.set_xticklabels(labels, minor=False, fontsize=FontSize, rotation=45)
                ax.set_yticklabels(labels, minor=False, fontsize=FontSize)
                ax.grid(False)
                ax.set_aspect(1)
                ax.set_frame_on(False)
                for t in ax.xaxis.get_major_ticks():
                    t.tick1On = False
                    t.tick2On = False
                for t in ax.yaxis.get_major_ticks():
                    t.tick1On = False
                    t.tick2On = False

                if len(ui.txtTitleCorr.text()):
                    plt.title(ui.txtTitleCorr.text())
                else:
                    plt.title('Correlation (' + ui.cbMethod.currentText() + \
                               ')\nTask: %s\nSub: %d, Counter: %d, Run: %d' % (TaskIDTitle, SubID, ConID, RunID))
                plt.show()


            if ui.cbCov.isChecked():
                NumData = np.shape(Cov)[0]
                fig2 = plt.figure(num=None, figsize=(NumData, NumData), dpi=100)
                plt.pcolor(Cov, vmin=np.min(Cov), vmax=np.max(Cov))
                plt.xlim([0, NumData])
                plt.ylim([0, NumData])
                cbar = plt.colorbar()
                cbar.ax.tick_params(labelsize=FontSize)
                ax = plt.gca()
                ax.invert_yaxis()
                ax.set_aspect(1)

                ax.set_yticks(np.arange(NumData) + 0.5, minor=False)
                ax.set_xticks(np.arange(NumData) + 0.5, minor=False)
                ax.set_xticklabels(labels, minor=False, fontsize=FontSize, rotation=45)
                ax.set_yticklabels(labels, minor=False, fontsize=FontSize)
                ax.grid(False)
                ax.set_aspect(1)
                ax.set_frame_on(False)
                for t in ax.xaxis.get_major_ticks():
                    t.tick1On = False
                    t.tick2On = False
                for t in ax.yaxis.get_major_ticks():
                    t.tick1On = False
                    t.tick2On = False
                if len(ui.txtTitleCov.text()):
                    plt.title(ui.txtTitleCov.text())
                else:
                    plt.title('Covariance (' + ui.cbMethod.currentText() + \
                               ')\nTask: %s\nSub: %d, Counter: %d, Run: %d' % (TaskIDTitle, SubID, ConID, RunID))

                plt.show()

            fig3 = plt.figure(figsize=(25, 10), )
            if len(ui.txtTitleDen.text()):
                plt.title(ui.txtTitleDen.text())
            else:
                plt.title('Similarity Analysis (' + ui.cbMethod.currentText() + \
                          ')\nTask: %s\nSub: %d, Counter: %d, Run: %d' % (TaskIDTitle, SubID, ConID, RunID))
            dn = dendrogram(Z, labels=labels, leaf_font_size=FontSize, color_threshold=1)
            plt.show()

        print("DONE.")
        msgBox.setText("Gradient Representational Similarity Analysis (RSA) is done.")
        msgBox.setIcon(QMessageBox.Information)
        msgBox.setStandardButtons(QMessageBox.Ok)
        msgBox.exec_()
    def btnConvert_click(self):
        msgBox = QMessageBox()
        tStart = time.time()
        Activation = ui.cbActivation.currentData()
        LossNorm = ui.cbLossNorm.currentData()
        try:
            Layers = strRange(ui.txtLayers.text(), Unique=False)
            if Layers is None:
                raise Exception('')

        except:
            msgBox.setText("Layers is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            KIter = np.int32(ui.txtKIter.text())
        except:
            msgBox.setText("Number of iteration is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            RIter = np.int32(ui.txtRIter.text())
        except:
            msgBox.setText("Number of iteration is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            BatchSize = np.int32(ui.txtBatch.text())
        except:
            msgBox.setText("Number of batch is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            ReportStep = np.int32(ui.txtReportStep.text())
        except:
            msgBox.setText("Number of Report Step is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            LearningRate = np.float32(ui.txtRate.text())
        except:
            msgBox.setText("Number of Report Step is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        if not ui.cbCov.isChecked() and not ui.cbCorr.isChecked():
            msgBox.setText("At least, you must select one metric!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Filter
        try:
            Filter = ui.txtFilter.text()
            if not len(Filter):
                Filter = None
            else:
                Filter = Filter.replace("\'", " ").replace(",", " ").replace(
                    "[", "").replace("]", "").split()
                Filter = np.int32(Filter)
        except:
            print("Filter is wrong!")
            return

        # OutFile
        OutFile = ui.txtOutFile.text()
        if not len(OutFile):
            msgBox.setText("Please enter out file!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        OutData = dict()

        # InFile
        InFile = ui.txtInFile.text()
        if not len(InFile):
            msgBox.setText("Please enter input file!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        if not os.path.isfile(InFile):
            msgBox.setText("Input file not found!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        print("Loading ...")
        InData = io.loadmat(InFile)

        # Data
        if not len(ui.txtData.currentText()):
            msgBox.setText("Please enter Input Data variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Label
        if not len(ui.txtLabel.currentText()):
            msgBox.setText("Please enter Train Label variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Design
        if not len(ui.txtDesign.currentText()):
            msgBox.setText("Please enter Input Design variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            Design = InData[ui.txtDesign.currentText()]
        except:
            msgBox.setText("Design value is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            X = InData[ui.txtData.currentText()]
            L = InData[ui.txtLabel.currentText()][0]
            if ui.cbScale.isChecked() and not ui.rbScale.isChecked():
                X = preprocessing.scale(X)
                print("Whole of data is scaled X~N(0,1).")

        except:
            print("Cannot load data or label")
            return

        # Task
        if not len(ui.txtTask.currentText()):
            msgBox.setText("Please enter Task variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            TaskTitle = InData[ui.txtTask.currentText()][0]
        except:
            msgBox.setText("Task variable name is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        TaskTitleUnique = np.unique(TaskTitle)
        Task = np.zeros(np.shape(TaskTitle))

        for ttinx, tt in enumerate(TaskTitle):
            for ttlinx, ttl in enumerate(TaskTitleUnique):
                if tt[0] == ttl:
                    Task[ttinx] = ttlinx + 1
                    break

        # Subject
        if not len(ui.txtSubject.currentText()):
            msgBox.setText("Please enter Subject variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            Sub = InData[ui.txtSubject.currentText()][0]
        except:
            msgBox.setText("Subject variable name is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Run
        if not len(ui.txtRun.currentText()):
            msgBox.setText("Please enter Run variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            Run = InData[ui.txtRun.currentText()][0]
        except:
            msgBox.setText("Run variable name is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Counter
        if not len(ui.txtCounter.currentText()):
            msgBox.setText("Please enter Counter variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            Con = InData[ui.txtCounter.currentText()][0]
        except:
            msgBox.setText("Counter variable name is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        if Filter is not None:
            for fil in Filter:
                # Remove Training Set
                labelIndx = np.where(L == fil)[0]
                Design = np.delete(Design, labelIndx, axis=0)
                X = np.delete(X, labelIndx, axis=0)
                L = np.delete(L, labelIndx, axis=0)
                Task = np.delete(Task, labelIndx, axis=0)
                Sub = np.delete(Sub, labelIndx, axis=0)
                Run = np.delete(Run, labelIndx, axis=0)
                Con = np.delete(Con, labelIndx, axis=0)
                print("Class ID = " + str(fil) + " is removed from data.")

        try:
            Unit = np.int32(ui.txtUnit.text())
        except:
            msgBox.setText("Unit for the test set must be a number!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        if Unit < 1:
            msgBox.setText("Unit for the test set must be greater than zero!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        print("Calculating Levels ...")
        GroupFold = None
        FoldStr = ""
        if ui.cbFSubject.isChecked():
            if not ui.rbFRun.isChecked():
                GroupFold = [Sub]
                FoldStr = "Subject"
            else:
                GroupFold = np.concatenate(([Sub], [Run]))
                FoldStr = "Subject+Run"

        if ui.cbFTask.isChecked():
            GroupFold = np.concatenate(
                (GroupFold, [Task])) if GroupFold is not None else [Task]
            FoldStr = FoldStr + "+Task"

        if ui.cbFCounter.isChecked():
            GroupFold = np.concatenate(
                (GroupFold, [Con])) if GroupFold is not None else [Con]
            FoldStr = FoldStr + "+Counter"

        if FoldStr == "":
            FoldStr = "Whole-Data"
            GUFold = [1]
            ListFold = [1]
            UniqFold = [1]
            GroupFold = [1]
            UnitFold = np.ones((1, np.shape(X)[0]))
        else:
            GroupFold = np.transpose(GroupFold)
            UniqFold = np.array(list(set(tuple(i)
                                         for i in GroupFold.tolist())))
            FoldIDs = np.arange(len(UniqFold)) + 1

            if len(UniqFold) <= Unit:
                msgBox.setText(
                    "Unit must be smaller than all possible levels! Number of all levels is: "
                    + str(len(UniqFold)))
                msgBox.setIcon(QMessageBox.Critical)
                msgBox.setStandardButtons(QMessageBox.Ok)
                msgBox.exec_()
                return False

            if np.mod(len(UniqFold), Unit):
                msgBox.setText(
                    "Unit must be divorceable to all possible levels! Number of all levels is: "
                    + str(len(UniqFold)))
                msgBox.setIcon(QMessageBox.Critical)
                msgBox.setStandardButtons(QMessageBox.Ok)
                msgBox.exec_()
                return False
            ListFold = list()
            for gfold in GroupFold:
                for ufoldindx, ufold in enumerate(UniqFold):
                    if (ufold == gfold).all():
                        currentID = FoldIDs[ufoldindx]
                        break
                ListFold.append(currentID)

            ListFold = np.int32(ListFold)
            if Unit == 1:
                UnitFold = np.int32(ListFold)
            else:
                UnitFold = np.int32((ListFold - 0.1) / Unit) + 1

            GUFold = np.unique(UnitFold)

        FoldInfo = dict()
        FoldInfo["Unit"] = Unit
        FoldInfo["Group"] = GroupFold
        FoldInfo["Order"] = FoldStr
        FoldInfo["List"] = ListFold
        FoldInfo["Unique"] = UniqFold
        FoldInfo["Folds"] = UnitFold

        OutData["FoldInfo"] = FoldInfo
        OutData["ModelAnalysis"] = "Tensorflow.Group.Single-Deep-Kernel.RSA"

        print("Number of all levels is: " + str(len(UniqFold)))

        # RSA Method
        OutData['Method'] = dict()
        OutData['Method']['Layers'] = ui.txtLayers.text()
        OutData['Method']['Activation'] = Activation
        OutData['Method']['LossNorm'] = LossNorm
        OutData['Method']['LearningRate'] = LearningRate
        OutData['Method']['KernelIter'] = KIter
        OutData['Method']['RSAIter'] = RIter
        OutData['Method']['BatchSize'] = BatchSize
        OutData['Method']['ReportStep'] = ReportStep
        OutData['Method']['Verbose'] = ui.cbVerbose.isChecked()
        TData = list()
        TReg = list()
        print("Reshaping Data ...")
        for foldID, fold in enumerate(GUFold):
            print("Reshaping level " + str(foldID + 1), " of ",
                  str(len(UniqFold)), " ...")
            Index = np.where(UnitFold == fold)
            # Whole-Data
            if FoldStr == "Whole-Data" and np.shape(Index)[0]:
                Index = [Index[1]]
            XLi = X[Index]
            RegLi = Design[Index]
            if ui.cbScale.isChecked() and ui.rbScale.isChecked():
                XLi = preprocessing.scale(XLi)
                print("Whole of data is scaled X%d~N(0,1)." % (foldID + 1))

            TData.append(XLi)
            TReg.append(RegLi)

        print("Running Deep Group RSA ...")
        rsa = DeepGroupRSA(layers=Layers, kernel_iter = KIter, rsa_iter = RIter, learning_rate=LearningRate,
                           loss_norm=LossNorm, activation=Activation, \
                          batch_size=BatchSize, report_step=ReportStep, verbose=ui.cbVerbose.isChecked(), \
                           NCat=np.shape(Design)[1], NVoxel=np.shape(X)[1], CPU=ui.cbDevice.currentData())
        Betas, Eps, Weights, Bias, MSE, loss_mat = rsa.fit(data_vals=TData,
                                                           design_vals=TReg)

        OutData["Weight"] = Weights
        OutData["Bias"] = Bias
        OutData["Perfromance"] = MSE
        OutData["Perfromance_Average"] = rsa.AMSE
        OutData["Perfromance_std"] = np.std(rsa.AMSE)
        OutData["LossMat"] = loss_mat
        print("Average Performance: %f" % (OutData["Perfromance"]))

        print("Calculating cov & corr ... ")
        AvgCov = None
        AvgCorr = None
        for beta_id, beta in enumerate(Betas):
            OutData["BetaL" + str(beta_id)] = beta
            if ui.cbCov.isChecked():
                co = np.cov(beta)
                OutData["Cov" + str(beta_id)] = co
                AvgCov = co if AvgCov is None else AvgCov + co

            if ui.cbCorr.isChecked():
                cr = np.corrcoef(beta)
                OutData["Corr" + str(beta_id)] = cr
                AvgCorr = cr if AvgCorr is None else AvgCorr + cr
        for eps_id, ep in enumerate(Eps):
            OutData["EpsL" + str(eps_id)] = ep

        if ui.cbCov.isChecked():
            AvgCov = AvgCov / len(TData)
            covClass = SimilarityMatrixBetweenClass(AvgCov)
            OutData["Covariance"] = AvgCov
            OutData["Covariance_min"] = covClass.min()
            OutData["Covariance_max"] = covClass.max()
            OutData["Covariance_mean"] = covClass.mean()
            OutData["Covariance_std"] = covClass.std()

        if ui.cbCorr.isChecked():
            AvgCorr = AvgCorr / len(TData)
            corClass = SimilarityMatrixBetweenClass(AvgCorr)
            OutData["Correlation"] = AvgCorr
            OutData["Correlation_min"] = corClass.min()
            OutData["Correlation_max"] = corClass.max()
            OutData["Correlation_mean"] = corClass.mean()
            OutData["Correlation_std"] = corClass.std()

        OutData["RunTime"] = time.time() - tStart
        print("Runtime (s): %f" % (OutData["RunTime"]))
        print("Saving results ...")
        io.savemat(OutFile, mdict=OutData, do_compression=True)
        print("Output is saved.")

        if ui.cbDiagram.isChecked():
            if ui.cbCorr.isChecked():
                fig1 = plt.figure(num=None, figsize=(5, 5), dpi=100)
                plt.pcolor(AvgCorr, vmin=-0.1, vmax=1)
                plt.xlim([0, np.shape(AvgCorr)[0]])
                plt.ylim([0, np.shape(AvgCorr)[0]])
                plt.colorbar()
                ax = plt.gca()
                ax.set_aspect(1)
                plt.title('Deep Group RSA: Correlation\nLevel: ' + FoldStr)
                plt.show()

            if ui.cbCov.isChecked():
                fig2 = plt.figure(num=None, figsize=(5, 5), dpi=100)
                plt.pcolor(AvgCov)
                plt.xlim([0, np.shape(AvgCov)[0]])
                plt.ylim([0, np.shape(AvgCov)[0]])
                plt.colorbar()
                ax = plt.gca()
                ax.set_aspect(1)
                plt.title('Deep Group RSA: Covariance\nLevel: ' + FoldStr)
                plt.show()
        print("DONE.")
        msgBox.setText(
            "Group Level Single-Deep-Kernel Representational Similarity Analysis is done."
        )
        msgBox.setIcon(QMessageBox.Information)
        msgBox.setStandardButtons(QMessageBox.Ok)
        msgBox.exec_()
Example #8
0
    def btnConvert_click(self):
        msgBox = QMessageBox()
        tStart = time.time()
        if not ui.cbCov.isChecked() and not ui.cbCorr.isChecked():
            msgBox.setText("At least, you must select one metric!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        Method          = ui.cbMethod.currentData()
        NuregMethod     = ui.cbNuregMethod.currentData()
        Tau2Prior       = ui.cbTau2Prior.currentData()
        SNRPrior        = ui.cbSNRPrior.currentData()

        GPS             = ui.cbGBS.isChecked()
        GPI             = ui.cbGPI.isChecked()
        BaselineSingle  = ui.cbBaselineSingle.isChecked()
        AutoNuisance    = ui.cbAutoNuisance.isChecked()
        NuregZscore     = ui.cbNuregZscore.isChecked()


        try:
            miter = np.int(ui.txtMaxIter.text())
            assert miter >= 1, None
        except:
            msgBox.setText("Max Iteration is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            iiter = np.int(ui.txtInitIter.text())
            assert iiter >= 1, None
        except:
            msgBox.setText("Init Iteration is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            Speed = np.int(ui.txtAnnealSpeed.text())
            assert Speed >= 1, None
        except:
            msgBox.setText("Anneal speed is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            rank = np.int(ui.txtRank.text())
            assert rank >= 0, None
            if rank == 0:
                rank = None
        except:
            msgBox.setText("Rank is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            NumReg = np.int(ui.txtNumReg.text())
            assert NumReg >= 0, None
            if NumReg == 0:
                NumReg = None
        except:
            msgBox.setText("Number of Reg is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            SNRBin = np.int(ui.txtSNRBins.text())
            assert SNRBin > 0, None
        except:
            msgBox.setText("SNR bin is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            rhoBin = np.int(ui.txtRhoBins.text())
            assert rhoBin > 0, None
        except:
            msgBox.setText("Rho bin is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            tol = np.float(ui.txtTole.text())
        except:
            msgBox.setText("Tolerance is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            eta = np.float(ui.txtEta.text())
        except:
            msgBox.setText("Eta is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            LogSRange = np.float(ui.txtLogSRange.text())
        except:
            msgBox.setText("LogS range is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            SpaceSmoothRange = np.float(ui.txtSpaceSmoothRange.text())
            if SpaceSmoothRange == 0:
                SpaceSmoothRange = None
        except:
            msgBox.setText("Space Smooth Range is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            IntenSmoothRange = np.float(ui.txtIntenSmoothRange.text())
            if IntenSmoothRange == 0:
                IntenSmoothRange = None
        except:
            msgBox.setText("Inten Smooth Range is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            Tau = np.float(ui.txtTauRange.text())
        except:
            msgBox.setText("Eta is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False


        try:
            CodeText  = ui.txtEvents.toPlainText()
            allvars = dict(locals(), **globals())
            exec(CodeText, allvars, allvars)
            Optimizer = allvars['optimizer']
            MinimizeOptions = allvars['minimize_options']
        except Exception as e:
            msgBox.setText("Optimizer is wrong!\n" + str(e))
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Filter
        try:
            Filter = ui.txtFilter.text()
            if not len(Filter):
                Filter = None
            else:
                Filter = Filter.replace("\'", " ").replace(",", " ").replace("[", "").replace("]","").split()
                Filter = np.int32(Filter)
        except:
            print("Filter is wrong!")
            return

        # OutFile
        OutFile = ui.txtOutFile.text()
        if not len(OutFile):
            msgBox.setText("Please enter out file!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        OutData = dict()

        # InFile
        InFile = ui.txtInFile.text()
        if not len(InFile):
            msgBox.setText("Please enter input file!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        if not os.path.isfile(InFile):
            msgBox.setText("Input file not found!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        print("Loading ...")
        InData = mainIO_load(InFile)

        # Data
        if not len(ui.txtData.currentText()):
            msgBox.setText("Please enter Input Data variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Label
        if not len(ui.txtLabel.currentText()):
                msgBox.setText("Please enter Train Label variable name!")
                msgBox.setIcon(QMessageBox.Critical)
                msgBox.setStandardButtons(QMessageBox.Ok)
                msgBox.exec_()
                return False

        # Design
        if not len(ui.txtDesign.currentText()):
            msgBox.setText("Please enter Input Design variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            Design = InData[ui.txtDesign.currentText()]
        except:
            msgBox.setText("Design value is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False


        # Condition
        if not len(ui.txtCond.currentText()):
            msgBox.setText("Please enter Condition variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            Cond = InData[ui.txtCond.currentText()]
            OutData[ui.txtCond.currentText()] = Cond
            labels = list()
            for con in Cond:
                labels.append(reshape_condition_cell(con[1]))

        except:
            msgBox.setText("Condition value is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        FontSize = ui.txtFontSize.value()

        try:
            X = InData[ui.txtData.currentText()]
            Intensity = np.mean(X, axis=0)
            L = InData[ui.txtLabel.currentText()][0]
            if ui.cbScale.isChecked() and not ui.rbScale.isChecked():
                X = preprocessing.scale(X)
                print("Whole of data is scaled X~N(0,1).")


        except:
            print("Cannot load data or label")
            return

        # Task
        if not len(ui.txtTask.currentText()):
                msgBox.setText("Please enter Task variable name!")
                msgBox.setIcon(QMessageBox.Critical)
                msgBox.setStandardButtons(QMessageBox.Ok)
                msgBox.exec_()
                return False
        try:
            TaskTitle = np.array(InData[ui.txtTask.currentText()][0])
        except:
            msgBox.setText("Task variable name is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        TaskTitleUnique = np.unique(TaskTitle)
        Task = np.zeros(np.shape(TaskTitle))

        for ttinx, tt in enumerate(TaskTitle):
            for ttlinx, ttl in enumerate(TaskTitleUnique):
                if tt[0] == ttl:
                    Task[ttinx] = ttlinx + 1
                    break

        # Subject
        if not len(ui.txtSubject.currentText()):
            msgBox.setText("Please enter Subject variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            Sub = InData[ui.txtSubject.currentText()][0]
        except:
            msgBox.setText("Subject variable name is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Run
        if not len(ui.txtRun.currentText()):
            msgBox.setText("Please enter Run variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            Run = InData[ui.txtRun.currentText()][0]
        except:
            msgBox.setText("Run variable name is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Counter
        if not len(ui.txtCounter.currentText()):
            msgBox.setText("Please enter Counter variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            Con = InData[ui.txtCounter.currentText()][0]
        except:
            msgBox.setText("Counter variable name is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            if len(ui.txtCoord.currentText()):
                Coord = np.transpose(InData[ui.txtCoord.currentText()])
            else:
                Coord = None
        except:
            msgBox.setText("Coordinate variable is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False


        if Filter is not None:
            for fil in Filter:
                # Remove Training Set
                labelIndx = np.where(L == fil)[0]
                Design = np.delete(Design, labelIndx, axis=0)
                X = np.delete(X, labelIndx, axis=0)
                L = np.delete(L, labelIndx, axis=0)
                Task = np.delete(Task, labelIndx, axis=0)
                Sub = np.delete(Sub, labelIndx, axis=0)
                Run = np.delete(Run, labelIndx, axis=0)
                Con = np.delete(Con, labelIndx, axis=0)
                print("Class ID = " + str(fil) + " is removed from data.")

        try:
            Unit = np.int32(ui.txtUnit.text())
        except:
            msgBox.setText("Unit for the test set must be a number!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        if Unit < 1:
            msgBox.setText("Unit for the test set must be greater than zero!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        print("Calculating Levels ...")
        GroupFold = None
        FoldStr = ""
        if ui.cbFSubject.isChecked():
            if not ui.rbFRun.isChecked():
                GroupFold = [Sub]
                FoldStr = "Subject"
            else:
                GroupFold = np.concatenate(([Sub],[Run]))
                FoldStr = "Subject+Run"

        if ui.cbFTask.isChecked():
            GroupFold = np.concatenate((GroupFold,[Task])) if GroupFold is not None else [Task]
            FoldStr = FoldStr + "+Task"

        if ui.cbFCounter.isChecked():
            GroupFold = np.concatenate((GroupFold,[Con])) if GroupFold is not None else [Con]
            FoldStr = FoldStr + "+Counter"

        if FoldStr == "":
            FoldStr = "Whole-Data"
            GUFold = [1]
            ListFold = [1]
            UniqFold = [1]
            GroupFold = [1]
            UnitFold = np.ones((1, np.shape(X)[0]))
        else:
            GroupFold = np.transpose(GroupFold)
            UniqFold = np.array(list(set(tuple(i) for i in GroupFold.tolist())))
            FoldIDs = np.arange(len(UniqFold)) + 1

            if len(UniqFold) <= Unit:
                msgBox.setText("Unit must be smaller than all possible levels! Number of all levels is: " + str(len(UniqFold)))
                msgBox.setIcon(QMessageBox.Critical)
                msgBox.setStandardButtons(QMessageBox.Ok)
                msgBox.exec_()
                return False

            if np.mod(len(UniqFold),Unit):
                msgBox.setText("Unit must be divorceable to all possible levels! Number of all levels is: " + str(len(UniqFold)))
                msgBox.setIcon(QMessageBox.Critical)
                msgBox.setStandardButtons(QMessageBox.Ok)
                msgBox.exec_()
                return False
            ListFold = list()
            for gfold in GroupFold:
                for ufoldindx, ufold in enumerate(UniqFold):
                    if (ufold == gfold).all():
                        currentID = FoldIDs[ufoldindx]
                        break
                ListFold.append(currentID)

            ListFold = np.int32(ListFold)
            if Unit == 1:
                UnitFold = np.int32(ListFold)
            else:
                UnitFold = np.int32((ListFold - 0.1) / Unit) + 1

            GUFold = np.unique(UnitFold)


        FoldInfo = dict()
        FoldInfo["Unit"]    = Unit
        FoldInfo["Group"]   = GroupFold
        FoldInfo["Order"]   = FoldStr
        FoldInfo["List"]    = ListFold
        FoldInfo["Unique"]  = UniqFold
        FoldInfo["Folds"]   = UnitFold

        OutData["FoldInfo"] = FoldInfo
        OutData["ModelAnalysis"] = "SK.Group.RSA." + ui.cbMethod.currentText()


        print("Number of all levels is: " + str(len(UniqFold)))

        Cov = None
        Corr = None
        AMSE = list()
        Beta = None

        for foldID, fold in enumerate(GUFold):
            print("Analyzing level " + str(foldID + 1)," of ", str(len(UniqFold)) , " ...")
            Index = np.where(UnitFold == fold)
            # Whole-Data
            if FoldStr == "Whole-Data" and np.shape(Index)[0]:
                Index = [Index[1]]
            XLi      = X[Index]
            if ui.cbScale.isChecked() and ui.rbScale.isChecked():
                XLi = preprocessing.scale(XLi)
                print("Whole of data is scaled X%d~N(0,1)." % (foldID + 1))
            #RegLi       =  np.insert(Design[Index], 0, 1, axis=1)
            RegLi       =  Design[Index]

            try:
                if Method == "brsa":
                    model = BRSA(n_iter=miter, rank=rank, auto_nuisance=AutoNuisance,
                                 n_nureg=NumReg, nureg_zscore=NuregZscore, nureg_method=NuregMethod,
                                 baseline_single=BaselineSingle, GP_space=GPS, GP_inten=GPI,
                                 space_smooth_range=SpaceSmoothRange, inten_smooth_range=IntenSmoothRange,
                                 tau_range=Tau,
                                 tau2_prior=Tau2Prior, eta=eta, init_iter=iiter, anneal_speed=Speed, tol=tol,
                                 optimizer=Optimizer, minimize_options=MinimizeOptions)
                    model.fit(XLi, RegLi, coords=Coord, inten=Intensity)
                else:
                    model = GBRSA(n_iter=miter, rank=rank, auto_nuisance=AutoNuisance,
                                  n_nureg=NumReg, nureg_zscore=NuregZscore, nureg_method=NuregMethod,
                                  baseline_single=BaselineSingle, tol=tol, anneal_speed=Speed,
                                  SNR_prior=SNRPrior, logS_range=LogSRange, SNR_bins=SNRBin, rho_bins=rhoBin,
                                  optimizer=Optimizer, minimize_options=MinimizeOptions)
                    model.fit(XLi, RegLi)
            except Exception as e:
                msgBox.setText(str(e))
                print(str(e))
                msgBox.setIcon(QMessageBox.Critical)
                msgBox.setStandardButtons(QMessageBox.Ok)
                msgBox.exec_()
                return False

            BetaLi = model.beta_
            Beta = BetaLi if Beta is None else Beta + BetaLi

            print("Calculating MSE for level %d ..." % (foldID + 1))
            MSE = mean_squared_error(XLi, np.matmul(RegLi, BetaLi))
            print("MSE%d: %f" % (foldID + 1, MSE))
            OutData["MSE" + str(foldID)] = MSE
            AMSE.append(MSE)
            if ui.cbBeta.isChecked():
                OutData["BetaL" + str(foldID + 1)] = BetaLi
            # Calculate Results
            if ui.cbCorr.isChecked():
                print("Calculating Correlation for level %d ..." % (foldID + 1))
                CorrLi = np.corrcoef(BetaLi)
                OutData["Corr" + str(foldID + 1)] = CorrLi
                if Corr is None:
                    Corr = CorrLi.copy()
                else:
                    if ui.rbAvg.isChecked():
                        Corr = np.add(Corr, CorrLi)
                    elif ui.rbMin.isChecked():
                        Corr = np.minimum(Corr, CorrLi)
                    else:
                        Corr = np.maximum(Corr, CorrLi)
            if ui.cbCov.isChecked():
                print("Calculating Covariance for level %d ..." % (foldID + 1))
                CovLi = np.cov(BetaLi)
                OutData["Cov" + str(foldID + 1)]  = CovLi
                if Cov is None:
                    Cov = CovLi.copy()
                else:
                    if ui.rbAvg.isChecked():
                        Cov = np.add(Cov, CovLi)
                    elif ui.rbMin.isChecked():
                        Cov = np.minimum(Cov, CovLi)
                    else:
                        Cov = np.maximum(Cov, CovLi)

        CoEff = len(UniqFold) - 1 if len(UniqFold) > 2 else 1
        if ui.cbCov.isChecked():
            if ui.rbAvg.isChecked():
                Cov = Cov / CoEff
            covClass = SimilarityMatrixBetweenClass(Cov)
            OutData["Covariance"]       = Cov
            OutData["Covariance_min"]   = covClass.min()
            OutData["Covariance_max"]   = covClass.max()
            OutData["Covariance_std"]   = covClass.std()
            OutData["Covariance_mean"]  = covClass.mean()
        if ui.cbCorr.isChecked():
            if ui.rbAvg.isChecked():
                Corr = Corr / CoEff
            corClass = SimilarityMatrixBetweenClass(Corr)
            OutData["Correlation"]      = Corr
            OutData["Correlation_min"]  = corClass.min()
            OutData["Correlation_max"]  = corClass.max()
            OutData["Correlation_std"]  = corClass.std()
            OutData["Correlation_mean"] = corClass.mean()


        # Calculating Distance Matrix
        dis = np.zeros((np.shape(Beta)[0], np.shape(Beta)[0]))

        for i in range(np.shape(Beta)[0]):
            for j in range(i + 1, np.shape(Beta)[0]):
                dis[i, j] = 1 - np.dot(Beta[i, :], Beta[j, :].T)
                dis[j, i] = dis[i, j]
        OutData["DistanceMatrix"] = dis
        Z = linkage(dis)
        OutData["Linkage"] = Z

        OutData["MSE"] = np.mean(AMSE)
        print("Average MSE: %f" % (OutData["MSE"]))
        OutData["RunTime"] = time.time() - tStart
        print("Runtime (s): %f" % (OutData["RunTime"]))
        print("Saving results ...")
        mainIO_save(OutFile, OutData)
        print("Output is saved.")


        if ui.cbDiagram.isChecked():
            if ui.cbCorr.isChecked():
                NumData = np.shape(Corr)[0]
                fig1 = plt.figure(num=None, figsize=(NumData, NumData), dpi=100)
                plt.pcolor(Corr, vmin=np.min(Corr), vmax=np.max(Corr))
                plt.xlim([0, NumData])
                plt.ylim([0, NumData])
                cbar = plt.colorbar()
                cbar.ax.tick_params(labelsize=FontSize)
                ax = plt.gca()
                ax.invert_yaxis()
                ax.set_aspect(1)

                ax.set_yticks(np.arange(NumData) + 0.5, minor=False)
                ax.set_xticks(np.arange(NumData) + 0.5, minor=False)
                ax.set_xticklabels(labels, minor=False, fontsize=FontSize, rotation=ui.txtXRotation.value())
                ax.set_yticklabels(labels, minor=False, fontsize=FontSize, rotation=ui.txtYRotation.value())
                ax.grid(False)
                ax.set_aspect(1)
                ax.set_frame_on(False)
                for t in ax.xaxis.get_major_ticks():
                    t.tick1On = False
                    t.tick2On = False
                for t in ax.yaxis.get_major_ticks():
                    t.tick1On = False
                    t.tick2On = False

                if len(ui.txtTitleCorr.text()):
                    plt.title(ui.txtTitleCorr.text())
                else:
                    plt.title('Group RSA: Correlation\nLevel: ' + FoldStr)
                plt.show()


            if ui.cbCov.isChecked():
                NumData = np.shape(Cov)[0]
                fig2 = plt.figure(num=None, figsize=(NumData, NumData), dpi=100)
                plt.pcolor(Cov, vmin=np.min(Cov), vmax=np.max(Cov))
                plt.xlim([0, NumData])
                plt.ylim([0, NumData])
                cbar = plt.colorbar()
                cbar.ax.tick_params(labelsize=FontSize)
                ax = plt.gca()
                ax.invert_yaxis()
                ax.set_aspect(1)

                ax.set_yticks(np.arange(NumData) + 0.5, minor=False)
                ax.set_xticks(np.arange(NumData) + 0.5, minor=False)
                ax.set_xticklabels(labels, minor=False, fontsize=FontSize, rotation=ui.txtXRotation.value())
                ax.set_yticklabels(labels, minor=False, fontsize=FontSize, rotation=ui.txtYRotation.value())
                ax.grid(False)
                ax.set_aspect(1)
                ax.set_frame_on(False)
                for t in ax.xaxis.get_major_ticks():
                    t.tick1On = False
                    t.tick2On = False
                for t in ax.yaxis.get_major_ticks():
                    t.tick1On = False
                    t.tick2On = False
                if len(ui.txtTitleCov.text()):
                    plt.title(ui.txtTitleCov.text())
                else:
                    plt.title('Group RSA: Covariance\nLevel: ' + FoldStr)
                plt.show()

            fig3 = plt.figure(figsize=(25, 10), )
            if len(ui.txtTitleDen.text()):
                plt.title(ui.txtTitleDen.text())
            else:
                plt.title('Group MP Gradient RSA: Similarity Analysis\nLevel: ' + FoldStr)

            dn = dendrogram(Z, labels=labels, leaf_font_size=FontSize, color_threshold=1, leaf_rotation=ui.txtXRotation.value())
            plt.show()


        print("DONE.")
        msgBox.setText("Group Representational Similarity Analysis (RSA) is done.")
        msgBox.setIcon(QMessageBox.Information)
        msgBox.setStandardButtons(QMessageBox.Ok)
        msgBox.exec_()
Example #9
0
    def btnConvert_click(self):
        msgBox = QMessageBox()
        tStart = time.time()

        if not ui.cbCov.isChecked() and not ui.cbCorr.isChecked():
            msgBox.setText("At least, you must select one metric!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Method
        gpu = ui.cbGPU.currentData()
        Verbose = ui.cbVerbose.isChecked()

        try:
            gamma = np.float(ui.txtGamma.text())
        except:
            msgBox.setText("Gamma is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            Iter = np.int(ui.txtIter.text())
        except:
            msgBox.setText("Max Iteration is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            NumFea = ui.txtNumFea.value()
        except:
            msgBox.setText("Number of features is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        if NumFea <= 0:
            NumFea = None

        # Filter
        try:
            Filter = ui.txtFilter.text()
            if not len(Filter):
                Filter = None
            else:
                Filter = Filter.replace("\'", " ").replace(",", " ").replace(
                    "[", "").replace("]", "").split()
                Filter = np.int32(Filter)
        except:
            print("Filter is wrong!")
            return

        # OutFile
        OutFile = ui.txtOutFile.text()
        if not len(OutFile):
            msgBox.setText("Please enter out file!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        OutData = dict()

        # InFile
        InFile = ui.txtInFile.text()
        if not len(InFile):
            msgBox.setText("Please enter input file!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        if not os.path.isfile(InFile):
            msgBox.setText("Input file not found!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        if not len(ui.txtSharedSpace.text()):
            msgBox.setText("Please enter Shared Space variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        if not len(ui.txtSharedVoxelSpace.text()):
            msgBox.setText("Please enter Shared Voxel Space variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        if not len(ui.txtViewSpaces.text()):
            msgBox.setText("Please enter View Space variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        if not len(ui.txtTransformMats.text()):
            msgBox.setText("Please enter Transform Matrices variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        print("Loading ...")
        InData = io.loadmat(InFile)
        OutData["imgShape"] = InData["imgShape"]

        # Data
        if not len(ui.txtData.currentText()):
            msgBox.setText("Please enter Input Data variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Label
        if not len(ui.txtLabel.currentText()):
            msgBox.setText("Please enter Train Label variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Coordinate
        if not len(ui.txtCoordinate.currentText()):
            msgBox.setText("Please enter Input Coordinate variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            Coordinate = InData[ui.txtCoordinate.currentText()]
            OutData["coordinate"] = Coordinate
        except:
            msgBox.setText("Coordinate value is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Condition
        if not len(ui.txtCond.currentText()):
            msgBox.setText("Please enter Condition variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            Cond = InData[ui.txtCond.currentText()]
            OutData["condition"] = Cond
            labels = list()
            for con in Cond:
                labels.append(con[1][0])
            labels = np.array(labels)
            OutData["labels"] = labels

        except:
            msgBox.setText("Condition value is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        FontSize = ui.txtFontSize.value()

        try:
            X = InData[ui.txtData.currentText()]
            L = InData[ui.txtLabel.currentText()][0]
            if ui.cbScale.isChecked() and not ui.rbScale.isChecked():
                X = preprocessing.scale(X)
                print("Whole of data is scaled X~N(0,1).")
        except:
            print("Cannot load data or label")
            return

        # Task
        if not len(ui.txtTask.currentText()):
            msgBox.setText("Please enter Task variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            TaskTitle = InData[ui.txtTask.currentText()][0]
        except:
            msgBox.setText("Task variable name is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        TaskTitleUnique = np.unique(TaskTitle)
        Task = np.zeros(np.shape(TaskTitle))

        for ttinx, tt in enumerate(TaskTitle):
            for ttlinx, ttl in enumerate(TaskTitleUnique):
                if tt[0] == ttl:
                    Task[ttinx] = ttlinx + 1
                    break

        # Subject
        if not len(ui.txtSubject.currentText()):
            msgBox.setText("Please enter Subject variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            Sub = InData[ui.txtSubject.currentText()][0]
        except:
            msgBox.setText("Subject variable name is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Run
        if not len(ui.txtRun.currentText()):
            msgBox.setText("Please enter Run variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            Run = InData[ui.txtRun.currentText()][0]
        except:
            msgBox.setText("Run variable name is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Counter
        if not len(ui.txtCounter.currentText()):
            msgBox.setText("Please enter Counter variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            Con = InData[ui.txtCounter.currentText()][0]
        except:
            msgBox.setText("Counter variable name is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        if Filter is not None:
            for fil in Filter:
                # Remove Training Set
                labelIndx = np.where(L == fil)[0]
                X = np.delete(X, labelIndx, axis=0)
                L = np.delete(L, labelIndx, axis=0)
                Task = np.delete(Task, labelIndx, axis=0)
                Sub = np.delete(Sub, labelIndx, axis=0)
                Run = np.delete(Run, labelIndx, axis=0)
                Con = np.delete(Con, labelIndx, axis=0)
                print("Class ID = " + str(fil) + " is removed from data.")

        try:
            Unit = np.int32(ui.txtUnit.text())
        except:
            msgBox.setText("Unit for the test set must be a number!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        if Unit < 1:
            msgBox.setText("Unit for the test set must be greater than zero!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        print("Calculating Levels ...")
        GroupFold = None
        FoldStr = ""
        if ui.cbFSubject.isChecked():
            if not ui.rbFRun.isChecked():
                GroupFold = [Sub]
                FoldStr = "Subject"
            else:
                GroupFold = np.concatenate(([Sub], [Run]))
                FoldStr = "Subject+Run"

        if ui.cbFTask.isChecked():
            GroupFold = np.concatenate(
                (GroupFold, [Task])) if GroupFold is not None else [Task]
            FoldStr = FoldStr + "+Task"

        if ui.cbFCounter.isChecked():
            GroupFold = np.concatenate(
                (GroupFold, [Con])) if GroupFold is not None else [Con]
            FoldStr = FoldStr + "+Counter"

        if FoldStr == "":
            FoldStr = "Whole-Data"
            GUFold = [1]
            ListFold = [1]
            UniqFold = [1]
            GroupFold = [1]
            UnitFold = np.ones((1, np.shape(X)[0]))
        else:
            GroupFold = np.transpose(GroupFold)
            UniqFold = np.array(list(set(tuple(i)
                                         for i in GroupFold.tolist())))
            FoldIDs = np.arange(len(UniqFold)) + 1

            if len(UniqFold) <= Unit:
                msgBox.setText(
                    "Unit must be smaller than all possible levels! Number of all levels is: "
                    + str(len(UniqFold)))
                msgBox.setIcon(QMessageBox.Critical)
                msgBox.setStandardButtons(QMessageBox.Ok)
                msgBox.exec_()
                return False

            if np.mod(len(UniqFold), Unit):
                msgBox.setText(
                    "Unit must be divorceable to all possible levels! Number of all levels is: "
                    + str(len(UniqFold)))
                msgBox.setIcon(QMessageBox.Critical)
                msgBox.setStandardButtons(QMessageBox.Ok)
                msgBox.exec_()
                return False
            ListFold = list()
            for gfold in GroupFold:
                for ufoldindx, ufold in enumerate(UniqFold):
                    if (ufold == gfold).all():
                        currentID = FoldIDs[ufoldindx]
                        break
                ListFold.append(currentID)

            ListFold = np.int32(ListFold)
            if Unit == 1:
                UnitFold = np.int32(ListFold)
            else:
                UnitFold = np.int32((ListFold - 0.1) / Unit) + 1

            GUFold = np.unique(UnitFold)

        FoldInfo = dict()
        FoldInfo["Unit"] = Unit
        FoldInfo["Group"] = GroupFold
        FoldInfo["Order"] = FoldStr
        FoldInfo["List"] = ListFold
        FoldInfo["Unique"] = UniqFold
        FoldInfo["Folds"] = UnitFold

        OutData["FoldInfo"] = FoldInfo
        OutData["ModelAnalysis"] = "SSA"
        print("Number of all levels is: " + str(len(UniqFold)))

        Xi = list()
        Yi = list()

        for foldID, fold in enumerate(GUFold):
            print("Extracting view " + str(foldID + 1), " of ",
                  str(len(UniqFold)), " ...")
            Index = np.where(UnitFold == fold)
            # Whole-Data
            if FoldStr == "Whole-Data" and np.shape(Index)[0]:
                Index = [Index[1]]

            Xi.append(X[Index])
            Yi.append(label_binarize(L[Index], np.unique(L)))

        try:
            ssa = SSA(gamma=gamma, gpu=gpu)
            Beta = ssa.run(X=Xi,
                           Y=Yi,
                           Dim=NumFea,
                           verbose=Verbose,
                           Iteration=Iter,
                           ShowError=ui.cbError.isChecked())
            if ui.cbError.isChecked():
                if ssa.LostVec is not None:
                    OutData["LossVec"] = ssa.LostVec
                    OutData["Error"] = ssa.Loss

        except Exception as e:
            msgBox.setText(str(e))
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        OutData["AlgorithmRuntime"] = ssa.Runtime
        OutData[ui.txtSharedSpace.text()] = Beta
        OutData[ui.txtSharedVoxelSpace.text()] = ssa.getSharedVoxelSpace()
        OutData[ui.txtTransformMats.text()] = ssa.getTransformMats()
        if ui.cbViewSpace.isChecked():
            for viewID, view in enumerate(ssa.getSubjectSpace()):
                OutData[ui.txtViewSpaces.text() + "_View" +
                        str(viewID + 1)] = np.transpose(view)
        else:
            OutData[ui.txtViewSpaces.text()] = ssa.getSubjectSpace()

        print("Calculating Distance Matrix ...")
        dis = np.zeros((np.shape(Beta)[0], np.shape(Beta)[0]))
        for i in range(np.shape(Beta)[0]):
            for j in range(i + 1, np.shape(Beta)[0]):
                dis[i, j] = 1 - np.dot(Beta[i, :], Beta[j, :].T)
                dis[j, i] = dis[i, j]
        # dis = dis - np.min(dis)
        # dis = dis / np.max(dis)
        OutData["DistanceMatrix"] = dis
        print("Applying linkage ...")
        Z = linkage(dis,
                    method=ui.cbLMethod.currentData(),
                    metric=ui.cbLMetric.currentData(),
                    optimal_ordering=ui.cbLOrder.isChecked())
        OutData["Linkage"] = Z

        if ui.cbCov.isChecked():
            Cov = np.cov(Beta)
            # OutData["Covariance"]       = Cov
            # OutData["Covariance_min"]   = np.min(Cov)
            # OutData["Covariance_max"]   = np.max(Cov)
            # OutData["Covariance_std"]   = np.std(Cov)
            # OutData["Covariance_mean"]  = np.mean(Cov)
            covClass = SimilarityMatrixBetweenClass(Cov)
            OutData["Covariance"] = Cov
            OutData["Covariance_min"] = covClass.min()
            OutData["Covariance_max"] = covClass.max()
            OutData["Covariance_std"] = covClass.std()
            OutData["Covariance_mean"] = covClass.mean()
        if ui.cbCorr.isChecked():
            Corr = np.corrcoef(Beta)
            # OutData["Correlation"]      = Corr
            # OutData["Correlation_min"]  = np.min(Corr)
            # OutData["Correlation_max"]  = np.max(Corr)
            # OutData["Correlation_std"]  = np.std(Corr)
            # OutData["Correlation_mean"] = np.mean(Corr)
            corClass = SimilarityMatrixBetweenClass(Corr)
            OutData["Correlation"] = Corr
            OutData["Correlation_min"] = corClass.min()
            OutData["Correlation_max"] = corClass.max()
            OutData["Correlation_std"] = corClass.std()
            OutData["Correlation_mean"] = corClass.mean()

        OutData["Runtime"] = time.time() - tStart
        print("Runtime: ", OutData["Runtime"])
        print("Algorithm Runtime: ", OutData["AlgorithmRuntime"])

        print("Saving results ...")
        io.savemat(OutFile, mdict=OutData, do_compression=True)
        print("Output is saved.")

        if ui.cbDiagram.isChecked():
            drawrsa = DrawRSA()

            if ui.cbCorr.isChecked():
                drawrsa.ShowFigure(Corr, labels, ui.txtTitleCorr.text(),
                                   FontSize, ui.txtXRotation.value(),
                                   ui.txtYRotation.value())

            if ui.cbCov.isChecked():
                drawrsa.ShowFigure(Cov, labels, ui.txtTitleCov.text(),
                                   FontSize, ui.txtXRotation.value(),
                                   ui.txtYRotation.value())

            drawrsa.ShowDend(Z, labels, ui.txtTitleDen.text(), FontSize,
                             ui.txtXRotation.value())

        print("DONE.")
        msgBox.setText("Shared Similarity Analysis (SSA) is done.")
        msgBox.setIcon(QMessageBox.Information)
        msgBox.setStandardButtons(QMessageBox.Ok)
        msgBox.exec_()
Example #10
0
    def btnConvert_click(self):
        msgBox = QMessageBox()
        tStart = time.time()
        if not ui.cbCov.isChecked() and not ui.cbCorr.isChecked():
            msgBox.setText("At least, you must select one metric!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Method
        method = ui.cbMethod.currentData()

        # Solver
        solver = ui.cbSolver.currentText()

        # Selection
        selection = ui.cbSelection.currentText()

        # Fit
        fit = ui.cbFit.isChecked()

        # normalize
        normalize = ui.cbNormalize.isChecked()

        try:
            alpha = np.float(ui.txtAlpha.text())
        except:
            msgBox.setText("Alpha is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            iter = np.int(ui.txtMaxIter.text())
        except:
            msgBox.setText("Max Iteration is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            tol = np.float(ui.txtTole.text())
        except:
            msgBox.setText("Tolerance is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            l1 = np.float(ui.txtL1.text())
        except:
            msgBox.setText("L1 is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            njob = np.float(ui.txtJobs.text())
        except:
            msgBox.setText("Number of jobs is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Filter
        try:
            Filter = ui.txtFilter.text()
            if not len(Filter):
                Filter = None
            else:
                Filter = Filter.replace("\'", " ").replace(",", " ").replace(
                    "[", "").replace("]", "").split()
                Filter = np.int32(Filter)
        except:
            print("Filter is wrong!")
            return

        # OutFile
        OutFile = ui.txtOutFile.text()
        if not len(OutFile):
            msgBox.setText("Please enter out file!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        OutData = dict()

        # InFile
        InFile = ui.txtInFile.text()
        if not len(InFile):
            msgBox.setText("Please enter input file!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        if not os.path.isfile(InFile):
            msgBox.setText("Input file not found!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        print("Loading ...")
        InData = io.loadmat(InFile)

        # Data
        if not len(ui.txtData.currentText()):
            msgBox.setText("Please enter Input Data variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Label
        if not len(ui.txtLabel.currentText()):
            msgBox.setText("Please enter Train Label variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Design
        if not len(ui.txtDesign.currentText()):
            msgBox.setText("Please enter Input Design variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            Design = InData[ui.txtDesign.currentText()]
        except:
            msgBox.setText("Design value is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        # Condition
        if not len(ui.txtCond.currentText()):
            msgBox.setText("Please enter Condition variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            Cond = InData[ui.txtCond.currentText()]
            OutData[ui.txtCond.currentText()] = Cond
            labels = list()
            for con in Cond:
                labels.append(con[1][0])
            labels = np.array(labels)
        except:
            msgBox.setText("Condition value is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            X = InData[ui.txtData.currentText()]
            L = InData[ui.txtLabel.currentText()][0]
        except:
            print("Cannot load data or label")
            return

        # Task
        if not len(ui.txtTask.currentText()):
            msgBox.setText("Please enter Task variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        # Task Val
        if not len(ui.txtTaskVal.currentText()):
            msgBox.setText("Please enter Task value!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            TaskIDTitle = ui.txtTaskVal.currentText()
        except:
            msgBox.setText("Task value is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        try:
            TaskTitle = InData[ui.txtTask.currentText()][0]
        except:
            msgBox.setText("Task variable name is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        TaskTitleUnique = np.unique(TaskTitle)
        Task = np.zeros(np.shape(TaskTitle))

        for ttinx, tt in enumerate(TaskTitle):
            for ttlinx, ttl in enumerate(TaskTitleUnique):
                if tt[0] == ttl:
                    Task[ttinx] = ttlinx + 1
                    break

        for ttlinx, ttl in enumerate(TaskTitleUnique):
            if TaskIDTitle == ttl:
                TaskID = ttlinx + 1
                break

        OutData["Task"] = TaskIDTitle

        # Subject
        if not len(ui.txtSubject.currentText()):
            msgBox.setText("Please enter Subject variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        # Subject Val
        if not len(ui.txtSubjectVal.currentText()):
            msgBox.setText("Please enter Subject value!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            SubID = np.int32(ui.txtSubjectVal.currentText())
        except:
            msgBox.setText("Subject value is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            Sub = InData[ui.txtSubject.currentText()][0]
        except:
            msgBox.setText("Subject variable name is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        OutData["SubjectID"] = SubID

        # Run
        if not len(ui.txtRun.currentText()):
            msgBox.setText("Please enter Run variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        # Run Val
        if not len(ui.txtRunVal.currentText()):
            msgBox.setText("Please enter Run value!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            RunID = np.int32(ui.txtRunVal.currentText())
        except:
            msgBox.setText("Run value is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            Run = InData[ui.txtRun.currentText()][0]
        except:
            msgBox.setText("Run variable name is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        OutData["RunID"] = RunID

        # Counter
        if not len(ui.txtCounter.currentText()):
            msgBox.setText("Please enter Counter variable name!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        # Counter Val
        if not len(ui.txtCounterVal.currentText()):
            msgBox.setText("Please enter Counter value!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            ConID = np.int32(ui.txtCounterVal.currentText())
        except:
            msgBox.setText("Counter value is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        try:
            Con = InData[ui.txtCounter.currentText()][0]
        except:
            msgBox.setText("Counter variable name is wrong!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False
        OutData["CounterID"] = ConID

        if Filter is not None:
            for fil in Filter:
                # Remove Training Set
                labelIndx = np.where(L == fil)[0]
                Design = np.delete(Design, labelIndx, axis=0)
                X = np.delete(X, labelIndx, axis=0)
                L = np.delete(L, labelIndx, axis=0)
                Task = np.delete(Task, labelIndx, axis=0)
                Sub = np.delete(Sub, labelIndx, axis=0)
                Run = np.delete(Run, labelIndx, axis=0)
                Con = np.delete(Con, labelIndx, axis=0)
                print("Class ID = " + str(fil) + " is removed from data.")

        # Select Task
        TaskIndex = np.where(Task == TaskID)
        Design = Design[TaskIndex, :][0]
        X = X[TaskIndex, :][0]
        L = L[TaskIndex]
        Sub = Sub[TaskIndex]
        Run = Run[TaskIndex]
        Con = Con[TaskIndex]
        # Select Subject
        SubIndex = np.where(Sub == SubID)
        Design = Design[SubIndex, :][0]
        X = X[SubIndex, :][0]
        L = L[SubIndex]
        Run = Run[SubIndex]
        Con = Con[SubIndex]
        # Select Counter
        ConIndex = np.where(Con == ConID)
        Design = Design[ConIndex, :][0]
        X = X[ConIndex, :][0]
        L = L[ConIndex]
        Run = Run[ConIndex]
        # Select Run
        RunIndex = np.where(Run == RunID)
        Design = Design[RunIndex, :][0]
        X = X[RunIndex, :][0]
        L = L[RunIndex]  # This will only use in supervised methods
        LUnique = np.unique(L)
        LNum = np.shape(LUnique)[0]
        OutData["Label"] = LUnique
        OutData["ModelAnalysis"] = "SK.Session.RSA." + ui.cbMethod.currentText(
        )

        if np.shape(X)[0] == 0:
            msgBox.setText("The selected data is empty!")
            msgBox.setIcon(QMessageBox.Critical)
            msgBox.setStandardButtons(QMessageBox.Ok)
            msgBox.exec_()
            return False

        if ui.cbScale.isChecked():
            X = preprocessing.scale(X)
            print("Data is scaled to N(0,1).")
        print("Running RSA ...")
        # RSA Method
        Reg = np.insert(Design, 0, 1, axis=1)

        if method == "ols":
            model = linmdl.LinearRegression(fit_intercept=fit,
                                            normalize=normalize,
                                            n_jobs=njob)
        elif method == "ridge":
            model = linmdl.Ridge(alpha=alpha,
                                 fit_intercept=fit,
                                 normalize=normalize,
                                 max_iter=iter,
                                 tol=tol,
                                 solver=solver)
        elif method == "lasso":
            model = linmdl.Lasso(alpha=alpha,
                                 fit_intercept=fit,
                                 normalize=normalize,
                                 max_iter=iter,
                                 tol=tol,
                                 selection=selection)
        elif method == "elast":
            model = linmdl.ElasticNet(alpha=alpha,l1_ratio=l1, fit_intercept=fit, normalize=normalize, \
                                               max_iter=iter, tol=tol,selection=selection)
        model.fit(Reg, X)
        Betas = np.transpose(model.coef_)[1:, :]

        print("Calculating MSE ...")
        MSE = mean_squared_error(X, np.matmul(Design, Betas))
        print("MSE: %f" % (MSE))
        OutData["MSE"] = MSE

        # Calculating Distance Matrix
        dis = np.zeros((np.shape(Betas)[0], np.shape(Betas)[0]))

        for i in range(np.shape(Betas)[0]):
            for j in range(i + 1, np.shape(Betas)[0]):
                dis[i, j] = 1 - np.dot(Betas[i, :], Betas[j, :].T)
                dis[j, i] = dis[i, j]
        OutData["DistanceMatrix"] = dis
        Z = linkage(dis)
        OutData["Linkage"] = Z

        if ui.cbBeta.isChecked():
            OutData["Betas"] = Betas
        # Calculate Results
        if ui.cbCorr.isChecked():
            print("Calculating Correlation ...")
            Corr = np.corrcoef(Betas)
            corClass = SimilarityMatrixBetweenClass(Corr)
            OutData["Correlation"] = Corr
            OutData["Correlation_min"] = corClass.min()
            OutData["Correlation_max"] = corClass.max()
            OutData["Correlation_std"] = corClass.std()
            OutData["Correlation_mean"] = corClass.mean()

        if ui.cbCov.isChecked():
            print("Calculating Covariance ...")
            Cov = np.cov(Betas)
            covClass = SimilarityMatrixBetweenClass(Cov)
            OutData["Covariance"] = Cov
            OutData["Covariance_min"] = covClass.min()
            OutData["Covariance_max"] = covClass.max()
            OutData["Covariance_std"] = covClass.std()
            OutData["Covariance_mean"] = covClass.mean()
        OutData["RunTime"] = time.time() - tStart
        print("Runtime (s): %f" % (OutData["RunTime"]))
        print("Saving results ...")
        io.savemat(OutFile, mdict=OutData, do_compression=True)
        print("Output is saved.")
        FontSize = ui.txtFontSize.value()

        if ui.cbDiagram.isChecked():
            if ui.cbCorr.isChecked():
                NumData = np.shape(Corr)[0]
                fig1 = plt.figure(num=None,
                                  figsize=(NumData, NumData),
                                  dpi=100)
                plt.pcolor(Corr, vmin=np.min(Corr), vmax=np.max(Corr))
                plt.xlim([0, NumData])
                plt.ylim([0, NumData])
                cbar = plt.colorbar()
                cbar.ax.tick_params(labelsize=FontSize)
                ax = plt.gca()
                ax.invert_yaxis()
                ax.set_aspect(1)

                ax.set_yticks(np.arange(NumData) + 0.5, minor=False)
                ax.set_xticks(np.arange(NumData) + 0.5, minor=False)
                ax.set_xticklabels(labels,
                                   minor=False,
                                   fontsize=FontSize,
                                   rotation=ui.txtXRotation.value())
                ax.set_yticklabels(labels,
                                   minor=False,
                                   fontsize=FontSize,
                                   rotation=ui.txtYRotation.value())
                ax.grid(False)
                ax.set_aspect(1)
                ax.set_frame_on(False)
                for t in ax.xaxis.get_major_ticks():
                    t.tick1On = False
                    t.tick2On = False
                for t in ax.yaxis.get_major_ticks():
                    t.tick1On = False
                    t.tick2On = False

                if len(ui.txtTitleCorr.text()):
                    plt.title(ui.txtTitleCorr.text())
                else:
                    plt.title('Correlation (' + ui.cbMethod.currentText() + \
                               ')\nTask: %s\nSub: %d, Counter: %d, Run: %d' % (TaskIDTitle, SubID, ConID, RunID))
                plt.show()

            if ui.cbCov.isChecked():
                NumData = np.shape(Cov)[0]
                fig2 = plt.figure(num=None,
                                  figsize=(NumData, NumData),
                                  dpi=100)
                plt.pcolor(Cov, vmin=np.min(Cov), vmax=np.max(Cov))
                plt.xlim([0, NumData])
                plt.ylim([0, NumData])
                cbar = plt.colorbar()
                cbar.ax.tick_params(labelsize=FontSize)
                ax = plt.gca()
                ax.invert_yaxis()
                ax.set_aspect(1)

                ax.set_yticks(np.arange(NumData) + 0.5, minor=False)
                ax.set_xticks(np.arange(NumData) + 0.5, minor=False)
                ax.set_xticklabels(labels,
                                   minor=False,
                                   fontsize=FontSize,
                                   rotation=ui.txtXRotation.value())
                ax.set_yticklabels(labels,
                                   minor=False,
                                   fontsize=FontSize,
                                   rotation=ui.txtYRotation.value())
                ax.grid(False)
                ax.set_aspect(1)
                ax.set_frame_on(False)
                for t in ax.xaxis.get_major_ticks():
                    t.tick1On = False
                    t.tick2On = False
                for t in ax.yaxis.get_major_ticks():
                    t.tick1On = False
                    t.tick2On = False

                if len(ui.txtTitleCov.text()):
                    plt.title(ui.txtTitleCov.text())
                else:
                    plt.title('Covariance (' + ui.cbMethod.currentText() + \
                               ')\nTask: %s\nSub: %d, Counter: %d, Run: %d' % (TaskIDTitle, SubID, ConID, RunID))
                plt.show()

        fig3 = plt.figure(figsize=(25, 10), )

        if len(ui.txtTitleDen.text()):
            plt.title(ui.txtTitleDen.text())
        else:
            plt.title('Similarity Analysis (' + ui.cbMethod.currentText() + \
                      ')\nTask: %s\nSub: %d, Counter: %d, Run: %d' % (TaskIDTitle, SubID, ConID, RunID))
        dn = dendrogram(Z,
                        labels=labels,
                        leaf_font_size=FontSize,
                        color_threshold=1,
                        leaf_rotation=ui.txtXRotation.value())
        plt.show()

        print("DONE.")
        msgBox.setText("Representational Similarity Analysis (RSA) is done.")
        msgBox.setIcon(QMessageBox.Information)
        msgBox.setStandardButtons(QMessageBox.Ok)
        msgBox.exec_()