From 40cee4ae0c8d961082fd60ea05f3ea2f8b5c66ff Mon Sep 17 00:00:00 2001 From: siddhant-0707 Date: Fri, 24 Nov 2023 04:28:26 +0530 Subject: [PATCH 1/2] enabled regression --- examples/pytorch/Bonsai/bonsai_example.py | 7 +- examples/pytorch/Bonsai/helpermethods.py | 54 +++--- pytorch/edgeml_pytorch/graph/bonsai.py | 11 +- .../edgeml_pytorch/trainer/bonsaiTrainer.py | 170 +++++++++++++----- pytorch/edgeml_pytorch/utils.py | 36 ++++ 5 files changed, 207 insertions(+), 71 deletions(-) diff --git a/examples/pytorch/Bonsai/bonsai_example.py b/examples/pytorch/Bonsai/bonsai_example.py index 29570f39f..1a59fd0d0 100644 --- a/examples/pytorch/Bonsai/bonsai_example.py +++ b/examples/pytorch/Bonsai/bonsai_example.py @@ -19,6 +19,9 @@ def main(): # Hyper Param pre-processing args = helpermethods.getArgs() + # Set 'isRegression' to be True, for regression. Default is 'False'. + isRegression = args.regression + sigma = args.sigma depth = args.depth @@ -37,7 +40,7 @@ def main(): outFile = args.output_file (dataDimension, numClasses, Xtrain, Ytrain, Xtest, Ytest, - mean, std) = helpermethods.preProcessData(dataDir) + mean, std) = helpermethods.preProcessData(dataDir, isRegression) sparZ = args.sZ @@ -74,7 +77,7 @@ def main(): # numClasses = 1 for binary case bonsaiObj = Bonsai(numClasses, dataDimension, - projectionDimension, depth, sigma).to(device) + projectionDimension, depth, sigma, isRegression).to(device) bonsaiTrainer = BonsaiTrainer(bonsaiObj, regW, regT, regV, regZ, diff --git a/examples/pytorch/Bonsai/helpermethods.py b/examples/pytorch/Bonsai/helpermethods.py index fbd7bbe55..f4d05bf3f 100644 --- a/examples/pytorch/Bonsai/helpermethods.py +++ b/examples/pytorch/Bonsai/helpermethods.py @@ -113,6 +113,11 @@ def getArgs(): help='Output file for dumping the program output, ' + '(default: stdout)') + parser.add_argument('-regression', type=str2bool, default=False, + help='boolean argument which controls whether to perform ' + + 'regression or classification.' + + 'default : False (Classification) values: [True, False]') + return parser.parse_args() @@ -159,7 +164,7 @@ def createTimeStampDir(dataDir): return None -def preProcessData(dataDir): +def preProcessData(dataDir, isRegression=False): ''' Function to pre-process input data Expects a .npy file of form [lbl feats] for each datapoint @@ -186,29 +191,35 @@ def preProcessData(dataDir): # End Mean Var normalisation # Classification. + if (isRegression == False): + numClasses = max(Ytrain_) - min(Ytrain_) + 1 + numClasses = int(max(numClasses, max(Ytest_) - min(Ytest_) + 1)) - numClasses = max(Ytrain_) - min(Ytrain_) + 1 - numClasses = int(max(numClasses, max(Ytest_) - min(Ytest_) + 1)) + lab = Ytrain_.astype('uint8') + lab = np.array(lab) - min(lab) - lab = Ytrain_.astype('uint8') - lab = np.array(lab) - min(lab) + lab_ = np.zeros((Xtrain.shape[0], numClasses)) + lab_[np.arange(Xtrain.shape[0]), lab] = 1 + if (numClasses == 2): + Ytrain = np.reshape(lab, [-1, 1]) + else: + Ytrain = lab_ - lab_ = np.zeros((Xtrain.shape[0], numClasses)) - lab_[np.arange(Xtrain.shape[0]), lab] = 1 - if (numClasses == 2): - Ytrain = np.reshape(lab, [-1, 1]) - else: - Ytrain = lab_ + lab = Ytest_.astype('uint8') + lab = np.array(lab) - min(lab) - lab = Ytest_.astype('uint8') - lab = np.array(lab) - min(lab) + lab_ = np.zeros((Xtest.shape[0], numClasses)) + lab_[np.arange(Xtest.shape[0]), lab] = 1 + if (numClasses == 2): + Ytest = np.reshape(lab, [-1, 1]) + else: + Ytest = lab_ - lab_ = np.zeros((Xtest.shape[0], numClasses)) - lab_[np.arange(Xtest.shape[0]), lab] = 1 - if (numClasses == 2): - Ytest = np.reshape(lab, [-1, 1]) - else: - Ytest = lab_ + elif (isRegression == True): + # The number of classes is always 1, for regression. + numClasses = 1 + Ytrain = Ytrain_ + Ytest = Ytest_ trainBias = np.ones([Xtrain.shape[0], 1]) Xtrain = np.append(Xtrain, trainBias, axis=1) @@ -218,7 +229,10 @@ def preProcessData(dataDir): mean = np.append(mean, np.array([0])) std = np.append(std, np.array([1])) - return dataDimension + 1, numClasses, Xtrain, Ytrain, Xtest, Ytest, mean, std + if (isRegression == False): + return dataDimension + 1, numClasses, Xtrain, Ytrain, Xtest, Ytest, mean, std + elif (isRegression == True): + return dataDimension + 1, numClasses, Xtrain, Ytrain.reshape((-1, 1)), Xtest, Ytest.reshape((-1, 1)), mean, std def dumpCommand(list, currDir): diff --git a/pytorch/edgeml_pytorch/graph/bonsai.py b/pytorch/edgeml_pytorch/graph/bonsai.py index f14b17ccb..f7a4feeb3 100644 --- a/pytorch/edgeml_pytorch/graph/bonsai.py +++ b/pytorch/edgeml_pytorch/graph/bonsai.py @@ -4,12 +4,13 @@ import torch import torch.nn as nn import numpy as np +import warnings class Bonsai(nn.Module): def __init__(self, numClasses, dataDimension, projectionDimension, - treeDepth, sigma, W=None, T=None, V=None, Z=None): + treeDepth, sigma, isRegression=False, W=None, T=None, V=None, Z=None): super(Bonsai, self).__init__() ''' Expected Dimensions: @@ -32,6 +33,11 @@ def __init__(self, numClasses, dataDimension, projectionDimension, self.dataDimension = dataDimension self.projectionDimension = projectionDimension + self.isRegression = isRegression + + if ((self.isRegression == True) & (numClasses != 1)): + warnings.warn("Number of classes cannot be greater than 1 for regression") + self.numClasses = 1 if numClasses == 2: self.numClasses = 1 @@ -126,6 +132,9 @@ def forward(self, X, sigmaI): return torch.t(self.score), self.X_ def assertInit(self): + errmsg = "Number of Classes for regression can only be 1." + if (self.isRegression == True): + assert (self.numClasses == 1), errmsg errRank = "All Parameters must has only two dimensions shape = [a, b]" assert len(self.W.shape) == len(self.Z.shape), errRank assert len(self.W.shape) == len(self.T.shape), errRank diff --git a/pytorch/edgeml_pytorch/trainer/bonsaiTrainer.py b/pytorch/edgeml_pytorch/trainer/bonsaiTrainer.py index 01c5ff5e0..dd672b43b 100644 --- a/pytorch/edgeml_pytorch/trainer/bonsaiTrainer.py +++ b/pytorch/edgeml_pytorch/trainer/bonsaiTrainer.py @@ -11,7 +11,7 @@ class BonsaiTrainer: def __init__(self, bonsaiObj, lW, lT, lV, lZ, sW, sT, sV, sZ, - learningRate, useMCHLoss=False, outFile=None, device=None): + learningRate, useMCHLoss=False, outFile=None, device=None, regLoss='huber'): ''' bonsaiObj - Initialised Bonsai Object and Graph lW, lT, lV and lZ are regularisers to Bonsai Params @@ -23,6 +23,7 @@ def __init__(self, bonsaiObj, lW, lT, lV, lZ, sW, sT, sV, sZ, ''' self.bonsaiObj = bonsaiObj + self.regressionLoss = regLoss self.lW = lW self.lV = lV @@ -67,15 +68,27 @@ def loss(self, logits, labels): self.lV * (torch.norm(self.bonsaiObj.V)**2) + self.lT * (torch.norm(self.bonsaiObj.T))**2) - if (self.bonsaiObj.numClasses > 2): - if self.useMCHLoss is True: - marginLoss = utils.multiClassHingeLoss(logits, labels) + # Loss functions for classification. + if (self.bonsaiObj.isRegression is False): + if (self.bonsaiObj.numClasses > 2): + if self.useMCHLoss is True: + marginLoss = utils.multiClassHingeLoss(logits, labels) + else: + marginLoss = utils.crossEntropyLoss(logits, labels) + loss = marginLoss + regLoss else: - marginLoss = utils.crossEntropyLoss(logits, labels) - loss = marginLoss + regLoss - else: - marginLoss = utils.binaryHingeLoss(logits, labels) - loss = marginLoss + regLoss + marginLoss = utils.binaryHingeLoss(logits, labels) + loss = marginLoss + regLoss + # Loss functions for regression. + elif (self.bonsaiObj.isRegression is True): + if(self.regressionLoss == 'huber'): + # Use of Huber Loss , because it is more robust to outliers. + marginLoss = utils.huberLoss(logits, labels) + loss = marginLoss + regLoss + elif (self.regressionLoss == 'l2'): + # L2 loss function. + marginLoss = utils.l2Loss(logits, labels) + loss = marginLoss + regLoss return loss, marginLoss, regLoss @@ -92,13 +105,17 @@ def accuracy(self, logits, labels): ''' Accuracy fucntion to evaluate accuracy when needed ''' - if (self.bonsaiObj.numClasses > 2): - correctPredictions = (logits.argmax(dim=1) == labels.argmax(dim=1)) - accuracy = torch.mean(correctPredictions.float()) - else: - pred = (torch.cat((torch.zeros(logits.shape), - logits), 1)).argmax(dim=1) - accuracy = torch.mean((labels.view(-1).long() == pred).float()) + if(self.bonsaiObj.isRegression is False): + if (self.bonsaiObj.numClasses > 2): + correctPredictions = (logits.argmax(dim=1) == labels.argmax(dim=1)) + accuracy = torch.mean(correctPredictions.float()) + else: + pred = (torch.cat((torch.zeros(logits.shape), + logits), 1)).argmax(dim=1) + accuracy = torch.mean((labels.view(-1).long() == pred).float()) + elif (self.bonsaiObj.isRegression is True): + # Accuracy for regression , in terms of mean absolute error. + accuracy = utils.mean_absolute_error(logits.view(-1, 1), labels.view(-1, 1)) return accuracy @@ -253,7 +270,10 @@ def train(self, batchSize, totalEpochs, trimlevel = 5 ihtDone = 0 - maxTestAcc = -10000 + if (self.bonsaiObj.isRegression is True): + maxTestAcc = 100000007 + else: + maxTestAcc = -10000 if self.isDenseTraining is True: ihtDone = 1 self.sigmaI = 1 @@ -264,6 +284,7 @@ def train(self, batchSize, totalEpochs, print("\nEpoch Number: " + str(i), file=self.outFile) ''' + trainAcc -> For Regression, it is 'Mean Absolute Error'. trainAcc -> For Classification, it is 'Accuracy'. ''' trainAcc = 0.0 @@ -325,9 +346,13 @@ def train(self, batchSize, totalEpochs, self.optimizer.step() # Classification. - - trainAcc += batchAcc.item() - trainLoss += batchLoss.item() + if (self.bonsaiObj.isRegression is False): + trainAcc += batchAcc.item() + trainLoss += batchLoss.item() + # Regression. + else: + trainAcc += np.mean(batchAcc.item()) + trainLoss += np.mean(batchLoss.item()) # Training routine involving IHT and sparse retraining if (counter >= int(totalBatches / 3.0) and @@ -352,11 +377,20 @@ def train(self, batchSize, totalEpochs, print("\n%s%s%s\n" % (header, msg, header), file=self.outFile) counter += 1 - - print("\nClassification Train Loss: " + str(trainLoss / numIters) + - "\nTraining accuracy (Classification): " + - str(trainAcc / numIters), - file=self.outFile) + + try: + if (self.bonsaiObj.isRegression is True): + print("\nRegression Train Loss: " + str(trainLoss / numIters) + + "\nTraining MAE (Regression): " + + str(trainAcc / numIters), + file=self.outFile) + else: + print("\nClassification Train Loss: " + str(trainLoss / numIters) + + "\nTraining accuracy (Classification): " + + str(trainAcc / numIters), + file=self.outFile) + except: + continue oldSigmaI = self.sigmaI self.sigmaI = 1e9 @@ -366,19 +400,41 @@ def train(self, batchSize, totalEpochs, testAcc = self.accuracy(logits, Ytest.to(self.device)).item() if ihtDone == 0: - maxTestAcc = -10000 - maxTestAccEpoch = i - else: - if maxTestAcc <= testAcc: + if (self.bonsaiObj.isRegression is False): + maxTestAcc = -10000 maxTestAccEpoch = i + elif (self.bonsaiObj.isRegression is True): maxTestAcc = testAcc - self.saveParams(currDir) - self.saveParamsForSeeDot(currDir) + maxTestAccEpoch = i - print("Test accuracy %g" % testAcc, file=self.outFile) + else: + if (self.bonsaiObj.isRegression is False): + if maxTestAcc <= testAcc: + maxTestAccEpoch = i + maxTestAcc = testAcc + self.saveParams(currDir) + self.saveParamsForSeeDot(currDir) + elif (self.bonsaiObj.isRegression is True): + print("Minimum Training MAE : ", np.mean(maxTestAcc)) + if maxTestAcc >= testAcc: + # For regression , we're more interested in the minimum + # MAE. + maxTestAccEpoch = i + maxTestAcc = testAcc + self.saveParams(currDir) + self.saveParamsForSeeDot(currDir) + + if (self.bonsaiObj.isRegression is False): + print("Test accuracy %g" % testAcc, file=self.outFile) + else: + print("Testing MAE %g" % np.mean(testAcc), file=self.outFile) - testAcc = testAcc - maxTestAcc = maxTestAcc + if (self.bonsaiObj.isRegression is True): + testAcc = np.mean(testAcc) + maxTestAcc = np.mean(maxTestAcc) + else: + testAcc = testAcc + maxTestAcc = maxTestAcc print("MarginLoss + RegLoss: " + str(marginLoss.item()) + " + " + str(regLoss.item()) + " = " + str(testLoss.item()) + "\n", @@ -394,20 +450,38 @@ def train(self, batchSize, totalEpochs, str(float(self.getModelSize()[1]) / 1024.0) + " KB hasSparse: " + str(self.getModelSize()[2]) + "\n", file=self.outFile) - print("For Classification, Maximum Test accuracy at compressed" + - " model size(including early stopping): " + - str(maxTestAcc) + " at Epoch: " + - str(maxTestAccEpoch + 1) + "\nFinal Test" + - " Accuracy: " + str(testAcc), file=self.outFile) - - resultFile.write("MaxTestAcc: " + str(maxTestAcc) + - " at Epoch(totalEpochs): " + - str(maxTestAccEpoch + 1) + - "(" + str(totalEpochs) + ")" + " ModelSize: " + - str(float(self.getModelSize()[1]) / 1024.0) + - " KB hasSparse: " + str(self.getModelSize()[2]) + - " Param Directory: " + - str(os.path.abspath(currDir)) + "\n") + if (self.bonsaiObj.isRegression is False): + print("For Classification, Maximum Test accuracy at compressed" + + " model size(including early stopping): " + + str(maxTestAcc) + " at Epoch: " + + str(maxTestAccEpoch + 1) + "\nFinal Test" + + " Accuracy: " + str(testAcc), file=self.outFile) + + resultFile.write("MaxTestAcc: " + str(maxTestAcc) + + " at Epoch(totalEpochs): " + + str(maxTestAccEpoch + 1) + + "(" + str(totalEpochs) + ")" + " ModelSize: " + + str(float(self.getModelSize()[1]) / 1024.0) + + " KB hasSparse: " + str(self.getModelSize()[2]) + + " Param Directory: " + + str(os.path.abspath(currDir)) + "\n") + + elif (self.bonsaiObj.isRegression is True): + print("For Regression, Minimum MAE at compressed" + + " model size(including early stopping): " + + str(maxTestAcc) + " at Epoch: " + + str(maxTestAccEpoch + 1) + "\nFinal Test" + + " MAE: " + str(testAcc), file=self.outFile) + + resultFile.write("MinTestMAE: " + str(maxTestAcc) + + " at Epoch(totalEpochs): " + + str(maxTestAccEpoch + 1) + + "(" + str(totalEpochs) + ")" + " ModelSize: " + + str(float(self.getModelSize()[1]) / 1024.0) + + " KB hasSparse: " + str(self.getModelSize()[2]) + + " Param Directory: " + + str(os.path.abspath(currDir)) + "\n") + print("The Model Directory: " + currDir + "\n") resultFile.close() diff --git a/pytorch/edgeml_pytorch/utils.py b/pytorch/edgeml_pytorch/utils.py index 120a13750..402e310d7 100644 --- a/pytorch/edgeml_pytorch/utils.py +++ b/pytorch/edgeml_pytorch/utils.py @@ -36,6 +36,36 @@ def findCUDA(): cuda_home = None return cuda_home + +def huberLoss(logits, labels, delta=1.0, reduction='mean'): + ''' + Huber Loss for Regression + ''' + loss = torch.where(torch.abs(logits - labels) < delta, + 0.5 * torch.square(logits - labels), + delta * (torch.abs(logits - labels) - 0.5 * delta)) + + if reduction == 'mean': + return torch.mean(loss) + elif reduction == 'sum': + return torch.sum(loss) + else: + return loss + + +def l2Loss(logits, labels, reduction='mean'): + ''' + L2 Loss for Regression + ''' + loss = torch.pow(logits - labels, 2) + if reduction == 'mean': + return torch.mean(loss) + elif reduction == 'sum': + return torch.sum(loss) + else: + return loss + + def multiClassHingeLoss(logits, labels): ''' MultiClassHingeLoss to match C++ Version - No pytorch internal version @@ -70,6 +100,12 @@ def binaryHingeLoss(logits, labels): return torch.mean(F.relu(1.0 - (2 * labels - 1) * logits)) +def mean_absolute_error(logits, labels): + ''' + Mean Absolute Error + ''' + return torch.mean(torch.abs(logits - labels)) + def hardThreshold(A: torch.Tensor, s): ''' Hard thresholds and modifies in-palce nn.Parameter A with sparsity s From bfcfd176cb69d7c850c69cd27b06d0d2ab183cd6 Mon Sep 17 00:00:00 2001 From: siddhant-0707 Date: Mon, 20 May 2024 17:58:21 +0530 Subject: [PATCH 2/2] tests --- .../Bonsai/abalone/PyTorchBonsaiResults.txt | 4 + .../04_42_31_30_11_23/SeeDot/Mean | 9 + .../04_42_31_30_11_23/SeeDot/Sigma | 1 + .../04_42_31_30_11_23/SeeDot/Std | 9 + .../04_42_31_30_11_23/SeeDot/T | 3 + .../04_42_31_30_11_23/SeeDot/V | 7 + .../04_42_31_30_11_23/SeeDot/W | 7 + .../04_42_31_30_11_23/SeeDot/Z | 2 + .../04_42_31_30_11_23/T.npy | Bin 0 -> 152 bytes .../04_42_31_30_11_23/V.npy | Bin 0 -> 184 bytes .../04_42_31_30_11_23/W.npy | Bin 0 -> 184 bytes .../04_42_31_30_11_23/Z.npy | Bin 0 -> 200 bytes .../04_42_31_30_11_23/command.txt | 1 + .../04_42_31_30_11_23/hyperParam.npy | Bin 0 -> 368 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8:0.2605 +9 1:1 2:0.6 3:0.475 4:0.205 5:1.176 6:0.5255 7:0.2875 8:0.308 +10 1:2 2:0.625 3:0.485 4:0.15 5:1.0945 6:0.531 7:0.261 8:0.296 +12 1:1 2:0.71 3:0.555 4:0.195 5:1.9485 6:0.9455 7:0.3765 8:0.495 diff --git a/examples/pytorch/Bonsai/abalone/pre_process.py b/examples/pytorch/Bonsai/abalone/pre_process.py new file mode 100644 index 000000000..fb9fbf373 --- /dev/null +++ b/examples/pytorch/Bonsai/abalone/pre_process.py @@ -0,0 +1,30 @@ +import random + +def split_data(input_file, train_file, test_file, test_ratio=0.2): + # Read the data from the file + with open(input_file, 'r') as file: + data = file.readlines() + + # Shuffle the data to randomize it + random.shuffle(data) + + # Split the data into training and testing sets + split_index = int(len(data) * test_ratio) + test_data = data[:split_index] + train_data = data[split_index:] + + # Write the training and testing data to separate files + with open(train_file, 'w') as file: + file.writelines(train_data) + + with open(test_file, 'w') as file: + file.writelines(test_data) + + print(f"Data split completed. Training data: {len(train_data)} lines, Testing data: {len(test_data)} lines") + +# Usage +input_file = 'abalone.txt' # Replace with the path to your input data file +train_file = 'train.txt' # Path to save the new training data +test_file = 'test.txt' # Path to save the testing data + +split_data(input_file, train_file, test_file) diff --git a/examples/pytorch/Bonsai/abalone/test.npy b/examples/pytorch/Bonsai/abalone/test.npy new file mode 100644 index 0000000000000000000000000000000000000000..7ecd31005632acb3216b706913469ec6651f4ee2 GIT binary patch literal 60248 zcmbWAcaWv&Ro+``it3^$ilQhR+cH`=m|bjPz=#4I>bn>l2TZWBUD!q0U`v#Wl0ja{ zYgvE@LST>uR!)RX((H=SXmXy}&S`pjI_Ge2=gGIbC+utxp1#lXJE!`)%s-w#bk*Da zz4v|3d%|;`bKdv+_)q-1AOETU@TQihTYlw{%^im==pE)IQ8Ne8vj;`UupcoLa{gM!vTxq{ZC)~ z)4z7zKhG8a>IZ)BhktIpztHy%d7L-LIMdI+`yYMX^s2vSDaLvIrR}jF|1aCc6W`l< z@Xi0RTJ-(c-awUMP__ta#miM#EUe{o8d1&w7pL_gz(U{K2T=BKP z`N<#o@Vk~v?|LfB$Uf+_}*IhjJcYgOT&kolM{eG$F7<~4hp4`1$Je>T) 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b/examples/pytorch/Bonsai/bank/bank-8nh/convert_to_libsvm.py new file mode 100644 index 000000000..0c48d33c0 --- /dev/null +++ b/examples/pytorch/Bonsai/bank/bank-8nh/convert_to_libsvm.py @@ -0,0 +1,20 @@ +def convert_to_libsvm(input_file, output_file): + with open(input_file, 'r') as infile, open(output_file, 'w') as outfile: + for line in infile: + # Split the line into a list of values + values = line.strip().split() + + # The last value is the target variable + target = values[-1] + + # Create a list of "index:value" pairs for the features + features = [f"{i + 1}:{v}" for i, v in enumerate(values[:-1])] + + # Join the target and features into the libsvm format + libsvm_line = f"{target} {' '.join(features)}\n" + + # Write the line to the output file + outfile.write(libsvm_line) + +# Example usage +convert_to_libsvm('bank-8nh.txt', 'libsvm-formatted.txt') diff --git a/examples/pytorch/Bonsai/bank/bank-8nh/pre_process.py b/examples/pytorch/Bonsai/bank/bank-8nh/pre_process.py new file mode 100644 index 000000000..077079cab --- /dev/null +++ b/examples/pytorch/Bonsai/bank/bank-8nh/pre_process.py @@ -0,0 +1,30 @@ +import random + +def split_data(input_file, train_file, test_file, test_ratio=0.2): + # Read the data from the file + with open(input_file, 'r') as file: + data = file.readlines() + + # Shuffle the data to randomize it + random.shuffle(data) + + # Split the data into training and testing sets + split_index = int(len(data) * test_ratio) + test_data = data[:split_index] + train_data = data[split_index:] + + # Write the training and testing data to separate files + with open(train_file, 'w') as file: + file.writelines(train_data) + + with open(test_file, 'w') as file: + file.writelines(test_data) + + print(f"Data split completed. Training data: {len(train_data)} lines, Testing data: {len(test_data)} lines") + +# Usage +input_file = 'bank-8nh.txt' # Replace with the path to your input data file +train_file = 'train.txt' # Path to save the new training data +test_file = 'test.txt' # Path to save the testing data + +split_data(input_file, train_file, test_file) diff --git a/examples/pytorch/Bonsai/bank/bank-8nh/test.npy b/examples/pytorch/Bonsai/bank/bank-8nh/test.npy new file mode 100644 index 0000000000000000000000000000000000000000..2cf9a3f49bc5272cc61470161ef81298cb26ba29 GIT binary patch literal 118064 zcmbSzcRbba`#%v;QAvBK6pBhI60VaL4VqdSN~Ni2p(UiDt*oXQZ4sevd+)tD$8j9a zvO~u2ex1|n^Ljkq=lj>sf7PSg{kpE}zQ*%^wX;4-{QWws)AmR4-`m?*ojZ#EtggL$sh+0F3Jpz_YbyVL zKk^d)e%hbYHgGG0VLv*gdwO3wq{|xm?VQ;N8=1G3_WVtPa;;%Uu1OWZLD}5NTc`Ac zTJ?esi<)K-{dW}_dCr4F4SkodHG$rS1Fw$I${@J?l55c|It*FrSQ~6$z!|B_Z_Rpp z;pD`kO@H)%LP)qe*WZRKcFxr!BP1UFVxSR5G7HQiUx3C}iQrMw1dx5rZoG;-53yuc zPq&CJhtkKuE?2PQ|T2TWBJ#{pLej( 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6:0.968744 7:0.572676 8:6 +0.069767 1:-0.658339 2:0.715641 3:0.510117 4:0.578895 5:3.316337 6:1.285620 7:0.400916 8:8 diff --git a/examples/pytorch/Bonsai/bank/convert_to_libsvm.py b/examples/pytorch/Bonsai/bank/convert_to_libsvm.py new file mode 100644 index 000000000..bacd80288 --- /dev/null +++ b/examples/pytorch/Bonsai/bank/convert_to_libsvm.py @@ -0,0 +1,20 @@ +def convert_to_libsvm(input_file, output_file): + with open(input_file, 'r') as infile, open(output_file, 'w') as outfile: + for line in infile: + # Split the line into a list of values + values = line.strip().split() + + # The last value is the target variable + target = values[-1] + + # Create a list of "index:value" pairs for the features + features = [f"{i + 1}:{v}" for i, v in enumerate(values[:-1])] + + # Join the target and features into the libsvm format + libsvm_line = f"{target} {' '.join(features)}\n" + + # Write the line to the output file + outfile.write(libsvm_line) + +# Example usage 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