One hot encoding

I have my label tensor of shape (1,1,128,128,128) in which the values might range from 0,24. I want to convert this to one hot encoded tensor, using the nn.fucntional.one_hot function n = 24 one_hot = torch.nn.functional.one_hot(indices, n) but this expects a tensor of indices, honestly, I am not sure how to get those. The only tensor I have is the label tensor of the shape described above and it contains values ranging from 1-24, not the indices How can I get a tensor of indices from my tensor? Thanks in advance. If the error you are getting is this one: Traceback (most recent call last): File "", line 1, in RuntimeError: one_hot is only applicable to index tensor. Maybe you just need to convert to int64: import torch # random Tensor with the shape you said indices = torch.Tensor(1, 1, 128, 128, 128).random_(1, 24) # indices.shape => torch.Size([1, 1, 128, 128, 128]) # indices.dtype => torch.float32 n = 24 one_hot = torch.nn.functional.one_hot(indices.to(torch.int64), n) # one_hot.shape => torch.Size([1, 1, 128, 128, 128, 24]) # one_hot.dtype => torch.int64 You can use indices.long() too. The torch.as_tensor function can also be helpful if your labels are stored in a list or numpy array: import torch import random n_classes = 5 n_samples = 10 # Create list n_samples random labels (can also be numpy array) labels = [random.randrange(n_classes) for _ in range(n_samples)] # Convert to torch Tensor labels_tensor = torch.as_tensor(labels) # Create one-hot encodings of labels ...

Often in machine learning, we want to convert There are two common ways to convert categorical variables into numeric variables: 1. Label Encoding: Assign each categorical value an integer value based on alphabetical order. 2. One Hot Encoding: Create new variables that take on values 0 and 1 to represent the original categorical values. For example, suppose we have the following dataset with two variables and we would like to convert the Team variable from a categorical variable into a numeric one: The following examples show how to use both label encoding and one hot encoding to do so. Example: Using Label Encoding Using label encoding, we would convert each unique value in the Team column into an integer value based on alphabetical order: In this example, we can see: • Each “A” value has been converted to 0. • Each “B” value has been converted to 1. • Each “C” value has been converted to 2. We have successfully converted the Team column from a categorical variable into a numeric variable. Example: Using One Hot Encoding Using one hot encoding, we would convert the Team column into new variables that contain only 0 and 1 values: When using this approach, we create one new column for each unique value in the original categorical variable. For example, the categorical variable Team had three unique values so we created three new columns in the dataset that all contain 0 or 1 values. Here’s how to interpret the values in the new columns: • The value in the new Team_A column...

One-hot encoding is used to convert categorical variables into a format that can be readily used by The basic idea of one-hot encoding is to create new variables that take on values 0 and 1 to represent the original categorical values. For example, the following image shows how we would perform one-hot encoding to convert a categorical variable that contains team names into new variables that contain only 0 and 1 values: The following step-by-step example shows how to perform one-hot encoding for this exact dataset in Python. Step 1: Create the Data First, let’s create the following pandas DataFrame: import pandas as pd #create DataFrame df = pd. DataFrame() #view DataFrame print(df) team points 0 A 25 1 A 12 2 B 15 3 B 14 4 B 19 5 B 23 6 C 25 7 C 29 Step 2: Perform One-Hot Encoding Next, let’s import the OneHotEncoder() function from the sklearn library and use it to perform one-hot encoding on the ‘team’ variable in the pandas DataFrame: from sklearn. preprocessing import OneHotEncoder #creating instance of one-hot-encoder encoder = OneHotEncoder(handle_unknown=' ignore') #perform one-hot encoding on 'team' column encoder_df = pd. DataFrame(encoder. fit_transform(df[[' team']]). toarray()) #merge one-hot encoded columns back with original DataFrame final_df = df. join(encoder_df) #view final df print(final_df) team points 0 1 2 0 A 25 1.0 0.0 0.0 1 A 12 1.0 0.0 0.0 2 B 15 0.0 1.0 0.0 3 B 14 0.0 1.0 0.0 4 B 19 0.0 1.0 0.0 5 B 23 0.0 1.0 0.0 6 C 25 0.0 0.0 1.0 7 C 29 0.0 0...

Machine learning has revolutionized the way we relate to software. It is, in many ways, a bridge between the physical and digital worlds. Machine learning systems can take in large amounts of data and highlight elements that might be invisible to the human eye. However, there’s one notable caveat to most forms of machine learning. Machine learning systems can’t simply take in undefined or unformatted data in the same way a human can. We need to specifically format data for machine learning systems. Every language and platform has specific methods that can be used to create a unified data format. And R, in particular, has some powerful built-in functionality that can be used to create compatible data sets for machine learning. One-shot encoding in particular is an easy-to-use solution within R. You’ll soon discover the best ways to leverage this functionality within your own code. An Overview of One-Hot Encoding One hot encoding tackles a fundamental problem with logic-based comparisons. How do you work with a binary comparison when you’re using an incompatible collection of data? The simple answer is that we just need to convert that data into a binary collection. With one-hot encoding, that means translating a data set into multiple fields consisting of either a 0 or 1 value. One-hot encoding brings with it a number of significant benefits. Of course, the most obvious benefit comes from pure compatibility with functions that require a particular formatting style. But you’...

I'm working on a prediction problem and I'm building a decision tree in R, I have several categorical variables and I'd like to one-hot encode them consistently in my training and testing set. I managed to do it on my training data with : temps <- X_train tt <- subset(temps, select = -output) oh <- data.frame(model.matrix(~ . -1, tt), CLASS = temps$output) But I can't find a way to apply the same encoding on my testing set, how can I do that? I recommend using the dummyVars function in the caret package: customers <- data.frame( id=c(10, 20, 30, 40, 50), gender=c('male', 'female', 'female', 'male', 'female'), mood=c('happy', 'sad', 'happy', 'sad','happy'), outcome=c(1, 1, 0, 0, 0)) customers id gender mood outcome 1 10 male happy 1 2 20 female sad 1 3 30 female happy 0 4 40 male sad 0 5 50 female happy 0 # dummify the data dmy <- dummyVars(" ~ .", data = customers) trsf <- data.frame(predict(dmy, newdata = customers)) trsf id gender.female gender.male mood.happy mood.sad outcome 1 10 0 1 1 0 1 2 20 1 0 0 1 1 3 30 1 0 1 0 0 4 40 0 1 0 1 0 5 50 1 0 1 0 0 example You apply the same procedure to both the training and validation sets. Here's a simple solution to one-hot-encode your category using no packages. Solution model.matrix(~0+category) It needs your categorical variable to be a factor. The factor levels must be the same in your training and test data, check with levels(train$category) and levels(test$category). It doesn't matter if some levels don't occur in your test s...