t-SNE | t-SNE in python from scratch | Natural Language Processing library
kandi X-RAY | t-SNE Summary
kandi X-RAY | t-SNE Summary
t-SNE in python from scratch
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Top functions reviewed by kandi - BETA
- Gradient descent
- Returns the k nearest neighbour neighbours of x1_index
- Compute the Q matrix of the covariance matrix
- Compute the qijth distance between two points
- Calculates the KL divergence between two values
- Compute the probability matrix
- R Compute the pij
- Compute the Q matrix
t-SNE Key Features
t-SNE Examples and Code Snippets
Community Discussions
Trending Discussions on t-SNE
QUESTION
I am trying to implement a t-SNE visualization in tensorflow for an image classification task. What I mainly found on the net have all been implemented in Pytorch. See here.
Here is my general code for training purposes which works completely fine, just want to add t-SNE visualization to it:
...ANSWER
Answered 2022-Mar-16 at 16:48You could try something like the following:
Train your model
QUESTION
I am using seaborn and and t-SNE to visualise class separability/overlap and in my dataset containing five classes. My plot is thus a 2x2 subplots. I used the following function which generates the figure below.
ANSWER
Answered 2022-Mar-08 at 15:54For this use case, seaborn allows a dictionary as palette. The dictionary will assign a color to each hue value.
Here is an example of how such a dictionary could be created for your data:
QUESTION
I've been working on t-SNE of my data using DBSCAN. I then assign the obtained values to the original dataframe and then plot it with seaborn scatterplot. This is the code:
...ANSWER
Answered 2022-Jan-05 at 09:58If it is the cluster size, you just need to tabulate the results of your DBSCAN, for example in this dataset:
QUESTION
How could I use t-SNE inside my pipeline?
I have managed without pipelining to successfully run t-SNE and on it a classification algorithm.
Do I need to write a custom method that can be called in the pipeline that returns a dataframe, or how does it work?
ANSWER
Answered 2021-Dec-07 at 03:34I think you misunderstood the use of pipeline. From help page:
Pipeline of transforms with a final estimator.
Sequentially apply a list of transforms and a final estimator. Intermediate steps of the pipeline must be ‘transforms’, that is, they must implement fit and transform methods. The final estimator only needs to implement fit
So this means if your pipeline is:
QUESTION
I am trying to use matplotlib scatter plot on Python (Jupyter Notebook) to create a t-sne visualization, with different colors for different points.
I am ashamed to admit that I have mostly borrowed prewritten code, so some of the nuance is far beyond me. However, I am running into a ValueError which I can't seem to solve (even after looking at solutions for similar instances of ValueErrors asked here on Stack Overflow).
Running the scatter (relevant code here) returns the ValueError: RGBA sequence should have length 3 or 4; although this is apparently directly caused by the ValueError: 'c' argument has 470000 elements, which is inconsistent with 'x' and 'y' with size 2500.
...ANSWER
Answered 2021-Oct-30 at 03:19The 4th parameter to pyplot.scatter is a color or set of colors, not a label. scatter has no parameter for labels. I'd just remove the 4th parameter altogether.
QUESTION
So I train a normal Random Forest in Scikit-Learn:
...ANSWER
Answered 2021-Oct-15 at 09:26import pandas as pd
feature_names = [f'feature {i}' for i in range(X_train.shape[1])]
def get_feature_importance(model):
importances = forest.feature_importances_
std = np.std([
tree.feature_importances_ for tree in forest.estimators_], axis=0)
elapsed_time = time.time() - start_time
print(f"Elapsed time to compute the importances: "
f"{elapsed_time:.3f} seconds")
return imporatnces
importances = get_feature_importance(clf_rf) # get important features to randomforest classifier
forest_importances = pd.Series(importances, index=feature_names) # convert to dataframe
# Let’s plot the impurity-based importance.
fig, ax = plt.subplots()
forest_importances.plot.bar(yerr=std, ax=ax)
ax.set_title("Feature importances using MDI")
ax.set_ylabel("Mean decrease in impurity")
fig.tight_layout()
QUESTION
Background:
I'm processing text (dataset with 1000 documents - applying Doc2Vec using Gensim lib), at the end I have a 300 dimension matrix for each doc.
So, I did this to have a 3 dimensional matrix:
...ANSWER
Answered 2021-Sep-21 at 03:08Because I don't have your tsne_x, tsne_y, tsne_z. I send example. In Your code you need split base your Label and use this code.
QUESTION
I used t-SNE to reduce the dimensionality of my data set from 18 to 2, then I used kmeans to cluster the 2D data points.
Using this, print(kmeans.cluster_centers_)
I now have an array of the 2D centroids of the clusters, but I want to get the 18D original data points that these centroids corresponds.
Is there a way to work t-SNE backwards? Thanks!
...ANSWER
Answered 2021-Jul-24 at 17:13Unfortunately the answer is no, there is not.
t-SNE computes a nonlinear mapping of each point individually, based on probability theory. It does not provide a continuously defined function nor its inverse.
You could try to interpolate the 18D coordinates based on the cluster members.
In general you might revisit how much sense it really makes to run k-means on a t-SNE result.
QUESTION
I have a Convolutional neural network (VGG16) that performs well on a classifying task on 26 image classes. Now I want to visualize the data distribution with t-SNE on tensorboard. I removed the last layer of the CNN, therefore the output is the 4096 features. Because the classification works fine (~90% val_accuracy) I expect to see something like a pattern in t-SNE. But no matter what I do, the distribution stays random (-> data is aligned in a circle/sphere and classes are cluttered). Did I do something wrong? Do I misunderstand t-SNE or tensorboard? It´s my first time working with that.
Here´s my code for getting the features:
...ANSWER
Answered 2021-May-15 at 09:31After weeks I stopped trying it with tensorboard. I reduced the number of features in the output layer to 256, 128, 64 and I previously reduced the features with PCA and Truncated SDV but nothing changed.
Now I use sklearn.manifold.TSNE and visualize the output with plotly. This is also easy, works fine and I can see appropriate patterns while t-SNE in tensorboard still produces a random distribution. So I guess for the algorithm in tensorboard it´s too many classes. Or I made a mistake when preparing the data and didn´t notice that (but then why does PCA work?)
If anyone knows what the problem was, I´m still curious. But in case someone else is facing the same problem, I´d recommend trying it with sklearn.
QUESTION
I'm trying to do an interactive plot with bokeh to visualize t-SNE data in a 2D chart. It should display 9 clothes categories. See my code and variables below.
df:
...ANSWER
Answered 2020-Nov-17 at 22:31Unfortunately legend hiding/muting is not compatible with automatically grouped legends. You will need to have a separate call to circle for each group (with a legend_label instead), in order for them to be individually hide-able.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
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Install t-SNE
You can use t-SNE like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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