tsne | A python wrapper for Barnes-Hut tsne | Machine Learning library

by danielfrg C++ Version: 0.3.1 License: Apache-2.0

X-Ray Key Features Code Snippets(1)Community Discussions(10)Vulnerabilities Install Support

kandi X-RAY | tsne Summary

tsne is a C++ library typically used in Artificial Intelligence, Machine Learning applications. tsne has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

Python library containing T-SNE algorithms. Note: Scikit-learn v0.17 includes TSNE algorithms and you should probably be using that instead.

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Quality

Security

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Support

tsne has a low active ecosystem.

It has 386 star(s) with 130 fork(s). There are 25 watchers for this library.

It had no major release in the last 12 months.

There are 7 open issues and 20 have been closed. On average issues are closed in 677 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of tsne is 0.3.1

Quality

tsne has no bugs reported.

Security

tsne has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

tsne is licensed under the Apache-2.0 License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

tsne releases are not available. You will need to build from source code and install.

Installation instructions are not available. Examples and code snippets are available.

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tsne Key Features

No Key Features are available at this moment for tsne.

tsne Examples and Code Snippets

Use TSNE .

python

Lines of Code : 7

License : No License

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def main():
    tsne = TSNE(perplexity=40)
    Z = tsne.fit_transform(X)
    plt.scatter(Z[:,0], Z[:,1])
    for i in range(D):
        plt.annotate(s=index_word_map[i], xy=(Z[i,0], Z[i,1]))
    plt.show()

Community Discussions

Trending Discussions on tsne

How we can check if TSNE results are real when we cluster data?

Notebook validation failed: Additional properties are not allowed ('id' was unexpected):

How to use fit_transform with an array?

Equalize the range of the values in legends for multiple ggplots

TSNE plot after clustering

python scatterplot - add legends

Error with matplotlib scatter plot due to color palette

R: formatting plotly hover text

R: Plotting 3D Objects in R (iris data)

R: "cex" option for ggplot2 and plotly

QUESTION

How we can check if TSNE results are real when we cluster data?

Asked 2021-May-24 at 16:08

I am apply TSNE for dimensionality reduction. I have several features that I reduce to 2 features. After, I use Kmeans to cluster the data. Finally, I use seaborn to plot the clustering results.

To import TSNE I use:

...

ANSWER

Answered 2021-May-24 at 14:53

Is something wrong with the procedure I follow?

Yes.

Using TSNE projects data onto another space, on which you have no real control.
Doing so is supposed to keep close points close, and far points far.

You then use KNN on the projected space to determine the groups.
This part loses any grouping information you previously had [citation needed, need to see what the data was beforehand]!

It would make much more sense to color the groups according to some prior labeled data, not according to KNN
-OR-
to use KNN on the original space for grouping, and then to color the projected space according to that grouping.

What you did in fact is meaningless, as it loses all prior information - labels and spatial.

To conclude:

If you have labels, use them.
If you don't, use a more sophisticated clustering algorithm, starting with KNN on the original space, as you can see KNN on the projected space is not enough.

Source https://stackoverflow.com/questions/67673905

QUESTION

Notebook validation failed: Additional properties are not allowed ('id' was unexpected):

Asked 2021-May-18 at 03:30

Getting below validation error on opening my notebook :

{ "metadata": { "trusted": true }, "id": "comparative-import", "cell_type": "code", "source": "import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport nltk\nimport re\nimport gensim \nfrom gensim.utils import simple_preprocess\nfrom gensim.models.word2vec import Word2Vec\nfrom nltk.stem.porter import PorterStemmer\nfrom nltk.corpus import stopwords\nfrom sklearn.decomposition import PCA,TruncatedSVD\nfrom sklearn.manifold import TSNE\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\nfrom wordcloud import WordCloud, STOPWORDS, ImageColorGenerator\n", "execution_count": 10, "outputs": [] }

...

ANSWER

Answered 2021-Apr-09 at 05:59

Your notebook is just a bunch of import you can easily recreate it for this time I did it for you :

Source https://stackoverflow.com/questions/67015684

QUESTION

How to use fit_transform with an array?

Asked 2021-May-13 at 20:42

Example of array content:

...

ANSWER

Answered 2021-May-13 at 12:26

Try this example:

Source https://stackoverflow.com/questions/67518777

QUESTION

Equalize the range of the values in legends for multiple ggplots

Asked 2021-Apr-26 at 12:30

I have a huge data frame with multiple columns that I would like to make multiple tsne plot based on two first columns and colored by other variables in each plot. Here is a sample of my data frame like this:

...

ANSWER

Answered 2021-Apr-26 at 12:30

You can easily do this with the wonderful patchwork package:

Source https://stackoverflow.com/questions/67266198

QUESTION

TSNE plot after clustering

Asked 2021-Apr-13 at 09:33

I applied K_Mean clustering on data and after I applied TSNE to plot the data. I have 4 dimension and 4 groups. The problem is my K_mean is correct but why with tsne, the same group are not all together?

...

ANSWER

Answered 2021-Apr-13 at 09:33

from sklearn.manifold import TSNE
import seaborn as sns

X_embedded = TSNE(n_components=2,random_state=42).fit_transform(X)
centers = np.array(kmeans.cluster_centers_)
model = KMeans(n_clusters = 4, init = "k-means++")
label = model.fit_predict(X_embedded)


plt.figure(figsize=(10,10))
uniq = np.unique(label)
for i in uniq:
   plt.scatter(data[label == i , 0] , data[label == i , 1] , label = i)
plt.scatter(centers[:,0], centers[:,1], marker="x", color='k')
#This is done to find the centroid for each clusters.
plt.legend()
plt.show()

Source https://stackoverflow.com/questions/67071758

QUESTION

python scatterplot - add legends

Asked 2021-Apr-09 at 16:25

I have written the following function to use scatterplot to plot the data

...

ANSWER

Answered 2021-Apr-08 at 09:53

You need to use your labels in order for your legend to appear. Try this:

Source https://stackoverflow.com/questions/67000807

QUESTION

Error with matplotlib scatter plot due to color palette

Asked 2021-Mar-31 at 00:59

I used this method to crate a scatter plot for another model for mnist dataset, and it works fine for the other model and I cannot figure out what I did wrong with this other model.

The method is

...

ANSWER

Answered 2021-Mar-31 at 00:59

I found out what was the issue after some googling and running into a few dead ins. The code I posted works fine. The labels I was using were converted into categorical using

Source https://stackoverflow.com/questions/66863527

QUESTION

R: formatting plotly hover text

Asked 2020-Dec-25 at 22:29

I am using the R programming language. I trying to learn how to customize hover text in 3d plotly objects as seen here: https://rstudio-pubs-static.s3.amazonaws.com/441420_9a7c15988f3c4f59b2d828eb87ba1634.html

Recently, I have learned how to create a 3d plotly object for some data that I simulated :

...

ANSWER

Answered 2020-Dec-25 at 22:29

Not sure what exactly you want to do with the predicted classes, but maybe something like this? (Color corresponds to real species, mouseover also shows prediction).

Source https://stackoverflow.com/questions/65434105

QUESTION

R: Plotting 3D Objects in R (iris data)

Asked 2020-Dec-24 at 02:42

I am using the R programming language. I am trying to follow this tutorial over here: http://www.semspirit.com/artificial-intelligence/machine-learning/regression/support-vector-regression/support-vector-regression-in-r/

For the famous Iris dataset, I am trying to plot the 3D decision surface for the random forest algorithm (using tsne dimensions):

...

ANSWER

Answered 2020-Dec-24 at 02:42

When you called add_trace(), z is not assigned correctly. The labels won't plot; you need to plot the probabilities you identified, z=df_m2$pred.

There are multiple ways to fix the issues with the mesh plot, but the easiest would be to use add_mesh instead of add_trace.

Source https://stackoverflow.com/questions/65432879

QUESTION

R: "cex" option for ggplot2 and plotly

Asked 2020-Dec-23 at 19:49

I am using the R programming language. I am trying to figure out how to "recreate" plots in ggplot2/plotly, once they have been created in base R.

For example, I created some data and made a plot :

...

ANSWER

Answered 2020-Dec-23 at 19:49

You can vary the point size based on lof. The tooltip in the ggplotly graph can also be adjusted to show lof and name.

Edit: Added var1, var2 and var3 to the tooltip

Source https://stackoverflow.com/questions/65420500

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install tsne

You can download it from GitHub.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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