t-distributed Stochastic Neighbor Embedding(T-SNE) is an unsupervised machine learning algorithm that is used for dimension reduction. More information about it can be found here.
You can learn more about t-SNE in the below video.
The below code snippet helps to reduce the dimensions of any given data using t-SNE.
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# Sample code to do T-SNE in Python %matplotlib inline from sklearn.manifold import TSNE import matplotlib.pyplot as plt #Creating the employee attitude survey data for t-SNE rating=[43,63,71,61,81,43,58,71,72,67,64,67,69,68,77,81,74,65,65,50,50,64,53,40,63,66,78,48,85,82] complaints=[51,64,70,63,78,55,67,75,82,61,53,60,62,83,77,90,85,60,70,58,40,61,66,37,54,77,75,57,85,82] privileges=[30,51,68,45,56,49,42,50,72,45,53,47,57,83,54,50,64,65,46,68,33,52,52,42,42,66,58,44,71,39] learning=[39,54,69,47,66,44,56,55,67,47,58,39,42,45,72,72,69,75,57,54,34,62,50,58,48,63,74,45,71,59] raises=[61,63,76,54,71,54,66,70,71,62,58,59,55,59,79,60,79,55,75,64,43,66,63,50,66,88,80,51,77,64] critical=[92,73,86,84,83,49,68,66,83,80,67,74,63,77,77,54,79,80,85,78,64,80,80,57,75,76,78,83,74,78] advance=[45,47,48,35,47,34,35,41,31,41,34,41,25,35,46,36,63,60,46,52,33,41,37,49,33,72,49,38,55,39] #Joining all the vectors together to form input matrix X SurveyData=list(zip(rating,complaints,privileges,learning,raises,critical,advance)) import pandas as pd InpData=pd.DataFrame(data=SurveyData, columns=["rating","complaints","privileges","learning","raises","critical","advance"]) print(InpData.head(10)) #Creating input data numpy array X=InpData.values ####################################################################### #Reducing the original data into 2 dimensions using T-SNE tsne = TSNE(n_components=2) ComponentValues=tsne.fit_transform(X) #Creating the dataframe ReducedData=pd.DataFrame(data=ComponentValues, columns=['Comp1','Comp2']) print(ReducedData.head(10)) #Visualizing the data in 2-dimensions plt.scatter(x=ReducedData['Comp1'],y=ReducedData['Comp2'] ) |
Sample Output
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