How to cluster my own Dataset

[hammad2008]

@DeMoriarty I hope you are doing well. I have the following Dataset. with four features I want to cluster the data.

Can you please provide a code for this one. I need to run it into anaconda

I will be very thankful to you

Dataset.csv

[hammad2008]

@TheProjectsGuy @DeMoriarty Can you please help me with that?

[hammad2008]

@DeMoriarty Can you please help me with this?

[DeMoriarty]

you can read and parse your file using the csv package, and represent your data as a nested list of floats, convert it into a pytorch tensor with torch.tensor(nested_list, device=device) then fit the kmeans clusterer with this tensor. You may also need to normalize/standardize your columns, so that all 4 features have similar ranges, in order to get better clustering results. The exact ways of reading csv files or doing feature preprocessing is beyond the scope of this repository, but there are a lot of useful resources on the internet.

[hammad2008]

@DeMoriarty, I just want to use two features 2nd column and 4th column. Then apply this K-means repo, can you please modified the code for that. I will be very thankful. I have tried many methods but still does not get result always get errors

[DeMoriarty]

there are many ways of choosing specific columns, if your data is already in numpy.ndarray or torch.tensor format, you can do:

indices = [1, 3]
data = data[:, indices ]

which will select the columns with specified indices.

[hammad2008]

But it does not work on your repo. The dataset has no applied using your kmeans function

[DeMoriarty]

can you provide a minimal code to reproduce the error?

[hammad2008]

@DeMoriarty Here is the following Code

and the error is AttributeError: 'numpy.ndarray' object has no attribute 'device'

import numpy as np
array = np.loadtxt('Dataset.csv', delimiter=',')

twofeaturs = array[:, [1, 3]]
print(twofeaturs)

from fast_pytorch_kmeans import KMeans
import torch

kmeans = KMeans(n_clusters=4, mode='euclidean', verbose=1)

labels = kmeans.fit_predict(twofeaturs)

after solving the error i want to plot the data into different cluster

[DeMoriarty]

fast_pytorch_kmeans only accepts pytorch tensors as input. all you have to do is:

twofeatures = torch.from_numpy(two_features)

and if you want to run the kmeans algorithm on gpu, you need to:

twofeatures = twofeatures.cuda()

before calling kmeans.fit_predict().

for plotting data with labels, see #7

[hammad2008]

@DeMoriarty when i use the following code it gives the error UnboundLocalError: local variable 'expected' referenced before assignment

from fast_pytorch_kmeans import KMeans
import torch
twofeaturs = torch.from_numpy(twofeaturs)
twofeaturs = twofeaturs.cuda()
kmeans = KMeans(n_clusters=8, mode='euclidean', verbose=1)
labels = kmeans.fit_predict(twofeaturs)

[hammad2008]

@DeMoriarty The following is the all code but i have get the error, as shown below can you please check it

UnboundLocalError: local variable 'expected' referenced before assignment

from fast_pytorch_kmeans import KMeans
import torch
import numpy as np
array = np.loadtxt('Dataset.csv', delimiter=',')

twofeatures = array[:, [1, 3]]
print(twofeatures)
torchfeatures = torch.from_numpy(twofeatures)
gpufeature = torchfeatures.cuda()
kmeans = KMeans(n_clusters=8, mode='euclidean', verbose=1)
labels = kmeans.fit_predict(gpufeature)
plt.scatter(array[:, 0], data[:, 1], c=labels, s=10, cmap="jet")
centroids = kmeans.centroids
plt.scatter(centroids[:, 0], centroids[:, 1], s=70)
plt.show()

[DeMoriarty]

that seems to be a bug in the KMeans, because it only recognizes float16 or float32 data, and your data is probably in float64. for a quick solution you can just convert your data into float32 or float16. to do so:

# float32
twofeature = twofeature.to(torch.float)
# or float16
twofeature = twofeature.to(torch.half)

in the meantime I will fix this bug and also add some assertions to make sure that user input is pytorch tensor. thanks for your contribution.

[hammad2008]

@DeMoriarty After that now getting the following error TypeError: can't convert cuda:0 device type tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.

from fast_pytorch_kmeans import KMeans
import torch
import numpy as np
import matplotlib.pyplot as plt

array = np.loadtxt('Dataset.csv', delimiter=',')

twofeatures = array[:, [1, 3]]
print(twofeatures)
torchfeatures = torch.from_numpy(twofeatures)
gpufeature = torchfeatures.cuda()
gpufeature = gpufeature.to(torch.float)
kmeans = KMeans(n_clusters=8, mode='euclidean', verbose=1)
labels = kmeans.fit_predict(gpufeature)
plt.scatter(array[:, 0], array[:, 1], c=labels, s=10, cmap="jet")
centroids = kmeans.centroids
plt.scatter(centroids[:, 0], centroids[:, 1], s=70)
plt.show()

[hammad2008]

@DeMoriarty Now getting this error ValueError: 'c' argument has 1 elements, which is inconsistent with 'x' and 'y' with size 6610.

from fast_pytorch_kmeans import KMeans
import torch
import numpy as np
import matplotlib.pyplot as plt

array = np.loadtxt('Dataset.csv', delimiter=',')

twofeatures = array[:, [1, 3]]
print(twofeatures)
torchfeatures = torch.from_numpy(twofeatures)
gpufeature = torchfeatures.cuda()
gpufeature = gpufeature.to(torch.float)
kmeans = KMeans(n_clusters=4, mode='euclidean', verbose=1)
labels = kmeans.fit_predict(gpufeature)
index = labels.cpu().data.numpy().argmax()
print(index)
plt.scatter(array[:, [0]], array[:, [1]], c=index, s=10, cmap="jet")
centroids = kmeans.centroids
plt.scatter(centroids[:, 0], centroids[:, 1], s=70)
plt.show()

[DeMoriarty]

does this work?

plt.scatter(twofeatures[:, 0], twofeatures[:, 1], c=labels.cpu(), s=10, cmap="jet")

[hammad2008]

@DeMoriarty no it does not work

[DeMoriarty]

@hammad2008 what is the error message?

[hammad2008]

@DeMoriarty The following are my full code. The error is

ValueError: 'c' argument has 1 elements, which is inconsistent with 'x' and 'y' with size 6610.

from fast_pytorch_kmeans import KMeans
import torch
import numpy as np
import matplotlib.pyplot as plt

array = np.loadtxt('Dataset.csv', delimiter=',')

twofeatures = array[:, [1, 3]]
print(twofeatures)
torchfeatures = torch.from_numpy(twofeatures)
gpufeature = torchfeatures.cuda()
gpufeature = gpufeature.to(torch.float)
kmeans = KMeans(n_clusters=4, mode='euclidean', verbose=1)
labels = kmeans.fit_predict(gpufeature)
print(labels)
index = labels.cpu().data.numpy().argmax()
print(index)
plt.scatter(array[:, [0]], array[:, [1]], c=index, s=10, cmap="jet")
centroids = kmeans.centroids
plt.scatter(centroids[:, 0], centroids[:, 1], s=70)
plt.show()

[DeMoriarty]

it's giving that error because you're taking argmax of the labels.

[hammad2008]

@DeMoriarty Can you please modified it and run it?

[DeMoriarty]

have you tried replacing

plt.scatter(array[:, [0]], array[:, [1]], c=index, s=10, cmap="jet")

with

plt.scatter(twofeatures[:, 0], twofeatures[:, 1], c=labels.cpu(), s=10, cmap="jet")

[hammad2008]

@DeMoriarty It also does not work.

[DeMoriarty]

@hammad2008 and the error message is?

[hammad2008]

@DeMoriarty AttributeError: 'numpy.ndarray' object has no attribute 'cpu'

[DeMoriarty]

@hammad2008 then just remove .cpu()

[DeMoriarty]

plt.scatter(twofeatures[:, 0], twofeatures[:, 1], c=labels, s=10, cmap="jet")

[DeMoriarty]

closing since no further issues reported