Trying to retrieve original file path names in the results

[Sid01123]

Is there a way to be able to get the original pathnames of images used post fit_transform?

I am uploading images onto google colab, and reading them in by their filepaths as "/content/name_of_image", and then I wish to be able to recover this "/content/name_of_image" post running clustering.

I tried to extract pathnames per label using the following code, but seemed to be getting the filepaths for images created in a temporary directory as follows:

CODE
Iloc = cl.results['labels']==0
cl.results['pathnames'][Iloc]

OUTPUT
array(['/tmp/clustimage/8732cb41-c72d-4266-b164-ff453d68428a.png',
'/tmp/clustimage/440fecd8-8a9c-49a0-b100-ccfb66107425.png',
'/tmp/clustimage/3c9c38d8-4da9-4e4f-9130-d3836182b8c6.png',
'/tmp/clustimage/85cc4848-1faf-44ea-ae4c-9d9d88bd6323.png',
'/tmp/clustimage/6127e4fb-1c25-4ba9-8d68-56ef482e3db4.png',
'/tmp/clustimage/abcf85e0-af1a-48f1-8861-122122b64e32.png',
'/tmp/clustimage/275bbde0-394d-4ba4-b4d0-1c67da323c8b.png',
'/tmp/clustimage/30b62285-2628-45c0-86b2-fea305cb8db3.png',
'/tmp/clustimage/c47a6867-3c8f-480c-a7bd-b3e7ec4ba334.png',
'/tmp/clustimage/da5c17fc-de2a-4375-b03c-066a0904428a.png'], dtype='<U56')

I wish to get the output as the original filenames that were in the pathnames list.

[erdogant]

Can you show with an example how this occurs?
When I try the flowers example, it stores the filenames and paths correctly.
The unique identifiers are only used if a data matrix is given as an input.

from clustimage import Clustimage
cl = Clustimage(method='pca', embedding
g='umap')
# Import data
Xlist = cl.import_example(data='flowers')
# Import data in a standardized manner
X = cl.import_data(Xlist)

X.keys()
dict_keys(['img', 'feat', 'xycoord', 'pathnames', 'labels', 'url', 'filenames'])
print(X['filenames'][0:5])
# array(['0001.png', '0002.png', '0003.png', '0004.png', '0005.png'],

What I can do for the datamatrix, is use the index names of a pandas dataframe for naming. In that way you can control the naming as you wish.

[erdogant]

I added this functionality the functionality to read pandas dataframes.
Update with: pip install -U clustimage

Example:

from clustimage import Clustimage
import pandas as pd
import numpy as np

# Initialize
cl = Clustimage()

# Import data
Xraw = cl.import_example(data='mnist')

print(Xraw)
# array([[ 0.,  0.,  5., ...,  0.,  0.,  0.],
#        [ 0.,  0.,  0., ..., 10.,  0.,  0.],
#        [ 0.,  0.,  0., ..., 16.,  9.,  0.],
#        ...,
#        [ 0.,  0.,  1., ...,  6.,  0.,  0.],
#        [ 0.,  0.,  2., ..., 12.,  0.,  0.],
#        [ 0.,  0., 10., ..., 12.,  1.,  0.]])

filenames = list(map(lambda x: str(x) + '.png', np.arange(0, Xraw.shape[0])))
Xraw = pd.DataFrame(Xraw, index=filenames)

print(Xraw)
#            0    1     2     3     4     5   ...   58    59    60    61   62   63
# 0.png     0.0  0.0   5.0  13.0   9.0   1.0  ...  6.0  13.0  10.0   0.0  0.0  0.0
# 1.png     0.0  0.0   0.0  12.0  13.0   5.0  ...  0.0  11.0  16.0  10.0  0.0  0.0
# 2.png     0.0  0.0   0.0   4.0  15.0  12.0  ...  0.0   3.0  11.0  16.0  9.0  0.0
# 3.png     0.0  0.0   7.0  15.0  13.0   1.0  ...  7.0  13.0  13.0   9.0  0.0  0.0
# 4.png     0.0  0.0   0.0   1.0  11.0   0.0  ...  0.0   2.0  16.0   4.0  0.0  0.0
#       ...  ...   ...   ...   ...   ...  ...  ...   ...   ...   ...  ...  ...
# 1792.png  0.0  0.0   4.0  10.0  13.0   6.0  ...  2.0  14.0  15.0   9.0  0.0  0.0
# 1793.png  0.0  0.0   6.0  16.0  13.0  11.0  ...  6.0  16.0  14.0   6.0  0.0  0.0
# 1794.png  0.0  0.0   1.0  11.0  15.0   1.0  ...  2.0   9.0  13.0   6.0  0.0  0.0
# 1795.png  0.0  0.0   2.0  10.0   7.0   0.0  ...  5.0  12.0  16.0  12.0  0.0  0.0
# 1796.png  0.0  0.0  10.0  14.0   8.0   1.0  ...  8.0  12.0  14.0  12.0  1.0  0.0

# Fit and transform data
results = cl.fit_transform(Xraw)

print(results['filenames'])
# array(['0.png', '1.png', '2.png', ..., '1794.png', '1795.png', '1796.png'],

[Sid01123]

Dear Mr. Taskesen, Thank you so much for taking the time to add this update. I shall try It out and let you know! Best, Sid

…

On Thu, Jun 9, 2022 at 10:25 AM Erdogan Taskesen ***@***.***> wrote: I added this functionality the functionality to read pandas dataframes. Update with: pip install -U clustimage Example: from clustimage import Clustimage import pandas as pd import numpy as np # Initialize cl = Clustimage() # Import data Xraw = cl.import_example(data='mnist') print(Xraw) # array([[ 0., 0., 5., ..., 0., 0., 0.], # [ 0., 0., 0., ..., 10., 0., 0.], # [ 0., 0., 0., ..., 16., 9., 0.], # ..., # [ 0., 0., 1., ..., 6., 0., 0.], # [ 0., 0., 2., ..., 12., 0., 0.], # [ 0., 0., 10., ..., 12., 1., 0.]]) filenames = list(map(lambda x: str(x) + '.png', np.arange(0, Xraw.shape[0]))) Xraw = pd.DataFrame(Xraw, index=filenames) print(Xraw) # 0 1 2 3 4 5 ... 58 59 60 61 62 63 # 0.png 0.0 0.0 5.0 13.0 9.0 1.0 ... 6.0 13.0 10.0 0.0 0.0 0.0 # 1.png 0.0 0.0 0.0 12.0 13.0 5.0 ... 0.0 11.0 16.0 10.0 0.0 0.0 # 2.png 0.0 0.0 0.0 4.0 15.0 12.0 ... 0.0 3.0 11.0 16.0 9.0 0.0 # 3.png 0.0 0.0 7.0 15.0 13.0 1.0 ... 7.0 13.0 13.0 9.0 0.0 0.0 # 4.png 0.0 0.0 0.0 1.0 11.0 0.0 ... 0.0 2.0 16.0 4.0 0.0 0.0 # ... ... ... ... ... ... ... ... ... ... ... ... ... # 1792.png 0.0 0.0 4.0 10.0 13.0 6.0 ... 2.0 14.0 15.0 9.0 0.0 0.0 # 1793.png 0.0 0.0 6.0 16.0 13.0 11.0 ... 6.0 16.0 14.0 6.0 0.0 0.0 # 1794.png 0.0 0.0 1.0 11.0 15.0 1.0 ... 2.0 9.0 13.0 6.0 0.0 0.0 # 1795.png 0.0 0.0 2.0 10.0 7.0 0.0 ... 5.0 12.0 16.0 12.0 0.0 0.0 # 1796.png 0.0 0.0 10.0 14.0 8.0 1.0 ... 8.0 12.0 14.0 12.0 1.0 0.0 # Or all in one run results = cl.fit_transform(Xraw) print(results['filenames']) # array(['0.png', '1.png', '2.png', ..., '1794.png', '1795.png', '1796.png'], — Reply to this email directly, view it on GitHub <https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_erdogant_clustimage_issues_4-23issuecomment-2D1151187795&d=DwMCaQ&c=009klHSCxuh5AI1vNQzSO0KGjl4nbi2Q0M1QLJX9BeE&r=D6W32UT11SWv3cCY-ZP9mPTas-ek59iSXpK9UYl1RaY&m=WhDAnk5ke6NJMlrh-fffAcVEv32VK7AnHw873swd82hTuVd5GgiwyBYA6JAZCljF&s=pGUi3Pn24RqdnaS_Vx33Xr3riARemSvRd6cmhTsr5d8&e=>, or unsubscribe <https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_notifications_unsubscribe-2Dauth_ATKYKE3OLHGWYLY2I4PEXPDVOH5EXANCNFSM5X7JH7RA&d=DwMCaQ&c=009klHSCxuh5AI1vNQzSO0KGjl4nbi2Q0M1QLJX9BeE&r=D6W32UT11SWv3cCY-ZP9mPTas-ek59iSXpK9UYl1RaY&m=WhDAnk5ke6NJMlrh-fffAcVEv32VK7AnHw873swd82hTuVd5GgiwyBYA6JAZCljF&s=kB6g7cPrLfl3WXv-2a14KQQDB8wPwkaiEuDgbeueI0s&e=> . You are receiving this because you authored the thread.Message ID: ***@***.***>

[erdogant]

I am closing this one. Re-open this issue if required.