Results quite different from the tutorial

[bitcometz]

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hello, I download the notebook from https://biolord.readthedocs.io/en/latest/tutorials/biolord_classify.html and run it with colab, but I got quite different results: my:

the tutorials:

Could you please help with this problem ?

Thanks !!!

Version information

Package Version

---

absl-py 1.4.0 aiohttp 3.9.3 aiosignal 1.3.1 alabaster 0.7.16 albumentations 1.3.1 altair 4.2.2 anndata 0.10.5.post1 annotated-types 0.6.0 anyio 3.7.1 appdirs 1.4.4 argon2-cffi 23.1.0 argon2-cffi-bindings 21.2.0 array_api_compat 1.4.1 array-record 0.5.0 arrow 1.3.0 arviz 0.15.1 astropy 5.3.4 astunparse 1.6.3 async-timeout 4.0.3 atpublic 4.0 attrs 23.2.0 audioread 3.0.1 autograd 1.6.2 Babel 2.14.0 backcall 0.2.0 backoff 2.2.1 beautifulsoup4 4.12.3 bidict 0.22.1 bigframes 0.20.0 biolord 0.0.2 bleach 6.1.0 blessed 1.20.0 blinker 1.4 blis 0.7.11 blosc2 2.0.0 bokeh 3.3.4 boto3 1.34.36 botocore 1.34.36 bqplot 0.12.42 branca 0.7.1 build 1.0.3 CacheControl 0.14.0 cachetools 5.3.2 catalogue 2.0.10 certifi 2024.2.2 cffi 1.16.0 chardet 5.2.0 charset-normalizer 3.3.2 chex 0.1.7 click 8.1.7 click-plugins 1.1.1 cligj 0.7.2 cloudpathlib 0.16.0 cloudpickle 2.2.1 cmake 3.27.9 cmdstanpy 1.2.0 colorcet 3.0.1 colorlover 0.3.0 colour 0.1.5 community 1.0.0b1 confection 0.1.4 cons 0.4.6 contextlib2 21.6.0 contourpy 1.2.0 croniter 1.4.1 cryptography 42.0.2 cufflinks 0.17.3 cupy-cuda12x 12.2.0 cvxopt 1.3.2 cvxpy 1.3.3 cycler 0.12.1 cymem 2.0.8 Cython 3.0.8 dask 2023.8.1 datascience 0.17.6 dateutils 0.6.12 db-dtypes 1.2.0 dbus-python 1.2.18 debugpy 1.6.6 decorator 4.4.2 deepdiff 6.7.1 defusedxml 0.7.1 diskcache 5.6.3 distributed 2023.8.1 distro 1.7.0 dlib 19.24.2 dm-tree 0.1.8 docrep 0.3.2 docutils 0.18.1 dopamine-rl 4.0.6 duckdb 0.9.2 earthengine-api 0.1.388 easydict 1.11 ecos 2.0.12 editdistance 0.6.2 editor 1.6.6 eerepr 0.0.4 en-core-web-sm 3.7.1 entrypoints 0.4 et-xmlfile 1.1.0 etils 1.6.0 etuples 0.3.9 exceptiongroup 1.2.0 fastai 2.7.14 fastapi 0.109.2 fastcore 1.5.29 fastdownload 0.0.7 fastjsonschema 2.19.1 fastprogress 1.0.3 fastrlock 0.8.2 filelock 3.13.1 fiona 1.9.5 firebase-admin 5.3.0 Flask 2.2.5 flatbuffers 23.5.26 flax 0.8.0 folium 0.14.0 fonttools 4.47.2 frozendict 2.4.0 frozenlist 1.4.1 fsspec 2023.6.0 future 0.18.3 gast 0.5.4 gcsfs 2023.6.0 GDAL 3.6.4 gdown 4.7.3 geemap 0.30.4 gensim 4.3.2 geocoder 1.38.1 geographiclib 2.0 geopandas 0.13.2 geopy 2.3.0 gin-config 0.5.0 glob2 0.7 google 2.0.3 google-ai-generativelanguage 0.4.0 google-api-core 2.11.1 google-api-python-client 2.84.0 google-auth 2.17.3 google-auth-httplib2 0.1.1 google-auth-oauthlib 1.2.0 google-cloud-aiplatform 1.39.0 google-cloud-bigquery 3.12.0 google-cloud-bigquery-connection 1.12.1 google-cloud-bigquery-storage 2.24.0 google-cloud-core 2.3.3 google-cloud-datastore 2.15.2 google-cloud-firestore 2.11.1 google-cloud-functions 1.13.3 google-cloud-iam 2.14.0 google-cloud-language 2.9.1 google-cloud-resource-manager 1.12.0 google-cloud-storage 2.8.0 google-cloud-translate 3.11.3 google-colab 1.0.0 google-crc32c 1.5.0 google-generativeai 0.3.2 google-pasta 0.2.0 google-resumable-media 2.7.0 googleapis-common-protos 1.62.0 googledrivedownloader 0.4 graphviz 0.20.1 greenlet 3.0.3 grpc-google-iam-v1 0.13.0 grpcio 1.60.1 grpcio-status 1.48.2 gspread 3.4.2 gspread-dataframe 3.3.1 gym 0.25.2 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numpy 1.23.5 numpyro 0.13.2 oauth2client 4.1.3 oauthlib 3.2.2 opencv-contrib-python 4.8.0.76 opencv-python 4.8.0.76 opencv-python-headless 4.9.0.80 openpyxl 3.1.2 opt-einsum 3.3.0 optax 0.1.8 orbax-checkpoint 0.4.4 ordered-set 4.1.0 osqp 0.6.2.post8 packaging 23.2 pandas 1.5.3 pandas-datareader 0.10.0 pandas-gbq 0.19.2 pandas-stubs 1.5.3.230304 pandocfilters 1.5.1 panel 1.3.8 param 2.0.2 parso 0.8.3 parsy 2.1 partd 1.4.1 pathlib 1.0.1 patsy 0.5.6 peewee 3.17.0 pexpect 4.9.0 pickleshare 0.7.5 Pillow 9.4.0 pins 0.8.4 pip 23.1.2 pip-tools 6.13.0 platformdirs 4.2.0 plotly 5.15.0 plotnine 0.12.4 pluggy 1.4.0 polars 0.20.2 pooch 1.8.0 portpicker 1.5.2 prefetch-generator 1.0.3 preshed 3.0.9 prettytable 3.9.0 proglog 0.1.10 progressbar2 4.2.0 prometheus-client 0.19.0 promise 2.3 prompt-toolkit 3.0.43 prophet 1.1.5 proto-plus 1.23.0 protobuf 3.20.3 psutil 5.9.5 psycopg2 2.9.9 ptyprocess 0.7.0 py-cpuinfo 9.0.0 py4j 0.10.9.7 pyarrow 10.0.1 pyarrow-hotfix 0.6 pyasn1 0.5.1 pyasn1-modules 0.3.0 pycocotools 2.0.7 pycparser 2.21 pyct 0.5.0 pydantic 2.1.1 pydantic_core 2.4.0 pydata-google-auth 1.8.2 pydot 1.4.2 pydot-ng 2.0.0 pydotplus 2.0.2 PyDrive 1.3.1 PyDrive2 1.6.3 pyerfa 2.0.1.1 pygame 2.5.2 Pygments 2.16.1 PyGObject 3.42.1 PyJWT 2.3.0 pymc 5.7.2 pymystem3 0.2.0 pynndescent 0.5.11 PyOpenGL 3.1.7 pyOpenSSL 24.0.0 pyparsing 3.1.1 pyperclip 1.8.2 pyproj 3.6.1 pyproject_hooks 1.0.0 pyro-api 0.1.2 pyro-ppl 1.8.6 pyshp 2.3.1 PySocks 1.7.1 pytensor 2.14.2 pytest 7.4.4 python-apt 0.0.0 python-box 7.1.1 python-dateutil 2.8.2 python-louvain 0.16 python-multipart 0.0.7 python-slugify 8.0.3 python-utils 3.8.2 pytorch-lightning 2.1.4 pytz 2023.4 pyviz_comms 3.0.1 PyWavelets 1.5.0 PyYAML 6.0.1 pyzmq 23.2.1 qdldl 0.1.7.post0 qudida 0.0.4 ratelim 0.1.6 readchar 4.0.5 referencing 0.33.0 regex 2023.12.25 requests 2.31.0 requests-oauthlib 1.3.1 requirements-parser 0.5.0 rich 13.7.0 rpds-py 0.17.1 rpy2 3.4.2 rsa 4.9 runs 1.2.2 s3transfer 0.10.0 safetensors 0.4.2 scanpy 1.9.8 scikit-image 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tensorflow-gcs-config 2.15.0 tensorflow-hub 0.16.1 tensorflow-io-gcs-filesystem 0.35.0 tensorflow-metadata 1.14.0 tensorflow-probability 0.23.0 tensorstore 0.1.45 termcolor 2.4.0 terminado 0.18.0 text-unidecode 1.3 textblob 0.17.1 tf-keras 2.15.0 tf-slim 1.1.0 thinc 8.2.2 threadpoolctl 3.2.0 tifffile 2024.1.30 tinycss2 1.2.1 tokenizers 0.15.1 toml 0.10.2 tomli 2.0.1 toolz 0.12.1 torch 2.1.0+cu121 torchaudio 2.1.0+cu121 torchdata 0.7.0 torchmetrics 1.3.0.post0 torchsummary 1.5.1 torchtext 0.16.0 torchvision 0.16.0+cu121 tornado 6.3.2 tqdm 4.66.1 traitlets 5.7.1 traittypes 0.2.1 transformers 4.35.2 triton 2.1.0 tweepy 4.14.0 typer 0.9.0 types-python-dateutil 2.8.19.20240106 types-pytz 2023.4.0.20240130 types-setuptools 69.0.0.20240125 typing_extensions 4.9.0 tzlocal 5.2 uc-micro-py 1.0.2 umap-learn 0.5.5 uritemplate 4.1.1 urllib3 2.0.7 uvicorn 0.27.0.post1 vega-datasets 0.9.0 wadllib 1.3.6 wasabi 1.1.2 wcwidth 0.2.13 weasel 0.3.4 webcolors 1.13 webencodings 0.5.1 websocket-client 1.7.0 websockets 12.0 Werkzeug 3.0.1 wheel 0.42.0 widgetsnbextension 3.6.6 wordcloud 1.9.3 wrapt 1.14.1 xarray 2023.7.0 xarray-einstats 0.7.0 xgboost 2.0.3 xlrd 2.0.1 xmod 1.8.1 xxhash 3.4.1 xyzservices 2023.10.1 yarl 1.9.4 yellowbrick 1.5 yfinance 0.2.36 zict 3.0.0 zipp 3.17.0

[zoepiran]

Hi, Could you please share the notebook you ran? The provided adata object at adata_abortive.h5ad contains the precomputed umap representation-in your plots above a different umap is presented. Given that I am not sure which evaluation your results depict and thus can not tell what is the expected outcome and do not think this is a bug.

[bitcometz]

@zoepiran thanks for your reply. Here is my notebook: https://colab.research.google.com/drive/1m8tIQy4HAx3euGm6u-kYuIQYw1wyXzME?usp=sharing

I just follow the tutorial but I got different results.

Best

[zoepiran]

Hi, I am afraid the model training here is still not over the desired data.
While the output of boxes [5] and [6] is accurate, the umap as well the expected 3024 cells, in box [7] the output indicates that a different adata is loaded as the print implies a total of 19053 cells:

train    17141
test      1905
ood          7
Name: split_random, dtype: int6

[bitcometz]

Hi, @zoepiran thanks for your help! But today I met another problem, after I install biolord there are some errors reporting for import biolord :

---------------------------------------------------------------------------
ImportError                               Traceback (most recent call last)
[<ipython-input-3-70f808061233>](https://localhost:8080/#) in <cell line: 5>()
      3 
      4 import scanpy as sc
----> 5 import biolord
      6 
      7 import seaborn as sns

2 frames
[/usr/local/lib/python3.10/dist-packages/biolord/_data.py](https://localhost:8080/#) in <module>
      4 from scvi.data import AnnDataManager
      5 from scvi.dataloaders import DataSplitter
----> 6 from scvi.model._utils import parse_use_gpu_arg
      7 
      8 

ImportError: cannot import name 'parse_use_gpu_arg' from 'scvi.model._utils' (/usr/local/lib/python3.10/dist-packages/scvi/model/_utils.py)

I haven't encountered this error message before.

Best

[zoepiran]

Thank you! This should be resolved now-we updated the version to the recent scvi(and PyTorch lightning).