Found array with 0 feature(s) (shape=(16752, 0)) while a minimum of 1 is required.

[xsun1229]

Hi PoPs team,

I encountered an error when replacing the example features with my own. My feature matrix is a binary (gene-by-GO terms) matrix derived from GO membership data. All other inputs remain the same as in the tutorial example.

python pops.py  --gene_annot_path example/data/utils/gene_annot_jun10.txt  \
--feature_mat_prefix GO/test  \
--num_feature_chunks 2  \
--magma_prefix example/data/magma_scores/PASS_Schizophrenia  \
--out_prefix LDL

However, I received the following error:

Traceback (most recent call last):
  File "pops.py", line 912, in <module>
    main(config_dict)
  File "pops.py", line 876, in main
    coefs_df = compute_coefficients(X_train, Y_train, cols, config_dict["method"], config_dict["random_seed"])
  File "pops.py", line 623, in compute_coefficients
    reg.fit(X_train, Y_train)
  File "/software/python-anaconda-2020.02-el7-x86_64/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py", line 2030, in fit
    estimator.fit(X, y, sample_weight=sample_weight)
  File "/software/python-anaconda-2020.02-el7-x86_64/lib/python3.7/site-packages/sklearn/linear_model/_ridge.py", line 1834, in fit
    y_numeric=True,
  File "/software/python-anaconda-2020.02-el7-x86_64/lib/python3.7/site-packages/sklearn/base.py", line 581, in _validate_data
    X, y = check_X_y(X, y, **check_params)
  File "/software/python-anaconda-2020.02-el7-x86_64/lib/python3.7/site-packages/sklearn/utils/validation.py", line 976, in check_X_y
    estimator=estimator,
  File "/software/python-anaconda-2020.02-el7-x86_64/lib/python3.7/site-packages/sklearn/utils/validation.py", line 817, in check_array
    % (n_features, array.shape, ensure_min_features, context)
ValueError: Found array with 0 feature(s) (shape=(16752, 0)) while a minimum of 1 is required.

Could you help me identify what might be causing this issue? Are there any preprocessing steps I might be missing?

Thanks for your help!

Best,
Xiaotong

[heomanism]

Hi, all !

I had the same issue using this command.

time python3 ../pops.py \

--gene_annot_path ../gencode.v45.gene_annot_PoPs.txt
--feature_mat_prefix ../features_munged/pops_features
--num_feature_chunks 2
--magma_prefix ../finngen_R12_F5_ALZHDEMENT_meta_out
--control_features_path ../pops/example/data/utils/features_jul17_control.txt
--out_prefix ../PoPs/output/finngen_R12_F5_ALZHDEMENT_meta_out
--verbose

/home2/mheo/program/pops/pops.py:94: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use sep='\s+' instead
gene_annot_df = pd.read_csv(gene_annot_path, delim_whitespace=True).set_index("ENSGID")
INFO: --project_out_covariates_chromosomes is None, defaulting to all chromosomes
INFO: --feature_selection_chromosomes is None, defaulting to all chromosomes
INFO: --training_chromosomes is None, defaulting to all chromosomes
INFO: MAGMA scores provided, loading MAGMA.
/home2/mheo/program/pops/pops.py:101: FutureWarning: The 'delim_whitespace' keyword in pd.read_csv is deprecated and will be removed in a future version. Use sep='\s+' instead
magma_df = pd.read_csv(magma_prefix + ".genes.out", delim_whitespace=True)
INFO: Using MAGMA covariates.
INFO: Using MAGMA error covariance.
INFO: Regularizing MAGMA error covariance.
INFO: Projecting 6 covariates out of target scores using genes on chromosome 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22. HLA region removed.
INFO: Computing marginal association table using genes on chromosome 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22. HLA region removed.

INFO: After filtering to features with p-value < 0.05 and unioning with non-constant control features, 0 features remain in model.
INFO: Building training X and Y using genes on chromosome 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22. HLA region removed.
INFO: X dimensions = (49432, 0). Y dimensions = (49432,)
INFO: Model = RidgeCV with 25 alphas, generalized leave-one-out cross-validation, NMSE as scoring metric.
INFO: Fitting model.
Traceback (most recent call last):
File "/home2/mheo/program/pops/pops.py", line 912, in
main(config_dict)
File "/home2/mheo/program/pops/pops.py", line 876, in main
coefs_df = compute_coefficients(X_train, Y_train, cols, config_dict["method"], config_dict["random_seed"])
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home2/mheo/program/pops/pops.py", line 623, in compute_coefficients
reg.fit(X_train, Y_train)
File "/data/global/bin/pkg_python311/sklearn/base.py", line 1363, in wrapper
return fit_method(estimator, *args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/data/global/bin/pkg_python311/sklearn/linear_model/_ridge.py", line 2694, in fit
super().fit(X, y, sample_weight=sample_weight, **params)
File "/data/global/bin/pkg_python311/sklearn/linear_model/_ridge.py", line 2425, in fit
estimator.fit(
File "/data/global/bin/pkg_python311/sklearn/linear_model/_ridge.py", line 2121, in fit
X, y = validate_data(
^^^^^^^^^^^^^^
File "/data/global/bin/pkg_python311/sklearn/utils/validation.py", line 2971, in validate_data
X, y = check_X_y(X, y, **check_params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/data/global/bin/pkg_python311/sklearn/utils/validation.py", line 1368, in check_X_y
X = check_array(
^^^^^^^^^^^^
File "/data/global/bin/pkg_python311/sklearn/utils/validation.py", line 1137, in check_array
raise ValueError(
ValueError: Found array with 0 feature(s) (shape=(49432, 0)) while a minimum of 1 is required by _RidgeGCV.

How could I solve it ?

Thanks for your help!