FileNotFoundError

[xuxif]

Traceback (most recent call last): File "/root/xTea/xtea/x_TEA_main.py", line 1031, in pkl_model = gc.load_model_from_file(sf_model) File "/root/xTea/xtea/x_genotype_classify.py", line 182, in load_model_from_file pickle_model = pickle.load(file, encoding='latin1') ModuleNotFoundError: No module named 'sklearn.ensemble.forest' Running command: sort -k1,1V -k2,2n -o ./xtea/HG002/Alu/candidate_disc_filtered_cns.txt.high_confident.post_filtering_with_gene_gntp.txt.sorted ./xtea/HG002/Alu/candidate_disc_filtered_cns.txt.high_confident.post_filtering_with_gene_gntp.txt

sort: cannot read: ./xtea/HG002/Alu/candidate_disc_filtered_cns.txt.high_confident.post_filtering_with_gene_gntp.txt: No such file or directory Traceback (most recent call last): File "/root/xTea/xtea/x_TEA_main.py", line 1065, in gvcf.cvt_raw_rslt_to_gvcf(s_sample_id, sf_bam, sf_raw_rslt, i_rep_type, sf_ref, sf_vcf) File "/root/xTea/xtea/x_gvcf.py", line 199, in cvt_raw_rslt_to_gvcf with open(sf_raw_rslt_sorted) as fin_rslt: FileNotFoundError: [Errno 2] No such file or directory: './xtea/HG002/Alu/candidate_disc_filtered_cns.txt.high_confident.post_filtering_with_gene_gntp.txt.sorted

I tried to reinstall xtea or install free same result were return. Python is 2.7.1 and 'from sklearn.ensemble import RandomForestClassifier' do not show any error or warnings!

[simoncchu]

Could you have a try with python3.7?

[xuxif]

Yes, I tried with python 3.7. But too much warnings let me give up!

[simoncchu]

Not sure what you mean by warnings. But I cannot repeat your errors. Anyway, depends on you.

[xuxif]

Could you please send me the result of HG002 with xTea illumina! Following are warning with python 3.7: /root/anaconda3/envs/your_env/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:30: DeprecationWarning: np.float is a deprecated alias for the builtin float. To silence this warning, use float by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use np.float64 here. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations method='lar', copy_X=True, eps=np.finfo(np.float).eps, /root/anaconda3/envs/your_env/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:167: DeprecationWarning: np.float is a deprecated alias for the builtin float. To silence this warning, use float by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use np.float64 here. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations method='lar', copy_X=True, eps=np.finfo(np.float).eps, /root/anaconda3/envs/your_env/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:284: DeprecationWarning: np.float is a deprecated alias for the builtin float. To silence this warning, use float by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use np.float64 here. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations eps=np.finfo(np.float).eps, copy_Gram=True, verbose=0, /root/anaconda3/envs/your_env/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:862: DeprecationWarning: np.float is a deprecated alias for the builtin float. To silence this warning, use float by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use np.float64 here. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations eps=np.finfo(np.float).eps, copy_X=True, fit_path=True, /root/anaconda3/envs/your_env/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1101: DeprecationWarning: np.float is a deprecated alias for the builtin float. To silence this warning, use float by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use np.float64 here. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations eps=np.finfo(np.float).eps, copy_X=True, fit_path=True, /root/anaconda3/envs/your_env/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1127: DeprecationWarning: np.float is a deprecated alias for the builtin float. To silence this warning, use float by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use np.float64 here. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations eps=np.finfo(np.float).eps, positive=False): /root/anaconda3/envs/your_env/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1362: DeprecationWarning: np.float is a deprecated alias for the builtin float. To silence this warning, use float by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use np.float64 here. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations max_n_alphas=1000, n_jobs=None, eps=np.finfo(np.float).eps, /root/anaconda3/envs/your_env/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1602: DeprecationWarning: np.float is a deprecated alias for the builtin float. To silence this warning, use float by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use np.float64 here. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations max_n_alphas=1000, n_jobs=None, eps=np.finfo(np.float).eps, /root/anaconda3/envs/your_env/lib/python3.7/site-packages/sklearn/linear_model/least_angle.py:1738: DeprecationWarning: np.float is a deprecated alias for the builtin float. To silence this warning, use float by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use np.float64 here. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations eps=np.finfo(np.float).eps, copy_X=True, positive=False): /root/anaconda3/envs/your_env/lib/python3.7/site-packages/sklearn/decomposition/online_lda.py:29: DeprecationWarning: np.float is a deprecated alias for the builtin float. To silence this warning, use float by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use np.float64 here. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations EPS = np.finfo(np.float).eps /root/anaconda3/envs/your_env/lib/python3.7/site-packages/sklearn/ensemble/gradientboosting.py:32: DeprecationWarning: np.bool is a deprecated alias for the builtin bool. To silence this warning, use bool by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.boolhere. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations from ._gradient_boosting import predict_stages /root/anaconda3/envs/your_env/lib/python3.7/site-packages/sklearn/ensemble/gradient_boosting.py:32: DeprecationWarning:np.boolis a deprecated alias for the builtinbool. To silence this warning, useboolby itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, usenp.bool_` here. Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations from ._gradient_boosting import predict_stages

[simoncchu]

Did you get the expected results with the warning?

You can find them here: https://github.com/parklab/xTea_paper

[xuxif]

Thank you very much! That is very helpful! But I see two vcf file for alu ( tprt and all) and do not find any descriptions. Could you please send me a short desctiption for each file!

[simoncchu]

I am not sure what's the goal here. If you are looking for a benchmark, then use "all". If you want to compare with xTea, then use those in "run_tools/xTea/HG002/".

[xuxif]

I'm going to compare genotype between xtea and benchmark of HG002. I downloaded HG002_hg38_Illumina_xTea_Alu.txt in ' xTea_paper/run_tools/xTea/HG002/'. When go through that file I only found 23 homo insertion (1/1 or 2). I don't know if that file should be further processed by other program. Here I focus on next-generateion sequence data from illumina (not long reads or 10X genomic).

[simoncchu]

In total? Should be more than that. You could send me email for more discussion on the paper related issues. Here, mainly for the technical issues.