huibinshen / fingerid Goto Github PK

~/fingerid/examples$ time python train_test.py 

real    0m55.436s
user    0m55.352s
sys 0m1.384s

real    0m55.337s
user    0m55.452s
sys 0m1.528s

$sudo python setup.py install Traceback (most recent call last):
  File "setup.py", line 3, in <module>
    from setuptools import setup, find_packages
ImportError: No module named setuptools

 # MODELS is the folder to store trained models.
    prob= True # Set prob=True if want probability output
    trainModels(train_ckm, labels, "MODELS", select_c=False, n_p=16, prob=prob)

~/fingerid/examples$ python train_test.py 

Computing combined kernel for ALIGN
train models and make prediction
Create directory MODELS to store the trained models
Traceback (most recent call last):
  File "train_test.py", line 93, in <module>
    trainModels(train_ckm, labels, "MODELS", select_c=False, n_p=16, prob=prob)
  File "../../fingerid/fingerid/model/trainSVM.py", line 76, in trainModels
    args=(x, labels, model_dir, task_dict[i], prob))
KeyError: 10

>>> import multiprocessing
>>> 
>>> multiprocessing.cpu_count()
16
>>> 

    # Set data info, default run on a small 50 compounds dataset.
    # To run the 978 componds dataset, change fgtree_folder to "metlin_trees".
    # The MS/MS used in the paper can be downloaded from METLIN database
    # with the same metlin id used in the filenames of fgtrees.
    # fgtree_folder = "test_data/train_trees/"

    fgtree_folder = "metlin_trees"
    ms_folder = "test_data/train_ms/"
    fingerprints = "test_data/train_output.txt" # output we want to predict

~/fingerid/examples$ python shen_ISMB2014.py 
computing kernel PPK
computing kernel NB
computing kernel NI
computing kernel LB
computing kernel LC
computing kernel CPC
computing kernel CP2
computing kernel CPK
computing kernel LI
computing kernel CSC
computing kernel RLB
computing kernel RLI
computing train kernel for 0'th example
computing train kernel for 1'th example
computing train kernel for 2'th example
computing train kernel for 3'th example
computing train kernel for 4'th example
computing train kernel for 5'th example
computing train kernel for 6'th example
computing train kernel for 7'th example
computing train kernel for 8'th example
computing train kernel for 9'th example
computing train kernel for 10'th example
computing train kernel for 11'th example
computing train kernel for 12'th example
computing train kernel for 13'th example
computing train kernel for 14'th example
computing train kernel for 15'th example
computing train kernel for 16'th example
computing train kernel for 17'th example
computing train kernel for 18'th example
computing train kernel for 19'th example
computing train kernel for 20'th example
computing train kernel for 21'th example
computing train kernel for 22'th example
computing train kernel for 23'th example
computing train kernel for 24'th example
computing train kernel for 25'th example
computing train kernel for 26'th example
computing train kernel for 27'th example
computing train kernel for 28'th example
computing train kernel for 29'th example
computing train kernel for 30'th example
computing train kernel for 31'th example
computing train kernel for 32'th example
computing train kernel for 33'th example
computing train kernel for 34'th example
computing train kernel for 35'th example
computing train kernel for 36'th example
computing train kernel for 37'th example
computing train kernel for 38'th example
computing train kernel for 39'th example
computing train kernel for 40'th example
computing train kernel for 41'th example
computing train kernel for 42'th example
computing train kernel for 43'th example
computing train kernel for 44'th example
computing train kernel for 45'th example
computing train kernel for 46'th example
computing train kernel for 47'th example
computing train kernel for 48'th example
computing train kernel for 49'th example
Writing PPK kernel to PPK_kernel.txt
Writing LB kernel to LB_kernel.txt
Writing NB kernel to NB_kernel.txt
Writing LC kernel to LC_kernel.txt
Writing RLB kernel to RLB_kernel.txt
Writing NI kernel to NI_kernel.txt
Writing LI kernel to LI_kernel.txt
Writing RLI kernel to RLI_kernel.txt
Writing CP2 kernel to CP2_kernel.txt
Writing CPC kernel to CPC_kernel.txt
Writing CSC kernel to CSC_kernel.txt
Writing CPK kernel to CPK_kernel.txt

Train SVM for kernel PPK
cv on 0'th fingerprint
cv on 1'th fingerprint
cv on 2'th fingerprint
cv on 3'th fingerprint
cv on 4'th fingerprint
cv on 5'th fingerprint
cv on 6'th fingerprint
cv on 7'th fingerprint
cv on 8'th fingerprint
cv on 9'th fingerprint
Writting prediction in cvpred_PPK.txt
Train SVM for kernel NB
cv on 0'th fingerprint
cv on 1'th fingerprint
cv on 2'th fingerprint
cv on 3'th fingerprint
cv on 4'th fingerprint
cv on 5'th fingerprint
cv on 6'th fingerprint
cv on 7'th fingerprint
cv on 8'th fingerprint
cv on 9'th fingerprint
Traceback (most recent call last):
  File "shen_ISMB2014.py", line 119, in <module>
    trainSVM(km_f, fingerprints, np=16, c_sel=True)
  File "shen_ISMB2014.py", line 91, in trainSVM
    cvpreds = internalCV_mp(train_km, labels, 5, select_c=c_sel, n_p=np, prob=prob)
  File "../../fingerid/fingerid/model/internalCV_mp.py", line 83, in internalCV_mp
    pred_fp[:,fp_ind] = res.pred_fp_
ValueError: could not broadcast input array from shape (978) into shape (50)