Usage
============

Data preprocessing 
------------------


Help ::

    python preprocessing -h 
    usage: preprocessing.py [-h] [--genome GENOME] [--tss TSS] [--outdir OUTDIR]
    
    optional arguments:
      -h, --help            show this help message and exit
      --genome GENOME, -g GENOME
                            genome fasta
      --tss TSS, -t TSS     tss path
      --outdir OUTDIR, -o OUTDIR
                            outdir

File format information 

* `genome` is a `fasta <http://genetics.bwh.harvard.edu/pph/FASTA.html>`_ format:: 
    
    # fasta format
    head tair10.fa
    >Chr1
    CCCTAAACCCTAAACCCTAAACCCTAAACCTCTGAATCCTTAATCCCTAAATCCCTAAAT
    CTTTAAATCCTACATCCATGAATCCCTAAATACCTAATTCCCTAAACCCGAAACCGGTTT
    CTCTGGTTGAAAATCATTGTGTATATAATGATAATTTTATCGTTTTTATGTAATTGCTTA
    TTGTTGTGTGTAGATTTTTTAAAAATATCATTTGAGGTCAATACAAATCCTATTTCTTGT
    GGTTTTCTTTCCTTCACTTAGCTATGGATGGTTTATCTTCATTTGTTATATTGGATACAA
    GCTTTGCTACGATCTACATTTGGGAATGTGAGTCTCTTATTGTAACCTTAGGGTTGGTTT
    
* `TSS` is a table format file::
    
    head cage_covered_5_leader.csv 
    Chr1,+,AT1G01010.1,3624
    Chr1,+,AT1G01010.1,3662
    Chr1,-,AT1G01020.2,8720
    Chr1,-,AT1G01020.6,8720
    Chr1,-,AT1G01020.1,8720
    Chr1,-,AT1G01020.3,8720
    Chr1,-,AT1G01020.4,8720
    Chr1,-,AT1G01020.5,8720 
    
Example ::
    
    mkdir data_cage_covered;
    python preprocessing.py -g ../../tair10.fa \ 
                            -t cage_covered_5_leader.csv \
                            -o data_cage_covered


`gffutils` is `tested <https://github.com/daler/gffutils/actions>`_ with Python
3.6, 3.7, 3.8, 3.9.

Model training
---------------------

* Ray 1.13.0 (Hyperparameters optimization)
* batch size 
* leanring rate

`TSARC`::

    python trainTSARC.py -h 
    usage: testTSARC.py [-h] [--model {lr,cnn,gru,lstm,attention}] [--test_data_path TEST_DATA_PATH] [--test_label_path TEST_LABEL_PATH]
                    [--model_dict_path MODEL_DICT_PATH]

    PyTorch Implementation of TSAR Predict

    optional arguments:
      -h, --help            show this help message and exit
      --model {lr,cnn,gru,lstm,attention}
                        model name
      --test_data_path TEST_DATA_PATH
                        test data saved in numpy ndarry
      --test_label_path TEST_LABEL_PATH
                        test label saved in numpy ndarray
      --model_dict_path MODEL_DICT_PATH
                        model saved name

Example ::
    
    python trainTSARC.py --model ResNet \
             --train_data_path ../../data_cage_covered/class_data.npy \
             --train_label_path ../../data_cage_covered/class_label.npy \
             --lr 0.001 --batch_size 128 \
             --model_save saved_model --epoch 100
`TSARL`::
    
    usage: testTSARL.py [-h] [--model {cnn,gru,attention}] [--test_data_path TEST_DATA_PATH] [--test_label_path TEST_LABEL_PATH]
                    [--scaler SCALER] [--model_dict_path MODEL_DICT_PATH]

    PyTorch Implementation of TSAR Predict
    
    optional arguments:
      -h, --help            show this help message and exit
      --model {cnn,gru,attention}
                            model name
      --test_data_path TEST_DATA_PATH
                            test data saved in numpy ndarry
      --test_label_path TEST_LABEL_PATH
                            test label saved in numpy ndarray
      --scaler SCALER       scaler for test data scale
      --model_dict_path MODEL_DICT_PATH
                            model saved name
   
Example ::
    
    python trainTSARL.py --model ResNet \
                  --train_data_path ../data/regress_data.npy \
                  --train_label_path ../data/regress_label.npy \
                  --lr 0.001 --batch_size 128 \
                  --model_saved saved_model --epoch 200

Model predicting
----------------

`TSARC`::

    conda install --channel conda-forge --channel bioconda numpy pandas biopython scikit-learn torch
    usage: predictTSARC.py [-h] [--model_id {lr,cnn,gru,lstm,attention,resnet}]
                       [--input_path INPUT_PATH] [--output_path OUTPUT_PATH]
                       [--model_path MODEL_PATH]

    PyTorch Implementation of TSARC Predict
    
    optional arguments:
      -h, --help            show this help message and exit
      --model_id {lr,cnn,gru,lstm,attention,resnet}
                            model name
      --input_path INPUT_PATH
                            input data csv
      --output_path OUTPUT_PATH
                            output path
      --model_path MODEL_PATH
                            model saved name
`TSARL`::
    
    python  predictTSARL.py  -h
    usage: predictTSARL.py [-h] [--model_id {lr,cnn,gru,lstm,attention,resnet}]
                           [--input_path INPUT_PATH] [--output_path OUTPUT_PATH]
                           [--model_path MODEL_PATH]
    
    PyTorch Implementation of TSARL Predict
    
    optional arguments:
      -h, --help            show this help message and exit
      --model_id {lr,cnn,gru,lstm,attention,resnet}
                            model name
      --input_path INPUT_PATH
                            input data csv
      --output_path OUTPUT_PATH
                            output path
      --model_path MODEL_PATH
                            model saved name