1. SVM_ROC.py¶
1.1. Description¶
Plot Receiver operating characteristic (ROC) curves using K-fold cross-validation.
- Options:
- --version
show program’s version number and exit
- -h, --help
show this help message and exit
- -i INPUT_FILE, --input_file=INPUT_FILE
Tab or space separated file. The first column contains sample IDs; the second column contains sample labels in integer (must be 0 or 1); the third column contains sample label names (string, must be consistent with column-2). The remaining columns contain featuers used to build SVM model.
- -o OUT_FILE, --output=OUT_FILE
The prefix of the output file.
- -n N_FOLD, --nfold=N_FOLD
The original sample is randomly partitioned into n equal sized subsamples (2 =< n <= 10). Of the n subsamples, a single subsample is retained as the validation data for testing the model, and the remaining n − 1 subsamples are used as training data. default=5.
- -C C_VALUE, --cvalue=C_VALUE
C value. default=1.0
- -s RAND_SEED, --seed=RAND_SEED
random_state seed used by the random number generator. default=0
- -k S_KERNEL, --kernel=S_KERNEL
Specifies the kernel type to be used in the algorithm. It must be one of ‘linear’, ‘poly’, ‘rbf’, ‘sigmoid’, ‘precomputed’ or a callable. If none is given, ‘rbf’ will be used. default=linear
- --xl=X_LOW
The lower limit of X-axis (false positive rate). default=-0.05
- --xu=X_UPPER
The upper limit of X-axis (false positive rate). default=0.5
- --yl=Y_LOW
The lower limit of Y-axis (true positive rate). default=0.5
- --yu=Y_UPPER
The upper limit of Y-axis (true positive rate). default=1.05
1.2. Input files format¶
ID |
Label |
Label_name |
feature_1 |
feature_2 |
feature_3 |
… |
feature_n |
sample_1 |
1 |
WT |
1560 |
795 |
0.9716 |
… |
feature_n |
sample_2 |
1 |
WT |
784 |
219 |
0.4087 |
… |
feature_n |
sample_3 |
1 |
WT |
2661 |
2268 |
1.1691 |
… |
feature_n |
sample_4 |
0 |
Mut |
643 |
198 |
0.5458 |
… |
feature_n |
sample_5 |
0 |
Mut |
534 |
87 |
1.0545 |
… |
feature_n |
sample_6 |
0 |
Mut |
332 |
75 |
0.5115 |
… |
feature_n |
1.3. Command¶
$ python3 SVM_ROC.py -i lung_CES_5features.tsv -o output_ROC -C 10