6. Advanced usage
leADS can be trained by users in multiple different ways. The training data made available to users consists of i) EC number indices with embedding ("biocyc21_Xe.pkl") and ii) pathway indices ("biocyc21_y.pkl"). To train leADS as per user specifications, a preprocessing step has to be first executed.
Note: Make sure to put the source code leADS/ (see Installing leADS) into the same directory as explained in the Download files section. Additionally, create a log/ and result/ folders (if you have not already created one during pathway prediction) in the same leADS_materials/ directory. The final structure should look like this:
leADS_materials/
├── objectset/
│ └── ...
├── model/
│ └── ...
├── dataset/
│ └── ...
├── result/
│ └── ...
├── log/
│ └── ...
└── leADS/
└── ...
For all experiments, using a terminal (On Linux and macOS) or an Anaconda command prompt (On Windows), navigate to the src/ folder in the leADS/ directory and then run the commands as shown in the Examples section of Preprocessing and Training .
To display leADS' running options use: python main.py --help. It should be self-contained.
This step is crucial and only performed if users wish to generate embeddings files such as "[DATANAME]_Xe.pkl", "[DATANAME]_Xa.pkl" etc., from a "[DATANAME]_X.pkl" file in order to use it for training.
The input file used for preprocessing is any matrix file containing EC number indices (e.g. biocyc21_X.pkl, cami_X.pkl)
- biocyc.pkl
- pathway2ec.pkl
- pathway2ec_idx.pkl
- pathway2vec_embeddings.npz
- hin.pkl
The basic command is represented below. Do not use this for preprocessing. This command is only a representation of all the flags used. See Example below on how to preprocess your datasets.
python main.py \
--preprocess-dataset \
--object-name "biocyc.pkl" \
--pathway2ec-name "pathway2ec.pkl" \
--pathway2ec-idx-name "pathway2ec_idx.pkl" \
--hin-name "hin.pkl" \
--features-name "pathway2vec_embeddings.npz" \
--X-name "[DATANAME]_X*.pkl" \
--file-name "[input (or save) file name]" \
--ospath "[absolute path to the object files directory (e.g. objectset)]" \
--dspath "[absolute path to the dataset directory (e.g. dataset)]" \
--batch 50 \
--num-jobs 2
The table below summarizes all the command-line arguments that are specific to this framework:
| Argument name | Description | Value |
|---|---|---|
| --preprocess-dataset | Preprocess inputs based on Biocyc collection | True |
| --object-name | The preprocessed MetaCyc database file | biocyc.pkl |
| --pathway2ec-name | The matrix file representing Pathway-EC association | pathway2ec.pkl |
| --pathway2ec-idx-name | The pathway2ec association indices file | pathway2ec_idx.pkl |
| --hin-name | The heterogeneous information network file | hin.pkl |
| --features-name | The features corresponding ECs and pathways | pathway2vec_embeddings.npz |
| --X-name | The Input file name to be provided for preprocessing | [DATANAME]_X*.pkl |
| --file-name | The names of input preprocessed files (without extension) | [input (or save) file name] |
| --ospath | The path to the data object that contains extracted information from the MetaCyc database (e.g. "biocyc.pkl") | Outside source code |
| --dspath | The path to the datasets | Outside source code |
| --batch | Batch size | 50 |
| --num-jobs | The number of parallel workers | 2 |
The output files generated after running the command are:
| File | Description |
|---|---|
| [FILENAME]_Xa.pkl | A matrix file (stored in the "dspath" location) representing information about organisms (or multi-organisms). Each row in this matrix represents an organism or multi-organisms and columns indicate EC number indices and abundance features. |
| [FILENAME]_Xc.pkl | A matrix file (stored in the "dspath" location) representing information about organisms (or multi-organisms). Each row in this matrix represents an organism or multi-organisms and columns indicate EC number indices and coverage features. |
| [FILENAME]_Xe.pkl | A matrix file (stored in the "dspath" location) representing information about organisms (or multi-organisms). Each row in this matrix represents an organism or multi-organisms and columns indicate EC number indices and embeddings. |
| [FILENAME]_Xea.pkl | A matrix file (stored in the "dspath" location) representing information about organisms (or multi-organisms). Each row in this matrix represents an organism or multi-organisms and columns indicate EC number indices and abundance features. |
| [FILENAME]_Xec.pkl | A matrix file (stored in the "dspath" location) representing information about organisms (or multi-organisms). Each row in this matrix represents an organism or multi-organisms and columns indicate EC number indices and coverage features. |
| [FILENAME]_Xm.pkl | A matrix file (stored in the "dspath" location) representing information about organisms (or multi-organisms). Each row in this matrix represents an organism or multi-organisms and columns indicate EC number indices, embeddings, abundance, and coverage features. |
| [FILENAME]_Xp.pkl | A matrix file (stored in the "dspath" location) representing information about organisms (or multi-organisms). Each row in this matrix represents an organism or multi-organisms and their embeddings in columns. |
Note: Each of these files differs in the total number of columns they contain, which is why the file used for training should also be used during prediction if one decides to train their own model based on certain specifications mentioned above.
Execute the following command to preprocess "cami" data (as an example):
python main.py --preprocess-dataset --object-name "biocyc.pkl" --pathway2ec-name "pathway2ec.pkl" --pathway2ec-idx-name "pathway2ec_idx.pkl" --hin-name "hin.pkl" --features-name "pathway2vec_embeddings.npz" --X-name "cami_X.pkl" --file-name "cami" --batch 50 --num-jobs 2
After running the command, the output will be saved to the dataset/ folder. All the feature files described in the table above are generated. The tree structure for the folder with the outputs will look like this:
leADS_materials/
├── objectset/
│ └── ...
├── model/
│ └── ...
├── dataset/
│ ├── cami_Xa.pkl
│ ├── cami_Xc.pkl
│ ├── cami_Xe.pkl
│ ├── cami_Xea.pkl
│ ├── cami_Xec.pkl
│ ├── cami_Xm.pkl
│ ├── cami_Xp.pkl
│ └── ...
├── result/
│ └── ...
└── leADS/
└── ...
Training can be done using one of the output files from the preprocessing step and the [DATANAME]_y.pkl file.
That being said, one has to keep in mind to use the same file during pathway predictions to avoid errors since each output file from the preprocessing step contains a different number of columns. Here we show you the recommended command but you can also use other flags as described in the argument descriptions table to suit your requirements.
The input to the command is the output obtained from the preprocessing step above (any one of the [DATANAME]_X*.pkl and the [DATANAME]_y.pkl)
The basic command is represented below. Do not use this for training. This command is only a representation of all the flags used. See Examples below on how to train a model.
python main.py \
--train \
--train-labels \
--calc-ads \
--ads-percent 0.7 \
--acquisition-type "psp" \
--top-k 50 \
--ssample-input-size 0.7 \
--ssample-label-size 2000 \
--calc-subsample-size 1000 \
--lambdas 0.01 0.01 0.01 0.01 0.01 10 \
--penalty "l21" \
--X-name "[DATANAME]_X*.pkl" \
--y-name "[DATANAME]_y.pkl" \
--model-name "[MODELNAME] (without extension)" \
--ospath "[absolute path to the object files directory (e.g. objectset)]" \
--dspath "[absolute path to the dataset directory (e.g. dataset)]" \
--mdpath "[absolute path to the model directory (e.g. model)]" \
--rspath "[absolute path to the result directory (e.g. result)]" \
--logpath "[absolute path to the log directory (e.g. log)]" \
--batch 50 \
--max-inner-iter 100 \
--num-epochs 10 \
--num-models 10 \
--num-jobs 2 \
The table below summarizes all the command-line arguments that are specific to this framework:
| Argument name | Description | Default Value |
|---|---|---|
| --train | Training the leADS model | True |
| --train-labels | A boolean variable to suggest training leADS using only class-labels data (e.g. "biocyc21_Xe.pkl" and "biocyc21_y.pkl") | False |
| --calc-ads | A boolean variable indicating whether to subsample dataset using active dataset subsampling (ADS) | False |
| --ads-percent | Corresponds the dataset subsampling size (within [0, 1]) | 0.7 |
| --acquisition-type | The acquisition function for estimating the predictive uncertainty (["entropy", "mutual", "variation", "psp"]) | "psp" |
| --top-k | The labels to be considered for variation ratio or psp acquisition functions | 10 |
| --ssample-input-size | Corresponds to the size of random subsampled inputs | 0.7 |
| --ssample-label-size | Corresponds to the size of random subsampled pathway labels | 2000 |
| --calc-subsample-size | The number of samples on which the cost function is computed | 1000 |
| --lambdas | Corresponds to the six hyper-parameters for constraints | 0.01, 0.01, 0.01, 0.01, 0.01, 10 |
| --penalty | The type of regularization term to be applied | l21 |
| --X-name | Input space of multi-label data | biocyc_Xe.pkl |
| --y-name | Pathway space of multi-label data | biocyc_y.pkl |
| --model-name | Corresponds to the name of the model excluding any EXTENSION. The model name will have .pkl extension | leADS |
| --mdpath | Path to store model | Outside source code |
| --rspath | Path to store costs and resulting samples indices | Outside source code |
| --logpath | path to the log directory | Outside source code |
| --ospath | The path to the data object that contains extracted information from the MetaCyc database (e.g. "biocyc.pkl") | Outside source code |
| --dspath | The path to the datasets | Outside source code |
| --batch | Batch size | 50 |
| --max-inner-iter | Corresponds to the number of inner iteration for logistic regression | 100 |
| --num-epochs | Corresponds to the number of iterations over the training set | 10 |
| --num-models | Corresponds to the number of base learners in an ensemble | 10 |
| --num-jobs | The number of parallel workers | 2 |
| File | Description |
|---|---|
| [MODELNAME].pkl | The trained model |
| [MODELNAME]_cost.txt | This file contains error values between predicted values and expected values |
| [MODELNAME]_samples.pkl | This file contains the sample indices that were produced during the training of the model. It is only created if the subsampling flag (--calc-ads) is applied. See Example 1 and 2
|
| log file | This file contains information regarding the run such as time taken to train the model, arguments applied and the files to which the results were stored |
If you wish to train a pathway dataset (e.g. "biocyc21_Xe.pkl" and "biocyc21_y.pkl") using the subsampling step with variation or psp as an acquisition function, you will need to provide an additional argument --top-k. Run the following command:
python main.py --train --train-labels --calc-ads --ads-percent 0.7 --acquisition-type "psp" --top-k 50 --ssample-input-size 0.7 --ssample-label-size 2000 --calc-subsample-size 1000 --lambdas 0.01 0.01 0.01 0.01 0.01 10 --penalty "l21" --X-name "biocyc21_Xe.pkl" --y-name "biocyc21_y.pkl" --model-name "leADS_retrained_1" --batch 50 --max-inner-iter 5 --num-epochs 10 --num-models 3 --num-jobs 2
After running the command, the output will be saved to the model/, result/, and log/ folders. A short description of the output is given in the table above. The tree structure for the folder with the outputs will look like this:
leADS_materials/
├── objectset/
│ └── ...
├── model/
│ ├── leADS_retrained_1.pkl
│ └── ...
├── dataset/
│ └── ...
├── result/
| ├── leADS_retrained_1_cost.txt
| ├── leADS_retrained_1_samples.pkl
│ └── ...
├── log/
| ├── leADS_events
│ └── ...
└── leADS/
└── ...
To train a pathway dataset using subsampling and a different acquisition function, execute the following command:
python main.py --train --train-labels --calc-ads --ads-percent 0.7 --acquisition-type "entropy" --ssample-input-size 0.7 --ssample-label-size 2000 --calc-subsample-size 1000 --lambdas 0.01 0.01 0.01 0.01 0.01 10 --penalty "l21" --X-name "biocyc21_Xe.pkl" --y-name "biocyc21_y.pkl" --model-name "leADS_retrained_2" --batch 50 --max-inner-iter 5 --num-epochs 10 --num-models 3 --num-jobs 2
After running the command, the output will be saved to the model/, result/, and log/ folders. A short description of the output is given in the table above. The tree structure for the folder with the outputs will look like this:
leADS_materials/
├── objectset/
│ └── ...
├── model/
│ ├── leADS_retrained_2.pkl
│ └── ...
├── dataset/
│ └── ...
├── result/
| ├── leADS_retrained_2_cost.txt
| ├── leADS_retrained_2_samples.pkl
│ └── ...
├── log/
| ├── leADS_events
│ └── ...
└── leADS/
└── ...
To train a pathway dataset without the subsampling step, execute the following command:
python main.py --train --train-labels --ssample-input-size 0.7 --ssample-label-size 2000 --calc-subsample-size 1000 --lambdas 0.01 0.01 0.01 0.01 0.01 10 --penalty "l21" --X-name "biocyc21_Xe.pkl" --y-name "biocyc21_y.pkl" --model-name "leADS_retrained_3" --batch 50 --max-inner-iter 5 --num-epochs 10 --num-models 3 --num-jobs 2
After running the command, the output will be saved to the model/, result/, and log/ folders. A short description of the output is given in the table above. The tree structure for the folder with the outputs will look like this:
leADS_materials/
├── objectset/
│ └── ...
├── model/
│ ├── leADS_retrained_3.pkl
│ └── ...
├── dataset/
│ └── ...
├── result/
| ├── leADS_retrained_3_cost.txt
│ └── ...
├── log/
| ├── leADS_events
│ └── ...
└── leADS/
└── ...
To train a pathway dataset using predefined samples, you need to provide an additional argument --train-selected-sample and the name of the file in --samples-ids (e.g."leADS_samples.pkl") that is stored in rspath:
python main.py --train --train-labels --train-selected-sample --ssample-input-size 0.7 --ssample-label-size 2000 --calc-subsample-size 1000 --lambdas 0.01 0.01 0.01 0.01 0.01 10 --penalty "l21" --X-name "biocyc21_Xe.pkl" --y-name "biocyc21_y.pkl" --samples-ids "leADS_samples.pkl" --model-name "leADS_retrained_4" --batch 50 --max-inner-iter 5 --num-epochs 10 --num-models 3 --num-jobs 2
After running the command, the output will be saved to the model/, result/, and log/ folders. A short description of the output is given in the table above. The tree structure for the folder with the outputs will look like this:
leADS_materials/
├── objectset/
│ └── ...
├── model/
│ ├── leADS_retrained_4.pkl
│ └── ...
├── dataset/
│ └── ...
├── result/
| ├── leADS_retrained_4_cost.txt
│ └── ...
├── log/
| ├── leADS_events
│ └── ...
└── leADS/
└── ...