Commit a87eed57 authored by Jana Schor's avatar Jana Schor
Browse files

updated example, not tested

parent c47aa70d
......@@ -38,7 +38,7 @@ or you can start a shell script (look at [run-all-publication-cases.sh](scripts/
example)
```shell script
singularity run --nv dfpl.sif ". ./scripts/run-all-cases.sh"
singularity run --nv dfpl.sif ". ./example/run-multiple-cases.sh"
```
It's also possible to get an interactive shell into the container
......
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\ No newline at end of file
{
"py/object": "dfpl.options.PredictOptions",
"py/object": "dfpl.options.Options",
"inputFile": "example/data/predict_data.csv",
"outputDir": "example/results_predict",
"ecWeightsFile": "example/models/generic_encoder.hdf5",
"model": "example/models/AR.model.hdf5",
"type": "smiles",
"fpType": "topological"
"outputFile": "predict_data_AR.csv",
"ecModelDir": "example/models/generic_encoder",
"fnnModelDir": "example/models/AR_saved_model",
"compressFeatures": true,
"trainAC": false,
"trainFNN": false
}
#!/usr/bin/env bash
# This script needs to be run from the deepFPlearn directory.
# Importantly, the conda environment needs to be set up and activated! For certain machines/HPC,
# we have a batch-job that does exactly that and then calls this file
function log_error() {
echo "$@" 1>&2
}
function call_convert() {
if [ -d "$1" ]; then
python -m dfpl convert -f "$1"
else
log_error "Could not find directory for data conversion $1"
fi
}
function call_train() {
if [ -f "$1" ]; then
python -m dfpl train -f "$1"
else
log_error "Could not find training file $1"
fi
}
function call_predict() {
if [ -f "$1" ]; then
python -m dfpl predict -f "$1"
else
log_error "Could not find prediction file $1"
fi
}
call_convert "data"
call_train "train.json"
call_predict "predict.json"
{
"py/object": "dfpl.options.TrainOptions",
"py/object": "dfpl.options.Options",
"inputFile": "example/data/train_data.csv",
"outputDir": "example/results_train/",
"ecWeightsFile": "encoder.hdf5",
"ecWeightsFile": "example/models/generic_encoder",
"type": "smiles",
"fpType": "topological",
"epochs": 3000,
"fpSize": 2048,
"encFPSize": 256,
"kFolds": 5,
"testingFraction": 0.2,
"enableMultiLabel": false,
"verbose": 2,
"trainAC": true,
"trainAC": false,
"trainFNN": true,
"compressFeatures": true
"compressFeatures": true,
"kFolds": 1,
"testSize": 0.2,
"optimizer": "Adam",
"lossFunction": "bce",
"epochs": 3000,
"batchSize": 128,
"activationFunction": "selu",
"dropout": 0.02382519002555714,
"learningRate": 0.00000783711232114358
}
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