Refactored scripts location

.github/workflows/build-and-run-tests-from-branch.yml

@@ -73,8 +73,8 @@ jobs:
     - name: Run monitoring
       run: |
         echo Find your Prometheus metrics using label {instance=\"${GITHUB_RUN_ID}-${HOSTNAME}\"}
-        chmod +x ./scripts/monitoring.sh
-        ./scripts/monitoring.sh ${{ secrets.PUSHGATEWAY_HOSTNAME }} ${{ secrets.PUSHGATEWAY_USER }} ${{ secrets.PUSHGATEWAY_PASSWORD }}
+        chmod +x ./scripts/project/monitoring.sh
+        ./scripts/project/monitoring.sh ${{ secrets.PUSHGATEWAY_HOSTNAME }} ${{ secrets.PUSHGATEWAY_USER }} ${{ secrets.PUSHGATEWAY_PASSWORD }}
     - name: Run tests
       run: |     
         gradle --no-daemon :utbot-framework:test ${{ matrix.project.TESTS_TO_RUN }}
@@ -131,8 +131,8 @@ jobs:
     - name: Run monitoring
       run: |
         echo Find your Prometheus metrics using label {instance=\"${GITHUB_RUN_ID}-${HOSTNAME}\"}
-        chmod +x ./scripts/monitoring.sh
-        ./scripts/monitoring.sh ${{ secrets.PUSHGATEWAY_HOSTNAME }} ${{ secrets.PUSHGATEWAY_USER }} ${{ secrets.PUSHGATEWAY_PASSWORD }}
+        chmod +x ./scripts/project/monitoring.sh
+        ./scripts/project/monitoring.sh ${{ secrets.PUSHGATEWAY_HOSTNAME }} ${{ secrets.PUSHGATEWAY_USER }} ${{ secrets.PUSHGATEWAY_PASSWORD }}
     - name: Run tests
       run: |
         cd ${{ matrix.project }}

.github/workflows/run-chosen-tests-from-branch.yml

@@ -45,8 +45,8 @@ jobs:
       - name: Run monitoring
         run: |
           echo Find your Prometheus metrics using label {instance=\"${GITHUB_RUN_ID}-${HOSTNAME}\"}
-          chmod +x ./scripts/monitoring.sh
-          ./scripts/monitoring.sh ${{ secrets.PUSHGATEWAY_HOSTNAME }} ${{ secrets.PUSHGATEWAY_USER }} ${{ secrets.PUSHGATEWAY_PASSWORD }}
+          chmod +x ./scripts/project/monitoring.sh
+          ./scripts/project/monitoring.sh ${{ secrets.PUSHGATEWAY_HOSTNAME }} ${{ secrets.PUSHGATEWAY_USER }} ${{ secrets.PUSHGATEWAY_PASSWORD }}
 
       - name: Run chosen tests
         run: |

docs/jlearch/pipeline-training-usage.md

@@ -32,6 +32,6 @@ Briefly:
 To do this, you should:
 * Be sure that you use `Java 8` by `java` command and set `JAVA_HOME` to `Java 8`. 
 * Put projects, on which you want to learn in `contest_input/projects` folder, then list classes, on which you want to learn in `contest_input/classes/<project name>/list` (if it is empty, than we will take all classes from project jar).
-* Run `pip install -r scripts/requirements.txt`. It is up to you to make it in virtual environment or not.
-* List selectors in `scripts/selector_list` and projects in `scripts/prog_list`
-* Run `./scripts/train_iteratively.sh `
+* Run `pip install -r scripts/ml/requirements.txt`. It is up to you to make it in virtual environment or not.
+* List selectors in `scripts/ml/selector_list` and projects in `scripts/ml/prog_list`
+* Run `./scripts/ml/train_iteratively.sh `

docs/jlearch/scripts.md

@@ -5,7 +5,7 @@ For each scenario: go to root of `UTBotJava` repository - it is `WORKDIR`.
 
 Before start of work run:
 ```bash
-./scripts/prepare.sh
+./scripts/ml/prepare.sh
 ```
 
 It will copy contest resources in `contest_input` folder and build the project, because we use jars, so if you want to change something in code and re-run scripts, then you should run:
@@ -16,11 +16,11 @@ It will copy contest resources in `contest_input` folder and build the project,
 ## To Train a few iterations of your models:
 By default features directory is `eval/features` - it should be created, to change it you should manually do it in source code of scripts.
 
-List projects and selectors on what you want to train in `scripts/prog_list` and `scripts/selector_list`. You will be trained on all methods of all classes from `contest_input/classes/<project name>/list`.
+List projects and selectors on what you want to train in `scripts/ml/prog_list` and `scripts/selector_list`. You will be trained on all methods of all classes from `contest_input/classes/<project name>/list`.
 
 Then just run:
 ```bash
-./scripts/train_iteratively.sh <time_limit> <iterations> <output_dir> <python_command>
+./scripts/ml/train_iteratively.sh <time_limit> <iterations> <output_dir> <python_command>
 ```
 Python command is your command for python3, in the end of execution you will get iterations models in `<output_dir>` folder and features for each selector and project in `<features_dir>/<selector>/<project>` for `selector` from `selectors_list` and in `<features_dir>/jlearch/<selector>/<prog>` for models.
 
@@ -29,15 +29,15 @@ Check that `srcTestDir` with your project exist in `build.gradle` of `utbot-juni
 
 Then just run: 
 ```bash
-./scripts/run_with_coverage.sh <project> <time_limit> <path_selector> <selector_alias>
+./scripts/ml/run_with_coverage.sh <project> <time_limit> <path_selector> <selector_alias>
 ``` 
 
 In the end of execution you will get jacoco report in `eval/jacoco/<project>/<selector_alias>/` folder.
 
 ## To estimate quality
 Just run:
 ```bash
-./scripts/quality_analysis.sh <project> <selector_aliases, separated by comma>
+./scripts/ml/quality_analysis.sh <project> <selector_aliases, separated by comma>
 ```
 It will take coverage reports from relative report folders (at `eval/jacoco/project/alias`) and generate charts in `$outputDir/<project>/<timestamp>.html`.
 `outputDir` can be changed in `QualityAnalysisConfig`. Result file will contain information about 3 metrics:

docs/jlearch/setup.md

@@ -1,7 +1,7 @@
 # How to setup environment for experiments on Linux
 
 * Clone repository, go to root
-* `chmod +x ./scripts/*` and `chmod +x gradlew`.
+* `chmod +x ./scripts/ml/*` and `chmod +x gradlew`.
 * Set `Java 8` as default and set `JAVA_HOME` to this `Java`.
   For example
   * Go through [this](https://sdkman.io/install) until `Windows installation`
@@ -17,19 +17,19 @@
   * `python3 -m venv /path/to/new/virtual/environment`
   * `source /path/to/venv/bin/activate`
   * Check `which python3`, it should be somewhere in `path/to/env` folder.
-  * `pip install -r scripts/requirements.txt`
-* `./scripts/prepare.sh`
-* Change `scripts/prog_list` to run on smaller project or delete some classes from `contest_input/classes/<project>/list`.
+  * `pip install -r scripts/ml/requirements.txt`
+* `./scripts/ml/prepare.sh`
+* Change `scripts/ml/prog_list` to run on smaller project or delete some classes from `contest_input/classes/<project>/list`.
 
 # Default settings and how to change it
-* You can reduce number of models in `models` variable in `scripts/train_iteratively.sh`
+* You can reduce number of models in `models` variable in `scripts/ml/train_iteratively.sh`
 * You can change amount of required RAM in `run_contest_estimator.sh`: `16 gb`  by default
 * You can change `batch_size` or `device` n `train.py`: `4096` and `gpu` by default
 * If you are completing setup on server, then you will need to uncomment tmp directory option in `run_contest_estimator.sh`
 
 # Continue setup
-* `scripts/train_iteratively.sh 30 2 models <your python3 command>`
+* `scripts/ml/train_iteratively.sh 30 2 models <your python3 command>`
 * In `models/` you should get models.
 * `mkdir eval/jacoco`
-* `./scripts/run_with_coverage.sh <any project (guava-26.0, for example)> 30 "NN_REWARD_GUIDED_SELECTOR path/to/model" some_alias`. `path/to/model` should be something like `models/nn32/0`, where `nn32` is a type of model and `0` is the iteration number
+* `./scripts/ml/run_with_coverage.sh <any project (guava-26.0, for example)> 30 "NN_REWARD_GUIDED_SELECTOR path/to/model" some_alias`. `path/to/model` should be something like `models/nn32/0`, where `nn32` is a type of model and `0` is the iteration number
 * You should get jacoco report in `eval/jacoco/guava-26.0/some_alias/`

scripts/run_with_coverage.sh → scripts/ml/run_with_coverage.sh

@@ -13,7 +13,7 @@ if [[ -n $COVERAGE_PROCESSING ]]; then
 fi
 
 WORKDIR="."
-$WORKDIR/scripts/run_contest_estimator.sh $PROJECT $TIME_LIMIT "$PATH_SELECTOR" "" "$COVERAGE_PROCESSING"
+$WORKDIR/scripts/ml/run_contest_estimator.sh $PROJECT $TIME_LIMIT "$PATH_SELECTOR" "" "$COVERAGE_PROCESSING"
 
 ./gradlew :utbot-junit-contest:test :utbot-junit-contest:jacocoTestReport
 

scripts/train_data.sh → scripts/ml/train_data.sh

@@ -9,6 +9,6 @@ while read prog; do
   while read selector; do
     echo "Starting features collection from $prog with $selector"
     selector="${selector%%[[:cntrl:]]}"
-    $WORKDIR/scripts/run_contest_estimator.sh "$prog" "$TIME_LIMIT" "$selector" true
-  done <"$WORKDIR/scripts/selector_list"
-done <"$WORKDIR/scripts/prog_list"
+    $WORKDIR/scripts/ml/run_contest_estimator.sh "$prog" "$TIME_LIMIT" "$selector" true
+  done <"$WORKDIR/scripts/ml/selector_list"
+done <"$WORKDIR/scripts/ml/prog_list"

scripts/train_iteratively.sh → scripts/ml/train_iteratively.sh

@@ -11,7 +11,7 @@ WORKDIR="."
 
 echo "Start training data on heuristical based selectors"
 
-$WORKDIR/scripts/train_data.sh $TIME_LIMIT
+$WORKDIR/scripts/ml/train_data.sh $TIME_LIMIT
 
 echo "Start iterative learning of models"
 
@@ -28,7 +28,7 @@ do
       echo "EXTRA_ARGS=$EXTRA_ARGS"
     fi
 
-    COMMAND="$PYTHON_COMMAND $WORKDIR/scripts/train.py --features_dir $WORKDIR/eval/features --output_dir $OUTPUT_DIR/$model/$i --prog_list $WORKDIR/scripts/prog_list --model $model $EXTRA_ARGS"
+    COMMAND="$PYTHON_COMMAND $WORKDIR/scripts/ml/train.py --features_dir $WORKDIR/eval/features --output_dir $OUTPUT_DIR/$model/$i --prog_list $WORKDIR/scripts/prog_list --model $model $EXTRA_ARGS"
     echo "TRAINING COMMAND=$COMMAND"
     $COMMAND
   done
@@ -44,7 +44,7 @@ do
         PREDICTOR="LINEAR"
       fi
 
-     $WORKDIR/scripts/run_contest_estimator.sh $prog $TIME_LIMIT "NN_REWARD_GUIDED_SELECTOR $OUTPUT_DIR/$model/$i $PREDICTOR" "true eval/features/jlearch/$model$i/$prog"
+     $WORKDIR/scripts/ml/run_contest_estimator.sh $prog $TIME_LIMIT "NN_REWARD_GUIDED_SELECTOR $OUTPUT_DIR/$model/$i $PREDICTOR" "true eval/features/jlearch/$model$i/$prog"
     done
-  done <"$WORKDIR/scripts/prog_list"
+  done <"$WORKDIR/scripts/ml/prog_list"
 done

scripts/monitoring.sh → scripts/project/monitoring.sh

@@ -53,9 +53,9 @@ if ! netstat -tulpn | grep -q ${PORT_NODE_EXPORTER} ; then
 fi
 
 # custom java processes memory metrics
-chmod +x scripts/ps_parser.sh
+chmod +x scripts/project/ps_parser.sh
 while true; do
-  ./scripts/ps_parser.sh | curl -u "${PUSHGATEWAY_USER}":"${PUSHGATEWAY_PASSWORD}" --data-binary @- "https://${PUSHGATEWAY_HOSTNAME}${PUSHGATEWAY_ADDITIONAL_PATH}/metrics/job/pushgateway/instance/${GITHUB_RUN_ID}-${HOSTNAME}${PROM_ADDITIONAL_LABELS}" 2>/dev/null
+  ./scripts/project/ps_parser.sh | curl -u "${PUSHGATEWAY_USER}":"${PUSHGATEWAY_PASSWORD}" --data-binary @- "https://${PUSHGATEWAY_HOSTNAME}${PUSHGATEWAY_ADDITIONAL_PATH}/metrics/job/pushgateway/instance/${GITHUB_RUN_ID}-${HOSTNAME}${PROM_ADDITIONAL_LABELS}" 2>/dev/null
   sleep ${SLEEP_TIME_SECONDS}
 done &