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  • JSON-based Command-line interface +
  • +
  • Run from Python/Jupyter Notebook
  • +
  • Running via CLI
  • -
  • Run from Python/Jupyter Notebook
  • +
  • Optional: inspect
  • Adding descriptors to QSARtuna
  • @@ -245,110 +247,6 @@

    Configuration file -

    Running via singulartity

    -

    QSARtuna can be deployed using Singularity container.

    -

    To run commands inside the container, Singularity uses the following syntax:

    -
    singularity exec <container.sif> <command>
    -
    -
    -

    We can run three-step-process from command line with the following command:

    -
    singularity exec /projects/cc/mai/containers/QSARtuna_latest.sif \
    -  /opt/qsartuna/.venv/bin/qsartuna-optimize \
    -  --config examples/optimization/regression_drd2_50.json \
    -  --best-buildconfig-outpath ~/qsartuna-target/best.json \
    -  --best-model-outpath ~/qsartuna-target/best.pkl \
    -  --merged-model-outpath ~/qsartuna-target/merged.pkl
    -
    -
    -

    Since optimization can be a long process, -we should avoid running it on the login node, -and we should submit it to the SLURM queue instead.

    - -
    -

    Submitting to SLURM

    -

    We can submit our script to the queue by giving sbatch the following script:

    -
    #!/bin/sh
    -#SBATCH --nodes=1
    -#SBATCH --ntasks=1
    -#SBATCH --cpus-per-task=5
    -#SBATCH --mem-per-cpu=4G
    -#SBATCH --time=100:0:0
    -#SBATCH --partition core
    -
    -# This script illustrates how to run one configuration from QSARtuna examples.
    -# The example we use is in examples/optimization/regression_drd2_50.json.
    -
    -# The example we chose uses relative paths to data files, change directory.
    -cd /{project_folder}/OptunaAZ-versions/OptunaAZ_latest
    -
    -singularity exec \
    -  /{project_folder}/containers/QSARtuna_latest.sif \
    -  /opt/qsartuna/.venv/bin/qsartuna-optimize \
    -  --config{project_folder}/examples/optimization/regression_drd2_50.json \
    -  --best-buildconfig-outpath ~/qsartuna-target/best.json \
    -  --best-model-outpath ~/qsartuna-target/best.pkl \
    -  --merged-model-outpath ~/qsartuna-target/merged.pkl
    -
    -
    -

    When the script is complete, it will create pickled model files inside your home directory under ~/qsartuna-target/.

    -
    -
    -

    Using the model

    -

    When the model is built, run inference:

    -
    singularity exec /{project_folder}/containers/QSARtuna_latest.sif \
    -  /opt/qsartuna/.venv/bin/qsartuna-predict \
    -  --model-file target/merged.pkl \
    -  --input-smiles-csv-file tests/data/DRD2/subset-50/test.csv \
    -  --input-smiles-csv-column "canonical" \
    -  --output-prediction-csv-file target/prediction.csv
    -
    -
    -

    Note that QSARtuna_latest.sif points to the most recent version of QSARtuna.

    -

    Legacy models require the inference with the same QSARtuna version used to train the model. -This can be specified by modifying the above command and supplying -/projects/cc/mai/containers/QSARtuna_<version>.sif (replace with the version of QSARtuna).

    -

    E.g:

    -
    singularity exec /{project_folder}/containers/QSARtuna_2.5.1.sif \
    -  /opt/qsartuna/.venv/bin/qsartuna-predict \
    -  --model-file 2.5.1_model.pkl \
    -  --input-smiles-csv-file tests/data/DRD2/subset-50/test.csv \
    -  --input-smiles-csv-column "canonical" \
    -  --output-prediction-csv-file target/prediction.csv
    -
    -
    -

    would generate predictions for a model trained with QSARtuna 2.5.1.

    -
    -
    -

    Optional: inspect

    -

    To inspect performance of different models tried during optimization, -use MLFlow Tracking UI:

    -
    module load mlflow
    -mlflow ui
    -
    -
    -

    Then open mlflow link your browser.

    -

    mlflow select experiment

    -

    If you run mlflow ui on SCP, -you can forward your mlflow port -with a separate SSH session started on your local (”non-SCP”) machine:

    -
    ssh -N -L localhost:5000:localhost:5000 user@login.intranet.net
    -
    -
    -

    (”-L” forwards ports, and “-N” just to not execute any commands).

    -

    In the MLFlow Tracking UI, select experiment to the left, -it is named after the input file path. -Then select all runs/trials in the experiment, and choose “Compare”. -You will get a comparison page for selected runs/trials in the experiment.

    -

    mlflow inspecting trials

    -

    Comparison page will show MLFlow Runs (called Trials in Optuna), -as well as their Parameters and Metrics. -At the bottom there are plots. -For X-axis, select “trial_number”. -For Y-axis, start with “optimization_objective_cvmean_r2”.

    -

    You can get more details by clicking individual runs. -There you can access run/trial build (training) configuration.

    -

    Run from Python/Jupyter Notebook

    @@ -359,7 +257,7 @@

    Run from Python/Jupyter Notebook activate my_env_with_qsartuna module purge # Just in case. which python # Check. Should output path that contains "my_env_with_qsartuna". -python -m pip install https://github.com/MolecularAI/QSARtuna/releases/download/3.1.0/qsartuna-3.1.0.tar.gz +python -m pip install https://github.com/MolecularAI/QSARtuna/releases/download/3.1.1/qsartuna-3.1.1.tar.gz

    Then you can use QSARtuna inside your Notebook:

    @@ -428,6 +326,154 @@

    Run from Python/Jupyter Notebook +

    Running via CLI

    +

    QSARtuna can be deployed directly from the CLI

    +

    To run commands QSARtuna uses the following syntax:

    +
    qsartuna-<optimize|build|predict|schemagen> <command>
    +
    +
    +

    We can run three-step-process from command line with the following command:

    +
      qsartuna-optimize \
    +  --config examples/optimization/regression_drd2_50.json \
    +  --best-buildconfig-outpath ~/qsartuna-target/best.json \
    +  --best-model-outpath ~/qsartuna-target/best.pkl \
    +  --merged-model-outpath ~/qsartuna-target/merged.pkl
    +
    +
    +

    Optimization accepts the following command line arguments:

    +
    shell
    +qsartuna-optimize -h 
    +usage: qsartuna-optimize [-h] --config CONFIG [--best-buildconfig-outpath BEST_BUILDCONFIG_OUTPATH] [--best-model-outpath BEST_MODEL_OUTPATH] [--merged-model-outpath MERGED_MODEL_OUTPATH] [--no-cache]
    +
    +optbuild: Optimize hyper-parameters and build (train) the best model.
    +
    +options:
    +  -h, --help            show this help message and exit
    +  --best-buildconfig-outpath BEST_BUILDCONFIG_OUTPATH
    +                        Path where to write Json of the best build configuration.
    +  --best-model-outpath BEST_MODEL_OUTPATH
    +                        Path where to write (persist) the best model.
    +  --merged-model-outpath MERGED_MODEL_OUTPATH
    +                        Path where to write (persist) the model trained on merged train+test data.
    +  --no-cache            Turn off descriptor generation caching
    +
    +required named arguments:
    +  --config CONFIG       Path to input configuration file (JSON): either Optimization configuration, or Build (training) configuration.
    +
    +
    +

    Since optimization can be a long process, +we should avoid running it on the login node, +and we should submit it to the SLURM queue instead.

    +
    +

    Submitting to SLURM

    +

    We can submit our script to the queue by giving sbatch the following script:

    +
    #!/bin/sh
    +#SBATCH --nodes=1
    +#SBATCH --ntasks=1
    +#SBATCH --cpus-per-task=5
    +#SBATCH --mem-per-cpu=4G
    +#SBATCH --time=100:0:0
    +#SBATCH --partition core
    +
    +# This script illustrates how to run one configuration from QSARtuna examples.
    +# The example we use is in examples/optimization/regression_drd2_50.json.
    +
    +module load Miniconda3
    +conda activate my_env_with_qsartuna
    +
    +# The example we chose uses relative paths to data files, change directory.
    +cd /{project_folder}/
    +
    +  /<your-project-dir>/qsartuna-optimize \
    +  --config {project_folder}/examples/optimization/regression_drd2_50.json \
    +  --best-buildconfig-outpath ~/qsartuna-target/best.json \
    +  --best-model-outpath ~/qsartuna-target/best.pkl \
    +  --merged-model-outpath ~/qsartuna-target/merged.pkl
    +
    +
    +

    When the script is complete, it will create pickled model files inside your home directory under ~/qsartuna-target/.

    +
    +
    +

    Using the model

    +

    When the model is built, run inference:

    +
      qsartuna-predict \
    +  --model-file target/merged.pkl \
    +  --input-smiles-csv-file tests/data/DRD2/subset-50/test.csv \
    +  --input-smiles-csv-column "canonical" \
    +  --output-prediction-csv-file target/prediction.csv
    +
    +
    +

    Note that prediction accepts a variety of command line arguments:

    +
     qsartuna-predict -h
    +usage: qsartuna-predict [-h] --model-file MODEL_FILE [--input-smiles-csv-file INPUT_SMILES_CSV_FILE] [--input-smiles-csv-column INPUT_SMILES_CSV_COLUMN] [--input-aux-column INPUT_AUX_COLUMN]
    +                        [--input-precomputed-file INPUT_PRECOMPUTED_FILE] [--input-precomputed-input-column INPUT_PRECOMPUTED_INPUT_COLUMN]
    +                        [--input-precomputed-response-column INPUT_PRECOMPUTED_RESPONSE_COLUMN] [--output-prediction-csv-column OUTPUT_PREDICTION_CSV_COLUMN]
    +                        [--output-prediction-csv-file OUTPUT_PREDICTION_CSV_FILE] [--predict-uncertainty] [--predict-explain] [--uncertainty_quantile UNCERTAINTY_QUANTILE]
    +
    +Predict responses for a given OptunaAZ model
    +
    +options:
    +  -h, --help            show this help message and exit
    +  --input-smiles-csv-file INPUT_SMILES_CSV_FILE
    +                        Name of input CSV file with Input SMILES
    +  --input-smiles-csv-column INPUT_SMILES_CSV_COLUMN
    +                        Column name of SMILES column in input CSV file
    +  --input-aux-column INPUT_AUX_COLUMN
    +                        Column name of auxiliary descriptors in input CSV file
    +  --input-precomputed-file INPUT_PRECOMPUTED_FILE
    +                        Filename of precomputed descriptors input CSV file
    +  --input-precomputed-input-column INPUT_PRECOMPUTED_INPUT_COLUMN
    +                        Column name of precomputed descriptors identifier
    +  --input-precomputed-response-column INPUT_PRECOMPUTED_RESPONSE_COLUMN
    +                        Column name of precomputed descriptors response column
    +  --output-prediction-csv-column OUTPUT_PREDICTION_CSV_COLUMN
    +                        Column name of prediction column in output CSV file
    +  --output-prediction-csv-file OUTPUT_PREDICTION_CSV_FILE
    +                        Name of output CSV file
    +  --predict-uncertainty
    +                        Predict with uncertainties (model must provide this functionality)
    +  --predict-explain     Predict with SHAP or ChemProp explainability
    +  --uncertainty_quantile UNCERTAINTY_QUANTILE
    +                        Apply uncertainty threshold to predictions
    +
    +required named arguments:
    +  --model-file MODEL_FILE
    +                        Model file name
    +
    +
    +
    +

    +
    +

    Optional: inspect

    +

    To inspect performance of different models tried during optimization, +use MLFlow Tracking UI:

    +
    module load mlflow
    +mlflow ui
    +
    +
    +

    Then open mlflow link your browser.

    +

    mlflow select experiment

    +

    If you run mlflow ui on SCP, +you can forward your mlflow port +with a separate SSH session started on your local (”non-SCP”) machine:

    +
    ssh -N -L localhost:5000:localhost:5000 user@login.intranet.net
    +
    +
    +

    (”-L” forwards ports, and “-N” just to not execute any commands).

    +

    In the MLFlow Tracking UI, select experiment to the left, +it is named after the input file path. +Then select all runs/trials in the experiment, and choose “Compare”. +You will get a comparison page for selected runs/trials in the experiment.

    +

    mlflow inspecting trials

    +

    Comparison page will show MLFlow Runs (called Trials in Optuna), +as well as their Parameters and Metrics. +At the bottom there are plots. +For X-axis, select “trial_number”. +For Y-axis, start with “optimization_objective_cvmean_r2”.

    +

    You can get more details by clicking individual runs. +There you can access run/trial build (training) configuration.

    +

    Adding descriptors to QSARtuna

    Add the descriptor code to the optunaz.descriptor.py file like so:

    @@ -489,7 +535,7 @@

    Adding descriptors to QSARtuna
    from qptuna.descriptors import YourNewDescriptor
    +
    from qsartuna.descriptors import YourNewDescriptor
     
     config = OptimizationConfig(
         data=Dataset(
    diff --git a/docs/sphinx-builddir/html/_sources/README.md.txt b/docs/sphinx-builddir/html/_sources/README.md.txt
    index 853781b..169e005 100644
    --- a/docs/sphinx-builddir/html/_sources/README.md.txt
    +++ b/docs/sphinx-builddir/html/_sources/README.md.txt
    @@ -135,26 +135,129 @@ and optimization is free to pair any specified descriptor with any of the algori
     
     When we have our data and our configuration, it is time to start the optimization.
     
    -### Running via singulartity
     
    -QSARtuna can be deployed using [Singularity](https://sylabs.io/guides/3.7/user-guide/index.html) container.
    +## Run from Python/Jupyter Notebook
     
    -To run commands inside the container, Singularity uses the following syntax:
    +Create conda environment with Jupyter and Install QSARtuna there:
     ```shell
    -singularity exec  
    +module purge
    +module load Miniconda3
    +conda create --name my_env_with_qsartuna python=3.10.10 jupyter pip
    +conda activate my_env_with_qsartuna
    +module purge  # Just in case.
    +which python  # Check. Should output path that contains "my_env_with_qsartuna".
    +python -m pip install https://github.com/MolecularAI/QSARtuna/releases/download/3.1.1/qsartuna-3.1.1.tar.gz
    +```
    +
    +Then you can use QSARtuna inside your Notebook:
    +```python
    +from qsartuna.three_step_opt_build_merge import (
    +    optimize,
    +    buildconfig_best,
    +    build_best,
    +    build_merged,
    +)
    +from qsartuna.config import ModelMode, OptimizationDirection
    +from qsartuna.config.optconfig import (
    +    OptimizationConfig,
    +    SVR,
    +    RandomForest,
    +    Ridge,
    +    Lasso,
    +    PLS,
    +    XGBregressor,
    +)
    +from qsartuna.datareader import Dataset
    +from qsartuna.descriptors import ECFP, MACCS_keys, ECFP_counts
    +
    +##
    +# Prepare hyperparameter optimization configuration.
    +config = OptimizationConfig(
    +    data=Dataset(
    +        input_column="canonical",
    +        response_column="molwt",
    +        training_dataset_file="tests/data/DRD2/subset-50/train.csv",
    +    ),
    +    descriptors=[ECFP.new(), ECFP_counts.new(), MACCS_keys.new()],
    +    algorithms=[
    +        SVR.new(),
    +        RandomForest.new(),
    +        Ridge.new(),
    +        Lasso.new(),
    +        PLS.new(),
    +        XGBregressor.new(),
    +    ],
    +    settings=OptimizationConfig.Settings(
    +        mode=ModelMode.REGRESSION,
    +        cross_validation=3,
    +        n_trials=100,
    +        direction=OptimizationDirection.MAXIMIZATION,
    +    ),
    +)
    +
    +##
    +# Run Optuna Study.
    +study = optimize(config, study_name="my_study")
    +
    +##
    +# Get the best Trial from the Study and make a Build (Training) configuration for it.
    +buildconfig = buildconfig_best(study)
    +# Optional: write out JSON of the best configuration.
    +import json
    +print(json.dumps(buildconfig.json(), indent=2))
    +
    +##
    +# Build (re-Train) and save the best model.
    +build_best(buildconfig, "target/best.pkl")
    +
    +##
    +# Build (Train) and save the model on the merged train+test data.
    +build_merged(buildconfig, "target/merged.pkl")
    +```
    +
    +## Running via CLI
    +
    +QSARtuna can be deployed directly from the CLI
    +
    +To run commands QSARtuna uses the following syntax:
    +```shell
    +qsartuna- 
     ```
     
     We can run three-step-process from command line with the following command:
     
     ```shell
    -singularity exec /projects/cc/mai/containers/QSARtuna_latest.sif \
    -  /opt/qsartuna/.venv/bin/qsartuna-optimize \
    +  qsartuna-optimize \
       --config examples/optimization/regression_drd2_50.json \
       --best-buildconfig-outpath ~/qsartuna-target/best.json \
       --best-model-outpath ~/qsartuna-target/best.pkl \
       --merged-model-outpath ~/qsartuna-target/merged.pkl
     ```
     
    +Optimization accepts the following command line arguments:
    +
    +```
    +shell
    +qsartuna-optimize -h 
    +usage: qsartuna-optimize [-h] --config CONFIG [--best-buildconfig-outpath BEST_BUILDCONFIG_OUTPATH] [--best-model-outpath BEST_MODEL_OUTPATH] [--merged-model-outpath MERGED_MODEL_OUTPATH] [--no-cache]
    +
    +optbuild: Optimize hyper-parameters and build (train) the best model.
    +
    +options:
    +  -h, --help            show this help message and exit
    +  --best-buildconfig-outpath BEST_BUILDCONFIG_OUTPATH
    +                        Path where to write Json of the best build configuration.
    +  --best-model-outpath BEST_MODEL_OUTPATH
    +                        Path where to write (persist) the best model.
    +  --merged-model-outpath MERGED_MODEL_OUTPATH
    +                        Path where to write (persist) the model trained on merged train+test data.
    +  --no-cache            Turn off descriptor generation caching
    +
    +required named arguments:
    +  --config CONFIG       Path to input configuration file (JSON): either Optimization configuration, or Build (training) configuration.
    +
    +```
    +
     Since optimization can be a long process,
     we should avoid running it on the login node, 
     and we should submit it to the SLURM queue instead. 
    @@ -176,13 +279,14 @@ We can submit our script to the queue by giving `sbatch` the following script:
     # This script illustrates how to run one configuration from QSARtuna examples.
     # The example we use is in examples/optimization/regression_drd2_50.json.
     
    +module load Miniconda3
    +conda activate my_env_with_qsartuna
    +
     # The example we chose uses relative paths to data files, change directory.
    -cd /{project_folder}/OptunaAZ-versions/OptunaAZ_latest
    +cd /{project_folder}/
     
    -singularity exec \
    -  /{project_folder}/containers/QSARtuna_latest.sif \
    -  /opt/qsartuna/.venv/bin/qsartuna-optimize \
    -  --config{project_folder}/examples/optimization/regression_drd2_50.json \
    +  //qsartuna-optimize \
    +  --config {project_folder}/examples/optimization/regression_drd2_50.json \
       --best-buildconfig-outpath ~/qsartuna-target/best.json \
       --best-model-outpath ~/qsartuna-target/best.pkl \
       --merged-model-outpath ~/qsartuna-target/merged.pkl
    @@ -195,33 +299,54 @@ When the script is complete, it will create pickled model files inside your home
     
     When the model is built, run inference:
     ```shell
    -singularity exec /{project_folder}/containers/QSARtuna_latest.sif \
    -  /opt/qsartuna/.venv/bin/qsartuna-predict \
    +  qsartuna-predict \
       --model-file target/merged.pkl \
       --input-smiles-csv-file tests/data/DRD2/subset-50/test.csv \
       --input-smiles-csv-column "canonical" \
       --output-prediction-csv-file target/prediction.csv
     ```
     
    -Note that QSARtuna_latest.sif points to the most recent version of QSARtuna.
    -
    -Legacy models require the inference with the same QSARtuna version used to train the model.
    -This can be specified by modifying the above command and supplying 
    -`/projects/cc/mai/containers/QSARtuna_.sif` (replace  with the version of QSARtuna).
    -
    -E.g:
    +Note that prediction accepts a variety of command line arguments:
     ```shell
    -singularity exec /{project_folder}/containers/QSARtuna_2.5.1.sif \
    -  /opt/qsartuna/.venv/bin/qsartuna-predict \
    -  --model-file 2.5.1_model.pkl \
    -  --input-smiles-csv-file tests/data/DRD2/subset-50/test.csv \
    -  --input-smiles-csv-column "canonical" \
    -  --output-prediction-csv-file target/prediction.csv
    + qsartuna-predict -h
    +usage: qsartuna-predict [-h] --model-file MODEL_FILE [--input-smiles-csv-file INPUT_SMILES_CSV_FILE] [--input-smiles-csv-column INPUT_SMILES_CSV_COLUMN] [--input-aux-column INPUT_AUX_COLUMN]
    +                        [--input-precomputed-file INPUT_PRECOMPUTED_FILE] [--input-precomputed-input-column INPUT_PRECOMPUTED_INPUT_COLUMN]
    +                        [--input-precomputed-response-column INPUT_PRECOMPUTED_RESPONSE_COLUMN] [--output-prediction-csv-column OUTPUT_PREDICTION_CSV_COLUMN]
    +                        [--output-prediction-csv-file OUTPUT_PREDICTION_CSV_FILE] [--predict-uncertainty] [--predict-explain] [--uncertainty_quantile UNCERTAINTY_QUANTILE]
    +
    +Predict responses for a given OptunaAZ model
    +
    +options:
    +  -h, --help            show this help message and exit
    +  --input-smiles-csv-file INPUT_SMILES_CSV_FILE
    +                        Name of input CSV file with Input SMILES
    +  --input-smiles-csv-column INPUT_SMILES_CSV_COLUMN
    +                        Column name of SMILES column in input CSV file
    +  --input-aux-column INPUT_AUX_COLUMN
    +                        Column name of auxiliary descriptors in input CSV file
    +  --input-precomputed-file INPUT_PRECOMPUTED_FILE
    +                        Filename of precomputed descriptors input CSV file
    +  --input-precomputed-input-column INPUT_PRECOMPUTED_INPUT_COLUMN
    +                        Column name of precomputed descriptors identifier
    +  --input-precomputed-response-column INPUT_PRECOMPUTED_RESPONSE_COLUMN
    +                        Column name of precomputed descriptors response column
    +  --output-prediction-csv-column OUTPUT_PREDICTION_CSV_COLUMN
    +                        Column name of prediction column in output CSV file
    +  --output-prediction-csv-file OUTPUT_PREDICTION_CSV_FILE
    +                        Name of output CSV file
    +  --predict-uncertainty
    +                        Predict with uncertainties (model must provide this functionality)
    +  --predict-explain     Predict with SHAP or ChemProp explainability
    +  --uncertainty_quantile UNCERTAINTY_QUANTILE
    +                        Apply uncertainty threshold to predictions
    +
    +required named arguments:
    +  --model-file MODEL_FILE
    +                        Model file name
     ```
     
    -would generate predictions for a model trained with QSARtuna 2.5.1.
     
    -### Optional: inspect
    +## Optional: inspect
     To inspect performance of different models tried during optimization,
     use [MLFlow Tracking UI](https://www.mlflow.org/docs/latest/tracking.html):
     ```bash
    @@ -258,86 +383,6 @@ You can get more details by clicking individual runs.
     There you can access run/trial build (training) configuration.
     
     
    -## Run from Python/Jupyter Notebook
    -
    -Create conda environment with Jupyter and Install QSARtuna there:
    -```shell
    -module purge
    -module load Miniconda3
    -conda create --name my_env_with_qsartuna python=3.10.10 jupyter pip
    -conda activate my_env_with_qsartuna
    -module purge  # Just in case.
    -which python  # Check. Should output path that contains "my_env_with_qsartuna".
    -python -m pip install https://github.com/MolecularAI/QSARtuna/releases/download/3.1.0/qsartuna-3.1.0.tar.gz
    -```
    -
    -Then you can use QSARtuna inside your Notebook:
    -```python
    -from qsartuna.three_step_opt_build_merge import (
    -    optimize,
    -    buildconfig_best,
    -    build_best,
    -    build_merged,
    -)
    -from qsartuna.config import ModelMode, OptimizationDirection
    -from qsartuna.config.optconfig import (
    -    OptimizationConfig,
    -    SVR,
    -    RandomForest,
    -    Ridge,
    -    Lasso,
    -    PLS,
    -    XGBregressor,
    -)
    -from qsartuna.datareader import Dataset
    -from qsartuna.descriptors import ECFP, MACCS_keys, ECFP_counts
    -
    -##
    -# Prepare hyperparameter optimization configuration.
    -config = OptimizationConfig(
    -    data=Dataset(
    -        input_column="canonical",
    -        response_column="molwt",
    -        training_dataset_file="tests/data/DRD2/subset-50/train.csv",
    -    ),
    -    descriptors=[ECFP.new(), ECFP_counts.new(), MACCS_keys.new()],
    -    algorithms=[
    -        SVR.new(),
    -        RandomForest.new(),
    -        Ridge.new(),
    -        Lasso.new(),
    -        PLS.new(),
    -        XGBregressor.new(),
    -    ],
    -    settings=OptimizationConfig.Settings(
    -        mode=ModelMode.REGRESSION,
    -        cross_validation=3,
    -        n_trials=100,
    -        direction=OptimizationDirection.MAXIMIZATION,
    -    ),
    -)
    -
    -##
    -# Run Optuna Study.
    -study = optimize(config, study_name="my_study")
    -
    -##
    -# Get the best Trial from the Study and make a Build (Training) configuration for it.
    -buildconfig = buildconfig_best(study)
    -# Optional: write out JSON of the best configuration.
    -import json
    -print(json.dumps(buildconfig.json(), indent=2))
    -
    -##
    -# Build (re-Train) and save the best model.
    -build_best(buildconfig, "target/best.pkl")
    -
    -##
    -# Build (Train) and save the model on the merged train+test data.
    -build_merged(buildconfig, "target/merged.pkl")
    -```
    -
    -
     ## Adding descriptors to QSARtuna
     
     
    @@ -407,7 +452,7 @@ CompositeCompatibleDescriptor = Union[
     
     Then you can use YourNewDescriptor inside your Notebook:
     ```python
    -from qptuna.descriptors import YourNewDescriptor
    +from qsartuna.descriptors import YourNewDescriptor
     
     config = OptimizationConfig(
         data=Dataset(
    diff --git a/docs/sphinx-builddir/html/index.html b/docs/sphinx-builddir/html/index.html
    index 65d09be..25728af 100644
    --- a/docs/sphinx-builddir/html/index.html
    +++ b/docs/sphinx-builddir/html/index.html
    @@ -114,6 +114,8 @@ 

    Welcome to QSARtuna Documentation!Background
  • JSON-based Command-line interface
  • Run from Python/Jupyter Notebook
  • +
  • Running via CLI
  • +
  • Optional: inspect
  • Adding descriptors to QSARtuna
  • diff --git a/docs/sphinx-builddir/html/objects.inv b/docs/sphinx-builddir/html/objects.inv index ed6e78ee458527a66db4f75be0a0fdbe2b5d56a0..3743e1035c480def8bf9fe7cc20b1337d1aa3374 100644 GIT binary patch delta 730 zcmV<00ww*>mIBL`0GfyySZ6Z56jE zu~}YY1>an3jb#--ex(!kb{U;SF+EL+)voO>TGCsuRJmMMipy0;dDdd)**nbb94iI(lA&g2+Mh0^!~{l3FC{;RjF;<|iPYN5c*# zhVc^(KE_gnRm-VnLSzPM}0uhus_cEHRFxw zd)e(jMOqcy4UpYWi{-T$SK+!z+*Kg@{PO2C=U*!y$;nKdU?+V*-S!Sa=*F+ds2alM z>UHnva_5snoh0pn?d-TXq^2;gUcWZ4-eU-tLG~IzdHL49y!GioQ-%(`S|o73np^7Z z#!O`hsa=wux6)%hDE~`^Gh123v z?Pc~)=W)~M*RywGB`H0(=GvD#t3)~Srdmdo%$Q}j zaz>fP4-T5eX41;d${S^!S>2aL-M9QX6rs+_R_tIWQ3h_VOX`kxd=qC@N>FQ3vLLxZE!$K-q?M=IVU&s|n$ zcM7M)rP_P$pUy+7(XVIk#7a_nZqeB-Z0{ty$|$piuAFaG&@Uy8lr!@;*jI^iIvDZ={q>t z?FO{Mt{JmdxwT{8?xt`rLFucK{W|hxK1N^9o)NXf-PLQa`L*1}%