diff --git a/docs/sphinx-builddir/doctrees/README.doctree b/docs/sphinx-builddir/doctrees/README.doctree index 65573d2..3dc37c4 100644 Binary files a/docs/sphinx-builddir/doctrees/README.doctree and b/docs/sphinx-builddir/doctrees/README.doctree differ diff --git a/docs/sphinx-builddir/doctrees/environment.pickle b/docs/sphinx-builddir/doctrees/environment.pickle index faadf32..294aca3 100644 Binary files a/docs/sphinx-builddir/doctrees/environment.pickle and b/docs/sphinx-builddir/doctrees/environment.pickle differ diff --git a/docs/sphinx-builddir/doctrees/notebooks/QSARtuna_Tutorial.doctree b/docs/sphinx-builddir/doctrees/notebooks/QSARtuna_Tutorial.doctree index 3d97099..fd52863 100644 Binary files a/docs/sphinx-builddir/doctrees/notebooks/QSARtuna_Tutorial.doctree and b/docs/sphinx-builddir/doctrees/notebooks/QSARtuna_Tutorial.doctree differ diff --git a/docs/sphinx-builddir/html/README.html b/docs/sphinx-builddir/html/README.html index a5ad32a..6dd8246 100644 --- a/docs/sphinx-builddir/html/README.html +++ b/docs/sphinx-builddir/html/README.html @@ -53,13 +53,15 @@
  • 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 ed6e78e..3743e10 100644
Binary files a/docs/sphinx-builddir/html/objects.inv and b/docs/sphinx-builddir/html/objects.inv differ
diff --git a/docs/sphinx-builddir/html/searchindex.js b/docs/sphinx-builddir/html/searchindex.js
index 3a903b4..1a6ca78 100644
--- a/docs/sphinx-builddir/html/searchindex.js
+++ b/docs/sphinx-builddir/html/searchindex.js
@@ -1 +1 @@
-Search.setIndex({"docnames": ["README", "algorithms", "deduplicator", "descriptors", "index", "modules", "notebooks/QSARtuna_Tutorial", "notebooks/preprocess_data", "optunaz", "optunaz.config", "optunaz.utils", "optunaz.utils.enums", "optunaz.utils.preprocessing", "splitters", "transform"], "filenames": ["README.md", "algorithms.rst", "deduplicator.rst", "descriptors.rst", "index.rst", "modules.rst", "notebooks/QSARtuna_Tutorial.ipynb", "notebooks/preprocess_data.ipynb", "optunaz.rst", "optunaz.config.rst", "optunaz.utils.rst", "optunaz.utils.enums.rst", "optunaz.utils.preprocessing.rst", "splitters.rst", "transform.rst"], "titles": ["QSARtuna \ud80c\udd9b: QSAR using Optimization for Hyperparameter Tuning (formerly Optuna AZ and QPTUNA)", "Available algorithms", "Available deduplicators", "Available descriptors", "Welcome to QSARtuna Documentation!", "optunaz", "QSARtuna CLI Tutorial", "Preprocessing data for QSARtuna", "optunaz package", "optunaz.config package", "optunaz.utils package", "optunaz.utils.enums package", "optunaz.utils.preprocessing package", "Available splitters", "Available transform"], "terms": {"build": [0, 4, 7, 8, 9, 10, 11], "predict": [0, 1, 3, 5, 6, 7, 9, 10, 11], "compchem": 0, "develop": [0, 3, 8], "uncertainti": [0, 1, 4, 7, 8, 9, 12, 14], "quantif": 0, "explain": [0, 1, 4, 5, 9], "mind": 0, "thi": [0, 1, 3, 4, 7, 8, 9, 10, 11, 12, 13], "librari": [0, 6, 7], "search": [0, 1, 3, 8, 9], "best": [0, 1, 8, 9], "ml": [0, 4, 9], "algorithm": [0, 3, 4, 7, 8, 9, 10, 11, 12, 13], "molecular": [0, 1, 3, 4, 6, 7, 8, 9], "given": [0, 1, 3, 6, 7, 8, 9, 10], "data": [0, 1, 3, 4, 8, 9, 11, 12, 13, 14], "itself": [0, 6], "done": [0, 6], "emploi": [0, 3, 6, 7, 8], "latest": [0, 9], "state": [0, 6, 12], "art": [0, 6], "estim": [0, 1, 3, 4, 8, 9, 11], "packag": [0, 1, 3, 5, 6, 7], "further": [0, 9], "document": [0, 6, 7], "github": [0, 6, 8, 12], "page": 0, "here": [0, 3, 6, 7, 8, 9, 12, 13], "public": [0, 1, 3, 4, 6, 8, 9], "avail": [0, 4, 6, 8], 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