dgdtl-lts

dgdtl-lts version 1.0.0 provides the deterministic DGDTL-LTS regression engine used by the reference workflow. Its public import is dgdtl_lts, which exports DGDTLEstimator. Reference use requires Python 3.12.12 and the frozen dgdtl_v2 scientific environment.

Linux installation

Prepare the environment using the environment instructions, including the check against the recorded scientific package versions. If that environment already exists, activate it instead of creating it again.

Run these commands from the software directory, not from this package directory:

mamba activate dgdtl_v2
python -m pip install --no-index --no-deps dist/dgdtl_lts-1.0.0-py3-none-any.whl
python -m pip check

The wheel installs the package; its dependencies must already be present in the environment. The supplied Hunter and Unified workflows also require u-maxp; the shared installation procedure installs both wheels. These commands use local artifacts and do not assume that either distribution has been published to a package index.

For an existing installation, confirm the interpreter, version, and public import location:

python --version
python -c "import sys, importlib.metadata as m, dgdtl_lts; print(sys.executable); print(m.version('dgdtl-lts')); print(dgdtl_lts.__file__)"

Expect Python 3.12.12, package version 1.0.0, and an import path beneath the active environment's site-packages.

Windows execution

Windows users run the supplied Hunter and Unified launchers through the Certified Runtime, which already contains the installed scientific packages. No host Python installation or package build is required. Keep the complete software distribution and use a separate working directory for inputs and new results. See the platform requirements and Windows example setup.

The existing Windows launchers expose Hunter and Unified. They do not provide a generic command for executing an arbitrary researcher script.

Public API

The following raw-mode usage pattern assumes that the researcher has already prepared the numeric arrays X_train, y_train, X_valid, y_valid, and X_test. It illustrates the estimator interface; reproducing a supplied domain requires that domain's full configuration and preprocessing.

from dgdtl_lts import DGDTLEstimator

estimator = DGDTLEstimator(
    mode="raw",
    fit_intercept=False,
    beta_sum_one=True,
    random_state=42,
)
estimator.fit(X_train, y_train, X_valid, y_valid)
prediction = estimator.predict(X_test)

X_train, X_valid, and X_test must use the same feature columns in the same order. Targets are separate one-dimensional arrays aligned with the corresponding rows. Do not include sample identifiers or the target column in the predictor matrix. If fit_intercept=True, the estimator adds its own intercept column.

The caller supplies feature construction, normalization, and consistent transforms between partitions. The estimator does not parse deployment guides or automatically reproduce the preprocessing performed by Unified.

In raw mode, predict(X) returns predictions on the scale of the supplied model inputs and coefficients. In error_matrix mode, X is the prepared error matrix and prediction requires y_reference; the method adds the coefficient-weighted correction to that reference. These modes are not interchangeable input representations.

After a successful fit, the existing attributes include baseline_coeffs_, baseline_mse_, champion_run1_, champion_run2_, champion_, run1_report_df_, and run2_report_df_. The estimator's default prediction uses its own selected champion. The complete cross-candidate U-MaxP structural selection is performed separately by the protocol and reference orchestrators; a direct fit call does not run the complete Hunter workflow.

The preserved estimator source defines the exact constructor parameters, method signatures, and behavior. Configuration choices such as bounds, thresholds, normalization, and process parallelism belong to the researcher's execution design. Training can be computationally intensive.

Use from a researcher script

On Linux, activate the prepared environment and run your script with its Python. The package is imported from site-packages; copying dgdtl_core.py into the working directory is unnecessary and can shadow the installed module. Your script remains responsible for loading inputs, preparing arrays, preserving configuration, and writing its outputs.

The package wheel supplies the engine and public interface. Hunter, Unified, reporting modules, launchers, datasets, and example results are separate parts of the distribution. Consult the file requirements for the selected workflow. For a complete worked example, use the example instructions and their supplied guides.

Reproducibility

The package includes dgdtl_core as a legacy compatibility module. New code should import DGDTLEstimator from dgdtl_lts. The packaged core is bytewise identical to the certified Golden Master. Artifact identities and original build details are preserved in the build record.

The documented certification scope applies to the recorded environment, artifacts, workflows, and comparison conditions. It does not automatically certify new researcher scripts, altered configurations, or new datasets. Use the existing reference comparison rules when interpreting a reproduction attempt.

Citation and license

Use the software citation for DGDTL-LTS / U-MaxP and the example references when using the associated datasets. License: MIT.

Contact

For questions regarding conceptualization and support, contact: jandrespmen@gmail.com