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Integrations with AI framework toolboxes

This directory contains examples for estimator level integration with common AI toolbox libraries such as scikit-learn or sktime.

Quick Start

For full code, see below.

You can also run any example directly:

python sklearn_classif_example.py
python sktime_forecasting_example.py
python sktime_tsc_example.py

Requires scikit-learn resp sktime installed.

Available Integrations

Integration Class name
sklearn classifier or regressor tuner OptCV
sktime forecasting tuner ForecastingOptCV
sktime time series classifier tuner TSCOptCV

Integration with sklearn

Any available tuning engine from hyperactive can be used, for example:

  • grid search - from hyperactive.opt import GridSearchSk as GridSearch
  • hill climbing - from hyperactive.opt import HillClimbing
  • optuna parzen-tree search - from hyperactive.opt.optuna import TPEOptimizer
# For illustration, we use grid search, this can be replaced by any other optimizer.

# 1. defining the tuned estimator:
from sklearn.svm import SVC
from hyperactive.integrations.sklearn import OptCV
from hyperactive.opt import GridSearchSk as GridSearch

param_grid = {"kernel": ["linear", "rbf"], "C": [1, 10]}
tuned_svc = OptCV(SVC(), GridSearch(param_grid))

# 2. fitting the tuned estimator:
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

tuned_svc.fit(X_train, y_train)

# 3. obtaining predictions
y_pred = tuned_svc.predict(X_test)

# 4. obtaining best parameters and best estimator
best_params = tuned_svc.best_params_
best_estimator = tuned_svc.best_estimator_

Integration with sktime - forecasting

Any available tuning engine from hyperactive can be used, for example:

  • grid search - from hyperactive.opt import GridSearchSk as GridSearch
  • hill climbing - from hyperactive.opt import HillClimbing
  • optuna parzen-tree search - from hyperactive.opt.optuna import TPEOptimizer

For illustration, we use grid search, this can be replaced by any other optimizer.

# 1. defining the tuned estimator:
from sktime.forecasting.naive import NaiveForecaster
from sktime.split import ExpandingWindowSplitter
from hyperactive.integrations.sktime import ForecastingOptCV
from hyperactive.opt import GridSearchSk as GridSearch

param_grid = {"strategy": ["mean", "last", "drift"]}
tuned_naive = ForecastingOptCV(
    NaiveForecaster(),
    GridSearch(param_grid),
    cv=ExpandingWindowSplitter(
        initial_window=12, step_length=3, fh=range(1, 13)
    ),
)

# 2. fitting the tuned estimator:
from sktime.datasets import load_airline
from sktime.split import temporal_train_test_split
y = load_airline()
y_train, y_test = temporal_train_test_split(y, test_size=12)

tuned_naive.fit(y_train, fh=range(1, 13))

# 3. obtaining predictions
y_pred = tuned_naive.predict()

# 4. obtaining best parameters and best forecaster
best_params = tuned_naive.best_params_
best_forecaster = tuned_naive.best_forecaster_

# Broadcasting options & diagnostics

``ForecastingOptCV`` mirrors ``ForecastingGridSearchCV`` by exposing
``tune_by_instance`` and ``tune_by_variable`` flags for automatic sktime
broadcasting over panel or multivariate data. After ``fit`` completes you also
gain access to ``best_score_``, ``cv_results_`` (per-fold backtesting results),
``n_splits_``, ``scorer_``, and ``refit_time_`` for downstream inspection.

Integration with sktime - time series classification

Any available tuning engine from hyperactive can be used, for example:

  • grid search - from hyperactive.opt import GridSearchSk as GridSearch
  • hill climbing - from hyperactive.opt import HillClimbing
  • optuna parzen-tree search - from hyperactive.opt.optuna import TPEOptimizer

For illustration, we use grid search, this can be replaced by any other optimizer.

# 1. defining the tuned estimator:
from sktime.classification.dummy import DummyClassifier
from sklearn.model_selection import KFold
from hyperactive.integrations.sktime import TSCOptCV
from hyperactive.opt import GridSearchSk as GridSearch

param_grid = {"strategy": ["most_frequent", "stratified"]}
tuned_naive = TSCOptCV(
    DummyClassifier(),
    GridSearch(param_grid),
    cv=KFold(n_splits=2, shuffle=False),
)

# 2. fitting the tuned estimator:
from sktime.datasets import load_unit_test
X_train, y_train = load_unit_test(
    return_X_y=True, split="TRAIN", return_type="pd-multiindex"
)
X_test, _ = load_unit_test(
    return_X_y=True, split="TEST", return_type="pd-multiindex"
)

tuned_naive.fit(X_train, y_train)

# 3. obtaining predictions
y_pred = tuned_naive.predict(X_test)

# 4. obtaining best parameters and best estimator
best_params = tuned_naive.best_params_
best_classifier = tuned_naive.best_estimator_