docs/quick_start.rst
Quick Start
***********
This section provides a jumping-off point for using HyperparameterHunter's main features.
Set Up an Environment
=====================
.. code-block:: python
from hyperparameter_hunter import Environment, CVExperiment
import pandas as pd
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import StratifiedKFold
from xgboost import XGBClassifier
data = load_breast_cancer()
df = pd.DataFrame(data=data.data, columns=data.feature_names)
df["target"] = data.target
env = Environment(
train_dataset=df,
results_path="path/to/results/directory",
metrics=["roc_auc_score"],
cv_type=StratifiedKFold,
cv_params=dict(n_splits=5, shuffle=True, random_state=32)
)
Individual Experimentation
--------------------------
.. code-block:: python
experiment = CVExperiment(
model_initializer=XGBClassifier,
model_init_params=dict(objective="reg:linear", max_depth=3, subsample=0.5)
)
Hyperparameter Optimization
---------------------------
.. code-block:: python
from hyperparameter_hunter import BayesianOptPro, Real, Integer, Categorical
optimizer = BayesianOptPro(iterations=10, read_experiments=True)
optimizer.forge_experiment(
model_initializer=XGBClassifier,
model_init_params=dict(
n_estimators=200,
subsample=0.5,
max_depth=Integer(2, 20),
learning_rate=Real(0.0001, 0.5),
booster=Categorical(["gbtree", "gblinear", "dart"]),
)
)
optimizer.go()
Plenty of examples for different libraries, and algorithms, as well as more advanced HyperparameterHunter features can be found
in the `examples <https://github.com/HunterMcGushion/hyperparameter_hunter/blob/master/examples>`__ directory.