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sklearn_example.py
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sklearn_example.py
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# Copyright 2023 Neal Lathia
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from libraries.util.datasets import load_regression_dataset
from libraries.util.domains import DIABETES_DOMAIN
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.metrics import mean_squared_error
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from modelstore.model_store import ModelStore
def _train_example_model() -> Pipeline:
X_train, X_test, y_train, y_test = load_regression_dataset()
# Train a model using an sklearn pipeline
params = {
"n_estimators": 250,
"max_depth": 4,
"min_samples_split": 5,
"learning_rate": 0.01,
"loss": "squared_error",
}
pipeline = Pipeline(
[
("scaler", StandardScaler()),
("regressor", GradientBoostingRegressor(**params)),
]
)
pipeline.fit(X_train, y_train)
results = mean_squared_error(y_test, pipeline.predict(X_test))
print(f"🔍 Trained model MSE={results}.")
return pipeline
def train_and_upload(modelstore: ModelStore) -> dict:
# Train a scikit-learn model
model = _train_example_model()
# Upload the model to the model store
print(f'⤴️ Uploading the sklearn model to the "{DIABETES_DOMAIN}" domain.')
meta_data = modelstore.upload(DIABETES_DOMAIN, model=model)
return meta_data
def load_and_test(modelstore: ModelStore, model_domain: str, model_id: str):
# Load the model back into memory!
print(f'⤵️ Loading the sklearn "{model_domain}" domain model={model_id}')
model = modelstore.load(model_domain, model_id)
# Run some example predictions
_, X_test, _, y_test = load_regression_dataset()
results = mean_squared_error(y_test, model.predict(X_test))
print(f"🔍 Loaded model MSE={results}.")