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causalml_example.py
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causalml_example.py
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# Copyright 2024 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.
import numpy as np
import pandas as pd
from libraries.util.datasets import load_causal_regression_dataset
from libraries.util.domains import DIABETES_DOMAIN
from causalml.inference.meta import XGBTRegressor, BaseSRegressor
from causalml.metrics import qini_score
from lightgbm.sklearn import LGBMRegressor
from modelstore.model_store import ModelStore
def _train_example_model() -> XGBTRegressor:
X_train, X_test, y_train, y_test, treatment_vector_train, treatment_vector_test = load_causal_regression_dataset()
params = {
"n_estimators": 250,
"max_depth": 4,
"learning_rate": 0.01,
"n_jobs": 1,
"verbose": -1
}
# Train causal regressor
lgbm = LGBMRegressor(**params)
model = BaseSRegressor(learner=lgbm)
model.fit(X_train, treatment_vector_train, y_train)
X_test = pd.DataFrame(X_test)
X_test["causal_scores"] = model.predict(X_test)
X_test["outcomes"] = y_test
X_test["treatment"] = treatment_vector_test
result = qini_score(
X_test[["causal_scores", "outcomes", "treatment"]],
outcome_col="outcomes",
treatment_col="treatment",
)
print(f"🔍 Trained model Qini score={result}.")
return model
def train_and_upload(modelstore: ModelStore) -> dict:
# Train a causalml regressor
model = _train_example_model()
# Upload the model to the model store
print(f'⤴️ Uploading the causalml 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 causalml "{model_domain}" domain model={model_id}')
model = modelstore.load(model_domain, model_id)
# Run some example predictions
_, X_test, _, y_test, _, treatment_vector_test = load_causal_regression_dataset()
X_test = pd.DataFrame(X_test)
X_test["causal_scores"] = model.predict(X_test)
X_test["outcomes"] = y_test
X_test["treatment"] = treatment_vector_test
result = qini_score(
X_test[["causal_scores", "outcomes", "treatment"]],
outcome_col="outcomes",
treatment_col="treatment",
)
print(f"🔍 Loaded model Qini score={result}.")