def score(data_conf, model_conf, **kwargs):
model = joblib.load("artifacts/input/model.joblib")
create_context(host=os.environ["AOA_CONN_HOST"],
username=os.environ["AOA_CONN_USERNAME"],
password=os.environ["AOA_CONN_PASSWORD"],
database=data_conf["schema"] if "schema" in data_conf
and data_conf["schema"] != "" else None)
predict_df = DataFrame(data_conf["table"])
# convert to pandas to use locally
predict_df = predict_df.to_pandas()
print("Scoring")
y_pred = model.predict(predict_df[model.feature_names])
print("Finished Scoring")
# create result dataframe and store in Teradata
y_pred = pd.DataFrame(y_pred, columns=["pred"])
y_pred["PatientId"] = predict_df["PatientId"].values
copy_to_sql(df=y_pred,
table_name=data_conf["predictions"],
index=False,
if_exists="replace")
def evaluate(data_conf, model_conf, **kwargs):
model = joblib.load('artifacts/input/model.joblib')
create_context(host=os.environ["AOA_CONN_HOST"],
username=os.environ["AOA_CONN_USERNAME"],
password=os.environ["AOA_CONN_PASSWORD"],
database=data_conf["schema"] if "schema" in data_conf and data_conf["schema"] != "" else None)
# Read test dataset from Teradata
# As this is for demo purposes, we simulate the test dataset changing between executions
# by introducing a random sample. Note that the sampling is performed in Teradata!
test_df = DataFrame(data_conf["table"]).sample(frac=0.8)
test_pdf = test_df.to_pandas()
X_test = test_pdf[model.feature_names]
y_test = test_pdf[model.target_name]
print("Scoring")
y_pred = model.predict(test_pdf[model.feature_names])
evaluation = {
'Accuracy': '{:.2f}'.format(metrics.accuracy_score(y_test, y_pred)),
'Recall': '{:.2f}'.format(metrics.recall_score(y_test, y_pred)),
'Precision': '{:.2f}'.format(metrics.precision_score(y_test, y_pred)),
'f1-score': '{:.2f}'.format(metrics.f1_score(y_test, y_pred))
}
with open("artifacts/output/metrics.json", "w+") as f:
json.dump(evaluation, f)
metrics.plot_confusion_matrix(model, X_test, y_test)
save_plot('Confusion Matrix')
metrics.plot_roc_curve(model, X_test, y_test)
save_plot('ROC Curve')
# xgboost has its own feature importance plot support but lets use shap as explainability example
import shap
shap_explainer = shap.TreeExplainer(model['xgb'])
shap_values = shap_explainer.shap_values(X_test)
shap.summary_plot(shap_values, X_test, feature_names=model.feature_names,
show=False, plot_size=(12, 8), plot_type='bar')
save_plot('SHAP Feature Importance')
feature_importance = pd.DataFrame(list(zip(model.feature_names, np.abs(shap_values).mean(0))),
columns=['col_name', 'feature_importance_vals'])
feature_importance = feature_importance.set_index("col_name").T.to_dict(orient='records')[0]
stats.record_stats(test_df,
features=model.feature_names,
predictors=["HasDiabetes"],
categorical=["HasDiabetes"],
importance=feature_importance,
category_labels={"HasDiabetes": {0: "false", 1: "true"}})