xgboost-inference-arm64-docker-lambda/train-code/bc_xgboost_train.py [9:39]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data', header = None)

# specify columns extracted from wbdc.names
data.columns = ["id","diagnosis","radius_mean","texture_mean","perimeter_mean","area_mean","smoothness_mean",
                "compactness_mean","concavity_mean","concave points_mean","symmetry_mean","fractal_dimension_mean",
                "radius_se","texture_se","perimeter_se","area_se","smoothness_se","compactness_se","concavity_se",
                "concave points_se","symmetry_se","fractal_dimension_se","radius_worst","texture_worst",
                "perimeter_worst","area_worst","smoothness_worst","compactness_worst","concavity_worst",
                "concave points_worst","symmetry_worst","fractal_dimension_worst"] 

# save the data
data.to_csv("data.csv", sep=',', index=False)

# print the shape of the data file
print(data.shape)

y = data["diagnosis"]
X = data.drop(["id", "diagnosis"], axis=1)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)

model = xgb.XGBClassifier().fit(X_train, y_train)

# make prediction
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Model Accuracy: %.2f%%" % (accuracy * 100.0))

# save the trained model file
model_file_name = "bc-xgboost-model"
joblib.dump(model, model_file_name) 
print("Model file {} saved successfully".format(model_file_name))
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



xgboost-inference-docker-lambda/train-code/bc_xgboost_train.py [9:39]:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data', header = None)

# specify columns extracted from wbdc.names
data.columns = ["id","diagnosis","radius_mean","texture_mean","perimeter_mean","area_mean","smoothness_mean",
                "compactness_mean","concavity_mean","concave points_mean","symmetry_mean","fractal_dimension_mean",
                "radius_se","texture_se","perimeter_se","area_se","smoothness_se","compactness_se","concavity_se",
                "concave points_se","symmetry_se","fractal_dimension_se","radius_worst","texture_worst",
                "perimeter_worst","area_worst","smoothness_worst","compactness_worst","concavity_worst",
                "concave points_worst","symmetry_worst","fractal_dimension_worst"] 

# save the data
data.to_csv("data.csv", sep=',', index=False)

# print the shape of the data file
print(data.shape)

y = data["diagnosis"]
X = data.drop(["id", "diagnosis"], axis=1)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=42)

model = xgb.XGBClassifier().fit(X_train, y_train)

# make prediction
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Model Accuracy: %.2f%%" % (accuracy * 100.0))

# save the trained model file
model_file_name = "bc-xgboost-model"
joblib.dump(model, model_file_name) 
print("Model file {} saved successfully".format(model_file_name))
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -