AttributeError: 'DecisionTreeRegressor' object has no attribute 'save' in GCS - docker

Question

I was trying to deploy my custom DecisionTreeRegressor for house price prediction to GCS Vertex AI. The tutorial I followed was tutorial for MPG dataset tutorial
However, when I tried to build and test the container locally using commands:
docker build ./ -t $IMAGE_URI
docker run $IMAGE_URI
The error message came out:
AttributeError: 'DecisionTreeRegressor' object has no attribute 'save'
The code I run as train.py:
# Import libraries necessary for this project
import numpy as np
import pandas as pd
from sklearn.model_selection import ShuffleSplit
# Load the Boston housing dataset
data = pd.read_csv('trainer/housing.csv')
prices = data['MEDV']
features = data.drop('MEDV', axis = 1)
# Import 'train_test_split'
from sklearn.model_selection import train_test_split
# Shuffle and split the data into training and testing subsets
X_train, X_test, y_train, y_test = train_test_split(features, prices, test_size=0.2, random_state = 42)
#Defining model fitting and tuning functions
# Import 'make_scorer', 'DecisionTreeRegressor', and 'GridSearchCV'
from sklearn.tree import DecisionTreeRegressor
from sklearn.metrics import make_scorer
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import r2_score # Import 'r2_score'
from sklearn.metrics import accuracy_score
# TODO: replace `your-gcs-bucket` with the name of the Storage bucket you created earlier
BUCKET = 'gs://gardena-dps-bucket'
def performance_metric(y_true, y_predict):
""" Calculates and returns the performance score between
true (y_true) and predicted (y_predict) values based on the metric chosen. """
score = r2_score(y_true, y_predict)
# Return the score
return score
def fit_model(X, y):
""" Performs grid search over the 'max_depth' parameter for a
decision tree regressor trained on the input data [X, y]. """
# Create cross-validation sets from the training data
cv_sets = ShuffleSplit(n_splits = 10, test_size = 0.20, random_state = 0)
# Create a decision tree regressor object
regressor = DecisionTreeRegressor()
# Create a dictionary for the parameter 'max_depth' with a range from 1 to 10
params = {'max_depth':[1,2,3,4,5,6,7,8,9,10]}
# Transform 'performance_metric' into a scoring function using 'make_scorer'
scoring_fnc = make_scorer(performance_metric)
# Create the grid search cv object --> GridSearchCV()
# Make sure to include the right parameters in the object:
# (estimator, param_grid, scoring, cv) which have values 'regressor', 'params', 'scoring_fnc', and 'cv_sets' respectively.
grid = GridSearchCV(estimator=regressor, param_grid=params, scoring=scoring_fnc, cv=cv_sets)
# Fit the grid search object to the data to compute the optimal model
grid = grid.fit(X, y)
# Return the optimal model after fitting the data
return grid.best_estimator_
# Fit the training data to the model using grid search
reg = fit_model(X_train, y_train)
# Produce a matrix for client data
client_data = [[12, 26.3, 16.99885]] # Client data in 2D array
# Show predictions
reprice = reg.predict(client_data).astype(int)
reprice
# Export model and save to GCS
reg.save(BUCKET + '/housing/model')

Answer 1

Scikit-learn estimators do not provide any method to save their states directly. From the Google documentation, the best way to store a fitted model to GCS is to use joblib to locally serialize your model and then upload it to GCS.
As follow:
from google.cloud import storage
from sklearn.externals import joblib
# Export the model to a file
model = 'model.joblib'
joblib.dump(pipeline, model)
# Upload the model to GCS
bucket = storage.Client().bucket(BUCKET_NAME)
blob = bucket.blob('{}/{}'.format(
datetime.datetime.now().strftime('model_%Y%m%d_%H%M%S'),
model))
blob.upload_from_filename(model)