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"""
Example extracted from https://www.mlflow.org/docs/latest/tutorials-and-examples/tutorial.html
To run this you need to have installed `sklearn` in your environment.
"""
import logging
import os
import sys
import warnings
from urllib.parse import urlparse
import mlflow.sklearn
import numpy as np
import pandas as pd
from mlflow.models import infer_signature
from sklearn.linear_model import ElasticNet
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
from sklearn.model_selection import train_test_split
logging.basicConfig(level=logging.WARN)
logger = logging.getLogger(__name__)
def eval_metrics(actual, pred):
rmse = np.sqrt(mean_squared_error(actual, pred))
mae = mean_absolute_error(actual, pred)
r2 = r2_score(actual, pred)
return rmse, mae, r2
if __name__ == "__main__":
mlflow_uri = "http://localhost:5000"
mlflow.set_tracking_uri(mlflow_uri)
os.environ["AWS_ACCESS_KEY_ID"] = "minio"
os.environ["AWS_SECRET_ACCESS_KEY"] = "password"
os.environ["MLFLOW_S3_ENDPOINT_URL"] = "http://localhost:9001"
warnings.filterwarnings("ignore")
np.random.seed(40)
# Read the wine-quality csv file from the URL
csv_url = "http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv"
try:
data = pd.read_csv(csv_url, sep=";")
except Exception as e:
logger.exception(
"Unable to download training & test CSV, check your internet connection. Error: %s",
e,
)
# Split the data into training and test sets. (0.75, 0.25) split.
train, test = train_test_split(data)
# The predicted column is "quality" which is a scalar from [3, 9]
train_x = train.drop(["quality"], axis=1)
test_x = test.drop(["quality"], axis=1)
train_y = train[["quality"]]
test_y = test[["quality"]]
alpha = float(sys.argv[1]) if len(sys.argv) > 1 else 0.5
l1_ratio = float(sys.argv[2]) if len(sys.argv) > 2 else 0.5
with mlflow.start_run():
lr = ElasticNet(alpha=alpha, l1_ratio=l1_ratio, random_state=42)
lr.fit(train_x, train_y)
signature = infer_signature(train_x, lr.predict(train_x))
predicted_qualities = lr.predict(test_x)
(rmse, mae, r2) = eval_metrics(test_y, predicted_qualities)
print("Elasticnet model (alpha=%f, l1_ratio=%f):" % (alpha, l1_ratio))
print(" RMSE: %s" % rmse)
print(" MAE: %s" % mae)
print(" R2: %s" % r2)
mlflow.log_param("alpha", alpha)
mlflow.log_param("l1_ratio", l1_ratio)
mlflow.log_metric("rmse", rmse)
mlflow.log_metric("r2", r2)
mlflow.log_metric("mae", mae)
tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme
# Model registry does not work with file store
if tracking_url_type_store != "file":
# Register the model
# There are other ways to use the Model Registry, which depends on the use case,
# please refer to the doc for more information:
# https://mlflow.org/docs/latest/model-registry.html#api-workflow
mlflow.sklearn.log_model(
lr,
"model",
registered_model_name="ElasticnetWineModel",
signature=signature,
)
else:
mlflow.sklearn.log_model(lr, "model")