mirror of
https://github.com/open-metadata/OpenMetadata.git
synced 2025-07-17 14:09:52 +00:00
104 lines
3.3 KiB
Python
104 lines
3.3 KiB
Python
|
"""
|
||
|
|
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")
|