1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
|
- # The data set used in this example is from http://archive.ics.uci.edu/ml/datasets/Wine+Quality
- # P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis.
- # Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553, 2009.
- import os
- import warnings
- import sys
- import pandas as pd
- import numpy as np
- from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
- from sklearn.model_selection import train_test_split
- from sklearn.linear_model import ElasticNet
- from urllib.parse import urlparse
- import mlflow
- from mlflow.models import infer_signature
- import mlflow.sklearn
- import logging
- 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__":
- warnings.filterwarnings("ignore")
- np.random.seed(40)
- # Read the wine-quality csv file from the URL
- csv_url = (
- "https://raw.githubusercontent.com/mlflow/mlflow/master/tests/datasets/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)
- predicted_qualities = lr.predict(test_x)
- (rmse, mae, r2) = eval_metrics(test_y, predicted_qualities)
- print("Elasticnet model (alpha={:f}, l1_ratio={:f}):".format(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)
- predictions = lr.predict(train_x)
- signature = infer_signature(train_x, predictions)
- 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", signature=signature)
|