├── LICENSE.txt ├── MLproject ├── README.md ├── conda.yaml ├── train.py └── wine-quality.csv /LICENSE.txt: -------------------------------------------------------------------------------- 1 | Copyright 2018 Databricks, Inc. All rights reserved. 2 | 3 | Apache License 4 | Version 2.0, January 2004 5 | http://www.apache.org/licenses/ 6 | 7 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 8 | 9 | 1. Definitions. 10 | 11 | "License" shall mean the terms and conditions for use, reproduction, 12 | and distribution as defined by Sections 1 through 9 of this document. 13 | 14 | "Licensor" shall mean the copyright owner or entity authorized by 15 | the copyright owner that is granting the License. 16 | 17 | "Legal Entity" shall mean the union of the acting entity and all 18 | other entities that control, are controlled by, or are under common 19 | control with that entity. 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We also recommend that a 186 | file or class name and description of purpose be included on the 187 | same "printed page" as the copyright notice for easier 188 | identification within third-party archives. 189 | 190 | Copyright [yyyy] [name of copyright owner] 191 | 192 | Licensed under the Apache License, Version 2.0 (the "License"); 193 | you may not use this file except in compliance with the License. 194 | You may obtain a copy of the License at 195 | 196 | http://www.apache.org/licenses/LICENSE-2.0 197 | 198 | Unless required by applicable law or agreed to in writing, software 199 | distributed under the License is distributed on an "AS IS" BASIS, 200 | WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 201 | See the License for the specific language governing permissions and 202 | limitations under the License. 203 | -------------------------------------------------------------------------------- /MLproject: -------------------------------------------------------------------------------- 1 | name: tutorial 2 | 3 | conda_env: conda.yaml 4 | 5 | entry_points: 6 | main: 7 | parameters: 8 | alpha: {type: float, default: 0.5} 9 | l1_ratio: {type: float, default: 0.1} 10 | command: "python train.py {alpha} {l1_ratio}" 11 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Example MLflow project 2 | -------------------------------------------------------------------------------- /conda.yaml: -------------------------------------------------------------------------------- 1 | name: tutorial 2 | channels: 3 | - defaults 4 | dependencies: 5 | - numpy>=1.14.3 6 | - pandas>=1.0.0 7 | - scikit-learn>0.19.1 8 | - pip 9 | - pip: 10 | - mlflow 11 | -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | # The data set used in this example is from http://archive.ics.uci.edu/ml/datasets/Wine+Quality 2 | # P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. 3 | # Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553, 2009. 4 | 5 | import os 6 | import warnings 7 | import sys 8 | 9 | import pandas as pd 10 | import numpy as np 11 | from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score 12 | from sklearn.model_selection import train_test_split 13 | from sklearn.linear_model import ElasticNet 14 | 15 | import mlflow 16 | import mlflow.sklearn 17 | 18 | 19 | def eval_metrics(actual, pred): 20 | rmse = np.sqrt(mean_squared_error(actual, pred)) 21 | mae = mean_absolute_error(actual, pred) 22 | r2 = r2_score(actual, pred) 23 | return rmse, mae, r2 24 | 25 | 26 | 27 | if __name__ == "__main__": 28 | warnings.filterwarnings("ignore") 29 | np.random.seed(40) 30 | 31 | # Read the wine-quality csv file (make sure you're running this from the root of MLflow!) 32 | wine_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "wine-quality.csv") 33 | data = pd.read_csv(wine_path) 34 | 35 | # Split the data into training and test sets. (0.75, 0.25) split. 36 | train, test = train_test_split(data) 37 | 38 | # The predicted column is "quality" which is a scalar from [3, 9] 39 | train_x = train.drop(["quality"], axis=1) 40 | test_x = test.drop(["quality"], axis=1) 41 | train_y = train[["quality"]] 42 | test_y = test[["quality"]] 43 | 44 | alpha = float(sys.argv[1]) if len(sys.argv) > 1 else 0.5 45 | l1_ratio = float(sys.argv[2]) if len(sys.argv) > 2 else 0.5 46 | 47 | with mlflow.start_run(): 48 | lr = ElasticNet(alpha=alpha, l1_ratio=l1_ratio, random_state=42) 49 | lr.fit(train_x, train_y) 50 | 51 | predicted_qualities = lr.predict(test_x) 52 | 53 | (rmse, mae, r2) = eval_metrics(test_y, predicted_qualities) 54 | 55 | print("Elasticnet model (alpha=%f, l1_ratio=%f):" % (alpha, l1_ratio)) 56 | print(" RMSE: %s" % rmse) 57 | print(" MAE: %s" % mae) 58 | print(" R2: %s" % r2) 59 | 60 | mlflow.log_param("alpha", alpha) 61 | mlflow.log_param("l1_ratio", l1_ratio) 62 | mlflow.log_metric("rmse", rmse) 63 | mlflow.log_metric("r2", r2) 64 | mlflow.log_metric("mae", mae) 65 | 66 | mlflow.sklearn.log_model(lr, "model") 67 | --------------------------------------------------------------------------------