src

+.ipynb_checkpoints

+name: tutorial
+
+conda_env: conda.yaml
+
+entry_points:
+  main:
+    parameters:
+      alpha: float
+      l1_ratio: {type: float, default: 0.1}
+    command: "python train.py {alpha} {l1_ratio}"

+name: tutorial
+channels:
+  - defaults
+dependencies:
+  - numpy>=1.14.3
+  - pandas>=1.0.0
+  - scikit-learn=0.19.1
+  - pip
+  - pip:
+    - mlflow

-print("hello world")

+# 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
+
+import mlflow
+import mlflow.sklearn
+
+
+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 (make sure you're running this from the root of MLflow!)
+    wine_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "wine-quality.csv")
+    data = pd.read_csv(wine_path)
+
+    # 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):" % (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)
+
+        mlflow.sklearn.log_model(lr, "model")