mlflow--mlflow
wehub-resource-sync
126 行
4.0 KiB
Python
126 行
4.0 KiB
Python
#
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# train_diabetes.py
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#
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# MLflow model using ElasticNet (sklearn) and Plots ElasticNet Descent Paths
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#
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# Uses the sklearn Diabetes dataset to predict diabetes progression using ElasticNet
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# The predicted "progression" column is a quantitative measure of disease progression one year after baseline
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# http://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_diabetes.html
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# Combines the above with the Lasso Coordinate Descent Path Plot
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# http://scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_coordinate_descent_path.html
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# Original author: Alexandre Gramfort <alexandre.gramfort@inria.fr>; License: BSD 3 clause
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#
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# Usage:
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# python train_diabetes.py 0.01 0.01
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# python train_diabetes.py 0.01 0.75
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# python train_diabetes.py 0.01 1.0
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#
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import sys
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import warnings
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from itertools import cycle
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import matplotlib.pyplot as plt
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import numpy as np
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import pandas as pd
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from sklearn import datasets
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from sklearn.linear_model import ElasticNet, enet_path
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from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
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from sklearn.model_selection import train_test_split
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# Load Diabetes datasets
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diabetes = datasets.load_diabetes()
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X = diabetes.data
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y = diabetes.target
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# Create pandas DataFrame for sklearn ElasticNet linear_model
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Y = np.array([y]).transpose()
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d = np.concatenate((X, Y), axis=1)
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cols = diabetes.feature_names + ["progression"]
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data = pd.DataFrame(d, columns=cols)
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# Import mlflow
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import mlflow
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import mlflow.sklearn
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from mlflow.models import infer_signature
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# Evaluate metrics
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def eval_metrics(actual, pred):
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rmse = np.sqrt(mean_squared_error(actual, pred))
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mae = mean_absolute_error(actual, pred)
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r2 = r2_score(actual, pred)
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return rmse, mae, r2
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if __name__ == "__main__":
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warnings.filterwarnings("ignore")
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np.random.seed(40)
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# Split the data into training and test sets. (0.75, 0.25) split.
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train, test = train_test_split(data)
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# The predicted column is "progression" which is a quantitative measure of disease progression one year after baseline
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train_x = train.drop(["progression"], axis=1)
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test_x = test.drop(["progression"], axis=1)
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train_y = train[["progression"]]
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test_y = test[["progression"]]
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alpha = float(sys.argv[1]) if len(sys.argv) > 1 else 0.05
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l1_ratio = float(sys.argv[2]) if len(sys.argv) > 2 else 0.05
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# Run ElasticNet
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lr = ElasticNet(alpha=alpha, l1_ratio=l1_ratio, random_state=42)
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lr.fit(train_x, train_y)
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predicted_qualities = lr.predict(test_x)
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(rmse, mae, r2) = eval_metrics(test_y, predicted_qualities)
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# Print out ElasticNet model metrics
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print(f"Elasticnet model (alpha={alpha:f}, l1_ratio={l1_ratio:f}):")
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print(f" RMSE: {rmse}")
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print(f" MAE: {mae}")
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print(f" R2: {r2}")
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# Infer model signature
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predictions = lr.predict(train_x)
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signature = infer_signature(train_x, predictions)
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# Log mlflow attributes for mlflow UI
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mlflow.log_param("alpha", alpha)
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mlflow.log_param("l1_ratio", l1_ratio)
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mlflow.log_metric("rmse", rmse)
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mlflow.log_metric("r2", r2)
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mlflow.log_metric("mae", mae)
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mlflow.sklearn.log_model(lr, name="model", signature=signature)
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# Compute paths
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eps = 5e-3 # the smaller it is the longer is the path
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print("Computing regularization path using the elastic net.")
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alphas_enet, coefs_enet, _ = enet_path(X, y, eps=eps, l1_ratio=l1_ratio)
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# Display results
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fig = plt.figure(1)
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ax = plt.gca()
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colors = cycle(["b", "r", "g", "c", "k"])
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neg_log_alphas_enet = -np.log10(alphas_enet)
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for coef_e, c in zip(coefs_enet, colors):
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l2 = plt.plot(neg_log_alphas_enet, coef_e, linestyle="--", c=c)
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plt.xlabel("-Log(alpha)")
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plt.ylabel("coefficients")
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title = "ElasticNet Path by alpha for l1_ratio = " + str(l1_ratio)
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plt.title(title)
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plt.axis("tight")
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# Save figures
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fig.savefig("ElasticNet-paths.png")
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# Close plot
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plt.close(fig)
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# Log artifacts (output files)
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mlflow.log_artifact("ElasticNet-paths.png")
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