mirror of
https://github.com/clearml/clearml
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59 lines
1.6 KiB
Python
59 lines
1.6 KiB
Python
import joblib
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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn.linear_model import LogisticRegression
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from clearml import Task
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# Connecting ClearML with the current process,
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# from here on everything is logged automatically
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task = Task.init(project_name="examples", task_name="Pipeline step 3 train model")
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# Arguments
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args = {
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'dataset_task_id': 'REPLACE_WITH_DATASET_TASK_ID',
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}
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task.connect(args)
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# only create the task, we will actually execute it later
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task.execute_remotely()
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print('Retrieving Iris dataset')
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dataset_task = Task.get_task(task_id=args['dataset_task_id'])
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X_train = dataset_task.artifacts['X_train'].get()
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X_test = dataset_task.artifacts['X_test'].get()
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y_train = dataset_task.artifacts['y_train'].get()
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y_test = dataset_task.artifacts['y_test'].get()
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print('Iris dataset loaded')
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model = LogisticRegression(solver='liblinear', multi_class='auto')
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model.fit(X_train, y_train)
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joblib.dump(model, 'model.pkl', compress=True)
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loaded_model = joblib.load('model.pkl')
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result = loaded_model.score(X_test, y_test)
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print('model trained & stored')
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x_min, x_max = X_test[:, 0].min() - .5, X_test[:, 0].max() + .5
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y_min, y_max = X_test[:, 1].min() - .5, X_test[:, 1].max() + .5
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h = .02 # step size in the mesh
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xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
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plt.figure(1, figsize=(4, 3))
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plt.scatter(X_test[:, 0], X_test[:, 1], c=y_test, edgecolors='k', cmap=plt.cm.Paired)
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plt.xlabel('Sepal length')
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plt.ylabel('Sepal width')
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plt.xlim(xx.min(), xx.max())
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plt.ylim(yy.min(), yy.max())
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plt.xticks(())
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plt.yticks(())
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plt.title('Iris Types')
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plt.show()
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print('Done')
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