try:
    import joblib
except ImportError:
    from sklearn.externals import joblib

from sklearn import datasets
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
import numpy as np
import matplotlib.pyplot as plt

from clearml import Task, Model

task = Task.init(project_name="examples 3", task_name="task name 5")

iris = datasets.load_diabetes()
X = iris.data
y = iris.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=12)

model = LogisticRegression(max_iter=20)  # sklearn LogisticRegression class
model.fit(X_train, y_train)

joblib.dump(model, 'model.pkl', compress=True) # it properly saves model locally, and sends to clearml as artifact

x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
h = .02  # step size in the mesh
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
plt.figure(1, figsize=(4, 3))

plt.scatter(X[:, 0], X[:, 1], c=y, edgecolors='k', cmap=plt.cm.Paired)
plt.xlabel('Sepal length')
plt.ylabel('Sepal width')

plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.xticks(())
plt.yticks(())

plt.show()
task.close()