mirror of
https://github.com/clearml/clearml
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61 lines
1.7 KiB
Python
61 lines
1.7 KiB
Python
import matplotlib.pyplot as plt
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import xgboost as xgb
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from sklearn import datasets
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from sklearn.metrics import accuracy_score
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from sklearn.model_selection import train_test_split
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from xgboost import plot_tree
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from clearml import Task
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task = Task.init(project_name='examples', task_name='XGBoost simple example')
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iris = datasets.load_iris()
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X = iris.data
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y = iris.target
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X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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dtrain = xgb.DMatrix(X_train, label=y_train)
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dtest = xgb.DMatrix(X_test, label=y_test)
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param = {
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'max_depth': 3, # the maximum depth of each tree
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'eta': 0.3, # the training step for each iteration
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'silent': 1, # logging mode - quiet
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'objective': 'multi:softprob', # error evaluation for multiclass training
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'num_class': 3} # the number of classes that exist in this datset
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num_round = 20 # the number of training iterations
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# noinspection PyBroadException
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try:
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# try to load a model
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bst = xgb.Booster(params=param, model_file='xgb.01.model')
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bst.load_model('xgb.01.model')
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except Exception:
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bst = None
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# if we dont have one train a model
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if bst is None:
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bst = xgb.train(param, dtrain, num_round)
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# store trained model model v1
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bst.save_model('xgb.01.model')
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bst.dump_model('xgb.01.raw.txt')
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# build classifier
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model = xgb.XGBClassifier()
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model.fit(X_train, y_train)
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# store trained classifier model
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model.save_model('xgb.02.model')
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# make predictions for test data
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y_pred = model.predict(X_test)
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predictions = [round(value) for value in y_pred]
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# evaluate predictions
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accuracy = accuracy_score(y_test, predictions)
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print("Accuracy: %.2f%%" % (accuracy * 100.0))
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labels = dtest.get_label()
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# plot results
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xgb.plot_importance(model)
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plot_tree(model)
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plt.show()
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