Refactor examples

examples/frameworks/xgboost/requirements.txt

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+sklearn
+trains
+xgboost>=0.90 ; python_version >= '3'
+xgboost>=0.82 ; python_version < '3'
+# sudo apt-get install graphviz
+graphviz>=0.8

examples/frameworks/xgboost/xgboost_sample.py

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