----------- Learning ------------------
"""
weights = 'distance'
n_neighbors = 15
# we create an instance of Neighbours Classifier and fit the data.
clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)
clf.fit(norm_data, labels)
# DetectionMethods.detect_with_cross_validation(clf, norm_data, labels)
"""
------------- Testing -------------
"""
norm_test_data, test_labels = Get_normalize_data.main2(final_path,
"test_connections.txt")
DetectionMethods.detect(clf, norm_test_data, test_labels)
X = np.array(norm_data)
y = np.array(labels)
h = .02
"""
------------- Ploting -------------
"""
# Create color maps
# cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])
# cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])
# # Plot the decision boundary. For that, we will assign a color to each
# # point in the mesh [x_min, x_max]x[y_min, y_max].
# x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
# y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
# xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
""" Read data model 1 """ X_train, X_test, y_train, y_test = Get_normalize_data.get_all_data(final_path) clf = MLPClassifier(solver='adam', alpha=1e-05, random_state=1) """ Crossvalidation """ DetectionMethods.detect_with_cross_validation(clf, X_train, y_train) """ Learning """ clf.fit(X_train, y_train) """ Detect """ DetectionMethods.detect(clf, X_test, y_test) # score = clf.score(X_test, y_test) # print score
# colsample_bytree=0.8,
# objective= 'binary:logistic',
# nthread=4,
# scale_pos_weight=1,
# seed=27)
# title = "Learning Curves ( XGBoost s)"
model = XGBClassifier(
learning_rate=0.1,
n_estimators=1000,
max_depth=3,
min_child_weight=5,
gamma=0.1,
subsample=0.8,
colsample_bytree=0.8,
objective='binary:logistic',
nthread=4,
scale_pos_weight=1,
seed=27)
"""
Crossvalidation
"""
DetectionMethods.detect_with_cross_validation(model, np_X_train, np_y_train)
"""
Detect model
"""
# model = XGBClassifier()
model.fit(np_X_train, np_y_train)
DetectionMethods.detect(model, np_X_test, np_y_test)