def __init__(self,
n_estimators=100,
max_features=None,
min_samples_split=2,
min_gain=0,
max_depth=float("inf")):
'''
:param n_estimators: number of trees
:param max_features: number of features for each tree generation, maxmium number of features
:param min_samples_split: used for decision tree split, param for decision tree
:param min_gain: Minimum information gain req. to continue, param for decision tree
:param max_depth: Maximum depth for tree, param for decision tree
'''
self.n_estimators = n_estimators
self.max_features = max_features
self.min_samples_split = min_samples_split
self.min_gain = min_gain
self.max_depth = max_depth
self.progressbar = progressbar.ProgressBar(widgets=bar_widgets)
self.trees_feature_idx = []
self.trees = [] # initial the list of trees
for _ in self.n_estimators:
self.trees.append(
ClassificationTree(min_samples_split=self.min_samples_split,
min_impurity=min_gain,
max_depth=self.max_depth))
def main():
print('-- Classification Tree')
data = datasets.load_iris()
X = data.data
y = data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4)
clf = ClassificationTree()
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print('Accuracy: ', accuracy)
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)
y_val = dt.predict(X_test)
acc = accuracy_score(y_test, y_val)
print('sklearn score:', acc)
def __init__(self, n_estimators=50, max_features=None, min_samples_split=2,
min_gain=1e-7, max_depth=float("inf"), debug=False):
self.n_estimators = n_estimators # Number of trees
self.max_features = max_features # Maxmimum number of features per tree
self.feature_indices = [] # The indices of the features used for each tree
self.min_samples_split = min_samples_split
self.min_gain = min_gain # Minimum information gain req. to continue
self.max_depth = max_depth # Maximum depth for tree
self.debug = debug
# Initialize decision trees
self.trees = []
for _ in range(n_estimators):
self.trees.append(
ClassificationTree(
min_samples_split=self.min_samples_split,
min_impurity=min_gain,
max_depth=self.max_depth))
def __init__(self,
n_estimators=100,
min_samples_split=2,
min_gain=0,
max_depth=float("inf"),
max_features=None):
self.n_estimators = n_estimators
self.min_samples_split = min_samples_split
self.min_gain = min_gain
self.max_depth = max_depth
self.max_features = max_features
self.trees = []
# 建立森林(bulid forest)
for _ in range(self.n_estimators):
tree = ClassificationTree(min_samples_split=self.min_samples_split,
min_impurity=self.min_gain,
max_depth=self.max_depth,
max_features=max_features)
self.trees.append(tree)
def main():
print("-- Classification Tree --")
data = datasets.load_iris()
X = data.data
y = data.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4)
clf = ClassificationTree(max_features=2)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("Our DTClassifier Accuracy:", accuracy)
clf = tree.DecisionTreeClassifier()
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print("sklearn DTClassifier Accuracy:", accuracy)
column = np.hstack((column, float(row['director_facebook_likes'])))
column = np.hstack((column, float(row['actor_1_facebook_likes'])))
column = np.hstack((column, float(row['actor_2_facebook_likes'])))
column = np.hstack((column, float(row['actor_3_facebook_likes'])))
column = np.hstack((column, float(row['budget'])))
column = np.hstack((column, float(row['facenumber_in_poster'])))
target = float(row['imdb_score'])
target = int(target)
if target >= 4 and target <= 8:
y = np.vstack((y, [target])) # scores
X = np.vstack((X, column))
# dataset.append(Instance( [ncfr, dur, dfl, a3fl, a1fl, gr], [target] ))
except Exception as e:
pass
model = ClassificationTree()
X = standardize(X)
# X = RobustScaler(quantile_range=(25, 75)).fit_transform(X)
# X = MinMaxScaler().fit_transform(X)
# # # print y
datasets = k_fold_cross_validation_sets(X, y, 3)
for data in datasets:
X_train, X_test, y_train, y_test = data
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
print(accuracy_score(y_test, y_pred))
# print accuracy_score(y_test, y_pred)
mse = mean_squared_error(y_test, y_pred)
print("Mean Squared Error:", mse)
# ..........................
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5)
# Rescaled labels {-1, 1}
rescaled_y_train = 2 * y_train - np.ones(np.shape(y_train))
rescaled_y_test = 2 * y_test - np.ones(np.shape(y_test))
# .......
# SETUP
# .......
adaboost = Adaboost(n_clf=8)
naive_bayes = NaiveBayes()
knn = KNN(k=4)
logistic_regression = LogisticRegression()
mlp = MultilayerPerceptron(n_hidden=20)
perceptron = Perceptron()
decision_tree = ClassificationTree()
random_forest = RandomForest(n_estimators=150)
support_vector_machine = SupportVectorMachine(C=1, kernel=rbf_kernel)
lda = LDA()
# ........
# TRAIN
# ........
print "Training:"
print "\tAdaboost"
adaboost.fit(X_train, rescaled_y_train)
print "\tNaive Bayes"
naive_bayes.fit(X_train, y_train)
print "\tLogistic Regression"
logistic_regression.fit(X_train, y_train)
print "\tLDA"
# ..........................
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5)
# Rescaled labels {-1, 1}
rescaled_y_train = 2*y_train - np.ones(np.shape(y_train))
rescaled_y_test = 2*y_test - np.ones(np.shape(y_test))
# .......
# SETUP
# .......
adaboost = Adaboost(n_clf = 8)
naive_bayes = NaiveBayes()
knn = KNN(k=4)
logistic_regression = LogisticRegression()
mlp = MultilayerPerceptron(n_hidden=20, n_iterations=20000, learning_rate=0.1)
perceptron = Perceptron()
decision_tree = ClassificationTree()
random_forest = RandomForest(n_estimators=50)
support_vector_machine = SupportVectorMachine()
lda = LDA()
gbc = GradientBoostingClassifier(n_estimators=50, learning_rate=.9, max_depth=2)
xgboost = XGBoost(n_estimators=50, learning_rate=0.5)
# ........
# TRAIN
# ........
print ("Training:")
print ("\tAdaboost")
adaboost.fit(X_train, rescaled_y_train)
print ("\tDecision Tree")
decision_tree.fit(X_train, y_train)
print ("\tGradient Boosting")
