self.plotMidText((self.xOff, self.yOff,), cntrPt, str(key))
self.yOff += 1.0 / self.totalD
def createPlot(self):
inTree = self._parameter['tree']
fig = plt.figure(1, facecolor='white')
fig.clf()
axprops = dict(xticks=[], yticks=[])
self.decisionNode = dict(boxstyle='sawtooth', fc='0.8')
self.leafNone = dict(boxstyle='round4', fc='0.8')
self.arrow_args = dict(arrowstyle='<-')
self.ax1 = plt.subplot(111, frameon=False, **axprops)
self.totalW = float(self.get_num_leafs(inTree))
self.totalD = float(self.get_tree_depth(inTree))
self.xOff = -0.5 / self.totalW
self.yOff = 1.0
self.plotTree(inTree, (0.5, 1.0), '')
plt.show()
if __name__ == '__main__':
path = os.getcwd() + '/../dataset/dataset_21_car.arff'
loader = DataLoader(path)
dataset = loader.load(target_col_name='class')
trainset, testset = dataset.cross_split()
dt = DecisionTreeClassifier(min_split=1, is_prune=False)
dt.fit(trainset[0], trainset[1])
predict = dt.predict(testset[0])
performance = accuracy_score(testset[1], predict)
print 'test accuracy:', performance
info = self._S
if cumulative is False:
return info
else:
return np.cumsum(info)
def plot(self, X):
nSize = X.shape[0]
nFeat = X.shape[1]
assert nFeat == 2, 'feature number should be 2.'
for i in xrange(nSize):
plt.plot(X[i, 0], X[i, 1], 'or')
plt.show()
if __name__ == '__main__':
path = os.getcwd() + '/../dataset/iris.arff'
loader = DataLoader(path)
dataset = loader.load(target_col_name='binaryClass')
trainset, testset = dataset.cross_split()
pca = PCA(2, whiten=False)
X = trainset[0][:, [0, 2]]
pca.fit(X)
_X = pca.transform(X)
print 'eigenvalue:', pca.information_distribution(percent=True)
pca.plot(_X)
pca = PCA(2, whiten=True)
pca.fit(X)
_X = pca.transform(X)
pca.plot(_X)
logger.info("progress: %.2f %%" % (float(i) / X.shape[0] * 100))
else:
raise ValueError
return pred
logger = get_logger("KNN")
if __name__ == "__main__":
from base.time_scheduler import TimeScheduler
scheduler = TimeScheduler()
# KNN for classification task
path = os.getcwd() + "/../dataset/electricity-normalized.arff"
loader = DataLoader(path)
dataset = loader.load(target_col_name="class")
trainset, testset = dataset.cross_split()
knn = KNNClassifier(search_mode="kd_tree")
knn.fit(trainset[0], trainset[1])
predict_kd_tree = scheduler.tic_tac("kd_tree", knn.predict, X=testset[0])
knn = KNNClassifier(search_mode="brutal")
knn.fit(trainset[0], trainset[1])
predict_brutal = scheduler.tic_tac("brutal", knn.predict, X=testset[0])
scheduler.print_task_schedule("brutal")
scheduler.print_task_schedule("kd_tree")
print accuracy_score(testset[1], predict_brutal), accuracy_score(testset[1], predict_kd_tree)
# KNN for regression task
# path = os.getcwd() + '/../dataset/winequality-white.csv'
# loader = DataLoader(path)
for irow in range(X.shape[0]):
_X = X[irow]
max_prob = None
label = None
for c in proba_y.keys():
p = proba_y[c]
for icol, feat in cond_proba_y[c].iteritems():
p += feat[_X[icol]]
if max_prob < p or max_prob is None:
max_prob = p
label = c
assert label is not None, 'label should be None. There must be some error. please check.'
pred.append(label)
return np.array(pred)
if __name__ == '__main__':
path = os.getcwd() + '/../dataset/dataset_21_car.arff'
loader = DataLoader(path)
dataset = loader.load(target_col_name='class')
trainset, testset = dataset.cross_split()
nb = NaiveBayes()
nb.fit(trainset[0], trainset[1])
predict = nb.predict(testset[0])
acc = accuracy_score(testset[1], predict)
print acc
nb.dump('NB.model')
# nb = NaiveBayes.load('NB.model')
# predict = nb.predict(testset[0])
# print accuracy_score(testset[1], predict)
else:
is_valid = False
nFeat = X.shape[1]
if nFeat == self._nFeat:
is_valid = True
return is_valid
def predict(self, X):
models = self._parameter['trees']
pred = np.zeros(X.shape[0])
for model in models:
pred += np.array(model.predict(X))
return pred
if __name__ == '__main__':
path = os.getcwd() + '/../dataset/winequality-white.csv'
loader = DataLoader(path)
dataset = loader.load(target_col_name='quality')
trainset, testset = dataset.cross_split()
gbdt = GradientBoostingDecisionTree(10)
gbdt.fit(trainset[0], trainset[1])
predict = gbdt.predict(testset[0])
print 'GBDT mean error:', mean_error(testset[1], predict)
dt = DecisionTreeRegressor()
dt.fit(trainset[0], trainset[1])
predict = dt.predict(testset[0])
print 'DecisionTree mean error:', mean_error(testset[1], predict)
