class KNN(object):
def __init__(self, task_type="cla", module_type="performance", **params):
assert task_type in ["cla", "reg"] # 两种类型
assert module_type in ["balance", "debug", "performance",
None] # 三种 性能模型
self.module_type = module_type
if self.module_type == "debug":
params["n_jobs"] = 1
elif self.module_type == "performance": # 性能模型
params["n_jobs"] = cpu_count() # cpu核心数
elif self.module_type == "balance": # 均衡模型
params["n_jobs"] = cpu_count() // 2
else:
params["n_jobs"] = None
self.task_type = task_type
# weights 取值{"uniform", "distance",None} # 默认使用的uniform
# "algorithm" 取值 {"auto", "ball_tree", "kd_tree", "brute",None}s
# 权重 uniform 均匀权重， distance 按照其距离的倒数
# "ball_tree" 使用BallTree算法，
# kd_tree 使用的KDTree 算法
# brute 使用暴力搜索 算法。
# p的取值， int 类型数据， 默认为2 马尔科夫功率参数
# p=1 时，等校于p=2使用manhattan_distance(l1)和euclidean_distance(l2).
# 对于任意的p， 使用minkowskidistance(l_p)
if self.task_type == "cla":
self.model = KNeighborsClassifier(
n_neighbors=params.get("n_neighbors", 5),
weights=params.get("weights", 'uniform'),
algorithm=params.get("algorithm", 'auto'),
leaf_size=params.get("leaf_size", 30), # 叶子大小
p=params.get("p", 2),
metric=params.get("metric", 'minkowski'),
metric_params=params.get("metric_params", None),
n_jobs=params.get("n_jobs", None) # 并行数
)
else:
self.model = KNeighborsRegressor(
n_neighbors=params.get("n_neighbors", 5),
weights=params.get("weights", 'uniform'),
algorithm=params.get("algorithm", 'auto'),
leaf_size=params.get("leaf_size", 30),
p=params.get("p", 2),
metric=params.get("metric", 'minkowski'),
metric_params=params.get("metric_params", None),
n_jobs=params.get("n_jobs", None))
def fit(self, x, y=None):
self.model.fit(X=x, y=y)
def get_params(self):
return self.model.get_params(deep=True)
def set_params(self, params):
self.model.set_params(**params)
def predict(self, x):
return self.model.predict(X=x)
def predict_proba(self, x):
if self.task_type == "cla":
return self.model.predict_proba(X=x)
else:
ValueError("回归任务无法使用")
def get_score(self, x, y, sample_weight):
return self.model.score(X=x, y=y, sample_weight=sample_weight)
def search_kneighbors(self,
x=None,
n_neighbors=None,
return_distance=True): # 查找K近邻居
return self.model.kneighbors(X=x,
n_neighbors=n_neighbors,
return_distance=return_distance)
def get_kneighbors_graph(self,
x=None,
n_neighbors=None,
mode='connectivity'): # 获取最近邻图
"""
:param x:
:param n_neighbors:
:param mode: "distance","connectivity"
:return:
"""
return self.model.kneighbors_graph(X=x,
n_neighbors=n_neighbors,
mode=mode)
x_train = x_train_with_names.drop('name', axis=1)
x_test = x_test_with_names.drop('name', axis=1)
# parameters
n_neighbors = 5
# building the model
neigh = KNeighborsRegressor(n_neighbors=n_neighbors)
# training the model
neigh.fit(x_train, y_train)
# looking at the methods
get_params = neigh.get_params() # returns the parameters of the model
kneighbours = neigh.kneighbors(
x_test.head(1), n_neighbors=n_neighbors
) # the first array gives the distance between the new data point and the k neighbours, and the second array gives the sample number of the k neighbours
kneighbours_graph = neigh.kneighbors_graph(
x_test.head(1), n_neighbors=n_neighbors, mode='distance'
) # returns a sparce matrix for the k neighbours for the new data points
prediction_array = neigh.predict(x_test) # predicted test values
train_score = neigh.score(x_train,
y_train) # the mean auuracy of the training dataset
test_score = neigh.score(x_test,
y_test) # the mean acccuracy for the test dataset
print(
'The mean accuracy of the train dataset is: %.3f and the mean accuracy of the test dataset is: %.3f'
% (train_score, test_score))
pdb.set_trace()
X = [[0], [1], [2], [3]]
y = [0, 0, 1, 1]
X_test = [[1.5]]
from sklearn.neighbors import KNeighborsRegressor
neigh = KNeighborsRegressor(n_neighbors=2)
neigh.fit(X, y)
print(neigh.predict(X_test))
samples = [[0., 0., 0.], [0., .5, 0.], [1., 1., .5]]
from sklearn.neighbors import NearestNeighbors
neigh = NearestNeighbors(n_neighbors=1)
neigh.fit(samples)
print("Q. who’s the closest point to [1,1,1] ?")
print("A. distance, index of point, datatype = ")
print(neigh.kneighbors([[1., 1., 1.]]))
X = [[0., 1., 0.], [1., 0., 1.]]
print("Q. who’s the closest points to", X, "? (multi version)")
print("A. index list of points = ")
print(neigh.kneighbors(X, return_distance=False))
X = [[0], [3], [1]]
from sklearn.neighbors import NearestNeighbors
neigh = NearestNeighbors(n_neighbors=2)
neigh.fit(X)
A = neigh.kneighbors_graph(X)
print("Computes the (weighted) graph of k-Neighbors for points in X")
print("k = ", 2-1, " X = ", X)
print("(point 1, 2) distance")
print(A)
print(A.toarray())
