def optimal_p(x, y):
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.neighbors import KNeighborsRegressor
from numpy import linspace
opt_p = 0
max_quality = 0
generator = KFold(n_splits=5, shuffle=True, random_state=42)
regressor = KNeighborsRegressor(5, weights="distance")
linspace = linspace(start=1, stop=10, num=200)
for __p in linspace:
regressor.p = __p
qualities = cross_val_score(estimator=regressor,
X=x,
y=y,
cv=generator,
scoring="neg_mean_squared_error")
avg_quality = sum(qualities) / float(len(qualities))
if avg_quality >= max_quality or max_quality == 0:
max_quality = avg_quality
opt_p = __p
return opt_p
from sklearn.cross_validation import cross_val_score
from sklearn.datasets import load_boston
from sklearn.model_selection import KFold
from sklearn.neighbors import KNeighborsRegressor
from sklearn.preprocessing import scale
bostonDataset = load_boston()
target = bostonDataset.target
data = scale(load_boston().data)
maxValue = -100000
maxP = -1
for i in linspace(start=1, stop=10, num=200):
regressionFun = KNeighborsRegressor(n_neighbors=5,
weights='distance',
metric='minkowski')
regressionFun.p = i
score = cross_val_score(estimator=regressionFun,
cv=KFold(n_splits=5, random_state=42,
shuffle=True).split(data),
X=data,
y=target,
scoring='neg_mean_squared_error')
meanScore = mean(score)
if meanScore > maxValue:
maxValue = meanScore
maxP = i
print(i, meanScore)
print(maxP, maxValue)
