我尝试了随机森林回归。
代码如下。
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import KFold, cross_val_predict
from sklearn.feature_selection import SelectKBest, f_regression
from sklearn.pipeline import make_pipeline, Pipeline
from sklearn.ensemble import RandomForestRegressor
from sklearn.feature_selection import RFECV
from sklearn.model_selection import GridSearchCV
np.random.seed(0)
d1 = np.random.randint(2, size=(50, 10))
d2 = np.random.randint(3, size=(50, 10))
d3 = np.random.randint(4, size=(50, 10))
Y = np.random.randint(7, size=(50,))
X = np.column_stack([d1, d2, d3])
n_smples, n_feats = X.shape
print (n_smples, n_feats)
kf = KFold(n_splits=5, shuffle=True, random_state=0)
regr = RandomForestRegressor(max_features=None,random_state=0)
pipe = make_pipeline(RFECV(estimator=regr, step=3, cv=kf, scoring =
'neg_mean_squared_error', n_jobs=-1),
GridSearchCV(regr, param_grid={'n_estimators': [100, 300]},
cv=kf, scoring = 'neg_mean_squared_error',
n_jobs=-1))
ypredicts = cross_val_predict(pipe, X, Y, cv=kf, n_jobs=-1)
rmse = mean_squared_error(Y, ypredicts)
print (rmse)
但是,我收到以下错误:sklearn.exceptions.NotFittedError:估算器未拟合,请在利用模型之前调用fit
。
我也试过:
model = pipe.fit(X,Y)
ypredicts = cross_val_predict(model, X, Y, cv=kf, n_jobs=-1)
但得到了同样的错误。
编辑 1:我也试过:
pipe.fit(X,Y)
但得到了同样的错误。
在Python 2.7(Sklearn 0.20(中,对于相同的代码,我得到了不同的错误:
TerminatedWorkerError: A worker process managed by the executor was unexpectedly terminated. This could be caused by a segmentation fault while calling the function or by an excessive memory usage causing the Operating System to kill the worker.
在 Python 2.7 (Sklearn 0.20.3( 中: NotFittedError: Estimator not fitted, call
适合before exploiting the model.
似乎您正在尝试使用网格搜索为分类器选择最佳参数,还有另一个可以这样做。您正在使用管道,但在此方法中我没有使用管道,但我通过随机搜索获得最佳参数。
import numpy as np
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import KFold, cross_val_predict
from sklearn.feature_selection import SelectKBest, f_regression
from sklearn.pipeline import make_pipeline, Pipeline
from sklearn.ensemble import RandomForestRegressor
from sklearn.feature_selection import RFECV
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import RandomizedSearchCV
from sklearn.ensemble import RandomForestClassifier
np.random.seed(0)
d1 = np.random.randint(2, size=(50, 10))
d2 = np.random.randint(3, size=(50, 10))
d3 = np.random.randint(4, size=(50, 10))
Y = np.random.randint(7, size=(50,))
X = np.column_stack([d1, d2, d3])
n_smples, n_feats = X.shape
print (n_smples, n_feats)
kf = KFold(n_splits=5, shuffle=True, random_state=0)
regr = RandomForestRegressor(max_features=None,random_state=0)
n_iter_search = 20
random_search = RandomizedSearchCV(regr, param_distributions={'n_estimators': [100, 300]},
n_iter=20, cv=kf,verbose=1,return_train_score=True)
random_search.fit(X, Y)
ypredicts=random_search.predict(X)
rmse = mean_squared_error(Y, ypredicts)
print(rmse)
print(random_search.best_params_)
random_search.cv_results_
试试这段代码。我希望这段代码能解决您的问题。
而不是
model = pipe.fit(X,Y)
你试过吗
pipe.fit(X,Y)
相反?
所以那将是
pipe.fit(X,Y)
# change model to pipe
ypredicts = cross_val_predict(pipe, X, Y, cv=kf, n_jobs=-1)