本文整理汇总了Python中sklearn.linear_model.ElasticNet.score方法的典型用法代码示例。如果您正苦于以下问题:Python ElasticNet.score方法的具体用法?Python ElasticNet.score怎么用?Python ElasticNet.score使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.linear_model.ElasticNet
的用法示例。
在下文中一共展示了ElasticNet.score方法的6个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: enet
# 需要导入模块: from sklearn.linear_model import ElasticNet [as 别名]
# 或者: from sklearn.linear_model.ElasticNet import score [as 别名]
def enet(a):
print ("Doing elastic net")
clf3 = ElasticNet(alpha=a)
clf3.fit(base_X, base_Y)
print ("Score = %f" % clf3.score(base_X, base_Y))
clf3_pred = clf3.predict(X_test)
write_to_file("elastic.csv", clf3_pred)
示例2: enet_granger_causality_test
# 需要导入模块: from sklearn.linear_model import ElasticNet [as 别名]
# 或者: from sklearn.linear_model.ElasticNet import score [as 别名]
def enet_granger_causality_test(X_t, y_t, top_df, max_iter=10000000):
"""
Return the cv-parameters tested across the whole data
:param X_t:
:param y_t:
:param top_df:
:return: res_df, test_betas
"""
test_errs = np.zeros(len(top_df))
scores = np.zeros(len(top_df))
dfs = np.zeros(len(top_df))
test_coefs = np.zeros((len(top_df), X_t.shape[1]))
for i in range(len(top_df)):
alpha = top_df.iloc[i]["alpha"]
lambda_min = top_df.iloc[i]["lambda.min"]
enet = ElasticNet(l1_ratio=alpha, alpha=lambda_min, max_iter=max_iter)
enet.fit(X_t, y_t)
y_pred = enet.predict(X_t)
test_errs[i] = np.average((y_t - y_pred)**2)
scores[i] = enet.score(X_t, y_t)
test_coefs[i] = enet.coef_
dfs[i] = len(np.where(enet.coef_)[0])
top_df["test_err"] = test_errs
top_df["score"] = scores
top_df["df"] = dfs
return top_df, test_coefs
示例3: ElasticNet
# 需要导入模块: from sklearn.linear_model import ElasticNet [as 别名]
# 或者: from sklearn.linear_model.ElasticNet import score [as 别名]
# ElasticNet Regression
import numpy as np
from sklearn import datasets
from sklearn.linear_model import ElasticNet
# load the diabetes datasets
dataset = datasets.load_diabetes()
# fit a model to the data
model = ElasticNet(alpha=0.1)
model.fit(dataset.data, dataset.target)
print(model)
# make predictions
expected = dataset.target
predicted = model.predict(dataset.data)
# summarize the fit of the model
mse = np.mean((predicted-expected)**2)
print(mse)
print(model.score(dataset.data, dataset.target))
示例4: ElasticNet
# 需要导入模块: from sklearn.linear_model import ElasticNet [as 别名]
# 或者: from sklearn.linear_model.ElasticNet import score [as 别名]
net = ElasticNet(alpha=1.5)
lasso = Lasso(alpha=5)
ridge = Ridge(alpha=3)
lr = LinearRegression()
dtr = DecisionTreeRegressor(max_depth=17)
bagger = BaggingRegressor(net, verbose = 1)
X_train, X_test, y_train, y_test = train_test_split(X_model, y)
dtr.fit(X_train,y_train)
dtr.score(X_test, y_test)
pred = dtr.predict(X_test)
plt.scatter(y_test, (pred*0.8)-y_test)
net.fit(X_train, y_train)
net.score(X_test, y_test)
preds = net.predict(X_test)
plt.scatter(y_test, (preds) - y_test, alpha = 0.7)
scores = cross_val_score(net, scale(X_model), y, cv=12)
scores.mean()
X2 = pivoted[['compilation_0', 'compilation_1', 'compilation_2']]
y2 = pivoted.compilation_3
X_train, X_test, y_train, y_test = train_test_split(X2, y2, test_size=0.2)
lr.fit(X_train, y_train)
lr.score(X_test, y_test)
pivoted.head()
mapped_pivot = pd.read_csv('pivot_catcherr.csv')
示例5: fit_linear_model
# 需要导入模块: from sklearn.linear_model import ElasticNet [as 别名]
# 或者: from sklearn.linear_model.ElasticNet import score [as 别名]
def fit_linear_model(X, y, results, keys,
alpha = np.logspace(-5,2,50),
l1_ratio = np.array([.1, .5, .7, .9, .95, .99, 1]),
num_cv = 5,
verbose = False,
intercept_scaling = 10,
plot_results = False,
labels = None
):
X = pp.scale(X)
clf = []
R2 = []
coef = []
prob = []
score = []
group_keys = []
if num_cv > 1:
num_cv2 = num_cv
else:
num_cv2 = 10
# Find best alpha and lambda
if (np.size(alpha)>1) or (np.size(l1_ratio)>1):
print "Determining best values for L1 ratio and alpha..."
clf_temp = ENCV(
l1_ratio = l1_ratio,
alphas = alpha,
cv = num_cv2,
fit_intercept = False,
verbose = verbose
)
clf_temp.fit(X,y)
best_alpha = clf_temp.alpha_
best_l1_ratio = clf_temp.l1_ratio_
print "Best L1 ratio: " + str(best_l1_ratio) + ", best alpha: " + str(best_alpha)
else:
best_alpha = alpha
best_l1_ratio = l1_ratio
# Now do cross-validation to estimate accuracy
if num_cv > 1:
if labels == None:
kf = KFold(n = len(y), n_folds = num_cv)
else:
kf = LOLO(labels)
#
for train, test in kf:
X_train, X_test, y_train, y_test, results_test, keys_test = X[train], X[test], y[train], y[test], results[test], keys[test]
clf_temp2 = EN(
l1_ratio = best_l1_ratio,
alpha = best_alpha,
fit_intercept = False)
clf_temp2.fit(X_train,y_train)
pred = clf_temp2.predict(X_test)
clf.append(clf_temp2)
R2.append(clf_temp2.score(X_test,y_test))
coef.append(clf_temp2.coef_)
prob.append(diff_to_prob(pred))
score.append(lossFx(results_test,pred))
group_keys.append(keys_test)
else:
clf_temp2 = EN(
l1_ratio = best_l1_ratio,
alpha = best_alpha,
fit_intercept = False)
clf_temp2.fit(X,y)
pred = clf_temp2.predict(X)
clf = clf_temp2
R2 = clf_temp2.score(X,y)
coef = clf_temp2.coef_
prob = diff_to_prob(pred)
score = lossFx(results,pred)
group_keys = keys
if num_cv > 1:
return clf, R2, score, coef, prob, kf, group_keys
else:
return clf, R2, score, coef, prob, group_keys
示例6: print
# 需要导入模块: from sklearn.linear_model import ElasticNet [as 别名]
# 或者: from sklearn.linear_model.ElasticNet import score [as 别名]
# print ('final score: ' + str(np.mean(final_score)))
# mean_coef = final_coef/10
# mean_intercept = np.mean(final_intercept)
# lin_reg.coef_ = mean_coef
# lin_reg.intercept_ = mean_intercept
# LABEL NEW ERROR TICKET LABELS ON EXISTING DATA
y_new = lin_reg.predict(X_test)
# print(lin_reg.coef_)
print('final score for linear Elastic Net: ' + str(lin_reg.score(X_test, y_test)))
# DEFINITION FOR ERROR TICKET
rate = 0.7
lower = y_new * (1-rate)
result = np.expand_dims((np.squeeze(y_test) - lower) < 0, axis=1)
# results = np.array(results)
# plt.scatter(X_test, y_test, color='black', s=1)
# plt.plot(X_test, y_new, color='blue')
# plt.ylabel('price (USD)')
# plt.xlabel('week score')
# plt.xticks()