我 Build 了一个回归模型来预测来自5个变量(5列)的能量(1列)...我使用我的实验数据来训练和拟合模型并且它的得分很好......
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
data = pd.read_csv('new.csv')
X = data.drop(['E'],1)
y = data['E']
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5 ,
random_state=2)
from sklearn import ensemble
clf1 = ensemble.GradientBoostingRegressor(n_estimators = 400, max_depth =5,
min_samples_split = 2, loss='ls',
learning_rate = 0.1)
clf1.fit(X_train, y_train)
clf1.score(X_test, y_test)
但现在我想添加一个新的csv文件,包含OrderedDict中提到的5个变量的新数据,并使用该模型预测能量......
使用代码波纹管我手动逐行插入并正确预测能量
from collections import OrderedDict
new_data = OrderedDict([('H',48.52512), ('A',169.8379), ('P',55.52512),
('R',3.058758), ('Q',2038.055)])
new_data = pd.Series(new_data)
data = new_data.values.reshape(1, -1)
clf1.predict(data)
但我不能用巨大的数据集做这个,需要导入csv文件...我做了下面但是无法弄清楚....
data_2 = pd.read_csv('new2.csv')
X_new = OrderedDict(data_2)
new_data = pd.Series(X_new)
data = new_data.values.reshape(1, -1)
clf1.predict(data)
但是给我:ValueError:用序列设置数组元素 .
谁能帮我 ??