train_feat1 = train_feat.iloc[train_index]
时间: 2024-06-07 09:08:37 浏览: 9
这段代码是在从 pandas.DataFrame 类型的 train_feat 变量中选择一部分数据,并将其赋值给 train_feat1 变量。其中,train_index 是一个指定了要选择哪些数据的索引列表,这些索引对应了 train_feat 中的行。具体来说,train_feat1 是 train_feat 中所有在 train_index 列表中的行所组成的新的 DataFrame 对象。这种操作常用于数据集划分,比如将数据集划分成训练集和测试集。
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下面这段代码用了哪种数学建模方法fold = 5 for model_seed in range(num_model_seed): print(seeds[model_seed],"--------------------------------------------------------------------------------------------") oof_cat = np.zeros(X_train.shape[0]) prediction_cat = np.zeros(X_test.shape[0]) skf = StratifiedKFold(n_splits=fold, random_state=seeds[model_seed], shuffle=True) for index, (train_index, test_index) in enumerate(skf.split(X_train, y)): train_x, test_x, train_y, test_y = X_train[feature_name].iloc[train_index], X_train[feature_name].iloc[test_index], y.iloc[train_index], y.iloc[test_index] dtrain = lgb.Dataset(train_x, label=train_y) dval = lgb.Dataset(test_x, label=test_y) lgb_model = lgb.train( parameters, dtrain, num_boost_round=10000, valid_sets=[dval], early_stopping_rounds=100, verbose_eval=100, ) oof_cat[test_index] += lgb_model.predict(test_x,num_iteration=lgb_model.best_iteration) prediction_cat += lgb_model.predict(X_test,num_iteration=lgb_model.best_iteration) / fold feat_imp_df['imp'] += lgb_model.feature_importance() del train_x del test_x del train_y del test_y del lgb_model oof += oof_cat / num_model_seed prediction += prediction_cat / num_model_seed gc.collect()
这段代码使用了交叉验证的方法(StratifiedKFold)来评估LightGBM模型的性能,并且使用了平均化的方法(num_model_seed)来减少模型的方差。其中,变量fold表示交叉验证折数,num_model_seed表示重复训练模型的次数。在每次交叉验证中,将训练数据(train_x)和测试数据(test_x)分别作为模型的训练集和验证集,使用LightGBM模型进行训练,并在验证集上进行早停策略(early_stopping_rounds),以避免模型过拟合。在训练过程中,记录并累加训练集和测试集的预测结果(oof_cat和prediction_cat),并计算特征重要性(feat_imp_df['imp'])。最后,使用平均化的方式计算oof和prediction,并释放不再使用的内存(gc.collect())。
用Python代码写一个朴素贝叶斯算法并实现可视化
由于朴素贝叶斯算法的实现需要根据具体的数据集进行不同的处理和预处理,因此下面提供的代码仅供参考,具体的实现需要根据实际情况进行调整和修改。
```python
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
# 加载数据
def load_data(file_path):
data = pd.read_csv(file_path, header=None)
return data
# 划分数据集
def split_data(data, ratio=0.8):
m, n = data.shape
idx = np.random.permutation(m)
train_idx = idx[:int(ratio*m)]
test_idx = idx[int(ratio*m):]
train_data = data.iloc[train_idx, :-1]
train_label = data.iloc[train_idx, -1]
test_data = data.iloc[test_idx, :-1]
test_label = data.iloc[test_idx, -1]
return train_data, train_label, test_data, test_label
# 计算先验概率和条件概率
def train(train_data, train_label):
m, n = train_data.shape
classes = train_label.unique()
class_num = len(classes)
prior_prob = np.zeros(class_num)
cond_prob = []
for i in range(class_num):
c = classes[i]
prior_prob[i] = np.sum(train_label==c) / m
sub_data = train_data[train_label==c]
cond_prob_c = []
for j in range(n):
cond_prob_c_j = {}
feat_vals = sub_data.iloc[:, j].unique()
for feat_val in feat_vals:
cond_prob_c_j[feat_val] = np.sum(sub_data.iloc[:, j]==feat_val) / len(sub_data)
cond_prob_c.append(cond_prob_c_j)
cond_prob.append(cond_prob_c)
return prior_prob, cond_prob
# 预测
def predict(test_data, prior_prob, cond_prob):
m, n = test_data.shape
classes = len(prior_prob)
preds = []
for i in range(m):
max_prob = -1
max_class = -1
for j in range(classes):
c = j
prob = prior_prob[j]
for k in range(n):
feat_val = test_data.iloc[i, k]
if feat_val in cond_prob[j][k]:
prob *= cond_prob[j][k][feat_val]
else:
prob = 0
if prob > max_prob:
max_prob = prob
max_class = c
preds.append(max_class)
return preds
# 计算准确率
def accuracy(preds, labels):
return np.sum(preds==labels) / len(labels)
# 可视化
def visualize(train_data, train_label):
plt.scatter(train_data.iloc[:, 0], train_data.iloc[:, 1], c=train_label)
plt.show()
# 主函数
def main():
file_path = 'data.csv'
data = load_data(file_path)
train_data, train_label, test_data, test_label = split_data(data)
visualize(train_data, train_label)
prior_prob, cond_prob = train(train_data, train_label)
preds = predict(test_data, prior_prob, cond_prob)
acc = accuracy(preds, test_label)
print('Accuracy:', acc)
if __name__ == '__main__':
main()
```
需要注意的是,上述代码中的`visualize`函数用于将数据可视化,方便观察数据的分布情况。在实际应用中,由于数据集的维度可能很高,因此无法直接进行可视化。此时可以考虑使用降维算法(如PCA等)将数据降到二维或三维,然后进行可视化。