我发现表单3的数据并没有被处理和分析。下面是对此的修改:
```python
def load_and_process_data(file_path, sheet_name):
"""加载和处理数据"""
try:
# 检查文件是否存在
if not os.path.exists(file_path):
log_message(f"错误: 文件 '{file_path}' 不存在")
return None
# 读取数据
data = pd.read_excel(file_path, sheet_name=sheet_name)
log_message(f"成功加载数据, 共 {len(data)} 行")
# 列名修复
col_renames = {
'表面风化化': '表面风化',
'采采样点风化类型': '采样点风化类型',
'样点风化类型': '采样点风化类型',
'总成分': '总含量'
}
new_columns = []
for col in data.columns:
if col in col_renames:
new_columns.append(col_renames[col])
else:
new_columns.append(col)
data.columns = new_columns
# 化学成分列重命名
rename_dict = {
'氧化硅(Si)': '二氧化硅(SiO2)',
'氧化锡(SnO)': '氧化锡(SnO2)',
'氧化硫(SO3)': '二氧化硫(SO2)',
'氧化亚铜(Cu2O)': '氧化亚铜(Cu2O)',
'氧化铜(CuO)': '氧化铜(CuO)',
'三氧化二铁(Fe2O3)': '三氧化二铁(Fe2O3)'
}
data = data.rename(columns=rename_dict)
# 删除总含量列
if '总含量' in data.columns:
data = data.drop('总含量', axis=1)
log_message("已删除'总含量'列")
# 添加缺失列的处理
required_cols = ['表面风化', '采样点风化类型', '类型']
for col in required_cols:
if col not in data.columns:
data[col] = np.nan
log_message(f"警告: 列 '{col}' 不存在，已创建空白列")
# 移除空白行
data.dropna(how='all', inplace=True)
log_message(f"处理后数据行数: {len(data)}")
return data
except Exception as e:
log_message(f"数据处理失败: {str(e)}")
traceback.print_exc()
return None
# 加载表单3的数据
unknown_file_path = r"D:\Users\86157\Desktop\数学建模\附件.xlsx"
unknown_data = load_and_process_data(unknown_file_path, sheet_name='表单3')
if unknown_data is not None:
# 在这里对表单3的数据进行分析和处理
log_message("成功加载并处理了表单3的数据")
else:
log_message("无法加载或处理表单3的数据")
```
添加了一个新的参数`sheet_name`到`load_and_process_data`函数中,以便加载特定的工作表。然后我直接调用了这个函数来加载表单3的数据,并在后续的代码中对其进行处理。
这样就可以确保表单3的数据也被正确地加载和处理了。请检查一下下面代码，不要修改文件地址，修改下面代码，并给出完整代码
代码如下：
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestClassifier
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import f1_score, adjusted_rand_score
from itertools import permutations
import os
import traceback
from mpl_toolkits.mplot3d import Axes3D
import warnings
from scipy.stats import f_oneway
# 设置中文显示
plt.rcParams['font.sans-serif'] = ['SimHei'] # 使用黑体
plt.rcParams['axes.unicode_minus'] = False # 解决负号显示问题
warnings.filterwarnings('ignore')
def log_message(message):
"""记录日志消息并打印到控制台"""
print(f"[INFO] {message}")
def get_colors(style='bright'):
"""获取颜色调色板"""
if style == 'bright':
return sns.color_palette('bright')
elif style == 'all':
return sns.color_palette('hsv', 15)
elif style == 'rainbow':
return sns.color_palette('rainbow')
else:
return sns.color_palette('deep')
def boxplot(data, rows, cols, hue=None, vars=None, figsize=(12, 8), subplots_adjust=(0.5, 0.5)):
"""创建箱线图"""
try:
if not vars:
numerical_cols = data.select_dtypes(include=['float64', 'int64']).columns.tolist()
if hue and hue in numerical_cols:
numerical_cols.remove(hue)
vars = numerical_cols
fig = plt.figure(figsize=figsize)
ax_num = 1
if hue:
palette = get_colors('rainbow')
else:
palette = None
for col in vars:
plt.subplot(rows, cols, ax_num)
if hue:
sns.boxplot(x=hue, y=col, data=data, palette=palette)
else:
sns.boxplot(y=data[col], color=np.random.choice(sns.color_palette()))
plt.title(col)
plt.xticks(rotation=45)
ax_num += 1
plt.tight_layout()
plt.subplots_adjust(hspace=subplots_adjust[0], wspace=subplots_adjust[1])
plt.savefig('化学成分箱线图分析.jpg', dpi=300, bbox_inches='tight')
plt.show()
return True
except Exception as e:
log_message(f"创建箱线图失败: {str(e)}")
return False
def distplot(data, rows=3, cols=4, bins=10, vars=None, hue=None, kind='hist', stat='count', shade=True,
figsize=(12, 5), color_style='all', alpha=0.7, subplots_adjust=(0.3, 0.2)):
"""创建分布图"""
try:
fig = plt.figure(figsize=figsize)
numerical_cols = data.select_dtypes(include=['float64', 'int64']).columns.tolist()
if not vars:
vars = numerical_cols
colors = get_colors(color_style)
ax_num = 1
for col in vars:
if col in numerical_cols and col != hue:
plt.subplot(rows, cols, ax_num)
col_data = data[col].dropna()
if kind == 'hist':
sns.histplot(data=data, x=col, bins=bins, color=np.random.choice(colors),
hue=hue, alpha=alpha, stat=stat)
elif kind == 'kde':
sns.kdeplot(data=data, x=col, color=np.random.choice(colors), alpha=alpha,
hue=hue, fill=shade)
elif kind == 'both':
sns.histplot(data=data, x=col, bins=bins, color=np.random.choice(colors),
alpha=alpha, hue=hue, stat='density')
sns.kdeplot(data=data, x=col, color='darkred', alpha=0.7, hue=hue, fill=False)
plt.xlabel(col)
ax_num += 1
plt.subplots_adjust(hspace=subplots_adjust[0], wspace=subplots_adjust[1])
plt.savefig('化学成分分布图.jpg', dpi=300, bbox_inches='tight')
plt.show()
return True
except Exception as e:
log_message(f"创建分布图失败: {str(e)}")
return False
def load_and_process_data(file_path):
"""加载和处理数据"""
try:
# 检查文件是否存在
if not os.path.exists(file_path):
log_message(f"错误: 文件 '{file_path}' 不存在")
return None
# 读取数据
data = pd.read_excel(file_path)
log_message(f"成功加载数据, 共 {len(data)} 行")
# 列名修复
col_renames = {
'表面风化化': '表面风化',
'采采样点风化类型': '采样点风化类型',
'样点风化类型': '采样点风化类型',
'总成分': '总含量'
}
new_columns = []
for col in data.columns:
if col in col_renames:
new_columns.append(col_renames[col])
else:
new_columns.append(col)
data.columns = new_columns
# 化学成分列重命名
rename_dict = {
'氧化硅(Si)': '二氧化硅(SiO2)',
'氧化锡(SnO)': '氧化锡(SnO2)',
'氧化硫(SO3)': '二氧化硫(SO2)',
'氧化亚铜(Cu2O)': '氧化亚铜(Cu2O)',
'氧化铜(CuO)': '氧化铜(CuO)',
'三氧化二铁(Fe2O3)': '三氧化二铁(Fe2O3)'
}
data = data.rename(columns=rename_dict)
# 删除总含量列
if '总含量' in data.columns:
data = data.drop('总含量', axis=1)
log_message("已删除'总含量'列")
# 添加缺失列的处理
required_cols = ['表面风化', '采样点风化类型', '类型']
for col in required_cols:
if col not in data.columns:
data[col] = np.nan
log_message(f"警告: 列 '{col}' 不存在，已创建空白列")
# 移除空白行
data.dropna(how='all', inplace=True)
log_message(f"处理后数据行数: {len(data)}")
return data
except Exception as e:
log_message(f"数据处理失败: {str(e)}")
traceback.print_exc()
return None
def select_subclass_features(data):
"""亚类划分特征选择"""
try:
# 分离高钾和铅钡玻璃数据
gaojia_data = data[data['类型'] == '高钾'].copy()
qianbai_data = data[data['类型'] == '铅钡'].copy()
log_message(f"高钾玻璃数据量: {len(gaojia_data)}")
log_message(f"铅钡玻璃数据量: {len(qianbai_data)}")
# 获取化学成分列
chem_cols = [col for col in data.columns if any(x in col for x in ['氧化', '二氧化', '化学'])]
log_message(f"找到 {len(chem_cols)} 个化学成分列")
# 高钾玻璃的特征选择
log_message("\n==== 高钾玻璃特征选择 ====")
if len(gaojia_data) > 0:
gaojia_x = gaojia_data[chem_cols]
gaojia_y = gaojia_data['采样点风化类型']
# 处理缺失值
for col in chem_cols:
if gaojia_x[col].isna().any():
median_val = gaojia_x[col].median()
gaojia_x[col].fillna(median_val, inplace=True)
# 特征重要性分析
model = RandomForestClassifier(random_state=42)
parameters = {'max_depth': range(1, 5), 'min_samples_leaf': [1, 2],
'criterion': ['gini', 'entropy'], 'min_impurity_decrease': [0.01, 0.02]}
grid_search = GridSearchCV(model, parameters, cv=min(5, len(gaojia_data)), n_jobs=-1)
grid_search.fit(gaojia_x, gaojia_y)
log_message(f'高钾玻璃特征选择精度: {grid_search.best_score_:.4f}')
log_message(f'最优参数: {grid_search.best_params_}')
best_model = grid_search.best_estimator_
best_model.fit(gaojia_x, gaojia_y)
# 特征重要性排序
gaojia_fea_df = pd.DataFrame({
'化学成分': chem_cols,
'特征重要性': best_model.feature_importances_
}).sort_values('特征重要性', ascending=False)
log_message("\n高钾玻璃特征重要性排序:")
log_message(gaojia_fea_df.head(10).to_string())
else:
log_message("警告: 没有高钾玻璃数据，跳过特征选择")
gaojia_fea_df = pd.DataFrame({'化学成分': [], '特征重要性': []})
# 铅钡玻璃的特征选择
log_message("\n==== 铅钡玻璃特征选择 ====")
if len(qianbai_data) > 0:
qianbai_x = qianbai_data[chem_cols]
qianbai_y = qianbai_data['采样点风化类型']
# 处理缺失值
for col in chem_cols:
if qianbai_x[col].isna().any():
median_val = qianbai_x[col].median()
qianbai_x[col].fillna(median_val, inplace=True)
# 特征重要性分析
grid_search = GridSearchCV(RandomForestClassifier(random_state=42),
parameters,
cv=min(5, len(qianbai_data)),
n_jobs=-1)
grid_search.fit(qianbai_x, qianbai_y)
log_message(f'铅钡玻璃特征选择精度: {grid_search.best_score_:.4f}')
log_message(f'最优参数: {grid_search.best_params_}')
best_model = grid_search.best_estimator_
best_model.fit(qianbai_x, qianbai_y)
# 特征重要性排序
qianbai_fea_df = pd.DataFrame({
'化学成分': chem_cols,
'特征重要性': best_model.feature_importances_
}).sort_values('特征重要性', ascending=False)
log_message("\n铅钡玻璃特征重要性排序:")
log_message(qianbai_fea_df.head(10).to_string())
else:
log_message("警告: 没有铅钡玻璃数据，跳过特征选择")
qianbai_fea_df = pd.DataFrame({'化学成分': [], '特征重要性': []})
return gaojia_data, qianbai_data, gaojia_fea_df, qianbai_fea_df, chem_cols
except Exception as e:
log_message(f"特征选择失败: {str(e)}")
traceback.print_exc()
return None, None, None, None, None
def optimize_features_and_cluster(gaojia_data, qianbai_data, gaojia_fea_df, qianbai_fea_df, chem_cols):
"""特征优化和聚类分析"""
try:
# 高钾玻璃优化的聚类
log_message("\n==== 高钾玻璃优化聚类 ====")
if len(gaojia_data) > 0:
def evaluate_gaojia(pred):
score = 0
for perm in permutations([0, 1]):
true_labels = gaojia_data['采样点风化类型'].replace({'未风化点': perm[0], '风化点': perm[1]})
score_ = f1_score(true_labels, pred, average='weighted')
score = max(score, score_)
return score
gaojia_fea_list = gaojia_fea_df['化学成分'].tolist()
best_score = 0
best_features = []
deleted_features = []
# 特征优化
for num_features in range(1, min(15, len(gaojia_fea_list))):
current_features = gaojia_fea_list[:num_features].copy()
for feat in deleted_features:
if feat in current_features:
current_features.remove(feat)
if not current_features:
continue
log_message(f"尝试特征数: {num_features}, 特征: {current_features}")
# 数据标准化
X = gaojia_data[current_features].values
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# 聚类分析
kmeans = KMeans(n_clusters=2, random_state=42, n_init=10)
cluster_labels = kmeans.fit_predict(X_scaled)
# 评估聚类效果
score = evaluate_gaojia(cluster_labels)
log_message(f"聚类评估得分: {score:.4f}")
if score > best_score:
best_score = score
best_features = current_features.copy()
log_message(f"新最佳得分: {score:.4f}，特征: {best_features}")
else:
last_feature = gaojia_fea_list[num_features - 1]
if last_feature not in deleted_features:
deleted_features.append(last_feature)
log_message(f"将特征 {last_feature} 添加到删除列表")
log_message(f"\n高钾玻璃最终选择的特征: {best_features}")
log_message(f"聚类评估得分: {best_score:.4f}")
# 最终聚类
if best_features:
X_final = gaojia_data[best_features].values
scaler_final = StandardScaler()
X_final_scaled = scaler_final.fit_transform(X_final)
kmeans_final = KMeans(n_clusters=2, random_state=42, n_init=10)
final_labels = kmeans_final.fit_predict(X_final_scaled)
gaojia_data['聚类标签'] = final_labels
# 保存聚类中心
cluster_centers = kmeans_final.cluster_centers_
gaojia_cluster_centers = pd.DataFrame(cluster_centers, columns=best_features)
gaojia_cluster_centers.index = ['亚类1', '亚类2']
else:
log_message("警告: 没有为高钾玻璃找到合适的聚类特征")
gaojia_cluster_centers = None
else:
log_message("没有高钾玻璃数据，跳过聚类")
gaojia_cluster_centers = None
best_features = []
# 铅钡玻璃优化的聚类
log_message("\n==== 铅钡玻璃优化聚类 ====")
if len(qianbai_data) > 0:
def evaluate_qianbai(pred):
score = 0
for perm in permutations([0, 1, 2]):
true_labels = qianbai_data['采样点风化类型'].replace({
'未风化点': perm[0],
'风化点': perm[1],
'严重风化点': perm[2]
})
score_ = f1_score(true_labels, pred, average='weighted')
score = max(score, score_)
return score
qianbai_fea_list = qianbai_fea_df['化学成分'].tolist()
best_score_qb = 0
best_features_qb = []
deleted_features_qb = []
# 特征优化
for num_features in range(1, min(15, len(qianbai_fea_list))):
current_features = qianbai_fea_list[:num_features].copy()
for feat in deleted_features_qb:
if feat in current_features:
current_features.remove(feat)
if not current_features:
continue
log_message(f"尝试特征数: {num_features}, 特征: {current_features}")
# 数据标准化
X = qianbai_data[current_features].values
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)
# 聚类分析
kmeans = KMeans(n_clusters=3, random_state=42, n_init=10)
cluster_labels = kmeans.fit_predict(X_scaled)
# 评估聚类效果
score = evaluate_qianbai(cluster_labels)
log_message(f"聚类评估得分: {score:.4f}")
if score > best_score_qb:
best_score_qb = score
best_features_qb = current_features.copy()
log_message(f"新最佳得分: {score:.4f}，特征: {best_features_qb}")
else:
last_feature = qianbai_fea_list[num_features - 1]
if last_feature not in deleted_features_qb:
deleted_features_qb.append(last_feature)
log_message(f"将特征 {last_feature} 添加到删除列表")
log_message(f"\n铅钡玻璃最终选择的特征: {best_features_qb}")
log_message(f"聚类评估得分: {best_score_qb:.4f}")
# 最终聚类
if best_features_qb:
X_final = qianbai_data[best_features_qb].values
scaler_final = StandardScaler()
X_final_scaled = scaler_final.fit_transform(X_final)
kmeans_final = KMeans(n_clusters=3, random_state=42, n_init=10)
final_labels = kmeans_final.fit_predict(X_final_scaled)
qianbai_data['聚类标签'] = final_labels
# 保存聚类中心
cluster_centers = kmeans_final.cluster_centers_
qianbai_cluster_centers = pd.DataFrame(cluster_centers, columns=best_features_qb)
qianbai_cluster_centers.index = ['亚类1', '亚类2', '亚类3']
else:
log_message("警告: 没有为铅钡玻璃找到合适的聚类特征")
qianbai_cluster_centers = None
else:
log_message("没有铅钡玻璃数据，跳过聚类")
qianbai_cluster_centers = None
best_features_qb = []
return gaojia_data, qianbai_data, best_features, best_features_qb, gaojia_cluster_centers, qianbai_cluster_centers
except Exception as e:
log_message(f"聚类优化失败: {str(e)}")
traceback.print_exc()
return None, None, [], [], None, None
def visualize_and_analyze_subclasses(gaojia_data, qianbai_data, gaojia_features, qianbai_features,
gaojia_cluster_centers, qianbai_cluster_centers):
"""亚类划分结果可视化与分析"""
try:
log_message("\n==== 亚类划分结果可视化与分析 ====")
# 高钾玻璃亚类划分可视化
if gaojia_data is not None and '聚类标签' in gaojia_data.columns:
plt.figure(figsize=(12, 8))
plt.suptitle("高钾玻璃亚类划分（基于聚类）")
# 3D散点图
ax1 = plt.subplot(121, projection='3d')
use_cols = gaojia_features[:3] if len(gaojia_features) >= 3 else gaojia_features
colors = get_colors('bright')
if use_cols:
for i in range(2): # 两个亚类
cluster_data = gaojia_data[gaojia_data['聚类标签'] == i]
label = f'亚类{i + 1}'
if len(use_cols) == 3:
ax1.scatter(cluster_data[use_cols[0]], cluster_data[use_cols[1]], cluster_data[use_cols[2]],
color=colors[i], label=label, s=50, alpha=0.7)
elif len(use_cols) == 2:
ax1.scatter(cluster_data[use_cols[0]], cluster_data[use_cols[1]],
np.zeros(len(cluster_data)),
color=colors[i], label=label, s=50, alpha=0.7)
else: # 只有1个特征
ax1.scatter(cluster_data[use_cols[0]],
np.zeros(len(cluster_data)),
np.zeros(len(cluster_data)),
color=colors[i], label=label, s=50, alpha=0.7)
if len(use_cols) >= 1: ax1.set_xlabel(use_cols[0])
if len(use_cols) >= 2: ax1.set_ylabel(use_cols[1])
if len(use_cols) >= 3: ax1.set_zlabel(use_cols[2])
ax1.legend()
else:
ax1.text(0.5, 0.5, "没有足够的特征进行可视化", ha='center', va='center')
# 箱线图比较亚类特征分布
ax2 = plt.subplot(122)
if gaojia_features:
box_data = gaojia_data.copy()
box_data['亚类'] = box_data['聚类标签'].apply(lambda x: f'亚类{x + 1}')
sns.boxplot(x='亚类', y=gaojia_features[0], data=box_data, palette=[colors[0], colors[1]])
plt.title(f'关键特征比较: {gaojia_features[0]}')
else:
ax2.text(0.5, 0.5, "没有可用特征", ha='center', va='center')
plt.tight_layout()
plt.savefig('高钾玻璃亚类划分结果.jpg', dpi=300)
plt.show()
else:
log_message("警告: 高钾玻璃聚类结果不可用，跳过可视化")
# 铅钡玻璃亚类划分可视化
if qianbai_data is not None and '聚类标签' in qianbai_data.columns:
plt.figure(figsize=(12, 8))
plt.suptitle("铅钡玻璃亚类划分（基于聚类）")
# 3D散点图
ax1 = plt.subplot(121, projection='3d')
use_cols = qianbai_features[:3] if len(qianbai_features) >= 3 else qianbai_features
colors = get_colors('bright')
if use_cols:
for i in range(3): # 三个亚类
cluster_data = qianbai_data[qianbai_data['聚类标签'] == i]
label = f'亚类{i + 1}'
if len(use_cols) == 3:
ax1.scatter(cluster_data[use_cols[0]], cluster_data[use_cols[1]], cluster_data[use_cols[2]],
color=colors[i], label=label, s=50, alpha=0.7)
elif len(use_cols) == 2:
ax1.scatter(cluster_data[use_cols[0]], cluster_data[use_cols[1]],
np.zeros(len(cluster_data)),
color=colors[i], label=label, s=50, alpha=0.7)
else: # 只有1个特征
ax1.scatter(cluster_data[use_cols[0]],
np.zeros(len(cluster_data)),
np.zeros(len(cluster_data)),
color=colors[i], label=label, s=50, alpha=0.7)
if len(use_cols) >= 1: ax1.set_xlabel(use_cols[0])
if len(use_cols) >= 2: ax1.set_ylabel(use_cols[1])
if len(use_cols) >= 3: ax1.set_zlabel(use_cols[2])
ax1.legend()
else:
ax1.text(0.5, 0.5, "没有足够的特征进行可视化", ha='center', va='center')
# 箱线图比较亚类特征分布
ax2 = plt.subplot(122)
if qianbai_features:
box_data = qianbai_data.copy()
box_data['亚类'] = box_data['聚类标签'].apply(lambda x: f'亚类{x + 1}')
sns.boxplot(x='亚类', y=qianbai_features[0], data=box_data, palette=colors[:3])
plt.title(f'关键特征比较: {qianbai_features[0]}')
else:
ax2.text(0.5, 0.5, "没有可用特征", ha='center', va='center')
plt.tight_layout()
plt.savefig('铅钡玻璃亚类划分结果.jpg', dpi=300)
plt.show()
else:
log_message("警告: 铅钡玻璃聚类结果不可用，跳过可视化")
# 合理性分析 - ANOVA检验特征显著差异
log_message("\n==== 合理性分析 - 亚类特征差异检验 ====")
# 高钾玻璃
if gaojia_data is not None and '聚类标签' in gaojia_data.columns and gaojia_features:
log_message("\n高钾玻璃:")
for i, feature in enumerate(gaojia_features[:3]):
try:
groups = [gaojia_data[gaojia_data['聚类标签'] == j][feature] for j in range(2)]
f_val, p_val = f_oneway(*groups)
log_message(f"{feature}: F值={f_val:.4f}, p值={p_val:.4f}{' (显著)' if p_val < 0.05 else ''}")
except Exception as e:
log_message(f"无法计算特征 {feature} 的ANOVA检验: {str(e)}")
# 铅钡玻璃
if qianbai_data is not None and '聚类标签' in qianbai_data.columns and qianbai_features:
log_message("\n铅钡玻璃:")
for i, feature in enumerate(qianbai_features[:3]):
try:
groups = [qianbai_data[qianbai_data['聚类标签'] == j][feature] for j in range(3)]
f_val, p_val = f_oneway(*groups)
log_message(f"{feature}: F值={f_val:.4f}, p值={p_val:.4f}{' (显著)' if p_val < 0.05 else ''}")
except Exception as e:
log_message(f"无法计算特征 {feature} 的ANOVA检验: {str(e)}")
# 保存聚类中心
try:
if gaojia_cluster_centers is not None:
gaojia_cluster_centers.to_excel("高钾玻璃亚类聚类中心.xlsx")
log_message("高钾玻璃聚类中心已保存")
if qianbai_cluster_centers is not None:
qianbai_cluster_centers.to_excel("铅钡玻璃亚类聚类中心.xlsx")
log_message("铅钡玻璃聚类中心已保存")
except Exception as e:
log_message(f"保存聚类中心失败: {str(e)}")
return gaojia_data, qianbai_data
except Exception as e:
log_message(f"可视化与分析失败: {str(e)}")
traceback.print_exc()
return None, None
def main():
"""主函数"""
try:
# 文件路径
file_path = r"D:\BianChen\python_studycode\tf_env\玻璃\分析结果.xlsx"
log_message(f"开始执行分析，数据文件: {file_path}")
# 1. 加载和处理数据
log_message("\n==== 步骤1: 数据加载与预处理 ====")
data = load_and_process_data(file_path)
if data is None or len(data) == 0:
log_message("错误: 数据处理失败或没有有效数据，程序终止")
return
# 初始数据可视化
log_message("\n执行初始数据可视化...")
plt.figure(figsize=(8, 6))
sns.countplot(x='类型', data=data, palette='Set2')
plt.title('玻璃类型分布')
plt.savefig('玻璃类型分布.jpg', dpi=300)
plt.show()
# 2. 亚类划分的特征选择
log_message("\n==== 步骤2: 亚类划分特征选择 ====")
gaojia_data, qianbai_data, gaojia_fea_df, qianbai_fea_df, chem_cols = select_subclass_features(data)
if gaojia_fea_df is not None and not gaojia_fea_df.empty:
gaojia_fea_df.to_excel("高钾玻璃特征重要性.xlsx", index=False)
log_message("高钾玻璃特征重要性已保存")
if qianbai_fea_df is not None and not qianbai_fea_df.empty:
qianbai_fea_df.to_excel("铅钡玻璃特征重要性.xlsx", index=False)
log_message("铅钡玻璃特征重要性已保存")
# 3. 聚类和优化特征选择
log_message("\n==== 步骤3: 特征优化与亚类聚类 ====")
(gaojia_with_subclasses, qianbai_with_subclasses,
gaojia_features, qianbai_features,
gaojia_centers, qianbai_centers) = optimize_features_and_cluster(
gaojia_data, qianbai_data, gaojia_fea_df, qianbai_fea_df, chem_cols
)
log_message(f"高钾玻璃最终使用特征: {gaojia_features}")
log_message(f"铅钡玻璃最终使用特征: {qianbai_features}")
# 4. 亚类划分的可视化与分析
log_message("\n==== 步骤4: 亚类划分可视化与分析 ====")
gaojia_final, qianbai_final = visualize_and_analyze_subclasses(
gaojia_with_subclasses, qianbai_with_subclasses,
gaojia_features, qianbai_features,
gaojia_centers, qianbai_centers
)
# 5. 保存最终结果
log_message("\n==== 步骤5: 保存结果 ====")
try:
if gaojia_final is not None and not gaojia_final.empty:
gaojia_final.to_excel("高钾玻璃亚类划分结果.xlsx", index=False)
log_message("高钾玻璃亚类划分结果已保存")
if qianbai_final is not None and not qianbai_final.empty:
qianbai_final.to_excel("铅钡玻璃亚类划分结果.xlsx", index=False)
log_message("铅钡玻璃亚类划分结果已保存")
except Exception as e:
log_message(f"保存最终结果失败: {str(e)}")
log_message("\n亚类划分分析完成！")
except Exception as e:
log_message(f"程序执行出错: {str(e)}")
traceback.print_exc()
if __name__ == "__main__":
main()
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