From 6d9db64a48095fb226b73c1fb69571928897975c Mon Sep 17 00:00:00 2001
From: mhz <cxyoz@outlook.com>
Date: Wed, 21 Aug 2024 10:26:02 +0200
Subject: [PATCH] explore the 201 space script

---
 graph_dit/exp_201/main.py | 82 +++++++++++++++++++++++++++++++++++++++
 1 file changed, 82 insertions(+)
 create mode 100644 graph_dit/exp_201/main.py

diff --git a/graph_dit/exp_201/main.py b/graph_dit/exp_201/main.py
new file mode 100644
index 0000000..2cad786
--- /dev/null
+++ b/graph_dit/exp_201/main.py
@@ -0,0 +1,82 @@
+
+import matplotlib.pyplot as plt
+import pandas as pd
+from nas_201_api import NASBench201API as API
+from naswot.score_networks import get_nasbench201_idx_score
+from naswot import datasets as dt
+from naswot import nasspace
+
+class Args():
+    pass
+args = Args()
+args.trainval = True
+args.augtype = 'none'
+args.repeat = 1
+args.score = 'hook_logdet'
+args.sigma = 0.05
+args.nasspace = 'nasbench201'
+args.batch_size = 128
+args.GPU = '0'
+args.dataset = 'cifar10'
+args.api_loc = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
+args.data_loc = '../cifardata/'
+args.seed = 777
+args.init = ''
+args.save_loc = 'results'
+args.save_string = 'naswot'
+args.dropout = False
+args.maxofn = 1
+args.n_samples = 100
+args.n_runs = 500
+args.stem_out_channels = 16
+args.num_stacks = 3
+args.num_modules_per_stack = 3
+args.num_labels = 1
+searchspace = nasspace.get_search_space(args)
+train_loader = dt.get_data(args.dataset, args.data_loc, args.trainval, args.batch_size, args.augtype, args.repeat, args)
+device = torch.device('cuda:2')
+
+
+         
+# source = '/nfs/data3/hanzhang/nasbenchDiT/graph_dit/NAS-Bench-201-v1_1-096897.pth'
+# api = API(source)
+
+
+
+# 示例百分数列表，精确到小数点后两位
+# percentages = [5.12, 15.78, 25.43, 35.22, 45.99, 55.34, 65.12, 75.68, 85.99, 95.25, 23.45, 12.34, 37.89, 58.67, 64.23, 72.15, 81.76, 99.99, 42.11, 61.58, 77.34, 14.56]
+percentages = []
+
+len_201 = 15625
+
+for i in range(len_201):
+    percentage = get_nasbench201_idx_score(i, train_loader, searchspace, args, device)
+    percentages.append(percentage)
+
+# 定义10%区间
+bins = [i for i in range(0, 101, 10)]
+
+# 对数据进行分箱，计算每个区间的数据量
+hist, bin_edges = pd.cut(percentages, bins=bins, right=False, retbins=True, include_lowest=True)
+bin_counts = hist.value_counts().sort_index()
+
+total_counts = len(percentages)
+percentages_in_bins = (bin_counts / total_counts) * 100
+
+# 绘制条形图
+plt.figure(figsize=(10, 6))
+bars = plt.bar(bin_counts.index.astype(str), bin_counts.values, width=0.9, color='skyblue')
+
+for bar, percentage in zip(bars, percentages_in_bins):
+    plt.text(bar.get_x() + bar.get_width() / 2, bar.get_height(),
+            f'{percentage:.2f}%', ha='center', va='bottom')
+
+# 添加标题和标签
+plt.title('Distribution of Percentages in 10% Intervals')
+plt.xlabel('Percentage Interval')
+plt.ylabel('Count')
+
+# 显示图表
+plt.xticks(rotation=45)
+plt.savefig('barplog.png')
+