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fixed notebooks
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@Alphonsce
Alphonsce committed May 9, 2024
1 parent 68d1607 commit b472415
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Showing 12 changed files with 69 additions and 3,169 deletions.
21 changes: 13 additions & 8 deletions plots/for_paper/for_paper.ipynb → plots/create_csvs/for_paper.ipynb
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"cells": [
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -11,14 +11,15 @@
"\n",
"import seaborn as sns\n",
"import numpy as np\n",
"import os\n",
"\n",
"import matplotlib.pyplot as plt\n",
"sns.set_style(\"white\", {\"grid.color\": \".6\", \"grid.linestyle\": \":\"})"
]
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
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},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -68,7 +69,7 @@
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -140,41 +141,45 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"for project in detection_projects:\n",
" os.makedirs(\"./detection\", exist_ok=True)\n",
" get_runs_df(project, resolution=True, runtime_limit=4 * 3600).to_csv(f\"./detection/{project}.csv\", index=False, columns=detection_cols)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"for project in fid_projects:\n",
" os.makedirs(\"./fid\", exist_ok=True)\n",
" get_runs_df(project).sort_values(by=\"name\", ascending=False).to_csv(f\"./fid/{project}.csv\", index=False, columns=fid_cols)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"for project in stable_signature_detection_projects:\n",
" os.makedirs(\"./detection\", exist_ok=True)\n",
" get_runs_df_stable_sig(project, runtime_limit=0).to_csv(f\"./detection/{project}.csv\", index=False, columns=stable_signature_detection_cols)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"clip_different_message = \"clip_different_msg\"\n",
"os.makedirs(\"./detection\", exist_ok=True)\n",
"get_runs_df(clip_different_message, runtime_limit=0, resolution=True).sort_values(by=\"name\", ascending=False).to_csv(f\"./detection/{clip_different_message}.csv\", index=False, columns=detection_cols)"
]
}
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337 changes: 0 additions & 337 deletions plots/diff_attacks/plotting.ipynb
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166 changes: 25 additions & 141 deletions ...stness/for_paper/diff_vae_for_paper.ipynb → ...nerative_attacks/diff_vae_for_paper.ipynb
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Expand Up @@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 166,
"execution_count": 25,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -20,7 +20,7 @@
},
{
"cell_type": "code",
"execution_count": 167,
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -49,7 +49,7 @@
},
{
"cell_type": "code",
"execution_count": 168,
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -61,7 +61,7 @@
},
{
"cell_type": "code",
"execution_count": 169,
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -72,7 +72,7 @@
},
{
"cell_type": "code",
"execution_count": 170,
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -81,15 +81,12 @@
"acc = df_msg[\"acc\"]\n",
"auc = df_msg[\"auc\"]\n",
"\n",
"detect_metrics = ['''TPR@1%FPR''', \"acc\", \"auc\"]\n",
"positions = [\n",
" [0, 1], [2, 0], [2, 2]\n",
"]"
"detect_metrics = ['''TPR@1%FPR''', \"acc\", \"auc\"]"
]
},
{
"cell_type": "code",
"execution_count": 171,
"execution_count": 30,
"metadata": {},
"outputs": [
{
Expand All @@ -107,7 +104,7 @@
"fig, ax = plt.subplots(figsize=(8, 6))\n",
"\n",
"\n",
"for metric, pos in zip(detect_metrics, positions):\n",
"for metric in detect_metrics:\n",
" # plt.subplot2grid((4, 4), pos, rowspan=2, colspan=2)\n",
"\n",
" ax.plot(\n",
Expand Down Expand Up @@ -146,68 +143,16 @@
},
{
"cell_type": "code",
"execution_count": 172,
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>TPR@1%FPR</th>\n",
" <th>acc</th>\n",
" <th>auc</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>490</td>\n",
" <td>0.099448</td>\n",
" <td>0.685083</td>\n",
" <td>0.690867</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name TPR@1%FPR acc auc\n",
"1 490 0.099448 0.685083 0.690867"
]
},
"execution_count": 172,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"df_detect = pd.read_csv(\"long_diff.csv\")[[\"Name\", '''TPR@1%FPR''', \"acc\", \"auc\"]].iloc[1::2, :]\n",
"\n",
"df_detect[\"Name\"] = df_detect[\"Name\"].map(lambda x: int(x.replace(\"s_\", \"\")))\n",
"df_detect.head(1)"
"df_detect = get_runs_df(\"new_big_diff_attacks\")[['''TPR@1%FPR''', \"acc\", \"auc\"]].iloc[1::2, :]"
]
},
{
"cell_type": "code",
"execution_count": 173,
"execution_count": 32,
"metadata": {},
"outputs": [
{
Expand All @@ -227,8 +172,7 @@
"fig, ax = plt.subplots(figsize=(8, 6))\n",
"\n",
"\n",
"for metric, pos in zip(detect_metrics, positions):\n",
" # plt.subplot2grid((4, 4), pos, rowspan=2, colspan=2)\n",
"for metric in detect_metrics:\n",
"\n",
" ax.plot(\n",
" steps, df_detect[metric], '--o', label=metric, markersize=markersize\n",
Expand Down Expand Up @@ -275,7 +219,7 @@
},
{
"cell_type": "code",
"execution_count": 174,
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -286,7 +230,7 @@
},
{
"cell_type": "code",
"execution_count": 175,
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -295,15 +239,12 @@
"acc = df_msg[\"acc\"]\n",
"auc = df_msg[\"auc\"]\n",
"\n",
"detect_metrics = ['''TPR@1%FPR''', \"acc\", \"auc\"]\n",
"positions = [\n",
" [0, 1], [2, 0], [2, 2]\n",
"]"
"detect_metrics = ['''TPR@1%FPR''', \"acc\", \"auc\"]"
]
},
{
"cell_type": "code",
"execution_count": 176,
"execution_count": 35,
"metadata": {},
"outputs": [
{
Expand All @@ -320,8 +261,7 @@
"source": [
"fig, ax = plt.subplots(figsize=(8, 6))\n",
"\n",
"for metric, pos in zip(detect_metrics, positions):\n",
" # plt.subplot2grid((4, 4), pos, rowspan=2, colspan=2)\n",
"for metric in detect_metrics:\n",
"\n",
" ax.plot(\n",
" quality, df_msg[metric], '--o', label=metric, markersize=markersize\n",
Expand Down Expand Up @@ -355,80 +295,25 @@
},
{
"cell_type": "code",
"execution_count": 177,
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>TPR@1%FPR</th>\n",
" <th>acc</th>\n",
" <th>auc</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>8</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name TPR@1%FPR acc auc\n",
"0 8 1.0 1.0 1.0"
]
},
"execution_count": 177,
"metadata": {},
"output_type": "execute_result"
}
],
"outputs": [],
"source": [
"df_detect = pd.read_csv(\"2018_detect.csv\")[[\"Name\", '''TPR@1%FPR''', \"acc\", \"auc\"]]\n",
"\n",
"df_detect[\"Name\"] = df_detect[\"Name\"].map(lambda x: int(x.replace(\"vae_2018_\", \"\")))\n",
"df_detect.head(1)"
"df_detect = get_runs_df(\"vae_2018_attacks\")[['''TPR@1%FPR''', \"acc\", \"auc\"]]"
]
},
{
"cell_type": "code",
"execution_count": 178,
"execution_count": 37,
"metadata": {},
"outputs": [],
"source": [
"detect_metrics = ['''TPR@1%FPR''', \"acc\", \"auc\"]\n",
"positions = [\n",
" [0, 1], [2, 0], [2, 2]\n",
"]"
"detect_metrics = ['''TPR@1%FPR''', \"acc\", \"auc\"]"
]
},
{
"cell_type": "code",
"execution_count": 179,
"execution_count": 38,
"metadata": {},
"outputs": [
{
Expand All @@ -445,8 +330,7 @@
"source": [
"fig, ax = plt.subplots(figsize=(8, 6))\n",
"\n",
"for metric, pos in zip(detect_metrics, positions):\n",
" # plt.subplot2grid((4, 4), pos, rowspan=2, colspan=2)\n",
"for metric in detect_metrics:\n",
"\n",
" ax.plot(\n",
" quality, df_detect[metric], '--o', label=metric, markersize=markersize\n",
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