Update repo

scripts/data.ipynb

@@ -0,0 +1,215 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "564cb208df3e42778a1f988e0e077eee",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading:   0%|          | 0.00/938 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using custom data configuration ejschwartz--oo-method-test-new-8eaca399917e96f5\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading and preparing dataset csv/default (download: 21.72 MiB, generated: 85.52 MiB, post-processed: Unknown size, total: 107.25 MiB) to /home/eschwartz/.cache/huggingface/datasets/ejschwartz___parquet/ejschwartz--oo-method-test-new-8eaca399917e96f5/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec...\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bb25d525ffb343779353edc479da48c6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading data files:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "208fc0a559044aab99013e47a81515ad",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading data:   0%|          | 0.00/22.8M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "19dbc119d6654a21b262770049848aae",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Extracting data files:   0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7b9ac6103de94be39157b3de21b49a3f",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "0 tables [00:00, ? tables/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Dataset parquet downloaded and prepared to /home/eschwartz/.cache/huggingface/datasets/ejschwartz___parquet/ejschwartz--oo-method-test-new-8eaca399917e96f5/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec. Subsequent calls will reuse this data.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "f2ab6ee941a24055aa309f89d1cace8d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/1 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import datasets\n",
+    "ds = datasets.load_dataset(\"ejschwartz/oo-method-test-new\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading cached processed dataset at /home/eschwartz/.cache/huggingface/datasets/ejschwartz___parquet/ejschwartz--oo-method-test-new-8eaca399917e96f5/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec/cache-2cacd54c29fd0da4.arrow\n",
+      "Loading cached processed dataset at /home/eschwartz/.cache/huggingface/datasets/ejschwartz___parquet/ejschwartz--oo-method-test-new-8eaca399917e96f5/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec/cache-aae4742ba8c97557.arrow\n",
+      "Loading cached processed dataset at /home/eschwartz/.cache/huggingface/datasets/ejschwartz___parquet/ejschwartz--oo-method-test-new-8eaca399917e96f5/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec/cache-65c638a5756f05c5.arrow\n",
+      "Loading cached processed dataset at /home/eschwartz/.cache/huggingface/datasets/ejschwartz___parquet/ejschwartz--oo-method-test-new-8eaca399917e96f5/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec/cache-e13e1e6cd866ea19.arrow\n",
+      "Loading cached processed dataset at /home/eschwartz/.cache/huggingface/datasets/ejschwartz___parquet/ejschwartz--oo-method-test-new-8eaca399917e96f5/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec/cache-8a6bb80fc2a77a30.arrow\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e7a6a640d0214fec87091e84af1abed9",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/84 [00:00<?, ?ba/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "bc64279aa0ee488f8d7e22b472524093",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/84 [00:00<?, ?ba/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Loading cached processed dataset at /home/eschwartz/.cache/huggingface/datasets/ejschwartz___parquet/ejschwartz--oo-method-test-new-8eaca399917e96f5/0.0.0/2a3b91fbd88a2c90d1dbbb32b460cf621d31bd5b05b934492fdef7d8d6f236ec/cache-5dd37e108f928473.arrow\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'2008': 27454,\n",
+       " '2010': 3691,\n",
+       " '2012': 5590,\n",
+       " '2013': 5908,\n",
+       " '2015': 9719,\n",
+       " '2017': 9919,\n",
+       " '2019': 10534,\n",
+       " '2023': 0}"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "{year: len(ds.filter(lambda r: year in r[\"Binary\"])['combined']) for year in [\"2008\", \"2010\", \"2012\", \"2013\", \"2015\", \"2017\", \"2019\", \"2022\"]}"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.8"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

scripts/gen-training.py

@@ -103,9 +103,6 @@ for addr, typ, name in parsed_data:
 
     df = df.append({'Binary': bname, 'Addr': addr, 'Name': name, 'Type': typ, 'Disassembly': dis}, ignore_index=True)
     
-    if False:
-        df.to_csv(oname, index=False)
-    
 df.to_csv(oname, index=False)
 
 #with open(jname, "w") as f: