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.ipynb_checkpoints/makedata-checkpoint.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7d404704-49f6-4f24-8341-a76b724a2d5d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "7295fc96-e8d2-48c6-8dde-9d6b93d6206b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "fp = \"/home/ma-user/work/lilong/OmniSQL/train_and_evaluate/data/processed/omni/dev/dev_bird.jsonl\"\n",
+    "# \"/home/ma-user/work/lilong/OmniSQL/train_and_evaluate/data/processed/omni/train_half_pi0/Omni_Q_result.jsonl\"\n",
+    "data = [json.loads(line) for line in open(fp).readlines()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "7bb5a56f-f33c-4171-8bc4-e5e3cbd71986",
+   "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>data_source</th>\n",
+       "      <th>prompt</th>\n",
+       "      <th>ability</th>\n",
+       "      <th>reward_model</th>\n",
+       "      <th>extra_info</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>zwhe99/DeepMath-103K</td>\n",
+       "      <td>[{'content': 'A conversation between User and ...</td>\n",
+       "      <td>math</td>\n",
+       "      <td>{'ground_truth': 'Yes', 'style': 'rule'}</td>\n",
+       "      <td>{'index': 0, 'question': 'Let $G$ be a finite ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>zwhe99/DeepMath-103K</td>\n",
+       "      <td>[{'content': 'A conversation between User and ...</td>\n",
+       "      <td>math</td>\n",
+       "      <td>{'ground_truth': '8', 'style': 'rule'}</td>\n",
+       "      <td>{'index': 1, 'question': 'Let $l,$ $m,$ and $n...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>zwhe99/DeepMath-103K</td>\n",
+       "      <td>[{'content': 'A conversation between User and ...</td>\n",
+       "      <td>math</td>\n",
+       "      <td>{'ground_truth': '8.45', 'style': 'rule'}</td>\n",
+       "      <td>{'index': 2, 'question': 'A triangular region ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>zwhe99/DeepMath-103K</td>\n",
+       "      <td>[{'content': 'A conversation between User and ...</td>\n",
+       "      <td>math</td>\n",
+       "      <td>{'ground_truth': '1', 'style': 'rule'}</td>\n",
+       "      <td>{'index': 3, 'question': 'Find the residue of ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>zwhe99/DeepMath-103K</td>\n",
+       "      <td>[{'content': 'A conversation between User and ...</td>\n",
+       "      <td>math</td>\n",
+       "      <td>{'ground_truth': 'B', 'style': 'rule'}</td>\n",
+       "      <td>{'index': 4, 'question': 'In the rhombus \\(ABC...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            data_source                                             prompt  \\\n",
+       "0  zwhe99/DeepMath-103K  [{'content': 'A conversation between User and ...   \n",
+       "1  zwhe99/DeepMath-103K  [{'content': 'A conversation between User and ...   \n",
+       "2  zwhe99/DeepMath-103K  [{'content': 'A conversation between User and ...   \n",
+       "3  zwhe99/DeepMath-103K  [{'content': 'A conversation between User and ...   \n",
+       "4  zwhe99/DeepMath-103K  [{'content': 'A conversation between User and ...   \n",
+       "\n",
+       "  ability                               reward_model  \\\n",
+       "0    math   {'ground_truth': 'Yes', 'style': 'rule'}   \n",
+       "1    math     {'ground_truth': '8', 'style': 'rule'}   \n",
+       "2    math  {'ground_truth': '8.45', 'style': 'rule'}   \n",
+       "3    math     {'ground_truth': '1', 'style': 'rule'}   \n",
+       "4    math     {'ground_truth': 'B', 'style': 'rule'}   \n",
+       "\n",
+       "                                          extra_info  \n",
+       "0  {'index': 0, 'question': 'Let $G$ be a finite ...  \n",
+       "1  {'index': 1, 'question': 'Let $l,$ $m,$ and $n...  \n",
+       "2  {'index': 2, 'question': 'A triangular region ...  \n",
+       "3  {'index': 3, 'question': 'Find the residue of ...  \n",
+       "4  {'index': 4, 'question': 'In the rhombus \\(ABC...  "
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "3c037aec-7d89-42b6-80f4-fe80447ec922",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# def make_map_fn(split, data_source):\n",
+    "system_prompt = \"You are a helpful assistant.\"\n",
+    "def process_fn(example, idx, split):\n",
+    "    question = example.get('question')\n",
+    "    answer = str(example.get('sql'))\n",
+    "    db = example.get('db_id')\n",
+    "    data = {\n",
+    "        \"data_source\": 'nl2sql',\n",
+    "        \"prompt\": [\n",
+    "        {\n",
+    "            \"role\": \"system\",\n",
+    "            \"content\": system_prompt\n",
+    "        },\n",
+    "        {\n",
+    "            \"role\": \"user\",\n",
+    "            \"content\": question\n",
+    "        }],\n",
+    "        \"ability\": \"nl2sql\",\n",
+    "        \"reward_model\": {\n",
+    "            \"style\": \"rule\",\n",
+    "            \"ground_truth\": answer\n",
+    "        },\n",
+    "        \"extra_info\": {\n",
+    "            'split': split,\n",
+    "            'index': idx,\n",
+    "            'question': question,\n",
+    "            'gt_sql': answer,\n",
+    "            'db_id': db\n",
+    "        }\n",
+    "    }\n",
+    "    return data\n",
+    "\n",
+    "        # return process_fn"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "fef55e7f-a359-4cce-871d-31b95d13e330",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import datasets\n",
+    "fp='/home/ma-user/work/haozhe/workspace/verl-tool/data/deep_math_tool_v9/train.parquet'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "9f58e2d1-0fcf-4855-b1cc-413cc4c40e4c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nd = [process_fn(entry,idx,'dev') for idx,entry in enumerate(data)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "04071ed9-2da7-40c7-91f8-5d366902ef70",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ndf = pd.DataFrame(nd)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "9dcc2cf9-cd4d-47e2-bfa0-daad4cd0a11a",
+   "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>data_source</th>\n",
+       "      <th>prompt</th>\n",
+       "      <th>ability</th>\n",
+       "      <th>reward_model</th>\n",
+       "      <th>extra_info</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>nl2sql</td>\n",
+       "      <td>[{'role': 'system', 'content': 'You are a help...</td>\n",
+       "      <td>nl2sql</td>\n",
+       "      <td>{'style': 'rule', 'ground_truth': 'SELECT `Fre...</td>\n",
+       "      <td>{'split': 'dev', 'index': 0, 'question': 'Task...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>nl2sql</td>\n",
+       "      <td>[{'role': 'system', 'content': 'You are a help...</td>\n",
+       "      <td>nl2sql</td>\n",
+       "      <td>{'style': 'rule', 'ground_truth': 'SELECT `Fre...</td>\n",
+       "      <td>{'split': 'dev', 'index': 1, 'question': 'Task...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>nl2sql</td>\n",
+       "      <td>[{'role': 'system', 'content': 'You are a help...</td>\n",
+       "      <td>nl2sql</td>\n",
+       "      <td>{'style': 'rule', 'ground_truth': 'SELECT T2.Z...</td>\n",
+       "      <td>{'split': 'dev', 'index': 2, 'question': 'Task...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>nl2sql</td>\n",
+       "      <td>[{'role': 'system', 'content': 'You are a help...</td>\n",
+       "      <td>nl2sql</td>\n",
+       "      <td>{'style': 'rule', 'ground_truth': 'SELECT T2.M...</td>\n",
+       "      <td>{'split': 'dev', 'index': 3, 'question': 'Task...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>nl2sql</td>\n",
+       "      <td>[{'role': 'system', 'content': 'You are a help...</td>\n",
+       "      <td>nl2sql</td>\n",
+       "      <td>{'style': 'rule', 'ground_truth': 'SELECT T2.P...</td>\n",
+       "      <td>{'split': 'dev', 'index': 4, 'question': 'Task...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  data_source                                             prompt ability  \\\n",
+       "0      nl2sql  [{'role': 'system', 'content': 'You are a help...  nl2sql   \n",
+       "1      nl2sql  [{'role': 'system', 'content': 'You are a help...  nl2sql   \n",
+       "2      nl2sql  [{'role': 'system', 'content': 'You are a help...  nl2sql   \n",
+       "3      nl2sql  [{'role': 'system', 'content': 'You are a help...  nl2sql   \n",
+       "4      nl2sql  [{'role': 'system', 'content': 'You are a help...  nl2sql   \n",
+       "\n",
+       "                                        reward_model  \\\n",
+       "0  {'style': 'rule', 'ground_truth': 'SELECT `Fre...   \n",
+       "1  {'style': 'rule', 'ground_truth': 'SELECT `Fre...   \n",
+       "2  {'style': 'rule', 'ground_truth': 'SELECT T2.Z...   \n",
+       "3  {'style': 'rule', 'ground_truth': 'SELECT T2.M...   \n",
+       "4  {'style': 'rule', 'ground_truth': 'SELECT T2.P...   \n",
+       "\n",
+       "                                          extra_info  \n",
+       "0  {'split': 'dev', 'index': 0, 'question': 'Task...  \n",
+       "1  {'split': 'dev', 'index': 1, 'question': 'Task...  \n",
+       "2  {'split': 'dev', 'index': 2, 'question': 'Task...  \n",
+       "3  {'split': 'dev', 'index': 3, 'question': 'Task...  \n",
+       "4  {'split': 'dev', 'index': 4, 'question': 'Task...  "
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ndf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "7130bc5b-4aa3-4860-9708-4ade22affb54",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ndf.to_parquet('dev.parquet')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "id": "c5f24e0e-c588-4650-87f4-4edacc51e85d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'SELECT movie_title, movie_release_year FROM movies ORDER BY LENGTH(movie_popularity) DESC LIMIT 1'"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data[0]['sql']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "ebca5d83-24c1-46fa-8f99-17facbad4281",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import requests\n",
+    "import json \n",
+    "\n",
+    "safe_payload = json.load(open(\"/home/ma-user/work/haozhe/workspace/verl-tool/action_logs2.json\"))\n",
+    "url = \"http://127.0.0.1:30978/get_observation\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "274c5d97-663b-4ba0-8dd5-77d7f9157ffe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "resp = requests.post(url, json=safe_payload)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "3f958278-9d8a-45ac-82bf-d98268e29b5a",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'To answer the question \"Among the movies from France, how many of them are drama?\", we need to follow these steps:\\n\\n1. Identify the movies from France.\\n2. Filter those movies to find the ones that are drama.\\n\\nHere\\'s the step-by-step process:\\n\\n1. **Identify the movies from France:**\\n   - We need to join the `movies` table with the `movies2directors` table to get the movies that have a director from France.\\n   - We will use the `country` column from the `movies` table to filter for movies from France.\\n\\n2. **Filter the drama movies:**\\n   - Once we have the movies from France, we need to filter those movies to find the ones that are drama.\\n\\nLet\\'s write the SQL query to achieve this:\\n\\n```sql\\n-- Step 1: Identify the movies from France\\nWITH france_movies AS (\\n    SELECT m.movieid\\n    FROM movies m\\n    JOIN movies2directors md ON m.movieid = md.movieid\\n    JOIN directors d ON md.directorid = d.directorid\\n    WHERE d.country = \\'France\\'\\n)\\n\\n-- Step 2: Filter the drama movies\\nSELECT COUNT(*) AS drama_movies_count\\nFROM france_movies\\nJOIN movies2actors ma ON france_movies.movieid = ma.movieid\\nWHERE ma.genre = \\'Drama\\';\\n```\\n\\n### Explanation:\\n1. **WITH clause:**\\n   - The `WITH` clause is used to define a temporary result set, which we call `france_movies`.\\n   - This temporary result set contains the movie IDs of movies from France.\\n\\n2. **Main query:**\\n   - We join the `movies` table with the `movies2directors` table to get the movies that have a director from France.\\n   - We then join the `movies2directors` table with the `directors` table to get the country of the director.\\n   - We filter the movies to find those with a country of \\'France\\'.\\n   - Finally, we join the `movies2actors` table to get the genre of the movies.\\n   - We count the number of drama movies from France.\\n\\nThis query will return the count of drama movies from France.<|im_end|>'"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "safe_payload['actions'][3]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "dee4debf-4a3b-4dda-8bd1-d9112a683fda",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       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+   "id": "5c3518f4-8d21-4fdb-a915-eba8831b5be9",
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+ "metadata": {
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+   "language": "python",
+   "name": "base"
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+}

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