{"behavior":"degenerate_repetition","label_filter":null,"limit":10,"next_offset":46946,"offset":46936,"previous_offset":null,"rows":[{"assistant_initiates":false,"asst_text":"\u0414\u043b\u044f \u043e\u043a\u0440\u0443\u0433\u043b\u0435\u043d\u0438\u044f \u0434\u043e 2 \u0437\u043d\u0430\u043a\u043e\u0432 \u043f\u043e\u0441\u043b\u0435 \u0437\u0430\u043f\u044f\u0442\u043e\u0439 \u043c\u043e\u0436\u043d\u043e \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u043c\u0435\u0442\u043e\u0434 `.round()` \u0438\u0437 \u0431\u0438\u0431\u043b\u0438\u043e\u0442\u0435\u043a\u0438 PySpark:\n\n```\nfrom pyspark.sql.functions import round\n\ndf_with_rounded_values = df.select(round('column_name', 2))\n```\n\n\u0417\u0430\u043c\u0435\u043d\u0438\u0442\u0435 `column_name` \u043d\u0430 \u0438\u043c\u044f \u0441\u0442\u043e\u043b\u0431\u0446\u0430, \u043a\u043e\u0442\u043e\u0440\u044b\u0439 \u0432\u044b \u0445\u043e\u0442\u0438\u0442\u0435 \u043e\u043a\u0440\u0443\u0433\u043b\u0438\u0442\u044c. \u0420\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u043e\u043c \u0431\u0443\u0434\u0435\u0442 \u043d\u043e\u0432\u044b\u0439 DataFrame \u0441 \u043e\u043a\u0440\u0443\u0433\u043b\u0435\u043d\u043d\u044b\u043c\u0438 \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f\u043c\u0438 \u0434\u043b\u044f \u0432\u044b\u0431\u0440\u0430\u043d\u043d\u043e\u0433\u043e \u0441\u0442\u043e\u043b\u0431\u0446\u0430.","behavior":"degenerate_repetition","conversation_hash":"1d7fa987a35b2d66b3fe38c9960759f4","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-3.5-turbo-0301","n_chars":402,"n_turns_in_conversation":4,"n_words":50,"source_row_offset":46936,"source_shard":"turns-00003.parquet","timestamp":"Sun, 28 May 2023 12:02:28 GMT","turn_id":"337df57df65b66c0d892b25f","turn_index":2,"user_text":"\u041e\u043a\u0440\u0443\u0433\u043b\u0438\u0442\u044c \u0434\u043e 2\u0445 \u0437\u043d\u0430\u043a\u043e\u0432 \u043f\u043e\u0441\u043b\u0435 \u0437\u0430\u043f\u044f\u0442\u043e\u0439 ? "},{"assistant_initiates":false,"asst_text":"\u0414\u043b\u044f \u043e\u043a\u0440\u0443\u0433\u043b\u0435\u043d\u0438\u044f \u0441\u0442\u0430\u043d\u0434\u0430\u0440\u0442\u043d\u043e\u0433\u043e \u043e\u0442\u043a\u043b\u043e\u043d\u0435\u043d\u0438\u044f \u0434\u043e \u0434\u0432\u0443\u0445 \u0437\u043d\u0430\u043a\u043e\u0432 \u043f\u043e\u0441\u043b\u0435 \u0437\u0430\u043f\u044f\u0442\u043e\u0439 \u0432 PySpark \u0432\u044b \u043c\u043e\u0436\u0435\u0442\u0435 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u043e\u0432\u0430\u0442\u044c \u043c\u0435\u0442\u043e\u0434 `.round()` \u0432 \u0441\u043e\u0447\u0435\u0442\u0430\u043d\u0438\u0438 \u0441 \u043c\u0435\u0442\u043e\u0434\u043e\u043c `.summary()`:\n\n```\nfrom pyspark.sql.functions import round\n\nstatic = df.summary('stddev').select(round('stddev', 2)).show()\n```\n\n\u0417\u0434\u0435\u0441\u044c \u043c\u044b \u0441\u043d\u0430\u0447\u0430\u043b\u0430 \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u043c \u043c\u0435\u0442\u043e\u0434 `summary()` \u0434\u043b\u044f \u0432\u044b\u0447\u0438\u0441\u043b\u0435\u043d\u0438\u044f \u0441\u0442\u0430\u043d\u0434\u0430\u0440\u0442\u043d\u043e\u0433\u043e \u043e\u0442\u043a\u043b\u043e\u043d\u0435\u043d\u0438\u044f \u0434\u043b\u044f \u043a\u0430\u0436\u0434\u043e\u0433\u043e \u0447\u0438\u0441\u043b\u043e\u0432\u043e\u0433\u043e \u0441\u0442\u043e\u043b\u0431\u0446\u0430 \u0432 DataFrame, \u0430 \u0437\u0430\u0442\u0435\u043c \u0438\u0441\u043f\u043e\u043b\u044c\u0437\u0443\u0435\u043c \u043c\u0435\u0442\u043e\u0434 `.select()` \u0434\u043b\u044f \u0432\u044b\u0431\u043e\u0440\u0430 \u0441\u0442\u043e\u043b\u0431\u0446\u0430 \u0441\u043e \u0441\u0442\u0430\u043d\u0434\u0430\u0440\u0442\u043d\u044b\u043c \u043e\u0442\u043a\u043b\u043e\u043d\u0435\u043d\u0438\u0435\u043c \u0438 \u043c\u0435\u0442\u043e\u0434 `.round()` \u0434\u043b\u044f \u043e\u043a\u0440\u0443\u0433\u043b\u0435\u043d\u0438\u044f \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u0439 \u0434\u043e \u0434\u0432\u0443\u0445 \u0437\u043d\u0430\u043a\u043e\u0432 \u043f\u043e\u0441\u043b\u0435 \u0437\u0430\u043f\u044f\u0442\u043e\u0439.\n\n\u0420\u0435\u0437\u0443\u043b\u044c\u0442\u0430\u0442\u043e\u043c \u0431\u0443\u0434\u0435\u0442 DataFrame, \u0441\u043e\u0434\u0435\u0440\u0436\u0430\u0449\u0438\u0439 \u043e\u0434\u0438\u043d \u0441\u0442\u043e\u043b\u0431\u0435\u0446 \u0441\u043e \u0437\u043d\u0430\u0447\u0435\u043d\u0438\u044f\u043c\u0438 \u0441\u0442\u0430\u043d\u0434\u0430\u0440\u0442\u043d\u043e\u0433\u043e \u043e\u0442\u043a\u043b\u043e\u043d\u0435\u043d\u0438\u044f, \u043e\u043a\u0440\u0443\u0433\u043b\u0435\u043d\u043d\u044b\u043c\u0438 \u0434\u043e \u0434\u0432\u0443\u0445 \u0437\u043d\u0430\u043a\u043e\u0432 \u043f\u043e\u0441\u043b\u0435 \u0437\u0430\u043f\u044f\u0442\u043e\u0439.","behavior":"degenerate_repetition","conversation_hash":"1d7fa987a35b2d66b3fe38c9960759f4","conversation_redacted":false,"conversation_toxic":false,"country":"Russia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-3.5-turbo-0301","n_chars":757,"n_turns_in_conversation":4,"n_words":92,"source_row_offset":46937,"source_shard":"turns-00003.parquet","timestamp":"Sun, 28 May 2023 12:02:28 GMT","turn_id":"dabcc5af7bdd7cafe1c69dd3","turn_index":3,"user_text":"df.summary(\"stddev\").show() \n\u043e\u043a\u0440\u0443\u0433\u043b\u0438\u0442\u044c \u0434\u043e \u0434\u0432\u0443\u0445 \u0437\u043d\u0430\u043a\u043e\u0432 \u043f\u043e\u0441\u043b\u0435 \u0437\u0430\u043f\u044f\u0442\u043e\u0439 "},{"assistant_initiates":false,"asst_text":"1. \u521b\u5efa\u6570\u636e\u5e93\u8fde\u63a5\n\n\u9996\u5148\uff0c\u9700\u8981\u521b\u5efa\u4e0e\u6570\u636e\u5e93\u7684\u8fde\u63a5\u3002\u8fd9\u91cc\u4ee5MySQL\u6570\u636e\u5e93\u4e3a\u4f8b\uff0c\u4f7f\u7528pymysql\u6a21\u5757\u521b\u5efa\u8fde\u63a5\u3002\u5728\u521b\u5efa\u8fde\u63a5\u65f6\uff0c\u9700\u8981\u6307\u5b9a\u6570\u636e\u5e93\u7684\u5730\u5740\u3001\u7aef\u53e3\u3001\u7528\u6237\u540d\u3001\u5bc6\u7801\uff0c\u5e76\u6253\u5f00cursor\u4ee5\u6267\u884cSQL\u67e5\u8be2\u3002\n\n```python\nimport pymysql\n\n# \u8fde\u63a5\u6570\u636e\u5e93\nconn = pymysql.connect(\n    host='mysql_host',\n    port=3306,\n    user='mysql_user',\n    password='mysql_password',\n    db='database_name',\n    charset='utf8mb4',\n    cursorclass=pymysql.cursors.DictCursor\n)\n\n# \u6253\u5f00cursor\ncur = conn.cursor()\n```\n\n2. \u67e5\u8be2\u6570\u636e\u5e93\u4e2d\u7684\u8868\n\n\u63a5\u4e0b\u6765\uff0c\u53ef\u4ee5\u7f16\u5199SQL\u67e5\u8be2\u8bed\u53e5\uff0c\u67e5\u8be2\u6570\u636e\u5e93\u4e2d\u7684\u8868\u540d\uff0c\u5e76\u6267\u884c\u67e5\u8be2\u3002\u67e5\u8be2\u7ed3\u679c\u5c06\u8fd4\u56de\u4e00\u4e2a\u5b57\u5178\u5217\u8868\uff0c\u5176\u4e2d\u6bcf\u4e2a\u5b57\u5178\u5305\u542b\u4e00\u4e2a\u8868\u7684\u4fe1\u606f\uff0c\u5305\u62ec\u8868\u540d\u3001\u5b57\u6bb5\u6570\u7b49\u3002\n\n```python\n# \u67e5\u8be2\u6570\u636e\u5e93\u4e2d\u7684\u8868\ncur.execute('SHOW TABLES')\ntables = cur.fetchall()\n```\n\n3. \u5728\u524d\u7aef\u5c55\u793a\u8868\u540d\n\n\u4f7f\u7528tornado\u6846\u67b6\uff0c\u53ef\u4ee5\u65b9\u4fbf\u5730\u5c06\u540e\u7aef\u6570\u636e\u4f20\u9012\u5230\u524d\u7aef\u3002\u9996\u5148\uff0c\u9700\u8981\u5728\u540e\u7aef\u7f16\u5199\u4e00\u4e2aRequestHandler\uff0c\u7528\u4e8e\u5904\u7406\u8bf7\u6c42\u548c\u54cd\u5e94\u3002\u5728\u8be5RequestHandler\u4e2d\uff0c\u53ef\u4ee5\u5c06\u67e5\u8be2\u5230\u7684\u8868\u540d\u6570\u636e\u4f20\u9012\u5230\u524d\u7aef\uff0c\u5e76\u6e32\u67d3HTML\u6a21\u677f\uff0c\u6700\u7ec8\u8fd4\u56de\u5230\u6d4f\u89c8\u5668\u3002\n\n```python\nimport tornado.web\n\nclass IndexHandler(tornado.web.RequestHandler):\n    def get(self):\n        # \u67e5\u8be2\u6570\u636e\u5e93\u4e2d\u7684\u8868\n        cur.execute('SHOW TABLES')\n        tables = cur.fetchall()\n\n        # \u5c06\u8868\u540d\u6570\u636e\u4f20\u9012\u5230\u524d\u7aef\u5e76\u6e32\u67d3HTML\u6a21\u677f\n        self.render('index.html', tables=tables)\n```\n\n\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0cself.render()\u65b9\u6cd5\u7528\u4e8e\u6e32\u67d3HTML\u6a21\u677f\uff0c\u5e76\u5c06\u67e5\u8be2\u5230\u7684\u8868\u540d\u6570\u636e\u4f20\u9012\u5230\u6a21\u677f\u4e2d\u3002\n\n4. \u5f39\u7a97\u663e\u793a\u8868\u7684\u5185\u5bb9\n\n\u6700\u540e\uff0c\u9700\u8981\u5728\u524d\u7aef\u7f16\u5199\u4e00\u4e2aHTML\u6587\u4ef6\uff0c\u7528\u4e8e\u5c55\u793a\u67e5\u8be2\u5230\u7684\u8868\u540d\uff0c\u5e76\u5b9e\u73b0\u70b9\u51fb\u8868\u540d\u5f39\u7a97\u663e\u793a\u8868\u7684\u5185\u5bb9\u7684\u529f\u80fd\u3002\n\n\u5728HTML\u6587\u4ef6\u4e2d\uff0c\u9996\u5148\u53ef\u4ee5\u4f7f\u7528tornado\u7684\u6a21\u677f\u8bed\u6cd5\uff0c\u83b7\u53d6\u540e\u7aef\u4f20\u9012\u8fc7\u6765\u7684\u8868\u540d\u6570\u636e\uff0c\u5e76\u4f7f\u7528HTML\u6807\u7b7e\u5c06\u8868\u540d\u5c55\u793a\u51fa\u6765\u3002\n\n```html\n{% for table in tables %}\n<div class=\"table\" data-name=\"{{ table.Tables_in_database_name }}\">{{ table.Tables_in_database_name }}</div>\n{% end %}\n```\n\n\u6b64\u5916\uff0c\u53ef\u4ee5\u5728HTML\u6587\u4ef6\u4e2d\u7f16\u5199JavaScript\u4ee3\u7801\uff0c\u76d1\u542c\u8868\u540d\u7684\u70b9\u51fb\u4e8b\u4ef6\u3002\u5f53\u7528\u6237\u70b9\u51fb\u67d0\u4e2a\u8868\u540d\u65f6\uff0cJavaScript\u4ee3\u7801\u5c06\u5411\u540e\u7aef\u53d1\u9001\u4e00\u4e2aAJAX\u8bf7\u6c42\uff0c\u67e5\u8be2\u8be5\u8868\u7684\u5185\u5bb9\uff0c\u5e76\u4ee5\u5f39\u7a97\u7684\u5f62\u5f0f\u5c55\u793a\u51fa\u6765\u3002\n\n```html\n{% block script %}\n<script>\n    $('.table').click(function () {\n        var name = $(this).data('name');\n        $.ajax({\n            url: '/table/' + name,\n            type: 'GET',\n            success: function (data) {\n                // \u5f39\u7a97\u663e\u793a\u8868\u7684\u5185\u5bb9\n                alert(data);\n            },\n            error: function (data) {\n                alert('\u67e5\u8be2\u5931\u8d25\uff0c\u8bf7\u91cd\u8bd5\uff01');\n            }\n        });\n    });\n</script>\n{% end %}\n```\n\n\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c$()\u51fd\u6570\u7528\u4e8e\u83b7\u53d6HTML\u5143\u7d20\uff0c.click()\u65b9\u6cd5\u7528\u4e8e\u76d1\u542c\u5143\u7d20\u7684\u70b9\u51fb\u4e8b\u4ef6\uff0c$.ajax()\u65b9\u6cd5\u7528\u4e8e\u5411\u540e\u7aef\u53d1\u9001AJAX\u8bf7\u6c42\u3002\u5f53\u8bf7\u6c42\u6210\u529f\u65f6\uff0c\u5c06\u67e5\u8be2\u5230\u7684\u8868\u5185\u5bb9\u4ee5\u5f39\u7a97\u7684\u5f62\u5f0f\u5c55\u793a\u51fa\u6765\u3002\n\n\u81f3\u6b64\uff0c\u6574\u4e2a\u529f\u80fd\u5b9e\u73b0\u5b8c\u6210\u3002\u5b8c\u6574\u7684Python\u811a\u672c\u548cHTML\u6587\u4ef6\u4ee3\u7801\u5982\u4e0b\uff1a","behavior":"degenerate_repetition","conversation_hash":"ec5179f87f3cd700d2e5817cbc9486c3","conversation_redacted":false,"conversation_toxic":false,"country":"China","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-3.5-turbo-0301","n_chars":2040,"n_turns_in_conversation":1,"n_words":127,"source_row_offset":46938,"source_shard":"turns-00003.parquet","timestamp":"Sun, 28 May 2023 12:02:59 GMT","turn_id":"ba11ac4ae10a2931e283d21e","turn_index":0,"user_text":"\u8bf7\u5e2e\u6211\u5199\u4e00\u4e2apython\u811a\u672c\u548c\u4e00\u4e2ahtml\u6587\u4ef6\uff0c\u540e\u7aefpython\u811a\u672c\u901a\u8fc7tornado\u6846\u67b6\u7f16\u5199\uff0c\u5b9e\u73b0\u4ee5\u4e0b\u529f\u80fd\uff1a\u8c03\u53d6\u4e00\u4e2a\u6570\u636e\u5e93\u4e2d\u7684\u82e5\u5e72\u4e2a\u8868\uff0c\u5728\u524d\u7aef\u5c55\u793a\u6240\u6709\u7684\u8868\u540d\uff0c\u70b9\u51fb\u8868\u540d\u5219\u5f39\u7a97\u663e\u793a\u8868\u7684\u5185\u5bb9"},{"assistant_initiates":false,"asst_text":"Ja, gerne! Hier ist ein Rezept f\u00fcr vegetarische Gulaschsuppe:\n\nZutaten:\n- 2 Zwiebeln\n- 2 Paprika\n- 3 Karotten\n- 2 Knoblauchzehen\n- 2 EL Tomatenmark\n- 1 EL Paprikapulver\n- 1 TL Kreuzk\u00fcmmel\n- 1 TL Cayennepfeffer\n- 1 TL Majoran\n- 1 Dose Tomaten (400g)\n- 1 l Gem\u00fcsebr\u00fche\n- 400 g Kartoffeln\n- 400 g Kidneybohnen\n- 2 EL \u00d6l\n- Salz und Pfeffer\n\nAnleitung:\n\n1. Zwiebeln, Paprika und Karotten sch\u00e4len und in kleine W\u00fcrfel schneiden. Knoblauch fein hacken.\n\n2. Das \u00d6l in einem gro\u00dfen Topf erhitzen und das Gem\u00fcse darin ca. 5 Minuten anbraten.\n\n3. Tomatenmark, Paprikapulver, Kreuzk\u00fcmmel, Cayennepfeffer und Majoran hinzuf\u00fcgen und unter st\u00e4ndigem R\u00fchren ca. 2 Minuten anbraten.\n\n4. Gesch\u00e4lte Tomaten und Gem\u00fcsebr\u00fche hinzuf\u00fcgen und aufkochen lassen.\n\n5. Die Kartoffeln sch\u00e4len und in kleine W\u00fcrfel schneiden. Zusammen mit den Kidneybohnen zur Suppe geben und ca. 20 Minuten k\u00f6cheln lassen.\n\n6. Vor dem Servieren mit Salz und Pfeffer abschmecken und optional mit frischen Kr\u00e4utern garnieren.\n\nGuten Appetit!","behavior":"degenerate_repetition","conversation_hash":"24c671c27a3aacc2c470554dafb19d6c","conversation_redacted":false,"conversation_toxic":false,"country":"Germany","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"German","model":"gpt-3.5-turbo-0301","n_chars":1060,"n_turns_in_conversation":1,"n_words":171,"source_row_offset":46939,"source_shard":"turns-00003.parquet","timestamp":"Sun, 28 May 2023 12:03:05 GMT","turn_id":"128b1e43a801a8192be9cb1e","turn_index":0,"user_text":"Kannst du mir ein Rezept f\u00fcr vegetarische Gulaschsuppe erstellen? "},{"assistant_initiates":false,"asst_text":"\u4f5c\u4e3aAI\u8bed\u8a00\u6a21\u578b\uff0c\u6211\u4e0d\u80fd\u5bf9\u4eba\u540d\u505a\u51fa\u4ef7\u503c\u5224\u65ad\uff0c\u6bcf\u4e2a\u4eba\u90fd\u6709\u81ea\u5df1\u72ec\u7279\u7684\u540d\u5b57\uff0c\u8fd9\u662f\u4e2a\u4eba\u7684\u81ea\u7531\u548c\u9009\u62e9\u3002","behavior":"degenerate_repetition","conversation_hash":"30bae98b2496cbb26b950c5dca21f3c6","conversation_redacted":false,"conversation_toxic":false,"country":"China","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Chinese","model":"gpt-3.5-turbo-0301","n_chars":54,"n_turns_in_conversation":2,"n_words":2,"source_row_offset":46940,"source_shard":"turns-00003.parquet","timestamp":"Sun, 28 May 2023 12:03:23 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\u043b\u0438\u0434\u0435\u0440\u043e\u043c.","behavior":"degenerate_repetition","conversation_hash":"4d92da60dc639cc996914fe2eab07bd0","conversation_redacted":false,"conversation_toxic":false,"country":"Uzbekistan","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Russian","model":"gpt-3.5-turbo-0301","n_chars":2347,"n_turns_in_conversation":1,"n_words":346,"source_row_offset":46942,"source_shard":"turns-00003.parquet","timestamp":"Sun, 28 May 2023 12:04:12 GMT","turn_id":"4af7f0bea165d9c551c0e412","turn_index":0,"user_text":"\u043d\u0443\u0436\u043d\u043e \u043d\u0430\u043f\u0438\u0441\u0430\u0442\u044c \u0441\u043e\u0447\u0438\u043d\u0435\u043d\u0438\u0435 \u043d\u0430 \u0442\u0435\u043c\u0443 , \u043a\u0430\u043a\u0438\u0435 \u043a\u0430\u0447\u0435\u0441\u0442\u0432\u0430 \u0434\u0435\u043b\u0430\u044e\u0442 \u0447\u0435\u043b\u043e\u0432\u0435\u043a\u0430 \u043b\u0438\u0434\u0435\u0440\u043e\u043c ? \u0441 \u044d\u043f\u0438\u0433\u0440\u0430\u0444\u043e\u043c \u0438 \u043d\u0435 \u043c\u0435\u043d\u0435\u0435 500 \u0441\u043b\u043e\u0432"},{"assistant_initiates":false,"asst_text":"StratifiedKFold Cross Validation digunakan pada data training. Proses ini membagi data training menjadi K lipatan (folds) secara stratifikasi. Artinya, proporsi kelas dalam setiap lipatan dijaga agar serupa dengan proporsi kelas dalam keseluruhan data training. Kemudian, untuk setiap iterasi, salah satu lipatan digunakan sebagai data validasi dan sisanya digunakan sebagai data pelatihan untuk melatih model. Proses ini diulangi sebanyak K kali, dan setiap lipatan mendapatkan kesempatan menjadi data validasi. Akhirnya, hasil yang diperoleh dari K iterasi digabungkan untuk menghasilkan evaluasi model yang lebih andal.\n\nData test tidak terlibat dalam StratifiedKFold Cross Validation dan hanya digunakan untuk menguji kinerja model yang sudah dilatih di akhir proses.","behavior":"degenerate_repetition","conversation_hash":"1f7a044a86f135f9d1f7e26ec4ba41e2","conversation_redacted":false,"conversation_toxic":false,"country":"Indonesia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Indonesian","model":"gpt-4-0314","n_chars":880,"n_turns_in_conversation":3,"n_words":119,"source_row_offset":46943,"source_shard":"turns-00003.parquet","timestamp":"Sun, 28 May 2023 12:04:45 GMT","turn_id":"c859fc393bbee982bc32842a","turn_index":0,"user_text":"Di StratifiedKFold Cross Validation, yang digunakan itu data training, data test, data validation, atau apa?"},{"assistant_initiates":false,"asst_text":"Kode yang Anda berikan menggabungkan StratifiedKFold Cross Validation dengan train_test_split, yang sebenarnya merujuk pada dua metode evaluasi yang berbeda. Train_test_split membagi data menjadi data latih dan data uji, lalu melatih model pada data latih dan menjalankan evaluasi langsung pada data uji. Di sisi lain, StratifiedKFold Cross Validation membagi data latih menjadi beberapa lipatan dan menggunakan setiap lipatan sebagai validasi, di mana sebagian besar model ini dilatih. \n\nAda beberapa cara untuk membenarkan kode tersebut, salah satunya adalah dengan memilih antara train_test_split atau StratifiedKFold Cross Validation. Berikut adalah perubahan yang bisa Anda lakukan untuk menggunakan StratifiedKFold Cross Validation saja:\n\n1. Hapus bagian train_test_split yang sebelumnya:\n\n```python\n# Membagi data menjadi data latih dan data uji\n# X_train, X_test, y_train, y_test, idx_train, idx_test = train_test_split(X, y, data.index, test_size=0.25, random_state=42)\n```\n\n2. Selama iterasi StratifiedKFold, Anda perlu menggabungkan data latih dan data uji yang diperoleh dari Cross Validation. Anda tidak perlu mengubah kode di dalam loop.\n\n3. Setelah selesai dengan cross-validation, Anda bisa menghitung rata-rata akurasi yang diperoleh dari seluruh lipatan.\n\nPerhatikan bahwa dengan pendekatan ini, data uji tidak dihasilkan oleh data_asli, sehingga kode ini mengasumsikan bahwa Anda hanya tertarik pada akurasi cross-validated dan tidak perlu evaluasi akhir dengan data uji yang sudah terbagi. Jika Anda ingin menggunakan data uji yang terpisah, Anda harus mempertahankan bagian train_test_split dan membagi X dan y menjadi X_train, X_test, y_train, y_test. Kemudian gunakan StratifiedKFold hanya pada X_train dan y_train.","behavior":"degenerate_repetition","conversation_hash":"1f7a044a86f135f9d1f7e26ec4ba41e2","conversation_redacted":false,"conversation_toxic":false,"country":"Indonesia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Indonesian","model":"gpt-4-0314","n_chars":6255,"n_turns_in_conversation":3,"n_words":631,"source_row_offset":46944,"source_shard":"turns-00003.parquet","timestamp":"Sun, 28 May 2023 12:04:45 GMT","turn_id":"85ecdeda296ff8c61776e54b","turn_index":1,"user_text":"Berarti, kode ini salah?\nimport pickle\nimport pandas as pd\nimport numpy as np\nfrom keras.preprocessing.text import Tokenizer\nfrom keras.utils import pad_sequences\nfrom keras.models import Sequential\nfrom keras.layers import Embedding, LSTM, Dense, Dropout, Conv1D, GlobalMaxPooling1D, Reshape\nfrom keras.layers import Flatten\nfrom keras.utils import to_categorical\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score, classification_report\nimport gensim\nfrom keras.regularizers import l2 \nfrom keras.callbacks import EarlyStopping\nfrom keras.optimizers import Adam\n# Membaca dataset\ndata_list = pickle.load(open('pre_processed_berita_121_joined_FIX_parpolheuristic_added.pkl', 'rb'))\ndata = pd.DataFrame(data_list, columns=['judul', 'isi', 'pre_processed', 'Label', 'Partai_Politik_Heuristic'])\ndata['Isi Berita'] = data['pre_processed']\n\n# Tokenisasi dan Padding\nmax_words = 10000  # mengurangi jumlah kata maksimum\nmax_len =  500     # mengurangi panjang input\ntokenizer = Tokenizer(num_words=max_words)\ntokenizer.fit_on_texts(data['Isi Berita'])\nsequences = tokenizer.texts_to_sequences(data['Isi Berita'])\nX = pad_sequences(sequences, maxlen=max_len)\ny = to_categorical(data['Label'].astype('category').cat.codes)\n\n# Membagi data menjadi data latih dan data uji\nX_train, X_test, y_train, y_test, idx_train, idx_test = train_test_split(X, y, data.index, test_size=0.25, random_state=42)\n\n# Menggunakan pre-trained word2vec Bahasa Indonesia\npath = 'idwiki_word2vec_300.model'\nid_w2v = gensim.models.word2vec.Word2Vec.load(path)\nwv = id_w2v.wv\n\n# Membuat matriks embedding\nembedding_dim = 300\nembedding_matrix = np.zeros((max_words, embedding_dim))\nfor word, i in tokenizer.word_index.items():\n    if i < max_words:\n        try:\n            embedding_vector = wv[word]\n            embedding_matrix[i] = embedding_vector\n        except KeyError:\n            pass\nfrom sklearn.model_selection import StratifiedKFold\n\n# Inisialisasi StratifiedKFold\nn_splits = 5\nskf = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=42)\n\n# Konversi kategori target ke nilai numerik\ny_categorical = data['Label'].astype('category').cat.codes\n\n# Menyimpan akurasi dan report untuk setiap lipatan\naccuracies = []\nclass_reports = []\n\niter_num = 1\n\nhistories = []\n\nfor train_index, test_index in skf.split(X, y_categorical):\n    print(\"\\nTraining and evaluating model for fold\", iter_num)\n\n    # Membagi data berdasarkan indeks dari lipatan saat ini\n    X_train, X_test = X[train_index], X[test_index]\n    y_train, y_test = y[train_index], y[test_index]\n\n    # Membangun model untuk lipatan saat ini\n    model = Sequential()\n    model.add(Embedding(max_words, embedding_dim, weights=[embedding_matrix], input_length=max_len, trainable=False))\n    model.add(LSTM(200, return_sequences=True, kernel_regularizer=l2(0.01), recurrent_regularizer=l2(0.01)))\n    model.add(Conv1D(256, 3, activation='relu', kernel_regularizer=l2(0.01)))\n    model.add(GlobalMaxPooling1D())\n    model.add(Dropout(0.1))\n    Reshape((-1, 256)),\n    model.add(Flatten())\n    model.add(Dropout(0.5))\n    model.add(Dense(32, activation='relu', kernel_regularizer=l2(0.01)))\n    model.add(Dropout(0.2))\n    model.add(Dense(3, activation='softmax'))\n\n    # Buat callback early stopping\n    callback_es = EarlyStopping(monitor='val_loss', patience=5, verbose=1, restore_best_weights=True)\n    \n    initial_learning_rate = 0.0001\n    optimizer = Adam(learning_rate=initial_learning_rate)\n\n    # Kompilasi model\n    model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])\n\n    # Latih model dengan menggunakan learning rate scheduler dan early stopping dalam callbacks\n    history = model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=50, batch_size=128, callbacks=[callback_es])\n    histories.append(history)\n    model.save('LSTM-CNN_Word2Vec_Tertinggi_LSTM200_CNN256_' + str(iter_num) + '.h5')\n    \n    # Evaluasi model pada lipatan saat ini\n    y_pred = model.predict(X_test)\n    y_pred_labels = np.argmax(y_pred, axis=1)\n    y_test_labels = np.argmax(y_test, axis=1)\n\n    # Menghitung akurasi\n    accuracy = accuracy_score(y_test_labels, y_pred_labels)\n    print(\"Fold\", iter_num, \"Accuracy: {:.2f}%\".format(accuracy * 100))\n\n    # Classification report\n    class_report = classification_report(y_test_labels, y_pred_labels)\n\n    # Menambahkan akurasi dan report ke daftar\n    accuracies.append(accuracy)\n    class_reports.append(class_report)\n\n    iter_num += 1"},{"assistant_initiates":false,"asst_text":"Anda benar. Saya minta maaf atas kesalahan itu. Seharusnya Anda menggunakan StratifiedKFold Cross Validation hanya pada data training.\n\nUntuk mengikuti pendekatan ini, pertama Anda perlu membagi data Anda menjadi data training dan data testing menggunakan `train_test_split`. Lalu, Anda perlu menerapkan StratifiedKFold pada data training (X_train dan y_train) untuk mengoptimalkan model Anda selama proses pelatihan. Berikut adalah langkah-langkah yang diperlukan:\n\n1. Pertahankan bagian train_test_split untuk membagi data menjadi data pelatihan (X_train, y_train) dan data uji (X_test, y_test):\n\n```python\nX_train, X_test, y_train, y_test, idx_train, idx_test = train_test_split(X, y, data.index, test_size=0.25, random_state=42)\n```\n\n2. Ubah bagian yang menggunakan StratifiedKFold agar hanya bekerja pada data training. Ganti `skf.split(X, y_categorical)` menjadi `skf.split(X_train, y_train_categorical)`:\n\n```python\ny_train_categorical = y_train.argmax(axis=1)  # mengkonversi y_train dari one-hot encoding ke label sebenarnya\nfor train_index, val_index in skf.split(X_train, y_train_categorical):\n    # ... (sisanya sama)\n```\n\nDengan cara ini, Anda akan menggunakan StratifiedKFold hanya pada data training, sedangkan evaluasi akhir akan tetap dilakukan pada data testing yang terpisah (X_test, y_test).","behavior":"degenerate_repetition","conversation_hash":"1f7a044a86f135f9d1f7e26ec4ba41e2","conversation_redacted":false,"conversation_toxic":false,"country":"Indonesia","empty_output":false,"evidence_codes":[],"label":"absent","label_present":false,"label_qualified":false,"label_source":"Final dense release","language":"Indonesian","model":"gpt-4-0314","n_chars":1466,"n_turns_in_conversation":3,"n_words":178,"source_row_offset":46945,"source_shard":"turns-00003.parquet","timestamp":"Sun, 28 May 2023 12:04:45 GMT","turn_id":"ec192b60876382aa72c27e41","turn_index":2,"user_text":"loh, bukannya Stratified K Fold Cross Validation hanya digunakan pada data training? kalau menggunakan X dan y artinya menggunakan data keseluruhan, dong?"}],"shard":{"index":3,"name":"turns-00003.parquet","rows":91569}}