testing generating examples

Pill_Ideologies-Post_Titles.py

@@ -191,27 +191,34 @@ class SubRedditPosts(datasets.GeneratorBasedBuilder):
         ]
 
     # method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
-    def _generate_examples(self, filepath, split):
+    def _generate_examples(self, data, split):
         # TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.
         # The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.
         print("inside generate examples")
         print(split, "is the split")
-        print(filepath.shape, "is the filepath")
-        # with open(filepath, encoding="utf-8") as f:
-        #     for key, row in enumerate(f):
-        #         data = json.loads(row)
-        #         if self.config.name == "first_domain":
-        #             # Yields examples as (key, example) tuples
-        #             yield key, {
-        #                 "sentence": data["sentence"],
-        #                 "option1": data["option1"],
-        #                 "answer": "" if split == "test" else data["answer"],
-        #             }
-        #         else:
-        #             yield key, {
-        #                 "sentence": data["sentence"],
-        #                 "option2": data["option2"],
-        #                 "second_domain_answer": (
-        #                     "" if split == "test" else data["second_domain_answer"]
-        #                 ),
-        #             }
+        print(data.shape, "is the filepath")
+        for key, row in data.iterrows():
+            # print(row, "is the row")
+            if self.config.name == "first_domain":
+                yield key, {
+                    "subreddit": row.get("subreddit"),
+                    "id": row.get("id"),
+                    "title": row.get("title"),
+                    "text": row("text"),
+                    "url": row("url"),
+                    "score": row.get("score"),
+                    "author": row.get("author"),
+                    "date": row.get("date"),
+                }
+
+            else:
+                yield key, {
+                    "subreddit": row.get("subreddit"),
+                    "id": row.get("id"),
+                    "title": row.get("title"),
+                    "text": row("text"),
+                    "url": row("url"),
+                    "score": row.get("score"),
+                    "author": row.get("author"),
+                    "date": row.get("date"),
+                }

test_notebook.ipynb

@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -11,29 +11,47 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Downloading builder script: 100%|██████████| 9.38k/9.38k [00:00<00:00, 9.38MB/s]\n"
+      "Downloading builder script: 100%|██████████| 10.1k/10.1k [00:00<00:00, 9.99MB/s]\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "https://huggingface.co/datasets/steamcyclone/Pill_Ideologies-Post_Titles/blob/main/reddit_posts_fm.csv\n"
+      "https://huggingface.co/datasets/steamcyclone/Pill_Ideologies-Post_Titles/raw/main/reddit_posts_fm.csv\n",
+      "C:\\Users\\ericr\\.cache\\huggingface\\datasets\\downloads\\0d4d993845dca9fad020a0cdc59de50781db2df85a57686ea495bc3a11e12dd8 <class 'datasets.utils.track.tracked_str'> checking type\n",
+      "no Error post pandas read csv\n",
+      "splits complete with scikit learn\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Downloading data: 100%|██████████| 48.0k/48.0k [00:00<00:00, 1.40MB/s]\n",
-      "Generating train split: 0 examples [00:00, ? examples/s]\n"
+      "Generating train split: 0 examples [00:00, ? examples/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "inside generate examples\n",
+      "train is the split\n",
+      "(4499, 8) is the filepath\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
      ]
     },
     {
@@ -42,15 +60,12 @@
      "output_type": "error",
      "traceback": [
       "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
       "File \u001b[1;32mc:\\Users\\ericr\\miniconda3\\envs\\sta663C\\Lib\\site-packages\\datasets\\builder.py:1726\u001b[0m, in \u001b[0;36mGeneratorBasedBuilder._prepare_split_single\u001b[1;34m(self, gen_kwargs, fpath, file_format, max_shard_size, split_info, check_duplicate_keys, job_id)\u001b[0m\n\u001b[0;32m   1725\u001b[0m _time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[1;32m-> 1726\u001b[0m \u001b[43m\u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrecord\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mgenerator\u001b[49m\u001b[43m:\u001b[49m\n\u001b[0;32m   1727\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mmax_shard_size\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mand\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mwriter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_num_bytes\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m>\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mmax_shard_size\u001b[49m\u001b[43m:\u001b[49m\n",
-      "File \u001b[1;32m~\\.cache\\huggingface\\modules\\datasets_modules\\datasets\\steamcyclone--Pill_Ideologies-Post_Titles\\b9769a66aafdd51743be385ed8b5c1188cf1bb911c2283a4d495a71e5eea207d\\Pill_Ideologies-Post_Titles.py:185\u001b[0m, in \u001b[0;36mSubRedditPosts._generate_examples\u001b[1;34m(self, filepath, split)\u001b[0m\n\u001b[0;32m    182\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_generate_examples\u001b[39m(\u001b[38;5;28mself\u001b[39m, filepath, split):\n\u001b[0;32m    183\u001b[0m     \u001b[38;5;66;03m# TODO: This method handles input defined in _split_generators to yield (key, example) tuples from the dataset.\u001b[39;00m\n\u001b[0;32m    184\u001b[0m     \u001b[38;5;66;03m# The `key` is for legacy reasons (tfds) and is not important in itself, but must be unique for each example.\u001b[39;00m\n\u001b[1;32m--> 185\u001b[0m     \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mfilepath\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mencoding\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mutf-8\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[0;32m    186\u001b[0m         \u001b[38;5;28;01mfor\u001b[39;00m key, row \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(f):\n",
-      "File \u001b[1;32mc:\\Users\\ericr\\miniconda3\\envs\\sta663C\\Lib\\site-packages\\datasets\\streaming.py:75\u001b[0m, in \u001b[0;36mextend_module_for_streaming.<locals>.wrap_auth.<locals>.wrapper\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     73\u001b[0m \u001b[38;5;129m@wraps\u001b[39m(function)\n\u001b[0;32m     74\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m---> 75\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[1;32mc:\\Users\\ericr\\miniconda3\\envs\\sta663C\\Lib\\site-packages\\datasets\\download\\streaming_download_manager.py:501\u001b[0m, in \u001b[0;36mxopen\u001b[1;34m(file, mode, download_config, *args, **kwargs)\u001b[0m\n\u001b[0;32m    500\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_local_path(main_hop):\n\u001b[1;32m--> 501\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mmain_hop\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    502\u001b[0m \u001b[38;5;66;03m# add headers and cookies for authentication on the HF Hub and for Google Drive\u001b[39;00m\n",
-      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'C:\\\\Users\\\\ericr\\\\.cache\\\\huggingface\\\\datasets\\\\downloads\\\\4b51286d8928be7cb69e9f832ace34b264a15b9a5d12d1f9c812eee79f9c19e9\\\\train.jsonl'",
+      "\u001b[1;31mTypeError\u001b[0m: 'NoneType' object is not iterable",
       "\nThe above exception was the direct cause of the following exception:\n",
       "\u001b[1;31mDatasetGenerationError\u001b[0m                    Traceback (most recent call last)",
-      "Cell \u001b[1;32mIn[6], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m test \u001b[38;5;241m=\u001b[39m \u001b[43mdatasets\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msteamcyclone/Pill_Ideologies-Post_Titles\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
+      "Cell \u001b[1;32mIn[32], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m test \u001b[38;5;241m=\u001b[39m \u001b[43mdatasets\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msteamcyclone/Pill_Ideologies-Post_Titles\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtrust_remote_code\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n",
       "File \u001b[1;32mc:\\Users\\ericr\\miniconda3\\envs\\sta663C\\Lib\\site-packages\\datasets\\load.py:2549\u001b[0m, in \u001b[0;36mload_dataset\u001b[1;34m(path, name, data_dir, data_files, split, cache_dir, features, download_config, download_mode, verification_mode, ignore_verifications, keep_in_memory, save_infos, revision, token, use_auth_token, task, streaming, num_proc, storage_options, trust_remote_code, **config_kwargs)\u001b[0m\n\u001b[0;32m   2546\u001b[0m try_from_hf_gcs \u001b[38;5;241m=\u001b[39m path \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m _PACKAGED_DATASETS_MODULES\n\u001b[0;32m   2548\u001b[0m \u001b[38;5;66;03m# Download and prepare data\u001b[39;00m\n\u001b[1;32m-> 2549\u001b[0m \u001b[43mbuilder_instance\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdownload_and_prepare\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   2550\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdownload_config\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_config\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2551\u001b[0m \u001b[43m    \u001b[49m\u001b[43mdownload_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdownload_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2552\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverification_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverification_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2553\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtry_from_hf_gcs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtry_from_hf_gcs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2554\u001b[0m \u001b[43m    \u001b[49m\u001b[43mnum_proc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_proc\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2555\u001b[0m \u001b[43m    \u001b[49m\u001b[43mstorage_options\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstorage_options\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2556\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2558\u001b[0m \u001b[38;5;66;03m# Build dataset for splits\u001b[39;00m\n\u001b[0;32m   2559\u001b[0m keep_in_memory \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m   2560\u001b[0m     keep_in_memory \u001b[38;5;28;01mif\u001b[39;00m keep_in_memory \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m is_small_dataset(builder_instance\u001b[38;5;241m.\u001b[39minfo\u001b[38;5;241m.\u001b[39mdataset_size)\n\u001b[0;32m   2561\u001b[0m )\n",
       "File \u001b[1;32mc:\\Users\\ericr\\miniconda3\\envs\\sta663C\\Lib\\site-packages\\datasets\\builder.py:1005\u001b[0m, in \u001b[0;36mDatasetBuilder.download_and_prepare\u001b[1;34m(self, output_dir, download_config, download_mode, verification_mode, ignore_verifications, try_from_hf_gcs, dl_manager, base_path, use_auth_token, file_format, max_shard_size, num_proc, storage_options, **download_and_prepare_kwargs)\u001b[0m\n\u001b[0;32m   1003\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m num_proc \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   1004\u001b[0m         prepare_split_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnum_proc\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m num_proc\n\u001b[1;32m-> 1005\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_download_and_prepare\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1006\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdl_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdl_manager\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1007\u001b[0m \u001b[43m        \u001b[49m\u001b[43mverification_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverification_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1008\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mprepare_split_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1009\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mdownload_and_prepare_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1010\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1011\u001b[0m \u001b[38;5;66;03m# Sync info\u001b[39;00m\n\u001b[0;32m   1012\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minfo\u001b[38;5;241m.\u001b[39mdataset_size \u001b[38;5;241m=\u001b[39m \u001b[38;5;28msum\u001b[39m(split\u001b[38;5;241m.\u001b[39mnum_bytes \u001b[38;5;28;01mfor\u001b[39;00m split \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39minfo\u001b[38;5;241m.\u001b[39msplits\u001b[38;5;241m.\u001b[39mvalues())\n",
       "File \u001b[1;32mc:\\Users\\ericr\\miniconda3\\envs\\sta663C\\Lib\\site-packages\\datasets\\builder.py:1767\u001b[0m, in \u001b[0;36mGeneratorBasedBuilder._download_and_prepare\u001b[1;34m(self, dl_manager, verification_mode, **prepare_splits_kwargs)\u001b[0m\n\u001b[0;32m   1766\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_download_and_prepare\u001b[39m(\u001b[38;5;28mself\u001b[39m, dl_manager, verification_mode, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mprepare_splits_kwargs):\n\u001b[1;32m-> 1767\u001b[0m     \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_download_and_prepare\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1768\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdl_manager\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1769\u001b[0m \u001b[43m        \u001b[49m\u001b[43mverification_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1770\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcheck_duplicate_keys\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mverification_mode\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mVerificationMode\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mBASIC_CHECKS\u001b[49m\n\u001b[0;32m   1771\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mverification_mode\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m==\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mVerificationMode\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mALL_CHECKS\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1772\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mprepare_splits_kwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1773\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n",
@@ -64,7 +79,7 @@
    "source": [
     "\n",
     "\n",
-    "test = datasets.load_dataset(\"steamcyclone/Pill_Ideologies-Post_Titles\")"
+    "test = datasets.load_dataset(\"steamcyclone/Pill_Ideologies-Post_Titles\", trust_remote_code=True)"
    ]
   },
   {
@@ -236,19 +251,96 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((4499, 8), (1125, 8), (625, 8))"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "# make stratified train, validation, and test sets\n",
     "\n",
     "from sklearn.model_selection import train_test_split\n",
     "\n",
-    "train, test = train_test_split(df, test_size=0.10, stratify=df['subreddit'])\n",
-    "train, val = train_test_split(train, test_size=0.20, stratify=train['subreddit'])\n",
+    "train, test = train_test_split(df, test_size=0.10, stratify=df['subreddit'], random_state=42)\n",
+    "train, val = train_test_split(train, test_size=0.20, stratify=train['subreddit'], random_state=42)\n",
     "\n",
     "train.shape, val.shape, test.shape"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['theredpillrebooted', '17c1wxt',\n",
+       "       'My name is Benjamin Persits and I am so sick of women.',\n",
+       "       'Yes, I\\'m using my full name because I\\'m so sick off all of this. The women in my dorm have become so obnoxious. They are so fucking annoying. I try to talk to any of them and it\\'s all \"sexual harassment\" and all that. I saw a cute girl by my dorm room sink the other day and we started talking. She wanders off and I walk after her while we chat. All of a sudden this is sexual harassment. Or when girls come on to me and when I lean in to kiss they don\\'t want to all of a sudden. Absolutely ridiculous. I\\'m not going to be quiet about feeling this way anymore. I\\'m sick of dealing with women, in my classes, anywhere. I\\'m so done. ',\n",
+       "       nan, 0, 'benjypersits', '2023-10-20 03:40:33'], dtype=object)"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "next(df.iterrows())[1].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<generator object DataFrame.iterrows at 0x000001A84F706F00>"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.iterrows()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "stop = 10\n",
+    "\n",
+    "for key, row in df.iterrows():\n",
+    "    # print(row.values)\n",
+    "    print(row.get('subreddit'))\n",
+    "    stop -= 1\n",
+    "    if stop == 0:\n",
+    "        break"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {