update crypto example preds to only download preds csv

Author: ndharasz

crypto/example_model.ipynb

@@ -52,14 +52,15 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 1,
       "metadata": {},
       "outputs": [
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            "crypto/v2.0_beta/train.parquet: 3.58MB [00:00, 14.5MB/s]                            \n"
+            "2025-10-08 10:50:06,923 INFO numerapi.utils: starting download\n",
+            "crypto/v2.0/train.parquet: 3.83MB [00:00, 13.9MB/s]                            \n"
           ]
         }
       ],
@@ -838,7 +839,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 10,
+      "execution_count": 2,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -852,8 +853,9 @@
           "name": "stdout",
           "output_type": "stream",
           "text": [
-            "[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.357879 seconds.\n",
-            "You can set `force_col_wise=true` to remove the overhead.\n",
+            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.000796 seconds.\n",
+            "You can set `force_row_wise=true` to remove the overhead.\n",
+            "And if memory is not enough, you can set `force_col_wise=true`.\n",
             "[LightGBM] [Info] Total Bins 112\n",
             "[LightGBM] [Info] Number of data points in the train set: 307157, number of used features: 22\n",
             "[LightGBM] [Info] Start training from score 0.499976\n",
@@ -3589,7 +3591,7 @@
               "              n_estimators=2000)"
             ]
           },
-          "execution_count": 5,
+          "execution_count": 2,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -3669,7 +3671,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 24,
+      "execution_count": null,
       "metadata": {},
       "outputs": [
         {
@@ -3687,7 +3689,28 @@
         "\n",
         "# generate live predictions\n",
         "live_data[\"prediction\"] = model.predict(live_data.filter(like=\"feature_\"))\n",
-        "live_data.to_parquet(\"predictions.parquet\")"
+        "submission = live_data[['symbol', 'prediction']]\n",
+        "submission"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "# Save and download your predictions\n",
+        "filename = f'crypto_example_preds.csv'\n",
+        "from numerai_tools.scoring import tie_kept_rank\n",
+        "submission[['prediction']] = tie_kept_rank(submission[['prediction']])\n",
+        "submission.to_csv(filename, index=False)\n",
+        "\n",
+        "# Download file if running in Google Colab\n",
+        "try:\n",
+        "    from google.colab import files\n",
+        "    files.download(filename)\n",
+        "except:\n",
+        "    pass"
       ]
     }
   ],