jim/openai-cookbook
1
0
Fork 0
mirror of https://github.com/james-m-jordan/openai-cookbook.git synced 2025-05-09 19:32:38 +00:00
Code Issues Packages Projects Releases Wiki Activity

adds data download from CDN with precomputed embeddings

Browse Source
This commit is contained in:
Ted Sanders 2022-07-11 17:02:00 -07:00
parent 6eae26d5cc
commit 350b9a7333
1 changed files with 21 additions and 16 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

37
examples/Regression_using_embeddings.ipynb
View File

@ -13,14 +13,14 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Babbage similarity embedding performance on 1k Amazon reviews: mse=0.38, mae=0.39\n"
"Babbage similarity embedding performance on 1k Amazon reviews: mse=0.39, mae=0.38\n"
]
}
],
@ -32,39 +32,41 @@
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
"\n",
"df = pd.read_csv('output/embedded_1k_reviews.csv')\n",
"df['babbage_similarity'] = df.babbage_similarity.apply(eval).apply(np.array)\n",
"datafile_path = \"https://cdn.openai.com/API/examples/data/fine_food_reviews_with_embeddings_1k.csv\" # for your convenience, we precomputed the embeddings\n",
"df = pd.read_csv(datafile_path)\n",
"df[\"babbage_similarity\"] = df.babbage_similarity.apply(eval).apply(np.array)\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(list(df.babbage_similarity.values), df.Score, test_size = 0.2, random_state=42)\n",
"X_train, X_test, y_train, y_test = train_test_split(list(df.babbage_similarity.values), df.Score, test_size=0.2, random_state=42)\n",
"\n",
"rfr = RandomForestRegressor(n_estimators=100)\n",
"rfr.fit(X_train, y_train)\n",
"preds = rfr.predict(X_test)\n",
"\n",
"\n",
"mse = mean_squared_error(y_test, preds)\n",
"mae = mean_absolute_error(y_test, preds)\n",
"\n",
"print(f\"Babbage similarity embedding performance on 1k Amazon reviews: mse={mse:.2f}, mae={mae:.2f}\")"
"print(f\"Babbage similarity embedding performance on 1k Amazon reviews: mse={mse:.2f}, mae={mae:.2f}\")\n"
]
},
{
"cell_type": "code",
"execution_count": 26,
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dummy mean prediction performance on Amazon reviews: mse=1.77, mae=1.04\n"
"Dummy mean prediction performance on Amazon reviews: mse=1.81, mae=1.08\n"
]
}
],
"source": [
"bmse = mean_squared_error(y_test, np.repeat(y_test.mean(), len(y_test)))\n",
"bmae = mean_absolute_error(y_test, np.repeat(y_test.mean(), len(y_test)))\n",
"print(f\"Dummy mean prediction performance on Amazon reviews: mse={bmse:.2f}, mae={bmae:.2f}\")"
"print(\n",
" f\"Dummy mean prediction performance on Amazon reviews: mse={bmse:.2f}, mae={bmae:.2f}\"\n",
")\n"
]
},
{
@ -83,11 +85,9 @@
}
],
"metadata": {
"interpreter": {
"hash": "be4b5d5b73a21c599de40d6deb1129796d12dc1cc33a738f7bac13269cfcafe8"
},
"kernelspec": {
"display_name": "Python 3.7.3 64-bit ('base': conda)",
"display_name": "Python 3.9.9 ('openai')",
"language": "python",
"name": "python3"
},
"language_info": {
@ -100,9 +100,14 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.3"
"version": "3.9.9"
},
"orig_nbformat": 4
"orig_nbformat": 4,
"vscode": {
"interpreter": {
"hash": "365536dcbde60510dc9073d6b991cd35db2d9bac356a11f5b64279a5e6708b97"
}
}
},
"nbformat": 4,
"nbformat_minor": 2