polars support (#829)

.github/workflows/ci.yaml

@@ -58,4 +58,4 @@ jobs:
         run: pip install ./ 
 
       - name: Tests
-        run: nbdev_test --do_print --timing --n_workers 1
+        run: nbdev_test --do_print --timing --n_workers 0 --flags polars

action_files/test_models/src/models.py

@@ -7,6 +7,7 @@
 import pytorch_lightning as pl
 import torch
 
+import neuralforecast
 from neuralforecast.core import NeuralForecast
 
 from neuralforecast.models.gru import GRU
@@ -34,6 +35,8 @@
 
 from src.data import get_data
 
+neuralforecast.config.id_as_index = False
+
 
 def main(dataset: str = 'M3', group: str = 'Monthly') -> None:
     train, horizon, freq, seasonality = get_data('data/', dataset, group)
@@ -82,7 +85,6 @@ def main(dataset: str = 'M3', group: str = 'Monthly') -> None:
         end = time.time()
         print(end - start)
 
-        forecasts = forecasts.reset_index()
         forecasts.columns = ['unique_id', 'ds', model_name]
         forecasts.to_csv(f'data/{model_name}-forecasts-{dataset}-{group}.csv', index=False)
         time_df = pd.DataFrame({'time': [end - start], 'model': [model_name]})
@@ -97,7 +99,6 @@ def main(dataset: str = 'M3', group: str = 'Monthly') -> None:
     end = time.time()
     print(end - start)
 
-    forecasts = forecasts.reset_index()
     forecasts = forecasts[['unique_id', 'ds', 'DeepAR-median']]
     forecasts.columns = ['unique_id', 'ds', 'DeepAR']
     forecasts.to_csv(f'data/{model_name}-forecasts-{dataset}-{group}.csv', index=False)

action_files/test_models/src/models2.py

@@ -7,6 +7,7 @@
 import pytorch_lightning as pl
 import torch
 
+import neuralforecast
 from neuralforecast.core import NeuralForecast
 
 from neuralforecast.models.gru import GRU
@@ -34,6 +35,8 @@
 
 from src.data import get_data
 
+neuralforecast.config.id_as_index = False
+
 
 def main(dataset: str = 'M3', group: str = 'Monthly') -> None:
     train, horizon, freq, seasonality = get_data('data/', dataset, group)
@@ -81,7 +84,6 @@ def main(dataset: str = 'M3', group: str = 'Monthly') -> None:
         end = time.time()
         print(end - start)
 
-        forecasts = forecasts.reset_index()
         forecasts.columns = ['unique_id', 'ds', model_name]
         forecasts.to_csv(f'data/{model_name}-forecasts-{dataset}-{group}.csv', index=False)
         time_df = pd.DataFrame({'time': [end - start], 'model': [model_name]})

environment.yml

@@ -10,11 +10,13 @@ dependencies:
   - numpy>=1.21.6
   - optuna
   - pandas>=1.3.5
+  - pyarrow
   - pytorch>=2.0.0
   - pytorch-lightning>=2.0.0
   - pip
-  - utilsforecast>=0.0.6
   - pip:
     - nbdev
     - black
+    - polars
     - "ray[tune]>=2.2.0"
+    - utilsforecast>=0.0.17

nbs/config.ipynb

@@ -0,0 +1,34 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "06dabc05-6dc5-4cb2-afde-62a1dd58f606",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| default_exp config"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e84cc420-9d82-4e69-abe2-87564faeb159",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#| export\n",
+    "id_as_index = True"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "python3",
+   "language": "python",
+   "name": "python3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}