Merge branch 'main' into feature/cv_tutorial

Nixtla · Oct 15, 2024 · 166ce16 · 166ce16
2 parents bdd8baf + 23c71bc
commit 166ce16
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Showing 5 changed files with 14 additions and 243 deletions.
diff --git a/nbs/docs/tutorials/20_conformal_prediction.ipynb b/nbs/docs/tutorials/20_conformal_prediction.ipynb
diff --git a/nbs/mint.json b/nbs/mint.json
@@ -64,7 +64,8 @@
           "group":"Probabilistic Forecasting",
           "pages":[
             "docs/tutorials/uncertainty_quantification.html",
-            "docs/tutorials/longhorizon_probabilistic.html"
+            "docs/tutorials/longhorizon_probabilistic.html",
+            "docs/tutorials/conformal_prediction.html"
           ]
         },   
         {

diff --git a/nbs/utils.ipynb b/nbs/utils.ipynb
@@ -627,7 +627,8 @@
     "    cuts = [alpha / 200 for alpha in reversed(alphas)]\n",
     "    cuts.extend(1 - alpha / 200 for alpha in alphas)\n",
     "    for model in model_names:\n",
-    "        scores = cs_df[model].to_numpy().reshape(cs_n_windows, n_series, horizon)\n",
+    "        scores = cs_df[model].to_numpy().reshape(n_series, cs_n_windows, horizon)\n",
+    "        scores = scores.transpose(1, 0, 2)\n",
     "        # restrict scores to horizon\n",
     "        scores = scores[:,:,:horizon]\n",
     "        mean = fcst_df[model].to_numpy().reshape(1, n_series, -1)\n",
@@ -670,7 +671,8 @@
     "    cuts = [lv / 100 for lv in level]\n",
     "    for model in model_names:\n",
     "        mean = fcst_df[model].to_numpy().ravel()\n",
-    "        scores = cs_df[model].to_numpy().reshape(cs_n_windows, n_series, horizon)\n",
+    "        scores = cs_df[model].to_numpy().reshape(n_series, cs_n_windows, horizon)\n",
+    "        scores = scores.transpose(1, 0, 2)\n",
     "        # restrict scores to horizon\n",
     "        scores = scores[:,:,:horizon]\n",
     "        quantiles = np.quantile(\n",

diff --git a/neuralforecast/utils.py b/neuralforecast/utils.py
@@ -502,7 +502,8 @@ def add_conformal_distribution_intervals(
     cuts = [alpha / 200 for alpha in reversed(alphas)]
     cuts.extend(1 - alpha / 200 for alpha in alphas)
     for model in model_names:
-        scores = cs_df[model].to_numpy().reshape(cs_n_windows, n_series, horizon)
+        scores = cs_df[model].to_numpy().reshape(n_series, cs_n_windows, horizon)
+        scores = scores.transpose(1, 0, 2)
         # restrict scores to horizon
         scores = scores[:, :, :horizon]
         mean = fcst_df[model].to_numpy().reshape(1, n_series, -1)
@@ -538,7 +539,8 @@ def add_conformal_error_intervals(
     cuts = [lv / 100 for lv in level]
     for model in model_names:
         mean = fcst_df[model].to_numpy().ravel()
-        scores = cs_df[model].to_numpy().reshape(cs_n_windows, n_series, horizon)
+        scores = cs_df[model].to_numpy().reshape(n_series, cs_n_windows, horizon)
+        scores = scores.transpose(1, 0, 2)
         # restrict scores to horizon
         scores = scores[:, :, :horizon]
         quantiles = np.quantile(

diff --git a/settings.ini b/settings.ini
@@ -15,7 +15,7 @@ language = English
 custom_sidebar = True
 license = apache2
 status = 2
-requirements = coreforecast>=0.0.6 fsspec numpy>=1.21.6 pandas>=1.3.5 torch>=2.0.0 pytorch-lightning>=2.0.0 ray[tune]>=2.2.0 optuna utilsforecast>=0.0.25
+requirements = coreforecast>=0.0.6 fsspec numpy>=1.21.6 pandas>=1.3.5 torch>=2.0.0 pytorch-lightning>=2.0.0 ray[tune]>=2.2.0 optuna utilsforecast>=0.2.3
 spark_requirements = fugue pyspark>=3.5
 aws_requirements = fsspec[s3]
 dev_requirements = black gitpython hyperopt matplotlib mypy nbdev==2.3.25 polars pre-commit pyarrow ruff s3fs transformers