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[FIX] Conformal prediction issues #1179

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merged 6 commits into from
Oct 15, 2024
Merged

[FIX] Conformal prediction issues #1179

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aedcc1b
fix_conformal_issues
elephaint Oct 14, 2024
67cbbe5
rerun_tutorial
elephaint Oct 15, 2024
a9b78a6
rerun_tutorial
elephaint Oct 15, 2024
7dcaa72
Merge branch 'main' into fix/conformal_issues
elephaint Oct 15, 2024
b5aca81
Merge branch 'main' into fix/conformal_issues
elephaint Oct 15, 2024
5d2ea22
fix_other_conformal_method
elephaint Oct 15, 2024
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240 changes: 3 additions & 237 deletions nbs/docs/tutorials/20_conformal_prediction.ipynb
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3 changes: 2 additions & 1 deletion nbs/mint.json
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Original file line number Diff line number Diff line change
Expand Up @@ -63,7 +63,8 @@
"group":"Probabilistic Forecasting",
"pages":[
"docs/tutorials/uncertainty_quantification.html",
"docs/tutorials/longhorizon_probabilistic.html"
"docs/tutorials/longhorizon_probabilistic.html",
"docs/tutorials/conformal_prediction.html"
]
},
{
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6 changes: 4 additions & 2 deletions nbs/utils.ipynb
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Expand Up @@ -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",
Expand Down Expand Up @@ -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",
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6 changes: 4 additions & 2 deletions neuralforecast/utils.py
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Expand Up @@ -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)
Expand Down Expand Up @@ -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(
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2 changes: 1 addition & 1 deletion settings.ini
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Expand Up @@ -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
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