diff --git a/README.md b/README.md
index b8533e5..f9654f5 100644
--- a/README.md
+++ b/README.md
@@ -56,23 +56,23 @@ conformal_predictor.fit(X_train, y_train)
# Predict quantiles with the conformal predictor
ŷ_test_quantiles = conformal_predictor.predict_quantiles(
- X_test, quantiles=(0.025, 0.05, 0.1, 0.9, 0.95, 0.975)
+ X_test, quantiles=(0.025, 0.05, 0.1, 0.5, 0.9, 0.95, 0.975)
)
```
When the input data is a pandas DataFrame, the output is also a pandas DataFrame. For example, printing the head of `ŷ_test_quantiles` yields:
-| house_id | 0.025 | 0.05 | 0.1 | 0.9 | 0.95 | 0.975 |
-|-----------:|---------:|---------:|---------:|---------:|---------:|---------:|
-| 1357 | 114784.0 | 120894.3 | 131618.0 | 175760.5 | 188052.0 | 205448.8 |
-| 2367 | 67416.6 | 80073.7 | 86754.0 | 117854.1 | 127582.6 | 142321.9 |
-| 2822 | 119422.7 | 132047.7 | 138724.6 | 178526.0 | 197246.2 | 214205.6 |
-| 2126 | 94030.6 | 99850.0 | 110891.3 | 150249.2 | 164703.0 | 182528.1 |
-| 1544 | 68996.2 | 81516.3 | 88231.6 | 121774.2 | 132425.1 | 147110.2 |
+| house_id | 0.025 | 0.05 | 0.1 | 0.5 | 0.9 | 0.95 | 0.975 |
+|-----------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|
+| 1357 | 114743.7 | 120917.9 | 131752.6 | 156708.2 | 175907.8 | 187996.1 | 205443.4 |
+| 2367 | 67382.7 | 80191.7 | 86871.8 | 105807.1 | 118465.3 | 127581.2 | 142419.1 |
+| 2822 | 119068.0 | 131864.8 | 138541.6 | 159447.7 | 179227.2 | 197337.0 | 214134.1 |
+| 2126 | 93885.8 | 100040.7 | 111345.5 | 134292.7 | 150557.1 | 164595.8 | 182524.1 |
+| 1544 | 68959.8 | 81648.8 | 88364.1 | 108298.3 | 122329.6 | 132421.1 | 147225.6 |
Let's visualize the predicted quantiles on the test set:
-
+
Expand to see the code that generated the graph above
@@ -84,7 +84,7 @@ import matplotlib.ticker as ticker
%config InlineBackend.figure_format = "retina"
plt.rc("font", family="DejaVu Sans", size=10)
plt.figure(figsize=(8, 4.5))
-idx = ŷ_test.sample(50, random_state=42).sort_values().index
+idx = ŷ_test_quantiles[0.5].sample(50, random_state=42).sort_values().index
x = list(range(1, len(idx) + 1))
x_ticks = [1, *list(range(5, len(idx) + 1, 5))]
for j in range(3):
@@ -217,15 +217,15 @@ Printing the head of the forecast quantiles time series `forecast.quantiles_df(q
| Timestamp | Value_NE5_0.025 | Value_NE5_0.05 | Value_NE5_0.1 | Value_NE5_0.25 | Value_NE5_0.5 | Value_NE5_0.75 | Value_NE5_0.9 | Value_NE5_0.95 | Value_NE5_0.975 |
|:---------------|------------------:|-----------------:|----------------:|-----------------:|----------------:|-----------------:|----------------:|-----------------:|------------------:|
-| 2022‑06‑01 01h | 19197.4 | 19262.5 | 19366.4 | 19612.7 | 19786.7 | 19996.5 | 20185.5 | 20293.3 | 20358.0 |
-| 2022‑06‑01 02h | 18963.2 | 19078.7 | 19263.3 | 19463.6 | 19706.0 | 19951.4 | 20125.2 | 20265.8 | 20353.4 |
-| 2022‑06‑01 03h | 19259.1 | 19372.3 | 19551.2 | 19846.4 | 20145.2 | 20401.1 | 20630.4 | 20814.0 | 20939.6 |
-| 2022‑06‑01 04h | 21537.8 | 21745.9 | 21958.0 | 22266.8 | 22600.7 | 22939.7 | 23356.0 | 23538.7 | 23691.7 |
-| 2022‑06‑01 05h | 24304.0 | 24503.6 | 24717.5 | 25029.4 | 25602.3 | 26266.4 | 26791.6 | 26963.8 | 27359.2 |
+| 2022‑06‑01 01h | 19165.2 | 19268.3 | 19435.7 | 19663.0 | 19861.7 | 20062.2 | 20237.9 | 20337.7 | 20453.2 |
+| 2022‑06‑01 02h | 19004.0 | 19099.0 | 19226.3 | 19453.7 | 19710.7 | 19966.1 | 20170.1 | 20272.8 | 20366.9 |
+| 2022‑06‑01 03h | 19372.6 | 19493.0 | 19679.4 | 20027.6 | 20324.6 | 20546.3 | 20773.2 | 20910.3 | 21014.1 |
+| 2022‑06‑01 04h | 21936.2 | 22105.6 | 22436.0 | 22917.5 | 23308.6 | 23604.8 | 23871.0 | 24121.7 | 24351.5 |
+| 2022‑06‑01 05h | 25040.5 | 25330.5 | 25531.1 | 25910.4 | 26439.4 | 26903.2 | 27287.4 | 27493.9 | 27633.9 |
Let's visualize the forecast and its prediction interval on the test set:
-
+
Expand to see the code that generated the graph above
diff --git a/notebooks/README.ipynb b/notebooks/README.ipynb
index 2845458..0615f50 100644
--- a/notebooks/README.ipynb
+++ b/notebooks/README.ipynb
@@ -66,7 +66,17 @@
"remove_output"
]
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Support for Torch based models not available. To enable them, install \"darts\", \"u8darts[torch]\" or \"u8darts[all]\" (with pip); or \"u8darts-torch\" or \"u8darts-all\" (with conda).\n",
+ "/opt/conformal-tights-env/lib/python3.10/site-packages/statsforecast/core.py:27: TqdmExperimentalWarning: Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n",
+ " from tqdm.autonotebook import tqdm\n"
+ ]
+ }
+ ],
"source": [
"from conformal_tights import ConformalCoherentQuantileRegressor\n",
"from sklearn.datasets import fetch_openml\n",
@@ -87,7 +97,7 @@
"\n",
"# Predict quantiles with the conformal predictor\n",
"ŷ_test_quantiles = conformal_predictor.predict_quantiles(\n",
- " X_test, quantiles=(0.025, 0.05, 0.1, 0.9, 0.95, 0.975)\n",
+ " X_test, quantiles=(0.025, 0.05, 0.1, 0.5, 0.9, 0.95, 0.975)\n",
")"
]
},
@@ -110,13 +120,13 @@
{
"data": {
"text/markdown": [
- "| house_id | 0.025 | 0.05 | 0.1 | 0.9 | 0.95 | 0.975 |\n",
- "|-----------:|---------:|---------:|---------:|---------:|---------:|---------:|\n",
- "| 1357 | 114784.0 | 120894.3 | 131618.0 | 175760.5 | 188052.0 | 205448.8 |\n",
- "| 2367 | 67416.6 | 80073.7 | 86754.0 | 117854.1 | 127582.6 | 142321.9 |\n",
- "| 2822 | 119422.7 | 132047.7 | 138724.6 | 178526.0 | 197246.2 | 214205.6 |\n",
- "| 2126 | 94030.6 | 99850.0 | 110891.3 | 150249.2 | 164703.0 | 182528.1 |\n",
- "| 1544 | 68996.2 | 81516.3 | 88231.6 | 121774.2 | 132425.1 | 147110.2 |"
+ "| house_id | 0.025 | 0.05 | 0.1 | 0.5 | 0.9 | 0.95 | 0.975 |\n",
+ "|-----------:|---------:|---------:|---------:|---------:|---------:|---------:|---------:|\n",
+ "| 1357 | 114743.7 | 120917.9 | 131752.6 | 156708.2 | 175907.8 | 187996.1 | 205443.4 |\n",
+ "| 2367 | 67382.7 | 80191.7 | 86871.8 | 105807.1 | 118465.3 | 127581.2 | 142419.1 |\n",
+ "| 2822 | 119068.0 | 131864.8 | 138541.6 | 159447.7 | 179227.2 | 197337.0 | 214134.1 |\n",
+ "| 2126 | 93885.8 | 100040.7 | 111345.5 | 134292.7 | 150557.1 | 164595.8 | 182524.1 |\n",
+ "| 1544 | 68959.8 | 81648.8 | 88364.1 | 108298.3 | 122329.6 | 132421.1 | 147225.6 |"
],
"text/plain": [
""
@@ -150,7 +160,7 @@
"outputs": [
{
"data": {
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