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split documentation notebooks by topic #223

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merged 3 commits into from
Sep 29, 2023
Merged

split documentation notebooks by topic #223

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3281059
split nbs/docs by topic
jmoralez Sep 29, 2023
c7ab7da
fix glob
jmoralez Sep 29, 2023
2555cd4
update setup
jmoralez Sep 29, 2023
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3 changes: 1 addition & 2 deletions .gitignore
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Original file line number Diff line number Diff line change
Expand Up @@ -149,12 +149,11 @@ Gemfile*
.jekyll-cache

# series files
nbs/data
/**/data/

# nbdev
nbs/_docs
_proc/
index_files
_docs
nbs/data
nbs/docs/**/*.xls*
2 changes: 1 addition & 1 deletion nbs/distributed.models.ray.xgb.ipynb
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Expand Up @@ -17,7 +17,7 @@
"source": [
"# RayXGBForecast\n",
"\n",
"> dask XGBoost forecaster"
"> ray XGBoost forecaster"
]
},
{
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503 changes: 0 additions & 503 deletions nbs/docs/electricity_peak_forecasting.ipynb
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This file was deleted.

128 changes: 97 additions & 31 deletions nbs/docs/end_to_end_walkthrough.ipynb → ...ting-started/end_to_end_walkthrough.ipynb
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Expand Up @@ -9,9 +9,7 @@
"source": [
"#| hide\n",
"%load_ext autoreload\n",
"%autoreload 2\n",
"import os\n",
"os.chdir('..')"
"%autoreload 2"
]
},
{
Expand Down Expand Up @@ -210,20 +208,30 @@
{
"cell_type": "code",
"execution_count": null,
"id": "d1f275bf-735f-43c7-994c-0e7430360f5d",
"id": "1b9587c4-3765-4891-9f56-5e45b7605ada",
"metadata": {},
"outputs": [],
"source": [
"fig = plot_series(df, max_insample_length=24 * 14)\n",
"fig.savefig('figs/end_to_end_walkthrough__eda.png', bbox_inches='tight')"
"fig = plot_series(df, max_insample_length=24 * 14)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6c3f8c45-add2-44ba-9bf5-8b24185fec89",
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"fig.savefig('../../figs/end_to_end_walkthrough__eda.png', bbox_inches='tight')"
]
},
{
"cell_type": "markdown",
"id": "2284936b-ac30-4b4b-b7c4-afa2b4059dc2",
"metadata": {},
"source": [
"![](../figs/end_to_end_walkthrough__eda.png)"
"![](../../figs/end_to_end_walkthrough__eda.png)"
]
},
{
Expand Down Expand Up @@ -392,20 +400,30 @@
{
"cell_type": "code",
"execution_count": null,
"id": "cbaad91f-7f67-4a25-a061-96c0f627d432",
"id": "a7a70560-e483-4b16-8a60-12dbc30363b7",
"metadata": {},
"outputs": [],
"source": [
"fig = plot_series(prep)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ec5116c3-6740-498e-95af-e8bdde4cdd9a",
"metadata": {},
"outputs": [],
"source": [
"fig = plot_series(prep)\n",
"fig.savefig('figs/end_to_end_walkthrough__differences.png', bbox_inches='tight')"
"#| hide\n",
"fig.savefig('../../figs/end_to_end_walkthrough__differences.png', bbox_inches='tight')"
]
},
{
"cell_type": "markdown",
"id": "8ab507ad-f2a8-4dde-87ad-5b102c91da0b",
"metadata": {},
"source": [
"![](../figs/end_to_end_walkthrough__differences.png)"
"![](../../figs/end_to_end_walkthrough__differences.png)"
]
},
{
Expand Down Expand Up @@ -1032,7 +1050,7 @@
"id": "7f29f543-ea21-48ca-bb3c-253ceb1be968",
"metadata": {},
"source": [
"If you want to do some transformation to your target before computing the features and then re-apply it after predicting you can use the `target_transforms` argument, which takes a list of transformations. You can find the implemented ones in `mlforecast.target_transforms` or you can implement your own as described in the [target transformations guide](target_transforms_guide.html#custom-transformations)."
"If you want to do some transformation to your target before computing the features and then re-apply it after predicting you can use the `target_transforms` argument, which takes a list of transformations. You can find the implemented ones in `mlforecast.target_transforms` or you can implement your own as described in the [target transformations guide](../how-to-guides/target_transforms_guide.html#custom-transformations)."
]
},
{
Expand Down Expand Up @@ -1394,12 +1412,20 @@
{
"cell_type": "code",
"execution_count": null,
"id": "50c9daf7-5002-4851-b03f-d9c62c3db433",
"id": "d95f87ca-ce35-4756-b4d4-34a6ef17bd01",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ef280687-ba68-4fb0-8c8b-1e3ff13b0056",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"np.testing.assert_allclose(preds['Naive'], last_vals['y'])"
]
},
Expand Down Expand Up @@ -1644,20 +1670,30 @@
{
"cell_type": "code",
"execution_count": null,
"id": "ed5ebbe2-5d9e-4898-b8be-6cefff2ac440",
"id": "0a0c694a-560b-4fa7-8193-f7c4eb5dba4a",
"metadata": {},
"outputs": [],
"source": [
"fig = plot_series(df, preds, max_insample_length=24 * 7)\n",
"fig.savefig('figs/end_to_end_walkthrough__predictions.png', bbox_inches='tight')"
"fig = plot_series(df, preds, max_insample_length=24 * 7)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "50b79ccf-1372-4eae-a8eb-6eec1999fea2",
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"fig.savefig('../../figs/end_to_end_walkthrough__predictions.png', bbox_inches='tight')"
]
},
{
"cell_type": "markdown",
"id": "2977ed1b-5b78-4ec5-a5bf-e91f57b374d6",
"metadata": {},
"source": [
"![](../figs/end_to_end_walkthrough__predictions.png)"
"![](../../figs/end_to_end_walkthrough__predictions.png)"
]
},
{
Expand Down Expand Up @@ -2065,20 +2101,30 @@
{
"cell_type": "code",
"execution_count": null,
"id": "e79a786f-a5f0-4034-be17-96283db304ab",
"id": "aa2d6a1a-e0f6-4d7f-9aac-92a19770f23b",
"metadata": {},
"outputs": [],
"source": [
"fig = plot_series(cv_result, cv_result.drop(columns='cutoff'), max_insample_length=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2a4528a9-6bde-430f-8bb3-977725ea057a",
"metadata": {},
"outputs": [],
"source": [
"fig = plot_series(cv_result, cv_result.drop(columns='cutoff'), max_insample_length=0)\n",
"fig.savefig('figs/end_to_end_walkthrough__cv.png', bbox_inches='tight')"
"#| hide\n",
"fig.savefig('../../figs/end_to_end_walkthrough__cv.png', bbox_inches='tight')"
]
},
{
"cell_type": "markdown",
"id": "c8d11f3d-0d7e-4c66-a5bb-acdaca176555",
"metadata": {},
"source": [
"![](../figs/end_to_end_walkthrough__cv.png)"
"![](../../figs/end_to_end_walkthrough__cv.png)"
]
},
{
Expand Down Expand Up @@ -2499,20 +2545,30 @@
{
"cell_type": "code",
"execution_count": null,
"id": "c28b5282-9731-4799-a23d-c7feef799206",
"id": "538ed4cb-2e96-4615-ae5f-bab230d80137",
"metadata": {},
"outputs": [],
"source": [
"fig = plot_series(cv.cv_preds_, cv.cv_preds_.drop(columns='window'), max_insample_length=0)\n",
"fig.savefig('figs/end_to_end_walkthrough__lgbcv.png', bbox_inches='tight')"
"fig = plot_series(cv.cv_preds_, cv.cv_preds_.drop(columns='window'), max_insample_length=0)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d5645508-7293-45d8-97a9-8f8c1bac63a0",
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"fig.savefig('../../figs/end_to_end_walkthrough__lgbcv.png', bbox_inches='tight')"
]
},
{
"cell_type": "markdown",
"id": "47e0fcfe-052b-4418-b2c5-62473425b42a",
"metadata": {},
"source": [
"![](../figs/end_to_end_walkthrough__lgbcv.png)"
"![](../../figs/end_to_end_walkthrough__lgbcv.png)"
]
},
{
Expand All @@ -2526,23 +2582,33 @@
{
"cell_type": "code",
"execution_count": null,
"id": "292d31f4-4980-444f-9124-9f4f4a6921ac",
"id": "5158cae8-1424-4c57-8f4a-02d0b65521ee",
"metadata": {},
"outputs": [],
"source": [
"final_fcst = MLForecast.from_cv(cv)\n",
"final_fcst.fit(df)\n",
"preds = final_fcst.predict(48)\n",
"fig = plot_series(df, preds, max_insample_length=24 * 14)\n",
"fig.savefig('figs/end_to_end_walkthrough__final_forecast.png', bbox_inches='tight')"
"fig = plot_series(df, preds, max_insample_length=24 * 14)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7e545230-deb7-41c7-9a87-d837262c2384",
"metadata": {},
"outputs": [],
"source": [
"#| hide\n",
"fig.savefig('../../figs/end_to_end_walkthrough__final_forecast.png', bbox_inches='tight')"
]
},
{
"cell_type": "markdown",
"id": "dced80f3-8424-4b0c-8bfc-5f8b36954826",
"metadata": {},
"source": [
"![](../figs/end_to_end_walkthrough__final_forecast.png)"
"![](../../figs/end_to_end_walkthrough__final_forecast.png)"
]
}
],
Expand Down
34 changes: 7 additions & 27 deletions nbs/docs/install.ipynb → nbs/docs/getting-started/install.ipynb
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Expand Up @@ -63,26 +63,6 @@
"* `pip install \"mlforecast<0.4.0\"` to install any version prior to 0.4.0"
]
},
{
"cell_type": "markdown",
"id": "3d3acf50-ace7-4c00-b935-36a801c79dc7",
"metadata": {},
"source": [
"#### Distributed training"
]
},
{
"cell_type": "markdown",
"id": "8912889b-cd26-4b37-a43f-bc672cdec9f0",
"metadata": {},
"source": [
"If you want to perform distributed training you have to include the [dask](https://www.dask.org/) extra:\n",
"\n",
"`pip install \"mlforecast[dask]\"`\n",
"\n",
"and also either [LightGBM](https://github.com/microsoft/LightGBM/tree/master/python-package) or [XGBoost](https://xgboost.readthedocs.io/en/latest/install.html#python)."
]
},
{
"cell_type": "markdown",
"id": "6772b367-7e1b-4612-8bcd-26039b2badf3",
Expand Down Expand Up @@ -132,22 +112,22 @@
},
{
"cell_type": "markdown",
"id": "40307de9-f0ec-4f84-a324-8a36d66e7fdb",
"id": "a7207f1c-1b37-4ec1-90fb-05b0ae372372",
"metadata": {},
"source": [
"#### Distributed training"
"### Distributed training"
]
},
{
"cell_type": "markdown",
"id": "83dab9f7-e614-42ef-95c0-a76ae20f74a7",
"id": "be5df835-f2bc-4be6-9ae3-e5723b84d97c",
"metadata": {},
"source": [
"If you want to perform distributed training you also have to install [dask](https://www.dask.org/):\n",
"\n",
"`conda install -c conda-forge dask`\n",
"If you want to perform distributed training you can use either dask, ray or spark. Once you know which framework you want to use you can include its extra:\n",
"\n",
"and also either [LightGBM](https://github.com/microsoft/LightGBM/tree/master/python-package) or [XGBoost](https://xgboost.readthedocs.io/en/latest/install.html#python)."
"* dask: `pip install \"mlforecast[dask]\"`\n",
"* ray: `pip install \"mlforecast[ray]\"`\n",
"* spark: `pip install \"mlforecast[spark]\"`"
]
},
{
Expand Down
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