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feature(STEF-274): Added support for builtin timeseries plotting. #14

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feature(STEF-274): Added support for builtin timeseries plotting. #14

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b968730
feature(STEF-274): Added support for builtin timeseries plotting.
egordm Mar 7, 2025
e316ff5
feature(STEF-274): Added support for builtin timeseries plotting.
egordm Mar 7, 2025
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105 changes: 35 additions & 70 deletions examples/02. Train a model and perform backtest.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -16,27 +16,23 @@
},
{
"cell_type": "code",
"execution_count": null,
"id": "066796c6",
"metadata": {
"ExecuteTime": {
"end_time": "2022-02-09T16:41:59.248166Z",
"start_time": "2022-02-09T16:41:59.231129Z"
}
},
"outputs": [],
"metadata": {},
"source": [
"import pandas as pd\n",
"from openstef.pipeline.train_create_forecast_backtest import train_model_and_forecast_back_test\n",
"from openstef.metrics.figure import plot_feature_importance\n",
"from openstef.data_classes.model_specifications import ModelSpecificationDataClass\n",
"from openstef.data_classes.prediction_job import PredictionJobDataClass # TODO, import from openstef when availavle\n",
"from openstef.plotting.load_forecast_plotter import LoadForecastPlotter\n",
"\n",
"# Set plotly as the default pandas plotting backend\n",
"pd.options.plotting.backend = 'plotly'\n",
"import plotly.io as pio\n",
"pio.renderers.default = \"plotly_mimetype+notebook\""
]
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
Expand All @@ -49,15 +45,8 @@
},
{
"cell_type": "code",
"execution_count": null,
"id": "86ba5377",
"metadata": {
"ExecuteTime": {
"end_time": "2022-02-09T16:43:47.845079Z",
"start_time": "2022-02-09T16:43:47.631699Z"
}
},
"outputs": [],
"metadata": {},
"source": [
"# Define properties of training/prediction. We call this a 'prediction_job' \n",
"pj=PredictionJobDataClass(id=287,\n",
Expand All @@ -79,7 +68,9 @@
"\n",
"# Load input data\n",
"input_data = pd.read_csv('data/get_model_input_pid_287.csv', index_col='index', parse_dates=True)\n"
]
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
Expand All @@ -94,15 +85,8 @@
},
{
"cell_type": "code",
"execution_count": null,
"id": "3695e036",
"metadata": {
"ExecuteTime": {
"end_time": "2022-02-09T16:48:17.967356Z",
"start_time": "2022-02-09T16:48:03.036309Z"
}
},
"outputs": [],
"metadata": {},
"source": [
"# Perform the backtest\n",
"n_folds = 2\n",
Expand All @@ -118,7 +102,9 @@
"# If n_folds>1, model is a list of models. In that case, only use the first model\n",
"if n_folds>1:\n",
" model=model[0]"
]
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
Expand All @@ -136,34 +122,22 @@
},
{
"cell_type": "code",
"execution_count": null,
"id": "4b0c71ae",
"metadata": {
"ExecuteTime": {
"end_time": "2022-02-09T16:48:22.244769Z",
"start_time": "2022-02-09T16:48:20.133250Z"
}
},
"outputs": [],
"metadata": {},
"source": [
"for horizon in set(forecast.horizon):\n",
" fig = forecast.loc[forecast.horizon==horizon,['quantile_P10','quantile_P30',\n",
" 'quantile_P50','quantile_P70','quantile_P90','realised','forecast']].plot(\n",
" title=f\"Horizon: {horizon}\")\n",
" fig.update_traces(\n",
" line=dict(color=\"green\", width=1), fill='tonexty', fillcolor='rgba(0, 255, 0, 0.1)',\n",
" selector=lambda x: 'quantile' in x.name and x.name != 'quantile_P10')\n",
" fig.update_traces(\n",
" line=dict(color=\"green\", width=1),\n",
" selector=lambda x: 'quantile_P10' == x.name)\n",
" fig.update_traces(\n",
" line=dict(color=\"red\", width=2),\n",
" selector=lambda x: 'realised' in x.name)\n",
" fig.update_traces(\n",
" line=dict(color=\"blue\", width=2),\n",
" selector=lambda x: 'forecast' in x.name)\n",
" data = forecast.loc[forecast.horizon==horizon]\n",
"\n",
" fig = LoadForecastPlotter().plot(\n",
" realized=data[\"realised\"],\n",
" forecast=data[\"forecast\"],\n",
" quantiles=data.filter(regex=\"quantile\")\n",
" )\n",
" fig.update_layout(dict(title=f'Horizon {horizon}'))\n",
" fig.show()"
]
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
Expand All @@ -175,20 +149,16 @@
},
{
"cell_type": "code",
"execution_count": null,
"id": "54426833",
"metadata": {
"ExecuteTime": {
"end_time": "2022-02-09T16:48:22.333170Z",
"start_time": "2022-02-09T16:48:22.246296Z"
},
"scrolled": false
},
"outputs": [],
"source": [
"forecast['err']=forecast['realised']-forecast['forecast']\n",
"forecast['err'].plot()"
]
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
Expand All @@ -200,18 +170,18 @@
},
{
"cell_type": "code",
"execution_count": null,
"id": "8e73f034",
"metadata": {},
"outputs": [],
"source": [
"mea = forecast.pivot_table(index='horizon', values=['err'], aggfunc=lambda x: x.abs().mean())\n",
"mea.index=mea.index.astype(str)\n",
"fig = mea.plot(kind='bar')\n",
"fig.update_layout(dict(title='MAE',\n",
" xaxis=dict(title='horizon'),\n",
" yaxis=dict(title='MAE [MW]')))"
]
],
"outputs": [],
"execution_count": null
},
{
"cell_type": "markdown",
Expand All @@ -223,18 +193,13 @@
},
{
"cell_type": "code",
"execution_count": null,
"id": "e6cb2814",
"metadata": {
"ExecuteTime": {
"end_time": "2022-02-09T16:48:31.314864Z",
"start_time": "2022-02-09T16:48:31.281873Z"
}
},
"outputs": [],
"metadata": {},
"source": [
"plot_feature_importance(model.feature_importance_dataframe)"
]
],
"outputs": [],
"execution_count": null
}
],
"metadata": {
Expand Down
2 changes: 1 addition & 1 deletion requirements.txt
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Original file line number Diff line number Diff line change
@@ -1,3 +1,3 @@
jupyter==1.0.0
jupyterlab~=3.6.2
openstef==3.4.7
openstef==3.4.64
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