4 merge requests!156include current development into release,!155Resolve "new release v0.12.1",!148Resolve "Create Quick-Start Notebook",!139Draft: Resolve "KZ filter"
"This notebook contains all examples as provided in Leufen et al. (2020). \n",
"Please follow the installation instructions provided in the [README](https://gitlab.version.fz-juelich.de/toar/mlair/-/blob/master/README.md) on gitlab. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Example 1\n",
"\n",
"The following cell imports MLAir and executes a minimalistic toy experiment. This cell is equivalent to Figure 2 in the manuscript."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import mlair\n",
"\n",
"# just give it a dry run without any modifications\n",
"mlair.run()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Example 2 \n",
"\n",
"In the following cell we use other station IDs provided as a list of strings (see also [JOIN-Web interface](https://join.fz-juelich.de/services/rest/surfacedata/) of the TOAR database for more details).\n",
"Moreover, we expand the `window_history_size` to 14 days and run the experiment. This cell is equivalent to Figure 3 in the manuscript."
"# expanded temporal context to 14 (days, because of default sampling=\"daily\")\n",
"window_history_size = 14\n",
"\n",
"# restart the experiment with little customisation\n",
"mlair.run(stations=stations, \n",
" window_history_size=window_history_size)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Example 3 \n",
"\n",
"The following cell loads the trained model from Example 2 and generates predictions for the two specified stations. \n",
"To ensure that the model is not retrained the keywords `create_new_model` and `train_model` are set to `False`. This cell is equivalent to Figure 4 in the manuscript. "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# our new stations to use\n",
"stations = ['DEBY002', 'DEBY079']\n",
"\n",
"# same setting for window_history_size\n",
"window_history_size = 14\n",
"\n",
"# run experiment without training\n",
"mlair.run(stations=stations, \n",
" window_history_size=window_history_size, \n",
" create_new_model=False, \n",
" train_model=False)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Example 4\n",
"\n",
"The following cell demonstrates how a user defined model can be implemented by inheriting from `AbstractModelClass`. Within the `__init__` method `super().__init__`, `set_model` and `set_compile_options` should be called. Moreover, it is possible to set custom objects by calling `set_custom_objects`. Those custom objects are used to re-load the model (see also Keras documentation). For demonstration, the loss is added as custom object which is not required because a Keras built-in function is used as loss.\n",
"\n",
"The Keras-model itself is defined in `set_model` by using the sequential or functional Keras API. All compile options can be defined in `set_compile_options`.\n",
"This cell is equivalent to Figure 5 in the manuscript."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Figure 5\n",
"import keras\n",
"from keras.losses import mean_squared_error as mse\n",
"Embedding of a custom Run Module in a modified MLAir workflow. In comparison to examples 1 to 4, this code example works on a single step deeper regarding the level of abstraction. Instead of calling the run method of MLAir, the user needs to add all stages individually and is responsible for all dependencies between the stages. By using the `Workflow` class as context manager, all stages are automatically connected with the result that all stages can easily be plugged in. This cell is equivalent to Figure 6 in the manuscript."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import logging\n",
"\n",
"class CustomStage(mlair.RunEnvironment):\n",
" \"\"\"A custom MLAir stage for demonstration.\"\"\"\n",
" def __init__(self, test_string):\n",
" super().__init__() # always call super init method\n",
" self._run(test_string) # call a class method\n",
" \n",
" def _run(self, test_string):\n",
" logging.info(\"Just running a custom stage.\")\n",
"# finally execute custom workflow in order of adding\n",
"CustomWorkflow.run()\n",
" "
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python (mlt_new)",
"language": "python",
"name": "venv"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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%% Cell type:markdown id: tags:
# MLAir (v1.0) - Examples
This notebook contains all examples as provided in Leufen et al. (2020).
Please follow the installation instructions provided in the [README](https://gitlab.version.fz-juelich.de/toar/mlair/-/blob/master/README.md) on gitlab.
%% Cell type:markdown id: tags:
## Example 1
The following cell imports MLAir and executes a minimalistic toy experiment. This cell is equivalent to Figure 2 in the manuscript.
%% Cell type:code id: tags:
``` python
importmlair
# just give it a dry run without any modifications
mlair.run()
```
%% Cell type:markdown id: tags:
## Example 2
In the following cell we use other station IDs provided as a list of strings (see also [JOIN-Web interface](https://join.fz-juelich.de/services/rest/surfacedata/) of the TOAR database for more details).
Moreover, we expand the `window_history_size` to 14 days and run the experiment. This cell is equivalent to Figure 3 in the manuscript.
# expanded temporal context to 14 (days, because of default sampling="daily")
window_history_size=14
# restart the experiment with little customisation
mlair.run(stations=stations,
window_history_size=window_history_size)
```
%% Cell type:markdown id: tags:
## Example 3
The following cell loads the trained model from Example 2 and generates predictions for the two specified stations.
To ensure that the model is not retrained the keywords `create_new_model` and `train_model` are set to `False`. This cell is equivalent to Figure 4 in the manuscript.
%% Cell type:code id: tags:
``` python
# our new stations to use
stations=['DEBY002','DEBY079']
# same setting for window_history_size
window_history_size=14
# run experiment without training
mlair.run(stations=stations,
window_history_size=window_history_size,
create_new_model=False,
train_model=False)
```
%% Cell type:markdown id: tags:
## Example 4
The following cell demonstrates how a user defined model can be implemented by inheriting from `AbstractModelClass`. Within the `__init__` method `super().__init__`, `set_model` and `set_compile_options` should be called. Moreover, it is possible to set custom objects by calling `set_custom_objects`. Those custom objects are used to re-load the model (see also Keras documentation). For demonstration, the loss is added as custom object which is not required because a Keras built-in function is used as loss.
The Keras-model itself is defined in `set_model` by using the sequential or functional Keras API. All compile options can be defined in `set_compile_options`.
This cell is equivalent to Figure 5 in the manuscript.
Embedding of a custom Run Module in a modified MLAir workflow. In comparison to examples 1 to 4, this code example works on a single step deeper regarding the level of abstraction. Instead of calling the run method of MLAir, the user needs to add all stages individually and is responsible for all dependencies between the stages. By using the `Workflow` class as context manager, all stages are automatically connected with the result that all stages can easily be plugged in. This cell is equivalent to Figure 6 in the manuscript.