Skip to content
Snippets Groups Projects
Commit 7bea9e64 authored by lukas leufen's avatar lukas leufen
Browse files

docs for preprocessing

parent fc400763
No related branches found
No related tags found
3 merge requests!125Release v0.10.0,!124Update Master to new version v0.10.0,!91WIP: Resolve "create sphinx docu"
Pipeline #35517 failed
Stage: test
Stage: docs
Stage: pages
Stage: deploy
Show whitespace changes
Inline Side-by-side
src/run_modules/pre_processing.py
+ 67
− 25
View file @ 7bea9e64
......@@ -22,18 +22,38 @@ DEFAULT_KWARGS_LIST = ["limit_nan_fill", "window_history_size", "window_lead_tim
class PreProcessing(RunEnvironment):
"""
Pre-process your data by using this class. It includes time tracking and uses the experiment setup to look for data
and stores it if not already in local disk. Further, it provides this data as a generator and checks for valid
stations (in this context: valid=data available). Finally, it splits the data into valid training, validation and
testing subsets.
Pre-process your data by using this class.
Schedule of pre-processing:
#. load and check valid stations (either download or load from disk)
#. split subsets (train, val, test, train & val)
#. create small report on data metrics
Required objects [scope] from data store:
* all elements from `DEFAULT_ARGS_LIST` in scope preprocessing for general data loading
* all elements from `DEFAULT_ARGS_LIST` in scopes [train, val, test, train_val] for custom subset settings
* `fraction_of_training` [.]
* `experiment_path` [.]
* `use_all_stations_on_all_data_sets` [.]
Optional objects
* all elements from `DEFAULT_KWARGS_LIST` in scope preprocessing for general data loading
* all elements from `DEFAULT_KWARGS_LIST` in scopes [train, val, test, train_val] for custom subset settings
Sets
* `stations` in [., train, val, test, train_val]
* `generator` in [train, val, test, train_val]
* `transformation` [.]
Creates
* all input and output data in `data_path`
* latex reports in `experiment_path/latex_report`
"""
def __init__(self):
# create run framework
"""Set up and run pre-processing."""
super().__init__()
#
self._run()
def _run(self):
......@@ -46,6 +66,7 @@ class PreProcessing(RunEnvironment):
self.report_pre_processing()
def report_pre_processing(self):
"""Log some metrics on data and create latex report."""
logging.debug(20 * '##')
n_train = len(self.data_store.get('generator', 'train'))
n_val = len(self.data_store.get('generator', 'val'))
......@@ -61,25 +82,33 @@ class PreProcessing(RunEnvironment):
def create_latex_report(self):
"""
This function creates tables with information on the station meta data and a summary on subset sample sizes.
Create tables with information on the station meta data and a summary on subset sample sizes.
* station_sample_size.md: see table below
* station_sample_size.tex: same as table below, but as latex table
* station_sample_size.md: see table below as markdown
* station_sample_size.tex: same as table below as latex table
* station_sample_size_short.tex: reduced size table without any meta data besides station ID, as latex table
All tables are stored inside experiment_path inside the folder latex_report. The table format (e.g. which meta
data is highlighted) is currently hardcoded to have a stable table style. If further styles are needed, it is
better to add an additional style than modifying the existing table styles.
+------------+-------------------------------------------+---------------+---------------+---------------+---------+-------+--------+
| stat. ID | station_name | station_lon | station_lat | station_alt | train | val | test |
|------------|-------------------------------------------|---------------|---------------|---------------|---------|-------|--------|
+============+===========================================+===============+===============+===============+=========+=======+========+
| DEBW013 | Stuttgart Bad Cannstatt | 9.2297 | 48.8088 | 235 | 1434 | 712 | 1080 |
+------------+-------------------------------------------+---------------+---------------+---------------+---------+-------+--------+
| DEBW076 | Baden-Baden | 8.2202 | 48.7731 | 148 | 3037 | 722 | 710 |
+------------+-------------------------------------------+---------------+---------------+---------------+---------+-------+--------+
| DEBW087 | Schwäbische_Alb | 9.2076 | 48.3458 | 798 | 3044 | 714 | 1087 |
+------------+-------------------------------------------+---------------+---------------+---------------+---------+-------+--------+
| DEBW107 | Tübingen | 9.0512 | 48.5077 | 325 | 1803 | 715 | 1087 |
+------------+-------------------------------------------+---------------+---------------+---------------+---------+-------+--------+
| DEBY081 | Garmisch-Partenkirchen/Kreuzeckbahnstraße | 11.0631 | 47.4764 | 735 | 2935 | 525 | 714 |
+------------+-------------------------------------------+---------------+---------------+---------------+---------+-------+--------+
| # Stations | nan | nan | nan | nan | 6 | 6 | 6 |
+------------+-------------------------------------------+---------------+---------------+---------------+---------+-------+--------+
| # Samples | nan | nan | nan | nan | 12253 | 3388 | 4678 |
+------------+-------------------------------------------+---------------+---------------+---------------+---------+-------+--------+
"""
meta_data = ['station_name', 'station_lon', 'station_lat', 'station_alt']
......@@ -113,9 +142,11 @@ class PreProcessing(RunEnvironment):
def split_train_val_test(self) -> None:
"""
Splits all subsets. Currently: train, val, test and train_val (actually this is only the merge of train and val,
but as an separate generator). IMPORTANT: Do not change to order of the execution of create_set_split. The train
subset needs always to be executed at first, to set a proper transformation.
Split data into subsets.
Currently: train, val, test and train_val (actually this is only the merge of train and val, but as an separate
generator). IMPORTANT: Do not change to order of the execution of create_set_split. The train subset needs
always to be executed at first, to set a proper transformation.
"""
fraction_of_training = self.data_store.get("fraction_of_training")
stations = self.data_store.get("stations")
......@@ -131,13 +162,16 @@ class PreProcessing(RunEnvironment):
@staticmethod
def split_set_indices(total_length: int, fraction: float) -> Tuple[slice, slice, slice, slice]:
"""
create the training, validation and test subset slice indices for given total_length. The test data consists on
(1-fraction) of total_length (fraction*len:end). Train and validation data therefore are made from fraction of
total_length (0:fraction*len). Train and validation data is split by the factor 0.8 for train and 0.2 for
validation. In addition, split_set_indices returns also the combination of training and validation subset.
Create the training, validation and test subset slice indices for given total_length.
The test data consists on (1-fraction) of total_length (fraction*len:end). Train and validation data therefore
are made from fraction of total_length (0:fraction*len). Train and validation data is split by the factor 0.8
for train and 0.2 for validation. In addition, split_set_indices returns also the combination of training and
validation subset.
:param total_length: list with all objects to split
:param fraction: ratio between test and union of train/val data
:return: slices for each subset in the order: train, val, test, train_val
"""
pos_test_split = int(total_length * fraction)
......@@ -147,11 +181,13 @@ class PreProcessing(RunEnvironment):
train_val_index = slice(0, pos_test_split)
return train_index, val_index, test_index, train_val_index
def create_set_split(self, index_list: slice, set_name) -> None:
def create_set_split(self, index_list: slice, set_name: str) -> None:
"""
Create subsets and store in data store.
Create the subset for given split index and stores the DataGenerator with given set name in data store as
`generator`. Checks for all valid stations using the default (kw)args for given scope and creates the
DataGenerator for all valid stations. Also sets all transformation information, if subset is training set. Make
`generator`. Check for all valid stations using the default (kw)args for given scope and create the
DataGenerator for all valid stations. Also set all transformation information, if subset is training set. Make
sure, that the train set is executed first, and all other subsets afterwards.
:param index_list: list of all stations to use for the set. If attribute use_all_stations_on_all_data_sets=True,
......@@ -161,16 +197,19 @@ class PreProcessing(RunEnvironment):
args = self.data_store.create_args_dict(DEFAULT_ARGS_LIST, scope=set_name)
kwargs = self.data_store.create_args_dict(DEFAULT_KWARGS_LIST, scope=set_name)
stations = args["stations"]
if self.data_store.get("use_all_stations_on_all_data_sets", scope=set_name):
if self.data_store.get("use_all_stations_on_all_data_sets"):
set_stations = stations
else:
set_stations = stations[index_list]
logging.debug(f"{set_name.capitalize()} stations (len={len(set_stations)}): {set_stations}")
# validate set
set_stations = self.check_valid_stations(args, kwargs, set_stations, load_tmp=False, name=set_name)
self.data_store.set("stations", set_stations, scope=set_name)
# create set generator and store
set_args = self.data_store.create_args_dict(DEFAULT_ARGS_LIST, scope=set_name)
data_set = DataGenerator(**set_args, **kwargs)
self.data_store.set("generator", data_set, scope=set_name)
# extract transformation from train set
if set_name == "train":
self.data_store.set("transformation", data_set.transformation)
......@@ -178,8 +217,10 @@ class PreProcessing(RunEnvironment):
def check_valid_stations(args: Dict, kwargs: Dict, all_stations: List[str], load_tmp=True, save_tmp=True,
name=None):
"""
Check if all given stations in `all_stations` are valid. Valid means, that there is data available for the given
time range (is included in `kwargs`). The shape and the loading time are logged in debug mode.
Check if all given stations in `all_stations` are valid.
Valid means, that there is data available for the given time range (is included in `kwargs`). The shape and the
loading time are logged in debug mode.
:param args: Dictionary with required parameters for DataGenerator class (`data_path`, `network`, `stations`,
`variables`, `interpolate_dim`, `target_dim`, `target_var`).
......@@ -187,6 +228,7 @@ class PreProcessing(RunEnvironment):
`window_lead_time`).
:param all_stations: All stations to check.
:param name: name to display in the logging info message
:return: Corrected list containing only valid station IDs.
"""
t_outer = TimeTracking()
......
src/run_modules/training.py
+ 4
− 2
View file @ 7bea9e64
......@@ -19,7 +19,9 @@ from src.run_modules.run_environment import RunEnvironment
class Training(RunEnvironment):
"""
Perform training.
Train your model with this module.
Schedule of training:
#. set_generators(): set generators for training, validation and testing and distribute according to batch size
#. make_predict_function(): create predict function before distribution on multiple nodes (detailed information
in method description)
......@@ -56,7 +58,7 @@ class Training(RunEnvironment):
"""
def __init__(self):
"""Set up training."""
"""Set up and run training."""
super().__init__()
self.model: keras.Model = self.data_store.get("model", "model")
self.train_set: Union[Distributor, None] = None
......
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Please register or to comment