diff --git a/.gitlab/issue_templates/release.md b/.gitlab/issue_templates/release.md
index a95cf033eed919339c6c1734638542c3e0cdbc57..c8289cdd1be4ab02a61c6feb0db9db7cb6ca40d3 100644
--- a/.gitlab/issue_templates/release.md
+++ b/.gitlab/issue_templates/release.md
@@ -14,6 +14,7 @@ vX.Y.Z
* [ ] Adjust `changelog.md` (see template for changelog)
* [ ] Update version number in `mlair/__ init__.py`
* [ ] Create new dist file: `python3 setup.py sdist bdist_wheel`
+* [ ] Add new dist file `mlair-X.Y.Z-py3-none-any.whl` to git
* [ ] Update file link `distribution file (current version)` in `README.md`
* [ ] Update file link in `docs/_source/installation.rst`
* [ ] Commit + push
diff --git a/CHANGELOG.md b/CHANGELOG.md
index b11a169c854465c5ea932f00f5da5a1688df7c18..34795b8333df846d5383fc2d8eca4b40517aab73 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,7 +1,25 @@
# Changelog
All notable changes to this project will be documented in this file.
-## v1.4.0 - 2021-07-27 - <release description>
+## v1.5.0 - 2021-11-11 - new uncertainty estimation
+
+### general:
+* introduces method to estimate sample uncertainty
+* improved multiprocessing
+* last release with tensorflow v1 support
+
+### new features:
+* test set sample uncertainty estmation during postprocessing (#333)
+* support of Kolmogorov Zurbenko filter for data handlers with filters (#334)
+
+### technical:
+* new communication scheme for multiprocessing (#321, #322)
+* improved error reporting (#323)
+* feature importance returns now unaggregated results (#335)
+* error metrics are reported for all competitors (#332)
+* minor bugfixes and refacs (#330, #326, #329, #325, #324, #320, #337)
+
+## v1.4.0 - 2021-07-27 - new model classes and data handlers, improved usability and transparency
### general:
* many technical adjustments to improve usability and transparency of MLAir
diff --git a/HPC_setup/create_runscripts_HPC.sh b/HPC_setup/create_runscripts_HPC.sh
index 61d361b5a6c737570d040aee9ec95f74c63439b4..730aa52ef42144826bd000d88c0fc81c9d508de0 100755
--- a/HPC_setup/create_runscripts_HPC.sh
+++ b/HPC_setup/create_runscripts_HPC.sh
@@ -102,6 +102,7 @@ cat <<EOT > ${cur}/run_${hpcsys}_batch.bash
#SBATCH --output=${hpclogging}mlt-out.%j
#SBATCH --error=${hpclogging}mlt-err.%j
#SBATCH --time=08:00:00
+#SBATCH --gres=gpu:4
#SBATCH --mail-type=ALL
#SBATCH --mail-user=${email}
diff --git a/README.md b/README.md
index 0e1df0561d15b743a85b0981b552a1444b6cc38c..a5fce2e53d82e3cff75a4f61000c616c62cbec69 100644
--- a/README.md
+++ b/README.md
@@ -25,13 +25,15 @@ HPC systems, see [here](#special-instructions-for-installation-on-jülich-hpc-sy
* Install all **requirements** from [`requirements.txt`](https://gitlab.version.fz-juelich.de/toar/mlair/-/blob/master/requirements.txt)
preferably in a virtual environment. You can use `pip install -r requirements.txt` to install all requirements at
once. Note, we recently updated the version of Cartopy and there seems to be an ongoing
- [issue](https://github.com/SciTools/cartopy/issues/1552) when installing numpy and Cartopy at the same time. If you
- run into trouble, you could use `cat requirements.txt | cut -f1 -d"#" | sed '/^\s*$/d' | xargs -L 1 pip install`
- instead.
+ [issue](https://github.com/SciTools/cartopy/issues/1552) when installing **numpy** and **Cartopy** at the same time.
+ If you run into trouble, you could use
+ `cat requirements.txt | cut -f1 -d"#" | sed '/^\s*$/d' | xargs -L 1 pip install` instead or first install numpy with
+ `pip install numpy==<version_from_reqs>` followed be the default installation of requirements. For the latter, you can
+ also use `grep numpy requirements.txt | xargs pip install`.
* Installation of **MLAir**:
* Either clone MLAir from the [gitlab repository](https://gitlab.version.fz-juelich.de/toar/mlair.git)
and use it without installation (beside the requirements)
- * or download the distribution file ([current version](https://gitlab.version.fz-juelich.de/toar/mlair/-/blob/master/dist/mlair-1.4.0-py3-none-any.whl))
+ * or download the distribution file ([current version](https://gitlab.version.fz-juelich.de/toar/mlair/-/blob/master/dist/mlair-1.5.0-py3-none-any.whl))
and install it via `pip install <dist_file>.whl`. In this case, you can simply import MLAir in any python script
inside your virtual environment using `import mlair`.
* (tf) Currently, TensorFlow-1.13 is mentioned in the requirements. We already tested the TensorFlow-1.15 version and couldn't
diff --git a/dist/mlair-1.5.0-py3-none-any.whl b/dist/mlair-1.5.0-py3-none-any.whl
new file mode 100644
index 0000000000000000000000000000000000000000..34495d960b009737fb40bec6dfe3a96effd14c02
Binary files /dev/null and b/dist/mlair-1.5.0-py3-none-any.whl differ
diff --git a/docs/_source/installation.rst b/docs/_source/installation.rst
index 27543ac109439e487756cc211ecc47be946c586c..c87e64b217b4207185cfc662fdf00d2f7e891cc5 100644
--- a/docs/_source/installation.rst
+++ b/docs/_source/installation.rst
@@ -26,7 +26,7 @@ Installation of MLAir
* Install all requirements from `requirements.txt <https://gitlab.version.fz-juelich.de/toar/machinelearningtools/-/blob/master/requirements.txt>`_
preferably in a virtual environment
* Either clone MLAir from the `gitlab repository <https://gitlab.version.fz-juelich.de/toar/machinelearningtools.git>`_
-* or download the distribution file (`current version <https://gitlab.version.fz-juelich.de/toar/mlair/-/blob/master/dist/mlair-1.4.0-py3-none-any.whl>`_)
+* or download the distribution file (`current version <https://gitlab.version.fz-juelich.de/toar/mlair/-/blob/master/dist/mlair-1.5.0-py3-none-any.whl>`_)
and install it via :py:`pip install <dist_file>.whl`. In this case, you can simply
import MLAir in any python script inside your virtual environment using :py:`import mlair`.
* (tf) Currently, TensorFlow-1.13 is mentioned in the requirements. We already tested the TensorFlow-1.15 version and couldn't
diff --git a/docs/requirements_docs.txt b/docs/requirements_docs.txt
index a39acca8a7cf887237f595d7992960ac10233a85..8ccf3ba6515d31ebd2b35901d3c9e58734d653d8 100644
--- a/docs/requirements_docs.txt
+++ b/docs/requirements_docs.txt
@@ -3,4 +3,5 @@ sphinx-autoapi==1.3.0
sphinx-autodoc-typehints==1.10.3
sphinx-rtd-theme==0.4.3
#recommonmark==0.6.0
-m2r2==0.2.5
\ No newline at end of file
+m2r2==0.2.5
+docutils<0.18
\ No newline at end of file
diff --git a/mlair/__init__.py b/mlair/__init__.py
index f760f9b0fa4b87bde1f6ee409626f4428083d895..75359e1773edea55ecc47556a83a465510fac6c8 100644
--- a/mlair/__init__.py
+++ b/mlair/__init__.py
@@ -1,6 +1,6 @@
__version_info__ = {
'major': 1,
- 'minor': 4,
+ 'minor': 5,
'micro': 0,
}
diff --git a/mlair/configuration/defaults.py b/mlair/configuration/defaults.py
index 242dcd31d01134b75f70a12c2708d4c99bb94301..ca569720dc41d95621d0613a2170cc4d9d46c082 100644
--- a/mlair/configuration/defaults.py
+++ b/mlair/configuration/defaults.py
@@ -44,14 +44,19 @@ DEFAULT_TEST_END = "2017-12-31"
DEFAULT_TEST_MIN_LENGTH = 90
DEFAULT_TRAIN_VAL_MIN_LENGTH = 180
DEFAULT_USE_ALL_STATIONS_ON_ALL_DATA_SETS = True
-DEFAULT_EVALUATE_BOOTSTRAPS = True
-DEFAULT_CREATE_NEW_BOOTSTRAPS = False
-DEFAULT_NUMBER_OF_BOOTSTRAPS = 20
-DEFAULT_BOOTSTRAP_TYPE = "singleinput"
-DEFAULT_BOOTSTRAP_METHOD = "shuffle"
+DEFAULT_DO_UNCERTAINTY_ESTIMATE = True
+DEFAULT_UNCERTAINTY_ESTIMATE_BLOCK_LENGTH = "1m"
+DEFAULT_UNCERTAINTY_ESTIMATE_EVALUATE_COMPETITORS = True
+DEFAULT_UNCERTAINTY_ESTIMATE_N_BOOTS = 1000
+DEFAULT_EVALUATE_FEATURE_IMPORTANCE = True
+DEFAULT_FEATURE_IMPORTANCE_CREATE_NEW_BOOTSTRAPS = False
+DEFAULT_FEATURE_IMPORTANCE_N_BOOTS = 20
+DEFAULT_FEATURE_IMPORTANCE_BOOTSTRAP_TYPE = "singleinput"
+DEFAULT_FEATURE_IMPORTANCE_BOOTSTRAP_METHOD = "shuffle"
DEFAULT_PLOT_LIST = ["PlotMonthlySummary", "PlotStationMap", "PlotClimatologicalSkillScore", "PlotTimeSeries",
- "PlotCompetitiveSkillScore", "PlotBootstrapSkillScore", "PlotConditionalQuantiles",
- "PlotAvailability", "PlotAvailabilityHistogram", "PlotDataHistogram", "PlotPeriodogram"]
+ "PlotCompetitiveSkillScore", "PlotFeatureImportanceSkillScore", "PlotConditionalQuantiles",
+ "PlotAvailability", "PlotAvailabilityHistogram", "PlotDataHistogram", "PlotPeriodogram",
+ "PlotSampleUncertaintyFromBootstrap"]
DEFAULT_SAMPLING = "daily"
DEFAULT_DATA_ORIGIN = {"cloudcover": "REA", "humidity": "REA", "pblheight": "REA", "press": "REA", "relhum": "REA",
"temp": "REA", "totprecip": "REA", "u": "REA", "v": "REA", "no": "", "no2": "", "o3": "",
diff --git a/mlair/data_handler/__init__.py b/mlair/data_handler/__init__.py
index 495b6e7c8604a839a084a2b78a54563c13eb06e6..d119977802038155af0d07d99c75c665622a148c 100644
--- a/mlair/data_handler/__init__.py
+++ b/mlair/data_handler/__init__.py
@@ -9,7 +9,7 @@ __author__ = 'Lukas Leufen, Felix Kleinert'
__date__ = '2020-04-17'
-from .bootstraps import BootStraps
+from .input_bootstraps import Bootstraps
from .iterator import KerasIterator, DataCollection
from .default_data_handler import DefaultDataHandler
from .abstract_data_handler import AbstractDataHandler
diff --git a/mlair/data_handler/data_handler_single_station.py b/mlair/data_handler/data_handler_single_station.py
index 8e95e76365181ee76f91a91319b912f2626a223a..88a57d108e4533968eeb9a65aabf575fae085704 100644
--- a/mlair/data_handler/data_handler_single_station.py
+++ b/mlair/data_handler/data_handler_single_station.py
@@ -593,6 +593,12 @@ class DataHandlerSingleStation(AbstractDataHandler):
non_nan_history = self.history.dropna(dim=dim)
non_nan_label = self.label.dropna(dim=dim)
non_nan_observation = self.observation.dropna(dim=dim)
+ if non_nan_label.coords[dim].shape[0] == 0:
+ raise ValueError(f'self.label consist of NaNs only - station {self.station} is therefore dropped')
+ if non_nan_history.coords[dim].shape[0] == 0:
+ raise ValueError(f'self.history consist of NaNs only - station {self.station} is therefore dropped')
+ if non_nan_observation.coords[dim].shape[0] == 0:
+ raise ValueError(f'self.observation consist of NaNs only - station {self.station} is therefore dropped')
intersect = reduce(np.intersect1d, (non_nan_history.coords[dim].values, non_nan_label.coords[dim].values,
non_nan_observation.coords[dim].values))
diff --git a/mlair/data_handler/bootstraps.py b/mlair/data_handler/input_bootstraps.py
similarity index 91%
rename from mlair/data_handler/bootstraps.py
rename to mlair/data_handler/input_bootstraps.py
index e03881484bfc9b8275ede8a4432072c74643994a..187f09050bb39a953ac58c2b7fca54b6a207aed1 100644
--- a/mlair/data_handler/bootstraps.py
+++ b/mlair/data_handler/input_bootstraps.py
@@ -28,12 +28,13 @@ class BootstrapIterator(Iterator):
_position: int = None
- def __init__(self, data: "BootStraps", method):
- assert isinstance(data, BootStraps)
+ def __init__(self, data: "Bootstraps", method, return_reshaped=False):
+ assert isinstance(data, Bootstraps)
self._data = data
self._dimension = data.bootstrap_dimension
self.boot_dim = "boots"
self._method = method
+ self._return_reshaped = return_reshaped
self._collection = self.create_collection(self._data.data, self._dimension)
self._position = 0
@@ -46,12 +47,15 @@ class BootstrapIterator(Iterator):
raise NotImplementedError
def _reshape(self, d):
- if isinstance(d, list):
- return list(map(lambda x: self._reshape(x), d))
- # return list(map(lambda x: np.rollaxis(x, -1, 0).reshape(x.shape[0] * x.shape[-1], *x.shape[1:-1]), d))
+ if self._return_reshaped:
+ if isinstance(d, list):
+ return list(map(lambda x: self._reshape(x), d))
+ # return list(map(lambda x: np.rollaxis(x, -1, 0).reshape(x.shape[0] * x.shape[-1], *x.shape[1:-1]), d))
+ else:
+ shape = d.shape
+ return np.rollaxis(d, -1, 0).reshape(shape[0] * shape[-1], *shape[1:-1])
else:
- shape = d.shape
- return np.rollaxis(d, -1, 0).reshape(shape[0] * shape[-1], *shape[1:-1])
+ return d
def _to_numpy(self, d):
if isinstance(d, list):
@@ -75,8 +79,8 @@ class BootstrapIterator(Iterator):
class BootstrapIteratorSingleInput(BootstrapIterator):
_position: int = None
- def __init__(self, *args):
- super().__init__(*args)
+ def __init__(self, *args, **kwargs):
+ super().__init__(*args, **kwargs)
def __next__(self):
"""Return next element or stop iteration."""
@@ -107,8 +111,8 @@ class BootstrapIteratorSingleInput(BootstrapIterator):
class BootstrapIteratorVariable(BootstrapIterator):
- def __init__(self, *args):
- super().__init__(*args)
+ def __init__(self, *args, **kwargs):
+ super().__init__(*args, **kwargs)
def __next__(self):
"""Return next element or stop iteration."""
@@ -140,8 +144,8 @@ class BootstrapIteratorVariable(BootstrapIterator):
class BootstrapIteratorBranch(BootstrapIterator):
- def __init__(self, *args):
- super().__init__(*args)
+ def __init__(self, *args, **kwargs):
+ super().__init__(*args, **kwargs)
def __next__(self):
try:
@@ -184,7 +188,7 @@ class MeanBootstraps:
return np.ones_like(data) * self._mean
-class BootStraps(Iterable):
+class Bootstraps(Iterable):
"""
Main class to perform bootstrap operations.
diff --git a/mlair/helpers/filter.py b/mlair/helpers/filter.py
index 543cff3624577ac617733b8b593c5f52f25196b3..36c93b04486fc9be013af2c4f34d2b3ee1bd84c2 100644
--- a/mlair/helpers/filter.py
+++ b/mlair/helpers/filter.py
@@ -768,6 +768,7 @@ class KolmogorovZurbenkoFilterMovingWindow(KolmogorovZurbenkoBaseClass):
def firwin_kzf(m, k):
+ m, k = int(m), int(k)
coef = np.ones(m)
for i in range(1, k):
t = np.zeros((m, m + i * (m - 1)))
diff --git a/mlair/helpers/helpers.py b/mlair/helpers/helpers.py
index 16b36921773a4af131065063de23963a56cb4c65..679f5a28fc564d56cd6f3794ee8fe8e1877b2b4c 100644
--- a/mlair/helpers/helpers.py
+++ b/mlair/helpers/helpers.py
@@ -118,8 +118,10 @@ def remove_items(obj: Union[List, Dict, Tuple], items: Any):
def remove_from_list(list_obj, item_list):
"""Remove implementation for lists."""
- if len(items) > 1:
+ if len(item_list) > 1:
return [e for e in list_obj if e not in item_list]
+ elif len(item_list) == 0:
+ return list_obj
else:
list_obj = list_obj.copy()
try:
diff --git a/mlair/helpers/statistics.py b/mlair/helpers/statistics.py
index 87f780f9a6133edfcb2f9c71c2956b92f332e915..af7975f3a042163a885f590c6624076fe91f03aa 100644
--- a/mlair/helpers/statistics.py
+++ b/mlair/helpers/statistics.py
@@ -287,7 +287,7 @@ class SkillScores:
combination_strings = [f"{first}-{second}" for (first, second) in combinations]
return combinations, combination_strings
- def skill_scores(self) -> pd.DataFrame:
+ def skill_scores(self) -> [pd.DataFrame, pd.DataFrame]:
"""
Calculate skill scores for all combinations of model names.
@@ -296,6 +296,7 @@ class SkillScores:
ahead_names = list(self.external_data[self.ahead_dim].data)
combinations, combination_strings = self.get_model_name_combinations()
skill_score = pd.DataFrame(index=combination_strings)
+ count = pd.DataFrame(index=combination_strings)
for iahead in ahead_names:
data = self.external_data.sel({self.ahead_dim: iahead})
skill_score[iahead] = [self.general_skill_score(data,
@@ -303,7 +304,12 @@ class SkillScores:
reference_name=second,
observation_name=self.observation_name)
for (first, second) in combinations]
- return skill_score
+ count[iahead] = [self.get_count(data,
+ forecast_name=first,
+ reference_name=second,
+ observation_name=self.observation_name)
+ for (first, second) in combinations]
+ return skill_score, count
def climatological_skill_scores(self, internal_data: Data, forecast_name: str) -> xr.DataArray:
"""
@@ -355,7 +361,7 @@ class SkillScores:
**kwargs)
def general_skill_score(self, data: Data, forecast_name: str, reference_name: str,
- observation_name: str = None) -> np.ndarray:
+ observation_name: str = None, dim: str = "index") -> np.ndarray:
r"""
Calculate general skill score based on mean squared error.
@@ -368,14 +374,29 @@ class SkillScores:
"""
if observation_name is None:
observation_name = self.observation_name
- data = data.dropna("index")
+ data = data.dropna(dim)
observation = data.sel(type=observation_name)
forecast = data.sel(type=forecast_name)
reference = data.sel(type=reference_name)
mse = mean_squared_error
- skill_score = 1 - mse(observation, forecast) / mse(observation, reference)
+ skill_score = 1 - mse(observation, forecast, dim=dim) / mse(observation, reference, dim=dim)
return skill_score.values
+ def get_count(self, data: Data, forecast_name: str, reference_name: str,
+ observation_name: str = None) -> np.ndarray:
+ r"""
+ Calculate general skill score based on mean squared error.
+
+ :param data: internal data containing data for observation, forecast and reference
+ :param observation_name: name of observation
+ :param forecast_name: name of forecast
+ :param reference_name: name of reference
+
+ :return: skill score of forecast
+ """
+ data = data.dropna("index")
+ return data.count("index").max().values
+
def skill_score_pre_calculations(self, data: Data, observation_name: str, forecast_name: str) -> Tuple[np.ndarray,
np.ndarray,
np.ndarray,
@@ -481,3 +502,58 @@ class SkillScores:
return monthly_mean
+
+def create_single_bootstrap_realization(data: xr.DataArray, dim_name_time: str) -> xr.DataArray:
+ """
+ Return a bootstraped realization of data
+ :param data: data from which to draw ONE bootstrap realization
+ :param dim_name_time: name of time dimension
+ :return: bootstrapped realization of data
+ """
+
+ num_of_blocks = data.coords[dim_name_time].shape[0]
+ boot_idx = np.random.choice(num_of_blocks, size=num_of_blocks, replace=True)
+ return data.isel({dim_name_time: boot_idx})
+
+
+def calculate_average(data: xr.DataArray, **kwargs) -> xr.DataArray:
+ """
+ Calculate mean of data
+ :param data: data for which to calculate mean
+ :return: mean of data
+ """
+ return data.mean(**kwargs)
+
+
+def create_n_bootstrap_realizations(data: xr.DataArray, dim_name_time: str, dim_name_model: str, n_boots: int = 1000,
+ dim_name_boots: str = 'boots') -> xr.DataArray:
+ """
+ Create n bootstrap realizations and calculate averages across realizations
+
+ :param data: original data from which to create bootstrap realizations
+ :param dim_name_time: name of time dimension
+ :param dim_name_model: name of model dimension
+ :param n_boots: number of bootstap realizations
+ :param dim_name_boots: name of bootstap dimension
+ :return:
+ """
+ res_dims = [dim_name_boots]
+ dims = list(data.dims)
+ coords = {dim_name_boots: range(n_boots), dim_name_model: data.coords[dim_name_model] }
+ if len(dims) > 1:
+ res_dims = res_dims + dims[1:]
+ res = xr.DataArray(np.nan, dims=res_dims, coords=coords)
+ for boot in range(n_boots):
+ res[boot] = (calculate_average(
+ create_single_bootstrap_realization(data, dim_name_time=dim_name_time),
+ dim=dim_name_time, skipna=True))
+ return res
+
+
+
+
+
+
+
+
+
diff --git a/mlair/model_modules/abstract_model_class.py b/mlair/model_modules/abstract_model_class.py
index 8898a6b2d0591328f2bb7010ccbfe144a48ca40b..7ecaad9cf077100f3b9a34b02c99e172d141a218 100644
--- a/mlair/model_modules/abstract_model_class.py
+++ b/mlair/model_modules/abstract_model_class.py
@@ -38,6 +38,13 @@ class AbstractModelClass(ABC):
self._input_shape = input_shape
self._output_shape = self.__extract_from_tuple(output_shape)
+ def load_model(self, name: str, compile: bool = False):
+ hist = self.model.history
+ self.model = keras.models.load_model(name)
+ self.model.history = hist
+ if compile is True:
+ self.model.compile(**self.compile_options)
+
def __getattr__(self, name: str) -> Any:
"""
Is called if __getattribute__ is not able to find requested attribute.
diff --git a/mlair/model_modules/keras_extensions.py b/mlair/model_modules/keras_extensions.py
index d890e7b0ff3beea812d8fc7766433a84d65a1ebe..8b99acd0f5723d3b00ec1bd0098712753da21b52 100644
--- a/mlair/model_modules/keras_extensions.py
+++ b/mlair/model_modules/keras_extensions.py
@@ -3,6 +3,7 @@
__author__ = 'Lukas Leufen, Felix Kleinert'
__date__ = '2020-01-31'
+import copy
import logging
import math
import pickle
@@ -199,12 +200,18 @@ class ModelCheckpointAdvanced(ModelCheckpoint):
if self.verbose > 0: # pragma: no branch
print('\nEpoch %05d: save to %s' % (epoch + 1, file_path))
with open(file_path, "wb") as f:
- pickle.dump(callback["callback"], f)
+ c = copy.copy(callback["callback"])
+ if hasattr(c, "model"):
+ c.model = None
+ pickle.dump(c, f)
else:
with open(file_path, "wb") as f:
if self.verbose > 0: # pragma: no branch
print('\nEpoch %05d: save to %s' % (epoch + 1, file_path))
- pickle.dump(callback["callback"], f)
+ c = copy.copy(callback["callback"])
+ if hasattr(c, "model"):
+ c.model = None
+ pickle.dump(c, f)
clbk_type = TypedDict("clbk_type", {"name": str, str: Callback, "path": str})
@@ -346,6 +353,8 @@ class CallbackHandler:
for pos, callback in enumerate(self.__callbacks):
path = callback["path"]
clb = pickle.load(open(path, "rb"))
+ if clb.model is None and hasattr(self._checkpoint, "model"):
+ clb.model = self._checkpoint.model
self._update_callback(pos, clb)
def update_checkpoint(self, history_name: str = "hist") -> None:
diff --git a/mlair/plotting/data_insight_plotting.py b/mlair/plotting/data_insight_plotting.py
index 6180493741c030d5dfdfcfa8972035619632c8aa..8d4ab2689b1eea24dc9d39d53b04e51405a3a874 100644
--- a/mlair/plotting/data_insight_plotting.py
+++ b/mlair/plotting/data_insight_plotting.py
@@ -21,6 +21,8 @@ from mlair.data_handler import DataCollection
from mlair.helpers import TimeTrackingWrapper, to_list, remove_items
from mlair.plotting.abstract_plot_class import AbstractPlotClass
+matplotlib.use("Agg")
+
@TimeTrackingWrapper
class PlotStationMap(AbstractPlotClass): # pragma: no cover
@@ -632,7 +634,10 @@ class PlotPeriodogram(AbstractPlotClass): # pragma: no cover
self._plot_total(raw=True)
self._plot_total(raw=False)
if multiple > 1:
- self._plot_difference(label_names)
+ self._plot_difference(label_names, plot_name_add="_last")
+ self._prepare_pgram(generator, pos, multiple, use_multiprocessing=use_multiprocessing,
+ use_last_input_value=False)
+ self._plot_difference(label_names, plot_name_add="_first")
@staticmethod
def _has_filter_dimension(g, pos):
@@ -649,7 +654,7 @@ class PlotPeriodogram(AbstractPlotClass): # pragma: no cover
return check_data.coords[filter_dim].shape[0], check_data.coords[filter_dim].values.tolist()
@TimeTrackingWrapper
- def _prepare_pgram(self, generator, pos, multiple=1, use_multiprocessing=False):
+ def _prepare_pgram(self, generator, pos, multiple=1, use_multiprocessing=False, use_last_input_value=True):
"""
Create periodogram data.
"""
@@ -663,7 +668,8 @@ class PlotPeriodogram(AbstractPlotClass): # pragma: no cover
plot_data_raw_single = dict()
plot_data_mean_single = dict()
self.f_index = np.logspace(-3, 0 if self._sampling == "daily" else np.log10(24), 1000)
- raw_data_single = self._prepare_pgram_parallel_gen(generator, m, pos, use_multiprocessing)
+ raw_data_single = self._prepare_pgram_parallel_gen(generator, m, pos, use_multiprocessing,
+ use_last_input_value=use_last_input_value)
for var in raw_data_single.keys():
pgram_com = []
pgram_mean = 0
@@ -715,7 +721,7 @@ class PlotPeriodogram(AbstractPlotClass): # pragma: no cover
raw_data_single[var_str] = raw_data_single[var_str] + [(f, pgram)]
return raw_data_single
- def _prepare_pgram_parallel_gen(self, generator, m, pos, use_multiprocessing):
+ def _prepare_pgram_parallel_gen(self, generator, m, pos, use_multiprocessing, use_last_input_value=True):
"""Implementation of data preprocessing using parallel generator element processing."""
raw_data_single = dict()
res = []
@@ -723,14 +729,15 @@ class PlotPeriodogram(AbstractPlotClass): # pragma: no cover
pool = multiprocessing.Pool(
min([psutil.cpu_count(logical=False), len(generator), 16])) # use only physical cpus
output = [
- pool.apply_async(f_proc_2, args=(g, m, pos, self.variables_dim, self.time_dim, self.f_index))
+ pool.apply_async(f_proc_2, args=(g, m, pos, self.variables_dim, self.time_dim, self.f_index,
+ use_last_input_value))
for g in generator]
for i, p in enumerate(output):
res.append(p.get())
pool.close()
else:
for g in generator:
- res.append(f_proc_2(g, m, pos, self.variables_dim, self.time_dim, self.f_index))
+ res.append(f_proc_2(g, m, pos, self.variables_dim, self.time_dim, self.f_index, use_last_input_value))
for res_dict in res:
for k, v in res_dict.items():
if k not in raw_data_single.keys():
@@ -816,8 +823,8 @@ class PlotPeriodogram(AbstractPlotClass): # pragma: no cover
pdf_pages.close()
plt.close('all')
- def _plot_difference(self, label_names):
- plot_name = f"{self.plot_name}_{self._sampling}_{self._add_text}_filter.pdf"
+ def _plot_difference(self, label_names, plot_name_add = ""):
+ plot_name = f"{self.plot_name}_{self._sampling}_{self._add_text}_filter{plot_name_add}.pdf"
plot_path = os.path.join(os.path.abspath(self.plot_folder), plot_name)
logging.info(f"... plotting {plot_name}")
pdf_pages = matplotlib.backends.backend_pdf.PdfPages(plot_path)
@@ -846,24 +853,33 @@ class PlotPeriodogram(AbstractPlotClass): # pragma: no cover
plt.close('all')
-def f_proc(var, d_var, f_index, time_dim="datetime"): # pragma: no cover
+def f_proc(var, d_var, f_index, time_dim="datetime", use_last_value=True): # pragma: no cover
var_str = str(var)
t = (d_var[time_dim] - d_var[time_dim][0]).astype("timedelta64[h]").values / np.timedelta64(1, "D")
if len(d_var.shape) > 1: # use only max value if dimensions are remaining (e.g. max(window) -> latest value)
to_remove = remove_items(d_var.coords.dims, time_dim)
for e in to_list(to_remove):
- d_var = d_var.sel({e: d_var[e].max()})
+ d_var = d_var.sel({e: d_var[e].max() if use_last_value is True else d_var[e].min()})
pgram = LombScargle(t, d_var.values.flatten(), nterms=1, normalization="psd").power(f_index)
# f, pgram = LombScargle(t, d_var.values.flatten(), nterms=1, normalization="psd").autopower()
return var_str, f_index, pgram
-def f_proc_2(g, m, pos, variables_dim, time_dim, f_index): # pragma: no cover
+def f_proc_2(g, m, pos, variables_dim, time_dim, f_index, use_last_value): # pragma: no cover
raw_data_single = dict()
if hasattr(g.id_class, "lazy"):
g.id_class.load_lazy() if g.id_class.lazy is True else None
if m == 0:
d = g.id_class._data
+ if d is None:
+ window_dim = g.id_class.window_dim
+ history = g.id_class.history
+ last_entry = history.coords[window_dim][-1]
+ d1 = history.sel({window_dim: last_entry}, drop=True)
+ label = g.id_class.label
+ first_entry = label.coords[window_dim][0]
+ d2 = label.sel({window_dim: first_entry}, drop=True)
+ d = (d1, d2)
else:
gd = g.id_class
filter_sel = {"filter": gd.input_data.coords["filter"][m - 1]}
@@ -871,7 +887,7 @@ def f_proc_2(g, m, pos, variables_dim, time_dim, f_index): # pragma: no cover
d = d[pos] if isinstance(d, tuple) else d
for var in d[variables_dim].values:
d_var = d.loc[{variables_dim: var}].squeeze().dropna(time_dim)
- var_str, f, pgram = f_proc(var, d_var, f_index)
+ var_str, f, pgram = f_proc(var, d_var, f_index, use_last_value=use_last_value)
raw_data_single[var_str] = [(f, pgram)]
if hasattr(g.id_class, "lazy"):
g.id_class.clean_up() if g.id_class.lazy is True else None
diff --git a/mlair/plotting/postprocessing_plotting.py b/mlair/plotting/postprocessing_plotting.py
index 7ba84656cc00a89a7ec88efcc30bf665e0849e2f..43f1864f7354c1f711bb886f4f97eda56439ab89 100644
--- a/mlair/plotting/postprocessing_plotting.py
+++ b/mlair/plotting/postprocessing_plotting.py
@@ -15,6 +15,7 @@ import pandas as pd
import seaborn as sns
import xarray as xr
from matplotlib.backends.backend_pdf import PdfPages
+from matplotlib.offsetbox import AnchoredText
from mlair import helpers
from mlair.data_handler.iterator import DataCollection
@@ -30,7 +31,7 @@ logging.getLogger('matplotlib').setLevel(logging.WARNING)
@TimeTrackingWrapper
-class PlotMonthlySummary(AbstractPlotClass):
+class PlotMonthlySummary(AbstractPlotClass): # pragma: no cover
"""
Show a monthly summary over all stations for each lead time ("ahead") as box and whiskers plot.
@@ -128,7 +129,7 @@ class PlotMonthlySummary(AbstractPlotClass):
logging.debug("... start plotting")
color_palette = [matplotlib.colors.cnames["green"]] + sns.color_palette("Blues_d",
self._window_lead_time).as_hex()
- ax = sns.boxplot(x='index', y='values', hue='ahead', data=data.compute(), whis=1., palette=color_palette,
+ ax = sns.boxplot(x='index', y='values', hue='ahead', data=data.compute(), whis=1.5, palette=color_palette,
flierprops={'marker': '.', 'markersize': 1}, showmeans=True,
meanprops={'markersize': 1, 'markeredgecolor': 'k'})
ylabel = self._spell_out_chemical_concentrations(target_var)
@@ -137,7 +138,7 @@ class PlotMonthlySummary(AbstractPlotClass):
@TimeTrackingWrapper
-class PlotConditionalQuantiles(AbstractPlotClass):
+class PlotConditionalQuantiles(AbstractPlotClass): # pragma: no cover
"""
Create cond.quantile plots as originally proposed by Murphy, Brown and Chen (1989) [But in log scale].
@@ -381,7 +382,7 @@ class PlotConditionalQuantiles(AbstractPlotClass):
@TimeTrackingWrapper
-class PlotClimatologicalSkillScore(AbstractPlotClass):
+class PlotClimatologicalSkillScore(AbstractPlotClass): # pragma: no cover
"""
Create plot of climatological skill score after Murphy (1988) as box plot over all stations.
@@ -448,7 +449,7 @@ class PlotClimatologicalSkillScore(AbstractPlotClass):
fig, ax = plt.subplots()
if not score_only:
fig.set_size_inches(11.7, 8.27)
- sns.boxplot(x="terms", y="data", hue="ahead", data=self._data, ax=ax, whis=1., palette="Blues_d",
+ sns.boxplot(x="terms", y="data", hue="ahead", data=self._data, ax=ax, whis=1.5, palette="Blues_d",
showmeans=True, meanprops={"markersize": 1, "markeredgecolor": "k"}, flierprops={"marker": "."})
ax.axhline(y=0, color="grey", linewidth=.5)
ax.set(ylabel=f"{self._label_add(score_only)}skill score", xlabel="", title="summary of all stations",
@@ -473,7 +474,7 @@ class PlotClimatologicalSkillScore(AbstractPlotClass):
@TimeTrackingWrapper
-class PlotCompetitiveSkillScore(AbstractPlotClass):
+class PlotCompetitiveSkillScore(AbstractPlotClass): # pragma: no cover
"""
Create competitive skill score plot.
@@ -491,12 +492,12 @@ class PlotCompetitiveSkillScore(AbstractPlotClass):
"""
- def __init__(self, data: pd.DataFrame, plot_folder=".", model_setup="NN"):
+ def __init__(self, data: Dict[str, pd.DataFrame], plot_folder=".", model_setup="NN"):
"""Initialise."""
super().__init__(plot_folder, f"skill_score_competitive_{model_setup}")
self._model_setup = model_setup
self._labels = None
- self._data = self._prepare_data(data)
+ self._data = self._prepare_data(helpers.remove_items(data, "total"))
default_plot_name = self.plot_name
# draw full detail plot
self.plot_name = default_plot_name + "_full_detail"
@@ -538,7 +539,7 @@ class PlotCompetitiveSkillScore(AbstractPlotClass):
fig, ax = plt.subplots(figsize=(size, size * 0.8))
data = self._filter_comparisons(self._data) if single_model_comparison is True else self._data
order = self._create_pseudo_order(data)
- sns.boxplot(x="comparison", y="data", hue="ahead", data=data, whis=1., ax=ax, palette="Blues_d",
+ sns.boxplot(x="comparison", y="data", hue="ahead", data=data, whis=1.5, ax=ax, palette="Blues_d",
showmeans=True, meanprops={"markersize": 3, "markeredgecolor": "k"}, flierprops={"marker": "."},
order=order)
ax.axhline(y=0, color="grey", linewidth=.5)
@@ -553,7 +554,7 @@ class PlotCompetitiveSkillScore(AbstractPlotClass):
fig, ax = plt.subplots()
data = self._filter_comparisons(self._data) if single_model_comparison is True else self._data
order = self._create_pseudo_order(data)
- sns.boxplot(y="comparison", x="data", hue="ahead", data=data, whis=1., ax=ax, palette="Blues_d",
+ sns.boxplot(y="comparison", x="data", hue="ahead", data=data, whis=1.5, ax=ax, palette="Blues_d",
showmeans=True, meanprops={"markersize": 3, "markeredgecolor": "k"}, flierprops={"marker": "."},
order=order)
ax.axvline(x=0, color="grey", linewidth=.5)
@@ -590,14 +591,9 @@ class PlotCompetitiveSkillScore(AbstractPlotClass):
@TimeTrackingWrapper
-class PlotBootstrapSkillScore(AbstractPlotClass):
+class PlotFeatureImportanceSkillScore(AbstractPlotClass): # pragma: no cover
"""
- Create plot of climatological skill score after Murphy (1988) as box plot over all stations.
-
- A forecast time step (called "ahead") is separately shown to highlight the differences for each prediction time
- step. Either each single term is plotted (score_only=False) or only the resulting scores CASE I to IV are displayed
- (score_only=True, default). Y-axis is adjusted following the data and not hard coded. The plot is saved under
- plot_folder path with name skill_score_clim_{extra_name_tag}{model_setup}.pdf and resolution of 500dpi.
+ Create plot of feature importance analysis.
By passing a list `separate_vars` containing variable names, a second plot is created showing the `separate_vars`
and the remaining variables side by side with different scaling.
@@ -612,7 +608,8 @@ class PlotBootstrapSkillScore(AbstractPlotClass):
def __init__(self, data: Dict, plot_folder: str = ".", model_setup: str = "", separate_vars: List = None,
sampling: str = "daily", ahead_dim: str = "ahead", bootstrap_type: str = None,
- bootstrap_method: str = None):
+ bootstrap_method: str = None, boot_dim: str = "boots", model_name: str = "NN",
+ branch_names: list = None, ylim: tuple = None):
"""
Set attributes and create plot.
@@ -625,24 +622,32 @@ class PlotBootstrapSkillScore(AbstractPlotClass):
:param bootstrap_annotation: additional information to use in the file name (default: None)
"""
annotation = ["_".join([s for s in ["", bootstrap_type, bootstrap_method] if s is not None])][0]
- super().__init__(plot_folder, f"skill_score_bootstrap_{model_setup}{annotation}")
+ super().__init__(plot_folder, f"feature_importance_{model_setup}{annotation}")
if separate_vars is None:
separate_vars = ['o3']
self._labels = None
self._x_name = "boot_var"
self._ahead_dim = ahead_dim
+ self._boot_dim = boot_dim
self._boot_type = self._set_bootstrap_type(bootstrap_type)
self._boot_method = self._set_bootstrap_method(bootstrap_method)
+ self._number_of_bootstraps = 0
+ self._branches_names = branch_names
+ self._ylim = ylim
- self._title = f"Bootstrap analysis ({self._boot_method}, {self._boot_type})"
self._data = self._prepare_data(data, sampling)
+ self._set_title(model_name)
if "branch" in self._data.columns:
plot_name = self.plot_name
for branch in self._data["branch"].unique():
- self._title = f"Bootstrap analysis ({self._boot_method}, {self._boot_type}, {branch})"
+ self._set_title(model_name, branch)
self._plot(branch=branch)
self.plot_name = f"{plot_name}_{branch}"
self._save()
+ if len(set(separate_vars).intersection(self._data[self._x_name].unique())) > 0:
+ self.plot_name += '_separated'
+ self._plot(branch=branch, separate_vars=separate_vars)
+ self._save(bbox_inches='tight')
else:
self._plot()
self._save()
@@ -655,6 +660,21 @@ class PlotBootstrapSkillScore(AbstractPlotClass):
def _set_bootstrap_type(boot_type):
return {"singleinput": "single input"}.get(boot_type, boot_type)
+ def _set_title(self, model_name, branch=None):
+ title_d = {"single input": "Single Inputs", "branch": "Input Branches", "variable": "Variables"}
+ base_title = f"{model_name}\nImportance of {title_d[self._boot_type]}"
+
+ additional = []
+ if branch is not None:
+ branch_name = self._branches_names[branch] if self._branches_names is not None else branch
+ additional.append(branch_name)
+ if self._number_of_bootstraps > 1:
+ additional.append(f"n={self._number_of_bootstraps}")
+ additional_title = ", ".join(additional)
+ if len(additional_title) > 0:
+ additional_title = f" ({additional_title})"
+ self._title = base_title + additional_title
+
@staticmethod
def _set_bootstrap_method(boot_method):
return {"zero_mean": "zero mean", "shuffle": "shuffled"}.get(boot_method, boot_method)
@@ -671,14 +691,16 @@ class PlotBootstrapSkillScore(AbstractPlotClass):
"""
station_dim = "station"
data = helpers.dict_to_xarray(data, station_dim).sortby(self._x_name)
+ data = data.transpose(station_dim, self._ahead_dim, self._boot_dim, self._x_name)
if self._boot_type == "single input":
number_tags = self._get_number_tag(data.coords[self._x_name].values, split_by='_')
new_boot_coords = self._return_vars_without_number_tag(data.coords[self._x_name].values, split_by='_',
keep=1, as_unique=True)
- values = data.values.reshape((data.shape[0], len(new_boot_coords), len(number_tags), data.shape[-1]))
- data = xr.DataArray(values, coords={station_dim: data.coords["station"], self._x_name: new_boot_coords,
- "branch": number_tags, self._ahead_dim: data.coords[self._ahead_dim]},
- dims=[station_dim, self._x_name, "branch", self._ahead_dim])
+ values = data.values.reshape((*data.shape[:3], len(number_tags), len(new_boot_coords)))
+ data = xr.DataArray(values, coords={station_dim: data.coords[station_dim], self._x_name: new_boot_coords,
+ "branch": number_tags, self._ahead_dim: data.coords[self._ahead_dim],
+ self._boot_dim: data.coords[self._boot_dim]},
+ dims=[station_dim, self._ahead_dim, self._boot_dim, "branch", self._x_name])
else:
try:
new_boot_coords = self._return_vars_without_number_tag(data.coords[self._x_name].values, split_by='_',
@@ -690,6 +712,7 @@ class PlotBootstrapSkillScore(AbstractPlotClass):
self._labels = [str(i) + sampling_letter for i in data.coords[self._ahead_dim].values]
if station_dim not in data.dims:
data = data.expand_dims(station_dim)
+ self._number_of_bootstraps = np.unique(data.coords[self._boot_dim].values).shape[0]
return data.to_dataframe("data").reset_index(level=np.arange(len(data.dims)).tolist())
@staticmethod
@@ -738,10 +761,10 @@ class PlotBootstrapSkillScore(AbstractPlotClass):
if separate_vars is None:
self._plot_all_variables(branch)
else:
- self._plot_selected_variables(separate_vars)
+ self._plot_selected_variables(separate_vars, branch)
- def _plot_selected_variables(self, separate_vars: List):
- data = self._data
+ def _plot_selected_variables(self, separate_vars: List, branch=None):
+ data = self._data if branch is None else self._data[self._data["branch"] == str(branch)]
self.raise_error_if_separate_vars_do_not_exist(data, separate_vars, self._x_name)
all_variables = self._get_unique_values_from_column_of_df(data, self._x_name)
remaining_vars = helpers.remove_items(all_variables, separate_vars)
@@ -749,22 +772,30 @@ class PlotBootstrapSkillScore(AbstractPlotClass):
data_second = self._select_data(df=data, variables=remaining_vars, column_name=self._x_name)
fig, ax = plt.subplots(nrows=1, ncols=2, gridspec_kw={'width_ratios': [len(separate_vars),
- len(remaining_vars)]})
+ len(remaining_vars)]},
+ figsize=(len(remaining_vars),len(remaining_vars)/2.))
if len(separate_vars) > 1:
first_box_width = .8
else:
- first_box_width = 2.
+ first_box_width = .8
- sns.boxplot(x=self._x_name, y="data", hue=self._ahead_dim, data=data_first, ax=ax[0], whis=1.,
+ sns.boxplot(x=self._x_name, y="data", hue=self._ahead_dim, data=data_first, ax=ax[0], whis=1.5,
palette="Blues_d", showmeans=True, meanprops={"markersize": 1, "markeredgecolor": "k"},
- flierprops={"marker": "."}, width=first_box_width)
+ showfliers=False, width=first_box_width)
ax[0].set(ylabel=f"skill score", xlabel="")
+ if self._ylim is not None:
+ _ylim = self._ylim if isinstance(self._ylim, tuple) else self._ylim[0]
+ ax[0].set(ylim=_ylim)
- sns.boxplot(x=self._x_name, y="data", hue=self._ahead_dim, data=data_second, ax=ax[1], whis=1.,
+ sns.boxplot(x=self._x_name, y="data", hue=self._ahead_dim, data=data_second, ax=ax[1], whis=1.5,
palette="Blues_d", showmeans=True, meanprops={"markersize": 1, "markeredgecolor": "k"},
- flierprops={"marker": "."})
+ showfliers=False)
ax[1].set(ylabel="", xlabel="")
ax[1].yaxis.tick_right()
+ if self._ylim is not None and isinstance(self._ylim, list):
+ _ylim = self._ylim[1]
+ ax[1].set(ylim=_ylim)
+
handles, _ = ax[1].get_legend_handles_labels()
for sax in ax:
matplotlib.pyplot.sca(sax)
@@ -772,7 +803,8 @@ class PlotBootstrapSkillScore(AbstractPlotClass):
plt.xticks(rotation=45, ha='right')
sax.legend_.remove()
- fig.legend(handles, self._labels, loc='upper center', ncol=len(handles) + 1, )
+ # fig.legend(handles, self._labels, loc='upper center', ncol=len(handles) + 1, )
+ ax[1].legend(handles, self._labels, loc='lower center', ncol=len(handles) + 1, fontsize="medium")
def align_yaxis(ax1, ax2):
"""
@@ -797,6 +829,7 @@ class PlotBootstrapSkillScore(AbstractPlotClass):
align_yaxis(ax[0], ax[1])
align_yaxis(ax[0], ax[1])
+ plt.subplots_adjust(right=0.8)
plt.title(self._title)
@staticmethod
@@ -826,12 +859,29 @@ class PlotBootstrapSkillScore(AbstractPlotClass):
"""
"""
- fig, ax = plt.subplots()
plot_data = self._data if branch is None else self._data[self._data["branch"] == str(branch)]
- sns.boxplot(x=self._x_name, y="data", hue=self._ahead_dim, data=plot_data, ax=ax, whis=1., palette="Blues_d",
- showmeans=True, meanprops={"markersize": 1, "markeredgecolor": "k"}, flierprops={"marker": "."})
+ if self._boot_type == "branch":
+ fig, ax = plt.subplots(figsize=(0.5 + 2 / len(plot_data[self._x_name].unique()) + len(plot_data[self._x_name].unique()),4))
+ sns.boxplot(x=self._x_name, y="data", hue=self._ahead_dim, data=plot_data, ax=ax, whis=1.,
+ palette="Blues_d", showmeans=True, meanprops={"markersize": 1, "markeredgecolor": "k"},
+ showfliers=False, width=0.8)
+ else:
+ fig, ax = plt.subplots()
+ sns.boxplot(x=self._x_name, y="data", hue=self._ahead_dim, data=plot_data, ax=ax, whis=1.5, palette="Blues_d",
+ showmeans=True, meanprops={"markersize": 1, "markeredgecolor": "k"}, showfliers=False)
ax.axhline(y=0, color="grey", linewidth=.5)
- plt.xticks(rotation=45)
+
+ if self._ylim is not None:
+ if isinstance(self._ylim, tuple):
+ _ylim = self._ylim
+ else:
+ _ylim = (min(self._ylim[0][0], self._ylim[1][0]), max(self._ylim[0][1], self._ylim[1][1]))
+ ax.set(ylim=_ylim)
+
+ if self._boot_type == "branch":
+ plt.xticks()
+ else:
+ plt.xticks(rotation=45)
ax.set(ylabel=f"skill score", xlabel="", title=self._title)
handles, _ = ax.get_legend_handles_labels()
ax.legend(handles, self._labels)
@@ -839,7 +889,7 @@ class PlotBootstrapSkillScore(AbstractPlotClass):
@TimeTrackingWrapper
-class PlotTimeSeries:
+class PlotTimeSeries: # pragma: no cover
"""
Create time series plot.
@@ -972,7 +1022,7 @@ class PlotTimeSeries:
@TimeTrackingWrapper
-class PlotSeparationOfScales(AbstractPlotClass):
+class PlotSeparationOfScales(AbstractPlotClass): # pragma: no cover
def __init__(self, collection: DataCollection, plot_folder: str = ".", time_dim="datetime", window_dim="window",
filter_dim="filter", target_dim="variables"):
@@ -998,6 +1048,80 @@ class PlotSeparationOfScales(AbstractPlotClass):
self._save()
+@TimeTrackingWrapper
+class PlotSampleUncertaintyFromBootstrap(AbstractPlotClass): # pragma: no cover
+
+ def __init__(self, data: xr.DataArray, plot_folder: str = ".", model_type_dim: str = "type",
+ error_measure: str = "mse", error_unit: str = None, dim_name_boots: str = 'boots',
+ block_length: str = None):
+ super().__init__(plot_folder, "sample_uncertainty_from_bootstrap")
+ default_name = self.plot_name
+ self.model_type_dim = model_type_dim
+ self.error_measure = error_measure
+ self.dim_name_boots = dim_name_boots
+ self.error_unit = error_unit
+ self.block_length = block_length
+ self.prepare_data(data)
+ self._plot(orientation="v")
+
+ self.plot_name = default_name + "_horizontal"
+ self._plot(orientation="h")
+
+ self._apply_root()
+
+ self.plot_name = default_name + "_sqrt"
+ self._plot(orientation="v")
+
+ self.plot_name = default_name + "_horizontal_sqrt"
+ self._plot(orientation="h")
+
+ self._data_table = None
+ self._n_boots = None
+
+ def prepare_data(self, data: xr.DataArray):
+ self._data_table = data.to_pandas()
+ if "persi" in self._data_table.columns:
+ self._data_table["persi"] = self._data_table.pop("persi")
+ self._n_boots = self._data_table.shape[0]
+
+ def _apply_root(self):
+ self._data_table = np.sqrt(self._data_table)
+ self.error_measure = f"root {self.error_measure}"
+ self.error_unit = self.error_unit.replace("$^2$", "")
+
+ def _plot(self, orientation: str = "v"):
+ data_table = self._data_table
+ n_boots = self._n_boots
+ size = len(np.unique(data_table.columns))
+ if orientation == "v":
+ figsize, width = (size, 5), 0.4
+ elif orientation == "h":
+ figsize, width = (6, (1+.5*size)), 0.65
+ else:
+ raise ValueError(f"orientation must be `v' or `h' but is: {orientation}")
+ fig, ax = plt.subplots(figsize=figsize)
+ sns.boxplot(data=data_table, ax=ax, whis=1.5, color="white",
+ showmeans=True, meanprops={"markersize": 6, "markeredgecolor": "k"},
+ flierprops={"marker": "o", "markerfacecolor": "black", "markeredgecolor": "none", "markersize": 3},
+ boxprops={'facecolor': 'none', 'edgecolor': 'k'},
+ width=width, orient=orientation)
+ if orientation == "v":
+ ax.set_ylabel(f"{self.error_measure} (in {self.error_unit})")
+ ax.set_xticklabels(ax.get_xticklabels(), rotation=45)
+ elif orientation == "h":
+ ax.set_xlabel(f"{self.error_measure} (in {self.error_unit})")
+ else:
+ raise ValueError(f"orientation must be `v' or `h' but is: {orientation}")
+ text = f"n={n_boots}" if self.block_length is None else f"{self.block_length}, n={n_boots}"
+ text_box = AnchoredText(text, frameon=True, loc=1, pad=0.5)
+ plt.setp(text_box.patch, edgecolor='k', facecolor='w')
+ ax.add_artist(text_box)
+ plt.setp(ax.lines, color='k')
+ plt.tight_layout()
+ self._save()
+ plt.close("all")
+
+
if __name__ == "__main__":
stations = ['DEBW107', 'DEBY081', 'DEBW013', 'DEBW076', 'DEBW087']
path = "../../testrun_network/forecasts"
diff --git a/mlair/plotting/training_monitoring.py b/mlair/plotting/training_monitoring.py
index b2b531b99c85bb43e4e758fd23045c9f0575cb24..39dd80651226519463d7b503fb612e43983d73cf 100644
--- a/mlair/plotting/training_monitoring.py
+++ b/mlair/plotting/training_monitoring.py
@@ -45,15 +45,18 @@ class PlotModelHistory:
self._additional_columns = self._filter_columns(history)
self._plot(filename)
- @staticmethod
- def _get_plot_metric(history, plot_metric, main_branch):
- if plot_metric.lower() == "mse":
- plot_metric = "mean_squared_error"
- elif plot_metric.lower() == "mae":
- plot_metric = "mean_absolute_error"
+ def _get_plot_metric(self, history, plot_metric, main_branch, correct_names=True):
+ _plot_metric = plot_metric
+ if correct_names is True:
+ if plot_metric.lower() == "mse":
+ plot_metric = "mean_squared_error"
+ elif plot_metric.lower() == "mae":
+ plot_metric = "mean_absolute_error"
available_keys = [k for k in history.keys() if
plot_metric in k and ("main" in k.lower() if main_branch else True)]
available_keys.sort(key=len)
+ if len(available_keys) == 0 and correct_names is True:
+ return self._get_plot_metric(history, _plot_metric, main_branch, correct_names=False)
return available_keys[0]
def _filter_columns(self, history: Dict) -> List[str]:
diff --git a/mlair/run_modules/experiment_setup.py b/mlair/run_modules/experiment_setup.py
index 28277d5057698c01594431008b81d959d415c3e2..63be6eb4c6e8b5f8d3149df023e07d23805f077f 100644
--- a/mlair/run_modules/experiment_setup.py
+++ b/mlair/run_modules/experiment_setup.py
@@ -18,10 +18,12 @@ from mlair.configuration.defaults import DEFAULT_STATIONS, DEFAULT_VAR_ALL_DICT,
DEFAULT_WINDOW_DIM, DEFAULT_DIMENSIONS, DEFAULT_TIME_DIM, DEFAULT_INTERPOLATION_METHOD, DEFAULT_INTERPOLATION_LIMIT, \
DEFAULT_TRAIN_START, DEFAULT_TRAIN_END, DEFAULT_TRAIN_MIN_LENGTH, DEFAULT_VAL_START, DEFAULT_VAL_END, \
DEFAULT_VAL_MIN_LENGTH, DEFAULT_TEST_START, DEFAULT_TEST_END, DEFAULT_TEST_MIN_LENGTH, DEFAULT_TRAIN_VAL_MIN_LENGTH, \
- DEFAULT_USE_ALL_STATIONS_ON_ALL_DATA_SETS, DEFAULT_EVALUATE_BOOTSTRAPS, DEFAULT_CREATE_NEW_BOOTSTRAPS, \
- DEFAULT_NUMBER_OF_BOOTSTRAPS, DEFAULT_PLOT_LIST, DEFAULT_SAMPLING, DEFAULT_DATA_ORIGIN, DEFAULT_ITER_DIM, \
+ DEFAULT_USE_ALL_STATIONS_ON_ALL_DATA_SETS, DEFAULT_EVALUATE_FEATURE_IMPORTANCE, DEFAULT_FEATURE_IMPORTANCE_CREATE_NEW_BOOTSTRAPS, \
+ DEFAULT_FEATURE_IMPORTANCE_N_BOOTS, DEFAULT_PLOT_LIST, DEFAULT_SAMPLING, DEFAULT_DATA_ORIGIN, DEFAULT_ITER_DIM, \
DEFAULT_USE_MULTIPROCESSING, DEFAULT_USE_MULTIPROCESSING_ON_DEBUG, DEFAULT_MAX_NUMBER_MULTIPROCESSING, \
- DEFAULT_BOOTSTRAP_TYPE, DEFAULT_BOOTSTRAP_METHOD, DEFAULT_OVERWRITE_LAZY_DATA
+ DEFAULT_FEATURE_IMPORTANCE_BOOTSTRAP_TYPE, DEFAULT_FEATURE_IMPORTANCE_BOOTSTRAP_METHOD, DEFAULT_OVERWRITE_LAZY_DATA, \
+ DEFAULT_UNCERTAINTY_ESTIMATE_BLOCK_LENGTH, DEFAULT_UNCERTAINTY_ESTIMATE_EVALUATE_COMPETITORS, \
+ DEFAULT_UNCERTAINTY_ESTIMATE_N_BOOTS, DEFAULT_DO_UNCERTAINTY_ESTIMATE
from mlair.data_handler import DefaultDataHandler
from mlair.run_modules.run_environment import RunEnvironment
from mlair.model_modules.fully_connected_networks import FCN_64_32_16 as VanillaModel
@@ -212,14 +214,17 @@ class ExperimentSetup(RunEnvironment):
sampling: str = None,
create_new_model=None, bootstrap_path=None, permute_data_on_training=None, transformation=None,
train_min_length=None, val_min_length=None, test_min_length=None, extreme_values: list = None,
- extremes_on_right_tail_only: bool = None, evaluate_bootstraps=None, plot_list=None,
- number_of_bootstraps=None, create_new_bootstraps=None, bootstrap_method=None, bootstrap_type=None,
+ extremes_on_right_tail_only: bool = None, evaluate_feature_importance: bool = None, plot_list=None,
+ feature_importance_n_boots: int = None, feature_importance_create_new_bootstraps: bool = None,
+ feature_importance_bootstrap_method=None, feature_importance_bootstrap_type=None,
data_path: str = None, batch_path: str = None, login_nodes=None,
hpc_hosts=None, model=None, batch_size=None, epochs=None, data_handler=None,
data_origin: Dict = None, competitors: list = None, competitor_path: str = None,
use_multiprocessing: bool = None, use_multiprocessing_on_debug: bool = None,
max_number_multiprocessing: int = None, start_script: Union[Callable, str] = None,
- overwrite_lazy_data: bool = None, **kwargs):
+ overwrite_lazy_data: bool = None, uncertainty_estimate_block_length: str = None,
+ uncertainty_estimate_evaluate_competitors: bool = None, uncertainty_estimate_n_boots: int = None,
+ do_uncertainty_estimate: bool = None, **kwargs):
# create run framework
super().__init__()
@@ -349,15 +354,28 @@ class ExperimentSetup(RunEnvironment):
default=DEFAULT_USE_ALL_STATIONS_ON_ALL_DATA_SETS)
# set post-processing instructions
- self._set_param("evaluate_bootstraps", evaluate_bootstraps, default=DEFAULT_EVALUATE_BOOTSTRAPS,
- scope="general.postprocessing")
- create_new_bootstraps = max([self.data_store.get("train_model", "general"),
- create_new_bootstraps or DEFAULT_CREATE_NEW_BOOTSTRAPS])
- self._set_param("create_new_bootstraps", create_new_bootstraps, scope="general.postprocessing")
- self._set_param("number_of_bootstraps", number_of_bootstraps, default=DEFAULT_NUMBER_OF_BOOTSTRAPS,
- scope="general.postprocessing")
- self._set_param("bootstrap_method", bootstrap_method, default=DEFAULT_BOOTSTRAP_METHOD)
- self._set_param("bootstrap_type", bootstrap_type, default=DEFAULT_BOOTSTRAP_TYPE)
+ self._set_param("do_uncertainty_estimate", do_uncertainty_estimate,
+ default=DEFAULT_DO_UNCERTAINTY_ESTIMATE, scope="general.postprocessing")
+ self._set_param("block_length", uncertainty_estimate_block_length,
+ default=DEFAULT_UNCERTAINTY_ESTIMATE_BLOCK_LENGTH, scope="uncertainty_estimate")
+ self._set_param("evaluate_competitors", uncertainty_estimate_evaluate_competitors,
+ default=DEFAULT_UNCERTAINTY_ESTIMATE_EVALUATE_COMPETITORS, scope="uncertainty_estimate")
+ self._set_param("n_boots", uncertainty_estimate_n_boots,
+ default=DEFAULT_UNCERTAINTY_ESTIMATE_N_BOOTS, scope="uncertainty_estimate")
+
+ self._set_param("evaluate_feature_importance", evaluate_feature_importance,
+ default=DEFAULT_EVALUATE_FEATURE_IMPORTANCE, scope="general.postprocessing")
+ feature_importance_create_new_bootstraps = max([self.data_store.get("train_model", "general"),
+ feature_importance_create_new_bootstraps or
+ DEFAULT_FEATURE_IMPORTANCE_CREATE_NEW_BOOTSTRAPS])
+ self._set_param("create_new_bootstraps", feature_importance_create_new_bootstraps, scope="feature_importance")
+ self._set_param("n_boots", feature_importance_n_boots, default=DEFAULT_FEATURE_IMPORTANCE_N_BOOTS,
+ scope="feature_importance")
+ self._set_param("bootstrap_method", feature_importance_bootstrap_method,
+ default=DEFAULT_FEATURE_IMPORTANCE_BOOTSTRAP_METHOD, scope="feature_importance")
+ self._set_param("bootstrap_type", feature_importance_bootstrap_type,
+ default=DEFAULT_FEATURE_IMPORTANCE_BOOTSTRAP_TYPE, scope="feature_importance")
+
self._set_param("plot_list", plot_list, default=DEFAULT_PLOT_LIST, scope="general.postprocessing")
self._set_param("neighbors", ["DEBW030"]) # TODO: just for testing
@@ -384,8 +402,10 @@ class ExperimentSetup(RunEnvironment):
if len(self.data_store.search_name(k)) == 0:
self._set_param(k, v)
else:
+ s = ", ".join([f"{k}({s})={self.data_store.get(k, scope=s)}"
+ for s in self.data_store.search_name(k)])
raise KeyError(f"Given argument {k} with value {v} cannot be set for this experiment due to a "
- f"conflict with an existing entry with same naming: {k}={self.data_store.get(k)}")
+ f"conflict with an existing entry with same naming: {s}")
def _set_param(self, param: str, value: Any, default: Any = None, scope: str = "general",
apply: Callable = None) -> Any:
diff --git a/mlair/run_modules/model_setup.py b/mlair/run_modules/model_setup.py
index 0b9e8ec56592901d9feba15eb50b6b21a0c53560..98263eb732d8067fba0950c7a4882fb3ef020995 100644
--- a/mlair/run_modules/model_setup.py
+++ b/mlair/run_modules/model_setup.py
@@ -84,7 +84,7 @@ class ModelSetup(RunEnvironment):
# load weights if no training shall be performed
if not self._train_model and not self._create_new_model:
- self.load_weights()
+ self.load_model()
# create checkpoint
self._set_callbacks()
@@ -131,13 +131,13 @@ class ModelSetup(RunEnvironment):
save_best_only=True, mode='auto')
self.data_store.set("callbacks", callbacks, self.scope)
- def load_weights(self):
- """Try to load weights from existing model or skip if not possible."""
+ def load_model(self):
+ """Try to load model from disk or skip if not possible."""
try:
- self.model.load_weights(self.model_name)
- logging.info(f"reload weights from model {self.model_name} ...")
+ self.model.load_model(self.model_name)
+ logging.info(f"reload model {self.model_name} from disk ...")
except OSError:
- logging.info('no weights to reload...')
+ logging.info('no local model to load...')
def build_model(self):
"""Build model using input and output shapes from data store."""
diff --git a/mlair/run_modules/post_processing.py b/mlair/run_modules/post_processing.py
index fa6326050b611ed4e27d75867e920b7e70d2fa9f..dbffc5ca206e022afbc1729d3589f287ccebdc11 100644
--- a/mlair/run_modules/post_processing.py
+++ b/mlair/run_modules/post_processing.py
@@ -16,13 +16,14 @@ import pandas as pd
import xarray as xr
from mlair.configuration import path_config
-from mlair.data_handler import BootStraps, KerasIterator
+from mlair.data_handler import Bootstraps, KerasIterator
from mlair.helpers.datastore import NameNotFoundInDataStore
from mlair.helpers import TimeTracking, statistics, extract_value, remove_items, to_list, tables
from mlair.model_modules.linear_model import OrdinaryLeastSquaredModel
from mlair.model_modules import AbstractModelClass
from mlair.plotting.postprocessing_plotting import PlotMonthlySummary, PlotClimatologicalSkillScore, \
- PlotCompetitiveSkillScore, PlotTimeSeries, PlotBootstrapSkillScore, PlotConditionalQuantiles, PlotSeparationOfScales
+ PlotCompetitiveSkillScore, PlotTimeSeries, PlotFeatureImportanceSkillScore, PlotConditionalQuantiles, \
+ PlotSeparationOfScales, PlotSampleUncertaintyFromBootstrap
from mlair.plotting.data_insight_plotting import PlotStationMap, PlotAvailability, PlotAvailabilityHistogram, \
PlotPeriodogram, PlotDataHistogram
from mlair.run_modules.run_environment import RunEnvironment
@@ -48,7 +49,7 @@ class PostProcessing(RunEnvironment):
* `target_var` [.]
* `sampling` [.]
* `output_shape` [model]
- * `evaluate_bootstraps` [postprocessing] and if enabled:
+ * `evaluate_feature_importance` [postprocessing] and if enabled:
* `create_new_bootstraps` [postprocessing]
* `bootstrap_path` [postprocessing]
@@ -83,11 +84,17 @@ class PostProcessing(RunEnvironment):
self._sampling = self.data_store.get("sampling")
self.window_lead_time = extract_value(self.data_store.get("output_shape", "model"))
self.skill_scores = None
- self.bootstrap_skill_scores = None
+ self.feature_importance_skill_scores = None
+ self.uncertainty_estimate = None
self.competitor_path = self.data_store.get("competitor_path")
self.competitors = to_list(self.data_store.get_default("competitors", default=[]))
self.forecast_indicator = "nn"
+ self.observation_indicator = "obs"
self.ahead_dim = "ahead"
+ self.boot_var_dim = "boot_var"
+ self.uncertainty_estimate_boot_dim = "boots"
+ self.model_type_dim = "type"
+ self.index_dim = "index"
self._run()
def _run(self):
@@ -101,25 +108,132 @@ class PostProcessing(RunEnvironment):
# calculate error metrics on test data
self.calculate_test_score()
- # bootstraps
- if self.data_store.get("evaluate_bootstraps", "postprocessing"):
- with TimeTracking(name="calculate bootstraps"):
- create_new_bootstraps = self.data_store.get("create_new_bootstraps", "postprocessing")
- bootstrap_method = self.data_store.get("bootstrap_method", "postprocessing")
- bootstrap_type = self.data_store.get("bootstrap_type", "postprocessing")
- self.bootstrap_postprocessing(create_new_bootstraps, bootstrap_type=bootstrap_type,
- bootstrap_method=bootstrap_method)
+ # sample uncertainty
+ if self.data_store.get("do_uncertainty_estimate", "postprocessing"):
+ self.estimate_sample_uncertainty()
+
+ # feature importance bootstraps
+ if self.data_store.get("evaluate_feature_importance", "postprocessing"):
+ with TimeTracking(name="calculate feature importance using bootstraps"):
+ create_new_bootstraps = self.data_store.get("create_new_bootstraps", "feature_importance")
+ bootstrap_method = self.data_store.get("bootstrap_method", "feature_importance")
+ bootstrap_type = self.data_store.get("bootstrap_type", "feature_importance")
+ self.calculate_feature_importance(create_new_bootstraps, bootstrap_type=bootstrap_type,
+ bootstrap_method=bootstrap_method)
+ if self.feature_importance_skill_scores is not None:
+ self.report_feature_importance_results(self.feature_importance_skill_scores)
# skill scores and error metrics
with TimeTracking(name="calculate skill scores"):
- skill_score_competitive, skill_score_climatological, errors = self.calculate_error_metrics()
+ skill_score_competitive, _, skill_score_climatological, errors = self.calculate_error_metrics()
self.skill_scores = (skill_score_competitive, skill_score_climatological)
self.report_error_metrics(errors)
- self.report_error_metrics(skill_score_climatological)
+ self.report_error_metrics({self.forecast_indicator: skill_score_climatological})
+ self.report_error_metrics({"skill_score": skill_score_competitive})
# plotting
self.plot()
+ def estimate_sample_uncertainty(self, separate_ahead=False):
+ """
+ Estimate sample uncertainty by using a bootstrap approach. Forecasts are split into individual blocks along time
+ and randomly drawn with replacement. The resulting behaviour of the error indicates the robustness of each
+ analyzed model to quantify which model might be superior compared to others.
+ """
+ n_boots = self.data_store.get_default("n_boots", default=1000, scope="uncertainty_estimate")
+ block_length = self.data_store.get_default("block_length", default="1m", scope="uncertainty_estimate")
+ evaluate_competitors = self.data_store.get_default("evaluate_competitors", default=True,
+ scope="uncertainty_estimate")
+ block_mse = self.calculate_block_mse(evaluate_competitors=evaluate_competitors, separate_ahead=separate_ahead,
+ block_length=block_length)
+ self.uncertainty_estimate = statistics.create_n_bootstrap_realizations(
+ block_mse, dim_name_time=self.index_dim, dim_name_model=self.model_type_dim,
+ dim_name_boots=self.uncertainty_estimate_boot_dim, n_boots=n_boots)
+ self.report_sample_uncertainty()
+
+ def report_sample_uncertainty(self, percentiles: list = None):
+ """
+ Store raw results of uncertainty estimate and calculate aggregate statistcs and store as raw data but also as
+ markdown and latex.
+ """
+ report_path = os.path.join(self.data_store.get("experiment_path"), "latex_report")
+ path_config.check_path_and_create(report_path)
+
+ # store raw results as nc
+ file_name = os.path.join(report_path, "uncertainty_estimate_raw_results.nc")
+ self.uncertainty_estimate.to_netcdf(path=file_name)
+
+ # store statistics
+ if percentiles is None:
+ percentiles = [.05, .1, .25, .5, .75, .9, .95]
+ df_descr = self.uncertainty_estimate.to_pandas().describe(percentiles=percentiles).astype("float32")
+ column_format = tables.create_column_format_for_tex(df_descr)
+ file_name = os.path.join(report_path, "uncertainty_estimate_statistics.%s")
+ tables.save_to_tex(report_path, file_name % "tex", column_format=column_format, df=df_descr)
+ tables.save_to_md(report_path, file_name % "md", df=df_descr)
+ df_descr.to_csv(file_name % "csv", sep=";")
+
+ def calculate_block_mse(self, evaluate_competitors=True, separate_ahead=False, block_length="1m"):
+ """
+ Transform data into blocks along time axis. Block length can be any frequency like '1m' or '7d. Data are only
+ split along time axis, which means that a single block can have very diverse quantities regarding the number of
+ station or actual data contained. This is intended to analyze not only the robustness against the time but also
+ against the number of observations and diversity ot stations.
+ """
+ path = self.data_store.get("forecast_path")
+ all_stations = self.data_store.get("stations")
+ start = self.data_store.get("start", "test")
+ end = self.data_store.get("end", "test")
+ index_dim = self.index_dim
+ coll_dim = "station"
+ collector = []
+ for station in all_stations:
+ # test data
+ external_data = self._get_external_data(station, path)
+ if external_data is not None:
+ pass
+ # competitors
+ if evaluate_competitors is True:
+ competitor = self.load_competitors(station)
+ combined = self._combine_forecasts(external_data, competitor, dim=self.model_type_dim)
+ else:
+ combined = external_data
+
+ if combined is None:
+ continue
+ else:
+ combined = self.create_full_time_dim(combined, index_dim, self._sampling, start, end)
+ # get squared errors
+ errors = self.create_error_array(combined)
+ # calc mse for each block (single station)
+ mse = errors.resample(indexer={index_dim: block_length}).mean(skipna=True)
+ collector.append(mse.assign_coords({coll_dim: station}))
+ # calc mse for each block (average over all stations)
+ mse_blocks = xr.concat(collector, dim=coll_dim).mean(dim=coll_dim, skipna=True)
+ # average also on ahead steps
+ if separate_ahead is False:
+ mse_blocks = mse_blocks.mean(dim=self.ahead_dim, skipna=True)
+ return mse_blocks
+
+ def create_error_array(self, data):
+ """Calculate squared error of all given time series in relation to observation."""
+ errors = data.drop_sel({self.model_type_dim: self.observation_indicator})
+ errors1 = errors - data.sel({self.model_type_dim: self.observation_indicator})
+ errors2 = errors1 ** 2
+ return errors2
+
+ @staticmethod
+ def create_full_time_dim(data, dim, sampling, start, end):
+ """Ensure time dimension to be equidistant. Sometimes dates if missing values have been dropped."""
+ start_data = data.coords[dim].values[0]
+ freq = {"daily": "1D", "hourly": "1H"}.get(sampling)
+ datetime_index = pd.DataFrame(index=pd.date_range(start, end, freq=freq))
+ t = data.sel({dim: start_data}, drop=True)
+ res = xr.DataArray(coords=[datetime_index.index, *[t.coords[c] for c in t.coords]], dims=[dim, *t.coords])
+ res = res.transpose(*data.dims)
+ res.loc[data.coords] = data
+ return res
+
def load_competitors(self, station_name: str) -> xr.DataArray:
"""
Load all requested and available competitors for a given station. Forecasts must be available in the competitor
@@ -139,10 +253,10 @@ class PostProcessing(RunEnvironment):
except (FileNotFoundError, KeyError):
logging.debug(f"No competitor found for combination '{station_name}' and '{competitor_name}'.")
continue
- return xr.concat(competing_predictions, "type") if len(competing_predictions) > 0 else None
+ return xr.concat(competing_predictions, self.model_type_dim) if len(competing_predictions) > 0 else None
- def bootstrap_postprocessing(self, create_new_bootstraps: bool, _iter: int = 0, bootstrap_type="singleinput",
- bootstrap_method="shuffle") -> None:
+ def calculate_feature_importance(self, create_new_bootstraps: bool, _iter: int = 0, bootstrap_type="singleinput",
+ bootstrap_method="shuffle") -> None:
"""
Calculate skill scores of bootstrapped data.
@@ -155,52 +269,64 @@ class PostProcessing(RunEnvironment):
:param _iter: internal counter to reduce unnecessary recursive calls (maximum number is 2, otherwise something
went wrong).
"""
- self.bootstrap_skill_scores = {}
+ if _iter == 0:
+ self.feature_importance_skill_scores = {}
for boot_type in to_list(bootstrap_type):
- self.bootstrap_skill_scores[boot_type] = {}
+ if _iter == 0:
+ self.feature_importance_skill_scores[boot_type] = {}
for boot_method in to_list(bootstrap_method):
try:
if create_new_bootstraps:
- self.create_bootstrap_forecast(bootstrap_type=boot_type, bootstrap_method=boot_method)
- boot_skill_score = self.calculate_bootstrap_skill_scores(bootstrap_type=boot_type,
- bootstrap_method=boot_method)
- self.bootstrap_skill_scores[boot_type][boot_method] = boot_skill_score
- except FileNotFoundError:
+ self.create_feature_importance_bootstrap_forecast(bootstrap_type=boot_type,
+ bootstrap_method=boot_method)
+ boot_skill_score = self.calculate_feature_importance_skill_scores(bootstrap_type=boot_type,
+ bootstrap_method=boot_method)
+ self.feature_importance_skill_scores[boot_type][boot_method] = boot_skill_score
+ except (FileNotFoundError, ValueError):
if _iter != 0:
- raise RuntimeError(f"bootstrap_postprocessing ({boot_type}, {boot_type}) was called for the 2nd"
- f" time. This means, that something internally goes wrong. Please check for "
- f"possible errors")
- logging.info(f"Could not load all files for bootstrapping ({boot_type}, {boot_type}), restart "
- f"bootstrap postprocessing with create_new_bootstraps=True.")
- self.bootstrap_postprocessing(True, _iter=1, bootstrap_type=boot_type, bootstrap_method=boot_method)
-
- def create_bootstrap_forecast(self, bootstrap_type, bootstrap_method) -> None:
+ raise RuntimeError(f"calculate_feature_importance ({boot_type}, {boot_type}) was called for the "
+ f"2nd time. This means, that something internally goes wrong. Please check "
+ f"for possible errors")
+ logging.info(f"Could not load all files for feature importance ({boot_type}, {boot_type}), restart "
+ f"calculate_feature_importance with create_new_bootstraps=True.")
+ self.calculate_feature_importance(True, _iter=1, bootstrap_type=boot_type,
+ bootstrap_method=boot_method)
+
+ def create_feature_importance_bootstrap_forecast(self, bootstrap_type, bootstrap_method) -> None:
"""
Create bootstrapped predictions for all stations and variables.
These forecasts are saved in bootstrap_path with the names `bootstraps_{var}_{station}.nc` and
`bootstraps_labels_{station}.nc`.
"""
+
+ def _reshape(d, pos):
+ if isinstance(d, list):
+ return list(map(lambda x: _reshape(x, pos), d))
+ else:
+ return d[..., pos]
+
# forecast
with TimeTracking(name=f"{inspect.stack()[0].function} ({bootstrap_type}, {bootstrap_method})"):
# extract all requirements from data store
forecast_path = self.data_store.get("forecast_path")
- number_of_bootstraps = self.data_store.get("number_of_bootstraps", "postprocessing")
- dims = ["index", self.ahead_dim, "type"]
+ number_of_bootstraps = self.data_store.get("n_boots", "feature_importance")
+ dims = [self.uncertainty_estimate_boot_dim, self.index_dim, self.ahead_dim, self.model_type_dim]
for station in self.test_data:
X, Y = None, None
- bootstraps = BootStraps(station, number_of_bootstraps, bootstrap_type=bootstrap_type,
+ bootstraps = Bootstraps(station, number_of_bootstraps, bootstrap_type=bootstrap_type,
bootstrap_method=bootstrap_method)
+ number_of_bootstraps = bootstraps.number_of_bootstraps
for boot in bootstraps:
X, Y, (index, dimension) = boot
# make bootstrap predictions
- bootstrap_predictions = self.model.predict(X)
- if isinstance(bootstrap_predictions, list): # if model is branched model
- bootstrap_predictions = bootstrap_predictions[-1]
+ bootstrap_predictions = [self.model.predict(_reshape(X, pos)) for pos in range(number_of_bootstraps)]
+ if isinstance(bootstrap_predictions[0], list): # if model is branched model
+ bootstrap_predictions = list(map(lambda x: x[-1], bootstrap_predictions))
# save bootstrap predictions separately for each station and variable combination
- bootstrap_predictions = np.expand_dims(bootstrap_predictions, axis=-1)
- shape = bootstrap_predictions.shape
- coords = (range(shape[0]), range(1, shape[1] + 1))
+ bootstrap_predictions = list(map(lambda x: np.expand_dims(x, axis=-1), bootstrap_predictions))
+ shape = bootstrap_predictions[0].shape
+ coords = (range(number_of_bootstraps), range(shape[0]), range(1, shape[1] + 1))
var = f"{index}_{dimension}" if index is not None else str(dimension)
tmp = xr.DataArray(bootstrap_predictions, coords=(*coords, [var]), dims=dims)
file_name = os.path.join(forecast_path,
@@ -208,12 +334,12 @@ class PostProcessing(RunEnvironment):
tmp.to_netcdf(file_name)
else:
# store also true labels for each station
- labels = np.expand_dims(Y, axis=-1)
+ labels = np.expand_dims(Y[..., 0], axis=-1)
file_name = os.path.join(forecast_path, f"bootstraps_{station}_{bootstrap_method}_labels.nc")
- labels = xr.DataArray(labels, coords=(*coords, ["obs"]), dims=dims)
+ labels = xr.DataArray(labels, coords=(*coords[1:], [self.observation_indicator]), dims=dims[1:])
labels.to_netcdf(file_name)
- def calculate_bootstrap_skill_scores(self, bootstrap_type, bootstrap_method) -> Dict[str, xr.DataArray]:
+ def calculate_feature_importance_skill_scores(self, bootstrap_type, bootstrap_method) -> Dict[str, xr.DataArray]:
"""
Calculate skill score of bootstrapped variables.
@@ -226,10 +352,11 @@ class PostProcessing(RunEnvironment):
with TimeTracking(name=f"{inspect.stack()[0].function} ({bootstrap_type}, {bootstrap_method})"):
# extract all requirements from data store
forecast_path = self.data_store.get("forecast_path")
- number_of_bootstraps = self.data_store.get("number_of_bootstraps", "postprocessing")
+ number_of_bootstraps = self.data_store.get("n_boots", "feature_importance")
forecast_file = f"forecasts_norm_%s_test.nc"
+ reference_name = "orig"
- bootstraps = BootStraps(self.test_data[0], number_of_bootstraps, bootstrap_type=bootstrap_type,
+ bootstraps = Bootstraps(self.test_data[0], number_of_bootstraps, bootstrap_type=bootstrap_type,
bootstrap_method=bootstrap_method)
number_of_bootstraps = bootstraps.number_of_bootstraps
bootstrap_iter = bootstraps.bootstraps()
@@ -240,16 +367,13 @@ class PostProcessing(RunEnvironment):
file_name = os.path.join(forecast_path, f"bootstraps_{str(station)}_{bootstrap_method}_labels.nc")
with xr.open_dataarray(file_name) as da:
labels = da.load()
- shape = labels.shape
# get original forecasts
- orig = self.get_orig_prediction(forecast_path, forecast_file % str(station), number_of_bootstraps)
- orig = orig.reshape(shape)
- coords = (range(shape[0]), range(1, shape[1] + 1), ["orig"])
- orig = xr.DataArray(orig, coords=coords, dims=["index", self.ahead_dim, "type"])
+ orig = self.get_orig_prediction(forecast_path, forecast_file % str(station), reference_name=reference_name)
+ orig.coords[self.index_dim] = labels.coords[self.index_dim]
# calculate skill scores for each variable
- skill = pd.DataFrame(columns=range(1, self.window_lead_time + 1))
+ skill = []
for boot_set in bootstrap_iter:
boot_var = f"{boot_set[0]}_{boot_set[1]}" if isinstance(boot_set, tuple) else str(boot_set)
file_name = os.path.join(forecast_path,
@@ -261,27 +385,34 @@ class PostProcessing(RunEnvironment):
for ahead in range(1, self.window_lead_time + 1):
data = boot_data.sel({self.ahead_dim: ahead})
boot_scores.append(
- skill_scores.general_skill_score(data, forecast_name=boot_var, reference_name="orig"))
- skill.loc[boot_var] = np.array(boot_scores)
+ skill_scores.general_skill_score(data, forecast_name=boot_var,
+ reference_name=reference_name, dim=self.index_dim))
+ tmp = xr.DataArray(np.expand_dims(np.array(boot_scores), axis=-1),
+ coords={self.ahead_dim: range(1, self.window_lead_time + 1),
+ self.uncertainty_estimate_boot_dim: range(number_of_bootstraps),
+ self.boot_var_dim: [boot_var]},
+ dims=[self.ahead_dim, self.uncertainty_estimate_boot_dim, self.boot_var_dim])
+ skill.append(tmp)
# collect all results in single dictionary
- score[str(station)] = xr.DataArray(skill, dims=["boot_var", self.ahead_dim])
+ score[str(station)] = xr.concat(skill, dim=self.boot_var_dim)
return score
- def get_orig_prediction(self, path, file_name, number_of_bootstraps, prediction_name=None):
+ def get_orig_prediction(self, path, file_name, prediction_name=None, reference_name=None):
if prediction_name is None:
prediction_name = self.forecast_indicator
file = os.path.join(path, file_name)
with xr.open_dataarray(file) as da:
- prediction = da.load().sel(type=prediction_name).squeeze()
- return self.repeat_data(prediction, number_of_bootstraps)
+ prediction = da.load().sel({self.model_type_dim: [prediction_name]})
+ if reference_name is not None:
+ prediction.coords[self.model_type_dim] = [reference_name]
+ return prediction.dropna(dim=self.index_dim)
@staticmethod
def repeat_data(data, number_of_repetition):
if isinstance(data, xr.DataArray):
data = data.data
- vals = np.tile(data, (number_of_repetition, 1))
- return vals[~np.isnan(vals).any(axis=1), :]
+ return np.repeat(np.expand_dims(data, axis=-1), number_of_repetition, axis=-1)
def _get_model_name(self):
"""Return model name without path information."""
@@ -342,20 +473,21 @@ class PostProcessing(RunEnvironment):
f"\n{sys.exc_info()[0]}\n{sys.exc_info()[1]}\n{sys.exc_info()[2]}")
try:
- if (self.bootstrap_skill_scores is not None) and ("PlotBootstrapSkillScore" in plot_list):
- for boot_type, boot_data in self.bootstrap_skill_scores.items():
+ if (self.feature_importance_skill_scores is not None) and ("PlotFeatureImportanceSkillScore" in plot_list):
+ for boot_type, boot_data in self.feature_importance_skill_scores.items():
for boot_method, boot_skill_score in boot_data.items():
try:
- PlotBootstrapSkillScore(boot_skill_score, plot_folder=self.plot_path,
- model_setup=self.forecast_indicator, sampling=self._sampling,
- ahead_dim=self.ahead_dim, separate_vars=to_list(self.target_var),
- bootstrap_type=boot_type, bootstrap_method=boot_method)
+ PlotFeatureImportanceSkillScore(
+ boot_skill_score, plot_folder=self.plot_path, model_setup=self.forecast_indicator,
+ sampling=self._sampling, ahead_dim=self.ahead_dim,
+ separate_vars=to_list(self.target_var), bootstrap_type=boot_type,
+ bootstrap_method=boot_method)
except Exception as e:
- logging.error(f"Could not create plot PlotBootstrapSkillScore ({boot_type}, {boot_method}) "
- f"due to the following error:\n{sys.exc_info()[0]}\n{sys.exc_info()[1]}\n"
- f"{sys.exc_info()[2]}")
+ logging.error(f"Could not create plot PlotFeatureImportanceSkillScore ({boot_type}, "
+ f"{boot_method}) due to the following error:\n{sys.exc_info()[0]}\n"
+ f"{sys.exc_info()[1]}\n{sys.exc_info()[2]}")
except Exception as e:
- logging.error(f"Could not create plot PlotBootstrapSkillScore due to the following error: {e}")
+ logging.error(f"Could not create plot PlotFeatureImportanceSkillScore due to the following error: {e}")
try:
if "PlotConditionalQuantiles" in plot_list:
@@ -453,6 +585,17 @@ class PostProcessing(RunEnvironment):
logging.error(f"Could not create plot PlotDataHistogram due to the following error: {e}"
f"\n{sys.exc_info()[0]}\n{sys.exc_info()[1]}\n{sys.exc_info()[2]}")
+ try:
+ if "PlotSampleUncertaintyFromBootstrap" in plot_list and self.uncertainty_estimate is not None:
+ block_length = self.data_store.get_default("block_length", default="1m", scope="uncertainty_estimate")
+ PlotSampleUncertaintyFromBootstrap(
+ data=self.uncertainty_estimate, plot_folder=self.plot_path, model_type_dim=self.model_type_dim,
+ dim_name_boots=self.uncertainty_estimate_boot_dim, error_measure="mean squared error",
+ error_unit=r"ppb$^2$", block_length=block_length)
+ except Exception as e:
+ logging.error(f"Could not create plot PlotSampleUncertaintyFromBootstrap due to the following error: {e}"
+ f"\n{sys.exc_info()[0]}\n{sys.exc_info()[1]}\n{sys.exc_info()[2]}")
+
def calculate_test_score(self):
"""Evaluate test score of model and save locally."""
@@ -512,10 +655,11 @@ class PostProcessing(RunEnvironment):
full_index = self.create_fullindex(observation_data.indexes[time_dimension], self._get_frequency())
prediction_dict = {self.forecast_indicator: nn_prediction,
"persi": persistence_prediction,
- "obs": observation,
+ self.observation_indicator: observation,
"ols": ols_prediction}
all_predictions = self.create_forecast_arrays(full_index, list(target_data.indexes[window_dim]),
time_dimension, ahead_dim=self.ahead_dim,
+ index_dim=self.index_dim, type_dim=self.model_type_dim,
**prediction_dict)
# save all forecasts locally
@@ -545,7 +689,7 @@ class PostProcessing(RunEnvironment):
with xr.open_dataarray(file) as da:
data = da.load()
forecast = data.sel(type=[self.forecast_indicator])
- forecast.coords["type"] = [competitor_name]
+ forecast.coords[self.model_type_dim] = [competitor_name]
return forecast
def _create_observation(self, data, _, transformation_func: Callable, normalised: bool) -> xr.DataArray:
@@ -675,7 +819,7 @@ class PostProcessing(RunEnvironment):
@staticmethod
def create_forecast_arrays(index: pd.DataFrame, ahead_names: List[Union[str, int]], time_dimension,
- ahead_dim="ahead", **kwargs):
+ ahead_dim="ahead", index_dim="index", type_dim="type", **kwargs):
"""
Combine different forecast types into single xarray.
@@ -688,7 +832,7 @@ class PostProcessing(RunEnvironment):
"""
keys = list(kwargs.keys())
res = xr.DataArray(np.full((len(index.index), len(ahead_names), len(keys)), np.nan),
- coords=[index.index, ahead_names, keys], dims=['index', ahead_dim, 'type'])
+ coords=[index.index, ahead_names, keys], dims=[index_dim, ahead_dim, type_dim])
for k, v in kwargs.items():
intersection = set(res.index.values) & set(v.indexes[time_dimension].values)
match_index = np.array(list(intersection))
@@ -727,18 +871,19 @@ class PostProcessing(RunEnvironment):
except (IndexError, KeyError, FileNotFoundError):
return None
- @staticmethod
- def _combine_forecasts(forecast, competitor, dim="type"):
+ def _combine_forecasts(self, forecast, competitor, dim=None):
"""
Combine forecast and competitor if both are xarray. If competitor is None, this returns forecasts and vise
versa.
"""
+ if dim is None:
+ dim = self.model_type_dim
try:
return xr.concat([forecast, competitor], dim=dim)
except (TypeError, AttributeError):
return forecast if competitor is None else competitor
- def calculate_error_metrics(self) -> Tuple[Dict, Dict, Dict]:
+ def calculate_error_metrics(self) -> Tuple[Dict, Dict, Dict, Dict]:
"""
Calculate error metrics and skill scores of NN forecast.
@@ -751,6 +896,7 @@ class PostProcessing(RunEnvironment):
path = self.data_store.get("forecast_path")
all_stations = self.data_store.get("stations")
skill_score_competitive = {}
+ skill_score_competitive_count = {}
skill_score_climatological = {}
errors = {}
for station in all_stations:
@@ -758,24 +904,61 @@ class PostProcessing(RunEnvironment):
# test errors
if external_data is not None:
- errors[station] = statistics.calculate_error_metrics(*map(lambda x: external_data.sel(type=x),
- [self.forecast_indicator, "obs"]),
- dim="index")
- # skill score
+ model_type_list = external_data.coords[self.model_type_dim].values.tolist()
+ for model_type in remove_items(model_type_list, self.observation_indicator):
+ if model_type not in errors.keys():
+ errors[model_type] = {}
+ errors[model_type][station] = statistics.calculate_error_metrics(
+ *map(lambda x: external_data.sel(**{self.model_type_dim: x}),
+ [model_type, self.observation_indicator]), dim=self.index_dim)
+
+ # load competitors
competitor = self.load_competitors(station)
- combined = self._combine_forecasts(external_data, competitor, dim="type")
- model_list = remove_items(list(combined.type.values), "obs") if combined is not None else None
+ combined = self._combine_forecasts(external_data, competitor, dim=self.model_type_dim)
+ if combined is not None:
+ model_list = remove_items(combined.coords[self.model_type_dim].values.tolist(),
+ self.observation_indicator)
+ else:
+ model_list = None
+
+ # test errors of competitors
+ for model_type in remove_items(model_list or [], list(errors.keys())):
+ if self.observation_indicator not in combined.coords[self.model_type_dim]:
+ continue
+ if model_type not in errors.keys():
+ errors[model_type] = {}
+ errors[model_type][station] = statistics.calculate_error_metrics(
+ *map(lambda x: combined.sel(**{self.model_type_dim: x}),
+ [model_type, self.observation_indicator]), dim=self.index_dim)
+
+ # skill score
skill_score = statistics.SkillScores(combined, models=model_list, ahead_dim=self.ahead_dim)
if external_data is not None:
- skill_score_competitive[station] = skill_score.skill_scores()
+ skill_score_competitive[station], skill_score_competitive_count[station] = skill_score.skill_scores()
internal_data = self._get_internal_data(station, path)
if internal_data is not None:
skill_score_climatological[station] = skill_score.climatological_skill_scores(
internal_data, forecast_name=self.forecast_indicator)
- errors.update({"total": self.calculate_average_errors(errors)})
- return skill_score_competitive, skill_score_climatological, errors
+ for model_type in errors.keys():
+ errors[model_type].update({"total": self.calculate_average_errors(errors[model_type])})
+ skill_score_competitive.update({"total": self.calculate_average_skill_scores(skill_score_competitive,
+ skill_score_competitive_count)})
+ return skill_score_competitive, skill_score_competitive_count, skill_score_climatological, errors
+
+ @staticmethod
+ def calculate_average_skill_scores(scores, counts):
+ avg_skill_score = 0
+ n_total = None
+ for vals in counts.values():
+ n_total = vals if n_total is None else n_total.add(vals, fill_value=0)
+ for station, station_scores in scores.items():
+ n = counts.get(station)
+ avg_skill_score = station_scores.mul(n.div(n_total, fill_value=0), fill_value=0).add(avg_skill_score,
+ fill_value=0)
+ return avg_skill_score
+
@staticmethod
def calculate_average_errors(errors):
@@ -788,28 +971,57 @@ class PostProcessing(RunEnvironment):
avg_error[error_metric] = new_val
return avg_error
+ def report_feature_importance_results(self, results):
+ """Create a csv file containing all results from feature importance."""
+ report_path = os.path.join(self.data_store.get("experiment_path"), "latex_report")
+ path_config.check_path_and_create(report_path)
+ res = []
+ for boot_type, d0 in results.items():
+ for boot_method, d1 in d0.items():
+ for station_name, vals in d1.items():
+ for boot_var in vals.coords[self.boot_var_dim].values.tolist():
+ for ahead in vals.coords[self.ahead_dim].values.tolist():
+ res.append([boot_type, boot_method, station_name, boot_var, ahead,
+ *vals.sel({self.boot_var_dim: boot_var,
+ self.ahead_dim: ahead}).values.round(5).tolist()])
+ col_names = [self.model_type_dim, "method", "station", self.boot_var_dim, self.ahead_dim,
+ *list(range(len(res[0]) - 5))]
+ df = pd.DataFrame(res, columns=col_names)
+ file_name = "feature_importance_skill_score_report_raw.csv"
+ df.to_csv(os.path.join(report_path, file_name), sep=";")
+
def report_error_metrics(self, errors):
report_path = os.path.join(self.data_store.get("experiment_path"), "latex_report")
path_config.check_path_and_create(report_path)
- metric_collection = {}
- for station, station_errors in errors.items():
- if isinstance(station_errors, xr.DataArray):
- dim = station_errors.dims[0]
- sel_index = [sel for sel in station_errors.coords[dim] if "CASE" in str(sel)]
- station_errors = {str(i.values): station_errors.sel(**{dim: i}) for i in sel_index}
- for metric, vals in station_errors.items():
- if metric == "n":
- continue
- pd_vals = pd.DataFrame.from_dict({station: vals}).T
- pd_vals.columns = [f"{metric}(t+{x})" for x in vals.coords["ahead"].values]
- mc = metric_collection.get(metric, pd.DataFrame())
- mc = mc.append(pd_vals)
- metric_collection[metric] = mc
- for metric, error_df in metric_collection.items():
- df = error_df.sort_index()
- if "total" in df.index:
- df.reindex(df.index.drop(["total"]).to_list() + ["total"], )
- column_format = tables.create_column_format_for_tex(df)
- file_name = f"error_report_{metric}.%s".replace(' ', '_')
- tables.save_to_tex(report_path, file_name % "tex", column_format=column_format, df=df)
- tables.save_to_md(report_path, file_name % "md", df=df)
+ for model_type in errors.keys():
+ metric_collection = {}
+ for station, station_errors in errors[model_type].items():
+ if isinstance(station_errors, xr.DataArray):
+ dim = station_errors.dims[0]
+ sel_index = [sel for sel in station_errors.coords[dim] if "CASE" in str(sel)]
+ station_errors = {str(i.values): station_errors.sel(**{dim: i}) for i in sel_index}
+ elif isinstance(station_errors, pd.DataFrame):
+ sel_index = station_errors.index.tolist()
+ ahead = station_errors.columns.values
+ station_errors = {k: xr.DataArray(station_errors[station_errors.index == k].values.flatten(),
+ dims=["ahead"], coords={"ahead": ahead}).astype(float)
+ for k in sel_index}
+ for metric, vals in station_errors.items():
+ if metric == "n":
+ metric = "count"
+ pd_vals = pd.DataFrame.from_dict({station: vals}).T
+ pd_vals.columns = [f"{metric}(t+{x})" for x in vals.coords["ahead"].values]
+ mc = metric_collection.get(metric, pd.DataFrame())
+ mc = mc.append(pd_vals)
+ metric_collection[metric] = mc
+ for metric, error_df in metric_collection.items():
+ df = error_df.sort_index()
+ if "total" in df.index:
+ df.reindex(df.index.drop(["total"]).to_list() + ["total"], )
+ column_format = tables.create_column_format_for_tex(df)
+ if model_type == "skill_score":
+ file_name = f"error_report_{model_type}_{metric}.%s".replace(' ', '_')
+ else:
+ file_name = f"error_report_{metric}_{model_type}.%s".replace(' ', '_')
+ tables.save_to_tex(report_path, file_name % "tex", column_format=column_format, df=df)
+ tables.save_to_md(report_path, file_name % "md", df=df)
diff --git a/mlair/run_modules/training.py b/mlair/run_modules/training.py
index 0696c2e7b8daa75925cf16096e183de94c21fe85..c076253d92a0e24f419046805687d2a80143176c 100644
--- a/mlair/run_modules/training.py
+++ b/mlair/run_modules/training.py
@@ -149,7 +149,7 @@ class Training(RunEnvironment):
logging.info("Found locally stored model and checkpoints. Training is resumed from the last checkpoint.")
self.callbacks.load_callbacks()
self.callbacks.update_checkpoint()
- self.model = keras.models.load_model(checkpoint.filepath)
+ self.model.load_model(checkpoint.filepath, compile=True)
hist: History = self.callbacks.get_callback_by_name("hist")
initial_epoch = max(hist.epoch) + 1
_ = self.model.fit(self.train_set,
@@ -179,6 +179,7 @@ class Training(RunEnvironment):
model_name = self.data_store.get("model_name", "model")
logging.debug(f"save best model to {model_name}")
self.model.save(model_name, save_format='h5')
+ self.model.save(model_name)
self.data_store.set("best_model", self.model)
def load_best_model(self, name: str) -> None:
@@ -189,8 +190,8 @@ class Training(RunEnvironment):
"""
logging.debug(f"load best model: {name}")
try:
- self.model.load_weights(name)
- logging.info('reload weights...')
+ self.model.load_model(name, compile=True)
+ logging.info('reload model...')
except OSError:
logging.info('no weights to reload...')
@@ -235,9 +236,11 @@ class Training(RunEnvironment):
if multiple_branches_used:
filename = os.path.join(path, f"{name}_history_main_loss.pdf")
PlotModelHistory(filename=filename, history=history, main_branch=True)
- if len([e for e in history.model.metrics_names if "mean_squared_error" in e]) > 0:
+ mse_indicator = list(set(history.model.metrics_names).intersection(["mean_squared_error", "mse"]))
+ if len(mse_indicator) > 0:
filename = os.path.join(path, f"{name}_history_main_mse.pdf")
- PlotModelHistory(filename=filename, history=history, plot_metric="mse", main_branch=multiple_branches_used)
+ PlotModelHistory(filename=filename, history=history, plot_metric=mse_indicator[0],
+ main_branch=multiple_branches_used)
# plot learning rate
if lr_sc:
diff --git a/mlair/workflows/default_workflow.py b/mlair/workflows/default_workflow.py
index 5894555a6af52299efcd8d88d76c0d3791a1599e..961979cb774e928bda96d4cd1a3a7b0f8565e968 100644
--- a/mlair/workflows/default_workflow.py
+++ b/mlair/workflows/default_workflow.py
@@ -31,7 +31,6 @@ class DefaultWorkflow(Workflow):
permute_data_on_training=None, extreme_values=None, extremes_on_right_tail_only=None,
transformation=None,
train_min_length=None, val_min_length=None, test_min_length=None,
- evaluate_bootstraps=None, number_of_bootstraps=None, create_new_bootstraps=None,
plot_list=None,
model=None,
batch_size=None,
diff --git a/requirements.txt b/requirements.txt
index c3deca92faa6363e6585b16ba1934039d6ad410a..c3e473b3ebe2829bd82b053306cf4d523cf43160 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,26 +1,32 @@
-## this list was generated using pipreqs on mlair/ directory
astropy==4.1
auto_mix_prep==0.2.0
Cartopy==0.18.0
dask==2021.3.0
dill==0.3.3
+fsspec==2021.11.0
keras==2.6.0
keras_nightly==2.5.0.dev2021032900
+locket==0.2.1
matplotlib==3.3.4
mock==4.0.3
+netcdf4==1.5.8
numpy==1.19.5
pandas==1.1.5
+partd==1.2.0
psutil==5.8.0
pydot==1.4.2
pytest==6.2.2
+pytest-lazy-fixture==0.6.3
requests==2.25.1
scipy==1.5.2
seaborn==0.11.1
setuptools==47.1.0
+--no-binary shapely Shapely==1.8.0
six==1.15.0
statsmodels==0.12.2
tabulate==0.8.9
tensorflow==2.5.0
+toolz==0.11.2
typing_extensions==3.7.4.3
wget==3.2
xarray==0.16.2
diff --git a/requirements_vm_local.txt b/requirements_vm_local.txt
deleted file mode 100644
index d57cfb8e0b75055e187816b9922f72ac510cbd7d..0000000000000000000000000000000000000000
--- a/requirements_vm_local.txt
+++ /dev/null
@@ -1,103 +0,0 @@
-absl-py==0.11.0
-appdirs==1.4.4
-astor==0.8.1
-astropy==4.1
-astunparse==1.6.3
-attrs==20.3.0
-Bottleneck==1.3.2
-cached-property==1.5.2
-cachetools==4.2.4
-Cartopy==0.18.0
-certifi==2020.12.5
-cftime==1.4.1
-chardet==4.0.0
-click==8.0.3
-cloudpickle==2.0.0
-coverage==5.4
-cycler==0.10.0
-dask==2021.10.0
-dill==0.3.3
-distributed==2021.10.0
-flatbuffers==1.12
-fsspec==0.8.5
-gast==0.4.0
-google-auth==2.3.0
-google-auth-oauthlib==0.4.6
-google-pasta==0.2.0
-greenlet==1.1.2
-grpcio==1.34.0
-h5py==3.1.0
-HeapDict==1.0.1
-idna==2.10
-importlib-metadata==3.4.0
-iniconfig==1.1.1
-Jinja2==3.0.2
-joblib==1.1.0
-keras-nightly==2.5.0.dev2021032900
-Keras-Preprocessing==1.1.2
-kiwisolver==1.3.1
-locket==0.2.1
-Markdown==3.3.3
-MarkupSafe==2.0.1
-matplotlib==3.3.4
-mock==4.0.3
-msgpack==1.0.2
-netCDF4==1.5.5.1
-numpy==1.19.5
-oauthlib==3.1.1
-opt-einsum==3.3.0
-ordered-set==4.0.2
-packaging==20.9
-pandas==1.1.5
-partd==1.1.0
-patsy==0.5.1
-Pillow==8.1.0
-pluggy==0.13.1
-protobuf==3.15.0
-psutil==5.8.0
-py==1.10.0
-pyasn1==0.4.8
-pyasn1-modules==0.2.8
-pydot==1.4.2
-pyparsing==2.4.7
-pyshp==2.1.3
-pytest==6.2.2
-pytest-cov==2.11.1
-pytest-html==3.1.1
-pytest-lazy-fixture==0.6.3
-pytest-metadata==1.11.0
-pytest-sugar==0.9.4
-python-dateutil==2.8.1
-pytz==2021.1
-PyYAML==5.4.1
-requests==2.25.1
-requests-oauthlib==1.3.0
-rsa==4.7.2
-scikit-learn==1.0.1
-scipy==1.5.2
-seaborn==0.11.1
-Shapely==1.7.1
-six==1.15.0
-sortedcontainers==2.4.0
-SQLAlchemy==1.4.26
-statsmodels==0.12.2
-tabulate==0.8.8
-tblib==1.7.0
-tensorboard==2.7.0
-tensorboard-data-server==0.6.1
-tensorboard-plugin-wit==1.8.0
-tensorflow==2.5.0
-tensorflow-estimator==2.5.0
-termcolor==1.1.0
-threadpoolctl==3.0.0
-toml==0.10.2
-toolz==0.11.1
-tornado==6.1
-typing-extensions==3.7.4.3
-urllib3==1.26.3
-Werkzeug==1.0.1
-wget==3.2
-wrapt==1.12.1
-xarray==0.16.2
-zict==2.0.0
-zipp==3.4.0
diff --git a/run.py b/run.py
index 1d607acfc8a789080a38da9ee546e516be845797..82bb0e2814d403b5be602eaebd1bc44b6cf6d6f9 100644
--- a/run.py
+++ b/run.py
@@ -28,8 +28,8 @@ def main(parser_args):
# stations=["DEBW087","DEBW013", "DEBW107", "DEBW076"],
stations=["DEBW013", "DEBW087", "DEBW107", "DEBW076"],
train_model=False, create_new_model=True, network="UBA",
- evaluate_bootstraps=False, # plot_list=["PlotCompetitiveSkillScore"],
- competitors=["test_model", "test_model2"], # model=chosen_model,
+ evaluate_feature_importance=False, # plot_list=["PlotCompetitiveSkillScore"],
+ competitors=["test_model", "test_model2"],
competitor_path=os.path.join(os.getcwd(), "data", "comp_test"),
**parser_args.__dict__, start_script=__file__)
workflow.run()
diff --git a/run_climate_filter.py b/run_climate_filter.py
index 5577375e2fc135676f71151791c1d564dcb25a2e..7f3fcbaaba94506faeadd884073620496155f8ea 100644
--- a/run_climate_filter.py
+++ b/run_climate_filter.py
@@ -65,9 +65,9 @@ def main(parser_args):
train_model=False, create_new_model=True,
epochs=1,
model=NN,
- evaluate_bootstraps=False,
- bootstrap_type=["singleinput", "branch", "variable"],
- bootstrap_method=["shuffle", "zero_mean"],
+ evaluate_feature_importance=False,
+ feature_importance_bootstrap_type=["singleinput", "branch", "variable"],
+ feature_importance_bootstrap_method=["shuffle", "zero_mean"],
plot_list=DEFAULT_PLOT_LIST,
# competitors=["IntelliO3-ts-v1", "MFCN_64_32_16_climFIR", "FCN_1449_512_32_4"], # "FCN_1449_16_8_4",],
lazy_preprocessing=True,
diff --git a/run_mixed_sampling.py b/run_mixed_sampling.py
index 819ef51129854b4539632ef91a55e33a2607eb55..47aa9b970c0e95ccadb60e8c090136c0fa6ceea4 100644
--- a/run_mixed_sampling.py
+++ b/run_mixed_sampling.py
@@ -4,8 +4,8 @@ __date__ = '2019-11-14'
import argparse
from mlair.workflows import DefaultWorkflow
-from mlair.data_handler.data_handler_mixed_sampling import DataHandlerMixedSampling, DataHandlerMixedSamplingWithFilter, \
- DataHandlerSeparationOfScales
+from mlair.data_handler.data_handler_mixed_sampling import DataHandlerMixedSampling
+
stats = {'o3': 'dma8eu', 'no': 'dma8eu', 'no2': 'dma8eu',
'relhum': 'average_values', 'u': 'average_values', 'v': 'average_values',
@@ -20,7 +20,7 @@ data_origin = {'o3': '', 'no': '', 'no2': '',
def main(parser_args):
args = dict(stations=["DEBW107", "DEBW013"],
network="UBA",
- evaluate_bootstraps=False, plot_list=[],
+ evaluate_feature_importance=True, # plot_list=[],
data_origin=data_origin, data_handler=DataHandlerMixedSampling,
interpolation_limit=(3, 1), overwrite_local_data=False,
sampling=("hourly", "daily"),
@@ -28,8 +28,6 @@ def main(parser_args):
create_new_model=True, train_model=False, epochs=1,
window_history_size=6 * 24 + 16,
window_history_offset=16,
- kz_filter_length=[100 * 24, 15 * 24],
- kz_filter_iter=[4, 5],
start="2006-01-01",
train_start="2006-01-01",
end="2011-12-31",
diff --git a/test/test_configuration/test_defaults.py b/test/test_configuration/test_defaults.py
index b6bdd9556f73ff711003b01c3a2b65a1c20c66d3..f6bc6d24724c2620083602d3864bcbca0a709681 100644
--- a/test/test_configuration/test_defaults.py
+++ b/test/test_configuration/test_defaults.py
@@ -62,10 +62,16 @@ class TestAllDefaults:
assert DEFAULT_HPC_LOGIN_LIST == ["ju", "hdfmll"]
def test_postprocessing_parameters(self):
- assert DEFAULT_EVALUATE_BOOTSTRAPS is True
- assert DEFAULT_CREATE_NEW_BOOTSTRAPS is False
- assert DEFAULT_NUMBER_OF_BOOTSTRAPS == 20
+ assert DEFAULT_DO_UNCERTAINTY_ESTIMATE is True
+ assert DEFAULT_UNCERTAINTY_ESTIMATE_BLOCK_LENGTH == "1m"
+ assert DEFAULT_UNCERTAINTY_ESTIMATE_EVALUATE_COMPETITORS is True
+ assert DEFAULT_UNCERTAINTY_ESTIMATE_N_BOOTS == 1000
+ assert DEFAULT_EVALUATE_FEATURE_IMPORTANCE is True
+ assert DEFAULT_FEATURE_IMPORTANCE_CREATE_NEW_BOOTSTRAPS is False
+ assert DEFAULT_FEATURE_IMPORTANCE_N_BOOTS == 20
+ assert DEFAULT_FEATURE_IMPORTANCE_BOOTSTRAP_TYPE == "singleinput"
+ assert DEFAULT_FEATURE_IMPORTANCE_BOOTSTRAP_METHOD == "shuffle"
assert DEFAULT_PLOT_LIST == ["PlotMonthlySummary", "PlotStationMap", "PlotClimatologicalSkillScore",
- "PlotTimeSeries", "PlotCompetitiveSkillScore", "PlotBootstrapSkillScore",
+ "PlotTimeSeries", "PlotCompetitiveSkillScore", "PlotFeatureImportanceSkillScore",
"PlotConditionalQuantiles", "PlotAvailability", "PlotAvailabilityHistogram",
- "PlotDataHistogram", "PlotPeriodogram"]
+ "PlotDataHistogram", "PlotPeriodogram", "PlotSampleUncertaintyFromBootstrap"]
diff --git a/test/test_data_handler/old_t_bootstraps.py b/test/test_data_handler/old_t_bootstraps.py
index 21c18c6c2d6f6a6a38a41250f00d3d14a29ed457..e21af9f614d4cfaaadf946cdab72cc69cd5b19a7 100644
--- a/test/test_data_handler/old_t_bootstraps.py
+++ b/test/test_data_handler/old_t_bootstraps.py
@@ -7,7 +7,7 @@ import numpy as np
import pytest
import xarray as xr
-from mlair.data_handler.bootstraps import BootStraps
+from mlair.data_handler.input_bootstraps import Bootstraps
from src.data_handler import DataPrepJoin
@@ -171,22 +171,22 @@ class TestBootStraps:
@pytest.fixture
def bootstrap(self, orig_generator, data_path):
- return BootStraps(orig_generator, data_path, 20)
+ return Bootstraps(orig_generator, data_path, 20)
@pytest.fixture
@mock.patch("mlair.data_handling.bootstraps.CreateShuffledData", return_value=None)
def bootstrap_no_shuffling(self, mock_create_shuffle_data, orig_generator, data_path):
shutil.rmtree(data_path)
- return BootStraps(orig_generator, data_path, 20)
+ return Bootstraps(orig_generator, data_path, 20)
def test_init_no_shuffling(self, bootstrap_no_shuffling, data_path):
- assert isinstance(bootstrap_no_shuffling, BootStraps)
+ assert isinstance(bootstrap_no_shuffling, Bootstraps)
assert bootstrap_no_shuffling.number_of_bootstraps == 20
assert bootstrap_no_shuffling.bootstrap_path == data_path
def test_init_with_shuffling(self, orig_generator, data_path, caplog):
caplog.set_level(logging.INFO)
- BootStraps(orig_generator, data_path, 20)
+ Bootstraps(orig_generator, data_path, 20)
assert caplog.record_tuples[0] == ('root', logging.INFO, "create / check shuffled bootstrap data")
def test_stations(self, bootstrap_no_shuffling, orig_generator):
@@ -213,9 +213,9 @@ class TestBootStraps:
@mock.patch("mlair.data_handling.data_generator.DataGenerator._load_pickle_data", side_effect=FileNotFoundError)
def test_get_generator_different_generator(self, mock_load_pickle, data_path, orig_generator):
- BootStraps(orig_generator, data_path, 20) # to create
+ Bootstraps(orig_generator, data_path, 20) # to create
orig_generator.window_history_size = 4
- bootstrap = BootStraps(orig_generator, data_path, 20)
+ bootstrap = Bootstraps(orig_generator, data_path, 20)
station = bootstrap.stations[0]
var = bootstrap.variables[0]
var_others = bootstrap.variables[1:]
diff --git a/test/test_helpers/test_statistics.py b/test/test_helpers/test_statistics.py
index 2a77f0806b886bee1a3961ff0a972e8f0ee62873..f5148cdc293939d5711afb57c2fa009c47b6c86d 100644
--- a/test/test_helpers/test_statistics.py
+++ b/test/test_helpers/test_statistics.py
@@ -4,7 +4,8 @@ import pytest
import xarray as xr
from mlair.helpers.statistics import standardise, standardise_inverse, standardise_apply, centre, centre_inverse, \
- centre_apply, apply_inverse_transformation, min_max, min_max_inverse, min_max_apply, log, log_inverse, log_apply
+ centre_apply, apply_inverse_transformation, min_max, min_max_inverse, min_max_apply, log, log_inverse, log_apply, \
+ create_single_bootstrap_realization, calculate_average, create_n_bootstrap_realizations
lazy = pytest.lazy_fixture
@@ -221,3 +222,36 @@ class TestLog:
data_ref, opts = log(data_orig, dim)
data_test = log_apply(data_orig, opts["mean"], opts["std"])
assert np.testing.assert_array_almost_equal(data_ref, data_test) is None
+
+
+class TestCreateBootstrapRealizations:
+
+ @pytest.fixture
+ def data(self):
+ return xr.DataArray(np.array(range(20)).reshape(2 , -1).T,
+ dims={'time': range(10), 'model': ['m1', 'm2']},
+ coords={'time': range(10), 'model': ['m1', 'm2']})
+
+ def test_create_single_bootstrap_realization(self, data):
+ np.random.seed(42)
+ proc_data = create_single_bootstrap_realization(data, "time")
+ assert isinstance(proc_data, xr.DataArray)
+ assert (proc_data.coords['time'].values == np.array([6, 3, 7, 4, 6, 9, 2, 6, 7, 4])).all()
+ # check if all time index values of proc_data are from data
+ assert np.in1d(proc_data.indexes['time'].values, data.indexes['time'].values).all()
+
+ def test_calculate_average(self, data):
+ assert isinstance(data, xr.DataArray)
+ assert calculate_average(data) == data.mean()
+ assert (calculate_average(data, axis=0) == data.mean(axis=0)).all()
+
+ def test_create_n_bootstrap_realizations(self, data):
+ boot_data = create_n_bootstrap_realizations(data, dim_name_time='time', dim_name_model='model',
+ n_boots=1000, dim_name_boots='boots')
+ assert isinstance(boot_data, xr.DataArray)
+ assert boot_data.shape == (1000, 2)
+
+ boot_data = create_n_bootstrap_realizations(data.sel(model='m1').squeeze(), dim_name_time='time',
+ dim_name_model='model', n_boots=1000, dim_name_boots='boots')
+ assert isinstance(boot_data, xr.DataArray)
+ assert boot_data.shape == (1000,)
diff --git a/test/test_run_modules/test_training.py b/test/test_run_modules/test_training.py
index 9d633a348bd1e24cd3f3abcdb83124f6107db2e9..b16c0c2586f87af8368ac0059edc8a3997780f69 100644
--- a/test/test_run_modules/test_training.py
+++ b/test/test_run_modules/test_training.py
@@ -1,8 +1,12 @@
+import copy
import glob
import json
+import time
+
import logging
import os
import shutil
+from typing import Callable
import tensorflow.keras as keras
import mock
@@ -11,6 +15,7 @@ from tensorflow.keras.callbacks import History
from mlair.data_handler import DataCollection, KerasIterator, DefaultDataHandler
from mlair.helpers import PyTestRegex
+from mlair.model_modules.fully_connected_networks import FCN
from mlair.model_modules.flatten import flatten_tail
from mlair.model_modules.inception_model import InceptionModelBase
from mlair.model_modules.keras_extensions import LearningRateDecay, HistoryAdvanced, CallbackHandler, EpoTimingCallback
@@ -76,10 +81,24 @@ class TestTraining:
obj.data_store.set("plot_path", path_plot, "general")
obj._train_model = True
obj._create_new_model = False
- yield obj
- if os.path.exists(path):
- shutil.rmtree(path)
- RunEnvironment().__del__()
+ try:
+ yield obj
+ finally:
+ if os.path.exists(path):
+ shutil.rmtree(path)
+ try:
+ RunEnvironment().__del__()
+ except AssertionError:
+ pass
+ # try:
+ # yield obj
+ # finally:
+ # if os.path.exists(path):
+ # shutil.rmtree(path)
+ # try:
+ # RunEnvironment().__del__()
+ # except AssertionError:
+ # pass
@pytest.fixture
def learning_rate(self):
@@ -144,7 +163,7 @@ class TestTraining:
@pytest.fixture
def model(self, window_history_size, window_lead_time, statistics_per_var):
channels = len(list(statistics_per_var.keys()))
- return my_test_model(keras.layers.PReLU, window_history_size, channels, window_lead_time, 0.1, False)
+ return FCN([(window_history_size + 1, 1, channels)], [window_lead_time])
@pytest.fixture
def callbacks(self, path):
@@ -174,7 +193,8 @@ class TestTraining:
obj.data_store.set("data_collection", data_collection, "general.train")
obj.data_store.set("data_collection", data_collection, "general.val")
obj.data_store.set("data_collection", data_collection, "general.test")
- obj.model.compile(optimizer=keras.optimizers.SGD(), loss=keras.losses.mean_absolute_error)
+ obj.model.compile(**obj.model.compile_options)
+ keras.utils.get_custom_objects().update(obj.model.custom_objects)
return obj
@pytest.fixture
@@ -209,6 +229,57 @@ class TestTraining:
if os.path.exists(path):
shutil.rmtree(path)
+ @staticmethod
+ def create_training_obj(epochs, path, data_collection, batch_path, model_path,
+ statistics_per_var, window_history_size, window_lead_time) -> Training:
+
+ channels = len(list(statistics_per_var.keys()))
+ model = FCN([(window_history_size + 1, 1, channels)], [window_lead_time])
+
+ obj = object.__new__(Training)
+ super(Training, obj).__init__()
+ obj.model = model
+ obj.train_set = None
+ obj.val_set = None
+ obj.test_set = None
+ obj.batch_size = 256
+ obj.epochs = epochs
+
+ clbk = CallbackHandler()
+ hist = HistoryAdvanced()
+ epo_timing = EpoTimingCallback()
+ clbk.add_callback(hist, os.path.join(path, "hist_checkpoint.pickle"), "hist")
+ lr = LearningRateDecay()
+ clbk.add_callback(lr, os.path.join(path, "lr_checkpoint.pickle"), "lr")
+ clbk.add_callback(epo_timing, os.path.join(path, "epo_timing.pickle"), "epo_timing")
+ clbk.create_model_checkpoint(filepath=os.path.join(path, "model_checkpoint"), monitor='val_loss',
+ save_best_only=True)
+ obj.callbacks = clbk
+ obj.lr_sc = lr
+ obj.hist = hist
+ obj.experiment_name = "TestExperiment"
+ obj.data_store.set("data_collection", data_collection, "general.train")
+ obj.data_store.set("data_collection", data_collection, "general.val")
+ obj.data_store.set("data_collection", data_collection, "general.test")
+ if not os.path.exists(path):
+ os.makedirs(path)
+ obj.data_store.set("experiment_path", path, "general")
+ os.makedirs(batch_path, exist_ok=True)
+ obj.data_store.set("batch_path", batch_path, "general")
+ os.makedirs(model_path, exist_ok=True)
+ obj.data_store.set("model_path", model_path, "general")
+ obj.data_store.set("model_name", os.path.join(model_path, "test_model.h5"), "general.model")
+ obj.data_store.set("experiment_name", "TestExperiment", "general")
+
+ path_plot = os.path.join(path, "plots")
+ os.makedirs(path_plot, exist_ok=True)
+ obj.data_store.set("plot_path", path_plot, "general")
+ obj._train_model = True
+ obj._create_new_model = False
+
+ obj.model.compile(**obj.model.compile_options)
+ return obj
+
def test_init(self, ready_to_init):
assert isinstance(Training(), Training) # just test, if nothing fails
@@ -223,9 +294,10 @@ class TestTraining:
assert ready_to_run._run() is None # just test, if nothing fails
def test_make_predict_function(self, init_without_run):
- assert hasattr(init_without_run.model, "predict_function") is False
+ assert hasattr(init_without_run.model, "predict_function") is True
+ assert init_without_run.model.predict_function is None
init_without_run.make_predict_function()
- assert hasattr(init_without_run.model, "predict_function")
+ assert isinstance(init_without_run.model.predict_function, Callable)
def test_set_gen(self, init_without_run):
assert init_without_run.train_set is None
@@ -242,10 +314,10 @@ class TestTraining:
[getattr(init_without_run, f"{obj}_set")._collection.return_value == f"mock_{obj}_gen" for obj in sets])
def test_train(self, ready_to_train, path):
- assert not hasattr(ready_to_train.model, "history")
+ assert ready_to_train.model.history is None
assert len(glob.glob(os.path.join(path, "plots", "TestExperiment_history_*.pdf"))) == 0
ready_to_train.train()
- assert list(ready_to_train.model.history.history.keys()) == ["val_loss", "loss"]
+ assert sorted(list(ready_to_train.model.history.history.keys())) == ["loss", "val_loss"]
assert ready_to_train.model.history.epoch == [0, 1]
assert len(glob.glob(os.path.join(path, "plots", "TestExperiment_history_*.pdf"))) == 2
@@ -260,8 +332,8 @@ class TestTraining:
def test_load_best_model_no_weights(self, init_without_run, caplog):
caplog.set_level(logging.DEBUG)
- init_without_run.load_best_model("notExisting")
- assert caplog.record_tuples[0] == ("root", 10, PyTestRegex("load best model: notExisting"))
+ init_without_run.load_best_model("notExisting.h5")
+ assert caplog.record_tuples[0] == ("root", 10, PyTestRegex("load best model: notExisting.h5"))
assert caplog.record_tuples[1] == ("root", 20, PyTestRegex("no weights to reload..."))
def test_save_callbacks_history_created(self, init_without_run, history, learning_rate, epo_timing, model_path):
@@ -290,3 +362,14 @@ class TestTraining:
history.model.metrics_names = mock.MagicMock(return_value=["loss", "mean_squared_error"])
init_without_run.create_monitoring_plots(history, learning_rate)
assert len(glob.glob(os.path.join(path, "plots", "TestExperiment_history_*.pdf"))) == 2
+
+ def test_resume_training1(self, path: str, model_path, batch_path, data_collection, statistics_per_var,
+ window_history_size, window_lead_time):
+
+ obj_1st = self.create_training_obj(2, path, data_collection, batch_path, model_path, statistics_per_var,
+ window_history_size, window_lead_time)
+ keras.utils.get_custom_objects().update(obj_1st.model.custom_objects)
+ assert obj_1st._run() is None
+ obj_2nd = self.create_training_obj(4, path, data_collection, batch_path, model_path, statistics_per_var,
+ window_history_size, window_lead_time)
+ assert obj_2nd._run() is None