diff --git a/.gitignore b/.gitignore
index e109cec7c7622cee9d9a635cc458b9c662fc4761..366a3e3907a4b0bed1bd400cc2e377b7cdbe92bc 100644
--- a/.gitignore
+++ b/.gitignore
@@ -60,7 +60,7 @@ Thumbs.db
htmlcov/
.pytest_cache
/test/data/
-/test/test_modules/data/
+/test/test_run_modules/data/
report.html
/TestExperiment/
/testrun_network*/
diff --git a/requirements.txt b/requirements.txt
index 71bb1338effff38092510982d4a2c1f37f7b026a..7da29a05b748531fd4ec327ff17f432ff1ecaabb 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -38,9 +38,9 @@ pydot==1.4.1
pyparsing==2.4.6
pyproj==2.5.0
pyshp==2.1.0
-pytest==5.3.5
-pytest-cov==2.8.1
-pytest-html==2.0.1
+pytest==6.0.0
+pytest-cov==2.10.0
+pytest-html==2.1.1
pytest-lazy-fixture==0.6.3
pytest-metadata==1.8.0
pytest-sugar
diff --git a/src/data_handler/__init__.py b/src/data_handler/__init__.py
index 9ce7307d87fea03c11066068d8eccd78a02ed0bf..451868b838ab7a0d165942e36b5ec6aa03e42721 100644
--- a/src/data_handler/__init__.py
+++ b/src/data_handler/__init__.py
@@ -11,5 +11,5 @@ __date__ = '2020-04-17'
from .bootstraps import BootStraps
from .iterator import KerasIterator, DataCollection
-from .advanced_data_handling import DefaultDataPreparation, AbstractDataPreparation
+from .advanced_data_handler import DefaultDataPreparation, AbstractDataPreparation
from .data_preparation_neighbors import DataPreparationNeighbors
diff --git a/src/data_handler/advanced_data_handling.py b/src/data_handler/advanced_data_handler.py
similarity index 98%
rename from src/data_handler/advanced_data_handling.py
rename to src/data_handler/advanced_data_handler.py
index 26cd3ca82d03d4f95703a6ed0ad0a4b1d28e09e4..c9c25ca7a6ce765db2eb67d9b6b7d9144e54987a 100644
--- a/src/data_handler/advanced_data_handling.py
+++ b/src/data_handler/advanced_data_handler.py
@@ -12,6 +12,7 @@ import pandas as pd
import datetime as dt
import shutil
import inspect
+import copy
from typing import Union, List, Tuple, Dict
import logging
@@ -109,9 +110,9 @@ class DefaultDataPreparation(AbstractDataPreparation):
@classmethod
def build(cls, station, **kwargs):
- sp_keys = {k: kwargs[k] for k in cls._requirements if k in kwargs}
+ sp_keys = {k: copy.deepcopy(kwargs[k]) for k in cls._requirements if k in kwargs}
sp = StationPrep(station, **sp_keys)
- dp_args = {k: kwargs[k] for k in cls.own_args("id_class") if k in kwargs}
+ dp_args = {k: copy.deepcopy(kwargs[k]) for k in cls.own_args("id_class") if k in kwargs}
return cls(sp, **dp_args)
def _create_collection(self):
@@ -274,7 +275,7 @@ class DefaultDataPreparation(AbstractDataPreparation):
@classmethod
def transformation(cls, set_stations, **kwargs):
- sp_keys = {k: kwargs[k] for k in cls._requirements if k in kwargs}
+ sp_keys = {k: copy.deepcopy(kwargs[k]) for k in cls._requirements if k in kwargs}
transformation_dict = sp_keys.pop("transformation")
if transformation_dict is None:
return
diff --git a/src/data_handler/bootstraps.py b/src/data_handler/bootstraps.py
index e3f7ff91f34ea4e6352a9809749973ca10bbc00f..4ccc1350ee36df49dba0683eea896fc4ed398b60 100644
--- a/src/data_handler/bootstraps.py
+++ b/src/data_handler/bootstraps.py
@@ -12,7 +12,6 @@ __author__ = 'Felix Kleinert, Lukas Leufen'
__date__ = '2020-02-07'
-import logging
import os
from collections import Iterator, Iterable
from itertools import chain
@@ -20,7 +19,7 @@ from itertools import chain
import numpy as np
import xarray as xr
-from src.data_handler.advanced_data_handling import AbstractDataPreparation
+from src.data_handler.advanced_data_handler import AbstractDataPreparation
class BootstrapIterator(Iterator):
diff --git a/src/data_handler/data_preparation_neighbors.py b/src/data_handler/data_preparation_neighbors.py
index 6d2099da9c0e6cac6b0dec6a0ee7fdd090d9df33..508716b14d085ab1bb2aaaeb02471480608b6a27 100644
--- a/src/data_handler/data_preparation_neighbors.py
+++ b/src/data_handler/data_preparation_neighbors.py
@@ -5,7 +5,7 @@ __date__ = '2020-07-17'
from src.helpers import to_list
from src.data_handler.station_preparation import StationPrep
-from src.data_handler.advanced_data_handling import DefaultDataPreparation
+from src.data_handler.advanced_data_handler import DefaultDataPreparation
import os
from typing import Union, List
diff --git a/src/run.py b/src/run.py
index 8a4ade33c0e5b260fafab58e76cf753455077d50..1244c25d1b67d1f80b7da2b1e18210186ac3a9f0 100644
--- a/src/run.py
+++ b/src/run.py
@@ -39,4 +39,6 @@ def run(stations=None,
if __name__ == "__main__":
- run(stations=["DEBW013","DEBW025"], statistics_per_var={'o3': 'dma8eu', "temp": "maximum"}, trainable=True, create_new_model=True)
+ from src.model_modules.model_class import MyBranchedModel
+ run(stations=["DEBW013","DEBW025"], statistics_per_var={'o3': 'dma8eu', "temp": "maximum"}, trainable=True,
+ create_new_model=True, model=MyBranchedModel)
diff --git a/src/run_modules/experiment_setup.py b/src/run_modules/experiment_setup.py
index 5d0e3743ac81262a74f28fd291193a6f10b8eeac..6fec871327d218cb2f42b84c518807c558c9c53d 100644
--- a/src/run_modules/experiment_setup.py
+++ b/src/run_modules/experiment_setup.py
@@ -18,7 +18,7 @@ from src.configuration.defaults import DEFAULT_STATIONS, DEFAULT_VAR_ALL_DICT, D
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
-from src.data_handler.advanced_data_handling import DefaultDataPreparation
+from src.data_handler.advanced_data_handler import DefaultDataPreparation
from src.run_modules.run_environment import RunEnvironment
from src.model_modules.model_class import MyLittleModel as VanillaModel
diff --git a/src/run_modules/pre_processing.py b/src/run_modules/pre_processing.py
index c5cebcf39093ff035536e9ce7838999769dc0cbd..4b6de8253a58ce0b65184ae506f198a8a6b17aad 100644
--- a/src/run_modules/pre_processing.py
+++ b/src/run_modules/pre_processing.py
@@ -157,7 +157,7 @@ class PreProcessing(RunEnvironment):
raise AssertionError(f"Make sure, that the train subset is always at first execution position! Given subset"
f"order was: {subset_names}.")
for (ind, scope) in zip([train_index, val_index, test_index, train_val_index], subset_names):
- self.create_set_split_new(ind, scope)
+ self.create_set_split(ind, scope)
@staticmethod
def split_set_indices(total_length: int, fraction: float) -> Tuple[slice, slice, slice, slice]:
@@ -181,7 +181,7 @@ class PreProcessing(RunEnvironment):
train_val_index = slice(0, pos_test_split)
return train_index, val_index, test_index, train_val_index
- def create_set_split_new(self, index_list: slice, set_name: str) -> None:
+ def create_set_split(self, index_list: slice, set_name: str) -> None:
# get set stations
stations = self.data_store.get("stations", scope=set_name)
if self.data_store.get("use_all_stations_on_all_data_sets"):
@@ -212,7 +212,7 @@ class PreProcessing(RunEnvironment):
:return: Corrected list containing only valid station IDs.
"""
t_outer = TimeTracking()
- logging.info(f"check valid stations started{' (%s)' % set_name if set_name is not None else 'all'}")
+ logging.info(f"check valid stations started{' (%s)' % (set_name if set_name is not None else 'all')}")
# calculate transformation using train data
if set_name == "train":
self.transformation(data_preparation, set_stations)
diff --git a/test/test_data_handling/old_t_bootstraps.py b/test/test_data_handler/old_t_bootstraps.py
similarity index 100%
rename from test/test_data_handling/old_t_bootstraps.py
rename to test/test_data_handler/old_t_bootstraps.py
diff --git a/test/test_data_handling/old_t_data_generator.py b/test/test_data_handler/old_t_data_generator.py
similarity index 100%
rename from test/test_data_handling/old_t_data_generator.py
rename to test/test_data_handler/old_t_data_generator.py
diff --git a/test/test_data_handling/old_t_data_preparation.py b/test/test_data_handler/old_t_data_preparation.py
similarity index 100%
rename from test/test_data_handling/old_t_data_preparation.py
rename to test/test_data_handler/old_t_data_preparation.py
diff --git a/test/test_data_handling/test_iterator.py b/test/test_data_handler/test_iterator.py
similarity index 100%
rename from test/test_data_handling/test_iterator.py
rename to test/test_data_handler/test_iterator.py
diff --git a/test/test_modules/test_experiment_setup.py b/test/test_run_modules/test_experiment_setup.py
similarity index 100%
rename from test/test_modules/test_experiment_setup.py
rename to test/test_run_modules/test_experiment_setup.py
diff --git a/test/test_modules/test_model_setup.py b/test/test_run_modules/test_model_setup.py
similarity index 100%
rename from test/test_modules/test_model_setup.py
rename to test/test_run_modules/test_model_setup.py
diff --git a/test/test_modules/test_partition_check.py b/test/test_run_modules/test_partition_check.py
similarity index 100%
rename from test/test_modules/test_partition_check.py
rename to test/test_run_modules/test_partition_check.py
diff --git a/test/test_modules/test_post_processing.py b/test/test_run_modules/test_post_processing.py
similarity index 100%
rename from test/test_modules/test_post_processing.py
rename to test/test_run_modules/test_post_processing.py
diff --git a/test/test_modules/old_t_pre_processing.py b/test/test_run_modules/test_pre_processing.py
similarity index 70%
rename from test/test_modules/old_t_pre_processing.py
rename to test/test_run_modules/test_pre_processing.py
index 63035d3858adb7c64265b27f87f8042c7f15a997..d08e3302fd55b8708e964bc5873209cc7d2dbbde 100644
--- a/test/test_modules/old_t_pre_processing.py
+++ b/test/test_run_modules/test_pre_processing.py
@@ -2,8 +2,7 @@ import logging
import pytest
-from src.data_handler import DataPrepJoin
-from src.data_handler.data_generator import DataGenerator
+from src.data_handler import DefaultDataPreparation, DataCollection, AbstractDataPreparation
from src.helpers.datastore import NameNotFoundInScope
from src.helpers import PyTestRegex
from src.run_modules.experiment_setup import ExperimentSetup
@@ -29,7 +28,7 @@ class TestPreProcessing:
def obj_with_exp_setup(self):
ExperimentSetup(stations=['DEBW107', 'DEBY081', 'DEBW013', 'DEBW076', 'DEBW087', 'DEBW001'],
statistics_per_var={'o3': 'dma8eu', 'temp': 'maximum'}, station_type="background",
- data_preparation=DataPrepJoin)
+ data_preparation=DefaultDataPreparation)
pre = object.__new__(PreProcessing)
super(PreProcessing, pre).__init__()
yield pre
@@ -48,19 +47,20 @@ class TestPreProcessing:
RunEnvironment().__del__()
def test_run(self, obj_with_exp_setup):
- assert obj_with_exp_setup.data_store.search_name("generator") == []
+ assert obj_with_exp_setup.data_store.search_name("data_collection") == []
assert obj_with_exp_setup._run() is None
- assert obj_with_exp_setup.data_store.search_name("generator") == sorted(["general.train", "general.val",
- "general.train_val", "general.test"])
+ assert obj_with_exp_setup.data_store.search_name("data_collection") == sorted(["general.train", "general.val",
+ "general.train_val",
+ "general.test"])
def test_split_train_val_test(self, obj_with_exp_setup):
- assert obj_with_exp_setup.data_store.search_name("generator") == []
+ assert obj_with_exp_setup.data_store.search_name("data_collection") == []
obj_with_exp_setup.split_train_val_test()
data_store = obj_with_exp_setup.data_store
- expected_params = ["generator", "start", "end", "stations", "permute_data", "min_length", "extreme_values",
- "extremes_on_right_tail_only", "upsampling"]
+ expected_params = ["data_collection", "start", "end", "stations", "permute_data", "min_length",
+ "extreme_values", "extremes_on_right_tail_only", "upsampling"]
assert data_store.search_scope("general.train") == sorted(expected_params)
- assert data_store.search_name("generator") == sorted(["general.train", "general.val", "general.test",
+ assert data_store.search_name("data_collection") == sorted(["general.train", "general.val", "general.test",
"general.train_val"])
def test_create_set_split_not_all_stations(self, caplog, obj_with_exp_setup):
@@ -69,9 +69,9 @@ class TestPreProcessing:
obj_with_exp_setup.create_set_split(slice(0, 2), "awesome")
assert ('root', 10, "Awesome stations (len=2): ['DEBW107', 'DEBY081']") in caplog.record_tuples
data_store = obj_with_exp_setup.data_store
- assert isinstance(data_store.get("generator", "general.awesome"), DataGenerator)
+ assert isinstance(data_store.get("data_collection", "general.awesome"), DataCollection)
with pytest.raises(NameNotFoundInScope):
- data_store.get("generator", "general")
+ data_store.get("data_collection", "general")
assert data_store.get("stations", "general.awesome") == ["DEBW107", "DEBY081"]
def test_create_set_split_all_stations(self, caplog, obj_with_exp_setup):
@@ -80,22 +80,22 @@ class TestPreProcessing:
message = "Awesome stations (len=6): ['DEBW107', 'DEBY081', 'DEBW013', 'DEBW076', 'DEBW087', 'DEBW001']"
assert ('root', 10, message) in caplog.record_tuples
data_store = obj_with_exp_setup.data_store
- assert isinstance(data_store.get("generator", "general.awesome"), DataGenerator)
+ assert isinstance(data_store.get("data_collection", "general.awesome"), DataCollection)
with pytest.raises(NameNotFoundInScope):
- data_store.get("generator", "general")
+ data_store.get("data_collection", "general")
assert data_store.get("stations", "general.awesome") == ['DEBW107', 'DEBY081', 'DEBW013', 'DEBW076', 'DEBW087']
@pytest.mark.parametrize("name", (None, "tester"))
- def test_check_valid_stations(self, caplog, obj_with_exp_setup, name):
+ def test_validate_station(self, caplog, obj_with_exp_setup, name):
pre = obj_with_exp_setup
caplog.set_level(logging.INFO)
- args = pre.data_store.create_args_dict(DEFAULT_ARGS_LIST)
- kwargs = pre.data_store.create_args_dict(DEFAULT_KWARGS_LIST)
stations = pre.data_store.get("stations", "general")
- valid_stations = pre.check_valid_stations(args, kwargs, stations, name=name)
+ data_preparation = pre.data_store.get("data_preparation")
+ collection, valid_stations = pre.validate_station(data_preparation, stations, set_name=name)
+ assert isinstance(collection, DataCollection)
assert len(valid_stations) < len(stations)
assert valid_stations == stations[:-1]
- expected = 'check valid stations started (tester)' if name else 'check valid stations started'
+ expected = "check valid stations started" + ' (%s)' % (name if name else 'all')
assert caplog.record_tuples[0] == ('root', 20, expected)
assert caplog.record_tuples[-1] == ('root', 20, PyTestRegex(r'run for \d+:\d+:\d+ \(hh:mm:ss\) to check 6 '
r'station\(s\). Found 5/6 valid stations.'))
@@ -107,3 +107,11 @@ class TestPreProcessing:
assert dummy_list[val] == list(range(10, 13))
assert dummy_list[test] == list(range(13, 15))
assert dummy_list[train_val] == list(range(0, 13))
+
+ def test_transformation(self):
+ pre = object.__new__(PreProcessing)
+ data_preparation = AbstractDataPreparation
+ stations = ['DEBW107', 'DEBY081']
+ assert pre.transformation(data_preparation, stations) is None
+ class data_preparation_no_trans: pass
+ assert pre.transformation(data_preparation_no_trans, stations) is None
diff --git a/test/test_modules/test_run_environment.py b/test/test_run_modules/test_run_environment.py
similarity index 100%
rename from test/test_modules/test_run_environment.py
rename to test/test_run_modules/test_run_environment.py
diff --git a/test/test_modules/old_t_training.py b/test/test_run_modules/test_training.py
similarity index 72%
rename from test/test_modules/old_t_training.py
rename to test/test_run_modules/test_training.py
index 0998ff67cb6d52ad24efaea3d5e404632331735a..5885accc87e9cd1b95cdfbd5c2a4dff65b3a2c18 100644
--- a/test/test_modules/old_t_training.py
+++ b/test/test_run_modules/test_training.py
@@ -9,7 +9,7 @@ import mock
import pytest
from keras.callbacks import History
-from src.data_handler import DataPrepJoin
+from src.data_handler import DataCollection, KerasIterator, DefaultDataPreparation
from src.helpers import PyTestRegex
from src.model_modules.flatten import flatten_tail
from src.model_modules.inception_model import InceptionModelBase
@@ -18,7 +18,7 @@ from src.run_modules.run_environment import RunEnvironment
from src.run_modules.training import Training
-def my_test_model(activation, window_history_size, channels, dropout_rate, add_minor_branch=False):
+def my_test_model(activation, window_history_size, channels, output_size, dropout_rate, add_minor_branch=False):
inception_model = InceptionModelBase()
conv_settings_dict1 = {
'tower_1': {'reduction_filter': 8, 'tower_filter': 8 * 2, 'tower_kernel': (3, 1), 'activation': activation},
@@ -27,7 +27,6 @@ def my_test_model(activation, window_history_size, channels, dropout_rate, add_m
X_input = keras.layers.Input(shape=(window_history_size + 1, 1, channels))
X_in = inception_model.inception_block(X_input, conv_settings_dict1, pool_settings_dict1)
if add_minor_branch:
- # out = [flatten_tail(X_in, 'Minor_1', activation=activation)]
out = [flatten_tail(X_in, inner_neurons=64, activation=activation, output_neurons=4,
output_activation='linear', reduction_filter=64,
name='Minor_1', dropout_rate=dropout_rate,
@@ -35,8 +34,7 @@ def my_test_model(activation, window_history_size, channels, dropout_rate, add_m
else:
out = []
X_in = keras.layers.Dropout(dropout_rate)(X_in)
- # out.append(flatten_tail(X_in, 'Main', activation=activation))
- out.append(flatten_tail(X_in, inner_neurons=64, activation=activation, output_neurons=4,
+ out.append(flatten_tail(X_in, inner_neurons=64, activation=activation, output_neurons=output_size,
output_activation='linear', reduction_filter=64,
name='Main', dropout_rate=dropout_rate,
))
@@ -46,7 +44,7 @@ def my_test_model(activation, window_history_size, channels, dropout_rate, add_m
class TestTraining:
@pytest.fixture
- def init_without_run(self, path: str, model: keras.Model, callbacks: CallbackHandler, model_path):
+ def init_without_run(self, path: str, model: keras.Model, callbacks: CallbackHandler, model_path, batch_path):
obj = object.__new__(Training)
super(Training, obj).__init__()
obj.model = model
@@ -60,15 +58,18 @@ class TestTraining:
obj.lr_sc = lr
obj.hist = hist
obj.experiment_name = "TestExperiment"
- obj.data_store.set("generator", mock.MagicMock(return_value="mock_train_gen"), "general.train")
- obj.data_store.set("generator", mock.MagicMock(return_value="mock_val_gen"), "general.val")
- obj.data_store.set("generator", mock.MagicMock(return_value="mock_test_gen"), "general.test")
+ obj.data_store.set("data_collection", mock.MagicMock(return_value="mock_train_gen"), "general.train")
+ obj.data_store.set("data_collection", mock.MagicMock(return_value="mock_val_gen"), "general.val")
+ obj.data_store.set("data_collection", mock.MagicMock(return_value="mock_test_gen"), "general.test")
os.makedirs(path)
obj.data_store.set("experiment_path", path, "general")
+ os.makedirs(batch_path)
+ obj.data_store.set("batch_path", batch_path, "general")
os.makedirs(model_path)
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)
obj.data_store.set("plot_path", path_plot, "general")
@@ -106,14 +107,35 @@ class TestTraining:
return os.path.join(path, "model")
@pytest.fixture
- def generator(self, path):
- return DataGenerator(os.path.join(os.path.dirname(__file__), 'data'), ['DEBW107'], ['o3', 'temp'], 'datetime',
- 'variables', 'o3', statistics_per_var={'o3': 'dma8eu', 'temp': 'maximum'},
- data_preparation=DataPrepJoin)
+ def batch_path(self, path):
+ return os.path.join(path, "batch")
+
+ @pytest.fixture
+ def window_history_size(self):
+ return 7
+
+ @pytest.fixture
+ def window_lead_time(self):
+ return 2
@pytest.fixture
- def model(self):
- return my_test_model(keras.layers.PReLU, 7, 2, 0.1, False)
+ def statistics_per_var(self):
+ return {'o3': 'dma8eu', 'temp': 'maximum'}
+
+ @pytest.fixture
+ def data_collection(self, path, window_history_size, window_lead_time, statistics_per_var):
+ data_prep = DefaultDataPreparation.build(['DEBW107'], data_path=os.path.join(os.path.dirname(__file__), 'data'),
+ statistics_per_var=statistics_per_var, station_type="background",
+ network="AIRBASE", sampling="daily", target_dim="variables",
+ target_var="o3", interpolate_dim="datetime",
+ window_history_size=window_history_size,
+ window_lead_time=window_lead_time, name_affix="train")
+ return DataCollection([data_prep])
+
+ @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)
@pytest.fixture
def callbacks(self, path):
@@ -127,29 +149,31 @@ class TestTraining:
return clbk, hist, lr
@pytest.fixture
- def ready_to_train(self, generator: DataGenerator, init_without_run: Training):
- init_without_run.train_set = Distributor(generator, init_without_run.model, init_without_run.batch_size)
- init_without_run.val_set = Distributor(generator, init_without_run.model, init_without_run.batch_size)
+ def ready_to_train(self, data_collection: DataCollection, init_without_run: Training, batch_path: str):
+ batch_size = init_without_run.batch_size
+ model = init_without_run.model
+ init_without_run.train_set = KerasIterator(data_collection, batch_size, batch_path, model=model, name="train")
+ init_without_run.val_set = KerasIterator(data_collection, batch_size, batch_path, model=model, name="val")
init_without_run.model.compile(optimizer=keras.optimizers.SGD(), loss=keras.losses.mean_absolute_error)
return init_without_run
@pytest.fixture
- def ready_to_run(self, generator, init_without_run):
+ def ready_to_run(self, data_collection, init_without_run):
obj = init_without_run
- obj.data_store.set("generator", generator, "general.train")
- obj.data_store.set("generator", generator, "general.val")
- obj.data_store.set("generator", generator, "general.test")
+ 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)
return obj
@pytest.fixture
- def ready_to_init(self, generator, model, callbacks, path, model_path):
+ def ready_to_init(self, data_collection, model, callbacks, path, model_path, batch_path):
os.makedirs(path)
os.makedirs(model_path)
obj = RunEnvironment()
- obj.data_store.set("generator", generator, "general.train")
- obj.data_store.set("generator", generator, "general.val")
- obj.data_store.set("generator", generator, "general.test")
+ 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")
model.compile(optimizer=keras.optimizers.SGD(), loss=keras.losses.mean_absolute_error)
obj.data_store.set("model", model, "general.model")
obj.data_store.set("model_path", model_path, "general")
@@ -164,6 +188,8 @@ class TestTraining:
obj.data_store.set("experiment_path", path, "general")
obj.data_store.set("trainable", True, "general")
obj.data_store.set("create_new_model", True, "general")
+ os.makedirs(batch_path)
+ obj.data_store.set("batch_path", batch_path, "general")
path_plot = os.path.join(path, "plots")
os.makedirs(path_plot)
obj.data_store.set("plot_path", path_plot, "general")
@@ -174,6 +200,13 @@ class TestTraining:
def test_init(self, ready_to_init):
assert isinstance(Training(), Training) # just test, if nothing fails
+ def test_no_training(self, ready_to_init, caplog):
+ caplog.set_level(logging.INFO)
+ ready_to_init.data_store.set("trainable", False)
+ Training()
+ message = "No training has started, because trainable parameter was false."
+ assert caplog.record_tuples[-2] == ("root", 20, message)
+
def test_run(self, ready_to_run):
assert ready_to_run._run() is None # just test, if nothing fails
@@ -185,8 +218,8 @@ class TestTraining:
def test_set_gen(self, init_without_run):
assert init_without_run.train_set is None
init_without_run._set_gen("train")
- assert isinstance(init_without_run.train_set, Distributor)
- assert init_without_run.train_set.generator.return_value == "mock_train_gen"
+ assert isinstance(init_without_run.train_set, KerasIterator)
+ assert init_without_run.train_set._collection.return_value == "mock_train_gen"
def test_set_generators(self, init_without_run):
sets = ["train", "val", "test"]
@@ -194,7 +227,7 @@ class TestTraining:
init_without_run.set_generators()
assert not all([getattr(init_without_run, f"{obj}_set") is None for obj in sets])
assert all(
- [getattr(init_without_run, f"{obj}_set").generator.return_value == f"mock_{obj}_gen" for obj in sets])
+ [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")