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test_training.py
test_training.py 13.59 KiB
import glob
import json
import logging
import os
import shutil
import keras
import mock
import pytest
from keras.callbacks import History
from mlair.data_handler import DataCollection, KerasIterator, DefaultDataHandler
from mlair.helpers import PyTestRegex
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
from mlair.run_modules.run_environment import RunEnvironment
from mlair.run_modules.training import Training
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},
'tower_2': {'reduction_filter': 8, 'tower_filter': 8 * 2, 'tower_kernel': (5, 1), 'activation': activation}, }
pool_settings_dict1 = {'pool_kernel': (3, 1), 'tower_filter': 8 * 2, 'activation': activation}
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, inner_neurons=64, activation=activation, output_neurons=4,
output_activation='linear', reduction_filter=64,
name='Minor_1', dropout_rate=dropout_rate,
)]
else:
out = []
X_in = keras.layers.Dropout(dropout_rate)(X_in)
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,
))
return keras.Model(inputs=X_input, outputs=out)
class TestTraining:
@pytest.fixture
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
obj.train_set = None
obj.val_set = None
obj.test_set = None
obj.batch_size = 256
obj.epochs = 2
clbk, hist, lr = callbacks
obj.callbacks = clbk
obj.lr_sc = lr
obj.hist = hist
obj.experiment_name = "TestExperiment"
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")
if not os.path.exists(path):
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")
obj._train_model = True
obj._create_new_model = False
yield obj
if os.path.exists(path):
shutil.rmtree(path)
RunEnvironment().__del__()
@pytest.fixture
def learning_rate(self):
lr = LearningRateDecay()
lr.lr = {"lr": [0.01, 0.0094]}
return lr
@pytest.fixture
def history(self):
h = History()
h.epoch = [0, 1]
h.history = {'val_loss': [0.5586272982587484, 0.45712877659670287],
'val_mean_squared_error': [0.5586272982587484, 0.45712877659670287],
'val_mean_absolute_error': [0.595368885413389, 0.530547587585537],
'loss': [0.6795708956961347, 0.45963566494176616],
'mean_squared_error': [0.6795708956961347, 0.45963566494176616],
'mean_absolute_error': [0.6523177288928538, 0.5363963260296364]}
h.model = mock.MagicMock()
return h
@pytest.fixture
def path(self):
return os.path.join(os.path.dirname(__file__), "TestExperiment")
@pytest.fixture
def model_path(self, path):
return os.path.join(path, "model")
@pytest.fixture
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 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 = DefaultDataHandler.build(['DEBW107'], data_path=os.path.join(path, 'data'),
experiment_path=os.path.join(path, 'exp_path'),
statistics_per_var=statistics_per_var, station_type="background",
network="AIRBASE", sampling="daily", target_dim="variables",
target_var="o3", time_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):
clbk = CallbackHandler()
hist = HistoryAdvanced()
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.create_model_checkpoint(filepath=os.path.join(path, "model_checkpoint"), monitor='val_loss',
save_best_only=True)
return clbk, hist, lr
@pytest.fixture
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, data_collection, init_without_run):
obj = init_without_run
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, data_collection, model, callbacks, path, model_path, batch_path):
if not os.path.exists(path):
os.makedirs(path)
os.makedirs(model_path)
obj = RunEnvironment()
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")
obj.data_store.set("model_name", os.path.join(model_path, "test_model.h5"), "general.model")
obj.data_store.set("batch_size", 256, "general")
obj.data_store.set("epochs", 2, "general")
clbk, hist, lr = callbacks
obj.data_store.set("callbacks", clbk, "general.model")
obj.data_store.set("lr_decay", lr, "general.model")
obj.data_store.set("hist", hist, "general.model")
obj.data_store.set("experiment_name", "TestExperiment", "general")
obj.data_store.set("experiment_path", path, "general")
obj.data_store.set("train_model", 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")
yield obj
if os.path.exists(path):
shutil.rmtree(path)
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("train_model", False)
Training()
message = "No training has started, because train_model 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
def test_make_predict_function(self, init_without_run):
assert hasattr(init_without_run.model, "predict_function") is False
init_without_run.make_predict_function()
assert hasattr(init_without_run.model, "predict_function")
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, 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"]
assert all([getattr(init_without_run, f"{obj}_set") is None for obj in sets])
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")._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 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 ready_to_train.model.history.epoch == [0, 1]
assert len(glob.glob(os.path.join(path, "plots", "TestExperiment_history_*.pdf"))) == 2
def test_save_model(self, init_without_run, model_path, caplog):
caplog.set_level(logging.DEBUG)
model_name = "test_model.h5"
assert model_name not in os.listdir(model_path)
init_without_run.save_model()
message = PyTestRegex(f"save best model to {os.path.join(model_path, model_name)}")
assert caplog.record_tuples[1] == ("root", 10, message)
assert model_name in os.listdir(model_path)
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"))
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, model_path):
init_without_run.save_callbacks_as_json(history, learning_rate)
assert "history.json" in os.listdir(model_path)
def test_save_callbacks_lr_created(self, init_without_run, history, learning_rate, model_path):
init_without_run.save_callbacks_as_json(history, learning_rate)
assert "history_lr.json" in os.listdir(model_path)
def test_save_callbacks_inspect_history(self, init_without_run, history, learning_rate, model_path):
init_without_run.save_callbacks_as_json(history, learning_rate)
with open(os.path.join(model_path, "history.json")) as jfile:
hist = json.load(jfile)
assert hist == history.history
def test_save_callbacks_inspect_lr(self, init_without_run, history, learning_rate, model_path):
init_without_run.save_callbacks_as_json(history, learning_rate)
with open(os.path.join(model_path, "history_lr.json")) as jfile:
lr = json.load(jfile)
assert lr == learning_rate.lr
def test_create_monitoring_plots(self, init_without_run, learning_rate, history, path):
assert len(glob.glob(os.path.join(path, "plots", "TestExperiment_history_*.pdf"))) == 0
history.model.output_names = mock.MagicMock(return_value=["Main"])
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