From 944d384ef6d2d97f0beb6ff255ca196769f772db Mon Sep 17 00:00:00 2001
From: lukas leufen <l.leufen@fz-juelich.de>
Date: Wed, 11 Dec 2019 13:46:49 +0100
Subject: [PATCH] two new plot routines: plot history and learning rate vs
epochs
---
requirements.txt | 1 +
src/modules/experiment_setup.py | 6 +-
src/modules/training.py | 17 +++-
src/plotting/__init__.py | 0
src/plotting/training_monitoring.py | 94 +++++++++++++++++++
test/test_modules/test_training.py | 35 +++++--
.../test_plotting/test_training_monitoring.py | 83 ++++++++++++++++
7 files changed, 226 insertions(+), 10 deletions(-)
create mode 100644 src/plotting/__init__.py
create mode 100644 src/plotting/training_monitoring.py
create mode 100644 test/test_plotting/test_training_monitoring.py
diff --git a/requirements.txt b/requirements.txt
index d2a7200b..7ac250ac 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -10,3 +10,4 @@ pytest-cov
pytest-html
pydot
mock
+matplotlib
diff --git a/src/modules/experiment_setup.py b/src/modules/experiment_setup.py
index 472173c7..5fdc1f1f 100644
--- a/src/modules/experiment_setup.py
+++ b/src/modules/experiment_setup.py
@@ -5,6 +5,7 @@ __date__ = '2019-11-15'
import logging
import argparse
from typing import Union, Dict, Any
+import os
from src import helpers
from src.modules.run_environment import RunEnvironment
@@ -32,7 +33,7 @@ class ExperimentSetup(RunEnvironment):
window_lead_time=None, dimensions=None, interpolate_dim=None, interpolate_method=None,
limit_nan_fill=None, train_start=None, train_end=None, val_start=None, val_end=None, test_start=None,
test_end=None, use_all_stations_on_all_data_sets=True, trainable=False, fraction_of_train=None,
- experiment_path=None):
+ experiment_path=None, plot_path=None):
# create run framework
super().__init__()
@@ -48,6 +49,9 @@ class ExperimentSetup(RunEnvironment):
self._set_param("experiment_name", exp_name)
self._set_param("experiment_path", exp_path)
helpers.check_path_and_create(self.data_store.get("experiment_path", "general"))
+ default_plot_path = os.path.join(exp_path, "plots")
+ self._set_param("plot_path", plot_path, default=default_plot_path)
+ helpers.check_path_and_create(self.data_store.get("plot_path", "general"))
# setup for data
self._set_param("var_all_dict", var_all_dict, default=DEFAULT_VAR_ALL_DICT)
diff --git a/src/modules/training.py b/src/modules/training.py
index 87dcf35e..ba70e3b4 100644
--- a/src/modules/training.py
+++ b/src/modules/training.py
@@ -9,6 +9,8 @@ import keras
from src.modules.run_environment import RunEnvironment
from src.data_handling.data_distributor import Distributor
+from src.plotting.training_monitoring import PlotModelHistory, PlotModelLearningRate
+from src.helpers import LearningRateDecay
class Training(RunEnvironment):
@@ -84,6 +86,7 @@ class Training(RunEnvironment):
callbacks=[self.checkpoint, self.lr_sc])
self.save_callbacks(history)
self.load_best_model(self.checkpoint.filepath)
+ self.create_monitoring_plots(history, self.lr_sc)
def save_model(self) -> None:
"""
@@ -121,5 +124,15 @@ class Training(RunEnvironment):
with open(os.path.join(path, "history_lr.json"), "w") as f:
json.dump(self.lr_sc.lr, f)
-
-
+ def create_monitoring_plots(self, history: keras.callbacks.History, lr_sc: LearningRateDecay) -> None:
+ """
+ Creates the history and learning rate plot in dependence of the number of epochs. The plots are saved in the
+ experiment's plot_path. History plot is named '<exp_name>_history_loss_val_loss.pdf', the learning rate with
+ '<exp_name>_history_learning_rate.pdf'.
+ :param history: keras history object with losses to plot (must include 'loss' and 'val_loss')
+ :param lr_sc: learning rate decay object with 'lr' attribute
+ """
+ path = self.data_store.get("plot_path", "general")
+ name = self.data_store.get("experiment_name", "general")
+ PlotModelHistory(filename=os.path.join(path, f"{name}_history_loss_val_loss.pdf"), history=history)
+ PlotModelLearningRate(filename=os.path.join(path, f"{name}_history_learning_rate.pdf"), lr_sc=lr_sc)
diff --git a/src/plotting/__init__.py b/src/plotting/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/src/plotting/training_monitoring.py b/src/plotting/training_monitoring.py
new file mode 100644
index 00000000..b18cce7a
--- /dev/null
+++ b/src/plotting/training_monitoring.py
@@ -0,0 +1,94 @@
+__author__ = 'Felix Kleinert, Lukas Leufen'
+__date__ = '2019-12-11'
+
+
+from typing import Union, Dict, List
+
+import keras
+import pandas as pd
+import matplotlib
+import matplotlib.pyplot as plt
+
+from src.helpers import LearningRateDecay
+
+
+matplotlib.use('Agg')
+history_object = Union[Dict, keras.callbacks.History]
+lr_object = Union[Dict, LearningRateDecay]
+
+
+class PlotModelHistory:
+ """
+ Plots history of all losses for a training event. For default loss and val_loss are plotted. If further losses are
+ provided (name must somehow include the word `loss`), this additional information is added to the plot with an
+ separate y-axis scale on the right side (shared for all additional losses). The plot is saved locally. For a proper
+ saving behaviour, the parameter filename must include the absolute path for the plot.
+ """
+ def __init__(self, filename: str, history: history_object):
+ """
+ Sets attributes and create plot
+ :param filename: saving name of the plot to create (preferably absolute path if possible), the filename needs a
+ format ending like .pdf or .png to work.
+ :param history: the history object (or a dict with at least 'loss' and 'val_loss' as keys) to plot loss from
+ """
+ if isinstance(history, keras.callbacks.History):
+ history = history.history
+ self._data = pd.DataFrame.from_dict(history)
+ self._additional_columns = self._filter_columns(history)
+ self._plot(filename)
+
+ @staticmethod
+ def _filter_columns(history: Dict) -> List[str]:
+ """
+ Select only columns named like %loss%. The default losses 'loss' and 'val_loss' are also removed.
+ :param history: a dict with at least 'loss' and 'val_loss' as keys (can be derived from keras History.history)
+ :return: filtered columns including all loss variations except loss and val_loss.
+ """
+ cols = list(filter(lambda x: "loss" in x, history.keys()))
+ cols.remove("val_loss")
+ cols.remove("loss")
+ return cols
+
+ def _plot(self, filename: str) -> None:
+ """
+ Actual plot routine. Plots loss and val_loss as default. If more losses are provided, they will be added with
+ an additional yaxis on the right side. The plot is saved in filename.
+ :param filename: name (including total path) of the plot to save.
+ """
+ ax = self._data[["loss", "val_loss"]].plot(linewidth=0.7)
+ if len(self._additional_columns) > 0:
+ self._data[self._additional_columns].plot(linewidth=0.7, secondary_y=True, ax=ax)
+ ax.set(xlabel="epoch", ylabel="loss", title=f"Model loss: best = {self._data[['val_loss']].min().values}")
+ ax.axhline(y=0, color="gray", linewidth=0.5)
+ plt.tight_layout()
+ plt.savefig(filename)
+ plt.close("all")
+
+
+class PlotModelLearningRate:
+ """
+ Plots the behaviour of the learning rate in dependence of the number of epochs. The plot is saved locally as pdf.
+ For a proper saving behaviour, the parameter filename must include the absolute path for the plot.
+ """
+ def __init__(self, filename: str, lr_sc: lr_object):
+ """
+ Sets attributes and create plot
+ :param filename: saving name of the plot to create (preferably absolute path if possible), the filename needs a
+ format ending like .pdf or .png to work.
+ :param lr_sc: the learning rate object (or a dict with `lr` as key) to plot from
+ """
+ if isinstance(lr_sc, LearningRateDecay):
+ lr_sc = lr_sc.lr
+ self._data = pd.DataFrame.from_dict(lr_sc)
+ self._plot(filename)
+
+ def _plot(self, filename: str) -> None:
+ """
+ Actual plot routine. Plots the learning rate in dependence of epoch.
+ :param filename: name (including total path) of the plot to save.
+ """
+ ax = self._data.plot(linewidth=0.7)
+ ax.set(xlabel="epoch", ylabel="learning rate")
+ plt.tight_layout()
+ plt.savefig(filename)
+ plt.close("all")
diff --git a/test/test_modules/test_training.py b/test/test_modules/test_training.py
index c91cb691..ddb301c6 100644
--- a/test/test_modules/test_training.py
+++ b/test/test_modules/test_training.py
@@ -6,6 +6,7 @@ import os
import json
import shutil
import logging
+import glob
from src.inception_model import InceptionModelBase
from src.flatten import flatten_tail
@@ -54,11 +55,23 @@ class TestTraining:
os.makedirs(path)
obj.data_store.put("experiment_path", path, "general")
obj.data_store.put("experiment_name", "TestExperiment", "general")
+ path_plot = os.path.join(path, "plots")
+ os.makedirs(path_plot)
+ obj.data_store.put("plot_path", path_plot, "general")
yield obj
if os.path.exists(path):
shutil.rmtree(path)
RunEnvironment().__del__()
+ @pytest.fixture
+ def learning_rate(self):
+ return {"lr": [0.01, 0.0094]}
+
+ @pytest.fixture
+ def init_with_lr(self, init_without_run, learning_rate):
+ init_without_run.lr_sc.lr = learning_rate
+ return init_without_run
+
@pytest.fixture
def history(self):
h = History()
@@ -120,6 +133,9 @@ class TestTraining:
obj.data_store.put("lr_decay", LearningRateDecay(), "general.model")
obj.data_store.put("experiment_name", "TestExperiment", "general")
obj.data_store.put("experiment_path", path, "general")
+ path_plot = os.path.join(path, "plots")
+ os.makedirs(path_plot)
+ obj.data_store.put("plot_path", path_plot, "general")
yield obj
if os.path.exists(path):
shutil.rmtree(path)
@@ -148,11 +164,13 @@ class TestTraining:
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])
- def test_train(self, ready_to_train):
+ 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, path, caplog):
caplog.set_level(logging.DEBUG)
@@ -172,8 +190,8 @@ class TestTraining:
init_without_run.save_callbacks(history)
assert "history.json" in os.listdir(path)
- def test_save_callbacks_lr_created(self, init_without_run, history, path):
- init_without_run.save_callbacks(history)
+ def test_save_callbacks_lr_created(self, init_with_lr, history, path):
+ init_with_lr.save_callbacks(history)
assert "history_lr.json" in os.listdir(path)
def test_save_callbacks_inspect_history(self, init_without_run, history, path):
@@ -182,10 +200,13 @@ class TestTraining:
hist = json.load(jfile)
assert hist == history.history
- def test_save_callbacks_inspect_lr(self, init_without_run, history, path):
- init_without_run.save_callbacks(history)
+ def test_save_callbacks_inspect_lr(self, init_with_lr, history, path):
+ init_with_lr.save_callbacks(history)
with open(os.path.join(path, "history_lr.json")) as jfile:
lr = json.load(jfile)
- assert lr == init_without_run.lr_sc.lr
-
+ assert lr == init_with_lr.lr_sc.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
+ init_without_run.create_monitoring_plots(history, learning_rate)
+ assert len(glob.glob(os.path.join(path, "plots", "TestExperiment_history_*.pdf"))) == 2
diff --git a/test/test_plotting/test_training_monitoring.py b/test/test_plotting/test_training_monitoring.py
new file mode 100644
index 00000000..d38fc623
--- /dev/null
+++ b/test/test_plotting/test_training_monitoring.py
@@ -0,0 +1,83 @@
+import keras
+import pytest
+import os
+
+from src.plotting.training_monitoring import PlotModelLearningRate, PlotModelHistory
+from src.helpers import LearningRateDecay
+
+
+@pytest.fixture
+def path():
+ p = os.path.join(os.path.dirname(__file__), "TestExperiment")
+ if not os.path.exists(p):
+ os.makedirs(p)
+ return p
+
+
+class TestPlotModelHistory:
+
+ @pytest.fixture
+ def history(self):
+ hist = keras.callbacks.History()
+ hist.epoch = [0, 1]
+ hist.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]}
+ return hist
+
+ @pytest.fixture
+ def history_var(self):
+ hist = keras.callbacks.History()
+ hist.epoch = [0, 1]
+ hist.history = {'val_loss': [0.5586272982587484, 0.45712877659670287],
+ 'test_loss': [0.595368885413389, 0.530547587585537],
+ 'loss': [0.6795708956961347, 0.45963566494176616],
+ 'mean_squared_error': [0.6795708956961347, 0.45963566494176616],
+ 'mean_absolute_error': [0.6523177288928538, 0.5363963260296364]}
+ return hist
+
+ @pytest.fixture
+ def no_init(self):
+ return object.__new__(PlotModelHistory)
+
+ def test_plot_from_hist_obj(self, history, path):
+ assert "hist_obj.pdf" not in os.listdir(path)
+ PlotModelHistory(os.path.join(path, "hist_obj.pdf"), history)
+ assert "hist_obj.pdf" in os.listdir(path)
+
+ def test_plot_from_hist_dict(self, history, path):
+ assert "hist_dict.pdf" not in os.listdir(path)
+ PlotModelHistory(os.path.join(path, "hist_dict.pdf"), history.history)
+ assert "hist_dict.pdf" in os.listdir(path)
+
+ def test_plot_additional_loss(self, history_var, path):
+ assert "hist_additional.pdf" not in os.listdir(path)
+ PlotModelHistory(os.path.join(path, "hist_additional.pdf"), history_var)
+ assert "hist_additional.pdf" in os.listdir(path)
+
+ def test_filter_list(self, no_init):
+ res = no_init._filter_columns({'loss': None, 'another_loss': None, 'val_loss': None, 'wrong': None})
+ assert res == ['another_loss']
+
+
+class TestPlotModelLearningRate:
+
+ @pytest.fixture
+ def learning_rate(self):
+ lr_obj = LearningRateDecay()
+ lr_obj.lr = {"lr": [0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.0094, 0.0094, 0.0094, 0.0094,
+ 0.0094, 0.0094, 0.0094, 0.0094, 0.0094, 0.0094]}
+ return lr_obj
+
+ def test_plot_from_lr_obj(self, learning_rate, path):
+ assert "lr_obj.pdf" not in os.listdir(path)
+ PlotModelLearningRate(os.path.join(path, "lr_obj.pdf"), learning_rate)
+ assert "lr_obj.pdf" in os.listdir(path)
+
+ def test_plot_from_lr_dict(self, learning_rate, path):
+ assert "lr_dict.pdf" not in os.listdir(path)
+ PlotModelLearningRate(os.path.join(path, "lr_dict.pdf"), learning_rate.lr)
+ assert "lr_dict.pdf" in os.listdir(path)
--
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