from collections.abc import Sequence
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import keras.src.callbacks
from ...utils.plot_utils import make_figure, add_titles_and_labels
[docs]
def loss(
history: keras.callbacks.History,
train_key: str = "loss",
val_key: str = "val_loss",
per_training_step: bool = False,
smoothing_factor: float = 0.8,
figsize: Sequence[float] = None,
train_color: str = "#132a70",
val_color: str = "black",
val_marker: str = "o",
val_marker_size: float = 5,
lw_train: float = 2.0,
lw_val: float = 2.0,
grid_alpha: float = 0.2,
legend_fontsize: int = 14,
label_fontsize: int = 14,
title_fontsize: int = 16,
) -> plt.Figure:
"""
A generic helper function to plot the losses of a series of training epochs and runs.
Parameters
----------
history : keras.src.callbacks.History
History object as returned by `keras.Model.fit`.
train_key : str, optional, default: "loss"
The training loss key to look for in the history
val_key : str, optional, default: "val_loss"
The validation loss key to look for in the history
per_training_step : bool, optional, default: False
A flag for making loss trajectory detailed (to training steps) rather than per epoch.
smoothing_factor : float, optional, default: 0.8
If greater than zero, smooth the loss curves by applying an exponential moving average.
figsize : tuple or None, optional, default: None
The figure size passed to the ``matplotlib`` constructor.
Inferred if ``None``
train_color : str, optional, default: '#132a70'
The color for the train loss trajectory
val_color : str, optional, default: None
The color for the optional validation loss trajectory
val_marker: str
Marker style for the validation loss curve. Default is "o".
val_marker_size: float
Marker size for the validation loss curve. Default is 5.
lw_train : int, optional, default: 2
The line width for the training loss curve
lw_val : int, optional, default: 2
The line width for the validation loss curve
grid_alpha : float, optional, default: 0.2
The transparency of the background grid
legend_fontsize : int, optional, default: 14
The font size of the legend text
label_fontsize : int, optional, default: 14
The font size of the y-label text
title_fontsize : int, optional, default: 16
The font size of the title text
Returns
-------
f : plt.Figure - the figure instance for optional saving
Raises
------
AssertionError
If the number of columns in ``train_losses`` does not match the
number of columns in ``val_losses``.
"""
train_losses = history.history.get(train_key)
val_losses = history.history.get(val_key)
train_losses = pd.DataFrame(np.array(train_losses))
val_losses = pd.DataFrame(np.array(val_losses)) if val_losses is not None else None
# Determine the number of rows for plot
num_row = len(train_losses.columns)
# Initialize figure
fig, axes = make_figure(num_row=num_row, num_col=1, figsize=(16, int(4 * num_row)) if figsize is None else figsize)
# Get the number of steps as an array
train_step_index = np.arange(1, len(train_losses) + 1)
if val_losses is not None:
val_step = int(np.floor(len(train_losses) / len(val_losses)))
val_step_index = train_step_index[(val_step - 1) :: val_step]
# If unequal length due to some reason, attempt a fix
if val_step_index.shape[0] > val_losses.shape[0]:
val_step_index = val_step_index[: val_losses.shape[0]]
# Loop through loss entries and populate plot
for i, ax in enumerate(axes.flat):
if smoothing_factor > 0:
# plot unsmoothed train loss
ax.plot(
train_step_index, train_losses.iloc[:, 0], color=train_color, lw=lw_train, alpha=0.3, label="Training"
)
# plot smoothed train loss
smoothed_train_loss = train_losses.iloc[:, 0].ewm(alpha=1.0 - smoothing_factor, adjust=True).mean()
ax.plot(
train_step_index,
smoothed_train_loss,
color=train_color,
lw=lw_train,
alpha=0.8,
label="Training (Moving Average)",
)
else:
# Plot unsmoothed train loss
ax.plot(
train_step_index, train_losses.iloc[:, 0], color=train_color, lw=lw_train, alpha=0.8, label="Training"
)
# Only plot if we actually have validation losses and a color assigned
if val_losses is not None and val_color is not None:
alpha_unsmoothed = 0.3 if smoothing_factor > 0 else 0.8
# Plot unsmoothed val loss
ax.plot(
val_step_index,
val_losses.iloc[:, 0],
color=val_color,
lw=lw_val,
alpha=alpha_unsmoothed,
linestyle="--",
marker=val_marker,
markersize=val_marker_size,
label="Validation",
)
# if requested, plot a second, smoothed curve
if smoothing_factor > 0:
smoothed_val_loss = val_losses.iloc[:, 0].ewm(alpha=1.0 - smoothing_factor, adjust=True).mean()
ax.plot(
val_step_index,
smoothed_val_loss,
color=val_color,
linestyle="--",
lw=lw_val,
alpha=0.8,
label="Validation (Moving Average)",
)
# rest of the styling
sns.despine(ax=ax)
ax.grid(alpha=grid_alpha)
ax.set_xlim(train_step_index[0], train_step_index[-1])
# legend only if there's at least one validation curve or smoothing was on
if val_losses is not None or smoothing_factor > 0:
ax.legend(fontsize=legend_fontsize)
# Add labels, titles, and set font sizes
add_titles_and_labels(
axes=axes,
num_row=num_row,
num_col=1,
title=["Loss Trajectory"],
xlabel="Training step #" if per_training_step else "Training epoch #",
ylabel="Value",
title_fontsize=title_fontsize,
label_fontsize=label_fontsize,
)
fig.tight_layout()
return fig
