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FIX Make cross-validation figures less inexact #765

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May 17, 2024
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Binary file modified figures/nested_cross_validation_diagram.png
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162 changes: 110 additions & 52 deletions figures/plot_parameter_tuning_cv.py
Original file line number Diff line number Diff line change
@@ -1,6 +1,6 @@
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.colors import LinearSegmentedColormap
from matplotlib.colors import ListedColormap
from matplotlib.patches import Patch
from pathlib import Path
from sklearn.model_selection import KFold
Expand All @@ -9,16 +9,18 @@
FIGURES_FOLDER = Path(__file__).parent
plt.style.use(FIGURES_FOLDER / "../python_scripts/matplotlibrc")

colors = ["#009e73ff", "#fd3c06ff", "#0072b2ff"]
cmap_name = "my_list"
cmap_cv = LinearSegmentedColormap.from_list(cmap_name, colors=colors, N=8)
colors_cv = ["#009e73ff", "#fd3c06ff", "white"]
colors_eval = ["#fd3c06ff", "#fd3c06ff", "#0072b2ff"]
cmap_cv = ListedColormap(colors=colors_cv)
cmap_eval = ListedColormap(colors=colors_eval)


def plot_cv_indices(cv, X, y, ax, lw=50):
def plot_cv_indices(cv, X, y, axs):
"""Create a sample plot for indices of a cross-validation object
embeded in a train-test split."""
splits = list(cv.split(X=X, y=y))
n_splits = len(splits)
ax1, ax2 = axs

# Generate the training/testing visualizations for each CV split
for ii, (train, test) in enumerate(splits):
Expand All @@ -28,120 +30,176 @@ def plot_cv_indices(cv, X, y, ax, lw=50):
indices[X.shape[0] : X.shape[0] + 10] = 2

# Visualize the results
ax.scatter(
ax1.scatter(
range(len(indices)),
[ii + 0.5] * len(indices),
c=indices,
marker="_",
lw=25,
cmap=cmap_cv,
)
ax2.scatter(
range(len(indices)),
[0.5] * len(indices),
c=indices,
marker="_",
lw=25,
cmap=cmap_eval,
)

# Formatting
yticklabels = list(range(n_splits))
ax.set(
ax1.set(
yticks=np.arange(n_splits) + 0.5,
yticklabels=yticklabels,
xlabel="Sample index",
ylabel="CV iteration",
ylim=[n_splits + 0.2, -0.2],
xlim=[0, 50],
)
ax.set_title(
"{} cross validation inside (non-shuffled-)train-test split".format(
type(cv).__name__
)

ax2.set(
yticks=[0.5],
yticklabels=[],
xlabel="Sample index",
ymargin=10,
ylim=[0.3, 0.7],
xlim=[0, 50],
)
ax.legend(
ax2.set_ylabel("refit +\nevaluation", labelpad=15)
ax2.legend(
[
Patch(color=cmap_cv(0.9)),
Patch(color=cmap_cv(0.5)),
Patch(color=cmap_cv(0.02)),
Patch(color=cmap_eval(0.9)),
],
[
"Training samples",
"Validation samples\n(for hyperparameter\ntuning)",
"Testing samples\n(reserved until\nfinal evaluation)",
],
["Testing samples", "Training samples", "Validation samples"],
loc=(1.02, 0.7),
loc=(1.02, 1.1),
labelspacing=1,
)
return ax
return


n_points = 40
X = np.random.randn(n_points, 10)
y = np.random.randn(n_points)

fig, ax = plt.subplots(figsize=(12, 4))
fig, axs = plt.subplots(
ncols=1,
nrows=2,
sharex=True,
figsize=(12, 5),
gridspec_kw={"height_ratios": [5, 1.5], "hspace": 0},
)
cv = KFold(5)
_ = plot_cv_indices(cv, X, y, ax)
plot_cv_indices(cv, X, y, axs)
plt.suptitle(
"Internal {} cross-validation in GridSearchCV".format(
type(cv).__name__),
y=0.95,
)
plt.tight_layout()
fig.savefig(FIGURES_FOLDER / "cross_validation_train_test_diagram.png")


def plot_cv_nested_indices(cv_inner, cv_outer, X, y, ax, lw=50):
def plot_cv_nested_indices(cv_inner, cv_outer, X, y, axs):
"""Create a sample plot for indices of a nested cross-validation object."""
splits_outer = list(cv_outer.split(X=X, y=y))
n_splits_outer = len(splits_outer)

# Generate the training/testing visualizations for each CV split
for ii, (train_outer, test_outer) in enumerate(splits_outer):

for outer_index, (train_outer, test_outer) in enumerate(splits_outer):
splits_inner = list(cv_inner.split(train_outer))
n_splits_inner = len(splits_inner)

# Fill in indices with the training/test groups
for jj, (train_inner, test_inner) in enumerate(splits_inner):
for inner_index, (train_inner, test_inner) in enumerate(splits_inner):
indices = np.zeros(shape=X.shape[0], dtype=np.int32)
indices[train_outer[train_inner]] = 1
indices[test_outer] = 2

# Visualize the results
ax.scatter(
axs[outer_index * 2].scatter(
range(len(indices)),
[n_splits_inner * ii + jj + 0.5] * len(indices),
[inner_index + 0.6] * len(indices),
c=indices,
marker="_",
lw=25,
cmap=cmap_cv,
)

axs[outer_index*2 + 1].scatter(
range(len(indices)),
[0.5] * len(indices),
c=indices,
marker="_",
lw=25,
cmap=cmap_eval,
)
axs[outer_index*2 + 1].set(
yticks=[0.5],
yticklabels=["refit +\nevaluation"],
xlabel="Sample index",
ymargin=10,
ylim=[0.3, 0.7],
xlim=[0, 50],
)

# Formatting
ax.set_title("{} nested cross-validation".format(type(cv_outer).__name__))
ax1 = ax.twinx()
yticklabels = n_splits_outer * list(range(n_splits_inner))
ax1.set(
yticks=np.arange(n_splits_outer * n_splits_inner) + 0.3,
yticklabels=yticklabels,
xlabel="Sample index",
ylabel="CV inner iteration",
ylim=[n_splits_outer * n_splits_inner + 0.2, -0.2],
xlim=[0, 50],
)
yticklabels = list(range(n_splits_outer))
ax.set(
yticks=n_splits_inner*np.arange(n_splits_outer) + 0.5,
yticklabels=yticklabels,
xlabel="Sample index",
ylabel="CV outer iteration",
ylim=[n_splits_outer * n_splits_inner + 0.2, 0.08],
xlim=[0, 50],
)
ax.legend(
ax_twin = axs[outer_index * 2].twinx()
yticklabels = list(range(n_splits_inner))
ax_twin.set(
yticks=np.arange(n_splits_inner) + 0.4,
yticklabels=yticklabels,
xlabel="Sample index",
ylabel="inner iteration",
ylim=[n_splits_inner + 0.2, -0.2],
xlim=[0, 50],
)

axs[outer_index * 2].set(
yticks=n_splits_inner * np.arange(n_splits_outer) + 0.5,
yticklabels=[outer_index] * n_splits_outer,
xlabel="Sample index",
ylim=[ n_splits_inner + 0.2, 0.08],
xlim=[0, 50],
)

axs[0].legend(
[
Patch(color=cmap_cv(0.9)),
Patch(color=cmap_cv(0.5)),
Patch(color=cmap_cv(0.02)),
Patch(color=cmap_eval(0.9)),
],
[
"Training samples",
"Validation samples\n(for hyperparameter\ntuning)",
"Testing samples\n(reserved until\nevaluation)",
],
["Testing samples", "Training samples", "Validation samples"],
loc=(1.06, .93),
loc=(1.2, -0.2),
labelspacing=1,
)
return ax
return


n_points = 50
X = np.random.randn(n_points, 10)
y = np.random.randn(n_points)

fig, ax = plt.subplots(figsize=(12, 12))
fig, axs = plt.subplots(
ncols=1,
nrows=10,
sharex=True,
figsize=(14, 15),
gridspec_kw={"height_ratios": [5, 1.5] * 5, "hspace": 0},
)
cv_inner = KFold(n_splits=4, shuffle=False)
cv_outer = KFold(n_splits=5, shuffle=False)
_ = plot_cv_nested_indices(cv_inner, cv_outer, X, y, ax)
plot_cv_nested_indices(cv_inner, cv_outer, X, y, axs)
plt.suptitle("{} nested cross-validation".format(type(cv_outer).__name__), y=0.97)
plt.tight_layout()
fig.text(0.0, 0.5, "outer iteration", va="center", rotation="vertical")
fig.savefig(FIGURES_FOLDER / "nested_cross_validation_diagram.png")
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