SeqOptPlot.hypervolume

SeqOptPlot.hypervolume(trajectory, ax=None, figsize=(6, 4))

Plot the per-generation hypervolume convergence trace.

Parameters:

• trajectory (array-like, shape (n_gen + 1,)) – Per-generation hypervolume of the front (SeqOpt.trajectory_ after a run).

• ax (matplotlib.axes.Axes, optional) – Axes to draw on. A new figure is created when None.

• figsize (tuple, default=(6, 4)) – Figure size when ax is None.

Returns:

• fig (matplotlib.figure.Figure) – The figure.

• ax (matplotlib.axes.Axes) – The axes the trace was drawn on.

Examples

import numpy as np, pandas as pd
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
import aaanalysis as aa
aa.options["verbose"] = False

# Gamma-secretase (GSEC) substrate data + the bundled interpretable CPP feature set.
df_feat = aa.load_features(name="DOM_GSEC")           # 150 CPP features (with positions, feat_importance)
df_seq  = aa.load_dataset(name="DOM_GSEC", n=50)      # 100 TMD sequences, label 1 = GSEC substrate
labels  = df_seq["label"].to_list()

# A simple RandomForest substrate classifier on the CPP feature matrix.
sf = aa.SequenceFeature()
X = np.asarray(sf.feature_matrix(features=df_feat["feature"],
                                 df_parts=sf.get_df_parts(df_seq=df_seq),
                                 df_scales=aa.load_scales()), dtype=float)
model = RandomForestClassifier(n_estimators=100, random_state=0).fit(X, labels)

# Pick a NON-substrate as the wild-type and design a "super substrate": mutate its TMD to
# maximize the predicted substrate probability with as few mutations as possible.
wt = df_seq[df_seq["label"] == 0].iloc[[0]].reset_index(drop=True)
objectives = [("substrate", "max", "delta_pred"),     # raise P(GSEC substrate) (RF prediction shift)
              ("parsimony", "min", "n_mut")]          # with as few mutations as possible

seqopt = aa.SeqOpt(mode="importance", model=model, target_class=1, random_state=42)
df_pareto = seqopt.run(df_seq=wt, df_feat=df_feat, objectives=objectives,
                       algorithm="nsga2", pop_size=40, n_gen=20, n_mut_max=5, region="tmd")
aa.display_df(df_pareto, n_rows=10, show_shape=True)

DataFrame shape: (7, 8)

	entry	variant	n_mut	sequence_mut	substrate	parsimony	rank	crowding
1	Q14802		0	MQKVTLGLLVFLAGF...PGETPPLITPGSAQS	0.000000	0.000000	0	inf
2	Q14802	L37T+I55T+V56L+S58Q+A59R	5	MQKVTLGLLVFLAGF...PGETPPLITPGSAQS	37.000000	5.000000	0	inf
3	Q14802	L37G+A59R	2	MQKVTLGLLVFLAGF...PGETPPLITPGSAQS	29.000000	2.000000	0	0.429730
4	Q14802	A59R	1	MQKVTLGLLVFLAGF...PGETPPLITPGSAQS	16.000000	1.000000	0	0.316216
5	Q14802	L37G+V56L+A59R	3	MQKVTLGLLVFLAGF...PGETPPLITPGSAQS	33.000000	3.000000	0	0.281081
6	Q14802	A59K	1	MQKVTLGLLVFLAGF...PGETPPLITPGSAQS	16.000000	1.000000	0	0.275676
7	Q14802	L37G+V56L+S58Q+A59R	4	MQKVTLGLLVFLAGF...PGETPPLITPGSAQS	35.000000	4.000000	0	0.254054

aa.plot_settings()
aa.SeqOptPlot().hypervolume(trajectory=seqopt.trajectory_)
plt.tight_layout(); plt.show()

# figsize sets the figure size in inches
aa.plot_settings()
aa.SeqOptPlot().hypervolume(trajectory=seqopt.trajectory_, figsize=(7, 4))
plt.tight_layout(); plt.show()