Sequence Metrics: LCP

This example demonstrates LCPSequenceMetric, which computes the Longest Common Prefix distance between two temporal sequences.

Setup

import matplotlib.pyplot as plt
import polars as pl

from tanat import build_states
from tanat.dataset import simulate_states
from tanat.metric.entity import HammingEntityMetric
from tanat.metric.sequence import LCPSequenceMetric

Generate synthetic data

SEED = 42
N_IDS = 80

raw_df = simulate_states(
    n_ids=N_IDS,
    seq_length_range=(3, 8),
    features=["score", "status"],
    seed=SEED,
)
pool = build_states(raw_df, id_column="id", start_column="start", end_column="end")

┌─ State SequenceStore
│
│ Step 1/4: Sorting & preparing data
│
│ Step 2/4: Building sequence index
│
│ Step 3/4: Writing entity & time index features
│
│ Step 4/4: Computing & writing metadata
│
└─ Done (80 sequences · 450 entities · 0.00s)

# Cast features to categorical
pool.cast_features({"status": pl.Categorical})
print(pool)

┌────────────────────────────────────────────────┐
│           StateSequencePool Summary            │
└────────────────────────────────────────────────┘

Overview
─────────────────────────
  Sequences          80
  Store              /home/runner/.tanat/_quick_state_d6c298e1
  id_column          id

Time Index
─────────────────────────
  Type               Datetime(time_unit='us', time_zone=None) [2000-01-12 06:14:52.240595 → 2025-05-09 18:37:51.409412]
  Columns            ['start', 'end']
  t0                 position=0, anchor=start

Entity Features (2)
─────────────────────────
  • score               Numerical [1 → 100]
  • status              Categorical (5 categories)

Define metric

hamming = HammingEntityMetric(entity_feature="status")
metric = LCPSequenceMetric(entity_metric=hamming, mode="normalized")
print(metric)

LCPSequenceMetric(settings=LCPSettings(entity_metric=HammingEntityMetric(settings=HammingSettings(entity_feature='status', cost=None, mismatch_cost=1.0)), equality_threshold=0.0, mode='normalized'))

Compute distance between a single pair

ids = pool.unique_ids
dist = metric(pool[ids[0]], pool[ids[1]])
print(f"Distance between {ids[0]} and {ids[1]}: {dist:.4f}")

Distance between 1 and 2: 1.0000

Compute full pairwise distance matrix

dm = metric.compute_matrix(pool)
print(f"Distance matrix shape: {dm.shape}")

┌─ LCPSequenceMetric
│

│ Chunks:   0%|          | 0/1 [00:00<?, ?it/s]
│ Chunks: 100%|██████████| 1/1 [00:00<00:00,  1.37it/s]
│ Chunks: 100%|██████████| 1/1 [00:00<00:00,  1.37it/s]
│
└─ Done (80 sequences · 0.73s)
Distance matrix shape: (80, 80)

Visualize distances

arr = dm.to_numpy()
fig, ax = plt.subplots(figsize=(6.5, 5.5))
im = ax.imshow(arr, cmap="viridis_r", vmin=0, vmax=1)
ax.set_title("LCP distance matrix", fontsize=12, fontweight="bold")
ax.set_xlabel("Sequence index")
ax.set_ylabel("Sequence index")
cbar = plt.colorbar(im, ax=ax)
cbar.set_label("Distance")
plt.tight_layout()
plt.show()