analyze_ablation.py

"""
Ablation analysis — mirrors all_chrom_evaluation.ipynb but for:
  - Ablation_no_partition, Ablation_no_priority, Ablation_no_length
  - Plus Baseline_bpe_5120 and Merged_uni_len2_5120 as references
Saves all plots to eval_outputs_ablation/plots/
"""

import os
import pandas as pd
import numpy as np
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import seaborn as sns

# ── Directories ──────────────────────────────────────────────────────────────
ABLATION_DIR = "/home/n5huang/dna_token/tokenizer_evaluation/eval_outputs_ablation"
MERGED_DIR   = "/home/n5huang/dna_token/tokenizer_evaluation/eval_outputs_all_chrom"
BASELINE_DIR = "/home/n5huang/dna_token/tokenizer_evaluation/eval_outputs_all_chrom_baseline_bpe"
PLOT_DIR     = os.path.join(ABLATION_DIR, "plots")
os.makedirs(PLOT_DIR, exist_ok=True)

pd.set_option("display.max_columns", 20)
pd.set_option("display.width", 200)
pd.set_option("display.float_format", "{:.6f}".format)

# ── Helper: compute summary row from an agg CSV ─────────────────────────────
def summarize_agg(agg: pd.DataFrame, name: str, scope: str, region: str = None):
    row = {
        "tokenizer": name,
        "scope": scope,
        "mean_of_mean_phyloP": agg["mean_mean"].mean(),
        "median_of_mean_phyloP": agg["mean_mean"].median(),
        "pct_mean_phyloP_above_0": (agg["mean_mean"] > 0).mean() * 100,
        "mean_of_variance": agg["mean_var"].mean(),
        "median_of_variance": agg["mean_var"].median(),
        "pct_variance_below_0.1": (agg["mean_var"] < 0.1).mean() * 100,
        "num_tokens": len(agg),
        "mean_token_count": agg["count"].mean(),
        "median_token_count": agg["count"].median(),
    }
    if region:
        row["region"] = region
    return row


# ── 1. Window-Based Summary ──────────────────────────────────────────────────
print("=" * 80)
print("1. WINDOW-BASED SUMMARY")
print("=" * 80)

ablation_names = ["Ablation_no_partition", "Ablation_no_priority", "Ablation_no_length"]
ref_names = {
    "Baseline_bpe_5120":    os.path.join(BASELINE_DIR, "agg_Baseline_bpe_5120.csv"),
    "Merged_uni_len2_5120": os.path.join(MERGED_DIR,   "agg_Merged_uni_len2_5120.csv"),
}

# Load all agg data (window)
agg_data = {}
window_summaries = []

for name in ablation_names:
    path = os.path.join(ABLATION_DIR, f"agg_{name}.csv")
    agg = pd.read_csv(path)
    agg_data[name] = agg
    window_summaries.append(summarize_agg(agg, name, "all_chrom_window"))

for name, path in ref_names.items():
    agg = pd.read_csv(path)
    agg_data[name] = agg
    window_summaries.append(summarize_agg(agg, name, "all_chrom_window"))

df_window = pd.DataFrame(window_summaries)
cols = ["tokenizer", "mean_of_mean_phyloP", "median_of_mean_phyloP",
        "pct_mean_phyloP_above_0", "mean_of_variance", "median_of_variance",
        "pct_variance_below_0.1", "num_tokens", "mean_token_count", "median_token_count"]
df_window = df_window[cols]
print(df_window.to_string(index=False))
df_window.to_csv(os.path.join(ABLATION_DIR, "summary_ablation_window.csv"), index=False)


# ── 2. Region-Based Summary ─────────────────────────────────────────────────
print("\n" + "=" * 80)
print("2. REGION-BASED SUMMARY")
print("=" * 80)

agg_region = {}
region_summaries = []

for name in ablation_names:
    for region in ["conserved", "neutral", "accelerated"]:
        path = os.path.join(ABLATION_DIR, f"agg_{name}_{region}.csv")
        if os.path.exists(path):
            agg = pd.read_csv(path)
            agg_region[(name, region)] = agg
            region_summaries.append(summarize_agg(agg, name, "all_chrom_region", region))

for name, base_path in ref_names.items():
    base_dir = os.path.dirname(base_path)
    for region in ["conserved", "neutral", "accelerated"]:
        path = os.path.join(base_dir, f"agg_{name}_{region}.csv")
        if os.path.exists(path):
            agg = pd.read_csv(path)
            agg_region[(name, region)] = agg
            region_summaries.append(summarize_agg(agg, name, "all_chrom_region", region))

df_region = pd.DataFrame(region_summaries)
cols_region = ["tokenizer", "region", "mean_of_mean_phyloP", "median_of_mean_phyloP",
               "pct_mean_phyloP_above_0", "mean_of_variance", "median_of_variance",
               "pct_variance_below_0.1", "num_tokens", "mean_token_count", "median_token_count"]
df_region = df_region[cols_region]

for region in ["conserved", "neutral", "accelerated"]:
    print(f"\n=== {region.upper()} ===")
    print(df_region[df_region["region"] == region].to_string(index=False))

df_region.to_csv(os.path.join(ABLATION_DIR, "summary_ablation_all_regions.csv"), index=False)


# ── Color scheme ─────────────────────────────────────────────────────────────
COLORS = {
    "Baseline_bpe_5120":      "steelblue",
    "Merged_uni_len2_5120":   "tomato",
    "Ablation_no_partition":  "#2ca02c",   # green
    "Ablation_no_priority":   "#9467bd",   # purple
    "Ablation_no_length":     "#ff7f0e",   # orange
}


# ── 3. Scatter: Mean phyloP vs Variance (Window) ────────────────────────────
print("\n" + "=" * 80)
print("3. SCATTER PLOTS: Mean phyloP vs Variance")
print("=" * 80)

fig, axes = plt.subplots(1, 2, figsize=(18, 8))

for ax_idx, (xcol, xlabel) in enumerate([("mean_mean", "Mean phyloP"), ("median_mean", "Median Mean phyloP")]):
    ax = axes[ax_idx]
    for name, color in COLORS.items():
        if name in agg_data:
            d = agg_data[name]
            ax.scatter(d[xcol], d["mean_var"], alpha=0.4, s=20, label=name, color=color)
    ax.set_xlabel(xlabel)
    ax.set_ylabel("Mean Variance")
    ax.set_title(f"Token Conservation: {xlabel} vs Variance")
    ax.legend(fontsize=8)

plt.tight_layout()
plt.savefig(os.path.join(PLOT_DIR, "scatter_mean_vs_var.png"), dpi=150, bbox_inches="tight")
plt.close()
print(f"  Saved scatter_mean_vs_var.png")


# ── 4. Histograms: Mean phyloP and Variance distributions ───────────────────
print("\n" + "=" * 80)
print("4. HISTOGRAMS")
print("=" * 80)

fig, axes = plt.subplots(1, 2, figsize=(18, 6))

ax = axes[0]
for name, color in COLORS.items():
    if name in agg_data:
        ax.hist(agg_data[name]["mean_mean"], bins=50, alpha=0.5, label=name, color=color)
ax.set_xlabel("Mean phyloP")
ax.set_ylabel("Token Count")
ax.set_title("Mean Conservation Distribution")
ax.legend(fontsize=8)

ax = axes[1]
for name, color in COLORS.items():
    if name in agg_data:
        ax.hist(agg_data[name]["mean_var"], bins=50, alpha=0.5, label=name, color=color)
ax.set_xlabel("Mean Variance")
ax.set_ylabel("Token Count")
ax.set_title("Variance Distribution")
ax.legend(fontsize=8)

plt.tight_layout()
plt.savefig(os.path.join(PLOT_DIR, "histograms_mean_var.png"), dpi=150, bbox_inches="tight")
plt.close()
print(f"  Saved histograms_mean_var.png")


# ── 5. Region scatter: by region ─────────────────────────────────────────────
print("\n" + "=" * 80)
print("5. REGION SCATTER PLOTS")
print("=" * 80)

fig, axes = plt.subplots(1, 3, figsize=(21, 7))

for i, region in enumerate(["conserved", "neutral", "accelerated"]):
    ax = axes[i]
    for name, color in COLORS.items():
        key = (name, region)
        if key in agg_region:
            d = agg_region[key]
            ax.scatter(d["mean_mean"], d["mean_var"], alpha=0.4, s=20, label=name, color=color)
    ax.set_xlabel("Mean phyloP")
    ax.set_ylabel("Mean Variance")
    ax.set_title(f"{region.capitalize()} Regions")
    ax.legend(fontsize=7, loc="upper left")

plt.suptitle("Region-Based: Ablation vs References", fontsize=14)
plt.tight_layout()
plt.savefig(os.path.join(PLOT_DIR, "scatter_by_region.png"), dpi=150, bbox_inches="tight")
plt.close()
print(f"  Saved scatter_by_region.png")


# ── 6. Bar charts: Summary metrics by tokenizer and region ──────────────────
print("\n" + "=" * 80)
print("6. BAR CHARTS: Summary metrics")
print("=" * 80)

metrics = ["mean_of_mean_phyloP", "pct_mean_phyloP_above_0", "mean_of_variance", "pct_variance_below_0.1"]
metric_labels = ["Mean of Mean phyloP", "% Mean phyloP > 0", "Mean of Variance", "% Variance < 0.1"]

fig, axes = plt.subplots(2, 2, figsize=(18, 12))

for idx, (metric, label) in enumerate(zip(metrics, metric_labels)):
    ax = axes[idx // 2, idx % 2]
    for region in ["conserved", "neutral", "accelerated"]:
        subset = df_region[df_region["region"] == region].copy()
        subset = subset.sort_values("tokenizer")
        x = range(len(subset))
        offset = {"conserved": -0.25, "neutral": 0, "accelerated": 0.25}[region]
        color = {"conserved": "forestgreen", "neutral": "gray", "accelerated": "firebrick"}[region]
        ax.bar([xi + offset for xi in x], subset[metric], width=0.25,
               label=region, color=color, alpha=0.8)
    ax.set_xticks(range(len(subset)))
    ax.set_xticklabels(subset["tokenizer"], rotation=45, ha="right", fontsize=8)
    ax.set_ylabel(label)
    ax.set_title(label)
    ax.legend(fontsize=8)

plt.suptitle("Ablation: Region-Based Metrics", fontsize=14)
plt.tight_layout()
plt.savefig(os.path.join(PLOT_DIR, "bar_region_metrics.png"), dpi=150, bbox_inches="tight")
plt.close()
print(f"  Saved bar_region_metrics.png")


# ── 7. Grouped bar: Ablation comparison (window metrics) ────────────────────
print("\n" + "=" * 80)
print("7. GROUPED BAR: Window metrics comparison")
print("=" * 80)

order = ["Baseline_bpe_5120", "Ablation_no_partition", "Ablation_no_priority",
         "Ablation_no_length", "Merged_uni_len2_5120"]
df_w_ordered = df_window.set_index("tokenizer").loc[order].reset_index()

fig, axes = plt.subplots(1, 3, figsize=(20, 6))

# Mean phyloP
ax = axes[0]
bars = ax.barh(df_w_ordered["tokenizer"], df_w_ordered["mean_of_mean_phyloP"],
               color=[COLORS[n] for n in df_w_ordered["tokenizer"]])
ax.set_xlabel("Mean of Mean phyloP")
ax.set_title("Conservation Signal (Higher is Better)")
ax.invert_yaxis()
for bar, val in zip(bars, df_w_ordered["mean_of_mean_phyloP"]):
    ax.text(bar.get_width() + 0.001, bar.get_y() + bar.get_height()/2,
            f"{val:.4f}", va="center", fontsize=9)

# % above 0
ax = axes[1]
bars = ax.barh(df_w_ordered["tokenizer"], df_w_ordered["pct_mean_phyloP_above_0"],
               color=[COLORS[n] for n in df_w_ordered["tokenizer"]])
ax.set_xlabel("% Mean phyloP > 0")
ax.set_title("Positive Conservation Rate (Higher is Better)")
ax.invert_yaxis()
for bar, val in zip(bars, df_w_ordered["pct_mean_phyloP_above_0"]):
    ax.text(bar.get_width() + 0.1, bar.get_y() + bar.get_height()/2,
            f"{val:.1f}%", va="center", fontsize=9)

# Mean variance
ax = axes[2]
bars = ax.barh(df_w_ordered["tokenizer"], df_w_ordered["mean_of_variance"],
               color=[COLORS[n] for n in df_w_ordered["tokenizer"]])
ax.set_xlabel("Mean of Variance")
ax.set_title("Internal Variance (Lower is Better)")
ax.invert_yaxis()
for bar, val in zip(bars, df_w_ordered["mean_of_variance"]):
    ax.text(bar.get_width() + 0.001, bar.get_y() + bar.get_height()/2,
            f"{val:.4f}", va="center", fontsize=9)

plt.suptitle("Ablation: Window-Based Summary Metrics", fontsize=14)
plt.tight_layout()
plt.savefig(os.path.join(PLOT_DIR, "bar_window_comparison.png"), dpi=150, bbox_inches="tight")
plt.close()
print(f"  Saved bar_window_comparison.png")


# ── 8. Print final comparison table ─────────────────────────────────────────
print("\n" + "=" * 80)
print("8. FINAL COMPARISON TABLE")
print("=" * 80)
print("\nWindow-based:")
print(df_w_ordered[["tokenizer", "mean_of_mean_phyloP", "pct_mean_phyloP_above_0",
                     "mean_of_variance", "pct_variance_below_0.1", "num_tokens"]].to_string(index=False))

print("\nRegion-based (conserved only):")
cons = df_region[df_region["region"] == "conserved"].copy()
cons = cons[cons["tokenizer"].isin(order)]
print(cons[["tokenizer", "mean_of_mean_phyloP", "pct_mean_phyloP_above_0",
            "mean_of_variance", "num_tokens"]].to_string(index=False))

print(f"\nAll plots saved to: {PLOT_DIR}/")
print("All summary CSVs saved to:", ABLATION_DIR)