# ════════════════════════════════════════════════════════════════════════════
#  SAE Bias Evaluation — Post-hoc Analysis & Plots  (matplotlib only)
#  Confirmed column schemas:
#    df_hc / df_neg:
#      index, feature, act_mean, ctrl_mean, ADS, selectivity_ratio,
#      group_label, final_concept, activation_label, social_attribute,
#      demographic_group, bias_direction, visual_stereotype, inferred_axis
#    df_sel:  feature, ADS, axis, selectivity_ratio, group_label, final_concept
#    df_all:  source, split, code, mean_activation, inferred_axis, bias_direction
#    df_sum:  final_concept, bias_direction, count, mean_act, demographic_group
#    df_cf:   feature, split, A, B, [C]
# ════════════════════════════════════════════════════════════════════════════

import os, json, warnings
import pandas as pd
import numpy as np
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import matplotlib.cm as cm
import seaborn as sns
import squarify   # pip install squarify

warnings.filterwarnings("ignore")

RESULTS_DIR = "./results"
PLOT_DIR    = f"{RESULTS_DIR}/analysis_plots"
os.makedirs(PLOT_DIR, exist_ok=True)

plt.rcParams.update({
    "figure.dpi": 150,
    "savefig.bbox": "tight",
    "savefig.pad_inches": 0.25,
    "font.size": 11,
    "axes.titlesize": 13,
    "axes.titleweight": "bold",
    "axes.spines.top": False,
    "axes.spines.right": False,
})
PALETTE = sns.color_palette("tab10")

# ── Load ──────────────────────────────────────────────────────────────────
df_all = pd.read_csv(f"{RESULTS_DIR}/all_features.csv")
df_sel = pd.read_csv(f"{RESULTS_DIR}/selectivity_live.csv")
df_hc  = pd.read_csv(f"{RESULTS_DIR}/high_confidence_features.csv")
df_neg = pd.read_csv(f"{RESULTS_DIR}/negative_bias_features.csv")
df_sum = pd.read_csv(f"{RESULTS_DIR}/concept_summary.csv")
df_cf  = pd.read_csv(f"{RESULTS_DIR}/counterfactual_delta.csv")

# Normalise string columns
for df in [df_all, df_sel, df_hc, df_neg, df_sum, df_cf]:
    df.columns = df.columns.str.strip()

for col in ["bias_direction", "inferred_axis", "demographic_group", "axis"]:
    for df in [df_all, df_sel, df_hc, df_neg]:
        if col in df.columns:
            df[col] = df[col].str.strip().str.lower()

print(f"all={len(df_all)}  sel={len(df_sel)}  hc={len(df_hc)}"
      f"  neg={len(df_neg)}  cf={len(df_cf)}")


# ════════════════════════════════════════════════════════════════════════════
# PLOT 1 — Bias Direction per Axis  (stacked bar)
#   source: df_hc  cols: inferred_axis, bias_direction
# ════════════════════════════════════════════════════════════════════════════
df_dir = (df_hc[df_hc["inferred_axis"] != "none"]
          .groupby(["inferred_axis", "bias_direction"])
          .size().reset_index(name="count"))
df_dir["pct"] = df_dir.groupby("inferred_axis")["count"] \
                       .transform(lambda x: x / x.sum() * 100)

pivot_dir = (df_dir.pivot_table(index="inferred_axis", columns="bias_direction",
                                 values="pct", aggfunc="sum")
             .reindex(columns=["positive", "neutral", "negative"], fill_value=0))

fig, ax = plt.subplots(figsize=(9, 5))
bar_colors = {"positive": "#2ecc71", "neutral": "#95a5a6", "negative": "#e74c3c"}
bottom = np.zeros(len(pivot_dir))
for direction in ["positive", "neutral", "negative"]:
    vals = pivot_dir.get(direction, pd.Series(0, index=pivot_dir.index)).values
    bars = ax.bar(pivot_dir.index, vals, bottom=bottom,
                  label=direction, color=bar_colors[direction])
    for bar, v in zip(bars, vals):
        if v > 5:
            ax.text(bar.get_x() + bar.get_width() / 2,
                    bar.get_y() + bar.get_height() / 2,
                    f"{v:.1f}%", ha="center", va="center",
                    fontsize=9, color="white", fontweight="bold")
    bottom += vals

ax.set_xlabel("Bias Axis"); ax.set_ylabel("% of Features")
ax.set_title("Bias Direction Distribution per Axis\n"
             "% of high-confidence features labelled by VLM")
ax.legend(loc="upper right", frameon=False)
plt.tight_layout()
plt.savefig(f"{PLOT_DIR}/01_bias_direction_by_axis.png")
plt.close(); print("Plot 1 saved")


# ════════════════════════════════════════════════════════════════════════════
# PLOT 2 — ADS Distribution per Axis  (box)
#   source: df_sel  cols: axis, ADS
# ════════════════════════════════════════════════════════════════════════════
df_ads = df_sel[df_sel["axis"] != "counterfactual"].copy()
axes_sorted = (df_ads.groupby("axis")["ADS"].median()
               .sort_values(ascending=False).index.tolist())
data_boxes = [df_ads[df_ads["axis"] == a]["ADS"].dropna().values
              for a in axes_sorted]

fig, ax = plt.subplots(figsize=(10, 5))
bp = ax.boxplot(data_boxes, patch_artist=True,
                flierprops=dict(marker="o", markersize=3, alpha=0.4),
                medianprops=dict(color="black", linewidth=2))
for patch, color in zip(bp["boxes"], PALETTE):
    patch.set_facecolor(color); patch.set_alpha(0.75)
ax.set_xticks(range(1, len(axes_sorted) + 1))
ax.set_xticklabels(axes_sorted, rotation=20, ha="right")
ax.set_xlabel("Bias Axis"); ax.set_ylabel("ADS")
ax.set_title("ADS Distribution per Bias Axis\n"
             "Activation Discriminativity Score (activator vs control)")
ax.axhline(0.05, color="grey", linestyle="--", linewidth=1, label="threshold 0.05")
ax.legend(frameon=False)
plt.tight_layout()
plt.savefig(f"{PLOT_DIR}/02_ADS_distribution_per_axis.png")
plt.close(); print("Plot 2 saved")


# ════════════════════════════════════════════════════════════════════════════
# PLOT 3 — Top-20 High-Confidence Features by ADS  (horizontal bar)
#   source: df_hc  cols: ADS, final_concept, group_label, inferred_axis
# ════════════════════════════════════════════════════════════════════════════
top20 = df_hc.nlargest(20, "ADS").copy()
top20["label"] = (top20["final_concept"].str[:36]
                  + "  [" + top20["group_label"].str[:14] + "]")
axis_color_map = {
    "gender": "#3498db", "caste_class": "#e74c3c",
    "religion": "#9b59b6", "region": "#f39c12",
    "skin_tone": "#1abc9c", "occupation": "#e67e22",
    "counterfactual": "#95a5a6"
}
bar_c = [axis_color_map.get(a, "#7f8c8d") for a in top20["inferred_axis"]]

fig, ax = plt.subplots(figsize=(12, 9))
y_pos = list(range(len(top20)))
ax.barh(y_pos, top20["ADS"].values, color=bar_c, alpha=0.85)
ax.set_yticks(y_pos)
ax.set_yticklabels(top20["label"].values, fontsize=9)
ax.invert_yaxis()
ax.set_xlabel("ADS Score"); ax.set_ylabel("Feature Concept")
ax.set_title("Top-20 High-Confidence Bias Features by ADS\n"
             "ADS ≥ 0.05 & selectivity_ratio ≥ 1.3")
for v, pos in zip(top20["ADS"].values, y_pos):
    ax.text(v + 0.001, pos, f"{v:.3f}", va="center", fontsize=8)
legend_patches = [mpatches.Patch(color=c, label=a)
                  for a, c in axis_color_map.items()
                  if a in top20["inferred_axis"].values]
ax.legend(handles=legend_patches, loc="lower right", frameon=False, fontsize=9)
plt.tight_layout()
plt.savefig(f"{PLOT_DIR}/03_top20_hc_features.png")
plt.close(); print("Plot 3 saved")


# ════════════════════════════════════════════════════════════════════════════
# PLOT 4 — Negative Stereotype Rate per Axis  (bar)
#   source: df_hc, df_neg  col: inferred_axis
# ════════════════════════════════════════════════════════════════════════════
total_per_axis = (df_hc[df_hc["inferred_axis"] != "none"]
                  .groupby("inferred_axis").size())
neg_per_axis   = df_neg.groupby("inferred_axis").size()
neg_rate = (neg_per_axis / total_per_axis * 100).dropna().reset_index()
neg_rate.columns = ["axis", "neg_rate"]
neg_rate = neg_rate.sort_values("neg_rate", ascending=False)

norm = plt.Normalize(neg_rate["neg_rate"].min(), neg_rate["neg_rate"].max())
bar_c4 = [cm.Reds(norm(v)) for v in neg_rate["neg_rate"]]

fig, ax = plt.subplots(figsize=(9, 5))
bars = ax.bar(neg_rate["axis"], neg_rate["neg_rate"], color=bar_c4)
for bar, v in zip(bars, neg_rate["neg_rate"]):
    ax.text(bar.get_x() + bar.get_width() / 2,
            bar.get_height() + 0.4, f"{v:.1f}%",
            ha="center", va="bottom", fontsize=10)
ax.set_xlabel("Bias Axis"); ax.set_ylabel("Negative Rate (%)")
ax.set_title("Negative Stereotype Rate per Bias Axis\n"
             "% of features labelled 'negative' by VLM")
plt.tight_layout()
plt.savefig(f"{PLOT_DIR}/04_negative_rate_per_axis.png")
plt.close(); print("Plot 4 saved")


# ════════════════════════════════════════════════════════════════════════════
# PLOT 5 — Counterfactual ADS Heatmap
#   source: df_sel  cols: axis=="counterfactual", feature, ADS, group_label
# ════════════════════════════════════════════════════════════════════════════
df_cf_ads = df_sel[df_sel["axis"] == "counterfactual"].copy()
if not df_cf_ads.empty:
    top_feats = (df_cf_ads.groupby("feature")["ADS"].max()
                 .nlargest(15).index.tolist())
    pivot = (df_cf_ads[df_cf_ads["feature"].isin(top_feats)]
             .pivot_table(index="group_label", columns="feature",
                          values="ADS", aggfunc="mean").fillna(0))
    pivot.columns = [f"F{c}" for c in pivot.columns]

    fig, ax = plt.subplots(figsize=(12, max(4, len(pivot) * 0.55)))
    sns.heatmap(pivot, ax=ax, cmap="RdBu_r", center=0,
                vmin=-0.2, vmax=0.2, annot=True, fmt=".2f",
                linewidths=0.4, cbar_kws={"label": "ADS"})
    ax.set_xlabel("Feature Index"); ax.set_ylabel("Counterfactual Pair")
    ax.set_title("Counterfactual ADS Heatmap\n"
                 "Red = group A fires more  |  Blue = group B fires more")
    plt.tight_layout()
    plt.savefig(f"{PLOT_DIR}/05_counterfactual_heatmap.png")
    plt.close(); print("Plot 5 saved")
else:
    print("Plot 5 skipped — no counterfactual rows in df_sel")


# ════════════════════════════════════════════════════════════════════════════
# PLOT 6 — Mean Activation per Group per Block  (grouped bar)
#   source: df_all  cols: source, split, code, mean_activation
# ════════════════════════════════════════════════════════════════════════════
code_col = "code" if "code" in df_all.columns else "split"
df_act = (df_all[df_all["source"] == "activator"]
          .groupby(["split", code_col])["mean_activation"]
          .mean().reset_index())
df_act["split_short"] = (df_act["split"]
                         .str.replace("_activator", "", regex=False)
                         .str.replace("_", " "))

codes  = df_act[code_col].unique()
groups = df_act["split_short"].unique()
x = np.arange(len(groups))
width = 0.8 / max(len(codes), 1)

fig, ax = plt.subplots(figsize=(13, 5))
for i, code in enumerate(codes):
    sub = df_act[df_act[code_col] == code].set_index("split_short")
    vals = [float(sub.loc[g, "mean_activation"])
            if g in sub.index else 0.0 for g in groups]
    offset = (i - len(codes) / 2 + 0.5) * width
    ax.bar(x + offset, vals, width=width * 0.9, label=str(code), alpha=0.85)

ax.set_xticks(x)
ax.set_xticklabels(groups, rotation=35, ha="right", fontsize=9)
ax.set_xlabel("Social Group"); ax.set_ylabel("Mean Activation")
ax.set_title("Mean SAE Activation per Group per Block\n"
             "Activator prompts — down.2.1 vs up.0.1")
ax.legend(frameon=False, title="Block")
plt.tight_layout()
plt.savefig(f"{PLOT_DIR}/06_mean_activation_per_group.png")
plt.close(); print("Plot 6 saved")


# ════════════════════════════════════════════════════════════════════════════
# PLOT 7 — High-Confidence Feature Count per Demographic Group (per-axis bars)
#   source: df_hc  cols: inferred_axis, group_label
# ════════════════════════════════════════════════════════════════════════════
df_demo = (df_hc.groupby(["inferred_axis", "group_label"])
           .size().reset_index(name="count")
           .sort_values(["inferred_axis", "count"], ascending=[True, False]))
axes_uniq = df_demo["inferred_axis"].unique()

fig, axes_arr = plt.subplots(1, len(axes_uniq),
                              figsize=(4.5 * len(axes_uniq), 5), sharey=False)
if len(axes_uniq) == 1:
    axes_arr = [axes_arr]

for i, (axis_name, grp) in enumerate(df_demo.groupby("inferred_axis")):
    ax = axes_arr[i]
    ax.barh(grp["group_label"], grp["count"],
            color=PALETTE[i % len(PALETTE)], alpha=0.82)
    ax.invert_yaxis()
    ax.set_title(axis_name, fontsize=10, fontweight="bold")
    ax.set_xlabel("Feature Count")
    ax.tick_params(axis="y", labelsize=8)

fig.suptitle("High-Confidence Features per Demographic Group",
             fontsize=13, fontweight="bold")
plt.tight_layout()
plt.savefig(f"{PLOT_DIR}/07_features_per_demo_group.png")
plt.close(); print("Plot 7 saved")


# ════════════════════════════════════════════════════════════════════════════
# PLOT 8 — ADS vs Selectivity Ratio Scatter
#   source: df_sel  cols: ADS, selectivity_ratio, axis, group_label
# ════════════════════════════════════════════════════════════════════════════
df_scat = df_sel[(df_sel["axis"] != "counterfactual")
                 & (df_sel["ADS"] > 0)].copy()
df_scat = df_scat.sample(min(2000, len(df_scat)), random_state=42)
axes_u = df_scat["axis"].unique()
cmap_s = {a: PALETTE[i % len(PALETTE)] for i, a in enumerate(axes_u)}

fig, ax = plt.subplots(figsize=(9, 6))
for axis_name, grp in df_scat.groupby("axis"):
    ax.scatter(grp["ADS"], grp["selectivity_ratio"],
               color=cmap_s[axis_name], alpha=0.4, s=20, label=axis_name)
ax.axhline(1.3, color="grey", linestyle="--", linewidth=1)
ax.axvline(0.05, color="grey", linestyle="--", linewidth=1)
ax.text(0.052, ax.get_ylim()[1] * 0.97, "ADS=0.05", fontsize=8, color="grey")
ax.text(df_scat["ADS"].min(), 1.32, "ratio=1.3", fontsize=8, color="grey")
ax.set_xlabel("ADS"); ax.set_ylabel("Selectivity Ratio")
ax.set_title("ADS vs Selectivity Ratio per Feature\n"
             "Top-right = most discriminative bias features")
ax.legend(frameon=False, markerscale=2, fontsize=9)
plt.tight_layout()
plt.savefig(f"{PLOT_DIR}/08_scatter_ADS_vs_selectivity.png")
plt.close(); print("Plot 8 saved")


# ════════════════════════════════════════════════════════════════════════════
# PLOT 9 — Counterfactual Delta A−B  (horizontal bar)
#   source: df_cf  cols: feature, split, A, B
# ════════════════════════════════════════════════════════════════════════════
if "A" in df_cf.columns and "B" in df_cf.columns:
    df_cf["delta_AB"] = pd.to_numeric(df_cf["A"], errors="coerce") \
                      - pd.to_numeric(df_cf["B"], errors="coerce")
    top_cf = df_cf.reindex(
        df_cf["delta_AB"].abs().nlargest(20).index).copy()
    top_cf["label"] = ("F" + top_cf["feature"].astype(str)
                       + "  |  " + top_cf["split"].str[:22])
    bar_c9 = ["#3498db" if v > 0 else "#e74c3c"
               for v in top_cf["delta_AB"]]

    fig, ax = plt.subplots(figsize=(11, 8))
    y_pos = list(range(len(top_cf)))
    ax.barh(y_pos, top_cf["delta_AB"].values, color=bar_c9, alpha=0.85)
    ax.set_yticks(y_pos)
    ax.set_yticklabels(top_cf["label"].values, fontsize=9)
    ax.invert_yaxis()
    ax.axvline(0, color="black", linewidth=1)
    ax.set_xlabel("Delta (A−B)")
    ax.set_title("Counterfactual Delta (A−B) per Feature\n"
                 "Blue = group A fires more  |  Red = group B fires more")
    ax.legend(handles=[
        mpatches.Patch(color="#3498db", label="Group A > Group B"),
        mpatches.Patch(color="#e74c3c", label="Group B > Group A")
    ], frameon=False)
    plt.tight_layout()
    plt.savefig(f"{PLOT_DIR}/09_counterfactual_delta_bar.png")
    plt.close(); print("Plot 9 saved")
else:
    print("Plot 9 skipped — A/B columns not found in counterfactual_delta.csv")


# ════════════════════════════════════════════════════════════════════════════
# PLOT 10 — Concept Treemap  (squarify)
#   source: df_sum  cols: final_concept, bias_direction, count, mean_act
# ════════════════════════════════════════════════════════════════════════════
df_tree = df_sum.copy()
df_tree["count"]    = pd.to_numeric(df_tree["count"], errors="coerce").fillna(1)
df_tree["mean_act"] = pd.to_numeric(df_tree["mean_act"], errors="coerce").fillna(0)
df_tree = df_tree.nlargest(40, "mean_act")
df_tree["label"] = df_tree["final_concept"].str[:28]

norm10   = plt.Normalize(df_tree["mean_act"].min(), df_tree["mean_act"].max())
t_colors = [cm.Oranges(norm10(v)) for v in df_tree["mean_act"]]

fig, ax = plt.subplots(figsize=(14, 8))
squarify.plot(sizes=df_tree["count"].values,
              label=df_tree["label"].values,
              color=t_colors, alpha=0.85, ax=ax,
              text_kwargs={"fontsize": 7})
ax.axis("off")
ax.set_title("Top Feature Concepts by Bias Direction\n"
             "Size = frequency  |  Color = mean activation (darker = higher)",
             fontsize=12, fontweight="bold")
sm = cm.ScalarMappable(cmap="Oranges", norm=norm10)
sm.set_array([])
plt.colorbar(sm, ax=ax, shrink=0.6, label="Mean Activation")
plt.tight_layout()
plt.savefig(f"{PLOT_DIR}/10_concept_treemap.png")
plt.close(); print("Plot 10 saved")

print(f"\n✓ All 10 plots saved → {PLOT_DIR}")