update att

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+*.xlsx* filter=lfs diff=lfs merge=lfs -text

src/bad_actor_simulation.xlsx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dd6875d023d101d090a6aa3b4f1ac6e7b26329447c686d78d1e96d17cbf613ef
+size 1212447

src/streamlit_app.py

@@ -1,40 +1,490 @@
-import altair as alt
-import numpy as np
 import pandas as pd
+import numpy as np
 import streamlit as st
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+st.set_page_config(
+    page_title="Bad Actor Simulation",
+    page_icon="⚠️",
+    layout="wide",
+    initial_sidebar_state="expanded",
+)
+
+st.markdown("""
+<style>
+/* ── Section card headers ─────────────────────────────────────────────────── */
+.section-card {
+    background: #f8f9fa;
+    border-left: 4px solid #e63946;
+    border-radius: 6px;
+    padding: 10px 16px;
+    margin: 16px 0 8px 0;
+}
+.section-card h3 { margin: 0; font-size: 1.05rem; font-weight: 700; color: #1d3557; }
+.section-card .sub { font-size: 0.78rem; color: #6c757d; margin-top: 3px; }
+
+/* ── KPI metric cards ─────────────────────────────────────────────────────── */
+[data-testid="metric-container"] {
+    background: #ffffff;
+    border: 1px solid #e9ecef;
+    border-radius: 10px;
+    padding: 14px 18px !important;
+    box-shadow: 0 1px 3px rgba(0,0,0,0.06);
+}
+[data-testid="stMetricValue"] { color: #1d3557; font-weight: 700; }
+[data-testid="stMetricLabel"] { color: #6c757d; font-size: 0.82rem; }
+
+/* ── Welcome card ─────────────────────────────────────────────────────────── */
+.welcome-card {
+    background: linear-gradient(135deg, #1d3557 0%, #457b9d 100%);
+    border-radius: 12px;
+    padding: 32px 36px;
+    color: white;
+    margin-bottom: 24px;
+}
+.welcome-card h2 { margin: 0 0 8px 0; font-size: 1.4rem; color: white; }
+.welcome-card p  { margin: 0 0 20px 0; color: rgba(255,255,255,0.8); font-size: 0.9rem; }
+.step-list { list-style: none; padding: 0; margin: 0; }
+.step-list li {
+    display: flex; align-items: center; gap: 10px;
+    padding: 7px 0; border-bottom: 1px solid rgba(255,255,255,0.15);
+    color: rgba(255,255,255,0.9); font-size: 0.88rem;
+}
+.step-list li:last-child { border-bottom: none; }
+.step-num {
+    background: #e63946; color: white; font-weight: 700;
+    border-radius: 50%; width: 22px; height: 22px;
+    display: flex; align-items: center; justify-content: center;
+    font-size: 0.75rem; flex-shrink: 0;
+}
+
+/* ── App header ───────────────────────────────────────────────────────────── */
+.app-header { margin-bottom: 4px; }
+.app-header h1 { margin: 0; font-size: 1.7rem; color: #1d3557; font-weight: 800; }
+.app-header .tagline { color: #6c757d; font-size: 0.85rem; margin-top: 2px; }
+.dataset-badge {
+    display: inline-block;
+    background: #e9ecef; color: #495057;
+    border-radius: 20px; padding: 4px 12px;
+    font-size: 0.78rem; margin-top: 4px;
+}
+
+/* ── Sidebar ──────────────────────────────────────────────────────────────── */
+[data-testid="stSidebar"] { background: #f8f9fa; }
+[data-testid="stSidebar"] .stButton > button {
+    background: #e63946 !important; color: white !important;
+    border: none !important; font-weight: 600 !important;
+    letter-spacing: 0.3px;
+}
+[data-testid="stSidebar"] .stButton > button:hover {
+    background: #c1121f !important;
+}
+</style>
+""", unsafe_allow_html=True)
+
+
+def _section(icon, title, subtitle=""):
+    sub_html = f'<div class="sub">{subtitle}</div>' if subtitle else ""
+    st.markdown(
+        f'<div class="section-card"><h3>{icon}&nbsp; {title}</h3>{sub_html}</div>',
+        unsafe_allow_html=True,
+    )
+
+REQUIRED_COLS = {"year", "month", "site_id", "section", "model", "eqm_no", "MA", "MTBF"}
+
+@st.cache_data
+def load_default():
+    df = pd.read_excel("bad_actor_simulation.xlsx", sheet_name="data_badactor")
+    df.columns = df.columns.str.strip()
+    df["model"] = df["model"].astype(str)
+    return df
+
+@st.cache_data
+def load_csv(data: bytes) -> pd.DataFrame:
+    import io
+    df = pd.read_csv(io.BytesIO(data))
+    df.columns = df.columns.str.strip()
+    df["model"] = df["model"].astype(str)
+    return df
+
+@st.cache_data
+def load_xlsx(data: bytes, sheet: str) -> pd.DataFrame:
+    import io
+    df = pd.read_excel(io.BytesIO(data), sheet_name=sheet)
+    df.columns = df.columns.str.strip()
+    df["model"] = df["model"].astype(str)
+    return df
+
+def get_xlsx_sheets(data: bytes) -> list:
+    import io
+    xf = pd.ExcelFile(io.BytesIO(data))
+    return xf.sheet_names
+
+# ── Helpers ────────────────────────────────────────────────────────────────────
+def minmax_norm(s):
+    lo, hi = s.min(), s.max()
+    if hi == lo:
+        return pd.Series(0.5, index=s.index)
+    return (s - lo) / (hi - lo)
+
+
+def flag_consecutive(group, col_below, col_period, threshold):
+    """Mark rows belonging to a consecutive-True run of length >= threshold."""
+    g       = group.sort_values(col_period)
+    below   = g[col_below].values
+    periods = g[col_period].values
+    result  = np.zeros(len(g), dtype=bool)
+    run_start = None
+    for i in range(len(g)):
+        if not below[i]:
+            run_start = None
+            continue
+        if run_start is None:
+            run_start = i
+        elif periods[i] != periods[i - 1] + 1:
+            run_start = i
+        if (i - run_start + 1) >= threshold:
+            result[run_start : i + 1] = True
+    return pd.Series(result, index=g.index)
+
+
+# ── Default filter values (edit here to change the initial widget state) ───────
+DEFAULT_YEARS         = [2026]
+DEFAULT_SITES         = [2009]
+DEFAULT_SECTIONS      = ["OBLOADER"]
+DEFAULT_MODELS        = ["6015B", "6020B", "EX2500-5", "EX3600-6", "PC1250SP-7", "PC1250SP-8", "PC2000-8", "PC4000-6"]
+DEFAULT_CONSECUTIVE_N = 2
+DEFAULT_OBS_MONTH     = 2
+
+def run_simulation(years, sites, sections, models, consecutive_n, obs_month):
+    # 1. Filter  (obs_month = observation cutoff: only months 1..obs_month)
+    mask = (
+        df["year"].isin(years) &
+        df["site_id"].isin(sites) &
+        df["section"].isin(sections) &
+        (df["month"] <= obs_month)
+    )
+    if "ALL" not in models:
+        mask &= df["model"].isin(models)
+    filt = df[mask].copy()
+
+    if filt.empty:
+        st.warning("No data found for the selected filters.")
+        return
+
+    # basis_key: used for per-model detail stats and Q1
+    # agg_key:   used for normalization min/max (matches the aggregated Normalisation Reference table)
+    basis_key = ["month", "site_id", "section", "model"]
+    agg_key   = ["month", "site_id", "section"]
+
+    # 2. Acuan Basis 1 — min-max normalise using section-level min/max (agg_key, no model)
+    filt["norm_MA"]   = filt.groupby(agg_key)["MA"]  .transform(minmax_norm)
+    filt["norm_MTBF"] = filt.groupby(agg_key)["MTBF"].transform(minmax_norm)
+
+    # Normalisation reference stats (min, max, avg) for display — detail per model
+    norm_stats = filt.groupby(basis_key).agg(
+        MA_min=("MA", "min"), MA_max=("MA", "max"), MA_avg=("MA", "mean"),
+        MTBF_min=("MTBF", "min"), MTBF_max=("MTBF", "max"), MTBF_avg=("MTBF", "mean"),
+    ).round(4).reset_index()
+
+    # 3. Bad actor score
+    filt["bad_actor_score"] = filt["norm_MA"] * filt["norm_MTBF"]
+
+    # 4. Acuan Basis 2 — Q1 threshold using the same basis_key
+    q1_df = (
+        filt.groupby(basis_key)["bad_actor_score"]
+        .quantile(0.25)
+        .reset_index()
+        .rename(columns={"bad_actor_score": "q1_threshold"})
+    )
+    filt = filt.merge(q1_df, on=basis_key, how="left")
+
+    # 5. Below-Q1 flag
+    filt["below_q1"] = filt["bad_actor_score"] < filt["q1_threshold"]
+
+    # 6. Consecutive detection (period = year*12 + month for cross-year safety)
+    filt["period"] = filt["year"] * 12 + filt["month"]
+    filt["is_bad_actor"] = (
+        filt.groupby("eqm_no", group_keys=False)
+        .apply(flag_consecutive,
+               col_below="below_q1",
+               col_period="period",
+               threshold=consecutive_n)
+    )
+
+    # ── Build bad actor summary ────────────────────────────────────────────────
+    bad_ids = filt.loc[filt["is_bad_actor"], "eqm_no"].unique()
+
+    def _streak(g):
+        periods = sorted(g.loc[g["below_q1"], "period"].tolist())
+        if not periods:
+            return 0
+        mx = cur = 1
+        for i in range(1, len(periods)):
+            cur = cur + 1 if periods[i] == periods[i - 1] + 1 else 1
+            mx = max(mx, cur)
+        return mx
+
+    rows = []
+    for eid, grp in filt[filt["eqm_no"].isin(bad_ids)].groupby("eqm_no"):
+        rows.append({
+            "eqm_no"          : eid,
+            "site_id"         : grp["site_id"].iloc[0],
+            "section"         : grp["section"].iloc[0],
+            "model"           : grp["model"].iloc[0],
+            "flagged_months"  : int(grp["below_q1"].sum()),
+            "max_streak"      : _streak(grp),
+            "bad_actor_months": ", ".join(
+                str(int(m)) for m in sorted(
+                    grp.loc[grp["is_bad_actor"], "month"].unique())),
+        })
+
+    summary = (
+        pd.DataFrame(rows)
+        .sort_values(["section", "max_streak"], ascending=[True, False])
+        .reset_index(drop=True)
+    )
+    summary["last_bad_actor_month"] = summary["bad_actor_months"].apply(
+        lambda s: int(s.split(", ")[-1]) if s else None
+    )
+    summary = summary[summary["last_bad_actor_month"] == obs_month].reset_index(drop=True)
+
+    # ── KPI row ────────────────────────────────────────────────────────────────
+    total_eqm  = filt["eqm_no"].nunique()
+    n_bad      = len(summary)
+    rate       = n_bad / total_eqm * 100 if total_eqm else 0
+    k1, k2, k3 = st.columns(3)
+    k1.metric("Equipment Evaluated", f"{total_eqm:,}")
+    k2.metric("Bad Actors Detected", f"{n_bad:,}")
+    k3.metric("Bad Actor Rate", f"{rate:.1f}%")
+    st.divider()
+
+    # ── Tabs ───────────────────────────────────────────────────────────────────
+    tab1, tab2, tab3 = st.tabs([
+        "⚠️  Bad Actors",
+        "📊  Reference Basis",
+        "🔢  Scored Data",
+    ])
+
+    # ── Tab 1: Bad actor list ──────────────────────────────────────────────────
+    with tab1:
+        _section("⚠️", "Bad Actor List",
+                 f"Min {consecutive_n} consecutive month(s)  ·  last flagged = Month {obs_month}")
+        if summary.empty:
+            st.success(f"No bad actors with last flagged month = Month {obs_month}.")
+        else:
+            st.markdown(
+                f'<p style="color:#e63946;font-weight:600;margin:4px 0 12px">'
+                f'{n_bad} equipment flagged</p>',
+                unsafe_allow_html=True,
+            )
+            st.dataframe(summary, use_container_width=True)
+
+            # Bad actor rate per section
+            _section("📈", "Bad Actor Rate by Section")
+            for sec, grp in summary.groupby("section"):
+                sec_total = filt.loc[filt["section"] == sec, "eqm_no"].nunique()
+                sec_rate  = len(grp) / sec_total if sec_total else 0
+                st.caption(f"{sec}  —  {len(grp)} / {sec_total} ({sec_rate*100:.1f}%)")
+                st.progress(sec_rate)
+
+    # ── Tab 2: Reference basis ─────────────────────────────────────────────────
+    with tab2:
+        norm_agg = (
+            filt.groupby(agg_key).agg(
+                MA_min=("MA", "min"), MA_max=("MA", "max"), MA_avg=("MA", "mean"),
+                MTBF_min=("MTBF", "min"), MTBF_max=("MTBF", "max"), MTBF_avg=("MTBF", "mean"),
+            )
+            .round(4)
+            .reset_index()
+        )
+        _section("📐", "Normalisation Reference",
+                 "min / max / avg of MA & MTBF used for normalization — aggregated across models")
+        st.dataframe(
+            norm_agg.style.format({
+                "MA_min": "{:.4f}", "MA_max": "{:.4f}", "MA_avg": "{:.4f}",
+                "MTBF_min": "{:.4f}", "MTBF_max": "{:.4f}", "MTBF_avg": "{:.4f}",
+            }),
+            use_container_width=True,
+        )
+        with st.expander("Detail per model"):
+            st.dataframe(
+                norm_stats.style.format({
+                    "MA_min": "{:.4f}", "MA_max": "{:.4f}", "MA_avg": "{:.4f}",
+                    "MTBF_min": "{:.4f}", "MTBF_max": "{:.4f}", "MTBF_avg": "{:.4f}",
+                }),
+                use_container_width=True,
+            )
+
+        pivot_idx = [k for k in basis_key if k not in ("model", "month")]
+        q1_pivot = q1_df.pivot_table(
+            index=pivot_idx, columns="month", values="q1_threshold", aggfunc="mean"
+        ).round(4)
+        q1_pivot.columns = [f"Month {int(c)}" for c in q1_pivot.columns]
+
+        _section("📉", "Q1 Threshold Table",
+                 "25th percentile of bad actor score — aggregated across models")
+        st.dataframe(q1_pivot, use_container_width=True)
+        with st.expander("Detail per model"):
+            q1_pivot_detail = q1_df.pivot_table(
+                index=[k for k in basis_key if k != "month"],
+                columns="month", values="q1_threshold"
+            ).round(4)
+            q1_pivot_detail.columns = [f"Month {int(c)}" for c in q1_pivot_detail.columns]
+            st.dataframe(q1_pivot_detail, use_container_width=True)
+
+    # ── Tab 3: Scored data ─────────────────────────────────────────────────────
+    with tab3:
+        _section("🔢", "Scored Data",
+                 "norm_MA × norm_MTBF = bad_actor_score  ·  rows in red = bad actor")
+        show_cols = [
+            "year", "month", "site_id", "section", "model", "eqm_no",
+            "MA", "MTBF", "norm_MA", "norm_MTBF",
+            "bad_actor_score", "q1_threshold", "below_q1", "is_bad_actor",
+        ]
+        scored = (
+            filt[show_cols]
+            .sort_values(["site_id", "section", "month", "eqm_no"])
+            .reset_index(drop=True)
+        )
+
+        def _highlight(row):
+            color = "background-color: #ffe0e0" if row["is_bad_actor"] else ""
+            return [color] * len(row)
+
+        st.dataframe(
+            scored.style
+            .format({
+                "norm_MA": "{:.4f}", "norm_MTBF": "{:.4f}",
+                "bad_actor_score": "{:.4f}", "q1_threshold": "{:.4f}",
+            })
+            .apply(_highlight, axis=1),
+            use_container_width=True,
+            height=420,
+        )
+
+
+# ── Sidebar controls ───────────────────────────────────────────────────────────
+with st.sidebar:
+    st.markdown(
+        '<p style="font-size:1.15rem;font-weight:800;color:#1d3557;'
+        'border-left:4px solid #e63946;padding-left:10px;margin-bottom:12px">'
+        'Simulation Controls</p>',
+        unsafe_allow_html=True,
+    )
+
+    # ── Dataset upload ─────────────────────────────────────────────────────────
+    df = None
+    data_source = "bad_actor_simulation.xlsx (default)"
+
+    with st.expander("Dataset", expanded=False):
+        uploaded = st.file_uploader(
+            "Upload CSV or XLSX  (leave empty to use default file)",
+            type=["csv", "xlsx"],
+        )
+
+        if uploaded is not None:
+            raw = uploaded.read()
+            ext = uploaded.name.rsplit(".", 1)[-1].lower()
+
+            if ext == "csv":
+                df = load_csv(raw)
+                data_source = uploaded.name
+                missing = REQUIRED_COLS - set(df.columns)
+                if missing:
+                    st.error(f"CSV missing columns: {', '.join(sorted(missing))}")
+                    df = None
+
+            elif ext == "xlsx":
+                sheets = get_xlsx_sheets(raw)
+                if len(sheets) == 1:
+                    sheet = sheets[0]
+                else:
+                    sheet = st.selectbox("Sheet name", sheets)
+                df = load_xlsx(raw, sheet)
+                data_source = f"{uploaded.name}  [sheet: {sheet}]"
+                missing = REQUIRED_COLS - set(df.columns)
+                if missing:
+                    st.error(f"XLSX missing columns: {', '.join(sorted(missing))}")
+                    df = None
+
+        if df is None:
+            df = load_default()
+
+        st.caption(f"Loaded: {data_source}  |  {len(df):,} rows")
+
+    st.divider()
+
+    # ── Filters ────────────────────────────────────────────────────────────────
+    with st.expander("🎛️  Filters", expanded=True):
+        all_years    = sorted(df["year"].dropna().unique().tolist())
+        all_sites    = sorted(df["site_id"].dropna().unique().tolist())
+        all_sections = sorted(df["section"].dropna().unique().tolist())
+        all_models   = ["ALL"] + sorted(df["model"].dropna().astype(str).unique().tolist())
+
+        def _default(lst, vals):
+            r = [v for v in vals if v in lst]
+            return r if r else lst[:1]
+
+        sel_years = st.multiselect(
+            "🗓️  Year(s)", all_years, default=_default(all_years, DEFAULT_YEARS))
+
+        obs_month = st.slider("📅  Observation Month (cutoff)", 1, 12, DEFAULT_OBS_MONTH,
+                              help="Only data up to this month is included in the evaluation.")
+
+        sel_sites = st.multiselect(
+            "🏭  Site(s)", all_sites, default=_default(all_sites, DEFAULT_SITES))
+
+        sel_sections = st.multiselect(
+            "🔧  Section(s)", all_sections,
+            default=_default(all_sections, DEFAULT_SECTIONS))
+
+        sel_models = st.multiselect(
+            "🚜  Model(s)  (ALL = no model filter)",
+            all_models, default=_default(all_models, DEFAULT_MODELS))
+
+        consecutive_n = st.slider("🔁  Min Consecutive Months", 1, 3, DEFAULT_CONSECUTIVE_N)
+
+    st.markdown("<br>", unsafe_allow_html=True)
+    run = st.button("▶  Run Simulation", type="primary", use_container_width=True)
+    st.caption("Adjust filters above, then click Run.")
+
+# ── Main area ──────────────────────────────────────────────────────────────────
+hcol1, hcol2 = st.columns([3, 1])
+with hcol1:
+    st.markdown(
+        '<div class="app-header">'
+        '<h1>⚠️ Bad Actor Simulation</h1>'
+        '<div class="tagline">Equipment reliability scoring based on normalised MA × MTBF</div>'
+        '</div>',
+        unsafe_allow_html=True,
+    )
+with hcol2:
+    st.markdown(
+        f'<div style="text-align:right;padding-top:8px">'
+        f'<span class="dataset-badge">📁 {data_source}</span><br>'
+        f'<span class="dataset-badge" style="margin-top:4px;display:inline-block">'
+        f'📅 Month 1 – {obs_month}</span>'
+        f'</div>',
+        unsafe_allow_html=True,
+    )
+
+st.divider()
+
+if run:
+    if not sel_years or not sel_sites or not sel_sections or not sel_models:
+        st.warning("Please select at least one value for each filter.")
+    else:
+        run_simulation(sel_years, sel_sites, sel_sections, sel_models,
+                       consecutive_n, obs_month)
+else:
+    st.markdown("""
+    <div class="welcome-card">
+      <h2>Welcome to the Simulation Console</h2>
+      <p>Identify equipment that consistently underperforms relative to its peers.</p>
+      <ul class="step-list">
+        <li><span class="step-num">1</span> Open <strong>Dataset</strong> in the sidebar to upload a CSV or XLSX file, or use the default.</li>
+        <li><span class="step-num">2</span> Expand <strong>Filters</strong> to set year, site, section, model, and observation month.</li>
+        <li><span class="step-num">3</span> Click <strong>Run Simulation</strong> to compute scores and flag bad actors.</li>
+      </ul>
+    </div>
+    """, unsafe_allow_html=True)