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# %% [markdown]
# # The conjugate VAR: fast Bayesian estimation
# ## When to reach for `ConjugateVAR` instead of the NUTS VAR
#
# Impulso estimates a reduced-form VAR two ways. Both return the *same* `FittedVAR`, so
# identification, impulse responses, FEVDs, and forecasts are byte-for-byte identical
# downstream. What differs is the **mode of inference**:
#
# - **The NUTS VAR** (`VAR`) places *independent-Normal* priors on the coefficients and
#   samples the full posterior with Hamiltonian Monte Carlo. Maximally flexible — it admits
#   per-equation shrinkage, stochastic volatility, sign restrictions, and external
#   instruments — but every coefficient is a sampled latent, so large systems are slow.
# - **The conjugate VAR** (`ConjugateVAR`) places a *Normal-Inverse-Wishart* prior, which is
#   conjugate to the VAR likelihood. The coefficient/covariance posterior is then available
#   in **closed form**: we draw $(\beta, \Sigma)$ analytically and reserve Monte Carlo for a
#   single low-dimensional hyperparameter — the Minnesota tightness $\lambda$ — which the
#   data *selects* by marginal likelihood ({cite:t}`giannoneLenzaPrimiceri2015`).
#
# This notebook fits both on the same series, shows they reach the same structural
# conclusions, times them, and ends with a rule for choosing between them. We use an
# **environmental** system — a German climate–energy VAR — rather than the usual
# macro data, to show the machinery is domain-agnostic.
#
# :::{admonition} Scope
# :class: note
# This is the estimator-first tour. For the conjugate VAR wearing a *deterministic
# volatility break* — the COVID application it was built for — see
# [Estimating a VAR after March 2020](post-march-2020.py). Why a conjugate estimator is a
# *sibling* of `VAR` rather than a mode of it is recorded in ADR 0004.
# :::

# %% tags=["remove-cell"]
import logging
import warnings

warnings.filterwarnings("ignore")
logging.getLogger("pytensor").setLevel(logging.ERROR)
logging.getLogger("matplotlib.font_manager").setLevel(logging.ERROR)

# %% tags=["remove-cell"]
import os

# Smoke-render flag: IMPULSO_DOCS_CI=1 shrinks MCMC for fast CI builds.
ci = os.environ.get("IMPULSO_DOCS_CI") == "1"

# %%
import time

import arviz as az
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from qc_core import plotting

from impulso import Cholesky, ConjugateVAR, MinnesotaPrior, NIWPrior, VAR, VARData, compare_evidence
from impulso.samplers import NUTSSampler

plotting.use_ledger_style()

# %% [markdown]
# ## Data: a German climate–energy system
#
# Weather is where the environment meets the economy: temperature drives heating and
# cooling demand, sunshine and wind set renewable supply, and rainfall feeds hydro and
# agriculture. We assemble a four-variable monthly system for Berlin (1980–2024) from the
# **ERA5 reanalysis** ({cite:t}`hersbach2020`), served by the
# [Open-Meteo](https://open-meteo.com/) historical archive.
#
# | Variable | ERA5 series (monthly mean of daily) | Unit | Economic reading |
# |----------|-------------------------------------|------|------------------|
# | `temperature` | `temperature_2m_mean` | °C | heating / cooling demand |
# | `radiation` | `shortwave_radiation_sum` | MJ/m² | solar-PV potential |
# | `wind` | `wind_speed_10m_mean` | km/h | wind-power potential |
# | `precipitation` | `precipitation_sum` | mm/day | hydro / runoff |
#
# The committed CSV is produced once by `scripts/fetch_berlin_climate.py` and read offline
# here — no network call at render time. That script targets Open-Meteo's free archive
# endpoint, so anyone can reproduce the file without credentials.

# %% mystnb={"figure": {"caption": "Raw monthly ERA5 series for Berlin, 1980–2024. Temperature and radiation are dominated by the seasonal cycle.", "name": "climate-raw"}} tags=["remove-input"]
raw = pd.read_csv("data/berlin_climate.csv", index_col="date", parse_dates=True)

fig, axes = plt.subplots(4, 1, figsize=(9, 6), sharex=True)
units = {"temperature": "°C", "radiation": "MJ/m²", "wind": "km/h", "precipitation": "mm/day"}
for ax, col in zip(axes, raw.columns, strict=True):
    ax.plot(raw.index, raw[col], linewidth=0.7, color=plotting.COLORS.body)
    ax.set_ylabel(f"{col}\n({units[col]})", fontsize=8)
_ = plotting.serif_title("Berlin climate — raw monthly means (1980–2024)", axes[0])

# %% [markdown]
# The raw series are overwhelmingly *seasonal* — a VAR fit on them would spend its
# coefficients re-learning the calendar. We model **anomalies** instead: each observation
# minus its month-of-year climatological mean, standardised to unit variance. The result is
# stationary, comparable across variables, and lets impulse responses read in standard
# deviations.

# %%
climatology = raw.groupby(raw.index.month).transform("mean")
anomalies = raw - climatology
anomalies = (anomalies - anomalies.mean()) / anomalies.std()
anomalies.describe().round(2)

# %% mystnb={"figure": {"caption": "Standardised monthly anomalies — the seasonal cycle removed. This is what the VAR sees.", "name": "climate-anomalies"}} tags=["remove-input"]
fig, axes = plt.subplots(4, 1, figsize=(9, 6), sharex=True)
for ax, col in zip(axes, anomalies.columns, strict=True):
    ax.plot(anomalies.index, anomalies[col], linewidth=0.6, color=plotting.COLORS.oxblood)
    ax.axhline(0, color=plotting.COLORS.hairline, linewidth=0.8)
    ax.set_ylabel(col, fontsize=8)
_ = plotting.serif_title("Berlin climate — standardised anomalies", axes[0])

# %% [markdown]
# ## Fitting the conjugate VAR
#
# We use twelve lags — enough to capture up to a year of dynamic feedback in monthly data —
# giving $4 \times 12 = 48$ coefficients per equation. The prior is the conjugate Minnesota
# prior `NIWPrior`; `select=True` asks the estimator to choose the overall tightness
# $\lambda$ by maximising the marginal likelihood and then sample its posterior, rather than
# fixing it by hand ({cite:t}`giannoneLenzaPrimiceri2015`; the Minnesota shrinkage idea goes
# back to {cite:t}`doan1984` and {cite:t}`litterman1986`).

# %%
LAGS = 12
data = VARData.from_df(anomalies, endog=list(anomalies.columns))

conjugate_prior = NIWPrior(select=True, decay=2.0, cross_shrinkage=1.0)
start = time.perf_counter()
fitted_conjugate = ConjugateVAR(lags=LAGS, prior=conjugate_prior, draws=2000, tune=1000, seed=0).fit(data)
conjugate_seconds = time.perf_counter() - start

lambda_hat = float(fitted_conjugate.idata.posterior["lambda_"].median())
print(f"data-selected Minnesota tightness  lambda = {lambda_hat:.3f}")
print(f"conjugate fit wall-clock                  = {conjugate_seconds:.2f} s")

# %% [markdown]
# The estimator reports a posterior for $\lambda$ (not a fixed value): the data speak to how
# much shrinkage the system needs. Everything downstream — coefficients, covariance, the base
# Cholesky factor — was drawn in closed form conditional on those hyperparameter draws.
#
# ## The same model by NUTS
#
# To make this a clean *inference-mode* comparison, we fit the NUTS VAR at the **same**
# tightness the conjugate estimator just selected (`MinnesotaPrior(tightness=lambda_hat)`).
# Now the only differences are the prior family (independent-Normal vs conjugate NIW) and
# the sampler — not the amount of shrinkage.

# %%
if ci:
    sampler = NUTSSampler(
        draws=50,
        tune=500,
        chains=1,
        cores=1,
        target_accept=0.9,
        random_seed=0,
        nuts_sampler_kwargs={"low_rank_modified_mass_matrix": True},
    )
else:
    sampler = NUTSSampler(draws=1000, tune=1500, chains=2, cores=1, random_seed=0)

start = time.perf_counter()
fitted_nuts = VAR(lags=LAGS, prior=MinnesotaPrior(tightness=lambda_hat)).fit(data, sampler=sampler)
nuts_seconds = time.perf_counter() - start
print(f"NUTS fit wall-clock = {nuts_seconds:.2f} s")
az.summary(fitted_nuts.idata, var_names=["intercept"], kind="diagnostics")

# %% [markdown]
# ### Speed
#
# Both estimators fit the identical 4-variable, 12-lag system. The conjugate path spends
# Monte Carlo only on a single scalar; NUTS explores a ~200-dimensional coefficient
# posterior. At full render the gap is an order of magnitude or more.

# %%
if ci:
    print("CI smoke mode: NUTS is shrunk to a few draws — these timings are NOT representative.")
else:
    print(
        f"conjugate: {conjugate_seconds:.2f} s | "
        f"NUTS: {nuts_seconds:.2f} s | "
        f"speed-up: {nuts_seconds / conjugate_seconds:.0f}x"
    )

# %% [markdown]
# ## Downstream parity: identical structural machinery
#
# Because both estimators return a `FittedVAR`, identification is the same call on each. We
# apply a Cholesky scheme with the ordering `radiation → temperature → wind → precipitation`
# (solar forcing is the most exogenous; rainfall the most responsive). The ordering encodes
# real assumptions — see [Monetary Policy Analysis](monetary-policy.py) for how much it can
# matter — but here we hold it fixed and vary only the estimator.

# %%
ordering = ["radiation", "temperature", "wind", "precipitation"]
irf_conjugate = fitted_conjugate.set_identification_strategy(Cholesky(ordering=ordering)).impulse_response(horizon=24)
irf_nuts = fitted_nuts.set_identification_strategy(Cholesky(ordering=ordering)).impulse_response(horizon=24)

# %% mystnb={"figure": {"caption": "Conjugate-VAR impulse responses (Cholesky). Column shock → row response, over 24 months.", "name": "irf-conjugate"}} tags=["remove-input"]
fig = irf_conjugate.plot()
_ = fig.suptitle(
    "Conjugate VAR — impulse responses",
    y=1.02,
    fontfamily=plotting.SERIF_STACK,
    fontweight=600,
)

# %% [markdown]
# Now overlay the two estimators on the same axes. If the conjugate VAR is a legitimate
# estimator and not a shortcut, its responses should track the NUTS responses in shape and
# sign, with band widths of the same order.

# %%
def irf_band(irf_result, shock, response, prob=0.9):
    """Return (horizons, median, hdi_low, hdi_high) for one shock→response pair."""
    draws = irf_result.idata.posterior_predictive["irf"].sel(shock=shock, response=response)
    median = draws.median(dim=("chain", "draw")).values
    hdi = az.hdi(draws, hdi_prob=prob)["irf"]
    return np.arange(median.shape[0]), median, hdi.sel(hdi="lower").values, hdi.sel(hdi="higher").values


# %% mystnb={"figure": {"caption": "Conjugate vs NUTS impulse responses at the same tightness. Medians (lines) and 90% bands (shaded).", "name": "irf-overlay"}} tags=["remove-input"]
pairs = [("radiation", "temperature"), ("temperature", "wind")]
fig, axes = plt.subplots(1, 2, figsize=(10, 4))
for ax, (shock, response) in zip(axes, pairs, strict=True):
    for result, color, label in [(irf_conjugate, "C0", "conjugate"), (irf_nuts, "C1", "NUTS")]:
        horizons, median, low, high = irf_band(result, shock, response)
        ax.plot(horizons, median, color=color, label=label)
        ax.fill_between(horizons, low, high, color=color, alpha=0.2)
    ax.axhline(0, color=plotting.COLORS.hairline, linewidth=0.8)
    plotting.serif_title(f"{shock} shock → {response}", ax)
    ax.set_xlabel("months")
    ax.legend()

# %% [markdown]
# The two estimators tell the same structural story: a positive radiation (sunshine) shock
# warms temperature; a warmth shock is followed by calmer winds. The medians track closely
# and the bands overlap. They are *not* identical — the conjugate NIW prior imposes a
# symmetric Kronecker structure across equations while the NUTS prior is independent-Normal —
# and that is exactly the point: the inference mode is a modelling choice, not a source of
# contradiction.

# %%
if not ci:
    correlations = [
        np.corrcoef(irf_band(irf_conjugate, shock, response)[1], irf_band(irf_nuts, shock, response)[1])[0, 1]
        for shock in ordering
        for response in ordering
    ]
    print(f"median-IRF shape correlation across all 16 shock/response pairs: {np.nanmean(correlations):.2f}")

# %% [markdown]
# ## Which lag order does the data prefer?
#
# The closed form gives us more than speed. Every conjugate fit reports its **marginal
# likelihood** — the density of the observed data under the model, with the coefficients and
# covariance integrated out — on `fitted.evidence`. Ratios of those numbers are Bayes
# factors, so the twelve-lag choice we made by convention can be put to the data instead.
#
# One alignment matters. A VAR($p$) conditions on its first $p$ rows and models the rest, so
# a VAR(1) and a VAR(12) on the same DataFrame are densities over *different* observations
# and their ratio means nothing. We therefore feed each candidate a series pre-trimmed to
# the longest lag order, `anomalies.iloc[LAGS - p:]`, so all three model exactly the same
# response window and differ only in how far back they look. `compare_evidence` refuses the
# comparison — loudly — if that alignment is missing.

# %%
comparison_draws = 50 if ci else 250
candidates = {}
for p in (1, 6, LAGS):
    aligned = VARData.from_df(anomalies.iloc[LAGS - p :], endog=list(anomalies.columns))
    candidates[f"p{p}"] = ConjugateVAR(
        lags=p,
        prior=NIWPrior(select=True, decay=2.0, cross_shrinkage=1.0),
        draws=comparison_draws,
        tune=comparison_draws,
        seed=0,
    ).fit(aligned)

# %%
evidence = compare_evidence(**candidates)
print(f"preferred lag order: {evidence.best}")
evidence.to_dataframe().round(3)

# %% [markdown]
# The `log_bayes_factor` column reads against the first model passed (`p1` here); the log10
# column is the unit Kass and Raftery tabulate, and `posterior_probability` converts the
# evidences to model weights under a flat prior over the three candidates. On these
# de-seasonalised anomalies the short model wins by tens of log points: once the calendar is
# removed, a month of Berlin weather carries little information about the next year of it,
# and the extra lags buy less than they cost. We keep twelve lags for the rest of the
# notebook so the estimator comparison stays on the system introduced above — but this is
# the number to quote when someone asks why.
#
# Two caveats travel with these values. Each is conditional on the presample the shared
# window leaves in front of it, and each is evaluated at the $\lambda$ its own fit selected,
# which makes the ratio an *empirical-Bayes* Bayes factor rather than a fully marginal one.
# Both are stated on `ModelEvidence`.
#
# ## When to reach for which
#
# Both estimators share the entire post-fitting pipeline — identification, IRFs, FEVDs,
# forecasts — so the choice is purely about the estimation path.
#
# | Reach for the **conjugate VAR** when… | Reach for the **NUTS VAR** when… |
# |----------------------------------------|-----------------------------------|
# | speed matters — hyperparameter selection, model comparison, or many refits | you need per-equation or asymmetric cross-variable shrinkage |
# | you want the tightness $\lambda$ chosen by the data (hierarchical) | you need stochastic volatility, sign restrictions, or external instruments |
# | the conjugate NIW (symmetric, Kronecker) prior suits the problem | you need arbitrary or non-conjugate priors |
# | the system is large and full MCMC over every coefficient is costly | you want full HMC convergence diagnostics on all coefficients |
#
# The conjugate VAR trades flexibility for closed-form speed and a data-driven prior. When
# your problem fits inside that trade — as macro and climate systems with symmetric Minnesota
# shrinkage usually do — it is the sharper tool. When you need volatility that moves or priors
# that bend per equation, the NUTS VAR is there, and everything you build on top is the same.
#
# <section class="consulting-cta">
#     <p>We currently have some <strong>availability for consulting</strong> on how Bayesian modelling, vector autoregressions, and impulso can be integrated into your team's macroeconomic, financial, and environmental forecasting work. If this sounds relevant, <a href="https://calendly.com/hello-1761-izqw/15-minute-meeting-clone-1">book an introductory call</a>. These calls are for consulting inquiries only. For technical usage questions and free community support, please use GitHub Discussions and the documentation.</p>
# </section>
#
# ## References
#
# The works cited above are collected on the [project bibliography](../references.md) page.