Audit des mots-clés ESG et comparaison des résultats du classificateur avec RAG

# 06_esg_rag_vs_finetune.py


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# %% [markdown]
# # ESG Screening and RAG: Implemented Classifier vs Interface Contract
#
# **Docker image**: `ml4t-gpu`
#
# **Book Reference**: Chapter 22, Section 22.8 (Applications and Strategic Choices)
#
# Two ways to do ESG analysis, and only one of them is implemented here.
#
# 1. **Keyword screening plus a pretrained sentiment model.** Built and run:
#    headlines are selected by an ESG keyword list, labelled E, S or G by a
#    second keyword list, and scored by FinBERT. It produces a row per headline
#    that a factor model can consume.
# 2. **A RAG assistant.** Not built here. What is recorded instead is the
#    contract such an assistant would have to satisfy - narrative answers,
#    cited spans, abstention - so that the comparison stays a comparison of
#    output types rather than an invented benchmark.
#
# The screening path turns out to demonstrate its own principal limitation
# without being asked to. Section 2 measures it: over the Bloomberg archive,
# the keyword screen that selects "ESG-relevant" headlines selects almost
# nothing but environmental ones, and two properties of the screen itself
# account for a good deal of that - its selection terms and the order its
# categoriser tests them in.
#
# **Learning objectives**
#
# After working through this notebook you will be able to:
#
# - Say what a fixed taxonomy costs, from a measurement rather than an
#   assertion.
# - Measure what a keyword screen actually selected, and name the properties of
#   the screen that account for the composition it returned.
# - Separate a model's inference cost from the one-off cost of loading it.
# - State the evidence contract a RAG producer has to meet before its output
#   can be compared with a classifier's.
#
# **Prerequisites**: the Bloomberg financial news archive
# (`data/alternative/news/bloomberg/`). No RAG answers are generated here.

# %% [markdown]
# ## 1. Setup

# %%
"""ESG Analysis: RAG vs Fine-Tuning - Comparing classification and retrieval approaches."""

import hashlib
import json
import time

import plotly.graph_objects as go

# Core imports
import polars as pl
import torch
from plotly.subplots import make_subplots
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
from transformers.utils import logging as transformers_logging

# ML4T configuration
from data import load_bloomberg_news
from utils.reproducibility import set_global_seeds
from utils.style import COLORS, show_plotly_with_alt

transformers_logging.set_verbosity_error()

# %% tags=["parameters"]
MAX_HEADLINES = 0  # total headlines to classify; 0 means 3 * PER_CATEGORY
MAX_QUESTIONS = 0
PER_CATEGORY = 7  # per-category default when MAX_HEADLINES is 0
SEED = 42
REQUIRE_GPU = True
FINBERT_MODEL = "ProsusAI/finbert"
FINBERT_REVISION = "4556d13015211d73dccd3fdd39d39232506f3e43"

# %%
set_global_seeds(SEED)
if REQUIRE_GPU and not torch.cuda.is_available():
    raise RuntimeError("FinBERT inference requires the ml4t-gpu service with CUDA.")
INFERENCE_DEVICE = 0 if torch.cuda.is_available() else -1
print(f"FinBERT device: {'cuda:0' if INFERENCE_DEVICE == 0 else 'cpu'}")

# %% [markdown]
# ## 2. What the keyword screen actually selects
#
# The screen is two keyword lists. The first decides which headlines are
# ESG-relevant at all; the second sorts those into Environmental, Social and
# Governance. Both are below, and the second is applied to the whole selected
# pool before any sampling, because what the pool contains decides what a
# sample can show.

# %%
news_df = load_bloomberg_news()
required_columns = {"timestamp", "headline"}
missing_columns = required_columns - set(news_df.columns)
if missing_columns:
    raise ValueError(f"Bloomberg news loader violates canonical schema: missing {missing_columns}.")

ESG_SELECTION_PATTERN = (
    r"(?i)(climate|carbon|emission|sustain|ESG|renewable|diversity|governance|environmental"
    r"|green.bond|pollution|social.responsibility|net.zero|solar|wind.energy|deforestation"
    r"|water.scarcity|labor.rights|board.independence|executive.compensation)"
)
esg_news = (
    news_df.filter(pl.col("headline").str.contains(ESG_SELECTION_PATTERN))
    .filter(pl.col("headline").str.len_chars() > 30)
    .sort(["timestamp", "headline"], descending=[True, False])
)
print(
    f"Archive spans {news_df['timestamp'].min():%Y-%m-%d} to {news_df['timestamp'].max():%Y-%m-%d}"
)
print(f"Headlines in archive: {news_df.height:,}")
print(f"Headlines the ESG screen selects: {esg_news.height:,}")


# %% [markdown]
# ### The E, S and G taxonomy
#
# A second keyword list sorts a selected headline into one category. The terms
# are the ones an analyst would reach for first, and the order matters:
# environmental is tested before social and social before governance, so a
# headline about a board's climate policy counts as environmental.


# %%
ESG_CATEGORY_TERMS = {
    "Environmental": [
        "carbon",
        "emission",
        "environmental",
        "environment",
        "solar",
        "sustainab",
        "net-zero",
        "net zero",
        "climate",
        "renewable",
        "wind",
        "pollution",
        "deforestation",
        "water scarcity",
        "green bond",
        "clean energy",
    ],
    "Social": [
        "worker",
        "safety",
        "labor",
        "labour",
        "diversity",
        "data breach",
        "customer",
        "human rights",
        "community",
        "social responsibility",
    ],
    "Governance": [
        "board",
        "ceo",
        "chief executive",
        "compensation",
        "shareholder",
        "governance",
        "audit",
        "disclosure",
    ],
}


def categorize_esg(text: str) -> str:
    """Assign one ESG category by keyword, or Other when no term matches."""
    lowered = text.lower()
    for category, terms in ESG_CATEGORY_TERMS.items():
        if any(term in lowered for term in terms):
            return category
    return "Other"


# %% [markdown]
# ### What the pool contains
#
# Apply the taxonomy to every selected headline before sampling any of them.


# %%
esg_news = esg_news.with_columns(
    pl.col("headline").map_elements(categorize_esg, return_dtype=pl.String).alias("category")
)
pool_counts = esg_news.group_by("category").len().sort("len", descending=True)
pool_counts

# %% [markdown]
# This screen is called an ESG screen and what it returns is environmental
# news. The counts are not close: social and governance headlines together are
# a rounding error against the environmental ones.
#
# Two properties of the screen itself account for a good deal of that, and both
# are visible in the code above rather than inferred.
#
# **The selection list is lopsided.** Its environmental terms are common single
# words - *climate*, *carbon*, *solar*, *renewable*, *emission* - and a
# headline needs only one of them. Its social and governance terms are mostly
# compound phrases that a headline has to contain intact: *social
# responsibility*, *labor rights*, *board independence*, *executive
# compensation*. The ordinary words those topics are actually written in -
# *board*, *pay*, *workers*, *safety* - appear in the categoriser's lists and
# not in the selection pattern, so a headline about a workforce dispute is
# never selected to be categorised in the first place.
#
# **The categoriser resolves ties towards environmental.** It tests
# environmental terms first, so a headline about a board's climate policy is
# environmental and not governance.
#
# What that does *not* establish is that keyword screening is intrinsically
# environmental. Deciding that would need alternative selection lists and a set
# of headlines labelled by someone other than this notebook, and neither is
# here. What it does establish is the thing worth carrying: a keyword screen's
# output composition is a property of the list, the imbalance can be large
# enough to make the label wrong, and it costs one group-by to check.
#
# `MAX_HEADLINES` is a budget for the whole sample, split evenly across the
# three categories, so a cap set for a fast test caps what a fast test runs.
#
# It also decides how to sample. On these proportions a uniform draw of twenty
# would be overwhelmingly environmental and would more likely than not contain
# no social or governance headline at all. The sample below is stratified
# instead, taking the same number from each category so all three are present -
# which is itself the admission that the screen could not supply them.

# %%
esg_categories = ("Environmental", "Social", "Governance")
if MAX_HEADLINES > 0:
    # Quotient and remainder, so a cap below the category count yields that many
    # headlines rather than one per category.
    quota, extra = divmod(MAX_HEADLINES, len(esg_categories))
    quotas = [quota + (1 if index < extra else 0) for index in range(len(esg_categories))]
else:
    quotas = [PER_CATEGORY] * len(esg_categories)

selected_headlines = pl.concat(
    [
        group.sample(min(quota, group.height), seed=SEED)
        for category, quota in zip(esg_categories, quotas, strict=True)
        if quota and (group := esg_news.filter(pl.col("category") == category)).height
    ]
).sort(["timestamp", "headline"], descending=[True, False])
ESG_HEADLINES = selected_headlines["headline"].to_list()
selection_sha256 = hashlib.sha256(
    "\n".join(
        f"{row['timestamp'].isoformat()}|{row['headline']}"
        for row in selected_headlines.iter_rows(named=True)
    ).encode()
).hexdigest()
print(f"Stratified sample: {len(ESG_HEADLINES)} headlines")
print(dict(selected_headlines.group_by("category").len().sort("category").iter_rows()))
print(f"Selected-row SHA-256: {selection_sha256}")


# %% [markdown]
# ### Sentiment-based headline classification
#
# Uses FinBERT as a sentiment proxy. In production, a dedicated ESG
# classifier (e.g., FinBERT-ESG) would replace this with a fine-grained
# taxonomy output.


# %% [markdown]
# ### Load the sentiment classifier
#
# Production requires the pinned FinBERT snapshot. A missing dependency fails
# closed instead of silently changing the model behind the reported results.
#


# %%
def load_finbert_pipeline():
    """Return the pinned FinBERT sentiment pipeline."""
    tokenizer = AutoTokenizer.from_pretrained(
        FINBERT_MODEL,
        revision=FINBERT_REVISION,
        local_files_only=True,
    )
    model = AutoModelForSequenceClassification.from_pretrained(
        FINBERT_MODEL,
        revision=FINBERT_REVISION,
        local_files_only=True,
    )
    classifier = pipeline(
        "sentiment-analysis",
        model=model,
        tokenizer=tokenizer,
        device=INFERENCE_DEVICE,
    )
    return classifier, FINBERT_MODEL


# %% [markdown]
# ### Classify the sample
#
# Apply FinBERT, attach the E/S/G label, and time the load separately from the
# inference. A throughput figure that includes the one-off cost of loading the
# weights is a statement about the batch size it happened to be measured at,
# not about the model - at twenty headlines the load dominates, and a reader
# sizing a nightly job off it would be out by an order of magnitude.


# %%
def classify_esg_headlines(headlines: list) -> pl.DataFrame:
    """Classify headlines with FinBERT, timing the load and the inference apart."""
    load_start = time.time()
    classifier, model_used = load_finbert_pipeline()
    load_seconds = time.time() - load_start

    parameter_device = next(classifier.model.parameters()).device
    if REQUIRE_GPU and parameter_device.type != "cuda":
        raise RuntimeError(f"FinBERT parameters are on {parameter_device}, not CUDA.")
    print(f"Sentiment model in use: {model_used} on {parameter_device}")

    inference_start = time.time()
    results = []
    for headline in headlines:
        result = classifier(headline[:512])[0]  # truncate to the model's maximum
        results.append(
            {
                "headline": headline,
                "model": model_used,
                "category": categorize_esg(headline),
                "sentiment": result["label"],
                "confidence": result["score"],
            }
        )
    inference_seconds = time.time() - inference_start

    per_headline_ms = 1_000 * inference_seconds / max(len(headlines), 1)
    print(f"Model load:  {load_seconds:.2f}s, paid once per process")
    print(f"Inference:   {inference_seconds:.2f}s for {len(headlines)} headlines")
    print(
        f"             {per_headline_ms:.1f} ms each, {len(headlines) / inference_seconds:.0f} per second"
    )
    print(
        f"Load is {load_seconds / (load_seconds + inference_seconds):.0%} of the wall clock at "
        f"this batch size, and a smaller share of it at every larger one."
    )

    return pl.DataFrame(results).with_columns(
        pl.lit(per_headline_ms).alias("inference_ms_per_headline"),
        pl.lit(load_seconds).alias("model_load_seconds"),
    )


# %% [markdown]
# One row per headline, with a category, a label and a confidence. That shape
# is what a factor pipeline can consume, and it is the structural advantage
# classification holds over a narrative answer however good the narrative is.
# %%
# Run classification
print("=== Approach A: Pretrained FinBERT Inference ===\n")

classification_results = classify_esg_headlines(ESG_HEADLINES)
print("\nClassification Results:")
classification_results

# %% [markdown]
# The `confidence` column is FinBERT's softmax over three sentiment classes. It
# is not a probability that the label is right, and it says nothing at all
# about the ESG category beside it, which came from a keyword match with no
# uncertainty attached.

# %% [markdown]
# ## Approach B: RAG Interface Contract
#
# A RAG implementation must answer open-ended questions with retrieved evidence,
# citations, and explicit abstention. This notebook records that contract without
# inventing answers, citations, confidence, or latency.

# %%
# Sample ESG questions for RAG
ESG_QUESTIONS = [
    "Summarize the company's strategy for reducing Scope 2 emissions and list any stated targets.",
    "What key performance indicators does the company use to track progress on sustainability?",
    "Identify governance concerns related to executive compensation or board independence.",
]

if MAX_QUESTIONS > 0:
    ESG_QUESTIONS = ESG_QUESTIONS[:MAX_QUESTIONS]


# %% [markdown]
# ### Declare the evidence contract
#
# Each question defines the evidence a live assistant must return before its
# output can enter a comparison.


# %%
def build_rag_contract(questions: list[str]) -> pl.DataFrame:
    """Return validation fields required from a future live RAG run."""
    return pl.DataFrame(
        {
            "question": questions,
            "required_output": ["grounded narrative"] * len(questions),
            "required_evidence": ["source id + quoted span"] * len(questions),
            "required_checks": ["citation support + abstention"] * len(questions),
            "measured_here": [False] * len(questions),
        }
    )


# %% [markdown]
# This is a specification and not a result. `05_10k_rag_assistant` implements
# the retrieval half; a live generation run would still have to be scored for
# citation support and abstention before either could be set against the
# classifier above.
# %%
# Run RAG analysis
print("\n=== Approach B: RAG Interface Contract ===\n")

rag_contract = build_rag_contract(ESG_QUESTIONS)
print("\nRequired RAG evidence:")
rag_contract

# %% [markdown]
# `measured_here` is false in every row, and that column exists so the table
# cannot be read as a result.

# %% [markdown]
# ## Comparison: Classification vs RAG
#
# This table separates the implemented screen from requirements that a future
# RAG producer must satisfy.

# %%
# Build comparison table
comparison = pl.DataFrame(
    {
        "Dimension": [
            "Primary Output",
            "Scalability",
            "Flexibility",
            "Verifiability",
            "Knowledge Updates",
            "Latency",
            "Best Use Case",
        ],
        "Implemented Screening": [
            "Keyword category + sentiment label",
            "High (batch processing)",
            "Low (fixed taxonomy)",
            "Indirect (confidence)",
            "Update rules or replace model",
            "Measured here, load and inference apart",
            "Systematic factor construction",
        ],
        "RAG Contract": [
            "Requires narrative + citations",
            "Not measured here",
            "Open-ended by design",
            "Requires cited source spans",
            "Requires corpus provenance",
            "Not measured here",
            "Due-diligence interface specification",
        ],
    }
)

print("\n=== Approach Comparison ===\n")
comparison

# %% [markdown]
# The Flexibility row is the one this run puts a number behind. A fixed
# taxonomy is low-flexibility in the sense measured in section 2: the screen
# could not supply social or governance headlines in proportion, and no
# reordering of the keyword list would have made it.

# %% [markdown]
# ## Decision Framework
#
# Use this framework to select the appropriate approach:
#
# | Question | Choose Classification | Choose RAG |
# |----------|----------------------|------------|
# | Need to process 1000s of documents? | **Yes** | No |
# | Need numeric time series? | **Yes** | No |
# | Need to explain the reasoning? | No | **Yes** |
# | Need to ask follow-up questions? | No | **Yes** |
# | Need real-time processing? | **Yes** | Maybe |
# | Knowledge changes frequently? | No | **Yes** |

# %%
# Performance comparison
print("\n=== Performance Statistics ===\n")

# Classification stats
if classification_results.height > 0:
    print("Classification Approach:")
    print(f"  Headlines processed: {classification_results.height}")
    print(f"  Unique categories: {classification_results['category'].n_unique()}")
    avg_confidence = classification_results["confidence"].mean()
    if avg_confidence is not None:
        print(f"  Average confidence: {avg_confidence:.2%}")

# RAG contract status
print("\nRAG Contract:")
print(f"  Questions specified: {rag_contract.height}")
print("  Answers generated: 0")
print("  Latency measured: No")

# %% [markdown]
# Two of the three ESG categories are present in the sample only because the
# sample was stratified to include them.

# %% [markdown]
# ## 4. The screen, in two pictures
#
# The sample's own category counts are equal by construction, so charting them
# would draw the stratification rather than anything about the data. The left
# panel shows the pool those categories were drawn from, on a log axis because
# the counts span three orders of magnitude. The right panel is FinBERT's
# sentiment over the stratified sample.

# %%
category_counts = classification_results.group_by("category").len().sort("len", descending=True)
sentiment_counts = classification_results.group_by("sentiment").len().sort("len", descending=True)

fig = make_subplots(
    rows=1,
    cols=2,
    subplot_titles=("Selected pool by category", "Sentiment in the stratified sample"),
    horizontal_spacing=0.16,
)
fig.add_trace(
    go.Bar(
        x=pool_counts["category"],
        y=pool_counts["len"],
        marker_color=COLORS["blue"],
        text=pool_counts["len"],
        textposition="outside",
    ),
    row=1,
    col=1,
)
fig.add_trace(
    go.Bar(
        x=sentiment_counts["sentiment"],
        y=sentiment_counts["len"],
        marker_color=COLORS["amber"],
        text=sentiment_counts["len"],
        textposition="outside",
    ),
    row=1,
    col=2,
)
fig.update_layout(
    title="What the ESG keyword screen selects, and how FinBERT scores a sample",
    height=430,
    showlegend=False,
    margin=dict(t=90),
)
fig.update_yaxes(title_text="Headlines (count, log scale)", type="log", row=1, col=1)
fig.update_yaxes(title_text="Headlines (count)", row=1, col=2)
show_plotly_with_alt(
    fig,
    "Two bar panels. Left, the selected pool by ESG category on a logarithmic count axis: "
    "the environmental bar runs off the top of the others by more than an order of "
    "magnitude, with uncategorised, governance and social following far below it in that "
    "order. Right, sentiment over the stratified sample on a linear axis: neutral is the "
    "tallest bar, positive next, negative slightly below it.",
)

# %%
print("\n=== ESG Analysis Comparison Summary ===")
print(f"Classification: {classification_results.height} headlines -> numeric scores")
print(f"RAG: {rag_contract.height} question contracts -> no generated answers")
print("\nBoundary: execute and evaluate a cited RAG producer before comparing performance")

# %%
completion_record = {
    "selected_rows_sha256": selection_sha256,
    "headlines": classification_results.height,
    "rag_questions": rag_contract.height,
    "models": classification_results["model"].unique().sort().to_list(),
    "model_revision": FINBERT_REVISION,
    "device": "cuda" if INFERENCE_DEVICE == 0 else "cpu",
    "category_counts": category_counts.to_dicts(),
    "sentiment_counts": sentiment_counts.to_dicts(),
}
print(f"COMPLETION_RECORD={json.dumps(completion_record, sort_keys=True)}")

# %% [markdown]
# ## Key takeaways
#
# 1. **Check what a keyword screen actually selected before naming it.**
#    Section 2 applies the categoriser to the whole selected pool, and this
#    screen returns environmental news by a margin that makes the label "ESG"
#    misleading. Two causes are visible in the screen itself: its environmental
#    terms are common single words while its social and governance terms are
#    compound phrases a headline must contain intact, and its categoriser
#    breaks ties towards environmental. Whether that generalises to keyword
#    screening as such is a question this notebook does not answer - it would
#    need other lists and independent labels. The check that catches it is one
#    group-by.
#
# 2. **A sample cannot show what its pool does not contain.** A uniform draw
#    of twenty from this pool would be overwhelmingly environmental and would
#    more likely than not contain no social or governance headline at all. The
#    stratified draw is what puts all three categories in front of the
#    classifier, and stratifying is an intervention that has to be declared,
#    because the resulting category counts are then a property of the sampling
#    rather than of the news.
#
# 3. **Time the load separately from the inference.** At this batch size the
#    one-off cost of loading the weights is a large share of the wall clock, so
#    a throughput figure computed over both describes the batch size rather
#    than the model.
#
# 4. **Classification produces portfolio-ready outputs**, which is the reason
#    to keep it: a row per document with a label and a score feeds a factor
#    model directly, and a narrative answer does not, however well cited.
#
# 5. **The RAG side of this notebook is a contract, not a result.** No answers
#    were generated, so no latency, no quality and no comparison is reported
#    for it. `05_10k_rag_assistant` implements the retrieval half;
#    `04_ragas_evaluation` is where the citation and abstention checks live.
#
# **Next**: [`07_institutional_holdings_graph`](07_institutional_holdings_graph.ipynb)
# builds graph-structured features from 13F filings.
#
# **Book reference**: Section 22.8, on choosing between RAG and fine-tuning.

```