用令牌级 SHAP 解释 FinBERT 金融情绪

# 10_shap_nlp_sentiment.py


```py
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# %% [markdown]
# # Interpreting Financial NLP Models with SHAP
#
# **Chapter 12: Advanced Models for Tabular Data**
# **Section 12.5: Model Explainability with SHAP**
#
# ## Purpose
#
# This notebook extends SHAP from tabular models to a pretrained financial language model. It
# examines whether token attributions support FinBERT's sentiment decisions and shows how a token's
# contribution can change with context.
#
# ## Learning objectives
#
# After completing this notebook, you will be able to:
#
# - explain Transformer sentiment probabilities with token-level SHAP values;
# - distinguish a token's contribution to one prediction from its standalone sentiment;
# - test a contextual explanation with a controlled text perturbation; and
# - state what attribution can, and cannot, establish in model validation.
#
# **Prerequisites**: Sections 12.5 on SHAP and Chapter 10 on financial text features. The notebook
# downloads the pinned FinBERT-tone checkpoint on first use. A CUDA-capable PyTorch environment is
# faster, but the same inference path runs on CPU.

# %%
"""Apply SHAP to FinBERT for token-level financial sentiment attribution."""

import matplotlib.pyplot as plt
import numpy as np
import polars as pl

import torch  # isort:skip  # Import before SHAP to initialize the CUDA runtime first.
import shap
from transformers import AutoModelForSequenceClassification, AutoTokenizer

from utils.reproducibility import set_global_seeds
from utils.style import COLORS, FIGSIZE, add_message_title, show_with_alt, zero_line

# %% tags=["parameters"]
MAX_SENTENCES = 0  # 0 uses the full teaching sample
SEED = 42

# %%
set_global_seeds(SEED)

MODEL_NAME = "yiyanghkust/finbert-tone"
MODEL_REVISION = "4921590d3c0c3832c0efea24c8381ce0bda7844b"
LABEL_ORDER = ("Negative", "Neutral", "Positive")
TORCH_DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")

print(f"Inference device: {'CUDA GPU' if TORCH_DEVICE.type == 'cuda' else 'CPU'}")
print(f"FinBERT revision: {MODEL_REVISION[:12]}")

# %% [markdown]
# ## Load a pinned FinBERT checkpoint
#
# FinBERT-tone is already fine-tuned for three-way financial sentiment. Pinning the model revision
# makes the weights and tokenizer part of the notebook's reproducibility contract. The checkpoint's
# native class indices are validated, then outputs are reordered once into the reader-facing order
# Negative, Neutral, Positive.

# %%
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, revision=MODEL_REVISION)
model = AutoModelForSequenceClassification.from_pretrained(
    MODEL_NAME,
    revision=MODEL_REVISION,
).to(TORCH_DEVICE)
model.eval()

model_labels = tuple(model.config.id2label[index] for index in range(model.config.num_labels))
if set(model_labels) != set(LABEL_ORDER):
    raise ValueError(f"Unexpected FinBERT labels: {model_labels}")

# %% [markdown]
# The probability wrapper is the single class-order boundary between FinBERT and SHAP. It batches
# inference, applies softmax to the native logits, and reorders columns by the checkpoint's label
# metadata rather than assuming that model indices have a particular meaning.


# %%
def predict_proba(texts):
    """Return FinBERT probabilities in LABEL_ORDER for SHAP."""
    text_batch = [str(text) for text in texts]
    encoded = tokenizer(text_batch, padding=True, return_tensors="pt").to(TORCH_DEVICE)
    with torch.inference_mode():
        native_probabilities = torch.softmax(model(**encoded).logits, dim=-1).cpu().numpy()
    display_indices = [model_labels.index(label) for label in LABEL_ORDER]
    return native_probabilities[:, display_indices]


# %% [markdown]
# Five short sentences establish the model's behavior before attribution. These are constructed
# teaching examples, not a labeled evaluation sample, so confidence describes model certainty on
# each example rather than out-of-sample accuracy.

# %%
test_sentences = [
    "Revenue growth exceeded analyst expectations.",
    "The company announced significant layoffs.",
    "Net loss narrowed from the prior year.",
    "Guidance was raised for the fiscal year.",
    "Management expressed concerns about margin pressure.",
]

test_probabilities = predict_proba(test_sentences)
winner_indices = test_probabilities.argmax(axis=1)

prediction_summary = pl.DataFrame(
    {
        "text": test_sentences,
        "prediction": [LABEL_ORDER[index] for index in winner_indices],
        "confidence": test_probabilities.max(axis=1),
    }
)
prediction_summary

# %% [markdown]
# ## Explain individual predictions
#
# The text masker creates coalitions by hiding token groups and querying the probability wrapper.
# Because the explained outputs are probabilities, each SHAP value is a contribution in probability
# points relative to the explainer's baseline. A positive value pushes toward the named class; a
# negative value pushes away from it.

# %%
# Each call passes silent=True: the partition explainer's tqdm bar writes to stderr.
explainer = shap.Explainer(
    predict_proba,
    tokenizer,
    output_names=list(LABEL_ORDER),
    algorithm="partition",
)

explain_sentences = [
    "Net loss narrowed significantly from the prior year.",
    "Revenue growth slowed amid weakening demand.",
    "The company raised its full-year guidance.",
]

explain_probabilities = predict_proba(explain_sentences)
explain_winners = explain_probabilities.argmax(axis=1)
shap_values = explainer(explain_sentences, silent=True)

# %% [markdown]
# Each panel ranks tokens by absolute contribution to that sentence's predicted class. Direction is
# relative to the predicted class: green pushes its probability higher and red pushes it lower.

# %%
fig, axes = plt.subplots(3, 1, figsize=FIGSIZE["grid_3x2"], sharex=True)

panel_rows = []
max_abs_contribution = 0.0
for sentence_index, (sentence, class_index) in enumerate(
    zip(explain_sentences, explain_winners, strict=True)
):
    tokens = shap_values[sentence_index].data
    values = shap_values[sentence_index, :, class_index].values
    contributions = [
        (str(token).strip(), float(value))
        for token, value in zip(tokens, values, strict=True)
        if str(token).strip() and str(token).strip() not in {"[CLS]", "[SEP]", "[PAD]"}
    ]
    strongest = sorted(contributions, key=lambda item: abs(item[1]), reverse=True)[:6]
    max_abs_contribution = max(max_abs_contribution, *(abs(value) for _, value in strongest))
    predicted = LABEL_ORDER[class_index]
    panel_rows.append((sentence, predicted, strongest))

for ax, (_, predicted, strongest) in zip(axes, panel_rows, strict=True):
    tokens = [token for token, _ in strongest]
    contributions = [value for _, value in strongest]
    colors = [COLORS["positive"] if value >= 0 else COLORS["negative"] for value in contributions]

    ax.barh(tokens, contributions, color=colors)
    ax.invert_yaxis()
    ax.set_xlim(-1.05 * max_abs_contribution, 1.05 * max_abs_contribution)
    ax.set_title(f"Predicted: {predicted}", loc="left")
    zero_line(ax, axis="x")

axes[1].set_ylabel("Token")
axes[-1].set_xlabel("SHAP contribution to predicted-class probability")
fig.suptitle("Token contributions behind each FinBERT decision", x=0.06, ha="left")
show_with_alt(
    fig,
    "One horizontal bar chart per sentence, each showing the tokens with the largest "
    "SHAP contributions to that sentence's predicted class, coloured by sign against a "
    "line at zero.",
)

# %% [markdown]
# ## Controlled context test: narrowed versus widened
#
# A token's SHAP value is a property of the complete input rather than of the token alone. To
# test that
# distinction, hold the sentence template fixed and replace only *narrowed* with *widened*. The
# resulting predictions and Positive-class attributions provide an adversarial check on the
# contextual interpretation. A sensible-looking local explanation does not guarantee that the model
# will respond sensibly to a nearby input.

# %%
context_sentences = [
    "Net loss narrowed significantly from the prior year.",
    "Net loss widened significantly from the prior year.",
]
context_probabilities = predict_proba(context_sentences)
context_shap = explainer(context_sentences, silent=True)
positive_index = LABEL_ORDER.index("Positive")

context_summary = pl.DataFrame(
    {
        "wording": ["narrowed", "widened"],
        "prediction": [LABEL_ORDER[index] for index in context_probabilities.argmax(axis=1)],
        "positive_probability": context_probabilities[:, positive_index],
    }
)
context_summary

# %%
fig, axes = plt.subplots(2, 1, figsize=FIGSIZE["dual_v"], sharex=True)

context_panels = []
context_limit = 0.0
for sentence_index, wording in enumerate(("narrowed", "widened")):
    tokens = context_shap[sentence_index].data
    values = context_shap[sentence_index, :, positive_index].values
    contributions = [
        (str(token).strip(), float(value))
        for token, value in zip(tokens, values, strict=True)
        if str(token).strip() and str(token).strip() not in {"[CLS]", "[SEP]", "[PAD]"}
    ]
    strongest = sorted(contributions, key=lambda item: abs(item[1]), reverse=True)[:7]
    context_limit = max(context_limit, *(abs(value) for _, value in strongest))
    context_panels.append((wording, strongest))

for ax, (wording, strongest) in zip(axes, context_panels, strict=True):
    tokens = [token for token, _ in strongest]
    contributions = [value for _, value in strongest]
    colors = [COLORS["positive"] if value >= 0 else COLORS["negative"] for value in contributions]

    ax.barh(tokens, contributions, color=colors)
    ax.invert_yaxis()
    ax.set_xlim(-1.05 * context_limit, 1.05 * context_limit)
    ax.set_title(wording.capitalize(), loc="left")
    zero_line(ax, axis="x")

fig.supylabel("Token")
fig.supxlabel("SHAP contribution to Positive probability")
fig.suptitle("Token contributions with one word changed", x=0.06, ha="left")
show_with_alt(
    fig,
    "Two horizontal bar charts of token SHAP contributions to the Positive class, one "
    "per wording of the same sentence template, coloured by sign against a line at zero.",
)

# %% [markdown]
# ## Aggregate a small teaching sample
#
# Aggregating signed Positive-class contributions can reveal recurring patterns, but the ten
# constructed sentences below are too small and too curated to support claims about a global finance
# vocabulary. The chart is therefore a diagnostic of this teaching sample only. Repeated corpus
# tokens contribute repeatedly to the sum.

# %%
teaching_sentences = [
    "Revenue exceeded expectations.",
    "Profit margins improved significantly.",
    "The company beat analyst estimates.",
    "Earnings per share increased.",
    "Growth accelerated in Q4.",
    "Sales declined sharply.",
    "Losses mounted during the quarter.",
    "Margins contracted due to costs.",
    "Revenue missed forecasts.",
    "Guidance was lowered.",
]
if MAX_SENTENCES > 0:
    teaching_sentences = teaching_sentences[:MAX_SENTENCES]

teaching_shap = explainer(teaching_sentences, silent=True)

token_totals: dict[str, float] = {}
for sentence_index in range(len(teaching_sentences)):
    tokens = teaching_shap[sentence_index].data
    values = teaching_shap[sentence_index, :, positive_index].values
    for token, value in zip(tokens, values, strict=True):
        normalized = str(token).strip().lower()
        if not normalized or normalized in {"[cls]", "[sep]", "[pad]"}:
            continue
        token_totals[normalized] = token_totals.get(normalized, 0.0) + float(value)

top_positive = sorted(
    ((token, value) for token, value in token_totals.items() if value > 0),
    key=lambda item: item[1],
    reverse=True,
)[:6]
top_negative = sorted(
    ((token, value) for token, value in token_totals.items() if value < 0),
    key=lambda item: item[1],
)[:6]
ranked_tokens = sorted(
    ((token, value) for token, value in top_negative + top_positive if abs(value) >= 0.01),
    key=lambda item: item[1],
)

# %%
fig, ax = plt.subplots(figsize=FIGSIZE["single_tall"])

tokens = [token for token, _ in ranked_tokens]
contributions = [value for _, value in ranked_tokens]
colors = [COLORS["positive"] if value >= 0 else COLORS["negative"] for value in contributions]

ax.barh(tokens, contributions, color=colors)
zero_line(ax, axis="x")
ax.set_xlabel("Summed SHAP contribution to Positive probability")
ax.set_ylabel("Token")
add_message_title(
    ax,
    "Summed token contribution to the Positive class",
    subtitle="Signed totals across the constructed teaching sentences",
)
show_with_alt(
    fig,
    "Horizontal bars of each token's summed SHAP contribution to the Positive class "
    "across the sample, coloured by sign against a line at zero.",
)

# %% [markdown]
# ## What attribution can support
#
# - **Model validation**: token attributions can reveal reliance on implausible artifacts or language
#   that deserves further testing. They cannot prove that training data were leak-free.
# - **Debugging**: controlled text perturbations can identify brittle or counterintuitive decisions,
#   but the explanation is still local to the model, masker, and input.
# - **Research hypotheses**: recurring attributions in a representative corpus may motivate a
#   candidate signal. They are predictive associations, not causal effects or evidence of alpha by
#   themselves.

# %% [markdown]
# ## Key takeaways
#
# 1. SHAP can decompose FinBERT class probabilities into token-level contributions using the same
#    coalition logic applied to tabular features.
# 2. The narrowed-versus-widened perturbation shows why attribution needs an adversarial check: a
#    locally plausible explanation can coexist with a counterintuitive nearby prediction.
# 3. Aggregated token scores depend on the sampled documents. This constructed sample demonstrates
#    the workflow, not a stable finance-domain vocabulary.
# 4. Attribution is a diagnostic layer. Leakage checks, representative validation, and economic
#    testing remain separate requirements before using text predictions in a strategy.
#
# These examples complete the token-attribution extension in **Section 12.5**. Next,
# `11_conformal_gbm` adds calibrated uncertainty intervals to gradient-boosting predictions.

```