Spaces:
Build error
Build error
File size: 9,765 Bytes
2153792 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 |
from typing import List, Dict, Any, Optional, Union, Callable, Tuple
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from collections import defaultdict
import torch
from sklearn.base import BaseEstimator
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.decomposition import PCA
from sklearn.manifold import TSNE
import warnings
warnings.filterwarnings("ignore")
SHAP_AVAILABLE = False
LIME_AVAILABLE = False
CAPTUM_AVAILABLE = False
UMAP_AVAILABLE = False
try:
import shap
SHAP_AVAILABLE = True
except ImportError:
pass
try:
import lime
import lime.lime_text
LIME_AVAILABLE = True
except ImportError:
pass
try:
import captum
import captum.attr
CAPTUM_AVAILABLE = True
except ImportError:
pass
try:
import umap
UMAP_AVAILABLE = True
except ImportError:
pass
def get_linear_feature_importance(
model: BaseEstimator,
feature_names: Optional[List[str]] = None,
class_index: int = -1
) -> pd.DataFrame:
if hasattr(model, "coef_"):
coef = model.coef_
if coef.ndim == 1:
weights = coef
else:
if class_index == -1:
weights = np.mean(coef, axis=0)
else:
weights = coef[class_index]
else:
raise ValueError("Model does not have coef_ attribute")
if feature_names is None:
feature_names = [f"feature_{i}" for i in range(len(weights))]
df = pd.DataFrame({"feature": feature_names, "weight": weights})
df = df.sort_values("weight", key=abs, ascending=False).reset_index(drop=True)
return df
def analyze_tfidf_class_keywords(
tfidf_matrix: np.ndarray,
y: np.ndarray,
feature_names: List[str],
top_k: int = 20
) -> Dict[Any, pd.DataFrame]:
classes = np.unique(y)
results = {}
for cls in classes:
mask = (y == cls)
avg_tfidf = np.mean(tfidf_matrix[mask], axis=0).A1 if hasattr(tfidf_matrix, 'A1') else np.mean(tfidf_matrix[mask], axis=0)
top_indices = np.argsort(avg_tfidf)[::-1][:top_k]
top_words = [feature_names[i] for i in top_indices]
top_scores = [avg_tfidf[i] for i in top_indices]
results[cls] = pd.DataFrame({"word": top_words, "tfidf_score": top_scores})
return results
def explain_with_shap(
model: BaseEstimator,
X_train: np.ndarray,
X_test: np.ndarray,
feature_names: Optional[List[str]] = None,
plot_type: str = "bar",
max_display: int = 20
):
if "tree" in str(type(model)).lower():
explainer = shap.TreeExplainer(model)
else:
explainer = shap.KernelExplainer(model.predict_proba, X_train[:100])
shap_values = explainer.shap_values(X_test[:100])
if feature_names is None:
feature_names = [f"feat_{i}" for i in range(X_test.shape[1])]
plt.figure(figsize=(10, 6))
if isinstance(shap_values, list):
shap.summary_plot(shap_values, X_test[:100], feature_names=feature_names, plot_type=plot_type, max_display=max_display, show=False)
else:
shap.summary_plot(shap_values, X_test[:100], feature_names=feature_names, plot_type=plot_type, max_display=max_display, show=False)
plt.tight_layout()
plt.show()
def explain_text_with_lime(
model: Any,
text: str,
tokenizer: Callable,
class_names: List[str],
num_features: int = 10,
num_samples: int = 5000
):
def predict_fn(texts):
tokenized = [tokenizer(t) for t in texts]
if hasattr(model, "vectorizer"):
X = model.vectorizer.transform(texts)
else:
raise NotImplementedError("Custom predict_fn needed for your pipeline")
return model.predict_proba(X.toarray())
explainer = lime.lime_text.LimeTextExplainer(class_names=class_names)
exp = explainer.explain_instance(text, predict_fn, num_features=num_features, num_samples=num_samples)
exp.show_in_notebook()
def visualize_attention_weights(
tokens: List[str],
attention_weights: np.ndarray,
layer: int = 0,
head: int = 0,
figsize: Tuple[int, int] = (10, 2)
):
if attention_weights.ndim != 4:
raise ValueError("attention_weights must be 4D: (layers, heads, seq, seq)")
weights = attention_weights[layer, head, :len(tokens), :len(tokens)]
plt.figure(figsize=figsize)
sns.heatmap(
weights,
xticklabels=tokens,
yticklabels=tokens,
cmap="viridis",
cbar=True
)
plt.title(f"Attention Layer {layer}, Head {head}")
plt.xticks(rotation=45, ha="right")
plt.yticks(rotation=0)
plt.tight_layout()
plt.show()
def get_transformer_attention(
model: 'torch.nn.Module',
tokenizer: 'transformers.PreTrainedTokenizer',
text: str,
device: str = "cpu"
) -> Tuple[List[str], np.ndarray]:
if not CAPTUM_AVAILABLE:
raise ImportError("Install Captum: pip install captum")
inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
input_ids = inputs["input_ids"].to(device)
model = model.to(device)
model.eval()
with torch.no_grad():
outputs = model(input_ids, output_attentions=True)
attentions = outputs.attentions
attn = torch.stack(attentions, dim=0).squeeze(1).cpu().numpy()
tokens = tokenizer.convert_ids_to_tokens(input_ids[0].cpu().numpy())
return tokens, attn
def analyze_errors(
y_true: np.ndarray,
y_pred: np.ndarray,
texts: List[str],
labels: Optional[List[Any]] = None
) -> pd.DataFrame:
errors = []
for i, (true, pred, text) in enumerate(zip(y_true, y_pred, texts)):
if true != pred:
errors.append({
"index": i,
"text": text,
"true_label": true,
"pred_label": pred
})
return pd.DataFrame(errors)
def compare_model_errors(
models: Dict[str, BaseEstimator],
X_test: np.ndarray,
y_test: np.ndarray,
texts: List[str]
) -> Dict[str, pd.DataFrame]:
results = {}
for name, model in models.items():
y_pred = model.predict(X_test)
errors = analyze_errors(y_test, y_pred, texts)
results[name] = errors
return results
def plot_embeddings(
embeddings: np.ndarray,
labels: np.ndarray,
method: str = "umap",
n_components: int = 2,
figsize: Tuple[int, int] = (12, 8),
title: str = "Embedding Projection"
):
if method == "tsne":
reducer = TSNE(n_components=n_components, random_state=42, n_jobs=-1)
elif method == "umap":
if not UMAP_AVAILABLE:
raise ImportError("Install UMAP: pip install umap-learn")
reducer = umap.UMAP(n_components=n_components, random_state=42, n_jobs=-1)
else:
raise ValueError("method must be 'tsne' or 'umap'")
proj = reducer.fit_transform(embeddings)
plt.figure(figsize=figsize)
scatter = plt.scatter(proj[:, 0], proj[:, 1], c=labels, cmap="tab10", alpha=0.7)
plt.colorbar(scatter)
plt.title(title)
plt.xlabel("Component 1")
plt.ylabel("Component 2")
plt.tight_layout()
plt.show()
def get_token_importance_captum(
model: 'torch.nn.Module',
tokenizer: 'transformers.PreTrainedTokenizer',
text: str,
device: str = "cpu"
) -> Tuple[List[str], np.ndarray]:
if not CAPTUM_AVAILABLE:
raise ImportError("Install Captum: pip install captum")
from captum.attr import LayerIntegratedGradients
import torch
inputs = tokenizer(
text,
return_tensors="pt",
truncation=True,
max_length=512,
padding=True
)
input_ids = inputs["input_ids"].to(device)
attention_mask = inputs["attention_mask"].to(device)
model = model.to(device)
model.eval()
with torch.no_grad():
outputs = model(input_ids=input_ids, attention_mask=attention_mask)
pred_class = torch.argmax(outputs.logits, dim=1).item()
def forward_func(input_ids):
return model(input_ids=input_ids, attention_mask=attention_mask).logits
baseline_ids = torch.zeros_like(input_ids).to(device)
baseline_ids[:, 0] = tokenizer.cls_token_id
baseline_ids[:, -1] = tokenizer.sep_token_id
lig = LayerIntegratedGradients(forward_func, model.bert.embeddings)
attributions, delta = lig.attribute(
inputs=input_ids,
baselines=baseline_ids,
target=pred_class,
return_convergence_delta=True
)
attributions = attributions.sum(dim=-1).squeeze(0).cpu().detach().numpy()
tokens = tokenizer.convert_ids_to_tokens(input_ids[0].cpu().numpy())
return tokens, attributions
def plot_token_importance(tokens: List[str], importance: np.ndarray, top_k: int = 20):
valid = [(t, imp) for t, imp in zip(tokens, importance) if t not in ["[CLS]", "[SEP]", "[PAD]"]]
if not valid:
return
tokens_clean, imp_clean = zip(*valid)
indices = np.argsort(np.abs(imp_clean))[-top_k:][::-1]
tokens_top = [tokens_clean[i] for i in indices]
imp_top = [imp_clean[i] for i in indices]
plt.figure(figsize=(10, 6))
colors = ["red" if x < 0 else "green" for x in imp_top]
plt.barh(range(len(imp_top)), imp_top, color=colors)
plt.yticks(range(len(imp_top)), tokens_top)
plt.gca().invert_yaxis()
plt.xlabel("Attribution Score")
plt.title("Token Importance (Green: positive, Red: negative)")
plt.tight_layout()
plt.show() |