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fix: nn models pad_sequences

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bernard-ng
2025-10-06 00:37:29 +02:00
parent cb22c06628
commit d3b3840278
7 changed files with 211 additions and 92 deletions
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src/ners/research/models/bigru_model.py
+6 -1
View File
@@ -23,6 +23,7 @@ class BiGRUModel(NeuralNetworkModel):
input_dim=vocab_size,
output_dim=params.get("embedding_dim", 64),
mask_zero=True,
input_length=params.get("max_len", 6),
),
# First recurrent block returns full sequences to allow stacking.
# Moderate dropout + optional recurrent_dropout to reduce overfitting
@@ -69,4 +70,8 @@ class BiGRUModel(NeuralNetworkModel):
sequences = self.tokenizer.texts_to_sequences(text_data)
max_len = self.config.model_params.get("max_len", 6)
return pad_sequences(sequences, maxlen=max_len, padding="post")
# Ensure padding and truncation are applied on the right to keep
# contiguous non-zero tokens on the left, matching RNN mask expectations.
return pad_sequences(
sequences, maxlen=max_len, padding="post", truncating="post"
)
src/ners/research/models/cnn_model.py
+4 -1
View File
@@ -83,4 +83,7 @@ class CNNModel(NeuralNetworkModel):
"max_len", 20
) # Longer for character level
return pad_sequences(sequences, maxlen=max_len, padding="post")
# Right-side padding and truncation ensure contiguous non-zero tokens on the left
return pad_sequences(
sequences, maxlen=max_len, padding="post", truncating="post"
)
src/ners/research/models/lstm_model.py
+4 -1
View File
@@ -68,4 +68,7 @@ class LSTMModel(NeuralNetworkModel):
sequences = self.tokenizer.texts_to_sequences(text_data)
max_len = self.config.model_params.get("max_len", 6)
return pad_sequences(sequences, maxlen=max_len, padding="post")
# Right-side padding and truncation to preserve contiguous non-zero tokens
return pad_sequences(
sequences, maxlen=max_len, padding="post", truncating="post"
)
src/ners/research/models/transformer_model.py
+4 -1
View File
@@ -88,4 +88,7 @@ class TransformerModel(NeuralNetworkModel):
sequences = self.tokenizer.texts_to_sequences(text_data)
max_len = int(self.config.model_params.get("max_len", 6))
return pad_sequences(sequences, maxlen=max_len, padding="post")
# Right-side padding and truncation for consistent masking/shape
return pad_sequences(
sequences, maxlen=max_len, padding="post", truncating="post"
)
src/ners/research/neural_network_model.py
+23
View File
@@ -149,6 +149,29 @@ class NeuralNetworkModel(BaseModel):
if invalid_mask.any():
arr[invalid_mask] = oov_index
# Enforce strictly right-padded masks for RNN/cuDNN compatibility.
# Any zero appearing before the last non-zero in a sequence will be
# replaced with the OOV index so the mask remains contiguous True->False.
try:
nz = arr != 0 # non-padding tokens
if nz.ndim == 2 and arr.shape[1] > 0:
# Identify rows that have at least one non-zero
has_nz = nz.any(axis=1)
# Compute last non-zero position per row; if none, set to -1
indices = np.arange(arr.shape[1], dtype=np.int64)
# Max of indices where nz is True gives last non-zero
last_pos = (nz * indices).max(axis=1)
last_pos = np.where(has_nz, last_pos, -1)
# Broadcast to mark the left region up to last non-zero (inclusive)
left_region = indices <= last_pos[:, None]
# Zeros inside the left region are invalid padding -> set to OOV
zero_inside = (~nz) & left_region
if zero_inside.any():
arr[zero_inside] = oov_index
except Exception:
# Best-effort; skip if any unexpected broadcasting issue occurs
pass
# Use int32 for TF embedding ops compatibility
return arr.astype(np.int32, copy=False)
except Exception as e:
src/notebooks/experiments.ipynb
+167 -87
View File
@@ -73,60 +73,82 @@
" cv = exp.get(\"cv_metrics\", {}) or {}\n",
"\n",
" cm = exp.get(\"confusion_matrix\")\n",
" tn=fp=fn=tp=np.nan\n",
" if isinstance(cm, list) and len(cm)==2 and all(isinstance(r, list) and len(r)==2 for r in cm):\n",
" tn = fp = fn = tp = np.nan\n",
" if (\n",
" isinstance(cm, list)\n",
" and len(cm) == 2\n",
" and all(isinstance(r, list) and len(r) == 2 for r in cm)\n",
" ):\n",
" # By inspection of the provided metrics, mapping is:\n",
" # rows = true [f, m]; cols = pred [f, m]\n",
" tn, fp = cm[0][0], cm[0][1] # true negatives and false positives for positive class 'm'\n",
" tn, fp = (\n",
" cm[0][0],\n",
" cm[0][1],\n",
" ) # true negatives and false positives for positive class 'm'\n",
" fn, tp = cm[1][0], cm[1][1]\n",
"\n",
" # Derived metrics from confusion matrix (where present)\n",
" def safe_div(a,b): \n",
" return float(a)/float(b) if (b not in (0, None) and not pd.isna(b)) else np.nan\n",
" def safe_div(a, b):\n",
" return (\n",
" float(a) / float(b) if (b not in (0, None) and not pd.isna(b)) else np.nan\n",
" )\n",
"\n",
" sensitivity = safe_div(tp, tp+fn) # TPR for 'm'\n",
" specificity = safe_div(tn, tn+fp) # TNR for 'm'\n",
" sensitivity = safe_div(tp, tp + fn) # TPR for 'm'\n",
" specificity = safe_div(tn, tn + fp) # TNR for 'm'\n",
" balanced_acc = np.nanmean([sensitivity, specificity])\n",
" mcc_num = (tp*tn - fp*fn)\n",
" mcc_den = sqrt((tp+fp)*(tp+fn)*(tn+fp)*(tn+fn)) if all(x==x for x in [tp+fp, tp+fn, tn+fp, tn+fn]) else np.nan\n",
" mcc_num = tp * tn - fp * fn\n",
" mcc_den = (\n",
" sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn))\n",
" if all(x == x for x in [tp + fp, tp + fn, tn + fp, tn + fn])\n",
" else np.nan\n",
" )\n",
" mcc = safe_div(mcc_num, mcc_den)\n",
"\n",
" n_test = exp.get(\"test_size\") or np.nansum([tn, fp, fn, tp])\n",
" test_acc = te.get(\"accuracy\", np.nan)\n",
" # 95% CI for accuracy via normal approximation (ok for n=2000)\n",
" if pd.notna(test_acc) and pd.notna(n_test) and n_test>0:\n",
" se = np.sqrt(test_acc*(1-test_acc)/n_test)\n",
" acc_ci_lo = test_acc - 1.96*se\n",
" acc_ci_hi = test_acc + 1.96*se\n",
" if pd.notna(test_acc) and pd.notna(n_test) and n_test > 0:\n",
" se = np.sqrt(test_acc * (1 - test_acc) / n_test)\n",
" acc_ci_lo = test_acc - 1.96 * se\n",
" acc_ci_hi = test_acc + 1.96 * se\n",
" else:\n",
" acc_ci_lo = acc_ci_hi = np.nan\n",
"\n",
" rows.append({\n",
" \"experiment_id\": exp_id,\n",
" \"model\": name or model_type,\n",
" \"model_family\": (model_type or \"\").upper(),\n",
" \"feature_set\": features,\n",
" \"train_accuracy\": tr.get(\"accuracy\", np.nan),\n",
" \"test_accuracy\": test_acc,\n",
" \"cv_accuracy_mean\": cv.get(\"accuracy\", np.nan),\n",
" \"cv_accuracy_std\": cv.get(\"accuracy_std\", np.nan),\n",
" \"train_f1\": tr.get(\"f1\", np.nan),\n",
" \"test_f1\": te.get(\"f1\", np.nan),\n",
" \"cv_f1_mean\": cv.get(\"f1\", np.nan),\n",
" \"cv_f1_std\": cv.get(\"f1_std\", np.nan),\n",
" \"TP\": tp, \"FP\": fp, \"TN\": tn, \"FN\": fn,\n",
" \"sensitivity_TPR_m\": sensitivity,\n",
" \"specificity_TNR_m\": specificity,\n",
" \"balanced_accuracy\": balanced_acc,\n",
" \"MCC\": mcc,\n",
" \"n_test\": n_test,\n",
" \"acc_95ci_lo\": acc_ci_lo,\n",
" \"acc_95ci_hi\": acc_ci_hi,\n",
" \"train_minus_test_gap\": (tr.get(\"accuracy\", np.nan) - test_acc) if pd.notna(tr.get(\"accuracy\", np.nan)) and pd.notna(test_acc) else np.nan,\n",
" \"test_minus_cv_gap\": (test_acc - cv.get(\"accuracy\", np.nan)) if pd.notna(test_acc) and pd.notna(cv.get(\"accuracy\", np.nan)) else np.nan,\n",
" \"start_time\": exp.get(\"start_time\"),\n",
" \"end_time\": exp.get(\"end_time\")\n",
" })\n",
" rows.append(\n",
" {\n",
" \"experiment_id\": exp_id,\n",
" \"model\": name or model_type,\n",
" \"model_family\": (model_type or \"\").upper(),\n",
" \"feature_set\": features,\n",
" \"train_accuracy\": tr.get(\"accuracy\", np.nan),\n",
" \"test_accuracy\": test_acc,\n",
" \"cv_accuracy_mean\": cv.get(\"accuracy\", np.nan),\n",
" \"cv_accuracy_std\": cv.get(\"accuracy_std\", np.nan),\n",
" \"train_f1\": tr.get(\"f1\", np.nan),\n",
" \"test_f1\": te.get(\"f1\", np.nan),\n",
" \"cv_f1_mean\": cv.get(\"f1\", np.nan),\n",
" \"cv_f1_std\": cv.get(\"f1_std\", np.nan),\n",
" \"TP\": tp,\n",
" \"FP\": fp,\n",
" \"TN\": tn,\n",
" \"FN\": fn,\n",
" \"sensitivity_TPR_m\": sensitivity,\n",
" \"specificity_TNR_m\": specificity,\n",
" \"balanced_accuracy\": balanced_acc,\n",
" \"MCC\": mcc,\n",
" \"n_test\": n_test,\n",
" \"acc_95ci_lo\": acc_ci_lo,\n",
" \"acc_95ci_hi\": acc_ci_hi,\n",
" \"train_minus_test_gap\": (tr.get(\"accuracy\", np.nan) - test_acc)\n",
" if pd.notna(tr.get(\"accuracy\", np.nan)) and pd.notna(test_acc)\n",
" else np.nan,\n",
" \"test_minus_cv_gap\": (test_acc - cv.get(\"accuracy\", np.nan))\n",
" if pd.notna(test_acc) and pd.notna(cv.get(\"accuracy\", np.nan))\n",
" else np.nan,\n",
" \"start_time\": exp.get(\"start_time\"),\n",
" \"end_time\": exp.get(\"end_time\"),\n",
" }\n",
" )\n",
"\n",
"df = pd.DataFrame(rows)"
]
@@ -139,23 +161,53 @@
"outputs": [],
"source": [
"# Clean and order categorical fields\n",
"df[\"feature_set\"] = df[\"feature_set\"].replace({\"full_name\":\"Full name\",\"native_name\":\"Native\",\"surname\":\"Surname\"})\n",
"order_features = [\"Full name\",\"Surname\",\"Native\"]\n",
"df[\"feature_set\"] = pd.Categorical(df[\"feature_set\"], categories=order_features, ordered=True)\n",
"df[\"feature_set\"] = df[\"feature_set\"].replace(\n",
" {\"full_name\": \"Full name\", \"native_name\": \"Native\", \"surname\": \"Surname\"}\n",
")\n",
"order_features = [\"Full name\", \"Surname\", \"Native\"]\n",
"df[\"feature_set\"] = pd.Categorical(\n",
" df[\"feature_set\"], categories=order_features, ordered=True\n",
")\n",
"\n",
"order_family = [\"LOGISTIC_REGRESSION\",\"LIGHTGBM\",\"LSTM\",\"CNN\",\"BIGRU\", \"RANDOM_FOREST\", \"TRANSFORMER\", \"NAIVE_BAYES\", \"XGBOOST\"]\n",
"df[\"model_family\"] = pd.Categorical(df[\"model_family\"], categories=order_family, ordered=True)\n",
"order_family = [\n",
" \"LOGISTIC_REGRESSION\",\n",
" \"LIGHTGBM\",\n",
" \"LSTM\",\n",
" \"CNN\",\n",
" \"BIGRU\",\n",
" \"RANDOM_FOREST\",\n",
" \"TRANSFORMER\",\n",
" \"NAIVE_BAYES\",\n",
" \"XGBOOST\",\n",
"]\n",
"df[\"model_family\"] = pd.Categorical(\n",
" df[\"model_family\"], categories=order_family, ordered=True\n",
")\n",
"\n",
"# Summary table (subset of most relevant columns)\n",
"summary_cols = [\n",
" \"experiment_id\",\"model_family\",\"feature_set\",\n",
" \"train_accuracy\",\"test_accuracy\",\"cv_accuracy_mean\",\"cv_accuracy_std\",\n",
" \"acc_95ci_lo\",\"acc_95ci_hi\",\n",
" \"balanced_accuracy\",\"MCC\",\n",
" \"train_minus_test_gap\",\"test_minus_cv_gap\",\n",
" \"n_test\"\n",
" \"experiment_id\",\n",
" \"model_family\",\n",
" \"feature_set\",\n",
" \"train_accuracy\",\n",
" \"test_accuracy\",\n",
" \"cv_accuracy_mean\",\n",
" \"cv_accuracy_std\",\n",
" \"acc_95ci_lo\",\n",
" \"acc_95ci_hi\",\n",
" \"balanced_accuracy\",\n",
" \"MCC\",\n",
" \"train_minus_test_gap\",\n",
" \"test_minus_cv_gap\",\n",
" \"n_test\",\n",
"]\n",
"summary = df[summary_cols].sort_values([\"model_family\",\"feature_set\",\"test_accuracy\"], ascending=[True, True, False]).reset_index(drop=True)\n",
"summary = (\n",
" df[summary_cols]\n",
" .sort_values(\n",
" [\"model_family\", \"feature_set\", \"test_accuracy\"], ascending=[True, True, False]\n",
" )\n",
" .reset_index(drop=True)\n",
")\n",
"\n",
"# Display the master summary table\n",
"display(summary)"
@@ -171,25 +223,37 @@
"# Build a pivot for plotting\n",
"plot_df = df.dropna(subset=[\"test_accuracy\"]).copy()\n",
"# Prepare positions\n",
"families = [f for f in order_family if f in plot_df[\"model_family\"].astype(str).unique()]\n",
"features = [f for f in order_features if f in plot_df[\"feature_set\"].astype(str).unique()]\n",
"families = [\n",
" f for f in order_family if f in plot_df[\"model_family\"].astype(str).unique()\n",
"]\n",
"features = [\n",
" f for f in order_features if f in plot_df[\"feature_set\"].astype(str).unique()\n",
"]\n",
"\n",
"# Bar positions\n",
"x = np.arange(len(families))\n",
"width = 0.8 / max(1,len(features)) # total width split by features\n",
"width = 0.8 / max(1, len(features)) # total width split by features\n",
"\n",
"fig1 = plt.figure(figsize=(10,6))\n",
"fig1 = plt.figure(figsize=(10, 6))\n",
"for i, feat in enumerate(features):\n",
" sub = plot_df[plot_df[\"feature_set\"].astype(str)==feat]\n",
" sub = plot_df[plot_df[\"feature_set\"].astype(str) == feat]\n",
" # Align to families\n",
" y = []\n",
" yerr = [[], []] # lower and upper errors for asymmetric CI\n",
" for fam in families:\n",
" row = sub[sub[\"model_family\"].astype(str)==fam]\n",
" row = sub[sub[\"model_family\"].astype(str) == fam]\n",
" if len(row):\n",
" val = float(row.iloc[0][\"test_accuracy\"])\n",
" lo = float(row.iloc[0][\"acc_95ci_lo\"]) if pd.notna(row.iloc[0][\"acc_95ci_lo\"]) else np.nan\n",
" hi = float(row.iloc[0][\"acc_95ci_hi\"]) if pd.notna(row.iloc[0][\"acc_95ci_hi\"]) else np.nan\n",
" lo = (\n",
" float(row.iloc[0][\"acc_95ci_lo\"])\n",
" if pd.notna(row.iloc[0][\"acc_95ci_lo\"])\n",
" else np.nan\n",
" )\n",
" hi = (\n",
" float(row.iloc[0][\"acc_95ci_hi\"])\n",
" if pd.notna(row.iloc[0][\"acc_95ci_hi\"])\n",
" else np.nan\n",
" )\n",
" else:\n",
" val, lo, hi = np.nan, np.nan, np.nan\n",
" y.append(val)\n",
@@ -201,7 +265,14 @@
" yerr[0].append(np.nan)\n",
" yerr[1].append(np.nan)\n",
"\n",
" plt.bar(x + i*width - (len(features)-1)*width/2, y, width, label=feat, yerr=yerr, capsize=4)\n",
" plt.bar(\n",
" x + i * width - (len(features) - 1) * width / 2,\n",
" y,\n",
" width,\n",
" label=feat,\n",
" yerr=yerr,\n",
" capsize=4,\n",
" )\n",
"\n",
"plt.xticks(x, families, rotation=0)\n",
"plt.ylabel(\"Test accuracy\")\n",
@@ -219,15 +290,15 @@
"metadata": {},
"outputs": [],
"source": [
"fig2 = plt.figure(figsize=(10,6))\n",
"fig2 = plt.figure(figsize=(10, 6))\n",
"for i, feat in enumerate(features):\n",
" sub = plot_df[plot_df[\"feature_set\"].astype(str)==feat]\n",
" sub = plot_df[plot_df[\"feature_set\"].astype(str) == feat]\n",
" y = []\n",
" for fam in families:\n",
" row = sub[sub[\"model_family\"].astype(str)==fam]\n",
" row = sub[sub[\"model_family\"].astype(str) == fam]\n",
" val = float(row.iloc[0][\"test_f1\"]) if len(row) else np.nan\n",
" y.append(val)\n",
" plt.bar(x + i*width - (len(features)-1)*width/2, y, width, label=feat)\n",
" plt.bar(x + i * width - (len(features) - 1) * width / 2, y, width, label=feat)\n",
"\n",
"plt.xticks(x, families, rotation=0)\n",
"plt.ylabel(\"Test F1\")\n",
@@ -245,14 +316,18 @@
"metadata": {},
"outputs": [],
"source": [
"fig3 = plt.figure(figsize=(7,7))\n",
"fig3 = plt.figure(figsize=(7, 7))\n",
"for feat in features:\n",
" sub = df[df[\"feature_set\"].astype(str)==feat]\n",
" sub = df[df[\"feature_set\"].astype(str) == feat]\n",
" plt.scatter(sub[\"train_accuracy\"], sub[\"test_accuracy\"], label=feat)\n",
"# y=x reference\n",
"lims = [min(df[\"train_accuracy\"].min(), df[\"test_accuracy\"].min())-0.02, max(df[\"train_accuracy\"].max(), df[\"test_accuracy\"].max())+0.02]\n",
"lims = [\n",
" min(df[\"train_accuracy\"].min(), df[\"test_accuracy\"].min()) - 0.02,\n",
" max(df[\"train_accuracy\"].max(), df[\"test_accuracy\"].max()) + 0.02,\n",
"]\n",
"plt.plot(lims, lims, linestyle=\"--\")\n",
"plt.xlim(lims); plt.ylim(lims)\n",
"plt.xlim(lims)\n",
"plt.ylim(lims)\n",
"plt.xlabel(\"Train accuracy\")\n",
"plt.ylabel(\"Test accuracy\")\n",
"plt.title(\"Overfitting analysis: Train vs Test accuracy\")\n",
@@ -268,22 +343,24 @@
"metadata": {},
"outputs": [],
"source": [
"best_rows = df.sort_values(\"test_accuracy\", ascending=False).groupby(\"feature_set\").head(1)\n",
"best_rows = (\n",
" df.sort_values(\"test_accuracy\", ascending=False).groupby(\"feature_set\").head(1)\n",
")\n",
"for _, row in best_rows.iterrows():\n",
" cm = np.array([[row[\"TN\"], row[\"FP\"]], [row[\"FN\"], row[\"TP\"]]], dtype=float)\n",
" if np.isnan(cm).any():\n",
" continue\n",
" fig = plt.figure(figsize=(5,5))\n",
" fig = plt.figure(figsize=(5, 5))\n",
" im = plt.imshow(cm, interpolation=\"nearest\")\n",
" plt.title(f\"Confusion Matrix — {row['model_family']} ({row['feature_set']})\")\n",
" plt.xticks([0,1], [\"Pred: f\",\"Pred: m\"])\n",
" plt.yticks([0,1], [\"True: f\",\"True: m\"])\n",
" plt.xticks([0, 1], [\"Pred: f\", \"Pred: m\"])\n",
" plt.yticks([0, 1], [\"True: f\", \"True: m\"])\n",
" # Annotate counts and rates\n",
" total = cm.sum()\n",
" for i in range(2):\n",
" for j in range(2):\n",
" val = cm[i,j]\n",
" plt.text(j, i, f\"{int(val)}\\n({val/total:.2%})\", ha=\"center\", va=\"center\")\n",
" val = cm[i, j]\n",
" plt.text(j, i, f\"{int(val)}\\n({val / total:.2%})\", ha=\"center\", va=\"center\")\n",
" plt.colorbar(im, fraction=0.046, pad=0.04)\n",
" plt.tight_layout()\n",
" plt.show()"
@@ -298,34 +375,37 @@
"source": [
"deltas = []\n",
"for fam in families:\n",
" fam_rows = df[df[\"model_family\"].astype(str)==fam]\n",
" base = fam_rows[fam_rows[\"feature_set\"]==\"Native\"]\n",
" fam_rows = df[df[\"model_family\"].astype(str) == fam]\n",
" base = fam_rows[fam_rows[\"feature_set\"] == \"Native\"]\n",
" if len(base):\n",
" base_acc = float(base.iloc[0][\"test_accuracy\"])\n",
" for feat in [\"Full name\",\"Surname\"]:\n",
" tgt = fam_rows[fam_rows[\"feature_set\"]==feat]\n",
" for feat in [\"Full name\", \"Surname\"]:\n",
" tgt = fam_rows[fam_rows[\"feature_set\"] == feat]\n",
" if len(tgt):\n",
" deltas.append({\n",
" \"model_family\": fam,\n",
" \"comparison\": f\"{feat} minus Native\",\n",
" \"delta_accuracy\": float(tgt.iloc[0][\"test_accuracy\"]) - base_acc\n",
" })\n",
" deltas.append(\n",
" {\n",
" \"model_family\": fam,\n",
" \"comparison\": f\"{feat} minus Native\",\n",
" \"delta_accuracy\": float(tgt.iloc[0][\"test_accuracy\"])\n",
" - base_acc,\n",
" }\n",
" )\n",
"\n",
"deltas_df = pd.DataFrame(deltas)\n",
"display(deltas_df)\n",
"\n",
"fig5 = plt.figure(figsize=(10,6))\n",
"fig5 = plt.figure(figsize=(10, 6))\n",
"# Make bars grouped by model_family\n",
"comp_types = deltas_df[\"comparison\"].unique().tolist() if not deltas_df.empty else []\n",
"x2 = np.arange(len(families))\n",
"width2 = 0.8 / max(1, len(comp_types))\n",
"for i, comp in enumerate(comp_types):\n",
" sub = deltas_df[deltas_df[\"comparison\"]==comp]\n",
" sub = deltas_df[deltas_df[\"comparison\"] == comp]\n",
" y = []\n",
" for fam in families:\n",
" row = sub[sub[\"model_family\"]==fam]\n",
" row = sub[sub[\"model_family\"] == fam]\n",
" y.append(float(row.iloc[0][\"delta_accuracy\"]) if len(row) else np.nan)\n",
" plt.bar(x2 + i*width2 - (len(comp_types)-1)*width2/2, y, width2, label=comp)\n",
" plt.bar(x2 + i * width2 - (len(comp_types) - 1) * width2 / 2, y, width2, label=comp)\n",
"\n",
"plt.xticks(x2, families)\n",
"plt.axhline(0, linestyle=\"--\")\n",
src/notebooks/names.ipynb
+3 -1
View File
@@ -113,7 +113,9 @@
"df_name_categories.head(12)\n",
"\n",
"# save data\n",
"df_name_categories.to_csv(\"../../assets/identified_category_distribution.csv\", index=False)"
"df_name_categories.to_csv(\n",
" \"../../assets/identified_category_distribution.csv\", index=False\n",
")"
]
},
{