fix: models

Makefile

@@ -1,52 +0,0 @@
-.PHONY: default
-default: help
-
-.PHONY: help
-help: ## Show this help message
-	@awk 'BEGIN {FS = ":.*?## "} /^[a-zA-Z_-]+:.*?## / {printf "\033[36m%-30s\033[0m %s\n", $$1, $$2}' $(MAKEFILE_LIST)
-
-# =============================================================================
-# ENVIRONMENT SETUP
-# =============================================================================
-
-.PHONY: setup
-setup: ## Setup virtual environment and install dependencies
-	python -m venv .venv
-	source .venv/bin/activate
-	.venv/bin/pip install --upgrade pip
-	.venv/bin/pip install -r requirements.txt
-
-.PHONY: install
-install: ## Install/update dependencies
-	pip install --upgrade pip
-	pip install -r requirements.txt
-
-# =============================================================================
-# DEVELOPMENT & CODE QUALITY
-# =============================================================================
-
-.PHONY: format
-format: ## Format code with black
-	black . --line-length 100
-
-.PHONY: lint
-lint: ## Lint code with flake8
-	flake8 . --max-line-length=100 --ignore=E203,W503 --exclude=.venv
-
-.PHONY: type-check
-type-check: ## Type check with mypy
-	mypy . --ignore-missing-imports
-
-.PHONY: notebook
-notebook: ## Start Jupyter notebook
-	jupyter notebook notebooks/
-
-# =============================================================================
-# DEPLOYMENT & PRODUCTION
-# =============================================================================
-
-.PHONY: backup
-backup: ## Backup datasets and results
-	@mkdir -p backups/$(shell date +%Y%m%d_%H%M%S)
-	@cp -r data/ backups/$(shell date +%Y%m%d_%H%M%S)/data/
-	@echo "Backup created in backups/$(shell date +%Y%m%d_%H%M%S)/"

README.md

@@ -91,11 +91,6 @@ uv run ners research train --name="random_forest" --type="baseline" --env="produ
 uv run ners research train --name="random_forest_native" --type="baseline" --env="production"
 uv run ners research train --name="random_forest_surname" --type="baseline" --env="production"
 
-# svm
-uv run ners research train --name="svm" --type="baseline" --env="production"
-uv run ners research train --name="svm_native" --type="baseline" --env="production"
-uv run ners research train --name="svm_surname" --type="baseline" --env="production"
-
 # naive bayes
 uv run ners research train --name="naive_bayes" --type="baseline" --env="production"
 uv run ners research train --name="naive_bayes_native" --type="baseline" --env="production"
@@ -112,46 +107,6 @@ uv run ners research train --name="xgboost_native" --type="baseline" --env="prod
 uv run ners research train --name="xgboost_surname" --type="baseline" --env="production"
 ```
 
-## TensorFlow on macOS (Intel) with uv
-
-TensorFlow no longer publishes wheels for macOS Intel. To keep using uv and run TF reliably, use a Linux container with TF preinstalled and install project code with minimal extras inside the container.
-
-### One-time build
-
-```bash
-docker compose -f docker/compose.tf.yml build
-
-If you see a message like `tensorflow/tensorflow:<tag>: not found`, update `docker/Dockerfile.tf-cpu` to a tag that exists (e.g., `2.17.0`) and rebuild:
-
-```bash
-sed -n '1,20p' docker/Dockerfile.tf-cpu  # verify the FROM line
-docker pull tensorflow/tensorflow:2.17.0 # quick availability check
-docker compose -f docker/compose.tf.yml build
-```
-```
-
-### Start a shell with uv and TF available
-
-```bash
-docker compose -f docker/compose.tf.yml run --rm tf bash
-```
-
-Inside the container:
-
-```bash
-# Install project in editable mode without pulling full deps
-uv pip install -e . --no-deps
-
-# Install only what research needs alongside TensorFlow
-uv pip install typer pandas scikit-learn seaborn plotly
-
-# Sanity check
-uv run python -c "import tensorflow as tf; print(tf.__version__)"
-
-# Run an experiment
-uv run ners research train --name="lstm" --type="baseline" --env="production"
-```
-
 ## Web Interface
 
 This project includes a user-friendly web interface built with Streamlit, allowing non-technical users to run

compose.yml

@@ -10,10 +10,12 @@ services:
     environment:
       NERS_ENV: production
       STREAMLIT_SERVER_ADDRESS: 0.0.0.0
+      PYTHONPATH: /app/src
     # expose Streamlit for `ners web run`
     ports:
       - "8501:8501"
     volumes:
+      - ./src:/app/src
       - ./assets:/app/assets
       - ./config:/app/config
       - ./data:/app/data

config/pipeline.development.yaml

@@ -30,7 +30,7 @@ llm:
 # Data handling configuration
 data:
   split_evaluation: false
-  max_dataset_size: 100_000
+  max_dataset_size: 10_000
   balance_by_sex: true
 
 # Enhanced logging for development

pyproject.toml

@@ -22,6 +22,7 @@ dependencies = [
     "streamlit>=1.50.0",
     "tqdm>=4.67.1",
     "typer>=0.19.2",
+    "tensorflow==2.20.0; sys_platform == 'linux' and platform_machine == 'x86_64'",
     "xgboost>=3.0.5",
 ]
 
@@ -36,6 +37,3 @@ build-backend = "uv_build"
 dev = [
     "ruff>=0.13.3",
 ]
-
-[tool.uv]
-required-environments = ["sys_platform == 'linux' and platform_machine == 'x86_64'"]

src/ners/cli.py

@@ -204,7 +204,6 @@ def web_run(
     config: Optional[Path] = typer.Option(None, help="Path to configuration file"),
     env: str = typer.Option("development", help="Environment name"),
 ) -> None:
-    """Launch the Streamlit web app via subprocess."""
     app_path = Path(__file__).parent / "web" / "app.py"
     cmd = [
         sys.executable,

src/ners/core/config/__init__.py

@@ -4,7 +4,6 @@ from typing import Optional, Union
 
 from ners.core.utils import ensure_directories
 from ners.core.config.config_manager import ConfigManager
-from ners.core.config.logging_config import LoggingConfig
 from ners.core.config.pipeline_config import PipelineConfig
 
 config_manager = ConfigManager()

src/ners/research/models/lightgbm_model.py

@@ -16,6 +16,7 @@ class LightGBMModel(TraditionalModel):
         # Store vectorizers and encoders to ensure consistent feature space
         self.vectorizers = {}
         self.label_encoders = {}
+        self.feature_columns = []
 
     def build_model(self) -> BaseEstimator:
         params = self.config.model_params
@@ -38,14 +39,16 @@ class LightGBMModel(TraditionalModel):
             random_state=self.config.random_seed,
             objective=params.get("objective", "binary"),
             n_jobs=params.get("n_jobs", -1),
-            verbose=2,
+            verbose=params.get("verbose", -1),
             device=device,
             gpu_platform_id=gpu_platform_id,
             gpu_device_id=gpu_device_id,
+            force_row_wise=params.get("force_row_wise", True),
         )
 
-    def prepare_features(self, X: pd.DataFrame) -> np.ndarray:
+    def prepare_features(self, X: pd.DataFrame) -> pd.DataFrame | np.ndarray:
         features = []
+        columns: list[str] = []
 
         for feature_type in self.config.features:
             if feature_type.value in X.columns:
@@ -53,7 +56,9 @@ class LightGBMModel(TraditionalModel):
 
                 if feature_type.value in ["name_length", "word_count"]:
                     # Numerical features
-                    features.append(column.fillna(0).values.reshape(-1, 1))
+                    arr = column.fillna(0).values.reshape(-1, 1)
+                    features.append(arr)
+                    columns.append(feature_type.value)
                 elif feature_type.value in ["full_name", "native_name", "surname"]:
                     # Character-level features for names
                     feature_key = f"vectorizer_{feature_type.value}"
@@ -63,20 +68,24 @@ class LightGBMModel(TraditionalModel):
                         self.vectorizers[feature_key] = CountVectorizer(
                             analyzer="char", ngram_range=(2, 3), max_features=50
                         )
-                        char_features = (
-                            self.vectorizers[feature_key]
-                            .fit_transform(column.fillna("").astype(str))
-                            .toarray()
-                        )
+                        vec = self.vectorizers[feature_key]
+                        char_features = vec.fit_transform(
+                            column.fillna("").astype(str)
+                        ).toarray()
+                        vocab_names = list(vec.get_feature_names_out())
                     else:
                         # Subsequent times - use existing vectorizer
-                        char_features = (
-                            self.vectorizers[feature_key]
-                            .transform(column.fillna("").astype(str))
-                            .toarray()
-                        )
+                        vec = self.vectorizers[feature_key]
+                        char_features = vec.transform(
+                            column.fillna("").astype(str)
+                        ).toarray()
+                        vocab_names = list(vec.get_feature_names_out())
 
                     features.append(char_features)
+                    # Prefix with feature name to avoid collisions
+                    columns.extend(
+                        [f"char_{feature_type.value}_{n}" for n in vocab_names]
+                    )
                 else:
                     # Categorical features
                     feature_key = f"encoder_{feature_type.value}"
@@ -111,5 +120,11 @@ class LightGBMModel(TraditionalModel):
                         )
 
                     features.append(encoded.reshape(-1, 1))
+                    columns.append(f"cat_{feature_type.value}")
+        if not features:
+            return pd.DataFrame(index=X.index)
 
-        return np.hstack(features) if features else np.array([]).reshape(len(X), 0)
+        matrix = np.hstack(features)
+        # Persist column order for consistency
+        self.feature_columns = columns
+        return pd.DataFrame(matrix, index=X.index, columns=columns)

src/ners/research/models/logistic_regression_model.py

@@ -1,3 +1,4 @@
+import logging
 import numpy as np
 import pandas as pd
 from sklearn.base import BaseEstimator
@@ -13,22 +14,38 @@ class LogisticRegressionModel(TraditionalModel):
 
     def build_model(self) -> BaseEstimator:
         params = self.config.model_params
-        # Character n-grams are strong signals for names; (2,5) balances
+        # Character n-grams are strong signals for names; (2,4) balances
         # capturing prefixes/suffixes with tractable feature size.
+        # Ensure tuple for sklearn API (YAML lists -> tuple)
+        ngram_range = params.get("ngram_range", (2, 4))
+        if isinstance(ngram_range, list):
+            ngram_range = tuple(ngram_range)
+
         vectorizer = CountVectorizer(
             analyzer="char",
-            ngram_range=params.get("ngram_range", (2, 5)),
+            ngram_range=ngram_range,
             max_features=params.get("max_features", 10000),
         )
 
-        # liblinear handles sparse, small-to-medium problems well; n_jobs parallelizes
-        # OvR across classes (no effect for binary). class_weight can mitigate imbalance.
+        # Choose solver and threads. liblinear ignores n_jobs>1 in recent sklearn
+        # versions, which raises a warning; clamp to 1 to avoid noise.
+        solver = params.get("solver", "liblinear")
+        n_jobs = params.get("n_jobs", -1)
+        if solver == "liblinear" and (n_jobs is None or n_jobs != 1):
+            if isinstance(n_jobs, int) and n_jobs != 1:
+                logging.info(
+                    "LogisticRegression(liblinear): forcing n_jobs=1 to avoid sklearn warning"
+                )
+            n_jobs = 1
+
+        # liblinear handles sparse, small-to-medium problems well; class_weight can
+        # mitigate imbalance. For very large, consider solver='saga'.
         classifier = LogisticRegression(
             max_iter=params.get("max_iter", 1000),
             random_state=self.config.random_seed,
             verbose=2,
-            solver=params.get("solver", "liblinear"),
-            n_jobs=params.get("n_jobs", -1),
+            solver=solver,
+            n_jobs=n_jobs,
             class_weight=params.get("class_weight", None),
         )
 

src/ners/research/models/naive_bayes_model.py

@@ -15,9 +15,14 @@ class NaiveBayesModel(TraditionalModel):
         params = self.config.model_params
         # Bag-of-character-ngrams aligns with Multinomial NB assumptions; (1,4)
         # includes unigrams for coverage and higher n for suffix/prefix cues.
+        # Ensure tuple for sklearn API (YAML lists -> tuple)
+        ngram_range = params.get("ngram_range", (2, 4))
+        if isinstance(ngram_range, list):
+            ngram_range = tuple(ngram_range)
+
         vectorizer = CountVectorizer(
             analyzer="char",
-            ngram_range=params.get("ngram_range", (2, 5)),
+            ngram_range=ngram_range,
             max_features=params.get("max_features", 8000),
         )
 

src/ners/research/models/svm_model.py

@@ -1,52 +0,0 @@
-import numpy as np
-import pandas as pd
-from sklearn.base import BaseEstimator
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.pipeline import Pipeline
-from sklearn.svm import SVC
-
-from ners.research.traditional_model import TraditionalModel
-
-
-class SVMModel(TraditionalModel):
-    """Support Vector Machine with character n-grams and RBF kernel"""
-
-    def build_model(self) -> BaseEstimator:
-        params = self.config.model_params
-        # TF-IDF downweights very common patterns; char n-grams (2,4) are effective
-        # for distinguishing name morphology under RBF kernels.
-        vectorizer = TfidfVectorizer(
-            analyzer="char",
-            ngram_range=params.get("ngram_range", (2, 4)),
-            max_features=params.get("max_features", 5000),
-        )
-
-        # RBF kernel captures non-linear interactions between n-grams; probability=True
-        # adds calibration at some cost. Larger cache helps speed kernel computations.
-        classifier = SVC(
-            kernel=params.get("kernel", "rbf"),
-            C=params.get("C", 1.0),
-            gamma=params.get("gamma", "scale"),
-            probability=True,  # Enable probability prediction
-            class_weight=params.get("class_weight", None),
-            cache_size=params.get("cache_size", 1000),
-            random_state=self.config.random_seed,
-            verbose=2,
-        )
-
-        return Pipeline([("vectorizer", vectorizer), ("classifier", classifier)])
-
-    def prepare_features(self, X: pd.DataFrame) -> np.ndarray:
-        text_features = []
-
-        for feature_type in self.config.features:
-            if feature_type.value in X.columns:
-                text_features.append(X[feature_type.value].astype(str))
-
-        if len(text_features) == 1:
-            return text_features[0].values
-        else:
-            combined = text_features[0].astype(str)
-            for feature in text_features[1:]:
-                combined = combined + " " + feature.astype(str)
-            return combined.values

src/ners/research/models/transformer_model.py

@@ -24,20 +24,21 @@ class TransformerModel(NeuralNetworkModel):
 
     def build_model_with_vocab(self, vocab_size: int, **kwargs) -> Any:
         params = kwargs
+        # Use a single resolved max_len everywhere to avoid shape mismatches
+        max_len = int(params.get("max_len", 6))
 
         # Build Transformer model
-        inputs = Input(shape=(params.get("max_len", 8),))
+        inputs = Input(shape=(max_len,))
         x = Embedding(
             input_dim=vocab_size,
             output_dim=params.get("embedding_dim", 64),
-            input_length=params.get("max_len", 8),
             mask_zero=True,
         )(inputs)
 
         # Add positional encoding
-        positions = tf.range(start=0, limit=params.get("max_len", 8), delta=1)
+        positions = tf.range(start=0, limit=max_len, delta=1)
         pos_embedding = Embedding(
-            input_dim=params.get("max_len", 8),
+            input_dim=max_len,
             output_dim=params.get("embedding_dim", 64),
         )(positions)
         x = x + pos_embedding
@@ -85,6 +86,6 @@ class TransformerModel(NeuralNetworkModel):
 
         # Convert to sequences
         sequences = self.tokenizer.texts_to_sequences(text_data)
-        max_len = self.config.model_params.get("max_len", 6)
+        max_len = int(self.config.model_params.get("max_len", 6))
 
         return pad_sequences(sequences, maxlen=max_len, padding="post")

src/ners/research/models/xgboost_model.py

@@ -20,13 +20,12 @@ class XGBoostModel(TraditionalModel):
     def build_model(self) -> BaseEstimator:
         params = self.config.model_params
 
-        # Optional GPU acceleration
+        # Optional GPU acceleration. With modern XGBoost, setting tree_method is
+        # sufficient and you typically don't need to pass `predictor`; doing so can
+        # trigger "Parameters ... are not used" warnings with the sklearn API.
         use_gpu = bool(params.get("use_gpu", False))
         default_tree_method = "gpu_hist" if use_gpu else "hist"
         tree_method = params.get("tree_method", default_tree_method)
-        predictor = params.get(
-            "predictor", "gpu_predictor" if tree_method.startswith("gpu") else "auto"
-        )
 
         # Histogram-based trees and parallelism provide fast training; default
         # logloss metric suits binary classification of gender.
@@ -40,8 +39,7 @@ class XGBoostModel(TraditionalModel):
             eval_metric="logloss",
             n_jobs=params.get("n_jobs", -1),
             tree_method=tree_method,
-            predictor=predictor,
-            verbosity=2,
+            verbosity=params.get("verbosity", 0),
         )
 
     def prepare_features(self, X: pd.DataFrame) -> np.ndarray:

src/ners/research/traditional_model.py

@@ -61,18 +61,22 @@ class TraditionalModel(BaseModel):
                 f"Fitting model with {X_prepared.shape[0]} samples and {X_prepared.shape[1]} features"
             )
 
+        try:
+            # Log a small sample safely for arrays or DataFrames
+            if hasattr(X_prepared, "iloc"):
+                logging.info(X_prepared.iloc[0].to_dict())
+            else:
                 logging.info(X_prepared[0])
+        except Exception:
+            pass
         logging.info(f"Model parameters: {self.config.model_params}")
 
-        history = self.model.fit(X_prepared, y_encoded)
+        # Fit scikit-learn compatible model. Unlike Keras, sklearn's fit returns
+        # the estimator itself and does not provide a training history object.
+        # We therefore do not populate training_history here.
+        self.model.fit(X_prepared, y_encoded)
         self.is_fitted = True
-
-        self.training_history = {
-            "accuracy": history.history["accuracy"],
-            "loss": history.history["loss"],
-            "val_accuracy": history.history.get("val_accuracy", []),
-            "val_loss": history.history.get("val_loss", []),
-        }
+        self.training_history = {}
 
         return self
 

uv.lock

@@ -2,10 +2,20 @@ version = 1
 revision = 3
 requires-python = ">=3.11"
 resolution-markers = [
-    "python_full_version >= '3.12'",
+    "python_full_version >= '3.13'",
+    "python_full_version == '3.12.*'",
     "python_full_version < '3.12'",
 ]
 
+[[package]]
+name = "absl-py"
+version = "2.3.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/10/2a/c93173ffa1b39c1d0395b7e842bbdc62e556ca9d8d3b5572926f3e4ca752/absl_py-2.3.1.tar.gz", hash = "sha256:a97820526f7fbfd2ec1bce83f3f25e3a14840dac0d8e02a0b71cd75db3f77fc9", size = 116588, upload-time = "2025-07-03T09:31:44.05Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/8f/aa/ba0014cc4659328dc818a28827be78e6d97312ab0cb98105a770924dc11e/absl_py-2.3.1-py3-none-any.whl", hash = "sha256:eeecf07f0c2a93ace0772c92e596ace6d3d3996c042b2128459aaae2a76de11d", size = 135811, upload-time = "2025-07-03T09:31:42.253Z" },
+]
+
 [[package]]
 name = "altair"
 version = "5.5.0"
@@ -45,6 +55,19 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/15/b3/9b1a8074496371342ec1e796a96f99c82c945a339cd81a8e73de28b4cf9e/anyio-4.11.0-py3-none-any.whl", hash = "sha256:0287e96f4d26d4149305414d4e3bc32f0dcd0862365a4bddea19d7a1ec38c4fc", size = 109097, upload-time = "2025-09-23T09:19:10.601Z" },
 ]
 
+[[package]]
+name = "astunparse"
+version = "1.6.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "six" },
+    { name = "wheel" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/f3/af/4182184d3c338792894f34a62672919db7ca008c89abee9b564dd34d8029/astunparse-1.6.3.tar.gz", hash = "sha256:5ad93a8456f0d084c3456d059fd9a92cce667963232cbf763eac3bc5b7940872", size = 18290, upload-time = "2019-12-22T18:12:13.129Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/2b/03/13dde6512ad7b4557eb792fbcf0c653af6076b81e5941d36ec61f7ce6028/astunparse-1.6.3-py2.py3-none-any.whl", hash = "sha256:c2652417f2c8b5bb325c885ae329bdf3f86424075c4fd1a128674bc6fba4b8e8", size = 12732, upload-time = "2019-12-22T18:12:11.297Z" },
+]
+
 [[package]]
 name = "attrs"
 version = "25.3.0"
@@ -344,6 +367,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/74/65/c162fbac63e867a055240b6600b92ef96c0eb7a1895312ac53c4be93d056/cymem-2.0.11-cp313-cp313-win_amd64.whl", hash = "sha256:25da111adf425c29af0cfd9fecfec1c71c8d82e2244a85166830a0817a66ada7", size = 39090, upload-time = "2025-01-16T21:50:24.239Z" },
 ]
 
+[[package]]
+name = "flatbuffers"
+version = "25.9.23"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/9d/1f/3ee70b0a55137442038f2a33469cc5fddd7e0ad2abf83d7497c18a2b6923/flatbuffers-25.9.23.tar.gz", hash = "sha256:676f9fa62750bb50cf531b42a0a2a118ad8f7f797a511eda12881c016f093b12", size = 22067, upload-time = "2025-09-24T05:25:30.106Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/ee/1b/00a78aa2e8fbd63f9af08c9c19e6deb3d5d66b4dda677a0f61654680ee89/flatbuffers-25.9.23-py2.py3-none-any.whl", hash = "sha256:255538574d6cb6d0a79a17ec8bc0d30985913b87513a01cce8bcdb6b4c44d0e2", size = 30869, upload-time = "2025-09-24T05:25:28.912Z" },
+]
+
 [[package]]
 name = "fonttools"
 version = "4.60.1"
@@ -393,6 +425,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/c7/93/0dd45cd283c32dea1545151d8c3637b4b8c53cdb3a625aeb2885b184d74d/fonttools-4.60.1-py3-none-any.whl", hash = "sha256:906306ac7afe2156fcf0042173d6ebbb05416af70f6b370967b47f8f00103bbb", size = 1143175, upload-time = "2025-09-29T21:13:24.134Z" },
 ]
 
+[[package]]
+name = "gast"
+version = "0.6.0"
+source = { registry = "https://pypi.org/simple" }
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