chore: Update gitignore and clean up code

This commit introduces several maintenance and cleanup changes:

- Updates the `.gitignore` file to exclude artifacts related to time-series experiments and data generation. This includes the time-series experiment results directory, generated synthetic data, a saved Keras model, and a new constraints file.

- Refactors `notebooks/unit_test_synthetic.ipynb` by removing redundant and unused imports.

- Cleans up `tests/test_tabpfn_classifier_class.py` by removing an unused variable.

Author: SamoraHunter

.gitignore

@@ -113,3 +113,9 @@ percent_missing_synthetic_data_generated.pkl
 synthetic_data_generated.csv
 synthetic_data_generated.csv
 notebooks/tabpfn-v2.5-classifier-v2.5_default-2.ckpt
+notebooks/HFE_ML_experiments_ts/*
+ts-constraints.txt
+notebooks/best_model.keras
+notebooks/percent_missing_synthetic_ts_data_generated.pkl
+notebooks/percent_missing_synthetic_ts_data.pkl
+notebooks/synthetic_ts_data_generated.csv

notebooks/unit_test_synthetic.ipynb

@@ -17,13 +17,11 @@
    "outputs": [],
    "source": [
     "import logging\n",
-    "import os\n",
     "import shutil\n",
     "from pathlib import Path\n",
     "import yaml\n",
     "# --- Make sure your notebook's working directory is the project root ---\n",
     "# (e.g., /home/samorah/_data/ml_binary_classification_gridsearch_hyperOpt/)\n",
-    "from pathlib import Path\n",
     "\n",
     "import pandas as pd\n",
     "from IPython.display import display\n",
@@ -102,7 +100,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import yaml\n",
     "\n",
     "CONFIG_HYPEROPT_PATH = Path(\"../config_hyperopt.yml\")\n",
     "\n",

tests/test_tabpfn_classifier_class.py

@@ -158,7 +158,6 @@ def test_fit_v2(self, mock_tabpfn_cls):
     @patch("ml_grid.model_classes.tabpfn_classifier_class.TabPFNClassifier")
     def test_predict_and_predict_proba_delegation(self, mock_tabpfn_cls):
         """Test that predict and predict_proba delegate to the internal estimator."""
-        mock_estimator_instance = mock_tabpfn_cls.return_value
 
         model_wrapper = TabPFNClassifierClass()