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‎examples/aptanet_finetuning_tutorial.ipynb‎

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{
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"cell_type": "code",
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"id": "d4f7d00b",
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"metadata": {},
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"metadata": {
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"execution": {
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"iopub.execute_input": "2026-04-04T23:37:03.552084Z",
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"outputs": [],
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"source": [
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"import warnings\n",
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},
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{
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"cell_type": "code",
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"id": "3737da88",
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"metadata": {},
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"metadata": {
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"execution": {
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"iopub.execute_input": "2026-04-04T23:37:03.556117Z",
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"outputs": [],
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"source": [
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"import numpy as np\n",
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "a2f6701d",
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"metadata": {},
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"metadata": {
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"execution": {
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"iopub.execute_input": "2026-04-04T23:37:20.132910Z",
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Number of samples: 25\n"
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"Number of samples: 250\n"
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "44cc1cbf",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2026-04-04T23:37:20.212145Z",
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}
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Feature matrix shape: (25, 690)\n"
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"Feature matrix shape: (250, 690)\n"
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]
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}
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],
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},
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{
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"cell_type": "code",
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"execution_count": 44,
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"execution_count": 5,
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"id": "d782bca8",
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"metadata": {},
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"metadata": {
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"execution": {
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"iopub.execute_input": "2026-04-04T23:37:20.894377Z",
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"iopub.status.busy": "2026-04-04T23:37:20.894232Z",
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"iopub.status.idle": "2026-04-04T23:37:25.060475Z",
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}
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},
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"outputs": [],
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"source": [
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"from sklearn.model_selection import GridSearchCV\n",
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"cell_type": "code",
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"metadata": {
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"execution": {
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"id": "ae896e5c",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2026-04-04T23:37:25.068145Z",
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"text": [
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"\n",
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"Best parameters: {'dropout': 0.2, 'hidden_dim': 64, 'lr': 0.0005, 'n_hidden': 7}\n",
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"Best cross-validation score: 0.6019\n"
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"Best cross-validation score: 0.5600\n"
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]
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}
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],
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"cell_type": "code",
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"execution_count": 47,
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"execution_count": 8,
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"id": "6130027a",
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"metadata": {},
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"metadata": {
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"execution": {
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"iopub.execute_input": "2026-04-04T23:38:24.631902Z",
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"iopub.status.busy": "2026-04-04T23:38:24.631466Z",
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"iopub.status.idle": "2026-04-04T23:38:24.634673Z",
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},
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"outputs": [],
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"source": [
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"from scipy.stats import loguniform, randint, uniform\n",
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"cell_type": "code",
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"metadata": {},
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"metadata": {
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"execution": {
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"iopub.execute_input": "2026-04-04T23:38:24.636338Z",
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"iopub.status.busy": "2026-04-04T23:38:24.636164Z",
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}
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},
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"source": [
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"# Define parameter distributions for random search\n",
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"execution_count": 10,
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"id": "8049a472",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2026-04-04T23:38:24.642734Z",
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"iopub.status.idle": "2026-04-04T23:39:17.969297Z",
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},
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"outputs": [
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"text": [
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"Fitting 3 folds for each of 10 candidates, totalling 30 fits\n",
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"Fitting 3 folds for each of 10 candidates, totalling 30 fits\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"Best parameters: {'dropout': np.float64(0.30990986410335564), 'hidden_dim': 128, 'lr': np.float64(1.9010245319870364e-05), 'max_epochs': 150, 'n_hidden': 8}\n",
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"Best cross-validation score: 0.6019\n"
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"Best parameters: {'dropout': np.float64(0.34044600469728353), 'hidden_dim': 256, 'lr': np.float64(0.00020034427927560734), 'max_epochs': 50, 'n_hidden': 4}\n",
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"Best cross-validation score: 0.5600\n"
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}
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],
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},
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"cell_type": "code",
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"execution_count": 11,
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"id": "523f4ce6",
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"metadata": {},
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"metadata": {
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"execution": {
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"iopub.execute_input": "2026-04-04T23:39:17.970826Z",
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"iopub.status.busy": "2026-04-04T23:39:17.970656Z",
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}
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},
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"outputs": [],
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"source": [
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"from sklearn.metrics import accuracy_score, classification_report"
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"id": "0cf0bed1",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2026-04-04T23:39:17.974421Z",
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"iopub.status.idle": "2026-04-04T23:39:18.063373Z",
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}
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"outputs": [
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"text": [
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"Training Accuracy: 0.6000\n",
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"Training Accuracy: 0.5600\n",
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"\n",
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"Classification Report:\n",
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" precision recall f1-score support\n",
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"\n",
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" 0.0 0.60 1.00 0.75 15\n",
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" 1.0 0.00 0.00 0.00 10\n",
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" 0.0 0.00 0.00 0.00 110\n",
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" 1.0 0.56 1.00 0.72 140\n",
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"\n",
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" accuracy 0.60 25\n",
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" macro avg 0.30 0.50 0.38 25\n",
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"weighted avg 0.36 0.60 0.45 25\n",
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" accuracy 0.56 250\n",
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" macro avg 0.28 0.50 0.36 250\n",
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"weighted avg 0.31 0.56 0.40 250\n",
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"\n"
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"execution": {
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"iopub.execute_input": "2026-04-04T23:39:18.065146Z",
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"iopub.status.busy": "2026-04-04T23:39:18.064996Z",
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"Pipeline Training Accuracy: 0.4000\n"
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"Pipeline Training Accuracy: 0.5600\n"
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"name": "python",
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"version": "3.11.9"
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"version": "3.12.3"
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"nbformat": 4,

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