JonnyBP commited on
Commit ·
e284b6a
1
Parent(s): c5d0f1a
feat: add new gap for rubric. #6
Browse files- notebooks/01_eda_v2.ipynb +0 -0
- notebooks/02_preprocessing_v2.ipynb +8 -9
- notebooks/03_vectorization_v2.ipynb +22 -14
- notebooks/04_baseline_v2.ipynb +26 -19
- notebooks/05_ensemble_v2.ipynb +18 -24
- notebooks/06_Optuna_v2_final.ipynb +33 -25
- reports/v2/03_wordclouds.png +2 -2
- reports/v2/14_optuna_comparativa.png +2 -2
- reports/v2/14_optuna_convergencia.png +0 -3
- reports/v2/15_optuna_convergencia.png +0 -3
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"Best trial: 37. Best value: 0.713928: 100%|██████████| 60/60 [00:
|
| 521 |
]
|
| 522 |
},
|
| 523 |
{
|
|
@@ -569,7 +577,7 @@
|
|
| 569 |
},
|
| 570 |
{
|
| 571 |
"cell_type": "code",
|
| 572 |
-
"execution_count":
|
| 573 |
"metadata": {
|
| 574 |
"id": "b5e6782e"
|
| 575 |
},
|
|
@@ -625,7 +633,7 @@
|
|
| 625 |
},
|
| 626 |
{
|
| 627 |
"cell_type": "code",
|
| 628 |
-
"execution_count":
|
| 629 |
"metadata": {
|
| 630 |
"id": "c8f54e1c"
|
| 631 |
},
|
|
@@ -640,7 +648,7 @@
|
|
| 640 |
"----------------------------------------------------------------------------------------------------\n",
|
| 641 |
" LR baseline 0.7531 0.8623 10.91 0.7015 0.0312 5.17 19 30 ⚠️ 5.2pp\n",
|
| 642 |
" LR tuned 0.7579 0.8987 14.07 0.7104 0.0353 4.76 18 30 ✅ OK\n",
|
| 643 |
-
" RF baseline 0.
|
| 644 |
" RF tuned 0.6924 0.8133 12.09 0.7139 0.0334 2.15 9 49 ✅ OK\n",
|
| 645 |
" LinearSVC 0.7250 0.9649 23.99 0.6847 0.0276 4.03 17 37 ✅ OK\n",
|
| 646 |
"\n",
|
|
@@ -688,14 +696,14 @@
|
|
| 688 |
},
|
| 689 |
{
|
| 690 |
"cell_type": "code",
|
| 691 |
-
"execution_count":
|
| 692 |
"metadata": {
|
| 693 |
"id": "243374d0"
|
| 694 |
},
|
| 695 |
"outputs": [
|
| 696 |
{
|
| 697 |
"data": {
|
| 698 |
-
"image/png": 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|
| 699 |
"text/plain": [
|
| 700 |
"<Figure size 1400x600 with 2 Axes>"
|
| 701 |
]
|
|
@@ -752,7 +760,7 @@
|
|
| 752 |
},
|
| 753 |
{
|
| 754 |
"cell_type": "code",
|
| 755 |
-
"execution_count":
|
| 756 |
"metadata": {
|
| 757 |
"id": "0d178b01"
|
| 758 |
},
|
|
@@ -830,7 +838,7 @@
|
|
| 830 |
},
|
| 831 |
{
|
| 832 |
"cell_type": "code",
|
| 833 |
-
"execution_count":
|
| 834 |
"metadata": {
|
| 835 |
"id": "8e205676"
|
| 836 |
},
|
|
@@ -875,7 +883,7 @@
|
|
| 875 |
},
|
| 876 |
{
|
| 877 |
"cell_type": "code",
|
| 878 |
-
"execution_count":
|
| 879 |
"metadata": {
|
| 880 |
"id": "6dc9f27a"
|
| 881 |
},
|
|
@@ -884,8 +892,8 @@
|
|
| 884 |
"name": "stderr",
|
| 885 |
"output_type": "stream",
|
| 886 |
"text": [
|
| 887 |
-
"2026/05/14
|
| 888 |
-
"2026/05/14
|
| 889 |
]
|
| 890 |
},
|
| 891 |
{
|
|
@@ -942,7 +950,7 @@
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},
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{
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"cell_type": "code",
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-
"execution_count":
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"metadata": {
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"id": "b77fe7cc"
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@@ -958,7 +966,7 @@
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"\n",
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"Mejora Optuna:\n",
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" LR: +0.48pp F1 test\n",
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-
" RF: -
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"\n",
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"Ganador: LR tuned\n",
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" F1 test : 0.7579\n",
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"id": "d57a4942"
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/under/miniconda3/envs/py310/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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" from .autonotebook import tqdm as notebook_tqdm\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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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"id": "44ca1814"
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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": 5,
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"metadata": {
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"id": "c2af1236"
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {
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"id": "8bc1d30a"
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {
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"id": "891692e9"
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},
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"output_type": "stream",
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"text": [
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" LR baseline F1=0.7531 | train-test=10.91pp | cv-test=5.17pp\n",
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" RF baseline F1=0.7531 | train-test=10.91pp | cv-test=5.17pp\n"
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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": 8,
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"metadata": {
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"id": "6c750ae7"
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {
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"id": "852e0804"
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},
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Best trial: 52. Best value: 0.710353: 100%|██████████| 60/60 [00:05<00:00, 10.25it/s]"
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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": 10,
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"metadata": {
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"id": "27c75e2a"
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {
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"id": "f37dc04c"
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},
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Best trial: 37. Best value: 0.713928: 100%|██████████| 60/60 [00:55<00:00, 1.09it/s]"
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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": 12,
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"metadata": {
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"id": "b5e6782e"
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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": 13,
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"metadata": {
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"id": "c8f54e1c"
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},
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"----------------------------------------------------------------------------------------------------\n",
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" LR baseline 0.7531 0.8623 10.91 0.7015 0.0312 5.17 19 30 ⚠️ 5.2pp\n",
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" LR tuned 0.7579 0.8987 14.07 0.7104 0.0353 4.76 18 30 ✅ OK\n",
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" RF baseline 0.7531 0.8623 10.91 0.7015 0.0312 5.17 19 30 ⚠️ 5.2pp\n",
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" RF tuned 0.6924 0.8133 12.09 0.7139 0.0334 2.15 9 49 ✅ OK\n",
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" LinearSVC 0.7250 0.9649 23.99 0.6847 0.0276 4.03 17 37 ✅ OK\n",
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"\n",
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {
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"id": "243374d0"
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},
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"outputs": [
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{
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"data": {
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|
| 707 |
"text/plain": [
|
| 708 |
"<Figure size 1400x600 with 2 Axes>"
|
| 709 |
]
|
|
|
|
| 760 |
},
|
| 761 |
{
|
| 762 |
"cell_type": "code",
|
| 763 |
+
"execution_count": 15,
|
| 764 |
"metadata": {
|
| 765 |
"id": "0d178b01"
|
| 766 |
},
|
|
|
|
| 838 |
},
|
| 839 |
{
|
| 840 |
"cell_type": "code",
|
| 841 |
+
"execution_count": 16,
|
| 842 |
"metadata": {
|
| 843 |
"id": "8e205676"
|
| 844 |
},
|
|
|
|
| 883 |
},
|
| 884 |
{
|
| 885 |
"cell_type": "code",
|
| 886 |
+
"execution_count": 17,
|
| 887 |
"metadata": {
|
| 888 |
"id": "6dc9f27a"
|
| 889 |
},
|
|
|
|
| 892 |
"name": "stderr",
|
| 893 |
"output_type": "stream",
|
| 894 |
"text": [
|
| 895 |
+
"2026/05/14 16:29:24 WARNING mlflow.models.model: `artifact_path` is deprecated. Please use `name` instead.\n",
|
| 896 |
+
"2026/05/14 16:29:25 WARNING mlflow.sklearn: Saving scikit-learn models in the pickle or cloudpickle format requires exercising caution because these formats rely on Python's object serialization mechanism, which can execute arbitrary code during deserialization. The recommended safe alternative is the 'skops' format. For more information, see: https://scikit-learn.org/stable/model_persistence.html\n"
|
| 897 |
]
|
| 898 |
},
|
| 899 |
{
|
|
|
|
| 950 |
},
|
| 951 |
{
|
| 952 |
"cell_type": "code",
|
| 953 |
+
"execution_count": 18,
|
| 954 |
"metadata": {
|
| 955 |
"id": "b77fe7cc"
|
| 956 |
},
|
|
|
|
| 966 |
"\n",
|
| 967 |
"Mejora Optuna:\n",
|
| 968 |
" LR: +0.48pp F1 test\n",
|
| 969 |
+
" RF: -6.07pp F1 test\n",
|
| 970 |
"\n",
|
| 971 |
"Ganador: LR tuned\n",
|
| 972 |
" F1 test : 0.7579\n",
|
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