From d7788e19a6219510b81b56391f28ea5f1922f94b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jo=C3=A3o=20Paulo?= <88985821+Joao-Paulo-Silva@users.noreply.github.com> Date: Tue, 7 Apr 2026 21:56:17 -0300 Subject: [PATCH 1/5] feat: package version dynamically from metadata --- aisp/__init__.py | 4 +++- pyproject.toml | 2 +- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/aisp/__init__.py b/aisp/__init__.py index fad8f7e..1363c0f 100644 --- a/aisp/__init__.py +++ b/aisp/__init__.py @@ -26,10 +26,12 @@ https://ais-package.github.io/docs/intro """ +from importlib.metadata import version + from . import csa from . import ina from . import nsa __author__ = "AISP Development Team" -__version__ = "0.5.1" +__version__ = version('aisp') __all__ = ["csa", "nsa", "ina"] diff --git a/pyproject.toml b/pyproject.toml index a46c054..81fb952 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -5,7 +5,7 @@ build-backend = "setuptools.build_meta" [project] name = "aisp" -version = "0.5.3" +version = "0.5.4" authors = [ { name="João Paulo da Silva Barros", email="jpsilvabarr@gmail.com" }, ] From e73771f55f2c3d2ff5f150a38c543a02ad3ea488 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jo=C3=A3o=20Paulo?= <88985821+Joao-Paulo-Silva@users.noreply.github.com> Date: Tue, 7 Apr 2026 22:04:36 -0300 Subject: [PATCH 2/5] fix: correctly assign no_label_sample_selection condition in BNSA class --- aisp/nsa/_binary_negative_selection.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/aisp/nsa/_binary_negative_selection.py b/aisp/nsa/_binary_negative_selection.py index 65b28bd..edc7d51 100644 --- a/aisp/nsa/_binary_negative_selection.py +++ b/aisp/nsa/_binary_negative_selection.py @@ -118,7 +118,7 @@ def __init__( self.no_label_sample_selection: str = sanitize_param( no_label_sample_selection, "max_average_difference", - lambda x: x == "nearest_difference", + lambda x: x == "max_nearest_difference", ) self.classes: Optional[npt.NDArray] = None @@ -308,7 +308,7 @@ def _assign_class_to_non_self_sample(self, line: npt.NDArray, c: list): distances = np.mean(line != self._detectors[_class_], axis=1) # Assign the label to the class with the greatest distance from # the nearest detector. - if self.no_label_sample_selection == "nearest_difference": + if self.no_label_sample_selection == "max_nearest_difference": class_differences[_class_] = distances.min() # Or based on the greatest distance from the average distances of the detectors. else: From 6e8f959b5094b37b72a06db58ef74654de81294c Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jo=C3=A3o=20Paulo?= <88985821+Joao-Paulo-Silva@users.noreply.github.com> Date: Tue, 7 Apr 2026 22:05:20 -0300 Subject: [PATCH 3/5] feat: example update BNSA --- ...e_with_randomly_generated_dataset-en.ipynb | 4 +- ...e_with_randomly_generated_dataset-en.ipynb | 48 +++++++------- .../BNSA/mushrooms_dataBase_example_en.ipynb | 49 ++++++++------- ...mple_with_randomly_generated_dataset.ipynb | 2 +- ...e_with_randomly_generated_dataset-pt.ipynb | 52 +++++++++------ .../mushrooms_dataBase_example_pt-br.ipynb | 63 ++++++++++--------- 6 files changed, 123 insertions(+), 95 deletions(-) diff --git a/examples/en/classification/AIRS/example_with_randomly_generated_dataset-en.ipynb b/examples/en/classification/AIRS/example_with_randomly_generated_dataset-en.ipynb index 70b251d..dc9c9f7 100644 --- a/examples/en/classification/AIRS/example_with_randomly_generated_dataset-en.ipynb +++ b/examples/en/classification/AIRS/example_with_randomly_generated_dataset-en.ipynb @@ -204,7 +204,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": ".venv (3.12.2)", "language": "python", "name": "python3" }, @@ -218,7 +218,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.12.2" }, "orig_nbformat": 4 }, diff --git a/examples/en/classification/BNSA/example_with_randomly_generated_dataset-en.ipynb b/examples/en/classification/BNSA/example_with_randomly_generated_dataset-en.ipynb index fbc1c87..453cbf8 100644 --- a/examples/en/classification/BNSA/example_with_randomly_generated_dataset-en.ipynb +++ b/examples/en/classification/BNSA/example_with_randomly_generated_dataset-en.ipynb @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -61,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -94,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -131,7 +131,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -181,12 +181,12 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -226,36 +226,36 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "Generating the detectors for the A class: ┇ ┇ 0/500 detectors" + "\u001b[92m✔ Non-self detectors for classes (A, B) successfully generated\u001b[0m: ┇██████████┇ 500/500 detectors" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "\u001b[92m✔ Non-self detectors for classes (A, B) successfully generated\u001b[0m: ┇██████████┇ 500/500 detectors\n" + "The accuracy is 0.945\n", + " precision recall f1-score support\n", + "\n", + " A 0.92 0.96 0.94 90\n", + " B 0.96 0.94 0.95 110\n", + "\n", + " accuracy 0.94 200\n", + " macro avg 0.94 0.95 0.94 200\n", + "weighted avg 0.95 0.94 0.95 200\n", + "\n" ] }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "The accuracy is 0.93\n", - " precision recall f1-score support\n", - "\n", - " A 0.93 0.91 0.92 90\n", - " B 0.93 0.95 0.94 110\n", - "\n", - " accuracy 0.93 200\n", - " macro avg 0.93 0.93 0.93 200\n", - "weighted avg 0.93 0.93 0.93 200\n", "\n" ] } @@ -282,12 +282,12 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -317,7 +317,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": ".venv (3.12.2)", "language": "python", "name": "python3" }, @@ -331,7 +331,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.12.2" }, "orig_nbformat": 4 }, diff --git a/examples/en/classification/BNSA/mushrooms_dataBase_example_en.ipynb b/examples/en/classification/BNSA/mushrooms_dataBase_example_en.ipynb index b9fae45..9573acf 100644 --- a/examples/en/classification/BNSA/mushrooms_dataBase_example_en.ipynb +++ b/examples/en/classification/BNSA/mushrooms_dataBase_example_en.ipynb @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -191,8 +191,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "The average accuracy is: 0.910721\n", - "Standard deviation of accuracies: 0.025371\n" + "The average accuracy is: 0.952513\n", + "Standard deviation of accuracies: 0.016990\n" ] } ], @@ -231,6 +231,13 @@ "execution_count": 6, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Generating the detectors for the edible class: ┇ ┇ 0/500 detectors" + ] + }, { "name": "stderr", "output_type": "stream", @@ -276,21 +283,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "The accuracy is 0.9081214109926169\n", + "The accuracy is 0.9528301886792453\n", " precision recall f1-score support\n", "\n", - " edible 0.86 0.99 0.92 1260\n", - " poisonous 0.98 0.82 0.90 1178\n", + " edible 0.92 1.00 0.96 1279\n", + " poisonous 1.00 0.91 0.95 1159\n", "\n", - " accuracy 0.91 2438\n", - " macro avg 0.92 0.91 0.91 2438\n", - "weighted avg 0.92 0.91 0.91 2438\n", + " accuracy 0.95 2438\n", + " macro avg 0.96 0.95 0.95 2438\n", + "weighted avg 0.96 0.95 0.95 2438\n", "\n" ] }, { "data": { - "image/png": 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ystasWaM5c+aUa0AAAIpTpgLr27evGjVqpBkzZmj58uWSpEaNGmnz5s32QgMA4GYq00Uczo6LOFDRcREHKrqbdhGHJKWnp2vs2LH605/+pFOnTkmSPvnkE33zzTdlXSUAACVWpgLbuHGjGjdurOTkZC1btkw5OTmSpD179mj8+PHlGhAAgOKUqcBGjRqliRMn6j//+Y/c3d3t461bt9a2bdvKLRwAANdTpgLbu3evHn300SLjVatW1ZkzZ351KAAAbqRMBValShVlZmYWGd+9e7dq1qz5q0MBAHAjZSqwJ598Ui+++KJOnjwpm82mwsJCbdmyRSNGjFDv3r3LOyMAAEWUqcAmTZqkO++8U2FhYcrJyVFkZKRiYmLUokULjR07trwzAgBQxK/6HNixY8e0d+9e5eTk6N5771WDBg3KM1uZ8TkwVHR8DgwV3U37HNgrr7yi3NxchYWFqUOHDurWrZsaNGigS5cu6ZVXXinLKgEAKJUy7YG5uroqMzNTVatWdRg/e/asqlatqoKCgnILWBbsgaGiYw8MFd1N2wMzDKPYpzPv2bNHgYGBZVklAAClUqqb+QYEBMhms8lms6lhw4YOJVZQUKCcnBwNGjSo3EMCAPC/SlVg06ZNk2EY6tevnxITE+Xv72+f5u7urjp16ig6OrrcQwIA8L9KVWB9+vSRJIWHh6tFixZyc3O7KaEAALiRMj0PLDY21v795cuXlZ+f7zDdz8/v16UCAOAGynQRR25uruLi4lS1alX5+PgoICDA4QsAgJutTAU2cuRIff7553rrrbfk4eGhd955R4mJiapRo4YWLFhQ3hkBACiiTIcQV61apQULFuihhx7SU089pQcffFD169dX7dq1tWjRIvXo0aO8cwIA4KBMe2BZWVmqW7eupGvnu7KysiRJDzzwgDZt2lR+6QAAuI4yFVjdunV15MgRSdKdd96pxYsXS7q2Z1alSpVyCwcAwPWUqcCeeuop7dmzR9K1pzO/8cYb8vT0VHx8vEaOHFmuAQEAKM6vuhv9T7777jvt2rVL9evXV5MmTcoj16/CvRBR0XEvRFR0JbkXYrkUmLOp5M5ToVGxXZjcyewIwE3lPfTNG85TpqsQJWnHjh1av369Tp06pcLCQodpU6ZMKetqAQAokTIV2KRJkzR27FhFRESoWrVqDjf1Le4u9QAAlLcyFdj06dM1d+5c9e3bt5zjAABQMmW6CtHFxUUtW7Ys7ywAAJRYmQosPj5eb7zxRnlnAQCgxMp0CHHEiBHq2LGj6tWrp8jIyCKPVVm+fHm5hAMA4HrKVGDDhg3T+vXr1apVKwUFBXHhBgDglitTgc2fP1/Lli1Tx44dyzsPAAAlUqZzYIGBgapXr155ZwEAoMTKVGATJkzQ+PHjlZubW955AAAokTIdQpwxY4bS09NVrVo11alTp8hFHCkpKeUSDgCA6ylTgXXu3LmcYwAAUDplKrDx48eXdw4AAEqlTOfAAAAwW4n3wAIDA5Wamqrg4GAFBAT84me/srKyyiUcAADXU+ICmzp1qnx9fe3f8+FlAICZeKAlYEE80BIVXUkeaFmmc2Curq46depUkfGzZ8/K1dW1LKsEAKBUylRg19tpy8vLk7u7+68KBABASZTqMvoZM2ZIuvbU5XfeeUeVK1e2TysoKNCmTZt05513lm9CAACKUaoCmzp1qqRre2CzZs1yOFzo7u6uOnXqaNasWeWbEACAYpSqwI4cOSJJatWqlZYvX66AgICbEgoAgBsp0zmw9evXO5RXQUGBvvrqK507d67cggEA8EvKVGDDhw/Xu+++K+laecXExKhp06YKCwvThg0byjMfAADFKlOBLVmyRFFRUZKkVatW6ejRo/r2228VHx+vl156qVwDAgBQnDIV2NmzZxUaGipJ+vjjj9W1a1c1bNhQ/fr10969e8s1IAAAxSlTgVWrVk379+9XQUGB1qxZo7Zt20qScnNz+SAzAOCWKNPjVJ566il169ZN1atXl81mU5s2bSRJycnJfA4MAHBLlKnAJkyYoLvvvlvHjh1T165d5eHhIenaLaZGjx5drgEBAChOqQ4hdujQQefPn5ckPf7448rLy3O4G8cf/vAHjRo1qnwTAgBQjFIV2Nq1a5WXl2d/PWnSJIdnf129elUHDx4sv3QAAFxHqQrsf2/iWwGfxAIAsIgyXYUIAIDZSlVgNputyJOYeTIzAMAMpboK0TAM9e3b137V4eXLlzVo0CD5+PhIksP5MQAAbqZSFVifPn0cXvfs2bPIPL179/51iQAAKIFSFdi8efNuVg4AAEqFizgAAJZEgQEALIkCAwBYEgUGALAkCgwAYEkUGADAkigwAIAlUWAAAEuiwAAAlkSBAQAsiQIDAFgSBQYAsCQKDABgSRQYAMCSKDAAgCVRYAAAS6LAAACWRIEBACyJAgMAWBIFBgCwJAoMAGBJFBgAwJIoMACAJTllgV24cEErVqzQgQMHzI4CAHBSTlFg3bp108yZMyVJly5dUrNmzdStWzc1adJEy5YtMzkdAMAZOUWBbdq0SQ8++KAk6YMPPpBhGMrOztaMGTM0ceJEk9MBAJyRUxTY+fPnFRgYKElas2aNunTpIm9vb3Xs2FFpaWkmpwMAOCOnKLCwsDBt3bpVFy9e1Jo1a/S73/1OknTu3Dl5enqanA4A4IwqmR1AkoYPH64ePXqocuXKql27th566CFJ1w4tNm7c2NxwAACn5BQFNmTIEP32t7/VsWPH1LZtW7m4XNsxrFu3LufAAADFshmGYZgdorxVcq9pdgTgprowuZPZEYCbynvomzecxyn2wPr16/eL0+fOnXuLkgAArMIpCuzcuXMOr69cuaJ9+/YpOztbrVu3NikVAMCZOUWBffDBB0XGCgsLNXjwYNWrV8+ERAAAZ+cUl9EXx8XFRQkJCZo6darZUQAATshpC0yS0tPTdfXqVbNjAACckFMcQkxISHB4bRiGMjMztXr1avXp08ekVAAAZ+YUBbZ7926H1y4uLgoJCdHkyZNveIUiAOD25BQFtn79erMjAAAsxikK7CenT5/WwYMHJUkREREKCQkxOREAwFk5xUUcFy9eVL9+/VS9enXFxMQoJiZGNWrU0NNPP63c3Fyz4wEAnJBTFFhCQoI2btyoVatWKTs7W9nZ2Vq5cqU2btyo559/3ux4AAAn5BT3QgwODtbSpUvtd6H/yfr169WtWzedPn26VOvjXoio6LgXIiq6ktwL0Sn2wHJzc1WtWrUi41WrVuUQIgCgWE5RYNHR0Ro/frwuX75sH7t06ZISExMVHR1tYjIAgLNyiqsQp0+frnbt2umOO+5QVFSUJGnPnj3y9PTU2rVrTU4HAHBGTlFgd999t9LS0rRo0SJ9++23kqTu3burR48e8vLyMjkdAMAZOUWBSZK3t7cGDBhgdgwAgEU4TYGlpaVp/fr1OnXqlAoLCx2mvfzyyyalAgA4K6cosDlz5mjw4MEKDg5WaGiobDabfZrNZqPAnMCDD9yv558frKb3NlaNGqF67PF++vDD/56ffPedqerTu5vDMmvXrlfHP/a81VGBErmYf1VvJh/W54dP61xuviJCfPXCgw11VzU/+zyHsy5q+peHlHLinK4WGqob6KO//76Jqvt6SpKOnc/V1C2HtPtEtq4UFKpF7SC9GNNQQd4eZm3WbcUpCmzixIn6y1/+ohdffNHsKLgOHx9vff31fs17719atuTdYudZs+ZzPT3gv08WyMvLv1XxgFJ75fMDOpR1URPbRCrEx0MfHzypQStTtOxPzVW1sqeOnc9Vv2U71TmyhgbfX1c+7q5Kz7ooD9drF29fulKgISu/UsPgyprduakk6c3kdD330dda0LWZXH72hzhuDqcosHPnzqlr165mx8AvWLN2vdas/eWbLufl5+uHH0r3oXPADJevFuiz9NOa2rGJ7qsZIEkadH9dbTp6Rkv2Hdezzetp5rZ0PVAnWMNbNrAvF+bvbf/+q8xsnfjxkv755G9V2f3af6WvtLlLsXM2avv359Q8LPDWbtRtyCk+B9a1a1d9+umnZsfArxQbE60T3+/RN/s2aebrSQoMDDA7ElCsgkJDBYYhd1fH/wI9Krlo94lsFRqGNh89q1pVvDVk5W61fneTei3ZofWH//sHWn5BoWyyOazDo5KLXGw2fXUi+1Ztym3NKfbA6tevr3Hjxmnbtm1q3Lix3NzcHKYPGzbsusvm5eUpLy/PYcwwDIfzaLj51n66Xh+s+FhHjx5T3bq1NfHPo7R61UK1fPCRIhflAGbzca+kJqH+mrPjiMIDfBTk7a41aSf19cnzCvP3VlZuvnKvFGjerqN6tnk9PdeivrZknNXzH3+t2Y82VbOaAWoc6i8vNxdN//KQ4prXkyRN//KQCgxDZ3LzbpAA5cEpCmz27NmqXLmyNm7cqI0bNzpMs9lsv1hgSUlJSkxMdFzGpbJsrn7XWQI3w+LFH9q/37fvW+3de0BpB7fqodgW+nz9ZhOTAcWb2DZSEz47oHbvbZarzaY7Q3zVvkGoDpy+oML/v0PsQ+Eh6nlPLUlSRIiv9mSe19J9x9WsZoACvdz1WvvGmrThoP6555hcbDa1b1hNjUJ8+QP6FnGKAjty5EiZlx09erQSEhIcxgKC7vy1kfArHTmSodOnz6pevToUGJxSmL+33n3sPl26UqCc/KsK8fHQi2v2qqaflwK83FTJxaa6gT4Oy9QN9NHunx0ejK4VpFW9W+jcpXxVcrHJ18NNbeZ+oXZ+3IDhVnCKAvu5n26OX9K/YDw8POTh4XjJKn/9mK9mzeoKCgpQ5skfzI4C/CIvN1d5ubnqwuUr+jIjS8Nb1Jebq4siq/rpu2zHm4l/l51rv4T+5wK83CVJ27/PUlZuvmLDg29J9tudU1zEIUkLFixQ48aN5eXlJS8vLzVp0kQLFy40Oxb+n4+Pt6Ki7lJU1F2SpPA6tRQVdZfCwmrIx8dbf00aq/t/21S1a9+h1q0e0PJlc3Uo/ag+/XTjDdYMmOPL785qy3dndfzCJW3LOKsBK1IUHuCtRxpVlyT1ubeW1qb9oOXfHFdGdq7+9fUxbTpyRt0a32Ffx8r9J/T1yfM6dj5Xqw9m6oVP9qrHPbVUJ8Dnej8W5cgp9sCmTJmicePGKS4uTi1btpQkbd68WYMGDdKZM2cUHx9vckI0uy9Kn61ban89+e8TJEnzFyzWs3Gj1bhxI/Xq1VVVqvjpxIkf9J91GzV+wt+Un89nweCccvKv6vWt6foh57L8Pd30cL2qerZ5Pbn9/1WFretV1UsP3am5u47qtU2pqh3grb/9vrHurVHFvo6j2bl6fVu6zl++ohq+nnq6Wbh63hNm0hbdfpzigZbh4eFKTExU7969Hcbnz5+vCRMmlPocGQ+0REXHAy1R0VnmgZaZmZlq0aJFkfEWLVooMzPThEQAAGfnFAVWv359LV68uMj4v//9bzVo0KCYJQAAtzunOAeWmJioJ554Qps2bbKfA9uyZYs+++yzYosNAACn2APr0qWLkpOTFRwcrBUrVmjFihUKDg7W9u3b9eijj5odDwDghJxiD0yS7rvvPv3jH/8wOwYAwCKcYg8sJSVFe/futb9euXKlOnfurDFjxnAZNgCgWE5RYAMHDlRqaqok6fDhw3riiSfk7e2tJUuW6IUXXjA5HQDAGTlFgaWmpuqee+6RJC1ZskSxsbF6//339d5772nZsmXmhgMAOCWnKDDDMOyP3Fi3bp06dOggSQoLC9OZM2fMjAYAcFJOUWDNmjXTxIkTtXDhQm3cuFEdO3aUdO0u9dWqVTM5HQDAGTlFgU2bNk0pKSmKi4vTSy+9pPr160uSli5dWuwdOgAAcIp7IV7P5cuX5erqWuQJzTfCvRBR0XEvRFR0JbkXotN8Dqw4np5Fn7sDAIBkYoEFBgYqNTVVwcHBCggI+MWHUGZlZd3CZAAAKzCtwKZOnSpfX19J186BAQBQGk59DqysOAeGio5zYKjoLHUOrKCgQCtWrNCBAwckSXfddZceeeQRubq6mpwMAOCMnKLADh06pA4dOuj48eOKiIiQJCUlJSksLEyrV69WvXr1TE4IAHA2TvE5sGHDhqlevXo6duyYUlJSlJKSooyMDIWHh2vYsGFmxwMAOCGn2APbuHGjtm3bpsDAQPtYUFCQXn31VfsDLgEA+Dmn2APz8PDQjz/+WGQ8JydH7u7uJiQCADg7pyiwP/zhD3rmmWeUnJwswzBkGIa2bdumQYMG6ZFHHjE7HgDACTlFgc2YMUP16tVTdHS0PD095enpqRYtWqh+/fqaPn262fEAAE7IKc6BValSRStXrtShQ4e0f/9+SVJkZKT9pr4AAPwvpygwSXr33Xc1depUpaWlSZIaNGig4cOHq3///iYnAwA4I6cosJdffllTpkzR0KFDFR0dLUnaunWr4uPjlZGRoVdeecXkhAAAZ+MUt5IKCQnRjBkz1L17d4fxf/7znxo6dGipn8rMraRQ0XErKVR0JbmVlFNcxHHlyhU1a9asyPh9992nq1evmpAIAODsnKLAevXqpbfeeqvI+OzZs9WjRw8TEgEAnJ1TnAOTrl3E8emnn6p58+aSpOTkZGVkZKh3795KSEiwzzdlyhSzIgIAnIhTFNi+ffvUtGlTSVJ6erokKTg4WMHBwdq3b599vl966CUA4PbiFAW2fv16syMAACzGKc6BAQBQWhQYAMCSKDAAgCVRYAAAS6LAAACWRIEBACyJAgMAWBIFBgCwJAoMAGBJFBgAwJIoMACAJVFgAABLosAAAJZEgQEALIkCAwBYEgUGALAkCgwAYEkUGADAkigwAIAlUWAAAEuiwAAAlkSBAQAsiQIDAFgSBQYAsCQKDABgSRQYAMCSKDAAgCVRYAAAS6LAAACWRIEBACyJAgMAWBIFBgCwJAoMAGBJFBgAwJIoMACAJVFgAABLosAAAJZEgQEALIkCAwBYEgUGALAkCgwAYEkUGADAkigwAIAlUWAAAEuiwAAAlkSBAQAsiQIDAFgSBQYAsCQKDABgSRQYAMCSKDAAgCVRYAAAS6LAAACWRIEBACyJAgMAWJLNMAzD7BCwtry8PCUlJWn06NHy8PAwOw5Qrnh/Oy8KDL/ahQsX5O/vr/Pnz8vPz8/sOEC54v3tvDiECACwJAoMAGBJFBgAwJIoMPxqHh4eGj9+PCe4USHx/nZeXMQBALAk9sAAAJZEgQEALIkCAwBYEgWGEtmwYYNsNpuys7MlSe+9956qVKlinz5hwgTdc889v7iOvn37qnPnzjctI1Cc/32vouKgwFAmTzzxhFJTU82OAdwQ79WKq5LZAWBNXl5e8vLyMjsGcEO8Vysu9sBuU4WFhUpKSlJ4eLi8vLwUFRWlpUuX2qd//PHHatiwoby8vNSqVSsdPXrUYfnrHZZ5++23FRYWJm9vb3Xr1k3nz58vcwZAkh566CHFxcUpLi5O/v7+Cg4O1rhx4/TTJ4DOnTun3r17KyAgQN7e3vr973+vtLQ0+/L/+17ds2ePWrVqJV9fX/n5+em+++7Tzp077dOXLVumu+66Sx4eHqpTp44mT57skKdOnTqaNGmS+vXrJ19fX9WqVUuzZ892mGfv3r1q3bq1vLy8FBQUpGeeeUY5OTkO2zR8+HCHZTp37qy+ffvaX7/55ptq0KCBPD09Va1aNT3++ONl/RVWWBTYbSopKUkLFizQrFmz9M033yg+Pl49e/bUxo0bdezYMT322GP64x//qK+++kr9+/fXqFGjbrjOQ4cOafHixVq1apXWrFmj3bt3a8iQIWXKAPzc/PnzValSJW3fvl3Tp0/XlClT9M4770i6dm51586d+vDDD7V161YZhqEOHTroypUrxa6rR48euuOOO7Rjxw7t2rVLo0aNkpubmyRp165d6tatm5588knt3btXEyZM0Lhx4/Tee+85rGPy5Mlq1qyZ/T0+ePBgHTx4UJJ08eJFtWvXTgEBAdqxY4eWLFmidevWKS4ursTbu3PnTg0bNkyvvPKKDh48qDVr1igmJqYMv7kKzsBt5/Lly4a3t7fx5ZdfOow//fTTRvfu3Y3Ro0cbkZGRDtNefPFFQ5Jx7tw5wzAMY968eYa/v799+vjx4w1XV1fj+++/t4998sknhouLi5GZmWkYhmH06dPH6NSpU4kyAD+JjY01GjVqZBQWFtrHXnzxRaNRo0ZGamqqIcnYsmWLfdqZM2cMLy8vY/HixYZhFH2v+vr6Gu+9916xP+tPf/qT0bZtW4exkSNHOvx7qF27ttGzZ0/768LCQqNq1arGW2+9ZRiGYcyePdsICAgwcnJy7POsXr3acHFxMU6ePGnfpueee87h53Tq1Mno06ePYRiGsWzZMsPPz8+4cOHCjX49tzX2wG5Dhw4dUm5urtq2bavKlSvbvxYsWKD09HQdOHBA999/v8My0dHRN1xvrVq1VLNmTYdlCgsL7X+ZliYD8HPNmzeXzWazv46OjlZaWpr279+vSpUqObxfg4KCFBERoQMHDhS7roSEBPXv319t2rTRq6++6vB+O3DggFq2bOkwf8uWLZWWlqaCggL7WJMmTezf22w2hYaG6tSpU/Z1REVFycfHx2Ed1/u3UJy2bduqdu3aqlu3rnr16qVFixYpNze3RMveTiiw29BPx+JXr16tr776yv61f//+W3YOyhky4PY0YcIEffPNN+rYsaM+//xzRUZG6oMPPijVOn465PgTm82mwsLCEi/v4uJiP4f3k58f8vT19VVKSor++c9/qnr16nr55ZcVFRVl/xgLrqHAbkORkZHy8PBQRkaG6tev7/AVFhamRo0aafv27Q7LbNu27YbrzcjI0IkTJxyWcXFxUURERKkzAD+XnJzs8Hrbtm1q0KCBIiMjdfXqVYfpZ8+e1cGDBxUZGXnd9TVs2FDx8fH69NNP9dhjj2nevHmSpEaNGmnLli0O827ZskUNGzaUq6tribI2atRIe/bs0cWLFx3W8fN/CyEhIcrMzLRPLygo0L59+xzWU6lSJbVp00avvfaavv76ax09elSff/55iTLcLriM/jbk6+urESNGKD4+XoWFhXrggQd0/vx5bdmyRX5+fho0aJAmT56skSNHqn///tq1a1eRk9jF8fT0VJ8+ffT3v/9dFy5c0LBhw9StWzeFhoaWOkOfPn1uwpbDqjIyMpSQkKCBAwcqJSVFr7/+uiZPnqwGDRqoU6dOGjBggN5++235+vpq1KhRqlmzpjp16lRkPZcuXdLIkSP1+OOPKzw8XN9//7127NihLl26SJKef/55/eY3v9Gf//xnPfHEE9q6datmzpypN998s8RZe/ToofHjx6tPnz6aMGGCTp8+raFDh6pXr16qVq2aJKl169ZKSEjQ6tWrVa9ePU2ZMsVh7+qjjz7S4cOHFRMTo4CAAH388ccqLCws9o/B25rZJ+FgjsLCQmPatGlGRESE4ebmZoSEhBjt2rUzNm7caBiGYaxatcqoX7++4eHhYTz44IPG3Llzb3gRR1RUlPHmm28aNWrUMDw9PY3HH3/cyMrKss/z84s4SpIBMIxrFzwMGTLEGDRokOHn52cEBAQYY8aMsV/UkZWVZfTq1cvw9/c3vLy8jHbt2hmpqan25X/+Xs3LyzOefPJJIywszHB3dzdq1KhhxMXFGZcuXbLPv3TpUiMyMtJwc3MzatWqZfztb39zyFO7dm1j6tSpDmNRUVHG+PHj7a+//vpro1WrVoanp6cRGBhoDBgwwPjxxx/t0/Pz843BgwcbgYGBRtWqVY2kpCSHizi++OILIzY21ggICDC8vLyMJk2aGP/+97/L4bdZsfA4FQBO7aGHHtI999yjadOmmR0FToZzYAAAS6LAAACWxCFEAIAlsQcGALAkCgwAYEkUGADAkigwAIAlUWAAAEuiwIAKrm/fvurcubPZMYByR4EBJurbt69sNptsNpvc3NwUHh6uF154QZcvXzY7GuD0uJkvYLL27dtr3rx5unLlinbt2qU+ffrIZrPpr3/9q9nRAKfGHhhgMg8PD4WGhiosLEydO3dWmzZt9J///EeSVFhYqKSkJIWHh8vLy0tRUVEOz0srKCjQ008/bZ8eERGh6dOnm7UpwC3FHhjgRPbt26cvv/xStWvXliQlJSXpH//4h2bNmqUGDRpo06ZN6tmzp0JCQhQbG6vCwkLdcccdWrJkiYKCgvTll1/qmWeeUfXq1dWtWzeTtwa4uSgwwGQfffSRKleurKtXryovL08uLi6aOXOm8vLyNGnSJK1bt07R0dGSpLp162rz5s16++23FRsbKzc3NyUmJtrXFR4erq1bt2rx4sUUGCo8CgwwWatWrfTWW2/p4sWLmjp1qipVqqQuXbrom2++UW5urtq2beswf35+vu6991776zfeeENz585VRkaGLl26pPz8fN1zzz23eCuAW48CA0zm4+Oj+vXrS5Lmzp2rqKgovfvuu7r77rslSatXr1bNmjUdlvHw8JAk/etf/9KIESM0efJkRUdHy9fXV3/729+UnJx8azcCMAEFBjgRFxcXjRkzRgkJCUpNTZWHh4cyMjIUGxtb7PxbtmxRixYtNGTIEPtYenr6rYoLmIqrEAEn07VrV7m6uurtt9/WiBEjFB8fr/nz5ys9PV0pKSl6/fXXNX/+fElSgwYNtHPnTq1du1apqakaN26cduzYYfIWALcGe2CAk6lUqZLi4uL02muv6ciRIwoJCVFSUpIOHz6sKlWqqGnTphozZowkaeDAgdq9e7eeeOIJ2Ww2de/eXUOGDNEnn3xi8lYANx8PtAQAWBKHEAEAlkSBAQAsiQIDAFgSBQYAsCQKDABgSRQYAMCSKDAAgCVRYAAAS6LAAACWRIEBACyJAgMAWNL/AQMgEzFum+H4AAAAAElFTkSuQmCC", + "image/png": 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", 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" ] @@ -351,8 +358,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "The mean accuracy is: 0.915641\n", - "Standard deviation of accuracies: 0.028040\n" + "The mean accuracy is: 0.953251\n", + "Standard deviation of accuracies: 0.017655\n" ] } ], @@ -434,21 +441,21 @@ "name": "stdout", "output_type": "stream", "text": [ - "The accuracy is 0.9052502050861362\n", + "The accuracy is 0.9663658736669402\n", " precision recall f1-score support\n", "\n", - " edible 0.85 1.00 0.92 1260\n", - " poisonous 1.00 0.80 0.89 1178\n", + " edible 0.94 1.00 0.97 1279\n", + " poisonous 0.99 0.93 0.96 1159\n", "\n", - " accuracy 0.91 2438\n", - " macro avg 0.92 0.90 0.90 2438\n", - "weighted avg 0.92 0.91 0.90 2438\n", + " accuracy 0.97 2438\n", + " macro avg 0.97 0.96 0.97 2438\n", + "weighted avg 0.97 0.97 0.97 2438\n", "\n" ] }, { "data": { - "image/png": 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", + "image/png": 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e9QMA4H8PsLVr1+rWV/369fV0Uvnz53fuU/1h/v7+Urx48dQcEgCAjA+wevXq6X+PHTsmpUqV0i0uAACskKZRiD/++KMsXrw4SfmiRYtk9uzZ6VEvAADSP8BCQkKkYMGCScrVAA5mowcA2DbATp48KWXKlElSrvrA1D4AAGwZYKqltW/fviTle/fulQIFCqRHvQAASP8Aa9OmjfTt21ePSlT3f6lN9YupCX5bt26dlkMCAJDxk/m+8847cvz4cT2Zb86c9w6hJvft0KEDfWAAAPtP5nv48GF92dDDw0OqVq2q+8DsIKdbCaurAGSo64sHWF0FIEN5vDQoY1pgCdRsHMnNyAEAQEZLcYANHDhQXzrMmzevfvxXxo8fnx51AwDgfw+wPXv2SGxsrPPxn2F2DgCArQJMjThM7jEAAMYMowcAwJgWWIsWLVJ80KVLl6a1PgAApG8LzMfHx7l5e3vLDz/8IDt37nTu37Vrly5T+wEAsE0LbObMmc7HQ4YMkVdeeUWmTp0qOXLk0GVqNo5evXrpcAMAwJY3MhcqVEg2bdoklSpVcik/dOiQ1K5dWy5duiRW4kZmZHbcyIzMLiU3MqdpEEdcXJwcPHgwSbkqU1NKAQCQ0dI0E8c///lP6dKlixw9elRq1qypy7Zt2ybjxo3T+wAAsGWAffjhh1K0aFH56KOPJDIyUpcVK1ZMBg8eLG+88UZ61xEAgPSdzFeJiorS/9pp8AZ9YMjs6ANDZpdhfWAJ/WDff/+9zJ8/3zl9VEREhERHR6f1kAAAZOwlxBMnTsjzzz8vJ0+elDt37kijRo3Ey8tL3nvvPf1cDa8HACAjpakFplZeDgwMlCtXrui1wBI0b95c38wMAIAtW2AbN26Un376Sdzc3FzKS5cuLWfOnEmvugEAkL4tMHWvl5p5436nT5/WlxIBALBlgD333HMyceJE53M1iEMN3hg5cqQEBQWlZ/0AAEjf+8DUII6AgAC5ffu2vPrqqxIeHi4FCxbUoxIBALBlgPn5+cnevXtlwYIF+l/V+lIzc7Rt29ZlUAcAALYJsNjYWHnooYdkxYoVOrDUBgCA7fvAcuXKpS8bAgBg3CCO119/Xd+0rGbjAADAmD6wHTt26BuW16xZI1WrVpW8efO67F+6dGl61Q8AgPQLMF9fXwkODk7LjwIA8OADTN3A/MEHH8jhw4clJiZG6tevL2+//TYjDwEA9u4De/fdd2X48OHi6ekpJUqUkMmTJ+v+MAAAbB1gc+bMkSlTpsjq1atl2bJlsnz5cgkLC9MtMwAAbBtgavmUxFNFNWzYUE8jpdYBAwDAtgGmhs3nzp07yX1h6uZmAABsO4jD4XBIp06dxN3d3Vmmbmru0aOHy1B6htEDAGwVYB07dkxS1q5du/SsDwAA6R9gM2fOTM3LAQCw11RSAABYjQADABiJAAMAGIkAAwAYiQADABiJAAMAGIkAAwAYiQADABiJAAMAGIkAAwAYiQADABiJAAMAGIkAAwAYiQADABiJAAMAGIkAAwAYiQADABiJAAMAGIkAAwAYiQADABiJAAMAGIkAAwAYiQADABiJAAMAGIkAAwAYiQADABjJlgEWFRUly5YtkwMHDlhdFQCATdkiwF555RX55JNP9ONbt25JYGCgLqtWrZosWbLE6uoBAGzIFgG2YcMGefrpp/Xjr776ShwOh1y9elUmT54sY8aMsbp6AAAbskWAXbt2TfLnz68ff/fddxIcHCx58uSRJk2aSHh4uNXVAwDYkC0CzM/PT7Zs2SI3btzQAfbcc8/p8itXrkju3Lmtrh4AwIZyig30799f2rZtK56enuLv7y/PPPOM89Ji1apVra4eAMCGbBFgvXr1kpo1a8qpU6ekUaNGkj37vYZh2bJl6QMDACQrm0ONmMhkcrqVsLoKQIa6vniA1VUAMpTHS4PMaIF17tz5L/fPmDHjgdUFAGAGWwSYGqyRWGxsrOzfv18Ppa9fv75l9QIA2JctAkzd+3W/+Ph46dmzp5QrV86SOgEA7M0Ww+iTowZyDBw4UCZMmGB1VQAANmTbAFOOHj0qcXFxVlcDAGBDtriEqFpaiamBkZGRkbJy5Urp2LGjZfUCANiXLQJsz549SS4fFipUSD766KO/HaEIAMiabBFga9eutboKAADD2CLAEly4cEEOHTqkH1eqVEm3wgAAsO0gDjWJr7pUWKxYMalbt67eihcvLl26dJGbN29aXT0AgA1lt8sgjvXr18vy5cv1zctq+/rrr3XZG2+8YXX1AAA2ZIu5EAsWLCiLFy92zkKfuG9MrcysLi2mBnMhIrNjLkRkdimZC9EWLTB1mbBIkSJJygsXLswlRACAfQOsVq1aMnLkSLl9+7az7NatWzJq1Ci9DwAAW45CnDRpkjRu3FhKliwpjzzyiC7bu3evXo159erVVlcPAGBDtgiwhx9+WMLDwyUsLEwOHjyoy9q0aaNXafbw8LC6egAAG7JFgCl58uSRbt26WV0NAIAhbBNgqgWmRh2eP39eL6WS2FtvvWVZvQAA9mSLAJs2bZpe+0sNpy9atKhky5bNuU89JsDsqXjxohIydrg837i+5MmTW44cPS5duw6UXbv3WV01wMWu3yNl9rp9cuDMRbkQdVPGd2wk9R8u7dyv7ib6bM0uWbrtoFy/FSOPli4iw1s8Jf6FfJyvOXHhqkxYsV1+Pn5WYu/GS4Vi+eX1xoHyePniev/VG7dl+Ly1En72sn6c39NDnqniL31eeFw8c7tZ8r4zO1sE2JgxY+Tdd9+VIUOGWF0VpJCvr49sWLdM1q3/SV5s2k4uXLwkFcqXkStXr1ldNSCJWzFxUrF4fmn2eEUZOOf7JPtnrdsr8zb9Ku+0qicl8nvJlNW7pNf0VbJ0UEtxz3Xvz2SfGaulVEEfCe3eRJeFbdyvy1YMbSUFvfNI9mzZdGC9/nyg5PPMLacuRknIV5vl2s07Mq4tK8tn2gC7cuWK/OMf/7C6GkiFfw3uJadPR0jXbn8shXP8+ClL6wT8mace8tNbclTrS4VRtwaPybP/3yp7p/Uz0mD0f2Ttryfk+UfLyZUbt+XkxSh5+x91pWLxAvo1/YIel4VbfpMjZ6/oAPPO4y6v1A5wHrd4Pi/9XLX8kInvA1PhtWbNGqurgVR48cXnZNeuffLl/M8l4vRe2bF9tXTp/KrV1QJS7czl63Lx+i15osIfM/h4ebhJ1VKFZO+Jc/q5bx53KV3IR5bvCpdbMbESdzdeFm89qC8TBpQsmOxxz1+7IT/8clxqlC32wN5LVmOLFlj58uVlxIgRsnXrVqlatarkypXLZX/fvn3/9Gfv3Lmjt/u/USXuR0P6K1umlHTv3l4mTpom496bLIE1HpWJE0ZLTGyszJ27yOrqASmmwksp4OV6y44Kp0v/v0/9Pfn8tSAZMPu/UvvNWfpyodo/pevzuuWV2NCwH2Xdr8flduxdqRdQSkb+4+kH+G6yFlsEWGhoqHh6eurJe9WWmDpx/irAQkJC9IwdLj+T3VOy5fDOsPri3qKjqgX25ohx+vnPP/8qVapUku7d2hNgyHTUl+KQr36SfJ4eMqNnU8mdK6cs3X5Q+s5cI2F9m0kh7zzO1w5q+qR0b1RdTly4JpNXbZcPl2+Vf7d4ytL6Z1a2CLBjx46l+WeHDRumZ7NPLF+Bh9KhVvgrkZHn5bcDh13KDh48Ii2aB1lWJyAtCv5/y0u1thIH0eXoW87+ru1HImTDgZOyYXQH54jCf5d8SraGL5DlOw9L5/qP/nE87zx6K1PYV3zyuMs/pyyX1xpWdzk2MlEf2P3fdFIzQb67u7t4e3u7bFw+zHg/bdkhlSqWcymrWKGsnDx5xrI6AWmhRh2qENt+5I9zN/p2jPxy8oI84n9vkvHbsXH6X3XpMDH1PP4v/l4l7IuJu5tBtc/abBNgc+bM0f1fauootVWrVk3mzp1rdbXwJyZNmiZPPFFdhg7pI+XKlZbWrZtJ165tZcrUWVZXDUji5p1YOXjmkt4SBm6ox5FXovUX3rZPPyzTftgj6349IeGRl+XNL9fpFtOzVfz166v5FxFvDzcZ8eU6ORRxSd8TNn7FNn2cpyuX0q/ZeOCkLNtxSI6cvazLVYvt3SWb9D1lKiSRSdcDGz9+vB7E0bt3b6lTp44u27Rpk3z66af6HrEBA1K39hHrgT0YTYIaypgxQ/X9X8eOn5KJE0PlixnzrK5WlsB6YKmz42iEdJu6Mkl50xoV9JD5hBuZl2w9KNdvx8hj+kbmOuJfyNf52l9PXZBPvtshv52+qEchliuST15rVN05PH/HkQj5+Lsd8vu5qxIbd1eK+OaVBlXLyD+ffUS8PVwHeiB91gOzRYCVKVNGD8To0KGDS/ns2bPl7bffTnUfGQGGzI4AQ2ZnzIKWkZGRUrt27STlqkztAwDAlgGm7gNbuHBhkvIFCxZIhQoVLKkTAMDebDGMXl0+bNWqlWzYsMHZB7Z582b54Ycfkg02AABs0QILDg6Wbdu26dnoly1bpjf1ePv27dK8eXOrqwcAsCFbtMCUGjVqyH/+8x+rqwEAMIQtWmC7d++WX375xfn866+/lmbNmsnw4cMlJibG0roBAOzJFgHWvXt3OXz43rREv//+u+4Py5MnjyxatEj+9a9/WV09AIAN2SLAVHg9+ui9ucRUaNWrV0/mzZsns2bNkiVLllhdPQCADdkiwNS91PHx8frx999/L0FB9yaE9fPzk4sXL1pcOwCAHdkiwAIDA/WUUWruQ7WcSpMmTXS5moGjSJF7k2kCAGC7AJs4caIeyKHmQvz3v/+tb2xWFi9enOwMHQAA2GIuxD9z+/ZtyZEjR5IVmv8OcyEis2MuRGR2KZkL0Tb3gSUnd+7cVlcBAGBTlgVY/vz59ehDNeNGvnz5/nIRysuXLz/QugEA7M+yAJswYYJ4eXk5+8AAAMg0fWBpRR8YMjv6wJDZGdUHdvfuXT2J74EDB/TzKlWqyEsvvaQHcQAAYMsAO3LkiL55+cyZM1KpUiVdFhISom9kXrlypZQrV87qKgIAbMYW94H17dtXh9SpU6f0/WBqO3nypJQpU0bvAwDAli0wNfvG1q1b9cjEBAUKFJBx48Y5F7gEAMB2LTB3d3e5fv16kvLo6Ghxc3OzpE4AAHuzRYC9+OKL8tprr+lVmdWgSLWpFlmPHj30QA4AAGwZYJMnT9Z9YLVq1dKzb6hNzYGo5kScNGmS1dUDANiQLfrAfH199SrMajTib7/9pssCAgKck/oCAGDLAFO++OILPTtHeHi4fl6hQgXp37+/dO3a1eqqAQBsyBYB9tZbb8n48eOlT58++jKismXLFhkwYIAeTj969GirqwgAsBlbTCVVqFAh3Q/Wpk0bl/L58+frUEvtqsxMJYXMjqmkkNmlZCopWwziiI2N1asy369GjRoSFxdnSZ0AAPZmiwBr3769fPbZZ0nKQ0NDpW3btpbUCQBgb7boA0sYxLFmzRp58skn9XN1T5jq/+rQoYMMHDjQ+TrVVwYAgC0CbP/+/VK9enX9+OjRo/pftdCl2tS+BH+16CUAIGuxRYCtXbvW6ioAAAxjiz4wAABSiwADABiJAAMAGIkAAwAYiQADABiJAAMAGIkAAwAYiQADABiJAAMAGIkAAwAYiQADABiJAAMAGIkAAwAYiQADABiJAAMAGIkAAwAYiQADABiJAAMAGIkAAwAYiQADABiJAAMAGIkAAwAYiQADABiJAAMAGIkAAwAYiQADABiJAAMAGIkAAwAYiQADABiJAAMAGIkAAwAYiQADABiJAAMAGIkAAwAYiQADABiJAAMAGIkAAwAYiQADABiJAAMAGIkAAwAYiQADABiJAAMAGIkAAwAYiQADABiJAAMAGIkAAwAYiQADABiJAAMAGIkAAwAYiQADABiJAAMAGIkAAwAYiQADABiJAAMAGIkAAwAYKZvD4XBYXQmY7c6dOxISEiLDhg0Td3d3q6sDpCvOb/siwPA/i4qKEh8fH7l27Zp4e3tbXR0gXXF+2xeXEAEARiLAAABGIsAAAEYiwPA/Ux3bI0eOpIMbmRLnt30xiAMAYCRaYAAAIxFgAAAjEWAAACMRYEiRdevWSbZs2eTq1av6+axZs8TX19e5/+2335ZHH330L4/RqVMnadasWYbXFUjs/nMVmQcBhjRp1aqVHD582OpqAH+LczXzyml1BWAmDw8PvQF2x7maedECy6Li4+P1BKVlypTRH+5HHnlEFi9e7Nz/7bffSsWKFfW+Z599Vo4fP56iyzKff/65+Pn5SZ48eeSVV17R88eltQ6A8swzz0jv3r31puYkLFiwoIwYMUIS7gC6cuWKdOjQQfLly6fPuxdeeEHCw8P/9Fzdu3evPqe9vLz03IY1atSQnTt3OvcvWbJEqlSpou/7Kl26tHz00Ucu9VFlY8eOlc6dO+tjlCpVSkJDQ11e88svv0j9+vX1eV2gQAF57bXXJDo62uU99e/f3+Vn1OV1dZk9wZQpU6RChQqSO3duKVKkiLRs2TJd/j8zEwIsi1LBMWfOHJk6dar8+uuvMmDAAGnXrp2sX79eTp06JS1atJCmTZvKzz//LF27dpWhQ4f+7TGPHDkiCxculOXLl8t3330ne/bskV69eqWpDkBis2fPlpw5c8r27dtl0qRJMn78eJk+fbrep/7oqwD65ptvZMuWLTrYgoKCJDY2NtljtW3bVkqWLCk7duyQXbt26XM7V65cep96rr54tW7dWoeQ6ttVYalCMDEVaoGBgc5zvGfPnnLo0CG978aNG9K4cWMdqOp3LFq0SL7//nsdwCml3k/fvn1l9OjR+rjq81S3bt3/4X8wk1I3MiNruX37tiNPnjyOn376yaW8S5cujjZt2jiGDRvmCAgIcNk3ZMgQ9XXXceXKFf185syZDh8fH+f+kSNHOnLkyOE4ffq0s2zVqlWO7NmzOyIjI/Xzjh07Ol5++eUU1QFIUK9ePUflypUd8fHxLuejKjt8+LA+Lzdv3uzcd/HiRYeHh4dj4cKFyZ6rXl5ejlmzZiX7u1599VVHo0aNXMoGDx7s8nnw9/d3tGvXzvlc1atw4cKOzz77TD8PDQ115MuXzxEdHe18zcqVK/Vn4ezZs8731K9fP5ffoz4b6jOiLFmyxOHt7e2IiopK9f9XVkILLAtSLaWbN29Ko0aNxNPT07mp1tDRo0flwIED8sQTT7j8TK1atf72uOpSSokSJVx+Rl0mTPhmmpo6AIk9+eSTehRs4nNLXSb87bffdMss8fmqLtlVqlRJn8fJGThwoL6q0LBhQxk3bpzL+aZ+pk6dOi6vV8/V77p7966zrFq1as7Hql5FixaV8+fPO4+hLofnzZvX5Rh/9llIjvpc+Pv7S9myZaV9+/YSFhamPy9wRYBlQQnX4leuXKkvESZs6o/Bg+qDskMdkDWpy4LqknWTJk3kxx9/lICAAPnqq69SdYyES46JQ0wFVEplz57d2YeXIPElT9W3tnv3bpk/f74UK1ZM3nrrLR2KCbex4B4CLAtSH1jVQX3y5EkpX768y6YGYFSuXFn3NSS2devWvz2uOl5ERITLz6gPqvo2nNo6AIlt27bN5bk6t9QAB3UexcXFuey/dOmSbumofX9GDVBSfa5r1qzR/b0zZ87U5erc37x5s8tr1XP1+hw5cqSoruoYaqCI6gtLfIzEn4VChQpJZGSkc79q3e3fv9/lOKplqVqJ77//vuzbt08PpFKBiz8wjD4LUt/uBg0apD/A6lvjU089pUcLqg+ZGpXVo0cP3Uk9ePBgfalFdWzf34mdHDVaqmPHjvLhhx/qVWxVJ7TqEFeXV1JbB3UcIIH6oqMu/XXv3l23TD7++GN9jqoQe/nll6Vbt256BKw6r9SgDHUpW5Xf79atW/q8ViP61OjX06dP64EWwcHBev8bb7whjz/+uLzzzjv6/jE1KOSTTz7RIwJTSg0SUbPXq3NYtfYuXLggffr00ZcC1WhCRY1QVO9HXYEoV66cHpSSuHW1YsUK+f333/XADTUYRI0KVp+T5L4MZmlWd8LBGqrjeeLEiY5KlSo5cuXK5ShUqJCjcePGjvXr1+v9y5cvd5QvX97h7u7uePrppx0zZsz420EcjzzyiGPKlCmO4sWLO3Lnzu1o2bKl4/Lly87XJB7EkZI6AAkDHnr16uXo0aOHHtigBkgMHz7cOahDnWPt27fX56MavKHOITW4I0Hic/XOnTuO1q1bO/z8/Bxubm76XO3du7fj1q1bztcvXrxYD9pQ52SpUqUcH3zwgUt91CCOCRMmuJSpc199BhLs27fP8eyzz+rPQf78+R3dunVzXL9+3bk/JibG0bNnT71PDQAJCQlxGcSxceNG/b7Ve1XvqVq1ao4FCxak+/+t6VhOBYCtqXum1DRlEydOtLoqsBn6wAAARiLAAABG4hIiAMBItMAAAEYiwAAARiLAAABGIsAAAEYiwAAARiLAgCxMTUK7bNkyq6sBpAkBBjwgal49NSGsmgU9NdQKwMxCASRFgAEPyBdffKEndd2wYYPLrP0A0oYAAx7Q+mcLFizQS8+rFtj9s/svX75cz4KuZvQvWLCgNG/e3DkP4IkTJ/Ss/epyX8KijmqWczU/YGKqlaZaawnULOtqYUR1PB8fH6lXr56eyR3ILAgw4AFYuHChPPTQQ3o5jHbt2smMGTOcCxqqJTVUYAUFBcmePXvkhx9+kJo1a+p9S5culZIlS8ro0aP1+lGJ15D6O9evX9dLemzatMm5fpb6HaocyAxYDwx4QJcPVXApzz//vF77bP369bqF9e6770rr1q1l1KhRzter1XeV/Pnz634ztc5Vcuuq/RW15lRioaGh4uvrq3/viy++mC7vC7ASLTAgg6nVgdUK123atHGutKsWS1Shpvz888/SoEGDdP+9586d0ws9qpaXuoSoFgpVlzLV4pBAZkALDMhgKqjUsvfFixd3lqnLh+7u7nq1Xw8Pj1QfUy1Pf/883LGxsS7P1eXDS5cuyaRJk8Tf31//vlq1aklMTMz/8G4A+6AFBmQgFVxz5syRjz76SLe0Era9e/fqQJs/f75Uq1ZN93v9GTc3N7l7965LWaFCheTs2bMuIaaOm9jmzZulb9++ut+rSpUqOsAuXryYAe8SsAYtMCADrVixQq5cuSJdunTRl/ESCw4O1q2zDz74QF9CLFeunO4LU6H37bffypAhQ/Tr1MhCNfRe7VMhpEYVqr6zCxcuyPvvvy8tW7aU7777TlatWqUvEyZQlw7nzp0rgYGBEhUVJYMHD05Taw+wK1pgQAZSAdWwYcMk4ZUQYDt37tQDNRYtWiTffPONHhqvBl+oPrMEagTi8ePHdcCplpdSuXJlmTJlinz66ad6wId6/aBBg5L8bhWe1atXl/bt2+vWWOHChR/AuwYeDBa0BAAYiRYYAMBIBBgAwEgEGADASAQYAMBIBBgAwEgEGADASAQYAMBIBBgAwEgEGADASAQYAMBIBBgAQEz0f+5tQdSloGNZAAAAAElFTkSuQmCC", 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" ] @@ -484,7 +491,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": ".venv (3.12.2)", "language": "python", "name": "python3" }, @@ -498,7 +505,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.12.2" }, "orig_nbformat": 4 }, diff --git a/examples/en/clustering/AiNet/example_with_randomly_generated_dataset.ipynb b/examples/en/clustering/AiNet/example_with_randomly_generated_dataset.ipynb index 543359a..5081d26 100644 --- a/examples/en/clustering/AiNet/example_with_randomly_generated_dataset.ipynb +++ b/examples/en/clustering/AiNet/example_with_randomly_generated_dataset.ipynb @@ -487,7 +487,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": ".venv (3.12.2)", "language": "python", "name": "python3" }, diff --git a/examples/pt-br/classification/BNSA/example_with_randomly_generated_dataset-pt.ipynb b/examples/pt-br/classification/BNSA/example_with_randomly_generated_dataset-pt.ipynb index c530a58..46f8f60 100644 --- a/examples/pt-br/classification/BNSA/example_with_randomly_generated_dataset-pt.ipynb +++ b/examples/pt-br/classification/BNSA/example_with_randomly_generated_dataset-pt.ipynb @@ -38,7 +38,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -65,7 +65,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -97,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -135,14 +135,14 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[92m✔ Non-self detectors for classes (A, B) successfully generated\u001b[0m: ┇██████████┇ 500/500 detectors\n" + "\u001b[92m✔ Non-self detectors for classes (A, B) successfully generated\u001b[0m: ┇██████████┇ 500/500 detectors" ] }, { @@ -160,6 +160,13 @@ "weighted avg 0.93 0.93 0.93 200\n", "\n" ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] } ], "source": [ @@ -185,12 +192,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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SpUpFXW/UqJEZGm3ZsqWkSJHCme0FACDBkt12HNMA48qXr7q6CUCi8n9jlqubACSaiBuhibu9GQAAXj3sWa9evaidXXTtnuPruFq0aFH8WgcAgKvC7+jRo6aoRas3HV/HlX4fAAAeF34TJ040jzlz5rzrawAAvDb87t255d6vAQDwJMnju85v/vz5cuPGDee3CAAAd1znp3t56q4u/v7+Zk2fnu1XokQJ57cOAAB36fn169dPypUrJ2FhYTJp0iRp0aKFOb1dP9fNrAEA8NpF7qGhoTJz5kyZPXt2VEWoj4+P1K5d2wRi9I2uPQWL3OHtWOQObxbXRe5O2+Fl+/btMmPGDFm4cKHp/WkQ6oG32iPs2bOneArCD96O8IM3S/Lwi3rjiAhzxNHgwYPlxIkTJgT37t0rnoLwg7cj/ODN4hp+KZ32hhER5qSHefPmmcfw8HDzvJ74DgCAO0lw+G3bts3M+S1YsMAMd2pHMlOmTNKqVSsz71ekSBHntBQAAFeGnxa3aODNmTNHjh8/bgJPjy6qVq2aCTw97DZlSqd1KgEAcKp4JZTjdHYNvXz58pl1flrYkjVrVue2DgAAdwm/1KlTmwDUw2rLly/v/FYBAOCOO7z4+fk5vzUAALhr+EUPPp3z27hxo1nWULBgQbPdWUhIiKny1B4iAADuJt5VKZcvX5b//Oc/psrTsVRQg08/PvvsMxOKo0aNkscff9yZ7QUAwDV7e+ppDh06dDBr+nx9fc02ZtHXyt+6dUvOnTtnTn/QHiEAAB4fft9//73s3LlTatSoIcuXL5dvvvnmrutTpkyR1q1bm97hhAkTnNVWAABcF35z586VDBkyyOeff26ONbqXrvH74IMPJDAw0Bx9BACAx4ffkSNHzJFGD6r41ADU+T6GPQEAXhF+uln19evXH3qfY39PAAA8PvwKFCggu3btkkuXLsV6jx50u3v3bnMvAAAeH34NGzY0m1i/++67cu3atfuua7Xnhx9+aApe6tWr54x2AgDg2nV+zz//vCl6WbJkidStW1fKli1rnt+/f78MGDBAVq5cKceOHTOL3vVeAADcSbwPs71w4YL06dNHVq9eHeP1oKAgGTZsmDnN3ZNwmC28HYfZwpsl+mG2GTNmlDFjxphT2nWvT63qjIyMNGFXqVIlUw0KAIA7ilf4ff3111K8eHGpXr26edQPAAC8Ovx01xbt+Wn4AQBgRbXnlStXpEiRIs5vDQAA7hp+eoDtli1bzHIHAACsqPbUZQxdunSRq1evSvPmzc2cX0BAgCRPnjzWyk9PQbUnvB3VnvBmiVrt6Vi4rrmpZ/Y9bCu0PXv2xOdtAABIFPEKvxw5cji/JQAAuHP46Rl+AABYVfACAIA1Pb+DBw/KwoUL5cyZM5IrVy6zwbU+AgDgleE3evRoGT58uDmxwWHEiBHyn//8R1q0aJFY7QMAwDXDnhs2bDCbVOvencWKFTPVnnpO340bN0z4Uc0JAPC68Js2bZpZsvD+++/LjBkz5L///a/Mnz9fXnnlFYmIiJCpU6cmfksBAEjK8Nu5c6cULlxYnnvuubue79GjhwQGBsq2bduc1R4AANwj/M6dO2eGOe+lvcGSJUvKyZMnE6NtAAC4Lvx0bi916tQxXkufPr3Z5gwAAK8KP63w1F5eTPT56BWgAAC4u3if5O6tMr81z9VNABLV1RNrXN0EwOXY4QUAYJ049/x0Xu/EiRMxPq+06CW205Fy5syZkDYCAJD05/npwvbY5vwe+gYedqRR2rT5XN0EIFGFHV/h6iYAicYnsKBze37xOPM2Qd8HAEBiiVP4LVu2LNEaAACAW4YfJzcAALwJ1Z4AAOsQfgAA6xB+AADrEH4AAOsQfgAA6xB+AADrEH4AAOsk6FQHPcpo8eLFsmHDBjl9+rQ5809Pdq9SpYpUr15dfHx8nNdSAACScm/PmBw7dky6du0qBw8evG8LM93PU09+HzFihBQsGLd91twFe3vC27G3J7xZXPf2jFf4Xbp0SZo2bSqhoaFm95f69etLnjx5TE9QQ3HRokXmBAh9bubMmeLn5yeegvCDtyP84M2cvrF1dOPHjzfB16BBAxk0aJCkSpXqruu9evWSvn37yvz582Xy5MnSuXPn+LwNAACJIl49v0aNGsnZs2dlxYoVkiZNmhjvuXbtmtSsWVOyZ88uM2bMEE9Bzw/ejp4fvFlce37xqvY8fvy4lCtXLtbgU3pN79FhUAAA3Em8wk8LWiIiIh56n96j84AAAHh8+Gkl55YtW+Ty5csPLIoJCQkx9wIA4PHhFxwcbMKtZ8+eMQagPqdFL+Hh4VKvXj1ntBMAANcWvFy/fl2aNWsmhw4dEn9/f6lWrZrkzZvXXNM5vtWrV0tYWJhZ4zd9+nTx9fUVT0HBC7wdBS/wZom6zk9ptedrr71mhjbNCyVLZh4dLxcUFCTDhg2TbNmyiSch/ODtCD94s0QPP4ft27fLxo0bzfZm+lIadpUrV5ayZcuKJyL84O0IP3izJAs/b0P4wdsRfvBmibrODwAATxan7c1KlSqVoDfZtWtXgr4fAIAkD7+4LGgHAMCrwm/ZsmWJ3xIAANwp/PTYIgAAvAUFLwAA68TrPL+OHTvG+V5d/D5u3Lj4vA0AAO4TfuvXr49T6OkSQsfOLwAAeHT4DRw4MMbnIyMjzZ6eeuLD8uXLpVWrVtKhQ4eEthEAAKdKtB1efvnlF3n33Xfl66+/lurVq4unYIcXeDt2eIE3c/kOL82bNzenOowdOzax3gIAAPer9ixUqJDs2bMnMd8CAAD3Cr/Dhw9HHXEEAIBHF7zE5aw/He48cOCAVKhQITHeAgCApA2/B210rRWfDrrM4YUXXohfywAAcKfwi8tG1zlz5pQuXbpInTp14vMWAAC4V/g9aKPr5MmTi6+vr2TIkCEh7QIAINHEK/weZaPrixcvEoQAAM+v9qxdu7YMHjz4off17t1bGjZsGJ+3AADAvcIvNDRUzp0799D7jh8/bnp+AAB41LCnrtN76aWXTJDdO+9Xr169WL/v0qVLcuHCBcmbN69zWgoAQFKFny5XaNOmjXTv3v2u58LDw83HA188Zcq7vg8AAI/a2DokJERu3bpleoK6dq9q1ary8ssvx/yiyZJJ6tSpTa8vICBAPAkbW8PbsbE1vFlcN7aOc7Vn+fLloz7XXVsqVqxoPgAA8DSJdqSRp6LnB29Hzw/ezOk9v5hoEcz48eNlw4YNcvr0aTPUGRgYKFWqVJFnnnnGHGkEAIDX9Py02vPNN9+Ua9euxXhyQ9q0aWXIkCFSt25d8ST0/ODt6PnBm8W15xen8Dt//rxkypQp6usjR45I06ZNTfDpIvYmTZpInjx5TEHMsWPHZPbs2bJgwQKzzdmsWbPMNU9B+MHbEX7wZk49yb1ly5bmeCIHPa5Ig+/tt9+Wzz//XKpVqyYFChQwh9fWrFlThg0bJn379pUrV67IxIkT4/+vAAAgEcQp/E6cOCHPPvusrFq1yny9fv16yZcvn7Rv3z7W79HlEHrP6tWrnddaAACSKvyef/55uXHjhnTt2tV8febMGSlevPhDv0/vOXXqVMJbCQBAUoffe++9J0uWLDEVnErn8nQe8GH0njRp0iS8lQAAuGJj62zZspkQdPTotm7dKn/88Ues9+s1vadYsWLOaSkAAK481aF169bmNPfOnTvL5s2b77uuz+nWZ5GRkdKiRQtntBMAANev8+vRo4csXrzY7OOZJUuWqNMbdKmDzgnqy9apU0dGjBghnoSlDvB2LHWAN3PqOr+YaK9u5MiR8t133913uoOfn5+0bdtWunXrZk528CSEH7wd4Qdvlujh53Dz5k3ZuXOn2d5MX0rnBkuXLi2pUqUST0T4wdsRfvBmSbK3p3kjHx8JCgqK9frKlSulRo0aCX0bAABcV/Cim1nv2LFDLl++/NBlDr169ZJXXnklIe0DAMDp4tzz08B7//33Zf/+/Xe+MWVK6dixowm4e82cOVMGDRokFy9edG5rAQBIqvA7evSo2a7s6tWrd831jRkzxhxj9Oqrr0b19vr06SNr164183+6wF2LXgAA8Lhhz9GjR5vg07m9OXPmmMXrelyRhts333xjrh08eFCaNWsWFXyVKlUypzt06tQp8f8VAAA4u+enYadBN3z4cHNYrWrcuLHp6Q0ePFgWLlwoQ4cONev7dJmDnvbQqlWrR2kHAADu1fPTZQwlSpSICj6H2rVrm16ezu9p8Dl6hgQfAMDjw0/P7suePft9z+uaPhUWFmYOtdWz+3LmzOn8VgIAkNThp727mHZqcSxk9/f3lwEDBnjcbi4AADvFa2Pre2lxi1Z9AgBgTfhxZh8AwLrwAwDAk8R5kk7X8p04ceKRrymKYAAA7iROpzroaex6bl+83iBZMtmzZ494Ck51gLfjVAd4M6ef6hDfk48SeGISAABOF6fwW7ZsmfPfGQAAdw6/XLlyJX5LAABIIlR7AgCsQ/gBAKxD+CHJVajwhEydOlqOHt0iYWEH5ciREPN1lSrlXd004IH+vhgmNRs/J5169o31nuOhJ+W9AUMluFUHCarZWOo2f0H6fz5Kzp6/EOf3GfPdVClVtb58MPC/Tmo57kX4IUm1adNEli//WZo0CZbTp8/IvHlL5cyZc+brpUt/kvbtn3F1E4EYhYdfkR5vfyxnzp2P9Z69+w9K647dZea8JZLOz1dqVK0kKVIklx9+mSMtXugqx/6MfT20w9Ydu2XUuElObj3uRfghyWTI4C/Dh/c3G6B36fKWVKwYLM8+20UqVKgn3br1leTJk8vQoR9J1qx3H50FuNqho8el3au9TTDFJjIyUt54f6BcuhwufXp0lukTRsrQT96VuT+MlVZNGsi58xfk/QHDHvg+F8MuyVsfDpaIyMhE+FcgOsIPSaZq1Qri759edu36Xb77btpd18aNmyJ79x6QtGnTSOXK5VzWRiC6sEuX5YvRE6RNx+6y7+AhyZs79t2qFi5bbXp2ZUoUlbZtmkU9nzJlCnnn9VckZ/assuW3XfLbrr2xvoaG419nzkn5sqWd/m/B3Qg/JJlbt+5seJAlS+ao47ActDcYEOBvPj/3gGElICl9/+NM+WbiNEmbNq0M/KC3dG4X+7D8irUbzWPdmk/dd80nZUqp9a8q/9y3Kcbvn/TTLFm+ZoN0fK6VVChbxmn/BnhR+IWHh8vu3bvl2LFjrm4KHsH69ZvlwoWLki1bFpky5SspUeIxcxRW0aKFZfLkkZIjRzZZs2ajrFu32dVNBYxsWQKl1ysdZOGP30qjerUeeO/+Pw6bx8cKFYjxeuGC+c2j9iBjmiv8fORYebxkMenWqa1T2o4Hc9vTZ3Wj7F9//dX0EKpXry5+fn7m+bFjx8rIkSPN6fLqsccek379+kmZMvyl5O7Cwi5Jq1adZOLEL6Vhwzrmw+HWrVsyZMgIGThwuEvbCETXsnFwnO/V4UqVPZY566yBmczj2XtGNq5cuSpvfjBI0qROLUM+etsMk8LS8Bs1apT50AlklSFDBvnyyy/l1KlT8tlnn5nnMmXKJFeuXJF9+/ZJhw4dZObMmZInTx4XtxwP8/vvB2Ty5J/l9de7yO7d++TIkWNSpEghKV68iLRt21q2bNkhs2cvcnUzgUd25eq1B55vquEW/T6Hjz79Uo4eD5VPP3pbcuXIlgQthVuG35IlS2T48OESGBgowcHBkiJFClm4cKH06NFDAgICzFZrI0aMkOLFi8uNGzdkzJgx5uvvvvtO3nvvPVc3Hw+QO3cOWbz4R8mePau0bv2SzJ//vz1jW7VqJGPHDpVJk0ZK/fr/J+vW/erStgKPSquVdQTjYaLfM2PeYpm3eIU0e/rfUr9O9URuIdx6zm/atGmSOXNmmTNnjgmzvn37yowZM8y1I0eOmK81+JQOiXbr1k2KFi0qmzbFPIkM9/Hhh72lQIG8MmjQl3cFn/rppzny2WdfiY+Pj7z9dneXtRGILz/ftObx2vXrMV53PO+b9s59fxw5JgOGjpICeXNL39deScKWwi17fnv37pVq1apJxowZo57TIc6aNWuaEAwKCrrve6pWrSpTp05N4pbiUdWq9S/zuGhRzOfJzZ+/VN55p6cEBTF/C8+TLWugWaenc3qF8ue97/pfZ+/MCWbNktk8Dh01Tq5euy7p0/nJR0OGx1g8s3nbDunz0RDJFJBB+vR8OUn+HbZwu/C7ePGiZMmS5b7nHc/p0Oe9fH19owpg4L4cSxlu3rwZ4/WIiDtzvD4+bvffEngorfLcf/CwHDh0VCqVe+K+6/q8Klr4zmGr4Veumscde/aZj9i2StMPXSNI+DmX2/2WiYiIuG8NmNLhMMe4ekw4NNf96SL2oKDS0rBhXdmzZ/991+vWvTPnsX37Lhe0DkgY3cps7qLlsmTFWnm+VZO7rt2MiJAVazaYz2s+Vck8ThgxJNbXGjluknz17WRp/nQ9+bjva4nccju53ZwfvNeoUePNY58+3aROnWp3XQsOriV9+/Ywn3/xxTcuaR+QELX+VVny5MphdnEZN+nHqOe1an3gsK/kxKm/pGLQ4/J4qTs1C3Att+v5wXvpEoeyZUtJ164dZc6c72X79t1y+PBRKVQov5QpU8Lc06/fULPZNeBpdMTK7ALz2rsy7KvxMnfRCimQL7fs2XdQ/jxxSrJnyyL9333d1c2EO4ffsmXLJDQ09K7nfv/9d/Oo1Z4xFcnAM7z55keyZMlq6dy5rTnaqGTJx8yuL3PmLJYRI76V1avvDA0BnuiJUsVl2rjh8vWEKbIxZLsc/TNUsmfNIv/XsrF0btdGAjPfWegO10t2280my4oVKxav70uWLJlTQjBt2nwJfg3AnYUdj7naFvAGPoF3Coo8ruc3cOBAVzcBAODl3K7n52r0/ODt6PnBm8W150e1JwDAOoQfAMA6hB8AwDqEHwDAOoQfAMA6hB8AwDqEHwDAOoQfAMA6hB8AwDqEHwDAOoQfAMA6hB8AwDqEHwDAOoQfAMA6hB8AwDqEHwDAOoQfAMA6hB8AwDqEHwDAOoQfAMA6hB8AwDqEHwDAOoQfAMA6hB8AwDqEHwDAOoQfAMA6hB8AwDqEHwDAOoQfAMA6hB8AwDqEHwDAOoQfAMA6hB8AwDqEHwDAOoQfAMA6hB8AwDqEHwDAOoQfAMA6hB8AwDqEHwDAOoQfAMA6hB8AwDqEHwDAOoQfAMA6hB8AwDqEHwDAOoQfAMA6hB8AwDqEHwDAOoQfAMA6hB8AwDqEHwDAOoQfAMA6hB8AwDqEHwDAOoQfAMA6hB8AwDqEHwDAOoQfAMA6hB8AwDqEHwDAOoQfAMA6hB8AwDqEHwDAOoQfAMA6hB8AwDqEHwDAOoQfAMA6hB8AwDqEHwDAOoQfAMA6hB8AwDqEHwDAOoQfAMA6hB8AwDqEHwDAOoQfAMA6hB8AwDqEHwDAOoQfAMA6hB8AwDqEHwDAOoQfAMA6yW7fvn3b1Y0AACAp0fMDAFiH8AMAWIfwAwBYh/ADAFiH8AMAWIfwAwBYh/ADAFiH8AMAWIfwAwBYh/ADAFiH8AMAWIfwAwBYh/ADAFiH8IPLzJkzR4oWLWo+QkJCXN0cIEF++eWXqP/P0T9KliwpTz75pLz44ouyePFiVzcT/0jp+ARIaj/++KP4+vrKlStXZMqUKVK+fHlXNwlIsGLFikmdOnWivr5+/bqcPXtWVq1aJd27dzcf3bp1c2kbQfjBRY4ePSqbN2+Wxo0by549e8xfxGfOnJEsWbK4umlAghQvXtwE3L3Onz9v/r+PGjVKmjZtKrlz53ZJ+3AHw55wienTp4ueo1ytWjWpX7++3Lx50/QEAW+VKVMmqVevnkRGRsqvv/7q6uZYj/BDkouIiJAZM2ZIypQppWrVquavYTVt2jRzDfBWKVKkMI+pU6d2dVOsR/ghya1cudIMcWqvL2PGjJInTx4z33f69GlZtmyZq5sHJAod9ly4cKFkyJBBqlev7urmWI85PyS5n376yTw2a9Ys6rkWLVqYis/JkyeboSHAU+3du1e+/PLLqK91NEP/2Fu+fLkZ6h8+fLikS5fOpW0E4Yckpr27NWvWSEBAgNSoUSPq+eDgYOnXr59s2rRJDh48KIULF3ZpO4H4+v33381HTPLnzy9//fVXkrcJ92PYE0le6KIT/k8//bSkSpUq6nld8qCFL0qXPQCeSkc09u3bF/Wxe/duWb9+vekNamFX7969ZcyYMa5upvWS3dZ+OJAE9L9a7dq1JTQ09IH3+fn5yerVqxkagsctcu/bt68Jv0GDBsV4jy7r0etp06aVtWvX8n/chRj2RJLRv341+HLkyGGKXWKioXfy5EmZNWuWPPfcc0neRiAxlShRwix50OKXw4cPS+nSpV3dJGsRfkjyQpd27dpJx44dY7xn4sSJ0r9/f/nhhx8IP3idGzdumB2NVPr06V3dHKsx54ckoX/pLl261Kztc6zri4nufJEmTRo5cOCAKX4BvInO9V27dk0ee+wxU/wC16HnhyShw5g62V+rVi0JDAyM9T5/f39p0KCBmT/RZQ+VKlVK0nYCzl7qoLS3t27dOlMAo8VdH3/8scvahzsIPyRZladjPd/DtGnTxoSfLnjXpRHZsmVLghYCibPUIVmyZKbAJVeuXGYov3379pI3b16XthFUewIALMScHwDAOoQfAMA6hB8AwDqEHwDAOoQfAMA6hB8AwDqEHwDAOoQf4EK3bt1ydRMAK7HDC6z2559/mmOWYuPYnSNr1qwSFBQkL7/8cpz2ZPzkk0/k+++/l5w5c5oNvWPa0m3JkiUydepUGTdunFP+DSlSpDBH5iSWokWLPvDnlDp1avPvLFOmjLz44otSqlQpcQXdWmzEiBHSsmVLs0k6EBPCD/hHnTp1TNBFFxERIadOnTKholuuLViwQCZMmCBPPPFErK+jW7L9+OOPZg/Hr776KsbgCwkJkW7dunnkNldPPvmkZM6c+b4erJ5Qrvtazp8/XxYuXGhCSH+mgDsi/IB/6EGkuXPnjvGa/mLv2bOnbN26Vd5//32ZPXu26e3EtnO/buL93//+V4oVKxbjPXqavafq0qVLrBuOh4WFybvvviuLFy82P6ennnrKnNIBuBvm/IA40GHPjz76yHy+f/9+OXbsWKz36i997QHpCRa20VM5BgwYIMmTJzfHWOkfC4A7oucHxJHuyu9w4cIFyZcv313XdbhPhzt1iFSPsNHArFKlinTq1EkKFCgQdV/btm3l119/NZ9riOpcmr728uXLHzp/p0Ov2kPV19Xh14fRs+P0gGAdhtSTw3V4Mk+ePFK3bl3p0KGDCStn00Na9XX//vtv83O61+7du808p/4M9J4MGTJIhQoV5KWXXpKSJUved78OPeuRWPrz1T8qtHeZKlUqM2Ssw6p6MLIOMQOPgvAD4kiPWFI+Pj5SqFChu345v/7667Jo0SJzWK8WxmTMmNEca6NHOc2dO1c+//zzqPkvnTPT+9avX29+aWvYZcqUyentPXPmjLzwwgvyxx9/SLp06aRixYomVLds2SKjRo0ygfLtt986/VBVfX0NtZiKZPSPgw8//NAM++o1/Vlp4OtcqhYAaaFQs2bNou7X+7TIaO3atWb4tFy5cubfcvLkSdmxY4cJww0bNsikSZNiHYYGYkL4AQ9w/fp1M9+nwffFF1+Y57THpL0bBw0SDT6t7NT5viJFipjn9bSw8ePHy+DBg+XNN980YaO9xVdeecX80tfw02KYzz77LFHa3qtXLxN8GrY6/6g9LKW90nfeeccETteuXU27NIwT4saNG3Lu3DlzYOvQoUPNcw0bNpTChQtH3bN9+3YTfNpr059l9erVo66tXLlSevToYYaMixcvHjVX+vPPP5vg057xtGnTJEuWLHcVDWm46+POnTtNlSkQV4Qf8I8HLXlQWgnavXt3efXVV+/6pe8Yfhw4cGBU8CntieiQ3K5du2TevHkmCPWXf1LYtm2bbN682YS0Bk304U3tbQ4ZMsT0nA4ePGiKUxo0aBDn127Xrt0Dr2vPWA9t7dOnz13Pjx492vTk9GcYPfhUjRo1zM9Kq2O1N6rtc9Aec/369e8KPlW+fHnTe9Rh1OPHjxN+eCSEHxDDUgft8W3atEkuXrxoguPtt9+W4OBg8fPzu+t7tMcRHh5ufjFXrlw5xtdt0qSJCT8dnksqGzduNI8aMjHN62nvSwNl7Nix5t5HCb/oSx00/DVotXes6/y0t9m0aVMz7BudzjU62lS1atUYX7dmzZom/LRH7NC6dWvzEZ1W0upcqf5R4Rhe1XYAj4LwA2JZ6nD16lUzPKiFFsOGDTM9i+g9O8eaPvWg9XpaYKI0IJKKo12O935Quxz3xnepg4aRzmlqz1YXl+uw5b1/CGhI6XCr44+Bh81V6mtqD1LpHyA6d6rDn1q0o+117IzjmOfTIWbgURB+QCy0F6jDb7rIXUv2dVhuxowZMS5afxDHL2btbSVUXNcHxiUMHAGiPbaE0JDSnrH+nHQeUec0NayiFwVFb3ejRo0e+ppaRKSvq/OEWgWqFZ7a69ZdY3QJic4l6rypDjU7epTAoyD8gAfQX8BakKK/sLXnpvNY0bcj0+UM6kHr/o4ePWoe4xKauj7OERYaYPdWMGoIxEW2bNnMo86FxcbR5nt3a4kvrdTUsNJKzNdee80UqzgCX4dB9XMdntSiFkfxzcNoqOq/WX/+ulXZvUEd158HcC8WuQMPoZWG+ktY6dCbrrVzKF26tOmR6FCdzhHGZM6cOeZR1+Y5xFaWH329mr7mveK6aNwx7Lhq1Sq5dOnSfdc1hLTQ5d52JYQuQXDspakbAWiBi4NWk+oyBbV06dIYv3/mzJlmHlKHmtXZs2fNMKfS5Q73Bp8Of+r7KDYIx6Mi/IA40KILxzyXLl3Qsn6lv5B10bpjzlCXFjhoz+27774zc4a6Ru3ZZ5+NuubY8kuDKfoQZUBAgFkyobQYJToNXcdaw4fRIUH90NfXXlj0HpLOZWpbQ0NDzRo/rbR0Fi1mad68uflcw0+rSR10+FINGjTILG2ITotX9PlDhw5JwYIFo8LU0XPUNYDR6dpAXaahw6OOAiXgUTDsCcRRv379pHHjxqZ4Qz/XtXNKN6g+cOCACSa9rqGji9Z1kfuRI0dMb07npqLPgWmxifaGdAeUZ555xnztWO+nBSUffPCBCU6tfNQw0BDRYNWTCnQ+LS60SKd9+/amt6qVlLqLimORu76v9mhHjhzplLnI6HRoWHuc+gfCe++9J1OmTDHDuRqMut5Ri2O0J6fFQ7rzjfZwdbhU/wjQnp+uo3T8gfD888+bpQ+6XEN7qlpYpD1CvV9peOvPWLdSAx4FPT8gjnSBugad0sIO3Y7MMS+oIaI9Qg0+DT29pkNx+stbh/N0mUR0OgemgaivqevUNOQcW4G1adPGnIig69i0d7ZmzRpzvy4D0KKbuMqePbsJSu35abhqYYi+j84H6nO6OXf0RejOor1XDT2lyyAmT558V+9Pw1B/HvpHxIoVK0wvTnef0cXxGowa0A69e/c2e4Xq8LL+LPTnqnOKGpK6W4zurKP0dYBHkew2NcIAAMvQ8wMAWIfwAwBYh/ADAFiH8AMAWIfwAwBYh/ADAFiH8AMAWIfwAwBYh/ADAFiH8AMAWIfwAwBYh/ADAFiH8AMAiG3+H57V8f8Td/0hAAAAAElFTkSuQmCC", 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" ] @@ -230,29 +237,36 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "\u001b[92m✔ Non-self detectors for classes (A, B) successfully generated\u001b[0m: ┇██████████┇ 500/500 detectors\n" + "\u001b[92m✔ Non-self detectors for classes (A, B) successfully generated\u001b[0m: ┇██████████┇ 500/500 detectors" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "A acurácia é 0.93\n", + "A acurácia é 0.945\n", " precision recall f1-score support\n", "\n", - " A 0.93 0.91 0.92 90\n", - " B 0.93 0.95 0.94 110\n", + " A 0.92 0.96 0.94 90\n", + " B 0.96 0.94 0.95 110\n", "\n", - " accuracy 0.93 200\n", - " macro avg 0.93 0.93 0.93 200\n", - "weighted avg 0.93 0.93 0.93 200\n", + " accuracy 0.94 200\n", + " macro avg 0.94 0.95 0.94 200\n", + "weighted avg 0.95 0.94 0.95 200\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ "\n" ] } @@ -279,12 +293,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -314,7 +328,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": ".venv (3.12.2)", "language": "python", "name": "python3" }, @@ -328,7 +342,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.12.2" }, "orig_nbformat": 4 }, diff --git a/examples/pt-br/classification/BNSA/mushrooms_dataBase_example_pt-br.ipynb b/examples/pt-br/classification/BNSA/mushrooms_dataBase_example_pt-br.ipynb index 8920363..1269b64 100644 --- a/examples/pt-br/classification/BNSA/mushrooms_dataBase_example_pt-br.ipynb +++ b/examples/pt-br/classification/BNSA/mushrooms_dataBase_example_pt-br.ipynb @@ -30,7 +30,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -147,7 +147,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -178,15 +178,15 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "A acurácia média é: 0.926186\n", - "Desvio padrão das acurácias: 0.020259\n" + "A acurácia média é: 0.953357\n", + "Desvio padrão das acurácias: 0.015282\n" ] } ], @@ -221,9 +221,16 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Generating the detectors for the Comestível class: ┇ ┇ 0/500 detectors" + ] + }, { "name": "stderr", "output_type": "stream", @@ -261,28 +268,28 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "A acuracia é 0.9159146841673503\n", + "A acuracia é 0.9606234618539786\n", " precision recall f1-score support\n", "\n", - " Comestível 0.87 0.98 0.92 1273\n", - " Venenoso 0.98 0.84 0.91 1165\n", + " Comestível 0.93 1.00 0.96 1294\n", + " Venenoso 1.00 0.92 0.96 1144\n", "\n", - " accuracy 0.92 2438\n", - " macro avg 0.92 0.91 0.91 2438\n", - "weighted avg 0.92 0.92 0.92 2438\n", + " accuracy 0.96 2438\n", + " macro avg 0.96 0.96 0.96 2438\n", + "weighted avg 0.96 0.96 0.96 2438\n", "\n" ] }, { "data": { - "image/png": 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", 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y5Mnl+PHj97VMAAB7OBZgOXPmNDNu3MqWLVske/bs97VMAAB7OBZgderUkV69eklUVFSCfZcuXTKLXNarV8+RsgEA3M+xqaS0CbFMmTLi7+8vHTt2lMKFC5vtOpR+7NixcvXqVTO8Xhe6vFNMJQVfx1RS8HWuXk5FgyksLEzat28v3bt3l9gc1SH1NWvWNCF2N+EFAPh3cHwyX3X69GnZs2ePCbGCBQtKhgwZ7ul41MDg66iBwde5ugYWlwbWjbPRAwDg6rkQAQC4GwQYAMBKBBgAwEoEGADASgQYAMBKBBgAwEoEGADASgQYAMBKBBgAwEoEGADASgQYAMBKBBgAwEoEGADASgQYAMBKBBgAwEoEGADASgQYAMBKBBgAwEoEGADASgQYAMBKBBgAwEoEGADASgQYAODfFWDLly+X+vXrS2hoqHk0aNBAVq5cmbilAwAgMQPsiy++kOrVq0uaNGmkc+fO5pE6dWqpVq2afPnll3dzSAAA7oifx+Px3NlHRIoWLSqvvfaadOnSJd724cOHy4QJE2THjh3ipMsn9jr684GklrdgfaeLACSpQ6e3JU0NbO/evab58EbajBgZGXk3hwQA4I7cVYDlzp1bfvrppwTblyxZYvYBAJDUkt/Nh7p27Wr6vTZt2iTly5c321avXi1Tp06VUaNGJXYZAQBInABr3769hISEyLBhw2TWrFnefrGZM2fK008/fTeHBAAg6QdxuB2DOODrGMQBX5dkgzgAALCmCTFDhgzi5+d3W+89derUvZQJAIDEC7CRI0d6n588eVL69+8vNWvWlHLlyplta9askcWLF0uvXr1u95AAANzfPrDGjRtL1apVpWPHjvG2f/zxx2Yo/ddffy1Oog8Mvo4+MPi6JOsD05pWrVq1EmzXbRpgAAAktbsKsEyZMsn8+fMTbNdtug8AAFfeB9a3b1959dVXZdmyZfLYY4+ZbWvXrpVFixaZuRABAHBlgLVs2dLcuDx69GiZN2+e2aavV61a5Q00AACSEjcyAxZiEAd83e0M4rirGlhcUVFREhMTE29bUFDQvR4WAIDEH8Rx8eJFM4Q+a9asEhgYaG5yjvsAAMCVAfb222/Lzz//LOPGjZOAgACZOHGiGdiRI0cO+eyzzxK/lAAAJEYT4rfffmuC6oknnpBWrVpJpUqVJDQ0VPLkySPTp0+XF1544W4OCwBA0tbAdK7D/Pnze/u7Yuc+rFixoqxYseJuDgkAQNIHmIZXZGSkeV6kSBHvmmBaMwsODr6bQwIAkPQBps2GmzdvNs/fffddGTt2rKRKlUq6dOli+scAALDiPrD9+/fLhg0bTD9YyZIlxWncBwZfx31g8HX35T4wpYM39AEAwP1y1wG2fv16Wbp0qRw7dkyuXbsWb9/w4cMTo2wAACRugA0cOFB69uwphQsXlmzZssVbqfl2V20GAOC+94FpaA0ePNhM6utGyVPmdLoIQJI6P9md1x6QWFK3GJA0oxCTJUsmFSpUuJuPAgCQKO4qwHS4vA6dBwDAqj6wt956S+rWrSsFChSQYsWKSYoUKeLtj10jDAAAVwVY586dzQjEqlWrSqZMmRi4AQCwI8CmTZsmc+fONbUwAACs6QPLmDGjaT4EAMCqAOvTp4/07t3bLGwJAIA1TYijR4+WiIgIcz9Y3rx5Ewzi2LhxY2KVDwCAxAuwhg0b3s3HAABwNsC0+RAAAOv6wAAAsKYGpiMPw8PDJXPmzJIhQ4Z/vPfr1KlTiVU+AADuLcBGjBgh6dKl8z7n5mUAgPUrMrsNs9HD1zEbPXxdks1G7+/vbxayvNHJkyfNPgAAktpdBditKm3R0dGSMmXKey0TAACJO4xeb2BW2v81ceJESZs2rXff1atXZcWKFVKkSJE7OSQAAEkfYDp4I7YGNn78+HjNhVrz0lk5dDsAAK4KsMjISPOvLqOia37pcHoAAKzpA9O1wOKGlzYfbtq0SU6fPp2YZQMAIHED7D//+Y9MmjTJG16VK1eWMmXKSO7cuWXZsmV3c0gAAJI+wGbPni2lSpUyz7/99lvZt2+f7Ny5U7p06SLvvffe3RwSAICkDzC93yskJMQ8X7hwoTRp0kQKFSokrVu3lq1bt97NIQEASPoA03XAtm/fbpoPFy1aJDVq1DDbdYFLbmQGALh2OZVWrVpJ06ZNJXv27OaesOrVq5vta9eu5T4wAIB7A6xPnz5SokQJ+eOPP0zzYUBAgNmuta/u3bsndhkBALi3JsQ6derI2bNnzfNnn33WTB0VdzaOevXqybvvvnsnhwQAIOkDbPHixSa0Yg0cODDe2l9XrlyRXbt23V1JAABIqgC7cRJfH1yJBQDgy6MQAQCwKsB0xOGNKzGzMjMAwPWjELXJsGXLlt5Rh1FRUdKuXTsJDAw0r+P2jwEA4JoAe/nll+O9btGiRYL3vPTSS/deKgAAEjPApkyZcidvBwAgyTCIAwBgJQIMAGAlAgwAYCUCDABgJQIMAGAlAgwAYCUCDABgJQIMAGAlAgwAYCUCDABgJQIMAGAlAgwAYCUCDABgJQIMAGAlAgwAYCUCDABgJQIMAGAlAgwAYCUCDABgJQIMAGAlAgwAYCUCDABgJQIMAGAlAgwAYCUCDABgJQIMAGAlAgwAYCVXBdjBgwfNAwAA1wfYtWvX5IMPPpD06dNLnjx5zCM4OFj69etn9gEAcDPJxWHvvfeeTJo0ST788EOpUKGC2bZq1Srp06ePREVFyYABA5wuIgDAhRwPsGnTpsnEiROlQYMG3m0lS5aUnDlzSocOHQgwAIA7mxBPnTolRYoUSbBdt+k+AABcGWClSpWSjz/+OMF23ab7AABwZRPikCFDpG7durJkyRIpV66c2bZmzRr5448/ZOHChU4XDwDgUo7XwKpUqSLh4eHSqFEjOXPmjHk888wzsmvXLqlUqZLTxQMAuJTjNTCVI0cOBmsAAOyqgS1atMgMm481duxYeeihh6R58+Zy+vRpR8sGAHAvxwPs7bfflnPnzpnnW7dulTfffFPq1KkjkZGR5jkAAK5sQtSgKlasmHk+d+5cqV+/vgwcOFA2btxoggwAAFfWwFKmTCkXL140z3Uk4lNPPWWeZ8yY0VszAwDAdTWwihUrmqZCnUZq3bp1MnPmTLNdRybmypXL6eIBAFzK8RqY3rCcPHlymTNnjowbN85MIaW+//57qVWrltPFAwC4lJ/H4/GIj0me8noIAr7q/OSWThcBSFKpWwxwfxOiunr1qnz99deyY8cO87p48eJmcl9/f3+niwYAcCnHA2zPnj1mtOGhQ4ekcOHCZtugQYMkd+7csmDBAilQoIDTRQQAuJDjfWCdO3c2IaVzH+rQeX0cOHBA8uXLZ/YBAODKGtjy5cvll19+McPmY2XKlCneApcAALiuBhYQECDnz59PsP3ChQvmHjEAAFwZYPXq1ZPXXntN1q5dKzogUh9aI2vXrl28VZoBAHBVgI0ePdr0gelaYKlSpTIPbToMDQ2VUaNGOV08AIBLOd4HFhwcLPPnz5fdu3fLzp07zbaiRYuaAAMAwLUBFqtgwYLmAQCAFQGmNzFPnTpVfvrpJzl27Jhcu3Yt3v6ff/7ZsbLh1t7v9aa836trvG07d+2REg9WcaxMwK1s2H9cpq0Jlx1HTsvxC1EyvEk5ebLI3zP2aN/7uOXbZd5vkXI+KkYeyp1ZetQuLXkypfO+p/bohXLk7PWJx2N1frKEtK5QxDzfd+K89F+4UfaeOCcXoi5LlnSppXaJ3NK2cjFJ4e94b41PcjzA3njjDRNgdevWlRIlSoifn5/TRcJt+n3bTqlZ6znv6ytXrjhaHuBWLl2+IoWypZeGD+WVN2evSbB/atgu+XLdHun39COSMziNfLJsm3T4cpXMa/+UBCT/e0agDlWKyTNl8ntfB6b8+09ocn8/qVcyjxQNCZZ0qVJI+NGz8sGCDXLN45HOTz6Y9L/kv5DjATZjxgyZNWsWa39Z6MqVq3L06HGniwH8TxVDs5vHzWjta/q6PdKmUhGpWjiH2dbv6Uel2vBvZenOw1KrRG7ve9MEpJDMaVPd9Di5MqQ1j1g5ggPl1/3H5bcDJxL998F1jtdr9V4vBmzYqWBoPjmwb4OE7wyTz6aNkdy5r1/8gE0OnflLTlyIksfyZfNu0xrUgzkzyuZDJ+O9d8rqnVLlo2+k2X+XmFrblRu6POI6cOqChEX8KWXzZEnS8v+bOV4D69q1qxkur8uq3E3zYXR0tHnc+I2KpsiktW7db9L61S4SHh4h2UOySq+eb8qyn7+SUqWflAsX/nK6eMBt0/BSmQID4m3PGJhKTv7/far5o6FSJCRY0qdOKZsPnpTRP/9uPvvWU6Xife6lKT/LziNnJObqNWlcJp90eKL4ffpN/n0cD7BVq1bJ0qVLzfpfOgt9ihQp4u2fN2/eP35eJ/7t27dvvG1+ydKKn39QkpQX1y1avNT7fOvWHbJ23W+yd89aafJsfZkydYajZQOSwouPF/I+L5Qt2AzM6L9goxnIkTJOP9mQZx6Xv2KuSPjRMzJiyVYzeKRV+esTlcMH7wNr1KjRXX++e/fuZkXnuDJkuj4qCPfP2bPnJHz3XgkNzet0UYA7EtundfKvaDNyMNapv6KkUEjwLT9XIkdGuXLNI4fPXJS8mf8erRiSPo35t0CWILl2zSP9FmyUlx4vJP7JaBXyuQCbMmXKPc+lqI+4aD68/wID00iB/Hlk+vS5ThcFuCM5gwNNiK2LPGaaCNWF6Muy9dApaVL21ss57Tp6VjSTMt7Q9BjXNY+YfjIdiegv/F3yuQCLHX69bNkyiYiIkObNm0u6dOnk8OHDEhQUJGnT/j2qB+4x5MNe8t2CH2X/gYOSI3uI9H6/q1y9ek1mzPza6aIBCVyMuWIGVcQduLHzzzOmPyt7+jTywqOhMmHVDnkgY1oTaGOXbTO1sapFrg9M0j4vDbRH8mSRwIDksvngKfnoh81S58E8EpT6+qTjC7YekOTJ/KRg1vSSMnky2Xb4tIxeulWeKpab+8B8NcD2798vtWrVMmuA6WCMGjVqmAAbPHiweT1+/Hini4ibyJkru3zx+VjJlCmDHD9+SlaHrZMKlerLiROnnC4akMC2w6ekzecrvK+H/bjF/Fu/ZB5z71fL8oXl0uWr0m/BBjkfdVlKP5BZPmle0XsPWEr/ZLJ42x8yfvl2uXz1qgm5Fo8VlBcf/3v2IA0vHZm4/9QFM5Ase/pAee7hUGkR5z1IXH4e/S/toIYNG5rAmjRpklkHbPPmzZI/f35TI2vTpo2ZI/FOJU/59x32gC86P7ml00UAklTqFgPcXwNbuXKlhIWFJVj7K2/evHLo0CHHygUAcDfHG2Z17kOdD/FGBw8eNDUzAABcGWBPPfWUjBw5Mt4IQl2NuXfv3kwvBQBwbxPisGHDpGbNmlKsWDGJiooyoxC13ytz5szyf//3f04XDwDgUo4HWK5cuczADZ3Ud8uWLab29corr8gLL7wgqVP/fVMhAACuCjCVPHlyadGihdPFAABYxBUBpk2GOh/izRa0fP/99x0rFwDAvRwPsAkTJkj79u1Nn1dISEi8aaD0OQEGAHBlgPXv318GDBgg3bp1c7ooAACLOD6M/vTp09KkSROniwEAsIzjAabh9cMPPzhdDACAZRxvQgwNDZVevXrJL7/8Ig8++GCCBS07d+7sWNkAAO7l+GS++fLlu+U+HcSxd+/eOz4mk/nC1zGZL3ydFZP5RkZGOl0EAICFHO8DixUTEyO7du0yi1sCAOD6ALt48aKZOipNmjRSvHhxs7Cl6tSpk3z44YdOFw8A4FKOB1j37t3NXIi6gGWqVKm826tXry4zZ850tGwAAPdyvA/s66+/NkH1+OOPx5uFQ2tjERERjpYNAOBejtfAjh8/LlmzZk2w/a+//ooXaAAAuCrAHn74YVmwYIH3dWxoTZw4UcqVK+dgyQAAbuZYE+Lvv/8uJUqUkEGDBkmtWrVk+/btcvnyZRk1apR5HhYWJsuXL3eqeAAAl3OsBlayZEl57LHHTFitXr3aDJ/XbTqtlDYprlmzRsqWLetU8QAALudYDUxrV1OmTJGuXbuaNcAaN24sH330kVSuXNmpIgEALOJYDaxSpUoyefJkOXLkiIwZM0b27dsnTzzxhBQqVEgGDx4sf/75p1NFAwBYwPFBHIGBgdKqVStTIwsPDzez048dO1YeeOABadCggdPFAwC4lOMBduPM9D169JCePXtKunTp4o1OBADAVTcyx1qxYoVpUpw7d64kS5ZMmjZtaqaYAgDAdQF2+PBhmTp1qnns2bNHypcvL6NHjzbhpU2LAAC4LsBq164tS5YskcyZM8tLL70krVu3lsKFCztVHACAZRwLMF15ec6cOVKvXj3x9/d3qhgAAEs5FmDffPONUz8aAOADXDUKEQCA20WAAQCsRIABAKxEgAEArESAAQCsRIABAKxEgAEArESAAQCsRIABAKxEgAEArESAAQCsRIABAKxEgAEArESAAQCsRIABAKxEgAEArESAAQCsRIABAKxEgAEArESAAQCsRIABAKxEgAEArESAAQCsRIABAKxEgAEArESAAQCsRIABAKxEgAEArESAAQCsRIABAKxEgAEArESAAQCsRIABAKxEgAEArESAAQCsRIABAKxEgAEArESAAQCsRIABAKxEgAEArESAAQCsRIABAKxEgAEArESAAQCsRIABAKxEgAEArESAAQCsRIABAKxEgAEArESAAQCsRIABAKxEgAEArESAAQCsRIABAKxEgAEArESAAQCs5OfxeDxOFwJ2i46OlkGDBkn37t0lICDA6eIAiYrz270IMNyzc+fOSfr06eXs2bMSFBTkdHGARMX57V40IQIArESAAQCsRIABAKxEgOGeacd279696eCGT+L8di8GcQAArEQNDABgJQIMAGAlAgyusHbtWhk1apTQog3gdhFgcNzBgweladOmUrp0afHz87vn4y1btswc58yZM4lSPgDuRIBZ6s8//5ROnTpJ/vz5zeio3LlzS/369eWnn34St5o6daoEBwfH2xYTE2PCa9iwYVK5cmXHygb30vO6Vq1aN923cuVK82Vly5Yt971ccF5ypwuAO7dv3z6pUKGCCYOhQ4fKgw8+KJcvX5bFixfL66+/Ljt37hRbpEyZUsLCwpwuBlzslVdekcaNG5uaeq5cueLtmzJlijz88MNSsmRJx8oHB+kwetildu3anpw5c3ouXLiQYN/p06fNv/v37/c0aNDAExgY6EmXLp2nSZMmnj///NP7vt69e3tKlSrlmTRpkid37tzmfe3bt/dcuXLFM3jwYE+2bNk8WbJk8fTv3z/B8V955RVP5syZzXGrVq3q2bRpk3e/Pn/iiSc8adOmNfvLlCnjWb9+vWfp0qXauRXvoWVQefLk8YwYMcI8f/755z1NmzaN9zNjYmI8mTJl8kybNs28vnr1qmfgwIGevHnzelKlSuUpWbKkZ/bs2d73x/6s2P8WsNvly5fN+divX79428+fP2/Os3HjxnlWrlzpqVixojkfcuXK5enUqVO860PPsQEDBnhatWplPqPn/KeffhrveAcOHDDXSfr06T0ZMmQw109kZKR3/8svv+x5+umnPUOHDvWEhIR4MmbM6OnQoYM5P2OdOnXK8+KLL3qCg4M9qVOn9tSqVcsTHh7u3b9v3z5PvXr1zP40adJ4ihUr5lmwYIF3/7JlyzyPPPKIJ2XKlOZndOvWzfz+uDkCzDInT570+Pn5mT/gt6J/4B966CFzQf/666+eX375xVO2bFlPlSpVvO/R8NAL+dlnn/Vs27bN880335iLpmbNmubi37lzp2fy5MkmCPTzsapXr+6pX7++CSW9MLt27WrCRculihcv7mnRooVnx44dZv+sWbNMqEVHR3tGjhzpCQoK8hw5csQ89A/QjQH23XffmQs/dp/69ttvzbZz586Z1xqqRYoU8SxatMgTERHhmTJliicgIMBc/IoA8z1vv/22p0CBAp5r1655t+n5qeeFnl/6BUzPIT3nVq9e7SldurSnZcuW3vfqOaaBM3bsWM/u3bs9gwYN8iRLlsyc50pDqGjRop7WrVt7tmzZ4tm+fbunefPmnsKFC5tzNzbA9Pxt166dOb/1vNQQ+u9//+v9ORp6epwVK1aYcun1FBoa6g25unXremrUqGF+hp67eozly5ebfQcPHjTH01DU43/11Vfmi2LsFz0kRIBZZu3ateaP87x58275nh9++MHj7+9vvlHG0pDSz61bt8681otCL5bYUFB6sWmtRgMwll7AerEr/ZarF3BUVFS8n6d/WGK/zWqta+rUqTctlwaNfru9UdwA02+betF+9tln3v1aK2vWrJl5rj9byx0WFhbvGFor1PcpAsz36B90/X+q/29jVapUyXxZ0v/3r732Wrz367mqAXXp0iXvOabvjaVBmDVrVlN7U59//rk51+MGpAaXBuTixYu9AabH0VaKWFpjiz03NTy1jBqgsU6cOGGOoV/k1IMPPujp06fPTX/HHj16JCiDBq5+0Yx7TeJvDOKwzO0MM9+xY4cZ1KGPWMWKFTN9ZrovVt68eSVdunTe19myZTPvS5YsWbxtx44dM883b94sFy5ckEyZMknatGm9j8jISImIiDDvefPNN+XVV1+V6tWry4cffujdfruSJ09uBnVMnz7dvP7rr79k/vz58sILL5jXe/bskYsXL0qNGjXileGzzz67458FexQpUkTKly8vkydP9p4HOoBD+8f0vNQBQnHPh5o1a8q1a9fMuRkrbj+ZDvwICQmJd27rMfV6iD1GxowZJSoqKt55Vbx4cfH39/e+zp49u/cYem3p+fvYY4959+u1UrhwYe9117lzZ+nfv7/pw9bpqeIOPtH3lCtXLt5IXH2fXnPa/4eEGMRhmYIFC5oTPDEGaqRIkSLeaz3uzbbpHwKlF5JesDpM/Uaxowv79OkjzZs3lwULFsj3339vLtIZM2ZIo0aNbrtcGlZVqlQxfxh+/PFHSZ06tXcUmpZB6fFz5swZ73PMVefbNKx05O3YsWPN4I0CBQqY80TPibZt25pwuNEDDzzgff6/zu2yZct6vzjFlSVLlts6xu3QL3carnr+/vDDD2ahTB2Bq78X7hw1MMvot0K9APQi1trJjfTep6JFi8off/xhHrG2b99u9mkN626VKVPGDN/Xb5mhoaHxHpkzZ/a+r1ChQtKlSxdzgT7zzDPmj03siMOrV6/+z5+j37S19jhz5kzzB6VJkybePxxafg2qAwcOJChD3BonfI/WzLV14MsvvzQ17tatW5sA0fNSz+8bzwd96Dl3O/QYu3fvlqxZsyY4hi5meTv0urty5Yq5KT/WyZMnZdeuXfGuOz1P27VrJ/PmzZOuXbvKhAkTvJ9fs2ZNvFaW1atXm1rhjaMvcR0BZiENLw2CRx99VObOnWsuPG1+GD16tGmC0OY7HVqvNZmNGzfKunXr5KWXXjLfVnXI8d3S4+rxGzZsaMJJh/PrEPj33ntPfv31V7l06ZJ07NjR1ND2799vLr7169ebCzO2yVK/6eq9aidOnDBNgbeitbjx48ebGlhs86HSi/mtt94yATlt2jTTvKO/45gxY8xr+C5t1mvWrJl0795djhw5Ii1btjTbu3XrZs5DPfc2bdpkrgdtdtbXt0vPMf0S9vTTT5umSW161PNYa3W323ynrSP6+TZt2siqVatMs2SLFi1MS4FuV//5z3/M7S56fD1vly5d6r0+OnToYL50am1MW1j0d9AWDG2Wj9usj7/xX8VCevOynvxVq1Y13+BKlChh+oQ0GMaNG2e+lerJnyFDBnNzsAaPfkZrNPdCj7tw4UJzzFatWpma1nPPPWfCSvvKtG9Av3FqWOo+/cZcu3Zt6du3r7dmpd889Y+QNssMGTLkH/+g6Ldqvfi1HyCufv36Sa9evUzzi1782ryoTTL58uW7p98PdjQjnj592rRC5MiRw9u3tXz5cgkPD5dKlSqZGV3ef/997/7bkSZNGlmxYoVpctRWAz2v9GdpH1hQUNBtH0dbG7Qpsl69eubLntam9JqJbUHQL556r2bseavXySeffGL26bmu79UvnKVKlTLXipahZ8+ed/zf6d+C5VQAAFaiBgYAsBIBBgCwEgEGALASAQYAsBIBBgCwEgEGALASAQYAsBIBBgCwEgEG+Didckmn/wJ8DQEGOBwuOkVX7EoAOh3WO++8Y6YwAvDPWE4FcJjOiadz6F2+fFk2bNggL7/8sgm0wYMHO100wNWogQEO0+VhdHFFXWZDm/p08mWdhV/pWlM6abHWzHRdNJ3kdc6cOd7P6uSwOuFr7H5dPHHUqFEO/jbA/UMNDHCR33//3SwNkidPHvNaw+uLL74wS8voch06Y7ou0aGz+evyOBpwulbU7Nmzzeq/+tnXXnvNLDyqqwEAvowAAxz23XffmbWudDHE6Ohos/bTxx9/bJ4PHDhQlixZYpbmULosjq419emnn5oA036z2OVqlNbEdFHEWbNmEWDweQQY4DBd103XcdMVtkeMGGFWvG7cuLFs27bNLPqpa73FFRMTY9a8irvA6eTJk80q1bqoqO5/6KGHHPhNgPuLAAMcFhgYaJauVxpE2s81adIks1Cp0sU6dbHDG/vN1IwZM8wK1cOGDTO1NF2xeujQofGWtQd8FQEGuIg2H/bo0cMsI68rDGtQac1KmwtvZvXq1Wala12OPlZERMR9LDHgHEYhAi7TpEkT8ff3N/1cWrvq0qWLTJs2zQTTxo0bZcyYMea10oEdv/76qyxevNgEXq9evWT9+vVO/wrAfUENDHAZ7QPr2LGjDBkyRCIjI82IQx2NuHfvXgkODpYyZcqYWppq27at/Pbbb9KsWTNz79jzzz9vamPff/+9078GkOT8PB6PJ+l/DAAAiYsmRACAlQgwAICVCDAAgJUIMACAlQgwAICVCDAAgJUIMACAlQgwAICVCDAAgJUIMACAlQgwAIDY6P8Beu7gTbtnPFsAAAAASUVORK5CYII=", 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" ] @@ -337,15 +344,15 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "A acurácia média é: 0.911424\n", - "Desvio padrão das acurácias: 0.023368\n" + "A acurácia média é: 0.951248\n", + "Desvio padrão das acurácias: 0.022352\n" ] } ], @@ -380,7 +387,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -421,28 +428,28 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "A acuracia é 0.961033634126333\n", + "A acuracia é 0.9667760459392944\n", " precision recall f1-score support\n", "\n", - " Comestível 0.94 0.99 0.96 1273\n", - " Venenoso 0.99 0.93 0.96 1165\n", + " Comestível 0.95 0.99 0.97 1294\n", + " Venenoso 0.99 0.94 0.96 1144\n", "\n", - " accuracy 0.96 2438\n", - " macro avg 0.96 0.96 0.96 2438\n", - "weighted avg 0.96 0.96 0.96 2438\n", + " accuracy 0.97 2438\n", + " macro avg 0.97 0.97 0.97 2438\n", + "weighted avg 0.97 0.97 0.97 2438\n", "\n" ] }, { "data": { - "image/png": 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", + "image/png": 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" ] @@ -476,7 +483,7 @@ ], "metadata": { "kernelspec": { - "display_name": ".venv", + "display_name": ".venv (3.12.2)", "language": "python", "name": "python3" }, @@ -490,7 +497,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.4" + "version": "3.12.2" }, "orig_nbformat": 4 }, From c03364c0985d20d3bb75d2e3d21ef1bbcc957351 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jo=C3=A3o=20Paulo?= <88985821+Joao-Paulo-Silva@users.noreply.github.com> Date: Tue, 7 Apr 2026 22:10:43 -0300 Subject: [PATCH 4/5] refactor: remove redundant validation from child class --- aisp/utils/display.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/aisp/utils/display.py b/aisp/utils/display.py index 0c6885e..817b8b5 100644 --- a/aisp/utils/display.py +++ b/aisp/utils/display.py @@ -161,8 +161,6 @@ class ProgressTable(TableFormatter): def __init__(self, headers: Mapping[str, int], verbose: bool = True) -> None: super().__init__(headers) - if not headers or not isinstance(headers, Mapping): - raise ValueError("'headers' must be a non-empty dictionary.") self.verbose: bool = verbose self.headers: Mapping[str, int] = headers self._ascii_only = not _supports_box_drawing() From 5d04b272f907678436efa3231f1f6b86bc4af411 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Jo=C3=A3o=20Paulo?= <88985821+Joao-Paulo-Silva@users.noreply.github.com> Date: Fri, 10 Apr 2026 19:14:10 -0300 Subject: [PATCH 5/5] refactor: remove kwargs usage from algoritmos --- aisp/base/core/_base.py | 3 +- aisp/csa/_ai_recognition_sys.py | 12 +++----- aisp/ina/_ai_network.py | 13 ++++---- aisp/nsa/_negative_selection.py | 37 +++++++++++------------ aisp/nsa/tests/test_negative_selection.py | 2 +- 5 files changed, 32 insertions(+), 35 deletions(-) diff --git a/aisp/base/core/_base.py b/aisp/base/core/_base.py index b1f300c..0ef16f5 100644 --- a/aisp/base/core/_base.py +++ b/aisp/base/core/_base.py @@ -3,6 +3,7 @@ from inspect import signature + class Base: """ Generic base class for models with a common interface. @@ -46,4 +47,4 @@ def get_params(self, deep: bool = True) -> dict: # pylint: disable=W0613 key: getattr(self, key) for key, _ in signature(self.__init__).parameters.items() if key != "self" and not key.startswith("_") and hasattr(self, key) - } + } diff --git a/aisp/csa/_ai_recognition_sys.py b/aisp/csa/_ai_recognition_sys.py index c503693..580d011 100644 --- a/aisp/csa/_ai_recognition_sys.py +++ b/aisp/csa/_ai_recognition_sys.py @@ -61,12 +61,10 @@ class AIRS(BaseClassifier): Distance metric used to compute affinity between cells and samples. seed : int Seed for the random generation of detector values. Defaults to None. - - **kwargs - p : float - This parameter stores the value of ``p`` used in the Minkowski distance. The default - is ``2``, which represents normalized Euclidean distance.\ - Different values of p lead to different variants of the Minkowski Distance. + p : float + This parameter stores the value of ``p`` used in the Minkowski distance. The default + is ``2``, which represents normalized Euclidean distance.\ + Different values of p lead to different variants of the Minkowski Distance. Attributes ---------- @@ -153,7 +151,7 @@ def __init__( self.metric = sanitize_choice(metric, ["manhattan", "minkowski"], "euclidean") - self.p = p + self.p: float = p self._cells_memory: Optional[Dict[str | int, list[BCell]]] = None self._all_class_cell_vectors: Optional[List[Tuple[Any, np.ndarray]]] = None diff --git a/aisp/ina/_ai_network.py b/aisp/ina/_ai_network.py index fb4f09f..3515c06 100644 --- a/aisp/ina/_ai_network.py +++ b/aisp/ina/_ai_network.py @@ -71,11 +71,10 @@ class AiNet(BaseClusterer): use_mst_clustering : bool, default=True If ``True``, performs clustering with **Minimum Spanning Tree** (MST). If ``False``, does not perform clustering and predict returns None. - **kwargs - p : float - This parameter stores the value of ``p`` used in the Minkowski distance. The default - is ``2``, which represents normalized Euclidean distance.\ - Different values of p lead to different variants of the Minkowski Distance. + p : float + This parameter stores the value of ``p`` used in the Minkowski distance. The default + is ``2``, which represents normalized Euclidean distance.\ + Different values of p lead to different variants of the Minkowski Distance. Attributes ---------- @@ -139,7 +138,7 @@ def __init__( metric: MetricType = "euclidean", seed: Optional[int] = None, use_mst_clustering: bool = True, - **kwargs, + p: float = 2.0 ): self.N: int = sanitize_param(N, 50, lambda x: x > 0) self.n_clone: int = sanitize_param(n_clone, 10, lambda x: x > 0) @@ -175,7 +174,7 @@ def __init__( metric, ["euclidean", "manhattan", "minkowski"], "euclidean" ) - self.p: np.float64 = np.float64(kwargs.get("p", 2.0)) + self.p: float = p self._metric_params = {} if self.metric == "minkowski": self._metric_params["p"] = self.p diff --git a/aisp/nsa/_negative_selection.py b/aisp/nsa/_negative_selection.py index babb4bd..5883124 100644 --- a/aisp/nsa/_negative_selection.py +++ b/aisp/nsa/_negative_selection.py @@ -2,7 +2,7 @@ from __future__ import annotations -from typing import Any, Dict, Literal, Optional, Union, List +from typing import Dict, Literal, Optional, Union, List import numpy as np import numpy.typing as npt @@ -57,20 +57,17 @@ class RNSA(BaseClassifier): * ``'default-NSA'``: Default algorithm with fixed radius. * ``'V-detector'``: This algorithm is based on the article Ji & Dasgupta (2004) [1]_ and uses a variable radius for anomaly detection in feature spaces. - - **kwargs : dict - Additional parameters. The following arguments are recognized: - * non_self_label : str, default='non-self' - This variable stores the label that will be assigned when the data has only one - output class, and the sample is classified as not belonging to that class. - * cell_bounds : bool, default=False - If set to ``True``, this option limits the generation of detectors to the space - within the plane between 0 and 1. This means that any detector whose radius exceeds - this limit is discarded, this variable is only used in the ``V-detector`` algorithm. - * p : float, default=2 - This parameter stores the value of ``p`` used in the Minkowski distance. The default - is ``2``, which represents Euclidean distance. Different values of p lead - to different variants of the Minkowski Distance. + * non_self_label : str, default='non-self' + This variable stores the label that will be assigned when the data has only one + output class, and the sample is classified as not belonging to that class. + * cell_bounds : bool, default=False + If set to ``True``, this option limits the generation of detectors to the space + within the plane between 0 and 1. This means that any detector whose radius exceeds + this limit is discarded, this variable is only used in the ``V-detector`` algorithm. + * p : float, default=2 + This parameter stores the value of ``p`` used in the Minkowski distance. The default + is ``2``, which represents Euclidean distance. Different values of p lead + to different variants of the Minkowski Distance. Attributes ---------- @@ -142,7 +139,9 @@ def __init__( max_discards: int = 1000, seed: Optional[int] = None, algorithm: Literal["default-NSA", "V-detector"] = "default-NSA", - **kwargs: Any, + p: float = 2.0, + non_self_label: str = 'non-self', + cell_bounds: bool = False ): self.metric: str = sanitize_choice( metric, ["manhattan", "minkowski"], "euclidean" @@ -160,9 +159,9 @@ def __init__( ) self.max_discards: int = sanitize_param(max_discards, 1000, lambda x: x > 0) - self.p: np.float64 = np.float64(kwargs.get("p", 2)) - self.cell_bounds: bool = bool(kwargs.get("cell_bounds", False)) - self.non_self_label: str = str(kwargs.get("non_self_label", "non-self")) + self.p: float = p + self.cell_bounds: bool = cell_bounds + self.non_self_label: str = non_self_label self._detectors: Optional[Dict[str | int, list[Detector]]] = None self.classes: Optional[npt.NDArray] = None diff --git a/aisp/nsa/tests/test_negative_selection.py b/aisp/nsa/tests/test_negative_selection.py index 1797896..823e9ab 100644 --- a/aisp/nsa/tests/test_negative_selection.py +++ b/aisp/nsa/tests/test_negative_selection.py @@ -103,7 +103,7 @@ def test_fit_raises_max_discards_error(self, rnsa_data): def test_predict_raises_feature_dimension_mismatch(self, rnsa_data): """Should raise FeatureDimensionMismatch when prediction input has wrong dimensions.""" X, y, seed = rnsa_data - model = RNSA(N=1000, aff_thresh=0.2, seed=seed) + model = RNSA(N=1000, seed=seed) model.fit(X, y, verbose=False) x_invalid = np.random.rand(5, 5) with pytest.raises(FeatureDimensionMismatch):