From ac41b0eb7a5784a510bb5dc5a6f58200d14c0afc Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=D0=90=D0=BB=D0=B5=D0=BA=D1=81=D0=B0=D0=BD=D0=B4=D1=80=20?= =?UTF-8?q?=D0=A1=D0=B2=D0=B5=D0=BA=D1=80=D0=BE=D0=B2=D0=B8=D0=BD?= Date: Wed, 14 May 2025 01:53:05 +0300 Subject: [PATCH] =?UTF-8?q?=D0=92=D1=8B=D0=BF=D0=BE=D0=BB=D0=BD=D0=B5?= =?UTF-8?q?=D0=BD=D0=BE=20=D0=B7=D0=B0=D0=B4=D0=B0=D0=BD=D0=B8=D0=B5=20?= =?UTF-8?q?=D0=B4=D0=BB=D1=8F=20=D0=B1=D0=B0=D0=B7=D0=BE=D0=B2=D0=BE=D0=B9?= =?UTF-8?q?=20=D0=BD=D0=B5=D0=B9=D1=80=D0=BE=D1=81=D0=B5=D1=82=D0=B8?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../week4_scikit_learn.ipynb-checkpoint.ipynb | 104 ++++++++++++++++++ week4_scikit_learn.ipynb.ipynb | 104 ++++++++++++++++++ 2 files changed, 208 insertions(+) create mode 100644 .ipynb_checkpoints/week4_scikit_learn.ipynb-checkpoint.ipynb create mode 100644 week4_scikit_learn.ipynb.ipynb diff --git a/.ipynb_checkpoints/week4_scikit_learn.ipynb-checkpoint.ipynb b/.ipynb_checkpoints/week4_scikit_learn.ipynb-checkpoint.ipynb new file mode 100644 index 0000000..f5de917 --- /dev/null +++ b/.ipynb_checkpoints/week4_scikit_learn.ipynb-checkpoint.ipynb @@ -0,0 +1,104 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "6af36011-9a8c-4dc1-85c5-910263c2d25e", + "metadata": {}, + "source": [ + "Базовая нейросеть" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2115d6e8-d6d0-4025-9ee0-32c46b20fe45", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 10\n", + " 1 1.00 1.00 1.00 9\n", + " 2 1.00 1.00 1.00 11\n", + "\n", + " accuracy 1.00 30\n", + " macro avg 1.00 1.00 1.00 30\n", + "weighted avg 1.00 1.00 1.00 30\n", + "\n" + ] + } + ], + "source": [ + "from sklearn.datasets import load_iris\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.neural_network import MLPClassifier\n", + "from sklearn.metrics import classification_report\n", + "\n", + "# Загрузка и разбиение данных\n", + "X, y = load_iris(return_X_y=True)\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", + "\n", + "# Нормализация данных\n", + "scaler = StandardScaler()\n", + "X_train = scaler.fit_transform(X_train)\n", + "X_test = scaler.transform(X_test)\n", + "\n", + "# Модель MLP — многослойный перцептрон\n", + "clf = MLPClassifier(hidden_layer_sizes=(10,), activation='relu', max_iter=2500, learning_rate_init=0.001, random_state=42)\n", + "clf.fit(X_train, y_train)\n", + "\n", + "# Отчёт о точности\n", + "print(classification_report(y_test, clf.predict(X_test)))\n" + ] + }, + { + "cell_type": "markdown", + "id": "125b3f46-cc81-4341-94f3-9de6dee8aff5", + "metadata": {}, + "source": [ + "Модель работает очень хорошо и достигла 100% точности на тестовых данных, что является отличным результатом для этого набора данных." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8d032afd-fc98-48cd-9ec8-7a742fcf8a50", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "62c7892d-b296-4f0c-8886-514b4ee2bad6", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/week4_scikit_learn.ipynb.ipynb b/week4_scikit_learn.ipynb.ipynb new file mode 100644 index 0000000..f5de917 --- /dev/null +++ b/week4_scikit_learn.ipynb.ipynb @@ -0,0 +1,104 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "6af36011-9a8c-4dc1-85c5-910263c2d25e", + "metadata": {}, + "source": [ + "Базовая нейросеть" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "2115d6e8-d6d0-4025-9ee0-32c46b20fe45", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " precision recall f1-score support\n", + "\n", + " 0 1.00 1.00 1.00 10\n", + " 1 1.00 1.00 1.00 9\n", + " 2 1.00 1.00 1.00 11\n", + "\n", + " accuracy 1.00 30\n", + " macro avg 1.00 1.00 1.00 30\n", + "weighted avg 1.00 1.00 1.00 30\n", + "\n" + ] + } + ], + "source": [ + "from sklearn.datasets import load_iris\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.preprocessing import StandardScaler\n", + "from sklearn.neural_network import MLPClassifier\n", + "from sklearn.metrics import classification_report\n", + "\n", + "# Загрузка и разбиение данных\n", + "X, y = load_iris(return_X_y=True)\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n", + "\n", + "# Нормализация данных\n", + "scaler = StandardScaler()\n", + "X_train = scaler.fit_transform(X_train)\n", + "X_test = scaler.transform(X_test)\n", + "\n", + "# Модель MLP — многослойный перцептрон\n", + "clf = MLPClassifier(hidden_layer_sizes=(10,), activation='relu', max_iter=2500, learning_rate_init=0.001, random_state=42)\n", + "clf.fit(X_train, y_train)\n", + "\n", + "# Отчёт о точности\n", + "print(classification_report(y_test, clf.predict(X_test)))\n" + ] + }, + { + "cell_type": "markdown", + "id": "125b3f46-cc81-4341-94f3-9de6dee8aff5", + "metadata": {}, + "source": [ + "Модель работает очень хорошо и достигла 100% точности на тестовых данных, что является отличным результатом для этого набора данных." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8d032afd-fc98-48cd-9ec8-7a742fcf8a50", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "62c7892d-b296-4f0c-8886-514b4ee2bad6", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}