{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "b90a9f64-8f6c-4766-949c-2d2b5b2f5cfb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 1.00 1.00 1.00 11\n", " 1 1.00 0.50 0.67 12\n", " 2 0.54 1.00 0.70 7\n", "\n", " accuracy 0.80 30\n", " macro avg 0.85 0.83 0.79 30\n", "weighted avg 0.89 0.80 0.80 30\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "D:\\Practice4\\.venv\\Lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (500) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n" ] } ], "source": [ "from sklearn.datasets import load_iris\n", "from sklearn.model_selection import train_test_split\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)\n", "\n", "# Модель MLP — многослойный перцептрон\n", "clf = MLPClassifier(hidden_layer_sizes=(10,), activation='relu', max_iter=500)\n", "clf.fit(X_train, y_train)\n", "\n", "# Отчёт о точности\n", "print(classification_report(y_test, clf.predict(X_test)))" ] }, { "cell_type": "code", "execution_count": 2, "id": "31c17449-d620-449b-ad0d-12375f567a70", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " precision recall f1-score support\n", "\n", " 0 0.89 1.00 0.94 8\n", " 1 0.38 0.89 0.53 9\n", " 2 0.00 0.00 0.00 13\n", "\n", " accuracy 0.53 30\n", " macro avg 0.42 0.63 0.49 30\n", "weighted avg 0.35 0.53 0.41 30\n", "\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "D:\\Practice4\\.venv\\Lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:691: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (100) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n", "D:\\Practice4\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", "D:\\Practice4\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n", "D:\\Practice4\\.venv\\Lib\\site-packages\\sklearn\\metrics\\_classification.py:1565: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n", " _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n" ] } ], "source": [ "from sklearn.datasets import load_iris\n", "from sklearn.model_selection import train_test_split\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)\n", "\n", "# Модель MLP — многослойный перцептрон\n", "clf = MLPClassifier(hidden_layer_sizes=(10,), activation='relu', max_iter=100)\n", "clf.fit(X_train, y_train)\n", "\n", "# Отчёт о точности\n", "print(classification_report(y_test, clf.predict(X_test)))" ] }, { "cell_type": "code", "execution_count": 3, "id": "5642dd14-042f-4895-ad2c-e04c919db0ed", "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 7\n", " 2 1.00 1.00 1.00 13\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.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)\n", "\n", "# Модель MLP — многослойный перцептрон\n", "clf = MLPClassifier(hidden_layer_sizes=(10,), activation='relu', max_iter=2500)\n", "clf.fit(X_train, y_train)\n", "\n", "# Отчёт о точности\n", "print(classification_report(y_test, clf.predict(X_test)))" ] }, { "cell_type": "code", "execution_count": 1, "id": "60f55406-d189-4b57-90d0-f0393235e99b", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import openml\n", "from sklearn.datasets import load_iris\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.neural_network import MLPClassifier\n", "from sklearn.metrics import classification_report, accuracy_score\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.pipeline import make_pipeline\n", "from sklearn.inspection import PartialDependenceDisplay" ] }, { "cell_type": "code", "execution_count": null, "id": "662ac0f8-7b50-42f3-b372-7c41aea3619e", "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": "3.13.3" } }, "nbformat": 4, "nbformat_minor": 5 }