Работа завершена

.ipynb_checkpoints/Untitled-checkpoint.ipynb

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+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

Untitled.ipynb

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+{
+ "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
+}