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.ipynb_checkpoints/Untitled-checkpoint.ipynb
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.ipynb_checkpoints/Untitled-checkpoint.ipynb
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{
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"cells": [],
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"metadata": {},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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.ipynb_checkpoints/week4_scikit_learn-checkpoint.ipynb
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.ipynb_checkpoints/week4_scikit_learn-checkpoint.ipynb
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Untitled.ipynb
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Untitled.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "b90a9f64-8f6c-4766-949c-2d2b5b2f5cfb",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" precision recall f1-score support\n",
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"\n",
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" 0 1.00 1.00 1.00 11\n",
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" 1 1.00 0.50 0.67 12\n",
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" 2 0.54 1.00 0.70 7\n",
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"\n",
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" accuracy 0.80 30\n",
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" macro avg 0.85 0.83 0.79 30\n",
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"weighted avg 0.89 0.80 0.80 30\n",
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"\n"
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]
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},
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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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"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",
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" warnings.warn(\n"
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]
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}
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],
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"source": [
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"from sklearn.datasets import load_iris\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.neural_network import MLPClassifier\n",
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"from sklearn.metrics import classification_report\n",
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"\n",
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"# Загрузка и разбиение данных\n",
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"X, y = load_iris(return_X_y=True)\n",
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"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
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"\n",
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"# Модель MLP — многослойный перцептрон\n",
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"clf = MLPClassifier(hidden_layer_sizes=(10,), activation='relu', max_iter=500)\n",
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"clf.fit(X_train, y_train)\n",
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"\n",
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"# Отчёт о точности\n",
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"print(classification_report(y_test, clf.predict(X_test)))"
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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": 2,
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"id": "31c17449-d620-449b-ad0d-12375f567a70",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" precision recall f1-score support\n",
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"\n",
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" 0 0.89 1.00 0.94 8\n",
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" 1 0.38 0.89 0.53 9\n",
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" 2 0.00 0.00 0.00 13\n",
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"\n",
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" accuracy 0.53 30\n",
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" macro avg 0.42 0.63 0.49 30\n",
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"weighted avg 0.35 0.53 0.41 30\n",
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"\n"
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]
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},
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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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"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",
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" warnings.warn(\n",
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"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",
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" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
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"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",
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" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n",
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"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",
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" _warn_prf(average, modifier, f\"{metric.capitalize()} is\", len(result))\n"
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]
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}
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],
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"source": [
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"from sklearn.datasets import load_iris\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.neural_network import MLPClassifier\n",
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"from sklearn.metrics import classification_report\n",
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"\n",
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"# Загрузка и разбиение данных\n",
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"X, y = load_iris(return_X_y=True)\n",
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"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
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"\n",
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"# Модель MLP — многослойный перцептрон\n",
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"clf = MLPClassifier(hidden_layer_sizes=(10,), activation='relu', max_iter=100)\n",
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"clf.fit(X_train, y_train)\n",
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"\n",
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"# Отчёт о точности\n",
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"print(classification_report(y_test, clf.predict(X_test)))"
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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": 3,
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"id": "5642dd14-042f-4895-ad2c-e04c919db0ed",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" precision recall f1-score support\n",
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"\n",
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" 0 1.00 1.00 1.00 10\n",
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" 1 1.00 1.00 1.00 7\n",
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" 2 1.00 1.00 1.00 13\n",
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"\n",
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" accuracy 1.00 30\n",
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" macro avg 1.00 1.00 1.00 30\n",
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"weighted avg 1.00 1.00 1.00 30\n",
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"\n"
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]
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}
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],
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"source": [
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"from sklearn.datasets import load_iris\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.neural_network import MLPClassifier\n",
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"from sklearn.metrics import classification_report\n",
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"\n",
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"# Загрузка и разбиение данных\n",
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"X, y = load_iris(return_X_y=True)\n",
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"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
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"\n",
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"# Модель MLP — многослойный перцептрон\n",
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"clf = MLPClassifier(hidden_layer_sizes=(10,), activation='relu', max_iter=2500)\n",
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"clf.fit(X_train, y_train)\n",
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"\n",
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"# Отчёт о точности\n",
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"print(classification_report(y_test, clf.predict(X_test)))"
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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": 1,
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"id": "60f55406-d189-4b57-90d0-f0393235e99b",
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import matplotlib.pyplot as plt\n",
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"import pandas as pd\n",
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"import openml\n",
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"from sklearn.datasets import load_iris\n",
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"from sklearn.model_selection import train_test_split\n",
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"from sklearn.neural_network import MLPClassifier\n",
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"from sklearn.metrics import classification_report, accuracy_score\n",
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"from sklearn.preprocessing import StandardScaler\n",
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"from sklearn.pipeline import make_pipeline\n",
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"from sklearn.inspection import PartialDependenceDisplay"
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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": null,
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"id": "662ac0f8-7b50-42f3-b372-7c41aea3619e",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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502
week4_scikit_learn.ipynb
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502
week4_scikit_learn.ipynb
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