Работа 4 сделана
This commit is contained in:
Alexey
parent
c54607d20c
commit
f3a9a1f7cb
550
.ipynb_checkpoints/Support_Vector_Machines-checkpoint.ipynb
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.ipynb_checkpoints/Support_Vector_Machines-checkpoint.ipynb
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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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{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "2703a83e-6b0b-4be3-9439-bf19482185ec",
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"metadata": {},
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"source": [
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"Выполнение кода из примера"
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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": 4,
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"id": "385a0e7d-27e3-4f28-8f00-5dc14227f034",
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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 14\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 9\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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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\Users\\1\\Desktop\\praktika04\\venv\\Lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:785: 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": 7,
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"id": "57225ea2-43d7-4399-834b-b2f121dd9a0c",
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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 0.91 0.95 11\n",
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" 1 0.20 0.12 0.15 8\n",
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" 2 0.53 0.73 0.62 11\n",
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"\n",
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" accuracy 0.63 30\n",
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" macro avg 0.58 0.59 0.57 30\n",
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"weighted avg 0.62 0.63 0.62 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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"C:\\Users\\1\\Desktop\\praktika04\\venv\\Lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:785: 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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]
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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": 8,
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"id": "5b9778ff-7d14-4d68-8119-2ebfdb468bf1",
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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 9\n",
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" 1 1.00 0.93 0.96 14\n",
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" 2 0.88 1.00 0.93 7\n",
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"\n",
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" accuracy 0.97 30\n",
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" macro avg 0.96 0.98 0.97 30\n",
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"weighted avg 0.97 0.97 0.97 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": null,
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"id": "8c642bb6-7583-466c-8864-2ee80d5accc7",
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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": "3.14.4"
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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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550
Support_Vector_Machines.ipynb
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550
Support_Vector_Machines.ipynb
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File diff suppressed because one or more lines are too long
180
week4_scikit_learn.ipynb
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week4_scikit_learn.ipynb
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@ -0,0 +1,180 @@
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{
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"cells": [
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||||
{
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||||
"cell_type": "markdown",
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"id": "2703a83e-6b0b-4be3-9439-bf19482185ec",
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"metadata": {},
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"source": [
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"Выполнение кода из примера"
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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": 4,
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"id": "385a0e7d-27e3-4f28-8f00-5dc14227f034",
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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 14\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 9\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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"name": "stderr",
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"output_type": "stream",
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"text": [
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"C:\\Users\\1\\Desktop\\praktika04\\venv\\Lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:785: 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": 7,
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"id": "57225ea2-43d7-4399-834b-b2f121dd9a0c",
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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 0.91 0.95 11\n",
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" 1 0.20 0.12 0.15 8\n",
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" 2 0.53 0.73 0.62 11\n",
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"\n",
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" accuracy 0.63 30\n",
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" macro avg 0.58 0.59 0.57 30\n",
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"weighted avg 0.62 0.63 0.62 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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"C:\\Users\\1\\Desktop\\praktika04\\venv\\Lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:785: 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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]
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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": 8,
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"id": "5b9778ff-7d14-4d68-8119-2ebfdb468bf1",
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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 9\n",
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" 1 1.00 0.93 0.96 14\n",
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" 2 0.88 1.00 0.93 7\n",
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"\n",
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" accuracy 0.97 30\n",
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" macro avg 0.96 0.98 0.97 30\n",
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"weighted avg 0.97 0.97 0.97 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": null,
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"id": "8c642bb6-7583-466c-8864-2ee80d5accc7",
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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": "3.14.4"
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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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