{ "cells": [ { "source": [ "# Постройте больше моделей классификации\n" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Обзор набора данных\n", "Этот набор данных содержит отдельные образцы (например, рецепты), помеченные по типу кухни.\n", "Каждая строка соответствует одному образцу/записи, а столбцы представляют ингредиенты или другие атрибуты, используемые для классификации, включая метку `cuisine`.\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "# Load dataset containing cuisine features\n", "cuisines_df = pd.read_csv(\"../../data/cleaned_cuisines.csv\")\n", "cuisines_df.head()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "0 indian\n", "1 indian\n", "2 indian\n", "3 indian\n", "4 indian\n", "Name: cuisine, dtype: object" ] }, "metadata": {}, "execution_count": 2 } ], "source": [ "cuisines_label_df = cuisines_df['cuisine']\n", "cuisines_label_df.head()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " almond angelica anise anise_seed apple apple_brandy apricot \\\n", "0 0 0 0 0 0 0 0 \n", "1 1 0 0 0 0 0 0 \n", "2 0 0 0 0 0 0 0 \n", "3 0 0 0 0 0 0 0 \n", "4 0 0 0 0 0 0 0 \n", "\n", " armagnac artemisia artichoke ... whiskey white_bread white_wine \\\n", "0 0 0 0 ... 0 0 0 \n", "1 0 0 0 ... 0 0 0 \n", "2 0 0 0 ... 0 0 0 \n", "3 0 0 0 ... 0 0 0 \n", "4 0 0 0 ... 0 0 0 \n", "\n", " whole_grain_wheat_flour wine wood yam yeast yogurt zucchini \n", "0 0 0 0 0 0 0 0 \n", "1 0 0 0 0 0 0 0 \n", "2 0 0 0 0 0 0 0 \n", "3 0 0 0 0 0 0 0 \n", "4 0 0 0 0 0 1 0 \n", "\n", "[5 rows x 380 columns]" ], "text/html": "
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" }, "metadata": {}, "execution_count": 3 } ], "source": [ "cuisines_features_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n", "cuisines_features_df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Попробуйте разные классификаторы\n" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.svm import SVC\n", "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier\n", "from sklearn.model_selection import train_test_split, cross_val_score\n", "from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = train_test_split(cuisines_features_df, cuisines_label_df, test_size=0.3)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "\n", "C = 10\n", "# Create different classifiers.\n", "classifiers = {\n", " 'Linear SVC': SVC(kernel='linear', C=C, probability=True,random_state=0),\n", " 'KNN classifier': KNeighborsClassifier(C),\n", " 'SVC': SVC(),\n", " 'RFST': RandomForestClassifier(n_estimators=100),\n", " 'ADA': AdaBoostClassifier(n_estimators=100)\n", " \n", "}\n" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Accuracy (train) for Linear SVC: 76.4% \n", " precision recall f1-score support\n", "\n", " chinese 0.64 0.66 0.65 242\n", " indian 0.91 0.86 0.89 236\n", " japanese 0.72 0.73 0.73 245\n", " korean 0.83 0.75 0.79 234\n", " thai 0.75 0.82 0.78 242\n", "\n", " accuracy 0.76 1199\n", " macro avg 0.77 0.76 0.77 1199\n", "weighted avg 0.77 0.76 0.77 1199\n", "\n", "Accuracy (train) for KNN classifier: 70.7% \n", " precision recall f1-score support\n", "\n", " chinese 0.65 0.63 0.64 242\n", " indian 0.84 0.81 0.82 236\n", " japanese 0.60 0.81 0.69 245\n", " korean 0.89 0.53 0.67 234\n", " thai 0.69 0.75 0.72 242\n", "\n", " accuracy 0.71 1199\n", " macro avg 0.73 0.71 0.71 1199\n", "weighted avg 0.73 0.71 0.71 1199\n", "\n", "Accuracy (train) for SVC: 80.1% \n", " precision recall f1-score support\n", "\n", " chinese 0.71 0.69 0.70 242\n", " indian 0.92 0.92 0.92 236\n", " japanese 0.77 0.78 0.77 245\n", " korean 0.87 0.77 0.82 234\n", " thai 0.75 0.86 0.80 242\n", "\n", " accuracy 0.80 1199\n", " macro avg 0.80 0.80 0.80 1199\n", "weighted avg 0.80 0.80 0.80 1199\n", "\n", "Accuracy (train) for RFST: 82.8% \n", " precision recall f1-score support\n", "\n", " chinese 0.80 0.75 0.77 242\n", " indian 0.90 0.91 0.90 236\n", " japanese 0.82 0.78 0.80 245\n", " korean 0.85 0.82 0.83 234\n", " thai 0.78 0.89 0.83 242\n", "\n", " accuracy 0.83 1199\n", " macro avg 0.83 0.83 0.83 1199\n", "weighted avg 0.83 0.83 0.83 1199\n", "\n", "Accuracy (train) for ADA: 71.1% \n", " precision recall f1-score support\n", "\n", " chinese 0.60 0.57 0.58 242\n", " indian 0.87 0.84 0.86 236\n", " japanese 0.71 0.60 0.65 245\n", " korean 0.68 0.78 0.72 234\n", " thai 0.70 0.78 0.74 242\n", "\n", " accuracy 0.71 1199\n", " macro avg 0.71 0.71 0.71 1199\n", "weighted avg 0.71 0.71 0.71 1199\n", "\n" ] } ], "source": [ "n_classifiers = len(classifiers)\n", "\n", "for index, (name, classifier) in enumerate(classifiers.items()):\n", " classifier.fit(X_train, np.ravel(y_train))\n", "\n", " y_pred = classifier.predict(X_test)\n", " accuracy = accuracy_score(y_test, y_pred)\n", " print(\"Accuracy (train) for %s: %0.1f%% \" % (name, accuracy * 100))\n", " print(classification_report(y_test,y_pred))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "---\n\n\n**Отказ от ответственности**: \nЭтот документ был переведен с помощью сервиса автоматического перевода [Co-op Translator](https://github.com/Azure/co-op-translator). Несмотря на наши усилия обеспечивать точность, обратите внимание, что автоматические переводы могут содержать ошибки или неточности. Оригинальный документ на исходном языке следует считать авторитетным источником. Для критически важной информации рекомендуется использовать профессиональный перевод, выполненный человеком. Мы не несем ответственности за любые недоразумения или неправильные толкования, возникшие в результате использования этого перевода.\n\n" ] } ], "metadata": { "interpreter": { "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d" }, "kernelspec": { "name": "python3", "display_name": "Python 3.7.0 64-bit ('3.7')" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.0" }, "metadata": { "interpreter": { "hash": "70b38d7a306a849643e446cd70466270a13445e5987dfa1344ef2b127438fa4d" } } }, "nbformat": 4, "nbformat_minor": 4 }