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" }, "metadata": {}, "execution_count": 1 } ], "source": [ "import pandas as pd\n", "cuisines_df = pd.read_csv(\"../../data/cleaned_cuisines.csv\")\n", "cuisines_df.head()" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from sklearn.linear_model import LogisticRegression\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", "from sklearn.svm import SVC\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 3, "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": 3 } ], "source": [ "cuisines_label_df = cuisines_df['cuisine']\n", "cuisines_label_df.head()" ] }, { "cell_type": "code", "execution_count": 4, "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": 4 } ], "source": [ "cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n", "cuisines_feature_df.head()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Accuracy is 0.8181818181818182\n" ] } ], "source": [ "lr = LogisticRegression(multi_class='ovr',solver='liblinear')\n", "model = lr.fit(X_train, np.ravel(y_train))\n", "\n", "accuracy = model.score(X_test, y_test)\n", "print (\"Accuracy is {}\".format(accuracy))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "ingredients: Index(['artemisia', 'black_pepper', 'mushroom', 'shiitake', 'soy_sauce',\n 'vegetable_oil'],\n dtype='object')\ncuisine: korean\n" ] } ], "source": [ "# test an item\n", "print(f'ingredients: {X_test.iloc[50][X_test.iloc[50]!=0].keys()}')\n", "print(f'cuisine: {y_test.iloc[50]}')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ " 0\n", "korean 0.392231\n", "chinese 0.372872\n", "japanese 0.218825\n", "thai 0.013427\n", "indian 0.002645" ], "text/html": "
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" }, "metadata": {}, "execution_count": 8 } ], "source": [ "#rehsape to 2d array and transpose\n", "test= X_test.iloc[50].values.reshape(-1, 1).T\n", "# predict with score\n", "proba = model.predict_proba(test)\n", "classes = model.classes_\n", "# create df with classes and scores\n", "resultdf = pd.DataFrame(data=proba, columns=classes)\n", "\n", "# create df to show results\n", "topPrediction = resultdf.T.sort_values(by=[0], ascending = [False])\n", "topPrediction.head()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " precision recall f1-score support\n\n chinese 0.75 0.73 0.74 223\n indian 0.93 0.88 0.90 255\n japanese 0.78 0.78 0.78 253\n korean 0.87 0.86 0.86 236\n thai 0.76 0.84 0.80 232\n\n accuracy 0.82 1199\n macro avg 0.82 0.82 0.82 1199\nweighted avg 0.82 0.82 0.82 1199\n\n" ] } ], "source": [ "y_pred = model.predict(X_test)\r\n", "print(classification_report(y_test,y_pred))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n---\n\n**免責聲明**： \n本文件已使用 AI 翻譯服務 [Co-op Translator](https://github.com/Azure/co-op-translator) 進行翻譯。儘管我們努力確保翻譯的準確性，但請注意，自動翻譯可能包含錯誤或不準確之處。原始文件的母語版本應被視為權威來源。對於關鍵資訊，建議尋求專業人工翻譯。我們對因使用此翻譯而引起的任何誤解或錯誤解釋不承擔責任。\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" } }, "coopTranslator": { "original_hash": "9408506dd864f2b6e334c62f80c0cfcc", "translation_date": "2025-08-29T23:41:13+00:00", "source_file": "4-Classification/2-Classifiers-1/solution/notebook.ipynb", "language_code": "mo" } }, "nbformat": 4, "nbformat_minor": 4 }