{ "cells": [ { "source": [ "# Sukurkite daugiau klasifikacijos modelių\n" ], "cell_type": "markdown", "metadata": {} }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Duomenų rinkinio apžvalga\n", "Šiame duomenų rinkinyje yra atskiri mėginiai (pavyzdžiui, receptai), pažymėti pagal virtuvę.\n", "Kiekviena eilutė atitinka vieną mėginį/įrašą, o stulpeliai atspindi ingredientus arba kitas ypatybes, naudojamas klasifikavimui, įskaitant `cuisine` žymą.\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": [ "# Išbandykite skirtingus klasifikatorius\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**Atsakomybės apribojimas**:\nŠis dokumentas buvo išverstas naudojant dirbtinio intelekto vertimo paslaugą [Co-op Translator](https://github.com/Azure/co-op-translator). Nors siekiame tikslumo, prašome atkreipti dėmesį, kad automatiniai vertimai gali turėti klaidų ar netikslumų. Originalus dokumentas jo gimtąja kalba turėtų būti laikomas autoritetingu šaltiniu. Kritinei informacijai rekomenduojama naudoti profesionalų žmogaus vertimą. Mes neatsakome už jokius nesusipratimus ar neteisingus aiškinimus, atsiradusius dėl šio vertimo naudojimo.\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 }