msb-public
/
ML-For-Beginners
mirror of https://github.com/microsoft/ML-For-Beginners.git
1
0
Code Issues Projects Releases Wiki Activity
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
Branches Tags
${ item.name }
Create tag ${ searchTerm }
Create branch ${ searchTerm }
from 'copilot/add-data-visualization-notebook'
${ noResults }
ML-For-Beginners/translations/ro/4-Classification/3-Classifiers-2/solution/notebook.ipynb

275 lines
13 KiB
Raw Permalink Blame History Escape

This file contains ambiguous Unicode characters!

This file contains ambiguous Unicode characters that may be confused with others in your current locale. If your use case is intentional and legitimate, you can safely ignore this warning. Use the Escape button to highlight these characters.

{
"cells": [
{
"source": [
"# Construiește Mai Multe Modele de Clasificare\n"
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Prezentare generală a datasetului\n",
"Acest dataset conține mostre individuale (de exemplu, rețete) etichetate după bucătărie.\n",
"Fiecare rând corespunde unei singure mostre/înregistrări, iar coloanele reprezintă ingrediente sau alte atribute folosite pentru clasificare, inclusiv eticheta `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": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>almond</th>\n <th>angelica</th>\n <th>anise</th>\n <th>anise_seed</th>\n <th>apple</th>\n <th>apple_brandy</th>\n <th>apricot</th>\n <th>armagnac</th>\n <th>artemisia</th>\n <th>artichoke</th>\n <th>...</th>\n <th>whiskey</th>\n <th>white_bread</th>\n <th>white_wine</th>\n <th>whole_grain_wheat_flour</th>\n <th>wine</th>\n <th>wood</th>\n <th>yam</th>\n <th>yeast</th>\n <th>yogurt</th>\n <th>zucchini</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>4</th>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>...</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n </tr>\n </tbody>\n</table>\n<p>5 rows × 380 columns</p>\n</div>"
},
"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": [
"# Încearcă diferiți clasificatori\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<!-- CO-OP TRANSLATOR DISCLAIMER START -->\n**Declinare a responsabilității**: \nAcest document a fost tradus folosind serviciul de traducere AI [Co-op Translator](https://github.com/Azure/co-op-translator). Deși ne străduim pentru acuratețe, vă rugăm să rețineți că traducerile automate pot conține erori sau inexactități. Documentul original în limba sa nativă trebuie considerat sursa autoritară. Pentru informații critice, se recomandă traducerea profesională realizată de un specialist uman. Nu ne asumăm răspunderea pentru orice neînțelegeri sau interpretări greșite rezultate din utilizarea acestei traduceri.\n<!-- CO-OP TRANSLATOR DISCLAIMER END -->\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
}
Reference in new issue View Git Blame Copy Permalink