wehub-resource-sync
281 lines
16 KiB
Plaintext
281 lines
16 KiB
Plaintext
{
|
||
"cells": [
|
||
{
|
||
"source": [
|
||
"مدلهای طبقهبندی بسازید\n"
|
||
],
|
||
"cell_type": "markdown",
|
||
"metadata": {}
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 1,
|
||
"metadata": {},
|
||
"outputs": [
|
||
{
|
||
"output_type": "execute_result",
|
||
"data": {
|
||
"text/plain": [
|
||
" Unnamed: 0 cuisine almond angelica anise anise_seed apple \\\n",
|
||
"0 0 indian 0 0 0 0 0 \n",
|
||
"1 1 indian 1 0 0 0 0 \n",
|
||
"2 2 indian 0 0 0 0 0 \n",
|
||
"3 3 indian 0 0 0 0 0 \n",
|
||
"4 4 indian 0 0 0 0 0 \n",
|
||
"\n",
|
||
" apple_brandy apricot armagnac ... 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 382 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>Unnamed: 0</th>\n <th>cuisine</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>...</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>indian</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>indian</td>\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>...</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>2</td>\n <td>indian</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>3</td>\n <td>indian</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>4</td>\n <td>indian</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 × 382 columns</p>\n</div>"
|
||
},
|
||
"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": "<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": 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": "<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>0</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>korean</th>\n <td>0.392231</td>\n </tr>\n <tr>\n <th>chinese</th>\n <td>0.372872</td>\n </tr>\n <tr>\n <th>japanese</th>\n <td>0.218825</td>\n </tr>\n <tr>\n <th>thai</th>\n <td>0.013427</td>\n </tr>\n <tr>\n <th>indian</th>\n <td>0.002645</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||
},
|
||
"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**سلب مسئولیت**: \nاین سند با استفاده از سرویس ترجمه هوش مصنوعی [Co-op Translator](https://github.com/Azure/co-op-translator) ترجمه شده است. در حالی که ما برای دقت تلاش میکنیم، لطفاً توجه داشته باشید که ترجمههای خودکار ممکن است شامل خطاها یا نادقتیهایی باشند. سند اصلی به زبان بومی آن باید به عنوان منبع معتبر در نظر گرفته شود. برای اطلاعات حساس، ترجمه حرفهای انسانی توصیه میشود. ما هیچ مسئولیتی در قبال سوءتفاهمها یا تفسیرهای نادرست ناشی از استفاده از این ترجمه نداریم.\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": "2025-09-04T02:23:32+00:00",
|
||
"source_file": "4-Classification/2-Classifiers-1/solution/notebook.ipynb",
|
||
"language_code": "fa"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 4
|
||
} |