wehub-resource-sync
268 lines
15 KiB
Plaintext
268 lines
15 KiB
Plaintext
{
|
||
"cells": [
|
||
{
|
||
"source": [
|
||
"# Build Classification Models"
|
||
],
|
||
"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))"
|
||
]
|
||
}
|
||
],
|
||
"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
|
||
} |