microsoft--ml-for-beginners
wehub-resource-sync
281 行
16 KiB
Plaintext
281 行
16 KiB
Plaintext
{
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"cells": [
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{
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"source": [
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"ماڈلز کی درجہ بندی بنائیں\n"
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],
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"cell_type": "markdown",
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"metadata": {}
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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" Unnamed: 0 cuisine almond angelica anise anise_seed apple \\\n",
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"0 0 indian 0 0 0 0 0 \n",
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"1 1 indian 1 0 0 0 0 \n",
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"2 2 indian 0 0 0 0 0 \n",
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"3 3 indian 0 0 0 0 0 \n",
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"4 4 indian 0 0 0 0 0 \n",
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"\n",
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" apple_brandy apricot armagnac ... whiskey white_bread white_wine \\\n",
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"0 0 0 0 ... 0 0 0 \n",
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"1 0 0 0 ... 0 0 0 \n",
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"2 0 0 0 ... 0 0 0 \n",
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"3 0 0 0 ... 0 0 0 \n",
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"4 0 0 0 ... 0 0 0 \n",
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"\n",
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" whole_grain_wheat_flour wine wood yam yeast yogurt zucchini \n",
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"0 0 0 0 0 0 0 0 \n",
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"1 0 0 0 0 0 0 0 \n",
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"2 0 0 0 0 0 0 0 \n",
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"3 0 0 0 0 0 0 0 \n",
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"4 0 0 0 0 0 1 0 \n",
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"\n",
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"[5 rows x 382 columns]"
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],
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"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>"
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},
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"metadata": {},
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"execution_count": 1
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}
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],
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"source": [
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"import pandas as pd\n",
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"cuisines_df = pd.read_csv(\"../../data/cleaned_cuisines.csv\")\n",
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"cuisines_df.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"from sklearn.linear_model import LogisticRegression\n",
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"from sklearn.model_selection import train_test_split, cross_val_score\n",
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"from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve\n",
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"from sklearn.svm import SVC\n",
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"import numpy as np"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"0 indian\n",
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"1 indian\n",
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"2 indian\n",
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"3 indian\n",
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"4 indian\n",
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"Name: cuisine, dtype: object"
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]
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},
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"metadata": {},
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"execution_count": 3
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}
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],
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"source": [
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"cuisines_label_df = cuisines_df['cuisine']\n",
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"cuisines_label_df.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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" almond angelica anise anise_seed apple apple_brandy apricot \\\n",
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"0 0 0 0 0 0 0 0 \n",
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"1 1 0 0 0 0 0 0 \n",
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"2 0 0 0 0 0 0 0 \n",
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"3 0 0 0 0 0 0 0 \n",
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"4 0 0 0 0 0 0 0 \n",
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"\n",
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" armagnac artemisia artichoke ... whiskey white_bread white_wine \\\n",
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"0 0 0 0 ... 0 0 0 \n",
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"1 0 0 0 ... 0 0 0 \n",
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"2 0 0 0 ... 0 0 0 \n",
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"3 0 0 0 ... 0 0 0 \n",
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"4 0 0 0 ... 0 0 0 \n",
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"\n",
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" whole_grain_wheat_flour wine wood yam yeast yogurt zucchini \n",
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"0 0 0 0 0 0 0 0 \n",
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"1 0 0 0 0 0 0 0 \n",
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"4 0 0 0 0 0 1 0 \n",
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"\n",
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"[5 rows x 380 columns]"
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],
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},
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"metadata": {},
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"execution_count": 4
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}
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],
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"source": [
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"cuisines_feature_df = cuisines_df.drop(['Unnamed: 0', 'cuisine'], axis=1)\n",
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"cuisines_feature_df.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"X_train, X_test, y_train, y_test = train_test_split(cuisines_feature_df, cuisines_label_df, test_size=0.3)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"Accuracy is 0.8181818181818182\n"
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]
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}
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],
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"source": [
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"lr = LogisticRegression(multi_class='ovr',solver='liblinear')\n",
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"model = lr.fit(X_train, np.ravel(y_train))\n",
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"\n",
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"accuracy = model.score(X_test, y_test)\n",
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"print (\"Accuracy is {}\".format(accuracy))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"ingredients: Index(['artemisia', 'black_pepper', 'mushroom', 'shiitake', 'soy_sauce',\n 'vegetable_oil'],\n dtype='object')\ncuisine: korean\n"
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]
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}
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],
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"source": [
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"# test an item\n",
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"print(f'ingredients: {X_test.iloc[50][X_test.iloc[50]!=0].keys()}')\n",
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"print(f'cuisine: {y_test.iloc[50]}')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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" 0\n",
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"korean 0.392231\n",
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"chinese 0.372872\n",
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"japanese 0.218825\n",
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"thai 0.013427\n",
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"indian 0.002645"
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],
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},
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"metadata": {},
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"execution_count": 8
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}
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],
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"source": [
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"#rehsape to 2d array and transpose\n",
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"test= X_test.iloc[50].values.reshape(-1, 1).T\n",
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"# predict with score\n",
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"proba = model.predict_proba(test)\n",
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"classes = model.classes_\n",
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"# create df with classes and scores\n",
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"resultdf = pd.DataFrame(data=proba, columns=classes)\n",
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"\n",
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"# create df to show results\n",
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"topPrediction = resultdf.T.sort_values(by=[0], ascending = [False])\n",
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"topPrediction.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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" 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"
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]
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}
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],
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"source": [
|
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"y_pred = model.predict(X_test)\r\n",
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"print(classification_report(y_test,y_pred))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"\n---\n\n**ڈس کلیمر**: \nیہ دستاویز AI ترجمہ سروس [Co-op Translator](https://github.com/Azure/co-op-translator) کا استعمال کرتے ہوئے ترجمہ کی گئی ہے۔ ہم درستگی کے لیے پوری کوشش کرتے ہیں، لیکن براہ کرم آگاہ رہیں کہ خودکار ترجمے میں غلطیاں یا عدم درستگی ہو سکتی ہیں۔ اصل دستاویز کو اس کی اصل زبان میں مستند ذریعہ سمجھا جانا چاہیے۔ اہم معلومات کے لیے، پیشہ ور انسانی ترجمہ کی سفارش کی جاتی ہے۔ اس ترجمے کے استعمال سے پیدا ہونے والی کسی بھی غلط فہمی یا غلط تشریح کے لیے ہم ذمہ دار نہیں ہیں۔\n"
|
||
]
|
||
}
|
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],
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