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ML-For-Beginners/4-Classification/1-Introduction/solution/intro-classification.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Build Classification Model"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.linear_model import LogisticRegression\r\n",
"from sklearn.model_selection import train_test_split, cross_val_score\r\n",
"from sklearn.metrics import accuracy_score,precision_score,confusion_matrix,classification_report, precision_recall_curve\r\n",
"from sklearn.svm import SVC\r\n",
"import pandas as pd\r\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [
{
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" <th>yam</th>\n",
" <th>yeast</th>\n",
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" almond angelica anise anise_seed apple apple_brandy apricot \\\n",
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"\n",
"[5 rows x 380 columns]"
]
},
"execution_count": 48,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"transformed_feature_df = pd.read_csv(\".data/features_dataset.csv\")\r\n",
"transformed_feature_df= transformed_feature_df.drop(['Unnamed: 0'], axis=1)\r\n",
"transformed_feature_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>cuisine</th>\n",
" </tr>\n",
" </thead>\n",
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" <td>indian</td>\n",
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" cuisine\n",
"0 indian\n",
"1 indian\n",
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"4 indian"
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},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"transformed_label_df = pd.read_csv(\".data/labels_dataset.csv\")\r\n",
"transformed_label_df= transformed_label_df.drop(['Unnamed: 0'], axis=1)\r\n",
"transformed_label_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"X_train, X_test, y_train, y_test = train_test_split(transformed_feature_df, transformed_label_df, test_size=0.3)"
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy is 0.8031693077564637\n"
]
}
],
"source": [
"lr = LogisticRegression(multi_class='ovr',solver='lbfgs')\r\n",
"model = lr.fit(X_train, np.ravel(y_train))\r\n",
"\r\n",
"accuracy = model.score(X_test, y_test)\r\n",
"print (\"Accuracy is {}\".format(accuracy))"
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"ingredients: Index(['corn'], dtype='object')\n",
"cusine: cuisine thai\n",
"Name: 3816, dtype: object\n"
]
}
],
"source": [
"# test an item\r\n",
"print(f'ingredients: {X_test.iloc[20][X_test.iloc[20]!=0].keys()}')\r\n",
"print(f'cusine: {y_test.iloc[20]}')"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
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" <th>thai</th>\n",
" <td>0.475724</td>\n",
" </tr>\n",
" <tr>\n",
" <th>chinese</th>\n",
" <td>0.201912</td>\n",
" </tr>\n",
" <tr>\n",
" <th>japanese</th>\n",
" <td>0.152046</td>\n",
" </tr>\n",
" <tr>\n",
" <th>korean</th>\n",
" <td>0.110980</td>\n",
" </tr>\n",
" <tr>\n",
" <th>indian</th>\n",
" <td>0.059338</td>\n",
" </tr>\n",
" </tbody>\n",
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"</div>"
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"text/plain": [
" 0\n",
"thai 0.475724\n",
"chinese 0.201912\n",
"japanese 0.152046\n",
"korean 0.110980\n",
"indian 0.059338"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#rehsape to 2d array and transpose\r\n",
"test= X_test.iloc[20].values.reshape(-1, 1).T\r\n",
"# predict with score\r\n",
"proba = model.predict_proba(test)\r\n",
"classes = model.classes_\r\n",
"# create df with classes and scores\r\n",
"resultdf = pd.DataFrame(data=proba, columns=classes)\r\n",
"\r\n",
"# create df to show results\r\n",
"topPrediction = resultdf.T.sort_values(by=[0], ascending = [False])\r\n",
"topPrediction.head()"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" chinese 0.77 0.70 0.74 239\n",
" indian 0.88 0.88 0.88 240\n",
" japanese 0.76 0.79 0.77 227\n",
" korean 0.86 0.78 0.82 240\n",
" thai 0.75 0.86 0.80 253\n",
"\n",
" accuracy 0.80 1199\n",
" macro avg 0.81 0.80 0.80 1199\n",
"weighted avg 0.81 0.80 0.80 1199\n",
"\n"
]
}
],
"source": [
"y_pred = model.predict(X_test)\r\n",
"print(classification_report(y_test,y_pred))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Try different classifiers"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [],
"source": [
"\r\n",
"C = 10\r\n",
"# Create different classifiers.\r\n",
"classifiers = {\r\n",
" 'L1 logistic': LogisticRegression(C=C, penalty='l1',\r\n",
" solver='saga',\r\n",
" multi_class='multinomial',\r\n",
" max_iter=10000),\r\n",
" 'L2 logistic (Multinomial)': LogisticRegression(C=C, penalty='l2',\r\n",
" solver='saga',\r\n",
" multi_class='multinomial',\r\n",
" max_iter=10000),\r\n",
" 'L2 logistic (OvR)': LogisticRegression(C=C, penalty='l2',\r\n",
" solver='saga',\r\n",
" multi_class='ovr',\r\n",
" max_iter=10000),\r\n",
" 'Linear SVC': SVC(kernel='linear', C=C, probability=True,\r\n",
" random_state=0)\r\n",
"}\r\n"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy (train) for L1 logistic: 79.9% \n",
"Accuracy (train) for L2 logistic (Multinomial): 79.7% \n",
"Accuracy (train) for L2 logistic (OvR): 79.8% \n",
"Accuracy (train) for Linear SVC: 77.9% \n"
]
}
],
"source": [
"n_classifiers = len(classifiers)\r\n",
"\r\n",
"for index, (name, classifier) in enumerate(classifiers.items()):\r\n",
" classifier.fit(X_train, np.ravel(y_train))\r\n",
"\r\n",
" y_pred = classifier.predict(X_test)\r\n",
" accuracy = accuracy_score(y_test, y_pred)\r\n",
" print(\"Accuracy (train) for %s: %0.1f%% \" % (name, accuracy * 100))\r\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
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"interpreter": {
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