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added kmeans clustering

master
mahehsma 2024-06-05 13:12:53 +02:00
parent e2b6e45cc6
commit 45195943d7
1 changed files with 190 additions and 11 deletions
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Experiments.ipynb
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@ -17,7 +17,7 @@
}, },
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"outputs": [ "outputs": [
@ -216,7 +216,7 @@
"[5 rows x 28 columns]" "[5 rows x 28 columns]"
] ]
}, },
"execution_count": 14, "execution_count": 21,
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"output_type": "execute_result" "output_type": "execute_result"
} }
@ -271,7 +271,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 20, "execution_count": 41,
"id": "38eb4f87-ca3c-4ecf-a8ca-29422822d933", "id": "38eb4f87-ca3c-4ecf-a8ca-29422822d933",
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
@ -282,7 +282,7 @@
"Training fold 0 for 20 epochs\n", "Training fold 0 for 20 epochs\n",
"Train samples:\t267\n", "Train samples:\t267\n",
"Test samples:\t30\n", "Test samples:\t30\n",
"Accuracy of fold 0: 0.8666666666666667\n", "Accuracy of fold 0: 0.9\n",
"Training fold 1 for 20 epochs\n", "Training fold 1 for 20 epochs\n",
"Train samples:\t267\n", "Train samples:\t267\n",
"Test samples:\t30\n", "Test samples:\t30\n",
@ -294,11 +294,11 @@
"Training fold 3 for 20 epochs\n", "Training fold 3 for 20 epochs\n",
"Train samples:\t267\n", "Train samples:\t267\n",
"Test samples:\t30\n", "Test samples:\t30\n",
"Accuracy of fold 3: 0.9333333333333333\n", "Accuracy of fold 3: 0.9\n",
"Training fold 4 for 20 epochs\n", "Training fold 4 for 20 epochs\n",
"Train samples:\t267\n", "Train samples:\t267\n",
"Test samples:\t30\n", "Test samples:\t30\n",
"Accuracy of fold 4: 0.8666666666666667\n", "Accuracy of fold 4: 0.9\n",
"Training fold 5 for 20 epochs\n", "Training fold 5 for 20 epochs\n",
"Train samples:\t267\n", "Train samples:\t267\n",
"Test samples:\t30\n", "Test samples:\t30\n",
@ -306,20 +306,20 @@
"Training fold 6 for 20 epochs\n", "Training fold 6 for 20 epochs\n",
"Train samples:\t267\n", "Train samples:\t267\n",
"Test samples:\t30\n", "Test samples:\t30\n",
"Accuracy of fold 6: 0.8666666666666667\n", "Accuracy of fold 6: 0.7666666666666667\n",
"Training fold 7 for 20 epochs\n", "Training fold 7 for 20 epochs\n",
"Train samples:\t268\n", "Train samples:\t268\n",
"Test samples:\t29\n", "Test samples:\t29\n",
"Accuracy of fold 7: 0.896551724137931\n", "Accuracy of fold 7: 0.8275862068965517\n",
"Training fold 8 for 20 epochs\n", "Training fold 8 for 20 epochs\n",
"Train samples:\t268\n", "Train samples:\t268\n",
"Test samples:\t29\n", "Test samples:\t29\n",
"Accuracy of fold 8: 0.7931034482758621\n", "Accuracy of fold 8: 0.7586206896551724\n",
"Training fold 9 for 20 epochs\n", "Training fold 9 for 20 epochs\n",
"Train samples:\t268\n", "Train samples:\t268\n",
"Test samples:\t29\n", "Test samples:\t29\n",
"Accuracy of fold 9: 0.7931034482758621\n", "Accuracy of fold 9: 0.7586206896551724\n",
"Avg accuracy 0.8582758620689654\n" "Avg accuracy 0.837816091954023\n"
] ]
} }
], ],
@ -371,6 +371,185 @@
"avg_accuracy = sum(accuracies) / len(accuracies)\n", "avg_accuracy = sum(accuracies) / len(accuracies)\n",
"print(f'Avg accuracy {avg_accuracy}')" "print(f'Avg accuracy {avg_accuracy}')"
] ]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "95215693-47c9-4202-92f5-efbc65bc32c9",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Training fold 0 for 20 epochs\n",
"Train samples:\t237\n",
"Test samples:\t60\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" warnings.warn(\n",
"C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 1 1 0 0 0 0 0 1 1 1 0 1 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 1 0 1 0 1 0 0 1\n",
" 1 1 1 1 1 0 0 0 1 0 1 0 0 0 1 0 0 1 1 1 1 0 1]\n",
"Accuracy of fold 0: 0.5833333333333334\n",
"Training fold 1 for 20 epochs\n",
"Train samples:\t237\n",
"Test samples:\t60\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" warnings.warn(\n",
"C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1 0 1 0 1 1 0 0 1 0 0 1 1 1 0 0 1 0 0 1 1 0 0 1 0 0 0 0 0 1 1 1 0 0 1 1 1\n",
" 0 0 0 0 0 0 1 0 1 1 1 1 1 1 1 1 1 0 0 0 1 1 1]\n",
"Accuracy of fold 1: 0.5\n",
"Training fold 2 for 20 epochs\n",
"Train samples:\t238\n",
"Test samples:\t59\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" warnings.warn(\n",
"C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 0 0 0 1 0 0 1 1 0 0 1 0 1 1 0 0 0 1 1 0 1 0 0 1 0 1 1 1 0 1 1 0 0 0 0 0\n",
" 0 1 1 0 1 1 1 0 1 0 1 0 0 0 1 0 0 0 0 1 1 0]\n",
"Accuracy of fold 2: 0.559322033898305\n",
"Training fold 3 for 20 epochs\n",
"Train samples:\t238\n",
"Test samples:\t59\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" warnings.warn(\n",
"C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0 1 0 1 1 1 0 0 0 1 0 1 1 0 1 0 1 1 1 1 0 1 0 0 0 0 1 1 1 0 1 0 1 0 1 0 1\n",
" 1 1 1 1 0 0 1 1 1 0 0 1 0 1 1 1 0 0 0 1 1 1]\n",
"Accuracy of fold 3: 0.576271186440678\n",
"Training fold 4 for 20 epochs\n",
"Train samples:\t238\n",
"Test samples:\t59\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
" warnings.warn(\n",
"C:\\Users\\maxwi\\anaconda3\\Lib\\site-packages\\sklearn\\cluster\\_kmeans.py:1382: UserWarning: KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting the environment variable OMP_NUM_THREADS=1.\n",
" warnings.warn(\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[1 1 1 1 1 0 0 0 1 0 0 0 1 1 1 1 1 1 1 1 1 1 1 0 0 0 1 1 0 1 0 1 1 0 1 1 1\n",
" 1 0 1 0 1 0 0 0 1 1 0 1 0 0 0 1 0 0 0 0 0 1]\n",
"Accuracy of fold 4: 0.5254237288135594\n",
"Avg accuracy 0.5488700564971751\n"
]
}
],
"source": [
"from sklearn.cluster import KMeans\n",
"\n",
"# number of components extracted from the pca\n",
"n_features = 10\n",
"\n",
"k_folds = 5\n",
"\n",
"# used to split the dataset into k folds\n",
"kf = KFold(n_splits=k_folds)\n",
"\n",
"accuracies = []\n",
"for i, (train_idx, test_idx) in enumerate(kf.split(X[numeric_columns])):\n",
" print(f'Training fold {i} for {epochs} epochs')\n",
"\n",
" # extract train and test data from the cleaned dataset\n",
" X_train, X_test = X.iloc[train_idx], X.iloc[test_idx]\n",
" y_train, y_test = y[train_idx], y[test_idx]\n",
"\n",
" print(f'Train samples:\\t{len(X_train)}')\n",
" print(f'Test samples:\\t{len(X_test)}')\n",
"\n",
" # do pca based on the train data of the given fold to extract 'n_features'\n",
" #pca = decomposition.PCA(n_components=n_features)\n",
" #pca.fit(X_train)\n",
" #X_train = pca.transform(X_train)\n",
"\n",
" model = KMeans(n_clusters=2)\n",
" model.fit(X_train)\n",
"\n",
" #X_test = pca.transform(X_test)\n",
" y_pred = model.predict(X_test)\n",
" print(y_pred)\n",
" \n",
"\n",
" # calculate the accuracy of the train data for the current fold\n",
" accuracy1 = sum(y_pred == y_test)[0] / len(y_pred)\n",
" accuracy2 = sum(y_pred != y_test)[0] / len(y_pred)\n",
" accuracy = max(accuracy1, accuracy2)\n",
" accuracies.append(accuracy)\n",
" print(f'Accuracy of fold {i}: {accuracy}')\n",
"\n",
"# calculate the average accuracy over all folds\n",
"avg_accuracy = sum(accuracies) / len(accuracies)\n",
"print(f'Avg accuracy {avg_accuracy}')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "880302e4-82c1-47b9-9fe3-cb3567511639",
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} }
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