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Combining several imperfect classifiers to reduce overall classification error.ipynb
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from sklearn.svm import SVC
from sklearn.ensemble import VotingClassifier
from sklearn.metrics import accuracy_score
from sklearn.model_selection import ParameterGrid
# Define the search space
subset_seed_range = range(0, 100) # Range of seeds to try
param_grid = {
'C': [0.1, 1, 10],
'gamma': [0.001, 0.01, 0.1, 1],
'kernel': ['rbf'],
'random_state': range(0,5)
}
all_combinations = [(seed, params) for seed in subset_seed_range for params in ParameterGrid(param_grid)]
# Initialize variables to track the best performance
best_ensemble_accuracy = 0
best_combination = None
n_passed = 0
# Perform the search
for seed, params in all_combinations:
#print(seed)
#print(params)
# Create the subsets using the seed
np.random.seed(seed)
subset_indices_1 = np.random.choice(range(len(X)), size=int(len(X)/3), replace=False)
subset_indices_2 = np.random.choice(list(set(range(len(X))) - set(subset_indices_1)), size=int(len(X)/3), replace=False)
subset_indices_3 = list(set(range(len(X))) - set(subset_indices_1) - set(subset_indices_2))
X_sub1, y_sub1 = X[subset_indices_1], y[subset_indices_1]
X_sub2, y_sub2 = X[subset_indices_2], y[subset_indices_2]
X_sub3, y_sub3 = X[subset_indices_3], y[subset_indices_3]
# Train SVC models on each subset with the given parameters
classifiers = []
for i, (X_sub, y_sub) in enumerate([(X_sub1, y_sub1), (X_sub2, y_sub2), (X_sub3, y_sub3)]):
clf = SVC(**params).fit(X_sub, y_sub)
classifiers.append(clf)
# Create the ensemble
ensemble = VotingClassifier(estimators=[(f'clf{i}', clf) for i, clf in enumerate(classifiers)], voting='hard')
ensemble.fit(X, y)
# Evaluate the ensemble and base classifiers
ensemble_accuracy = accuracy_score(y, ensemble.predict(X))
base_accuracies = [accuracy_score(y, clf.predict(X)) for clf in classifiers]
# We don't want 'bad' examples
passed = all(acc < ensemble_accuracy for acc in base_accuracies)
if passed:
n_passed = n_passed + 1
# Check if this combination is the best so far
if ensemble_accuracy > best_ensemble_accuracy and passed:
best_ensemble_accuracy = ensemble_accuracy
best_combination = (seed, params)
#print("----")
# Output the results
print(f"Best ensemble accuracy: {best_ensemble_accuracy:.2f}")
print(f"Best combination: Seed={best_combination[0]}, Parameters={best_combination[1]}")
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{
"cells": [
{
"cell_type": "markdown",
"id": "ef07293c",
"metadata": {},
"source": [
"## Combining several imperfect classifiers to reduce overall classification error\n",
"\n",
"This notebook illustrates one of the basic concepts in ensemble learning.\n",
"\n",
"By combining classifiers that are trained on different subsets of the training data, it is possible to acheive superior classifier performance.\n",
"\n",
"Figure is adapted from an example published in:\n",
"- Robi Polikar. Ensemble based systems in decision making. Circuits and Systems Magazine, IEEE, 6(3):21–45, 2006.\n",
" Link to the original publication: https://doi.org/10.1109/MCAS.2006.1688199\n",
" \n",
"\n",
"Code author: [smihael](https://github.com/smihael)\n",
"\n",
"Licence: CC-BY-SA 4.0"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "6d2876f3",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from sklearn import datasets\n",
"from sklearn.svm import SVC\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.ensemble import VotingClassifier\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.colors import ListedColormap\n",
"\n",
"from matplotlib.patches import Patch\n",
"from matplotlib.lines import Line2D\n",
"from matplotlib.legend_handler import HandlerTuple"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "101bdb33",
"metadata": {},
"outputs": [],
"source": [
"# Create a meshgrid for plotting\n",
"def create_meshgrid(X, h=.02):\n",
" x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5\n",
" y_min, y_max = X[:, 1].min() - 0.5, X[:, 1].max() + 0.5\n",
" xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n",
" return xx, yy"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "6487a6ad",
"metadata": {},
"outputs": [],
"source": [
"def plot_decision_boundaries(clf, X, y, xx, yy, Z, title, subplot, \n",
" X_sub=None,\n",
" color='0.9',\n",
" marker_types=['^', 's', 'o'],\n",
" fill_areas=False,\n",
" plot_boundary=True,\n",
" show_legend=False,\n",
" cs='solid', correct=None):\n",
" \n",
" s=30\n",
" \n",
" if fill_areas:\n",
" subplot.contourf(xx, yy, Z, alpha=0.1, cmap=ListedColormap(['lightgray','gray','darkgray']))\n",
" elif plot_boundary:\n",
" subplot.contour(xx, yy, Z, alpha=0.8, colors='black', linestyles=cs)\n",
"\n",
" # Plot all points first, make them transparent\n",
" for idx, cl in enumerate(np.unique(y)):\n",
" if True:#correct is not None:\n",
" correctly_classified_points = X[np.all([y == cl, np.isin(np.arange(len(X)), correct)], axis=0)]\n",
" incorrectly_classified_points = X[np.all([y == cl, ~np.isin(np.arange(len(X)), correct)], axis=0)]\n",
" subplot.scatter(x=correctly_classified_points[:,0], y=correctly_classified_points[:,1], c='lightgreen', marker=marker_types[idx],\n",
" label=f'Razred {cl}', s=s, alpha=1,edgecolors='gray',linewidths=1)\n",
" if len(incorrectly_classified_points) > 0:\n",
" subplot.scatter(x=incorrectly_classified_points[:,0], y=incorrectly_classified_points[:,1], c='lightgray' if correct is None else 'red', marker=marker_types[idx],\n",
" label=f'Razred {cl}', s=s, alpha=1,edgecolors='gray',linewidths=1)\n",
" \n",
" # Plot points from the training subset with specified shape\n",
" if X_sub is not None:\n",
" for idx, cl in enumerate(np.unique(y)):\n",
" indices = np.where(np.logical_and(y == cl, np.isin(range(len(X)), X_sub)))[0]\n",
" if len(indices) > 0:\n",
" subplot.scatter(x=X[indices, 0], y=X[indices, 1], c='None', marker=marker_types[idx],\n",
" label=f'Razred {cl} (podmnožica)', edgecolors='k', s=s, alpha=1,linewidths=2)\n",
" \n",
" #if correct is not None:\n",
" # Infer incorrectly classified indices from correctly classified indices\n",
" #incorrectly_classified_indices = np.setdiff1d(np.arange(len(X)), correct)\n",
" #subplot.scatter(X[correct, 0], X[correct, 1], label='Correctly Classified', c='green')\n",
" #subplot.scatter(X[incorrectly_classified_indices, 0], X[incorrectly_classified_indices, 1], marker='x', label='Incorrectly Classified', c='red')\n",
"\n",
"\n",
" subplot.set_xlim(xx.min(), xx.max())\n",
" subplot.set_ylim(yy.min(), yy.max())\n",
" subplot.set_xlabel('Značilka 1')\n",
" subplot.set_ylabel('Značilka 2')\n",
" subplot.set_title(title)\n",
" if show_legend:\n",
" subplot.legend()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "7f193585",
"metadata": {},
"outputs": [],
"source": [
"# Create a synthetic dataset\n",
"X, y = datasets.make_classification(n_samples=75, n_features=2, n_redundant=0, n_clusters_per_class=1, n_classes=3, random_state=5)\n",
"\n",
"# ... or use iris dataset\n",
"#iris = datasets.load_iris()\n",
"#X = iris.data[:, [0, 3]]\n",
"#y = iris.target\n",
"\n",
"# Split the data into three different subsets\n",
"np.random.seed(2)\n",
"subset_indices_1 = np.random.choice(range(len(X)), size=int(len(X)/3), replace=False)\n",
"subset_indices_2 = np.random.choice(list(set(range(len(X))) - set(subset_indices_1)), size=int(len(X)/3), replace=False)\n",
"subset_indices_3 = list(set(range(len(X))) - set(subset_indices_1) - set(subset_indices_2))\n",
"\n",
"X_sub1, y_sub1 = X[subset_indices_1], y[subset_indices_1]\n",
"X_sub2, y_sub2 = X[subset_indices_2], y[subset_indices_2]\n",
"X_sub3, y_sub3 = X[subset_indices_3], y[subset_indices_3]\n",
"\n",
"# Define the base classifiers, trained on different subsets\n",
"clf=[None]*3\n",
"params={'kernel': 'poly','random_state': 55} #poly,n=50 ok\n",
"clf[0] = SVC(**params).fit(X_sub1, y_sub1)\n",
"clf[1] = SVC(**params).fit(X_sub2, y_sub2)\n",
"clf[2] = SVC(**params).fit(X_sub3, y_sub3)\n",
"\n",
"# Define an ensemble of the three classifiers\n",
"ensemble = VotingClassifier(estimators=[('a', clf[0]), ('b', clf[1]), ('c', clf[2])], voting='hard')\n",
"ensemble.fit(X, y)\n",
"\n",
"# Ceate meshgrid\n",
"xx, yy = create_meshgrid(X)\n",
"\n",
"# Get decision boundaries and predict labels for original dataset\n",
"Z=[None]*3\n",
"correctly_classified_indices=[None]*3\n",
"for i in range(3):\n",
" Z[i] = clf[i].predict(np.c_[xx.ravel(), yy.ravel()])\n",
" Z[i] = Z[i].reshape(xx.shape)\n",
" \n",
" y_pred = clf[i].predict(X)\n",
" correctly_classified_indices[i] = np.where(y == y_pred)[0]\n",
" \n",
"Z_ensamble = ensemble.predict(np.c_[xx.ravel(), yy.ravel()])\n",
"Z_ensamble = Z_ensamble.reshape(xx.shape)\n",
"\n",
"y_pred_ensemble = ensemble.predict(X)\n",
"correctly_classified_indices_ens = np.where(y == y_pred_ensemble)[0]\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "c23e1d12",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 1080x720 with 6 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Plotting decision boundaries\n",
"fig, axs = plt.subplots(2, 3, figsize=(15, 10), gridspec_kw={'height_ratios': [1, 1], 'hspace': 0.75})\n",
"\n",
"# Define colors for different subsets\n",
"colors = ['blue', 'red', 'green']\n",
"ls= ['--','-.',':']\n",
"cs= ['dashed', 'dashdot', 'dotted']\n",
" \n",
"\n",
"plot_decision_boundaries(clf[0], X, y, xx, yy, Z[0], 'Model 1', axs[0, 0], subset_indices_1, cs=cs[0], correct=correctly_classified_indices[0])\n",
"plot_decision_boundaries(clf[1], X, y, xx, yy, Z[1], 'Model 2', axs[0, 1], subset_indices_2, cs=cs[1], correct=correctly_classified_indices[1])\n",
"plot_decision_boundaries(clf[2], X, y, xx, yy, Z[2], 'Model 3', axs[0, 2], subset_indices_3, cs=cs[2], correct=correctly_classified_indices[2])\n",
"\n",
"# Plot all decision boundaries together\n",
"plot_decision_boundaries(ensemble, X, y, xx, yy, Z_ensamble, 'Odločitvene meje\\nrazličnih modelov', axs[1, 0], plot_boundary=False)\n",
"d=[None]*3\n",
"for i in range(3):\n",
" d[i] = axs[1, 0].contour(xx, yy, Z[i], alpha=0.8, linestyles=cs[i], colors='black')\n",
" #axs[1, 0].clabel(d[i], fmt=f'Classifier {i}', inline_spacing=10, fontsize=8)\n",
"\n",
"\n",
"# Plot ensamble\n",
"plot_decision_boundaries(ensemble, X, y, xx, yy, Z_ensamble, 'Ansambelski model in\\nskupna odločitvena meja', axs[1, 1],correct=correctly_classified_indices_ens)\n",
"\n",
"\n",
"# Clear the last subplot for the legend\n",
"axs[1, 2].clear()\n",
"axs[1, 2].axis('off')\n",
"\n",
"# Custom legend for the entire figure\n",
"legend_elements = [\n",
" Line2D([0], [0], color='w', markerfacecolor='lightgray', markeredgecolor='None', marker='^', markersize=10, label='Razred 0'),\n",
" Line2D([0], [0], color='w', markerfacecolor='lightgray', markeredgecolor='None', marker='s', markersize=10, label='Razred 1'),\n",
" Line2D([0], [0], color='w', markerfacecolor='lightgray', markeredgecolor='None', marker='o', markersize=10, label='Razred 2'),\n",
" Line2D([0], [0], color='w', markerfacecolor='lightgray', marker='*', markeredgecolor='k', markersize=10, label='Primer je vključen\\nv učno množico'), \n",
" Line2D([0], [0], color='w', markerfacecolor='lightgray', marker='*', markeredgecolor='gray', markersize=10, label='Primer ni vključen\\nv učno množico'), \n",
" Line2D([0], [0], color='w', markerfacecolor='green', marker='*', markersize=10, label='Pravilno razvrščen primer'), \n",
" Line2D([0], [0], color='w', markerfacecolor='red', marker='*', markersize=10, label='Napačno razvrščen primer'), \n",
"\n",
" Line2D([0], [0], ls=ls[0], color='k', lw=2, label='Odločitvena meja \\npri modelu 1'),\n",
" Line2D([0], [0], ls=ls[1], color='k', lw=2, label='Odločitvena meja \\npri modelu 2'),\n",
" Line2D([0], [0], ls=ls[2], color='k', lw=2, label='Odločitvena meja \\npri modelu 3'),\n",
"]\n",
"axs[1, 2].legend(handles=legend_elements, loc='right',handler_map={tuple: HandlerTuple(ndivide=None)})\n",
"\n",
"\n",
"# The second subplot in the second row should be positioned to make space for the horizontal arrow\n",
"axs[1, 1].set_position([0.5, axs[1,0].get_position().y0,\n",
" axs[0, 0].get_position().width,\n",
" axs[0, 0].get_position().height])\n",
"\n",
"\n",
"# Add arrows from the top row to the 4th subplot, shortening them so the heads are not overlapped (patchB)\n",
"\n",
"arrow_end_x = axs[1, 0].get_position().x0 + 0.6 * axs[1, 0].get_position().width\n",
"arrow_end_y = axs[1, 0].get_position().y0 + 0.7 * axs[1, 0].get_position().height\n",
"\n",
"for i in range(3):\n",
" axs[0, i].annotate(\"\", xy=(arrow_end_x, arrow_end_y), xycoords='figure fraction', \n",
" xytext=(0.5, -0.2), textcoords='axes fraction',\n",
" arrowprops=dict(arrowstyle=\"simple\",\n",
" color='0.3',# colors[i],\n",
" patchB=axs[1,0],\n",
" shrinkB=50 if i==0 else 30))\n",
"\n",
" \n",
"# Add a big horizontal arrow between the subplots in the second row\n",
"\n",
"arrow_start_x = axs[1, 0].get_position().x1 # x position where the fourth subplot ends\n",
"arrow_end_x = axs[1, 1].get_position().x0 # x position where the fifth subplot starts\n",
"\n",
"arrow_y = axs[1, 0].get_position().y0 + axs[1, 0].get_position().height/3\n",
"\n",
"ma=axs[1, 0].annotate(\"\", xy=(arrow_end_x-0.14, arrow_y), xycoords='figure fraction', \n",
" xytext=(arrow_start_x-0.07, arrow_y), textcoords='figure fraction',\n",
" arrowprops=dict(arrowstyle=\"simple,head_width=0.8,head_length=0.8\", color=\"0.3\", lw=5))\n",
"\n",
"\n",
"# Save and display the plot\n",
"plt.savefig(\"Združevanje_več_klasifikatorjev.svg\",bbox_inches='tight', pad_inches=0)\n",
"plt.show()\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "7e92efa4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Classifier 1 accuracy: 0.95\n",
"Classifier 2 accuracy: 0.91\n",
"Classifier 3 accuracy: 0.91\n",
"Ensemble classifier accuracy: 0.96\n"
]
}
],
"source": [
"from sklearn.metrics import accuracy_score\n",
"\n",
"# We won't do the classical train/test split with train_test_split(X, y, test_size=0.3), \n",
"# as this is specific example, where we want to see graphically\n",
"# what training data went in each of the base classifiers and\n",
"# how the classifier performs on the overall dataset. \n",
"\n",
"X_test = X\n",
"y_test = y\n",
"\n",
"accuracies = []\n",
"\n",
"for i, classifier in enumerate(clf):\n",
" y_pred = classifier.predict(X_test)\n",
" accuracy = accuracy_score(y_test, y_pred)\n",
" accuracies.append(accuracy)\n",
" print(f\"Classifier {i+1} accuracy: {accuracy:.2f}\")\n",
"\n",
"# Print accuracy of the ensemble classifier\n",
"y_pred_ensemble = ensemble.predict(X_test)\n",
"accuracy_ensemble = accuracy_score(y_test, y_pred_ensemble)\n",
"print(f\"Ensemble classifier accuracy: {accuracy_ensemble:.2f}\")\n"
]
}
],
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