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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": "1f325928",
"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": "5708604b",
"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"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "7e1b96a5",
"metadata": {},
"outputs": [],
"source": [
"# Create a meshgrid for plotting\n",
"def create_meshgrid(X, h=.02):\n",
" x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
" y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\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": "40a2cb04",
"metadata": {},
"outputs": [],
"source": [
"def plot_decision_boundaries(clf, X, y, xx, yy, Z, title, subplot, \n",
" X_sub=None,\n",
" color='lightgray',\n",
" marker_types=['^', 's', 'o'],\n",
" fill_areas=False,\n",
" plot_boundary=True,\n",
" show_legend=False):\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=0.5, cmap=ListedColormap([color]))\n",
"\n",
" # Plot all points with gray color first\n",
" for idx, cl in enumerate(np.unique(y)):\n",
" subplot.scatter(x=X[y == cl, 0], y=X[y == cl, 1], c='0.5', marker=marker_types[idx],\n",
" label=f'Razred {cl}', edgecolors='k', s=20, alpha=0.5)\n",
"\n",
" # Plot points from the training subset with specified color\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=color, marker=marker_types[idx],\n",
" label=f'Razred {cl} (podmnožica)', edgecolors='k', s=20, alpha=1)\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": "46596e11",
"metadata": {},
"outputs": [],
"source": [
"# Create a synthetic dataset\n",
"# X, y = datasets.make_classification(n_samples=100, 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, 2]]\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': 'rbf','random_state': 0}\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 \n",
"Z=[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",
" \n",
"Z_ensamble = ensemble.predict(np.c_[xx.ravel(), yy.ravel()])\n",
"Z_ensamble = Z_ensamble.reshape(xx.shape)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "eb28ef55",
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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\n",
"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",
"\n",
"plot_decision_boundaries(clf[0], X, y, xx, yy, Z[0], 'Model 1', axs[0, 0], subset_indices_1, color=colors[0])\n",
"plot_decision_boundaries(clf[1], X, y, xx, yy, Z[1], 'Model 2', axs[0, 1], subset_indices_2, color=colors[1])\n",
"plot_decision_boundaries(clf[2], X, y, xx, yy, Z[2], 'Model 3', axs[0, 2], subset_indices_3, color=colors[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=0.1, colors=colors[i])\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])\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='k', marker='^', markersize=10, label='Razred 0'),\n",
" Line2D([0], [0], color='w', markerfacecolor='lightgray', markeredgecolor='k', marker='s', markersize=10, label='Razred 1'),\n",
" Line2D([0], [0], color='w', markerfacecolor='lightgray', markeredgecolor='k', marker='o', markersize=10, label='Razred 2'),\n",
" Line2D([0], [0], color='w', markerfacecolor=colors[0], marker='*', markersize=10, label='Množica za \\ntreniranje modela 1'), \n",
" Line2D([0], [0], color='w', markerfacecolor=colors[1], marker='*', markersize=10, label='Množica za \\ntreniranje modela 2'), \n",
" Line2D([0], [0], color='w', markerfacecolor=colors[2], marker='*', markersize=10, label='Množica za \\ntreniranje modela 3'), \n",
"\n",
" Line2D([0], [0], color=colors[0], lw=2, label='Odločitvena meja \\npri modelu 1'),\n",
" Line2D([0], [0], color=colors[1], lw=2, label='Odločitvena meja \\npri modelu 2'),\n",
" Line2D([0], [0], color=colors[2], lw=2, label='Odločitvena meja \\npri modelu 3'),\n",
"]\n",
"axs[1, 2].legend(handles=legend_elements, loc='right')\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=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=\"0.5\", lw=5))\n",
"\n",
"\n",
"# Save and display the plot\n",
"plt.savefig(\"Združevanje_več_klasifikatorjev.svg\")\n",
"plt.show()\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "01a96382",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Classifier 1 accuracy: 0.93\n",
"Classifier 2 accuracy: 0.93\n",
"Classifier 3 accuracy: 0.95\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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