examples/learning_curve.ipynb
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"# Estimate the number for required samples"
],
"cell_type": "markdown",
"metadata": {}
},
{
"source": [
"A key question to address before using an active learning loop is if the number of initialization samples is \"sufficient\". One heuristic to estimate this is to perform learning curve analysis. \n",
"\n",
"- The cross-validation error of the model trained with all initialization samples will indicate if the model has any predictive power\n",
"- The shape of the learning curve will indicate if the model is already saturated or if additional samples (e.g., acquired via the active learning) can increase the performance\n",
"\n",
"One still has to note though that a model that is not predictive at intialization may become predictive at some point in the active learning process. This might be connected to the danger of underestimating uncertainties, for which reason we print a warning if the cross-validation error is greater than the variance. "
],
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"source": [
"from pyepal import PALSklearn\n",
"from sklearn.gaussian_process import GaussianProcessRegressor\n",
"from sklearn.gaussian_process.kernels import RBF\n",
"from sklearn.preprocessing import StandardScaler\n",
"from pyepal.models.gpr import build_coregionalized_model\n",
"from pyepal.pal.utils import exhaust_loop\n",
"from pyepal.plotting import plot_learning_curve\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt \n",
"plt.style.use('ggplot')"
]
},
{
"source": [
"We will use the [Binh-Korn test function](https://en.wikipedia.org/wiki/Test_functions_for_optimization), which defines our Pareto front."
],
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"execution_count": 15,
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"source": [
"def binh_korn(x, y): # pylint:disable=invalid-name\n",
" \"\"\"https://en.wikipedia.org/wiki/Test_functions_for_optimization\"\"\"\n",
" obj1 = 4 * x ** 2 + 4 * y ** 2\n",
" obj2 = (x - 5) ** 2 + (y - 5) ** 2\n",
" return -obj1, -obj2"
]
},
{
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"execution_count": 16,
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"source": [
"def binh_korn_points():\n",
" \"\"\"Create a dataset based on the Binh-Korn test function\"\"\"\n",
" x = np.linspace(0, 5, 100) # pylint:disable=invalid-name\n",
" y = np.linspace(0, 3, 100) # pylint:disable=invalid-name\n",
" array = np.array([binh_korn(xi, yi) for xi, yi in zip(x, y)])\n",
" return np.hstack([x.reshape(-1, 1), y.reshape(-1, 1)]), array"
]
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"execution_count": 17,
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"source": [
"x, points = binh_korn_points()"
]
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"execution_count": 18,
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"outputs": [],
"source": [
"palinstance = PALSklearn(x, [GaussianProcessRegressor(RBF(length_scale=0.01, length_scale_bounds=(1e-03, 1e3)), alpha=0.01), \n",
" GaussianProcessRegressor(RBF(length_scale=0.01, length_scale_bounds=(1e-03, 1e3)), alpha=0.01)], 2)"
]
},
{
"source": [
"To plot a learning curve we can use the `plot_learning_curve` function from the `pyepal.plotting` subpackage. This function takes a `PALBase` instance and the labels. You can optionally also provide the indices, the number of points on the learning curve, a `KFold` object, and a list of metrics. \n",
"\n",
"The function will output a plot of a learning curve and a dictionary with the metrics."
],
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"cell_type": "code",
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},
"metadata": {}
}
],
"source": [
"fig, metrics = plot_learning_curve(palinstance, points, num_steps=10)"
]
},
{
"source": [
"Often, tuning some hyperparameters and scaling of the data can improve the performance."
],
"cell_type": "markdown",
"metadata": {}
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"palinstance_scaled = PALSklearn(StandardScaler().fit_transform(x), [GaussianProcessRegressor(RBF(length_scale=0.05, length_scale_bounds=(1e-02, 1e1)), alpha=0.00001, normalize_y=True), \n",
" GaussianProcessRegressor(RBF(length_scale=0.05, length_scale_bounds=(1e-02, 1e1)), alpha=0.00001, normalize_y=True)], 2)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"output_type": "display_data",
"data": {
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\n"
},
"metadata": {}
}
],
"source": [
"fig, metrics = plot_learning_curve(palinstance_scaled, points, num_steps=10)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
]
}