{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Active Learning Introduction\n", "\n", "The LECA workflow object nativeley supports basic Bayesian optimization and Active Learning routines, which are covered in the general introduction notebook.\n", "\n", "For advanced Bayesian Optimization and Active Learning LECA provides an interface to the [PyALAF](https://pythonal.readthedocs.io/en/latest/) library. \n", "This allows for population-based active learning without the need to predefine a pool of possible interesting data points. Furthemore, batch-wise active learning is supported to allow for several suggestions simulatenously. This allows to take full advantage of high throughput experimentation setups. \n", "It is possible to chose either from pre-defined acquisition functions or define custom ones.\n", "\n", "Data suggestion and acquisition can be easily automated by storing the suggested and acquired data for inspection first and easily adding it to the LECA workflow afterwards. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Import LECA and Standard Python Notebooks" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from LECA import prep, fit, analyze # LECA Modules\n", "from LECA import active_learning as al # LECA active learning module\n", "import pandas as pd # Pandas DataFrames\n", "import numpy as np # Numpy for standard math operations mostly\n", "import matplotlib.pyplot as plt # Matplotlib for plotting\n", "random_state=0 # (int) random_state for reproduceability, set to None if this is not desired" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Define Synthetic Experimental Setup\n", "\n", "The setup is similar to the setup in the Synthetic LECA 1D introduction. \n", "However, we start with a lower number of initial data points and reduced noise" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# True underlying function f(x) -> y\n", "def f(x):\n", " \"\"\"1D Polynomial function for our toy data.\"\"\"\n", " return np.ravel(np.exp(-(x - 2)**2) + np.exp(-(x - 6)**2/10) + 1/ (x**2 + 1))\n", "\n", "\n", "# Synthetic experimental setup with normal measurement error sigma\n", "def run_experiment(x, sigma=0.1, n_resample=3): ## take dataframe -> output dataframe of experimental results\n", " result = pd.DataFrame(columns=['x','y'])\n", " for _ in range(n_resample):\n", " noisy_y = [y + np.random.normal(0, sigma) for y in f(x)]\n", " single_sample = pd.concat([x, pd.Series(noisy_y, name='y')],axis=1)\n", " result = pd.concat([result, single_sample],axis=0)\n", " return result.reset_index(drop=True)\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Combined 30 datapoints down to 10\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "training_data_size = 10\n", "sigma = 0.05 # Measurement noise\n", "n_resample = 3 # How many times a given x is tested for objective function y (generating statistics)\n", "\n", "# Generate training dataset\n", "features, objective_functions = 'x', 'y'\n", "starting_data = pd.DataFrame({features: np.linspace(-2,10,training_data_size)}) # sample points for initial model training\n", "df = run_experiment(starting_data[features], sigma, n_resample)\n", "# Use prep.combine_cut to combine repeated measurements\n", "combined_df = prep.combine_cut(df, objective_functions, features)\n", "\n", "%matplotlib inline\n", " \n", "# Initialize plot figure\n", "fig = plt.figure(figsize=(6,4))\n", "ax = fig.add_subplot(111)\n", "\n", "\n", "n_samples = 100\n", "X = np.linspace(-2, 10, n_samples)\n", "ax.errorbar(X,f(X),label='Ground truth', fmt='--', alpha=0.5,capsize=1)\n", "ax.errorbar(combined_df['x'],combined_df['y'],yerr=1.96*(combined_df['y_std_mean']),xerr=None,label='Training Data', fmt='o', c='black', alpha=0.5,capsize=1)\n", "\n", "ax.set_ylabel('y', fontsize=18)\n", "ax.set_xlabel('x', fontsize=18)\n", "ax.tick_params(labelsize=16)\n", "ax.legend()\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Start workflow and add a regression model" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "wf = fit.WorkFlow(combined_df,\n", " [features],\n", " objective_functions,\n", " random_state=random_state,\n", " polynomial_degree=3,\n", " validation_holdout=0.0)\n", "\n", "# Add a simple LR model\n", "wf.add_regr('GPR', 'gpr_Matern_iso')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "n_samples = 50\n", "X = np.linspace(-2, 10, n_samples)\n", "x_input = pd.DataFrame({'x':X})\n", "pred = wf.predict(x_input, X_scaled=False, min_max=True, return_std=True)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Initialize plot figure\n", "fig = plt.figure(figsize=(6,4))\n", "ax = fig.add_subplot(111)\n", "\n", "ax.errorbar(X,pred['y'],yerr=1.96*(pred['y_std']),xerr=None,label='prediction', fmt='o-', alpha=0.2,capsize=3)\n", "ax.errorbar(X,f(X),yerr=1.96*(sigma)/np.sqrt(3),xerr=None,label='Ground truth', fmt='.--', alpha=0.5,capsize=1)\n", "ax.errorbar(wf.data['x'],wf.data['y'],yerr=1.96*(wf.data['y_std_mean']),xerr=None,label='Measured', fmt='o', c='black', alpha=0.5,capsize=1)\n", "\n", "\n", "ax.set_ylabel('y', fontsize=18)\n", "ax.set_xlabel('x', fontsize=18)\n", "ax.tick_params(labelsize=16)\n", "ax.legend()\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Use Active Learning to suggest new data\n", "\n", "Demonstration of iterative semi-automatic active learning for LECA workflows.\n", "After new data is suggested, it can be inspected, measurements can be done and then manually added to the ```ActiveLearner```." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Perform Active Learning" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of Data points in active set: 10\n" ] } ], "source": [ "active_learner = al.ActiveLearner(wf)\n", "print(f'Number of Data points in active set: {len(active_learner.X)}')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Perform the active learning experiment\n", "# The ideal acquisition function is used as default\n", "result = active_learner.single_step_al('GPR', \n", " acquisition_function='ideal',\n", " lim = [[-2], [10]], \n", " batch_size=3,\n", " random_state=random_state)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Combined 9 datapoints down to 3\n" ] }, { "data": { "text/html": [ "
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xyy_stdy_std_meancount
0-1.3361760.3738000.0132120.0076283
11.3313341.1557920.0663730.0383203
22.5793411.1844060.0232780.0134403
\n", "
" ], "text/plain": [ " x y y_std y_std_mean count\n", "0 -1.336176 0.373800 0.013212 0.007628 3\n", "1 1.331334 1.155792 0.066373 0.038320 3\n", "2 2.579341 1.184406 0.023278 0.013440 3" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get the AL suggestions\n", "suggested_X = active_learner.get_suggested_data()\n", "\n", "# Evaluate suggested data\n", "suggested_data = pd.DataFrame({features: suggested_X.flatten()}) # sample points for initial model training\n", "df_suggested = run_experiment(suggested_data[features], sigma, n_resample)\n", "\n", "# Use prep.combine_cut to combine repeated measurements\n", "combined_df_suggested = prep.combine_cut(df_suggested, objective_functions, features)\n", "combined_df_suggested" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of Data points in active set: 13\n" ] } ], "source": [ "# Add the AL suggestions to the data set \n", "# Once the data is added, it is removed from the suggestions \n", "# In that case, no data can be added\n", "\n", "active_learner.add_suggested_data(combined_df_suggested)\n", "print(f'Number of Data points in active set: {len(active_learner.X)}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Update Workflow" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "New indices: RangeIndex(start=10, stop=13, step=1)\n", "Data successfully added to the LECA workflow\n" ] } ], "source": [ "#Update the workflow and retrain it afterwards\n", "active_learner.update_wf()\n", "wf.retrain()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "n_samples = 50\n", "X = np.linspace(-2, 10, n_samples)\n", "x_input = pd.DataFrame({'x':X})\n", "pred = wf.predict(x_input, X_scaled=False, min_max=True, return_std=True)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Initialize plot figure\n", "fig = plt.figure(figsize=(6,4))\n", "ax = fig.add_subplot(111)\n", "\n", "ax.errorbar(X,pred['y'],yerr=1.96*(pred['y_std']),xerr=None,label='prediction', fmt='o-', alpha=0.2,capsize=3)\n", "ax.errorbar(X,f(X),yerr=1.96*(sigma)/np.sqrt(3),xerr=None,label='Ground truth', fmt='.--', alpha=0.5,capsize=1)\n", "ax.errorbar(wf.data['x'],wf.data['y'],yerr=1.96*(wf.data['y_std_mean']),xerr=None,label='Measured', fmt='o', c='black', alpha=0.5,capsize=1)\n", "ax.errorbar(combined_df_suggested['x'],combined_df_suggested['y'],yerr=1.96*(combined_df_suggested['y_std_mean']),xerr=None,\n", " label='Added via AL', fmt='o', c='red', alpha=1,capsize=1)\n", "\n", "\n", "ax.set_ylabel('y', fontsize=18)\n", "ax.set_xlabel('x', fontsize=18)\n", "ax.tick_params(labelsize=16)\n", "ax.legend()\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example for automated active learning and experimentation\n", "\n", "Active Learning and data acquisition can be automated via data evaluation models. We define such a model and then use the automatic_al tool of LECA." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Definition of data evaluation model" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "# Example class to evaluate the suggested data points\n", "# The data evaluation model could be coupled to some experiments\n", "# Whenever data is suggested by the AL routine, experiments can be performed and feed back into the active learner\n", "\n", "class TestFunctionEvaluator():\n", " def __init__(self):\n", " self.n_features = 1\n", " \n", " def evaluate(self, x, **kwargs):\n", " x = np.array(x)\n", " if len(x.shape)==1:\n", " x = x.reshape(1,-1)\n", " \n", " noisy_y = [y + np.random.normal(0, sigma) for y in f(x)]\n", " return noisy_y" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Automatic Active Learning" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Perform automatic active learning for two iterations\n", "result = active_learner.automatic_al('GPR', \n", " acquisition_function='ideal',\n", " lim = [[-2], [10]], \n", " batch_size=3,\n", " active_learning_steps=2,\n", " random_state=random_state,\n", " data_evaluation_models=[TestFunctionEvaluator()]\n", " )" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " x y\n", "0 -0.014587 1.076292\n", "1 8.006089 0.703244\n", "2 9.346408 0.335327\n", "3 6.667496 1.018420\n", "4 3.978556 0.751315\n", "5 -1.005299 0.520838" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#Inspect automatically collected data\n", "collected_data = active_learner.get_collected_data()\n", "collected_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Update workflow" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "New indices: RangeIndex(start=13, stop=19, step=1)\n", "Data successfully added to the LECA workflow\n" ] } ], "source": [ "#Update the workflow and retrain it afterwards\n", "active_learner.update_wf()\n", "wf.retrain()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "n_samples = 50\n", "X = np.linspace(-2, 10, n_samples)\n", "x_input = pd.DataFrame({'x':X})\n", "pred = wf.predict(x_input, X_scaled=False, min_max=True, return_std=True)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ " # Initialize plot figure\n", "fig = plt.figure(figsize=(6,4))\n", "ax = fig.add_subplot(111)\n", "\n", "ax.errorbar(X,pred['y'],yerr=1.96*(pred['y_std']),xerr=None,label='prediction', fmt='o-', alpha=0.2,capsize=3)\n", "ax.errorbar(X,f(X),yerr=1.96*(sigma)/np.sqrt(3),xerr=None,label='Ground truth', fmt='.--', alpha=0.5,capsize=1)\n", "ax.errorbar(wf.data['x'],wf.data['y'],yerr=1.96*(wf.data['y_std_mean']),xerr=None,label='Measured', fmt='o', c='black', alpha=0.5,capsize=1)\n", "ax.plot(collected_data['x'],collected_data['y'], 'ro', label='Added via AL')\n", "\n", "\n", "ax.set_ylabel('y', fontsize=18)\n", "ax.set_xlabel('x', fontsize=18)\n", "ax.tick_params(labelsize=16)\n", "ax.legend()\n", "plt.tight_layout()\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "LECA_dev", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.0" } }, "nbformat": 4, "nbformat_minor": 2 }