{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Informed Search\n", "> In this final chapter you will be given a taste of more advanced hyperparameter tuning methodologies known as ''informed search''. This includes a methodology known as Coarse To Fine as well as Bayesian & Genetic hyperparameter tuning algorithms. You will learn how informed search differs from uninformed search and gain practical skills with each of the mentioned methodologies, comparing and contrasting them as you go. This is the Summary of lecture \"Hyperparameter Tuning in Python\", via datacamp.\n", "\n", "- toc: true \n", "- badges: true\n", "- comments: true\n", "- author: Chanseok Kang\n", "- categories: [Python, Datacamp, Machine_Learning]\n", "- image: images/acc_for_lr.png" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Informed Search - Coarse to Fine\n", "- Coarse to fine tuning\n", " 1. Random search\n", " 2. Find promising areas\n", " 3. Grid search in the smaller area\n", " 4. Continue until optimal score obtained\n", "- Why Coarse to Fine?\n", " - Utilizes the advantanges of grid and random search\n", " - Wide search to begin with\n", " - Deeper search once you know where a good spot is likely to be\n", " - Better spending of time and computational efforts mean you can iterate quicker\n", " " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Visualizing Coarse to Fine\n", "You're going to undertake the first part of a Coarse to Fine search. This involves analyzing the results of an initial random search that took place over a large search space, then deciding what would be the next logical step to make your hyperparameter search finer." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "def visualize_hyperparameter(name):\n", " plt.clf()\n", " plt.scatter(results_df[name],results_df['accuracy'], c=['blue']*500)\n", " plt.gca().set(xlabel='{}'.format(name), ylabel='accuracy', title='Accuracy for different {}s'.format(name))\n", " plt.gca().set_ylim([0,100])" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "11000\n", " max_depth min_samples_leaf learn_rate accuracy\n", "1 10 14 0.477450 97\n", "4 6 12 0.771275 97\n", "2 7 14 0.050067 96\n", "3 5 12 0.023356 96\n", "5 13 11 0.290470 96\n", "6 6 10 0.317181 96\n", "7 19 10 0.757919 96\n", "8 2 16 0.931544 96\n", "9 16 13 0.904832 96\n", "10 12 13 0.891477 96\n", "Index(['max_depth', 'min_samples_leaf', 'learn_rate', 'accuracy'], dtype='object')\n" ] }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from itertools import product\n", "\n", "max_depth_list = range(1, 6)\n", "min_samples_leaf_list = range(3, 14)\n", "learn_rate_list = np.linspace(0.01, 1.33, 200)\n", "\n", "combinations_list = [list(x) for x in product(max_depth_list, \n", " min_samples_leaf_list, \n", " learn_rate_list)]\n", "\n", "results_df = pd.read_csv('./dataset/results_df.csv')\n", "\n", "# Confirm the size fo the combinations_list\n", "print(len(combinations_list))\n", "\n", "# Sort the results_df by accuracy and print the top 10 rows\n", "print(results_df.sort_values(by='accuracy', ascending=False).head(10))\n", "\n", "# Confirm which hyperparameters were used in this search\n", "print(results_df.columns)\n", "\n", "# Call visualize_hyperparameter() with each hyperparameter in turn\n", "visualize_hyperparameter('max_depth')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "visualize_hyperparameter('min_samples_leaf')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "visualize_hyperparameter('learn_rate')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have undertaken the first step of a Coarse to Fine search. Results clearly seem better when `max_depth` is below 20. learn_rates smaller than 1 seem to perform well too. There is not a strong trend for `min_samples` leaf though." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Coarse to Fine Iterations\n", "You will now visualize the first random search undertaken, construct a tighter grid and check the results." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def visualize_first():\n", " for name in results_df.columns[0:2]:\n", " plt.clf()\n", " plt.scatter(results_df[name],results_df['accuracy'], c=['blue']*500)\n", " plt.gca().set(xlabel='{}'.format(name), ylabel='accuracy', title='Accuracy for different {}s'.format(name))\n", " plt.gca().set_ylim([0,100])\n", " x_line = 20\n", " if name == \"learn_rate\":\n", " x_line = 1\n", " plt.axvline(x=x_line, color=\"red\", linewidth=4)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Use the provided function to visualize the first results\n", "visualize_first()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "def visualize_second():\n", " for name in results_df2.columns[0:2]:\n", " plt.clf()\n", " plt.scatter(results_df[name],results_df['accuracy'], c=['blue']*500)\n", " plt.gca().set(xlabel='{}'.format(name), ylabel='accuracy', title='Accuracy for different {}s'.format(name))\n", " plt.gca().set_ylim([0,100])\n", " x_line = 20\n", " if name == \"learn_rate\":\n", " x_line = 1\n", " plt.axvline(x=x_line, color=\"red\", linewidth=4)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Create some combination lists & combine\n", "max_depth_list = list(range(1, 21))\n", "learn_rate_list = np.linspace(0.001, 1, 50)\n", "\n", "results_df2 = pd.read_csv('./dataset/results_df2.csv')\n", "\n", "visualize_second()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Informed Search - Bayesian Statistics\n", "- Bayes rule\n", " - A statistical method of using new evidence to iteratively update our beliefs about some outcome\n", "$$ P(A \\vert B) = \\frac{P(B \\vert A) P(A)}{P(B)} $$\n", " - LHS = the probability of A given B has occurred. B is some new evidence (**Posterior**)\n", " - RHS = how to calculate LHS\n", " - $P(A)$ is the **'prior'**. The initial hypothesis about the event. \n", " - $P(A\\vert B)$ is the probability given new evidence\n", " - $P(B)$ is the **'marginal likelihood'**. It is the probability of observing this new evidence\n", " - $P(B \\vert A)$ is the **likelihood** which is the probability of observing the evidence, given the event we care about\n", "- Bayes in Hyperparameter Tuning\n", " - Pick a hyperparameter combination\n", " - Build a model\n", " - Get new evidence (the score of the model)\n", " - Update our belief and chose better hyperparamters next round" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Bayes Rule in Python\n", "In this exercise you will undertake a practical example of setting up Bayes formula, obtaining new evidence and updating your 'beliefs' in order to get a more accurate result. The example will relate to the likelihood that someone will close their account for your online software product.\n", "\n", "These are the probabilities we know:\n", "\n", "- 7% (0.07) of people are likely to close their account next month\n", "- 15% (0.15) of people with accounts are unhappy with your product (you don't know who though!)\n", "- 35% (0.35) of people who are likely to close their account are unhappy with your product" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.16333333333333336\n" ] } ], "source": [ "# Assign probabilities to variables\n", "p_unhappy = 0.15\n", "p_unhappy_close = 0.35\n", "\n", "# Probability someone will close\n", "p_close = 0.07\n", "\n", "# Probability unhappy person will close\n", "p_close_unhappy = (p_unhappy_close * p_close) / p_unhappy\n", "print(p_close_unhappy)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Bayesian Hyperparameter tuning with Hyperopt\n", "In this example you will set up and run a bayesian hyperparameter optimization process using the package `Hyperopt`. You will set up the domain (which is similar to setting up the grid for a grid search), then set up the objective function. Finally, you will run the optimizer over 20 iterations.\n", "\n", "You will need to set up the domain using values:\n", "\n", "- `max_depth` using quniform distribution (between 2 and 10, increasing by 2)\n", "- `learning_rate` using uniform distribution (0.001 to 0.9)\n", "\n", "Note that for the purpose of this exercise, this process was reduced in data sample size and hyperopt & GBM iterations. If you are trying out this method by yourself on your own machine, try a larger search space, more trials, more cvs and a larger dataset size to really see this in action!\n", "\n", "> Note: This session requires `Hyperopt` packages" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\n", "\n", "credit_card = pd.read_csv('./dataset/credit-card-full.csv')\n", "# To change categorical variable with dummy variables\n", "credit_card = pd.get_dummies(credit_card, columns=['SEX', 'EDUCATION', 'MARRIAGE'], drop_first=True)\n", "\n", "X = credit_card.drop(['ID', 'default payment next month'], axis=1)\n", "y = credit_card['default payment next month']\n", "\n", "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, shuffle=True)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "100%|██████████| 20/20 [02:16<00:00, 6.83s/trial, best loss: 0.18080952380952375]\n", "{'learning_rate': 0.0128515490384306, 'max_depth': 6.0}\n" ] } ], "source": [ "import hyperopt as hp\n", "from sklearn.ensemble import GradientBoostingClassifier\n", "from sklearn.model_selection import cross_val_score\n", "\n", "# Set up space dictionary with specified hyperparamters\n", "space = {'max_depth': hp.hp.quniform('max_depth', 2, 10, 2),\n", " 'learning_rate': hp.hp.uniform('learning_rate', 0.001, 0.9)}\n", "\n", "# Set up objective function\n", "def objective(params):\n", " params = {'max_depth': int(params['max_depth']), \n", " 'learning_rate': params['learning_rate']}\n", " gbm_clf = GradientBoostingClassifier(n_estimators=100, **params)\n", " best_score = cross_val_score(gbm_clf, X_train, y_train, \n", " scoring='accuracy', cv=2, n_jobs=4).mean()\n", " loss = 1 - best_score\n", " return loss\n", "\n", "# Run the algorithm\n", "best = hp.fmin(fn=objective, space=space, max_evals=20, \n", " rstate=np.random.RandomState(42), algo=hp.tpe.suggest)\n", "print(best)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Informed Search - Genetic Algorithms\n", "- Genetics in Machine Learning\n", " 1. Create some models (that have hyperparameter settings)\n", " 2. Pick the best (by our scoring function)\n", " : these are the ones that \"survive\"\n", " 3. Create new models that are similar to the best ones\n", " 4. Add in some randomness so we don't reach a local optimum\n", " 5. Repeat until we are happy!\n", "- Advantages\n", " - It allows us to learn from previous iterations, just like bayesian hyperparameter tuning\n", " - It has the additional advantage of some randomness\n", " - Takes care of many tedious aspects of machine learning" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Genetic Hyperparameter Tuning with TPOT\n", "You're going to undertake a simple example of genetic hyperparameter tuning. `TPOT` is a very powerful library that has a lot of features. You're just scratching the surface in this lesson, but you are highly encouraged to explore in your own time.\n", "\n", "This is a very small example. In real life, TPOT is designed to be run for many hours to find the best model. You would have a much larger population and offspring size as well as hundreds more generations to find a good model.\n", "\n", "You will create the estimator, fit the estimator to the training data and then score this on the test data.\n", "\n", "For this example we wish to use:\n", "\n", "- 3 generations\n", "- 4 in the population size\n", "- 3 offspring in each generation\n", "- `accuracy` for scoring\n", "\n", "> Note: This session requires `tpot` packages" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Optimization Progress', max=13.0, style=ProgressStyle(des…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Generation 1 - Current best internal CV score: 0.8204285714285714\n", "Generation 2 - Current best internal CV score: 0.8204285714285714\n", "Generation 3 - Current best internal CV score: 0.8204285714285714\n", "Best pipeline: DecisionTreeClassifier(input_matrix, criterion=gini, max_depth=1, min_samples_leaf=10, min_samples_split=9)\n", "0.8176666666666667\n" ] } ], "source": [ "from tpot import TPOTClassifier\n", "\n", "# Assign the values outlined to the inputs\n", "number_generations = 3\n", "population_size = 4\n", "offspring_size = 3\n", "scoring_function = 'accuracy'\n", "\n", "# Create the tpot classifier\n", "tpot_clf = TPOTClassifier(generations=number_generations, population_size=population_size,\n", " offspring_size=offspring_size, scoring=scoring_function,\n", " verbosity=2, random_state=2, cv=2)\n", "\n", "# Fit the classifier to the training data\n", "tpot_clf.fit(X_train, y_train)\n", "\n", "# Score on the test set\n", "print(tpot_clf.score(X_test, y_test))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can see in the output the score produced by the chosen model (in this case a version of Naive Bayes) over each generation, and then the final accuracy score with the hyperparameters chosen for the final model. This is a great first example of using TPOT for automated hyperparameter tuning." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Analysing TPOT's stability\n", "You will now see the random nature of TPOT by constructing the classifier with different random states and seeing what model is found to be best by the algorithm. This assists to see that TPOT is quite unstable when not run for a reasonable amount of time.\n", "\n" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Optimization Progress', max=10.0, style=ProgressStyle(des…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Generation 1 - Current best internal CV score: 0.8213809523809524\n", "Generation 2 - Current best internal CV score: 0.8213809523809524\n", "Best pipeline: XGBClassifier(input_matrix, learning_rate=0.001, max_depth=9, min_child_weight=7, n_estimators=100, nthread=1, subsample=0.45)\n", "0.8195555555555556\n" ] } ], "source": [ "# Create the tpot classifier\n", "tpot_clf = TPOTClassifier(generations=2, population_size=4, offspring_size=3,\n", " scoring='accuracy', cv=2, verbosity=2, random_state=42)\n", "\n", "# Fit the classifier to the training data\n", "tpot_clf.fit(X_train, y_train)\n", "\n", "# Score on the test set\n", "print(tpot_clf.score(X_test, y_test))" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Optimization Progress', max=10.0, style=ProgressStyle(des…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Generation 1 - Current best internal CV score: 0.7811904761904762\n", "Generation 2 - Current best internal CV score: 0.7811904761904762\n", "Best pipeline: LogisticRegression(input_matrix, C=0.5, dual=False, penalty=l2)\n", "0.7724444444444445\n" ] } ], "source": [ "# Create the tpot classifier\n", "tpot_clf = TPOTClassifier(generations=2, population_size=4, offspring_size=3,\n", " scoring='accuracy', cv=2, verbosity=2, random_state=122)\n", "\n", "# Fit the classifier to the training data\n", "tpot_clf.fit(X_train, y_train)\n", "\n", "# Score on the test set\n", "print(tpot_clf.score(X_test, y_test))" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "", "version_major": 2, "version_minor": 0 }, "text/plain": [ "HBox(children=(FloatProgress(value=0.0, description='Optimization Progress', max=10.0, style=ProgressStyle(des…" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\n", "Generation 1 - Current best internal CV score: 0.7960476190476191\n", "Generation 2 - Current best internal CV score: 0.8049523809523809\n", "Best pipeline: GradientBoostingClassifier(input_matrix, learning_rate=0.5, max_depth=3, max_features=0.6000000000000001, min_samples_leaf=15, min_samples_split=17, n_estimators=100, subsample=0.6000000000000001)\n", "0.812\n" ] } ], "source": [ "# Create the tpot classifier\n", "tpot_clf = TPOTClassifier(generations=2, population_size=4, offspring_size=3,\n", " scoring='accuracy', cv=2, verbosity=2, random_state=99)\n", "\n", "# Fit the classifier to the training data\n", "tpot_clf.fit(X_train, y_train)\n", "\n", "# Score on the test set\n", "print(tpot_clf.score(X_test, y_test))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can see that TPOT is quite unstable when only running with low generations, population size and offspring. The first model chosen was a Decision Tree, then a K-nearest Neighbor model and finally a Random Forest. Increasing the generations, population size and offspring and running this for a long time will assist to produce better models and more stable results." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "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": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }