{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Hyperparameters and Parameters\n", "> In this introductory chapter you will learn the difference between hyperparameters and parameters. You will practice extracting and analyzing parameters, setting hyperparameter values for several popular machine learning algorithms. Along the way you will learn some best practice tips & tricks for choosing which hyperparameters to tune and what values to set & build learning curves to analyze your hyperparameter choices. 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/accuracy_learning_curve.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": [ "## Introduction\n", "- Parameters\n", " - Components of the model learned during the modeling process\n", " - Do not set these manually\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Extracting a Logistic Regression parameter\n", "You are now going to practice extracting an important parameter of the logistic regression model. The logistic regression has a few other parameters you will not explore here but you can review them in the [scikit-learn.org](https://scikit-learn.org/) documentation for the `LogisticRegression()` module under 'Attributes'.\n", "\n", "This parameter is important for understanding the direction and magnitude of the effect the variables have on the target.\n", "\n", "In this exercise we will extract the coefficient parameter (found in the `coef_` attribute), zip it up with the original column names, and see which variables had the largest positive effect on the target variable." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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IDLIMIT_BALAGEPAY_0PAY_2PAY_3PAY_4PAY_5PAY_6BILL_AMT1...SEX_2EDUCATION_1EDUCATION_2EDUCATION_3EDUCATION_4EDUCATION_5EDUCATION_6MARRIAGE_1MARRIAGE_2MARRIAGE_3
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5 rows × 32 columns

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" ], "text/plain": [ " ID LIMIT_BAL AGE PAY_0 PAY_2 PAY_3 PAY_4 PAY_5 PAY_6 BILL_AMT1 \\\n", "0 1 20000 24 2 2 -1 -1 -2 -2 3913 \n", "1 2 120000 26 -1 2 0 0 0 2 2682 \n", "2 3 90000 34 0 0 0 0 0 0 29239 \n", "3 4 50000 37 0 0 0 0 0 0 46990 \n", "4 5 50000 57 -1 0 -1 0 0 0 8617 \n", "\n", " ... SEX_2 EDUCATION_1 EDUCATION_2 EDUCATION_3 EDUCATION_4 \\\n", "0 ... 1 0 1 0 0 \n", "1 ... 1 0 1 0 0 \n", "2 ... 1 0 1 0 0 \n", "3 ... 1 0 1 0 0 \n", "4 ... 0 0 1 0 0 \n", "\n", " EDUCATION_5 EDUCATION_6 MARRIAGE_1 MARRIAGE_2 MARRIAGE_3 \n", "0 0 0 1 0 0 \n", "1 0 0 0 1 0 \n", "2 0 0 0 1 0 \n", "3 0 0 1 0 0 \n", "4 0 0 1 0 0 \n", "\n", "[5 rows x 32 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "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", "credit_card.head()" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split\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": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " Variable Coefficient\n", "0 LIMIT_BAL -3.146007e-06\n", "1 AGE -1.667351e-02\n", "2 PAY_0 1.180600e-03\n", "3 PAY_2 8.930544e-04\n", "4 PAY_3 7.965670e-04\n", "5 PAY_4 7.851508e-04\n", "6 PAY_5 7.263725e-04\n", "7 PAY_6 6.704895e-04\n", "8 BILL_AMT1 -6.855808e-06\n", "9 BILL_AMT2 4.410500e-06\n", "10 BILL_AMT3 2.179079e-06\n", "11 BILL_AMT4 5.482805e-07\n", "12 BILL_AMT5 2.105253e-06\n", "13 BILL_AMT6 2.514244e-06\n", "14 PAY_AMT1 -3.239663e-05\n", "15 PAY_AMT2 -2.570219e-05\n", "16 PAY_AMT3 -6.103578e-06\n", "17 PAY_AMT4 -7.670497e-06\n", "18 PAY_AMT5 -5.088686e-06\n", "19 PAY_AMT6 -2.729188e-06\n", "20 SEX_2 -3.806425e-04\n", "21 EDUCATION_1 -1.122823e-04\n", "22 EDUCATION_2 -2.846602e-04\n", "23 EDUCATION_3 -1.137210e-04\n", "24 EDUCATION_4 -7.239638e-06\n", "25 EDUCATION_5 -2.047148e-05\n", "26 EDUCATION_6 -1.976180e-06\n", "27 MARRIAGE_1 -1.068800e-04\n", "28 MARRIAGE_2 -4.193479e-04\n", "29 MARRIAGE_3 -1.006513e-05\n", " Variable Coefficient\n", "2 PAY_0 0.001181\n", "3 PAY_2 0.000893\n", "4 PAY_3 0.000797\n" ] } ], "source": [ "from sklearn.linear_model import LogisticRegression\n", "\n", "log_reg_clf = LogisticRegression(max_iter=1000)\n", "log_reg_clf.fit(X_train, y_train)\n", "\n", "# Create a list of original variable names from the training DataFrame\n", "original_variables = X_train.columns\n", "\n", "# Extract the coefficients of the logistic regression estimator\n", "model_coefficients = log_reg_clf.coef_[0]\n", "\n", "# Create a dataframe of the variables and coefficients & print it out\n", "coefficient_df = pd.DataFrame({'Variable': original_variables, \n", " 'Coefficient': model_coefficients})\n", "print(coefficient_df)\n", "\n", "# Print out the top 3 positive variables\n", "top_three_df = coefficient_df.sort_values(by='Coefficient', axis=0, ascending=False)[0:3]\n", "print(top_three_df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Extracting a Random Forest parameter\n", "You will now translate the work previously undertaken on the logistic regression model to a random forest model. A parameter of this model is, for a given tree, how it decided to split at each level.\n", "\n", "This analysis is not as useful as the coefficients of logistic regression as you will be unlikely to ever explore every split and every tree in a random forest model. However, it is a very useful exercise to peak under the hood at what the model is doing.\n", "\n", "In this exercise we will extract a single tree from our random forest model, visualize it and programmatically extract one of the splits." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "This node split on feature PAY_4, at a value of 1.0\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.tree import export_graphviz\n", "import os\n", "import pydot\n", "\n", "rf_clf = RandomForestClassifier(max_depth=4, criterion='gini', n_estimators=10);\n", "rf_clf.fit(X_train, y_train)\n", "\n", "# Extract the 7th (index 6) tree from the random forest\n", "chosen_tree = rf_clf.estimators_[6]\n", "\n", "# Convert tree to dot object\n", "export_graphviz(chosen_tree,\n", " out_file='tree6.dot',\n", " feature_names=X_train.columns,\n", " filled=True,\n", " rounded=True)\n", "(graph, ) = pydot.graph_from_dot_file('tree6.dot')\n", "\n", "# Convert dot to png\n", "graph.write_png('tree_viz_image.png')\n", "\n", "# Visualize the graph using the provided image\n", "tree_viz_image = plt.imread('tree_viz_image.png')\n", "plt.figure(figsize = (16,10))\n", "plt.imshow(tree_viz_image, aspect='auto');\n", "plt.axis('off')\n", "\n", "# Extract the parameters and level of the top (index 0) node\n", "split_column = chosen_tree.tree_.feature[0]\n", "split_column_name = X_train.columns[split_column]\n", "split_value = chosen_tree.tree_.threshold[0]\n", "\n", "# Print out the feature and level\n", "print('This node split on feature {}, at a value of {}'.format(split_column_name, split_value))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introducing Hyperparameters\n", "- Hyperparameters\n", " - Something you set before the modelling process (need to tune)\n", " - The algorithm does not learn these" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Exploring Random Forest Hyperparameters\n", "Understanding what hyperparameters are available and the impact of different hyperparameters is a core skill for any data scientist. As models become more complex, there are many different settings you can set, but only some will have a large impact on your model.\n", "\n", "You will now assess an existing random forest model (it has some bad choices for hyperparameters!) and then make better choices for a new random forest model and assess its performance." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "RandomForestClassifier(n_estimators=5, random_state=42)\n", "Confusion Matrix: \n", "\n", " [[6336 667]\n", " [1249 748]] \n", " Accuracy Score: \n", "\n", " 0.7871111111111111\n" ] } ], "source": [ "from sklearn.metrics import confusion_matrix, accuracy_score\n", "\n", "rf_clf_old = RandomForestClassifier(min_samples_leaf=1, min_samples_split=2, \n", " n_estimators=5, oob_score=False, random_state=42)\n", "\n", "rf_clf_old.fit(X_train, y_train)\n", "rf_old_predictions = rf_clf_old.predict(X_test)\n", "\n", "# Print out the old estimator, notice which hyperparameter is badly set\n", "print(rf_clf_old)\n", "\n", "# Get confusion matrix & accuracy for the old rf_model\n", "print('Confusion Matrix: \\n\\n {} \\n Accuracy Score: \\n\\n {}'.format(\n", " confusion_matrix(y_test, rf_old_predictions),\n", " accuracy_score(y_test, rf_old_predictions)\n", "))" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Confusion Matrix: \n", "\n", " [[6638 365]\n", " [1235 762]]\n", "Accuracy Score: \n", "\n", " 0.8222222222222222\n" ] } ], "source": [ "# Create a new random forest classifier with better hyperparameters\n", "rf_clf_new = RandomForestClassifier(n_estimators=500)\n", "\n", "# Fit this to the data and obtain predictions\n", "rf_new_predictions = rf_clf_new.fit(X_train, y_train).predict(X_test)\n", "\n", "# Assess the new model (using new predictions!)\n", "print('Confusion Matrix: \\n\\n', confusion_matrix(y_test, rf_new_predictions))\n", "print('Accuracy Score: \\n\\n', accuracy_score(y_test, rf_new_predictions))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Hyperparameters of KNN\n", "To apply the concepts learned in the prior exercise, it is good practice to try out learnings on a new algorithm. The k-nearest-neighbors algorithm is not as popular as it used to be but can still be an excellent choice for data that has groups of data that behave similarly. Could this be the case for our credit card users?\n", "\n", "In this case you will try out several different values for one of the core hyperparameters for the knn algorithm and compare performance.\n", "\n" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The accuracy of 5, 10, 20 neighbors was 0.755, 0.7764444444444445, 0.7804444444444445\n" ] } ], "source": [ "from sklearn.neighbors import KNeighborsClassifier\n", "\n", "# Build a knn estimator for each value of n_neighbors\n", "knn_5 = KNeighborsClassifier(n_neighbors=5)\n", "knn_10 = KNeighborsClassifier(n_neighbors=10)\n", "knn_20 = KNeighborsClassifier(n_neighbors=20)\n", "\n", "# Fit each to the training data & produce predictions\n", "knn_5_predictions = knn_5.fit(X_train, y_train).predict(X_test)\n", "knn_10_predictions = knn_10.fit(X_train, y_train).predict(X_test)\n", "knn_20_predictions = knn_20.fit(X_train, y_train).predict(X_test)\n", "\n", "# Get an accuracy score for each of the models\n", "knn_5_accuracy = accuracy_score(y_test, knn_5_predictions)\n", "knn_10_accuracy = accuracy_score(y_test, knn_10_predictions)\n", "knn_20_accuracy = accuracy_score(y_test, knn_20_predictions)\n", "print('The accuracy of 5, 10, 20 neighbors was {}, {}, {}'.format(knn_5_accuracy,\n", " knn_10_accuracy,\n", " knn_20_accuracy))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setting & Analyzing Hyperparameter Values\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Automating Hyperparameter Choice\n", "Finding the best hyperparameter of interest without writing hundreds of lines of code for hundreds of models is an important efficiency gain that will greatly assist your future machine learning model building.\n", "\n", "An important hyperparameter for the GBM algorithm is the learning rate. But which learning rate is best for this problem? By writing a loop to search through a number of possibilities, collating these and viewing them you can find the best one.\n", "\n", "Possible learning rates to try include 0.001, 0.01, 0.05, 0.1, 0.2 and 0.5" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " learning_rate accuracy\n", "0 0.001 0.778111\n", "1 0.010 0.823000\n", "2 0.050 0.826000\n", "3 0.100 0.825556\n", "4 0.200 0.823333\n", "5 0.500 0.818778\n" ] } ], "source": [ "from sklearn.ensemble import GradientBoostingClassifier\n", "\n", "# Set the learning rates & results storage\n", "learning_rates = [0.001, 0.01, 0.05, 0.1, 0.2, 0.5]\n", "results_list = []\n", "\n", "# Create the for loop to evaluate model predictions for each learning rate\n", "for learning_rate in learning_rates:\n", " model = GradientBoostingClassifier(learning_rate=learning_rate)\n", " predictions = model.fit(X_train, y_train).predict(X_test)\n", " \n", " # Save the learning rate and accuracy score\n", " results_list.append([learning_rate, accuracy_score(y_test, predictions)])\n", " \n", "# Gather everything into a DataFrame\n", "results_df = pd.DataFrame(results_list, columns=['learning_rate', 'accuracy'])\n", "print(results_df)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Building Learning Curves\n", "If we want to test many different values for a single hyperparameter it can be difficult to easily view that in the form of a DataFrame. Previously you learned about a nice trick to analyze this. A graph called a 'learning curve' can nicely demonstrate the effect of increasing or decreasing a particular hyperparameter on the final result.\n", "\n", "Instead of testing only a few values for the learning rate, you will test many to easily see the effect of this hyperparameter across a large range of values. A useful function from NumPy is `np.linspace(start, end, num)` which allows you to create a number of values (`num`) evenly spread within an interval (`start`, `end`) that you specify." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Set the learning rates & accuracies list\n", "learn_rates = np.linspace(0.01, 2, num=30)\n", "accuracies = []\n", "\n", "# Create the for loop\n", "for learn_rate in learn_rates:\n", " # Create the model, predictions & save the accuracies as before\n", " model = GradientBoostingClassifier(learning_rate=learn_rate)\n", " predictions = model.fit(X_train, y_train).predict(X_test)\n", " accuracies.append(accuracy_score(y_test, predictions))\n", " \n", "# Plot results\n", "plt.plot(learn_rates, accuracies);\n", "plt.gca().set(xlabel='learning_rate', ylabel='Accuracy', title='Accuracy for different learning_rates');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can see that for low values, you get a pretty good accuracy. However once the learning rate pushes much above 1.5, the accuracy starts to drop. " ] } ], "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 }