{ "cells": [ { "cell_type": "markdown", "id": "28a775e9", "metadata": {}, "source": [ "# Model Comparison" ] }, { "cell_type": "markdown", "id": "a6d185a2", "metadata": {}, "source": [ "To compare models, we will first optimize the parameters of two diffrent models and look at how the different parameters settings impact the model comparison. Later, we'll see how to compare across models of different classes." ] }, { "cell_type": "code", "execution_count": 1, "id": "28da5e4b", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import seaborn as sns\n", "import pandas as pd\n", "from sklearn import datasets\n", "from sklearn import cluster\n", "from sklearn import svm\n", "from sklearn import tree\n", "# import the whole model selection module\n", "from sklearn import model_selection\n", "sns.set_theme(palette='colorblind')" ] }, { "cell_type": "markdown", "id": "bf2b95e0", "metadata": {}, "source": [ "We'll use the iris data again." ] }, { "cell_type": "code", "execution_count": 2, "id": "e8e74039", "metadata": {}, "outputs": [], "source": [ "iris_X, iris_y = datasets.load_iris(return_X_y=True)" ] }, { "cell_type": "markdown", "id": "e80f934e", "metadata": {}, "source": [ "Remember, we need to split the data into training and test. The cross validation step will hep us optimize the parameters, but we don't want *data leakage* where the model has seen the test data multiple times. So, we split the data here for train and test annd the cross validation splits the training data into train and \"test\" again, but this test is better termed validation." ] }, { "cell_type": "code", "execution_count": 3, "id": "c042d51d", "metadata": {}, "outputs": [], "source": [ "iris_X_train, iris_X_test, iris_y_train, iris_y_test = model_selection.train_test_split(\n", " iris_X,iris_y, test_size =.2)" ] }, { "cell_type": "markdown", "id": "245eacdc", "metadata": {}, "source": [ "Then we can make the object, the parameter grid dictionary and the Grid Search object. We split these into separate cells, so that we can use the built in help to see more detail." ] }, { "cell_type": "code", "execution_count": 4, "id": "356a8948", "metadata": {}, "outputs": [], "source": [ "dt = tree.DecisionTreeClassifier()" ] }, { "cell_type": "code", "execution_count": 5, "id": "06e00905", "metadata": {}, "outputs": [], "source": [ "params_dt = {'criterion':['gini','entropy'],\n", " 'max_depth':[2,3,4],\n", " 'min_samples_leaf':list(range(2,20,2))}" ] }, { "cell_type": "code", "execution_count": 6, "id": "bc77f267", "metadata": {}, "outputs": [], "source": [ "dt_opt = model_selection.GridSearchCV(dt,params_dt)" ] }, { "cell_type": "markdown", "id": "a7b5fa39", "metadata": {}, "source": [ "Then we fit the Grid search using the training data, and remember this actually resets the parameters and then cross validates multiple times." ] }, { "cell_type": "code", "execution_count": 7, "id": "8f4f0d24", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
GridSearchCV(estimator=DecisionTreeClassifier(),\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'max_depth': [2, 3, 4],\n",
       "                         'min_samples_leaf': [2, 4, 6, 8, 10, 12, 14, 16, 18]})
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
" ], "text/plain": [ "GridSearchCV(estimator=DecisionTreeClassifier(),\n", " param_grid={'criterion': ['gini', 'entropy'],\n", " 'max_depth': [2, 3, 4],\n", " 'min_samples_leaf': [2, 4, 6, 8, 10, 12, 14, 16, 18]})" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dt_opt.fit(iris_X_train,iris_y_train)" ] }, { "cell_type": "markdown", "id": "76aa865c", "metadata": {}, "source": [ "adn look at the results" ] }, { "cell_type": "code", "execution_count": 8, "id": "d820c781", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'mean_fit_time': array([0.00039306, 0.00034938, 0.00034437, 0.000349 , 0.00034513,\n", " 0.00034647, 0.00034838, 0.00034761, 0.00035162, 0.00038896,\n", " 0.00035572, 0.00034981, 0.00035625, 0.00034728, 0.00034947,\n", " 0.000349 , 0.00034842, 0.00034723, 0.00035934, 0.00035663,\n", " 0.00035768, 0.00035028, 0.00035329, 0.00034971, 0.00035257,\n", " 0.00035281, 0.0003489 , 0.00035853, 0.00035596, 0.00035648,\n", " 0.0003509 , 0.00035429, 0.00035157, 0.00035481, 0.000353 ,\n", " 0.000348 , 0.00036955, 0.00036836, 0.00036283, 0.00036693,\n", " 0.00035868, 0.00036645, 0.0003583 , 0.00035987, 0.00035768,\n", " 0.0003799 , 0.00037074, 0.00036554, 0.00036526, 0.00036535,\n", " 0.00036039, 0.00036349, 0.0003541 , 0.00035987]),\n", " 'std_fit_time': array([6.72941413e-05, 8.11268045e-06, 3.81648499e-06, 8.66270211e-06,\n", " 4.09525119e-06, 7.54458255e-06, 3.91587547e-06, 3.24809768e-06,\n", " 8.48636188e-06, 3.16120380e-05, 9.03854864e-06, 5.72204590e-07,\n", " 1.01033231e-05, 1.15430054e-06, 3.76187952e-06, 2.99839209e-06,\n", " 3.01050074e-06, 5.62304040e-06, 3.37646503e-06, 2.38609238e-06,\n", " 8.43530189e-06, 3.58548569e-06, 9.80059873e-06, 3.46618306e-06,\n", " 7.93385961e-06, 8.40289362e-06, 4.56769181e-06, 8.42451299e-06,\n", " 1.52289576e-06, 9.17140216e-06, 1.24709099e-06, 9.49133553e-06,\n", " 3.84911271e-06, 9.27249058e-06, 1.04203921e-05, 5.76164530e-07,\n", " 2.71839005e-06, 7.51680506e-06, 4.13338336e-06, 1.24590533e-05,\n", " 2.32430603e-06, 1.30709058e-05, 4.18259854e-06, 8.42208358e-06,\n", " 3.00218129e-06, 8.78804544e-06, 8.06545774e-06, 5.73474746e-06,\n", " 6.13435761e-06, 6.88794535e-06, 3.65208705e-06, 1.15504882e-05,\n", " 3.65768603e-06, 1.35648229e-05]),\n", " 'mean_score_time': array([0.00024743, 0.00022221, 0.00022173, 0.00022287, 0.00021906,\n", " 0.0002223 , 0.00025768, 0.00025663, 0.00026088, 0.00023623,\n", " 0.00022044, 0.00022583, 0.0002223 , 0.00022435, 0.00022206,\n", " 0.00022521, 0.0002264 , 0.00022044, 0.00022712, 0.00022411,\n", " 0.0002233 , 0.00022063, 0.00022211, 0.00022278, 0.00022445,\n", " 0.00022144, 0.00022278, 0.00022326, 0.00022783, 0.00021963,\n", " 0.00022578, 0.00021935, 0.00022659, 0.00022411, 0.00022068,\n", " 0.00022483, 0.00022292, 0.00022302, 0.00022135, 0.0002223 ,\n", " 0.00022497, 0.00022588, 0.00022087, 0.00022306, 0.00022216,\n", " 0.00022306, 0.00022664, 0.00022197, 0.00022216, 0.00022125,\n", " 0.00022173, 0.00022187, 0.00022726, 0.00022092]),\n", " 'std_score_time': array([4.32319092e-05, 3.19160472e-06, 3.24809768e-06, 3.58548569e-06,\n", " 8.03580262e-07, 3.42062119e-06, 1.14242063e-06, 2.86102295e-07,\n", " 6.98432161e-06, 1.09738869e-05, 9.84180805e-07, 7.53855268e-06,\n", " 3.78657946e-06, 8.77561758e-06, 3.19017957e-06, 1.06726340e-05,\n", " 1.00810187e-05, 1.04033586e-06, 8.75174819e-06, 5.33759511e-06,\n", " 6.38961792e-06, 1.78161065e-06, 2.86340614e-06, 2.90675176e-06,\n", " 3.54466773e-06, 1.61280961e-06, 3.74431046e-06, 4.77218475e-06,\n", " 7.51014740e-06, 1.01601008e-06, 8.88633093e-06, 5.43678010e-07,\n", " 6.88794535e-06, 8.95641852e-06, 8.86968386e-07, 8.29751462e-06,\n", " 1.60291064e-06, 1.86271906e-06, 1.24891289e-06, 1.30238536e-06,\n", " 9.11569983e-06, 9.07445041e-06, 9.36836372e-07, 2.85545443e-06,\n", " 5.09122765e-07, 2.26986508e-06, 7.90112121e-06, 1.28834306e-06,\n", " 2.66943646e-06, 1.16800773e-06, 7.83523403e-07, 1.68991519e-06,\n", " 8.43260596e-06, 1.16410786e-06]),\n", " 'param_criterion': masked_array(data=['gini', 'gini', 'gini', 'gini', 'gini', 'gini', 'gini',\n", " 'gini', 'gini', 'gini', 'gini', 'gini', 'gini', 'gini',\n", " 'gini', 'gini', 'gini', 'gini', 'gini', 'gini', 'gini',\n", " 'gini', 'gini', 'gini', 'gini', 'gini', 'gini',\n", " 'entropy', 'entropy', 'entropy', 'entropy', 'entropy',\n", " 'entropy', 'entropy', 'entropy', 'entropy', 'entropy',\n", " 'entropy', 'entropy', 'entropy', 'entropy', 'entropy',\n", " 'entropy', 'entropy', 'entropy', 'entropy', 'entropy',\n", " 'entropy', 'entropy', 'entropy', 'entropy', 'entropy',\n", " 'entropy', 'entropy'],\n", " mask=[False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False],\n", " fill_value='?',\n", " dtype=object),\n", " 'param_max_depth': masked_array(data=[2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n", " mask=[False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False],\n", " fill_value='?',\n", " dtype=object),\n", " 'param_min_samples_leaf': masked_array(data=[2, 4, 6, 8, 10, 12, 14, 16, 18, 2, 4, 6, 8, 10, 12, 14,\n", " 16, 18, 2, 4, 6, 8, 10, 12, 14, 16, 18, 2, 4, 6, 8, 10,\n", " 12, 14, 16, 18, 2, 4, 6, 8, 10, 12, 14, 16, 18, 2, 4,\n", " 6, 8, 10, 12, 14, 16, 18],\n", " mask=[False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False],\n", " fill_value='?',\n", " dtype=object),\n", " 'params': [{'criterion': 'gini', 'max_depth': 2, 'min_samples_leaf': 2},\n", " {'criterion': 'gini', 'max_depth': 2, 'min_samples_leaf': 4},\n", " {'criterion': 'gini', 'max_depth': 2, 'min_samples_leaf': 6},\n", " {'criterion': 'gini', 'max_depth': 2, 'min_samples_leaf': 8},\n", " {'criterion': 'gini', 'max_depth': 2, 'min_samples_leaf': 10},\n", " {'criterion': 'gini', 'max_depth': 2, 'min_samples_leaf': 12},\n", " {'criterion': 'gini', 'max_depth': 2, 'min_samples_leaf': 14},\n", " {'criterion': 'gini', 'max_depth': 2, 'min_samples_leaf': 16},\n", " {'criterion': 'gini', 'max_depth': 2, 'min_samples_leaf': 18},\n", " {'criterion': 'gini', 'max_depth': 3, 'min_samples_leaf': 2},\n", " {'criterion': 'gini', 'max_depth': 3, 'min_samples_leaf': 4},\n", " {'criterion': 'gini', 'max_depth': 3, 'min_samples_leaf': 6},\n", " {'criterion': 'gini', 'max_depth': 3, 'min_samples_leaf': 8},\n", " {'criterion': 'gini', 'max_depth': 3, 'min_samples_leaf': 10},\n", " {'criterion': 'gini', 'max_depth': 3, 'min_samples_leaf': 12},\n", " {'criterion': 'gini', 'max_depth': 3, 'min_samples_leaf': 14},\n", " {'criterion': 'gini', 'max_depth': 3, 'min_samples_leaf': 16},\n", " {'criterion': 'gini', 'max_depth': 3, 'min_samples_leaf': 18},\n", " {'criterion': 'gini', 'max_depth': 4, 'min_samples_leaf': 2},\n", " {'criterion': 'gini', 'max_depth': 4, 'min_samples_leaf': 4},\n", " {'criterion': 'gini', 'max_depth': 4, 'min_samples_leaf': 6},\n", " {'criterion': 'gini', 'max_depth': 4, 'min_samples_leaf': 8},\n", " {'criterion': 'gini', 'max_depth': 4, 'min_samples_leaf': 10},\n", " {'criterion': 'gini', 'max_depth': 4, 'min_samples_leaf': 12},\n", " {'criterion': 'gini', 'max_depth': 4, 'min_samples_leaf': 14},\n", " {'criterion': 'gini', 'max_depth': 4, 'min_samples_leaf': 16},\n", " {'criterion': 'gini', 'max_depth': 4, 'min_samples_leaf': 18},\n", " {'criterion': 'entropy', 'max_depth': 2, 'min_samples_leaf': 2},\n", " {'criterion': 'entropy', 'max_depth': 2, 'min_samples_leaf': 4},\n", " {'criterion': 'entropy', 'max_depth': 2, 'min_samples_leaf': 6},\n", " {'criterion': 'entropy', 'max_depth': 2, 'min_samples_leaf': 8},\n", " {'criterion': 'entropy', 'max_depth': 2, 'min_samples_leaf': 10},\n", " {'criterion': 'entropy', 'max_depth': 2, 'min_samples_leaf': 12},\n", " {'criterion': 'entropy', 'max_depth': 2, 'min_samples_leaf': 14},\n", " {'criterion': 'entropy', 'max_depth': 2, 'min_samples_leaf': 16},\n", " {'criterion': 'entropy', 'max_depth': 2, 'min_samples_leaf': 18},\n", " {'criterion': 'entropy', 'max_depth': 3, 'min_samples_leaf': 2},\n", " {'criterion': 'entropy', 'max_depth': 3, 'min_samples_leaf': 4},\n", " {'criterion': 'entropy', 'max_depth': 3, 'min_samples_leaf': 6},\n", " {'criterion': 'entropy', 'max_depth': 3, 'min_samples_leaf': 8},\n", " {'criterion': 'entropy', 'max_depth': 3, 'min_samples_leaf': 10},\n", " {'criterion': 'entropy', 'max_depth': 3, 'min_samples_leaf': 12},\n", " {'criterion': 'entropy', 'max_depth': 3, 'min_samples_leaf': 14},\n", " {'criterion': 'entropy', 'max_depth': 3, 'min_samples_leaf': 16},\n", " {'criterion': 'entropy', 'max_depth': 3, 'min_samples_leaf': 18},\n", " {'criterion': 'entropy', 'max_depth': 4, 'min_samples_leaf': 2},\n", " {'criterion': 'entropy', 'max_depth': 4, 'min_samples_leaf': 4},\n", " {'criterion': 'entropy', 'max_depth': 4, 'min_samples_leaf': 6},\n", " {'criterion': 'entropy', 'max_depth': 4, 'min_samples_leaf': 8},\n", " {'criterion': 'entropy', 'max_depth': 4, 'min_samples_leaf': 10},\n", " {'criterion': 'entropy', 'max_depth': 4, 'min_samples_leaf': 12},\n", " {'criterion': 'entropy', 'max_depth': 4, 'min_samples_leaf': 14},\n", " {'criterion': 'entropy', 'max_depth': 4, 'min_samples_leaf': 16},\n", " {'criterion': 'entropy', 'max_depth': 4, 'min_samples_leaf': 18}],\n", " 'split0_test_score': array([0.95833333, 0.95833333, 0.95833333, 0.95833333, 0.95833333,\n", " 0.95833333, 0.95833333, 0.95833333, 0.95833333, 0.95833333,\n", " 0.95833333, 0.95833333, 0.95833333, 0.95833333, 0.95833333,\n", " 0.95833333, 0.95833333, 0.95833333, 0.95833333, 0.95833333,\n", " 0.95833333, 0.95833333, 0.95833333, 0.95833333, 0.95833333,\n", " 0.95833333, 0.95833333, 0.95833333, 0.95833333, 0.95833333,\n", " 0.95833333, 0.95833333, 0.95833333, 0.95833333, 0.95833333,\n", " 0.95833333, 0.95833333, 0.95833333, 0.95833333, 0.95833333,\n", " 0.95833333, 0.95833333, 0.95833333, 0.95833333, 0.95833333,\n", " 0.95833333, 0.95833333, 0.95833333, 0.95833333, 0.95833333,\n", " 0.95833333, 0.95833333, 0.95833333, 0.95833333]),\n", " 'split1_test_score': array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", " 1., 1., 1.]),\n", " 'split2_test_score': array([0.91666667, 0.91666667, 0.91666667, 0.91666667, 0.91666667,\n", " 0.91666667, 0.91666667, 0.91666667, 0.91666667, 0.91666667,\n", " 0.91666667, 0.91666667, 0.91666667, 0.91666667, 0.91666667,\n", " 0.91666667, 0.91666667, 0.91666667, 0.91666667, 0.91666667,\n", " 0.91666667, 0.91666667, 0.91666667, 0.91666667, 0.91666667,\n", " 0.91666667, 0.91666667, 0.91666667, 0.91666667, 0.91666667,\n", " 0.91666667, 0.91666667, 0.91666667, 0.91666667, 0.91666667,\n", " 0.91666667, 0.91666667, 0.91666667, 0.91666667, 0.91666667,\n", " 0.91666667, 0.91666667, 0.91666667, 0.91666667, 0.91666667,\n", " 0.91666667, 0.91666667, 0.91666667, 0.91666667, 0.91666667,\n", " 0.91666667, 0.91666667, 0.91666667, 0.91666667]),\n", " 'split3_test_score': array([0.91666667, 0.91666667, 0.91666667, 0.91666667, 0.91666667,\n", " 0.91666667, 0.91666667, 0.91666667, 0.91666667, 0.95833333,\n", " 0.95833333, 0.95833333, 0.91666667, 0.91666667, 0.91666667,\n", " 0.91666667, 0.91666667, 0.91666667, 0.91666667, 0.95833333,\n", " 0.95833333, 0.91666667, 0.91666667, 0.91666667, 0.91666667,\n", " 0.91666667, 0.91666667, 0.91666667, 0.91666667, 0.91666667,\n", " 0.91666667, 0.91666667, 0.91666667, 0.91666667, 0.91666667,\n", " 0.91666667, 0.95833333, 0.95833333, 0.95833333, 0.91666667,\n", " 0.91666667, 0.91666667, 0.91666667, 0.91666667, 0.91666667,\n", " 0.91666667, 0.95833333, 0.95833333, 0.91666667, 0.91666667,\n", " 0.91666667, 0.91666667, 0.91666667, 0.91666667]),\n", " 'split4_test_score': array([0.95833333, 0.95833333, 0.95833333, 0.95833333, 0.95833333,\n", " 0.95833333, 0.95833333, 0.95833333, 0.95833333, 0.95833333,\n", " 0.95833333, 0.95833333, 0.95833333, 0.95833333, 0.95833333,\n", " 0.95833333, 0.95833333, 0.95833333, 1. , 0.95833333,\n", " 0.95833333, 0.95833333, 0.95833333, 0.95833333, 0.95833333,\n", " 0.95833333, 0.95833333, 0.95833333, 0.95833333, 0.95833333,\n", " 0.95833333, 0.95833333, 0.95833333, 0.95833333, 0.95833333,\n", " 0.95833333, 0.95833333, 0.95833333, 0.95833333, 0.95833333,\n", " 0.95833333, 0.95833333, 0.95833333, 0.95833333, 0.95833333,\n", " 0.95833333, 0.95833333, 0.95833333, 0.95833333, 0.95833333,\n", " 0.95833333, 0.95833333, 0.95833333, 0.95833333]),\n", " 'mean_test_score': array([0.95 , 0.95 , 0.95 , 0.95 , 0.95 ,\n", " 0.95 , 0.95 , 0.95 , 0.95 , 0.95833333,\n", " 0.95833333, 0.95833333, 0.95 , 0.95 , 0.95 ,\n", " 0.95 , 0.95 , 0.95 , 0.95833333, 0.95833333,\n", " 0.95833333, 0.95 , 0.95 , 0.95 , 0.95 ,\n", " 0.95 , 0.95 , 0.95 , 0.95 , 0.95 ,\n", " 0.95 , 0.95 , 0.95 , 0.95 , 0.95 ,\n", " 0.95 , 0.95833333, 0.95833333, 0.95833333, 0.95 ,\n", " 0.95 , 0.95 , 0.95 , 0.95 , 0.95 ,\n", " 0.95 , 0.95833333, 0.95833333, 0.95 , 0.95 ,\n", " 0.95 , 0.95 , 0.95 , 0.95 ]),\n", " 'std_test_score': array([0.03118048, 0.03118048, 0.03118048, 0.03118048, 0.03118048,\n", " 0.03118048, 0.03118048, 0.03118048, 0.03118048, 0.02635231,\n", " 0.02635231, 0.02635231, 0.03118048, 0.03118048, 0.03118048,\n", " 0.03118048, 0.03118048, 0.03118048, 0.0372678 , 0.02635231,\n", " 0.02635231, 0.03118048, 0.03118048, 0.03118048, 0.03118048,\n", " 0.03118048, 0.03118048, 0.03118048, 0.03118048, 0.03118048,\n", " 0.03118048, 0.03118048, 0.03118048, 0.03118048, 0.03118048,\n", " 0.03118048, 0.02635231, 0.02635231, 0.02635231, 0.03118048,\n", " 0.03118048, 0.03118048, 0.03118048, 0.03118048, 0.03118048,\n", " 0.03118048, 0.02635231, 0.02635231, 0.03118048, 0.03118048,\n", " 0.03118048, 0.03118048, 0.03118048, 0.03118048]),\n", " 'rank_test_score': array([12, 12, 12, 12, 12, 12, 12, 12, 12, 1, 1, 1, 12, 12, 12, 12, 12,\n", " 12, 11, 1, 1, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12,\n", " 12, 12, 1, 1, 1, 12, 12, 12, 12, 12, 12, 12, 1, 1, 12, 12, 12,\n", " 12, 12, 12], dtype=int32)}" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dt_opt.cv_results_" ] }, { "cell_type": "markdown", "id": "5ed77f59", "metadata": {}, "source": [ "We can reformat it into a dataframe for further analysis." ] }, { "cell_type": "code", "execution_count": 9, "id": "15225c88", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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mean_fit_timestd_fit_timemean_score_timestd_score_timeparam_criterionparam_max_depthparam_min_samples_leafparamssplit0_test_scoresplit1_test_scoresplit2_test_scoresplit3_test_scoresplit4_test_scoremean_test_scorestd_test_scorerank_test_score
00.0003930.0000670.0002470.000043gini22{'criterion': 'gini', 'max_depth': 2, 'min_sam...0.9583331.00.9166670.9166670.9583330.950.0311812
10.0003490.0000080.0002220.000003gini24{'criterion': 'gini', 'max_depth': 2, 'min_sam...0.9583331.00.9166670.9166670.9583330.950.0311812
\n", "
" ], "text/plain": [ " mean_fit_time std_fit_time mean_score_time std_score_time \\\n", "0 0.000393 0.000067 0.000247 0.000043 \n", "1 0.000349 0.000008 0.000222 0.000003 \n", "\n", " param_criterion param_max_depth param_min_samples_leaf \\\n", "0 gini 2 2 \n", "1 gini 2 4 \n", "\n", " params split0_test_score \\\n", "0 {'criterion': 'gini', 'max_depth': 2, 'min_sam... 0.958333 \n", "1 {'criterion': 'gini', 'max_depth': 2, 'min_sam... 0.958333 \n", "\n", " split1_test_score split2_test_score split3_test_score split4_test_score \\\n", "0 1.0 0.916667 0.916667 0.958333 \n", "1 1.0 0.916667 0.916667 0.958333 \n", "\n", " mean_test_score std_test_score rank_test_score \n", "0 0.95 0.03118 12 \n", "1 0.95 0.03118 12 " ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dt_df = pd.DataFrame(dt_opt.cv_results_)\n", "dt_df.head(2)" ] }, { "cell_type": "markdown", "id": "801a49f6", "metadata": {}, "source": [ "```{admonition} Correction\n", "The parameters in this function were in the wrong \n", "order in this function in class\n", "```\n", "I changed the markers and the color of the error bars for readability." ] }, { "cell_type": "code", "execution_count": 10, "id": "da8d2d53", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "filenames": { "image/png": "/home/runner/work/BrownFall21/BrownFall21/_build/jupyter_execute/notes/2021-11-15_18_0.png" }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.errorbar(x=dt_df['mean_fit_time'],y=dt_df['mean_score_time'],\n", " xerr=dt_df['std_fit_time'],yerr=dt_df['std_score_time'],\n", " marker='s',ecolor='r')\n", "plt.xlabel('fit time')\n", "plt.ylabel('score time')\n", "# save the limits so we can reuse them\n", "xmin, xmax, ymin, ymax = plt.axis()" ] }, { "cell_type": "markdown", "id": "287d1a1f", "metadata": {}, "source": [ "The \"points\" are at the mean fit and score times. The lines are the \"standard deviation\" or how much we expect that number to vary, since means are an estimate. \n", "Because the data shows an upward trend, this plot tells us that mostly, the models that are slower to fit are also slower to apply. This makes sense for decision trees, deeper trees take longer to learn and longer to traverse when predicting. \n", "Because the error bars mostly overlap the other points, this tells us that mostly the variation in time is not a reliable difference. If we re-ran the GridSearch, we could get them in different orders. \n", "\n", "To interpret the error bar plot, let's look at a line plot of just the means, with the same limits so that it's easier to compare to the plot above." ] }, { "cell_type": "code", "execution_count": 11, "id": "816c4900", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.0003190333141088638, 0.00046708042490480804)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "filenames": { "image/png": "/home/runner/work/BrownFall21/BrownFall21/_build/jupyter_execute/notes/2021-11-15_20_1.png" }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(dt_df['mean_fit_time'],\n", " dt_df['mean_score_time'], marker='s')\n", "plt.xlabel('fit time')\n", "plt.ylabel('score time')\n", "# match the axis limits to above\n", "plt.ylim(ymin, ymax)\n", "plt.xlim(xmin,xmax)" ] }, { "cell_type": "markdown", "id": "eb506ead", "metadata": {}, "source": [ "this plot shows the mean times, without the error bars." ] }, { "cell_type": "code", "execution_count": 12, "id": "a9f0c649", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "filenames": { "image/png": "/home/runner/work/BrownFall21/BrownFall21/_build/jupyter_execute/notes/2021-11-15_22_1.png" }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "dt_df['mean_test_score'].plot(kind='bar')" ] }, { "cell_type": "code", "execution_count": 13, "id": "33453cf4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 0.950000\n", "1 0.950000\n", "2 0.950000\n", "3 0.950000\n", "4 0.950000\n", "5 0.950000\n", "6 0.950000\n", "7 0.950000\n", "8 0.950000\n", "9 0.958333\n", "10 0.958333\n", "11 0.958333\n", "12 0.950000\n", "13 0.950000\n", "14 0.950000\n", "15 0.950000\n", "16 0.950000\n", "17 0.950000\n", "18 0.958333\n", "19 0.958333\n", "20 0.958333\n", "21 0.950000\n", "22 0.950000\n", "23 0.950000\n", "24 0.950000\n", "25 0.950000\n", "26 0.950000\n", "27 0.950000\n", "28 0.950000\n", "29 0.950000\n", "30 0.950000\n", "31 0.950000\n", "32 0.950000\n", "33 0.950000\n", "34 0.950000\n", "35 0.950000\n", "36 0.958333\n", "37 0.958333\n", "38 0.958333\n", "39 0.950000\n", "40 0.950000\n", "41 0.950000\n", "42 0.950000\n", "43 0.950000\n", "44 0.950000\n", "45 0.950000\n", "46 0.958333\n", "47 0.958333\n", "48 0.950000\n", "49 0.950000\n", "50 0.950000\n", "51 0.950000\n", "52 0.950000\n", "53 0.950000\n", "Name: mean_test_score, dtype: float64" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dt_df['mean_test_score']" ] }, { "cell_type": "markdown", "id": "5f76bcb3", "metadata": {}, "source": [ "Now let's compare with a different model, we'll use the parameter optimized version for that model." ] }, { "cell_type": "code", "execution_count": 14, "id": "d6707a20", "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'GridSearchCV' is not defined", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", "Input \u001b[0;32mIn [14]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m svm_clf \u001b[38;5;241m=\u001b[39m svm\u001b[38;5;241m.\u001b[39mSVC()\n\u001b[1;32m 2\u001b[0m param_grid \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mkernel\u001b[39m\u001b[38;5;124m'\u001b[39m:[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mlinear\u001b[39m\u001b[38;5;124m'\u001b[39m,\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrbf\u001b[39m\u001b[38;5;124m'\u001b[39m], \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mC\u001b[39m\u001b[38;5;124m'\u001b[39m:[\u001b[38;5;241m.5\u001b[39m, \u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m10\u001b[39m]}\n\u001b[0;32m----> 3\u001b[0m svm_opt \u001b[38;5;241m=\u001b[39m \u001b[43mGridSearchCV\u001b[49m(svm_clf,param_grid,)\n", "\u001b[0;31mNameError\u001b[0m: name 'GridSearchCV' is not defined" ] } ], "source": [ "svm_clf = svm.SVC()\n", "param_grid = {'kernel':['linear','rbf'], 'C':[.5, 1, 10]}\n", "svm_opt = GridSearchCV(svm_clf,param_grid,)" ] }, { "cell_type": "markdown", "id": "652438d7", "metadata": {}, "source": [ "The error above is because we didn't import `GridSearchCV` directly today, we imported the whole `model_selection` module, so we have to use that in order to access the class." ] }, { "cell_type": "code", "execution_count": 15, "id": "c2d72dbb", "metadata": {}, "outputs": [], "source": [ "svm_clf = svm.SVC()\n", "param_grid = {'kernel':['linear','rbf'], 'C':[.5, .75,1,2,5,7, 10]}\n", "svm_opt = model_selection.GridSearchCV(svm_clf,param_grid,cv=10)" ] }, { "cell_type": "code", "execution_count": 16, "id": "db1fa584", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "module" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(model_selection)" ] }, { "cell_type": "code", "execution_count": 17, "id": "608d4625", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'scoring': None,\n", " 'estimator': DecisionTreeClassifier(),\n", " 'n_jobs': None,\n", " 'refit': True,\n", " 'cv': None,\n", " 'verbose': 0,\n", " 'pre_dispatch': '2*n_jobs',\n", " 'error_score': nan,\n", " 'return_train_score': False,\n", " 'param_grid': {'criterion': ['gini', 'entropy'],\n", " 'max_depth': [2, 3, 4],\n", " 'min_samples_leaf': [2, 4, 6, 8, 10, 12, 14, 16, 18]},\n", " 'multimetric_': False,\n", " 'best_index_': 9,\n", " 'best_score_': 0.9583333333333334,\n", " 'best_params_': {'criterion': 'gini', 'max_depth': 3, 'min_samples_leaf': 2},\n", " 'best_estimator_': DecisionTreeClassifier(max_depth=3, min_samples_leaf=2),\n", " 'refit_time_': 0.00036978721618652344,\n", " 'scorer_': ,\n", " 'cv_results_': {'mean_fit_time': array([0.00039306, 0.00034938, 0.00034437, 0.000349 , 0.00034513,\n", " 0.00034647, 0.00034838, 0.00034761, 0.00035162, 0.00038896,\n", " 0.00035572, 0.00034981, 0.00035625, 0.00034728, 0.00034947,\n", " 0.000349 , 0.00034842, 0.00034723, 0.00035934, 0.00035663,\n", " 0.00035768, 0.00035028, 0.00035329, 0.00034971, 0.00035257,\n", " 0.00035281, 0.0003489 , 0.00035853, 0.00035596, 0.00035648,\n", " 0.0003509 , 0.00035429, 0.00035157, 0.00035481, 0.000353 ,\n", " 0.000348 , 0.00036955, 0.00036836, 0.00036283, 0.00036693,\n", " 0.00035868, 0.00036645, 0.0003583 , 0.00035987, 0.00035768,\n", " 0.0003799 , 0.00037074, 0.00036554, 0.00036526, 0.00036535,\n", " 0.00036039, 0.00036349, 0.0003541 , 0.00035987]),\n", " 'std_fit_time': array([6.72941413e-05, 8.11268045e-06, 3.81648499e-06, 8.66270211e-06,\n", " 4.09525119e-06, 7.54458255e-06, 3.91587547e-06, 3.24809768e-06,\n", " 8.48636188e-06, 3.16120380e-05, 9.03854864e-06, 5.72204590e-07,\n", " 1.01033231e-05, 1.15430054e-06, 3.76187952e-06, 2.99839209e-06,\n", " 3.01050074e-06, 5.62304040e-06, 3.37646503e-06, 2.38609238e-06,\n", " 8.43530189e-06, 3.58548569e-06, 9.80059873e-06, 3.46618306e-06,\n", " 7.93385961e-06, 8.40289362e-06, 4.56769181e-06, 8.42451299e-06,\n", " 1.52289576e-06, 9.17140216e-06, 1.24709099e-06, 9.49133553e-06,\n", " 3.84911271e-06, 9.27249058e-06, 1.04203921e-05, 5.76164530e-07,\n", " 2.71839005e-06, 7.51680506e-06, 4.13338336e-06, 1.24590533e-05,\n", " 2.32430603e-06, 1.30709058e-05, 4.18259854e-06, 8.42208358e-06,\n", " 3.00218129e-06, 8.78804544e-06, 8.06545774e-06, 5.73474746e-06,\n", " 6.13435761e-06, 6.88794535e-06, 3.65208705e-06, 1.15504882e-05,\n", " 3.65768603e-06, 1.35648229e-05]),\n", " 'mean_score_time': array([0.00024743, 0.00022221, 0.00022173, 0.00022287, 0.00021906,\n", " 0.0002223 , 0.00025768, 0.00025663, 0.00026088, 0.00023623,\n", " 0.00022044, 0.00022583, 0.0002223 , 0.00022435, 0.00022206,\n", " 0.00022521, 0.0002264 , 0.00022044, 0.00022712, 0.00022411,\n", " 0.0002233 , 0.00022063, 0.00022211, 0.00022278, 0.00022445,\n", " 0.00022144, 0.00022278, 0.00022326, 0.00022783, 0.00021963,\n", " 0.00022578, 0.00021935, 0.00022659, 0.00022411, 0.00022068,\n", " 0.00022483, 0.00022292, 0.00022302, 0.00022135, 0.0002223 ,\n", " 0.00022497, 0.00022588, 0.00022087, 0.00022306, 0.00022216,\n", " 0.00022306, 0.00022664, 0.00022197, 0.00022216, 0.00022125,\n", " 0.00022173, 0.00022187, 0.00022726, 0.00022092]),\n", " 'std_score_time': array([4.32319092e-05, 3.19160472e-06, 3.24809768e-06, 3.58548569e-06,\n", " 8.03580262e-07, 3.42062119e-06, 1.14242063e-06, 2.86102295e-07,\n", " 6.98432161e-06, 1.09738869e-05, 9.84180805e-07, 7.53855268e-06,\n", " 3.78657946e-06, 8.77561758e-06, 3.19017957e-06, 1.06726340e-05,\n", " 1.00810187e-05, 1.04033586e-06, 8.75174819e-06, 5.33759511e-06,\n", " 6.38961792e-06, 1.78161065e-06, 2.86340614e-06, 2.90675176e-06,\n", " 3.54466773e-06, 1.61280961e-06, 3.74431046e-06, 4.77218475e-06,\n", " 7.51014740e-06, 1.01601008e-06, 8.88633093e-06, 5.43678010e-07,\n", " 6.88794535e-06, 8.95641852e-06, 8.86968386e-07, 8.29751462e-06,\n", " 1.60291064e-06, 1.86271906e-06, 1.24891289e-06, 1.30238536e-06,\n", " 9.11569983e-06, 9.07445041e-06, 9.36836372e-07, 2.85545443e-06,\n", " 5.09122765e-07, 2.26986508e-06, 7.90112121e-06, 1.28834306e-06,\n", " 2.66943646e-06, 1.16800773e-06, 7.83523403e-07, 1.68991519e-06,\n", " 8.43260596e-06, 1.16410786e-06]),\n", " 'param_criterion': masked_array(data=['gini', 'gini', 'gini', 'gini', 'gini', 'gini', 'gini',\n", " 'gini', 'gini', 'gini', 'gini', 'gini', 'gini', 'gini',\n", " 'gini', 'gini', 'gini', 'gini', 'gini', 'gini', 'gini',\n", " 'gini', 'gini', 'gini', 'gini', 'gini', 'gini',\n", " 'entropy', 'entropy', 'entropy', 'entropy', 'entropy',\n", " 'entropy', 'entropy', 'entropy', 'entropy', 'entropy',\n", " 'entropy', 'entropy', 'entropy', 'entropy', 'entropy',\n", " 'entropy', 'entropy', 'entropy', 'entropy', 'entropy',\n", " 'entropy', 'entropy', 'entropy', 'entropy', 'entropy',\n", " 'entropy', 'entropy'],\n", " mask=[False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False],\n", " fill_value='?',\n", " dtype=object),\n", " 'param_max_depth': masked_array(data=[2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3,\n", " 4, 4, 4, 4, 4, 4, 4, 4, 4, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n", " 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4],\n", " mask=[False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False],\n", " fill_value='?',\n", " dtype=object),\n", " 'param_min_samples_leaf': masked_array(data=[2, 4, 6, 8, 10, 12, 14, 16, 18, 2, 4, 6, 8, 10, 12, 14,\n", " 16, 18, 2, 4, 6, 8, 10, 12, 14, 16, 18, 2, 4, 6, 8, 10,\n", " 12, 14, 16, 18, 2, 4, 6, 8, 10, 12, 14, 16, 18, 2, 4,\n", " 6, 8, 10, 12, 14, 16, 18],\n", " mask=[False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False, False, False,\n", " False, False, False, False, False, False],\n", " fill_value='?',\n", " dtype=object),\n", " 'params': [{'criterion': 'gini', 'max_depth': 2, 'min_samples_leaf': 2},\n", " {'criterion': 'gini', 'max_depth': 2, 'min_samples_leaf': 4},\n", " {'criterion': 'gini', 'max_depth': 2, 'min_samples_leaf': 6},\n", " {'criterion': 'gini', 'max_depth': 2, 'min_samples_leaf': 8},\n", " {'criterion': 'gini', 'max_depth': 2, 'min_samples_leaf': 10},\n", " {'criterion': 'gini', 'max_depth': 2, 'min_samples_leaf': 12},\n", " {'criterion': 'gini', 'max_depth': 2, 'min_samples_leaf': 14},\n", " {'criterion': 'gini', 'max_depth': 2, 'min_samples_leaf': 16},\n", " {'criterion': 'gini', 'max_depth': 2, 'min_samples_leaf': 18},\n", " {'criterion': 'gini', 'max_depth': 3, 'min_samples_leaf': 2},\n", " {'criterion': 'gini', 'max_depth': 3, 'min_samples_leaf': 4},\n", " {'criterion': 'gini', 'max_depth': 3, 'min_samples_leaf': 6},\n", " {'criterion': 'gini', 'max_depth': 3, 'min_samples_leaf': 8},\n", " {'criterion': 'gini', 'max_depth': 3, 'min_samples_leaf': 10},\n", " {'criterion': 'gini', 'max_depth': 3, 'min_samples_leaf': 12},\n", " {'criterion': 'gini', 'max_depth': 3, 'min_samples_leaf': 14},\n", " {'criterion': 'gini', 'max_depth': 3, 'min_samples_leaf': 16},\n", " {'criterion': 'gini', 'max_depth': 3, 'min_samples_leaf': 18},\n", " {'criterion': 'gini', 'max_depth': 4, 'min_samples_leaf': 2},\n", " {'criterion': 'gini', 'max_depth': 4, 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'min_samples_leaf': 6},\n", " {'criterion': 'entropy', 'max_depth': 4, 'min_samples_leaf': 8},\n", " {'criterion': 'entropy', 'max_depth': 4, 'min_samples_leaf': 10},\n", " {'criterion': 'entropy', 'max_depth': 4, 'min_samples_leaf': 12},\n", " {'criterion': 'entropy', 'max_depth': 4, 'min_samples_leaf': 14},\n", " {'criterion': 'entropy', 'max_depth': 4, 'min_samples_leaf': 16},\n", " {'criterion': 'entropy', 'max_depth': 4, 'min_samples_leaf': 18}],\n", " 'split0_test_score': array([0.95833333, 0.95833333, 0.95833333, 0.95833333, 0.95833333,\n", " 0.95833333, 0.95833333, 0.95833333, 0.95833333, 0.95833333,\n", " 0.95833333, 0.95833333, 0.95833333, 0.95833333, 0.95833333,\n", " 0.95833333, 0.95833333, 0.95833333, 0.95833333, 0.95833333,\n", " 0.95833333, 0.95833333, 0.95833333, 0.95833333, 0.95833333,\n", " 0.95833333, 0.95833333, 0.95833333, 0.95833333, 0.95833333,\n", " 0.95833333, 0.95833333, 0.95833333, 0.95833333, 0.95833333,\n", " 0.95833333, 0.95833333, 0.95833333, 0.95833333, 0.95833333,\n", " 0.95833333, 0.95833333, 0.95833333, 0.95833333, 0.95833333,\n", " 0.95833333, 0.95833333, 0.95833333, 0.95833333, 0.95833333,\n", " 0.95833333, 0.95833333, 0.95833333, 0.95833333]),\n", " 'split1_test_score': array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", " 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n", " 1., 1., 1.]),\n", " 'split2_test_score': array([0.91666667, 0.91666667, 0.91666667, 0.91666667, 0.91666667,\n", " 0.91666667, 0.91666667, 0.91666667, 0.91666667, 0.91666667,\n", " 0.91666667, 0.91666667, 0.91666667, 0.91666667, 0.91666667,\n", " 0.91666667, 0.91666667, 0.91666667, 0.91666667, 0.91666667,\n", " 0.91666667, 0.91666667, 0.91666667, 0.91666667, 0.91666667,\n", " 0.91666667, 0.91666667, 0.91666667, 0.91666667, 0.91666667,\n", " 0.91666667, 0.91666667, 0.91666667, 0.91666667, 0.91666667,\n", " 0.91666667, 0.91666667, 0.91666667, 0.91666667, 0.91666667,\n", " 0.91666667, 0.91666667, 0.91666667, 0.91666667, 0.91666667,\n", " 0.91666667, 0.91666667, 0.91666667, 0.91666667, 0.91666667,\n", " 0.91666667, 0.91666667, 0.91666667, 0.91666667]),\n", " 'split3_test_score': array([0.91666667, 0.91666667, 0.91666667, 0.91666667, 0.91666667,\n", " 0.91666667, 0.91666667, 0.91666667, 0.91666667, 0.95833333,\n", " 0.95833333, 0.95833333, 0.91666667, 0.91666667, 0.91666667,\n", " 0.91666667, 0.91666667, 0.91666667, 0.91666667, 0.95833333,\n", " 0.95833333, 0.91666667, 0.91666667, 0.91666667, 0.91666667,\n", " 0.91666667, 0.91666667, 0.91666667, 0.91666667, 0.91666667,\n", " 0.91666667, 0.91666667, 0.91666667, 0.91666667, 0.91666667,\n", " 0.91666667, 0.95833333, 0.95833333, 0.95833333, 0.91666667,\n", " 0.91666667, 0.91666667, 0.91666667, 0.91666667, 0.91666667,\n", " 0.91666667, 0.95833333, 0.95833333, 0.91666667, 0.91666667,\n", " 0.91666667, 0.91666667, 0.91666667, 0.91666667]),\n", " 'split4_test_score': array([0.95833333, 0.95833333, 0.95833333, 0.95833333, 0.95833333,\n", " 0.95833333, 0.95833333, 0.95833333, 0.95833333, 0.95833333,\n", " 0.95833333, 0.95833333, 0.95833333, 0.95833333, 0.95833333,\n", " 0.95833333, 0.95833333, 0.95833333, 1. , 0.95833333,\n", " 0.95833333, 0.95833333, 0.95833333, 0.95833333, 0.95833333,\n", " 0.95833333, 0.95833333, 0.95833333, 0.95833333, 0.95833333,\n", " 0.95833333, 0.95833333, 0.95833333, 0.95833333, 0.95833333,\n", " 0.95833333, 0.95833333, 0.95833333, 0.95833333, 0.95833333,\n", " 0.95833333, 0.95833333, 0.95833333, 0.95833333, 0.95833333,\n", " 0.95833333, 0.95833333, 0.95833333, 0.95833333, 0.95833333,\n", " 0.95833333, 0.95833333, 0.95833333, 0.95833333]),\n", " 'mean_test_score': array([0.95 , 0.95 , 0.95 , 0.95 , 0.95 ,\n", " 0.95 , 0.95 , 0.95 , 0.95 , 0.95833333,\n", " 0.95833333, 0.95833333, 0.95 , 0.95 , 0.95 ,\n", " 0.95 , 0.95 , 0.95 , 0.95833333, 0.95833333,\n", " 0.95833333, 0.95 , 0.95 , 0.95 , 0.95 ,\n", " 0.95 , 0.95 , 0.95 , 0.95 , 0.95 ,\n", " 0.95 , 0.95 , 0.95 , 0.95 , 0.95 ,\n", " 0.95 , 0.95833333, 0.95833333, 0.95833333, 0.95 ,\n", " 0.95 , 0.95 , 0.95 , 0.95 , 0.95 ,\n", " 0.95 , 0.95833333, 0.95833333, 0.95 , 0.95 ,\n", " 0.95 , 0.95 , 0.95 , 0.95 ]),\n", " 'std_test_score': array([0.03118048, 0.03118048, 0.03118048, 0.03118048, 0.03118048,\n", " 0.03118048, 0.03118048, 0.03118048, 0.03118048, 0.02635231,\n", " 0.02635231, 0.02635231, 0.03118048, 0.03118048, 0.03118048,\n", " 0.03118048, 0.03118048, 0.03118048, 0.0372678 , 0.02635231,\n", " 0.02635231, 0.03118048, 0.03118048, 0.03118048, 0.03118048,\n", " 0.03118048, 0.03118048, 0.03118048, 0.03118048, 0.03118048,\n", " 0.03118048, 0.03118048, 0.03118048, 0.03118048, 0.03118048,\n", " 0.03118048, 0.02635231, 0.02635231, 0.02635231, 0.03118048,\n", " 0.03118048, 0.03118048, 0.03118048, 0.03118048, 0.03118048,\n", " 0.03118048, 0.02635231, 0.02635231, 0.03118048, 0.03118048,\n", " 0.03118048, 0.03118048, 0.03118048, 0.03118048]),\n", " 'rank_test_score': array([12, 12, 12, 12, 12, 12, 12, 12, 12, 1, 1, 1, 12, 12, 12, 12, 12,\n", " 12, 11, 1, 1, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12,\n", " 12, 12, 1, 1, 1, 12, 12, 12, 12, 12, 12, 12, 1, 1, 12, 12, 12,\n", " 12, 12, 12], dtype=int32)},\n", " 'n_splits_': 5}" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dt_opt.__dict__" ] }, { "cell_type": "markdown", "id": "1390ca8e", "metadata": {}, "source": [ "This doesn't have attributes yet, even though they are the same type, because we have not fit it tot data yet." ] }, { "cell_type": "code", "execution_count": 18, "id": "909d33b5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(sklearn.model_selection._search.GridSearchCV,\n", " sklearn.model_selection._search.GridSearchCV)" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(svm_opt), type(dt_opt)" ] }, { "cell_type": "markdown", "id": "1102e164", "metadata": {}, "source": [ "Now we can fit the model to the training data of this second model." ] }, { "cell_type": "code", "execution_count": 19, "id": "6901d229", "metadata": {}, "outputs": [], "source": [ "# fit the model and put the CV results in a dataframe\n", "svm_opt.fit(iris_X_train,iris_y_train)\n", "sv_df = pd.DataFrame(svm_opt.cv_results_)" ] }, { "cell_type": "code", "execution_count": 20, "id": "912d4c56", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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mean_fit_timestd_fit_timemean_score_timestd_score_timeparam_Cparam_kernelparamssplit0_test_scoresplit1_test_scoresplit2_test_scoresplit3_test_scoresplit4_test_scoresplit5_test_scoresplit6_test_scoresplit7_test_scoresplit8_test_scoresplit9_test_scoremean_test_scorestd_test_scorerank_test_score
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10.0006060.0000090.0002970.0000080.5rbf{'C': 0.5, 'kernel': 'rbf'}1.00.9166670.9166671.01.00.9166670.9166670.9166670.9166671.00.9500000.04082514
\n", "
" ], "text/plain": [ " mean_fit_time std_fit_time mean_score_time std_score_time param_C \\\n", "0 0.000508 0.000057 0.000272 0.000016 0.5 \n", "1 0.000606 0.000009 0.000297 0.000008 0.5 \n", "\n", " param_kernel params split0_test_score \\\n", "0 linear {'C': 0.5, 'kernel': 'linear'} 1.0 \n", "1 rbf {'C': 0.5, 'kernel': 'rbf'} 1.0 \n", "\n", " split1_test_score split2_test_score split3_test_score split4_test_score \\\n", "0 1.000000 1.000000 1.0 1.0 \n", "1 0.916667 0.916667 1.0 1.0 \n", "\n", " split5_test_score split6_test_score split7_test_score split8_test_score \\\n", "0 1.000000 0.916667 0.916667 1.000000 \n", "1 0.916667 0.916667 0.916667 0.916667 \n", "\n", " split9_test_score mean_test_score std_test_score rank_test_score \n", "0 1.0 0.983333 0.033333 1 \n", "1 1.0 0.950000 0.040825 14 " ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sv_df.head(2)" ] }, { "cell_type": "code", "execution_count": 21, "id": "88013de9", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "filenames": { "image/png": "/home/runner/work/BrownFall21/BrownFall21/_build/jupyter_execute/notes/2021-11-15_35_1.png" }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.errorbar(x=sv_df['mean_fit_time'],xerr=sv_df['std_fit_time'],\n", " y=sv_df['mean_score_time'],yerr=sv_df['std_score_time'])" ] }, { "cell_type": "code", "execution_count": 22, "id": "974c9fb8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time',\n", " 'param_C', 'param_kernel', 'params', 'split0_test_score',\n", " 'split1_test_score', 'split2_test_score', 'split3_test_score',\n", " 'split4_test_score', 'split5_test_score', 'split6_test_score',\n", " 'split7_test_score', 'split8_test_score', 'split9_test_score',\n", " 'mean_test_score', 'std_test_score', 'rank_test_score'],\n", " dtype='object')" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sv_df.columns" ] }, { "cell_type": "markdown", "id": "be40ab10", "metadata": {}, "source": [ "We can see if the models that take longer to fit or score perform better." ] }, { "cell_type": "code", "execution_count": 23, "id": "3f8723c4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "filenames": { "image/png": "/home/runner/work/BrownFall21/BrownFall21/_build/jupyter_execute/notes/2021-11-15_38_1.png" }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "svm_time = sv_df.melt(id_vars=['param_C', 'param_kernel', 'params',],\n", " value_vars=['mean_fit_time', 'std_fit_time', 'mean_score_time', 'std_score_time'])\n", "sns.lmplot(data=sv_df, x='mean_fit_time',y='mean_test_score',\n", " hue='param_kernel',fit_reg=False)" ] }, { "cell_type": "markdown", "id": "46959e80", "metadata": {}, "source": [ "This looks like mostly no." ] }, { "cell_type": "code", "execution_count": 24, "id": "faa2a5f6", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "filenames": { "image/png": "/home/runner/work/BrownFall21/BrownFall21/_build/jupyter_execute/notes/2021-11-15_40_1.png" }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.lmplot(data=sv_df, x='mean_score_time',y='mean_test_score',\n", " hue='param_kernel',fit_reg=False)" ] }, { "cell_type": "markdown", "id": "ad6c3ffe", "metadata": {}, "source": [ "Again, for score time, the slower models don't appear to be better. Remember though the time differences weren't that different. \n", "\n", "```{admonition} Try it yourself\n", "Try this same analysis for the decision tree, does it matter there?\n", "```" ] }, { "cell_type": "code", "execution_count": 25, "id": "8bdf2e6a", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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param_Cparam_kernelparamsvariablescore
00.5linear{'C': 0.5, 'kernel': 'linear'}split0_test_score1.0
10.5rbf{'C': 0.5, 'kernel': 'rbf'}split0_test_score1.0
20.75linear{'C': 0.75, 'kernel': 'linear'}split0_test_score1.0
30.75rbf{'C': 0.75, 'kernel': 'rbf'}split0_test_score1.0
41linear{'C': 1, 'kernel': 'linear'}split0_test_score1.0
\n", "
" ], "text/plain": [ " param_C param_kernel params variable \\\n", "0 0.5 linear {'C': 0.5, 'kernel': 'linear'} split0_test_score \n", "1 0.5 rbf {'C': 0.5, 'kernel': 'rbf'} split0_test_score \n", "2 0.75 linear {'C': 0.75, 'kernel': 'linear'} split0_test_score \n", "3 0.75 rbf {'C': 0.75, 'kernel': 'rbf'} split0_test_score \n", "4 1 linear {'C': 1, 'kernel': 'linear'} split0_test_score \n", "\n", " score \n", "0 1.0 \n", "1 1.0 \n", "2 1.0 \n", "3 1.0 \n", "4 1.0 " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sv_df_scores = sv_df.melt(id_vars=['param_C', 'param_kernel', 'params',],\n", " value_vars=['split0_test_score',\n", " 'split1_test_score', 'split2_test_score', 'split3_test_score',\n", " 'split4_test_score'], value_name='score')\n", "sv_df_scores.head()" ] }, { "cell_type": "code", "execution_count": 26, "id": "7d10a20a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "filenames": { "image/png": "/home/runner/work/BrownFall21/BrownFall21/_build/jupyter_execute/notes/2021-11-15_43_1.png" }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sns.catplot(data=sv_df_scores,x='param_C',y='score',\n", " col='param_kernel')" ] }, { "cell_type": "markdown", "id": "293ca1b4", "metadata": {}, "source": [ "```{admonition} Try it yourself\n", "Try interpretting the plot above, what does it say? what can you conclude from it. \n", "```" ] }, { "cell_type": "code", "execution_count": 27, "id": "89ae55e8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "filenames": { "image/png": "/home/runner/work/BrownFall21/BrownFall21/_build/jupyter_execute/notes/2021-11-15_45_1.png" }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "dt_df['mean_test_score'].plot(kind='bar')" ] }, { "cell_type": "code", "execution_count": 28, "id": "cb49a6bb", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "filenames": { "image/png": "/home/runner/work/BrownFall21/BrownFall21/_build/jupyter_execute/notes/2021-11-15_46_1.png" }, "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sv_df['mean_test_score'].plot(kind='bar')" ] }, { "cell_type": "markdown", "id": "bcc1cc9a", "metadata": {}, "source": [ "From these last two plots we see that the SVM performance is more sensitive to its parameters, where for the parameters tested, the decision tree is not impacted. \n", "\n", "What can we say based on this? We'll pick up from here on Wednesday." ] } ], "metadata": { "jupytext": { "text_representation": { "extension": ".md", "format_name": "myst", "format_version": 0.13, "jupytext_version": "1.10.3" } }, "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": "3.8.13" }, "source_map": [ 12, 16, 20, 32, 36, 38, 42, 45, 49, 53, 59, 61, 65, 67, 71, 73, 77, 80, 88, 96, 104, 112, 116, 120, 122, 126, 130, 134, 140, 144, 146, 150, 152, 156, 162, 166, 171, 173, 177, 182, 186, 189, 197, 206, 209, 215, 219, 221 ] }, "nbformat": 4, "nbformat_minor": 5 }