{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "5a1fa66c-e538-4215-9ad6-ceedecef6645", "metadata": {}, "outputs": [], "source": [ "from sklearn.datasets import load_iris" ] }, { "cell_type": "code", "execution_count": 2, "id": "17a95bdb-6bb2-4c58-bacc-2ffa368acfdf", "metadata": {}, "outputs": [], "source": [ "iris = load_iris()" ] }, { "cell_type": "code", "execution_count": 3, "id": "fd41f149-4238-41a2-81b6-ce69d2a7e416", "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 4, "id": "4b907051-63e5-4c3f-a0e5-b951d5060b69", "metadata": {}, "outputs": [], "source": [ "df = pd.DataFrame(iris.data, columns=iris.feature_names)" ] }, { "cell_type": "code", "execution_count": 5, "id": "dc2c3117-18f3-4ab8-b00d-046afc9bb204", "metadata": {}, "outputs": [], "source": [ "df[\"flower\"] = iris.target" ] }, { "cell_type": "code", "execution_count": 6, "id": "c51f710f-5fdb-4ef8-8108-f9030698c6c7", "metadata": {}, "outputs": [], "source": [ "df[\"flower\"] = df[\"flower\"].apply(lambda x: iris.target_names[x])" ] }, { "cell_type": "code", "execution_count": 7, "id": "259932d6-19fd-4113-917b-55c3d4c0643c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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sepal length (cm)sepal width (cm)petal length (cm)petal width (cm)flower
05.13.51.40.2setosa
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" ], "text/plain": [ " sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) \\\n", "0 5.1 3.5 1.4 0.2 \n", "1 4.9 3.0 1.4 0.2 \n", "2 4.7 3.2 1.3 0.2 \n", "3 4.6 3.1 1.5 0.2 \n", "4 5.0 3.6 1.4 0.2 \n", "\n", " flower \n", "0 setosa \n", "1 setosa \n", "2 setosa \n", "3 setosa \n", "4 setosa " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 8, "id": "d8e7edb4-7ae3-4baf-b196-2e6082c8dc5d", "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import train_test_split" ] }, { "cell_type": "code", "execution_count": 9, "id": "30bc7e98-6509-49b4-a88d-652437b308b5", "metadata": {}, "outputs": [], "source": [ "x_train, x_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2)" ] }, { "cell_type": "code", "execution_count": 10, "id": "2699689a-4f13-4186-92f8-ce325bac4e3a", "metadata": {}, "outputs": [], "source": [ "from sklearn.svm import SVC" ] }, { "cell_type": "code", "execution_count": 11, "id": "887940a9-5f38-408d-9b6c-771e51bef55e", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9333333333333333" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model = SVC(kernel='rbf', C=20, gamma='auto')\n", "model.fit(x_train, y_train)\n", "model.score(x_test, y_test)" ] }, { "cell_type": "code", "execution_count": 12, "id": "70fc50cc-f802-43b7-8d99-267569e9f920", "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import cross_val_score" ] }, { "cell_type": "code", "execution_count": 13, "id": "e5e5ef79-ed19-49d9-8b85-945c30a305f4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1. , 1. , 0.9 , 0.93333333, 1. ])" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cross_val_score(SVC(kernel='linear', C=20, gamma='auto'), iris.data, iris.target, cv=5)" ] }, { "cell_type": "code", "execution_count": 14, "id": "bc36f23b-8e1f-4d90-a1da-6541035bde30", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.96666667, 1. , 0.96666667, 0.96666667, 1. ])" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cross_val_score(SVC(kernel='rbf', C=10, gamma='auto'), iris.data, iris.target, cv=5)" ] }, { "cell_type": "code", "execution_count": 15, "id": "2d2e4747-7ca2-4195-9ea3-e4b473cb2ca5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.96666667, 1. , 0.9 , 0.96666667, 1. ])" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cross_val_score(SVC(kernel='rbf', C=20, gamma='auto'), iris.data, iris.target, cv=5)" ] }, { "cell_type": "code", "execution_count": 16, "id": "b3b7fb8d-f0df-48e3-b15d-0df4fbc13dcb", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "kernels = [\"rbf\", \"linear\"]\n", "C = [1, 10, 20]\n", "avg_score = {}\n", "for k in kernels:\n", " for c in C:\n", " cv_scores = cross_val_score(SVC(kernel=k, C=c, gamma='auto'), iris.data, iris.target, cv=5)\n", " avg_score[k+\"_\"+str(c)] = np.mean(cv_scores)" ] }, { "cell_type": "code", "execution_count": 17, "id": "897ec1e1-e14d-44c1-9a56-0181575d69d2", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'rbf_1': 0.9800000000000001,\n", " 'rbf_10': 0.9800000000000001,\n", " 'rbf_20': 0.9666666666666668,\n", " 'linear_1': 0.9800000000000001,\n", " 'linear_10': 0.9733333333333334,\n", " 'linear_20': 0.9666666666666666}" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "avg_score" ] }, { "cell_type": "code", "execution_count": 18, "id": "76c2fcbc-ea50-45e2-8994-8ec375807da2", "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import GridSearchCV" ] }, { "cell_type": "code", "execution_count": 19, "id": "740bea6d-442b-4c02-8037-986a50eff030", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
GridSearchCV(cv=5, estimator=SVC(gamma='auto'),\n",
       "             param_grid={'C': [1, 10, 20], 'kernel': ['rbf', 'linear']})
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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" ], "text/plain": [ "GridSearchCV(cv=5, estimator=SVC(gamma='auto'),\n", " param_grid={'C': [1, 10, 20], 'kernel': ['rbf', 'linear']})" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf = GridSearchCV(SVC(gamma='auto'),\n", " {'C': [1, 10, 20], \n", " 'kernel': ['rbf', 'linear']},\n", " cv=5,\n", " return_train_score=False)\n", "clf.fit(iris.data, iris.target)" ] }, { "cell_type": "code", "execution_count": 20, "id": "df47a4e7-bfbd-4670-a0b2-47acf9de4344", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'mean_fit_time': array([0.00140004, 0.00140204, 0.00119882, 0.00080657, 0.00059376,\n", " 0.00059996]),\n", " 'std_fit_time': array([0.00049091, 0.00049348, 0.00040064, 0.00040348, 0.00048495,\n", " 0.00048986]),\n", " 'mean_score_time': array([0.00060101, 0.0012032 , 0.00079985, 0.00060096, 0.00060592,\n", " 0.00039415]),\n", " 'std_score_time': array([0.00049072, 0.00040417, 0.00040044, 0.00049068, 0.00049487,\n", " 0.00048292]),\n", " 'param_C': masked_array(data=[1, 1, 10, 10, 20, 20],\n", " mask=[False, False, False, False, False, False],\n", " fill_value='?',\n", " dtype=object),\n", " 'param_kernel': masked_array(data=['rbf', 'linear', 'rbf', 'linear', 'rbf', 'linear'],\n", " mask=[False, False, False, False, False, False],\n", " fill_value='?',\n", " dtype=object),\n", " 'params': [{'C': 1, 'kernel': 'rbf'},\n", " {'C': 1, 'kernel': 'linear'},\n", " {'C': 10, 'kernel': 'rbf'},\n", " {'C': 10, 'kernel': 'linear'},\n", " {'C': 20, 'kernel': 'rbf'},\n", " {'C': 20, 'kernel': 'linear'}],\n", " 'split0_test_score': array([0.96666667, 0.96666667, 0.96666667, 1. , 0.96666667,\n", " 1. ]),\n", " 'split1_test_score': array([1., 1., 1., 1., 1., 1.]),\n", " 'split2_test_score': array([0.96666667, 0.96666667, 0.96666667, 0.9 , 0.9 ,\n", " 0.9 ]),\n", " 'split3_test_score': array([0.96666667, 0.96666667, 0.96666667, 0.96666667, 0.96666667,\n", " 0.93333333]),\n", " 'split4_test_score': array([1., 1., 1., 1., 1., 1.]),\n", " 'mean_test_score': array([0.98 , 0.98 , 0.98 , 0.97333333, 0.96666667,\n", " 0.96666667]),\n", " 'std_test_score': array([0.01632993, 0.01632993, 0.01632993, 0.03887301, 0.03651484,\n", " 0.0421637 ]),\n", " 'rank_test_score': array([1, 1, 1, 4, 5, 6])}" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf.cv_results_" ] }, { "cell_type": "code", "execution_count": 21, "id": "3842bced-5ac0-4bf2-9adf-e463bfa26db8", "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_scoremean_test_scorestd_test_scorerank_test_score
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" ], "text/plain": [ " mean_fit_time std_fit_time mean_score_time std_score_time param_C \\\n", "0 0.001400 0.000491 0.000601 0.000491 1 \n", "1 0.001402 0.000493 0.001203 0.000404 1 \n", "2 0.001199 0.000401 0.000800 0.000400 10 \n", "3 0.000807 0.000403 0.000601 0.000491 10 \n", "4 0.000594 0.000485 0.000606 0.000495 20 \n", "5 0.000600 0.000490 0.000394 0.000483 20 \n", "\n", " param_kernel params split0_test_score \\\n", "0 rbf {'C': 1, 'kernel': 'rbf'} 0.966667 \n", "1 linear {'C': 1, 'kernel': 'linear'} 0.966667 \n", "2 rbf {'C': 10, 'kernel': 'rbf'} 0.966667 \n", "3 linear {'C': 10, 'kernel': 'linear'} 1.000000 \n", "4 rbf {'C': 20, 'kernel': 'rbf'} 0.966667 \n", "5 linear {'C': 20, 'kernel': 'linear'} 1.000000 \n", "\n", " split1_test_score split2_test_score split3_test_score split4_test_score \\\n", "0 1.0 0.966667 0.966667 1.0 \n", "1 1.0 0.966667 0.966667 1.0 \n", "2 1.0 0.966667 0.966667 1.0 \n", "3 1.0 0.900000 0.966667 1.0 \n", "4 1.0 0.900000 0.966667 1.0 \n", "5 1.0 0.900000 0.933333 1.0 \n", "\n", " mean_test_score std_test_score rank_test_score \n", "0 0.980000 0.016330 1 \n", "1 0.980000 0.016330 1 \n", "2 0.980000 0.016330 1 \n", "3 0.973333 0.038873 4 \n", "4 0.966667 0.036515 5 \n", "5 0.966667 0.042164 6 " ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.DataFrame(clf.cv_results_)\n", "df" ] }, { "cell_type": "code", "execution_count": 23, "id": "9c093072-048b-4187-9dd4-b4042edd0633", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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param_Cparam_kernelmean_test_score
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" ], "text/plain": [ " param_C param_kernel mean_test_score\n", "0 1 rbf 0.980000\n", "1 1 linear 0.980000\n", "2 10 rbf 0.980000\n", "3 10 linear 0.973333\n", "4 20 rbf 0.966667\n", "5 20 linear 0.966667" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[[\"param_C\", \"param_kernel\", \"mean_test_score\"]]" ] }, { "cell_type": "code", "execution_count": 24, "id": "c184bb27-fc8b-45d4-80fd-39251da1ef3b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['__abstractmethods__',\n", " '__annotations__',\n", " '__class__',\n", " '__delattr__',\n", " '__dict__',\n", " '__dir__',\n", " '__doc__',\n", " '__eq__',\n", " '__format__',\n", " '__ge__',\n", " '__getattribute__',\n", " '__getstate__',\n", " '__gt__',\n", " '__hash__',\n", " '__init__',\n", " '__init_subclass__',\n", " '__le__',\n", " '__lt__',\n", " '__module__',\n", " '__ne__',\n", " '__new__',\n", " '__reduce__',\n", " '__reduce_ex__',\n", " '__repr__',\n", " '__setattr__',\n", " '__setstate__',\n", " '__sizeof__',\n", " '__sklearn_clone__',\n", " '__str__',\n", " '__subclasshook__',\n", " '__weakref__',\n", " '_abc_impl',\n", " '_build_request_for_signature',\n", " '_check_feature_names',\n", " '_check_n_features',\n", " '_check_refit_for_multimetric',\n", " '_estimator_type',\n", " '_format_results',\n", " '_get_default_requests',\n", " '_get_metadata_request',\n", " '_get_param_names',\n", " '_get_tags',\n", " '_more_tags',\n", " '_parameter_constraints',\n", " '_repr_html_',\n", " '_repr_html_inner',\n", " '_repr_mimebundle_',\n", " '_required_parameters',\n", " '_run_search',\n", " '_select_best_index',\n", " '_validate_data',\n", " '_validate_params',\n", " 'best_estimator_',\n", " 'best_index_',\n", " 'best_params_',\n", " 'best_score_',\n", " 'classes_',\n", " 'cv',\n", " 'cv_results_',\n", " 'decision_function',\n", " 'error_score',\n", " 'estimator',\n", " 'fit',\n", " 'get_metadata_routing',\n", " 'get_params',\n", " 'inverse_transform',\n", " 'multimetric_',\n", " 'n_features_in_',\n", " 'n_jobs',\n", " 'n_splits_',\n", " 'param_grid',\n", " 'pre_dispatch',\n", " 'predict',\n", " 'predict_log_proba',\n", " 'predict_proba',\n", " 'refit',\n", " 'refit_time_',\n", " 'return_train_score',\n", " 'score',\n", " 'score_samples',\n", " 'scorer_',\n", " 'scoring',\n", " 'set_fit_request',\n", " 'set_params',\n", " 'transform',\n", " 'verbose']" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dir(clf)" ] }, { "cell_type": "code", "execution_count": 25, "id": "451a4f85-38e9-4928-a43c-29a98ef11b9a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.9800000000000001" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf.best_score_" ] }, { "cell_type": "code", "execution_count": 26, "id": "0a18515f-7651-43a0-a8c6-b10888140ab1", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'C': 1, 'kernel': 'rbf'}" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "clf.best_params_" ] }, { "cell_type": "markdown", "id": "0bf652ae-3c18-4340-ae99-006ab45e1088", "metadata": {}, "source": [ "不同模型，谁最好" ] }, { "cell_type": "code", "execution_count": 27, "id": "478f9706-f3a2-4bd6-9e90-da52e7f28ccc", "metadata": {}, "outputs": [], "source": [ "from sklearn.svm import SVC\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.linear_model import LogisticRegression" ] }, { "cell_type": "code", "execution_count": 28, "id": "8398663b-7d4e-4d0b-9a2c-69c7934a5084", "metadata": {}, "outputs": [], "source": [ "model_params = {\n", " 'svm':{\n", " 'model': SVC(gamma='auto'),\n", " 'params': {\n", " 'C': [1, 10, 20],\n", " 'kernel': ['rbf', 'linear']\n", " }\n", " },\n", " 'random_forest':{\n", " 'model': RandomForestClassifier(),\n", " 'params': {\n", " 'n_estimators': [1, 5, 10]\n", " }\n", " },\n", " 'logistic_regression': {\n", " 'model': LogisticRegression(solver='liblinear', multi_class='auto'),\n", " 'params': {\n", " 'C': [1, 5, 10]\n", " }\n", " }\n", "}" ] }, { "cell_type": "code", "execution_count": 30, "id": "f072707d-a124-4f61-bbb3-2f26b5c10eec", "metadata": {}, "outputs": [], "source": [ "scores = []\n", "for model, mp in model_params.items():\n", " clf = GridSearchCV(mp['model'], mp['params'], cv=5, return_train_score=False)\n", " clf.fit(iris.data, iris.target)\n", " scores.append({'model': model,\n", " 'best_score': clf.best_score_,\n", " 'best_params': clf.best_params_})\n", " " ] }, { "cell_type": "code", "execution_count": 31, "id": "e68db3aa-6a69-4662-899b-e73690272851", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[{'model': 'svm',\n", " 'best_score': 0.9800000000000001,\n", " 'best_params': {'C': 1, 'kernel': 'rbf'}},\n", " {'model': 'random_forest',\n", " 'best_score': 0.9600000000000002,\n", " 'best_params': {'n_estimators': 5}},\n", " {'model': 'logistic_regression',\n", " 'best_score': 0.9666666666666668,\n", " 'best_params': {'C': 5}}]" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "scores" ] }, { "cell_type": "code", "execution_count": 32, "id": "0279fa4c-7058-41c7-ad16-369bf4206794", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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modelbest_scorebest_params
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1random_forest0.960000{'n_estimators': 5}
2logistic_regression0.966667{'C': 5}
\n", "
" ], "text/plain": [ " model best_score best_params\n", "0 svm 0.980000 {'C': 1, 'kernel': 'rbf'}\n", "1 random_forest 0.960000 {'n_estimators': 5}\n", "2 logistic_regression 0.966667 {'C': 5}" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = pd.DataFrame(scores, columns=['model', 'best_score', 'best_params'])\n", "df" ] }, { "cell_type": "markdown", "id": "f396a9c8-256e-4d36-813c-9a39e67e39a3", "metadata": {}, "source": [ "作业：使用手写体数据，找出前面我们课程中学习的最好模型。" ] }, { "cell_type": "code", "execution_count": null, "id": "47dab0d6-5a29-4e8b-a74e-24a6239d0e56", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.8.0" } }, "nbformat": 4, "nbformat_minor": 5 }