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@kirikei記事にそって、AutoGluon使ってみた
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-kirikei-autogluon.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "@kirikei記事にそって、AutoGluon使ってみた",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/tomo-makes/d388f40632d553778e59ab5552b5cdcb/-kirikei-autogluon.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "BwPFzJD8ZrxX",
"colab_type": "text"
},
"source": [
"# AutoGluonを試す\n",
"\n",
"[データとfitだけで始めるAutoML - AutoGluon使ってみた - - Qiita](https://qiita.com/kirikei/items/f879eb2cfbaf3d37ee0f)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TSGrRaR8fRCe",
"colab_type": "text"
},
"source": [
"### 前提\n",
"\n",
"- GPUあり、Python3ランタイムを選ぶ\n",
" - ただし、 `mxnet-cu100` をインストールに選ばなければGPUなしでも動くと思われる"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pwkN1h0SaAxa",
"colab_type": "text"
},
"source": [
"## インストール"
]
},
{
"cell_type": "code",
"metadata": {
"id": "tq7qZ2_kZoWT",
"colab_type": "code",
"outputId": "638d058a-b38b-4eb8-9d73-7dc565554aca",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
}
},
"source": [
"!pip install --upgrade mxnet-cu100"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Collecting mxnet-cu100\n",
"\u001b[?25l Downloading https://files.pythonhosted.org/packages/3d/84/d098e0607ee6207448b6af65315f5d45946b49e4f48160eade6cdd64ce4e/mxnet_cu100-1.5.1.post0-py2.py3-none-manylinux1_x86_64.whl (540.1MB)\n",
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"Installing collected packages: graphviz, mxnet-cu100\n",
" Found existing installation: graphviz 0.10.1\n",
" Uninstalling graphviz-0.10.1:\n",
" Successfully uninstalled graphviz-0.10.1\n",
"Successfully installed graphviz-0.8.4 mxnet-cu100-1.5.1.post0\n",
"Collecting autogluon\n",
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"\u001b[?25l Downloading https://files.pythonhosted.org/packages/bf/b3/9175539d5a43b0bb1fe6d9729613a9639dd78a0e13c3fa7fc1ba702e56fa/dask-2.9.0-py3-none-any.whl (770kB)\n",
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"\u001b[?25l Downloading https://files.pythonhosted.org/packages/8e/7b/88f10115b4748f86be6b7b1d8761ba5023fccf6e6cbe762e368f63eddcf9/botocore-1.12.253-py2.py3-none-any.whl (5.7MB)\n",
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"Building wheels for collected packages: ConfigSpace, gluonnlp\n",
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"Installing collected packages: tqdm, ConfigSpace, scikit-learn, dask, distributed, gluoncv, lightgbm, scikit-optimize, pandas, catboost, gluonnlp, botocore, boto3, bcrypt, pynacl, cryptography, paramiko, autogluon\n",
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],
"name": "stdout"
},
{
"output_type": "display_data",
"data": {
"application/vnd.colab-display-data+json": {
"pip_warning": {
"packages": [
"pandas",
"tqdm"
]
}
}
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "AxEJ6a9vdNPE",
"colab_type": "code",
"outputId": "c3a01f6d-f655-4d22-d11c-e3e1fe310ada",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
}
},
"source": [
"!pip install autogluon"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Requirement already satisfied: autogluon in /usr/local/lib/python3.6/dist-packages (0.0.3)\n",
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],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "uyUG_yY6aCQ-",
"colab_type": "text"
},
"source": [
"## テーブルデータで試す\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "1aTzzwWRdg5p",
"colab_type": "text"
},
"source": [
"国税調査のデータadultデータセットを用いて分類問題と回帰問題を解く。"
]
},
{
"cell_type": "code",
"metadata": {
"id": "HonOkoU0Z5iI",
"colab_type": "code",
"outputId": "706ab950-fa18-4b5a-dcf7-ecc5e1057955",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
}
},
"source": [
"import autogluon as ag\n",
"from autogluon import TabularPrediction as task\n",
"\n",
"train_data = task.Dataset(file_path='https://autogluon.s3.amazonaws.com/datasets/Inc/train.csv')\n",
"train_data = train_data.head(2000) # subsample 2000 data points for faster demo"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Loaded data from: https://autogluon.s3.amazonaws.com/datasets/Inc/train.csv | Columns = 15 / 15 | Rows = 39073 -> 39073\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "pI4yRw38Z56v",
"colab_type": "code",
"outputId": "013946ff-c350-4d4a-a93d-92b69de4556d",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 377
}
},
"source": [
"train_data.head()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age</th>\n",
" <th>workclass</th>\n",
" <th>fnlwgt</th>\n",
" <th>education</th>\n",
" <th>education-num</th>\n",
" <th>marital-status</th>\n",
" <th>occupation</th>\n",
" <th>relationship</th>\n",
" <th>race</th>\n",
" <th>sex</th>\n",
" <th>capital-gain</th>\n",
" <th>capital-loss</th>\n",
" <th>hours-per-week</th>\n",
" <th>native-country</th>\n",
" <th>class</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>25</td>\n",
" <td>Private</td>\n",
" <td>178478</td>\n",
" <td>Bachelors</td>\n",
" <td>13</td>\n",
" <td>Never-married</td>\n",
" <td>Tech-support</td>\n",
" <td>Own-child</td>\n",
" <td>White</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>United-States</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>23</td>\n",
" <td>State-gov</td>\n",
" <td>61743</td>\n",
" <td>5th-6th</td>\n",
" <td>3</td>\n",
" <td>Never-married</td>\n",
" <td>Transport-moving</td>\n",
" <td>Not-in-family</td>\n",
" <td>White</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>35</td>\n",
" <td>United-States</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>46</td>\n",
" <td>Private</td>\n",
" <td>376789</td>\n",
" <td>HS-grad</td>\n",
" <td>9</td>\n",
" <td>Never-married</td>\n",
" <td>Other-service</td>\n",
" <td>Not-in-family</td>\n",
" <td>White</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>15</td>\n",
" <td>United-States</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>55</td>\n",
" <td>?</td>\n",
" <td>200235</td>\n",
" <td>HS-grad</td>\n",
" <td>9</td>\n",
" <td>Married-civ-spouse</td>\n",
" <td>?</td>\n",
" <td>Husband</td>\n",
" <td>White</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>50</td>\n",
" <td>United-States</td>\n",
" <td>&gt;50K</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>36</td>\n",
" <td>Private</td>\n",
" <td>224541</td>\n",
" <td>7th-8th</td>\n",
" <td>4</td>\n",
" <td>Married-civ-spouse</td>\n",
" <td>Handlers-cleaners</td>\n",
" <td>Husband</td>\n",
" <td>White</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>El-Salvador</td>\n",
" <td>&lt;=50K</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age workclass fnlwgt ... hours-per-week native-country class\n",
"0 25 Private 178478 ... 40 United-States <=50K\n",
"1 23 State-gov 61743 ... 35 United-States <=50K\n",
"2 46 Private 376789 ... 15 United-States <=50K\n",
"3 55 ? 200235 ... 50 United-States >50K\n",
"4 36 Private 224541 ... 40 El-Salvador <=50K\n",
"\n",
"[5 rows x 15 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 5
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TseD-HCGaIdB",
"colab_type": "text"
},
"source": [
"### 分類\n",
"\n",
"classカラムの分類，すなわち収入が50k以上か未満かの二値分類を行う。"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "f8I_vW_aaue6",
"colab_type": "text"
},
"source": [
"クラスを確認する。"
]
},
{
"cell_type": "code",
"metadata": {
"id": "xuW0zRSdZ5_9",
"colab_type": "code",
"outputId": "b67794f5-98e5-4394-9f08-d591abcb0aa2",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 119
}
},
"source": [
"label_column = 'class'\n",
"print(\"Summary of class variable: \\n\", train_data[label_column].describe())"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Summary of class variable: \n",
" count 2000\n",
"unique 2\n",
"top <=50K\n",
"freq 1551\n",
"Name: class, dtype: object\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "kUdFkEEbav0b",
"colab_type": "text"
},
"source": [
"とりあえずAutoGluonを実行する。"
]
},
{
"cell_type": "code",
"metadata": {
"id": "fhT0RKuoacps",
"colab_type": "code",
"outputId": "2fa7082b-c172-4017-ff5a-c7b60f727c4b",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 785
}
},
"source": [
"result_dir = 'agModels-predictClass' # specifies folder where to store trained models\n",
"predictor = task.fit(train_data=train_data, label=label_column, output_directory=result_dir)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Beginning AutoGluon training ...\n",
"Preprocessing data ...\n",
"Here are the first 10 unique label values in your data: [' <=50K' ' >50K']\n",
"AutoGluon infers your prediction problem is: binary (because only two unique label-values observed)\n",
"If this is wrong, please specify `problem_type` argument in fit() instead (You may specify problem_type as one of: ['binary', 'multiclass', 'regression'])\n",
"\n",
"Selected class <--> label mapping: class 1 = >50K, class 0 = <=50K\n",
"\tData preprocessing and feature engineering runtime = 0.08s ...\n",
"AutoGluon will gauge predictive performance using evaluation metric: accuracy\n",
"To change this, specify the eval_metric argument of fit()\n",
"Fitting model: RandomForestClassifierGini ...\n",
"\t0.72s\t = Training runtime\n",
"\t0.8725\t = Validation accuracy score\n",
"Fitting model: RandomForestClassifierEntr ...\n",
"\t0.8s\t = Training runtime\n",
"\t0.875\t = Validation accuracy score\n",
"Fitting model: ExtraTreesClassifierGini ...\n",
"\t0.6s\t = Training runtime\n",
"\t0.8475\t = Validation accuracy score\n",
"Fitting model: ExtraTreesClassifierEntr ...\n",
"\t0.6s\t = Training runtime\n",
"\t0.8525\t = Validation accuracy score\n",
"Fitting model: KNeighborsClassifierUnif ...\n",
"\t0.01s\t = Training runtime\n",
"\t0.78\t = Validation accuracy score\n",
"Fitting model: KNeighborsClassifierDist ...\n",
"\t0.01s\t = Training runtime\n",
"\t0.7325\t = Validation accuracy score\n",
"Fitting model: LightGBMClassifier ...\n",
"\t0.39s\t = Training runtime\n",
"\t0.8775\t = Validation accuracy score\n",
"Fitting model: CatboostClassifier ...\n",
"\t2.38s\t = Training runtime\n",
"\t0.8725\t = Validation accuracy score\n",
"Fitting model: NeuralNetClassifier ...\n",
"\t15.16s\t = Training runtime\n",
"\t0.8675\t = Validation accuracy score\n",
"Fitting model: LightGBMClassifierCustom ...\n",
"\t0.72s\t = Training runtime\n",
"\t0.855\t = Validation accuracy score\n",
"Fitting model: weighted_ensemble_l1 ...\n",
"\t0.45s\t = Training runtime\n",
"\t0.88\t = Validation accuracy score\n",
"AutoGluon training complete, total runtime = 24.09s ...\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "LPucLx6ha429",
"colab_type": "text"
},
"source": [
"テストデータをダウンロードする。"
]
},
{
"cell_type": "code",
"metadata": {
"id": "rHfXqg_EagA7",
"colab_type": "code",
"outputId": "00eed8c8-1eaa-4eb0-cbc3-70c3bf3b8271",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 54
}
},
"source": [
"test_data = task.Dataset(file_path='https://autogluon.s3.amazonaws.com/datasets/Inc/test.csv')\n",
"y_test = test_data[label_column] # values to predict\n",
"test_data_nolab = test_data.drop(labels=[label_column],axis=1) "
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Loaded data from: https://autogluon.s3.amazonaws.com/datasets/Inc/test.csv | Columns = 15 / 15 | Rows = 9769 -> 9769\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5tTNcSYRa7sg",
"colab_type": "text"
},
"source": [
"モデルを評価する。"
]
},
{
"cell_type": "code",
"metadata": {
"id": "23IKFo1gagUS",
"colab_type": "code",
"outputId": "07474158-ede6-4350-ba6a-05aa63869c4f",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 663
}
},
"source": [
"# unnecessary, just demonstrates how to load previously-trained predictor from file\n",
"predictor = task.load(result_dir) \n",
"\n",
"y_pred = predictor.predict(test_data_nolab)\n",
"print(\"Predictions: \", y_pred)\n",
"perf = predictor.evaluate_predictions(y_true=y_test, y_pred=y_pred, auxiliary_metrics=True)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Evaluation: accuracy on test data: 0.856280\n",
"Evaluations on test data:\n",
"{\n",
" \"accuracy\": 0.8562800696079435,\n",
" \"accuracy_score\": 0.8562800696079435,\n",
" \"balanced_accuracy_score\": 0.7625358844305661,\n",
" \"matthews_corrcoef\": 0.5769073882148189,\n",
" \"f1_score\": 0.8562800696079435\n",
"}\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"Predictions: [' <=50K' ' <=50K' ' >50K' ... ' <=50K' ' <=50K' ' <=50K']\n"
],
"name": "stdout"
},
{
"output_type": "stream",
"text": [
"Detailed (per-class) classification report:\n",
"{\n",
" \" <=50K\": {\n",
" \"precision\": 0.8791222570532915,\n",
" \"recall\": 0.9409475238223057,\n",
" \"f1-score\": 0.9089848308051341,\n",
" \"support\": 7451\n",
" },\n",
" \" >50K\": {\n",
" \"precision\": 0.7547380156075808,\n",
" \"recall\": 0.5841242450388265,\n",
" \"f1-score\": 0.6585603112840467,\n",
" \"support\": 2318\n",
" },\n",
" \"accuracy\": 0.8562800696079435,\n",
" \"macro avg\": {\n",
" \"precision\": 0.8169301363304362,\n",
" \"recall\": 0.7625358844305661,\n",
" \"f1-score\": 0.7837725710445904,\n",
" \"support\": 9769\n",
" },\n",
" \"weighted avg\": {\n",
" \"precision\": 0.8496082155269166,\n",
" \"recall\": 0.8562800696079435,\n",
" \"f1-score\": 0.8495638014009084,\n",
" \"support\": 9769\n",
" }\n",
"}\n"
],
"name": "stderr"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "lD0LNVMJaqRI",
"colab_type": "text"
},
"source": [
"探索の様子を確認する。"
]
},
{
"cell_type": "code",
"metadata": {
"id": "tAjW4yaaaliD",
"colab_type": "code",
"outputId": "0f4a0147-1c09-456b-f293-5ec47cce6fe1",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 258
}
},
"source": [
"results = predictor.fit_summary()\n"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"*** Summary of fit() ***\n",
"Number of models trained: 11\n",
"Types of models trained: \n",
"{'KNNModel', 'TabularNeuralNetModel', 'CatboostModel', 'LGBModel', 'WeightedEnsembleModel', 'RFModel'}\n",
"Validation performance of individual models: {'RandomForestClassifierGini': 0.8725, 'RandomForestClassifierEntr': 0.875, 'ExtraTreesClassifierGini': 0.8475, 'ExtraTreesClassifierEntr': 0.8525, 'KNeighborsClassifierUnif': 0.78, 'KNeighborsClassifierDist': 0.7325, 'LightGBMClassifier': 0.8775, 'CatboostClassifier': 0.8725, 'NeuralNetClassifier': 0.8675, 'LightGBMClassifierCustom': 0.855, 'weighted_ensemble_l1': 0.88}\n",
"Best model (based on validation performance): weighted_ensemble_l1\n",
"Hyperparameter-tuning used: False\n",
"Bagging used: False \n",
"Stack-ensembling used: False \n",
"User-specified hyperparameters:\n",
"{'NN': {'num_epochs': 500}, 'GBM': {'num_boost_round': 10000}, 'CAT': {'iterations': 10000}, 'RF': {'n_estimators': 300}, 'XT': {'n_estimators': 300}, 'KNN': {}, 'custom': ['GBM']}\n",
"Plot summary of models saved to file: SummaryOfModels.html\n",
"*** End of fit() summary ***\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "SYQIMvyDbEBS",
"colab_type": "text"
},
"source": [
"モデルがタスクと、カラムをどう認識したか確認する。"
]
},
{
"cell_type": "code",
"metadata": {
"id": "dcVTZ4qjbDM4",
"colab_type": "code",
"outputId": "9f480390-c6b8-40c8-8a26-df74c3d64aaf",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 71
}
},
"source": [
"print(\"AutoGluon infers problem type is: \", predictor.problem_type)\n",
"print(\"AutoGluon categorized the features as: \", predictor.feature_types)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"AutoGluon infers problem type is: binary\n",
"AutoGluon categorized the features as: {'nlp': [], 'vectorizers': [], 'int': ['age', 'fnlwgt', 'education-num', 'capital-gain', 'capital-loss', 'hours-per-week'], 'object': ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country']}\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5C2lmOHUaMt7",
"colab_type": "text"
},
"source": [
"### 回帰\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vChVHi2bbM3k",
"colab_type": "text"
},
"source": [
"年齢をtarget varとして回帰する。"
]
},
{
"cell_type": "code",
"metadata": {
"id": "An2PitqfaNBr",
"colab_type": "code",
"outputId": "8c0f3880-e5a3-4ef5-d356-b8a4d72b0d32",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 187
}
},
"source": [
"age_column = 'age'\n",
"print(\"Summary of age variable: \\n\", train_data[age_column].describe())"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Summary of age variable: \n",
" count 2000.000000\n",
"mean 38.375000\n",
"std 13.781195\n",
"min 17.000000\n",
"25% 27.000000\n",
"50% 37.000000\n",
"75% 47.000000\n",
"max 90.000000\n",
"Name: age, dtype: float64\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_wkdvWk6eIoh",
"colab_type": "text"
},
"source": [
"`evaluate()` で評価する。\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "xxT2HQOkeFAr",
"colab_type": "code",
"outputId": "844fea81-f834-406a-de24-a47d661ee43f",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 632
}
},
"source": [
"predictor_age = task.fit(train_data=train_data, \n",
" output_directory=\"agModels-predictAge\", \n",
" label=age_column,\n",
" problem_type='regression')\n",
"performance = predictor_age.evaluate(test_data)"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"Beginning AutoGluon training ...\n",
"Preprocessing data ...\n",
"\tData preprocessing and feature engineering runtime = 0.08s ...\n",
"AutoGluon will gauge predictive performance using evaluation metric: root_mean_squared_error\n",
"To change this, specify the eval_metric argument of fit()\n",
"Fitting model: RandomForestRegressorMSE ...\n",
"NumExpr defaulting to 2 threads.\n",
"\t1.51s\t = Training runtime\n",
"\t-11.08\t = Validation root_mean_squared_error score\n",
"Fitting model: ExtraTreesRegressorMSE ...\n",
"\t1.1s\t = Training runtime\n",
"\t-11.2898\t = Validation root_mean_squared_error score\n",
"Fitting model: KNeighborsRegressorUnif ...\n",
"\t0.01s\t = Training runtime\n",
"\t-14.5251\t = Validation root_mean_squared_error score\n",
"Fitting model: KNeighborsRegressorDist ...\n",
"\t0.01s\t = Training runtime\n",
"\t-15.2104\t = Validation root_mean_squared_error score\n",
"Fitting model: LightGBMRegressor ...\n",
"\t0.34s\t = Training runtime\n",
"\t-10.311\t = Validation root_mean_squared_error score\n",
"Fitting model: CatboostRegressor ...\n",
"\t2.16s\t = Training runtime\n",
"\t-10.4003\t = Validation root_mean_squared_error score\n",
"Fitting model: NeuralNetRegressor ...\n",
"\t13.07s\t = Training runtime\n",
"\t-10.6561\t = Validation root_mean_squared_error score\n",
"Fitting model: LightGBMRegressorCustom ...\n",
"\t0.61s\t = Training runtime\n",
"\t-10.5824\t = Validation root_mean_squared_error score\n",
"Fitting model: weighted_ensemble_l1 ...\n",
"\t0.43s\t = Training runtime\n",
"\t-10.193\t = Validation root_mean_squared_error score\n",
"AutoGluon training complete, total runtime = 21.12s ...\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"Predictive performance on given dataset: root_mean_squared_error = 10.27827195078937\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XNGO3Ds-ePk5",
"colab_type": "text"
},
"source": [
"結果を確認する。"
]
},
{
"cell_type": "code",
"metadata": {
"id": "pu3yKie4ePsk",
"colab_type": "code",
"outputId": "ede76091-30ce-4302-f27e-0cd50dece0a4",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 258
}
},
"source": [
"results = predictor_age.fit_summary()"
],
"execution_count": 0,
"outputs": [
{
"output_type": "stream",
"text": [
"*** Summary of fit() ***\n",
"Number of models trained: 9\n",
"Types of models trained: \n",
"{'KNNModel', 'TabularNeuralNetModel', 'CatboostModel', 'LGBModel', 'WeightedEnsembleModel', 'RFModel'}\n",
"Validation performance of individual models: {'RandomForestRegressorMSE': -11.079997378910637, 'ExtraTreesRegressorMSE': -11.289767107134379, 'KNeighborsRegressorUnif': -14.525081755363722, 'KNeighborsRegressorDist': -15.210358581022946, 'LightGBMRegressor': -10.31100588616801, 'CatboostRegressor': -10.400331629210777, 'NeuralNetRegressor': -10.656088855066407, 'LightGBMRegressorCustom': -10.582398535859294, 'weighted_ensemble_l1': -10.193049558344967}\n",
"Best model (based on validation performance): weighted_ensemble_l1\n",
"Hyperparameter-tuning used: False\n",
"Bagging used: False \n",
"Stack-ensembling used: False \n",
"User-specified hyperparameters:\n",
"{'NN': {'num_epochs': 500}, 'GBM': {'num_boost_round': 10000}, 'CAT': {'iterations': 10000}, 'RF': {'n_estimators': 300}, 'XT': {'n_estimators': 300}, 'KNN': {}, 'custom': ['GBM']}\n",
"Plot summary of models saved to file: SummaryOfModels.html\n",
"*** End of fit() summary ***\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "dKWhS31qeM-W",
"colab_type": "code",
"colab": {}
},
"source": [
""
],
"execution_count": 0,
"outputs": []
}
]
}
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