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interpretable_ml_xai_regression_2023-10.ipynb
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"<a href=\"https://colab.research.google.com/gist/alonsosilvaallende/8b1bda4a2ae83c0a1e8eba72947f8749/interpretable_ml_xai_regression_2023-10.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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{
"cell_type": "markdown",
"metadata": {
"id": "VCV1UZu-NTgr"
},
"source": [
"# Regression for Iris dataset with 'sepal width' as the target"
]
},
{
"cell_type": "code",
"metadata": {
"id": "vVdIF-vCOYWB"
},
"source": [
"import sklearn\n",
"\n",
"assert sklearn.__version__ >= \"1.0\"\n",
"# %pip install -q -U scikit-learn>=1.0.0"
],
"execution_count": 1,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "9ypieMc3NQ-M"
},
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt"
],
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "-atdbdkNNQ-i"
},
"source": [
"from sklearn.datasets import load_iris\n",
"\n",
"iris = load_iris()"
],
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"scrolled": false,
"id": "o-LwD0hQNQ-4",
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"base_uri": "https://localhost:8080/"
},
"outputId": "767ceb84-3b55-4e4f-e7c0-452b75c36533"
},
"source": [
"print(iris.DESCR)"
],
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
".. _iris_dataset:\n",
"\n",
"Iris plants dataset\n",
"--------------------\n",
"\n",
"**Data Set Characteristics:**\n",
"\n",
" :Number of Instances: 150 (50 in each of three classes)\n",
" :Number of Attributes: 4 numeric, predictive attributes and the class\n",
" :Attribute Information:\n",
" - sepal length in cm\n",
" - sepal width in cm\n",
" - petal length in cm\n",
" - petal width in cm\n",
" - class:\n",
" - Iris-Setosa\n",
" - Iris-Versicolour\n",
" - Iris-Virginica\n",
" \n",
" :Summary Statistics:\n",
"\n",
" ============== ==== ==== ======= ===== ====================\n",
" Min Max Mean SD Class Correlation\n",
" ============== ==== ==== ======= ===== ====================\n",
" sepal length: 4.3 7.9 5.84 0.83 0.7826\n",
" sepal width: 2.0 4.4 3.05 0.43 -0.4194\n",
" petal length: 1.0 6.9 3.76 1.76 0.9490 (high!)\n",
" petal width: 0.1 2.5 1.20 0.76 0.9565 (high!)\n",
" ============== ==== ==== ======= ===== ====================\n",
"\n",
" :Missing Attribute Values: None\n",
" :Class Distribution: 33.3% for each of 3 classes.\n",
" :Creator: R.A. Fisher\n",
" :Donor: Michael Marshall (MARSHALL%[email protected])\n",
" :Date: July, 1988\n",
"\n",
"The famous Iris database, first used by Sir R.A. Fisher. The dataset is taken\n",
"from Fisher's paper. Note that it's the same as in R, but not as in the UCI\n",
"Machine Learning Repository, which has two wrong data points.\n",
"\n",
"This is perhaps the best known database to be found in the\n",
"pattern recognition literature. Fisher's paper is a classic in the field and\n",
"is referenced frequently to this day. (See Duda & Hart, for example.) The\n",
"data set contains 3 classes of 50 instances each, where each class refers to a\n",
"type of iris plant. One class is linearly separable from the other 2; the\n",
"latter are NOT linearly separable from each other.\n",
"\n",
".. topic:: References\n",
"\n",
" - Fisher, R.A. \"The use of multiple measurements in taxonomic problems\"\n",
" Annual Eugenics, 7, Part II, 179-188 (1936); also in \"Contributions to\n",
" Mathematical Statistics\" (John Wiley, NY, 1950).\n",
" - Duda, R.O., & Hart, P.E. (1973) Pattern Classification and Scene Analysis.\n",
" (Q327.D83) John Wiley & Sons. ISBN 0-471-22361-1. See page 218.\n",
" - Dasarathy, B.V. (1980) \"Nosing Around the Neighborhood: A New System\n",
" Structure and Classification Rule for Recognition in Partially Exposed\n",
" Environments\". IEEE Transactions on Pattern Analysis and Machine\n",
" Intelligence, Vol. PAMI-2, No. 1, 67-71.\n",
" - Gates, G.W. (1972) \"The Reduced Nearest Neighbor Rule\". IEEE Transactions\n",
" on Information Theory, May 1972, 431-433.\n",
" - See also: 1988 MLC Proceedings, 54-64. Cheeseman et al\"s AUTOCLASS II\n",
" conceptual clustering system finds 3 classes in the data.\n",
" - Many, many more ...\n"
]
}
]
},
{
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"source": [
"df_raw = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
"df_raw.head()"
],
"execution_count": 5,
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{
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" 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"
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" [theme=dark] .colab-df-convert {\n",
" background-color: #3B4455;\n",
" fill: #D2E3FC;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-convert:hover {\n",
" background-color: #434B5C;\n",
" box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
" filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
" fill: #FFFFFF;\n",
" }\n",
" </style>\n",
"\n",
" <script>\n",
" const buttonEl =\n",
" document.querySelector('#df-91e0c3a2-ccd4-44d8-b3e7-23c503d546a7 button.colab-df-convert');\n",
" buttonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
"\n",
" async function convertToInteractive(key) {\n",
" const element = document.querySelector('#df-91e0c3a2-ccd4-44d8-b3e7-23c503d546a7');\n",
" const dataTable =\n",
" await google.colab.kernel.invokeFunction('convertToInteractive',\n",
" [key], {});\n",
" if (!dataTable) return;\n",
"\n",
" const docLinkHtml = 'Like what you see? Visit the ' +\n",
" '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
" + ' to learn more about interactive tables.';\n",
" element.innerHTML = '';\n",
" dataTable['output_type'] = 'display_data';\n",
" await google.colab.output.renderOutput(dataTable, element);\n",
" const docLink = document.createElement('div');\n",
" docLink.innerHTML = docLinkHtml;\n",
" element.appendChild(docLink);\n",
" }\n",
" </script>\n",
" </div>\n",
"\n",
"\n",
"<div id=\"df-e4d0e0ef-98fa-4368-ac88-b97ec5b0261d\">\n",
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e4d0e0ef-98fa-4368-ac88-b97ec5b0261d')\"\n",
" title=\"Suggest charts.\"\n",
" style=\"display:none;\">\n",
"\n",
"<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
" width=\"24px\">\n",
" <g>\n",
" <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
" </g>\n",
"</svg>\n",
" </button>\n",
"\n",
"<style>\n",
" .colab-df-quickchart {\n",
" --bg-color: #E8F0FE;\n",
" --fill-color: #1967D2;\n",
" --hover-bg-color: #E2EBFA;\n",
" --hover-fill-color: #174EA6;\n",
" --disabled-fill-color: #AAA;\n",
" --disabled-bg-color: #DDD;\n",
" }\n",
"\n",
" [theme=dark] .colab-df-quickchart {\n",
" --bg-color: #3B4455;\n",
" --fill-color: #D2E3FC;\n",
" --hover-bg-color: #434B5C;\n",
" --hover-fill-color: #FFFFFF;\n",
" --disabled-bg-color: #3B4455;\n",
" --disabled-fill-color: #666;\n",
" }\n",
"\n",
" .colab-df-quickchart {\n",
" background-color: var(--bg-color);\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: var(--fill-color);\n",
" height: 32px;\n",
" padding: 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-quickchart:hover {\n",
" background-color: var(--hover-bg-color);\n",
" box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: var(--button-hover-fill-color);\n",
" }\n",
"\n",
" .colab-df-quickchart-complete:disabled,\n",
" .colab-df-quickchart-complete:disabled:hover {\n",
" background-color: var(--disabled-bg-color);\n",
" fill: var(--disabled-fill-color);\n",
" box-shadow: none;\n",
" }\n",
"\n",
" .colab-df-spinner {\n",
" border: 2px solid var(--fill-color);\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" animation:\n",
" spin 1s steps(1) infinite;\n",
" }\n",
"\n",
" @keyframes spin {\n",
" 0% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" border-left-color: var(--fill-color);\n",
" }\n",
" 20% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 30% {\n",
" border-color: transparent;\n",
" border-left-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 40% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-top-color: var(--fill-color);\n",
" }\n",
" 60% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" }\n",
" 80% {\n",
" border-color: transparent;\n",
" border-right-color: var(--fill-color);\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" 90% {\n",
" border-color: transparent;\n",
" border-bottom-color: var(--fill-color);\n",
" }\n",
" }\n",
"</style>\n",
"\n",
" <script>\n",
" async function quickchart(key) {\n",
" const quickchartButtonEl =\n",
" document.querySelector('#' + key + ' button');\n",
" quickchartButtonEl.disabled = true; // To prevent multiple clicks.\n",
" quickchartButtonEl.classList.add('colab-df-spinner');\n",
" try {\n",
" const charts = await google.colab.kernel.invokeFunction(\n",
" 'suggestCharts', [key], {});\n",
" } catch (error) {\n",
" console.error('Error during call to suggestCharts:', error);\n",
" }\n",
" quickchartButtonEl.classList.remove('colab-df-spinner');\n",
" quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
" }\n",
" (() => {\n",
" let quickchartButtonEl =\n",
" document.querySelector('#df-e4d0e0ef-98fa-4368-ac88-b97ec5b0261d button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
" </div>\n",
" </div>\n"
]
},
"metadata": {},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "ZAthwy2MNQ_3",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "669dd893-29e5-490a-f117-78bcc55a80b9"
},
"source": [
"df_raw['class'].unique()"
],
"execution_count": 7,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([0, 1, 2])"
]
},
"metadata": {},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"source": [
"list(iris.target_names)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "NJfKYx5u-kss",
"outputId": "898f47a5-2720-4c96-edba-2d62f82225ce"
},
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['setosa', 'versicolor', 'virginica']"
]
},
"metadata": {},
"execution_count": 8
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "d0MYo8ixNRAA"
},
"source": [
"from sklearn.model_selection import train_test_split\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(\n",
" df_raw.drop(columns='sepal width (cm)'), df_raw['sepal width (cm)'], random_state=42)"
],
"execution_count": 9,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "nY28r0Z6NRAK",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "896847a4-1be0-46f4-d130-4a0ce84f8b20"
},
"source": [
"X_train.columns"
],
"execution_count": 10,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Index(['sepal length (cm)', 'petal length (cm)', 'petal width (cm)', 'class'], dtype='object')"
]
},
"metadata": {},
"execution_count": 10
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "TSYfiyqPNRAT",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
},
"outputId": "1aebc9fd-7886-40c3-bbe3-6c7b68c64778"
},
"source": [
"y_train.name"
],
"execution_count": 11,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'sepal width (cm)'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 11
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "_Nn9wkJINRAj"
},
"source": [
"categorical_columns = ['class']\n",
"numerical_columns = ['sepal length (cm)', 'petal length (cm)', 'petal width (cm)']"
],
"execution_count": 12,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "SHNFVFSCNRAt"
},
"source": [
"from sklearn.pipeline import Pipeline\n",
"from sklearn.preprocessing import OneHotEncoder\n",
"\n",
"categorical_pipe = Pipeline([\n",
" ('onehot', OneHotEncoder())\n",
"])"
],
"execution_count": 13,
"outputs": []
},
{
"cell_type": "code",
"source": [
"pd.DataFrame(X_train['class']).head(5).style.hide(axis=\"index\")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "Svs5Vu0IClPD",
"outputId": "53343cd2-6d5f-4094-e401-aeb9e222feef"
},
"execution_count": 14,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7a68e10cfe80>"
],
"text/html": [
"<style type=\"text/css\">\n",
"</style>\n",
"<table id=\"T_64278\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th id=\"T_64278_level0_col0\" class=\"col_heading level0 col0\" >class</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td id=\"T_64278_row0_col0\" class=\"data row0 col0\" >0</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_64278_row1_col0\" class=\"data row1 col0\" >0</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_64278_row2_col0\" class=\"data row2 col0\" >2</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_64278_row3_col0\" class=\"data row3 col0\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_64278_row4_col0\" class=\"data row4 col0\" >1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
]
},
"metadata": {},
"execution_count": 14
}
]
},
{
"cell_type": "code",
"source": [
"encoder = OneHotEncoder(sparse_output=False)\n",
"X = encoder.fit_transform(pd.DataFrame(X_train['class']))"
],
"metadata": {
"id": "RHVVQuInDJ9H"
},
"execution_count": 15,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"Scikit-Learn loves Pandas: https://colab.research.google.com/gist/ageron/d4e0d3caed542b8a4285ef433f650a8d/scikit-learn-pandas.ipynb#scrollTo=9jZTcN0OC8ZP"
],
"metadata": {
"id": "N70viswyma4H"
}
},
{
"cell_type": "code",
"source": [
"encoder.feature_names_in_"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "qbJssFuqDQ6b",
"outputId": "e4349648-c5fe-4c9a-b514-a0bf6b9b8caf"
},
"execution_count": 16,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array(['class'], dtype=object)"
]
},
"metadata": {},
"execution_count": 16
}
]
},
{
"cell_type": "code",
"source": [
"encoder.get_feature_names_out()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "y-MD3HyjDYqW",
"outputId": "2234f2bd-3b0f-4693-a23b-bddac421dff5"
},
"execution_count": 17,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array(['class_0', 'class_1', 'class_2'], dtype=object)"
]
},
"metadata": {},
"execution_count": 17
}
]
},
{
"cell_type": "code",
"source": [
"X_train_df = pd.DataFrame(X, columns=encoder.get_feature_names_out(), index=X_train.index)\n",
"formatdict = {col: '{:.1f}' for col in X_train_df.columns.values}\n",
"X_train_df[:5].style.hide(axis=\"index\").format(formatdict)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 206
},
"id": "XrVNezgkUavU",
"outputId": "6409937c-b2b1-4755-e3e9-25785d81ab4f"
},
"execution_count": 18,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7a68aab45030>"
],
"text/html": [
"<style type=\"text/css\">\n",
"</style>\n",
"<table id=\"T_0ba9f\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th id=\"T_0ba9f_level0_col0\" class=\"col_heading level0 col0\" >class_0</th>\n",
" <th id=\"T_0ba9f_level0_col1\" class=\"col_heading level0 col1\" >class_1</th>\n",
" <th id=\"T_0ba9f_level0_col2\" class=\"col_heading level0 col2\" >class_2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td id=\"T_0ba9f_row0_col0\" class=\"data row0 col0\" >1.0</td>\n",
" <td id=\"T_0ba9f_row0_col1\" class=\"data row0 col1\" >0.0</td>\n",
" <td id=\"T_0ba9f_row0_col2\" class=\"data row0 col2\" >0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_0ba9f_row1_col0\" class=\"data row1 col0\" >1.0</td>\n",
" <td id=\"T_0ba9f_row1_col1\" class=\"data row1 col1\" >0.0</td>\n",
" <td id=\"T_0ba9f_row1_col2\" class=\"data row1 col2\" >0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_0ba9f_row2_col0\" class=\"data row2 col0\" >0.0</td>\n",
" <td id=\"T_0ba9f_row2_col1\" class=\"data row2 col1\" >0.0</td>\n",
" <td id=\"T_0ba9f_row2_col2\" class=\"data row2 col2\" >1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_0ba9f_row3_col0\" class=\"data row3 col0\" >0.0</td>\n",
" <td id=\"T_0ba9f_row3_col1\" class=\"data row3 col1\" >1.0</td>\n",
" <td id=\"T_0ba9f_row3_col2\" class=\"data row3 col2\" >0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_0ba9f_row4_col0\" class=\"data row4 col0\" >0.0</td>\n",
" <td id=\"T_0ba9f_row4_col1\" class=\"data row4 col1\" >1.0</td>\n",
" <td id=\"T_0ba9f_row4_col2\" class=\"data row4 col2\" >0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
]
},
"metadata": {},
"execution_count": 18
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "DZIhlDdKNRA8"
},
"source": [
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"numerical_pipe = Pipeline([\n",
" ('scaler', StandardScaler())\n",
"])"
],
"execution_count": 19,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "RwfiM8TKNRBD"
},
"source": [
"from sklearn.compose import ColumnTransformer\n",
"\n",
"preprocessing = ColumnTransformer(\n",
" [('cat', categorical_pipe, categorical_columns),\n",
" ('num', numerical_pipe, numerical_columns)])"
],
"execution_count": 20,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from sklearn.neighbors import KNeighborsRegressor\n",
"\n",
"knn = Pipeline([\n",
" ('preprocess', preprocessing),\n",
" ('knn', KNeighborsRegressor(n_neighbors=1))\n",
"])"
],
"metadata": {
"id": "q90veasnBAGS"
},
"execution_count": 21,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from sklearn import set_config\n",
"\n",
"set_config(display='diagram')\n",
"\n",
"knn.fit(X_train, y_train)"
],
"metadata": {
"id": "adLj3uqmBT1X",
"outputId": "3675e47f-8733-43ae-a450-7dbee7975987",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 192
}
},
"execution_count": 22,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Pipeline(steps=[('preprocess',\n",
" ColumnTransformer(transformers=[('cat',\n",
" Pipeline(steps=[('onehot',\n",
" OneHotEncoder())]),\n",
" ['class']),\n",
" ('num',\n",
" Pipeline(steps=[('scaler',\n",
" StandardScaler())]),\n",
" ['sepal length (cm)',\n",
" 'petal length (cm)',\n",
" 'petal width (cm)'])])),\n",
" ('knn', KNeighborsRegressor(n_neighbors=1))])"
],
"text/html": [
"<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocess&#x27;,\n",
" ColumnTransformer(transformers=[(&#x27;cat&#x27;,\n",
" Pipeline(steps=[(&#x27;onehot&#x27;,\n",
" OneHotEncoder())]),\n",
" [&#x27;class&#x27;]),\n",
" (&#x27;num&#x27;,\n",
" Pipeline(steps=[(&#x27;scaler&#x27;,\n",
" StandardScaler())]),\n",
" [&#x27;sepal length (cm)&#x27;,\n",
" &#x27;petal length (cm)&#x27;,\n",
" &#x27;petal width (cm)&#x27;])])),\n",
" (&#x27;knn&#x27;, KNeighborsRegressor(n_neighbors=1))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocess&#x27;,\n",
" ColumnTransformer(transformers=[(&#x27;cat&#x27;,\n",
" Pipeline(steps=[(&#x27;onehot&#x27;,\n",
" OneHotEncoder())]),\n",
" [&#x27;class&#x27;]),\n",
" (&#x27;num&#x27;,\n",
" Pipeline(steps=[(&#x27;scaler&#x27;,\n",
" StandardScaler())]),\n",
" [&#x27;sepal length (cm)&#x27;,\n",
" &#x27;petal length (cm)&#x27;,\n",
" &#x27;petal width (cm)&#x27;])])),\n",
" (&#x27;knn&#x27;, KNeighborsRegressor(n_neighbors=1))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocess: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;cat&#x27;,\n",
" Pipeline(steps=[(&#x27;onehot&#x27;, OneHotEncoder())]),\n",
" [&#x27;class&#x27;]),\n",
" (&#x27;num&#x27;,\n",
" Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler())]),\n",
" [&#x27;sepal length (cm)&#x27;, &#x27;petal length (cm)&#x27;,\n",
" &#x27;petal width (cm)&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>[&#x27;class&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>[&#x27;sepal length (cm)&#x27;, &#x27;petal length (cm)&#x27;, &#x27;petal width (cm)&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsRegressor</label><div class=\"sk-toggleable__content\"><pre>KNeighborsRegressor(n_neighbors=1)</pre></div></div></div></div></div></div></div>"
]
},
"metadata": {},
"execution_count": 22
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.metrics import median_absolute_error\n",
"\n",
"print(\"train error: %0.3f, test error: %0.3f\" %\n",
" (median_absolute_error(y_train, knn.predict(X_train)),\n",
" median_absolute_error(y_test, knn.predict(X_test))))"
],
"metadata": {
"id": "CmbjfLmABb1F",
"outputId": "f4e33eb0-1b32-4562-a2dd-29565bc312dd",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 23,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"train error: 0.000, test error: 0.300\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"def scatter_predictions(y_pred, y_true):\n",
" plt.figure(figsize=(6, 6))\n",
" plt.xlabel('true target')\n",
" plt.ylabel('prediction')\n",
" plt.scatter(y_true, y_pred, color=\"C0\")\n",
" plt.plot(y_true,y_true, color=\"C1\")\n",
"\n",
"scatter_predictions(knn.predict(X_test), y_test)"
],
"metadata": {
"id": "_qVERwk2BsR-",
"outputId": "b9862eb6-eaf7-4909-8c4c-c2f3ea185c60",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 545
}
},
"execution_count": 24,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 600x600 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"from sklearn.inspection import permutation_importance\n",
"\n",
"def boxplot_pi(model, X_test, y_test):\n",
" result = permutation_importance(model, X_test, y_test, n_repeats=10,\n",
" random_state=42, n_jobs=-1)\n",
" sorted_idx = result.importances_mean.argsort()\n",
"\n",
" fig, ax = plt.subplots(figsize=(8,6))\n",
" ax.boxplot(result.importances[sorted_idx].T,\n",
" vert=False, labels=X_test.columns[sorted_idx])\n",
" ax.set_title(\"Permutation Importances (test set)\")\n",
" fig.tight_layout()\n",
" plt.show()\n",
"\n",
"boxplot_pi(knn, X_test, y_test)"
],
"metadata": {
"id": "0eCWxLhCEMuj",
"outputId": "f48ff83a-caa9-4e70-ea43-75759dbdf68e",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 607
}
},
"execution_count": 25,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 800x600 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"y_pred = knn.predict(X_test)"
],
"metadata": {
"id": "-CVcFyCSwIbb"
},
"execution_count": 26,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from sklearn.metrics import r2_score"
],
"metadata": {
"id": "wOkdo_DgwbZA"
},
"execution_count": 27,
"outputs": []
},
{
"cell_type": "code",
"source": [
"diff = pd.DataFrame()\n",
"for col in X_test.columns:\n",
" diff_col = []\n",
" for n in range(100):\n",
" X_aux = X_test.copy()\n",
" X_aux[col] = np.random.permutation(X_aux[col])\n",
" y_pred_aux = knn.predict(X_aux)\n",
" diff_col.append(r2_score(y_test, y_pred) - r2_score(y_test, y_pred_aux))\n",
" diff[col] = diff_col"
],
"metadata": {
"id": "D9R4ZZu1vtx6"
},
"execution_count": 28,
"outputs": []
},
{
"cell_type": "code",
"source": [
"diff.median().sort_values(ascending=False)"
],
"metadata": {
"id": "EAbyIN4wwQSh",
"outputId": "64f472f6-0e1a-4622-b4a5-6f9b60029bb5",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 29,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"sepal length (cm) 0.448502\n",
"class 0.311440\n",
"petal length (cm) 0.132039\n",
"petal width (cm) 0.114099\n",
"dtype: float64"
]
},
"metadata": {},
"execution_count": 29
}
]
},
{
"cell_type": "code",
"source": [
"import seaborn as sns\n",
"\n",
"sorted_idx = diff.median().sort_values(ascending=False).index\n",
"sns.boxplot(diff[sorted_idx], orient=\"h\");"
],
"metadata": {
"id": "ndQy-OQTwuNa",
"outputId": "9de7c449-dff4-47ce-f352-dbdb41cfd9e2",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 430
}
},
"execution_count": 30,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "dzk8LAsaNRBR"
},
"source": [
"from sklearn.linear_model import LinearRegression\n",
"\n",
"lm = Pipeline([\n",
" ('preprocess', preprocessing),\n",
" ('regressor', LinearRegression())\n",
"])"
],
"execution_count": 31,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"scrolled": true,
"id": "UMRyr788NRBc",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 192
},
"outputId": "fd44c594-7643-4658-c341-4e8bdbba4918"
},
"source": [
"from sklearn import set_config\n",
"\n",
"set_config(display='diagram')\n",
"\n",
"lm.fit(X_train, y_train)"
],
"execution_count": 32,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Pipeline(steps=[('preprocess',\n",
" ColumnTransformer(transformers=[('cat',\n",
" Pipeline(steps=[('onehot',\n",
" OneHotEncoder())]),\n",
" ['class']),\n",
" ('num',\n",
" Pipeline(steps=[('scaler',\n",
" StandardScaler())]),\n",
" ['sepal length (cm)',\n",
" 'petal length (cm)',\n",
" 'petal width (cm)'])])),\n",
" ('regressor', LinearRegression())])"
],
"text/html": [
"<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocess&#x27;,\n",
" ColumnTransformer(transformers=[(&#x27;cat&#x27;,\n",
" Pipeline(steps=[(&#x27;onehot&#x27;,\n",
" OneHotEncoder())]),\n",
" [&#x27;class&#x27;]),\n",
" (&#x27;num&#x27;,\n",
" Pipeline(steps=[(&#x27;scaler&#x27;,\n",
" StandardScaler())]),\n",
" [&#x27;sepal length (cm)&#x27;,\n",
" &#x27;petal length (cm)&#x27;,\n",
" &#x27;petal width (cm)&#x27;])])),\n",
" (&#x27;regressor&#x27;, LinearRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocess&#x27;,\n",
" ColumnTransformer(transformers=[(&#x27;cat&#x27;,\n",
" Pipeline(steps=[(&#x27;onehot&#x27;,\n",
" OneHotEncoder())]),\n",
" [&#x27;class&#x27;]),\n",
" (&#x27;num&#x27;,\n",
" Pipeline(steps=[(&#x27;scaler&#x27;,\n",
" StandardScaler())]),\n",
" [&#x27;sepal length (cm)&#x27;,\n",
" &#x27;petal length (cm)&#x27;,\n",
" &#x27;petal width (cm)&#x27;])])),\n",
" (&#x27;regressor&#x27;, LinearRegression())])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocess: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;cat&#x27;,\n",
" Pipeline(steps=[(&#x27;onehot&#x27;, OneHotEncoder())]),\n",
" [&#x27;class&#x27;]),\n",
" (&#x27;num&#x27;,\n",
" Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler())]),\n",
" [&#x27;sepal length (cm)&#x27;, &#x27;petal length (cm)&#x27;,\n",
" &#x27;petal width (cm)&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>[&#x27;class&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>[&#x27;sepal length (cm)&#x27;, &#x27;petal length (cm)&#x27;, &#x27;petal width (cm)&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div></div></div>"
]
},
"metadata": {},
"execution_count": 32
}
]
},
{
"cell_type": "code",
"source": [
"!pip install -q shap"
],
"metadata": {
"id": "aEnZLzSgxgMe",
"outputId": "0c86325e-7b25-4052-9e6a-e1feaac39e2f",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 33,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/532.9 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m41.0/532.9 kB\u001b[0m \u001b[31m1.1 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━\u001b[0m \u001b[32m409.6/532.9 kB\u001b[0m \u001b[31m5.7 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m532.9/532.9 kB\u001b[0m \u001b[31m5.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h"
]
}
]
},
{
"cell_type": "code",
"source": [
"import shap\n",
"\n",
"explainer = shap.Explainer(lm.predict, X_train)"
],
"metadata": {
"id": "eM9xA30DxaU8"
},
"execution_count": 34,
"outputs": []
},
{
"cell_type": "code",
"source": [
"shap_values = explainer(X_test)"
],
"metadata": {
"id": "vTVBK1MVyH0T",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "971f7e4c-1b91-465c-b1f1-115b76209257"
},
"execution_count": 35,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"ExactExplainer explainer: 39it [00:11, 3.23it/s] \n"
]
}
]
},
{
"cell_type": "code",
"source": [
"shap.plots.waterfall(shap_values[0])"
],
"metadata": {
"id": "yKkqztX-ydYx",
"outputId": "0d517fd0-5766-4bcb-ce8c-f88372cf65ef",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 386
}
},
"execution_count": 36,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 800x350 with 3 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"shap.plots.waterfall(shap_values[5])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 386
},
"id": "2TTNLtkDH8rt",
"outputId": "cc349dbd-999f-4e13-f7eb-2322723dbebe"
},
"execution_count": 37,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 800x350 with 3 Axes>"
],
"image/png": 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h8PHxQUxMDMLDw7meDxERNQ1VaVUS2VBTQ4GzCX8lkQAQdws4HAVMD9Mv72ADvP8Y8NbPQF6R/n4ikk6lBthyGmZmZlJHYpKYSFKD5OTkICYmBl5eXhg5ciS6deuG0NBQhIaGIi0tDdeuXZM6RCIiIl2CAPT0BM7V8B51NhHo6gbYKHW3vzcTyMgD1h5olhCJyEhxtxBk5SJ1FCaJiSQ1SHWiGBQUpLM9ICAAcrkcCQkJUoQFALh+/TqmTZsGV1dXKBQK2NnZoU+fPtiwYUOdx509exaTJ0+Gu7s7lEolLCws0KVLF7z33nt6ZdPS0jBjxgy0b98eCoUCNjY26NKlCxYuXKhTbsWKFfD19YWVlRUsLCzg6uqKESNGIDk5uVGvmYiIDOBoAyjNgfQaxlNVb+vo+Ne2IE/g2ZHAK+sAjaZ5YiQi48gEpK/ZKXUUJoljJKlBbt++DUEQ4OrqqrNdLpfDyckJt2/frrcOURRRVlZm8DktLCzqnSAnJiYGQ4YMQX5+PkaOHIng4GCoVCpERERg3759eOyxx2o9du/evTh//jyGDRsGHx8fqFQq7NmzB2+99RaysrLwxRdfaMuOGTMGMTExmDx5Mnr27ImSkhJcuXIFv/32m7bMihUr8Prrr6N79+54+eWXYWlpieTkZBw7dgypqansy09EJDcD7K10tynkgIVCf2bUHBVwv8MmLM2rvpdV6O8rLdctAwD/fhLYdwE4GHl/5yWipiMCtgdjpY7CJDGRpAYpKiqCUqmssU+6tbU1MjMzUVlZWWefdZVKhY0bNxp8zpkzZ8LWtu4p1+fNm4fc3FysX78es2fP1tlXWVlZ57ELFy7EsmXLdLatWLECvXv3xnfffYePP/4YFhYWuH37NqKiojBp0iRs2bKl1vp27doFpVKJixcvwtzcvNZy9cnJyYG1tTUsLCwAVD1voihqn4vy8nIUFhbCyclJe0x6ejrc3NxqfZyRkYH27dtrE3NTOYdGI0IAwPl6iVqIQQHAMf1eHxgUAMwcrLvN61ngZv0fUtap5M9k0aKGCeGU5rplpg8CwvyBHn+/v3MSUdMSRVim5bWIv0tMDRNJahC1Wg2ZrOae0dXJo1qtrjORtLS0xNixYw0+p6WlZZ3709LScP78efTr108vibw7rtq0a9dO+3+VSoX8/HyIooihQ4ciJiYGEREReOCBB2BjYwO5XI6oqCjExMSgR48eNdZna2uL8vJy/PDDD3jqqadqfb7q4+joqPPYxkZ3enpzc3OdFzkAei9q9z7u0KGDSZ5DJmMKSdSiRN4ARryju+2TuUBGLrDynq5qGXn3f74cVVXLo5uD/r7qbWk5Vd9XzgE2nwbK1YDnn+Ov2llXfXd3BszlNXeRJaLmJTdD5fjgFvF3ialhIkkNIpfLUVpaWuO+6pY/ubzuHy+5XI7OnTs3WkxRUVEQRbHWxK4+ubm5eOGFF3DgwAHcuXNHb391d11LS0ssWbIEH374IYKCgtC5c2cMHDgQ06ZNw4wZM7TlP/jgA1y4cAHPPvssXn31VfTq1QujRo3C888/r/fCRERkkvKKqmZLvVuuqipBu3d7YxBFIDoZCK5h8fKBvsC1jKpZYQHAwwWY5QLMGqJf9uInwKUkoM+rjR8jERlHXYmUvm6o4beamhgTSWoQa2tr5OXl1dh9ta5ur3fTaDS1JqM1USqVDW7VM8SYMWNw9uxZjB8/HkOHDoWrqyvMzMywc+dObNq0Sadr7LJlyzBnzhxs2LABJ06cwKFDh7B161asWbMGR48eBQD07dsXSUlJ+OWXXxAeHo6IiAi89dZb+Oyzz3DkyBH07t27ya6FiIhQ1XJoZVG1vEe1LaeB5Y8D/bpUrUkJVK0rOTwI+PiuVtBJH+nX97cHqr4e/xxI1f/AkYgkYGUBVX8vqaMwSUwkqUFcXFyQmpqKrKwsnWZ9tVqNO3fuGNTUX1RU1KhjJHv27AlBEHD58mWD66yWmZmJs2fP4uGHH8bOnbrdqfbt21fjMV26dMGbb74JAKioqMCoUaNw9OhRhIeHY9SoUQAAKysrzJ07F3PnzgUA/PDDD5g3bx7effddbNu2zeg4iYgIwNKpVd8D3au+Pz4UeKBb1f8/uGvs+k8vAcN6AMKUv7Z9tQ94egSwd2lV4lhRCbwyHsjMAz7Z9Ve5nWf1z9vbu+r7vgvAncJGuxwiaiAzGfBIX3j5+0odiUliIkkN0qVLF1y8eBHR0dE6SePVq1ehVqvRtWvXeuto7DGSHTt2RHBwMCIiIrBx40bMnDlTZ79Go6m1RbO6G654z4yA165dw/bt23W2FRQUAADs7Oy02xQKBXr06IGjR49qu8Cmpqbqdd0dMqSqi1ReXl6d10JERHV4/54ZuJ8c8df/P6h9EjQAVV1Xh70FfDYPeGMqIJMBx2KAheuA7ILGj5WImk6lBpg0EIWFhbC3t5c6GpMjiPf+5UxkoN9++w2XL1+Gl5cXPDw8kJubi5iYGHTo0AHjxo2rd6mOphAVFYUhQ4agsLAQo0ePRr9+/VBcXIxz587B3d0d69evBwCMHj0a4eHhOoljv379cPHiRYwbNw79+/fHjRs3sHnzZri6uuLatWvYvHkzpk6diqNHj+KRRx7BkCFD0L17dzg6OiI2NhZbt26Fvb09EhMTYWdnBx8fH9ja2mLAgAHa52fLli1ITU3F999/r22lpGY0+SNgRw2tDERERNT6mMmA7B9x6UYChwxJgC2S1GChoaGwtbXFlStXkJycDKVSiR49eiA4OFiSJBKo6t4aERGB1157Db/99hvCw8NhbW2NLl26YNy4cXUeu3PnTjz77LM4efIk9u3bBzc3N7z22mtQKBRYsmSJtpyfnx/Gjx+Ps2fP4sSJE1Cr1XB0dMTEiRPxr3/9S9tSOXfuXGzfvh2bN2+GSqWCjY0NfH198cknn2DatGlN+jwQERERtWkyARjSHWhnLdnfnaaOLZJEZDrYIklERNQ2CALwxVPAC2OkjsRkNd0UmERERERERE1BFIEJ/QGgQRMt0v1jIklERERERK1LT8+qJX5QNXs+NT8mkkRERERE1HqYyYCpodqH7dq1ky4WE8ZEkoiIiIiIWo9KDTBxgPahi4uLhMGYLiaSRERERETUenR2AoI8tQ8TEhIkDMZ0MZEkIiIiIqLWQW5W1a2VS35IjokkERERERG1DupKYNJAnU2enp61FKamxESSiIiIiIhaBzsrYFCAzqaSkhKJgjFtTCSJiIiIiKjlk8uAif2rurfeJSsrS6KATBsTSSIiIiIiavnUGmDywPrLUbNgIklERERERC2fuRwY2Vtvc8+ePZs/FmIiSURERERELZyZDHi4F2Ct1NsVFxcnQUDERJKIiIiIiFo2Te3dWsvKypo5GAKYSBIRERERUWswLrjGzXZ2ds0cCAFMJImIiIiIqCUTAAzwA9q3q3G3m5tbs4ZDVZhIEhERERFRyyUIwKMhte7mGElpMJEkIiIiIqKWSyMCkwZIHQXdQy51AEREzeblcUBxudRREBERkTHa2wO+HWvd7e7u3ozBUDVBFEVR6iCIiIiIiIgaIiMjAx06dJA6DJPDrq1ERERERNRqZWRkSB2CSWIiSUREREREREZh11YiIiIiImq11Go15HJO/dLc2CJJREREREStVmJiotQhmCQmkkRERERE1GqVlpZKHYJJYiJJREREREStlo2NjdQhmCSOkSQiIiIiolarrKwMFhYWUodhctgiSURERERErdaVK1ekDsEkMZEkIiIiIiIiozCRJCIiIiKiVqtTp05Sh2CSuOAKEdGf3jtdiZ2JHDZORDXrbAP89xEz2JgLUodCRHfhlC/SYCJJRARAI4pYfUFEVonUkRBRS3U+E4i6DYSx8YOoRUlLS4Orq6vUYZgcdm0lIgJwLgNMIomIiIgMxESSiAjAzkQNzNhbjYiIqNXp3r271CGYJCaSREQAtsSLqOQQCyIiolbn5s2bUodgkphIEpHJS8wVEZ8rdRRERETUEEVFRVKHYJKYSBKRyduZKELGbq1EREStkpWVldQhmCQmkkRk8rbGa8CZw4mIiFonb29vqUMwSUwkicik3S4WcSYdYB5JRETUOl2+fFnqEEwSE0kiMml7rolMIomIiIiMxESSiEzatgQu+0FERNSaubm5SR2CSWIiSUQmq7hCxMGb4LIfRERErZiZmZnUIZgkJpJEZLIO3BBRVil1FERERHQ/UlNTpQ7BJDGRJCKTtTNRhJzdWomIiIiMxkSSiEySWiNie6IINbu1EhERtWoBAQFSh2CSmEgSkUn6/RaQXyZ1FERERHS/bt26JXUIJomJJBGZpJ2JGsj5CkhERNTqFRYWSh2CSeKfUURkckRRxOZ4EWqN1JEQERHR/VIqlVKHYJKYSBKRybmcDaTww0siIqI2wdfXV+oQTJJc6gCIiJrbjkQRZgLXjzQ19hbAiiEyTPYVYKUAzqaLePWYBhez6j923WgZ5vbQ/+z16h0R3dbpryHjYw+894AMIzwF2CqAVBXwS5yIN06xGZyIqLFFR0ejd+/eUodhctgi2QadO3cO/v7+8PT0bNLzfP755xAEAe+88069ZePi4hAWFgZBMGythca8hpycHMyaNQu2tra4c+fOfddnLLVajQ8++AAymQzHjx9v9vOTvq3xGiaRJkYAsHeKGR7rJmD1RQ0WHdfA1UrAsRlm6NrOsDpK1SJm763U+frHcf3EsJcLcP5xM/RyEfDJOQ1ePKLBxqsiOto06iURERFJqslaJEVRRHR0NK5cuQKVSgWlUgkfHx8EBwdDoVAYXZ9arcbmzZtRWFiI7t2744EHHmiCqKUVFxeHVatWYdSoUZg0aZLU4TS5tLQ0rF27Fra2tnjttdea7DyHDh3C3r178corr8DJyanJzlMbuVyOiRMnYtu2bVi4cCHOnz9vcEJNjS+1UMSl21JHQc1tqr+AQZ0ETN1Via3xVZ8i/BJXifgnzbBskAyz9tbfUqjWAP+7UvcnEAKA9WPNcDUHePCXSpSqq/fwkwsioqbSvn17qUMwSU3WInn69GmcOXMGDg4OCAsLg4+PD2JiYhAeHg5RNP4N9dy5cygtLW2CSFuO+Ph4rFmzBjt27JA6FIO88MILUKlUeOONNxp0fFpaGn7++Wd88cUXjRzZXzIzM7Fx40YUFxdjwYIFTXae+vj7++Phhx/GxYsX8euvv0oWBwG7EkUwjTc9U/0EZBSJ2Bb/1/tPdklVd9OJXQWYmxlWj0wAbM1r3z/SS0CQi4BlpzUoVQOW8qpjiIio6VhYWEgdgklqkkQyJycHMTEx8PLywsiRI9GtWzeEhoYiNDQUaWlpuHbtmlH1ZWdnIzo6Gv369WuKcKmB5HI5rK2tIZe33KG20dHROHXqFEaPHg0XFxfJ4lAoFBg5ciScnZ3x9ddfSxYHAdsSNGCDsOnp4yrgQqao1y54Nl2EtUKAn0P9dVgpgIKXzFDwkhx3XjDD6odksL6ng80Iz6ofrjI1EDHbDMV/l6P4ZTNsHCeDAycVJCJqEsnJyVKHYJKaJJGsThSDgoJ0tgcEBEAulyMhIcHgujQaDU6cOAF3d3d4e3s3apwNcfdYv4MHD2Lw4MGwsbGBUqlEWFgYLly4oHfM7du3sWTJEvj5+UGpVMLCwgI9evTAL7/8oi3z+eefY8KECQCAH3/8EYIgaL8AICsrC2+99RaCg4Ph5OQEhUIBOzs7jBw5ElFRUQ26lvPnz+uNQywuLsZTTz0FQRDQs2dP7fbc3FzMmjULgiDgjz/+0MZc0xjJS5cuYfz48WjXrh3Mzc0RFBSEU6dO6ZQ5duwYRo8ejfj4eCQnJ+tc77Fjx7TlRFHE3r17ERoaCmtra1hYWGDo0KGIj4+v9/o0Gg2OHTuG7OxsPPLII3r7KysrcejQIYwdOxYuLi5QKBSwt7dHaGgoDh48COCvsZoeHh7YunUrgoODYWVlBRsbGzz22GNQqVS4du0aHnvsMbi4uMDc3Bzdu3fHb7/9pnc+Dw8PdO/eHeHh4VCpVPXGT40vv0zE8RRAw16GJsfNGkgv0t9eva2jTd2fLqQXASvOipi3X4O/7a7ErmsiXugjw/5HzWB216G+fyakv4yX4WqOiEd3VmJ5hIhHfQXsnmxgsycREVEr0CRNSbdv34YgCHB1ddU9mVwOJycn3L5t+ACl6Oho5OXl4eGHH25QLGVlZQZ3pVUoFDAzM/yN/tlnn4WrqyteeOEFxMfHY//+/Rg8eDD++OMP9OjRA0DVJyTPPfccfv31V4SFhWHKlCkoKirCgQMHMGPGDNy+fRsvvPACHn74YSxYsACrV69GcHAwZsyYAQBwcKj6q+TGjRvYsmULXFxcMHv2bNjb2yMqKgqHDh1CWFgYLl68aPTUxx07dkRAQAB27dqFa9euoUuXLrh16xbi4uIgCAJiYmKQnZ0NZ2dnpKWl4erVq7C1tUVwcHCtdUZHR2PevHm4dOkShg4dipCQEERGRmLBggVwdHTUluvWrRsWLFiAtWvXoqysDEuWLAEAWFtbo1u3bkhJSQEA5Ofn46mnnkK/fv3w8ssvIzIyEgcOHMD48eMRGxtb5/3Kzs5GbGwsAGDAgAE6+yorK7Fu3Tr84x//QEFBAcaPH48ePXrgzp07iIyMxKFDh3R+5vLz8/HCCy8gJCQEDz/8ME6dOoWNGzciPz8fZWVlSEtLw9y5c5Geno6dO3di3LhxSE5Ohq2trbaO9u3bw8fHBydOnNC2klLz2pckQs0kstUTAIO7opb9OaGqpfyv/9+tegyjZT3vhktO6o6h3BQnIj5XxIeDzTDVT8CmuKofLJs/WygjMkU8/mvVMdsSRBRXCPhoiBke8hBwOJk/hEREjcnPz0/qEExSkySSRUVFUCqVNf6Rb21tjczMTFRWVtabtBUUFOD8+fPo27cvbG1tUVho/MJvW7duNbj1Z+jQofD39ze47p49e2L79u0QBAHl5eVYuXIl3nzzTbz66qsIDw+HWq3Gjz/+iF9//RWLFy/Gv/71L+2xUVFRmDNnDhYvXow5c+age/fuGDlyJFavXo3AwEC9yWf69OmDiIgIWFtba7ep1Wr8+9//xuuvv47PPvsMX331lcGxA1WJTffu3bFr1y4cOXJEm0jGx8fjgQcewMmTJ3HkyBFMnz4dqampiI+Px9ChQ2u9b2VlZVi3bh0uXbqExx9/HD/99BMAQKVSYfHixfjyyy91zj1u3Dhs3LgRpaWletdbnUgWFBRgyZIleP311wFUtcw+99xz2LZtGw4dOoRRo0bVen25ublIT08HAHTp0kVnX3R0ND7//HPk5eVh//79OvWIoqj34UNBQQGWLl2KRYsWAQBiYmLw+OOPY9++fejduzciIyOhUChQUlICBwcHrF69Ghs2bMCzzz6rrcPW1lbbvfby5ctMJCWwP0mEXACTyVZuiHvVbKuGCPhejbgcoEQNWNRwiPLPd8EStf6++nx2XsR7g0SM8PwrkayuZ+M9k/JsuCLioyFAWCfgMHtgERE1qoyMDPj4+Egdhslpkq6tarUaMlnNVVcnIWp1/e/ap06dgq2trU4XS2MNHz4cY8eONejL3d3dqLoXL16s7Xpqbm6OsWPHolu3bjh8+DBUKhVu3bqFEydOQBAEzJw5E6mpqdova2tr9OrVCyqVCqdPn673XAqFQptEVlRUICsrCxkZGfD19UX79u1x5swZo58bmUyG0NBQKJVKHDlyBEBVgpuVlYUJEybA3d0dhw8fhiiKiIiIgEqlwkMPPVRrfampqdquvdWJHwDY2Nhg+vTpDZpRSyaT4eWXX9Y+dnV11bb21tdFuqSkBCqVCnK5HHZ2djr7Ll68iMuXL2PUqFF6yaggCHo/v2ZmZnjppZe0jzt06ABfX1+Ioojnn39eOxOxpaWldixvTfFVt8pmZRmwcN2fcnJyUFZWpn2sUql0PlQpLy/XW9akOoGu7XFGRoZOsmwq5+hqVwkNZ89s9a7eETF3X6VBX+l/fo6YXlTVvfVe1dvSVMb/XJSqgTulgONdYx/T/jxfZrFu2aw/HztYcIBuW9BWXhN5Dp6jrZyjoKCgRVyHqWmSFkm5XF7rDKuVlZXaMnVJSEhAamoqJkyYUGtSaogOHTo0+Nj6dOvWTeexg4MD3NzcEBsbi5s3b6KiokL7Q9urV69a68nMzKz3XCUlJfjf//6H1atX4+rVqzo/+AAatKQKUDVur0uXLjhy5Ii2BVgul2P06NHYt28fDh8+jDt37mjHYQ4fPrzWugoLC3H79m3IZDK9LgbVz40h13o3Nzc3KJW6M1RUJ2P1rQlZ1xIbiYmJEEURffv2NSiODh066MRhYWEBKysrANAbu1u9xEhN8VW/gBmz/MfdXYKBqsT8bubm5nrLmri5udX5+N7fC1M5x6MBCrx5uob+jdSqZBYDP142LvG7lCVicGcBAnQX4hjoJqCoQkR8rvFx2CgAZ0vgdslf285nVtXe6Z41I6vXkLxdwg8y2oK28prIc/AcbeUc5ubmLeI6TE2TJJLW1tbIy8ursftqXd1eq1VWVuL06dPw8PCApaUl8vPztccCVZ8Q5OfnayeuqUtJSYnBYyTNzc0bfQZSURQhCAJ++umnGq9ZqVQiJCSk3jp+/PFH/P3vf4etrS1eeukl+Pn5wcrKCrdu3cKqVaug0dS/BlpNOnbsCH9/f1y+fBnHjx9HXFwcgoOD4evri4CAABw5cgTnz59HXFwcnJyc7qt1uCHq+jmp774qlUrY2tpCrVYjPz8f9vb2TRJHbftqii83t+qvVSlnkDVlAY6Alx1wo0DqSKi5bYkXMc1fhil+gnYdSSdLYJq/gN3XRJTf9fmCz58vFder3npgYQYoZICqQrfON0NlkAkC9if99fq7M1HE58NFzOshww8xldqk9amgqg9ED95kIklE1Njubdyh5tEkiaSLiwtSU1ORlZWlk6mr1WrcuXOn3uxdrVajtLQUycnJNU7nm5iYiMTERAwcOLDOlj4A2L59e5ONkbxy5YpOElg9Js/MzAyenp64ffs2XF1dcfnyZfTr16/BP+Q5OTk4evQoysrKsG/fPjz44IPafYcOHYJKpUK7du0aVLezszOCgoKwbds2hIeHIz4+Hi+88AIsLCwQEhKCNWvWYN++fUhMTMTYsWPrbEmrHgMYGxuL+Ph4BAYGavfdPV6xmjGtcg3h6OiIjh07Aqhq4b57kiBfX18IgoCLFy82aQx3Kyws1HZpre6eS81LEARM9ROw6jwn3TE1W+JFnE4TsW60DN2dNMguAZ7vLYOZALz9m+4HcYenV3045P1NVXbZwRq4OMcMG6+KuHqn6gdnlLeAR3xk2Jekwc7Ev36YMouBD85o8N4DZtg/VYYdiSJ6uQh4uqeADVc0OJfRTBdMRGRCIiMj0bt3b6nDMDlNkkh26dIFFy9eRHR0tE7SePXqVajVanTt2lWnfEFBATQajTYZUigUGDFihF69paWlOHXqFNzd3eHv76/XJF2T4cOHGzQeE9Bv4q7PRx99pDPZzq+//oorV65gxIgRsLGx0S5VcfToUbz++uvYvn27TuuVWq1Genq6dmymQqGAhYUFcnJydM5z95i9e/t2//TTTygoKGhwIimTyTBw4EBYW1tj+/btyM3N1XZf9fX1hZeXFzZv3ozi4uI6u7UCQOfOndGnTx8cP34cy5cv15lsZ/PmzXrdWquvNz09Xdty25icnJzQvXt3bN26FWfOnNFJJPv06YPAwEDs378fhw4d0vl5a0j3U0NkZmYiKSkJcrkcgwYNatS6yXCTfGX4+By7t5oajQiM3VqJlUNleKmPDJYKICIDmLuvst5urXllwJ5rIh72FPBEoAAzAUjMA/55ohIfn9Nfm/L9MyJyyyrxYh8ZVj0oIKMI+OCMiHdPN6znCBERUUvUJImko6MjAgMDcfnyZRw4cAAeHh7Izc1FTEwM3Nzc9BLJPXv2QKVS4ZlnngFQldzUNPNS9QBYW1tbg2dmasoxklFRUQgLC8PQoUMRFxeH/fv3w9LSEh9//DGAqkRp/vz5uHDhAnbt2gV/f3+MGTMGjo6OSE1NxcWLFxETE4Py8nIAVRPJ+Pj44MCBA/jHP/6Bjh07wsnJCbNnz0ZYWBi2bduGGTNmYPbs2VAoFDh16hSio6Pvu5tk586d4evri0uXLmnXwwSATp06wdfXF+Hh4QDqHh8JVI0bnDdvHo4cOYL169cjOTkZISEhiIqKwtGjR9GxY0ekpaVpy1dfb3R0NGbPno2+ffvC3t5eu57m/ZLJZHjwwQexZs0a7N27FwsWLNDu69GjB1566SW89tprGD16NCZMmIDAwEDk5uYiKioKoaGhWL58eaPEUS05ORmxsbEYNWqUXj98aj4hboCDBZBbVn9ZalvyyoCnD2jw9IG6y1W3RFbLLwPm7DMuCfzyoogvL/IDCyKi5sAhQ9JokkQSAEJDQ2Fra4srV64gOTkZSqUSPXr0QHBwcJN3aWwua9euxbJly7B69WpUVFSgb9++WL16tc44Qnd3d6xbtw5r1qzBzz//jO+//x7l5eWwt7eHv78/PvnkE23Znj17YunSpfjoo4+wevVq7YRFc+bMwdy5c1FeXo61a9fi66+/hpmZGQYMGIBvvvkGb7/9dq2TGxmiU6dO8Pf3x6VLlxAaGqodd9qhQwd0794d4eHh2rGU9enZsyfWrVuHt956CydPnsTvv/8Of39/rF69Gt99951OItmhQwe8/vrryM/Px+7du7Fx40aIooiuXbs2WqIVGBiIwYMHY+fOncjMzNTOHGtmZoZ58+bB3d0dn3zyCY4fP45du3bBxsZGuxRLY6qoqEB4eDiys7Px3HPPNWrdZBwzmYDJvgJ+ihWhZgMRERFRq3f38njUfATR0JloCAAQFxeHefPm4fTp0wZP4kPS2rJlC5566iksWLAA77//viQxREdH44knngAAnD9/vs18mNJa7b6mwYTtzCKJyHi/zTRDWCe+hhO1JJcuXeIYSQk0yTqSRC3J8OHDMW7cOHz++ef1LhnSFNRqNXbs2IFLly7hs88+YxLZAozwEKA0bD17IiIiIqoBWySNxBZJorZh0g419lwDKvlrTERGYIskUcujUqk4/4QE2CJJRCZpclcZk0giIqI2IDs7W+oQTFKTTbbTVvn7++P333+XOgwiuk/juggQAL2lG4iIiKh1ycvLkzoEk8QWSSIySU6WAgZ1AthBjYiIqHWTy9k2JgUmkkRksqb4ysC5j4iIiFq3Hj16SB2CSWIiSUQma2JXARr2bSUiImrVIiMjpQ7BJDGRJCKT5dNOQDdHqaMgIiKi+8GVFKTBRJKITNpUPwFm7N5KRETUajk5OUkdgkliIklEJm0ilwEhIiJq1ezs7KQOwSQxkSQik9a3PdDBSuooiIiIqKGSkpKkDsEkMZEkIpMmCAKm+AmQ89WQiIiIyGD804mITN6krgLUGqmjICIioobw8fGROgSTxESSiEzeUHcBNgqpoyAiIqKGyMvLkzoEk8REkohMnrmZgHE+AuScvZWIiKjVycnJkToEk8REkogIwCRfAWrO3kpERNTqyGRMaaTAZ52ICMAYb7ZIEhERtUY9e/aUOgSTxESSiAiAnYWAYR5SR0FELZm5DHDlckFELU50dLTUIZgkudQBEBG1FD+MNkNWsdRREFFL5agEPO3ZdYGopamsrJQ6BJPERJKI6E+dbAV0spU6CiIiIjKGg4OD1CGYJHZtJSIiIiKiVsvJyUnqEEwSE0kiIiIiImq1EhMTpQ7BJDGRJCIiIiIiIqMwkSQiIiIiolbLy8tL6hBMEhNJIiIiIiJqtVQqldQhmCQmkkRERERE1GplZ2dLHYJJYiJJRERERERERhFEURSlDoKIiIiIiIhaD7ZIEhERERFRq3X58mWpQzBJcqkDICIiahIxN4HrmVJHQa3NyN6A0lzqKIjICBUVFVKHYJKYSBIRUduTUwgELwLK+McFGWn3EmBcsNRREJER7O3tpQ7BJLFrKxERtT2lFUwiyXhyGbDjrNRREJGR2rdvL3UIJomJJBEREREAqDXA9jNAZaXUkRCREeLj46UOwSQxkSQiIiKqlqMC/kiQOgoiohaPiSQRERFRNbkM2MnurUStiYeHh9QhmCQmkkRERETV1Bpg82mpoyAiI5SWlkodgkliIklERER0t6RM4Gqq1FEQkYGysrKkDsEkMZEkIiIiuptM4OytRET1YCJJREREdDeNCGxl91ai1iIoKEjqEEwSE0kiIiKie52/BqTnSB0FERmAy39Ig4kkERER0b1EALvPSR0FERmgrKxM6hBMEhNJIiIionvJBGD7H1JHQUQGsLW1lToEk8REkoiIiOheGhE4FAUUlkgdCRHVo1OnTlKHYJKYSBIRERHVRF0JhF+UOgoiqsfVq1elDsEkMZEkIiIiqolcxmVAiIhqwUSSiIiIqCZqDbDrLFChljoSIqpD586dpQ7BJDGRJCIiIqpNYSlw8orUURBRHdRqftgjBSaSRERERLWRmwE7OHsrUUuWkZEhdQgmiYkkERERUW3UlcDW04AoSh0JEVGLIpc6ACIiImoi9lbAijnA5IGAlQVwNgF49Ufg4vX6j31qBDB7KBDQCWhnDaTlAMcuA8s2ATdv65b9v1HA8CBgoC/g4QL8cASYt7pprkkKablA5A2gt7fUkRBRDXr06CF1CCaJLZKtUFxcHPz9/eHu7l5v2XPnzsHZ2RmDBw9uhsgaz7FjxyAIAubOnXvfdSUnJ6N///4YOHDg/QfWAMXFxZg5cyYcHByQm5srSQxEZIIEAdj7BvDYYGD1PmDRT4CrPXDsXaCrW/3H9/EBkjKBFTuA59YC/z0BjOkDRKwA3Bx0y74+uSqRvJzSNiemMZOxeytRC3bt2jWpQzBJDW6RvHjxIrKzs5GdnY3CwkLY2NjgscceM6qODRs2QKVS1bhvzpw5UCqVDQ2vRbpx4wY+/PBDjB07FpMmTZI6HMk1x/Oh0Wjwww8/4Ny5czhy5EiTnKM+VlZWePbZZ7F//34sW7YMq1atkiQOImpjjr4L3MiqveVvaigwKACYurKqayYA/PI7EL8aWDYDmLWq7vpf+I/+th1/AOc/BuYMA5Zv/2v70DeB5D9bKQv/Z+yVtHyVmqrn8J2/SR0JEdWgpKRE6hBMUoMTyYiICFhYWMDZ2Rnl5eUNDqBdu3bo06eP3naFQtHgOluqGzdu4JtvvkF5eTkTSTTP83H9+nVs2rQJQUFBePDBB5vkHIbo168fBg8ejK+//hpvvvkmnJycJIuFiEzE1FAgIxfYduavbdkFVcnk7CGAuRwoN7L18EZW1fd21rrbk2/rl21rYlKqrt/LVepIiOge1tbW9ReiRtfgrq1/+9vf8MQTT+CRRx6BlZVVgwOwtLSEr6+v3peZmVmD6ySqdvjwYVy5cgXz5s2TNA5bW1uMHz8e5eXl+OGHHySNhYhMRB9v4MJ1/UliziYA1krAr6Nh9TjaAC72QL8uwLoXq7Ydjm7cWFsDQQB2RUgdBRHVwMPDQ+oQTFKDE0k7O7tGC0Kj0dxXq2ZjSktLQ1hYGARBwIkTJzBkyBDY2NhAqVQiJCQEFy5c0DsmPz8fb775Jvz8/KBUKmFhYYHAwED8/PPP2jLffvuttkXsxx9/hCAI2i8AyMvLw7JlyxAcHAwnJycoFArY2dlhxIgRiIqKavTrrE5o+vfvD1tbWygUCri7u+PNN9/UWYvn7vGYBw8eRFhYGKytrWFhYYHBgwcjPj5ep16NRoM9e/ZgwIABsLS0hJWVFUaMGIGdO3fqjNWs7/m4u76vv/4aAQEBsLCwgJ2dHWbNmlVrl+i7FRcXIzw8HKIoYuzYsTU+B+vXr0dISAjs7OygUCjg7OyMcePGafvaV4/VfPzxx/Hpp5+ia9eusLCwgIuLC9577z0AwJkzZzB8+HDY2dnBwsICDz30ENLS0vTO179/f7Rv317n54KIqMm4OQDpNYzLrt7W0dGwem59C2StA86tBML8gRe/BQ5FNl6crcndrbtE1GJcucK1XqUg+aytWVlZ+P7776HRaGBubg5PT08MGDDA4Cbq8vJyaDQag8rK5XLI5YZf8hNPPAFXV1csWLAAiYmJ2Lt3LwYPHow//vhDOzvU7du3MX/+fOzZswehoaF49NFHUVJSgv3792PmzJnIzs7GggULMGTIECxcuBCfffYZgoODMWPGDACAvb09gKoE9ueff4arqytmzZoFR0dHREdHIzw8HKGhobh06RJ8fX0Njr0u5eXlePvtt/Hxxx+jc+fOeOqpp2BpaYmTJ0/igw8+QExMDLZv365zTEFBAR5//HH069cPCxcuRFRUFPbt24dHHnkEV69e1bYg//rrr5gzZw4KCwsxffp0uLu74/jx43j66adRVlamra++56PayZMnsWPHDkyZMgVTpkzB4cOHsWHDBiiVSnz33Xd1Xmd6ejri4+NhZ2cHPz8/nX1lZWV4++238cknn8DS0hJTp05F586dkZKSgoiICMTExKBLly46cezcuRPTp0+Hg4MDdu3ahbfeeguFhYXYvHkzfHx88OKLL+LixYsIDw/HY489hmPHjumc09XVFT4+PoiIiIBKpYKNjY1hN4yISG5WNQPr3RRywEIBONnqbs9RVbVCWpoDZTV0XS3984NbS3PDzj3mfUCpALp1rprF1drC+PjbAlEETsYCOYWAo2395YmI2jhJE0kHBwcEBASgXbt20Gg0SE9Px9WrV5GWloZJkyYZlEyGh4cjPT3doPP17dsXwcHBBscXFBSEnTt3QhAEVFZW4rPPPsOiRYuwcOFCHDx4EACwbt067NmzB4sWLcLy5cu1xyYlJWHy5MlYvHgxnnjiCfj5+WHChAn47LPPEBgYiNdee03nXL6+vjh37pzONYuiiK+//hovv/wyPv30U3z99dcGx16X3377DV999RWCg4Nx8uRJbXJdWFiIZ555Bj///DOOHTuGYcOGaY8pKCjAP//5TyxevBgAUFRUhLlz52LLli04dOgQRo0ahZycHHzxxRfIzc3F+vXrMXv2bABATk4OZs6ciQMHDmjrq+/5qHbz5k2cPn1aO+Pq9evXMXHiRPz000/4/PPP60zGcnNzkZ6eDk9PT72WzpMnT+LLL7+EnZ0doqKi0KlTJ+0+URQh3tMVLDk5WSeO8ePHY/z48fj444/x3HPP4csvvwQA3LlzBzNmzMDhw4e1rbnV7Ozs4OLiArVajbi4OPTr16/W2O+Wk5OjbQUGAJVKBVEUYWtb9YdMeXk5CgsLdcZdpqenw83NrdbHGRkZaN++vfZ54Tl4jjZ3DrQxgwKAY+/VvH3mPbNyez1btTxHSTlgUcPbvPLPBLLEwJ5Ax2Kqvu+/COw8C8SsAlSlwJf7DA6/zdCIwO0ClNtYtO7fD56D52hj5+jYsWOLuA5TI2kiOWbMGJ3HXbt2hZubG44cOYLz589jyJAh9dYREhKi09JVF2O74y5ZskT7w2ZmZoZJkyZh3bp1OHr0KFQqFcrLy3Hw4EEIgoBZs2YhNTVVe6xcLkefPn0QGRmJ06dPY+TIkXWeS6FQaCcYqqioQG5uLsrLyxEQEID27dvj9OnTRsVeG1EUsXPnThQUFGDevHnIyMjQ2T9s2DD8/PPPOHDggE4iKZPJ8Pe//1372NraGsHBwdiyZQsSEhIwatQoJCcnIyoqCq6urjoz+Do6OmLu3Lk6iaShBgwYoLNsR8eOHREQEICYmBjcuHGjznWDSktL9V4Aqp+DXbt2QaVS4aOPPtJJIgHU2MX23jjc3d3h7e2NyMhILFy4ULvdyckJAQEBOHz4MBISEnQSSaVSqU18s7KyDH4OHB11u5/dmzybm5vrXeO9L2r3Pu7QoQPPwXO07XMU5qBNibwBjHhHd9snc6sm01m5U3d7Rl7V9/Rc/WU6gL+2pTXgObqeCVxMAmYNMc1E0ssV8OsIc0Fo3b8fPAfPwXM0yTlMjeRdW+/VtWtXREREIDk52aDyLi4uTRZLt27ddB47ODjAzc0NsbGxuHnzJpRKJTIyMiCKInr16lVrPZmZmfWeq6KiAhs2bMCqVatw5coVveS4sSYfKi0tRUpKCgDg2WefrbXcvTG3b99ebzmW6l+uO3fuAKhq0czOzsaAAQMgk+kOv/X09GzQpExdu3bVeaxUKrWtttXnrYsgCHqti6Wlpbh16xYA1DhjsCFxWFhYaK/H21t3gep27drVGl91LPcmqkREdcorAg7fM14+V1WVLN67vdqlG8DgblWTxNz9OjjQFygqBeL1x3IbxNK8qkutqZHLqmbC5es3UYuTlpYGV1fOqNzcWlwiCVR9ImBI8gVUJQWGjpG8u9WvsYiiCEEQ8NNPP9WY7CkUCgwaNKjeejZu3Ihnn30Wtra2ePHFF+Hv7w8rKytkZ2dj+fLlBl+jIfFWJzNLly5FYGBgjeXuTYzrSmTvTdQaU11jWus7r1KphK2tLXJy7r9loq44antuakpgi4qKADTtByBERACALaeBaWHAlJC/1pF0sq3atvuc7tIfPu2rvl//873XTAbYWlYlsHfr3xUI8gQ2nGz6+FsatQaYNEDqKIiIWowWmUgWFBTA0tLSoLIHDx5ssjGSV65cQUhIiPZx9Zg7MzMzeHp6orS0FK6urrh8+TL69eun14JpqLKyMuzduxdlZWXYt2+fznqHf/zxB1QqVaPNkmtpaYmOHaumfO/UqRNmzpzZKPUCVUtcODk5ITExERqNRqdV8ubNmyguLm60cxnC0dERHTt2RFJSkk48SqVS25310qVL9XY7biwFBQW4ffs25HK5TpdXIqImseU0cDoOWLcA6N4ZyC4Enh9dlSS+fc/s0YeXVX33/r+q7zZKIOU/wKbfgMspQFEZEOQBzBsO5BcD723WPX5cMNDLq+r/CjnQ0wtYOrXq8a4IIPpmU11l83GwBkL86i9HRM2uoX+D0/1plkRSpVJBrVbDzs5O+8d8aWmpXldJALh8+TKKiorQvXt3g+puyjGSH374oc5kOzt27MCVK1cwfPhw2NjYwNraGg8//DCOHj2KRYsWYceOHTqtUxqNBikpKfD09ARQ1TppYWGB7OxsnfMIgqB9Xu5uxSouLsaaNWtQUFDQaImkIAjayWref/99zJgxQ6+PeE5ODhQKhXawsaE8PDzQs2dPHDx4EBs2bNCZbKemtRNrez4ai5ubG/z8/BAdHY3Y2FjteEpBEDBhwgSsW7cOK1aswOOPP67Tx72pup9mZWXh+vXr6N27N2dsJaKmp9EAY98HVj4BvPRIVZfUiERg7hf1d2stLge+PQw82KOqO6elOZCWC2w8Bby/uWoyn7s9GgLMHf7X474+VV8AkHqn9SeScjNgcgjANa6JWqTk5ORGW92ADNfgRDI+Pl67ll9paSkqKyu1ayza2NjoLLdw9OhRpKenY+bMmdrkJCEhAVevXoW7uztsbW21s7beuHEDdnZ2Bs9o2ZRdBKOjoxEaGorhw4cjPj4ee/fuhVKpxKeffgqgKtF4+umncfbsWezYsQN+fn4YM2YMnJ2dkZqaiosXLyI6Olq7Rmb18g+HDh3Ca6+9hk6dOsHe3h7z58/HiBEjsG3bNsyYMQOPP/44zM3NcfLkSURFRTX6NQ4dOhQvv/wyli9fDh8fH0yZMgWenp7Izs7G5cuXERERgd27d+tMtmMIR0dHLFiwABEREZg3bx7279+Pzp074/jx47h27RpsbGx0krO6no/GYGlpiVGjRmHbtm349ddfdSbmGTx4MJ577jl8+umn8Pf3x9SpU+Hh4YHU1FRERETg3XffxcSJExsljmp//PEHMjMz8Y9//KNR6yUiE/XgW/WXySsCnv6q6qsu1S2R1SrUwMLvDY9l3uqqr7ZKXclurUQtWPXQIWpeDU4k4+Li9LqUnjt3DsBfLUF1cXFxwa1bt3Dt2jWUlpYCqOoa2atXL/Tu3Vs7Fa+UfvzxRyxduhT//ve/UVFRgV69euHrr79Gz549tWWcnZ3x008/4euvv8Z///tfrFu3DuXl5bC3t4efnx9WrlypLdu1a1csW7YM7777Lr788kvtdc+fPx+zZs1CUVERvvzyS3z11VcwMzNDcHAwNmzYgNdee61Ru4Wam5vj7bffRlBQEL744gts374dhYWFsLGxQefOnfHCCy/oXKMxxo0bhx9//BHvvfcetmzZAplMhrCwMKxZswZz5szR6bJc1/PRWIYPH47AwED88MMPWLRokXa7hYUF3nvvPfj7+2PNmjXYvHkzSktL0a5dO4SFhSEoKKjRYgCqJiLas2cPzM3NMXfu3Eatm4iImphSAYxo2PsiETU9Q4fEUeMSxKacKaUVSktLw9SpU3H69OkmnUTG1Pzyyy+YMWMGnnnmGaxdu7bZzqvRaPCvf/0Lb7zxBg4ePIgRI0Y027nvdvToUUyZMgVPPPEEVq1aJUkMRCYlLQfo9JTUUVBbYCarGgO6Y7HUkRBRLdRqdZ0TI1LTkNVfhMhwarUaJSUlOtvu3LmD7777DgCabWKbajKZDHPmzEFwcDCWLFnSrOeuVlxcjLVr10IURbz99tuSxEBERA1UqQEmD6y/HBFJJiYmRuoQTBJTd2pU8fHxmDFjBry8vBAUFITi4mIcO3YMkZGR6N+/PyZNmtTsMbm7uyMiIqLZz1vNysoKP//8c/0FiYio5RGEqhZJIiLSwUSSGpWzszMGDhyIAwcO4NChQ6ioqICLiwuee+45LF++vM71KImIiFoUQQAGBVStv0lELVaHDh2kDsEkcYwkERG1PRwjSY1BJgCfzAX+Pl7qSIioDtnZ2XB2dpY6DJPDMZJERERENdGIwEQu+0HU0qWmpkodgkliIklERERUk26dAO/2UkdBRNQiMZEkIiIiupeZDJgaJnUURGSAgIAAqUMwSUwkiYiIiO5VqWG3VqJW4tatW1KHYJKYSBIRERHdq0M7oK+P1FEQkQEKCwulDsEkMZEkIiIiupvcDJgSUrX8BxG1eBYWFlKHYJKYSBIRERHdTV0JTGK3VqLWws/PT+oQTBITSSIiIqK72SiBoYFSR0FEBoqOjpY6BJPERJKIiIiomlwGjOsHmCukjoSIqEVjIklERERUTa0BJodIHQURGcHV1VXqEEwSE0kiIiKianIZMLqP1FEQkRGUSqXUIZgkJpJEREREACATgGE9ADsrqSMhIiMkJydLHYJJkksdABERUZMRtP8Q1U8UgUdDpY6CiKhVYCJJRERtj5sD8O7fgGh+Sk1GmtBf6giIyEhc/kMagiiKotRBEBERERERNURSUhK8vb2lDsPkcIwkERERERG1Wvn5+VKHYJKYSBIRERERUaulUHDdVymwaysREREREREZhS2SRERERETUal26dEnqEEwSE0kiIiIiIiIyChNJIiIiIiJqtZydnaUOwSQxkSQiIiIiolbLxsZG6hBMEhNJIiIiqlNGRobUIRAR1erGjRtSh2CSmEgSERFRnZhIEhHRvbj8BxEREdVJpVKx6xgRtVh8jZKGXOoAiIiImlthuYiQ/1WiQiN1JK1DgI0G26eJMJMJUodCRKTnzp07TCQlwESSiIhMzv4kEbF3pI6i9UjItUJBOeCglDoSIiJ9ubm58PT0lDoMk8MxkkREZHK2J4iQs3GNiKhNMDMzkzoEk8REkoiITEpFpYjd10SoOUMAEVGbEBQUJHUIJomJJBERmZTjqSJUFVJHQUREjSUqKkrqEEwSE0kiIjIpOxNFyPnuR0TUZmg0nDlNCnwrJSIikyGKIrbGi1Dzbw4iojbD0dFR6hBMEhNJIiIyGRezgPQiqaMgIqLG1K5dO6lDMElMJImIyGTsTNTAjLO1EhG1KdevX5c6BJPERJKIiEzGlngRlZytlYiI6L4xkSQiIpOQlCci9o7UURARUWPz9vaWOgSTxESSiIhMwq5rIt/0iIjaoIKCAqlDMEl8TyUiIpOwNV4D9molImp77txhdxMpMJEkIqI2706JiN/SwESSiKgNEgTOoiYFJpJERNTm7b0uQsMskoioTerVq5fUIZgkJpJERNTmbU/gsh9ERG1VTEyM1CGYJCaSRETUppVUiNifBC77QUTURqnVaqlDMElMJImIqE07nCyitFLqKIiIqKm0a9dO6hBMEhNJIiJq03YkipCzWysRUZvl7OwsdQgmSS51AERERE2lUiNie4IItQTdWu0tgBVDZJjsK8BKAZxNF/HqMQ0uZtV/bP8OwNweMgzsIKCnC6AwEyB8rN9164lAAT+MMau1nll7K7HhCvv0ElHblpiYiN69e0sdhslhi2Qr9eyzz0IQBBw7dqzesuPHj4cgCLhx40aTx9VYCgsLERYW1ijTOYuiiDfffBNmZma4evVqI0RnvN27d8PCwgLr1q2T5PxEpuqPdCCntPnPKwDYO8UMj3UTsPqiBouOa+BqJeDYDDN0bVf/8WN9ZHgqSIAI4Hp+7eVOpIqYvbdS7+t8hgi1RsThm0wiiYioaRjdIvmf//yn5orkcsyfP79BQRw6dAjXr1+Hg4MDpk2b1qA6qPUpKyvDF198gfT0dHzyySdNdp64uDh89913mD59OgICAprsPHV56KGHMHToUCxevBgzZsyAlZWVJHEQmZodiRrIBTR7i+RUfwGDOgmYuqsSW+OrTv5LXCXinzTDskEyzNqrqfP4ry9psPwsUKoGvnhIBn/Hmj9US8oHkvJ1L04pB74aARxJFpFZ3DjXQ0TUknl5eUkdgklqUNfWDh06oFu3bjrbZLKGNW7evHkTSUlJMDOrvWsOtU3l5eXYtm0bTp8+3aSJ5FdffYX09HQsXry4yc5RHysrK8yfPx+PPfYYvv/+eyxYsECyWIhMyZZ4abq1TvUTkFEkYlv8XyfPLgF+iRMxu7sAczOgvI4JgLLuIwEc30WAnYWA/12pO1klImorioqKOOGOBBqU/dnZ2cHX11fnq0uXLkbXU1FRgVOnTqF79+6wtLRsSChEdbp16xb27duHgIAAyRerHTJkCLp27YrVq1dLGgeRqbh6R0RSHd1Cm1IfVwEXMkXcm8OeTRdhrRDg59B0557VTUBxhW4SS0TUlt2+fVvqEExSg8dIVlZWoqKi4r5OHhERAVEU0b9///uqp7EcP34cDz30EJydnaFQKGBvb4/AwEB88803OuXy8vLw97//Hd7e3rCwsIBSqUS/fv1w8OBBnXI///wzBEHA0qVL8cYbb8Dd3R0KhQIuLi5YtGiR3po3u3fvxiOPPILOnTtDqVTCwsICfn5+TZZ4xMbGYurUqWjfvj0UCgXs7OwwduxYXL9+Xadc9XjMXbt24amnnoKrqysUCgU6d+5cY1fnrKwszJ49G05OTlAoFOjSpQvWr1+vM1YzLS0NwcHBOH36NABAEATt1w8//KBTX3R0NEaPHg07OzuYm5ujZ8+e2uPqExERgcTERIwfP77G/XFxcfjb3/6Gjh07QqFQwNbWFkFBQfjpp58A6I7VPHfuHB588EHY2trC0tISI0aMQEZGBlQqFf7+979r6/Dw8MCWLVv0ztWhQwf07NkTcXFxko3VJDIlOxJFyCSardXNGkgv0t9eva2jTdME5qAERnsJ2H1NhOr+3qKJiIjq1KCurdevX0dCQgJEUYRSqUSXLl3Qv39/mJubG1xHVlYWLl++jOHDhxt1XLWKigpUVhq2MJiZmRkUCkWdZWJjYzFjxgzk5+dj5syZ8PHxwZ07d3Dp0iWcOHECTz/9NICqTzwmTJiAP/74Aw899BBmzZqFvLw87N69G6NHj8bWrVsxadIknbo3btyIrKwsPPbYY7Czs8O+ffuwcuVKpKamYsOGDdpyO3bsQGxsLB5++GF4eHggJycHv/76K1588UXk5ubizTffNO5JqsPZs2cxadIk5ObmYvLkyfDz88P169exY8cO9OvXD5cuXYKnp6fOMa+88goA4PHHH0dFRQV++eUXPPvsswgMDMSgQYMAAPn5+ZgxYwaOHTuGfv364eGHH8bNmzfx/PPPw9HRUVtXu3bt8Oqrr2LlypVITEzEypUrAVTdqyFDhuicd/LkyXBzc8OLL76ImzdvYuvWrRg7diySk5Nha2tb53VWT0YUGhqqt+/MmTN49NFHkZaWhuHDh2Pu3LkoKSnBhQsXsHv3bsyZM0en/PTp0+Hp6YmXX34ZkZGR+PXXXzFmzBh069YNJ06cwIwZM1BRUYFNmzbhb3/7GxISEuDt7a09XiaToV+/fti6dSuOHTsm2XhNIlOxNV4DTSM0ygkAzA0cfVH259uSpfyv/9+tVP3X/qYw1U+AhZzdWonItEjd68xUGf1W5uLiAh8fH9jb26O8vBzJycm4fPky0tPTMXHixHoTNgDQaDQ4ceIEOnfu3KAusQDw22+/IT4+3qCyfn5+GDZsWJ1lTpw4gczMTHz00Ud4/fXXay23YsUKnDlzBl9++SWef/557faXX34ZI0aMwIIFCzBx4kSd2UZv3ryJs2fPol+/fgCARYsWYdSoUdi4cSNeeuklhISEAAA++eQTvf7dixYtwogRI7BixQosXrzYoOe3PsXFxViyZAnu3LmDs2fP6vzybdu2DbNmzcKSJUvwv//9T+c4c3NzXLp0SZv4P/jgg5g+fTo+/fRTbSK5efNmHDt2DMOGDcPhw4e1Y2e/+eYbPPvss9q6rKysMHPmTPzwww9ITEzEa6+9pnOuwsJC7f+HDRuGb7/9FkDVDKyurq747LPPsGHDBp0676XRaHDt2jUA0Ps5U6lU+Oc//4m0tDSsXr0aL7zwgs7+mj6keOihh7St0yUlJZg4cSIOHjyIjIwMxMXFwc7ODgDQo0cPPPfcc/j666+xYsUKnTq6du0KAIiMjKw1biK6f/llIs5lNk5dQ9yrZls1RMD3asTlACVqwKKGQ5R/vuuW6K/k0ShmdZPhTomIfUns1kpEpuPKlSvo3r271GGYHKO7tk6ePBm9evWCl5cX/Pz8MGLECPTv3x85OTmIjo42qI7IyEjk5+drk4+G6NWrF8aOHWvQlyGfUlQncPv27UNmZs1/feTk5ODw4cOwtrbGuHHjkJqaqv1SKpXo3bs3bt26hYSEBJ3jhg4dqk0iAcDV1RWzZ88GAJ0ukNUxiKKIgoIC3Lp1C4IgICgoCCqVqtG6Q167dg1nzpxBaGgonJycdK6ja9eu8Pb21uumCwALFy7UaT3u1asXXF1dtder0Wiwf/9+AFUJ8N0TME2cOBGBgYENivfuxF4QBIwYMQJAVbfUupSVlWkT0rtbQ4Gq9YbOnj2LLl266CWRAGqc/GnRokXa/1taWmp/rubNm6dNIgEgLCwMSqWyxvvl6uoKAMjIyKgz9rvl5OSgrKxM+1ilUukk2uXl5bhz547OMenp6XU+zsjIgCj+9Ycmz8FztLVz2JkDnW3QKK7eETF3X6VBX+mqP+Muqureeq/qbWmqxk/03G2BwZ2BzXEi1E3UINmS7znPwXPwHKZ7jvLy8hZxHaamUTrX9OrVC+fPn0dKSgr69u1bZ9n8/HxcuHABffr00fnj21gODg5wcGi82QrGjBmDSZMmYefOnXBzc0OXLl0wZMgQPPnkkwgLCwMA5ObmIiMjA0VFRXrdPu+WmZkJPz8/7eMePXrolQkKCgIAnaQzPj4er7/+Ok6cOIGcnBy9Y3Jzcxt8fXe7ceMGioqKcPz4cbi7u9dYpqZZeH18fHQeK5VK2NjYaGMtLS3VDnb29/fXKduuXTu0b98eMTExRsd773mrk7GsLANW9f7T3S8cAJCSkoLi4mKjFq+9N47qn797WzstLCxgbW2N7OzsWuMwZn3Me5NgGxvdv47Nzc3h5OSks83Nza3Oxx06dOA5eI42f45H/QR8efH+Z23NLAZ+vGxcJZeyRAzuLEAAdCbcGegmoKhCRHzjvJzrmNlNgEwQ8L8rhg37aIiWfs95Dp6D5zDNc9jZ2bWI6zA1jZJIymQyWFtbo7S0/lWfz5w5AwsLC3h7eyM//6/p9ERRhEajQX5+PhQKRb3r7JWXl+tNVlMbuVxe7zhMe3t7bNmyBadPn8a2bdtw5swZ/Pzzz/j+++/x8ssvY9WqVdo4HRwc8OWXX9ZYj0KhqDFxrE9WVhamTZuG6OhoTJ06FYMGDYKTkxPMzMzw448/Ijw8HBpN43zEXF1PWFhYrctQ1PT817ZEy71JWmNr6HmVSqV2DGVOTk6tSfP9xmHM0jXViXb79u3vKxYiqt/ErgI+vyBNF88t8SKm+cswxU/QriPpZAlM86+aCOfupT987Ku+X7/PGWYfC5DhZoGIU7furx4iotbm3iSRmkejJJJqtRoqlcqgP45VKhWKi4uxefPmGvdv2rQJHh4eGD16dJ31/P777406RhKoSggeeOABPPDAAwCqWgjHjRuHzz//HEuWLIG9vT1cXV0RExOD8ePH631yUZuaWuGquwH7+voCAC5evIjo6Gj87W9/05mAp6KiQjuDaGPx9PSElZUViouLMXPmzEarV6lUwsXFBUBVt9O7W/Dy8vJq7TLcVARB0LYMJyQk6HRx9vDwgJWVVbOPVaxugTamJZSIGmZwZwF25kBBefOfe0u8iNNpItaNlqG7kwbZJcDzvWUwE4C3f9P9UPDw9KoPo7y/+Su79LADHu9e1XMhuH3V96UhVd9vFgD/jdVNkAOdgV6uAv71ByfZISLTEx8fz7+tJGBUIllaWgqlUqm3/dy5cxBFER4eHjrbi4uLUV5eDhsbG8jlVacaOHAgysv139VPnToFMzMzhIaG1tsaCVR1p62euKQ+1tY1DFS5R2ZmJhwdHXUms+ncuTPc3NyQkJCA3Nxc7ZjQqKgovPrqq1i7dq1OHRqNBikpKXrdXo8fP47z589rx0nevn0b//3vfwEAjz76KABon597W9kOHjyIkydPGnSdhuratSsGDhyIo0ePYtOmTZgxY4bO/uo+4cY218tkMowaNQpbt27FihUrMGrUKG0X2Z07d+Ly5cs65RUKBSwsLABUtRje28WgMQwbNgyffvopfvvtN0ydOlW7vUuXLhgwYACOHTuGtWvX6k3ao9Foauzeez80Gg0uXLgAoGrcLBE1LblMwISuAn6+cv/dW42lEYGxWyuxcqgML/WRwVIBRGQAc/dVGtSt1dtewPsP6PZ2qH58LEXEf2N1u6/O6lb1erWBs7USEVEzMSqRvHDhArKystCxY0fY2NigoqICKSkpSEtLg6urq16XzrNnz2pb9Tp27AigKjmryZkzZ6BQKPTGodWmscdIfvrpp1i7di1GjBgBX19fmJub4/Tp0zh16hT69OmjHfP3z3/+ExEREfjPf/6D06dP46GHHoKdnR2Sk5Nx7tw55OXlISUlRaduT09PDB06FI899hjs7e3x66+/IjY2FtOnT9cuSxEYGIju3btj06ZNqKioQFBQEOLi4rB37164uroiKSmp0a7VxsYGK1aswJQpUzBz5kx899132iQ3KSkJp06dwtChQ/VmbTXEtGnT8L///Q/Hjh3DgAEDMHLkSNy4cQO7d++Gu7s7kpOTteMDlUolAgMDcezYMcyaNQsPPfQQLCwsMG7cODg7OzfKtfbr1w9+fn7YvXs3PvvsM53n4MMPP8SUKVPwf//3f9iyZQsGDhyIsrIynD9/Hs7Ozvjll18aJYZq6enpiIyMhL+/P5f+IGomk7sKeq13zSWvDHj6gAZPH6i73N0tkdWOp4gQPjZ8atclJzVYcpJJJBGZpnsbs6h5GJVIduzYEXl5eYiPj0dZWRkEQYC9vT369++PoKAgbataa1Td0vj7779j79690Gg0aN++PV566SW888472nLOzs7Yu3cvli9fji1btmDNmjVQq9VwcHBAYGCg3jIWADBz5kwIgoAff/wRGRkZaNeuHV555RV89NFH2jIdOnTATz/9hEWLFuHQoUPYuXMnOnfujPfffx8xMTH4z3/+06jXGxwcjGPHjmHZsmU4cuQIjh07BjMzMzg7O2PgwIE6S5sYo127dti0aRNeeeUV7N+/HytXroSHhwe++OILbNiwAcnJybC0tNSWf+ONN5CamooTJ04gPDwcoijC1tZW21J7vzp27IixY8di1apVOq3CQNXakocOHcJbb72FU6dO4ejRo7C0tIS3tzfmz5/fKOe/28mTJ3Ht2jX8+9//bvS6iahmI70EKGRABXMsIqI26+7ZV6n5CGJTz5Riwn7++WfMnDkTb7/9tk4yaopu376Nhx56CAkJCVCpVEZNUHO/4uLiMHz4cAwaNKjRWxkNVVxcjIkTJyIqKgpJSUkGdd8mosYxdqsa4TequptSw+UsMIOD0vAZp4mImsulS5c4RlICjTsIjAjQWYMHqBr3uX37dly+fBlDhgxp1iQSqFqK5Mknn8TWrVtx5cqVZj13tUOHDuH48eP46KOPmEQSNbMpvjImkURERI2s9fZFpRZr4cKFOHPmDIYPHw57e3tERUUhPDwcCoVCpztvc3r33Xfx7rvvSnJuAJgwYUKNk0wRUdMb30V/PUciImo7qtdnp+bFFklqdKGhodr1L//1r3/h8OHDGDhwIE6ePIk+ffpIHR4RmZj21gL6dwDYKZOIqG2qXl6NmhfHSBIRUZu34qwG/zypYRfX+8AxkkTUUnGMpDTYIklERG3exK4Ck0giojbK1tZW6hBMEhNJIiJq8/wdBXRpJ3UURETUFDp16iR1CCaJiSQREZmEqX4C5OyZSUTU5ly9elXqEEwSE0kiIjIJk7rKoGb3ViIiokbBRJKIiEzCADfA2VLqKIiIqLF17txZ6hBMEhNJIiIyCTJBwKSuAuR85yMialMqKyulDsEk8e2UiIhMxmRfAWqN1FEQEVFjSk9PlzoEk8REkoiITMZwDwFKudRREBERtX5MJImIyGQo5QIe8QbMOHsrEVGbERgYKHUIJomJJBERmZRJvjJUcvZWIqI2IykpSeoQTBITSSIiMiljvQXI2CJJRNRmFBcXSx2CSWIiSUREJsXRUsDgTgBzSSKitsHa2lrqEEwSE0kiIjI5U/z49kdE1FZ4enpKHYJJ4jspERGZnIldBHCYpOGczSugNJM6CiKimsXGxkodgkniJOhERGRyPO0FXH/KDFxS0jBpCbGwVPSUOgwiImpBmEgSEZFJ8m7HUZKGsvXsIHUIRES16tixo9QhmCR2bSUiIqI6CQKTbiJqufgaJQ0mkkRERFSnW7duSR0CEVGt+BolDSaSREREREREZBRBFEVOXEdERES1Kisrg4WFhdRhEBHViK9R0mCLJBEREdUpJSVF6hCIiGrF1yhpMJEkIiKiOqlUKqlDICKqFV+jpMFEkoiIiOqkVCqlDoGIqFZ8jZIGx0gSERFRndRqNeRyLj1NRC0TX6OkwRZJIiIiqlNMTIzUIRAR1YqvUdJg6k7UglVWViI+Pl7qMIjIxF2/fp0zIhJRi9VSXqP8/PxgZmYmdRjNhokkUQsWHx+P7t27Sx0GEREREdUjNjYW3bp1kzqMZsMxkkQtGFsk66dSqTBgwACcPXsWNjY2UodjkngPpNeU9yAjIwPDhw/HkSNH0KFDh0atuy3h74H0eA9aBlO+D6bWIslEkohatYKCAtjb2yM/Px92dnZSh2OSeA+k15T3IDU1Fe7u7khJSUHnzp0bte62hL8H0uM9aBl4H0wHJ9shIiIiIiIiozCRJCIiIiIiIqMwkSSiVs3CwgJvv/12i5itzVTxHkivKe+BnZ0dhg4dyi5q9eDvgfR4D1oG3gfTwTGSREREREREZBS2SBIREREREZFRmEgSERERERGRUZhIEhERERERkVGYSBIREREREZFRmEgSUYsSHx+Pt956CyEhIXBxcYGtrS169+6NDz74AEVFRQbXo1ar8e9//xt9+/aFtbU17O3t0bdvX6xdu7YJo28bGuMeiKKIDRs2ICwsDM7OzrC1tUVgYCDeffddFBQUNPEVtH5xcXGYNWsWunXrBnt7e1hZWSEgIACvvPIK0tPTDa7n119/RVhYGKytreHo6Ihp06YhKSmpCSNvO+73HuTm5uLzzz/HyJEj4e7uDktLS/j7++OZZ55BSkpKM1xB69dYvwd3mzFjBgRBQI8ePRo52rarse4D35fbHs7aSkQtyuLFi/Hll19iwoQJCAkJgUKhwNGjR/HLL7+gZ8+eOHPmDCwtLeuso7y8HBMmTMDRo0cxa9YshISEQK1WIyEhAZaWlvjwww+b6Wpap8a4B0uXLsWHH36I4cOHY9KkSVAoFDh27Bg2bdqEgQMH4vTp0xAEoZmuqPU5fPgwPvjgA4SEhKBz586Qy+WIjo7GunXrYGdnh0uXLsHV1bXOOrZt24apU6eiV69eePrpp5Gfn49Vq1bBzMwM586dQ8eOHZvpalqn+70H+/fvx7hx4/DQQw9h+PDhcHZ2RkxMDNauXQtzc3P8/vvv6N69ezNeUevTGL8Hd9uzZw8mTpwICwsL+Pj4ICYmpgmjbzsa4z7wfbmNEomIWpCIiAgxLy9Pb/vSpUtFAOIXX3xRbx1vvPGGaGZmJh45cqQpQmzz7vceVFRUiFZWVmLfvn3FyspKnX2zZs0SAYgXL15szJBNxi+//CICEJcvX15nufLycrFjx46ih4eHWFhYqN1+8eJFUSaTiU8//XRTh9pmGXoPkpKSxMTERL3tBw8eFAGIjz76aFOF2OYZeg/uVlhYKLq7u4svvvii6OnpKQYGBjZhhKbBmPvA9+W2iV1biahFCQ4Ohr29vd72GTNmAEC9nyAXFRXh888/x8SJE/Hggw9CFEUUFhY2Saxt1f3eg4qKCpSUlKBDhw6QyXTfZqpbwaytrRspWtPi6ekJoKrbZF2OHz+OtLQ0PPXUU7CxsdFu7927N4YNG4ZNmzahoqKiSWNtqwy9B15eXujSpYve9hEjRsDR0ZGtYffB0Htwt6VLl6KyshLvv/9+U4Vlcgy9D3xfbruYSBJRq5CamgoAaN++fZ3lTp48icLCQvTr1w8vv/wy7OzsYGdnBxcXFyxZsgRqtbo5wm2TDL0HlpaWGDJkCPbv34/ly5cjMTERN27cwA8//ICvvvoKs2fPhq+vb3OE3OqVlpYiOzsbqampOHDgAJ599lkAwNixY+s8LiIiAgAQGhqqty8kJAQFBQWIj49v/IDboIbeg9rk5+ejsLCw3t8j+sv93oOzZ89i9erV+Oyzz2BnZ9eUobZpDb0PfF9uw6RuEiUiqo9arRZDQ0NFuVwuXr16tc6yq1atEgGILi4uYqdOncSvvvpK3LRpkzhhwgQRgDhnzpxmirptMeYeiKIopqamiiNGjBABaL8EQRDfeOMNUaPRNEPEbcMXX3yh8xx6eXmJ//3vf+s9bsGCBSIAMTY2Vm/fl19+KQIQw8PDmyLkNqeh96A2//jHP0QA4nfffdeIUbZt93MPKioqxKCgIHH06NHabeza2jANvQ98X2675M2ZtBIRNcTf//53nD59Gh9++CH8/f3rLFvdXSYnJweXL1/Wlp8+fToefPBB/PTTT1i8eDG6devW5HG3JcbcAwCwsLCAt7c35syZgzFjxgAAtm7divfffx9KpRJLly5t6pDbhEmTJiEgIAAqlQoXL17Erl27kJ2dXe9xxcXFAKruw72USqVOGapbQ+9BTbZs2YKPP/4Yo0ePxrx58xo50rbrfu7BypUrkZiYiB07djRtkCagofeB78ttmNSZLBFRXd544w0RgPjMM88YVP7jjz8WAYiDBg3S2/f999+LAMSvvvqqscNs04y9B0VFRaKvr684Y8YMvX0zZswQZTKZQa2apC8yMlI0NzcXP/zwwzrLsUWy6Rh6D+61d+9e0dzcXAwODhbz8/ObKDrTYOg9SEhIEJVKpfj+++/rbGeLZOMw9D7wfbnt4hhJImqx3nnnHbz//vuYN28e1qxZY9AxnTt3BgB06NBBb5+bmxsA4yZoMHUNuQdbtmxBQkICpk2bprdv2rRp0Gg0OHXqVGOHahJ69uyJPn364KuvvqqzXPWkRrdu3dLbV72tU6dOjR+gCTD0Htxt//79mDJlCgIDA3HgwAGO07tPht6DV199FY6Ojpg8eTISExO1X2q1GuXl5UhMTGzwepRk+H3g+3Lbxa6tRNQivfPOO1i2bBmeeOIJfPvttwavOThgwAAAf00Mc7fqbcasO2bKGnoPqhOVyspKvX3VkypwcoWGKykpQU5OTp1l+vfvDwA4ffo0RowYobPvzJkzsLOzg5+fX5PF2NYZcg+q7d+/X9sl8NChQ3BwcGji6EyDIffg5s2bSEtLQ2BgYI37fX198cgjj2DPnj1NEaJJMOQ+8H25DZO6SZSI6F7Lli0TAYiPP/643jqEd0tLSxOvXLkiFhUV6WwfNGiQKAiCeP78ee02tVotDhgwQJTL5eLNmzebLPa24n7uwY4dO0QA4tixY/XKjxkzRgSgc29IX3p6eo3bjxw5IspkMnH48OHabTXdg/LyctHNzU1vHclLly6JMplMfPLJJ5su+Dbifu+BKIpieHi4qFQqxZ49e4rZ2dlNGm9bdL/34ODBg+LmzZv1vlxcXER3d3dx8+bN4qlTp5r8Olq7xvhd4Pty2ySIoihKm8oSEf3lyy+/xIIFC+Dh4YH33ntPbx3C9u3b4+GHHwYAzJ07Fz/++COOHj2KYcOGactcvHgRgwcPhrm5OV566SU4OTlh06ZN+O233/DWW29h2bJlzXlJrc793oPKykqEhYXh7NmzGDx4MKZMmQIA2LZtG06ePIlp06bhl19+adZram0mT56M9PR0DB8+HJ6enigtLcX58+fx888/w8rKCseOHUPv3r0B1P57sHnzZsyYMQO9evXC008/jYKCAnz22WcQBAHnz59n19Z63O89OHfuHAYPHgxRFPHRRx/B2dlZ7xyzZ89uxitqfRrj96AmXl5esLGx4VqeBmqM+8D35TZK4kSWiEjHE088oTO9+L1fQ4cO1St79OhRvXoiIyPF8ePHi/b29qKFhYXYu3dvcd26dc12Ha1ZY9yDgoIC8Z///Kfo7+8vmpubixYWFmKPHj3E5cuXixUVFc17Qa3Qpk2bxEceeUTs3LmzaGFhISqVStHf319csGCB3if3df0e7N69Wxw4cKBoaWkptmvXTnz00UfFxMTEZrqK1u1+78G6devq/D3in2D1a6zfg3txsh3jNNZ94Pty28MWSSIiIiIiIjIKZ20lIiIiIiIiozCRJCIiIiIiIqMwkSQiIiIiIiKjMJEkIiIiIiIiozCRJCIiIiIiIqMwkSQiIiIiIiKjMJEkIiIiIiIiozCRJCIiIiIiIqPIpQ6AiIiImt7cuXPx448/AgACAwMRExOjs1+j0eDDDz/EunXrkJycDA8PD1y7dg0rVqzA999/j9jYWMhkxn/+vGbNGnz44YdISEiAhYWFzr5Vq1Zh4cKF2se3b9+Gs7NzA67OeDk5OTh//jyys7NRXFwMuVwOBwcH9OrVC56engbVUVlZiXPnziEhIQFlZWVwdHRE//790blz51qPuXDhAs6dOwcHBwdMmzZNb39+fj4iIiKQmZmJ0tJS2NjYoGvXrujVqxfkcv7ZRkQtB1skiYiIWrnw8HAIglDr108//QQAcHZ2xvr16/HRRx/p1fHVV1/hrbfewpQpU/D9999j7dq1KCgowPLly/H666/rJZHLli2DTCbDlStX9OqaP38+zMzMsHfvXsydOxfl5eVYu3atXrnRo0dj/fr1mDx5ciM9E4ZTqVSoqKiAn58fwsLC0LdvXwBVz2VN11STY8eOISoqCl27dkVYWBhkMhn27duHjIyMWs956dKlWhNClUqF7du3IysrC4GBgQgLC0P79u1x/vx5HD58uGEXSkTURPjRFhERUSsXGRkJAPj3v/8NBwcHvf2jRo3CkSNHYG1tjdmzZ9dYx7p16/Dwww9j5cqV2m2rVq2CWq3GzJkz9co/99xz+Oijj7Bq1SqdJPGLL77AunXr8P777+ORRx4BADzxxBP49NNP8eKLL0IQBG3ZgIAABAQEIDExEdu3b2/YxTeQh4cHPDw8dLYFBgZi+/btiIqKQrdu3eo8PisrC9euXcPAgQPRq1cvAICvry+2bNmCP/74AxMnTtQ75syZM3B1dYUoiigtLdXbn5CQgPLyckyYMAGOjo4AgG7dukEURW2r572tukREUmGLJBERUSsXFRUFe3t7LFiwALNnz9b7cnFxqfP40tJSREZGYsiQITrb161bhwkTJkCpVOod4+rqilmzZmH9+vXIzs4GABw/fhyvvPIKHn30USxdulRbdvr06bh58yaOHj3aCFfbdGQyGaytrVFeXl5v2evXr0MQBJ2EUy6Xw9/fH5mZmVCpVDrl09PTkZSUhLCwsFrrrD6vlZWVznYrKysIgtCgrsVERE2Fr0hEREStXGRkJPr06aPT2meoJ598EpaWlqisrMQbb7wBQRAQGhqKpKQkREVFYcSIEbUeu3DhQpSUlGDNmjVISUnB9OnTERAQgB9++EGnXL9+/eDo6IidO3caHV9NNBoNSktLDfoSRbHOuioqKlBaWoqCggJERUUhJSUFnTp1qjeGO3fuwN7eHubm5jrbXV1dtfvvjve3335DQECAtqWxJh07dgRQlZBnZ2dDpVLh2rVriI2NRWBgIBQKRb1xERE1F3ZtJSIiasXKy8sRFxeHBx54QNsyeDd7e/s6E5BZs2ZBoVBg7dq1+Pzzz+Ho6AhPT0/8/vvvAKAdO1iTwMBAjBw5El9++SV27NiBiooK7NixAzY2Nnpl+/bti99++60BV6gvIyMDe/bsMajszJkzYWtrW+v+M2fOaMdECoIALy8vDBo0qN56i4uL9VoOgb9aE4uKirTbrly5ApVKpe3qWxt3d3cEBwfj4sWLuHnzpnZ7nz590L9//3pjIiJqTkwkiYiIWrHY2FhUVFRgzZo1WLNmjd7+uLg4+Pn51Xr88OHDcfjwYVhbW2PBggXa7pNvvvkmAMDb27vO87/yyisYPXo0srKy8Ouvv6JLly41lvPx8cH69esNvaw6OTk5YezYsQaVtbS0rHN/UFAQvL29UVxcjOvXr0MURVRWVtZbr1qthpmZmd726m3VdZSWluLcuXPo27dvvbEAgK2tLdzc3ODt7Q2lUonk5GRcvHgRlpaW6NGjR73HExE1FyaSRERErVhUVBQA4IcffqixS6avr69BdQQGBuqMwbtz5w7kcnmNrYt3q27N8/HxwahRo2ot5+DggJKSklpb8oxhYWFR5xIbxmjXrh3atWsHAPDz88PevXsRHh6OSZMm1dlVWC6X15hwVm+rTigjIiJgYWGBwMDAemNJTEzEiRMnMGPGDO3z7u3tDVEUcfbsWXTt2rXG8apERFJgIklERNSKRUZGQi6XY+bMmXrj9Yypo64ksDaHDh3Ca6+9Bl9fXyQkJODAgQMYOXJkjWWrxyo2ZBznvSorK1FWVmZQWaVSadQkNT4+Pjh58iTy8/O1CWZNrKysdLqvVisuLgYAWFtbIz8/H1evXkVoaKh2e3X8Go0GhYWFUCgU2uQwNjYWzs7Oesm7p6cn4uPjkZ2d3WgJNBHR/WIiSURE1IpFRUXB29u7wUlkXl4eUlJSEBQUpLPdyckJarUahYWFNY4xvH79OmbMmIE+ffrg0KFD8PPzw2effVZrIpmbmwsrKyuDunfWJzMzs9HGSN5LrVYDQL0ztzo5OSEtLQ3l5eU6z31WVpZ2f0FBAURRxO+//64dc3q3jRs3okePHtqZXEtKSmpc3kOj0QBAvRMHERE1JyaSRERErVhUVBRCQkLu63gA6Nmzp872gIAAAEBSUpLePpVKhYkTJ0KhUGD79u2wt7fH888/j2XLluHKlSs1rsGYlJRU79qMhmqMMZIlJSV6+zQaDRISEmBmZqZdj1OtVkOlUkGpVOp0K/Xx8UFUVBSuXLmiXUeysrIScXFxcHV1hY2NDeRyeY2JdUREBCoqKhAWFgY7Ozvtdnt7e6SmpiIvL0+nNfTatWsQBKHOGV+JiJobE0kiIqJWKiMjA1lZWdqkryEiIyMB6CeSoaGhAIBz587p7BNFEY8//jji4uJw9OhRbVfL559/Hh999BFWrVqFtWvX6p3nwoULmDVrVoPjvFtjjJE8efIkysvL4ebmBmtraxQXFyMxMRF5eXkICQnRznSblZWFPXv2oG/fvggODtYe7+rqCh8fH5w9exYlJSWwt7dHfHw8CgsLMXToUABV3Wq9vLz0zh0dHQ0Aevt69eqFlJQU7N69G4GBgbCwsEBycjJSUlIQEBAAa2vr+7pmIqLGxESSiIiolapOAm/fvo3//ve/evt79eql12X1XlFRUejUqZNea5ePjw969OiBQ4cOYf78+drt77zzDnbs2IG1a9fqLJPh4uKC2bNnY/369fjwww/h5OSk3Xf+/Hnk5ORg4sSJDbrOpuDj44O4uDjExsaitLQU5ubmcHZ2xoABA2pM/moybNgw2NjYICEhAeXl5XB0dMTo0aPh5ubWoJjc3NwwceJEnD9/HpcvX0ZZWRlsbW3Rv39/basnEVFLwUSSiIiolarulrpu3TqsW7dOb/9PP/1kUCJ5b2tktfnz5+Ott97SdgPdvn073nvvPfzf//0fnnnmGb3yCxcuxHfffYc1a9Zg6dKl2u2bN2+Gh4cHhg8fbszlNamuXbuia9eu9Zbr2LFjjdcKVM3cGhISYnTX4vHjx9e6z9XVFWPGjDGqPiIiKQgiR24TERG1eXPnzsWRI0dw4cIFyOXyOmckrZafnw8fHx+sWLECTz75ZIPOW1ZWBi8vLyxevBgvv/yyzr7S0lKoVCqsWLECK1euxO3bt+Hs7Nyg8xARUfMyfD5sIiIiatVSUlLg4uKCBx54wKDy9vb2WLRoEVauXKmdOdRY69atg0KhwP/93//p7VuzZg1cXFywcuXKBtVNRETSYYskERGRCYiNjUVaWhoAwMbG5r5mem0sKSkpiIuL0z4eOnSodpIbIiJq2ZhIEhERERERkVHYtZWIiIiIiIiMwkSSiIiIiIiIjMJEkoiIiIiIiIzCRJKIiIiIiIiMwkSSiIiIiIiIjMJEkoiIiIiIiIzCRJKIiIiIiIiMwkSSiIiIiIiIjMJEkoiIiIiIiIzy/zCqHYuITdTrAAAAAElFTkSuQmCC\n"
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"- The plot is dubbed “waterfall” because each step resembles flowing\n",
"water. Water can flow in either direction, just as SHAP values can be\n",
"positive or negative. Positive SHAP values point to the right.\n",
"- The y-axis exhibits the individual features, along with the values for\n",
"the selected data instance.\n",
"- The feature values are ordered by the magnitudes of their SHAP values.\n",
"- The x-axis is on the scale of SHAP values.\n",
"- Each bar signifies the SHAP value for that specific feature value.\n",
"- The x-axis also shows the estimated expected prediction 𝔼(𝑓(𝑋)) and\n",
"the actual prediction of the instance 𝑓(𝑥(𝑖)).\n",
"- The bars start at the bottom from the expected prediction and add up\n",
"to the actual prediction."
],
"metadata": {
"id": "nx0JDAUVQn9O"
}
},
{
"cell_type": "code",
"source": [
"shap.plots.beeswarm(shap_values)"
],
"metadata": {
"id": "FWz7SBt0ytMv",
"outputId": "2d8c221c-65ee-4c3b-dd4c-f62bcbd76232",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 329
}
},
"execution_count": 38,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 800x310 with 2 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"- The x-axis represents the SHAP values, while the y-axis shows the\n",
"features, and the color indicates the feature’s value.\n",
"- Each row corresponds to a feature.\n",
"- The feature order is determined by importance, defined as the average\n",
"of absolute SHAP values: $𝐼_𝑗 =\\frac1n\\sum_{i=1}^n\\phi_j^{(i)}$\n",
"- Each dot represents the SHAP value of a feature for a data point,\n",
"resulting in a total of 𝑝 ⋅ 𝑛 dots."
],
"metadata": {
"id": "Dx2ZAkoiRKBI"
}
},
{
"cell_type": "code",
"source": [
"shap.plots.partial_dependence(\n",
" \"sepal length (cm)\", lm.predict, X_test, ice=False,\n",
" model_expected_value=True, feature_expected_value=True\n",
")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 474
},
"id": "F-MSZ5XUHRbr",
"outputId": "c4cd49cb-42d2-4078-9c8d-3bc086858642"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 4 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"shap.plots.scatter(shap_values[:,\"sepal length (cm)\"], color=shap_values)"
],
"metadata": {
"id": "cyPGqs1CgSeO"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"shap.plots.beeswarm(shap_values)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 237
},
"id": "uy26MdaIHqVk",
"outputId": "028b0d3e-4492-4902-bf71-d42c16024666"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 576x223.2 with 2 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"source": [
"shap.plots.bar(shap_values)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 255
},
"id": "OV2zeVLzIp2X",
"outputId": "44d810d0-4d0b-4d79-8572-61f4e1c6b96d"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 576x252 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "o9rVdLtCNRBv",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "4953305e-751a-4d32-b1da-efc4ef2f0c35"
},
"source": [
"from sklearn.metrics import median_absolute_error\n",
"\n",
"print(\"train error: %0.3f, test error: %0.3f\" %\n",
" (median_absolute_error(y_train, lm.predict(X_train)),\n",
" median_absolute_error(y_test, lm.predict(X_test))))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"train error: 0.163, test error: 0.193\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "53e0p0D8NRBz",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 391
},
"outputId": "7324f019-9ab4-4b2e-9cd7-672bc445f643"
},
"source": [
"scatter_predictions(lm.predict(X_test), y_test)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x432 with 1 Axes>"
],
"image/png": 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gryE0SY0o9yA2B540s5eBFwnmIKaZ2QQzaz69c2R4COxcYCRwYoT1iJSmJW+3CIcdmu7h/aVfp3psv9NDaJIqFhxslMeGZjXAlmTtdbj70xHV1aba2lqfOXNm3C8rkoyPX4EbB63qDur5UM5f5jW9qnlu9OA4K+uQjmJKFzOb5e61hTwmryEmM7sCOBZ4DWieeXIg9oAQqRjvPg+/y5q2G7+UD0c/knPTNI7tFzSEJqmU7xzEMGC7rAllEYnSgkfh7uGZ/vilgMb2JV75zkG8RXCim4hEbc5dOcMBNLYv8cp3D+IrYI6ZPUGwhAYA7j4ykqpEKtVz18P0CzL9rHCAAi4YJFIE+QbE1PBPRKIy/UJ47rpMv1U4NNPYvsQlr4Bw90lmtibQfKzdAndvjK4skUDFHAnz0Kkw9+5Mv41wEIlTvkcx7U+wDMY7gAF9zeyEJA5zlcrRvJ5P85INzcf8A+UVErcNg7eezPQVDpIS+Q4x/Rw4yN0XAJjZN4G7aWNpDIlXuf7Kbm89n3L43we0XK4bFA6SKvkGRPfmcABw93+YmY5qSoFy/pVd9uv5KBwk5fI9zHWmmU00s/3Dv5sBnc6cAuW8amaXlsROO4WDlIB8A+I0grOoR4Z/r4W3ScLK+Vd22R7zr3CQEpHvUUzLgWvCP0mRcj6ztiyP+Vc4SAlpNyDM7F53/66ZzSPH5ULDC/1IgkYdvF2LOQgok1/Zoc4e85/KiXuFg5SYjvYgzgj/HRJ1IdI5Zfkru4tSOXGvcJAS1G5AZF3M53R3Pzf7vnCF13NXf5TETWfWtpS6w2MVDlKi8p2kPjDHbf9ZzEJEiiVVE/cVHA5TZtcx6PIZbDX6EQZdPiN1FzWSjnU0B3EacDqwTXhluGbrAn+NsjCRzkrNxH3KwiHOeZlUDvNJwTrag7gLOAz4Q/hv89/u7n5cxLWJdMqog7ejezdrcVv3bhbvxH0e4RDnL+zmL+y6+oZYLlVazufnVJJ2A8Ldl7r7O8B1wBJ3f9fd3wVWmNmecRQo0inWQT9KeYZDOX9hp2qYTzot3zmI3wBfZvW/DG8TSZ2rHl9AY1PLo7IbmzyeX695DiuV+xd2WZ8FX0HyDQhz91X/j3P3leS/jpNIrHLNP7R3e9EUMOdQ7l/YZXsWfIXJ+5KjZjbSzLqHf2cQXIZUJHWqLPd4Ulu3Z+v0vECBE9Ll/oU9bGANlx05gJpe1RhQ06uay44coAnqEpPvXsCpwPXAWIIzqp8ATomqKJFmnTnypslXO+m/3duzX6tTR9504miluM+AT+KESp2fU/ryXYtpITC8ww1FimjK7DpG3TeXxpXBF3tdfQOj7psLtP+FXdPGYa41Hfw679QJdp08lFVf2FIKOjoP4hx3v9LMfknutZhGRlaZVLzxU19dFQ7NGlc646e+2u4XXWd/nRc8L5AdDmv3hlFvtvv8rekLW9Kuoz2I+eG/uvaDxK6+Ifdlz9u6vVlnf50XdIJddjj03RNO+lO7zy1Sijpai+nh8N9J8ZQjUhyd+XWe955HdjgMOAaOmtiVUkVSq6MhpofJMbTUzN2HFr0ikdAGa3Xns69W31vYYK1ornab155HdjjseyZ8Z3wktYikQUdDTFeH/x4JbAbcEfa/B3wSVVEiAOMO24lR989tcdJb9ypj3GE7Rfaa7e55ZIfDf14Fe+pAPilvHQ0xPQVgZj9399qsux42M81LSKRSda2L7HA4ZhLsNCz+GkRilu95EGub2dbu/haAmW0FrB1dWSKBuI/0GTtlHne/8D5N7lSZ8b09+nDJ3P0yG5z4CPTfN7Z6RJKUb0CcCfzFzN4iWPZsS+DHkVUlkoCxU+Zxx9/eW9Vf6U0tw+G0v8Km0Q1viaRNvifKPWZm3wC2D2963d2XR1eWSPzufuH9Ve01aeQfPU/I3PmTedCrXwJViSQnr7WYzGwtYBQwwt3nAv3MTNeplrLSvBTHunzVIhx2W/ZbhYNUpHwX6/sd8DWwd9ivAy6JpCKRhFSZ0c8+YV7Pk1fdtv2y3/GFrZdgVSLJyTcgtnH3K4FGAHf/ingvwSISuVE7f87TPc5c1d9m2e0sowff27NvglWJJCffSeqvzaya8KQ5M9sG0ByElI8Fj3HqG5njLvovu5Mq68bxe/blkmEDEixMJDn5BsQ44DGgr5ndCQwCToyqKJFYzbwVpmX2HBi/lHcSK0YkPToMCDPrBmxAcDb1XgRDS2e4+6cR1yYSvScmwDM/z/TzXK5bpBJ0GBDuvjJc9vte4JEYahKJx30nwqsPZfoKB5EW8h1i+rOZnQ1MBv7VfKO7L4mkKpGo/WYQfPJKpq9wEFlNvgFxLMEE9emtbt+6uOWIxGDCRrByRaavcBDJKd+A2JEgHPYlCIpngBujKkokMp28RKhIJco3ICYBnwPXh/3vh7d9N4qiRCKhcBApSL4BsbO775jVf9LMXouiIJFIKBxECpbvmdQvmdlezR0z2xNdp1pKhcJBpFPy3YPYHfirmTWvhdwPWGBm8wB3910iqU6kqxQOIp2Wb0AcEmkVIlFQOIh0Sb7Xg3g36kJEikrhINJl+c5BiJQOhYNIUSggpLwoHESKJt85CJFETJldx1WPL+DD+ga26FXNqIO3Y9jAmtwbtwqHQT0f4sPRj3T8OBHJSQEhBSnoC7sIrzXmwXk0NDYBUFffwJgH5wGs/pqtwmGHpsk01Dd0/DgRaZOGmCRvzV/YdfUNOJkv3imz6yJ5vaseX7AqHJo1NDZx1eMLWm6YY88hr8eJSLsUEJK3vL+wi+TDcA+g3dtzzDnk9TgR6ZACQvJW18YXbFu3d9UWvarbv72NCekOHycieVFASN6qzAq6vatGHbwd1d2rWtxW3b2KUQdv1+7RSu0+TkTypklqyVuTe0G3d1XzhPJqk+J/2LHlhq0OZW3zcXlMUHd2Ej7OyXuRuEQWEGbWE3ga6BG+zv3uPq7VNj2A2wjWeloMHOvu70RVk3RNTa/qnMNJNREO3QwbWNPyizbP8xxWe1wepsyuY9R9c2lcGQReXX0Do+6bu+r52ntc3kdbiZSQKIeYlgOD3X1XYDfgkOwVYUMnAZ+5+7bAL4ArIqxHuijxoZuIT4IbP/XVVeHQrHGlM37qq+0+Lu7Je5G4RBYQHvgy7HYP/1qPRRxOcOEhgPuBb5tFNKAtXTZsYA2XHTmAml7VGMGew2VHDojnV3IMZ0jXNzQWdHszHTUl5SrSSWozqzKzOcBCYLq7v9BqkxrgfQB3XwEsBTaKsiYpQSlfPkNHTUm5ijQg3L3J3XcD+gB7mNnOnXkeMzvFzGaa2cxFixYVt0jJW9wnygGxhsMGa3Uv6PZmiQ+9iUQklsNc3b0eeJLVrytRB/QFMLM1gPUJJqtbP/4md69199revXtHXa60Ifax9uxw2PmoyPccxh22E92rWo5wdq8yxh22U7uPS3ToTSRCUR7F1BtodPd6M6sGDmT1SeipwAnA88DRwAz3iI6ZlC6Ldaw9Oxz2HgEHX1r812ilK4fHduaoKZG0i/I8iM2BSWZWRbCncq+7TzOzCcBMd58K3ALcbmZvAkuA4RHWI120RRuHuRZ9rD07HA66FPYZUdznb4e+6EUyIgsId38ZGJjj9guz2suAY6KqQYpr1MHbtTjeHyIYa88OhyMnwi76eIgkpSLOpNZZrsXRlSGYvGSHww8egm0GF+d5RaRTyj4gdJZrcUUyBOMOF/XK9H/0JNT8W3FfQ0QKVvaL9eks15Rb2dQyHEbMUjiIpETZ70HoLNcUa1wGl26a6Z+1ANbdLLl6RKSFst+D0FmuKdVQ3zIcRr+vcBBJmbIPCJ3lmkKffwRXbJnpj10IPddLrh4Ryansh5giP/KmiCriaKtP34Bf1Wb6Fy6BblVtby8iiSn7gIDSOPmpIo62+mAWTMw6dHVcPWjxXpHUKvshplJR9kdbvfHnluEwfqnCQSTlFBApUdZHW829B+48alV3UM+Hol0BVkSKQgGREmV7tNVz18FDP17V7b/srniWCReRLlNApMQB2+dexryt20vCY2Ng+qqlt+i/7K5V7bIaPhMpUxUxSV0Knnw994WQ2ro99e49AV6bsqqbHQ7NymL4TKSMKSBSoqzmIG4eDHWzVnUH9XwIlsWwTLiIFJWGmFKibOYgrtiqRTgwfqlOVhQpUQqIlCiLL9Hx60PDkqx+cIlQXZJTpDRpiCklSumM75yyr+UAq10/uhROVhSRlhQQKVKyX6IdhIOIlCYNMUnXKBxEypYCQjpP4SBS1hQQ0jkKB5Gyp4CQwikcRCqCJqmlMDGHQ0VcI0MkpRQQkr8EwqHsr5EhkmIaYpL8JDCsVPbXyBBJOQWEdCyhOYeyWp9KpAQpIKR9CU5Il836VCIlSgEhbUv4aKWyWJ9KpIRpklpyyw4H6wbjPou9hJJfn0qkxCkgZHXZ4bB+XzjzlS4/ZWcPVy3Z9alEyoACQlrKDoc+e8DJ07v8lDpcVaQ0aQ5CMrLDYYfDihIOoMNVRUqVAkIC2eHwrR/BsXcU7al1uKpIaVJASMtw2H8MHHp1UZ9eh6uKlCYFRAWbMruuRTjMHTAW9h/d4WMGXT6DrUY/wqDLZwTP0QEdripSmhQQFWrK7DqG/WHHVf3/+3oEw+cMaPcLv3myua6+AScz2dxRSOia1CKlydw96RoKUltb6zNnzky6jNKXtedwwtfn8tTKXYHgy/u50YNzPmTQ5TOoyzFv0N5jRCQdzGyWu9cW8hgd5lqJssLhiOUXMdu/sarf3sSxJptFKouGmCpNVjgcuPzKFuEA7U8ca7JZpLIoICqFe4tw+NOB0/lgjS1bbNLRxLEmm0UqiwKiEqxsgot6Zfpnv8FBg/YoeOJ42MAajtq9hiozAKrMOGp3LYUhUq40B1HuViyHSzbJ9Ee/Bz2DPYlC1zmaMruOB2bV0RQe2NDkzgOz6qjdckOFhEgZ0h5EOVv+ZctwOP+TVeHQGVoyQ6SyaA+iXH21BK7cKtO/YDFUde0/t45iEqks2oMoR0vrWobDuPouhwPoKCaRSqOAKDefvgG/yJwhzfilEE4qd5WOYhKpLBpiKid1L8HNB2T6Rb5EqK7wJlJZFBDl4q2n4LahmX5E14/WFd5EKoeGmMrB20/HEg4iUlkUEKVu/jSYdFimr3AQkSJRQJSyOXfB5OMyfYWDiBSRAqJUPX8DTDkt01c4iEiRaZI6RabMrsvvCKEZl8LTV2b6CgcRiYACIiWar9bWvJRF89XagJYhMe2nMPOWTF/hICIR0RBTSuS1ztHkHygcRCQ22oNIiQ7XObr1EHjv+cwdCgcRiZj2IFKi3XWOrh+ocBCR2CkgUqKtdY6eavw+LHkrc6PCQURioiGmlMi1ztFzy46A7GkJhYOIxCiygDCzvsBtwKaAAze5+3Wtttkf+APwdnjTg+4+Iaqa0q7FOkfjW13YR+EgIjGLcg9iBXCWu79kZusCs8xsuru/1mq7Z9x9SIR1lB6Fg4ikQGRzEO7+kbu/FLa/AOYDWga0IwoHEUmJWCapzaw/MBB4Icfde5vZXDN71Mx2iqOe1Hrp9pZ9hYOIJCjygDCzdYAHgJ+4++et7n4J2NLddwV+CUxp4zlOMbOZZjZz0aJF0RaclL/fDFNHBO19z1Q4iEjiIg0IM+tOEA53uvuDre9398/d/cuw/Uegu5ltnGO7m9y91t1re/fuHWXJyfjrL+GPZwftE/8I3xmfZDUiIkCEAWFmBtwCzHf3a9rYZrNwO8xsj7CexVHVlEpPXQl/Ghu0T/oz9B+UbD0iIqEoj2IaBPwAmGdmc8LbzgP6Abj7jcDRwGlmtgJoAIa7u0dYU7r8+SJ4NszOHz8Nm++abD0iIlkiCwh3fxawDrb5FfCrqGpItcfGwN9+HbRPex423THZekREWtGZ1EmYOhJemhS0R8yCjbdNth4RkRwUEHF74GSYd1/QHjkHNtwq2XpERNqggIjT3d+HBY8E7TNfhfX7JFuPiEg7FBBxue1weOsvQfusBbDuZomWIyLSEQVEHCYeCB/8PWiP+iesvdqpHiIiqaOAiNqv94GFrwbtc9+B6g0SLUdEJF8KiChdOwDq3wvao9+HnuslW4+ISAEUEFG5cpJEcE4AAAiHSURBVGv4Kjwp/LwPYc21k61HRKRACogoXNwbmr4O2ud/At17JluPiEgnKCCKyR0u6pXpj10Ea6yZXD0iIl2ggCiW1uFwwWKo0tsrIqVL32DFsHIlTMg6OunCJdCtKrl6RESKIJYrypW1lU2twuEzhYOIlAXtQXRFUyNcnHXS27h6sHYXsBURKRnag+isFcsVDiJS1hQQndHYAJdsErTX6BlcP1rhICJlRgFRqK//BZeGC+2ttTGM/STZekREIqKAKMSyz+FnWwTtDfrDOf9MtBwRkSgpIPL11RK4vG/Q3nQAnDE32XpERCKmgMjHvz6FK8Mrv/XdC057Ntl6RERioIDoyBcfw1XbBO2tD4CTHk+2HhGRmCgg2rP0A/j5dkF7+yHw31OSrUdEJEYKiLYseRt+sVPQ3uVYGH5nsvWIiMRMAZHLon/A9bsF7d1PhCNvSrQcEZEkKCBa++RVuOFbQXuv0+Gw65KtR0QkIQqIbB/Ogd/sE7T3OwsOuSzZekREEqTF+pq9/yLc8p2gfcBY+I9RydYjIpIwBQTAO8/C7w8N2gddCvuMSLYeEZEUUEC8+QTccWTQ/q+rYY8fJVuPiEhKVHZALHgU7h4etA+/AQYen2w9IiIpUrkB8eoUuO+EoH3ULTDg6GTrERFJmcoMiLmT4aFTgvaxd8AOhyVbj4hIClVeQMyaBA+PDNrfvw++eVCy9YiIpFRlBcQLv4VHzwnaP5gC2xyQbD0iIilWOQFR/14mHP7nUdhyn2TrERFJuco5k7pqTdhyXzh5hsJBRCQPlbMHse5m8D+PJF2FiEjJqJw9CBERKYgCQkREclJAiIhITgoIERHJSQEhIiI5KSBERCQnBYSIiOSkgBARkZwUECIikpMCQkREclJAiIhITgoIERHJSQEhIiI5mbsnXUNBzGwR8G7SdURsY+DTpItIGb0nq9N7sjq9J7ltDKzt7r0LeVDJBUQlMLOZ7l6bdB1povdkdXpPVqf3JLfOvi8aYhIRkZwUECIikpMCIp1uSrqAFNJ7sjq9J6vTe5Jbp94XzUGIiEhO2oMQEZGcFBAJMbO+Zvakmb1mZq+a2Rk5ttnfzJaa2Zzw78Ikao2LmfU0s7+b2dzwPbkoxzY9zGyymb1pZi+YWf/4K41Pnu/JiWa2KOtzcnIStcbNzKrMbLaZTctxX0V9Tpp18J4U/DlZI5oyJQ8rgLPc/SUzWxeYZWbT3f21Vts94+5DEqgvCcuBwe7+pZl1B541s0fd/W9Z25wEfObu25rZcOAK4Ngkio1JPu8JwGR3H5FAfUk6A5gPrJfjvkr7nDRr7z2BAj8n2oNIiLt/5O4vhe0vCP6j1iRbVbI88GXY7R7+tZ4kOxyYFLbvB75tZhZTibHL8z2pOGbWBzgUmNjGJhX1OYG83pOCKSBSINz9HQi8kOPuvcPhhUfNbKdYC0tAuIs8B1gITHf31u9JDfA+gLuvAJYCG8VbZbzyeE8AjjKzl83sfjPrG3OJSbgWOAdY2cb9Ffc5oeP3BAr8nCggEmZm6wAPAD9x989b3f0SsKW77wr8EpgSd31xc/cmd98N6APsYWY7J11T0vJ4Tx4G+rv7LsB0Mr+cy5KZDQEWuvuspGtJizzfk4I/JwqIBIVjyg8Ad7r7g63vd/fPm4cX3P2PQHcz2zjmMhPh7vXAk8Ahre6qA/oCmNkawPrA4nirS0Zb74m7L3b35WF3IrB73LXFbBAw1MzeAe4BBpvZHa22qbTPSYfvSWc+JwqIhITjobcA8939mja22ax53NTM9iD471W2H3Iz621mvcJ2NXAg8HqrzaYCJ4Tto4EZXsYn8+TznpjZ5lndoQTzWWXL3ce4ex937w8MJ/gMHN9qs4r6nOTznnTmc6KjmJIzCPgBMC8cXwY4D+gH4O43EnywTzOzFUADMLycP+TA5sAkM6siCMN73X2amU0AZrr7VIJQvd3M3gSWEPyfoZzl856MNLOhBEfGLQFOTKzaBFX45ySnrn5OdCa1iIjkpCEmERHJSQEhIiI5KSBERCQnBYSIiOSkgBARkZwUEFL2zKyXmZ0e4fPvb2b7RPX8rV7rvDheRwQUEFIZegE5AyI8y7ar9gcKCoguvK4CQmKjgJBKcDmwTbgG/lXhL/5nzGwq8JqZ9TezV5o3NrOzzWx82N7GzB4zs1nhY7bPfuJwocVTgTPD59/PzA4Lr0Ew28z+bGabhtuON7Pbzew5gpO4epvZ9PA6DxPN7N3mpVTM7HgLrgMxx8x+Gy7YdzlQHd52Zwzvm1Q4nUktlWA0sHO44B1mtj/wb+Ftb3dwMZmbgFPd/Q0z2xP4NTC4+U53f8fMbgS+dPerw+ffANjL3T28KMs5wFnhQ3YE9nX3BjP7FcGSCJeZ2SEE1zDAzHYguHbBIHdvNLNfA8e5+2gzG9H8v0MkagoIqVR/d/e329sgXGl3H+C+rEsJ9MjjufsAk8O1b9YEsl9nqrs3hO19gSMA3P0xM/ssvP3bBAupvRi+bjXBUt8isVJASKX6V1Z7BS2HW3uG/3YD6jvxi/2XwDXuPjXcWxnfxuu2xYBJ7j6mwNcVKSrNQUgl+AJYt537PwE2MbONzKwHMASC5daBt83sGAhW4DWzXfN4/vUJlpuGzIqiuTwHfDd87oOADcLbnwCONrNNwvs2NLMtw/saw2XiRSKngJCy5+6LgefM7BUzuyrH/Y3ABODvBBdSyV5O+zjgJDObC7xKcCnL1h4GjmiepCbYY7jPzGYBn7ZT2kXAQeEE+THAx8AX4XXJxwJ/MrOXw5qal2q+CXhZk9QSB63mKpKQcG+lyd1XmNnewG80AS1pojkIkeT0A+41s27A18CPEq5HpAXtQYiISE6agxARkZwUECIikpMCQkREclJAiIhITgoIERHJSQEhIiI5/T84hgwn1AxOxwAAAABJRU5ErkJggg==\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"source": [
"boxplot_pi(lm, X_test, y_test)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 441
},
"id": "X8bwlzZDcu2G",
"outputId": "70181b44-e26a-499c-c5ff-c08c45cc042d"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "U-WxMmdxNRB_",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "b5fc0af6-4ae5-4054-c4df-e83aa7227fbf"
},
"source": [
"print(\"The hyper-parameters for a linear model are:\")\n",
"for param_name in LinearRegression().get_params().keys():\n",
" print(param_name)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"The hyper-parameters for a linear model are:\n",
"copy_X\n",
"fit_intercept\n",
"n_jobs\n",
"normalize\n",
"positive\n"
]
}
]
},
{
"cell_type": "code",
"source": [
"!pip install -q interpret"
],
"metadata": {
"id": "Z2_s7mrGO3Ro"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from interpret.glassbox import ExplainableBoostingRegressor\n",
"\n",
"model = ExplainableBoostingRegressor(interactions=0)\n",
"model.fit(X_train, y_train)"
],
"metadata": {
"id": "_rPpM48ZO6K7"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"print(\"train error: %0.3f, test error: %0.3f\" %\n",
" (median_absolute_error(y_train, model.predict(X_train)),\n",
" median_absolute_error(y_test, model.predict(X_test))))"
],
"metadata": {
"id": "X4UJo6OQO-qY"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"explainer = shap.Explainer(model.predict, X_train)\n",
"shap_values = explainer(X_test)"
],
"metadata": {
"id": "bLLMyhyBPLdu"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"shap.plots.waterfall(shap_values[0])"
],
"metadata": {
"id": "kSZVYLjxPPIq"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"shap.plots.scatter(shap_values[:,\"sepal length (cm)\"])"
],
"metadata": {
"id": "In-eiIZjPPqJ"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"shap.plots.scatter(shap_values[:,\"sepal length (cm)\"], show=False)\n",
"# First get the index of the alcohol feature\n",
"idx = model.explain_global().data()['names'].index('sepal length (cm)')\n",
"# extract the relevant data from the tree-based GAM\n",
"explain_data = model.explain_global().data(idx)\n",
"# the alcohol feature values\n",
"x_data = explain_data[\"names\"]\n",
"# the part of the prediction function for alcohol\n",
"y_data = explain_data[\"scores\"]\n",
"y_data = np.r_[y_data, y_data[np.newaxis, -1]]\n",
"plt.plot(x_data, y_data, color='red')\n",
"plt.show()"
],
"metadata": {
"id": "9b3OIgfjPR-h"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "2xkjKorMNRCT"
},
"source": [
"from sklearn.ensemble import RandomForestRegressor\n",
"\n",
"rf = Pipeline([\n",
" ('preprocess', preprocessing),\n",
" ('regressor', RandomForestRegressor(n_estimators=100, n_jobs=-1, random_state=42))\n",
"])"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"scrolled": false,
"id": "pAckw-G4NRCf",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 192
},
"outputId": "89a37b81-709d-4c1c-9f78-0a05de5131c9"
},
"source": [
"rf.fit(X_train, y_train)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Pipeline(steps=[('preprocess',\n",
" ColumnTransformer(transformers=[('cat',\n",
" Pipeline(steps=[('onehot',\n",
" OneHotEncoder())]),\n",
" ['class']),\n",
" ('num',\n",
" Pipeline(steps=[('scaler',\n",
" StandardScaler())]),\n",
" ['sepal length (cm)',\n",
" 'petal length (cm)',\n",
" 'petal width (cm)'])])),\n",
" ('regressor',\n",
" RandomForestRegressor(n_jobs=-1, random_state=42))])"
],
"text/html": [
"<style>#sk-6e6a82bc-5b2a-459b-87c1-a4ea86d8a213 {color: black;background-color: white;}#sk-6e6a82bc-5b2a-459b-87c1-a4ea86d8a213 pre{padding: 0;}#sk-6e6a82bc-5b2a-459b-87c1-a4ea86d8a213 div.sk-toggleable {background-color: white;}#sk-6e6a82bc-5b2a-459b-87c1-a4ea86d8a213 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-6e6a82bc-5b2a-459b-87c1-a4ea86d8a213 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-6e6a82bc-5b2a-459b-87c1-a4ea86d8a213 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-6e6a82bc-5b2a-459b-87c1-a4ea86d8a213 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-6e6a82bc-5b2a-459b-87c1-a4ea86d8a213 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-6e6a82bc-5b2a-459b-87c1-a4ea86d8a213 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-6e6a82bc-5b2a-459b-87c1-a4ea86d8a213 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-6e6a82bc-5b2a-459b-87c1-a4ea86d8a213 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-6e6a82bc-5b2a-459b-87c1-a4ea86d8a213 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-6e6a82bc-5b2a-459b-87c1-a4ea86d8a213 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-6e6a82bc-5b2a-459b-87c1-a4ea86d8a213 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-6e6a82bc-5b2a-459b-87c1-a4ea86d8a213 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-6e6a82bc-5b2a-459b-87c1-a4ea86d8a213 div.sk-estimator:hover {background-color: #d4ebff;}#sk-6e6a82bc-5b2a-459b-87c1-a4ea86d8a213 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-6e6a82bc-5b2a-459b-87c1-a4ea86d8a213 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-6e6a82bc-5b2a-459b-87c1-a4ea86d8a213 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-6e6a82bc-5b2a-459b-87c1-a4ea86d8a213 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-6e6a82bc-5b2a-459b-87c1-a4ea86d8a213 div.sk-item {z-index: 1;}#sk-6e6a82bc-5b2a-459b-87c1-a4ea86d8a213 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-6e6a82bc-5b2a-459b-87c1-a4ea86d8a213 div.sk-parallel::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-6e6a82bc-5b2a-459b-87c1-a4ea86d8a213 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-6e6a82bc-5b2a-459b-87c1-a4ea86d8a213 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-6e6a82bc-5b2a-459b-87c1-a4ea86d8a213 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-6e6a82bc-5b2a-459b-87c1-a4ea86d8a213 div.sk-parallel-item:only-child::after {width: 0;}#sk-6e6a82bc-5b2a-459b-87c1-a4ea86d8a213 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-6e6a82bc-5b2a-459b-87c1-a4ea86d8a213 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-6e6a82bc-5b2a-459b-87c1-a4ea86d8a213 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-6e6a82bc-5b2a-459b-87c1-a4ea86d8a213 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-6e6a82bc-5b2a-459b-87c1-a4ea86d8a213 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-6e6a82bc-5b2a-459b-87c1-a4ea86d8a213\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocess&#x27;,\n",
" ColumnTransformer(transformers=[(&#x27;cat&#x27;,\n",
" Pipeline(steps=[(&#x27;onehot&#x27;,\n",
" OneHotEncoder())]),\n",
" [&#x27;class&#x27;]),\n",
" (&#x27;num&#x27;,\n",
" Pipeline(steps=[(&#x27;scaler&#x27;,\n",
" StandardScaler())]),\n",
" [&#x27;sepal length (cm)&#x27;,\n",
" &#x27;petal length (cm)&#x27;,\n",
" &#x27;petal width (cm)&#x27;])])),\n",
" (&#x27;regressor&#x27;,\n",
" RandomForestRegressor(n_jobs=-1, random_state=42))])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"c633e83b-a0b1-4563-8ca9-0b9db2cf05aa\" type=\"checkbox\" ><label for=\"c633e83b-a0b1-4563-8ca9-0b9db2cf05aa\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocess&#x27;,\n",
" ColumnTransformer(transformers=[(&#x27;cat&#x27;,\n",
" Pipeline(steps=[(&#x27;onehot&#x27;,\n",
" OneHotEncoder())]),\n",
" [&#x27;class&#x27;]),\n",
" (&#x27;num&#x27;,\n",
" Pipeline(steps=[(&#x27;scaler&#x27;,\n",
" StandardScaler())]),\n",
" [&#x27;sepal length (cm)&#x27;,\n",
" &#x27;petal length (cm)&#x27;,\n",
" &#x27;petal width (cm)&#x27;])])),\n",
" (&#x27;regressor&#x27;,\n",
" RandomForestRegressor(n_jobs=-1, random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"4ae69dd9-1ed8-41ca-b2e8-418b94941707\" type=\"checkbox\" ><label for=\"4ae69dd9-1ed8-41ca-b2e8-418b94941707\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocess: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;cat&#x27;,\n",
" Pipeline(steps=[(&#x27;onehot&#x27;, OneHotEncoder())]),\n",
" [&#x27;class&#x27;]),\n",
" (&#x27;num&#x27;,\n",
" Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler())]),\n",
" [&#x27;sepal length (cm)&#x27;, &#x27;petal length (cm)&#x27;,\n",
" &#x27;petal width (cm)&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"a8318421-2b62-411d-a869-9883341f5277\" type=\"checkbox\" ><label for=\"a8318421-2b62-411d-a869-9883341f5277\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>[&#x27;class&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"8438c20c-e881-4905-a358-e34e71257049\" type=\"checkbox\" ><label for=\"8438c20c-e881-4905-a358-e34e71257049\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"dd0a9c83-cce7-4a85-93e4-e6e1b0403009\" type=\"checkbox\" ><label for=\"dd0a9c83-cce7-4a85-93e4-e6e1b0403009\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>[&#x27;sepal length (cm)&#x27;, &#x27;petal length (cm)&#x27;, &#x27;petal width (cm)&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"09a1dbce-f69b-4922-868f-261ab19aa8c8\" type=\"checkbox\" ><label for=\"09a1dbce-f69b-4922-868f-261ab19aa8c8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"2b0db141-667a-4c1a-8ea9-18f72d14e5b3\" type=\"checkbox\" ><label for=\"2b0db141-667a-4c1a-8ea9-18f72d14e5b3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(n_jobs=-1, random_state=42)</pre></div></div></div></div></div></div></div>"
]
},
"metadata": {},
"execution_count": 52
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "6onlZOMeNRCj",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "ee3d5a1c-7797-428f-a76c-74e9b909b5e6"
},
"source": [
"from sklearn.metrics import median_absolute_error\n",
"\n",
"print(\"train error: %0.3f, test error: %0.3f\" %\n",
" (median_absolute_error(y_train, rf.predict(X_train)),\n",
" median_absolute_error(y_test, rf.predict(X_test))))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"train error: 0.060, test error: 0.275\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"scrolled": false,
"id": "xLGOVM3TNRCs",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 391
},
"outputId": "d25268a9-2662-46aa-f44c-19d6ba541e82"
},
"source": [
"scatter_predictions(rf.predict(X_test), y_test)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 432x432 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "EqZqJRZmNRC1",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "63b466ef-65e5-46ee-b1c9-e6cf4d41d1a9"
},
"source": [
"print(\"The hyper-parameters for a random forest model are:\")\n",
"for param_name in rf.get_params().keys():\n",
" print(param_name)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"The hyper-parameters for a random forest model are:\n",
"memory\n",
"steps\n",
"verbose\n",
"preprocess\n",
"regressor\n",
"preprocess__n_jobs\n",
"preprocess__remainder\n",
"preprocess__sparse_threshold\n",
"preprocess__transformer_weights\n",
"preprocess__transformers\n",
"preprocess__verbose\n",
"preprocess__verbose_feature_names_out\n",
"preprocess__cat\n",
"preprocess__num\n",
"preprocess__cat__memory\n",
"preprocess__cat__steps\n",
"preprocess__cat__verbose\n",
"preprocess__cat__onehot\n",
"preprocess__cat__onehot__categories\n",
"preprocess__cat__onehot__drop\n",
"preprocess__cat__onehot__dtype\n",
"preprocess__cat__onehot__handle_unknown\n",
"preprocess__cat__onehot__sparse\n",
"preprocess__num__memory\n",
"preprocess__num__steps\n",
"preprocess__num__verbose\n",
"preprocess__num__scaler\n",
"preprocess__num__scaler__copy\n",
"preprocess__num__scaler__with_mean\n",
"preprocess__num__scaler__with_std\n",
"regressor__bootstrap\n",
"regressor__ccp_alpha\n",
"regressor__criterion\n",
"regressor__max_depth\n",
"regressor__max_features\n",
"regressor__max_leaf_nodes\n",
"regressor__max_samples\n",
"regressor__min_impurity_decrease\n",
"regressor__min_samples_leaf\n",
"regressor__min_samples_split\n",
"regressor__min_weight_fraction_leaf\n",
"regressor__n_estimators\n",
"regressor__n_jobs\n",
"regressor__oob_score\n",
"regressor__random_state\n",
"regressor__verbose\n",
"regressor__warm_start\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Xeklg8zNNRDA",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 193
},
"outputId": "8f27e800-8030-4525-8553-5e8aae37c8c8"
},
"source": [
"from sklearn.model_selection import GridSearchCV\n",
"\n",
"param_grid = {\n",
" 'regressor__max_features': (2, 3, 4),\n",
" 'regressor__max_depth': (2, 3, 5),\n",
" 'regressor__min_samples_leaf': (1, 3, 5),\n",
"}\n",
"\n",
"model_grid_search = GridSearchCV(rf, param_grid=param_grid,\n",
" n_jobs=-1, cv=3)\n",
"model_grid_search.fit(X_train, y_train)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"GridSearchCV(cv=3,\n",
" estimator=Pipeline(steps=[('preprocess',\n",
" ColumnTransformer(transformers=[('cat',\n",
" Pipeline(steps=[('onehot',\n",
" OneHotEncoder())]),\n",
" ['class']),\n",
" ('num',\n",
" Pipeline(steps=[('scaler',\n",
" StandardScaler())]),\n",
" ['sepal '\n",
" 'length '\n",
" '(cm)',\n",
" 'petal '\n",
" 'length '\n",
" '(cm)',\n",
" 'petal '\n",
" 'width '\n",
" '(cm)'])])),\n",
" ('regressor',\n",
" RandomForestRegressor(n_jobs=-1,\n",
" random_state=42))]),\n",
" n_jobs=-1,\n",
" param_grid={'regressor__max_depth': (2, 3, 5),\n",
" 'regressor__max_features': (2, 3, 4),\n",
" 'regressor__min_samples_leaf': (1, 3, 5)})"
],
"text/html": [
"<style>#sk-dccfe0c3-2ebb-4508-a6b6-a1741249642d {color: black;background-color: white;}#sk-dccfe0c3-2ebb-4508-a6b6-a1741249642d pre{padding: 0;}#sk-dccfe0c3-2ebb-4508-a6b6-a1741249642d div.sk-toggleable {background-color: white;}#sk-dccfe0c3-2ebb-4508-a6b6-a1741249642d label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-dccfe0c3-2ebb-4508-a6b6-a1741249642d label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-dccfe0c3-2ebb-4508-a6b6-a1741249642d label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-dccfe0c3-2ebb-4508-a6b6-a1741249642d div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-dccfe0c3-2ebb-4508-a6b6-a1741249642d div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-dccfe0c3-2ebb-4508-a6b6-a1741249642d div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-dccfe0c3-2ebb-4508-a6b6-a1741249642d input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-dccfe0c3-2ebb-4508-a6b6-a1741249642d input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-dccfe0c3-2ebb-4508-a6b6-a1741249642d div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-dccfe0c3-2ebb-4508-a6b6-a1741249642d div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-dccfe0c3-2ebb-4508-a6b6-a1741249642d input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-dccfe0c3-2ebb-4508-a6b6-a1741249642d div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-dccfe0c3-2ebb-4508-a6b6-a1741249642d div.sk-estimator:hover {background-color: #d4ebff;}#sk-dccfe0c3-2ebb-4508-a6b6-a1741249642d div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-dccfe0c3-2ebb-4508-a6b6-a1741249642d div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-dccfe0c3-2ebb-4508-a6b6-a1741249642d div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-dccfe0c3-2ebb-4508-a6b6-a1741249642d div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-dccfe0c3-2ebb-4508-a6b6-a1741249642d div.sk-item {z-index: 1;}#sk-dccfe0c3-2ebb-4508-a6b6-a1741249642d div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-dccfe0c3-2ebb-4508-a6b6-a1741249642d div.sk-parallel::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-dccfe0c3-2ebb-4508-a6b6-a1741249642d div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-dccfe0c3-2ebb-4508-a6b6-a1741249642d div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-dccfe0c3-2ebb-4508-a6b6-a1741249642d div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-dccfe0c3-2ebb-4508-a6b6-a1741249642d div.sk-parallel-item:only-child::after {width: 0;}#sk-dccfe0c3-2ebb-4508-a6b6-a1741249642d div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-dccfe0c3-2ebb-4508-a6b6-a1741249642d div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-dccfe0c3-2ebb-4508-a6b6-a1741249642d div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-dccfe0c3-2ebb-4508-a6b6-a1741249642d div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-dccfe0c3-2ebb-4508-a6b6-a1741249642d div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-dccfe0c3-2ebb-4508-a6b6-a1741249642d\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=3,\n",
" estimator=Pipeline(steps=[(&#x27;preprocess&#x27;,\n",
" ColumnTransformer(transformers=[(&#x27;cat&#x27;,\n",
" Pipeline(steps=[(&#x27;onehot&#x27;,\n",
" OneHotEncoder())]),\n",
" [&#x27;class&#x27;]),\n",
" (&#x27;num&#x27;,\n",
" Pipeline(steps=[(&#x27;scaler&#x27;,\n",
" StandardScaler())]),\n",
" [&#x27;sepal &#x27;\n",
" &#x27;length &#x27;\n",
" &#x27;(cm)&#x27;,\n",
" &#x27;petal &#x27;\n",
" &#x27;length &#x27;\n",
" &#x27;(cm)&#x27;,\n",
" &#x27;petal &#x27;\n",
" &#x27;width &#x27;\n",
" &#x27;(cm)&#x27;])])),\n",
" (&#x27;regressor&#x27;,\n",
" RandomForestRegressor(n_jobs=-1,\n",
" random_state=42))]),\n",
" n_jobs=-1,\n",
" param_grid={&#x27;regressor__max_depth&#x27;: (2, 3, 5),\n",
" &#x27;regressor__max_features&#x27;: (2, 3, 4),\n",
" &#x27;regressor__min_samples_leaf&#x27;: (1, 3, 5)})</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"6c075c67-d6dc-40c4-8bf7-de8a9d2c220f\" type=\"checkbox\" ><label for=\"6c075c67-d6dc-40c4-8bf7-de8a9d2c220f\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=3,\n",
" estimator=Pipeline(steps=[(&#x27;preprocess&#x27;,\n",
" ColumnTransformer(transformers=[(&#x27;cat&#x27;,\n",
" Pipeline(steps=[(&#x27;onehot&#x27;,\n",
" OneHotEncoder())]),\n",
" [&#x27;class&#x27;]),\n",
" (&#x27;num&#x27;,\n",
" Pipeline(steps=[(&#x27;scaler&#x27;,\n",
" StandardScaler())]),\n",
" [&#x27;sepal &#x27;\n",
" &#x27;length &#x27;\n",
" &#x27;(cm)&#x27;,\n",
" &#x27;petal &#x27;\n",
" &#x27;length &#x27;\n",
" &#x27;(cm)&#x27;,\n",
" &#x27;petal &#x27;\n",
" &#x27;width &#x27;\n",
" &#x27;(cm)&#x27;])])),\n",
" (&#x27;regressor&#x27;,\n",
" RandomForestRegressor(n_jobs=-1,\n",
" random_state=42))]),\n",
" n_jobs=-1,\n",
" param_grid={&#x27;regressor__max_depth&#x27;: (2, 3, 5),\n",
" &#x27;regressor__max_features&#x27;: (2, 3, 4),\n",
" &#x27;regressor__min_samples_leaf&#x27;: (1, 3, 5)})</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"816267fb-1988-467c-afb4-f45ae0b73c1b\" type=\"checkbox\" ><label for=\"816267fb-1988-467c-afb4-f45ae0b73c1b\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocess: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;cat&#x27;,\n",
" Pipeline(steps=[(&#x27;onehot&#x27;, OneHotEncoder())]),\n",
" [&#x27;class&#x27;]),\n",
" (&#x27;num&#x27;,\n",
" Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler())]),\n",
" [&#x27;sepal length (cm)&#x27;, &#x27;petal length (cm)&#x27;,\n",
" &#x27;petal width (cm)&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"54d01f43-9062-456c-a7a9-02b45b6fad6d\" type=\"checkbox\" ><label for=\"54d01f43-9062-456c-a7a9-02b45b6fad6d\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>[&#x27;class&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"d21a9f13-e085-4de0-ba2c-b6eb1b2f6012\" type=\"checkbox\" ><label for=\"d21a9f13-e085-4de0-ba2c-b6eb1b2f6012\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"92f07b94-ae84-4f3d-8b98-b02fd3e76dcc\" type=\"checkbox\" ><label for=\"92f07b94-ae84-4f3d-8b98-b02fd3e76dcc\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>[&#x27;sepal length (cm)&#x27;, &#x27;petal length (cm)&#x27;, &#x27;petal width (cm)&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"f3be7a87-9255-43b8-99e8-ba325b6b5f98\" type=\"checkbox\" ><label for=\"f3be7a87-9255-43b8-99e8-ba325b6b5f98\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"59b1b5d8-d034-4453-b2c0-d01db98b9f88\" type=\"checkbox\" ><label for=\"59b1b5d8-d034-4453-b2c0-d01db98b9f88\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(n_jobs=-1, random_state=42)</pre></div></div></div></div></div></div></div></div></div></div></div></div>"
]
},
"metadata": {},
"execution_count": 56
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "T_pgWdWVNRDG",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "3c8eff42-1db3-49e5-f438-3a0d51e4369f"
},
"source": [
"print(\"train error: %0.3f, test error: %0.3f\" %\n",
" (median_absolute_error(y_train, model_grid_search.predict(X_train)),\n",
" median_absolute_error(y_test, model_grid_search.predict(X_test))))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"train error: 0.120, test error: 0.201\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "Fmxbr-noNRDL",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "085498bf-40b1-49a9-8249-27c759c90e7b"
},
"source": [
"print(f\"The best set of hyperparameters is: \"\n",
" f\"{model_grid_search.best_params_}\")"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"The best set of hyperparameters is: {'regressor__max_depth': 5, 'regressor__max_features': 3, 'regressor__min_samples_leaf': 3}\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "28wkVwv2NRDU"
},
"source": [
"rf_best = Pipeline([\n",
" ('preprocess', preprocessing),\n",
" ('regressor', RandomForestRegressor(\n",
" n_estimators=100, max_depth=5, max_features=3, min_samples_leaf=3, n_jobs=-1, random_state=42))\n",
"])"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "uvSX8FmHNRDZ",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 192
},
"outputId": "13ded2fb-db7f-45cc-fbdc-d305a2a587a6"
},
"source": [
"rf_best.fit(X_train, y_train)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Pipeline(steps=[('preprocess',\n",
" ColumnTransformer(transformers=[('cat',\n",
" Pipeline(steps=[('onehot',\n",
" OneHotEncoder())]),\n",
" ['class']),\n",
" ('num',\n",
" Pipeline(steps=[('scaler',\n",
" StandardScaler())]),\n",
" ['sepal length (cm)',\n",
" 'petal length (cm)',\n",
" 'petal width (cm)'])])),\n",
" ('regressor',\n",
" RandomForestRegressor(max_depth=5, max_features=3,\n",
" min_samples_leaf=3, n_jobs=-1,\n",
" random_state=42))])"
],
"text/html": [
"<style>#sk-28126540-c632-420e-a8f5-7a3690bd7822 {color: black;background-color: white;}#sk-28126540-c632-420e-a8f5-7a3690bd7822 pre{padding: 0;}#sk-28126540-c632-420e-a8f5-7a3690bd7822 div.sk-toggleable {background-color: white;}#sk-28126540-c632-420e-a8f5-7a3690bd7822 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-28126540-c632-420e-a8f5-7a3690bd7822 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-28126540-c632-420e-a8f5-7a3690bd7822 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-28126540-c632-420e-a8f5-7a3690bd7822 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-28126540-c632-420e-a8f5-7a3690bd7822 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-28126540-c632-420e-a8f5-7a3690bd7822 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-28126540-c632-420e-a8f5-7a3690bd7822 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-28126540-c632-420e-a8f5-7a3690bd7822 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-28126540-c632-420e-a8f5-7a3690bd7822 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-28126540-c632-420e-a8f5-7a3690bd7822 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-28126540-c632-420e-a8f5-7a3690bd7822 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-28126540-c632-420e-a8f5-7a3690bd7822 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-28126540-c632-420e-a8f5-7a3690bd7822 div.sk-estimator:hover {background-color: #d4ebff;}#sk-28126540-c632-420e-a8f5-7a3690bd7822 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-28126540-c632-420e-a8f5-7a3690bd7822 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-28126540-c632-420e-a8f5-7a3690bd7822 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-28126540-c632-420e-a8f5-7a3690bd7822 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;}#sk-28126540-c632-420e-a8f5-7a3690bd7822 div.sk-item {z-index: 1;}#sk-28126540-c632-420e-a8f5-7a3690bd7822 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;}#sk-28126540-c632-420e-a8f5-7a3690bd7822 div.sk-parallel::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 2em;bottom: 0;left: 50%;}#sk-28126540-c632-420e-a8f5-7a3690bd7822 div.sk-parallel-item {display: flex;flex-direction: column;position: relative;background-color: white;}#sk-28126540-c632-420e-a8f5-7a3690bd7822 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-28126540-c632-420e-a8f5-7a3690bd7822 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-28126540-c632-420e-a8f5-7a3690bd7822 div.sk-parallel-item:only-child::after {width: 0;}#sk-28126540-c632-420e-a8f5-7a3690bd7822 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;position: relative;}#sk-28126540-c632-420e-a8f5-7a3690bd7822 div.sk-label label {font-family: monospace;font-weight: bold;background-color: white;display: inline-block;line-height: 1.2em;}#sk-28126540-c632-420e-a8f5-7a3690bd7822 div.sk-label-container {position: relative;z-index: 2;text-align: center;}#sk-28126540-c632-420e-a8f5-7a3690bd7822 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-28126540-c632-420e-a8f5-7a3690bd7822 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-28126540-c632-420e-a8f5-7a3690bd7822\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocess&#x27;,\n",
" ColumnTransformer(transformers=[(&#x27;cat&#x27;,\n",
" Pipeline(steps=[(&#x27;onehot&#x27;,\n",
" OneHotEncoder())]),\n",
" [&#x27;class&#x27;]),\n",
" (&#x27;num&#x27;,\n",
" Pipeline(steps=[(&#x27;scaler&#x27;,\n",
" StandardScaler())]),\n",
" [&#x27;sepal length (cm)&#x27;,\n",
" &#x27;petal length (cm)&#x27;,\n",
" &#x27;petal width (cm)&#x27;])])),\n",
" (&#x27;regressor&#x27;,\n",
" RandomForestRegressor(max_depth=5, max_features=3,\n",
" min_samples_leaf=3, n_jobs=-1,\n",
" random_state=42))])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"47a0de4f-104f-43c2-b2e3-2fc40484c8cf\" type=\"checkbox\" ><label for=\"47a0de4f-104f-43c2-b2e3-2fc40484c8cf\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">Pipeline</label><div class=\"sk-toggleable__content\"><pre>Pipeline(steps=[(&#x27;preprocess&#x27;,\n",
" ColumnTransformer(transformers=[(&#x27;cat&#x27;,\n",
" Pipeline(steps=[(&#x27;onehot&#x27;,\n",
" OneHotEncoder())]),\n",
" [&#x27;class&#x27;]),\n",
" (&#x27;num&#x27;,\n",
" Pipeline(steps=[(&#x27;scaler&#x27;,\n",
" StandardScaler())]),\n",
" [&#x27;sepal length (cm)&#x27;,\n",
" &#x27;petal length (cm)&#x27;,\n",
" &#x27;petal width (cm)&#x27;])])),\n",
" (&#x27;regressor&#x27;,\n",
" RandomForestRegressor(max_depth=5, max_features=3,\n",
" min_samples_leaf=3, n_jobs=-1,\n",
" random_state=42))])</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"68debd82-1f1a-4c5e-b30d-b0697be4f2ed\" type=\"checkbox\" ><label for=\"68debd82-1f1a-4c5e-b30d-b0697be4f2ed\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">preprocess: ColumnTransformer</label><div class=\"sk-toggleable__content\"><pre>ColumnTransformer(transformers=[(&#x27;cat&#x27;,\n",
" Pipeline(steps=[(&#x27;onehot&#x27;, OneHotEncoder())]),\n",
" [&#x27;class&#x27;]),\n",
" (&#x27;num&#x27;,\n",
" Pipeline(steps=[(&#x27;scaler&#x27;, StandardScaler())]),\n",
" [&#x27;sepal length (cm)&#x27;, &#x27;petal length (cm)&#x27;,\n",
" &#x27;petal width (cm)&#x27;])])</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"73cfdbb8-8855-4068-9878-1e164f3cfc4e\" type=\"checkbox\" ><label for=\"73cfdbb8-8855-4068-9878-1e164f3cfc4e\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">cat</label><div class=\"sk-toggleable__content\"><pre>[&#x27;class&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"4ce30119-fcc1-409f-b3dc-2478709da57e\" type=\"checkbox\" ><label for=\"4ce30119-fcc1-409f-b3dc-2478709da57e\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">OneHotEncoder</label><div class=\"sk-toggleable__content\"><pre>OneHotEncoder()</pre></div></div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"e74a3cc8-c602-4c9e-8126-ede33edfd876\" type=\"checkbox\" ><label for=\"e74a3cc8-c602-4c9e-8126-ede33edfd876\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">num</label><div class=\"sk-toggleable__content\"><pre>[&#x27;sepal length (cm)&#x27;, &#x27;petal length (cm)&#x27;, &#x27;petal width (cm)&#x27;]</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"6a932f69-dc77-49cb-8cdc-8322919f761b\" type=\"checkbox\" ><label for=\"6a932f69-dc77-49cb-8cdc-8322919f761b\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"a8fca666-57a5-464a-ab01-ae535f2719b5\" type=\"checkbox\" ><label for=\"a8fca666-57a5-464a-ab01-ae535f2719b5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor(max_depth=5, max_features=3, min_samples_leaf=3,\n",
" n_jobs=-1, random_state=42)</pre></div></div></div></div></div></div></div>"
]
},
"metadata": {},
"execution_count": 60
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "5Xryqy6lNRDh",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "75870e34-e3dd-4dd1-f57e-ceea9c4fd52d"
},
"source": [
"print(\"train error: %0.3f, test error: %0.3f\" %\n",
" (median_absolute_error(y_train, rf_best.predict(X_train)),\n",
" median_absolute_error(y_test, rf_best.predict(X_test))))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"train error: 0.120, test error: 0.201\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "qNNb1Y20NRDp",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 441
},
"outputId": "679e4a78-d7d5-4d2b-8636-23e306b180e0"
},
"source": [
"boxplot_pi(rf_best, X_test, y_test)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 576x432 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "NOux8KgNaFvu"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"!pip install -q shap"
],
"metadata": {
"id": "GQ_CIt4pEKSK",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "45286a48-e62c-486a-80e5-1014d30e1bf5"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/572.6 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[91m━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.2/572.6 kB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m225.3/572.6 kB\u001b[0m \u001b[31m3.8 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m \u001b[32m563.2/572.6 kB\u001b[0m \u001b[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m572.6/572.6 kB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h"
]
}
]
},
{
"cell_type": "code",
"source": [
"import shap"
],
"metadata": {
"id": "48_o0D-fEIEz"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"X_train.shape"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "tcUndYYWZU1l",
"outputId": "1835ee52-6093-4e0e-e880-e05cdc8eb499"
},
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(112, 4)"
]
},
"metadata": {},
"execution_count": 30
}
]
},
{
"cell_type": "code",
"source": [
"X100 = shap.utils.sample(X_train, 100)"
],
"metadata": {
"id": "o4s6zh4xEzye"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"shap.plots.partial_dependence(\n",
" \"sepal length (cm)\", knn.predict, X_train, ice=False,\n",
" model_expected_value=True, feature_expected_value=True\n",
")"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 474
},
"id": "T3-Mbq-gEZTG",
"outputId": "077c6eb4-577e-4f8a-e2c8-4b06837a5b4c"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 4 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"# compute the SHAP values for the linear model\n",
"explainer = shap.Explainer(knn.predict, X_train)\n",
"shap_values = explainer(X_train)"
],
"metadata": {
"id": "ksgts-UgFlLL"
},
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"source": [
"shap.plots.beeswarm(shap_values)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 346
},
"id": "281B69ZbGYbJ",
"outputId": "518a8000-6773-42b9-ada9-94c13cafd677"
},
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"text": [
"No data for colormapping provided via 'c'. Parameters 'vmin', 'vmax' will be ignored\n"
]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 800x310 with 2 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"shap.plots.bar(shap_values)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 354
},
"id": "HdRqxDO2ntoe",
"outputId": "35e4cdd5-6d58-498a-f43e-b507c12ab46d"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 800x350 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"shap.plots.waterfall(shap_values[5])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 386
},
"id": "JVJpQANrFz56",
"outputId": "3e4ba5af-29eb-4046-f148-f8f1bb65c27a"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 800x350 with 3 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
}
]
}
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