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{
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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\", \"Please upgrade scikit-learn with %pip install --quiet --upgrade scikit-learn>=1.0\""
],
"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": "2713e2e1-d490-47d7-8591-e2b62736b34c"
},
"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%PLU@io.arc.nasa.gov)\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": 6,
"outputs": [
{
"output_type": "execute_result",
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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",
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"metadata": {},
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"\n",
" <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
" <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
" </svg>\n",
" </button>\n",
"\n",
" <style>\n",
" .colab-df-container {\n",
" display:flex;\n",
" gap: 12px;\n",
" }\n",
"\n",
" .colab-df-convert {\n",
" background-color: #E8F0FE;\n",
" border: none;\n",
" border-radius: 50%;\n",
" cursor: pointer;\n",
" display: none;\n",
" fill: #1967D2;\n",
" height: 32px;\n",
" padding: 0 0 0 0;\n",
" width: 32px;\n",
" }\n",
"\n",
" .colab-df-convert:hover {\n",
" background-color: #E2EBFA;\n",
" box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
" fill: #174EA6;\n",
" }\n",
"\n",
" .colab-df-buttons div {\n",
" margin-bottom: 4px;\n",
" }\n",
"\n",
" [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-a81ed572-006f-46cc-af41-eef7fff3e369 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-a81ed572-006f-46cc-af41-eef7fff3e369');\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-c0461fe6-1e8f-4a71-8f15-2d725940cc3a\">\n",
" <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c0461fe6-1e8f-4a71-8f15-2d725940cc3a')\"\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-c0461fe6-1e8f-4a71-8f15-2d725940cc3a button');\n",
" quickchartButtonEl.style.display =\n",
" google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
" })();\n",
" </script>\n",
"</div>\n",
" </div>\n",
" </div>\n"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "dataframe",
"variable_name": "df_raw",
"summary": "{\n \"name\": \"df_raw\",\n \"rows\": 150,\n \"fields\": [\n {\n \"column\": \"sepal length (cm)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.828066127977863,\n \"min\": 4.3,\n \"max\": 7.9,\n \"num_unique_values\": 35,\n \"samples\": [\n 6.2,\n 4.5,\n 5.6\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"sepal width (cm)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.4358662849366982,\n \"min\": 2.0,\n \"max\": 4.4,\n \"num_unique_values\": 23,\n \"samples\": [\n 2.3,\n 4.0,\n 3.5\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"petal length (cm)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 1.7652982332594662,\n \"min\": 1.0,\n \"max\": 6.9,\n \"num_unique_values\": 43,\n \"samples\": [\n 6.7,\n 3.8,\n 3.7\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"petal width (cm)\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.7622376689603465,\n \"min\": 0.1,\n \"max\": 2.5,\n \"num_unique_values\": 22,\n \"samples\": [\n 0.2,\n 1.2,\n 1.3\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"class\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 2,\n \"num_unique_values\": 3,\n \"samples\": [\n 0,\n 1,\n 2\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}"
}
},
"metadata": {},
"execution_count": 7
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "ZAthwy2MNQ_3",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "07838f1b-b188-4374-992b-eb8603ec2343"
},
"source": [
"df_raw['class'].unique()"
],
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([0, 1, 2])"
]
},
"metadata": {},
"execution_count": 8
}
]
},
{
"cell_type": "code",
"source": [
"list(iris.target_names)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "NJfKYx5u-kss",
"outputId": "cad3a343-f13b-4ff0-8644-2ebe0315b56f"
},
"execution_count": 9,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['setosa', 'versicolor', 'virginica']"
]
},
"metadata": {},
"execution_count": 9
}
]
},
{
"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": 10,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "nY28r0Z6NRAK",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "fe4a9ace-64d0-43c1-dc14-afe7165a1652"
},
"source": [
"X_train.columns"
],
"execution_count": 11,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Index(['sepal length (cm)', 'petal length (cm)', 'petal width (cm)', 'class'], dtype='object')"
]
},
"metadata": {},
"execution_count": 11
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "TSYfiyqPNRAT",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 36
},
"outputId": "11ff9324-61ce-4f72-bd74-01fcb1b14770"
},
"source": [
"y_train.name"
],
"execution_count": 12,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"'sepal width (cm)'"
],
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
}
},
"metadata": {},
"execution_count": 12
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "_Nn9wkJINRAj"
},
"source": [
"categorical_columns = ['class']\n",
"numerical_columns = ['sepal length (cm)', 'petal length (cm)', 'petal width (cm)']"
],
"execution_count": 13,
"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": 14,
"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": "a3fac7a8-9a16-4546-804b-deeca768bc28"
},
"execution_count": 15,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7f31e436e0e0>"
],
"text/html": [
"<style type=\"text/css\">\n",
"</style>\n",
"<table id=\"T_f0a85\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th id=\"T_f0a85_level0_col0\" class=\"col_heading level0 col0\" >class</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td id=\"T_f0a85_row0_col0\" class=\"data row0 col0\" >0</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_f0a85_row1_col0\" class=\"data row1 col0\" >0</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_f0a85_row2_col0\" class=\"data row2 col0\" >2</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_f0a85_row3_col0\" class=\"data row3 col0\" >1</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_f0a85_row4_col0\" class=\"data row4 col0\" >1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
]
},
"metadata": {},
"execution_count": 15
}
]
},
{
"cell_type": "code",
"source": [
"encoder = OneHotEncoder(sparse_output=False)\n",
"X = encoder.fit_transform(pd.DataFrame(X_train['class']))"
],
"metadata": {
"id": "RHVVQuInDJ9H"
},
"execution_count": 16,
"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": "c0e0314a-bc40-428d-d982-eb71936f0397"
},
"execution_count": 17,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array(['class'], dtype=object)"
]
},
"metadata": {},
"execution_count": 17
}
]
},
{
"cell_type": "code",
"source": [
"encoder.get_feature_names_out()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "y-MD3HyjDYqW",
"outputId": "3f13ffbc-b26b-49a7-9ca2-fcff3c70bed5"
},
"execution_count": 18,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array(['class_0', 'class_1', 'class_2'], dtype=object)"
]
},
"metadata": {},
"execution_count": 18
}
]
},
{
"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": "1691f791-b89c-41db-9210-94e3a6af8782"
},
"execution_count": 20,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7f31dd24ecb0>"
],
"text/html": [
"<style type=\"text/css\">\n",
"</style>\n",
"<table id=\"T_28371\" class=\"dataframe\">\n",
" <thead>\n",
" <tr>\n",
" <th id=\"T_28371_level0_col0\" class=\"col_heading level0 col0\" >class_0</th>\n",
" <th id=\"T_28371_level0_col1\" class=\"col_heading level0 col1\" >class_1</th>\n",
" <th id=\"T_28371_level0_col2\" class=\"col_heading level0 col2\" >class_2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td id=\"T_28371_row0_col0\" class=\"data row0 col0\" >1.0</td>\n",
" <td id=\"T_28371_row0_col1\" class=\"data row0 col1\" >0.0</td>\n",
" <td id=\"T_28371_row0_col2\" class=\"data row0 col2\" >0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_28371_row1_col0\" class=\"data row1 col0\" >1.0</td>\n",
" <td id=\"T_28371_row1_col1\" class=\"data row1 col1\" >0.0</td>\n",
" <td id=\"T_28371_row1_col2\" class=\"data row1 col2\" >0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_28371_row2_col0\" class=\"data row2 col0\" >0.0</td>\n",
" <td id=\"T_28371_row2_col1\" class=\"data row2 col1\" >0.0</td>\n",
" <td id=\"T_28371_row2_col2\" class=\"data row2 col2\" >1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_28371_row3_col0\" class=\"data row3 col0\" >0.0</td>\n",
" <td id=\"T_28371_row3_col1\" class=\"data row3 col1\" >1.0</td>\n",
" <td id=\"T_28371_row3_col2\" class=\"data row3 col2\" >0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td id=\"T_28371_row4_col0\" class=\"data row4 col0\" >0.0</td>\n",
" <td id=\"T_28371_row4_col1\" class=\"data row4 col1\" >1.0</td>\n",
" <td id=\"T_28371_row4_col2\" class=\"data row4 col2\" >0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
]
},
"metadata": {},
"execution_count": 20
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "DZIhlDdKNRA8"
},
"source": [
"from sklearn.preprocessing import StandardScaler\n",
"\n",
"numerical_pipe = Pipeline([\n",
" ('scaler', StandardScaler())\n",
"])"
],
"execution_count": 21,
"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)]\n",
")"
],
"execution_count": 22,
"outputs": []
},
{
"cell_type": "markdown",
"source": [
"## KNeighborsRegressor"
],
"metadata": {
"id": "DV5JV17b3jQz"
}
},
{
"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": 23,
"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": "ef4aa950-78d6-427d-b966-095bb48d282f",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 386
}
},
"execution_count": 24,
"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": 24
}
]
},
{
"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": "5fb073d4-0b1f-493b-d4b6-968865580c6d",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 25,
"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": "c00c5a35-46c4-443c-93de-d7e3a75c7f7d",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 545
}
},
"execution_count": 26,
"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": "3a86e613-3a73-4fdf-c491-a0c799bbfc02",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 607
}
},
"execution_count": 27,
"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": 28,
"outputs": []
},
{
"cell_type": "code",
"source": [
"from sklearn.metrics import r2_score"
],
"metadata": {
"id": "wOkdo_DgwbZA"
},
"execution_count": 29,
"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": 30,
"outputs": []
},
{
"cell_type": "code",
"source": [
"diff.median().sort_values(ascending=False)"
],
"metadata": {
"id": "EAbyIN4wwQSh",
"outputId": "02ad7ed8-333e-4345-d46c-e4689b51d330",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"execution_count": 31,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"sepal length (cm) 0.438456\n",
"class 0.284889\n",
"petal length (cm) 0.151414\n",
"petal width (cm) 0.083960\n",
"dtype: float64"
]
},
"metadata": {},
"execution_count": 31
}
]
},
{
"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": "323bd3bc-6c40-4282-a3d3-eeb8e67e55e2",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 430
}
},
"execution_count": 32,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "markdown",
"source": [
"## LinearRegression"
],
"metadata": {
"id": "AMh5g6oW34-x"
}
},
{
"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": 33,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"scrolled": true,
"id": "UMRyr788NRBc",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 190
},
"outputId": "93132086-2a86-4681-b6f1-4df19923df8b"
},
"source": [
"from sklearn import set_config\n",
"\n",
"set_config(display='diagram')\n",
"\n",
"lm.fit(X_train, y_train)"
],
"execution_count": 34,
"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": 34
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "o9rVdLtCNRBv",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "c5013c03-56df-4ac5-a92e-cb6c5d899e3b"
},
"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": 35,
"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": 545
},
"outputId": "90570293-7417-4705-8d5d-1c059103be2e"
},
"source": [
"scatter_predictions(lm.predict(X_test), y_test)"
],
"execution_count": 36,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 600x600 with 1 Axes>"
],
"image/png": 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RQgAA0pSmkqPc3b5xuZTR3X9/wAKEEABIdPfZPdunnUUAQUQwOwYAEpl3ADm9nTR+k5lakHC4EwIAico7gDTLkkZ9aKYWWMrhdOmTHfu192CZmjZMVffWjZWcZDNdVhWEEABIRN4BpPn50sgVRkqBtZZuLtKkxVtVVFJWeay5PVUTB2aqX1Zzg5VVxeMYAEg03gGkZXcCSJxYurlIo+au9wggklRcUqZRc9dr6eYiQ5X5RggBgETiHUBaXSjdtNxMLbCUw+nSpMVb5fLxvYpjkxZvlcPpq4cZhBAASBTeAeScXtL1bxkpBdb7ZMf+KndATuaSVFRSpk927I9cUTUghABAIvAOIO0uP7EjLuLG3oP+A0go/SKBEAIA8c47gHT4jTT0VTO1IGyaNky1tF8kEEIAIJ55B5DMfOmaF42UgvDq3rqxmttT5W8irk0nZsl0b904kmVVixACAPHKO4B0vlq6eo6ZWhB2yUk2TRyYKUlVgkhFe+LAzKhaL4QQAgDxyDuAnD9MuvJpM7UgYvplNdfMYTlKt3s+ckm3p2rmsJyoWyeExcoAIN54B5Cc66RBj5upBRHXL6u5+mSms2IqACDCvANIt5ukAY+aqQXGJCfZlNfmdNNl1IjHMQAQL7wDSI//RwBBVCOEAEA88A4gPcdK/QrM1AIEiBACALHOO4BccKt0+f1magGCwJgQAIhl3gHk4julSyaYqQUIEiEEAGKVdwC55O/SxX8zUwsQAkIIAMQi7wDSe6J04a21OqXD6YqJaZ2IH4QQAIg13gHk8vtPDESthaWbizRp8VaPXVib21M1cWBm1C1whfjBwFQAccnhdGnN9n1atGG31mzfJ4fTZboka3gHkH7TLAkgo+aur7INfHFJmUbNXa+lm4tqdX7AH+6EAIg7cfup3juAXPGI1H1ErU7pcLo0afFW+YpoLp3Yc2TS4q3qk5nOoxlYjjshAOJK3H6q9w4gv3ms1gFEkj7Zsb/Ke3Uyl6SikjJ9smN/rX8W4I0QAiBu1PSpXjrxqT7mHs14B5BB/5K63mDJqfce9B9AQukHBIMQAiBuxOWneu8Akv+klDPcstM3bZhac6cg+gHBIIQAiBtx96neO4D87hnp/CGW/ojurRuruT1V/kZ72HRiPE331o0t/bmARAgBEEfi6lO9dwC56jkp+/eW/5jkJJsmDsyUpCpBpKI9cWAmg1IRFoQQAHEjbj7VeweQq/9PyroybD+uX1ZzzRyWo3S7ZzhLt6dq5rCc2J5RhKjGFF0AcaPiU/2ouetlkzwGqMbMp3rvAHLNS1KHK8L+Y/tlNVefzHRWTEVE2VwuV4wNE6+d0tJS2e12lZSUKC0tzXQ5AMIgZtcJ8Q4gQ16W2vczUwvgg9XXUO6EAIg7Mfmp3juADHtNanuZmVqACCGEAIhLyUk25bU53XQZgfEOINculNpcYqQUIJIIIQBgkncAuW6x1PoiM7UAEUYIAQBTvAPIDUuks3uaqQUwgBACADVwOF3Wjy/xDiB/elc661e1OycQYwghAFCNsMy08Q4gN/5HyuhWiyqB2MRiZQDgR1h25PUOICP+SwBBwiKEAIAPYdmR1zuAjFwptcgJtUQg5hFCAMAHy3fk9Q4gf1klNT8v9AKBOMCYEADwwdIdeb0DyP/7SGraMYSqgPhCCAEAHyzbkdc7gIxeKzU5N8SqgPjC4xgA8MGSHXm9A8iYTwkgwEkIIQDgQ8WOvJKqBJGAduT1DiBj10tntLO0RiDWEUIAwI9+Wc01c1iO0u2ej1zS7amaOSzH/zoh3gFk3Ebp9DZhqhKIXYwJAYBqBL0jr3cAGb9JOu2s8BcKxCBCCADUIOAdeb0DyC1bJHvL8BQFxAFCCABYwTuA3PqllBbisu5AgiCEAEBteQeQv26TGqabqQWIIYQQAHEpLDvf+uIdQG77RmrQxPqfA8QhQgiAuBOWnW998Q4gt38r1Q9g7AgASUzRBRBnwrLzrS/eAeRvOwggQJAIIQDiRlh2vvXFO4Dc8Z1Ur5qVUwH4ZDSEzJw5U9nZ2UpLS1NaWpry8vK0ZMkSv/1nz54tm83m8ZWaGtj+DgDin+U73/riHUDuLJRObRT6+aKIw+nSmu37tGjDbq3Zvq/2YQ2ogdExIS1bttS0adPUrl07uVwuzZkzR4MHD9Znn32mTp06+XxNWlqatm3bVtm22cIw0AxATLJ051tfqgSQXVJqWmjnijIRG0cDnMRoCBk4cKBH+4EHHtDMmTP10Ucf+Q0hNptN6elMfQNQlWU73/riHUAm7JZSGgR/nihUMY7G+75HxTiaapeoB2ohasaEOBwOzZ8/X4cPH1ZeXp7ffocOHdLZZ5+tjIwMDR48WFu2bKn2vOXl5SotLfX4AhCfLNn51hfvAHLXD3ETQCI2jgbwwXgI2bRpkxo0aKCUlBT95S9/0RtvvKHMzEyffdu3b6/nnntOixYt0ty5c+V0OtWzZ099//33fs9fUFAgu91e+ZWRkRGuvwoAw2q9860vvgJI3foh1xhtIjKOBvDD5nK5jMbbo0ePqrCwUCUlJVqwYIGeeeYZrVixwm8QOdmxY8fUsWNHDRkyRFOmTPHZp7y8XOXl5ZXt0tJSZWRkqKSkRGlp8fEsF4Any8Y3xHkAkaRFG3Zr3PwNNfabfs35Gnx+i/AXhKhWWloqu91u2TXU+GJldevWVdu2bSVJubm5Wrt2raZPn65Zs2bV+No6deqoS5cu+uabb/z2SUlJUUpKimX1Aoh+Qe9864tXAPl4yBYVbz2gpg3Lwrf6qgFhHUcD1MB4CPHmdDo97lxUx+FwaNOmTbriiivCXBWAWBPwzre+eAWQXnVf0nfPb6xsx9OskYpxNMUlZT7HhdgkpYcyjgYIgNExIRMmTNDKlSv13XffadOmTZowYYI++OADDR06VJI0fPhwTZgwobL/5MmT9e677+rbb7/V+vXrNWzYMO3cuVM33XSTqb8CgHjjFUDal83Rd6Wel2fLV181KCzjaIAAGb0TsnfvXg0fPlxFRUWy2+3Kzs7WsmXL1KdPH0lSYWGhkpLcOennn3/WiBEjVFxcrEaNGik3N1erV68OaPwIANTIK4BcWPdllZc5qnRz6cQFetLireqTmR7zF+h+Wc01c1hOlXE06XF0xwfRyfjA1EizelANgDjhFUA+GrpN1zy7rsaXvTSiR+iPfaJMxHYeRsyKu4GpAGCc9yyYe/Zpz6Y9Ab005NVXo1CtxtEAIQgphDgcDs2ePVvvvfee9u7dK6fT6fH9999/35LiACDsvAPIvfulpGRmjQAREFIIGTdunGbPnq0BAwYoKyuL/VsAxKYqAeRn6X/j0Jg1AoRfSCFk/vz5euWVV5gaCyB2VRNAJPeskVFz18smeQQRZo0A1ghpiu7JC4wBQMzxDiATD3gEkAoVs0bS7Z6PXNLtqWzqBlggpNkxjz76qL799lvNmDEj5h7FMDsGSHC+AkgN/x9j1ghwQlTMjlm1apX++9//asmSJerUqZPq1Knj8f3XX3+91oUBcOMiaJEQAojErBEgXEIKIaeddpp++9vfWl0LAB8s24wt0YUYQACED4uVAVFs6eYijZq7vsrsjIpLJ+MSAuQdQO4rMVMHEOOi4nFMhR9//FHbtm2TJLVv315NmjSpdUGIfzxaCIzD6dKkxVt9Tg+Nt2XDw4oAAkStkELI4cOHNXbsWL3wwguVC5UlJydr+PDh+te//qV69epZWiTiB48WAvfJjv0e75M3l6SikjJ9smM/4xX8IYAAUS2kKbq33nqrVqxYocWLF+vAgQM6cOCAFi1apBUrVuivf/2r1TUiTlQ8WvC+sMbTjqRWCnQ58HhaNtxSBBAg6oUUQl577TU9++yz6t+/v9LS0pSWlqYrrrhCTz/9tBYsWGB1jYgDNT1akE48WnA4E2qIUrVYNrwWCCBATAgphBw5ckTNmjWrcrxp06Y6cuRIrYtC/Anm0QJOqFg23N9oD5tOPMpi2XAvBBAgZoQUQvLy8jRx4kSVlbkvKr/88osmTZqkvLw8y4pD/ODRQvAqlg2XVCWIsGy4Dy4XAQSIMSENTJ0+fbr69u2rli1b6rzzzpMkbdy4UampqVq2bJmlBSI+8GghNBXLhnsP5k1nMK8nl0uadJrnMQIIEPVCXifkyJEjevHFF/Xll19Kkjp27KihQ4fq1FNPtbRAq7FOiBkOp0sXPPh+jTuSrrrjUj7Z+8C05moQQICIsfoaymJliJiK2TGS7x1JWXgrMuIq0Did0uRGnscIIEDYGFus7M0331T//v1Vp04dvfnmm9X2HTRoUK0LQ/zh0YJ5cbVOi9MhTfYalEsAAWJKwHdCkpKSVFxcrKZNmyrJx5bXlSe02eRwOCwr0GrcCTEvrj6Jx5C4WgKeAAIYYexOSMXKqN5/BoLFjqSRF1dLwDuOSVPO8DxGAAFiUkhTdF944QWVl5dXOX706FG98MILtS4KgLXiZp2W40djPoA4nC6t2b5Pizbs1prt+6Jqgb5org3xKaQpujfccIP69eunpk2behw/ePCgbrjhBg0fPtyS4gBYIy7WaTleLt3v+f+cWAsg0TwmJ5prQ/wK6U6Iy+WSzVb1lu33338vu93u4xUATIr5dVqO/RJ0AIm2T/XRvHdSNNeG+BbUnZAuXbrIZrPJZrOpd+/eOuUU98sdDod27Nihfv36WV4kgNqpWAK+ukcyUbsE/NHD0tQzPY/VEECi7VN9NI/JiebaEP+CCiH5+fmSpA0bNqhv375q0KBB5ffq1q2rVq1a6corr7S0QAC1l5xk06DzmmvWyh1++ww6r3n0XWTKD0oFLT2PBRBAfM0CqvhUb2IWUDBjciI9aDuaa0P8CyqETJw4UZLUqlUrXXPNNUpJSQlLUQCs5XC69ObG6m+pv7mxSH/r1zF6gkhZiTTtLM9jATyCicZP9dE8Jieaa0P8C2lMSGZmpjZs2FDl+Mcff6xPP/20tjUBsFhNn3alKJsdc2R/0AFEit5ZQNE8Jieaa0P8CymEjB49Wrt27apyfPfu3Ro9enStiwJgrZj6tHt4n/RQa89jAc6Cida/Z8WYHH/3XmwyNyYnmmtD/AsphGzdulU5OTlVjnfp0kVbt26tdVEArHVGg8AenQbar4LlM1AO7ZUePsfzWBDTcKP1U31ykk0TB2ZKUpWLfUV74sBMI4/Cork2xL+Q1glJSUnRnj17dM45nv+zKCoq8pgxA8AatV7qPtBsEESGsHwGSmmR9I8OnseCXAek4lN9Tbs1m/hUH817J0VzbYhvISWGyy+/XBMmTNCiRYsq1wU5cOCA7rrrLvXp08fSAoFEZ8XF/qfDVVc4rk0/y2eglHwv/bOT57EQFiKr+FQ/au562eR7t2aTn+r7ZTVXn8z0qNw7KZprQ/wKeAO7k+3evVsXXXSR9u3bpy5dukg6MW23WbNmWr58uTIyMiwv1CpsYIdY4u9iL524qAZ6sV+zfZ+GPP1Rjf1eGtGjxmmYDqdLFzz4vt8BoBV3G1bdcWlgF7Cfd0rTsz2P1XIl1GhbJwSIF8Y2sDtZixYt9Pnnn+vFF1/Uxo0bdeqpp+qGG27QkCFDVKdOnVoXBaD66abSiU/5gU43tfIxhaXrSuzbLv3La3yZBUux86keiA0hD+CoX7++/vznP1tZC4CTBDOttqaLvZWPKSybgfLT19KMrp7HLNwLht2agegXcAh588031b9/f9WpU0dvvvlmtX0HDRpU68KARFdc8oul/awafGjJDJS9X0hP9PA8FmOb0QGovYBDSH5+voqLi9W0adPK5dt9sdlscjgcVtQGJLT9h49a2k+y5jFFrR/tFH0uzbrQ8xgBBEhIAYcQp9Pp888AwqNxgGt2BNqvQm0fU9Tq0c4Pn0lP9fI8RgABElZIi5UBCL/0tMAeewTaz0oVj3bS7Z4/O92e6n/Gzq61BBAAHgK+E/L4448HfNKbb745pGIAuFU89qhucKrJ5bSDerSzc7X0fH/PYwQQIOEFvE5I69aeezn8+OOPOnLkiE477TRJJxYrq1evnpo2bapvv/3W8kKtwjohiCUV64RIvh97mNiWPmg7VkpzBnoeI4AAMcnqa2jAj2N27NhR+fXAAw/o/PPP1xdffKH9+/dr//79+uKLL5STk6MpU6bUuigAJ4T02COCatw75pv/EEAA+BXSiqlt2rTRggULKldLrbBu3TpdddVV2rFjh2UFWo07IYhFtd47JgyWbi7SfW9uVXHpSdN901J136D/TffdtlR66Q+eLyKAADEtKlZMLSoq0vHjx6scdzgc2rNnT62LAuAp2hbeWrq5SH/532OikxWXlukvc9fr9Uv2K2fNGM9vEkAAeAlpdkzv3r01cuRIrV/v/p/QunXrNGrUKF122WWWFQcg+jicLt35+ia/3x+Q9BEBBEBAQgohzz33nNLT09W1a1elpKQoJSVF3bt3V7NmzfTMM89YXSOAKPLRt/t04Mgxn9/LT1qlf9f1mklHAAHgR0iPY5o0aaJ33nlHX331lb788ktJUocOHXTuuedaWhyA6LNm+z6fx3+f/IEervOU50ECCIBqhLyBnSS1atVKLpdLbdq00Smn1OpUAGJG1bHsQ5P/owfqPOdx7JG8j3VbpEoCEJNCehxz5MgR3XjjjapXr546deqkwsJCSdLYsWM1bdo0SwsEEF3yzjnDo31j8jtVAkirsnlV+gGAt5BCyIQJE7Rx40Z98MEHSk11r19w2WWX6eWXX7asOADRp0eb03VavTqSpAdPeUr31Jnr8f1WZfN0Wr066hFFs3kARKeQnqEsXLhQL7/8snr06CGbzb1WQadOnbR9+3bLigMQfZKTbJr2u846+vINGpS8xuN7rcrmSZKm/a6z8XVMAES/kELIjz/+qKZNm1Y5fvjwYY9QAiA+9dt8m+QjgKSnpei+QZ2Mr+QKIDaEFEK6du2qt99+W2PHjpWkyuDxzDPPKC8vz7rqAESf//udtP09j0OL8rfqpShZyRVA7AgphEydOlX9+/fX1q1bdfz4cU2fPl1bt27V6tWrtWLFCqtrBBAtnusnFXreAdF9JRpsphoAMS6kgakXXHCBNm7cqOPHj6tz585699131bRpU61Zs0a5ublW1wggGsy6yGcAAYBQBX0n5NixYxo5cqTuuecePf300+GoCUC0mdFd+mmb5zECCIBaCvpOSJ06dfTaa6+FoxYA0egfnQggAMIipMcx+fn5WrhwocWlAIg6D7aWSr/3PEYAAWCRkAamtmvXTpMnT9aHH36o3Nxc1a9f3+P7N998syXFATDo/nTp+C+exwggACxkc7lcVTeCqEHr1q39n9Bm07ffflurosKptLRUdrtdJSUlSktLM10OEJ3us/s4RgABEp3V19CQ7oTs2LGj8s8VGYZFyoA4QQABECEhjQmRpGeffVZZWVlKTU1VamqqsrKy9Mwzz1hZG4BII4AAiKCQ7oTce++9+sc//qGxY8dWrpC6Zs0a3XLLLSosLNTkyZMtLRJABBBAAERYSGNCmjRposcff1xDhgzxOP7SSy9p7Nix+umnnywr0GqMCQF8IIAACIDV19CQHsccO3ZMXbt2rXI8NzdXx48fr3VRACKIAALAkJBCyLXXXquZM2dWOf7UU09p6NChtS4KQIQQQAAYFNKYEOnEwNR3331XPXr0kCR9/PHHKiws1PDhw3XrrbdW9vvHP/5R+yoBWI8AAsCwkELI5s2blZOTI0navn27JOmMM87QGWecoc2bN1f2Y9ouEKUIIACiQEgh5L///a/VdQCIFAIIgCgR8johAGIQAQRAFCGEAImCAAIgyhBCgERAAAEQhUKeHQMgchxOlz7ZsV97D5apacNUdW/dWMlJAQ789hFA1lz7rfZu2B38uQDAQoQQ4CS1utiHydLNRZq0eKuKSsoqjzW3p2riwEz1y2pe/Yt9BJC8lNdV9PRHwZ8LACwW0rLtsYxl2+FPrS72Yaxp1Nz18v4lrYhFM4fl+K/NRwBpXTYvtHMBgKJk2XYg3lRc7E8OIJJUXFKmUXPXa+nmoojX5HC6NGnx1iqhQVLlsUmLt8rh9NHDzx2QkM4FAGFCCEHCq+li75KZC/QnO/ZXCUUnc0kqKinTJzv2e37DzxiQkM4FAGFECEHCq+liL5m5QO89WH1NPvv5mQUT0rkAIMwIIUh4xaWBXXgD7WeVpg1Tg+tXzTTcoM8FABFACEHC23+o3NJ+VuneurGa21Plb26OTScGznZv3bjGdUCCOlcQHE6X1mzfp0UbdmvN9n2MKQEQFKboIuE1rl/X0n5WSU6yaeLATI2au142yWPMSkWYmDgwU8mTT6v6Yq+FyAI+VxDTkcMxmygap0gDCB/uhCDhpdtPtbSflfplNdfMYTlKt3s+Jkm3p56YUrugQ9UX+VkJtcZzBREc/M0mKqrFbKKlm4t0wYPva8jTH2nc/A0a8vRHuuDB943MTAIQGawTgoTncLp0wYPvVzs4tbk9VavuuNTYp3KfdwgCuAMS8LmC+HuF4/2q1XooACLG6msoj2OQ8E5+VOErkdsU/KMKqyUn2ZTX5nT3gVrsBVPlXEEKZjZRID+npinSNp2YIt0nM51HM0CcMfo4ZubMmcrOzlZaWprS0tKUl5enJUuWVPuaV199VR06dFBqaqo6d+6sd955J0LVIp5VPKpo7vWoonkIjyrCzvBmdMUlv1jaL+T1UADEPKN3Qlq2bKlp06apXbt2crlcmjNnjgYPHqzPPvtMnTp1qtJ/9erVGjJkiAoKCvSb3/xG8+bNU35+vtavX6+srCwDfwPEk35ZzdUnMz26B0ZGwW64+w8ftbQfa5gAicvonZCBAwfqiiuuULt27XTuuefqgQceUIMGDfTRRx/57D99+nT169dPt99+uzp27KgpU6YoJydHM2bMiHDlgAFREEAkqXGDFEv7sYYJkLiiZkyIw+HQq6++qsOHDysvL89nnzVr1ujWW2/1ONa3b18tXLjQ73nLy8tVXu5e36G0tNSSehF/onEDu0pREkAkKT0tsDAQaL+KNUyKS8r8jslJD2ENEwDRz/gU3U2bNqlBgwZKSUnRX/7yF73xxhvKzMz02be4uFjNmjXzONasWTMVFxf7PX9BQYHsdnvlV0ZGhqX1Iz5E4wZ2laIogEju0FCdYBY+qxgYLKnKYmqhrmECIDYYDyHt27fXhg0b9PHHH2vUqFG67rrrtHXrVsvOP2HCBJWUlFR+7dq1y7JzIz7UarfacIuyACK5Q4NNvkNDKLOJrFzDBEDsMP44pm7dumrbtq0kKTc3V2vXrtX06dM1a9asKn3T09O1Z88ej2N79uxRenq63/OnpKQoJSWwZ9NITMHMzqjN1NagRWEAqVARGrwfX6XX4vFVTAwMBmAp4yHEm9Pp9BjDcbK8vDy99957Gj9+fOWx5cuX+x1DAgQiKmdnRHEAqRCO0FDbNUwAxBajIWTChAnq37+/zjrrLB08eFDz5s3TBx98oGXLlkmShg8frhYtWqigoECSNG7cOF188cV69NFHNWDAAM2fP1+ffvqpnnrqKZN/DcS4qJudEQMBpAKhAUBtGA0he/fu1fDhw1VUVCS73a7s7GwtW7ZMffr0kSQVFhYqKck9bKVnz56aN2+e7r77bt11111q166dFi5cyBohqJWomp0RQwEEAGqLvWNqiV0/40PF7BjJ9w6zERkcSQABEOXYOyaKRPW6EghKOAZaBoUAAiABcSckROz6GZ+M3NkigACIEdwJiQLs+hm/Ij7QkgACIIEZX6wsFrHrJyxBAAGQ4AghIYjKdSUQWwggAEAICUXUrSuB2EIAAQBJhJCQVKwr4W+0h03BbeCFBEIAAYBKhJAQJOKunw6nS2u279OiDbu1Zvs+M5u5xToCCAB4YHZMiIyvKxFBrIdiAQIIAFTBOiG1FO8rprIeigUIIADiBOuERJl43sCL9VAsQAABAL8YEwK/WA+llrwDSHJdAggAnIQQAr9YD6UWvALI8ToN5fj7XkPFAEB0IoTAL9ZDCZFXANnrOk1tD87SBQ++r6WbiwwVBQDRhxACvyrWQ6kO66F48Qoghc4m6l7+hCSpuKRMo+auJ4gAwP8QQuBXcpJNg86rfubLoPOaMyi1glcA+cZ5pi46Or2yXTHAd9LirayzAgAihKAaDqdLb26s/lP7mxuLuKBKVQLIJmcrXXb0kSrdGMwLAG6EEPhV0+wYiQuqpCoBZJ2znQYenVrtSxjMCwCEEFSD2TEB8AogJc3ydOXRSTW+jMG8AEAIQTWYHVMD73VA2l2uBiOXsLkhAASIEAK/2C24Gt4BpMNvpKGvJuTmhgAQKkII/OKC6od3AMm6UrrmxcpmxeaG6V7Tm9Ptqey1AwAnYQM71IhddE/iHUDOHyrlP+Gza7xvbggg8Vh9DSWEICBcUFU1gOTeIA18zEgpAGACu+jCiHjeLTgg3gGk+0jpiofM1AIAcYIxIUBNvANIz7EEEACwAHdCgOp4B5ALb5N632OmFgCIM4QQwB/vANLrLqnXHWZqAYA4RAgBfPEOIJfdJ11wi5FSACBeEUIAb94BpO9UKW+0mVoAII4RQoCTeQeQ/g9Lv/qzmVpOwhRpAPGIEAJU8A4gA6dLudcbKeVkLBYHIF4xRReQqgaQoQuiJoCMmrveI4BIUnFJmUbNXa+lm4sMVQYAtUcIAbwDyA1LpHZ9zNRyEofTpUmLt8rXksYVxyYt3iqHM6EWPQYQRwghSGzeAeTG5dLZPc3U4uWTHfur3AE5mUtSUUmZPtmxP3JFAYCFGBOCxOUdQG56X2qZa6YWH/Ye9B9AQukHANGGEILE5B1A/rxCOvN8I6X407RhqqX9ACDa8DgGicc7gPxlVdQFEEnq3rqxmttT5W8irk0nZsl0b904kmUBgGUIIUgs3gFk1BopvbOZWmqQnGTTxIGZklQliFS0Jw7MZL0QADGLEILE4R1ARn8iNcs0U0uA+mU118xhOUq3ez5ySbenauawHNYJARDTGBOCxOAdQMask85oa6aWIPXLaq4+memsmAog7hBCEP+8A8jY9dLpbcL+Y61caj05yaa8NqdbXCEAmEUIQXzzDiDjNkqNWoX9x7LUOgDUjDEhiF/eAWT85ogFEJZaB4CaEUIQn7wDyC1bpdMywv5jWWodAAJHCEH88Q4gf90m2VtE5Eez1DoABI4xIYgv3gHktq+lBk0j9uNZah0AAkcIQUw7eQbK4IVea37cvl2qf0bI5wtlRgtLrQNA4AghiFknz0D5LvWPnt/82w6pXnDLmVsxo6ViqfXikjKf40JsOrHQGEutAwBjQhCjTp6B4h1Azit7Sku/LQ/5fCcLdkYLS60DQOAIIYg5J89A8Q4gncueUakaBDUDxeoZLSy1DgCB4XEMYk7FDBTvANKp7Fkd1qmS3DNQAlllNJgZLYGuWspS6wBQM0IIYs7eg1UDSGbZczqi1Cr9Aj2flf0qsNQ6AFSPEIKY4z0LpkPZ8ypTSpV+Vs9UYUYLAFiLMSGILV7rgLQvm10lgNh0YlZLoDNQKma0+HtQEuz5AACBIYQgdlQJIHN0VHU9joUyA4UZLQBgBiEEscF7JdS7f9T0Yb+ybAZKxYyWZmmed1WapaUwowUAwoQxIYh+3gHknp+k5DphmoHi714IAMBqNpfLlVDbeZaWlsput6ukpERpaWmmy0FNqgSQfVKy9dm5YrEy71+GigjC3RAAsP4ayuMYRK8IBRCrFysDAASGEILo5B1A7t0flgAiBbdYGQDAOowJQfSpEkB+lpLCl5fDtVgZAKB63AlBdIlwAJFYrAwATCGEIHp4B5CJB8IeQCQWKwMAUwghiA6+AogtMtNjWawMAMwghMA8gwGkQsViZVYtfgYAqBkDU2FWFASQCuFZ/AwA4A8hBOZ4B5D7SszUcZLkJJvy2pxuugwASAg8joEZURhAAACRRQhB5BFAAAAihCDSCCAAgP8hhCByCCAAgJMQQhAZBBAAgBdmxyAgDqcrtKmrLpc06TTPYwQQAIAIIQjA0s1FmrR4q8dOs83tqZo4MLP6RbwIIACAavA4BtVaurlIo+aur7LVfXFJmUbNXa+lm4t8v5AAAgCoASEEfjmcLk1avFUuH9+rODZp8VY5nF49CCAAgAAQQuDXJzv2V7kDcjKXpKKSMn2yY/9JBwkgAIDAEELg196D/gOIz35OJwEEABAwQgj8atowteZOFf2cTmlyI89vEEAAANUghMCv7q0bq7k9Vf4m4tp0YpZM97PtBBAAQNAIIfArOcmmiQMzJalKEKlo3zfgXCXf77XrLAEEABAAQgiq1S+ruWYOy1G63fPRTLo9VU/+MVt9X8/yfAEBBAAQIBYrQ436ZTVXn8x0zxVTz2qo5AeaeHYkgAAAgkAIQUCSk2zKa/O/xy7Hj0r3E0AAALVj9HFMQUGBunXrpoYNG6pp06bKz8/Xtm3bqn3N7NmzZbPZPL5SUwObxQELHC8ngAAALGE0hKxYsUKjR4/WRx99pOXLl+vYsWO6/PLLdfjw4Wpfl5aWpqKiosqvnTt3RqjiBHesTLq/qecxAggAIERGH8csXbrUoz179mw1bdpU69at00UXXeT3dTabTenp6QH9jPLycpWXl1e2S0tLQys20Tkd0gPNPI8RQAAAtRBVs2NKSk5c1Bo3blxtv0OHDunss89WRkaGBg8erC1btvjtW1BQILvdXvmVkZFhac0JwemQJnv9NyGAAABqyeZyuXztTxZxTqdTgwYN0oEDB7Rq1Sq//dasWaOvv/5a2dnZKikp0SOPPKKVK1dqy5YtatmyZZX+vu6EZGRkqKSkRGlpaWH5u8QVAggA4H9KS0tlt9stu4ZGTQgZNWqUlixZolWrVvkME/4cO3ZMHTt21JAhQzRlypQa+1v9BsY1x3FpykkLkbXpLV37url6AABGWX0NjYopumPGjNFbb72llStXBhVAJKlOnTrq0qWLvvnmmzBVl6C8A0i7y6Whr5qrBwAQd4yOCXG5XBozZozeeOMNvf/++2rdunXQ53A4HNq0aZOaN28ehgoTlHcAObc/AQQAYDmjd0JGjx6tefPmadGiRWrYsKGKi4slSXa7Xaeeeqokafjw4WrRooUKCgokSZMnT1aPHj3Utm1bHThwQA8//LB27typm266ydjfI644jklTznC32w+QhswzVw8AIG4ZDSEzZ86UJPXq1cvj+PPPP6/rr79eklRYWKikJPcNm59//lkjRoxQcXGxGjVqpNzcXK1evVqZmZmRKjt+ea+E2nGg9Ie55uoBAMS1qBmYGikMTPXDO4Bk5ktXzzFWDgAg+lh9DY2qdUJgiHcAybqSAAIACDtCSKLz3gum8++lq54zVw8AIGEQQhLZ8XLPvWCyr5GufMZcPQCAhEIISVTem9Gd90fpd7PM1QMASDiEkER07BfPzei6DJN+O9NcPQCAhEQISTTHfpEeOGkH4pzrpMH/NlcPACBhEUISydEjngEk9wZp0OPm6gEAJDRCSKI4eliaetLS9t1ukgY+ZqwcAAAIIYng6GFp6pnudvc/SwMeNVcPAAAihMS/8kOeAeRXo6QrHjZXDwAA/0MIiWflB6WCFu52j9FS/2nm6gEA4CSEkHhVflAqaOlu9xwr9Ztqrh4AALwQQuJRWalnAPn1OOny+83VAwCAD4SQeFNWIk3LcLcvuFXqM9lcPQAA+HGK6QJgoV8OSA+e7W5feJvU+x5j5QAAUB1CSLzwDiAX/U269O/GygEAoCaEkHjwy8/Sg63c7V4TpF53GisHAIBAEEJi3ZH90kOt3e1L/i5d/Ddz9QAAECBCSCzzDiCX3i1ddLu5egAACAIhJFYd3ic9fI673XuidOGt5uoBACBIhJBYdPgn6eE27vZlk6QLxhsrBwCAUBBCYs2hH6VH2rrbfaZIv77ZXD0AAISIEBJLvANI36lS3mhz9QAAUAuEkFhxcI/06Lnudr9pUo9R5uoBAKCWCCGx4GCx9Gh7d7v/Q9KvRpqrBwAACxBCop13ALniEan7CHP1AABgEUJINCv9QfpHR3d7wKNSt5vM1QMAgIUIIdGqZLf0z0x3+zf/lLr+yVw9AABYjBASjUq+l/7Zyd0e+LiUe525egAACANCSLQ5sEt6LMvdHvQvKWe4uXoAAAgTQkg0OVAoPdbZ3R78b6nLMHP1AAAQRoSQaPHzTml6trud/6R0/hBz9QAAEGaEkGiwf4f0+Pnu9m9nSeddY6wcAAAigRBi2v5vpce7uNu/e1rKvtpcPQAARAghxKR926V/5bjbVz4rdb7KXD0AAEQQIcQU7wBy1XNS1pXm6gEAIMIIISb89LU0o6u7/fvZUqffGisHAAATCCGR9uNX0r+7udtXvyBlDjZXDwAAhhBCIunHbdK/u7vbf5grdRxorh4AAAwihETK3i+kJ3q429fMkzoMMFcPAACGEUIiYc9WaWaeuz1kvtS+v7l6AACIAoSQcNuzRZrZ093+4yvSuX3N1QMAQJRIMl1AXCve5BVAXiWAAADwP9wJCZeiz6VZF7rbQ1+T2l1mrh4AAKIMISQcijZKsy5yt4e9LrXtba4eAACiECHEaj98Jj3Vy92+9g2pzaXGygEAIFoRQqy0e7309CXu9vBF0jm9jJUDAEA0I4RY5ft10jMn3fG4brHU+iL//QEASHDMjrHC9596BZC3CCAAANSAOyFWeP9+95+vf0dq9WtztQAAECMIIVboMUpyOaRed0ln59XcHwAAEEIscW5fFiEDACBIjAkBAABGEEIAAIARhBAAAGAEIQQAABhBCAEAAEYQQgAAgBGEEAAAYAQhBAAAGEEIAQAARhBCAACAEYQQAABgBCEEAAAYQQgBAABGEEIAAIARhBAAAGAEIQQAABhBCAEAAEYQQgAAgBGnmC4g0lwulySptLTUcCUAAMSWimtnxbW0thIuhBw8eFCSlJGRYbgSAABi08GDB2W322t9HpvLqjgTI5xOp3744Qc1bNhQNpvNdDkxpbS0VBkZGdq1a5fS0tJMl5NweP/N4v03i/ffrIr3v7CwUDabTWeeeaaSkmo/oiPh7oQkJSWpZcuWpsuIaWlpafxPwCDef7N4/83i/TfLbrdb+v4zMBUAABhBCAEAAEYQQhCwlJQUTZw4USkpKaZLSUi8/2bx/pvF+29WuN7/hBuYCgAAogN3QgAAgBGEEAAAYAQhBAAAGEEIAQAARhBCIEkqKChQt27d1LBhQzVt2lT5+fnatm1bta+ZPXu2bDabx1dqamqEKo4vM2fOVHZ2duVCTHl5eVqyZEm1r3n11VfVoUMHpaamqnPnznrnnXciVG38Cfb9599+eE2bNk02m03jx4+vth+/A+ERyPtv1e8AIQSSpBUrVmj06NH66KOPtHz5ch07dkyXX365Dh8+XO3r0tLSVFRUVPm1c+fOCFUcX1q2bKlp06Zp3bp1+vTTT3XppZdq8ODB2rJli8/+q1ev1pAhQ3TjjTfqs88+U35+vvLz87V58+YIVx4fgn3/Jf7th8vatWs1a9YsZWdnV9uP34HwCPT9lyz6HXABPuzdu9clybVixQq/fZ5//nmX3W6PXFEJplGjRq5nnnnG5/euvvpq14ABAzyO/epXv3KNHDkyEqUlhOref/7th8fBgwdd7dq1cy1fvtx18cUXu8aNG+e3L78D1gvm/bfqd4A7IfCppKREktS4ceNq+x06dEhnn322MjIyavzkiMA4HA7Nnz9fhw8fVl5ens8+a9as0WWXXeZxrG/fvlqzZk0kSoxrgbz/Ev/2w2H06NEaMGBAlX/bvvA7YL1g3n/Jmt+BhNvADjVzOp0aP368fv3rXysrK8tvv/bt2+u5555Tdna2SkpK9Mgjj6hnz57asmULmwSGYNOmTcrLy1NZWZkaNGigN954Q5mZmT77FhcXq1mzZh7HmjVrpuLi4kiUGpeCef/5t2+9+fPna/369Vq7dm1A/fkdsFaw779VvwOEEFQxevRobd68WatWraq2X15enscnxZ49e6pjx46aNWuWpkyZEu4y40779u21YcMGlZSUaMGCBbruuuu0YsUKvxdCWCuY959/+9batWuXxo0bp+XLlzPA14BQ3n+rfgcIIfAwZswYvfXWW1q5cmXQn+jq1KmjLl266JtvvglTdfGtbt26atu2rSQpNzdXa9eu1fTp0zVr1qwqfdPT07Vnzx6PY3v27FF6enpEao1Hwbz/3vi3Xzvr1q3T3r17lZOTU3nM4XBo5cqVmjFjhsrLy5WcnOzxGn4HrBPK++8t1N8BxoRAkuRyuTRmzBi98cYbev/999W6deugz+FwOLRp0yY1b948DBUmHqfTqfLycp/fy8vL03vvvedxbPny5dWOYUBwqnv/vfFvv3Z69+6tTZs2acOGDZVfXbt21dChQ7VhwwafF0B+B6wTyvvvLeTfgVoPbUVcGDVqlMtut7s++OADV1FRUeXXkSNHKvtce+21rjvvvLOyPWnSJNeyZctc27dvd61bt851zTXXuFJTU11btmwx8VeIaXfeeadrxYoVrh07drg+//xz15133umy2Wyud9991+VyVX3vP/zwQ9cpp5zieuSRR1xffPGFa+LEia46deq4Nm3aZOqvENOCff/5tx9+3rMz+B2IrJref6t+B3gcA0knFmuSpF69enkcf/7553X99ddLkgoLC5WU5L559vPPP2vEiBEqLi5Wo0aNlJubq9WrVzOGIQR79+7V8OHDVVRUJLvdruzsbC1btkx9+vSRVPW979mzp+bNm6e7775bd911l9q1a6eFCxdWO5AY/gX7/vNvP/L4HTArXL8DNpfL5bK6WAAAgJowJgQAABhBCAEAAEYQQgAAgBGEEAAAYAQhBAAAGEEIAQAARhBCAACAEYQQAABgBCEEAAAYQQgBElyvXr00fvx4ozW0atVKjz32mNEavEVjTUC8IYQAqJHL5dLx48dNl1Gjo0ePmi4BQBAIIUACu/7667VixQpNnz5dNptNNptN3333nT744APZbDYtWbJEubm5SklJ0apVq3T99dcrPz/f4xzjx4/32PjQ6XSqoKBArVu31qmnnqrzzjtPCxYs8FtDr169tHPnTt1yyy2VNUjSvn37NGTIELVo0UL16tVT586d9dJLL1V57ZgxYzR+/HidccYZ6tu3ryTpzTffVLt27ZSamqpLLrlEc+bMkc1m04EDBypfu2rVKl144YU69dRTlZGRoZtvvlmHDx+utiYA1iKEAAls+vTpysvL04gRI1RUVKSioiJlZGRUfv/OO+/UtGnT9MUXXyg7OzugcxYUFOiFF17Qk08+qS1btuiWW27RsGHDtGLFCp/9X3/9dbVs2VKTJ0+urEGSysrKlJubq7ffflubN2/Wn//8Z1177bX65JNPPF4/Z84c1a1bVx9++KGefPJJ7dixQ1dddZXy8/O1ceNGjRw5Un//+989XrN9+3b169dPV155pT7//HO9/PLLWrVqlcaMGVNtTQCsdYrpAgCYY7fbVbduXdWrV0/p6elVvj958uTK7ewDUV5erqlTp+o///mP8vLyJEnnnHOOVq1apVmzZuniiy+u8prGjRsrOTlZDRs29KihRYsWuu222yrbY8eO1bJly/TKK6+oe/fulcfbtWunhx56qLJ95513qn379nr44YclSe3bt9fmzZv1wAMPVPYpKCjQ0KFDK8fCtGvXTo8//rguvvhizZw5029NAKxFCAHgV9euXYPq/8033+jIkSNVgsvRo0fVpUuXoM7lcDg0depUvfLKK9q9e7eOHj2q8vJy1atXz6Nfbm6uR3vbtm3q1q2bx7GTQ4skbdy4UZ9//rlefPHFymMul0tOp1M7duxQx44dg6oVQGgIIQD8ql+/vkc7KSlJLpfL49ixY8cq/3zo0CFJ0ttvv60WLVp49EtJSQnqZz/88MOaPn26HnvsMXXu3Fn169fX+PHjqww+9a4xEIcOHdLIkSN18803V/neWWedFfT5AISGEAIkuLp168rhcATUt0mTJtq8ebPHsQ0bNqhOnTqSpMzMTKWkpKiwsNDno5dgavjwww81ePBgDRs2TNKJAa9fffWVMjMzqz1X+/bt9c4773gcW7t2rUc7JydHW7duVdu2bYOqCYC1GJgKJLhWrVrp448/1nfffaeffvpJTqfTb99LL71Un376qV544QV9/fXXmjhxokcoadiwoW677TbdcsstmjNnjrZv367169frX//6l+bMmVNtDStXrtTu3bv1008/SToxTmP58uVavXq1vvjiC40cOVJ79uyp8e8zcuRIffnll7rjjjv01Vdf6ZVXXtHs2bMlqXKWyx133KHVq1drzJgx2rBhg77++mstWrSocmCqv5oAWIsQAiS42267TcnJycrMzFSTJk1UWFjot2/fvn11zz336G9/+5u6deumgwcPavjw4R59pkyZonvuuUcFBQXq2LGj+vXrp7ffflutW7f2e97Jkyfru+++U5s2bdSkSRNJ0t13362cnBz17dtXvXr1Unp6epXpwb60bt1aCxYs0Ouvv67s7GzNnDmzcnZMxSOh7OxsrVixQl999ZUuvPBCdenSRffee6/OPPPMamsCYC2by/sBLwDEmQceeEBPPvmkdu3aZboUACdhTAiAuPPEE0+oW7duOv300/Xhhx/q4Ycf9njUAiA6EEIAxJ2vv/5a999/v/bv36+zzjpLf/3rXzVhwgTTZQHwwuMYAABgBANTAQCAEYQQAABgBCEEAAAYQQgBAABGEEIAAIARhBAAAGAEIQQAABhBCAEAAEb8f/I3okI31FyYAAAAAElFTkSuQmCC\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"boxplot_pi(lm, X_test, y_test)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 607
},
"id": "X8bwlzZDcu2G",
"outputId": "5d87a346-2925-4d3d-efa3-e991656bfca7"
},
"execution_count": 37,
"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": [
"%pip install --quiet shap"
],
"metadata": {
"id": "aEnZLzSgxgMe"
},
"execution_count": 40,
"outputs": []
},
{
"cell_type": "code",
"source": [
"import shap\n",
"\n",
"explainer = shap.Explainer(lm.predict, X_train)"
],
"metadata": {
"id": "eM9xA30DxaU8"
},
"execution_count": 41,
"outputs": []
},
{
"cell_type": "code",
"source": [
"shap_values = explainer(X_test)"
],
"metadata": {
"id": "vTVBK1MVyH0T"
},
"execution_count": 42,
"outputs": []
},
{
"cell_type": "code",
"source": [
"shap.plots.waterfall(shap_values[0])"
],
"metadata": {
"id": "yKkqztX-ydYx",
"outputId": "176c6d74-e518-4e8c-9cef-65917fea1a59",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 386
}
},
"execution_count": 43,
"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": [
"X_test.mean()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "cX70r-wm3er7",
"outputId": "a23b1a80-572b-46d2-943a-2902e9d2f007"
},
"execution_count": 45,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"sepal length (cm) 5.881579\n",
"petal length (cm) 3.613158\n",
"petal width (cm) 1.155263\n",
"class 0.921053\n",
"dtype: float64"
]
},
"metadata": {},
"execution_count": 45
}
]
},
{
"cell_type": "code",
"source": [
"y_train.mean()"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "aUVr_KHG31bi",
"outputId": "a2f3e584-918f-41fe-f09e-1a691a0662a9"
},
"execution_count": 48,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"3.0401785714285716"
]
},
"metadata": {},
"execution_count": 48
}
]
},
{
"cell_type": "code",
"source": [
"lm.predict(X_test)[:10]"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "G47VzGtk5Tzw",
"outputId": "2cd5ffa3-50c2-4b2b-dcdf-285ade32dd56"
},
"execution_count": 67,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([2.68882271, 3.67002562, 3.38976912, 2.87491372, 3.0414682 ,\n",
" 3.65914168, 2.72435311, 3.34795138, 2.94223694, 2.68893794])"
]
},
"metadata": {},
"execution_count": 67
}
]
},
{
"cell_type": "code",
"source": [
"X_test.iloc[0]"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "aGwZbxlI5MBM",
"outputId": "5adf020b-7db7-42d4-b81c-9fbbec716950"
},
"execution_count": 59,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"sepal length (cm) 6.1\n",
"petal length (cm) 4.7\n",
"petal width (cm) 1.2\n",
"class 1.0\n",
"Name: 73, dtype: float64"
]
},
"metadata": {},
"execution_count": 59
}
]
},
{
"cell_type": "code",
"source": [
"shap.plots.waterfall(shap_values[5])"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 386
},
"id": "2TTNLtkDH8rt",
"outputId": "a7987685-9907-4318-d8f1-11d0bcb3fbe5"
},
"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": {}
}
]
},
{
"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": "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.beeswarm(shap_values)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 329
},
"id": "uy26MdaIHqVk",
"outputId": "4c14d47e-9922-4a77-9a60-c1237bb3a1ef"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 800x310 with 2 Axes>"
],
"image/png": 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CZF/5UubTK1hIfwxIz4cQQlgnyEtFAzw4MLWcZ0HUJm9Iz4dlRSO8FELkr0u3s5f+GFDNLD8scz6EEMJUWIxiEngAXCtC0wbVLH/CmAQfQojsqxMEzg8MmXF2TE9/TJWo7WuaVtc0TQghHnfB//V8PKio9XzIalfmSfAhhMi+4l7w5QuZAYizI8x8AUp4F2y9ClClHoEEtfc3PHb3d6XJpMdz/osQQjzMlcei50OCD0tkzocQImeGPwW9msJfV9J7PB7jwANA46ih3ZxmRJ6LISkqhVIN/MzOAxFCiMddgKf51a7KuBdEbfKKBB2WSPAhhMi5Et6P7epWlvhW8SroKgghRKF2K978XIg7CflelTyjl+DDIrksJ4QQQggh8o2vC5jrGC5q9/mQYVfmSfAhhBBCCCHyTQk3hVfqGZ+Uty6n0Kpc0TlRl+DDMhl2JYQQQggh8tXMNhralFPZc12lVnGFgdUVFKXonKjb+xK74eHh7N27lzt37tC7d28CAgLQ6XRER0fj7e2NVpvz+1hJ8CGEEEIIIfKVoij0qKzQo3JB1ySv2Gcgpaoq48ePZ9asWaSlpaEoCrVr1yYgIIC4uDiCg4N5//33GTt2bI7LkGFXQgghhBBC2JC9Drv69NNP+fLLL5kwYQI7duxAVTP7cLy9venVqxdr1qzJVRkSfAghhBBCCGFD9hp8zJ8/n8GDB/PRRx9Rr149k+fr1KnDuXPnclWGDLsSQgghhBDChux1zse1a9d44oknLD7v7u5OTExMrsqQ4EMIIYQQQggbsrcejwwlS5bk2rVrFp8/fvw4gYGBuSpDhl0JIYQQQghhQ/Y67KpXr17MnTuXS5cuGdIyViHbvn07ixcvpm/fvrkqQ4IPIYQQQgghbEjN8mdPpk6dir+/P/Xq1WPw4MEoisInn3xCixYtePrpp6lTpw5vv/12rsqQ4EMIIYQQQggbsteeD29vbw4dOsSbb75JeHg4Li4u7Nmzh6ioKCZPnsy+fftwc8vdrehlzocQQgghhBA2ZG9BR1aurq688847vPPOO3mSvwQfQgghhBBC2JC9DbfKTxJ8CCGEEEIIYUN6O53Z8MILLzxyG0VRWLBgQY7LkOBDCCGEEEIIG7LXno/ffvvNsLpVBp1Ox82bN9HpdJQoUQJ3d/dclSHBhxBCCCGEEDZkr3M+wsLCzKanpqYyb948Zs6cyY4dO3JVhn32CQkhuBmncjPOXq+tCCGEEEWZkuXP/jk6OjJ69Gg6dOjA6NGjc5WXBB9C2JmEVJWeP+soO0dHmTk6uv+sIz5FghAhhBCisLDXpXYfpW7duuzduzdXeUjwIYSdmX5Ez7oLqmE86YYLKtOO6Au0TkIIIYTIZK83GXyUHTt2yH0+hHjc7L5m+lW262pR+3oTQggh7Je99ni8//77ZtOjoqLYu3cvJ06cYOLEibkqQ4IPIexMdT+FPQ8EIDWK2eeXnBBCCFEU2eslwSlTpphN9/X1pWLFisydO5dhw4blqgwJPoSwM2831bDyrI6IpPTHvs4wqamMoBRCiFxJTYP/rYI1h6CEF0zqDe3rFXSthJ3S22nPh16f98O4JfgQws7sD1cNgQdAZDLsu64S5G2fX3RCCFEoTFwKX2zMfPz7WTj+KdQOKrg6Cbtlr8Ou8oMEH0LYme9Pm3bmLj6tMrBmAVRGCCGKisW7jB+npsGKffCxBB8i++xl2NXVq1dztF9gYGCOy5TgQwg74+ZomubulP/1EEKIIsXNGSLijNPcnQumLsLu2UvPR3BwsMkdza2h0+lyXKYEH0IUgF+v6Nl6WaWCt8LgmgoeTtZ/8F9rqGHDBR26/y6raBUY0yB7XxzxKSpLTum5GKnSobyGDhWzP2dk63kdOy7oqFJcYVBdB9yy0QZRdCTHpHJ2/TXibycR/GQp/BsUy9b+qqpybWs4d4/dx7eaN0HdA9E6Ze/9GHM6khurwnDwcCRgYAVc/LO3DGRqRDK3F58j5VYixXsF49W0ZLb2Lwx09xJIWPwnursJuPWpjlOjMvlfh2NXSVv9J4qfG45Dm6CU8MjX8tU0Hfx0CI6HQdNK0LsRijYb76Xx3eD1RZmPvd3h+TY2r6d4PNhL8LFw4cIcBR+58dgGH/PmzWP+/Pls2LCBMmXy7ku6a9eu+Pv78+233z5y240bNzJ16lTmzp1LSEjII7e3ZRsuXLjAgAED+PLLL2natGmu8sqJpKQkevToQa9evXj55Zfzvfz8NO2wnv/blzGhS2X+KTj0nBZnB+s+/F5O4KiBjIsODhrwdrb+iyNFpxK6JJUTt9Kjl88P6fhfKy3vtLT+62DKbylM3ZVmeLzguI7fhznjoLWPL1thGylxqazpv4+Yq/EA/Ln4IqGT61Cjr/XDVI5OOsH5ZZcMj8M2XOXJpaFW739n+w2O9d2Fmpb+fr486wzN9z2NW6B1J76pEcmcaLiO5LD0K97XPztFlUWhlB5S2eo6FDTd3XjuNFyA7loMAHGfHsRvRU/c+uffWMy0VSdJ6r8E9OnHIfWrfbgeH4+mlGe+1YF+s2DtsczHg1vA98Ot37+EV/rNqDPGy3g4g6v0fIicsZdhV88//3y+l2l3S+Rs3LiRFStWFHQ18s2xY8eYN28esbGxeVrOjBkzqFu3boEEHgAuLi48//zzLF26lHv37hVIHfJDcprKx4eNV5I4eQfWX7D+a+rTo3qSsvR2Juvg02zcZHDjOb0h8MjwyUEdianW1SEhReXT/WlGaUfD9Ww+l/MuWGGfzm0KNwQeGY7NOWf1/gm3Ermw4pJR2s09t7l34r7VeVyYfsoQeACk3EvmytyzVu9/+/vzhsADABWuTv3D6v0Lg/jvThoCDwBUiHl/X77WIeX97YbAA0ANjyZt/sF8K189dc048ABYsh/10h3rM5my0viMMTwCFu20Sf3E40ePxvAnjNldz8fGjRu5efMmzz33XEFXxSpr1qzJVXfW8ePHmT9/Pl27dsXTM2+uIP31118cPnyYzz77LE/yt1b37t2ZPXs2y5cv57XXXivQuuSVxDSISTFNv51gfR534k3TbidYH7zcMVNWXAokpIKrmfkklrY1qUOcaZoo2hLvJ5ukJUUko6qqVd97yVEpqGbi5sR7SaaJFqTcNd02+Y71+6feSTTN87ZpWmGmN/OloL9t5osiD6l3TC+Qqbfz9qKZkdvR5tPvxEAFK4fR3TGTh6V8hXgEe+n5sOTAgQOcOHGC6Ohok+V3FUXh3XffzXHedhd82Bsnp8I/E3jVqlX4+PjQokWLAq2Hq6srbdq0YePGjYwcOdIuXrvs8nFRaBuosDPLHckdNdC1ovUBaq8qxvsD9K5i/ZWVLpU0jNVCSpaOitZBCsXcrKtDSQ+F5oEaDlzN/DJydoAuVeXqjj1S9So3/4nBwUlDySrZu8BRvm1pTsw7ZxRAlG/rb/UFF5+qXnhW8CD2Umbk6uTlSOknrJ9zUbpbIBe/+Nsozb+n9auwFOsZzLVP/jI6UyjeJ9jq/TOkRSSRePIeLrX8cCyZvTknueXauxpxM4+YpGWX/l48aX/dwqFOaTTF3bO1r7ZXHdLm/m6c1rtu9uvw9w2ITEBpWh7FQWv9ji2qQClv42AhsBiElLc+j95NYdFvmY8VBXoVzGgAYf/sZc7HgyIiIujcuTNHjhwxXEhS1fQvyIx/5zb4yJOzhY0bNxISEsLhw4eZN28eXbp0oVmzZvTv359t27aZ3eeff/5hwoQJtG3blmbNmtGrVy8WLFhAWlrm8I6uXbty4sQJbt68SUhIiOHv2LH0rtbTp08zZcoUevXqRfPmzQkNDeWFF15g165dZsu0xrfffktISAjh4eGGtHv37hESEkKjRo2Ijs78ort8+TIhISEsXrzYqM7m5jD8/PPP9O7dm2bNmtGjRw9WrFhhOLgZpkyZwvz58wHo1q2bob3z5s0z2i4lJYVvvvmGTp060axZM5599ln2799vVfvS0tLYs2cPjRs3xsHBNBZNTU3l+++/57nnnqN58+a0atWKQYMG8dNPPxm2mTdvHiEhIVy6dInPP/+cp556iubNmzNy5EjCwsIA+O233xgwYADNmzena9eurF271mx9nnjiCaKiogzHtCgaWTc94MjwdHkIzsY9Op6pCmWyDGf3d4d+Va0vv5y3QpfKmRVw1MCIBtn4kQdeaazFIUsbulXTUsZLgg97E3M7icWDjvDD8BMsHXqMla/+QWqi9cPnilXxonSWCeZaJw3VszHfQ1EUKg+oYPRLFNwzEEcPK7rg/lN2UAUcvDO3d6/iRfEn/a3e37NhMTyzTDDXuGrxfzkbHyjg/pJ/OVV2Mefbrud0wPfc+erPbO2fW05NA3AMyWyz4u6I27D62cojaf5RIgKmE9N2IREB00mcezhb+zsOawoemReMNPXLom1u/Ym/mpJGavfZpNWaSlrLT0mt/C7q2VtW76+4OMHzLTCc72kUGNY6ewHMy+3TV7zK0LgSNKpk/f5CZKFm+bMnb7zxBn/99RcrVqzg0qVLqKrKtm3bOHfuHCNGjKBevXrcuHEjV2Xk6dnC119/zfbt2+nTpw/Dhw8nNTWVSZMmsXHjRqPt9u/fz4svvsjVq1cZOHAgEyZMoE6dOsybN49JkyYZths/fjzBwcH4+Pjw/vvvG/7Kl0//gtu9ezdhYWG0a9eOCRMm8MILLxATE8Mbb7zB1q1bc9SGRo0aAXD06FFD2pEjR9BoNKiqanSSnLFNxj6WrFixgg8//BBnZ2dGjRpFly5dWLZsmdEJPUCvXr1o0yZ9pY1x48YZ2vvkk08abTdlyhT++OMPBg4cyIgRI4iMjGTChAlWvTn+/fdfEhISqFnTdGJiamoqo0eP5uuvv8bPz48RI0bwyiuvUK1aNbMB3ZQpUzh37hxDhw5l0KBBnDp1ildffZXNmzfzySef0Lp1a8aMGYOnpycfffQRJ0+eNMmjTp06QPpws6JIVVXe3KuSmuVK8YaLsP+69V9PnxxRuZFliNPNePj4sPX7H7quZ+2/mRVI1cPE39LQq9blodOrTNyRRlqWNqw6reNYuMz5sDcHvr1MRFjmOLxrJ6I4sfq61fuH7b7NzWOZ8zN0KXoOz/jH6v3TEtI4NfMfyPJeOr/0InHXrB8ydP6Dv0iLzhwHGH8uhivfnbd6/3trwog9mDkvQJ+o4/JE6y9+6KKTufbKHtT/JmKpqXquj/+dlBv5N+wpYcVpUo/dNDxW41OJ+b/fHrKHMf39BOJe3QTJ/13sS04j/rXN6O9YP5Yy5e1f0sdkZuT5Rzhpy6x/HfXfH0TdkCVoC7tP2vjVVu+v3oqCL7ZmnunpVfhoI2pkNo7DxGWQkGUo4eHzsPKA9fsLkYWKYvizJ7/88gvDhw+nX79+huH+Go2GSpUq8c033xAcHMzYsWNzVUaeDruKiorixx9/xMMj/TJtnz596N+/PzNmzKB9+/a4uLiQnJzM//73P2rVqsWcOXMMV9979+5N5cqVmTFjBseOHSMkJITWrVuzYsUKkpOT6dSpk0l5L774IqNHjzZK69+/P8899xwLFiygY8eO2W5D7dq1cXFx4dixY/To0QNIDzKqVKlCcnIyR48epW3btoZ0Dw8PqlWz3N0dGxvL7NmzKV++PAsXLsTFxQVI7yHp06eP0bZ16tShUqVK7Nq1i9atW1tc0crHx4cZM2YYhjqEhIQwZMgQ1q5da/J6POjSpfTJngEBASbPrVixguPHjzN06FBGjRpl9NyD4/8AihUrxhdffGGoh4+PD5999hnTp0/np59+onTp0gB06NCBzp07s3LlSurVq2eUh7+/P1qt1lCvouZ+IlwyM4T46C2VFgHWfUEduWkaJBy9ZX3wceSG6bELi4a78VDKigWCbsWpXIs2Le/IdT0hZbPXgyIK1q0zMSZpt89YP07/zqlIk7S7f0dbPecj5mIsqbHGixeoeog4FYlHOeuG/UQdM12gIvq49RPWY4/ctSrNksR/ItHHG7eBND2JJ+7iVCZ7Q5dyKuWI6YWmlKM3zWxpXtpftzIDD0MGOtL+uoVTO+uu/OuOmN6oTHfkKo5Dm1i1v3okzKo0i05ehdQHLoAkpsDf16GFlT1ZR8wErUcvwHPWr74mRAZ7CzoyREVFGS5IZ5y/x8VlXojo0KEDb7/9dq7KyNOejz59+hgqDumN6N27NzExMYYr24cPH+b+/ft07dqVuLg4oqKiDH/Nmzc3bGMNV1dXw7+TkpKIiooiKSmJRo0acfnyZaMXz1oODg7Uq1fPqIfj+PHjNGrUiEaNGnHkSPo4W1VVOXHiBA0aNECrtXwCdujQIZKSkujbt68h8AAoVapUjoIjSA+wsv7Q16xZEzc3N6vuWhkZmX7y4O3tbfLc1q1b8fLy4qWXXjJ5TqMxfev069fPqB4ZgUVoaKgh8ADw9fUlKCiIa9euma2Tt7c3ERERj6x7fomIiCA5OfNqWFxcnNHqYykpKdy/b3yyc/PmTbOPi7lCBdOXmipusVaX0ai06RdaSGnFpMxbt24ZDeXLaEejMqbHLtgbvB2ta0dpD4UAL9M6NCqrselr9ah2SBm5L6NEFdOTY7eyxo8fVkbJWj4m+/tV8zT6rjXXjozvA6+Knjh6Gl8DUzSgK5VmdTt8GpreV8S7YTGrXyuPRiVM9vdsVMLq4+FawxeN2wPX8Rw0uNYvnm/HPOuQqwxO/6VZU8b9EqRP3DLKQItDndKGMh7VDrW+aR20jTLn3jyqHbFVfE32VxuWMyrjoe2oF4jqaPzbq7o4Qs3MC2uPaoeugekwsdiqpR5ab3v4nD/OZRQkfZY/e1KmTBlu3Uof8ujs7EzJkiX588/MXsnw8PBc3xckT3s+goODTdIyhkhlzKG4fPkyAO+//77FfB58s1kSERHBnDlz2LNnj9mT17i4OKNgyFohISEcOnSIy5cv4+joyI0bN2jUqBHJycmsXLmSO3fuEBkZSXR09COHXGW029xrU6FChWzXDcz3Wnh7exvNR7Ek4w304HwTgKtXr1K1alWcna1b5/zBenh5eQGY7bHx9PQ0vLkfZO1V0/zi5+dn9PjB95CTkxPFihmfAPn7+5t9rCgK85/S0Hu9nqjk9OHJYxoodK5hHJE8rIyJTRR++Ddz6FVpd5jURKGEu3GZWQO+rO1oFgC9qmpYezb9K9FBA9OedMDFWYvLA8faUjs+6eDIkLUphqFXfWtqaBSgBWz3Wj2qHVJG7ssIHVmJu+fiibiSPvSqXH0fQoca99w+rIzgNqWp0j2Ac+vTh2q5FnOm9eR6RivzmWtHxmffwc2Bxh815NAbx9Al6VAcFOq+UYvyDYOtbke1/zUg5q9I4i+kn6QUf9KfoJcqo3U1/nmz1I4SfYIJb1aC2IPpvR0aVy3lPwnJ1vEoN7sVV0fsRk3SoThqKPvpEziV9aA0xnnk1TFXB5YkedN5Etf8C4A2wBOfLztYX0atCiR93SVz6JWTFvcvO6Mp6WH0Wj2sHfpZfUnsOA/1avoFLW2P2jgMyrxf1aPa4fNaR9L2X80cehVcDKeZ/YzKeFg7lNI+8MUAGL8CUtLAxRHlmyEovpkB9qPaof3seWg7JXPoVePKeL5kfFHQHj/nj3MZBUnVFJ7zmOwIDQ1lx44dhmkP/fr1Y/r06Wi1WvR6PTNnzuSpp57KVRkFvtpVxknva6+9RpUqVcxuU6KE6ZUpc/mMHj2ay5cv079/f2rUqIGHhwcajYaNGzeydetWs0OFrJF13oeTkxMODg7Ur1+f1NRUNBoNR44cISoqymjb/GSuFwLMBxQP8vVNv9pkTaCS03pkt34xMTHUqlUr1/UprJ4M1BA+QuHwTZVgL4XyPtn7glp1FqM5H7fi4aezMLqBdftfi1bZfCHzs5Cmh7kndPSraf2QqTlHjed8bDyr50aMXiad2xmvUi48v6wxN/6OwdE5+6tdKRqFJz+sT4OXKxN/O4nS9XzROmVv6F1w90DKtC5NxKlIvCt74VrK9dE7ZeEW7EGrE12JPHIPBw9HvGqbXkF/mLgT94k9lDnMSp+o49b8c3g3L/2QvYwVG1IN767BJPxxF9daxXAslb+rXSkOGoqt7kPqmXvo78bj1CwAxTF7x8FlWCOcetYg7c//Vrsqkb0hY5rqpXC7OAn9wTCUYu5oalj/+gEoTg44rn8F/enw9NWumlXI3mRxQBndHvWZxnDqOtQLRCmWzeXp5/9qPOfj6AU4dgGaZm8BAiEAVPuMPRg3bhw7duwgOTkZZ2dnpkyZwt9//21Y3So0NJSvv/46V2XkafCRsdJRVhk9HWXLpvftBwamd8u6urrSpMmjx4ZauiJ+/vx5zp07x7Bhwxg+3PiOpuvWrctGrU1Vq1YNDw8Pjh49iqOjI7Vq1cLV1RVXV1eqVq3K0aNHiYmJwc/Pj4oVKz40r4x2h4WF0bhxY6PnzM1zyOsegIz6mhsCFRQURFhYGCkpKfm27O2NGzfQ6XSPfB3tnZujQpvAnB3bNedMg7bV5/SMbmDdif+mC3qSHxgavfuKyv0E1arldu/Eqey/YhzIJ6XB5nN6hoVI8GFvFI1C2dpmxgJmg0+QBz5B2e9VzuDk7UTpFqUevaEFilaDXzPrl+fN6t7aMJPlaO6uvkzVxdkb5+/g54JX23KP3jAPOVYvDtWL53h/TXF3nNrm/LtXcdCibZm7725NrbKP3uhhdSjpDW1z+H5e/cBNEVUV1hyS4EPkiL32fNSuXZvatWsbHvv6+vLrr78SFRWFVqu1yT3n8vRMYfXq1UZjf+Pi4lizZg2enp40bNgQgGbNmuHn58fixYvNXn1PSkoiPj5ztQo3NzdiYmJMrppnXF1/MP3ChQvs3r07V+3QarU0aNCAEydOGOZ7ZAgJCeHo0aOcOHGChg0bPjJYaNKkCc7OzqxatYqkpMwbYd2+fdvsMsRubulX0GJiTCeG2kLVqlVxd3fn1KlTJs917NiRmJgYFixYYPKcNb0qOZFRjwYNrLyM/xgqaeaCZEkr79GRvq1pmocTuFm5uqmlbc3VS4jCzrGkaU+Lk5k08RgoaSZoMZcmhBVUTeafPfnnH/MrFvr4+NjsZtd52vPh4+PDkCFD6Nq1K5B+/49bt27xzjvvGCZbu7q6MnXqVCZMmEDv3r3p1q0b5cqVIzY2lrCwMHbt2sWnn35KSEj62NFatWqxb98+pk+fTp06ddBoNDRq1Ijy5ctToUIFlixZQlJSEkFBQVy9epW1a9dSqVIlzpw5k6u2NGrUiL179wIY6pKRvnTpUpN0S7y8vBg5ciQzZ87khRdeoFOnTiQlJbF27VrKlSvH2bNnjbbPGH701Vdf8fTTT+Pk5ETFihWpVMk2a49rtVqefPJJdu/ebdLD8eyzz7Jv3z4WLFjAP//8YwicLl26xJUrV5g9e7ZN6pDVgQMH8PHxseq1fFy90UjDuvM6Ev9bnMZFC282tv7brWsVDQ1KK5zIskLWm820uDpaF8C4OSm80cKBqbsyV8dpVFZD5yqy0pWwP6WGVCb8q79JDvvvQpkCgZOzd48MUURMfgYGf5Xe4wEQWByGPvnwfYSwQNXaZ89HrVq1qFWrFv379+eZZ56x2flmVnkafLz66qucPHmSVatWERERQWBgIB988IHJqk7NmjXj+++/5/vvv2fLli1ERkbi5eVFQEAAAwYMoHLlyoZtBwwYQHh4ODt37mTNmjXo9Xrmzp1LSEgIX375JTNnzmTTpk0kJiZSsWJFw70nbBF8QPrM/4x7UQDUr18fBwcH0tLSrJ7vMXDgQFxdXVm+fDnffPMNpUqVYuDAgXh4eJhMvK9Xrx6vvvoqa9eu5YMPPkCn0zFs2DCbvhl69+7Nxo0b2bdvn2HZYABHR0dmzZrFsmXL2LZtG7Nnz8bJyYnAwEBDQGlLiYmJ7Nq1iz59+hTJu5vbSkyK8d3JU3QQk6yClcv6OWkV9g52ZMkpPecjVJ6qoOGpitm7NDPlSSealtOy/YKOKsUUBtVzwMFOv2jF483Rz5kGx3twe9E5Um4lUrxXEF7Ncj4ETNixga2gsn/6UKuSXvD8k1Dcq6BrJeyU3k6HXc2ZM4eVK1fy3nvv8e6771KvXj1DIBIUZP1NZB9GUfNg/MzGjRuZOnWqISgQhd+rr75KYmIi3333XYHV4YcffmD27Nn8/PPPFC+e83HLRV2vdTp+Pm/8se1WSWF9T+l5EIVbamoqixYtAmDo0KE4Olp/J3MhhLAnG7yXG/7dLXpAAdYkZ27fvs2qVatYuXIlBw6k32yzcePG9O/fn759+1q895w17GwkmsgrY8eO5dSpUxw6dKhAyk9KSmLx4sUMGjRIAo9HSEg1TYtPMU0TQgghRMHQaxXDnz0qVaoUo0ePZu/evVy9epXPP/8cRVEYP358rntACnypXVE4VKxY0eqbOeYFFxcXsxPuhanBNRW2hRn3fAypZZ9fbkIIIURRZK+rXZnj7+9PzZo1qV69OqdPnzZaCConJPgQws60DFAo7gr3EtMf+7lAaEDR+ZITQggh7J3ezn+WVVVl9+7d/PTTT/z888/cu3cPX19f+vfvT79+/R6dwUPkSfDRtWvXPJmQLISAjw/rDYEHQEQSfHhIz7dPyZwPIYQQojCw156Pffv2sXLlSlavXs2dO3fw8vKiR48e9OvXj3bt2uHgkPvQQXo+hLAz/9w3XSPCXJoQQgghCoa93uG8VatWeHh40LVrV/r160fHjh1tvgKpBB9C2JnW5RT2XFNN0oQQQghROKiPuOl0YbVq1So6d+5suB9fXpDVroSwM2821tCjkmK4q0fXigr/10Q+ykIIIURhoVcy/+xJ79698zTwAOn5EMLuuDkq/NxTy804FRUo42Fn32xCCCFEEWevcz7ygwQfQtgpfwk6hBBCiELJXud85AcJPoQQQgghhLAhe53zkR8k+BBCCCGEEMKG7G2uR36S4EMIIYQQQggbkp4Py2SJHCGEEEIIIWxIVTL/7E1MTAzTpk3jqaeeon79+hw5cgSAiIgIvvjiCy5cuJCr/KXnQwghhBBCCBvS22nPx/Xr12nVqhXXrl2jcuXK/Pvvv8TFxQHg5+fHvHnzuHLlCl9++WWOy5DgQwghhBBCCBvS2+lSu2+88QaxsbGcPHmSkiVLUrJkSaPne/TowaZNm3JVhgy7EkIIIYQQwoZURTH82ZPt27czZswYatSogWKm7hUqVODatWu5KkN6PoQQQgghhLAhe5zrAZCYmEiJEiUsPh8bG5vrMqTnQwghhBBCCBtSNYrhz57UqFGDvXv3Wnx+3bp11K9fP1dlSPAhhBBCCCGEDdnrsKuxY8fy448/8sknnxAdHQ2AXq/nwoULDBo0iIMHD/L666/nqgwZdiWEEEIIIYQN2VuPR4aBAwdy5coV3nnnHSZNmgRAx44dUVUVjUbDRx99RI8ePXJVhgQfQgghhBBC2JKd9XhkNWnSJAYNGsSaNWu4cOECer2eihUr0qtXLypUqJDr/CX4EEIIIYQQwobssecjISGBli1bMmzYMEaMGJHr4VWWSPAhhBBCCCGEDdnbXA8ANzc3Ll++bHaJXVuSCedCCCGEEELYkKpoDH/2pGPHjmzbti1Py7CvV0QIIYQQQohCzl6X2n333Xc5d+4cgwYNYv/+/YSHhxMREWHylxsy7EoI8dj54rCOzw7piEuBwbU1fN5Oi7ODff1ACCGEKLzscdgVQM2aNQH4559/WLFihcXtdDpdjsuQ4EMI8Vj5+V8943/N/NL85rgeT2f4uI18HQohhLAR+4w9eO+99/J8zof82gohHitrz+pN0tb8q+fjNgVQGSGEEEWSXmufMxumTJmS52VI8CGEeKyU9jCT5m6nl6iEEEIUSvY67Co/SPAhhHisjA7R8v1feu4mpD921MA7LbQFWykhhBBFir0GH++///4jt1EUhXfffTfHZUjwIYR4rAR5K/w1zJHpB3VEJam83kRL7ZL22T1uK6qqcuNsPA7OGkqVdzO7Tdy9ZCKvJVCqqidObgX305F4JY7kq3F4NiqO1iVn9Ui5FE3qjThcG5dG42QaeKrJaSQduYlDOS8cg73N5pF2/j66O/E4NQlAccjZ+0f/723UyAQ0jYNQzAzRUOOTUY9dQalcEqWMT47KEEIUDHsNPh427EpRFFRVleBDCCGyIzFVZejGNLZeUgE4GJ7G1mcdCfK2zx+K3Iq5m8yKt//l7pVEACo08OaZKVVwdMk8KT+8OIyDCy6j16k4uWt5enJNKrYonu91PffaIa7P+hf0Ko7Fnan9c1t8WpSyen9VVbnx8k4iF/wNKjiUdiNoYzdcQzLzSDoUzu3ua9HdSQAFvEbUp/jsDpl56PREDVlP4vJTAGjLeeH3y3M41ippfT1S0kjuvwTdz38BoFQohsvWEWgqlzBso99ymrRnv4PoRNBq0LzTCYcpXa0uQwhRsOw1+NDrTedF6vV6rly5wjfffMPevXvZsmVLrsp4vC/3CSEeO/P+0BsCD4B/78PE33K+ZKC927XomiHwALh0IpqjG24bHkeExXPg20vodemvWUq8jh0fn0GXavoDlZcid9/k+ldnQJ9ej9R7yfw77EC28ojdfJnI79IDD4C0WwncGPmb0Tb3Xt6WHngAqBAz5w8Sdlw2PJ+06h9D4AGguxZD9KvZ+yFOW3LUEHgAqJfukzJuXebjNB1pLy5JDzwAdHr0UzehPxWerXKEEAVHVRTDn73TaDSUL1+ezz77jMqVK/Pqq6/mLj8b1avQuXHjBiEhIcybN++R2x47doyQkBA2btyYDzWznY0bNxISEsKxY8dynVdERAStWrXi559/tkHNsk9VVQYMGMDUqVMLpHzx+DgcrpqkHbmRvyfShUn4v3EPTbt1Jsbk+YTIVKJvJJqk56WYw3dN6/FvNGkxKVbnkXj4tmnasTuo/wU0+sRUUk6ZlpN8+Kbh3ymHTQOAVDNpD6M/fMUkTZc17Vok3Iw22UY9ctkkTQhROBWl4COr0NBQfvnll1zlUWSDj6Li7NmzzJs3jxs3buRpOXPmzMHX15euXQumW19RFF5++WU2b97M2bNnC6QO4vEQ4m/6Q9DQ//H9KvSvYrr8l39ld8O/S1XzMnne1ccRL3/XPK3XgzxDTId5uVb2QuvpaHUeriGmQ6Nc6pdA+e8OxBpXRxxrFDPZxjmktOHfjiFlTJ43l/YwmpByJmnarGkBvlDK9HVXGgZlqxwhRMGx1zucP8qxY8fQaHL3m/n4/uLaiXPnzjF//vw8DT5u377Nhg0b6NevHw4OBTcNqFWrVvj7+7Nw4cICq4Mo+kY21NAmKPPHoKIvTGvz+K521WZoOfwCXAyPA2t70rhH5sl2sfLuNH0hGOW/XwsHFw3t3qyKg1P+/nz4tS1DmeFVDY8dfJyo9u0T2boZlmfXCvgMrm54rC3uSpnZxjd4KTGvIxq/zNfD88U6uD5V3vDYtV9NXPpk5qEp7YH3Vx2z1RaH55ug7VzD8FgJ9MXp8+6Zjx21OHw7ENyd/ytEQTOxI5p6pkGLEKJwsteejyVLlpj9++qrr+jTpw8LFiygT58+uSpDJpwL1q5dC8BTTz1VwDWBTp06sWjRIu7du0fx4vk/oVXkrehkleX/qNxJUOlZWUPdktn/Uo5IVFl2RiUyCfpWUahR3DSP367o+e2qSjU/hWeqKThpM7dxc1T4qacD037XEZUEE5pqqOBrmse2Czr2XdFTq6RCnxpaHLTG29yK1bPsjzRSdCr96zhSoZjxybiqqmz+J5WDYWnUL+tAzzqOaB+4AnYnUsfWo0mgQodGLpT2y34QdO9uKkcPxKLRQpPmXvj4GX+t63Qqpw9Gc/NKEsHV3KjW0PiKuk8pZ/pNrcrBVTdwdtXSvH9ZnFyN69Hk+WBcvBy59XcMVdqWpFJoCR4UeTGWi9tv4uThQJWuAbj4OGW7LTF/R3Fj41Wc/Jwp1688jt7GeVT+ojEuQe7E/x2F/4tV8G3tb5JH/Im7RK4Pw8nfjWIDKqP1zMxD0Sj4f9Max4pepJyPwm9EHdyaGufh0iKAUmt7EjP/TxyrFcPn9RCjAEdx0ODzXVfiqhVDdzUG91cb41ivNNmhODvgvGQAKZ/sRL0di+PrrdFUN85D060u2h9eRP/DUZTGwWhfaW2Sj3o3BpYdgLhk6NcEpYrp6/FYuXATftwPbs4wMBRK+mQ/jzPXYdXv4O0GA1tBMU+bV1M8Huwt6Mjw/PPPW3yuePHiTJw4kffeey9XZWQr+EhOTmbx4sVs27aN27dv4+joSKlSpXjiiSd47bXXjLY9fPgwS5Ys4e+//yYlJYXAwED69OljEi117doVf39/xo0bx8yZM/n7779xdHSkZcuWvPbaa/j5+Rm2jY+P5/vvv+fw4cNcv36dhIQESpUqRdu2bRk2bBguLi7YkqqqrFmzhnXr1nH58mU0Gg01atRg2LBhhISEGLa7ceMG3bp1Y9iwYdSoUYP58+dz4cIFPD096dSpE6NGjTLpUdi5cyffffcdV65cwdfXl+7du1O3bl1GjRrF5MmT6dq1K/PmzWP+/PkAjBgxwrBvly5djJZCU1WVpUuXsnr1au7cuYO/vz8vvPACXbp0saqdv/76KzVq1DB6rbPmvW7dOtatW8elS5cAKFOmDG3atDHUaePGjUydOpXZs2fz559/sn79eiIjI6lUqRITJkygdu3aHD9+nNmzZ3P27Fnc3d3p27cvL730kkl5TzzxBPPnz2f37t25jqxF4RKZpNJ4mY4LUemP/3dIxw+dNTxTzfor6LfjVRot03EtNv3xB4fg5+4aulTMzOP93/VMPpAxh0NlwSmFnf00aP77IbgWo9JoYSq349O3WPa3ni39HXgyODOPN7al8tnvaYbHSyvr2DzQ2fD4wj09TWcncD8hfa7AB7+lsPMlN5oFZZ60v7I6gbm/JxseP1PPiZ+GZA5xungjjZc/iyA+KT2PxVvjmTvOlyrlrB9GFHYxic//d43k5PQ8tqyP4K2pgfiXzTzhXjb9Cn8fzpy30aJLcbq9lDlM6OqpGJZNPIMuNT2PU7/d48Wva+FTOv37VFVV1o3/kytHIwH4d8dtmgwJovnwioY8rh24w5ZRR9Cnpefx5+KL9PqxJe4lrP9OvrHpGsee34/638T2i7P/pdVvHXHyS3/d9ck6TrT8hdgT9wG4vfwSlb9qQrlXM3sQ7i07x6XBvxkmlN+aeYqaR3qh9Up/PXRxKVxqupLkv9PziF5+lrIL2uE7tKYhj5h5J7k3YpvhccLqs5Q5OBCNa/px0UcmcrfxAnQXIgBIXHYK3x964fpMZh6Pot6OJbHR56jXotLrtfw4zj+/iEOXzDx0729CNzl9HqL6w1HU9X/isPN1lP+GO6jX7kOjyXD7v7khH6xH3TIB5Unr61Gk7PsH2k+F5NT0x9PWwpHpEGz9KmRs+wO6fgyp/332P10HR6eDv+lvoxCPYq/Bx+XLpnPLFEXB19cXT0/bBOPZ6jf/5JNPmD9/PrVr12bcuHG88sorNG7cmKNHjxptt3btWkaPHk1iYiIvvPACr7/+OgEBAUybNo0vv/zSJN87d+4wcuRIypYty5gxY2jTpg2//PILI0aMICkpybDd3bt3Wb9+PTVq1OCll17i9ddfp1q1aixZsoQJEybk8CWw7L333mP69OmUK1eOMWPGMHz4cOLi4hg1ahR79uwx2f7AgQO8//77PPHEE4wbN44qVaqwdOlSlixZYrTd9u3bmThxIklJSQwbNox+/frx66+/MmvWLKPtnnzySXr27AnA0KFDef/993n//ffp1auX0XbffPMNv/zyC7169WLMmDEoisKUKVM4efLkI9t4//59rly5Qs2a5n+w3nvvPT788EMUReGFF17gtddeo1GjRuzcudNk21mzZrF792769+/PsGHDCA8PZ/To0ezevZs333yT+vXrM3bsWIKDg5k7d67ZCUvVqlXDycmJ48ePP7Luwr4sPq0aAg9IX7Tovd+zN9H7279UQ+ABkKaHKVnyiE1RmXbYOM/d11R+DcucZD7rmM4QeACk6OD9fZmrXd2NV/nycFrWLPjlvJ7fr2Zu88X+FEPgAZCYCh/uygw0rkbqmHcw2SiPlSdT+OtGZr7LtscbAg+AhGSVpTviyY4t6yMMgQdAQryeHZsjDI+vXUgwCjwAfv/lHrGRqYbH+5aHGwIPgIToNI78fMvw+PofUYbAI8OxH66RFJuZx7HZ5wyBB0D87ST+/jEsW23596O/DIEHQMKVeK4suWh4fGftFUPgkSFs6kn0aZnH+/q7Rw2BB0DS2SjuLTtneBz9wzlD4AGACrffPZT5UK8S+d4+ozJS/rxD/KrMeWgJi/80BB4A6FVi39ttdTsBUr/93RB4AJCmJ3XK1sx6xCahm7bVaB919znUX//NTPh6e2bgAZCSBu+vy1Y9ipT/rcoMPADuxsCXm7KXx5SfMgMPgPAImL3V8vZCPIS9DrtSFIWSJUsSFBRk+AsMDDQEHomJiVy9ejVXZWSr52P37t088cQTD12R6N69e3z22Wd06NCBDz/80JDet29fPvvsM5YvX07v3r0JCAgwPHf9+nXGjRvHc889Z0irUKECM2bM4McffzR0AZUtW5bNmzcb9SI888wzzJkzhwULFnD69Glq1aqVnSZZtGvXLrZs2cLbb79tdLLfv39/hg4dyueff05oaKhRd/ylS5dYuXIlZcqkX1Xs3bs3/fr146effuKFF14AIC0tjRkzZuDr68v333+Pl1f6EIg+ffrw7LPPGtWhcuXK1KlTh59//pkmTZoY9bZklZKSwpIlS3B0TL8y17ZtW7p3787KlSupV6/eQ9uZEeFmPR4ZduzYwZYtW3j66aeZOnWq0QQjc+tA63Q6Fi9ebKhH+fLlGT9+PG+99RaLFi2iRo30K5Tdu3enS5curFq1ik6dOhnl4ejoSMmSJQ29LIVBREQE7u7uODunX4GNi4tDVVXDBzElJYXY2FiKFcucqHrz5k38/f0tPr516xalSpUyvH8ehzJuxJmuMhUem70yws3kcT1WJTY2Fk9PTyKTIDHNZBP+Do+iQ3k/Q5kPuhaVhqo6oCgKd+NVUs2svHvxTiJPBKb3XFyPMt3gyv0UIP0GfbdiVFTTqhIepafOf50ON+8lmzx/637myZM1x+Pu7QSTPKIiMl+AG1ciTZ7X6yE2Mg1PX0dSUlKIvG26alXM/cwVpK6fN10hSpeiJzE6FRdPRyIiIogzk0fCnSSL7Shd2niI0c2bN0m6ZZpH0q309kVERJBw1XTVrdT7yeiTdKQ5pREbG0vqDdPgLSU83lCG9obpyl5pN+PTg46oSNwcnNHdNX1N08JjDe1wuGH6BtL996ay9vNBuJmVrMKjDWX4xSvpEe2D29yIyizjRpTJ89yIKvDPeYGVEZ4lIPxP4sVwsi6L8Kgy9OH3Ta7IJl2+Sdb+uyLxWj1GZRQkews6MpQvX56lS5canZNntWHDBp577jl0upwvUZ+tng8PDw8uXbrEhQsXLG7z66+/kpKSQvfu3YmKijL6a9myJXq9niNHjhjtkzEMJ6u+ffvi7u7Orl27DGmOjo6GwCMtLY2YmBiioqJo3LgxAKdPn85Ocx7ql19+wd3dndatWxu1IS4ujpYtW3Ljxg2TyK9169aGwAPSo8eQkBDu379PQkL6D9q///7L3bt36dKliyHwAHBzczPp0bBW3759DSf8ACVLliQwMJBr1649ct/IyPSTk6x1yZBxE5mxY8earGxgbqWDPn36GNWjfv36ANSqVcsQeED6caxZs6bFyNnb29tQr8LAz8/P8GUI6Z+DrF2PTk5ORl+GgMmX34OPS5cubRS4Pg5ldK9k+p7pUVnJVhk9Kpl+mfesrDGUEeilUP+BURbOWni2nm9mmVVM69GrhqOhjOolFKoUMy7Hwwm61MxcAapXLdP5DH3qZp7mNAjQUs7HuBxfN4VWlTI/H082NF1lqk2DzLuLW3M8GjUzHQ5Sr1FmvnWblcbFzbgefqWcKB3sYiijRqjp3KqqT2TmW7dDBRycjfMoVsEd34D0uvr5+VG+remPffCTpS2249atW0aP/f398e9segGkdKcAQxmlepRHeWDejW9bfxw8HA1l+HYPNs5AwZDm7++PZ9cK8MBbyLNbBRSNgp+fHy5e7rh2rGC8gUbBvVslQxku3avyIJceVQ1lGNXfwudD26O2SR7aHrUMZSiBfij1H5hc7uyApmPNzDJ6NDTJg+4NCvxzXmBl9GjMg1z7t8pWGZqeTU3ycHmmZf62Q8qwaRkFyV57PlRzV86ySE1NzfVqV9nq+Rg3bhyTJ0+mf//+lC1blpCQEFq2bEloaKihImFhYQC88sorFvOJiDC+QlG2bFmjk1ZIf5OVLVuW8HDj9dNXrVrFmjVruHTpksnV99hYM5c0cygsLIz4+Hg6dOhgcZuIiAiCgjKXPixbtqzJNt7e3gBER0fj5uZmaE/W/TKYS7OGpXIf/IE3J+ODb+7Ndu3aNYoXL27yYbe2HhkBTdaALOtz0dGmV/8y6pKdFWyEfWgRoDC3vYapv+u5mwi9Kyt8/WT2vsA6ltcwsw18dFhPVDL0q6rwaSvjPFZ31zJsm55dV1Wq+MGMNhpKe2S+n/pU1/BRay2fH9YRnwoDa2l4PzRzroaiKKx71omXN6Sy/6qemiUVvnraEV/XzDyGNHTgcqQTXx1IIUUHQ0Mc+b/WmQGJg1Zhw0sejFgVz+ErOuqV1fJNbzfcnLLUo5Urd6J0rN2XCCp0b+FKvzaZwYc1OnT1JSY6jf27otFoFVp38KHlk96G513ctDw/KZj1829wMyyJoGpu9B4ZgCbLxPfQAQEkxabx5457ODgpNO3lT+0nMwMSN18nuk2rze4vzxNxJYGAej60n1jNqB5NxlYjNT6NC7+E4+juQL0XKhHcJnuTsGt92BB9ko7w9ddw8nOiyvhalAjNzMO9qjc1VrTi4sRjJF2Jp1jHslSb39woj6A5oah6iFofhkMpVwLeb4RH48y7l7s2KEnZxR24887vpN6Ix7NrBcp++6RRHiUWdeLesK0kbL6IQzlP/D5qhVPNzAn2zi0C8Z7bmdipe9DfTcCld3W8v346W2116FgddWZPUj7aAVGJOPSrj9On3Y23WT2ctJeWou4+B1VK4jDjGZTSmcdW6dMY9aO+8PkWiE+Ggc3h/d7ZqkeR8t4zEBEH3+8CFycY1xWeC81eHh8PhJgE+GE/eLrCWz2gm2lQI4Q19Ha0xG7GxfwM9+/fN3uBOCoqih9//DHXQV62go/WrVuzYcMGDhw4wIkTJzhy5Ajr16+nfv36zJ49G0dHR8NJ7NSpUy2uVmTuZNkay5YtY+bMmTRt2pT+/ftTvHhxHB0duXv3LlOmTDE7FCinVFXF19eXDz74wOI2FStWNHr8sEjwUZFkblgq15oyfXx8gPQ3Xl7VQ6vN3go+MTExhnqJomV4XQ3D62pyFWC+1lDDaw0t51HBR2FnP+1DywjxV2hcRiEuBZqUUXB1NN6uegkN+150tpiHoihMbe/M1PaWt6lX1oFDY70tPq/RKIzu6cnonjmfwKfVKvQbUpJ+QyxPqq1Q04PXZ1axWA8HJw2dxlTg6VfLW3y9fALc8K/hhaOrltI1vHD3M+75cXR1oM0H9Wj9v7o5Pq6OXo40nN+cBt9aPm6lnilPqWfKW2yLYzEXKq/u8NBj7zu4Or6Dq1vcRuvngnMTf9Jux+EY5I1jTdPfMffhDXEf3jBX72NN/QC0jYPQR8SjaRIE7savqVKhBI6/jXtoGcr/dYP/6yYXbACcHWHOcJj9MuT0tXBzhkWvwsLROc9DiP/YU4/HjBkzeP/994H037exY8cyduxYs9uqqvrQc2NrZHupXW9vbzp16kSnTp1QVZWvv/6aJUuWsGfPHtq1a0e5culdxT4+PjRp0sSqPMPDw0lNTTXq/UhJSSE8PJzg4GBD2i+//EKZMmX46quvjE50f//99+w245HKlSvH1atXqV27Nm5u2bsa+TAZvQBXrpje4dZcWl7/oGQEUOYi3MDAQPbs2cP9+/et7v3IrZSUFG7fvk2bNm0evbGwW7Z4Xz8qD0vPH7yu5+kf08iY27zvmo6kNBjdyDRItqaeOa1HfstpPVOTdawcdYK4O+lzVG6fieX+pXh6fl4322XYop7WbJObPO6N+ZXYuScBSDlyi4Rtlyn37zAc/E2HyeW0vbo/w0lqN5uMyUUpv4ehxibjNLFdjsooLO+xQsEWr4W8nsIG9Hb0PurQoQMeHh6oqsqbb77Js88+S4MGDYy2URQFd3d3GjZsaHEOsrWsDj50Oh0JCQlG4+8URaFq1fSxrhlDaNq3b8/s2bOZN28eDRs2NFn+Ni4uDicnJ5ycMq/yxMfHs2rVKqPJLatWrSI+Pp7WrVsb0rRaLYqiGF3RT0tLY/HixdY2w2qdO3dm3759zJo1izfffNPk+ZyekFevXp3ixYuzadMmnn/+ecPQpISEBMP9NrJydU0fR26LnglzfH19qVChgtn5Mk8//TR79uzhq6++YvLkyUYBX15daTt79iypqakmb3ohbGXhn3p0D3QKfntSbzb4EBB28L4h8Mhw+eB9Yu8k4VnStsubFzQ1VUfcolPGaTEpxP90Bu+xjWxWTtriIzy4qkHatwfNBh9CCPukPjjBrBBr1qwZzZo1A9LPyXv37m2zBZzMsTr4SEhIoGPHjoSGhlK1alV8fX25ceMGq1evxsvLi9DQ9LGVpUqVYuLEiXzwwQf07duXTp064e/vT2RkJBcuXGD37t2sWrXKaB5AQEAA8+fP5+LFi1SvXp0zZ86wYcMGgoOD6d+/v2G7tm3bMmvWLMNyvPHx8Wzbti1P7srdrl07unbtysqVK/n3339p2bIlPj4+3Llzh7/++ovr16+zfv36bOfr4ODA2LFjeeeddxgyZAjdu3dHq9WyceNGvL29CQ8PNzqpr1mzJhqNhoULFxITE4Orqytly5a16ZuiXbt2LFiwwOTGfu3ataN9+/Zs3ryZa9euERoaiqenJ1evXuXgwYOsXLnSZnXIcODAARwcHIyCTiFsSWvm98DBfn4j8p1ibtyyYiHd3ikKaDXAA6u4aG18N3dz+Zl7Ywoh7JY9DbvKavLkyXlehtVn7S4uLjz77LMcOXKEI0eOkJCQQPHixQkNDWXo0KGUKJE5Ia9bt24EBgaybNky1q5dS2xsLD4+PgQFBTFy5EiTHoOSJUsybdo0Zs6cybZt23B0dKRjx46MHTvWcOUfYNCgQaiqyvr16/n8888pVqwY7du3p1u3biarZdnC5MmTCQkJ4eeff2bx4sWkpqZSrFgxqlWrxqhRo3Kcb8eOHXFwcOC7775j3rx5+Pn50b17dypXrswbb7xhtNpD6dKlee+99/j++++ZNm0aaWlpdOnSxabBR8+ePVmwYAFbt25l4MCBRs99+OGH1K9fn/Xr1zN//ny0Wi1lypShXbu8uUK3ZcsWWrVqJXc3F3nm5foaFv6pJzXLFLFRITY+uSxCgpsWw7usK9HhmUvhVm5VAo/izg/Zyz4pDhq8htclesYxQ5qmuCse/avbtBzHF5uQNnu/0XK6jqNbPmQPIYS9sdfgI0PG/O7o6GiTOdWKovDuu+/mOG9FzcuZ0FbIuMP5t99+W5DVKBQyJtQvWrSI2rVNl2LMSx999BGHDx9mzZo1edKTZI2MmxEuXbrUMJxPiLxw8Lqer47qiUtRGVxHS9/qEnw8TNzdZI4uv0LE5XgCGvjSsH85HJyzP0wtNTWVRYsWAek3Tn1wlcPCQNWrxMw+QcLGCzgEeePzRmMcK9v+Dte6E9dInbEHIhJweK4BDgNyN4ZaCFG4TOryh+HfH26qX4A1yZ6IiAg6d+7MkSNHDEPsM0KFjH8ripKr+3wUzFnmYy5jjeSsq0AlJCSwatUqvL29qVat2kP2zhsjRoxg27ZtbNiwIcf3G8kNVVX59ttv6dy5swQeIs81C9DQLEACDmt5lHCmzdgqBV2NfKFoFLxHN8R7tJn7aNiQtkE5tEsHPnpDIYRdUu204+ONN97gr7/+YsWKFTRp0oQKFSqwbds2ypcvz4wZMzh48KDhPnA5JcFHAQgPD2fMmDF06NCBMmXKcO/ePTZv3kx4eDgTJ04skKuBfn5+7NmzJ9/LzaAoCitWrCiw8oUQQgghbMWeVrvK6pdffmH48OH069eP+/fvA+m3UqhUqRLffPMNvXr1YuzYsfzwww85LkOCjwLg4+NDrVq12LJlC5GRkWi1WipVqsTo0aNp3759QVdPCCGEEELkgr3O+YiKiqJmzZpA+l3mIX2l2gwdOnTg7bffzlUZBR58bNy4saCrkO98fHz46KOPCroaQgghhBAiD9hr8FGmTBlu3boFgLOzMyVLluTPP/+ke/fuACarsuZEgQcfQgghhBBCFCX2OuwqNDSUHTt2MGnSJAD69evH9OnT0Wq16PV6Zs6cyVNPPZWrMiT4EEIIIYQQwobsdcL5uHHj2LFjB8nJyTg7OzNlyhT+/vtvw9K6oaGhfP3117kqQ4IPIYQQQgghbMie7nCeVe3atY1u9+Dr68uvv/5KVFQUWq0WT0/PXJchwYcQQgghhBA2pNMUreXcfXx8bJZX0XplhBBCCCGEKGCqohj+7M3Vq1cZMWIEVatWxc/Pj7179wJw7949xowZwx9//PGIHB5Oej6EEEIIIYSwIb39xRwA/PPPP7Rs2RK9Xk+TJk24cOECaWlpABQvXpz9+/cTHx/PggULclyGBB9CCCGEEELYkD32eAC8+eab+Pj4cOjQIRRFoWTJkkbPd+7cmZ9++ilXZciwKyGEEEIIIWxIj2L4syd79+5l5MiRlChRwuz9PAIDAwkPD89VGdLzIYQQQgghhA3Za8+HXq/Hzc3N4vN3797F2dk5V2VIz4cQQgghhBA2pFcy/+xJgwYN2Lx5s9nn0tLS+PHHH2natGmuypDgQwghhBBCCBvSK4rhz5783//9H1u3bmXkyJGcPn0agNu3b/Prr7/SoUMHzpw5w8SJE3NVhgy7EkIIIYQQwobsddjV008/zeLFi3nttdf49ttvARg4cCCqquLl5cWSJUsIDQ3NVRkSfAghhBBCCGFD9jbcKqtBgwbRq1cvtm/fzoULF9Dr9VSsWJGnnnpK7nAuhBBCCCFEYaPa0SpXb7/9Nv3796dOnTqGNHd3d3r27Jkn5cmcDyGEEEIIIWzInuZ8TJs2zTC/A+D+/ftotVp+++23PClPej6EEEIIIYSwIXsIOh5GVdU8y1uCDyGEEEIIIWzInud85DUJPoQQQgghhLAhnSIzGyyR4EMIIYQQQggbsreej7CwME6cOAFAdHQ0AOfPn8fHx8fs9g0aNMhxWYqal4O6hBBCiP+kpqayaNEiAIYOHYqjo2MB10gIIfJG7xeuGv69ZmFgAdbk0TQaDcoDc1RUVTVJy5qu0+lyXJ70fAghhHgoXUwySX/dx7maLw7FXQu6OkWeXlU5dkPF1wUqF9Nw6o4enQr1SskwDiHshd6OltrNuCiUXyT4EEIIYVHUynNcf3En+rhUFGct/p+2oPirdQu6WkXW+ft6Ov2YxoWI9EEJxT0U7iWmP9e4jMLmZxwo7mY/JzVCPK50dvQxHTJkSL6WJ5dRhBBCmKWLS+H6sN/Qx6UCoCbruPH6XlKuxxZwzYqu13foDIEHCobAA+DIDZUPDuR8qIMQIv/Y030+8psEH0IIIcxKPhOJPibFOFGnknjsTsFU6DFw6Lo+84GZc5ZD4TJNUwh7oFcy/4QxCT6EEEKY5VzVB43HA5PCNQqu9UoUTIUeAw39s5ypmIkzGpaWMxkh7IEexfAnjEnwIYQQwiytlzNlvm6F4vTfT4VWofQHTXEK9irYihVhX7R3oKznfw9U8HLKfK5mcYV3W2gLpF5CiOzRKYrhTxiTCedCCFFQYhJgxe9wNwZ6NYaaAYan9FtPoR68iFK3HEr3+iha668VqXo9KevPoPvjBg7NAnHsWMXskolZ6WOSiV1xBt3dBNx7Vsa5Vnrvht/zNdC4OhCz4RJuoWUp9lLNnLX1P46XtThe1HLd6ypBvYLROMnJdFY1S2rY3N+BmUf0lPZQGFxbYfYJFZ0KoxoqrL+g5048dK6kcC4SztxXaVNOoXWgXEsUojCR4VaWyX0+hBCiIETEQeN34eLt9MdaDax6DXo2Qjf2B9QvfzVsqvRpiHbVK1ZnHfvsj6T8+JfhsfOopnjM6mZxe11kEtcbLyX1QtR/dVEovbIbHr2qcOP1vdybedKwrXfvigSt7mx1XbI688Up/v3otOFxsWYlaLnuyWwFVkXdpnM6eq5KI+2/qR8abeaSnYoW1IwhHBogS0A5uZnClOYSyAlRWLQeftPw793z/AuwJoWPfOMLIURBWLg7M/AA0OnhvdWoNyJRZ/1mtKm6+jjqH1esyjbt1C2jwAMgec5hdNejLe4Ts/BUZuABoFOJeO8AqTfjuff1n0bbRq+5SMKJ7E84T0tI4/yX/xql3T94l9u/3cp2XkXZ5D06Q+CBkuVeAUqWwAOMAg+AT46qRCfLtUQhCgudkvknjBXZ4GPevHmEhIRw48aNR247ZcoUQkJC8qFWtvXyyy/TtWtXm+S1evVqWrVqRVRUlE3yy66zZ8/SqFEjjh8/XiDlC5HvrkeYSbsPN6PTA5EHqOFRVmWrNxdk6FXUm5aXx00zs3Ru2vVYUm/Gg870hDYtPM6qumSVGp2CLj7NJD3xZkK28yrKrsdmeb0tnbSYSU9Kg/uJpulCiIIhS+1aVuiCj40bN7JixYqCrkahkR+vR1xcHPPmzeO5557Dx8cnT8uypGrVqrRu3ZqZM2ciIwHFY6FbQ9O07iFQLxCCihmne7uitKpiVbaOoeVRfFyM0jQB3mjrW+72d+9WyTSteyVc6xbH8YHJ5RovJ9xbB5hs/yiu/m541/U1SlMcNZRqK8MRsupWJcvPctavwgf//cD3ZO3iUMFHTnKEKCxkwrllhTL4+OGHHwq6GoVGfrweq1atIjY2lmeeeSZPy3mUZ599ljNnznDgwIECrYcQ+eLJmvD1ECjpBQ5aeKYpzByEotWgXf8qNC6fvl2tsmjXv4ri6WpVtoq7E54bB6OtXRoAbaMAPDcMQnGwPB/ArU0gxWe1RVvSDbQKHs9UpfiX6XMxgtd3wa1JKQCca/oRvKELWk8ni3k9TIN5TUipkN774RbsTuMFzXEr656jvIqqz9s78FwtDQ4aKOYCbQLBwwncHKFNOSiVfohoXkahkk/6PqEBsLqbzPcQojBJVhTDnzAmq1095vR6PWvXruWJJ57A19f30Tvkofr161OmTBnWrFlDixYtCrQuogg7dBamr4N7sfDMEzDqaZPx8w/1658wcxPEJ8OQ1vD8k9kr/+cjMHdH+pXrJ2tBi6pwJyb9/95u6dsE+qG0qIyqgFI/CCqXfGS2utO3SProN/RXInFoVQFts3Lg5oBDowASV/xF6qjNaCr7oSlfjOTfwtAUc8WhSTkSd10FVcWlTTAuLcqiu5OAS4sANN7OADiW88CtRRlUwLVOcaK3Xef620dxKudB6f+rj1vd4lY33aWkK6lBOpRUBb/GxfGq5m12uzt/RnDyu/MkRaZQoUMZag6sgKIpuj/giakq0/anseOinsrFoIqvlmYBCsVcFZqWVXDSqqSp0DZYg5+ryq14lScDFa7HqRRzgRZlFcp4mM972T96Fp1WcdbCmAYKHcsXumuOQhRJaUX3KyvXchR8bNy4kalTp/LNN99w8uRJNm7cyP379wkKCmLo0KE89dRTJvv8888/LFy4kD/++IOEhAT8/f3p3LkzQ4YMwcEhvRpdu3bl5s301QGyzsGYO3cuISEhnD59mtWrV/PXX39x+/ZttFotlSpVYtCgQbRp0yYnTXmoe/fuMX/+fPbv38/9+/fx8fGhZcuWjBw5Ej8/P8N28+bNY/78+axevZrNmzezefNmIiMjCQ4OZtSoUSYn0klJScyePZtt27YRFxdH5cqVeeWVV/jll1/YtGkTx44ds+r1yHD37l1mzJjBwYMHSUlJoX79+rzxxhsEBQU9so1///03N2/e5Pnnn7f4GixatIj9+/dz584dPDw8qFy5MoMHD6Zp06ZA+tyTmzdvMm/ePL744guOHTuGoii0atWKN998ExcXFxYvXsy6deu4d+8e5cuX54033qBevXpGZSmKQtOmTVm/fj0JCQm4ubk9sv5CZMuZ69BmMiT9d9fuff+krzr1npW9fr//Cx3/lzknY/dpSE6F4abfeWZtOAa9Pst8vOMv0jugNbD/bHpdJvdG1/1r2HceAPXwZXS7/kV7+n2LvRf6W7HEtZyDGpU+6F/3+xX0KKhoSTl8A/W/Tm79wevoycxDXXeWNBwAhaQdYejQoKIhaX84uvuJFJvSnCs9NhG/N33uXOLh26hAGg7EoxC95So1z/THqYx1vRcnxx7FfVd6UHP92hXu7r5N+8NdcPTMvJFh1KVYNg7ejy45/TW+dew+SVEphIypblUZ9uiF9an8eFoHwMHrAHrQKKAorDuLYWWrnVd1hkD5QLg+fe6HRuHwLZVT9/Rs6Gn8/lh0Ss8L2zLnD20LU9nZF1mWV4h8kCY3F7QoVz0fX3/9NYmJifTp0wdID0omTZpESkqK0UTo/fv388Ybb1CuXDkGDhyIl5cXp06dYt68eZw7d45PPvkEgPHjxzNr1iyioqIYN26cYf/y5dOHH+zevZuwsDDatWuHv78/0dHRbNq0iTfeeIMPPviAjh075qY5Rm7dusXQoUNJTU2le/fuBAQEcO3aNdasWcOxY8dYunQpHh7Gl5qmTJmCg4MDAwcOJDU1lR9++IEJEyawdu1aypQpY9jurbfe4sCBA7Ru3ZrGjRtz48YN3njjDaNtrHk9ABITExk2bBi1a9dm1KhRhIeH8+OPPzJ+/Hh++ukntNqHd8VnTPCuWdN07f4bN27w4osvEhERQadOnahRowaJiYmcOnWKI0eOGIKPjHqMHDmSBg0aMHr0aP755x82bNhAcnIyPj4+nD59mmeeeYa0tDSWLVvGuHHj2LhxI+7uxictderUYe3atZw8eZInnnjioXUXItuW7M4MPDLM22598PHdr6aTwedttz74mLfDTGKWsfvzdqI+08wQeBicvYW65yxK2xpms01d+ach8MigoKJHNVohSf/ASFsF0KA3BCQKqqE2MfP+xL1/dUPgkXUf5b989TEpRP5wnlLj61locKaUyGRubrpulJZ8J4mbW64T+Ezmd9r5DdcMgUeGMyvDimzwEZmosvJvnekTKpkTy1VMltY1pP9n40WVm3Eq/h6Z23z7l/HrqFfhu1MqrQNtUXMhxMOkSuxhUa6Cj6ioKH788UfDSXifPn3o378/M2bMoH379ri4uJCcnMz//vc/atWqxZw5cwy9HL1796Zy5crMmDGDY8eOERISQuvWrVmxYgXJycl06tTJpLwXX3yR0aNHG6X179+f5557jgULFtg0+Jg+fTppaWksX76cUqVKGdLbtWvH0KFDWb58OcOHDzfax8fHhxkzZhhu5hUSEsKQIUNYu3atod779+/nwIED9OjRg3feecewb0hICGPHjjXK71GvB6Qfg0GDBjFkyBBDmq+vL1999RVHjhyhWbNmD23n5cuXAQgIMJ1AOm3aNO7evcvXX39tko9eb/yjFhUVxeDBgxk8eLAhLTY2ll9//ZVq1aqxaNEiw7EvX74848ePZ+vWrfTu3dson4x6XLp0qdAEHxEREbi7u+PsnH7FNi4uDlVV8fRMvw1xSkoKsbGxFCuWOUn45s2b+Pv7W3x869YtSpUqZXivSBn5VIa5oTsaxfoyzO6vsb4dGuWh18L0CigaC1elNRrLZeTFkCSNYtVQp4SkRJKTkx95PDy1nihK1vAmnaJRjNplrkwV1aoy7PG9G5eoothgBLRCemyStQyzh0/Vc/9+lF2+VlKGlJHdMgpSqsz1sChXfa99+vQxuvrv4eFB7969iYmJMVxRP3z4MPfv36dr167ExcURFRVl+GvevLlhG2u4umZOuExKSiIqKoqkpCQaNWrE5cuXiYvL/vKP5sTFxbF//35CQ0NxdnY2qnOZMmUICAgwW+f+/fsb3UW4Zs2auLm5cfXqVUPavn37ABgwYIDRvi1atDDq0bCWRqOhf//+RmmNGjUCMCrXksjISLRarUkvTnR0NAcPHuSJJ54wG8BoHjhB0mq19OvXzyitXr16qKpK7969DYEHpM/tALh27ZpJvt7e6WPAIyLMLENaQPz8/AxfhpD+Ps/4MgRwcnIy+jIETL78HnxcunRpo/eKlJFPZTzfBtycjZ5j1NPWl/Fyh/TJ4Vm90tH6drzylJn5JZmPNaOfQqlaGqXtA1f5a5ZBaVXFYhmO/eqiFDMepqii/Pdf5sm+Bv0D2xj3hmTtJfEeVR/nKr54tCtnsk/GdlpfZwJeqmvV8XDycaJMD+O8XPxd8X86wKhdVXoE4uBm/BrXGlCxcL+vclFGuRKeDKhjpodaeeDfZla4yrpNr8oKpd0VozJeqffA97QCI+s72O1rJWVIGdktoyClZvkTxnJ1uSU4ONgkLeMEOjw8HMi8sv7+++9bzOf+/ftWlRcREcGcOXPYs2eP2ZPTuLg4k5PonAgLC0Ov17N+/XrWr19vdpuyZcuapJnrPfD29iY6OnPd/Rs3bqDRaChXrpzJtkFBQYbXy1olSpQw+rBmlAkYlWuJYiEyv3btGqqqUrVqVavqUbx4cZN6eHmlL9H54HCyjHRz9ctYZtdSvYTIlcplYN8H8MVGuBuTPuH8xXbW79+4Mvw2Fb7+BeKTYEgbeKa59fs/XR9++T+Yuz39ZLJ9HTh08b+6NIUX0+euadaOQv/pVjhwAeoHonnracs9IoCmhAceB14hefoe9Fci0baugC4iGd3Jm7g0CkB1cSJ1/xW0VYujlC9G8s7L6RPOmwaQuPMqqODSLpjEw7fQ3U3Eo29VvF+qA0DQ2s7cnX6cuD3huNQvjrakB7G7b+JUzoNSb9TDsZT1c7PqfN6Qs3HncTqvpXKLKlR7vRYO7sY/Q16B7nRb3pK/Fl0g6X4KFTqWoWqfR89fs2fzuzpSo4TC9ot6qhRTqOinYdslHcVcFVoEatgRpidNDx0raDlyW+V2PLQLVrgaq3ImQqFNOYXxIabfmQNqaHB1gIWnVVy0MLq+QvOy8t0qRH5IkPMYi/J8tauMk8nXXnuNKlXMr1NfokQJq/IZPXo0ly9fpn///tSoUQMPDw80Gg0bN25k69atJkOBcuvpp5+mS5cuZp978EQbTHsDstY9r1gq09pyfX190el0uQ7cHlaP7LwuMTExhnoJkScaVIRlY3O+f8sa6X851bFe+l+G0aabKF6uaP/XM1vZaquWxG1BX6u29Xwjc0ij16uNDf/2ftV02+SzkcSsv0TSqfuk3Yin7OzW+E/K2U1Ztc5aElulkNgKag9tgKOjo9ntilf34cnp9nfj15xyclB4q4Ujb7VInwPywsY0fgsDD0eVKzF6Tt5Nn69R1lNlwdNanLTWn9T0qqKhl3W3iBFC2FCixB4W5Sr4CAsLM0nLuHKf0TMQGJg+s83V1ZUmTZo8Mk9LV7zPnz/PuXPnGDZsmMlci3Xr1mWj1o8WEBCAoiikpaVZVefs8Pf3R6/Xc+3aNZNhVleuXDHZPq97ACpWrAikD9GqUSPzhKpcuXIoisLZs2fztPwHZQzFyqiXEKLgqGl6rvTcTOr19CGtKRejudLrF6pffwGtj+kFGJF743akse5s+oW0mFQ4dDPzufkn9QR5K0xqLvf0EKKwS5HVrizK1ZyP1atXG82ziIuLY82aNXh6etKwYfrde5s1a4afnx+LFy82O8wmKSmJ+Ph4w2M3NzdiYmJMropnXD1/MP3ChQvs3r07N80w4ePjQ/Pmzfntt984deqUyfOqqhIZGZmjvENDQwFM7lq+f/9+s0OuLL0etpJxnB5sp7e3N0888QS///672fkteVWfU6dOodVqqVu3bp7kL4SwXuKf9wyBRwZ9fCpxe8MLqEZF3+bzWXrwzZy7bL5g2x5+IUQeUbL8CSO56vnw8fFhyJAhhmV1N27cyK1bt3jnnXdwcXEB0ns8pk6dyoQJE+jduzfdunWjXLlyxMbGEhYWxq5du/j0008N962oVasW+/btY/r06dSpUweNRkOjRo0oX748FSpUYMmSJSQlJREUFMTVq1dZu3YtlSpV4syZM7l8KYxNnDiRl156iWHDhtG5c2eqVq2KXq8nPDycvXv30qlTJ5MeGGs0b96cZs2a8fPPPxMVFWVYanft2rVUrlyZ8+eNl9i09Hpkvc9IblSvXp2yZcty4MABkwnjb775Ji+88AJjxoyhS5cuVK9enaSkJP7++2/8/f0ZM2aMTeqQQVVVwyR3uceHEAXPsaw7OGggzfiE1ynI08IeIreCfRTuJvx3cSfrcrsZz3vLmYwQdkHmfFiUq+Dj1Vdf5eTJk6xatYqIiAgCAwPN3m+jWbNmfP/993z//fds2bKFyMhIvLy8CAgIYMCAAVSuXNmw7YABAwgPD2fnzp2sWbMGvV5vuKnel19+ycyZM9m0aROJiYlUrFiRKVOmcO7cOZsHH6VLl2bZsmV8//337Nmzhy1btuDk5ESpUqVo2bIl7du3z1G+iqIwffp0w00Gf//9dypVqsRnn33GqlWrTFaosvR62Cr4UBSFXr16MXv2bO7fv2+0kkTZsmVZunQp3333HQcOHGDz5s14eXlRuXJlevbM3nh0a5w4cYKbN2/y1ltv2TxvIUT2OZZ2p8T4+tz95LghzWdAVVzrPnqensiZD1o70PWnVFJ0gJq+QpXuv1jExwXefkJuECiEsG+KmoPxMxl3OH/wTtsid/r160daWhpr1qzJ13Lj4uLo1asXPXr04JVXXsnXsrOaMGECt2/fZsmSJbLalRCFSNzecBJ+v4lL3eJ4dgzK8eczNTWVRYsWATB06FCLE84fd5ciVX7+V4efq0KrIJh5VEWnh7ebayjrKcGHEPZAGR9l+Lf6uU+B1aMwyvPVroSppKQkw7C0DPv37+fixYs884yVd1q2IQ8PD15++WW+/vprnnvuOXx8fPK9Dv/++y979uxh7ty5EngIUch4hJbFI9R0eXGRNyr4Koxv5sCdeJU2y9P45176NcIdYXp+e86RAC/5jhSi0JNzGYsk+CgA3333HWfPnqVhw4Z4eHhw7tw5NmzYgLe3t9GdyvNTnz596NOnT4GUDVCtWjWOHj1aYOULIURhM+2gzhB4AJyPgA8O6Jj7tPx0C1HoSexhkXyDFYB69erx559/snTpUuLi4vD29ubJJ59k5MiRlCpVqqCrJ4QQohD487bpqOiTZtKEEIWQBB8W5Sj46Nq1q2GFK5F9LVq0oEWLFgVdDSGEEIVYi3IKv11RTdKEEPZAPquWyMw1IYQQohB6o6mWDuUzT2BaBSq8IzcYFMI+yH0+LJJhV0IIIUQh5OGksO1ZRy5EqOhUqFpMzmKEsB/yebVEgg8hhBCiEKvkJycxQtgd+dhaJMGHEEIIIYQQtiTBh0USfAghhBBCCGFTEn1YIsGHEEIIIYQQtiSxh0USfAghhBBCCGFLcodzi2SpXSGEEEIIIUS+kJ4PIYQQQgghbEk6PiyS4EMIIYQQQgibkujDEgk+hBBCCCGEsCWZ2GCRBB9CCCGEEELYlPR8WCLBhxBCCCGEELYksYdF0ikkhBBCCCGEyBfS8yGEEEIIIYQtSc+HRRJ8CCGEEEIIYUtyk0GLZNiVEEIIIYQQIl9Iz4cQQgghhBC2JB0fFknwIYQQQgghhE1J9GGJBB9CCCGEEELYksQeFsmcDyGEEEIIIUS+kJ4PIYQQQgghbEl6PiySng8hhBBCCCFEvpCeDyGEEEIIIWxJ7vNhkQQfQgghhBBC2JLEHhZJ8CGEEEIIIYQtSfBhkQQfQgghhBBC2JREH5ZI8CGEEEIIIYQtSexhkax2JYQQQgghhMgX0vMhhBBCCCGELUnPh0XS8yGEEEIIIUQ+mzJlCh4eHgVdjXwnPR9CCCGEEELYkvR8WCQ9H0IIIYQQQoh8IcGHEEIIIYQQtqQomX85dOrUKZ566inc3d3x9vamT58+XL161fD8iy++SMuWLQ2P7927h0ajoVGjRoa0uLg4HB0dWbVqVY7rYWsy7EqIh1BVldjY2IKuhhBFQmpqKomJiQDExMTg6OhYwDUSQhR1np6eKLkIAHIsl0Veu3aN0NBQKlasyLJly0hKSmLSpEm0atWKv/76C09PT0JDQ1m+fDlJSUm4uLiwd+9enJ2d+eOPP4iNjcXT05Pff/+dtLQ0QkNDbdMuG5DgQ4iHiI2Nxdvbu6CrIUSRM3bs2IKughDiMRAdHY2Xl1e+l6tOyN0p9owZM0hNTWX79u34+fkBUL9+fWrUqMHixYt59dVXCQ0NJTk5mcOHD9OqVSv27t1Lz5492b59OwcOHKBjx47s3buXKlWqUKpUKVs0yyYk+BDiITw9PYmOjs6z/OPi4ujcuTObN29+7Fa8kLZL26Xtjw9pu7S9oNru6elZIOXm1r59+3jyyScNgQdAtWrVqFu3Lvv37+fVV1+lfPnyBAQEsHfvXkPwMWLECBITE9mzZ48h+ChMvR4gwYcQD6UoSp5eMdFoNGi1Wry8vB67HyVpu7Rd2v74kLZL2x+3tudWZGQk9erVM0kvVaoUERERhscZQUdMTAx//vknoaGhxMfHs3r1apKTkzly5AjDhg3Lx5o/mkw4F0IIIYQQohDx8/Pjzp07Jum3b9826g0JDQ3l4MGD7N69m+LFi1OtWjVCQ0M5evQou3btIjk52WhSemEgwYcQQgghhBCFSIsWLdi5cyeRkZGGtLNnz/LXX3/RokULQ1pGT8cXX3xhGF5Vr149XF1dmTZtGuXKlSM4ODi/q/9QMuxKiALk5OTEsGHDcHJyKuiq5Dtpu7T9cSNtl7Y/bh7ntltLp9OxevVqk/TXXnuNRYsW0aFDByZNmkRSUhLvvPMOgYGBPP/884btqlWrRsmSJdmzZw9fffUVAFqtlubNm7NlyxYGDBiQX02xmqKqqlrQlRBCCCGEEOJxMmXKFKZOnWr2uaVLl1KnTh0mTJjAgQMH0Gq1tG/fni+++IKgoCCjbfv27cvq1as5efIkdevWBeCTTz5h4sSJzJs3j5dffjnP25IdEnwIIYQQQggh8oXM+RBCCCGEEELkCwk+hBBCCCGEEPlCJpwLkYf27t3LnDlzuHLlCqVLl+b555+nW7duD91n3rx5zJ8/3+xzvXr14u23337odhMnTqRPnz65r3wu5aTtN27cMLtNrVq1WLx4sVHan3/+ycyZMzl37hy+vr706dOHIUOGoCiKLZuRIzlp+99//83q1av5448/uHv3LiVLlqRt27a8+OKLuLq6GrYrLMc9LCyM6dOn89dff+Hu7k6nTp145ZVXcHR0fOh+qqry/fffs2rVKqKioqhSpQrjxo2jdu3aRtvdvXuX6dOnc/jwYRwcHGjTpg2vv/56obhXQE7afu/ePZYvX87hw4e5fv06Hh4e1K9fn9GjR+Pv72/Y7tixY4wYMcJk//bt2/Pxxx/nSXuyI6fHvWvXrty8edMk/cCBAzg7OxseF7Xjbul4AgQFBbFmzZqHblcYjvu1a9dYunQpp0+f5uLFiwQFBbFy5cpH7lcUPusib0jwIUQeOXnyJG+88Qbdu3dn/PjxHD16lP/973+4ubnRrl07i/v16NGDJ554wijtxIkTfP311ybpzs7OzJ071yitbNmytmtEDuW07RlGjRpFSEiI4bGbm5vR89euXePVV1+lSZMmjBw5kvPnzzNr1iy0Wi2DBg2yeXuyI6dt37FjB9euXWPw4MEEBgZy6dIl5s2bx+nTp02OcUEf95iYGEaMGEFgYCCffvopd+7cYcaMGSQlJfHWW289dN/vv/+eefPmMXr0aCpXrsyqVasYPXo0y5cvJyAgAIC0tDRGjx4NwAcffEBSUhJffvkl77zzDjNnzszr5j1UTtt+5swZdu3aRbdu3ahduzZRUVF89913DBkyhJ9++glfX1+j7SdPnmy0PKaPj08etch6uTnuAG3btmXgwIFGaVlXQSqKx71atWosWrTIKC0+Pp4xY8aYfJ9D4TzuFy9e5MCBA9SsWRO9Xo9er7dqP3v/rIs8pAoh8sSoUaPUoUOHGqW9/fbbap8+fbKd1+TJk9U2bdqoKSkphrS5c+eqLVq0yHU980JO2x4eHq42bNhQ3bFjx0O3++CDD9QuXboYvR6zZs1SW7durSYnJ+e84jaQ07ZHRESYpG3ZskVt2LCh+s8//xjSCsNxX7hwodqiRQs1KirKkLZmzRq1cePG6p07dyzul5SUpIaGhqqzZs0ypKWkpKhdunRRP/74Y0Pali1b1JCQEPXy5cuGtIMHD6oNGzZUT506ZdvGZFNO2x4TE6OmpqYapd26dUsNCQlRly5dakg7evSo2rBhQ/Xvv/+2feVzKadtV1VV7dKlizpt2rSHblMUj7s5GzZsUBs2bKiePn3akFaYj7tOpzP8e/LkyWrfvn0fuU9R+KyLvCNzPoTIAykpKRw7dszkSneHDh24fPkyN27csDqv5ORkdu3aRdu2bR85tKEwsGXbLfn9999p3bq10evRoUMHYmNj+euvv3Kdf07lpu0PXvkGqFq1KpA+LKEw+f3332ncuDHe3t6GtPbt26PX6zl06JDF/f766y/i4+ONXh9HR0fatGnDgQMHjPKvXLmy0RXgJk2a4O3tbbRdQchp2z09PXFwMB5sUKpUKXx9fQvd8bUkp23PTv5F7bibs3XrVgIDA6lZs6atq5knNJrsnyoWhc+6yDsSfAiRB65fv05aWprJXUXLly8PpI8dtta+ffuIj4/nqaeeMnkuOTmZdu3a0aRJE/r27cvPP/+cm2rbhC3aPm3aNBo3bkz79u354IMPiI6ONjyXmJjI7du3TdY5Dw4ORlGUbL22tmbL4w7pQ7gAk/wK+riHhYWZ1MnT05PixYs/tI0Zz5l7fW7dukVSUpJhuwePr6IoBAUFFejxhZy33ZwrV64QERFheH9k9dprr9G4cWM6derEl19+aXhtClJu275161aaNWtGy5YtGTNmDBcuXDDJv6gf9/v373Ps2DGz3+dQOI97ThSFz7rIOzLnQ4g8EBMTA6T/OGXl5eVl9Lw1tm3bRsmSJWnQoIFRerly5Xj11VepWrUqKSkpbN26lQ8//JC4uLgCnfeQm7Y7OTnRp08fmjZtiqenJ6dPn2bhwoX8888/LFmyBAcHB2JjY83m7+joiIuLS7ZeW1uz5XGPiori22+/pVWrVgQGBhrSC8Nxj4mJMWkjpLf7YW2MiYnBycnJaIJxxn6qqhIbG4uLiwuxsbFm8/fy8irQ4ws5b/uDVFXls88+o0SJEkYnoh4eHgwePJgGDRrg7OzM0aNHWbZsGZcvXy7wMfC5aXtoaCi1atWidOnShIeHs3DhQl588UWj8f+Pw3HfsWMHOp2Ojh07GqUX5uOeE0Xhsy7yjgQfQlgpLi6Oe/fuPXI7W078jY2N5cCBAzzzzDMmXd+dOnUyetyiRQtSU1NZsGABzz77rMkQj9zIr7YXL16ciRMnGh43bNiQihUrMnbsWHbt2kX79u1zlX9OFMRxT0tLM6xq9n//939Gz+XncRd559tvv+XIkSN8/fXXRquZVatWjWrVqhkeN2rUiOLFizN9+nROnz5NrVq1CqK6ufbGG28Y/l2/fn2aNm1K7969WbZsmdFnvqjbsmUL1atXN7naX1SPuxDmyK+UEFb69ddf+eCDDx653erVqw1XuuPi4oyey7iSk/H8o+zcuZOUlBSTq2SWtG/fnp07d3Lt2jWzQzlyqiDanqF58+a4urpy5swZ2rdvb7hK9mD+qampJCUlZTv/R8nvtquqytSpU/n777+ZP38+xYsXf+Q+eXXcLfHy8jJpI6QHyw9ro5eXFykpKSQnJxtdEY2NjUVRFMOx9fT0NJt/TEwMpUqVskELci6nbc/q559/Zv78+bz77rs0btz4kdu3b9+e6dOn8++//xboSagt2p6hePHi1KtXjzNnzhjSivpxv379On///Tevv/66VdsXluOeE0Xhsy7yjgQfQlipR48e9OjRw6ptU1JScHBwICwsjGbNmhnSLY2DtWTbtm0EBwcbXRErCAXRdktcXV0pVaqUyXjgK1euoKpqrvN/UH63febMmfz66698+eWXVKlSJQc1znvBwcEmr39GD9HD2pjx3JUrV4zaFhYWRunSpXFxcTFs9+B8AFVVuXLlCk2aNLFJG3Iqp23PsGvXLqZNm8aIESPo3r173lQyj+S27dbkX1SPO6TPedFoNBbnexQlReGzLvKOTDgXIg84OTkREhLCzp07jdJ37NhB+fLlKVOmzCPzuHfvHsePH7e61wPSgxVPT0/KlSuX7Trbii3antW+fftITEykRo0ahrQnnniCvXv3kpaWZkjbvn07np6e1K1bN3cNyIXctn3x4sWsWLGCyZMnW3VFPEN+H/cnnniCI0eOGObfQHoPkUajoWnTphb3q1OnDu7u7vz666+GtLS0NHbt2kXz5s2N8j9//jxXr141pB05coTo6Gij7QpCTtsO6TeSmzRpEj169OCll16yusxt27YBGH0GCkJu2v6gu3fvcvLkSZPPdVE87hm2bdtGw4YNrerNzNgeCv6450RR+KyLvCM9H0LkkZdeeonhw4czbdo02rVrx/Hjx9m6davJ3WqbNGlC586dee+994zSt23bhl6vtxh8DBw4kC5duhAcHExSUhJbt25l165djB8/vsDH/ee07TNmzECj0VCrVi08PT35+++/Wbx4MTVq1KB169aG/QYPHszWrVt5++236du3LxcuXGDp0qVW3Wk5r+W07Vu3bmXWrFk8/fTTlC1bllOnThm2DQgIMCzFWxiOe+/evfnpp58YP348L7zwAnfu3OHLL7+kV69elChRwrDdyJEjuXnzJuvWrQPSb444dOhQvv32W3x9falUqRKrVq0iOjra6OZz7dq1Y9GiRbz55puMGjWKpKQkZs6cSYsWLQp8+ElO23758mUmTJhAuXLl6NSpk9Hx9fX1NUy6fvfddwkICKBatWqGiccrVqygdevWBX4SmtO2b926lf3799O8eXNKlCjB9evXWbx4MVqttsgf9wz//vsvly9fZsCAAWbzL8zHPSkpif379wNw8+ZN4uPjDUFFw4YN8fX1LZKfdZF3JPgQIo/Uq1eP6dOnM2fOHNavX0/p0qV55513TO4BodPpzN4xdtu2bdSsWdNwUvKgcuXKsWLFCu7fvw9ApUqV+N///sfTTz9t+8ZkU07bXr58eVavXs3atWtJSkqiZMmSdOvWjeHDhxudWJcrV45Zs2YxY8YMXnvtNXx9fRk+fLjJ3ZMLQk7bnnGvgC1btrBlyxajbSdPnkzXrl2BwnHcvby8mDNnDp9++injx4/H3d2dHj168Morrxhtp9Pp0Ol0RmlDhgxBVVWWLVtGZGQkVapU4euvvzZ6nzs4OPD111/z6aefMmnSJLRaLW3atGHcuHH50r6HyWnbT58+TVxcHHFxcbz44otG23bp0oUpU6YAUKFCBbZs2cLy5ctJSUmhTJkyDB06lKFDh+Z52x4lp20vW7Ysd+/e5fPPPzesbtSoUSOGDx9utFBDUTzuGbZt24aTkxNt27Y1m39hPu4REREmiwJkPJ47dy4hISFF8rMu8o6iqqpa0JUQQgghhBBCFH0y50MIIYQQQgiRLyT4EEIIIYQQQuQLCT6EEEIIIYQQ+UKCDyGEEEIIIUS+kOBDCCGEEEIIkS8k+BBCCCGEEELkCwk+hBBCCCGEEPlCgg8hhBBCCCFEvpDgQwghConnn38eRVEKuhpA+h25HRwc2LFjhyFt9+7dKIrC4sWLC65iolBYvHgxiqKwe/fuHO0v7yXzTp48iUajYc+ePQVdFSHyjAQfQog8denSJV5++WWqVauGm5sbvr6+VK9enSFDhrBr1y6jbYODg6lVq5bFvDJOzu/du2f2+TNnzqAoCoqisG/fPov5ZGyT8efi4kLlypUZN24cEREROWtoETNu3DiaN29O+/btC7oq+SIsLIwpU6Zw8uTJgq6KyCdRUVFMmTIlxwFUTj3svVavXj169OjB+PHjUVU1X+slRH5xKOgKCCGKrmPHjtGqVSscHR0ZPHgwNWvWJDExkfPnz7N9+3Y8PT1p06aNzcpbsGABnp6euLq6snDhQlq2bGlx23r16jF+/HgAIiIi+OWXX5gxYwY7duzg+PHjODk52axe9ubgwYPs2LGDdevWGaWHhoaSmJiIo6NjwVQsD4WFhTF16lSCg4OpV69eQVdH5IOoqCimTp0KQOvWrfOt3Ee918aOHUurVq345Zdf6Ny5c77VS4j8IsGHECLPTJ06lYSEBE6ePEndunVNnr9165bNykpNTWXp0qX07dsXb29vvv32W7766is8PT3Nbl+2bFkGDhxoeDxmzBi6du3Kpk2bWL9+PX379rVZ3ezN7NmzKV68OJ06dTJK12g0uLi4FFCthHg8tGzZkuDgYObOnSvBhyiSZNiVECLPnD9/nmLFipkNPABKly5ts7I2btzInTt3GDJkCM8//zzx8fH89NNP2crjqaeeAuDChQsWt5kzZw6KorBhwwaT5/R6PQEBAUZXM7dv306/fv2oUKECrq6u+Pj40KFDB6vHdLdu3Zrg4GCT9LCwMBRFYcqUKUbpqqoyZ84cGjZsiJubGx4eHrRp08ZkiJslaWlprFu3jnbt2pn0cJgbp581bfbs2VStWhUXFxdq167Npk2bADh16hQdO3bEy8uLYsWKMWbMGFJTU82289KlS3Tv3h1vb2+8vLzo2bMnly5dMtpWr9fz4YcfEhoaSunSpXFyciIwMJCRI0dy//59s+1as2YNrVu3xsfHBzc3N6pWrcqYMWNISUlh8eLFhh64oUOHGobjWXM1PCwsjEGDBlGqVCmcnZ2pWLEib7/9NgkJCUbbTZkyBUVROHv2LG+//TYBAQE4OztTt25dfvnll0eWA5nzLHbu3Mn7779PUFAQrq6uNGnShEOHDgGwZ88eWrRogbu7O/7+/vzvf/8zm9e6deto3rw57u7ueHh40Lx5c9avX2922/nz51OtWjWcnZ2pVKkSM2fOtDgkKDo6mrfeeotKlSrh7OxMiRIlePbZZ02OYXZZ+zo/bN6Uoig8//zzQPr7tnz58kD6RZKMY57xWcv6+frhhx+oU6cOLi4uBAYGMmXKFNLS0ozytvZzas17TVEUnnrqKbZu3UpcXFw2XykhCj/p+RBC5JmKFSty9uxZ1q5dS69evazaR6fTWZzTkZycbHG/BQsWUL58eVq2bImiKNSvX5+FCxfy0ksvWV3f8+fPA1C8eHGL2/Tv35/XX3+dJUuW0K1bN6Pndu7cSXh4uGE4F6SfbERERDB48GACAgIIDw/nu+++o23btuzateuhQ8NyYtCgQfzwww/06dOHoUOHkpyczPLly2nfvj1r1641qfODjh8/TlxcHI0bN85Wud988w2RkZG89NJLuLi48NVXX9GzZ09WrVrFsGHDePbZZ+nRowfbt2/n66+/pmTJkrzzzjtGecTHx9O6dWuaNGnCxx9/zPnz55k9ezaHDh3ijz/+MASrKSkpfPrpp/Tu3Zvu3bvj7u7O0aNHWbBgAfv37zcZNjdp0iQ++ugjatSoweuvv46/vz8XL15kzZo1vP/++4SGhvL222/z0Ucf8fLLLxuOSalSpR7a5itXrtC4cWOio6N55ZVXqFy5Mrt37+bjjz/mwIED7Ny5EwcH45/ZIUOG4OjoyIQJE0hJSWHmzJn06NGDc+fOmT15NWfixInodDpee+01UlJS+Pzzz+nQoQNLlizhxRdf5OWXX2bAgAGsXLmS9957j/Llyxv18s2ePZtRo0ZRrVo13nvvPSD9fdqjRw/mzZvHyy+/bNh25syZvP7669StW5ePPvqIhIQEPvvsM0qWLGlSr+joaJ544gmuXr3KCy+8QM2aNbl58yazZ8+mSZMmHDt2jKCgIKvamNvX+VGqV6/OjBkzeP311+nZs6fh+8nDw8Nouw0bNnDp0iVGjRpF6dKl2bBhA1OnTuXKlSssWrQo222x9r3WrFkz5s2bx/79++nYsWO2yxGiUFOFECKP/P7776qjo6MKqJUrV1aHDh2qzp49W/3nn3/Mbh8UFKQCj/y7e/eu0X7h4eGqVqtVJ0+ebEibOXOmCpgtC1A7dOig3r17V71796567tw59YsvvlAdHR1Vb29v9fbt2w9tV58+fVRnZ2c1IiLCKH3gwIGqg4OD0f5xcXEm+9+6dUstVqyY+vTTTxulDxkyRH3wa7lVq1ZqUFCQSR6XL19WAaM2r127VgXUefPmGW2bmpqqNmzYUA0ODlb1ev1D27Zw4UIVUNevX2/y3K5du1RAXbRokUlamTJl1KioKEP6n3/+qQKqoijqmjVrjPJp0KCBWrp0aZN2Auprr71mlJ7RpuHDhxvS9Hq9mpCQYFK/7777TgXUn376yZB2+PBhFVDbtGmjJiYmGm2v1+sNr4e5tj3Kc889pwLq5s2bjdInTJigAup3331nSJs8ebIKqJ07dzY6BkeOHFEBdeLEiY8sb9GiRSqg1q9fX01OTjakr1+/XgVUBwcH9ejRo4b05ORktXTp0mrTpk0NaREREaq7u7tasWJFNTo62pAeHR2tVqhQQfXw8FAjIyNVVVXVyMhI1c3NTa1evboaHx9v2PbatWuqu7u7Cqi7du0ypI8ZM0Z1cXFRT548aVTvsLAw1dPTUx0yZIghLTuvd3ZeZ3OfoQyAUR3MfYYefE6j0ajHjx83pOv1erVHjx4qoB48eNCQnp3PqTVt37dvnwqon332mcVthLBXMuxKCJFnmjVrxvHjxxkyZAjR0dEsWrSIV155hRo1ahAaGmp2KEZwcDA7duww+9ehQwez5SxevBi9Xs/gwYMNaQMGDMDR0ZGFCxea3Wf79u2UKFGCEiVKUKVKFcaNG0eNGjXYvn272au6WQ0ZMoTk5GSjYV1xcXH8/PPPdOzY0Wh/d3d3o23u37+PVqulSZMmHD58+KHlZNeyZcvw9PSkR48e3Lt3z/AXFRVF165dCQsLM/TuWHL37l0A/Pz8slX2888/j7e3t+FxnTp18PLyokyZMia9Xi1atODWrVtmh5RMnDjR6HHPnj2pWrWq0eR3RVFwdXUF0nvKoqKiuHfvHk8++SSA0eu6fPlyAD7++GOT+SoZQ15yQq/Xs2HDBurXr28yN+b//u//0Gg0/Pzzzyb7vfbaa0ZlNmrUCA8Pj0cel6xGjhxp1LOTcfW8SZMmhISEGNKdnJxo3LixUd47duwgPj6eMWPG4OXlZUj38vJizJgxxMXF8euvvwLpn5GEhARGjRqFm5ubYduAgAAGDBhgVCdVVVm+fDmhoaGULVvW6P3n7u5O06ZN2b59u9VtzJDT19lW2rdvT4MGDQyPFUXhzTffBMjTcosVKwbAnTt38qwMIQqKDLsSQuSp2rVrG+YIXLlyhT179vDdd9+xb98+unfvbjJExt3dnXbt2pnNa9myZSZpqqqycOFC6tSpg16vN5qv0bx5c5YuXcrHH39sMiyjSZMmfPDBBwA4OzsTFBREYGCgVW3KCDCWLFnCiBEjgPQ5BfHx8UYBEMDFixeZNGkS27ZtIyoqyug5W9/T48yZM8TGxj50uNDt27epUqWKxecz6qRmc5nPChUqmKT5+vpSrlw5s+kA9+/fNxrm4uPjY3YeUPXq1Vm3bh3x8fGGYG7lypV8/vnn/PHHHybzRyIjIw3/Pn/+PIqiWJx3lFN3794lLi6OmjVrmjzn5+eHv7+/2eDa3OtUrFgxi3NVzHkwj4zXM2MOw4PPZc378uXLAGbrnZGWUe+M/1erVs1k2xo1ahg9vnv3Lvfv3zcE9eZoNNm/3pnT19lWqlevbpKW0fa8LDfj81dY7vsjhC1J8CGEyDdBQUEMHjyYQYMG0bJlSw4cOMCRI0do0aJFjvPcs2cPFy9eBKBy5cpmt9m0aRM9evQwSitevLjFIOdRHBwceO6555g5cyYXLlygUqVKLFmyBF9fX6M5FXFxcYSGhhIfH8/YsWOpXbs2np6eaDQaPv74Y3777bdHlmXp5OPBCa+QfsJSokQJVqxYYTG/h91HBTCcOGb3fidarTZb6ZD9ACfD2rVr6devH40bN+bLL7+kXLlyuLi4oNPp6NixI3q93mj73PRw2Jql1yM7r0VOXuu8llH/du3a8dZbbxVYPbLzeSnM5WZ8/iwFckLYMwk+hBD5TlEUmjRpwoEDBwgPD89VXgsXLsTZ2ZklS5aYvbI6fPhwFixYYBJ85NaQIUOYOXMmS5YsYdiwYezevZuXX34ZZ2dnwzY7d+7kxo0bLFy4kKFDhxrt/+Bka0v8/Pw4fvy4Sbq5q66VK1fm/9u7u5Aovj4O4N/Udm3djc13QRDRthdbzYrSTVIMy4tc2hSjt/UmvdALCaNQiKA3Uli8sMKCwDLMYPMFMrcMCiM0A03C3MxyQ0m7MCsxy2h/z80zQ+Osuev/3z7V8/uAyM4cztmZnbk4c875zsDAAJKSkmQLZ90ldE48mQb0b/nw4QPGxsZkox/9/f0IDQ0VRz1qa2vh7++P+/fvS6YD2e12WZ06nQ6tra3o7e396SJ6TzsnISEh0Gg06Ovrk+2bmJjA6Ojob/m+EGHUpK+vD1u3bpXse/78uaSM8N9ut89ZVhASEgKtVotPnz4tuFPviqfnWZgu+P79e8nUQVf3izu/eX9/v2zb7PMktOvufepOu8II7nwPCxj7E/GaD8bYL9PW1ubyyd/09LQ4/3v29A1PfPz4EVarFdu2bUNubi5ycnJkf0ajEa2trRgdHV1wO66sXbsW8fHxuHbtGmpra+F0OpGXlycpIzyJnv1U++7du26v99DpdJicnERXV5e4zel0orKyUlbWbDbD6XSitLTUZV3v3r2bt73ExEQsXbpUjG71trNnz0o+NzY24sWLF5LOo6+vLxYtWiQZ4SAicRrdj/bu3QsAKCsrw8zMjGy/8NsInTV3R3x8fHyQlZWFnp4e2Gw22TE4nU6YTCa36vKmjIwMBAQEoKqqCpOTk+L2yclJVFVVQa1Wi2+1z8jIwJIlS3D+/HlJpO3IyIhsdM3Hxwf79u1DV1cXrFary7YXsn7B0/MsTCkU1q0ILBaLrG53fvO2tjZ0d3eLn4kIFRUVACC5Jj25T91pt7OzE35+fti8efOcZRj7U/HIB2Pslzl06BDGx8dhNBqh1+uhUqkwPDyMuro6DAwMwGw2Q6/XL7j+69evY3p6GtnZ2XOWyc7ORk1NDa5cuSJbzPxP5eXloaSkBOXl5dDpdEhKSpLsT0lJQXh4OEpKSuBwOBAZGYmnT5+itrYWer0ez549m7eNgoICWCwWmEwmFBcXQ6FQwGq1uuzUCfG6586dQ3d3N3bs2IHg4GCMjIygo6MDg4OD885T9/X1xa5du9DU1ISvX79KRnJ+teDgYDQ0NODt27dIS0sTo3bDwsIk7zPJycnBzZs3kZ6eDrPZjG/fvqGpqUn2zgcA2LhxI44ePYry8nKsW7cOu3fvRnh4OIaGhmC1WtHV1QWtVovVq1dDo9HgwoULUKlU0Gq1CA0NFRexu3LmzBm0tbVh586dKCwsRGxsLNrb23Hjxg1s2bJF1hn9HWi1WlRUVKCoqAibNm0S33tRU1ODwcFBXLx4UQwOWLZsGU6ePInDhw/DYDDAbDbj8+fPqK6uxvLly9HT0yOp+/Tp03j06BFyc3ORm5uLpKQkKBQKvHnzBrdv38b69esl74hxlyfnec+ePSgrK0NBQQHsdjsCAwNhs9lcxncHBQUhNjYW9fX1iImJQVhYGAICApCVlSWWSUhIQHp6OoqKihAREYHm5mbcu3cPBw4cQHJysljOk/t0vmuNiGCz2ZCZmbngEUzGfmv/k4wtxtj/hTt37lBhYSHFx8dTUFAQ+fr6UmBgIKWlpdHly5fp+/fvkvJRUVEUFxc3Z31CjKYQtbthwwby8/OTRd7+6MuXL6TRaEin04nb8N/I039qbGyM/Pz8CACdOnXKZZne3l7avn07abVaUqvVlJqaSu3t7S4jQeeKCW1paaGEhARSKBQUERFBR44cIbvdPmdM6NWrVyklJYU0Gg0plUqKiooik8lE9fX1bh2XEE9rtVol238WtesqNjQqKopSU1Nl24XY2aGhIXGbEFX66tUrMhqNpNFoSK1Wk9FopJcvX8rquHTpEq1atYqUSiWFh4dTfn4+jY+Py+JUBXV1dWQwGEitVpNKpaIVK1ZQcXGxJLK2paWFEhMTSalUEgCX3322169f0/79+ykkJIQWL15M0dHRVFpaKommneuY5ztPswlRuz/G2wrmOu65rqmGhgZKTk4mlUpFKpWKkpOTqbGx0WW71dXVpNPpSKFQUExMDFVWVoqRzLO/y9TUFJ04cYLWrFlD/v7+pFaraeXKlXTw4EHq7OwUy3kabezueSYi6uzsJIPBQEqlkoKCgig/P58mJiZcnqPHjx+TwWAglUpFAMS43B8jcuvq6kiv15NCoaDIyEg6duwYzczMyNr15D792bX24MEDAkC3bt1y69ww9qdZRLTAFX+MMcb+WpmZmZiamsLDhw+90l5aWhocDgccDodX2mPsZxwOB6Kjo3H8+HHJqJs3mEwmDA8P48mTJ79NUAJj/yZe88EYY0zGYrGgo6NjQe9mYIwtTE9PD5qbm2GxWLjjwf5avOaDMcaYTFxc3C+PJ2WMSSUmJsqiohn72/DIB2OMMcYYY8wreM0HY4wxxhhjzCt45IMxxhhjjDHmFdz5YIwxxhhjjHkFdz4YY4wxxhhjXsGdD8YYY4wxxphXcOeDMcYYY4wx5hXc+WCMMcYYY4x5BXc+GGOMMcYYY17BnQ/GGGOMMcaYV/wHBezaU4A6BLMAAAAASUVORK5CYII=\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"shap.plots.bar(shap_values)"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 354
},
"id": "OV2zeVLzIp2X",
"outputId": "33806c8a-b04b-4c04-8330-0dcc76def9d8"
},
"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.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": "0bb43aa6-4071-4d65-9f8a-5462e9a21e41"
},
"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",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 470
},
"outputId": "ef5ff1b7-d361-4527-e0bb-ead5495fba26"
},
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/plain": [
"<Figure size 750x500 with 3 Axes>"
],
"image/png": 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\n"
},
"metadata": {}
}
]
},
{
"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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h7je6e7271/fq1SveYkXS6pOlcMmW+f5578G6G4SrRzIvtoAws15m1jPX7gF8A3i51TZbFXSHAXPiqkekqn30Hozvm++fvxi6dQ9Xj9SEOGcxbQVMNLM6oiC6092nmtlYoMHdpwCnm9kwYBWwGDghxnqkCmlFUGDx63D1rvn+BUvALFw9UjMsmmxUwoZmvYFtKQgVd38sprraVF9f7w0NDUm/rATQekVQiM7GHXfowNoJiXdfgBsG5/u6RKh0kpnNcPf6ch5T0h6EmV0KHAm8BDT/1+pEJ8KJxKK9FUFrIiDmPwW/KRi2UzhIwko9xDQcGFAwoCwSu5peEfSVB+COo/J9hYMEUOog9VyiE91EElOzK4LOul3hIKlQ6h7Ex8AsM/sL0RIaALj76bFUJWXJ6kDuiKEDio5BZHpF0CevhmkFF/dROEhApQbElNyfpEyWL+1YcyuCTjsfnvxFvq9wkMDKmcW0DvCFXPcVd18ZW1Xt0CymlgaPn05jkWPyvXv24MmRQwJUJJ1y78nw3B35vsJBKizOWUz7ES2DMQ8woI+ZHR9imqu0VNMDuVnxu+Ew95F8X+EgKVHqIaafAfu7+ysAZvYF4A7aWBpDkrN1zx5F9yAyP5DbgaoZlylcrhsUDpIqpc5i6tYcDgDu/k80qykVdGnHNTWPyzQuWY6TH5eZPLOxw8cmSuEgKVdqQDSY2QQz2y/392tAAwEpMHxQb8YdOpDePXtgRGMPNXWmcRHtnWCXGgoHqQKlHmI6Bfg/oHla6+PA9bFUJGXTpR1bSv24jMJBqkRJAZE7g/rK3J9IqqV6XEbhIFWk3UNMZnZn7t/Zuau+tfhLpkSR8qR2XEbhIFWmoz2IM3L/HhR3ISKVksoT7BQOUoXaDQh3fyfXPNXdf1x4X26F1x+v+SiR8FI1LtNGOFTNVFypWaXOYvpGkdv+u5KFiGRSO+FQFVNxpaZ1NAZxipnNBnZsNf7wOjA7mRJFqlQ7h5WqYiqu1LyOxiBuBx4AxgEjC27/yN0Xx1aVSLXrYMwh9VNxRehgD8Ldl7r7POAXwGJ3n+/u84FVZrZnEgWKVJ0SBqRr9loXUlVKHYP4JbCsoL8sd5uIFCpxtlJqp+KKFCj1TGrzgnXB3X21mZX6WJHaUMZU1lROxRVppdQv+blmdjr5vYZTiS5DKhKr0ZNnc8czb9LkTp0ZR+/Zh4uHDwxd1po6cZ5DqqbiihRR6iGmk4F9gEbgLWBP4KS4ihKBKBxuffoNmnI7r03u3Pr0G4yenLIJdDoJTjKq1LWYFgBHdbihZF6SJ3fd8cybbd6emr0IhYNkWLsBYWbnuPtlZnYNsMa1Sd399CIPk4xK+vrXTW1cDret2xNXGA7r94IRr4WrRSQGHe1BzMn9q2s/SLsnd8UREHVmRcOgzqzir1W2wnDosyec+OdwtYjEpKO1mO7L/TsxmXIkzZI+uevoPftw69NvFL09qMJwGHgEHDYhXC0iMeroENN9FDm01Mzdh1W8IkmtpK+z0DzOkKpZTIXhsO+Z8PUxoSoRiV1Hh5iuyP17KLAlcGuufzTwXlxFSTqNGDqgxRgExH9y18XDB6ZzQPq/L4c9NZFPsq2jQ0yPApjZz9y9vuCu+8xM4xI1pqZP7ioMhyMmwheHh6tFJCGlnii3vpl93t3nAphZf2D9+MqStKq5k7vc4cKe+f4J90O/fcPVI5KgUgPiTOCvZjYXMGBb4AexVSWSBqtXw9iN8/1T/gZbfDFcPSIJK/VEuQfNbAdgx9xNL7v7ivjKEgls1Qq4ePN8/4ezoWffcPWIBFDSUhtmth4wAjjN3Z8D+pqZrlMt2fTJ0pbhcM7rCgepSaWuxfQb4FNg71y/Ebg4lopEQlo8F8YXhMG578B6m4SrRySgUgNiO3e/DFgJ4O4fE41FiGTHWw1w9aB8/yeLYJ31wtUjElipAfGpmfUgd9KcmW0HaAxCsuOVB2HC1z7r9v/kNgZf/hiTZzYGLEokrFJnMV0APAj0MbPbgMHACXEVJZKohpth6pmfdft9cjsQ/2KESUtyJV7Jhg4DwszWAjYmOpt6L6JDS2e4+/sx1yYplLkvmb+Mhcd/9lm3ORyaxbkYYZKSXolXsqHDQ0zuvho4x90Xufv97j5V4VCbmr9kGpcsx8l/yVTtYZg/nNAiHPq3CodmcS1GmKT2VuIVaUupYxAPm9nZZtbHzDZp/ou1MkmdTH3J/HIwvHhvvj9maZuLDsa1GGGSkl6JV7Kh1IA4kug61I8SXRui+U9qSGa+ZMZuCu+9kO/nrgI3YugAenSra7Fp3IsRJiXL4SfxKTUgdgauA54DZgHXAFpzoMZk4ktmzEawelVBP3+J0OGDejPu0IH07tkDA3r37MG4Qwdm4hh9lsNP4lPqLKaJwIfA1bn+Mbnb/ieOoiSdQiz33VlFB9P/uHPLjYpcPzrJxQiTHPCv6ZV4pdNKDYhd3L3wv65HzOylOAqS9Bo+qDcN8xe3uIDPYbulb3XXYjN2SgmHJIWYVVRzK/FKl5V6iOlZM9uruWNme6IxiJozeWYjd89o/Ow60U3u3D2jMXWzmFoPps/rfkzLDQKHA2RswF8yq9SA2A34m5nNM7N5wFPA7mY228yej606SZVq+VIrHDRPYzhAhgb8JdNKPcR0QKxVSFUI8aXWmeP0zdfObh0Og7vfy5MxvF5nJH19b5HOKGkPwt3nt/cXd5GSDknPYursiXkjhg5YIxx2WHlHh4PpSZ4IqFlFUg1KPcQkCZg8s5HB46fTf+T9DB4/PXXH9pP+UuvsIa3WA9L9Prk9t8xkPK/XGVmeUivZUeohJolZNayVk/RUyU4d0hqzUYtu89pKK1d7h2sqJX0ITbOKJO0UECnR3q/XNH2JJPmlVvZx+jbCoVlHX/QaFxBpSYeYUkKzWtZU1iGtDsIBOv6i17iASEsKiJTIxDIWFVbycfpW4TD5kJc69UWvcQGRlsy9hNG7FKmvr/eGhuydo9d6DAKiLzV9QXWgVTg0n+eQuetWiHSRmc1w9/pyHqMxiJTQWjmd0EY4gAaARSpBAZEi+lIrQzvhANqDEKkEBYRUnxLCIe1ThkWqQWyD1GbW3cz+bmbPmdmLZnZhkW3WNbNJZvaamT1jZv3iqkcyooNwgOpZM0ok7eKcxbQCGOLuXwZ2BQ4oXBE250TgA3ffHvg5cGmM9Ui1KyEcQFOGRSoltkNMHk2PWpbrdsv9tZ4ydQgwJte+C7jWzMyrbWqVdKjLYwIlhgPohDeRSon1PAgzqzOzWcACYJq7P9Nqk97AmwDuvgpYCmwaZ02SvC4vgldGOIBOeBOplFgDwt2b3H1XYBtgDzPbpTPPY2YnmVmDmTUsXLiwskVK7Lo0JlBmOIBOeBOplERmMbn7EjN7hOi6Ei8U3NUI9AHeMrO1gY2ARUUefyNwI0QnysVfsVRSp8cECsNhl8Pg8JtLfk1NGRbpujhnMfUys565dg/gG8DLrTabAhyfax8OTNf4Q/Z0ahmRwnDY+7SywkFEKiPOPYitgIlmVkcURHe6+1QzGws0uPsU4CbgFjN7DVgMHBVjPRLIiKEDii4j0uaYQGE47H8J7HNazBXmjZ48mzueeZMmd+rMOHrPPlw8fGBiry+SJnHOYnoeGFTk9vML2p8AR8RVg6RDWcuIFIbDoRPgS8l9PEZPns2tT7/xWb/J/bO+QkJqkRbrk/QoDIdv3wvbDUn05bcb9Seaivz3UGfGv8Z9M9FaRCpNi/VJdXKHC3vm+99/BHr/R+JlFAuH9m4XyToFhIS1ugnGbpLvnzYDNts+SCl1Zm3uQYjUopoICK3smVIrP4FLtsj3z3oFNtwyWDlH79mnxRhE4e0itSjzAaGVPVNq+RK4dNt8f+Sb0P1z4eohPxCtWUwikcwPUg8eP73oujy9e/bgyZHJDoJKzofvwJU75vujF8Da64arR6QGdGaQOvPXpNbKninz/qstw+H8xQoHkZTK/CEmreyZIm/NgAkFe20XLAENAGeWxv6qX+b3ILSyZ0q8+nDLcBizVOGQYV1ewVdSIfN7EGWdxSvxeO73cO8P8v0SVmStNvq13FJ7K/jW8vtSbTIfEKCVPYN68hcw7fx8P6PhoJlyLWnsLxsyf4hJKmvyzEYGj59O/5H3M3j89PYPGTw4KvPhALoGdjGdWsFXUkcBISUr67jyncfD09fn+xkNB9Cv5WI09pcNCggpWcm/lH89BF6anO9nOBxAv5aL0VX9sqEmxiCkMkr6pXxpf1i+ON/PeDhAJ653USM09lf9FBBSsg7PKenE9aPTpjOzkTRTTrJKASEla/eXckbCobOzkfRrWbJIYxBSsuGDenPYbr0/W/66zozDduvN8D/u3HLDKgwH0GwkkdYUEFKyyTMbuXtG42fXTGhy5+JZ+7bcqErDATQbSaQ1BYSUrPUv7Hndj2m5QRWHA2g2kkhrCggpWeEv6ayFA2juvkhrCggpWfMv6dbhMLj7vSHKqTjN3RdpSbOYpGQjhg5YY0B6p6ZJjMvQL2zNRhLJU0BIyVqHw+Du9zJO8/1FMksBIaUpcp7Dk2EqEZGEaAxCOpaBk+BEpHwKCGmfwkGkZikgpG0KB5GapoCQ4grDwdZSOIjUIA1Sy5oKw2GjPnDmC8FK0bWeRcJRQEhLheGwzR7wvWnBStG1nkXC0iEmySsMh50ODhoOoNVVRUJTQEikMBx2/z4ceWu4WnK0uqpIWAoIaRkO+42CA68IV0sBra4qEpYCotYVhsM3r4D9RoarpRWtrioSlgapUyTxGTuF4XDYTTDw8PheqxN0rWeRsBQQKZH4jJ3CcDj2btjh65V/jQrQ6qoi4egQU0okOmOnMBxOfDi14SAiYWkPIiUSm7FTGA6nPg2b71TZ5xeRzNAeRErEPmPHvWU4nPG8wkFE2qWASIlYZ+ysboILe+b7Z78KG2/b9ecVkUzTIaaUiG3GzqoVcPHm+f7IN6D7Rm1vLyKSo4BIkYrP2FmxDMYVPN9570G37pV7fhHJNAVEVn28GC7rn+//ZBHU6f9uESmdvjGyaGkj/HznfP+CJWAWrh4RqUoapM6a919tGQ5jliocRKRTFBBZ0vgsXFuf7+sqcCLSBTrElBVzH4XfDcv3SwiHJNd+0pXhRKqPAiILXn+sU+GQ1NpPujKcSHXSIaZqN2cqTDw43y/xsFKSaz/pynAi1UkBUc1m3Q6Tjs33yxhzSPJqbboynEh1UkBUq6eug8mn5PtlDkgnebU2XRlOpDopIKrR9EvgoXPz/U7MVkryam26MpxIddIgdbWZ+iNouCnf7+RU1iSv1qYrw4lUJ3P30DWUpb6+3hsaGkKXEcakb8OcKfm+znMQkRKZ2Qx3r+94yzztQVSLmw+AN57K9xUOIhIzBUQ1uHoQLJ6b7yscRCQBCogUKXq28Z92h5X/zm+kcBCRhCggUqLY2cbD/7hzy40UDiKSoNimuZpZHzN7xMxeMrMXzeyMItvsZ2ZLzWxW7u/8uOpJu9ZnG8/rfkzLDRQOIpKwOPcgVgFnufuzZrYhMMPMprn7S622e9zdD4qxjqpQeFaxwkFE0iC2PQh3f8fdn821PwLmAJr43obms4pbh8Pg7veGKEdEJJkzqc2sHzAIeKbI3Xub2XNm9oCZfTGJetJoxNABHLvOYy1u26lpks42FpFgYh+kNrMNgLuBH7r7h63ufhbY1t2Xmdk3gcnADkWe4yTgJIC+ffvGXHEYw1f+ieFr3QDA9auGcdsG32GczjYWkYBiPZPazLoBU4GH3P3KErafB9S7+/ttbZPJM6n/dg38eXTUPuFP0G9w2HpEJHM6cyZ1nLOYDLgJmNNWOJjZlrntMLM9cvUsiqumVHr0snw4nPiwwkFEUiPOQ0yDgW8Ds81sVu62c4G+AO5+A3A4cIqZrQKWA0d5tS0O1RUPXwhP5LLzB4/BVl8OW4+ISIHYAsLdnwCsg22uBa6Nq4ZUe3AUPH191D7lKdhi5/a3FxFJmM6kDmHK6fDsxKh92gzYbPuw9YiIFKGASNrd34PZf4jap8+CTfqHrUdEpA0KiCTdcQy8cn/UPvNF2GibsPWIiLRDAZGU3x0Cc/8atc96BTbcMmg5IiIdUUAkYcAsgkoAAAjNSURBVMI34K2/R+0R/4L1Nwtbj4hICRQQcbt+H1jwYtT+8TzosXHQckRESqWAiNNVA2HJG1F75JvQ/XNh6xERKYMCIi6XfR4+zp0Ufu7bsM76YesRESmTAiIOF/WCpk+j9nnvQbfuYesREekEBUQlucOFPfP90Qth7XXC1SMi0gUKiEppHQ4/WQR1entFpHrpG6wSVq+GsQWzk85fDGvVhatHRKQCErmiXKatbmoVDh8oHEQkE7QH0RVNK+GigpPeLlgC1u4CtiIiVUN7EJ21aoXCQUQyTQHRGSuXw8WbR+21u8OYpQoHEckcBUS5Pv03XJJbaG+9zWD0e2HrERGJiQKiHJ98CD/dOmpv3A/O+VfQckRE4qSAKNXHi2F8n6i9xUA447mw9YiIxEwBUYp/vw+X5a781mcvOOWJsPWIiCRAAdGRj96Fy7eL2p//Kpz4UNh6REQSooBoz9K34GcDovaOB8H/Tg5bj4hIghQQbVn8Ovz8i1H7S0fCUbeFrUdEJGEKiGIW/hOu3jVq73YCHHpj0HJEREJQQLT23otw3e5Re69T4eBfhK1HRCQQBUSht2fBL/eJ2l85Cw4YF7YeEZGAtFhfszf/ATd9PWp/dTT814iw9YiIBKaAAJj3BPz2wKi9/yWwz2lh6xERSQEFxGt/gVsPjdrfvAL2+H7YekREUqK2A+KVB+COo6L2IdfBoOPC1iMikiK1GxAvToY/HB+1D7sJBh4eth4RkZSpzYB4bhLce1LUPvJW2OngsPWIiKRQ7QXEjIlw3+lR+5g/wBf2D1uPiEhK1VZAPPMreOCcqP3tybDdV8PWIyKSYrUTEEveyIfDdx6AbfcJW4+ISMrVzpnUdevAtvvC96YrHERESlA7exAbbgnfuT90FSIiVaN29iBERKQsCggRESlKASEiIkUpIEREpCgFhIiIFKWAEBGRohQQIiJSlAJCRESKUkCIiEhRCggRESlKASEiIkUpIEREpCgFhIiIFGXuHrqGspjZQmB+6DpithnwfugiUkbvyZr0nqxJ70lxmwHru3uvch5UdQFRC8yswd3rQ9eRJnpP1qT3ZE16T4rr7PuiQ0wiIlKUAkJERIpSQKTTjaELSCG9J2vSe7ImvSfFdep90RiEiIgUpT0IEREpSgERiJn1MbNHzOwlM3vRzM4oss1+ZrbUzGbl/s4PUWtSzKy7mf3dzJ7LvScXFtlmXTObZGavmdkzZtYv+UqTU+J7coKZLSz4nHwvRK1JM7M6M5tpZlOL3FdTn5NmHbwnZX9O1o6nTCnBKuAsd3/WzDYEZpjZNHd/qdV2j7v7QQHqC2EFMMTdl5lZN+AJM3vA3Z8u2OZE4AN3397MjgIuBY4MUWxCSnlPACa5+2kB6gvpDGAO8Lki99Xa56RZe+8JlPk50R5EIO7+jrs/m2t/RPR/au+wVYXlkWW5brfcX+tBskOAibn2XcDXzMwSKjFxJb4nNcfMtgEOBCa0sUlNfU6gpPekbAqIFMjt/g4Cnily9965wwsPmNkXEy0sgNwu8ixgATDN3Vu/J72BNwHcfRWwFNg02SqTVcJ7AnCYmT1vZneZWZ+ESwzhKuAcYHUb99fc54SO3xMo83OigAjMzDYA7gZ+6O4ftrr7WWBbd/8ycA0wOen6kubuTe6+K7ANsIeZ7RK6ptBKeE/uA/q5+5eAaeR/OWeSmR0ELHD3GaFrSYsS35OyPycKiIByx5TvBm5z93ta3+/uHzYfXnD3PwHdzGyzhMsMwt2XAI8AB7S6qxHoA2BmawMbAYuSrS6Mtt4Td1/k7ity3QnAbknXlrDBwDAzmwf8HhhiZre22qbWPicdvied+ZwoIALJHQ+9CZjj7le2sc2WzcdNzWwPov+/MvshN7NeZtYz1+4BfAN4udVmU4Djc+3Dgeme4ZN5SnlPzGyrgu4wovGszHL3Ue6+jbv3A44i+gwc12qzmvqclPKedOZzollM4QwGvg3Mzh1fBjgX6Avg7jcQfbBPMbNVwHLgqCx/yIGtgIlmVkcUhne6+1QzGws0uPsUolC9xcxeAxYT/ceQZaW8J6eb2TCimXGLgROCVRtQjX9Oiurq50RnUouISFE6xCQiIkUpIEREpCgFhIiIFKWAEBGRohQQIiJSlAJCMs/MeprZqTE+/35mtk9cz9/qtc5N4nVEQAEhtaEnUDQgcmfZdtV+QFkB0YXXVUBIYhQQUgvGA9vl1sC/PPeL/3EzmwK8ZGb9zOyF5o3N7GwzG5Nrb2dmD5rZjNxjdix84txCiycDZ+ae/ytmdnDuGgQzzexhM9sit+0YM7vFzJ4kOomrl5lNy13nYYKZzW9eSsXMjrPoOhCzzOxXuQX7xgM9crfdlsD7JjVOZ1JLLRgJ7JJb8A4z2w/4j9xtr3dwMZkbgZPd/VUz2xO4HhjSfKe7zzOzG4Bl7n5F7vk3BvZyd89dlOUc4KzcQ3YG9nX35WZ2LdGSCOPM7ACiaxhgZjsRXbtgsLuvNLPrgWPdfaSZndb8v0MkbgoIqVV/d/fX29sgt9LuPsAfCi4lsG4Jz70NMCm39s06QOHrTHH35bn2vsC3ANz9QTP7IHf714gWUvtH7nV7EC31LZIoBYTUqn8XtFfR8nBr99y/awFLOvGL/RrgSnefkttbGdPG67bFgInuPqrM1xWpKI1BSC34CNiwnfvfAzY3s03NbF3gIIiWWwdeN7MjIFqB18y+XMLzb0S03DTkVxQt5kngf3LPvT+wce72vwCHm9nmufs2MbNtc/etzC0TLxI7BYRknrsvAp40sxfM7PIi968ExgJ/J7qQSuFy2scCJ5rZc8CLRJeybO0+4FvNg9REewx/MLMZwPvtlHYhsH9ugPwI4F3go9x1yUcDfzaz53M1NS/VfCPwvAapJQlazVUkkNzeSpO7rzKzvYFfagBa0kRjECLh9AXuNLO1gE+B7weuR6QF7UGIiEhRGoMQEZGiFBAiIlKUAkJERIpSQIiISFEKCBERKUoBISIiRf1/NbD3gLGPZisAAAAASUVORK5CYII=\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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