{ "cells": [ { "cell_type": "markdown", "metadata": { "toc": true }, "source": [ "

Table of Contents

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" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Price prediction with k-nearest neighbors algorithm\n", "\n", "In this study, we'll cover how to:\n", "- load the data\n", "- impute missing values\n", "- encode categorical features\n", "- standardize numerical values\n", "- train a k-nearest neighbors model and plot the predictions\n", "- tune the hyperparameters of an estimator\n", "- evaluate a model using cross-validation and error minimization\n", "- improve the model with custom feature selection\n", "- find the elbow point of a curve\n", "\n", "\n", "## Loading the data\n", "The [UCI website](https://archive.ics.uci.edu/ml/datasets/automobile) provides everything you need to get started:\n", "- Training features, consisting of three types of entities: a) the specification of an auto in terms of various characteristics; b) its assigned insurance risk rating; c) its normalized losses in use as compared to other cars. \n", "- Target Feature, *i. e.* the variable which we want to predict: the price of the car, ranging from 5118 to 45400." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from pathlib import Path\n", "\n", "import pandas as pd\n", "import numpy as np\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "\n", "plt.style.use('fivethirtyeight')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "#dataquest solution\n", "#https://github.com/dataquestio/solutions/blob/master/Mission155Solutions.ipynb\n", "#about the dataset\n", "#https://archive.ics.uci.edu/ml/datasets/automobile\n", "#https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.names\n", "#download the dataset\n", "#https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "attributes=[\"symboling\",\n", " \"normalized_losses\",\n", " \"make\",\n", " \"fuel_type\",\n", " \"aspiration\",\n", " \"doors\",\n", " \"body_style\",\n", " \"drive_wheels\",\n", " \"engine_location\",\n", " \"wheel_base\",\n", " \"length\",\n", " \"width\",\n", " \"height\",\n", " \"curb_weight\",\n", " \"engine_type\",\n", " \"num_cylinders\",\n", " \"engine_size\",\n", " \"fuel_system\",\n", " \"bore\",\n", " \"stroke\",\n", " \"compression_ratio\",\n", " \"horsepower\",\n", " \"peak_rpm\",\n", " \"city_mpg\",\n", " \"highway_mpg\",\n", " \"price\"]" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "cars = pd.read_csv('data/imports-85.data',\n", " header=None,\n", " names=attributes)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's take a quick look at the raw data." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "def highlight_missing_value(val):\n", " \"\"\" \n", " return the css property `'background-color: orange'` for \"?\" value, white otherwise.\n", " \"\"\"\n", " color = 'orange' if val==\"?\" else 'white'\n", " return 'background-color: %s' % color" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
symboling normalized_losses make fuel_type aspiration doors body_style drive_wheels engine_location wheel_base length width height curb_weight engine_type num_cylinders engine_size fuel_system bore stroke compression_ratio horsepower peak_rpm city_mpg highway_mpg price
03?alfa-romerogasstdtwoconvertiblerwdfront88.6168.864.148.82548dohcfour130mpfi3.472.6891115000212713495
13?alfa-romerogasstdtwoconvertiblerwdfront88.6168.864.148.82548dohcfour130mpfi3.472.6891115000212716500
21?alfa-romerogasstdtwohatchbackrwdfront94.5171.265.552.42823ohcvsix152mpfi2.683.4791545000192616500
32164audigasstdfoursedanfwdfront99.8176.666.254.32337ohcfour109mpfi3.193.40101025500243013950
42164audigasstdfoursedan4wdfront99.4176.666.454.32824ohcfive136mpfi3.193.4081155500182217450
" ], "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cars.head().style.applymap(highlight_missing_value)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The dataset contains categorical and numerical variables. We will need to take this into account when preprocessing the dataset thereafter. Since we plan to perform a *linear regression*, it's imperative to work with *numerical values* only. The dataset has also missing values we need to fix." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Imputation of missing values\n", "\n", "According to the [Data Set Description](https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.names), the missing values are denoted by an interrogation point on the following attributes:\n", "- normalized losses (41 missing values)\n", "- doors (2)\n", "- bore (4)\n", "- stroke (4)\n", "- horsepower (2)\n", "- peak-rpm (2)\n", "- price (4)\n", "\n", "Unfortunately, this causes this attributes have been cast to Pandas objects even when they contain continuous values.\n", "\n", "So we will do the following:\n", "1. Remove the normalized losses column, since this attribute contains 20% of missing values.\n", "2. Impute the continuous missing values by np.nan values.\n", "3. Change the data type to float.\n", "4. Remove the 4 observations with an unknown price since this attribute is our target.\n", "5. Replace the remaining missing values by the mean of the attribute." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "def remove_attribute(attribute, df):\n", " \"\"\"\n", " Remove column from a dataframe\n", " \n", " attribute (string): the column name to remove\n", " df (DataFrame): the data\n", " \n", " \"\"\"\n", " \n", " df.drop(attribute,axis=1,inplace=True)\n", " \n", "\n", "def clean_continuous_attributes(mv_str, cols_index, df):\n", " \"\"\"\n", " Replace missing vaalues by np.nan value and change object columns to float data type\n", " \n", " mv_str (string): the string denoting the missing value\n", " cols_index (list of integers) : the columns index we want to clean\n", " df (DataFrame) : the data \n", " \n", " \"\"\"\n", " \n", " for idx, col in enumerate(df.columns):\n", " if idx in cols_index:\n", " df[col]=df[col].replace(mv_str, np.nan)\n", " df[col]=df[col].astype(np.float64)\n", " return df\n", "\n", "def remove_rows(target, df):\n", " \"\"\"\n", " Remove rows with np.nan values from a DataFrame and reset the index\n", " \n", " target (array-like of strings): the column name which we want to remove np.nan values \n", " df (DataFrame) : the data \n", " \n", " \"\"\"\n", " df.dropna(subset=target,inplace=True)\n", " df.reset_index(inplace=True)\n", " df.drop(\"index\",axis=1,inplace=True)\n", " \n", "\n", "def impute_missing_values(cols_index, df):\n", " \"\"\"\n", " Replace np.nan values by the mean of the attribute \n", " \n", " cols_index (list of integers) : the columns index we want to replace np.nan values by the mean\n", " df (DataFrame) : the data \n", " \n", " \"\"\"\n", " for idx, col in enumerate(df.columns):\n", " if idx in cols_index: \n", " m = df[col].mean()\n", " df[col] = df[col].fillna(m) \n", " return df" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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symbolingmakefuel_typeaspirationdoorsbody_styledrive_wheelsengine_locationwheel_baselength...engine_sizefuel_systemborestrokecompression_ratiohorsepowerpeak_rpmcity_mpghighway_mpgprice
03alfa-romerogasstdtwoconvertiblerwdfront88.6168.8...130mpfi3.472.689.0111.05000.0212713495.0
13alfa-romerogasstdtwoconvertiblerwdfront88.6168.8...130mpfi3.472.689.0111.05000.0212716500.0
21alfa-romerogasstdtwohatchbackrwdfront94.5171.2...152mpfi2.683.479.0154.05000.0192616500.0
32audigasstdfoursedanfwdfront99.8176.6...109mpfi3.193.4010.0102.05500.0243013950.0
42audigasstdfoursedan4wdfront99.4176.6...136mpfi3.193.408.0115.05500.0182217450.0
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5 rows × 25 columns

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" ], "text/plain": [ " symboling make fuel_type aspiration doors body_style \\\n", "0 3 alfa-romero gas std two convertible \n", "1 3 alfa-romero gas std two convertible \n", "2 1 alfa-romero gas std two hatchback \n", "3 2 audi gas std four sedan \n", "4 2 audi gas std four sedan \n", "\n", " drive_wheels engine_location wheel_base length ... engine_size \\\n", "0 rwd front 88.6 168.8 ... 130 \n", "1 rwd front 88.6 168.8 ... 130 \n", "2 rwd front 94.5 171.2 ... 152 \n", "3 fwd front 99.8 176.6 ... 109 \n", "4 4wd front 99.4 176.6 ... 136 \n", "\n", " fuel_system bore stroke compression_ratio horsepower peak_rpm city_mpg \\\n", "0 mpfi 3.47 2.68 9.0 111.0 5000.0 21 \n", "1 mpfi 3.47 2.68 9.0 111.0 5000.0 21 \n", "2 mpfi 2.68 3.47 9.0 154.0 5000.0 19 \n", "3 mpfi 3.19 3.40 10.0 102.0 5500.0 24 \n", "4 mpfi 3.19 3.40 8.0 115.0 5500.0 18 \n", "\n", " highway_mpg price \n", "0 27 13495.0 \n", "1 27 16500.0 \n", "2 26 16500.0 \n", "3 30 13950.0 \n", "4 22 17450.0 \n", "\n", "[5 rows x 25 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "remove_attribute(\"normalized_losses\", cars)\n", "missing_values_col_index=[17,18,20,21,24]\n", "cars = clean_continuous_attributes(\"?\", missing_values_col_index, cars)\n", "remove_rows([\"price\"], cars)\n", "cars = impute_missing_values(missing_values_col_index, cars)\n", "cars.head()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "#safety check\n", "assert cars.isnull().sum().sum()==0" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 201 entries, 0 to 200\n", "Data columns (total 25 columns):\n", "symboling 201 non-null int64\n", "make 201 non-null object\n", "fuel_type 201 non-null object\n", "aspiration 201 non-null object\n", "doors 201 non-null object\n", "body_style 201 non-null object\n", "drive_wheels 201 non-null object\n", "engine_location 201 non-null object\n", "wheel_base 201 non-null float64\n", "length 201 non-null float64\n", "width 201 non-null float64\n", "height 201 non-null float64\n", "curb_weight 201 non-null int64\n", "engine_type 201 non-null object\n", "num_cylinders 201 non-null object\n", "engine_size 201 non-null int64\n", "fuel_system 201 non-null object\n", "bore 201 non-null float64\n", "stroke 201 non-null float64\n", "compression_ratio 201 non-null float64\n", "horsepower 201 non-null float64\n", "peak_rpm 201 non-null float64\n", "city_mpg 201 non-null int64\n", "highway_mpg 201 non-null int64\n", "price 201 non-null float64\n", "dtypes: float64(10), int64(5), object(10)\n", "memory usage: 39.4+ KB\n" ] } ], "source": [ "cars.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Feature construction with one-hot encoding\n", "We need also to replace the two missing values for the doors column by \"unknown\" and then to one-hot encode (*i.e.*, generate a column by category) the categorical columns (including the symboling attribute).\n", "Categorical variables cannot be included in a linear model if not coded as integers first and we want to avoid categorical features to be treated as ordered values. Since the symboling feature has ordered integer values, we will also one-hot encode this numerical attribute." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "from sklearn.preprocessing import OneHotEncoder" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "#replace the two missing values for the doors column by \"unknown\"\n", "cars[\"doors\"]=cars[\"doors\"].replace(\"?\", \"unknown\")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "#replace symboling attribute ordered values by letters\n", "symboling_dict = { 3:\"G\", 2:\"F\", 1:\"E\", 0:\"D\", -1:\"C\", -2:\"B\", -3:\"A\" }\n", "cars[\"symboling\"] = cars[\"symboling\"].apply(lambda x: symboling_dict[x])" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " symboling_B symboling_C symboling_D symboling_E symboling_F \\\n", "0 0.0 0.0 0.0 0.0 0.0 \n", "1 0.0 0.0 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 1.0 0.0 \n", "3 0.0 0.0 0.0 0.0 1.0 \n", "4 0.0 0.0 0.0 0.0 1.0 \n", "\n", " symboling_G make_alfa-romero make_audi make_bmw make_chevrolet ... \\\n", "0 1.0 1.0 0.0 0.0 0.0 ... \n", "1 1.0 1.0 0.0 0.0 0.0 ... \n", "2 0.0 1.0 0.0 0.0 0.0 ... \n", "3 0.0 0.0 1.0 0.0 0.0 ... \n", "4 0.0 0.0 1.0 0.0 0.0 ... \n", "\n", " num_cylinders_twelve num_cylinders_two fuel_system_1bbl \\\n", "0 0.0 0.0 0.0 \n", "1 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 \n", "\n", " fuel_system_2bbl fuel_system_4bbl fuel_system_idi fuel_system_mfi \\\n", "0 0.0 0.0 0.0 0.0 \n", "1 0.0 0.0 0.0 0.0 \n", "2 0.0 0.0 0.0 0.0 \n", "3 0.0 0.0 0.0 0.0 \n", "4 0.0 0.0 0.0 0.0 \n", "\n", " fuel_system_mpfi fuel_system_spdi fuel_system_spfi \n", "0 1.0 0.0 0.0 \n", "1 1.0 0.0 0.0 \n", "2 1.0 0.0 0.0 \n", "3 1.0 0.0 0.0 \n", "4 1.0 0.0 0.0 \n", "\n", "[5 rows x 66 columns]" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#select columns object\n", "cars_object = cars.select_dtypes('object')\n", "#one-hot encoding\n", "enc=OneHotEncoder().fit(cars_object)\n", "#pass to array\n", "encoded = enc.transform(cars_object).toarray()\n", "#create a dataframe with the binary features\n", "one_hot_encoded_features = pd.DataFrame(index=cars.index)\n", "#put binary features names in a readable fashion\n", "binary_col_names = enc.get_feature_names(cars_object.columns)\n", "\n", "for idx, feature in enumerate(binary_col_names):\n", " one_hot_encoded_features[feature]=encoded[:,idx]\n", "one_hot_encoded_features.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The mean value of binary features shows us the prevalence, meaning what proportion of the described object has that characteristic.\n", "The plot below shows that a dozen characteristics are common across cars, but most of them concern less than 20% of the dataset." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "one_hot_encoded_features.apply(pd.value_counts)\n", "sorted_one_hot_encoded_features = one_hot_encoded_features.iloc[:, 1:].mean().sort_values()\n", "ax = sorted_one_hot_encoded_features.plot(kind='barh',\n", " stacked=True,\n", " figsize=(5, 12),\n", " title='Prevalence of Binary Features')\n", "ax.set_xlabel('Proportion of cars');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Standardization of numerical values" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As mentioned by the [scikit-learn documentation](https://scikit-learn.org/stable/modules/preprocessing.html):\n", "> If a feature has a variance that is orders of magnitude larger than others, it might dominate the objective function and make the estimator unable to learn from other features correctly as expected.\n", "\n", "That's why we need to transform the data so that each attribute will be like standard normally distributed data (Gaussian) with zero mean and unit variance." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "#remove all object features since we constructed binary features from them\n", "cars=cars.drop(cars.select_dtypes('object').columns,axis=1)\n", "#join binary features and continuous features\n", "all_features = one_hot_encoded_features.join(cars)" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "scrolled": false }, "outputs": [], "source": [ "#normalize the numerical values\n", "standardized_cars = (all_features - all_features.mean())/(all_features.std())\n", "#reintroduce the non normalized target column\n", "standardized_cars[\"price\"] = all_features[\"price\"]\n", "#safety check if mean = 0 and variance = 1 for each column\n", "assert np.isclose(standardized_cars.std(),1.0).sum()==len(standardized_cars.columns)-1\n", "assert np.isclose(standardized_cars.mean(),0).sum()==len(standardized_cars.columns)-1" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let’s get some insights by looking at the variables' distributions and at the pairwise relationships between them. Only main continuous variables will be used. The following plot shows their Pearson standard correlation coefficients." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.style.use('fivethirtyeight')\n", "plt.figure(figsize=(10,10))\n", "continuous_idx = len(one_hot_encoded_features.columns)\n", "ticks = range(len(standardized_cars.columns)-continuous_idx)\n", "dt = standardized_cars.iloc[:,continuous_idx:]\n", "plt.grid(False)\n", "plt.imshow(dt.corr())\n", "plt.xticks(ticks,dt.columns,rotation=90)\n", "plt.yticks(ticks,dt.columns)\n", "plt.colorbar()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that some variables are strongly linearly correlated, exhibiting clusters (length, width, wheelbase, or between city-mpg and highway-mpg). Risk of redundancy here and that may decrease the performance of the model since correlated features are known to induce instabilities.\n", "Regarding price target, the strongest correlation occurs with the engine size attribute, so we can expect this feature to be a good predictor." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Build the model\n", "Now we have removed the discrete variables, we are ready to perform a linear regression based on the k-nearest neighbors (KNN) algorithm using the root of mean squared error (RMSE) as error metric during test/train validation. First, we will fit a univariate model, useful to know which features are the best predictors.\n", "### Univariate Model" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "from sklearn.neighbors import KNeighborsRegressor\n", "from sklearn.metrics import mean_squared_error" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "def knn_train_test(col_names, target, df, k = 5):\n", " \"\"\"\n", " Trains a KNN model and returns the RMSE\n", " \n", " col_names (string): the training features\n", " target (string): the column target\n", " df (DataFrame): the data\n", " k (int) : the number of neighbors\n", " \n", " output (float) : RMSE\n", " \"\"\"\n", " #initiate a seed for reproductibility \n", " np.random.seed(123)\n", " #randomization\n", " p = np.random.permutation(len(df))\n", " df = df.loc[p]\n", " #using 50/50 split for training and testing data\n", " l = len(df)//2\n", " #train only col_name\n", " train_feature = df.iloc[0:l][col_names]#.values.reshape(l,1)\n", " test_feature = df.iloc[l:][col_names]#.values.reshape(len(df)-l,1)\n", " #target\n", " train_target = df.iloc[0:l][target]\n", " test_target = df.iloc[l:][target]\n", " #instantiate KNeighborsRegressor class with default parameters\n", " knn = KNeighborsRegressor(n_neighbors = k)\n", " # Pass everything into the fit method.\n", " knn.fit(train_feature, train_target)\n", " #making predictions on the testing set using the training data \n", " predictions = knn.predict(test_feature)\n", " #calculating mean squared error (MSE) and root mean squared error (RMSE)\n", " predictions_mse = mean_squared_error(test_target, predictions)\n", " predictions_rmse = predictions_mse ** (1/2)\n", " return predictions_rmse" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "horsepower 4367.420884\n", "engine_size 4622.893168\n", "curb_weight 4741.616768\n", "highway_mpg 5077.530212\n", "city_mpg 5746.501341\n", " ... \n", "fuel_system_spdi 9783.758088\n", "make_plymouth 9787.573337\n", "make_isuzu 9807.865944\n", "make_mitsubishi 9808.004146\n", "make_chevrolet 9918.293043\n", "Length: 79, dtype: float64" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "target = \"price\"\n", "rmse=[]\n", "for col in standardized_cars.columns[:-1]: \n", " rmse.append(knn_train_test([col], target, standardized_cars))\n", "best_attributes = pd.Series(data=rmse, index=standardized_cars.columns[:-1]).sort_values()\n", "best_attributes " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Plotting the predictions\n", "Since the car prices are ranging from 5118 to 45400, the best error score of 4367 seems pretty bad. Let's create a new function, namely knn_predictions, that will return the predictions and the testing set. Then we will plot the results with another function, plot_pred.\n", "\n", "We also create another error score function returning the Mean Absolute Percentage Error (MAPE), fitting better with past results (Kibler *et al.*, 1989) for comparison purposes: 11,84% for their instance-based technique (IBL) algorithm derived from KNN algorithm, and 14,12 % for the resulting linear regression equation, according to [UCI website](https://archive.ics.uci.edu/ml/datasets/automobile)." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "def knn_predictions(col_names, target, df, k = 5):\n", " \"\"\"\n", " Trains a KNN model, returns the predictions & testing set\n", " \n", " col_names (string): the training features\n", " target (string): the column target\n", " df (DataFrame): the data\n", " k (int) : the number of neighbors\n", " \n", " output: \n", " test_target (Pandas series): the testing set\n", " predictions (array): model predictions\n", " \n", " \"\"\"\n", " \n", " #initiate a seed for reproductibility \n", " np.random.seed(123)\n", " #randomization\n", " p = np.random.permutation(len(df))\n", " df = df.loc[p]\n", " #using 50/50 split for training and testing data\n", " l = len(df)//2\n", " #train only col_name\n", " train_feature = df.iloc[0:l][col_names]#.values.reshape(l,1)\n", " test_feature = df.iloc[l:][col_names]#.values.reshape(len(df)-l,1)\n", " #target\n", " train_target = df.iloc[0:l][target]\n", " test_target = df.iloc[l:][target]\n", " #instantiate KNeighborsRegressor class with default parameters\n", " knn = KNeighborsRegressor(n_neighbors = k)\n", " # Pass everything into the fit method.\n", " knn.fit(train_feature, train_target)\n", " #making predictions on the testing set using the training data \n", " predictions = knn.predict(test_feature) \n", " return test_target, predictions\n", "\n", "def mean_absolute_percentage_error(target, predictions):\n", " \"\"\"\n", " Calculate MAPE error metric\n", " \n", " target (string): the attribute we want to predict\n", " predictions \n", " \n", " output (float): MAPE score\n", " \"\"\" \n", " return np.mean(np.abs((target - predictions) / target))\n", "\n", "def plot_pred(target, predictions, title=\"\"):\n", " \"\"\"\n", " Scatter plot of model predictions (y-axis) vs target (x-axis)\n", " \n", " target (1d array): the true values of the attribute we want to predict\n", " predictions (1d array): the predictions made by the model\n", " title (string, optional): the title of the chart\n", " \n", " \"\"\"\n", " \n", " fig, ax = plt.subplots(figsize=(8, 8))\n", " mape = mean_absolute_percentage_error(target, predictions)\n", " string_score = f'MAPE on testing set: {mape:.2%}'\n", " ax.scatter(target, predictions)\n", " ax.plot([0, 1], [0, 1], transform=ax.transAxes,ls=\"--\", c=\"red\")\n", " plt.text(10000, 27000, string_score)\n", " plt.title(title)\n", " plt.ylabel('Model predictions')\n", " plt.xlabel('Truths')\n", " plt.xlim(5000,46000)\n", " plt.ylim(5000,46000)\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "image/png": 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RgorcGqBt27Zo1qwZTp48icePH+ONN97Ajh07YGpqioEDBwrqrlmzBikpKVizZg1GjBghOLZnz54Sr9wDSk4GtQkNDcWlS5fw/vvvF1sA/vDhw5d+uRVV8FqPGTMGAQEBZXpuRSuILSkpSevxR48eCeoV/Lekq/m0tVPwHH9/f83UaHkr+JIt+KJ+XWV9XQp70Wetbdu22L59O3Jzc3Ht2jWcPn0aGzZswNSpU2FqagpfX19Nm82bN3+l9T5JSUmCiwOA51eMpqeno3bt2pqyV+mji4sLzM3NERERgfT0dFy6dAljx44F8HxkS09PDxEREbh37x4A4bRe4bb+/vtvQSyl8bLfYZVKhbS0NK1rJKn6YApM1UaTJk1gZGSEP//8U5MwFVWwjUHLli0rNJb3338f+fn52LVrFyIjI/HPP//A29u72KhKwaXR2i5v/+WXX8otnlc5T8Ff3dpG+N555x3o6enh/Pnz5RZjeWndujUAaN2RPScnRzMVU1CvYDoxOjpa63492l6fgs0xK7L/BZ/R15keLKxwP7VtG1AwsqZtqrA0DA0N8c4772DGjBlYv349AODIkSMAgFq1aqF58+aIi4sr8XfzRbS9B+fOnYNarRZMB5f1vQeef87fe+89JCQkYOvWrcjPz0fXrl0BPJ9Wa9WqFc6cOaNZq1g0kXr33Xc18ZS3uLg4qNVqrdPaVH0wkaJqw9jYGD4+PsjPz8eXX35ZLAF4+vSpZj1L0dGf8jZs2DBIpVKEhIRoFplrO2fBtgJF/+E/depUud5nq6Tz3Llzp8Rbj1haWgKA5i/xwt544w0MGzYM169fx+LFi7UmIPfv3y+3JKAshg4dCkNDQ2zatKnY+VesWIHExET06tULdnZ2AJ6vJ+rWrRvu3btXbLQuLCxM65eyi4sLOnbsiNDQUGzevFnrnll//vmn1teutJo1awZra2v8+uuvr9xGYXXr1kWPHj1w//59rFq1SnDs1q1bCAoKgpGREYYOHVrqNs+dO6d17WDByE/B9CQATJgwAXl5eRg/frzWUeP09PQS+7ps2TLBebKysuDv7w9A+HtV1ve+QEHitGLFChgaGgr2guvSpQuuXr2KkydPwsbGptg6pzFjxsDQ0BBfffWV1s+7Uql85dssXbx4EQC0XvBA1Qen9qhaWbhwIa5cuYIdO3bg4sWL6N69OywsLPDgwQOEhobi6dOnGDRoEHx9fSs0DltbW/To0QMnTpzAH3/8UWzvqAKffPIJtm3bhlGjRqFfv36ws7PDrVu3cPLkSQwcOBD79u0rl3g8PDzg6OiItWvX4tatW2jVqhX++ecfhIWFoVevXporxArr3r07Vq1ahQULFuDWrVuaRc/Tp08HAAQEBCA+Ph5Lly7Fzp070aFDB9jY2ODRo0f4888/cfHiRSxcuLDYwuOK5uDggKVLl2Lq1Kno1q0bBgwYABsbG0RHR+OXX35B3bp18d133wmes3z5cvTs2RNz587FmTNn0KpVK9y5cweHDh2Ch4eH1o05N27ciP79+2PSpElYv3493n33XdSuXRuJiYm4ffs2rl27hq1btxabkiqLfv36YePGjbh27dorb6NQ2IoVK+Dh4YGFCxciMjIS7777rmYfqaysLKxatarY1gcv8uOPP+L06dN477330LBhQ5iZmeHPP/9EWFgYTExMBFdBjhgxAr/99hv++9//wsXFBT169ICDgwPS0tKQkJCAc+fOoVu3bsX2hQIAJycntG/fHv369dPsI3Xnzh306dNHcNXkq7z3wP9GmR4/fowOHToI9u3q2rUrVq1ahZSUFK1JZpMmTbB27VpMmDAB7du3h7u7Oxo1agSlUon79+8jOjoaOTk5SEhIKPXrWuD06dPQ09N76f1DqWpjIkXVSp06dXDq1Cn897//xeHDh7Fz505kZmbCwsICLi4uGD58OIYMGVIpsYwYMQInTpxAXl5esb2jCrRs2RKHDx+Gv78/Tpw4AaVSiZYtW+Lnn3+GhYVFuSVSMpkMhw4dwvz583H27FmcP38eDRs2xPTp0zFhwgSt5+nSpQsCAgIQHByMjRs3ahY9FyRSZmZmOHLkCH7++Wfs3r0bR44cQXZ2NqysrODg4IB58+ZhwIAB5RJ/WY0aNQqOjo5YvXo1jh49CoVCATs7O4wZM6bYFgsA0KhRI5w8eRLffPMNIiIicO7cObRo0QLbtm3DkydPtCZSdnZ2CA8Px4YNG3Dw4EHs3bsXeXl5sLa2RuPGjbFkyZJiO1WX1aeffoqNGzciJCSkXBKpBg0aICIiAsuXL8fx48dx4cIFyGQydOzYEZ9//nmZRz4+/fRT1K5dG5cuXUJMTAzy8vJgZ2cHX19fTJw4sVgSHRAQgF69emHTpk04e/Ysnj59CgsLC9jb2+OTTz4p8XczODgYAQEB2LVrFx49egQ7OzvMnj0bU6ZMKbbOqKzvPfA8UbOzs8ODBw80o1MF3NzcYGRkhJycHK1XAwLPt6Bo2bIl1qxZgzNnziA8PBzGxsawtbWFu7s7+vfvX4ZX9bmCDXx79uxZpm1OqOqRpKamlv4+D0REVK6GDRuGixcv4rffftN65WJN5uzsjHv37pVq65GaJjAwELNnz8aRI0deOyEncXGNFBGRiL799ls8e/YMa9euFTsUqiSZmZlYuXIlvL29mUTVAJzaIyISUdOmTREYGFjiJf1U89y9excff/xxsbsgUPXEqT0iIhKFLk/tUc3BRIqIiIjoFXGNFBEREdErYiJFRERE9IqYSBERERG9IiZSVCpxcXFih1Dp2GfdwD7rBvZZd2jtd24uzFq3hoVcLvgpD0ykiIiIqGYzNERWQADU+v/b9SmvnG7Nw0SKiIiIarz83r2RFRgItUSC3PffR+bmzeXSLjfkJCIiIp2QN2QIVPXqQenqCmi5P+qr4IgUERER1SwqVYmHlO3bl1sSBVShROq7776DXC7X3HkeAPz8/CCXywU/7u7ugufl5ORg+vTpcHR0hL29PXx9fXH//n1BnXv37mHYsGGwt7eHo6MjZsyYgdzcXEGds2fPokuXLrCxsUHr1q0RFBRUcZ0lIiKiCmG7ZQtMP/gAyM+vlPNViUTq4sWL2Lx5M1q0aFHsWNeuXREbG6v52b17t+D47NmzcfjwYWzatAmhoaFIT0/HsGHDoFQqAQBKpRLDhg1DRkYGQkNDsWnTJhw6dAhffvmlpo07d+5g6NChaNeuHSIjIzF16lTMmDEDBw8erNiOExERUflQq2H0zTeot3o1DEJDYTJhwgtHpsqL6Guk0tLS8J///AerV69GQEBAseNGRkawsbEp8bk///wz1qxZg27dugEA1q9fD2dnZ0RERKBHjx44ffo0bt26hevXr6NevXoAgPnz5+Pzzz/H3LlzYW5ujuDgYNja2mLZsmUAACcnJ/z666/48ccf0b9//wrqOREREZULpRLGX3wBo59+0hQZ7twJtZUVsv39K/TUoo9ITZ48Gf3790eXLl20Hj9//jwaN26Mtm3b4vPPP8fjx481x65evYq8vDx0795dU1avXj04OTkhOjoaABATEwMnJydNEgUAPXr0QE5ODq5evaqpU7iNgjpXrlxBXl5eufWViIiIylluLkw+/VSQRAGAulYt5PXqVeGnF3VEavPmzYiPj8f69eu1Hnd3d0ffvn3RoEEDJCQkwN/fH/369UNERASMjIyQlJQEqVQKS0tLwfOsrKyQlJQEAEhKSoKVlZXguKWlJaRSqaBO165di7WRn5+P5ORk2Nraao1P1zY707X+AuyzrmCfdQP7XPPoZWej0YwZMDx/XlCeZ2GBuB9+QKadHfCC16BJkyavHYNoiVRcXBwWLFiAY8eOwdDQUGudQYMGaf6/RYsWcHFxgbOzM8LCwtCvX78S21ar1ZBIJJrHhf+/sBfVUavVL3wuUD5vQHURFxenU/0F2GddwT7rBva5BkpNhczXF/oXLgiKc62tkXP4MOo6OVVKGKJN7cXExCA5ORnt27eHpaUlLC0t8csvv2Djxo2wtLRETk5OsefY2dnB3t4e8fHxAABra2solUokJycL6j158kQzCmVtba0ZeSqQnJwMpVL5wjpPnjyBvr4+6tSpU259JiIiotcnSUpCLW/vYkmUslEj3N64EapKSqIAERMpLy8vnDt3DlFRUZqfNm3aYNCgQYiKitI6SpWcnIwHDx5oFp+7uLjAwMAA4eHhmjr3799HbGwsXF1dAQDt2rVDbGysYEuE8PBwGBkZwcXFRVMnIiJCcK7w8HC0adMGBgYG5d11IiIiekWShATIPDwgvXFDUK50dobi2DHk2tlVajyiTe0V7AtVmKmpKWrXro3mzZsjIyMDS5YsQb9+/WBjY4OEhAQsWLAAVlZW8Pb2BgBYWFjgww8/xLx582BlZYXatWvjyy+/RIsWLTRrnrp374633noL48aNg7+/P54+fYp58+Zh5MiRMDc3BwCMGjUKGzZswKxZszBq1ChER0cjJCQEGzdurNTXhIiIiEqmFxsL2cCB0EtMFJTnt28PxfbtgFwOpKVVakyib39QEqlUit9//x07duxAWloabGxs0KlTJwQHB8PMzExTb9GiRZBKpRg1ahSys7PRuXNnrFu3DlKpVNPOzp07MW3aNHh4eMDY2BiDBw+Gf6HLIRs2bIhdu3Zhzpw5CAoKgq2tLZYuXcqtD4iIiKoI6ZUrMB00CHopKYLyvJ49n983z9RUlLiqVCJ19OhRzf+bmJhg3759L32OsbExli1bptkDSpv69etj586dL2znvffeQ2RkZOmDJSIiosqTmQlJZqagKNfHB1nr1gElXLRWGUTfR4qIiIjoZZQdOyLzp5+g1n8+BpQzejSyNmwQNYkCqtiIFBEREVFJ8j08kBUYCL3bt5Hz1VfAC7YoqixMpIiIiKjayBsyROwQBDi1R0RERFWHWg2DkBAgO1vsSEqFiRQRERFVDSoVjGfPhun48TAdNQqoBve7ZSJFRERE4svPh8mECTBatw4AYHDsGEwmTgRUKpEDezEmUkRERCSu7GyYjhwJw+3bBcUGx49D784dcWIqJS42JyIiIvGkp0M2fDj0o6IExSprayj27YPK0VGkwEqHiRQRERGJQpKSAtPBg6F/+bKgXOXgAMWBA1U+iQKYSBEREZEIJImJkPn4QHr7tqBc2awZFPv2QW1vL1JkZcNEioiIiCqVXnw8ZP37Q+/ePUF5/ttvI3PPHqjr1BEpsrLjYnMiIiKqNHrXr0Pm4VE8iercGYqDB6tVEgUwkSIiIqJKIo2ORi1vb+glJQnK8/r0gWLXLsDMTKTIXh0TKSIiIqp4WVkwHTkSkrQ0QXGury8yt2wBjI1FCuz1MJEiIiKiimdigsxNm6AulDDljB2LrLVrAf3qu2SbiRQRERFVCuV77yHzp5+g1tdH9uzZyF6yBNCr3qlI9U0BiYiIqNrJ9/BAxoULUDVuLHYo5aJ6p4FERERU9ajVQFZWiYdrShIFMJEiIiKi8qRUwnjqVMiGDgWys8WOpsIxkSIiIqLykZsLk//8B0bBwdCPioLpqFFAfr7YUVUoJlJERET0+jIzYTpiBAz37dMUGRw7BuM5c0QMquJxsTkRERG9ntRUyHx9oX/hgqBYZW+P3E8+ESmoysFEioiIiF6ZJCnp+c2Hb9wQlCsdHaE4cABqBweRIqscnNojIiKiVyJJSIDMw6N4EtWyJRTHj9f4JApgIkVERESvQC82FrU8PCCNjxeU57u5IePIEaitrUWKrHIxkSIiIqIykV65ApmnJ/QSEwXlee7uUOzbB8jlIkVW+ZhIERERUalJo6Ig69sXeikpgvJcHx9khoQApqYiRSYOJlJERERUKvphYZANHgxJRoagPGfUKGRt2AAYGooUmXiYSBEREVGpqGxsiiVL2VOmIHvFCkAqFSkqcTGRIiIiolJRubhAsX071MbGAICs+fOR8/XXgEQicmTi4T5SREREVGrK995D5k8/QfLoEfI++kjscETHRIqIiIjKJN/DQ+wQqgxO7REREZFQfj6Mli0Dnj0TO5Iqj4kUERER/U92Nkw/+gjGCxdCNnw4kJ0tdkRVGhMpIiIiei49HbKhQ2Fw9CgAQP/sWU8xxZ4AACAASURBVJiOGgXk5YkcWNXFRIqIiIggSUmBrH9/6EdGCsqlN29C8vixSFFVfUykiIiIdJwkMRGyPn2gf/myoFzZrBkyjh+H2t5epMiqPl61R0REpMP04uMhGzAAegkJgvL8t99G5p49UNepI1Jk1QNHpIiIiHSU3o0bkHl4FE+iOneG4uBBJlGlwESKiIhIB0mjo1HLywt6SUmC8jwvLyh27QLMzESKrHphIkVERKRj9E+dgmzgQEjS0gTlue+/j8zNm4F/bwFDL8dEioiISIfoHzgAU19fSDIzBeU548Yha80aQJ/Lp8uCrxYREZGuyMiAyYwZkBTZFyp79mzkzJih0zcfflUckSIiItIVtWpBsWsX1IXWP2UtWYKcmTOZRL0iJlJEREQ6ROXiAsX27VDXqoXMdeuQO26c2CFVa5zaIyIi0jHK995D+m+/QW1pKXYo1V6VGZH67rvvIJfLMX36dE2ZWq3G4sWL0axZM9ja2sLLywu3bt0SPC81NRVjxoyBg4MDHBwcMGbMGKSmpgrq3Lx5E3369IGtrS3eeustLF26FGq1WlDn4MGDcHV1hbW1NVxdXXH48OGK6ywREVEFk+TlAUW+DwtjElU+qkQidfHiRWzevBktWrQQlK9atQpr1qzB0qVLcfr0aVhZWWHgwIFIT0/X1Pn0009x7do17N69G3v27MG1a9cwduxYzfFnz55h4MCBsLa2xunTp7FkyRKsXr0aP/74o6ZOTEwMRo8ejSFDhiAqKgpDhgzBxx9/jF9//bXiO09ERFTeMjPReNo0yAYOBJ49EzuaGk30RCotLQ3/+c9/sHr1asjlck25Wq1GYGAgJk+ejP79+6N58+YIDAxERkYG9uzZAwCIjY3FyZMnsXLlSri6uqJdu3b4/vvvERYWhri4OADA7t27kZWVhcDAQDRv3hz9+/fHpEmTsHbtWs2oVGBgIDp16oRp06bByckJ06ZNw3vvvYfAwMDKf0GIiIheR2oqZD4+sDh3DvpXrkA2fDiQnS12VDWW6IlUQaLUpUsXQfndu3fx6NEjdO/eXVNmYmKCDh06IDo6GsDzkaRatWrB1dVVU8fNzQ0ymUxQp3379jAxMdHU6dGjBx48eIC7d+8CeD4iVvg8BXUK2iAiIqoOJElJqOXtDf0LFzRl+mfPwnjuXBGjqtlEXWy+efNmxMfHY/369cWOPXr0CABgZWUlKLeyssKDBw8AAElJSbC0tISk0CWbEokEb7zxBpL+3fI+KSkJ9kXuWl3QZlJSEho2bIhHjx5pPU9SkW3ziyoY9dIVutZfgH3WFeyzbqjpfTZ88ABNJ0yA9N49QXlmkyb4w8cH+TW8/4WV9r1u0qTJa59LtEQqLi4OCxYswLFjx2BoaFhiPUmRfS3UanWxxKmol9UpmNJ7WR1tbRdWHm9AdREXF6dT/QXYZ13BPuuGmt5nvdhYyMaNg15ioqA8380NeTt24M1CS2dqusp+r0Wb2ouJiUFycjLat28PS0tLWFpa4pdffsHGjRthaWmJOv/ecbroqNCTJ080o0fW1tZ48uSJ4Ao8tVqN5ORkQR1tbQD/G5mysbF54XmIiIiqKumVK5B5ehZLotLat4di3z5Ah5IoMYiWSHl5eeHcuXOIiorS/LRp0waDBg1CVFQUGjduDBsbG4SHh2uek52djfPnz2vWRLVr1w4ZGRmIiYnR1ImJiYFCoRDUOX/+PLILLbQLDw+HnZ0dGjRoAAB49913BecpqFN47RUREVFVI42KgqxvX+ilpAjKc3188Od33wGmpiJFpjtEm9qTy+WCq/QAwNTUFLVr10bz5s0BAH5+fvjuu+/QpEkTNG7cGMuXL4dMJsPgwYMBAE5OTnB3d8eUKVOwatUqqNVqTJkyBb1799YM6w0ePBhLly7F+PHjMW3aNPz5559YuXIlZsyYoZm6GzduHPr06YMVK1bA29sbR44cQVRUFI4fP16JrwgREVHp6YeGwnTUKEhycgTlOaNGIXv5cqjj40WKTLdU6Z3NJ02ahKysLEyfPh2pqalo27Yt9u3bB7NC9wjasGEDZs6cCR8fHwCAp6cnAgICNMctLCywf/9+TJs2Dd26dYNcLseECRMwceJETR1XV1cEBQXB398fixcvxptvvomgoCC88847lddZIiKiUjLYuRMm48dDolQKyrOnTEHOvHm8b14lkqSmpqpfXo10XU1fqKkN+6wb2GfdUJP6bLB1K0wLDQYUyJo/H7mTJmke16Q+l4XOLDYnIiKislO2awdVodu7qCUSZK5aJUiiqPIwkSIiIqpGVE2bQrF3L9RmZlAbGCAzOBh5H30kdlg6q0qvkSIiIqLiVC4uUOzYAUl2NvJ79BA7HJ3GRIqIiKgaUnbsKHYIBE7tERERVU3p6TCePBmS5GSxI6EXYCJFRERUxUhSUiDr3x9GP/0E08GDgWfPxA6JSsBEioiIqAqRJCZC1qcP9C9fBgDoX7kC2fDhQKE7dFDVwUSKiIioitCLj0ctDw9Ib98WHlAoIMnKEicoeiEmUkRERFWA3o0bkHl4QC8hQVCe36kTFIcOQV27tkiR0YswkSIiIhKZNDoatby8oJeUJCjP69MHit27gUK3RqOqhYkUERGRiPRPnYJs4EBI0tIE5bm+vsjcsgUwNhYpMioNJlJEREQi0T9wAKa+vpBkZgrKc8aORdbatYA+t3us6phIERERicBgyxaYjh4NSV6eoDx79mxkL1kC6PErujpgqktERFTJDH/4ASbz5hUrz1qyBLnjxokQEb0qJlJERESVKTUVRuvXC4rUUimy1qxBnq+vSEHRq+K4IRERUWWSy6HYvx8qS0sAgNrICJk//8wkqppiIkVERFTJVE2bQrF3L1T29lDs3o38Pn3EDoleEaf2iIiIRKBycUH6lSuAkZHYodBr4IgUERFRRUlNhSQxseTjTKKqPSZSREREFUCSlIRa3t7PN9tMThY7HKogTKSIiIjKmSQhATIPD0hv3IA0NhamgwcD6elih0UVgIkUERFROdKLjUUtDw9I4+M1ZfpXrsB4yRIRo6KKwkSKiIionEivXIHM0xN6RdZF5bm7I/vLL0WKiioSEykiIqJyII2KgqxvX+ilpAjKc318kBkSApiaihQZVSQmUkRERK9JPzQUssGDIcnIEJTnjBqFrA0bAENDkSKjisZEioiI6DUY7NgB0w8/hCQnR1CePWUKslesAKRSkSKjysANOYmIiF6R4fr1MJk5s1h51vz5yJ00SYSIqLIxkSIiIiortRpGAQEwXrxYWCyRIGvlSuR99JFIgVFlYyJFRERURoarVhVPogwMkLlhA/IHDBApKhID10gRERGVUd6gQVDVrat5rDY1ReaOHUyidBATKSIiojJS168Pxf79UFlaQm1hAcX+/cjv0UPssEgEnNojIiJ6BaqmTaHYuxfQ14eqZUuxwyGRMJEiIiJ6RSoXF7FDIJFxao+IiKgEksREmA4dCsn9+2KHQlUUEykiIiIt9OLjUcvDAwYnTkDm4wNJcrLYIVEVxESKiIioCL0bNyDz8IBeQgIAQBobC9PBg4H0dJEjo6qGiRQREVEh0uho1PLygl5SkqBcbW8PGBiIFBVVVUykiIiI/qV/6hRkAwdCkpYmKM99/31kbt4MGBuLFBlVVUykiIiIAOgfOABTX19IMjMF5TnjxiFrzRpAnxe6U3FMpIiISOcZbNkC09GjIcnLE5Rnz5mD7MWLAT1+XZJ2TK+JiEinGf7wA0zmzStWnrV0KXLHjhUhIqpOmEgREZFuUqthtGABjL//XlgslSJr7VrkDRsmUmBUnTCRIiIi3aNUwnjaNBgFBwuK1UZGyPzpJ+R7eooUGFU3TKSIiEjnSJ49g35UlKBMbWYGRUgIlJ06iRQVVUdcPUdERDpHXbs2FPv3Q1W3LgBAVacOFIcOMYmiMmMiRUREOkldvz4U+/dD2aoVFMeOQdmmjdghUTUkWiK1YcMGdOjQAfXr10f9+vXRs2dPhIWFaY77+flBLpcLftzd3QVt5OTkYPr06XB0dIS9vT18fX1xv8iNJe/du4dhw4bB3t4ejo6OmDFjBnJzcwV1zp49iy5dusDGxgatW7dGUFBQxXWciIiqDFXTpsg4cwYqJyexQ6FqSrREyt7eHvPnz8eZM2cQHh6Ozp07Y8SIEbhx44amTteuXREbG6v52b17t6CN2bNn4/Dhw9i0aRNCQ0ORnp6OYcOGQalUAgCUSiWGDRuGjIwMhIaGYtOmTTh06BC+/PJLTRt37tzB0KFD0a5dO0RGRmLq1KmYMWMGDh48WDkvBBERVShJQgKM7t17QQVJ5QVDNY5oi829vLwEj+fOnYtNmzbh4sWLaNmyJQDAyMgINjY2Wp+flpaGn3/+GWvWrEG3bt0AAOvXr4ezszMiIiLQo0cPnD59Grdu3cL169dRr149AMD8+fPx+eefY+7cuTA3N0dwcDBsbW2xbNkyAICTkxN+/fVX/Pjjj+jfv39FdZ+IiCqBXmwsZAMHoqlajZxTp57fL4+oHFWJNVJKpRJ79+6FQqFAu3btNOXnz59H48aN0bZtW3z++ed4/Pix5tjVq1eRl5eH7t27a8rq1asHJycnREdHAwBiYmLg5OSkSaIAoEePHsjJycHVq1c1dQq3UVDnypUryCuywy0REVUf0itXIPP0hF5iIowePHh+D73kZLHDohpG1O0Pbt68iV69eiE7OxsymQxbt25FixYtAADu7u7o27cvGjRogISEBPj7+6Nfv36IiIiAkZERkpKSIJVKYWlpKWjTysoKSf/esTspKQlWVlaC45aWlpBKpYI6Xbt2LdZGfn4+kpOTYWtrW2L8cXFxr/sSVCu61l+AfdYV7HPNY3bpEhpPnQq9QvfNk8bGImPxYiTq0G7lNf19Lklp+92kSZPXPpeoiVSTJk0QFRWFtLQ0HDp0CH5+fjhy5AiaN2+OQYMGaeq1aNECLi4ucHZ2RlhYGPr161dim2q1GpJC892SEua+X1RHrVa/8LmF49cVcXFxOtVfgH3WFexzzaMfGgrTSZMgyckRlOeMGgXZ0qVoIpWKFFnlqunvc0kqu9+iTu0ZGhrC0dERbdq0wddffw1nZ2esXbtWa107OzvY29sjPj4eAGBtbQ2lUonkIsO0T5480YxCWVtba0aeCiQnJ0OpVL6wzpMnT6Cvr486deqUSz+JiKhyGOzYAdMPPyyWRD346CNkr1gB6EgSRZWnSqyRKqBSqYptTVAgOTkZDx480Cw+d3FxgYGBAcLDwzV17t+/j9jYWLi6ugIA2rVrh9jYWMGWCOHh4TAyMoKLi4umTkREhOBc4eHhaNOmDQwMDMqze0REVIEM16+H6bhxkPx75XaBrPnzcX/iRF6dRxVCtETqm2++wblz53D37l3cvHkT8+fPx9mzZzFkyBBkZGTgq6++QkxMDO7evYuoqCj4+vrCysoK3t7eAAALCwt8+OGHmDdvHiIiIvDbb79h7NixaNGihWbNU/fu3fHWW29h3Lhx+O233xAREYF58+Zh5MiRMDc3BwCMGjUKiYmJmDVrFmJjY7FlyxaEhIRg4sSJYr00RERUFmo1jJYuhcnMmcJiiQSZq1Yhd9IkkQIjXSDaGqlHjx5hzJgxSEpKgrm5OVq0aIE9e/agR48eyMrKwu+//44dO3YgLS0NNjY26NSpE4KDg2FmZqZpY9GiRZBKpRg1ahSys7PRuXNnrFu3DtJ/h26lUil27tyJadOmwcPDA8bGxhg8eDD8/f01bTRs2BC7du3CnDlzEBQUBFtbWyxdupRbHxARVQcqFYznzIHRunWCYrWBATI3bED+gAEiBUa6QpKamqoWOwiq+nRx0SL7rBvY52osPx8mn30Gw+3bBcVqU1Nk/vwz8nv00JTVmD6XgS72Gaj8fot61R4REdGrMv722+JJlIUFFLt2QfnvWlmiilalFpsTERGVVs6ECVA2aqR5rLK2RsbRo0yiqFIxkSIiompJbW0Nxf79UNWtC5WDAxTHj0P17y3GiCoLp/aIiKjaUjs4QHHgANQyGe+jR6JgIkVERFWfWl3iPlAqHVxQTVUHp/aIiKhK07t+HbU6dYLeX3+JHQpRMUykiIioypJGR6OWtzekN25ANmAAJIXuVEFUFTCRIiKiKkn/1KnnyVNaGgBA7949yHx8gH8fE1UFTKSIiKjK0T9wAKa+vpBkZQnK87t1Awrd4YJIbFxsTqSD7qbnwf9yOh5kKmFnKsVXb5uhgRlv0k3PVcTnoyxtGmzZApPJkyFRqQTl2XPm4I9xk+EflVqlPrvV/fdJW/wAqnWfKhMTKSIdczc9DwPCkvF3ulJT9uvjXBzobSliVFRVvOjz8apfpGVp0/CHH2Ayb16xNrKWLkXc8NHlHtvrqojXqzJpi//8w2xAIsE/iv8lstWpT5WNU3tEOsb/crrgH00A+DtdCf/L6SJFRFVJRXw+StWmWg2j+fOLJVFqqRSZ69cjd+zYKvnZrYoxlYW2+P/JVAuSKKB69amycUSKSMc8yFRqLX9YQjnplor4fLy0TaUSxtOmwSg4WHBcbWSEzJ9+Qr6nZ4XF9rqqYkxlUVL82lSXPlU2JlJEOsbOVKq13LaEctItFfH5eGGbubkwGTcOhvv2CY6pzcygCAmBslOnCo3tdVXFmMqipPi1qS59qmyc2iPSMV+9bYY3zYT/IL5p9r8FpqTbKuLzUVKbc9+SwnTEiGJJlKpOHSgOHRIkURUV2+uqijGVhbb465lKUE8mTA+qU58qG0ekiHRMAzMDHOhtCf/L6XiYqYRtoSty4h6KHR2J7UWfj/Ju00GVCb2Hwg+dqm5dKPbtg8rJqVJie11VMaayKCl+ANW2T5WNiRSRDmpgZoANXeqIHQZVURXx+dDepgUUe/dC5ukJ6V9/QdmoERT790Pt4FCpsb2uqhhTWZQUf3XuU2Xi1B4REYlGbW0NxYEDyOvdG4pjx16YRBFVRRyRIiIiUanr10fmzp1ih0H0SjgiRUREFU565Qr0rl8XOwyicsdEioiIKpQ0Kgqyvn0hGzQIen/9JXY4ROWKiRQREVUY/dBQyAYPhiQjA3pJSZANGABJYqLYYRGVGyZSRERUIQx27IDphx9CkpOjKdO7dw8GBw+KGBVR+WIiRURE5c5w/XqYjhsHiVJ4W5Gs+fOR6+cnUlRE5Y9X7RERUflRq2EUEADjxYuFxRIJslauRN5HH4kUGFHFYCJFRETlQ6WC8Zw5MFq3TlCsNjBA5oYNyB8wQKTAiCoOEykiInp9+fkw+ewzGG7fLihWm5oi8+efkd+jh0iBEVUsJlJERPR6srNhOno0DEJDBcVqCwsodu2C0tVVpMCIKt5rLzZ/+PAhbt++XR6xEBFRdZOeDtmQIcWSKJW1NTKOHmUSRTVeqROp4OBgjB07VlD2xRdfoHnz5ujQoQM6deqE5OTkcg+QiIiqLuMFC6AfFSUoUzk4QHH8OFQtW4oUFVHlKXUitXnzZpiZmWkeR0ZGIigoCIMHD8a8efPw999/Y/ny5RUSJBERVU3ZX34JpbOz5rGyWTNkHD8OlaOjiFERVZ5Sr5G6e/cuPvjgA83jAwcOoG7duli3bh309PSQlpaG/fv3Y3GRS16JiKgGk8uh2LcPMk9PqM3NkblnD9R16ogdFVGlKXUilZubCwMDA83j8PBwuLu7Q0/v+aCWo6MjHj58WP4REhFRlaa2soLi4EGozc2BQjMXRLqg1FN7DRo0QEREBADg8uXLuHPnDrp37645npSUJJj6IyKiGiY7u8RD6rp1mUSRTip1IjV69GgcOHAAHTp0gI+PD+rWrYuePXtqjl+4cAHNmjWrkCCJiEhc+qdOwaxNG+hdvy52KERVSqmn9j799FMYGhrixIkTaN26NSZPngwTExMAwNOnT/H48WOMHj26wgIlIiJxGOzfD5MxYyDJy4Ns0CAojh2DqlEjscMiqhLKtCHnyJEjMXLkyGLltWvX1kz7ERFRzWGweTNMJk+GRK0GAOglJUE2YADSz53jVB4RymFDTiIiqpkMV62C6aRJmiSqQM6ECUyiiP5VphGpiIgIbN68GXfu3MHTp0+hLvLLJZFIcPXq1XINkIiIKplaDaMFC2D8/ffCYqkUWWvWIM/XV6TAiKqeUidSgYGB+PLLL/HGG2/gnXfewVtvvVWRcRERkRiUShhPmwaj4GBBsdrICJnBwcjv00ekwIiqplInUmvWrEHHjh2xd+9eGBoaVmRMREQkhtxcmIwbB8N9+wTF6lq1oAgJgbJzZ5ECI6q6Sr1GKjk5GT4+PkyiiIhqosxMmI4YUSyJUtWpA8Xhw0yiiEpQ6hEpFxcXJCQkVGQsREQkhtRUyHx9oX/hgqBYZW8Pxf79UDk5iRQYUdVX6hGphQsXIiQkBJGRkRUZDxERVSLJkyeo5e1dLIlSOjo+v/kwkyiiFyr1iNTixYthbm6OAQMGoFGjRqhfvz6kUqmgjkQiwa5du8o9SCIiqhhqU1Ooi2xloGzZEop9+6C2thYpKqLqo9SJ1O3btyGRSFCvXj3k5OTgzz//LFZHIpGUa3BERFTBTE2h2L4dtfr2hfT6deS7uUGxYwcgl4sdGVG1UOqpvevXr+PatWsv/Pntt99KfeINGzagQ4cOqF+/PurXr4+ePXsiLCxMc1ytVmPx4sVo1qwZbG1t4eXlhVu3bgnaSE1NxZgxY+Dg4AAHBweMGTMGqampgjo3b95Enz59YGtri7feegtLly4ttv/VwYMH4erqCmtra7i6uuLw4cOl7gcRUbUnl0Oxdy9yRo+GYt8+JlFEZSDazub29vaYP38+zpw5g/DwcHTu3BkjRozAjRs3AACrVq3CmjVrsHTpUpw+fRpWVlYYOHAg0tPTNW18+umnuHbtGnbv3o09e/bg2rVrGDt2rOb4s2fPMHDgQFhbW+P06dNYsmQJVq9ejR9//FFTJyYmBqNHj8aQIUMQFRWFIUOG4OOPP8avv/5aeS8GEZHI1NbWyF6xAjA1FTsUomqlTDubA8CZM2dw4sQJzRV8Dg4O6NWrF7p06VKmdry8vASP586di02bNuHixYto0aIFAgMDMXnyZPTv3x/A8w1BmzRpgj179mDUqFGIjY3FyZMncfz4cbi6ugIAvv/+e3h6eiIuLg5NmjTB7t27kZWVhcDAQJiYmKB58+b4448/sHbtWkycOBESiQSBgYHo1KkTpk2bBgBwcnJCVFQUAgMDsWnTprK+PEREVZb+sWNQ164NWFqKHQpRjVHqEanc3Fx8+OGHGDhwINauXYuoqChERkZi7dq1GDhwID788EPk5eW9UhBKpRJ79+6FQqFAu3btcPfuXTx69Ajdu3fX1DExMUGHDh0QHR0N4PlIUq1atTRJFAC4ublBJpMJ6rRv3x4mJiaaOj169MCDBw9w9+5dAMDFixcF5ymoU9AGEVFNYLBzJ0w/+ACyoUNh8scfYodDVGOUekQqICAAR44cgZ+fHyZNmgQbGxsAQFJSElatWoW1a9di2bJlmDNnTqlPfvPmTfTq1QvZ2dmQyWTYunUrWrRooUlirKysBPWtrKzw4MEDzXktLS0FC9wlEgneeOMNJCUlaerY29sXa6PgWMOGDfHo0SOt5ylo40Xi4uJK3deaQNf6C7DPuqKm99lq1y40WLbs+YNnz9D0s89w29gYOQ4O4gZWyWr6+6yNLvYZKH2/mzRp8trnKnUitXv3bgwZMgSLFi0SlFtbW2PhwoV4/Pgxdu7cWaZEqkmTJoiKikJaWhoOHToEPz8/HDlyRHO86FWAarW6WOJU1MvqFCw0f1md0lyBWB5vQHVRMF2qS9hn3VCj+6xWw2jZMhgXJFH/0n/6FI1SUpDXo4dIgVW+Gv0+l0AX+wxUfr9LPbX38OFDuLm5lXjc1dUVDx8+LNPJDQ0N4ejoiDZt2uDrr7+Gs7Mz1q5dKxjtKuzJkyea0SNra2s8efJEcAWeWq1GcnKyoI62NoD/jUzZ2Ni88DxERNWSSgXjOXNgXOSPX7WBAeIXLULekCEiBUZUs5Q6kbKxscGlS5dKPH758mVYv+bmbSqVCrm5uWjQoAFsbGwQHh6uOZadnY3z589r1kS1a9cOGRkZiImJ0dSJiYmBQqEQ1Dl//jyys7M1dcLDw2FnZ4cGDRoAAN59913BeQrqFF57RURUreTnw2TiRBgFBgqK1SYmyNyxA0/d3UUKjKjmKXUi5ePjg+3bt2Px4sV49uyZpvzZs2dYsmQJtm/fjsGDB5f6xN988w3OnTuHu3fv4ubNm5g/fz7Onj2LIUOGQCKRwM/PDytXrsShQ4fw+++/Y/z48ZDJZJpzODk5wd3dHVOmTMHFixcRExODKVOmoHfv3pohvcGDB8PExATjx4/H77//jkOHDmHlypUYP368Zupu3LhxiIyMxIoVK/DHH39gxYoViIqKgp+fX6n7QkRUZWRnw/Sjj2AYEiIoVpubQ7F/P/J1aDqPqDKUeo3UrFmzcOPGDQQEBGD58uWa6bdHjx5BpVLB3d0ds2bNKvWJHz16hDFjxiApKQnm5uZo0aIF9uzZgx7//pJPmjQJWVlZmD59OlJTU9G2bVvs27cPZoVuZbBhwwbMnDkTPj4+AABPT08EBARojltYWGD//v2YNm0aunXrBrlcjgkTJmDixImaOq6urggKCoK/vz8WL16MN998E0FBQXjnnXdK3RcioiohPR2y4cOhHxUlKFZZW0Oxdy9Uzs4iBUZUc0lSU1PVL6/2P8ePHy+2j1Tv3r3Ru3fvCgmQqgZdXLTIPuuGmtJnSUoKTAcPhv7ly4JyVf36UBw4AFWjRpqymtLnsmCfdUdl97vMG3J6eHjAw8OjImIhIqJXIElMhMzHB9LbtwXlSicnKPbvh7rINjBEVH5Eu0UMERGVD6OAgGJJVP7bb0MRGsokiqiClTgilnTd+gAAIABJREFU5e3tDT09Pezbtw/6+vro27fvSxuTSCQ4dOhQuQZIREQvlr1oEaSxsdA/fx4AkN+pExQhIUChNaVEVDFKTKTUajVUKpXmsUqleukmlYX3dCIiokpiagrF9u2o1bcvVPXrIzMoCDA2FjsqIp1QYiJ19OjRFz4mIqIqRC6H4tAhqM3MAP0yL38loldU6jVSv/zyi2ZXcG2Sk5Pxyy+/lEtQRESknSQ5ucRj6tq1mUQRVbJSJ1J9+/YttgN4YWfOnCnVOioiIno1Blu2wKx1a0gvXBA7FCL6V6kTqZetf8rNzYWeHi8CJCKqCIY//ADTzz+HJCMDsqFDoXf9utghERFeso/Us2fPkJaWpnmckpKCe/fuFauXmpqKvXv3ws7OrvwjJCLSZWo1jBYsgPH332uKJM+eQTZsGNIvXQJMTEQMjohemEitXbtWc8sViUSC2bNnY/bs2VrrqtVqzJ07t/wjJCLSVUoljKdNg1FwsKBYbWSErOXLmUQRVQEvTKS6du0KY2NjqNVqLFiwAD4+PnAucq8miUQCU1NTtGnThvenIyIqL7m5MBk3Dob79gmK1bVqQRESAmXnziIFRkSFvTCRcnNzg5ubGwAgJycHffv2RYsWLSolMCIinZWZCdOPPoLB//2foFhVpw4y9+6Fsk0bkQIjoqJKfZ3srFmzKjIOIiICgNRUyN5/X7NLeQGVvT0U+/dD5eQkUmBEpE2pL7ObPXs23n777RKPt23blmukiIhegyQpCbX69i2WRCkdHZFx/DiTKKIqqNSJ1IkTJ+Dj41Pi8YEDB+L48ePlEhQRka6RJCRA5ukJaZFtDZQtW0Jx/DjUDg4iRUZEL1Lqqb379+/D4QW/yA4ODrh//365BEVEpEskCQmo5ekJvSL/hua7uUGxYwcgl4sUGRG9TKlHpMzMzHDnzp0Sj//9998w5k0yicpNVFQU5HI5kl9wSxCqGdS2tlA2by4oy3N3h2LfPiZRRFVcqROpzp07IygoSGsydefOHQQHB6MzL8et0fz8/CCXy/HZZ58VOzZv3jzI5XIMGzas2LHHjx/DxsYGLVu2hEqlKnbc2dkZcrkccrkcdnZ2aN++PYIL7Zuzbds2zfGiP9nZ2eXbyZfYtm0b6tatW+7tOjs7Y/Xq1YIyV1dXxMbGok6dOuV+vvJ09+5dyOVyXLny/+zdd1RUR/vA8e+ywFJEsCBIFBVFrIklSuw19ihiIxoLGntii70lRn5Ro9ForC+WRGMLBltsSdTYXrvmTVERQ2KJxoKiwFKW3fv7Q9m41EWBpTyfczxH5s7OfQZc92Fm7szFLL82Pj6e4cOH07BhQ0qWLEnHjh3TrBcSEkLjxo0pXbo0lStXZsiQIdy9ezfddg0GAwEBAdSoUQM3Nzd8fHwYMmQIt2/fNtZ58OAB/v7+tG/fnlKlSlG9enXGjx9vsgnx9evXad++PR4eHrRv357r16+b3Oedd97hyy+/zHK/U7G1RfvVVyQ1aABAor8/2k2bwMHh5dsWQuQosxOpqVOnYjAYaNSoERMmTGDdunV8+eWXTJgwgcaNG6MoCtOmTcvJWEUeUKZMGbZv305sbKyxLCkpia1bt1KmTJk0X7Np0ybatWuHRqPh4MGDadaZOHEiYWFhnDhxgo4dOzJ27FhCn9s/x8HBgbCwsFR/CvIoqK2tLW5ubqhUKkuHkmP0ej12dnYMGTKENm3apFnn1KlTDB06lLfffpuTJ0+yceNGrly5wuDBgzNsu2nTpqxbt46zZ8+yfv16/vrrL9555x3jdSsrKzp16sTChQs5d+4cy5cv58iRI4wePdpYZ/r06Xh4eHDs2DHc3d1NHqj57rvviIyMpH///i/5XXjGwYHYLVuInzmTuOBgsLXNnnaFEDnK7ESqYsWKHDhwgFq1arF69WrGjRvH2LFjWb16NbVq1WLfvn14e3vnZKwiD6hevTpeXl5s377dWHbgwAE0Gg2NGzdO8zVff/01AQEB9OrViw0bNqRZx8nJCTc3N7y8vJg+fToVK1Zkz549xusqlQo3N7dUfzJy4sQJWrVqhZubG97e3kyZMoXExETj9Y4dO/LBBx/w8ccf4+XlRaVKlZg+fXqao2bwdKpt5MiRxMbGGkfE5syZAzw9a/LDDz+kWrVqeHh40KJFC5OkUafTMXHiRKpUqWIc/fjoo4+Mcdy8eZMZM2YY202+3/NTe8mjYUeOHKFBgwZ4eHjQqVOnVKPECxcuxNvbm1deeYWhQ4cyd+7cVBvppjRv3jxq1KhBw4YNqVy5MkOHDjVeUxSFxYsXU6tWLdzd3WnYsCFbt241Xn/ttdcAaNGiBS4uLumOKqXF0dGRRYsWMWDAgHRH+s6ePYuHhwcjR46kfPny1KtXjyFDhnD+/Pl027WysmLEiBHUq1cPT09PfH19GTNmDBcuXDCOYhYvXpyBAwdStWpVPD09adasGYMGDeLkc0/MXb16lbfffpuKFSvSu3dvrl69Cjw9Pmv69OksXrw4exNdZ2cSxo0DtTr72hRC5KgsnTJctWpV9uzZw7Vr1/jxxx/54Ycf+OOPP/juu++olmJ+XxRcffv2ZePGjcavv/76a/r06ZPmB8p///tfHj58SOvWrenZsyf79+/nwYMHmd5Do9Gg0+leOMbbt2/To0cPXn31VY4ePcoXX3zBt99+y6xZs0zqhYSEoFar+f7775k/fz4rVqwwGQl7nq+vL3PmzDEZHUue5hw5ciQnTpwgODiY//73v7z99tsEBATw67MnsFauXMmePXtYs2YN58+fZ+3atVSqVAl4+v175ZVXjKNyYWFh6fYrISGBhQsXsnTpUr7//nseP37MuHHjjNe//fZb5s2bx4wZMzhy5Ag+Pj4sX748w+/Vzp07Wbp0KZ999hmhoaFs3bqVunXrGq8HBQWxYcMGFixYwKlTpxg7dixjx47lwIEDABw6dMh477CwML7++mvg30Tw2LFjGd4/M76+vty9e5d9+/ahKAqRkZGEhoby5ptvmt3Go0ePCAkJ4fXXX093FPPOnTvs3r2bRo0aGctq1KjBTz/9hMFg4NChQ8YNiWfNmkXv3r2pXLlylvtjGxyM9Y8/Zvl1Qoi8KUuJVLISJUpQt25dXn/99Ty/fkNkvx49enDx4kX++OMP7t69y8GDB+ndu3eaddevX4+/vz82NjaUL1+eunXrsnnz5nTbTkpKYuPGjVy6dIlmzZoZy2NjY3nllVdM/qQ3FQSwZs0a3Nzc+Oyzz/Dx8aFdu3Z8+OGHBAcHo9VqjfV8fHyYNm0alSpVomvXrjRp0oQjR46k2aatrS1FixY1GR0rUqQIf/75J9u2bWPdunU0atSI8uXLM2TIEN58803j+pmbN29SsWJFGjZsSNmyZfH19TVOMxUrVgwrKyvjqFxGI21JSUksWLCAunXrUqNGDd5//32OHTtmHEVbuXIlvXv3pl+/flSqVIlx48aZJEVpuXnzJm5ubrRs2RJ3d3dq167NkCFDjN/3ZcuWsWTJElq3bk358uXp0aMH/fr1Y/Xq1cDT/w/g6QiPm5sbxYoVA55Ox3p7e+Pwkut86tevz+rVqxkyZAiurq5UrFgRRVFYsWJFpq/98MMP8fDwoEKFCty6dctkJC3ZtGnTKF26NFWrVqVIkSIsW7bMeG327NmEh4fz6quvEhERwezZszl9+jQnT55k0KBBDB48mNdee413332XJ0+eZByMoqD59FPsJ0zAoW9f1KdOZfl7IYTIe9Ld/uDEiRMAxt/Okr/OzPO/zYmCycXFhU6dOvH111/j7OxM48aNKVu2bKp6T548YdeuXezatctYFhAQwPLly1MtWJ89ezZz584lISEBW1tbRo0aRWBgoPG6g4NDqpEN2wzWkISFhVGvXj2srP79XaFBgwYkJiYSERFBjRo1AFIdeeTu7s79+/fN+C7863//+x+KohiPU0qWkJBgfACjd+/edO3albp169KyZUvefPNN3nzzTZP4zKHRaEym0N3d3dHpdDx+/JhixYpx9epV+vXrZ/KaunXrcu3atXTb9PPzY+XKlbz22mu8/vrrxgXYGo2GsLAw4uPj6d69u8mIo06ny3A7lOT7nj17Nkv9S8uVK1eYPHkyEyZMoGXLlty9e5cZM2YwZswYVq1aleFrR40aRd++fbl58ybz5s1jyJAhbNu2zaQvY8eOJSgoiPDwcGbPns2UKVP4/PPPAfDw8DBJvhITE+nevTuLFi1i0aJFqNVqzp8/z4gRI5g/fz6zZ89OOxCDAbtp09A8S/5UcXE49uxJzJ49GDKZdhVC5G3pJlKdOnVCpVLxzz//YGtra/w6PYqioFKpePjwYY4EKvKWd955h+HDh+Po6MjUqVPTrLNt2za0Wi1t27Y1Kdfr9Zw6dcok8Rg5ciR9+/bF3t4ed3f3VP/WVCoVXl5eZseX/O8xLc+X29jYpLqmKIrZ94GnT4ipVCoOHTqUqr3kaaRatWrxyy+/cPDgQY4ePcrw4cOpUaMGO3bsyFIyZW1t+pZN7svz67qyumanTJkynDt3jiNHjrBjxw6mT5/OvHnz+PHHH43tbt68OVWynDKWnLJw4ULq1KnDqFGjgKfTbQ4ODrRv354ZM2ak+5ADPB0tK1GiBJUqVaJy5cpUr16dkydP0rBhQ2OdkiVL4u3tTeXKlSlevDjt27dn/Pjxaba7aNEiGjZsiK+vLxMmTGDatGlYW1vTrVs3Pvnkk7SDSErCftQobDdtSlWukq0thMj30v2fcPfu3cC/v/Unfy0EQLNmzbCxsSEyMjLdxcUbNmxg8ODBJiNL8HR9yYYNG0wSqeLFi2cpUcpMlSpV2L59OwaDwZionDx5EltbWypUqPDC7dra2qLX603KXn31VRRF4e7duxluAeLk5ISfnx9+fn707t2b1q1bExERQaVKldJs90VUrlyZCxcumDydduHChUxfZ2dnR9u2bfHy8mLWrFlUrlyZ06dPU69ePTQaDTdv3jSZan1e8v8R2RF/WuLi4lCnWHyd/HVWkt7kpPD5Bw6yUufq1ats3ryZo0ePGusmr+NLTExMu//x8TgMGoTNcw9OACjOzsSGhKCvX9/s+IUQeVO6iVTKJ7DSeyJLFE4qlYoTJ06gKAoajSbV9d9++42LFy/yxRdfpHoQoVevXrz//vvMnTsXJycns+6XnKikVLJkyVQfsgCDBg1ixYoVfPDBBwwbNoy//vqLWbNmMXjw4Jdas+Pp6Ul8fDyHDx/m1Vdfxd7enkqVKtGzZ09GjBjB//3f//Haa6/x6NEjjh8/Trly5ejcuTNLly7F3d2dmjVrYmNjQ0hICEWLFsXDw8PY7smTJ+nZsycajca47iirhg0bxsiRI6lduzYNGzbku+++49y5c8YnAdOyceNG9Ho9devW5f79+3z33XfY2Njg5eWFk5MT77//PjNmzEBRFBo1akRMTAznzp3DysqKAQMG4Orqir29PQcPHsTT0xONRoOzszPnz59n2LBhrFy5MsN1WleuXCExMZHIyEhiY2P55ZdfgKcJKkC7du0YPXo0a9asoVWrVvzzzz9MmTKF1157zThKtnv3bj7++GN27tyJh4cHZ86c4X//+x9vvPEGzs7O/Pnnn3zyySd4enoaE/j9+/fz8OFDihUrhq2tLVeuXGHmzJnUq1cvVVKvKAqjR48mKCiIokWLAhj3O/Px8WHt2rU0eLYHlFF0NI59+mD9LPFKZihVitjQUAzPppeFEPlb7ozNiwIpoyRo/fr1VKxY0bgW6Xlt27bFYDDw7bffMmDAALPupdVq8UnjwNYLFy6kOZLl4eFBSEgIM2fOpEmTJjg7O9O9e3dmzpxp1v3S4+vry8CBAxk0aBAPHz5k0qRJTJkyhWXLlrFgwQJmzpzJ7du3KVasGHXq1KFJkybA0+/VkiVLiIiIQKVSUbNmTUJCQoxJ3dSpUxkzZgy1a9cmISGBqKioF4qvW7duxqQxLi6OTp06MXDgQPbu3Zvua5ydnVm8eDHTp08nMTGRqlWrsmHDBsqXLw88XYzt6urK0qVL+eCDD3BycqJmzZrG/Zasra2ZN28en376KfPmzaNBgwbs2bMHrVZLeHi4yeL+tPTo0YObN28av04e1Uv+HvTp04eYmBiCg4OZPn06RYsWpUmTJiZPYD558oTw8HDjCJGdnR07d+7kk08+ITY2Fnd3d1q3bs3atWuN0612dnasW7eOy5cvk5SUxCuvvEKnTp0YO3Zsqhi//PJLSpYsSadOnYxlkydPZujQobRo0QJfX18mT55svKZ6+BCH7t2xTjEaaPD0JHbHDgzZOPoqhLAsVVRUVJpj4yNHjsx6YyoVS5cufemgRN4THh5e6PYJKyh97tOnj3HT1MwUlD5nRXb3WXX7No7+/qivXDEp11epQmxoKMqzUUhLkp9z4VAY+wy53+90R6SOHj2aatFqXFyccQ8gFxcXFEUxHqdQsmTJl37MWQjxcrRaLWvWrKF169ZYW1uza9cu9u7dy/r16y0dWqFgFRGBo58fVjdumJQn1amDdts2FNkuRogCJ91EKnkjwWTh4eF07dqVcePGMWLECOMajsjISJYtW8Y333zDt99+m7PRCiEypFKp+PHHH1m4cCHx8fF4eXmxatUq3nrrLUuHVuBZ/fYbjv7+WN27Z1Ke1LQpsRs3gpnrAYUQ+YvZa6QmTpxI8+bNTc6agqePF8+cOZO7d+8yceLEdHeFFkLkPHt7e3bu3GnpMAol29WrUyVRuo4d0a5ZAwX4TEghCjuzN7A5c+YMderUSfd6nTp1OHPmTLYEJYQQ+U38p5+ie+7YmsS330b71VeSRAlRwJmdSBUpUoTjx4+ne/3YsWOyRkoIUXjZ2qL96iuSGjQgYdgw4pYtg1zatFQIYTlmv8sDAgJYsmQJTk5ODBs2zHjg6rVr11ixYgW7du1KdeyHEEIUKg4OxIaGPh2FyuIO80KI/MnsRGr69Oncv3+f9evXs2HDBuMTfYqioCgKvXr1SrV+SgghCiKr8HAM6T1ebW+fu8EIISzK7ETKxsaG5cuX895777F//35u3bqFoih4enrSpk2bVIe/CiFEgaMoaGbNQrNsGdotW0hq1crSEQkhLCzLE/jVqlVLdeSHEEIUeHo9dh98gObLLwFw6NuX2O3b0fv6WjYuIYRFmX/s/DOnTp3i008/ZcKECVy7dg2A2NhYzp8/z5MnT7I9QCGEsLjEROzffdeYRAGotFocBg2CDA5BFkIUfGaPSCUmJhrP7FIUBZVKRadOnahUqRJqtZru3bszcuRIxo8fn5PxCiFE7tJqcejXD5sffzQpNhQvjnbDBrC1tVBgQoi8wOwRqTlz5nDgwAHmz5/P2bNnUZR/j+izs7PDz8+Pffv25UiQQghhEVFROPr7p06iPDyI3bcPfe3aFgpMCJFXmJ1IhYSEMGDAAAYNGkTxNM6L8vb25q+//srO2IQQwmJU9+5RpFMnrE+dMinXe3kRs38/Bh8fC0UmhMhLzE6k7t+/T82aNdO9rtFoiI2NzZaghBDCklQ3buDYrh3q334zKdfXqEHs/v0onp4WikwIkdeYnUi5ubllOOJ0/vx5ypUrlx0xCSGExViFhVGkXTvUEREm5UlvvEHMd9+hlCplociEEHmR2YlU586dWbdunfFJPcC4Kee+ffsICQnB398/+yMUQohcor54Ecf27bG6fdukXNe69dMdy11cLBSZECKvMjuRmjRpEmXLlqVZs2a8++67qFQqFi5cSOvWrenTpw+1atVi9OjRORmrEELkGKuwMBw7d8bq4UOT8kR/f7SbNoGcJSqESIPZiZSTkxPff/8948aN4/79+9jZ2XHq1CliY2OZMmUKu3fvxk5OORdC5FOGSpXQvfmmSVnCwIHEBQfLFgdCiHSZlUglJCSwefNmfv/9dz744AOOHTvG7du3+eeffzh58iQTJkzIchK1cOFCWrRoQdmyZalYsSK9evXi0qVLJnWGDx+Oi4uLyZ/WrVunim3ChAl4eXnh4eFBQEAAf//9t0mdmzdv0qtXLzw8PPDy8mLixIkkpthE7/jx4zRr1gw3Nzdee+011q5dm6X+CCHyObWauJUrjclU/LhxxH/2GajVFg5MCJGXmZVIaTQaRo8eza+//pptNz5+/DiDBg3iwIED7Nq1C2tra/z8/Hj06JFJvebNmxMWFmb8ExISYnI9eTRszZo17N27l+joaHr16oVerwdAr9fTq1cvYmJi2Lt3L2vWrGHXrl1MmzbN2MZff/1Fz549qV+/PkePHmXcuHFMnDiRnTt3Zlt/hRD5gK0t2q++Qvuf/5AwcyY8WwcqhBDpMXtnc29vb+7evZttNw4NDTX5etWqVXh6enLq1Cnat29vLNdoNLi5uaXZxuPHj9mwYQPLli2jRYsWxnZq1qzJTz/9RKtWrTh06BCXL1/m119/pUyZMgDMmjWLUaNGMWPGDIoWLcq6detwd3dn/vz5APj4+HDu3DmWLl1Kly5dsq3PQoh8wMEBXc+elo5CCJFPmL1GauLEiQQHB/P777/nSCAxMTEYDAZcUjwVc/LkSSpVqkTdunUZNWoU9+/fN177+eef0el0tGzZ0lhWpkwZfHx8OH36NABnzpzBx8fHmEQBtGrVioSEBH7++WdjnefbSK5z8eJFdDpdtvdVCGFBBgN2U6dis327pSMRQhQAZo9IHT16FFdXV5o2bUr9+vWpUKEC9vb2JnVUKhULFix4oUAmT55MzZo1qV+/vrGsdevWvPXWW5QrV44bN24QFBRE586d+emnn9BoNNy7dw+1Wk2JEiVM2nJ1deXevXsA3Lt3D1dXV5PrJUqUQK1Wm9Rp3rx5qjaSkpKIjIzE3d09zZjDw8NfqK/5VWHrL0ifC5ykJMoHBeG8Zw8Ga2tuRUdDgwYFu8/pkD4XDoWxz2B+v729vV/6XmYnUs8vvj516hSnUhybAC+eSE2dOpVTp06xf/9+1M8t7OzWrZvx79WrV6dWrVrUrFmTAwcO0Llz53TbSz5U+fm40pJRneSzBNN7LWTPDyC/CA8PL1T9BelzgRMfj8PAgdjs3QuAVVIS3pMnc2XJEjy6d7dwcLmrQP+c0yF9Ljxyu99mJ1IpF4FnlylTphAaGsru3bspX758hnVLly6Nh4cHEc92HC5VqhR6vZ7IyEhKlixprPfgwQMaNmxorJM8zZcsMjISvV5vHKkqVaqUcXTq+Tasra3TPFdQCJHPREfj2Ls31seOmRQrRYpgkP2hhBAvwew1Ujlh0qRJbNu2jV27dlG5cuVM60dGRnLnzh3j4vNatWphY2PD4cOHjXX+/vtvwsLC8PX1BaB+/fqEhYWZbIlw+PBhNBoNtWrVMtb56aefTO51+PBhateujY2Nzct2UwhhQaqHD3Hs0iVVEmXw9CR2/37iCuFv7EKI7GP2iFSyP/74gx9++IGbN28CULZsWVq3bk2lSpWy1M748ePZunUrX3/9NS4uLsYnAh0dHSlSpAgxMTHMnTuXzp074+bmxo0bN/j4449xdXWlU6dOADg7O9O3b19mzpyJq6srxYoVY9q0aVSvXt245qlly5ZUrVqVYcOGERQUxKNHj5g5cyb9+vWjaNGiAAQGBhIcHMzkyZMJDAzk9OnTbNq0idWrV2f12yOEyENUt2/j6O+P+soVk3J9lSrEhoaieHhAIV1DIoTIHmYnUklJSUyYMIH169djMBhMrk2dOpW+ffvy2WefYW1tXpPJSUrK7QUmTZrElClTUKvVXLp0iS1btvD48WPc3Nxo0qQJ69atw8nJyVj/k08+Qa1WExgYSHx8PE2bNmXlypXGtVZqtZqtW7cyfvx42rVrh52dHd27dycoKMjYRvny5fnmm2+YOnUqa9euxd3dnXnz5snWB0LkY1YRETh26YLVs1/6kiXVqYN22zYUmbYXQmQDsxOp2bNn8+WXXxIQEMDQoUOpWLEiANeuXWPVqlWsX78eFxcXZs2aZVZ7UVFRGV63t7dPtddUWuzs7Jg/f75xD6i0lC1blq1bt2bYTuPGjTl69Gim9xNC5H1Wv/6KY7duWKVY+5jUpAmxmzbBc7+MCSHEyzA7kdq8eTN+fn6sWLHCpLx27dqsXLmSuLg4Nm3aZHYiJYQQOUF9+jSOPXuievzYpFzXoQPatWtBzgQVQmQjsxeba7VaGjdunO71pk2bEh8fny1BCSHEi7A+eBBHP79USVRiQADa9esliRJCZDuzE6mGDRumuXdUslOnThm3HBBCCEuw3r8fVVycSVnC0KHELV8OZq7fFEKIrDA7kfrss8/45Zdf+OCDDwgLC0On06HT6QgLC2PcuHH8+uuvfPbZZzkZqxBCZCh+7lwS/f3//XrKFOLnzgUri+70IoQowMz+Fa1evXooisLVq1dZt26dccfv5B3Ara2tqVevnslrVCoVt2/fzsZwhRAiA2o1cStXooqNJalFCxKHDbN0REKIAs7sRKpr164ZHpcihBB5gq0t2s2bZRRKCJErzE6kUj6tJ4QQFqPXoz59Gn166zIliRJC5BL530YIkb8kJmI/eDCOHTtivWOHpaMRQhRykkgJIfIPrRaHPn2wDQ1FpSg4DB6M9cGDlo5KCFGISSIlhMgfoqJw9PfH5ocfjEUqnQ67SZMgKcmCgQkhCjPZWEUIkeep7t17evjwb7+ZlOu9vIgNDZU9ooQQFiMjUkKIPE114waO7dqlTqJq1CB2/34UT08LRSaEEJJICSHyMKuwMIq0a4c6IsKkPOmNN4j57juUUqUsFJkQQjwliZQQIk9SX7yIY/v2WKXY1FfXuvXT6TwXFwtFJoQQ/0p3YcGrr76a5Q04VSoVP//880sHJYQo3NTHjuH49tuoYmJMyhP9/YlbuRJsbS0UmRBCmEo3kWrUqJHsZC6EyHXWe/fiEBiIKiHBpDwhMJD4BQtArbYfYXgoAAAgAElEQVRQZEIIkVq6iZTsZC6EyG1WP/+MQ9++qPR6k/L4sWNJmDkT5Jc7IUQeI2ukhBB5huG110js18+kLG7WLBI+/FCSKCFEnpSlROrJkycsWLCAzp0707BhQ86dOwfAw4cPWbx4MX/88UeOBCmEKCRUKuIXLCDR3x9FpUK7eDGJo0dbOiohhEiX2bvY3b59mw4dOvD3339TsWJFrl69SmxsLADFixdn/fr13L59m3nz5uVYsEKIQkCtJm7lShIDA9E3aWLpaIQQIkNmJ1IfffQRT5484ciRI7i5uVGpUiWT6x07duT777/P9gCFEAWUoqQ/XWdrK0mUECJfMHtq78cff2To0KFUq1Ytzaf5ypcvz+0U+70IIUSa4uNx6NsXm6++snQkQgjxUswekdJqtbi5uWV43WAwZEtQQogCLDoaxz59sD56FOs9e1CcnUny87N0VEII8ULMHpGqWLEi58+fT/f6jz/+SLVq1bIlKCFEwaR6+BDHLl2wPnr06deKgsPgwah/+smygQkhxAsyO5Hq378/W7ZsYcuWLcaRJ5VKRWxsLDNmzODo0aMMGjQoxwIVQuRvqtu3cezQAesLF0zKDRUrYqhc2UJRCSHEyzF7am/IkCFcvnyZ4cOH4+TkBMDAgQOJiopCr9czdOhQevXqlWOBCiHyL6uICBz9/LC6ccOkPKlOHbTbtqEUL26hyIQQ4uWYnUgBLFq0iICAALZv305ERAQGg4EKFSrg7+9PgwYNcipGIUQ+ZvXbbzj6+2N1755JeVLTpsRu3AjPfjETQoj8KEuJFICvry++vr45EYsQooBRnz6NY8+eqB4/NinXdeyIds0asLOzUGRCCJE95IgYIUSOsD54EMeuXVMlUYkBAWi/+kqSKCFEgZDuiNSrr76a5n5RGVGpVPz8888vHZQQIn+z3rEDh8GDUel0JuUJQ4cSP2cOWMnvcEKIgiHdRKpRo0apEqmff/6Zy5cvU6VKFSpVqoSiKPzxxx9cuXKFqlWrUqtWrRwPWAiRt9msX4/9mDGoUuwrFz9lCgkTJ8rhw0KIAiXdRGrFihUmX+/fv589e/awfft2mjdvbnLt0KFDBAYG8uGHH+ZIkEKI/MPqr79SJVFxc+eSOGyYhSISQoicY/b4+v/93/8xePDgVEkUQMuWLXn33XeZPXt2dsYmhMiHEmbMICEwEABFrUa7cqUkUUKIAsvsp/bCw8N555130r3u6urKtWvXsiUoIUQ+plIRv2ABqoQEdJ06kdShg6UjEkKIHGP2iFSZMmXYtm0biYmJqa4lJiYSEhJCmTJlsjU4IUQ+pVYTt3y5JFFCiALP7BGpMWPG8P7779O8eXMGDhxIpUqVUKlUXL16lXXr1nHlyhWWLFmSk7EKIfISrRbrH34gqUsXY9H1aB1BF6K5o9VT2kHN9DpOlHOysWCQQgiRs8xOpN555x2srKyYNWsWEyZMMD7RpygKrq6uLFmyJMOpPyFEARIVhWNAANanTqFdvBhd//5cj9bhdyCSP6P1xmrn7ieyo20JSaaEEAVWlnY27927N7169eLChQvcvHkTRVHw9PSkdu3aWFtneZN0IUQ+pLp3D0d/f9S//QaA/ZgxULQoQcWbmSRRAH9G6wm6EE1wMzlLTwhRMGU5+1Gr1dSrV4969erlRDxCiDxMdeMGjn5+qCMi/i1TFGy/+IK7H6R93uY/Wn2a5UIIURBkKZHS6/Vs2rSJ77//nhvPTnH39PSkbdu2vP3226jV6hwJUghheVZhYTh27YrV7dsm5UlvvEHsli24/c8A9+JSvc7dQf5fEEIUXGY/tffkyRPatm3L6NGjOXLkCIqiYDAYOHLkCKNGjaJdu3ZER0fnZKxCCAtRX7yIY/v2qZIoXevWxIaGgosL0+s4UcHJNGmq4PR0wbkQQhRUZidSQUFBXLx4kU8++YRr165x9OhRjh07xh9//MGcOXO4cOECQUFBORmrEMIC1MeO4fjWW1g9fGhSnujvj3bTJnBwAKCckw072pagh5c9Tdxt6eFlLwvNhRAFntlTe9999x2BgYEMS7FDsY2NDUOHDuXq1avs3r2befPmZXuQQgjLsN63D4cBA1AlJJiUJwQGEr9gAaSYzi/nZCMLy4UQhYrZI1KRkZFUrVo13evVqlUjMjIyW4ISQliezdatOLzzTqokKn7sWOIXLkyVRAkhRGFkdiJVtmxZDh8+nO71w4cPU7Zs2WwJSghhWbb/+Q8OQ4ei0ps+cRc3axYJH34Iz/aRE0KIws7sROqdd95hz549DB8+nMuXL6PT6dDpdFy6dImRI0eyd+9e+vXrl5OxCiFygfrMGewnTjQpU1QqtIsXkzh6tIWiEkKIvMnsRGr06NEMGDCALVu20KhRI9zd3XF3d6dx48Zs2rSJAQMGMGrUKLNvvHDhQlq0aEHZsmWpWLEivXr14tKlSyZ1FEVhzpw5VKlSBXd3dzp27Mjly5dN6kRFRTFkyBA8PT3x9PRkyJAhREVFmdT5/fff6dChA+7u7lStWpV58+ahKIpJnZ07d+Lr60upUqXw9fVl9+7dZvdFiIJEX78+8ePGGb9WbGzQrluHrn9/C0YlhBB5k9mLzVUqFYsWLWLIkCEcOHDAZB+pNm3aUK1atSzd+Pjx4wwaNIg6deqgKAqffPIJfn5+nD59mmLFigGwePFili1bxrJly/D29ubTTz+la9eunD17Fienp49Uv/vuu9y6dYuQkBBUKhWjRo1i6NChbN26FXi6bUPXrl1p2LAhhw4dIjw8nJEjR+Lg4MD7778PwJkzZxg4cCBTpkzhrbfeYvfu3QwYMIADBw7w+uuvZ6lfQhQECTNmoIqKwnbLFrQbNpDUqpWlQxJCiDxJFRUVpWReLefFxMTg6enJxo0bad++PYqiUKVKFQYPHsz48eMBiIuLw9vbm9mzZxMYGEhYWBi+vr7s37+fN954A4CTJ0/Svn17zp49i7e3N2vWrOGjjz7i6tWr2NvbAzB//nzWrl3LpUuXUKlUBAYG8ujRI3bs2GGMp0uXLpQsWZI1a9bk/jcjDwoPD8fb29vSYeSqQt9nvR6ra9cw+PhYNqgcVuh/zoWE9LnwyO1+mz21l9NiYmIwGAy4uLgAcP36de7evUvLli2Ndezt7WnYsCGnT58Gno4kFSlSBF9fX2OdN954A0dHR5M6DRo0MCZRAK1ateLOnTtcv34dgLNnz5rcJ7lOchtCFFgpnsgzoVYX+CRKCCFeVoZTe88nKOZQqVScOnXqhQKZPHkyNWvWpH79+gDcvXsXAFdXV5N6rq6u3LlzB4B79+5RokQJVM89QaRSqShZsiT37t0z1vHw8EjVRvK18uXLc/fu3TTvk9xGesLDw7PazXytsPUXCnaf1VFRVB49mketWvHPcw+KFOQ+p0f6XDhInwsPc/udHSNXGSZSydNhtWrVwsoq5wavpk6dyqlTp9i/f3+q8/pUKR6zVhQlVeKUUmZ1kheaZ1YnrbafV5iGTAvjEHFB7rPq9m0c+/ZFfeUKjpcuUbxSJXT9+xfoPqdH+lw4SJ8Lj9zud4aJVO3atbl48SIRERH4+/vTs2dPatWqla0BTJkyhdDQUHbv3k358uWN5W5ubsDTUaMyZcoYyx88eGAcPSpVqhQPHjwwSXoURSEyMtKkTsqRpQcPHgD/jky5ubmlWSflKJUQBYFVRASOfn5YPXtgBMB+zBiUV16BcuUsGJkQQuQ/GQ4zHTp0iPPnz9O/f38OHDhAy5YtqVevHvPnz+evv/566ZtPmjSJbdu2sWvXLipXrmxyrVy5cri5uZlsAhofH8/JkyeNU47169cnJiaGM2fOGOucOXOG2NhYkzonT54kPj7eWOfw4cOULl2acs8+NOrVq5dqs9HDhw9neWpTiLzO6rffcGzXziSJAtA3bkyS/HsXQogsy3S+zsvLiylTpnD+/Hm+//57mjdvTnBwMHXq1OHNN98kODiYhykOMzXH+PHj2bRpE6tXr8bFxYW7d+9y9+5dYmJigKdTbcOHD+fzzz9n165dXLp0iREjRuDo6Ej37t0B8PHxoXXr1owdO5azZ89y5swZxo4dS9u2bY3Det27d8fe3p4RI0Zw6dIldu3axeeff86IESOMo1jDhg3j6NGjLFy4kKtXr7Jw4UKOHTvG8OHDs9wvIfIq9enTFOnYEasUo6+6Dh2IDQmBZ1uKCCGEMF+WFj69/vrrzJ8/n8uXL/PNN9+g0WiYNGkSwcHBWb7x6tWriY6OpkuXLvj4+Bj/fPHFF8Y6o0ePZsSIEUyYMIEWLVrwzz//EBoaatxDCiA4OJgaNWrg7+9Pt27dqFGjBqtWrTJed3Z2Zvv27dy5c4cWLVowYcIERo4cyXvvvWes4+vry9q1a9m8eTONGjViy5YtrF27VvaQEgWG9cGDOHbtiurxY5PyxIAAtOvXg52dhSITQoj8zewNOZM9fvyYnTt3EhISwn//+1+KFi36Qou6Uu4+nhaVSsWUKVOYMmVKunWKFSvGf/7znwzbqV69Ovv27cuwTpcuXejSpUumMQmR31jv2IHD4MGodDqT8oShQ4mfMwdy8EESIYQo6MxKpHQ6Hfv37yckJITvv/8egDZt2vDVV1/Rtm1bbG1tczRIIcSLsVm/HvsxY1AZDCbl8VOmkDBxohw+LIQQLynDROr48eOEhISwc+dOoqOjadSoEfPnz6dLly4ULVo0t2IUQrwA2yVLsJ85M1V53Ny5JA4bZoGIhBCi4MkwkXrrrbewt7enTZs2dOvWzbixZUYbXdWtWzd7IxRCZJnm44+xW7jQpExRq4lbtgxdQICFohJCiIIn06m9uLg4du7cya5duzKsl7yX04s8wSeEyF7Ks6OWjF9rNGjXrSOpQwcLRSSEEAVThonUsmXLcisOIUQ2Shw1ClVUFHYLF6IUKULspk3omza1dFhCCFHgZJhI9e7dO7fiEEJks4QZM8BgIKlLF/S1a1s6HCGEKJCyvP2BECKfUKlI+OgjS0chhBAFmmwgI0Q+prp3D9tM9lETQgiRc2RESoh8SnXjBo5+fqgjIiAujsTRoy0dkhBCFDoyIiVEPmQVFkaRdu2eJlGA/YcfYrN+vYWjEkKIwkcSKSHyGfXFizi2b4/V7dsm5TZ79oCiWCgqIYQonCSREiIfUR87huNbb2GVYr+2RH9/tBs2yJEvQgiRy2SNlBD5hPXevTgEBqJKSDApTwgMJH7BAlCrcy2W69E6gi5EE/FEx/14hVL2VlRwsmZ6HSfKOdnkWhw5Jbl/d7R6Sjuoc71flr6/ufJLnELkJEmkhMgiS3x42GzZgv3Ikaj0epPy+LFjSZg5M1dHoq5H6/A7EMmf0f/GciNGz7n7Os7dT2RH2xL5+sM0rf7lZr8sfX9z5Zc4hchpMrUnRBYkf3iERMRx/J9EQiLi8DsQyfVoXY7d03bVKhyGDUuVRMXNmkXChx/m+nRe0IVokw/P5/0ZrSfoQnSuxpPd0upfbvbL0vc3V36JU4icJomUEFmQqx8eioJm3jzsJ00yLVap0C5ebLHtDu5o006ikv2TyfW8Lr3+5Va/LH1/c+WXOIXIaTK1J0QW5NqHh8GA3bRpaFasMClWbGzQBgeT5OeXvffLgtIOGa/Fcs/kel6XXv9yq1+Wvr+58kucQuQ0GZESIgty68NDfeJE6iTKwQHtli0WTaIAptdxooJT2v2t4PR0zVh+llb/crNflr6/ufJLnELkNEmkhMiC3Prw0DdpQtzHHxu/Vpydid2+naRWrbL1Pi+inJMNO9qWoIeXPa+XtMaziJp6rjb08LIvEAuNn+9fE3fbXO+Xpe9vrvwSpxA5Tab2hMiC5A+PoAvR/KPV456DT+0ljhqF6tEjbDduJDY0FEONGtl+jxdVzsmG4GbFLR1GjrF0/yx9f3PllziFyEmSSAmRRbn54ZEwcyaJw4ahuLnlyv2EEEJkjUztCWFhqkeP0j/aRaWSJEoIIfIwSaSEsCCriAiKNGuGZvZsS4cihBDiBUgiJYSFWP32G47t2mF14wZ2Cxdiu2SJpUMSQgiRRZJICWEB6tOnKdKxI1b37hnL7GfOxPrgQQtGJYQQIqskkRIil1kfPIhj166oHj82KU98+22SmjWzUFRCCCFehCRSQuQi6x07cAgIQKXVmpQnDBtG3LJlYC0P0gohRH4iiZQQucRm/XocBg5EpTM94Dh+6lTi58wBK3k7CiFEfiO//gqRC2yXLMF+5sxU5XHz5pE4dKgFIhJCCJEdJJESIicpCpqPP8Zu0SLTYrWauOXL0fXqZaHAhBBCZAdJpITIKXo9duPHo1m3zqRY0WjQrltHUocOFgpMCCFEdpFESogXcD1aR9CFaO5o9ZRO67w9RcHw7lA027eZvE7v6Ejc5s3omzY1r50M7j/51GPOPdABCvVcbZnj60w5JxuuR+uYcvoxZ+8nAipeL2nD3Dec5TBZUaBk5b1zPVrHjDAbYq7dz9L7TAhzSCIlRBZdj9bhdyCSP6P1xrJz9xPZ0fbfk++vxySxocirzOffROqBowv9Rwczp3YDypnZTnr377j3Pre0/x4rs/dmAr88fMCqJi4MPRbFrVjDsysK+24l8Ove++zp4CofHqJAyMp759+6NkBihnWFeBHymJAQWRR0IdrkP3CAP6P1BF2INqmzoE43Jrz1AQC3nN1o8v569rpWM9Yzp5307v98EpXsVqyB4ccfP5dEPXdNq2TarhD5RVbeOy/6PhPCXDIiJUQW3dHq0yz/57ny5DoLWg5Eb2VF6Ktvcr34Kyb1zGknK/cHeJyQOokyt10h8ousvHde9H0mhLkkkRIii0o7qNMsd3+u/Pk6i5oPSLOeOe1k5f4AzhorHuvS/oDIrF0h8ousvHde9H0mhLlkak+ILJpex4kKTv/+J1zl7h8sObiU6bWLmNQp46BK9doyjlZMr+OUZjsAFZzUxusZ3T+9tlc0dqaMY+q3dRkHVabtCpFfZOW986LvMyHMpZ48efJHlg5C5H0PHz6kRIkSlg4jV6XXZxeNmvZlNUTGG2h463dCFgXS9LdjOKv06J+dleeiUdOxnB3XnyTxOFHB0Rqal9awtnlx4wLX59spobHCt5Qty5u4ZLoANqO2a5bQ0NHTjhvRSUQlGnC0tqKZuy3rWhQ3a2Gt/JwLh/ze56y8d5Lr/nU/itJF7c1+nxUE+f3n/KJyu98ytSfECyjnZMNaq99xnNsPVUwMAHYLF6K4uJA4apSxzuY3S2baTnCz4i90//TaLudkw6bWGd9XiPwuK++dck42zPbR4e3tmsNRicJIpvaEeAHWe/fi2L27MYlKZvXnn6CkfqJOCCFEwSSJlBBZZLNlCw59+6JKSDApjx87lviFC0GVev2SEEKIgkmm9oTIAttVq7CfNClVedysWSSOHm2BiIQQQliSJFJCmENR0Hz6KXZz5pgWq1TEff45uv79LRSYEEIIS5JESojMGAzYTZ2KZuVKk2LFxgZtcDBJfn4WCkwIIYSlSSIlREaSkrB//31sN282KVYcHNBu2EBSq1YWCkwIIUReYNHF5idOnCAgIICqVavi4uLCxo0bTa4PHz4cFxcXkz+tW7c2qZOQkMCECRPw8vLCw8ODgIAA/v77b5M6N2/epFevXnh4eODl5cXEiRNJTEw0qXP8+HGaNWuGm5sbr732GmvXrs2ZTot8Q5WQgEP//qmTKGdnYrdvlyRKCCGEZROp2NhYqlWrxty5c7G3t0+zTvPmzQkLCzP+CQkJMbk+ZcoUdu/ezZo1a9i7dy/R0dH06tULvf7pMRl6vZ5evXoRExPD3r17WbNmDbt27WLatGnGNv766y969uxJ/fr1OXr0KOPGjWPixIns3Lkz5zov8rwi//sf1nv3mpQZSpUiZs8e9L6+FopKCCFEXmLRqb02bdrQpk0bAEaMGJFmHY1Gg5ubW5rXHj9+zIYNG1i2bBktWrQAYNWqVdSsWZOffvqJVq1acejQIS5fvsyvv/5KmTJlAJg1axajRo1ixowZFC1alHXr1uHu7s78+fMB8PHx4dy5cyxdupQuXbpkd7ct5nq0jqAL0dzR6int8PSIhOTdfTO6ltdizS3R9esT9/nnODx7Gs/g6Unsjh0YvLxyNQ4hhBB5V55fI3Xy5EkqVaqEs7MzjRo1YsaMGbi6Pt2d9ueff0an09GyZUtj/TJlyuDj48Pp06dp1aoVZ86cwcfHx5hEAbRq1YqEhAR+/vlnmjZtypkzZ0zaSK6zefNmdDodNjb5/yiB69E6/A5E8mf0vwfanrufyI62T7fRT+9abiUvzydOTtYqfn2k41aswWLxJNP1709cVBS2mzcTtv4bPrpZhDth93M8ucsLiaQQQojM5elEqnXr1rz11luUK1eOGzduEBQUROfOnfnpp5/QaDTcu3cPtVqd6kwdV1dX7t27B8C9e/eMiVeyEiVKoFarTeo0b948VRtJSUlERkbi7u6ec53MJUEXok0SJYA/o/UEXYg2/j2tay9yfElmUiYJAyrb896Jx6liyK14MpM4ejTX3h5Al6Nx/BkdZyzPLLl70WQoo6RXkikhhMhb8nQi1a1bN+Pfq1evTq1atahZsyYHDhygc+fO6b5OURRUz+0urUpnp+mM6ijPjvlI77UA4eHhGXcgD4l4oAHUqcsjY0Ah3Wvh4ZHGr7Ojv3/HqXjvdw234v9dnvfdX7HEGTJfrpcynuxke+sWiaVLg9r0+5Dc5xlhNvwZbZrE/BmtZ9KRv5nto0vVXlr9PHk7lqXVE3jFPuMjZLJ6r+yWn/5dZxfpc+EgfS48zO23t7f3S98rTydSKZUuXRoPDw8iIiIAKFWqFHq9nsjISEqW/PeQ1gcPHtCwYUNjndOnT5u0ExkZiV6vN45UlSpVyjg69Xwb1tbWFC+e/ghIdvwAcovX7YecfxKXurxEEYB0r3l7ewJP/1FmR38/PfKQW/Gm9zIniUoZT3ZSnz6N44ABJPr7mxzx8nyfY67dBxJTvTZW7ZjmQahp9fNWvBUbHxUn+NWMR9Wyeq/slF0/5/xE+lw4SJ8Lj9zud75KpCIjI7lz545x8XmtWrWwsbHh8OHD9OjRA4C///6bsLAwfJ89VVW/fn0WLFjA33//zSuvvALA4cOH0Wg01KpVy1hnz549Jvc6fPgwtWvXLhDrowCm13Hi3P1Ek+miCk5Pp5uADK9lpzva9KfvMpJWPCnXValU8ESnZGkazfrgwafn5mm1aNatQylWjISZM1PVK+2QesQOwD2d8vT6+eOtOLw330FvULCzVlHawQqvojYm8aZ3rytROgYfeVgg10vJmjAhRH5l0UQqJibGOLpkMBi4desWv/zyC8WKFaNYsWLMnTuXzp074+bmxo0bN/j4449xdXWlU6dOADg7O9O3b19mzpyJq6srxYoVY9q0aVSvXt245qlly5ZUrVqVYcOGERQUxKNHj5g5cyb9+vWjaNGiAAQGBhIcHMzkyZMJDAzk9OnTbNq0idWrV1vk+5ITyjnZsKNtCYIuRPOPVo97ig+rjK5lp/SSBEdriE369+syDipeLWFLtE5JM5601hE9z5w1RdY7duAweDAq3b/TZXYLF5LUrBn6Zs1M6maWiJrbz0eJAM8W0Scq3NYaOP8gySTetO4FcD9eISQirsCtl5I1YUKI/EwVFRWV8YKNHHTs2DHeeuutVOVvv/02CxcupE+fPvzyyy88fvwYNzc3mjRpwrRp00yewIuPj2fGjBls27aN+Ph4mjZtymeffWZS5+bNm4wfP56jR49iZ2dH9+7dCQoKQqPRGOscP36cqVOncuXKFdzd3RkzZgwDBw7M2W9APpJdQ6VpfWhWcFKztJEzX16NMzuRG3zkISERqacjn9fDyz7dxek269djP2YMKoPBpDx+6lQSJkwAlSpVn5NHTcyJMbNEL7N4k+/10+0E7scbMqybnSwxFZDezzKn+phSYZz+kD4XDoWxz1DIpvaaNGlCVFRUutdDQ0MzbcPOzo758+cb94BKS9myZdm6dWuG7TRu3JijR49mej/xcjIaGWtUOu1NWdNizhThP+nUsV2yBPs0pu/i5s0jcejQDGM394M9ZT+vRCWlmRClF2/yvTrtu8/9f1Kvl0qvb/lRej/LgtRHIUTBla/WSImCISsJSXrSmzp7Xqr1S4qC5uOPsVu0yLRYrSZu+XJ0vXq9VEwpPd9Pc0bQ0lpvldW1WflRYeijEKLgsugRMUK8qOl1nKjglP4Hbar1S3o9duPGpU6iNBq0X3+d7UlUStPrOFHGIf2tNNJbb5VWP3PqQQBLKQx9FEIUXDIiJfKllFNnRZ49tZfm4vTEROyHDcM2xVSx4uRE7KZN6Js0yZV493RwZfKpx5x7oDM+tefhYEWFFE/tZdTPnHwQwFIKQx+FEAWXJFIi3zJrilCrxaF/f2x++MGk2FC8ONpvv0Vfu3YORmiqnJMNm98smXnFNF5niR3dc1Nh6KMQomCSREoUGKn2IqpdhGrvBGCd4iECg4cH4V+HMONJae7sy/lz84QQQhRckkiJAiG9vYgOv90Pz2PHUD078kfv5cXVjdvo+LtDls7NE0IIIdIii81FgZDeocwTPFoS9/nnAOhr1CB2/34+vOeS4QHOQgghhLlkREoUCBntRaTr3x+tgwO6N98EFxfunL+fbl0hhBAiKySREgVCZnsR6Z6dxWhOXSGEEMJcMrUnCoQ5Cb+wOfQjrAyZn4Un+xYJIYTILjIiJfI967178QoMpGJCAsUdbQjq9xHujtaFem8mIYQQuUMSKWGWv+NUfHrk4b9bC+SRxMNmyxbsR45EpX86EtVm/yaaVnMnIY2z9J4n+xYJIYTIDpJIiUxdj9bx3u8absXnre0CbFetwn7SpFTlStGiFohGvIhUe3/lkQRdCCHMJYmUyFTQhWhuxZsup0veLm7pbGUAACAASURBVMAiozqKgubTT7GbM8e0WKUi7vPP0fXvn/sxiSxLb+8vSyfoQgiRFbLYvBC7Hq1j8JGHdNp3n8FHHnI9WpdmvYy2Fsh1BgN2U6akTqJsbNCuWydJVD6S3t5fsp+XECI/kRGpQiorowF5ZruApCTs338f282bTYoVBwe0GzaQ1KpV7sYjXkqeStCFEOIFyYhUIZWV0YDpdZwoY2cwKcv17QLi43Ho1y91EuXsTOz27ZJE5UN5JkEXQoiXICNShVRWRgPKOdmwtHoCGx8Vt8x2AdHROPbujfWxYybFhlKliA0NxVCjRu7EIbLV9DpOnLufaJLQy35eQoj8RhKpQiqrowGv2CsEv5r7C8tVDx/i0L071hcumJQbPD2J3bEDg5eXSXleeQosr8SRl8l+XkKIgkASqUIqv4wGWP3+O+pffzUp01epQmxoKIqHh0l5XnkKLK/EkR/Ifl5CiPxO1kgVUsmjAT287GnibksPL/s8+UGvb9IEbXAwikoFQFKdOsTu3ZsqiYK88xRYXolDCCFEzpMRqUIsv4wGJPn5Eff4Mbbffkvsxo3glPaoWV55CiyvxCGEECLnSSIl8gVd//7o+vYFq/QHUfPKU2B5JQ4hhBA5T6b2RJ6hPncOEhPTr5BBEgVP131VcDJNViyx7iuvxCGEECLnSSIl8gSb7dtxbN8e+2HDQP9iU2B5Zd1XXolDCCFEzpOpPWFxNl99hf2YMagUBdvQUBQXF+I/+wyeLTDPiryy7iuvxCGEECJnyYiUsCjbJUtwGD0alaIYyzRr16I+c8aCUQkhhBDmkREpYRmKgubjj7FbtMi0WK0mbtky9L6+FgpMCCGEMJ8kUiL36fXYjR+PZt06k2JFo0G7bh1JHTpYKDAhhBAiaySRErkrMRH7YcOwDQ01KVaKFCF20yb0TZtaKDAhhBAi6ySRErlHq8Whf39sfvjBpNhQvDjab79FX7u2hQITQgghXowkUiJ3REXhGBCA9alTJsUGDw9it2/H4ONjocCEEEKIFyeJlMhxqnv3cPT3R/3bbyblei8vYnfsQPH0tFBkQgghxMuRRCqfuR6tI+hCNHe0eoraqFAUiE5SKO3wdOfsrGz6+Hxbab3++etF9DbMc9dlfVNJgwHHbt1SJ1E1ahAbGopSqlTW2suizPoohBBCvAxJpPKR69E6/A5E8md02jt/n7ufaPYO2mm19fzrU1+3IexAZNZ36LayIv6jj3AICECl0wGQ5OtL7Nat4OJifjsvILM+CiGEEC9LNuTMR4IuRKebRAH8Ga0n6EL0C7f1/Oszu54VSa1aoQ0ORlGp0LVuTWxoaI4nUZC9fRBCCCHSIiNS+cgdbeZn0P1jRp2M2kp+fWbXsyrJz49YFxf0DRuCre0LtZFV2d0HIYQQIiVJpPKR0g7qTOu4m1Eno7aSX5/Z9XQpCtdjktJclxRRtxFBJ3NvvdIL90EIIYQwkyRS+cj0Ok6cu5+Y7vReBaenycmLtvX86zO7nhabrVvRbdtOj26fcjXu32Tl3P1EljZy5r0Tj3N1vdKL9EEIIYTICkmk8pFyTjbsaFuCoAvR/KPV4/Tsqb2YJAX3LI7wpGwr5etTXnfUxzKvWal027f9z3+wnzgRgFkPVPR5Zx4Gq6fJ1J/ReoYff8yNmLTXKwU3K/6i35KX6qMQQgjxsiSRymfKOdlkW+KRWVvPXw8Pj0o7AVEUNPPnY/fJJ8aigIv7iHRw5r3uM4xljxMMad4jp9crZef3SwghhEhJEinx4gwG7KZNQ7NihUlxotqaIxXrmZQ5a6x4rEudNMl6JSGEEPmZbH8gXkxSEvbvvZcqiTLY2TPk/RWE1G5nLKvgpGZFY2cqOJkmTbJeSQghRH4nI1KFSLbt8h0fj8OgQdjs2WNSrBQtivabbxhXvS7aNNYl7WhrLeuVhBBCFCiSSOUxOXWkSbbt8h0djWOfPlgfPWpSbChVithvv8VQsyblIM11SbJeSQghREEjiVQekpNHmmS0y7e5yY3q4UMcunfH+sIFk3JD2bLE7tiBoWLFl4pRCCGEyG8sukbqxIkTBAQEULVqVVxcXNi4caPJdUVRmDNnDlWqVMHd3Z2OHTty+fJlkzpRUVEMGTIET09PPD09GTJkCFFRUSZ1fv/9dzp06IC7uztVq1Zl3rx5KIpiUmfnzp34+vpSqlQpfH192b17d850OgM5eaTJy+7ybXPvHo4dOqRKovQ+PsQcOCBJlBBCiELJoolUbGws1apVY+7cudjb26e6vnjxYpYtW8a8efM4dOgQrq6udO3alejofxOLd999l19++YWQkBC2bdvGL7/8wtChQ43Xnzx5QteuXSlVqhSHDh1i7ty5fPHFFyxdutRY58yZMwwcOJAePXpw7NgxevTowYABAzh37lzOfgNSyMkjTV5ml2+riAiqDB6M+soVk/KkOnWI3bsXxcPjpeMTQggh8iOLTu21adOGNm3aADBixAiTa4qisGLFCsaMGUOXLl0AWLFiBd7e3mzbto3AwEDCwsL48ccf2b9/P76+vgAsWrSI9u3bEx4ejre3NyEhIcTFxbFixQrs7e2pVq0aV69eZfny5bz33nuoVCpWrFhBkyZNGD9+PAA+Pj4cO3aMFStWsGbNmlz7fuTkkSYvs8u36sEDrB8+NClLatKE2E2bwEmeuhNCCFF45dntD65fv87du3dp2bKlscze3p6GDRty+vRp4OlIUpEiRYxJFMAbb7yBo6OjSZ0GDRqYjHi1atWKO3fucP36dQDOnj1rcp/kOslt5JbpdZxybIuA5F2+e3jZ08Tdlh5e9mavvdLXr88fn36KYvO0rq5DB2JDQiSJEkIIUejl2cXmd+/eBcDV1dWk3NXVlTt37gBw7949SpT4//buPS6qOv8f+GsYBhwQGQUZIUREEfG26opmtppFuWVqoEhuF63UhOyC3/C+W1oPtW1VLBLN1VVbtIeoJbqoa0mKSqFlXlKR/SGggqOigzBcZ+b8/nDnLAdmBIEZYHg9H495PHbO+cw5n/f5bPHucz4XD8hkMvG8TCaDp6cnbt68KZbxqfHqyXTNmzdvwt/fHxqNxux9TNewJCsrqwGRPdjqXjKsy3PErUoHdHYyYpZfGSpvFCPrRtNcf261R3Hlyh3My3PErQoHdHY24gUvA769KRe/z/LT4xHlf8eSDR+O7KVL4X7iBHIWLgSuXn3oe18v+29s5q7fQlmjjeujOZ9Vc8XcnBhz28CY2476xh0YGNjoe7XYRMqkepIE3H/lVzNxqqmuMqaB5nWVMXft6pqiAWpdE8ATA5r8srXkFlchRjJDUI7vbiugF/9Wy5FZ3k7stcrKyoJnVBQQFYWGRG3uftWv3xKZXg/bWnM+q+aKuTkx5raBMbcdto67xb7aU6vVAFCrV+j27dti75GXlxdu374tmYEnCAIKCwslZcxdA/hfz5RarX7gfeyRuRmCegEYf+4wlJVlAJpuxqCl+zXl9e0JnxURUevRYhOpbt26Qa1WIzU1VTxWXl6O9PR0cUzU0KFDUVJSgoyMDLFMRkYGdDqdpEx6ejrKy8vFMqmpqfD29ka3bt0AACEhIZL7mMpUH3tlb8zNEHz/8Cbs2fQ2dm6OgUJfCaDpNhW25oxEe8NnRUTUejRrIlVSUoKzZ8/i7NmzMBqNuHbtGs6ePYurV69CJpMhKioKcXFxSE5OxoULFxAdHQ1XV1dMmjQJwP3ZdaGhoYiJicHJkyeRkZGBmJgYjBkzRuzWmzRpEpRKJaKjo3HhwgUkJycjLi4O0dHR4qu7WbNm4ejRo1i1ahUuX76MVatWIS0tDVFRUc32bKxNMkNQELBs32p8unclAOC5i2nYsm0hHIyGJttU2JozEu0NnxURUevRrInU6dOnMXLkSIwcORJlZWVYvnw5Ro4ciWXLlgEA3n33XURHRyM2NhajR4/GjRs3sHv3brhVmy22YcMG9OvXD+Hh4Zg4cSL69euH9evXi+fd3d3xzTffoKCgAKNHj0ZsbCzeeustzJ49WywzbNgwbNq0Cdu3b8eIESPw9ddfY9OmTRgyZIjtHoaNmWYIOhgNSEhaigXf/11yPuzc9/hj0f9rsk2FrTkj0d7wWRERtR4yrVbbsqdNkdXkFepwb+qbGHFsn+S4TumKTxdtQPjUp8XBzU0xeM+0j2Br2bS4OQdqNtezaouDUxlz28CY2w5bx93iZ+2RlZSWovesaVAcOyQ5bOzUCcKuXYgZNKjJb8lNi+uPz4qIqHVgImUnTD0YBaUGdFDIIAhAsV6Ad7XeDFOZ4lt3sXrlTPS++LPkGsZHHoFu924Yg4KaKQoiIqLWhYmUHcgtrsILknWHpE7dqkT8CHfMPl4EXb4GB9fNRO/8TEkZQ48e0H3zDQQ/P1tUmYiIyC4wkbID5tYdqu5KsQFRx4qAvKs4lvAGAm/nSc4b+veHbtcuCF5e1q4qERGRXWmx60hR/Vlad6g6dW4Wjn/2cq0k6kzQEJTs3cskioiIqAHYI9UCVR/v5O0ix7ReSmy+XGbxewfFg7eykRv0+Hr9W/At0kiOpwT/AduWrMMXKlWT1KvmzLKa5Vv6LD0iIqKHxUSqhTE33umbK2XV9sCr/d3XRQZfVwdc0xnNXtMgd8TLU5bhu4TpcKm6v8L714OeRezry/Cv4fXribpeJqux/1vtepy6VSnuB2cujurniYiI7AFf7bUwlvbAe9D3a6UC+ndUICJACWcLLZrefRDCXl+DSrkj1g2fjJde/gQGRbt6JzXr8hzrrFf1/eC4XxwREbUF7JFqYeoz3smcEr2A7U93wsGr+agwml9j9d+9H8eQOTtwzrsXIJOhtGYm9AC3KuqXc5v2g+N+cURE1BawR6qFsbTPWl1M+7CpnB3EDYfNOecTBPx3j0F3S91XZnR2Nv/a0FI9uF8cERG1BUykWhhz+6w51hhLXvO7uA+bIODQ2Y04nPAGlJVl4nk5gJrD0R1lQMLj7vWu1yw/fZ31qr4fHPeLIyKitoCv9hrhYWel1ad8NzcFvh3jIdlnzTQ7ztL3xYPd0M1VjnYLF6JXQgJ6AfjXV3MwefpncHFtJyZMUceKUFRhhLuzAxIed8cIb2W9Y31EKdRZr+rxmIuDs/aIiMjeMJFqhIeZlfYws9jM7bNWM+mRfNfroZw9G07btomHRp8/iuun/oay+Hjx2NmI+idO5tSnXnWVJyIisid8tdcIDzMrzWqz2MrL4TJ1qiSJAgDB3R2Vr77auGsTERHRA7FHqolZmpVmlVlsxcVwfeklOB49Kjls9PK6v/lwv34NvzYRERHViYlUE7M0K62pZ7HJ7tyBy6RJcPzlF8lxo58fdN9+C2NAQIOuS0RERPXHV3uN8DCz0szNYnN2AHRVRuQWV5n9TW5xFWYcuYPn99/CjCN3xHKy/Hy4PvdcrSTK0Ls3Sg4cYBJFRERkI+yRaoSHmZVmmsU2/8cipBZUoNwAVBiBlKsVuKgtrDXo3NLg9JReRej1cgQc8qSbD+sHD0bpzp0QOnFwNxERka0wkWqEhsxK+02rR3mNYVGmQefVrzX/x6Jag9PbZ15Al/dnwkF7W3JcP3IkdImJgBvXaCIiIrIlvtqzEVMPU15J3YPOc4urkFpQITk/LOcMjsRPg6pGElU1dix0O3YwiSIiImoG7JFqhBlH7ohjoupaaNPc8gfV5ZYY8Pz+W/B2kUNXZazVa1Xq1K7Wb/b9IQzvjPsInQ4VIaBDKRe8JCIisjEmUo2QlF2GdE0FIAi4Vvq/DYDNLbT5oM2IHWVAXolB7K0ytwXeOZ8ghL+5Ft+tmw55eTniRr6MORPmQSiT4UqZHj/f1j9wQVAiIiJqeny110jXdEZJEgWYX2jT0vIHLo4y6KU/R4WF/YFdHn8UK+atw+Jn30bMC/MhOEibr0kW+CQiIqJ6Y4+UldRcaHPxYDeculUpeb3X3U2OTs4y/HxbX+v37eSQvN7r7ibH8mHueKtqBI6pQup9XyIiIrIe9khZSc2FNk3LH0QEKPGHLk6ICFDi2zEeCOhQ4zWcIGD20UR4VRbDr70cIZ0VYtlubgqLPVuW7ktERETWwx4pK3B1lGFar/ub+eYWV2HBT0VI11SgVH+/p8lV4YBSvRHvpN3FxaL/9SA5GA1Yu/MjvJmehIgzB/HHN9fDqHJB53YOeOuYFo4QcKbQ/OKdwP2xVqb70v1nX9ckACIiosZgImUFOr2A2ceLED8CeDNNi2u6/w16qjACRVVG5JdKB0Ip9JX4KnEBIn89AAAYmf0zkjbPQdjra5BSYwyWJXoB2Hy5DCO8mUxZWtCUg/GJiKgp8dWelVwpNiDqWJEkibJEWVmGPRvfFpMok2F5Z+F/J/+h7ssxUveZW26Cg/GJiKipsUfKioosTb+rxr3sHvZtiMbjV05Ljl9zV+PpqA3I8vJ/qHtyjNR9lpabYKJJRERNiYmUFbk7O6CoyvIfbq/i2zi4biYG5mdKjmd5+iE0aiPyOvk81P0etGlyW2NpUD4TTSIiakp8tWcl3d3kSHjcHb6u5h+x3518HPvslVpJ1K8+QRgT8xW8evnhWV9ni7838WonwxBPR8nMPrq/3ER3N2nSxESTiIiaGnukGiGkswKaMiO82sngpZRDEIASvYAu1WaI/etZx1qz9n53+wp2xL8B9V2N5HqnAwdjzYeb8O3IR8SEyDTz7EapAXIIyCwyoFQvwN3ZAQmPu3NguQWm5SZMz64LZ+0REZEVMJFqhEPPe9VZppubAttCPcXv8tOn4TLxVTjcvSMpVxUaioCtW7HGxaXW7zeM6tQ0FW5j+OyIiMja+GrPhuRpaXAdNw4Od6RJVGV4OEq3bQNqJFFERETUsjGRspXKSrhER0NWUiI5XPHaayjbsAFwcmqmihEREVFDMZGyFScn6LZtg9Chg3ioPCYG5atWAXLOJCMiImqNmEjZkLF/f+h27IDg4oKyJUtQ8cEHgEzW3NUiIiKiBuJgcxszPPooin/+GYK3d3NXhYiIiBqJPVLWYDQCRUUWTzOJIiIisg9MpJqaXg/lW2+h/fPPPzCZIiIiotaPiVRTKi+Hy6uvwmn7dsjPnYPriy8CpaXNXSsiIiKyEiZSTaW4GK4REVCkpIiHHNPTofy//2vGShEREZE1cbB5E5DduQOXSZPg+MsvkuNGPz9UxMY2U62IiIjI2phINZIsPx+u4eGQX7okOW7o3Ru63bsh+Pg0U82IiIjI2phINYJDdjZcX3gBDnl5kuP6wYNRunMnhE7c542IiMieMZFqBNc//hEON29KjulHjoQuMRFwc2umWhEREZGttOjB5suXL4dKpZJ8evXqJZ4XBAHLly9H79690aVLF4wdOxYXL16UXEOr1WLmzJnw8/ODn58fZs6cCa1WKynz22+/4bnnnkOXLl0QHByMTz75BIIg1Fm/mklU1dix0O3YwSSKiIiojWjRiRQABAYGIjMzU/ycOHFCPLdmzRp88cUX+OSTT3D48GF07twZYWFhKC4uFstMnz4dZ8+eRVJSEnbu3ImzZ8/izTffFM/fu3cPYWFh8PLywuHDh7FixQp8/vnniI+Pf6h6Vk6ZgtItW4B27RofNBEREbUKLf7VnqOjI9Rqda3jgiAgISEB7733HiZMmAAASEhIQGBgIHbu3InXXnsNmZmZ+O6773DgwAEMGzYMALB69Wo8++yzyMrKQmBgIJKSklBWVoaEhAQolUr06dMHly9fxtq1azF79mzI6rEXXsWsWShftgxwaPF5KRERETWhFv+XPycnB8HBwRgwYABef/115OTkAAByc3Oh0Wjw5JNPimWVSiUee+wx/PTTTwCAjIwMtG/fXkyiAODRRx+Fq6urpMzw4cOhVCrFMk899RQKCgqQm5v7wLoVabUo0mpRvmKF3SdRgYGBzV0Fm2PMbQNjbhsYc9th67hb9F//IUOGYO3atUhKSsJnn30GjUaDZ555Bnfu3IFGowEAdO7cWfKbzp074+Z/xy7dvHkTHh4ekl4lmUwGT09PSRlz1zCdIyIiIrKkRb/ae/rppyXfhwwZgoEDB2Lbtm0ICQkBgFqv3gRBqJU41VRXGdNA8/q81iMiIqK2q0X3SNXUvn179O7dG9nZ2eK4qZq9Rrdv3xZ7lLy8vHD79m3JDDxBEFBYWCgpY+4aQO3eLiIiIqLqWlUiVV5ejqysLKjVanTr1g1qtRqpqamS8+np6eKYqKFDh6KkpAQZGRlimYyMDOh0OkmZ9PR0lJeXi2VSU1Ph7e2Nbt262SgyIiIiao1adCK1ePFiHDt2DDk5OTh16hSmTp2K0tJSTJkyBTKZDFFRUYiLi0NycjIuXLiA6OhouLq6YtKkSQCAoKAghIaGIiYmBidPnkRGRgZiYmIwZswYcTDapEmToFQqER0djQsXLiA5ORlxcXGIjo7mqz0iIiJ6oBadSOXn52P69OkICQnBK6+8AicnJxw6dAh+fn4AgHfffRfR0dGIjY3F6NGjcePGDezevRtu1RbE3LBhA/r164fw8HBMnDgRAHDw4EFxgc9u3bpBr9ejoKAAo0ePRmxsLAYMGID4+PhmWeSzKRw/fhwvvvgigoODoVKpkJiYKDlvy4VM9+zZg2HDhsHLywvDhg3D3r17myXmqKioWou7hoaGSspUVFQgNjYWAQEB8PHxwYsvvojr169Lyly9ehWRkZHw8fFBQEAA5s6di8rKSkmZY8eOYdSoUVCr1fjd736HTZs2WSXmVatWYfTo0ejatSt69OiByMhIXLhwQVLG3tq6PjHbW1tv2LABjz32GLp27YquXbvi6aefxsGDB8Xz9tbG9YnZ3trYnJUrV0KlUiG22sb39tjW1ZmLuTW0dYtOpDZt2oRLly7h1q1buHjxIr766iv07t1bPC+TybBgwQJkZmZCo9EgJSUFffr0kVyjY8eO+PLLL3H16lVcvXoV48aNq7XIZ0ZGBvbv3w+NRoOoqCicOXMGf/3rX5t9kc+G0ul06NOnD1asWCFZ1sHEVguZZmRk4PXXX0dERATS0tIQERGBadOm4dSpUzaPGQCeeOIJSbsnJSVJzi9YsAB79+7Fxo0bkZKSguLiYkRGRsJgMAAADAYDIiMjUVJSgpSUFGzcuBHJyclYtGiReI2cnBxMnjwZQ4cOxdGjRzFnzhzMnTsXe/bsafKYjx07hjfeeAMHDx5EcnIyHB0d8cILL+Du3btiGXtr6/rEDNhXW/v4+GDJkiU4cuQIUlNTMXLkSLz00ks4f/48APtr4/rEDNhXG9d08uRJbNmyBX379pUct8e2ritmoBW0tVarFdrSZ968eUJwcLDZc3fv3hXUarWwePFi8VhBQYHQvn17YfXq1YJWqxV++uknAYBw4MABscz+/fsFAMLJkycFrVYrrFy5UnBzcxMKCgrEMosWLRK8vb2Fu3fv2jReV1dX4YsvvmiWGMPCwoQnnnhCUp9Ro0YJEydOtGnMWq1WmDJlijBmzBiLv8nNzRUUCoXw5ZdfisfOnz8vyGQyYdeuXYJWqxWSkpIEmUwmnD9/Xiyzfv16wdnZWcjLyxO0Wq3w7rvvCgEBAZJrv/LKK0JISIjV2/ratWuCg4ODsH379jbT1jVjbittrVKphNWrV7eJNq4Zs723cW5uruDv7y/s2bNHGDFihDBjxgxBq7Xvf54txdxa2rpF90hZS0te5NPabBnjyZMnJfcxlTFdw9bS09PRs2dP/P73v8c777yDW7duied+/fVXVFVVSerr6+uLoKAgScxBQUHw9fUVyzz11FOoqKjAr7/+KpYxF/Pp06dRVVVlzfBQUlICo9EIlUoFoG20dc2YTey1rQ0GA3bt2gWdToehQ4e2iTauGbOJvbaxabeOUaNGSY7bc1tbitmkpbd1m0uk2voin7aMUaPRPPA+thQaGop169Zhz549+Pjjj/Hzzz9j/PjxqKioEOssl8vh4eFhsb7mYvbw8IBcLq/zuej1ehQWFlorPADA/Pnz0b9/f/GPTVto65oxA/bZ1r/99hseeeQReHl5ISYmBv/85z/Rt29fu25jSzED9tnGALBlyxZkZ2dLXjmZ2GtbPyhmoHW0dYtekNMauMjnfbaKsa772IppogEA9O3bFwMHDkT//v1x8OBBjB8/3uLv6vNcah5vjrZfuHAhfvzxRxw4cAByudxi3Uz1sYe2thSzPbZ1YGAg0tLSUFRUhOTkZERFRWHfvn0PrEdrb2NLMffp08cu2zgrKwtLly7F/v374eTkZLGcPbV1fWJuDW3d5nqkampri3zaMka1Wv3A+zQnb29v+Pj4IDs7G8D9eAwGQ63/8qj5XGrGU1hYCIPBUOdzcXR0RKdOnawSy4IFC7Br1y4kJyfD399fPG7PbW0pZnPsoa2dnJwQEBCAQYMG4YMPPkD//v2xdu1au25jSzGbYw9tnJGRgcLCQgwfPhweHh7w8PDA8ePH8fe//x0eHh7i/eypreuK2dTrVF1LbOs2n0i1tUU+bRljSEiI5D6mMtXf3zeXwsJCFBQUiH+IBg4cCIVCIanv9evXkZmZKYk5MzNTMq02NTUVzs7OGDhwoFjmhx9+kNwrNTUVgwYNgkKhaPI45s2bh507dyI5ORm9evWSnLPXtn5QzObYS1tXZzQaUVlZabdtbI4pZnPsoY3Hjh2LEydOIC0tTfwMGjQIEydORFpaGnr27Gl3bV1XzOZ6qVpiW8vnz5//YUMeQGu1ePFiODk5wWg04j//+Q9iY2ORnZ2N1atXQ6VSwWAwYPXq1ejZsycMBgMWLVoEjUaDuLg4ODs7w9PTE6dOncLOnTsxYMAAXL9+HTExMRg8eLA4xbRHjx74xz/+gXPnziEwMBDp6en4y1/+gvfee88m/9IpKSnBpUuXoNFo8NVXX6FPnz7o0KEDKisr4e7ubrMYvb29sWzZMigUCnh4eGDLli1I4uJ0VAAABi9JREFUTEzEmjVr4OPjY7OY5XI5li5divbt20Ov1+PcuXN4++23YTAY8Omnn8LZ2Rnt2rXDjRs3xHXHioqKEBMTgw4dOmDJkiVwcHCAv78/9u7di8OHD6Nv3764dOkS3n//fURERGDcuHEAgO7duyMuLg63bt1C165dkZKSgpUrV+Ljjz+WLN3RFN5//318/fXX2Lx5M3x9faHT6aDT6QDc/695mUxmd21dV8wlJSV219Yffvih+O+s69evIyEhATt27MCHH36IHj162F0b1xWzWq22uzYGgHbt2qFz586ST1JSEvz8/PDSSy/Z5T/PdcWs0+laR1vbYtpqS/qEh4cLXbp0ERQKheDt7S2MGzdO+PHHH8Xzd+/eFebNmyeo1WrB2dlZeOyxx4QTJ05IrnHlyhVh8uTJgpubm+Dm5iZMnjxZyMnJkZQ5fvy4MHz4cMHZ2VlQq9XC/Pnzbbb0wd69ewUAtT5TpkyxeYxbtmwRAgMDBYVCIfTq1UvYunWrzWMuKCgQnnzyScHT01NQKBSCr6+vMGXKFMlUWK1WK9y4cUOYMWOG0LFjR0GpVApjxoypVebcuXPCmDFjBKVSKXTs2FGYMWOGoNFoJGX27dsnDBgwQHBychL8/PyEVatWWSVmc/ECEObNm9cs/3+2RVvXFbM9tvWUKVMEX19fwcnJSfD09BRGjRolTuu2xzauK2Z7bGNLn5pLAdhjWz8o5tbS1jKtVmub5baJiIiI7EybHyNFRERE1FBMpIiIiIgaiIkUERERUQMxkSIiIiJqICZSRERERA3ERIqIiIiogZhIERE1QGJiIlQqFU6ePNncVSGiZsREiohaHZVKVa9PYmJio+/16aefSjYIJiKqzrG5K0BE9LDWr18v+b5582acOnUK8fHxkuNNsSXT3/72N4SHh+P5559v9LWIyP4wkSKiVicyMlLy/YcffsAvv/xS63hNpaWlcHFxsWbViKiN4as9IrJLUVFRUKvVyMvLw5/+9Cf4+fkhIiICwP1d58eOHVvrN8uXL4dKpRK/q1QqVFRUYPv27eLrwpq/q6qqwtKlSxEUFIQuXbogLCwMOTk5kjLZ2dmYNm0agoKCoFar0bdvX0ydOhX5+flNHzgR2RR7pIjIbhmNRoSHh2Pw4MFYsmQJ5HL5Q/1+/fr1mD17NoYMGYJp06YBALy8vCRlFi5cCKVSiZiYGBQWFiI+Ph4zZ87Ev//9bwD3E63w8HCUl5dj+vTpUKvV0Gg0OHz4MPLz8+Hj49MksRJR82AiRUR2q6qqCs888wyWLVvWoN9HRkbinXfegb+/v8XXhi4uLti3bx8cHO538Hfs2BELFy7ExYsXERwcjEuXLiEnJwdbtmzBhAkTxN/FxsY2qE5E1LLw1R4R2bXp06db9fqvvfaamEQBwIgRIwBAfL3n5uYGAPj++++h0+msWhcisj0mUkRktxwcHODn52fVe3Tt2lXy3TTG6u7duwAAf39/zJo1C1u3bkWPHj0wYcIErF27FoWFhVatFxHZBhMpIrJbCoUCjo61RzDIZDKz5Q0Gw0Pfw9K4K0EQxP+9YsUKpKenY+7cuTAYDPjzn/+MkJAQXLx48aHvR0QtCxMpImpzVCoVioqKah3Py8urdcxS0vWwgoODMWfOHOzbtw9HjhzBvXv3kJCQ0CTXJqLmw0SKiNqcgIAAXL58GTdv3hSP5efnIyUlpVZZFxcXaLXaBt/r3r170Ov1kmNBQUFQKpWNui4RtQyctUdEbc6rr76K+Ph4hIWFYerUqSgqKsKmTZvQo0cPnDlzRlJ20KBBOHLkCD7//HP4+PjA09MTo0aNqve9jh49itjYWIwfPx6BgYEQBAG7d+9GcXExJk6c2NShEZGNMZEiojanZ8+e2LhxIz7++GMsWrQI3bt3x0cffYSsrKxaidSKFSswZ84crFixAjqdDiNGjHioRKpfv34IDQ3FoUOHsHXrVjg7OyM4OBiJiYlmFwUlotZFptVqhbqLEREREVFNHCNFRERE1EBMpIiIiIgaiIkUERERUQMxkSIiIiJqICZSRERERA3ERIqIiIiogZhIERERETUQEykiIiKiBmIiRURERNRATKSIiIiIGuj/A6+2E1i/ZBq6AAAAAElFTkSuQmCC\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tt, pred = knn_predictions([\"horsepower\"], \"price\", standardized_cars)\n", "plot_pred(tt, pred, \"Univariate model (horsepower)\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The figure confirms our first intuition: the best univariate model is not very predictive. Good predictions should lie on the red line.\n", "#### Hyperparameter optimization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since the KNN algorithm is based on finding the $k$-neighbors of a data point, it may be possible to improve the model by tuning the $k$ parameter (namely n_neighbors parameter). As pointed in the [scikit-learn documentation](https://scikit-learn.org/stable/modules/neighbors.html#unsupervised-neighbors):\n", "> The optimal choice of the [k] value is highly data-dependent: in general a larger suppresses the effects of noise, but makes the classification boundaries less distinct.\n", "\n", "In other words, a smaller $k$ may lead to overfitting, while a larger value may come at the cost of predictive power (underfitting).\n", "\n", "Shortcut:\n", "- small $k$ value = low bias + high variance\n", "- large $k$ value = high bias + low variance\n", "\n", "So, this is about the famous [bias-variance tradeoff](http://scott.fortmann-roe.com/docs/BiasVariance.html). How to minimize bias without increasing variance, and *vice versa*?" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "#tuning k parameter and save the results into a DataFrame\n", "univariate_model_df = pd.DataFrame(index=standardized_cars.columns[:-1])\n", "for k in [1,3,5,7,9]:\n", " rmse=[]\n", " col_name=\"k\"+str(k)\n", " for col in standardized_cars.columns[:-1]: \n", " rmse.append(knn_train_test([col], target, standardized_cars, k))\n", " univariate_model_df[col_name]=rmse" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "k1 engine_size\n", "k3 engine_size\n", "k5 horsepower\n", "k7 engine_size\n", "k9 engine_size\n", "dtype: object" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#label the best RMSE scores by k-neighbors values\n", "univariate_model_df.idxmin(axis=0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Hyperparameter optimization doesn't look to upset dramatically the features importance, though the horsepower leadership is now challenged by the engine size attribute. Let's take a look." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,5))\n", "x=[1,3,5,7,9]\n", "plt.plot(x,univariate_model_df.loc[\"horsepower\"],label=\"horsepower\")\n", "plt.plot(x,univariate_model_df.loc[\"engine_size\"],label=\"engine size\")\n", "plt.title(\"Univariate Model, tuning $k$\")\n", "plt.legend(loc='lower right')\n", "plt.ylabel(\"RMSE\")\n", "plt.xlabel(\"k-neighbors\")\n", "plt.xticks(x)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Indeed, the univariate model based on the horsepower attribute didn't improve its initial error score of 4367 (with $k$ = 5, the default value). Remember that the engine size attribute has the best Pearson score (highly correlated to price variable). The figure confirms this fact since this attribute improves notably the univariate model by reaching the 4200 levels when $1\\leq k \\leq3$. But, as said before, low $k$ values present a non-negligible risk of overfitting, so it is preferable to be cautious with this result.\n", "\n", "Let's now plot again the prediction and calculate the MAPE score." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tt, pred = knn_predictions([\"engine_size\"], \"price\", standardized_cars, k = 3)\n", "plot_pred(tt, pred, \"Univariate model (engine size, $k$ = 3)\") " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Surprisingly, the MAPE score is worst than expected! It means there is no linear relationship between RMSE and MAPE scores, at least for the engine size attribute. It's still unclear if the engine size attribute is more informative than the horsepower one.\n", "Finally, despite hyperparameter optimization, the best univariate models are still poorly predictive." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "### Multivariate Model\n", "Since the univariate models are getting poor results, we will now build several multivariate models and work on feature selection.\n", "Possible approaches consist of testing all attributes together or only the continuous ones.\n", "However, by selecting the best attributes obtained during the hyperparameter optimization step (saved into univariate_model_df DataFrame), we can build several other multivariate models (with $k$ = 5, the default value) and save again the results." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5748.238867240251\n" ] } ], "source": [ "#useful variable\n", "total_features = len(standardized_cars.columns)-1\n", "\n", "#full feature set model\n", "target = \"price\"\n", "full_feature_set = standardized_cars.columns[:-1].tolist()\n", "print(knn_train_test(full_feature_set, target, standardized_cars))" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "4935.325727330618\n" ] } ], "source": [ "#only continuous features\n", "target = \"price\"\n", "continuous_features = standardized_cars.iloc[:,66:-1].columns.tolist()\n", "print(knn_train_test(continuous_features, target, standardized_cars))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "At first glance, the binary features are not adding value to the model, at least when used indiscriminately. Indeed, the results above shows that the full feature set (*i.e.* absence of feature selection) has a larger score error than the model based on continuous features only. " ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#print the top 10 obtained from previous hyperparameter optimization step\n", "sorted_univariates = univariate_model_df.mean(axis=1).sort_values(ascending=False)\n", "ax = sorted_univariates[-10:].plot(kind='barh',\n", " stacked=True,\n", " figsize=(5, 5),\n", " title='Univariate model')\n", "ax.set_xlabel('Average RMSE, tuning $k$');\n", "ax.set_ylabel('Attribute');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will now build new models based on univariate_model_df sorted values, building feature sets to test by selecting the top-$X$ best features where $2\\leq X \\leq 77$ (total number of attributes)." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "#select the top-X index from univariate_model_df and build a model\n", "rmse=[]\n", "for i in range(2,total_features):\n", " col_names = univariate_model_df.mean(axis=1).sort_values().head(i).index\n", " rmse.append(knn_train_test(col_names, target, standardized_cars))" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "image/png": 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EgwcPws3NDQsWLGjpUyGiFsBMLxERERG1ecz0EhEREVGbx6CXiIiIiNo8Br1ERERE1OYx6CUiIiKiNo9BLxERERG1eQx6W1hGRkZLn0KrwXtlP94r+/Fe2Y/3yn68V/bjvbIf79XNYdBLRERERG0eg14iIiIiavMY9BIRERFRm8egl4iIiIjaPAa9RERERNTmMeglIiIiojavxYLelStXQqVSiX7CwsLM68vKyvDss88iKioKHTp0QGxsLNasWSPah1arxbPPPovu3bsjODgYkydPRnZ2tmibrKwsJCQkIDg4GN27d8dzzz0HnU7XLNdIRERERK5B3pIH12g02Llzp/m1TCYz/3nRokXYv38/3n//fXTp0gWHDh3CU089hYCAAEyePBkA8OKLLyIpKQnr16+Hn58fFi1ahISEBBw4cAAymQxGoxEJCQnw8/NDUlISCgsLMWfOHAiCgMTExGa/XiIiImo5JkGAwQToTQKEerZzk0rgLpM023lR82jRoFculyMoKKjOdceOHUNCQgIGDx4MAOjSpQs2bdqE48ePY/LkySguLsamTZuwZs0aDB06FACwbt069O7dG/v378ewYcOwb98+pKWl4dSpUwgJCQEALFu2DPPnz8eSJUvg4+PTPBdKREREIt9f1WLRsWJklSgh+/Gqw/cvADD8EeQaTAL0JtQb6NYmkwD3dVHivXv84CFn8NtWtGhNb2ZmJiIjI9GnTx/MmDEDmZmZ5nUDBgzArl27cPnyZQDA0aNHcfr0aQwbNgwAkJqaCr1ej7i4OPN7QkJCEB4ejqNHjwKoCpzDw8PNAS8ADBs2DFqtFqmpqc1whURERGTpwBUtHtybh1MFehQZJMjXmhz+U6A1oUQnoNwgQNeIgBcAjALw38wKvH6y1Gn3gJpfi2V6Y2NjsXbtWmg0GuTl5SExMREjR47EkSNH4O/vj1WrVuFvf/sboqOjIZdXneb//d//IT4+HgCQk5MDmUyGgIAA0X7VajVycnLM26jVatH6gIAAyGQy8za2NOdUf5xW0H68V/bjvbIf75X9eK/sx3tVt5MlUsw97Q6tyfUzqG+dLMFdbjno5NGYkNm5+FzZptFo6l3fYkHviBEjRK9jY2MRExODLVu2YO7cuVi3bh2OHj2KrVu3onPnzjh06BCWLFmC0NBQDB8+3OZ+BUGARFLzF6n2n2uztbxaQzfOUTIyMprtWK0d75X9eK/sx3tlP94r+/Fe1e1Evg5/O5aHClPLBZFuUkAukUBqIwyoMAqoPj2dIMG/cvyweVhA3Rs3Mz5XN6dFa3pr8/b2RkREBM6fP4+KigosX74c//nPfzB69GgAQHR0NE6dOoV33nkHw4cPR2BgIIxGI/Lz89G+fXvzfvLy8jBo0CAAQGBgoLnUoVp+fj6MRqNVBpiIiIic57ciPcbvzkeJThzwzuuqw1MDQ51yTLlEArm0amCaXAJIJQ0nvbaeK8eclELz668vVWJfdiXiOnk45Ryp+bhMn97KykpkZGQgKCgIer0eer1e1M0BqOruYDKZAAAxMTFwc3NDcnKyeX12djbS09PRv39/AEC/fv2Qnp4uamOWnJwMd3d3xMTENMNVERER0YUSA8btykO+1iRa/kJMO0wLMaC9h8wpPyp3KbzdpHCXSSCTShoMeAEgoYcSfdVu4vM8Wgyd0XVKHKhpWizoXbx4MX744QdkZmbip59+wsMPP4zy8nJMmTIFPj4+uOuuu7Bs2TKkpKQgMzMTmzdvxieffIKxY8cCAHx9fTF16lQsXboU+/fvx4kTJ/D444+jV69eGDJkCAAgLi4OkZGRmD17Nk6cOIH9+/dj6dKlmDZtGjs3EBERNYPLZQbctzsP1yrEAe+8aG88H9Ouhc7KNqlEgv8boELt8PhssQEfpJW12DmRY7RYecOVK1cwc+ZMc3lCbGws9u7di9DQqq84NmzYgGXLlmHWrFkoLCxE586dsWjRIsyaNcu8j1dffRUymQzTp09HZWUlBg8ejPfff9+cIZbJZNi2bRsWLFiA+Ph4eHh4YOLEiVixYkWLXDMREdGt5Hq5EffvzkNWmVG0fEa4F5bH+tiVeW0Jt7dXYGqYJzaeLTcvW5Vaige7eyLIU1bPO8mVSYqKipivb0EsSrcf75X9eK/sx3tlP94r+/FeATqjgCFf5eDXQoNoeUKPqv630j8CXle9V7kVRtz5xXVRDfJfenpi7T1+LXZOrnqvWguXqeklIiKituO/mRVWAe99XTyw5u6agNeVqZUyLLxdXAq55Vw5fsrVtdAZ0c1i0EtEREQOdzJfL3o9NNgdH/7JH3JbvcJc0KMRXohUiStBnztSBJPAL8lbIwa9RERE5HAFFp0aHuimhELWegJeoKrV2T/7q0TLfs7TY3NGuY133HqEVvQBwGX69BIREVHbYRn0+rm3zjzbn4LdcX9XD+zIrDQvW3a8BO09pE0q0+jWToYwlVvDG7qASoOA3ZcrkVFsQH6lsWp658o/pnqurPqzRAJM7umJf/bzhczFs/gMeomIiMjhCivFQa9/Kw16AeAffX2xJ0uLij969eZVmjDlu4Im72/JHT74+22u166tNkEQMC05H3suaxvc9l9pN9CtnRxP9PJuhjNrutb7BBIREZHLssz0tuagN9Rbjqf7OC6g+8fPJUi6VOGw/TnDoes6uwLeaq/8XILLZYaGN2xBrfcJJCIiIpfVloJeAJgf3Q7d2zmuR+/slEJklrpukLj+txuN2v6GQcDzR4uddDaOwfIGIiIiciijSUBhG6npraaUS/BlfHu8daoMWU3IaAoCsO+KFtWzGZfoBEzbV4A9Y9TwkLtWLez1ciP+lynORM+P9ka4So4ADykC3GUI8JBid1YlXjxWE+h+fakSOy9WYGwXZXOfsl0Y9BIREZFDFetMqD2mv52bpNV1bqhLqLccrw9UNbyhDe+eLsXiH0vMr08W6PHc0SK8fVfLTXhRl41nb8BQ63+gxleOZXXMoPd4lBe+uFCOH3Nr2tM9f6QYfwp2Rzs31/uQ43pnRERERK1aWyttcJQne3njvi4eomUbz5Zjc0bjSgmcyWAS8J90cUu2RyO86pwyWiqR4I1Bfqj9eSa73IhXfy6x2tYV8CkkIiIih7IKej0YbgCARCLBu3f7oYePuDb474eLcKpAb+NdzWtXViWyy43m155yCSb38LS5fbS/G+ZadG1Yl3YDqXmuN3Mdn0IiIiJyKGZ6bfNRSLFxaACUtdKjlUZg2r58FFnct5ZgOYDtwe5KqBr4//dcTDuEetcE8iYBePpQEYwm15q4gk8hEREROVRBG+rR6wy9/N2wepC4NvhCqRFP/lDYojOcnSvWI/mKuE3ZoxFeDb7Py01qVeucmq/HvxrZAcLZ+BQSERGRQ7WV2dicaUpPTzwSJi4b+PpSJeYfLMLGszeQdKkCP+bokFlqQJne1CzB8IZ0cZDaT61AnwCFXe8dEeKBcV3FXRtWHC9B9g2jjXc0P3ZvICIiIoeybFfGTG/d/tlfhV/y9TiRX1PPuymjHJsyyq22Vcok6OXtjld9tegX6O7wcyk3mLDZ4riPRjac5a1tZX9f7MuuRIm+KkAvMwh4/kgRPh4W4LDzvBl8ComIiMihWNNrHw+5BB8N9YdK0XA7twqjgJ+KZRj7TR4+/d06KL5Zn5+vQLGuJpvs7y7F/Y3st9vRU4ald/qIlu28VIlvXGT2OT6FRERE5FDs3mC/ru3k+GCwP+ydn0JnAmZ9X4iVv5Q4tOTBcgDbVI1nkybNmB7uhVi1m/n1w2GeGBDk+Mx0U7C8gYiIiByKA9kaZ2RnDxy4LxDfZVcip8KEvEoj8ipN5p/cSiO0FqWxq1JLcb7EgHfu8rvpGd1+ztUhtVaJhQTAdDsGsNVFJq3q3TsnpRCJA3wx0EUCXoBBLxERETkYyxsar5e/G3r5u9W5ThAEfHa+Ak+mFEAv1AS4n52vwKUyIzYP80d7D1md77XHhxZZ3hEh7ujarukhYm9/N6Tcp65zQouWxKeQiIiIHMpyIBu7N9wciUSCST08sTZaiwCLe3k0R4dhX+Uivahpk1sUVBrxxQXLGdi8bWxtP1cLeAEGvURERORgrOl1jhhfE74dq4bGV5yFvVhmxIivc7Hht6qZ0CoN9tf6bj5XjspapROh3jIM7+Q6JQmOxPIGIiIicphyg0kURLlJAe+brDmlGt185Ng7Ro1pyQX4/mrNRBIlOgHPHC4CAMgkQJivHL383RDt54ZofzcEKqUwmAC9SYBeAIwmAXqT9QC2GeFekEnb5v8vBr1ERETkMHUNYnPFr7pbM5W7FJ+PDMAzh4rq7OlrFIC0IgPSigzYDvvbhSmkwF8tJsxoS/h9AxERETkMB7E1DzepBG/fpcLyWB846iPFuG7KmxoQ5+qY6SUiIiKH4SC25iORSDC/dzsMD/HAfy9U4EyhHqcL9LhU1vipf92kwJO9bn4AmytrsaB35cqVWLVqlWhZYGAgzp49a3597tw5vPzyy/j++++h1+uh0Wjwr3/9C+Hh4QAArVaLxYsX4/PPP0dlZSUGDx6M119/HZ06dTLvIysrCwsWLEBKSgo8PDwwceJErFixAgqFfXNJExERkf2Y6W1+UX5uiPKraXdWrDPhTIHeHAT/WqhHuUGAm1QCubQqSyyXVP3XTQr4KqSY3NMTtwW07dioRTO9Go0GO3fuNL+WyWpS6pmZmRg1ahQmT56M//3vf1CpVDh79iy8vGqaJb/44otISkrC+vXr4efnh0WLFiEhIQEHDhyATCaD0WhEQkIC/Pz8kJSUhMLCQsyZMweCICAxMbFZr5WIiOhWwM4NLc9XIcWgDu4Y1KFtdmFoqhYNeuVyOYKCgupct2LFCsTFxeGVV14xL+vatav5z8XFxdi0aRPWrFmDoUOHAgDWrVuH3r17Y//+/Rg2bBj27duHtLQ0nDp1CiEhIQCAZcuWYf78+ViyZAl8fMTzQxMREdHN4Wxs5Kpa9EnMzMxEZGQk+vTpgxkzZiAzMxMAYDKZsGvXLoSHh2PChAno0aMHhg4dii+++ML83tTUVOj1esTFxZmXhYSEIDw8HEePHgUAHDt2DOHh4eaAFwCGDRsGrVaL1NTU5rlIIiKiWwjLG8hVtVimNzY2FmvXroVGo0FeXh4SExMxcuRIHDlyBHq9HmVlZVi9ejUWLlyIl156Cd9//z0ee+wxeHp6Ij4+Hjk5OZDJZAgICBDtV61WIycnBwCQk5MDtVotWh8QEACZTGbexpaMjAzHXrCLHKu1472yH++V/Xiv7Md7Zb9b9V5dzFWgdnihL8pFRsa1et9zq96rpuC9sk2j0dS7vsWC3hEjRohex8bGIiYmBlu2bMGECRMAAPfeey/mzp0LAOjTpw9SU1Px4YcfIj4+3uZ+BUEQ9QO01RuwoZ6BDd04R8nIyGi2Y7V2vFf2472yH++V/Xiv7Hcr3ytDZh6AmkkTIkI7QtNFaXP7W/leNRbv1c1xme8cvL29ERERgfPnzyMgIAByudzcpaFaWFgYLl++DKCq04PRaER+fr5om7y8PHN2NzAw0Cqjm5+fD6PRaJUBJiIiopvHgWzkqlzmSaysrERGRgaCgoKgUChwxx13WKXwz507h86dOwMAYmJi4ObmhuTkZPP67OxspKeno3///gCAfv36IT09HdnZ2eZtkpOT4e7ujpiYmGa4KiIiolsLB7KRq2qx8obFixcjPj4eISEh5pre8vJyTJkyBQAwf/58TJ8+HYMGDcLgwYORkpKCL774Aps3bwYA+Pr6YurUqVi6dCnUarW5ZVmvXr0wZMgQAEBcXBwiIyMxe/ZsrFixAoWFhVi6dCmmTZvGzg1EREROwIFs5KpaLOi9cuUKZs6cifz8fLRv3x6xsbHYu3cvQkNDAQBjx47Fm2++idWrV+OFF15A9+7d8f7772PUqFHmfbz66quQyWSYPn26eXKK999/39zvVyaTYdu2bViwYAHi4+NFk1MQERFRlbNFeggAwnzlDY55qY/RJKBYJ4iWcUY2chUtFvRu2LChwW0eeughPPTQQzbXe3h4IDExsd6JJjp37oxt27Y16RyJiIjaMoNJwN8PF+Gjs+UAgGdva4dFd7IfPOsAACAASURBVDT9m9AinQm1Q14fhQRyadODaCJH4scvIiKiW1CFQcDUfQXmgBcAXjtRit+K9E3ep2VpQwCzvORC+DQSERHdYop1Jkzcm4dvsipFywUAiamlTd4vB7GRK+PTSEREdAvJrTDiz9/k4eA1XZ3rv7hQgbNNzPZyEBu5Mj6NREREt4hLZQbEJ+XiZIHtoFZAVZlDU1gGvX7s0UsuhE8jERHRLeC3Ij3iv87F7yVG0fK+aje8MVAlWrb9QgXOFTc+28tML7kyPo1ERERt3PFcHe5NysOVcnFQGhfsji9HtcfD4Z6IVNU0dDIJTcv2FjLoJRfGp5GIiKgNy60w4oE9eVZZ2Ae6KvHJ8AB4uUkhlUjw7G3tROs/O1+B8yWGRh2LA9nIlfFpJCIiasM2ni1HicWEEdPDPfHhn/ygkNX00L2/qxJhvjXZXqMAvH6ycdleljeQK+PTSERE1Ial5ou7NDwW4YXVA1WQWUwaIZNaZ3s/OVeOzFL7s735lkEvB7KRC+HTSERE1IadzBcPSJvS09PmVMPjuynR08ci29uI2t5Ci/IGTkFMroRPIxERURtVpDXhYllNtwaZBIj0c7O5vUwqwQKLbO/Wc+W4aGe2l+UN5Mr4NFKz2X+lEm+eLG30wAgiImqa04XiLG+YrxxKed1Z3moTuyvRvZ3M/NogAG/YUdsrCAKDXnJpfBqpWSRdqsC43fl4+XgJ7tmRg/xKY8NvIiKim3LKorSht7/tLG81eR3Z3s3nypFVVn/C4oZBgK5WzOsuAzwbCLCJmhODXmoWX2ZWmP98wyBgz2VtC54NEdGt4ZTFzGu9AxoOegFgUg9PdK2V7dWbgDdPldX7nrqyvLZqh4laAoNeahbXLRqiXytnppeIyNkspxvuY0emF6jK9v69jzjbu+nsDWTfsP2727JHLwexkavhE0nNIs+inCGX5Q1ERE6lMwpIL2p8eUO1yT09Eepdk+3VmYDPfi+3uT1nYyNXxyeSmkW+RQYgt8JkY0siInKEtCI99LV+1YZ4yeDvIbP9BgtuUglmRXqJlv1qEUTXxkFs5Or4RJLTCYKAPIugN4dBLxGRU1nW80Y3IstbzTIz/Hux7cFsDHrJ1fGJJKcr1gkwiGfARG4FyxuIiJzJclKKPnYOYqutR62JKgAgo8QAQRDq3NYq6OVsbORi+ESS01nW8wJAbiUzvUREzmTVuaEJmd5gL5mo7ViJzvqbu2ocyEaujk8kOV1dAW6+1gSjqe5sARER3RyTIOB0Ezs31CaVSNDdItt7zsYEQxzIRq6OTyQ5XV1ZAZNQFfgSEZHjXSw1olRfk1jwUUhEnRgaQ2NZ4mCjrpc1veTq+ESS01l2bqjGDg5ERM5h2Z+3t79bkyeK6OErDnptDWZj0Euujk8kOZ2tQWvs1UtE5ByW0w83pbShmsbXejBbXTiQjVwdn0hyOluDHti2jIjIOU4V6ESvmzKIrVpPH2Z6qW3gE0lOZ6t2N4dty4iInMKyc0OfAEWT92XZtux8qQEGi4HIBpOAEl3NMgkAlYIhBrmWFnsiV65cCZVKJfoJCwurc9unnnoKKpUK77zzjmi5VqvFs88+i+7duyM4OBiTJ09Gdna2aJusrCwkJCQgODgY3bt3x3PPPQedTvwJmJzLVu2urQwwERE1XV6lEVfKa36/KqRAmEWJQmOo3KVQ1ypV0JuArDJx0sKyc4OvQgKZtGk1xETO0vS/BQ6g0Wiwc+dO82uZzHpk6Y4dO/Dzzz+jY8eOVutefPFFJCUlYf369fDz88OiRYuQkJCAAwcOQCaTwWg0IiEhAX5+fkhKSkJhYSHmzJkDQRCQmJjo1GujGnX16QVY3kBE5AyW9bwRKjcoZDcXgPb0lSO3siZhlFFsQLdaGWDL0oYA1vOSC2rRp1IulyMoKMj80759e9H6S5cu4YUXXsCHH34IuVwcnxcXF2PTpk1Yvnw5hg4dipiYGKxbtw5nzpzB/v37AQD79u1DWloa1q1bh5iYGAwdOhTLli3Dxo0bUVJS0lyXecuz3b2B5Q1ERI5m2bmhKTOxWbKs67Xs1ct6XmoNWvSpzMzMRGRkJPr06YMZM2YgMzPTvM5gMGDmzJlYsGABwsPDrd6bmpoKvV6PuLg487KQkBCEh4fj6NGjAIBjx44hPDwcISEh5m2GDRsGrVaL1NRU510YmQmC7dl7OCsbEZHjOWImNks9LcojzlkMZrOcjY1BL7miFitviI2Nxdq1a6HRaJCXl4fExESMHDkSR44cgb+/P1auXAk/Pz88+uijdb4/JycHMpkMAQEBouVqtRo5OTnmbdRqtWh9QEAAZDKZeRtbMjIybuLqGqc5j9XcSgyAQfCsc92VUm2jr70t3ytH472yH++V/Xiv7NdS9+r4VQ/Uzmn5l19HRsbVm9qn1w0ZAHfz65PXSpCRkWt+/ds18Xq57gYyMgrt3j+fK/vxXtmm0WjqXd9iQe+IESNEr2NjYxETE4MtW7aY/5uSktLo/QqCIGrAbasZd0NNuhu6cY6SkZHRbMdqCeeK9QDq/oBRZJCiZ8+edjdMb+v3ypF4r+zHe2U/3iv7tdS9KjeYcPGgOMAd1acbfG6yk4JJrQfSan6XX9UroNGEml+7VZYCqCkb7KL2hUajsmvffK7sx3t1c1zm+wdvb29ERETg/PnzSElJwbVr1xAeHo6AgAAEBAQgKysLL730EqKiogAAgYGBMBqNyM/PF+0nLy/PnN0NDAy0yujm5+fDaDRaZYDJOeorYdCZgGKdYHM9ERE1zq+FBtTuJtatneymA96q/chRuxlDdrkRN/Q1v9+ta3qbNuUxkTO5TNBbWVmJjIwMBAUFYebMmTh48CBSUlLMPx07dsQTTzyBHTt2AABiYmLg5uaG5ORk8z6ys7ORnp6O/v37AwD69euH9PR0URuz5ORkuLu7IyYmpnkv8BbVUFsyzspGROQ4Jy1nYnPAIDYAUMgk6OItDmR/rzWYjQPZqDVosfKGxYsXIz4+HiEhIeaa3vLyckyZMgVqtdoqE1vd6aE6re/r64upU6di6dKlUKvV5pZlvXr1wpAhQwAAcXFxiIyMxOzZs7FixQoUFhZi6dKlmDZtGnx8fJr7km9Jtjo3VMupMEHj20wnQ0TUxlnPxNb0SSks9fSR40JpTaLiXLHBPOkFB7JRa9BiQe+VK1cwc+ZM5Ofno3379oiNjcXevXsRGhra8Jv/8Oqrr0Imk2H69OmorKzE4MGD8f7775v7/cpkMmzbtg0LFixAfHw8PDw8MHHiRKxYscJZl0UWGmpLZmviCiIiajyrmdgc0LmhWk9fOfZma82vz9WT6fVj0EsuqMWC3g0bNjRq+1OnTlkt8/DwQGJiYr0TTXTu3Bnbtm1r9PmRY7C8gYioeRhNAs4UiFuJ9XZQeQNQf9syyxnZ/Dk5BbkgPpXkVPkWvwhDvMQ1YZyVjYjIMc6VGFBhrBnFpvaQooPScf/M9/QRB9D1ZXpZ3kCuiE8lOZVl+UKUn9xiPTO9RESOYDmIrbe/m90tIe1RV6ZXEAQIgsCgl1oFPpXkVHkW5QtRfuJMQQ5nZSMicghnzMRWW7CnFJ7ymiC6RC8gt9KEMoOAWt3LoJRJoJQ7LtgmchQGveRUlt0bIlTiX8J5LG8gInKIk5aD2BxYzwtUTerUw0ec7c0oNrBzA7UafDLJaQRBsBrIFmlR3pDDgWxERDdNEAScqqO8wdE0FiUOv5cYrAax+XEQG7koPpnkNMU6AYZaMwN5ySXo4m1Z08tMLxHRzbpabhINHPaUW2dlHcFyn+eKDaznpVaDTyY5jWU9b4CHFL4KCWrPiHnDIIimsiQiosY7aTEpRS8/OWRSx9fVWg5my2DQS60In0xyGsvSBrWHFBKJBGoPcduyXA5mIyK6KdalDY6bia02jY91eQNreqm14JNJTmMZzLb/o86rvUXfSJY4EBHdHGcPYqvWwyLTe6HUYNVvnUEvuaoWm5GN2j7Lzg0Bf2R4Ay0GOeSwVy8R3SIEQcD5EiNK9CaYBMAoCDAKqPoxAQIEqD1k6NWIQWiXygw4eE1c3uCMQWwA4KuQIlApNQe6ehOQmi8+Ngeykati0EtOU1d5AwColbJ6tyMiaou0RgH378rDkRxdg9tO6q7EO3f7wV1Wf11uboURD+zOE9XVesise6I7Ug8fOXIqaq7hx1zx9TDTS66KTyY5jeVsa9XlDWpmeonoFvSf9Bt2BbwA8On5CjywO8+qHVhtxToTxu/Jx+8l4t+hT/TydurkEJZty4p1gug1g15yVXwyyWnytZblDdWZXougl5leIroFJF2qbNT2h67rMOrrXFwsNVitKzeYMPnbfKtZ2B7oqsSi231u6jwb0rOBVmgMeslVsbyBnMaqvOGPsoZAi/IGDmQjorauRGfCoeta0bKYADfIpYBMIoFUAsgkQGapEZdv1GRuzxYbMOLrXHw6PAAx7as6MuiMAh5JLsDh6+Ks8fBO7lg32M8prcpqs2xbZolBL7kqBr3kNLbKGwItuzdwVjYiauOSr2hRuyW5xleO/fcFWm1XqDXhL9/liwLanAoT7v0mD/8e4o/hndwxJ6UQey6LA+j+gQp8NNQfigZqgB2hwUwvB7KRi+KTSU5j3b3hj5Zlln16meklojZud5a4tGFkiEed2/m5S/Hfke0xvptStLzcIGDKd/kYtzsPn1+oEK3r5SfHtuEB8HJrnn/Su7aTw1ZsLZUAvgrnB95ETcGgl5xCEASr8gZbmV4OZCOitswkCNh7WRz0jupcd9ALAB5yCT78kx+eiva22A+QYtGarHs7Gf47qj1UzVhSoJBJ0MVbVuc6P4UUUgmDXnJNDHrJKYp1Agy1BvR6ySXwlFc9bgHuUtQuOSvSCdAZBRARtUW/5OlFk/X4uEkwMKj+GdOkEgmW9fVF4gBf2CrR7egpxX9HtbcaJ9EcbNX1srSBXBmfTnKKPIs63YBavwhlUgkCLLIS7NVLRG3VLovShrhOHnCzc7DZY5He+DjOH0qLegI/dwn+O6o9urRrmaE5NoNeDmIjF8ank5zC1sQUtl6zxIGIXJnBJOCXPB3eOV2KhG/zMXhHDl48WgSjqeFvqSzreesrbajLvaFK7BzdHh2U1eMipNg+oj0iVM6bgKIhPX3qPrYfg15yYezeQE6Ra6Oet5paKQOKDDa3JyJqSQaTgFMFevxwVYsfrmlx+LoOJXpxgHuyQA9/dymejbHdF/fKDSNO1uqlKwEwIsS90edzp1qBnycG4US+HpEqt2at4a0LM73UGjHoJaew7twgrjmzalvGTC8RuYgDV7R4/PsCXLOjs8zaX8vwRC9vm50TLAew9VUrrDrY2MtTLsXAoMYHzM5gq20Zg15yZXw6ySkaKm+wzPwy00vUsEqDgHdOlWLxsWL8Xmw9SxfdvAqDYHfACwCFWgEbz5bbXG9ZzzuykaUNrqqjpxRedUx1zIFs5Mr4dJJT2JqYoprlaOMc9uolatDSn4qx5KcSvHumDBP35qHCwK4njvaf9Bs2A15/dyn+3MUD8RaB65ozZdDXUdtbaRBw4Kp4EonG1vO6KolEgh51ZHuZ6SVXxvIGcop8bd0TU1RTc1Y2okYp0Zmw8ewN8+sLpUbsy67EmC7Ket5FjVFhEPDmqVLRsgGBCozvpsTdHd0RoZJDKpEgt8KI3p9dQ/Wvrcs3jNh+vgJTenqK3vvDNS3Ka30w6eQpQ7Rf2/lnV+MrF9UrAxzIRq6txZ7OlStXQqVSiX7CwsIAAHq9Hi+99BIGDRqE4OBghIeHY+bMmcjKyhLtQ6vV4tlnn0X37t0RHByMyZMnIzs7W7RNVlYWEhISEBwcjO7du+O5556DTidu7k2OZ1XeYJHZDeSsbESN8tXFClh+Nvz6UmXdG1OT/Dv9Bq7X+l3kKZdgU5w/ZkV5I8rPzTzpglopw181XqL3vn2qFCZBnO21moWtszskbWjihh51DGZjppdcWYs+nRqNBunp6eafQ4cOAQDKy8tx4sQJLFiwAAcOHMCWLVuQnZ2NiRMnwmCoqWN78cUX8dVXX2H9+vVISkpCaWkpEhISYDRW/ctgNBqRkJCAsrIyJCUlYf369fjf//6HRYsWtcj13koaKm+wzPSyZRlR/T79vcJq2a6sShjsaJlFDSs3mKyyvDMjvKw+sFebG+0tmjQircggCnIFAdjViFnYWqO6BrMx6CVX1qLfs8jlcgQFBVkt9/X1xZdffila9sYbb2DAgAFIT09Hr169UFxcjE2bNmHNmjUYOnQoAGDdunXo3bs39u/fj2HDhmHfvn1IS0vDqVOnEBISAgBYtmwZ5s+fjyVLlsDHx3abGbo51t0b6u/Ty4FsRLZdLTfie4vaUAAo0JpwJEeHuzu4xoj+1uzf6eWisQWecgnmWUwDXFvXdnKM76bE9vM1H0beOlWG0aFV5SbnyyXIKqv5MO8hAwZ3bFv/nzR1ZXo5kI1cWIs+nZmZmYiMjESfPn0wY8YMZGZm2ty2tLTqE7hKpQIApKamQq/XIy4uzrxNSEgIwsPDcfToUQDAsWPHEB4ebg54AWDYsGHQarVITU11whURAAiCYFXeUGef3lryKk1WXw0SUZXt58th629H0iXrDDA1TrnBhLcssryP1ZPlrTbfIig+kqPD4etVH05+KBS/908d3c1TsbcVHMhGrU2LZXpjY2Oxdu1aaDQa5OXlITExESNHjsSRI0fg7+8v2lan02Hx4sWIj49Hp06dAAA5OTmQyWQICAgQbatWq5GTk2PeRq1Wi9YHBARAJpOZt7ElIyPjZi/Rbs15rOZQYgAMQs2ADqVUQPaF3622aydTotRY9f2gSQB+SjsHvwYmGGpr98qZeK/s5+r36uNfPWArR/HluVI8ospFc5WKuvq9aorN2XLkVCjMr5VSAWO8cpCRUf+/E0oAA/3ccbhWgPvK4et4o5cWPxSIs7ox7qXIyCh06Hm7gjt83PFzSdX13+5jxKXz55q0n7b4XDkL75VtGo2m3vUtFvSOGDFC9Do2NhYxMTHYsmUL5s6da15uMBgwa9YsFBcXY+vWrQ3uVxAE0UABW4MGGhpM0NCNc5SMjIxmO1ZzOVesB1Dzj4XaU17nNQadvI7Skpoabe+OXaGpJ+pti/fKWXiv7Ofq9+q3Ij3Sb9T8fZJKALkE0P3xZcoVrRS69l0R7e/8KWld/V41xQ29CZt/ug6g5tupx3u1Q7+oENtvqmWhtxZ/3pVnfv1DoQw57UJxsiRXtN1f7+iMzt5tp3NDtc2djFh9oipLvuC2dgjybPzEG23xuXIW3qub4zLfQ3h7eyMiIgLnz583LzMYDHj00Udx5swZ7NixQ5QBDgwMhNFoRH5+vmg/eXl55uxuYGCgVUY3Pz8fRqPRKgNMjtPQxBTm5VazsrGul8jSZ7+LJz4Y3NEdQzuJB0R9zRKHJtuQfkM0psCrgVpeS3d3UCBWLf7A8ej+AphQk1jp5SdvkwEvAHT0lCFxoAqJA1VNCniJmpPLBL2VlZXIyMgwD2zT6/WYPn06zpw5g6+++spqwFtMTAzc3NyQnJxsXpadnY309HT0798fANCvXz+kp6eL2pglJyfD3d0dMTExzXBVt6aG6nmrWU1FzF69RCImQcBn58UB7aTuSowJtQh6L7J1WVPc0Jvw1qky0bJZkV5W06bXRyKR4Kne7UTLLCe3sJzMgohahl0fPcvLy+Hh4QGptOEYuaioCJmZmQ0GldU1uiEhIeaa3vLyckyZMgUGgwEPP/wwfvnlF2zduhUSiQTXr18HAPj4+ECpVMLX1xdTp07F0qVLoVar4efnh0WLFqFXr14YMmQIACAuLg6RkZGYPXs2VqxYgcLCQixduhTTpk1j5wYnsgx6bf0DovbgrGxE9Tmao8Mliw4AY7sooTUKkADmwW0nC/S4VGZAaBvNJjrLht9uiH5fecslmNuILG+1MaEe0PjKkWFjauiRIQx6iVyBXZnekJAQfP755+bXJSUliIuLwy+//GK17Z49e0QdFWy5cuUKZs6cib59+2Lq1KlQKBTYu3cvQkNDkZ2djaSkJFy9ehVDhgxBeHi4+eeLL74w7+PVV1/F2LFjMX36dMTHx8PLywuffPIJZLKqYEomk2Hbtm3w9PREfHw8pk+fjrFjx2LFihX2XDY1kb2ZXuvyBmZ6iWr7zKI37+jOSvgopFArZegfqBCtS3LRiSoEQcA3lyrw799u4FiOFpXNNHVykbZqBrvE1BLsyKxAZqkBQq0OMTf0Jrx12iLLG9W4LG81qURi1cmhmr+7FLFqRZ3riKh52ZUWECxaSRkMBvzyyy8oKSlp8oE3bNhgc12XLl1QVFTU4D48PDyQmJiIxMREm9t07twZ27Zta9I5UtPkWZQp2KrptZqVjb16icx0RgH/zRTX807qUTPl8JhQDxzJqZldMulSJWZHNT5L6UyCIGD+wSJsyqi5Djcp0MffDX0DFeirVqBvoAKdvWQOm6msRGfC+7+W4d0zZSjRif/t8lFI0NvfDbcFuKFIK1hneXs1/f5N6uGJV38pwdVy8e+xESHukEnbzixsRK0Zvwsjh7Mub2Cml6ixvs2uRKG2Jmjzc5dgWK0BbPeGKrHkp5rEw8FrWhRqTfBzoT6pa86UiQJeANCbgON5ehzP0+N93ABQVd8fUM95+yqkuKuDAsM6eaBvoAJudQSRZXoT/pV2A2+fLhXdt9pKdAIOXtPh4DXrqegfj/KCfxOyvNXcZRI80csbS34UJ4NGsbSByGUw6CWHsy5vsFXTazEVMTO9RGaWpQ3ju3lCIasJ9nr4yhGpkiOtqKqO1CgAu7MqMbmnJ1zBd9mVWPqTfd8G5lSYGqzpP5Kjw+sny+DjJsE9Hd0xvJMH4jq5Q62UYsNvN/DmqTKr3z32aucmwdzodg1v2IBHwr3w2olSFP+RYXaXAXGdGPQSuQoGveRwVi3LlLa6N1iUN3AgGxGAqq/ov8kSB70PdldabTcmVIm0opqZxL6+VOESQe/vxQbM2F8AU62Eq7dcAj8PqWhq3qYo0Qv4+lIlvv6jhlkpk6DCWHdmV6WQ4M9dlDhfasCpfD1K9HVvN6eXt0My5O3cpHjvHj/MPFAIo8mEV/upoHKhzDvRrc7uoLeueitH1WBR25JnUaZgd3lDpdFqchGiW9FXFytQuzQ+1Nt64BoAjOnigddO1gS932VrUWEQoJRb/x26Wm7Eip9LkFdpwpwoLwwJdk4GslhnwpTv8s3ZTgCQAPj3UH+MCPHAtXIjfsrV4cccHX7M1eGXPL3NoNUedb3Xx62q1GBOL2/4Kqp+z5gEARdLjThZoMeJfB1O5uuRU2HCXR0UeP62m8/yVrs3VImLD3ngTPo5xETYN8EFETUPu4PeJ598EvPmzRMte/DBB63amJlMzNbdygRBQL7Wvu4N3m5SeMolKP9jNLfWWJXF8VUw6KVb26e/W/bm9azzw2BMgBuCPaW48sfgqXKDgP1XKjE6VJwVvlBiwH2788xZ1t1ZlXimjzcW3u4DuQMHWRlNAmYdKMBZi9Zdy2J9MOKP2tYOnjKM7aLE2C5V56g3CcgsNUBv458OQQDOFOrxbXYlkrO19Q549ZZLMDvKG3Ojva0yrFKJBN185OjmI8f9Xa2z5o7kJpXAi9+jErkcu/5aTpkyxdnnQW1EsU4Q/ePlJZfAU2776732HlJRH9LcCqM5M0PUWulNAlKuanG9wgS9SYDOKEBngujPSrkEd7ZXoF+gQpSZvVpuxPdXtaL9Pdij7iBNIpHg3lAlPvzthnlZ0iVx0PtbkR7jduVZTZiw+mQZDl/XYf2f/BHs5ZiZtFb8XILdl8XnPqmHst4ZztykEmh8659CuZe/Gyb18IRJEHAyX499V7T49nIljuXoYBAAT7kEj0V4YX5v7ya1HCOiW4NdQe/atWudfR7URli2K7NV2lAtUGkR9Faa0NPXKadG1Cz0JgHDd+biRL7eru0VUuBOtQJ3Bbnjrg4K/JSrQ+0v7Pv4uyFcZTsoHBPqIQp6v8mqhNEkQCaV4ES+DuN351t9+1Lt8HUd7tmRgw/+5CfqDNEU28+X4w2L2c1ub++Gtwb5OaxkSSqRIKa9AjHtFXimTzuU6ExILzJA4ytn7SwRNYhfwJBD2TsxRbWqWdlqggPOykatXdKlSrsDXgDQmaqCz8PXdXjtpPX6STayvNXu6uAOH4XE3JM2r9KEY7k6yCTAxL35Vr1qPWQQ1Qvna02YuCcff+/TDi/c3q7Ocof8SiPOFRtwpkCK3y0G2AFAkVbA04cKRcuClFJsjguos77YUXwUUvSto9aZiKgudk9DnJ+fj6CgICgU4l8wW7ZswbZt23Dt2jWEhYXhmWeewe233+6UkyXXZ9W5wY5Mb23s1Uut3Q/XtA1vZCcJgAnd6+/GoJBJMCrEA5+drwlG//lLKX7K1eGGxexnM8K98HxMO8xOKUTylZrzFAC8drIUh65r8XiUNy6UGJBRYsC5YgPOFutr9b31AH4taPC8FVLg47gAh5VNEBE5gl1Bb2JiIt5//32kpaWJgt433ngD//jHPwAAfn5+OHv2LPbt24e9e/ciKirKOWdMLs16Yor6/9FTc1Y2amOOXBdPfDCqswdCvGRwkwIKqQQKqQRyKZB1w4iD17TILLX9QW9wR3d09Gw4cBwTqhQFvQeuWgfec3t54x99fSCRSPD5yACsPlmGV38pEbUVO3Rdh0PXGw5qG/LmIBUzsETkcuwKen/44QeMGjUKKpXKvKysrAyrVq1Chw4d8PXXX6Nbt244fvw4xo8fjzfffBMffPCB006aXFejyxusMr0Meqn1KtaZcKZQXNrw3t2qemf6ulxmwKHrOhy8psUP17T4vaQqCHaXAS/ebl8rrWEh7lBIq0ol6vJCTDs8H9POXFsrlUiw4LZ26B+owMwDBbjuwL93WxhQ6wAAIABJREFUT/Tywl80Xg7bHxGRo9gV9F68eBFjxowRLfvuu++g1Woxb948dOvWDQBw5513YsqUKdi5c6fjz5RaBcuBbI0tb8hheQO1Yj/m6ESZ0wiVvMGpbUO85ZjkLcekHlVlDNfKjUgv0qOnrxs62Vke0M5NiiHB7thz2TrD+49YH8zrXXfwfE9Hd6TcH4hZ3xdi/xXbZRnuMqBHOzlUEi3aedVdbiGTSjC4oztmRTLgJSLXZFfQW1JSAn9/f9GygwcPQiKRYNiwYaLlUVFR2LBhg+POkFoV6/KG+oNeyymKWd5ArZllacOAJnzF38FThg52lDRYGhOqtAp6Xx/oi0cjbLcLA6pmRvx8RADe+7UM289XwEchhcZXjp4+8qr/+srR2UsGmVSCjIwMaDShjT43IiJXYFfQGxwcjAsXLoiWHTp0CCqVCmFhYaLlBoMBXl78pH+rsi5vqP8fbw5ko7bk0HVx0Dmwg3uzHXtSD0/867cbOF2gh4cMeGOQH6bYOSWxTCrB3Oh2mBvtuJnJiIhcjV2NDQcOHIjNmzcjMzMTALB//36cOXMGI0aMsNr29OnT6NSpk0NPkloPq+4NyobKG5jppbZBaxTwc97NZ3qbSimXYO8YNf4X3x6nJ3WwO+AlIrpV2BX0Pv/886isrERsbCyio6MxceJEKJVKPPPMM6Lt9Ho9du7cibvvvtspJ0uuL6+icZNTqBQS1G7jWaoXUGHRZomoNUjN04n633bylCHUu3lbdinlVXW1DX3DQkR0K7Ir6A0NDcWBAwfwyCOPICIiAtOmTcP+/fsRHh4u2u7HH3/E7bffjgkTJjjlZMm1CYJgNfNTQ90bJBKJdQeHSpY4UOtzJMciyxukcNhMZEREdPPsnpGtW7dueO211+rdZtCgQRg0aNBNnxS1TsU6AfpaMa+XXAJPecOfq9QeMlwtr3ljboUJofWPvSFyOYctBrENDGKfWiIiV8LJyslhLNuVNVTaUI1ty6i1MwkCjlgMYhsQ1HyD2IiIqGF2ZXrfeuutRu1UIpFg/vz5TTohar0aOzFFNTUHs1Erl15kQJGuphbdRyFBpMruL9KIiKgZ2PVb+eWXXzbXpglCw4OMGPTemqw6N9gb9HpwVjZq3SxLGwYEKiCTsp6XiMiV2J2KcHd3R3x8PB588EFER0c785yolbKemMK+EeSWA9lY3kCtDUsbiIhcn11B79GjR/Hpp5/is88+w44dOxAREYFJkyZh4sSJCAkJcfY5UivR1PIG9uql1u4QB7EREbk8u6KSsLAwLF68GCdOnMDXX3+NAQMG4J133sFtt92G0aNH4z//+Q8KCwudfa7k4iwHsjW1vCGj2IByAwNfah2yygy4fKPm2VdIgdsDGPQSEbmaRndvGDhwIFavXo309HRs2rQJHTt2xMKFCxEREYGNGzc64xyplci3Km9o2kC2UwV69PsiBzsyK+yqISdqSUcssrx3qhXwkLOel4jI1TS5ZZlcLsfgwYMxfPhwREdHQ6fT4dq1a3a/f+XKlVCpVKKfsLAw83pBELBy5UpERESgQ4cOGDNmDNLS0kT7KCoqwqxZsxAaGorQ0FDMmjULRUVFom3OnDmDe++9Fx06dEBkZCRWrVrFQMpJLMsS7J0VSuMjh4+bOEi4fMOIh5MLMG53Pn4r0jvsHIkczWpSimacepiIiOzX6KDXaDRi165dmDFjBsLCwvC3v/0NAQEB2LBhQ6M7Nmg0GqSnp5t/Dh06ZF731ltvYc2aNVi1ahX27dsHtVqNBx54AKWlpeZtZs6ciZMnT+Kzzz7D9u3bcfLkSTz++OPm9SUlJXjggQcQGBiIffv24Z///CfeeecdvPvuu429bLKDVfcGpX2Pl4dcgo+G+lv16wWAA1e1uPvLHCw6Vowyg0NOk8ihDnMQGxFRq2B394bqwWxffvklCgsL0b9/f7zyyisYN24c/Pz8mnZwuRxBQUFWywVBwHvvvYenn34a999/PwDgvffeg0ajwfbt2zF9+nSkp6fj22+/xa5du9C/f38AwBtvvIHRo0cjIyMDGo0Gn332GSoqKvDee+9BqVQiKioKZ8+exdq1azF37tw2O0VohUHAtt/LoVJIMaqzB5TN9FVrfhMnpwCAoZ088OP4IPxfainW/VoGQ61kvEEA1pwpw1Y3Jd7xrMCYLkpHnTLRTSnSmpBWWPNpTAKgPzO9REQuya6oJCYmBqNHj8bhw4cxb948nDx5Et988w2mT5/e5IAXADIzMxEZGYk+ffpgxowZyMzMBABcvHgR169fR1xcnHlbpVKJQYMG4ejRowCAY8eOwdvb2xzwAsCAAQPg5eUl2mbgwIFQKmuCpGHDhuHq1au4ePFik8/b1f3lu3w8fagIj+wvwID/XsfurEqnH1MQhCZ3b6jmq5DilX6++GFcIP7U0TpbVqCXYGpyAc6XMOVLruFojg61i6Ui/eRQuXOiSyIiV2RXpvfixYtQKpUwGo3YunUrtm7dWu/2EokER44cqXeb2NhYrF27FhqNBnl5eUhMTMTIkSNx5MgRXL9+HQCgVqtF71Gr1bh69Srw/+3deVxU9foH8M8wLMM+7EpssYiCGCqKWrmndtW00NBrVt7Um9ktK0hNc8sr16WuLZqUVl6Xqy1WlqjVDQ33suyn5YIhqKhsssMMMHN+fxADZwaQfQ4zn/fr5evlnHPmzJnvDPDMM8/3+QLIzs6Gm5ubKFsrk8ng7u6O7Oxs3THe3t4G56jZFxAQ0OD1paamNnr9baktHyuvAki+Yae7nVGiQex3eRjiWoWXAivRVdE+9czf58pRqa0NVG0tBGRe+aNF55IDWBsIJDvL8e8rVrilrg0itALw+f9dxUNe7OV7Jx35Hu7sWjpW+9KtAFjpbocpyk1+3E39+bUljlXTcayajmPVsJCQkEb3NynoHTRoUJuXAjzwwAOi21FRUYiMjMTOnTvRr18/ADB4TEEQDIJcfXc6pmYS252ez50Grq3UlGK0lar8SgDZBtsP37bEqUIrxEc64tlwB1jL2+b1rNIKWHG6CG9dKBFt93awbPXz6gZgej8t/nYoHwfqZKstnDwQEuLYqnOburZ+X5my1ozVpUs5AGonso3p5omQQLuG79DJ8X3VdByrpuNYNR3HqnWaFPTu27evWSfVaJqfhXNwcED37t2RlpaGcePGAajOxtZd/CI3N1eXqfX09ERubq4oyBUEAXl5eaJjarK+dc8BGGaRTUVhRcP9bcs11QHqfy+XYd0AZwzxVrTqsW6VafC3Q7cNGvMDwGMh9q06dw07SwsM9LIWBb1csY2kQFUl4Odcdm4gIuos2rT4TK1W4/3330efPn2afV+VSoXU1FR4eXnB398fXl5eSE5OFu0/fvy4roa3f//+KCkpwalTp3THnDp1CqWlpaJjjh8/DpWqNmBKTk5G165d4e/v39KnKWmFFeLyhfryuamFVZhwMA+Tv8nFVxnlqNQ2v+ThyC01Bu/NNgh4LWVAQn9nzItwaPY5G6K/eEV2OReuIOP7ObcCdT9j+jrI4ePQ5LnBRETUwZr8G1qtVuPAgQO4cuUKXFxcMGbMGF3nhbKyMiQmJuLdd99FTk4OAgMD73i+xYsXY8yYMfDx8dHV9JaVlWHq1KmQyWSYM2cOXn/9dYSEhCA4OBjr1q2Dvb09Jk2aBAAIDQ3FyJEj8cILL+DNN9+EIAh44YUXMHr0aF3qf9KkSVi9ejWeeeYZxMXF4fLly1i/fj1efvllk+3coJ/pnRhgi2F32WDZT0W4rRbv+zZTjW8z1fBQWCA2yA7Tu9khVGmFxgiCgLfOlWDF6SJo9GJlbzsLfDjUFdFt3LJJf5liZnpJCvT78w5klpeISNKaFPTevHkTY8eORXp6uq4m1t7eHrt27YKFhQVmzZqFGzduoH///nj99dd15QmNuXHjBmbOnIm8vDy4u7sjKioK3377Lfz8/AAAzz//PMrLyxEfH4+CggL07dsXe/bsgaNjbS3n+++/j/nz5+ORRx4BADz44INYs2aNbr+zszM+//xzxMXFYdiwYVAqlZg7dy6effbZpo9QJ6Mf9LrYWODxbvYY56fA8tNF2HqpzOA+OSot3vmtBO/8VoJ+HlaY3s0efd2tkV2uwa1yLW6VaXCrTIOsci0uF1Xh3G3DxSKGdLXB5iEuBqurtQX9fr/6i2AQGcPxW+L+vAPZn5eISNKaFPS+9tpruHr1Kp577jkMGjQIGRkZWLNmDZ5//nnk5OQgLCwMW7ZswYABA5r8wB988EGj+2UyGRYuXIiFCxc2eIyLiwvee++9Rs8THh6O/fv3N/m6Ojv98gZn6+qMtqtCjjfvdcH0bvaYf6IAp3PrX+Xsx5xK/JhTUO++hsTd44iFkY6QW7RP9tww08ugl4xLoxVwMkevnteLmV4iIilrUtB76NAhTJs2DcuWLdNt8/T0xJNPPolRo0bhv//9Lyws2JtSCvQzvc7W4tclysMa343zwK95ldiWWoZP0spQVNGyNmZKaxneG+yKUb6tmxB3J/r9fm+rtajUCrBqpyCb6E5+L6gS/dy42MgQqmQ9LxGRlDUpUs3OzkZUVJRoW01bsccee4wBr4TcKegFqrPoke7WeH2gEhdju+K9wS64v0vzslSDvKxx+CHPdg94AcDKQgZXvYb/+gthEHWkE3pLD0d72sDCROcJEBGZiialJjQaDRQKcXBTc9vJyantr4pazDDobfwPsa2lDI8G2eHRIDukF1dhe2oZDlxTobxKCy9bObrYydHFzgJdbOXwspOji60c/o5yBDh2bFbL09ZCNBEvu1yDrnZtXz9M5uVMbgV+ypPD1VcDN8Wd30+qKgFbLpbi9V+LRdsHsrSBiEjymhy5pKen4/Tp07rbRUVFAKobJTs4GLan6tu3bxtcHjWXQU1vM5ZEDXC0xOI+TljcR3ofZDxt5bhQULv8cA7reqmVdqaW4pkjBQBssPDiLYzyUWBqsB1G+SgMFm/RaAXs+qMMCb8U43qpYfcQBr1ERNLX5KA3ISEBCQkJBttffvll0e2axSJu377d+qujZmtKeUNn5Gmr36uXbcuodd74v9pVBCu1wL6rKuy7qoKbjQUmBdpiarAd7nGzQtJVFV77uUj0oauufh5WiPJg0EtEJHVNCno3bNjQ3tdBbaRArxev8g7lDZ2F/gIVbFtGrZFeXIXLRfUHsXlqLRLPlyLxfCk8FBYNvtccLGV4tqcD/tHTgfW8RESdQJOC3r/+9a/tfR3URgxblplKpldcb5nFTC+1wrfXVXc+CPV/uLK2AP7W3R5x9zjCvQl1wEREJA3ssWNCBEEwKG9wMpGg12CBCtb0UivoB71PdrOD3EKGz9LKUNBACz8ZgCnBdlgQ6Qj/Dp7ISURErcff3CaktEoQLQ1sK5fBRm4aX7t6KrhABbUNVZWAlJvihSX+HuaAHi5WWNXfGQeuqbAztRTfZap1P08P+irwal8nhLk0vkw3ERFJF4NeE9LQamymQH8iWw7LG6iFjmapUV7n06GXjRbd/1xYwkYuw4QAW0wIsEVWmQYnsisQ5GSJnq4MdomIOjsGvSbEVDs3AICH/lLEnMhGLfTNNXFpwyAXDWT1TETzspNjQoBtR10WERG1M9OJisi0g1697g15Ki2qtC1bPpnM23eZ4qD3Xhd+gCIiMgemExVRs1dj60ys5TI4WdYGuQKqA1+i5kgrqsIfRbWlMVYWQJQzS2WIiMwBg14T0prV2DoDNyvx82OJAzWXfteGgV42sGeRFxGRWTCtqMjMFapNt7wBAFytxUEvJ7OZh1PZajyTko/1/1cMTStLWr7TC3of8LFp1fmIiKjzYI7DhJhyeQMAuOpnetm2zOTdLNNg0jd5KKqsfu0LKrRYFuXconOVVwlIuaUWbXvARwHktPoyiYioEzCtVKCZM9XV2GoYBr3M9Jq6nalluoAXADb9XoJbZS173Y/cUkNV564+9nKEOvNzPxGRuTCtqMjMmXL3BgBws2am15wIgoDdf5SJtqk0wJtni1t0Pv163lE+inpblRERkWkyrajIzJldeYOKmV5TdiavEpcKqwy2f3ixFFktyPbqB70jWc9LRGRWGPSaEJMvbzCYyMZMryn77+WyererNMCb55qX7f2jsApXimsDZWsLYHBXBr1ERObEtKIiM2fq5Q36K8Gyptd0VWoF7LlS3uD+Dy+UNev1/1ZvQYpBXWzgYGVaPx9ERNQ4/tY3IaZe3qDfpzeHfXpN1v8yVcit8/o6W8vQ1a7211W5RsBbZ0uafD6D0oa7mOUlIjI3DHpNiLmVN+SqtK3u20rStPuyOMv7cIAt5kU4irZtuVDapF7NZVVaHKmvVRkREZkV04qKzJggCAaZXicTC3qtLcTZa60A3FYz22tqCtRaJF0TB72xwXZ4ops9utiKs71vn7tztvfIzQqo68TGfg5ydGOrMiIis2NaUZEZK60SoKmT9LSVy2AjN63yBgDwtJWLbrNtmenZm1EuClL9HeQY4GkNhaUMz+tlezdfKEXuHbp46Jc2PMBWZUREZolBr4kwLG0wzT/qHgrxWzaHbctMzi69rg2xwXa6IPXJUHt41cn2llUJeLuR2l5BEAwmsbGel4jIPEkm6H399dehVCoRHx+v21ZSUoL4+HiEhYWhS5cuiIqKwoYNG0T3U6vViI+PR2BgILy9vTFlyhRkZmaKjrl27RpiY2Ph7e2NwMBAvPzyy6ioqOiQ59VRTL1zQw39TG8WM70mJaO4CseyxD+bsYF2uv/bWsrwXD3Z3rwGPvz8UVSFdLYqIyIiSCTo/fHHH7F161aEh4eLti9atAjffPMNNm3ahJMnT+Kll17C8uXLsWvXLt0xCxcuxFdffYUtW7YgKSkJxcXFiI2NhUZT/YdOo9EgNjYWJSUlSEpKwpYtW7B3714sWrSoQ59jezOXoNfDVvy82LbMtHystwJbPw8rBOnV384ItYNnnfdBaZWAdxqo7f3mungC271dbGDPVmVERGbJ6L/9CwsLMWvWLLz99ttQKpWifadOnUJsbCwGDx4Mf39/TJ06FVFRUTh9+rTuvtu2bcOKFSswbNgwREZGIjExEb/99hsOHToEAPj+++9x/vx5JCYmIjIyEsOGDcPy5cvxn//8B0VFRR39dNuNqbcrq+GpX97ATK/JqF52WG8CW5CdwXF2lhb4R08H0bb3z5fidp1sryAIKFBrceCa/ips7NpARGSujB70zps3DxMmTMCQIUMM9g0YMAAHDhzA9evXAQAnT57EuXPnMGLECADAmTNnUFlZieHDh+vu4+Pjg9DQUJw8eRJAdeAcGhoKHx8f3TEjRoyAWq3GmTNn2vOpdSiDml4bo7+07cJwIhszvabi59xKXC6qXXbYygJ45G7beo/9W6g93Ot8ACqpEvCX/bm494sshO66CY+tNxCw8yZ+uCnO9I7i0sNERGbLqH17tm7dirS0NCQmJta7f/Xq1XjhhRfQs2dPWFpWX+qaNWswZswYAEB2djbkcjnc3NxE9/Pw8EB2drbuGA8PD9F+Nzc3yOVy3TH1SU1NbfHzaq62eKzUG5YArHW3hbIipKbmtfq8UlOZnwWgNnBJzysxyefZFjryPdwWEv+wAlC77N69yirkXUtDQ6/uX7tY4q302vf8hYKqBo6s5m2jhZCVjtR6fuw721gZE8eq6ThWTcexajqOVcNCQkIa3W+0oDc1NRUrVqzA/v37YW1tXe8xiYmJOHnyJP773//C19cXx44dw6uvvgo/Pz+MHDmywXMLgiBqSdRQe6LG2hbdaeDaSmpqaps8lnVpEYBi3e0ATxeEhDi3+rxSkpqaisggH+B8jm5bqYUCISF+RrwqaWqr91VHqdAI+O7HWwBqy1X+do8nQgLqz/QCwPwALXbeyhKt3NaYJ8Kc0a2br8H2zjZWxsSxajqOVdNxrJqOY9U6Rgt6T506hby8PAwcOFC3TaPR4NixY/jggw+QlpaGFStW4KOPPsKDDz4IAOjZsyfOnj2Lt99+GyNHjoSnpyc0Gg3y8vLg7u6uO09ubi4GDRoEAPD09NSVOtTIy8uDRqMxyAB3Zqa+GlsNT1v9ml6WN5iC7zJVooVGlNYyjPZtvP7W3soC/4p2xszD+fXud7CUwd3WAu4KC4y4S2GwohsREZkXowW9Y8eORe/evUXb5s6di6CgILz44osAgMrKSsjl4hpOuVwOrbb6j2NkZCSsrKyQnJyMyZMnAwAyMzNx8eJFREdHAwD69++PdevWITMzE3fddRcAIDk5GTY2NoiMjGzX59iRzKV7g6dC/H7IUWmhFQRYcLGBTm23XteGh++2bdLiKpMC7RDmYoXf8yvhbG0BD4UF3BQWcFfIYWvJ9wQREdUyWtCrVCoNujXY2dnBxcUFYWFhAIB7770Xy5cvh729PXx9fXH06FHs2rULy5cvBwA4Oztj+vTpWLJkCTw8PODi4oJFixYhPDwcQ4cOBQAMHz4cPXr0wNNPP42VK1ciPz8fS5YsweOPPw4nJ6cOfc7tyVyCXoWlDE5WMhRVVme2NQKQr9bCTS8Yps6jvi4L9XVtaEiYixXCXKzufCAREZk1SS9A/8EHH2D58uWYPXs28vPz4evri0WLFmH27Nm6Y1atWgW5XI4ZM2ZApVJh8ODB2LRpky5DLJfLsXv3bsTFxWHMmDFQKBSYNGkSVq5caayn1S7MZUU2oLpXb1FlbVlDdjmDXqkrrtTi0z/KcbmoCjkqDXLKtchRaZFTrkGuSitaQjvAUY5oz/rr/ImIiFpKUkHvvn37RLe9vLywcePGRu+jUCiwdu1arF27tsFjfH19sXv37ja5Rqkyl0wvUN227I+iukGvBj2Y6ZMsjVbAiK9ycKmw8e4KNWKD7BqdZEpERNQSphsZmRlzCno9FPqrsnGBCik7cquiyQGvtQUwNbjppQ1ERERNJalML7WcQdBrY7qZMoMFKprYsoqM40rxnQNeJysZAhwt8dI9jghw5K8lIiJqe/zrYgIEQTCo6XWyMuFML9uWdSpXS8RB70P+CkwLsYfnn+3E2GmBiIg6AoNeE1BSJUBbJ+a1s5TBugntnjor/bZlLG+Qtqsl4g8lD/go7tiDl4iIqK2ZbjrQjBSq9et5TTfgBbhARWeToVfe4OfAz9pERNTxGPSaAHNZja0Ga3o7F/1Mr78j28sREVHHM+3oyEyYU+cGgDW9nUl5lYCsOuUnFjLgLnsGvURE1PFMOzoyE4ZBr5mVN6i0EAShgaPJmK6XiksbvO3ksLIw7fcnERFJE4NeE2Bu5Q12lhZwqDPbv1ILFFQw6JWijGJxFt7PgVleIiIyDtOOjsyEuZU3AIYlDlkscZAk/XpeBr1ERGQsph8dmQFzK28A6pnMxrZlkqTfo9ePC08QEZGRMOg1AeZW3gAYLkXMyWzSZNC5gZleIiIyEtOPjsyAOZY3MNPbObBHLxERSYXpR0dmwByDXoO2ZSrzyvReKqhEgVr6gT5reomISCqYdjEBhuUNpl/T62Wmmd7yKgFPHb6NpKsqWMiAKUF2mB/pCH8J1sqWVWmRU2fhEDl79BIRkRGZfkrQDDDTax41vRqtgFl/BrwAoBWAnZfLELUnC/EnCpBVJq0xuKaX5b3LXg5L9uglIiIjMf3oyAyYY9DrqTeRzdSXIhYEAXEnCvD1nwFvXZVa4P3zpej9WRZWnC6UTNkDe/QSEZGUmH50ZAb0gxxnG9PPpulPZMsx8fKGNb8W48OLZY0eU1Yl4I3/K0GvT2/hw2uWKK007pgYtCvjJDYiIjIiBr2dnFYQUFRphi3L9Mobsss1JrsU8UcXS5HwS7Fom4+9HOsGONebPS2qELAxwxp/2Z+LKq3xxoST2IiISEpMPzoycSWVAurGNfaWMliZQd2kg5UF7OosRVyhNZzQZwq+zijHi8cLRNuU1jJ8NsoNM3s44MdHvLB2gDM8bQ1/lH/Nq8QPN9UddakGDHr0SnCyHRERmQ8GvZ2cOa7GVkN/gYrsTjaZTVUlNFqCcDxLjZmHb4s+1CjkwO6RbghVWgEAbOQyzOrhgF9ivLCsrxOUeq//2duV7XLtTWFY3sBMLxERGQ9TL52cOa7GVsPT1gIZdbKJ2SotuhnxeppKEAQs+6kIb54rAVBdqhDibIluzpboprREiLMVrCyAKd/loW77YQsZ8MFQV0R72Ric097KAvN6OUJhKcOCk4W67b/lGy/o5UQ2IiKSEga9nZw5dm6o4WErB1Ab1HWWtmU7L5fpAl4AuF6qwfVSDZJvNF6KsH6QEn/xs230mDAXK9Ht34yU6S2p1CKvzgRLSxngbcegl4iIjMd8IiQTZc7lDV4Gk9mk38Eho7hKlIltqld6O+LxbvZ3PC7cRfw59lJhFSqNMJlNv0evj4MccjOoNSciIuli0NvJmXN5g0cna1um0Qp4OiUfxZXNC0JndrdH/D2OTTrWTSFHV7va90ClFkgtrGrkHu3DsHMDv1QiIiLjkkyE9Prrr0OpVCI+Pl60/fLly3jsscfg5+eHrl27YvDgwbh48aJuv1qtRnx8PAIDA+Ht7Y0pU6YgMzNTdI5r164hNjYW3t7eCAwMxMsvv4yKiooOeV7tzZzLGwwXqJB2ecOG30pwPEv8vkvo74zjEz2xdZgrFvdxwqOBtoh0s4K9pQyWMmBWD3usjnaGTNb0LKl+icPvRqjrzSjmJDYiIpIWSaRffvzxR2zduhXh4eGi7enp6Rg9ejSmTJmCvXv3QqlU4tKlS7C3r/2ad+HChUhKSsKWLVvg4uKCRYsWITY2FocPH4ZcLodGo0FsbCxcXFyQlJSE/Px8zJkzB4IgYO3atR39VNucOQe9+pleKZc3nLtdiZU/F4m2jbjLBk+H2UMmk6GHXqAqCAIEABbNCHZrhLtY4X+ZtfXBv92uxKTAFl12i7FHLxERSY0qbgsLAAAgAElEQVTRg97CwkLMmjULb7/9NtasWSPat3LlSgwfPhz//Oc/ddsCAgJE9922bRs2bNiAYcOGAQASExMRERGBQ4cOYcSIEfj+++9x/vx5nD17Fj4+PgCA5cuX47nnnsOrr74KJyen9n+S7cica3r1e9NKdSKbWiNg9g+3UfelcrGR4Z37XBrM4MpkMrT0lZRCple/XRl79BIRkbEZPS04b948TJgwAUOGDBFt12q1OHDgAEJDQxETE4OgoCAMGzYMe/bs0R1z5swZVFZWYvjw4bptPj4+CA0NxcmTJwEAp06dQmhoqC7gBYARI0ZArVbjzJkz7fzs2p9BTa+N0V/SDuOp0Mv0qqSZ6f3nz0X4PV8cBP57oAu6tlM3g3BXvQ4O+VKo6WWml4iIjMuo6ZetW7ciLS0NiYmJBvtycnJQUlKCN954A6+88gqWLl2KH374AbNmzYKdnR3GjBmD7OxsyOVyuLm5ie7r4eGB7OxsAEB2djY8PDxE+93c3CCXy3XH1Cc1NbUNnmHTtOaxMm9bo+7LWJaXhdRUaWY820LdsapOJtrpbmeVVuHSpVS0oCKg3ZwutMDb52yAOnnbBz2qEF51He31FrPQAnLYQvPnY14v1eDn86noyGTrlUJb1H3OmpxrSC2S7op5Hfnz3tlxrJqOY9V0HKum41g1LCQkpNH9Rgt6U1NTsWLFCuzfvx/W1tYG+7Xa6qzdX/7yFzz77LMAgF69euHMmTPYvHkzxowZ0+C5BUEQfW3c2FfIDbnTwLWV1NTUVj2W5nIOgNrJUd39vRHirWiDK5Me/bESBAGKH2/oFnCoEGTwCgiSTF1zUYUW//wyGwJqP4T42Mvx7gNdoWznjLz/mWtIK6t9f6td/dCnnkUt2kNRhRaFR27qbltZAAPDgiTbsqy1P4PmhGPVdByrpuNYNR3HqnWMFh2cOnUKeXl5GDhwINzc3ODm5oajR49i8+bNcHNzg6urKywtLREaGiq6X7du3XD9+nUAgKenJzQaDfLy8kTH5Obm6rK7np6eBhndvLw8aDQagwxwZ2TOLctkMlk9bcukk+VecLLQoF/thvtc2j3gBYBgO3GpR0cuUqH/nH3t2aOXiIiMz2gR0tixY3Hs2DGkpKTo/vXu3RsxMTFISUmBtbU1+vTpY5DGv3z5Mnx9fQEAkZGRsLKyQnJysm5/ZmYmLl68iOjoaABA//79cfHiRVEbs+TkZNjY2CAyMrIDnmn7MufuDUA9bcsk0sHh4DUVdl4uE217JtweQ7w7JtsabC8eB/2a4vakP4nNj5PYiIhIAoz210ipVEKpVIq22dnZwcXFBWFhYQCA5557DjNmzMCgQYMwePBgpKSkYM+ePdixYwcAwNnZGdOnT8eSJUvg4eGha1kWHh6OoUOHAgCGDx+OHj164Omnn8bKlSuRn5+PJUuW4PHHH+/0nRsA8+7eAACe+ksRS2Qy29vnikW3uystsaSPc4c9vn7Q+1sHdnDI4CQ2IiKSIEmnYMaNG4f169fjjTfewIIFCxAYGIhNmzZh9OjRumNWrVoFuVyOGTNmQKVSYfDgwdi0aRPk8uo/tHK5HLt370ZcXBzGjBkDhUKBSZMmYeXKlcZ6Wm1GKwgo0lvdy8ncMr0GSxEbv7whu1yDY3qLULx7vwsUlh33gSTETvy++D2/0qDWvb0YZHq5GhsREUmApP4a7du3z2DbtGnTMG3atAbvo1AosHbt2kYXmvD19cXu3bvb5BqlpKRSgLZObGNvKYOVmdVOSnGBiq8yykWvS4SrFXq7G07WbE9eNgKcrGUo+rPmu7hSwNUSTYf0y71aLP7g4c9MLxERSYB5pQVNjLmXNgCGNb1SmMj2xZVy0e2JAbYdfg0yWfXKbHV11CIV7NFLRERSxKC3EzPnzg01PPUzvUau6c0p1+CoXmmDMYJewHBlto5apIIT2YiISIrML0qSoAK1Flllzc9QmnvnBgDw0KvpPZ1TgcuFHb/sbo2vMlSi0oaerlYIcjZO0GeMTG9hhRYFdT6M2cgBL1vze18SEZH08K+RkWSXa7D5fAmePWeD4P/exLpfi+98Jz0sb6he7KGurHItRnydg0M3VEa5ni/SjV/aUCPMRRxsd0SvXv3SBl97S1hIaYk8IiIyWwx6jeR0TgXiThTiZIEcVQKQdFUFQWjeMq0sbwD8HS0x2le8Al1hhYCYb/Lw/vmSDr2WnHINjtxSi7ZNDDDe6ng99DK9l4uqoKpq36WArxbrd25gPS8REUmD+UVJEjHUWwG7Oi2sMss0+DWveZm4AjXLGwDgP8NcMSVInFHVCED8iUK8dLwAldr2DfRqfF1PaUOws1XDd2hnztYW8K0TdGoE4GI7l35wEhsREUmVeUZJEmBrKcNwvdW5vr7avK/kWdNbzUYuw7v3u2BFlBP0v0jfcqEUMd/kIV/d/hPcpFTaUMOwrrd9J7NlcBIbERFJlHlGSRIx1l8cFCVdLW/gyPqxpreWTCbDcxGO2DnCFQ56i0D8cFONEV9l41JB+2U5c1UapEiotKFGT/0ODu1c16uf6WWPXiIikgoGvUY02scGctR+H/57fhWuFDU9E2dQ02vDl/NBP1scHOth8LV6WrEGEw/mGpSEtBX90oZwF0ujljbU0J/M1t4dHAzLG5jpJSIiaWCUZESuCjkincVB2L5mZHtZ3lC/cFcrfD/eAwO9xKug3SjTYvcfZe3ymFIsbQCqx6Ku39o96OVENiIikiZGSUY2xFWcGUtqRl2vftCrNOPyBn3uCjm+GO2OmLvFweeX6c0rIWmKPJUGKTf1ShvulkbQG+RkCZs6cWdWuRa5qvZZta5ArdUtewwACjngyR69REQkEfyLZGRD3MQByInsiiYHJWxZ1jgbuQyv9nUSbTueVYFbLVgIpDFfZ6igqfNShLlYIkQCpQ0AYGkhQ6izfl1v+0xmM5jE5mAJGXv0EhGRRDBKMjJvhYCedb6C1grAgWtNy/ayvOHOAhwtEelWO74CgK8z2jbbK9XShhodVdfLdmVERCRljJIkYKyfeJZ/U0sc2L2haSYEtF+JQ55Kgx/0SxskFvR2VF0vJ7EREZGUMeiVAP2gNzlTjbKqxrsMaAVBVD8JAE7M9NZLP+g9mlWBnPK2KXHYd1WvtEFpiW5KaZQ21DDs1ds+QW+G3mps/o7M9BIRkXQwSpKACFcr0cpZ5RoB32eqG7kHUFwpoG7I62Apg6UFM731CXSyRIReCcnXGc1bCKQhX1wRZ40nSGQCW136Qe/5/Cpo2mGVOpY3EBGRlDHolQCZTIa/+DavxIH1vM1jUOLQBnW9t1UaHJZ4aQNQ3UHBrU4P53KNgPTitu/gYNiujOUNREQkHYyUJEJ/dbYD11SoaiQbZ9i5gVnexuivjpZyU428Vrbu+lqvtKGH0hKhEittAKo/VOnX9Z5r4xIHQRBwjZleIiKSMAa9EjHIy1rUZ/e2WosT2RUNHm+Q6eVqbI0KdrYSdTHQCNX1uK1hUNogwSxvjfbu4FBQIaC4svYTgJ2lDO4KvieJiEg6+FdJIiwtZBhtUOLQ8FfwhXrL6XIS253pB6X6QWtz7E0vxyGJLkhRH/263t9ut23Qqz+Jzc9Bzh69REQkKYyUJGSsnzho2pehgiDUX+LAdmXNpx/0Hr6pxu0WlDh8caUcMw7dRt3qk+5KS3SXYGlDjfbu4JDB0gYiIpI4zjSRkOF32cBGDqj/jB8ySjT4Lb9KtHhFDa7G1nzdlVborrTEhYLqrKRGAJKuqfBYiH2Tz/FpWhn+/kO+qJZXBmCp3spvUtPdxRIyQNfx40qxBqWVWthbNfy+EQQBmaUapBZW4VJhFVILq3CluAo2chm62snRxdYCXezk6Gonxym9UhxOYiMiIqnhXyYJcbCywFBvBQ7WWZEt6Wp5A0Evuze0xEMBtrhwplh3+8sr5U0Oenf/UYY5KfmiDK8MwMb7XfCgn3RLGwDAztICgU5y/FFU/YlKAHChoAp9PawhCAJulWtxNq8S5/IrcT6/EpcKq3C5sAqlVS1rbebPTC8REUkMg16JGesnDnr3XVXh5UjDLCLLG1pmgr8t1tQJeg/dVKNArYXyDhMBd6aWYu6RAlFvZAsZsOl+FzwaZNdOV9u2wl2sdEEvAPzz5yJoAZy7XYlcVeOLoTSXnyN/tRARkbQwPSgxD/oqUDd8/TWvEtf0+p8CLG9oqTAXS4Q41wZkldrGJwwCwLZLhgGvXAa8P7jzBLyA4XLE399Q49ANdZsHvABwj5t065uJiMg8SSZSev3116FUKhEfH1/v/ueffx5KpRJvv/22aLtarUZ8fDwCAwPh7e2NKVOmIDMzU3TMtWvXEBsbC29vbwQGBuLll19GRUXD7cCMycNWjmhPa9G2/fW01mJ5Q8vIZDJM8NdfqKLh1mUfXSzFP44aBrxbhrgiJrDzBLwAEObS/EDUyVqGfh5W+GuwHZb1dcK24a74aKgrEvo74/meDng0yBb3d7FGiLMlHK1kcLaW4dU+TghgppeIiCRGEn+ZfvzxR2zduhXh4eH17v/yyy/x888/o2vXrgb7Fi5ciKSkJGzZsgUuLi5YtGgRYmNjcfjwYcjlcmg0GsTGxsLFxQVJSUnIz8/HnDlzIAgC1q5d295PrUXG+ilEPXr3XCnHrB72ohZQ+kGvkuUNTfZQgALr/q+2xCE5U4XCCq3og8NtlQZLfirC9tQy0X0tZcAHQ13xkIR78jZkkJc1bOUylGsM63TtLGUIc6lerjncxQqhSiuEKi3hobBoVusxQRDYqoyIiCTJ6EFvYWEhZs2ahbfffhtr1qwx2H/16lUsWLAAX3zxBSZNmmRw323btmHDhg0YNmwYACAxMRERERE4dOgQRowYge+//x7nz5/H2bNn4ePjAwBYvnw5nnvuObz66qtwcpLerPux/rZ49aci3e0T2RVIuqoSrdrG8oaWi3C1QqCjHGl/LsVboa1eAS82yA6CIGD3H+VYdKoQeXq9kK0sgI+GuhqsntdZuCnk+M9wV2z4rQRWMqCnqxUiXK0Q4WaFQEdLyC1aH6wy4CUiIqkyeqQ0b948TJgwAUOGDDHYV1VVhZkzZyIuLg6hoaEG+8+cOYPKykoMHz5ct83HxwehoaE4efIkAODUqVMIDQ3VBbwAMGLECKjVapw5c6YdnlHrBTpZYkhXG9G2hacKUV5nJj3LG1pOJpMZ9Oz9Mr0clwsrMeFgHp5OyTcIeK0tgG3DO2/AW+MBHwW+GO2OT0a5Y2mUMx4JtEOIs1WbBLxERERSZtRM79atW5GWlobExMR69yckJMDFxQVPPfVUvfuzs7Mhl8vh5uYm2u7h4YHs7GzdMR4eHqL9bm5ukMvlumPqk5qa2pyn0ir1PdbTXWQ4clMBzZ/T2q6WaLD0cDpm+VVPassvtwXqTHnLvX4FVWYwd6itXpfechmA2gD2m2vl+O5aOSoEw+DvblstFoVUIEh1HR34tmi1jnwPd3Ycq6bjWDUdx6rpOFZNx7FqWEhISKP7jRb0pqamYsWKFdi/fz+sra0N9h85cgQ7d+5ESkpKs8+tX1fY0FeujX0Ve6eBayupqan1PlYIgFnqAmz6vVS37T+Z1pjb3xe+DnKUHLkhOr5392BYmni2rqGxaolgQYD/5SzdSmJV9QS7NnIg/h4nPNfTAdbyzjW2bTlWpo5j1XQcq6bjWDUdx6rpOFatY7TvxE+dOoW8vDwMHDgQbm5ucHNzw9GjR7F582a4ubnhf//7H27duoXQ0FDd/mvXrmHp0qUICwsDAHh6ekKj0SAvL0907tzcXF1219PT0yCjm5eXB41GY5ABlpoFkU5wV9S+RCoNsPjHQhRVCKJuAg6WMpMPeNtafSUOdQ3ztsHxiV6Iu8ex0wW8REREZMhomd6xY8eid+/eom1z585FUFAQXnzxRbi7u2Py5Mmi/TExMYiJicETTzwBAIiMjISVlRWSk5N1x2ZmZuLixYuIjo4GAPTv3x/r1q1DZmYm7rrrLgBAcnIybGxsEBkZ2d5Ps1WUNhZYFuWEZ48U6LZ9laHCyLvEfWVZz9syEwJs8da5EtE2D4UFVvV3xqRAW07KIiIiMiFGC3qVSiWUSqVom52dHVxcXESZ3LosLS3h5eWlS+07Oztj+vTpWLJkCTw8PHQty8LDwzF06FAAwPDhw9GjRw88/fTTWLlyJfLz87FkyRI8/vjjkuzcoO+vwXb46GIpfsqp1G179cdC0TFcja1l+rhb4dFAW3ycVv0h4oludlgW5QyXO6zORkRERJ2P0VuWtdaqVasgl8sxY8YMqFQqDB48GJs2bYJcLgcAyOVy7N69G3FxcRgzZgwUCgUmTZqElStXGvnKm8ZCJsOaaCVGfJ2jK2koqtRrV8YgrUVkMhkSB7vgHxGO8FRYwMtObuxLIiIionYiqaB33759je4/e/aswTaFQoG1a9c2utCEr68vdu/e3errM5Y+HtaY3s0O/7lUVu9+lje0nEwmQ4SrGbS9ICIiMnOMljqJJX2dGixjYHkDERERUeMY9HYS7go5FvWuvwaZmV4iIiKixjFa6kT+1t0eYS6GFSkMeomIiIgax2ipE7G0kGHtAKXBdpY3EBERETWOQW8nc28XG0wKFC+qEO7CiVhEREREjZFU9wZqmnUDlMhXa3EiqwKTAm0xxNvG2JdEREREJGkMejshpY0FPhvlbuzLICIiIuo0WN5ARERERCaPQS8RERERmTwGvURERERk8hj0EhEREZHJY9BLRERERCaPQS8RERERmTwGvURERERk8hj0EhEREZHJY9BLRERERCZPVlBQIBj7IoiIiIiI2hMzvURERERk8hj0EhEREZHJY9BLRERERCaPQS8RERERmTwGvURERERk8hj0GtHmzZvRq1cveHl5YciQITh27JixL8nojh49iilTpqBHjx5QKpXYsWOHaL8gCEhISED37t3RpUsXjB07FufPnzfS1RrPG2+8gWHDhsHX1xdBQUGIjY3F77//LjqGY1Xt/fffx6BBg+Dr6wtfX1888MADOHjwoG4/x6lhr7/+OpRKJeLj43XbOF7VEhISoFQqRf+6deum289xErt16xaefvppBAUFwcvLC9HR0Thy5IhuP8erWkREhMH7SqlU4tFHH9Udw9ih5Rj0GsmePXuwYMECvPTSS/jhhx/Qv39/TJ48GdeuXTP2pRlVaWkpwsLC8K9//Qu2trYG+998801s2LABq1evxvfffw8PDw88/PDDKC4uNsLVGs+RI0fw1FNP4eDBg9i7dy8sLS0xceJE5Ofn647hWFXz9vbG8uXLcfjwYSQnJ2Pw4MGYNm0azp07B4Dj1JAff/wRW7duRXh4uGg7x6tWSEgILl68qPtXN/jgONUqKCjA6NGjIQgCPv74Y5w8eRJr1qyBh4eH7hiOV7Xk5GTRe+rw4cOQyWSYOHEiAMYOrcU+vUYyYsQIhIeH46233tJt69OnDyZMmIClS5ca8cqk46677sKaNWswbdo0ANWZgO7du2PWrFmIi4sDAJSXlyMkJASvvfYaZsyYYczLNaqSkhL4+flhx44dePDBBzlWdxAQEIClS5fiySef5DjVo7CwEEOGDMGbb76JNWvWICwsDGvXruX7qo6EhATs3bsXx48fN9jHcRJbsWIFjh49KvqGpS6OV8PWrVuHt956CxcuXICdnR1jh1ZiptcIKioqcObMGQwfPly0ffjw4Th58qSRrkr6MjIykJWVJRo3W1tbDBo0yOzHraSkBFqtFkqlEgDHqiEajQafffYZSktL0b9/f45TA+bNm4cJEyZgyJAhou0cL7H09HT06NEDvXr1wt/+9jekp6cD4Djp27dvH/r27YsZM2YgODgY9913H9577z0IQnXOjeNVP0EQsG3bNsTGxsLOzo6xQxuwNPYFmKO8vDxoNBrRVzsA4OHhgezsbCNdlfRlZWUBQL3jdvPmTWNckmQsWLAAERER6N+/PwCOlb7ffvsNo0aNgkqlgr29PbZv347w8HDdHwqOU62tW7ciLS0NiYmJBvv4vqoVFRWFjRs3IiQkBLm5uVi7di1GjRqFEydOcJz0pKenY8uWLXjmmWcwb948nD17FvPnzwcAzJ49m+PVgOTkZGRkZGD69OkAGDu0BQa9RiSTyUS3BUEw2EaGOG5ir7zyCk6cOIEDBw5ALpeL9nGsqoWEhCAlJQWFhYXYu3cv5syZg6+//lq3n+NULTU1FStWrMD+/fthbW3d4HEcL+CBBx4Q3Y6KikJkZCR27tyJfv36AeA41dBqtejdu7fu6/d77rkHaWlp2Lx5M2bPnq07juMltnXrVvTp0we9evUSbec4tRzLG4zAzc0Ncrnc4JNZbm6uwSc4quXl5QUAHLc6Fi5ciM8++wx79+5FQECAbjvHSsza2hqBgYG6P7wRERHYuHEjx0nPqVOnkJeXh4EDB8LNzQ1ubm44evQoNm/eDDc3N7i6ugLgeNXHwcEB3bt3R1paGt9Xery8vBAaGira1q1bN1y/fl23H+B41ZWTk4OkpCQ88cQTum2MHVqPQa8RWFtbIzIyEsnJyaLtycnJiI6ONtJVSZ+/vz+8vLxE46ZSqXD8+HGzHLf58+fj008/xd69e0WtkgCO1Z1otVpUVFRwnPSMHTsWx44dQ0pKiu5f7969ERMTg5SUFAQHB3O8GqBSqZCamgovLy++r/QMGDAAly9fFm27fPkyfH19AfD3VX127NgBGxsbPPLII7ptjB1aT75gwYJlxr4Ic+To6IiEhAR06dIFCoUCa9euxbFjx/DOO+/A2dnZ2JdnNCUlJbhw4QKysrKwbds2hIWFwcnJCRUVFXB2doZGo8G///1vBAcHQ6PRYNGiRcjKysL69ethY2Nj7MvvMHFxcdi1axc++ugj+Pj4oLS0FKWlpQCqfzHKZDKO1Z+WLVsGa2traLVaZGZm4t1338XHH3+MZcuWISgoiONUh0KhgIeHh+jfJ598Aj8/P0ybNo3vqzoWL16se19dvnwZ8fHxSEtLw7///W8olUqOUx0+Pj5YvXo1LCws0KVLFxw+fBgrV67ECy+8gL59+/J9pUcQBMydOxejR4/WtSqrwdihdVjTaySPPPIIbt++jbVr1yIrKws9evTAxx9/DD8/P2NfmlH98ssvGD9+vO52QkICEhISMHXqVLz77rt4/vnnUV5ejvj4eBQUFKBv377Ys2cPHB0djXjVHW/z5s0AgAkTJoi2z58/HwsXLgQAjtWfsrKyMHv2bGRnZ8PJyQnh4eH49NNPMWLECAAcp+bieFW7ceMGZs6ciby8PLi7uyMqKgrffvut7nc4x6lWnz59sGPHDqxYsQJr166Fj48PXnnlFcycOVN3DMerVkpKCtLS0vD+++8b7GPs0Drs00tEREREJo81vURERERk8hj0EhEREZHJY9BLRERERCaPQS8RERERmTwGvURERERk8hj0EhEREZHJY9BLRNRMSqUSL7zwgrEvo8muXLmCSZMmwd/fH0qlEjt27DD2JRnYsWMHlEolMjIydNvGjh2Lfv36GfGqiMiUMOglIsmpCYA8PT1x/fp1g/0xMTGIiIgwwpV1Tv/4xz/w888/Y8GCBUhMTMS9997b6nPu3r0bGzduNNheUlKChIQEpKSktPox2sPx48eRkJCAgoICY18KEXUwBr1EJFkVFRV44403jH0ZnZpGo8Hx48fx6KOPYs6cOYiNjUVAQECrz/vxxx/j3XffNdheWlqK1atX48iRI80635QpU3Dr1q12X1nqxIkTWL16NQoLC9v1cYhIehj0EpFkRUREYPv27fVme02dIAhQqVStPs/t27eh0Wjg7OzcBlfV9srKygAAcrkcCoUCMpnMyFdERKaKQS8RSdaLL74IAHfM9mZkZDRYqxoREYE5c+bobteUThw5cgSvvPIKgoOD4efnh7lz50KlUqG0tBTz5s1DYGAg/Pz8EBcXh6qqqnofd8+ePYiOjoaXlxcGDRqEgwcPGhxTVFSExYsXIyIiAp6enujZsyeWLVsGtVotOq6mTviLL77AoEGD4Onpic8++6zR5338+HGMHz8ed911F3x8fDBx4kT89NNPuv0JCQkICQkBAKxevRpKpRJKpbLRc+7YsQMTJkxAt27d4Onpib59+2L9+vXQarW6Y8aOHYv//e9/uHbtmu6cNfW4oaGhBo9XM/4JCQlQKpW4cOECnn76adx9990YMGCA6HWpW9Nb4+zZs3jwwQfRtWtXhIeHY/369aL9KSkpUCqV9ZZUKJVKJCQk6B5/+fLlAIB77rlHd31175ecnIxx48bBx8cH3t7eGDduHE6ePCk6Z0lJCRYvXoxevXrBy8sLISEhGD9+vGRLOoiomqWxL4CIqCE+Pj7461//iu3bt+PFF1+Ej49Pm5174cKFcHd3x/z583HmzBns2LEDdnZ2SE9Ph62tLRYtWoQffvgBmzdvRmBgIJ555hnR/U+ePInPP/8cf//73+Hg4ICtW7di2rRp+PLLL3U1s+Xl5Rg3bhwyMjLw5JNP4u6778bZs2fxzjvv4NKlS9i5c6fonMePH8eXX36JWbNmwcvLC926dWvw+o8ePYqHH34Y3t7eiIuLg1arxYcffoixY8di3759iIqKwvjx4+Hu7o74+HiMGzcO48ePv+O4vP/++wgJCcHIkSNha2uL5ORkLFu2DEVFRViyZAkAIC4uDgUFBbh16xZWrVqlu6+7uzvWrl1r8Hh333236DFmzJgBPz8/LFq0CBUVFY1eT1FREWJiYjBu3Dg8/PDDSEpKwrJly6DRaPDSSy/d8fnUNX78eKSmpmLPnj1YtWoV3NzcAEAXqH/66aeYPXs27r//fixatAharRY7duzAQw89pBtToPrD2BdffIGZM2eie/fuKCwsxE8//YSzZ8/i/vvvb9Y1EVHHYdBLRJL20ksvYefOnWw4J7wAAAfLSURBVHjjjTfatL7Xzc0Ne/bs0X2dfvXqVWzevBmTJ0/Ge++9BwB46qmnEB0dje3btxsEvb///jsOHjyI6OhoAMC0adPQp08fLF++HN988w0AYOPGjUhNTcWhQ4d0gRUA9OjRA3FxcTh27BgGDRqk237x4kUcPnwYvXr1uuP1L1q0CPb29vjuu+/g7u4OAJg6dSr69++PxYsX48CBA+jZsyc8PDwQHx+P8PBwxMbG3vG8SUlJsLOz092eOXMm/vGPfyAxMRHz58+HjY0Nhg0bhi5duqCoqMjgnA899NAdHy84OBjbtm2747UAQFZWFpYsWaLL+s+cORMPPfQQ1q1bh5kzZzarbKNnz56IiIjAnj17MHbsWPj7++v2lZaWIi4uDrGxsaJa5RkzZmDAgAFYsWIF9u7dCwA4ePAgnnjiCVHAT0TSx/IGIpI0X19fXba3LWt7H3vsMVH9aFRUFARBwPTp00XH9e3bF1euXDG4f+/evXUBLwC4urpi8uTJOHXqlK4zwOeff47o6Gi4u7sjLy9P92/o0KEAgB9++EF0zujo6CYFvFlZWThz5gymTp2qC3gBwNvbG5MmTcLJkydb3J2gJuDVaDQoKChAXl4e7rvvPpSWliI1NbVF59T31FNPNflYCwsLzJw5U3R71qxZKC8vb9NyguTkZBQUFODRRx8VvVbl5eUYOnQojh8/jsrKSgCAo6MjTp8+jRs3brTZ4xNR+2Oml4gkrz2yvfqlEk5OTg1uLy8vh1qtho2NjW57UFCQwTlrttXUuv7xxx84d+5cvccCQG5uruh2U7sqXL16FQDqLX8IDQ2FIAi6a2iu48ePY8WKFTh9+rRB6UFbdTxoTvcIT09P3WtTo+44t5U//vgDAPDwww83eExhYSHc3d2xfPlyzJ07Fz179kSvXr0wcuRITJ48WZTNJyLpYdBLRJJXN9tb8zV3XY3N+K87AasuuVxe73YLi/q/ABME4Y6PqX+MVqvF4MGD671moDozW5etrW29xzWH/jU0R3p6Oh5++GEEBgYiISEBPj4+sLGxwa+//oqlS5c2OJbN1Zzn2ZRxbuj112g0TX6cmue2ceNGg9elRk3wHRMTg3vvvRf79+/H999/j8TERKxfvx4bNmxoUgkJERkHg14i6hTqZnv1ubi4ADDMRKrVaty6datdrufy5csG29LS0gBUB+lA9QSukpISXTlDW6npZXvp0iWDfampqZDJZLpraI6kpCSoVCrs2rVL1C+3vo4KDQWabd1yLCsrC0VFRaJsr/4412S09V//mox4U66vZrKdu7t7k16vLl26YMaMGZgxYwYKCgrwwAMPYPXq1Qx6iSSMNb1E1CnUzfZmZmaK9jk6OsLd3d2gxvODDz5oVravOX755RecOnVKd/v27dv45JNP0K9fP10Q9sgjj+Dnn39GUlKSwf3Ly8tRUlLSosf28vJCZGQkdu3ahby8PN32mzdv4pNPPkF0dHSLShtqst91M6lqtVo3sa8uOzu7essdamqC22rFM61Wi82bNxvcVigUuO+++wBUfwiQy+UGr39D113f9Y0YMQLOzs5Yt26dQTs5oLYURaPRGDxvpVIJf39/rvJGJHHM9BJRp1GT7b1w4YJBJvPJJ5/EunXr8Mwzz6Bfv3745ZdfcPjwYV1bqrYWFhaG2NhYzJ49W9eyrLi4WNfWC6he/vebb77B9OnT8eijj6Jv375Qq9W4fPkyPv/8c12Q3BL//Oc/MXHiRIwcORJPPPEEBEHAli1bUFlZiddee61F5xwxYgSsra0xZcoUPPnkk6ioqMCuXbvqLfno3bs39u7di/nz5yMqKgoWFhaIiYmBg4MDQkJCsGfPHgQHB8PV1RX+/v66dl/N5eXlhU2bNuH69evo0aMH9u3bh5SUFLzyyiu6wN7JyQkxMTHYvHkzZDIZQkJCkJKSgvT09HqvGwBee+01xMTEwNraGoMHD4aHhwfefPNNPPXUU7jvvvswefJkeHl5ITMzEykpKbC3t8enn36K4uJihIWFYfz48ejZsyecnJxw4sQJfPfdd5g1a1aLniMRdQwGvUTUafj6+mLatGn48MMPDfbFxcXh9u3b2LNnD7744gvcd999+PLLL5vUm7YloqOjcf/99+Nf//oX0tPTERQUhO3bt4v6tNra2mLv3r148803sWfPHnz22Wewt7dHQEAA5syZo1s4oiXuvfdefPnll1i1ahXWrFkDmUyGqKgofPjhhy0OpIODg7Fjxw6sWLECS5cuhZubG6ZMmYL77rvPYILX7NmzceHCBXz88cd47733IAgCYmJiAAAbNmzAwoULsXjxYqjVakydOrXFQa+TkxM++OADvPzyy9i5cydcXV2xZMkSvPDCC6LjVq9ejaqqKmzfvh0WFhYYNWoUPv30UwQHB4uO69evHxYvXoyPPvoIc+fOhVarxVdffQUPDw9MnDgRXbt2xRtvvIGNGzeivLwcXl5eiIqKwuOPPw6gOlM8c+ZMJCcnY//+/aiqqoK/vz9ee+010SIoRCQ9soKCgpbPeiAiIiIi6gRY00tEREREJo9BLxERERGZPAa9RERERGTyGPQSERERkclj0EtEREREJo9BLxERERGZPAa9RERERGTyGPQSERERkclj0EtEREREJo9BLxERERGZvP8HQ+5EQvmEzpoAAAAASUVORK5CYII=\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,5))\n", "plt.plot(rmse)\n", "plt.title(\"Multivariate model, $k$ = 5\")\n", "plt.xlabel(\"Number of attributes\")\n", "plt.ylabel(\"RMSE\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "By using this hierarchical approach, we still don't see any significant improvement. The best univariate model (horsepower with default values) is still leading the competition in terms of RMSE (4367).\n", "As for the univariate models, we will now tune the $k$ parameter and save the results into a DataFrame." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "#tuning k parameter and save the results into a DataFrame\n", "multivariate_model_df = pd.DataFrame(index=range(2,total_features))\n", "for k in range(1,20):\n", " rmse=[]\n", " col_name=\"k\"+str(k)\n", " for i in range(2,total_features):\n", " col_names = univariate_model_df.mean(axis=1).sort_values().head(i).index \n", " rmse.append(knn_train_test(col_names, target, standardized_cars, k))\n", " multivariate_model_df[col_name]=rmse" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#print the top 10 obtained from previous hyperparameter optimization step\n", "sorted_multivariates = multivariate_model_df.mean(axis=1).sort_values(ascending=False)\n", "ax = sorted_multivariates[-10:].plot(kind='barh',\n", " stacked=True,\n", " figsize=(5, 5),\n", " title='Multivariate model, tuning $k$')\n", "ax.set_xlabel('Average RMSE');\n", "ax.set_ylabel('Number of attributes');" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "image/png": 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evBkmJiYqS4Z13Y8yxz9q1Ch07doVcXFxOHbsGFJSUtC8eXO0adMGvr6+UpMlNLaYmBh069YN33zzDRITE1FWVoZ27dohIiICH3zwgUyHnC+++AKOjo7YsGGD5JwZMmQIoqOj8frrr8vdh6qO//fff4eOjg5GjRpVp2Osvkc2f/58bN68GcXFxejUqRNmzpyJoKCgesW6YMECHD16FJcvX8bPP/8MHR0d2NraYu7cuXj33Xcllwhf9HNy9epVlJeXyyS79u3bY/v27Rg6dCiCg4Nx+PBhSUtOGY39mavrd01DvQ+NRZCfny9u1D0SkVZ49OgRHBwc8M4772D16tXqDoeoRrxnSEQN4sSJE9DV1UVERIS6QyGqFVuGRESk9dgyJCIircdkSEREWo/JkIiItB6TIRERaT0mQyIi0npMhg0gMzNT3SEQKYXnKmmKhj5XmQyJiEjrMRkSEZHWYzIkIiKtx2RIRERaj8mQiIi0Hh/hRERETdbNogrE/lqErAf6cLjzEFE9jNDOqG7Px1QGkyERETVJN4sqMDw5D9eLRACEOF/4BOful2Ovv7nKEyIvkxIRUZMU+2vR/yXCf10vEiH21yKV74vJkIiImqS7j0Vyy/9RUF4fTIZERNQkWbcQyi1vo6C8PpgMiYioSYrqYYRXjKQT3ytGQkT1MFL5vpgMibTQzaIKTDv2ENMv6WPasYe4WVSh7pCIZLQz0sVef3OMdjBATxMRRjsYNEjnGQAQ5Ofni1W+VS2XmZkJJycndYdBJJd0D72nXjESNtiXDJEqNPT3KluGRFqmMXvoEWkKJkMiLdOYPfSINEWTSYbLly+HqakpZs+eLSkzNTWV+xMRESGpExYWJrPc19dXattlZWWYPXs2HBwcYGNjg7FjxyInJ6fRjo2oKWnMHnpEmqJJzEBz9uxZJCQkwMXFRao8IyND6vWFCxcwduxYDB8+XKrcy8sL8fHxktd6enpSyyMjI5GUlISNGzfCzMwM8+bNQ3BwMI4dOwahUHVfAI01bRBRfUT1MMK5++Uy9wwboocekaZQezIsKCjAtGnTsGbNGixZskRqmZWVldTrpKQkODo64vXXX5cq19fXl6n77PY3b96MuLg4DBgwAAAQHx8PV1dXpKamwsfHRyXH0ZjTBhHVR3UPvdhfi5CVVwwH85b8w420ntovk86aNQvDhg1D//79a6xXVFSEPXv2YMKECTLLTp06BUdHR/Ts2RMzZ87E/fv3JcsuXryIiooKeHt7S8psbW3h7OyMM2fOqOw42CmBNEk7I12s798K61zLsL5/KyZC0npqbRkmJCQgKytL6hKnIrt370ZZWRnGjRsnVe7r64uhQ4eiXbt2yM7ORmxsLAIDA5Gamgp9fX3k5uZCKBTC3Nxcaj0LCwvk5uaq7FjYKYGISHOpLRlmZmZi0aJF+PHHH2Xu8cmTkJCAgIAAtG7dWqo8KChI8n8XFxe4ubnB1dUVycnJCAwMVLg9sVgMgUBQY3x10VKkC0D2r2tDUQkyM/PrtC2ixlTXc51IXepzrtY2RlFtyTA9PR15eXnw8PCQlIlEIpw8eRLffvst7ty5A319fQDApUuXcOHCBcTExNS6XWtra9jY2CArKwsAYGlpCZFIhLy8PKlE+uDBA3h6eircTl0Hd37RpgIZcgYyf9HfkpegqMniBBGkKV7aQfcBAQE4efIk0tLSJD/du3dHUFAQ0tLSpFqLCQkJsLe3h5eXV63bzcvLw927dyUdatzc3KCrq4uUlBRJnZycHGRkZKBPnz4qO552RrpY29cE9i2FaCkUw76lEGv7mjAREhFpALW1DKvHBD6rRYsWMDMzQ5cuXSRljx8/RmJiImbOnClzWbO4uBiLFy9GYGAgrKyskJ2djUWLFsHCwgJDhgwBAJiYmOCdd95BTEwMLCwsJEMrXFxclEquyrpZVIEZJwqQXSwCIEBxsQgzThRgr78OEyIRUROn9qEVtdmzZw9KSkoQEhIis0woFOLq1avYvn07CgoKYGVlhX79+mHTpk0wMvp3zNRnn30GoVCISZMmobS0FG+88QbWrVun0jGGNfUmXd+/lcr2Q0REqseJulVkyI/3cfyfcpnyfm30cGCwhRoiIqod7xmSpnhp7xm+bDjFFRGR5mIyVJHGfAglERGpFpOhijTmQyiJiEi1mAwbAu/CEhFplCbfm1RTcKJuIiLNxZahinCibiIizcVkqCKcqJuISHMxGaoIh1YQEWkuJkMViephBFtD6V+nrWEzDq0gItIATIaqJBbX/JqIiJokJkMVif21CLcfSye/24/F7EBDRKQBmAxVhB1oiIg0F5OhirADDRGR5mIyVBHOTUpEpLmYDFWEc5MSEWkuTsemQu2MdLG+fytkZubBycle3eEQEZGS2DIkIiKtx2RIRERaj8mQiIi0Hu8ZqtDNogrE/lqErAf6cLjzEFE9jNiBhohIAzAZqgifZ0hEpLl4mVRF+DxDIiLNxWSoIpyOjYhIczEZqginYyMi0lw1JsMNGzYgKytL8rqqqgp//PEHnjx5IlP3woULmDNnzgsHsnz5cpiammL27NmSsrCwMJiamkr9+Pr6Sq1XVlaG2bNnw8HBATY2Nhg7dixycnKk6ty6dQvBwcGwsbGBg4MD5syZg/Ly8heOVR5Ox0ZEpLlqTIZz5szB+fPnJa/z8/Ph6emJ9PR0mbqZmZnYsGHDCwVx9uxZJCQkwMXFRWaZl5cXMjIyJD+JiYlSyyMjI3HgwAFs3LgRSUlJKCoqQnBwMESip5cnRSIRgoODUVxcjKSkJGzcuBH79+/HvHnzXihWRTgdGxGR5qqxN6lYzsNp5ZXVR0FBAaZNm4Y1a9ZgyZIlMsv19fVhZWWlcN3NmzcjLi4OAwYMAADEx8fD1dUVqamp8PHxwdGjR3Ht2jVcvnwZtra2AICFCxdi5syZiI6OhrGxscqOhdOxERFpJrXfM5w1axaGDRuG/v37y11+6tQpODo6omfPnpg5cybu378vWXbx4kVUVFTA29tbUmZrawtnZ2ecOXMGAJCeng5nZ2dJIgQAHx8flJWV4eLFiw10VEREpEnUOs4wISEBWVlZiI+Pl7vc19cXQ4cORbt27ZCdnY3Y2FgEBgYiNTUV+vr6yM3NhVAohLm5udR6FhYWyM3NBQDk5ubCwsJCarm5uTmEQqGkjjyZmZn1Orb6rk/UWHiukqaoz7nq5ORU43K1JcPMzEwsWrQIP/74I/T09OTWCQoKkvzfxcUFbm5ucHV1RXJyMgIDAxVuWywWQyAQSF4/+/9nKSoHav/F1SQzM7Ne6xM1Fp6rpCka+lytNRmeO3cOOjpPqxUVFUEgEODEiRN4+PChVL2zZ8/Wacfp6enIy8uDh4eHpEwkEuHkyZP49ttvcefOHejr60utY21tDRsbG0kPV0tLS4hEIuTl5aF169aSeg8ePICnp6ekTvUl02p5eXkQiUQyLUYiItJOtSbDb775Bt98841U2dKlS+XWraml9byAgAB0795dqiw8PBwdOnTARx99JLe1mJeXh7t370o61Li5uUFXVxcpKSkYPXo0ACAnJwcZGRno06cPAKB3795YtmwZcnJy0LZtWwBASkoK9PX14ebmpnS8RET08qoxGR44cKDBdlw9bvBZLVq0gJmZGbp06YLi4mIsXrwYgYGBsLKyQnZ2NhYtWgQLCwsMGTIEAGBiYoJ33nkHMTExsLCwgJmZGebNmwcXFxd4eXkBALy9vdG5c2dMnz4dsbGxePToEWJiYjB+/HiV9iQlIiLNVWMyfP311xsrDhlCoRBXr17F9u3bUVBQACsrK/Tr1w+bNm2CkdG/A9k/++wzCIVCTJo0CaWlpXjjjTewbt06CIVCyXZ27NiBiIgIDBo0CM2bN8eoUaMQGxurrkMjIqImRpCfn//CAwdzcnLwzz//oEOHDjKtPG3GTgmkKXiukqZo6HO1xnGG586dwxdffCE1tg8A7t27hyFDhsDV1RUDBw6Ek5MT5s+f32BBEhERNaQak+HGjRuxefNmmV6X4eHhOHHiBDw8PBAeHo5OnTphzZo12LZtW4MGS0RE1BBqvGd47tw5BAQESJVdv34dP//8M7y9vbF7924AQEVFBQYMGIDNmzdj3LhxDRctERFRA6ixZXjv3j2Za7TJyckQCASYOHGipExXVxejRo3C1atXGyRIIiKihlTr3KTPT8xd/cSKvn37SpVbWFjIfbQTERFRU1djMnzllVdw+vRpyevKykqkpaWhY8eOaNWqlVTd52eBISIi0hQ13jMcN24coqKi4OTkBE9PT+zatQt5eXmYPn26TN0TJ07A0dGxwQIlIiJqKDUmwylTpiAlJQVffPEFBAIBxGIx+vXrh/DwcKl6t27dwtGjRxETE9OgwRIRETWEGpOhrq4uduzYgQsXLuD69euwt7dHr169ZOpVVFRgw4YNMvcRiYiINIFSj3Dq3r27zKTaz3JwcICDg4PKgiIiImpMan/SPRERkbrV2DKsfgySsgQCgVTvUyIiIk1QYzL8888/YWBgADc3NzRrxkYkERG9nGpMht27d8eFCxeQlZWFkSNHYsyYMXwgLhERvXRqbO4dPXoU58+fx4QJE5CcnAxvb2+89tprWLp0KW7cuNFIIRIRETWsWq99Ojg4IDIyEufPn8fhw4fh5eWF9evXo0ePHhg4cCDWr1+Phw8fNkasREREDaJONwJ79eqFpUuX4tq1a9i5cyf09fUxd+5crF+/vqHiIyIianBKjTN8VkFBAfbt24fExEScPHkSxsbGfFI2ERFpNKWSYUVFBQ4dOoTExEQcPnwYAODn54eEhAT4+/tDT0+vQYMkIiJqSDUmw+PHjyMxMRH79u1DUVER+vbti6VLl2LYsGEwNjZurBiJiIgaVI3JcOjQoTAwMICfnx+CgoJgY2MDAMjMzFS4Ts+ePVUbIRERUQOr9TLpkydPsG/fPuzfv7/GemKxGAKBgD1LiYhI49SYDOPi4horDiIiIrWpMRm+9dZbjRUHli9fjv/85z+YNm0ali5dioqKCsTGxuKnn37CjRs3YGRkhH79+mH+/Pmws7OTrBcQEIATJ05IbWvkyJH49ttvJa/z8/MxZ84cHDp0CAAwaNAgLFmyBKampo1zcERE1KSpdMLRlJSUF1rv7NmzSEhIgIuLi6Ts8ePH+O233xAREYFjx45h69atyMnJwahRo1BZWSm1fkhICDIyMiQ/K1eulFo+depUXLp0CYmJidi1axcuXbqEd99994ViJSKil0+dxxnKs3//fqxcuRK//fZbne8ZFhQUYNq0aVizZg2WLFkiKTcxMcHevXul6q5cuRLu7u7IyMiQSpwtWrSAlZWV3O1nZGTgyJEjOHTokOQpHCtXrsTgwYORmZnJMZJERFR7yzAlJQXBwcHo3bs3/P39ER8fL1n2v//9D+7u7pg4cSJycnLwySef1DmAWbNmYdiwYejfv3+tdYuKigBA5vLm7t274eDgAHd3d0RFRUnqAUB6ejpatmwp9Tgqd3d3GBoa4syZM3WOl4iIXj41tgwPHz6MsWPHQiwWw9zcHFlZWTh79izy8vJQWFiI+Ph4ODo6YuXKlRg7diz09fXrtPOEhARkZWVJJVhFysvLERUVhUGDBqFt27aS8tGjR8POzg5t2rTBH3/8gYULF+L333+XtCpzc3Nhbm4OgUAgWUcgEKB169bIzc1VuL+aho8oo77rEzUWnqukKepzrtZ2FbDGZLhq1SpYW1vjhx9+QMeOHVFQUIApU6Zg9erVAJ52epk4ceILPeswMzMTixYtwo8//ljrDDaVlZUIDQ1FQUEBtm3bJrVs4sSJkv+7uLigffv28PHxwcWLFyWPm3o2EVarHgqiSH0un/LyK2kKnqukKRr6XK0xi12+fBmTJ09Gx44dATy9jxcVFYWysjJ8+OGHmDx58gs/9Dc9PR15eXnw8PCAubk5zM3NceLECWzYsAHm5uYoKysD8DQRTpkyBVeuXMG+ffvQqlWrGrfbvXt3CIVCZGVlAQAsLS3x4MEDiMViSR2xWIy8vDxYWFi8UOxERPRyqbFlWFRUJDWMAYDkda9eveq144CAAHTv3l2qLDw8HB06dMBHH+lRsK4AACAASURBVH0EPT09VFRUYPLkybh27RoOHjyosJPMs65cuQKRSCSp27t3bxQXFyM9PV1y3zA9PR0lJSVS9xGJiEh71dqb9PlLidWvdXTq1xHV1NRUpiNMixYtYGZmhi5duqCyshITJkzAhQsXsG3bNggEAty7dw8AYGxsDAMDA1y/fh07d+6En58fWrVqhYyMDERFRaFbt25wd3cHADg7O8PX1xcffvghVq1aBbFYjA8//BD+/v68PERERACUSIbff/89Tp8+LXldWloKgUCAdevWyUzRJhAIsGzZMpUElpOTg6SkJACAl5eX1LK4uDiEhIRAV1cXx44dw7p161BSUoK2bdvCz88Pn3zyCYRCoaT++vXrMXfuXIwcORIAMHjwYKlhHEREpN0E+fn5YkULzczM6rYxzk0KgJ0SSHPwXCVN0dDnao0tw0ePHjXYjomIiJoKlU7HRkREpImYDImISOsxGRIRkdZjMiQiIq3HZEhERFqPyZCIiLQekyEREWm9Os2pVlFRgUOHDuHGjRvIz8+XmvwaeDroPjo6WqUBEhERNTSlk2F6ejrGjx+P3NxcmSRYTduT4c2iCsT+WoSsB/pwuPMQUT2M0M5IV91hERFRLZROhrNmzYJIJMK3336Lnj17wtjYuCHj0jg3iyowPDkP14tEAIQ4X/gE5+6XY6+/ORMiEVETp3QyzMrKQnR0NIYPH96Q8Wis2F+L/i8R/ut6kQixvxZhff+an8FIRETqpXQHGmdnZ5SXlzdkLBrt7mOR3PJ/FJQTEVHToXQyjI6OxoYNGyRPkCdpxroCueVGCsqJiKjpUPoyqa+vL2JjY+Hh4YE+ffrAxsZG6pmBwNMONGvXrlV5kJpAQZ8iheVERNR0KJ0MU1NTER4ejvLycqSlpUFfX1+mjjYnw6JK+VmvWEE5ERE1HUonw8jISLRu3Rrr1q1Dr169oKen15BxaRzrFkK55W0UlBMRUdOh9D3DGzduIDw8HJ6enkyEckT1MMIrRtKJ7xUjIaJ6GKkpIiIiUpbSydDFxQUPHz5syFg0WjsjXaztawL7lkK0FIph31KItX1NOMaQiEgDKJ0MY2NjsXnzZpw+fboh49FYN4sqMONEAbKLRSgWCZBdLMKMEwW4WVSh7tCIiKgWSt8zXL58OQwNDfHmm2/C0dERtra2cnuT7ty5U+VBagIOuici0lxKJ8M//vgDAoEAtra2KC0txV9//SVTRyDQ3jF1HHRPRKS5lE6Gly9fbsg4NB4H3RMRaa4m8zzD5cuXw9TUFLNnz5aUicVifP755+jUqRPatGmDgIAAXLt2TWq9/Px8hIaGwt7eHvb29ggNDUV+fr5UnStXruDNN99EmzZt0LlzZ3zxxRcKn7zxojjonohIcyndMrx165ZS9ezs7OocxNmzZ5GQkAAXFxep8lWrViEuLg5xcXFwcnLCkiVLMGLECJw9exZGRk+HLEydOhW3b99GYmIiBAIBZs6ciXfffRc7duwAABQWFmLEiBHw9PTE0aNHkZmZifDwcLRo0QLvv/9+nWNVJKNAfkcZReVERNR0KJ0Mu3XrptQ9wboOvygoKMC0adOwZs0aLFmyRFIuFovx9ddfY9asWRg2bBgA4Ouvv4aTkxN27dqFSZMmISMjA0eOHMGhQ4fQp08fAMDKlSsxePBgZGZmwsnJCYmJiXjy5Am+/vprGBgYoEuXLvjzzz/x1VdfYcaMGSq7z5lVVFWnciIiajqUToZr166VSRwikQg3b97E9u3bYWlpialTp9Y5gOpk179/f6lkePPmTdy7dw/e3t6SMgMDA3h6euLMmTOYNGkS0tPT0bJlS0kiBAB3d3cYGhrizJkzcHJyQnp6Ojw8PGBgYCCp4+Pjg//+97+4efMm2rdvX+eYiYjo5aJ0MgwJCVG4bNasWfD29kZxcXGddp6QkICsrCzEx8fLLLt37x4AwMLCQqrcwsICd+/eBQDk5ubC3NxcKkkLBAK0bt0aubm5kjo2NjYy26hepigZZmZm1ulYAAMA8lqZ4hfYFlHj4flJmqI+56qTk1ONy5VOhjVp2bIlQkJC8NVXXyE0NFSpdTIzM7Fo0SL8+OOPNU7v9nxrVCwWyyS/59VWp7rzTE2XSGv7xck4nqNggaDu2yJqJNW3E4iauoY+V1XWm1RXV1fSYlNGeno68vLy4OHhAXNzc5ibm+PEiRPYsGEDzM3N0arV04Hq1S28ag8ePJC07CwtLfHgwQOpnqFisRh5eXlSdeRtA5BtddbHq2byf5WKyomIqOlQyTf15cuXsW7dOjg7Oyu9TkBAAE6ePIm0tDTJT/fu3REUFIS0tDQ4OjrCysoKKSkpknVKS0tx6tQpyT3C3r17o7i4GOnp6ZI66enpKCkpkapz6tQplJaWSuqkpKTA2toa7dq1q++hS+SWyh9DoaiciIiajnr3Ji0oKEBhYSFatmyJuLg4pXdsamoKU1NTqbIWLVrAzMwMXbp0AQCEhYVh+fLlcHJygqOjI5YtWwZDQ0OMGjUKAODs7AxfX198+OGHWLVqFcRiMT788EP4+/tLmtOjRo3CF198gffeew8RERH466+/8OWXX2LOnDkqnTGnpFJ++WMF5URE1HQonQz79u0rkzwEAgFMTU3h4OCAoKAgmeRWXx988AGePHmC2bNnIz8/Hz179sSePXskYwwBYP369Zg7dy5GjhwJABg8eLBUr1QTExP88MMPiIiIwIABA2Bqaorw8HDMmDFDpbGa6jdDYYXs1Gsm+rxMSkTU1Any8/N5HU8FTtx9gmHJD/Hsg+11BMA+/1boa22geEUiNWIHGtIUGtOBRtv1tTbAPv9WUs8zZCIkItIMdRpaUVFRgUOHDuHGjRvIz8+Xmd9TIBAgOjpapQFqkr7WBrg02oB/bRMRaRilk2F6ejrGjx+P3NxchZNca3syJCIizaR0Mpw1axZEIhG+/fZb9OzZE8bGxg0ZFxERUaNROhlmZWUhOjoaw4cPb8h4NNrNogrE/lqErAf6cLjzEFE9jNDOSFfdYRERUS2UTobOzs4oLy9vyFg02s2iCgT8+AC3S6oACHG+8AlO3SvD/wa3ZkIkImrilO5NGh0djQ0bNiArK6sh49FYkWcK/i8R/ut2SRUizxSoKSIiIlKW0i1DX19fxMbGwsPDA3369IGNjQ2EQqFUHYFAgLVr16o8SE1w+p78VvPpXLamiYiaOqWTYWpqKsLDw1FeXo60tDTo6+vL1NHmZFhYIb+HbWE55zQgImrqlE6GkZGRaN26NdatW4devXrV+NglbVSpIOcpKicioqZD6XuGN27cQHh4ODw9PZkIiYjopaJ0MnRxccHDhw8bMhYiIiK1UDoZxsbGYvPmzTh9+nRDxqOxzBSMnlBUTkRETYfS9wyXL18OQ0NDvPnmm3B0dIStra3c3qQ7d+5UeZCawMFEB+cfyD68sINJnaZ/JSIiNVD6m/qPP/6AQCCAra0tSktL8ddff8nUUeXDcjWNg7Gu3GT4ijGbhkRETZ3SyfDy5csNGYfGG2Sri8SsJ3LLiYioaePzDFUk+lxxncqJiKjpYDJUkX8eV9WpnIiImg6Fl0nNzMzQrFkz3L17F3p6ejAzM6v1nqBAIEBeXp7Kg9QEilIeUyERUdOnMBnOmTMHAoEAOjo6Uq+JiIheNgqTYWRkZI2viYiIXha8Z0hERFqvTiPCRSIRjh49ihs3buDRo0cQi6VnoRYIBJgzZ45KAyQiImpoSifDS5cu4e2338bt27dlkmC1uiTD9evXY9OmTbh16xYAoFOnToiIiIC/vz8AwNTUVO56U6dOxbJlywAAYWFh2LZtm9TyXr164ciRI5LXZWVliIqKwu7du1FaWoo33ngDy5cvR9u2bZWKk4iIXn5KJ8OIiAgUFxdj8+bN6Nu3r8JkpSwbGxssXLgQHTp0QFVVFbZt24aQkBCkpqaia9euyMjIkKp/4cIFjB07FsOHD5cq9/LyQnx8vOT180/UiIyMRFJSEjZu3AgzMzPMmzcPwcHBOHbsmMx0ckREpJ3q1DKMjIxEQECASnb8/Haio6OxceNGnD17Fl27doWVlZXU8qSkJDg6OuL111+XKtfX15epW62goACbN29GXFwcBgwYAACIj4+Hq6srUlNT4ePjo5JjISIizaZ0BxpLS0vJMAtVE4lE2L17N0pKStC7d2+Z5UVFRdizZw8mTJggs+zUqVNwdHREz549MXPmTNy/f1+y7OLFi6ioqIC3t7ekzNbWFs7Ozjhz5kyDHAsREWkepbNbaGgotm/fjtDQUOjqqma+zStXrsDPzw+lpaUwNDTEli1b4OLiIlNv9+7dKCsrw7hx46TKfX19MXToULRr1w7Z2dmIjY1FYGAgUlNToa+vj9zcXAiFQpibm0utZ2Fhgdzc3Bpjy8zMrOPRGACQNw5T/ALbImo8PD9JU9TnXHVycqpxudLJ0MbGBjo6OvDw8MDbb78t9xFOADBixIg6BZeWloaCggLs378fYWFhOHjwILp06SJVLyEhAQEBAWjdurVUeVBQkOT/Li4ucHNzg6urK5KTkxEYGKhwv2KxuNYJBGr7xck4nqNggaDu2yJqJJmZmTw/SSM09LmqdDKcMmWK5P8LFy6UW0cgENQpGerp6cHBwQEA0L17d/z666/46quvsHbtWkmdS5cu4cKFC4iJial1e9bW1rCxsUFWVhaAp5d2RSIR8vLypBLpgwcP4OnpqXScRET0clM6GR44cKAh4wAAVFVVoby8XKosISEB9vb28PLyqnX9vLw83L17V9Khxs3NDbq6ukhJScHo0aMBADk5OcjIyECfPn1UGrsAgLwBJ5zAjoio6VM6GT7fi7O+FixYAD8/P7Rt2xbFxcXYtWsXjh8/jp07d0rqPH78GImJiZg5c6bMZc3i4mIsXrwYgYGBsLKyQnZ2NhYtWgQLCwsMGTIEAGBiYoJ33nkHMTExsLCwkAytcHFxUSq51oWBjgCPK2XToYEO0yERUVPXMN1DlXDv3j2EhoYiNzcXxsbGcHFxwa5du6SGO+zZswclJSUICQmRWV8oFOLq1avYvn07CgoKYGVlhX79+mHTpk0wMjKS1Pvss88gFAoxadIkyaD7devWqXyM4RpPI0z5pVBuORERNW2C/Px8udPJhIeHQyAQYNWqVRAKhQgPD699YwKB1P0+bbP77yK8f7IIpZVVaK7TDGs8jRDUgcmQmi52oCFNobYONL/88guaNWuGqqoqCIVC/PLLL0o9z1CbBXV4mvz4BUNEpFkUJsPLly/X+JqIiOhlwUc4ERGR1mMyJCIirVdjb9K6jsUTCAQ4ffp0vQLSZDeLKhD7axGyHujD4c5DRPUwQjsj1UxdR0REDafGZPjnn3/CwMAAbm5uaNaMjcia3CyqwPDkPFwvEgEQ4nzhE5y7X469/uZMiERETVyNybB79+64cOECsrKyMHLkSIwZMwZubm6NFZtGif216P8S4b+uF4kQ+2sR1vdvpaaoiIhIGTU2944ePYrz589jwoQJSE5Ohre3N1577TUsXboUN27caKQQNUNGfkWdyomIqOmo9dqng4MDIiMjcf78eRw+fBheXl5Yv349evTogYEDB2L9+vV4+PBhY8TapP1VKKpTORERNR11uhHYq1cvLF26FNeuXcPOnTuhr6+PuXPnYv369Q0Vn8YQiOVO5INmCsqJiKjpqPPcpAUFBdi3bx8SExNx8uRJGBsbc7YVAOYGQpQUy7YCWxmodg5UIiJSPaWSYUVFBQ4dOoTExEQcPnwYAODn54eEhAT4+/tDT0+vQYPUBF+/boJhyQ/x7IMrdARPy4mIqGmrMRkeP34ciYmJ2LdvH4qKitC3b18sXboUw4YNg7GxcWPFqBH6Whtgn38rhB0vwMMnlWhloIOvXzdBX2sDdYdGRES1UPjUCgAwMzODgYEB/Pz8EBQUBBsbm1o32LNnT5UGqIk4UTdpCp6rpCnU9tSKak+ePMG+ffuwf//+GuuJxWIIBAL2LCUiIo1TYzKMi4trrDiIiIjUpsZk+NZbbzVWHERERGrDCUeJiEjrMRkSEZHWYzIkIiKtx2RIRERaj8mQiIi0HpMhERFpPbUlw/Xr18PT0xN2dnaws7PDwIEDkZycLFkeFhYGU1NTqR9fX1+pbZSVlWH27NlwcHCAjY0Nxo4di5ycHKk6t27dQnBwMGxsbODg4IA5c+agvLy8UY6RiIg0Q52fWqEqNjY2WLhwITp06ICqqips27YNISEhSE1NRdeuXQEAXl5eiI+Pl6zz/ITgkZGRSEpKwsaNG2FmZoZ58+YhODgYx44dg1AohEgkQnBwMMzMzJCUlIRHjx4hLCwMYrEYS5cubdTjJSKipkttyTAgIEDqdXR0NDZu3IizZ89KkqG+vj6srKzkrl9QUIDNmzcjLi4OAwYMAADEx8fD1dUVqamp8PHxwdGjR3Ht2jVcvnwZtra2AICFCxdi5syZiI6O5mTjREQEoIncMxSJRNi9ezdKSkrQu3dvSfmpU6fg6OiInj17YubMmbh//75k2cWLF1FRUQFvb29Jma2tLZydnXHmzBkAQHp6OpydnSWJEAB8fHxQVlaGixcvNsKRERGRJlBbyxAArly5Aj8/P5SWlsLQ0BBbtmyBi4sLAMDX1xdDhw5Fu3btkJ2djdjYWAQGBiI1NRX6+vrIzc2FUCiEubm51DYtLCyQm5sLAMjNzYWFhYXUcnNzcwiFQkkdRTIzM+t1bPVdn6ix8FwlTVGfc7W2J16oNRk6OTkhLS0NBQUF2L9/P8LCwnDw4EF06dIFQUFBknouLi5wc3ODq6srkpOTERgYqHCb1U/PqPbs/5+lqPzZ2F4UH4tDmoLnKmmKhj5X1XqZVE9PDw4ODujevTvmz58PV1dXfPXVV3LrWltbw8bGBllZWQAAS0tLiEQi5OXlSdV78OCBpDVoaWkp0wLMy8uDSCSSaTESEZH2ahL3DKtVVVUpHPaQl5eHu3fvSjrUuLm5QVdXFykpKZI6OTk5yMjIQJ8+fQAAvXv3RkZGhtRwi5SUFOjr68PNza0Bj4SIiDSJ2i6TLliwAH5+fmjbti2Ki4uxa9cuHD9+HDt37kRxcTEWL16MwMBAWFlZITs7G4sWLYKFhQWGDBkCADAxMcE777yDmJgYWFhYSIZWuLi4wMvLCwDg7e2Nzp07Y/r06YiNjcWjR48QExOD8ePHsycpERFJqC0Z3rt3D6GhocjNzYWxsTFcXFywa9cu+Pj44MmTJ7h69Sq2b9+OgoICWFlZoV+/fti0aROMjIwk2/jss88gFAoxadIklJaW4o033sC6desgFAoBAEKhEDt27EBERAQGDRqE5s2bY9SoUYiNjVXXYRMRURMkyM/PF6s7iJcNOyWQpuC5Spripe5AQ0RE1BQwGRIRkdZjMiQiIq3HZEhERFqPyZCIiLQekyEREWk9JkMiItJ6TIZERKT1mAyJiEjrMRkSEZHWYzIkIiKtx2RIRERaj8mQiIi0HpMhERFpPSZDIiLSekyGRESk9ZgMiYhI6zEZEhGR1mMyJCIircdkSEREWk9H3QG8TG4WVSD21yJkPdCHw52HiOphhHZGuuoOi4iIasFkqCI3iyowPDkP14tEAIQ4X/gE5+6XY6+/ORMiEVETx8ukKhL7a9H/JcJ/XS8SIfbXIjVFREREylJbMly/fj08PT1hZ2cHOzs7DBw4EMnJyQCAiooKzJ8/H56enrCxsYGzszOmTp2KW7duSW0jICAApqamUj+TJ0+WqpOfn4/Q0FDY29vD3t4eoaGhyM/PV/nx3H0sklv+j4JyIiJqOtR2mdTGxgYLFy5Ehw4dUFVVhW3btiEkJASpqamws7PDb7/9hoiICLi6uqKwsBBRUVEYNWoUTpw4AR2df8MOCQlBTEyM5HXz5s2l9jN16lTcvn0biYmJEAgEmDlzJt59913s2LFDpcdj3UIot7yNgnIiImo61JYMAwICpF5HR0dj48aNOHv2LLp27Yq9e/dKLV+5ciXc3d2RkZEBFxcXSXmLFi1gZWUldx8ZGRk4cuQIDh06hD59+ki2M3jwYGRmZsLJyUllxxPVwwjn7pdLXSp9xUiIqB5GKtsHERE1jCZxz1AkEmH37t0oKSlB79695dYpKnp6783U1FSqfPfu3XBwcIC7uzuioqIk9QAgPT0dLVu2lCRCAHB3d4ehoSHOnDmj0mNoZ6SLvf7mGO1ggJ4mIox2MGDnGSIiDaHW3qRXrlyBn58fSktLYWhoiC1btki1+qqVl5cjKioKgwYNQtu2bSXlo0ePhp2dHdq0aYM//vgDCxcuxO+//y5pVebm5sLc3BwCgUCyjkAgQOvWrZGbm1tjbJmZmS90THNsANgAQBnK/8lD5j8vtBmiRvOi5zpRY6vPuVrblUC1JkMnJyekpaWhoKAA+/fvR1hYGA4ePIguXbpI6lRWViI0NBQFBQXYtm2b1PoTJ06U/N/FxQXt27eHj48PLl68CDc3NwCQSoTVxGKx3PLnY3tRqr4ES9RQeK6Spmjoc1Wtl0n19PTg4OCA7t27Y/78+XB1dcVXX30lWV5ZWYkpU6bgypUr2LdvH1q1alXj9rp37w6hUIisrCwAgKWlJR48eACxWCypIxaLkZeXBwsLi4Y5KCIi0jhN4p5htaqqKpSXlwN4Orxi0qRJuHLlCg4cOKCwk8yzrly5ApFIJKnbu3dvFBcXIz09XVInPT0dJSUlUvcRiYhIu6ntMumCBQvg5+eHtm3bori4GLt27cLx48exc+dOVFZWYsKECbhw4QK2bdsGgUCAe/fuAQCMjY1hYGCA69evY+fOnfDz80OrVq2QkZGBqKgodOvWDe7u7gAAZ2dn+Pr64sMPP8SqVasgFovx4Ycfwt/fn5eGiIhIQpCfny+uvZrqhYWFIS0tDbm5uTA2NoaLiwtmzpwJHx8f3Lx5E6+++qrc9eLi4hASEoLbt28jNDQU165dQ0lJCdq2bQs/Pz988sknMDMzk9R/9OgR5s6dix9//BEAMHjwYCxZskSmVyoREWkvtSVDIiKipqJJ3TMkIiJSByZDIiLSekyGRESk9ZgMiYhI6zEZqsiJEycwduxYdO7cGaampvj+++/VHRKRXCtWrMCAAQNgZ2eHDh06IDg4GFevXlV3WEQyPv/8c5nH9HXs2LFB9sVkqCIlJSXo0qULFi9eDAMDA3WHQ6TQ8ePHMWXKFCQnJ2P//v3Q0dHB8OHD8ejRI3WHRiTDyckJGRkZkp+TJ082yH7UOjfpy8TPzw9+fn4AgPfee0/N0RAptmfPHqnX8fHxsLe3x+nTpzF48GA1RUUkn46OjlIzkNUXW4ZEWq64uBhVVVWciIKapBs3bqBz587o1q0bJk+ejBs3bjTIfpgMibTcJ598AldXV4XPEiVSl169euGrr75CYmIiVq9ejXv37sHPzw8PHz5U+b54mZRIi3366ac4ffo0Dh06BKFQqO5wiKQMHDhQ6nWvXr3g5uaGrVu3YsaMGSrdF5MhkZaKjIzEnj17cODAAbRv317d4RDVqmXLlujUqZPkMX2qxMukRFpo7ty52LVrF/bv399gXdWJVK20tBSZmZkN0qGGLUMVKS4ulvy1UlVVhdu3b+PSpUswMzODnZ2dmqMj+ldERAR27NiBLVu2wNTUVPJ4NENDQ7Rs2VLN0RH9KyoqCoMGDYKtrS0ePHiApUuX4vHjxxg3bpzK98WnVqhIWloahg4dKlM+btw4fP3112qIiEg+Rb1G586di8jIyEaOhkixyZMn4+TJk8jLy0Pr1q3Rq1cvzJs3D506dVL5vpgMiYhI6/GeIRERaT0mQyIi0npMhkREpPWYDImISOsxGRIRkdZjMiQiIq3HZEhUD9UPH60euK5u1fG8iLS0NJiammL37t211g0LC2uUx+oQNRYmQyIi0npMhkQvkdmzZ+Off/5RdxhEGofJkOgloqOjg+bNm6s7DJV5/PixukMgLcFkSKRi9+7dg4eHB9zc3HDz5k2F9arvu+Xm5mLSpEmws7NDu3bt8MEHH6C0tFSm/u7du+Hj4wNra2vY29sjODgYf/zxh1QdefcMxWIxli1bBhcXF1hbW8PPzw/p6ekICAhAQECAzH7EYjHWrl0LV1dXWFlZYeDAgfjtt9/kHkN2djbGjBmDtm3bwsnJCTExMaioqJCqU1VVhS+//BI9e/aEpaUlOnfujNmzZ6OgoECqXkBAAF577TX8/vvvGDp0KGxsbPDxxx8DALKysjBx4kQ4OzvDysoKLi4umDBhAu7cuaPw90tUF3xqBZEK3b59G8OGDUOzZs2QlJQEGxubGutXVVVhxIgRcHFxwcKFC3Hu3DkkJCTA3NwcMTExknpffvklFixYgKFDh2Ls2LEoKSnBhg0b4O/vj2PHjtX4PML//Oc/WLFiBXx8fDBo0CD8/fffGDNmDMzMzOTGt3btWlRUVCA0NBSVlZVYvXo1QkJCcOHCBejq6krFPmrUKLi6umLBggU4fvw4Vq9ejYKCAqxatUpS7+OPP8amTZswePBgTJ8+HdeuXcPGjRtx/vx5JCcnS22zoKAAI0eOxNChQxEUFAQTExNUVFRg5MiRKC0txdSpU2FlZYV79+7h6NGjuHPnTq2/YyJlMBkSqcj169cRGBgIIyMj7N27F5aWlrWuU1FRgcGDByMqKgrA01n68/PzkZCQIEmGt27dQmxsrMxTJcaOHYvevXtj2bJlWLt2rdzt379/H2vWrIGfnx927NgBgUAAAOjSpQtmzpwpN5EUFhbi5MmTksutTk5OePvtt3H06FH4+/tLxe7p6Ykvv/wSADBt2jRMnz4d3333HWbMmAEnJydcvXoVmzZtwpgxY/DNN99I1nVyckJkZCS2bduG8ePHS8pzc3OxePFiTJ8+XVJ2+fJl3LhxAwkJCRg2bJikfPbs2bX+fomUo33+qQAABPtJREFUxcukRCqQkZGBN998E+bm5jh48KBSibDalClTpF737dsXeXl5KCoqAgAcOHAAlZWVCAoKQl5enuRHV1cXvXr1wi+//KJw26mpqaioqMDUqVMliRAA3nrrLZiYmMhdJyQkROq+4+uvvw4AuHHjhkzdd999V+a1WCzGTz/9BABITk4GAMycOVOq3uTJk2FsbCxZXk1HRwcTJ06UKjMyMgIA/PzzzygpKVF0qET1wpYhkQq89dZbMDU1xb59+6SSzJMnT1BYWChV99nxebq6urC2tpZaXn3P79GjRzAyMsLff/8NAOjdu7fcfbdo0UJhXLdu3QIAdOjQQapcR0cH7dq1k7vO8w+jfjaeZwkEAjg4OEiVVe+ner/Z2dkQCARwcnKSqqevr4927dohOztbqrxNmzYyHYDat2+P6dOnY926ddi5cyf69OkDf39/BAcHw9zcXP6BE9URkyGRCgQGBmLr1q3YsmULwsPDJeV79uyReg0A+fn5kv83a6b44oxY/PRRo1VVVQCAXbt2QUdH9iNb0zZqUr395wmFwjrVf1FisViqtQoABgYGcusuXrwYEyZMwI8//oijR48iOjoay5Ytw//+9z907txZpXGRdmIyJFKB+fPnw9DQEPPmzYOhoaHkUp+Pjw/27t1br22/8sorAABbW9s6P+G7upX3999/S7UOKysrkZ2dja5du75wXGKxGFlZWVLJqLoVW71fe3t7iMViZGZmSu2rvLwc2dnZ6Nevn9L769y5Mzp37oyPPvoIv//+O7y8vPD1119j9erVL3wMRNV4z5BIRZYsWYKQkBB89NFH2LlzJ4Cnl/28vLykfuoqMDAQOjo6+PzzzyWtxGc9ePBA4bpeXl7Q0dHBhg0bpFp2W7dulRna8CLi4+Plvvb19QUA+Pn5AQDi4uKk6m3atAmFhYVSHXIUKSwsRGVlpVSZs7MzDAwMpFrZRPXBliGRiggEAqxZswZPnjzBe++9BwMDAwwdOrTe223fvj0WLlyIefPmwdfXF0OHDoWZmRlu3bqFw4cPo1evXli5cqXcdS0sLDBjxgx8+eWXGDVqFAYNGoSsrCxs374dr7zyisxlyrrQ1dXFyZMnMXXqVLi7uyMtLQ379u3D22+/jY4dOwIAXFxcMGnSJEnyGzBgAK5du4ZNmzahR48eGDduXK37+eWXXzB79mwEBgbCyckJYrEYe/bsQVFREYKCgl44fqJnMRkSqVCzZs3wzTff4MmTJ5gyZQq2bt0qaSXVR3h4OBwdHbFmzRqsWLEClZWVsLa2hru7O955550a142JiUGLFi3w//7f/8OJEyfQrVs37Ny5E7Nnz67XbDXNmjXDrl27EBERgfnz56N58+aYMWOG1PhIAFi+fDn+fzt3jFohEABheNIo2D20t7ERPYOVYKVHsPEaeg17F7Z9B9BDvKM8rayE1CFJEwKK+3/lVtMN7LAbx7GMMZrnWWEYqus69X3/5Y3hb/I8V1mWWpZFxhj5vq80TWWt/fHTAOAvPtZ1/d9VHMDlHcehJElU1zWbGyA2Q+D29n3/dmat1fv9VlEUJyQCrodrUuDmns+npmlSVVV6PB56vV6y1irPczVNc3Y84BIoQ+DmsixTEAQax1HbtimKIrVtq2EY5Hne2fGAS2AzBAA4j80QAOA8yhAA4DzKEADgPMoQAOA8yhAA4DzKEADgvE/Br9jIF7J0JwAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = multivariate_model_df.idxmin(axis=1).sort_values().apply(lambda x: x[1])\n", "y = multivariate_model_df.min(axis=1).sort_values()\n", "plt.scatter(x,y)\n", "plt.xlabel(\"k-neighbors\")\n", "plt.ylabel(\"Minimum RMSE\")\n", "plt.title(\"Multivariate model, best $k$ values\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The model with the 15th best attributes has the best average score over all $k$ values, and this time we managed to obtain better RMSEs overall, but it looks suspicious since most of the minimal values occur when $k=1$. " ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,5))\n", "x=range(1,20)\n", "line1 = int(best_attributes[0])\n", "plt.plot(x,multivariate_model_df.min(axis=0))\n", "plt.xticks(x)\n", "plt.xlabel(\"k-neighbors\")\n", "plt.ylabel(\"Minimum RMSE\")\n", "plt.hlines(line1, 1, 19, linestyle='dashed',linewidth=2)\n", "plt.text(14, 4400, \"horsepower, $k$ = 5\")\n", "plt.title(\"Multivariate model, best scores, tuning $k$\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Discarding the risk of overfitting at $k <3$, we think there is maybe a good window at $3\\leq k \\leq6$ where the best scores are under the horsepower score dashed line, our best univariate model with default parameters. Let's find and display the set of attributes candidates. Note that we start now to store the most relevant feature sets into a Python set object (advantage compared to list, tuple, or dictionary built-in data types: *sets do not allow duplicate values*), namely feature_sets, for future usage." ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best feature set for n_neighbors = 3: ['engine_size', 'horsepower']\n", "\n", "Best feature set for n_neighbors = 4: ['engine_size', 'horsepower', 'curb_weight']\n", "\n", "Best feature set for n_neighbors = 5: ['engine_size', 'horsepower', 'curb_weight', 'highway_mpg']\n", "\n", "Best feature set for n_neighbors = 6: ['engine_size', 'horsepower', 'curb_weight', 'highway_mpg', 'length', 'city_mpg', 'width', 'compression_ratio', 'bore', 'wheel_base', 'num_cylinders_four', 'fuel_system_2bbl', 'peak_rpm', 'drive_wheels_rwd', 'make_mercedes-benz']\n", "\n" ] } ], "source": [ "#initiate feature_sets\n", "feature_sets = set()\n", "#add the best attribute\n", "feature_sets.add(tuple([\"horsepower\"]))\n", "#add the 2nd best attribute\n", "feature_sets.add(tuple([\"engine_size\"]))\n", "#add the full feature set\n", "feature_sets.add(tuple(full_feature_set))\n", "#add a feature set containing only continuous values\n", "feature_sets.add(tuple(continuous_features))\n", "\n", "#store best features for k-neighbors values between 3 and 6\n", "for idx, i in enumerate(multivariate_model_df.idxmin(axis=0)[2:6]): \n", " features = list(univariate_model_df.mean(axis=1).sort_values().head(i).index)\n", " feature_sets.add(tuple(features))\n", " print(f'Best feature set for n_neighbors = {idx+3}: {features}')\n", " print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Interesting to notice, the fourth set (15 attributes) contains binary features - four cylinders, 2bbl fuel system, rear-wheel drive and Mercedes-Benz. Binary features are finally showing some predictive power.\n", "\n", "Now, let's take an ultimate look at some of the multivariate models we've just built." ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'RMSE')" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,5))\n", "\n", "for idx in range(0,10): \n", " plt.plot(range(1,20),multivariate_model_df.iloc[idx])\n", "plt.title(\"Multivariate models, tuning $k$\")\n", "plt.xticks(range(1,20))\n", "plt.xlabel(\"k-neighbors\")\n", "plt.ylabel(\"RMSE\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In conclusion, hierarchical feature selection and train/test validation are a bit deceptive, leading to poor results compared to the best univariate model with default parameters. Even after tuning the $k$ parameter, the small improvements noted seem to be negligible and let us a bit septic. We need to improve the validation strategy." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Improving the model with K-fold cross-validation\n", "As mentioned by the [scikit-learn documentation](https://scikit-learn.org/stable/module/cross_validation.html), the cross-validation technique splits the training set into $k$ smaller sets and $k$-folds follow the same procedure:\n", "\n", "- a model is trained using $k-1$ of the folds as training data;\n", "\n", "- the resulting model is validated on the remaining part of the data (*i.e.*, it is used as a test set to compute a performance measure such as accuracy)." ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import KFold, cross_val_score, cross_validate" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "def cross_validation(col_names, target, df, k=5, n_splits=5):\n", " \"\"\"\n", " Trains a KNN model following the cross validation procedure and returns the RMSE\n", " \n", " col_name (string): the training features\n", " target (string): the column target\n", " df (DataFrame): the data\n", " k (int, optional): the n_neighbors parameter of KNN model we want to optimize\n", " n_splits (int, optional, min = 2): the number of folds\n", " \n", " output (float) : the average RMSE over n_splits\n", " \"\"\"\n", " #instantiate KNeighborsRegressor class with default parameters\n", " knn = KNeighborsRegressor(n_neighbors = k)\n", " #initiate KFold, shuffle and set a seed for reproductibility\n", " kf = KFold(n_splits=n_splits,shuffle=True,random_state=123) \n", " # Pass everything into the fit method.\n", " mses = cross_val_score(knn,df[col_names],df[target],\n", " scoring =\"neg_mean_squared_error\", cv = kf)\n", " rmses = [np.sqrt(np.abs(mse)) for mse in mses]\n", " avg_rmse = np.mean(rmses) \n", " return avg_rmse" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Cross validation with ('wheel_base', 'length', 'width', 'height', 'curb_weight', 'engine_size', 'bore', 'stroke', 'compression_ratio', 'horsepower', 'peak_rpm', 'city_mpg', 'highway_mpg'): 3535\n", "\n", "Cross validation with ('symboling_B', 'symboling_C', 'symboling_D', 'symboling_E', 'symboling_F', 'symboling_G', 'make_alfa-romero', 'make_audi', 'make_bmw', 'make_chevrolet', 'make_dodge', 'make_honda', 'make_isuzu', 'make_jaguar', 'make_mazda', 'make_mercedes-benz', 'make_mercury', 'make_mitsubishi', 'make_nissan', 'make_peugot', 'make_plymouth', 'make_porsche', 'make_renault', 'make_saab', 'make_subaru', 'make_toyota', 'make_volkswagen', 'make_volvo', 'fuel_type_diesel', 'fuel_type_gas', 'aspiration_std', 'aspiration_turbo', 'doors_four', 'doors_two', 'doors_unknown', 'body_style_convertible', 'body_style_hardtop', 'body_style_hatchback', 'body_style_sedan', 'body_style_wagon', 'drive_wheels_4wd', 'drive_wheels_fwd', 'drive_wheels_rwd', 'engine_location_front', 'engine_location_rear', 'engine_type_dohc', 'engine_type_l', 'engine_type_ohc', 'engine_type_ohcf', 'engine_type_ohcv', 'engine_type_rotor', 'num_cylinders_eight', 'num_cylinders_five', 'num_cylinders_four', 'num_cylinders_six', 'num_cylinders_three', 'num_cylinders_twelve', 'num_cylinders_two', 'fuel_system_1bbl', 'fuel_system_2bbl', 'fuel_system_4bbl', 'fuel_system_idi', 'fuel_system_mfi', 'fuel_system_mpfi', 'fuel_system_spdi', 'fuel_system_spfi', 'wheel_base', 'length', 'width', 'height', 'curb_weight', 'engine_size', 'bore', 'stroke', 'compression_ratio', 'horsepower', 'peak_rpm', 'city_mpg', 'highway_mpg'): 4029\n", "\n", "Cross validation with ('engine_size', 'horsepower'): 2977\n", "\n", "Cross validation with ('engine_size',): 3304\n", "\n", "Cross validation with ('engine_size', 'horsepower', 'curb_weight'): 3045\n", "\n", "Cross validation with ('horsepower',): 3896\n", "\n", "Cross validation with ('engine_size', 'horsepower', 'curb_weight', 'highway_mpg', 'length', 'city_mpg', 'width', 'compression_ratio', 'bore', 'wheel_base', 'num_cylinders_four', 'fuel_system_2bbl', 'peak_rpm', 'drive_wheels_rwd', 'make_mercedes-benz'): 3295\n", "\n", "Cross validation with ('engine_size', 'horsepower', 'curb_weight', 'highway_mpg'): 3022\n", "\n" ] } ], "source": [ "#applying cross validation on each stored feature set and print the results\n", "for feature_set in feature_sets:\n", " col_names=list(feature_set)\n", " score = cross_validation(col_names, target, standardized_cars, k=5, n_splits=5)\n", " print(f'Cross validation with {feature_set}: {score:.0f}')\n", " print() " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first results show pretty interesting improvements in model performance. The worst value is now under 3900!\n", "\n", "Like in the previous section, by selecting the best features from univariate_model_df sorted values, 77 feature sets of increasing size go to the oven and the scores after tuning the number of folds are stored into a new DataFrame named cross_validation_df.\n", "\n", "After analysis, the best sets of attributes will be added to feature_sets. The score obtained previously with engine size and horsepower features is now temporarily our new benchmark (RMSE = 2977)." ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "#cross validation with top-𝑋 feature selection from univariate_model_df sorted values\n", "cross_validation_df = pd.DataFrame(index=range(2,total_features))\n", "for k in range(2,20):\n", " rmse=[]\n", " col_name=\"k\"+str(k)\n", " for i in range(2,total_features):\n", " col_names = univariate_model_df.mean(axis=1).sort_values().head(i).index \n", " rmse.append(cross_validation(col_names, target, standardized_cars, n_splits=k))\n", " cross_validation_df[col_name]=rmse" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "labels = [\"2 features\", \"3 features\", \"4 features\", \"5 features\", \"6 features\"]\n", "\n", "plt.figure(figsize=(10,5))\n", "\n", "for idx in range(0,5): \n", " plt.plot(range(2,20),cross_validation_df.iloc[idx],label=labels[idx])\n", "plt.hlines(2977, 1, 19, linestyle='dashed',linewidth=2)\n", "plt.text(14, 3010, \"2 features, folds = 5\")\n", "plt.xticks(range(2,20))\n", "plt.title(\"Multivariate models, cross-validation\")\n", "plt.xlabel(\"Number of folds\")\n", "plt.ylabel(\"Average RMSE\") \n", "plt.legend(loc=\"upper right\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The figure shows that the model is now starting to be much more predictive. We seat now around the 3000 levels. Since all the curves are showing the same decreasing convexity, one may be tempted to use a large number of folds since larger values visibly converge toward lower error scores. Once again, it's about finding the right balance between bias and variance. We will address this question *infra*." ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best feature set for 2 folds: ['engine_size', 'horsepower', 'curb_weight', 'highway_mpg', 'length', 'city_mpg', 'width', 'compression_ratio', 'bore', 'wheel_base', 'num_cylinders_four', 'fuel_system_2bbl', 'peak_rpm']\n", "\n", "Best feature set for 3 folds: ['engine_size', 'horsepower', 'curb_weight']\n", "\n", "Best feature set for 4 folds: ['engine_size', 'horsepower']\n", "\n", "Best feature set for 5 folds: ['engine_size', 'horsepower']\n", "\n", "Best feature set for 6 folds: ['engine_size', 'horsepower']\n", "\n", "Best feature set for 7 folds: ['engine_size', 'horsepower', 'curb_weight', 'highway_mpg']\n", "\n", "Best feature set for 8 folds: ['engine_size', 'horsepower']\n", "\n", "Best feature set for 9 folds: ['engine_size', 'horsepower']\n", "\n", "Best feature set for 10 folds: ['engine_size', 'horsepower']\n", "\n", "Best feature set for 11 folds: ['engine_size', 'horsepower', 'curb_weight', 'highway_mpg']\n", "\n", "Best feature set for 12 folds: ['engine_size', 'horsepower']\n", "\n", "Best feature set for 13 folds: ['engine_size', 'horsepower']\n", "\n", "Best feature set for 14 folds: ['engine_size', 'horsepower', 'curb_weight', 'highway_mpg']\n", "\n", "Best feature set for 15 folds: ['engine_size', 'horsepower', 'curb_weight', 'highway_mpg']\n", "\n", "Best feature set for 16 folds: ['engine_size', 'horsepower']\n", "\n", "Best feature set for 17 folds: ['engine_size', 'horsepower', 'curb_weight', 'highway_mpg']\n", "\n", "Best feature set for 18 folds: ['engine_size', 'horsepower', 'curb_weight', 'highway_mpg']\n", "\n", "Best feature set for 19 folds: ['engine_size', 'horsepower']\n", "\n", "------------------------------------------\n", "Actual number of stored feature sets: 9\n" ] } ], "source": [ "#adding best sets to feature_sets\n", "for idx, i in enumerate(cross_validation_df.idxmin(axis=0)): \n", " features = list(univariate_model_df.mean(axis=1).sort_values().head(i).index)\n", " feature_sets.add(tuple(features))\n", " print(f'Best feature set for {idx+2} folds: {features}')\n", " print()\n", " \n", "print('------------------------------------------')\n", "print(f'Actual number of stored feature sets: {len(feature_sets)}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Forward selection & backward elimination\n", "We introduce two additional algorithms selecting features iteratively.\n", "- forward_selection: starts with an empty set and adds progressively new features following the RMSE criterion.\n", "- backward_elimination: starts with a full feature set and removes at each iteration the least useful feature, whose absence improves the performance following the same RMSE criterion.\n" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "def forward_selection(features, target, df, min_rmse):\n", " \"\"\"\n", " Trains a KNN model following the train/test validation procedure selecting features iteratively\n", " Starts with empty set and adds progressively new features following the RMSE criterion.\n", " \n", " features (list of string): the training features\n", " target (string): the column target\n", " df (DataFrame): the data\n", " min_rmse (float): the minimum RSME value we want to minimize \n", " \n", " outputs :\n", " RMSE (float)\n", " featurees (list of string): the best feature set found by the algorithm\n", " \"\"\" \n", " all_features = df.drop(target,axis=1, inplace = False).columns\n", " l=len(all_features)-len(features)\n", " updated_features = [feature for feature in all_features if feature not in features]\n", " selected_features = []\n", " for i in range(0,l): \n", " features.append(updated_features[i]) \n", " avg_rmse = knn_train_test(features, target, df)\n", " if avg_rmse < min_rmse:\n", " min_rmse = avg_rmse \n", " selected_features.append(updated_features[i])\n", " features.remove(updated_features[i])\n", " else:\n", " features.remove(updated_features[i])\n", " if len(selected_features)>0:\n", " features.append(selected_features[-1]) \n", " forward_selection(features, target, df, min_rmse)\n", " \n", " return(knn_train_test(features, target, df), features)\n", "\n", "def backward_elimination(features, target, df, min_rmse):\n", " \"\"\"\n", " Trains a KNN model following the train/test validation procedure selecting features iteratively\n", " Starts with full feature set and removes at each iteration the least useful feature\n", " following the RMSE criterion.\n", " \n", " features (list of string): the training features\n", " target (string): the column target\n", " df (DataFrame): the data\n", " min_rmse (float): the minimum RSME value we want to minimize \n", " \n", " outputs :\n", " RMSE (float) \n", " featurees (list of string): the best feature set found by the algorithm\n", " \"\"\" \n", " l=len(features) \n", " eliminated_features = [] \n", " for i in range(0,l): \n", " f = [feature for feature in features if feature!=features[i]] \n", " avg_rmse = knn_train_test(f, target, df)\n", " \n", " if avg_rmse < min_rmse:\n", " min_rmse = avg_rmse \n", " eliminated_features.append(features[i]) \n", " \n", " if len(eliminated_features)>0: \n", " features.remove(eliminated_features[-1]) \n", " backward_elimination(features, target, df, min_rmse)\n", " \n", " return(knn_train_test(features, target, df), features) " ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Forward Selection: ['horsepower', 'highway_mpg', 'make_mercedes-benz', 'drive_wheels_rwd', 'make_peugot', 'make_volkswagen', 'make_audi', 'make_porsche', 'city_mpg', 'make_alfa-romero', 'engine_type_rotor', 'fuel_system_2bbl', 'make_saab', 'make_isuzu', 'make_chevrolet']\n", "\n", "RMSE: 2815.095801475529\n" ] } ], "source": [ "#train/test validation with default parameters using forward feature selection\n", "#store the features set obtained\n", "min_rmse = knn_train_test(standardized_cars.columns[:-1].tolist(), \"price\", standardized_cars)\n", "fs_best_rmse, fs_f = forward_selection(features=[],\n", " target=\"price\",\n", " df=standardized_cars,\n", " min_rmse=min_rmse)\n", "print(f'Forward Selection: {fs_f}')\n", "print()\n", "print(f'RMSE: {fs_best_rmse}')\n", "#store the feature set\n", "feature_sets.add(tuple(fs_f))" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Backward Elimination: ['symboling_B', 'symboling_C', 'symboling_F', 'make_alfa-romero', 'make_audi', 'make_honda', 'make_isuzu', 'make_mazda', 'make_mercury', 'make_nissan', 'make_renault', 'make_saab', 'make_toyota', 'make_volkswagen', 'doors_two', 'body_style_convertible', 'body_style_hatchback', 'body_style_sedan', 'drive_wheels_4wd', 'drive_wheels_fwd', 'drive_wheels_rwd', 'engine_location_front', 'engine_location_rear', 'engine_type_dohc', 'engine_type_l', 'engine_type_rotor', 'num_cylinders_five', 'num_cylinders_four', 'num_cylinders_three', 'num_cylinders_two', 'fuel_system_1bbl', 'fuel_system_4bbl', 'fuel_system_mpfi', 'fuel_system_spdi', 'fuel_system_spfi', 'length', 'height', 'curb_weight', 'engine_size', 'bore', 'stroke', 'horsepower', 'highway_mpg']\n", "\n", "RMSE: 2348.328023370358\n" ] } ], "source": [ "#train/test validation with default parameters using backward elimination\n", "#store the feature set obtained\n", "features = standardized_cars.columns[:-1].tolist()\n", "min_rmse = knn_train_test(features, \"price\", standardized_cars)\n", "#min_rmse = standardized_cars[\"price\"].max()\n", "#print(min_rmse)\n", "be_best_rmse, be_f = backward_elimination(features=features,\n", " target=\"price\",\n", " df=standardized_cars,\n", " min_rmse=min_rmse)\n", "print(f'Backward Elimination: {be_f}')\n", "print()\n", "print(f'RMSE: {be_best_rmse}')\n", "#store the feature set\n", "feature_sets.add(tuple(be_f))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Composed of 43 attributes, the feature set obtained by backward elimination is doing the best RMSE score we made until now. This is very promising, showing that feature selection is a very important step in machine learning.\n", "\n", "Let's now build another DataFrame tuning the number of folds into cross_validation function and using each set of attributes stored in feature_sets as parameter.\n", "\n", "Then, we will use this DataFrame to find the optimal number of folds for each series of RMSEs values and plot the best result in MAPE/RMSE terms after writing a new cross-validation function." ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "#construct a dataframe with the scores of the stored feature sets\n", "final_df = pd.DataFrame()\n", "#keep into memory the features names\n", "features_list = list(feature_sets)\n", "for k in range(2,20):\n", " rmse=[]\n", " col_name=\"k\"+str(k)\n", " for feature_set in features_list:\n", " features = list(feature_set)\n", " score = cross_validation(features, target, standardized_cars, k=5, n_splits=k)\n", " rmse.append(score) \n", " final_df[col_name]=rmse" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Average RMSE')" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10,5))\n", "for idx in range(0,len(feature_sets)):\n", " plt.plot(range(2,20),final_df.iloc[idx,:])\n", "plt.title(\"Selected feature sets, cross-validation\")\n", "plt.xlabel(\"Number of folds\")\n", "plt.xticks(range(2,20))\n", "plt.ylabel(\"Average RMSE\")\n", "#plt.legend(loc=\"upper right\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The chart above shows clearly there is a substantial *gap* between two series and the others ones regardless the cross-validation strategy. The full feature set is one of them. Enough to say that feature selection is not optional here!\n", "\n", "Note that the second worst series is the best univariate model (horsepower), showing now its intrinsic limits." ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Number of features: 79\n", "('symboling_B', 'symboling_C', 'symboling_D', 'symboling_E', 'symboling_F', 'symboling_G', 'make_alfa-romero', 'make_audi', 'make_bmw', 'make_chevrolet', 'make_dodge', 'make_honda', 'make_isuzu', 'make_jaguar', 'make_mazda', 'make_mercedes-benz', 'make_mercury', 'make_mitsubishi', 'make_nissan', 'make_peugot', 'make_plymouth', 'make_porsche', 'make_renault', 'make_saab', 'make_subaru', 'make_toyota', 'make_volkswagen', 'make_volvo', 'fuel_type_diesel', 'fuel_type_gas', 'aspiration_std', 'aspiration_turbo', 'doors_four', 'doors_two', 'doors_unknown', 'body_style_convertible', 'body_style_hardtop', 'body_style_hatchback', 'body_style_sedan', 'body_style_wagon', 'drive_wheels_4wd', 'drive_wheels_fwd', 'drive_wheels_rwd', 'engine_location_front', 'engine_location_rear', 'engine_type_dohc', 'engine_type_l', 'engine_type_ohc', 'engine_type_ohcf', 'engine_type_ohcv', 'engine_type_rotor', 'num_cylinders_eight', 'num_cylinders_five', 'num_cylinders_four', 'num_cylinders_six', 'num_cylinders_three', 'num_cylinders_twelve', 'num_cylinders_two', 'fuel_system_1bbl', 'fuel_system_2bbl', 'fuel_system_4bbl', 'fuel_system_idi', 'fuel_system_mfi', 'fuel_system_mpfi', 'fuel_system_spdi', 'fuel_system_spfi', 'wheel_base', 'length', 'width', 'height', 'curb_weight', 'engine_size', 'bore', 'stroke', 'compression_ratio', 'horsepower', 'peak_rpm', 'city_mpg', 'highway_mpg')\n", "\n", "Number of features: 1\n", "('horsepower',)\n", "\n" ] } ], "source": [ "#display the 2 worst scores series\n", "for idx in final_df.idxmax().unique():\n", " feature_set = features_list[idx]\n", " print(f'Number of features: {len(feature_set)}')\n", " print(feature_set)\n", " print()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Elbow detection point\n", "It's possible to find the point of maximum curvature (elbow point) in a system with the [Kneedle algorithm](https://www1.icsi.berkeley.edu/~barath/papers/kneedle-simplex11.pdf). A Python package, [kneed](https://github.com/arvkevi/kneed), has been created by Kevin Arvai. For more information about parameters and how it works, see this [Kaggle notebook](https://www.kaggle.com/kevinarvai/knee-elbow-point-detection).\n", "Applied to the RMSE curves, the elbow point will be equal to the appropriate number of folds after which information gain becomes negligible. Indeed, a large(r) number of folds will increase the variance of the estimate, and that's not desirable. As pointed by [Sebastian Raschka](https://sebastianraschka.com/faq/docs/number-of-kfolds.html):\n", "> The reason for the increasing variance of the estimate is that the overlap between training sets increases with an increasing size of [folds].\n", "\n", "Example of elbow point detection with the first feature set stored in final_df:" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "from kneed import KneeLocator" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Feature set: ('symboling_B', 'symboling_C', 'symboling_F', 'make_alfa-romero', 'make_audi', 'make_honda', 'make_isuzu', 'make_mazda', 'make_mercury', 'make_nissan', 'make_renault', 'make_saab', 'make_toyota', 'make_volkswagen', 'doors_two', 'body_style_convertible', 'body_style_hatchback', 'body_style_sedan', 'drive_wheels_4wd', 'drive_wheels_fwd', 'drive_wheels_rwd', 'engine_location_front', 'engine_location_rear', 'engine_type_dohc', 'engine_type_l', 'engine_type_rotor', 'num_cylinders_five', 'num_cylinders_four', 'num_cylinders_three', 'num_cylinders_two', 'fuel_system_1bbl', 'fuel_system_4bbl', 'fuel_system_mpfi', 'fuel_system_spdi', 'fuel_system_spfi', 'length', 'height', 'curb_weight', 'engine_size', 'bore', 'stroke', 'horsepower', 'highway_mpg')\n", "\n", "Elbow point: 5\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "kl = KneeLocator(\n", " range(2,20),\n", " final_df.iloc[0,:],\n", " curve=\"convex\",\n", " direction=\"decreasing\",\n", " S=0.0,\n", " interp_method=\"polynomial\"\n", ")\n", "kl.plot_knee()\n", "\n", "print(f'Feature set: {features_list[0]}')\n", "print()\n", "print(f'Elbow point: {kl.elbow}')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here, the first significant elbow point, or the first point of maximum curvature from a polynomial interpolation point of view, is marked by the dashed line. The error is still decreasing after the elbow point, but at the cost of an increasing variance that can lead to worst performances when the model will be applied to a new dataset. \n", "\n", "Now, we will write a new cross-validation function, cross_validation_predictions, that uses cross_val_predict scikit-learn function instead of cross_val_score. We will use the predictions output to calculate the MAPE score in conjunction with the RMSE. Remember that we want *in the last instance* to compare our results with Kibler *et al.* (1989).\n", "\n", "To end this study, the last charts below show the best feature sets found minimizing respectively the MAPE criterion and the RMSE criterion." ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "from sklearn.model_selection import cross_val_predict" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "def cross_validation_predictions(col_names, target, df, k = 5, n_splits = 5):\n", " \"\"\"\n", " Trains a KNN model following the cross validation procedure\n", " and returns the predictions & testing set\n", " \n", " col_names (string): the training features\n", " target (string): the column target\n", " df (DataFrame): the data\n", " k (int): the n_neighbors parameter of KNN model we want to optimize\n", " n_splits (int, min = 2): the number of folds \n", " \n", " output: \n", " predictions (array): model predictions\n", " \n", " \"\"\"\n", " knn = KNeighborsRegressor(n_neighbors = k)\n", " #initiate KFold, shuffle and set a seed for reproductibility \n", " kf = KFold(n_splits=n_splits,shuffle=True,random_state=123) \n", " predictions = cross_val_predict(knn,df[col_names],df[target],cv = kf)\n", " return predictions\n", "\n", "def plot_pred(target, predictions, title=\"\"):\n", " \"\"\"\n", " Scatter plot of model predictions (y-axis) vs target (x-axis)\n", " \n", " target (1d array): the true values of the attribute we want to predict\n", " predictions (1d array): the predictions made by the model \n", " title (string, optional): the title of the chart\n", " \n", " \"\"\"\n", " \n", " fig, ax = plt.subplots(figsize=(8, 8))\n", " \n", " mape = mean_absolute_percentage_error(target, predictions)\n", " rmse = mean_squared_error(target, predictions) ** (1/2)\n", " string_mape = f'MAPE on dataset: {mape:.2%}'\n", " string_rmse = f'RMSE on dataset: {rmse:.0f}'\n", " ax.scatter(target, predictions)\n", " ax.plot([0, 1], [0, 1], transform=ax.transAxes,ls=\"--\", c=\"red\")\n", " plt.text(30000, 10100, string_mape)\n", " plt.text(30000, 11500, string_rmse)\n", " plt.title(title)\n", " plt.ylabel('Model predictions')\n", " plt.xlabel('Truths')\n", " plt.xlim(5000,46000)\n", " plt.ylim(5000,46000)\n", " plt.show()\n", "\n", "def elbow(x, y, curve=\"convex\", direction=\"decreasing\", s = 0.0, interp_method='polynomial'):\n", " \"\"\"\n", " Calculate the elbow point of a curve.\n", " Optional parameters: see https://www.kaggle.com/kevinarvai/knee-elbow-point-detection\n", " \n", " x (1d array): the x-coordinates of the curve we want to calculate the elbow point\n", " y (1d array): the y-coordinates of the curve we want to calculate the elbow point \n", " \n", " output: the elbow point (int)\n", " \n", " \"\"\" \n", " \n", " kl = KneeLocator(\n", " x,\n", " y,\n", " curve=curve,\n", " direction=direction,\n", " S=s,\n", " interp_method=interp_method\n", " )\n", " \n", " return kl.elbow" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Folds: 6\n", "Feature set: ['engine_size', 'horsepower', 'curb_weight', 'highway_mpg']\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "------------------------------------------------------------------\n", "\n", "Folds: 5\n", "Feature set: ['symboling_B', 'symboling_C', 'symboling_F', 'make_alfa-romero', 'make_audi', 'make_honda', 'make_isuzu', 'make_mazda', 'make_mercury', 'make_nissan', 'make_renault', 'make_saab', 'make_toyota', 'make_volkswagen', 'doors_two', 'body_style_convertible', 'body_style_hatchback', 'body_style_sedan', 'drive_wheels_4wd', 'drive_wheels_fwd', 'drive_wheels_rwd', 'engine_location_front', 'engine_location_rear', 'engine_type_dohc', 'engine_type_l', 'engine_type_rotor', 'num_cylinders_five', 'num_cylinders_four', 'num_cylinders_three', 'num_cylinders_two', 'fuel_system_1bbl', 'fuel_system_4bbl', 'fuel_system_mpfi', 'fuel_system_spdi', 'fuel_system_spfi', 'length', 'height', 'curb_weight', 'engine_size', 'bore', 'stroke', 'horsepower', 'highway_mpg']\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "elbows=[]\n", "\n", "mape = np.zeros(shape=(len(feature_sets),))\n", "rmse = np.zeros(shape=(len(feature_sets),))\n", "predictions_df = pd.DataFrame()\n", "for idx in range(0,len(feature_sets)):\n", " elbows.append(elbow(range(2,20),final_df.iloc[idx,:]))\n", "for idx in range(0,len(feature_sets)):\n", " col_name=\"feature_sets\"+str(idx) \n", " pred = cross_validation_predictions(list(features_list[idx]),\n", " \"price\",\n", " standardized_cars,\n", " k = 5,\n", " n_splits = elbows[idx])\n", " mape[idx]+=mean_absolute_percentage_error(standardized_cars[\"price\"], pred)\n", " rmse[idx]+=mean_squared_error(standardized_cars[\"price\"], pred) ** (1/2)\n", " predictions_df[col_name] = pred\n", "\n", "for i in range(0,2):\n", " if i == 0: \n", " idx_min = np.argmin(mape) #first chart = best MAPE\n", " title = \"Best model (MAPE criterion)\"\n", " else:\n", " idx_min = np.argmin(rmse) #2nd chart = best RMSE\n", " title = \"Best model (RMSE criterion)\"\n", " print(f'Folds: {elbows[idx_min]}')\n", " print(f'Feature set: {list(features_list[idx_min])}') \n", " plot_pred(standardized_cars[\"price\"], predictions_df.iloc[:,idx_min], title)\n", " if i == 0:\n", " print('------------------------------------------------------------------') \n", " print() " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## Final Results\n", "After a long process, we obtained finally an error score very closed to Kibler *et al.* (1989) best past result (IBL algorithm, 11,84%).\n", "\n", "It's interesting to note that when minimizing the MAPE score, the best result is obtained with continuous features only (engine size, horsepower, curb weight, highway mpg), found using *univariate feature selection*.\n", "\n", "But when minimizing the RMSE, the binary features are playing a more important role (in particular, the *make* of the car), and this time this is the *backward elimination* technique that worked better for us. \n", "\n", "An interesting fact we have discovered during the process: [MAPE and RMSE scores don't have clear linear relationship](https://community.dataquest.io/t/linear-relationship-between-rmse-and-mean-absolute-percentage-error/548378/4). In other words, scoring parameter is a non trivial task, and the choice of the scoring function we want to optimize need to be thought carefully since the very start of the project.\n", "\n", "Also, since the target (car prices) is skewed to the right, the biggest prices are \"high variance\", being the most challenging values to predict. It would be interesting, for further investigations, to model *the logarithm of the price* rather than the price in monetary unit.\n", "\n", "Some changes could improve the model. Between several ideas:\n", "- at the first stage of data preprocessing, narrow the data removing all rows with missing values. That's what Kibler *et al.* (1989) did, working with a final dataset of only 159 observations;\n", "- if we don't want to narrow the data such drastically, it's also possible to try other [imputation of missing values techniques](https://scikit-learn.org/stable/modules/impute.html);\n", "- use cross-validation elbow point to find then the optimal number of neighbors for each feature set." ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "text/html": [ "/* Copyright (c) 2013 Cameron Davidson-Pilon */\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from IPython.core.display import HTML\n", "\n", "def css_styling():\n", " styles = open(\"css/custom.css\", \"r\").read() #or edit path to custom.css\n", " return HTML(styles)\n", "css_styling()" ] } ], "metadata": { "hide_input": false, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": true, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": true, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }