From e7cc240b0113eb947bb2fd5682561d59efdb20a7 Mon Sep 17 00:00:00 2001 From: Sander Vanden Hautte Date: Fri, 12 Mar 2021 15:52:17 +0100 Subject: [PATCH 1/8] Preventing PyCharm to commit IDEA settings files. --- .gitignore | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.gitignore b/.gitignore index b2a2749..ce2acda 100644 --- a/.gitignore +++ b/.gitignore @@ -106,6 +106,9 @@ ENV/ # vscode settings .vscode/ +# JetBrains IDEs (PyCharm etc.) +.idea/ + # Other ignore files *.pptx *.ppt From 0a6e130fcb8a1e8f467b9ac5d1aa7e5d10c048a5 Mon Sep 17 00:00:00 2001 From: Sander Vanden Hautte Date: Thu, 1 Apr 2021 10:42:05 +0200 Subject: [PATCH 2/8] .idea folder ignore was mentioned twice --- .gitignore | 1 - 1 file changed, 1 deletion(-) diff --git a/.gitignore b/.gitignore index 89abbb8..3a9c002 100644 --- a/.gitignore +++ b/.gitignore @@ -112,4 +112,3 @@ ENV/ # Other ignore files *.pptx *.ppt -.idea/ From c787e8bfbc1e8eb3d648f6e77042621c0b1a670b Mon Sep 17 00:00:00 2001 From: Sander Vanden Hautte Date: Thu, 1 Apr 2021 16:56:12 +0200 Subject: [PATCH 3/8] Dataset creation method for a very large house prices dataset (about 2Mx300) + preprocessor performance test method. --- .gitignore | 3 + cobra/datasets/__init__.py | 219 +++++++++++++++++++++++ requirements.txt | 1 + tests/preprocessing/test_preprocessor.py | 67 +++++++ 4 files changed, 290 insertions(+) create mode 100644 cobra/datasets/__init__.py diff --git a/.gitignore b/.gitignore index 3a9c002..94f578c 100644 --- a/.gitignore +++ b/.gitignore @@ -109,6 +109,9 @@ ENV/ # JetBrains IDEs (PyCharm etc.) .idea/ +# Downloaded datasets (see cobra.datasets module): +datasets/ + # Other ignore files *.pptx *.ppt diff --git a/cobra/datasets/__init__.py b/cobra/datasets/__init__.py new file mode 100644 index 0000000..36e51ff --- /dev/null +++ b/cobra/datasets/__init__.py @@ -0,0 +1,219 @@ +""" +Dataset creation methods for specific test cases, e.g. memory consumption. + +Many machine learning libraries provide methods already to create datasets to +quickly run some experiments, +e.g. sklearn.datasets.make_classification() +(https://scikit-learn.org/stable/modules/generated/sklearn.datasets.make_classification.html#sklearn.datasets.make_classification). + +This package provides additional dataset creation methods for specific test +cases, e.g. very large datasets to try out Cobra's memory consumption. +""" +import os + +import numpy as np +import pandas as pd +from tqdm.auto import tqdm + + +def make_large_house_prices_dataset( + data_folder: str = './data/argentina-venta-de-propiedades', + ask_download_confirmation: bool = True) -> pd.DataFrame: + """ + Create a very large house prices dataset (shape: (2126816, 340)) + for classification and regression purposes, based on the Kaggle dataset at + https://www.kaggle.com/msorondo/argentina-venta-de-propiedades. + + This method downloads the dataset from Kaggle, + since including it directly in this repository would make the repository + very large, while only a few users of this repository will use this dataset. + + To make the download work, you need to have a Kaggle API Token saved + in a file on your computer. + If this is not the case, a warning will be thrown to help you set this up. + + To execute this, we advise that your PC should have >= 16 GB RAM. + + Parameters + ---------- + data_folder : str + path of the folder in which the CSV files can be written + ask_download_confirmation : bool + whether a confirmation must be asked before the 2.47 GB of CSV files + are downloaded. Set to False to run this method from a pytest unit test, + because those don't suppport input() calls. + + Returns + ------- + pd.DataFrame + a 2Mx340 basetable, ready for classification or regression experiments. + + Raises + ------ + ModuleNotFoundError + In case the kaggle package is not installed. + This is a dependency solely for this method, so it is not included + in cobra's requirements.txt. + IOError + In case a Kaggle API token file is not available on your machine. + See our help message printed in this case for how to solve this. + """ + # Importing the following modules at the top of the file is not useful, + # since they are only required for THIS specific dataset creation method. + # We don't want to make other dataset creation methods crash on + # the unavailability of the following modules if they don't use them. + from kaggle.api.kaggle_api_extended import KaggleApi + from zipfile import ZipFile + + setup_help_msg = r""" + This method downloads the dataset from Kaggle, + since including it directly in this repository would make the repository + very large, while only a few users of this repository will use this dataset. + + To make the download work, you need to have a Kaggle API Token saved + in a file on your computer. + If this is not yet the case: + 1. Create a Kaggle account, if you don't have one yet. + 2. Log in on Kaggle's website. + 3. On your Kaggle account, under "API", select "Create New API Token" and + a file "kaggle.json" will be downloaded on your computer. + 4. Move that "kaggle.json" file to the following path: + "C:\Users\\.kaggle". + 5. Run this method.""" + # Authenticate to Kaggle: + try: + api = KaggleApi() + api.authenticate() + except IOError: + print(setup_help_msg) + raise + + # Download and unzip the CSV files from Kaggle: + if ask_download_confirmation: + download_consent = input("Warning: 2.47 GB of CSV files will be " + "downloaded from Kaggle. " + "Is this OK? Type 'y' to continue:") + if download_consent != 'y': + raise RuntimeError("Stopped creating the houses dataset, " + "you did not consent to download it.") + dataset = 'msorondo/argentina-venta-de-propiedades' + # api.dataset_list_files(dataset) is buggy + we're discarding one file + # (ar_properties.csv), so let's specify them manually: + csv_files = [ + 'uy_properties_crude.csv', # smallest first for debugging + 'ar_properties_crude.csv', + 'co_properties_crude.csv', + 'ec_properties_crude.csv', + 'pe_properties_crude.csv', + #'uy_properties_crude.csv' + ] + os.makedirs(data_folder, exist_ok=True) + for csv_file in tqdm(csv_files, + desc="Downloading CSV files of the Kaggle dataset..."): + api.dataset_download_file(dataset, csv_file, data_folder) + zip_file = os.path.join(data_folder, csv_file + ".zip") + with ZipFile(zip_file) as zf: + zf.extract(csv_file, data_folder) + os.remove(zip_file) + + # Combine all CSVs into 1 big dataframe + # & add the country of each loaded CSV file: + print("Combining the CSVs into one basetable...") + dfs = [] + for csv_file in csv_files: + df = pd.read_csv(os.path.join(data_folder, csv_file)) + country = csv_file.split("_")[0] + df["country"] = country + dfs.append(df) + basetable = pd.concat(dfs, axis=0, ignore_index=True) + del dfs + + # Keep only houses for sale. (Some are paid for per month, we assume those + # are houses for rent and less applicable in that case, for this toy + # dataset). + print("Filtering only houses for sale...") + basetable = basetable[basetable.price_period.isna()] + + # The houses are from 5 different countries, with different currencies, + # so create one target column (house price) in a single currency (EUR): + print("Converting house prices in different currencies to EUR...") + + def price_to_eur(price, currency): + # 1 Argentine Peso equals 0,0092 Euro + if currency == "ARS": + return price * 0.0092 + # 1 United States Dollar equals 0,84 Euro + elif currency == "USD": + return price * 0.84 + elif pd.isna(currency): + return np.nan + # 1 Colombian Peso equals 0,00024 Euro + elif currency == "COP": + return price * 0.00024 + # 1 Sol equals 0,23 Euro + elif currency == "PEN": + return price * 0.23 + # 1 Uruguayan Peso equals 0,019 Euro + elif currency == "UYU": + return price * 0.019 + else: + raise ValueError("Unexpected currency.") + + basetable["price_EUR"] = basetable[["price", "currency"]].apply( + lambda row: price_to_eur(row[0], row[1]), axis=1) + + # Create a target column for a classification problem: + # which houses are more expensive than 300K? + # A Cobra model will then tell which features explain WHY this is the case. + basetable["price_EUR_>300K"] = basetable.price_EUR > 300_000 + + # Derived features for the datetime columns: + print("Creating derived features for the datetime columns...") + basetable["start_date"] = pd.to_datetime(basetable["start_date"], + format="%Y-%m-%d", + errors='coerce') # avoid OutOfBoundsDatetime + basetable["end_date"] = pd.to_datetime(basetable["end_date"], + format="%Y-%m-%d", + errors='coerce') + basetable["created_on"] = pd.to_datetime(basetable["created_on"], + format="%Y-%m-%d", + errors='coerce') + datetime_features = ["start_date", "end_date", "created_on"] + derived_datetime_features = [] + for datetime_feature in datetime_features: + basetable[datetime_feature + "_year"] = basetable[datetime_feature].dt.year + basetable[datetime_feature + "_month"] = basetable[datetime_feature].dt.month + basetable[datetime_feature + "_day"] = basetable[datetime_feature].dt.day + basetable[datetime_feature + "_quarter"] = basetable[datetime_feature].dt.quarter + derived_datetime_features += [ + datetime_feature + "_year", + datetime_feature + "_month", + datetime_feature + "_day", + datetime_feature + "_quarter" + ] + + # To reproduce the cobra performance issues under the same circumstances, + # we need the dataframe to have 300 columns. + print("Adding extra columns with random values, " + "to reproduce bigger dataframes...") + # => Add some irrelevant features (features with random values): + num_random_features = 150 + random_feature_cols = [f"random_feature_{feat_idx}" + for feat_idx in range(num_random_features)] + df_randoms = pd.DataFrame( + np.random.rand(basetable.shape[0], num_random_features) * 100, + columns=random_feature_cols) + basetable = pd.concat([basetable, df_randoms], axis=1) + print("Dataframe shape after adding irrelevant features:", basetable.shape) + # => ... and add some correlated (in this case identical) features: + random_feature_corr_cols = [col + "_corr" for col in random_feature_cols] + df_corr = df_randoms.copy().rename(columns={ + random_feature_col: random_feature_corr_col + for random_feature_col, random_feature_corr_col + in zip(random_feature_cols, random_feature_corr_cols) + }) + basetable = pd.concat([basetable, df_corr], axis=1) + del df_randoms, df_corr + print("Dataframe shape after adding correlated features:", basetable.shape) + + return basetable diff --git a/requirements.txt b/requirements.txt index 8ee7c41..799d87a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,3 +4,4 @@ scipy>=1.5.4 scikit-learn>=0.23.1 matplotlib>=3.3.3 seaborn>=0.11.0 +tqdm>=4.59.0 diff --git a/tests/preprocessing/test_preprocessor.py b/tests/preprocessing/test_preprocessor.py index 80f6d73..ecbebff 100644 --- a/tests/preprocessing/test_preprocessor.py +++ b/tests/preprocessing/test_preprocessor.py @@ -7,6 +7,7 @@ import pandas as pd from cobra.preprocessing.preprocessor import PreProcessor +from cobra.datasets import make_large_house_prices_dataset @contextmanager @@ -147,3 +148,69 @@ def test_get_variable_list(self, continuous_vars: list, discrete_vars) assert actual == expected + + @pytest.mark.skip() # Only meant to be run manually. + def test_preprocessor_performance_on_large_dataset(self, + data_folder='../../datasets/argentina-venta-de-propiedades'): + """ + Download a large housing dataset and run the PreProcessor on it, + to check and debug its performance. + + This test is meant to be run manually only, + since it would slow down the automatic tests if included + *and* there are no real assertions to be made here - it's meant more + as a thing to run manually and go over (or debug) the code. + + To run: + - disable the skip marking of this test and debug it + - call this test from a python console (this will print progress + messages, the first option won't): + from + """ + print("Creating basetable...") + basetable = make_large_house_prices_dataset( + data_folder, + ask_download_confirmation=False) # input() call doesn't work in pytest. + + # Preparing the preprocessor to do the performance testing: + preprocessor = PreProcessor.from_params() + basetable = preprocessor.train_selection_validation_split(basetable, + train_prop=0.7, + selection_prop=0.15, + validation_prop=0.15) + + # Setting which vars are discrete and which are continuous: + derived_datetime_features = [col for col in basetable.columns + if col.startswith("start_date") + or col.startswith("end_date") + or col.startswith("created_on")] + hierarchical_location_features = ["l1", "l2", "l3", "l4", "l5", "l6"] + rooms_features = ["rooms", "bedrooms", "bathrooms"] + discrete_vars = ["ad_type"] + \ + derived_datetime_features + \ + hierarchical_location_features + \ + rooms_features + \ + ["property_type", "operation_type", "country"] + # Note: "title" and "description" are not included here, they would help + # create a better model, but our primary interest here is testing + # just the preprocessing performance instead... + + random_feature_cols = [col for col in basetable.columns + if col.startswith("random_feature")] + continuous_vars = ["lat", "lon", "surface_total", "surface_covered"] + \ + random_feature_cols + + # Setting the target column: + target_clf = "price_EUR_>300K" + target_regr = "price_EUR" + + print("Fitting the preprocessor...") + preprocessor.fit(basetable[basetable["split"] == "train"], + continuous_vars=continuous_vars, + discrete_vars=discrete_vars, + target_column_name=target_clf) + + print("Transforming the preprocessor...") + basetable = preprocessor.transform(basetable, + continuous_vars=continuous_vars, + discrete_vars=discrete_vars) From 17f3bb7cca607b7bcafce948445577dd65bc96ec Mon Sep 17 00:00:00 2001 From: Sander Vanden Hautte Date: Thu, 1 Apr 2021 17:10:06 +0200 Subject: [PATCH 4/8] Forgot to complete calling instructions for dataset creation (helper for issue #27). --- tests/preprocessing/test_preprocessor.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/tests/preprocessing/test_preprocessor.py b/tests/preprocessing/test_preprocessor.py index ecbebff..58311fd 100644 --- a/tests/preprocessing/test_preprocessor.py +++ b/tests/preprocessing/test_preprocessor.py @@ -165,7 +165,9 @@ def test_preprocessor_performance_on_large_dataset(self, - disable the skip marking of this test and debug it - call this test from a python console (this will print progress messages, the first option won't): - from + from tests.preprocessing.test_preprocessor import TestPreProcessor + tpp = TestPreProcessor() + tpp.test_preprocessor_performance_on_large_dataset(data_folder='./datasets/argentina-venta-de-propiedades') """ print("Creating basetable...") basetable = make_large_house_prices_dataset( From 66a71c779f9d09b551eaf56c037f3677cdeb6a60 Mon Sep 17 00:00:00 2001 From: "hendrik.dewinter" Date: Wed, 8 Sep 2021 15:14:31 +0200 Subject: [PATCH 5/8] updating the readme for linear regression --- README.rst | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/README.rst b/README.rst index 22bf451..d2a3503 100644 --- a/README.rst +++ b/README.rst @@ -15,7 +15,7 @@ cobra .. image:: material\logo.png :width: 300 -**cobra** is a Python package to build predictive models using logistic regression with a focus on performance and interpretation. It consists of several modules for data preprocessing, feature selection and model evaluation. The underlying methodology was developed at Python Predictions in the course of hundreds of business-related prediction challenges. It has been tweaked, tested and optimized over the years based on feedback from clients, our team, and academic researchers. +**cobra** is a Python package to build predictive models using linear or logistic regression with a focus on performance and interpretation. It consists of several modules for data preprocessing, feature selection and model evaluation. The underlying methodology was developed at Python Predictions in the course of hundreds of business-related prediction challenges. It has been tweaked, tested and optimized over the years based on feedback from clients, our team, and academic researchers. Main Features ============= @@ -28,7 +28,7 @@ Main Features - replace missing values and - add columns with incidence rate per category/bin -- Perform univariate feature selection based on AUC +- Perform univariate feature selection based on RMSE (linear regression) or AUC (logistic regression) - Compute correlation matrix of predictors - Find the suitable variables using forward feature selection - Evaluate model performance and visualize the results @@ -79,10 +79,15 @@ Help and Support Documentation ------------- +**Logistic Regression** -- HTML documentation of the `individual modules `_ +- HTML documentation of the `individual modules `_ for logistic regression - A step-by-step `tutorial `_ +**Linear Regression** + +- HTML documentation of the individual modules for linear regression +- A step-by-step tutorial Outreach ------------- From b81365b2469541ebc75654e5483f47ea7bf5947d Mon Sep 17 00:00:00 2001 From: "hendrik.dewinter" Date: Mon, 20 Sep 2021 14:45:40 +0200 Subject: [PATCH 6/8] changed a small reference bug in the evaluator after changing from 1 Evaluator class to 2, for log and linear regression respectively --- cobra/evaluation/evaluator.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/cobra/evaluation/evaluator.py b/cobra/evaluation/evaluator.py index b395711..7ef8437 100644 --- a/cobra/evaluation/evaluator.py +++ b/cobra/evaluation/evaluator.py @@ -133,7 +133,7 @@ def compute_scalar_metrics(y_true: np.ndarray, "recall": recall_score(y_true, y_pred_b), "F1": f1_score(y_true, y_pred_b, average=None)[1], "matthews_corrcoef": matthews_corrcoef(y_true, y_pred_b), - "lift at {}".format(lift_at): np.round(Evaluator + "lift at {}".format(lift_at): np.round(ClassificationEvaluator ._compute_lift( y_true=y_true, y_pred=y_pred, From de3874f626280590d9c4759c5b63e52d979953e5 Mon Sep 17 00:00:00 2001 From: "hendrik.dewinter" Date: Mon, 20 Sep 2021 15:24:39 +0200 Subject: [PATCH 7/8] Updated Readme also applicable to linear regression. (only thing remaining is the tutorial link in the bottom, where I want to put a link to a ipynb file for both logistic and linear regression) --- README.rst | 3 +- docs/Cobra_Tutorial_LinearRegression.ipynb | 1861 +++++++++++++ docs/Cobra_Tutorial_LogisticRegression.ipynb | 2501 ++++++++++++++++++ 3 files changed, 4364 insertions(+), 1 deletion(-) create mode 100644 docs/Cobra_Tutorial_LinearRegression.ipynb create mode 100644 docs/Cobra_Tutorial_LogisticRegression.ipynb diff --git a/README.rst b/README.rst index d2a3503..afa60b0 100644 --- a/README.rst +++ b/README.rst @@ -86,8 +86,9 @@ Documentation **Linear Regression** -- HTML documentation of the individual modules for linear regression +- HTML documentation of the individal modules for linear regression - A step-by-step tutorial + Outreach ------------- diff --git a/docs/Cobra_Tutorial_LinearRegression.ipynb b/docs/Cobra_Tutorial_LinearRegression.ipynb new file mode 100644 index 0000000..214a956 --- /dev/null +++ b/docs/Cobra_Tutorial_LinearRegression.ipynb @@ -0,0 +1,1861 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], + "source": [ + "# Optional: import local development version of Cobra:\n", + "import sys\n", + "import os\n", + "sys.path.append(r\"C:\\Users\\hendrik.dewinter\\PycharmProjects\\cobra\")\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tutorial for Linear Regression" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This section we will walk you through all the required steps to build a predictive linear regression model using **Cobra**. All classes and functions used here are well-documented. In case you want more information on a class or function, run the next cell:" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": {}, + "outputs": [], + "source": [ + "#help(function_or_class_you_want_info_from)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Building a good model involves three steps\n", + "\n", + "1. **Preprocessing**: properly prepare the predictors (a synonym for “feature” or variable that we use throughout this tutorial) for modelling.\n", + "\n", + "2. **Feature Selection**: automatically select a subset of predictors which contribute most to the target variable or output in which you are interested.\n", + "\n", + "3. **Model Evaluation**: once a model has been build, a detailed evaluation can be performed by computing all sorts of evaluation metrics.\n", + "\n", + "\n", + "\n", + "Let's dive in!!!\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Miles Per Gallon Prediction\n", + "- GOAL : Predict the distance, measured in miles, that a car can travel per gallon of fuel\n", + "- BASETABLE : seaborn dataset MPG" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "from pandas.api.types import is_datetime64_any_dtype\n", + "\n", + "pd.set_option('display.max_columns', 50)\n", + "pd.set_option(\"display.max_rows\", 50)\n", + "from cobra.preprocessing import PreProcessor\n", + "from cobra.evaluation import generate_pig_tables, plot_incidence\n", + "from cobra.evaluation import evaluator" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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mpgcylindersdisplacementhorsepowerweightaccelerationmodel_yearoriginname
018.08307.0130.0350412.070usachevrolet chevelle malibu
115.08350.0165.0369311.570usabuick skylark 320
218.08318.0150.0343611.070usaplymouth satellite
316.08304.0150.0343312.070usaamc rebel sst
417.08302.0140.0344910.570usaford torino
\n", + "
" + ], + "text/plain": [ + " mpg cylinders displacement horsepower weight acceleration \\\n", + "0 18.0 8 307.0 130.0 3504 12.0 \n", + "1 15.0 8 350.0 165.0 3693 11.5 \n", + "2 18.0 8 318.0 150.0 3436 11.0 \n", + "3 16.0 8 304.0 150.0 3433 12.0 \n", + "4 17.0 8 302.0 140.0 3449 10.5 \n", + "\n", + " model_year origin name \n", + "0 70 usa chevrolet chevelle malibu \n", + "1 70 usa buick skylark 320 \n", + "2 70 usa plymouth satellite \n", + "3 70 usa amc rebel sst \n", + "4 70 usa ford torino " + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import seaborn as sns\n", + "df=sns.load_dataset('mpg')\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the example below, we assume the data for model building is available in a pandas DataFrame. This DataFrame should contain a an ID column, a target column (e.g. “**mpg**”) and a number of candidate predictors (features) to build a model with.\n", + "\n", + "***\n" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "mpg float64\n", + "cylinders int64\n", + "displacement float64\n", + "horsepower float64\n", + "weight int64\n", + "acceleration float64\n", + "model_year int64\n", + "origin object\n", + "name object\n", + "dtype: object" + ] + }, + "execution_count": 56, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.dtypes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "it is required to set all category vars to object dtype\n" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [], + "source": [ + "df.loc[:, df.dtypes == 'category'] =\\\n", + " df.select_dtypes(['category'])\\\n", + " .apply(lambda x: x.astype('object'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data preprocessing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### The first part focusses on preparing the predictors for modelling by:\n", + "\n", + "1. Defining the ID column, the target, discrete and contineous variables\n", + "\n", + "2. Splitting the dataset into training, selection and validation datasets.\n", + "\n", + "3. Binning continuous variables into discrete intervals\n", + "\n", + "4. Replacing missing values of both categorical and continuous variables (which are now binned) with an additional “Missing” bin/category\n", + "\n", + "5. Regrouping categories in new category “other”\n", + "\n", + "6. Replacing bins/categories with their corresponding incidence rate per category/bin." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this toy dataset, the index will serve as ID," + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "df[\"id\"] = df.index + 1\n", + "id_col = \"id\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "and MPG is the target,\n" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "count 398.000000\n", + "mean 23.514573\n", + "std 7.815984\n", + "min 9.000000\n", + "25% 17.500000\n", + "50% 23.000000\n", + "75% 29.000000\n", + "max 46.600000\n", + "Name: mpg, dtype: float64" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "target_col = \"mpg\"\n", + "df[target_col].describe()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finding out which variables are categorical (\"discrete\") and which are continous:\n", + "\n", + "\n", + " => discrete are definitely those that contain strings:" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['origin', 'name']\n", + "\n", + "origin\n", + "usa 249\n", + "japan 79\n", + "europe 70\n", + "Name: origin, dtype: int64\n", + "\n", + "name\n", + "ford pinto 6\n", + "amc matador 5\n", + "toyota corolla 5\n", + "ford maverick 5\n", + "peugeot 504 4\n", + " ..\n", + "dodge colt hardtop 1\n", + "fiat 131 1\n", + "chrysler lebaron salon 1\n", + "plymouth custom suburb 1\n", + "ford ranger 1\n", + "Name: name, Length: 305, dtype: int64\n", + "\n" + ] + } + ], + "source": [ + "col_dtypes = df.dtypes\n", + "discrete_vars = [col for col in col_dtypes[col_dtypes==object].index.tolist() if col not in [id_col, target_col]] \n", + "print(discrete_vars)\n", + "print()\n", + "for col in discrete_vars:\n", + " print(col)\n", + " print(df[col].value_counts())\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we also check for numerical columns that only contain a few different values, thus to be interpreted as discrete, categorical variables\n" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cylinders\n", + "4 204\n", + "8 103\n", + "6 84\n", + "3 4\n", + "5 3\n", + "Name: cylinders, dtype: int64\n", + "\n" + ] + } + ], + "source": [ + "for col in df.columns:\n", + " if col not in discrete_vars and col not in [id_col, target_col]: # if we didn't mark it as discrete already because it was string typed, or also excluding it if it is the target:\n", + " val_counts = df[col].value_counts()\n", + " if len(val_counts) > 1 and len(val_counts) <= 10: # The column contains less than 10 different values. \n", + " print(col)\n", + " print(val_counts)\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By taking a look at the printed variables, it is clear that we have to include those in the list of discrete variables. This can be done as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['origin', 'name', 'cylinders']" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "discrete_vars.extend([\"cylinders\"])\n", + "discrete_vars" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The remaining variables can be labelled continous predictors, without including the target variable.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [], + "source": [ + "continuous_vars = list(set(df.columns)\n", + " - set(discrete_vars) \n", + " - set([id_col, target_col]))\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we can prepare **Cobra's Preprocessor**" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The target encoder's additive smoothing weight is set to 0. This disables smoothing and may make the encoding prone to overfitting.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on method from_params in module cobra.preprocessing.preprocessor:\n", + "\n", + "from_params(model_type: str = 'classification', n_bins: int = 10, strategy: str = 'quantile', closed: str = 'right', auto_adapt_bins: bool = False, starting_precision: int = 0, label_format: str = '{} - {}', change_endpoint_format: bool = False, regroup: bool = True, regroup_name: str = 'Other', keep_missing: bool = True, category_size_threshold: int = 5, p_value_threshold: float = 0.001, scale_contingency_table: bool = True, forced_categories: dict = {}, weight: float = 0.0, imputation_strategy: str = 'mean') method of builtins.type instance\n", + " Constructor to instantiate PreProcessor from all the parameters\n", + " that can be set in all its required (attribute) classes\n", + " along with good default values.\n", + " \n", + " Parameters\n", + " ----------\n", + " model_type : str\n", + " Model type (``classification`` or ``regression``).\n", + " n_bins : int, optional\n", + " Number of bins to produce. Raises ValueError if ``n_bins < 2``.\n", + " strategy : str, optional\n", + " Binning strategy. Currently only ``uniform`` and ``quantile``\n", + " e.g. equifrequency is supported.\n", + " closed : str, optional\n", + " Whether to close the bins (intervals) from the left or right.\n", + " auto_adapt_bins : bool, optional\n", + " Reduces the number of bins (starting from n_bins) as a function of\n", + " the number of missings.\n", + " starting_precision : int, optional\n", + " Initial precision for the bin edges to start from,\n", + " can also be negative. Given a list of bin edges, the class will\n", + " automatically choose the minimal precision required to have proper\n", + " bins e.g. ``[5.5555, 5.5744, ...]`` will be rounded\n", + " to ``[5.56, 5.57, ...]``. In case of a negative number, an attempt\n", + " will be made to round up the numbers of the bin edges\n", + " e.g. ``5.55 -> 10``, ``146 -> 100``, ...\n", + " label_format : str, optional\n", + " Format string to display the bin labels\n", + " e.g. ``min - max``, ``(min, max]``, ...\n", + " change_endpoint_format : bool, optional\n", + " Whether or not to change the format of the lower and upper bins\n", + " into ``< x`` and ``> y`` resp.\n", + " regroup : bool\n", + " Whether or not to regroup categories.\n", + " regroup_name : str\n", + " New name of the non-significant regrouped variables.\n", + " keep_missing : bool\n", + " Whether or not to keep missing as a separate category.\n", + " category_size_threshold : int\n", + " All categories with a size (corrected for incidence if applicable)\n", + " in the training set above this threshold are kept as a separate category,\n", + " if statistical significance w.r.t. target is detected. Remaining\n", + " categories are converted into ``Other`` (or else, cf. regroup_name).\n", + " p_value_threshold : float\n", + " Significance threshold for regrouping.\n", + " forced_categories : dict\n", + " Map to prevent certain categories from being grouped into ``Other``\n", + " for each column - dict of the form ``{col:[forced vars]}``.\n", + " scale_contingency_table : bool\n", + " Whether contingency table should be scaled before chi^2.\n", + " weight : float, optional\n", + " Smoothing parameters (non-negative). The higher the value of the\n", + " parameter, the bigger the contribution of the overall mean.\n", + " When set to zero, there is no smoothing (e.g. the pure target incidence is used).\n", + " imputation_strategy : str, optional\n", + " In case there is a particular column which contains new categories,\n", + " the encoding will lead to NULL values which should be imputed.\n", + " Valid strategies are to replace with the global mean of the train\n", + " set or the min (resp. max) incidence of the categories of that\n", + " particular variable.\n", + " \n", + " Returns\n", + " -------\n", + " PreProcessor\n", + " class encapsulating CategoricalDataProcessor,\n", + " KBinsDiscretizer, and TargetEncoder instances\n", + "\n" + ] + } + ], + "source": [ + "preprocessor = PreProcessor.from_params(\n", + " model_type=\"regression\"\n", + ")\n", + "\n", + "# These are the options though:\n", + "help(PreProcessor.from_params)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "split data into train-selection-validation set:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "from cobra.preprocessing import PreProcessor\n", + "basetable = preprocessor.train_selection_validation_split(\n", + " data=df,\n", + " train_prop=0.6,\n", + " selection_prop=0.2,\n", + " validation_prop=0.2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And fit the preprocessor pipeline:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "12ea98963cc948c88ceea3e81d3da7d0", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Computing discretization bins...: 0%| | 0/5 [00:00\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", + " \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", + "
mpgcylindersdisplacementhorsepowerweightaccelerationmodel_yearoriginnameidsplitmodel_year_binhorsepower_bindisplacement_binacceleration_binweight_binorigin_processedname_processedcylinders_processedorigin_encname_enccylinders_encmodel_year_enchorsepower_encdisplacement_encacceleration_encweight_enc
018.08307.0130.0350412.070usachevrolet chevelle malibu1train70.0 - 71.0110.0 - 132.0302.0 - 350.08.0 - 12.23369.0 - 3731.0usachevrolet chevelle malibu820.59127517.5000014.98333319.23529419.55714315.44583315.50818.208333
115.08350.0165.0369311.570usabuick skylark 3202train70.0 - 71.0151.0 - 225.0302.0 - 350.08.0 - 12.23369.0 - 3731.0usabuick skylark 320820.59127515.0000014.98333319.23529413.75833315.44583315.50818.208333
218.08318.0150.0343611.070usaplymouth satellite3train70.0 - 71.0132.0 - 151.0302.0 - 350.08.0 - 12.23369.0 - 3731.0usaplymouth satellite820.59127518.0000014.98333319.23529415.32500015.44583315.50818.208333
316.08304.0150.0343312.070usaamc rebel sst4validation70.0 - 71.0132.0 - 151.0302.0 - 350.08.0 - 12.23369.0 - 3731.0usaamc rebel sst820.59127523.8337514.98333319.23529415.32500015.44583315.50818.208333
417.08302.0140.0344910.570usaford torino5selection70.0 - 71.0132.0 - 151.0232.0 - 302.08.0 - 12.23369.0 - 3731.0usaford torino820.59127523.8337514.98333319.23529415.32500018.02173915.50818.208333
\n", + "" + ], + "text/plain": [ + " mpg cylinders displacement horsepower weight acceleration \\\n", + "0 18.0 8 307.0 130.0 3504 12.0 \n", + "1 15.0 8 350.0 165.0 3693 11.5 \n", + "2 18.0 8 318.0 150.0 3436 11.0 \n", + "3 16.0 8 304.0 150.0 3433 12.0 \n", + "4 17.0 8 302.0 140.0 3449 10.5 \n", + "\n", + " model_year origin name id split \\\n", + "0 70 usa chevrolet chevelle malibu 1 train \n", + "1 70 usa buick skylark 320 2 train \n", + "2 70 usa plymouth satellite 3 train \n", + "3 70 usa amc rebel sst 4 validation \n", + "4 70 usa ford torino 5 selection \n", + "\n", + " model_year_bin horsepower_bin displacement_bin acceleration_bin \\\n", + "0 70.0 - 71.0 110.0 - 132.0 302.0 - 350.0 8.0 - 12.2 \n", + "1 70.0 - 71.0 151.0 - 225.0 302.0 - 350.0 8.0 - 12.2 \n", + "2 70.0 - 71.0 132.0 - 151.0 302.0 - 350.0 8.0 - 12.2 \n", + "3 70.0 - 71.0 132.0 - 151.0 302.0 - 350.0 8.0 - 12.2 \n", + "4 70.0 - 71.0 132.0 - 151.0 232.0 - 302.0 8.0 - 12.2 \n", + "\n", + " weight_bin origin_processed name_processed \\\n", + "0 3369.0 - 3731.0 usa chevrolet chevelle malibu \n", + "1 3369.0 - 3731.0 usa buick skylark 320 \n", + "2 3369.0 - 3731.0 usa plymouth satellite \n", + "3 3369.0 - 3731.0 usa amc rebel sst \n", + "4 3369.0 - 3731.0 usa ford torino \n", + "\n", + " cylinders_processed origin_enc name_enc cylinders_enc model_year_enc \\\n", + "0 8 20.591275 17.50000 14.983333 19.235294 \n", + "1 8 20.591275 15.00000 14.983333 19.235294 \n", + "2 8 20.591275 18.00000 14.983333 19.235294 \n", + "3 8 20.591275 23.83375 14.983333 19.235294 \n", + "4 8 20.591275 23.83375 14.983333 19.235294 \n", + "\n", + " horsepower_enc displacement_enc acceleration_enc weight_enc \n", + "0 19.557143 15.445833 15.508 18.208333 \n", + "1 13.758333 15.445833 15.508 18.208333 \n", + "2 15.325000 15.445833 15.508 18.208333 \n", + "3 15.325000 15.445833 15.508 18.208333 \n", + "4 15.325000 18.021739 15.508 18.208333 " + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basetable = preprocessor.transform(basetable,\n", + " continuous_vars=continuous_vars,\n", + " discrete_vars=discrete_vars)\n", + "\n", + "\n", + "basetable.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Feature selection" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once the predictors are properly prepared, we can start building a predictive model, which boils down to selecting the right predictors from the dataset to train a model on.\n", + "As a dataset typically contains many predictors, **we first perform a univariate preselection** to rule out any predictor with little to no predictive power. Later, using the list of preselected features, we build a multiple regression model using **forward feature selection** to choose the right set of predictors." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In previous steps, these were the predictors, as preprocessed so far:" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['acceleration_bin',\n", + " 'cylinders_processed',\n", + " 'displacement_bin',\n", + " 'horsepower_bin',\n", + " 'model_year_bin',\n", + " 'name_processed',\n", + " 'origin_processed',\n", + " 'weight_bin']" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "preprocessed_predictors = [\n", + " col for col in basetable.columns\n", + " if col.endswith(\"_bin\") or col.endswith(\"_processed\")\n", + "]\n", + "sorted(preprocessed_predictors)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But for feature selection, we use the target encoded version of each of these." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['acceleration_enc',\n", + " 'cylinders_enc',\n", + " 'displacement_enc',\n", + " 'horsepower_enc',\n", + " 'model_year_enc',\n", + " 'name_enc',\n", + " 'origin_enc',\n", + " 'weight_enc']" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "preprocessed_predictors = [col for col in basetable.columns.tolist()\n", + " if '_enc' in col]\n", + "sorted(preprocessed_predictors)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A univariate selection on the preprocessed predictors can be conducted. The thresholds for retaining a feature can be changed by the user, for instance both at 10 for now.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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predictorRMSE trainRMSE selectionpreselection
0weight4.1656364.091908True
1displacement4.3600214.276909True
2cylinders4.6300864.894663True
3horsepower4.4065534.956710True
4model_year5.9175876.294349True
5origin6.5787106.664076True
6name1.2803737.429626True
7acceleration6.9092797.632203True
\n", + "
" + ], + "text/plain": [ + " predictor RMSE train RMSE selection preselection\n", + "0 weight 4.165636 4.091908 True\n", + "1 displacement 4.360021 4.276909 True\n", + "2 cylinders 4.630086 4.894663 True\n", + "3 horsepower 4.406553 4.956710 True\n", + "4 model_year 5.917587 6.294349 True\n", + "5 origin 6.578710 6.664076 True\n", + "6 name 1.280373 7.429626 True\n", + "7 acceleration 6.909279 7.632203 True" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from cobra.model_building import univariate_selection\n", + "\n", + "\n", + "df_rmse = univariate_selection.compute_univariate_preselection(\n", + " target_enc_train_data=basetable[basetable[\"split\"] == \"train\"],\n", + " target_enc_selection_data=basetable[basetable[\"split\"] == \"selection\"],\n", + " predictors=preprocessed_predictors,\n", + " target_column=target_col,\n", + " model_type=\"regression\",\n", + " preselect_rmse_threshold = 10, #RMSE maximum for selection\n", + " preselect_overtrain_threshold = 10) #difference between RMSE on train and selection set\n", + "df_rmse\n", + "\n", + "#As the square root of a variance, RMSE can be interpreted as the standard deviation of \n", + "# the unexplained variance, and has the useful property of being in the same units as the response variable. \n", + "#Lower values of RMSE indicate better fit." + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# TODO: this does not work yet for regression\n", + "from cobra.evaluation import plot_univariate_predictor_quality\n", + "\n", + "# Plot df_rmse to get a horizontal barplot:\n", + "plot_univariate_predictor_quality(df_rmse)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we compute correlations between the preprocessed predictors and plot it using a correlation matrix:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " origin name cylinders model_year horsepower \\\n", + "origin 1.000000 0.546835 0.572771 0.183962 0.481406 \n", + "name 0.546835 1.000000 0.809813 0.630115 0.820650 \n", + "cylinders 0.572771 0.809813 1.000000 0.430675 0.830014 \n", + "model_year 0.183962 0.630115 0.430675 1.000000 0.424169 \n", + "horsepower 0.481406 0.820650 0.830014 0.424169 1.000000 \n", + "displacement 0.665415 0.832378 0.908653 0.378712 0.869803 \n", + "acceleration 0.242806 0.468041 0.558839 0.285982 0.630786 \n", + "weight 0.631066 0.847656 0.879737 0.353530 0.877309 \n", + "\n", + " displacement acceleration weight \n", + "origin 0.665415 0.242806 0.631066 \n", + "name 0.832378 0.468041 0.847656 \n", + "cylinders 0.908653 0.558839 0.879737 \n", + "model_year 0.378712 0.285982 0.353530 \n", + "horsepower 0.869803 0.630786 0.877309 \n", + "displacement 1.000000 0.530417 0.939289 \n", + "acceleration 0.530417 1.000000 0.479246 \n", + "weight 0.939289 0.479246 1.000000 \n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from cobra.evaluation import plot_correlation_matrix\n", + "\n", + "# compute correlations between preprocessed predictors:\n", + "df_corr = (univariate_selection\n", + " .compute_correlations(basetable[basetable[\"split\"] == \"train\"],\n", + " preprocessed_predictors))\n", + "print(df_corr)\n", + "\n", + "# plot correlation matrix\n", + "plot_correlation_matrix(df_corr)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To get a list of the selected predictors after the univariate selection, run the following call:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['weight_enc',\n", + " 'displacement_enc',\n", + " 'cylinders_enc',\n", + " 'horsepower_enc',\n", + " 'model_year_enc',\n", + " 'origin_enc',\n", + " 'name_enc',\n", + " 'acceleration_enc']" + ] + }, + "execution_count": 73, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "preselected_predictors = (univariate_selection\n", + " .get_preselected_predictors(df_rmse))\n", + "preselected_predictors" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After an initial preselection on the predictors, we can start building the model itself using forward feature selection to choose the right set of predictors. Since we use target encoding on all our predictors, we will only consider models with positive coefficients (no sign flip should occur) as this makes the model more interpretable." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Modeling" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Based on the performance curves (AUC per model with a particular number of predictors in case of logistic regression), a final model can then be chosen and the variables importance can be plotted:\n", + "Note: variable importance is based on correlation of the predictor with the model scores (and not the true labels!)." + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3580586ecd4e4bbfb2b5d1e629047b70", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sequentially adding best predictor...: 0%| | 0/8 [00:00\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", + "
predictorslast_added_predictortrain_performanceselection_performancevalidation_performancemodel_type
0[name_enc]name_enc1.2803737.4296266.833512regression
1[name_enc, model_year_enc]model_year_enc1.2409087.1898596.638183regression
2[name_enc, model_year_enc, weight_enc]weight_enc1.2003626.5880476.047175regression
3[name_enc, model_year_enc, weight_enc, horsepo...horsepower_enc1.1924046.5467835.974150regression
4[name_enc, model_year_enc, horsepower_enc, wei...cylinders_enc1.1891536.5613925.974802regression
5[name_enc, model_year_enc, horsepower_enc, wei...acceleration_enc1.1871926.5510465.965723regression
6[name_enc, model_year_enc, horsepower_enc, wei...origin_enc1.1870336.5467685.957807regression
7[name_enc, model_year_enc, origin_enc, horsepo...displacement_enc1.1869746.5473385.955585regression
\n", + "" + ], + "text/plain": [ + " predictors last_added_predictor \\\n", + "0 [name_enc] name_enc \n", + "1 [name_enc, model_year_enc] model_year_enc \n", + "2 [name_enc, model_year_enc, weight_enc] weight_enc \n", + "3 [name_enc, model_year_enc, weight_enc, horsepo... horsepower_enc \n", + "4 [name_enc, model_year_enc, horsepower_enc, wei... cylinders_enc \n", + "5 [name_enc, model_year_enc, horsepower_enc, wei... acceleration_enc \n", + "6 [name_enc, model_year_enc, horsepower_enc, wei... origin_enc \n", + "7 [name_enc, model_year_enc, origin_enc, horsepo... displacement_enc \n", + "\n", + " train_performance selection_performance validation_performance \\\n", + "0 1.280373 7.429626 6.833512 \n", + "1 1.240908 7.189859 6.638183 \n", + "2 1.200362 6.588047 6.047175 \n", + "3 1.192404 6.546783 5.974150 \n", + "4 1.189153 6.561392 5.974802 \n", + "5 1.187192 6.551046 5.965723 \n", + "6 1.187033 6.546768 5.957807 \n", + "7 1.186974 6.547338 5.955585 \n", + "\n", + " model_type \n", + "0 regression \n", + "1 regression \n", + "2 regression \n", + "3 regression \n", + "4 regression \n", + "5 regression \n", + "6 regression \n", + "7 regression " + ] + }, + "execution_count": 85, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from cobra.model_building import ForwardFeatureSelection\n", + "\n", + "forward_selection = ForwardFeatureSelection(model_type=\"regression\",\n", + " #model_name=\"linear-regression\",\n", + " max_predictors=30,\n", + " pos_only=True)\n", + "\n", + "# fit the forward feature selection on the train data\n", + "# has optional parameters to force and/or exclude certain predictors (see docs)\n", + "forward_selection.fit(basetable[basetable[\"split\"] == \"train\"],\n", + " target_column_name = target_col,\n", + " predictors = preselected_predictors)\n", + " #forced_predictors: list = [],\n", + " #excluded_predictors: list = [])\n", + "\n", + "# compute model performance\n", + "performances = (forward_selection\n", + " .compute_model_performances(basetable, target_column_name = target_col))\n", + "performances" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As can be seen, we have completed 7 steps till no further improvement can be observed" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\hendrik.dewinter\\PycharmProjects\\cobra\\cobra\\evaluation\\plotting_utils.py:137: RuntimeWarning: divide by zero encountered in double_scalars\n", + " ax.set_yticks(np.arange(0, max_metric*1.02, np.floor(max_metric/10)))\n" + ] + }, + { + "ename": "ValueError", + "evalue": "Maximum allowed size exceeded", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;31m# plot performance curves\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mplot_performance_curves\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mperformances\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32m~\\PycharmProjects\\cobra\\cobra\\evaluation\\plotting_utils.py\u001b[0m in \u001b[0;36mplot_performance_curves\u001b[1;34m(model_performance, dim, path, colors)\u001b[0m\n\u001b[0;32m 135\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_yticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmax_metric\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m0.02\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.05\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 136\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mmodel_type\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"regression\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 137\u001b[1;33m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_yticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmax_metric\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m1.02\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmax_metric\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 138\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 139\u001b[0m \u001b[1;31m# Make pretty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: Maximum allowed size exceeded" + ] + }, + { + "data": { + "image/png": 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VoQilKqQdhBMD8Nnwi5y73DIccfoSMZFx6W4zwe7g9PELnD5+IdU8m4/1SteIYiFJ3uclpEAQVuu1P0ck4tQlVv6xnc2rDxAfa8cvYBM1G5ancZvMdRExhYACUOdl141q+3+ATR9C+Mor84//7XqFVIAa/SHsSfAL8VpxRURExEVh14sC8/hTsrw/JcsXTjUvaRBO2SIcnUEQdiQ4OX3iAqdPpB2ECxTJm2rotEJFQwgpmHYQ3rPlWKqb/+Jj7axfsYdNK/fxcJ/mVK5R8jpqIZex+kClR1yv8NWuocv2zb4ydNnF/fDPC/DfMFff3xrPQb4KXi2yiIjIzUxhN4fKKAjHRse7w+9Z96gRrp+jL2UchM+cuMCZ9IJw4eRdI3z9bCz4+j8S7KmHdXM6DJwOB7Mn/02fN+67eVp4kyrWEFp/A43fhS2TYPunEHf5RkT7JdcNbps/cj2VrdYAKN5UQ5eJiIjcYAq7uVBAkB8lyhWiRLlCqebFRse7xg4+dYmIFH2Foy7FprtNR4KTMycvcubkReBYpsvicDhZtWg793RtmJWvYg7BpaDxGLhtGOye6ericH7n5ZkGHJjnehWuc3noskfB5u+afWEfbBwLu78i1B4JK4KhSjeoPRDyVfTK1xERETEThV2TCQjyo0TZQpQomzoIx8XEX+kjfLlFOOLyWMJRF9MPwhlxOgw2rzpwc4fdRL5BUP0ZqNYbjvzhCr1Hfr8y/8wG+KsHrBwEt/aFfJVhaS9w2MGwYwFXi/D2aa6b3u6eA2XbeenLiIiImIPC7k3EP9CP4mULUTytIBxrv9IKHO56stzGf/ZlartxsXYcCU5sPtd+85spWaxQpq3rFbEdNo+H3V9CQoxrfkw4rHkj/fUNOyTY4fdO8OhmtfBKzqMrEp6juhXJdhbDMIyrL3bt1q1bR7169Tyx6avK7nF2b1ZjnpuV4SOUk8qbP5DbWlSlXrPK5Mmrp4ulEnsWtk91PX44KpPdRCw2KN3WNY6vTwDYAlzdH2xJ3idOt/qqP3BKSUKDYY/E4qvQkC0OLXSdiF2+IuFm8QWbr65IXA/VrWfpmOA5OaBuM8qdCruSrgVfrWb9ij3JHsF8NTYfKzUalqdhy1BuKVPQg6XLpRx22D8H/uwGhjMbN2xJHn7TC8VJ5/mksZx7eoqfrWlsO9Vn+eecwK3Q4BkX9sF3Na88YCUtPkG6IpEVqlvP0jHBc3JI3SrsSpZEnLrE5Dd/xh6fejSGRFabBf9AX2IiUz8go0zlojRsFUpo7dJYberikMzHVsAjv3reZfVLO3BnGKyzMZjb/OHSwewLDYYTnA7g8r9G4st55b0zxc8p5yVbN+lyjivbT2vdxPeZWjeNeemum4ntpll2h6tbTtQxMt53LZCnJBSs5npvsSZ/Yc3ktCyum9Z66a2bcrpX1rW43q8d6RrG0EjIoGp9oEpXaDTm8s8W1+diSf6zJcm0pD9b0piW5XVzEZ1IeE4OqluFXcmytMbZBVfItdmsPNynOeVDb2H7ukOs/nMnxw+dTbWNkIJB1G9RlbpNKxMU7H8ji59zTQ1x3Yx2NRYfKNEMHHHgiL38ioOEJO8dseDMXHeTm4OFTJ1I2ALAN086gdWRzS3vImaVTiBOGZyThuu0Qndm1036c2aDecxZsF+8+lfxyweBRZNMyMRxJNMRKhuXy+7PzNT20lkmNgISojJe1eIL1Z+GZhMzV54sUtiV6xJx6hKrFm1n86oDxMXa8Q/wpWaj8jRqnfwJaoZhcGz/GVYv3sn29YdSdX/w8bVRo5Gri0OxUgVu9NfIWf7u6xp1wcggpF7LAcLpuBx8U4RiR+zlYJxkesqfM1rOmUawTvmz+336YzyLiMhNzC8EeqUe4z87KexKtsls3V48F83av3ezbtnuNB90Ua5qMRq2CqVKrVJZeoRxrpeDLv1kG8MJjvisB+vMLJdey3bS6dndPcRiu/yyuv61pvg56fyM5qW7bpL31qusS9LPSGc7aX1GqnWzUD6rDX57KON9NpFPELT94XLruHG51TzJC2fqaWlN98a6aa13I9Y9v8f189V3SAgqdrk1LvHFlZ+NJNOS/mykMS0r64pkiRX6pt8lMjtklDs19Jh4REiBIFp2rE2ze2uwdc1BVi/eycnDEe75B3eFc3BXOPkL5aH+nVWpc0clAvPcRF0c8lV0ddq/Wqf+3BJ0wRWKfC73oyWfd8pgGDAtX+a6iPjmhW77rxImc1nfRE+r2iNzVyRCn4Qyd9+4cplBdl/t8aS0gnOyQJwiOCcN19ezbtKfr2X739W6+qV2AN9geHhdiomZOAZk+jiRjctl92dmantpLDOrOtgjr76qX3DmyuEhCrviUT6+NmrfXpFajStwZN9pVv+5kx0bDmM4XQek82ejWDRnPUt/2kTNRhVo2CqUIiXye7fQN0rZdq6W203jYPdMjPhLWPzyQpXurscL56agm1NYLK7hbjITGqo+DoGpH8ctGag90PXAk4QM6tbm69p/5drkprpN1ifWqyXJnKqPZ/KY0APyV7lx5TKDKt0zV7dVut+4MqVVBHVjkGuRHXV7ISKKtUt3s275HmIiU3dxqBB2Cw1ahVK5RsmbqouD9ttsYsYuIjlJDhlmyJRUt56hY4Ln5KC6zSh33jxJQnKMfAXz0OrBOgx450Hue7xRqpvV9u84yayJS5k4dD4rF20nNjr1sGYi6UrsIuIT5AoJSVl8XdNzWxeRnCTxikT1p8EvBAOL6+aT6k+7piuMZZ3q1jN0TPCcXFK3atmVa+KJujUMg0O7T/HfXzvZueEIKXdJX38fat9ekQZ3VqVwcS/1Bb0BtN9mswv71EXkBtB+6zmq22ymY4Ln5IC61Q1qkqNZLBbKVS1GuarFOH82kjVLdrN++R53i649LoE1S3axZskuKlYvQcNWoVSqXgKLNTd0FhOvyVfRdSNPs4nsVGgQER0TPCeH163CruQo+QsF07pTXVrcV5PNq/ezevFOTh+/Mjbfvm3H2bftOAWL5qVBy1Bq314B/0A/L5ZYREREcjKFXcmRfP19qNesCnWbVubgzpOs/msnuzYddY80E3HqEr/NWsNf8zZSu4mri0OhYiHeLbSIiIjkOAq7kqNZLBbKhxWnfFhxzp2+xH9LdrFhxV7iYlx3KsfH2vlv8U7++2snlW8tScNWoVSoVhyLxkcVERERFHYlFylQJC93P3Ibd95fi02r9vPf4p2cOXn5eecG7NlyjD1bjlH4lhAatAylVuMK+AX4ZrxRERERMTWFXcl1/AJ8qd+iKrc1r8L+HSf4b/FOdm855u7icObkRX795j8W/7iBOk0q0aBlVQoUyevdQouIiIhXKOxKrmWxWKhYrQQVq5Ug4tRF/vtrFxv+2Ud8rKuLQ1yMnVV/7mDV4h1UrVmKBq1CKR96i7o4iIiI3EQUdsUUChYNoe1j9bmzY202/buP1Yt3EnHqkmumAbs2HWXXpqMUKZGPBi1DqdmoPH7+6uIgIiJidgq7Yir+Ab40aBlK/RZV2bvtOP/9tZO9W4+7558+foEFX61m8Q8bqNu0EvVbVCV/4WAvllhEREQ8SWFXTMlitVC5Rkkq1yjJmZMX+O+vXWz6dx/xcQkAxEbH8+/v21n5xw6q1i5Fw1ahlK1STF0cRERETEZhV0yv8C35uKdLA1p2rM3Gf/fx31+7OHfa1cXBMAx2bjjCzg1HKFaqAA1aVqVGw/L4+ulXQ0RExAz0F11uGgFBfjS6K4wGLauyd8txVv+1k/3bT7jnhx89x89fruLPueup26wy9VtUJV/BPF4ssYiIiFwvhV256VitVqrUKkWVWqU4ffw8/y1xdXGwxzsAiImK55+F2/j39+2E1SlNw1ZhlK5URF0cREREciGFXbmpFSmRn3u7NqRlx9ps+Gcfa/7ayfmzUQAYToPt6w6zfd1hbilTkIYtQ7m1QTl8fG1eLrWIiIhklsKuCBCYx5/b21Sj0V2h7N58jP8W7+TAzpPu+ScPRzB/xr8smrOOes0qc1uLqoQUCPJiiUVERCQzFHZFkrBarYTWLk1o7dKEHz3Hf3/tZPPqAyRc7uIQHRnH8l+38s/v26hWtywNWoVSqkJhdXEQERHJoRR2RdJRrFQB7nu8Ma0erMuGFXtZs2QXFyJcXRycDoOtaw6ydc1BSpQtRINWoVS/ray6OIiIiOQwCrsiVxEU7E+TttVp3DqMXZuOsnrxTg7tDnfPP37oLPM+/4dFc9ZxW/Mq1GtWmbz51cVBREQkJ1DYFckkq81KWN0yhNUtw8nDEaz+aydbVh/AkeAEIOpiLH//vJnlv26l+m1ladgqlJLlC3u51CIiIjc3hV2RLLilTEE6PHE7rR+qy7rle1izZDeXzkcD4HQ42bL6AFtWH6Bk+cI0bBVKtXplsPmoi4OIiMiNprArch2C8gbQ9J4a3N6mOjs3HGb1Xzs5sve0e/6xA2f4YdoK/pgdyG0tXF0cgkMC3fMjTl1i5R/b2bz6APGxdvwCNlGzYXkat6lGwaJ5vfGVRERETEVhVyQb2HysVK9fjur1y3H80Fn+W7yTrWsOurs4RF6IYen8TSxfsIVb65ejQatQoi7GMnvy3zgcTpwOA4D4WDvrV+xh08p9PNynOZVrlPTm1xIREcn1FHZFslmJsoXo2LMJrTvVZd2yPaxZupvICzEAOBKcbFq5n00r92OxgGGkXt/pMHA6HMye/Dd93rhPLbwiIiLXwertAoiYVZ6QQJq1r8kLbz/Ag73uoFSF5DerpRV0k3I4nKxatN2DJRQRETE/teyKeJjNx0aNhuWp0bA8xw6ccT2oYtWBq67ndBisWbqbfdtP4Bfgi3+AL/4BPvgH+OIX6Od+7x/om2S+b6ppfgE+WK06rxURkZtTlsPuxYsXGTp0KLt378ZisTB69Gjq1KmTnWUTMZ2S5QvzwFN3ZCrsJoo4dem6P9fX3ydZGPYL8ME/0Bf/AL8r7/19L0+7HJQDU4dnPz8fLNbc8bQ43fznOapbz1HdimS/LIfdt956i6ZNmzJ+/Hji4+OJjY3NznKJmJpfgC/xsfYb9nn2uATscQnuvsNZZgE//ysB2N3SnEaLctKwnCo8B/ji42fz2GOW92w5ppv/PER16zmqW8/SiYTn5PS6tRjG1XoOphYZGcn999/P4sWL0/1jtW7dOurVq3fdBcyKHTt2EBYW5pXPNjvVbfZY8NVq1q/Y4/6Dlhar1UKNRhVo2u5W4uLsxMXYiYu1Ex975X3c5ffxsVd+vjI/wf1zTmSxWlK0NPvh7++TKjBn1EUjMUDbfKzuY1HEqUtMfvNn7PGOdD/b18+mm/+yQHXrOapbz0rrRALAarNgs1l1InEdckrdZpQ7sxR2d+zYwbBhw6hUqRI7d+6kevXqDBkyhKCgK49IXbduXbKfb6TY2FgCAgK88tlmp7rNHpHnY/n9y+3uocnSYvOxcvfj1QjOf331bRgGCXYn9jgHCXaH6994J/Z4BwnxDuzJ3l+ZlvJne5wjw/J6k9VqwcfPiq+fDXucg/i49ANDopBCARQp6QoNmToIXvuh0hObyFRhjcx9o2v+rLMnIrl0Lu6qq4UUCqBIqbxYACwWLBZcJyOu/9zTSJzumpTsvXsZXNPTWj7NZZItb7n8eUk+3wIWrrxPuUzSbaRX3tTbzNx3cH9+GstsXnaUQzvOZriPWKxQ4dbC1GlZNvl2JUM38nh7s8lJdRsdHZ1u2M1SN4aEhAS2b9/OsGHDqFWrFqNGjWLKlCm88MILyZbzVgugWh89R3WbffIHF80RZ8PXwulwEh+X4G5RdrckJ2llTtq6nGxebMLl+fHExyaQYL96IM10uZwG8bEO4mMzv82LZ2O5eFbdrzxBdesZhhP2bT7Dvs1nks9IEtSvBOkkgT/lPKsrsCcN65YU4d1yeTnIaF4667p/tqQ4ucjKthJPGCxYrOlsK+k2kn031zKHdofjcGR8ou5wOFm36BgVwoq7TyTclQvJpyWeaFx5k3JWshWuvE1rWuLPqTeSvBiWFNtPZ92U0zIoWxpfM51tpf7QxGlb15zCeZW6NQyD0/vjqd+1TobLXa9169alOy9LYfeWW27hlltuoVatWgC0bduWKVOmZK10IjepyjVK0ueN+1i1aDubVx0gLtaOf4AvNRuVp1HrnNHPKSWrzUpAkB8BQX7XvS1HgpP4uAy6ZMSk6Jrhnp+QJEC7pl3tYCtiaoYrUBiJP8i1MyD8yDnCj5zzdklMx+kw2LzqAPd0bei1MmQp7BYpUoRbbrmF/fv3U6FCBVauXEnFihWzu2wiplewaF7u6dqQe7o2vOlazW0+VgJ9/AnM43/d20qwO9zBd/Kbv2CPT7jqOj6+Nto8cuWSl4XULRqpXGWRzF1RvvpC2XFlOlOXtzO1yJWFFny9OlMt8j6+Nlo/XA/DaQAGhgGG03AHssT37pCW4l+STUv+PlPrOC/HvsvvDfd7kpcnyftkyycr5+V1nGRQ/tTlJY0yZrRO9KWrdw9x/z+xWFxdVZRrJZeIj/PuvSNZHo1h2LBhvPTSS9jtdkqXLs2YMWOys1wiIpnm42vDx9dGnrwB1Gpc4eo3/9ks1GlSkfotqt7AUuZ+xw6cyXTdNrhTdXstMnXTqs1CvaaVk7WQpXcSkKy1N0Xwxkge6IFkJwVpbosU66bxOZnb1lU+J+W20jmhyVSZL2/rj+/XZup+A5uPlTs71E5Wtym5pyX/J3FmGtMy2laKhZJOS7J8ysWS9ck3Ui3unphW/++U5U+2vdSbvfKd0tnW+uV7M3Vlzc/f96rLeFKWw25YWBg//PBDdpZFROS6NW5TjU0r9+F0pN8CabNZadS62g0slTmobj0nq3Wb2K9V0nf62PlMnUjUvaMSTdpWv4ElMwGDTNVtzUblb2Ch0iiDVz9dRCSbFSyal4f7NMfXz4bVljwEWG0WfP1sPNyneY7sE53TqW49R3XrOY3bVMNmyzju6CQta3JL3SrsiojpJN78V69pZfwDXJfP/AN8qde0Mn3euC/HjXKRm6huPUd16xk6kfCc3FK3WRpnNzP0UAlzUt16jurWc1S3nqO69RzVbfaKOHUpV41+k5vkhLrNKHdmuc+uiIiISG5xM49+42k5vW7VjUFERERETEthV0RERERMS2FXRERERExLYVdERERETEthV0RERERMS2FXRERERExLYVdERERETEthV0RERERMS2FXRERERExLYVdERERETEthV0RERERMS2FXRERERExLYVdERERETEthV0RERERMS2FXRERERExLYVdERERETEthV0RERERMS2FXRERERExLYVdERERETEthV0RERERMS2FXRERERExLYVdERERETEthV0RERERMS2FXRERERExLYVdERERETEthV0RERERMS2FXRERERExLYVdERERETEthV0RERERMS2FXRERERExLYVdERERETEthV0RERERMS2FXRERERExLYVdERERETEthV0RERERMS2FXRERERExLYVdERERETEthV0RERERMS2FXRERERExLYVdERERETEthV0RERERMS2FXRERERExLYVdERERETEthV0RERERMS2FXRERERExLYVdERERETEthV0RERERMS2FXRERERExLYVdERERETEthV0RERERMyyerK7Zs2ZI8efJgtVqx2Wz88MMP2VkuEREREZHrluWwC/DFF19QsGDB7CqLiIiIiEi2UjcGERERETGt6wq7Tz31FA8++CDfffdddpVHRERERCTbWAzDMLKyYnh4OMWKFePs2bM8+eSTDBs2jPr167vnr1u3jqCgoGwr6LWIjY0lICDAK59tdqpbz1Hdeo7q1nNUt56juvUc1a3neKtuo6OjqVevXprzstxnt1ixYgAUKlSI1q1bs3nz5mRhFyAsLCyrm78uO3bs8Npnm53q1nNUt56juvUc1a3nqG49R3XrOd6q23Xr1qU7L0vdGKKjo4mMjHS//+eff6hcuXLWSiciIiIi4iFZatk9e/Ys/fr1A8DhcNC+fXuaNWuWrQUTEREREbleWQq7pUuX5qeffsrusoiIiIiIZCsNPSYiIiIipqWwKyIiIiKmpbArIiIiIqalsCsiIiIipqWwKyIiIiKmpbArIiIiIqalsCsiIiIipqWwKyIiIiKmpbArIiIiIqalsCsiIiIipqWwKyIiIiKmpbArIiIiIqalsCsiIiIipqWwKyIiIiKmpbArIiIiIqalsCsiIiIipqWwKyIiIiKmpbArIiIiIqalsCsiIiIipqWwKyIiIiKmpbArIiIiIqalsCsiIiIipqWwKyIiIiKmpbArIiIiIqalsCsiIiIipqWwKyIiIiKmpbArIiIiIqalsCsiIiIipqWwKyIiIiKmpbArIiIiIqalsCsiIiIipqWwKyIiIiKmpbArIiIiIqalsCsiIiIipqWwKyIiIiKmpbArIiIiIqalsCsiIiIipqWwKyIiIiKmpbArIiIiIqalsCsiIiIipqWwKyIiIiKmpbArIiIiIqalsCsiIiIipqWwKyIiIiKmpbArIiIiIqalsCsiIiIipqWwKyIiIiKmpbArIiIiIqalsCsiIiIipqWwKyIiIiKmpbArIiIiIqalsCsiIiIipqWwKyIiIiKmpbArIiIiIqZ1XWHX4XDQsWNHnnnmmewqj4iIiIhItrmusPvll19SsWLF7CqLiIiIiEi2ynLYPXnyJEuXLqVTp07ZWR4RERERkWyT5bA7evRoXn75ZaxWdfsVERERkZzJJysrLVmyhIIFC3LrrbeyevXqdJfbsWNHlgt2PWJjY7322WanuvUc1a3nqG49R3XrOapbz1Hdek5OrNsshd3169fz119/sWzZMuLi4oiMjOSll17i/fffT7ZcWFhYthTyWu3YscNrn212qlvPUd16jurWc1S3nqO69RzVred4q27XrVuX7rwshd2BAwcycOBAAFavXs3nn3+eKuiKiIiIiHibOtyKiIiIiGllqWU3qYYNG9KwYcPsKIuIiIiISLZSy66IiIiImJbCroiIiIiYlsKuiIiIiJiWwq6IiIiImJbCroiIiIiYlsKuiIiIiJiWwq6IiIiImJbCroiIiIiYlsKuiIiIiJiWwq6IiIiImJbCroiIiIiYlsKuiIiIiJiWwq6IiIiImJbCroiIiIiYlsKuiIiIiJiWwq6IiIiImJbCroiIiIiYlsKuiIiIiJiWwq6IiIiImJbCroiIiIiYlsKuiIiIiJiWwq6IiIiImJbCroiIiIiYlsKuiIiIiJiWwq6IiIiImJbCroiIiIiYlsKuiIiIiJiWwq6IiIiImJbCroiIiIiYlsKuiIiIiJiWwq6IiIiImJbCroiIiIiYlsKuiIiIiJiWwq6IiIiImJbCroiIiIiYlsKuiIiIiJiWwq6IiIiImJbCroiIiIiYlsKuiIiIiJiWwq6IiIiImJbCroiIiIiYlsKuiIiIiJiWwq6IiIiImJbCroiIiIiYlsKuiIiIiJiWwq6IiIiImJbCroiIiIiYlsKuiIiIiJiWwq6IiIiImJbCroiIiIiYlsKuiIiIiJiWwq6IiIiImJbCroiIiIiYlsKuiIiIiJiWT1ZWiouLo2vXrsTHx+NwOLj77rvp379/dpdNREREROS6ZCns+vn58cUXX5AnTx7sdjtdunShWbNm1K5dO5uLJyIiIiKSdVnqxmCxWMiTJw8ACQkJJCQkYLFYsrVgIiIiIiLXK8t9dh0OBx06dOD222/n9ttvp1atWtlZLhERERGR62YxDMO4ng1cvHiRfv36MWzYMKpUqeKevm7dOoKCgq67gFkRGxtLQECAVz7b7FS3nqO69RzVreeobj1Hdes5qlvP8VbdRkdHU69evTTnZanPblIhISE0bNiQ5cuXJwu7AGFhYde7+SzZsWOH1z7b7FS3nqO69RzVreeobj1Hdes5qlvP8Vbdrlu3Lt15WerGEBERwcWLFwFXgv/333+pUKFC1konIiIiIuIhWWrZPXXqFK+++ioOhwPDMGjbti133nlndpdNREREROS6ZCnshoaGMm/evGwuioiIiIhI9tIT1ERERETEtBR2RURERMS0FHZFRERExLQUdkVERETEtBR2RURERMS0FHZFRERExLQUdkVERETEtBR2RURERMS0FHZFRERExLQUdkVERETEtBR2RURERMS0FHZFRERExLQUdkVERETEtBR2RURERMS0FHZFRERExLQUdkVERETEtBR2RURERMS0FHZFRERExLQUdkVERETEtBR2RURERMS0FHZFRERExLQUdkVERETEtBR2RURERMS0FHZFRERExLQUdkVERETEtBR2RURERMS0FHZFRERExLQUdkVERETEtBR2RURERMS0FHZFRERExLQUdkVERETEtBR2RURERMS0FHZFRERExLQUdkVERETEtBR2RURERMS0FHZFRERExLQUdkVERETEtBR2RURERMS0FHZFRERExLQUdkVERETEtBR2RURERMS0FHZFRERExLQUdkVERETEtBR2RURERMS0FHZFRERExLQUdkVERETEtBR2RURERMS0FHZFRERExLQUdkVERETEtBR2RURERMS0FHZFRERExLQUdkVERETEtBR2RURERMS0FHZFRERExLQUdkVERETEtHyystKJEycYNGgQZ86cwWq18sgjj9CjR4/sLpuIiIiIyHXJUti12Wy8+uqrVK9encjISB566CGaNGlCpUqVsrt8IiIiIiJZlqVuDEWLFqV69eoABAcHU6FCBcLDw7O1YCIiIiIi1+u6++wePXqUHTt2UKtWrewoj4iIiIhItrEYhmFkdeWoqCi6d+9Onz59aNOmTbJ569atIygo6LoLmBWxsbEEBAR45bPNTnXrOapbz1Hdeo7q1nNUt56juvUcb9VtdHQ09erVS3NelvrsAtjtdvr37899992XKugmCgsLy+rmr8uOHTu89tlmp7r1HNWt56huPUd16zmqW89R3XqOt+p23bp16c7LUjcGwzAYMmQIFSpU4Mknn8xywUREREREPClLYXfdunXMnz+fVatW0aFDBzp06MDff/+d3WUTEREREbkuWerGcNttt7Fr167sLouIiIiISLbSE9RERERExLQUdkVERETEtBR2RURERMS0FHZFRERExLQUdkVERETEtBR2RURERMS0FHZFRERExLQUdkVERETEtBR2RURERMS0FHZFRERExLQUdkVERETEtBR2RURERMS0FHZFRERExLQUdkVERETEtBR2RURERMS0FHZFRERExLQUdkVERETEtBR2RURERMS0FHZFRERExLQUdkVERETEtBR2RURERMS0FHZFRERExLQUdkVERETEtBR2RURERMS0FHZFRERExLQUdkVERETEtBR2RURERMS0FHZFRERExLQUdkVERETEtBR2RURERMS0FHZFRERExLQUdkVERETEtBR2RURERMS0FHZFRERExLQUdkVERETEtHy8XYDssu9iDGM3H+GrPeFE2h0ErzpDt8rFGFizNBVDAr1dPBERERHxAlO07C48fJaac9YwbcdxLtkdGMAlu4NpO45Tc84aFh4+6+0iioiIiIgX5Pqwu+9iDJ3+3EZ0ghO7kXye3YDoBCed/tzGvosx3imgiIiIiHhNru/GMHbzEewOZ4bLxDucDF2zn5G3lcffZsXfZiXg8r9+VgsWi+UGlTZ3UhcRERERya1yfdj9ak94qhbdlBIMmLXvNLP2nU5zvr/Ngr81eQh2/WtJNi3ZdKuVAB9rivUsaSyb/vSU2/Kx5LzgvfDwWTr9uQ2740rLeWIXkS92n2TOXdVpV6aQdwspIiIiko5cH3Yj7Y7r3kacwyDO4YBs2Nb1sECmgnaALWXIzlzQdk9zB/X0P8PHak3WRSQluwH2y11ENneqrxbeLFKruYiIiGdZDMO4Srto1qxbt4569ep5YtPJhExfzqVMhFQrUC5vAHFOJ3EOg1iHkziHE7vTI18/17NdbmB2XKV6LECFkAAaF83nDtCJLd4BPilbsVO3gAekmpd8GR9rru9Wnq60Ws0BfC3ga7Oq1fw6pTqR8LXpRCKbqG49R3XrOapbz8kJdZtR7sz1Ybfvit1M23E8w64MvhZ4OqwEE++okmqe0zCIuxx8XQHYSPLeSZzTSWyCkzhniunpLZ90unvdpPONNJZ1TU/wzP+KXM1q4aqBODOh+XqXsVmzt3vJvosx1JyzJs1W80RBPla1mmeRTiQ8R3XrOapbz1Hdek5OqVtTh10zhQaH07jc8nz1cJwtwTyd5WMdTtTgnZztcujOTGhOe17yZeYcOM2KExfI6JqEjwXuL1uYgTVLY7WAxWLBClgsYLVYsHDlX4sFrFguzwMLlsv/pljWwuVtpDMfrvpZybafw/qYg7mOCTmN6tZzVLeeo7r1nJxUt6YOu5BzzirMJMHppOAX/2Sqi0igzcqnTaskC8uxKYJ66nlpL5PyvUJ37pA9wTlpYE9jPkkDu+vftD7r8KVYTsXar1rmEkF+VMkX5N4Ol8sIrs9MPe1KuHfPS7FO6mmZXMeSfD6X10tahivz01jnOsuddNl017HAkuPn2RYRRUbj31iBGoXy0LpkwWTrJy1T6umW1NPSWM+SZGq620qxzfS3lXSdjLebfFrq7V7teyVbNo2yAMzZf5qV4RevWre3F8vHIxWLZLCU9+S8U1+X7/ad4t9M1G2TW/LxaMWiN6pYpvDdvnD+OZlx3WZ0dT07mT7sguvsYtzmI8zcE84lu4O8vja6Vy7GAPXFybLr7SKSHRKcGQfilKE57XmX17vciu3uWpKii0l6y8Q6nChzi4iIZE2Ir40LTzb16GdklDtz/WgMiSqGBDLxjipMvKMKO3bsICwszNtFyvUG1izNF7tPYs/g8oSvzcqAmqU9VgYfqxUfK+TxtXnsM67GMFz9qZOH5NSBOL0AnlZIn7rzRKZujrRZoH6REAwMnAYYuPqZGwY4ufyvQer5cHmekWxasmWTbSON+Zn4LBERkavJjpGzrodpwq5kv4ohgcy5q/pVu4iYveXcYrHga7Hg62clbzZt0wCvt5pnByNFmL6e4JwsmBvgvLz9NOcnbj+Nz7rzl40Z9h9LFORj5ee7a7hDe+JFLgOSTLv87+Uyk3ReinUSl78yP411Lpcz3XXSKYN7nSTbS3OdVGW8ehlSzzeSfcek67z6337irjZEC66xy0feVt697aTbSiyX+72Rxnwj9bLpz086PY1l01gvrc9Pb72kFz/T3lbqsqa33bS+V+L8T7Yfz9QJsJ/VwtNhJa663I3moYvE2WJKJhsX/KwWeoUWvwElMo/MNtwEe7HBChR25SralSnE5k711UUkm+WEVvPskNiv1JqDblTrUeWWTJ1IPFnlFlqWLHDjCmYCuy/EZKpue1Utzsu1yty4gpmA3Wlkqm57hxZnQpPKN65gJuAkc40LvUOL5+jGhZwosw033SsXu2FlSot5BzGVbJPYReTCk03Z1rQYF55sysQ7qijoXofEVvMgHyu+KXKir8XV6ngztJp7wsCapfG1ZXxoyw0nEjmR6tZzVLeeo7r1nNxStwq7Il6S2Gr+dFgJQnxtWHB14n86rASbO9XXCCJZpBMJz1Hdeo7q1nNUt56TW+rWNKMxJKUb1DxHdes5qtvspRFaPEd16zmqW89R3XpOTqjbm2LosaQUGjxHdes5qlvPUd16jurWc1S3nqO69Rxv1W1GuVPdGERERETEtLIcdgcPHkzjxo1p3759dpZHRERERCTbZDnsPvjgg0ybNi07yyIiIiIikq2yHHbr169Pvnz5srMsIiIiIiLZSn12RURERMS0PPoEtR07dnhy8+mKjY312mebnerWc1S3nqO69RzVreeobj1Hdes5ObFuPRp2vTWsh4YU8RzVreeobj1Hdes5qlvPUd16jurWc7w59Fh61I1BREREREwry2H3xRdf5LHHHuPAgQM0a9aM2bNnZ2e5RERERESuW5a7MXzwwQfZWQ4RERERkWynbgwiIiIiYloKuyIiIiJiWgq7IiIiImJaFsMwDE9sOKMhIEREREREslO9evXSnO6xsCsiIiIi4m3qxiAiIiIipqWwKyIiIiKmpbArIiIiIqalsCtyAzidTm8XwbR024HkRtpvRW6cXBt2d+zYwcKFCxUisknSeoyOjvZiSczH4XBgtbp+1SIiIrxcGnNxOp1YLBZAdesJKY+vCmjZI3G/jYmJ8XZRTClxP3U4HMl+luuT9HiQWLe5Ra4Muz/99BMvvPACQUFB3i6KKRiG4Q5jM2bMYO7cucTFxXm5VOZhs9kAePfddxk+fDixsbFeLpF5JO63U6dO5Z133tGJWjZKPC4cPnyY7777LtmJhVwfq9XK5s2beeKJJ9i+fbu3i2M6FouFdevWMWTIEOLj47XfZoOkOeH3339n1qxZXi7Rtcl1YTc8PJyff/6Zjz/+mObNm7srX7Iu8UAwatQolixZQps2bfD39/dyqczlrbfe4tChQ4waNYqAgABvF8dUxo4dy7///suLL76oE+BsZLFYWL58Oc8884yOB9lsw4YNvPzyyzz55JNUq1bN28UxnYULFzJ8+HCaNm2Kn5+ft4tjCok5Yfz48UydOpU6dep4uUTXxsfbBbhWefLkwWazER8fj2EYOJ1ObDYbkZGRBAcHe7t4uVZ4eDiHDx/m008/JSAgAIfD4W6RlOvjdDqJjY1l6NChhISEEBcXh7+/P4ZhqMXhOsXExHD8+HFGjx5NsWLFiI+P1x+3bBIZGcmPP/7IiBEjqF+/vreLk6s5nc5kDTPHjh3joYceom3btu5uTonHAh0Xrl3KOtu6dSvDhg2jQYMG+luWjU6cOMH27dv5+uuv8ff3T7Vf52S5opTnzp1j+fLl7N69m+DgYPLly8fRo0eJiorCZrOxceNGvvrqKyIjI71d1Fwhrf5LgYGB+Pv7s2fPHuLj490Hh8OHD9/o4uVqSev2woULgOuS5YkTJ/jmm28A3K1kmzZtuvEFzMWS1m18fDzg2m8BFixYAOAOulu2bLnBpcv9UvbBCwwM5MKFC+7jakJCAqA+/dcqaZ/9xP02ISGBxYsXc/HiRWw2GxaLhaNHj+qSexY4HI5UdbZ3715Wr14NoD791yFln/2goCAiIyPdXW9y070oOT7sHjp0iMcee4w5c+YwZMgQVqxYQffu3Zk3bx5vv/02kyZNYuDAgVSrVk0tu5mQtN/d77//zoQJE5g3bx4hISEUKlSIdevWceLECQCmTJnCd999p5soMilp3c6bN48+ffrwzjvvcPz4cV566SX27t3L119/DcC4ceN4++23dYKWSUlbbn799VcGDx7MyJEjAWjVqhUXL15k3rx5gKv/7qRJkzh//ryXSpv7JF4hi4+P5++//2bz5s3YbDZq165NeHg44eHh+Pj4sG/fPiZOnJgr/rjlBIZhYLPZuHDhAi+//DKDBw9m9+7d3H777TRq1IipU6cCsHPnTp599ln+/fdfL5c4d0ms34iICD766CNmz54NwBNPPMGZM2dYtmwZVquV9evX069fP44ePerlEucOiQ0LVquVLVu2MGfOHP7991/y5cvHrbfeysGDBzl+/DgA8+fPZ8qUKTk+J+ToxwX/999/LF26lLCwMO677z6WLl3KyJEjmTZtGoGBgaxatYojR47Qtm1bKleu7O3i5irLli1j7NixdOzYkcWLF9OsWTMeffRR3n77bc6dO4fFYuH8+fNMmjSJggULeru4ucoPP/zA33//Tffu3fn+++8pUaIEd955J06nkyFDhlClShViY2P54IMP1Mf0Gv3111989tlnPPvss3z00UfUqlWLnj17snr1ar744gtuueUWIiIitN9mwenTp3nmmWcoWbIkW7duZciQIVSuXJnPPvuMM2fOUK9ePb777jv69etHhw4dvF3cXOPEiROMHj2aOnXqcOHCBXbt2kXv3r2x2Wx8/vnnREdHc/LkSZ599lnatm3r7eLmOgcPHmTYsGHceuutLF++nA4dOtCkSRN2797N+PHjufPOO1mxYgUvv/wyd911l7eLm6usWrWKoUOHcvfddzN37lzef/99ihcvzsyZM9m3bx/ly5dn/fr1fPTRR1SsWNHbxc2YkYPNmDHDaNOmjTFnzhwjISHBMAzD+Prrr417773XOHnypGEYhuF0Og3DMAyHw+G1cuYWiXX1+eefG88++6zxzz//GIZhGEePHjXatm1r/Prrr4ZhGMbevXuNpUuXutdT3WYssV4NwzA2bdpk3HXXXcZnn31mGIZhHD582Bg1apQxefJk48KFC0Z8fLxx4cIF9/KJ+7WkLWndLlmyxOjVq5cxdepUwzAMIyoqyujcubPx+eefG4ZhGOfPnzcOHTrkXl51m3l79+413n//ffd++88//xh169Y19u3bZ8TExBjz5883Pv30U2PNmjVeLmnOl3SfPX36tDFixAija9eu7mnjxo0zhg0bZhw4cMC9THh4eJrrS8YOHTpkPP/888YHH3xgGIZrP+7Xr58xZ84cw263G3v37jXWrFlj7N+/372O6jdzFi1aZLz66qvuLLB06VKjXr167mPs33//bcybN8+4dOmSYRg5PyfkyG4MCxcu5PDhw/To0YNHH32UlStXcurUKQC6dOlC3bp1mTt3brJ1cksnaW9I7HeTeBk4NDSU48ePc/DgQeLj4ylZsiSjRo1i1KhRLF26lIoVK9K8eXMgeX8zSS2x64LdbsfhcBAaGsoDDzzAwoULOXLkCKVLl6Zjx47s37+f2bNnEx8fT0hIiHtd3TiRvsS6NS5ffAoNDaVMmTLs3buX/fv3ExQUxFtvvcWsWbP47rvvyJcvH2XKlAHQTSlXkbJ/7rZt21i1ahXnz58nJiaG22+/nQEDBvDEE08QHR3N/fffT+/evbntttswDEPjlqYjZf/R/PnzU69ePRISEvj5558BeOGFFwBXN7GzZ89SuHBhihYt6q5T9dlNX1pjuwYHB7N+/XrCw8OpWLEiXbp04Y8//uDXX3+lbNmy3HbbbZQvXz7V30FJLmXdXrhwgQMHDnDgwAFiYmJo3rw5/fr147HHHuPixYs0a9aMDh06EBwcnCtyQo7qxhAZGclLL71EwYIFCQwMJC4ujlGjRvHSSy9RrFgxnnrqKV2azKJ9+/bx7bffUqtWLRo0aOC+o3LAgAGEhYVhtVqZN28ewcHButRzjTZs2MDnn39O4cKFufvuuylVqhTz58/n4MGDjBw5koCAAJYtW0bRokUJDQ31dnFzlT179jBx4kRKly5NixYtKFasGF999RW33HIL999/P4UKFWLZsmU4nU5atGjh7eLmCklPBP7880/Kly9P2bJl+f333/n333958MEHqVu3LhaLhUGDBhEUFMTw4cM1SsBVJK2fESNGkCdPHooVK8YDDzzAggUL2Lp1K+3bt6dBgwZERETwyy+/8Pjjj3u51LlH0jv/P/vsM/Lnz0/Tpk2Jiopizpw5BAQE8Mwzz+Dn58c333xDQkKC6jeTEus28d6HSpUq0aBBA+bPn8/WrVu57777qFu3LgAvv/wyDRs2pFOnTl4u9bXJUWH3888/p0CBArRp04Y+ffpQu3ZtBg4cyIULF+jXrx/NmzenZ8+e7gN1bhr2wptWrlzJO++8Q/fu3dm0aRPHjh3js88+46OPPuLw4cM8++yzlC9f3tvFzJU2bdrE6NGjeeutt/j222/ZsGEDEydOxOFw8MUXXxAVFcWYMWO8XcxcacOGDYwZM4YBAwawbt06vv76a5YsWcLKlStZuXIlJUqUoHPnzhoDNoteeuklHA4HhQsX5sCBA0ybNo33338fgLZt23Lrrbd6uYS5j91u57XXXqNIkSJ06tSJe+65h88++4xy5cqxaNEidu/eTY8ePahatap7HZ1EZF50dDQvv/wyZcuWxdfXl19//ZXp06dz+PBhlixZQtGiRendu7e3i5kr7d69m9dff53WrVuzZcsWDMPgww8/5N1338XX15c2bdrk6mNCjkqKFy5c4OjRo/Tt25c77riDgQMHuu+oHj16NK1atUp2aVJBN3P279/PBx98QOXKldm0aROdO3cG4Pnnn8cwDH7//Xddlsyic+fO8dBDD3H69Gk2btzIgAEDKFGiBEWLFqVjx46ULFnSPdyQXJvz58/z/PPP4+Pjw7Jlyxg2bBgBAQHceeedhIaG6il/12HOnDlUrlyZsWPHEhERQb58+QDo1asXZ8+e5c8//+TixYvu5XPbo0G9JSoqinLlytGtWzemTp3KQw89RJMmTShZsiQtW7akcOHC7iHcEinoZt7atWupUaMGAwYM4PDhwzRp0oRSpUrRqFEj6taty8GDBzl27Jj775n+rmXekiVLePHFF2nTpg0nTpzgtttuw2Kx8PTTT3P06FHWrl3rfr4B5L66zVEtu8uXL+eFF17gxRdfpGvXrgAMGTKEatWquX/WWfC1GzVqFFu2bMHX15dBgwZRs2ZNdu/ejdPppGLFivj6+nq7iLnWkiVL+Pzzz7Hb7bz11ltUrFiRzZs3s2vXLh5++GFvFy9XmzFjBr/++iuBgYEMHDiQmjVrsnPnTvbs2cN9993n7eLlajNnziQmJoa1a9dSoUIFXn31VWJjY7lw4QJOp5OLFy8ma32UqzMMg0OHDvHRRx+xf/9+2rdv725lnDp1Kj169MAwDF2JuA7Lli3jzz//ZNu2bbRp04ZnnnnGPe5rgwYNOHfuHAUKFPB2MXOlUaNGYbfb2bVrFz169KBdu3YcOHCAgIAArFYrwcHB5MmTx9vFzLIc1TTasGFD+vTpw9y5c1mwYAG9evUiNjbWHXRBZ8HXIvE8pnPnzkRHR/Pggw9Ss2ZN9u/fz6uvvsrevXvdQVctN1lTrVo18uTJw3333UdwcDAHDhxg+PDhXLp0ydtFy/UeffRRoqKiaNy4MTVr1uTIkSMMGzaMM2fOuJfJQefquUpYWBjjx4/ntttu49VXXwXgo48+4ttvv6VYsWIKullgsVgoV64cJUqUoFChQjz66KOA66rk8uXL3U9OlKwrX748W7dupXXr1jzzzDMAjBkzhq+//hq73a6gex1atGjBnDlzePbZZ2nXrh3x8fG89dZbrFixgmLFipEnT55UD5nITXJUy26ib7/9FqfTid1u54knngDUP/dqElu802r5djgcLFmyhLfeeotWrVqxevVqunfvziOPPOKl0uYeMTEx7qd0JSQk4ONz5QnbiXW9du1aFi1axIEDB4iKiuLRRx/l/vvv91aRc5XEOkz5mN/Em6h27drFG2+8QZkyZdi/fz+dOnXiscce82KJc7/EOp8+fTrff/89vXr14u+//yYmJobx48e793e5Nol/owzDYPDgwcTHx7tHW3j//ffTPT5L5iTW3aJFi/j2228pVKgQp0+fplChQowdO9bbxcvxMtr3Eud9+umn/PTTT7Rr146lS5dyxx13uEcPye1ueNgNDw8nLi6O4ODgTI+soGGE0pf0JGDdunUkJCRQv379NE8Mdu/e7W7BDQsLS7W+JBcVFcWiRYvcg2Xv2bOHjh07pllf8fHxxMbGEhcXR5EiRQB1uclI0rpZu3YtGzZsoGvXrskespH4e3/27FksFguXLl2ibNmygPbbq8nsMXPu3LkkJCRw7tw5+vTpc03r3ozSqpuk+2Li/Li4OOx2O0ePHnWPwKJ6vbq06iit4+iePXuIiYnh9OnTtGrVKt11xSUyMpLjx49TpUoVIiIi8PPzS/eJs3/99ReGYWC1WrnzzjsBcxxvb2jYXbJkCZMmTaJ+/fqcPXuWzp07U6dOnVTLaae9duvWreObb76hR48e1KxZ86rLa0zHq3M4HPzzzz+MHDkSi8XC559/TqlSpdJdNnGfVd1m3pYtW/jiiy948sknqV69eprLpDzQmuHA60mJ9RMREcG3337LPffck2q0lfROxHTsTVvS+jp8+DDz5s2jZs2a1K9f3315N6N9MuVVIUkuaf0eOXKEo0ePUrx4ccqVK5fucklpv83Y/v37mT9/Pk6nk4ULF/LRRx+lOt6mtw+b5Xh7w75BeHg4X331FVOmTKFixYrs2LEjzRujjMvPuj579iybN29Wn7xM2LZtG127dqVy5crUrFkzzbv/k/a1SRz4XGEsbYl3S9tsNhwOB4ZhULZsWW655ZZk8xMlfThEdHS06jYDifuhYRjs27ePV199lfj4+HQf953YwgC4n71uhgOvJyQeK61WKzt37qRbt24EBQUlC7opT8RS9tVXYEhb0iA2aNAgTpw4weLFixk9ejTgqvOUx9ikFHQzlli/27dvp0ePHnz77be8++67/Pnnn8DV91sdE9KWuE9WqFCB6OhovvzyS7p27Zpmw0JiF5yUoy2YpW5v2LeIjIykaNGiLF68mO+//54RI0Zw6623sn//fux2u3s5i8XChg0bePzxx8mXL59CQxpS/qJXr16dxx9/nG+//RYAPz+/ZMskPTP7888/+fvvv3USkYb4+Hji4uLw8fEhNjaWEydOcOedd/LLL79Qq1YthgwZQnx8PD4+PkRFRQHJnzA3e/Zs3n33XdVtOpLWld1up2LFigwYMIDTp0+zf//+VMsnPkEN4LfffuOzzz7TjZTpSNriFR8fz+zZs+nSpQtPPvkk+/btY+HChRw4cACLxeL+A5h4khYTE8O///6bq28+uRGWLFnC0KFD6dq1K2PGjKF3797Y7XY++OAD4ErgTazXM2fOMGTIEE6cOOHlkudcW7dudQ9xN3/+fH766SfGjBnD+PHjuffee/niiy/YunUrFovF/buf2CAWHh7O5MmTsdvtyglpSPlUs06dOvHUU09x+vRpNmzYkGajTWJDzbZt2/j7779vdJE9yqNh1+FwsGrVKgAqVqxIREQEU6ZM4YsvvqBOnTrs2bOH6dOnc/r0afc6P/30E++//z5ffPGFu3+eXJH4i37ixAnGjRvH559/jsPh4LXXXqNy5cruO1RtNhtOpzPZDj9lyhS+/vprmjRpooNDCmfOnGHcuHFs27aNw4cP8+CDDzJ8+HC6dOlCQEAADz/8MD4+PkyYMIGVK1cybdo0Lly44G4JGzduHKtWreK1115T3abDZrNx8uRJevbsyYcffsiwYcNo2rQpTZo0Ydq0aRw7dsy9bMr9du7cufTu3Vstj2lIGnSnTJnC7NmzqVGjBl9//TX9+/dnypQpzJ8/n1dffZWYmBh3C47VamX//v107dqV4OBg07TgZJeUJ1ZhYWGcPHmSrVu3AlCiRAl69uzJzp07mTJlCuAKvFarla1bt9KnTx8effRRihcvfsPLnhvs2bOHzZs3ux+fHh4ezo8//uj+HW/RogVt2rThgw8+IDw83D09sUGsb9++tGjRQkNnpiHxhCs+Pp4BAwYwYcIETp06Rf/+/bFYLCxevJhz585x8uRJdu/enewK2rx58xg2bJj7iWlm4bE+u6dPn6Z///7Y7XbKli3Lk08+yYkTJ1i0aBGVKlWiQoUKTJw4kUceeYQuXboArnE1165dywcffJDszmxJbsOGDYwcOZJevXoxe/ZsgoKCePPNNylcuDAPPvggYWFhvPXWW8nWef311wkKCmLQoEH6o5aOt956C5vNht1up0aNGnTs2NF9087kyZM5ePAgH374IXv37mXkyJHUqVMHwzB4/vnnqVKlCv369VPQTSHpKCERERH07duX3r17kydPHgYPHszbb79No0aNGDp0KE6n0/248ESvvfYaISEh2m8z4eOPP+bcuXP07duXAgUKsHDhQkqUKEFYWBgXLlxg7NixDBs2zD1W5rJly5g0aRLvvPNOqr6RN7ukfUC//vprSpUqRa1atTh79iy9e/dm9OjRNGrUiISEBP7991+OHDniHiLzl19+4csvv2TixIkULVrUm18jV/j2228pWrQorVq1YuTIkRw/fpxPPvkEcD20Z9KkSdSoUYMOHToA8OOPPzJ37lw+/PBDChcu7M2i52hnzpxh1qxZnD17ltKlS7Nr1y5atWpFkyZNePvttwHXFbMxY8Zw1113ATB+/HgOHDjAO++8Y7oM5pGwGxkZyX///ed+utSoUaMICQmhadOmBAYGMnPmTGw2G23atOGOO+5wr7d9+3bCwsIUGFJIOqSNxWJh3LhxNGrUiBIlSvDCCy/g7+9PaGgow4cP5+zZs/Tp04fp06cTGBiI1Wrl7bffpkKFCu5xH+WKpF08oqKi+PDDD9m6dSt9+/aladOmAHTo0IFGjRoxePBgnE4nUVFR5M2bF4fDwfLly7Hb7bRu3dqbXyPHMwyDqKgoZsyYQfv27Rk4cCCPPfYYDz/8MLGxsSQkJPDuu+8ycOBA99O85s6di81mo2PHjt4tfA6VtEX30qVLdOvWjZCQEGbOnJksrK1du5b333+fpk2b0q9fPwB+//13Zs2axfjx48mbN6/XvkNOk/RGsoiICJ5++ml3n+e4uDj69evH8ePHef/995k4cSLly5dPdgw5cOAA33//Pc8//zwBAQFe+x45WeJ+Gx0dTVBQEJ988gnHjh2ja9euhIWF8eSTT1KpUiWGDBkCJB/+cdWqVcyePZvRo0drzOIUEus1ISEBu91Ou3btqFatGh9//DHR0dEsX76cP/74w/246o0bNxIYGOi+oX3ixInY7XZeeOEFc2YwI5t9//33xr333ms88sgjxqRJkwzDMIyTJ08aw4YNMyZNmmScPHnSMAzDcDqd7n8TEhKyuximkbRuzp8/bxiGYTgcDuPkyZNG586djV27dhlRUVFG7dq1jbFjxxqGcaVuE0VGRt64AuciSev2p59+MlatWmWcO3fOGDRokDF9+nTj2LFjhmEYRkREhFG1alXjr7/+ci/vcDgMwzAMu91+YwudSyTWj2EYxqJFi4yhQ4ca8fHxRps2bYwmTZoYO3fuNAzD9f/ggw8+cO/bhnFl/42Li7uxhc6FTp48aezbt88wDMPYu3evcffddxtz5841DMNVtydPnjQGDx5szJ8/P9l627Zt03E3hS1bthi//fab+3d66dKlxujRow3DMIyzZ88aCxcuNPr37284nU7j7bffNrp165ZsPzcM136f8vgrLknrZd++fcbo0aONpUuXGoZhGOPGjTNGjhxpnDx50rh06ZLRtGlTY+bMmam2cfbsWdVvGpL+Lifuv4sWLTKqV69u7N271zAMwwgPDze+++474+mnn3ZPM4wr/1+OHj16A0t842XrLaJLly5l1apV7r6Pn376KY0bN6ZOnTp07dqVcePGUbVqVYoVK+Y+c7BYLOqHl4HEunn99ddxOp2EhYXRokULfHx8sNlslCxZEl9fX+rWreseLSDlWVlufsSfpxiX+z4nJCQwYcIENmzYQL58+XjllVfo1asXkydPJn/+/DRt2pRChQqxdOlSd/3ClTtUdZd12pJ2OahatSrLli3j6NGjDBgwgOnTp+N0Ojl48CDDhg2jatWq7tZcuLL/mu0y2vUyUgy7tHDhQqZOnUrlypXJly8fnTt35o033mDMmDGULl2a+vXrU6hQIYYNG+ZuGUts8a1WrZq3vkaOZbfbqV+/PhcvXiRPnjycO3eONWvWAFCwYEGqVavGwoULOXbsGK+88gqnTp1K1bVGXW3Sl7jvfvfddyxatIjw8HCOHj1KwYIFefrpp3n//ff57rvv6NWrF1999VWafXEzOzb/zSTxbxnAJ598QkREBLVr16Zdu3a8+OKL9O7dm99++42iRYtSv359QkJC0uy2VLJkyRtc8hsr234zN2zYwNSpUwGoXLkyHTt2pHv37owZM4aTJ09StWpVBg0a5B4AWjInPj6egQMHUrJkSR5++GE++eQTNmzYgL+/P9WrV+eFF17gwQcf5J577nH3fZb0Xbp0yX3XaUxMDD179sTX15fu3btToEABPvvsM4oVK8aDDz7In3/+yfr160lISHD3vdNoAJkTHx9P3759mTRpEoGBgYSEhHDw4EHatm3L/fffz+jRo3nrrbdo2bIlQ4cOBfTo36vZtm0b4NoHd+3axbx585g1axZ16tRh+fLlADRu3JiePXvy4osvEh4ejo+PT7InoqlhIbXE0QDq1KnD+fPnGT58OP/99x9t27alatWqTJgwAYAyZcpw/vx59+OqixYtqhEsMmHlypXu9zt37mTu3LmMHz+eadOmUalSJRYuXMiFCxd46qmn2L59O//88w9lypShePHiOt5mQuJJxODBgzlx4gQNGzZk8uTJ/PDDD/Ts2ZMGDRrQuXNnwPW45bZt27pvYE+6vtldd9hNHPuyTp06dO7cmYSEBNauXYvT6aRLly7UqlXL/bi5xL5POkBkTnx8vDto3XPPPcyYMYN27drRvn17goODefLJJ3n44YcZPHgwDz30EKC6TY9hGO6+don77MWLFzEMg2effZbWrVvTuXNnHA4H06dPp0mTJtx1112UL18eHx8fd4uNwsLVxcXFYbVa8fX15a+//mLLli2cPXuWGTNmEBMTQ9euXZk6dSrjxo3jySefBJIPMyap7d27l06dOrFu3TpsNhsWi4Vy5coxY8YM5s2bx9ixYylfvjzh4eF07NiRoUOHUqxYMW8XO8fbuXMn//77LwcOHOD9998nICCAatWqsXTpUrZu3Ur37t1Zvnw5r7zyCo899hiVK1emdu3a7vXVkpuxo0ePsmXLlmTT7HY7CQkJFCtWjPbt27Np0yZmzpxJwYIFef3115Pd/6DjbeacOnWK/PnzM2LECFasWEGVKlV44IEHAHj77bcxDIN///03WYPCzbbvXte3Xb9+PX369GHixIn8+uuvtG/fnrCwMH777Td27NgBwKBBgxg+fDhw5QziZqvkrNi3bx9ffvklcXFxnD9/nu7du1OnTh13p/2kN/ndfvvtQPIB+CU5i8VCiRIlGDRoEIcPH2b//v0UK1aMPHnyMGPGDMA1tFC+fPnYvn078+bNo2PHjlSqVMm7Bc8Fkh5ADxw4wHvvvcfevXt56qmn3EOIlSlThrVr1zJ58mQAAgIC3I+r1H6bMbvdTqVKlRgxYgQvvvgiERERlChRggMHDrBs2TI++eQTqlWrxpo1axg3bhwXL17k7rvvBnTyezUlS5bkt99+o3fv3hw4cIASJUrw9NNP4+fnx99//03evHn5/PPPefDBB3nmmWfcVyFUr1dnt9spVaoUTz31FC+++CIbN26kSpUq3Hbbbfz555/uh8nUqlWLc+fOsWrVKkqUKAGofjOS9Hib2HBTsGBBDh06RPPmzcmfPz9jx47FZrMxf/58YmNjmTt3LrfffvtN3aCQ5b8wK1as4I033uCFF15w32X9448/0rt3b3x8fPjxxx85cuQIvr6+7meDS/pS/nLv37+fv//+mwIFCnDrrbfSpEkTd/AaPHgwK1asSNUX92bekTOS9FJYYridNGkSp06dolOnThw8eJBZs2YBuIPE1q1bdQktExKfxpcob968VK9enZdffpl8+fLRtGlTzp07R7du3ahRowYJCQmp6lX7bcZ8fX05ePAgMTExBAUF8b///Y/g4GCaN29OaGgoc+bM4ffff2fEiBE0aNDAPW4pqGEhLUnDQt68ealWrRp58uShXbt2REZGYrVaeeqpp7h48SLff/89Fy9epGHDhtx5552AeR6f6imJ9evr68vGjRvZsGED5cqV46233uL8+fPUrVuXLVu2MGLECGbNmsXWrVvJmzcvixcvdm9D9Zu+xOPlDz/8wJAhQ5g8eTI2m43GjRvToEED2rZtC8B7773HN998Q0xMjHudm7mrWJb3qNjYWD799FOioqJYsWIFrVu35q+//mL9+vU8/fTTFCtWLNkNPZKxxF/uI0eOANC6dWvy5s3LL7/8QocOHahWrRqfffYZzzzzDIZh8NlnnxEYGHhT77yZkXQIpk2bNmEYBmPGjMHHx4fvv/+eKlWq0KpVK2bNmkXXrl0pU6YMPXv25MSJE+6zZklf0odqDB8+nFWrVtGuXTv69+/PuHHjOHnyJOvXrydv3rxMmDCBl19+WZcmr9Hx48d55plnyJcvH++++y7+/v7079+frl270qpVK44fP84///zDsGHDePDBB71d3BwtaXeZbdu2cfHiRbp27cqIESNYunQpy5Ytw+FwULhwYR566CFiY2MJCgpKtg0FsYwl1u/KlSuZOXMmUVFR9O/fn7CwMF5//XVatGhB9+7dyZcvHzt27HCPo+vv75/mo+7FJWkjwc6dO5kzZw6dOnXit99+46uvvqJWrVrUrl2bF154gX79+rF7926mT59OgQIF3OvdzA0L1zzO7siRIylWrBh16tShSpUq7tbdcuXK8dhjj+Hn58e7777rDrop7yCW9M2aNYs1a9ZQqlQpBgwYwOzZs4mPj6dr167Y7XYcDgfnzp1zP5EnaZCT9IWHh/Paa69RtGhRbDYbTZo0oWHDhrz00kvuvrqRkZHuPtIvvPACd9xxB3379vV20XM8h8PBgAEDyJMnD/Xq1WPLli1YrVYGDhzI0aNHmTt3LjNnzmTRokWUKlXK/dhP7bfpS1k/4eHhjBs3zj0QPMBdd91Fhw4deO6554Ar48PqeJs5EydOZNmyZdStW5fg4GCeeeYZFi1axLJly2jVqhULFizgscceo379+tpXMynpfhsREUGXLl2oXr06Y8eOdS/Tu3dvihcvzogRIwDXfSmfffYZf/31Fx999JG7G4Mkl/RqwoQJEyhYsCB+fn48/PDD7Nq1i9GjR/PII4/Qrl07Tp8+TWRkJBUrVgSUExJl+hQ1MjKS9957j6ioKE6dOsXChQv54Ycf2L9/P/ny5ePQoUOUKFGCZ599NlmLrg68mde2bVsGDhzI1q1bmTJlCsuXL+enn34iKioKX19fAgIC3EE38XGAkrG4uDimTp1Kly5dePrpp9mwYQMXL16kYMGCvPjii8yaNYvffvuN4OBgfHx8mDRpEg8//LCCbjpSnhufO3eOuLg4xowZQ6dOnejevTuBgYGsWLGC0NBQhgwZwrx58yhdurT7WKD9Nn1Jf69PnjwJuFoSN2zYwObNm93LdezYkUmTJrFixQrgyhB4Ot6mLWmr2Ny5c9mzZw/ff/89Z8+eZcmSJUycOJF77rmHRo0asXz5cmw2G40aNdK+mklJ99uff/6Zbdu2ubvb7dy5073chAkTWL58OUuXLgVc/198fHyYNWuWgm4GrFYrMTExvPnmm2zbto1Nmzbx9ddfc+TIEapWrcpTTz3F9OnTWbJkCcWKFVPQTUOmWnbj4uJ4/vnncTgcTJ061d3hOTY2ll9++YWCBQty8OBBBg0a5L4xQi0M6UtvB0yss3PnzrFv3z7mzp3LypUrGThwIPfdd58XSpr7JN3vtm/fzvz584mLi6NBgwZ89dVX3HPPPXTr1o3w8HCKFSvGP//8Q/ny5d0H2osXLybr8yhXJK3biIgI95iXvXv3pkmTJjzxxBMAvP/++wQGBrqf1pXW+pJa4nHB4XDQr18/d//G7t27s3HjRkaPHs2ECRPw8/Pjp59+StaPVNKXuN9FRkaycuVKgoODqVGjBp988gl79uyhe/fuzJgxg5YtW9K1a1fi4+PdYzyrf27mRUVF8dRTT1GmTBkOHTpE27Zt2bZtG0ePHuXjjz92Hy8uXLiQbFxtyZhhGJw9e5Y333yTQoUKMXz4cC5cuMDMmTM5dOgQI0aMIDAwkO+//57KlStTp04dbxc5R8rUb7G/vz/t27dn3bp1bNq0iYCAAJo1awa4xnV87LHHmDVrljvogloY0vPLL7+k22posVgwDIMCBQpw2223MXz4cNq3b8/BgwdvbCFzqZRhKiEhgYCAAOLi4hg+fDh9+vShW7dugKuFYe3atTRp0oQSJUq4W34UdNOWtK/jjBkz6N+/P6+//jp//vknjz76KMePH+fHH38E4Pz58+6WxqTn0jompC1x7GebzcbJkyf5/PPPadSoEW+99Rb58+dn/PjxNG/enH79+vH9998zdOhQypQp4w66upEyYxaLhTNnzvDBBx9w/vx5GjduTHx8PKdOnWLKlCk0bdoUwzBYvXo1R44cUdDNolWrVlG+fHneffddPv30U/z8/KhTpw6NGjXimWeecS+XeIzVfpu+pDesWywWChcuzC233MK+ffs4evQoefPmpUOHDoSEhLi7hDzyyCMKuhnI9G9y+/bt6d+/P6NGjSIiIoLSpUtz2223Ub58eZo2bUqRIkV0s1QmtG/fnvPnzzNu3Lg05ycGAqfTib+/Pw888ADr168nMjLyRhYzV0qsuwkTJvDll18SGhrKqVOnKF++PI8//jgLFixgy5YtPPHEExiGwW233eZeV5d6Mpb4R3/GjBls27aNESNGUKJECdatW8fu3bu54447mDp1Kn369OHcuXPuP24KuBkLDw/nvffec98MOX/+fMaOHUv9+vWpWLEirVq1okSJEnz44Yfcd999jB49mmnTpiV7gIz23dQS/xY5HA5OnTrFY489RlxcHJ06dQJcx9e///6bP/74gy+//JICBQowZMgQSpcu7d6Ggu61CQoK4sCBA4SHh5M/f34aNWrE3Llzuf/++6lTpw4nTpwAUHemq0g6FOOMGTOYMWMGixYtYsiQIRQtWpQffviBmJgYSpcuTceOHSlZsiTx8fHKX1dxzTeovffee2zfvp3p06cnm65LlBlL2koQHh7OE088QY8ePXjsscfSXD5xSKfx48cTERHhPnuT9BmGQVRUFM8//zz79+9n9OjRbNmyhZ9++omPPvqIOXPm4HQ6KVKkCL169QLUenM1SX+v169fz4ABA+jcuTN9+vThwoUL/PPPP2zcuJFXX32ViIgIYmNjKVWqFKD+Yldz6NAhypYtS3x8PLt37yZv3ryULVuWvn37EhMT4z7Gbtu2jW+++YaKFSvSs2dP9/8THXPTltZ+99VXX/Hll18yc+ZM98M2/vzzT3766SdiYmIYPXq0u8FGdZo158+fZ+rUqZQrV46HHnoIq9XK//73P/r168ett94KKCdci9dee43o6Gg6dOjAmDFjGDJkCFWqVGHYsGG0aNGCRx99FB8fH9VnJl1z2I2JiWHAgAE88sgjtGzZ0lPlMpXEX/AzZ85gt9spXrw4O3fupG/fvowcOZImTZokOwgkPVgn/kGUjJ0+fZpFixbRvHlzzp07x+DBg7n33nspVKgQH330Ec8++2yqEwsF3Ywl7pPR0dH4+Phgs9mYPn06CxYsYNy4cZQrV47jx4/zyiuv8NFHHyV7br3qNmOrV6/m66+/pnPnzjRu3JghQ4YQHR3NqFGjyJMnD/fddx+NGzfmtddewzAM9uzZQ4UKFdzdQyRtSY+jY8eOxTAMypYtS6dOnXjrrbc4evQokyZNch9fo6Oj3UOL6eTs+q1atYpffvmF48ePY7FYCAkJSfcqpiSXdN+9ePEi06ZN48UXX2TEiBGcPXuWt99+m8DAQDZv3szw4cN5//33qVChgpdLnXtcc9gF1w1r/v7+niiPaf31119MmTKF0NBQgoKC6Nu3L8uWLWPChAl8/PHHlC9fHsMw3H33nE4nzz77LM2bN+fRRx/1dvFzvNOnT/Pjjz+ycuVKXnvtNbZs2YLD4aBBgwY8+eST1KtXj1GjRrn3W7UwZM7ixYv59ttvKVy4MHfffTeVK1fml19+YdWqVYwYMcJ9V/DEiRMpXLiwt4ubq0yYMIHz58/TrVs3ypQpwyuvvEKRIkV45ZVXiIiIoE2bNjz77LPuG/9AJxHpSRpUExISeP755yldujT169fnww8/pG/fvrRr145evXpxyy23MGrUqGTrq16zT2RkJP/99x8Wi0UP4siEpH+LTp48SWxsLHny5KFnz57kyZOHOnXq8MorrwCwcOFC2rVr577BWjIvS3tf0sAgaQsPD3e/379/P1988QWTJ0+mdOnSrFixghMnTnDPPffQvn17evfuTVRUFBaLBZvNRnh4OL169eK+++5T0M2kIkWK8PTTT3P//ffz6quvsn79eo4dO0bZsmX55ptveP3115OdoCnoXt3KlSuZPn067777Ln5+fnzyySf4+/vTsWNH/P396du3L+vXr+fjjz9W0L1Gdrud//3vf/j7+zNnzhzOnz/Pa6+9xu7du/niiy8oWLAg06ZNc98slUiBIbXjx48zZswYjh07BrhaxSpVqsSrr77Kzz//TN26dWnXrh0AH374IRs3bmTv3r3JtqF6zT7BwcG0bNky2Q2Uqt/0Jf4t+v3333nuuec4duwYRYoUoU2bNkRHR7uD7uTJk/nuu+84e/asO+gqg2Xede2BCgxpW758OQ899JA78MbExFC5cmWWLl3K77//zsiRI6lcuTLh4eH069ePxx9/3H0pbfPmzfTs2ZNBgwa5D9CSeQ888ADPPfccMTExTJ48mf3793PLLbeQN29e3f17FSnrJ7G/2D///MPOnTsZOnQoRYoUoUCBAgwaNIiGDRtSuHDhZN0XJHOef/55vvzyS3r16sW5c+eYO3cuwcHBvPzyy8ydO5dff/2V2rVrJ7sRTdJWokQJjh07xqxZs7h06RL+/v4sX76cFi1aUKtWLd58803ANb6un58fc+fOdT96XTxPXUOubuvWrUyfPt3drRGgc+fO1KxZk0ceeYQBAwawZs0a3nvvPQoVKuReTxks83S6lc2cTidNmzalXbt2jBgxgvj4eMqWLcuuXbuYMWMGn3/+ObVq1WLr1q1MnTqV6OhoHn/8cfdOGxcXxxdffEFoaKiXv0nu1aJFC4YMGcKnn36arE+TDrrpS7wMnJCQwMyZMwFXq8HcuXP58ccf+eSTT6hZsyYrVqzg+++/p3z58rRr145Vq1axevVqL5c+Z0s6jFCizp07Ex0dTcGCBXn44YfZu3cv8+fPJzQ0lMGDB1O7du0bX9Bcxul0ulu2Ro0axY4dO/j+++/x9fXloYceomrVqu4Gg+HDh/PLL78QHx+vK5PidSmPCfHx8RQuXJjQ0FB3d8bChQszatQo3njjDR555BE+++wzihQpokabLFLYzWZWq5UzZ86QN29eNm7cyEsvvUSePHlo1aoVdevW5Y8//mDFihUMHTqUSpUqpXruev369XVJOBsULFiQ5s2bA2mHDUnOZrNx5MgRevTowZ49ewCoVasWBQoUoEWLFpw7d461a9fyzjvvEBgYiNVqpUaNGowcOZKGDRt6ufQ5V9JLuN988w2HDx/GMAxuueUWli1bxrlz56hTpw5t2rThjz/+YM2aNTRu3JgSJUpov81AYr1aLBYiIiIoVKgQQ4YMYenSpfz55580atSIxo0b061bN/dweNOmTSM4ONi9DbWKiTck9l+22+3Y7XYcDgfBwcH4+flx/PhxwJUjtm/fzj///EP16tVp3LgxoJsor0eWblCT9B05coT//e9/9OrVi9KlS/PKK6/w0EMP0aNHD/777z9+/PFHfHx86NixI02bNvV2ceUmlvTGiOjoaCZNmkSVKlXo0KEDcXFxnDp1iqioKBYuXMiePXuIjo7mqaee0n6bSSmfZ79o0SIqVapEyZIl6datG7NmzcLPz4///e9/AKxduzbZ2M+SMbvdzmuvvYaPjw+FCxemU6dOHD9+nE8//ZS+ffvSoEEDjh8/TlxcHOXLlwcUFsS7Eve/AwcOMHjwYBo0aEBUVBTDhg1jxIgR+Pr6EhYWRoECBXjnnXcYOnQot99+u7eLbQoax+Y6pXWXaeXKlbn//vuxWq3MmjWLe++9lyJFitCpUyeaNm2K0+nE19fXfRlNLQxyoyXdbxcsWMCFCxc4fPgwTqeTVatW4ePjw88//8wrr7zCgAEDiIqKwjAMgoODtd9mktVqJTo6mqeffpqKFSvy008/sX//flauXMn//vc/ypQpk+yJfYlBV3euX118fDwvvvgijRs3pkaNGgwePJiSJUvy2GOPceDAAcaPH8/QoUOTdQdT0BVv2bJlCzVq1MBms7F9+3ZeeeUVXn75ZaxWKwMHDiQgIIBhw4bx7bffsmbNGs6fP88bb7yhq2bZSGH3OiQ9eEZGRuLj44O/vz8RERGcOHGC4sWLU7hwYR544AGGDh1KnTp1qFixonsdhQXxlsQwtXLlSn7//XfGjx9Ps2bNmD17Ns2aNaNFixZ06NCBmTNn8sADD5AnTx5AQ7Zdqx07dlC6dGn3TVLlypWjQoUKVKlShTVr1jBlyhS6du1K1apV3eso6KaW9ATAMAwcDgclS5akVatWDB06lLvuuovHHnuMyMhIunTpQkRERKpgq6Ar3hAREcFnn31G2bJlGTBgAEFBQbz33nsYhsErr7zCsGHD+Oyzz/j666/dj7OPjY0lICBAJ77ZSLV4HRIPngMGDOC1116jZ8+exMbG0qBBA0aOHMnWrVs5cOAAFouF119/nYoVK3q5xHKzS9pradmyZQwfPtzdR7xUqVIMGDCAdu3acfr0aT755BMqV65MQECAex0F3WtjGAY7duxwj8xitVoxDIPQ0FD69u1L37592bVrl5dLmbMl/YO/aNEi/vnnHwIDAzl27Bjt27fnwQcfZMCAAQB88cUXHDp0iGeffZbKlSt7s9giAOTLl4/HH3+cgwcPMnv2bMqVK0flypWZOXMmTz31FO3bt6dOnTqMHj3afbNvQEBAsscGy/VTTWZR4h2Tr7/+Onnz5mXcuHHcdddd9O/fn65du1K3bl2+++47+vfvT6lSpdxDCOmmE/EWp9OJxWIhJiYGgGbNmtGlSxcOHz7svjECYN++fQwePJhmzZrx7LPPArpzPauqVq1KkyZNWLZsGQkJCYBrZIAffvgBcD2COemY3JJa4h/88ePHM2nSJIoVK4ZhGHTq1Ik77riDhIQE4uLieOGFF9iyZQslSpTwcolFrgzlaLPZqFGjBh07dmTRokUsWbIEm82GYRj4+fm5x3yeMGFCsm4LaljIXurGkEV2ux0/Pz+KFi1KtWrVsNls9OzZk+PHj/Paa6/x8ccfA7i7MwA6UxOvslqtbNy4kY8++ogyZcpQrFgx+vbty8GDB/n888/p27cvBQsWpGLFirzzzjuUKlUKUB/S65E3b16aN2/OTz/9xLx58/D19aVYsWL06NGDyMhIwsLCePzxx71dzBwp6X63c+dO1q9fz3fffeceOqxevXr4+/vzwQcf8Oeff1K0aFGGDh0KqLuNeJdhGNhsNk6ePMnRo0cpWLAgd955J+fPn2fWrFmEhobSrFkzFi9ezNixY3nqqado1aoVoL7lnqLRGDIp6cFz9uzZ/PHHH4SGhrJ161buuusuunbtCsCZM2f48MMPGTFiRLKAoIOveEPS/W7Pnj0899xzDBw4kODgYObOnUtISAivvfYaL7zwApUqVaJXr17JhmfSfps94uLi2LRpEw6Hwz2MkKQvMehGRkYSHBzMoUOHGD9+PG+88QZ+fn7urjWJrWdxcXHuYRwVFiQn2L59Oy+88AJ169Zl+fLljBgxgurVq/PLL7+wc+dOhg8fTnBwMMePH3dfjdDx1nPUXJMJDofDvQOGh4ezfPlyHnnkEQoXLszBgweZPn06s2fPZu3atbz33nvY7fZUO6x2YPGGpPtdQkICd9xxB61bt6ZRo0aMGDGCQ4cOsX37dvr37090dHSy/rkp15es8/f3p0GDBsnGy5T0Wa1WNm3axOOPP8769esJCAjg+PHjbNmyxb2Pzps3j1mzZuFwONxB1+l0KuiKVyRtN/zvv//4+uuvefnll3n77bcZNmwYs2fP5uzZszz22GP4+/szdepUAAXdG0TdGK4iaSvBhAkTiIyMpGbNmrRu3Rq73c6lS5eYO3cu4eHhbNiwgZCQEIYMGeLlUsvNLCEhgVmzZrnv7B05ciTFihWjQIEC/Pbbb3Tq1InQ0FCCgoIoX768e59+7bXXvFzym4cCWca2bdvGK6+8wksvvUTdunUB6NmzJxMnTmTp0qXExcWxYcMGxo8fj5+fn3s9dbcRb0h5NWH79u1s2bKFSpUq4XQ6adu2Lbt27eK9995jxowZPPfcc6keHqWg61kKu1dhs9mIjIxk0qRJnDt3jqioKE6cOMHu3bupUqUKPXr0YMeOHZw+fZrRo0e719OlNPGW8+fP89VXXxEVFUV0dLR7nwW49dZbGTRoEGPHjiU2Npb169dz5513utdV64J4Q8rj5ZkzZ2jevDl33XUXCQkJWCwWWrduTZEiRTh16hQHDx5k0KBBBAcHq0+5eFXSfXfUqFE0atSIHj16cOHCBU6dOsXWrVupWbMm7dq1Izw83P0ERdD9EDeS+uxeRVxcHM8//zxOp5MpU6Zw/vx5xo0bR4kSJbj33nspVaoUx44d4+jRo+47KbUDi7dt27aNkSNHUrhwYSZOnEhMTAw//PADERER/Pfff5QvX55du3bxxBNP0K5dO28XV25iScPCL7/8QokSJciXLx8vvfQS48ePp3Tp0gAcOnSI4OBgChUqlOa6IjdSVFQUvr6++Pn5cenSJXr37k2dOnXo378/gYGBXLhwgQ8//JADBw5Qv359Fi9eTMeOHXVDqpcokV2Fv78/7du3Z+3atWzcuJH8+fPz6KOPcuDAAX7//XciIiIoWbJksiFDFHTF26pXr07Pnj1ZtmwZmzZtIjAwkGbNmhEUFET16tXp0qUL06dPdwddnfOKt9hsNs6fP0+PHj3YsmULY8aM4csvv6Rly5ZMmzaNs2fPsn37dp5//nn3OKRJ1xW50fbs2cPs2bOJjo4GYOPGjRQvXpxXXnmFvXv38tVXX7Fy5UoGDx5MoUKFOHfuHAMHDlTQ9SK17GbSjBkzWLBgAZMnT6ZQoUIsWrSIXbt28fTTTyfrMyaSkyTut59++ikFCxZk8+bN7Nixgw4dOqS6GU3EG5xOJ2+++SahoaF07tyZLl26cP78ebp168a+ffs4ePAgERERPPPMM7Rt29bbxZWb3IYNG1izZg0tW7YkJCSEiIgIihcvTpcuXQgICKBWrVrYbDZ++OEHvv32Wy5dusT3339P48aNueuuu5KNdiM3jsLuNXjvvffYvn0706dP93ZRRDItvf1W/XMlp7h48SIJCQkMGDCAjh07UrVqVZ544gleeukl7rjjDoKCgsifPz+g/Va8a8mSJSxfvpyKFSuydu1aypQpQ7du3bBYLBw/fpyaNWsC8PLLL9OtWzdq1arF7NmzOXr0KH379nWPEy03lq63X4Nnn30Wf39//vrrL/c0nStITpfWfgu6+1dyjpCQEM6fP4/NZuOBBx6gWrVqVKhQgZMnT1K0aFHy58/vPtZqvxVvSHwC4p133omfnx/79u2jZs2anD17lgULFuBwOKhZsyY7d+6kR48e2Gw2atWqBcDDDz/MgAEDFHS9SC271yguLk47rOQ62m8lp7t06RJvvvkmgYGBhIeHU6NGDZ577jlvF0vE7eLFizz33HNUrlyZrVu3cuuttxISEsLJkyfd42ivXbuWY8eO8fTTTwOukOzjo4GvvE1hN4t0KU1yI+23kpPt27eP3377jaCgIJ588klAIy5IzvHrr7+6H/EbERHBnDlziI+Px2q1sm/fPu6++25atWrl3l81MlPOof8LWaTAILmR9lvJySpWrEi/fv3cQVdPRJOcpECBAhw7dozw8HAKFixIy5YtWbhwIdHR0ZQuXdp9c1oiBd2cQ/8nREQkxzEMQ2FBcpTQ0FDq1q3LsmXLMAyDSpUqUbFiRUqUKEHfvn0pVqyY7uPJodSNQURERCQTVq1axYIFCzh69ChOp5OKFSvy+uuvA+omlpMp7IqIiIhkUmRkJP/++y+GYXD33XcD6p+b0ynsioiIiGSRbqLM+RR2RURERMS01OYuIiIiIqalsCsiIiIipqWwKyIiIiKmpbArIiIiIqalsCsiIiIipqWwKyIiIiKmpbArIiIiIqb1fzSnPz7LwzKnAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from cobra.evaluation import plot_performance_curves\n", + "\n", + "# plot performance curves\n", + "plot_performance_curves(performances)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Based on the performance curves (AUC per model with a particular number of predictors in case of logistic regression), a final model can then be chosen and the variables importance can be plotted:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model = forward_selection.get_model_from_step(7)\n", + "final_predictors = model.predictors\n", + "final_predictors\n", + "from cobra.evaluation import plot_variable_importance\n", + "variable_importance = model.compute_variable_importance(\n", + " basetable[basetable[\"split\"] == \"selection\"])\n", + "plot_variable_importance(variable_importance)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we can again export the model to a dictionary to store it as JSON" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: 'output\\\\model.json'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mmodel_path\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"output\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"model.json\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel_path\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"w\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mfile\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5\u001b[0m \u001b[0mjson\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'output\\\\model.json'" + ] + } + ], + "source": [ + "model_dict = model.serialize()\n", + "\n", + "model_path = os.path.join(\"output\", \"model.json\")\n", + "with open(model_path, \"w\") as file:\n", + " json.dump(model_dict, file)\n", + "\n", + "# To reload the model again from a JSON file, run the following snippet:\n", + "# from cobra.model_building import LinearRegressionModel\n", + "# with open(model_path, \"r\") as file:\n", + "# model_dict = json.load(file)\n", + "# model = LinearRegressionModel()\n", + "# model.deserialize(model_dict)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have build and selected a final model, it is time to evaluate its predictions on the test set against various evaluation metrics. The used evaluation metrics are:\n", + "1. Coefficient of Determination: R2\n", + "2. Mean Absolute Error: MAE\n", + "3. Mean Squared Error: MSE\n", + "4. Root Mean Squared Error: RMSE\n", + "\n", + "Furthermore, plotting makes the evaluation of a linear regression model a lot easier. We will use a **prediction plot**, which presents predictions from the model against actual values and a **Q-Q plot** from the standardized prediction residuals." + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [], + "source": [ + "from cobra.evaluation import RegressionEvaluator\n", + "# get numpy array of True target labels and predicted scores:\n", + "y_true = basetable[basetable[\"split\"] == \"selection\"][target_col].values\n", + "y_pred = model.score_model(basetable[basetable[\"split\"] == \"selection\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [], + "source": [ + "evaluator = RegressionEvaluator()\n", + "evaluator.fit(y_true, y_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "R2 0.363109\n", + "MAE 4.927506\n", + "MSE 42.867631\n", + "RMSE 6.547338\n", + "dtype: float64" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluator.compute_scalar_metrics(y_true, y_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "quantiles [-2.23653945714085, -1.9545779044793146, -1.77...\n", + "residuals [-1.890723531717944, -1.874030710025782, -1.64...\n", + "dtype: object" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluator.compute_qq_residuals(y_true, y_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "evaluator.plot_predictions(y_true=y_true, y_pred=y_pred)" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "ename": "ValueError", + "evalue": "Illegal format string \"o--.\"; two marker symbols", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# does not work yet!\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mevaluator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot_qq\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32m~\\PycharmProjects\\cobra\\cobra\\evaluation\\evaluator.py\u001b[0m in \u001b[0;36mplot_qq\u001b[1;34m(self, path, dim)\u001b[0m\n\u001b[0;32m 683\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mqq\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"residuals\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 684\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 685\u001b[1;33m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"o--.\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"cornflowerblue\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlinewidth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 686\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"r--.\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"cornflowerblue\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlinewidth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 687\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1741\u001b[0m \"\"\"\n\u001b[0;32m 1742\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# does not work yet!\n", + "evaluator.plot_qq()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "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": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/Cobra_Tutorial_LogisticRegression.ipynb b/docs/Cobra_Tutorial_LogisticRegression.ipynb new file mode 100644 index 0000000..8fdcc10 --- /dev/null +++ b/docs/Cobra_Tutorial_LogisticRegression.ipynb @@ -0,0 +1,2501 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Optional: import local development version of Cobra:\n", + "import sys\n", + "import os\n", + "sys.path.append(r\"C:\\Users\\hendrik.dewinter\\PycharmProjects\\cobra\")\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Tutorial for Logistic Regression" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This section we will walk you through all the required steps to build a predictive logistic regression model using **Cobra**. All classes and functions used here are well-documented. In case you want more information on a class or function, run the next cell:" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "#help(function_or_class_you_want_info_from)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Building a good model involves three steps\n", + "\n", + "1. **Preprocessing**: properly prepare the predictors (a synonym for “feature” or variable that we use throughout this tutorial) for modelling.\n", + "\n", + "2. **Feature Selection**: automatically select a subset of predictors which contribute most to the target variable or output in which you are interested.\n", + "\n", + "3. **Model Evaluation**: once a model has been build, a detailed evaluation can be performed by computing all sorts of evaluation metrics.\n", + "\n", + "\n", + "\n", + "Let's dive in!!!\n", + "***" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Survival Prediction using Titanic data\n", + "- GOAL : Predict if individuals survives in titanic sinking\n", + "- BASETABLE : seaborn dataset Titanic" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "import the necessary libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "import json\n", + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "from pandas.api.types import is_datetime64_any_dtype\n", + "\n", + "pd.set_option('display.max_columns', 50)\n", + "pd.set_option(\"display.max_rows\", 50)\n", + "from cobra.preprocessing import PreProcessor\n", + "from cobra.evaluation import generate_pig_tables, plot_incidence\n", + "from cobra.evaluation import evaluator" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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survivedpclasssexagesibspparchfareembarkedclasswhoadult_maledeckembark_townalivealone
003male22.0107.2500SThirdmanTrueNaNSouthamptonnoFalse
111female38.01071.2833CFirstwomanFalseCCherbourgyesFalse
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403male35.0008.0500SThirdmanTrueNaNSouthamptonnoTrue
\n", + "
" + ], + "text/plain": [ + " survived pclass sex age sibsp parch fare embarked class \\\n", + "0 0 3 male 22.0 1 0 7.2500 S Third \n", + "1 1 1 female 38.0 1 0 71.2833 C First \n", + "2 1 3 female 26.0 0 0 7.9250 S Third \n", + "3 1 1 female 35.0 1 0 53.1000 S First \n", + "4 0 3 male 35.0 0 0 8.0500 S Third \n", + "\n", + " who adult_male deck embark_town alive alone \n", + "0 man True NaN Southampton no False \n", + "1 woman False C Cherbourg yes False \n", + "2 woman False NaN Southampton yes True \n", + "3 woman False C Southampton yes False \n", + "4 man True NaN Southampton no True " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import seaborn as sns\n", + "df=sns.load_dataset('titanic')\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In the example below, we assume the data for model building is available in a pandas DataFrame. This DataFrame should contain a an ID column, a target column (e.g. “**survived**”) and a number of candidate predictors (features) to build a model with.\n", + "\n", + "***\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "survived int64\n", + "pclass int64\n", + "sex object\n", + "age float64\n", + "sibsp int64\n", + "parch int64\n", + "fare float64\n", + "embarked object\n", + "class category\n", + "who object\n", + "adult_male bool\n", + "deck category\n", + "embark_town object\n", + "alive object\n", + "alone bool\n", + "dtype: object" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.dtypes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "it is required to set all category vars to object dtype\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "df.loc[:, df.dtypes == 'category'] =\\\n", + " df.select_dtypes(['category'])\\\n", + " .apply(lambda x: x.astype('object'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data preprocessing" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### The first part focusses on preparing the predictors for modelling by:\n", + "\n", + "1. Defining the ID column, the target, discrete and contineous variables\n", + "\n", + "2. Splitting the dataset into training, selection and validation datasets.\n", + "\n", + "3. Binning continuous variables into discrete intervals\n", + "\n", + "4. Replacing missing values of both categorical and continuous variables (which are now binned) with an additional “Missing” bin/category\n", + "\n", + "5. Regrouping categories in new category “other”\n", + "\n", + "6. Replacing bins/categories with their corresponding incidence rate per category/bin." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this toy dataset, the index will serve as ID," + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "df[\"id\"] = df.index + 1\n", + "id_col = \"id\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "and survived is the target,\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "target_col = \"survived\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we remove the columns 'who' and 'adult_male' since they are duplicate of 'sex', and also 'alive', which seems to be a duplicate of 'survived'\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "del df['who']\n", + "del df['adult_male']\n", + "del df['alive']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finding out which variables are categorical (\"discrete\") and which are continous:\n", + "\n", + "\n", + " => discrete are definitely those that contain strings:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['sex', 'embarked', 'class', 'deck', 'embark_town']\n", + "\n", + "sex\n", + "male 577\n", + "female 314\n", + "Name: sex, dtype: int64\n", + "\n", + "embarked\n", + "S 644\n", + "C 168\n", + "Q 77\n", + "Name: embarked, dtype: int64\n", + "\n", + "class\n", + "Third 491\n", + "First 216\n", + "Second 184\n", + "Name: class, dtype: int64\n", + "\n", + "deck\n", + "C 59\n", + "B 47\n", + "D 33\n", + "E 32\n", + "A 15\n", + "F 13\n", + "G 4\n", + "Name: deck, dtype: int64\n", + "\n", + "embark_town\n", + "Southampton 644\n", + "Cherbourg 168\n", + "Queenstown 77\n", + "Name: embark_town, dtype: int64\n", + "\n" + ] + } + ], + "source": [ + "col_dtypes = df.dtypes\n", + "discrete_vars = [col for col in col_dtypes[col_dtypes==object].index.tolist() if col not in [id_col, target_col]] \n", + "print(discrete_vars)\n", + "print()\n", + "for col in discrete_vars:\n", + " print(col)\n", + " print(df[col].value_counts())\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we also check for numerical columns that only contain a few different values, thus to be interpreted as discrete, categorical variables\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pclass\n", + "3 491\n", + "1 216\n", + "2 184\n", + "Name: pclass, dtype: int64\n", + "\n", + "sibsp\n", + "0 608\n", + "1 209\n", + "2 28\n", + "4 18\n", + "3 16\n", + "8 7\n", + "5 5\n", + "Name: sibsp, dtype: int64\n", + "\n", + "parch\n", + "0 678\n", + "1 118\n", + "2 80\n", + "3 5\n", + "5 5\n", + "4 4\n", + "6 1\n", + "Name: parch, dtype: int64\n", + "\n", + "alone\n", + "True 537\n", + "False 354\n", + "Name: alone, dtype: int64\n", + "\n" + ] + } + ], + "source": [ + "for col in df.columns:\n", + " if col not in discrete_vars and col not in [id_col, target_col]: # if we didn't mark it as discrete already because it was string typed, or also excluding it if it is the target:\n", + " val_counts = df[col].value_counts()\n", + " if len(val_counts) > 1 and len(val_counts) <= 10: # The column contains less than 10 different values. \n", + " print(col)\n", + " print(val_counts)\n", + " print()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By taking a look at the printed variables, it is clear that we have to include those in the list of discrete variables. This can be done as follows:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['sex',\n", + " 'embarked',\n", + " 'class',\n", + " 'deck',\n", + " 'embark_town',\n", + " 'pclass',\n", + " 'sibsp',\n", + " 'parch',\n", + " 'class',\n", + " 'deck',\n", + " 'alone']" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "discrete_vars.extend([\"pclass\",\"sibsp\",\"parch\",\"class\",\"deck\",\"alone\"])\n", + "discrete_vars" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The remaining variables can be labelled continous predictors, without including the target variable.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['fare', 'age']" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "continuous_vars = list(set(df.columns)\n", + " - set(discrete_vars) \n", + " - set([id_col, target_col]))\n", + "continuous_vars " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, we can prepare **Cobra's Preprocessor**" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "The target encoder's additive smoothing weight is set to 0. This disables smoothing and may make the encoding prone to overfitting.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on method from_params in module cobra.preprocessing.preprocessor:\n", + "\n", + "from_params(model_type: str = 'classification', n_bins: int = 10, strategy: str = 'quantile', closed: str = 'right', auto_adapt_bins: bool = False, starting_precision: int = 0, label_format: str = '{} - {}', change_endpoint_format: bool = False, regroup: bool = True, regroup_name: str = 'Other', keep_missing: bool = True, category_size_threshold: int = 5, p_value_threshold: float = 0.001, scale_contingency_table: bool = True, forced_categories: dict = {}, weight: float = 0.0, imputation_strategy: str = 'mean') method of builtins.type instance\n", + " Constructor to instantiate PreProcessor from all the parameters\n", + " that can be set in all its required (attribute) classes\n", + " along with good default values.\n", + " \n", + " Parameters\n", + " ----------\n", + " model_type : str\n", + " Model type (``classification`` or ``regression``).\n", + " n_bins : int, optional\n", + " Number of bins to produce. Raises ValueError if ``n_bins < 2``.\n", + " strategy : str, optional\n", + " Binning strategy. Currently only ``uniform`` and ``quantile``\n", + " e.g. equifrequency is supported.\n", + " closed : str, optional\n", + " Whether to close the bins (intervals) from the left or right.\n", + " auto_adapt_bins : bool, optional\n", + " Reduces the number of bins (starting from n_bins) as a function of\n", + " the number of missings.\n", + " starting_precision : int, optional\n", + " Initial precision for the bin edges to start from,\n", + " can also be negative. Given a list of bin edges, the class will\n", + " automatically choose the minimal precision required to have proper\n", + " bins e.g. ``[5.5555, 5.5744, ...]`` will be rounded\n", + " to ``[5.56, 5.57, ...]``. In case of a negative number, an attempt\n", + " will be made to round up the numbers of the bin edges\n", + " e.g. ``5.55 -> 10``, ``146 -> 100``, ...\n", + " label_format : str, optional\n", + " Format string to display the bin labels\n", + " e.g. ``min - max``, ``(min, max]``, ...\n", + " change_endpoint_format : bool, optional\n", + " Whether or not to change the format of the lower and upper bins\n", + " into ``< x`` and ``> y`` resp.\n", + " regroup : bool\n", + " Whether or not to regroup categories.\n", + " regroup_name : str\n", + " New name of the non-significant regrouped variables.\n", + " keep_missing : bool\n", + " Whether or not to keep missing as a separate category.\n", + " category_size_threshold : int\n", + " All categories with a size (corrected for incidence if applicable)\n", + " in the training set above this threshold are kept as a separate category,\n", + " if statistical significance w.r.t. target is detected. Remaining\n", + " categories are converted into ``Other`` (or else, cf. regroup_name).\n", + " p_value_threshold : float\n", + " Significance threshold for regrouping.\n", + " forced_categories : dict\n", + " Map to prevent certain categories from being grouped into ``Other``\n", + " for each column - dict of the form ``{col:[forced vars]}``.\n", + " scale_contingency_table : bool\n", + " Whether contingency table should be scaled before chi^2.\n", + " weight : float, optional\n", + " Smoothing parameters (non-negative). The higher the value of the\n", + " parameter, the bigger the contribution of the overall mean.\n", + " When set to zero, there is no smoothing (e.g. the pure target incidence is used).\n", + " imputation_strategy : str, optional\n", + " In case there is a particular column which contains new categories,\n", + " the encoding will lead to NULL values which should be imputed.\n", + " Valid strategies are to replace with the global mean of the train\n", + " set or the min (resp. max) incidence of the categories of that\n", + " particular variable.\n", + " \n", + " Returns\n", + " -------\n", + " PreProcessor\n", + " class encapsulating CategoricalDataProcessor,\n", + " KBinsDiscretizer, and TargetEncoder instances\n", + "\n" + ] + } + ], + "source": [ + "# using all Cobra's default parameters for preprocessing for now:\n", + "preprocessor = PreProcessor.from_params(\n", + " model_type=\"classification\")\n", + "\n", + "# These are the options though:\n", + "help(PreProcessor.from_params)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "split data into train-selection-validation set:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "from cobra.preprocessing import PreProcessor\n", + "basetable = preprocessor.train_selection_validation_split(\n", + " data=df,\n", + " train_prop=0.6,\n", + " selection_prop=0.2,\n", + " validation_prop=0.2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And fit the preprocessor pipeline:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a16c079bbf1b4b8a932e7c60fcaa81cf", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Computing discretization bins...: 0%| | 0/2 [00:00\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", + " \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", + "
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311female35.01053.1000SFirstCSouthamptonFalse4train39.8 - 76.131.0 - 35.0femaleOtherFirstCOther11OtherFalse0.7445650.3125000.6349210.6410260.3125000.6349210.5692310.3311550.5229360.5471700.525000
403male35.0008.0500SThirdNaNSouthamptonTrue5train7.9 - 8.131.0 - 35.0maleOtherThirdMissingOther3OtherOtherTrue0.1652420.3125000.2178220.2703350.3125000.2178220.2987650.3311550.2555210.1627910.525000
\n", + "" + ], + "text/plain": [ + " survived pclass sex age sibsp parch fare embarked class deck \\\n", + "0 0 3 male 22.0 1 0 7.2500 S Third NaN \n", + "1 1 1 female 38.0 1 0 71.2833 C First C \n", + "2 1 3 female 26.0 0 0 7.9250 S Third NaN \n", + "3 1 1 female 35.0 1 0 53.1000 S First C \n", + "4 0 3 male 35.0 0 0 8.0500 S Third NaN \n", + "\n", + " embark_town alone id split fare_bin age_bin sex_processed \\\n", + "0 Southampton False 1 train 7.2 - 7.9 18.0 - 22.0 male \n", + "1 Cherbourg False 2 validation 39.8 - 76.1 35.0 - 40.0 female \n", + "2 Southampton True 3 validation 7.9 - 8.1 24.0 - 27.0 female \n", + "3 Southampton False 4 train 39.8 - 76.1 31.0 - 35.0 female \n", + "4 Southampton True 5 train 7.9 - 8.1 31.0 - 35.0 male \n", + "\n", + " embarked_processed class_processed deck_processed embark_town_processed \\\n", + "0 Other Third Missing Other \n", + "1 C First C Cherbourg \n", + "2 Other Third Missing Other \n", + "3 Other First C Other \n", + "4 Other Third Missing Other \n", + "\n", + " pclass_processed sibsp_processed parch_processed alone_processed sex_enc \\\n", + "0 3 1 Other False 0.165242 \n", + "1 1 1 Other False 0.744565 \n", + "2 3 Other Other True 0.744565 \n", + "3 1 1 Other False 0.744565 \n", + "4 3 Other Other True 0.165242 \n", + "\n", + " embarked_enc class_enc deck_enc embark_town_enc pclass_enc sibsp_enc \\\n", + "0 0.312500 0.217822 0.270335 0.312500 0.217822 0.569231 \n", + "1 0.542373 0.634921 0.641026 0.542373 0.634921 0.569231 \n", + "2 0.312500 0.217822 0.270335 0.312500 0.217822 0.298765 \n", + "3 0.312500 0.634921 0.641026 0.312500 0.634921 0.569231 \n", + "4 0.312500 0.217822 0.270335 0.312500 0.217822 0.298765 \n", + "\n", + " parch_enc alone_enc fare_enc age_enc \n", + "0 0.331155 0.522936 0.198198 0.333333 \n", + "1 0.331155 0.522936 0.547170 0.395349 \n", + "2 0.331155 0.255521 0.162791 0.384615 \n", + "3 0.331155 0.522936 0.547170 0.525000 \n", + "4 0.331155 0.255521 0.162791 0.525000 " + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basetable = preprocessor.transform(basetable,\n", + " continuous_vars=continuous_vars,\n", + " discrete_vars=discrete_vars)\n", + "basetable.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Feature selection" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once the predictors are properly prepared, we can start building a predictive model, which boils down to selecting the right predictors from the dataset to train a model on.\n", + "As a dataset typically contains many predictors, **we first perform a univariate preselection** to rule out any predictor with little to no predictive power. Later, using the list of preselected features, we build a logistic regression model using **forward feature selection** to choose the right set of predictors." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In previous steps, these were the predictors, as preprocessed so far:" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['age_bin',\n", + " 'alone_processed',\n", + " 'class_processed',\n", + " 'deck_processed',\n", + " 'embark_town_processed',\n", + " 'embarked_processed',\n", + " 'fare_bin',\n", + " 'parch_processed',\n", + " 'pclass_processed',\n", + " 'sex_processed',\n", + " 'sibsp_processed']" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "preprocessed_predictors = [\n", + " col for col in basetable.columns\n", + " if col.endswith(\"_bin\") or col.endswith(\"_processed\")]\n", + "sorted(preprocessed_predictors)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "But for feature selection, we use the target encoded version of each of these." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "preprocessed_predictors = [col for col in basetable.columns.tolist()\n", + " if '_enc' in col]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A univariate selection on the preprocessed predictors can be conducted. The thresholds for retaining a feature are now on default but can be changed by the user.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from cobra.model_building import univariate_selection\n", + "\n", + "df_auc = univariate_selection.compute_univariate_preselection(\n", + " target_enc_train_data=basetable[basetable[\"split\"] == \"train\"],\n", + " target_enc_selection_data=basetable[basetable[\"split\"] == \"selection\"],\n", + " predictors=preprocessed_predictors,\n", + " target_column=target_col,\n", + " preselect_auc_threshold=0.53, # if auc_selection <= 0.53 exclude predictor\n", + " preselect_overtrain_threshold=0.05 # if (auc_train - auc_selection) >= 0.05 --> overfitting!\n", + " )\n", + "from cobra.evaluation import plot_univariate_predictor_quality\n", + "plot_univariate_predictor_quality(df_auc)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we compute correlations between the preprocessed predictors and plot it using a correlation matrix:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from cobra.evaluation import plot_correlation_matrix\n", + "df_corr = (univariate_selection\n", + " .compute_correlations(basetable[basetable[\"split\"] == \"train\"],\n", + " preprocessed_predictors))\n", + "plot_correlation_matrix(df_corr)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To get a list of the selected predictors after the univariate selection, run the following call:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['class_enc', 'pclass_enc', 'embarked_enc', 'embark_town_enc']" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "preselected_predictors = (univariate_selection\n", + " .get_preselected_predictors(df_auc))\n", + "preselected_predictors" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After an initial preselection on the predictors, we can start building the model itself using forward feature selection to choose the right set of predictors. Since we use target encoding on all our predictors, we will only consider models with positive coefficients (no sign flip should occur) as this makes the model more interpretable." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Modeling" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c783c5374c3d4018b390aacba01fe65a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Sequentially adding best predictor...: 0%| | 0/4 [00:00\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", + "
predictorslast_added_predictortrain_performanceselection_performancevalidation_performancemodel_type
0[class_enc]class_enc0.6966890.6549300.659056classification
1[class_enc, embarked_enc]embarked_enc0.7151210.6625640.676793classification
2[class_enc, embarked_enc, pclass_enc]pclass_enc0.7151210.6625640.676793classification
3[embarked_enc, pclass_enc, class_enc, embark_t...embark_town_enc0.7151210.6625640.676793classification
\n", + "" + ], + "text/plain": [ + " predictors last_added_predictor \\\n", + "0 [class_enc] class_enc \n", + "1 [class_enc, embarked_enc] embarked_enc \n", + "2 [class_enc, embarked_enc, pclass_enc] pclass_enc \n", + "3 [embarked_enc, pclass_enc, class_enc, embark_t... embark_town_enc \n", + "\n", + " train_performance selection_performance validation_performance \\\n", + "0 0.696689 0.654930 0.659056 \n", + "1 0.715121 0.662564 0.676793 \n", + "2 0.715121 0.662564 0.676793 \n", + "3 0.715121 0.662564 0.676793 \n", + "\n", + " model_type \n", + "0 classification \n", + "1 classification \n", + "2 classification \n", + "3 classification " + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from cobra.model_building import ForwardFeatureSelection\n", + "\n", + "forward_selection = ForwardFeatureSelection(model_type=\"classification\",\n", + " max_predictors=30,\n", + " pos_only=True)\n", + "\n", + "# fit the forward feature selection on the train data\n", + "# has optional parameters to force and/or exclude certain predictors (see docs)\n", + "forward_selection.fit(basetable[basetable[\"split\"] == \"train\"],\n", + " target_column_name = target_col,\n", + " predictors = preselected_predictors)\n", + " #forced_predictors: list = [],\n", + " #excluded_predictors: list = [])\n", + "\n", + "# compute model performance\n", + "performances = (forward_selection\n", + " .compute_model_performances(basetable, target_column_name = target_col))\n", + "performances" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As can be seen, we have completed 4 steps till no further improvement can be observed" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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USbt377b0oRUrVkiSJkyYYFXH37VrV40YMcLynpZfP/30k0qWLKlXX31VLi7/znFQv359Pfroo7py5YpWr15ttU9+3teAgsAsHHB4v//+u37//XfLz66urvL19VXr1q312GOP6d5777Xct3fvXknp5RFr1661Os65c+ckKUstY35n7zhw4ICk9Dfs6/n5+al69erav3+/YmNj5e3tbWlr+fLlsz2ev7+/1c8eHh6S0j/0nZ2dre5zd3fXlStXLD9nfE0eHx+vXbt26cSJEzp+/Lh2796tEydOKC0tLcv57rzzziy3ZbQzJSXF6jFmLjXJkDnU79mzR1J6nWXmQCrJ8lV0xrGyk5iYqBMnTujuu++Wu7u71X2enp4aN25cjvvmR3a/0x49emjJkiX65ZdfLIFqxYoVcnNzs/yRs2/fPhmGoZiYGH3yySdZjuHq6qqzZ8/q/PnzqlChgrp06aIVK1booYceUuPGjXXffffpgQceuOHwf+zYMSUmJqpJkyZWASxD06ZN9euvv+rAgQNq0aJFjo+3VKlSatiwoTZv3izDMGQ2m7Vt2zb17dtXLi4u2rZtm+Ux/Pnnn6pVq5aqVq0qKf1r9urVqyspKUm7du3SsWPH9M8//2jv3r2W0o/rX1/Xn//kyZO6evWq6tevn+Ux1K9fX66urjf0/Ei29fGKFSvq4YcfVmpqqvbu3Wt5LPv379dff/0l6d9geitd/3zY+rrKzsMPP6yFCxfqo48+0qJFi3TffffpvvvuU+vWrS3vHdLN9c34+HgdO3ZM5cqVy7bU48KFC5LSn99atWrpwIEDqly5cpY2m0wmm6cWjYuL08mTJ9WkSRN5eXllub9p06aaM2dOlrbn530NKAgEaDi8d999N9tp7LITGxsrSZo9e3aO21w/enZ9gMtJRk1udm/uklS+fHnt379fCQkJljfwEiVK5Hi8zKNnmeVnRoakpCR98MEHWrRokeVimQoVKujuu+9W6dKlFRUVla/jZowwGYYhSZaQntNjzJDxPH///fc5bnP985zdfXmd50Zl97y3aNFCFSpUsAToS5cuadOmTWrXrp18fHwk/fv4d+7cqZ07d+Z4/JiYGFWoUEFTp05V/fr1tWTJEm3dulVbt27V+++/r/r162vy5Mk51nrmJOM1lvH6uV7GH2OJiYl5Pt62bdtq+/btOnjwoJKSkhQXF6fmzZvLxcVFX375pbZt26Z77rlHBw4c0PDhwy37mc1mff7555o7d67l91SmTBk1btxYVapUUUREhOX1kuH6PpSxn6enZ5Z2OTs739Tv3dY+/v333+vTTz9VZGSkpPT66oYNG6pmzZratWtXlsdyK1z/+7D1dZWd2rVr64cfftCsWbO0bt06/fDDD/rhhx/k4eGhxx9/3DJifDN9M+P1FxUVpZkzZ+a5/5UrV1S2bNkct7NFxoWWtr728/O+BhQEAjRuKx4eHnJ2dtauXbtuapQrOxlhIOOD+HoZH5K+vr639LzZCQ4O1oIFC9SxY0c99thjCgwMtJy3c+fO2Qbo/MgYyYqPj88ydVhiYqLc3Nzk5ORk2S40NFTVqlW7qfNk5+rVq5ZtcvowtPUqeycnJ3Xt2tUyirVr1y6lpqZarW6Xcc5nnnkmy9f+2XF1ddWwYcM0bNgwnTlzRhs3btSqVau0YcMGPf300/r9999teh3eytfYfffdpxkzZmjTpk1KSUmRk5OT7r77bjk5OcnZ2Vnbtm1TamqqDMPQ/fffb9lvzpw5+vDDD9W8eXM9+eSTqlOnjqU84z//+Y8iIiLyPHepUqUk/Rt2MzMM46ZmSLClj69cuVJvvvmmAgMD9eabb6pevXqqVKmSJOnNN9/M9oLM692K15+tr6uc1K5dWx9++KGSk5O1Y8cOrV+/XkuWLNGsWbNUoUIFPfroozfVNzP2bdasmdVFy7ltn58+nB+O9P4K5Ac10LitBAYGKi0tLdspp3bs2KH3338/S/1ofmWMJmZM0ZVZXFyc9u/fL39//0KZ03f58uUqU6aMPvroI7Vo0cLyoZKYmGiZJuxGRl8CAgIkpU/Vd73JkyerYcOGOnnypGXKteunlpKk48ePa+rUqfrjjz9yPI+3t7cqVaqk/fv3Z5m5JDk5Wa1bt9awYcMkyRKSrp+lIPMME/mVMeXY77//rlWrVsnHx8cqPGY8royvwa/38ccfa/bs2UpOTtbJkyf1wQcfaM2aNZKkypUrW2bPuOeee3T+/HmdOnXKpvbVqFFD7u7uCg8Pz3aFtoza5fyUiNStW1fly5fX5s2btX37dgUGBsrHx0deXl6qV6+etm3bpj///FM+Pj5WNaTLly+Xs7OzPvvsM913332W8GwYhmUawLxeW3fccYe8vb21Y8eOLPcdOXIkyyiiLWzp48uXL5ckTZ8+Xe3bt7eEZ0n5fiy34vVny+sqJ0uXLtXbb78twzDk5uamFi1aaNy4cZaSkIz3pZvpm97e3qpcuXKOv6OlS5fqk08+sbyuAwICdObMmWz/YO/Vq5elNCpzLXVOvLy8VLVqVR07dizbazhsee0DhYEAjdtKxgVLU6ZMsZoGLS4uThMnTtQXX3yRbX1wfrRv317e3t5asGCBpQ5TSp8L+J133lFiYqLVnLAFyd3dXUlJSVZ10WlpaZZ2SDdW/9etWzc5OTlp1qxZlinPpPSwsHLlSlWrVk3VqlVTjx495OzsrA8//NDqwzM1NVVvv/225syZk2Vu7uv16NFDsbGx+vTTT61u//bbb3X16lW1bNlSUnqolGQJqlJ6mUF+psq7Xu3atRUQEKBffvlF27ZtU8eOHa3+4KlWrZruvvturV+/PsucuUuXLtWnn36qP//8U25ubipRooS++OILffTRR1bBJzk5WVFRUXJzc8vXxamZubm5qWvXroqMjNTHH39sdd/69eu1cuVK+fv75/virDZt2mjbtm3asWOHmjdvbrm9efPmioiI0Nq1a9WmTRurC7bc3d2VlpaWJcT897//tVzIev2c5NdzdXVVt27d9M8//1jmNpfSn5sbubgyM1v6eEZpSUbtboalS5da6rkzP5aMsJy579yK158tr6uc7Ny5U/Pnz89yEXRGmK1cubIk3XTffPjhhxUTE6P333/fqj78yJEjmjRpkubOnWv5g71Hjx4yDEPvv/++1fvqypUrdeLECUsfznh95fWe9PDDDysxMVFTpkyx+r3s3btX8+fPl4+PT5b5zQF7oYQDt5V77rlHgwcP1rx589S1a1e1bdtWbm5uCg0N1dmzZzVgwACri69s4eXlpSlTpmj06NEaMGCAHnroIZUpU0abN2/WoUOH1KxZM6t5ZQtS9+7dNWfOHPXp00ft27dXamqqNmzYoGPHjsnPz0/R0dGKiYnJ8QLGnNSsWVMjR47Uxx9/rJ49e+qBBx6QYRj65ZdflJSUpHfffVdS+oU748aNU3BwsLp166Z27dqpVKlSWr9+vSIiIvTAAw9YZrbIydNPP621a9dq1qxZ2rZtmxo2bKijR49q7dq1atCggYYMGSIpPdR/9NFH+uqrr3Ty5ElVrVpVGzdu1JUrVyyhwdbnLiPEZS7fyDBp0iQ99thjev7553XfffepVq1aOnbsmNauXStfX1+9+eabkqRy5cppyJAhmjt3rrp166a2bdvKyclJf/75pyIiIvTMM8/cUK3vuHHjtH37dn3xxRfatm2bGjdurJMnT+qPP/6Qp6en3nvvvXyN6EnpddAZM8NcH6C//PJLxcXFWWbfyNCjRw/t3LlTAwcOVOfOneXq6qotW7Zo7969KlOmjC5evJjnH0eSNHr0aG3atEnBwcHasGGDatasqU2bNikmJibf1x1kx5Y+3qNHD61YsUIjR45U165d5eXlpd27d2vr1q3ZPpaM+uMJEyaodevWlvnVb8XrL7+vq5z85z//0cqVK/Xiiy9q1apV8vf31+nTp7V69WqVK1dOgwYNknTzffOpp57Shg0bNG/ePIWFhal58+a6cuWKVq1apYSEBL333nuW13Xfvn21evVqLV26VAcPHlSLFi10/vx5rV69WlWqVLFcSFi6dGm5ublpy5Ytevfdd/XQQw9lWUBFSp9/e8OGDVq2bJkOHjyoe+65RxcvXlRoaKgMw9CMGTMK7LoJwFaMQOO289prr2natGmqVKmSfv75Z/30008qW7aspkyZkueHVF46dOigBQsWqHXr1vrzzz/1ww8/SJJeeuklff3114W2JPPo0aP13HPPycnJSQsWLFBoaKiqVKmir776SiNGjJAkrVu37oaO/eyzz2rGjBmqVKmS/ve//2nZsmUKCgrS/Pnz1bBhQ8t2TzzxhGbPnq3atWtr9erVWrRokVxcXDR+/Hh9/PHHVqOa2fH09NSCBQs0fPhwnTt3Tt9++6327t2rQYMGae7cuZbnsmzZsvr222/VsmVLrV+/XiEhIapZs6YWLlxoufjPFj169JCTk5MqVqyY7YwqNWrU0JIlS/TII4/o4MGD+vbbb3Xw4EH17NlTixcvtvoKedy4cZo4caK8vLz0008/6YcffpCnp2e2U6fll5+fn3744QcNGzZMUVFRmj9/vnbv3q1evXppyZIlVr+DvLRu3Vqurq4ymUxWj7VZs2ZycXGRk5OT2rRpY7XPo48+qtdff12+vr4KCQnRsmXL5OnpqQ8++ECTJk2SlL/XVqlSpbRw4UINGDBABw8e1KJFi1S2bNlb0k/y28fvv/9+zZgxQ3fccYeWLVumn376SUlJSXrjjTcsK09mfiwjRoxQw4YNtXHjRksN8K16/dnyuspO1apVtXDhQnXp0kV79uzR3LlztW3bNvXo0UM//PCD1cWHN9M3S5QooW+//VbPPfeckpKStGDBAq1bt05NmjTRt99+a/VHZ0apzwsvvKDExER999132rx5s7p3766FCxdaauHd3Nz0xhtvqFSpUlqwYEG2K0ZK6d8YfP311xo1apRSUlK0cOFCbd68WQ888IAWLVpkNe0nYG8mg8tUAQAAgHxjBBoAAACwAQEaAAAAsAEBGgAAALABARoAAACwAQEaAAAAsAEBGgAAALABARoAAACwAQEaAAAAsAEBGgAAALABARoAAACwAQEaAAAAsAEBGgAAALABARoAAACwAQEaAAAAsAEBGgAAALABARoAAACwAQEaAAAAsAEBGgAAALABARoAAACwAQEaAAAAsAEBGgAAALABARoAAACwAQEaAAAAsAEBGgAAALABARoAAACwAQEaAAAAsAEBGgAAALABARoAAACwAQEaAAAAsAEBGgAAALABARoAAACwAQEaAAAAsAEBGgAAALABARoAAACwAQEaAAAAsAEBGgAAALABARoAAACwAQEaAAAAsAEBGgAAALABARoAAACwAQEaAAAAsAEBGgAAALCBi70bYIuwsDB7NwEAAADFRNOmTbO9vUgFaCnnB1LQ9u/frzp16tjl3ICji7iSoOnhJzX/8HnFpaTJy9VZg2pV0NigaqrpU9LezQMcBn0FyJuj9JPcBm5NhmEYhdaSmxQWFkaABhzMyn8uqm/oXqWkmZWS6d3E1SS5Ojtpcft66nxHGfs1EHAQ9BUgb47UT3LLndRAA7hhEVcS1Dd0r66mWr/RSVKKIV1NNatv6F5FXEmwTwMBB0FfAfJWlPpJkSvhAOA4gnf+o+Q0c67bXE01697/7dBdpfh6GsXXkcsJuppKXwFyk59+kpJm1ozwk5p5b0AhtSp7BGgAeTIMQ8djExUeHa9dF+MUHh2n8Oh4Hb6cv1GAcwnJOpeQXMCtBIo++gqQuxRDmnf4PAEagGOJTU7VnksZQTle4df+H5uSZu+mAQCgOAf4PCJAA8WU2TB0LDZR4Rfj/g3L0XGKuJJ4y8/l6eKklZ2DbvlxgaKi08rwPL+alugrKN7y20+8XJ0LoTW5I0ADxcCV5FTtvq78Ynd0vE1/xfu5u6hhGS819PNSUBlPBfl56fP9Z/T1wbNZLvbIzNUkDQ2oqDaVfG/+gQBF1JCAivpy/xn6CpCL/PaTwbUqFF6jckCABm4jZsNQxJUEhV+M167ouPTR5eh4HY/N/6iys0mq7euhhmW8FOTnpSA/TzUs46VKHm4ymUxW277sfoe+O3JeKbmMGLg6O2l0ULUbfkzA7WBsUDV9c+gcfQXIRVHqJwRooIiKSUrJNKqcXn6xOzo+X19/ZShXwvVaUPZUUBkvNfTzVJ3SnnJ3zt8MlzV9Smpx+3p5ztnJAhEo7ugrQN6KUj8hQAMOLs1s6MiVBIVHX6tVvja6/E9cUr6P4WIyqU5pD6vyi4ZlPFWhZNZRZVt1vqOMwvverRnhJzXv8HnFpqTJ29VZg2tV0GhWVwMs6CtA3opKP2ElwnxiJUIUhujEa6PK18ovwqPjtSc6Xgl5zLWcWYWSrlnKL2r7esgtn6PKN4u+AuQPfQXImz37SW65kxFowA5SzWYdvpxgVX6x62K8TsXnf1TZ1cmkupZR5fTyiwZ+Xqrg4VaALQcAAARooIBdTEzJUn6x91K8ktLy/+VPJQ+3f2uVr5VfBPp6yNWpcEaVAQDAvwjQwC2SYjbrUEyCVfnFrotxOnM1/6uKuTmZVK+0pyUsNyzjpQZ+nipXklFlAAAcBQEauAFRCclWy1rvuhivfZfilWzO/6hyFU83S/lFRlgOKFVSLowqAwDg0AjQQC6S08w6ePmqpfwiIyyfS8j/qHIJZyfV9/O0Kr9o4OelMiVcC7DlAACgoBCggWvOX03+t1b52ujy/pirSrFhVPkOL3er2S+CynjqLh9GlQEAuJ0QoFHsJKWZdSDm6r/LWl+7sC8yISXfxyjp7KQG1xYfyVyrXNqdUWUAAG53BGjctgzD0LmE5PSAnKlW+UDMVaXaMP35nd4lrMovgvy8VNOnpJydbm4BEgAAUDQRoHFbSExN037LqPK/tcoXEvM/quzp4qQG15VfNPDzUik3ugkAAPgXyQBFimEYOnM1OUv5xcGYq7JhWmXV8C5hVX4R5OepGj4l5XSTy1oDAIDbHwEaDishNU37LmWqVb52YV90Umq+j+Hl6pyl/KK+n6d8GFUGAAA3iBQBuzMMQ6fik/4tv7gYp13R8Tp0+apsmABDd/mUTA/LZf4Ny3d6l2BUGQAA3FIEaBSqq6lp2hsdf221vn//H5Oc/1FlH1fnLOUX9f085eXKyxkAABQ8EgcKhGEY+icuKUv5xeHLCcrvoLJJUq1SJa3KL4LKeMrfq4RMjCoDAAA7IUDjpsWnpGnPpWtTxV0rvwi/GKcrKWn5Poavm4uCyljXKtcr7SlPV+cCbDkAAIDtCNDIN7Nh6ERsoqXsImOquIgr+R9VdjJJAaU8rMovgsp4qZqnO6PKAACgSCBAI1uxyanafW0+5Yzyi93R8Yq1YVS5tLuLGl4ru8j4f73SnirpwqgyAAAougjQxZzZMHQsNtFSfpERlo/GJub7GM4mKdDXw7pW2c9TVRhVBgAAtyECdDFyJTnVsvhIRvnF7ug4xaea832MMu4ualjGy6r8oq6vh0owqgwAAIqJAgnQZrNZEydO1MGDB+Xm5qbJkyfL399fkhQVFaUxY8ZYtt2/f7/Gjh2r/v3757gPbJNmNnQ0NiHLvMrHbRhVdjGZVNvXw6r8IsjPS5U83BhVBgAAxVqBBOjQ0FAlJydr0aJF2rlzp4KDg/XZZ59JksqVK6d58+ZJknbs2KEZM2bokUceyXUf5CwmKcUSkjPKL/ZcitdVG0aVy5VwvTaq/G/5RZ3SnnJ3dirAlgMAABRNBRKgw8LC1KZNG0lSo0aNtGfPnizbGIaht99+W++//76cnZ3ztU9xlmY2dORKwr/zKl9bhOSfuKR8H8PVyaQ6vh5W5RcN/bxUwcOtAFsOAABweymQAB0XFycvLy/Lz87OzkpNTZWLy7+n++OPP1SrVi3VqFEj3/sUF9GJKZbZLzLKL/ZExysxLf+jyhVLumUpv6jt6yE3RpUBAABuSoGkUy8vL8XHx1t+NpvNWYLwzz//rMcff9ymfaT0mml7SExMvOXnTjUMnUhI08G4VB28mqJD8ak6GJeqc8n5D8quJqmmh4tqe7oo4Np/gZ6uKuOWEZSTpdRkKfKSIiJvafOBbBVEXwFuR/QVIG+O2k8KJEA3adJEa9asUZcuXbRz504FBARk2Wbv3r1q0qSJTftIUp06dQqiydmKuJKg6eEnNf/wecWlpMnLNU6DalXQ2KBqqulT0qZjXUxMyVJ+sfdSvJLS8rsEiVTZw+1a2cW/5RcBviXl6sSoMhzH/v37C7WfAkUVfQXImz37SVhYWI73FUiAfuihh7Rx40YNGDBAhmFoypQpWrZsma5evar+/fsrOjpanp6eVrM5ZLePPa3856L6hu5VSppZKdcybmxKmr7cf0bfHDqnxe3rqfMdZbLsl2I261BMwrXV+v5d1vrM1eR8n9vd2aR6pa3LL4LKeKpsCWqVAQAA7M1kGEb+h0DtLCwsTE2bNi3w80RcSVDQ4m25zmTh4eKkNd0aXZtb+d95lfddileyOf9PaVVPd6tlrRuW8VKtUiXlwqgyiihG1YD8oa8AebP3CHROubP4XaGXD9PDTyoljwv2rqaa1WLp9nwfs4Szk+r7eVrKL4L80keW/Uq43mxzAQAAUIgI0NmYf/i8pWzjRtzh5W5VftGwjJfu8ikpZycWIAEAACjqCNDZiEtJy/e2Lcp7WxYfaVjGSw38POXrzqgyAADA7YoAnQ0vV2fF5iNE+7g6a3Ovgq/JBgAAgOPgSrVsDKpVQa55VFu4mqTBtSoUToMAAADgMAjQ2RgbVE2ueazY5+rspNFB1QqpRQAAAHAUBOhs1PQpqcXt68nDxSnLSLSrKX0Ku8Xt69m8mAoAAACKPgJ0DjrfUUbhfe/WU3Uqy8fVWSal1zw/Vaeywvvene0iKgAAALj9cRFhLmr6lNTMewM0894AJrwHAACAJEagAQAAAJsQoAEAAAAbEKABAAAAGxCgAQAAABsQoAEAAAAbEKABAAAAGxCgAQAAABsQoAEAAAAbEKABAAAAGxCgAQAAABsQoAEAAAAbEKABAAAAGxCgAQAAABsQoAEAAAAbEKABAAAAGxCgAQAAABsQoAEAAAAbEKABAAAAGxCgAQAAABsQoAEAAAAbEKABAAAAGxCgAQAAABsQoAEAAAAbEKABAAAAGxCgAQAAABsQoAEAAAAbEKABAAAAGxCgAQAAABu42LsBAIq+6MhYbVq9T+Fbjik5MUVuJXYpqEV1texQV37lve3dPMBh0FeAvBWFfmIyDMOwdyPyKywsTE2bNrXLuffv3686derY5dyAIzu8+7RCZq1TWppZ5rR/306cnE1ydnZSvxFtVatBFTu2EHAM9BUgb47UT3LLnZRwALhh0ZGxCpm1TinJaVZvdJJkTjOUkpymkFnrFB0Za6cWAo6BvgLkrSj1E0o4ANywTav3KS3NnOs2qSlpWjF/sxq1vquQWgU4nh0bjyg1JS3XbegrKO7y00/S0sza/Ns+dXmsRSG1KnsEaAD5ZpgNXY6O1/nTlxR5Kkbb/zwsszn3KjDDkI7uP6ej+88VUiuBoom+AuTNnGYofPMxAjQAxxQfm6jI0zGKPH1J50/HKOp0jCJPxyg5KdXeTQMAFGPJSSn2bgIBGijukpNSFHXmsiJPx+j86UuKOh2j86djFH8l8Zadw8nZpLpN/W/Z8YCiZt/fJ/L8tkair6B4y28/cXN3LYTW5I4ADRQTaalmRUde0flrI8mRpy8p8vRlXboQK9kwF09JL3dVqOKr8lV8df7UJf1zJEpGLm94Ts4mNW1Ty+5ftwH2VKKkm7ZvOJzlwqjM6Cso7vLbT4LuqV6IrcoeARq4zRiGoSvRV9PrlC1hOUYXzl1WWmruF/xl5urmrHKV04Nyxn8VqpSWp08JmUwmSelXTM96a5lSknO+6MPZ2Un3PFT3ph8XUJS17FBXuzZFyJxGXwFyUpT6CQEaKMKuxiVlGk2+FpbPxCgpIf/1YSYnk8pU8FH5yr4qX9XXMrpcuqy3TE6mXPf1K++tfiPa5jlnp6NMfA/YC30FyFtR6icspJJPLKQCe0pJSlXU2ax1ynGXE2w6jo+fhypUKa1ylX1VoWp6UC5bsZRcXJ1vqn3RkbHa/Ns+hW8+pqTEFLmXcFXQPdV1z0OOs2oU4AjoK0DeHKWf5JY7CdD5RIBGYTCnmRUdGavIMzE6f+rfkeXoKNvqlEt4uF0LyKUtI8vlK/uqhIdbwTX+GvoKkD/0FSBv9uwnueVOSjgAOzAMQ7GXrqZf0HcmRpHXwnLUWdvqlF1cnVWuUqn0GuWq6WG5QlVfeZUqaalTBgAAtxYBGihgCfFJVvXJkafS/594NTnfxzCZTPKr4J0+mlzFVxWuheXS5b3k5ORUgK0HAADXI0ADt0hqSlp6nfKpS+lB+XR6GUZszFWbjuPt66EKVX3T65SvXdBXtlIpubrRXQEAcAR8IgM2MpvNuhQZd61O+ZKizqRf0Bd9Pla2XFLgXtI1/YK+Kv8G5fJVfFXS070AWw8AAG4WARrIgWEYiruckH4x35kYy8hy1NnLSs1l3uPrObs4XatTLp1pPmVfeZf2oE4ZAIAiiAANSEq8mmwpu8g8r3JCfP7rlGWS/Mp5XwvJpS1B2a+8t5ycqVMGAOB2QYBGsZKakqYL5y5brdB3/vQlXYm2rU7Zq1RJq9X5ylfxVblKpeTqTpcCAOB2x6c9bkuG2dClC3GKPH1J50/HWBYeuXj+igyzbXXK5Sv7ZqpTTg/LHl7UKQMAUFwRoFGkGYah+CuJlpHkjFHlqDMxSrGhTtnJ2enf+ZQzjSz7+FGnDAAArBGgUWQkJaZkqVGOPB2jq3FJNh2ndDmvbOqUfeTsQp0yAADIGwEaDictNU0Xzl2xCsvnT8fo8sV4m47j6VMi2zpltxKuBdRyAABQHBCgYTeG2VDMxbhr5RcZdcqXdPH8FZnT8l+n7Obukh6OM+qUK6cva+3pXaIAWw8AAIorAjQKRfyVBEtQzljOOupMjJKTUvN9DCdnk8pWvFanXNlX5aum/9+3jJdMTtQpAwCAwkGAxi2VnJiiqLOXdf7UtRrla2E5PjbRpuP4lvFU+aqlLWG5QlVflangI2cX5wJqOQAAQP4QoHFD0lLNunj+yr8r9F0Ly5ei4mw6joeXe3pIrlo6PShfK8Vwp04ZAAA4KAI0cmUYhi5Hxysy03LW50/H6MK5KzKnmfN9HFc35/Q65cq+qnAtLJev6isvn5IF2HoAAIBbjwANi6txSekzXmSE5WuzYCQnpuT7GCYnk8pW8LHUJ2eMLJcuS50yAAC4PRCgi6GUpNT0OuXTl/4dWT4do7jLCTYdp5Sfp/XCI1VLq0wFH7m4Uqdc7FyOkHZOlw7NV+2UOGmDlxQwSGo0VipV096tAxwHfQXIWxHoJwTo25g5zayLkbH/LjxyLSxHR8VK+Z8lTiU93SyLjmQsPFKusq9KeLgVXONRdJxYKf3aV0pLkYwUmSQpJVba96V08Bup42LJv7O9WwnYH30FyFsR6ScE6NuAYRi6culqllX6os5eVlpq/uuUXdycVa5SKcuiIxn/eZUqyXLWyN7liPQ3utSrWe8zUqTUlPT7+4c7zKgBYBf0FSBvRaifEKCLmIT4JEtQPp8pLCcl2FCnbDKpTAVvq+Wsy1fxVelyXnJyYjlrXGNOk5IuSYkXpMSL1/137bZTv2f/RpdZ6lXpu7skE6U9KMaMtLy3oa+guMtPP0lLkXbNkO6bWfDtyQUB2kGlJKfqwtnL10LyvyPLsTG21Sn7lPbINJr873LW1CkXM6mJ/4bfpExBOOGC9c+Zw3FSjGyq9clLft4YAdBXgNwYKdKheQTo4s5sNutSZFz6BX2ZwnJ0ZKwMI//hpYSHm/UFfVVKq1zlUirp6V6ArUehM4z0WrDsAm9ut6XG27vlAADcGsm2rTlREAjQhcQwDMXGJFjVKGfUKaem5H+0wdnFKX0u5WsLjlS4NrLs7UudcpFjKZHIIwRfP0Jszn+5zk1z95Xcy0gly6b/v0TGf2XT/79xjJSWj29FXH2k4RcLvLmAw/rKL/2P37zQV1Cc5befuHkVfFvyQIDORXRkrDat3qfwLceUnJgitxK7FNSiulp2qCu/8t457pd4NTlTnfK/YTnxanK+z20ymeRX3ttqVLl8FV/5lfOWkzN1yg4nLSnvkojrb7vVJRK5cXK5LgBfF4Szu829dPp+ubkYnn5ltJFLqDe5SoGD8z4WcDsLGERfAfKS334SMLjw2pQDemkODu8+rZBZ65SWZpY5LT3kJCemaPuGw9q1KUL9RrRV9doVr6tTvqTIMzG6Ep3HRVXX8fYt+e+iI9eCcrlKpeTqxq+n0GVbIpGPMonCLJFw8cg5BGeE5OtHjN18pIL4hqLR2PRphVJzebNzdpUajr715waKEvoKkLci1E9IaNmIjoxVyKx1SknOWlphTjNkTkvTgo//kEyyaQDRvaRrekC+FpYz5lP28KJOuUBYlUhkE4KTchgxtkeJRLajwdmNDpeRXBxo+fNSNdPn5Mw0Z6eFyTX9ja7jYrtPNwTYHX0FyFsR6icE6GxsWr1PaWn5mD85h/Ds7OKkshVLqULV9IBc4drIsk9pD+qUb1RGiUR+L5pLvJgengurRMLknHdJROZR4pJl81ciURT4d06fk3PXDOnQPBnJsTK5ead/xdZwtEO80QEOgb4C5K2I9BOTYctUD3YWFhampk2bFvh53n3ueyUn5m8UsnQ57/QL+ar+O7Jcpjx1yjkyDCklLn8hOGOE2N4lEhmBN3OJxPXBuKBKJIqg/fv3q06dOvZuBuDw6CtA3uzZT3LLnbfB8Netl9/wLJM0akqvAm2LQ7u+RCKjFCLh+tkjLjhIiUQ+R4gdqUQCAAA4HAJ0NtxKuOYrRLu7uxZCawqJVYlEPssk7FIikY8QbAnMfrdHiQQAAHAopItsBLWoru0bDltm38iOk7NJQfdUL8RW5ZOlRCIfITjziHGhlkiUzF9ZROafKZEAAAAOggCdjZYd6mrXpgiZ03Je4MTZ2Un3PFS3YBtiTpOSY7Ipichu9ogLDlIikY8RYkokAABAEUaAzoZfeW/16+2pkB+ilGZ2ljnT0+SkVDk7palf73K5LqaSRZYSifyMENurRCKXmSMokQAAAMUc6Sc7lyNUK+JRjajtpc2RHRUe3VrJ5hJyc0pUkN9G3VP+V/kdiZWqL5RcSuQ8c0TmEeKUQly3PaNEIrsFNXIaIXYrRYkEAABAPhCgs7NzupSWIj/3SHWpNk9dqs3Luk2apFW9Cr4tbqXyN3NE5tsokQAAACgwBOjsHJqf+zrsNyJLiUR+5hemRAIAAMDRkM6yY0u5RZV2+RshpkQCAADgtkCAzo6rl5QSm/d2bj5Sz98Lvj0AAABwGKw3nZ2AQZIpj0VSTK7p67IDAACgWCFAZ6fRWMk5jwDt7Co1HF047QEAAIDDIEBnp1RNqeNiycUj60i0yTX99o6L07cDAABAsUKAzol/Z6l/uFTvKcnNR4ZM6TXP9Z5Kv92/s71bCAAAADsokIsIzWazJk6cqIMHD8rNzU2TJ0+Wv7+/5f7w8HAFBwfLMAyVK1dO7733ntzd3dWrVy95e6ev7le1alW9++67BdG8/CtVU7pvpnTfTB3Yv1916tSxb3sAAABgdwUSoENDQ5WcnKxFixZp586dCg4O1meffSZJMgxDr7/+uj7++GP5+/srJCREp0+fVpUqVSRJ8+Zls2gJAAAA4CAKpIQjLCxMbdq0kSQ1atRIe/bssdx37Ngx+fr66ptvvtGgQYMUExOjGjVq6MCBA0pISNCwYcP0+OOPa+fOnQXRNAAAAOCmFMgIdFxcnLy8vCw/Ozs7KzU1VS4uLrp06ZJ27Nih119/Xf7+/hoxYoTq168vPz8/DR8+XP369dPx48f15JNPatWqVXJxYapqAAAAOI4CSadeXl6Kj4+3/Gw2my1B2NfXV/7+/rrrrrskSW3atNGePXs0ZMgQ+fv7y2QyqXr16vL19VVUVJQqVapkdez9+/cXRJPzlJiYaLdzA0UJfQXIH/oKkDdH7ScFEqCbNGmiNWvWqEuXLtq5c6cCAgIs91WrVk3x8fE6ceKE/P399ffff6tv375avHixDh06pIkTJ+r8+fOKi4tTuXLlshzbXhfy7eciQiBf6CtA/tBXgLzZs5+EhYXleF+BBOiHHnpIGzdu1IABA2QYhqZMmaJly5bp6tWr6t+/v9555x2NHTtWhmGocePGuv/++5WcnKxXXnlFAwcOlMlk0pQpUyjfAAAAgMMpkITq5OSkSZMmWd1Ws+a/i460bNlSixcvtrrfzc1N06dPL4jmAAAAALcMC6kAAAAANiBAAwAAADYgQAMAAAA2IEADAAAANiBAAwAAADYgQAMAAAA2IEADAAAANiBAAwAAADYgQAMAAAA2IEADAAAANiBAAwAAADYgQAMAAAA2IEADAAAANiBAAwAAADYgQAMAAAA2IEADAAAANiBAAwAAADYgQAMAAAA2IEADAAAANiBAAwAAADYgQAMAAAA2IEADAAAANiBAAwAAADYgQAMAAAA2IEADAAAANiBAAwAAADYgQAMAAAA2IEADAAAANiBAAwAAADYgQAMAAAA2IEADAAAANiBAAwAAADYgQAMAAAA2IEADAAAANsh3gL58+XJBtgMAAAAoElzy2mDr1q2aNGmS0tLS1KlTJ1WuXFn9+vUrjLYBAAAADifPEeiPPvpI8+fPV9myZTVixAgtXLiwMNoFAAAAOKQ8A7STk5N8fX1lMpnk7u4uT0/PwmgXAAAA4JDyDNB33HGHpk+frpiYGM2ePVuVK1cujHYBAAAADinPAP3WW2+pcuXKatq0qTw8PPT2228XRrsAAAAAh5RngN67d69SUlL05ptvaseOHTpy5EhhtAsAAABwSHkG6LffflutWrWSJL3wwgt65513CrxRAAAAgKPKM0C7uLjorrvukiRVq1ZNTk6svQIAAIDiK895oCtXrqwPPvhAjRo1Unh4uMqXL18Y7QIAAAAcUp7Dye+++678/Py0bt06lSlTRu+++25htAsAAABwSHmOQLu5ualJkyaqV6+eJGnXrl26++67C7xhAAAAgCPKM0A/99xzio6OVqVKlWQYhkwmEwEaAAAAxVaeAfrChQv6/vvvC6MtAAAAgMPLswa6evXqOn/+fGG0BQAAAHB4eY5Ab9++XQ888ID8/Pwst23YsKFAGwUAAAA4qjwD9K+//loY7QAAAACKhDwD9M6dO7VkyRKlpKRIkiIjI/XVV18VeMMAAAAAR5RnDfTkyZPVvHlzxcXFqXLlyvL19S2EZgEAAACOKc8A7ePjo27dusnLy0vPPfccFxQCAACgWMszQJtMJh0+fFgJCQk6evSooqKiCqNdAAAAgEPKM0CPHz9ehw8f1uDBg/Xiiy9q4MCBhdEuAAAAwCHleRFhrVq1VKlSJSUlJWn27NkymUyF0S4AAADAIeUZoF966SWFhYXJx8fHspT3Tz/9VBhtAwAAABxOngH62LFj+v333wujLQAAAIDDy7MGOigoSEePHi2MtgAAAAAOL88RaC8vL/Xt21ceHh6W21jKGwAAAMVVngF6y5Yt2rp1q1xc8twUAAAAuO3lWcLh7++vixcvFkZbAAAAAIeX57Dyjh071K5dO5UuXdpyGyUcAAAAKK7yDNBvvfWWWrZsWRhtAQAAABxeniUcM2fOLIx2AAAAAEVCniPQJpNJzz77rKpXry4np/S8PWbMmAJvGAAAAOCI8gzQffr0KYx2AAAAAEVCniUc3bt319WrVxUeHq4rV66oa9euhdEuAAAAwCHlGaDfeOMNnTx5Uq1bt9bp06f12muvFUa7AAAAAIeUZwnHiRMn9N1330mS2rdvrwEDBhR4owAAAABHlecIdFJSkhISEiRJiYmJSktLK/BGAQAAAI4qzxHoxx9/XD179lStWrV05MgRjRo1qjDaBQAAADikHAP0/PnzNWjQIPn7++uHH37QyZMnVbVqVasVCQEAAIDiJscAvWjRIlWtWlUzZszQuHHjJEmXL1+WJN17772F0zoAAADAweQYoJ9//nmFhobq4sWLWrFihdV9BGgAAAAUVzkG6Pbt26t9+/ZaunSpevXqVYhNAgAAABxXnrNwLF68uDDaAQAAABQJec7CkZycrF69eql69epyckrP29OnTy/whgEAAACOKM8A/eKLLxZGOwAAAIAiIc8Sjrp162rjxo1aunSpYmJiVKFChcJoFwAAAOCQ8gzQEyZMULVq1XT8+HGVLVtWr776amG0CwAAAHBIeQbomJgY9e3bVy4uLmrSpIkMwyiMdgEAAAAOKc8ALUkRERGSpHPnzlkuJAQAAACKozzT8GuvvaYJEyZo3759GjVqlMaPH18Y7QIAAAAcUp6zcAQEBOizzz7TP//8ozvvvFO+vr6F0CwAAADAMeUZoL/77jt9++23uuuuu3TkyBE988wz6tmzZ2G0DQAAAHA4eQbokJAQ/fzzz3J3d1dCQoIGDRpEgAYAAECxlWcNdJkyZeTs7CxJKlGiBCUcAAAAKNbyHIE2DEO9evVS48aNtW/fPqWmpmrs2LGSWNIbAAAAxU+eAXrEiBGWf3fv3r1AGwMAAAA4ujwDdPPmzQujHQAAAECRkGeAvhFms1kTJ07UwYMH5ebmpsmTJ8vf399yf3h4uIKDg2UYhsqVK6f33ntPrq6uue4DAAAAOIICCdChoaFKTk7WokWLtHPnTgUHB+uzzz6TlF5T/frrr+vjjz+Wv7+/QkJCdPr0aR05ciTHfQAAAABHkWOAHjNmjEwmU7b35XXxYFhYmNq0aSNJatSokfbs2WO579ixY/L19dU333yjQ4cOqW3btqpRo4YWLVqU4z4AAACAo8gxQA8YMOCGDxoXFycvLy/Lz87OzkpNTZWLi4suXbqkHTt26PXXX5e/v79GjBih+vXr57pPZvv377/hdt2MxMREu50bKEroK0D+0FeAvDlqP8kxQGdcPBgXF6cvvvhCUVFRuv/++xUYGJjnQb28vBQfH2/52Ww2W4Kwr6+v/P39ddddd0mS2rRpoz179uS6T2Z16tTJ50O7tfbv32+3cwNFCX0FyB/6CpA3e/aTsLCwHO/LcyGVCRMmqFq1ajp+/LjKli2rV199Nc8TNmnSROvXr5ck7dy5UwEBAZb7qlWrpvj4eJ04cUKS9Pfff6tWrVq57gMAAAA4ijwvIoyJiVHfvn31888/q0mTJjIMI8+DPvTQQ9q4caMGDBggwzA0ZcoULVu2TFevXlX//v31zjvvaOzYsTIMQ40bN9b9998vs9mcZR8AAADA0eRrFo6IiAhJ0rlz5+TklOegtZycnDRp0iSr22rWrGn5d8uWLbV48eI89wEAAAAcTZ5p+NVXX9WECRO0b98+jRo1SuPHjy+MdgEAAAAOKc8R6MDAQC1atKgw2gIAAAA4vBwDdLt27azmgXZxcVFqaqrc3Ny0cuXKQmkcAAAA4GhyDNCrVq2SYRh66623NGDAAAUFBWnfvn1asGBBYbYPAAAAcCg5Bmg3NzdJ0smTJxUUFCRJqlu3ro4dO1Y4LQMAAAAcUJ410N7e3vrwww8VFBSkHTt2qEqVKoXRLgAAAMAh5TkLx/vvv69y5crpzz//VPny5fXuu+8WRrsAAAAAh5RngHZ3d5ckGYYhs9mstLS0Am8UAAAA4KjyDNCvv/66Tp48qXvvvVenT5/Wa6+9VhjtAgAAABxSnjXQJ06c0HfffSdJat++vQYMGFDgjQIAAAAcVZ4j0ElJSUpISJAkJSYmUsIBAACAYi3PEejHH39cPXv2VK1atXTkyBE999xzhdEuAAAAwCHlGaB79Oih++67TydPnlTVqlVVunTpwmgXAAAA4JByDNCvvPJKjjsxlR0AAACKqxwD9J49e5SYmKgePXqocePGMgyjMNsFAAAAOKQcLyJctmyZPv30UyUlJWn27NnauXOn7rjjDrVp06Yw2wcAAAA4lFxroAMCAvTiiy9KkrZt26bp06fr3Llz+uGHHwqlcQAAAICjyfMiwri4OP32229avny5EhIS1KNHj8JoFwAAAOCQcgzQK1eu1IoVK3TmzBl16NBBb731lqpWrVqYbQMAAAAcTo4BevTo0apRo4Zq166tQ4cOacaMGZb7pk+fXiiNAwAAABxNjgH622+/Lcx2AAAAAEVCjgG6efPmhdkOAAAAoEjIcRo7AAAAAFkRoAEAAAAbEKABAAAAGxCgAQAAABsQoAEAAAAbEKABAAAAGxCgAQAAABsQoAEAAAAbEKABAAAAGxCgAQAAABsQoAEAAAAbEKABAAAAGxCgAQAAABsQoAEAAAAbEKABAAAAGxCgAQAAABsQoAEAAAAbEKABAAAAGxCgAQAAABsQoAEAAAAbEKABAAAAGxCgAQAAABsQoAEAAAAbEKABAAAAGxCgAQAAABsQoAEAAAAbEKABAAAAGxCgAQAAABsQoAEAAAAbEKABAAAAGxCgAQAAABsQoAEAAAAbEKABAAAAGxCgAQAAABsQoAEAAAAbEKABAAAAGxCgAQAAABsQoAEAAAAbEKABAAAAGxCgAQAAABsQoAEAAAAbEKABAAAAGxCgAQAAABsQoAEAAAAbEKABAAAAGxCgAQAAABsQoAEAAAAbEKABAAAAGxCgAQAAABsQoAEAAAAbEKABAAAAGxCgAQAAABsQoAEAAAAbEKABAAAAGxCgAQAAABsQoAEAAAAbEKABAAAAGxCgAQAAABsQoAEAAAAbEKABAAAAGxCgAQAAABsQoAEAAAAbEKABAAAAGxCgAQAAABsQoAEAAAAbEKABAAAAGxCgAQAAABsQoAEAAAAbEKABAAAAG7gUxEHNZrMmTpyogwcPys3NTZMnT5a/v7/l/rlz52rx4sXy8/OTJL311luqUaOGevXqJW9vb0lS1apV9e677xZE8wAAAIAbViABOjQ0VMnJyVq0aJF27typ4OBgffbZZ5b79+7dq6lTp6p+/fqW25KSkiRJ8+bNK4gmAQAAALdEgZRwhIWFqU2bNpKkRo0aac+ePVb37927V7Nnz9bAgQP1+eefS5IOHDighIQEDRs2TI8//rh27txZEE0DAAAAbkqBjEDHxcXJy8vL8rOzs7NSU1Pl4pJ+uq5du+rRRx+Vl5eXRo4cqTVr1qhy5coaPny4+vXrp+PHj+vJJ5/UqlWrLPsAAAAAjqBA0qmXl5fi4+MtP5vNZksQNgxDQ4YMsdQ6t23bVvv27VPr1q3l7+8vk8mk6tWry9fXV1FRUapUqZLVsffv318QTc5TYmKi3c4NFCX0FSB/6CtA3hy1nxRIgG7SpInWrFmjLl26aOfOnQoICLDcFxcXp27duumXX36Rh4eHtmzZoj59+mjx4sU6dOiQJk6cqPPnzysuLk7lypXLcuw6deoURJPztH//frudGyhK6CtA/tBXgLzZs5+EhYXleF+BBOiHHnpIGzdu1IABA2QYhqZMmaJly5bp6tWr6t+/v0aPHq3HH39cbm5uatmypdq2bavk5GS98sorGjhwoEwmk6ZMmUL5BgAAABxOgSRUJycnTZo0yeq2mjVrWv7dq1cv9erVy+p+Nzc3TZ8+vSCaAwAAANwyLKQCAAAA2IAADQAAANiAAA0AAADYgAANAAAA2IAADQAAANiAAA0AAADYgAANAAAA2IAADQAAANiAAA0AAADYgAANAAAA2IAADQAAANiAAA0AAADYgAANAAAA2IAADQAAANiAAA0AAADYgAANAAAA2IAADQAAANiAAA0AAADYgAANAAAA2IAADQAAANiAAA0AAADYgAANAAAA2IAADQAAANiAAA0AAADYgAANAAAA2IAADQAAANiAAA0AAADYgAANAAAA2IAADQAAANiAAA0AAADYgAANAAAA2IAADQAAANiAAA0AAADYgAANAAAA2IAADQAAANiAAA0AAADYgAANAAAA2IAADQAAANiAAA0AAADYgAANAAAA2IAADQAAANiAAA0AAADYgAANAAAA2IAADQAAANiAAA0AAADYgAANAAAA2IAADQAAANiAAA0AAADYwMXeDQAAAEBWEVcSND38pOYfPq+4lDR5uTprUK0KGhtUTTV9Stq7ecUaI9AAAAAOZuU/FxW0eJu+3H9GsSlpMiTFpqTpy/1nFLR4m1b+c9HeTSzWCNAAAAAOJOJKgvqG7tXVVLNSDOv7UgzpaqpZfUP3KuJKwk2dZ/bs2br33nuVlJQkSRo/frzWr19vtU3r1q0t/w4NDdXgwYM1ePBg9evXT6tWrcpyzIMHD2rbtm35bsPo0aOVnJx8g4/AfgjQAAAADmR6+EmlpJlz3SYlzawZ4Sdv6jzLli1Tly5dtGLFijy33b59u77++mvNmjVL8+bN0+zZs/XBBx/oyJEjVtutXr06y225mTFjhtzc3Gxuu71RAw0AAGAH08NPamLYccWlpNm8b4ohfbrvjD7dd8bqdi9XZ01seqfGBlXLdf8tW7bojjvu0IABAzRu3Dj17t071+1DQkI0ZMgQeXp6SpJKly6tkJAQ+fj4WLY5f/68fvrpJ7m6uqpevXqaMGGC7rzzTrm5uemll17SxIkTlZSUpJiYGD377LNq37692rVrp5UrV+rNN9+Um5ubTp8+rcjISAUHB6tevXo2Py+FhRFoAAAAO5gefvKGwnNu4lLSND0fI9MhISHq16+fatSoITc3N+3atSvb7UwmkyQpMjJS1apZh/JSpUpZ7pekChUq6OGHH9bQoUMVFBSkq1ev6plnntEHH3ygo0eP6oknntDcuXP1+uuv67vvvstyrsqVK+urr77S4MGDtWjRIlsedqFjBBoAAMAOxgZVu+ER6Jx4uTrnOfp8+fJlrV+/XtHR0Zo3b57i4uI0f/58eXh4ZKlHTk1NlZQebs+ePavatWtb7gsLC1PZsmXl7++f47mqV68uSSpXrpw+++wzLV68WCaTyXLczOrUqSNJqlixorZv356/B2wnBGgAAAA7GBtULduw+8yGQ/py/5ksFxBm5mqSnqpTWTPvDbD5vD///LP69Omjl19+WZKUkJCgBx98UMOGDdNvv/2m9u3bS5L+/vtv3XXXXZKk3r17a/r06WrRooU8PDx08eJFTZgwQR999JHVsU0mk8zmf+u3nZzSix0++ugj9evXT23bttWPP/6on376KUu7Mo9mOzoCNAAAgAMZG1RN3xw6p5TUnC8kdHV20ug8RppzEhISomnTpll+LlmypDp06KDExER5eHioZ8+e8vT0lKurqyZNmiRJaty4sR555BENGzZMLi4uSkxM1JgxY6xGpCWpfv36mjZtmmrWrGl1e6dOnfTOO+/o888/V6VKlXTp0qUbarujMBmGkcvfN44lLCxMTZs2tcu59+/fb/lqAUDO6CtA/tBXkJuV/1xU39C9SkmznsrO1ZQenhe3r6fOd5SxXwMLiT37SW65k4sIAQAAHEznO8oovO/deqpOZfm4OstJko+rs56qU1nhfe8uFuHZkVHCAQAA4IBq+pTUzHsDbqjOGQWLEWgAAADABgRoAAAAwAaUcAAAADiiyxHSzunSoflSSpzk6iUFDJIajZVK1cx7fxQYRqABAAAczYmV0qIgad+XUkqsJCP9//u+TL/9xEp7t7BYI0ADAAA4kssR0q99pdSrkpFifZ+Rkn77r33Tt7sJs2fP1r333qukpCRJ0vjx47V+/XqrbVq3bm35d2hoqAYPHqzBgwerX79+WrVq1Q2f+9SpU3rkkUds2icpKUkhISGSpCVLluj333+/4fPfLEo4AAAAHMnO6VJaSu7bpKVIu2ZI98284dMsW7ZMXbp00YoVK9S7d+9ct92+fbu+/vprff755/L09NSlS5fUv39/3XXXXZbVCgtaVFSUQkJC1K9fvzzbW9AI0AAAAPawc7q0bWJ6fbOtjBRpz6fp/2Xm6iXdPTG9TjoXW7Zs0R133KEBAwZo3LhxeQbSkJAQDRkyRJ6enpKk0qVLKyQkRD4+Plbbfffdd1q6dKmcnJzUpEkTvfzyyzp79qxef/11JSUlyd3dXW+//bbVPlu3btWMGTPk7OysatWqadKkSUpLS9Mrr7yiiIgIubi46PXXX9ePP/6oI0eOaObMmTIMQ2XLltXAgQMVHByssLAwSVK3bt00ZMgQjR8/Xm5ubjp9+rQiIyMVHBysevXq5eOJzR9KOAAAAOxh5/QbC8+5SYlLP24eMkZya9SoITc3N+3atSvb7UwmkyQpMjJS1apZLx1eqlQpy/0ZlixZoldffVWLFi1StWrVlJqaqqlTp2rw4MGaN2+ehg8frvfff9+yvWEYev311zVz5kzNnz9fFSpU0E8//aTvv/9eVapU0dSpUxUcHKxdu3ZpxIgRuuuuuzRy5EjL/mvWrNGpU6f0ww8/aMGCBVq+fLkOHjwoSapcubK++uorDR48WIsWLcrf85dPjEADAADYQ6OxNz4CnRNXrzxHny9fvqz169crOjpa8+bNU1xcnObPny8PDw8lJydbbZuamiopPYyePXtWtWvXttwXFhamsmXLyt/f33Lbu+++qzlz5uj9999Xo0aNZBiGDh06pM8//1xffvmlDMOQq6urZfvo6GhFRkbqhRdekCQlJiaqdevWio6O1n333SdJCggIUEBAgE6dOpXlsURERKhZs2YymUxydXVVw4YNFRGRXhuesQR4xYoVtX379vw+g/lCgAYAALCHRmOzD7vrnkmfbeP6CwgzM7lK9Z66oRron3/+WX369NHLL78sSUpISNCDDz6oYcOG6bffflP79u0lSX///belvrl3796aPn26WrRoIQ8PD128eFETJkzQRx99ZHXsH374QW+99Zbc3d01fPhw7dixQzVq1NCwYcPUpEkTRUREaNu2bZbtS5curYoVK+q///2vvL299fvvv8vDw0OHDh3S7t27VaVKFZ08eVIffvihxo4dK7PZbHW+mjVrasmSJRo6dKhSUlK0Y8cOPfzww+lP0XWj47cSARoAAMCRNBorHfxGSs0lQDu7Sg1H39DhQ0JCNG3aNMvPJUuWVIcOHZSYmCgPDw/17NlTnp6ecnV11aRJkyRJjRs31iOPPKJhw4bJxcVFiYmJGjNmjNWItCQFBgaqb9++Kl26tCpUqKCGDRvq5Zdf1sSJE5WUlKTExES9+uqrlu2dnJz06quv6qmnnpJhGPL09NS0adPUpEkTTZgwQa+++qrc3d01YcIElSlTRikpKXrvvfdUokQJSdIDDzygrVu3qn///kpJSVGnTp1uaa1zTkyGYRgFfpZbJCwsTE2bNrXLuffv32/5KgBAzugrQP7QV5CrEyvTp6pLS7EeiTa5pofnjosl/872a18hsWc/yS13chEhAACAo/HvLPUPTy/TcPOR5JT+/3pPpd9eDMKzI6OEAwAAwBGVqple43wTcz2jYDACDQAAANiAAA0AAADYgBIOAAAABxQdGatNq/cpfMsxJSemyK2Eq4JaVFfLDnXlV97b3s0r1gjQAAAADubw7tMKmbVOaWlmmdPSJ0xLTkzR9g2HtWtThPqNaKtaDarYuZXFFyUcAAAADiQ6MlYhs9YpJTnNEp4zmNMMpSSnKWTWOkVHxt7UeWbPnq17771XSUlJkqTx48dr/fr1Vtu0bt3a8u/Q0FANHjxYgwcPVr9+/bRq1aobPvepU6f0yCOPSJJGjx6dZQXE9evXa/z48Tnun5SUpJCQEEnpy4f//vvvN9yWG0GABgAAcCCbVu9TWpo5123S0sza/Nu+mzrPsmXL1KVLF61YsSLPbbdv366vv/5as2bN0rx58zR79mx98MEHOnLkyE21QZJmzJghNzc3m/aJioqyBOjevXvrwQcfvOl22IISDgAAADv4a/U+rft5l5KTUm3e15xmaNvaQ9q29pDV7W7uLmrbo6Fadaib6/5btmzRHXfcoQEDBmjcuHHq3bt3rtuHhIRoyJAh8vT0lJS+BHdISIh8fHws20RHR+uxxx7TL7/8IpPJpLfeekutWrVSqVKlNHNm+lR8iYmJmjp1qlxdXS37tWvXTitXrtSpU6c0YcIElSxZUiVLllSpUqUkSfPnz9fq1auVmpoqb29vffLJJ5o1a5aOHDmimTNnyjAMlS1bVgMHDlRwcLDCwsIkSd26ddOQIUM0fvx4ubm56fTp04qMjFRwcPBNr1bICDQAAIAdbFq974bCc26Sk1K1aXXeI9MhISHq16+fatSoITc3N+3atSvb7UwmkyQpMjJS1apVs7qvVKlSlvslyc/PT4GBgfr777+VnJysrVu36oEHHtDhw4f13nvv6dtvv1W7du1yLP346KOPNGrUKH399ddq3LixJMlsNismJkZff/21FixYoNTUVO3evVsjRozQXXfdpZEjR1r2X7NmjU6dOqUffvhBCxYs0PLly3Xw4EFJUuXKlfXVV19p8ODBWrRoUZ7PT14YgQYAALCDlh3q3vAIdE7c3F3UMo/R58uXL2v9+vWKjo7WvHnzFBcXp/nz58vDwyNLLXJqanrbKleurLNnz6p27dqW+8LCwlS2bFn5+/tbbnvkkUf0008/KSoqSu3atZOLi4sqVKigd955Rx4eHjp//ryaNGmSbbsOHz6soKAgSVKTJk109OhROTk5ydXVVWPGjJGHh4fOnTtnadP1IiIi1KxZM5lMJrm6uqphw4aKiIiQJMty4BUrVtT27dtzfX7ygwANAABgB6061M221GLF/C3avuFwlgsIM3NyNqlpm1rq8lgLm8/7888/q0+fPnr55ZclSQkJCXrwwQc1bNgw/fbbb2rfvr0k6e+//9Zdd90lKb3OePr06WrRooU8PDx08eJFTZgwQR999JHVsVu2bKn33ntP58+f1xtvvCFJeu211xQaGiovLy+9/PLLMozsH1eNGjW0Y8cO3XfffdqzZ48k6fjx4woNDVVISIgSEhLUu3dvGYYhJycnmc3WdeI1a9bUkiVLNHToUKWkpGjHjh16+OGHJclqpPxWIEADAAA4kJYd6mrXpgiZ09Jy3MbZ2Un3PJT7SHNOQkJCNG3aNMvPJUuWVIcOHZSYmCgPDw/17NlTnp6ecnV11aRJkyRJjRs31iOPPKJhw4bJxcVFiYmJGjNmjNWItJQeVDt27Ki//vrLMjLds2dPPfLII/Lx8VHZsmUVGRmZbbvefPNNjR49Wl999ZX8/Pzk7u6uSpUqqWTJkurdu7fc3NxUrlw5RUZGqnHjxkpJSdF7772nEiVKSJIeeOABbd26Vf3791dKSoo6dep007XOOTEZOf0Z4IDCwsLUtGlTu5x7//79luF/ADmjrwD5Q19BbrKbB1pKH3l2dnYqNvNA27Of5JY7GYEGAABwMLUaVNGIN7tr82/7FL75mJKTUuTm7qqge6rrnodYidDeCNAAAAAOyK+8t7o81uKG6pxRsJjGDgAAALABARoAAACwAQEaAAAAsAEBGgAAALBBgVxEaDabNXHiRB08eFBubm6aPHmy1So1c+fO1eLFi+Xn5ydJeuutt3TnnXfmug8AAADgCAokQIeGhio5OVmLFi3Szp07FRwcrM8++8xy/969ezV16lTVr1/fctvq1atz3QcAAABwBAUSoMPCwtSmTRtJUqNGjSzLMWbYu3evZs+eraioKN1///16+umn89wHAAAAcAQFEqDj4uLk5eVl+dnZ2VmpqalycUk/XdeuXfXoo4/Ky8tLI0eO1Jo1a/LcBwAAAHAEBZJOvby8FB8fb/nZbDZbgrBhGBoyZIi8vdNX0Gnbtq327duX6z6ZhYWFFUST88We5waKEvoKkD/0FSBvjthPCiRAN2nSRGvWrFGXLl20c+dOBQQEWO6Li4tTt27d9Msvv8jDw0NbtmxRnz59lJiYmOM+GXJajxwAAAAoLCbDMIxbfdCMWTgOHTokwzA0ZcoU7du3T1evXlX//v21dOlSzZs3T25ubmrZsqVGjRqV7T41a9a81U0DAAAAbkqBBGgAAADgdsVCKgAAFACz2WzvJgAoIARoAABusbS0NDk5OckwDJ0+fdrezQEcWlEshiBAX5Pxy0tNTbVzSwDHld2IWlF84wMKmrOzs06fPq0nn3xS06ZN0+bNm+3dJMAhpaWlyWQyWf5dVFADncmWLVv0448/6sEHH1SrVq0sU+0BSA/PTk7pf3OvXLlSJpNJnTp1snOrAMcUHx+vCRMmqF27dnrwwQdVsmRJnTt3TlWqVJFhGJbAABRnmfvCZ599Jnd3dzVr1kxBQUF2blneGIG+5vfff9eHH36oVq1a6c4775S3t7eSkpLs3SzAYWSE5zfeeEOLFy9WRESE/vnnHzu3CnAM1387k5aWpvPnz6tFixby8vJSdHS0Jk2apLi4OMIzcE1GX5g8ebL279+vBg0ayM/PT8nJyZIc+zqCYhugr/+aICoqSp07d1avXr0UGBioVatW6ddff7VT6wDHkflLqn379ikqKkpfffWVnn32WTk5Oenrr7+2X+MAB5D525lvvvlGv/zyi2JjY9W9e3d98MEHkqSIiAiVKFHCEgyA4izz58qFCxd06tQpTZo0SXfffbd8fHz0/PPPKyEhwdKvHJHjtqwAmc1mOTs7S5L++usvS93zuXPnFBsbK0naunWrSpYsabc2Ao7CZDIpOjpaklSxYkWlpqZq06ZNlvsjIiL4tgbFWsaH/MyZM7V27Vrt379fM2bMUEBAgCpVqqT/+7//08yZM/V///d/8vPzs3NrAfvJCM4mk0kRERE6f/68XF1dFRAQoDVr1kiSPD09VapUKYe/vqZAViJ0dE5OToqOjtb48eN16dIlVapUSePGjdPmzZv1xRdf6MSJE3J2dlarVq3s3VTALjLXpa1fv17vvfee7r33XtWpU0ddu3bV999/r1OnTmnp0qXq2LGj3N3d7dxiwH5SUlI0Y8YMxcTEaM6cOYqLi1NISIh+/fVXjRkzRikpKSpRooTc3d2tRquB4ibjc+WPP/7Qe++9pzvvvFMPPfSQypUrp0OHDumNN97Q8ePH1bZtW3l4eNi5tbkrlhcRnjlzRq+//rr69++vDh06aNSoUQoKClKnTp0UFxenAwcOqFevXpLExR4o1tasWaMNGzaoffv2OnbsmM6fP68GDRrI19dXf/31lwIDA9WxY0d7NxMoVJk/F+Li4uTl5aV58+Zp586devzxx9WgQQP9888/mjt3rlq3bq0OHTpIEuEZkDRv3jydPHlS//nPf7Rz507t3btXgYGBCgwM1MGDB1W+fHk1a9bM3s3MU7EYgb7+TctsNuvcuXOKiIiQJL3zzjt68sknlZaWpqefflq1a9fOdj/gdpeWlmYpb4qKitIHH3yg+++/Xy1btlT16tX122+/aePGjXryySc1atQoy378oYniJOO1vnTpUq1bt04VKlRQly5ddPr0af3555/y9vZWjRo19Nxzz6ls2bKW/fg8QXGU+XNFSr+W5tixYypfvrw6dOigixcvavPmzapataq6dOlix5ba5rbvzYZhWN605syZo5CQEIWHh+vDDz/UX3/9pT/++EPe3t5655131LhxY6t9ebNDcZJxbUBiYqL++usvSdKbb76pPXv2aN++fapYsaLuuece1a5dWz4+Plb7Ep5R3Pz111/6+eefNW3aNJ04cUKLFy9Wt27ddPr0af36669KTk62hOdi+EUvICn9te/s7KwLFy5ozpw5Wrlypd59912VKFFCM2fOlCT17t1bTZs2VWBgoJ1ba5vbPiFmfLBPnDhRx48fl7+/v7788ktFRETomWee0dSpUxUREaGaNWuqefPmdm4tYD9OTk46efKkBg0aZPkqOjExUW3bttXHH3+s48ePq1atWurfv7+8vLwIBSjWLly4oHr16mnlypUqUaKEAgMDlZiYqBdffFHdu3eXm5ubZVv+wERxlXGx4OjRo1WxYkX9+OOPCg4O1tSpU7Vs2TItWLBA7u7u6tmzp9zd3YvU58ptH6AlKSEhQSVKlNC4ceMUGhqqBg0aKDo6WnXr1tXo0aNVoUIFezcRsCvDMGQ2m7V48WINGzZMzzzzjFxdXbV69Wo9+uij8vf31549e6z2IRSgOKtTp442bNigxYsXa8aMGYqOjtZvv/2m0qVLq2rVqkUqCAAFaevWrWrXrp1atGghs9msixcv6vLly3rvvfeyrP5clD5XisVFhGfOnNGrr74qwzDUvn179e7dW2+//bZGjBghf39/SdRwApK0YMECrV+/XnFxcQoODtbmzZvl4eFRpOrSgMIQGxur1atXW0JzcnKypk+fbu9mAQ4jI1f99ttvWrJkiS5evKj//ve/OnDggMLDw/XMM8/Yu4k3pVhcRFi5cmW1b99eP/74o+68806NGTNG9erVs4RnqWj91QMUFH9/f5nNZgUFBenkyZP69ttvNXnyZHs3Cyh0eQ2qeHt7q0+fPmrZsqWioqLUsGFDSVkvmAKKq4z+c+edd8rT01PVq1fXr7/+qiVLlmjSpEl2bt3Nu+1HoDO/Ca5atUpJSUkqUaIEU2+hWIqLi1NUVJSqV6+e7SwzycnJ2rZtm/bu3asTJ07oqaeesvpDEygOMofg6OjobBc/ybwgRMb2zNwEZG/37t2Kjo7W1q1bNWzYMJUpU6bIf/N/WwTovP7iz+5NjTc6FDeGYeh///uf5s+fr9mzZ8vPz8+q71z/ZpbRR+grKE4y94Pg4GC5ubmpS5cululNJevPj9WrV+vq1avq2rWrXF1d7dJmwF7yG4Kzm064qH+uFO3W69+pt+Li4jRjxgxFREQoOTnZapvMfyOsWrVKV69eLfK/OMAWGW9yvXr1UuPGjS21mhmjZlL6SFrGv5OSkhQXF2c1DSRQHGSMKE+ePFlOTk567LHHFB4eblk3ICUlxdInvvnmG82bN08NGjQgPKPYyRyeN23apA0bNujYsWOW+zJkhOXo6GgdOHBA0u0xTXCRfwQZU28999xz8vb21tmzZxUfH6+kpCRJUmpqqmWE7Z133tHRo0dVsmRJezYZKHQZb3LLly/XpUuXdPLkSQUHB0tK70MZV0I7OTnpxIkTGjZsmC5cuFCkv14D8uv6D/uEhARt3bpVjRo10rRp03T48GE999xzOnnypCUoBwcHKzw8XP/9739Vs2ZNezUdsIvM4XnFihUKDg7Wb7/9ppkzZyoiIkImk8nqc+XAgQN65plnbqvPlCIZoDNGyTJs2bJFPj4+ateunebMmaO5c+dq9uzZMpvNcnFx0aVLlzR69Gg1atTotvsFAvm1bt06zZkzR88//7wmT56sc+fO6ZtvvpEkubikX0+8efNmTZo0ScHBwapRo4Y9mwsUisxBYOPGjfr+++/l7Oysl19+WWazWb169dKrr76qqlWr6sqVK0pKStIbb7yh1NRUTZ8+Xd7e3nZ+BEDhy7wa5/r16/XZZ59pwoQJqlevnr777jtdvHjR8rkSGhqq999/XzNnzixyi6XkpsgF6Mx1M+vXr9eVK1dUvnx51a9fX99++61efPFFVatWTSVKlJCTk5P27dunF154QcOHD1fXrl3t3Hqg8Fz/h2bp0qXVrl07VatWTXfccYd69OihBQsWaPPmzZKkkJAQzZ8/Xx999JGqVatmjyYDhS4jCPz888/65JNPdMcdd2jjxo1q1KiRateurdWrV6t379568MEHVa9ePcXHx6tLly567bXX7NxywH7MZrOWLl2qnTt3KiEhQadPn5arq6seeughmUwmLV++XJL09ddfa9WqVfr000+tlrW/HRTZiwinTp2qo0ePauDAgWrevLnc3d21detWHTp0SKtXr9a4cePUqFEj/fLLL2ratCmLpaBYyRhVi4qK0qJFi1S/fn0lJCRoxYoVGjhwoOrUqaNvvvlGJpNJTz/9tA4fPqy1a9fqmWeesYwaALezzBfQxsXFacSIERo6dKgSEhK0du1aHTlyRN98840iIiLk4eGhOnXq2LnFgGNITk5WSkqKPv30Uw0aNEirVq3SxYsX1adPH9WoUUOnTp1SlSpVdOnSJS1fvlyDBg26LWqer1dkAnTmr9lWr16tBQsW6KuvvtL777+v8uXLS5ICAwP1xx9/aOjQoapatao9mwvY3ZEjRzRmzBgNGTJE69evV926dZWYmKiEhAQdPnxYNWvW1IQJEySlXxjFRVAoLjK+yUxJSdGRI0dUq1Yt/fLLLzpw4ICcnZ01duxYjR49Wo899piaNWsmyXraOqA4yZy/jh49qk8//VSvvvqqfv75Z505c0YTJkzQxIkT5ePjo6eeekpeXl52bnHhcPgAnflNKy4uTl5eXlq3bp3Wr1+v5ORkVaxYUeXLl9eFCxf0f//3f5b9bocpUoCbsWLFCqWlpemhhx7S4MGDdf/996tz586qWbOmIiMjLX940ldQnGS83k+dOqXXXntNZcqUkaurq0aPHi1nZ2ctX75cJ0+eVFRUlD744AO+kQGuSUtL065duzRq1Ci1adNGvXv31hdffKF3331XCQkJOnPmjJo3b27vZhaaIvGpaTKZtHHjRo0aNUoTJ05UvXr1NGbMGHXs2FH9+vXTn3/+mWX0jECA4mTdunWWf//44486fPiwnJ2dNW3aNP3nP//RN998oypVqigkJERms1llypSRJKapQ7Gxbt06bdy4UU5OTrpw4YLefPNNDR8+XE8++aQuX76sr7/+WikpKfLz81OFChX08ccfy8XFJcu1BEBxkfm1v2bNGk2bNk1NmjRR7969FRcXp0OHDmn9+vXatGmTqlatWqzCs+TAAfrvv/9WXFycTCaTQkNDNWfOHL3xxhuKiIjQZ599JsMwdPz4cU2cOFF33323/vOf/9i7yYBdREdHa+TIkVq2bJmk9FlpKlSooE6dOqldu3ZycnJSaGioFi5cqIcfflhOTk6W2k++jkZxERcXp/Hjx+vEiRMqW7asOnfuLCcnJ3399ddq0aKFDh06pM8++0w9evTQU089JSl9xI0/MFEcZXxTk5aWpvPnz8vHx0clS5a0DGLWqlVLAwYM0FtvvWVZxr64ccgSjoiICM2fP1/+/v4aOnSoFi5cqLJly6pChQpavHixNmzYoB49emjAgAHy8vKy1NsU9WUhAVslJyfLzc1Nmzdv1ksvvaSJEydqy5YtatSokTp27Ciz2ay1a9fq7Nmz6tq1q/z8/CjZQLGSmppqKcP47rvv9OOPP2rhwoWKiorSkiVLNHDgQB0+fFj/+9//1L9/fzVp0kQSnycovjJe+2fPntW4cePk7++v/fv368svv9Tvv/+uHTt26Oeff9aaNWtUrlw5ScWzFNAhA7SU/nVBWFiY6tWrp1atWmnp0qWKiIjQpEmT9O677+rUqVOaOnVqsSlWB3KyZ88e/fPPP4qMjNTHH3+satWqqVatWoqLi1NqaqpmzJhhmau2OL7JofjKeL2npqZq6dKl6tixo4KDgxUTE6NPP/1UI0aMkLOzs/755x998sknuvPOO+3dZMBuLly4oFKlSsnV1VWXL1/Wc889p549e6pPnz6aMWOGwsPDNXfuXEnS999/rwEDBti5xfblkJ+kFy5c0NKlS+Xp6amwsDAdOnRILVq0UGxsrMaOHauLFy9q+vTphGcUS5n/5j1w4IBef/11mUwmDR06VIMHD1ZSUpLef/99vf/+++rbt6/VQg+EZxQnGfXOjz/+uDZt2qRly5bpnXfe0dWrV/X5559r+vTpGjBggL7//nvdeeed1DujWNu7d6+mTp2qvXv3ymQyKSAgwFLXPHr0aLm4uFiWtM8Izw46BlsoHO7TNCIiQi+88IIaN26s9u3bq0GDBlq+fLnOnj2rdu3aqUqVKnr//fdVokQJ3uxQ7KSlpVl9rXzp0iVVrVpVUVFRktLf5CpUqKDnnntOXl5e6tSpk72aCtjF9R/oW7ZsUZs2bTR9+nT17dtXx44d06hRo/T9998rPDxcbdq0kaenJ/XOKPZatWqljRs36rXXXpNhGIqJidGmTZsUGRmp33//XZcvX5avr6/VPsW5zMnh5ue5ePGi7rjjDg0dOlRS+pyD69evl6urq8aMGaPu3btL4qtoFD+GYVgu/ps+fbpcXV3VuXNnde3aVbt27dLq1avVoUMHffPNNzp06JCdWwsUvsyfCytWrFBMTIwWL16sunXrSpLc3Nw0f/58PfDAA1q1apXc3d0t+2b0LaC4io+PV4cOHbRjxw79/fffGjdunD766CPt2LFDkZGRmjJlimUGJzhggC5VqpRKliyp3bt3q0GDBkpKSlL79u01fPhweXh4WLYjPKO4MZlMSkxM1EsvvWSZy3nJkiXq2bOn4uPjtW7dOlWqVEkNGjRQQEAAf2Si2Ml4vS9cuNCy6mZsbKx+/PFH+fv7q0aNGjpw4IC6d+9uCc/0EyB9gMbX11ejR4/WoUOH9MYbb2jcuHF65JFHVKtWLTk7O1u++ae/pHO4iwgNw9CcOXN09uxZxcbGKioqSq+++qpq1qxp76YBdpH5Deuvv/7S9u3bNXLkSE2bNk3r16/Xgw8+qEaNGsnFxUVt2rSxc2sB+1qzZo0+//xzvfLKK2rYsKHCw8M1fPhwtW/fXqVLl1bXrl1Vr149ezcTcGi///673n77bT366KOWaR2ZmcaawwVoKT0wnD59Wtu3b9eDDz4oLy8v/upBsXP+/HlVqFBBknTmzBmVLVtWkrRgwQLt27dPTz/9tE6dOqUPPvhAb7zxhpo2bSqJNzkUb6dOndJXX32l8uXLa8SIETKZTHrjjTf03HPPqWzZsjKZTHyeALnI+Aw5ceKE/P397d0ch+WQAfp6vNmhuNm2bZvWrl2r3r17KywsTIsXL1a5cuXUqFEjPfnkkxo1apQ6d+6shQsXqn///uratau9mww4jP3792vZsmWKj49XQkKC3NzcNHnyZHs3C7CrdevWqW3btpLSV6wNCAhQgwYNrLbJnLdiYmLk6+tLBstBkXhG+MWhuKlYsaJcXV319ddfa82aNZo7d64mTpyo77//XkuWLFGLFi00f/58jRw5kvAMXKdOnTq6//77denSJXl6elrCMzM3objKbsXa60eXMwflJUuWaNKkSYTnXPCsAA4kLS1NklStWjX1799f7u7u8vX1VUpKisqVK6dXXnlFMTExeuyxx/Tll1+qefPmxXoeTiAnzZo1s/xx+eeff0piMAbFU3Jysvz8/PTFF1/ovffe0x9//KHSpUtr48aNMpvNls+QjP4xa9Ys7d69W8HBwfSZXPDMAA7E2dlZJ06c0Pjx43XmzBk9+OCDKlu2rNauXavIyEgtX77c8oZWsmRJ6p2BHDg5OemBBx5Q3bp1ValSJXs3B7AbNzc37dmzR9HR0Ro2bJhefPFFbd68WX/88YdGjhypJ554QqmpqTIMQxMnTpSzs7PeeOMNubm52bvpDq1I1EADt7PMIfjkyZOaOXOmSpUqpSNHjujJJ59UdHS01q5dqwsXLqhZs2Z69tln7dxioOjgK2gUR5k/Vw4cOKBXXnlFTz31lDp37qwZM2bo119/1apVqxQXF6c///xTnTt31pQpU9S8eXO1b9/ezq0vGgjQgB2lpaVZFnBYuXKl/vrrLwUEBGjw4MH63//+p82bN6tfv346ePCg7rzzTrVs2VISoQAAkL3MnyuStGnTJi1YsEB33323Hn/8cUnSkCFD5OPjo08++cSyXWxsrLy9vQu9vUUVARpwAMuWLdPatWtVqVIlbdu2TbNmzVLp0qX18ccf68qVK3rhhRfk5eVl72YCABxY5pHnzCvWRkREaNeuXWrcuLE6dOggSTp06JACAgLs2dwijQAN2EHmEeSff/5Zn376qWbOnKlatWrpzTffVGpqqiZNmiRnZ2dFR0fLz8/Pzi0GABQFmVesjYqKkre3t3r27Km9e/dq+/btGjBggGX6Or7NvHE8a4AdODk56dSpU3r99df1zz//6PTp0/rpp58kSW+99ZaOHTtm+dnPz4+ZNgAAOco8ReP27dsVEBCg559/Xj4+Pvrzzz+1cuVK+fn5qVOnTlZzPxOebxzPHGAHx44d0+uvv66OHTvK399fvr6+Wrhwob777jtJ0uzZs9W3b1/L9sy0AQC43vnz5yWlB+EzZ84oOTlZzZo1k5eXl1566SX16dNH48aN09q1a+Xj46M2bdpIEoMyt4CLvRsAFEdXrlxR3bp15enpqblz5+qFF15QeHi4PvnkE913332qUqWKJL5eAwBkLz8r1h46dEgLFy7UU089paZNm1r2ZVDm5hGgATvw8vLS8ePH9fbbb+ujjz7S2bNndfr0aS1YsEDVqlWzbEd4BgBkJ/OKtRcuXNDcuXN19epVDRgwQGXKlLGsWPv888+refPm9m7ubYeLCAE7mT9/vo4dOyYPDw/t3LlTr776qmrXrm3vZgEAHFjmaerOnj2rr776SvHx8Xr55Zfl6+ur0NBQ/fPPPxo2bJgSEhJYdKuAEKABOzGbzTp9+rS2b9+uBx98UF5eXpRsAADydOLECX322Wfq16+fkpOT9ddff6lmzZpq1aqVpkyZokaNGmno0KGSRHguIARowEHwJgcAyA4r1joeAjQAAICDYsVax8QzCwAA4KAywvOyZcsUGhqqUqVKafny5bp06ZJ69uypSpUq6ZdfflH37t0t4VniIvSCxgg0AACAg2HFWsfGnycAAAAOhhVrHRvzQAMAADiYY8eOadKkSRo+fLguXbpkWbG2SpUqeuyxxzR79mx5eXlZtuci9MLFCDQAAICDybxi7dKlS/XCCy+oe/fu+uSTT3Ty5El5eHhISi/1QOFjBBoAAMDBsGKtY+MiQgAAAAfEirWOiwANAADggFix1nERoAEAAIoAVqx1HPwJAwAAUAQQnh0HARoAAACwAQEaAAAAsAEBGgAAALABARoAAACwAQEaAAAAsAEBGgAAALDB/wOHDuFD70la4QAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from cobra.evaluation import plot_performance_curves\n", + "\n", + "# plot performance curves\n", + "plot_performance_curves(performances)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Based on the performance curves (AUC per model with a particular number of predictors in case of logistic regression), a final model can then be chosen and the variables importance can be plotted:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['embarked_enc', 'pclass_enc', 'class_enc', 'embark_town_enc']\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "model = forward_selection.get_model_from_step(3)\n", + "\n", + "# Note that chosen model the following variables:\n", + "final_predictors = model.predictors\n", + "print(final_predictors)\n", + "from cobra.evaluation import plot_variable_importance\n", + "\n", + "variable_importance = model.compute_variable_importance(\n", + " basetable[basetable[\"split\"] == \"selection\"]\n", + ")\n", + "plot_variable_importance(variable_importance)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note**: variable importance is based on correlation of the predictor with the model scores (and not the true labels!)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "Finally, we can again export the model to a dictionary to store it as JSON" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "ename": "FileNotFoundError", + "evalue": "[Errno 2] No such file or directory: 'output\\\\model.json'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mmodel_path\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"output\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"model.json\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel_path\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"w\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mfile\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5\u001b[0m \u001b[0mjson\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'output\\\\model.json'" + ] + } + ], + "source": [ + "model_dict = model.serialize()\n", + "\n", + "model_path = os.path.join(\"output\", \"model.json\")\n", + "with open(model_path, \"w\") as file:\n", + " json.dump(model_dict, file)\n", + "\n", + "# To reload the model again from a JSON file, run the following snippet:\n", + "# from cobra.model_building import LinearRegressionModel\n", + "# with open(model_path, \"r\") as file:\n", + "# model_dict = json.load(file)\n", + "# model = LinearRegressionModel()\n", + "# model.deserialize(model_dict)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have build and selected a final model, it is time to evaluate its predictions on the test set against various evaluation metrics. The used evaluation metrics are:\n", + "1. Accuracy\n", + "2. AUC: Area Under Curve\n", + "3. Precision\n", + "4. Recall\n", + "5. F1\n", + "6. Matthews Correlation Coefficient\n", + "7. Lift\n", + "\n", + "Furthermore, we can evaluate the classification performance using a confusion matrix.\n", + "\n", + "\n", + "Also plotting makes the evaluation of a logistic regression model a lot easier. We will first use a **Receiver Operating Characteristic (ROC) curve**, which is a plot of the false positive rate (x-axis) versus the true positive rate (y-axis). Next, the **Cumulative Gains curve**, **Cumulative Lift curve** and **Cumulative Response curve** can be called." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "from cobra.evaluation import ClassificationEvaluator\n", + "\n", + "# get numpy array of True target labels and predicted scores:\n", + "y_true = basetable[basetable[\"split\"] == \"selection\"][target_col].values\n", + "y_pred = model.score_model(basetable[basetable[\"split\"] == \"selection\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [], + "source": [ + "evaluator = ClassificationEvaluator()\n", + "evaluator.fit(y_true, y_pred) # Automatically find the best cut-off probability" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "accuracy 0.646067\n", + "AUC 0.662564\n", + "precision 0.551282\n", + "recall 0.605634\n", + "F1 0.577181\n", + "matthews_corrcoef 0.274883\n", + "lift at 0.05 1.880000\n", + "dtype: float64" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evaluator.scalar_metrics" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "evaluator.plot_cumulative_gains()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "evaluator.plot_lift_curve()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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1sibspOther0.8202250.3988760.390411
\n", + "
" + ], + "text/plain": [ + " variable label pop_size global_avg_target avg_target\n", + "0 age 1.0 - 10.0 0.067416 0.398876 0.416667\n", + "1 age 10.0 - 18.0 0.078652 0.398876 0.500000\n", + "2 age 18.0 - 22.0 0.112360 0.398876 0.300000\n", + "3 age 22.0 - 24.0 0.022472 0.398876 0.500000\n", + "4 age 24.0 - 27.0 0.061798 0.398876 0.454545\n", + "5 age 27.0 - 31.0 0.151685 0.398876 0.407407\n", + "6 age 31.0 - 35.0 0.106742 0.398876 0.421053\n", + "7 age 35.0 - 40.0 0.061798 0.398876 0.181818\n", + "8 age 40.0 - 50.0 0.084270 0.398876 0.400000\n", + "9 age 50.0 - 80.0 0.073034 0.398876 0.461538\n", + "10 age Missing 0.179775 0.398876 0.406250\n", + "0 alone False 0.331461 0.398876 0.423729\n", + "1 alone True 0.668539 0.398876 0.386555\n", + "0 class First 0.213483 0.398876 0.631579\n", + "1 class Other 0.224719 0.398876 0.475000\n", + "2 class Third 0.561798 0.398876 0.280000\n", + "0 deck B 0.067416 0.398876 0.666667\n", + "1 deck C 0.039326 0.398876 0.428571\n", + "2 deck E 0.039326 0.398876 0.571429\n", + "3 deck Missing 0.786517 0.398876 0.350000\n", + "4 deck Other 0.067416 0.398876 0.583333\n", + "0 embark_town Cherbourg 0.112360 0.398876 0.550000\n", + "1 embark_town Missing 0.005618 0.398876 1.000000\n", + "2 embark_town Other 0.882022 0.398876 0.375796\n", + "0 embarked C 0.112360 0.398876 0.550000\n", + "1 embarked Missing 0.005618 0.398876 1.000000\n", + "2 embarked Other 0.882022 0.398876 0.375796\n", + "0 fare 0.0 - 7.2 0.050562 0.398876 0.111111\n", + "1 fare 7.2 - 7.9 0.207865 0.398876 0.297297\n", + "2 fare 7.9 - 8.1 0.073034 0.398876 0.384615\n", + "3 fare 8.1 - 10.5 0.129213 0.398876 0.434783\n", + "4 fare 10.5 - 14.5 0.089888 0.398876 0.437500\n", + "5 fare 14.5 - 21.1 0.061798 0.398876 0.272727\n", + "6 fare 21.1 - 27.9 0.134831 0.398876 0.416667\n", + "7 fare 27.9 - 39.8 0.073034 0.398876 0.384615\n", + "8 fare 39.8 - 76.1 0.078652 0.398876 0.428571\n", + "9 fare 76.1 - 512.3 0.101124 0.398876 0.722222\n", + "0 parch 1 0.095506 0.398876 0.470588\n", + "1 parch Other 0.904494 0.398876 0.391304\n", + "0 pclass 1 0.213483 0.398876 0.631579\n", + "1 pclass 3 0.561798 0.398876 0.280000\n", + "2 pclass Other 0.224719 0.398876 0.475000\n", + "0 sex female 0.297753 0.398876 0.754717\n", + "1 sex male 0.702247 0.398876 0.248000\n", + "0 sibsp 1 0.179775 0.398876 0.437500\n", + "1 sibsp Other 0.820225 0.398876 0.390411" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from cobra.evaluation import generate_pig_tables\n", + "predictor_list = [col for col in basetable.columns\n", + " if col.endswith(\"_bin\") or col.endswith(\"_processed\")]\n", + "pig_tables = generate_pig_tables(basetable[basetable[\"split\"] == \"selection\"],\n", + " id_column_name=id_col,\n", + " target_column_name=target_col,\n", + " preprocessed_predictors=predictor_list)\n", + "pig_tables" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "age\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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za9cuUlNT6d69uzUQdHV15a233uLuu+/mhRde4JNPPqF169akpaWxY8cOwNhItE2bNgBs376djz/+GDc3twqB7v/93//x448/smPHDj788EPuuuuu83pPIiIXO424XCCHCuC+OPDaDnaRxn/vizPKL4SjR48SHh7OkSNH/lY7mzZtIjw8vMrFy2+++SaTJk36W9eoD9LT0/npp5/quhs2LVmypMq1FWB8AHriiSdqsUeXpgEDBrB48WJGjBhBQkICq1atwmw2c9ttt/HFF19U2JPElmHDhvHZZ58xYMAAoqKi2LBhAz179uSzzz6zpsk9VWhoKN9++y1TpkzB2dmZdevWcejQIbp3786///1vm/u/1NScOXN4+eWXCQ8PZ/PmzWzevJnQ0FBeeeUVHn30UWs9Ozs73nrrLebMmUNERAR79uxh48aN+Pr6cu+997J48eIa7UbfqlUrPvvsM/z8/FizZg05OTlMmjSJxYsXV5lx7FTDhg3j66+/5tprryU1NZVVq1aRlZXF+PHjWbJkSaW01RMmTOC6666juLiYtWvXcvDgweq/SNXk5OTE+++/z1133YWPjw8bNmxgw4YNNGrUiMcff5yPPvqoQsKA7t278+233zJ+/HgKCwv5/fffSUhIYMCAAXz88cfcfvvtgDEV8cknn6SkpISHH364QjY1R0dHnn/+eUwmE//+979tTuUTEbmcmSy2JlzL3/LzSbghBopKoOiUckfA0Q6+bgnDG5zfa5rNZjIyMmjYsOFZM/ycSWFhISdPnqwyBeqbb77Jtm3b+PTTT8/5GvXBzJkzKSoqOm+Lk8+nJUuW8NZbb1U5X75seoyt1L8itWnTpk3cdttt9O7dm48//riuuyMiIpc4jbicZ4cKjKAl97SgBYznuSXG8fM98mJvb4+fn9/fClrA+JaxOvs2XOwu5njd09NTQYuIiIhcdhS4nGevJxsjLWdSVAJv2s4ees5OnyoWHh7Ot99+y6hRo+jSpQuTJk0iLi7OWn/v3r3ceuutdOrUiSFDhvD1118DlaeKRUdHc/PNN9OpUyduv/12687RZbZu3coNN9xAx44dGTlyJN9++6312JNPPsmsWbN47LHH6Ny5M1dffTVLliyxHs/Ly+OFF16gV69e9OjRg+nTp5OdnQ0YIz+zZ8+mV69e9OzZk4cffrjKDeg2bdrEgAEDeOGFF+jWrRtz586lqKiIV155hQEDBtCuXTsGDRrE//73PwDmzp3L0qVL+f777607e2dlZTFjxgy6detG3759+cc//mHty+mWLFnChAkTePPNN+natSsDBw6stMHdl19+yZAhQ+jSpQs333wzO3futB4bPHgwr776Kv369WPEiBFnnJbXtWtX+vXrx+LFiyu8rmVTxebOncujjz5qvfdBgwYxf/58m+2JiIiIXMwUuFTT68nguR1MkWd+vJtaeaTldEXAO6lnb8tzu3HdczVv3jyeeuopFi5cSFpaGm+88QZgZD6aMmUKLVu2ZOnSpTz66KM899xzFdJ+ghE8TJ06laZNm7JkyRKGDh1a4QN0amoqU6dOZdSoUXz//ffcf//9zJo1y5oCFYw0r23atGHJkiX069eP5557zhr8/POf/2TDhg3MmzePhQsXcvDgQV5++WUA3njjDXbs2MH8+fP59NNPsVgs3H333VWOlCQnJ5Odnc3SpUsZO3YsCxYsYPXq1fz73//ml19+YezYscyaNYvk5GTuuOMOhg8fztVXX20N2J566imOHz/O559/zvz58zl8+DAzZ86s8rXdu3cvu3fvZtGiRTz00EPMmjXLml1q9erVvP3228ycOZOlS5cyYMAAJk+eXGH38O+++47333+f119/vdKeG2X3s3//fr788ksef/xxnn/+edavX2+zLytXrsTe3p7Fixdz44038sYbbxAdHV1l30VEREQuRsoqVk2vJ0P2WUZSzrfsEuO6j/ufva4tkydPtu43cfPNN/PJJ58Axr4I7u7uPPvss9jb29OyZUtOnDhh3f25zPr16zl+/DjPPfcc7u7uhIaGsmnTJo4fPw7A559/Ts+ePZk8eTJgZPqJiYnhk08+sY5khIWFWTPjPProo3z22WccPHiQiIgIfvrpJxYsWGDdA+H5559n8+bN5OXl8dlnn/HVV1/Rtm1bAF599VV69uxJZGSktf7p7rzzTutC4LCwMGbPnk3nzp0BuOeee3jnnXc4fPgwvXr1wsXFheLiYho2bEhcXBwrV65k48aNeHt7A/DKK68wePBgEhMTCQwMtHm9V155BV9fX8LCwti8eTNffvklAwcO5P3332fq1KnWDFH33nsv69evZ/Hixdx///2AkQHqTCliHR0deemll2jYsCGtW7dm8+bNLFq0yOZO556enjz55JPY29tzzz338NFHH7F7926bu3SLnE89e/bkwIEDdd0NERG5TChwqabH/eG5hNoNXjzszj1oASpk8/Hw8Kgw/SsiIqLCephbb70VMKZdlYmOjqZZs2YV9nJo3749a9euBSAmJoa1a9fSpUsX6/GyYKDMqTthl2VpKi4u5vDhwxQXF9OuXTvr8Y4dO9KxY0eioqIoKiqy7ktRpqCggMOHD1cZuJyanWfo0KH8+eefvPzyy8TExLB3716ASsEZwKFDh7BYLJUyFwHExsbaDFyaNWtWIetS+/bt+eyzz6ztvfHGG7z99tvW44WFhRV2Dz+1r7Y0bdq0wuvYtm3bStPRTm3r1N+lu7s7RUVnG/cTERERubgocKmmx/2rF0TcFwfvn2W6mCMw1Q/mnT1L6N9y+uZ5ZdOsqtpUz5bTp2adOq2puLiYkSNHct9991WoY2dXPgPR1rUsFkuFNKKnK9t47dNPP620CP3UD/OnK9uUDoz1IV9++SXXX389o0eP5tlnn7WOAtm6npubW4X1OWWqSlRw+vQus9lsvW+z2cyMGTMq7RZ+aorcU/tqy6mvIRgBV1W/t5r8PkVEREQuVlrjcp497m+kPD4TRzt49G+MpPxdwcHBHDhwoMLow8yZMyuMEAC0bt2auLg4Tp48aS0rG7kACAkJITY2luDgYOtj3bp11nUjZ9K0aVPs7e0rtLd+/Xquvvpq67Hjx49b223YsCEvvfQSx44dq9Y9Llq0iGeeeYZp06YxcuRI8vLygPJAzGQyVbiP3NxczGaz9XoAL730UpUL9OPj4ysc2717t3XX85CQEJKSkiq8Lh9++CGbN2+uVt/BSLZw6m7dO3fuJDQ0tNrni4iIiFxqFLicZ6HOxj4tbnbGyMqpHDHKv25p1Ksr1113HTk5OcyZM4fDhw/zww8/8MMPP9C/f/8K9fr06UNQUBBPPfUU0dHRfP311yxfvtx6fOLEiezbt4/XX3+d2NhYfvnll0o7XFfFw8ODcePGMWfOHHbs2MHevXt57bXX6NWrF56enowfP54XX3yRDRs2cOjQIWbMmEFUVBQtWrSo1j16e3uzZs0a4uPjiYyMtO6CXlhYCBijHwkJCSQnJxMaGkr//v2ZPn06f/31F/v372fGjBmkp6fTuHFjm+3n5eXxz3/+k0OHDvHVV1/xyy+/WKe23X777Xz66acsXbqUuLg45s2bxzfffEPLli2r1XeAoqIinnzySaKioli0aBHLly9nypQp1T5fRERE5FKjwOUCGN4AdrY1poN52Rkvsped8Xxn2/O/+WRNeXp68t5777Fz506uu+465s6dy5w5c+jatWuFeo6Ojrz33ntkZ2czbtw4vvrqKyZOnGg93qRJE+bPn8/69eu59tpreeWVV3jwwQcr1DmTmTNn0qFDB+68805uv/122rdvz4wZM6zH+vXrx6OPPsoNN9xAQUEBH3zwAS4uLtVqe86cOURFRTFy5EhmzJjBNddcQ+fOna0jPKNHjyYuLo7rrrsOi8XCq6++SnBwMHfccQe33norjRs35j//+U+V7Tdu3JgmTZpwww038P777/Pqq69yxRVXADBixAgef/xx5s2bx8iRI1m5ciXvvPMObdq0qVbfAdq0aUNQUBA33ngj8+fPZ86cOXTo0KHa54uIiIhcakyWi3knPpE6cLad7UVERETk/NOIi4iIiIiI1HsKXEREREREpN7TVDEREREREan3NOIiIiIiIiL1ngIXERERERGp9xS4iIiIiIhIvafARURERERE6j0FLiIiIiIiUu8pcBERERERkXpPgYvUe5MmTeLNN9/82+0MHjyYxYsX2zx25MgRwsPDOXr06N++Tl2yWCx88cUXlJSU1HVXRERERM4r7eMi9d6JEydwdHTE3d39b7WTkZGBm5sbLi4ulY4dOXKEYcOGsWrVKpo2bfq3rlOXNm/ezKRJk9izZw8ODg513R0RERGR80afbKTe8/b2Pi/tNGzY8Ly0U5/pewgRERG5VGmqWA3Yh15/To8rrptWZZtXXDetyvNqavv27UycOJFOnTrRuXNn/u///o/k5GRKSkro379/pWlSI0aM4PPPPwdg69at3HDDDXTs2JGRI0fy7bffWus9+eSTzJgxgzFjxtCzZ08OHDjAoUOHuPPOO+nSpQsdOnTg5ptv5uDBg9Zzdu/ezYQJE+jYsSM33XQTb7/9NpMmTbIeP9P1TnfqVLEnn3ySWbNm8dhjj9G5c2euvvpqlixZYq2bl5fHCy+8QK9evejRowfTp08nOzsbqDhVrKioiBdeeIHu3bszcOBA1q5dW+GaWVlZzJgxg27dutG3b1/+8Y9/WNvZtGkTAwYM4Msvv2TAgAH07NmTadOmkZ+fbz3/xx9/ZOTIkXTq1IkbbriB7du3W4/9+uuv1mNjx47ljz/+qPLeBw8ezKuvvkq/fv0YMWIExcXFrFmzhrFjx9KhQwe6devGI488QnZ2NkePHuW2224DoF27dmzatAmAL7/8kiFDhtClSxduvvlmdu7cWeX1REREROorBS6XiOzsbO6++2769OnDDz/8wAcffMDRo0d59913sbOz45prrmH58uXW+lFRUcTGxnL11VeTmprK1KlTGTVqFN9//z33338/s2bNYvXq1db63333Hffffz8LFiygVatW3HfffQQFBbFs2TIWLVpESUkJr776KmB86L/zzjtp06YNS5cu5dprr+W9996ztlWd653JokWLaNOmDUuWLKFfv34899xznDhxAoB//vOfbNiwgXnz5rFw4UIOHjzIyy+/XKmNuXPn8ttvv/Huu+/y1ltv8emnn1Y4/tRTT3H8+HE+//xz5s+fz+HDh5k5c6b1eHp6Oj/99BPvvfces2fPZsWKFdYAasOGDUyfPp2JEyfy3Xff0bNnT+6++26ys7PZv38/06ZN46677uL7779nwoQJPPDAA+zbt6/K+/3uu+94//33ef3110lMTOTBBx/kpptu4ueff+btt99m48aNfPHFFwQGBjJ37lwA/vjjD7p06cLq1at5++23mTlzJkuXLmXAgAFMnjyZlJSUar3WIiIiIvWFpopdIvLy8rj77ru54447MJlMNGvWjGHDhlm/6b/22mu55ZZbyMrKwtPTk19++YWePXvi6+vLW2+9Rc+ePZk8eTIAwcHBxMTE8MknnzB48GAA2rRpw1VXXQVAbm4uN9xwAxMnTrSuOxk7dizz588H4KeffsLFxYV//OMfODg4EBoayrZt20hNTQXg888/P+v1ziQsLIy77roLgEcffZTPPvuMgwcPEhERwU8//cSCBQvo3r07AM8//zybN2+ucL7FYmHx4sVMmzaNK664AjBGcu655x4A4uLiWLlyJRs3brROU3vllVcYPHgwiYmJABQXF/PUU08RHh5OREQE/fv3Z9euXQB88cUXDB8+nFtuuQWAxx9/HIvFQmZmJh988AHXX389Y8aMAaB58+bs3LmTTz/9lDlz5ti831GjRhEREQFAbGwsTz/9NDfeeCMATZs2pU+fPkRHR2Nvb0+DBg0AaNSoEQ4ODrz//vtMnTqVoUOHAnDvvfeyfv16Fi9ezP3333/W11pERESkvlDgconw8/Nj7NixfPzxx+zbt4/o6GgOHDhAx44dAejUqRP+/v6sXr2a0aNH88svv3D77bcDEBMTw9q1a+nSpYu1veLi4gprQk5dsO7m5sbEiRNZtmwZu3fvJiYmhr1791o/5B84cIA2bdpUWBzeuXNnVq5cWe3rnUmzZs2sP3t4eFjPP3z4MMXFxbRr1856vGPHjtbXoMzx48fJyMiwBgMA7du3t/586NAhLBYLgwYNqnTt2NhY7OyMgcrmzZtX6EdxcbH1/PHjx1uP2dnZMX36dOuxqKgovvnmG+vxoqKiSn08VZMmTaw/t2jRAicnJ959910OHjzIwYMHiY6OZuTIkTbPPXToEG+88QZvv/22taywsJCAgIAqryciIiJSHylwqQHzoW/OXqmGtnz32nlpJzk5meuvv542bdrQr18/JkyYwG+//UZkZKS1zsiRI1mxYgVt27YlLi6OYcOGAcaH/pEjR3LfffdVaLPsAzqAk5OT9eecnBxuuOEGGjRowNChQ7n22muJiYmxTgezt7evtEj81OfVud6ZODo6ViqzWCwV+lgdp/bp1CDLbDbj5uZmc92Nn5+fdWTl9H6UtWerf6e2/X//93+MGzeuQvmZ+u7s7Gz9ef/+/dx8880MGjSIbt26MWXKFD755JMzXm/GjBn069evQrmbm1uV54iIiIjUR1rjcolYuXIl7u7uLFiwgMmTJ9O9e3fi4+MrfDgfMWIE69ev56effqJfv37WaUUhISHExsYSHBxsfaxbt46vv/7a5rU2b95MUlISn376KXfeeSd9+vQhISHBeq3WrVtz4MABzGaz9Zw9e/ZYf67p9aqradOm2Nvbs3fvXmvZ+vXrufrqqyvsa+Lj44Ovr681AAEqrDEJCQkhNzcXs9ls7R/ASy+9ZF2gfybBwcEV+mCxWBgxYgR//PEHISEhxMfHV7j3ZcuWWUejzmbZsmV07dqVN954g1tuuYWOHTty5MgR62tvMpkq1A8JCSEpKanC9T788MNK0+dERERE6jsFLpcIb29vUlJS+PPPP4mPj+e9995jxYoVFBYWWutEREQQFBTExx9/zIgRI6zlEydOZN++fbz++uvExsbyyy+/8Nprr+Hv71/ltfLy8li5ciVHjx5l8eLFfP7559ZrjRw5ktzcXObMmUNMTAyLFy/mp59+OufrVZeHhwfjxo1jzpw57Nixg7179/Laa6/Rq1evCqM5JpOJiRMnMm/ePP7880927txZYQF/aGgo/fv3Z/r06fz111/s37+fGTNmkJ6eTuPGjc/aj9tuu42ffvqJxYsXc+TIEf71r39x8uRJunTpwpQpU/jll1/4+OOPOXLkCF988QX//e9/K0w7OxNvb2+ioqL466+/iI2N5eWXX2bXrl0UFRUB5SMpe/fupaCggNtvv51PP/2UpUuXEhcXx7x58/jmm29o2bJlTV5aERERkTqnwOUSMXz4cK677joeeeQRxo0bx8aNG5k5cyaHDx+ukKZ35MiRlJSUMGTIEGtZkyZNmD9/PuvXr+faa6/llVde4cEHH2TixIk2r9WlSxceeOABXnzxRa677jq++eYbnn32WU6cOEFCQgLu7u7897//JTIykuuuu44lS5YwatQo63Soml6vJmbOnEmHDh248847uf3222nfvj0zZsyoVO/ee+9l7NixPPbYY9xzzz3Wxe5lXn31VYKDg7njjju49dZbady4Mf/5z3+q1Ydu3brx4osvMn/+fEaNGkVkZCTz58/H09OTzp07869//YuvvvqKkSNH8vHHHzNnzhyuvPLKarU9adIkunbtyu23385NN93EsWPHKmQlCwsLo1+/fkycOJE//viDESNG8PjjjzNv3jxGjhzJypUreeedd2jTpk21riciIiJSX5gs2rFOzrP4+HiSk5Otmb3AyO6Vl5dnMzWxiIiIiMjZaMRFzrvs7GzrlKhjx46xYsUKli1bxjXXXFPXXRMRERGRi5RGXOSCWLx4MQsWLCAxMZGgoCDuvPPOCimCRURERERqQoGLiIiIiIjUe5oqJiIiIiIi9Z4CFxERERERqfcUuIiIiIiISL2nwEVEREREROo9BS4iIiIiIlLvKXAREREREZF6T4HLJeLo0aOEh4dz5MgRm8eXLFnCgAEDzvt1n3zySZ544onz3q6IiIiIyKm0j8sl4ujRowwZMoQVK1YQHBxc6Xh+fj65ubk0bNjwvF43KysLAE9Pz/ParoiIiIjIqRzqugP1ncViYfOWrRyIOkhmZlatXtvLy5PwsNb0uKI7JpPpb7Xl4uKCi4vLeepZOQUsIiIiIlIbNFXsLDZv2cqWrdtqPWgByMzMYsvWbWzesrXa56xYsYKBAwfStWtXZs2aRXFxMVBxqtimTZsYMGAAX375JQMGDKBnz55MmzaN/Px8m20mJiZy55130rVrV3r06MHMmTPJyckBKk4VGzx4MOHh4ZUeZb788kuGDBlCly5duPnmm9m5c+c5vS4iIiIicvlR4HIWB6IO1nUXatSHxYsX88Ybb/Df//6XX3/9lblz59qsl56ezk8//cR7773H7NmzWbFiBUuWLLFZ94UXXsDBwYFvvvmGDz/8kO3bt/Pf//63Ur2vv/6adevWsW7dOlauXEmTJk244447AFi9ejVvv/02M2fOZOnSpQwYMIDJkyeTkpJS7XsTERERkcuXApdLzJNPPkm3bt3o0aMHDz/8MIsWLbJZr7i4mKeeeoqIiAiGDh1K//792bVrl826x44dw9PTkyZNmtC+fXvmzZvHmDFjKtVr2LAhfn5++Pn58dZbb9G4cWMef/xxAN5//32mTp3K0KFDadGiBffeey/t27dn8eLF5+3eRUREROTSpcDlLMLDWtd1F2rUhw4dOlh/btu2LSdOnCAjI8Nm3ebNm1t/9vDwsE4rO91DDz3EL7/8Qq9evXjooYfYv38/ISEhVfZh4cKFrF+/nrfeegsHB2MZ1aFDh3jjjTfo0qWL9bFt2zZiY2OrfW8iIiIicvnS4vyz6HFFd4A6X5xfXXZ25bFoWcI4R0dHm3VPL68qwdzQoUP5/fff+fXXX/njjz+YOXMm69at4+WXX65Ud9u2bbz22mv85z//ISAgwFpuNpuZMWMG/fr1q1Dfzc2tejcmIiIiIpc1BS5nYTKZ6NnjCnr2uKKuu1ItUVFR9O7dG4CdO3fi5+f3tzN/vfnmm1x99dVMmDCBCRMmsGzZMv7xj39UClzS09N5+OGH+b//+z/69+9f4VhISAhJSUkVUjU/++yz9OjRg5EjR/6t/omIiIjIpU9TxS4xs2bNYseOHaxfv55///vf1sXxf0dMTAwvvPACe/fuJSYmhhUrVtCuXbsKdcxmM4888ggtWrRg0qRJpKamWh+FhYXcfvvtfPrppyxdupS4uDjmzZvHN998Q8uWLf92/0RERETk0qcRl0vMrbfeyv33309hYSHjx49nypQpf7vN5557jhdeeIEpU6ZQWFhIr169eP311yvUSUxMZPPmzQD06dOnwrGFCxcyYsQI0tPTmTdvHikpKbRs2ZJ33nmHNm3a/O3+iYiIiMilz2SpamGDiIiIiIhIPaGpYiIiIiIiUu8pcBERERERkXpPgYuIiIiIiNR7ClxERERERKTeU+AiIiIiIiL1ngIXERERERGp92o9cDleDLcehoY7oMlOePIomEsTMh8pgGFR4L4d2uyBn0+eua2vMqDVbnDbBqOjIaWo/Nif2RC6C/z+ggWpFc97PB4Wpp/X2xIRERERkQuo1gOX++LgaCH8EQ6fhcAn6fBGMlgsMPoQNHKALREwuRFcfwgOF9huZ0sOTI6FZwJgYwRkmuG22PLjD8TBXX6wKAQeioe0YqM8qQhWZcEtDS/0nYqIiIiIyPniUNsX/OkkfBIC7V2N5xMbwuos6OYGB/JhbTh42kNbV/g1Ez5Ig1lNKrczNwWu94EpvsbzhSHQfBdE50MrF9ifD+O8IcwFvO0hpgB8HeClJHjCH+xNtXbLIiIiIiLyN9X6iEsjB/g8HXJLIKEQfsk0gpaNOdDFzQhayvTzgA05ttvZmAMDPMqfN3OCYKfy+s2dYFuuMf3suBmaOhrX+z0LbtZoi4iIiIjIRaXWA5f/NIffssFzOzTZBQGO8FwQJBZBkGPFuv6OxrQyW2zWdyiv/3ITuCMWQnfD9AAIcoLZScbPGm0REREREbm41PpUsegC6OIKzwYZ61IeiIMnjhojMM6nhVHOJiiw2G7HZn278vpjfSC9ARSUgLcDxBcaC/bnNoOnjsFn6dDHAz5sAW7KrSYiIiIiUq/V6kf2QwXwSLwRLPT1gOEN4P0WMC8FnExGkHGqAkvVQYWLnY36JRXru9oZQQvA7ER4MgA25cAXGbCnHRRbjLUyIiIiIiJSv9Vq4BKZY6xhaepUXtbNDcxAoKOR8etUSUVGuS1NHCGp+LT6xbbrHykwApYJPsYamJ7uRj+GehlrZUREREREpH6r1cAlyAlOmI1pW2X25Rv/jXCBHXmQYy4/ti4bernbbquXu3G8THwhxBXarv9iIswMADsTmICygZpii5GGWURERERE6rdaDVx6uUNnV7g9FnbmwsZsmHoEJjU0UhsHO8GUWNiTB68kGaMhd5WmOy4sMUZgyjarvNcP/pdhbC65K8/Y02W4F7R2qXjNmAKIzIXxPsbz7m5GZrG/cmHpCWP0RURERERE6rdaDVwcTPBjK2joAIOjYFwMDPSA+cFGpq9loZBSDN32GTvbLw2FFs7GuetzIHBn+WhNbw9YEAyzkqD3fmhgD5+0qHzNFxPh6UAwlWYS6+9ppEMecADc7eDBxrVy6yIiIiIi8jeYLBZNlhIRERERkfpNiYBFRERERKTeU+AiIiIiIiL1ngIXERERERGp9xS4iIiIiIhIvafARURERERE6j0FLiIiIiIiUu8pcBERERERkXpPgYuIiIiIiNR7ClxERERERKTeU+AiIiIiIiL1nkNdd0BERERERM5s3/4DrFr9m81jkydNxAKsWfMHiUlJeHp40K9vb4KDm1fZ3sHoQ2zcuJmc3FyaNW3CoCsH4ubmCkBiYhIrV62hsLCQ3r160K5tG+t56/7cgG+jRkREhJ3P26sWBS4iIiIiIvVc61ahNG/ezPrcYrHw40+/4OXpiYeHB19+9Q0+Pt5MuGEch2Nj+Xn5SibeNB4vL69KbSUnp/DrqjVcObA/fr6+rF23nl9Xrea6USMB+H3tOtq1jaBxYz9++PEXWoa0wNXVlZzcXI4ePUaf3j1r7b5PpaliIiIiIiL1nIODA+5ubtZHTMxhsrKyGXTlQI4dS+D4iRMMunIADRv60K1rFwIC/Nm7b7/Ntnbu2k1oyxDaRITj69uIoUMGERd/lBMnTwJw/PgJWrYMoVnTpjg7O5OZmQVAZOR2unTuiJ1d3YQQClxE6rFDBXBfHHhtB7tI47/3xRnlIiIicnkqLCxk85ZIevbojouLM0nJyfj5+uLk5GStExgQQFJSss3zk5JTCAoKtD739PTA09PDWt/T04PU1DQys7IoKCjA3cOd7JwcEhISad261YW9uTPQVDGReurnk3BDDBSVQFFpWVYJvJ8Kn6TD1y1heIM67aJIvTTvP/PrugsicpF54L6767oLNbJ7zz7s7e1p2yYCgJzcXNzd3SrUcXNzJTsnx+b5ubm5uLu7V6zv6kZ2tlG/d6+erPx1NWazme7duuDh7s7vf6ylS5dOdTbaAgpcROqlQwVG0JJbUvlYEUYwc0MM7GwLoc613j0RERGpIxaLhT1799KxQ3vs7e0BKC4qtv5cxt7eHrPZbLON4mJb9e2s9UNbhhB8x2TMZjPOzs5kZWWTmJjMgP792LBxEweiogkM8GfwoIE4OjpegLu0rVZDpo/TwBRp+xFXCFOPVC5/y/YIFwBfZUCr3eC2DUZHQ0pR+bE/syF0F/j9BQtSK573eDwsTL8w9yhyPryebAQnZ1JUAm+e4d+HiIiIXHpSU9M4eTKT8PDW1jIHh8pBitlsxsHB9hiFraDGbC7B0bG8voODA87OxrejWyO30bVrZ5KSk4k6eIiJN42npKSEnbv2nK/bqpZaDVxubAiJHcsfxzpAVze43huaO8HePHitScU6U/1st7UlBybHwjMBsDECMs1wW2z58Qfi4C4/WBQCD8VDWrFRnlQEq7LgloYX+GZF/obP0sunh1WlCPhUAbiIiMhl5UhcPP7+jfE4ZaqXu7s7ubl5Ferl5ubi7uZ2+umn1M+tVN/NRv3MrCySU1Jo3SqUpKQUAvwb4+TkRLOmTUlOrt1vUGs1cHG1gwDH8seSE8ZIy4Jg4/i+fOjuXrGOWxU9nJsC1/vAFF/o6AYLQ2B5JkTnG8f358M4bxjiBd72EFO6mPmlJHjCH+xNF/puRc5d9llGW2paT0RERC4NScnJNAkKqlAW4O9PWloaRUXlX3smJibh7+9vs40A/8YkJiZZn2dlZZOVnU2Ajfpbt26jW9cumEwmTCZjqhpAiaUEy/m4oRqos9U1WWZ4PhFeCAIfB2MkJMMM4S7VO39jDgzwKH/ezAmCnWBD6Rqk5k6wLReOFMBxMzR1hIRC+D0LbtZoi9RzHtX8l+muvIAiIiKXlYyMDBo29KlQFhQUiKenJ7+u/o30jAwit+0gKTnFunGk2WwmJzeXkhLjG8/27dsSdTCaPXv3kZaezq+r1xDcvBne3hWz/pw8mUlKaiqtQlsC0NjPj2MJiaSlpRMTcxj/xo1r4Y7L1dnHnvmp4GyCO32N53vzjEwB/zgGTXZCp73GmpiqJBZB0Glrgfwd4Gih8fPLTeCOWAjdDdMDIMgJZicZP2u0Req7WxtBdZa6WYAduWetJiIiIpeI3Nw8XJwrZuaxs7NjxIirycvL46vFSzhwIIoR1wzDy8sTgMSkZD76+FNr1rDAgAAGXTmArVu38c033+Ls5MTQIYMqXWtLZCTdu3XFZDI+PAcFBRLWOpQl336Hg6MjHTu2v8B3W5HJUjbeU4ssFgjbYwQtMwKMsndS4JF4eKMZDPSA37Jh2lH4LATG+1Ruwz4SfmkNV52yGeiAAzDYE54rHT3LK4GCEvB2gPhCGBUN29rAMwnGGoI+HvBhi6qno4nUlUMF0HGv7axip3Ozg49b2P53InI5UjpkEampiy0d8uWqTj6yb8s1PphNOmXK1n1+kNwJHmxsrFl5qDFM9YV3U2234WJnBCWnKiipGIS42hlBC8DsRHgyADblwBcZsKcdFFuMtTIi9U2os7FPi5td5ZEXR4zRStfSkcPcEpgQY4xWltT61xAiIiIitaNOApefM6GnuzF9q4zJBA1Py9jWxgWOFdpuo4kjJBVXLEsqhkAb82uOFBgBywQfYw1MT3fwtIehXsZaGZH6aHgDY5+WqX7gZWf8Y/WyM57vaQeRbaD1KSPFs5Jg7CEjw56IiIjIpaZOApeNOcZ0sFM9Hg/XRlcs254HEVUs1u/lDuuyy5/HFxoZynq5V677YiLMDAA7E5iAsoGaYosxbU2kvgp1hnnN4WQXMHcz/juvuVHexhU2RcDVp0yX/O4k9N5fnl1PRERE5FJRJ4HL7jxo51qx7Dpv+Pkk/DvFmEb2ToqxSeS00jUwhSVG5jFzaaBxrx/8L8PYXHJXnrGny3AvaH1aoBNTAJG55fP/u7sZmcX+yoWlJ4zRF5GLlY8D/NgKpp2SvXBvPvTYDysz665fIiIiIudbnQQuyUWVp4UN9IQvQuC9VGi3B95JNZ73Kx2ZWZ8DgTuNkRWA3h7G/i+zkoxvmBvYwyctKl/rxUR4OtCYigbQ39NIhzzggJFK9sHazeImct7Zm+DVpvBpC2PtCxgpwK85CG8ma1RRRERELg11klVMRC6MLTkw5hAklO8/xeRG8N/mRkILkcuBsoqJSE0pq9jFQR9lRC4hV7jD1jbQ+5QpkJ+kw5VRxgasIiIiIhcrBS4il5hAR1gTBnc0Ki/blAPd9xv/FREREbkYKXARuQQ528H7wfB2M7AvLUssMtZ2fZJep10TEREROScKXEQuUSaTsZHr8tbQsDR6KbTAlFh4LN5IBy4iIiJysVDgInKJG+IFW9pAu1NShb+ZAiMOwvHiqs8TERERqU8UuIhcBlo6w4YIGONdXrYyy9jvZW9enXVLREREpNoUuIhcJjzt4ZuW8GxgeVl0AfTcD9+dqLNuiYiIiFSLAheRy4idCZ4Lgq9bGhuwAmSXGHu/zE7UZpUiIiJSfylwEbkMXe8D68OhhZPx3AI8kwA3HoYcc512TURERMQmBS4il6mObsai/Ss9yssWH4e+B+BIQd31S0RERMQWBS4ilzFfB1gRBg/4lZf9lWdsVvlHVt31S0REROR0ClxELnOOJpjbHBYEGz8DpBXDkCh4N7Vu+yYiIiJSRoGLiABwpy+sCYPGDsbzYuC+OLjnCBSW1GnXRERERBS4iEi5vh6wtQ10cysvm58GQw9CSlHd9UtEREREgYuIVNDMCdaGw8SG5WVrs+GK/bA9t+76JSIiIpe3Wg1cPk4DU6TtR1yhkcloWBS4b4c2e+Dnk2du76sMaLUb3LbB6OiK3wj/mQ2hu8DvL1hw2jz9x+NhYfr5vz+RS4WrHXzWAl5pAqXLXogrhL774cuMuuyZiIiIXK5qNXC5sSEkdix/HOsAXd3gem9o5gijD0EjB9gSAZMbwfWH4HAVaVm35MDkWHgmADZGQKYZbostP/5AHNzlB4tC4KF4Y7ExQFIRrMqCWxraalVEyphMMD0AfmgFDeyNsjwL3HQYnjoGJdqsUkRERGpRrQYurnYQ4Fj+WHLC+BZ3QTCsyYID+fBeMLR1hScDoI8HfJBmu625KcYmelN8jf0oFobA8kyIzjeO78+Hcd4wxAu87SGmNAB6KQme8Ad7k+12RaSiEQ1gUwSEOZeXvZRkfNGQqc0qRUREpJbU2RqXLDM8nwgvBIGPA2zMgS5u4GlfXqefB2zIsX3+xhwYcMrGec2cINipvH5zJ9iWa0w/O26Gpo6QUAi/Z8HNGm0RqZFwFyN4Ge5VXvbDSei1Hw7m112/RERE5PJRZ4HL/FRwNhkpWAESiyDIsWIdf0c4Wmj7fJv1Hcrrv9wE7oiF0N3GdJcgJ5idZPys0RaRmvN2gO9bwXT/8rJ9+dBjPyw/y3o0ERERkb+rTgIXi8VIsfpg4/IN73JLwPm03jiboKCKefQ269uV1x/rA+mdIa2TMaoTX2gs2L/Jx5if33wn3BRjtCMi1WNvgleawuch4FL6b/eEGUZEw+vJxr9tERERkQuhTgKXbblwqAAmnTJly8UOCk4LIgos4FZFD23WL6lY39XO+JYYYHaisW5mUw58kQF72kGxxVgrIyI1M7EhrAs3pmAClABPHDUSZuTpywARERG5AOokcPk5E3q6G9O3yjRxNDJ+nSqpCAJPmw5WoX7xafWLbdc/UmAELBN8jDUwPd2NtTRDvYy1MiJSc93cYUsb6ONeXvZpBgw8AMeqmOIpIiIicq7qJHDZmAMDPSqW9XKHHXmQc0qWonXZRrktvdyN42XiC40MZbbqv5gIMwPAzmTsSVH2hXCxRVNbRP6OAEdYHQb/16i8bEsudN8HG7OrPk9ERESkpuokcNmdB+1cK5YN9DSygk2JhT158EqSEeDcVbp4v7DEGIExlwYa9/rB/zKMzSV35RlTVIZ7QWuXiu3GFEBkLoz3MZ53dzMyi/2VC0tPGKMvInLunO2MlObzmkFZUsCkYhgYZWw6KyIiInI+1EngklwEDR0qltmbYFkopBRDt33GzvZLQ6FF6d4R63MgcKcxsgLQ28P4sDQrCXrvNzbI+6RF5Wu9mAhPBxqb6QH09zTSIQ84AO52RoIAEfl7TCa4vzGsDINGpdFLoQVuPwKPxBujmyIiIiJ/h8li0WQpETl/DhcYm1PuyisvG+IJX7aERg5Vnydyvsz7z/y67oKIXGQeuO/uuu6CVEOd7eMiIpemEGdYHw7jvMvLVmVBj33GNFARERGRc6HARUTOOw97WNwSng8sL4sphF774dsTddYtERERuYgpcBGRC8LOBP8MgiUtjfVkANklMPYQvJAAJZqkKiIiIjWgwEVELqixPrAhAkJO2bfp2USYEAPZ5qrPExERETmVAhcRueA6uBqbVQ72LC/75gT0PWAs5hcRERE5GwUuIlIrGjnA8tbw0CkpyHfmwRX7YE1W3fVLRERELg4KXESk1jiY4O1m8EEwOJXurZRuhqui4J0UUHJ2ERERqYoCFxGpdXf4wm9h4F+6r4sZeCAe7o6DwpI67ZqIiIjUUwpcRKRO9PaArW2gu1t52YI0GBwFyUV11y8RERGpnxS4iEidaeoEf4TDrQ3Ly/7Mge77IDKn7volIiIi9Y8CFxGpU652sLAFvNak/A3paBH0OwBfZNRlz0RERKQ+UeAiInXOZIInAuDHVtDA3ijLt8DEwzDzGJi1aF9EROSyp8BFROqNaxrA5ggIdy4vezkJrouGk9qsUkRE5LKmwEVE6pUwF9jUBkY2KC/7KRN67oOo/Lrrl4iIiNQth7rugIjI6RrYw7JQ+EcCvJRklB0ogB774YsQGN7gzOeLiIhcisxmM+s3buLAgYNYLBZatwqlf78+2Nvbk5mVxZo1f5CYlISnhwf9+vYmOLh5lW0djD7Exo2bycnNpVnTJgy6ciBubq4AJCYmsXLVGgoLC+ndqwft2raxnrfuzw34NmpERETYBb/f09X6iEuRBR6LB98d0GgH3HsECkr3bZh6BEyRFR9vJVfd1lcZ0Go3uG2D0dGQckoK1T+zIXQX+P0FC1Irnvd4PCxMP993JiLnk70J5jQxAhXX0s0qT5phZDS8lqTNKkVE5PKzfsNGYmIOM3L41Vw74hqOxMWxZWskFouFn35ajouLMxNuGEdERBg/L19JZmamzXaSk1P4ddUaunfvyg3jxlBYWMSvq1Zbj/++dh3t2kZw9bAh/LH2T/Ly8gDIyc3l6NFjhIW1qpX7PV2tBy7TjsKSE7CsFXzfCn7OhBcTjWN784zMQokdyx9T/Wy3syUHJsfCMwGwMQIyzXBbbPnxB+LgLj9YFAIPxUNasVGeVASrsuCWhrZaFZH65qaGsC4Cmjkazy3A9GMwKRbytFmliIhcJgoKCti1ey+DrhxIYGAAgYEB9LiiOympaRw7lsDxEycYdOUAGjb0oVvXLgQE+LN3336bbe3ctZvQliG0iQjH17cRQ4cMIi7+KCdOngTg+PETtGwZQrOmTXF2diYzMwuAyMjtdOncETu7ulltUqtXPVEM76bCgmDo6wF9POC5QIjMNY7vy4fu7hDgWP5wq6KHc1Pgeh+Y4gsd3WBhCCzPhOjSOfD782GcNwzxAm97iCkwyl9Kgif8jW9zReTi0NUNtrSBfh7lZZ9nwIADcLSw7volIiJSWxISk3BwcKBZ0ybWsjYR4Vx37QiSkpPx8/XFycnJeiwwIICkJNtTl5KSUwgKCrQ+9/T0wNPTw1rf09OD1NQ0MrOyKCgowN3DneycHBISEmndum5GW6CW17isyzb2bBjqWV42xdd4JBVBhhnCXarX1sYcIwAp08wJgp1gQw60coHmTrAtF5xNcNwMTR0hoRB+z4I3mp7f+xKRC8/fEVa1hgfj4b00o2xrrrFZ5ZJQ44sQERGRS1XmyUy8PD2JOhhNZOR2ioqKCA1tSe9ePcjJzcXd3a1CfTc3V7JzbO/mnJubi7u7e8X6rm5kZxv1e/fqycpfV2M2m+nerQse7u78/sdaunTpVGejLVDLgcuhAmjhBF8ch9mJkF0C431gTpAxTcwB+McxY/qYrwM82tgIamxJLIIgx4pl/g7l376+3ARuOQyFFngqEIKc4P44mB6g0RaRi5WTHfy3OXR2g4fioBhILoYro+Dd5vB/VbxfiIiIXOwKi4rIzMpi567dXHllf4oKi/jtj7VYLCUUFxVjb29fob69vT1ms+29BIqLbdW3s9YPbRlC8B2TMZvNODs7k5WVTWJiMgP692PDxk0ciIomMMCfwYMG4ujoaOsSF0SthkxZJXC4wJjmNT/Y+ADy9XFjvvq+0ilendzg51bGB5C742Dxcdtt5ZaA82m9d7aDgtIFu2N9IL0zpHWCF4IgvtBYsH+TDzx1DJrvhJtijHZE5OJhMsG9fvBrmPEFBxhJP+48YgQzRVq0LyIilyA7OxOFhYUMGzqYoMBAgoOb07dPb3bv2WczSDGbzTg42B6jsF2/BEfH8voODg44Oxsbq22N3EbXrp1JSk4m6uAhJt40npKSEnbu2nOe7/LMajVwcTBBZgl8FmLMVR/eAP7VFOanwj1+kNwJHmxsrFl5qDFM9TXWxNjiYleejaxMQUnFNTGuduBd+vrPToQnA2BTDnyRAXvaQbHFCKJE5OIz0BO2REBH1/KyualwzUFIL667fomIiFwI7m7u2NnZ0aBB+Z4A3t4NMJvNuLm5kpubV6F+bm4u7m5upzdjtOXuTm5ubqX6bjbqZ2ZlkZySQutWoSQlpRDg3xgnJyeaNW1KcvIZ0v9eALUauAQ5GtPBQk/ZFTvcBfItRtavhqcFhW1c4FgVC2+bOELSaR9Okooh0MZo1ZECI2CZ4GOsgenpDp72MNTLWCsjIhenFs6wPhxu8C4vW50FV+yDXXlVniYiInLRCQjwp6SkhLT08j09jmccx9HRkQB/f9LS0igqKt8bJDExCX9/f1tNEeDfmMTEJOvzrKxssrKzCbBRf+vWbXTr2gWTyYTJBJbS/QhKLCXU9iSHWg1cersbc9JP/UCxNw887eDVJLg2umL97XkQUcVi/V7uxmL/MvGFEFdolJ/uxUSYGQB2JjABZQM1xRbtBSFysXO3h69awotB5WWHC6H3flhaxVRTERGRi423dwNCQlqwavVvpKSkkpCQyPqNm2nXtg1NmzbB09OTX1f/RnpGBpHbdpCUnGLdONJsNpOTm0tJifEpuH37tkQdjGbP3n2kpafz6+o1BDdvhrd3xR2eT57MJCU1lVahLQFo7OfHsYRE0tLSiYk5jH/jxrX6GtRq4NLaBUY3gNtjITIH1mbBk8fgLl+4zht+Pgn/TjEW8b+TYmwSOS3AOLewxMg8Zi4NNO71g/9lGJtL7soz9nQZ7mVc41QxBUa65fE+xvPubkZmsb9yYekJY/RFRC5uJhM8EwjfhoJH6btaTgmMi4HnE6BEX1CIiMgl4Kohg/Bt1Ihvv/uBH39eTsuWLejdqwd2dnaMGHE1eXl5fLV4CQcORDHimmF4eRmpfBOTkvno40+tWcMCAwIYdOUAtm7dxjfffIuzkxNDhwyqdL0tkZF079YVk8nIbBUUFEhY61CWfPsdDo6OdOzYvvZuHjBZLLU75pBlhofj4ZvjxpqXyY3glabgaIKvMuCFRIgugJbOMCsIxpUGHL9lwaAoONzemB4C8Ek6/DPBmM9+lRe81xz8TpsqdnssjGwAN/iUlz0SDx+lGXPk/xcCHhWTKojIRWxPHow+ZHwBUmasNyxsoX/rl4t5/5lf110QkYvMA/fdXdddkGqo9cBFRORCyyiGG2Pg16zysvYusKyV8aWIXNoUuIhITSlwuTjU3Q4yIiIXSEMH+Lk1PHLK1Nvd+cai/dWZddcvEREROXcKXETkkuRggjebwUfB4FS66WyGGYYdNNKga6xZRETk4qLARUQuaVN84few8lTpZuCheLjrSOW9oERERKT+UuAiIpe8Xh6wNQJ6nLKv1gfpMDjKyFYoIiIi9Z8CFxG5LAQ5we/hMKlhedn6HOi+D7ZqI1oREZF6T4GLiFw2XOzgkxbwetPyN79jRdD/AHyefqYzRUREpK4pcBGRy4rJBI/5G1nHvEv3dcm3wK2xMP1o+Sa3IiIiUr8ocBGRy9IwL9gcAW1cysteS4Zro+FEcd31S0RERGxT4CIil63WLrAxAkY1KC/7JRN67of9+XXXLxEREalMgYuIXNa87OHbUHg6oLwsqgB67oOfTtZdv0RERKQiBS4ictmzM8GsJvBlCLiWblaZWWJMG3slSZtVioiI1AcKXERESk1oCOsjoLmT8dwCPHkMbjkMudqsUkREpE4pcBEROUVnN9gSAf09ysu+OG6kTI4vrLt+iYiIXO4UuIiInKaxI/zaGu7xLS/blmtsVvlndt31S0RE5HKmwEVExAYnO3g3GN5tDg6lZSnFMCgKFqTWaddEREQuS7UeuBRZ4LF48N0BjXbAvUegoHTu+JECGBYF7tuhzR74+SwZfb7KgFa7wW0bjI6GlKLyY39mQ+gu8Pur8oeMx+NhoXbJFpFquMcPVoWBb2n0UmSBqXHwQJzxs4iIiNSOWg9cph2FJSdgWSv4vhX8nAkvJhpZe0YfgkYOxvzyyY3g+kNwuMB2O1tyYHIsPBNg7MOQaYbbYsuPPxAHd/nBohB4KB7SSjeUSyqCVVlwS8MLfKMicskY4AlbI6Cza3nZO6nGFy1p2qxSRESkVtRq4HKiGN5NhQXB0NcD+njAc4EQmQtrsuBAPrwXDG1d4ckA4/gHabbbmpsC1/vAFF/o6AYLQ2B5JkSXbhq3Px/GecMQL/C2h5jSAOilJHjCH+xNtXLLInKJCHaGdeEwwae87LdsuGIf/JVbd/0SERG5XNRq4LIuG1ztYKhnedkUX/i5NWzMgS5u4GlffqyfB2zIsd3WxhwYcErWn2ZOEOxUXr+5k7GY9kgBHDdDU0dIKITfs+BmjbaIyDlwtzdGcWcHQdl3H7GF0OcAfHO8TrsmIiJyyavVwOVQAbRwMlKLttsDwbvgiaNQWAKJRRDkWLG+vyMcrSL9qM36DuX1X24Cd8RC6G6YHgBBTjA7yfhZoy0icq5MJngqEJaFgmfpO2huCdwQA/9MgBKtexEREbkgHM5e5fzJKjHWrMxNgfnBkGWGe+Og2GL84Xc+LYxyNkFBFR8CbNa3K68/1gfSGxgL/70djP0X/syGuc3gqWPwWboxFe3DFuCm3GoiUkOjvI31daMPQXTpVNQXE2FXHixsUXH0WERERP6+Wv3I7mCCzBL4LMSYBja8AfyrKcxPBSdTeXaxMgWWqoMKFzsb9Usq1ne1M4IWgNmJxrqZTTnwRQbsaWcETHNTzt/9icjlpa0rbI6AYV7lZd+egN77jRFmEREROX9qNXAJcjSGeEKdy8vCXSDfAgGORsavUyUVQeBp08HKNHGEpNOy+SQV265/pMAIWCb4GGtgerob34YO9TLWyoiInCsfB/ixFTzuX162J99YtP9rZt31S0RE5FJTq4FLb3coxphKUWZvnjFPvLcH7MiDHHP5sXXZ0Mvddlu93I3jZeILIa7Qdv0XE2FmANiZjAW1ZQM1xRYjDbOIyN/hYDJGjz9pYUxxBSMpyDUH4e1kvc+IiIicD7UauLR2gdEN4PZYiMyBtVnw5DG4yxeGeBpZwabEwp48eCXJGA25y9c4t7DEGIExl34AuNcP/pdhbC65K8/Y02W4l3GNU8UUGOmWx5emMO3uZmQW+ysXlp4wRl9ERM6H2xrBH+HlI79m4JGj8H9HKk9tFRERkZqp9WXpn4ZAR1cYHAVjDsFYb3i5qZHpa1kopBRDt33GzvZLQ6FF6bSy9TkQuNMYWQFjhGZBMMxKMuaTN7A3vu083YuJ8HSgkQkIoL+nkQ55wAFwt4MHG9fGXYvI5aKHu7FZ5alfinyUDldGGdkQRURE5NyYLBZNYhAROd/yS+CeOPgkvbwsyBG+DYUrNNJ7Qc37z/y67oKIXGQeuO/uuu6CVIMSAYuIXAAudvBRMLzZtPyNNqEI+h+AT9PPeKqIiIjYoMBFROQCMZngEX9Y3hp8Svd1KbDAbbHG5rvFGu8WERGpNgUuIiIX2FAvY7+XtqckD3k9GUZGw/Hiqs8TERGRcgpcRERqQSsX2BhhZFYssyITeu6HfXlVnyciIiIGBS4iIrXE0x6WhMI/AsvLDhYYwcsPJ+qsWyIiIhcFBS4iIrXIzgQvBMHiluBW+g6cVQLXHYI5idqsUkREpCoKXERE6sANPrA+3Nh4F8ACPJ0ANx+GXG1WKSIiUokCFxGROtLJDbZEwECP8rIvj0O//RBXWHf9EhERqY8UuIiI1CE/R1gZBvf5lZdtz4Pu+2BtVt31S0REpL5R4CIiUsccTfBOc5jf3PgZILUYBkfB/NS67ZuIiEh9ocBFRKSemOoHq8PAz8F4XgzcEwf3xUGh1r2IiMhlToGLiEg90s8DtraBLq7lZe+mwlUHIbWo7volIiJS1xS4iIjUM82dYF0E3ORTXvZHNnTfDzty665fIiIidUmBi4hIPeRmB/8LgZeaQOmyF+IKoe8BWHy8TrsmIiJSJxS4iIjUUyYTPBkA37cCr9J369wSmBAD/zgGJdqsUkRELiO1Hrj8LwNMkRUfY6KNY1OPVD72VnLVbX2VAa12g9s2GB0NKafM//4zG0J3gd9fsOC0rDyPx8PC9PN/byIiF8LIBrAxAlo7l5fNSoIxhyDTXHf9EhERqU21HrjszYOx3pDYsfzxcYvyY681qXhsqp/tdrbkwORYeCbA+IOeaYbbYsuPPxAHd/nBohB4KB7Sio3ypCJYlQW3NLxw9ygicr61cYXNEXCNV3nZ9yeh936Izq+7fomIiNSW2g9c8qGjKwQ4lj+8S1N/7suH7u4Vj7lV0cO5KXC9D0zxhY5usDAElmeW/wHfnw/jvGGIF3jbQ0yBUf5SEjzhD/Ym2+2KiNRX3g7wQyuY5l9etjcfrtgPKzLrrl8iIiK1oU5GXMJdKpcnFUGG2fYxWzbmwACP8ufNnCDYCTbkGM+bO8G2XDhSAMfN0NQREgrh9yy4WaMtInKRsjfBq03h0xbgXPoFzAkzDD8IbyaDReteRETkEuVQmxcrLIFDBfDDSfhngrGwdLwPPB9kBDQOGAtOf84EXwd4tLExomJLYhEEOVYs83eAo4XGzy83gVsOQ6EFngqEICe4Pw6mB2i0RUQufrc2Mr7oGXsIjhVBCfDYUfgrD/7bHFyUekVE5JJzIOogK39dXaEsJKQFI4dfTWZWFmvW/EFiUhKeHh7069ub4ODmVbZ1MPoQGzduJic3l2ZNmzDoyoG4uRmbiCUmJrFy1RoKCwvp3asH7dq2sZ637s8N+DZqRERE2IW5yTOo1cDlYIGxE7S7HXzd0ghiHo6HrBJoWzrS0skNHmoMv2XD3XHgbm8EN6fLLQHn0/4wO9tBQem3jWN9IL0BFJQY0yviC40F+3ObwVPH4LN06OMBH7aoejqaiEh9doU7bGkD1x8qH23+JB325cHSUOMLGxERuXQcP36cliEtGDiwv7XMwd4ei8XCTz8tx8fHmwk3jONwbCw/L1/JxJvG4+XlVamd5OQUfl21hisH9sfP15e169bz66rVXDdqJAC/r11Hu7YRNG7sxw8//kLLkBa4urqSk5vL0aPH6NO7Z63d86lq9SN7O1dI6wTvBRsByjgfeKsZLEgzFuEnd4IHGxtrVh5qDFN9jR2jbXGxM4KSUxWUVAxCXO3K18/MTjTSim7KgS8yYE87KLYYa2VERC5WgY6wJgzuaFRetjnX2KxyU07d9UtERM6/jIzjNGrUCHc3N+vD2dmZY8cSOH7iBIOuHEDDhj5069qFgAB/9u7bb7Odnbt2E9oyhDYR4fj6NmLokEHExR/lxMmTABw/foKWLUNo1rQpzs7OZGZmARAZuZ0unTtiZ1c33/rX+lUbnTbG08YFiixG1q+GNo4dK7TdThNHSCquWJZUbPwRP92RAuMP+AQf41vJnu7gaQ9DvYy1MiIiFzNnO3g/GP7dDOxLyxKLYMABYwRGREQuDRnHT+Dj412pPCk5GT9fX5ycyofaAwMCSEqyva9IUnIKQUGB1ueenh54enpY63t6epCamkZmVhYFBQW4e7iTnZNDQkIirVu3Or83VQO1OlVsyXG4Nw7iO4BTaci0PdfI+vWvJDhQYGTMKbM9DyKqWKzfyx3WZcOdpWtg4guNXaV7uVeu+2IizAwAO5OxA3XZQE2xRQtZReTSYDIZI9ZtXYwNKjPMxhq/KbHwV66xoN9B6/tERC5aZrOZzMxMYmOPsGnzFiwWaBXakp49upOTm4u7u1uF+m5urmTn2P6GPjc3F3f3ih+a3VzdyM426vfu1ZOVv67GbDbTvVsXPNzd+f2PtXTp0qnORluglgOXgZ5gAabGwVMBxpqXaceM1J59PWBwFPw7xdhs7ZeTxiaRq0rX/RSWGH+I/RyMxfX3+sHAKOjrDr08jLUyw72g9WmBTkwBRObCgmDjeXc3eDnJ+EO+9AQM9azNV0BE5MIa4mWse7kuGvaUpod/MwV258GilpVHtkVE5OJw4uRJSkpKcHB0YPjVwzh58iRr162nqKiQ4mIz9vb2Ferb29tjNtvepbi4uNhGfTtr/dCWIQTfMRmz2YyzszNZWdkkJiYzoH8/NmzcxIGoaAID/Bk8aCCOjjamO10gtRoyNXKA5a2NqVtd98HUI3CPrzEaMtATvgiB91Kh3R54J9V43q805fH6HAjcaYysAPT2MIKRWUnGBmwN7OGTFpWv+WIiPB1ofBsJ0N/TSIc84ICRJODBxrVy6yIitaalM2yIMDb7LbMyC3rshz15ddYtERH5Gxo1bMj/3TGZwVcOxNe3EaGhLenXrw979u7HwaFykGI2m3FwsP1tla2gxmwuwdGxvL6DgwPOzs4AbI3cRteunUlKTibq4CEm3jSekpISdu7ac57v8sxq/bu3Lm6wJtz2sQkNjYctV3qCpVvFssmNjMeZfNSictlbzYyHiMilytPeyN74QiI8n2iUHSqAXvvh8xC4zrtOuyciIufA1aXi1KKGPj6UlJTg7uZOWlpGhWO5ubm4u1WcPlbG3d2d3NzcSvXdbNTPzMoiOSWFKwf2Z8dfuwjwb4yTkxPNmjYlLj7+b95RzSgRsIjIJcrOBM8FGQGMe+m7fXYJjDlkZFrUGj8RkYvHoUMxfPDRwgojJalpaTg7O+Ef0Ji0tDSKioqsxxITk/D397fZVoB/YxITk6zPs7KyycrOJsBG/a1bt9GtaxdMJhMmE1hK/3iUWEqo7T8jClxERC5x1/vA+nBoUZpsxgI8kwA3HoYc29OfRUSknglqEgRYWPPbHxw/cYLY2CP8uX4jXTp3oklQEJ6envy6+jfSMzKI3LaDpOQU68aRZrOZnNxcSkqMFFXt27cl6mA0e/buIy09nV9XryG4eTO8vRtUuObJk5mkpKbSKrQlAI39/DiWkEhaWjoxMYfxb1y7ay4UuIiIXAY6uhmL9gedkpBk8XHoe8BYdygiIvWbq4sL1107kqysbL766hvW/P4H7du1pVvXLtjZ2TFixNXk5eXx1eIlHDgQxYhrhuHlZbzpJyYl89HHn1qzhgUGBDDoygFs3bqNb775FmcnJ4YOGVTpmlsiI+nerSum0sXiQUGBhLUOZcm33+Hg6EjHju1r7wUATBaLJguIiFwuiizwWDzMO2VzX18HYzrZwEsky+K8/8yv6y6IyEXmgfvurusuSDVoxEVE5DLiaIK5zY2sjI6l2RbTimFoFLybeuZzRURE6pICFxGRy9CdvrAmDPxLc0sWA/fFwT1HjH2zRERE6hsFLiIil6m+Hsa6l26nZL+cnwZDD0JKUdXniYiI1AUFLiIil7FmTrA2HCaesofW2mzovg+251Z9noiISG1T4CIicplztYPPWsCrTaB02QvxRdB3P3yZcaYzRUREao8CFxERwWSCaQHwYytoYG+U5VngpsPw1DEoUf5JERGpYwpcRETEangD2BQBYc7lZS8lwehDcFKbVYqISB1S4CIiIhWEuxjBy3Cv8rIfTkKv/XAwv+76JSIilzcFLiIiUom3A3zfCmb4l5ftz4ce+2H5ybrrl4iIXL4UuIiIiE32Jni5KXweAi6lq/ZPmGFENLyeDBatexERkVqkwEVERM5oYkNYFw5NHY3nJcATR+G2WMjTZpUiIlJLaj1w+V8GmCIrPsZEG8eOFMCwKHDfDm32wM9nmY7wVQa02g1u22B0dMUN0/7MhtBd4PcXLEiteN7j8bAw/fzel4jIpaybu7FZZR/38rLPMmDgAThWWHf9EhGRy0etBy5782CsNyR2LH983MKYcjD6EDRygC0RMLkRXH8IDhfYbmdLDkyOhWcCYGMEZJqNb//KPBAHd/nBohB4KB7Sio3ypCJYlQW3NLTVqoiIVCXAEVaHwZ2+5WVbco3NKjdm112/RETk8lD7gUs+dHQ1/gCWPbwdYE0WHMiH94KhrSs8GQB9POCDNNvtzE2B631gii90dIOFIbA8E6JLM97sz4dx3jDEC7ztIaY0AHopCZ7wN+Zui4hIzTjbwXvNYV4zKN3uhaRiGBgFH1Xxfi0iInI+1MmIS7hL5fKNOdDFDTzty8v6ecCGHNvtbMyBAR7lz5s5QbBTef3mTrAt15h+dtxszM1OKITfs+BmjbaIiJwzkwnubwwrw6BR6Xt2oQXuOAKPxEOxFu2LiMgFUKuBS2EJHCow9gNovdtYg/LkUSgogcQiCHKsWN/fEY5WMXfaZn2H8vovN4E7YiF0N0wPgCAnmJ1k/KzRFhGRv2+Qp7HupYNrednbKXDNQUgvrrt+iYjIpcmhNi92sACKAXc7+LqlEcQ8HA9ZJZBfYkxBOJWzCQqq+OYu11Z9u/L6Y30gvYERFHk7QHyhsWB/bjN46hh8lm5MRfuwBbgpt5qIyDkJcYb14caawyUnjLJVWdBjHyxrBe1dz3S2iIhI9dXqR/Z2rpDWyVjH0skNxvnAW81gQRq42BlBxqkKLFUHFTbrl1Ss72pnBC0AsxONdTObcuCLDNjTzpjOMDfl/N2fiMjlyMMeFreE5wPLy2IKofd++PZEnXVLREQuMbU+1tDotDGeNi5QZDGmfSUVVTyWVASBp00HK9PE0VgQWqF+se36RwqMgGWCj7EGpqe7sZZmqJexVkZERP4eOxP8MwiWhoJH6V+W7BIYewheSIASrXsREZG/qVYDlyXHwf8vY61Lme25RtavXu6wIw9yzOXH1mUb5bb0cjeOl4kvhLhC2/VfTISZAcYfVhPG5mlgjLho52cRkfNnjDdsiIAQp/KyZxNhQgxkm6s8TURELnElJSXk5OZSUnLuOxfXauAy0BMswNQ4iMqHH0/CtGMwzR+u9DSygk2JhT158EqSMRpyV+l+AYUlxgiMuTTQuNfP2MxyQSrsyjPmVw/3gtanZSyLKYDIXBjvYzzv7mZkFvsrF5aeMEZfRETk/GnvaizaH+xZXvbNCeh7oOq9uURE5NKUlJTMt9/9wPwFH/LxJ5+Rlp7OipWr2Lhpc43bqtXApZEDLG9tTN3qug+mHoF7fI3REHsTLAuFlGLots/Y2X5pKLRwNs5dnwOBO42RFYDeHrAgGGYlGfOoG9jDJy0qX/PFRHg60EjfCdDf00iHPOCAkSTgwca1cusiIpeVsvf7h095j92ZB1fsM/btEhGRS9/Ro8dY8u13APTqeYW1vFGjhkRu28H2HTtr1J7JYtFkKRERuXA+SoN74oy9XsDYuPLtZnCfX/mXSufTvP/MP/+Nisgl7YH77q7rLlySvlq8BB8fb64aOpiSkhL+898FTBg/jsZ+fmzavIWD0THcOvHGarenRMAiInJB3e4Lv4UZe20BmIEH4uHuuIprHkVE5NKSnpFBeFhrm8eaNGlCVlbNhuAVuIiIyAXX2wO2tjHWGZZZkAaDoyC5qOrzRETk4uXm6krG8eM2jx0/fhw315pt9qXARUREakVTJ/gjHG5tWF72Zw503weRSk0vInLJiYgIY9Pmrezdt5+8/HwALCUW4uOPsnlLJK1bt6pRew5nryIiInJ+uNrBwhbQyRVmHDPS0x8tgn4H4MMWRvIUERG5NFzRvRtZ2dmsXvM7ptJFjV8v+RaAli1D6Nmje43aU+AiIiK1ymSCJwKMtMk3HYaTZsi3wMTDRqr62U2MTJMiInJxs7OzY+jgQXTr0oVjCQnk5+fj5OREUGAgvr6NatyeAhcREakT1zSAzREw+hDsN2YQ8EqysTfX/1oaae5FROTidSwhAf/GjfHx8cbHx7vCsezsHPbu20+PK7pVuz2tcRERkToT5gIbI2Bkg/KynzKh5z44kF93/RIRkb9v6bffs/ibpZw4ebLSseycbLZsjaxRewpcRESkTjWwNzYgnhlQXnagAHruh58r/60TEZGLSFFREV8tXsKhQzF/uy0FLiIiUufsTTCnCXwRAq6l61tOmmFkNLyWBNoqWUTk4nTVkMGEh7fmlxW/8uf6DVj+xhu6AhcREak3bmoI6yKgmaPx3AJMPwaTYiFPm1WKiFx07OzsGNi/H4MHDWTnrj0sXfY9ubm5mEw1D0MUuIiISL3S1Q22tIF+HuVln2dA/wNwtLDu+iUiIueuTUQ448ZcR+bJTL5c/A1paWk1bkOBi4iI1Dv+jrCqNUz1LS+LzDU2q1yfXXf9EhGRc+fv35gJ48fh5eXFmt/+qPH5ClxERKRecrKD+cHwn+blufuTi+HKKPig5l/UiYhILetxRTfcPdwrlLm5uTF29Cg6tG+Hh4dHFWfapn1cRESkXrvXD9q6wA0xkFYMRRa484ixWeXrzcBRm1WKiNRLPa7obrPczs6OgQP6MbCG7SlwERGRem+gJ2wp3axyZ55RNjcVdufD4pbQSH/NRETqhS8WLWbYVUNo1KghXyxafObKJrj5xvHVbltv9SIiclFo4Qzrw2FKLHx9wihbkwVX7INlraCDa132TkREAPwa++LoaIQYfn6+cB5HxU2Wv5NM+W+4MxaiC+C3cOP51COw4LQ5y282hUf8bZ//VQY8lQAJhXCVFywIhsal6TP/zIbbDkNmCcwJgrv8ys97PB46ucFtjc77LYmISC2wWGB2EvwjobzM3Q4WtoBxPjDvP/PrrG8icnF64L6767oLUg01WpxfbIHP0uH2WBh+EA7mw0dpsDO3ZhddlQkfpFcs25sHrzWBxI7lj6l+ts/fkgOTY+GZANgYAZlmuC22/PgDcUawsigEHoo35kQDJBXBqiy4pWHN+isiIvWHyQTPBMK3oeBR+lcspwSuj4HnEkDbvYiI1B/Hj58gPSMDgMKiIn7/Yy0//Pgz+/YfqHFb1Q5c0ouh134jaNmWCysyIasElpyAPgdgU0712skxG6MrfSsmGGBfPnR3hwDH8odbFb2bmwLX+8AUX+joBgtDYHkmROcbx/fnwzhvGOIF3vYQU2CUv5QET/gbOzSLiMjFbbS38eVVqHN52fOJ8EHrYRTYaSa0iEhdOxRzmC++XMz+0iBl1erf2LvvAEXFxaz57Q927tpdo/aqHbg8Gg8nzRDdHiLbGLsZA3zdEnq6w1PHqtfO0wlwpafxKJNUBBlmCHepXhsbc2DAKdnTmjlBsBNsKA2emjsZwdWRAjhuhqaOxpSy37PgZo22iIhcMtq5wuYIGHrK35SdDUN4vd0Y0pw9qz5RREQuuK2R22jdKpTevXqSk5NDTMxhrujejbGjR9GzR3d27tpTo/aqHbh8fxJmB0Gwc8U1Ns528Li/sTHY2WzIhsXH4V9NK5bvzTOyBPzjGDTZCZ32wsdnyNGfWARBjhXL/B3Kd1R+uQncEQuhu2F6AAQ5GfOhpwdotEVE5FLT0AF+bg2PNC4vS3RrxGvtxnHAK6juOiYicpk7fvwEbdpEYGdnR+yROABahbYEIMDfn6ysrBq1V+3AxWwBlypqF1uMxZJnUlAC/3cE3moGPqeN4O8rneLVyQ1+bgX/5wt3xxlBji25JUbAdCpnOygo7cNYH0jvDGmd4IUgiC80Fuzf5GOMDDXfCTfFGO2IiMjFz8EEbzaDj4LBocQMQK6jC/+JGMnv/u2okyw0IiKXOScnJwryjQ/6sbFH8PL0xNu7AQDHT5zAzbVm6SCrHbgM9jTmDh8vLi8zYWwE9naKkWP/TF5IhNbOMN6n8rH7/CC5EzzY2Fiz8lBjmOoL76babsvFzgiETlVQUnFNjKsdeJcGSLMT4ckAYx3OFxmwp50RbM1NOetti4jIRWSKLzy89zu8Co25wyUmO75u0Y8vQgZQZKpRPhoREfmbWgQ3Z/2GTfz2+1pij8QRHt4agL/+2sWGjZtp2bJFjdqr9rv4683gWKEx/eq6aCNo+UcCtNlj7F78atMzn/+/DGMBvcd24/FqMqzNNn42mYyh/lO1cTGuZ0sTR0gqrliWVAyBjpXrHikwApYJPsYamJ7u4GkPQ72MtTIiInJpaZGTwvTdSwjOTraWbWjchrltRpHpqM1eRERqS/9+fWjWrCkJiYm0aRNOt65dANizbz+hLUPo3atnjdqrdtqVUGfY2RbeTIHfsoznyUUwqgE85m8skD+T38KM0Zkyb6bA1hz4PMTYW+VAAfzQqvz49jyIqGKxfi93WJcNd/oaz+MLIa7QKD/di4kwMwDsTEawVTZQU53pbSIicnFqUJTLw3u/54uQAWzxCwPgsGcAr7Ufx11Ry2mec4aFlCIiF4HVa37nxMmTjBtzHQCZWVmsWfMHiUlJeHp40K9vb4KDm1d5/sHoQ2zcuJmc3FyaNW3CoCsH4uZmfLmTmJjEylVrKCwspHevHrRr28Z63ro/N+DbqBEREWFn7aOjoyNXDuxfqfzmG2/AZKr5wvMajZs3doTp/rAhAqLaw69hcL/f2YMWMBb1t3Ipf/jYG9O5WrnAdd7w80n4dwocKoB3UmBhOkwLMM4tLDEyj5lLA417/YwRnAWpsCvP2NNluBe0Pi3QiSkwkgaUTU/r7mZkFvsrF5aeMEZfRETk0uRoMTMpZg1jj6zHZDG+tjrh5MFbbUezpVGrs5wtIlJ/xR89yt59+63PLRYLP/20HBcXZybcMI6IiDB+Xr6SzMxMm+cnJ6fw66o1dO/elRvGjaGwsIhfV622Hv997TratY3g6mFD+GPtn+Tl5QGQk5vL0aPHCAv7e++h5xK0QA0Cl+PFMOIg9C5/jdiQDWF7YMLfXOg+0BO+CIH3UqHdHngn1XjerzTl8focCNxpjKwA9PaABcEwK8noTwN7+KRF5XZfTISnA42paAD9PY10yAMOGLssP9i48jkiInLpMAGDk3Zx74GfcS02NvUqsnNgYashfNusJyUo1aSIXFyKiopY89taAgMCrGXHjiVw/MQJBl05gIYNfejWtQsBAf4VgptT7dy1m9CWIbSJCMfXtxFDhwwiLv4oJ06eBIxsYC1bhtCsaVOcnZ3JzDSyf0VGbqdL547Y2dXNmsFqTxV74qgxuvGfU0acBnvCj63gnjh4+piR0aW6ZjWp+HxCQ+Nhy5WeYOlWsWxyI+NxJh+1qFz2VjPjISIil482J4/yxO4lLAi/hiRXYxh+VVBnEtwaMiV6FW7mKhZViojUMxs3baZJUCDu7m4kJCYBkJScjJ+vL05O5dOgAgMCSExMtNlGUnIKXTp3tD739PTA09ODpKRkvBs0wNPTg9TUNOzt7SkoKMDdw53snBwSEhLp17f3hb3BM6h2uPTjSXijGYzyLi9zsoPhDYx9U6pKXSwiIlIfNC7I5LE9S2l/PNZats+7Oa+3G0uSi3ed9UtEpLoSk5KIPhRD3z4Vg4ec3Fzc3d0qlLm5uZKdYzsTVW5uLu7uFddMuLm6kZ1t1O/dqyer1/zOp599QdcunfBwdycychtdunSqs9EWqMGIS14JuFQxou5pByfM56tLIiIiF4aruYi7opbzU9MrWN6kKwAprt683m4MU6JX0e5kfB33UETENrPZzOo1v9O/bx9cXJwrHCsuKsbe3r5Cmb29PWaz7Q/oxcW26ttZ64e2DCH4jsmYzWacnZ3JysomMTGZAf37sWHjJg5ERRMY4M/gQQNxdLSR1vcCqXbg0scDXkkypoe5n3KfuSXwr2To63EhuiciInJ+2QHXHt1CUG46n7W8kiJ7R/IdnJkfPpxR8ZsYmviXVr6ISL2zeUsk3g0a0KpVaKVjDg72FBZWnPJqNptxcLD9Ud9WUGM2l+DoWF7fwcHBev7WyG107dqZpORkog4eYuJN41m1+jd27tpDt66dq+yzxWJhz959HDkSR1FxMZbTUvqagDGjR53ptiveZ3UrvtIE+h+A5ruMxfSNHSC12MjSZQZ+P3tGNBERkXqja0YMjfNP8l7Y1Rx39sRiMvFd814cc2vExMN/4FRSfPZGRERqSdTBaHJzc5n/3gcAmEtKsFgszH/vA7p160JaWkaF+rm5ubi7udlqCnd3d3JzcyvVd7NRPzMri+SUFK4c2J8df+0iwL8xTk5ONGvalLj4M49Sr/tzA3/t3IWfny8e7u7nnE2sTLUDl45usKsdvJlsbOS4O8/I5nVLI3i0MbRwPnsbIiIi9UnT3HSm7V7Ch62vItorCIBI39akuHpzV9RyfAq1U7GI1A9jx4yipKQ8je9ff+0kJSWNq64aTHZWNpGR2ykqKrJO3UpMTMLf399mWwH+jUlMTLLuz5KVlU1WdjYBNupv3bqNbl27YDKZMJmwjpqUWEo425aIB6IO0uOKbvS4ovs53HFlNVpd09zJyBy2sXQfly1t4O1mClpEROTi5Vmcz/37f6Rf8h5rWby7H6+1H8chj4AznCkiUnu8PD3xbtDA+nB2dsbewR7vBg0ICgrE09OTX1f/RnpGBpHbdpCUnGINTMxmMzm5udbAp337tkQdjGbP3n2kpafz6+o1BDdvhrd3gwrXPHkyk5TUVFqFtgSgsZ8fxxISSUtLJybmMP6Nz7y3iNlsJigo8Ly9BtUecQE4UQxrsiCnpHwH+lPddpb0xCIiIvWRg6WEG2PX0SQ3ncXBfSmxsyfL0Y25ba5lfOw6+qba3gtBRKQ+sLOzY8SIq1m95ne+WryEBl5ejLhmGF5engAkJiXz7bLvue3WiXh5eRIYEMCgKwewefNW8vPzadasKYOuHFCp3S2RkXTv1tU6xSsoKJCw1qEs+fY7goIC6dix/Rn71SK4OTExsTRt0uSM9arLZDl9lUwVvj8BNx02sovZbAgwd7N9TEREpLbM+8/8v3V+tGcgH7S+imxHV2tZ/6TdXB+3AXvL39htWUTqrQfuu7uuu3BJ2rf/AOv+3EBggD/+/v6VkgWYTNC5U8cqzq6s2iMuTx6D7m4wrzk0dQQ7pVwREZFLUKusRKbtXsKCsKs56u4LwNqA9iS5+XD7wV/xLM6v4x6KiFwcVq3+DYDYI3HEHomrdNxkMtUocKn2iIvLNvixFQzxqnbbIiIite7vjriUKbBz4POWV7K9UXnq0YYFWdwV9QtNczPOcKaIXGw04nJxqPbi/DYucKTw7PVEREQuBc4lxdwe/Suj4jdhKv2OL8PZkzfbjmGHT0gd905E5OKRn59P7JE4og5GEx9/tNKeM9VV7alirzeF/ztipEDu6Q5uNkKehjVa6i8iIlK/mYBhCTsIzD3OwlaDybd3otDekQ/ChnHN0UiGH9tas/ScIiKXmU2bt7Bt+18VNry0s7OjS+eO9O7Vs0ZtVTvUuPEwZJlhQkzVdbQ4X0RELkUdThzhsT1LWRB2DakuRrrQX5p245hbQ247tAaXkqI67qGISP3z11+7iNy2gy6dO9K6VSvc3FzJzc0j6mA023fsxM3NnU5nyUx2qmoHLv9qek79FRERuSQE5p3gid1L+KjVUPZ7NwNgV8MQ3nBpwF1Ry/EryKzjHoqI1C87d++ha5dO9OrZw1rm5uaGr28j7Ozs2L17z4UJXCZrjxYREbnMuZkLuefAz3zXvCerAzsBkOjWkH+1H8vtB38lIvNYHfdQRKT+yMnJoUlQkM1jTYIC2b7jrxq1V6OpuYcL4M5YCNkFrttgWy48Fg/vp9XomoDRzpUHyp8fKYBhUeC+HdrsgZ9Pnvn8rzKg1W5w2wajoyHllFH6P7MhdBf4/QULUiue93g8LEyveX9FREQA7LEwNm4jtx5ajUNJMQC5Di68GzGC3/zbU61UnSIil4EGXl4kJCbaPJaQmIiHu3uN2qt24LIjF7rsg3XZMKoBFJa+M1uAu4/ApzUIBlZlwgen1LdYYPQhaOQAWyKM0Z3rDxmBki1bcmByLDwTABsjINMMt8WWH38gDu7yg0Uh8FA8pBl/V0gqglVZcEvD6vdVRETElp5pB3l473d4FeYAUGKy45sWffm85ZUUmbRkX0SkY8f2RG7bwcZNm0lNSyMnJ4fUtDQ2bNxM5LYdtGvXpkbtVXuq2KPx0Nvd2MulBJhXOpLxZjMossDryTCpGtPJcsww9Qj0PSXAWpMFB/JhbTh42kNbV/g1Ez5Ig1lNKrcxNwWu94Epxr5gLAyB5rsgOh9aucD+fBjnDWEu4G0PMQXg6wAvJcET/mCvzTNFROQ8aJGTyvTdS3g/bBixHv4AbPILJ9nFmzsPrqBBUW4d91BEpO60a9uGkycz2b5jJ5HbdljLTSYTnTq2p2uXzjVqr9qBy6Yc+DoU7EzGCMmpbvCBj6o54vJ0AlzpCYGOxugNwMYc6OJmBC1l+nnA2mzbbWzMMQKQMs2cINgJNuQYgUtzJ2Mam7MJjpuhqSMkFMLvWfCGkgzIRco+9PpzOq9ru5Zs+e41m8euuG4a2/acIVXgGZgPfWOz/O6n3uX9L389pzY3f/sq3TqEVip/74sV3PvMuW0q+O6su5l687BK5ZG7DtFjzPRzavPOG4cyf869No/p91R/fk+9OzThpmHtbB57+PUV59Rm08aeTJvUu0JZg6JcHtr7Pf/88i9y4xMAiAP+WYN233688r0DLFqxhw27zm3dzBO39qKZf+Vdo9fvPMqXK/eeU5s3XtWWPh0r/yGNT87kX59tPKc2a+v3JCJ1o0/vnnTt0omk5BQKCgpwcXamsX9jXF1catxWtQMXL3tjqpUtcYXgVY1R8Q3ZsPg47G5rjNCUSSyCIMeKdf0d4WgVe9PYrO9QXv/lJnDLYWM621OBEOQE98fB9ACNtoiIyPnnaDHTsCALja+IyOUuPz8fZ2dnTCYT+fn51vIA/8aV6gG41CCAqXbgMt4HZh6DEGfo72GUmYCofHg+AcZ4n/n8ghJjA8u3moHPaVfNLQHn0wIfZxMUVLHC0WZ9u/L6Y30gvYFxTW8HiC80FuzPbQZPHYPP0qGPB3zYwvZGmiIiIiIiUnMffLSQG8aNwd+/Me9/+Akm05lHDe6/d2q126524PJKU9ibD0OiwL30w/6Ig5BaDN3djeNn8kIitHY2AqDTudjBydNGcwosVQcVLnZGUFKhfknF+q52xgNgdiI8GWBMd/siA/a0g9tjjbUyMwLO3G8REREREamewYMG4tXAmKY6ZPCV57Vtk8Vy+oqVM1t+0lhMn2GGBvbGWpRRDYy1L2cSssuY4uVQWq/QAmaLEVw8FQA/nYR1EeX1n00w1sCsCqvcVthuY9rXnb7lZcG7YFZQ5QQBRwpgzCGIbANvpcDmHFjUEv6bCsszYWnladoiInIRm/efc1tnc77l2zmyMHQQuxqGWMv88k8y9cAvBOSfqLuOiUglD9x3d1134ZJ0LCEBPz8/nBwdKx0rKCggPv4orVpV/8N4jSdKXd0AXm4K7wXDa01htPfZgxaA38KMtS072hiPu3yhu5vxcy932JFnZBwrsy7bKLell3v5wn4wpoLFFdqu/2IizAww+mjCyIgGUGypnGRARETkfHEpKeLOgyu45miktSzVpQGvtx/Lbu/mddgzEZHa8e2yHziecdzmsZSUVFauWlOj9s44VeyhuBq1xb/P8D4c7FzxuY+9MdrSysVYNxPsBFNi4bkg+OGkkTnsg2CjbmGJMcLj52Asrr/XDwZGGSmVe3nAw/Ew3Atan7a2J6YAInNhQWk73d3g5ST4KxeWnoChnjW7PxERkZqwA0Ye20pQXjqftRxEob0j+fZOvBd2DSOPbmFYwnaUM0ZELiU//7KCEyeMneQtFgsrfl2Fg33lkCM7Jwcvz5p9GD9j4PL9abvXJxQZe7a0cIIAR0gvhkMFxsL4Tq41um4F9iZYFmos3u+2D0KdjSlcLUqDnfU5MCgKDrc3ynp7GMHIPxOMPlzlBe/ZCJpeTISnA6FsTVB/T7i5IQw4AAM94cHGlc8RERE537pkHMYv/yQLwq4hw9kTi8nED816kODWkIkxv+NcUlzXXRQROS86d+rInn37AEjPyMDb2xtX14qjC3YmO5o5OdG+XdsatV3tNS6fphvrTr4JNfZcKROVD2MPwe2N4AktdBcRkTpWX9a42JLl4MKHra8i2ivIWtY0J427opbTsLCKzctE5ILTGpcL49fVa7iiWzcaNKi8p9S5qPYal6eOGfujnBq0gLE7/YtB8K9k2+eJiIiIwbM4nwf2/0j/pN3WsqPuvrzWfhzRnvr2T0QuLUMHDzpj0JKcnFKj9qqdDjmrpOpF+LklxhQyEREROTN7SwkTjvxJk9x0Frfoh9nOnmxHV+ZGXMv4I3/SL2VfXXdRROS8yM7OYe2f60lISMBsLuHUiV5msxmLxVKjfVyqPeJylSfMOApbciqW/5YFM46dfQNKERERKdc3dT8P7vsBj6I8AErs7PkyZABftuhHsUm7I4vIxe+PdX8SH3+UsNataNDAC1/fRnRo35YGDbywWCxcc/VVNWqv2u+M85qDhz302g++OyBiNzTcYWxI2doZ3mpWwzsRERG5zIVmJzFt9xKa5aRay9b5t+OdiJFkObic4UwRkfrv2LEE+vTqSf9+fWnbJgJ7Ozv69O7FjeOvp1nTJsTEHK5Re9UOXPwdYXsbI9vXnb5GVq57/OCX1vBbOHja1/heRERELnsNC7N5ZO93dE2LtpZFewXxWvtxHHVrdIYzRUTqt+LiYho29AHAx8eb1LR0AEwmEx3atyMhIbFG7VV7jQsYa1yu8zYeIiIicn44lRQz5dAqmuSm80OzHlhMJo47e/Jm29HcGvMbXTJi6rqLIiI15unpwcnMTIKCAvHx9qagoIDMzEy8vLxwcHAgLz+/Ru2dMXDpuBf+FwLtXaHDnvL9UGwxAX/VLBWziIiIlDIBwxJ3EJSXwSehg8l3cKbQ3pEPW1/F1ce2MeLolupPkxARqQdatwrlz/UbAWgTEU6jRg1Z9+cGOnbswNZt2/Hx9q5Re2cMXLq5gXvpu2Q3d7S7r4iIyAXW/kQcj+/5lgVhV5Pi6g3A8iZdOebWkNsOrcbVXFS3HRQRqaYruncjP7+AuLh42kSEc+WA/vzw0y/EHI7FycmJ4dfUbHF+tTegLGOxlI+85JVAiQXctb5FRETqifq8AWVN5No78XGrIezzbm4tC8jNYGrUcvwKMuuwZyKXHm1AWXsKCws5fvwEPj7eODk51ejcao86F5bAfXFGVrEyf2aD718w/SiYtY+LiIjIeeNmLuSeA78wJGGHtSzJrSGvtR/HvgZN665jIiJ/g5OTE/7+jWsctEANFuc/lQD/y4DZQeVl3dzgjWbwzDEjq9g/Amt8fREREamCHRbGxG+iSW4G/2s5gGI7B/IcnHk3fDhj4jYyKGmXpnGLSL0yf8GHNap/9113VLtutQOXLzPgzaZwu295mY8D3OtnNDInSYGLiIjIhXBF+kH884+zIOxqTjh5YDHZsTS4D8fcGnHT4bU4Wsx13UUREQA6d+p4xoRef0e1A5cTZghwtH2suRMka62giIjIBdM8J40ndi/lg9ZXcdgzAIDNfuEku3pzV9QKGhTl1nEPRUSgZ4/uF6ztagcuXd1gfipc41U5LfJ7adDF7Xx3TURERE7VoCiXB/d9z1ct+rOxcQQARzz8ebX9OO6KWkGLnJQ67qGISLn9+6POWiciIqza7VU7cHk+CIYdhDZ7YEQDaOwIqUXwcyYcKoAVrat9TRERETlHjpYSJh7+naa5aSwJ7kOJyY5MJ3febnsdNx3+g55pZ/+gICJSG35dvcZmuclkwt7eHkdHhwsTuFzpCevC4aUkY5F+hhka2EMfd/ioBfR0r147+/PhgTjYmAONHOABP5hmjHgz9QgsSKtY/82m8Ii/7ba+yjCSBiQUwlVesCDYCKjAyHh222HILIE5QXCXX/l5j8dDJze4rVF1715ERKT+MAEDk/cQkHeCD1sNJdfRhWI7ez4LHcQxt0aMjtuIPUr3KSJ1667/m1KprKiomITERDZs3MywoYNr1F6N93H5O4osELYbBnnCUwFGEDPxMLzbHG5pBP32wxhvuPWUgMLLHtxsJG3ekgMDDhjndnWDh+PB2Q5+KR356bIXbmwIV7jBtdEQ3xF8HSCpCK45CJH/3959x1ddHf4ff917k5vk3uw9WWEHw5QhQ5DpFhS/1oVba8WqqNWq9VfRVuuorVIFnDhorYpaq1CWbJS9VwgjhNyEDLJu5r3398cHEkICgkLuDb6fj8d9lM/5nHvu+dCHl7xzVhewaCsWEZFzzrlyjsupyg8IYVrH0eTY6v/x7FycxS275mN3VXmxZyIth85xaX5bt25n05Yt/N/4q0/5Pad8jgsYh02uc8LSMlhc2vj1Y7Kroa8dprSC9oFwWTiMCIVFZcb9bZXQx25sAnD01VRoAXgtD66OgFuiId0GM9rCnBLIqDTub6+EceEwPBTCLZB55Lv7zw54OE6hRUREzg3RVaVM2vIF6YV76sq2h6XwUrex5ARFeLFnIiInFhISTGFh0Wm955Sniq0og2sz4WANTQ4+mwBX75O30SYA/tXO+LPHA8vLjcAzpZUxElLogk6Bp9afleVGADkqxQqtrbCi3AhFrayw1gkBJihyQbK/MaVsUSm8onO7RETkHBLgruX2Xf9jdlJvvk02dvTJDwzj5bSrmJCxgPMO7/NyD0Xkl6iysrJRmcfjobzcyeo16wgLCz2t9k45uPw2yxi5+EcrSLae5lBNE5I3GSHosjC4JsIIFH7AU9nGgv9oP3gw1hhRaUpODSQetz1znB8cqDb+/HwS3LAHqj3w+wRItMJv9sOj8RptERGRc48ZuCR7DYnOQj5IHUa1xZ8qi5VpncZwadYPjD64TodVikizeuud9zGd4FAXi8XCmFEjTqu9Uw4umyrg81S4OOy02j+hL1ON4PLr/fBgVv1IS3cb3B8L35XB3fvBboHxTYx0O93GmpZjBZih6shw0NgIKAiDKjeE+0FWtbFg/7UU+H02fFgAFwTDO21OPB1NRESkpelRtIeYLcVM6ziawkDjt5n/TenLQVsUN2R+R4C71ss9FJFfiuEXDW1UZsKE1epPUlIiAQEBp9XeKQeXVlYoOYMH8/Y5sguZ0w0T9kJJD/hVJEQe6VG6DXZVwhuHmg4ugWYjlByryt0whASZjRfAcznwWDx8Xw4zC2FLGty611gr87v4M/dcIiIi3pZUUcgjW2bxTvsR7ApLAmBdVCp5gWHcuXMOUdVlXu6hiPwSdOnc6Yy2d1rnuPzhILQPgN6nuPXx8bKrYY0TrgivL+saaEznKnUb08OO1SUQ/lfSdFtJ/uA47pdGjlpI8G9cd1+VEVj+0QpezTO2bg6xGBsDzDlB+yIiIi1ZcG0lv9nxDZ+3GsDi+G4AZNujeanbOG7bNZcOpTle7qGI/BIUFhaxbv0GDuY4qKqqIigokOSkJHr27E5oSMhptXXKk6RezjUW0PfdDgFrIXRdw1fYuh9vY1sljNsNeTX1ZWucEOMHf84xti0+1roK6HyCxfr97cbuZkdlVcP+aqP8eJNz4PF4MJuMTQSODtTUeoxNAkRERM5FFo+b8fuW8avMRVjcxrSJMv8gXu98KUtiu3q5dyJyrjuQnc2//v0ZWVkHSEpMIK1rZxLi48ncs4d//utT8gsKTqu9Ux5xuSwM+JnrWy4Mga5BcMteeDkZdlfBY9nwRDz0sMFFO+HveXBpGMwuhhkFMP/IYZrVbmPXsRg/Y3H9r2Pgwp0w0A79g43NAy4OhQ7HBZ3MKiMcTW9tXPexwfMO2OCEWYdhxOkFPRERkRbngkPbia8o4q0Ooyi12nCbLXzSdjDZtiiu2bcMP4/7xxsRETlNy5d/T3JSEpdcPAqLxVJX7nK5+Pqb2SxdtoKrrrjslNs75eDydOLpdbQp/ib4uj3ctx/6bTemaz0QayzGN5lgZlt4JgcePQDtAozrQcHGe5eXw7CdsKebsa3ygGAjjPzhIBTUwshQmNaq8WdOzoEnEoz2AQaHGGtphuwwgtTE2J//XCIiIr6uXVkuj2z5nOkdRpMVHAPAsriu5ARFcMeu/xFS23jbUhGRn6OwqIiL+/VpEFrA2FGsR/p5zJ4z97TaM3k8J54s9Uou3BAJcf7Gn0/aEPBg3MnriIiInG2v/2Oqt7vg06pNFma2u5DV0R3qyiKqSrlz5xxSnKc3bUPkXHHfvXd7uwvnpH/9+zPatW3L+X16Nbq3fsNGtm7bzvXXXXvK7Z10xOXhA8aIR5y/8eeTUXARERHxfVaPi5t3LyDJWcBXKf3wmEwUBYTw165XckPmInoX7vZ2F0XkHDGgfz/+N3c+tbW1dGifit1uo7Kykj1797Nq9RoGXtCfvEOH6urHxsSctL2TjriIiIi0NBpxOXVbwlJ4v/1wKvzqz1IYmb2Oyw6swox+PJBfjpYy4lJUVMSiJcvIdeQSGBjIeeel0atnDwBKSktZuHAxOQ4HIcHBDBo4gNatm1hHccSujN2sXPkD5U4nKclJDBt6ITZbEAA5OQ7mzl9IdXU1A/r3Ja1rl7r3LV22guioKDp37vij/T32+/jYgyiPxo+jZR6PB5PJxG9+fddJ2zvlNS4iIiJybkkrzmLSlllM6ziGvKBwAOYm9eSgLZIJuxcQ5Kr2bgdFpI7L5eKrr78lOSmRof83mKKiw/xv7nzsdjsdO7Tnm2/mEBERzrXXjGPP3r18O2cu1183ntDQ0EZt5ebmMW/+QoZeOJiY6GiWLF3OvPkLuOLySwFYtGQpaV07Exsbw9f/nU27tm0ICgqi3OnkwIFsLhjQ75T6PPbKy8/o34GCi4iIyC9YXGUxk7bM4v3Ui9gaYWzBuSWiNS+nXcVdO2YTW6UDz0R8QXm5k7jYGC4cMgg/Pz/Cw8JISU4i++BB7DYbRYcPM27sFVitViIjI8g6kM3Wbdvp369vo7Y2btpMaru2dQdEjhg+jPc/+IjDxcWEh4VRVHSYdu3aEhEeTkBAACUlpQQFBbFmzTp69kjHbD61E1WSkup396qpqaG6pobAgIBGi/VPlYKLiIjIL5zNVc3dO+fwn5TzmZfYE4DcoAhe6jaOWzPm0aX4Rxa6ishZFxoawpjRIwFjapXDkcvBHAcXDh6IIzeXmOhorFZrXf2E+Hhycpo+aNaRm0fPHul11yEhwYSEBONw5BIeFkZISDCHDuVjsVioqqrCHmynrLycgwdzGDRwwGn1e3/WAVau/IFD+fl1ZbExMfTt25vWrU48la0pp3wApYiIiJy7zHi4MusHJmTMx99dC0CFXwBvdLqY+fHpWvEi4kPem/Ehn836kvi4WFJT21HudGK32xrUsdmCKCsvb/L9TqcTu73hqe22IBtlZUb9Af37sWDhIj74cCa9enYn2G5nzZq19OzZ/ZRHW8AILV//91vMZjODBg5g1IiLGHhBfzDB1/+dzf6s0/uliEZcREREpE6fggxiKw4zveNoDgcE4zGZ+aL1ALLtUVyXuRirx+XtLor84l1y8Ric5eV8t3gJS5etoLamtsmzUlyupv97ra1tqr65rn5qu7a0vm0CLpeLgIAASkvLyMnJZcjgQaxY+T07dmaQEB/HRcMuxN/f/4T9/P77VbRr26ZupOioHt3TmT1nLqtWraFVSvIpP7eCi4/Rbjgicrpaym440nK0cubzyObPebvjSDJDEgBYFd2R3MBw7tw5h/Aap5d7KPLLFhcbA8RQU1vLvPkL6dqlM9XVDTfTcLlc+Pk1/aN+U6HG5XLj719f38/Pr+79q9espVevHjhyc9m5azfXXzee+Qu+Y+OmLfTu1eOE/SwoLKRv3z5N3uvSpTOzZ//vFJ62nqaKiYiISCOhtRXct+1rBuRtqyvbHxzLi93GsSdYB7eJNLeysnL27NnboCwyIgK3243NZsPprGhwz+l0Yrc1nD52lN1ux+l0Nqpva6J+SWkpuXl5dGifisORR3xcLFarlZTkZHJzT35Cvc0WRFlZ2Qmepwy/k4zWNEXBRURERJrk73Hzqz2LGb93KWaPG4ASq52/d7mcldGdvNw7kV+WoqIivpn9vwYBJe/QIYKCAklMiCc/P5+ampq6ezk5DuLimv4lQ3xcLDk5jrrr0tIySsvKiG+i/urVa+ndqycmkwmTqf4MFrfH/aNr31LbtWXl9z+Qddxalv1ZB1j5/SpS27X9scduQFPFRERE5IRMwJDcLSQ4C3m7w0jK/YOoNVv4KHUo2bYortq/AouW7oucdYmJCURGRDB/wUIGXjCA4pISVqz8gT69epGYmEBISAjzFnxH3/N7s3fvfhy5eVw0bChgTBurrKoiKDAQs9lMt25dmfXFf0hIiCcuLpYlS5fTulUK4eFhDT6zuLiEvEOHGDZ0CGDsBrZm7Xry8wvIzNxDcvLJ16f0Pb8PDkceX/7nv1itVmy2IJzOCmpqaoiNjTnl82COMnmOxibxCVrjIiKnS2tcGtL36NmTHxDC9I6jOWiLqivrWHyA2zLmYa+t8mLPRH6elvI9WlpayqIly8jOPojV6s953brRu1cPTCYTh4uLWbBwEbm5eYSFhjJo4ABatUoB4ED2Qb748j/cfOP1hIaGALBt+w5++GE1lZWVpKQkM2zoEIKCghp83rwFC2nTujXtU9vVlS1Zuoxt23eSmJjAqJHDsf7IdC+Px8Peffs4eNBBVVUVgYEBJCTE06Z1a0wm02k9v4KLj9E/uCJyulrKP7jNRd+jZ1eV2Y8PUoexIbL+B5noymLu3DmHxIoiL/ZM5KfT9+jZV1ZeTlVlFUFBgU2upTkVmiomIiIipyzAXcttu+byv8Re/DflfADyA8N4Je0qbtq9kO5Fe73bQRHxKVu3bmf12nWUlpbWlUWEh9Ov3/mnvcZFi/NFRETktJiBMQfXcsfOOQS4jC1YqyxW3uo4mm+TeuH2bvdExEds3LSZBd8tIjYmmhHDh3H5pRcz4qKhhEeEM3vOXHbvzjyt9po9uGyvhBE7IXgdtN4EL9ZvaMC+Khi1E+zroMsW+Lb45G19UgjtN4NtLVyZAXn1GymwrAxSN0HMBph+qOH7JmXBjIIz90wiIiK/RN2L9vLQli+IqiypK/sm+Xze7TCSKrMmdYj80q3fsJEe3dMZM3oknTp2oFWrFDp16sglY0ZxXrc0fli15rTaa9bgUuOBi3dBKyus7wJTUmByDnxUAB4PXLkbovxgVWeYEAVX74Y9J1jrt6ocJuyFJ+NhZWcoccHNe+vv37cf7oyBf7aF+7Mgv9Yod9TA/FK4IfJsP62IiMi5L7GiiEe2fE7H4vrtTtdHtuOVrleRHxDixZ6JiLc5nRW0Sml657E2bVpTXFLS5L0Tadbgkl0Nfe0wpRW0D4TLwmFEKCwqg4WlsKMSprWGrkHwWDxcEAxv5zfd1mt5cHUE3BIN6TaY0RbmlEBGpXF/eyWMC4fhoRBugcwjAejPDng4Diynt4mBiIiInIC9top7t3/D0JxNdWUH7VG8lDaWnaGJXuyZiHhTSkoyO3buavLenr17SUpMOK32mjW4tAmAf7WDILMxwrKsDBaXwvAQWFkOPW0QYqmvPygYVpQ33dbKchgSXH+dYoXW1vr6rayw1mlMPytyQbI/HKyGRaXwK422iIiInFEWPFy9fzk37P4OP7cLgHL/IKZ0vpTFcWk66UXkF6h1qxT27tvPp59/wYaNm8nI2M3mzVv5z3+/ZfPmrURHR7Fu/UbWrd/I+g0bf7Q9r01ATd4EB2vgsjC4JgIeyILE47aBjvOHA9VNvz+npon6fvX1n0+CG/ZAtQd+nwCJVvjNfng0XqMtIiIiZ0v//B3EVR7mrQ4jKbHacZvM/LvNIA7Yorh271L8PFq6L/JL8d2iJQA4HLk4HLmN7q9Zu77uzyaTiR7d00/anteCy5epRnD59X54MAucbgg4bvwnwARVJ/gVTZP1zfX1x0ZAQRhUuSHcD7KqjRGe11Lg99nwYYExFe2dNmDT3moiIiJnTNuyXB7Z/DnTO45mf3AsACtiu5AbFM7tO+cSWlvh5R6KSHM40+fjeO1H9j52uCIcXk6GqflgNRkh41hVnhOHikBzE/XdDesHmY3QAvBcjrFu5vtymFkIW9Kg1mOslREREZEzK7zGyW+3fsX5+TvryjJDEnix2zj226K92DMRaamafXH+V4cblnUNNKZzJfgbO34dy1FjlDclyR8ctcfVr226/r4qI7BcG2GsgelnN9bSjAg11sqIiIjImWf1uLhp90Ku2rcC05EpYocDgnk17UpWR6V6uXci0tI0a3DZVgnjdjc8b2WNE2L8jIX46yug3FV/b2kZ9Lc33VZ/u3H/qKxq2F/ddP3JOfB4PJhNYIK6g7FqPcYmASIiInJ2mIDhjo3cs2M2QbXGFp81Zj/ebz+CL1P64kYLT0Xk1DRrcLkwxNjq+Ja9sK0Cvj4Mj2XDE/HGvdZW496WCnjBYYyG3HlkNLnabYzAuI4EjV/HwMeFxuGSmyqMM10uDoUOgQ0/M7PKCEfjI4zrPjZjZ7ENTph12Bh9ERERkbOra3EWD2+ZRVxFUV3ZvMSeTO04mgqL1Ys9E5GWolmDi78Jvm4Pfibotx3u3g8PxML9scZOX1+mQl4t9N5mnGw/K9XYQhlgeTkkbDRGVgAGBMP01vCsAwZshzALvN+m8WdOzoEnEsB05Bc6g0OM7ZCH7AC7GSbGNsuji4iI/OLFVhYzacsXpBXtqyvbGtGal9KuIjcwzIs9E5GWwOTxaLKUL3n9H1O93QURaWHO9K4tLZ2+R32fGxNfJ5/P3KSedWVBtVVMyJhPWnGWF3smv1T6Hm0ZtBGwiIiINCszHq448AO37JqHv8tY+FrhF8DUThczL6G7DqsUkSYpuIiIiIhX9C7czYNbvyKiqhQAj8nEl636MyP1IqpNFi/3TkR8jYKLiIiIeE2KM59HNn9Ou5KcurLV0R14teuVFFm1g46I1FNwEREREa8Kqa1k4vavGZi7ta4sKziGF9PGkRkc58WeiYgvUXARERERr/PzuLlu7xKu3bMEs9s41K3UauPvXS5nRUwnL/dORHyBgouIiIj4jMF5W7lv+38JrqkAwGW28HG7ofy79UBcJv3YIvJLpm8AERER8SkdSnN4ePPnJJXn15Utju/GlE6XUO4X4MWeiYg3KbiIiIiIz4mqLuPBrV/Ss2B3XdmusCReTBvHwaBIL/ZMRLxFwUVERER8UoC7llsz5nFp1g91ZQWBobycdhXrI9p6sWci4g0KLiIiIuKzTMCYg+u4c+dsAlzVAFRb/Hm74yi+SeqN27vdE5FmpOAiIiIiPi+9aB+TtnxBdGVxXdm3yX14u8MoKs3+XuyZiDQXBRcRERFpERIqinh48yw6FR+oK9sY2ZZX0q4kPyDEiz0Tkeag4CIiIiItht1Vxa+3f8OwnI11ZTm2KF5MG8eO0EQv9kxEzjYFFxEREWlRLHgYt38FN+5eiN+Rwyqd/oH8o/OlLIpLw+Pl/onI2aHgIiIiIi1Sv/yd/HbrV4RWlwPgNpn5tM0gPm57ITU6rFLknNPs/1XvroLLMyBiPSRvhElZUHlkS5C79oFpTcPXq7knbuuTQmi/GWxr4coMyKupv7esDFI3QcwGmH6o4fsmZcGMgjP+aCIiItLM2pTn8ejmz2ldVv8Dw8rYzrzW5XJK/IO82DMROdP8mvPDqt1GaOkaCMs7QV4t3LbXuPdyCmytgBeT4Mao+veEWppua1U5TNgLb7SCXjb4bRbcvBdmdzDu37cf7oyB821wWQaMjYBoP3DUwPxS+EvyWXxQERERaTZhNU5+u/U//LPtYH6I6QTAnpB4Xuw2jmv2LmV7WAqrozpQafEn0FVDn4JdXJSzkZiqEi/3XEROR7OOuPzghIwqeK8NdAmCC0NgchJ8VGjc31YJfewQ71//sp2gh6/lwdURcEs0pNtgRluYUwIZlcb97ZUwLhyGh0K4BTKrjPI/O+DhOLCYzvbTioiISHPx97i4MfM7xu5bjsljTOU4bA3mrQ6jWRbbhUo/K5hMVPpZWR7TmefPu4YtYSle7rWInI5mDS6dAuCb9hB8zCiKCajyGCMhhS7oFHhqba0shyHB9dcpVmhthRXGNFdaWWGtE/ZVQZELkv3hYDUsKoVfRZ6xRxIREREfYQIucmzi1zu+JaD2yG8sTSY8x613cZstVFv8eafDSA4FhDZ/R0XkJ2nW4BLjDyOO+X5we+D1PBgcbEwT8wOeyoakjdB9K7yXf+K2cmog8bjzpuL84IBxqC7PJxnT0FI3w6PxkGiF5xzGnzXaIiIicu7qUnyAbkX7wXPy/cVqTWYWxp/XTL0SkZ/Lq1tuPHQA1jnhz0nGNDGA7jb4tj3cHg1374d/FzX9XqcbAo7rfYDZGL0BY01LQQ/I7w7PJEJWtbFg/7oI+H02tNoI12Ua7YiIiMi5ZUtEazCd/DeVbrOFVdEdm6lHIvJzNevi/KM8HnjgAPwjDz5NhbQgY8H+ryIh8kiP0m2wqxLeOATjIxq3EWiGquNCR5W74ZqYILPxAnguBx6Lh+/LYWYhbEmDW/caa2V+F39WHlNERES8pNLi/+OVgKpTrCci3tfsIy5uD9y2zwgk/2oHV4Yb5SZTfWg5qksgZFc33U6SPzhqG5Y5aiGhie+ffVVGYLk2wlgD088OIRZj2trK8p/9SCIiIuJjAl01P14JCDjFeiLifc0eXCYdgI8L4fN2MO6YkZRJWca2xcdaVwGdT7BYv78dlpbVX2dVw/5qo/x4k3Pg8Xgwm4yFe0cHamo9Pzr9VURERFqgPgW7MLtdJ61jdrs4P39nM/VIRH6uZg0uK8vg1Tz4Y6Kx7bGjpv51RTh8Wwx/zzMOqZySZxwS+ciRaVzVbqOe60jQ+HWMEYCmH4JNFcaZLheHQofjgk5mFaxx1k8362Mzdhbb4IRZh43RFxERETm3XJSzET/PyRey+nncDHNsaqYeicjP1azB5dPDxv8+ng0JGxu+BgbDzLYw7RCkbYEph4zrQUe2PF5ebtTLOjJ1bEAwTG8NzzpgwHYIs8D7bRp/5uQceCKhfn3e4BBjLc2QHWA3w8TYs/3UIiIi0txiqkq4bddcrK6aRiMvZrcLq6uG23bN1SGUIi2IyePRZClf8vo/pnq7CyLSwtx3793e7oJP0feoHOtQQCgL489jVXRHqiz+BLhqOD9/J8McmxRapI6+R1sGr+wqJiIiItIcYqpKuHbfMq7dt8zbXRGRn8mr57iIiIiIiIicCgUXERERERHxeQouIiIiIiLi8xRcRERERETE5ym4iIiIiIiIz9OuYiIiIiIiLUBxcTFLli4nx+HAz8+fDu1T6d/vfPz8/CgpLWXhwsXkOByEBAczaOAAWrdudcK2dmXsZuXKHyh3OklJTmLY0Aux2YIAyMlxMHf+QqqrqxnQvy9pXbvUvW/pshVER0XRuXPHs/68x9OIi4iIiIiIj3O5XHz9zWwsFgtXj72KUSMuInPPXlZ+vwqPx8M338whMDCAa68ZR+fOHfl2zlxKSpo+qyg3N4958xfSp08vrhl3FdXVNcybv6Du/qIlS0nr2pnRo4azeMkyKioqACh3OjlwIJuOHds3yzMfT8FFRERERMTH5eblUVxcwvDhw4iMjCApKZF+ffuwc+cusrMPUnT4MMOGDiEyMoLevXoSHx/H1m3bm2xr46bNpLZrS5fOnYiOjmLE8GHszzrA4eJiAIqKDtOuXVtSkpMJCAigpKQUgDVr1tGzRzpms3cihIKLiIiIiIiPiwgP5/JLL8bq719XZjKZcLldOHJziYmOxmq11t1LiI/H4chtsi1Hbh6JiQl11yEhwYSEBNfVDwkJ5tChfEpKS6mqqsIebKesvJyDB3Po0ME7oy2gNS4iIiIiIj4vKCiIlJTkumuPx8PGTZtJSEig3OnEbrc1qG+zBVFWXt5kW06nE7vd3rB+kI2yMqP+gP79mDtvAS6Xiz69exJst7No8RJ69uzutdEWUHAREREREWlxli5bTn5+AeOvGcv69RuxWCwN7lssFlwuV5Pvra2tbaK+ua5+aru2tL5tAi6Xi4CAAEpLy8jJyWXI4EGsWPk9O3ZmkBAfx0XDLsT/mBGgs01TxUREREREWgiPx8PiJcvYtHkro0YOJyoyEj+/xiHF5XLh59f0GEVTocblcuPvX1/fz8+PgIAAAFavWUuvXj1w5Oayc9durr9uPG63m42btpzhpzs5BRcRERERkRbA4/Ewf+F3bN6yldEjh9OubRsA7HY7TmdFg7pOpxO7zdZEK0frOxvVtzVRv6S0lNy8PDq0T8XhyCM+Lhar1UpKcjK5uU2voTlbFFxERERERFqApctWsHNnBhePGUVqaru68vi4OPLz86mpqakry8lxEBcX12Q78XGx5OQ46q5LS8soLSsjvon6q1evpXevnphMJkwmIzwBuD1uPGfqwU5RsweX3VVweQZErIfkjTApCyrdxr19VTBqJ9jXQZct8G3xydv6pBDabwbbWrgyA/Lq/79iWRmkboKYDTD9UMP3TcqCGQVn9LFERERERM4ahyOXDRs30a9vH2JjYyh3OuteiYkJhISEMG/BdxQUFrJm7XocuXl1B0e6XC7KnU7cbuOH7m7durJzVwZbtm4jv6CAeQsW0rpVCuHhYQ0+s7i4hLxDh2h/JCTFxsSQfTCH/PwCMjP3EBcb26x/B826OL/abYSWroGwvBPk1cJte417LyXDlbuhSyCs6gxfFcPVu2FLGrQNaNzWqnKYsBfeaAW9bPDbLLh5L8zuYNy/bz/cGQPn2+CyDBgbAdF+4KiB+aXwl+TGbYqIiIiI+KKM3ZkArFj5AytW/tDg3r333Mkll4xmwcJFfPLvzwkLDeWSMaMIDQ0BIMeRyxdf/oebb7ye0NAQEuLjGTZ0CD/8sJrKykpSUpIZNnRIo89ctWYNfXr3wmQyAZCYmEDHDql8/sVXJCYmkJ7e7Sw/dUMmz9HxnmawtAwu2gmF3SH4yEYGHxfCQ1nwcVu4NAPyukPIkXsjdkJ/Ozyb1Litm/eAG/iwrXGdVQ2tNsGuNGgfCEFrYUNX6BgICRvgy/bQ124EnPNtcGNUszzyaXv9H1O93QURaWHuu/dub3fBp+h7VEROl75HW4ZmnSrWKQC+aV8fWgBMQJUHVpZDT1t9aAEYFAwrmt5+mpXlMCS4/jrFCq2t9fVbWWGt05h+VuSCZH84WA2LSuFXkWf80URERERE5Cxq1qliMf4w4pitnt0eeD0PBgdDTg0kHrcNdJw/HKhuuq0m6/vV138+CW7YA9Ue+H0CJFrhN/vh0XiwmM7cM4mIiIiIyNnn1QMoHzoA65ywqgu8kgsBx43/BJiM0ZimON1N1DfX1x8bAQVhUOWGcD9jKtmyMngtBX6fDR8WwAXB8E4bsGlvNRERERERn+aVH9k9HmOtyZQ8mNkO0oIg0GyEjGNVeU4cKpqs725YP8hshBaA53LgsXj4vhxmFhqL/ms98FremXsuERERERE5O5o9uLg9cNs+eOMQ/KsdXBlulCf5Gzt+HctRAwn+jZqor197XP3apuvvqzICy7URxhqYfnZjLc2IUGOtjIiIiIiI+LZmDy6TDhg7iX3eDsZF1Jf3t8P6Cih31ZctLTPKm9Lfbtw/Kqsa9lc3XX9yDjweD2aTsRnA0YGaWo8x+iMiIiIiIr6tWYPLyjJ4NQ/+mAh97MaIytHXhSHGrmC37IUtFfCCwxgNuTPaeG+126jnOhI0fh1jBKDph2BThXGmy8Wh0CGw4WdmVsEaJ4w/EpL62IydxTY4YdZhY/RFRERERER8W7MGl08PG//7eDYkbGz48gBfphqHUvbeZpxsPysV2hw5fHJ5uVEv68iuYQOCYXpreNYBA7ZDmAXeb9P4MyfnwBMJcOTcHAaHGNshD9kBdjNMbN4DP0VERERE5Cdo1l3FXko2XifSPhAWdWr63tAQ8PRuWDYhynidzLttGpe9mmK8RERERESkZdBGwCIiIiIi4vMUXERERERExOcpuIiIiIiIiM9TcBEREREREZ+n4CIiIiIiIj5PwUVERERERHyegouIiIiIiPg8BRcREREREfF5Ci4iIiIiIuLzFFxERERERMTnKbiIiIiIiIjPU3ARERERERGfp+AiIiIiIiI+T8FFRERERER8noKLiIiIiIj4PAUXERERERHxeV4LLlVu6LYF5pXUl921D0xrGr5ezT1xG58UQvvNYFsLV2ZAXk39vWVlkLoJYjbA9EMN3zcpC2YUnNnnERERERGRs8crwaXSDb/aA1sqG5ZvrYAXkyAnvf51V0zTbawqhwl74cl4WNkZSlxw8976+/fthztj4J9t4f4syK81yh01ML8Ubog8G08mIiIiIiJng19zf+DWCrh+D3iauLetEp61Q7z/j7fzWh5cHQG3RBvXM9pCq02QUQntA2F7JYwLh46BEG6BzCqI9oM/O+DhOLCYzuRTiYiIiIjI2dTsIy5LymBUKKzo3LDcUQOFLugUeGrtrCyHIcH11ylWaG2FFeXGdSsrrHXCvioockGyPxyshkWl8CuNtoiIiIiItCjNPuJy9wmmfm2tMDrzVDZ8W2KMjjwYWz+icrycGkg8bmQmzg8OVBt/fj4JbtgD1R74fQIkWuE3++HReI22iIiIiIi0NM0eXE5k25H1Lt1tcH8sfFcGd+8HuwXGRzSu73RDwHHjRQFmqDoyB21sBBSEGZsAhPtBVrWxYP+1FPh9NnxYABcEwzttwKa91UREREREfJrP/Mh+bwzkdoeJsZB+JLzcFQ1vHGq6fqDZCCXHqnI3DCFBZiO0ADyXA4/Fw/flMLMQtqRBrcdYKyMiIiIiIr7NZ4KLyQSRx43/dAmE7Oqm6yf5g6O2YZmjFhKaWNi/r8oILNdGGGtg+tkhxAIjQo21MiIiIiIi4tt8JrhMyoLLMhqWrauAzidYrN/fDkvL6q+zqmF/tVF+vMk58Hg8mE1gAo4O1NR6wNPU9mYiIiIiIuJTfCa4XBEO3xbD3/NgdxVMyTMOiXwk3rhf7TZ2HnMdCRq/joGPC43DJTdVGGe6XBwKHY4LOplVsMZZv06mj83YWWyDE2YdNkZfRERERETEt/lMcLkwBGa2hWmHIG0LTDlkXA86suXx8nJI2GiMrAAMCIbpreFZBwzYDmEWeL9N43Yn58ATCcZUNIDBIcZ2yEN2gN1srKkRERERERHf5tVdxTy9G15fG2m8mjI0pHH9CVHG62TebdO47NUU4yUiIiIiIi2Dz4y4iIiIiIiInIiCi4iIiIiI+DwFFxERERER8XkKLiIiIiIi4vMUXERERERExOcpuIiIiIiIiM9TcBEREREREZ+n4CIiIiIiIj5PwUVERERERHyegouIiIiIiPg8P293QERERERETp3L5eJf//6MwQMvICUlGYCS0lIWLlxMjsNBSHAwgwYOoHXrVidsY1fGblau/IFyp5OU5CSGDb0Qmy0IgJwcB3PnL6S6upoB/fuS1rVL3fuWLltBdFQUnTt3PLsP2QSNuIiIiIiItBC1tbXM+d88CguL6so8Hg/ffDOHwMAArr1mHJ07d+TbOXMpKSlpso3c3DzmzV9Inz69uGbcVVRX1zBv/oK6+4uWLCWta2dGjxrO4iXLqKioAKDc6eTAgWw6dmx/dh/yBBRcRERERERagMLCIj797AuKjwsk2dkHKTp8mGFDhxAZGUHvXj2Jj49j67btTbazcdNmUtu1pUvnTkRHRzFi+DD2Zx3gcHExAEVFh2nXri0pyckEBARQUlIKwJo16+jZIx2z2TsRQsFFRERERKQFOJiTQ0pKMteMu6pBuSM3l5joaKxWa11ZQnw8Dkduk+04cvNITEyouw4JCSYkJLiufkhIMIcO5VNSWkpVVRX2YDtl5eUcPJhDhw7eGW0BrXEREREREWkRuqV1bbK83OnEbrc1KLPZgigrL2+yvtPpxG63N6wfZKOszKg/oH8/5s5bgMvlok/vngTb7SxavISePbt7bbQFvDjiUuWGbltg3jEjXfuqYNROsK+DLlvg2+KTt/FJIbTfDLa1cGUG5NXU31tWBqmbIGYDTD/U8H2TsmBGwZl7FhERERERb6mtqcVisTQos1gsuFyupuvXNlXfXFc/tV1b7rhtAnfcNoF+fc+ntLSMnJxcOnZoz4qV3/PejI+Y87951NTUNNX8WeOV4FLphl/tgS2V9WUeD1y5G6L8YFVnmBAFV++GPVVNt7GqHCbshSfjYWVnKHHBzXvr79+3H+6MgX+2hfuzIL/WKHfUwPxSuCHybD2diIiIiEjz8fNrHFJcLhd+fk1Prmoq1Lhcbvz96+v7+fkREBAAwOo1a+nVqweO3Fx27trN9deNx+12s3HTljP8JCfX7MFlawX03w67jwskC0thRyVMaw1dg+CxeLggGN7Ob7qd1/Lg6gi4JRrSbTCjLcwpgYwjYWh7JYwLh+GhEG6BzCOf92cHPBwHFtNZe0QRERERkWZjt9txOisalDmdTuw220nqOxvVtzVRv6S0lNy8PDq0T8XhyCM+Lhar1UpKcjK5uU2voTlbmj24LCmDUaGwonPD8pXl0NMGIceMWg0KhhVNT81jZTkMCa6/TrFCa2t9/VZWWOs0pp8VuSDZHw5Ww6JS+JVGW0RERETkHBEfF0d+fn6DqVs5OQ7i4uJOUD+WnBxH3XVpaRmlZWXEN1F/9eq19O7VE5PJhMlkbL0M4Pa48Zzh5/gxzR5c7o6BvySD7bhPzqmBRP+GZXH+cKC66XaarO9XX//5JLhtL6RuhkfjIdEKzzmMP2u0RURERETOFYmJCYSEhDBvwXcUFBayZu16HLl5dQdHulwuyp1O3G43AN26dWXnrgy2bN1GfkEB8xYspHWrFMLDwxq0W1xcQt6hQ7RPbQdAbEwM2QdzyM8vIDNzD3Gxsc36nD6zHbLTDQHH9SbABFUniHJN1jfX1x8bAQU9IL87PJMIWdXGgv3rIuD32dBqI1yXabQjIiIiItJSmc1mLrlkNBUVFXzy78/ZsWMnl4wZRWhoCAA5jlzefe+Dul3DEuLjGTZ0CKtXr+Wzz74gwGplxPBhjdpdtWYNfXr3wmQyfuufmJhAxw6pfP7FV/j5+5Oe3q35HhIf2g450AzFx21MUOVpPDJzbP2q40JHlbth/SCz8QJ4LsdYN/N9OcwshC1pcOteY63M7+LP2GOIiIiIiJx19917d4Pr8LAwxl11RZN1k5MSG9Xv0rkTXTp3OulnjLiocZgZPGgggwcNPM3enhk+M+KS5G/s+HUsRw0k+J+kfu1x9Wubrr+vyggs10YYa2D62Y21NCNCjbUyIiIiIiLi23wmuPS3w/oKKD9mZ7alZUb5ieovLau/zqqG/dVN15+cA4/Hg9kEJuDoQE2tx9iGWUREREREfJvPBJcLQ4xdwW7ZC1sq4AWHMRpyZ7Rxv9ptjMC4jgSNX8fAx4XG4ZKbKowzXS4OhQ6BDdvNrII1ThgfYVz3sRk7i21wwqzDxuiLiIiIiIj4Np8JLhYTfJkKebXQe5txsv2sVGhjnHvD8nJI2GiMrAAMCIbpreFZBwzYDmEWeL9N43Yn58ATCXBkTRGDQ4ztkIfsALsZJjbvZggiIiIiIvITeHVxvqd3w+v2gbDoBGuEhoY0rj8hynidzLttGpe9mmK8RERERESkZfCZERcREREREZETUXARERERERGfp+AiIiIiIiI+T8FFRERERER8noKLiIiIiIj4PAUXERERERHxeQouIiIiIiLi8xRcRERERETE5ym4iIiIiIiIz1NwERERERERn6fgIiIiIiIiPk/BRUREREREfJ6Ci4iIiIiI+DwFFxERERER8XkKLiIiIiIi4vN8Krh8XAimNQ1fV2U0XXeDEwZsB9ta6L0NVpXX33PUwOAdELoO7toHHk/9vf8chtv2ns2nEBERERGRM82ngsvWChgbDjnp9a/32jSuV+6CizOgvx3WdIHBwXBpBpS6jPsvOCDGD1Z2hrkl8J/i+vc+mwNPJjTH04iIiIiIyJniW8GlEtKDIN6//hXu17jev4rA3wQvJ0OXIPhrMoRZjHKA7ZUwJhS6BhnhZnulUT6rCM4LgnYBzfdMIiIiIiLy8/lWcKmAToE/Xm9lOQy0g9lkXJtMMDAYVpQZ162ssM4JlW7YUmlcezzwJwc8odEWEREREZEWx2eCS7UbdlfB18XQYTOkboLHDkCVu3HdnBpItDYsi/ODAzXGnx+Og/8Wg30dxPrB1RHw6WHoaYO2Gm0REREREWlxmpiI5R27qqAWsJvh03ZGiPltFpS6YUqrhnWdbggwNSwLMNeHnA6BsOc8KKiFWH9we+DPOTArFT4tgsezIdoPZrQx6oqIiIiIiG/zmeCSFgT53SHqSI+628AD/GoP/C0F/I4JKoEmqPI0fH+VG2zHjB9ZTEZoAfikCPrajeu79sGiTrCwFO7Pgm87nNXHEhERERGRM8BnpopBfWg5qksg1HjgUG3D8iSrseXxsRw1kODfuE23B553wO8TYFslWE3GAv1RocZaGRERERER8X0+E1w+L4K4DcZal6PWOSHcAvHHBZr+dlheVn8+i8cDy8uN8uN9XAgXBBsL9E3A0eZrPUaoERERERER3+czweXCEGNq2F37YWelsbj+kWx4JM7YNcxRAxVHUsc1EVDmholZxk5kDx2AEhdcF9mwTZcH/pILv483rjsGGFPKvjwMHxVC/+DmfEIREREREfmpfCa4RPnBnA6wrwp6bTPWotwTDY8fCR0JG+FfhcafQy3w3/bGqEuvbbCsDL5pDyGWhm1+WAhDgiH5yA5kdgv8oxXcuc8IRq8mN9/ziYiIiIjIT+czi/PB2K54Yaem73l6N7w+3w5ru568vQlRxutYN0QZLxERERERaTl8ZsRFRERERETkRBRcRERERETE5ym4iIiIiIiIz1NwERERERERn6fgIiIiIiIiPk/BRUREREREfJ6Ci4iIiIiI+DwFFxERERER8XkKLiIiIiIi4vMUXERERERExOcpuIiIiIiIiM9TcBEREREREZ+n4CIiIiIiIj5PwUVERERERHyegouIiIiIiPg8P2934HhVbpiYBf8uggATPBQHj8Y3XXeDE+7Zb/xvlyB4sxWcbzfuOWpgfKZx77pImNoKTCbj3n8Ow6zD8E6bZnggEREREZEzwOVysWjJUnbvzsRittCjRzq9evZosm5+fgELFy2moKCQiIhwhg4ZTFxcLADlTiez58wlP7+ADh1SGXbhEExHflDes3cvmZl7GX7R0GZ6qlPncyMujxyA5WUwrwNMbQ3P5sA/CxvXK3fBxRnQ3w5rusDgYLg0A0pdxv0XHBDjBys7w9wS+E9x/XufzYEnE5rneUREREREzoRly1ficORy5eWXMXToEFatXsvOXRmN6tXU1PDV198QHxfHtdeMIzEhnq+/+Zbq6moA1q5dT1BgIOOvHktWVjZ79+6re+/q1evo07tXsz3T6fCp4FLugun58GoK9LbDleHGaMvreY3r/qsI/E3wcrIx2vLXZAizGOUA2ythTCh0DTLCzfZKo3xWEZwXBO0Cmu2xRERERER+lpqaGrZs3cbggRcQGxtDu7Zt6NWzB5s2bWlUd1fGbixmM4MGDiAyMoJBAy/AarWyK2M3AEWHD9OqVQqRkRHEx8VSdPgwALsz9xAVFUlYWGhzPtop86ngsqECqjwwKLi+bFAwrHJCradh3ZXlMNAO5iPTv0wmGBgMK8qM61ZWWOeESjdsqTSuPR74kwOe0GiLiIiIiLQg+fkFuFwuEhLq11AkJMSTm5eH2+1uUNeRm0t8Qnzd9C+TyURCfDwORy4AIcHB5OcXUFtbS2FhEcHBwXg8HtasWUef3j2b76FOk0+tccmpgUgLBB4Tp+L8oNoDh2ohwb9h3U6BDd8f5wfrK4w/PxwHw3fCtHwYFgJXR8Cnh6GnDdqeZLSlsrKSzZs3n7FnOl0Hsw947bNFpGVavXq1t7vgU/Q9KiKny9vfo926dSMwMPCkdcqdTgIDA/Hzq//x3RYUhNvtpqKiArvdXlfuLHcSHh7e4P02WxCH8gsA6NmjO1989TVbtm4jKSmR1HZt2b07k+iYKEJDfXO0BXwsuDjdEHDcGNDR6yp3E3VNjeserdchEPacBwW1EOsPbg/8OQdmpcKnRfB4NkT7wYw2Rt2jNm/ezA033HBGn0tE5Gz67NNPvN0FEZEWzdvfox999BF9+vQ5aZ3a2losloY/KFssFgBcLncTdS2N6rpcxmLw8PAwbr7xV1RWVmGzBRmjLWvXc/HFo8jYncmKld8TGBjIyOEXER4e9nMf74zxqeASaG4cUI5e244LNIEmY1rZ8XWPrWcxGaEF4JMi6Gs3ru/aB4s6wcJSuD8Lvu1Q/55u3brx0UcfnZkHEhERERH5Ed26dfvROn4WS6OAcjSIHDsKA2Cx+NXdO7au/zH1zGYzNlsQYKyJiY2NwRYUxMLvFjP2qsvJzj7I4qXLuOKyS37SM50NPhVckvyhyAXVbrAeCSCOWmNkJfK4niZZjS2Pj+WoaTid7Ci3B553wFftYVslWE3GAn1/Ezx9sGHdwMDAH028IiIiIiLNyW63U1VVhcvlqhtNcVY4sVgsBAY2XAdhD7bhdDoblJU7K7DZbI3a9Xg8rF27nksvGU1hUREWs5noqCgsZjM/rPKtqcg+tTi/h80IFcvL68uWlkFvG/gdNy2sv93YNtlzZNTF4zHe199OIx8XwgXBxgJ9E3A0q9Z6jFAjIiIiIuLLoqOjMJvN5BxZYA+Qk+MgJiYas7nhj/TxcXE4cnPxHPlB2ePx4HA4iI+Pa9Tuzp27iI+PIyQkBBMmPBjvcbs9dT9n+wqfCi42M0yIgnv3ww/l8NVheMkB9xtn5eCogYojqeOaCCg7cljl1gp46ACUuIzDJo/l8sBfcuH3RzZg6BhgTCn78jB8VAj9gxERERER8Wn+/v507tSRRYuXkJubx549e1m3fiPdzzOmmZU7ndTW1gLQPrUdNTU1LF6yjMLCIpYuW0F1dQ0d2qc2aNPtdrN23Ya6ncTCw8Nwudxk7tnLjp276g6s9BUmj8e3spTTDb/eB58dhlALTIozXgCmNfBua7gl2rheVQ5374OtlZAeBG+0Ms5/Odb7BUa911vVl31UAA8egHh/+Fdb4xwYERERERFfVlNTw3eLl5K5OxN/q5WePdLp2aM7AK//YyrDLxpKl86dAMjNzeO7RUsoLCoiKiqSoUMGExsb06C9bdt3kJd3iAuHDKor27FzF0uXLccWZGP0qBFERkY03wP+CJ8LLiIiIiIiIsfzqaliIiIiIt5y/CF+IuJbfGpXMRE5c9xud6PFeiIiYvB4PJhMJhwOR11gSUxM9HKvRORkFFxEWrij//hu2rSJXbt2UVZWRv/+/enYsaO3uyYi4pOOfm/OmzePl156icDAQA4dOsTFF1/M5ZdfTvfu3b3dRRFpgn4dK9LCmUwm5syZwz333MOsWbNYuXIlV1xxBR999BGVlZXe7p6IiE+orq6u+7PJZGLjxo089thj3HjjjXz66afcf//9fPjhhxQUFDSoKyK+Q8FFpIUpKytrcJ2VlcVLL73Egw8+yAcffMALL7xQd+/oP76aty0iv2QzZ87k008/bfD9uXXrVrp3786NN96Iw+HgnXfe4YYbbqBNmzb897//9WJvReREFFxEWpAvvviCMWPGkJOTU1dmMpkIDg7miiuuYP/+/Vx22WWMHz+eSy65hKeffpqdO3dqrYuI/KJt2LCBt99+m7lz59aFl8DAQEJDQ8nMzOSGG26gX79+PPXUU+Tn5zN58mQyMzO93GsROZ5+mhFpQYYNG0Z8fDx33XVXXXhxOp0UFxezZs0abr/9dgYPHswzzzxDSEgIGRkZLFy40Mu9FhHxjqMnPjz//PNcdNFFvPnmm8yZM4eKigpat27N0qVLufrqqxk+fDjPPPMMAKGhoSQkJGC1Wr3ZdRFpgoKLSAsSFhbGu+++i91u5/bbb8fhcNCxY0f69evHrbfeSnp6Os8++ywmkwmPx0NAQACxsb516q2ISHMxmUy4XC4AnnjiCQYOHMjUqVP55ptv6NmzJ5MmTaKiooL09HTy8vKorq7m22+/xePxYLfbf6R1EWlu2lVMpIUJCQlh+vTp3H777dxyyy2899573H///ZSXl/PDDz+wcuVKPB4P33//PTk5OfTp08fbXRYR8QqPx4PFYqGqqoqAgAD+8Ic/8OyzzzJlyhQA/u///o+SkhKee+45IiMjsdvt5ObmMn36dCIifOe0cBExmDxHx1FFxGcd3bqzpKQEl8tFREQETqeTm2++maqqKqZOnYrFYuHVV19l8eLFhIaGYrVa+fOf/0zXrl293X0RkWZ39Htz5cqVLFq0CKvVym9+8xusVivPPfccCxYs4N577+Xqq69my5Yt7Nu3j5qaGvr06UNSUpK3uy8iTVBwEfFxR//xXbBgAVOnTsXlcjFgwAAmTZpEeXk5N998M9XV1UyfPp34+Hj27NmDzWbDarXqN4Yi8os2e/ZsHn74Yfr27cu6devo1KkTL774IikpKTz33HPMnz+fiRMnMmrUKE0NE2kBtMZFxMeZTCbmz5/P/fffT9euXbnooot49913+etf/4rdbueDDz4gICCAO+64g+zsbNq2bUtcXJxCi4j8omVkZPCXv/yFZ599lnfeeYdPP/2U9evX89RTT5GTk8MTTzzBqFGj+NOf/sT8+fNxu93od7kivk3BRcTHHT58mGnTpvHII4/w9NNPM27cOOx2O1OnTuX//b//h81mY8aMGbhcLh544AFqa2u93WUREa85Gj5ycnIICgrisssuo7CwkJdeeonrr78eh8PBAw88wMGDB3nssceYMGEC3bt3x2w2YzKZvNx7ETkZLc4X8XF+fn7U1NTQvn17SktLefXVV7nssssYMmQI99xzD1FRUUycOJFPPvmEkpIS/Pz0n7WI/PIcnVZbWFhIVFQUtbW1pKenc+DAAebNm0d4eDiPPPIIe/fuZezYsTz44IPccsst3Hfffd7uuoicIv2EI+JDjv6m8Oh2xkd/+5eamkpAQACLFy+mtraWcePGkZSURFxcHFOmTGHXrl28/PLLhISEeLP7IiJeYzKZmDdvHi+//DLR0dH079+fMWPGkJKSwqJFixg9ejRBQUE4nU66du1KcnIy6enp3u62iJwGBRcRH2MymVi1ahVLliyhffv2XHHFFfzpT3/C39+fxx57jJCQENLS0vB4PPTp04cxY8bQsWNH/P39vd11ERGvycrK4qmnnuKmm25ix44drF69mvLyckJCQnA6nXTp0gWAZcuW0aZNG/74xz8SHBzs5V6LyOlQcBHxISaTiWXLlnHHHXeQnp7OtGnTWLduHbfeeiutWrVi7969dOzYkbKyMqZNm8aaNWt4/PHHiYqK8nbXRUSa3bHTwyoqKrjyyiu59957AZg+fTpLliyhrKyM3NxcXnjhBUwmE5mZmcyYMUOhRaQF0nbIIl527PQwl8vFzJkzAbjxxhtZvnw5jz76KMOHD+eBBx5g+/bt3H777aSkpFBeXs60adN0TouI/KLNnj2bKVOmkJeXR0hICO+++y4pKSkAvPHGG2zbtg273Y6/vz+pqakMGjSI1NRUL/daRH4KBRcRH7FixQo2btzI7NmzufTSS7njjjsAWLJkCY8//jgjR47k3nvvpba2lv3799OmTRvi4uK83GsRkeZ3dKSloKCAsWPHMn78eJxOJ5999hm33XYbY8eOrft+fPPNN1m0aBH9+vVj4sSJWCwWL/deRH4qTRUT8QGrV6/m1ltvpU+fPmRlZbFixQpuuukmAgICGDx4MM8//zxPPvkkpaWlPProo/Tr18/bXRYR8RqTycTSpUv57rvvGD16NBMnTgQgISGB6dOnYzKZuOqqq4iLi+Oee+4hMDCQkSNHKrSItHA6x0XEy3bs2MGqVat46qmnmDFjBjNmzGDHjh1MmjSJ6upqAAYNGsTTTz/N5s2bdc6AiAjgdDr58MMPmT9/PkVFRQDcfPPN3HHHHXzwwQd89dVXOBwOAG655RaSkpK82V0ROQMUXES8qLCwkDfeeIM333yTmpoazGYzXbt2Zfr06axbt46HHnqoLrwMGzaMWbNmERMT4+Vei4h436hRo5g6dSp5eXm8/fbbdeUTJkzg7rvv5u9//zuzZ8/G5XJ5sZciciZpjYuIF9XU1PDDDz/wyiuvUFVVxeeff47VagVg+/bt3H333bRt25Zp06ZhtVobnO0iIiLwv//9j4ceeojbb7+dBx98sK585syZDBgwgDZt2nivcyJyRim4iDSjo8EjOzubqqoqTCYTbdu2ZePGjTzzzDMEBgYyffp0goKCANi8eTMPP/ww7733HvHx8V7uvYiIbzoaXu68805++9vfers7InKWKLiInGUrVqygU6dOREZGAsY/sFOmTKGyspKAgACSkpL405/+xPbt2/nb3/6Gn59fg/BSXV1dNwojIiJNmzdvHvfddx/3339/3VkuInJu0RoXkbPor3/9K4888kjdTjbr1q3jscce46abbmLmzJlMmjSJhQsXsnnzZvr27cvDDz+My+Xiuuuuo7KyEkChRUTkFIwYMYI33niD0aNHe7srInKWKLiInCU1NTVs376dYcOGERYWRk1NDU6nk169enHNNddQXl7OH//4R2688UaSkpL4+OOP6dOnDxMnTiQ6OpqCggJvP4KISIsybNgwHS4pcg7TOS4iZ1FsbCwHDx7khRdeYOHChYwZM4bq6mqqq6u56aabGDx4ME8++SSbNm3i3Xff5fzzz6dv37707NmzbqqYiIiIiGjEReSs8ff356mnnqKoqIj333+fkSNHcscdd3Dw4EHS09O56KKLmDx5MgButxuz2UxgYCB+fn4KLSIiIiLHUXAROYtqamrIyMigffv27Nq1i4ULF3LrrbfSsWNHwsLCACgpKWHJkiUEBQURGhrq5R6LiIiI+CbtKiZylpWVlVFdXc3vfvc7AgMD6d+/P263m3feeQe3201kZCR5eXlMmzaNtLQ0b3dXRERExCcpuIg0k/379zN58mSCgoIYPXo0ffv2ZenSpdhsNtLS0khOTvZ2F0VERER8loKLSDPav38/zz77LCaTieuuu45hw4Z5u0siIiIiLYLWuIg0o1atWvHUU09RWlrKl19+SVlZmbe7JCIiItIiaMRFxAuys7Mxm80kJCR4uysiIiIiLYKCi4iIiIiI+DxNFRMREREREZ+n4CIiIiIiIj5PwUVERERERHyegouIiIiIiPg8BRcREREREfF5Ci4iInLGPPbYY1x22WUnrXPTTTdx9913N1OPRETkXOHn7Q6IiMgvy9NPP43ZrN+biYjI6VFwERGRZtW+fXtvd0FERFog/cpLROQctWHDBm644QZ69uxJ3759uf/++8nOzm5QZ8aMGYwaNYpu3bpx6aWX8s0339Tde+utt+jUqROLFy+uK5s2bRpdu3Zl/fr1J/3st956iwsuuIBevXoxadIkCgoK6u4dO1Xs+++/p1OnTqxevZrrrruO8847j+HDh/Pvf/+7UXsjR47kvPPOY8SIEUyZMgW32/1T/2pERKQFUnARETkHVVRUcNdddxEXF8c//vEPJk+ezNatW3nooYfq6rz++uu88MILXHLJJbz55ptccMEFPPTQQ3z77bcA3HrrrXTr1o3nnnuO6upqMjMzef3117n99tvp0aPHCT87MzOTTz75hD/84Q/84Q9/YOXKlUycOPGk/X3ooYcYPXp0XTB68sknycjIAOCbb77hb3/7G7fccgtvv/0248eP57XXXuOTTz75+X9RIiLSYmiqmIjIOWjXrl0cPnyYm266iZ49ewIQERHBypUrcbvdlJWVMW3aNO644w4eeOABAAYNGkR5eTkvv/wyF198MRaLheeee45rrrmGd955h8WLF9O6desfDSEAU6dOpW3btnWfe9ddd7F69Wr69OnTZP2bbrqJW2+9FYC0tDTmzp3L4sWLad++PatWrSIpKYnrr78ek8lE37598fPzIzY29gz8TYmISEuh4CIicg5q164d4eHh3HPPPVx66aVceOGFDBgwgL59+wKwfv16qqqqGDp0KLW1tXXvGzJkCJ999hlZWVmkpKTQuXNn7rzzTv72t79hsVj45JNPsFqtJ/3sDh061IWWo236+/uzcePGEwaXY0dwQkNDsdlsOJ1OAHr27MnHH3/M1VdfzZgxYxg6dCi33377T/2rERGRFkpTxUREzkHBwcF8+OGHDBgwgFmzZnHXXXcxcOBAPv74YwAOHz4MwHXXXUdaWlrd67e//S0Ahw4dqmvriiuuwO12k5iYSIcOHX70s6Ojoxtcm0wmwsPDycvLO+F7AgMDG1ybzWY8Hk/d5z///POYzWZeeeUVLr/8cq688kp27979438RIiJyztCIi4jIOapDhw68+uqrVFdXs2bNGt5//33++Mc/kpaWRkhICABTpkwhLi6u0XuPHTGZPHkybdq0IScnh6lTp3Lfffed9HOLi4sbXLvdboqKiggPD//JzzJ27FjGjh1LQUEBCxYsYMqUKdx3331163FEROTcpxEXEZFz0OLFixkwYACFhYVYrVYGDBjAU089BcDBgwfp3r07/v7+FBQUcN5559W9du3axZQpU+ra+eyzz1i+fDnPPfccd999N2+++WbdovkT2b59O/n5+XXX8+fPp7a2tm6a2ul64oknuP/++wGIiopi/PjxXHPNNeTk5Pyk9kREpGXSiIuIyDkoPT0dj8fDfffdx5133om/vz/vv/8+oaGh9OvXj8jISG666Saef/55iouLSU9PZ/v27fz1r39l+PDhBAcHc+jQIV544QXGjh1Lnz59SE9P56uvvuKJJ55g5syZJzxE0mw2c8899zBx4kQOHTrEiy++yJAhQ+jVq9dPepbzzz+f3/3ud7zyyitccMEFOBwOZs6cyciRI3/OX5GIiLQwCi4iIueg8PBw3nrrLV5++WUeffRRampqSE9P59133yUyMhKARx55hMjISD755BP+/ve/Exsby4QJE+qmgj3zzDOYTCYeeeQRAKxWK0899RS33347M2bM4JZbbmnys88//3x69uzJo48+isvl4pJLLuGxxx77yc9y1VVXUVZWxkcffcR7771HSEgIo0ePZtKkST+5TRERaXlMnqOrH0VERERERHyU1riIiIiIiIjPU3ARERERERGfp+AiIiIiIiI+T8FFRERERER8noKLiIiIiIj4PAUXERERERHxeQouIiIiIiLi8xRcRERERETE5/1/0xgPWIwns0IAAAAASUVORK5CYII=\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from cobra.evaluation import plot_incidence\n", + "for predictor in list(pig_tables.variable.unique()):\n", + " print(predictor)\n", + " try:\n", + " if predictor + \"_bin\" in basetable.columns:\n", + " column_order = list(basetable[predictor + \"_bin\"].unique().sort_values())\n", + " else:\n", + " column_order = None #sorted(list(basetable[predictor].unique())) # e.g. just binary variable\n", + " plot_incidence(pig_tables,\n", + " variable=predictor,\n", + " model_type=\"classification\",\n", + " column_order=column_order)\n", + " except ValueError as ve:\n", + " print(f\"Can't plot PIG for {predictor}. Error was: {ve}\")\n", + " except TypeError as ve:\n", + " print(f\"Can't plot PIG for {predictor}. Error was: {ve}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "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": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 0b21aa12d4ded5044a3bbd4cb7fe5dbcb41583af Mon Sep 17 00:00:00 2001 From: "hendrik.dewinter" Date: Mon, 27 Sep 2021 10:56:44 +0200 Subject: [PATCH 8/8] updated documentation. Deleted the old tutorial, final update of the readme with current html links, inclusion of two ipynb files within a tutorials folder for in the repo. --- README.rst | 20 +- docs/Cobra_Tutorial_LinearRegression.ipynb | 1861 ------------- docs/Cobra_Tutorial_LogisticRegression.ipynb | 2501 ------------------ docs/Makefile | 44 - docs/make.bat | 35 - docs/source/conf.py | 83 - docs/source/index.rst | 37 - docs/source/tutorial.rst | 224 -- 8 files changed, 9 insertions(+), 4796 deletions(-) delete mode 100644 docs/Cobra_Tutorial_LinearRegression.ipynb delete mode 100644 docs/Cobra_Tutorial_LogisticRegression.ipynb delete mode 100644 docs/Makefile delete mode 100644 docs/make.bat delete mode 100644 docs/source/conf.py delete mode 100644 docs/source/index.rst delete mode 100644 docs/source/tutorial.rst diff --git a/README.rst b/README.rst index afa60b0..52282d4 100644 --- a/README.rst +++ b/README.rst @@ -9,13 +9,10 @@ ------------------------------------------------------------------------------------------------------------------------------------ -===== -cobra -===== -.. image:: material\logo.png +.. image:: C:/Users/hendrik.dewinter/PycharmProjects/cobra/material/logo.png :width: 300 -**cobra** is a Python package to build predictive models using linear or logistic regression with a focus on performance and interpretation. It consists of several modules for data preprocessing, feature selection and model evaluation. The underlying methodology was developed at Python Predictions in the course of hundreds of business-related prediction challenges. It has been tweaked, tested and optimized over the years based on feedback from clients, our team, and academic researchers. +**Cobra** is a Python package to build predictive models using linear or logistic regression with a focus on performance and interpretation. It consists of several modules for data preprocessing, feature selection and model evaluation. The underlying methodology was developed at Python Predictions in the course of hundreds of business-related prediction challenges. It has been tweaked, tested and optimized over the years based on feedback from clients, our team, and academic researchers. Main Features ============= @@ -25,8 +22,8 @@ Main Features - partition into train/selection/validation sets - create bins from continuous variables - regroup categorical variables based on statistical significance - - replace missing values and - - add columns with incidence rate per category/bin + - replace missing values + - add columns with average of target values (linear regression) or incidence rate per category/bin (logistic regression) - Perform univariate feature selection based on RMSE (linear regression) or AUC (logistic regression) - Compute correlation matrix of predictors @@ -79,15 +76,16 @@ Help and Support Documentation ------------- + +HTML documentation of the `individual modules `_ + **Logistic Regression** -- HTML documentation of the `individual modules `_ for logistic regression -- A step-by-step `tutorial `_ +- A step-by-step tutorial ``_ **Linear Regression** -- HTML documentation of the individal modules for linear regression -- A step-by-step tutorial +- A step-by-step tutorial ``_ Outreach ------------- diff --git a/docs/Cobra_Tutorial_LinearRegression.ipynb b/docs/Cobra_Tutorial_LinearRegression.ipynb deleted file mode 100644 index 214a956..0000000 --- a/docs/Cobra_Tutorial_LinearRegression.ipynb +++ /dev/null @@ -1,1861 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 52, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], - "source": [ - "# Optional: import local development version of Cobra:\n", - "import sys\n", - "import os\n", - "sys.path.append(r\"C:\\Users\\hendrik.dewinter\\PycharmProjects\\cobra\")\n", - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tutorial for Linear Regression" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This section we will walk you through all the required steps to build a predictive linear regression model using **Cobra**. All classes and functions used here are well-documented. In case you want more information on a class or function, run the next cell:" - ] - }, - { - "cell_type": "code", - "execution_count": 53, - "metadata": {}, - "outputs": [], - "source": [ - "#help(function_or_class_you_want_info_from)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Building a good model involves three steps\n", - "\n", - "1. **Preprocessing**: properly prepare the predictors (a synonym for “feature” or variable that we use throughout this tutorial) for modelling.\n", - "\n", - "2. **Feature Selection**: automatically select a subset of predictors which contribute most to the target variable or output in which you are interested.\n", - "\n", - "3. **Model Evaluation**: once a model has been build, a detailed evaluation can be performed by computing all sorts of evaluation metrics.\n", - "\n", - "\n", - "\n", - "Let's dive in!!!\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Miles Per Gallon Prediction\n", - "- GOAL : Predict the distance, measured in miles, that a car can travel per gallon of fuel\n", - "- BASETABLE : seaborn dataset MPG" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "import the necessary libraries" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "from pandas.api.types import is_datetime64_any_dtype\n", - "\n", - "pd.set_option('display.max_columns', 50)\n", - "pd.set_option(\"display.max_rows\", 50)\n", - "from cobra.preprocessing import PreProcessor\n", - "from cobra.evaluation import generate_pig_tables, plot_incidence\n", - "from cobra.evaluation import evaluator" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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mpgcylindersdisplacementhorsepowerweightaccelerationmodel_yearoriginname
018.08307.0130.0350412.070usachevrolet chevelle malibu
115.08350.0165.0369311.570usabuick skylark 320
218.08318.0150.0343611.070usaplymouth satellite
316.08304.0150.0343312.070usaamc rebel sst
417.08302.0140.0344910.570usaford torino
\n", - "
" - ], - "text/plain": [ - " mpg cylinders displacement horsepower weight acceleration \\\n", - "0 18.0 8 307.0 130.0 3504 12.0 \n", - "1 15.0 8 350.0 165.0 3693 11.5 \n", - "2 18.0 8 318.0 150.0 3436 11.0 \n", - "3 16.0 8 304.0 150.0 3433 12.0 \n", - "4 17.0 8 302.0 140.0 3449 10.5 \n", - "\n", - " model_year origin name \n", - "0 70 usa chevrolet chevelle malibu \n", - "1 70 usa buick skylark 320 \n", - "2 70 usa plymouth satellite \n", - "3 70 usa amc rebel sst \n", - "4 70 usa ford torino " - ] - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import seaborn as sns\n", - "df=sns.load_dataset('mpg')\n", - "df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the example below, we assume the data for model building is available in a pandas DataFrame. This DataFrame should contain a an ID column, a target column (e.g. “**mpg**”) and a number of candidate predictors (features) to build a model with.\n", - "\n", - "***\n" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "mpg float64\n", - "cylinders int64\n", - "displacement float64\n", - "horsepower float64\n", - "weight int64\n", - "acceleration float64\n", - "model_year int64\n", - "origin object\n", - "name object\n", - "dtype: object" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.dtypes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "it is required to set all category vars to object dtype\n" - ] - }, - { - "cell_type": "code", - "execution_count": 57, - "metadata": {}, - "outputs": [], - "source": [ - "df.loc[:, df.dtypes == 'category'] =\\\n", - " df.select_dtypes(['category'])\\\n", - " .apply(lambda x: x.astype('object'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data preprocessing" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### The first part focusses on preparing the predictors for modelling by:\n", - "\n", - "1. Defining the ID column, the target, discrete and contineous variables\n", - "\n", - "2. Splitting the dataset into training, selection and validation datasets.\n", - "\n", - "3. Binning continuous variables into discrete intervals\n", - "\n", - "4. Replacing missing values of both categorical and continuous variables (which are now binned) with an additional “Missing” bin/category\n", - "\n", - "5. Regrouping categories in new category “other”\n", - "\n", - "6. Replacing bins/categories with their corresponding incidence rate per category/bin." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this toy dataset, the index will serve as ID," - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [], - "source": [ - "df[\"id\"] = df.index + 1\n", - "id_col = \"id\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "and MPG is the target,\n" - ] - }, - { - "cell_type": "code", - "execution_count": 59, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "count 398.000000\n", - "mean 23.514573\n", - "std 7.815984\n", - "min 9.000000\n", - "25% 17.500000\n", - "50% 23.000000\n", - "75% 29.000000\n", - "max 46.600000\n", - "Name: mpg, dtype: float64" - ] - }, - "execution_count": 59, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "target_col = \"mpg\"\n", - "df[target_col].describe()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finding out which variables are categorical (\"discrete\") and which are continous:\n", - "\n", - "\n", - " => discrete are definitely those that contain strings:" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['origin', 'name']\n", - "\n", - "origin\n", - "usa 249\n", - "japan 79\n", - "europe 70\n", - "Name: origin, dtype: int64\n", - "\n", - "name\n", - "ford pinto 6\n", - "amc matador 5\n", - "toyota corolla 5\n", - "ford maverick 5\n", - "peugeot 504 4\n", - " ..\n", - "dodge colt hardtop 1\n", - "fiat 131 1\n", - "chrysler lebaron salon 1\n", - "plymouth custom suburb 1\n", - "ford ranger 1\n", - "Name: name, Length: 305, dtype: int64\n", - "\n" - ] - } - ], - "source": [ - "col_dtypes = df.dtypes\n", - "discrete_vars = [col for col in col_dtypes[col_dtypes==object].index.tolist() if col not in [id_col, target_col]] \n", - "print(discrete_vars)\n", - "print()\n", - "for col in discrete_vars:\n", - " print(col)\n", - " print(df[col].value_counts())\n", - " print()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we also check for numerical columns that only contain a few different values, thus to be interpreted as discrete, categorical variables\n" - ] - }, - { - "cell_type": "code", - "execution_count": 61, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "cylinders\n", - "4 204\n", - "8 103\n", - "6 84\n", - "3 4\n", - "5 3\n", - "Name: cylinders, dtype: int64\n", - "\n" - ] - } - ], - "source": [ - "for col in df.columns:\n", - " if col not in discrete_vars and col not in [id_col, target_col]: # if we didn't mark it as discrete already because it was string typed, or also excluding it if it is the target:\n", - " val_counts = df[col].value_counts()\n", - " if len(val_counts) > 1 and len(val_counts) <= 10: # The column contains less than 10 different values. \n", - " print(col)\n", - " print(val_counts)\n", - " print()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "By taking a look at the printed variables, it is clear that we have to include those in the list of discrete variables. This can be done as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['origin', 'name', 'cylinders']" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "discrete_vars.extend([\"cylinders\"])\n", - "discrete_vars" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The remaining variables can be labelled continous predictors, without including the target variable.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [], - "source": [ - "continuous_vars = list(set(df.columns)\n", - " - set(discrete_vars) \n", - " - set([id_col, target_col]))\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we can prepare **Cobra's Preprocessor**" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "The target encoder's additive smoothing weight is set to 0. This disables smoothing and may make the encoding prone to overfitting.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on method from_params in module cobra.preprocessing.preprocessor:\n", - "\n", - "from_params(model_type: str = 'classification', n_bins: int = 10, strategy: str = 'quantile', closed: str = 'right', auto_adapt_bins: bool = False, starting_precision: int = 0, label_format: str = '{} - {}', change_endpoint_format: bool = False, regroup: bool = True, regroup_name: str = 'Other', keep_missing: bool = True, category_size_threshold: int = 5, p_value_threshold: float = 0.001, scale_contingency_table: bool = True, forced_categories: dict = {}, weight: float = 0.0, imputation_strategy: str = 'mean') method of builtins.type instance\n", - " Constructor to instantiate PreProcessor from all the parameters\n", - " that can be set in all its required (attribute) classes\n", - " along with good default values.\n", - " \n", - " Parameters\n", - " ----------\n", - " model_type : str\n", - " Model type (``classification`` or ``regression``).\n", - " n_bins : int, optional\n", - " Number of bins to produce. Raises ValueError if ``n_bins < 2``.\n", - " strategy : str, optional\n", - " Binning strategy. Currently only ``uniform`` and ``quantile``\n", - " e.g. equifrequency is supported.\n", - " closed : str, optional\n", - " Whether to close the bins (intervals) from the left or right.\n", - " auto_adapt_bins : bool, optional\n", - " Reduces the number of bins (starting from n_bins) as a function of\n", - " the number of missings.\n", - " starting_precision : int, optional\n", - " Initial precision for the bin edges to start from,\n", - " can also be negative. Given a list of bin edges, the class will\n", - " automatically choose the minimal precision required to have proper\n", - " bins e.g. ``[5.5555, 5.5744, ...]`` will be rounded\n", - " to ``[5.56, 5.57, ...]``. In case of a negative number, an attempt\n", - " will be made to round up the numbers of the bin edges\n", - " e.g. ``5.55 -> 10``, ``146 -> 100``, ...\n", - " label_format : str, optional\n", - " Format string to display the bin labels\n", - " e.g. ``min - max``, ``(min, max]``, ...\n", - " change_endpoint_format : bool, optional\n", - " Whether or not to change the format of the lower and upper bins\n", - " into ``< x`` and ``> y`` resp.\n", - " regroup : bool\n", - " Whether or not to regroup categories.\n", - " regroup_name : str\n", - " New name of the non-significant regrouped variables.\n", - " keep_missing : bool\n", - " Whether or not to keep missing as a separate category.\n", - " category_size_threshold : int\n", - " All categories with a size (corrected for incidence if applicable)\n", - " in the training set above this threshold are kept as a separate category,\n", - " if statistical significance w.r.t. target is detected. Remaining\n", - " categories are converted into ``Other`` (or else, cf. regroup_name).\n", - " p_value_threshold : float\n", - " Significance threshold for regrouping.\n", - " forced_categories : dict\n", - " Map to prevent certain categories from being grouped into ``Other``\n", - " for each column - dict of the form ``{col:[forced vars]}``.\n", - " scale_contingency_table : bool\n", - " Whether contingency table should be scaled before chi^2.\n", - " weight : float, optional\n", - " Smoothing parameters (non-negative). The higher the value of the\n", - " parameter, the bigger the contribution of the overall mean.\n", - " When set to zero, there is no smoothing (e.g. the pure target incidence is used).\n", - " imputation_strategy : str, optional\n", - " In case there is a particular column which contains new categories,\n", - " the encoding will lead to NULL values which should be imputed.\n", - " Valid strategies are to replace with the global mean of the train\n", - " set or the min (resp. max) incidence of the categories of that\n", - " particular variable.\n", - " \n", - " Returns\n", - " -------\n", - " PreProcessor\n", - " class encapsulating CategoricalDataProcessor,\n", - " KBinsDiscretizer, and TargetEncoder instances\n", - "\n" - ] - } - ], - "source": [ - "preprocessor = PreProcessor.from_params(\n", - " model_type=\"regression\"\n", - ")\n", - "\n", - "# These are the options though:\n", - "help(PreProcessor.from_params)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "split data into train-selection-validation set:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [], - "source": [ - "from cobra.preprocessing import PreProcessor\n", - "basetable = preprocessor.train_selection_validation_split(\n", - " data=df,\n", - " train_prop=0.6,\n", - " selection_prop=0.2,\n", - " validation_prop=0.2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And fit the preprocessor pipeline:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "12ea98963cc948c88ceea3e81d3da7d0", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Computing discretization bins...: 0%| | 0/5 [00:00\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", - " \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", - "
mpgcylindersdisplacementhorsepowerweightaccelerationmodel_yearoriginnameidsplitmodel_year_binhorsepower_bindisplacement_binacceleration_binweight_binorigin_processedname_processedcylinders_processedorigin_encname_enccylinders_encmodel_year_enchorsepower_encdisplacement_encacceleration_encweight_enc
018.08307.0130.0350412.070usachevrolet chevelle malibu1train70.0 - 71.0110.0 - 132.0302.0 - 350.08.0 - 12.23369.0 - 3731.0usachevrolet chevelle malibu820.59127517.5000014.98333319.23529419.55714315.44583315.50818.208333
115.08350.0165.0369311.570usabuick skylark 3202train70.0 - 71.0151.0 - 225.0302.0 - 350.08.0 - 12.23369.0 - 3731.0usabuick skylark 320820.59127515.0000014.98333319.23529413.75833315.44583315.50818.208333
218.08318.0150.0343611.070usaplymouth satellite3train70.0 - 71.0132.0 - 151.0302.0 - 350.08.0 - 12.23369.0 - 3731.0usaplymouth satellite820.59127518.0000014.98333319.23529415.32500015.44583315.50818.208333
316.08304.0150.0343312.070usaamc rebel sst4validation70.0 - 71.0132.0 - 151.0302.0 - 350.08.0 - 12.23369.0 - 3731.0usaamc rebel sst820.59127523.8337514.98333319.23529415.32500015.44583315.50818.208333
417.08302.0140.0344910.570usaford torino5selection70.0 - 71.0132.0 - 151.0232.0 - 302.08.0 - 12.23369.0 - 3731.0usaford torino820.59127523.8337514.98333319.23529415.32500018.02173915.50818.208333
\n", - "" - ], - "text/plain": [ - " mpg cylinders displacement horsepower weight acceleration \\\n", - "0 18.0 8 307.0 130.0 3504 12.0 \n", - "1 15.0 8 350.0 165.0 3693 11.5 \n", - "2 18.0 8 318.0 150.0 3436 11.0 \n", - "3 16.0 8 304.0 150.0 3433 12.0 \n", - "4 17.0 8 302.0 140.0 3449 10.5 \n", - "\n", - " model_year origin name id split \\\n", - "0 70 usa chevrolet chevelle malibu 1 train \n", - "1 70 usa buick skylark 320 2 train \n", - "2 70 usa plymouth satellite 3 train \n", - "3 70 usa amc rebel sst 4 validation \n", - "4 70 usa ford torino 5 selection \n", - "\n", - " model_year_bin horsepower_bin displacement_bin acceleration_bin \\\n", - "0 70.0 - 71.0 110.0 - 132.0 302.0 - 350.0 8.0 - 12.2 \n", - "1 70.0 - 71.0 151.0 - 225.0 302.0 - 350.0 8.0 - 12.2 \n", - "2 70.0 - 71.0 132.0 - 151.0 302.0 - 350.0 8.0 - 12.2 \n", - "3 70.0 - 71.0 132.0 - 151.0 302.0 - 350.0 8.0 - 12.2 \n", - "4 70.0 - 71.0 132.0 - 151.0 232.0 - 302.0 8.0 - 12.2 \n", - "\n", - " weight_bin origin_processed name_processed \\\n", - "0 3369.0 - 3731.0 usa chevrolet chevelle malibu \n", - "1 3369.0 - 3731.0 usa buick skylark 320 \n", - "2 3369.0 - 3731.0 usa plymouth satellite \n", - "3 3369.0 - 3731.0 usa amc rebel sst \n", - "4 3369.0 - 3731.0 usa ford torino \n", - "\n", - " cylinders_processed origin_enc name_enc cylinders_enc model_year_enc \\\n", - "0 8 20.591275 17.50000 14.983333 19.235294 \n", - "1 8 20.591275 15.00000 14.983333 19.235294 \n", - "2 8 20.591275 18.00000 14.983333 19.235294 \n", - "3 8 20.591275 23.83375 14.983333 19.235294 \n", - "4 8 20.591275 23.83375 14.983333 19.235294 \n", - "\n", - " horsepower_enc displacement_enc acceleration_enc weight_enc \n", - "0 19.557143 15.445833 15.508 18.208333 \n", - "1 13.758333 15.445833 15.508 18.208333 \n", - "2 15.325000 15.445833 15.508 18.208333 \n", - "3 15.325000 15.445833 15.508 18.208333 \n", - "4 15.325000 18.021739 15.508 18.208333 " - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basetable = preprocessor.transform(basetable,\n", - " continuous_vars=continuous_vars,\n", - " discrete_vars=discrete_vars)\n", - "\n", - "\n", - "basetable.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Feature selection" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once the predictors are properly prepared, we can start building a predictive model, which boils down to selecting the right predictors from the dataset to train a model on.\n", - "As a dataset typically contains many predictors, **we first perform a univariate preselection** to rule out any predictor with little to no predictive power. Later, using the list of preselected features, we build a multiple regression model using **forward feature selection** to choose the right set of predictors." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In previous steps, these were the predictors, as preprocessed so far:" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['acceleration_bin',\n", - " 'cylinders_processed',\n", - " 'displacement_bin',\n", - " 'horsepower_bin',\n", - " 'model_year_bin',\n", - " 'name_processed',\n", - " 'origin_processed',\n", - " 'weight_bin']" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "preprocessed_predictors = [\n", - " col for col in basetable.columns\n", - " if col.endswith(\"_bin\") or col.endswith(\"_processed\")\n", - "]\n", - "sorted(preprocessed_predictors)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "But for feature selection, we use the target encoded version of each of these." - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['acceleration_enc',\n", - " 'cylinders_enc',\n", - " 'displacement_enc',\n", - " 'horsepower_enc',\n", - " 'model_year_enc',\n", - " 'name_enc',\n", - " 'origin_enc',\n", - " 'weight_enc']" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "preprocessed_predictors = [col for col in basetable.columns.tolist()\n", - " if '_enc' in col]\n", - "sorted(preprocessed_predictors)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A univariate selection on the preprocessed predictors can be conducted. The thresholds for retaining a feature can be changed by the user, for instance both at 10 for now.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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predictorRMSE trainRMSE selectionpreselection
0weight4.1656364.091908True
1displacement4.3600214.276909True
2cylinders4.6300864.894663True
3horsepower4.4065534.956710True
4model_year5.9175876.294349True
5origin6.5787106.664076True
6name1.2803737.429626True
7acceleration6.9092797.632203True
\n", - "
" - ], - "text/plain": [ - " predictor RMSE train RMSE selection preselection\n", - "0 weight 4.165636 4.091908 True\n", - "1 displacement 4.360021 4.276909 True\n", - "2 cylinders 4.630086 4.894663 True\n", - "3 horsepower 4.406553 4.956710 True\n", - "4 model_year 5.917587 6.294349 True\n", - "5 origin 6.578710 6.664076 True\n", - "6 name 1.280373 7.429626 True\n", - "7 acceleration 6.909279 7.632203 True" - ] - }, - "execution_count": 70, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from cobra.model_building import univariate_selection\n", - "\n", - "\n", - "df_rmse = univariate_selection.compute_univariate_preselection(\n", - " target_enc_train_data=basetable[basetable[\"split\"] == \"train\"],\n", - " target_enc_selection_data=basetable[basetable[\"split\"] == \"selection\"],\n", - " predictors=preprocessed_predictors,\n", - " target_column=target_col,\n", - " model_type=\"regression\",\n", - " preselect_rmse_threshold = 10, #RMSE maximum for selection\n", - " preselect_overtrain_threshold = 10) #difference between RMSE on train and selection set\n", - "df_rmse\n", - "\n", - "#As the square root of a variance, RMSE can be interpreted as the standard deviation of \n", - "# the unexplained variance, and has the useful property of being in the same units as the response variable. \n", - "#Lower values of RMSE indicate better fit." - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# TODO: this does not work yet for regression\n", - "from cobra.evaluation import plot_univariate_predictor_quality\n", - "\n", - "# Plot df_rmse to get a horizontal barplot:\n", - "plot_univariate_predictor_quality(df_rmse)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we compute correlations between the preprocessed predictors and plot it using a correlation matrix:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 72, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - " origin name cylinders model_year horsepower \\\n", - "origin 1.000000 0.546835 0.572771 0.183962 0.481406 \n", - "name 0.546835 1.000000 0.809813 0.630115 0.820650 \n", - "cylinders 0.572771 0.809813 1.000000 0.430675 0.830014 \n", - "model_year 0.183962 0.630115 0.430675 1.000000 0.424169 \n", - "horsepower 0.481406 0.820650 0.830014 0.424169 1.000000 \n", - "displacement 0.665415 0.832378 0.908653 0.378712 0.869803 \n", - "acceleration 0.242806 0.468041 0.558839 0.285982 0.630786 \n", - "weight 0.631066 0.847656 0.879737 0.353530 0.877309 \n", - "\n", - " displacement acceleration weight \n", - "origin 0.665415 0.242806 0.631066 \n", - "name 0.832378 0.468041 0.847656 \n", - "cylinders 0.908653 0.558839 0.879737 \n", - "model_year 0.378712 0.285982 0.353530 \n", - "horsepower 0.869803 0.630786 0.877309 \n", - "displacement 1.000000 0.530417 0.939289 \n", - "acceleration 0.530417 1.000000 0.479246 \n", - "weight 0.939289 0.479246 1.000000 \n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "from cobra.evaluation import plot_correlation_matrix\n", - "\n", - "# compute correlations between preprocessed predictors:\n", - "df_corr = (univariate_selection\n", - " .compute_correlations(basetable[basetable[\"split\"] == \"train\"],\n", - " preprocessed_predictors))\n", - "print(df_corr)\n", - "\n", - "# plot correlation matrix\n", - "plot_correlation_matrix(df_corr)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To get a list of the selected predictors after the univariate selection, run the following call:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 73, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['weight_enc',\n", - " 'displacement_enc',\n", - " 'cylinders_enc',\n", - " 'horsepower_enc',\n", - " 'model_year_enc',\n", - " 'origin_enc',\n", - " 'name_enc',\n", - " 'acceleration_enc']" - ] - }, - "execution_count": 73, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "preselected_predictors = (univariate_selection\n", - " .get_preselected_predictors(df_rmse))\n", - "preselected_predictors" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After an initial preselection on the predictors, we can start building the model itself using forward feature selection to choose the right set of predictors. Since we use target encoding on all our predictors, we will only consider models with positive coefficients (no sign flip should occur) as this makes the model more interpretable." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Modeling" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Based on the performance curves (AUC per model with a particular number of predictors in case of logistic regression), a final model can then be chosen and the variables importance can be plotted:\n", - "Note: variable importance is based on correlation of the predictor with the model scores (and not the true labels!)." - ] - }, - { - "cell_type": "code", - "execution_count": 85, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "3580586ecd4e4bbfb2b5d1e629047b70", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Sequentially adding best predictor...: 0%| | 0/8 [00:00\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", - "
predictorslast_added_predictortrain_performanceselection_performancevalidation_performancemodel_type
0[name_enc]name_enc1.2803737.4296266.833512regression
1[name_enc, model_year_enc]model_year_enc1.2409087.1898596.638183regression
2[name_enc, model_year_enc, weight_enc]weight_enc1.2003626.5880476.047175regression
3[name_enc, model_year_enc, weight_enc, horsepo...horsepower_enc1.1924046.5467835.974150regression
4[name_enc, model_year_enc, horsepower_enc, wei...cylinders_enc1.1891536.5613925.974802regression
5[name_enc, model_year_enc, horsepower_enc, wei...acceleration_enc1.1871926.5510465.965723regression
6[name_enc, model_year_enc, horsepower_enc, wei...origin_enc1.1870336.5467685.957807regression
7[name_enc, model_year_enc, origin_enc, horsepo...displacement_enc1.1869746.5473385.955585regression
\n", - "" - ], - "text/plain": [ - " predictors last_added_predictor \\\n", - "0 [name_enc] name_enc \n", - "1 [name_enc, model_year_enc] model_year_enc \n", - "2 [name_enc, model_year_enc, weight_enc] weight_enc \n", - "3 [name_enc, model_year_enc, weight_enc, horsepo... horsepower_enc \n", - "4 [name_enc, model_year_enc, horsepower_enc, wei... cylinders_enc \n", - "5 [name_enc, model_year_enc, horsepower_enc, wei... acceleration_enc \n", - "6 [name_enc, model_year_enc, horsepower_enc, wei... origin_enc \n", - "7 [name_enc, model_year_enc, origin_enc, horsepo... displacement_enc \n", - "\n", - " train_performance selection_performance validation_performance \\\n", - "0 1.280373 7.429626 6.833512 \n", - "1 1.240908 7.189859 6.638183 \n", - "2 1.200362 6.588047 6.047175 \n", - "3 1.192404 6.546783 5.974150 \n", - "4 1.189153 6.561392 5.974802 \n", - "5 1.187192 6.551046 5.965723 \n", - "6 1.187033 6.546768 5.957807 \n", - "7 1.186974 6.547338 5.955585 \n", - "\n", - " model_type \n", - "0 regression \n", - "1 regression \n", - "2 regression \n", - "3 regression \n", - "4 regression \n", - "5 regression \n", - "6 regression \n", - "7 regression " - ] - }, - "execution_count": 85, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from cobra.model_building import ForwardFeatureSelection\n", - "\n", - "forward_selection = ForwardFeatureSelection(model_type=\"regression\",\n", - " #model_name=\"linear-regression\",\n", - " max_predictors=30,\n", - " pos_only=True)\n", - "\n", - "# fit the forward feature selection on the train data\n", - "# has optional parameters to force and/or exclude certain predictors (see docs)\n", - "forward_selection.fit(basetable[basetable[\"split\"] == \"train\"],\n", - " target_column_name = target_col,\n", - " predictors = preselected_predictors)\n", - " #forced_predictors: list = [],\n", - " #excluded_predictors: list = [])\n", - "\n", - "# compute model performance\n", - "performances = (forward_selection\n", - " .compute_model_performances(basetable, target_column_name = target_col))\n", - "performances" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As can be seen, we have completed 7 steps till no further improvement can be observed" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\hendrik.dewinter\\PycharmProjects\\cobra\\cobra\\evaluation\\plotting_utils.py:137: RuntimeWarning: divide by zero encountered in double_scalars\n", - " ax.set_yticks(np.arange(0, max_metric*1.02, np.floor(max_metric/10)))\n" - ] - }, - { - "ename": "ValueError", - "evalue": "Maximum allowed size exceeded", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;31m# plot performance curves\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0mplot_performance_curves\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mperformances\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32m~\\PycharmProjects\\cobra\\cobra\\evaluation\\plotting_utils.py\u001b[0m in \u001b[0;36mplot_performance_curves\u001b[1;34m(model_performance, dim, path, colors)\u001b[0m\n\u001b[0;32m 135\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_yticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmax_metric\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m0.02\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0.05\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 136\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mmodel_type\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"regression\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 137\u001b[1;33m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mset_yticks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmax_metric\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m1.02\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfloor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmax_metric\u001b[0m\u001b[1;33m/\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 138\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 139\u001b[0m \u001b[1;31m# Make pretty\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mValueError\u001b[0m: Maximum allowed size exceeded" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from cobra.evaluation import plot_performance_curves\n", - "\n", - "# plot performance curves\n", - "plot_performance_curves(performances)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Based on the performance curves (AUC per model with a particular number of predictors in case of logistic regression), a final model can then be chosen and the variables importance can be plotted:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "model = forward_selection.get_model_from_step(7)\n", - "final_predictors = model.predictors\n", - "final_predictors\n", - "from cobra.evaluation import plot_variable_importance\n", - "variable_importance = model.compute_variable_importance(\n", - " basetable[basetable[\"split\"] == \"selection\"])\n", - "plot_variable_importance(variable_importance)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we can again export the model to a dictionary to store it as JSON" - ] - }, - { - "cell_type": "code", - "execution_count": 78, - "metadata": {}, - "outputs": [ - { - "ename": "FileNotFoundError", - "evalue": "[Errno 2] No such file or directory: 'output\\\\model.json'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mmodel_path\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"output\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"model.json\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel_path\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"w\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mfile\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5\u001b[0m \u001b[0mjson\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'output\\\\model.json'" - ] - } - ], - "source": [ - "model_dict = model.serialize()\n", - "\n", - "model_path = os.path.join(\"output\", \"model.json\")\n", - "with open(model_path, \"w\") as file:\n", - " json.dump(model_dict, file)\n", - "\n", - "# To reload the model again from a JSON file, run the following snippet:\n", - "# from cobra.model_building import LinearRegressionModel\n", - "# with open(model_path, \"r\") as file:\n", - "# model_dict = json.load(file)\n", - "# model = LinearRegressionModel()\n", - "# model.deserialize(model_dict)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Evaluation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have build and selected a final model, it is time to evaluate its predictions on the test set against various evaluation metrics. The used evaluation metrics are:\n", - "1. Coefficient of Determination: R2\n", - "2. Mean Absolute Error: MAE\n", - "3. Mean Squared Error: MSE\n", - "4. Root Mean Squared Error: RMSE\n", - "\n", - "Furthermore, plotting makes the evaluation of a linear regression model a lot easier. We will use a **prediction plot**, which presents predictions from the model against actual values and a **Q-Q plot** from the standardized prediction residuals." - ] - }, - { - "cell_type": "code", - "execution_count": 79, - "metadata": {}, - "outputs": [], - "source": [ - "from cobra.evaluation import RegressionEvaluator\n", - "# get numpy array of True target labels and predicted scores:\n", - "y_true = basetable[basetable[\"split\"] == \"selection\"][target_col].values\n", - "y_pred = model.score_model(basetable[basetable[\"split\"] == \"selection\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 80, - "metadata": {}, - "outputs": [], - "source": [ - "evaluator = RegressionEvaluator()\n", - "evaluator.fit(y_true, y_pred)" - ] - }, - { - "cell_type": "code", - "execution_count": 81, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "R2 0.363109\n", - "MAE 4.927506\n", - "MSE 42.867631\n", - "RMSE 6.547338\n", - "dtype: float64" - ] - }, - "execution_count": 81, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "evaluator.compute_scalar_metrics(y_true, y_pred)" - ] - }, - { - "cell_type": "code", - "execution_count": 82, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "quantiles [-2.23653945714085, -1.9545779044793146, -1.77...\n", - "residuals [-1.890723531717944, -1.874030710025782, -1.64...\n", - "dtype: object" - ] - }, - "execution_count": 82, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "evaluator.compute_qq_residuals(y_true, y_pred)" - ] - }, - { - "cell_type": "code", - "execution_count": 83, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "evaluator.plot_predictions(y_true=y_true, y_pred=y_pred)" - ] - }, - { - "cell_type": "code", - "execution_count": 86, - "metadata": {}, - "outputs": [ - { - "ename": "ValueError", - "evalue": "Illegal format string \"o--.\"; two marker symbols", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# does not work yet!\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mevaluator\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot_qq\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32m~\\PycharmProjects\\cobra\\cobra\\evaluation\\evaluator.py\u001b[0m in \u001b[0;36mplot_qq\u001b[1;34m(self, path, dim)\u001b[0m\n\u001b[0;32m 683\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mqq\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"residuals\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 684\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 685\u001b[1;33m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"o--.\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"cornflowerblue\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlinewidth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 686\u001b[0m \u001b[0max\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"r--.\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"cornflowerblue\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlinewidth\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 687\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py\u001b[0m in \u001b[0;36mplot\u001b[1;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[0;32m 1741\u001b[0m \"\"\"\n\u001b[0;32m 1742\u001b[0m \u001b[0mkwargs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m,\u001b[0m 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\u001b[0m_process_plot_format\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 370\u001b[0m \u001b[0mtup\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtup\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 371\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py\u001b[0m in \u001b[0;36m_process_plot_format\u001b[1;34m(fmt)\u001b[0m\n\u001b[0;32m 144\u001b[0m \u001b[1;32melif\u001b[0m \u001b[0mc\u001b[0m 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# does not work yet!\n", - "evaluator.plot_qq()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "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": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/Cobra_Tutorial_LogisticRegression.ipynb b/docs/Cobra_Tutorial_LogisticRegression.ipynb deleted file mode 100644 index 8fdcc10..0000000 --- a/docs/Cobra_Tutorial_LogisticRegression.ipynb +++ /dev/null @@ -1,2501 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Optional: import local development version of Cobra:\n", - "import sys\n", - "import os\n", - "sys.path.append(r\"C:\\Users\\hendrik.dewinter\\PycharmProjects\\cobra\")\n", - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Tutorial for Logistic Regression" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "This section we will walk you through all the required steps to build a predictive logistic regression model using **Cobra**. All classes and functions used here are well-documented. In case you want more information on a class or function, run the next cell:" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [], - "source": [ - "#help(function_or_class_you_want_info_from)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Building a good model involves three steps\n", - "\n", - "1. **Preprocessing**: properly prepare the predictors (a synonym for “feature” or variable that we use throughout this tutorial) for modelling.\n", - "\n", - "2. **Feature Selection**: automatically select a subset of predictors which contribute most to the target variable or output in which you are interested.\n", - "\n", - "3. **Model Evaluation**: once a model has been build, a detailed evaluation can be performed by computing all sorts of evaluation metrics.\n", - "\n", - "\n", - "\n", - "Let's dive in!!!\n", - "***" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Survival Prediction using Titanic data\n", - "- GOAL : Predict if individuals survives in titanic sinking\n", - "- BASETABLE : seaborn dataset Titanic" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "import the necessary libraries" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "import json\n", - "import pandas as pd\n", - "import numpy as np\n", - "\n", - "from pandas.api.types import is_datetime64_any_dtype\n", - "\n", - "pd.set_option('display.max_columns', 50)\n", - "pd.set_option(\"display.max_rows\", 50)\n", - "from cobra.preprocessing import PreProcessor\n", - "from cobra.evaluation import generate_pig_tables, plot_incidence\n", - "from cobra.evaluation import evaluator" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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403male35.0008.0500SThirdmanTrueNaNSouthamptonnoTrue
\n", - "
" - ], - "text/plain": [ - " survived pclass sex age sibsp parch fare embarked class \\\n", - "0 0 3 male 22.0 1 0 7.2500 S Third \n", - "1 1 1 female 38.0 1 0 71.2833 C First \n", - "2 1 3 female 26.0 0 0 7.9250 S Third \n", - "3 1 1 female 35.0 1 0 53.1000 S First \n", - "4 0 3 male 35.0 0 0 8.0500 S Third \n", - "\n", - " who adult_male deck embark_town alive alone \n", - "0 man True NaN Southampton no False \n", - "1 woman False C Cherbourg yes False \n", - "2 woman False NaN Southampton yes True \n", - "3 woman False C Southampton yes False \n", - "4 man True NaN Southampton no True " - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import seaborn as sns\n", - "df=sns.load_dataset('titanic')\n", - "df.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In the example below, we assume the data for model building is available in a pandas DataFrame. This DataFrame should contain a an ID column, a target column (e.g. “**survived**”) and a number of candidate predictors (features) to build a model with.\n", - "\n", - "***\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "survived int64\n", - "pclass int64\n", - "sex object\n", - "age float64\n", - "sibsp int64\n", - "parch int64\n", - "fare float64\n", - "embarked object\n", - "class category\n", - "who object\n", - "adult_male bool\n", - "deck category\n", - "embark_town object\n", - "alive object\n", - "alone bool\n", - "dtype: object" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "df.dtypes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "it is required to set all category vars to object dtype\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "df.loc[:, df.dtypes == 'category'] =\\\n", - " df.select_dtypes(['category'])\\\n", - " .apply(lambda x: x.astype('object'))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data preprocessing" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### The first part focusses on preparing the predictors for modelling by:\n", - "\n", - "1. Defining the ID column, the target, discrete and contineous variables\n", - "\n", - "2. Splitting the dataset into training, selection and validation datasets.\n", - "\n", - "3. Binning continuous variables into discrete intervals\n", - "\n", - "4. Replacing missing values of both categorical and continuous variables (which are now binned) with an additional “Missing” bin/category\n", - "\n", - "5. Regrouping categories in new category “other”\n", - "\n", - "6. Replacing bins/categories with their corresponding incidence rate per category/bin." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this toy dataset, the index will serve as ID," - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "df[\"id\"] = df.index + 1\n", - "id_col = \"id\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "and survived is the target,\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "target_col = \"survived\"" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we remove the columns 'who' and 'adult_male' since they are duplicate of 'sex', and also 'alive', which seems to be a duplicate of 'survived'\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "del df['who']\n", - "del df['adult_male']\n", - "del df['alive']" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finding out which variables are categorical (\"discrete\") and which are continous:\n", - "\n", - "\n", - " => discrete are definitely those that contain strings:" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['sex', 'embarked', 'class', 'deck', 'embark_town']\n", - "\n", - "sex\n", - "male 577\n", - "female 314\n", - "Name: sex, dtype: int64\n", - "\n", - "embarked\n", - "S 644\n", - "C 168\n", - "Q 77\n", - "Name: embarked, dtype: int64\n", - "\n", - "class\n", - "Third 491\n", - "First 216\n", - "Second 184\n", - "Name: class, dtype: int64\n", - "\n", - "deck\n", - "C 59\n", - "B 47\n", - "D 33\n", - "E 32\n", - "A 15\n", - "F 13\n", - "G 4\n", - "Name: deck, dtype: int64\n", - "\n", - "embark_town\n", - "Southampton 644\n", - "Cherbourg 168\n", - "Queenstown 77\n", - "Name: embark_town, dtype: int64\n", - "\n" - ] - } - ], - "source": [ - "col_dtypes = df.dtypes\n", - "discrete_vars = [col for col in col_dtypes[col_dtypes==object].index.tolist() if col not in [id_col, target_col]] \n", - "print(discrete_vars)\n", - "print()\n", - "for col in discrete_vars:\n", - " print(col)\n", - " print(df[col].value_counts())\n", - " print()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we also check for numerical columns that only contain a few different values, thus to be interpreted as discrete, categorical variables\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pclass\n", - "3 491\n", - "1 216\n", - "2 184\n", - "Name: pclass, dtype: int64\n", - "\n", - "sibsp\n", - "0 608\n", - "1 209\n", - "2 28\n", - "4 18\n", - "3 16\n", - "8 7\n", - "5 5\n", - "Name: sibsp, dtype: int64\n", - "\n", - "parch\n", - "0 678\n", - "1 118\n", - "2 80\n", - "3 5\n", - "5 5\n", - "4 4\n", - "6 1\n", - "Name: parch, dtype: int64\n", - "\n", - "alone\n", - "True 537\n", - "False 354\n", - "Name: alone, dtype: int64\n", - "\n" - ] - } - ], - "source": [ - "for col in df.columns:\n", - " if col not in discrete_vars and col not in [id_col, target_col]: # if we didn't mark it as discrete already because it was string typed, or also excluding it if it is the target:\n", - " val_counts = df[col].value_counts()\n", - " if len(val_counts) > 1 and len(val_counts) <= 10: # The column contains less than 10 different values. \n", - " print(col)\n", - " print(val_counts)\n", - " print()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "By taking a look at the printed variables, it is clear that we have to include those in the list of discrete variables. This can be done as follows:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['sex',\n", - " 'embarked',\n", - " 'class',\n", - " 'deck',\n", - " 'embark_town',\n", - " 'pclass',\n", - " 'sibsp',\n", - " 'parch',\n", - " 'class',\n", - " 'deck',\n", - " 'alone']" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "discrete_vars.extend([\"pclass\",\"sibsp\",\"parch\",\"class\",\"deck\",\"alone\"])\n", - "discrete_vars" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The remaining variables can be labelled continous predictors, without including the target variable.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['fare', 'age']" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "continuous_vars = list(set(df.columns)\n", - " - set(discrete_vars) \n", - " - set([id_col, target_col]))\n", - "continuous_vars " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, we can prepare **Cobra's Preprocessor**" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "The target encoder's additive smoothing weight is set to 0. This disables smoothing and may make the encoding prone to overfitting.\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Help on method from_params in module cobra.preprocessing.preprocessor:\n", - "\n", - "from_params(model_type: str = 'classification', n_bins: int = 10, strategy: str = 'quantile', closed: str = 'right', auto_adapt_bins: bool = False, starting_precision: int = 0, label_format: str = '{} - {}', change_endpoint_format: bool = False, regroup: bool = True, regroup_name: str = 'Other', keep_missing: bool = True, category_size_threshold: int = 5, p_value_threshold: float = 0.001, scale_contingency_table: bool = True, forced_categories: dict = {}, weight: float = 0.0, imputation_strategy: str = 'mean') method of builtins.type instance\n", - " Constructor to instantiate PreProcessor from all the parameters\n", - " that can be set in all its required (attribute) classes\n", - " along with good default values.\n", - " \n", - " Parameters\n", - " ----------\n", - " model_type : str\n", - " Model type (``classification`` or ``regression``).\n", - " n_bins : int, optional\n", - " Number of bins to produce. Raises ValueError if ``n_bins < 2``.\n", - " strategy : str, optional\n", - " Binning strategy. Currently only ``uniform`` and ``quantile``\n", - " e.g. equifrequency is supported.\n", - " closed : str, optional\n", - " Whether to close the bins (intervals) from the left or right.\n", - " auto_adapt_bins : bool, optional\n", - " Reduces the number of bins (starting from n_bins) as a function of\n", - " the number of missings.\n", - " starting_precision : int, optional\n", - " Initial precision for the bin edges to start from,\n", - " can also be negative. Given a list of bin edges, the class will\n", - " automatically choose the minimal precision required to have proper\n", - " bins e.g. ``[5.5555, 5.5744, ...]`` will be rounded\n", - " to ``[5.56, 5.57, ...]``. In case of a negative number, an attempt\n", - " will be made to round up the numbers of the bin edges\n", - " e.g. ``5.55 -> 10``, ``146 -> 100``, ...\n", - " label_format : str, optional\n", - " Format string to display the bin labels\n", - " e.g. ``min - max``, ``(min, max]``, ...\n", - " change_endpoint_format : bool, optional\n", - " Whether or not to change the format of the lower and upper bins\n", - " into ``< x`` and ``> y`` resp.\n", - " regroup : bool\n", - " Whether or not to regroup categories.\n", - " regroup_name : str\n", - " New name of the non-significant regrouped variables.\n", - " keep_missing : bool\n", - " Whether or not to keep missing as a separate category.\n", - " category_size_threshold : int\n", - " All categories with a size (corrected for incidence if applicable)\n", - " in the training set above this threshold are kept as a separate category,\n", - " if statistical significance w.r.t. target is detected. Remaining\n", - " categories are converted into ``Other`` (or else, cf. regroup_name).\n", - " p_value_threshold : float\n", - " Significance threshold for regrouping.\n", - " forced_categories : dict\n", - " Map to prevent certain categories from being grouped into ``Other``\n", - " for each column - dict of the form ``{col:[forced vars]}``.\n", - " scale_contingency_table : bool\n", - " Whether contingency table should be scaled before chi^2.\n", - " weight : float, optional\n", - " Smoothing parameters (non-negative). The higher the value of the\n", - " parameter, the bigger the contribution of the overall mean.\n", - " When set to zero, there is no smoothing (e.g. the pure target incidence is used).\n", - " imputation_strategy : str, optional\n", - " In case there is a particular column which contains new categories,\n", - " the encoding will lead to NULL values which should be imputed.\n", - " Valid strategies are to replace with the global mean of the train\n", - " set or the min (resp. max) incidence of the categories of that\n", - " particular variable.\n", - " \n", - " Returns\n", - " -------\n", - " PreProcessor\n", - " class encapsulating CategoricalDataProcessor,\n", - " KBinsDiscretizer, and TargetEncoder instances\n", - "\n" - ] - } - ], - "source": [ - "# using all Cobra's default parameters for preprocessing for now:\n", - "preprocessor = PreProcessor.from_params(\n", - " model_type=\"classification\")\n", - "\n", - "# These are the options though:\n", - "help(PreProcessor.from_params)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "split data into train-selection-validation set:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "from cobra.preprocessing import PreProcessor\n", - "basetable = preprocessor.train_selection_validation_split(\n", - " data=df,\n", - " train_prop=0.6,\n", - " selection_prop=0.2,\n", - " validation_prop=0.2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "And fit the preprocessor pipeline:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "a16c079bbf1b4b8a932e7c60fcaa81cf", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Computing discretization bins...: 0%| | 0/2 [00:00\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", - " \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", - "
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003male22.0107.2500SThirdNaNSouthamptonFalse1train7.2 - 7.918.0 - 22.0maleOtherThirdMissingOther31OtherFalse0.1652420.3125000.2178220.2703350.3125000.2178220.5692310.3311550.5229360.1981980.333333
111female38.01071.2833CFirstCCherbourgFalse2validation39.8 - 76.135.0 - 40.0femaleCFirstCCherbourg11OtherFalse0.7445650.5423730.6349210.6410260.5423730.6349210.5692310.3311550.5229360.5471700.395349
213female26.0007.9250SThirdNaNSouthamptonTrue3validation7.9 - 8.124.0 - 27.0femaleOtherThirdMissingOther3OtherOtherTrue0.7445650.3125000.2178220.2703350.3125000.2178220.2987650.3311550.2555210.1627910.384615
311female35.01053.1000SFirstCSouthamptonFalse4train39.8 - 76.131.0 - 35.0femaleOtherFirstCOther11OtherFalse0.7445650.3125000.6349210.6410260.3125000.6349210.5692310.3311550.5229360.5471700.525000
403male35.0008.0500SThirdNaNSouthamptonTrue5train7.9 - 8.131.0 - 35.0maleOtherThirdMissingOther3OtherOtherTrue0.1652420.3125000.2178220.2703350.3125000.2178220.2987650.3311550.2555210.1627910.525000
\n", - "" - ], - "text/plain": [ - " survived pclass sex age sibsp parch fare embarked class deck \\\n", - "0 0 3 male 22.0 1 0 7.2500 S Third NaN \n", - "1 1 1 female 38.0 1 0 71.2833 C First C \n", - "2 1 3 female 26.0 0 0 7.9250 S Third NaN \n", - "3 1 1 female 35.0 1 0 53.1000 S First C \n", - "4 0 3 male 35.0 0 0 8.0500 S Third NaN \n", - "\n", - " embark_town alone id split fare_bin age_bin sex_processed \\\n", - "0 Southampton False 1 train 7.2 - 7.9 18.0 - 22.0 male \n", - "1 Cherbourg False 2 validation 39.8 - 76.1 35.0 - 40.0 female \n", - "2 Southampton True 3 validation 7.9 - 8.1 24.0 - 27.0 female \n", - "3 Southampton False 4 train 39.8 - 76.1 31.0 - 35.0 female \n", - "4 Southampton True 5 train 7.9 - 8.1 31.0 - 35.0 male \n", - "\n", - " embarked_processed class_processed deck_processed embark_town_processed \\\n", - "0 Other Third Missing Other \n", - "1 C First C Cherbourg \n", - "2 Other Third Missing Other \n", - "3 Other First C Other \n", - "4 Other Third Missing Other \n", - "\n", - " pclass_processed sibsp_processed parch_processed alone_processed sex_enc \\\n", - "0 3 1 Other False 0.165242 \n", - "1 1 1 Other False 0.744565 \n", - "2 3 Other Other True 0.744565 \n", - "3 1 1 Other False 0.744565 \n", - "4 3 Other Other True 0.165242 \n", - "\n", - " embarked_enc class_enc deck_enc embark_town_enc pclass_enc sibsp_enc \\\n", - "0 0.312500 0.217822 0.270335 0.312500 0.217822 0.569231 \n", - "1 0.542373 0.634921 0.641026 0.542373 0.634921 0.569231 \n", - "2 0.312500 0.217822 0.270335 0.312500 0.217822 0.298765 \n", - "3 0.312500 0.634921 0.641026 0.312500 0.634921 0.569231 \n", - "4 0.312500 0.217822 0.270335 0.312500 0.217822 0.298765 \n", - "\n", - " parch_enc alone_enc fare_enc age_enc \n", - "0 0.331155 0.522936 0.198198 0.333333 \n", - "1 0.331155 0.522936 0.547170 0.395349 \n", - "2 0.331155 0.255521 0.162791 0.384615 \n", - "3 0.331155 0.522936 0.547170 0.525000 \n", - "4 0.331155 0.255521 0.162791 0.525000 " - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "basetable = preprocessor.transform(basetable,\n", - " continuous_vars=continuous_vars,\n", - " discrete_vars=discrete_vars)\n", - "basetable.head()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Feature selection" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once the predictors are properly prepared, we can start building a predictive model, which boils down to selecting the right predictors from the dataset to train a model on.\n", - "As a dataset typically contains many predictors, **we first perform a univariate preselection** to rule out any predictor with little to no predictive power. Later, using the list of preselected features, we build a logistic regression model using **forward feature selection** to choose the right set of predictors." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In previous steps, these were the predictors, as preprocessed so far:" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['age_bin',\n", - " 'alone_processed',\n", - " 'class_processed',\n", - " 'deck_processed',\n", - " 'embark_town_processed',\n", - " 'embarked_processed',\n", - " 'fare_bin',\n", - " 'parch_processed',\n", - " 'pclass_processed',\n", - " 'sex_processed',\n", - " 'sibsp_processed']" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "preprocessed_predictors = [\n", - " col for col in basetable.columns\n", - " if col.endswith(\"_bin\") or col.endswith(\"_processed\")]\n", - "sorted(preprocessed_predictors)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "But for feature selection, we use the target encoded version of each of these." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "preprocessed_predictors = [col for col in basetable.columns.tolist()\n", - " if '_enc' in col]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A univariate selection on the preprocessed predictors can be conducted. The thresholds for retaining a feature are now on default but can be changed by the user.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from cobra.model_building import univariate_selection\n", - "\n", - "df_auc = univariate_selection.compute_univariate_preselection(\n", - " target_enc_train_data=basetable[basetable[\"split\"] == \"train\"],\n", - " target_enc_selection_data=basetable[basetable[\"split\"] == \"selection\"],\n", - " predictors=preprocessed_predictors,\n", - " target_column=target_col,\n", - " preselect_auc_threshold=0.53, # if auc_selection <= 0.53 exclude predictor\n", - " preselect_overtrain_threshold=0.05 # if (auc_train - auc_selection) >= 0.05 --> overfitting!\n", - " )\n", - "from cobra.evaluation import plot_univariate_predictor_quality\n", - "plot_univariate_predictor_quality(df_auc)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we compute correlations between the preprocessed predictors and plot it using a correlation matrix:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "from cobra.evaluation import plot_correlation_matrix\n", - "df_corr = (univariate_selection\n", - " .compute_correlations(basetable[basetable[\"split\"] == \"train\"],\n", - " preprocessed_predictors))\n", - "plot_correlation_matrix(df_corr)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "To get a list of the selected predictors after the univariate selection, run the following call:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['class_enc', 'pclass_enc', 'embarked_enc', 'embark_town_enc']" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "preselected_predictors = (univariate_selection\n", - " .get_preselected_predictors(df_auc))\n", - "preselected_predictors" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "After an initial preselection on the predictors, we can start building the model itself using forward feature selection to choose the right set of predictors. Since we use target encoding on all our predictors, we will only consider models with positive coefficients (no sign flip should occur) as this makes the model more interpretable." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Modeling" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "c783c5374c3d4018b390aacba01fe65a", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Sequentially adding best predictor...: 0%| | 0/4 [00:00\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", - "
predictorslast_added_predictortrain_performanceselection_performancevalidation_performancemodel_type
0[class_enc]class_enc0.6966890.6549300.659056classification
1[class_enc, embarked_enc]embarked_enc0.7151210.6625640.676793classification
2[class_enc, embarked_enc, pclass_enc]pclass_enc0.7151210.6625640.676793classification
3[embarked_enc, pclass_enc, class_enc, embark_t...embark_town_enc0.7151210.6625640.676793classification
\n", - "" - ], - "text/plain": [ - " predictors last_added_predictor \\\n", - "0 [class_enc] class_enc \n", - "1 [class_enc, embarked_enc] embarked_enc \n", - "2 [class_enc, embarked_enc, pclass_enc] pclass_enc \n", - "3 [embarked_enc, pclass_enc, class_enc, embark_t... embark_town_enc \n", - "\n", - " train_performance selection_performance validation_performance \\\n", - "0 0.696689 0.654930 0.659056 \n", - "1 0.715121 0.662564 0.676793 \n", - "2 0.715121 0.662564 0.676793 \n", - "3 0.715121 0.662564 0.676793 \n", - "\n", - " model_type \n", - "0 classification \n", - "1 classification \n", - "2 classification \n", - "3 classification " - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from cobra.model_building import ForwardFeatureSelection\n", - "\n", - "forward_selection = ForwardFeatureSelection(model_type=\"classification\",\n", - " max_predictors=30,\n", - " pos_only=True)\n", - "\n", - "# fit the forward feature selection on the train data\n", - "# has optional parameters to force and/or exclude certain predictors (see docs)\n", - "forward_selection.fit(basetable[basetable[\"split\"] == \"train\"],\n", - " target_column_name = target_col,\n", - " predictors = preselected_predictors)\n", - " #forced_predictors: list = [],\n", - " #excluded_predictors: list = [])\n", - "\n", - "# compute model performance\n", - "performances = (forward_selection\n", - " .compute_model_performances(basetable, target_column_name = target_col))\n", - "performances" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As can be seen, we have completed 4 steps till no further improvement can be observed" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from cobra.evaluation import plot_performance_curves\n", - "\n", - "# plot performance curves\n", - "plot_performance_curves(performances)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Based on the performance curves (AUC per model with a particular number of predictors in case of logistic regression), a final model can then be chosen and the variables importance can be plotted:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['embarked_enc', 'pclass_enc', 'class_enc', 'embark_town_enc']\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "model = forward_selection.get_model_from_step(3)\n", - "\n", - "# Note that chosen model the following variables:\n", - "final_predictors = model.predictors\n", - "print(final_predictors)\n", - "from cobra.evaluation import plot_variable_importance\n", - "\n", - "variable_importance = model.compute_variable_importance(\n", - " basetable[basetable[\"split\"] == \"selection\"]\n", - ")\n", - "plot_variable_importance(variable_importance)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Note**: variable importance is based on correlation of the predictor with the model scores (and not the true labels!)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "Finally, we can again export the model to a dictionary to store it as JSON" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "ename": "FileNotFoundError", - "evalue": "[Errno 2] No such file or directory: 'output\\\\model.json'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mFileNotFoundError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mmodel_path\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"output\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"model.json\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel_path\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"w\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mfile\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 5\u001b[0m \u001b[0mjson\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodel_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfile\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'output\\\\model.json'" - ] - } - ], - "source": [ - "model_dict = model.serialize()\n", - "\n", - "model_path = os.path.join(\"output\", \"model.json\")\n", - "with open(model_path, \"w\") as file:\n", - " json.dump(model_dict, file)\n", - "\n", - "# To reload the model again from a JSON file, run the following snippet:\n", - "# from cobra.model_building import LinearRegressionModel\n", - "# with open(model_path, \"r\") as file:\n", - "# model_dict = json.load(file)\n", - "# model = LinearRegressionModel()\n", - "# model.deserialize(model_dict)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Evaluation" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have build and selected a final model, it is time to evaluate its predictions on the test set against various evaluation metrics. The used evaluation metrics are:\n", - "1. Accuracy\n", - "2. AUC: Area Under Curve\n", - "3. Precision\n", - "4. Recall\n", - "5. F1\n", - "6. Matthews Correlation Coefficient\n", - "7. Lift\n", - "\n", - "Furthermore, we can evaluate the classification performance using a confusion matrix.\n", - "\n", - "\n", - "Also plotting makes the evaluation of a logistic regression model a lot easier. We will first use a **Receiver Operating Characteristic (ROC) curve**, which is a plot of the false positive rate (x-axis) versus the true positive rate (y-axis). Next, the **Cumulative Gains curve**, **Cumulative Lift curve** and **Cumulative Response curve** can be called." - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [], - "source": [ - "from cobra.evaluation import ClassificationEvaluator\n", - "\n", - "# get numpy array of True target labels and predicted scores:\n", - "y_true = basetable[basetable[\"split\"] == \"selection\"][target_col].values\n", - "y_pred = model.score_model(basetable[basetable[\"split\"] == \"selection\"])" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "evaluator = ClassificationEvaluator()\n", - "evaluator.fit(y_true, y_pred) # Automatically find the best cut-off probability" - ] - }, - { - "cell_type": "code", - "execution_count": 35, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "accuracy 0.646067\n", - "AUC 0.662564\n", - "precision 0.551282\n", - "recall 0.605634\n", - "F1 0.577181\n", - "matthews_corrcoef 0.274883\n", - "lift at 0.05 1.880000\n", - "dtype: float64" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "evaluator.scalar_metrics" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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1sibspOther0.8202250.3988760.390411
\n", - "
" - ], - "text/plain": [ - " variable label pop_size global_avg_target avg_target\n", - "0 age 1.0 - 10.0 0.067416 0.398876 0.416667\n", - "1 age 10.0 - 18.0 0.078652 0.398876 0.500000\n", - "2 age 18.0 - 22.0 0.112360 0.398876 0.300000\n", - "3 age 22.0 - 24.0 0.022472 0.398876 0.500000\n", - "4 age 24.0 - 27.0 0.061798 0.398876 0.454545\n", - "5 age 27.0 - 31.0 0.151685 0.398876 0.407407\n", - "6 age 31.0 - 35.0 0.106742 0.398876 0.421053\n", - "7 age 35.0 - 40.0 0.061798 0.398876 0.181818\n", - "8 age 40.0 - 50.0 0.084270 0.398876 0.400000\n", - "9 age 50.0 - 80.0 0.073034 0.398876 0.461538\n", - "10 age Missing 0.179775 0.398876 0.406250\n", - "0 alone False 0.331461 0.398876 0.423729\n", - "1 alone True 0.668539 0.398876 0.386555\n", - "0 class First 0.213483 0.398876 0.631579\n", - "1 class Other 0.224719 0.398876 0.475000\n", - "2 class Third 0.561798 0.398876 0.280000\n", - "0 deck B 0.067416 0.398876 0.666667\n", - "1 deck C 0.039326 0.398876 0.428571\n", - "2 deck E 0.039326 0.398876 0.571429\n", - "3 deck Missing 0.786517 0.398876 0.350000\n", - "4 deck Other 0.067416 0.398876 0.583333\n", - "0 embark_town Cherbourg 0.112360 0.398876 0.550000\n", - "1 embark_town Missing 0.005618 0.398876 1.000000\n", - "2 embark_town Other 0.882022 0.398876 0.375796\n", - "0 embarked C 0.112360 0.398876 0.550000\n", - "1 embarked Missing 0.005618 0.398876 1.000000\n", - "2 embarked Other 0.882022 0.398876 0.375796\n", - "0 fare 0.0 - 7.2 0.050562 0.398876 0.111111\n", - "1 fare 7.2 - 7.9 0.207865 0.398876 0.297297\n", - "2 fare 7.9 - 8.1 0.073034 0.398876 0.384615\n", - "3 fare 8.1 - 10.5 0.129213 0.398876 0.434783\n", - "4 fare 10.5 - 14.5 0.089888 0.398876 0.437500\n", - "5 fare 14.5 - 21.1 0.061798 0.398876 0.272727\n", - "6 fare 21.1 - 27.9 0.134831 0.398876 0.416667\n", - "7 fare 27.9 - 39.8 0.073034 0.398876 0.384615\n", - "8 fare 39.8 - 76.1 0.078652 0.398876 0.428571\n", - "9 fare 76.1 - 512.3 0.101124 0.398876 0.722222\n", - "0 parch 1 0.095506 0.398876 0.470588\n", - "1 parch Other 0.904494 0.398876 0.391304\n", - "0 pclass 1 0.213483 0.398876 0.631579\n", - "1 pclass 3 0.561798 0.398876 0.280000\n", - "2 pclass Other 0.224719 0.398876 0.475000\n", - "0 sex female 0.297753 0.398876 0.754717\n", - "1 sex male 0.702247 0.398876 0.248000\n", - "0 sibsp 1 0.179775 0.398876 0.437500\n", - "1 sibsp Other 0.820225 0.398876 0.390411" - ] - }, - "execution_count": 41, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from cobra.evaluation import generate_pig_tables\n", - "predictor_list = [col for col in basetable.columns\n", - " if col.endswith(\"_bin\") or col.endswith(\"_processed\")]\n", - "pig_tables = generate_pig_tables(basetable[basetable[\"split\"] == \"selection\"],\n", - " id_column_name=id_col,\n", - " target_column_name=target_col,\n", - " preprocessed_predictors=predictor_list)\n", - "pig_tables" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "age\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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effZZHnroIe6++2569uxJSEgIa9euJSgoiNDQUKfF5CaTiVdeeYXJkyczY8YMvv76azp06EBpaamj8tOkSZMcZY7PxJgxY1i1ahWzZ8/myiuvpHfv3litVlJSUrBYLPzud79zjDRNnjyZTZs28eOPPzJq1Cg6d+5MYGAgmzdvpqCggNjYWP7+9783+NphYWH88MMPrF+/nk6dOrFv3z727dtHdHS0o7xyfSIiInjhhRd46KGHeOSRR3jvvfeIjY0lNTWVXbt24e/vz0svveQ0pap2Qfqbb75JSkoKkydPpkePHmfwXavfhAkT+Pbbb1m/fj1Dhw6la9eueHh4sHXrVvLy8ujZs6cjCJ5KWFgYJSUljBw5kt69e1NUVERKSgqenp688MILjilwCQkJ3Hbbbbz//vuMHj2aHj160KRJE/bt28f+/fsJDQ3l97//fZ3+mzVrxo033kjfvn3x8vJi9erVVFRU8Lvf/c4xmiMi8muiEZdzZH8l3JsGwZvAnGL8eW+acfxcyMjIIDExsc5v8xpr7dq1JCYm1rvA+dVXX2XSpEm/6BruID8/n7lz517o23Bp9uzZ9a6/AGO39drpJeLawIEDmTlzJqNGjSIzM5NFixZhtVq59dZb+fTTT532JHFlxIgRfPzxxwwcOJA9e/awevVq+vTpw8cff+yyTG58fDxfffUVt912Gz4+PqxYsYL9+/fTs2dP/vnPf7rc/6Wxpk2bxvPPP09iYiLr1q1j3bp1xMfH88ILL/Dggw862pnNZl577TWmTZtGUlIS27dvZ82aNYSGhnLPPfcwc+bMRu1G37ZtWz7++GPCwsJYsmQJZWVlTJo0iZkzZ9ZbcexEI0aM4Msvv+Tqq68mLy+PRYsWUVJSwvjx45k9e3adstUTJkzgmmuuoaamhuXLl7N3796Gf5MayNvbm3fffZff/va3NG3alNWrV7N69WqaN2/Oww8/zH//+98GFQwIDAzkk08+oW/fvqxevZqdO3fSv39//ve//9UJqlOmTOHJJ5+kffv2bNmyhcWLF1NZWcktt9zCV1995aiwdqJnnnmGO+64g127drF27Vo6dOjA9OnTnf6+RUR+TUx2V5Oy5Rf54SiMOwDVNqg+4bgX4GWGL+PgqiZn95pWq5WCggKaNWt22ipAp1JVVcXRo0frLZP66quvsnHjRj766KMzvoY7mDp1KtXV1WdtAfPZNHv2bF577TWX89vh+BQaV+WBRc6WtWvXcuutt9KvXz/ef//9C307vyqTJk1i3bp1fPLJJ441VCIiohGXs25/pRFaLCeFFjDeW2zG+bM98uLh4UFYWNgvCi1g/CayIXs7XOwu5rweFBSk0CIiIiK/OgouZ9nLOcZIy6lU2+BV1xVGz9jJU8USExP56quvGDNmDMnJyUyaNIm0tDRH+x07dnDLLbfQtWtXhg0bxpdffgnUnSq2b98+brrpJrp27crtt9/u2F261oYNGxg3bhxdunRh9OjRfPXVV45zjz32GM888wwPPfQQ3bp1Y+TIkcyePdtxvry8nKeeeoq+ffvSu3dvHn30UUpLSwFj5OfZZ5+lb9++9OnThz/+8Y/1blK3du1aBg4cyFNPPUWPHj144403qK6u5oUXXmDgwIF07NiRIUOG8L///Q+AN954gzlz5vDtt986dv8uKSlhypQp9OjRg8svv5y//OUvjns52ezZs5kwYQKvvvoq3bt3Z9CgQXU2wfv8888ZNmwYycnJ3HTTTWzZssVxbujQobz44ov079+fUaNGnXJaXvfu3enfvz8zZ850+r7WThV74403ePDBBx3PPmTIEGbMmOGyPxEREZGLmYJLA72cA0GbwJRy6te/8uqOtJysGngz7/R9BW0yrnumpk+fzuOPP86HH37IkSNHeOWVVwCjOtJtt91GXFwcc+bM4cEHH+Rvf/ubU2lQMMLDXXfdRUxMDLNnz2b48OFOP0Dn5eVx1113MWbMGL799lvuu+8+nnnmGUeZVDBKwbZv357Zs2fTv39//va3vznCz1//+ldWr17N9OnT+fDDD9m7dy/PP/88AK+88gqbN292bM5mt9u5++676x0pycnJobS0lDlz5nD99dfzzjvvsHjxYv75z38yb948rr/+ep555hlycnK44447uOqqqxg5cqQjsD3++OMUFhbyySefMGPGDA4ePMjUqVPr/d7u2LGDbdu28dlnn/GHP/yBZ555xlGBavHixbz++utMnTqVOXPmMHDgQCZPnuy0w/g333zDu+++y8svv+xy5+2cnBx27drF559/zsMPP8zf//53Vq1a5fJeFixYgIeHBzNnzuTGG2/klVdeYd++ffXeu4iIiMjFSFXFGujlHCg9zUjK2VZqM677cMTp27oyefJkx54UN910Ex988AFg7CkQEBDAk08+iYeHB3FxcRQVFTl2sa61atUqCgsL+dvf/kZAQADx8fGsXbuWwsJCAD755BP69OnD5MmTAaMa0IEDB/jggw8cIxkJCQn89re/BeDBBx/k448/Zu/evSQlJTF37lzeeecdxxzuv//976xbt47y8nI+/vhjvvjiCzp06ADAiy++SJ8+fUhJSal3zvedd97pWCyckJDAs88+S7du3QD43e9+x5tvvsnBgwfp27cvvr6+1NTU0KxZM9LS0liwYAFr1qxxbOj2wgsvMHToULKysoiKinJ5vRdeeIHQ0FASEhJYt24dn3/+OYMGDeLdd9/lrrvucizOveeee1i1ahUzZ87kvvvuA4wqUacqI+vl5cVzzz1Hs2bNaNeuHevWreOzzz5zuXdDUFAQjz32GB4eHvzud7/jv//9L9u2baNt27b19i9yOn369GH37t0X+jZ+lS72NYQiIueKgksDPRwBf8s8v+El0HzmoQVwqvgTGBjoNP0rKSnJaT3MLbfcAhjTrmrt27ePli1bOu330KlTJ5YvXw7AgQMHWL58OcnJyY7ztWGg1om7ZddWcqqpqeHgwYPU1NTQsWNHx/kuXbrQpUsX9uzZQ3V1tWPvilqVlZUcPHiw3uDSokULx9fDhw9n5cqVPP/88xw4cIAdO3YA1AlnAPv378dut9epbgSQmprqMri0bNnSqTJTp06d+Pjjjx39vfLKK7z++uuO81VVVU47jJ94r67ExMQ4fR87dOhQZzraiX2d+HcZEBBAdfXpxv1ERERELi4KLg30cETDQsS9afDuaaaLeQF3hcH001cS/UVO3mCvdppVfRvvuXLy1KwTpzXV1NQwevRo7r33Xqc2ZvPxGYiurmW3209ZatRqtQLGbx1PXoR+4g/zJ6vduA6M9SGff/45N9xwA9deey1PPvmkYxTI1fX8/f2d1ufUqq9QwcnTu6xWq+O5rVYrU6ZMqbOj+IlldE+8V1dO/B6CEbjq+3trzN+niIiIyMVKa1zOsocjjJLHp+Jlhgd/wUjKLxUbG8vu3budRh+mTp3qNEIA0K5dO9LS0jh69KjjWO3IBUCbNm1ITU0lNjbW8VqxYoVj3cipxMTE4OHh4dTfqlWrGDlypONcYWGho99mzZrx3HPPcfjw4QY942effcaf//xnHnnkEUaPHk15eTlwPIiZTCan57BYLFitVsf1AJ577rl6F+inp6c7ndu2bZtjZ/Q2bdqQnZ3t9H157733WLduXYPuHYxiCyfu6L1lyxbi4+Mb/HkRERGRS42Cy1kW72Ps0+JvNkZWTuSFcfzLOKPdhXLNNddQVlbGtGnTOHjwIN999x3fffcdAwYMcGp32WWXER0dzeOPP86+ffv48ssv+fHHHx3nJ06cyM6dO3n55ZdJTU1l3rx5dXbBrk9gYCBjx45l2rRpbN68mR07dvDSSy/Rt29fgoKCGD9+PE8//TSrV69m//79TJkyhT179tC6desGPWNISAhLliwhPT2dlJQUx07pVVVVgDH6kZmZSU5ODvHx8QwYMIBHH32Un3/+mV27djFlyhTy8/MJDw932X95eTl//etf2b9/P1988QXz5s1zTG27/fbb+eijj5gzZw5paWlMnz6dWbNmERcX16B7B6iuruaxxx5jz549fPbZZ/z444/cdtttDf68iIiIyKVGweUcuKoJbOlgTAcLNhvf5GCz8X5Lh7O/+WRjBQUF8fbbb7NlyxauueYa3njjDaZNm0b37t2d2nl5efH2229TWlrK2LFj+eKLL5g4caLjfIsWLZgxYwarVq3i6quv5oUXXuD3v/+9U5tTmTp1Kp07d+bOO+/k9ttvp1OnTkyZMsVxrn///jz44IOMGzeOyspK/vOf/+Dr69ugvqdNm8aePXsYPXo0U6ZM4corr6Rbt26OEZ5rr72WtLQ0rrnmGux2Oy+++CKxsbHccccd3HLLLYSHh/PWW2/V2394eDgtWrRg3LhxvPvuu7z44ov06tULgFGjRvHwww8zffp0Ro8ezYIFC3jzzTdp3759g+4doH379kRHR3PjjTcyY8YMpk2bRufOnRv8eREREZFLjcl+Me/EJ3IBnG5nexERERE5+zTiIiIiIiIibk/BRURERERE3J6miomIiIiIiNvTiIuIiIiIiLg9BRcREREREXF7Ci4iIiIiIuL2FFxERERERMTtKbiIiIiIiIjbU3ARERERERG3p+Aibm/SpEm8+uqrv7ifoUOHMnPmTJfnDh06RGJiIhkZGb/4OheS3W7n008/xWazXehbERERETmrtI+LuL2ioiK8vLwICAj4Rf0UFBTg7++Pr69vnXOHDh1ixIgRLFq0iJiYmF90nQtp3bp1TJo0ie3bt+Pp6Xmhb0dERETkrNFPNuL2QkJCzko/zZo1Oyv9uDP9HkJEREQuVZoq1gge8Tec0avXNY/U22evax6p93ONtWnTJiZOnEjXrl3p1q0b//d//0dOTg42m40BAwbUmSY1atQoPvnkEwA2bNjAuHHj6NKlC6NHj+arr75ytHvssceYMmUK1113HX369GH37t3s37+fO++8k+TkZDp37sxNN93E3r17HZ/Ztm0bEyZMoEuXLvzmN7/h9ddfZ9KkSY7zp7reyU6cKvbYY4/xzDPP8NBDD9GtWzdGjhzJ7NmzHW3Ly8t56qmn6Nu3L7179+bRRx+ltLQUcJ4qVl1dzVNPPUXPnj0ZNGgQy5cvd7pmSUkJU6ZMoUePHlx++eX85S9/cfSzdu1aBg4cyOeff87AgQPp06cPjzzyCBUVFY7Pf//994wePZquXbsybtw4Nm3a5Di3cOFCx7nrr7+eZcuW1fvsQ4cO5cUXX6R///6MGjWKmpoalixZwvXXX0/nzp3p0aMHDzzwAKWlpWRkZHDrrbcC0LFjR9auXQvA559/zrBhw0hOTuamm25iy5Yt9V5PRERExF0puFwiSktLufvuu7nsssv47rvv+M9//kNGRgb/+te/MJvNXHnllfz444+O9nv27CE1NZWRI0eSl5fHXXfdxZgxY/j222+57777eOaZZ1i8eLGj/TfffMN9993HO++8Q9u2bbn33nuJjo7m66+/5rPPPsNms/Hiiy8Cxg/9d955J+3bt2fOnDlcffXVvP32246+GnK9U/nss89o3749s2fPpn///vztb3+jqKgIgL/+9a+sXr2a6dOn8+GHH7J3716ef/75On288cYbLF26lH/961+89tprfPTRR07nH3/8cQoLC/nkk0+YMWMGBw8eZOrUqY7z+fn5zJ07l7fffptnn32W+fPnOwLU6tWrefTRR5k4cSLffPMNffr04e6776a0tJRdu3bxyCOP8Nvf/pZvv/2WCRMmcP/997Nz5856n/ebb77h3Xff5eWXXyYrK4vf//73/OY3v+GHH37g9ddfZ82aNXz66adERUXxxhtvALBs2TKSk5NZvHgxr7/+OlOnTmXOnDkMHDiQyZMnk5ub26DvtYiIiIi70FSxS0R5eTl33303d9xxByaTiZYtWzJixAjHb/qvvvpqbr75ZkpKSggKCmLevHn06dOH0NBQXnvtNfr06cPkyZMBiI2N5cCBA3zwwQcMHToUgPbt23PFFVcAYLFYGDduHBMnTnSsO7n++uuZMWMGAHPnzsXX15e//OUveHp6Eh8fz8aNG8nLywPgk08+Oe31TiUhIYHf/va3ADz44IN8/PHH7N27l6SkJObOncs777xDz549Afj73//OunXrnD5vt9uZOXMmjzzyCL169QKMkZzf/e53AKSlpbFgwQLWrFnjmKb2wgsvMHToULKysgCoqanh8ccfJzExkaSkJAYMGMDWrVsB+PTTT7nqqqu4+eabAXj44Yex2+0UFxfzn//8hxtuuIHrrrsOgFatWrFlyxY++ugjpk2b5vJ5x4wZQ1JSEgCpqak88cQT3HjjjQDExMRw2WWXsW/fPjw8PGjSpAkAzZs3x9PTk3fffZe77rqL4cOHA3DPPfewatUqZs6cyX333Xfa77WIiIiIu1BwuUSEhYVx/fXX8/7777Nz50727dvH7t276dKlCwBdu3YlIiKCxYsXc+211zJv3jxuv/12AA4cOMDy5ctJTk529FdTU+O0JuTEBev+/v5MnDiRr7/+mm3btnHgwAF27Njh+CF/9+7dtG/f3mlxeLdu3ViwYEGDr3cqLVu2dHwdGBjo+PzBgwepqamhY8eOjvNdunRxfA9qFRYWUlBQ4AgDAJ06dXJ8vX//fux2O0OGDKlz7dTUVMxmY6CyVatWTvdRU1Pj+Pz48eMd58xmM48++qjj3J49e5g1a5bjfHV1dZ17PFGLFi0cX7du3Rpvb2/+9a9/sXfvXvbu3cu+ffsYPXq0y8/u37+fV155hddff91xrKqqisjIyHqvJyIiIuKOFFwawbp/1ukbNdL6b146K/3k5ORwww030L59e/r378+ECRNYunQpKSkpjjajR49m/vz5dOjQgbS0NEaMGAEYP/SPHj2ae++916nP2h/QAby9vR1fl5WVMW7cOJo0acLw4cO5+uqrOXDggGM6mIeHR51F4ie+b8j1TsXLy6vOMbvd7nSPDXHiPZ0YsqxWK/7+/i7X3YSFhTlGVk6+j9r+XN3fiX3/3//9H2PHjnU6fqp79/HxcXy9a9cubrrpJoYMGUKPHj247bbb+OCDD055vSlTptC/f3+n4/7+/vV+RkRERMQdaY3LJWLBggUEBATwzjvvMHnyZHr27El6errTD+ejRo1i1apVzJ07l/79+zumFbVp04bU1FRiY2MdrxUrVvDll1+6vNa6devIzs7mo48+4s477+Syyy4jMzPTca127dqxe/durFar4zPbt293fN3Y6zVUTEwMHh4e7Nixw3Fs1apVjBw50mlfk6ZNmxIaGuoIIIDTGpM2bdpgsViwWq2O+wN47rnnHAv0TyU2NtbpHux2O6NGjWLZsmW0adOG9PR0p2f/+uuvHaNRp/P111/TvXt3XnnlFW6++Wa6dOnCoUOHHN97k8nk1L5NmzZkZ2c7Xe+9996rM31ORERExN0puFwiQkJCyM3NZeXKlaSnp/P2228zf/58qqqqHG2SkpKIjo7m/fffZ9SoUY7jEydOZOfOnbz88sukpqYyb948XnrpJSIiIuq9Vnl5OQsWLCAjI4OZM2fyySefOK41evRoLBYL06ZN48CBA8ycOZO5c+ee8fUaKjAwkLFjxzJt2jQ2b97Mjh07eOmll+jbt6/TaI7JZGLixIlMnz6dlStXsmXLFqcF/PHx8QwYMIBHH32Un3/+mV27djFlyhTy8/MJDw8/7X3ceuutzJ07l5kzZ3Lo0CH+8Y9/cPToUZKTk7ntttuYN28e77//PocOHeLTTz/l3//+t9O0s1MJCQlhz549/Pzzz6SmpvL888+zdetWqqurgeMjKTt27KCyspLbb7+djz76iDlz5pCWlsb06dOZNWsWcXFxjfnWioiIiFxwCi6XiKuuuoprrrmGBx54gLFjx7JmzRqmTp3KwYMHncr0jh49GpvNxrBhwxzHWrRowYwZM1i1ahVXX301L7zwAr///e+ZOHGiy2slJydz//338/TTT3PNNdcwa9YsnnzySYqKisjMzCQgIIB///vfpKSkcM011zB79mzGjBnjmA7V2Os1xtSpU+ncuTN33nknt99+O506dWLKlCl12t1zzz1cf/31PPTQQ/zud79zLHav9eKLLxIbG8sdd9zBLbfcQnh4OG+99VaD7qFHjx48/fTTzJgxgzFjxpCSksKMGTMICgqiW7du/OMf/+CLL75g9OjRvP/++0ybNo3Bgwc3qO9JkybRvXt3br/9dn7zm99w+PBhp6pkCQkJ9O/fn4kTJ7Js2TJGjRrFww8/zPTp0xk9ejQLFizgzTffpH379g26noiIiIi7MNm1Y52cZenp6eTk5Dgqe4FR3au8vNxlaWIRERERkdPRiIucdaWlpY4pUYcPH2b+/Pl8/fXXXHnllRf61kRERETkIqURFzknZs6cyTvvvENWVhbR0dHceeedTiWCRUREREQaQ8FFRERERETcnqaKiYiIiIiI21NwERERERERt6fgIiIiIiIibk/BRURERERE3J6Ci4iIiIiIuD0FFxERERERcXsKLpeIjIwMEhMTOXTokMvzs2fPZuDAgWf9uo899hh/+tOfznq/IiIiIiIn0j4ul4iMjAyGDRvG/PnziY2NrXO+oqICi8VCs2bNzup1S0pKAAgKCjqr/YqIiIiInMjzQt+Au7Pb7axbv4Hde/ZSXFxyXq8dHBxEYkI7evfqiclk+kV9+fr64uvre5bu7DgFFhERERE5HzRV7DTWrd/A+g0bz3toASguLmH9ho2sW7+hwZ+ZP38+gwYNonv37jzzzDPU1NQAzlPF1q5dy8CBA/n8888ZOHAgffr04ZFHHqGiosJln1lZWdx55510796d3r17M3XqVMrKygDnqWJDhw4lMTGxzqvW559/zrBhw0hOTuamm25iy5YtZ/R9EREREZFfHwWX09i9Z++FvoVG3cPMmTN55ZVX+Pe//83ChQt54403XLbLz89n7ty5vP322zz77LPMnz+f2bNnu2z71FNP4enpyaxZs3jvvffYtGkT//73v+u0+/LLL1mxYgUrVqxgwYIFtGjRgjvuuAOAxYsX8/rrrzN16lTmzJnDwIEDmTx5Mrm5uQ1+NhERERH59VJwucQ89thj9OjRg969e/PHP/6Rzz77zGW7mpoaHn/8cZKSkhg+fDgDBgxg69atLtsePnyYoKAgWrRoQadOnZg+fTrXXXddnXbNmjUjLCyMsLAwXnvtNcLDw3n44YcBePfdd7nrrrsYPnw4rVu35p577qFTp07MnDnzrD27iIiIiFy6FFxOIzGh3YW+hUbdQ+fOnR1fd+jQgaKiIgoKCly2bdWqlePrwMBAx7Syk/3hD39g3rx59O3blz/84Q/s2rWLNm3a1HsPH374IatWreK1117D09NYRrV//35eeeUVkpOTHa+NGzeSmpra4GcTERERkV8vLc4/jd69egJc8MX5DWU2H8+itQXjvLy8XLY9+Xh9BeaGDx/OTz/9xMKFC1m2bBlTp05lxYoVPP/883Xabty4kZdeeom33nqLyMhIx3Gr1cqUKVPo37+/U3t/f/+GPZiIiIiI/KopuJyGyWSiT+9e9Ond60LfSoPs2bOHfv36AbBlyxbCwsJ+ceWvV199lZEjRzJhwgQmTJjA119/zV/+8pc6wSU/P58//vGP/N///R8DBgxwOtemTRuys7OdSjU/+eST9O7dm9GjR/+i+xMRERGRS5+mil1innnmGTZv3syqVav45z//6Vgc/0scOHCAp556ih07dnDgwAHmz59Px44dndpYrVYeeOABWrduzaRJk8jLy3O8qqqquP322/noo4+YM2cOaWlpTJ8+nVmzZhEXF/eL709ERERELn0acbnE3HLLLdx3331UVVUxfvx4brvttl/c59/+9jeeeuopbrvtNqqqqujbty8vv/yyU5usrCzWrVsHwGWXXeZ07sMPP2TUqFHk5+czffp0cnNziYuL480336R9+/a/+P5ERERE5NJnste3sEFERERERMRNaKqYiIiIiIi4PQUXERERERFxewouIiIiIiLi9hRcRERERETE7Sm4iIiIiIiI21NwERERERERt3fBgsudqTB49/H3dx0CU4rz67Wc+j//RQG03Qb+G+HafZBbffzcylKI3wphP8M7ec6fezgdPsw/q48iIiIiIiLn2AXZx2VRMQzfC4MCYWmicaz/LrguBG5pfrxdsAf4u4hW68tg4G74Vyvo7g9/TAcfM8xrZ5xP3gE3NoNe/nD1PkjvAqGekF0NV+6FlPbgYTrnjykiIiIiImeJ5/m+YJnVGF25PMD5+M4KeCYAIr1O38cbuXBDU7gt1Hj/YRtotRX2VUBbX9hVAWNDIMEXQjzgQKURXJ7Lhj9FKLSIiIiIiFxszvtUsScyYXCQ8aqVXQ0FVkj0bVgfa8pgYODx9y29IdYbVpcZ71t5w0YLHKqEQivEeEFmFfxUAjc1O3vPIiIiIiIi58d5DS6rS2FmIfwjxvn4jnJj6Ocvh6HFFui6A94/Un8/WdUQfdLITIQnZFQZXz/fAu5Ihfht8GgkRHvDs9nG1xptERERERG5+Jy3qWKVNvi/Q/BaS2h60lV3Vhh/dvWHP4TD0lK4Ow0CPGB807p9WWzGmpYT+Zih8thqneubQn4T45ohnpBeZSzYf6MlPH4YPs6HywLhvdau19CIiIiIiIh7OW8/tj+VBe18XAeRe8Mgpyv8Phy6HAsvd4XCv/LqtgXwNRuh5ESVNucQ4mc2QgvAs1nwWCSsLYNPC2B7R6ixG2tlRERERETE/Z23EZf/FRhTvAI3Ge+r7GC1G+9Lk6HZSXfS3hfmF7vuq4UXZNc4H8uugSgXC/sPVRqB5a1W8Fou9AmAIA8YHgw/1tO/iIiIiIi4l/MWXJYmQPUJhZdfzYUNZfBJG2Nvld2V8F3b4+c3lUNSPYv1+wbAilK481hVsfQqSKsyjp/s6SyYGglmE5iA2oGaGjuc/0LQIiIiIiJyJs5bcIn1cX7f1MOYztXWF64JgaF74J+5MLoJzDtqbBK5KMFoW2Uzqo6FeRqL6+8Jg0F7jJLKfQONfVyuCoZ2JwWdA5WQYoF3Yo33Pf3h+Wz42QJzimB4ECIiIiIichFwi6Xpg4Lg0zbwdh503A5v5hnv+x8rebyqDKK2GCMrAP0CjTDyTDb02wVNPOCD1nX7fToLnogC07FKYgOCjHLIA3dDgNlYUyMiIiIiIu7PZLdrwpSIiIiIiLg3txhxERHX9lfCvWkQvAnMKcaf96YZx0VERER+TTTiIuKmfjgK4w5AtQ2qTzjuBXiZ4cs4uKrJhbo7ERERkfNLwUXEDe2vhC47jM1W6+Nvhi0dIN6n/jYiIiIilwpNFRNxQy/nGCMtp1Jtg1dzzs/9iIiIiFxoGnERcUPBm6DkNMEFjN88XBlsjLrE+Rz/s42PMSIjIiIicqlQcBFxQ+YU+KX/w4zygjjvuqEm3gfCPY+XCRcRERG5GCi4iLihho64nCl/c/2hJtYbfDRaIyIiIm5GwUXEDd2bBu/mOVcTO5kXcH0I3NjMWMx/oPLYn1VwqBJqzvDaJqClt3OwOTHcNPPQaI2IiIicfwouIm7ol1YVq7FDetXxQHNiqNlfCUetZ35vTTxcj9bE+UArb/BUqBEREZFzQMFFxE2dq31c7HYotJ40SnNCqEmvOvP1NR4YU81cTUGL84FgjzPsWERERH71FFxE3Nj+SqPk8Uf5UGqDQDNMag4PRpy7/VsqbXCoyjnUnDhac6pRoNMJ9TRGa1yFmhZeYNZojYiIiNRDwUVEGsxuh9ya+kNN1qkW5ZyGtwna1LOuJk7lnUVERH71FFxE5Kyx2OBgPetqDlZC5S/4r02kZ/1T0CJU3llEROSSp+AiIueFzQ6Z1a7X1RyohLwzLYPG8fLOrkJNa5V3FhERuSQouIiIWyi2GqMyrqag/dLyzjFerkNNvMo7X5KmvzXjQt+CiFxk7r/37gt9C9IAnhf6BkREwKg41tXfeJ2sxg4ZVa5DzenKO9uB9Grj9VOpi+uaXa+rifcx9rPxUqgRERFxCwouIuL2PE3Q2sd4DXNxvqDG9bqaA8fKO5+qEFqxDTaVG6+T1ZZ3PrlQQO3XTVTeWURE5LxRcBGRi14zT+PVM6Duuapj5Z1PHq2p/brsFKnGyrG2VbCwpO755h71T0FTeWcREZGzS8FFRC5p3mZo52u8Tma3G0UBXE1BO1BpFBM4lXwr5FtgvcXFdY+Vd3Y1Ba2NNwRotEZERKRRFFxE5FfLZIJwL+PVL7Du+fITyjufHGoOnKa8c5UddlcaL1ciPV2Hmjgf45wKBoiIyMkqKipZtnwFh9LS8fT0IDEhgb59emE2mykuKWHJkmVkZWcTFBhI/8v7ERvbqt6+9u7bz5o16yizWGgZ04Ihgwfh7+8HQFZWNgsWLaGqqop+fXvTsUN7x+dWrFxNaPPmJCUlnPPnPZmCi4hIPfzM0MHPeJ3MZjc23HRV2nl/A8o7Z9cYr1VlLq5rch1o4o6N1qi8s4jIr9NPy5ZTZrEw9rprKK8oZ/6Cxfj6+pDcrStz5/5I06YhTBg3loOpqfzw4wIm/mY8wcHBdfrJycll4aIlDB40gLDQUJavWMXCRYu5Zsxo4zrLV9CxQxLh4WF89/084tq0xs/PjzKLhYyMw1zWr8/5fnRAwUVE5IyYTdDC23gNDKp7vsRat2BA7deppynvXG6H7RXG62QmjPUz9W3G2VzlnUVELlmH0tIZPnQwzZs3AyChXVsyDmcSHhZGYVERY6+/Bm9vb5o1a0p6xmF27NxF3z696/SzZes24uPa0D4pEYDhw4bwwUefUHT0KCFNmlBYWERcXBuahoTg4+NDcXEJfn5+pKRsIrlbF8zmC/MbNAUXEZFzIKgB5Z1dTUHbXwlFpynvnFFtvOor71zfFLRWKu8sInJR8/X1YffefbRsGUNlVRVpaenExbUmOyeHsNBQvL29HW2jIiPJyspy2U92Ti7J3bo43gcFBRIUFEh2dg4hTZoQFBRIXt4RPDw8qKysJCAwgNKyMjIzs+h/eb9z/pz1UXARETnPTizvPNTF+cKa+kNNQ8o7by43XifzwAgv9e1bo/LOIiLubdDA/ixYuIS33/0vdrudmBbR9O7Vk+UrVxEQ4PybMn9/P0rLXMxHBiwWCwEBzqU4/f38KS012vfr24cFCxdjtVrp2SOZwIAAflq2nOTkrhdstAUUXERE3E5TT+jhCT3qKe+cVuU61DSkvPPBKuOFi/LOzTxcr6upLe/sodEaEZEL6ujRYsJCQ+ndqwdVVVX8tHwlK1etpqa6Bg8P598+eXh4YLW6HsKvqXHV3uxoHx/Xhtg7JmO1WvHx8aGkpJSsrBwGDujP6jVr2b1nH1GREQwdMggvL69z87AuKLiIiFxEvM3Q1td4nay2vLOrdTUHKuHwaco7F1ih4BTlnVt711M0QOWdRUTOuaNHj7J8xSomT5pIYKBRCnOopwdff/M9HTu0p6qqyqm91WrF09P1j/quQo3VasPL63h7T09Px+c3pGyke/duZOfksGfvfib+ZjyLFi9ly9bt9Oje7Sw+5akpuIiIXCJOLO/ct57yzqn1lHY+UAkVpynvvKfSeLkS4Vn/Zpwq7ywi8svl5h3B29vLEVoAwsPCsNvt+Pv7k59f4NTeYrEQ4O9ioSUQEBCAxWKp097fRfvikhJycnMZPGgAm3/eSmREON7e3rSMiSEtPf0sPFnDKbiIiPxK+JmhvZ/xOpnNDtnVrqegHaiE3NOUd86pMV6rT1He2dW6mtbe4KvyziIipxUQ4E9lZRUlJaUEBRnhpaCwEICmTUPYtGkz1dXVjqlbWVnZREREuOwrMiKcrKxsx/4sJSWllJSWEumi/YYNG+nRPRmTyYTJBHa78Vsum93GKX7fdU4ouIiICGYTRHsbrwEuzpdYj23G6SLUpFZB9Sn+36sh5Z1drauJ84ZQjdaIiAAQGRFBaGhzFi1ZSv/L+lFTU8OSn5aRmNCO+Lg2rF+fwsLFS+ndqwepqWlk5+QydMhgwJg2VlFZiZ+vL2azmU6dOjDnq2+JiookIiKc5StWEduqJSEhTZyuefRoMbl5eQwZPBAwRnhSNm7myJF8Dhw4SExMzHn9HpjstbFJRETkDFiPlXd2ta5mfyUUnqK88+kEmeufglZfeefpb8048wuKyK/S/ffefaFvoUFKy8pYsWIVGYczMZvNtI2P47J+ffD09KTo6FEWL/mJnJxcmgQH0//yfrRq1RKAjMOZfPX1t9x6y0SCg43Nx3bu2s26dRuoqKigZcsYhgweiJ+f85D8wsVLaB0bS9v4OMex5StWsnPXHqKjoxhxxTC8z+PifAUXERE5p2rLO7sarUk7TXnnUzHjXN659s8NX8+ieWUx/taq0/YhIgIXT3D5tVNwERGRC6baDofqCTX7K6H0TFMN4F9dQWhlMc0rSwitKCa08tiropiQqjLM5312toi4KwWXi4PWuIiIyAXjZTp1eecjNfWHmtOVd7Z4+ZLm5UtaYHidcx42K80qS46FmRJCK446BRwf22mqEYiIyHmn4CIiIm7JZIIwL+PVx8VmnBW24wUDTgw167MKyPcNptpc///FWc0e5PmFkOcX4vJ8ULWF0IqTRmuO/RlUbUGF0EREzj8FFxERuSj51lPeefr8mdiAEi9/jvgEc8Q32PFnvk8QR3yCKfF2vbdBrRIvf0q8/DkYFFnnnJethuYVtSM1xTQ/YQpa88oSvOy/oBqBiIjUS8FFREQuOWagSbWFJtUW4kuz65yvNHuS7xNMXm2Y8W3CkWOhpsAnCKvZo96+q82eZPs3I9u/mcvzIVWldUZrmh8LNoE1Fai6s4jImVFwERGRXx0fWw3R5QVElxfUOWfDRJF3gGOEJs+nCfm+Rqg54hOMxcvFgpwTFHkHUuQdyD5X17VWOUJN2EmjNU2rSvG0/4JqBCIil7gLFlzuTIV9lbA00Xh/qBJ+ewhWlhnlLV+Jgaua1P/5Lwrg8UzIrIIrguGdWAg/VkZ6ZSncehCKbTAtGn4bdvxzD6dDV3+4tfk5ezQREbmImbHTrKqUZlWlLs9bPLzJd0xBCzph5CaYAp9A7Kb6V8BUenhzOCCUwwGhdc6Z7DaaVpYaU9Aqi2leUUzYsT9DVd5ZROTCBJdFxfCffBgUaLy32+Ha/dDeF9YnwTdH4Yb9sL0jtPGp+/n1ZTA5Ff7VCrr7wx/T4dZUmNfOOH9/mhFWevnD1fvg+qbG7svZ1bCoBF48v5t8iojIJcTfWoW/5QgtLUfqnLOazBR4B56wnqYJR3yDHEGnwsO73n7tJjMFvsEU+AazhxZ1r1tTQfOKEqeyzs0riwlTeWcR+ZU478GlzAp3HYLLT6gQs6QEdlfA8kQI8oAOfrCwGP5zBJ6p+99u3siFG5rCbcd+YfVhG2i1FfZVGCU1d1XA2BBI8IUQD6PKTKgnPJcNf4oAD00wFhGRc8DDbiOs0hgpOZkdKPP0PVYowJh6ln9C8YAin8BT9m3x9MUS6Et6YFidcx42K80ca2uK61RCU3lnEbkUnPfg8kQmDA6CKC9YcWwUfk0ZJPsboaVW/0BY7nqUnjVlRgCp1dIbYr1hdZkRXFp5w0YL+Jig0AoxXsaUsp9KjCloIhcjj/gbzuhz3TvGsf6bl1ye63XNI2zcfuCM+rXun+Xy+N2P/4t3P194Rn2u++pFenSOr3P87U/nc8+fZ5xRn/965m7uumlEneMpW/fT+7pHz6jPO28czoxp97g8p78n9/l76te5Bb8Z0dHluT++PP+M+owJD+KRSf1cnnvpo9Vk5JacUb+vPzyCapMH+T5Bx4oFGGFmy8wfKVqx5rSftwP5x161bM+/CF2Mv6egaotjtKZiyU/smPnDGd3njVd04LIudf+PND2nmH98fPr7dOV8/z2JyMXrvAaX1aUwsxC2dYCXc44fz6qGaC/nthFekFHPdF6X7T2Pt3++Bdx8EKrs8HgURHvDfWnwaKRGW0RExD152a1EVhQRWVEER41j1UfTWX0W+q4t75waFAFND2ofGhG5KJ234FJpg/87BK+1hKYnXdViA5+T/ivqY4LKeqbrumxvPt7++qaQ38S4ZognpFcZC/bfaAmPH4aP8+GyQHivNfjrv94iInIJalOSTVl5c/JPU965MbY1aYVX8zjHNDSVdxaR8+m8BZensqCdD4xvWvecrxmOVjsfq7TXHyp8zUYocWpvc27vZzZeAM9mwWORsLYMPi0wFv3fnmqslZlSd28xERGRi974Q6toWbHNUd65dgra5sI0dp1hn1ubxbK17TDHex9rFc0rSgjw3gGc2VQxEZGGMtnt9vNShqTNVmOKl+exX81U2cFqN8LF45Ew9yisSDre/slMYw3MooS6fSVsM6Z93XlCNcnYrfBMNEw6qczxoUq4bj+ktIfXcmFdGXwWB//Ogx+LYU7dadoiInIRm/7Wma2z+bUyyjsHHauEFnyseIDxZ6FPILZTlHc+FZPdRtOqsuMFA04qGqDyzuJO7r/37gt9C9IA523EZWkCVJ8QkV7NhQ1l8EkbSKuCadlGxbGAY6PZK0qhb4DrvvoGGOdrg0t6ldGHq/ZPZ8HUSDCbwATUDtTU2I0yzCIiIr9mRnnnfFpa8uucqy3vfGLBgHzf4+HmtOWdfYIo8AkCF+Wd/WoqjwcZx341JYRWHCWkqgyPs1TeOc8nmMVRXdjQvB0VHl74Wqvpmb+XoVlbXFZ/ExH3dd6CS+xJ+7E09TBGW9r6Gnu1xHrDbanwt2j47qhROew/sUbbKhsUWCHM01hcf08YDNpjlFTuG2js43JVMLQ7aTPjA5WQYjE2pwTo6Q/PZ8PPFphTBMODzvVTi4iIXLycyjsXH3Y6d7y8c93RmnyfIIq8A7Gb6l8BU+7pQ3pgmMvyzmableZVpY7NN2sDjlHquQRfW7WLHuva3qQl77W7ghqTGduxdT4Vnt6sCktiXWgCd+xdQMej6Q3/hojIBXVBNqA8mYcJvo43Fu/32AnxPsYUrtbHws6qMhiyBw52Mo71CzTCyF8zIb8GrgiGt1vV7ffpLHgiCmr/uzkgCG5qBgN3w6Ag+H34+XtGERGRS4kJCKypILCmgtZleXXOV5s8KPAJdJp65hit8Qmi2sOrbqfH2Mwe5Pk2Ic+3icvzgdXlTptwhp4wWhNcbcGMMdLyXrsrqHJxHZvZgyo8eK/dFTy29UuNvIhcJM7bGhcREZHzQWtc3J8dKPHycwo1J47WFHvXM1e8AbxsNTSvLKHS5EGhTyCcYo2O2Wbl8tydTDi08oyvJ5cGrXG5OLjFiIuIiIj8epiA4OpygqvLiSvNqXO+0ux5bDPOYPJqp6H5GiM1BT7B1JyivHO12ZNsPxclTF2wmT1YH5qg4CJykVBwEREREbfiY6shuryQ6PLCOudsmDjq7V9ntCbfN4gjPsGUefk16lqVp5iyJiLuRcFFRERELhpm7DStKqNpVRntSrLqnC/38OaITxCvdriOao/T/5jjY23YQn8RufC0b7yIiIhcMvysVbS05NPnyG7MNusp25ptVnod2XOe7kxEfikFFxEREbnkDM3agqfddso2nnYbQ7K3nqc7EpFfSsFFRERELjlhlcXcsXcB3tbqOiMvZpsVb2s1d+xdoFLIIhcRrXERERGRS1LHo+k8tvVLlkR2Zn1oApUeXvhYq+l1ZA9DsrcqtIhcZBRcRERE5JIVVlnMhEMrVfJY5BKgqWIiIiIiIuL2FFxERERERMTtKbiIiIiIiIjbU3ARERERERG3p+AiIiIiIiJuT8FFRERERETcnoKLiIiIiIi4PQUXERERERFxewouIiIiIiLi9hRcRERERETE7Sm4iIiIiIiI21NwERERERERt6fgIiIiIiIibs/zQt+AiIiIiIic2s5du1m0eKnLc5MnTcQOLFmyjKzsbIICA+l/eT9iY1vV29/efftZs2YdZRYLLWNaMGTwIPz9/QDIyspmwaIlVFVV0a9vbzp2aO/43IqVqwlt3pykpISz+XgNouAiIiIiIuLm2rWNp1Wrlo73drud7+fOIzgoiMDAQD7/YhZNm4YwYdxYDqam8sOPC5j4m/EEBwfX6SsnJ5eFi5YweNAAwkJDWb5iFQsXLeaaMaMB+Gn5Cjp2SCI8PIzvvp9HXJvW+Pn5UWaxkJFxmMv69Tlvz30iTRUTEREREXFznp6eBPj7O14HDhykpKSUIYMHcfhwJoVFRQwZPJBmzZrSo3sykZER7Ni5y2VfW7ZuIz6uDe2TEgkNbc7wYUNIS8+g6OhRAAoLi4iLa0PLmBh8fHwoLi4BICVlE8ndumA2X5gIoeAiIiIiInIRqaqqYt36FPr07omvrw/ZOTmEhYbi7e3taBMVGUl2do7Lz2fn5BIdHeV4HxQUSFBQoKN9UFAgeXlHKC4pobKykoDAAErLysjMzKJdu7bn9uFOQVPFREREREQuItu278TDw4MO7ZMAKLNYCAjwd2rj7+9HaVmZy89bLBYCAgKc2/v5U1pqtO/Xtw8LFi7GarXSs0cygQEB/LRsOcnJXS/YaAsouIiIiIiIXDTsdjvbd+ygS+dOeHh4AFBTXeP4upaHhwdWq9VlHzU1rtqbHe3j49oQe8dkrFYrPj4+lJSUkpWVw8AB/Vm9Zi279+wjKjKCoUMG4eXldQ6e0jVNFRMRERERuUjk5R3h6NFiEhPbOY55etYNKVarFU9P12MUrkKN1WrDy+t4e09PT3x8fADYkLKR7t27kZ2Tw569+5n4m/HYbDa2bN1+th6rQRRcREREREQuEofS0omICCfwhKleAQEBWCzlTu0sFgsB/v4nf/yE9pY67f1dtC8uKSEnN5d2bePJzs4lMiIcb29vWsbEkJPjeg3NuaLgIiIiIiJykcjOyaFFdLTTsciICI4cOUJ1dbXjWFZWNhERES77iIwIJysr2/G+pKSUktJSIl2037BhIz26J2MymTCZjKlqADa7DfvZeKBGUHAREREREblIFBQU0KxZU6dj0dFRBAUFsXDxUvILCkjZuJnsnFzHxpFWq5UyiwWbzQZAp04d2LN3H9t37ORIfj4LFy8htlVLQkKaOPV79GgxuXl5tI2PAyA8LIzDmVkcOZLPgQMHiQgPPw9PfJyCi4iIiIjIRcJiKcf32NqTWmazmVGjRlJeXs4XM2eze/ceRl05guDgIACysnP47/sfOaqGRUVGMmTwQDZs2MisWV/h4+3N8GFD6lxrfUoKPXt0x2QyAUZASmgXz+yvvsHTy4suXTqd46d1ZrLXjveIiIhcAqa/NeNC34KIXGTuv/fuC30L0gAacREREREREben4CIiIiIiIm5PwUVERERERNzeeQ8uuypg+B4I3ASxW+Gl45XYuOsQmFKcX6+dojz0FwXQdhv4b4Rr90Hu8QpwrCyF+K0Q9jO8k+f8uYfT4cP8s/tcIiIiIiJy7pzX4FJth6v2Qitv2Nwe3mwJT2fBJ8dCxI5yeKkFZHU5/rorzHVf68tgcir8ORLWJEGxFW5NPX7+/jT4bRh81gb+kA5Haozj2dWwqARubnYun1RERERERM4mz/N5scNV0DsA3mwFfmZo6wvDg+GnUri5OeysgGcCINLr9H29kQs3NIXbQo33H7aBVlthX4XR764KGBsCCb4Q4gEHKiHUE57Lhj9FgIfpnD6qiIiIiIicRed1xKW1D3weZ4QWu92YzrWsBIYFGSMhBVZI9G1YX2vKYGDg8fctvSHWG1Yb5alp5Q0bLXCoEgqtEOMFmVXwUwncpNEWEREREZGLynkdcTlRzFbIrIarm8C4pkag8AT+chh+KDZGRx4MPz6icrKsaog+aWQmwhMyqoyvn28BNx+EKjs8HgXR3nBfGjwaqdEWEREREZGLzQULLl/HG8HlnjR4MP34SEtXf/hDOCwthbvTIMADxjet+3mLDXxOGi/yMUPlse00r28K+U2g0gYhnpBeZYzwvNESHj8MH+fDZYHwXmvwV201ERERERG3dsGCS88A40+LzVhkX9zNmMLV7NgddfGHvRXwrzzXwcXXbISSE1XanEOIn9l4ATybBY9Fwtoy+LQAtneE21ONtTJTIs/yw4mIiIiIyFl1XscaDlfBN0XOxzr4GtO5SmzHQ0ut9r7GZ1xp4QXZNc7HsmsgysXC/kOVRmCZ0NRYA9MnAII8jMIAa8rO+HFEREREROQ8Oa/BZWcFjN3vvN9KigXCPOG5LLh6n3P7TeWQVM9i/b4BsKL0+Pv0KkirMo6f7OksmBoJZhOYgNqBmhq7USRARERERETc23kNLoOCoIMf3JYKO8vhuyJ47DA8EQnXhMAPR+GfubC/Et7MNTaJfOTYNK4qm1F5zHosaNwTBv8rMDaX3FpuTDe7KhjanRR0DlQa4ah2ullPf6MQwM8WmFNkjL6IiIiIiIh7O6/BxcsE37UFTxP02WUsvn8g3FiMPygIPm0Db+dBx+3wZp7xvv+xkseryiBqizGyAtAvEN6JhWeyod8uaOIBH7Sue82ns+CJKDAdqyQ2IMhYSzNwNwSY4ffh5+XRRURERETkFzDZ7ZosJSIil47pb8240LcgIheZ+++9+0LfgjSACgGLiIiIiIjbU3ARERERERG3p+AiIiIiIiJuT8FFRERERETcnoKLiIiIiIi4PQUXERERERFxewouIiIiIiLi9hRcRERERETE7Sm4iIiIiIiI21NwERERERERt6fgIiIiIiIibk/BRURERERE3J6Ci4iIiIiIuD0FFxERERERcXsKLiIiIiIi4vYUXERERERExO0puIiIiIiIiNtTcBEREREREben4CIiIiIiIm5PwUVERERERNyegouIiIiIiLg9BRcREREREXF7Ci4iIiIiIuL2PC/0DYiIiIiIyOlZrVZWrVnL7t17sdvttGsbz4D+l+Hh4UFxSQlLliwjKzuboMBA+l/ej9jYVvX2tXffftasWUeZxULLmBYMGTwIf38/ALKyslmwaAlVVVX069ubjh3aOz63YuVqQps3Jykp4Zw/78k04iIiIiIichFYtXoNBw4cZPRVI7l61JUcSktj/YYU7HY7c+f+iK+vDxPGjSUpKYEfflxAcXGxy35ycnJZuGgJPXt2Z9zY66iqqmbhosWO8z8tX0HHDkmMHDGMZctXUl5eDkCZxUJGxmESEtqel+c9mYKLiIiIiIibq6ysZOu2HQwZPIioqEiioiLp3asnuXlHOHw4k8KiIoYMHkizZk3p0T2ZyMgIduzc5bKvLVu3ER/XhvZJiYSGNmf4sCGkpWdQdPQoAIWFRcTFtaFlTAw+Pj4UF5cAkJKyieRuXTCbL0yEUHAREREREXFzmVnZeHp60jKmheNY+6RErrl6FNk5OYSFhuLt7e04FxUZSXZ2jsu+snNyiY6OcrwPCgokKCjQ0T4oKJC8vCMUl5RQWVlJQGAApWVlZGZm0a7dhRltAa1xERERERFxe8VHiwkOCmLP3n2kpGyiurqa+Pg4+vXtTZnFQkCAv1N7f38/SsvKXPZlsVgICAhwbu/nT2mp0b5f3z4sWLgYq9VKzx7JBAYE8NOy5SQnd71goy2g4CIiIiIi4vaqqqspLilhy9ZtDB48gOqqapYuW47dbqOmugYPDw+n9h4eHlitVpd91dS4am92tI+Pa0PsHZOxWq34+PhQUlJKVlYOAwf0Z/Watezes4+oyAiGDhmEl5fXuXlgFzRVTERERETEzZnNJqqqqhgxfCjRUVHExrbi8sv6sW37TpchxWq14unpeozCdXsbXl7H23t6euLj4wPAhpSNdO/ejeycHPbs3c/E34zHZrOxZev2s/yUp6bgIiIiIiLi5gL8AzCbzTRp0sRxLCSkCVarFX9/PyyWcqf2FouFAH//k7sx+goIwGKx1Gnv76J9cUkJObm5tGsbT3Z2LpER4Xh7e9MyJoacHNdraM4VBRcRERERETcXGRmBzWbjSH6+41hhQSFeXl5ERkRw5MgRqqurHeeysrKJiIhw3VdEOFlZ2Y73JSWllJSWEumi/YYNG+nRPRmTyYTJBHa7HQCb3Yb9bD1cAym4iIiIiIi4uZCQJrRp05pFi5eSm5tHZmYWq9aso2OH9sTEtCAoKIiFi5eSX1BAysbNZOfkOjaOtFqtlFks2Gw2ADp16sCevfvYvmMnR/LzWbh4CbGtWhIS0sTpmkePFpObl0fb+DgAwsPCOJyZxZEj+Rw4cJCI8PDz+j1QcBERERERuQhcMWwIoc2b89U33/H9Dz8SF9eafn17YzabGTVqJOXl5Xwxcza7d+9h1JUjCA4OAiArO4f/vv+Ro2pYVGQkQwYPZMOGjcya9RU+3t4MHzakzvXWp6TQs0d3TCYTANHRUSS0i2f2V9/g6eVFly6dzt/DAyZ77XiPiIjIJWD6WzMu9C2IyEXm/nvvvtC3IA2gERcREREREXF75z247KqA4XsgcBPEboWXjq8L4lAljNgDAZug/Xb44eip+/qiANpuA/+NcO0+yD2+HomVpRC/FcJ+hnfynD/3cDp8mI+IiIiIiFwkzmtwqbbDVXuhlTdsbg9vtoSns+CTfLDb4dr90NwT1ifB5OZww344WOm6r/VlMDkV/hwJa5Kg2Aq3ph4/f38a/DYMPmsDf0iHIzXG8exqWFQCNzc7108rIiIiIiJni+tdac6Rw1XQOwDebAV+ZmjrC8OD4adSiPKC3RWwPBGCPKCDHywshv8cgWda1O3rjVy4oSncFmq8/7ANtNoK+yqMfndVwNgQSPCFEA84UAmhnvBcNvwpAjxM5/PJRURERETklzivIy6tfeDzOCO02O3GdK5lJTAsCNaUQbK/EVpq9Q+E1WWu+1pTBgMDj79v6Q2x3sfbt/KGjRZj+lmhFWK8ILMKfiqBmzTaIiIiIiJyUTmvIy4nitkKmdVwdRMY1xQeSIdoL+c2EV6QUeX681nVLtp7Hm//fAu4+SBU2eHxKIj2hvvS4NFIjbaIiIiIiFxsLlhw+TreCC73pMGD6WCxgc9J4z8+Jqisp1izy/bm4+2vbwr5TaDSBiGekF5ljPC80RIePwwf58NlgfBea/BXbTUREREREbd2wX5k7xkA14TAyzEw4wh4m4yQcaJKe/2hwtfsor3Nub2f2QgtAM9mwWORsLYMPi2A7R2hxm6slREREREREfd2XoPL4Sr4psj5WAdfYzpXlJdR8etE2dXGcVdaeEF2zUnta1y3P1RpBJYJTY01MH0CjLU0w4ONtTIiIiIiIuLezmtw2VkBY/c777eSYoEwT2Mh/uZyKLMeP7eiFPoGuO6rb4BxvlZ6FaRVuW7/dBZMjQSzCUxA7UBNjd0oEiAiIiIiIu7tvAaXQUFGmePbUmFnOXxXBI8dhicijXOx3sa57eXwQrYxGvLbY+WOq2zGCIz1WNC4Jwz+V2BsLrm13NjT5apgaOfrfM0DlUY4Gt/UeN/T36gs9rMF5hQZoy8iIiIiIuLezmtw8TLBd23B0wR9dsHdafBAOPwh3Kj09XU85NZAj53GzvZz4o0SygCryiBqizGyAtAvEN6JhWeyod8uaOIBH7Sue82ns+CJKDAdqyQ2IMgohzxwNwSY4ffh5+XRRURERETkFzDZ7ZosJSIil47pb8240LcgIheZ+++9+0LfgjSACgGLiIiIiIjbU3ARERERERG3p+AiIiIiIiJuT8FFRERERETcnoKLiIiIiIi4PQUXERERERFxewouIiIiIiLi9hRcRERERETE7Sm4iIiIiIiI21NwERERERERt6fgIiIiIiIibk/BRURERERE3J6Ci4iIiIiIuD0FFxERERERcXsKLiIiIiIick7ZbDbKLBZsNtsZ9+F5Fu9HRERERETEITs7hzXr1pOVlY3NZmP8uOvZvHkLwcFB9O3Tu1F9acRFRERERETOuoyMw8z+6hsA+vbp5TjevHkzUjZuZtPmLY3qT8FFRERERETOulWr19KubTzXXXM1Xbt0xm63A9CjezI9eySzfcfORvWn4CIiIiIiImddfkEBiQntXJ5r0aIFJSUljepPwUVERERERM46fz8/CgoLXZ4rLCzE38+vUf1pcb6IiIiIiJx1SUkJrF23AW9vb2JjWwFgt9lJT89g3foU2iclNqo/BRcRERERETnrevXsQUlpKYuX/ITJZALgy9lfARAX14Y+vXs2qj8FFxEREREROevMZjPDhw6hR3IyhzMzqaiowNvbm+ioKEJDmze6PwUXERERERE56w5nZhIRHk7TpiE0bRridK60tIwdO3fRu1ePBvenxfkiIiIiInLWzfnqW2bOmkPR0aN1zpWWlbJ+Q0qj+lNwERERERGRc6K6upovZs5m//4Dv7gvTRUTEREREbkI7N6zlwULFzsda9OmNaOvGklxSQlLliwjKzuboMBA+l/ez1HJy5W9+/azZs06yiwWWsa0YMjgQfj7G+WJs7KyWbBoCVVVVfTr25uOHdo7Prdi5WpCmzcnKSmhQfd8xbCh7Nm3j3nzF9Kta2cu69fXsVC/sRRcREREREQuAoWFhcS1ac2gQQMcxzw9PLDb7cyd+yNNm4YwYdxYDqam8sOPC5j4m/EEBwfX6ScnJ5eFi5YweNAAwkJDWb5iFQsXLeaaMaMB+Gn5Cjp2SCI8PIzvvp9HXJvW+Pn5UWaxkJFxmMv69WnwPZvNZgYN6E94WBhLf1pOTm4eV44YjsnU+IlfmiomIiIiInIRKCgopHnz5gT4+ztePj4+HD6cSWFREUMGD6RZs6b06J5MZGQEO3buctnPlq3biI9rQ/ukREJDmzN82BDS0jMca1EKC4uIi2tDy5gYfHx8KC42drhPSdlEcrcumM2NjxDtkxIZe901FB8t5vOZszhy5Eij+1BwERERERG5CBQUFtWpzgWQnZNDWGgo3t7ejmNRkZFkZ+e47Cc7J5fo6CjH+6CgQIKCAh3tg4ICycs7QnFJCZWVlQQEBlBaVkZmZhbt2rU94/uPiAhnwvixBAcHs2TpskZ/XlPFRERERETcnNVqpbi4mNTUQ6xdtx67HdrGx9Gnd0/KLBYCAvyd2vv7+1FaVuayL4vFQkBAgHN7P39KS432/fr2YcHCxVitVnr2SCYwIICfli0nOblro0ZbevfqQUDgSdfx9+f6a8ewfMUqUg+lNbgvUHAREREREXF7RUePYrPZ8PTy5KqRIzh69CjLV6yiurqKmhorHh4eTu09PDywWq0u+6qpqXHR3uxoHx/Xhtg7JmO1WvHx8aGkpJSsrBwGDujP6jVr2b1nH1GREQwdMggvL69677l3r54uj5vNZgYN7M+gxnwDUHAREREREXF7zZs14//umIyfry8AoaHNsQPzFyyiY4ckqqqqnNpbrVY8PV3/qO8q1FitNry8jrf39PR0fH5Dyka6d+9Gdk4Oe/buZ+JvxrNo8VK2bN1Oj+7dnPr59LOZjLhiGM2bN+PTz2ae+qFMcNON4xvw9MfuqcEtRURERETkgqkNLbWaNW2KzWYjwD+AI0cKnM5ZLBYC/J2nj9UKCAjAYrHUae/von1xSQk5ubkMHjSAzT9vJTIiHG9vb1rGxJCWnl6nfVh4qCMAhYWFwplVPnZJwUVERERExM3t33+ApctWcNutNzumeeUdOYKPjzcRkeGkbNxEdXW1Y+pWVlY2ERERLvuKjAgnKyvbsT9LSUkpJaWlRLpov2HDRnp0T8ZkMmEygd1uB8Bmt2F30ffwoUOOfz1siIsWZ65RVcVq7PBxPtyeClfthb0V8N8jsMVy2o+KiIiIiMgZim4RDdhZsnQZhUVFpKYeYuWqNSR360qL6GiCgoJYuHgp+QUFpGzcTHZOriOYWK1WyiwWbDYbAJ06dWDP3n1s37GTI/n5LFy8hNhWLQkJaeJ0zaNHi8nNy6NtfBwA4WFhHM7M4siRfA4cOEhEePhp77uwsIj8AmM0qKq6mp+WLee7739g567djf4eNDi45NdA311GaNlogfnFUGKD2UVw2W5Y67pogYiIiIiI/EJ+vr5cc/VoSkpK+eKLWSz5aRmdOnagR/dkzGYzo0aNpLy8nC9mzmb37j2MunIEwcFBAGRl5/Df9z9yVA2LioxkyOCBbNiwkVmzvsLH29vl6Mj6lBR69uju2Ok+OjqKhHbxzP7qGzy9vOjSpdMp73n/gYN8+vlMdh0LKYsWL2XHzt1U19SwZOkytmzd1qjvgcleO95zGrcehNVlsLAdtPAG742woT109IVR+4w2ixJO38/+SnggHVaUQoAZbmwKz7YAXzPcdQjeOWkvmldj4AHXo1x8UQCPZ0JmFVwRDO/EQvixwgYrS417LrbBtGj4bdjxzz2cDl394dbmDXlyERG5mEx/a8aFvgURucjcf+/dF/oWLkmfz5xFs6ZNGTZ0MOXl5bz/4Sf06d2Lnj2SSdm4iZ279nDLxBsb3F+DR1y+PQrPRkOsj/MaGx8zPBwBKQ2YLlZlgzH7wMcEqxLhkzbwVRE8cdg4v6McXmoBWV2Ov+4Kc93X+jKYnAp/joQ1SVBshVtTj5+/P80IK5+1gT+kw5Ea43h2NSwqgZubNfTJRURERESksQoLi2jfPgmz2ezYs6V22llkRAQlJSWN6q/BwcVqN0ZFXKmxQ0PGbdZZYF8lvN8a2vvBoCB4ugV8cqwIws4K6BkAkV7HX/71XPONXLihKdwWCl384cM28GMx7Kswzu+qgLEhMCwYQjzgQKVx/Lls+FMEeJzFCgciIiIiIuLM29ubygrjh/PU1EMEBwU51tEUFhXh7+fXqP4aHFyGBsHfs6Cw5vgxE1Bth9dzjRByOok+MLctBJ6w340JqLQbIyEFVkj0rffjTtaUwcDA4+9bekOstzGdDaCVt7EW51AlFFohxsuYUvZTCdyk0RYRERERkXOqdWwrVq1ey9KflpN6KI3ExHYA/PzzVlavWUdcXOtG9dfgcsgvt4TLd0H8NugXYASOv2QaIxtFNbAi6fR9hHnB8BM217TZYXouDAg0pol5An85DD8UQ6gnPBhujKi4klUN0Sdt1BnhCRnH9t55vgXcfBCq7PB4FER7w31p8GikRltERERERM61Af0vY+WqNWRmZdG+fSI9uicDsH3nLuLj2tCvb59G9dfg4BLvA1s6wKu5sLTEeJ9TDWOawEMRxohHYz2UAZsssL690ScYi+b/EA5LS+HuNAjwgPFN637WYjPW15zIx2yM3gBc3xTym0ClDUI8Ib3KWLD/Rkt4/LBR1vmyQHivdf3T0URERERE5Mx4eXkxeNCAOsdvunGco1JZYzRqA8pwL3g0Ap5rYbw/aoW86saHFrsdHsiAt3Lhy3jo6AcdfI0pXM2O3VEXf2OfmH/luQ4uvmYjlJyo0uYcQvzMxgvg2Sx4LNIo2/xpAWzvaJR2fiMXpkQ27v5FREREROTMnElogUascSmsgVF7od+u48dWl0LCdphwwBgBaQibHe44ZASSz+Pg2hDjuMl0PLTUau8Lh6tc99PCC7JrnI9l10CUV922hyqNwDKhqbEGpk8ABHnA8GBjrYyIiIiIiLi3BgeXP2XA1nJ4Keb4saFB8H1bIxTUljQ+nYcz4H8FMDsOxp4wkvJwOly9z7ntpnJIqmexft8AYy+YWulVkFZlHD/Z01kwNRLMJmNtTm3Gamg1NBERERERubAaHFy+PwqvtIQxIcePeZvhqibGQviZhafvY00pvJYLf482yh5nVx9/XRMCPxyFf+Yam1S+mQsf5sMjx6ZxVdmMdtZjQeOeMCMAvZNnBKrJqXBVMLQ7KegcqDT2mKmdbtbT36gs9rMF5hQZoy8iIiIiIuLeGhxcym3gW890tCAzFFlP38eXRcafUw9D1Bbn1+WB8GkbeDsPOm6HN/OM9/2PlTxeVWa0Sz82daxfILwTC89kG9PXmnjAB63rXvPpLHgiypiKBjAgyFhLM3A3BJjh9+EN/Q6IiIiIiMiFYrLbGzZZ6qq9UGKFH9sZlb5qWWzG2hcfs3FORETkQpr+1owLfQsicpG5/967L/QtXJLsdjvbd+zk0KE0qmtqODl2mIDrrh3T4P4aXFXshRYwYDe02mpsNhnuCXk1xrQrK/BTQoOvKSIiIiIil7gVK1fz85athIWFEhgQcMbVxGo1OLh08YetHeHVHKMy17ZyY3rWzc2NjSJb+/yi+xARERERkUvI7j176d2rB7179Twr/TVqH5dW3vBqy7NyXRERERERuYRZrVaio6POWn+NCi5FNbCkBMpsx0sKn+jW5mfprkRERERE5KLWOrYVBw6kEtOixVnpr8HB5dsi+M1Bo7qYKyYUXERERERExNCqVUtWrFxNcXExEREReHo6Rw+TCbp17dLg/hocXB47bOyBMr0VxHgZmzmKiIiIiIi4smjxUgBSD6WReiitznmTyXRugsv+SvhnS+js1+C+RURERETkV+psl5lucHBp7wuHqs7qtUVERERE5BJXUVFBdk4uVVVV+Pn6EhERjre3d6P7aXBweTkG/u+QUQK5TwD4m+u2adaopf4iIiIiInIpW7tuPRs3/YzVanUcM5vNJHfrQr++fRrVV4Ojxo0HocQKEw7U38bao1HXFhERERGRS9TPP28lZeNmkrt1oV3btvj7+2GxlLNn7z42bd6Cv38AXbt0anB/DQ4u/4g5o/sVEREREZFfoS3bttM9uSt9+/R2HPP39yc0tDlms5lt27afm+AyWaWORURERESkgcrKymgRHe3yXIvoKDZt/rlR/blYqVK/g5VwZyq02Qp+G2GjBR5Kh3ePNOqaIiIiIiJyiWsSHExmVpbLc5lZWQQGBDSqvwaPuGy2wOA9EOkJY5rAm3nGcTtw9yHwMcEkjcqIiIiIiAjQpUsnflq2ArvdTnx8HP5+fljKy9m37wCbNv9M3z69GtVfg4PLg+nQLwC+bws2YPqx4PJqS6i2w8s5Ci4iIiIiImLo2KE9R48Ws2nzFlI2bnYcN5lMdO3Sie7J3RrVX4ODy9oy+DIezCaw253PjWsK/81v1HVFREREROQSd1m/PnRP7kp2Ti6VlZX4+vgQHhGOn69vo/tqcHAJ9oDsatfn0qoguFGrZURERERE5FJTUVGBj48PJpOJiooKx/HIiPA67QB8GxFgGhxcxjeFqYehjQ8MCDSOmYA9FfD3TLgupMHXFBERERGRS9B//vsh48ZeR0REOO++9wEmk+mU7e+7564G993g4PJCDOyogGF7IODY6MqovZBXAz0DjPMiIiIiIvLrNXTIIIKbBAMwbOjgs9p3g4OLvxkWJcCPR2FJCRRYoYkH9A80qoyZTx2mRERERETkEtc+KdHxdXBwEGFhYXh7edVpV1lZSXp6RqP6bnBwqTWyifESERERERGpz1dff+eYNnay3Nw8FixaQtu28Q3u75TB5Q9pjbu5f7ZqXHsREREREbl0/DBvPkVFRwGw2+3MX7gIT4+6kaO0rIzgoKBG9X3K4PLtUef3mdXGni2tvSHSC/JrYH8l+Jihq1+jrisiIiIiIpeYbl27sH3nTgDyCwoICQnBz8+5cpjZZKaltzedOnZoVN+nDC4HOx//+qN8eDITZsVDsv/x43sq4Pr9MDakUdcVEREREZFLTFRUJFFRkY73vXr0oMmxxfq/VIN3X3n8MDzfwjm0ACT4wtPR8I+cs3I/IiIiIiJyCRg+dMgpQ0tOTm6j+mvw4vwSW/2Vwyw2YwqZiIiIiIgIQGlpGctXriIzMxOr1YbdfjwwWK1W7HZ7o/ZxafCIyxVBMCUD1pc5H19aAlMOawNKERERERE5btmKlaSnZ5DQri1NmgQTGtqczp060KRJMHa7nStHXtGo/ho84jK9FYzYC313QVMPCPWE3Bo4aoUBgfBay0Y/i4iIiIiInIHFS36i6OhRxl53DQDFJSUsWbKMrOxsggID6X95P2Jj6y/5u3ffftasWUeZxULLmBYMGTwIf3+j2lZWVjYLFi2hqqqKfn1707FDe8fnVqxcTWjz5iQlJZz2Hg8fzuSyvn3o1KkDW7dtZ//+A1zWry/9+tr59ru5HDhwkPi4Ng1+5gaPuER4wab2MCce7gyFQUHwuzCY1w6WJkKQR4OvKSIiIiIiZyg9I4MdO3c53tvtdubO/RFfXx8mjBtLUlICP/y4gOLiYpefz8nJZeGiJfTs2Z1xY6+jqqqahYsWO87/tHwFHTskMXLEMJYtX0l5eTkAZRYLGRmHSUho26D7rKmpoVmzpgA0bRpC3pF8AEwmE507dSQzM6tRz92oDSjNJrgmxHiJiIiIiMj5VV1dzZKly4mKPF656/DhTAqLihh7/TV4e3vTrFlT0jMOs2PnLvr26V2njy1btxEf18axy/3wYUP44KNPKDp6lJAmTSgsLCIurg1NQ0Lw8fGhuLgEPz8/UlI2kdytC2Zzw8Y+goICOVpcTHR0FE1DQqisrKS4uJjg4GA8PT0pr6ho1LOfMrh02QH/awOd/KDzdjDVszgfwAT83LhSzCIiIiIi0ghr1q6jRXQUAQH+ZGZlA5Cdk0NYaCje3t6OdlGRkWRluR7RyM7JJblbF8f7oKBAgoICyc7OIaRJE4KCAsnLO4KHhweVlZUEBAZQWlZGZmYW/S/v1+B7bdc2npWr1gDQPimR5s2bsWLlarp06cyGjZtoGhLSqGc/ZXDp4Q8BxwJVjwAjnIiIiIiIyPmXlZ3Nvv0HuOnGCWz++WfH8TKLhYAA5z1L/P39KC0rO7kLACwWCwEBAc7t/fwpLTXa9+vbhwULF2O1WunZI5nAgAB+Wrac5OSuDR5tAejVswcVFZWkpaXTPimRwQMH8N3ceRw4mIq3tzdXXXkWF+f/t/Xxr98/9rXdfnzkpdwGNjsEaH2LiIiIiMg5Y7VaWbzkJwZcfhm+vj5O52qqa/DwcP6B3MPDA6vV6rKvmhpX7c2O9vFxbYi9YzJWqxUfHx9KSkrJysph4ID+rF6zlt179hEVGcHQIYPw8vKq957NZjODBvZ3vI+KimTypIkUFhbRtGmI0whRQzQ4MlXZ4N40o6pYrZWlEPozPJoBVu3jIiIiIiJyTqxbn0JIkya0bRtf55ynZ92QYrVa8fR0PUbhKtRYrTa8vI639/T0xMfHCEgbUjbSvXs3snNy2LN3PxN/Mx6bzcaWrdsb/Rze3t5ERIQ3OrRAIxbnP54J/yuAZ6OPH+vhD6+0hD8fNqqK/SWq0dcXEREREZHT2LN3HxaLhRlv/wcAq83Y0HHG2/+hR49kjhwpcGpvsVgI8Pd31RUBAQFYLJY67f1dtC8uKSEnN5fBgwaw+eetRB4LHS1jYkhLT6/TfsY77zXque7+7R0Nbtvg4PJ5AbwaA7eHHj/W1BPuCTM6mZat4CIiIiIici5cf90YbDab4/3PP28hN/cIV1wxlNKSUlJSNlFdXe2YupWVlU1ERITLviIjwsnKynbsz1JSUkpJaSmRLtpv2LCRHt2TMZlMmExG6WUAm92GqwlX3bp2OWVBr1+iwcGlyAqR9Uxha+UNOdUN62d/JTyQDitKjYX/NzaFZ1uArxkOVcJvD8HKMqPPV2Lgqib19/VFgTESlFkFVwTDO7EQfuweV5bCrQeh2AbTouG3Ycc/93A6dPWHW5s37J5FRERERC6k4KAgp/c+Pj54eHoQ0qQJwUFBBAUFsXDxUnr36kFqahrZObkMHTIYMKaNVVRW4ufri9lsplOnDsz56luioiKJiAhn+YpVxLZqSUiI8w/eR48Wk5uXx5DBAwEIDwsjZeNmjhzJ58CBg8TExNS5zz69e56T54dGBJfu/jAjD64MrlsW+e0jkOx6JMpJlQ3G7IMOvrAqEXJr4I5U49w/YuDa/dDeF9YnwTdH4Yb9sL0jtPGp29f6MpicCv9qZdzbH9Ph1lRjQ0yA+9OMsNLLH67eB9c3hVBPyK6GRSXwYt3vs4iIiIjIRcdsNjNq1EgWL/mJL2bOpklwMKOuHEFwsBF2srJz+Orrb7n1lokEBwcRFRnJkMEDWbduAxUVFbRsGeMIJydan5JCzx7dMR374T86OoqEdvHM/uoboqOj6NKl0ynva9euPae996SkhAY/p8leO95zGktLYMReiPOGUU2MkY28avih2BhFmd8OBgWduo8VpTB0DxR0hcBjhQz+VwAPpRv7xYzeB7ldjfUyAMP3QN8AeKZF3b5uPQg24OM2xvv0Kmi1FfZ2hLa+4LfR2FcmwReifoav20LvACPg9PKHWzTaIiJySZr+1owLfQsicpG5/967L/QtXJLq+++xyWTCw8MDLy9P/u/2yQ3ur8EjLoODYEUiPJdthI0CKzTxgMsCjLLJfQJO2wWJPjC37fHQAsbeMJV2WFNmjNoEnXCufyAsL3Xd15oy+NMJ0/BaekOsN6wuM4JLK2/YaAEfExRaIcbLmFL2U4kxBU1ERERERM6d3/7fbXWOVVfXkJmVxeo16xgxfGij+mtwcAFjxGJO3QpsDRbmBcNPWCdjs8P0XBgQCFnVEH3SGpoIL8ioct2Xy/aex9s/3wJuPghVdng8CqK94b40eDQSPLSTpoiIiIjIOVVbTvnkYwnt2lJTXcOyFSu5cfwNDe6vUcHFZoefy6Hs2MaTJxt4mqliJ3soAzZZYH17eCUHfE7aVcbHZIzGuGKxuWhvPt7++qaQ3wQqbRDiaUwlW1kKb7SExw/Dx/lwWSC81xr8G74BqIiIiIiI/EJBQYEUFBQ26jMNDi6rS2HCAcisxmXpMxNg7dGwvux2eCAD3sqFL+Oho59RVezoSZXJKu31hwpfsxFKnNrbnNv7mY0XwLNZ8FgkrC2DTwuMRf+3p8IbuTAlsmH3LSIiIiIiDVNRUVHnmN1up6zMwoaUTTRpEtyo/hocXP6YDiEe8FYriPGGMx2ksNnh/w7BJwXweRxcG2Icb+EFPzvvg0N2NUTVU4K5hRdk15zUvsZ1+0OVRmB5qxW8lmusxwnygOHB8GPxGT6IiIiIiIjU6933PnBUJDuZh4cHV44Y3qj+GhxctpbD7PhT76vSEA9nGIv7Z8fB1SHHj/cNMDaxLLNCwLEF+itKjeOu9A0wzt95bEPM9CpIq3Ld/uksmBoJZpMxMlQ7UFNjN0Z/RERERETk7Bo2dHCdYyZMeHt70aJFtMs1MKfS4ODSyhuKrY3qu441pcaIx3MtoGeAMaJSa1CQURXstlT4WzR8d9SoHPafWON8lc2oZBbmaSyuvycMBu2BywOgb6AxInRVMLTzdb7mgUpIsRibUwL09Ifns43RnTlFMLyR63JEREREROT02iclntX+Ghxc/h4Nf82Etj7QowGlj135ssj4c+ph43Wi6u7wdbwxjazHToj3MSqYtT4WxFaVwZA9cLCTcaxfoBFG/poJ+TVwRTC83aruNZ/Ogieijm+aOSAIbmoGA3cbYen34Wf2LCIiIiIicmoFBYVs2vwzmVnZVFZW4ufnS0yLFiQndyU4qHEjCA3egLLXTthTAaU28DQZFb+cOgKOJjfq2iIiImedNqAUkcbSBpTnRsbhw3z73Q/4+frSqlVL/Px8KS+v4FBaGtXVNYy9/hpCmzd8V/gGj7hc3QT4hetbRERERETk12HVqrXEtGjBqKtG4OFxfJd5q9XKd3PnsWLlaq675uoG99fg4PJkdONuVEREREREfr0KCgu5qk9Pp9ACRkWxbl06M+/HBY3q75TB5ZUcuLmZsYP9Kzmn7sgEPBjRqGuLiIiIiMglqmnTEHJzjxDbqu5C9MKiIoKCG7fG5ZTB5U8Z0D/QCC5/yjh1RwouIiIiIiJSq1/fPsxfsIiamhratY0nIMCfiooKDqamsX5DCpdf1pfcvDxH+/CwsFP2d8rgYuvh+msREREREZFT+ebb7wFI2biJjZs2O47X1gb7adkKx3uTycR999x1yv4avMZFRERERESkoa6/dsxZ7U/BRUREREREzroWLY5X96qurqaquhpfH586i/UbSsFFRERERETOibT0DNasWUfekSOOY+FhYfTu3cPlov1TMZ/tmxMREREREUlLz+C773/AbDbT//J+jBg+lMsv6wsm+O77eaSln6b610k04iIiIiIiImfd2rXriWvTmitHXuF0vFvXLsz7cQHr16fQqmVMg/vTiIuIiIiIiJx1+QUFtG+f5PJc+/ZJHDlh+lhDKLiIiIiIiMhZ5+/vR2lpqctzpaWleHp5Nao/BRcRERERETnr4uPasGbtOtJPWsuSlp7BmrXriY9r06j+tMZFRERERETOut69epKdncvX336Pt7c3/v5+WCzlVFdXEx4exmX9+jSqPwUXERERERE567y8vBh7/TWkHjpEZmY2lZWV+Pr6EBUVSevYWEwmU6P6U3AREREREZFzwmQy0aZ1a8LCwqisqMTPzxd/f/8z6kvBRUREREREzokdO3axYeMmSkpKHMeahoTQp08vrXEREREREZELb8vWbSxbvpK28XH06d0TP19fysvL2X8wlXk/LuDKEcOJj49rcH8KLiIiIiIictZt/nkL3bp2of/l/ZyOJyYmsGz5StatT2lUcFE5ZBEREREROesslnJatYxxea5161iOFhc3qj8FFxEREREROetatoxh9569Ls8dTE2lRXRUo/rTVDERERERETnrYlu1ZPWadXw5+yvatW1LgL8fFRWVHDx0iLS0dLond2XT5i0AmEzQrWuXU/an4CIiIiIiImfd0p+WA5CdnUN2dk6d8ykbNzu+NplMCi4iIiIiInL+3X/v3We1P61xERERERERt6fgIiIiIiIibk/BRURERERE3J6Ci4iIiIiIuD0FFxERERERcXsKLiIiIiIi4vZUDllERERE5CJQWFjIT8tXkpOdg6+vL507d6R7cjcAiktKWLJkGVnZ2QQFBtL/8n7Exraqt6+9+/azZs06yiwWWsa0YMjgQfj7+wGQlZXNgkVLqKqqol/f3nTs0N7xuRUrVxPavDlJSQnn9Fld0YiLiIiIiIibs1qtfPPdDwQFBnLjjeMYOLA/6zdsZPeevdjtdubO/RFfXx8mjBtLUlICP/y4gOLiYpd95eTksnDREnr27M64sddRVVXNwkWLHed/Wr6Cjh2SGDliGMuWr6S8vByAMouFjIzDJCS0PS/PfDIFFxERERERN1dWZiEiPIxBA/sT0qQJbVrH0jKmBYczMzl8OJPCoiKGDB5Is2ZN6dE9mcjICHbs3OWyry1btxEf14b2SYmEhjZn+LAhpKVnUHT0KACFhUXExbWhZUwMPj4+FBeXAJCSsonkbl0wmy9MhFBwERERERFxc8HBQVw58go8PT2x2+1kZWWTmZVNyxYtyM7JISw0FG9vb0f7qMhIsrNzXPaVnZNLdHSU431QUCBBQYGO9kFBgeTlHaG4pITKykoCAgMoLSsjMzOLdu0uzGgLaI2LiIiIiMhF5f0PP6aszELr2FbEx8exfOUqAgL8ndr4+/tRWlbm8vMWi4WAgADn9n7+lJYa7fv17cOChYuxWq307JFMYEAAPy1bTnJy1ws22gIKLiIiIiIiF5VRV12JpayMpcuWs2Llamqqa/Dw8HBq4+HhgdVqdfn5mhpX7c2O9vFxbYi9YzJWqxUfHx9KSkrJysph4ID+rF6zlt179hEVGcHQIYPw8vI6Nw/pwgWLTJU26LQdFp6wZuiuQ2BKcX695nqEC4AvCqDtNvDfCNfug9zq4+dWlkL8Vgj7Gd7Jc/7cw+nwYf7ZfR4RERERkfMhIjyMNm1ac/ll/di2fYfLkGK1WvH0dD1G4bq9DS+v4+09PT3x8fEBYEPKRrp370Z2Tg579u5n4m/GY7PZ2LJ1+1l+slO7IMGlwgY3HYTtFc7Hd5TDSy0gq8vx111hrvtYXwaTU+HPkbAmCYqtcGvq8fP3p8Fvw+CzNvCHdDhSYxzProZFJXBzs3PxZCIiIiIiZ19paRkHD6Y6HWvWtCk2mw1/f38slnKncxaLhQB/5+ljtQICArBYLHXa+7toX1xSQk5uLu3axpOdnUtkRDje3t60jIkhJ+cUIwznwHkPLjvKoe8u2F9Z99zOCugZAJFex1/+9dzhG7lwQ1O4LRS6+MOHbeDHYth3LAztqoCxITAsGEI84MCx6z2XDX+KAA/TOXk8EREREZGzrrCwkLnz5jsFlNy8PPz8fImOiuTIkSNUVx+ffpSVlU1ERITLviIjwsnKyna8LykppaS0lEgX7Tds2EiP7smYTCZMJrDb7QDY7DbsZ+vhGui8B5flpTAiGFYnOR/ProYCKyT6NqyfNWUwMPD4+5beEOsNq4+tQWrlDRstcKgSCq0Q4wWZVfBTCdyk0RYRERERuYhER0fRrGlTFi1eQkFBIQdTD7F6zTp6du9OdHQUQUFBLFy8lPyCAlI2biY7J9excaTVaqXMYsFmswHQqVMH9uzdx/YdOzmSn8/CxUuIbdWSkJAmTtc8erSY3Lw82sbHARAeFsbhzCyOHMnnwIGDRISHn9fvwXkPLneHwYsxdUdSdpQblQL+chhabIGuO+D9I/X3k1UN0SetBYrwhIwq4+vnW8AdqRC/DR6NhGhveDbb+FqjLSIiIiJyMfHw8ODq0VdiMpuZOWsOS39aRtcunenSpRNms5lRo0ZSXl7OFzNns3v3HkZdOYLg4CAAsrJz+O/7HzmqhkVFRjJk8EA2bNjIrFlf4ePtzfBhQ+pcc31KCj17dMdkMn54jo6OIqFdPLO/+gZPLy+6dOl0/r4BgMleO95zAZhSYEE7GB4Mb+bCA+nwSksYFAhLS+GRDPi4DYxvWvezHikwrx1cEXz82MDdMDQI/hZtvC+3GUUAQjwhvQrG7ION7eHPmfBxPlwWCO+1rn86moiIXHymvzXjQt+CiFxk7r/37gt9C9IAbvMj+71hkNMVfh9urFn5QzjcFQr/ynPd3tdshJITVdqcQ4if2QgtAM9mwWORsLYMPi2A7R2hxm6slREREREREffmNsHFZIJmJ1Vsa+8Lh6tct2/hBdk1zseyayDKRSnpQ5VGYJnQ1FgD0ycAgjyMkZ41rvflERERERERN+I2weXhdLh6n/OxTeWQVM9i/b4BsKL0+Pv0KkirMo6f7OksmBoJZhOYgNqBmho7XLiJciIiIiIi0lBuE1yuCYEfjsI/c41SyW/mGptEPhJpnK+yGZXHrMeCxj1h8L8CY3PJreXGni5XBUO7k4LOgUpIsRxfJ9PT36gs9rMF5hQZoy8iIiIiIuLe3Ca4DAqCT9vA23nQcTu8mWe873+s5PGqMojaYoysAPQLhHdi4Zls6LcLmnjAB63r9vt0FjwRZUxFAxgQZJRDHrgbAszGmhoREREREXFvF7SqmIiIyNmmqmIi0liqKnZxcJsRFxERERERkfoouIiIiIiIiNtTcBEREREREben4CIiIiIiIm5PwUVERERERNyegouIiIiIiLg9BRcREREREXF7Ci4iIiIiIuL2FFxERERERMTtKbiIiIiIiIjbU3ARERERERG3p+AiIiIiIiJuT8FFRERERETcnoKLiIiIiIi4PQUXERERERFxewouIiIiIiLi9hRcRERERETE7Sm4iIiIiIiI21NwERERERERt6fgIiIiIiIibk/BRURERERE3J6Ci4iIiIiIuD0FFxERERERcXsKLiIiIiIi4vYUXERERERExO0puIiIiIiIiNtTcBEREREREben4CIiIiIiIm5PwUVERERERNyegouIiIiIiLg9BRcREREREXF7Ci4iIiIiIuL2PC/0DYiIiIiIyOkdPXqU5StWkZWdjaenF+3axtO3Ty88PT0pLilhyZJlZGVnExQYSP/L+xEb26revvbu28+aNesos1hoGdOCIYMH4e/vB0BWVjYLFi2hqqqKfn1707FDe8fnVqxcTWjz5iQlJZzz5z2ZRlxERERERNzc/7d359FR1ff/x5+TSSbJTBKykUwCYUtCwmIkwBeJIARQrFuLVDxfxWqLX0CoUn5WK9alR+3Xtm71W6hCcUEs/CqurahVEAQEQRMwQABJ2GTJJGRfJuvM/P64MBAScPlCcuPv9Thnjrmf+547nw/nmDOvfD6fez0eDyvf/zdWq5WfXj+JiZePZ/+Bg2ze8gU+n4/33/+QkJBgbrxhMunp/fngw1VUV1e3e63i4hJWf7yW4cOHcsPkSTQ1NbP64zX+8+s2fMqggelcOXEC6zdspL6+HoA6t5sjR47Sv39Kh4z5TAouIiIiIiImV1xSQlVVNRMmjCM6OooePRK5ZMRw9u4t4OjRY1RUVjIuewzR0VEMG5qJ0xnPrt172r3W9h07Se7XlwHpacTGxnD5hHF8ffgIlVVVAFRUVNKvX1+SevYkODiY6uoaAHJzt5E5JIOAgM6JEAouIiIiIiImFxUZyXXXXIUtKMjfZrFY8Hg9uIqL6R4bi81m859LcDpxuYrbvZaruITExAT/cXh4GOHhYf768PAwjh8vpbqmhsbGRhxhDmrr6jh2rIjU1M6ZbQHtcRERERERMb3Q0FCSknr6j30+H9t37CQhIYE6txuHw96q3m4Ppbaurt1rud1uHA5H6/pQO7W1Rn3WyEtYtXoNHo+H4cMyCXM4WLd+A5mZF3fabAt04oxLoxcG58Pq05beHWqEiXvBsQ0G5MMHVee+xopySNkJ9q3wk0IoaT51bmMtJO+A7nmw+Hjr9/36MCwtO39jERERERHpSJ9u3ERpaRlZI0fQ0tyC1Wptdd5qteLxeNp9b0tLe/UB/vrkfn35r2m38V/TbuOSEf9BTU0tRUXF9E9N4bPNW1iydBkffrSa5ubm9i5/wXRKcGnwwk0HIL/hVJvPBz/ZBzGB8EU63BYDP90HBxrbv8YXdXDbQXjQCZvTodoDtx48df7Or2F6d/hHX5hzGEpbjHZXM3xcA1OjL9ToREREREQuDJ/Px/oNG9mxcxcTr5hATHQ0gYFtQ4rH4yEwsP3FVe2FGo/HS1DQqfrAwECCg4MByMndytChQ3AVF7O3YB83/+cUvF4v23fkn+fRnVuHB5dd9TByD+w7I5CsrYGvGuBvvWFgKMxzwqVh8GJp+9eZXwI/jYKfx0KGHZb2hQ+rofBEGNrTAJMjYUIERFph/4nP+4ML7okHq+WCDVFERERE5Lzz+Xx8vPYTdubv4sorJtCvbx8AHA4Hbnd9q1q3243Dbm/nKifr3W3q7e3UV9fUUFxSQmpKMi5XCc74OGw2G0k9e1Jc3P4emgulw4PLhlqYGAGfpbdu31wHmXYIP23WanQYfNb+0jw218GYsFPHSTbobTtV38sGW93G8rMKD/QMgmNNsK4GbtJsi4iIiIh0MZ9u/Iy9ewu56kcTSU7u5293xsdTWlraaulWUZGL+Pj4dq/jjI+jqMjlP66pqaWmthZnO/U5OVsZNjQTi8WCxWKEJwCvz4vvfA3sW+rw4DKzOzzRE+xnfHJRMyQGtW6LD4IjTe1fp936wFP1f+wB0w5C8k74jRMSbfDfLuNnzbaIiIiISFfichWTt30Hl4wYTlxcd+rcbv8rMTGB8PBwVq/5hLLycnK3fomruMT/4EiPx0Od243X6wVg8OCB7C0oJH/XbkrLyli9Zi29eyURGdmt1WdWVVVTcvw4KSdCUlz37hw9VkRpaRn79x8gPi6uQ/8NTHNXMbcXgs8IM8EWaDxLlGu3PuBU/fVRUNbNuAlAZCAcbjI27M9Pgt8ehb+XGUvRXurTNkSJiIiIiJhJ4b79AHy2+XM+2/x5q3Oz75jO1VdfyZq161jx+lt0i4jg6h9NJCIiHIAiVzHv/PNdbr3lZiIiwklwOhmXPYbPP8+hoaGBpKSejMse0+Yzv8jNZfiwoVgsxl/9ExMT6J+azFvv/IvExAQyMgZf4FG3ZprgEhIAVWfcmKDRd/ZQERJghJJW9d7W9aEBxgvgv4uMfTNb6uD/lkP+IPjFQWOvzH3O8zYMEREREZHzbvSoLEaPyjrr+chu3Zg86cftnuvZI5E7Z89s1TYgPY0B6Wnn/MzLx49r03bZ6FFcNnrUt+jx+WeauYYeQcYdv07naoaEoHPUt5xR39J+/aFGI7DcGGXsgbnEYeyluTzC2CsjIiIiIiLmZprgMtIBX9ZD3Wl3Zvu01mg/W/2ntaeODzfB103t1z9WBPc7IcACFuDkRE2Lz7gNs4iIiIiImJtpgsvYcOOuYD8/CPn18CeXMRsyPdY43+Q1ZmA8J4LGrO6wvNx4uOSOeuOZLldFQGpI6+vub4RcN0yJMo6H2407i+W54e1KY/ZFRERERETMzTR7XKwW+Gcy3H4Ihu2G5GB4Oxn6GM+9YVMdjNsLBwYbbVlhsLg3PHwMylrgigj4W6+2132sCB5IgBN7irgs3Lgd8pivjLB0V8feDOEbLXhuUWd3QUS6mDPXLYuIiPwQdWpw8Q1rfZwSAuvOskcoO7xt/W0xxutcXu7Ttu3ZJOMlIiIiIiJdg2mWiomIiIiIiJyNgouIiIiIiJiegouIiIiIiJiegouIiIiIiJiegouIiIiIiJiegouIiIiIiJiegouIiIiIiJiegouIiIiIiJiegouIiIiIiJiegouIiIiIiJiegouIiIiIiJiegouIiIiIiJiegouIiIiIiJiegouIiIiIiJiegouIiIiIiJiegouIiIiIiJiegouIiIiIiJiegouIiIiIiJiegouIiIiIiJiegouIiIiIiJiegouIiIiIiJiegouIiIiIiJiegouIiIiIiJiegouIiIiIiJiegouIiIiIiJiegouIiIiIiJiegouIiIiIiJiegouIiIiIiJiegouIiIiIiJiegouIiIiIiJiegouIiIiIiJiegouIiIiIiJiegouIiIiIiJiegouIiIiIiJheYGd3QEREREREvj2Px8Nrr7/JZaMuJSmpJwDVNTWsXbueIpeL8LAwRo/KonfvXme9RkHhPjZv/pw6t5uknj0Ylz0Wuz0UgKIiF6s+XktTUxNZI0cwaOAA//s+3fgZsTExpKf3v7CDbIdmXEREREREuoiWlhY+/Gg15eUV/jafz8f7739ISEgwN94wmfT0/nzw4Sqqq6vbvUZxcQmrP17L8OFDuWHyJJqamln98Rr/+XUbPmXQwHSunDiB9Rs2Ul9fD0Cd282RI0fp3z/lwg7yLEwVXJaXgyW39WtSYfu1eW7I2gP2rTBsN3xRd+qcqxku+woitsGMQ+DznTr3biVMO3ghRyEiIiIicv6Vl1fwxpvvUHVGIDl69BgVlZWMyx5DdHQUw4Zm4nTGs2v3nnavs33HTpL79WVAehqxsTFcPmEcXx8+QmVVFQAVFZX069eXpJ49CQ4Oprq6BoDc3G1kDskgIKBzIoSpgsuuerg+EooyTr2W9GlbV+eBqwphpANyB8BlYXBNIdR4jPN/ckH3QNicDquq4d2qU+/9fRE8mNARoxEREREROX+OFRWRlNSTGyZPatXuKi6me2wsNpvN35bgdOJyFbd7HVdxCYmJp74Qh4eHER4e5q8PDw/j+PFSqmtqaGxsxBHmoLaujmPHikhN7ZzZFjDZHpddDZARCs6gc9e9VgFBFni6JwRY4M894b0qo/2/YmFPgxGABoYa4WZPA/wYeLsCLgqFfsEdMRoRERERkfNn8KCB7bbXud04HPZWbXZ7KLV1de3Wu91uHA5H6/pQO7W1Rn3WyEtYtXoNHo+H4cMyCXM4WLd+A5mZF3fabAuYLbjUw5Sob67bXAejHEZoAbBYYFQYfFZrBJdeNtjmhgYv5DfATyKN5WKPu2BFvws6BBERERGRDtXS3ILVam3VZrVa8Xg87de3tFcf4K9P7teX3tNuw+PxEBwcTE1NLUVFxYy5bDSfbd7CV3sLSXDGM37cWIKCvmHG4TwyzVKxJi/sa4SVVZC6E5J3wLwj0OhtW1vUDIm21m3xgXCk2fj5nnhjBsaxDeIC4adR8EYlZNqhr2ZbREREROQHJDCwbUjxeDwEBrY/R9FeqPF4vAQFnaoPDAwkONj44pyTu5WhQ4fgKi5mb8E+bv7PKXi9XrbvyD/PIzk30wSXgkZoARwB8EY/eLInLCuHu4+0rXV7IdjSui044FTISQ2BAxcZe2RW9wcr8IcieMAJb1QYwShrDxQ0XOhRiYiIiIhcWA6HA7e7vlWb2+3GYbefo97dpt7eTn11TQ3FJSWkpiTjcpXgjI/DZrOR1LMnxcXt76G5UEwTXAaFQunF8LfecLEdJkfBs0mwuBRafK1rQyzQeEZboxfsp43GaoG4EzNXKypghMM4nnEI3kqGm6JhzuELOyYRERERkQvNGR9PaWkpzc3N/raiIhfx8fFnqY+jqMjlP66pqaWmthZnO/U5OVsZNjQTi8WCxWLcehnA6/Pia1N9YZkmuADEnDGbNSAEmn1wvKV1ew+bccvj07maIaGdJXZeH/zRBb9NgN0NYLMYG/QnRhh7ZUREREREurLExATCw8NZveYTysrLyd36Ja7iEv+DIz0eD3VuN16vsTxp8OCB7C0oJH/XbkrLyli9Zi29eyURGdmt1XWrqqopOX6clGRjk3hc9+4cPVZEaWkZ+/cfID4urkPHaZrg8lYFxOcZe11O2uaGSCs4zwg0Ix2wqfbU81l8PthUZ7SfaXk5XBpmbNi3ACcv3+IzQo2IiIiISFcWEBDA1VdfSX19PStef4uvvtrL1T+aSEREOABFrmJeXvKq/65hCU4n47LHkJOzlTfffIdgm43LJ4xrc90vcnMZPmwoFouxRyMxMYH+qcm89c6/CAwKIiNjcMcNErD4fD5TfH0va4EB+XB1N/it09jzMv0Q3NndmC1xNUM3K4QGQLUHUnbCjVEwu7uxnGx5ORQOhvDTbpDg8UHmbng/BXrajOe/JG6HpX2N2ZatbvgwtfPG3J4Fzy3q7C6ISBdz5+yZnd0FU9HvURH5rvR7tGswzYxLTKARIg41wtDdxl6UO2LhfqdxPmE7vFZu/BxhhfdSjFmXobthY60RTsJb39WNv5fDmDAjtAA4rPBcLyMQvVcFz/bsuPGJiIiIiMj3Z6rnuGTaYW1a++d8w1of/4cDtrb/DB6/22KM1+mmxhgvERERERHpOkwz4yIiIiIiInI2Ci4iIiIiImJ6Ci4iIiIiImJ6Ci4iIiIiImJ6Ci4iIiIiImJ6Ci4iIiIiImJ6Ci4iIiIiImJ6Ci4iIiIiImJ6Ci4iIiIiImJ6Ci4iIiIiImJ6Ci4iIiIiImJ6Ci4iIiIiImJ6Ci4iIiIiImJ6Ci4iIiIiImJ6Ci4iIiIiImJ6Ci4iIiIiImJ6Ci4iIiIiImJ6Ci4iIiIiImJ6Ci4iIiIiImJ6Ci4iIiIiImJ6Ci4iIiIiImJ6Ci4iIiIiImJ6Ci4iIiIiImJ6Ci4iIiIiImJ6Ci4iIiIiImJ6Ci4iIiIiImJ6Ci4iIiIiImJ6Ci4iIiIiImJ6Ci4iIiIiImJ6Ci4iIiIiImJ6Ci4iIiIiImJ6Ci4iIiIiImJ6Ci4iIiIiImJ6Ci4iIiIiImJ6Ci4iIiIiImJ6Ci4iIiIiImJ6gZ3dgTM1euGuw/B6BQRb4O54+I2z/do8N9zxtfHfAaGwsBf8h8M452qGKfuNc/8ZDYt6gcVinHu3Et6uhJf6dMCARERERETOA4/Hw7oNn7Jv336sAVaGDMlgaOaQdmtLS8tYu249ZWXlREVFkj3mMuLj4wCoc7v594erKC0tIzU1mXFjx2A58UX5wMGD7N9/kAnjsztoVN+e6WZc7j0Cm2phdSos6g2/L4J/lLetq/PAVYUw0gG5A+CyMLimEGo8xvk/uaB7IGxOh1XV8G7Vqff+vggeTOiY8YiIiIiInA8bN23G5SrmJ9ddS3b2GL7I2cregsI2dc3Nzfxr5fs44+O58YbJJCY4Wfn+BzQ1NQGwdeuXhIaEMOWn13P48FEOHjzkf29OzjaGDxvaYWP6LkwVXOo8sLgUnk2CYQ74SaQx27KgpG3taxUQZIGnexqzLX/uCd2sRjvAngb4UQQMDDXCzZ4Go/3tCrgoFPoFd9iwRERERET+V5qbm8nftZvLRl1KXFx3+vXtw9DMIezYkd+mtqBwH9aAAEaPyiI6OorRoy7FZrNRULgPgIrKSnr1SiI6OgpnfBwVlZUA7Nt/gJiYaLp1i+jIoX1rpgouefXQ6IPRYafaRofBF25o8bWu3VwHoxwQcGL5l8UCo8Lgs1rjuJcNtrmhwQv5DcaxzwePu+ABzbaIiIiISBdSWlqGx+MhIeHUHoqEBCfFJSV4vd5Wta7iYpwJTv/yL4vFQoLTictVDEB4WBilpWW0tLRQXl5BWFgYPp+P3NxtDB+W2XGD+o5MtcelqBmirRByWpyKD4QmHxxvgYSg1rVpIa3fHx8IX9YbP98TDxP2wt9KYVw4/DQK3qiETDv0PcdsS0NDAzt37jxvY/qujh090mmfLSJdU05OTmd3wVT0e1REvqvO/j06ePBgQkJCzllT53YTEhJCYOCpr+/20FC8Xi/19fU4HA5/u7vOTWRkZKv32+2hHC8tAyBzyMW886+V5O/aTY8eiST368u+ffuJ7R5DRIQ5Z1vAZMHF7YXgM+aATh43etuptbStPVmXGgIHLoKyFogLAq8P/lAEbyfDGxVw/1GIDYSlfYzak3bu3MnUqVPP67hERC6kN99Y0dldEBHp0jr79+iyZcsYPnz4OWtaWlqwWlt/UbZarQB4PN52aq1taj0eYzN4ZGQ3br3lJhoaGrHbQ43Zlq1fctVVEynct5/PNm8hJCSEKyaMJzKy2/92eOeNqYJLSEDbgHLy2H5GoAmxGMvKzqw9vc5qMUILwIoKGOEwjmccgnVpsLYG5hyGD1JPvWfw4MEsW7bs/AxIREREROQbDB48+BtrAq3WNgHlZBA5fRYGwGoN9J87vTbotLqAgADs9lDA2BMTF9cde2goaz9Zz/WTruPo0WOs/3QjP7726u81pgvBVMGlRxBUeKDJC7YTAcTVYsysRJ/R0x4245bHp3M1t15OdpLXB390wb9SYHcD2CzGBv0gC/zuWOvakJCQb0y8IiIiIiIdyeFw0NjYiMfj8c+muOvdWK1WQkJa74NwhNlxu92t2urc9djt9jbX9fl8bN36JddcfSXlFRVYAwKIjYnBGhDA51+YaymyqTbnD7EboWJT3am2T2thmB0Cz1gWNtJh3DbZd2LWxecz3jfSQRvLy+HSMGODvgU4mVVbfEaoERERERExs9jYGAICAig6scEeoKjIRffusQQEtP5K74yPx1VcjO/EF2Wfz4fL5cLpjG9z3b17C3A64wkPD8eCBR/Ge7xen/97tlmYKrjYA+C2GJj9NXxeB/+qhKdcMMd4Vg6uZqg/kTpuiILaEw+r3FUPdx+Bao/xsMnTeXzwRDH89sQNGPoHG0vK/lkJy8phZBgiIiIiIqYWFBREelp/1q3fQHFxCQcOHGTbl9u5+CJjmVmd201LSwsAKcn9aG5uZv2GjZSXV/Dpxs9oamomNSW51TW9Xi9bt+X57yQWGdkNj8fL/gMH+Wpvgf+BlWZh8fnMlaXcXph1CN6shAgr/DreeAFYcuHl3vDzWOP4izqYeQh2NUBGKDzfy3j+y+leKTPqFvQ61basDP7PEXAGwWt9jefAiIiIiIiYWXNzM5+s/5T9+/YTZLOROSSDzCEXA7DguUVMGJ/NgPQ0AIqLS/hk3QbKKyqIiYkme8xlxMV1b3W93Xu+oqTkOGPHjPa3fbW3gE83bsIeaufKiZcTHR3VcQP8BqYLLiJiyMvLo3///oSGKlmLiHQkn8/nf/6FiJiHqZaKiYgxbVtWVsaMGTN49dVXaWho6OwuiYj8oJ38G+6BAwcAFFpETErBRcRkfD4fMTExREdH85e//IWlS5fS1NTU2d0SEfnBslgsrF27lsmTJ3f6gwhF5OwUXERM5uQtDiMiIrjkkkt45plnWLhwocKLiMgFUlhYyCeffMK9997L8OHD0Sp6EXMy1XNcRMSYcTl8+DB1dXU899xzfPnll/zyl7/EYrEwc+ZMbDZbZ3dRROQHwefzUV9fz1133cWBAweYPn06YMzAaJ+LiPloxkXEZCwWC3FxcVxzzTU0NjYyYcIEnn76af7617+yaNEizbyIiJwnFosFu93OggULGDx4MDk5OeTm5vrPaeZFxFx0VzERk2pqasJms/mfkLty5UruuecefvnLX2rmRUTkezo5k3JyZtvr9TJw4EBcLhfTp0+nV69ezJo1i8GDB7eqF5HOp+AiYnJerxeLxYLFYmHlypXMmzePW2+9lblz5yq8iIh8D6tWreIPf/gDUVFRlJSUkJmZyfTp04mMjGTWrFn06dOHWbNmMWjQoM7uqoicRkvFREwuIMD439Tn83Httdfy6KOP8sYbb1BbW9vJPRMR6Xr27NnDgw8+yO23386bb77J/fffz0cffURhYSFJSUk8//zzHD58mKeeeordu3d3dndF5DQKLiJdwMllCj6fj8mTJ7NmzRqio6M7uVciIl3P119/TUpKClOnTuXo0aP8+c9/5uabb2b48OEsWbKEpKQknnjiCdxuN1FR5nliuIgouIh0GaevsXY4HJ3YExGRrstutxMYGMi2bduYOnUqWVlZPPzww1RVVfE///M/5OTkkJaWxquvvorT6ezs7orIaRRcRLqQk+FFG0VFRM6tpaUFr9cLQHV1tb+9Z8+elJaWMm3aNEaNGsWjjz4KQGxsLElJSdjtdgCCgoI6vtMick4KLiIiIvKDsWzZMvLy8ggMDCQgIIDVq1cza9YsbrzxRl5//XV69+7N3LlzaWxsJCkpiT179lBTU8Py5cupra0lLi4O0B+IRMxIdxUTERGRH4SKigruv/9+vvrqK1544QUcDgcTJ07kF7/4BQUFBRw+fJiJEydy11138c4777B48WKqqqqIiYmhoqKChQsXMnDgwM4ehoichYKLiIiIdHl1dXU4HA4KCgpYuHAh27dvZ9y4cdjtdubOnQvA4sWL+eCDD8jOzmb27NmUlZVx4MABmpqaSEtLIz4+vnMHISLnFNjZHRARERH532hoaGDNmjUMHTqU1NRUbr31Vt59912WL1/O5MmT/XXTp0/H5/Px73//m4CAAKZMmcLIkSM7seci8l0ouIiIiEiX5fP58Pl8zJ8/n4SEBH784x9z6NAhrr32WkpLS3nvvfe46aabGDBgAAAzZswgICCAFStWEBQUxO23347VatWeFpEuQEvFREREpMvyer3+B/WOGTOGkpISHn74YW6++Wb279/PX/7yF/Lz83n22WcZNGiQ/32vvPIK48ePJykpqbO6LiLfkYKLiIiIdDmrV69m79695Obm0qtXL/r06cPLL79Mc3MzAwYM4NFHHyUxMZHCwkKef/558vLymD9/vn/mRUS6HgUXERER6VKefPJJVq1axYABA4iJiWHXrl243W5CQkL44x//yG233UZycjKPP/64P7wsXLiQdevWsXTpUoUXkS5KwUVERES6jCVLlvDCCy/w3HPPkZ6ejs1mo7y8nMLCQu677z769+/Pr371K+666y4SExN58skncTqdbNmyhbVr13LTTTfRu3fvzh6GiHwPCi4iIiJiej6fj+bmZubMmcOoUaP42c9+1qbm4MGD3HLLLYwfP57777+fq6++mvj4eFJSUggJCWHu3LmEhYV1Qu9F5HwI6OwOiIiIiHwTi8VCTU0N27dvJzk5ud2aPn368PDDD/PRRx9x6NAhXn/9dbp160ZBQQFTpkxRaBHp4nQ7ZBEREekSwsPDiYyM5Pjx44AxC3PmbYyHDRtGcHAwOTk53HLLLSxYsICWlhZCQ0M7o8sich5pxkVERERMz+fz0dLSQnh4OJs2bQKMWZgzV7zHxMTgdDqpqKgAICgoSKFF5AdCwUVERERMz2KxYLfbufPOO/nnP//JsmXL/O2AP8BUVFRgs9nIyMjotL6KyIWh4CIiIiJdxqWXXsrMmTN57LHHWLx4MU1NTcCpALN06VKOHz9Oenp6Z3ZTRC4A7XERERGRLsNqtTJjxgxsNhtPP/00mzdvpn///oSEhHDs2DHWr1/Piy++SHx8fGd3VUTOM90OWURERLqkvLw8VqxYwddff01ISAgDBw5k0qRJ9O3bt7O7JiIXgIKLiIiIiIiYnva4iIiISJd1+t9f9bdYkR82zbiIiIiIiIjpacZFRERERERMT8FFRERERERMT8FFRERERERMT8FFRERERERMT8FFRERERERMT8FFROQHLi0tjRdffBGA+fPnk5mZ2eF92LJlC2lpaezYseOsNW+99RZpaWmUl5d3YM9ERKSrCOzsDoiIyIX12muvkZiY2Nnd+EbZ2dm89tprREREdHZXRETEhBRcRER+4IYMGdLZXfhWoqOjiY6O7uxuiIiISWmpmIhIF5eXl8fUqVPJzMxkxIgRzJkzh6NHj/rPn75U7KR33nmHcePGcfHFFzNz5kwOHTrkP+d2u3nggQcYPXo0GRkZXH/99Xz00Uf+8/PmzWPmzJm89NJLXHrppQwfPpxf//rXVFZWfmNf8/PzmTx5MhdddBGTJ09m48aN/nNnLhUbP348ixcv5ne/+x0jRoxg6NCh3HfffdTW1n7rsYuIyA+HgouISBdWX1/PjBkziI+P57nnnuOxxx5j165d3H333ed8z1NPPcWcOXN44oknOHjwINOmTaO5uRmAP/3pT2zevJkHHniARYsWkZyczK9+9Sv27dvnv0Zubi7Lly/noYce4sEHH2TTpk3MmjXrG/v7+OOPc/nll7NgwQJiY2OZOXNmq+ueadGiRVRXV/PMM88wd+5c3nvvPZ5//vnvPXYREem6tFRMRKQLKygooLKykp/97Gf+TfdRUVFs3rwZr9dLQEDbv0/5fD6efPJJsrKyAOjXrx/XXXcd7733HpMmTSInJ4dRo0Zx1VVXATBs2DBiY2NpaWnxX6O2tpZ//OMfpKSkABAZGcnMmTP5/PPPGTFixFn7O23aNGbPng1AVlYWV1xxBS+++CKPP/54u/VOp5NnnnkGi8XC6NGj+fzzz1m/fj333nvv9xq7iIh0XQouIiJdWL9+/YiMjOSOO+7gmmuuYezYsWRlZZ0zPISHh/tDC0BqaipJSUns2LGDSZMmkZmZyYoVKygpKWHcuHFkZ2czb968VtdIS0vzhxaAsWPHEhQURE5Ozjk/+8orr/T/bLPZGD16NNu3bz9r/UUXXYTFYvEfO51Odu/e/b3HLiIiXZf+HCUi0oWFhYXx97//naysLN5++21mzJjBqFGjWL58+VnfExMT06YtOjqakpISAB588EFmz55NQUEBDz/8MNnZ2cyZM6fV3pLu3bu3er/FYiEyMpKqqqpz9vfMzz79c9sTGhra5nN8Ph/w/cYuIiJdl4KLiEgXl5qayrPPPsuWLVtYsmQJw4YN45FHHiEvL6/d+urq6jZtpaWlREZGAhASEsKcOXP4+OOP+eCDD5gzZw5r167lySef9NefuRHf6/VSUVHRbig612ef/rnfx3cdu4iIdF0KLiIiXdj69evJysqivLwcm81GVlYWDz30EADHjh1r9z3l5eXk5+f7j/Pz8zly5AgjRozA4/Fw7bXXsmTJEsBYjjVr1iyGDBlCUVGR/z179uzB5XL5jz/55BNaWlq45JJLztnfDRs2+H9uaGhg/fr133tp1/cZu4iIdF3a4yIi0oVlZGTg8/m48847mT59OkFBQbzyyitEREScNUTYbDbuvvtu7rnnHpqbm3nqqadIT0/nyiuvxGq1kpGRwV//+leCg4Pp168feXl55Obm8sgjj/iv0dLSwh133MGdd95JVVUVTz31FNnZ2Vx88cXn7O+iRYsIDg6mR48evPTSS9TX1zN9+vQOG7uIiHRdCi4iIl1YZGQkL7zwAk8//TS/+c1vaG5uJiMjg5dffvmsD3Ps0aMHP//5z3nkkUeoq6tj7NixPPTQQ9hsNsDY42K321m4cCFlZWX06NGD++67jylTpvivkZKSwlVXXcVvf/tbLBYL1113Hffcc8839veRRx5h/vz5HDp0iEGDBrF06VJ69erVYWMXEZGuy+I7uctRRETkW5g3bx47d+5k5cqVnd0VERH5/4j2uIiIiIiIiOkpuIiIiIiIiOlpqZiIiIiIiJieZlxERERERMT0FFxERERERMT0FFxERERERMT0FFxERERERMT0FFxERERERMT0/h9ZVZIQpH5nGwAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from cobra.evaluation import plot_incidence\n", - "for predictor in list(pig_tables.variable.unique()):\n", - " print(predictor)\n", - " try:\n", - " if predictor + \"_bin\" in basetable.columns:\n", - " column_order = list(basetable[predictor + \"_bin\"].unique().sort_values())\n", - " else:\n", - " column_order = None #sorted(list(basetable[predictor].unique())) # e.g. just binary variable\n", - " plot_incidence(pig_tables,\n", - " variable=predictor,\n", - " model_type=\"classification\",\n", - " column_order=column_order)\n", - " except ValueError as ve:\n", - " print(f\"Can't plot PIG for {predictor}. Error was: {ve}\")\n", - " except TypeError as ve:\n", - " print(f\"Can't plot PIG for {predictor}. Error was: {ve}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "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": "3.8.8" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/docs/Makefile b/docs/Makefile deleted file mode 100644 index cb7f749..0000000 --- a/docs/Makefile +++ /dev/null @@ -1,44 +0,0 @@ -# Minimal makefile for Sphinx documentation -# - -# You can set these variables from the command line, and also -# from the environment for the first two. -SPHINXOPTS ?= -SPHINXBUILD ?= sphinx-build -SOURCEDIR = source -PAPER = -BUILDDIR = build - -# Internal variables. -PAPEROPT_a4 = -D latex_paper_size=a4 -PAPEROPT_letter = -D latex_paper_size=letter - -.PHONY: help Makefile clean html man changes linkcheck doctest - - -# Put it first so that "make" without argument is like "make help". -help: - @echo "Please use \`make ' where is one of" - @echo " html to make standalone HTML files" - @echo " man to make manual pages" - @echo " changes to make an overview of all changed/added/deprecated items" - @echo " linkcheck to check all external links for integrity" - @echo " doctest to run all doctests embedded in the documentation (if enabled)" - -clean: - -rm -rf $(BUILDDIR)/* - -html: - $(SPHINXBUILD) -b html $(SOURCEDIR) $(BUILDDIR)/html $(SPHINXOPTS) - -man: - $(SPHINXBUILD) -b man $(SPHINXOPTS) $(BUILDDIR)/man - -changes: - $(SPHINXBUILD) -b changes $(SPHINXOPTS) $(BUILDDIR)/changes - -linkcheck: - $(SPHINXBUILD) -b linkcheck $(SPHINXOPTS) $(BUILDDIR)/linkcheck - -doctest: - $(SPHINXBUILD) -b doctest $(SPHINXOPTS) $(BUILDDIR)/doctest diff --git a/docs/make.bat b/docs/make.bat deleted file mode 100644 index 6247f7e..0000000 --- a/docs/make.bat +++ /dev/null @@ -1,35 +0,0 @@ -@ECHO OFF - -pushd %~dp0 - -REM Command file for Sphinx documentation - -if "%SPHINXBUILD%" == "" ( - set SPHINXBUILD=sphinx-build -) -set SOURCEDIR=source -set BUILDDIR=build - -if "%1" == "" goto help - -%SPHINXBUILD% >NUL 2>NUL -if errorlevel 9009 ( - echo. - echo.The 'sphinx-build' command was not found. Make sure you have Sphinx - echo.installed, then set the SPHINXBUILD environment variable to point - echo.to the full path of the 'sphinx-build' executable. Alternatively you - echo.may add the Sphinx directory to PATH. - echo. - echo.If you don't have Sphinx installed, grab it from - echo.http://sphinx-doc.org/ - exit /b 1 -) - -%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% -goto end - -:help -%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O% - -:end -popd diff --git a/docs/source/conf.py b/docs/source/conf.py deleted file mode 100644 index 449fd5a..0000000 --- a/docs/source/conf.py +++ /dev/null @@ -1,83 +0,0 @@ -# Configuration file for the Sphinx documentation builder. -# -# This file only contains a selection of the most common options. For a full -# list see the documentation: -# https://www.sphinx-doc.org/en/master/usage/configuration.html - -# -- Path setup -------------------------------------------------------------- - -# If extensions (or modules to document with autodoc) are in another directory, -# add these directories to sys.path here. If the directory is relative to the -# documentation root, use os.path.abspath to make it absolute, like shown here. - -import os -import sys -sys.path.insert(0, os.path.abspath('../../')) - - -# -- Project information ----------------------------------------------------- - -project = 'cobra' -copyright = '2020, Python Predictions' -author = 'Python Predictions' - -# The full version, including alpha/beta/rc tags -release = '1.0.0' - - -# -- General configuration --------------------------------------------------- - -# Add any Sphinx extension module names here, as strings. They can be -# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom -# ones. -extensions = [ - 'sphinx.ext.todo', - 'sphinx.ext.viewcode', - 'sphinx.ext.autodoc', - 'sphinx.ext.githubpages', - 'sphinx.ext.napoleon' -] - -autodoc_member_order = 'bysource' - -# Napoleon settings -napoleon_google_docstring = True -napoleon_numpy_docstring = True -napoleon_include_init_with_doc = True -napoleon_include_private_with_doc = False -napoleon_include_special_with_doc = True -napoleon_use_admonition_for_examples = False -napoleon_use_admonition_for_notes = False -napoleon_use_admonition_for_references = False -napoleon_use_ivar = False -napoleon_use_param = True -napoleon_use_rtype = True -napoleon_type_aliases = None - -# Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] - -# The suffix(es) of source filenames. -source_suffix = ['.rst', '.md'] - -# The master toctree document. -master_doc = 'index' - -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -# This pattern also affects html_static_path and html_extra_path . -exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] - -# -- Options for HTML output ------------------------------------------------- - -# The theme to use for HTML and HTML Help pages. See the documentation for -# a list of builtin themes. -# -html_theme = 'sphinx_rtd_theme' - -# Add any paths that contain custom static files (such as style sheets) here, -# relative to this directory. They are copied after the builtin static files, -# so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ['_static'] - -html_favicon = 'images/cobra_icon.png' diff --git a/docs/source/index.rst b/docs/source/index.rst deleted file mode 100644 index 92055b9..0000000 --- a/docs/source/index.rst +++ /dev/null @@ -1,37 +0,0 @@ -.. cobra documentation master file, created by - sphinx-quickstart on Thu Dec 3 11:55:07 2020. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - -********************************* -Welcome to cobra's documentation! -********************************* - -.. include:: ../../README.rst - -.. toctree:: - :maxdepth: 2 - :hidden: - :caption: Contents: - -.. toctree:: - :maxdepth: 4 - :hidden: - :caption: Tutorial - - tutorial - -.. toctree:: - :maxdepth: 4 - :hidden: - :caption: API Reference - - docstring/modules - - -Indices and tables -================== - -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` diff --git a/docs/source/tutorial.rst b/docs/source/tutorial.rst deleted file mode 100644 index d8d69b1..0000000 --- a/docs/source/tutorial.rst +++ /dev/null @@ -1,224 +0,0 @@ -Tutorial -======== - -This section we will walk you through all the required steps to build a predictive model using cobra. All classes and functions used here are well-documented. In case you want more information on a class or function, simply read the corresponding parts in the documentation or run the following python snippet from e.g. a notebook: - -.. code-block:: python - - help(function_or_class_you_want_info_from) - -Building a good model involves three steps - - - preprocessing: properly prepare the predictors (a synonym for "feature" or variable that we use throughout this tutorial) for modelling. - - feature selection: automatically select a subset of predictors which contribute most to the target variable or output in which you are interested. - - model evaluation: once a model has been build, a detailed evaluation can be performed by computing all sorts of evaluation metrics. - -In the examples below, we assume the data for model building is available in a pandas DataFrame called ``basetable``. This DataFrame should at least contain an ID column (e.g. "customernumber"), a target column (e.g. "TARGET") and a number of candidate predictors (features) to build a model with. - -Preprocessing -------------- - -The first part focusses on preparing the predictors for modelling by: - -- Splitting the dataset into training, selection and validation datasets. -- binning continuous variables into discrete intervals -- Replacing missing values of both categorical and continuous variables (which are now binned) with an additional "Missing" bin/category -- Regrouping categories in new category "other" -- Replacing bins/categories with their corresponding incidence rate per category/bin. - -This will be taken care of by the ``PreProcessor`` class, which has a scikit-learn like interface (i.e. ``fit`` & ``transform``) - -.. code-block:: python - - import json - from cobra.preprocessing import PreProcessor - - # Prepare data - # create instance of PreProcessor from parameters - # There are many options possible, see API reference, but here - # we will use all the defaults - preprocessor = PreProcessor.from_params() - - # split data into train-selection-validation set - # in the result, an additional column "split" will be created - # containing each of those values - basetable = preprocessor.train_selection_validation_split( - basetable, - train_prop=0.6, selection_prop=0.2, - validation_prop=0.2) - - # create list containing the column names of the discrete resp. - # continuous variables - continuous_vars = [] - discrete_vars = [] - - # fit the pipeline - preprocessor.fit(basetable[basetable["split"]=="train"], - continuous_vars=continuous_vars, - discrete_vars=discrete_vars, - target_column_name=target_column_name) - - # store fitted preprocessing pipeline as a JSON file - pipeline = preprocessor.serialize_pipeline() - - # I/O outside of PreProcessor to allow flexibility (e.g. upload to S3, ...) - path = "path/to/store/preprocessing/pipeline/as/json/file/for/later/re-use.json" - with open(path, "w") as file: - json.dump(pipeline, file) - - # transform the data (e.g. perform discretisation, incidence replacement, ...) - basetable = preprocessor.transform(basetable, - continuous_vars=continuous_vars, - discrete_vars=discrete_vars) - - # When you want to reuse the pipeline the next time, simply load it back in again - # using the following snippet: - # with open(path, "r") as file: - # pipeline = json.load(file) - # preprocessor = PreProcessor.from_pipeline(pipeline) and you're good to go! - -Feature selection ------------------ - -Once the predictors are properly prepared, we can start building a predictive model, which boils down to selecting the right predictors from the dataset to train a model on. As a dataset typically contains many predictors, we can first perform a univariate preselection to rule out any predictor with little to no predictive power. - -This preselection is based on an AUC threshold of a univariate model on the train and selection datasets. As the AUC just calculates the quality of a ranking, all monotonous transformations of a given ranking (i.e. transformations that do not alter the ranking itself) will lead to the same AUC. Hence, pushing a categorical variable (incl. a binned continuous variable) through a logistic regression will produce exactly the same ranking as using target encoding, as it will produce the exact same output: a ranking of the categories on the training/selection set. Therefore, no univariate model is trained here as the target encoded train and selection data is used as predicted scores to compute the AUC with against the target. - -.. code-block:: python - - from cobra.model_building import univariate_selection - from cobra.evaluation import plot_univariate_predictor_quality - from cobra.evaluation import plot_correlation_matrix - - # Get list of predictor names to use for univariate_selection - preprocessed_predictors = [col for col in basetable.columns if col.endswith("_enc")] - - # perform univariate selection on preprocessed predictors: - df_auc = univariate_selection.compute_univariate_preselection( - target_enc_train_data=basetable[basetable["split"] == "train"], - target_enc_selection_data=basetable[basetable["split"] == "selection"], - predictors=preprocessed_predictors, - target_column=target_column_name, - preselect_auc_threshold=0.53, # if auc_selection <= 0.53 exclude predictor - preselect_overtrain_threshold=0.05 # if (auc_train - auc_selection) >= 0.05 --> overfitting! - ) - - # Plot df_auc to get a horizontal barplot: - plot_univariate_predictor_quality(df_auc) - - # compute correlations between preprocessed predictors: - df_corr = (univariate_selection - .compute_correlations(basetable[basetable["split"] == "train"], - preprocessed_predictors)) - - # plot correlation matrix - plot_correlation_matrix(df_corr) - - # get a list of predictors selection by the univariate selection - preselected_predictors = (univariate_selection - .get_preselected_predictors(df_auc)) - -After an initial preselection on the predictors, we can start building the model itself using forward feature selection to choose the right set of predictors. Since we use target encoding on all our predictors, we will only consider models with positive coefficients (no sign flip should occur) as this makes the model more interpretable. - -.. code-block:: python - - from cobra.model_building import ForwardFeatureSelection - from cobra.evaluation import plot_performance_curves - from cobra.evaluation import plot_variable_importance - - forward_selection = ForwardFeatureSelection(max_predictors=30, - pos_only=True) - - # fit the forward feature selection on the train data - # has optional parameters to force and/or exclude certain predictors (see docs) - forward_selection.fit(basetable[basetable["split"] == "train"], - target_column_name, - preselected_predictors) - - # compute model performance (e.g. AUC for train-selection-validation) - performances = (forward_selection - .compute_model_performances(basetable, target_column_name)) - - # plot performance curves - plot_performance_curves(performances) - -Based on the performance curves (AUC per model with a particular number of predictors in case of logistic regression), a final model can then be chosen and the variables importance can be plotted: - -.. code-block:: python - - # After plotting the performances and selecting the model, - # we can extract this model from the forward_selection class: - model = forward_selection.get_model_from_step(5) - - # Note that chosen model has 6 variables (python lists start with index 0), - # which can be obtained as follows: - final_predictors = model.predictors - # We can also compute and plot the importance of each predictor in the model: - variable_importance = model.compute_variable_importance( - basetable[basetable["split"] == "selection"] - ) - plot_variable_importance(variable_importance) - -**Note**: variable importance is based on correlation of the predictor with the *model scores* (and not the true labels!). - -Finally, we can again export the model to a dictionary to store it as JSON - -.. code-block:: python - - model_dict = model.serialize() - - with open(path, "w") as file: - json.dump(model_dict, file) - - # To reload the model again from a JSON file, run the following snippet: - # from cobra.model_building import LogisticRegressionModel - # with open(path, "r") as file: - # model_dict = json.load(file) - # model = LogisticRegressionModel() - # model.deserialize(model_dict) - -Evaluation ----------- - -Now that we have build and selected a final model, it is time to evaluate it against various evaluation metrics: - -.. code-block:: python - - from cobra.evaluation import Evaluator - - # get numpy array of True target labels and predicted scores: - y_true = basetable[basetable["split"] == "selection"][target_column_name].values - y_pred = model.score_model(basetable[basetable["split"] == "selection"]) - - evaluator = Evaluator() - evaluator.fit(y_true, y_pred) # Automatically find the best cut-off probability - - # Get various scalar metrics such as accuracy, AUC, precision, recall, ... - evaluator.scalar_metrics - - # Plot non-scalar evaluation metrics: - evaluator.plot_roc_curve() - - evaluator.plot_confusion_matrix() - - evaluator.plot_cumulative_gains() - - evaluator.plot_lift_curve() - - evaluator.plot_cumulative_response_curve() - -Additionally, we can also compute the output needed to plot the so-called Predictor Insights Graphs (PIGs in short). These are graphs that represents the insights of the relationship between a single predictor (e.g. age) and the target (e.g. burnouts). This is a graph where the predictor is binned into groups, and where we represent group size in bars and group (target) incidence in a colored line. We have the option to force order of predictor values. - -.. code-block:: python - - from cobra.evaluation import generate_pig_tables - from cobra.evaluation import plot_incidence - - predictor_list = [col for col in basetable.columns - if col.endswith("_bin") or col.endswith("_processed")] - pig_tables = generate_pig_tables(basetable[basetable["split"] == "selection"], - id_column_name=id_column_name, - target_column_name=target_column_name, - preprocessed_predictors=predictor_list) - # Plot PIGs - plot_incidence(pig_tables, 'predictor_name', predictor_order) \ No newline at end of file