diff --git a/README.rst b/README.rst index 360064972..8b2d61a01 100644 --- a/README.rst +++ b/README.rst @@ -79,19 +79,25 @@ Enhanced Machine Learning Workflow ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ To demonstrate the model inspection capability of FACET, we first create a -pipeline to fit a learner. In this simple example using the Boston housing -data, we will train a Random Forest regressor using 10 repeated 5-fold CV -to predict median house price. With the use of *sklearndf* we can create a -*pandas* DataFrame compatible workflow. However, FACET provides additional -enhancements to keep track of our feature matrix and target vector using a -sample object (`Sample`) and easily compare hyperparameter configurations -and even multiple learners with the `LearnerRanker`. +pipeline to fit a learner. In this simple example we use the +`diabetes dataset `__ +which contains age, sex, BMI and blood pressure along with 6 blood serum +measurements as features. A transformed version of this dataset is also available +on scikit-learn +`here `__. + + +In this quickstart we will train a Random Forest regressor using 10 repeated +5-fold CV to predict disease progression after one year. With the use of +*sklearndf* we can create a *pandas* DataFrame compatible workflow. However, +FACET provides additional enhancements to keep track of our feature matrix +and target vector using a sample object (`Sample`) and easily compare +hyperparameter configurations and even multiple learners with the `LearnerRanker`. .. code-block:: Python # standard imports import pandas as pd - from sklearn.datasets import load_boston from sklearn.model_selection import RepeatedKFold # some helpful imports from sklearndf @@ -102,14 +108,11 @@ and even multiple learners with the `LearnerRanker`. from facet.data import Sample from facet.selection import LearnerRanker, LearnerGrid - # load Boston housing dataset - boston = load_boston() - boston_df = pd.DataFrame(data=boston.data, columns=boston.feature_names).assign( - MEDIAN_HOUSE_PRICE=boston.target - ) + # load the diabetes dataset + diabetes_df = pd.read_csv('diabetes_quickstart.csv') # create FACET sample object - boston_sample = Sample(observations=boston_df, target_name="MEDIAN_HOUSE_PRICE") + diabetes_sample = Sample(observations=diabetes_df, target_name="Disease_progression") # create a (trivial) pipeline for a random forest regressor rnd_forest_reg = RegressorPipelineDF( @@ -132,7 +135,7 @@ and even multiple learners with the `LearnerRanker`. # rank your candidate models by performance (default is mean CV score - 2*SD) ranker = LearnerRanker( grids=rnd_forest_grid, cv=rkf_cv, n_jobs=-3 - ).fit(sample=boston_sample) + ).fit(sample=diabetes_sample) # get summary report ranker.summary_report() @@ -140,9 +143,10 @@ and even multiple learners with the `LearnerRanker`. .. image:: sphinx/source/_static/ranker_summary.png :width: 600 -We can see based on this minimal workflow that a value of 8 for minimum samples -in the leaf was the best performing of the three considered values. This approach -easily extends to multiple hyperparameters for the learner and multiple learners. +We can see based on this minimal workflow that a value of 11 for minimum +samples in the leaf was the best performing of the three considered values. +This approach easily extends to multiple hyperparameters for the learner +and multiple learners. Model Inspection ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ @@ -209,28 +213,30 @@ features in a model are: # visualise synergy as a matrix from pytools.viz.matrix import MatrixDrawer - synergy_matrix = inspector.feature_synergy_matrix(symmetrical=True) + synergy_matrix = inspector.feature_synergy_matrix() MatrixDrawer(style="matplot%").draw(synergy_matrix, title="Synergy Matrix") .. image:: sphinx/source/_static/synergy_matrix.png :width: 600 -As before the matrix row represents the "perspective from" feature in the pair. -Looking across the row for `LSTAT` there is relatively minimal synergy (≤14%) -with other features in the model. However, looking down the column for `LSTAT` -(i.e., perspective of other features in a pair with `LSTAT`) we find many -features (the rows) are synergistic (12% to 47%) with `LSTAT`. We can conclude that: +For any feature pair (A, B), the first feature (A) is the row, and the second +feature (B) the column. For example, looking across the row for `LTG` (Lamotrigine) +there is relatively minimal synergy (≤14%) with other features in the model. +However, looking down the column for `LTG` (i.e., perspective of other features +in a pair with `LTG`) we find many features (the rows) are synergistic (12% to 34%) +with `LTG`. We can conclude that: + -- `LSTAT` is a strongly autonomous feature, displaying minimal synergy with other - features for predicting median house price. -- The contribution of other features to predicting median house price is partly - enabled by the strong contribution from `LSTAT`. +- `LTG` is a strongly autonomous feature, displaying minimal synergy with other + features for predicting disease progression after one year. +- The contribution of other features to predicting disease progression after one + year is partly enabled by the strong contribution from `LTG`. -High synergy features must be considered carefully when investigating business -impact, as they work together to predict the outcome. It would not make much -sense to consider `ZN` (proportion of residential land zoned for lots over -25,000 sq.ft) without `LSTAT` given the 47% synergy of `ZN` with `LSTAT` for -predicting median house price. + +High synergy features must be considered carefully when investigating impact, +as they work together to predict the outcome. It would not make much sense to +consider `TC` (T-Cells) without `LTG` given the 34% synergy of `TC` with `LTG` +for predicting progression after one year. **Redundancy** @@ -243,15 +249,20 @@ predicting median house price. .. image:: sphinx/source/_static/redundancy_matrix.png :width: 600 -For any feature pair (A, B), the first feature (A) is the row, and the second -feature (B) the column. For example, if we look at the feature pair (`LSTAT`, `RM`) -from the perspective of `LSTAT` (percentage of lower status of the population), -then we look-up the row for `LSTAT` and the column for `RM` (average number of -rooms per dwelling) and find 39% redundancy. This means that 39% of the -information in `LSTAT` is duplicated with `RM` to predict median house price. -We can also see looking across the row for `LSTAT` that apart from the 39% -redundancy with `RM`, `LSTAT` has minimal redundancy (<5%) with any of the -other features included in the model. +For any feature pair (A, B), the first feature (A) is the row, and the second feature +(B) the column. For example, if we look at the feature pair (`LDL`, `TC`) from the +perspective of `LDL` (Low-Density Lipoproteins), then we look-up the row for `LDL` +and the column for `TC` and find 47% redundancy. This means that 47% of the +information in `LDL` is duplicated with `TC` to predict disease progression +after one year. This redundancy is similar when looking "from the perspective" +of `TC` for (`TC`, `LDL`) which is 50%. + + +If we look across the columns for the `LTG` row we can see that apart from the +32% redundancy with `BMI`, `LTG` has minimal redundancy (<9%) with the other +features included in the model. Further, if we look cross the rows for the +`LTG` column we can see a number of the features have moderate redundancy +with `LTG`. **Clustering redundancy** @@ -278,12 +289,12 @@ Let's look at the example for redundancy. .. image:: sphinx/source/_static/redundancy_dendrogram.png :width: 600 -Based on the dendrogram we can see that the feature pairs (`LSTAT`, `RM`) -and (`CRIM`: per capita crime rate by town, `NOX`: nitric oxides concentration -in parts per 10 million) each represent a cluster in the dendrogram and -that `LSTAT` and `RM` have high importance. As a next action we could -remove RM (and maybe NOX) to further simplify the model and obtain a -set of independent features. +Based on the dendrogram we can see that the feature pairs (`LDL`, `TC`) +and (`LTG`, `BMI`: body mass index) each represent a cluster in the +dendrogram and that `LTG` and `BMI` have high the highest importance. +As potential next actions we could remove `TC` and explore the impact of +removing one of `LTG` or `BMI` to further simplify the model and obtain a +reduced set of independent features. Please see the `API reference `__ @@ -292,10 +303,10 @@ for more detail. Model Simulation ~~~~~~~~~~~~~~~~~~ -Taking the LSTAT feature as an example, we do the following for the simulation: +Taking the `BMI` feature as an example, we do the following for the simulation: - We use FACET's `ContinuousRangePartitioner` to split the range of observed values of - LSTAT into intervals of equal size. Each partition is represented by the central + `BMI` into intervals of equal size. Each partition is represented by the central value of that partition. - For each partition, the simulator creates an artificial copy of the original sample assuming the variable to be simulated has the same value across all observations - @@ -303,8 +314,8 @@ Taking the LSTAT feature as an example, we do the following for the simulation: acquired from the ranker, the simulator now re-predicts all targets using the models trained for all folds and determines the average uplift of the target variable resulting from this. -- The FACET `SimulationDrawer` allows us to visualise the result; both in a matplotlib - and a plain-text style. +- The FACET `SimulationDrawer` allows us to visualise the result; both in a + matplotlib and a plain-text style. Finally, because FACET can use bootstrap cross validation, we can create a crossfit from our previous `LearnerRanker` best model to perform the simulation so we can @@ -328,9 +339,9 @@ quantify the uncertainty by using bootstrap confidence intervals. cv=bscv, n_jobs=-3, verbose=False, - ).fit(sample=boston_sample) + ).fit(sample=diabetes_sample) - SIM_FEAT = "LSTAT" + SIM_FEAT = "BMI" simulator = UnivariateUpliftSimulator(crossfit=boot_crossfit, n_jobs=-3) # split the simulation range into equal sized partitions @@ -344,9 +355,10 @@ quantify the uncertainty by using bootstrap confidence intervals. .. image:: sphinx/source/_static/simulation_output.png -We would conclude from the figure that lower values of `LSTAT` are associated with -an increase in median house price, and that the lower `LSTAT` of 8% or less results -in a significant uplift in median house price. +We would conclude from the figure that higher values of `BMI` are associated with +an increase in disease progression after one year, and that for a `BMI` of 29 +and above, there is a significant increase in disease progression after one year +of at least 26 points. Contributing diff --git a/azure-pipelines.yml b/azure-pipelines.yml index 39440075b..a8b53f616 100644 --- a/azure-pipelines.yml +++ b/azure-pipelines.yml @@ -1,10 +1,10 @@ trigger: - master - - 1.1 + - 1.1.x pr: - master - - 1.1 + - 1.1.x # set the build name name: $[ variables['branchName'] ] @@ -15,7 +15,7 @@ schedules: displayName: Nightly full build branches: include: - - 1.1 + - 1.1.x resources: repositories: @@ -23,12 +23,12 @@ resources: type: github endpoint: BCG-Gamma name: BCG-Gamma/sklearndf - ref: 1.1 # todo - update to stable release + ref: 1.1.x # todo - update to stable release - repository: pytools type: github endpoint: BCG-Gamma name: BCG-Gamma/pytools - ref: 1.1 # todo - update to stable release + ref: 1.1.x # todo - update to stable release variables: ${{ if not(startsWith(variables['Build.SourceBranch'], 'refs/pull/')) }}: @@ -114,7 +114,7 @@ stages: jobs: - job: - displayName: 'pytest @ 1.1 environment' + displayName: 'pytest @ develop environment' condition: > and( ne(variables.master_or_release, 'True'), @@ -216,7 +216,7 @@ stages: - script: dir $(Build.SourcesDirectory) - script: | - conda install -y conda-build toml=0.10.* flit=3.0.* + conda install -y -c anaconda conda-build=3.20.5 toml=0.10.* flit=3.0.* displayName: 'Install conda-build, flit, toml' condition: eq(variables['BUILD_SYSTEM'], 'conda') @@ -276,10 +276,10 @@ stages: FACET_V_PYTHON_BUILD: '=3.8.*' BUILD_SYSTEM: 'conda' PKG_DEPENDENCIES: 'max' - unconstrained_dependencies_conda: - FACET_V_PYTHON_BUILD: '>=3.6' - PKG_DEPENDENCIES: 'unconstrained' - BUILD_SYSTEM: 'conda' +# unconstrained_dependencies_conda: +# FACET_V_PYTHON_BUILD: '>=3.6' +# PKG_DEPENDENCIES: 'unconstrained' +# BUILD_SYSTEM: 'conda' default_dependencies_tox: FACET_V_PYTHON_BUILD: '=3.7.*' BUILD_SYSTEM: 'tox' @@ -292,10 +292,10 @@ stages: FACET_V_PYTHON_BUILD: '=3.8.*' BUILD_SYSTEM: 'tox' PKG_DEPENDENCIES: 'max' - unconstrained_dependencies_tox: - FACET_V_PYTHON_BUILD: '>=3.6' - PKG_DEPENDENCIES: 'unconstrained' - BUILD_SYSTEM: 'tox' +# unconstrained_dependencies_tox: +# FACET_V_PYTHON_BUILD: '>=3.6' +# PKG_DEPENDENCIES: 'unconstrained' +# BUILD_SYSTEM: 'tox' steps: - task: UsePythonVersion@0 @@ -310,7 +310,7 @@ stages: - script: dir $(Build.SourcesDirectory) - script: | - conda install -y conda-build toml=0.10.* flit=3.0.* + conda install -y -c anaconda conda-build=3.20.5 toml=0.10.* flit=3.0.* displayName: 'Install conda-build, flit, toml' condition: eq(variables['BUILD_SYSTEM'], 'conda') diff --git a/environment.yml b/environment.yml index f3873010f..9a50b06d9 100644 --- a/environment.yml +++ b/environment.yml @@ -7,9 +7,9 @@ dependencies: - conda-build - conda-verify - docutils - - flit = 3.0 - flake8 = 3.8.* - flake8-comprehensions = 3.2.* + - flit = 3.0 - isort = 5.5.* - joblib = 0.16.* - jupyter >= 1.0 @@ -34,8 +34,8 @@ dependencies: - sphinx = 3.2.* - sphinx-autodoc-typehints = 1.11.* - tableone = 0.7.* - - typing_inspect = 0.6.* - toml = 0.10.* - tox = 3.20.* + - typing_inspect = 0.6.* - xlrd = 1.2.* - yaml = 0.1.* \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index a5eae0548..7a8604367 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -23,12 +23,12 @@ requires = [ "numpy >=1.16,<1.20", "scipy >=1.2,<1.6", "pyyaml >=5.0", - "joblib >=0.13,<1.0", + "joblib >=0.13,<=1.0", "gamma-pytools >=1.0.1", "sklearndf >=1.0.1", ] -requires-python = ">=3.6,<4" +requires-python = ">=3.6,<3.9" classifiers = [ "Development Status :: 5 - Production/Stable", @@ -90,7 +90,7 @@ shap = "=0.35" matplotlib = "=3.3.*" [build.matrix.unconstrained] -python = ">=3.6,<4" +python = ">=3.6,<3.9" pandas = ">=0.24" numpy = ">=1.16" scipy = ">=1.2,<1.6" diff --git a/sphinx/auxiliary/Boston_getting_started_example.ipynb b/sphinx/auxiliary/Boston_getting_started_example.ipynb deleted file mode 100644 index 30f4c378e..000000000 --- a/sphinx/auxiliary/Boston_getting_started_example.ipynb +++ /dev/null @@ -1,597 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n" - ] - }, - { - "attachments": { - "Gamma_Facet_Logo_RGB_LB.svg": { - "image/svg+xml": [ - "<svg width="781" height="134" viewBox="0 0 781 134" fill="none" xmlns="http://www.w3.org/2000/svg">
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" - ] - } - }, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![Gamma_Facet_Logo_RGB_LB.svg](attachment:Gamma_Facet_Logo_RGB_LB.svg)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "FACET is composed of the following key components:\n", - "\n", - "- **Model Inspection**\n", - "\n", - " FACET introduces a new algorithm to quantify dependencies and interactions between features in ML models. This new tool for human-explainable AI adds a new, global perspective to the observation-level explanations provided by the popular [SHAP](https://shap.readthedocs.io/en/latest/) approach. To learn more about FACET's model inspection capabilities, see the getting started example below.\n", - "\n", - "\n", - "- **Model Simulation**\n", - "\n", - " FACET's model simulation algorithms use ML models for *virtual experiments* to help identify scenarios that optimise predicted outcomes. To quantify the uncertainty in simulations, FACET utilises a range of bootstrapping algorithms including stationary and stratified bootstraps. For an example of FACET’s bootstrap simulations, see the getting started example below. \n", - " \n", - " \n", - "- **Enhanced Machine Learning Workflow** \n", - "\n", - " FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-Gamma/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features. " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "delete_for_interactive": true, - "nbsphinx": "hidden" - }, - "outputs": [], - "source": [ - "# this cell's metadata contains\n", - "# \"nbsphinx\": \"hidden\" so it is hidden by nbsphinx\n", - "\n", - "import warnings\n", - "\n", - "warnings.filterwarnings(\"ignore\")\n", - "\n", - "from matplotlib import pyplot as plt\n", - "\n", - "def _configure_matplotlib():\n", - " # set global options for matplotlib\n", - "\n", - " import matplotlib\n", - "\n", - " matplotlib.rcParams[\"figure.figsize\"] = (16.0, 8.0)\n", - " matplotlib.rcParams[\"figure.dpi\"] = 72\n", - "\n", - "\n", - "_configure_matplotlib()\n", - "\n", - "del _configure_matplotlib" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Pipelining & Model Ranking\n", - "\n", - "To demonstrate the model inspection capability of FACET, we first create a pipeline to fit a learner. In this simple example using the Boston housing data, we will train a Random Forest regressor using 10 repeated 5-fold CV to predict median house price. With the use of *sklearndf* we can create a *pandas* DataFrame compatible workflow. However, FACET provides additional enhancements to keep track of our feature matrix and target vector using a sample object (`Sample`) and easily compare hyperparameter configurations and even multiple learners with the `LearnerRanker`." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " ranking_score r2_score regressor \\\n", - " mean std type \n", - "rank \n", - "0 0.721234 0.813158 0.045962 RandomForestRegressorDF \n", - "1 0.706528 0.801775 0.047623 RandomForestRegressorDF \n", - "2 0.691872 0.788968 0.048548 RandomForestRegressorDF \n", - "\n", - " \n", - " min_samples_leaf \n", - "rank \n", - "0 8 \n", - "1 11 \n", - "2 15 " - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# standard imports\n", - "import pandas as pd\n", - "from sklearn.datasets import load_boston\n", - "from sklearn.model_selection import RepeatedKFold\n", - "\n", - "# some helpful imports from sklearndf\n", - "from sklearndf.pipeline import RegressorPipelineDF\n", - "from sklearndf.regression import RandomForestRegressorDF\n", - "\n", - "# relevant FACET imports\n", - "from facet.data import Sample\n", - "from facet.selection import LearnerRanker, LearnerGrid\n", - "\n", - "# load Boston housing dataset\n", - "boston = load_boston()\n", - "boston_df = pd.DataFrame(data=boston.data, columns=boston.feature_names).assign(\n", - " MEDIAN_HOUSE_PRICE=boston.target\n", - ")\n", - "\n", - "# create FACET sample object\n", - "boston_sample = Sample(observations=boston_df, target_name=\"MEDIAN_HOUSE_PRICE\")\n", - "\n", - "# create pipeline for random forest regressor\n", - "rforest_reg = RegressorPipelineDF(regressor=RandomForestRegressorDF(random_state=42))\n", - "\n", - "# define grid of models which are \"competing\" against each other\n", - "rnd_forest_grid = [\n", - " LearnerGrid(\n", - " pipeline=rforest_reg, learner_parameters={\"min_samples_leaf\": [8, 11, 15]}\n", - " )\n", - "]\n", - "\n", - "# create repeated k-fold CV iterator\n", - "rkf_cv = RepeatedKFold(n_splits=5, n_repeats=10, random_state=42)\n", - "\n", - "# rank your candidate models by performance (default is mean CV score - 2*SD)\n", - "ranker = LearnerRanker(grids=rnd_forest_grid, cv=rkf_cv, n_jobs=-3).fit(sample=boston_sample)\n", - "\n", - "# get summary report\n", - "ranker.summary_report()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can see based on this minimal workflow that a value of 8 for minimum samples in the leaf was the best performing of the three considered values. This approach easily extends to multiple hyperparameters for the learner and multiple learners." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Model inspection\n", - "\n", - "FACET implements several model inspection methods for\n", - "[scikit-learn]() estimators.\n", - "FACET enhances model inspection by providing global metrics that complement \n", - "the local perspective of SHAP. The key global metrics for each pair of \n", - "features in a model are:\n", - "\n", - "- **Synergy**\n", - "\n", - " The degree to which the model combines information from one feature with \n", - " another to predict the target. For example, let's assume we are predicting \n", - " cardiovascular health using age and gender and the fitted model includes \n", - " a complex interaction between them. This means these two features are \n", - " synergistic for predicting cardiovascular health. Further, both features \n", - " are important to the model and removing either one would significantly \n", - " impact performance. Let's assume age brings more information to the joint\n", - " contribution than gender. This asymmetric contribution means the synergy for\n", - " (age, gender) is less than the synergy for (gender, age). To think about it\n", - " another way, imagine the prediction is a coordinate you are trying to reach.\n", - " From your starting point, age gets you much closer to this point than \n", - " gender, however, you need both to get there. Synergy reflects the fact \n", - " that gender gets more help from age (higher synergy from the perspective \n", - " of gender) than age does from gender (lower synergy from the perspective of\n", - " age) to reach the prediction. *This leads to an important point: synergy \n", - " is a naturally asymmetric property of the global information two interacting \n", - " features contribute to the model predictions.* Synergy is expressed as a \n", - " percentage ranging from 0% (full autonomy) to 100% (full synergy).\n", - "\n", - "\n", - "- **Redundancy**\n", - "\n", - " The degree to which a feature in a model duplicates the information of a \n", - " second feature to predict the target. For example, let's assume we had \n", - " house size and number of bedrooms for predicting house price. These \n", - " features capture similar information as the more bedrooms the larger \n", - " the house and likely a higher price on average. The redundancy for \n", - " (number of bedrooms, house size) will be greater than the redundancy \n", - " for (house size, number of bedrooms). This is because house size \n", - " \"knows\" more of what number of bedrooms does for predicting house price \n", - " than vice-versa. Hence, there is greater redundancy from the perspective \n", - " of number of bedrooms. Another way to think about it is removing house \n", - " size will be more detrimental to model performance than removing number \n", - " of bedrooms, as house size can better compensate for the absence of \n", - " number of bedrooms. This also implies that house size would be a more \n", - " important feature than number of bedrooms in the model. *The important \n", - " point here is that like synergy, redundancy is a naturally asymmetric \n", - " property of the global information feature pairs have for predicting \n", - " an outcome.* Redundancy is expressed as a percentage ranging from 0% \n", - " (full uniqueness) to 100% (full redundancy)." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# fit the model inspector\n", - "from facet.inspection import LearnerInspector\n", - "inspector = LearnerInspector()\n", - "inspector.fit(crossfit=ranker.best_model_crossfit_)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Synergy**" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# visualise synergy as a matrix\n", - "from pytools.viz.matrix import MatrixDrawer\n", - "synergy_matrix = inspector.feature_synergy_matrix()\n", - "MatrixDrawer(style=\"matplot%\").draw(synergy_matrix, title=\"Synergy Matrix\")\n", - "\n", - "# save copy of plot to _static directory for documentation\n", - "plt.savefig(\n", - " \"facet/sphinx/source/_static/synergy_matrix.png\", bbox_inches=\"tight\", pad_inches=0\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As before the matrix row represents the \"perspective from\" feature in the pair. Looking across the row for `LSTAT` there is relatively minimal synergy (≤14%) with other features in the model. However, looking down the column for `LSTAT` (i.e., perspective of other features in a pair with `LSTAT`) we find many features (the rows) are synergistic (12% to 47%) with `LSTAT`. We can conclude that:\n", - "\n", - "- `LSTAT` is a strongly autonomous feature, displaying minimal synergy with other features for predicting median house price.\n", - "- The contribution of other features to predicting median house price is partly enabled by the strong contribution from `LSTAT`.\n", - "\n", - "High synergy features must be considered carefully when investigating business impact, as they work together to predict the outcome. It would not make much sense to consider `ZN` (proportion of residential land zoned for lots over 25,000 sq.ft) without `LSTAT` given the 47% synergy of `ZN` with `LSTAT` for predicting median house price." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Redundancy**" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# visualise redundancy as a matrix\n", - "redundancy_matrix = inspector.feature_redundancy_matrix()\n", - "MatrixDrawer(style=\"matplot%\").draw(redundancy_matrix, title=\"Redundancy Matrix\")\n", - "\n", - "# save copy of plot to _static directory for documentation\n", - "plt.savefig(\n", - " \"facet/sphinx/source/_static/redundancy_matrix.png\",\n", - " bbox_inches=\"tight\",\n", - " pad_inches=0,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For any feature pair (A, B), the first feature (A) is the row, and the second feature (B) the column. For example, if we look at the feature pair (`LSTAT`, `RM`) from the perspective of `LSTAT` (percentage of lower status of the population), then we look-up the row for `LSTAT` and the column for `RM` (average number of rooms per dwelling) and find 39% redundancy. This means that 39% of the information in `LSTAT` is duplicated with `RM` to predict median house price.\n", - "We can also see looking across the row for `LSTAT` that apart from the 39% redundancy with `RM`, `LSTAT` has minimal redundancy (<5%) with any of the other features included in the model.\n", - "\n", - "**Clustering redundancy**\n", - "\n", - "As detailed above redundancy and synergy for a feature pair is from the \"perspective\" of one of the features in the pair, and so yields two distinct values. However, a symmetric version can also be computed that provides not only a simplified perspective but allows the use of (1 - metric) as a feature distance. With this distance hierarchical, single linkage clustering is applied to create a dendrogram visualization. This helps to identify groups of low distance, features which activate \"in tandem\" to predict the outcome. Such information can then be used to either reduce clusters of highly redundant features to a subset or highlight clusters of highly synergistic features that should always be considered together.\n", - "\n", - "Let's look at the example for redundancy." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# visualise redundancy using a dendrogram\n", - "from pytools.viz.dendrogram import DendrogramDrawer\n", - "redundancy = inspector.feature_redundancy_linkage()\n", - "DendrogramDrawer().draw(data=redundancy, title=\"Redundancy Dendrogram\")\n", - "\n", - "# save copy of plot to _static directories for documentation\n", - "plt.savefig(\n", - " \"facet/sphinx/source/_static/redundancy_dendrogram.png\",\n", - " bbox_inches=\"tight\",\n", - " pad_inches=0,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Based on the dendrogram we can see that the feature pairs (`LSTAT`, `RM`) and (`CRIM`: per capita crime rate by town, `NOX`: nitric oxides concentration in parts per 10 million) each represent a cluster in the dendrogram and that `LSTAT` and `RM` have high importance. As a next action we could remove RM (and maybe NOX) to further simplify the model and obtain a set of independent features." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Simulation\n", - "\n", - "Taking the LSTAT feature as an example, we do the following for the simulation: \n", - "\n", - "- We use FACET's `ContinuousRangePartitioner` to split the range of observed values of LSTAT into intervals of equal size. Each partition is represented by the central value of that partition. \n", - "- For each partition, the simulator creates an artificial copy of the original sample assuming the variable to be simulated has the same value across all observations - which is the value representing the partition. Using the best `LearnerCrossfit` acquired from the ranker, the simulator now re-predicts all targets using the models trained for all folds and determines the average uplift of the target variable resulting from this.\n", - "- The FACET `SimulationDrawer` allows us to visualise the result; both in a matplotlib and a plain-text style.\n", - "\n", - "Finally, because FACET can use bootstrap cross validation, we can create a crossfit from our previous `LearnerRanker` best model to perform the simulation so we can quantify the uncertainty by using bootstrap confidence intervals." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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5GcePH691zqKiIri5ucHe3h5nzpzB33//3cS9aPkYXi2gT2gI7hkYiW+3HcDFazmWLoeIiIiIiMwICAjAvHnzam1/9dVXoVarERERgfDwcLz66qsAgMcffxylpaWIiIjAO++8gz59+tQ69s4774RGo0FERAReffVV9OvXr8n70dIJYvWfCVqImJgYwz2SWrL84jKMeeETdAnwxsoXH7nhtAIiIiIiorZk8eLFWLx4saXLoCZ2Mz9njrxaiLuzAxZMiUX82cvYfOCYpcshIiIiIiKyagyvFjRhcE9Edw7Cu2v/QGFpuaXLISIiIiIisloMrxYkkUiwaOY4lJQr8f66HZYuh4iIiIiIyGoxvFpYl0AfzBzdHxv2JuDoucuWLoeIiIiIiMgqMbxagcfvHQp/T1e8vmIrVLyHExERERERUS0Mr1bA3kaBV2aMwYX0bHz/xyFLl0NERERERGR1GF6txNCorhjZKxSf//IX0nMKLF0OEREREVGb9/HHHyM8PBxhYWH46KOPDNvvv/9+REVFISoqCsHBwYiKiqrz+ODgYPTo0QNRUVGIiYkxbF+xYgWuXbtm0i43N7eputGoCgsL8fnnnxteX7t2DZMmTQIA7NmzB+PGjWuy92Z4tSIvTr8LgiDgvz9sQwu7/S4RERERUauSnJyMr7/+GocPH8axY8ewdetWnD9/HgCwbt06JCUlISkpCRMnTsSECRPqPc/u3buRlJSE+Ph4w7aa4bU5aLXaRjlPzfDq7++P9evXN8q5b6RB4TU+Ph4ffvghFi5ciEWLFuGnn35Cfn5+U9fW5vh5uOCpCcPw17FziDt62tLlEBERERG1WadPn0a/fv1gb28PmUyGIUOGYNOmTSZtRFHETz/9hGnTpjX4vOvXr0d8fDymT5+OqKgoVFRUAAA++eQTREdHo0ePHjhz5kyt41asWIF77rkHd955J7p27YrXX3/dsO/HH39Enz59EBUVhccee8wQVB0dHbFo0SL07dsXhw4dwvfff4+IiAhERkZixowZAICcnBxMnDgRvXv3Ru/evXHgwAEAwOLFizFr1iwMHToUHTp0wNKlSwEAL7zwAi5evIioqCgsXLgQqampCA8Pr1VvWVkZZs2ahd69e6Nnz57YvHlzg79H9ZGZ27lixQosXboUISEh6NWrF7p27QqlUon9+/fjf//7H8LDw/Gf//wHQUFBt10I6T0Y2xebDxzDmz9ux4CwjnCws7F0SUREREREFvXWqu04k5bZqOfsFuSLF6ffVe/+8PBwvPzyy8jLy4OdnR22bdtmMvUXAPbt2wcfHx907ty5znMIgoBRo0ZBEAQ89thjmDNnDiZNmoRPP/0U7733nsn5PD09kZCQgM8//xzvvfcevvnmm1rnO3z4MJKTk2Fvb4/evXtj7NixcHBwwLp163DgwAHI5XI88cQTWLVqFR566CGUlZUhPDwcb7zxBk6ePIklS5bgwIED8PT0NAxGzps3D88++ywGDRqEtLQ0jB49GqdP6wfSzpw5g927d6OkpARdu3bF448/jrfffhvJyclISkoCAKSmptbZ9yVLlmD48OFYvnw5CgsL0adPH4wcORIODg71fs9vxGx4LSsrw4EDB2BnZ1fn/qSkJJw/f57htRHJpFIsfvhuPPCfb/DJxl14wcw/KCIiIiIiahqhoaF4/vnnERsbC0dHR0RGRkImM41Pa9asMTvqeuDAAfj7+yM7OxuxsbHo1q0bBg8eXGfb6qnHvXr1wsaNG+tsExsbCw8PD0P7/fv3QyaT4ejRo+jduzcAoKKiAt7e3gAAqVSKiRMnAgB27dqFSZMmwdPTEwDg7u4OAIiLi8OpU6cM71FcXIySkhIAwNixY2FjYwMbGxt4e3sjKyvLzHfM1J9//oktW7bgvffeAwAolUqkpaUhNDS0weeoyWx4ffLJJ80eXN+FyXR7IjsGYMrQXvhxxz8YPygK3dv7WbokIiIiIiKLMTdC2pRmz56N2bNnAwBeeuklBAQEGPZpNBps3LgRR48erfd4f39/AIC3tzfuu+8+HD58uN7wamOjn3EplUqhqef2mYIg1HotiiJmzpyJt956q1Z7W1tbSKVSAPopzjWPBwCdTodDhw7VOWBZXdON6qqLKIrYsGEDunbt2uBjbsTsNa9TpkwxPH/++edN9o0aNarRiqDanp08Em5O9nj9u1+h1eksXQ4RERERUZuTnZ0NAEhLS8PGjRtNRlnj4uLQrVs3k0BrrKyszDCCWVZWhj///NNwbaiTk5Nh383YsWMH8vPzUVFRgV9++QUDBw7EiBEjsH79ekOt+fn5uHz5cq1jR4wYgZ9++gl5eXmGdoA+13366aeGdtXTgevT0NpHjx6NTz75xLAQbWJiYoP6aI7Z8Fq9mhag/0YZy8nJue03p/o5O9jh+QfuxImUdPy0O/7GBxARERERUaOaOHEiunfvjrvvvhufffYZ3NzcDPvWrl1ba8rwtWvXMGbMGABAVlYWBg0ahMjISPTp0wdjx47FnXfeCQB4+OGHMXfuXJMFmxpi0KBBmDFjBqKiojBx4kTExMSge/fu+O9//4tRo0YhIiICsbGxyMjIqHVsWFgYXn75ZQwZMgSRkZGYP38+AGDp0qWIj49HREQEunfvji+//NJsDR4eHhg4cCDCw8OxcOHCetu9+uqrUKvViIiIQHh4OF599dUG97M+gmjmnizR0dFISEio9byu180lJibGZJnp1kwURTz67vc4cSkdv739FLxcnSxdEhERERFRs1i8eDEWL15s6TKsxooVKxAfH28yStoa3MzP2ezIa3l5ORITE3H06FFUVFQgMTERCQkJhtfUtARBwKsPjYNKo8U7a/6wdDlEREREREQWY3bBJl9fX8NwsvHz6tfU9IJ9PfCvsYPw2S97cO+gKAzs0cnSJRERERERUTN7+OGH8fDDD1u6DIsyG1737NnTTGWQOY+OHYSth07gje9/w+YlT8BWIbd0SURERERERM3K7LThH3/8ET/88EOt7V9//TVWr17dZEWRKRuFHItmjsOV7Hx8vXWfpcshIiIiIiJqdmbD6/vvv49777231vb7778f77//flPVRHXoH9YB4/pH4Jvf9iMlI9fS5RARERERETUrs+FVq9XCyan2CrfOzs5Qq9VNVhTV7blpo2GnkOONlVthZpFoIiIiIiKiVsdseFWr1SgrK6u1vaSkBCqVqsmKorp5ujji2Skj8c/pFPx68LilyyEiIiIiatWkUimioqIQGRmJ6OhoHDx4sFHP//DDD2P9+vUAgEcffRSnTp1q1PO3NmbD6+zZszFp0iSkpqYatqWmpmLq1KmYPXt2U9dGdZg8pBciOwbgnTV/oLC03NLlEBERERG1WnZ2dkhKSsKxY8fw1ltv4cUXX2yy9/rmm2/QvXv3Jjt/a2A2vC5YsAD33HMPhgwZAg8PD3h4eGDIkCEYN24cFi5c2Fw1khGJRILXHr4bRWUV+PDnOEuXQ0RERETUJhQXF8PNzQ0AUFpaihEjRiA6Oho9evTA5s2bAQBlZWUYO3YsIiMjER4ejnXr1gEAjh49iiFDhqBXr14YPXo0MjIyap1/6NChiI+PBwA4Ojri5ZdfRmRkJPr164esrCwAQE5ODiZOnIjevXujd+/eOHDgQHN03WqYvVUOAMydOxdz585FaWkpRFGs8xpYal7dgnwxY1Q/rPj9IO4dFIWenYMsXRIRERERUatTUVGBqKgoKJVKZGRkYNeuXQAAW1tbbNq0Cc7OzsjNzUW/fv0wfvx4/P777/D398dvv/0GACgqKoJarcZTTz2FzZs3w8vLC+vWrcPLL7+M5cuX1/u+ZWVl6NevH5YsWYLnnnsOX3/9NV555RXMmzcPzz77LAYNGoS0tDSMHj0ap0+fbpbvhTUwG14/+OADswfPnz+/UYuhhnvyvqH4/XAyXl+xFT+//hjkMqmlSyIiIiIiajJ/iBeRKZY22vl8BUeMFjqabVM9bRgADh06hIceegjJyckQRREvvfQS9u7dC4lEgvT0dGRlZaFHjx5YsGABnn/+eYwbNw533HEHkpOTkZycjNjYWAD6RXH9/PzMvq9CocC4ceMAAL169cKOHTsAAHFxcSbXxRYXF6OkpKTNDDCaDa8lJSXNVQfdJAdbG7z04Bg8vXQtfvjzb8waM9DSJRERERERNZnRQkdAsNz79+/fH7m5ucjJycG2bduQk5ODo0ePQi6XIzg4GEqlEl26dMHRo0exbds2vPjiixg1ahTuu+8+hIWF4dChQw1+L7lcDkHQd1YqlUKj0QAAdDodDh06BDs7uybpo7UzG15fe+21evfVtQoxNa+RvUIxrGdXfLZpN+7sGwZ/D1dLl0RERERE1CqdOXMGWq0WHh4eKCoqgre3N+RyOXbv3o3Lly8DAK5duwZ3d3c8+OCDcHR0xIoVK/DCCy8gJycHhw4dQv/+/aFWq3Hu3DmEhYXddA2jRo3Cp59+alh/KCkpCVFRUY3ZTatmdsEmAEhPT0d8fLzh1jjZ2dl46aWX0Llz5yYvjm7spQfHAACW/LDNwpUQEREREbUu1de8RkVF4f7778fKlSshlUoxffp0xMfHIyYmBqtWrUK3bt0AACdOnECfPn0QFRWFJUuW4JVXXoFCocD69evx/PPPIzIyElFRUbd8y52lS5ciPj4eERER6N69O7788svG7K7VE0RRFOvb+dFHH2HJkiXo1KkTKisrMW/ePMyfPx8PPfQQnnvuuRvO1W4KMTExhlW4SO/bbfvx/rod+GTeNIyI7mbpcoiIiIiIbtvixYuxePFiS5dBTexmfs5mpw0vW7YMZ8+ehbu7O9LS0tCpUyfs3bsX/fr1a4w6qZE8NKo/fj1wHEt+2IZ+3UPgYGtj6ZKIiIiIiIgaldlpw7a2tnB3dwcABAUFoUuXLgyuVkguk+K1h8chM78In23aY+lyiIiIiIiIGp3ZkderV6/i6aefNrzOzs42eb106dKmq4xuSs/OQZg8pBd++PNvjB8YiW5BvpYuiYiIiIiIqNGYDa/vvvuuyetevXo1aTF0e56dMhI7E87g9RW/YtUrsyGR3HA9LiIiIiIiqyQIArRaLaRSqaVLoSai1WoNtwRqCLPhdebMmcjJycHly5fRqVMnuLq63m591IRcHe3x3LTReGHZRvz811HcP6y3pUsiIiIiIrolfn5+OHjwIAYMGMAA2wpptVocPHjwphYBNrva8DfffIOXXnoJHTt2REpKCpYtW4bx48c3SrG3iqsNmyeKImb9byVOX87A1refgqeLo6VLIiIiIiK6acXFxVi7di0yMjJgJrJQCyUIAvz8/DB16lQ4Ozs37Bhz4TU8PBy7d++Gl5cXLl26hOnTp+PQoUONVvCtYHi9sZSMXNz7yue4s08Y/vfYREuXQ0REREREdNvMXhSpUCjg5eUFAOjQoQMqKyubpSi6PSF+nnh07CD8evA4Dp28ZOlyiIiIiIiIbpvZkVdvb29MnTrV8Hrt2rUmry2x2jBHXhtGqVLjnpc/h0QQ8Mt/H4eNQm7pkoiIiIiIiG5Zs6w2PGvWLGzduhXe3t5ITk4GAOTn5+P+++9HamoqgoOD8dNPP8HNze2Wzk+12SrkWPTQWPzrvR/w7bYDeOLeoZYuiYiIiIiI6JaZHXltqKeeegqffPJJvfv37t0LR0dHPPTQQ4bw+txzz8Hd3R0vvPAC3n77bRQUFOB///vfDd+LI683Z8HnPyMu4Qx++e8TCPb1sHQ5REREREREt6RRbgR64MABs/sHDx4Md3d3k22bN2/GzJkzAehvyfPLL780RilUw/MP3AmFTIr/fL+Vq7QREREREVGL1Sjh9VZkZWUZ7unj5+eH7OzsetsuW7YMMTExiImJQU5OTnOV2Cp4uTrh2ckjcejkJWz7O9nS5RAREREREd0Si4XXmzFnzhzEx8cjPj7esPoxNdyUYTHoEdIOb6/5HcVlFZYuh4iIiIiI6KY1Sni9lemoPj4+yMjIAABkZGTA29u7MUqhOkglErz28DgUFJfho/U7LV0OERERERHRTbvl8KrRaAzP582bd9PHjx8/HitXrgQArFy5Evfcc8+tlkIN0D3YHw/G9sW63fE4dvGqpcshIiIiIiK6KWbD66BBgwzPZ8yYYbKvT58+hucPP/yw2TeZNm0a+vfvj7NnzyIgIADffvstXnjhBezYsQOdO3fGjh078MILL9xC+XQznpowHN6uTnh9xa/QaLWWLoeIiIiIiKjBzN7ntayszPD85MmTJvtuZqrwmjVr6ty+cyensDYnBzsbvDj9Ljzz6Tqs2vEPZt45wNIlERERERERNYjZkVdBEG5pH1mv2JhQDInsgqUbdyMjr8jS5RARERERETWI2ZHXwsJCbNq0CTqdDoWFhdi4cSMA/ahrURGDT0skCAJenjEG41/6DG+t2o6lT0+1dElEREREREQ3JIhm5v8+8sgjZg/+7rvvGr2gG4mJiUF8fHyzv29r8/XWffjw5zh89swDGNazq6XLISIiIiIiMstseLVGDK+NQ63RYuKiL1FeqcKWN5+EvY3C0iURERERERHVy+w1r7/++isuX75seP3GG28gMjIS48ePR0pKSpMXR01HLpNi0cxxuJZbiC9+2WPpcoiIiIiIiMwyG15ffvlleHl5AQC2bt2KH3/8EcuXL8f48eMxd+7cZimQmk5M1/aYMLgnVv5xCOeuZFm6HCIiIiIionrdcLVhe3t7AMDGjRsxe/Zs9OrVC48++ihycnKapUBqWgvuHwVHO1u8sXIrdDqdpcshIiIiIiKqk9nwKooiSktLodPpsHPnTowYMcKwT6lUNnlx1PRcHe2xcOooJJxPw8a9iZYuh4iIiIiIqE5mw+szzzyDqKgoxMTEIDQ0FDExMQCAxMRE+Pn5NUuB1PTuHRSFmK7t8d5PO5BfXGbpcoiIiIiIiGq54WrD6enpyM7ORmRkJCQSfdbNyMiAWq1GUFBQsxRpjKsNN42L13Jw3ytfYFz/HnjzX/dZuhwiIiIiIiITMnM7ExISDM+TkpIgCAI8PT0RGBjY5IVR8+ro74VZYwZg2a/7cO+gKPQJDbF0SURERERERAZmR16HDRtWa1t+fj5UKhXWrFmDqKiopqytThx5bToVlSrc8/LnkMuk2PSfx6GQm/3bBhERERERUbO54bThusTHx2P+/PnYu3dvU9RkFsNr09p3/Dwee/9HPD1xOOaOH2LpcoiIiIiIiADcYMGm+sTExKC0tLSxayErcEdEZ4zuHYavtuxFWla+pcshIiIiIiICcIvhNSsrC4IgNHYtZCVenH4nZFIp/vP9VtzCwDwREREREVGjM3tR41NPPVUrpObn5+PgwYP4+OOPm7QwshxvN2fMmzQcb/64Hb8fPom7+oZbuiQiIiIiImrjzIbX6vu6VhMEAR4eHvjggw/g7e3dpIW1RFpRxIH8Mgz2cLR0Kbdt2og+2Lz/GN5atR2DenSCk72tpUsiIiIiIqI2rEELNimVSly4cAGCIKBjx46wtbVckLHmBZu2ZBbitXOZuM/XBS908oFCckuzsq1Gcko6pr7+NaaO6I1XZoy1dDlERERERNSGmU1XGo0Gzz33HAICAjBz5kw8+OCDCAwMxHPPPQe1Wt1cNbYYY31cMDvQA5syizD7WBqyKlv29yg8pB2mjeyDNTuPIDkl3dLlEBERERFRG2Y2vC5cuBD5+flISUnB0aNHkZiYiIsXL6KwsBALFixorhpbDKkg4P9CvPBe93a4VK7CAwmpOFpYbumybsvTE4bD08URi7/7FRqt1tLlEBERERFRG2V22nDnzp1x7ty5Wos2abVadOvWDefPn2/yAmuy5mnDxi6WVeLfp9KRrlTh2Q7emObv1mJXaN7+TzL+/fnPeHH6XZgxqp+lyyEiIiIiojbI7MirIAh1Bi6pVNpig1hz6ehggx96tsdAd0e8ezEbr57NQIVWZ+mybsmdfcIwqEcnLN2wC1n5xZYuh4iIiIiI2iCz4bV79+74/vvva23/8ccf0a1btyYrqrVwkknxQfd2eLy9J7ZlF+ORpMu4plRZuqybJggCXpkxFhqtFm+t3m7pcoiIiIiIqA0yO204PT0dEyZMgJ2dHXr16gVBEHDkyBFUVFRg06ZNaNeuXXPWCqDlTBuuaV9eKV46cw1SAXg7tB36uTlYuqSb9uWWv7B0wy58MX86hkR2sXQ5RERERETUhjToVjm7du3CyZMnIYoiwsLCMGLEiOaorU4tNbwCwOUKFf598ipSylV4KsQLMwPcW9T0a5VGgwmvfgmVWoPNS56AnY3C0iUREREREVEbYTa85ufnmz3Y3d290Qu6kZYcXgGgXKvD4rMZ2JFbglhPJyzu6gd7acu5H+yRM6mY+dZ3+Ne4O/Ds5JGWLoeIiIiIiNoImbmd1VOFRVFERkYG/P39AQCiKEIQBFy6dKlZimxN7KUS/C/UH2FX87E0JQeXyivxflgA2tu1jFHM3t2Cce+gKHy3/QDG9Y9A5wBvS5dERERERERtQIOmDQNAz549kZiY2NT13FBLH3k19ndBGV44nQ6tCCzp5o/BHo6WLqlB8ovLMPaFT9CpnTe+f+mRFjX1mYiIiIiIWqYGz1dlQGl8/dwcsDo6GAF2csw7eRVfXc6FrmF/S7Aod2cHLLg/FkfPXcamfUmWLoeIiIiIiNqAlnOxZSvlb6vAd5HtMdbbGV9ezsX8U+ko0WgtXdYN3XdHT0R3DsJ76/5EQUmZpcshIiIiIqJWzuw1rx988IHheXZ2tslrAJg/f37TVNXG2Eol+E9XP4Q52eKDS9mYkXgZ73dvh44ONpYurV4SiQSvPTwOExd9ifd/2oH/zr7X0iUREREREVErZnbktaSkxPD1r3/9y+R1SUlJc9XYJgiCgGnt3PFljyCUaLR4KOky4nKKLV2WWZ0DfPDwnQOwcW8i4s9etnQ5RERERETUijV4wSZz3nrrLbz44ouNUc8NtaYFm+qTXanGglPpOFGixKxADzwR7AmplV5zXF6pwviXPoOdQo4N/5kLhczsYD4REREREdEtaZTwGh0djYSEhMao54baQngFAJVOh/9dyMLGzCIMcHPAm9384SKXWrqsOv2VdA6Pf7gKUZ0C0b29H4J83PVf3u4I8HKDQs5AS0REREREt6dRUkUj5F+qQSGR4NUufghzssPbF7IwPTEV73dvh66OtpYurZYhUV3w9IThiDt6GlsOHkNpRaVhnyAI8HV31odZL3dDsA30dkegtxscbK33ul4iIiIiIrIeHHltAY4XV2DhqXQUa7RY1MUXd3m7WLqkeomiiMLScqRl5eNyVj6uZOcjLTsfaVn5uJJdgPwaKxN7ujgaRmmDvK8H2yAfd7g42FmoF0REREREZG0aJbz27NkTiYmJjVHPDbXF8AoAeSoNFp5KR2JxBaa3c8MzHbwhs9LrYM0pKVdWBdoCpGXlIS1bH2rTsvKRVWC6QJWLg931YFv1WB1sPZwdeO9hIiIiIqI2pFGmDU+ePLkxTkNmeChk+CoiCB9cysaq9AKcLa3E/0L94a5oWdeTOtnbonuwP7oH+9faV1GpwtWcguvBtmq09tjFq9j+TzJ0Rn9nsbdVmIzWBnm7I7Dq0cfNCRIJb2FMRERERNSamB15feONN+o/UBDw6quvNklR5rTVkVdjW7OK8N/zmXCVSfF+WDuEObX+6bUqjQbXcouQlpWPtOzrwbZ65Faj1RraKuQyBHi5ob236TW2Qd7u8Pd0gUxqnQtfERERERFR/cyG1/fff7/WtvLycnzzzTfIy8tDaWlpkxZXF4ZXvdMlSvz71FXkqbR4sbMP7vV1tXRJFqPV6ZCZX2wIttXTkNOqrrmtUKkNbWVSCfw8XNHep/Y1tgGerrBRyC3YEyIiIiIiqk+Dr3ktKSnBxx9/jG+//RZTpkzBv//9b3h7ezd1fbUwvF5XoNbgxdPX8E9hOSb5ueK5jj6QS3gdqDFRFJFbVIrLRmG2egGptOx8lJQrDW0FQYCPm3OdwZYrIxMRERERWdYNL5jMz8/HBx98gFWrVmHmzJlISEiAm5tbc9RGN+Aml+HTHoH4LCUHK67m43xZJd4J9Ye3DUcPqwmCAC9XJ3i5OiGma3uTfaIooqis4vqqyFnXQ+2uxDPIKzZdGdnDxdEwFTnQ2w1B3h4I8nFHiJ8HHO2s7xZGREREREStidmR14ULF2Ljxo2YM2cOnnzySTg6OjZnbXXiyGvd/swpxuKzGXCQSfBuaDtEudhbuqQWr7RCibTsApNgWz1ym5l/fWVkO4Ucb865D6N7h1mwWiIiIiKi1s1seJVIJLCxsYFMJjO5LYkoihAEAcXFxfUd2mQYXut3oawS809eRUalGgs7+mCynytvJ9NElCo1ruYU4HJWPpb/th+JF67gyXuH4vF7hnClYyIiIiKiJtAo93ltTgyv5pVotHjpzDXszy/DeB8XvNTZBzYMU01KpdZg8Ypf8cv+JIzuHYY3/3Uv7GwUli6LiIiIiKhVuelUU1ZWhlWrVmHs2LFNUQ/dJieZFB+HBWBOkAe2ZBVhVlIaMpTqGx9It0whl2HJo/di4dRR+DP+FGYsWY7M/CJLl0VERERE1Ko0KLyqVCr88ssvmDJlCvz8/BAXF4e5c+c2dW10iySCgMeDvfBB93a4XKHCA4mpOFJYduMD6ZYJgoBH7hqIz599AJez8jFl8TIcu3DF0mUREREREbUaZsPrjh07MGvWLISEhGD9+vWYMWMG3N3d8d133+Huu+9urhrpFg3zdMKPPdvDTS7F48ev4Ier+Whhs8RbnCGRXbBm0aOwU8gx8+0V2HLgmKVLIiIiIiJqFW64YNMdd9yBFStWICQkBADQoUMHXLp0qdkKrInXvN68Mo0Wi85lYFduKe70csaiLr6wk/I62KZUWFqOZz5Zh8NnUvHo2EGYN2kEpLz2mIiIiIjolpn9bfro0aPo168fRo4cidjYWHz77bfQarXNVRs1EgeZFO+FtsP/BXvhj5xizEy6jKsVKkuX1aq5Otrj64UP4f5hMfjmt/14+uO1KKuotHRZREREREQtVoNXGz5w4ADWrFmDDRs2ICoqCvfddx/mzJnT1PXVwpHX23MgvxQvnbkGAHizmz8Gulv+3r2t3Zqdh/Hmj9vRwd8Tnz3zAAK83CxdEhERERFRi3PTt8rR6XTYsWMH1q5di++++66p6qoXw+vtu1qhwvxT6bhQVokngz0xK9CD94NtYodOXsKzn/0EiSDg46fuR+9uwZYuiYiIiIioRTEbXhMSEsweHB0d3egF3QjDa+Oo0OrwxrlM/J5TjOEejnijqx8cZFJLl9WqpWbm4cmPVuNKdj5efWgsJg+NsXRJREREREQthtnwOmzYsPoPFATs2rWrSYoyh+G18YiiiFXpBfjoUjaC7BT4IKwdgu1tLF1Wq1ZcVoEFX6zH/hMX8GBsXzw3bTRkUv7RgIiIiIjoRm562rClMbw2viOFZXju9DWodSL+09UPwzydLF1Sq6bV6fDeuj+x8vdDGBDWEe8/ORkuDnaWLouIiIiIyKrdMLzm5eVh9erVOHPmDAAgNDQUDzzwANzd3ZulwJoYXptGhlKNBafScapUiX8FeWBue09IeB1sk9qwNwGvr9iKAC9XfP7sAwj29bR0SUREREREVsvsrXJOnz6N8PBwHD16FF26dEHnzp1x5MgRhIeHG8IstQ5+tnIsjwrCPT4u+DotD/NOXkWJhrdFakoTB0fjuxdmorhMiamvf40DJy5YuiQiIiIiIqtlduR10qRJmDJlCqZMmWKyfcOGDVi9ejU2bNhw2wUEBwfDyckJUqkUMpnshqOqHHltWqIo4ueMQrx7MQt+NnJ8EBaATg68DrYppecU4MmP1uBCejaef+BOPBjbl6s/ExERERHVYDa8du3aFWfPnr3pfTcjODgY8fHx8PRs2JRJhtfmkVRUjoWn01Gq0eH1rn4Y5eVs6ZJatTJlJZ7/aiN2JZzB5CG98PJDY6CQySxdFhERERGR1TA7bdjBweGW9lHLF+Vij9U9Q9DV0RbPn76Gjy5lQ9Oy1vZqURxsbbD0qfsx5+478PNfR/HoO9+joKTM0mUREREREVkNsyOvAQEBmD9/fq3toijio48+wpUrV267gJCQELi5uUEQBDz22GOYM2dOrTbLli3DsmXLAAA5OTm4fPnybb8vNYxaJ+Ldi1n4OaMQfVzt8XaoP9zkHBFsSlsPHccr326Gl4sjPn/2AXQO8LF0SUREREREFmc2vL7++utmD37ttdduu4Br167B398f2dnZiI2NxSeffILBgwfX257Thi1jc2Yh3jyfBQ+FFO93D0Cok62lS2rVjl+8iqeWrkWZshLvPDYRw6O7WbokIiIiIiKLsqr7vC5evBiOjo5YsGBBvW0YXi3nZEkFFpxKR4Fai1c6+2Kcj4ulS2rVsvKL8dTSNTiZmoFnJ4/A7DGDuJATEREREbVZZq95NV5l+PnnnzfZN2rUqNt+87KyMpSUlBie//nnnwgPD7/t81LTCHOyw6qewejhZIdXz2bgfxeyoNZZzd8+Wh0fd2esfPER3NknDB/8FIcXlm1EpUpt6bKIiIiIiCzCbHg9f/684fmOHTtM9uXk5Nz2m2dlZWHQoEGIjIxEnz59MHbsWNx55523fV5qOu4KGb6ICMT0dm5Ye60Ajx1PQ65KY+myWi07GwXee3wSnp4wHL8ePI6Zb69ATmGJpcsiIiIiImp2ZlfeMTdFsTGmL3bo0AHHjh277fNQ85IJAhZ09EGYky3eOJeJBxJS8V73dohwtrN0aa2SIAiYe88QdGrnjee/2oD7X1+GT+dNQ/dgf0uXRkRERETUbMyOvJaXlyMxMRFHjx5FRUUFEhMTkZCQYHhNbdtd3i5YEdUeComAR4+l4c3zmdiaVYTU8krorOdS6lZjZEwofnxlNgABDy5Zjt8Pn7R0SUREREREzcbsgk1Dhw41O8K6e/fuJinKHC7YZH2K1Fq8eSET+/JKUVF1DayjVILuTrYIc7JFuJMdwpxs4a2QccGhRpBbVIp5S9ci8cIVPHHvUDxxzxBIJGb/DkVERERE1OJZ1WrDDcHwar20oojUchWSSypwskSJ5BIlzpcpoan6f5inQlYVZm0R5qgPtM5yqWWLbqFUag0Wr/gVv+xPwqje3fHmv+6DvY3C0mURERERETUZs+F148aNZg+eMGFCoxd0IwyvLUulTodzpZVVYVYfalMrVIb9gbZyw8hsmJMtujnawlbKUcSGEEURK34/iPfW7UC3IF98Om8a/Dx4+yIiIiIiap3MhleJRIKoqChERUUB0P+ybDhQELB8+fImL7Amaw6vOYUl2HLwGGJ7dUeQj7uly7FaJRotTpUocbJEiZNVgTarasViKYBODjYId7JD96pR2g4ONpBxunG9/jp2Dgu/WA8bhRyfPD0VUZ0CLV0SEREREVGjMxteN23ahHXr1uHChQu45557MG3aNHTq1Kk566vFmsPrtr9PYMEX6wEAXQN9MLJXKGJjuqNzgDev9byBnEoNTpZW4GSxEidL9aO0JRodAMBWIiDU0bYqzOpHaQNs5fyeGrmQno0nP1qDzPwivPHIeNwzKMrSJRERERERNaoGXfNaVlaGzZs3Y926dcjLy8OSJUswZMiQ5qivFmsOrwCQnlOAuKOnsePoaSSevwJRFBHk447YXqEYGdMdPUL8ubhOA4iiiCtKtWFkNrlEiTOlSlRWLQjlIpMgrCrIVgdaD4XZOz+1eoWl5Xj205/wz+kUzB4zEM9MHgkp/79GRERERK1Eg8KrVqvF77//jrVr1yI5ORlvv/02Ro8e3Rz11WLt4dVYTmEJdiWeQVz8afxzOgUarQ4+bs4Y0asbYmO6o1eXIMikXLCoodQ6EZfKK5FcNd04uUSJi2WV0FXt97WRIczJTr8glJMtQh1t4ShrW99ftUaLt1Ztx9pdRzA0qgvemTsRjna2li6LiIiIiOi2mQ2vu3fvxpo1a3D48GGMHDkSU6dORUxMTHPWV0tLCq/Gisoq8FfSOeyIP4X9Jy6gUq2Bq6M9hkd3RWyv7ujXPQQ2Crmly2xxKrQ6nClVGhaEOlWixBWlGgAgAAi2V1SFWX2o7exgA0UbGI1cs/Mw3vxxO0L8PPHZM9MQ6M1rsImIiIioZbvhgk0REREYNGgQBEGodY3h0qVLm7zAmlpqeDVWXqnCgRMXsCP+NPYknUVpRSUcbG0wOLIzRvYKxeCIznCws7F0mS1WoVqLU1Ujs9XTjvPUWgCAXBDQxdEG4U626F51u54QewUkrfD62UMnL+HZz36CRBDw0f9NQZ/QEEuXRERERER0y8yG1xUrVphdFGfmzJlNUpQ5rSG8GlNpNPjnVAri4k9jZ8IZ5JeUQSGXYWBYR4yMCcWwnl3h6mhv6TJbNFEUkVmpMQTZkyVKnCpVokyrn3DsIJWgu6Ot4XY9YU528LWRtYoFoS5n5eHJD1cjLTsfr8wYiynDLDtzgoiIiIjoVjXomldr0trCqzGtTofEc2nYcfQ0dsSfRmZ+EaQSCXp3C0ZsTChGRHeDt5uzpctsFXSiiNQKFZKLlThVqh+lPVuqhKbqX4OHXGoIstWPrvKWef1sSbkS//78Z+w/cQHTY/vi+Wmjea01EREREbU4ZsPrnDlz8PTTTyM8PLzWvrKyMqxbtw42NjaYPn16kxZprDWHV2OiKOJk6jXsiD+NHfGnkJqZBwCI6hSIkb1CMbJXKO8l28hUOh3Ol5kuCJVarkL1P5AAW7nJ6sbdHG1hJ20Z189qdTq8t+5PrPz9EPqHdcAHT06Bi4OdpcsiIiIiImows+E1KSkJb775Jk6cOIHw8HB4eXlBqVTi/PnzKC4uxqxZszB37lzY2DTf9ZltJbwaE0URF6/lIC5efwue05czAABdg3wR2ysUsTGh6NSO95JtCqUaLU4bFoTSh9rMSg0AQAqgg4MNernYY1agB7xsrP9WPRv3JmLxil/RztMVnz/7AEL8PC1dEhERERFRgzRo2nBpaSni4+ORkZEBOzs7hIaGomvXrs1RXy1tMbzWdLXqXrJx8aeReEF/L9n2Ph4Y2asb7yXbDPJUGsPqxidLlDhSWA65RMCcIA880M4dcol1/xHh6LnLmLd0HdRaLd5/YjIG9ehk6ZKIiIiIiG6I17y2cDmFJdiZcAY7j/JespZypUKF9y5mY29+KYLtFFjY0RsD3B0tXZZZ6bmF+L+PVuP81Ww8/8BoPBjbjyP3RERERGTVGF5bkRvdS7Z/WAco5NY/tbWl2p9fincvZiGtQo0hHo5Y0MEbAXYKS5dVrzJlJV74aiN2JpzBpCHReOWhsVDI+P8PIiIiIrJODK+tVHmlCvuPX8COo6fwV9I5w71kh0R2xsiY7rgjohMcbHkv2cam0umwKr0AX1/OhVYEHgp0x6xAD6td2Emn0+GTjbvx1a970atLe3z81P1wd3awdFlERERERLU0KLz+/PPPmDx58g23NQeG15unUmvw96kUxB09hZ0JZ1BQUq6/l2x4R8TGdMfQqC68l2wjy65U46NLOdieUwxfGxnmd/DGSE8nq52a+9uhE3jl21/g6eKIz555AF0CfSxdEhERERGRiQaF1+joaCQkJNxwW3NgeL09Gq0WieevYEf8KcQdPY3M/GJIJRL0CQ1GbK9QjOgVCi9XJ0uX2WokFpXjfxeycLasEr1d7PF8Jx90dLDOEe8Tl9Lxfx+vQZmyEu88NhHDo7tZuiQiIiIiIgOz4XX79u3Ytm0bfvrpJ9x///2G7cXFxTh16hQOHz7cLEUaY3htPKIoIjnlmiHI1ryXbGxMKAK9eS/Z26UVRWzIKMTnqTko1ehwfzs3zG3vCSeZ9S2klZVfjKeWrsHJ1Aw8M2kEHh07yGpHi4mIiIiobTEbXv/55x+cOXMGixYtwhtvvGHY7uTkhGHDhsHNza1ZijTG8No0eC/Zpleo1uKz1BxsyCiEq1yKp4K9cI+vCyRW9j1VqtR45ZtfsO2fZIzrH4H/zBoPG4Xc0mURERERURtnNrxWTw1+4IEHsHr16uasq14Mr82jvnvJxsaEYmSvUPTo0I5B9hadKVXifxeykFRcgTAnW7zQ0QfhznaWLsuEKIr46te9WLphF3p0aIdP503jdHIiIiIisiiz4TU8PBwLFy7EG2+8gXfffbfW/gkTJjRpcXVheG1+1feSjYs/jcNn9PeS9XV3xoheoYjtFYpo3kv2pomiiO3ZxfgwJQe5Kg3u8XHBUyFe8FBY161q4uJP4/llG+Fsb4tP501DWIi/pUsiIiIiojbKbHjdv38/Vq1ahZ9++gnjx483PVAQsHz58iYvsCaGV8sqLC3X30v26GkcqLqXrJuTPYb37Ia7+oWjX2gIJBLrvC2MNSrTaPFNWh5+TM+HrUSCue09McXfDXKJ9Yxqn0nLxJMfrUZBSTmWPHov7uobbumSiIiIiKgNatBqw99++y1mz57dHPXcEMOr9ShTVmL/iQuIiz+NPUnnUKasRDtPV9x3R0/ce0cU/D1cLV1ii5FaXol3L2bjYEEZOtgr8HxHH/Rxs577reYVl2Le0nVIOJ+Gx+8ZgifvHco/UhARERFRszIbXnft2oXhw4dj48aNde7ntGGqVqlSI+7oGWzYm4C/T12CIAgYENYBEwZHY0R0Nyjk1jUd1hqJooi/8kvx/sVsXFWqMdLTCfM7eMPP1joWS1KpNXh95VZs2peIUTHd8eac+2Bvo7B0WURERETURpgNr6+99hpef/11PPLII7UP5LRhqsfVnAJs2peITfsSkZlfDFdHe9w9IAITB0ejS6CPpcuzepU6Hb6/ko/lV/S3Lno40AMzA9xhK7X8SKcoilj5xyG8t/ZPdAn0wdzxQxDi54Egb3euSExERERETapB04atCcNry6HV6XAw+SI27E3AroSz0Gi16BHSDhOG9MSYvj3gZG9r6RKtWoZSjY9SsvFnTgn8beRY0NEbQz0crWKV573HzmPhl+tRUq4EoP9jVjtPVwT7eiDEzxMhvh5o7+uJED8P+Lg5W0XNRERERNSymQ2vH3zwgdmD58+f3+gF3QjDa8tUUFKGXw8ex4a9CTh/NRu2CjlG9+6OiYOj0atre4YbM44UluGdC9m4UF6J/m4OWNjRGyH2NpYuC+WVKqRcy0VKZi5SMnKRmpmHlIxcXM7MQ4VKbWhnZ6PQh1pfDwT7eSKkKtS29/WAg63l+0FERERELYPZ8Pr666+bPfi1115r9IJuhOG1ZRNFEScupWPD3gRs+zsZZcpKtPfxwITBPXHvoCjeS7QeGlHET9cK8EVqLpQ6Haa1c8ecIA84yqzvFkWiKCKroBgpGXlIrRFsr+UVwfgjx9vVCSF+noYR2+CqYOvv6QopF4QiIiIiIiOcNkwWU16pwp9HTmHDXwk4eu4ypBIJ7ojohAmDozEksgvkVhjMLC1fpcEnqTnYnFkED4UUT4d4Y6y3MyQtZORaqVLjSnY+UjLykJKZi9SMXKRk5iE1IxfFVVOQAUAhlyHI210/QutTNRW5KuS6OtpbsAdEREREZCkNCq+XLl3CvHnz8Pfff0MQBPTv3x8ffvghOnTo0Bw1mmB4bZ1SM3OxcW8iftmfhNyiUni4OOKegZGYODgaIX6eli7P6pwsqcDbF7KQXKJEhLMdXujog1CnlnsNsSiKyC8pQ0pGHi5n6gNt9Yjtlex8aLQ6Q1s3J3uE+Hoi2M+j6lEfagO93aCQcVVrIiIiotaqQeG1X79+ePLJJzFt2jQAwNq1a/HJJ5/gn3/+afICa2J4bd00Wi32Hb+ADXsT8FfSOWh1OkR3DsKEwdEY3ac7r5E0ohNF/JpVhKUpOShQazHB1xVPhnjCrZXdlkit0SI9t8AwDTm1atQ2JTMPeUWlhnZSiQTtvFxNgm31aK2ni3UsdEVEREREt65B4bVv3761gmq/fv3w999/N1lh9WF4bTtyCkuw5cAxbNibgNTMPNjbKjCmbzgmDo5GRMcAhpEqJRotvrqci7XpBXCQSfBEsBcm+rlC1ga+P8VlFUjNzDNcU6t/rn+sVGsM7RztbBDs62G4pja4Kti293GHHe9VS0RERNQiNCi8vvDCC3B1dcXUqVMhCALWrVuHyspKPPnkkwAAd3f3Ji+0GsNr2yOKIhLOp2HDXwn44/BJVKjU6OjvhYlDonH3gAh4ODtaukSrcLGsEu9czMLhwnJ0drDB8x190Mu1bV4fqtPpkJFfXHVN7fXR2tTMPGTkFZm09fNw0Y/W+nqYTEX2c3eGhItGEREREVmNBoXXkJCQ+k8gCLh06VKjFmUOw2vbVlqhxPZ/krFhbyKOX7wKmVSCYT27YsLgaAzq0anNr1AriiJ25pbg/UvZyKzUYLSXE57t4A0fG7mlS7MaFZUqXM7KrxqpzTVaFTkPZcpKQztbhRztfdxNRmurV0XmPYqJiIiImh9XG6YW6/zVbGzcl4AtB46hoKQcPm7OuHdQFCYM7olA7+abDWCNKrQ6rLiShxVX8iEVgNlBnpgR4AZFGw/35oiiiNyiUsMUZOMR2/ScQmh11xeNcrK3ha1CDlu5DDYKOewUctgoZLBVyGEj1z/a1thmZyOHjVwOW4UMNnK56Wvjc8j1j3YKORRyGafHExEREVVpUHj9/vvv69z+0EMPNXpBN8LwSjWpNBrsSTyHDXsTcODEBehEEX1DQzBhcDRiY0Jhq2i7o47pFSq8fykbu/NKEWgrx8KOPrjDg9Osb5ZKo8HV7ALDbX2yCopRqdKgQqVGpVoNpUqDSpVa/1qlgVKtvr5fpTa5/vZmCIIAG7msdvitI+waArNReK5uayuXw9bmeti2rac9b09FRERE1qxB4fWpp54yPFcqldi5cyeio6Oxfv36Ji2uLgyvZE5mfhF+2ZeEjfsScTWnAM72thjbvwcmDo5G92B/S5dnMYfyy/DOxSykVqhwh7sDFnT0QZAdFypqLjqdDiqNFkqVuupLH3aVKjWUag2U1aG3alt1IDa8rgrEykp9+0pVjf1qjcm5NVrtLdUplUgMgdgk7MplsLWRw8HWBvcMisLwnl05IkxERETN7pamDRcVFWHGjBnYsmVLU9RkFsMrNYROp8PhM6nYsDcBO+JPQ6XWoFuQLyYM7olx/SPg6tj2FjJS60SsSc/HV2l5UOtEzAhwx+wgD9hLOZW4tdFotUaBtirsqjVV4bdGUDYK0MYhulKtQUWl6chyRn4xMvOL0LNTIJ6dEouYru0t3VUiIiJqQ24pvKrVakREROD06dNNUZNZDK90s4rKKrDt7xPY8FcCTl3OgEIuw8jobpgwJBr9QkPa3IqyOZUaLE3JxtbsYngrZHimgzfu9HLiSBrdkEarxS/7kvDppt3ILizB0KgueHbySHQO8LF0aURERNQGNCi83n333YZfbHU6HU6dOoUpU6bg7bffbvICa2J4pdtx+nIGNuxNwNZDJ1BcVoF2nq64746euPeOKPh7uFq6vGaVVFSOdy5m4XRpJaJd7PB8Rx90ceQqunRjFZUq/LjjH3zz236UVlTinoGR+L8Jw9rcvyEiIiJqXg0Kr3/99ZfhuUwmQ/v27REQENCkhdWH4ZUaQ6VKjbijZ7BhbwL+PnUJgiBgYHhHTBgcjeE9u0Ihl1m6xGahFUX8klmET1NyUKzRYrK/Kx5v7wUXORfuoRsrLC3H11v3YVXcYQDAAyP6YM7dd7TJaflERETU9HirHGrzruYUYNO+RGzal4jM/GK4Otpj/IAITBgcjS6BbWM6ZJFaiy8u5+Dna4VwlknxfyFeuNfXBVJOJaYGyMgrwqebdmPz/iTY2yowe+wgzBjVD/Y2XBSMiIiIGg/DK1EVrU6Hg8kXsWFvAnYlnIVGq0WPkHaYMKQnxvbrAUe71j+l9lypEv+7mIWEogqEOtrg+U6+iHS2s3RZ1EKcv5qNj9bHYXfiWXi5OuHJe4diwuCekEk5kk9ERES3j+GVqA4FJWX49eBxbNibgPNXs2GrkGN07+6YODgavbq2b9WLG4miiD9ySvDhpWxkqzQY5+2Mp0O84WXTNqZS0+1LOJeGD37agYTzaQj29cAzk0YiNia0Vf+7ISIioqbH8EpkhiiKOHEpHRv2JmDb38koU1aivY8HJgzuiXsHRcHL1cnSJTaZcq0O36bl4Yer+VBIBMwJ8sC0du6QSxhA6MZEUcTuxLP48Oc4XLyWgx4d2uHfU2LRJzTE0qURERFRC2U2vPbo0cPsX8qPHz/eJEWZw/BKllJeqcKfR05hw18JOHruMqQSCQZHdMbEIdEYHNm51U6NTKtQ4b2LWdiXX4ZgOwWe6+iD/u4Oli6LWgitToctB47hk427kJlfjEE9OmH+lFh0C/K1dGlERETUwpgNr5cvXwYAfPbZZwCAGTNmAABWrVoFe3t7LFq0qBlKNMXwStYgNTMXG/cm4pf9ScgtKoWfhwumDu+NSUOi4ebUOoPdvrxSvHsxC1eUagzzcMS/O3ijnR0X5KGGUarUWB13GMu27kNJuRJj+/XA0xOHI8DLzdKlERERUQvRoGnDAwcOxIEDB264rTkwvJI10Wi12J14Fqt3HsY/p1KgkMswpm84HhjZB+Eh7SxdXqNT6XT48WoBvknLhVYEIp3t4G8rRztbucmjp0IGCa9vpDoUl1Xgm9/248cd/0Cj1WHq8BjMHT8E7s6t848+RERE1HgaFF6joqLw6aefYtCgQQCAgwcP4oknnkBSUlJT11cLwytZqwvp2Viz8zB+2X8MFZUqRHQMwAMj+uDOPmGt7r6xWZVqfJuWh7NllbimVCFXpTXZrxAE+NcItNcfFXCRSbh4TxuXlV+Mz3/Zg437EmGrkGPWmAF4aHR/ONjaWLo0IiIislINCq9Hjx7FrFmzUFRUBEEQ4OLiguXLlyM6Oro5ajTB8ErWrqRcic0HkrA67jBSM/Pg7uSASUOjcf+w3vDzcLF0eU1CqdXhWqUa15T6r/Sqr2tKFdKVahRrdCbtHaSSeoKtPtzaSyUW6gk1t0vXcvDxhp3YEX8aHi6OeHz8YEwa2gsKWev6gw8RERHdvptabbi4uBiiKMLFxXK/gDO8Ukuh0+nw96kUrN55GHsSz0IQBAyP7ooHRvZFn27BbWrksUSjrTfYpivVUOpMP4Zc5VL421SHWdNg62crg0LCcNvaHLtwBR/8HIcjZ1IR6O2OeROH484+YZDwZ01ERERVGhRes7Ky8NJLL+HatWvYvn07Tp06hUOHDmH27NnNUaMJhldqidJzCrBudzx+3nMURWUV6Ojvhekj++DugZFtfpqkKIooUGtrBFs10qvCbUalGpoan1JeClkdwVYOf1sFfGxkkLahPwy0JqIoYt/xC/jgpx04dzUL3dv7Yf79sRgQ1tHSpREREZEVaFB4veuuu/DII49gyZIlOHbsGDQaDXr27IkTJ040R40mGF6pJVOq1Nj+TzJW7fgHpy5nwNHOBvcOisK0EX0Q4udp6fKskk4UkaPS1BlsrynVyK7UwHhSskwAfGzqDrb+NnJ4KqRtatS7JdLqdPjt0Aks3bgL13IL0T+sA+ZPjkVYiL+lSyMiIiILalB47d27N44cOYKePXsiMTERgH4RJy7YRHRrRFHE8YtXsSruMH4/fBIarRYDwzti2og+GBLVBVJOlWwwtU5EZqVxsL0ecK8p1chTmy4mZSMR4G9TM9jqw207WzmcuZiU1VCpNVi76wi+3LIXhaXluKtvOJ6eOBztfTwsXRoRERFZQINWxHBwcEBeXp7hF7q///670a57/f333zFv3jxotVo8+uijeOGFFxrlvETWTBAERHYKRGSnQDw3bTTW7zmKdbvj8X8fr4G/pyumDe+NiUOi4epob+lSrZ5cIiDQToHAeu45W6HVmVxve63yerA9XlKBkhqLSTnWu5iUPtzacTGpZqOQy/DQ6P64746e+G77Aaz8/RB2xJ/C5KG9MHf8EHi5Olm6RCIiImpGDRp5TUhIwFNPPYXk5GSEh4cjJycHP//8MyIjI2/rzbVaLbp06YIdO3YgICAAvXv3xpo1a9C9e/d6j+HIK7VWak3VPWPj/sHhM6mwkcswpl8PTB/ZB92DOV2yqZRotLWmJBuP4NZcTMpNLoW3QgZ7qQR2UknVo2B4bSfRb6vebieVwF4iub7faJ9CEDjKexNyCkvwxea/sP6vo5DLZHj4zv545K4BcLSztXRpRERE1AwaFF4rKyshlUpx9uxZiKKIrl27QqfTwcbm9haaOXToEBYvXow//vgDAPDWW28BAF588cV6j2F4pbbg/NUsrI47jC0HjqFCpUZUp0A8MLIPRvXuzluINCNRFJFfazEpFXJVGlRoRZTrdKjQ6lCu1T9WaEWoGr6AO6SASaC1rwq7xgHYtioY21dtrxmY6wrNcknrDsSpmXlYumEnfj98Em5O9pg7fjDuH9a71d1PmYiIiEw1KLxGR0cjISHhhttu1vr16/H777/jm2++AQD88MMP+Oeff/Dpp5/WewzDK7UlxWUVVfeMPYLLWXnwcHHElKG9MGVoDHzcnS1dHtVBrROhNAq15VpRH25rBN1yragPvLrqdjoo62lfrtXVWnHZHJkAw4jv9XBce3TYrp7AbFczHFedx9pCcXJKOt7/aQf+OZWCdp6ueGrCcIzr34O31yEiImqlzP6ZOjMzE+np6aioqEBiYiKqc25xcTHKy8tv+83rys11TaFbtmwZli1bBgBIL8nFSt2x235vohbBDsBIe9w/cjCuZBfgxKWr+DHrBFb9lYwO/p7o0aEd/D1cAFhXqGjzJFVf8vqbyAA4VX01hA4i1DoRalGERjR6rgPUYt371Lqq16KIAp2IbBHQ1Gin0wHQ3fDt9TULQFcHW/RytYedNQTE9sDQhVHomN0eh05dwpKsPfhydzz6dQ9Bex938N8FERFRyzRTUvflqWbD6x9//IEVK1bg6tWrmD9/vmG7s7Mz3nzzzdsuKiAgAFeuXDG8vnr1Kvz9a1/bN2fOHMyZMweAfuS1vs4QtWq++q+rOQVYu+sI1q9MQFLZWXQO8MYDI/tgXP+INn/P2FavCfKiWicaRndNR4eNtlftS6tQYdvlYpyRSjAryAMPtHODjTWEWF9A5z0E2/85iaUbdmLt93+hT7dgzJ8Si4iOAZaujoiIiBpJg6YNb9iwARMnTmz0N9doNOjSpQt27tyJdu3aoXfv3li9ejXCwsLqPYbThon0KipV2FZ1z9gzaZlwsrfFfYOiMHVEHwT78lYi1DQulVfi40vZ2JtfBl8bGZ4O8cJoL2dIrGThKZVGg593H8UXm/9CfkkZRsV0x7xJI3gfZSIiolagQeH1pZdewnPPPQdXV1cAQEFBAd5//33897//ve0Ctm3bhmeeeQZarRazZs3Cyy+/bLY9wyuRKVEUkXThClbHHcYfR05Co9VhUI9OmD6yLwZFdOI9Y6lJHC4ow4cp2ThTWokwJ1v8u4M3erpYz62dyioqseL3g/ju94OoVGkwYXBPPHnvUHi78VpxIiKilqpB4bVnz55ITEw02dYYCzbdCoZXovrlFJbg56p7xuYUliDQyw33D++NCYN78p6x1Oh0ooitWcX4LDUH2SoNhns6Yl6IN4LqueeuJeQVl+LLLXuxblc8ZFIJZozqh9ljBsLZwc7SpREREdFNalB4jYiIwJEjRwy3xqmoqEBMTAxOnjzZ5AXWxPBKdGNqjRZxR09jddxhHD13GTZyGcb1j8ADI/sgtL2fpcujVqZCq8OPV/Px3ZU8qEURU/zdMCfIEy5yqaVLM7iSnY+lG3fht0Mn4OJghzl334EHRvSBjcLMqlpERERkVRoUXt955x1s2bIFjzzyCARBwPLlyzF+/Hg899xzzVGjCYZXoptzNi0Tq3cextaDx1GhUiO6cxAeGNkHI2NCec9YalS5Kg2+SM3BL5lFcJBJ8K8gT9zv7wqFFU1dP3U5Ax/9HIf9Jy7A190FT00YhvEDIzm9noiIqAVoUHgFgO3bt2Pnzp0QRRGjRo3C6NGjm7q2OjG8Et2aorIK/LIvEat3HsGV7Hx4ujhiyrAYTBnai9cBUqO6UFaJDy9l42BBGQJs5Xg6xAsjPZ3qvBWapfx96hI++GkHklOuoVM7b8yfMhJDIrtYVY1ERERkqsHh1VowvBLdHp1Oh/0nLmL1zn+w7/gFSCUCYmO644GRfRDdOYi/vFOjOZSvX9TpfFklIp3tML+DNyKcredaU1EU8ceRU/h4/U5czspDdJcg/HtKLHp2DrJ0aURERFQHs+F10KBB2L9/P5ycTP9iLooiBEFAcXFxsxRpjOGVqPFczsrD2l1HsGlvIorLlega6IMHRvbB2P4RsLexnkV3qOXSiiI2Zxbhi8s5yFVpMcrLCU8He6GdFS3qpNZosWFvAj7/ZQ9yi0oxPLobnpk0Ap3aeVu6NCIiIjLCkVciQkWlCr8dOoFVcf/g7JUsONvb4r7BPTFteB8E+bhbujxqBcq1Oqy8kofvr+ZDKwLT2rnh0SAPOMmsZ1Gn8koVvv/jEL797QAqKlW4944oPHnvMPh5uFi6NCIiIsINwmt+fr7Zg93dm/+XWoZXoqYjiiISzqdhddxh7Ig/Ba1OxB0RnfDAiL4Y1KMjJFzUhm5TdqUan6fmYktWEZxlEsxp74nJfm6QS6xnunpBSRmW/boPq3cehiAIeDC2Lx4a1Q9ertZ13S4REVFbYza8hoSEQBAEiKKItLQ0uLm5QRRFFBYWIigoCCkpKc1ZKwCGV6Lmkl1QjJ/2HMVPu+ORW1SKQG93PDCiN+69oydceI9Muk1nS5X44FI2DheWI8hOjmdCvDHUw9GqwmF6TgE+3bQbWw4ehyiKcHawQwc/TwT7eqCDvydC/LzQwc8TAV5ukFvRCDIREVFr1aBpw3PnzsX48eMxZswYAPqVh+Pi4vD+++83eYE1MbwSNS+VRoO4eP09YxPOp8FWIcfdVfeM7Rrka+nyqAUTRRH7qxZ1SilXIdpFv6hTmJN1/XHk/NVs/H3qElIycpGSkYtLGbnIKSwx7JdJJQj0dkeInyc6+HkixOiLf+ghIiJqPA0Kr7169cLRo0dNtlkqRDK8ElnO6csZWLPzMLYeOgGlSg1ne1v4uDvD190Fvu7O8HN3Mbz283CGj5sz7LjwE92ARhSxKaMQX1zORYFaizHezvi/YC/42cotXVq9SsqVSM3MqwqzOfrHa7m4nJUPjVZraOfh7GASZqsDrr+nK+8tS0REdJMaFF5Hjx6NO+64Aw8++CAEQcCPP/6IvXv34o8//miOGk0wvBJZXmFpObb9fQKXMnKRmV+MzLwiZOYXI7+krFZbFwc7+Hq4wK8q1PpUhVxfo9CrkMss0AuyNqUaLb67ko9V6fr1Fqa3c8cjge5wbEFTcjVaLdJzCw1hNjVTP1KbkpGLgpJyQzuFXIZgHw8E+3lUjdbqpyAH+3nAwdbGgj0gIiKyXg0Kr/n5+Xj99dexd+9eCIKAwYMHY9GiRVywiYhMVKrUyCooQWa+PswaP2ZUhdyisopax7k7OejDrEd1qK0OtvrQ6+XmBIWMAbetyFCq8VlqDn7LLoabXIq57T0xwc8VMiu6HvZWFJSUISUjDymZ+jCbck0fbK/mFECr0xna+bg51zkF2dfd2aquCSYiImpuN3WrnNLSUjg6OjZlPTfE8ErUslVUqpBVUIyMPH2ozcovRkaNsFtSrjQ5RhAEeDg76Edsq6Yj66cm68Ouj7szvF2dIJO2nBE6urGTJRX44FI2EooqEGKvwLMh3hjk7tDqApxKo8GV7IKqMKufgpxSNSXZ+N+CnY0CIb4etaYgt/f1gK3CeqdYExERNZYGhdeDBw/i0UcfRWlpKdLS0nDs2DF89dVX+Pzzz5ujRhMMr0StX1lFJTILqqcjVwdboxHc/CKUK1Umx0gEAV6uToYwa5ia7OEC36qw6+nqyOsMWxhRFLEnrxQfp+TgcoUKfVztMb+DN7o62lq6tCYniiJyi0qrwuz1kdqUjFxcyytC9X++BUGAv4fL9dFaf0+E+OrDraeLda3gTEREdDsaFF779u2L9evXY/z48UhMTAQAhIeHIzk5uckLrInhlYhEUURpRaU+zFaP4BYUIyPPdARXqVKbHCeTSvQB1814irLpo4ezA+9na4XUOhHrMwqw7HIeijRa3O3jgieDPeFt0zZHHJUqNS5n5hnCrPFXhdH/753sbQ2hNtjXAx38vBDi74lAbzdOxSciohanwf/lCgwMNHkt5fQ8IrIQQRDgZG8LJ3tbdA7wqbONKIooKqu4HmbziqpGc/Wvk1OuYWfCGajUGpPjZFIpfNyc9GHW4/oIro/79UWn3JzsOZrVzOQSAdPauWOcjwu+TcvD6vQC/JlTjBkB7ng40AP20rb1BwdbhRxdg3xr3a5Kp9Mhq6DEsApyaoY+4B46eQm/7E8ytJNKJAj0dtOP0Pp7mlxj6+po38y9ISIiapgGhdfAwEAcPHgQgiBApVJh6dKlCA0NberaiIhumSAIcHW0h6ujPbrVcz9aURRRUFJusqBUVkHVNbh5xUg6fwV/FJwyufUJoA+4bk72cHW0g5ujPVyd7OHmaA83J/uq7Q7659X7nOxhp5Az8DYCJ5kUz3TwxmQ/VyxNzcHXaXnYlFmIx9t74R5fF0jb+PdYIpHAz0N/PfiA8I4m+8oqKqsWi8pDSkaOYdT2wMmLJn/EcXOyNxqt9UQ7L1f4ubvA39MVHs6t75pjIiJqORo0bTg3Nxfz5s1DXFwcRFHEqFGj8PHHH8PDw6M5ajTBacNE1Jx0Oh3yisuMpiMXIaewFIWl5SgoKUdBaTkKqx9LK1DfR6pCLqsddGu8NnnuaM9FeBrgeLF+UadjxRXoZG+DZzt4YYC7ZRcWbGm0Oh2u5RbWOQU5r9j09lcKuQx+VYul+Xu6GEKtn4cL/D1ceesrIiJqUjcMr1qtFjNnzsSPP/7YXDWZxfBKRNZKq9OhpFypD7WGQHs93BaUmD4vKC1HcR23Dqpmp5DXG271wVc/wuvqYGcIvG0xOIiiiLjcEixNycFVpRoD3BzwbAdvdHLg/VJvV3FZBTLyinAtrwgZeYVVj0W4lqt/nlNYYtJeEAR4ujhWhdmqYGsScF3gZG/L0VsiIrolDRp5HT16NH799VcoFIrmqMkshlciak00Wi2KyipQWFphFG7Lao3qVu8rLKuodSshYw62Ng0a1XU1PLdrNbcYUul0WHetEF+n5aJMo8O9vi54PNgLnoq2F+ibi0qtQWbV7a6u5RZWBd2qx9wiZOQX1bqu3MHWRj9qWzVaa/Lo6QIvVyeuCk5ERHVqUHh97LHHkJCQgPHjx8PBwcGwff78+U1aXF0YXomorVNpNCgqrTCZulxzVLewatS3en/NWwsZc7a3rRVuDdfvOly/btfD2RHtfdytftSsSK3F12m5WHetAHJBwMOBHpgR4A67NraokzUQRRF5xWWGUHs94F4fwS2qMftAJpXAx83ZaNTWNOj6ebjAzsbyf0wnIqLm16Dw+vrrr9e5/bXXXmv0gm6E4ZWI6OZVqtT60V2jcFtQUmbYVlhiGoQLSspQWWPEDAC6Bfli1piBGN07DHKZdY/YplWosDQlBztzS+CtkOHJYC+M83GGxMrDd1tTpqxERnWYNRq1rX6eXVACrU5ncoybk73RqG31NbjXR3G5IjgRUevUoPBarbi4WH+LCienpqzJLIZXIqLmUVGpMgTbwtIKpGbmYvXOI7h0LQd+Hi54+M7+mDA4Gg621n1taWJROT64lI3kEiW6Othgfgdv9HFzuPGBZBU0Wi2yC0v005BNAu71UdyKStOZBbYKuWmwNUxL1j/6uDlb/R9fiIiotgaF1/j4eDzyyCMoKdEvzODi4oLly5ejV69eTV5gTQyvRESWo9Pp8Nex81i+7QCOnrsMZwc7TB0eg+kj+8LL1XJ/2LwRnSjij5wSfJKSjYxKDQa7O2BeB290sLfu4E03Vn1P52t5ta+7zcgtwrX8IuQVlZocIwgCvF2d6hy1rb721tHO1kI9IiKi+jQovEZEROCzzz7DHXfcAQDYv38/nnjiCRw/frzJC6yJ4ZWIyDocu3AFy7cfQNzRM5DLpLhnQCQevmsAQvw8LV1avSp1OqxJL8C3aXmo0Oowwc8Vc9t7wp2LOrVqSpUamfnFtUZtDc/zi2vdz1kuk8LB1gYOtgo42NrA3lZh+tzu+j7TNvrX9rY2cLC7/rq1LIxGRGRJDQqvAwcOxIEDB264rTkwvBIRWZfUzDys+P0gftmfBLVGi+E9u2LWmIHo2TnI0qXVK1+lwbK0XKy/VghbqQSzAj3wQDs32HJRpzZJp9Mht6jUMA25eiGpMmUlyipU+kfl9cfy6tcVldA18OorG7ms7hBcI+QabzcOw8Zh2d5WwRWZiahNalB4ffbZZ1FeXo5p06ZBEASsW7cObm5umDhxIgAgOjq6yQutxvBKRGSd8opLsTruMFbHHUZRWQV6dgrErLGDMCyqCyRW+ot2anklPkrJwV95pfC1keGpYC/c6c1FnahhRFFEpVpTZ8gtrwq35ZVG2ytq7Dc8Xm/T0KVIbBXy6yO8dYRgext94L0egOsIwTYKONjp21rrv1EiImMNCq/Dhg2r/wSCgF27djVqUeYwvBIRWbfyShU27k3Ayt8PIT23ECF+nnj4zv4YPyASNgq5pcur05HCMnx4KRunSysR5mSL+R28Ee1ib+myqI0RRREVKrUh5JYbRnqrw23DQ3D1toays9GPCNvZKCCTSiARBEglEkgk1x9lEgkkEgmkEsHkUVajnfFjXdvMnUta33GCBFJp/dtrva9QdT6p0bkFo/cwPpegf5TLpJBJJZBLpZDJpBzdJrJCN7XasDVgeCUiahk0Wi3+PHIKy7cdwKnLGfBwccSDsX0xdXhvuDjYWbq8WnSiiN+yi/FpSg6yVRoM93DE0x280d6O9xSllkmn06GiUq0PspWqOqZAXx8xLq8KyeUqFbRaHXQ6EVpd1aOof9RoddDpdNDqRP2jqH/UGLcXjY7T6aDVXm9nOM5wbv1za/1VVBBqB1q50XPT7VLIZPrX1c9lNZ8bHWN4LpNCJjV9fuP99Ww3rkkq4Wg6tUoMr0RE1KREUcQ/p1OwfNsB7D9xAXY2CkweEo0Zo/ujnaerpcurpUKrw6r0fHx3JR8qnQ6T/dzwYIAbfGzkkHI6MVGjE2sG3hpB1zjwGrczftTodLXa63S6qu21218/1/W2aq0OGq0Wao0WmhrP1fVt12ih0V5/rtbWvb36GOP3aGpSiaTOcFv9XF71vHqkXZAIkAhC7edVX/Xtk0iMnle9rtleAOrc3tDzCDf5fjXPK6D+ugBU9REwbDF6LghV+43aXm8mGPZXHWbyHFXnNW5reL/qk9c8TjBtixrvXbMtzNR5/T9ZpnUo5FL4urvc7P+lrALDKxERNZuzaZn4bvtBbPvnBEQRuLNvGGbdNRCh7f0sXVoteSoNvrici00ZhdABkAmAt0IOX1sZ/Gzk8LWRw89WDl8bmeG5PRd8IqIGqA7sNYOuyijomgvQpuFYY2irqj6uriCtqTqH0fPq7SJE6EQRok7/WOu5KEKnEyEC9e4zPK9uK9Y+lyjWvb3meahpdQ7wxuYlT1q6jFvC8EpERM0uI68I3/95CD/vOYpypQoDwjpi1piB6B/WwfAXZWuRUl6Jo4XlyKzUIKNSjQylGpmVamRXalBz7MRFJoGvjRy+VaHWzxBw9a89FTIuBkVEdAP1huCbDMu1w7XxuXVV7wWI0O+rfm/xeiEQq7YZt61+DqO2Ys3nRm0hGp3DaL/+tQgYPRfraGs4B0yPM67Z9NG0TuO4J4oinOxtMTy62y38ZCyvweH14MGDSE1NhUajMWx76KGHmqyw+jC8EhG1HsVlFVi3Ox4//Pk3cotK0S3IF7PGDMTo3mGQy6z7vphaUUROpQaZlWpkVD1mKtXIqFTrg65SjVKtzuQYmYCqIFs1Ymsr1wdcG/2Irq+NHHYcvSUiIqpTg8LrjBkzcPHiRURFRUFadZNtQRCwdOnSJi+wJoZXIqLWR6XW4NdDx/HdtgO4lJELPw8XzBzdHxOHRMPB1sbS5d2yEo22KtRqqkKtGhnKqqBbNXqrq3GMq0wKv6og62tzfZqyX9WIrrtcytFbIiJqkxoUXkNDQ3Hq1CmrmMrF8EpE1HrpdDr8dew8vt22Hwnn0uDsYIepw2MwfWRfeLk6Wbq8RqepGr3NMIzaVo3kVk1NzqjUoLzG6K1cEPRTko2mI1dfc+tnI4ePjQy2HL0lIqJWqEHhdfLkyVi6dCn8/Cy/oAbDKxFR23DswhUs334AcUfPQC6T4p4BkXj4rgEI8fO0dGnNRhRFlGp1uFYVZjMrNchUqnHN6HmOSoOa/yF3k0sNU5H9jKYpVwdcN7nUKv4gTUREdDMaFF6HDRuGpKQk9OnTBzY216dvbdmypUmLqwvDKxFR25KamYsVvx/CL/uToNZoMaxnV8weMxA9OwdZujSroNaJyFGpcc1oOnL1NOXqBaaUOtP/1NtIBPhUj9jWsWqyj40MNrxHJBERWZkGhde//vqrzu1Dhgxp9IJuhOGViKhtyi0qxeq4f7B65xEUl1WgZ6dAPDJmIIb37AoJg1a9RFFEsUZnslJyptH05IxKDXJVmlrHecil8LOVI8BWgSA7OQLtFAi00z93lXHkloiImh9vlUNERC1KeaUKG/cmYOXvh5CeW4gQP088fGd/jB8QCRuF3NLltUgqnQ7ZhlsBXR/BvaZU40qF/rnxlbeOUokhyOofFQi0lSPITsEpyURE1GQaFF7//vtvPPXUUzh9+jRUKhW0Wi0cHBxQXFzcHDWaYHglIiIA0Gi1+PPIKSzfdgCnLmfAw8URD8b2xdThveHiYGfp8loVlU5/3W1ahRpXKlS4olQZnl9T1hVsq0KtrQKBdvpQG2ingDuDLRER3YYGhdeYmBisXbsWkydPRnx8PL7//nucP38eb775ZnPUWKsWhlciIqomiiL+PpWC77YfwP4TF2Bno8CkIdF4aHR/tPN0tXR5rZ5aJ+qDrVKFKxUqpFWocMUo2GqN2jpUB1tbhcnIbaCtAp4KBlsiIjKvweE1Pj4eEREROH78OABgwIABOHjwYJMXWF8tRERENZ1Jy8R32w9g+z/JEEXgzr5hmHXXQIS2t/xq+W2RWicio1JdFWhVJiO315RqaIx+A7GTCNenIFcF3OrnXgoZgy0REUHWkEb29vZQqVSIiorCc889Bz8/P5SVlTV1bURERDelW5Av/vfYRDwzaSS+//MQft5zFL8dOoEBYR0xa8xA9A/rwBDUjOQSAUFVgbQmtU5EZqX6eqhV6kdtz5dVYk9eiUmwta0KtoYpyFWLSAXZKeCpkEHCnykRUZvQoJHXy5cvw8fHByqVCh9++CGKiorwxBNPoFOnTs1RowmOvBIRUUMVl1Vg3e54/PDn38gtKkW3IF/MGjMQo3uHQS6TWro8qodGFJFZtVhUdai9UjUd+apSDbXRry62EqHOFZEDbRXwtmGwJSJqTRq82nBFRQXS0tLQtWvXpq7JLIZXIiK6WSq1Br8ePIbvth/EpYxc+Hm4YObo/pg4JBoOtjY3PgFZDa0oIqtSv3iUyXRkpQpXK0yDrY1EQICt6YrI1c99GGyJiFqcBoXXX3/9FQsWLIBKpUJKSgqSkpKwaNEibNmypTlqNMHwSkREt0qn0+GvY+fx7bb9SDiXBmcHO0wdHoPpI/vCy9XJ0uXRbdIHW03VKK0KaUq14fmVCjVURr/yKAQB7QzTkK+H2iA7OXxs5JAy2BIRWZ0GhddevXph165dGDp0KBITEwHAZPGm5sTwSkREjeHYhStYvv0A4o6egUwqwb0Do/DwXQMQ4udp6dKoCehEEdmVGv1orVJtujKyUoVK3fVfh+SCAE+FFC5yKVxkdT+6yqRwNjxK4CyTMvASETWxBi3YJJPJ4OLi0tS1EBERNZvIToH4+KmpSM3MxYrfD+GX/UlYvzcBw3p2xay7BiK6S5ClS6RGJBEE+NrK4WsrR58a+3SiiByVxmRF5FyVBkUaLYo0OmSWVqJIrUWxRmtyT1tjAgAnmcQQbp1lUrgaPeq3S2qFYEephIuIERE1UIPCa3h4OFavXg2tVovz589j6dKlGDBgQFPXRkRE1OSCfT2x+OG78X/3DcPquH+weucR7Eo4g56dAvHImIEY3rMrJBKJpcukJiQRBPjY6KcLx7jW304niijV6PShVq1FoUaL4lqP+v2Fai3SKlQoVGtRqq0v8gJSAM61RnZNQ65rHWHYViIw9BJRm9OgacPl5eVYsmQJ/vzzT4iiiNGjR+PVV1+Fra1tc9RogtOGiYioKZVXqrBxbwJW/H4I13ILEezrgfEDI9HJ3xvBfh4I8naHQt6gv/0SAdCvnlys1hpCr/5RH3KrQ29Rrf1aVOjq/xVNIQgm05aNpzGbG/lV8A8xRNSCNXi1YWvB8EpERM1Bo9XizyOn8N32gziZes2wXSIIaOflhhA/D4T4eiLYzwMd/LwQ7OsBTxdHjoZRo1HpdIaQW1e4rRmGq0Ow2syvdnYSwey1vE4yCRSCALlE/2V4bvSoMH5ttE0mCFzBmYialNnwOn78eLMHc7VhIiJqC0orlEjJyENKRi5SM3ORkpGH1MxcpGbmoVKtMbRztLMxBFr9oydCfD3Q3tcDtgq5BXtAbYUoilDqxFrhtrDqml39ow6FVduNpz1rG+H9ZQIMwbY60BoCr7kQXPMYo+CsEATIbhCca52nRpvqR4ZropbNbHj18vJCYGAgpk2bhr59+6Jm0yFDhjR5gTUxvBIRkbXQ6XTIyC9GakYuLtUItpn5xYZ2giDA38MFIX6eCPb1qHr0RIifB3zcnDlaSxYniiJKtTqUaHRQiyLUOv2Xquq5RhSh0omGfao6thn21Xht7jzG51OLtY9pbMbhuq5HaVVQlgmATBCuf0lgCOJSwWi/xKhN1TapUXvjL7nRNrPnkJie73oN17cxhFNbZTa8arVa7NixA2vWrMHx48cxduxYTJs2DWFhYc1ZowmGVyIiagnKK1VIzcxDakZu1YhtXlXAzUNFpcrQzs5GoQ+0VaE2xE8/Yhvs6wF7G4UFe0BkWaIoQiPCJPBWP2p0DQzO1cfU07b6PCpRH6y1Vduq39fwpavxuro2nenr5iJBdVBGnQG6IQG4+ksi6FfLFgRAgADjWCxU76t+Df2G6m2CUbvq/UKNY1HdtqqRybGC0f6qY2tvNz3eZLvRe6D6+JrvW/XEpDajmo3fv2bdRqXU6oPJsTXqNdRTx3bT9667ptrnN/3eGfbV+P4Zjq3vfate2cskiHS2Q0vU4GteKysrsWbNGixcuBCLFi3CU0891dS11YnhlYiIWjJRFJFVUGwYoa0OtikZubiWV2Qyy8nX3dkwQhvs64kOVcHWz92ZKyATWRlRFKGFcaCF2UCsrrFNW08g1hiOr7GtrvPqarxvnec03aYVAREiRJO+ACKufxn+t9Z2/ZPq40Wj42HUrvpzTYTRdpM2puc3PpYaXyd7G/wcE2LpMm7JDZdLrKysxG+//YY1a9YgNTUVTz/9NCZMmNActREREbU6giDA190Fvu4u6B/WwWSfUqXG5cw8pFSP2GbmIjUjD78ePI7SikpDO1uFHO193E2CrX7U1gOOds1/JwAi0v/blgGQSTmlt7EZh9/rgdhof3V4Fk0Db3VwrhmEa4fk6+HZ5FiT9qLJvrrOUzPk1zqP0cHGfzCo3R+jNqLptprtjetHre3G7a+3sWnBf/w0O/I6c+ZMJCcn46677sLUqVMRHh7enLXViSOvRETU1oiiiNyiUsMIbUpVsE3JyEN6TgF0Rv8p93J1Qoivh2GxKP2jJ9p5uULagn9hISIiMhteJRIJHBwc9A2NJoaLoghBEFBcXFzfoTe0ePFifP311/Dy8gIAvPnmmxgzZswNj2N4JSIiuk6l1iAtOx+pGXlVgbY63OahuKzC0E4ukyLI291wXW2IUbh1cWiZ1z4REVHbYnbasE6na9I3f/bZZ7FgwYImfQ8iIqLWTCGXoVM7b3Rq522yXRRFFJSUV0091ofZlIxcXLyWgz1JZ6HRXv9vvLuTg9HtffSPHfw90c7TDXKZtLm7REREVKcbXvNKRERELY8gCHB3doC7swN6dWlvsk+t0eJqToFh+nH1qO3uxLPI31tmaCeXSdG9vR+iOgeiZ6cg9OwcCC9Xp+buChEREYCbWG24sS1evBgrVqyAs7MzYmJi8P7778PNze2Gx3HaMBERUdMpKqswjNSev5qFYxevIjnlGlRqDQCgnadrVZgNRFTnIHQJ8IZMytFZIiJqek0aXkeOHInMzMxa25csWYJ+/frB09MTgiDg1VdfRUZGBpYvX17neZYtW4Zly5YBAHJycnD58uWmKpmIiIhqUGk0OH05E0nn05B44QoSz19BTmEJAP19aiM6tENUp0BEdQ5EZMcAuDraW7hiIiJqjSw28mosNTUV48aNQ3Jy8g3bcuSViIjIskRRxLW8IiSdv4LEC2lIOn8FZ69kQVu1VkYHf6+qkVn9CG2In6fJwo9ERES3wmLXvGZkZMDPzw8AsGnTJqu4DQ8RERHdmCAIaOfpinaerhjbvwcAoExZieSUa0g8n4akC1ew4+hpbNibAABwcbAzjMz27BSI8A7tYG+jsGQXiIioBbLYyOuMGTOQlJQEQRAQHByMr776yhBmzeHIKxERkfXT6XRIzcxD4oUrVSO0V3DpWg4AQCqRoFuQr9G1s4Hwc3fh6CwREZllFdOGbwbDKxERUctUWFqOYxevIun8FSRduILjF6+iQqUGAPi4OSOyU4BhIajQ9r5QyHhTBCIiuo7hlYiIiCxCo9Xi3JUsJFaF2cQLV3AttxAAYCOXISzE3xBmozoFwMPZ0bIFExGRRTG8EhERkdXIyi9G0kV9mE06fwUnUzOg0WoBAEE+7kYLQQWhYzsvSCUSC1dMRETNheGViIiIrFalSo2TqRmGVY2TLlxBXnEZAMDRzgaRHQMQ1SkQPTsHIaJjOzja2Vq4YiIiaioMr0RERNRiiKKIK9kF+mnGVSsbn7uaDVEUIQgCugR4G8JsVKdABHq7cSEoIqJWguGViIiIWrTSCiWOX0w3hNljF6+itKISAODh7KC/TU/VdOOwYH/YKuQWrpiIiG4FwysRERG1KlqdDhfTc5B4IQ3HLlxF4vkruJyVBwCQSaXoHuxndO1sILzdnC1cMRERNQTDKxEREbV6ecWlOHbhatV04ytITklHpVoDAPD3dNWH2U6B6Nk5EF0CfSCTSi1cMRER1cTwSkRERG2OSqPBmcuZhjCbeD4N2YUlAAA7hRwhfp7wcXeGj5szvN2c4Ovuon90029zsLOxcA+IiNoehlciIiJq80RRREZ+EZLO6+83eyU7H1n5xcgqKEFhaXmt9o52NvB2c4avIdw6619XPfq4OcHN0R4S3sqHiKjRMLwSERERmaFUqZFdUIKsgmL9V37x9ecFJcjKL0ZOYQl0NX6lksuk8HZ1Mozg+lSFWh93F/i4OcHbzRnerk6QyzhFmYioIWSWLoCIiIjImtkq5AjycUeQj3u9bbQ6HfKKSpGZX4zsghJkFhTpH/OLkV1QjJOp17A78SyUKrXJcYIgwN3ZofYIrpvxSK4THGw5TZmIiOGViIiI6DZJJRL9SKqZlYtFUURRWYVJqM0sqHrML0Z6TiGOnktDcVlFrWMd7Wyuj966V4/gGo/oOsPNyZ73tCWiVo3hlYiIiKgZCIIAV0d7uDrao0ugT73tKipVyC4sMVxzm1VQZJienFVQjAsnspFbVFprmrJCLoO3q5NhYSnja3CrR3C9XDhNmYhaLoZXIiIiIitiZ6NAex8PtPfxqLeNRqtFblGpSag1vib3RMo1ZCWcgarqdkDVBEGAh7OD6Qiu0Yiup4sj7G0VsFPIYauQw0Yu46JTRGQ1GF6JiIiIWhiZVApfdxf4ursAHetuUz1NOSvfdHpy9eJTV7LzEX8mFcXlSrPvZaeQw9ZGH2arQ62djQJ2Vduqt9vZ1GhnI4ed4no7u6rjaraxZUAmogZieCUiIiJqhYynKXcN8q23XXmlCtlVKyfnFpZCqVKhQqWGUqVGRaX+S2n8WqWCslKtD8YFxVBWqlGuUkNZqUZFparWdOaGqA7BtlUh+Hr4rdpuI4e9onbwtasKyNXH2tcVoG0UDMhErQTDKxEREVEbZm+jQLCvJ4J9PW/7XKIoQq3VVgVZtSEEXw/CKv3zqrBb/dywvUZQLqmoRE5hqVGYVqGiUg2tTnfTtdnIZdfDsY3CZCTZ1kYOmUQCCIBEECAIAgTo/wCg/wIECJBI9AtiVW+TCAIECKbHVbUVJNfPIdFv1G83agsAEolg2C7Uem/TbdVtYVRPXXVWvwdq1CnoG9fqY1tn/L3Tv77+fan5fTV8/4Xq1jWON25r/D2u4/yGn13VRqGOY6sPEOqoBTXOcb1mAHXUUL1dIZfC38O1Sb+nTYXhlYiIiIgahSAIUMhkUMhkcHawa7L3UWu0hpBbXqkyCcKmI8YqQ1A2eax6Xq5SoUxZibziUmi0OogAIIrQiSJEERBR9SiK+i8AOp0IGG3XVW03Oa5qm+E4EfoRaaPt1W0BETrd9e1ETa1zgDc2L3nS0mXcEoZXIiIiImpR5DIp5DIpnOxtLV1KozMOyqJYR3iueo6qcG0cfMX6QndVeK7eXjOgG43xtSmG74fhseYfHq7vM9mub4jqB7FGG9T8+RmdHzXOY9wGNd7H+Pyo432M/39gqN+oDeqoQRRFODu03H83DK9ERERERFaiegowEdXGK9eJiIiIiIjI6jG8EhERERERkdVjeCUiIiIiIiKrx/BKREREREREVo/hlYiIiIiIiKwewysRERERERFZPYZXIiIiIiIisnoMr0RERERERGT1GF6JiIiIiIjI6jG8EhERERERkdVjeCUiIiIiIiKrx/BKREREREREVk8QRVG0dBE3w9PTE8HBwZYuo145OTnw8vKydBkW0Vb73lb7DbTdvrfVfgNtt+/sd9vTVvveVvsNtN2+t9V+A2237y2h356envj9999rbW9x4dXaxcTEID4+3tJlWERb7Xtb7TfQdvveVvsNtN2+s99tT1vte1vtN9B2+95W+w203b635H5z2jARERERERFZPYZXIiIiIiIisnoMr41szpw5li7BYtpq39tqv4G22/e22m+g7fad/W572mrf22q/gbbb97bab6Dt9r0l95vXvBIREREREZHV48grERERERERWT2G19ug1WrRs2dPjBs3rta+yspK3H///ejUqRP69u2L1NTU5i+wiRQWFmLSpEno1q0bQkNDcejQIZP9oiji6aefRqdOnRAREYGEhAQLVdq4PvzwQ4SFhSE8PBzTpk2DUqk02d+afuazZs2Ct7c3wsPDDdvy8/MRGxuLzp07IzY2FgUFBXUeu3LlSnTu3BmdO3fGypUrm6vkRlFXvxcuXIhu3bohIiIC9913HwoLC+s89vfff0fXrl3RqVMnvP32281UceOpq+/V3nvvPQiCgNzc3DqPbW0/cwD45JNP0LVrV4SFheG5556r89jW+DNPSkpCv379EBUVhZiYGBw+fLjOY1vyz/zKlSsYNmwYQkNDERYWho8//hhA6/+Mq6/frf0zrr5+V2vNn2/m+t6aP+Pq63db+HxTKpXo06cPIiMjERYWhtdeew0AkJKSgr59+6Jz5864//77oVKp6jz+rbfeQqdOndC1a1f88ccfzVl6w4l0y95//31x2rRp4tixY2vt++yzz8THHntMFEVRXLNmjThlypTmLq/JPPTQQ+LXX38tiqIoVlZWigUFBSb7f/vtN/HOO+8UdTqdeOjQIbFPnz4WqLJxXb16VQwODhbLy8tFURTFyZMni999951Jm9b0M//rr7/Eo0ePimFhYYZtCxcuFN966y1RFEXxrbfeEp977rlax+Xl5YkhISFiXl6emJ+fL4aEhIj5+fnNVvftqqvff/zxh6hWq0VRFMXnnnuuzn5rNBqxQ4cO4sWLF8XKykoxIiJCPHnyZLPV3Rjq6rsoimJaWpo4atQoMSgoSMzJyal1XGv8me/atUscMWKEqFQqRVEUxaysrFrHtdafeWxsrLht2zZRFPWf5UOGDKl1XEv/mV+7dk08evSoKIqiWFxcLHbu3Fk8efJkq/+Mq6/frf0zrr5+i2Lr/3yrr++t/TOuvn63hc83nU4nlpSUiKIoiiqVSuzTp4946NAhcfLkyeKaNWtEURTFxx57TPz8889rHXvy5EkxIiJCVCqV4qVLl8QOHTqIGo2mWetvCI683qKrV6/it99+w6OPPlrn/s2bN2PmzJkAgEmTJmHnzp0QW8HlxcXFxdi7dy9mz54NAFAoFHB1dTVps3nzZjz00EMQBAH9+vVDYWEhMjIyLFBt49JoNKioqIBGo0F5eTn8/f1N9remn/ngwYPh7u5uss24fzNnzsQvv/xS67g//vgDsbGxcHd3h5ubG2JjY+u8wbS1qqvfo0aNgkwmAwD069cPV69erXXc4cOH0alTJ3To0AEKhQJTp07F5s2bm6XmxlJX3wHg2WefxTvvvANBEOo8rjX+zL/44gu88MILsLGxAQB4e3vXOq61/swFQUBxcTEAoKioqNbnHNDyf+Z+fn6Ijo4GADg5OSE0NBTp6emt/jOuvn639s+4+voNtP7Pt/r63to/4+rrd1v4fBMEAY6OjgAAtVoNtVoNQRCwa9cuTJo0CUD9n2+bN2/G1KlTYWNjg5CQEHTq1Kne0WlLYni9Rc888wzeeecdSCR1fwvT09MRGBgIAJDJZHBxcUFeXl5zltgkLl26BC8vLzzyyCPo2bMnHn30UZSVlZm0Me47AAQEBBj+Q9FStWvXDgsWLEBQUBD8/Pzg4uKCUaNGmbRprT/zallZWfDz8wOg/w9DdnZ2rTat8WdvbPny5bjrrrtqbW+t/d6yZQvatWuHyMjIetu0xr6fO3cO+/btQ9++fTFkyBAcOXKkVpvW2G8A+Oijj7Bw4UIEBgZiwYIFeOutt2q1aU19T01NRWJiIvr27dumPuOM+22stX/GGfe7rX2+Gfe9LX3GGfe7rXy+abVaREVFwdvbG7GxsejYsSNcXV0Nf6Sqr08tpe8Mr7dg69at8Pb2Rq9eveptU9eIW31/2WtJNBoNEhIS8PjjjyMxMREODg61roNojX0vKCjA5s2bkZKSgmvXrqGsrAw//vijSZvW2O+b1Zq/B0uWLIFMJsP06dNr7WuN/S4vL8eSJUvwxhtvmG3XGvuu0WhQUFCAv//+G++++y6mTJlSq5+tsd+AftT5ww8/xJUrV/Dhhx8aZtkYay19Ly0txcSJE/HRRx/B2dm5Qce0hr7X1+/W/hln3G+ZTNamPt9q/szbymdczX63lc83qVSKpKQkXL16FYcPH8bp06drtamrTy2l7wyvt+DAgQPYsmULgoODMXXqVOzatQsPPvigSZuAgABcuXIFgP4XoaKiojqn5LU0AQEBCAgIMPy1dtKkSbUWZDLuO6CfYl3X1IyWJC4uDiEhIfDy8oJcLseECRNw8OBBkzat9WdezcfHxzD9OyMjo85pRq3xZw/oF2/YunUrVq1aVecHeWvs98WLF5GSkoLIyEgEBwfj6tWriI6ORmZmpkm71tj3gIAATJgwAYIgoE+fPpBIJLUWc2mN/Qb0/1+fMGECAGDy5Ml1ThlrDX1Xq9WYOHEipk+fbuhvW/iMq6vfQOv/jKvZ77b0+VbXz7wtfMbV1e+28vlWzdXVFUOHDsXff/+NwsJCaDQaAPX3qcX0vbkvsm1tdu/eXeeCTZ9++qnJ4j2TJ09u7tKazKBBg8QzZ86IoiiKr732mrhgwQKT/Vu3bjVZsKl3796WKLNR/f3332L37t3FsrIyUafTiQ899JC4dOlSkzat7WeekpJispDLggULTBYzWbhwYa1j8vLyxODgYDE/P1/Mz88Xg4ODxby8vGaruTHU7Pf27dvF0NBQMTs7u95j1Gq1GBISIl66dMmwsEVycnJzlNuoavbdWPv27etd0KS1/cy/+OIL8dVXXxVFURTPnj0rBgQEiDqdzuSY1voz79atm7h7925RFEUxLi5OjI6OrnVMS/+Z63Q6ccaMGeK8efNMtrf2z7j6+t3aP+Pq67ex1vr5Vl/fW/tnXH39bgufb9nZ2YaFVMvLy8VBgwaJv/76qzhp0iSTBZs+++yzWscmJyebLNgUEhJilQs2MbzeJuPw+uqrr4qbN28WRVEUKyoqxEmTJokdO3YUe/fuLV68eNGSZTaqxMREsVevXmKPHj3Ee+65R8zPzxe/+OIL8YsvvhBFUf+h8cQTT4gdOnQQw8PDxSNHjli44saxaNEisWvXrmJYWJj44IMPikqlstX+zKdOnSr6+vqKMplMbNeunfjNN9+Iubm54vDhw8VOnTqJw4cPN3yYHzlyRJw9e7bh2G+//Vbs2LGj2LFjR3H58uWW6sItqavfHTt2FAMCAsTIyEgxMjLS8AeK9PR08a677jIc+9tvv4mdO3cWO3ToIP73v/+1VBduWV19N2b8y11r/5lXVlaK06dPF8PCwsSePXuKO3fuFEWxbfzM9+3bJ0ZHR4sRERFinz59xPj4eFEUW9fPfN++fSIAsUePHoZ/17/99lur/4yrr9+t/TOuvn4ba62fb/X1vbV/xtXX77bw+Xbs2DExKipK7NGjhxgWFia+/vrroiiK4sWLF8XevXuLHTt2FCdNmmRYaXrz5s3/397dx1RZ93Ec/xwkcEbe5JRMj5NZ8iAnPE7HwU2RtcGk+Qco0yktFXCz5rItNRezsnJof+RTDStTWPjUTKeBo6aoMKcJIRKlSDKWLAt1Mhw+AMb9h3dME7sFj/wuzvV+/YWHs/m5do1rfvx9z5fO/8jo6Ojo+PDDDztGjRrVERYW1rmZ2WocHR19dB0qAAAAAMA2+MwrAAAAAMDyKK8AAAAAAMujvAIAAAAALI/yCgAAAACwPH/TAUz44YcfTEfwir9/12p3+MK1c90PzxeuW+r+tXPdfRvX/XDset2Sb1w71/3wfOG6JX7GHxbX3bf15Ge8Ozh5BQAAAABYHuUVAAAAAGB5lFcAAAAAgOVRXgEAAAAAlkd5BQAAAABYHuUVAAAAAGB5lFcAAAAAgOVRXgEAAAAAludvOgAAAAAASJLH4zEdARbGySsAAAAAwPIorwAAAAAAy6O8AgAAALCFpqYmpaamKiIiQpGRkTp+/LjpSOgGPvMKAAAAwBYWL16sqVOnavfu3WptbdX169dNR0I3UF4BAAAAi2Fxkfc1NzerpKREubm5kqSAgAAFBASYDYVuYWwYAAAAgM+rq6vTkCFDNH/+fI0bN06ZmZlqaWkxHQvdQHkFAAAAbGT9+vVyuVyKiorSunXrTMfpNe3t7aqoqNCrr76qU6dO6cknn9Tq1atNx0I3UF4BAAAAm6iurtYXX3yhkydP6vTp0yooKFBtba3pWL3C6XTK6XR2jmSnpqaqoqLCcCp0B595BQAAAGzizJkzio2N1YABAyRJU6ZM0d69e7Vs2TLDyR6/oUOHasSIEaqpqVF4eLgOHTqkMWPGmI4lic84PyxOXgEAAACbcLlcKikp0ZUrV3T9+nUdOHBAFy5cMB2r12zcuFFpaWmKjo5WZWWl3n77bdOR0A2cvAIAAAA2ERkZqbfeeksJCQkKCgrS2LFj5e9vn0rgdrtVXl5uOgZ6iJNXAAAAwEYyMjJUUVGhkpISDRo0SKNHjzYdCV6Wnp6ukJAQuVyuzteWLl2qiIgIRUdHKyUlRU1NTeYC9hDlFQAAALCRxsZGSdJvv/2mPXv2aPbs2YYTwdvmzZunoqKie15LSEhQdXW1qqqqFBYWpuzsbEPpes4+MwIAAAAANGPGDF25ckVPPPGEPv30Uz399NOmI8HL4uLiVF9ff89riYmJnV/HxsZq9+7dvZzq0VFeAQAA+gC2kcJbSktLTUeAYVu2bNGsWbNMx+g2xoYBAAAAwCZWrVolf39/paWlmY7SbZy8AgCAPoUTSHvhfgPek5eXp4KCAh06dEgOh8N0nG7j5BUAAMBmutpEKt35HZjh4eGKiorSsmXLDKUD8DgUFRVpzZo12r9/vwYMGGA6To9QXgEAgG11VeJmzZolt9stt9ut0NBQud1ucwEfk642kR4+fFj79u1TVVWVfv75Zy1ZssRQusfLrvcc9jJ79mxNnDhRNTU1cjqd+vLLL7Vo0SJdu3ZNCQkJcrvdWrhwoemY3cbYMAAAsK158+Zp0aJFeuWVVzpf27VrV+fXb775pv7zn/+YiPZYdbWJNCcnR8uXL1dgYKAkKSQkxECyx8+u9xz2smPHjvtey8jIMJDEuzh5BQAAthUXF6dBgwZ1+b2Ojg59/fXXtvkdmOfOnVNpaak8Ho+mTJmisrIy05EeC+450Hdx8goAANCF0tJSPfPMMxo9erTpKL2ivb1dV69e1YkTJ1RWVqaZM2eqrq6uTy516Sm73XOgr+HkFQAAoAs7duyw1Qmc0+nU9OnT5XA4FBMTIz8/P12+fNl0rF5lt3sO9DWcvAIAAPxDe3u79uzZox9//NF0lF6TnJys4uJixcfH69y5c2ptbdXgwYNNx+o1drznQF9DeQUAAPiHgwcPKiIiQk6n03SUx2L27Nk6cuSILl++LKfTqZUrVyo9PV3p6elyuVwKCAhQXl6erUaGff2eA76A8goAAGyrqxKXkZGhnTt3+vT4aFebSCUpPz+/l5P0Prvec8AXUF4BAIBtPajE5ebm9m4Q9BruOdB3sbAJAAAAAGB5lFcAAAAAgOVRXgEAAAAAlkd5BQAAAABYHuUVAAAAAGB5lFcAAAAAgOVRXgEAAAAAlkd5BQAAAABYHuUVAAAAAGB5lFcAAAAAgOVRXgEAAAAAlkd5BQAAAABYHuUVAAAAAGB5lFcAAAAAgOVRXgEAAAAAlkd5BQAAAABYHuUVAAAAAGB5lFcAAAAAgOX5mw4AAMCj8Hg8piMAAIBewMkrAAAAAMDyKK8AANzl5s2biomJ0dixYxUVFaV3331XktTR0aGsrCyFhYUpMjJSGzZsMJwUAAB7YWwYAIC7BAYGqri4WEFBQWpra9OkSZOUlJSkM2fO6MKFCzp79qz8/PzU2NhoOioAALZCeQUA4C4Oh0NBQUGSpLa2NrW1tcnhcCgnJ0fbt2+Xn9+doaWQkBCTMQEAsB3GhgEA+Ifbt2/L7XYrJCRECQkJ8ng8On/+vHbt2qUJEyYoKSlJtbW1pmMCAGArlFcAAP6hX79+qqysVENDg06ePKnq6mrdunVL/fv3V3l5uRYsWKD09HTTMQEAsBXKKwAADxAcHKz4+HgVFRXJ6XRqxowZkqSUlBRVVVUZTgcAgL1QXgEAuMulS5fU1NQkSbpx44YOHjyoiIgIJScnq7i4WJJ09OhRhYWFGUwJAID9sLAJAIC7XLx4UXPnztXt27f1119/aebMmZo2bZomTZqktLQ0rV27VkFBQdq8ebPRnB6Px+jfDwBAb6O8AgBwl+joaJ06deq+14ODg1VYWGggEQAAkBgbBgAA/3P79m2NGzdO06ZNkySlpaUpPDxcLpdL6enpamtrM5wQAGBnlFcAACBJWr9+vSIjIzv/nJaWprNnz+qnn37SjRs3jI9KAwDsjfIKAADU0NCgwsJCZWZmdr720ksvyeFwyOFwKCYmRg0NDQYTAgDsjvIKAAD0xhtv6KOPPpKf3/3/NGhra9NXX32lqVOnGkgGAMAdLGwC4HPYwgp0T0FBgUJCQjR+/HgdOXLkvu+/9tpriouL0+TJk3s/HAAA/0N5BQDA5o4dO6b9+/frwIEDunnzppqbm/Xyyy8rPz9fK1eu1KVLl/TZZ5+ZjgkAsDnGhgHgIfxzC2tHR4eysrIUFhamyMhIbdiwwXBCoOeys7PV0NCg+vp67dy5Uy+++KLy8/O1efNmfffdd9qxY0eX48QAAPQmTl4B4CH8vYW1ublZkpSbm6sLFy7o7Nmz8vPzU2Njo+GEgPctXLhQI0eO1MSJEyVJ06dP1zvvvGM4FQDAriivAPB//L2FNSsrSx9//LEkKScnR9u3b+88jQoJCTEZEfCa+Ph4xcfHS5La29vNhgEA4C7MAAHA/9HVFtbz589r165dmjBhgpKSklRbW2swIQAAgO+jvALAv7h7C+vdbt26pf79+6u8vFwLFixQenq6oYQAAAD2wNgwAPyLB21hdTqdmjFjhiQpJSVF8+fPN5wUAADAt3HyCgD/4kFbWJOTk1VcXCxJOnr0qMLCwgwnBQAA8G22PHn1eDymIwDo45YvX660tDStXbtWQUFB2rx5s+lIPNsAAIBPs2V5BYCeuHsLa3BwsAoLC80GAgAAsBHKq5eEhobqqaeeUr9+/eTv76/y8nItXbpU3377rQICAvTcc89p69atCg4ONh0VAB4KzzUAAGAlfObViw4fPqzKykqVl5dLkhISElRdXa2qqiqFhYUpOzvbcEIA6B6eawAAwCoor49RYmKi/P3vHG7HxsaqoaHBcCIAeDQ81wAAgCmUVy9xOBxKTEzU+PHj9fnnn9/3/S1btigpKclAMgDoGZ5rAADASvjMq5ccO3ZMw4YNU2NjoxISEhQREaG4uDhJ0qpVq+Tv76+0tDTDKe2LLaxA9/FcAwAAVsLJq5cMGzZMkhQSEqKUlBSdPHlSkpSXl6eCggJt27ZNDofDZEQA6BaeawAAwEoor17Q0tKia9eudX79/fffy+VyqaioSGvWrNH+/fs1YMAAwynhTaGhoXrhhRfkdrs1YcKEztc3btyo8PBwRUVFadmyZQYTAo+G5xoAALAaxoa94M8//1RKSookqb29XXPmzNHUqVP1/PPP69a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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# FACET imports\n", - "from facet.validation import BootstrapCV\n", - "from facet.crossfit import LearnerCrossfit\n", - "from facet.simulation import UnivariateUpliftSimulator\n", - "from facet.simulation.partition import ContinuousRangePartitioner\n", - "from facet.simulation.viz import SimulationDrawer\n", - "\n", - "# create bootstrap CV iterator\n", - "bscv = BootstrapCV(n_splits=1000, random_state=42)\n", - "\n", - "# create a bootstrap CV crossfit for simulation using best model\n", - "boot_crossfit = LearnerCrossfit(\n", - " pipeline=ranker.best_model_,\n", - " cv=bscv,\n", - " n_jobs=-3,\n", - " verbose=False,\n", - ").fit(sample=boston_sample)\n", - "\n", - "SIM_FEAT = \"LSTAT\"\n", - "simulator = UnivariateUpliftSimulator(crossfit=boot_crossfit, n_jobs=-3)\n", - "\n", - "# split the simulation range into equal sized partitions\n", - "partitioner = ContinuousRangePartitioner()\n", - "\n", - "# run the simulation\n", - "simulation = simulator.simulate_feature(feature_name=SIM_FEAT, partitioner=partitioner)\n", - "\n", - "# visualise results\n", - "SimulationDrawer().draw(data=simulation, title=SIM_FEAT)\n", - "\n", - "# save copy of plot to _static directory for documentation\n", - "plt.savefig(\n", - " \"facet/sphinx/source/_static/simulation_output.png\",\n", - " bbox_inches=\"tight\",\n", - " pad_inches=0,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We would conclude from the figure that lower values of `LSTAT` are associated with an increase in median house price, and that the lower `LSTAT` of 8% or less results in a significant uplift in median house price." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "facet-develop", - "language": "python", - "name": "facet-develop" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.6" - }, - "toc": { - "base_numbering": 1, - "nav_menu": {}, - "number_sections": false, - "sideBar": true, - "skip_h1_title": false, - "title_cell": "Table of Contents", - "title_sidebar": "Contents", - "toc_cell": false, - "toc_position": { - "height": "calc(100% - 180px)", - "left": "10px", - "top": "150px", - "width": "191.594px" - }, - "toc_section_display": true, - "toc_window_display": false - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/sphinx/auxiliary/Diabetes_getting_started_example.ipynb b/sphinx/auxiliary/Diabetes_getting_started_example.ipynb new file mode 100644 index 000000000..b255a1220 --- /dev/null +++ b/sphinx/auxiliary/Diabetes_getting_started_example.ipynb @@ -0,0 +1,619 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n" + ] + }, + { + "attachments": { + "Gamma_Facet_Logo_RGB_LB.svg": { + "image/svg+xml": [ + "<svg width="781" height="134" viewBox="0 0 781 134" fill="none" xmlns="http://www.w3.org/2000/svg">
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" + ] + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![Gamma_Facet_Logo_RGB_LB.svg](attachment:Gamma_Facet_Logo_RGB_LB.svg)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + " \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "FACET is composed of the following key components:\n", + "\n", + "- **Model Inspection**\n", + "\n", + " FACET introduces a new algorithm to quantify dependencies and interactions between features in ML models. This new tool for human-explainable AI adds a new, global perspective to the observation-level explanations provided by the popular [SHAP](https://shap.readthedocs.io/en/latest/) approach. To learn more about FACET's model inspection capabilities, see the getting started example below.\n", + "\n", + "\n", + "- **Model Simulation**\n", + "\n", + " FACET's model simulation algorithms use ML models for *virtual experiments* to help identify scenarios that optimise predicted outcomes. To quantify the uncertainty in simulations, FACET utilises a range of bootstrapping algorithms including stationary and stratified bootstraps. For an example of FACET’s bootstrap simulations, see the getting started example below. \n", + " \n", + " \n", + "- **Enhanced Machine Learning Workflow** \n", + "\n", + " FACET offers an efficient and transparent machine learning workflow, enhancing [scikit-learn]( https://scikit-learn.org/stable/index.html)'s tried and tested pipelining paradigm with new capabilities for model selection, inspection, and simulation. FACET also introduces [sklearndf](https://github.com/BCG-Gamma/sklearndf), an augmented version of *scikit-learn* with enhanced support for *pandas* dataframes that ensures end-to-end traceability of features. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "delete_for_interactive": true, + "nbsphinx": "hidden" + }, + "outputs": [], + "source": [ + "# this cell's metadata contains\n", + "# \"nbsphinx\": \"hidden\" so it is hidden by nbsphinx\n", + "\n", + "import warnings\n", + "\n", + "warnings.filterwarnings(\"ignore\")\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "def _configure_matplotlib():\n", + " # set global options for matplotlib\n", + "\n", + " import matplotlib\n", + "\n", + " matplotlib.rcParams[\"figure.figsize\"] = (16.0, 8.0)\n", + " matplotlib.rcParams[\"figure.dpi\"] = 72\n", + "\n", + "\n", + "_configure_matplotlib()\n", + "\n", + "del _configure_matplotlib" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Pipelining & Model Ranking\n", + "\n", + "To demonstrate the model inspection capability of FACET, we first create a pipeline to fit a learner. In this simple example we use the [diabetes dataset](#https://www4.stat.ncsu.edu/~boos/var.select/diabetes.tab.txt) which contains age, sex, BMI and blood pressure along with 6 blood serum measurements as features. A transformed version of this dataset is also available on scikit-learn [here](#https://scikit-learn.org/stable/datasets/toy_dataset.html#diabetes-dataset).\n", + "\n", + "In this quickstart we will train a Random Forest regressor using 10 repeated 5-fold CV to predict disease progression after one year. With the use of *sklearndf* we can create a *pandas* DataFrame compatible workflow. However, FACET provides additional enhancements to keep track of our feature matrix and target vector using a sample object (`Sample`) and easily compare hyperparameter configurations and even multiple learners with the `LearnerRanker`." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " ranking_score r2_score regressor \\\n", + " mean std type \n", + "rank \n", + "0 0.316643 0.443901 0.063629 RandomForestRegressorDF \n", + "1 0.315783 0.442023 0.063120 RandomForestRegressorDF \n", + "2 0.314089 0.442691 0.064301 RandomForestRegressorDF \n", + "\n", + " \n", + " min_samples_leaf \n", + "rank \n", + "0 11 \n", + "1 8 \n", + "2 15 " + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# standard imports\n", + "import pandas as pd\n", + "from sklearn.model_selection import RepeatedKFold\n", + "\n", + "# some helpful imports from sklearndf\n", + "from sklearndf.pipeline import RegressorPipelineDF\n", + "from sklearndf.regression import RandomForestRegressorDF\n", + "\n", + "# relevant FACET imports\n", + "from facet.data import Sample\n", + "from facet.selection import LearnerRanker, LearnerGrid\n", + "\n", + "# load the diabetes dataset\n", + "diabetes_df = pd.read_csv('diabetes_quickstart.csv')\n", + "\n", + "# create FACET sample object\n", + "diabetes_sample = Sample(observations=diabetes_df, target_name=\"Disease_progression\")\n", + "\n", + "# create a (trivial) pipeline for a random forest regressor\n", + "rnd_forest_reg = RegressorPipelineDF(\n", + " regressor=RandomForestRegressorDF(random_state=42)\n", + ")\n", + "\n", + "# define grid of models which are \"competing\" against each other\n", + "rnd_forest_grid = [\n", + " LearnerGrid(\n", + " pipeline=rnd_forest_reg,\n", + " learner_parameters={\n", + " \"min_samples_leaf\": [8, 11, 15]\n", + " }\n", + " ),\n", + "]\n", + "\n", + "# create repeated k-fold CV iterator\n", + "rkf_cv = RepeatedKFold(n_splits=5, n_repeats=10, random_state=42)\n", + "\n", + "# rank your candidate models by performance (default is mean CV score - 2*SD)\n", + "ranker = LearnerRanker(\n", + " grids=rnd_forest_grid, cv=rkf_cv, n_jobs=-3\n", + ").fit(sample=diabetes_sample)\n", + "\n", + "# get summary report\n", + "ranker.summary_report()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see based on this minimal workflow that a value of 11 for minimum samples in the leaf was the best performing of the three considered values. This approach easily extends to multiple hyperparameters for the learner and multiple learners." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Model inspection\n", + "\n", + "FACET implements several model inspection methods for\n", + "[scikit-learn]() estimators.\n", + "FACET enhances model inspection by providing global metrics that complement \n", + "the local perspective of SHAP. The key global metrics for each pair of \n", + "features in a model are:\n", + "\n", + "- **Synergy**\n", + "\n", + " The degree to which the model combines information from one feature with \n", + " another to predict the target. For example, let's assume we are predicting \n", + " cardiovascular health using age and gender and the fitted model includes \n", + " a complex interaction between them. This means these two features are \n", + " synergistic for predicting cardiovascular health. Further, both features \n", + " are important to the model and removing either one would significantly \n", + " impact performance. Let's assume age brings more information to the joint\n", + " contribution than gender. This asymmetric contribution means the synergy for\n", + " (age, gender) is less than the synergy for (gender, age). To think about it\n", + " another way, imagine the prediction is a coordinate you are trying to reach.\n", + " From your starting point, age gets you much closer to this point than \n", + " gender, however, you need both to get there. Synergy reflects the fact \n", + " that gender gets more help from age (higher synergy from the perspective \n", + " of gender) than age does from gender (lower synergy from the perspective of\n", + " age) to reach the prediction. *This leads to an important point: synergy \n", + " is a naturally asymmetric property of the global information two interacting \n", + " features contribute to the model predictions.* Synergy is expressed as a \n", + " percentage ranging from 0% (full autonomy) to 100% (full synergy).\n", + "\n", + "\n", + "- **Redundancy**\n", + "\n", + " The degree to which a feature in a model duplicates the information of a \n", + " second feature to predict the target. For example, let's assume we had \n", + " house size and number of bedrooms for predicting house price. These \n", + " features capture similar information as the more bedrooms the larger \n", + " the house and likely a higher price on average. The redundancy for \n", + " (number of bedrooms, house size) will be greater than the redundancy \n", + " for (house size, number of bedrooms). This is because house size \n", + " \"knows\" more of what number of bedrooms does for predicting house price \n", + " than vice-versa. Hence, there is greater redundancy from the perspective \n", + " of number of bedrooms. Another way to think about it is removing house \n", + " size will be more detrimental to model performance than removing number \n", + " of bedrooms, as house size can better compensate for the absence of \n", + " number of bedrooms. This also implies that house size would be a more \n", + " important feature than number of bedrooms in the model. *The important \n", + " point here is that like synergy, redundancy is a naturally asymmetric \n", + " property of the global information feature pairs have for predicting \n", + " an outcome.* Redundancy is expressed as a percentage ranging from 0% \n", + " (full uniqueness) to 100% (full redundancy)." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# fit the model inspector\n", + "from facet.inspection import LearnerInspector\n", + "inspector = LearnerInspector()\n", + "inspector.fit(crossfit=ranker.best_model_crossfit_)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Synergy**" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# visualise synergy as a matrix\n", + "from pytools.viz.matrix import MatrixDrawer\n", + "synergy_matrix = inspector.feature_synergy_matrix()\n", + "MatrixDrawer(style=\"matplot%\").draw(synergy_matrix, title=\"Synergy Matrix\")\n", + "\n", + "# save copy of plot to _static directory for documentation\n", + "plt.savefig(\n", + " \"facet/sphinx/source/_static/synergy_matrix.png\", bbox_inches=\"tight\", pad_inches=0\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For any feature pair (A, B), the first feature (A) is the row, and the second feature (B) the column. For example, looking across the row for `LTG` (Lamotrigine) there is relatively minimal synergy (≤14%) with other features in the model. However, looking down the column for `LTG` (i.e., perspective of other features in a pair with `LTG`) we find many features (the rows) are synergistic (12% to 34%) with `LTG`. We can conclude that:\n", + "\n", + "- `LTG` is a strongly autonomous feature, displaying minimal synergy with other features for predicting disease progression after one year.\n", + "- The contribution of other features to predicting disease progression after one year is partly enabled by the strong contribution from `LTG`.\n", + "\n", + "High synergy features must be considered carefully when investigating impact, as they work together to predict the outcome. It would not make much sense to consider `TC` (T-Cells) without `LTG` given the 34% synergy of `TC` with `LTG` for predicting progression after one year." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Redundancy**" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# visualise redundancy as a matrix\n", + "redundancy_matrix = inspector.feature_redundancy_matrix()\n", + "MatrixDrawer(style=\"matplot%\").draw(redundancy_matrix, title=\"Redundancy Matrix\")\n", + "\n", + "# save copy of plot to _static directory for documentation\n", + "plt.savefig(\n", + " \"facet/sphinx/source/_static/redundancy_matrix.png\",\n", + " bbox_inches=\"tight\",\n", + " pad_inches=0,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For any feature pair (A, B), the first feature (A) is the row, and the second feature (B) the column. For example, if we look at the feature pair (`LDL`, `TC`) from the perspective of `LDL` (Low-Density Lipoproteins), then we look-up the row for `LDL` and the column for `TC` and find 47% redundancy. This means that 47% of the information in `LDL` is duplicated with `TC` to predict disease progression after one year. This redundancy is similar when looking \"from the perspective\" of `TC` for (`TC`, `LDL`) which is 50%.\n", + "\n", + "If we look across the columns for the `LTG` row we can see that apart from the 32% redundancy with `BMI`, `LTG` has minimal redundancy (<9%) with the other features included in the model. Further, if we look cross the rows for the `LTG` column we can see a number of the features have moderate redundancy with `LTG`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Clustering redundancy**\n", + "\n", + "As detailed above redundancy and synergy for a feature pair is from the \"perspective\" of one of the features in the pair, and so yields two distinct values. However, a symmetric version can also be computed that provides not only a simplified perspective but allows the use of (1 - metric) as a feature distance. With this distance hierarchical, single linkage clustering is applied to create a dendrogram visualization. This helps to identify groups of low distance, features which activate \"in tandem\" to predict the outcome. Such information can then be used to either reduce clusters of highly redundant features to a subset or highlight clusters of highly synergistic features that should always be considered together.\n", + "\n", + "Let's look at the example for redundancy." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# visualise redundancy using a dendrogram\n", + "from pytools.viz.dendrogram import DendrogramDrawer\n", + "redundancy = inspector.feature_redundancy_linkage()\n", + "DendrogramDrawer().draw(data=redundancy, title=\"Redundancy Dendrogram\")\n", + "\n", + "# save copy of plot to _static directories for documentation\n", + "plt.savefig(\n", + " \"facet/sphinx/source/_static/redundancy_dendrogram.png\",\n", + " bbox_inches=\"tight\",\n", + " pad_inches=0,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Based on the dendrogram we can see that the feature pairs (`LDL`, `TC`) and (`LTG`, `BMI`: body mass index) each represent a cluster in the dendrogram and that `LTG` and `BMI` have high the highest importance. As potential next actions we could remove `TC` and explore the impact of removing one of `LTG` or `BMI` to further simplify the model and obtain a reduced set of independent features." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Simulation\n", + "\n", + "Taking the `BMI` feature as an example, we do the following for the simulation: \n", + "\n", + "- We use FACET's `ContinuousRangePartitioner` to split the range of observed values of `BMI` into intervals of equal size. Each partition is represented by the central value of that partition. \n", + "- For each partition, the simulator creates an artificial copy of the original sample assuming the variable to be simulated has the same value across all observations - which is the value representing the partition. Using the best `LearnerCrossfit` acquired from the ranker, the simulator now re-predicts all targets using the models trained for all folds and determines the average uplift of the target variable resulting from this.\n", + "- The FACET `SimulationDrawer` allows us to visualise the result; both in a matplotlib and a plain-text style.\n", + "\n", + "Finally, because FACET can use bootstrap cross validation, we can create a crossfit from our previous `LearnerRanker` best model to perform the simulation so we can quantify the uncertainty by using bootstrap confidence intervals." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# FACET imports\n", + "from facet.validation import BootstrapCV\n", + "from facet.crossfit import LearnerCrossfit\n", + "from facet.simulation import UnivariateUpliftSimulator\n", + "from facet.simulation.partition import ContinuousRangePartitioner\n", + "from facet.simulation.viz import SimulationDrawer\n", + "\n", + "# create bootstrap CV iterator\n", + "bscv = BootstrapCV(n_splits=1000, random_state=42)\n", + "\n", + "# create a bootstrap CV crossfit for simulation using best model\n", + "boot_crossfit = LearnerCrossfit(\n", + " pipeline=ranker.best_model_,\n", + " cv=bscv,\n", + " n_jobs=-3,\n", + " verbose=False,\n", + ").fit(sample=diabetes_sample)\n", + "\n", + "SIM_FEAT = \"BMI\"\n", + "simulator = UnivariateUpliftSimulator(crossfit=boot_crossfit, n_jobs=-3)\n", + "\n", + "# split the simulation range into equal sized partitions\n", + "partitioner = ContinuousRangePartitioner()\n", + "\n", + "# run the simulation\n", + "simulation = simulator.simulate_feature(feature_name=SIM_FEAT, partitioner=partitioner)\n", + "\n", + "# visualise results\n", + "SimulationDrawer().draw(data=simulation, title=SIM_FEAT)\n", + "\n", + "# save copy of plot to _static directory for documentation\n", + "plt.savefig(\n", + " \"facet/sphinx/source/_static/simulation_output.png\",\n", + " bbox_inches=\"tight\",\n", + " pad_inches=0,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We would conclude from the figure that higher values of `BMI` are associated with an increase in disease progression after one year, and that for a `BMI` of 29 and above, there is a significant increase in disease progression after one year of at least 26 points." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "facet-develop", + "language": "python", + "name": "facet-develop" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.6" + }, + "toc": { + "base_numbering": 1, + "nav_menu": {}, + "number_sections": false, + "sideBar": true, + "skip_h1_title": false, + "title_cell": "Table of Contents", + "title_sidebar": "Contents", + "toc_cell": false, + "toc_position": { + "height": "calc(100% - 180px)", + "left": "10px", + "top": "150px", + "width": "191.594px" + }, + "toc_section_display": true, + "toc_window_display": false + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/sphinx/auxiliary/diabetes_quickstart.csv b/sphinx/auxiliary/diabetes_quickstart.csv new file mode 100644 index 000000000..1db035307 --- /dev/null +++ b/sphinx/auxiliary/diabetes_quickstart.csv @@ -0,0 +1,443 @@ +Age,Sex,BMI,Blood_Pressure,TC,LDL,HDL,TSH,LTG,GLU,Disease_progression +59,1,32.1,101,157,93.2,38,4,4.8598,87,151 +48,0,21.6,87,183,103.2,70,3,3.8918,69,75 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+60,1,28.2,112,185,113.8,42,4,4.9836,93,178 +47,1,24.9,75,225,166,42,5,4.4427,102,104 +60,1,24.9,99.67,162,106.6,43,3.77,4.1271,95,132 +36,0,30,95,201,125.2,42,4.79,5.1299,85,220 +36,0,19.6,71,250,133.2,97,3,4.5951,92,57 diff --git a/sphinx/source/_static/ranker_summary.png b/sphinx/source/_static/ranker_summary.png index ff327f6ae..3bb03700f 100644 Binary files a/sphinx/source/_static/ranker_summary.png and b/sphinx/source/_static/ranker_summary.png differ diff --git a/sphinx/source/_static/redundancy_dendrogram.png b/sphinx/source/_static/redundancy_dendrogram.png index 27f31e84c..8fec4b4e5 100644 Binary files a/sphinx/source/_static/redundancy_dendrogram.png and b/sphinx/source/_static/redundancy_dendrogram.png differ diff --git a/sphinx/source/_static/redundancy_matrix.png b/sphinx/source/_static/redundancy_matrix.png index 3d2c93169..549e10c41 100644 Binary files a/sphinx/source/_static/redundancy_matrix.png and b/sphinx/source/_static/redundancy_matrix.png differ diff --git a/sphinx/source/_static/simulation_output.png b/sphinx/source/_static/simulation_output.png index 7ba6b7202..fedae59cc 100644 Binary files a/sphinx/source/_static/simulation_output.png and b/sphinx/source/_static/simulation_output.png differ diff --git a/sphinx/source/_static/synergy_matrix.png b/sphinx/source/_static/synergy_matrix.png index fda83ca1b..60e563c9b 100644 Binary files a/sphinx/source/_static/synergy_matrix.png and b/sphinx/source/_static/synergy_matrix.png differ diff --git a/src/facet/selection/_selection.py b/src/facet/selection/_selection.py index 7201fe6f1..9420418a4 100644 --- a/src/facet/selection/_selection.py +++ b/src/facet/selection/_selection.py @@ -81,8 +81,9 @@ def __init__( preprocessing_parameters: Optional[Dict[str, Sequence]] = None, ) -> None: """ - :param pipeline: the :class:`.RegressorPipelineDF` or - :class:`.ClassifierPipelineDF` to which the hyper-parameters will be applied + :param pipeline: the :class:`~.sklearndf.pipeline.RegressorPipelineDF` or + :class:`~.sklearndf.pipeline.ClassifierPipelineDF` to which the + hyper-parameters will be applied :param learner_parameters: the hyper-parameter grid in which to search for the optimal parameter values for the pipeline's final estimator :param preprocessing_parameters: the hyper-parameter grid in which to search @@ -116,7 +117,9 @@ def _prefix_parameter_names( @property def parameters(self) -> Mapping[str, Sequence[Any]]: - """The parameter grid for the pipeline representing the entire pipeline.""" + """ + The parameter grid for the entire pipeline. + """ return MappingProxyType(self._grid_dict) def __iter__(self) -> Iterable[Dict[str, Any]]: