{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Intro\n", "\n", "TPOT gives the user a lot of options for customizing the search space, from hyperparameter ranges to model selection to pipeline configuration. TPOT is able to select models, optimize their hyperparameters, and build a complex pipeline structure. Each level of detail has multiple customization options. This tutorial will first explore how to set up a hyperparameter search space for a single method. Next, we will describe how to set up simultaneous model selection and hyperparameter tuning. Finally, we will cover how to utilize these steps to configure a search space for a fixed pipeline of multiple steps, as well as having TPOT optimize the pipeline structure itself.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Hyperparameter Search Spaces with ConfigSpace\n", "\n", "Hyperparameter search spaces are defined using the [ConfigSpace package found here](https://github.com/automl/ConfigSpace). More information on how to set up a hyperparameter space can be found in their [documentation here](https://automl.github.io/ConfigSpace/main/guide.html).\n", "\n", "TPOT uses `ConfigSpace.ConfigurationSpace` objects to define the hyperparameter search space for individual models. This object can be used to keep track of the desired hyperparameters as well as provide functions for random sampling from this space.\n", "\n", "In short, you can use the `Integer`, `Float`, and `Categorical` functions of `ConfigSpace` to define a range of values used for each param. Alternatively, a tuple with (min,max) ints or floats can be used to specify an int/float search space and a list can be used to specify a categorical search space. A fixed value can also be provided for parameters that are not tunned. The space parameter of `ConfigurationSpace` takes in a dictionary of param names to these ranges.\n", "\n", "Note: If you want reproducible results, you need to set a fixed random_state in the search space.\n", "\n", "Here is an example of a hyperparameter range for RandomForest" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sampled hyperparameters\n", "{'bootstrap': True, 'criterion': 'gini', 'max_features': 0.8874647037836, 'min_samples_leaf': 2, 'min_samples_split': 5, 'n_estimators': 128}\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/opt/anaconda3/envs/tpotenv/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n" ] }, { "data": { "text/html": [ "
RandomForestClassifier(max_features=0.8874647037836, min_samples_leaf=2,\n",
       "                       min_samples_split=5, n_estimators=128)
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" ], "text/plain": [ "RandomForestClassifier(max_features=0.8874647037836, min_samples_leaf=2,\n", " min_samples_split=5, n_estimators=128)" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from ConfigSpace import ConfigurationSpace\n", "from ConfigSpace import ConfigurationSpace, Integer, Float, Categorical, Normal\n", "from sklearn.ensemble import RandomForestClassifier\n", "import tpot\n", "import numpy as np\n", "import sklearn\n", "import sklearn.datasets\n", "\n", "rf_configspace = ConfigurationSpace(\n", " space = {\n", " 'n_estimators': 128, #as recommended by Oshiro et al. (2012\n", " 'max_features': Float(\"max_features\", bounds=(0.01,1), log=True), #log scale like autosklearn?\n", " 'criterion': Categorical(\"criterion\", ['gini', 'entropy']),\n", " 'min_samples_split': Integer(\"min_samples_split\", bounds=(2, 20)),\n", " 'min_samples_leaf': Integer(\"min_samples_leaf\", bounds=(1, 20)),\n", " 'bootstrap': Categorical(\"bootstrap\", [True, False]),\n", " #random_state = 1, # If you want results to be reproducible, you can set a fixed random_state.\n", " }\n", ")\n", "\n", "hyperparameters = dict(rf_configspace.sample_configuration())\n", "print(\"sampled hyperparameters\")\n", "print(hyperparameters)\n", "\n", "rf = RandomForestClassifier(**hyperparameters)\n", "rf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "More simply:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sampled hyperparameters\n", "{'bootstrap': False, 'criterion': 'entropy', 'max_features': 0.8418685817308, 'min_samples_leaf': 5, 'min_samples_split': 2, 'n_estimators': 128}\n" ] }, { "data": { "text/html": [ "
RandomForestClassifier(bootstrap=False, criterion='entropy',\n",
       "                       max_features=0.8418685817308, min_samples_leaf=5,\n",
       "                       n_estimators=128)
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" ], "text/plain": [ "RandomForestClassifier(bootstrap=False, criterion='entropy',\n", " max_features=0.8418685817308, min_samples_leaf=5,\n", " n_estimators=128)" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "rf_configspace = ConfigurationSpace(\n", " space = {\n", " 'n_estimators': 128, #as recommended by Oshiro et al. (2012\n", " 'max_features':(0.01,1), #not log scaled\n", " 'criterion': ['gini', 'entropy'],\n", " 'min_samples_split': (2, 20),\n", " 'min_samples_leaf': (1, 20),\n", " 'bootstrap': [True, False],\n", " #random_state = 1, # If you want results to be reproducible, you can set a fixed random_state.\n", " }\n", ")\n", "\n", "hyperparameters = dict(rf_configspace.sample_configuration())\n", "print(\"sampled hyperparameters\")\n", "print(hyperparameters)\n", "\n", "rf = RandomForestClassifier(**hyperparameters)\n", "rf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# TPOT Search spaces\n", "\n", "TPOT allows you to create hyperparameter search spaces for individual methods and pipeline structure search spaces. For example, TPOT can create linear pipelines, trees, or graphs. \n", "\n", "TPOT search spaces are found in the `search_spaces` module. There are two primary kinds of search spaces, node and pipeline. Node search spaces specify a single sklearn `BaseEstimator` search space. Pipeline search spaces define the possible structures for a group of node search spaces. These take in node search spaces and produce a pipeline using nodes from that search space. Since sklearn Pipelines are also `BaseEstimator`, pipeline search spaces are also technically node search spaces. This means that pipeline search spaces can take in other pipeline search spaces in order to define more complex structures. The primary differentiating factor between node and pipeline search spaces is that pipeline search spaces must take in another search space as input to feed its individual nodes. Therefore, all search spaces eventually end in a node search space at the lowest level. Note that parameters for pipeline search spaces can differ, some take in only a single search space, some take in a list, or some take in multiple defined parameters.\n", "\n", "## node search spaces\n", "\n", "\n", "| Name | Info |\n", "| :--- | :----: |\n", "| EstimatorNode | Takes in a ConfigSpace along with the class of the method. This node will optimize the hyperparameters for a single method. |\n", "| GeneticFeatureSelectorNode | Uses evolution to optimize a set of features, exports a basic sklearn Selector that simply selects the features chosen by the node. |\n", "| FSSNode | FSS stands for FeatureSetSelector. This node takes in a list of user-defined subsets of features and selects a single predefined subset. Note that TPOT will not create new subsets nor will it select multiple subsets per node. If using a linear pipeline, this node should be set as the first step. In linear pipelines it is recommended that you only use a small number of feature sets. I recommend exploring using FSSNodes in pipelines that allow TPOT to select more than one FSSNode at a time. For example, DynamicUnionPipeline and GraphPipeline are both excellent combos for FSSNode. Use FFSNode inside a DynamicUnionPipeline at the start of linear pipeline to explore optimal combinations of subsets in linear pipelines. Set the leaf_search_space of GraphSearchPipeline TPOT can use multiple feature sets in different ways, for example, with different transformers for different sets. |\n", "\n", "\n", "\n", "## pipeline search spaces\n", "\n", "found in tpot2.search_spaces.pipelines\n", "\n", "WrapperPipeline - This search space is for wrapping a sklearn estimator with a method that takes another estimator and hyperparameters as arguments.\n", " For example, this can be used with sklearn.ensemble.BaggingClassifier or sklearn.ensemble.AdaBoostClassifier.\n", "\n", "\n", "| Name | Info |\n", "| :--- | :----: |\n", "| ChoicePipeline | Takes in a list of search spaces. Will select one node from the search space. |\n", "| SequentialPipeline | Takes in a list of search spaces. will produce a pipeline of Sequential length. Each step in the pipeline will correspond to the the search space provided in the same index. |\n", "| DynamicLinearPipeline | Takes in a single search space. Will produce a linear pipeline of variable length. Each step in the pipeline will be pulled from the search space provided. |\n", "| UnionPipeline | Takes in a list of search spaces. The returned pipeline will include one estimator per search space joined in an sklearn FeatureUnion. Useful for having many steps in one layer. |\n", "| DynamicUnionPipeline | Takes in a single search space. It will pull anywhere from 1 to max_estimators number of estimators from the search space and concatenate them in a FeatureUnion. |\n", "| TreePipeline |Generates a pipeline of variable length. Pipeline will have a tree structure similar to TPOT1. |\n", "| GraphSearchPipeline | Generates a directed acyclic graph of variable size. Search spaces for root, leaf, and inner nodes can be defined separately if desired. |\n", "| WrapperPipeline | This search space is for wrapping a sklearn estimator with a method that takes another estimator and hyperparameters as arguments. For example, this can be used with sklearn.ensemble.BaggingClassifier or sklearn.ensemble.AdaBoostClassifier. |\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Node Search Space Examples\n", "\n", "Node search spaces represent the smallest unit of an sklearn pipeline. All node search spaces create and optimize a single node which exports a single estimator object. For example this could be a KNeighborsClassifier or a FeatureSetSelector.\n", "\n", "### EstimatorNode\n", "\n", "The EstimatorNode represents the hyperparameter search space for a scikit-learn estimator. " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "import tpot\n", "from ConfigSpace import ConfigurationSpace\n", "from ConfigSpace import ConfigurationSpace, Integer, Float, Categorical, Normal\n", "from sklearn.neighbors import KNeighborsClassifier\n", "\n", "knn_configspace = ConfigurationSpace(\n", " space = {\n", "\n", " 'n_neighbors': Integer(\"n_neighbors\", bounds=(1, 10)),\n", " 'weights': Categorical(\"weights\", ['uniform', 'distance']),\n", " 'p': Integer(\"p\", bounds=(1, 3)),\n", " 'metric': Categorical(\"metric\", ['euclidean', 'minkowski']),\n", " 'n_jobs': 1,\n", " }\n", ")\n", "\n", "\n", "knn_node = tpot.search_spaces.nodes.EstimatorNode(\n", " method = KNeighborsClassifier,\n", " space = knn_configspace,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can sample generate an individual with the generate() function. This individual samples from the search space as well as provides mutation and crossover functions to modify the current sample." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "knn_individual = knn_node.generate()\n", "knn_individual" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sampled hyperparameters\n", "{'metric': 'minkowski', 'n_jobs': 1, 'n_neighbors': 4, 'p': 1, 'weights': 'uniform'}\n" ] } ], "source": [ "print(\"sampled hyperparameters\")\n", "print(knn_individual.hyperparameters)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All Individual objects have mutation and crossover operators that TPOT uses to optimize the pipelines." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mutated hyperparameters\n", "{'metric': 'minkowski', 'n_jobs': 1, 'n_neighbors': 6, 'p': 2, 'weights': 'distance'}\n" ] } ], "source": [ "knn_individual.mutate() # mutate the individual\n", "print(\"mutated hyperparameters\")\n", "print(knn_individual.hyperparameters)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In TPOT, crossover only modifies the individual calling the crossover function, the second individual remains the same" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "original hyperparameters for individual 1\n", "{'metric': 'euclidean', 'n_jobs': 1, 'n_neighbors': 8, 'p': 3, 'weights': 'uniform'}\n", "original hyperparameters for individual 2\n", "{'metric': 'minkowski', 'n_jobs': 1, 'n_neighbors': 3, 'p': 2, 'weights': 'distance'}\n", "\n", "post crossover hyperparameters for individual 1\n", "{'metric': 'minkowski', 'n_jobs': 1, 'n_neighbors': 3, 'p': 2, 'weights': 'distance'}\n", "post crossover hyperparameters for individual 2\n", "{'metric': 'minkowski', 'n_jobs': 1, 'n_neighbors': 3, 'p': 2, 'weights': 'distance'}\n" ] } ], "source": [ "knn_individual1 = knn_node.generate()\n", "knn_individual2 = knn_node.generate()\n", "\n", "print(\"original hyperparameters for individual 1\")\n", "print(knn_individual1.hyperparameters)\n", "\n", "print(\"original hyperparameters for individual 2\")\n", "print(knn_individual2.hyperparameters)\n", "\n", "print()\n", "\n", "knn_individual1.crossover(knn_individual2) # crossover the individuals\n", "print(\"post crossover hyperparameters for individual 1\")\n", "print(knn_individual1.hyperparameters)\n", "print(\"post crossover hyperparameters for individual 2\")\n", "print(knn_individual2.hyperparameters)\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All search spaces have an export_pipeline function that returns an sklearn `BaseEstimator`" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
KNeighborsClassifier(n_jobs=1, n_neighbors=3, weights='distance')
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" ], "text/plain": [ "KNeighborsClassifier(n_jobs=1, n_neighbors=3, weights='distance')" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "est = knn_individual1.export_pipeline()\n", "est" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If a dictionary of parameters is passed instead of of a ConfigSpace object, then the hyperparameters will always be fixed and not learned." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
KNeighborsClassifier(n_neighbors=10)
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" ], "text/plain": [ "KNeighborsClassifier(n_neighbors=10)" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import tpot\n", "from ConfigSpace import ConfigurationSpace\n", "from ConfigSpace import ConfigurationSpace, Integer, Float, Categorical, Normal\n", "from sklearn.neighbors import KNeighborsClassifier\n", "\n", "space = {\n", "\n", " 'n_neighbors':10,\n", "}\n", "\n", "knn_node = tpot.search_spaces.nodes.EstimatorNode(\n", " method = KNeighborsClassifier,\n", " space = space,\n", ")\n", "\n", "knn_node.generate().export_pipeline()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### FSSNode and GeneticFeatureSelectorNode\n", "\n", "Both of these are given their own tutorials. See Tutorial 3 for FFSNode and Tutorial 5 for GeneticFeatureSelectorNode" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Pipeline Search Space Examples\n", "\n", "Pipeline search spaces are used to define the structure and restrictions of the pipelines TPOT can search. Unlike Node search spaces, all pipeline search spaces take in other search spaces as inputs. Rather than sample hyperparameters, pipeline search spaces can select models from the input search spaces and organize them within a linear sklearn Pipeline or a TPOT GraphPipeline." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### ChoicePipeline\n", "\n", "The simplest pipeline search space is the ChoicePipeline. This takes in a list of search spaces and simply selects and samples from one. In this example, we will construct a search space that takes in several options for a classifier. The resulting search space will then first select a model from KNeighborsClassifier, LogisticRegression or DecisionTreeClassifier, and then select the hyperparameters for the given model." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import tpot\n", "from ConfigSpace import ConfigurationSpace\n", "from ConfigSpace import ConfigurationSpace, Integer, Float, Categorical, Normal\n", "from sklearn.neighbors import KNeighborsClassifier\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.tree import DecisionTreeClassifier\n", "\n", "knn_configspace = ConfigurationSpace(\n", " space = {\n", "\n", " 'n_neighbors': Integer(\"n_neighbors\", bounds=(1, 10)),\n", " 'weights': Categorical(\"weights\", ['uniform', 'distance']),\n", " 'p': Integer(\"p\", bounds=(1, 3)),\n", " 'metric': Categorical(\"metric\", ['euclidean', 'minkowski']),\n", " 'n_jobs': 1,\n", " }\n", ")\n", "\n", "lr_configspace = ConfigurationSpace(\n", " space = {\n", " 'solver': Categorical(\"solver\", ['newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga']),\n", " 'penalty': Categorical(\"penalty\", ['l1', 'l2']),\n", " 'dual': Categorical(\"dual\", [True, False]),\n", " 'C': Float(\"C\", bounds=(1e-4, 1e4), log=True),\n", " 'class_weight': Categorical(\"class_weight\", ['balanced']),\n", " 'n_jobs': 1,\n", " 'max_iter': 1000,\n", " }\n", " )\n", "\n", "dt_configspace = ConfigurationSpace(\n", " space = {\n", " 'criterion': Categorical(\"criterion\", ['gini', 'entropy']),\n", " 'max_depth': Integer(\"max_depth\", bounds=(1, 11)),\n", " 'min_samples_split': Integer(\"min_samples_split\", bounds=(2, 21)),\n", " 'min_samples_leaf': Integer(\"min_samples_leaf\", bounds=(1, 21)),\n", " 'max_features': Categorical(\"max_features\", ['sqrt', 'log2']),\n", " 'min_weight_fraction_leaf': 0.0,\n", " }\n", " )\n", "\n", "knn_node = tpot.search_spaces.nodes.EstimatorNode(\n", " method = KNeighborsClassifier,\n", " space = knn_configspace,\n", ")\n", "\n", "lr_node = tpot.search_spaces.nodes.EstimatorNode(\n", " method = LogisticRegression,\n", " space = lr_configspace,\n", ")\n", "\n", "dt_node = tpot.search_spaces.nodes.EstimatorNode(\n", " method = DecisionTreeClassifier,\n", " space = dt_configspace,\n", ")\n", "\n", "classifier_node = tpot.search_spaces.pipelines.ChoicePipeline(\n", " search_spaces=[\n", " knn_node,\n", " lr_node,\n", " dt_node,\n", " ]\n", ")\n", "\n", "\n", "tpot.search_spaces.pipelines.ChoicePipeline(\n", " search_spaces = [\n", " tpot.search_spaces.nodes.EstimatorNode(\n", " method = KNeighborsClassifier,\n", " space = knn_configspace,\n", " ),\n", " tpot.search_spaces.nodes.EstimatorNode(\n", " method = LogisticRegression,\n", " space = lr_configspace,\n", " ),\n", " tpot.search_spaces.nodes.EstimatorNode(\n", " method = DecisionTreeClassifier,\n", " space = dt_configspace,\n", " ),\n", " ]\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Search space objects provided by pipeline search spaces work the same as with node search spaces. Note that crossover only works when both individuals have sampled the same method. " ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sampled pipeline\n" ] }, { "data": { "text/html": [ "
KNeighborsClassifier(n_jobs=1, n_neighbors=3)
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" ], "text/plain": [ "KNeighborsClassifier(n_jobs=1, n_neighbors=3)" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "classifier_individual = classifier_node.generate()\n", "\n", "print(\"sampled pipeline\")\n", "classifier_individual.export_pipeline()" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mutated pipeline\n" ] }, { "data": { "text/html": [ "
KNeighborsClassifier(metric='euclidean', n_jobs=1, n_neighbors=9)
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" ], "text/plain": [ "KNeighborsClassifier(metric='euclidean', n_jobs=1, n_neighbors=9)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(\"mutated pipeline\")\n", "classifier_individual.mutate()\n", "classifier_individual.export_pipeline()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Built in search spaces for EstimatorNode and ChoicePipeline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "TPOT also comes with predefined hyperparameter search spaces. The current search spaces were adapted from a combination of the original TPOT package as well as the search spaces used in [AutoSklearn](https://github.com/automl/auto-sklearn/tree/development/autosklearn/pipeline/components). The helper function `tpot.config.get_search_space` takes in a string or a list of strings, and returns either a EstimatorNode or a ChoicePipeline (including all methods in the list), respectively. \n", "\n", "| String | Corresponding Method |\n", "| --- | ----- |\n", "| SGDClassifier | |\n", "| RandomForestClassifier | |\n", "| ExtraTreesClassifier | |\n", "| GradientBoostingClassifier | |\n", "| MLPClassifier | |\n", "| DecisionTreeClassifier | |\n", "| XGBClassifier | |\n", "| KNeighborsClassifier | |\n", "| SVC | |\n", "| LogisticRegression | |\n", "| LGBMClassifier | |\n", "| LinearSVC | |\n", "| GaussianNB | |\n", "| BernoulliNB | |\n", "| MultinomialNB | |\n", "| ExtraTreesRegressor | |\n", "| RandomForestRegressor | |\n", "| GradientBoostingRegressor | |\n", "| BaggingRegressor | |\n", "| DecisionTreeRegressor | |\n", "| KNeighborsRegressor | |\n", "| XGBRegressor | |\n", "| ZeroCount | |\n", "| ColumnOneHotEncoder | |\n", "| Binarizer | |\n", "| FastICA | |\n", "| FeatureAgglomeration | |\n", "| MaxAbsScaler | |\n", "| MinMaxScaler | |\n", "| Normalizer | |\n", "| Nystroem | |\n", "| PCA | |\n", "| PolynomialFeatures | |\n", "| RBFSampler | |\n", "| RobustScaler | |\n", "| StandardScaler | |\n", "| SelectFwe | |\n", "| SelectPercentile | |\n", "| VarianceThreshold | |\n", "| SGDRegressor | |\n", "| Ridge | |\n", "| Lasso | |\n", "| ElasticNet | |\n", "| Lars | |\n", "| LassoLars | |\n", "| LassoLarsCV | |\n", "| RidgeCV | |\n", "| SVR | |\n", "| LinearSVR | |\n", "| AdaBoostRegressor | |\n", "| ElasticNetCV | |\n", "| AdaBoostClassifier | |\n", "| MLPRegressor | |\n", "| GaussianProcessRegressor | |\n", "| HistGradientBoostingClassifier | |\n", "| HistGradientBoostingRegressor | |\n", "| AddTransformer | |\n", "| mul_neg_1_Transformer | |\n", "| MulTransformer | |\n", "| SafeReciprocalTransformer | |\n", "| EQTransformer | |\n", "| NETransformer | |\n", "| GETransformer | |\n", "| GTTransformer | |\n", "| LETransformer | |\n", "| LTTransformer | |\n", "| MinTransformer | |\n", "| MaxTransformer | |\n", "| ZeroTransformer | |\n", "| OneTransformer | |\n", "| NTransformer | |\n", "| PowerTransformer | |\n", "| QuantileTransformer | |\n", "| ARDRegression | |\n", "| QuadraticDiscriminantAnalysis | |\n", "| PassiveAggressiveClassifier | |\n", "| LinearDiscriminantAnalysis | |\n", "| DominantEncoder | |\n", "| RecessiveEncoder | |\n", "| HeterosisEncoder | |\n", "| UnderDominanceEncoder | |\n", "| OverDominanceEncoder | |\n", "| GaussianProcessClassifier | |\n", "| BaggingClassifier | |\n", "| LGBMRegressor | |\n", "| Passthrough | |\n", "| SkipTransformer | |\n", "| PassKBinsDiscretizer | |\n", "| SimpleImputer | |\n", "| IterativeImputer | |\n", "| KNNImputer | |\n", "| MDR | |\n", "| ContinuousMDR | |\n", "| ReliefF | |\n", "| SURF | |\n", "| SURFstar | |\n", "| MultiSURF | |\n", "| LinearRegression_sklearnex | |\n", "| Ridge_sklearnex | |\n", "| Lasso_sklearnex | |\n", "| ElasticNet_sklearnex | |\n", "| SVR_sklearnex | |\n", "| NuSVR_sklearnex | |\n", "| RandomForestRegressor_sklearnex | |\n", "| KNeighborsRegressor_sklearnex | |\n", "| RandomForestClassifier_sklearnex | |\n", "| KNeighborsClassifier_sklearnex | |\n", "| SVC_sklearnex | |\n", "| NuSVC_sklearnex | |\n", "| LogisticRegression_sklearnex | |\n", "\n", "Some methods require a wrapped estimator. To account for both regression and classification, these have been grouped separately with their own special strings.\n", "\n", "| Wrapper Special String | Notes |\n", "| :--- | :----: |\n", "| RFE_classification | FRE with learned ExtraTreesClassifier |\n", "| RFE_regression | RFE with learned ExtraTreesRegressor |\n", "| SelectFromModel_classification | SelectFromModel with learned ExtraTreesClassifier |\n", "| SelectFromModel_regression | SelectFromModel with learned ExtraTreesRegressor |\n", "| IterativeImputer_learned_estimators | IterativeImputer with learned ExtraTreesRegressor |\n", "\n", "\n", "There are also special strings that include a predefined lists of methods. These will return a ChoicePipeline of the included methods.\n", "\n", "| List Special String | Included methods |\n", "| :--- | :----: |\n", "| \"selectors\" | [\"SelectFwe\", \"SelectPercentile\", \"VarianceThreshold\",] |\n", "| \"selectors_classification\" | [\"SelectFwe\", \"SelectPercentile\", \"VarianceThreshold\", \"RFE_classification\", \"SelectFromModel_classification\"] |\n", "| \"selectors_regression\" | [\"SelectFwe\", \"SelectPercentile\", \"VarianceThreshold\", \"RFE_regression\", \"SelectFromModel_regression\"] |\n", "| \"classifiers\" | [\"LGBMClassifier\", \"BaggingClassifier\", 'AdaBoostClassifier', 'BernoulliNB', 'DecisionTreeClassifier', 'ExtraTreesClassifier', 'GaussianNB', 'HistGradientBoostingClassifier', 'KNeighborsClassifier','LinearDiscriminantAnalysis', 'LogisticRegression', \"LinearSVC\", \"SVC\", 'MLPClassifier', 'MultinomialNB', \"QuadraticDiscriminantAnalysis\", 'RandomForestClassifier', 'SGDClassifier', 'XGBClassifier'] |\n", "| \"regressors\" | [\"LGBMRegressor\", 'AdaBoostRegressor', \"ARDRegression\", 'DecisionTreeRegressor', 'ExtraTreesRegressor', 'HistGradientBoostingRegressor', 'KNeighborsRegressor', 'LinearSVR', \"MLPRegressor\", 'RandomForestRegressor', 'SGDRegressor', 'SVR', 'XGBRegressor'] |\n", "| \"transformers\" | [\"PassKBinsDiscretizer\", \"Binarizer\", \"PCA\", \"ZeroCount\", \"ColumnOneHotEncoder\", \"FastICA\", \"FeatureAgglomeration\", \"Nystroem\", \"RBFSampler\", \"QuantileTransformer\", \"PowerTransformer\"] |\n", "| \"scalers\" | [\"MinMaxScaler\", \"RobustScaler\", \"StandardScaler\", \"MaxAbsScaler\", \"Normalizer\", ] |\n", "| \"all_transformers\" | [\"transformers\", \"scalers\"] |\n", "| \"arithmatic\" | [\"AddTransformer\", \"mul_neg_1_Transformer\", \"MulTransformer\", \"SafeReciprocalTransformer\", \"EQTransformer\", \"NETransformer\", \"GETransformer\", \"GTTransformer\", \"LETransformer\", \"LTTransformer\", \"MinTransformer\", \"MaxTransformer\"] |\n", "| \"imputers\" | [\"SimpleImputer\", \"IterativeImputer\", \"KNNImputer\"] |\n", "| \"skrebate\" | [\"ReliefF\", \"SURF\", \"SURFstar\", \"MultiSURF\"] |\n", "| \"genetic_encoders\" | [\"DominantEncoder\", \"RecessiveEncoder\", \"HeterosisEncoder\", \"UnderDominanceEncoder\", \"OverDominanceEncoder\"] |\n", "| \"classifiers_sklearnex\" | [\"RandomForestClassifier_sklearnex\", \"LogisticRegression_sklearnex\", \"KNeighborsClassifier_sklearnex\", \"SVC_sklearnex\",\"NuSVC_sklearnex\"] |\n", "| \"regressors_sklearnex\" | [\"LinearRegression_sklearnex\", \"Ridge_sklearnex\", \"Lasso_sklearnex\", \"ElasticNet_sklearnex\", \"SVR_sklearnex\", \"NuSVR_sklearnex\", \"RandomForestRegressor_sklearnex\", \"KNeighborsRegressor_sklearnex\"] |\n", "| \"genetic encoders\" | [\"DominantEncoder\", \"RecessiveEncoder\", \"HeterosisEncoder\", \"UnderDominanceEncoder\", \"OverDominanceEncoder\"] |" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here are some examples of how to get search spaces using the `get_search_space` function. " ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sampled pipeline 1\n" ] }, { "data": { "text/html": [ "
KNeighborsClassifier(n_jobs=1, n_neighbors=15, p=1, weights='distance')
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" ], "text/plain": [ "KNeighborsClassifier(n_jobs=1, n_neighbors=15, p=1, weights='distance')" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#same pipeline search space as before.\n", "classifier_choice = tpot.config.get_search_space([\"KNeighborsClassifier\", \"LogisticRegression\", \"DecisionTreeClassifier\"])\n", "\n", "print(\"sampled pipeline 1\")\n", "classifier_choice.generate().export_pipeline()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sampled pipeline 2\n" ] }, { "data": { "text/html": [ "
LogisticRegression(C=5.9018435257131, max_iter=1000, n_jobs=1, solver='saga')
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" ], "text/plain": [ "LogisticRegression(C=5.9018435257131, max_iter=1000, n_jobs=1, solver='saga')" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(\"sampled pipeline 2\")\n", "classifier_choice.generate().export_pipeline()" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sampled pipeline 1\n" ] }, { "data": { "text/html": [ "
SGDClassifier(alpha=0.0007786971309, class_weight='balanced',\n",
       "              eta0=0.0209976430718, l1_ratio=0.8571538017043,\n",
       "              learning_rate='constant', loss='modified_huber', n_jobs=1,\n",
       "              penalty='elasticnet')
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" ], "text/plain": [ "SGDClassifier(alpha=0.0007786971309, class_weight='balanced',\n", " eta0=0.0209976430718, l1_ratio=0.8571538017043,\n", " learning_rate='constant', loss='modified_huber', n_jobs=1,\n", " penalty='elasticnet')" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#search space for all classifiers\n", "classifier_choice = tpot.config.get_search_space(\"classifiers\")\n", "\n", "print(\"sampled pipeline 1\")\n", "classifier_choice.generate().export_pipeline()" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sampled pipeline 2\n" ] }, { "data": { "text/html": [ "
BernoulliNB(alpha=0.0667141454883, fit_prior=False)
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" ], "text/plain": [ "BernoulliNB(alpha=0.0667141454883, fit_prior=False)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(\"sampled pipeline 2\")\n", "classifier_choice.generate().export_pipeline()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### A note on reproducibility \n", "Many sklearn estimators, like RandomForestClassifier, are stochastic and require a random_state parameter in order to have deterministic results. If you want TPOT runs to be reproducible, it is important that the estimators used by TPOT have a random state set. TPOT will not automatically set this value. This can either be set manually in each search space, or by passing in the random state to the `get_search_space` function. For example: " ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
RandomForestClassifier(bootstrap=False, max_features=0.0234127070363,\n",
       "                       min_samples_leaf=3, min_samples_split=8,\n",
       "                       n_estimators=128, n_jobs=1, random_state=1)
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" ], "text/plain": [ "RandomForestClassifier(bootstrap=False, max_features=0.0234127070363,\n", " min_samples_leaf=3, min_samples_split=8,\n", " n_estimators=128, n_jobs=1, random_state=1)" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "reproducible_random_forest = tpot.config.get_search_space(\"RandomForestClassifier\", random_state=1)\n", "reproducible_random_forest.generate().export_pipeline()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## SequentialPipeline\n", "\n", "SequentialPipelines are of fixed length and sample from a predefined distribution for each step. " ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sampled pipeline\n" ] }, { "data": { "text/html": [ "
Pipeline(steps=[('variancethreshold',\n",
       "                 VarianceThreshold(threshold=0.00023551581)),\n",
       "                ('pca', PCA(n_components=0.9764631370244)),\n",
       "                ('logisticregression',\n",
       "                 LogisticRegression(C=1.9396611393109, max_iter=1000, n_jobs=1,\n",
       "                                    penalty='l1', solver='saga'))])
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" ], "text/plain": [ "Pipeline(steps=[('variancethreshold',\n", " VarianceThreshold(threshold=0.00023551581)),\n", " ('pca', PCA(n_components=0.9764631370244)),\n", " ('logisticregression',\n", " LogisticRegression(C=1.9396611393109, max_iter=1000, n_jobs=1,\n", " penalty='l1', solver='saga'))])" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "selector_choicepipeline = tpot.config.get_search_space(\"VarianceThreshold\")\n", "transformer_choicepipeline = tpot.config.get_search_space(\"PCA\")\n", "classifier_choicepipeline = tpot.config.get_search_space(\"LogisticRegression\")\n", "\n", "stc_pipeline = tpot.search_spaces.pipelines.SequentialPipeline([\n", " selector_choicepipeline,\n", " transformer_choicepipeline,\n", " classifier_choicepipeline,\n", "])\n", "\n", "print(\"sampled pipeline\")\n", "stc_pipeline.generate().export_pipeline()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is an example of the form Selector-Transformer-Classifier.\n", "\n", "Note that each step in the sequence is a ChoicePipeline this time. Here, the SequentialPipeline can sample from search provided search space in order." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sampled pipeline\n" ] }, { "data": { "text/html": [ "
Pipeline(steps=[('variancethreshold',\n",
       "                 VarianceThreshold(threshold=0.0004317798946)),\n",
       "                ('kbinsdiscretizer',\n",
       "                 KBinsDiscretizer(encode='onehot-dense', n_bins=77)),\n",
       "                ('lgbmclassifier',\n",
       "                 LGBMClassifier(boosting_type='dart', max_depth=5,\n",
       "                                n_estimators=76, n_jobs=1, num_leaves=192,\n",
       "                                verbose=-1))])
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" ], "text/plain": [ "Pipeline(steps=[('variancethreshold',\n", " VarianceThreshold(threshold=0.0004317798946)),\n", " ('kbinsdiscretizer',\n", " KBinsDiscretizer(encode='onehot-dense', n_bins=77)),\n", " ('lgbmclassifier',\n", " LGBMClassifier(boosting_type='dart', max_depth=5,\n", " n_estimators=76, n_jobs=1, num_leaves=192,\n", " verbose=-1))])" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "selector_choicepipeline = tpot.config.get_search_space(\"selectors\")\n", "transformer_choicepipeline = tpot.config.get_search_space(\"transformers\")\n", "classifier_choicepipeline = tpot.config.get_search_space(\"classifiers\")\n", "\n", "stc_pipeline = tpot.search_spaces.pipelines.SequentialPipeline([\n", " selector_choicepipeline,\n", " transformer_choicepipeline,\n", " classifier_choicepipeline,\n", "])\n", "\n", "print(\"sampled pipeline\")\n", "stc_pipeline.generate().export_pipeline()" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sampled pipeline\n" ] }, { "data": { "text/html": [ "
Pipeline(steps=[('selectpercentile',\n",
       "                 SelectPercentile(percentile=4.5788544361168)),\n",
       "                ('columnonehotencoder', ColumnOneHotEncoder()),\n",
       "                ('decisiontreeclassifier',\n",
       "                 DecisionTreeClassifier(criterion='entropy', max_depth=10,\n",
       "                                        min_samples_split=13))])
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" ], "text/plain": [ "Pipeline(steps=[('selectpercentile',\n", " SelectPercentile(percentile=4.5788544361168)),\n", " ('columnonehotencoder', ColumnOneHotEncoder()),\n", " ('decisiontreeclassifier',\n", " DecisionTreeClassifier(criterion='entropy', max_depth=10,\n", " min_samples_split=13))])" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(\"sampled pipeline\")\n", "stc_pipeline.generate().export_pipeline()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## DynamicLinearPipeline\n", "\n", "DynamicLinearPipeline takes in a single search space and randomly samples and places estimators in a list without a predefined sequence. DynamicLinearPipeline are most often used when paired with LinearPipeline. A common strategy is to use DynamicLinearPipeline to optimize a series of preprocessing or feature engineering steps, followed by a final classifier or regressor." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sampled pipeline\n" ] }, { "data": { "text/html": [ "
Pipeline(steps=[('pca-1', PCA(n_components=0.6376571946485)),\n",
       "                ('pca-2', PCA(n_components=0.7836827180307)),\n",
       "                ('quantiletransformer',\n",
       "                 QuantileTransformer(n_quantiles=334,\n",
       "                                     output_distribution='normal'))])
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" ], "text/plain": [ "Pipeline(steps=[('pca-1', PCA(n_components=0.6376571946485)),\n", " ('pca-2', PCA(n_components=0.7836827180307)),\n", " ('quantiletransformer',\n", " QuantileTransformer(n_quantiles=334,\n", " output_distribution='normal'))])" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import tpot.config\n", "\n", "\n", "linear_feature_engineering = tpot.search_spaces.pipelines.DynamicLinearPipeline(search_space = tpot.config.get_search_space([\"all_transformers\",\"selectors_classification\"]), max_length=10)\n", "print(\"sampled pipeline\")\n", "linear_feature_engineering.generate().export_pipeline()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sampled pipeline\n" ] }, { "data": { "text/html": [ "
Pipeline(steps=[('selectfwe', SelectFwe(alpha=0.0004164619371)),\n",
       "                ('binarizer', Binarizer(threshold=0.2392693027442)),\n",
       "                ('rbfsampler',\n",
       "                 RBFSampler(gamma=0.3669672326084, n_components=35))])
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" ], "text/plain": [ "Pipeline(steps=[('selectfwe', SelectFwe(alpha=0.0004164619371)),\n", " ('binarizer', Binarizer(threshold=0.2392693027442)),\n", " ('rbfsampler',\n", " RBFSampler(gamma=0.3669672326084, n_components=35))])" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(\"sampled pipeline\")\n", "linear_feature_engineering.generate().export_pipeline()" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sampled pipeline\n" ] }, { "data": { "text/html": [ "
Pipeline(steps=[('pipeline',\n",
       "                 Pipeline(steps=[('binarizer',\n",
       "                                  Binarizer(threshold=0.2150677779496)),\n",
       "                                 ('maxabsscaler', MaxAbsScaler()),\n",
       "                                 ('columnonehotencoder',\n",
       "                                  ColumnOneHotEncoder())])),\n",
       "                ('gaussiannb', GaussianNB())])
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" ], "text/plain": [ "Pipeline(steps=[('pipeline',\n", " Pipeline(steps=[('binarizer',\n", " Binarizer(threshold=0.2150677779496)),\n", " ('maxabsscaler', MaxAbsScaler()),\n", " ('columnonehotencoder',\n", " ColumnOneHotEncoder())])),\n", " ('gaussiannb', GaussianNB())])" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "full_search_space = tpot.search_spaces.pipelines.SequentialPipeline([\n", " linear_feature_engineering,\n", " tpot.config.get_search_space(\"classifiers\"),\n", "])\n", "\n", "print(\"sampled pipeline\")\n", "full_search_space.generate().export_pipeline()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "sampled pipeline\n" ] }, { "data": { "text/html": [ "
Pipeline(steps=[('pipeline',\n",
       "                 Pipeline(steps=[('zerocount', ZeroCount()),\n",
       "                                 ('selectfrommodel',\n",
       "                                  SelectFromModel(estimator=ExtraTreesClassifier(class_weight='balanced',\n",
       "                                                                                 max_features=0.1619832293406,\n",
       "                                                                                 min_samples_leaf=7,\n",
       "                                                                                 min_samples_split=7,\n",
       "                                                                                 n_jobs=1),\n",
       "                                                  threshold=0.6414209870839)),\n",
       "                                 ('variancethreshold',\n",
       "                                  VarianceThreshold(threshold=0.0113542845765))])),\n",
       "                ('multinomialnb', MultinomialNB(alpha=0.0815128367119))])
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" ], "text/plain": [ "Pipeline(steps=[('pipeline',\n", " Pipeline(steps=[('zerocount', ZeroCount()),\n", " ('selectfrommodel',\n", " SelectFromModel(estimator=ExtraTreesClassifier(class_weight='balanced',\n", " max_features=0.1619832293406,\n", " min_samples_leaf=7,\n", " min_samples_split=7,\n", " n_jobs=1),\n", " threshold=0.6414209870839)),\n", " ('variancethreshold',\n", " VarianceThreshold(threshold=0.0113542845765))])),\n", " ('multinomialnb', MultinomialNB(alpha=0.0815128367119))])" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "print(\"sampled pipeline\")\n", "full_search_space.generate().export_pipeline()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### UnionPipeline\n", "\n", "Union pipelines can be useful when you want to either do multiple transformations in a single layer. Another common strategy is to do a union with a transformer and a passthrough for when you want to keep the original data in addition to the transformation. " ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
FeatureUnion(transformer_list=[('pca', PCA(n_components=0.7674007136568)),\n",
       "                               ('passthrough', Passthrough())])
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" ], "text/plain": [ "FeatureUnion(transformer_list=[('pca', PCA(n_components=0.7674007136568)),\n", " ('passthrough', Passthrough())])" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "transform_and_passthrough = tpot.search_spaces.pipelines.UnionPipeline([\n", " tpot.config.get_search_space(\"transformers\"),\n", " tpot.config.get_search_space(\"Passthrough\"),\n", "])\n", "\n", "transform_and_passthrough.generate().export_pipeline()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "UnionPipelines are an excellent tool to expand the capabilities of the linear search spaces." ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Pipeline(steps=[('selectpercentile',\n",
       "                 SelectPercentile(percentile=29.1049436421441)),\n",
       "                ('featureunion',\n",
       "                 FeatureUnion(transformer_list=[('powertransformer',\n",
       "                                                 PowerTransformer()),\n",
       "                                                ('passthrough',\n",
       "                                                 Passthrough())])),\n",
       "                ('extratreesclassifier',\n",
       "                 ExtraTreesClassifier(max_features=0.8376611419015,\n",
       "                                      min_samples_leaf=9, min_samples_split=17,\n",
       "                                      n_jobs=1))])
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" ], "text/plain": [ "Pipeline(steps=[('selectpercentile',\n", " SelectPercentile(percentile=29.1049436421441)),\n", " ('featureunion',\n", " FeatureUnion(transformer_list=[('powertransformer',\n", " PowerTransformer()),\n", " ('passthrough',\n", " Passthrough())])),\n", " ('extratreesclassifier',\n", " ExtraTreesClassifier(max_features=0.8376611419015,\n", " min_samples_leaf=9, min_samples_split=17,\n", " n_jobs=1))])" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stc_pipeline2 = tpot.search_spaces.pipelines.SequentialPipeline([\n", " tpot.config.get_search_space(\"selectors\"),\n", " transform_and_passthrough,\n", " tpot.config.get_search_space(\"classifiers\"),\n", "])\n", "\n", "stc_pipeline2.generate().export_pipeline()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Union pipelines can also be used to create \"branches\" if you are trying to create a tree-like search space. This can be particularly useful when paired with the FeatureSetSelector node (FSSNode) as each branch can learn different feature engineering for different subsets of the features, for example." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Pipeline(steps=[('featureunion',\n",
       "                 FeatureUnion(transformer_list=[('pipeline-1',\n",
       "                                                 Pipeline(steps=[('selectfwe',\n",
       "                                                                  SelectFwe(alpha=0.0080564930162)),\n",
       "                                                                 ('quantiletransformer',\n",
       "                                                                  QuantileTransformer(n_quantiles=450,\n",
       "                                                                                      output_distribution='normal'))])),\n",
       "                                                ('pipeline-2',\n",
       "                                                 Pipeline(steps=[('variancethreshold',\n",
       "                                                                  VarianceThreshold(threshold=0.155443085484)),\n",
       "                                                                 ('columnonehotencoder...\n",
       "                               feature_types=None, gamma=14.5866790094856,\n",
       "                               grow_policy=None, importance_type=None,\n",
       "                               interaction_constraints=None,\n",
       "                               learning_rate=0.2226908938347, max_bin=None,\n",
       "                               max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "                               max_delta_step=None, max_depth=11,\n",
       "                               max_leaves=None, min_child_weight=3, missing=nan,\n",
       "                               monotone_constraints=None, multi_strategy=None,\n",
       "                               n_estimators=100, n_jobs=1, nthread=1,\n",
       "                               num_parallel_tree=None, ...))])
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" ], "text/plain": [ "Pipeline(steps=[('featureunion',\n", " FeatureUnion(transformer_list=[('pipeline-1',\n", " Pipeline(steps=[('selectfwe',\n", " SelectFwe(alpha=0.0080564930162)),\n", " ('quantiletransformer',\n", " QuantileTransformer(n_quantiles=450,\n", " output_distribution='normal'))])),\n", " ('pipeline-2',\n", " Pipeline(steps=[('variancethreshold',\n", " VarianceThreshold(threshold=0.155443085484)),\n", " ('columnonehotencoder...\n", " feature_types=None, gamma=14.5866790094856,\n", " grow_policy=None, importance_type=None,\n", " interaction_constraints=None,\n", " learning_rate=0.2226908938347, max_bin=None,\n", " max_cat_threshold=None, max_cat_to_onehot=None,\n", " max_delta_step=None, max_depth=11,\n", " max_leaves=None, min_child_weight=3, missing=nan,\n", " monotone_constraints=None, multi_strategy=None,\n", " n_estimators=100, n_jobs=1, nthread=1,\n", " num_parallel_tree=None, ...))])" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "st_pipeline = tpot.search_spaces.pipelines.SequentialPipeline([\n", " tpot.config.get_search_space(\"selectors\"),\n", " tpot.config.get_search_space(\"transformers\"),\n", "])\n", "\n", "branched_pipeline = tpot.search_spaces.pipelines.SequentialPipeline([\n", " tpot.search_spaces.pipelines.UnionPipeline([\n", " st_pipeline,\n", " st_pipeline,\n", " ]),\n", " tpot.config.get_search_space(\"classifiers\"),\n", "])\n", "\n", "branched_pipeline.generate().export_pipeline()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### DynamicUnionPipeline\n", "\n", "DynamicUnionPipeline works similarly as UnionPipeline. Whereas UnionPipeline is fixed length, with each index corresponding to the search space provided as a list, DynamicUnionPipeline takes in a single search space and will sample 1 or more estimators/pipelines and concatenate them with a FeatureUnion. \n", "\n", "Note that DynamicUnionPipeline will check for pipeline uniqueness, so it will never concatenate two completely identical pipelines. In other words, all steps within the feature union will be unique.\n", "\n", "This can be useful when you want multiple transformers (or in some cases, pipelines), but are not sure how many or which ones." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
FeatureUnion(transformer_list=[('fastica', FastICA(n_components=4))])
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" ], "text/plain": [ "FeatureUnion(transformer_list=[('fastica', FastICA(n_components=4))])" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dynamic_transformers = tpot.search_spaces.pipelines.DynamicUnionPipeline(tpot.config.get_search_space(\"transformers\"), max_estimators=4)\n", "dynamic_transformers.generate().export_pipeline()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One good strategy could be to pair this with Passthrough in a feature union so that you output all the transformations along with the original data." ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
FeatureUnion(transformer_list=[('featureunion',\n",
       "                                FeatureUnion(transformer_list=[('pca',\n",
       "                                                                PCA(n_components=0.9386236966835)),\n",
       "                                                               ('zerocount',\n",
       "                                                                ZeroCount()),\n",
       "                                                               ('featureagglomeration',\n",
       "                                                                FeatureAgglomeration(n_clusters=94,\n",
       "                                                                                     pooling_func=<function max at 0x1048f3470>))])),\n",
       "                               ('passthrough', Passthrough())])
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" ], "text/plain": [ "FeatureUnion(transformer_list=[('featureunion',\n", " FeatureUnion(transformer_list=[('pca',\n", " PCA(n_components=0.9386236966835)),\n", " ('zerocount',\n", " ZeroCount()),\n", " ('featureagglomeration',\n", " FeatureAgglomeration(n_clusters=94,\n", " pooling_func=))])),\n", " ('passthrough', Passthrough())])" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dynamic_transformers_with_passthrough = tpot.search_spaces.pipelines.UnionPipeline([\n", " dynamic_transformers,\n", " tpot.config.get_search_space(\"Passthrough\")],\n", " )\n", "\n", "dynamic_transformers_with_passthrough.generate().export_pipeline()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Pipeline(steps=[('variancethreshold',\n",
       "                 VarianceThreshold(threshold=0.0003352949622)),\n",
       "                ('featureunion',\n",
       "                 FeatureUnion(transformer_list=[('featureunion',\n",
       "                                                 FeatureUnion(transformer_list=[('featureagglomeration',\n",
       "                                                                                 FeatureAgglomeration(linkage='complete',\n",
       "                                                                                                      metric='cosine',\n",
       "                                                                                                      n_clusters=25)),\n",
       "                                                                                ('columnordinalencoder',\n",
       "                                                                                 ColumnOrdinalEncoder())])),\n",
       "                                                ('passthrough',\n",
       "                                                 Passthrough())])),\n",
       "                ('mlpclassifier',\n",
       "                 MLPClassifier(activation='identity', alpha=0.000256185492,\n",
       "                               early_stopping=True,\n",
       "                               hidden_layer_sizes=[146, 146, 146],\n",
       "                               learning_rate='invscaling',\n",
       "                               learning_rate_init=0.0006442167601,\n",
       "                               n_iter_no_change=32))])
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" ], "text/plain": [ "Pipeline(steps=[('variancethreshold',\n", " VarianceThreshold(threshold=0.0003352949622)),\n", " ('featureunion',\n", " FeatureUnion(transformer_list=[('featureunion',\n", " FeatureUnion(transformer_list=[('featureagglomeration',\n", " FeatureAgglomeration(linkage='complete',\n", " metric='cosine',\n", " n_clusters=25)),\n", " ('columnordinalencoder',\n", " ColumnOrdinalEncoder())])),\n", " ('passthrough',\n", " Passthrough())])),\n", " ('mlpclassifier',\n", " MLPClassifier(activation='identity', alpha=0.000256185492,\n", " early_stopping=True,\n", " hidden_layer_sizes=[146, 146, 146],\n", " learning_rate='invscaling',\n", " learning_rate_init=0.0006442167601,\n", " n_iter_no_change=32))])" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stc_pipeline3 = tpot.search_spaces.pipelines.SequentialPipeline([\n", " tpot.config.get_search_space(\"selectors\"),\n", " dynamic_transformers_with_passthrough,\n", " tpot.config.get_search_space(\"classifiers\"),\n", "])\n", "\n", "stc_pipeline3.generate().export_pipeline()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### WrapperPipeline\n", "\n", "Some sklearn estimators take in other sklearn estimators as a parameter. The wrapper pipeline is used to tune both the original estimators hyperparameters simultaneously with the inner estimators hyperparameters. In fact, the inner estimator in WrapperPipeline can be any search space defined with any of the methods described in this Tutorial.\n", "\n", "The `get_search_space` will automatically create an inner search space for sklearn estimators that do use require an inner estimator. For example \"SelectFromModel_classification\" will return the following search space" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
ExtraTreesClassifier(class_weight='balanced', max_features=0.9851993193336,\n",
       "                     min_samples_leaf=5, min_samples_split=6, n_jobs=1)
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" ], "text/plain": [ "ExtraTreesClassifier(class_weight='balanced', max_features=0.9851993193336,\n", " min_samples_leaf=5, min_samples_split=6, n_jobs=1)" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "SelectFromModel_configspace_part = ConfigurationSpace(\n", " space = {\n", " 'threshold': Float('threshold', bounds=(1e-4, 1.0), log=True),\n", " }\n", " )\n", "\n", "extratrees_estimator_node = tpot.config.get_search_space(\"ExtraTreesClassifier\") #this exports an ExtraTreesClassifier node\n", "extratrees_estimator_node.generate().export_pipeline()" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
SelectFromModel(estimator=ExtraTreesClassifier(max_features=0.277440186742,\n",
       "                                               min_samples_leaf=9,\n",
       "                                               min_samples_split=17, n_jobs=1),\n",
       "                threshold=0.0032005860778)
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" ], "text/plain": [ "SelectFromModel(estimator=ExtraTreesClassifier(max_features=0.277440186742,\n", " min_samples_leaf=9,\n", " min_samples_split=17, n_jobs=1),\n", " threshold=0.0032005860778)" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.ensemble import ExtraTreesClassifier\n", "from sklearn.feature_selection import SelectFromModel\n", "\n", "select_from_model_wrapper_searchspace = tpot.search_spaces.pipelines.WrapperPipeline(\n", " method=SelectFromModel,\n", " space = SelectFromModel_configspace_part,\n", " estimator_search_space= extratrees_estimator_node,\n", " )\n", "\n", "select_from_model_wrapper_searchspace.generate().export_pipeline()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### WrapperPipeline strategy for ensembles/inner classifiers and regressors (EstimatorTransformer)\n", "\n", "Sklearn Pipelines only allow classifiers/regressors as the final step. All other steps are expected to implement a transform function. We can get around this by wrapping it in another transformer class that returns the output of predict or predict_proba inside the transform() function.\n", "\n", "To wrap classifiers as transfomers, you can use the following class: `tpot.builtin_modules.EstimatorTransformer`. You can specify whether to pass the outputs of predict, predict_proba, or decision function with the `method` parameter. \n", "\n", "#### cross_val_predict_cv\n", "\n", "An additional consideration is whether or not to use `cross_val_predict_cv`. If this parameter is set, during model training any classifiers or regressors that is not the final predictor will use `sklearn.model_selection.cross_val_predict` to pass out of sample predictions into the following steps of the model. The model will still be fit to the full data which will be used for predictions after training. Training downstream models on out of sample predictions can often prevent overfitting and increase performance. The reason is that this gives downstream models a estimate of how upstream models compare on unseen data. Otherwise, if an upsteam model heavily overfits the data, downsteam models may simply learn to blindly trust the seemingly well-predicting model, propagating the over-fitting through to the end result.\n", "\n", "The downside is that cross_val_predict_cv is significantly more computationally demanding, and may not be necessary for your given dataset. \n", "\n", "Note: This is not necessary for `GraphSearchPipeline` as the exported GraphPipeline estimator does have builtin support for inner/regressors. Instead of using a wrapper, you can set the `cross_val_predict_cv` param when initializing the `GraphSearchPipeline` object." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
EstimatorTransformer(estimator=MLPClassifier(alpha=0.000648285661,\n",
       "                                             hidden_layer_sizes=[380],\n",
       "                                             learning_rate='invscaling',\n",
       "                                             learning_rate_init=0.0008851810314,\n",
       "                                             n_iter_no_change=32))
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" ], "text/plain": [ "EstimatorTransformer(estimator=MLPClassifier(alpha=0.000648285661,\n", " hidden_layer_sizes=[380],\n", " learning_rate='invscaling',\n", " learning_rate_init=0.0008851810314,\n", " n_iter_no_change=32))" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "classifiers = tpot.config.get_search_space(\"classifiers\")\n", "wrapped_estimators = tpot.search_spaces.pipelines.WrapperPipeline(tpot.builtin_modules.EstimatorTransformer, {}, classifiers)\n", "\n", "est = wrapped_estimators.generate().export_pipeline() #returns an estimator with a transform function\n", "est" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/anaconda3/envs/tpotenv/lib/python3.10/site-packages/sklearn/neural_network/_multilayer_perceptron.py:690: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (200) reached and the optimization hasn't converged yet.\n", " warnings.warn(\n" ] }, { "data": { "text/plain": [ "array([[0.34363566, 0.65636434],\n", " [0.14785295, 0.85214705],\n", " [0.45816571, 0.54183429],\n", " [0.81083741, 0.18916259],\n", " [0.56944478, 0.43055522]])" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "X, y = np.random.rand(100, 10), np.random.randint(0, 2, 100)\n", "\n", "est.fit_transform(X, y)[0:5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "you can manually set the settings for an estimator the same way you would do it for an EstimatorNode. Here's another example with cross_val_predict and method being used." ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[0],\n", " [0],\n", " [0],\n", " [0],\n", " [0]])" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "classifiers = tpot.config.get_search_space(\"classifiers\")\n", "wrapped_estimators_cv = tpot.search_spaces.pipelines.WrapperPipeline(tpot.builtin_modules.EstimatorTransformer, {'cross_val_predict_cv':10, 'method':'predict'}, classifiers)\n", "est = wrapped_estimators_cv.generate().export_pipeline() #returns an estimator with a transform function\n", "est.fit_transform(X, y)[0:5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These can now be used inside a linear pipeline. This is fairly similar to the default linear pipeline search space." ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Pipeline(steps=[('robustscaler',\n",
       "                 RobustScaler(quantile_range=(0.2632669052042,\n",
       "                                              0.892009308738))),\n",
       "                ('featureunion-1',\n",
       "                 FeatureUnion(transformer_list=[('featureunion',\n",
       "                                                 FeatureUnion(transformer_list=[('columnonehotencoder',\n",
       "                                                                                 ColumnOneHotEncoder()),\n",
       "                                                                                ('kbinsdiscretizer',\n",
       "                                                                                 KBinsDiscretizer(encode='onehot-dense',\n",
       "                                                                                                  n_bins=58,\n",
       "                                                                                                  strategy='kmeans'))])),\n",
       "                                                ('passthrough',\n",
       "                                                 Passth...\n",
       "                                                                                                      estimator=LogisticRegression(C=334.8557628287718,\n",
       "                                                                                                                                   max_iter=1000,\n",
       "                                                                                                                                   n_jobs=1,\n",
       "                                                                                                                                   solver='saga'),\n",
       "                                                                                                      method='predict')),\n",
       "                                                                                ('estimatortransformer-2',\n",
       "                                                                                 EstimatorTransformer(cross_val_predict_cv=10,\n",
       "                                                                                                      estimator=QuadraticDiscriminantAnalysis(reg_param=0.0011738914966),\n",
       "                                                                                                      method='predict'))])),\n",
       "                                                ('passthrough',\n",
       "                                                 Passthrough())])),\n",
       "                ('lineardiscriminantanalysis', LinearDiscriminantAnalysis())])
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" ], "text/plain": [ "Pipeline(steps=[('robustscaler',\n", " RobustScaler(quantile_range=(0.2632669052042,\n", " 0.892009308738))),\n", " ('featureunion-1',\n", " FeatureUnion(transformer_list=[('featureunion',\n", " FeatureUnion(transformer_list=[('columnonehotencoder',\n", " ColumnOneHotEncoder()),\n", " ('kbinsdiscretizer',\n", " KBinsDiscretizer(encode='onehot-dense',\n", " n_bins=58,\n", " strategy='kmeans'))])),\n", " ('passthrough',\n", " Passth...\n", " estimator=LogisticRegression(C=334.8557628287718,\n", " max_iter=1000,\n", " n_jobs=1,\n", " solver='saga'),\n", " method='predict')),\n", " ('estimatortransformer-2',\n", " EstimatorTransformer(cross_val_predict_cv=10,\n", " estimator=QuadraticDiscriminantAnalysis(reg_param=0.0011738914966),\n", " method='predict'))])),\n", " ('passthrough',\n", " Passthrough())])),\n", " ('lineardiscriminantanalysis', LinearDiscriminantAnalysis())])" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dynamic_wrapped_classifiers_with_passthrough = tpot.search_spaces.pipelines.UnionPipeline([\n", " tpot.search_spaces.pipelines.DynamicUnionPipeline(wrapped_estimators_cv, max_estimators=4),\n", " tpot.config.get_search_space(\"Passthrough\")\n", " ])\n", "\n", "stc_pipeline4 = tpot.search_spaces.pipelines.SequentialPipeline([\n", " tpot.config.get_search_space(\"scalers\"),\n", " dynamic_transformers_with_passthrough,\n", " dynamic_wrapped_classifiers_with_passthrough,\n", " tpot.config.get_search_space(\"classifiers\"),\n", "])\n", "\n", "stc_pipeline4.generate().export_pipeline()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### GraphSearchPipeline\n", "\n", "The GraphSearchPipeline is a flexible search space without a prior restriction of pipeline structure. With GraphSearchPipeline, TPOT will create a pipeline in the shape of a directed acyclic graph. Throughout the optimization process, TPOT may add/remove nodes, add/remove edges, and performs model selection and hyperparameter tuning for each node.\n", "\n", "The primary parameters for the graph_search_space are the root_search_space, inner_search_space, and leaf_search_space.\n", "\n", "| Parameter | Type | Description |\n", "|------------------------|-------------------------------------|-----------------------------------------------------------------------------------------------------------|\n", "| root_search_space | SklearnIndividualGenerator | The search space for the root node of the graph. This node will be the final estimator in the pipeline. |\n", "| inner_search_space | SklearnIndividualGenerator, optional| The search space for the inner nodes of the graph. If not defined, there will be no inner nodes. |\n", "| leaf_search_space | SklearnIndividualGenerator, optional| The search space for the leaf nodes of the graph. If not defined, the leaf nodes will be drawn from the inner_search_space. |\n", "| crossover_same_depth | bool, optional | If True, crossover will only occur between nodes at the same depth in the graph. If False, crossover will occur between nodes at any depth. |\n", "| cross_val_predict_cv | int, cross-validation generator or an iterable, optional | Determines the cross-validation splitting strategy used in inner classifiers or regressors. |\n", "| method | str, optional | The prediction method to use for the inner classifiers or regressors. If 'auto', it will try to use predict_proba, decision_function, or predict in that order. |\n", "\n", "This search space exports a `tpot.GraphPipeline`. This is similar to a scikit-learn Pipeline, but for directed acyclic graph pipelines. You can learn more about using this module in Tutorial 6." ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "graph_search_space = tpot.search_spaces.pipelines.GraphSearchPipeline(\n", " root_search_space= tpot.config.get_search_space([\"KNeighborsClassifier\", \"LogisticRegression\", \"DecisionTreeClassifier\"]),\n", " leaf_search_space = tpot.config.get_search_space(\"selectors\"), \n", " inner_search_space = tpot.config.get_search_space([\"transformers\"]),\n", " max_size = 10,\n", ")\n", "\n", "ind = graph_search_space.generate()" ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "est1 = ind.export_pipeline()\n", "est1.plot() #GraphPipelines have a helpful plotting function to visualize the pipeline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Lets add a few more mutations and plot the final pipeline to get a sense of the diversity of pipelines that can be generated with this search space" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "image/png": 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8+fOquAKjzYiI7AUDOyIiC/Tt21cee+wxo2s7d+6Ut956S7M1ERGZ4lYsEZGF8vPzpV27dkazZL28vFSAh61aIiKtMWNHRGQhX19fWbBggbi7//fSWVRUpLZkCwsLNV0bEREwsCMiugzdunWTqVOnGl3bu3evzJw5U7M1ERHpcCuWiOgyoXCiU6dOcuDAAf01Dw8P2bp1q3Tu3FnTtRGRa2NgR0R0BdDqpEuXLkZVsWh9Eh0dLT4+PpqujYhcF7diiYiuQPv27WX69OlG1w4dOmR2jYioMjFjR0R0hVA40b17d4mKitJfc3Nzkw0bNkivXr00XRsRuSYGdkREV+HgwYPSoUMHo6rYxo0bq4IKf39/TddGRK6HW7FERFehZcuWZhWxx44dk2eeeUazNRGR62LGjojoKpWUlEjv3r1VVayhv//+W/r166fZuojI9TCwIyKygri4ODV9AtMpdCIiImT//v0SGBio6dqIyHVwK5aIyAoiIyPlzTffNLqWkJAgTzzxhGZrIiLXw4wdEZGVlJaWyg033CBr1641uv7777/Lrbfeqtm6iMh1MLAjIrKikydPSuvWrSUnJ0d/LSwsTE2pCAkJ0XRtROT8uBVLRGRFDRo0kHfffdfoWkpKijz66KOarYmIXAczdkREVoaX1VtuuUVWrlxpdP3HH3+UO++8U7N1EZHzY2BHRGQDycnJ0qpVK8nKytJfCw0NVQ2Na9asqenaiMh5cSuWiMgGwsPD5aOPPjK6lp6eLg899JDK6BER2QIDOyIiGxk1apQMHjzY6NpPP/0k3333nWZrIiLnxq1YIiIbSktLU1uyyNbpBAUFqS3ZOnXqaLo2InI+zNgREdlQrVq15NNPPzW6hnN39913H7dkicjqGNgREdnYXXfdJSNHjjS6tmLFCpk3b55mayIi58StWCKiSpCRkSEtW7aU1NRU/bWqVauqWbL169fXdG1E5DyYsSMiqgSYOjF37lyja+fOnZPx48erUWRERNbAwI6IqJLcdtttMm7cOKNra9askdmzZ2u2JiJyLtyKJSKqRNnZ2WqWbGJiov6an5+f7NmzRyIjIzVdGxE5PmbsiIgqUbVq1cyKJvLy8mTs2LFSUlKi2bqIyDkwsCMiqmQ33HCDPPzww0bXtmzZIu+9955mayIi58CtWCIiDeTk5Ei7du3k2LFj+mtVqlSR6OhoadGihaZrIyLHxYwdEZEGAgIC5KuvvhI3Nzf9tfPnz8u9994rRUVFmq6NiBwXAzsiIo306tVLnnjiCaNru3btkjfeeEOzNRGRY+NWLBGRhvLz86VDhw5y+PBh/TVPT0/ZuXOn2qolIroczNgREWnI19dXFixYIB4eHvprxcXFMmbMGLU1S0R0ORjYERFprEuXLvLss88aXcOosZdfflmzNRGRY+JWLBGRHSgsLJTOnTvLvn379Nfc3d1VG5SuXbtqujYichwM7IiI7MTevXtVcGdYFdu0aVPZvXu32rIlIroUbsUSEdmJtm3byosvvmh07ciRIzJt2jTN1kREjoUZOyIiO4LCiR49eqiqWB30ulu3bp306dNH07URkf1jYEdEZGcOHTok7du3N6qKbdiwoTp/h8bGREQV4VYsEZGdad68ubz66qtG106cOCFPP/20ZmsiIsfAjB0RkR0qKSmRa6+9VjZt2mR0fdWqVdK/f3/N1kVE9o2BHRGRnTp27Ji0adNG8vLy9Nfq1q2retwFBQVpujYisk/ciiUislONGzeWWbNmGV1LSkqSxx9/XLM1EZF9Y8aOiMiOlZaWqq3X1atXG13/9ddfZdCgQZqti4jsEwM7IiI7l5CQIK1atZJz587pr9WqVUsOHjwo1atX13RtRGRfuBVLRGTnIiIi5P333ze6lpaWJpMmTdJsTURkn5ixIyJyAHipHjhwoPzxxx9G1xcvXizDhg3TbF1EZF8Y2BEROYiUlBRp2bKlZGZm6q9hKxZbstiaJSLiViwRkYMICwuTTz75xOjamTNn5IEHHlAZPSIiBnZERA5kxIgRcueddxpdW7ZsmXz99dearYmI7Ae3YomIHMzp06fVliz+q1OtWjU5cOCAamBMRK6LGTsiIgdTo0YN+eyzz4yuZWdny4QJE7glS+TiGNgRETmgO+64Q0aPHm107c8//5S5c+dqtiYi0h63YomIHBSqY9G4+NSpU/pr/v7+apZsw4YNNV0bEWmDGTsiIgcVHBwsX3zxhdG13NxcGTdunBpFRkSuh4EdEZEDu/nmm+W+++4zurZ+/Xr56KOPNFsTEWmHW7FERA7u7Nmz0qZNG4mPj9df8/HxkT179kjTpk01XRsRVS5m7IiIHFxgYKDMnz/f6FpBQYGMHTtWSkpKNFsXEVU+BnZERE7guuuuk0cffdTo2rZt2+Ttt9/WbE1EVPm4FUtE5CRQONGuXTs5evSo/pq3t7dERUWp6lkicn7M2BEROQm0OlmwYIG4u//30l5YWChjxoyRoqIiTddGRJWDgR0RkRPp0aOHPPnkk0bXdu/eLa+++qpmayKiysOtWCIiJ4PCiY4dO0pMTIz+mqenpzpzh+tE5LyYsSMicjJodbJw4ULx8PDQXysuLpZ7771Xzp8/r+naiMi2GNgRETkhZOamTZtmdO3gwYPy4osvarYmIrI9bsUSETkpFE507dpVNSrWQWHFpk2bpHv37pqujYhsg4EdEZET279/v8reGVbFRkZGqmDPz89P07URkfVxK5aIyIm1bt1aXnrpJaNrcXFx8txzz2m2JiKyHWbsiIicHAonevXqJdu3bze6vmbNGjWxgoicBwM7IiIXcOTIETWVAq1QdBo0aCD79u2TqlWraro2IrIebsUSEbmApk2byuuvv2507eTJk/LUU09ptiYisj5m7IiIXERpaalcf/31sn79eqPrK1askAEDBmi2LiKyHgZ2REQu5Pjx49KmTRvJzc3VXwsPD1fVs8HBwZqujYiuHrdiiYhcSKNGjeSdd94xupacnCxTpkzRbE1EZD3M2BFprKSkRDIyMiQtLU19nU5NlfP5+VJaUiLuHh5SxddXatSuLbVq1VJfISEhRqOiiC4XXvax9frnn38aXf/5559l8ODBmq2LiK4eAzsijWRmZsrevXtlf3S0FOTmSllxsQTk50u1jAzxKi4W97IyKXVzkyJPT8kOCZEcX19x8/QUH39/ad2hg7Rt25ZbZ3TFEhMTVY+77Oxs/bWaNWuqsWOhoaGaro2IrhwDO6JKdurUKdmyaZOciIsTr7w8iUhIlLCMDKmWmyteJSUV3q/Iw0Oy/f0lJSREEiLqSZGfnzSMjJSevXtLWFhYpf4eyDksWLBAxo4da3TtrrvukiVLloibm5tm6yKiK8fAjqgSm8Ru3rxZdm7eLAHp6XJNfILUTU8Xj9LSy/5ZJe7ukhQaKkfrR0hOaKh07tlTevbsKZ6enjZZOzknvPxj63XZsmVG1xctWiQjRozQbF1EdOUY2BFVgtTUVPlj2TLJTEqWZnFxEpmcrLZarxa2auPCw+VwZKSE1A2XWwYNktq1a1tlzeQ6/zZbtmypznnq4BzngQMHmAkmckAM7IhsLD4+Xn5evFj8TqVIx0OHJDAvz+qPcdbPT6KaN5e8OnXkjuHDpH79+lZ/DHJe2HodPny40bXbbrtNZfK4JUvkWBjYEdk4qFu6aJFUj0+QLjEx4nkF266WKnZ3l+0tW0hGRITcOXIkgzu6LAjsEOAZmj9/vtkZPCKybwzsiGy4xfX9woUSdOKkdD940Cpbr5ZszW5t1VKyGjSUEWPu4bYsWSw9PV1atWqlWu7oBAYGqi3ZevXqabo2IrIcGxQT2ahQAmfqsP3aNSamUoI6wON0PRgjvimnZPmyZWodRJZAi5PPP//c6NrZs2dl/PjxqsiCiBwDAzsiG0D1KwolcKbOltuv5cHjdYw5JBnJybJly5ZKfWxybIMGDZJ7773X6Nrff/8tc+bM0WxNRHR5GNgR2aBPHVqaoPrVFoUSlqiWlydNY+Nkx6ZNkpKSoskayDG9//77anasoaefflqOHTum2ZqIyHIM7IisDM2H0acOLU201CQ5Wa1j86ZNmq6DHEtQUJDMmzfP6Fpubq6MGzdOSis5+0xEl4+BHZGVx4RhogSaD1fWubqK4PEbxyfIidhYtS4iS/Xv318efPBBo2sbN26UDz74QLM1EZFlGNgRWRFmv2JMGCZK2IN66enimZcn+/bt03op5GBmzZolDRs2NLr23HPPyeHDhzVbExFdGgM7IispKSmR/dHRavbrlYwJswWso35iouyLilLrI7JU1apVVR87Q+fPn1fFFay2JrJfDOzIKWFmart27VRfrqFDh0qejYsYPvzwQ2nbtq28PmuWDPv2Gxm0O1p9/WzQE8xadmZny63RUXLXnj0WfX/YmQwpyM01GhllbTk5OdKvXz8JCAiQp556ymaPQ5Wrb9++8thjjxld27Fjh7z11luarYmILo4Nislpe3Kh4SqMGjVKOnbsKE888YTNHg/ZsEOHDsnyH36Q1197TXZ06250e2lZmbhbaTTTC0fjpEdQkAwIrWHR9xe4u8uKa/vKLUOHqkD3auDwvLu7+edBZHK2b98uBw8eVNWTb7/99lU9DtmP/Px89SEpNjZWf83Ly0t27dolbdq00XRtRGTOs5xrRE6ld+/e6owZAj1U9mHMF4acf/XVV1K3bl1p0aKFetPCV9OmTSUhIUFdj4yMVNfOnDmjDpLjOt7QZs+eLe3bt1ejlnx9fSUqKkpuv/126datmwTk5+sfN6mgQCbGHJRr/PzkUG6u/NquvUw+fFhOFxZKYVmpPFi3ngyqWVN930MxMdI8wF/2nTsnTf395f2mzdSMzjdPHJc1GRni7eYuN4eGSq0q3rIiPV02ZWbJlqws+V/DRvL80aNyJDdHvN3dZeY1kdIiIEA+jI+XpPMFEp+fL80DAuTYmXTZsnevan2CzN3ChQvVQfjdu3fLkCFD5PXXX1dr/vrrr1X2sbCwUGXg3n33XTl58qQMHDhQDYrfs2ePug9+34aqVKkiffr0kePHj1f63y/ZFv6uFyxYID179tRXxRYVFcmYMWNU9s7b21vrJRKRAQZ25NRwFmjFihUyYMAAmTFjhgryfvvtN1m8eLFMnjxZDTlHEHfixAnZtGmTdOjQQf0X26pNmjRR2SlsReHQeOfOnSUuLk5Gjx6tslOAoA+/RhD2/bffSjWT7c5jeXnydtNm0szfX/3/W02aSJCXl+SVlMide3bLgNBQdf14fp6816ypNPb1k3v275ddZ8+qgHB5erqs7dRZZfvOFRdLVU9P2ZGdre53XUh1+TIpSQI8POS3Dh1lz9mz8kxsrPzWoYP6mQn5BfJ16zYq4BuflioZF9b67bffqkANAWlYWJg0a9ZMnnzySTl9+rT8+uuvsnXrVrWVjTfuP/74QwV0yEbifszQuCZ8aJk6daq88cYbRoVCM2fOVF9EZD8Y2JFTysrKUttHgEzShAkTpEuXLrJ8+XJ1bdiwYTJlyhT16169eqlgDl/PPPOMrF+/Xs6dO6cyFLrO+9hi1DFsHXLXXXepoA7O5+eLr8mh8ga+vvqgDr46lSyrz/wb/KWcPy+nzp8XTzc3aejrK9f4/ft9LQL8Jfl8gbQPDJSqHh7yXFys3FC9ugrkTCEAvL9uXfXrdoGBcr60VAWA0K96iArqdEUUrVu3Vr/Gf5GNrF+/vvr/a665RhITE9W0jG3btkmnTp3UdZxLxBY2AjsEuQzqXBs+GP3+++9qdqwOMr2YVoEPPURkHxjYkdM2WcW24cXoAjIEdj/99JPadp07d6588sknqhgA27Y6OE+ELJYpPz8//a9LS0rMetf5enjof70tK0uiz56VH9u1kyru7jJkz24pLC0VTw8PfQAGyM6VlokK+H5q1142ZWbKH+mnZdk//8hHzVtY/Gfg4/7fY+t+rvqvu7vaOtVfd3dXZwSxzXb//ffLiy++aHQ/bMUa/j7JNeHfDLbw8QFJVxWLfzeoko2OjhYfHx+tl0hErIolV4IA7rvvvlO//vHHH9UbFHTv3l1WrlwpNWrUEA8PD9XmAVm7rl27qtuvu+46+fTTT422oMrj7uEhpRcpkMgpKZEgTy8V1MXk5Mjh3NyLrje3pERl366vXl2ea9hIndMz1SkwUH47/c+/6zp3Tnw83NV2bXnwe7sYnKnDFjW2l+Gff/7hODIygrOl06dPN7qGbXrTa0SkHQZ25FJbSevWrVNbisjK6broI5BDMUWPHj3U/+O/tWrV0hcIfPTRR+p+OHfXvHlzfXBoqoqvrxRVEFRBn+BgFazdHLVL5iQmSsuAgIuuF9/7QMxBGRgdLeMOHJCnGxg3i4VRYWEq+BsYHSUvHzsqb0Q2Kfdnlbi7i+clDrljy3XatGkqwMOf0a233npZLVJQeILK488++0ydW0xKSrL4vuQ4cN4UW/SG3nnnHXWUgYi0x3YnRFayevVqObJqldy4dZvYm7+6d5OmN92kgjaiq4Uzpyg0QvW0TuPGjVU229/gTCkRVT5m7IisBFm+HGTtLrHlWdmwHqwL6yOyBmR3Tath0b8QxUdEpC0GdkRWgsDJzdNTsispY5GTmyP/nD6tqnSLLzIuDOvBuqwR2OH8HaqNDb90ZxHJtaBFDs6nGsIRB2SuiUg7rIolshKc0/Px95eUkBAJPXvWpo+FLbCzFx6juLhICs6fl6CgauLrY9w4GFKq/7surO9qVa9e/ZLVxuQaUIyDxsU4e4rpFDrjx4+X/fv3S2BgoKbrI3JVzNgRWfGNrnWHDpIQUU8VK9iSbgKATllZqcrcZWVni+GxWawjvl49adOx4yWrYokuF/ohvvnmm0bXMKHFluP7iOjiGNgRWRGyF0V+fpJ0YaKErVTx8REvL/Mq17y8XDmdni5FF/qMJYaGSrGfH5sLk81MmjRJtQQy9OWXX6qpJURU+RjYEVlRcHCwNIyMlKP1Iy7a0+5quV3YFvX1NW8cjK3Z9NOnJSc/X47Vj5CGTZqodRHZAhpcz5s3TwJM2veg2fXltMshIutgYEdkZT1795ac0FCJCw+36eNgkkRwUJAEBQWLm5vxU7lMyiSmVk1J9fGRdu3b23QdRA0aNJB3333X6BqaWz/66KOarYnIVTGwI7KysLAw6dyzpxyOjJSzJqO4cP6toKBASkzOyF0NP19fqREaKl6eXvpruYGBEtesmazZtEkGDBggUVFRVns8ovLcd9996t+aITTzXrp0qWZrInJFDOyIbKBnz54SXDdcopo3l2J3d0E5Q25urqSkpkpGZoakpaVJfkGB1R4Pc2xDa4SKv5+/lHh4yOGOnSQ5I0O2bNmi+ouhLcX7779vVFhBZE2YvfzFF1+oOc2GJk6cqMbTEVHlYGBHZAMItG4dNEjy6tSRLc2ayj/p6ZJ9Nlttkv6rTM6dO2fVx3QTN6kaFCQn+vaV1AB/WbZ8uRrSDkVFRfL444/L7bffrp8FS2Rt4eHhagSfofT0dHnooYf4oYKokjCwI7KR7Oxs2bFnt8T6+cnezp1UJs2QtUsrkBnc2qqlZDdqJHeOGCGNGjUy+57ffvtNNRXeuHGjlR+d6F+jRo2SwYMHG1376aefKpyxTETWxVmxRDbw+++/y5AhQ1SmLCIiQoYOHix18vKkeVSU+F1oLBzgH2C1Jq7Zfn4S1aK55IfVkTuGD5P69eurx54+fbpZnzFdJeNLL72kBrqzvx1ZG44atGrVSmXrdLBFixmzderU0XRtRM6OgR2RDWDM1o4dO/T/X7NmTRl0660SHhwskYcPS53YWAmpFqQKH64GWqrEhofLkSaREhIeLrcMGiS1a9c2+p5Vq1bJPffcI6dPnza7//XXXy/ffPONKvggsqYff/xRhg4danTt5ptvVv3tcB6PiGyDgR2RDeANbOXKlUbXkBnr0aOH9OzcWUJzcqRlapo0yMoSjyuokMVECTQfRp86tFbp0quX+tk421cetJ5AcFfeHM8aNWrI119/LTfddNNlr4PoYu6++25ZtGiR0TUUWEyYMEGzNRE5OwZ2RDaASlR0409MTDS7DRm1nj16SOd27cS7oEDqJyZK2JkMqZabK14Xih3KU+ThIdmYRVs9RI0Jw0QJNB/u2auXRRk3FFK88cYb8sILL5iNJIOpU6fKK6+8Il5e/7VNIboaaFDcsmVLSU1N1V+rWrWqmiWL4wJEZH0M7IhsAIHTtddeW2GRgq+vryQnJ8u+fftkX1SUFOTmSllxsQTk50tgRqZ4FxeLe1mplLq5S6Gnp5wNCZYcX19x8/QUH39/NfsVY8KuZKLEpk2bZOTIkZKUlGR2W7du3VSGBQ1niax13nTgwIFmRwD++usvddaTiKyLgR2RDXz88ccX7brfu3dv2bBhgz6ThswGDpzj63RqqhSiiXFxsXh4eoq3j4/UqF1batWqpb5CQkKuuuABLU/Gjx8vy5YtM7sNh9wx6xPFH0TWgK1XjB0zhLYojzzyiGZrInJWDOyIrCwuLk7atm0r+fn5+mt169aVfv36yeLFi1Vw9sMPP0jnzp01XSee+h9++KE8/fTTqoLW1MMPPyzvvPOO+Pj4aLI+cq7WP61btzY6muDn5yd79uyRyMhITddG5GwY2BFZEbJvffr0URMfDP39998qsCsuLq6wwEErGDc2YsQIOXr0qNltCFARjDZt2lSTtZHzwHPgxhtvNLqGgh9krtlyh8h6eMCByIowCN00qJs0aZIK6sDegjro2LGjCu5QwWhq79696vYFCxZosjZyHjfccIPKAhvCc+W9997TbE1EzogZOyIrQfPVDh06SGFhof5a48aNVXDk7+8v9g4vBfPnz1fnngy3kXXQLmX27NkSEBCgyfrI8eXk5KjJJ6ga16lSpYpER0dLixYtNF0bkbNgxo7ICnBG7d577zUK6tCE9auvvnKIoE63XhRU7Nq1S00NMIVed8je4VwU0ZXAhwI8JwwbFJ8/f149d8o750lEl4+BHZEVoD8ctjMNPfHEE9KrVy9xNMicYGrGAw88YHZbbGysaonyySefcKg7XRE8J/DcMIQPE3gOEdHV41Ys0VXavXu3dOnSRRVG6DRv3lxtLzl6RemSJUvk/vvvl7MX5tsauuOOO1RblCvppUeuDVv9OLZw+PBh/TWcP925c6faqiWiK8fAjugqYBsJbUvQSV8HFX5bt27VvJ2JtRw/flxVzeJN11RERIR8//330r17d03WRo4LWWFUxaKSXActUfDvDOfuiOjKcCuW6Cq89NJLRkEdPPfcc04T1EGjRo3UtIonn3zS7LaEhATVbBnbaOWNKSOqCLLczz77rNE1PJdefvllzdZE5AyYsSO6Qtu3b1cZB8OABn3fkInw9vYWZ/THH3+og+6YXGGqf//+snDhQtWAmcgSKDbChyCM1tPBmDG0QenataumayNyVAzsiK7wjFD79u3lyJEj+mteXl5qGwnBnTPDjNtRo0bJ+vXrzW6rXbu2fPPNN/q+fUSXgnZACO4Mq2LREBtnVzFTmYguD7diia7AtGnTjII6ePHFF50+qIPw8HBZvXq1+v2aDnFPTU1V0wWef/55o2ISoorgOYN/S4bw3MJzjIguHzN2RJcJI5CuvfZao3YfyDhg+8geJ0vY0rp161T27tSpU2a39ezZUxYtWiT16tXTZG3kOPAhAMcaDAt00OsO/74woo+ILMfAjugyO+e3adNGTpw4ob+GCj5sG6HFiSs6ffq0jB07VpYvX252G1qhoCHtoEGDNFkbOY5Dhw6p4w2oNNdp2LChOn/HaSdEluNWLNFlePrpp42COnjttddcNqiDGjVqyG+//SZvv/22WcYyMzNTbr/9dpkyZYrRGzaRKTyHXn31VaNreK5NnTpVszUROSJm7Igs9Oeff8pNN91k1kUf20XoXUf/9iZDzzvT4BfQkBY97yIjIzVZG9k/9LTDMQe01zF97uHsJhFdGgM7IgtkZWWp5qlJSUn6a35+fmqbqHHjxpquzd5kZ2eraRU//PCD2W3YUvvss8/k7rvv1mRtZP+OHTumjjvk5eXpr9WtW1cOHDgg1apV03RtRI6AW7FEFnj88ceNgjqYNWsWg7py4M138eLFKoAzHamGM4ootpgwYYLk5uZqtkayX3hO4bllCM+9xx57TLM1ETkSZuyILmHZsmXqnJihG264QVatWmXW7oPEbJLA8OHD1cH48s5UIQBEJpTIEJp+o+E12uqYPhcHDhyo2bqIHAEDO6KLwISFli1bSlpamv5aYGCgClgwJ5UuDZm5yZMny7x588xuQ0bv/ffflwceeEC1tyAyHFfXqlUrOXfunP4appocPHhQqlevrunaiOwZ0w1EFzFp0iSjoA4QiDCos5y/v798+eWX8u2335q1rSgoKJCJEyeqrB7O5hHp4DmG55ohPBcfeeQRzdZE5AiYsSOqwJIlS1TAYei2225T20HMLl2ZuLg4VTUbHR1tdht6lqFqFsPhiQBvT9h6xYxi0+fm0KFDNVsXkT1jYEdUDozGwjaQ4bB7NNvFNlBYWJima3N06GeH3mQffvih2W3og/fGG2+oYhWeXyRISUlRxyHQE1EHW7F4LmJrloiM8ZWTyAQ+6zz44INGQR188sknDOqsAJM6PvjgA/n1119VsGw6Wuqpp55SWRpMtCDCcw7PPUN4buI5yrwEkTkGdkQmvv76a7Xdauiuu+5SW4hkPRgztnfvXjVT1hTGk7Vr1041fybCc+/OO+80uoYPBt98841mayKyV9yKJTLpl4UtWMOD/BiZhW0f/JesD1m6GTNmqNFspi9H2I6dPn26+uJ0D9eGDC62ZA0zueiZiMbFaGBMRP9ixo7oAgQV9913n1l15ueff86gzoZwru6VV15RY6NMz0yhn9lLL70k/fr1k+TkZM3WSNrDcxBNrw3huYrnLPMTRP9hYEdkEMCh6bCh0aNHy+DBgzVbkytB02dszaIxran169errVls0ZLruuOOO9Rz0hCes3PnztVsTUT2hluxRCJy/PhxNZ/ScMxVnTp11DaP6QF/si1k6TBSatq0aWoovKknn3xSbdt6e3trsj7SFqpjcVzi1KlT+mvoj4i5zWiZQ+TqmLEjl4dAYvz48WazS9FUl0Fd5cO5umeeeUY2btxYbiPod955R3r16qWCcXI9eE5+8cUXZjOIx40bp57LRK6OgR25vI8++kht9Rm6//77ZcCAAZqtiUS6d+8ue/bsUdtvpnbu3Cnt27dXjWrJ9dx8883qbJ0hPIfxXCZyddyKJZd25MgRdXYLo610GjRooLZ1qlatquna6F94iZo9e7Y88cQTUlhYaHY75sxi9JSvr68m6yNtnD17Vh2fiI+PN5o9jA8DTZs21XRtRFpixo5cus3G2LFjjYI6wLB6BnX2A+PbMLN3+/bt0qRJk3KLXjCGLCYmRpP1kTYCAwNl/vz5RtfwXMZzuryzmUSugoEduay3335btm3bZnRt8uTJct1112m2JqoYMqtRUVFyzz33mN2GIpdOnTqpc5HchHAdeK4++uijRtfwnMZzm8hVcSuWXNL+/ftVIGC4tRcZGam2cfz8/DRdG13awoUL5eGHHzYreIGRI0fKnDlzVEaHnB/+DSDoP3r0qP4aKqbxIQDVs0SuhoEduZyioiLp2rWr7N6926gSE1WYPXr00HRtdHnnI4cNG6bOQ5pq3LixLF68WDp27KjJ2qhybdmyRXr37m1UFYviGmzfe3l5abo2osrGrVhyOa+++qpRUAcYPM+gzrHggDzeuJG5M3Xs2DFVVYuiCn52dX547qK/oSE8x/FcJ3I1zNiRS8H2DLJ1hoerMX9y165dqqKOHNPSpUtlwoQJZuPgYODAgeqQffXq1TVZG1UOFE4gQ2tYRINxdThzx8wtuRIGduRSL/w4V3fw4EH9NQyWR9aHL/yO7+TJk+p8nWlBDGBI/Hfffae268j1PrjhepUqVTRdG1Fl4VYsuYwXX3zRKKiD559/nkGdk0D/wQ0bNqipFaaSkpLk2muvlVdeeYWtMJwYnssYRWcIz3k894lcBTN25BJ4uNq1YDA82qKcPn3a7Lbrr79evvnmGwkLC9NkbWRbqHRH1g4V7obFUZs2bVLnLomcHQM7cnp5eXmqHUJcXJxROwScq2vdurWmayPbSUlJkdGjR8uaNWvMbqtRo4Z8/fXXctNNN2myNrJ9OyNk71ABr8N2RuQquBVLTu+5554zCurgpZdeYlDn5JCR+/PPP2XmzJkqY2MImTzMAsa2reGbPzkHPLdffvllo2t4DcBrAZGzY8aOnNratWvV1puhbt26qZ51qJgj14C/77vvvludtTOFfw+LFi1SZ/TIuUYG9urVSx23MIQMLqfLkDNjYEdOi0PCydCZM2dk3Lhx8ttvv5ndFhQUpMaRDRkyRJO1ke2aWOMYhuE8aATwaGrNedDkrLgVS04LTYcNgzp44403GNS5KPSx+/XXX1XTYtOCmaysLLnzzjtl0qRJRkEAOTY8119//XWztjh4bSByVszYkVNasWKF3HLLLUbX+vbtq7ZhTM9bketBX7Phw4erCRWm2rZtq8aR8QOAc0AlPI5jrF+/3uw1AucsiZwNAztyOpmZmWr496lTp/TX/P39VaVcw4YNNV0b2ddW/cSJE9X5OlP49/LJJ5/Ivffeq8nayLqOHz+ujmXk5ubqr4WHh6vXhODgYE3XRmRtTF2Q05k8ebJRUAfvvPMOgzoyEhgYKN9++6188cUX4uvra3QbAoCxY8fKmDFjJCcnR7M1knU0atRIvQYYSk5OlilTpmi2JiJbYcaOnMrPP/9sdgC+f//+snLlSnFzc9NsXWTfMF8UW7MHDhwwu61JkyZqaxaH8Mlx4a0OW69ogWP6mjF48GDN1kVkbQzsyGmgNxnmQhpOG6hWrZp6s8asUKKLyc/Pl8cee0w+//xzs9swZxQZn4cffpgfEBxYYmKi6nGXnZ2tv1azZk01diw0NFTTtRFZC7diySng88lDDz1kNkLqww8/ZFBHFsF27Geffaayc9imNXT+/Hl55JFHVOUsznCSY6pXr5588MEHRtf++ecf9drBHAc5C2bsyCngADwa0BoaNGiQ/PLLL8yw0BUdth8xYoTs3LnT7LaIiAj5/vvvOXfUQeEtD1uvy5YtM3sNwd85kaNjYEdOMRMUW7CGmRT0LMMWbO3atTVdGzn2MPn//e9/ZofuwcPDQ1555RWZOnUq2+c4oNTUVPWakZGRob8WEhKiXjMwio7IkfEViRwaPpfcf//9Zttjn376KYM6uire3t7y9ttvy++//64+KBgqKSlRc0dvvvlmSUtL02yNdGXw2oDXCEMI8h544AFuyZLDY2BHDu2rr76SP/74w+gaqhuHDh2q2ZrIudx6662yd+9e1eDaFCosUS27evVqTdZGV27YsGHqyxCC+AULFmi2JiJr4FYsOayEhATViPjcuXP6a7Vq1VIVbqYZFqKrhSzdzJkz1RemGRjCOU5s286YMUM8PT01WyNdnvT0dPUaYph1ReEMtmRRaEHkiJixI4eEN9YJEyYYBXUwd+5cBnVkEzhXh8AN2bk6deoY3YbPx6+++qpce+21qqUGOQa0ODFtb4OJJOPHj+eWLDksBnbkkObMmSN///230TVMChg4cKBmayLXgOBtz549ZrOIYfPmzWrWrGnFJdkvVM+bjo7DawteY4gcEbdiyeFgcDvmPubl5emvoVcdtk/QkJiosrLG7733njz77LNSXFxc7mi7t956SzU3JvuWlZWltmQxZsxwXjDOVjZu3FjTtRFdLmbsyOHOOSEzZxjUwZdffsmgjioV2pw8+eSTKktX3hxiNMfu0aOHxMXFabI+slxQUJDMmzfPbF7wuHHjzM5TEtk7BnbkUN5//33ZtGmT0bWJEyeqebBEWujSpYvs3r273Ers6Oho6dChg3z33XearI0sh9eQBx980Ojaxo0bzSZVENk7bsWSwzh06JC0b99ejXfSQaZk3759EhAQoOnaiPBSiuKdKVOmSEFBgdntOJCPLB62+Mg+oRgLZyRPnDihv4atdJypbNasmaZrI7IUM3bkEHCGCQecDYM6tJhAHzsGdWQP8O8RDW537NghzZs3N7sdW32dO3eW/fv3a7I+urSqVavK/Pnzja7hNQevPeWdoySyRwzsyCG8+eabZnM7H3vsMenTp49mayIqT+vWrdW/VWToyss6Y+v2s88+YzsNO4VG1HhtMYRgHYUwRI6AW7Fk91CZhkxHUVGR/lrTpk3VuSZfX19N10Z0MThbh3NbOTk5ZrfhTB62bln0Y3/y8/PVRJHY2Fj9NS8vL9m1a5eqyCeyZwzsyO4HsSOowzk6w2rELVu2SNeuXTVdG5ElUBU7YsQIVUhhCmdEv//+e5XFI/uybds26dmzp1FVLM7fIXuHOcJE9opbsWTXXn75ZaOgDp555hkGdeQwIiMj1QcR9LUzhUP6CB7eeecdttWwM926dZOpU6ea7R688sormq2JyBLM2JHdwidj9AFD7zrT80ts+kqOCBMp0IcxMzPT7DZMskAxUI0aNTRZG5lD4USnTp1U83PD0XJbt25VOwlE9oiBHdntGRf0/zp8+LD+GoarI6jD2RciR4VZsiNHjlSNjU1hBu23336rxpaRfcBZXmyVG1bFouoZW+s+Pj6aro2oPNyKJbs0ffp0o6AOXnjhBQZ15PDq1asn69atk2nTpqkWKYZOnTol/fr1kxkzZhhlqkk76J2J1yPT6mbTa0T2ghk7sjvo9o6WA4b/NDt27Ki2P1CZRuQsMGx+9OjRkpaWZnYbngPI3oWHh2uyNvoPKvK7d+8uUVFR+msIyvFahTOSRPaEgR3ZFbSFQOXZ8ePH9ddwng4vqC1bttR0bUS2gKBuzJgx8ueff5rdFhoaKgsWLFDn70hbBw8eVMdDUKmv07hxY1VQwWkiZE+4FUt2BRWvhkEdzJw5k0EdOa1atWrJihUr5I033lAH8w2lp6fLrbfeKk899ZRRQEGVD69BeC0ydOzYMXn22Wc1WxNReZixI7valrrxxhuNrqEqdsOGDWZveETOCMcN0PMuISHB7DZUYaLnXaNGjTRZG4k699i7d2/192T62oWzkUT2gIEd2YXs7GzVygQVgzp+fn5qm+Oaa67RdG1ElQmtUCZMmCA///yz2W2BgYFqWsWwYcM0WRv923Aax0VQua8TERGhZgDj74dIa9yKJbvwxBNPGAV1uvmwDOrI1QQHB8vSpUvl448/NptwcPbsWRk+fLgaU2YYWFDlNpzGa5MhZFjxGkZkD5ixI839/vvvMnDgQKNr119/vfz1119qfBiRq9qzZ48K5Axnluq0atVKFi9eLC1atNBkba4MU0JuuOEGWbt2rdH1P/74g4UupDkGdqSpM2fOqDeo1NRU/bWqVauqbY369etrujYie6kUf/jhh+Xrr782u83X11dl9saNG2fWE49s6+TJk+r4CP5+dMLCwtSUipCQEE3XRq6N6RDS1KOPPmoU1MF7773HoI7ogoCAAFm4cKFqe2LaVgPbsTiPh1542KalytOgQQN59913ja6lpKSUOxOYqDIxY0ea+fHHH2Xo0KFG17CNga1ZZh+IzB05ckQVTuzbt8/sNpxHRdUsmnlT5cDbJ16zVq5caXQdZySHDBmi2brItTGwI82asmILFn26DA+NYxsD8zKJqHwFBQXy5JNPyuzZs81uw2SWWbNmqawRPxxVjuTkZPValpWVpb9Wo0YN9VpWs2ZNTddGrolbsVTp8Fli4sSJRkEdfPTRRwzqiC4Bg+c/+eQTlfGuVq2a2eirxx57TAYPHqzOr5LtYeQbXrsMnT59Wh566CGjsYhElYUZO7qiJp0ZGRkq64av06mpcj4/X0pLSsTdw0Oq+PpKjdq1VUd9fOEgsWGD4W+++Ubuueceo5+JbQu8UTHLQHR5B/hHjhwp27ZtM7utbt26smjRIunVq5cma3MleBvFa9gvv/xidB2zfu+++27N1kWuiYEdXVbjVDQM3h8dLQW5uVJWXCwB+flSLSNDvIqLxb2sTErd3KTI01OyQ0Ikx9dX3Dw9xcffX1p36KCaeubl5anRPGhIbDgPE3MYuW1BdPmQpZs+fbpZbzXAB6qXXnpJjb3i9JbKP14SFBSkXtu4E0GViYEdXdKpU6dky6ZNciIuTrzy8iQiIVHCMjKkWm6ueJWUVHi/Ig8Pyfb3l5SQEEmIqCdFfn6SmJIiPyxdalQJy4PGRFdv1apVKhOObUBTGHeFdilox0GVWxB28803q/523I2gysLAjipUXFwsmzdvlp2bN0tAerpcE58gddPTxaO09LJ/Vom7uxyrWlUOh9eR9IAA2bxzp2zZskU1X8V2BRFdPbTbQOuTNWvWmN2GjDjaptx0002arM1VYOsVW+CGvvjiC9WWhqgyMLCjciGj9seyZZKZlCzN4uIkMjlZbbVeqeKSEpVJKJEyOdWkicQ1ayanc3JkypNPSpMmTay6diJXPwP7+uuvy4svvqgmJJh65plnZObMmaqClqwP549x3IRN10krDOzITHx8vPy8eLH4nUqRjocOSWBe3lX9PPwDQ4VeYeF5/bW8wEA50bOnFNaLkDuGD+MLHpGVbdy4UWWPkpKSzG7r3r27yirxeWcb2Hq97bbbjK5xTCJVFv4LI7OgbumiRRJ84qT03r37qoM6yMvNNQrqILS4WK7bt1+CTp5Qj4fHJSLr6d27t5o1azqHGbZu3Srt2rWTn376SZO1Obtbb71Vxo8fb3QN2+Pl9R4ksjZm7EgPWwffL1woQSdOSveDB69q61WnuKRYTv9zWspU3u5fqM6rUaOmuLu5qSrara1aSlaDhjJizD1Su3btq35MIvoPXuI//PBDefrpp1UFralJkybJ22+/rfrjkfWg8h+zZBMTE/XX/Pz8VLAdGRmp6drIuTFjR/pCCZypw/Zr15gYqwR1+AlZmVlGQZ2uBQCCOsDjdD0YI74pp2T5smVqHURkPajGnDJlisrSNW7c2Ox2NDvu1q2bGldG1oPm0fPmzTO6hnZPY8eOVecgiWyFgR0pqH5FoQTO1HleQdVreZAdKCwqNLrm7+cvVbyrGF3D43WMOSQZycmqUpaIrA8zZKOjo1VDY1PoT4nbUTVL1nPDDTfIww8/bHQNr3HvvfeeZmsi58fAjlSfOrQ0QfWrNc7U6Zlk/Tw8PCUwMLDcb62WlydNY+Nkx6ZNqmUDEVkfnn9oL4T2G76+vka35ebmyr333qu+cnJyNFujs0HjaNNM6fPPPy8xMTGarYmcGwM7Us2H0acOLU2sycvbW/z8/PVBHUaLXaxJZ5PkZLWOzZs2WXUdRPQfPAfRU23nzp2qLYcpZO06deqksnh09QICAuSrr74yeu07f/68CqDLO/NIdLUY2Lk4jAnDRAk0H7bGuTpDeBkLqlZNwsLqSK2aNcXL0/Oi34/HbxyfICdiY9W6iMh2ENTt2LFDHnjgAbPbcN6ua9euqoqT9XVXD/N6n3jiCaNru3btkjfeeEOzNZHzYmDn4vCpHGPCMFHCVi5nkE699HTxzMuTffv22Ww9RPRfleZnn30m33//vdkxCWSVUDF711138YOWFaApdLNmzYyuvfzyy6pKlsiaGNi5MFRm7Y+OVrNfr2RMmC1gHfUTE2VfVBQrx4gqCUb77d69W23BmkKvu/bt26uqWrpyONO4YMEC1e5JB10AxowZo4JoImthYGfn8IkOWyboh4QX3RMnTlT4vaGhoZc9+qYgN1c2REVJoUFgd93OHTIwOkp9jTuwX04XGle22lrYmQxZuXKlWp+uuGPUqFHq1zir8tRTT132z8RhcfSOwjkXHgwnMteoUSNVHW+6ZQhoII6GxygEKG9MGVmmS5cu8uyzzxpdw6gxvM4TWQsDOzuGsvi1a9eqVD2e/L/88ovqAWctaWlpUlZcLD8cPyZFJudovm/bTn7r0FFaBVSVOQYNNi+mxEpncarl5sq6TZvU+qBOnTqqku9q4LzQn3/+yRFKRBfh7e0t77zzjvz+++9SvXp1o9uQQUdQcvPNN+ufm3T5XnjhBWnTpo3RNZy12759u2ZrIufCwM7OJ0EgC6cb1l23bl0JDg6WVatWqVmP2B4ZPXq0FJaTUcMn686dO6sXEHSV13n11VdV9g/X0Zg0avNmlZEbsXePTIw5aPZzOlcLlPiCfBW0vX78uAzZs1sGRkfLsn/+Ubf/lJYmkw7FyOh9+2Ty4UPqZ+Hn4Htu3x0tJ/Pz1fd9npR44b5R8uWF2ZXbs7Jk7IH98lBMjPTftUteO35cXf/o2DEpKCiQwYMHy8SJE+XkyZPlbhGdPn1ahgwZom7Dnwe2kiqC33PDhg2v4G+ByDVHYuEDZZ8+fcxuwwckjCNbvXq1JmtzhuAZlce613VAFhRVsvkXXi+JrgYDOzt24403yuHDh6VFixaqczyqqNLT02XWrFlq7iACGWyfzJ071+yFF4O/UfGG71m+fLkcOHBA/Rf3w89BcULHdu1kUIMGUtPbW2Xo5rQwb32wJiNDmvr5yw9pqer7fmrXXn5o21bmJiVJ5oVS/cO5uTKnRQv5pHkLeeX4MbkuJER+69BBfmjbTt1nU2ampJ4/L0vbtpNf2neQ9ZkZEpubq+4bk5MjM6+5Rn7v0EHWZpyRUwUF8kSDBuLn7S2vvPSSzJkzp8I/n8cee0yee+459fvBCyWCQCKyDnyQxOvFiy++aNamCB868fqEfmycFnP52rZtq/5cTSuRp02bptmayHlcvP8Eaapq1aoqMMN2LD4d44UUAQyCMmSoAIdu8enaNLD7448/ZOPGjer/z507J7GxsbJp0yYZN26cVKny7+QHLw8P8argRRkZPLyYI6h7onEDmRYXK7F5efLr6X8zdTklxZJYUKB+3TsoWAIutDLZlZ0t7zX9t/LL291dvEVkU1amrMvIlF1n/82o5ZaUyIn8fAny9JT2VQMl1BvfJRLp5y/J589LHR8fVUlbeOHnV+Tvv/+Wgwf/yzKyco/IunDQf8aMGXLttdfK3XffbdQ8HG1QsAOwbt06WbRokdSrV0/TtTqaZ555Rn799VfVT1Dn/fffVzsV5WVKiSzFwM7OeXp6qoAOX9iWReYOgdz8+fMrvA/S+vg0iNS+IQR2Rt9XUlJh7zpk8PwNqrdwXBqZtS7VjM/4Hc3LEx+Piyd+S8tEHomIkCG1ahldx1ast/t/mQAPN3zvf+spsSATgGwd/oyIyHYQ2KE1El5TVqxYYXQbCi6QgUJh06BBgzRbo6PB6xaqZHGkRlcVi2AZs2Tx4R2NjYmuBLdi7RhS88eOHdM/4bGd+uCDD6oMHqrU4OzZs2aVsv3791dVoBg4DTijlp2dreYWIiDUvYjkFRRIqZubCuCQRbuYXkHB8m1Kir5AAlup5RVLdKpWTW3bAipt80pKpFdwkLqWf+ExjmZnS1JGhuTl50spor5yuLu5iZv7xf95XnfddfLpp5/q/5+d8olsp0aNGqqoAkdBTD9MIVt+++23qw+ebN1huebNm6uspyG8nk+dOlWzNZHjY2Bnx9CWA8URaHfSqlUrlYmbPHmyOlN35513qgIIpOx1QZ7OgAED5I477pBu3bqp++FnoBjhlltuUZ+8O3TooA4/79i1S4o8PWVY7dpyz/595RZP6OB76lbxkcG7o+XW6Ch57cRxKS8km9aosfx95owqkhi+d6/8U1gofYJD5Mbq1WXY3j1yc9QumRoXK1m5OZKXnyfnC89LalqaZGVnS0lpqZRd+Kk9IyPl+RkzLnpu7qOPPlLbQMgW4AXyu+++q/B70YQVZ4Zw9rBp06bltnQgootzd3dX7YaQ/W/QoIHZ7R9++KH06NFD4uLiNFmfI8JZYUymMIQPrH/99ZdmayLH5lbGeTEuC+f2jqxaJTdu3VZpj3n23DnJyTlX4e1u4ibeVarI9htvkLq9e6sAlojsT1ZWlhpH9sMPP5jdhm1EfJjCuTy6NOzM4IO6bpcF8EEUuzTVqlXTdG3keJixc2G1atWSHF9fKTI4S2drvqowouIhY8jY5ZYUS6aXlzoniGwltiU2bNjA6jsiO4KemosXL1aV6z4+Pma7DWgqPmHCBMm9UAFPFWvcuLHa4jaE3QVk84guFwM7Fw/s3Dw9Jdvfv9IeE72batSsIf7+AeLhUX7RQ25QkBSXlakmqDExMeoFr2/fvuqMz8iRI1Wz4jNnzpR7X5xXwTaz4dfFCk2I6Mqhch7nftFayXQOKsybN0/100SDdbo4HDvp16+f0TUUpPz222+arYkcE7diXRg6yc/+4AMJ371HWp88qckakIUrOH9enQH8t9FymZxo3Vr2hofLB7Nnq6KRis76oOULKoRvu+02dZbQtNcWEVUeZOYeffTRcj9IIaOHVh7YuuXztGIJCQnqtQwtqgw/gKOtk+kkEKKKMGPn4j2qWnfoIAkR9aTkEhWotoLqugB/fwmtXl1q164tgdVDJbVxYzl87FiFQR2gkARtFv73v/+psykYFfbwww+r/n3s3k5U+fz9/VWG7ptvvjFr1YEPbshIDR8+XFXoU/kiIiJUAGwIOxePPPKIZmsix8OMnYtDm4IvZs+W9tG7pf6FMWFaOlmzpuzp0F7GT5yoKusQqKHFAsYbWcrX11dtaSCbhy82TiWqXHjuIogrb8wfRvt9//330qVLF03WZu/wljxw4ED12mdoyZIlMnToUM3WRY6DgR3Jj0uWSPq2bXLdrqgKGxZXBvTUW9upo4R27y53mbyA4SAxRqIhyMPEicvJyiGjh+1aBHldu3ZVmUoisi30s0PhE1qglJepx+D7xx9/XB2rIGOY8IHCMcNpOtiKxZYstmaJLoaBHakXkW/nz5dm+w9I06QkzdZxuG5dOdK6lYwaN07CwsIq/D4Edehfp8vmmfbxuxi8ON58880q0LvppptUZR8R2Q7GZmGUYXkj/9BbEwUCKIwiYxjTZtouBk2gf/75Z55TpItiYEfK+vXrZefqNXLd9u0SaNBLqbJk+/nJum5dpUu/fpc1JxH/fFE5iwAPgR7O3eH8nSWQuUNjUF0BBqr6+IJJZJuiAAQpeH6aqlOnjqp0R/N0Mn5tw9br0qVLja5jXvg999yj2brI/jGwI3116oJ586Qk5pD03r1bPC0Mjqzy2O7usqFDe/Fq3lzGjB9/VbNfMzIyZNWqVSrQw0zL8rIEFcHZHwR4+EJ7lSpVqlzxOojI/DUGvSlff/11s8IobMdOnz5dffGoxH9Onz6ttmTxXx00LEbjYjQwJioPAzvSS01Nle8Xfi1BJ09I9wMHK+W8Hc7VbW3VUrIaNJQRY+5RlbHWfCPZtm2bPpuHF8PLqfC78cYbVTYP20XIKhDR1cMZWYw5RLWnKXygQvYuPDxck7XZI2y9DhkyxOgajpHggyt3GKg8DOzICM6rLV20SEISEqTrwRibZu6QqdvesoVkRETInSNHqpYltv696c7lrVmz5rKGlWO+rq4Ao1OnTjzwTXQVENRhO7G8eaihoaGyYMEC9YGK/oU/K7SRMYSRbegLSGSKgR2VGwD9vHiJ+J06JR0PHbLJmTucqYtq0Vzyw+rIHcOH2TyoK6+ZKoI7XaCXnJxs8X1RlaYrwEBWLzAw0KZrJXJGOAv71ltvyfPPP6+apZt68skn5bXXXhNvb29xdThSgsbFp06d0l9Dr8B9+/apIyREhhjYUYXbsn8sWyaZScnSLC5OIpOTrbI1i63X2PBwOdIkUkLCw+WWQYOsuv16JfAU2Lt3rz7I2759+0WbI5uOSEOxhy6bFxkZafP1EjmTLVu2qFGBKLAwhXFk6HnXqFEjcXXYejXNYmLrGh9QuYNAhhjY0UXPqKGKbefmzRKQni6N4xOkXnq6eFzB9iwmWySGhsqx+hGSExoqXXr1kh49elxVoYSt4KAyXkQR6K1cuVLOnj1r8X2bNGmir7JFxS2zDUSWFT1NmDBBfvnlF7PbkBGfO3euDBs2TFzd/fffL1988YXRtQ8++EAmT56s2ZrI/jCwo0tC+n/L5s1yIjZWPPPypH5iooSdyZBqubniVc4Wik6Rh4dk+/tLSvUQia9XT4r9/KRhkybSs1evi/apsydFRUUquNUVYBw+fNji+1atWlUdckaQh63bmjVr2nStRI4Mb0WffPKJ2oL9d260MZwnw7gtTJZxVfiQiYbrhr078eeByTz4UEkEDOzoss554EzHvqgoKcjNlbLiYgnIz5fAjEzxLi4W97JSKXVzl0JPTzkbEiw5vr7i5ukpPv7+0qZjR/WCFBwcLI7s2LFj+i1b9P4r7w2oPKhewwglXTavXbt2rGgjKgfGkGEcGcaSmcI5s8WLF0uLFi3EVa1du1auv/56o2vdunWTTZs2sVUMKQzs6LLhoDO2TlDZhq/TqalSWFAgJcXF4uHpKd4+PlKjdm1VZICvkJAQp3zBOXfunGrdgEAPXziXaCm0T9HNsr3hhhtUexUi+u+5NWnSJPn666/NbkOG6uOPP1bTLFz1wxG2Xj/66COjaxjR9swzz2i2JrIfDOyIrFThFx0drc/m7dq1y+L7ohEyuu7rCjBY5Ub0L7Q9efjhhyWvnMp8TLL49NNPXbIqHVX9yPofPXpUfw3neaOiolRWk1wbAzsiG0D2bvny5SrQ+/PPPyUnJ8fi+2KbSbdla68FJkSV5ciRI6pwAsdATF1zzTWqarZjx47iitXEvXv3Nhqh2L59e1XVj2p9cl0M7IhsDI2QN27cqDJ5+MI5PUsFBQXJgAEDVJCH/1avXt2mayWyRwUFBaqoYvbs2Wa3IYiZNWuW2p50ta3ZqVOnqt+7oRkzZqjRbeS6GNgRVSI83WJjY/Vbtgj40FbGEuhV1b17d302D1survZGRq5t6dKlqi1Kdna22W2DBg2SefPmudSHHwS8yFbGxMToryHDj6wdpuWQa2JgR6QhvEFhqxZBHrZu09PTLb5vvXr19OfyUCXnym0gyHWcPHlSNTTGHGhTdevWlUWLFqkekq4C5+q6du1qNL2jZcuW6jrO75LrYWBHZCfwwrxz5059Ng+9qSyFoK5fv376SlsEfUTOCv0lp0+fLm+++abZbajAf+mll+TZZ591ymr88mDr9eWXXza6hgpZVMqS62FgR2SnkpKS9AUYaKtSXmVgRdAzUJfNw6d5V3mDI9eyatUqueeee9S0GFP4oIN2KY7SDP1qoJ8metmhB6Dh0Q30tsPxDXItDOyIHOQszbp16/QFGIad5y8FZ44w+QKBHiZhoCCDyFmkpKTI6NGj1cxUU5j2snDhQvXv3tnt379fnbdDNlMHs6uR+ffz89N0bVS5GNgRORg8ZXFYWjfmDCPPDFseXAwydzh/pCvAaNasGQswyCmOMbz++utqS7K85wK2JWfOnOn0bUCw9frcc88ZXZsyZYoaxUaug4EdkYPDFBBsSSHQW7FihRr9Zik0Q0aAh6++ffvysDU5NFSZo3ExjjGYwlYlCisaNGggzgoV9vjghqpYQ8hmXnfddZqtiyoXAzsiJ3thR7WgrgDjwIEDFt8XY81uvPFGlc275ZZb1NgzIkdz5swZNW7st99+M7sNxxC+/PJLGTJkiDhzQ2dMpcDxDR0Es2jwXLVqVU3XRpWDgR2RE8NZPN0s29WrV6tmyZZCHyxdAUanTp3UYWwiR4C3tQ8//FCefvppozNnOhhT9s4774iPj484I2y9Pv7440bXHnjgAfnss880WxNVHgZ2RC4CVbXYktEVYCQnJ1t831q1aukLMJDVc8X5nOR40Mtt+PDh5U57adu2rSxevFiaNm0qzgbnDNHbcv369UbXcVQDE2zIuTGwI3JBeNpja0ZXgIHtW0tfCnAAvU+fPvpsHirviOzV2bNnZeLEiep8XXnHDz755BO59957xdkcP35ctT3Kzc3VXwsPD1fVs8HBwZqujWyLgR0RqT5gK1euVIEeCjHKG9lUkSZNmuirbHFw29vb26ZrJbpceJvDuLFHH31U8vPzzW5HLzzMoQ0ICBBngq1XBLWmv1e0gCHnxcCOiIzgTBJaqOiyeYcPH7b4vjicjZ5hCPKwdYs+YkT24uDBg2prFv8t7wMKtmZReOAs8PaOrVeMLTT0888/y+DBgzVbF9kWAzsiuiicT9JV2eLMDrrcWwL98bp06aLP5uENkz3zyB7OmqKw4PPPPze7De1+UFSB4gpn+beamJgorVu3NsrC4wMXgtvQ0FBN10a2wcCOiCx27tw5Nd5MV2mbmppq8X3RPkU3y/aGG25Q55uItILsHCpFcQbP1B133KHaojjLWbQFCxbI2LFjja7dddddsmTJEqcJYOk/DOyI6Ior7zCbUrdlu3PnTovvi8zItddeqy/AQKNkIi0KDLA1u2vXLrPbIiIi5Pvvv3eKWat4m8fW67Jly4yuo6BkxIgRmq2LbIOBHRFZBbJ3aKeAQA9nenJyciy+b4sWLfRbtj169BBPT0+brpVIB0cLMIbr3XffLXcE3yuvvCJTp051+D6OeH62bNlSTarRCQkJUU3Mw8LCNF0bWRcDOyKyOjRCxngnXc+88vqIVQTTAXDgG0Ee/lu9enWbrpUIkHVG2xNMrjDVv39/VUmKfo6ODFuvyFAawvMMmTxuyToPBnZEZFN4iYmNjdUXYCDgw+gzSyBLgq0wXTavVatWfAMim8GM2VGjRsmGDRvMbqtdu7Z888030q9fP3FkCOwQ4BmaP3++2Rk8clwM7IioUqE6D1u1CPSWL1+ueuhZql69evpzeeis7+vra9O1kuspKSmRmTNnyssvv2zWtBsfKv73v//JjBkzHPa4QHp6uvqAlJaWpr+GSTLYksXzixwfAzsi0vRNFAfXdQUYKMawFII6ZE90lbZ8UyJrWrdundx9992SkpJidlvPnj1V4YGj/pvD1uvtt99udA2V6vjAxYy442NgR0R2tRWGLB6CPLRVQc8xS2F8ki6b17VrV3XwnehqIJuMc3coCjKFVihfffWVDBo0SBwRtl7RBsUQpm889NBDmq2JrIOBHRHZpYKCApU10WXzTp48afF9UXCByRcI9DAJAwUZRFfa1gcVs6icLe9s6OTJk+Wtt95SLXwcSVZWltqSTU5O1l9Db8m9e/dK48aNNV0bXR0GdkRk9/AyFRMToy/AwMgzvOFaApk7zLDVZfOaNWvG7Sa6bDt27FA9306cOGF2W4cOHVTPu8jISHEk2HrFBx9DvXv3Vh+oHL29iytjYEdEDge9uFatWqWCvJUrVxr15rqURo0a6ats+/bt63CZFtK28Of++++XH374wey2gIAA+eyzz9S5PEcyceJEtW5DyFBi7Bo5JgZ2ROTQsD22bds2fTYP1X2WwtbTjTfeqAK9W265RY09I7oYvGXOnTtXpkyZoo4LmBo/frx8+OGHDjMyD2MC27Zta5SJxIedPXv2qOw2OR4GdkTkVOLj4/WzbFevXq2aJVsKW2q6LdtOnTpxO4oqtH//ftUT7tChQ2a3NW/eXM2ibd26tTiC9evXqxF/hrp06aKOPDhqWxdXxsCOiJwWqmrXrFmjL8BA1a2lMGVAV4CBrB56fREZys3NVcUT8+bNM7vNx8dH3n//fXnggQcc4kwntl6xXkOvvvqq6ttHjoWBHRG5BLzU7du3Tx/kYfvW0pc/Ly8v6dOnjz6b52iH5Mm2vvvuO3nwwQfLnY88dOhQtXVbrVo1sWf5+fnSrl07NSXG8N89+kyilRA5DgZ2ROSyPcpQeIFAD4UYOBhvqSZNmugLMFBx6+3tbdO1kv2Li4tTVbPR0dFmtzVs2FBVzWJ7057hww6aLxtWnOP8HSqC+W/ccTCwIyKXV1RUpM4T6QowDh8+bPF9q1atqlpGIMjD1m3NmjVtulayXzjPOXXqVFU8YQpn1d544w215WnPZzfRrw/rNDR9+nQ1Yo0cAwM7IiITx44d0wd5OFheWFho0f1wlgpZGV02D1tbjnC+iqw/sguTHTIzM81uQ/U1JlbUqFFD7DU4ReGQYXU5ekFu3bpVOnfurOnayDIM7IiILtEOAuPNdJW2qampFt8X7VN0s2wxi9NRWmDQ1UtMTJSRI0eqTHB5/y6+/fZbs0pUe4GZzfiAYjhpA5W+2GZGUQjZNwZ2REQWwtkjvOnpCjB27txp8X3RGwxv5LoCDJy7IueGwGjGjBny2muvmRXqYDsWW5z4sse5xth6ffHFF42uPfXUUzJr1izN1kSWYWBHRHSFkL3DgHgEehjPVF5VZEVatGih37Lt0aMH+4U5MWR8R48eLWlpaWa3YfoJsnfh4eFib+dOu3fvLlFRUfprOFawceNGVWBB9ouBHRGRlc4m4U0PQR6+cE7PUkFBQTJgwAAV5OG/1atXt+laqfIhqBszZoz6AGAqNDRUFixYoM7f2ZODBw+qpt2GZ0wbN24se/fu5bECO8bAjojIyvCyin5gugIMBHyG55UuBlt0yJTosnmtWrViAYYTbeVjK3PatGlSUlJidvuTTz6ptm3tqbXIW2+9Jc8884zRtUmTJsnHH3+s2Zro4hjYERHZGHrkIVODQG/58uWqh56l6tWrpz+Xd/3114uvr69N10q2hwpT9LxLSEgwuw2Vp+h516hRI7EHCEB79+6t1my6vdyvXz/N1kUVY2BHRFTJb5To5q8rwEAxhqUQ1OHNVFdpi6CPHBNaoUyYMEF+/vlns9swvu7zzz9Xs2jtpfkyGhVjOoVORESEmpfLUXv2h4EdEZGGML8WWTwEeciCYL6tpTDqSZfN69q1q11WV1LF8PY7e/ZseeKJJ8rtlXj//fer+a1+fn6itY8++kjNxTWEwPSLL77QbE1UPgZ2RER2oqCgQNatW6fP5p08edLi+6LgApMvEOhhEgYKMsgx7NmzR2XnDOe06rRs2VKWLFmiqqi1Ph+IXoxr1641uo5/q/hgQfaDgR0RkR3CS3NMTIy+AAONbg1neF4MMneYYavL5jVr1owFGHYOrXIefvhh+frrr8vdgkfGbPz48Zr+PeKDRuvWrY3a+oSFhakpFSEhIZqti4wxsCMicgAZGRmyatUqFeStXLlS/b+lcBBfV2WLvmlolkz2aeHChSrAy83NNbsNkyzmzJmj6bm2uXPnygMPPGB07e6771a9+Mg+MLAjInIwaJ2ybds2fTbPcK7npaD/2I033qgCPfRNw3grsi9HjhyRYcOGyb59+8xuQx+5xYsXS8eOHTVZG0IG/LvBhwtDP/74o9x5552arImMMbAjInJw8fHx+lm2q1evVs2SLYUGtLotWwx/Rx89so/zluhrh+IKU15eXqq/3JQpUzTZmk1OTlb9FbOysoyaLKOhcc2aNSt9PWSMgR0RkRNBVe2aNWv0EzDwJmypWrVq6QswkNVjKwvtLV26VFWfoheiqYEDB8r8+fM1mVTyzTffyD333GN0bciQISpzx/Oc2mJgR0TkpPDyju08XZUttm8tfclHVqhPnz76bF5kZKTN10sVFy3gfB3+/kzVrVtXvvvuO9VEuDLh3xECuV9++cUs4Bs1alSlroWMMbAjInIRmHiBs1EI9FCIUV4WqCJNmjTRF2Cg4taexl65gqKiIpk+fbq8+eabZrdh+/yll16S5557rlJ7GWL+LbZk09PT9dfQZgdbsjy7qR0GdkRELhoooIWKLpt3+PBhi+9btWpV1SsPQR62bnmuqvIgIMcWaHlj6TByDhkztCCpLNh6HTp0qNE1/JvAvyluyWqDgR0REcmxY8f0Vbbr168vdxJCefDm3aVLF302r127dnxDt7GUlBQZPXq0OktpqkaNGqoXHgLvyoJ2J4sWLTK6hokUOBtIlY+BHRERGTl37pwab6artE1NTbX4vtiC082yxaQCtFch28wcfuONN+SFF14ot3H11KlT5ZVXXlFnJW0NPRUxIcPw3wmyupglW79+fZs/PhljYEdERBVC0LB79279lu3OnTstvi8aIV977bX6AoyGDRvadK2uaNOmTaqwAjOHTXXr1k1l0ho0aGDzdeDfBv6eTbeG//rrL7bQqWQM7IiIyGLIyqxYsUIFen/++afReKlLwbxT3ZZtjx49xNPT06ZrdRVnzpxR48aWLVtmdhuKGb788ktVwWpr2HqdN2+e0TWMQnvkkUds/tj0HwZ2RER0RdAIeePGjfqeeTinZykEHAMGDFBBHv6rRS82Z4K38g8//FCefvppVRhjCmPK3nnnHfHx8bHZGlBljVmyiYmJ+mt+fn6yZ88etsupRAzsiIjoquGtJDY2Vl+AgYAPo88sga267t2767N5aKHBAowrExUVJSNGjJCjR4+a3da2bVs1jqxp06Y2e3yczURza0PIzm7YsKFSW7G4MgZ2RERkk+wNtmoR6C1fvrzc9hwVqVevnv5cHs5p+fr62nStzubs2bPy0EMPqcbFplDM8sknn8i9995rs8efNGmS2Si0WbNmyVNPPWWzx6T/MLAjIiKbV3Ci6EKXzcPWnKUQ1PXr109faYugjy4Nb+0YN4bzbfn5+Wa3oxcegq+AgACrPzbOXaLtjeHWPAppoqOj1TlLsi0GdkREVKlQwYksHgI9bN1hvq2l2rRpo8/mde3aldt7lxATEyPDhw+XAwcOlDtNBFuzCMJsUa2LkXSGIUanTp1ky5YtldKCxZUxsCMiIs0UFBTIunXr9AUY8fHxFt8XBReYcoBADw15UZBB5pCxe+yxx+Tzzz83uw2ZNBRVoLjC2ucasfWKn23o5ZdfVqPRyHYY2BERkV3A2xEyTLqeeRh5Vl7z3fIgc4cZtroCjGbNmrEAw8SSJUvk/vvvV2fwTN1xxx2qLUpwcLBVA8oOHToYjatDixtsy9siS0j/YmBHRER2CRMNMBsVgR5652VmZlp8XzRDRoCHr759+6rMFIkcP35cVc2W12g6IiJCvv/+e1WhbC07duxQVbE4Z6mDlih4fP6d2AYDOyIisntonbJt2zZ9AUZ5Z8YqgkpQtOBANu+WW25RY89cGeYA/+9//zPbJtVlPjGKDCPJrDUx4vnnn5dXX33V6Boe3/QaWQcDOyIicjg4i6ebZbt69WrVLNlS2B7UFWDgQL+rjrzCnx3anmByhan+/fvLwoULpVatWlYJJDt37iz79u3TX8OfOQopUABD1sXAjoiIHBqqatesWaMvwEhOTrb4vghcdAUYyOoFBgaKK8Gf1ahRo2T9+vVmt9WuXVu++eYb1W7mau3du1cFd4ZTMdAoGXOI2afQuhjYERGR08BbGjJDugIMbN9a+jaHNhxo0aErwHCVMVg4/zZz5kz1ZVqsggIUbJvOmDHjqmf7YusV27KGHn/8cXn33Xev6ueSMQZ2RETktDDxYuXKlSrQQyEGJmJYCn3edEEeKm69vb3FmaHtDLJ3p06dMrutZ8+esmjRoqtqEI1zkiikMCzcQOCIx0VATdbBwI6IiFwCtgHRQkWXzTNsw3EpVatWVb3ydAUYNWvWFGcNhMeOHasaSJtCK5SvvvpKBg0adMU//9ChQ9K+fXujM5GoYEaW1RZTMFwRAzsiInJJGHmlq7LFGTMc8rcEskw4L6Zrp4KebM7UMw/bse+99548++yzKstmavLkyfLWW29dcbsSVOOazo2dOHGifPrpp1e8ZvoPAzsiInJ5586dU+PNdJW2qampFt8X7VN0s2xvuOEG1V7FGaAHHXrenThxwuw2VBaj592VnEPEmb5rr71WjR0zhK1yVOPS1WFgR0REZJKxQrWmrsp2165dFt8XWSwELbp2KthmdGQ4k4hpFT/88IPZbdg6/eyzz+Tuu+++omwp5v4azgmuW7eu7N+/n6PhrhIDOyIiootA9g5nzpDJ+/PPPyUnJ8fi+7Zo0UJfgIHCgautLNUCwoS5c+fKlClT1GxfU+PGjZOPPvrosjOVs2fPlkmTJhldw/m++fPnX/WaXRkDOyIiIgvh0P/GjRv12TxkniyFTNSAAQNUoIfeedWrVxdHgmza8OHDVQGEqebNm8vixYvVuLDLyYxi6xUNpg39+uuvV1Wg4eoY2BEREV0BvH3GxsbqCzAQ8JVXbFAeTF7ATFZdNq9Vq1YOUYCRm5uriifmzZtndpuPj4+8//778sADD1j8e0lISFC/d5xxNGwaffDgQYcLfO0FAzsiIiIrnUfDVi0CPWzdonWIpdAfTncu7/rrr7f7aQzfffedPPjgg+VuSw8dOlQ+//xzi8/KIUicMGGC0TVkBlGcQZePgR0REZGVofITjXh12bw9e/ZYfF8EdQjudIHe1TQFtqW4uDhVNRsdHW12W4MGDdTWbJcuXS75cxCGDBw4UP1ZGcL9hw0bZtU1uwIGdkRERDaWlJSkL8BAWxXDatBLQfWoLsjr2rWreHh4iD2dOZw6dap8+OGHZrehUOT111+XJ554Qm09X0xKSoq0bNlSMjMz9dewFYstWWzNkuUY2BEREVUiVJZijJauACM+Pt7i+yLYQeEFAj1MwrCX1iDLli1T1bEZGRlmt2G9CxYskBo1alz0Z2BkmWnrFBRR/PLLLw5x/tBeMLAjIiLSCN6CY2Ji9GPOMPIM1aKWQOYOM2x1BRjNmjXTNABKTExUgZlp42FdE+dvv/1W9fi72J8FzuctXbrU6DqCwjFjxkh+fr7qE3ip7J+rY2BHRERkJ5DxwgQGBHorVqww2pq8FDRD1m3Z9u3bV1WpVjZUBb/00kvy6quvqkDNEILO6dOnywsvvFDhdjIKTrAla1h4EhgYKLfffrssWbJEnT9EgIh5vVQ+BnZERER2CEHStm3b9AUYBw4csPi+aBaM8WYI9BAEIWNWmdCbbvTo0eWOZuvTp4+qqg0PDy/3vj///LMMGTKkwp/duHFjOXr0qFXX60xcIrBDdRI+BaWlpamv06mpcj4/X0pLSsTdw0Oq+PpKjdq11QFNfIWEhNjV4VQiIiKcxdMFeWvWrFGFC5bCbFddNq9Tp06Vsp2J91tsoaIFTHlnBbHFivWUZ+TIkRdtd5KVlSXVqlXj+7urBXZIYe/du1f2R0dLQW6ulBUXS0B+vlTLyBCv4mJxLyuTUjc3KfL0lOyQEMnx9RU3T0/x8feX1h06SNu2bSU4OFjr3wYREZFZo2AEd7pALzk52eL71qxZU2XxEOjdeOONaqvTVnBecNasWTJt2jQVhJlCxSwqZ729vfXX8Hu57rrrVDuViuAcX1FREd/fXSWwO3XqlGzZtElOxMWJV16eRCQkSlhGhlTLzRWvcv5h6RR5eEi2v7+khIRIQkQ9KfLzk4aRkdKzd28JCwur1N8DERGRJfA2vm/fPn0BBrZvLX1r9/LyUlujugKMyMhIm6xx69atKgtXXgVw586dVXauUaNGqgADPe2QlClP7dq1pVePHtKudWvxLyri+7uzB3Y4j4CKop2bN0tAerpcE58gddPTxcPCCiNDJe7ukhQaKkfrR0hOaKh07tlTevbs6ZADnImIyHWg8ACFFwjyVq5cKWfPnrX4vk2aNNEHeai4NcykWWMX7b777pOffvrJ7DZkDZG5e/nll9WWqilsn/bo0UN6du4soTk50jQ5Wa45l8P3d2cO7HBA849lyyQzKVmaxcVJZHKySsVeLaRy48LD5XBkpITUDZdbBg1SnxiIiIjsHbYrkfDQZfMOHz5s8X2rVq2qeuUh0MPWLbZwrxZCjk8//VRtwVp6RhCPO+jWWyU8OFgiDx+WOrGx4u/jI8FBV7eVWuqk7+9OEdghtfvz4sXidypFOh46JIGX0dHbUmf9/CSqeXPJq1NH7hg+TOrXr2/1xyAiIrKlY8eO6c/lrV+/XgoLCy26H1qVYMtUV4DRvn37q+qZhxFrmAcbGxt70e+LiIiQYYMHS1henjSPihK/C9lHD3cPq02kOOtk7+8OH9ghqFu6aJFUj0+QLjEx4nkFaVlLFbu7y/aWLSQjIkLuHDnS4f/yiYjIdZ07d06NN0Ogh6/yWpNUBO1TdAUYaKuC9iqXKycnRyZNmiQLFy6sMKgbMWSINMjMkqZbt4iHwRk6d3cPqW3FUWPFTvT+7tCBHf4Rfr9woQSdOCndDx60ytarJanbra1aSlaDhjJizD1OkbYlIiLXhurV3bt368ec7dq1y+L7YhoEJkrosnlolGypM2fOyDXXXKPal5huv44ZMUIFdR2io8Xf11d9T1kZkjduElStmvj5+Yk1lTrJ+7vDBnYolFgwb56UxByS3rt32zRTZ/bY7u6yoUN78WreXMaMH+80By6JiIh0iZPly5erTB760CG7ZqkWLVroCzBQ8HCx98h3331XnnzySbNCiXFjxkhzDw9pt2GDytRVrx6qCjmwdYyf52GjPnzFTvD+7rCBHc4G7Fy9Rq7bvt0mZ+ouJdvPT9Z16ypd+vVTpeJERETOCEUOGzdu1GfzcE7PUkFBQTJgwAAV6N18882qMbHhVjAaJ5tOkejdu7dc37mzdF27Vn+mzsvTS2rUqCGVIdvB398dMrBDn7rvvvpKmu0/IE2TkjRbx+G6deVI61Yyatw4p+uDQ0REZAohAwoedAUYCPiwg2YJTLvo3r27PpuH0WEvvvii0fdg+3Ps3XdLq8OHpe6RI/rrnp5eUrOSAjtHf393yMDuxyVLJH3bNrluV1SlnKu72H782k4dJbR7d7lr6FDN1kFERKSF7OxstVWLIA9bt+np6RbfF82R0Y7F0IihQ6VraKh0XLtOStVteI93U5m+KlbsqefM7+8Ot3mMBoeYKNE+PkHToA7w+I3jE2RP9epqXc46noSIiKg8mNc6dOhQ9YWRYTt37tRn89DS5GJMgzr8rIYRERK+e48EBwaqM3W6ma9X3ljF9d7fbT8F2MowZgRjwjBRwh7US08Xz7w8Nc6FiIjIVaHooVu3bjJz5kxVYYvxYJ999pkMGjRIfH19L3l/zG/1KyqS6okJqlo2LzdX/czKDuoc/f3doQI7fBrAwF/MhruSMSK2gHXUT0yUfVFR5Q44JiIickV169aVBx54QH799VcVqGGrFn3ryusRh2bHHVq3lnoJCRfe38sk++xZi2fe2oKHg76/X1ZghyaC6DaNtOTYsWNVrxrdockDBw6oPjYXs2zZMnnvvfcu+j0zZsyQjz/+2Oz6unXrZPDgwVKQm6sG/lrLueJieTY2Vq7fuVOG7NktEw4ekBP5ebI9K0sePRRj0c8IO5Oh1rV48WK5/vrrpU2bNmqgsQ76AT399NP6GX5du3ZVf46o7B01atRV/x527NghnTp1UucVkP4mIiKyJ+g5h9FmeH8/cuSIWRsRnKFTY8JOnTK4Wialqm/df75KTpbCSkzshF14f88wiTsQoGLyBd57HTaww9DeN998U1atWqXfa0ZQt2jRIosfDOnYxx9//KpKrsuKiyXoMvrpQOlFIv5nYmMlrIq3rO7USX5q116eadBQ0guN9/0vBj+5ClLG53Lktddek7Vr18r+/ftVwKYbZIy/+FmzZqlfr169Wo1lQZq6b9++8u2331r8WBV9YkAH8C+//FJGjhxp8c8iIiKqLAEBAer9Di1O8vLyzCppESR5urlJgEGjYlTCYnSYoQWnkqWogvf0i73XX6lqubkq7tC9n+vcfffdKgPp0MUTzz77rApKDIcAP/bYYypgGT16tFkAMnXqVNmwYYNqJohfI9D56quvVGbv7bffVuXS+IPB4cl+/fqp79V1usaBS/SOSUpKUsHSiBEj1HVU23y1cKHMSUuT60NC5JmGjdT1X/5Jky+SklSQdUfNWnJf3bqSVFAgE2MOyjV+fnIoN1eWtm0nUw4flrTCf4cO4771fHzkcG6ufNy8uX7mXZMLY1GQsdPZc/asvHbiuPqU4O/hIW81aSp1fHxkwz9p8vrJeHGTMik4dkyatmktBw8e1Hfx7tKli/zwww+qR8/XX38tkydP1g8+Rrbuk08+UVE/0tT4M0PgjOwb/kyQvr799tvlxx9/VH/uqDzCwVIMT65oWHNubq5qKnn8+HFL/1qJiIhsDjtKGEH2yiuvyIMPPigNGjSQkydP6m9HSxH/3FyJOV8oP2dmyJuNGstLSYkSkxAvOGU3LjxcCkpL5Z/CQhmxd4+E+/jInBYtpcu2rXJrjRqyMztb3m/WXBanpsiWrCzxcHOTZxo2lJ5BwVJSViZvnTghO89mS1Fpmdxft64MqllTfkpLk7UZZyS7uFgSCgpkckR9FTv8dSZdQr295bMWLQV1uAH5+Sqwa9WqlX69PXv2NFq/QwZ2qHKpV6+e0bWmTZuqLwQmGAmig+wR/pJQHZOfn68OU6JBoSEEhc8//7zaXsV/DaH5IYKZhIQEuemmm/SBXUxMjLw5eLD0T0qWMfv3qeCrvq+vfJSQoAI3Xw8PGb53j3QLqiZBnl5yLC9P3m7aTJr5+8uq9HQJ8vKUL1u1Unv2uSUlsj07W93mfolBxggOF7Vpq/6hrD5zRmYnJsijgdVkXnKSPFw9RDr5+cmedu1ku4fxJwusH9uuOitXrtT/GmldZOygcePGZo+JABBfpsr7XtPMKhERkb2ZPXu2+u9bb71ldlsVLy+plpUlP51JlxsCAiQ6/bSczMmReeHh6na8Z9cJDpYvvb3l+7btVJIFsoqLpU9wiLzY+BpZmX5a4vML5Lf2HeTU+fNyz/59srJjJ/n5nzSp6e2tduUKSkpk6N690vvCzuPRvDx1HT/n5qhd8nqTJjK5fkd5/PBhWZeRIf1DQyUwI1NOX8YcXYfZiv3mm2/Kvf7cc8/J66+/bnQNPW2++OILadeunWpGiGyTaRYpKipKZaRg+PDhRrehcSGiewQxhvPjrmncWMJ8fFS6dkBoqESdPSv7c85J92pBEuTlJVXc3eUmXM/+t1N1A19fFbhBE38/FdEjat9z7pwEXMaYEETzjxyKkVujo+TtkyfkyLlzUlpaIq18fOTzM2dkaVaWlOTni08l9tghIiJyFqigLcnLk/0FBdLFz0/qeHpKenGxvH/6tOzMyxN/dzcVS5jycXeX60JC1K8REwysUUMla+r6+KgY4HhenmzOzJQlaakyaHe0DNu3V3JKiiWxoEDdp3tQkEoKhVWpIl7u7tIvpLo+Zkg+/+8On3dxsRRe+H5HYHF0g6wcMnb33Xef0XWMA8GZO2TYdLANiRJnXUZKR7dNaclA4YoY9q67RKJN/WXpNPT1k1/bd5C1GRny+onjMrBGTRWxH8nLVfvyF8vafZAQL31DQmRE7TCJzc2Vpw8fUtdHBQdLVz8/2ZqXJ6+tXSs33XqrRb8/IiIi+k9CYqJEVakiPfz9VfKmqoeHzKtXT7bn5ckPWVkquJtU47+jYIaB3aWUisjMa66RLtWCjK4jW+dtcH9EAbr/dxc3/Zk997JSKbFwuoZDZexwSBDn3bAla+p///ufOjen079/f5Vy1R32x7k604P/CAh/++039WucQ7PE0WPH5DQOXZaVyZ/pZ6RjYKC0CagqW7OzJLu4SJ2B++vMGelUrZrZfdPOnxc/Dw8ZUquW3FsnXA7l5qhovomfv3ySmKAvqY7LzZVdJp8KcopLpJb3v8HmT/+kqWaJqOhJLiqSa6pUkXuCgyUsMFCyLsy0IyIiIsuFhobKjwcPyk1Vq6r/zyopUefmrwsIkLEhIXKssEjNncUWLLZly4OY4I/00+r9PLmgQOLz86WRn5/0CgqWb1NS1Fk7QIJG92tLlLq5i8dl7PJpzeKVovISrTRw5s30HBcKHSIiIvT/f//998uJEydUSw9k73DebsWKFUb3QdsTFF1Mnz5dDfwNDAy85BqwFfvp1q0yKyNDFU/oou9H6kXIqH379MUTLQMC1AFIQ7F5efLmieMqM4cI/7XISHX9jSaR8urx49Jv1y7x83CX2lWqyPONGqtAUP/7qVtXVc9+EH9SegeHqKi+Zo2a8nFsrGxPTVPFE2F160pVk98DzhYieMW285w5c+S7775TRRQ4K4jt6/j4eFVAghJwBL6YmYdtbPyZYV7eL7/8oqqOdd9fEVTh3nHHHWrbGk0gsYWN9jBERET2ADt+2G4FFE8iuYNiwHvuuUddm/HCC7Loyy+lT/0G6v+zc3Pkqbg4lTVDBu9/11wjvj4+Mqx2bXV2rqGvryqeMNS/+r9HtG7bHa3OxM+MjFRHtHAfxASDd0er7F0Nb2/5ouV/hRCXUujpKd4+PkbX0PINXULQnw/9+hDTYPqGS8+KRbkzghBUo6KyFhUnhlm/8mC798iqVXLj1m1iTwqLCmVlp06y/NAhWbNmjX47OSUlxaHGkBAREWlhzJgxkp+cLK9dRruxyvJX927S9KabVAcPR6BZbhFtPVAZi0wVol00P74U9LmJ8vWVIg8P8bKjLtBuPr5SUr26al2CzCaaED/11FMM6oiIiC7h5ptvVtm84QMHStHWbXb1/l7k4SE5vr4q/nAUmmXsrgQCpq/mzJFe27ZLqB2dZ0sPDJRN3brK2IkTpUaNGjZ7HKR9n3nmGaNr6KWDfnhERESOyl7f3++Li5U4NzcJqV5dPy0DR6pat24t9spxTgOKSEhIiPj4+0tKSIhd/cWnVP93XVifLeF8I76IiIicib2+v0/p31+S27WTh6dMEQ+TXrVOMStWa/hDbd2hgyRE1JMSC0qcKwPWEV+vnrTp2NFh/tKJiIjsCd/frcc+/vQuQ9u2baXIz0+SQkPFHiSGhkqxn5+0adNG66UQERE5LL6/u2hgh4KEhpGRcrR+hJReqkOxjeHxj9WPkIZNmrBQgoiI6Crw/d1FAzvo2bu35ISGStyFGXJaiQ0PV+vo2auXpusgIiJyBnx/d9HADg2PO/fsKYcjI+Wsn58ma8j285MjTSKlS69eaj1ERER0dfj+7qKBna7NR3DdcIlq3lyKK/mgJR4vqkVzCQkPlx49elTqYxMRETkzvr+7aGCHfjK3DhokeXXqyPaWLSptPx6Pg8fLD6sjtwwapO9rQ0RERFeP7+8uGtgB5qneMXyYZEREyNZWLW0e2ePn43HweHhcPD4RERFZF9/fXWTyREXi4+Pl58VLxO/UKel46JAE5uXZZM8d6VlE8vhLr1+/vtUfg4iIiP7D93cXDewgNTVV/li2TDKTkqVZXJxEJieLuxV+a0jNojoGBymx5470rCNH8kRERI6E7+8uGthBcXGxbN68WXZu3iwB6enSOD5B6qWni0dp6RV1nEZzQvSxQckzqmNwkNJR99yJiIgcFd/fXTSw0zl16pRs2bxZTsTGimdentRPTJSwMxlSLTdXvEpKKrxfkYeHZGNWXfUQNUYEHafRnLCng5Y8ExERORO+v7toYKeTmZkp+/btk31RUVKQmytlxcUSkJ8vgRmZ4l1cLO5lpVLq5i6Fnp5yNiRYcnx9xc3TUw0ixmw4jBFxtI7TREREzo7v7y4a2OmUlJRIRkaGpKWlqa/TqalSWFAgJcXF4uHpKd4+PlKjdm2pVauW+goJCXGogb9ERESuiO/vLhrYEREREbkCh+5jR0RERET/YWBHRERE5CQY2BERERE5CQZ2RERERE6CgR0RERGRk2BgR0REROQkGNgREREROQkGdkREREROgoEdERERkZNgYEdERETkJBjYERERETkJBnZEREREToKBHREREZGTYGBHRERE5CQY2BERERE5CQZ2RERERE6CgR0RERGRk2BgR0REROQkGNgREREROQkGdkREREROgoEdERERkZNgYEdERETkJBjYERERETkJBnZEREREToKBHREREZGTYGBHRERE5CQY2BERERGJc/g/XTIGj5FEzGgAAAAASUVORK5CYII=", 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", 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", 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", 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", 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", 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", 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", 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", 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "for i in range(0,50):\n", " ind.mutate()\n", " if i%5==0:\n", " est = ind.export_pipeline()\n", " est.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### TreePipeline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "TreePipelines work the same way as GraphPipelines, but they are limited to a tree structure. This is similar to the search space in the original TPOT.\n", "\n", "(This search space is still experimental and currently built off GraphSearchPipeline. It may be rewritten with its own code in the future.)" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tree_search_space = tpot.search_spaces.pipelines.TreePipeline(\n", " root_search_space= tpot.config.get_search_space([\"KNeighborsClassifier\", \"LogisticRegression\", \"DecisionTreeClassifier\"]),\n", " leaf_search_space = tpot.config.get_search_space(\"selectors\"), \n", " inner_search_space = tpot.config.get_search_space([\"transformers\"]),\n", " max_size = 10,\n", ")\n", "\n", "ind = graph_search_space.generate()\n", "exp = ind.export_pipeline()\n", "exp.plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tips and Tricks\n", "\n", "* Two very helpful transformers to use with search spaces are `tpot.buildin_models.Passthrough` and `tpot.builtin_models.SkipTransformer`. \n", " Passthrough will simply pass through the exact inputs it receives into the next step. This is particularly useful inside UnionSearchSpace as it allows for both the transformed data as well as the original data to be passed into the next step.\n", " SkipTransformer will always return nothing. This is helpful when inside a union with Passthrough and an optional second method. For example, if you are unsure of whether or not you will need a transformer, you can have SkipTransformer be one option that will skip the transformation step if selected." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this example, the FeatureUnion layer will always have at least one transformer selected and will always have one passthrough" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
FeatureUnion(transformer_list=[('featureunion',\n",
       "                                FeatureUnion(transformer_list=[('kbinsdiscretizer',\n",
       "                                                                KBinsDiscretizer(encode='onehot-dense',\n",
       "                                                                                 n_bins=80,\n",
       "                                                                                 strategy='kmeans')),\n",
       "                                                               ('fastica',\n",
       "                                                                FastICA(algorithm='deflation',\n",
       "                                                                        n_components=91))])),\n",
       "                               ('passthrough', Passthrough())])
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" ], "text/plain": [ "FeatureUnion(transformer_list=[('featureunion',\n", " FeatureUnion(transformer_list=[('kbinsdiscretizer',\n", " KBinsDiscretizer(encode='onehot-dense',\n", " n_bins=80,\n", " strategy='kmeans')),\n", " ('fastica',\n", " FastICA(algorithm='deflation',\n", " n_components=91))])),\n", " ('passthrough', Passthrough())])" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from tpot.search_spaces.pipelines import *\n", "from tpot.config import get_search_space\n", "\n", "#This FeatureUnion layer will always have at least one transformer selected and will always have one passthrough\n", "transformers_with_passthrough = UnionPipeline([\n", " DynamicUnionPipeline(get_search_space([\"transformers\"])),\n", " get_search_space(\"Passthrough\")\n", " ]\n", " )\n", "\n", "transformers_with_passthrough.generate().export_pipeline()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this example, the FeatureUnion layer will always one passthrough. In addition, it may select one or more transformer, but it may skip transformers altogether and only include a Passthrough. " ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
FeatureUnion(transformer_list=[('skiptransformer', SkipTransformer()),\n",
       "                               ('passthrough', Passthrough())])
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" ], "text/plain": [ "FeatureUnion(transformer_list=[('skiptransformer', SkipTransformer()),\n", " ('passthrough', Passthrough())])" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "final_transformers_layer =UnionPipeline([\n", " ChoicePipeline([\n", " DynamicUnionPipeline(get_search_space([\"transformers\"])),\n", " get_search_space(\"SkipTransformer\"),\n", " ]),\n", " get_search_space(\"Passthrough\")\n", " ]\n", " )\n", "\n", "final_transformers_layer.generate().export_pipeline()" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
FeatureUnion(transformer_list=[('skiptransformer', SkipTransformer()),\n",
       "                               ('passthrough', Passthrough())])
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" ], "text/plain": [ "FeatureUnion(transformer_list=[('skiptransformer', SkipTransformer()),\n", " ('passthrough', Passthrough())])" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "inner_estimators_layer = UnionPipeline([\n", " ChoicePipeline([\n", " DynamicUnionPipeline(wrapped_estimators, max_estimators=4),\n", " get_search_space(\"SkipTransformer\"),\n", " ]),\n", " get_search_space(\"Passthrough\")]\n", " )\n", "\n", "inner_estimators_layer.generate().export_pipeline()" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Pipeline(steps=[('normalizer', Normalizer(norm='l1')),\n",
       "                ('featureunion-1',\n",
       "                 FeatureUnion(transformer_list=[('skiptransformer',\n",
       "                                                 SkipTransformer()),\n",
       "                                                ('passthrough',\n",
       "                                                 Passthrough())])),\n",
       "                ('featureunion-2',\n",
       "                 FeatureUnion(transformer_list=[('skiptransformer',\n",
       "                                                 SkipTransformer()),\n",
       "                                                ('passthrough',\n",
       "                                                 Passthrough())])),\n",
       "                ('bernoullinb',\n",
       "                 BernoulliNB(alpha=5.0573782838899, fit_prior=False))])
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" ], "text/plain": [ "Pipeline(steps=[('normalizer', Normalizer(norm='l1')),\n", " ('featureunion-1',\n", " FeatureUnion(transformer_list=[('skiptransformer',\n", " SkipTransformer()),\n", " ('passthrough',\n", " Passthrough())])),\n", " ('featureunion-2',\n", " FeatureUnion(transformer_list=[('skiptransformer',\n", " SkipTransformer()),\n", " ('passthrough',\n", " Passthrough())])),\n", " ('bernoullinb',\n", " BernoulliNB(alpha=5.0573782838899, fit_prior=False))])" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "final_linear_pipeline = SequentialPipeline([\n", " get_search_space(\"scalers\"),\n", " final_transformers_layer,\n", " inner_estimators_layer,\n", " get_search_space(\"classifiers\"),\n", " ])\n", "\n", "final_linear_pipeline.generate().export_pipeline()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Template Search Spaces\n", "\n", "As mentioned in Tutorial 1, TPOT has several buildin search spaces. Here is the same table:\n", "\n", "| String | Description |\n", "| :--- | :----: |\n", "| linear | A linear pipeline with the structure of \"Selector->(transformers+Passthrough)->(classifiers/regressors+Passthrough)->final classifier/regressor.\" For both the transformer and inner estimator layers, TPOT may choose one or more transformers/classifiers, or it may choose none. The inner classifier/regressor layer is optional. |\n", "| linear-light | Same search space as linear, but without the inner classifier/regressor layer and with a reduced set of faster running estimators. |\n", "| graph | TPOT will optimize a pipeline in the shape of a directed acyclic graph. The nodes of the graph can include selectors, scalers, transformers, or classifiers/regressors (inner classifiers/regressors can optionally be not included). This will return a custom GraphPipeline rather than an sklearn Pipeline. More details in Tutorial 6. |\n", "| graph-light | Same as graph search space, but without the inner classifier/regressors and with a reduced set of faster running estimators. |\n", "| mdr |TPOT will search over a series of feature selectors and Multifactor Dimensionality Reduction models to find a series of operators that maximize prediction accuracy. The TPOT MDR configuration is specialized for genome-wide association studies (GWAS), and is described in detail online here. |\n", "\n", "Rather than create your own search space, you can simply pass the string into the `search_space` param. Alternatively, you can access tpot.config.`template_search_spaces.get_template_search_spaces` directly which offers a few more customizable options for each template including `cross_val_predict_cv` and whether or not stacked classifiers/regressors are allowed. Or you can copy the code and customize it manually!\n", "\n", " `tpot.config.template_search_spaces.get_template_search_spaces`\n", " Returns a search space which can be optimized by TPOT.\n", "\n", " Parameters\n", " ----------\n", " search_space: str or SearchSpace\n", " The default search space to use. If a string, it should be one of the following:\n", " - 'linear': A search space for linear pipelines\n", " - 'linear-light': A search space for linear pipelines with a smaller, faster search space\n", " - 'graph': A search space for graph pipelines\n", " - 'graph-light': A search space for graph pipelines with a smaller, faster search space\n", " - 'mdr': A search space for MDR pipelines\n", " If a SearchSpace object, it should be a valid search space object for TPOT.\n", " \n", " classification: bool, default=True\n", " Whether the problem is a classification problem or a regression problem.\n", "\n", " inner_predictors: bool, default=None\n", " Whether to include additional classifiers/regressors before the final classifier/regressor (allowing for ensembles). \n", " Defaults to False for 'linear-light' and 'graph-light' search spaces, and True otherwise. (Not used for 'mdr' search space)\n", " \n", " cross_val_predict_cv: int, default=None\n", " The number of folds to use for cross_val_predict. \n", " Defaults to 0 for 'linear-light' and 'graph-light' search spaces, and 5 otherwise. (Not used for 'mdr' search space)\n", "\n", " get_search_space_params: dict\n", " Additional parameters to pass to the get_search_space function." ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Pipeline(steps=[('standardscaler', StandardScaler()),\n",
       "                ('rfe',\n",
       "                 RFE(estimator=ExtraTreesClassifier(max_features=0.8009842720563,\n",
       "                                                    min_samples_leaf=4,\n",
       "                                                    min_samples_split=9,\n",
       "                                                    n_jobs=1),\n",
       "                     step=0.4315847507401)),\n",
       "                ('featureunion-1',\n",
       "                 FeatureUnion(transformer_list=[('skiptransformer',\n",
       "                                                 SkipTransformer()),\n",
       "                                                ('passthrough',\n",
       "                                                 Passthrough())])),\n",
       "                ('featureunion-2',\n",
       "                 FeatureUnion(tran...\n",
       "                                                                                                                                       max_features='sqrt',\n",
       "                                                                                                                                       min_samples_leaf=17,\n",
       "                                                                                                                                       min_samples_split=8))),\n",
       "                                                                                ('estimatortransformer-2',\n",
       "                                                                                 EstimatorTransformer(cross_val_predict_cv=5,\n",
       "                                                                                                      estimator=LGBMClassifier(max_depth=3,\n",
       "                                                                                                                               n_estimators=84,\n",
       "                                                                                                                               n_jobs=1,\n",
       "                                                                                                                               num_leaves=244,\n",
       "                                                                                                                               verbose=-1)))])),\n",
       "                                                ('passthrough',\n",
       "                                                 Passthrough())])),\n",
       "                ('lineardiscriminantanalysis',\n",
       "                 LinearDiscriminantAnalysis(shrinkage=0.369619691802,\n",
       "                                            solver='eigen'))])
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" ], "text/plain": [ "Pipeline(steps=[('standardscaler', StandardScaler()),\n", " ('rfe',\n", " RFE(estimator=ExtraTreesClassifier(max_features=0.8009842720563,\n", " min_samples_leaf=4,\n", " min_samples_split=9,\n", " n_jobs=1),\n", " step=0.4315847507401)),\n", " ('featureunion-1',\n", " FeatureUnion(transformer_list=[('skiptransformer',\n", " SkipTransformer()),\n", " ('passthrough',\n", " Passthrough())])),\n", " ('featureunion-2',\n", " FeatureUnion(tran...\n", " max_features='sqrt',\n", " min_samples_leaf=17,\n", " min_samples_split=8))),\n", " ('estimatortransformer-2',\n", " EstimatorTransformer(cross_val_predict_cv=5,\n", " estimator=LGBMClassifier(max_depth=3,\n", " n_estimators=84,\n", " n_jobs=1,\n", " num_leaves=244,\n", " verbose=-1)))])),\n", " ('passthrough',\n", " Passthrough())])),\n", " ('lineardiscriminantanalysis',\n", " LinearDiscriminantAnalysis(shrinkage=0.369619691802,\n", " solver='eigen'))])" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "linear_search_space = tpot.config.template_search_spaces.get_template_search_spaces(\"linear\", inner_predictors=True, cross_val_predict_cv=5)\n", "linear_search_space.generate().export_pipeline()" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [], "source": [ "linear_search_space = tpot.config.template_search_spaces.get_template_search_spaces(\"linear\", inner_predictors=True, cross_val_predict_cv=5)\n", "linear_est = tpot.TPOTEstimator(\n", " search_space = linear_search_space,\n", " scorers=['roc_auc_ovr',tpot.objectives.complexity_scorer],\n", " scorers_weights=[1,-1],\n", " classification=True,\n", " verbose=1,\n", " )\n", "\n", "#alternatively, you can use the template search space to generate a pipeline\n", "linear_est = tpot.TPOTEstimator(\n", " search_space = \"linear\",\n", " scorers=['roc_auc_ovr',tpot.objectives.complexity_scorer],\n", " scorers_weights=[1,-1],\n", " n_jobs=32,\n", " classification=True,\n", " verbose=1,\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Optimize Search Space with TPOTEstimator\n", "\n", "Once you have constructed a search space, you can use TPOTEstimator to optimize a pipeline within that space. Simply pass that search space into the `search_space` parameter. Here is a cell where you can select different search spaces that we created in this tutorial." ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [], "source": [ "all_search_spaces ={\n", " \"classifiers_only\" : classifier_choice,\n", " \"stc_pipeline\" : stc_pipeline,\n", " \"stc_pipeline2\": stc_pipeline2,\n", " \"stc_pipeline3\": stc_pipeline3,\n", " \"stc_pipeline4\": stc_pipeline4,\n", " \"final_linear_pipeline\": final_linear_pipeline,\n", " \"graph_pipeline\": graph_search_space,\n", "}\n", "\n", "X, y = sklearn.datasets.load_breast_cancer(return_X_y=True)\n", "X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, test_size=0.5)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Generation: : 5it [00:58, 11.77s/it]\n" ] }, { "data": { "text/html": [ "
TPOTEstimator(classification=True, cv=5, early_stop=2, max_time_mins=10,\n",
       "              n_jobs=4,\n",
       "              scorers=['roc_auc_ovr',\n",
       "                       <function complexity_scorer at 0x32f4e0550>],\n",
       "              scorers_weights=[1.0, -1.0],\n",
       "              search_space=<tpot.search_spaces.pipelines.sequential.SequentialPipeline object at 0x32f7692d0>,\n",
       "              verbose=2)
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" ], "text/plain": [ "TPOTEstimator(classification=True, cv=5, early_stop=2, max_time_mins=10,\n", " n_jobs=4,\n", " scorers=['roc_auc_ovr',\n", " ],\n", " scorers_weights=[1.0, -1.0],\n", " search_space=,\n", " verbose=2)" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "selected_search_space = all_search_spaces[\"stc_pipeline\"] #change this to select a different search space\n", "\n", "\n", "est = tpot.TPOTEstimator(\n", " scorers=[\"roc_auc_ovr\", tpot.objectives.complexity_scorer],\n", " scorers_weights=[1.0, -1.0],\n", " classification = True,\n", " cv = 5,\n", " search_space = selected_search_space,\n", " max_time_mins=10,\n", " max_eval_time_mins = 10,\n", " early_stop = 2,\n", " verbose = 2,\n", " n_jobs=4,\n", ")\n", "\n", "est.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "auroc score 0.9947351959966638\n" ] } ], "source": [ "# score the model\n", "auroc_scorer = sklearn.metrics.get_scorer(\"roc_auc\")\n", "auroc_score = auroc_scorer(est, X_test, y_test)\n", "\n", "print(\"auroc score\", auroc_score)" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Pipeline(steps=[('selectfwe', SelectFwe(alpha=0.0001569023321)),\n",
       "                ('powertransformer', PowerTransformer()),\n",
       "                ('mlpclassifier',\n",
       "                 MLPClassifier(activation='identity', alpha=0.0008696190619,\n",
       "                               hidden_layer_sizes=[203, 203],\n",
       "                               learning_rate_init=0.0135276110446,\n",
       "                               n_iter_no_change=32))])
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" ], "text/plain": [ "Pipeline(steps=[('selectfwe', SelectFwe(alpha=0.0001569023321)),\n", " ('powertransformer', PowerTransformer()),\n", " ('mlpclassifier',\n", " MLPClassifier(activation='identity', alpha=0.0008696190619,\n", " hidden_layer_sizes=[203, 203],\n", " learning_rate_init=0.0135276110446,\n", " n_iter_no_change=32))])" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#plot the best pipeline\n", "if isinstance(est.fitted_pipeline_, tpot.GraphPipeline):\n", " est.fitted_pipeline_.plot()\n", " \n", "est.fitted_pipeline_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Transformer-only pipelines - imputation optimization example\n", "\n", "Pipelines don't necessarily need to end in a classifier or regressor. Transformer only pipelines are possible as long as you have a custom objective function to match. " ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.04690299241236334" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import sklearn\n", "import sklearn.datasets\n", "import numpy as np\n", "import tpot\n", "\n", "#in practice, cross validation is likely better, but this simple example is fine for demonstration purposes\n", "def rmse_obective(est, X, missing_add=.2, rng=1, fitted=False):\n", " rng = np.random.default_rng(rng)\n", " X_missing = X.copy()\n", " missing_idx = rng.random(X.shape) < missing_add\n", " X_missing[missing_idx] = np.nan\n", " \n", " if not fitted:\n", " est.fit(X_missing)\n", " \n", " X_filled = est.transform(X_missing)\n", " return np.sqrt(np.mean((X_filled[missing_idx] - X[missing_idx])**2))\n", "\n", "from sklearn.impute import SimpleImputer\n", "\n", "X, y = sklearn.datasets.load_diabetes(return_X_y=True)\n", "\n", "imp = SimpleImputer(strategy=\"mean\")\n", "\n", "rmse_obective(imp, X)" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
SimpleImputer()
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" ], "text/plain": [ "SimpleImputer()" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import tpot.search_spaces\n", "from ConfigSpace import ConfigurationSpace, Integer, Float, Categorical, Normal\n", "\n", "#set up an imputation search space that includes simple imputer, knn imputer, and iterative imputer (with an optimized ExtraTreesRegressor)\n", "\n", "simple_imputer = tpot.config.get_search_space(\"SimpleImputer\")\n", "knn_imputer = tpot.config.get_search_space(\"KNNImputer\")\n", "\n", "space = ConfigurationSpace({ 'initial_strategy' : Categorical('initial_strategy', \n", " ['mean', 'median', \n", " 'most_frequent', 'constant']),\n", " 'n_nearest_features' : Integer('n_nearest_features', \n", " bounds=(1, X.shape[1])),\n", " 'imputation_order' : Categorical('imputation_order', \n", " ['ascending', 'descending', \n", " 'roman', 'arabic', 'random']),\n", "})\n", "\n", "# This optimizes both the iterative imputer parameters and the ExtraTreesRegressor parameters\n", "iterative_imputer_sp = tpot.search_spaces.pipelines.WrapperPipeline(\n", " method = sklearn.impute.IterativeImputer,\n", " space = space,\n", " estimator_search_space = tpot.config.get_search_space(\"ExtraTreesRegressor\"),\n", ")\n", "#this is equivalent to\n", "# iterative_imputer_sp = tpot.config.get_search_space(\"IterativeImputer_learned_estimators\")\n", "\n", "imputation_search_space = tpot.search_spaces.pipelines.ChoicePipeline(\n", " search_spaces = [simple_imputer, knn_imputer, iterative_imputer_sp],\n", ")\n", "imputation_search_space.generate().export_pipeline()" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/ketrong/Desktop/tpotvalidation/tpot/tpot/tpot_estimator/estimator.py:535: UserWarning: Labels are not encoded as ints from 0 to N. For compatibility with some classifiers such as sklearn, TPOT has encoded y with the sklearn LabelEncoder. When using pipelines outside the main TPOT estimator class, you can encode the labels with est.label_encoder_\n", " warnings.warn(\"Labels are not encoded as ints from 0 to N. For compatibility with some classifiers such as sklearn, TPOT has encoded y with the sklearn LabelEncoder. When using pipelines outside the main TPOT estimator class, you can encode the labels with est.label_encoder_\")\n", "Generation: : 1it [00:19, 19.42s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Generation: 1\n", "Best rmse score: 0.03494378757292814\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Generation: : 2it [00:35, 17.45s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Generation: 2\n", "Best rmse score: 0.03494378757292814\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Generation: : 3it [00:51, 16.76s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Generation: 3\n", "Best rmse score: 0.034787576318641794\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "Generation: : 3it [01:10, 23.47s/it]" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Generation: 4\n", "Best rmse score: 0.034283600126080886\n", "Early stop\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "\n" ] }, { "data": { "text/html": [ "
TPOTEstimator(classification=True, early_stop=2, max_eval_time_mins=300,\n",
       "              max_time_mins=10, n_jobs=20, objective_function_names=['rmse'],\n",
       "              other_objective_functions=[functools.partial(<function rmse_obective at 0x33edfd480>, X=array([[ 0.03807591,  0.05068012,  0.06169621, ..., -0.00259226,\n",
       "         0.01990749, -0.01764613],\n",
       "       [-0.00188202, -0.04464164, -0.05147406, ..., -0.03949338,\n",
       "        -...\n",
       "        -0.04688253,  0.01549073],\n",
       "       [-0.04547248, -0.04464164,  0.03906215, ...,  0.02655962,\n",
       "         0.04452873, -0.02593034],\n",
       "       [-0.04547248, -0.04464164, -0.0730303 , ..., -0.03949338,\n",
       "        -0.00422151,  0.00306441]]), missing_add=0.2)],\n",
       "              other_objective_functions_weights=[-1], scorers=[],\n",
       "              scorers_weights=[],\n",
       "              search_space=<tpot.search_spaces.pipelines.choice.ChoicePipeline object at 0x36ff3e770>,\n",
       "              verbose=3)
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" ], "text/plain": [ "TPOTEstimator(classification=True, early_stop=2, max_eval_time_mins=300,\n", " max_time_mins=10, n_jobs=20, objective_function_names=['rmse'],\n", " other_objective_functions=[functools.partial(, X=array([[ 0.03807591, 0.05068012, 0.06169621, ..., -0.00259226,\n", " 0.01990749, -0.01764613],\n", " [-0.00188202, -0.04464164, -0.05147406, ..., -0.03949338,\n", " -...\n", " -0.04688253, 0.01549073],\n", " [-0.04547248, -0.04464164, 0.03906215, ..., 0.02655962,\n", " 0.04452873, -0.02593034],\n", " [-0.04547248, -0.04464164, -0.0730303 , ..., -0.03949338,\n", " -0.00422151, 0.00306441]]), missing_add=0.2)],\n", " other_objective_functions_weights=[-1], scorers=[],\n", " scorers_weights=[],\n", " search_space=,\n", " verbose=3)" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from functools import partial\n", "\n", "final_objective = partial(rmse_obective, X=X, missing_add=.2)\n", "\n", "est = tpot.TPOTEstimator(\n", " scorers = [],\n", " scorers_weights = [],\n", " other_objective_functions = [final_objective],\n", " other_objective_functions_weights = [-1],\n", " objective_function_names = [\"rmse\"],\n", " classification = True,\n", " search_space = imputation_search_space,\n", " max_time_mins=10,\n", " max_eval_time_mins = 60*5,\n", " verbose = 3,\n", " early_stop = 2,\n", " n_jobs=20,\n", ")\n", "\n", "est.fit(X, y=y)" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "final rmse score 0.02796745384428642\n" ] } ], "source": [ "# score the model\n", "rmse_score = final_objective(est, fitted=True)\n", "print(\"final rmse score\", rmse_score)" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
IterativeImputer(estimator=ExtraTreesRegressor(criterion='friedman_mse',\n",
       "                                               max_features=0.6404215718013,\n",
       "                                               min_samples_leaf=2,\n",
       "                                               min_samples_split=10, n_jobs=1),\n",
       "                 imputation_order='arabic', n_nearest_features=9)
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" ], "text/plain": [ "IterativeImputer(estimator=ExtraTreesRegressor(criterion='friedman_mse',\n", " max_features=0.6404215718013,\n", " min_samples_leaf=2,\n", " min_samples_split=10, n_jobs=1),\n", " imputation_order='arabic', n_nearest_features=9)" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "est.fitted_pipeline_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Combined Search Space Example" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Generation: : 25it [10:00, 24.01s/it]\n" ] }, { "data": { "text/html": [ "
TPOTEstimator(classification=True, cv=5, max_eval_time_mins=300,\n",
       "              max_time_mins=10, n_jobs=20, scorers=['roc_auc'],\n",
       "              scorers_weights=[1],\n",
       "              search_space=<tpot.search_spaces.pipelines.sequential.SequentialPipeline object at 0x35798c880>,\n",
       "              verbose=2)
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" ], "text/plain": [ "TPOTEstimator(classification=True, cv=5, max_eval_time_mins=300,\n", " max_time_mins=10, n_jobs=20, scorers=['roc_auc'],\n", " scorers_weights=[1],\n", " search_space=,\n", " verbose=2)" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from tpot.search_spaces.pipelines import *\n", "from tpot.config import get_search_space\n", "\n", "selectors = get_search_space([\"selectors_classification\", \"Passthrough\"])\n", "estimators = get_search_space([\"classifiers\"])\n", "\n", "\n", "# this allows us to wrap the classifiers in the EstimatorTransformer\n", "# this is necessary so that classifiers can be used inside of sklearn pipelines\n", "wrapped_estimators = WrapperPipeline(tpot.builtin_modules.EstimatorTransformer, {}, estimators)\n", "\n", "scalers = get_search_space([\"scalers\",\"Passthrough\"])\n", "\n", "transformers_layer =UnionPipeline([\n", " ChoicePipeline([\n", " DynamicUnionPipeline(get_search_space([\"transformers\"])),\n", " get_search_space(\"SkipTransformer\"),\n", " ]),\n", " get_search_space(\"Passthrough\")\n", " ]\n", " )\n", "\n", "inner_estimators_layer = UnionPipeline([\n", " ChoicePipeline([\n", " DynamicUnionPipeline(wrapped_estimators),\n", " get_search_space(\"SkipTransformer\"),\n", " ]),\n", " get_search_space(\"Passthrough\")]\n", " )\n", "\n", "\n", "search_space = SequentialPipeline(search_spaces=[\n", " scalers,\n", " selectors, \n", " transformers_layer,\n", " inner_estimators_layer,\n", " estimators,\n", " ])\n", "\n", "est = tpot.TPOTEstimator(\n", " scorers = [\"roc_auc\"],\n", " scorers_weights = [1],\n", " classification = True,\n", " cv = 5,\n", " search_space = search_space,\n", " max_time_mins=10,\n", " max_eval_time_mins = 60*5,\n", " verbose = 2,\n", " n_jobs=20,\n", ")\n", "\n", "est.fit(X_train, y_train)" ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
Pipeline(steps=[('maxabsscaler', MaxAbsScaler()),\n",
       "                ('selectfwe', SelectFwe(alpha=0.0004883916878)),\n",
       "                ('featureunion-1',\n",
       "                 FeatureUnion(transformer_list=[('featureunion',\n",
       "                                                 FeatureUnion(transformer_list=[('powertransformer',\n",
       "                                                                                 PowerTransformer())])),\n",
       "                                                ('passthrough',\n",
       "                                                 Passthrough())])),\n",
       "                ('featureunion-2',\n",
       "                 FeatureUnion(transformer_list=[('featureunion',\n",
       "                                                 FeatureUnion(transformer_list=[('estimatortransformer',\n",
       "                                                                                 EstimatorTransformer(estimator=LinearDiscriminantAnalysis(shrinkage=0.5801392483719,\n",
       "                                                                                                                                           solver='lsqr')))])),\n",
       "                                                ('passthrough',\n",
       "                                                 Passthrough())])),\n",
       "                ('mlpclassifier',\n",
       "                 MLPClassifier(activation='identity', alpha=0.0310773820788,\n",
       "                               hidden_layer_sizes=[54, 54, 54],\n",
       "                               learning_rate_init=0.0017701050157,\n",
       "                               n_iter_no_change=32))])
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" ], "text/plain": [ "Pipeline(steps=[('maxabsscaler', MaxAbsScaler()),\n", " ('selectfwe', SelectFwe(alpha=0.0004883916878)),\n", " ('featureunion-1',\n", " FeatureUnion(transformer_list=[('featureunion',\n", " FeatureUnion(transformer_list=[('powertransformer',\n", " PowerTransformer())])),\n", " ('passthrough',\n", " Passthrough())])),\n", " ('featureunion-2',\n", " FeatureUnion(transformer_list=[('featureunion',\n", " FeatureUnion(transformer_list=[('estimatortransformer',\n", " EstimatorTransformer(estimator=LinearDiscriminantAnalysis(shrinkage=0.5801392483719,\n", " solver='lsqr')))])),\n", " ('passthrough',\n", " Passthrough())])),\n", " ('mlpclassifier',\n", " MLPClassifier(activation='identity', alpha=0.0310773820788,\n", " hidden_layer_sizes=[54, 54, 54],\n", " learning_rate_init=0.0017701050157,\n", " n_iter_no_change=32))])" ] }, "execution_count": 57, "metadata": {}, "output_type": "execute_result" } ], "source": [ "est.fitted_pipeline_" ] } ], "metadata": { "kernelspec": { "display_name": "tpotenv", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.16" } }, "nbformat": 4, "nbformat_minor": 2 }