Add the new feature of customized initial population.

PULL_REQUEST_TEMPLATE.md

@@ -1,30 +1,116 @@
-[please review the [Contribution Guidelines](http://epistasislab.github.io/tpot/contributing/) prior to submitting your pull request. go ahead and delete this line if you've already reviewed said guidelines.]
-
 ## What does this PR do?
 
-
+Add the new feature of allowing users to specify customized initial pipeline population for TPOT2.
 
 ## Where should the reviewer start?
 
-
+- tpot2/tests/test_customized_iniPop.py
+Contains the SequentialPipeline initialization method, which consists of scalers, selectors, transformers_layer, inner_estimators_layer, estimators and a sample of initializing this TPOTClassifier in a customized_initial_population parameter.
+- tpot2/config/get_configspace.py
+A new set_node() function has been added, containing mainly operations for adding new nodes in pipeline.
+- tpot2/evolvers/base_evolver.py
+Add some judgments about the number of initialized populations and the number of populations that need to be generated by crushed gold.
+- tpot2/tpot_estimator/estimator.py
+Add passing of customized_initial_population parameter
 
 ## How should this PR be tested?
 
+The test code is at tpot2/tests/test_customized_iniPop.py:
+
+**pytest test_customized_iniPop.py**
+
+```
+import pytest
+
+
+@pytest.fixture
+def test_customized_iniPop():
+    import tpot2
+    import sklearn
+    import sklearn.datasets
+
+    scorer = sklearn.metrics.get_scorer('roc_auc_ovo')
+
+    X, y = sklearn.datasets.load_iris(return_X_y=True)
+
+    X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, train_size=0.75, test_size=0.25)
+
+    from tpot2.config.get_configspace import set_node
+    from tpot2.search_spaces.pipelines.union import UnionPipeline
+    from tpot2.search_spaces.pipelines.choice import ChoicePipeline
+    from tpot2.search_spaces.pipelines.sequential import SequentialPipeline
+    from tpot2.config.get_configspace import get_search_space
 
+    scalers = set_node("MinMaxScaler", {})
+    selectors = set_node("SelectFwe", {'alpha': 0.0002381268562})
+    transformers_layer =UnionPipeline([
+                            ChoicePipeline([
+                                set_node("SkipTransformer", {})
+                            ]),
+                            get_search_space("Passthrough",)
+                            ]
+                        )
+
+    inner_estimators_layer = UnionPipeline([
+                                get_search_space("Passthrough",)]
+                            )
+    estimators = set_node("HistGradientBoostingClassifier", 
+                        {'early_stop': 'valid', 
+                        'l2_regularization': 0.0011074158219, 
+                        'learning_rate': 0.0050792320068, 
+                        'max_depth': None, 
+                        'max_features': 0.3430178535213, 
+                        'max_leaf_nodes': 237, 
+                        'min_samples_leaf': 63, 
+                        'tol': 0.0001, 
+                        'n_iter_no_change': 14, 
+                        'validation_fraction': 0.2343285974496})
+
+    pipeline = SequentialPipeline(search_spaces=[
+                                        scalers,
+                                        selectors, 
+                                        transformers_layer,
+                                        inner_estimators_layer,
+                                        estimators,
+                                        ])
+    ind = pipeline.generate()
+
+    est = tpot2.TPOTClassifier(search_space="linear", n_jobs=40, verbose=5, generations=1, population_size=5, customized_initial_population=[ind])
+
+    est.fit(X_train, y_train)
+
+    print(str(est.fitted_pipeline_))
+
+    print(scorer(est, X_test, y_test))
+```
 
 ## Any background context you want to provide?
 
+In this version, users can specify a well-defined initial pipeline population, currently limited to the *SequentialPipeline* type. This update has the potential to improve algorithm performance and reduce evolutionary time.
 
+Several Tips:
 
-## What are the relevant issues?
+1. These SequentialPipeline pipelines can be obtained:
+
+Referencing the examples in customized_initial_population.py and modifying them according to TPOT2's config_dict.
 
-[you can link directly to issues by entering # then the number of the issue]
+2. We consider the relationship between #customized initial pipelines and #population_size as follows:
 
-## Screenshots (if appropriate)
+```
+init_population_size = len(customized_initial_population)
+if self.cur_population_size <= init_population_size:
+    initial_population = customized_initial_population[:self.cur_population_size]
+else:
+    initial_population = [next(self.individual_generator) for _ in range(self.cur_population_size - init_population_size)]
+    initial_population = customized_initial_population + initial_population
+```
+3. The current version is only applicable to solve the problem where search_spaces is linear and the initialized pipeline is of type SequentialPipeline. We will continue to refine the scenario where search_spaces is graph and the pipeline is of type GraphPipeline in the near future if you think our approach is appropriate.
 
 
+## What are the relevant issues?
+
+[issue-61](https://github.com/EpistasisLab/tpot2/issues/61)
 
-## Questions:
+## Main Contributors
 
-- Do the docs need to be updated?
-- Does this PR add new (Python) dependencies?
+@peiyanpan @t-harden