AutoML on Titanic dataset samples

scripts/run_auto_ml_titanic.py

@@ -0,0 +1,32 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Apr 20 21:13:14 2019
+
+@author: Lionel Massoulard
+"""
+
+import os.path
+
+from aikit.datasets import load_dataset, DatasetEnum
+from aikit.ml_machine import MlMachineLauncher
+
+from aikit.logging import _set_logging_to_console
+
+def loader():
+    """ this is the function that should return the DataSet """
+    dfX,y,_,_,_ = load_dataset(DatasetEnum.titanic)
+
+    return dfX,y
+
+
+if __name__ == "__main__":
+
+    _set_logging_to_console()
+
+    base_folder = os.path.join(os.path.expanduser('~'), "automl","titanic")
+    launcher = MlMachineLauncher(base_folder=base_folder,
+                                 name="titanic",
+                                 loader=loader)
+
+
+    launcher.execute_processed_command_argument()

scripts/run_auto_ml_titanic_advanced.py

@@ -0,0 +1,108 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sat Apr 20 21:13:14 2019
+
+@author: Lionel Massoulard
+"""
+import os.path
+
+from aikit.datasets import load_dataset, DatasetEnum
+from aikit.ml_machine import MlMachineLauncher
+
+from aikit.logging import _set_logging_to_console
+
+from sklearn.model_selection import StratifiedKFold
+
+from collections import OrderedDict
+
+def loader():
+    """ this is the function that should return the DataSet """
+    dfX,y,_,_,_ = load_dataset(DatasetEnum.titanic)
+
+    return dfX,y
+
+
+if __name__ == "__main__":
+
+    _set_logging_to_console()
+
+    def set_configs(launcher):
+        """ this is the function that will set the different configurations """
+        # Change the CV here :
+        launcher.job_config.cv = StratifiedKFold(n_splits=10, shuffle=True, random_state=123) # specify CV
+
+        # Change the scorer to use :
+        launcher.job_config.scoring = ['accuracy', 'log_loss_patched', 'avg_roc_auc', 'f1_macro']
+
+        # Change the main scorer (linked with )
+        launcher.job_config.main_scorer = 'accuracy'
+
+        # Change the base line (for the main scorer)
+        launcher.job_config.score_base_line = 0.8
+
+        # Allow 'approx cv or not : 
+        launcher.job_config.allow_approx_cv = False
+
+        # Allow 'block search' or not :
+        launcher.job_config.do_blocks_search = True
+
+        # Start with default models or not :
+        launcher.job_config.start_with_default = True
+
+        # Change default 'columns block' : use for block search
+        launcher.auto_ml_config.columns_block = OrderedDict([
+              ('NUM', ['pclass', 'age', 'sibsp', 'parch', 'fare', 'body']),
+             ('TEXT', ['name', 'ticket']),
+             ('CAT', ['sex', 'cabin', 'embarked', 'boat', 'home_dest'])])
+
+        # Change the list of models/transformers to use :
+        launcher.auto_ml_config.models_to_keep = [
+                     #('Model', 'LogisticRegression'),
+                     ('Model', 'RandomForestClassifier'),
+                     #('Model', 'ExtraTreesClassifier'),
+
+                     # Example : keeping only RandomForestClassifer
+
+                     ('FeatureSelection', 'FeaturesSelectorClassifier'),
+
+                     ('TextEncoder', 'CountVectorizerWrapper'),
+
+                     #('TextPreprocessing', 'TextNltkProcessing'),
+                     #('TextPreprocessing', 'TextDefaultProcessing'),
+                     #('TextPreprocessing', 'TextDigitAnonymizer'),
+
+                     # => Example: removing TextPreprocessing
+
+                     ('CategoryEncoder', 'NumericalEncoder'),
+                     ('CategoryEncoder', 'TargetEncoderClassifier'),
+
+                     ('MissingValueImputer', 'NumImputer'),
+
+                     ('DimensionReduction', 'TruncatedSVDWrapper'),
+                     ('DimensionReduction', 'PCAWrapper'),
+
+                     ('TextDimensionReduction', 'TruncatedSVDWrapper'),
+                     ('DimensionReduction', 'KMeansTransformer'),
+                     ('Scaling', 'CdfScaler')
+                     ]
+
+        # Specify the type of problem
+        launcher.auto_ml_config.type_of_problem = 'CLASSIFICATION'
+
+        # Specify special hyper parameters : Example 
+        launcher.auto_ml_config.specific_hyper = {
+                ('Model', 'RandomForestClassifier') : {"n_estimators":[10,20]}
+                }
+        # Example : only test n_estimators to be 10 or 20
+
+        return launcher
+
+    base_folder = os.path.join(os.path.expanduser('~'), "automl","titanic")
+    launcher = MlMachineLauncher(base_folder=base_folder,
+                                 name="titanic",
+                                 loader=loader,
+                                 set_configs=set_configs)
+
+
+
+    launcher.execute_processed_command_argument()