Family features

README.md

@@ -21,9 +21,17 @@ In this open source solution you will find references to the [neptune.ml](https:
 # How to start?
 ## Learn about our solutions
 1. Check [Kaggle forum](https://www.kaggle.com/c/home-credit-default-risk/discussion/57175) and participate in the discussions.
-1. Check our [Wiki pages :page_facing_up:](https://github.com/neptune-ml/open-solution-home-credit/wiki), where we document our work. Click on the house to get started [:house_with_garden:](https://github.com/neptune-ml/open-solution-home-credit/wiki).
+1. Check our [Wiki pages :house_with_garden:](https://github.com/neptune-ml/open-solution-home-credit/wiki), where we document our work. See solutions below:
 
-## Start experimenting with our ready-to-use code
+| link to code | name | CV | LB | link to description |
+|:---:|:---:|:---:|:---:|:---:|
+|[solution 1](https://github.com/neptune-ml/open-solution-home-credit/tree/solution-1)|*chestnut* :chestnut:|?|0.742|[LightGBM and basic features](https://github.com/neptune-ml/open-solution-home-credit/wiki/LightGBM-and-basic-features)|
+|[solution 2](https://github.com/neptune-ml/open-solution-home-credit/tree/solution-2)|*seedling* :seedling:|?|0.747|[Sklearn and XGBoost algorithms and groupby features](https://github.com/neptune-ml/open-solution-home-credit/wiki/Sklearn-and-XGBoost-algorithms-and-groupby-features)|
+|[solution 3](https://github.com/neptune-ml/open-solution-home-credit/tree/solution-3)|*blossom* :blossom:|0.7840|0.790|[LightGBM on selected features](https://github.com/neptune-ml/open-solution-home-credit/wiki/LightGBM-on-selected-features)|
+|[solution 4](https://github.com/neptune-ml/open-solution-home-credit/tree/solution-4)|*tulip* :tulip: |0.7905|0.801|[LightGBM with smarter features](https://github.com/neptune-ml/open-solution-home-credit/wiki/LightGBM-with-smarter-features)|
+|solution 5|*sunflower* :sunflower:|||*(in progress...)* :construction_worker_woman:|
+
+## Start experimenting with ready-to-use code
 You can jump start your participation in the competition by using our starter pack. Installation instruction below will guide you through the setup.
 
 ### Installation *(fast track)*
@@ -39,7 +47,7 @@ neptune run --config neptune_random_search.yaml main.py train_evaluate_predict -
 
 :snake:
 ```bash
-python main.py train_evaluate_predict --pipeline_name lightGBM
+python main.py -- train_evaluate_predict --pipeline_name lightGBM
 ```
 
 ### Installation *(step by step)*
@@ -49,7 +57,7 @@ git clone https://github.com/minerva-ml/open-solution-home-credit.git
 ```
 2. Install requirements in your Python3 environment
 ```bash
-pip3 install requirements.txt
+pip3 install -r requirements.txt
 ```
 3. Register to the [neptune.ml](https://neptune.ml) _(if you wish to use it)_
 4. Update data directories in the [neptune.yaml](https://github.com/minerva-ml/open-solution-home-credit/blob/master/neptune.yaml) configuration file.
@@ -63,7 +71,7 @@ neptune run --config neptune_random_search.yaml main.py train_evaluate_predict -
 
 :snake:
 ```bash
-python main.py train_evaluate_predict --pipeline_name lightGBM
+python main.py -- train_evaluate_predict --pipeline_name lightGBM
 ```
 
 6. collect submit from `experiment_directory` specified in the [neptune.yaml](https://github.com/minerva-ml/open-solution-home-credit/blob/master/neptune.yaml)

src/feature_extraction.py

@@ -255,7 +255,14 @@ def __init__(self, categorical_columns, numerical_columns):
                                              'external_sources_mean',
                                              'external_sources_nanmedian',
                                              'short_employment',
-                                             'young_age']
+                                             'young_age',
+                                             'cnt_non_child',
+                                             'child_to_non_child_ratio',
+                                             'income_per_non_child',
+                                             'credit_per_person',
+                                             'credit_per_child',
+                                             'credit_per_non_child',
+                                             ]
 
     def transform(self, X, **kwargs):
         X['annuity_income_percentage'] = X['AMT_ANNUITY'] / X['AMT_INCOME_TOTAL']
@@ -273,6 +280,12 @@ def transform(self, X, **kwargs):
         X['phone_to_birth_ratio'] = X['DAYS_LAST_PHONE_CHANGE'] / X['DAYS_BIRTH']
         X['phone_to_employ_ratio'] = X['DAYS_LAST_PHONE_CHANGE'] / X['DAYS_EMPLOYED']
         X['external_sources_weighted'] = X.EXT_SOURCE_1 * 2 + X.EXT_SOURCE_2 * 3 + X.EXT_SOURCE_3 * 4
+        X['cnt_non_child'] = X['CNT_FAM_MEMBERS'] - X['CNT_CHILDREN']
+        X['child_to_non_child_ratio'] = X['CNT_CHILDREN'] / X['cnt_non_child']
+        X['income_per_non_child'] = X['AMT_INCOME_TOTAL'] / X['cnt_non_child']
+        X['credit_per_person'] = X['AMT_CREDIT'] / X['CNT_FAM_MEMBERS']
+        X['credit_per_child'] = X['AMT_CREDIT'] / (1 + X['CNT_CHILDREN'])
+        X['credit_per_non_child'] = X['AMT_CREDIT'] / X['cnt_non_child']
         for function_name in ['min', 'max', 'sum', 'mean', 'nanmedian']:
             X['external_sources_{}'.format(function_name)] = eval('np.{}'.format(function_name))(
                 X[['EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3']], axis=1)

src/pipeline_config.py

@@ -139,13 +139,14 @@
                                        'YEARS_BUILD_MEDI',
                                        'YEARS_BUILD_MODE']
 
+cols_to_agg = ['AMT_CREDIT', 'AMT_ANNUITY', 'AMT_INCOME_TOTAL', 'EXT_SOURCE_1', 'EXT_SOURCE_2', 'EXT_SOURCE_3']
+aggs = ['min', 'mean', 'max', 'sum', 'var']
+aggregation_pairs = [(col, agg) for col in cols_to_agg for agg in aggs]
+
 APPLICATION_AGGREGATION_RECIPIES = [
-    (['CODE_GENDER', 'NAME_EDUCATION_TYPE'], [('AMT_ANNUITY', 'max'),
-                                              ('AMT_CREDIT', 'max'),
-                                              ('EXT_SOURCE_1', 'mean'),
-                                              ('EXT_SOURCE_2', 'mean'),
-                                              ('OWN_CAR_AGE', 'max'),
-                                              ('OWN_CAR_AGE', 'sum')]),
+    (['NAME_EDUCATION_TYPE', 'CODE_GENDER'],  aggregation_pairs),
+    (['NAME_FAMILY_STATUS', 'NAME_EDUCATION_TYPE'], aggregation_pairs),
+    (['NAME_FAMILY_STATUS', 'CODE_GENDER'], aggregation_pairs),
     (['CODE_GENDER', 'ORGANIZATION_TYPE'], [('AMT_ANNUITY', 'mean'),
                                             ('AMT_INCOME_TOTAL', 'mean'),
                                             ('DAYS_REGISTRATION', 'mean'),