Update g1_v1 graph processing and schemas
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62dfadde20
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@ -26,6 +26,6 @@ def post_processing(data):
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for key in [
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for key in [
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"hd_score_m1", "hd_score_g1", "cluster_size_users_v2",
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"hd_score_m1", "hd_score_g1", "cluster_size_users_v2",
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"target_connected_30_sum", "email_cnt", "rejected_app_count",
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"target_connected_30_sum", "email_cnt", "rejected_app_count",
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"app_dt_day_cnt"
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"app_dt_day_cnt", "hd_score_iso_m2"
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]
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]
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}
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}
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@ -15,7 +15,8 @@ def pre_processing(results):
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"target_connected_30_sum": float,
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"target_connected_30_sum": float,
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"email_cnt": float,
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"email_cnt": float,
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"rejected_app_count": float,
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"rejected_app_count": float,
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"app_dt_day_cnt": float
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"app_dt_day_cnt": float,
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"hd_score_iso_m2": float
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}
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}
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data = {
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data = {
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"hd_score_m1": result["hd_score_m1"],
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"hd_score_m1": result["hd_score_m1"],
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@ -25,6 +26,7 @@ def pre_processing(results):
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"rejected_app_count": result["rejected_app_count"],
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"rejected_app_count": result["rejected_app_count"],
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"app_dt_day_cnt": result["app_dt_day_cnt"],
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"app_dt_day_cnt": result["app_dt_day_cnt"],
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"cluster_size": result["cluster_size"],
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"cluster_size": result["cluster_size"],
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"hd_score_iso_m2": result["hd_score_iso_m2"],
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}
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}
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for col, dtype in dtypes.items():
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for col, dtype in dtypes.items():
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@ -1,5 +1,5 @@
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joblib==1.4.2
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joblib==1.4.2
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pandas==2.2.3
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pandas==2.2.3
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xgboost==2.1.3
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xgboost==2.1.3
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typing==3.6.1
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typing==3.6.1
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@ -29,6 +29,10 @@
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"app_dt_day_cnt": {
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"app_dt_day_cnt": {
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"type": ["number", "null"],
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"type": ["number", "null"],
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"description": "Number of application days counted."
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"description": "Number of application days counted."
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},
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"hd_score_iso_m2": {
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"type": ["number", "null"],
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"description": "HD fraud Score M2"
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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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@ -11,7 +11,8 @@ data = [{
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"email_cnt": 1,
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"email_cnt": 1,
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"rejected_app_count": 2,
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"rejected_app_count": 2,
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"app_dt_day_cnt": 2,
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"app_dt_day_cnt": 2,
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"cluster_size": 3
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"cluster_size": 3,
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"hd_score_iso_m2": 1202
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}]
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}]
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class TestBlock(unittest.TestCase):
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class TestBlock(unittest.TestCase):
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