Autogluon fraud

# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

# Input data files are available in the read-only "../input/" directory
# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory

import os
for dirname, _, filenames in os.walk('/kaggle/input'):
    for filename in filenames:
        print(os.path.join(dirname, filename))

# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" 
# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session

import pandas as pd
import numpy as np
from autogluon.tabular import TabularPredictor

# directory = '~/IEEEfraud/'  # directory where you have downloaded the data CSV files from the competition
label = 'isFraud'  # name of target variable to predict in this competition
eval_metric = 'roc_auc'  # Optional: specify that competition evaluation metric is AUC
# save_path = directory + 'AutoGluonModels/'  # where to store trained models

!kaggle competitions download -c ieee-fraud-detection

Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /home/coco/.kaggle/kaggle.json'
Downloading ieee-fraud-detection.zip to /home/coco/Dropbox/coco/posts/study
 99%|███████████████████████████████████████▌| 117M/118M [00:09<00:00, 18.2MB/s]
100%|████████████████████████████████████████| 118M/118M [00:09<00:00, 13.5MB/s]

!unzip ieee-fraud-detection.zip -d ./ieee-fraud-detection

Archive:  ieee-fraud-detection.zip
  inflating: ./ieee-fraud-detection/sample_submission.csv  
  inflating: ./ieee-fraud-detection/test_identity.csv  
  inflating: ./ieee-fraud-detection/test_transaction.csv  
  inflating: ./ieee-fraud-detection/train_identity.csv  
  inflating: ./ieee-fraud-detection/train_transaction.csv  

train_identity = pd.read_csv('ieee-fraud-detection/train_identity.csv')
train_transaction = pd.read_csv('ieee-fraud-detection/train_transaction.csv')

# !kaggle competitions download -c ieee-fraud-detection
# !unzip titanic.zip -d ./ieee-fraud-detection
# df_train = pd.read_csv('titanic/train.csv')
# df_test = pd.read_csv('titanic/test.csv')
# !rm titanic.zip
# !rm -rf titanic/

train_data = pd.merge(train_transaction, train_identity, on='TransactionID', how='left')

train_data

	TransactionID	isFraud	TransactionDT	TransactionAmt	ProductCD	card1	card2	card3	card4	card5	...	id_31	id_32	id_33	id_34	id_35	id_36	id_37	id_38	DeviceType	DeviceInfo
0	2987000	0	86400	68.50	W	13926	NaN	150.0	discover	142.0	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
1	2987001	0	86401	29.00	W	2755	404.0	150.0	mastercard	102.0	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
2	2987002	0	86469	59.00	W	4663	490.0	150.0	visa	166.0	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
3	2987003	0	86499	50.00	W	18132	567.0	150.0	mastercard	117.0	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
4	2987004	0	86506	50.00	H	4497	514.0	150.0	mastercard	102.0	...	samsung browser 6.2	32.0	2220x1080	match_status:2	T	F	T	T	mobile	SAMSUNG SM-G892A Build/NRD90M
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
590535	3577535	0	15811047	49.00	W	6550	NaN	150.0	visa	226.0	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
590536	3577536	0	15811049	39.50	W	10444	225.0	150.0	mastercard	224.0	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
590537	3577537	0	15811079	30.95	W	12037	595.0	150.0	mastercard	224.0	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
590538	3577538	0	15811088	117.00	W	7826	481.0	150.0	mastercard	224.0	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN
590539	3577539	0	15811131	279.95	W	15066	170.0	150.0	mastercard	102.0	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

590540 rows × 434 columns

predictor = TabularPredictor(label=label, eval_metric=eval_metric, verbosity=3).fit(
    train_data, presets='best_quality', time_limit=1200
)

results = predictor.fit_summary()

No path specified. Models will be saved in: "AutogluonModels/ag-20231012_071002"
Presets specified: ['best_quality']
============ fit kwarg info ============
User Specified kwargs:
{'auto_stack': True}
Full kwargs:
{'_feature_generator_kwargs': None,
 '_save_bag_folds': None,
 'ag_args': None,
 'ag_args_ensemble': None,
 'ag_args_fit': None,
 'auto_stack': True,
 'calibrate': 'auto',
 'excluded_model_types': None,
 'feature_generator': 'auto',
 'feature_prune_kwargs': None,
 'holdout_frac': None,
 'hyperparameter_tune_kwargs': None,
 'included_model_types': None,
 'keep_only_best': False,
 'name_suffix': None,
 'num_bag_folds': None,
 'num_bag_sets': None,
 'num_stack_levels': None,
 'pseudo_data': None,
 'refit_full': False,
 'save_space': False,
 'set_best_to_refit_full': False,
 'unlabeled_data': None,
 'use_bag_holdout': False,
 'verbosity': 3}
========================================
/home/coco/anaconda3/envs/ag/lib/python3.10/site-packages/autogluon/core/utils/utils.py:564: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context("mode.use_inf_as_na", True):  # treat None, NaN, INF, NINF as NA
Stack configuration (auto_stack=True): num_stack_levels=0, num_bag_folds=8, num_bag_sets=20
Saving AutogluonModels/ag-20231012_071002/learner.pkl
Saving AutogluonModels/ag-20231012_071002/predictor.pkl
Beginning AutoGluon training ... Time limit = 1200s
AutoGluon will save models to "AutogluonModels/ag-20231012_071002"
AutoGluon Version:  0.8.2
Python Version:     3.10.13
Operating System:   Linux
Platform Machine:   x86_64
Platform Version:   #26~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Thu Jul 13 16:27:29 UTC 2
Disk Space Avail:   715.54 GB / 982.82 GB (72.8%)
Train Data Rows:    590540
Train Data Columns: 433
Label Column: isFraud
Preprocessing data ...
/home/coco/anaconda3/envs/ag/lib/python3.10/site-packages/autogluon/core/utils/utils.py:564: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context("mode.use_inf_as_na", True):  # treat None, NaN, INF, NINF as NA
AutoGluon infers your prediction problem is: 'binary' (because only two unique label-values observed).
    2 unique label values:  [0, 1]
    If 'binary' is not the correct problem_type, please manually specify the problem_type parameter during predictor init (You may specify problem_type as one of: ['binary', 'multiclass', 'regression'])
/home/coco/anaconda3/envs/ag/lib/python3.10/site-packages/autogluon/tabular/learner/default_learner.py:215: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context("mode.use_inf_as_na", True):  # treat None, NaN, INF, NINF as NA
Selected class <--> label mapping:  class 1 = 1, class 0 = 0
Using Feature Generators to preprocess the data ...
Fitting AutoMLPipelineFeatureGenerator...
    Available Memory:                    35500.76 MB
    Train Data (Original)  Memory Usage: 2715.97 MB (7.7% of available memory)
    Warning: Data size prior to feature transformation consumes 7.7% of available memory. Consider increasing memory or subsampling the data to avoid instability.
    Inferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.
    Stage 1 Generators:
        Fitting AsTypeFeatureGenerator...
            Original Features (exact raw dtype, raw dtype):
                ('float64', 'float') : 399 | ['TransactionAmt', 'card2', 'card3', 'card5', 'addr1', ...]
                ('int64', 'int')     :   3 | ['TransactionID', 'TransactionDT', 'card1']
                ('object', 'object') :  31 | ['ProductCD', 'card4', 'card6', 'P_emaildomain', 'R_emaildomain', ...]
            Types of features in original data (raw dtype, special dtypes):
                ('float', [])  : 399 | ['TransactionAmt', 'card2', 'card3', 'card5', 'addr1', ...]
                ('int', [])    :   3 | ['TransactionID', 'TransactionDT', 'card1']
                ('object', []) :  31 | ['ProductCD', 'card4', 'card6', 'P_emaildomain', 'R_emaildomain', ...]
            Types of features in processed data (raw dtype, special dtypes):
                ('float', [])  : 399 | ['TransactionAmt', 'card2', 'card3', 'card5', 'addr1', ...]
                ('int', [])    :   3 | ['TransactionID', 'TransactionDT', 'card1']
                ('object', []) :  31 | ['ProductCD', 'card4', 'card6', 'P_emaildomain', 'R_emaildomain', ...]
            1.2s = Fit runtime
            433 features in original data used to generate 433 features in processed data.
    Stage 2 Generators:
        Fitting FillNaFeatureGenerator...
/home/coco/anaconda3/envs/ag/lib/python3.10/site-packages/autogluon/features/generators/fillna.py:58: FutureWarning: The 'downcast' keyword in fillna is deprecated and will be removed in a future version. Use res.infer_objects(copy=False) to infer non-object dtype, or pd.to_numeric with the 'downcast' keyword to downcast numeric results.
  X.fillna(self._fillna_feature_map, inplace=True, downcast=False)
            Types of features in original data (raw dtype, special dtypes):
                ('float', [])  : 399 | ['TransactionAmt', 'card2', 'card3', 'card5', 'addr1', ...]
                ('int', [])    :   3 | ['TransactionID', 'TransactionDT', 'card1']
                ('object', []) :  31 | ['ProductCD', 'card4', 'card6', 'P_emaildomain', 'R_emaildomain', ...]
            Types of features in processed data (raw dtype, special dtypes):
                ('float', [])  : 399 | ['TransactionAmt', 'card2', 'card3', 'card5', 'addr1', ...]
                ('int', [])    :   3 | ['TransactionID', 'TransactionDT', 'card1']
                ('object', []) :  31 | ['ProductCD', 'card4', 'card6', 'P_emaildomain', 'R_emaildomain', ...]
            0.7s = Fit runtime
            433 features in original data used to generate 433 features in processed data.
    Stage 3 Generators:
        Fitting IdentityFeatureGenerator...
            Types of features in original data (raw dtype, special dtypes):
                ('float', []) : 399 | ['TransactionAmt', 'card2', 'card3', 'card5', 'addr1', ...]
                ('int', [])   :   3 | ['TransactionID', 'TransactionDT', 'card1']
            Types of features in processed data (raw dtype, special dtypes):
                ('float', []) : 399 | ['TransactionAmt', 'card2', 'card3', 'card5', 'addr1', ...]
                ('int', [])   :   3 | ['TransactionID', 'TransactionDT', 'card1']
            0.2s = Fit runtime
            402 features in original data used to generate 402 features in processed data.
        Fitting CategoryFeatureGenerator...
            Fitting CategoryMemoryMinimizeFeatureGenerator...
                Types of features in original data (raw dtype, special dtypes):
                    ('category', []) : 31 | ['ProductCD', 'card4', 'card6', 'P_emaildomain', 'R_emaildomain', ...]
                Types of features in processed data (raw dtype, special dtypes):
                    ('category', []) : 31 | ['ProductCD', 'card4', 'card6', 'P_emaildomain', 'R_emaildomain', ...]
                0.0s = Fit runtime
                31 features in original data used to generate 31 features in processed data.
            Types of features in original data (raw dtype, special dtypes):
                ('object', []) : 31 | ['ProductCD', 'card4', 'card6', 'P_emaildomain', 'R_emaildomain', ...]
            Types of features in processed data (raw dtype, special dtypes):
                ('category', []) : 31 | ['ProductCD', 'card4', 'card6', 'P_emaildomain', 'R_emaildomain', ...]
            0.6s = Fit runtime
            31 features in original data used to generate 31 features in processed data.
        Skipping DatetimeFeatureGenerator: No input feature with required dtypes.
        Skipping TextSpecialFeatureGenerator: No input feature with required dtypes.
        Skipping TextNgramFeatureGenerator: No input feature with required dtypes.
        Skipping IdentityFeatureGenerator: No input feature with required dtypes.
        Skipping IsNanFeatureGenerator: No input feature with required dtypes.
    Stage 4 Generators:
        Fitting DropUniqueFeatureGenerator...
            Types of features in original data (raw dtype, special dtypes):
                ('category', []) :  31 | ['ProductCD', 'card4', 'card6', 'P_emaildomain', 'R_emaildomain', ...]
                ('float', [])    : 399 | ['TransactionAmt', 'card2', 'card3', 'card5', 'addr1', ...]
                ('int', [])      :   3 | ['TransactionID', 'TransactionDT', 'card1']
            Types of features in processed data (raw dtype, special dtypes):
                ('category', []) :  31 | ['ProductCD', 'card4', 'card6', 'P_emaildomain', 'R_emaildomain', ...]
                ('float', [])    : 399 | ['TransactionAmt', 'card2', 'card3', 'card5', 'addr1', ...]
                ('int', [])      :   3 | ['TransactionID', 'TransactionDT', 'card1']
            1.6s = Fit runtime
            433 features in original data used to generate 433 features in processed data.
    Stage 5 Generators:
        Fitting DropDuplicatesFeatureGenerator...
            5 duplicate columns removed: ['V28', 'V113', 'V119', 'V122', 'V154']
            Types of features in original data (raw dtype, special dtypes):
                ('category', []) :  31 | ['ProductCD', 'card4', 'card6', 'P_emaildomain', 'R_emaildomain', ...]
                ('float', [])    : 394 | ['TransactionAmt', 'card2', 'card3', 'card5', 'addr1', ...]
                ('int', [])      :   3 | ['TransactionID', 'TransactionDT', 'card1']
            Types of features in processed data (raw dtype, special dtypes):
                ('category', []) :  31 | ['ProductCD', 'card4', 'card6', 'P_emaildomain', 'R_emaildomain', ...]
                ('float', [])    : 394 | ['TransactionAmt', 'card2', 'card3', 'card5', 'addr1', ...]
                ('int', [])      :   3 | ['TransactionID', 'TransactionDT', 'card1']
            2.7s = Fit runtime
            428 features in original data used to generate 428 features in processed data.
    Unused Original Features (Count: 5): ['V28', 'V113', 'V119', 'V122', 'V154']
        These features were not used to generate any of the output features. Add a feature generator compatible with these features to utilize them.
        Features can also be unused if they carry very little information, such as being categorical but having almost entirely unique values or being duplicates of other features.
        These features do not need to be present at inference time.
        ('float', []) : 5 | ['V28', 'V113', 'V119', 'V122', 'V154']
    Types of features in original data (exact raw dtype, raw dtype):
        ('float64', 'float') : 394 | ['TransactionAmt', 'card2', 'card3', 'card5', 'addr1', ...]
        ('int64', 'int')     :   3 | ['TransactionID', 'TransactionDT', 'card1']
        ('object', 'object') :  31 | ['ProductCD', 'card4', 'card6', 'P_emaildomain', 'R_emaildomain', ...]
    Types of features in original data (raw dtype, special dtypes):
        ('float', [])  : 394 | ['TransactionAmt', 'card2', 'card3', 'card5', 'addr1', ...]
        ('int', [])    :   3 | ['TransactionID', 'TransactionDT', 'card1']
        ('object', []) :  31 | ['ProductCD', 'card4', 'card6', 'P_emaildomain', 'R_emaildomain', ...]
    Types of features in processed data (exact raw dtype, raw dtype):
        ('category', 'category') :  31 | ['ProductCD', 'card4', 'card6', 'P_emaildomain', 'R_emaildomain', ...]
        ('float64', 'float')     : 394 | ['TransactionAmt', 'card2', 'card3', 'card5', 'addr1', ...]
        ('int64', 'int')         :   3 | ['TransactionID', 'TransactionDT', 'card1']
    Types of features in processed data (raw dtype, special dtypes):
        ('category', []) :  31 | ['ProductCD', 'card4', 'card6', 'P_emaildomain', 'R_emaildomain', ...]
        ('float', [])    : 394 | ['TransactionAmt', 'card2', 'card3', 'card5', 'addr1', ...]
        ('int', [])      :   3 | ['TransactionID', 'TransactionDT', 'card1']
    10.4s = Fit runtime
    428 features in original data used to generate 428 features in processed data.
/home/coco/anaconda3/envs/ag/lib/python3.10/site-packages/autogluon/common/utils/pandas_utils.py:50: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '591140.6153846154' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
  memory_usage[column] = (
    Train Data (Processed) Memory Usage: 1895.06 MB (5.3% of available memory)
Data preprocessing and feature engineering runtime = 11.64s ...
AutoGluon will gauge predictive performance using evaluation metric: 'roc_auc'
    This metric expects predicted probabilities rather than predicted class labels, so you'll need to use predict_proba() instead of predict()
    To change this, specify the eval_metric parameter of Predictor()
Saving AutogluonModels/ag-20231012_071002/learner.pkl
User-specified model hyperparameters to be fit:
{
    'NN_TORCH': {},
    'GBM': [{'extra_trees': True, 'ag_args': {'name_suffix': 'XT'}}, {}, 'GBMLarge'],
    'CAT': {},
    'XGB': {},
    'FASTAI': {},
    'RF': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],
    'XT': [{'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass']}}, {'criterion': 'squared_error', 'ag_args': {'name_suffix': 'MSE', 'problem_types': ['regression', 'quantile']}}],
    'KNN': [{'weights': 'uniform', 'ag_args': {'name_suffix': 'Unif'}}, {'weights': 'distance', 'ag_args': {'name_suffix': 'Dist'}}],
}
Saving AutogluonModels/ag-20231012_071002/utils/data/X.pkl
Saving AutogluonModels/ag-20231012_071002/utils/data/y.pkl
Model configs that will be trained (in order):
    KNeighborsUnif_BAG_L1:  {'weights': 'uniform', 'ag_args': {'valid_stacker': False, 'name_suffix': 'Unif', 'model_type': <class 'autogluon.tabular.models.knn.knn_model.KNNModel'>, 'priority': 100}, 'ag_args_ensemble': {'use_child_oof': True}}
    KNeighborsDist_BAG_L1:  {'weights': 'distance', 'ag_args': {'valid_stacker': False, 'name_suffix': 'Dist', 'model_type': <class 'autogluon.tabular.models.knn.knn_model.KNNModel'>, 'priority': 100}, 'ag_args_ensemble': {'use_child_oof': True}}
    LightGBMXT_BAG_L1:  {'extra_trees': True, 'ag_args': {'name_suffix': 'XT', 'model_type': <class 'autogluon.tabular.models.lgb.lgb_model.LGBModel'>, 'priority': 90}}
    LightGBM_BAG_L1:    {'ag_args': {'model_type': <class 'autogluon.tabular.models.lgb.lgb_model.LGBModel'>, 'priority': 90}}
    RandomForestGini_BAG_L1:    {'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass'], 'model_type': <class 'autogluon.tabular.models.rf.rf_model.RFModel'>, 'priority': 80}, 'ag_args_ensemble': {'use_child_oof': True}}
    RandomForestEntr_BAG_L1:    {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass'], 'model_type': <class 'autogluon.tabular.models.rf.rf_model.RFModel'>, 'priority': 80}, 'ag_args_ensemble': {'use_child_oof': True}}
    CatBoost_BAG_L1:    {'ag_args': {'model_type': <class 'autogluon.tabular.models.catboost.catboost_model.CatBoostModel'>, 'priority': 70}}
    ExtraTreesGini_BAG_L1:  {'criterion': 'gini', 'ag_args': {'name_suffix': 'Gini', 'problem_types': ['binary', 'multiclass'], 'model_type': <class 'autogluon.tabular.models.xt.xt_model.XTModel'>, 'priority': 60}, 'ag_args_ensemble': {'use_child_oof': True}}
    ExtraTreesEntr_BAG_L1:  {'criterion': 'entropy', 'ag_args': {'name_suffix': 'Entr', 'problem_types': ['binary', 'multiclass'], 'model_type': <class 'autogluon.tabular.models.xt.xt_model.XTModel'>, 'priority': 60}, 'ag_args_ensemble': {'use_child_oof': True}}
    NeuralNetFastAI_BAG_L1:     {'ag_args': {'model_type': <class 'autogluon.tabular.models.fastainn.tabular_nn_fastai.NNFastAiTabularModel'>, 'priority': 50}}
    XGBoost_BAG_L1:     {'ag_args': {'model_type': <class 'autogluon.tabular.models.xgboost.xgboost_model.XGBoostModel'>, 'priority': 40}}
    NeuralNetTorch_BAG_L1:  {'ag_args': {'model_type': <class 'autogluon.tabular.models.tabular_nn.torch.tabular_nn_torch.TabularNeuralNetTorchModel'>, 'priority': 25}}
    LightGBMLarge_BAG_L1:   {'learning_rate': 0.03, 'num_leaves': 128, 'feature_fraction': 0.9, 'min_data_in_leaf': 5, 'ag_args': {'model_type': <class 'autogluon.tabular.models.lgb.lgb_model.LGBModel'>, 'name_suffix': 'Large', 'hyperparameter_tune_kwargs': None, 'priority': 0}}
Fitting 13 L1 models ...
Fitting model: KNeighborsUnif_BAG_L1 ... Training model for up to 1188.36s of the 1188.35s of remaining time.
    Fitting KNeighborsUnif_BAG_L1 with 'num_gpus': 0, 'num_cpus': 16
Saving AutogluonModels/ag-20231012_071002/models/KNeighborsUnif_BAG_L1/utils/model_template.pkl
Loading: AutogluonModels/ag-20231012_071002/models/KNeighborsUnif_BAG_L1/utils/model_template.pkl
    Warning: Not enough memory to safely train model. Estimated to require 3.640 GB out of 32.686 GB available memory (55.676%)... (20.000% of avail memory is the max safe size)
    To force training the model, specify the model hyperparameter "ag.max_memory_usage_ratio" to a larger value (currently 1.0, set to >=0.61 to avoid the error)
        To set the same value for all models, do the following when calling predictor.fit: `predictor.fit(..., ag_args_fit={"ag.max_memory_usage_ratio": VALUE})`
    Not enough memory to train KNeighborsUnif_BAG_L1... Skipping this model.
Saving AutogluonModels/ag-20231012_071002/models/trainer.pkl
Fitting model: KNeighborsDist_BAG_L1 ... Training model for up to 1185.58s of the 1185.58s of remaining time.
    Fitting KNeighborsDist_BAG_L1 with 'num_gpus': 0, 'num_cpus': 16
Saving AutogluonModels/ag-20231012_071002/models/KNeighborsDist_BAG_L1/utils/model_template.pkl
Loading: AutogluonModels/ag-20231012_071002/models/KNeighborsDist_BAG_L1/utils/model_template.pkl
    Warning: Not enough memory to safely train model. Estimated to require 3.640 GB out of 32.688 GB available memory (55.673%)... (20.000% of avail memory is the max safe size)
    To force training the model, specify the model hyperparameter "ag.max_memory_usage_ratio" to a larger value (currently 1.0, set to >=0.61 to avoid the error)
        To set the same value for all models, do the following when calling predictor.fit: `predictor.fit(..., ag_args_fit={"ag.max_memory_usage_ratio": VALUE})`
    Not enough memory to train KNeighborsDist_BAG_L1... Skipping this model.
Saving AutogluonModels/ag-20231012_071002/models/trainer.pkl
Fitting model: LightGBMXT_BAG_L1 ... Training model for up to 1182.87s of the 1182.86s of remaining time.
    Fitting LightGBMXT_BAG_L1 with 'num_gpus': 0, 'num_cpus': 16
Saving AutogluonModels/ag-20231012_071002/models/LightGBMXT_BAG_L1/utils/model_template.pkl
Loading: AutogluonModels/ag-20231012_071002/models/LightGBMXT_BAG_L1/utils/model_template.pkl
Will use sequential fold fitting strategy because import of ray failed. Reason: ray is required to train folds in parallel for TabularPredictor or HPO for MultiModalPredictor. A quick tip is to install via `pip install ray==2.6.3`
    Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
/home/coco/anaconda3/envs/ag/lib/python3.10/site-packages/autogluon/common/utils/pandas_utils.py:50: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '517312.76923076925' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
  memory_usage[column] = (
    Fitting 10000 rounds... Hyperparameters: {'learning_rate': 0.05, 'extra_trees': True}
    Ran out of time, early stopping on iteration 5877. Best iteration is:
    [5876]  valid_set's binary_logloss: 0.044724
/home/coco/anaconda3/envs/ag/lib/python3.10/site-packages/autogluon/common/utils/pandas_utils.py:50: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '517312.76923076925' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
  memory_usage[column] = (
    Fitting 10000 rounds... Hyperparameters: {'learning_rate': 0.05, 'extra_trees': True}
    Ran out of time, early stopping on iteration 6210. Best iteration is:
    [6184]  valid_set's binary_logloss: 0.044593
/home/coco/anaconda3/envs/ag/lib/python3.10/site-packages/autogluon/common/utils/pandas_utils.py:50: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '517312.76923076925' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
  memory_usage[column] = (
    Fitting 10000 rounds... Hyperparameters: {'learning_rate': 0.05, 'extra_trees': True}
    Ran out of time, early stopping on iteration 6268. Best iteration is:
    [6262]  valid_set's binary_logloss: 0.0450604
/home/coco/anaconda3/envs/ag/lib/python3.10/site-packages/autogluon/common/utils/pandas_utils.py:50: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '517312.76923076925' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
  memory_usage[column] = (
    Fitting 10000 rounds... Hyperparameters: {'learning_rate': 0.05, 'extra_trees': True}
    Ran out of time, early stopping on iteration 6519. Best iteration is:
    [6510]  valid_set's binary_logloss: 0.0438093
/home/coco/anaconda3/envs/ag/lib/python3.10/site-packages/autogluon/common/utils/pandas_utils.py:50: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '517313.76923076925' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
  memory_usage[column] = (
    Fitting 10000 rounds... Hyperparameters: {'learning_rate': 0.05, 'extra_trees': True}
    Ran out of time, early stopping on iteration 6926. Best iteration is:
    [6898]  valid_set's binary_logloss: 0.0450263
/home/coco/anaconda3/envs/ag/lib/python3.10/site-packages/autogluon/common/utils/pandas_utils.py:50: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '517313.76923076925' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
  memory_usage[column] = (
    Fitting 10000 rounds... Hyperparameters: {'learning_rate': 0.05, 'extra_trees': True}
    Ran out of time, early stopping on iteration 7246. Best iteration is:
    [7246]  valid_set's binary_logloss: 0.04315
/home/coco/anaconda3/envs/ag/lib/python3.10/site-packages/autogluon/common/utils/pandas_utils.py:50: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '517313.76923076925' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
  memory_usage[column] = (
    Fitting 10000 rounds... Hyperparameters: {'learning_rate': 0.05, 'extra_trees': True}
    Ran out of time, early stopping on iteration 7562. Best iteration is:
    [7545]  valid_set's binary_logloss: 0.0449857
/home/coco/anaconda3/envs/ag/lib/python3.10/site-packages/autogluon/common/utils/pandas_utils.py:50: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '517313.76923076925' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
  memory_usage[column] = (
    Fitting 10000 rounds... Hyperparameters: {'learning_rate': 0.05, 'extra_trees': True}
    Ran out of time, early stopping on iteration 9106. Best iteration is:
    [7836]  valid_set's binary_logloss: 0.0452498
Saving AutogluonModels/ag-20231012_071002/models/LightGBMXT_BAG_L1/utils/oof.pkl
Saving AutogluonModels/ag-20231012_071002/models/LightGBMXT_BAG_L1/model.pkl
    0.9713   = Validation score   (roc_auc)
    1113.06s     = Training   runtime
    28.14s   = Validation runtime
Saving AutogluonModels/ag-20231012_071002/models/trainer.pkl
Fitting model: LightGBM_BAG_L1 ... Training model for up to 37.45s of the 37.44s of remaining time.
    Fitting LightGBM_BAG_L1 with 'num_gpus': 0, 'num_cpus': 16
Saving AutogluonModels/ag-20231012_071002/models/LightGBM_BAG_L1/utils/model_template.pkl
Loading: AutogluonModels/ag-20231012_071002/models/LightGBM_BAG_L1/utils/model_template.pkl
    Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
/home/coco/anaconda3/envs/ag/lib/python3.10/site-packages/autogluon/common/utils/pandas_utils.py:50: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '517312.76923076925' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
  memory_usage[column] = (
    Fitting 10000 rounds... Hyperparameters: {'learning_rate': 0.05}
    Ran out of time, early stopping on iteration 9. Best iteration is:
    [9] valid_set's binary_logloss: 0.11505
    Time limit exceeded... Skipping LightGBM_BAG_L1.
Saving AutogluonModels/ag-20231012_071002/models/trainer.pkl
Fitting model: RandomForestGini_BAG_L1 ... Training model for up to 29.33s of the 29.32s of remaining time.
    Fitting RandomForestGini_BAG_L1 with 'num_gpus': 0, 'num_cpus': 16
Saving AutogluonModels/ag-20231012_071002/models/RandomForestGini_BAG_L1/utils/model_template.pkl
Loading: AutogluonModels/ag-20231012_071002/models/RandomForestGini_BAG_L1/utils/model_template.pkl
    Warning: Model is expected to require 262.4s to train, which exceeds the maximum time limit of 29.3s, skipping model...
    Time limit exceeded... Skipping RandomForestGini_BAG_L1.
Saving AutogluonModels/ag-20231012_071002/models/trainer.pkl
Fitting model: RandomForestEntr_BAG_L1 ... Training model for up to 22.0s of the 22.0s of remaining time.
    Fitting RandomForestEntr_BAG_L1 with 'num_gpus': 0, 'num_cpus': 16
Saving AutogluonModels/ag-20231012_071002/models/RandomForestEntr_BAG_L1/utils/model_template.pkl
Loading: AutogluonModels/ag-20231012_071002/models/RandomForestEntr_BAG_L1/utils/model_template.pkl
    Warning: Model is expected to require 192.7s to train, which exceeds the maximum time limit of 22.0s, skipping model...
    Time limit exceeded... Skipping RandomForestEntr_BAG_L1.
Saving AutogluonModels/ag-20231012_071002/models/trainer.pkl
Fitting model: CatBoost_BAG_L1 ... Training model for up to 15.66s of the 15.65s of remaining time.
    Fitting CatBoost_BAG_L1 with 'num_gpus': 0, 'num_cpus': 16
Saving AutogluonModels/ag-20231012_071002/models/CatBoost_BAG_L1/utils/model_template.pkl
Loading: AutogluonModels/ag-20231012_071002/models/CatBoost_BAG_L1/utils/model_template.pkl
    Fitting 8 child models (S1F1 - S1F8) | Fitting with SequentialLocalFoldFittingStrategy
/home/coco/anaconda3/envs/ag/lib/python3.10/site-packages/autogluon/common/utils/pandas_utils.py:50: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '517312.76923076925' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
  memory_usage[column] = (
    Catboost model hyperparameters: {'iterations': 10000, 'learning_rate': 0.05, 'random_seed': 0, 'allow_writing_files': False, 'eval_metric': 'Logloss', 'thread_count': 8}
    Time limit exceeded... Skipping CatBoost_BAG_L1.
Saving AutogluonModels/ag-20231012_071002/models/trainer.pkl
Fitting model: ExtraTreesGini_BAG_L1 ... Training model for up to 10.19s of the 10.19s of remaining time.
    Fitting ExtraTreesGini_BAG_L1 with 'num_gpus': 0, 'num_cpus': 16
Saving AutogluonModels/ag-20231012_071002/models/ExtraTreesGini_BAG_L1/utils/model_template.pkl
Loading: AutogluonModels/ag-20231012_071002/models/ExtraTreesGini_BAG_L1/utils/model_template.pkl
    Warning: Model is expected to require 155.7s to train, which exceeds the maximum time limit of 10.2s, skipping model...
    Time limit exceeded... Skipping ExtraTreesGini_BAG_L1.
Saving AutogluonModels/ag-20231012_071002/models/trainer.pkl
Fitting model: ExtraTreesEntr_BAG_L1 ... Training model for up to 4.32s of the 4.31s of remaining time.
    Fitting ExtraTreesEntr_BAG_L1 with 'num_gpus': 0, 'num_cpus': 16
Saving AutogluonModels/ag-20231012_071002/models/ExtraTreesEntr_BAG_L1/utils/model_template.pkl
Loading: AutogluonModels/ag-20231012_071002/models/ExtraTreesEntr_BAG_L1/utils/model_template.pkl
    Warning: Model is expected to require 154.8s to train, which exceeds the maximum time limit of 4.3s, skipping model...
    Time limit exceeded... Skipping ExtraTreesEntr_BAG_L1.
Saving AutogluonModels/ag-20231012_071002/models/trainer.pkl
Skipping NeuralNetFastAI_BAG_L1 due to lack of time remaining.
Saving AutogluonModels/ag-20231012_071002/models/trainer.pkl
Skipping XGBoost_BAG_L1 due to lack of time remaining.
Saving AutogluonModels/ag-20231012_071002/models/trainer.pkl
Skipping NeuralNetTorch_BAG_L1 due to lack of time remaining.
Saving AutogluonModels/ag-20231012_071002/models/trainer.pkl
Skipping LightGBMLarge_BAG_L1 due to lack of time remaining.
Saving AutogluonModels/ag-20231012_071002/models/trainer.pkl
Not enough time left to finish repeated k-fold bagging, stopping early ...
Completed 1/20 k-fold bagging repeats ...
Loading: AutogluonModels/ag-20231012_071002/models/LightGBMXT_BAG_L1/utils/oof.pkl
Model configs that will be trained (in order):
    WeightedEnsemble_L2:    {'ag_args': {'valid_base': False, 'name_bag_suffix': '', 'model_type': <class 'autogluon.core.models.greedy_ensemble.greedy_weighted_ensemble_model.GreedyWeightedEnsembleModel'>, 'priority': 0}, 'ag_args_ensemble': {'save_bag_folds': True}}
Fitting model: WeightedEnsemble_L2 ... Training model for up to 360.0s of the -1.54s of remaining time.
    Fitting WeightedEnsemble_L2 with 'num_gpus': 0, 'num_cpus': 16
Saving AutogluonModels/ag-20231012_071002/models/WeightedEnsemble_L2/utils/model_template.pkl
Loading: AutogluonModels/ag-20231012_071002/models/WeightedEnsemble_L2/utils/model_template.pkl
Ensemble size: 1
Ensemble weights: 
[1.]
    0.37s   = Estimated out-of-fold prediction time...
Saving AutogluonModels/ag-20231012_071002/models/WeightedEnsemble_L2/utils/oof.pkl
Saving AutogluonModels/ag-20231012_071002/models/WeightedEnsemble_L2/model.pkl
    0.9713   = Validation score   (roc_auc)
    0.07s    = Training   runtime
    0.07s    = Validation runtime
Saving AutogluonModels/ag-20231012_071002/models/trainer.pkl
Saving AutogluonModels/ag-20231012_071002/models/trainer.pkl
Saving AutogluonModels/ag-20231012_071002/models/trainer.pkl
AutoGluon training complete, total runtime = 1203.47s ... Best model: "WeightedEnsemble_L2"
Loading: AutogluonModels/ag-20231012_071002/models/trainer.pkl
Saving AutogluonModels/ag-20231012_071002/models/trainer.pkl
Saving AutogluonModels/ag-20231012_071002/learner.pkl
Saving AutogluonModels/ag-20231012_071002/predictor.pkl
Saving AutogluonModels/ag-20231012_071002/__version__ with contents "0.8.2"
Saving AutogluonModels/ag-20231012_071002/metadata.json
TabularPredictor saved. To load, use: predictor = TabularPredictor.load("AutogluonModels/ag-20231012_071002")
Loading: AutogluonModels/ag-20231012_071002/models/LightGBMXT_BAG_L1/model.pkl
Loading: AutogluonModels/ag-20231012_071002/models/WeightedEnsemble_L2/model.pkl
/home/coco/anaconda3/envs/ag/lib/python3.10/site-packages/autogluon/core/utils/plots.py:169: UserWarning: AutoGluon summary plots cannot be created because bokeh is not installed. To see plots, please do: "pip install bokeh==2.0.1"
  warnings.warn('AutoGluon summary plots cannot be created because bokeh is not installed. To see plots, please do: "pip install bokeh==2.0.1"')

[50]    valid_set's binary_logloss: 0.0936737
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[1800]  valid_set's binary_logloss: 0.055691
[1850]  valid_set's binary_logloss: 0.0553846
[1900]  valid_set's binary_logloss: 0.0551046
[1950]  valid_set's binary_logloss: 0.0548127
[2000]  valid_set's binary_logloss: 0.0545277
[2050]  valid_set's binary_logloss: 0.0542339
[2100]  valid_set's binary_logloss: 0.0539807
[2150]  valid_set's binary_logloss: 0.0537343
[2200]  valid_set's binary_logloss: 0.0534642
[2250]  valid_set's binary_logloss: 0.0532622
[2300]  valid_set's binary_logloss: 0.0530425
[2350]  valid_set's binary_logloss: 0.0528564
[2400]  valid_set's binary_logloss: 0.0526538
[2450]  valid_set's binary_logloss: 0.0524329
[2500]  valid_set's binary_logloss: 0.0521854
[2550]  valid_set's binary_logloss: 0.0519891
[2600]  valid_set's binary_logloss: 0.0518008
[2650]  valid_set's binary_logloss: 0.0516694
[2700]  valid_set's binary_logloss: 0.0514709
[2750]  valid_set's binary_logloss: 0.0512856
[2800]  valid_set's binary_logloss: 0.0511404
[2850]  valid_set's binary_logloss: 0.0509686
[2900]  valid_set's binary_logloss: 0.0508043
[2950]  valid_set's binary_logloss: 0.0506575
[3000]  valid_set's binary_logloss: 0.0504941
[3050]  valid_set's binary_logloss: 0.0503218
[3100]  valid_set's binary_logloss: 0.0501923
[3150]  valid_set's binary_logloss: 0.050039
[3200]  valid_set's binary_logloss: 0.0498999
[3250]  valid_set's binary_logloss: 0.0497412
[3300]  valid_set's binary_logloss: 0.0496217
[3350]  valid_set's binary_logloss: 0.0495241
[3400]  valid_set's binary_logloss: 0.0494252
[3450]  valid_set's binary_logloss: 0.0493008
[3500]  valid_set's binary_logloss: 0.0491831
[3550]  valid_set's binary_logloss: 0.0490707
[3600]  valid_set's binary_logloss: 0.0489746
[3650]  valid_set's binary_logloss: 0.0488395
[3700]  valid_set's binary_logloss: 0.0487442
[3750]  valid_set's binary_logloss: 0.0486169
[3800]  valid_set's binary_logloss: 0.0485238
[3850]  valid_set's binary_logloss: 0.048429
[3900]  valid_set's binary_logloss: 0.0483117
[3950]  valid_set's binary_logloss: 0.0482465
[4000]  valid_set's binary_logloss: 0.0481866
[4050]  valid_set's binary_logloss: 0.0480781
[4100]  valid_set's binary_logloss: 0.0479657
[4150]  valid_set's binary_logloss: 0.0479174
[4200]  valid_set's binary_logloss: 0.0478491
[4250]  valid_set's binary_logloss: 0.0477598
[4300]  valid_set's binary_logloss: 0.0476699
[4350]  valid_set's binary_logloss: 0.0475929
[4400]  valid_set's binary_logloss: 0.0475102
[4450]  valid_set's binary_logloss: 0.0474583
[4500]  valid_set's binary_logloss: 0.0473897
[4550]  valid_set's binary_logloss: 0.0473208
[4600]  valid_set's binary_logloss: 0.0472478
[4650]  valid_set's binary_logloss: 0.0471707
[4700]  valid_set's binary_logloss: 0.0470975
[4750]  valid_set's binary_logloss: 0.0470223
[4800]  valid_set's binary_logloss: 0.046933
[4850]  valid_set's binary_logloss: 0.0468955
[4900]  valid_set's binary_logloss: 0.0467916
[4950]  valid_set's binary_logloss: 0.0467316
[5000]  valid_set's binary_logloss: 0.0466726
[5050]  valid_set's binary_logloss: 0.0466299
[5100]  valid_set's binary_logloss: 0.0465787
[5150]  valid_set's binary_logloss: 0.0465216
[5200]  valid_set's binary_logloss: 0.0464555
[5250]  valid_set's binary_logloss: 0.0464032
[5300]  valid_set's binary_logloss: 0.0463644
[5350]  valid_set's binary_logloss: 0.046345
[5400]  valid_set's binary_logloss: 0.0463021
[5450]  valid_set's binary_logloss: 0.0462572
[5500]  valid_set's binary_logloss: 0.0462455
[5550]  valid_set's binary_logloss: 0.046189
[5600]  valid_set's binary_logloss: 0.0461821
[5650]  valid_set's binary_logloss: 0.0461512
[5700]  valid_set's binary_logloss: 0.0461375
[5750]  valid_set's binary_logloss: 0.0460782
[5800]  valid_set's binary_logloss: 0.046044
[5850]  valid_set's binary_logloss: 0.0460181
[5900]  valid_set's binary_logloss: 0.0459886
[5950]  valid_set's binary_logloss: 0.0459739
[6000]  valid_set's binary_logloss: 0.0459499
[6050]  valid_set's binary_logloss: 0.0459214
[6100]  valid_set's binary_logloss: 0.0458806
[6150]  valid_set's binary_logloss: 0.0458286
[6200]  valid_set's binary_logloss: 0.0458169
[6250]  valid_set's binary_logloss: 0.0458025
[6300]  valid_set's binary_logloss: 0.045789
[6350]  valid_set's binary_logloss: 0.045785
[6400]  valid_set's binary_logloss: 0.0457392
[6450]  valid_set's binary_logloss: 0.0457052
[6500]  valid_set's binary_logloss: 0.0456856
[6550]  valid_set's binary_logloss: 0.0456551
[6600]  valid_set's binary_logloss: 0.0456049
[6650]  valid_set's binary_logloss: 0.0455808
[6700]  valid_set's binary_logloss: 0.0455627
[6750]  valid_set's binary_logloss: 0.0455433
[6800]  valid_set's binary_logloss: 0.0455255
[6850]  valid_set's binary_logloss: 0.0454953
[6900]  valid_set's binary_logloss: 0.0454847
[6950]  valid_set's binary_logloss: 0.0454637
[7000]  valid_set's binary_logloss: 0.0454715
[7050]  valid_set's binary_logloss: 0.0454629
[7100]  valid_set's binary_logloss: 0.0454673
[7150]  valid_set's binary_logloss: 0.0454424
[7200]  valid_set's binary_logloss: 0.0454276
[7250]  valid_set's binary_logloss: 0.0454074
[7300]  valid_set's binary_logloss: 0.045406
[7350]  valid_set's binary_logloss: 0.0454003
[7400]  valid_set's binary_logloss: 0.045375
[7450]  valid_set's binary_logloss: 0.0453647
[7500]  valid_set's binary_logloss: 0.0453676
[7550]  valid_set's binary_logloss: 0.0453669
[7600]  valid_set's binary_logloss: 0.0453402
[7650]  valid_set's binary_logloss: 0.045347
[7700]  valid_set's binary_logloss: 0.0453404
[7750]  valid_set's binary_logloss: 0.0452905
[7800]  valid_set's binary_logloss: 0.0452678
[7850]  valid_set's binary_logloss: 0.0452621
[7900]  valid_set's binary_logloss: 0.0452721
[7950]  valid_set's binary_logloss: 0.0453034
[8000]  valid_set's binary_logloss: 0.0453192
[8050]  valid_set's binary_logloss: 0.0453174
[8100]  valid_set's binary_logloss: 0.04531
[8150]  valid_set's binary_logloss: 0.0453097
[8200]  valid_set's binary_logloss: 0.0453064
[8250]  valid_set's binary_logloss: 0.0453017
[8300]  valid_set's binary_logloss: 0.0453148
[8350]  valid_set's binary_logloss: 0.0453153
[8400]  valid_set's binary_logloss: 0.0453138
[8450]  valid_set's binary_logloss: 0.0453403
[8500]  valid_set's binary_logloss: 0.0453684
[8550]  valid_set's binary_logloss: 0.0453877
[8600]  valid_set's binary_logloss: 0.0454095
[8650]  valid_set's binary_logloss: 0.0453896
[8700]  valid_set's binary_logloss: 0.0453852
[8750]  valid_set's binary_logloss: 0.045368
[8800]  valid_set's binary_logloss: 0.0453844
[8850]  valid_set's binary_logloss: 0.0453988
[8900]  valid_set's binary_logloss: 0.0454074
[8950]  valid_set's binary_logloss: 0.0453968
[9000]  valid_set's binary_logloss: 0.0454079
[9050]  valid_set's binary_logloss: 0.0454114
[9100]  valid_set's binary_logloss: 0.045438
*** Summary of fit() ***
Estimated performance of each model:
                 model  score_val  pred_time_val     fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order
0    LightGBMXT_BAG_L1   0.971274      28.143910  1113.060551               28.143910        1113.060551            1       True          1
1  WeightedEnsemble_L2   0.971274      28.211386  1113.131086                0.067476           0.070535            2       True          2
Number of models trained: 2
Types of models trained:
{'StackerEnsembleModel_LGB', 'WeightedEnsembleModel'}
Bagging used: True  (with 8 folds)
Multi-layer stack-ensembling used: False 
Feature Metadata (Processed):
(raw dtype, special dtypes):
('category', []) :  31 | ['ProductCD', 'card4', 'card6', 'P_emaildomain', 'R_emaildomain', ...]
('float', [])    : 394 | ['TransactionAmt', 'card2', 'card3', 'card5', 'addr1', ...]
('int', [])      :   3 | ['TransactionID', 'TransactionDT', 'card1']
*** End of fit() summary ***

test_identity = pd.read_csv('ieee-fraud-detection/test_identity.csv')
test_transaction = pd.read_csv('ieee-fraud-detection/test_transaction.csv')
test_data = pd.merge(test_transaction, test_identity, on='TransactionID', how='left')  # same join applied to training files

y_predproba = predictor.predict_proba(test_data)
y_predproba.head(5)  # some example predicted fraud-probabilities

KeyError: "38 required columns are missing from the provided dataset to transform using AutoMLPipelineFeatureGenerator. 38 missing columns: ['id_01', 'id_02', 'id_03', 'id_04', 'id_05', 'id_06', 'id_07', 'id_08', 'id_09', 'id_10', 'id_11', 'id_12', 'id_13', 'id_14', 'id_15', 'id_16', 'id_17', 'id_18', 'id_19', 'id_20', 'id_21', 'id_22', 'id_23', 'id_24', 'id_25', 'id_26', 'id_27', 'id_28', 'id_29', 'id_30', 'id_31', 'id_32', 'id_33', 'id_34', 'id_35', 'id_36', 'id_37', 'id_38'] | 433 available columns: ['TransactionID', 'TransactionDT', 'TransactionAmt', 'ProductCD', 'card1', 'card2', 'card3', 'card4', 'card5', 'card6', 'addr1', 'addr2', 'dist1', 'dist2', 'P_emaildomain', 'R_emaildomain', 'C1', 'C2', 'C3', 'C4', 'C5', 'C6', 'C7', 'C8', 'C9', 'C10', 'C11', 'C12', 'C13', 'C14', 'D1', 'D2', 'D3', 'D4', 'D5', 'D6', 'D7', 'D8', 'D9', 'D10', 'D11', 'D12', 'D13', 'D14', 'D15', 'M1', 'M2', 'M3', 'M4', 'M5', 'M6', 'M7', 'M8', 'M9', 'V1', 'V2', 'V3', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9', 'V10', 'V11', 'V12', 'V13', 'V14', 'V15', 'V16', 'V17', 'V18', 'V19', 'V20', 'V21', 'V22', 'V23', 'V24', 'V25', 'V26', 'V27', 'V28', 'V29', 'V30', 'V31', 'V32', 'V33', 'V34', 'V35', 'V36', 'V37', 'V38', 'V39', 'V40', 'V41', 'V42', 'V43', 'V44', 'V45', 'V46', 'V47', 'V48', 'V49', 'V50', 'V51', 'V52', 'V53', 'V54', 'V55', 'V56', 'V57', 'V58', 'V59', 'V60', 'V61', 'V62', 'V63', 'V64', 'V65', 'V66', 'V67', 'V68', 'V69', 'V70', 'V71', 'V72', 'V73', 'V74', 'V75', 'V76', 'V77', 'V78', 'V79', 'V80', 'V81', 'V82', 'V83', 'V84', 'V85', 'V86', 'V87', 'V88', 'V89', 'V90', 'V91', 'V92', 'V93', 'V94', 'V95', 'V96', 'V97', 'V98', 'V99', 'V100', 'V101', 'V102', 'V103', 'V104', 'V105', 'V106', 'V107', 'V108', 'V109', 'V110', 'V111', 'V112', 'V113', 'V114', 'V115', 'V116', 'V117', 'V118', 'V119', 'V120', 'V121', 'V122', 'V123', 'V124', 'V125', 'V126', 'V127', 'V128', 'V129', 'V130', 'V131', 'V132', 'V133', 'V134', 'V135', 'V136', 'V137', 'V138', 'V139', 'V140', 'V141', 'V142', 'V143', 'V144', 'V145', 'V146', 'V147', 'V148', 'V149', 'V150', 'V151', 'V152', 'V153', 'V154', 'V155', 'V156', 'V157', 'V158', 'V159', 'V160', 'V161', 'V162', 'V163', 'V164', 'V165', 'V166', 'V167', 'V168', 'V169', 'V170', 'V171', 'V172', 'V173', 'V174', 'V175', 'V176', 'V177', 'V178', 'V179', 'V180', 'V181', 'V182', 'V183', 'V184', 'V185', 'V186', 'V187', 'V188', 'V189', 'V190', 'V191', 'V192', 'V193', 'V194', 'V195', 'V196', 'V197', 'V198', 'V199', 'V200', 'V201', 'V202', 'V203', 'V204', 'V205', 'V206', 'V207', 'V208', 'V209', 'V210', 'V211', 'V212', 'V213', 'V214', 'V215', 'V216', 'V217', 'V218', 'V219', 'V220', 'V221', 'V222', 'V223', 'V224', 'V225', 'V226', 'V227', 'V228', 'V229', 'V230', 'V231', 'V232', 'V233', 'V234', 'V235', 'V236', 'V237', 'V238', 'V239', 'V240', 'V241', 'V242', 'V243', 'V244', 'V245', 'V246', 'V247', 'V248', 'V249', 'V250', 'V251', 'V252', 'V253', 'V254', 'V255', 'V256', 'V257', 'V258', 'V259', 'V260', 'V261', 'V262', 'V263', 'V264', 'V265', 'V266', 'V267', 'V268', 'V269', 'V270', 'V271', 'V272', 'V273', 'V274', 'V275', 'V276', 'V277', 'V278', 'V279', 'V280', 'V281', 'V282', 'V283', 'V284', 'V285', 'V286', 'V287', 'V288', 'V289', 'V290', 'V291', 'V292', 'V293', 'V294', 'V295', 'V296', 'V297', 'V298', 'V299', 'V300', 'V301', 'V302', 'V303', 'V304', 'V305', 'V306', 'V307', 'V308', 'V309', 'V310', 'V311', 'V312', 'V313', 'V314', 'V315', 'V316', 'V317', 'V318', 'V319', 'V320', 'V321', 'V322', 'V323', 'V324', 'V325', 'V326', 'V327', 'V328', 'V329', 'V330', 'V331', 'V332', 'V333', 'V334', 'V335', 'V336', 'V337', 'V338', 'V339', 'id-01', 'id-02', 'id-03', 'id-04', 'id-05', 'id-06', 'id-07', 'id-08', 'id-09', 'id-10', 'id-11', 'id-12', 'id-13', 'id-14', 'id-15', 'id-16', 'id-17', 'id-18', 'id-19', 'id-20', 'id-21', 'id-22', 'id-23', 'id-24', 'id-25', 'id-26', 'id-27', 'id-28', 'id-29', 'id-30', 'id-31', 'id-32', 'id-33', 'id-34', 'id-35', 'id-36', 'id-37', 'id-38', 'DeviceType', 'DeviceInfo']"