02wk-05: 타이타닉 / Autogluon

Author

최규빈

Published

September 12, 2023

1. 강의영상

2. Import

# 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

/kaggle/input/titanic/train.csv
/kaggle/input/titanic/test.csv
/kaggle/input/titanic/gender_submission.csv

#pip install autogluon

from autogluon.tabular import TabularDataset, TabularPredictor

/home/cgb2/anaconda3/envs/ag/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html
  from .autonotebook import tqdm as notebook_tqdm

3. 분석의 절차

A. 데이터

- 비유: 문제를 받아오는 과정으로 비유할 수 있다.

!kaggle competitions download -c titanic
!unzip titanic.zip -d ./titanic
df_train = TabularDataset('titanic/train.csv')
df_test = TabularDataset('titanic/test.csv')
!rm titanic.zip
!rm -rf titanic/

Downloading titanic.zip to /home/cgb2/Dropbox/07_lectures/2023-09-MP2023/posts
  0%|                                               | 0.00/34.1k [00:00<?, ?B/s]
100%|██████████████████████████████████████| 34.1k/34.1k [00:00<00:00, 5.82MB/s]
Archive:  titanic.zip
  inflating: ./titanic/gender_submission.csv  
  inflating: ./titanic/test.csv      
  inflating: ./titanic/train.csv

B. Predictor 생성

- 비유: 문제를 풀 학생을 생성하는 과정으로 비유할 수 있다.

predictr = TabularPredictor("Survived")

No path specified. Models will be saved in: "AutogluonModels/ag-20231024_084026"

C. 적합(fit)

- 비유: 학생이 공부를 하는 과정으로 비유할 수 있다.

- 학습

predictr.fit(df_train) 
# 학생(predictr)에게 문제(tr)를 줘서 학습을 시킴(predictr.fit())

/home/cgb2/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
Beginning AutoGluon training ...
AutoGluon will save models to "AutogluonModels/ag-20231024_084026"
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:   265.30 GB / 490.57 GB (54.1%)
Train Data Rows:    891
Train Data Columns: 11
Label Column: Survived
Preprocessing data ...
/home/cgb2/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/cgb2/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:                    124529.88 MB
    Train Data (Original)  Memory Usage: 0.31 MB (0.0% of available memory)
    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...
            Note: Converting 1 features to boolean dtype as they only contain 2 unique values.
    Stage 2 Generators:
        Fitting FillNaFeatureGenerator...
/home/cgb2/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)
    Stage 3 Generators:
        Fitting IdentityFeatureGenerator...
        Fitting CategoryFeatureGenerator...
            Fitting CategoryMemoryMinimizeFeatureGenerator...
        Fitting TextSpecialFeatureGenerator...
            Fitting BinnedFeatureGenerator...
            Fitting DropDuplicatesFeatureGenerator...
        Fitting TextNgramFeatureGenerator...
            Fitting CountVectorizer for text features: ['Name']
            CountVectorizer fit with vocabulary size = 8
    Stage 4 Generators:
        Fitting DropUniqueFeatureGenerator...
    Stage 5 Generators:
        Fitting DropDuplicatesFeatureGenerator...
    Types of features in original data (raw dtype, special dtypes):
        ('float', [])        : 2 | ['Age', 'Fare']
        ('int', [])          : 4 | ['PassengerId', 'Pclass', 'SibSp', 'Parch']
        ('object', [])       : 4 | ['Sex', 'Ticket', 'Cabin', 'Embarked']
        ('object', ['text']) : 1 | ['Name']
    Types of features in processed data (raw dtype, special dtypes):
        ('category', [])                    : 3 | ['Ticket', 'Cabin', 'Embarked']
        ('float', [])                       : 2 | ['Age', 'Fare']
        ('int', [])                         : 4 | ['PassengerId', 'Pclass', 'SibSp', 'Parch']
        ('int', ['binned', 'text_special']) : 9 | ['Name.char_count', 'Name.word_count', 'Name.capital_ratio', 'Name.lower_ratio', 'Name.special_ratio', ...]
        ('int', ['bool'])                   : 1 | ['Sex']
        ('int', ['text_ngram'])             : 9 | ['__nlp__.henry', '__nlp__.john', '__nlp__.master', '__nlp__.miss', '__nlp__.mr', ...]
    0.2s = Fit runtime
    11 features in original data used to generate 28 features in processed data.
/home/cgb2/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 '2417.259259259259' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
  memory_usage[column] = (
    Train Data (Processed) Memory Usage: 0.07 MB (0.0% of available memory)
Data preprocessing and feature engineering runtime = 0.19s ...
AutoGluon will gauge predictive performance using evaluation metric: 'accuracy'
    To change this, specify the eval_metric parameter of Predictor()
Automatically generating train/validation split with holdout_frac=0.2, Train Rows: 712, Val Rows: 179
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'}}],
}
Fitting 13 L1 models ...
Fitting model: KNeighborsUnif ...
    0.6536   = Validation score   (accuracy)
    0.01s    = Training   runtime
    0.03s    = Validation runtime
Fitting model: KNeighborsDist ...
    0.6536   = Validation score   (accuracy)
    0.01s    = Training   runtime
    0.02s    = Validation runtime
Fitting model: LightGBMXT ...
/home/cgb2/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 '2059.259259259259' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
  memory_usage[column] = (
    0.8156   = Validation score   (accuracy)
    0.48s    = Training   runtime
    0.0s     = Validation runtime
Fitting model: LightGBM ...
/home/cgb2/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 '2059.259259259259' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
  memory_usage[column] = (
    0.8212   = Validation score   (accuracy)
    0.16s    = Training   runtime
    0.0s     = Validation runtime
Fitting model: RandomForestGini ...
    0.8156   = Validation score   (accuracy)
    0.54s    = Training   runtime
    0.2s     = Validation runtime
Fitting model: RandomForestEntr ...
    0.8156   = Validation score   (accuracy)
    0.61s    = Training   runtime
    0.18s    = Validation runtime
Fitting model: CatBoost ...
/home/cgb2/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 '2059.259259259259' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
  memory_usage[column] = (
    0.8268   = Validation score   (accuracy)
    0.56s    = Training   runtime
    0.01s    = Validation runtime
Fitting model: ExtraTreesGini ...
    0.8156   = Validation score   (accuracy)
    0.82s    = Training   runtime
    0.22s    = Validation runtime
Fitting model: ExtraTreesEntr ...
    0.8101   = Validation score   (accuracy)
    0.7s     = Training   runtime
    0.23s    = Validation runtime
Fitting model: NeuralNetFastAI ...
/home/cgb2/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 '2059.259259259259' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
  memory_usage[column] = (
/home/cgb2/anaconda3/envs/ag/lib/python3.10/site-packages/autogluon/tabular/models/fastainn/tabular_nn_fastai.py:192: 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.
  df = df.fillna(column_fills, inplace=False, downcast=False)
/home/cgb2/anaconda3/envs/ag/lib/python3.10/site-packages/autogluon/tabular/models/fastainn/tabular_nn_fastai.py:192: 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.
  df = df.fillna(column_fills, inplace=False, downcast=False)
/home/cgb2/anaconda3/envs/ag/lib/python3.10/site-packages/fastai/data/transforms.py:225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if is_categorical_dtype(col):
/home/cgb2/anaconda3/envs/ag/lib/python3.10/site-packages/fastai/tabular/core.py:233: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if not is_categorical_dtype(c):
/home/cgb2/anaconda3/envs/ag/lib/python3.10/site-packages/fastai/tabular/core.py:233: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if not is_categorical_dtype(c):
/home/cgb2/anaconda3/envs/ag/lib/python3.10/site-packages/fastai/tabular/core.py:233: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if not is_categorical_dtype(c):
/home/cgb2/anaconda3/envs/ag/lib/python3.10/site-packages/fastai/data/transforms.py:225: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if is_categorical_dtype(col):
/home/cgb2/anaconda3/envs/ag/lib/python3.10/site-packages/fastai/tabular/core.py:233: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if not is_categorical_dtype(c):
No improvement since epoch 9: early stopping
/home/cgb2/anaconda3/envs/ag/lib/python3.10/site-packages/autogluon/tabular/models/fastainn/tabular_nn_fastai.py:192: 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.
  df = df.fillna(column_fills, inplace=False, downcast=False)
/home/cgb2/anaconda3/envs/ag/lib/python3.10/site-packages/fastai/tabular/core.py:233: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if not is_categorical_dtype(c):
/home/cgb2/anaconda3/envs/ag/lib/python3.10/site-packages/fastai/tabular/core.py:233: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if not is_categorical_dtype(c):
/home/cgb2/anaconda3/envs/ag/lib/python3.10/site-packages/fastai/tabular/core.py:233: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if not is_categorical_dtype(c):
    0.8324   = Validation score   (accuracy)
    2.15s    = Training   runtime
    0.01s    = Validation runtime
Fitting model: XGBoost ...
/home/cgb2/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 '2059.259259259259' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
  memory_usage[column] = (
/home/cgb2/anaconda3/envs/ag/lib/python3.10/site-packages/xgboost/data.py:440: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.
  if is_sparse(data):
    0.8101   = Validation score   (accuracy)
    0.23s    = Training   runtime
    0.01s    = Validation runtime
Fitting model: NeuralNetTorch ...
/home/cgb2/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 '2059.259259259259' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
  memory_usage[column] = (
    0.8212   = Validation score   (accuracy)
    3.4s     = Training   runtime
    0.01s    = Validation runtime
Fitting model: LightGBMLarge ...
/home/cgb2/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 '2059.259259259259' has dtype incompatible with int64, please explicitly cast to a compatible dtype first.
  memory_usage[column] = (
    0.8324   = Validation score   (accuracy)
    0.42s    = Training   runtime
    0.0s     = Validation runtime
Fitting model: WeightedEnsemble_L2 ...
    0.8324   = Validation score   (accuracy)
    0.48s    = Training   runtime
    0.0s     = Validation runtime
AutoGluon training complete, total runtime = 12.03s ... Best model: "WeightedEnsemble_L2"
TabularPredictor saved. To load, use: predictor = TabularPredictor.load("AutogluonModels/ag-20231024_084026")

<autogluon.tabular.predictor.predictor.TabularPredictor at 0x7fd1a92d4af0>

- 리더보드확인 (모의고사 채점)

predictr.leaderboard()

                  model  score_val  pred_time_val  fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order
0         LightGBMLarge   0.832402       0.003578  0.422645                0.003578           0.422645            1       True         13
1       NeuralNetFastAI   0.832402       0.011829  2.153854                0.011829           2.153854            1       True         10
2   WeightedEnsemble_L2   0.832402       0.012396  2.629154                0.000567           0.475301            2       True         14
3              CatBoost   0.826816       0.007282  0.558903                0.007282           0.558903            1       True          7
4              LightGBM   0.821229       0.003267  0.163059                0.003267           0.163059            1       True          4
5        NeuralNetTorch   0.821229       0.014016  3.402719                0.014016           3.402719            1       True         12
6            LightGBMXT   0.815642       0.003467  0.479265                0.003467           0.479265            1       True          3
7      RandomForestEntr   0.815642       0.175467  0.606128                0.175467           0.606128            1       True          6
8      RandomForestGini   0.815642       0.204887  0.539022                0.204887           0.539022            1       True          5
9        ExtraTreesGini   0.815642       0.224873  0.821764                0.224873           0.821764            1       True          8
10              XGBoost   0.810056       0.005781  0.229575                0.005781           0.229575            1       True         11
11       ExtraTreesEntr   0.810056       0.229908  0.699347                0.229908           0.699347            1       True          9
12       KNeighborsDist   0.653631       0.016264  0.011564                0.016264           0.011564            1       True          2
13       KNeighborsUnif   0.653631       0.028849  0.011524                0.028849           0.011524            1       True          1

	model	score_val	pred_time_val	fit_time	pred_time_val_marginal	fit_time_marginal	stack_level	can_infer	fit_order
0	LightGBMLarge	0.832402	0.003578	0.422645	0.003578	0.422645	1	True	13
1	NeuralNetFastAI	0.832402	0.011829	2.153854	0.011829	2.153854	1	True	10
2	WeightedEnsemble_L2	0.832402	0.012396	2.629154	0.000567	0.475301	2	True	14
3	CatBoost	0.826816	0.007282	0.558903	0.007282	0.558903	1	True	7
4	LightGBM	0.821229	0.003267	0.163059	0.003267	0.163059	1	True	4
5	NeuralNetTorch	0.821229	0.014016	3.402719	0.014016	3.402719	1	True	12
6	LightGBMXT	0.815642	0.003467	0.479265	0.003467	0.479265	1	True	3
7	RandomForestEntr	0.815642	0.175467	0.606128	0.175467	0.606128	1	True	6
8	RandomForestGini	0.815642	0.204887	0.539022	0.204887	0.539022	1	True	5
9	ExtraTreesGini	0.815642	0.224873	0.821764	0.224873	0.821764	1	True	8
10	XGBoost	0.810056	0.005781	0.229575	0.005781	0.229575	1	True	11
11	ExtraTreesEntr	0.810056	0.229908	0.699347	0.229908	0.699347	1	True	9
12	KNeighborsDist	0.653631	0.016264	0.011564	0.016264	0.011564	1	True	2
13	KNeighborsUnif	0.653631	0.028849	0.011524	0.028849	0.011524	1	True	1

D. 예측 (predict)

- 비유: 학습이후에 문제를 푸는 과정으로 비유할 수 있다.

- training set 을 풀어봄 (predict) \(\to\) 점수 확인

(df_train.Survived == predictr.predict(df_train)).mean()

/home/cgb2/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)
/home/cgb2/anaconda3/envs/ag/lib/python3.10/site-packages/autogluon/tabular/models/fastainn/tabular_nn_fastai.py:192: 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.
  df = df.fillna(column_fills, inplace=False, downcast=False)
/home/cgb2/anaconda3/envs/ag/lib/python3.10/site-packages/fastai/tabular/core.py:233: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if not is_categorical_dtype(c):
/home/cgb2/anaconda3/envs/ag/lib/python3.10/site-packages/fastai/tabular/core.py:233: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if not is_categorical_dtype(c):
/home/cgb2/anaconda3/envs/ag/lib/python3.10/site-packages/fastai/tabular/core.py:233: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if not is_categorical_dtype(c):

0.8810325476992144

(df_train.Survived == (df_train.Sex == "female")).mean() # 예전점수와 비교

0.7867564534231201

- test set 을 풀어봄 (predict) \(\to\) 점수 확인 하러 캐글에 결과제출

df_test.assign(Survived = predictr.predict(df_test)).loc[:,['PassengerId','Survived']]

/home/cgb2/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)
/home/cgb2/anaconda3/envs/ag/lib/python3.10/site-packages/autogluon/tabular/models/fastainn/tabular_nn_fastai.py:192: 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.
  df = df.fillna(column_fills, inplace=False, downcast=False)
/home/cgb2/anaconda3/envs/ag/lib/python3.10/site-packages/fastai/tabular/core.py:233: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if not is_categorical_dtype(c):
/home/cgb2/anaconda3/envs/ag/lib/python3.10/site-packages/fastai/tabular/core.py:233: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if not is_categorical_dtype(c):
/home/cgb2/anaconda3/envs/ag/lib/python3.10/site-packages/fastai/tabular/core.py:233: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if not is_categorical_dtype(c):

	PassengerId	Survived
0	892	0
1	893	0
2	894	0
3	895	0
4	896	0
...	...	...
413	1305	0
414	1306	1
415	1307	0
416	1308	0
417	1309	0

418 rows × 2 columns

# df_test.assign(Survived = predictr.predict(df_test)).loc[:,['PassengerId','Survived']]\
# .to_csv("autogluon_submission.csv",index=False)