02wk-06: 타이타닉 / Autogluon (Fsize)

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, 395kB/s]
Archive:  titanic.zip
  inflating: ./titanic/gender_submission.csv  
  inflating: ./titanic/test.csv      
  inflating: ./titanic/train.csv

- 피처엔지니어링

df_train.eval('Fsize = SibSp + Parch')
df_test.eval('Fsize = SibSp + Parch')

	PassengerId	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	Fsize
0	892	3	Kelly, Mr. James	male	34.5	0	0	330911	7.8292	NaN	Q	0
1	893	3	Wilkes, Mrs. James (Ellen Needs)	female	47.0	1	0	363272	7.0000	NaN	S	1
2	894	2	Myles, Mr. Thomas Francis	male	62.0	0	0	240276	9.6875	NaN	Q	0
3	895	3	Wirz, Mr. Albert	male	27.0	0	0	315154	8.6625	NaN	S	0
4	896	3	Hirvonen, Mrs. Alexander (Helga E Lindqvist)	female	22.0	1	1	3101298	12.2875	NaN	S	2
...	...	...	...	...	...	...	...	...	...	...	...	...
413	1305	3	Spector, Mr. Woolf	male	NaN	0	0	A.5. 3236	8.0500	NaN	S	0
414	1306	1	Oliva y Ocana, Dona. Fermina	female	39.0	0	0	PC 17758	108.9000	C105	C	0
415	1307	3	Saether, Mr. Simon Sivertsen	male	38.5	0	0	SOTON/O.Q. 3101262	7.2500	NaN	S	0
416	1308	3	Ware, Mr. Frederick	male	NaN	0	0	359309	8.0500	NaN	S	0
417	1309	3	Peter, Master. Michael J	male	NaN	1	1	2668	22.3583	NaN	C	2

418 rows × 12 columns

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_084218"

C. 적합(fit)

- 비유: 학생이 공부를 하는 과정으로 비유할 수 있다.

- 학습

predictr.fit(df_train.eval('Fsize = SibSp + Parch'))

/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_084218"
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:   265.27 GB / 490.57 GB (54.1%)
Train Data Rows:    891
Train Data Columns: 12
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:                    124116.32 MB
    Train Data (Original)  Memory Usage: 0.32 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', [])          : 5 | ['PassengerId', 'Pclass', 'SibSp', 'Parch', 'Fsize']
        ('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', [])                         : 5 | ['PassengerId', 'Pclass', 'SibSp', 'Parch', 'Fsize']
        ('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
    12 features in original data used to generate 29 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: 0.08 MB (0.0% of available memory)
Data preprocessing and feature engineering runtime = 0.22s ...
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 ...
    0.648    = Validation score   (accuracy)
    0.01s    = Training   runtime
    0.02s    = Validation runtime
Fitting model: KNeighborsDist ...
    0.648    = Validation score   (accuracy)
    0.01s    = Training   runtime
    0.0s     = 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)
    0.4s     = 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)
    0.19s    = Training   runtime
    0.0s     = Validation runtime
Fitting model: RandomForestGini ...
    0.8156   = Validation score   (accuracy)
    0.42s    = Training   runtime
    0.14s    = Validation runtime
Fitting model: RandomForestEntr ...
    0.8212   = Validation score   (accuracy)
    0.54s    = Training   runtime
    0.11s    = 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)
    0.54s    = Training   runtime
    0.0s     = Validation runtime
Fitting model: ExtraTreesGini ...
    0.8045   = Validation score   (accuracy)
    0.84s    = Training   runtime
    0.24s    = Validation runtime
Fitting model: ExtraTreesEntr ...
    0.8101   = Validation score   (accuracy)
    0.77s    = Training   runtime
    0.18s    = 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):
/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)
    1.82s    = 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):
    0.8268   = Validation score   (accuracy)
    0.2s     = Training   runtime
    0.0s     = 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] = (
    0.8324   = Validation score   (accuracy)
    3.7s     = Training   runtime
    0.02s    = 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)
    0.4s     = Training   runtime
    0.0s     = Validation runtime
Fitting model: WeightedEnsemble_L2 ...
    0.8547   = Validation score   (accuracy)
    0.48s    = Training   runtime
    0.0s     = Validation runtime
AutoGluon training complete, total runtime = 11.61s ... Best model: "WeightedEnsemble_L2"
TabularPredictor saved. To load, use: predictor = TabularPredictor.load("AutogluonModels/ag-20231024_084218")

<autogluon.tabular.predictor.predictor.TabularPredictor at 0x7f29d8593b80>

- 리더보드확인 (모의고사채점)

predictr.leaderboard()

                  model  score_val  pred_time_val  fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order
0   WeightedEnsemble_L2   0.860335       0.016608  2.934600                0.000400           0.236214            2       True         14
1        NeuralNetTorch   0.837989       0.008671  1.382263                0.008671           1.382263            1       True         12
2         LightGBMLarge   0.832402       0.003077  0.377827                0.003077           0.377827            1       True         13
3       NeuralNetFastAI   0.832402       0.007537  1.316123                0.007537           1.316123            1       True         10
4              CatBoost   0.826816       0.003649  0.528946                0.003649           0.528946            1       True          7
5               XGBoost   0.826816       0.004545  0.150251                0.004545           0.150251            1       True         11
6              LightGBM   0.821229       0.003294  0.180805                0.003294           0.180805            1       True          4
7      RandomForestEntr   0.821229       0.194986  0.478185                0.194986           0.478185            1       True          6
8            LightGBMXT   0.815642       0.003372  0.198229                0.003372           0.198229            1       True          3
9      RandomForestGini   0.815642       0.097445  0.306731                0.097445           0.306731            1       True          5
10       ExtraTreesEntr   0.810056       0.076015  0.512563                0.076015           0.512563            1       True          9
11       ExtraTreesGini   0.804469       0.118582  0.881634                0.118582           0.881634            1       True          8
12       KNeighborsDist   0.648045       0.001869  0.007722                0.001869           0.007722            1       True          2
13       KNeighborsUnif   0.648045       0.004271  0.092099                0.004271           0.092099            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	WeightedEnsemble_L2	0.860335	0.016608	2.934600	0.000400	0.236214	2	True	14
1	NeuralNetTorch	0.837989	0.008671	1.382263	0.008671	1.382263	1	True	12
2	LightGBMLarge	0.832402	0.003077	0.377827	0.003077	0.377827	1	True	13
3	NeuralNetFastAI	0.832402	0.007537	1.316123	0.007537	1.316123	1	True	10
4	CatBoost	0.826816	0.003649	0.528946	0.003649	0.528946	1	True	7
5	XGBoost	0.826816	0.004545	0.150251	0.004545	0.150251	1	True	11
6	LightGBM	0.821229	0.003294	0.180805	0.003294	0.180805	1	True	4
7	RandomForestEntr	0.821229	0.194986	0.478185	0.194986	0.478185	1	True	6
8	LightGBMXT	0.815642	0.003372	0.198229	0.003372	0.198229	1	True	3
9	RandomForestGini	0.815642	0.097445	0.306731	0.097445	0.306731	1	True	5
10	ExtraTreesEntr	0.810056	0.076015	0.512563	0.076015	0.512563	1	True	9
11	ExtraTreesGini	0.804469	0.118582	0.881634	0.118582	0.881634	1	True	8
12	KNeighborsDist	0.648045	0.001869	0.007722	0.001869	0.007722	1	True	2
13	KNeighborsUnif	0.648045	0.004271	0.092099	0.004271	0.092099	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.eval('Fsize = SibSp + Parch'))).mean()

0.9169472502805837

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

# df_test.assign(Survived = predictr.predict(df_test.eval('Fsize = SibSp + Parch'))).loc[:,['PassengerId','Survived']]\
# .to_csv("autogluon(Fsize)_submission.csv",index=False)