13wk-57: House Prices / 자료분석(Autogluon)

Author

최규빈

Published

December 1, 2023

1. 강의영상

2. Imports

#!pip install autogluon.eda

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt 
import seaborn as sns
#---#
from autogluon.tabular import TabularPredictor
import autogluon.eda.auto as auto
#---#
import warnings
warnings.filterwarnings('ignore')

3. Data

ref: https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques/overview

!kaggle competitions download -c house-prices-advanced-regression-techniques

2023-12-01 22:24:10,010 WARNING Retrying (Retry(total=2, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7ff5450cc250>: Failed to establish a new connection: [Errno -3] Temporary failure in name resolution')': /api/v1/competitions/data/download-all/house-prices-advanced-regression-techniques
2023-12-01 22:24:30,028 WARNING Retrying (Retry(total=1, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7ff545062020>: Failed to establish a new connection: [Errno -3] Temporary failure in name resolution')': /api/v1/competitions/data/download-all/house-prices-advanced-regression-techniques
Downloading house-prices-advanced-regression-techniques.zip to /home/cgb2/Dropbox/07_lectures/2023-09-MP2023/posts
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100%|█████████████████████████████████████████| 199k/199k [00:00<00:00, 833kB/s]

!unzip house-prices-advanced-regression-techniques.zip

Archive:  house-prices-advanced-regression-techniques.zip
  inflating: data_description.txt    
  inflating: sample_submission.csv   
  inflating: test.csv                
  inflating: train.csv

df_submission = pd.read_csv("sample_submission.csv")
df_train = pd.read_csv("train.csv")
df_test = pd.read_csv("test.csv")

!rm sample_submission.csv
!rm train.csv
!rm test.csv
!rm data_description.txt
!rm house-prices-advanced-regression-techniques.zip

4. 적합

set(df_train.columns) - set(df_test.columns)

{'SalePrice'}

# step1 -- pass
# step2
predictr = TabularPredictor(label='SalePrice')
# step3
predictr.fit(df_train)
# step4
yhat = predictr.predict(df_train)
yyhat = predictr.predict(df_test)

No path specified. Models will be saved in: "AutogluonModels/ag-20231201_132915/"
Beginning AutoGluon training ...
AutoGluon will save models to "AutogluonModels/ag-20231201_132915/"
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:   248.13 GB / 490.57 GB (50.6%)
Train Data Rows:    1460
Train Data Columns: 80
Label Column: SalePrice
Preprocessing data ...
AutoGluon infers your prediction problem is: 'regression' (because dtype of label-column == int and many unique label-values observed).
    Label info (max, min, mean, stddev): (755000, 34900, 180921.19589, 79442.50288)
    If 'regression' 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'])
Using Feature Generators to preprocess the data ...
Fitting AutoMLPipelineFeatureGenerator...
    Available Memory:                    123443.91 MB
    Train Data (Original)  Memory Usage: 4.06 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 3 features to boolean dtype as they only contain 2 unique values.
    Stage 2 Generators:
        Fitting FillNaFeatureGenerator...
    Stage 3 Generators:
        Fitting IdentityFeatureGenerator...
        Fitting CategoryFeatureGenerator...
            Fitting CategoryMemoryMinimizeFeatureGenerator...
    Stage 4 Generators:
        Fitting DropUniqueFeatureGenerator...
    Stage 5 Generators:
        Fitting DropDuplicatesFeatureGenerator...
    Types of features in original data (raw dtype, special dtypes):
        ('float', [])  :  3 | ['LotFrontage', 'MasVnrArea', 'GarageYrBlt']
        ('int', [])    : 34 | ['Id', 'MSSubClass', 'LotArea', 'OverallQual', 'OverallCond', ...]
        ('object', []) : 43 | ['MSZoning', 'Street', 'Alley', 'LotShape', 'LandContour', ...]
    Types of features in processed data (raw dtype, special dtypes):
        ('category', [])  : 40 | ['MSZoning', 'Alley', 'LotShape', 'LandContour', 'LotConfig', ...]
        ('float', [])     :  3 | ['LotFrontage', 'MasVnrArea', 'GarageYrBlt']
        ('int', [])       : 34 | ['Id', 'MSSubClass', 'LotArea', 'OverallQual', 'OverallCond', ...]
        ('int', ['bool']) :  3 | ['Street', 'Utilities', 'CentralAir']
    0.2s = Fit runtime
    80 features in original data used to generate 80 features in processed data.
    Train Data (Processed) Memory Usage: 0.52 MB (0.0% of available memory)
Data preprocessing and feature engineering runtime = 0.23s ...
AutoGluon will gauge predictive performance using evaluation metric: 'root_mean_squared_error'
    This metric's sign has been flipped to adhere to being higher_is_better. The metric score can be multiplied by -1 to get the metric value.
    To change this, specify the eval_metric parameter of Predictor()
Automatically generating train/validation split with holdout_frac=0.2, Train Rows: 1168, Val Rows: 292
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 11 L1 models ...
Fitting model: KNeighborsUnif ...
    -52278.8213  = Validation score   (-root_mean_squared_error)
    0.01s    = Training   runtime
    0.15s    = Validation runtime
Fitting model: KNeighborsDist ...
    -51314.2734  = Validation score   (-root_mean_squared_error)
    0.01s    = Training   runtime
    0.06s    = Validation runtime
Fitting model: LightGBMXT ...
    -27196.7065  = Validation score   (-root_mean_squared_error)
    2.72s    = Training   runtime
    0.02s    = Validation runtime
Fitting model: LightGBM ...
    -28692.2871  = Validation score   (-root_mean_squared_error)
    5.95s    = Training   runtime
    0.06s    = Validation runtime
Fitting model: RandomForestMSE ...
    -32785.3519  = Validation score   (-root_mean_squared_error)
    0.44s    = Training   runtime
    0.04s    = Validation runtime
Fitting model: CatBoost ...
    -28465.6966  = Validation score   (-root_mean_squared_error)
    50.09s   = Training   runtime
    0.02s    = Validation runtime
Fitting model: ExtraTreesMSE ...
    -32045.9062  = Validation score   (-root_mean_squared_error)
    0.33s    = Training   runtime
    0.04s    = Validation runtime
Fitting model: NeuralNetFastAI ...
    -33846.1203  = Validation score   (-root_mean_squared_error)
    3.1s     = Training   runtime
    0.03s    = Validation runtime
Fitting model: XGBoost ...
    -27778.2437  = Validation score   (-root_mean_squared_error)
    0.9s     = Training   runtime
    0.01s    = Validation runtime
Fitting model: NeuralNetTorch ...
    -35786.956   = Validation score   (-root_mean_squared_error)
    6.1s     = Training   runtime
    0.04s    = Validation runtime
Fitting model: LightGBMLarge ...
    -32084.1712  = Validation score   (-root_mean_squared_error)
    9.08s    = Training   runtime
    0.05s    = Validation runtime
Fitting model: WeightedEnsemble_L2 ...
    -26322.571   = Validation score   (-root_mean_squared_error)
    0.21s    = Training   runtime
    0.0s     = Validation runtime
AutoGluon training complete, total runtime = 80.78s ... Best model: "WeightedEnsemble_L2"
TabularPredictor saved. To load, use: predictor = TabularPredictor.load("AutogluonModels/ag-20231201_132915/")
WARNING: Int features without null values at train time contain null values at inference time! Imputing nulls to 0. To avoid this, pass the features as floats during fit!
WARNING: Int features with nulls: ['BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'BsmtFullBath', 'BsmtHalfBath', 'GarageCars', 'GarageArea']

[1000]  valid_set's rmse: 27505.1
[2000]  valid_set's rmse: 27240.4
[3000]  valid_set's rmse: 27201.5
[4000]  valid_set's rmse: 27197.3
[5000]  valid_set's rmse: 27197.2
[1000]  valid_set's rmse: 29499.8
[2000]  valid_set's rmse: 28896.4
[3000]  valid_set's rmse: 28752.1
[4000]  valid_set's rmse: 28705.7
[5000]  valid_set's rmse: 28695.2
[6000]  valid_set's rmse: 28693
[7000]  valid_set's rmse: 28692.5
[8000]  valid_set's rmse: 28692.3
[9000]  valid_set's rmse: 28692.3
[10000] valid_set's rmse: 28692.3
[1000]  valid_set's rmse: 32134.9
[2000]  valid_set's rmse: 32087.8
[3000]  valid_set's rmse: 32084.2
[4000]  valid_set's rmse: 32084.2
[5000]  valid_set's rmse: 32084.2

5. 제출

df_submission['SalePrice'] = yyhat 
df_submission.to_csv("submission.csv",index=False)

!kaggle competitions submit -c house-prices-advanced-regression-techniques -f submission.csv -m "오토글루온을 이용하여 첫제출"
!rm submission.csv

  0%|                                               | 0.00/21.1k [00:00<?, ?B/s]2023-12-01 22:35:47,374 WARNING Retrying (Retry(total=9, connect=None, read=None, redirect=None, status=None)) after connection broken by 'NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x7fab8d916500>: Failed to establish a new connection: [Errno -3] Temporary failure in name resolution')': /upload/storage/v1/b/kaggle-competitions-submissions/o?uploadType=resumable&upload_id=ABPtcPrQPMZgrOes6mIirhgo1brnKPVPDPdDAa0UrFknmizjXgeg2BBIaNtyZYx7a5ZMz70lq_iPJQj-hoNcwHQjzA7_ajRVGka6C29N5aBKnRKBGg
100%|████████████████████████████████████████| 21.1k/21.1k [00:26<00:00, 821B/s]
Successfully submitted to House Prices - Advanced Regression Techniques

958/4955

0.19334006054490413

나쁘지 않은 순위..

6. 해석 및 시각화 (HW)

- 변수들중에서 SalePrice를 예측하기에 적절한 변수들을 조사해볼것.