13wk-51: 아이스크림(type무의미) / 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

df_train = pd.read_csv('https://raw.githubusercontent.com/guebin/MP2023/main/posts/mid/icesales_train.csv')

df_train.head()

	temp	type	sales
0	19.4	choco	64.807407
1	0.9	vanilla	25.656697
2	7.4	vanilla	34.756650
3	4.5	choco	27.265442
4	21.1	choco	70.606946

sns.scatterplot(df_train,x='temp',y='sales',hue='type')

4. 적합

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

No path specified. Models will be saved in: "AutogluonModels/ag-20231201_105859/"
Beginning AutoGluon training ...
AutoGluon will save models to "AutogluonModels/ag-20231201_105859/"
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:   248.43 GB / 490.57 GB (50.6%)
Train Data Rows:    280
Train Data Columns: 2
Label Column: sales
Preprocessing data ...
AutoGluon infers your prediction problem is: 'regression' (because dtype of label-column == float and many unique label-values observed).
    Label info (max, min, mean, stddev): (88.99437629756306, 10.335207096486446, 51.10189, 21.16757)
    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:                    126321.75 MB
    Train Data (Original)  Memory Usage: 0.02 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...
    Stage 3 Generators:
        Fitting IdentityFeatureGenerator...
    Stage 4 Generators:
        Fitting DropUniqueFeatureGenerator...
    Stage 5 Generators:
        Fitting DropDuplicatesFeatureGenerator...
    Types of features in original data (raw dtype, special dtypes):
        ('float', [])  : 1 | ['temp']
        ('object', []) : 1 | ['type']
    Types of features in processed data (raw dtype, special dtypes):
        ('float', [])     : 1 | ['temp']
        ('int', ['bool']) : 1 | ['type']
    0.0s = Fit runtime
    2 features in original data used to generate 2 features in processed data.
    Train Data (Processed) Memory Usage: 0.0 MB (0.0% of available memory)
Data preprocessing and feature engineering runtime = 0.02s ...
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: 224, Val Rows: 56
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 ...
    -2.7316  = Validation score   (-root_mean_squared_error)
    0.01s    = Training   runtime
    0.02s    = Validation runtime
Fitting model: KNeighborsDist ...
    -3.5558  = Validation score   (-root_mean_squared_error)
    0.01s    = Training   runtime
    0.01s    = Validation runtime
Fitting model: LightGBMXT ...
    -3.1036  = Validation score   (-root_mean_squared_error)
    0.4s     = Training   runtime
    0.0s     = Validation runtime
Fitting model: LightGBM ...
    -3.0864  = Validation score   (-root_mean_squared_error)
    0.18s    = Training   runtime
    0.0s     = Validation runtime
Fitting model: RandomForestMSE ...
    -2.9027  = Validation score   (-root_mean_squared_error)
    0.27s    = Training   runtime
    0.04s    = Validation runtime
Fitting model: CatBoost ...
    -2.7878  = Validation score   (-root_mean_squared_error)
    0.28s    = Training   runtime
    0.0s     = Validation runtime
Fitting model: ExtraTreesMSE ...
    -2.88    = Validation score   (-root_mean_squared_error)
    0.39s    = Training   runtime
    0.03s    = Validation runtime
Fitting model: NeuralNetFastAI ...
    -2.6541  = Validation score   (-root_mean_squared_error)
    2.25s    = Training   runtime
    0.01s    = Validation runtime
Fitting model: XGBoost ...
    -3.061   = Validation score   (-root_mean_squared_error)
    0.11s    = Training   runtime
    0.0s     = Validation runtime
Fitting model: NeuralNetTorch ...
    -2.6146  = Validation score   (-root_mean_squared_error)
    1.91s    = Training   runtime
    0.01s    = Validation runtime
Fitting model: LightGBMLarge ...
    -2.9062  = Validation score   (-root_mean_squared_error)
    0.25s    = Training   runtime
    0.0s     = Validation runtime
Fitting model: WeightedEnsemble_L2 ...
    -2.5554  = Validation score   (-root_mean_squared_error)
    0.23s    = Training   runtime
    0.0s     = Validation runtime
AutoGluon training complete, total runtime = 6.52s ... Best model: "WeightedEnsemble_L2"
TabularPredictor saved. To load, use: predictor = TabularPredictor.load("AutogluonModels/ag-20231201_105859/")

[1000]  valid_set's rmse: 3.11194

sns.scatterplot(df_train,x='temp',y='sales',hue='type',alpha=0.3)
sns.lineplot(df_train,x='temp',y=yhat,hue='type')

predictr.leaderboard(silent=True)

	model	score_val	pred_time_val	fit_time	pred_time_val_marginal	fit_time_marginal	stack_level	can_infer	fit_order
0	WeightedEnsemble_L2	-2.555379	0.067882	4.665440	0.000242	0.226336	2	True	12
1	NeuralNetTorch	-2.614644	0.007492	1.911196	0.007492	1.911196	1	True	10
2	NeuralNetFastAI	-2.654070	0.007698	2.245980	0.007698	2.245980	1	True	8
3	KNeighborsUnif	-2.731556	0.017128	0.009127	0.017128	0.009127	1	True	1
4	CatBoost	-2.787790	0.000878	0.278754	0.000878	0.278754	1	True	6
5	ExtraTreesMSE	-2.879997	0.025122	0.387946	0.025122	0.387946	1	True	7
6	RandomForestMSE	-2.902703	0.035322	0.272800	0.035322	0.272800	1	True	5
7	LightGBMLarge	-2.906174	0.000696	0.253458	0.000696	0.253458	1	True	11
8	XGBoost	-3.061021	0.001687	0.114215	0.001687	0.114215	1	True	9
9	LightGBM	-3.086423	0.000693	0.181278	0.000693	0.181278	1	True	4
10	LightGBMXT	-3.103597	0.000973	0.395612	0.000973	0.395612	1	True	3
11	KNeighborsDist	-3.555839	0.013983	0.005915	0.013983	0.005915	1	True	2

5. 해석 및 시각화

A. y의 분포, (X,y)의 관계 시각화

auto.target_analysis(
    train_data=df_train,
    label='sales',
    fit_distributions=False
)

Target variable analysis

	count	mean	std	min	25%	50%	75%	max	dtypes	unique	missing_count	missing_ratio	raw_type	special_types
sales	280	51.101886	21.167573	10.335207	33.053077	47.844021	70.451589	88.994376	float64	280			float

Target variable correlations

train_data - spearman correlation matrix; focus: absolute correlation for sales >= 0.5

Feature interaction between temp/sales in train_data

B. 중요한 설명변수

auto.quick_fit(
    train_data=df_train,
    label='sales',
    show_feature_importance_barplots=True
)

No path specified. Models will be saved in: "AutogluonModels/ag-20231201_110230/"

Model Prediction for sales

Using validation data for Test points

Model Leaderboard

	model	score_test	score_val	pred_time_test	pred_time_val	fit_time	pred_time_test_marginal	pred_time_val_marginal	fit_time_marginal	stack_level	can_infer	fit_order
0	LightGBMXT	-3.549018	-4.206044	0.001961	0.000846	0.218935	0.001961	0.000846	0.218935	1	True	1

Feature Importance for Trained Model

	importance	stddev	p_value	n	p99_high	p99_low
temp	25.164809	1.617020	0.000002	5	28.494276	21.835342
type	-0.048470	0.059119	0.929654	5	0.073258	-0.170197

Rows with the highest prediction error

Rows in this category worth inspecting for the causes of the error

	temp	type	sales	sales_pred	error
73	-3.7	vanilla	12.432354	24.724379	12.292025
191	-0.3	vanilla	16.436525	24.724379	8.287854
218	14.7	choco	60.178468	52.961044	7.217424
166	16.1	choco	66.821367	59.932861	6.888506
5	23.2	vanilla	75.697957	82.155197	6.457240
118	8.3	choco	45.364110	38.923119	6.440991
198	4.4	vanilla	24.924572	31.039103	6.114530
7	11.2	choco	45.593168	51.416027	5.822859
89	25.7	vanilla	87.788320	82.155197	5.633123
109	2.0	vanilla	19.398204	24.724379	5.326174

C. 관측치별 해석

auto.explain_rows(
    train_data=df_train,
    model=predictr,
    rows=df_train.iloc[[0]],
    display_rows=True,
    plot='waterfall'
)

	temp	type	sales
0	19.4	choco	64.807407

auto.explain_rows(
    train_data=df_train,
    model=predictr,
    rows=df_train.iloc[[1]],
    display_rows=True,
    plot='waterfall'
)

	temp	type	sales
1	0.9	vanilla	25.656697