[STBDA2023] 14wk-59: 자전거대여 / 시계열자료분석?(Autogluon)

Author

김보람

Published

December 10, 2023

14wk-59: 자전거대여 / 시계열자료분석?(Autogluon)

최규빈
2023-12-01

1. 강의영상

???

2. Imports

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import sklearn.preprocessing
#---#}
from autogluon.tabular import TabularPredictor
from autogluon.timeseries import TimeSeriesDataFrame, TimeSeriesPredictor
from autogluon.common import space
#---#
import warnings
warnings.filterwarnings('ignore')

3. Data

ref: https://www.kaggle.com/competitions/bike-sharing-demand

- 자료 다운로드

!kaggle competitions download -c bike-sharing-demand
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 bike-sharing-demand.zip to /home/coco/Dropbox/Class/STBDA23/posts
100%|█████████████████████████████████████████| 189k/189k [00:00<00:00, 459kB/s]
100%|█████████████████████████████████████████| 189k/189k [00:00<00:00, 459kB/s]
!unzip bike-sharing-demand.zip -d data
Archive:  bike-sharing-demand.zip
  inflating: data/sampleSubmission.csv  
  inflating: data/test.csv           
  inflating: data/train.csv          
sampleSubmission = pd.read_csv('data/sampleSubmission.csv')
df_train = pd.read_csv('data/train.csv')
df_test = pd.read_csv('data/test.csv') 
!rm -rf data
!rm bike-sharing-demand.zip

- 자료관찰

set(df_train['weather'])
{1, 2, 3, 4}
display("train",df_train,"test",df_test)
'train'
datetime season holiday workingday weather temp atemp humidity windspeed casual registered count
0 2011-01-01 00:00:00 1 0 0 1 9.84 14.395 81 0.0000 3 13 16
1 2011-01-01 01:00:00 1 0 0 1 9.02 13.635 80 0.0000 8 32 40
2 2011-01-01 02:00:00 1 0 0 1 9.02 13.635 80 0.0000 5 27 32
3 2011-01-01 03:00:00 1 0 0 1 9.84 14.395 75 0.0000 3 10 13
4 2011-01-01 04:00:00 1 0 0 1 9.84 14.395 75 0.0000 0 1 1
... ... ... ... ... ... ... ... ... ... ... ... ...
10881 2012-12-19 19:00:00 4 0 1 1 15.58 19.695 50 26.0027 7 329 336
10882 2012-12-19 20:00:00 4 0 1 1 14.76 17.425 57 15.0013 10 231 241
10883 2012-12-19 21:00:00 4 0 1 1 13.94 15.910 61 15.0013 4 164 168
10884 2012-12-19 22:00:00 4 0 1 1 13.94 17.425 61 6.0032 12 117 129
10885 2012-12-19 23:00:00 4 0 1 1 13.12 16.665 66 8.9981 4 84 88

10886 rows × 12 columns

'test'
datetime season holiday workingday weather temp atemp humidity windspeed
0 2011-01-20 00:00:00 1 0 1 1 10.66 11.365 56 26.0027
1 2011-01-20 01:00:00 1 0 1 1 10.66 13.635 56 0.0000
2 2011-01-20 02:00:00 1 0 1 1 10.66 13.635 56 0.0000
3 2011-01-20 03:00:00 1 0 1 1 10.66 12.880 56 11.0014
4 2011-01-20 04:00:00 1 0 1 1 10.66 12.880 56 11.0014
... ... ... ... ... ... ... ... ... ...
6488 2012-12-31 19:00:00 1 0 1 2 10.66 12.880 60 11.0014
6489 2012-12-31 20:00:00 1 0 1 2 10.66 12.880 60 11.0014
6490 2012-12-31 21:00:00 1 0 1 1 10.66 12.880 60 11.0014
6491 2012-12-31 22:00:00 1 0 1 1 10.66 13.635 56 8.9981
6492 2012-12-31 23:00:00 1 0 1 1 10.66 13.635 65 8.9981

6493 rows × 9 columns

- train/test가 나누어진 시점 해석

display("train",df_train[::24][:20], "test",df_test[::24][:10])
'train'
datetime season holiday workingday weather temp atemp humidity windspeed casual registered count
0 2011-01-01 00:00:00 1 0 0 1 9.84 14.395 81 0.0000 3 13 16
24 2011-01-02 00:00:00 1 0 0 2 18.86 22.725 88 19.9995 4 13 17
48 2011-01-03 01:00:00 1 0 1 1 8.20 8.335 44 27.9993 0 2 2
72 2011-01-04 04:00:00 1 0 1 1 5.74 9.090 63 6.0032 0 2 2
96 2011-01-05 05:00:00 1 0 1 1 9.02 11.365 47 11.0014 0 3 3
120 2011-01-06 06:00:00 1 0 1 2 5.74 8.335 63 7.0015 0 36 36
144 2011-01-07 07:00:00 1 0 1 1 8.20 10.605 69 8.9981 8 76 84
168 2011-01-08 07:00:00 1 0 0 2 6.56 9.090 74 7.0015 1 8 9
192 2011-01-09 07:00:00 1 0 0 1 3.28 4.545 53 12.9980 1 5 6
216 2011-01-10 07:00:00 1 0 1 1 4.92 6.060 50 15.0013 2 75 77
240 2011-01-11 09:00:00 1 0 1 2 7.38 9.850 51 11.0014 3 127 130
264 2011-01-12 11:00:00 1 0 1 1 8.20 9.090 51 26.0027 3 32 35
288 2011-01-13 11:00:00 1 0 1 2 8.20 8.335 44 30.0026 4 33 37
312 2011-01-14 12:00:00 1 0 1 1 8.20 9.850 44 16.9979 2 59 61
336 2011-01-15 12:00:00 1 0 0 1 9.84 11.365 48 15.0013 15 74 89
360 2011-01-16 12:00:00 1 0 0 1 9.84 10.605 41 19.0012 29 75 104
384 2011-01-17 12:00:00 1 1 0 2 7.38 9.850 47 8.9981 10 70 80
408 2011-01-19 00:00:00 1 0 1 2 9.02 13.635 93 0.0000 0 3 3
432 2011-02-01 01:00:00 1 0 1 2 6.56 9.090 69 7.0015 0 3 3
456 2011-02-02 02:00:00 1 0 1 3 9.02 11.365 93 8.9981 4 0 4 
'test'
datetime season holiday workingday weather temp atemp humidity windspeed
0 2011-01-20 00:00:00 1 0 1 1 10.66 11.365 56 26.0027
24 2011-01-21 00:00:00 1 0 1 2 9.84 11.365 70 16.9979
48 2011-01-22 00:00:00 1 0 0 1 1.64 1.515 45 16.9979
72 2011-01-23 01:00:00 1 0 0 1 1.64 3.790 57 7.0015
96 2011-01-24 03:00:00 1 0 1 1 1.64 1.515 45 16.9979
120 2011-01-25 04:00:00 1 0 1 1 5.74 8.335 74 7.0015
144 2011-01-26 06:00:00 1 0 1 3 8.20 9.090 86 19.0012
168 2011-01-28 05:00:00 1 0 1 2 7.38 10.605 80 7.0015
192 2011-01-29 06:00:00 1 0 0 1 6.56 9.090 64 8.9981
216 2011-01-30 07:00:00 1 0 0 1 5.74 10.605 86 0.0000
  • 자료가 세개의 토막으로 되어있어..!!

- 시계열분석을 해야하나?

코드들을 확인 (https://www.kaggle.com/c/bike-sharing-demand/code?competitionId=3948&sortBy=voteCount) -> 시계열분석은 아닌것 같지않어?

- 데이터분석전략: 딱히 기세를 모델링할 필요를 못느끼겠음.

  • 오히려 시계열을 피처엔지어링하여 회귀문제로 바꾸는게 적절하다.
  • 시계열 -> 요일,시간의 피처추출 + 외부자료를 활용하여 휴일유무 체크 + 외부자료를 해당요일의 날씨체크 -> lm(y~X)의 회귀문제로 해석!

4. 적합1 -> 제출1

A. 적합

set(df_train.columns) - set(df_test.columns)
{'casual', 'count', 'registered'}
sampleSubmission.head()
datetime count
0 2011-01-20 00:00:00 0
1 2011-01-20 01:00:00 0
2 2011-01-20 02:00:00 0
3 2011-01-20 03:00:00 0
4 2011-01-20 04:00:00 0
  • count를 맞추자!

- 데이터 전처리(step1)

df_train_featured = df_train.copy()
df_test_featured = df_test.copy()
#---# 
df_train_featured = df_train_featured.drop(['casual','registered'],axis=1)

- step2~4

# step1 -- pass 
# step2
predictr = TabularPredictor(label='count')
# step3
predictr.fit(df_train_featured)
# step4 
yhat = predictr.predict(df_train_featured)
yyhat = predictr.predict(df_test_featured)
No path specified. Models will be saved in: "AutogluonModels/ag-20231210_063818/"
Beginning AutoGluon training ...
AutoGluon will save models to "AutogluonModels/ag-20231210_063818/"
AutoGluon Version:  0.8.2
Python Version:     3.8.18
Operating System:   Linux
Platform Machine:   x86_64
Platform Version:   #38~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Thu Nov  2 18:01:13 UTC 2
Disk Space Avail:   651.50 GB / 982.82 GB (66.3%)
Train Data Rows:    10886
Train Data Columns: 9
Label Column: count
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): (977, 1, 191.57413, 181.14445)
    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:                    128823.96 MB
    Train Data (Original)  Memory Usage: 1.52 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 2 features to boolean dtype as they only contain 2 unique values.
    Stage 2 Generators:
        Fitting FillNaFeatureGenerator...
    Stage 3 Generators:
        Fitting IdentityFeatureGenerator...
        Fitting DatetimeFeatureGenerator...
    Stage 4 Generators:
        Fitting DropUniqueFeatureGenerator...
    Stage 5 Generators:
        Fitting DropDuplicatesFeatureGenerator...
    Types of features in original data (raw dtype, special dtypes):
        ('float', [])                      : 3 | ['temp', 'atemp', 'windspeed']
        ('int', [])                        : 5 | ['season', 'holiday', 'workingday', 'weather', 'humidity']
        ('object', ['datetime_as_object']) : 1 | ['datetime']
    Types of features in processed data (raw dtype, special dtypes):
        ('float', [])                : 3 | ['temp', 'atemp', 'windspeed']
        ('int', [])                  : 3 | ['season', 'weather', 'humidity']
        ('int', ['bool'])            : 2 | ['holiday', 'workingday']
        ('int', ['datetime_as_int']) : 5 | ['datetime', 'datetime.year', 'datetime.month', 'datetime.day', 'datetime.dayofweek']
    0.0s = Fit runtime
    9 features in original data used to generate 13 features in processed data.
    Train Data (Processed) Memory Usage: 0.98 MB (0.0% of available memory)
Data preprocessing and feature engineering runtime = 0.05s ...
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=0.1, Train Rows: 9797, Val Rows: 1089
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 ...
    -109.7394    = Validation score   (-root_mean_squared_error)
    0.08s    = Training   runtime
    0.01s    = Validation runtime
Fitting model: KNeighborsDist ...
    -92.4421     = Validation score   (-root_mean_squared_error)
    0.04s    = Training   runtime
    0.02s    = Validation runtime
Fitting model: LightGBMXT ...
    -135.958     = Validation score   (-root_mean_squared_error)
    0.83s    = Training   runtime
    0.01s    = Validation runtime
Fitting model: LightGBM ...
    -134.0804    = Validation score   (-root_mean_squared_error)
    0.59s    = Training   runtime
    0.01s    = Validation runtime
Fitting model: RandomForestMSE ...
    -122.0128    = Validation score   (-root_mean_squared_error)
    1.43s    = Training   runtime
    0.03s    = Validation runtime
Fitting model: CatBoost ...
    -134.2362    = Validation score   (-root_mean_squared_error)
    2.65s    = Training   runtime
    0.0s     = Validation runtime
Fitting model: ExtraTreesMSE ...
    -128.4294    = Validation score   (-root_mean_squared_error)
    0.96s    = Training   runtime
    0.04s    = Validation runtime
Fitting model: NeuralNetFastAI ...
    -136.6546    = Validation score   (-root_mean_squared_error)
    7.3s     = Training   runtime
    0.01s    = Validation runtime
Fitting model: XGBoost ...
    -135.0751    = Validation score   (-root_mean_squared_error)
    0.73s    = Training   runtime
    0.01s    = Validation runtime
Fitting model: NeuralNetTorch ...
    -138.9909    = Validation score   (-root_mean_squared_error)
    23.89s   = Training   runtime
    0.01s    = Validation runtime
Fitting model: LightGBMLarge ...
    -132.1736    = Validation score   (-root_mean_squared_error)
    0.84s    = Training   runtime
    0.0s     = Validation runtime
Fitting model: WeightedEnsemble_L2 ...
    -92.4421     = Validation score   (-root_mean_squared_error)
    0.17s    = Training   runtime
    0.0s     = Validation runtime
AutoGluon training complete, total runtime = 40.62s ... Best model: "WeightedEnsemble_L2"
TabularPredictor saved. To load, use: predictor = TabularPredictor.load("AutogluonModels/ag-20231210_063818/")
[1000]  valid_set's rmse: 136.065

- 적합한것을 관찰해보자.

plt.plot(df_train['count'][:300],'--',label='y')
plt.plot(yhat[:300],alpha=0.5,lw=4,label='yhat')
plt.legend()
<matplotlib.legend.Legend at 0x7f2d0dede160>

  • 잘 맞추는데?.. (수상할 정도로)

B. 제출

- 제출

sampleSubmission['count'] = yyhat 
sampleSubmission.to_csv("submission.csv",index=False)
!kaggle competitions submit -c bike-sharing-demand -f submission.csv -m "Message"
!rm submission.csv
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'
100%|████████████████████████████████████████| 188k/188k [00:02<00:00, 83.1kB/s]
Successfully submitted to Bike Sharing Demand

- 순위확인

3171/3242 # 냈다면
0.9780999383096853

- yyhat을 살펴봄

plt.plot(yyhat[:300])

  • yhat이랑 모양자체가 너무 다름

5. 적합2 -> 제출2

A. 피처엔지니어링

- 이미 시계열로 적합할 의지가 없으므로 datetime열은 삭제하는게 좋겠음. (인덱스의 역할만 하는 쓸모없는 변수)

df_train_featured = df_train.copy()
df_test_featured = df_test.copy()
#----# 
df_train_featured = df_train_featured.drop(['casual','registered'],axis=1)
#--#
df_train_featured = df_train_featured.drop(['datetime'],axis=1)
df_test_featured = df_test_featured.drop(['datetime'],axis=1)

B. 적합

- 조용히 적합 (verbosity=False)

# step1 -- pass 
# step2
predictr = TabularPredictor(label='count',verbosity=False)
# step3
predictr.fit(df_train_featured)
# step4 
yhat = predictr.predict(df_train_featured)
yyhat = predictr.predict(df_test_featured)

- 적합결과 시각화

plt.plot(df_train['count'][:300],'--',label='y')
plt.plot(yhat[:300],alpha=0.5,lw=4,label='yhat')
plt.legend()
<matplotlib.legend.Legend at 0x7f2d0c215e20>

  • 오히려 좋아
plt.plot(yyhat[:300],alpha=0.5,lw=4,color='C1')

- 더 예쁜 시각화

df = pd.concat([
    df_train.assign(count_hat = yhat, dataset_type = 'train'),
    df_test.assign(count_hat = yyhat, dataset_type = 'test')
])
df['datetime'] = pd.to_datetime(df['datetime'])
sns.lineplot(
    df.sort_values('datetime')[:(24*28)],
    x='datetime',y='count',
    hue='dataset_type',
    linestyle='--',
    lw=0.8
)
sns.lineplot(
    df.sort_values('datetime')[:(24*28)],
    x='datetime',y='count_hat',
    hue='dataset_type',
    alpha=0.5,
    lw=3
)
fig = plt.gcf()
fig.set_size_inches(8,2)  # x축 y축 너비조정
plt.xticks(rotation=15);  # 글자 기울기 조정

시각화코드를 함수로 구현

def plot(yhat,yyhat):
    df = pd.concat([
        df_train.assign(count_hat = yhat, dataset_type = 'train'),
        df_test.assign(count_hat = yyhat, dataset_type = 'test')
    ])
    df['datetime'] = pd.to_datetime(df['datetime'])
    sns.lineplot(
        df.sort_values('datetime')[:(24*28)],
        x='datetime',y='count',
        hue='dataset_type',
        linestyle='--',
        lw=0.8
    )
    sns.lineplot(
        df.sort_values('datetime')[:(24*28)],
        x='datetime',y='count_hat',
        hue='dataset_type',
        alpha=0.5,
        lw=3
    )
    fig = plt.gcf()
    fig.set_size_inches(8,2)
    plt.xticks(rotation=15); 
    fig.show()
plot(yhat,yyhat)

C. 제출

- 제출

sampleSubmission['count'] = yyhat 
sampleSubmission.to_csv("submission.csv",index=False)
!kaggle competitions submit -c bike-sharing-demand -f submission.csv -m "Message"
!rm submission.csv
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'
100%|█████████████████████████████████████████| 188k/188k [00:01<00:00, 102kB/s]
Successfully submitted to Bike Sharing Demand

- 순위확인

2951/3242 # 냈다면
0.9102405922270204

D. Pipeline Automation – 싹다 함수로 구현

def fit_predict(df_train_featured, df_test_featured):
    # step1 -- pass 
    # step2
    predictr = TabularPredictor(label='count',verbosity=False)
    # step3
    predictr.fit(df_train_featured)
    # step4 
    yhat = predictr.predict(df_train_featured)
    yyhat = predictr.predict(df_test_featured)
    # display
    display(predictr.leaderboard())
    return yhat, yyhat 
def submit(yyhat):
    sampleSubmission['count'] = yyhat 
    sampleSubmission['count'] = sampleSubmission['count'].apply(lambda x: x if x>0 else 0)
    sampleSubmission.to_csv("submission.csv",index=False)
    !kaggle competitions submit -c bike-sharing-demand -f submission.csv -m "Message"
    !rm submission.csv
def auto(df_train_featured, df_test_featured):
    yhat,yyhat = fit_predict(df_train_featured, df_test_featured)
    plot(yhat,yyhat)
    submit(yyhat)
auto(df_train_featured,df_test_featured)
                  model   score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order
0   WeightedEnsemble_L2 -146.942817       0.093126  19.834418                0.000256           0.173803            2       True         12
1              CatBoost -148.454154       0.001523   0.862499                0.001523           0.862499            1       True          6
2       NeuralNetFastAI -148.887325       0.009749   5.793004                0.009749           5.793004            1       True          8
3         LightGBMLarge -149.213280       0.002825   0.750371                0.002825           0.750371            1       True         11
4            LightGBMXT -149.261116       0.011158   0.808311                0.011158           0.808311            1       True          3
5               XGBoost -149.642096       0.006088   0.241403                0.006088           0.241403            1       True          9
6              LightGBM -149.739171       0.001782   0.447107                0.001782           0.447107            1       True          4
7        NeuralNetTorch -151.552860       0.005795   9.768490                0.005795           9.768490            1       True         10
8         ExtraTreesMSE -156.627917       0.030079   0.714512                0.030079           0.714512            1       True          7
9       RandomForestMSE -157.475877       0.031739   0.963428                0.031739           0.963428            1       True          5
10       KNeighborsUnif -165.533975       0.007050   0.052896                0.007050           0.052896            1       True          1
11       KNeighborsDist -176.146340       0.006670   0.050870                0.006670           0.050870            1       True          2
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'
100%|████████████████████████████████████████| 243k/243k [00:02<00:00, 98.7kB/s]
Successfully submitted to Bike Sharing Demand
model score_val pred_time_val fit_time pred_time_val_marginal fit_time_marginal stack_level can_infer fit_order
0 WeightedEnsemble_L2 -146.942817 0.093126 19.834418 0.000256 0.173803 2 True 12
1 CatBoost -148.454154 0.001523 0.862499 0.001523 0.862499 1 True 6
2 NeuralNetFastAI -148.887325 0.009749 5.793004 0.009749 5.793004 1 True 8
3 LightGBMLarge -149.213280 0.002825 0.750371 0.002825 0.750371 1 True 11
4 LightGBMXT -149.261116 0.011158 0.808311 0.011158 0.808311 1 True 3
5 XGBoost -149.642096 0.006088 0.241403 0.006088 0.241403 1 True 9
6 LightGBM -149.739171 0.001782 0.447107 0.001782 0.447107 1 True 4
7 NeuralNetTorch -151.552860 0.005795 9.768490 0.005795 9.768490 1 True 10
8 ExtraTreesMSE -156.627917 0.030079 0.714512 0.030079 0.714512 1 True 7
9 RandomForestMSE -157.475877 0.031739 0.963428 0.031739 0.963428 1 True 5
10 KNeighborsUnif -165.533975 0.007050 0.052896 0.007050 0.052896 1 True 1
11 KNeighborsDist -176.146340 0.006670 0.050870 0.006670 0.050870 1 True 2

6. 적합3 -> 제출3

A. 시간정보 피처엔지니어링

df_train_featured = df_train.copy()
df_test_featured = df_test.copy()
#----# 
df_train_featured = df_train_featured.drop(['casual','registered'],axis=1)
#--#
df_train_featured['hour'] = df_train_featured['datetime'].apply(pd.to_datetime).dt.hour
df_test_featured['hour'] = df_test_featured['datetime'].apply(pd.to_datetime).dt.hour
df_train_featured['weekday'] = df_train_featured['datetime'].apply(pd.to_datetime).dt.weekday
df_test_featured['weekday'] = df_test_featured['datetime'].apply(pd.to_datetime).dt.weekday
#--#
df_train_featured = df_train_featured.drop(['datetime'],axis=1)
df_test_featured = df_test_featured.drop(['datetime'],axis=1)

B. 적합 -> 시각화 -> 제출

auto(df_train_featured,df_test_featured)
                  model   score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order
0   WeightedEnsemble_L2  -59.456941       0.102968  64.794636                0.000255           0.167149            2       True         12
1         LightGBMLarge  -60.899261       0.007749   1.221596                0.007749           1.221596            1       True         11
2              CatBoost  -61.268467       0.003084  24.933573                0.003084          24.933573            1       True          6
3              LightGBM  -61.447456       0.021623   1.687860                0.021623           1.687860            1       True          4
4               XGBoost  -61.749260       0.007222   0.476288                0.007222           0.476288            1       True          9
5            LightGBMXT  -62.400538       0.057122   3.474121                0.057122           3.474121            1       True          3
6       RandomForestMSE  -67.993149       0.038693   1.092855                0.038693           1.092855            1       True          5
7         ExtraTreesMSE  -68.246627       0.034320   0.640087                0.034320           0.640087            1       True          7
8        NeuralNetTorch  -68.927865       0.005912  32.834049                0.005912          32.834049            1       True         10
9       NeuralNetFastAI  -70.979733       0.010306   7.108909                0.010306           7.108909            1       True          8
10       KNeighborsDist -115.023130       0.008851   0.055676                0.008851           0.055676            1       True          2
11       KNeighborsUnif -117.802477       0.008756   0.056952                0.008756           0.056952            1       True          1
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'
100%|█████████████████████████████████████████| 240k/240k [00:01<00:00, 124kB/s]
Successfully submitted to Bike Sharing Demand
model score_val pred_time_val fit_time pred_time_val_marginal fit_time_marginal stack_level can_infer fit_order
0 WeightedEnsemble_L2 -59.456941 0.102968 64.794636 0.000255 0.167149 2 True 12
1 LightGBMLarge -60.899261 0.007749 1.221596 0.007749 1.221596 1 True 11
2 CatBoost -61.268467 0.003084 24.933573 0.003084 24.933573 1 True 6
3 LightGBM -61.447456 0.021623 1.687860 0.021623 1.687860 1 True 4
4 XGBoost -61.749260 0.007222 0.476288 0.007222 0.476288 1 True 9
5 LightGBMXT -62.400538 0.057122 3.474121 0.057122 3.474121 1 True 3
6 RandomForestMSE -67.993149 0.038693 1.092855 0.038693 1.092855 1 True 5
7 ExtraTreesMSE -68.246627 0.034320 0.640087 0.034320 0.640087 1 True 7
8 NeuralNetTorch -68.927865 0.005912 32.834049 0.005912 32.834049 1 True 10
9 NeuralNetFastAI -70.979733 0.010306 7.108909 0.010306 7.108909 1 True 8
10 KNeighborsDist -115.023130 0.008851 0.055676 0.008851 0.055676 1 True 2
11 KNeighborsUnif -117.802477 0.008756 0.056952 0.008756 0.056952 1 True 1

7. 추가적인 피처엔지니어링

A. Step1 – 관련없는 변수 삭제

- 지금까지 수행한 피처엔지니어링

df_train_featured = df_train.copy()
df_test_featured = df_test.copy()
#----# 
df_train_featured = df_train_featured.drop(['casual','registered'],axis=1)
#--#
df_train_featured['hour'] = df_train_featured['datetime'].apply(pd.to_datetime).dt.hour
df_test_featured['hour'] = df_test_featured['datetime'].apply(pd.to_datetime).dt.hour
df_train_featured['weekday'] = df_train_featured['datetime'].apply(pd.to_datetime).dt.weekday
df_test_featured['weekday'] = df_test_featured['datetime'].apply(pd.to_datetime).dt.weekday
#--#
df_train_featured = df_train_featured.drop(['datetime'],axis=1)
df_test_featured = df_test_featured.drop(['datetime'],axis=1)
sns.heatmap(df_train_featured.set_index('count').reset_index().corr(),vmin=-1,cmap='bwr')
<AxesSubplot: >

  • holiday, workingday, weekday는 count와 관련이 없어보인다. –> 제외하고 분석
auto(
    df_train_featured.drop(['holiday', 'workingday', 'weekday'],axis=1),
    df_test_featured.drop(['holiday', 'workingday', 'weekday'],axis=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 -100.669244       0.052277  41.755835                0.000254           0.174775            2       True         12
1              CatBoost -101.264453       0.001435   1.276704                0.001435           1.276704            1       True          6
2            LightGBMXT -102.499627       0.011276   0.884664                0.011276           0.884664            1       True          3
3         LightGBMLarge -102.767101       0.002414   0.788208                0.002414           0.788208            1       True         11
4               XGBoost -103.481823       0.002760   0.260296                0.002760           0.260296            1       True          9
5              LightGBM -103.565687       0.004105   0.565680                0.004105           0.565680            1       True          4
6        NeuralNetTorch -104.992742       0.005868  30.550567                0.005868          30.550567            1       True         10
7       NeuralNetFastAI -105.342264       0.009668   7.785035                0.009668           7.785035            1       True          8
8       RandomForestMSE -106.378229       0.032638   1.180547                0.032638           1.180547            1       True          5
9         ExtraTreesMSE -106.730503       0.039648   0.762568                0.039648           0.762568            1       True          7
10       KNeighborsUnif -128.806002       0.007811   0.067189                0.007811           0.067189            1       True          1
11       KNeighborsDist -128.946333       0.007767   0.064981                0.007767           0.064981            1       True          2
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'
100%|█████████████████████████████████████████| 242k/242k [00:02<00:00, 113kB/s]
Successfully submitted to Bike Sharing Demand
model score_val pred_time_val fit_time pred_time_val_marginal fit_time_marginal stack_level can_infer fit_order
0 WeightedEnsemble_L2 -100.669244 0.052277 41.755835 0.000254 0.174775 2 True 12
1 CatBoost -101.264453 0.001435 1.276704 0.001435 1.276704 1 True 6
2 LightGBMXT -102.499627 0.011276 0.884664 0.011276 0.884664 1 True 3
3 LightGBMLarge -102.767101 0.002414 0.788208 0.002414 0.788208 1 True 11
4 XGBoost -103.481823 0.002760 0.260296 0.002760 0.260296 1 True 9
5 LightGBM -103.565687 0.004105 0.565680 0.004105 0.565680 1 True 4
6 NeuralNetTorch -104.992742 0.005868 30.550567 0.005868 30.550567 1 True 10
7 NeuralNetFastAI -105.342264 0.009668 7.785035 0.009668 7.785035 1 True 8
8 RandomForestMSE -106.378229 0.032638 1.180547 0.032638 1.180547 1 True 5
9 ExtraTreesMSE -106.730503 0.039648 0.762568 0.039648 0.762568 1 True 7
10 KNeighborsUnif -128.806002 0.007811 0.067189 0.007811 0.067189 1 True 1
11 KNeighborsDist -128.946333 0.007767 0.064981 0.007767 0.064981 1 True 2

  • 안좋아졌음..

- 왜 이런 결과가 나오는가?

- 참고: https://guebin.github.io/DV2023/posts/02wk-1.html

sex =  np.array([0,0,0,0]*100+[0] + [1]+[1,1,1,1]*100 + [2]*401)
surv = np.array([0,0,0,0]*100+[1] + [0]+[1,1,1,1]*100 + [0]*401) 
surv_conti = surv + np.random.randn(len(surv))*0.1
_df = pd.DataFrame({'sex':sex, 'surv':surv, 'surv_conti':surv_conti})
_df.corr()
sex surv surv_conti
sex 1.00000 -0.002160 0.006710
surv -0.00216 1.000000 0.978614
surv_conti 0.00671 0.978614 1.000000 
sns.scatterplot(_df, x='sex',y='surv_conti',alpha=0.5)
<AxesSubplot: xlabel='sex', ylabel='surv_conti'>

surv_conti.mean()
0.33106272309886425
sns.heatmap(_df.corr(),cmap='bwr',vmin=-1)
<AxesSubplot: >

- 우리의 예제

sns.scatterplot(
    df_train_featured,
    x='holiday',
    y='count',
    alpha=0.1
)
<AxesSubplot: xlabel='holiday', ylabel='count'>

sns.scatterplot(
    df_train_featured,
    x='weekday',
    y='count',
    alpha=0.1
)
<AxesSubplot: xlabel='weekday', ylabel='count'>

sns.scatterplot(
    df_train_featured,
    x='workingday',
    y='count',
    alpha=0.1
)
<AxesSubplot: xlabel='workingday', ylabel='count'>

B. Step2 – atemp 혹은 temp 삭제

- 지금까지 한 피처엔지니어링

df_train_featured = df_train.copy()
df_test_featured = df_test.copy()
#----# 
df_train_featured = df_train_featured.drop(['casual','registered'],axis=1)
#--#
df_train_featured['hour'] = df_train_featured['datetime'].apply(pd.to_datetime).dt.hour
df_test_featured['hour'] = df_test_featured['datetime'].apply(pd.to_datetime).dt.hour
df_train_featured['weekday'] = df_train_featured['datetime'].apply(pd.to_datetime).dt.weekday
df_test_featured['weekday'] = df_test_featured['datetime'].apply(pd.to_datetime).dt.weekday
#--#
df_train_featured = df_train_featured.drop(['datetime'],axis=1)
df_test_featured = df_test_featured.drop(['datetime'],axis=1)
sns.heatmap(df_train_featured.set_index('count').reset_index().corr(),vmin=-1,cmap='bwr')
<AxesSubplot: >

  • temp와 atemp가 동시에 있어서 공선성 문제를 만들 수 있어보임.
  • 둘중 하나를 제거하는게 좋을것 같음.
auto(
    df_train_featured.drop(['temp'],axis=1),
    df_test_featured.drop(['temp'],axis=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  -59.511984       0.097477  58.259779                0.000259           0.167847            2       True         12
1         LightGBMLarge  -61.029920       0.008677   1.338407                0.008677           1.338407            1       True         11
2              LightGBM  -61.580307       0.019255   1.537220                0.019255           1.537220            1       True          4
3              CatBoost  -61.849961       0.001914  17.761223                0.001914          17.761223            1       True          6
4               XGBoost  -62.741724       0.007695   0.495664                0.007695           0.495664            1       True          9
5            LightGBMXT  -63.351618       0.053134   3.360234                0.053134           3.360234            1       True          3
6         ExtraTreesMSE  -67.852239       0.036566   0.597020                0.036566           0.597020            1       True          7
7        NeuralNetTorch  -68.046825       0.006544  33.599183                0.006544          33.599183            1       True         10
8       RandomForestMSE  -68.525817       0.037002   0.909598                0.037002           0.909598            1       True          5
9       NeuralNetFastAI  -72.517457       0.009779   6.932450                0.009779           6.932450            1       True          8
10       KNeighborsDist -114.334789       0.008465   0.068125                0.008465           0.068125            1       True          2
11       KNeighborsUnif -116.835940       0.008546   0.069387                0.008546           0.069387            1       True          1
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'
100%|█████████████████████████████████████████| 241k/241k [00:01<00:00, 173kB/s]
Successfully submitted to Bike Sharing Demand
model score_val pred_time_val fit_time pred_time_val_marginal fit_time_marginal stack_level can_infer fit_order
0 WeightedEnsemble_L2 -59.511984 0.097477 58.259779 0.000259 0.167847 2 True 12
1 LightGBMLarge -61.029920 0.008677 1.338407 0.008677 1.338407 1 True 11
2 LightGBM -61.580307 0.019255 1.537220 0.019255 1.537220 1 True 4
3 CatBoost -61.849961 0.001914 17.761223 0.001914 17.761223 1 True 6
4 XGBoost -62.741724 0.007695 0.495664 0.007695 0.495664 1 True 9
5 LightGBMXT -63.351618 0.053134 3.360234 0.053134 3.360234 1 True 3
6 ExtraTreesMSE -67.852239 0.036566 0.597020 0.036566 0.597020 1 True 7
7 NeuralNetTorch -68.046825 0.006544 33.599183 0.006544 33.599183 1 True 10
8 RandomForestMSE -68.525817 0.037002 0.909598 0.037002 0.909598 1 True 5
9 NeuralNetFastAI -72.517457 0.009779 6.932450 0.009779 6.932450 1 True 8
10 KNeighborsDist -114.334789 0.008465 0.068125 0.008465 0.068125 1 True 2
11 KNeighborsUnif -116.835940 0.008546 0.069387 0.008546 0.069387 1 True 1 

auto(
    df_train_featured.drop(['atemp'],axis=1),
    df_test_featured.drop(['atemp'],axis=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  -59.317972       0.070543  52.287304                0.000294           0.195872            2       True         12
1         LightGBMLarge  -60.667457       0.008713   1.374132                0.008713           1.374132            1       True         11
2              CatBoost  -60.919338       0.001944  11.113472                0.001944          11.113472            1       True          6
3            LightGBMXT  -61.740606       0.037555   2.373389                0.037555           2.373389            1       True          3
4              LightGBM  -62.028032       0.016164   1.334948                0.016164           1.334948            1       True          4
5               XGBoost  -62.503591       0.006645   0.457786                0.006645           0.457786            1       True          9
6       RandomForestMSE  -67.814371       0.032844   0.992604                0.032844           0.992604            1       True          5
7         ExtraTreesMSE  -67.843089       0.032126   0.775694                0.032126           0.775694            1       True          7
8        NeuralNetTorch  -68.163726       0.005872  35.895491                0.005872          35.895491            1       True         10
9       NeuralNetFastAI  -71.526328       0.009695   6.962153                0.009695           6.962153            1       True          8
10       KNeighborsDist -112.678494       0.008421   0.068928                0.008421           0.068928            1       True          2
11       KNeighborsUnif -115.103505       0.008441   0.070454                0.008441           0.070454            1       True          1
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'
100%|█████████████████████████████████████████| 241k/241k [00:02<00:00, 108kB/s]
Successfully submitted to Bike Sharing Demand
model score_val pred_time_val fit_time pred_time_val_marginal fit_time_marginal stack_level can_infer fit_order
0 WeightedEnsemble_L2 -59.317972 0.070543 52.287304 0.000294 0.195872 2 True 12
1 LightGBMLarge -60.667457 0.008713 1.374132 0.008713 1.374132 1 True 11
2 CatBoost -60.919338 0.001944 11.113472 0.001944 11.113472 1 True 6
3 LightGBMXT -61.740606 0.037555 2.373389 0.037555 2.373389 1 True 3
4 LightGBM -62.028032 0.016164 1.334948 0.016164 1.334948 1 True 4
5 XGBoost -62.503591 0.006645 0.457786 0.006645 0.457786 1 True 9
6 RandomForestMSE -67.814371 0.032844 0.992604 0.032844 0.992604 1 True 5
7 ExtraTreesMSE -67.843089 0.032126 0.775694 0.032126 0.775694 1 True 7
8 NeuralNetTorch -68.163726 0.005872 35.895491 0.005872 35.895491 1 True 10
9 NeuralNetFastAI -71.526328 0.009695 6.962153 0.009695 6.962153 1 True 8
10 KNeighborsDist -112.678494 0.008421 0.068928 0.008421 0.068928 1 True 2
11 KNeighborsUnif -115.103505 0.008441 0.070454 0.008441 0.070454 1 True 1

C. Step3 – season을 범주로?

- 지금까지한 피처엔지니어링

df_train_featured = df_train.copy()
df_test_featured = df_test.copy()
#----# 
df_train_featured = df_train_featured.drop(['casual','registered'],axis=1)
#--#
df_train_featured['hour'] = df_train_featured['datetime'].apply(pd.to_datetime).dt.hour
df_test_featured['hour'] = df_test_featured['datetime'].apply(pd.to_datetime).dt.hour
df_train_featured['weekday'] = df_train_featured['datetime'].apply(pd.to_datetime).dt.weekday
df_test_featured['weekday'] = df_test_featured['datetime'].apply(pd.to_datetime).dt.weekday
#--#
df_train_featured = df_train_featured.drop(['datetime'],axis=1)
df_test_featured = df_test_featured.drop(['datetime'],axis=1)
#--#
df_train_featured = df_train_featured.drop(['atemp'],axis=1)
df_test_featured = df_test_featured.drop(['atemp'],axis=1)

- 사실 season의 의미는 season - 1 = spring, 2 = summer, 3 = fall, 4 = winter 임

  • 지금은 season이 1,2,3,4로 코딩되어 있는데, 이것을 문자열로 바꾸면 더 좋지 않을까?
auto(
    df_train_featured.assign(season = df_train_featured.season.map({1:'spring',2:'summer',3:'fall',4:'winter'})),
    df_test_featured.assign(season = df_train_featured.season.map({1:'spring',2:'summer',3:'fall',4:'winter'}))
)
                  model   score_val  pred_time_val   fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order
0   WeightedEnsemble_L2  -58.809100       0.124894  88.477947                0.000289           0.195235            2       True         12
1            LightGBMXT  -61.072683       0.087661   4.569525                0.087661           4.569525            1       True          3
2         LightGBMLarge  -61.188052       0.012944   1.727106                0.012944           1.727106            1       True         11
3              CatBoost  -62.040378       0.006600  47.570805                0.006600          47.570805            1       True          6
4              LightGBM  -62.161719       0.018669   1.587194                0.018669           1.587194            1       True          4
5               XGBoost  -62.183089       0.010632   0.598557                0.010632           0.598557            1       True          9
6        NeuralNetTorch  -67.358419       0.006769  33.816719                0.006769          33.816719            1       True         10
7       RandomForestMSE  -68.346914       0.032818   1.102551                0.032818           1.102551            1       True          5
8         ExtraTreesMSE  -68.569688       0.035215   0.590428                0.035215           0.590428            1       True          7
9       NeuralNetFastAI  -72.355712       0.012358   7.412937                0.012358           7.412937            1       True          8
10       KNeighborsDist -113.647125       0.008502   0.069228                0.008502           0.069228            1       True          2
11       KNeighborsUnif -116.110797       0.008447   0.071381                0.008447           0.071381            1       True          1
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'
100%|█████████████████████████████████████████| 240k/240k [00:01<00:00, 124kB/s]
Successfully submitted to Bike Sharing Demand
model score_val pred_time_val fit_time pred_time_val_marginal fit_time_marginal stack_level can_infer fit_order
0 WeightedEnsemble_L2 -58.809100 0.124894 88.477947 0.000289 0.195235 2 True 12
1 LightGBMXT -61.072683 0.087661 4.569525 0.087661 4.569525 1 True 3
2 LightGBMLarge -61.188052 0.012944 1.727106 0.012944 1.727106 1 True 11
3 CatBoost -62.040378 0.006600 47.570805 0.006600 47.570805 1 True 6
4 LightGBM -62.161719 0.018669 1.587194 0.018669 1.587194 1 True 4
5 XGBoost -62.183089 0.010632 0.598557 0.010632 0.598557 1 True 9
6 NeuralNetTorch -67.358419 0.006769 33.816719 0.006769 33.816719 1 True 10
7 RandomForestMSE -68.346914 0.032818 1.102551 0.032818 1.102551 1 True 5
8 ExtraTreesMSE -68.569688 0.035215 0.590428 0.035215 0.590428 1 True 7
9 NeuralNetFastAI -72.355712 0.012358 7.412937 0.012358 7.412937 1 True 8
10 KNeighborsDist -113.647125 0.008502 0.069228 0.008502 0.069228 1 True 2
11 KNeighborsUnif -116.110797 0.008447 0.071381 0.008447 0.071381 1 True 1

  • 이건 적용하지 말자.
  • 어차피 트리계열은 명목형변수를 순서형변수로 잘못 적용해도 크게 상관없음.

D. Step4 – \(y\)의 분포

- 지금까지한 피처엔지니어링

df_train_featured = df_train.copy()
df_test_featured = df_test.copy()
#----# 
df_train_featured = df_train_featured.drop(['casual','registered'],axis=1)
#--#
df_train_featured['hour'] = df_train_featured['datetime'].apply(pd.to_datetime).dt.hour
df_test_featured['hour'] = df_test_featured['datetime'].apply(pd.to_datetime).dt.hour
df_train_featured['weekday'] = df_train_featured['datetime'].apply(pd.to_datetime).dt.weekday
df_test_featured['weekday'] = df_test_featured['datetime'].apply(pd.to_datetime).dt.weekday
#--#
df_train_featured = df_train_featured.drop(['datetime'],axis=1)
df_test_featured = df_test_featured.drop(['datetime'],axis=1)
#--#
df_train_featured = df_train_featured.drop(['atemp'],axis=1)
df_test_featured = df_test_featured.drop(['atemp'],axis=1)
df_train_featured['count'].hist() # 정규분포가 아니네
<AxesSubplot: >

transfomr = sklearn.preprocessing.PowerTransformer(method='box-cox')
count2 = transfomr.fit_transform(df_train_featured[['count']]).reshape(-1)
plt.hist(count2);

df_train_featured.assign(count = count2)
season holiday workingday weather temp humidity windspeed count hour weekday
0 1 0 0 1 9.84 81 0.0000 -1.255010 0 5
1 1 0 0 1 9.02 80 0.0000 -0.801417 1 5
2 1 0 0 1 9.02 80 0.0000 -0.924248 2 5
3 1 0 0 1 9.84 75 0.0000 -1.340805 3 5
4 1 0 0 1 9.84 75 0.0000 -2.043720 4 5
... ... ... ... ... ... ... ... ... ... ...
10881 4 0 1 1 15.58 50 26.0027 0.928271 19 2
10882 4 0 1 1 14.76 57 15.0013 0.576172 20 2
10883 4 0 1 1 13.94 61 15.0013 0.233448 21 2
10884 4 0 1 1 13.94 61 6.0032 0.006178 22 2
10885 4 0 1 1 13.12 66 8.9981 -0.291061 23 2

10886 rows × 10 columns

- 적합

# step1 -- pass 
# step2 
predictr = TabularPredictor(label='count',verbosity=False)
# step3
predictr.fit(df_train_featured.assign(count = count2))
# step4
yhat = predictr.predict(df_train_featured) 
yyhat = predictr.predict(df_test_featured)
yhat = transfomr.inverse_transform(yhat.to_frame()).reshape(-1)
yyhat = transfomr.inverse_transform(yyhat.to_frame()).reshape(-1)
plot(yhat,yyhat)

submit(yyhat)
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'
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8. HW

box-cox transform이 아닌 log1p변환을 취해서 결과를 구해볼 것

- y에 0값이 많으므로 log에 1더한 log1p함수를 이용하는 것임.

np.log1p(0.1234), np.log(0.1234+1)
np.expm1(0.11635980111619529)

결과는 아래와 같음

count3 = np.log1p(df_train_featured['count'])
count3
0        2.833213
1        3.713572
2        3.496508
3        2.639057
4        0.693147
           ...   
10881    5.820083
10882    5.488938
10883    5.129899
10884    4.867534
10885    4.488636
Name: count, Length: 10886, dtype: float64
plt.hist(count3)
(array([ 237.,  462.,  625.,  626.,  805., 1076., 1768., 2544., 2058.,
         685.]),
 array([0.69314718, 1.31238343, 1.93161968, 2.55085593, 3.17009218,
        3.78932843, 4.40856467, 5.02780092, 5.64703717, 6.26627342,
        6.88550967]),
 <BarContainer object of 10 artists>)

df_train_featured.assign(count = count3)
season holiday workingday weather temp humidity windspeed count hour weekday
0 1 0 0 1 9.84 81 0.0000 2.833213 0 5
1 1 0 0 1 9.02 80 0.0000 3.713572 1 5
2 1 0 0 1 9.02 80 0.0000 3.496508 2 5
3 1 0 0 1 9.84 75 0.0000 2.639057 3 5
4 1 0 0 1 9.84 75 0.0000 0.693147 4 5
... ... ... ... ... ... ... ... ... ... ...
10881 4 0 1 1 15.58 50 26.0027 5.820083 19 2
10882 4 0 1 1 14.76 57 15.0013 5.488938 20 2
10883 4 0 1 1 13.94 61 15.0013 5.129899 21 2
10884 4 0 1 1 13.94 61 6.0032 4.867534 22 2
10885 4 0 1 1 13.12 66 8.9981 4.488636 23 2

10886 rows × 10 columns

- 적합

# step1 -- pass 
# step2 
predictr = TabularPredictor(label='count',verbosity=False)
# step3
predictr.fit(df_train_featured.assign(count = count3))
# step4
yhat = predictr.predict(df_train_featured) 
yyhat = predictr.predict(df_test_featured)
yhat = np.expm1(yhat)
yyhat = np.expm1(yyhat)
plot(yhat,yyhat)

submit(yyhat)
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'
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