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shirapon24


folder_special 0
baseline LightGBM Python

初心者用サンプルコード

初心者用サンプルコード

  • LightGBMを用いた初心者用のサンプルコード

  • 初手にLightGBMを使用する理由についてはu++さんの記事が参考になります

  • 簡単なコードが読めることとデータコンペの流れを理解していることを前提としています

  • 比較的simpleに実装したつもりです
  • ライブラリのインストールは各自で実施してください
  • 質問はスレッドまで

ディレクトリ構成

root ├── input │ ├── station_list.csv │ ├── submission.csv │ ├── test_data.csv │ └── train_data.csv ├── notebook │ ├── sample_for_beginner.ipynb ---> このノートブック ├── output ├── submission
１．ライブラリやデータの準備
# -*- coding: utf-8 -*-
import numpy as np
import pandas as pd

pd.set_option('max_colwidth', 500)
pd.set_option('max_columns', 500)
pd.set_option('max_rows', 500)

%matplotlib inline
from matplotlib import pyplot as plt
import seaborn as sns
import japanize_matplotlib

import json, os, gc, math, time
import datetime
import collections
from tqdm import tqdm
import glob

from statistics import mean
from sklearn.preprocessing import MultiLabelBinarizer
from sklearn.preprocessing import LabelEncoder

from sklearn.model_selection import KFold, GroupKFold, StratifiedKFold

from sklearn import metrics

import lightgbm as lgb

import warnings
warnings.filterwarnings("ignore")
class Config():
    NAME = 'sample_for_beginner'
    INPUT_PATH = '../input/'
    OUTPUT_PATH = '../output/'
    SUBMIT_PATH = '../submission/'
    TARGET = 'y' # this is fare
    NUM_FOLD = 5
    SEED_FOLD = 71
    SEED_MODEL = 42
    
CFG = Config()
train = pd.read_csv(os.path.join(CFG.INPUT_PATH, 'train_data.csv'))
test = pd.read_csv(os.path.join(CFG.INPUT_PATH, 'test_data.csv'))
sample_submission = pd.read_csv(os.path.join(CFG.INPUT_PATH, 'submission.csv'))
train.head()
id name host_id neighbourhood latitude longitude room_type minimum_nights number_of_reviews last_review reviews_per_month availability_365 y
0 1 KiyosumiShirakawa 3min|★SkyTree★|WIFI|Max4|Tree102 242899459 Koto Ku 35.68185 139.80310 Entire home/apt 1 55 2020-04-25 2.21 173 12008
1 2 Downtown Tokyo Iriya next to Ueno 308879948 Taito Ku 35.72063 139.78536 Entire home/apt 6 72 2020-03-25 2.11 9 6667
2 3 Japan Style,Private,Affordable,4min to Sta. 300877823 Katsushika Ku 35.74723 139.82349 Entire home/apt 1 18 2020-03-23 3.46 288 9923
3 4 4 min to Shinjuku Sta. by train / 2 ppl / Wi-fi 236935461 Shibuya Ku 35.68456 139.68077 Entire home/apt 1 2 2020-04-02 1.76 87 8109
4 5 LICENSED SHINJUKU HOUSE: Heart of the action! 243408889 Shinjuku Ku 35.69840 139.70467 Entire home/apt 1 86 2020-01-30 2.00 156 100390
２．EDA　データを簡単に見てみます
from matplotlib_venn import venn2
def plot_venn_train_test(tra, val, col):
    """trainとtestのベン図をplotする
    """
    fig, ax = plt.subplots(figsize=(6,9))
    plt.title(col, fontsize=10)
    train_unique = tra[col].unique()
    test_unique = val[col].unique()
    common_num = len(set(train_unique) & set(test_unique))
    venn2(subsets=(len(train_unique)-common_num, len(test_unique)-common_num, common_num),set_labels=('Train', 'Test'))
    return fig, ax
fig, ax = plot_venn_train_test(train, test, "host_id")
# ホストＩＤはかぶってないので不要
fig, ax = plot_venn_train_test(train, test, "name")
#　めっさ少しかぶってる
fig, ax = plot_venn_train_test(train, test, "neighbourhood")
# 完全に一致
3.特徴量エンジニアリング
  • 今回は基本的なカテゴリーエンコーディングの手法である下記を使用する
    • ラベルエンコーディング
    • カウントエンコーディング
    • ワンホットエンコーディング
  • 日付についても単純に年月週等を取り出します
def get_label_encoding(df , cols):
    """label_encoding
    """
    for col in cols:
        df[col].fillna("missing", inplace=True)
        le = LabelEncoder()
        le = le.fit(df[col])
        df["LE=" + col] = le.transform(df[col])
            
    return df
    
def get_count_encoding(df, cols):
    """count_encoding
    """
    for col in cols:
        counter = collections.Counter(df[col].values)
        count_dict = dict(counter.most_common())
        encoded = df[col].map(lambda x: count_dict[x]).values
        df["CE=" + col] = encoded
        
    return df
def get_date_feature(_df, col):
    """date to feature
    """

    date_siries = pd.to_datetime(_df[col])
    _df[col + "_year"] = date_siries.dt.year
    _df[col + "_month"] = date_siries.dt.month 
    _df[col + "_day"] = date_siries.dt.day 
    _df[col + "_week"] = date_siries.dt.dayofweek 
    _df[col + "_yymmdd"] = date_siries.dt.strftime('%Y%m%d').astype(np.int32) 

    return _df
def create_feature(_tra, _val):
    """特徴量生成

    """
    
    # 訓練データとテストデータの結合
    _df = pd.concat([_tra, _val], axis=0).reset_index(drop=True)
    
    # 日付データの処理
    _df["last_review"] = _df["last_review"].fillna(0)
    _df = get_date_feature(_df, "last_review")
    
    # カテゴリーエンコーディング
    cat_cols = ["name", "neighbourhood"]
    _df = get_label_encoding(_df, cat_cols)
    _df = get_count_encoding(_df, cat_cols)
    _df = pd.get_dummies(_df, columns=["room_type"])
    
    # 不要なカラムの削除
    _df = _df.drop(["id", "host_id", "last_review", "name", "neighbourhood"], axis=1)

    # 再度訓練データとテストデータを分割
    _tra = _df.iloc[:_tra.shape[0], :]
    _val = _df.iloc[_tra.shape[0]:, :]
    
    # 目的変数を取り出して返す
    target = _tra[CFG.TARGET]
    _tra = _tra.drop(CFG.TARGET, axis=1)
    _val = _val.drop(CFG.TARGET, axis=1)
    
    return _tra, _val, target
train_df, test_df, target = create_feature(train.copy(), test.copy())
train_df.head()
latitude longitude minimum_nights number_of_reviews reviews_per_month availability_365 last_review_year last_review_month last_review_day last_review_week last_review_yymmdd LE=name LE=neighbourhood CE=name CE=neighbourhood room_type_Entire home/apt room_type_Hotel room room_type_Private room room_type_Shared room
0 35.68185 139.80310 1 55 2.21 173 2020 4 25 5 20200425 6130 9 1 242 1 0 0 0
1 35.72063 139.78536 6 72 2.11 9 2020 3 25 2 20200325 4559 21 1 2126 1 0 0 0
2 35.74723 139.82349 1 18 3.46 288 2020 3 23 0 20200323 5891 7 1 433 1 0 0 0
3 35.68456 139.68077 1 2 1.76 87 2020 4 2 3 20200402 1999 16 4 1095 1 0 0 0
4 35.69840 139.70467 1 86 2.00 156 2020 1 30 3 20200130 6181 18 1 2803 1 0 0 0
４．モデリング
  • 冒頭で記載した通りLightGBMを使用
  • StratifiedKFoldで5つに分割して交差検証を行っている
  • 交差検証につてはu++さんの記事が非常に参考になる 
def fit_lgbm(train, test, y, groups=None, params: dict=None, n_splits=5, verbose=100, early_stopping_rounds=100):
    """train lightgbm
    """
    
    models = []
    scores = []
    iterations = []
    oof_preds = np.zeros((train.shape[0],))
    sub_preds = np.zeros((test.shape[0],))
    
    folds = StratifiedKFold(n_splits=n_splits, random_state=CFG.SEED_FOLD, shuffle=True)
    for n_fold, (trn_idx, val_idx) in enumerate(folds.split(train, target)):
        
        print("Fold is :", n_fold+1)
        trn_x, trn_y = train.iloc[trn_idx], y.iloc[trn_idx]
        val_x, val_y = train.iloc[val_idx], y.iloc[val_idx]
        trn_x = trn_x.values
        val_x = val_x.values
        
        clf = lgb.LGBMRegressor(**params)
        
        clf.fit(trn_x, trn_y, 
                eval_set= [(trn_x, trn_y), (val_x, val_y)], 
                eval_metric="rmse", 
                verbose=verbose, early_stopping_rounds=early_stopping_rounds
               )
    
        oof_preds[val_idx] = clf.predict(val_x, num_iteration=clf.best_iteration_)
        sub_preds += clf.predict(test, num_iteration=clf.best_iteration_) / n_splits
        
        gc.collect()
        
        oof_preds = np.clip(oof_preds, 0, np.inf)
        sub_preds = np.clip(sub_preds, 0, np.inf)
        score = np.sqrt(metrics.mean_squared_error(y[val_idx], oof_preds[val_idx]))

        
        print("CV:{} RMSLE:{}".format(n_fold+1,score))
        
        iterations.append(clf.best_iteration_)
        scores.append(score)
        models.append(clf)
    
    return oof_preds, sub_preds, models, scores
params = {
    'objective': 'rmse', 
    'boosting_type': 'gbdt',
    'learning_rate': 0.01,
    'max_depth': -1,
    'num_leaves': 31,
    'n_estimators': 100000,
    "importance_type": "gain",
    'random_state': CFG.SEED_MODEL,
}

oof_pred, sub_pred, models, fold_scores = fit_lgbm(train_df, test_df, np.log1p(target),
                                                   params=params,
                                                   n_splits=5,
                                                   early_stopping_rounds=100)
Fold is : 1
Training until validation scores don't improve for 100 rounds
[100]	training's rmse: 0.789085	valid_1's rmse: 0.802772
[200]	training's rmse: 0.716151	valid_1's rmse: 0.745864
[300]	training's rmse: 0.674589	valid_1's rmse: 0.72145
[400]	training's rmse: 0.645677	valid_1's rmse: 0.706381
[500]	training's rmse: 0.624051	valid_1's rmse: 0.695621
[600]	training's rmse: 0.605819	valid_1's rmse: 0.687508
[700]	training's rmse: 0.589237	valid_1's rmse: 0.680485
[800]	training's rmse: 0.574465	valid_1's rmse: 0.675827
[900]	training's rmse: 0.559317	valid_1's rmse: 0.670026
[1000]	training's rmse: 0.546112	valid_1's rmse: 0.66458
[1100]	training's rmse: 0.532641	valid_1's rmse: 0.6606
[1200]	training's rmse: 0.521237	valid_1's rmse: 0.656926
[1300]	training's rmse: 0.511559	valid_1's rmse: 0.65418
[1400]	training's rmse: 0.500988	valid_1's rmse: 0.650753
[1500]	training's rmse: 0.490622	valid_1's rmse: 0.647755
[1600]	training's rmse: 0.481013	valid_1's rmse: 0.645375
[1700]	training's rmse: 0.472153	valid_1's rmse: 0.643278
[1800]	training's rmse: 0.463638	valid_1's rmse: 0.641326
[1900]	training's rmse: 0.455072	valid_1's rmse: 0.639423
[2000]	training's rmse: 0.447044	valid_1's rmse: 0.637786
[2100]	training's rmse: 0.439478	valid_1's rmse: 0.636327
[2200]	training's rmse: 0.432123	valid_1's rmse: 0.63539
[2300]	training's rmse: 0.425764	valid_1's rmse: 0.634364
[2400]	training's rmse: 0.41998	valid_1's rmse: 0.633321
[2500]	training's rmse: 0.413686	valid_1's rmse: 0.632166
[2600]	training's rmse: 0.407444	valid_1's rmse: 0.631164
[2700]	training's rmse: 0.401614	valid_1's rmse: 0.629924
[2800]	training's rmse: 0.395729	valid_1's rmse: 0.629011
[2900]	training's rmse: 0.389618	valid_1's rmse: 0.628166
[3000]	training's rmse: 0.384	valid_1's rmse: 0.627495
[3100]	training's rmse: 0.379002	valid_1's rmse: 0.626662
[3200]	training's rmse: 0.374175	valid_1's rmse: 0.625922
[3300]	training's rmse: 0.369425	valid_1's rmse: 0.625368
[3400]	training's rmse: 0.364725	valid_1's rmse: 0.625202
[3500]	training's rmse: 0.3602	valid_1's rmse: 0.624725
[3600]	training's rmse: 0.355911	valid_1's rmse: 0.624396
[3700]	training's rmse: 0.350962	valid_1's rmse: 0.624025
[3800]	training's rmse: 0.34651	valid_1's rmse: 0.623659
[3900]	training's rmse: 0.341824	valid_1's rmse: 0.623331
[4000]	training's rmse: 0.337538	valid_1's rmse: 0.623044
[4100]	training's rmse: 0.333395	valid_1's rmse: 0.622746
[4200]	training's rmse: 0.329732	valid_1's rmse: 0.622372
[4300]	training's rmse: 0.325499	valid_1's rmse: 0.622099
[4400]	training's rmse: 0.321386	valid_1's rmse: 0.621748
[4500]	training's rmse: 0.317763	valid_1's rmse: 0.621697
[4600]	training's rmse: 0.314315	valid_1's rmse: 0.621193
[4700]	training's rmse: 0.310391	valid_1's rmse: 0.621195
Early stopping, best iteration is:
[4637]	training's rmse: 0.312854	valid_1's rmse: 0.620984
CV:1 RMSLE:0.6209840569002207
Fold is : 2
Training until validation scores don't improve for 100 rounds
[100]	training's rmse: 0.783872	valid_1's rmse: 0.828208
[200]	training's rmse: 0.713494	valid_1's rmse: 0.767349
[300]	training's rmse: 0.672645	valid_1's rmse: 0.73795
[400]	training's rmse: 0.645067	valid_1's rmse: 0.720696
[500]	training's rmse: 0.622988	valid_1's rmse: 0.708938
[600]	training's rmse: 0.603118	valid_1's rmse: 0.699293
[700]	training's rmse: 0.585443	valid_1's rmse: 0.690751
[800]	training's rmse: 0.569988	valid_1's rmse: 0.683753
[900]	training's rmse: 0.555947	valid_1's rmse: 0.678555
[1000]	training's rmse: 0.543755	valid_1's rmse: 0.675211
[1100]	training's rmse: 0.532986	valid_1's rmse: 0.671581
[1200]	training's rmse: 0.521339	valid_1's rmse: 0.667332
[1300]	training's rmse: 0.510563	valid_1's rmse: 0.663425
[1400]	training's rmse: 0.500514	valid_1's rmse: 0.659543
[1500]	training's rmse: 0.491226	valid_1's rmse: 0.656081
[1600]	training's rmse: 0.48254	valid_1's rmse: 0.653445
[1700]	training's rmse: 0.474068	valid_1's rmse: 0.651548
[1800]	training's rmse: 0.465859	valid_1's rmse: 0.64939
[1900]	training's rmse: 0.458085	valid_1's rmse: 0.6469
[2000]	training's rmse: 0.449935	valid_1's rmse: 0.644753
[2100]	training's rmse: 0.442791	valid_1's rmse: 0.643242
[2200]	training's rmse: 0.435491	valid_1's rmse: 0.641088
[2300]	training's rmse: 0.427976	valid_1's rmse: 0.63923
[2400]	training's rmse: 0.421414	valid_1's rmse: 0.637892
[2500]	training's rmse: 0.41499	valid_1's rmse: 0.636368
[2600]	training's rmse: 0.408449	valid_1's rmse: 0.634886
[2700]	training's rmse: 0.40237	valid_1's rmse: 0.633343
[2800]	training's rmse: 0.396734	valid_1's rmse: 0.631861
[2900]	training's rmse: 0.390865	valid_1's rmse: 0.630505
[3000]	training's rmse: 0.385082	valid_1's rmse: 0.629458
[3100]	training's rmse: 0.379436	valid_1's rmse: 0.628069
[3200]	training's rmse: 0.374271	valid_1's rmse: 0.627231
[3300]	training's rmse: 0.368944	valid_1's rmse: 0.626139
[3400]	training's rmse: 0.363675	valid_1's rmse: 0.625123
[3500]	training's rmse: 0.358755	valid_1's rmse: 0.624234
[3600]	training's rmse: 0.354449	valid_1's rmse: 0.623696
[3700]	training's rmse: 0.350146	valid_1's rmse: 0.623421
[3800]	training's rmse: 0.345843	valid_1's rmse: 0.623195
[3900]	training's rmse: 0.341444	valid_1's rmse: 0.622701
[4000]	training's rmse: 0.337261	valid_1's rmse: 0.622132
[4100]	training's rmse: 0.333238	valid_1's rmse: 0.621441
[4200]	training's rmse: 0.329461	valid_1's rmse: 0.620881
[4300]	training's rmse: 0.325267	valid_1's rmse: 0.620612
[4400]	training's rmse: 0.321385	valid_1's rmse: 0.620239
[4500]	training's rmse: 0.318	valid_1's rmse: 0.620172
[4600]	training's rmse: 0.314528	valid_1's rmse: 0.619981
[4700]	training's rmse: 0.311251	valid_1's rmse: 0.619665
[4800]	training's rmse: 0.30759	valid_1's rmse: 0.619104
[4900]	training's rmse: 0.304468	valid_1's rmse: 0.619008
[5000]	training's rmse: 0.301197	valid_1's rmse: 0.618731
[5100]	training's rmse: 0.298144	valid_1's rmse: 0.618284
[5200]	training's rmse: 0.295067	valid_1's rmse: 0.61768
[5300]	training's rmse: 0.291439	valid_1's rmse: 0.617327
[5400]	training's rmse: 0.288039	valid_1's rmse: 0.617103
[5500]	training's rmse: 0.285077	valid_1's rmse: 0.616997
[5600]	training's rmse: 0.282244	valid_1's rmse: 0.616739
[5700]	training's rmse: 0.279029	valid_1's rmse: 0.616504
[5800]	training's rmse: 0.275853	valid_1's rmse: 0.616322
Early stopping, best iteration is:
[5778]	training's rmse: 0.276497	valid_1's rmse: 0.61623
CV:2 RMSLE:0.6162296952189872
Fold is : 3
Training until validation scores don't improve for 100 rounds
[100]	training's rmse: 0.790352	valid_1's rmse: 0.797812
[200]	training's rmse: 0.717196	valid_1's rmse: 0.739492
[300]	training's rmse: 0.67635	valid_1's rmse: 0.710418
[400]	training's rmse: 0.648113	valid_1's rmse: 0.694873
[500]	training's rmse: 0.625695	valid_1's rmse: 0.684574
[600]	training's rmse: 0.606326	valid_1's rmse: 0.676143
[700]	training's rmse: 0.589447	valid_1's rmse: 0.669576
[800]	training's rmse: 0.573177	valid_1's rmse: 0.663278
[900]	training's rmse: 0.556794	valid_1's rmse: 0.656926
[1000]	training's rmse: 0.542608	valid_1's rmse: 0.651551
[1100]	training's rmse: 0.530601	valid_1's rmse: 0.64843
[1200]	training's rmse: 0.518413	valid_1's rmse: 0.644849
[1300]	training's rmse: 0.507341	valid_1's rmse: 0.641505
[1400]	training's rmse: 0.497983	valid_1's rmse: 0.63892
[1500]	training's rmse: 0.488871	valid_1's rmse: 0.636364
[1600]	training's rmse: 0.479129	valid_1's rmse: 0.63335
[1700]	training's rmse: 0.470326	valid_1's rmse: 0.63149
[1800]	training's rmse: 0.462039	valid_1's rmse: 0.629597
[1900]	training's rmse: 0.454314	valid_1's rmse: 0.628031
[2000]	training's rmse: 0.446283	valid_1's rmse: 0.627229
[2100]	training's rmse: 0.438736	valid_1's rmse: 0.626146
[2200]	training's rmse: 0.431104	valid_1's rmse: 0.625039
[2300]	training's rmse: 0.423795	valid_1's rmse: 0.624958
Early stopping, best iteration is:
[2245]	training's rmse: 0.427896	valid_1's rmse: 0.624697
CV:3 RMSLE:0.6246969157999711
Fold is : 4
Training until validation scores don't improve for 100 rounds
[100]	training's rmse: 0.78891	valid_1's rmse: 0.785152
[200]	training's rmse: 0.718129	valid_1's rmse: 0.730267
[300]	training's rmse: 0.675322	valid_1's rmse: 0.703504
[400]	training's rmse: 0.644702	valid_1's rmse: 0.687821
[500]	training's rmse: 0.623176	valid_1's rmse: 0.679494
[600]	training's rmse: 0.604177	valid_1's rmse: 0.671663
[700]	training's rmse: 0.588387	valid_1's rmse: 0.664594
[800]	training's rmse: 0.573005	valid_1's rmse: 0.658622
[900]	training's rmse: 0.558595	valid_1's rmse: 0.65412
[1000]	training's rmse: 0.545672	valid_1's rmse: 0.649986
[1100]	training's rmse: 0.533605	valid_1's rmse: 0.646376
[1200]	training's rmse: 0.522669	valid_1's rmse: 0.643622
[1300]	training's rmse: 0.512197	valid_1's rmse: 0.641161
[1400]	training's rmse: 0.501655	valid_1's rmse: 0.637781
[1500]	training's rmse: 0.491799	valid_1's rmse: 0.635533
[1600]	training's rmse: 0.483138	valid_1's rmse: 0.634113
[1700]	training's rmse: 0.475249	valid_1's rmse: 0.632729
[1800]	training's rmse: 0.467591	valid_1's rmse: 0.630967
[1900]	training's rmse: 0.459909	valid_1's rmse: 0.629078
[2000]	training's rmse: 0.452522	valid_1's rmse: 0.627366
[2100]	training's rmse: 0.444864	valid_1's rmse: 0.626045
[2200]	training's rmse: 0.437101	valid_1's rmse: 0.624389
[2300]	training's rmse: 0.428759	valid_1's rmse: 0.622805
[2400]	training's rmse: 0.421691	valid_1's rmse: 0.621507
[2500]	training's rmse: 0.415013	valid_1's rmse: 0.620529
[2600]	training's rmse: 0.407942	valid_1's rmse: 0.619293
[2700]	training's rmse: 0.40124	valid_1's rmse: 0.617741
[2800]	training's rmse: 0.395035	valid_1's rmse: 0.616626
[2900]	training's rmse: 0.388784	valid_1's rmse: 0.615661
[3000]	training's rmse: 0.382344	valid_1's rmse: 0.61482
[3100]	training's rmse: 0.376878	valid_1's rmse: 0.614296
[3200]	training's rmse: 0.371426	valid_1's rmse: 0.614205
[3300]	training's rmse: 0.365927	valid_1's rmse: 0.613641
[3400]	training's rmse: 0.36085	valid_1's rmse: 0.613228
[3500]	training's rmse: 0.355385	valid_1's rmse: 0.612286
[3600]	training's rmse: 0.349678	valid_1's rmse: 0.611869
[3700]	training's rmse: 0.344328	valid_1's rmse: 0.611266
[3800]	training's rmse: 0.339463	valid_1's rmse: 0.611105
[3900]	training's rmse: 0.335176	valid_1's rmse: 0.610702
[4000]	training's rmse: 0.330141	valid_1's rmse: 0.610428
[4100]	training's rmse: 0.325849	valid_1's rmse: 0.60994
[4200]	training's rmse: 0.321711	valid_1's rmse: 0.609504
[4300]	training's rmse: 0.317556	valid_1's rmse: 0.609444
[4400]	training's rmse: 0.313544	valid_1's rmse: 0.609221
[4500]	training's rmse: 0.309808	valid_1's rmse: 0.609034
Early stopping, best iteration is:
[4495]	training's rmse: 0.309971	valid_1's rmse: 0.609012
CV:4 RMSLE:0.6090120445655745
Fold is : 5
Training until validation scores don't improve for 100 rounds
[100]	training's rmse: 0.785527	valid_1's rmse: 0.806675
[200]	training's rmse: 0.714646	valid_1's rmse: 0.748941
[300]	training's rmse: 0.672232	valid_1's rmse: 0.723304
[400]	training's rmse: 0.642285	valid_1's rmse: 0.707281
[500]	training's rmse: 0.618514	valid_1's rmse: 0.696302
[600]	training's rmse: 0.599658	valid_1's rmse: 0.68958
[700]	training's rmse: 0.582668	valid_1's rmse: 0.683641
[800]	training's rmse: 0.567869	valid_1's rmse: 0.678612
[900]	training's rmse: 0.552817	valid_1's rmse: 0.673357
[1000]	training's rmse: 0.540353	valid_1's rmse: 0.669522
[1100]	training's rmse: 0.527337	valid_1's rmse: 0.665172
[1200]	training's rmse: 0.515884	valid_1's rmse: 0.661924
[1300]	training's rmse: 0.506013	valid_1's rmse: 0.660081
[1400]	training's rmse: 0.495232	valid_1's rmse: 0.657109
[1500]	training's rmse: 0.485785	valid_1's rmse: 0.655172
[1600]	training's rmse: 0.476823	valid_1's rmse: 0.653265
[1700]	training's rmse: 0.467386	valid_1's rmse: 0.651088
[1800]	training's rmse: 0.458662	valid_1's rmse: 0.649493
[1900]	training's rmse: 0.450426	valid_1's rmse: 0.647798
[2000]	training's rmse: 0.442206	valid_1's rmse: 0.646554
[2100]	training's rmse: 0.434259	valid_1's rmse: 0.644975
[2200]	training's rmse: 0.427574	valid_1's rmse: 0.643702
[2300]	training's rmse: 0.420428	valid_1's rmse: 0.641861
[2400]	training's rmse: 0.413929	valid_1's rmse: 0.640826
[2500]	training's rmse: 0.407319	valid_1's rmse: 0.639825
[2600]	training's rmse: 0.400933	valid_1's rmse: 0.63867
[2700]	training's rmse: 0.394746	valid_1's rmse: 0.638278
[2800]	training's rmse: 0.388922	valid_1's rmse: 0.637967
[2900]	training's rmse: 0.383226	valid_1's rmse: 0.637335
[3000]	training's rmse: 0.377749	valid_1's rmse: 0.636588
[3100]	training's rmse: 0.372394	valid_1's rmse: 0.636091
[3200]	training's rmse: 0.367216	valid_1's rmse: 0.636029
Early stopping, best iteration is:
[3140]	training's rmse: 0.370351	valid_1's rmse: 0.635991
CV:5 RMSLE:0.635991426846803

fold_scores
[0.6209840569002207,
 0.6162296952189872,
 0.6246969157999711,
 0.6090120445655745,
 0.635991426846803]
mean(fold_scores)
0.6213828278663113
５．特徴量重要度
  • 特徴量重要度を確認し仮設を立てる
  • なぜこの特徴量は効いているのか、効いていないのか
import japanize_matplotlib
dt_now = datetime.datetime.now()
today = dt_now.strftime('%Y-%m-%d %H-%M')
feature_importance_df = pd.DataFrame()
for i, model in enumerate(models):
    _df = pd.DataFrame()
    _df['feature_importance'] = model.feature_importances_
    _df['column'] = test_df.columns
    _df['fold'] = i + 1
    feature_importance_df = pd.concat([feature_importance_df, _df], axis=0, ignore_index=True)

order = feature_importance_df.groupby('column')\
    .sum()[['feature_importance']]\
    .sort_values('feature_importance', ascending=False).index[:100]

fig, ax = plt.subplots(figsize=(12, 12))
plt.title(CFG.TARGET, fontsize=24)
sns.set_theme(style="whitegrid")
sns.barplot(data=feature_importance_df, x='feature_importance', y='column', order=order, palette='husl')
ax.tick_params(axis='x', rotation=90)
fig.tight_layout()
plt.savefig(os.path.join(CFG.OUTPUT_PATH, CFG.NAME+"_feature_importance.png"))
  • importanceが高い（深堀りした方が良さそうなところ）
    • LabelEncodingしただけでnameが上位に入っている
    • room_type間での何が違うのか
    • 経度、緯度やその他数値特徴量
６．サブミットファイルの作成
sample_submission["y"] = np.expm1(sub_pred)
sample_submission.to_csv(os.path.join(CFG.SUBMIT_PATH, CFG.NAME+".csv"), index=False)

今後スコアを上げるにあたって参考になるもの

添付データ

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