Kaggle-Predicting Optimal Fertilizers-(多分类+xgboost+同一特征值多样性)

Predicting Optimal Fertilizers

题意：

给出土壤的特性，预测出3种最佳的肥料

数据处理：

1.有数字型和类别型，类别不能随意换成数字，独热编码。cat可以直接处理category类型。
2.构造一些相关土壤特性特征
3.由于label是category类型，但是xgb不可以处理category类型，因此需要先编码，最后求出结果之后再解码。

建立模型：

1.catboost交叉验证、xgboost交叉验证

代码：

import os
import sys
import warnings
import numpy as np
import pandas as pd
import seaborn
from catboost import CatBoostRegressor, CatBoostClassifier
from lightgbm import LGBMRegressor
from matplotlib import pyplot as plt
import lightgbm
from mlxtend.regressor import StackingCVRegressor
from sklearn import clone
from sklearn.ensemble import VotingRegressor, StackingClassifier, StackingRegressor
from sklearn.linear_model import Lasso, LogisticRegression, RidgeCV
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, make_scorer, mean_squared_log_error
from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score, StratifiedKFold
from sklearn.preprocessing import StandardScaler, LabelEncoder, PolynomialFeatures
from xgboost import XGBRegressor, XGBClassifier
from sklearn.preprocessing import RobustScaler
from sklearn.model_selection import KFold
from sklearn.linear_model import Ridge
from catboost import Pool, CatBoostClassifierdef init():os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'  # 仅输出错误日志warnings.simplefilter('ignore')  # 忽略警告日志pd.set_option('display.width', 1000)pd.set_option('display.max_colwidth', 1000)pd.set_option("display.max_rows", 1000)pd.set_option("display.max_columns", 1000)def show_dataframe(df):print("查看特征值和特征值类型\n" + str(df.dtypes) + "\n" + "-" * 100)print("查看前10行信息\n" + str(df.head()) + "\n" + "-" * 100)print("查看每个特征值的各种数据统计信息\n" + str(df.describe()) + "\n" + "-" * 100)print("输出重复行的个数\n" + str(df.duplicated().sum()) + "\n" + "-" * 100)print("查看每列的缺失值个数\n" + str(df.isnull().sum()) + "\n" + "-" * 100)print("查看缺失值的具体信息\n" + str(df.info()) + "\n" + "-" * 100)# print("输出X所有值出现的是什么，还有对应出现的次数\n" + str(df['X'].value_counts()) + "\n" + "-" * 100)def show_relation(data, colx, coly):if data[colx].dtype == 'object' or data[colx].dtype == 'category' or len(data[colx].unique()) < 20:seaborn.boxplot(x=colx, y=coly, data=data)else:plt.scatter(data[colx], data[coly])plt.xlabel(colx)plt.ylabel(coly)plt.show()def mapk(actual, predicted, k=3):def apk(a, p, k):score = 0.0for i in range(min(k, len(p))):if p[i] == a:score += 1.0 / (i + 1)breakreturn scorereturn np.mean([apk(a, p, k) for a, p in zip(actual, predicted)])if __name__ == '__main__':init()df_train = pd.read_csv('/kaggle/input/playground-series-s5e6/train.csv')df_test = pd.read_csv('/kaggle/input/playground-series-s5e6/test.csv')df_train_additional = pd.read_csv('/kaggle/input/fertilizer-prediction/Fertilizer Prediction.csv')pd.concat([df_train, df_train_additional], ignore_index=True)print("Start Feature enggering" + "-" * 70 + "\n")df_all = pd.concat([df_train.drop(['id', 'Fertilizer Name'], axis=1), df_test.drop(['id'], axis=1)], axis=0)df_all['Temp_Humidity_Interaction'] = df_all['Temparature'] * df_all['Humidity']df_all['N_P_Ratio'] = df_all['Nitrogen'] / (df_all['Phosphorous'].replace(0, 1e-6))df_all['K_P_Ratio'] = df_all['Potassium'] / (df_all['Phosphorous'].replace(0, 1e-6))df_all['Soil_Crop_Combination'] = df_all['Soil Type'].astype(str) + '_' + df_all['Crop Type'].astype(str)df_all['P_to_K'] = df_all['Phosphorous'] / (df_all['Potassium'] + 1e-5)df_all['Total_NPK'] = df_all['Nitrogen'] + df_all['Phosphorous'] + df_all['Potassium']df_all['Climate_Index'] = (df_all['Temparature'] + df_all['Humidity']) / 2df_all['Water_Stress'] = df_all['Humidity'] - df_all['Moisture']original_numerical_cols = ['Temparature', 'Humidity', 'Moisture', 'Nitrogen', 'Potassium', 'Phosphorous']for col in original_numerical_cols:df_all[f'{col}_Binned'] = df_all[col].astype(str)numerical_features = ['Temparature', 'Humidity', 'Moisture', 'Nitrogen', 'Potassium', 'Phosphorous','Temp_Humidity_Interaction', 'N_P_Ratio', 'K_P_Ratio']categorical_features = ['Soil Type', 'Crop Type', 'Soil_Crop_Combination']categorical_features.extend([f'{col}_Binned' for col in original_numerical_cols])poly_features_to_transform = original_numerical_colspoly = PolynomialFeatures(degree=2, include_bias=False)df_all_transformers = poly.fit_transform(df_all[poly_features_to_transform])poly_feature_names = poly.get_feature_names_out(poly_features_to_transform)df_all = df_all.drop(columns=poly_features_to_transform)df_all = pd.concat([df_all, pd.DataFrame(df_all_transformers, columns=poly_feature_names,index=df_all.index)], axis=1)numerical_features = df_all.select_dtypes(include=['int64', 'float64']).columns.tolist()categorical_features = df_all.select_dtypes(exclude=['int64', 'float64']).columns.tolist()all_features_ordered = numerical_features + categorical_featuresdf_all = df_all[all_features_ordered]all_categories_union = {}for col in categorical_features:if col in df_all.columns:all_categories_union[col] = pd.concat([df_all[col],], axis=0).astype(str).unique()else:print(f"Warning: Categorical column '{col}' not found after feature engineering. Skipping conversion.")for col in categorical_features:if col in df_all.columns:df_all[col] = pd.Categorical(df_all[col], categories=all_categories_union[col])le = LabelEncoder()X_train = df_all[:df_train.shape[0]]Y_train = df_train['Fertilizer Name']Y_train = le.fit_transform(Y_train)X_test = df_all[df_train.shape[0]:]print("Training model" + "-" * 70 + "\n")model_xgb = XGBClassifier(max_depth=8,  # 降低树深度colsample_bytree=0.5,  # 控制特征采样比例subsample=0.7,  # 控制数据采样比例n_estimators=3000,  # 减少迭代轮数learning_rate=0.03,  # 降低学习率gamma=0.5,  # 增加分裂难度max_delta_step=2,  # 限制权重更新步长reg_alpha=5,  # 增强L1正则化reg_lambda=3,  # 增强L2正则化early_stopping_rounds=100,  # 更早停止训练objective='multi:softprob',random_state=13,enable_categorical=True,tree_method='hist',device='cuda')kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=42)pred_xgb = np.zeros((X_test.shape[0], len(le.classes_)))for fold, (train_idx, val_idx) in enumerate(kfold.split(X_train, Y_train)):print(f"\nFold {fold + 1}/{kfold.n_splits}")x_fold_train, x_fold_val = X_train.iloc[train_idx], X_train.iloc[val_idx]y_fold_train, y_fold_val = Y_train[train_idx], Y_train[val_idx]model_xgb.fit(x_fold_train, y_fold_train,eval_set = [(x_fold_val, y_fold_val)],verbose = 100,)pred_xgb += model_xgb.predict_proba(X_test) / kfold.n_splitspred_top3_xgb = np.argsort(pred_xgb, axis=1)[:, -3:][:, ::-1]top3_label = []for row in pred_top3_xgb:converted = [le.classes_[i] for i in row]top3_label.append(converted)submission = pd.DataFrame({'id': df_test['id'],'Fertilizer Name': [' '.join(preds) for preds in top3_label],})submission.to_csv('/kaggle/working/submission.csv', index=False)
#xgb0.35642