专门做高端网站设计的云华设计,wordpress彩色标签固定宽度代码,qq群优惠券里面网站怎么做的,网站源码超市文章目录1. 导入工具包2. 读取数据3. 数字特征、文字特征分离4. 数据处理Pipeline5. 尝试不同的模型6. 参数搜索7. 特征重要性筛选8. 最终完整Pipeline使用 sklearn 的 pipeline 搭建机器学习的流程 本文例子为 [Kesci] 新人赛 员工满意度预测 参考 [Hands On ML] 2. 一个完整…
文章目录1. 导入工具包2. 读取数据3. 数字特征、文字特征分离4. 数据处理Pipeline5. 尝试不同的模型6. 参数搜索7. 特征重要性筛选8. 最终完整Pipeline使用 sklearn 的 pipeline 搭建机器学习的流程 本文例子为 [Kesci] 新人赛 · 员工满意度预测 参考 [Hands On ML] 2. 一个完整的机器学习项目加州房价预测
1. 导入工具包
import numpy as np
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
from sklearn.preprocessing import LabelBinarizer
from sklearn.base import BaseEstimator, TransformerMixin
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import FeatureUnion
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_val_score2. 读取数据
data pd.read_csv(../competition/Employee_Satisfaction/train.csv)
test pd.read_csv(../competition/Employee_Satisfaction/test.csv)
data.columnsIndex([id, last_evaluation, number_project, average_monthly_hours,time_spend_company, Work_accident, package,promotion_last_5years, division, salary, satisfaction_level],dtypeobject)训练数据标签分离
y data[satisfaction_level]
X data.drop([satisfaction_level], axis1)3. 数字特征、文字特征分离
def num_cat_splitor(X):s (X.dtypes object)object_cols list(s[s].index)# object_cols # [package, division, salary]num_cols list(set(X.columns) - set(object_cols))# num_cols# [Work_accident, time_spend_company, promotion_last_5years, id,# average_monthly_hours, last_evaluation, number_project]return num_cols, object_cols
num_cols, object_cols num_cat_splitor(X)
# print(num_cols)
# print(object_cols)
# X[object_cols].values特征数值筛选器
class DataFrameSelector(BaseEstimator, TransformerMixin):def __init__(self, attribute_names):self.attribute_names attribute_namesdef fit(self, X, yNone):return selfdef transform(self, X):return X[self.attribute_names].values4. 数据处理Pipeline
数字特征
num_pipeline Pipeline([(selector, DataFrameSelector(num_cols)),(imputer, SimpleImputer(strategymedian)),(std_scaler, StandardScaler()),])文字特征
cat_pipeline Pipeline([(selector, DataFrameSelector(object_cols)),(cat_encoder, OneHotEncoder(sparseFalse)),])组合数字和文字特征
full_pipeline FeatureUnion(transformer_list[(num_pipeline, num_pipeline),(cat_pipeline, cat_pipeline),])
X_prepared full_pipeline.fit_transform(X)5. 尝试不同的模型
from sklearn.ensemble import RandomForestRegressor
forest_reg RandomForestRegressor()
forest_scores cross_val_score(forest_reg,X_prepared,y,scoringneg_mean_squared_error,cv3)
forest_rmse_scores np.sqrt(-forest_scores)
print(forest_rmse_scores)
print(forest_rmse_scores.mean())
print(forest_rmse_scores.std())还可以尝试别的模型
6. 参数搜索
param_grid [{n_estimators : [3,10,30,50,80],max_features:[2,4,6,8]},{bootstrap:[False], n_estimators : [3,10],max_features:[2,3,4]},
]
forest_reg RandomForestRegressor()
grid_search GridSearchCV(forest_reg, param_grid, cv5,scoringneg_mean_squared_error)
grid_search.fit(X_prepared,y)最佳参数
grid_search.best_params_最优模型
grid_search.best_estimator_搜索结果
cv_result grid_search.cv_results_
for mean_score, params in zip(cv_result[mean_test_score], cv_result[params]):print(np.sqrt(-mean_score), params)0.2129252723367584 {max_features: 2, n_estimators: 3}
0.19276874697889504 {max_features: 2, n_estimators: 10}
0.1865548358477794 {max_features: 2, n_estimators: 30}
.......7. 特征重要性筛选
feature_importances grid_search.best_estimator_.feature_importances_选择前 k 个最重要的特征
k 3
def indices_of_top_k(arr, k):return np.sort(np.argpartition(np.array(arr), -k)[-k:])class TopFeatureSelector(BaseEstimator, TransformerMixin):def __init__(self, feature_importances, k):self.feature_importances feature_importancesself.k kdef fit(self, X, yNone):self.feature_indices_ indices_of_top_k(self.feature_importances, self.k)return selfdef transform(self, X):return X[:, self.feature_indices_]8. 最终完整Pipeline
prepare_select_and_predict_pipeline Pipeline([(preparation, full_pipeline),(feature_selection, TopFeatureSelector(feature_importances, k)),(forst_reg, RandomForestRegressor())
])参数搜索
param_grid [{preparation__num_pipeline__imputer__strategy: [mean, median, most_frequent],feature_selection__k: list(range(5, len(feature_importances) 1)),forst_reg__n_estimators : [200,250,300,310,330],forst_reg__max_features:[2,4,6,8]
}]grid_search_prep GridSearchCV(prepare_select_and_predict_pipeline, param_grid, cv10,scoringneg_mean_squared_error, verbose2, n_jobs-1)训练
grid_search_prep.fit(X,y)
grid_search_prep.best_params_
final_model grid_search_prep.best_estimator_预测
y_pred_test final_model.predict(test)
result pd.DataFrame()
result[id] test[id]
result[satisfaction_level] y_pred_test
result.to_csv(rf_ML_pipeline.csv,indexFalse)以上只是粗略的大体框架还有很多细节大家多指教 我的CSDN博客地址 https://michael.blog.csdn.net/
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