update, add random forest

rf.py

@@ -0,0 +1,149 @@
+# _*_coding:utf-8_*_
+import numpy as np
+import pandas as pd
+
+
+def load_data(trainfile, testfile):
+	traindata = pd.read_csv(trainfile)
+	testdata = pd.read_csv(testfile)
+	feature_data = traindata.iloc[:, 1:-1]
+	label_data = traindata.iloc[:, -1]
+	test_feature = testdata.iloc[:, 1:]
+	return feature_data, label_data, test_feature
+
+
+def random_forest_train(feature_data, label_data, test_feature, submitfile):
+	from sklearn.ensemble import RandomForestRegressor
+	from sklearn.model_selection import train_test_split
+	from sklearn.metrics import mean_squared_error
+
+	X_train, X_test, y_train, y_test = train_test_split(feature_data, label_data, test_size=0.01)
+	params = {
+		'n_estimators': 70,
+		'max_depth': 13,
+		'min_samples_split': 10,
+		'min_samples_leaf': 5,  # 10
+		'max_features': 7
+	}
+	print(X_test)
+	model = RandomForestRegressor(**params)
+	model.fit(X_train, y_train)
+	# 对测试集进行预测
+	y_pred = model.predict(X_test)
+	# 计算准确率
+	MSE = mean_squared_error(y_test, y_pred)
+	RMSE = np.sqrt(MSE)
+	print(RMSE)
+
+	submit = pd.read_csv(submitfile)
+	print(submit)
+	print(model.predict(test_feature))
+	submit['CPU'] = model.predict(test_feature)
+	submit.to_csv('my_random_forest_prediction1.csv', index=False)
+	print(submit)
+
+
+def random_forest_parameter_tuning1(feature_data, label_data, test_feature):
+	from sklearn.ensemble import RandomForestRegressor
+	from sklearn.model_selection import train_test_split
+	from sklearn.metrics import mean_squared_error
+	from sklearn.model_selection import GridSearchCV
+
+	X_train, X_test, y_train, y_test = train_test_split(feature_data, label_data, test_size=0.23)
+	param_test1 = {
+		'n_estimators': range(10, 71, 10)
+	}
+	model = GridSearchCV(estimator=RandomForestRegressor(
+		min_samples_split=100, min_samples_leaf=20, max_depth=8, max_features='sqrt',
+		random_state=10), param_grid=param_test1, cv=5
+	)
+	model.fit(X_train, y_train)
+	# 对测试集进行预测
+	y_pred = model.predict(X_test)
+	# 计算准确率
+	MSE = mean_squared_error(y_test, y_pred)
+	RMSE = np.sqrt(MSE)
+	print(RMSE)
+	return model.best_score_, model.best_params_
+
+
+def random_forest_parameter_tuning2(feature_data, label_data, test_feature):
+	from sklearn.ensemble import RandomForestRegressor
+	from sklearn.model_selection import train_test_split
+	from sklearn.metrics import mean_squared_error
+	from sklearn.model_selection import GridSearchCV
+
+	X_train, X_test, y_train, y_test = train_test_split(feature_data, label_data, test_size=0.23)
+	param_test2 = {
+		'max_depth': range(3, 14, 2),
+		'min_samples_split': range(50, 201, 20)
+	}
+	model = GridSearchCV(estimator=RandomForestRegressor(
+		n_estimators=70, min_samples_leaf=20, max_features='sqrt', oob_score=True,
+		random_state=10), param_grid=param_test2, cv=5
+	)
+	model.fit(X_train, y_train)
+	# 对测试集进行预测
+	y_pred = model.predict(X_test)
+	# 计算准确率
+	MSE = mean_squared_error(y_test, y_pred)
+	RMSE = np.sqrt(MSE)
+	print(RMSE)
+	return model.best_score_, model.best_params_
+
+
+def random_forest_parameter_tuning3(feature_data, label_data, test_feature):
+	from sklearn.ensemble import RandomForestRegressor
+	from sklearn.model_selection import train_test_split
+	from sklearn.metrics import mean_squared_error
+	from sklearn.model_selection import GridSearchCV
+
+	X_train, X_test, y_train, y_test = train_test_split(feature_data, label_data, test_size=0.23)
+	param_test3 = {
+		'min_samples_split': range(10, 90, 20),
+		'min_samples_leaf': range(10, 60, 10),
+	}
+	model = GridSearchCV(estimator=RandomForestRegressor(
+		n_estimators=70, max_depth=13, max_features='sqrt', oob_score=True,
+		random_state=10), param_grid=param_test3, cv=5
+	)
+	model.fit(X_train, y_train)
+	# 对测试集进行预测
+	y_pred = model.predict(X_test)
+	# 计算准确率
+	MSE = mean_squared_error(y_test, y_pred)
+	RMSE = np.sqrt(MSE)
+	print(RMSE)
+	return model.best_score_, model.best_params_
+
+
+def random_forest_parameter_tuning4(feature_data, label_data, test_feature):
+	from sklearn.ensemble import RandomForestRegressor
+	from sklearn.model_selection import train_test_split
+	from sklearn.metrics import mean_squared_error
+	from sklearn.model_selection import GridSearchCV
+
+	X_train, X_test, y_train, y_test = train_test_split(feature_data, label_data, test_size=0.23)
+	param_test4 = {
+		'max_features': range(3, 9, 2)
+	}
+	model = GridSearchCV(estimator=RandomForestRegressor(
+		n_estimators=70, max_depth=13, min_samples_split=10, min_samples_leaf=10, oob_score=True,
+		random_state=10), param_grid=param_test4, cv=5
+	)
+	model.fit(X_train, y_train)
+	# 对测试集进行预测
+	y_pred = model.predict(X_test)
+	# 计算准确率
+	MSE = mean_squared_error(y_test, y_pred)
+	RMSE = np.sqrt(MSE)
+	print(RMSE)
+	return model.best_score_, model.best_params_
+
+
+if __name__ == '__main__':
+	trainfile = 'data/train.csv'
+	testfile = 'data/test.csv'
+	submitfile = 'data/sample_submit.csv'
+	feature_data, label_data, test_feature = load_data(trainfile, testfile)
+	random_forest_train(feature_data, label_data, test_feature, submitfile)