数据标准化+网格搜索+交叉验证+预测(Python)
2017-02-09 03:14
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Download datasets iris_training.csv from: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/tutorials/monitors
Method: SVR
Neural Network:
Method: SVR
# -*- coding: utf-8 -*- import pandas as pd from sklearn.grid_search import GridSearchCV from sklearn import svm, datasets from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.utils import shuffle import numpy as np from sklearn import metrics df = pd.read_csv('iris_training.csv', header=0) parameters = {'kernel':['rbf'], 'gamma':np.logspace(-5, 0, num=6, base=2.0),'C':np.logspace(-5, 5, num=11, base=2.0)} grid_search = GridSearchCV(svm.SVR(), parameters, cv=10, n_jobs=4, scoring='mean_squared_error') X = df[df.columns.drop('virginica')] y = df['virginica'] X_train, X_test, y_train, y_test = train_test_split(\ X, y, test_size=0.3, random_state=42) random_seed = 13 X_train, y_train = shuffle(X_train, y_train, random_state=random_seed) X_scaler = StandardScaler() X_train = X_scaler.fit_transform(X_train) X_test = X_scaler.transform(X_test) grid_search.fit(X_train,y_train) y_pred = grid_search.predict(X_test) print 'mean_squared_error:'+str(metrics.mean_squared_error(y_test,y_pred)),\ 'r2_score:'+str(metrics.r2_score(y_test,y_pred))
Neural Network:
# -*- coding: utf-8 -*- import pandas as pd from sklearn.grid_search import GridSearchCV from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.utils import shuffle import numpy as np from sklearn import metrics from sklearn.neural_network import MLPRegressor df = pd.read_csv('iris_training.csv', header=0) #neural networks for regresion parameters = {'hidden_layer_sizes':[200,250,300,400,500,600], 'activation':['relu']} grid_search = GridSearchCV(MLPRegressor(), parameters, cv=10, n_jobs=4, scoring='mean_squared_error') X = df[df.columns.drop('virginica')] y = df['virginica'] X_train, X_test, y_train, y_test = train_test_split(\ X, y, test_size=0.3, random_state=42) random_seed = 13 X_train, y_train = shuffle(X_train, y_train, random_state=random_seed) X_scaler = StandardScaler() X_train = X_scaler.fit_transform(X_train) X_test = X_scaler.transform(X_test) grid_search.fit(X_train,y_train) y_pred = grid_search.predict(X_test) print 'mean_squared_error:'+str(metrics.mean_squared_error(y_test,y_pred)),\ 'r2_score:'+str(metrics.r2_score(y_test,y_pred))
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