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model_fusion.py
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model_fusion.py
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import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression,LogisticRegression
from sklearn.svm import SVR
from sklearn.model_selection import train_test_split, cross_val_score, cross_val_predict
from mlxtend.regressor import StackingRegressor, StackingCVRegressor
from mlxtend.classifier import StackingClassifier, StackingCVClassifier
from models import Models
model = Models()
# ---------------------------------------定义模型融合类----------------------------------------
class RegressorBlender:
def __init__(self, x_train, x_test, y_train, y_test=None):
x_train.drop(['Unnamed: 0', 'Id'], axis=1, inplace=True)
x_test.drop(['Unnamed: 0', 'Id'], axis=1, inplace=True)
self.x_train = x_train
self.x_test = x_test
self.y_train = y_train['y'].values
if self.y_train is not None:
self.y_test = y_test['y'].values
def reg_blend(self):
mete_reg = LinearRegression()
reg1 = model.svm_regressor()
reg2 = model.randomforest_regressor()
reg3 = model.xgb_regressor()
self.blend = StackingRegressor(regressors=[reg1, reg2, reg3], meta_regressor=mete_reg)
self.blend.fit(self.x_train, self.y_train)
return self.blend
def score(self):
scores = cross_val_score(self.blend, X=self.x_train, y=self.y_train, cv=10,
verbose=2)
return scores
def prediction(self):
y_pred = self.blend.predict(self.x_test)
return y_pred
import matplotlib
class ClassifierBlender:
def __init__(self, x_train, x_test, y_train, y_test=None):
x_train.drop(['Unnamed: 0', 'Id'], axis=1, inplace=True)
x_test.drop(['Unnamed: 0', 'Id'], axis=1, inplace=True)
self.x_train = x_train
self.x_test = x_test
self.y_train = y_train['y'].values
if self.y_train is not None:
self.y_test = y_test['y'].values
def clf_blend(self):
mete_clf = LinearRegression()
clf1 = model.svm_regressor()
clf2 = model.randomforest_regressor()
clf3 = model.xgb_regressor()
self.blend = StackingClassifier(classifiers=[clf1, clf2, clf3], meta_classifier=mete_clf)
self.blend.fit(self.x_train, self.y_train)
return self.blend
def score(self):
scores = cross_val_score(self.blend, X=self.x_train, y=self.y_train, cv=10,
verbose=2)
return scores
def prediction(self):
y_pred = self.blend.predict(self.x_test)
return y_pred