在Python中使用标准偏差的熊猫移动平均值

Question

I want to smooth a noise using a moving average filter after fitting a regression model using a RandomForestRegressor for a data set I am considering using found in this link

import pandas as pd
import math
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import r2_score, mean_squared_error, make_scorer
from sklearn.model_selection import train_test_split
from math import sqrt
from sklearn.cross_validation import train_test_split


n_features=3000

df = pd.read_csv('cubic32.csv')

for i in range(1,n_features):
    df['X_t'+str(i)] = df['X'].shift(i)

print(df)

df.dropna(inplace=True)


X = df.drop('Y', axis=1)
y = df['Y']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.40)

X_train = X_train.drop('time', axis=1)
X_test = X_test.drop('time', axis=1)


parameters = {'n_estimators': [10]}
clf_rf = RandomForestRegressor(random_state=1)
clf = GridSearchCV(clf_rf, parameters, cv=5, scoring='neg_mean_squared_error', n_jobs=-1)
model = clf.fit(X_train, y_train)
model.cv_results_['params'][model.best_index_]
math.sqrt(model.best_score_*-1)
model.grid_scores_

#####
print()
print(model.grid_scores_)
print("The best score: ",model.best_score_)

print("RMSE:",math.sqrt(model.best_score_*-1))

clf_rf.fit(X_train,y_train)
modelPrediction = clf_rf.predict(X_test)
print(modelPrediction)

print("Number of predictions:",len(modelPrediction))

meanSquaredError=mean_squared_error(y_test, modelPrediction)
print("Mean Square Error (MSE):", meanSquaredError)
rootMeanSquaredError = sqrt(meanSquaredError)
print("Root-Mean-Square Error (RMSE):", rootMeanSquaredError)

fig, ax = plt.subplots()
index_values=range(0,len(y_test))

y_test.sort_index(inplace=True)
X_test.sort_index(inplace=True)

modelPred_test = clf_rf.predict(X_test)
ax.plot(pd.Series(index_values), y_test.values)

smoothed=pd.rolling_mean(modelPred_test, 90, min_periods=90, freq=None, center=False, how=None)
PlotInOne=pd.DataFrame(pd.concat([pd.Series(smoothed), pd.Series(y_test.values)], axis=1))
plt.figure(); PlotInOne.plot(); plt.legend(loc='best')

However, the plot of the predicted values seems (as shown below) to be very coarse (the blue line).

The orange line is a plot of the actual value.

How can we calculate the standard deviation of the prediction (blue line) in the plot shown above and pass it as an interval parameter to the moving average that the window runs on? Currently, I am setting the size of the moving window manually as 50 but I wanted to pass the value of the standard deviation instead.

smoothed=pd.rolling_mean(modelPred_test, 50, min_periods=50, freq=None, center=False, how=None)

Answer 1

smoothed=pd.rolling(modelPred_test, 50, min_periods=50, freq=None, center=False, how=None).std()

Answer 2

pd.rolling_mean(modelPred_test,windows=round(np.std(modelPred_test)))

您也可以将标准偏差插入最小窗口中〜