Python、机器学习和线性回归

Question

我是 Python 和机器学习的新手，我下周要交作业。这是我到现在为止的代码：

# to get in-line plots
%matplotlib inline
import matplotlib.pyplot as plt

import numpy as np
import scipy as sp
from scipy import stats

# Load the data
IDnumber = 0000001
np.random.seed(IDnumber)

filename = "ccpp_Data_clean2018.csv"

Data = np.genfromtxt(filename, delimiter=';',skip_header=1)
dataDescription = stats.describe(Data)
print(dataDescription)

Data.shape

#get number of total samples
num_total_samples = Data.shape[0]

print("Total number of samples: "+str(num_total_samples))

#size of each chunk of data for training, validation, testing
size_chunk = int(num_total_samples/3.)

print("Size of each chunk of data: "+str(size_chunk))

#shuffle the data
np.random.shuffle(Data)

#training data
X_training = np.delete(Data[:size_chunk], 4, 1)
Y_training = Data[:size_chunk, 4]
print("Training data input size: "+str(X_training.shape))
print("Training data output size: "+str(Y_training.shape))

#validation data, to be used to choose among different models
X_validation = np.delete(Data[size_chunk:size_chunk*2], 4, 1)
Y_validation = Data[size_chunk:size_chunk*2, 4]
print("Validation data input size: "+str(X_validation.shape))
print("Validation data ouput size: "+str(Y_validation.shape))

#test data, to be used to estimate the true loss of the final model(s)
X_test = np.delete(Data[size_chunk*2:num_total_samples], 4, 1)
Y_test = Data[size_chunk*2: num_total_samples, 4]
print("Test data input size: "+str(X_test.shape))
print("Test data output size: "+str(Y_test.shape))
#scale the data

# standardize the input matrix
from sklearn import preprocessing
scaler = preprocessing.StandardScaler().fit(X_training)
X_training = scaler.transform(X_training)
print("Mean of the training input data:"+str(X_training.mean(axis=0)))
print("Std of the training input data:"+str(X_training.std(axis=0)))
X_validation = scaler.transform(X_validation) # use the same transformation on validation data
print("Mean of the validation input data:"+str(X_validation.mean(axis=0)))
print("Std of the validation input data:"+str(X_validation.std(axis=0)))
X_test = scaler.transform(X_test) # use the same transformation on test data
print("Mean of the test input data:"+str(X_test.mean(axis=0)))
print("Std of the test input data:"+str(X_test.std(axis=0)))
#compute linear regression coefficients for training data

#add a 1 at the beginning of each sample for training, validation, and testing
m_training = # COMPLETE: NUMBER OF POINTS IN THE TRAINING SET
X_training = np.hstack((np.ones((m_training,1)),X_training))

m_validation = # COMPLETE: NUMBER OF POINTS IN THE VALIDATION SET
X_validation = np.hstack((np.ones((m_validation,1)),X_validation))

m_test = # COMPLETE: NUMBER OF POINTS IN THE TEST SET
X_test = np.hstack((np.ones((m_test,1)),X_test))

# Compute the coefficients for linear regression (LR) using linalg.lstsq
w_np, RSStr_np, rank_X_tr, sv_X_tr = #COMPLETE

print("LR coefficients with numpy lstsq: "+ str(w_np))

# compute Residual sums of squares by hand
print("RSS with numpy lstsq: "+str(RSStr_np))
print("Empirical risk with numpy lstsq:"+str(RSStr_np/m_training))

我拆分集合的方式是分配的一部分，我必须预测的数据在最后一列，这是数据集：http://archive.ics.uci.edu/ml/datasets/Combined+Cycle+Power+Plant。

我的问题是：在代码的最后一部分（“完整”行在哪里）m_training、m_validation 和 m_test 只是对应 X 的形状？我的意思是：

m_training = X_training.shape

等等。我不确定。 最后，我必须在输入中传递给 linalg.lstsq 函数的参数是什么？

更新 我正在继续编写代码，但我又卡住了，这次我必须：

#compute predictions on training set, validation set, and test set
prediction_training = # COMPLETE
prediction_validation = # COMPLETE
prediction_test = # COMPLETE

#what about the RSS and loss for points in the validation data?
RSS_validation =# COMPLETE
RSS_test = # COMPLETE

print("RSS on validation data: "+str(RSS_validation))
print("Loss estimated from validation data:"+str(RSS_validation/m_validation))


#another measure of how good our linear fit is given by the following (that is 1 - R^2)
#compute 1 -R^2 for training, validation, and test set
Rmeasure_training = #COMPLETE
Rmeasure_validation = #COMPLETE 
Rmeasure_test = #COMPLETE

我发现很多困难，所以如果您对我可以在哪里找到并学习我需要的东西有什么好的建议，我将非常感激。我有一本教科书，但没有编程，只有理论。

Answer 1

你可以使用

 m_training=len(X_training)

但更好的方法确实是使用形状

X_training.shape

这将返回一个元组 (m, n)，其中 m 是行数，n 是列数。那么

m_training = X_training.shape[0]

是您正在寻找的。实际上，为了在数据的第一行中添加 1 列，您需要指明行数。

对于函数 linalg.lstsq，您可以查看以下示例： https://docs.scipy.org/doc/numpy-1.15.0/reference/generated/numpy.linalg.lstsq.html

在你的情况下应该是：

linalg.lstsq(X_training,y_training)