【发布时间】：2019-04-24 14:05:00
【问题描述】：
这个学期我开始使用机器学习。我们只使用过微软的 Azure 和亚马逊的 AWS 等 API，但我们还没有深入了解这些服务的工作原理。我的好朋友是数学专业的大四学生，他让我帮助他根据他提供给我的文件.csv 使用 TensorFlow 创建一个股票预测器。
我有几个问题。第一个是他的.csv 文件。该文件只有日期和结束值，它们没有分开，因此我不得不手动分开日期和值。我已经设法做到了，现在我遇到了 MinMaxScaler() 的问题。有人告诉我，我几乎可以忽略日期，只测试结束值，将它们标准化，然后根据它们做出预测。
我不断收到此错误：
ValueError: 找到包含 0 个样本的数组 (shape=(0, 1)) 而一个 MinMaxScaler() 要求最小值为 1
老实说，我以前从未使用过SKLearning 和 TensorFlow，这是我第一次从事这样的项目。我在该主题上看到的所有指南都使用 pandas，但就我而言，.csv 文件一团糟，我不相信我可以使用 pandas。
我正在关注this 指南：
但不幸的是，由于我缺乏经验，有些事情并不适合我，如果我能更清楚地了解我应该如何处理我的案件，我将不胜感激。
下面附上我的（凌乱的）代码：
import pandas as pd
import numpy as np
import tensorflow as tf
import sklearn
from sklearn.model_selection import KFold
from sklearn.preprocessing import scale
from sklearn.preprocessing import MinMaxScaler
import matplotlib
import matplotlib.pyplot as plt
from dateutil.parser import parse
from datetime import datetime, timedelta
from collections import deque
stock_data = []
stock_date = []
stock_value = []
f = open("s&p500closing.csv","r")
data = f.read()
rows = data.split("\n")
rows_noheader = rows[1:len(rows)]
#Separating values from messy `.csv`, putting each value to it's list and also a combined list of both
for row in rows_noheader:
[date, value] = row[1:len(row)-1].split('\t')
stock_date.append(date)
stock_value.append((value))
stock_data.append((date, value))
#Numpy array of all closing values converted to floats and normalized against the maximum
stock_value = np.array(stock_value, dtype=np.float32)
normvalue = [i/max(stock_value) for i in stock_value]
#Number of closing values and days. Since there is one closing value for each, they both match and there are 4528 of them (each)
nclose_and_days = 0
for i in range(len(stock_data)):
nclose_and_days+=1
train_data = stock_value[:2264]
test_data = stock_value[2264:]
scaler = MinMaxScaler()
train_data = train_data.reshape(-1,1)
test_data = test_data.reshape(-1,1)
# Train the Scaler with training data and smooth data
smoothing_window_size = 1100
for di in range(0,4400,smoothing_window_size):
#error occurs here
scaler.fit(train_data[di:di+smoothing_window_size,:])
train_data[di:di+smoothing_window_size,:] = scaler.transform(train_data[di:di+smoothing_window_size,:])
# You normalize the last bit of remaining data
scaler.fit(train_data[di+smoothing_window_size:,:])
train_data[di+smoothing_window_size:,:] = scaler.transform(train_data[di+smoothing_window_size:,:])
# Reshape both train and test data
train_data = train_data.reshape(-1)
# Normalize test data
test_data = scaler.transform(test_data).reshape(-1)
# Now perform exponential moving average smoothing
# So the data will have a smoother curve than the original ragged data
EMA = 0.0
gamma = 0.1
for ti in range(1100):
EMA = gamma*train_data[ti] + (1-gamma)*EMA
train_data[ti] = EMA
# Used for visualization and test purposes
all_mid_data = np.concatenate([train_data,test_data],axis=0)
window_size = 100
N = train_data.size
std_avg_predictions = []
std_avg_x = []
mse_errors = []
for pred_idx in range(window_size,N):
std_avg_predictions.append(np.mean(train_data[pred_idx-window_size:pred_idx]))
mse_errors.append((std_avg_predictions[-1]-train_data[pred_idx])**2)
std_avg_x.append(date)
print('MSE error for standard averaging: %.5f'%(0.5*np.mean(mse_errors)))
【问题讨论】：
-
请发布一些数据样本，以及错误的完整堆栈跟踪。
标签： python tensorflow scikit-learn