预处理要与tflearn一起使用的csv文件 [英] Preprocessing csv files to use with tflearn

问题描述

我的问题是关于在将csv文件输入到神经网络之前对其进行预处理.

My question is about preprocessing csv files before inputing them into a neural network.

我想在python 3中使用tflearn为著名的虹膜数据集建立一个深度神经网络.

I want to build a deep neural network for the famous iris dataset using tflearn in python 3.

数据集: http://archive.ics .uci.edu/ml/machine-learning-databases/iris/iris.data

我正在使用tflearn加载csv文件.但是，我的数据集的类别"列中有诸如鸢尾花，鸢尾花，鸢尾花等词.

I'm using tflearn to load the csv file. However, the classes column of my data set has words such as iris-setosa, iris-versicolor, iris-virginica.

神经网络仅适用于数字.因此，我必须找到一种方法来将类从单词更改为数字.由于它是一个非常小的数据集，因此我可以使用Excel/文本编辑器手动进行操作.我为不同的班级手动分配了数字.

Nueral networks work only with numbers. So, I have to find a way to change the classes from words to numbers. Since it is a very small dataset, I can do it manually using Excel/text editor. I manually assigned numbers for different classes.

但是，我不可能为我处理的每个数据集都做到这一点.因此，我尝试使用熊猫执行一次热编码.

But, I can't possibly do it for every dataset I work with. So, I tried using pandas to perform one hot encoding.

preprocess_data = pd.read_csv("F:\Gautam\.....\Dataset\iris_data.csv")
preprocess_data = pd.get_dummies(preprocess_data)

但是现在，我无法使用这段代码:

But now, I can't use this piece of code:

data, labels = load_csv('filepath', categorical_labels=True,
                     n_classes=3)

"filepath"应仅是csv文件的目录，而不是诸如preprocess_data之类的任何变量.

'filepath' should only be a directory to the csv file, not any variable like preprocess_data.

原始数据集:

     Sepal Length  Sepal Width  Petal Length  Petal Width  Class
89            5.5          2.5           4.0          1.3  iris-versicolor
85            6.0          3.4           4.5          1.6  iris-versicolor
31            5.4          3.4           1.5          0.4  iris-setosa
52            6.9          3.1           4.9          1.5  iris-versicolor
111           6.4          2.7           5.3          1.9  iris-virginica

手动修改的数据集:

     Sepal Length  Sepal Width  Petal Length  Petal Width  Class
89            5.5          2.5           4.0          1.3      1
85            6.0          3.4           4.5          1.6      1
31            5.4          3.4           1.5          0.4      0
52            6.9          3.1           4.9          1.5      1
111           6.4          2.7           5.3          1.9      2

这是我的代码，可以很好地运行，但是，我手动修改了数据集.

Here's my code which runs perfectly, but, I have modified the dataset manually.

import numpy as np
import pandas as pd
import tflearn
from tflearn.layers.core import input_data, fully_connected
from tflearn.layers.estimator import regression
from tflearn.data_utils import load_csv


data_source = 'F:\Gautam\.....\Dataset\iris_data.csv'

data, labels = load_csv(data_source, categorical_labels=True,
                         n_classes=3)


network = input_data(shape=[None, 4], name='InputLayer')

network = fully_connected(network, 9, activation='sigmoid', name='Hidden_Layer_1')

network = fully_connected(network, 3, activation='softmax', name='Output_Layer')

network = regression(network, batch_size=1, optimizer='sgd', learning_rate=0.2)

model = tflearn.DNN(network)
model.fit(data, labels, show_metric=True, run_id='iris_dataset', validation_set=0.1, n_epoch=2000)

我想知道tflearn中是否有其他内置函数(或其他任何模块)，可以用来将类的值从单词更改为数字.我认为手动修改数据集不会产生效果.

I want to know if there's any other built-in function in tflearn (or in any other module, for that matter) that I can use to modify the value of my classes from words to numbers. I don't think manually modifying the datasets would be productive.

我也是tflearn和神经网络的初学者.任何帮助，将不胜感激.谢谢.

I'm a beginner in tflearn and neural networks also. Any help would be appreciated. Thanks.

推荐答案

使用sklearn库中的标签编码器:

Use label encoder from sklearn library:

from sklearn.preprocessing import LabelEncoder,OneHotEncoder

df = pd.read_csv('iris_data.csv',header=None)
df.columns=[Sepal Length,Sepal Width,Petal Length,Petal Width,Class]

enc=LabelEncoder()
df['Class']=enc.fit_transform(df['Class'])
print df.head(5)

如果要One-hot encoding，则首先需要labelEncode，然后执行OneHotEncoding:

if you want One-hot encoding then first you need to labelEncode then do OneHotEncoding :

enc=LabelEncoder()
enc_1=OneHotEncoder()
df['Class']=enc.fit_transform(df['Class'])
df['Class']=enc_1.fit_transform([df['Class']]).toarray()
print df.head(5)

这些编码器首先按字母顺序对单词进行排序，然后为其分配标签.如果要查看将哪个标签分配给哪个类，请执行以下操作:

These encoders first sort the words in alphabetical order then assign them labels. If you want to see which label is assigned to which class, do:

for k in list(enc.classes_) :
   print 'name ::{}, label ::{}'.format(k,enc.transform([k]))

如果要将此数据帧另存为csv文件，请执行以下操作:

If you want to save this dataframe as a csv file, do:

df.to_csv('Processed_Irisdataset.csv',sep=',')

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