Preprocessing csv files to use with tflearn

Question

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

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

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

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.

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.

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)

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

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

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

Original Dataset:

     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

Manually modified dataset:

     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

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)

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.

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

Answer 1

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)

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)

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]))

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

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

Answer 2

The simpliest solution is map by dict of all possible values:

df['Class'] = df['Class'].map({'iris-versicolor': 1, 'iris-setosa': 0, 'iris-virginica': 2})
print (df)
   Sepal Length  Sepal Width  Petal Length  Petal  Width  Class
0            89          5.5           2.5    4.0    1.3      1
1            85          6.0           3.4    4.5    1.6      1
2            31          5.4           3.4    1.5    0.4      0
3            52          6.9           3.1    4.9    1.5      1
4           111          6.4           2.7    5.3    1.9      2

If want generate dictionary by all unique values:

d = {v:k for k, v in enumerate(df['Class'].unique())}
print (d)
{'iris-versicolor': 0, 'iris-virginica': 2, 'iris-setosa': 1}

df['Class'] = df['Class'].map(d)
print (df)
   Sepal Length  Sepal Width  Petal Length  Petal  Width  Class
0            89          5.5           2.5    4.0    1.3      0
1            85          6.0           3.4    4.5    1.6      0
2            31          5.4           3.4    1.5    0.4      1
3            52          6.9           3.1    4.9    1.5      0
4           111          6.4           2.7    5.3    1.9      2