# Onehot Conversion for Categorical Data ¶

The good point of onehot vector to learn

At first, onehot encoding is one of the conversion method for categorical data, and good representation of the data on neural network learning.(except random forest and other method which can use the categorical data.
There are impossible classification pattern problem to solve without onehot encoding or embedding as bellow.

So, onehot encoding and embedding encoding is needed for neural network classification.

## Required Libaries ¶

• matplotlib 2.0.2
• numpy 1.12.1
• scikit-learn 0.18.2
• pandas 0.20.3
In [1]:

from __future__ import division, print_function
import numpy as np
import pandas as pd

import renom as rm
from renom.cuda import set_cuda_active

from sklearn.preprocessing import LabelBinarizer, label_binarize
from sklearn.metrics import confusion_matrix, classification_report
import matplotlib.pyplot as plt
# If you would like to use GPU, set True, otherwise you should be set to False.
set_cuda_active(False)


## Import column infos ¶

Import column names and return the column names and the dictionary which column is numeric column or not.

In [2]:

def make_col_names():
col_names = []
continuous_dict = {}
if i > 96:
line = line.rstrip()
name = line.split(":")[0]
col_names.append(name)
line = line.replace(" ","").replace(".","")
continuous = line.split(":")[1] == "continuous"
continuous_dict[name] = continuous
col_names.append("label")
continuous_dict["label"] = False
return col_names, continuous_dict


## Get the column names and if whether column is continuous ¶

In [3]:

n_id = 0
col_names, continuous_dicts = make_col_names()


## Load the data and make Index ¶

We show the some information about missing values and some preprocessing.

In [4]:

def load_data(filename, col_names, n):
# Display the number of records before delete missing valeus.
print("the number of {} records:{}\n".format(filename, len(df.index)))
df.columns = col_names

# Replace the missing value's character to np.nan.
df = df.applymap(lambda d: np.nan if d==" ?" else d)

# Unify the different written forms.
df = df.applymap(lambda d: " <=50K" if d==" <=50K." else d)
df = df.applymap(lambda d: " >50K" if d==" >50K." else d)

# Display the information about missing values and
print("missing value info:\n{}\n".format(df.isnull().sum(axis=0)))
df = df.dropna(axis=0)

# the number of records after delete missing valeus.
print("the number of {} records after trimming:{}\n".format(filename, len(df.index)))
ids = list(np.arange(n, n+len(df.index)))
df["ID"] = np.array(ids)
n = n+len(df.index)
return df,n


## Load the data and some preprocessing for pandas data frame ¶

The adult.data is for the training, the adult.test is for the prediction.

In [5]:

df_train,n_id_train = load_data("adult.data", col_names, n_id)

the number of adult.data records:32561

missing value info:
age                  0
workclass         1836
fnlwgt               0
education            0
education-num        0
marital-status       0
occupation        1843
relationship         0
race                 0
sex                  0
capital-gain         0
capital-loss         0
hours-per-week       0
native-country     583
label                0
dtype: int64

the number of adult.data records after trimming:30162

missing value info:
age                 0
workclass         963
fnlwgt              0
education           0
education-num       0
marital-status      0
occupation        966
relationship        0
race                0
sex                 0
capital-gain        0
capital-loss        0
hours-per-week      0
native-country    274
label               0
dtype: int64

the number of adult.test records after trimming:15060



## Get not continious columns ¶

In [6]:

def get_not_continuous_columns(continuous_dict):
categorical_names = [k for k, v in continuous_dict.items() if not v]
return categorical_names


## Display label information ¶

In [7]:

def print_labelinfo(labelnames):
for i in range(len(labelnames)):
print("label{}:{}".format(i,labelnames[i]))


## Onehot encoding ¶

We convert the data from the categorical data to one hot vector representation.

In [8]:

def convert_data(df_train, df_test, n_id_train, n_id_test, continuous_dicts):
categorical_names = get_not_continuous_columns(continuous_dicts)
df = pd.concat((df_train, df_test), axis=0)

# Get the dummy for the categorical data.
for name in categorical_names:
if name=="label":
labelnames = list(pd.get_dummies(df[name]).columns)
print("labelname:{}".format(labelnames))
dummy_df = pd.get_dummies(df[name])
df = pd.concat((df, dummy_df), axis=1)
df = df.drop(name, axis=1)

# Convert the data type.
for name in df.columns:
df[name] = df[name].astype(float)

# Reguralize the data.
for name in df.columns:
if name=="ID":
df[name] = df[name]
else:
df[name] = (df[name] - df[name].min()) / (df[name].max() - df[name].min())

df_train = df[df["ID"]<n_id_train].drop("ID", axis=1)
df_test = df[df["ID"]>=n_id_train].drop("ID", axis=1)

y_train = df_train[labelnames].values
y_test = df_test[labelnames].values
print_labelinfo(labelnames)
X_train = df_train.drop(labelnames, axis=1).values
X_test = df_test.drop(labelnames, axis=1).values
return X_train, y_train, X_test, y_test


## Confirm the shape of the data ¶

In [9]:

X_train, y_train, X_test, y_test = \
convert_data(df_train, df_test, n_id_train, n_id_test, continuous_dicts)
print("X_train:{} y_train:{} X_test:{} y_test:{}".format(X_train.shape, y_train.shape, X_test.shape, y_test.shape))

labelname:[' <=50K', ' >50K']
label0: <=50K
label1: >50K
X_train:(30162, 104) y_train:(30162, 2) X_test:(15060, 104) y_test:(15060, 2)