scikit sehr geringe Genauigkeit auf Klassifikatoren (Naive Bayes, DecisionTreeClassifier) ​​

Question

Ich verwende diesen Datensatz Weath Based on age und die Dokumentation besagt, dass die Genauigkeit um 84% sein sollte. Leider ist die Genauigkeit meines Programms bei 25%

die Daten, die ich folgendes getan zu verarbeiten:

1. Loaded the .txt data file and converted it to a .csv 
2. Removed data with missing values 
3. Extracted the class values: <=50K >50 and convert it to 0 and 1 respectively 
4. For each attribute and for each string value of that attribute I 
    mapped it to an integer value. Example att1{'cs':0, 'cs2':1}, 
    att2{'usa':0, 'greece':1} ... and so on 
5. Called naive bayes on the new integer data set 

Python-Code:

import load_csv as load #my functions to do [1..5] of the list 
import numpy as np 

my_data = np.genfromtxt('out.csv', dtype = dt, delimiter = ',', skip_header = 1) 

data = np.array(load.remove_missing_values(my_data))      #this funcion removes the missing data 
features_train = np.array(load.remove_field_num(data, len(data[0]) - 1)) #this function extracts the data, e.g removes the class in the end of the data 

label_train = np.array(load.create_labels(data)) 
features_train = np.array(load.convert_to_int(features_train)) 


my_data = np.genfromtxt('test.csv', dtype = dt, delimiter = ',', skip_header = 1) 

data = np.array(load.remove_missing_values(my_data)) 
features_test = np.array(load.remove_field_num(data, len(data[0]) - 1)) 

label_test = np.array(load.create_labels(data))       #extracts the labels from the .csv data file 
features_test = np.array(load.convert_to_int(features_test))    #converts the strings to ints(each unique string of an attribute is assigned a unique integer value 

from sklearn import tree 
from sklearn.naive_bayes import GaussianNB 
from sklearn import tree 
from sklearn.metrics import accuracy_score 

clf = tree.DecisionTreeClassifier() 
clf.fit(features_train, label_train) 
predict = clf.predict(features_test) 

score = accuracy_score(predict, label_test) #Low accuracy score 

load_csv Modul:

import numpy as np 

attributes = { 'Private':0, 'Self-emp-not-inc':1, 'Self-emp-inc':2, 'Federal-gov':3, 'Local-gov':4, 'State-gov':5, 'Without-pay':6, 'Never-worked':7, 
      'Bachelors':0, 'Some-college':1, '11th':2, 'HS-grad':3, 'Prof-school':4, 'Assoc-acdm':5, 'Assoc-voc':6, '9th':7, '7th-8th':8, '12th':9, 'Masters':10, '1st-4th':11, '10th':12,     'Doctorate':13, '5th-6th':14, 'Preschool':15, 
      'Married-civ-spouse':0, 'Divorced':1, 'Never-married':2, 'Separated':3, 'Widowed':4, 'Married-spouse-absent':5, 'Married-AF-spouse':6, 
      'Tech-support':0, 'Craft-repair':1, 'Other-service':2, 'Sales':3, 'Exec-managerial':4, 'Prof-specialty':5, 'Handlers-cleaners':6, 'Machine-op-inspct':7, 'Adm-clerical':8, 
      'Farming-fishing':9, 'Transport-moving':10, 'Priv-house-serv':11, 'Protective-serv':12, 'Armed-Forces':13, 
      'Wife':0, 'Own-child':1, 'Husband':2, 'Not-in-family':4, 'Other-relative':5, 'Unmarried':5, 
      'White':0, 'Asian-Pac-Islander':1, 'Amer-Indian-Eskimo':2, 'Other':3, 'Black':4, 
      'Female':0, 'Male':1, 
      'United-States':0, 'Cambodia':1, 'England':2, 'Puerto-Rico':3, 'Canada':4, 'Germany':5, 'Outlying-US(Guam-USVI-etc)':6, 'India':7, 'Japan':8, 'Greece':9, 'South':10, 'China':11,     'Cuba':12, 'Iran':13, 'Honduras':14, 'Philippines':15, 'Italy':16, 'Poland':17, 'Jamaica':18, 'Vietnam':19, 'Mexico':20, 'Portugal':21, 'Ireland':22, 'France':23,     'Dominican-Republic':24, 'Laos':25, 'Ecuador':26, 'Taiwan':27, 'Haiti':28, 'Columbia':29, 'Hungary':30, 'Guatemala':31, 'Nicaragua':32, 'Scotland':33, 'Thailand':34, 'Yugoslavia':35,     'El-Salvador':36, 'Trinadad&Tobago':37, 'Peru':38, 'Hong':39, 'Holand-Netherlands':40 
     } 



def remove_field_num(a, i):                  #function to strip values 
    names = list(a.dtype.names) 
    new_names = names[:i] + names[i + 1:] 
    b = a[new_names] 
    return b 

def remove_missing_values(data): 
    temp = [] 
    for i in range(len(data)): 
     for j in range(len(data[i])): 
      if data[i][j] == '?':                 #If a missing value '?' is encountered do not append the line to temp 
       break; 
      if j == (len(data[i]) - 1) and len(data[i]) == 15: 
       temp.append(data[i])                #Append the lines that do not contain '?' 
    return temp 

def create_labels(data): 
    temp = [] 
    for i in range(len(data)):                 #Iterate through the data 
     j = len(data[i]) - 1                  #Extract the labels 
     if data[i][j] == '<=50K': 
      temp.append(0) 
     else: 
      temp.append(1) 
    return temp 

def convert_to_int(data): 

    my_lst = [] 
    for i in range(len(data)): 
     lst = [] 
     for j in range(len(data[i])): 
      key = data[i][j] 
      if j in (1, 3, 5, 6, 7, 8, 9, 13, 14): 
       lst.append(int(attributes[key])) 
      else: 
       lst.append(int(key))  
     my_lst.append(lst) 

    temp = np.array(my_lst) 
    return temp 

Ich habe versucht, beide tree undzu verwendenaber die Genauigkeit ist sehr niedrig. Irgendwelche Vorschläge von was vermisse ich?

Answer 1

Ich vermute, das Problem liegt in der Vorverarbeitung. Es ist besser, die kategorialen Variablen als one_hot-Vektoren (Vektoren mit nur Null oder Eins, wobei eins dem gewünschten Wert für diese Klasse entspricht) anstelle von rohen Zahlen zu codieren. Sklearn DictVectorizer kann Ihnen dabei helfen. Sie können die Klassifizierung viel effizienter mit der pandas-Bibliothek durchführen.

Die folgende Tabelle zeigt, wie einfach das mit Hilfe der Bibliothek pandas erreicht werden kann. Es funktioniert sehr gut neben Scikit-lernen. Dies erreicht eine Genauigkeit von 81,6 bei einem Testsatz, der 20% der gesamten Daten darstellt.

from __future__ import division 

from sklearn.cross_validation import train_test_split 
from sklearn.feature_extraction.dict_vectorizer import DictVectorizer 
from sklearn.linear_model.logistic import LogisticRegression 
from sklearn.metrics.classification import classification_report, accuracy_score 
from sklearn.naive_bayes import GaussianNB 
from sklearn.tree.tree import DecisionTreeClassifier 

import numpy as np 
import pandas as pd 


# Read the data into a pandas dataframe 
df = pd.read_csv('adult.data.csv') 

# Columns names 
cols = np.array(['age', 'workclass', 'fnlwgt', 'education', 'education-num', 
       'marital-status', 'occupation', 'relationship', 'race', 'sex', 
       'capital-gain', 'capital-loss', 'hours-per-week', 'native-country', 
       'target']) 

# numeric columns 
numeric_cols = ['age', 'fnlwgt', 'education-num', 
       'capital-gain', 'capital-loss', 'hours-per-week'] 

# assign names to the columns in the dataframe 
df.columns = cols 

# replace the target variable to 0 and 1 for <50K and >50k 
df1 = df.copy() 
df1.loc[df1['target'] == ' <=50K', 'target'] = 0 
df1.loc[df1['target'] == ' >50K', 'target'] = 1 

# split the data into train and test 
X_train, X_test, y_train, y_test = train_test_split(
    df1.drop('target', axis=1), df1['target'], test_size=0.2) 


# numeric attributes 

x_num_train = X_train[numeric_cols].as_matrix() 
x_num_test = X_test[numeric_cols].as_matrix() 

# scale to <0,1> 

max_train = np.amax(x_num_train, 0) 
max_test = np.amax(x_num_test, 0)  # not really needed 

x_num_train = x_num_train/max_train 
x_num_test = x_num_test/max_train  # scale test by max_train 

# labels or target attribute 

y_train = y_train.astype(int) 
y_test = y_test.astype(int) 

# categorical attributes 

cat_train = X_train.drop(numeric_cols, axis=1) 
cat_test = X_test.drop(numeric_cols, axis=1) 

cat_train.fillna('NA', inplace=True) 
cat_test.fillna('NA', inplace=True) 

x_cat_train = cat_train.T.to_dict().values() 
x_cat_test = cat_test.T.to_dict().values() 

# vectorize (encode as one hot) 

vectorizer = DictVectorizer(sparse=False) 
vec_x_cat_train = vectorizer.fit_transform(x_cat_train) 
vec_x_cat_test = vectorizer.transform(x_cat_test) 

# build the feature vector 

x_train = np.hstack((x_num_train, vec_x_cat_train)) 
x_test = np.hstack((x_num_test, vec_x_cat_test)) 


clf = LogisticRegression().fit(x_train, y_train.values) 
pred = clf.predict(x_test) 
print classification_report(y_test.values, pred, digits=4) 
print accuracy_score(y_test.values, pred) 

clf = DecisionTreeClassifier().fit(x_train, y_train) 
predict = clf.predict(x_test) 
print classification_report(y_test.values, pred, digits=4) 
print accuracy_score(y_test.values, pred) 

clf = GaussianNB().fit(x_train, y_train) 
predict = clf.predict(x_test) 
print classification_report(y_test.values, pred, digits=4) 
print accuracy_score(y_test.values, pred)