MLP NN

import pandas as pd

# Location of dataset
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data"

# Assign colum names to the dataset
names = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'Class']

# Read dataset to pandas dataframe
irisdata = pd.read_csv(url, names=names) 

In [2]:

irisdata.head()  

Out[2]:

	sepal-length	sepal-width	petal-length	petal-width	Class
0	5.1	3.5	1.4	0.2	Iris-setosa
1	4.9	3.0	1.4	0.2	Iris-setosa
2	4.7	3.2	1.3	0.2	Iris-setosa
3	4.6	3.1	1.5	0.2	Iris-setosa
4	5.0	3.6	1.4	0.2	Iris-setosa

In [3]:

# Assign data from first four columns to X variable
X = irisdata.iloc[:, 0:4]

# Assign data from first fifth columns to y variable
y = irisdata.select_dtypes(include=[object])  

In [4]:

y.head() 

Out[4]:

	Class
0	Iris-setosa
1	Iris-setosa
2	Iris-setosa
3	Iris-setosa
4	Iris-setosa

In [5]:

y.Class.unique()  

Out[5]:

array(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'], dtype=object)

In [6]:

from sklearn import preprocessing  
le = preprocessing.LabelEncoder()

y = y.apply(le.fit_transform)  

In [7]:

y

Out[7]:

	Class
0	0
1	0
2	0
3	0
4	0
5	0
6	0
7	0
8	0
9	0
10	0
11	0
12	0
13	0
14	0
15	0
16	0
17	0
18	0
19	0
20	0
21	0
22	0
23	0
24	0
25	0
26	0
27	0
28	0
29	0
...	...
120	2
121	2
122	2
123	2
124	2
125	2
126	2
127	2
128	2
129	2
130	2
131	2
132	2
133	2
134	2
135	2
136	2
137	2
138	2
139	2
140	2
141	2
142	2
143	2
144	2
145	2
146	2
147	2
148	2
149	2

150 rows × 1 columns

In [8]:

from sklearn.model_selection import train_test_split  
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20)  

In [9]:

from sklearn.preprocessing import StandardScaler  
scaler = StandardScaler()  
scaler.fit(X_train)

X_train = scaler.transform(X_train)  
X_test = scaler.transform(X_test)  

In [10]:

from sklearn.neural_network import MLPClassifier  
mlp = MLPClassifier(hidden_layer_sizes=(10, 10, 10), max_iter=1000)  
mlp.fit(X_train, y_train.values.ravel())  

Out[10]:

MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,
       beta_2=0.999, early_stopping=False, epsilon=1e-08,
       hidden_layer_sizes=(10, 10, 10), learning_rate='constant',
       learning_rate_init=0.001, max_iter=1000, momentum=0.9,
       nesterovs_momentum=True, power_t=0.5, random_state=None,
       shuffle=True, solver='adam', tol=0.0001, validation_fraction=0.1,
       verbose=False, warm_start=False)

In [11]:

predictions = mlp.predict(X_test)  

In [12]:

predictions

Out[12]:

array([0, 2, 1, 2, 0, 0, 2, 2, 2, 0, 1, 1, 0, 1, 1, 2, 0, 0, 2, 1, 1, 1,
       1, 0, 1, 1, 2, 2, 1, 0])

In [13]:

#Evaluating the Algorithm
from sklearn.metrics import classification_report, confusion_matrix  
print(confusion_matrix(y_test,predictions))  
print(classification_report(y_test,predictions))

[[ 9  0  0]
 [ 0 12  1]
 [ 0  0  8]]
             precision    recall  f1-score   support

          0       1.00      1.00      1.00         9
          1       1.00      0.92      0.96        13
          2       0.89      1.00      0.94         8

avg / total       0.97      0.97      0.97        30