In [18]:

    

import pandas as pd               
import numpy as np

from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, LSTM
from keras.optimizers import SGD
from keras.callbacks import History 
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.layers import Embedding
from keras.layers import MaxPooling1D

%matplotlib inline                
plt.rcParams["figure.figsize"] = [16, 8]

import matplotlib
import matplotlib.pyplot as plt

history = History()
history_testing = History()


    

In [19]:

    

df_init = df = pd.read_csv('maintenance_data.csv')
#Convert strings to discrete integers
try:
    mymap = {'TeamA':1, 'TeamB':2, 'TeamC':3, 'Provider1':1, 'Provider2':2, 'Provider3':3, 'Provider4':4}
    df = df.applymap(lambda s: mymap.get(s) if s in mymap else s)
except:
    pass  

X = df.drop('broken', axis=1).values
Y = df.broken.values[np.newaxis].T
df.head()


    

    
Out[19]:







  

    

      

      
lifetime
      
broken
      
pressureInd
      
moistureInd
      
temperatureInd
      
team
      
provider
    
  
  

    

      
0
      
56
      
0
      
92.178854
      
104.230204
      
96.517159
      
1
      
4
    
    

      
1
      
81
      
1
      
72.075938
      
103.065701
      
87.271062
      
3
      
4
    
    

      
2
      
60
      
0
      
96.272254
      
77.801376
      
112.196170
      
1
      
1
    
    

      
3
      
86
      
1
      
94.406461
      
108.493608
      
72.025374
      
3
      
2
    
    

      
4
      
34
      
0
      
97.752899
      
99.413492
      
103.756271
      
2
      
1
    
  

In [20]:

    

model = Sequential()

model.add(Dense(64, activation='relu', input_dim=6))
model.add(Dropout(0.5))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(2, activation='softmax'))
model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])
model.fit(X, Y, epochs=200, verbose=0, validation_split=0.15, callbacks=[history])

#Accuracy during training epochs
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('Model Accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='lower right')
plt.grid()
plt.show()

#Loss during training epochs
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Model Loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper right')
plt.grid()
plt.show()


    

In [21]:

    

print(pd.DataFrame(history.history['val_acc']).mean(), pd.DataFrame(history.history['val_acc']).max())


    

    




0    0.831033
dtype: float64 0    0.86
dtype: float64

In [ ]: