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predictor.py
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predictor.py
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'''
Script to make predictions on raw readings streaming
over a UDP socket
'''
import pandas as pd
import os, pickle, socket
import numpy as np
import scipy.stats as stats
from statsmodels.robust.scale import mad as mediad
from sklearn.cross_validation import train_test_split
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import confusion_matrix
import seaborn as sb
from matplotlib import pyplot as plt
import itertools
from sklearn import preprocessing
from collections import deque
UDPSocket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
UDPSocket.bind(('0.0.0.0', 5000))
X = deque(maxlen=400)
Y = deque(maxlen=400)
Z = deque(maxlen=400)
NBModel = pickle.load(open('NBModel2.pkl', 'rb'))
def get_parameters(data):
'''
perform aggregates on
rolling windows of 410
With scaling and normalizing options
'''
func_dict = {
'min': np.min,
'max': np.max,
'diff': lambda x: np.max(x) - np.min(x),
'std': np.std,
'iqr': stats.iqr,
'rms': lambda x: np.sqrt(np.mean(np.square(x))),
'mad': lambda x: x.mad(),
'mediad': mediad
}
aggregations = {
'X': func_dict,
'Y': func_dict,
'Z': func_dict
}
#Preprocessing Options
#data[['X', 'Y', 'Z']] = preprocessing.scale(preprocessing.MinMaxScaler().fit_transform(data[['X', 'Y', 'Z']]))
#data[['X', 'Y', 'Z']] = preprocessing.scale(data[['X', 'Y', 'Z']])
data['temp'] = 10
data = data.groupby('temp', as_index=False)
stats_data = data.agg(aggregations)
stats_data.columns = [''.join(col).strip() for col in stats_data.columns.values]
del stats_data['temp']
return stats_data
def predict():
count = 0
while True:
data, _ = UDPSocket.recvfrom(4096)
readings = str(data, 'utf-8').split(',')
X.append(float(readings[0]))
Y.append(float(readings[1]))
Z.append(float(readings[2]))
count += 1
if count == 200:
data_frame = pd.DataFrame({
'X': X,
'Y': Y,
'Z': Z,
}
)
print(NBModel.predict(get_parameters(data_frame)))
count = 0
if __name__ == '__main__':
predict()