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simulation_of_soleinoid_data.py
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simulation_of_soleinoid_data.py
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# -*- coding: utf-8 -*-
"""
Created on Fri Dec 17 13:33:55 2021
@author: Mc Zie
"""
import pandas as pd
from sklearn.preprocessing import LabelEncoder,OneHotEncoder
from sklearn.model_selection import train_test_split
import pandas as pd
from sklearn import model_selection
import sklearn as read_csv
import numpy as np
from sklearn.metrics import classification_report,accuracy_score,confusion_matrix
from matplotlib import pyplot as plt
from sklearn import svm
from sklearn import metrics
from sklearn.svm import SVC
from sklearn.metrics import roc_curve, auc
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import label_binarize
from sklearn.multiclass import OneVsRestClassifier
from scipy import interp
from sklearn.preprocessing import LabelEncoder
from sklearn import preprocessing
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from sklearn.neural_network import MLPClassifier
import numpy as np
from random import shuffle
from operator import itemgetter
import pandas as pd
from sklearn.model_selection import train_test_split
import pandas as pd
from sklearn import model_selection
import sklearn as read_csv
import numpy as np
from sklearn.metrics import classification_report,accuracy_score,confusion_matrix
from matplotlib import pyplot as plt
from sklearn import svm
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.metrics import roc_curve, auc
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import label_binarize
from sklearn.multiclass import OneVsRestClassifier
from scipy import interp
from sklearn.preprocessing import LabelEncoder
from sklearn import preprocessing
from sklearn.decomposition import PCA
from sklearn.preprocessing import StandardScaler
from sklearn.neural_network import MLPClassifier
import numpy as np
from random import shuffle
from operator import itemgetter
from sklearn import preprocessing
from gplearn.genetic import SymbolicRegressor
from sklearn.utils.random import check_random_state
import numpy as np
from sklearn.model_selection import KFold
from sklearn.model_selection import cross_val_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import BaggingClassifier
from sklearn.model_selection import cross_val_score, train_test_split
from mlxtend.plotting import plot_learning_curves
from mlxtend.plotting import plot_decision_regions
import itertools
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split, GridSearchCV
import numpy as np
import xgboost as xgb
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from mlxtend.classifier import StackingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import GridSearchCV
from matplotlib import pyplot
#We choose 5 cross validation for our machine learning model
n_folds = KFold(n_splits =5,shuffle= False )
from sklearn.model_selection import RandomizedSearchCV
from sklearn.metrics import jaccard_score
from keras.models import Sequential
from keras.layers.core import Dense,Activation
from keras.utils import np_utils
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
import xgboost as xgb
from sklearn.metrics import accuracy_score
from numpy import mean
from numpy import std
from sklearn.model_selection import RepeatedStratifiedKFold # evaluate a given model using cross-validation
# import packages for hyperparameters tuning
from hyperopt import STATUS_OK, Trials, fmin, hp, tpe
import keras
import keras.utils
from keras import utils as np_utils
from tensorflow.keras.utils import to_categorical
from keras.layers import Dropout
# Example of Dropout on the Sonar Dataset: Hidden Layer
from pandas import read_csv
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.wrappers.scikit_learn import KerasClassifier
from keras.constraints import maxnorm
from keras.optimizers import SGD
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import StratifiedKFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
import os
import pickle
import random
import datetime
import time
import board
import busio
i2c = busio.I2C(board.SCL, board.SDA)
import csv
import adafruit_ads1x15.ads1015 as ADS
#import adafruit_ads1x15.ads1115 as ADS
from adafruit_ads1x15.analog_in import AnalogIn
slope = 1.48; #slope from linear fit
intercept = -1.56 # intercept from linear fit
ads = ADS.ADS1015(i2c)
chan = AnalogIn(ads, ADS.P0)
#voltage=chan.voltage
while True:
try:
voltage = round((chan.voltage),2)
print( 'voltage:')
print(f'{chan.voltage} Volt')
vol_water_cont = ((1.0/chan.voltage)*slope)+intercept #calc of theta_v (vol. water content)
vol_water_cont= round((vol_water_cont),2)
print(" V, Theta_v: ")
print(f'{vol_water_cont} cm^3/cm^3')
if vol_water_cont>-0.40:
print('wet')
else:
print('dry')
def get_voltage():
voltage = round((chan.voltage),2)
voltage = str(voltage)
return(voltage)
def get_humidity():
humidity = vol_water_cont
humidity= round((humidity),2)
humidity = str(humidity)
return(humidity)
def date_now():
today = datetime.datetime.now().strftime("%Y-%m-%d")
today = str(today)
return(today)
def time_now():
now = datetime.datetime.now().strftime("%H:%M:%S")
now = str(now)
return(now)
def get_humidity_binary():
if vol_water_cont>-0.40:
return str('wet')
else:
return str('dry')
def write_to_csv():
#the a is for append, if w for write is used then it overwrites the file
with open('/home/pi/sensor_readings.csv', mode='a') as sensor_readings:
sensor_write = csv.writer(sensor_readings, delimiter=',', quotechar='"', quoting=csv.QUOTE_MINIMAL)
write_to_log = sensor_write.writerow([date_now(),time_now(),get_voltage(), get_humidity(),get_humidity_binary()])
return(write_to_log)
print( write_to_csv())
from numpy import loadtxt
from keras.models import load_model
# load model
model = load_model('/home/pi/model_rwet_dry.h5')
colnames=['date','time','voltage','humidity','target']
df1=pd.read_csv("/home/pi/sensor_readings.csv",names=colnames, header=None)
encoded=df1[['date','time','target']].apply(LabelEncoder().fit_transform)
remain=df1[['voltage','humidity']]
# Adding both the dataframes encoded and remaining (without encoding)
data=pd.concat([remain,encoded], axis=1)
X=data[['voltage', 'humidity', 'date', 'time']]
y=data['target']
result = model.predict(X)
from sklearn.preprocessing import binarize
#everything together
predict = np.ravel(binarize(result.reshape(-1,1), 0.5))
def soleinoi_off_on():
if predict[1]>0.5:
print( 'soleinoid valve is off' )
else:
print('soleinoid valve is on')
time.sleep(1)
except KeyboardInterrupt:
break
except IOError:
print ("Error")