Project human sensor interface #

#test.py
from pandas import read_csv
from pandas.plotting import scatter_matrix
from matplotlib import pyplot
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import StratifiedKFold
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC

# Load dataset
names = ['moisture','floating', 'raw_pulse', 'pulse', 'class']
# dataset = read_csv(url, names=names)
dataset = read_csv("all.csv", names=names)

#print(dataset.shape)
#print(dataset.head(20))
print("The sensors used in test")
print(dataset.describe())
print("Two test people and ambient/no person")
print(dataset.groupby('class').size())

...
# box and whisker plots
dataset.plot(kind='box', subplots=True, layout=(2,2), sharex=False, sharey=False)
#pyplot.show()
# histograms
dataset.hist()
#pyplot.show()

# scatter plot
scatter_matrix(dataset)
#pyplot.show()

# Split-out validation dataset
array = dataset.values
X = array[:,0:4]
y = array[:,4]
X_train, X_validation, Y_train, Y_validation = train_test_split(X, y, test_size=0.20, random_state=1)

# Spot Check Algorithms
models = []
models.append(('LogisticRegression', LogisticRegression(solver='liblinear', multi_class='ovr')))
models.append(('LinearDiscriminantAnalysis', LinearDiscriminantAnalysis()))
models.append(('KNeighborsClassifier', KNeighborsClassifier()))
models.append(('DecisionTreeClassifier', DecisionTreeClassifier()))
models.append(('GaussianNB', GaussianNB()))
models.append(('SVC', SVC(gamma='auto')))
# evaluate each model in turn
results = []
names = []
for name, model in models:
	kfold = StratifiedKFold(n_splits=20, random_state=1, shuffle=True)
	cv_results = cross_val_score(model, X_train, Y_train, cv=kfold, scoring='accuracy')
	results.append(cv_results)
	names.append(name)
	print('%s: %f (%f)' % (name, cv_results.mean(), cv_results.std()))

# Compare Algorithms
pyplot.boxplot(results, labels=names)
pyplot.title('Algorithm Comparison')
#pyplot.show()

# Make predictions on validation dataset
#model = SVC(gamma='auto')
#model = GaussianNB()
# choose a model for predicting
print("Using DecisionTreeClassifier()")
model = DecisionTreeClassifier()
#model = LogisticRegression(solver='liblinear', multi_class='ovr')

print("Fitting the training data...")
model.fit(X_train, Y_train)
predictions = model.predict(X_validation)

# Evaluate predictions
print(accuracy_score(Y_validation, predictions))
print(confusion_matrix(Y_validation, predictions))
print(classification_report(Y_validation, predictions))

#challenge test using live data stream or saved csv
#['peter' 'peter' 'peter' 'peter' 'peter' 'peter' 'peter' 'peter' 'peter'
# 'peter' 'peter' 'peter' 'peter' 'peter' 'peter' 'peter' 'peter' 'peter'
# 'peter' 'peter' 'peter' 'alex' 'alex' 'alex' 'alex' 'alex' 'alex' 'alex'
# 'alex']

print("External data challenge...")
challenge = read_csv("challenge.csv")
prediction = model.predict(challenge)
print(prediction)

#print(model.predict_proba(challenge))