ML-tutorial/nb.py at master · aprameyabharadwaj/ML-tutorial · GitHub

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import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import json

f = open("test.txt", 'r+')
data = json.load(f)

dataset= np.array([data["Max"],data["time"],data["tagid"]])
dataset = np.transpose(dataset)

X = dataset[: ,:-1]

y = dataset[: , 2]


from sklearn.cross_validation import train_test_split
X_train , X_test , y_train , y_test = train_test_split(X , y , test_size = 0.25 , random_state = 0)


from sklearn.preprocessing import StandardScaler
sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)


from sklearn.naive_bayes import GaussianNB
classifier  = GaussianNB()
classifier.fit(X_train,y_train)


y_pred = classifier.predict(X_test)


from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test , y_pred)


from matplotlib.colors import ListedColormap
X_set, y_set = X_train, y_train
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
             alpha = 0.75, cmap = ListedColormap(('red', 'green','blue','black','purple','yellow')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
                c = ListedColormap(('red', 'green','blue','black','purple','yellow'))(i), label = j)
plt.title(' (Test set)')
plt.xlabel('time')
plt.ylabel('max')
plt.legend()
plt.show()


from matplotlib.colors import ListedColormap
X_set, y_set = X_test, y_test
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1, stop = X_set[:, 0].max() + 1, step = 0.01),
                     np.arange(start = X_set[:, 1].min() - 1, stop = X_set[:, 1].max() + 1, step = 0.01))
plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(), X2.ravel()]).T).reshape(X1.shape),
             alpha = 0.75, cmap = ListedColormap(('red', 'green','blue','black','purple','yellow')))
plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())
for i, j in enumerate(np.unique(y_set)):
    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
                c = ListedColormap(('red', 'green','blue','black','purple','yellow'))(i), label = j)
plt.title('K-NN (Test set)')
plt.xlabel('Age')
plt.ylabel('Estimated Salary')
plt.legend()
plt.show()
"""max and time is input and time is x axis"""