In [5]:
import random
def makeTerrainData(n_points=1000):
###############################################################################
### make the toy dataset
random.seed(42)
grade = [random.random() for ii in range(0,n_points)]
bumpy = [random.random() for ii in range(0,n_points)]
error = [random.random() for ii in range(0,n_points)]
y = [round(grade[ii]*bumpy[ii]+0.3+0.1*error[ii]) for ii in range(0,n_points)]
for ii in range(0, len(y)):
if grade[ii]>0.8 or bumpy[ii]>0.8:
y[ii] = 1.0
### split into train/test sets
X = [[gg, ss] for gg, ss in zip(grade, bumpy)]
split = int(0.75*n_points)
X_train = X[0:split]
X_test = X[split:]
y_train = y[0:split]
y_test = y[split:]
grade_sig = [X_train[ii][0] for ii in range(0, len(X_train)) if y_train[ii]==0]
bumpy_sig = [X_train[ii][1] for ii in range(0, len(X_train)) if y_train[ii]==0]
grade_bkg = [X_train[ii][0] for ii in range(0, len(X_train)) if y_train[ii]==1]
bumpy_bkg = [X_train[ii][1] for ii in range(0, len(X_train)) if y_train[ii]==1]
# training_data = {"fast":{"grade":grade_sig, "bumpiness":bumpy_sig}
# , "slow":{"grade":grade_bkg, "bumpiness":bumpy_bkg}}
grade_sig = [X_test[ii][0] for ii in range(0, len(X_test)) if y_test[ii]==0]
bumpy_sig = [X_test[ii][1] for ii in range(0, len(X_test)) if y_test[ii]==0]
grade_bkg = [X_test[ii][0] for ii in range(0, len(X_test)) if y_test[ii]==1]
bumpy_bkg = [X_test[ii][1] for ii in range(0, len(X_test)) if y_test[ii]==1]
test_data = {"fast":{"grade":grade_sig, "bumpiness":bumpy_sig}
, "slow":{"grade":grade_bkg, "bumpiness":bumpy_bkg}}
return X_train, y_train, X_test, y_test
# return training_data, test_data
In [6]:
def classify(features_train, labels_train):
### import the sklearn module for GaussianNB
### create classifier
### fit the classifier on the training features and labels
### return the fit classifier
from sklearn.naive_bayes import GaussianNB
bayesGausiano = GaussianNB()
bayesGausiano.fit(features_train,labels_train)
return(bayesGausiano)
In [7]:
import matplotlib
import matplotlib.pyplot as plt
import pylab as pl
import numpy as np
def prettyPicture(clf, X_test, y_test):
x_min = 0.0; x_max = 1.0
y_min = 0.0; y_max = 1.0
# Plot the decision boundary. For that, we will assign a color to each
# point in the mesh [x_min, m_max]x[y_min, y_max].
h = .01 # step size in the mesh
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.pcolormesh(xx, yy, Z, cmap=pl.cm.seismic)
# Plot also the test points
grade_sig = [X_test[ii][0] for ii in range(0, len(X_test)) if y_test[ii]==0]
bumpy_sig = [X_test[ii][1] for ii in range(0, len(X_test)) if y_test[ii]==0]
grade_bkg = [X_test[ii][0] for ii in range(0, len(X_test)) if y_test[ii]==1]
bumpy_bkg = [X_test[ii][1] for ii in range(0, len(X_test)) if y_test[ii]==1]
plt.scatter(grade_sig, bumpy_sig, color = "b", label="fast")
plt.scatter(grade_bkg, bumpy_bkg, color = "r", label="slow")
plt.legend()
plt.xlabel("bumpiness")
plt.ylabel("grade")
plt.savefig("test.png")
import base64
import json
import subprocess
def output_image(name, format, bytes):
image_start = "BEGIN_IMAGE_f9825uweof8jw9fj4r8"
image_end = "END_IMAGE_0238jfw08fjsiufhw8frs"
data = {}
data['name'] = name
data['format'] = format
data['bytes'] = base64.encodestring(bytes)
print image_start+json.dumps(data)+image_end
In [8]:
import numpy as np
import pylab as pl
from sklearn.metrics import accuracy_score
features_train, labels_train, features_test, labels_test = makeTerrainData()
# the training data (features_train, labels_train) have both "fast" and "slow" points mixed
# in together--separate them so we can give them different colors in the scatterplot,
# and visually identify them
grade_fast = [features_train[ii][0] for ii in range(0, len(features_train)) if labels_train[ii]==0]
bumpy_fast = [features_train[ii][1] for ii in range(0, len(features_train)) if labels_train[ii]==0]
grade_slow = [features_train[ii][0] for ii in range(0, len(features_train)) if labels_train[ii]==1]
bumpy_slow = [features_train[ii][1] for ii in range(0, len(features_train)) if labels_train[ii]==1]
# Classifier creation
clf = classify(features_train, labels_train)
# draw the decision boundary with the text points overlaid
prettyPicture(clf, features_test, labels_test)
# Accuracy estimation
labelsPredicted = clf.predict(features_test)
accuracy = accuracy_score(labels_test,labelsPredicted)
print "Estimated accuracy = " + str(accuracy)