def classification_regression_test():
import code_exam
x,y,dates,movies = load_data()
#add intercept to x matrix
x["intercept"] = np.ones(len(x))
test_x, train_x, test_y, train_y = create_test_train_set(x, y)
train_y.columns = ["y"]
train_y.index = range(len(train_y))
Y = np.zeros((len(train_x), 5))
for i in [1,2,3,4,5]:
expr = "y==" + str(i)
Y[train_y.query(expr).index, i-1] = 1
X = train_x
Xt = X.transpose()
XtX = Xt.dot(X)
XtY = Xt.dot(Y)
B = np.linalg.inv(XtX).dot(XtY)
preds = test_x.dot(B)
#predict on highest score
p1 = preds.apply(lambda x: np.argmax(x) + 1, 1)
print "Highest score prediction summary"
code_exam.summary(p1)
print "###################################\n\n"
#predict on excpected score
p2 = preds.apply(lambda x: x.dot([1,2,3,4,5]) / np.sum(x), 1)
print "Expected score prediction summary"
code_exam.summary(p2)
print "###################################\n\n"
print "Coorelattion between two scores is: ",\
np.corrcoef(p1, p2)[0][1]
#MSE
print "Highest score predict mse:", np.sqrt(np.mean((p1-test_y.ix[:,0])**2))
print "Expected score predict mse:", np.sqrt(np.mean((p2-test_y.ix[:,0])**2))
def logistic_regression_test():
from sklearn.linear_model import LogisticRegression
import code_exam
x,y,dates,movies = load_data()
#add intercept to x matrix
x["intercept"] = np.ones(len(x))
test_x, train_x, test_y, train_y = create_test_train_set(x, y)
fit = LogisticRegression(
fit_intercept=False,
multi_class='multinomial',
solver='newton-cg',
max_iter=300).fit(X=train_x,y=train_y.ix[:,0])
#predict on highest score
p1 = fit.predict(test_x)
print "Highest score prediction summary"
code_exam.summary(p1)
print "###################################\n\n"
#predict on expected score
p_proba = fit.predict_proba(test_x)
p2 = np.apply_along_axis(lambda x: x.dot([1,2,3,4,5]), 1, p_proba)
print "Expected score prediction summary"
code_exam.summary(p2)
print "###################################\n\n"
print "Coorelattion between two scores is: ",\
np.corrcoef(p1, p2)[0][1]
#MSE
print "Highest score predict mse:", np.sqrt(np.mean((p1-test_y.ix[:,0])**2))
print "Expected score predict mse:", np.sqrt(np.mean((p2-test_y.ix[:,0])**2))
#use statmodels package in order to intepret results of the logistic regression
import statsmodels.api as sm
train_y.columns = ["y"]
logit = sm.MNLogit(train_y, train_x.ix[:, range(14)+[99]])
return logit.fit()
