def test_quantile_regression_list2(self):
X = random(1000)
eps1 = (random(900) - 0.5) * 0.1
eps2 = random(100) * 2
eps = numpy.hstack([eps1, eps2])
X = X.reshape((1000, 1)) # pylint: disable=E1101
Y = X * 3.4 + 5.6 + eps
clq = QuantileLinearRegression(verbose=False, fit_intercept=True)
self.assertRaise(lambda: clq.fit(X, Y), ValueError)
Y = X.ravel() * 3.4 + 5.6 + eps
clq = QuantileLinearRegression(verbose=False, fit_intercept=True)
clq.fit(X, Y)
clr = LinearRegression(fit_intercept=True)
clr.fit(X, Y)
self.assertNotEqual(clr.intercept_, 0)
self.assertNotEqual(clq.intercept_, 0)
self.assertNotEqualArray(clr.coef_, clq.coef_)
self.assertNotEqualArray(clr.intercept_, clq.intercept_)
self.assertLesser(clq.n_iter_, 10)
pr = clr.predict(X)
pq = clq.predict(X)
self.assertEqual(pr.shape, pq.shape)
def test_quantile_regression_quantile_check(self):
n = 100
X = (numpy.arange(n) / n)
Y = X + X * X / n
X = X.reshape((n, 1))
for q in [0.1, 0.5, 0.9]:
clq = QuantileLinearRegression(verbose=False,
fit_intercept=True,
quantile=q,
max_iter=10)
clq.fit(X, Y)
y = clq.predict(X)
diff = y - Y
sign = numpy.sign(diff) # pylint: disable=E1111
pos = (sign > 0).sum() # pylint: disable=W0143
neg = (sign < 0).sum() # pylint: disable=W0143
if q < 0.5:
self.assertGreater(neg, pos * 4)
if q > 0.5:
self.assertLesser(neg * 7, pos)
plt.plot(X, y) ols_model = lm.LinearRegression() ols_model.fit(X, y) ols_trend = ols_model.predict(X) print(ols_model.coef_) print(ols_trend[-1] - ols_trend[0]) plt.plot(X, ols_trend, color="r") X_lad = np.array(call_center_data.index).reshape(-1, 1) y_lad = np.array(call_center_data["calls"]) # print(X_lad) lad_model = QuantileLinearRegression(verbose=True) lad_model.fit(X_lad, y_lad) lad_trend = lad_model.predict(X_lad) print(lad_model.coef_) print(lad_trend[-1] - lad_trend[0]) plt.plot(X, lad_trend, color="g") plt.show()
})
df_results.plot('Actual MSRP', 'Predicted MSRP', kind='scatter')
# In[ ]:
#from mlinsights.mlmodel import PiecewiseRegressor
#from sklearn.tree import DecisionTreeRegressor
clqs = {}
for qu in [0.25, 0.5, 0.85]:
clq = QuantileLinearRegression(quantile=qu)
clq.fit(X_tr, Y_tr)
clqs['q=%1.2f' % qu] = clq
print(clq)
print('Training Mean Absolute Error:',
metrics.mean_absolute_error(Y_tr, clq.predict(X_tr)))
print('Testing Mean Squared Error:',
metrics.mean_squared_error(Y_te, clq.predict(X_te)))
print('Training Root Mean Squared Error:',
np.sqrt(metrics.mean_squared_error(Y_tr, clq.predict(X_tr))))
print('Testing Root Mean Squared Error:',
np.sqrt(metrics.mean_squared_error(Y_te, clq.predict(X_te))))
R2_tr = r2_score(Y_tr, clq.predict(X_tr))
print(R2_tr)
R2 = r2_score(Y_te, clq.predict(X_te))
print(R2)
#let's look at the residuals as well:
matplotlib.rcParams['figure.figsize'] = (6.0, 6.0)
