def test_rolling_r2():
def tail(iterable, n):
return collections.deque(iterable, maxlen=n)
r2 = metrics.Rolling(metric=metrics.R2(), window_size=3)
n = r2.window_size
sk_r2 = sk_metrics.r2_score
y_true = [
0.4656520648923188,
0.5768996330715701,
0.045385529424484594,
0.31852843450357393,
0.8344133739124894,
]
y_pred = [
0.5431172475992199,
0.2436885541729249,
0.20238076597257637,
0.6173775443360237,
0.9194776501054074,
]
for i, (yt, yp) in enumerate(zip(y_true, y_pred)):
r2.update(yt, yp)
if i >= 2:
assert math.isclose(
r2.get(),
sk_r2(tail(y_true[:i + 1], n), tail(y_pred[:i + 1], n)))
def test_r2():
r2 = metrics.R2()
sk_r2 = sk_metrics.r2_score
y_true = [
0.8454795371447003, 0.36530165758399, 0.32733508302313696,
0.3907841858998481, 0.33367434897950754, 0.10209784710790504,
0.9537676025825098, 0.49208175447064406, 0.25808584318657635,
0.22114819033795075
]
y_pred = [
0.28023834604821274, 0.8799362767074241, 0.08515114818265701,
0.04474250926418322, 0.34180002419963607, 0.7018106760663595,
0.4650385019574035, 0.8556417963590652, 0.6818470809869084,
0.9232617479260311
]
weights = [
0.8977831327937194, 0.9059323375861669, 0.6403106244128447,
8.703927525188782e-05, 0.6043234651744177, 0.09393312409759613,
0.24795625986595893, 0.28872232042874824, 0.6618185762206685,
0.14885033958068794
]
for i, (yt, yp, w) in enumerate(zip(y_true, y_pred, weights)):
r2.update(yt, yp, w)
if i >= 1:
assert math.isclose(
r2.get(),
sk_r2(y_true[:i + 1],
y_pred[:i + 1],
sample_weight=weights[:i + 1]))
def reset(self):
self.mae = metrics.Rolling(metrics.MAE(), window_size=self.window_size)
self.mse = metrics.Rolling(metrics.MSE(), window_size=self.window_size)
self.r2 = metrics.Rolling(metrics.R2(), window_size=self.window_size)
self.sample_count = 0
self.last_true_label = None
self.last_prediction = None
def __init__(self):
super().__init__()
self.mae = metrics.MAE()
self.mse = metrics.MSE()
self.r2 = metrics.R2()
self.last_true_label = None
self.last_prediction = None
def __init__(self, window_size=200):
super().__init__()
self.window_size = window_size
self.mae = metrics.Rolling(metrics.MAE(), window_size=self.window_size)
self.mse = metrics.Rolling(metrics.MSE(), window_size=self.window_size)
self.r2 = metrics.Rolling(metrics.R2(), window_size=self.window_size)
self.sample_count = 0
self.last_true_label = None
self.last_prediction = None
from river import datasets
from river import metrics
from river.evaluate import Track
def trump_mse_track(n_samples=10_000, seed=42):
dataset = datasets.TrumpApproval().take(n_samples)
track = Track("TRUMP Approval + R2", dataset, metrics.R2(), n_samples)
return track
def chickweights_mse_track(n_samples=10_000, seed=42):
dataset = datasets.ChickWeights().take(n_samples)
track = Track("ChickWeights + R2", dataset, metrics.R2(), n_samples)
return track
def reset(self):
self.mae = metrics.MAE()
self.mse = metrics.MSE()
self.r2 = metrics.R2()
self.last_true_label = None
self.last_prediction = None
