21c16153ecd473a027df2af1e9a4fd3c71810e1a,pymc3/variational/callbacks.py,CheckLossConvergence,__call__,#CheckLossConvergence#Any#Any#Any#,26

Before Change

    def __call__(self, approx, hist, i):
        if hist is None or i < self.window_size or i % self.every:
            return
        diff = ((hist[-self.window_size:] - hist[-self.window_size-1:-1])
                / hist[-self.window_size-1:-1])
        mean = diff.mean()
        // unbiased std of mean
        std = diff.std() / (self.window_size - 1)**.5
        t = abs(mean / std)
        p = stats.t.cdf(t, df=self.window_size) - .5
        // 1 - confidence is lower allowed p
        if p < self.critical:
            raise StopIteration

After Change

    def __call__(self, approx, hist, i):
        if hist is None or i < self.every or i % self.every:
            return
        if self.steps is None:
            window = int(max(0.1 * hist.size // self.every, 2.0))
        else:
            window = int(max(0.1 * self.steps // self.every, 2.0))
        losses = hist[::self.every][-window:]
        diff = np.abs((losses[1:]-losses[:-1])/losses[:-1])
        mean = np.mean(diff)
        med = np.median(diff)

Instances