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Question

在此示例的帮助下，我正在尝试实现部分Facebook先知。

https://github.com/luke14free/pm-prophet/blob/master/pmprophet/model.py

这很顺利:)，但是我不了解的点积产品存在一些问题。请注意，我正在实现线性趋势。

ds = pd.to_datetime(df['dagindex'], format='%d-%m-%y')


m = pm.Model()
changepoint_prior_scale = 0.05
n_changepoints = 25
changepoints = pd.date_range(
    start=pd.to_datetime(ds.min()),
    end=pd.to_datetime(ds.max()),
    periods=n_changepoints + 2
)[1: -1]

with m:

    # priors
    sigma = pm.HalfCauchy('sigma', 10, testval=1)
    #trend
    growth = pm.Normal('growth', 0, 10)

    prior_changepoints = pm.Laplace('changepoints', 0, changepoint_prior_scale, shape=len(changepoints))

    y = np.zeros(len(df))

    # indexes x_i for the changepoints. 
    s = [np.abs((ds - i).values).argmin() for i in changepoints]

    g = growth
    x = np.arange(len(ds))
    # delta
    d = prior_changepoints

    regression = x * g

    base_piecewise_regression = []

    for i in s:
        local_x = x.copy()[:-i]
        local_x = np.concatenate([np.zeros(i), local_x])
        base_piecewise_regression.append(local_x)

    piecewise_regression = np.array(base_piecewise_regression)

#  this dot product doesn't work?
    piecewise_regression = pm.math.dot(theano.shared(piecewise_regression).T, d)

#  If I comment out this line and use that one as dot product. It works fine
#     piecewise_regression = (piecewise_regression.T * d[None, :]).sum(axis=-1)
    regression += piecewise_regression

    y += regression

    obs = pm.Normal('y',
                   mu=(y - df.gebruikers.mean()) / df.gebruikers.std(),
                   sd=sigma,
                   observed=(df.gebruikers - df.gebruikers.mean()) / df.gebruikers.std())

    start = pm.find_MAP(maxeval=10000)
    trace = pm.sample(500, step=pm.NUTS(), start=start)

如果我使用

运行上面的代码段

piecewise_regression = (piecewise_regression.T * d[None, :]).sum(axis=-1)

该模型按预期工作。但是，我无法使其与点产品一起使用。 NUTS采样器根本不采样。

piecewise_regression = pm.math.dot(theano.shared(piecewise_regression).T, d)

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我有一个最小的工作示例

theano.shared仍然出现问题。我有一个最小的工作示例：

np.random.seed(5)

n_changepoints = 10
t = np.arange(1000)
s = np.sort(np.random.choice(t, size=n_changepoints, replace=False))
a = (t[:, None] > s) * 1

real_delta = np.random.normal(size=n_changepoints)
y = np.dot(a, real_delta) * t

with pm.Model():
    sigma = pm.HalfCauchy('sigma', 10, testval=1)
    delta = pm.Laplace('delta', 0, 0.05, shape=n_changepoints)
    g = tt.dot(a, delta) * t
    obs = pm.Normal('obs',
                   mu=(g - y.mean()) / y.std(),
                   sd=sigma,
                   observed=(y - y.mean()) / y.std())
    trace = pm.sample(500)

似乎与矩阵a的大小有关。如果我以NUTS开头，则NUTS不会采样

t = np.arange（1000）

但是，当我将t的大小减小为：

t = np.arange（100）

为什么NUTS不使用tt.dot或pm.math.dot进行采样？

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0 个答案: