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Dec 28, 2022
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Replace at -> pt. #485

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5abda1e
Replace at -> pt.
twiecki Dec 27, 2022
c646546
Rename two remaining at->pt.
twiecki Dec 28, 2022
f9b7823
Add pytensor to intersphinx.
twiecki Dec 28, 2022
638fe29
Add pytensor to pre-commit.
twiecki Dec 28, 2022
062f210
remove aesara from intersphinx mapping
OriolAbril Dec 28, 2022
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2 changes: 1 addition & 1 deletion examples/case_studies/GEV.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -50,7 +50,7 @@
"import numpy as np\n",
"import pymc as pm\n",
"import pymc_experimental.distributions as pmx\n",
"import pytensor.tensor as at\n",
"import pytensor.tensor as pt\n",
"\n",
"from arviz.plots import plot_utils as azpu"
]
Expand Down
2 changes: 1 addition & 1 deletion examples/case_studies/GEV.myst.md
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Original file line number Diff line number Diff line change
Expand Up @@ -45,7 +45,7 @@ import matplotlib.pyplot as plt
import numpy as np
import pymc as pm
import pymc_experimental.distributions as pmx
import pytensor.tensor as at
import pytensor.tensor as pt

from arviz.plots import plot_utils as azpu
```
Expand Down
34 changes: 17 additions & 17 deletions examples/case_studies/binning.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -72,7 +72,7 @@
"We are now in a position to sketch out a generative PyMC model:\n",
"\n",
"```python\n",
"import pytensor.tensor as at\n",
"import pytensor.tensor as pt\n",
"\n",
"with pm.Model() as model:\n",
" # priors\n",
Expand All @@ -81,7 +81,7 @@
" # generative process\n",
" probs = pm.math.exp(pm.logcdf(pm.Normal.dist(mu=mu, sigma=sigma), cutpoints))\n",
" probs = pm.math.concatenate([[0], probs, [1]])\n",
" probs = at.extra_ops.diff(probs)\n",
" probs = pt.extra_ops.diff(probs)\n",
" # likelihood\n",
" pm.Multinomial(\"counts\", p=probs, n=sum(counts), observed=counts)\n",
"```\n",
Expand All @@ -98,7 +98,7 @@
"simply concatenates the cumulative density at $-\\infty$ (which is zero) and at $\\infty$ (which is 1).\n",
"The third line\n",
"```python\n",
"probs = at.extra_ops.diff(probs)\n",
"probs = pt.extra_ops.diff(probs)\n",
"```\n",
"calculates the difference between consecutive cumulative densities to give the actual probability of a datum falling in any given bin.\n",
"\n",
Expand All @@ -125,7 +125,7 @@
"import numpy as np\n",
"import pandas as pd\n",
"import pymc as pm\n",
"import pytensor.tensor as at\n",
"import pytensor.tensor as pt\n",
"import seaborn as sns\n",
"\n",
"warnings.filterwarnings(action=\"ignore\", category=UserWarning)"
Expand Down Expand Up @@ -320,7 +320,7 @@
" mu = pm.Normal(\"mu\")\n",
"\n",
" probs1 = pm.math.exp(pm.logcdf(pm.Normal.dist(mu=mu, sigma=sigma), d1))\n",
" probs1 = at.extra_ops.diff(pm.math.concatenate([[0], probs1, [1]]))\n",
" probs1 = pt.extra_ops.diff(pm.math.concatenate([[0], probs1, [1]]))\n",
" pm.Multinomial(\"counts1\", p=probs1, n=c1.sum(), observed=c1.values)"
]
},
Expand Down Expand Up @@ -841,7 +841,7 @@
" mu = pm.Normal(\"mu\")\n",
"\n",
" probs2 = pm.math.exp(pm.logcdf(pm.Normal.dist(mu=mu, sigma=sigma), d2))\n",
" probs2 = at.extra_ops.diff(pm.math.concatenate([[0], probs2, [1]]))\n",
" probs2 = pt.extra_ops.diff(pm.math.concatenate([[0], probs2, [1]]))\n",
" pm.Multinomial(\"counts2\", p=probs2, n=c2.sum(), observed=c2.values)"
]
},
Expand Down Expand Up @@ -1238,11 +1238,11 @@
" mu = pm.Normal(\"mu\")\n",
"\n",
" probs1 = pm.math.exp(pm.logcdf(pm.Normal.dist(mu=mu, sigma=sigma), d1))\n",
" probs1 = at.extra_ops.diff(pm.math.concatenate([np.array([0]), probs1, np.array([1])]))\n",
" probs1 = pt.extra_ops.diff(pm.math.concatenate([np.array([0]), probs1, np.array([1])]))\n",
" probs1 = pm.Deterministic(\"normal1_cdf\", probs1)\n",
"\n",
" probs2 = pm.math.exp(pm.logcdf(pm.Normal.dist(mu=mu, sigma=sigma), d2))\n",
" probs2 = at.extra_ops.diff(pm.math.concatenate([np.array([0]), probs2, np.array([1])]))\n",
" probs2 = pt.extra_ops.diff(pm.math.concatenate([np.array([0]), probs2, np.array([1])]))\n",
" probs2 = pm.Deterministic(\"normal2_cdf\", probs2)\n",
"\n",
" pm.Multinomial(\"counts1\", p=probs1, n=c1.sum(), observed=c1.values)\n",
Expand Down Expand Up @@ -1719,7 +1719,7 @@
" mu = pm.Normal(\"mu\")\n",
" # study 1\n",
" probs1 = pm.math.exp(pm.logcdf(pm.Normal.dist(mu=mu, sigma=sigma), d1))\n",
" probs1 = at.extra_ops.diff(pm.math.concatenate([np.array([0]), probs1, np.array([1])]))\n",
" probs1 = pt.extra_ops.diff(pm.math.concatenate([np.array([0]), probs1, np.array([1])]))\n",
" probs1 = pm.Deterministic(\"normal1_cdf\", probs1)\n",
" pm.Multinomial(\"counts1\", p=probs1, n=c1.sum(), observed=c1.values)\n",
" # study 2\n",
Expand Down Expand Up @@ -2149,12 +2149,12 @@
"\n",
" # Study 1\n",
" probs1 = pm.math.exp(pm.logcdf(pm.Normal.dist(mu=mu[0], sigma=sigma[0]), d1))\n",
" probs1 = at.extra_ops.diff(pm.math.concatenate([np.array([0]), probs1, np.array([1])]))\n",
" probs1 = pt.extra_ops.diff(pm.math.concatenate([np.array([0]), probs1, np.array([1])]))\n",
" probs1 = pm.Deterministic(\"normal1_cdf\", probs1, dims=\"bin1\")\n",
"\n",
" # Study 2\n",
" probs2 = pm.math.exp(pm.logcdf(pm.Normal.dist(mu=mu[1], sigma=sigma[1]), d2))\n",
" probs2 = at.extra_ops.diff(pm.math.concatenate([np.array([0]), probs2, np.array([1])]))\n",
" probs2 = pt.extra_ops.diff(pm.math.concatenate([np.array([0]), probs2, np.array([1])]))\n",
" probs2 = pm.Deterministic(\"normal2_cdf\", probs2, dims=\"bin2\")\n",
"\n",
" # Likelihood\n",
Expand Down Expand Up @@ -2392,12 +2392,12 @@
"\n",
" # Study 1\n",
" probs1 = pm.math.exp(pm.logcdf(pm.Normal.dist(mu=mu[0], sigma=sigma[0]), d1))\n",
" probs1 = at.extra_ops.diff(pm.math.concatenate([np.array([0]), probs1, np.array([1])]))\n",
" probs1 = pt.extra_ops.diff(pm.math.concatenate([np.array([0]), probs1, np.array([1])]))\n",
" probs1 = pm.Deterministic(\"normal1_cdf\", probs1, dims=\"bin1\")\n",
"\n",
" # Study 2\n",
" probs2 = pm.math.exp(pm.logcdf(pm.Normal.dist(mu=mu[1], sigma=sigma[1]), d2))\n",
" probs2 = at.extra_ops.diff(pm.math.concatenate([np.array([0]), probs2, np.array([1])]))\n",
" probs2 = pt.extra_ops.diff(pm.math.concatenate([np.array([0]), probs2, np.array([1])]))\n",
" probs2 = pm.Deterministic(\"normal2_cdf\", probs2, dims=\"bin2\")\n",
"\n",
" # Likelihood\n",
Expand Down Expand Up @@ -2927,12 +2927,12 @@
" \n",
" # Study 1\n",
" probs1 = pm.math.exp(pm.logcdf(pm.Normal.dist(mu=mu[0], sigma=sigma[0]), d1))\n",
" probs1 = at.extra_ops.diff(pm.math.concatenate([np.array([0]), probs1, np.array([1])]))\n",
" probs1 = pt.extra_ops.diff(pm.math.concatenate([np.array([0]), probs1, np.array([1])]))\n",
" probs1 = pm.Deterministic(\"normal1_cdf\", probs1, dims='bin1')\n",
"\n",
" # Study 2\n",
" probs2 = pm.math.exp(pm.logcdf(pm.Normal.dist(mu=mu[1], sigma=sigma[1]), d2))\n",
" probs2 = at.extra_ops.diff(pm.math.concatenate([np.array([0]), probs2, np.array([1])]))\n",
" probs2 = pt.extra_ops.diff(pm.math.concatenate([np.array([0]), probs2, np.array([1])]))\n",
" probs2 = pm.Deterministic(\"normal2_cdf\", probs2, dims='bin2')\n",
"\n",
" # Likelihood\n",
Expand Down Expand Up @@ -3091,11 +3091,11 @@
" beta = pm.HalfNormal(\"beta\", 10)\n",
"\n",
" probs1 = pm.math.exp(pm.logcdf(pm.Gumbel.dist(mu=mu, beta=beta), d1))\n",
" probs1 = at.extra_ops.diff(pm.math.concatenate([np.array([0]), probs1, np.array([1])]))\n",
" probs1 = pt.extra_ops.diff(pm.math.concatenate([np.array([0]), probs1, np.array([1])]))\n",
" probs1 = pm.Deterministic(\"gumbel_cdf1\", probs1)\n",
"\n",
" probs2 = pm.math.exp(pm.logcdf(pm.Gumbel.dist(mu=mu, beta=beta), d2))\n",
" probs2 = at.extra_ops.diff(pm.math.concatenate([np.array([0]), probs2, np.array([1])]))\n",
" probs2 = pt.extra_ops.diff(pm.math.concatenate([np.array([0]), probs2, np.array([1])]))\n",
" probs2 = pm.Deterministic(\"gumbel_cdf2\", probs2)\n",
"\n",
" pm.Multinomial(\"counts1\", p=probs1, n=c1.sum(), observed=c1.values)\n",
Expand Down
34 changes: 17 additions & 17 deletions examples/case_studies/binning.myst.md
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -69,7 +69,7 @@ In ordinal regression, the cutpoints are treated as latent variables and the par
We are now in a position to sketch out a generative PyMC model:

```python
import pytensor.tensor as at
import pytensor.tensor as pt

with pm.Model() as model:
# priors
Expand All @@ -78,7 +78,7 @@ with pm.Model() as model:
# generative process
probs = pm.math.exp(pm.logcdf(pm.Normal.dist(mu=mu, sigma=sigma), cutpoints))
probs = pm.math.concatenate([[0], probs, [1]])
probs = at.extra_ops.diff(probs)
probs = pt.extra_ops.diff(probs)
# likelihood
pm.Multinomial("counts", p=probs, n=sum(counts), observed=counts)
```
Expand All @@ -95,7 +95,7 @@ probs = pm.math.concatenate([[0], probs, [1]])
simply concatenates the cumulative density at $-\infty$ (which is zero) and at $\infty$ (which is 1).
The third line
```python
probs = at.extra_ops.diff(probs)
probs = pt.extra_ops.diff(probs)
```
calculates the difference between consecutive cumulative densities to give the actual probability of a datum falling in any given bin.

Expand All @@ -115,7 +115,7 @@ import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pymc as pm
import pytensor.tensor as at
import pytensor.tensor as pt
import seaborn as sns

warnings.filterwarnings(action="ignore", category=UserWarning)
Expand Down Expand Up @@ -226,7 +226,7 @@ with pm.Model() as model1:
mu = pm.Normal("mu")

probs1 = pm.math.exp(pm.logcdf(pm.Normal.dist(mu=mu, sigma=sigma), d1))
probs1 = at.extra_ops.diff(pm.math.concatenate([[0], probs1, [1]]))
probs1 = pt.extra_ops.diff(pm.math.concatenate([[0], probs1, [1]]))
pm.Multinomial("counts1", p=probs1, n=c1.sum(), observed=c1.values)
```

Expand Down Expand Up @@ -331,7 +331,7 @@ with pm.Model() as model2:
mu = pm.Normal("mu")

probs2 = pm.math.exp(pm.logcdf(pm.Normal.dist(mu=mu, sigma=sigma), d2))
probs2 = at.extra_ops.diff(pm.math.concatenate([[0], probs2, [1]]))
probs2 = pt.extra_ops.diff(pm.math.concatenate([[0], probs2, [1]]))
pm.Multinomial("counts2", p=probs2, n=c2.sum(), observed=c2.values)
```

Expand Down Expand Up @@ -426,11 +426,11 @@ with pm.Model() as model3:
mu = pm.Normal("mu")

probs1 = pm.math.exp(pm.logcdf(pm.Normal.dist(mu=mu, sigma=sigma), d1))
probs1 = at.extra_ops.diff(pm.math.concatenate([np.array([0]), probs1, np.array([1])]))
probs1 = pt.extra_ops.diff(pm.math.concatenate([np.array([0]), probs1, np.array([1])]))
probs1 = pm.Deterministic("normal1_cdf", probs1)

probs2 = pm.math.exp(pm.logcdf(pm.Normal.dist(mu=mu, sigma=sigma), d2))
probs2 = at.extra_ops.diff(pm.math.concatenate([np.array([0]), probs2, np.array([1])]))
probs2 = pt.extra_ops.diff(pm.math.concatenate([np.array([0]), probs2, np.array([1])]))
probs2 = pm.Deterministic("normal2_cdf", probs2)

pm.Multinomial("counts1", p=probs1, n=c1.sum(), observed=c1.values)
Expand Down Expand Up @@ -519,7 +519,7 @@ with pm.Model() as model4:
mu = pm.Normal("mu")
# study 1
probs1 = pm.math.exp(pm.logcdf(pm.Normal.dist(mu=mu, sigma=sigma), d1))
probs1 = at.extra_ops.diff(pm.math.concatenate([np.array([0]), probs1, np.array([1])]))
probs1 = pt.extra_ops.diff(pm.math.concatenate([np.array([0]), probs1, np.array([1])]))
probs1 = pm.Deterministic("normal1_cdf", probs1)
pm.Multinomial("counts1", p=probs1, n=c1.sum(), observed=c1.values)
# study 2
Expand Down Expand Up @@ -612,12 +612,12 @@ with pm.Model(coords=coords) as model5:

# Study 1
probs1 = pm.math.exp(pm.logcdf(pm.Normal.dist(mu=mu[0], sigma=sigma[0]), d1))
probs1 = at.extra_ops.diff(pm.math.concatenate([np.array([0]), probs1, np.array([1])]))
probs1 = pt.extra_ops.diff(pm.math.concatenate([np.array([0]), probs1, np.array([1])]))
probs1 = pm.Deterministic("normal1_cdf", probs1, dims="bin1")

# Study 2
probs2 = pm.math.exp(pm.logcdf(pm.Normal.dist(mu=mu[1], sigma=sigma[1]), d2))
probs2 = at.extra_ops.diff(pm.math.concatenate([np.array([0]), probs2, np.array([1])]))
probs2 = pt.extra_ops.diff(pm.math.concatenate([np.array([0]), probs2, np.array([1])]))
probs2 = pm.Deterministic("normal2_cdf", probs2, dims="bin2")

# Likelihood
Expand Down Expand Up @@ -645,12 +645,12 @@ with pm.Model(coords=coords) as model5:

# Study 1
probs1 = pm.math.exp(pm.logcdf(pm.Normal.dist(mu=mu[0], sigma=sigma[0]), d1))
probs1 = at.extra_ops.diff(pm.math.concatenate([np.array([0]), probs1, np.array([1])]))
probs1 = pt.extra_ops.diff(pm.math.concatenate([np.array([0]), probs1, np.array([1])]))
probs1 = pm.Deterministic("normal1_cdf", probs1, dims="bin1")

# Study 2
probs2 = pm.math.exp(pm.logcdf(pm.Normal.dist(mu=mu[1], sigma=sigma[1]), d2))
probs2 = at.extra_ops.diff(pm.math.concatenate([np.array([0]), probs2, np.array([1])]))
probs2 = pt.extra_ops.diff(pm.math.concatenate([np.array([0]), probs2, np.array([1])]))
probs2 = pm.Deterministic("normal2_cdf", probs2, dims="bin2")

# Likelihood
Expand Down Expand Up @@ -748,12 +748,12 @@ with pm.Model(coords=coords) as model5:

# Study 1
probs1 = pm.math.exp(pm.logcdf(pm.Normal.dist(mu=mu[0], sigma=sigma[0]), d1))
probs1 = at.extra_ops.diff(pm.math.concatenate([np.array([0]), probs1, np.array([1])]))
probs1 = pt.extra_ops.diff(pm.math.concatenate([np.array([0]), probs1, np.array([1])]))
probs1 = pm.Deterministic("normal1_cdf", probs1, dims='bin1')

# Study 2
probs2 = pm.math.exp(pm.logcdf(pm.Normal.dist(mu=mu[1], sigma=sigma[1]), d2))
probs2 = at.extra_ops.diff(pm.math.concatenate([np.array([0]), probs2, np.array([1])]))
probs2 = pt.extra_ops.diff(pm.math.concatenate([np.array([0]), probs2, np.array([1])]))
probs2 = pm.Deterministic("normal2_cdf", probs2, dims='bin2')

# Likelihood
Expand Down Expand Up @@ -855,11 +855,11 @@ with pm.Model() as model6:
beta = pm.HalfNormal("beta", 10)

probs1 = pm.math.exp(pm.logcdf(pm.Gumbel.dist(mu=mu, beta=beta), d1))
probs1 = at.extra_ops.diff(pm.math.concatenate([np.array([0]), probs1, np.array([1])]))
probs1 = pt.extra_ops.diff(pm.math.concatenate([np.array([0]), probs1, np.array([1])]))
probs1 = pm.Deterministic("gumbel_cdf1", probs1)

probs2 = pm.math.exp(pm.logcdf(pm.Gumbel.dist(mu=mu, beta=beta), d2))
probs2 = at.extra_ops.diff(pm.math.concatenate([np.array([0]), probs2, np.array([1])]))
probs2 = pt.extra_ops.diff(pm.math.concatenate([np.array([0]), probs2, np.array([1])]))
probs2 = pm.Deterministic("gumbel_cdf2", probs2)

pm.Multinomial("counts1", p=probs1, n=c1.sum(), observed=c1.values)
Expand Down
30 changes: 15 additions & 15 deletions examples/case_studies/blackbox_external_likelihood_numpy.ipynb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -47,7 +47,7 @@
"import numpy as np\n",
"import pymc as pm\n",
"import pytensor\n",
"import pytensor.tensor as at\n",
"import pytensor.tensor as pt\n",
"\n",
"print(f\"Running on PyMC v{pm.__version__}\")"
]
Expand Down Expand Up @@ -180,7 +180,7 @@
"outputs": [],
"source": [
"# define a pytensor Op for our likelihood function\n",
"class LogLike(at.Op):\n",
"class LogLike(pt.Op):\n",
"\n",
" \"\"\"\n",
" Specify what type of object will be passed and returned to the Op when it is\n",
Expand All @@ -189,8 +189,8 @@
" log-likelihood)\n",
" \"\"\"\n",
"\n",
" itypes = [at.dvector] # expects a vector of parameter values when called\n",
" otypes = [at.dscalar] # outputs a single scalar value (the log likelihood)\n",
" itypes = [pt.dvector] # expects a vector of parameter values when called\n",
" otypes = [pt.dscalar] # outputs a single scalar value (the log likelihood)\n",
"\n",
" def __init__(self, loglike, data, x, sigma):\n",
" \"\"\"\n",
Expand Down Expand Up @@ -336,7 +336,7 @@
" c = pm.Uniform(\"c\", lower=-10.0, upper=10.0)\n",
"\n",
" # convert m and c to a tensor vector\n",
" theta = at.as_tensor_variable([m, c])\n",
" theta = pt.as_tensor_variable([m, c])\n",
"\n",
" # use a Potential to \"call\" the Op and include it in the logp computation\n",
" pm.Potential(\"likelihood\", logl(theta))\n",
Expand Down Expand Up @@ -426,10 +426,10 @@
"outputs": [],
"source": [
"# define a pytensor Op for our likelihood function\n",
"class LogLikeWithGrad(at.Op):\n",
"class LogLikeWithGrad(pt.Op):\n",
"\n",
" itypes = [at.dvector] # expects a vector of parameter values when called\n",
" otypes = [at.dscalar] # outputs a single scalar value (the log likelihood)\n",
" itypes = [pt.dvector] # expects a vector of parameter values when called\n",
" otypes = [pt.dscalar] # outputs a single scalar value (the log likelihood)\n",
"\n",
" def __init__(self, loglike, data, x, sigma):\n",
" \"\"\"\n",
Expand Down Expand Up @@ -473,15 +473,15 @@
" return [g[0] * self.logpgrad(theta)]\n",
"\n",
"\n",
"class LogLikeGrad(at.Op):\n",
"class LogLikeGrad(pt.Op):\n",
"\n",
" \"\"\"\n",
" This Op will be called with a vector of values and also return a vector of\n",
" values - the gradients in each dimension.\n",
" \"\"\"\n",
"\n",
" itypes = [at.dvector]\n",
" otypes = [at.dvector]\n",
" itypes = [pt.dvector]\n",
" otypes = [pt.dvector]\n",
"\n",
" def __init__(self, data, x, sigma):\n",
" \"\"\"\n",
Expand Down Expand Up @@ -609,7 +609,7 @@
" c = pm.Uniform(\"c\", lower=-10.0, upper=10.0)\n",
"\n",
" # convert m and c to a tensor vector\n",
" theta = at.as_tensor_variable([m, c])\n",
" theta = pt.as_tensor_variable([m, c])\n",
"\n",
" # use a Potential\n",
" pm.Potential(\"likelihood\", logl(theta))\n",
Expand Down Expand Up @@ -710,7 +710,7 @@
" c = pm.Uniform(\"c\", lower=-10.0, upper=10.0)\n",
"\n",
" # convert m and c to a tensor vector\n",
" theta = at.as_tensor_variable([m, c])\n",
" theta = pt.as_tensor_variable([m, c])\n",
"\n",
" # use a Normal distribution\n",
" pm.Normal(\"likelihood\", mu=(m * x + c), sd=sigma, observed=data)\n",
Expand Down Expand Up @@ -947,7 +947,7 @@
"pytensor.config.compute_test_value = \"ignore\"\n",
"pytensor.config.exception_verbosity = \"high\"\n",
"\n",
"var = at.dvector()\n",
"var = pt.dvector()\n",
"test_grad_op = LogLikeGrad(data, x, sigma)\n",
"test_grad_op_func = pytensor.function([var], test_grad_op(var))\n",
"grad_vals = test_grad_op_func([mtrue, ctrue])\n",
Expand All @@ -956,7 +956,7 @@
"\n",
"# test the gradient called through LogLikeWithGrad\n",
"test_gradded_op = LogLikeWithGrad(my_loglike, data, x, sigma)\n",
"test_gradded_op_grad = at.grad(test_gradded_op(var), var)\n",
"test_gradded_op_grad = pt.grad(test_gradded_op(var), var)\n",
"test_gradded_op_grad_func = pytensor.function([var], test_gradded_op_grad)\n",
"grad_vals_2 = test_gradded_op_grad_func([mtrue, ctrue])\n",
"\n",
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