Replace at -> pt. (pymc-devs#485)

* Replace at -> pt.

* Rename two remaining at->pt.

* Add pytensor to intersphinx.

* Add pytensor to pre-commit.

* remove aesara from intersphinx mapping

Co-authored-by: Oriol (ZBook) <[email protected]>

Author: twiecki

.pre-commit-config.yaml

@@ -83,15 +83,14 @@ repos:
                examples/howto/custom_distribution.ipynb)
       entry: >
           (?x)(arviz-devs.github.io|
-               aesara.readthedocs.io|
-               aeppl.readthedocs.io|
                pymc-experimental.readthedocs.io|
                docs.pymc.io|
                numpy.org/doc|
                pymc-examples.readthedocs.io|
                docs.python.org|
                xarray.pydata.org
                python.arviz.org|
+               pytensor.readthedocs.io|
                docs.xarray.dev|
                www.pymc.io|
                docs.scipy.org/doc)

examples/case_studies/GEV.ipynb

@@ -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"
    ]

examples/case_studies/GEV.myst.md

@@ -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
 ```

examples/case_studies/binning.ipynb

@@ -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",
@@ -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",
@@ -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",
@@ -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)"
@@ -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)"
    ]
   },
@@ -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)"
    ]
   },
@@ -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",
@@ -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",
@@ -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",
@@ -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",
@@ -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",
@@ -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",

examples/case_studies/binning.myst.md

@@ -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
@@ -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)
 ```
@@ -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.
 
@@ -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)
@@ -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)
 ```
 
@@ -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)
 ```
 
@@ -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)
@@ -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
@@ -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
@@ -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
@@ -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
@@ -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)