| Name | scmodels JSON |
| Version |
0.3.2
JSON |
| download |
| home_page | None |
| Summary | Structural Causal Models |
| upload_time | 2024-04-04 10:25:46 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | None |
| license | The MIT License (MIT) Copyright (c) 2019 Michael Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
bayesian-graphs
graphical-models
scm
sem
fcm
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
numpy
scipy
pandas
matplotlib
sympy
networkx
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
| OS | Status |
| :-------------: |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| Linux | [![Py 3.[7-12]](https://github.com/maichmueller/scmodels/actions/workflows/pytest_linux.yml/badge.svg)](https://github.com/maichmueller/scmodels/actions/workflows/pytest_linux.yml) |
| Mac | [![Py 3.[7-12]](https://github.com/maichmueller/scmodels/actions/workflows/pytest_mac.yml/badge.svg)](https://github.com/maichmueller/scmodels/actions/workflows/pytest_mac.yml) |
| Windows | [![Py 3.[7-12]](https://github.com/maichmueller/scmodels/actions/workflows/pytest_win.yml/badge.svg)](https://github.com/maichmueller/scmodels/actions/workflows/pytest_win.yml) |
A Python package implementing Structural Causal Models (SCM).
The library uses the CAS library [SymPy](https://github.com/sympy/sympy) to allow the user to state arbitrary assignment functions and noise distributions as supported by SymPy and builds the DAG with [networkx](https://github.com/networkx/networkx).
It supports the features:
- Sampling
- Intervening
- Plotting
- Printing
and by extension all methods on a DAG provided by networkx after accessing the member variable dag
# Installation
Either install via pip
```
pip install scmodels
```
or via cloning the repository and running the installation locally
```
git clone https://github.com/maichmueller/scmodels
cd scmodels
pip install .
```
# Building an SCM
In order to declare the SCM
$X ~ \leftarrow ~ Z ~ \rightarrow ~ V ~ \rightarrow ~ W$
$\downarrow ~ ~ \swarrow$
$Y$
with the assignments
$Z \sim \text{LogLogistic} (\alpha=1,\beta=1)$
$X=3Z^2 \cdot N \quad [N \sim \text{LogNormal}(\mu=1,\sigma=1)]$
$Y = 2Z + \sqrt{X} + N \quad [N \sim \text{Normal}(\mu=2,\sigma=1)]$
$V = Z + N^P \quad [N \sim \text{Normal}(\mu=0,\sigma=1), P \sim \text{Ber}(0.5)]$
$W = V + \exp(T) - \log(M) * N \quad [N \sim \text{Normal}(\mu=0,\sigma=1), T \sim \text{StudentT}(\nu = 0.5), M \sim \text{Exp}(\lambda = 1)]$
there are 2 different ways implemented.
## 1. List Of Strings
Describe the assignments as strings of the form:
'VAR = FUNC(Noise, parent1, parent2, ...), Noise ~ DistributionXYZ'
Note that in this case, one must not restate the noise symbol string in the distribution (as would otherwise be necessary in constructing sympy distributions).
```python
from scmodels import SCM
assignment_seq = [
"Z = M; M ~ LogLogistic(alpha=1, beta=1)",
"X = N * 3 * Z ** 2; N ~ LogNormal(mean=1, std=1)",
"Y = P + 2 * Z + sqrt(X); P ~ Normal(mean=2, std=1)",
"V = N**P + Z; N ~ Normal(0,1) / P ~ Bernoulli(0.5)",
"W = exp(T) - log(M) * N + V; M ~ Exponential(1) / T ~ StudentT(0.5) / N ~ Normal(0, 2)",
]
myscm = SCM(assignment_seq)
```
Agreements:
- The name of the noise variable in the distribution specification
(e.g. `P ~ Normal(mean=2, std=1)`) has to align with the noise variable
name (`P`) of the assignment string.
- Multiple noise models can be composited as done for variable `W` in the above example.
The noise model string segment must specify all individual noise models separated by a '/'
## 2. Assignment Map
One can construct the SCM via an assignment map with the variables as keys
and a tuple defining the assignment and the noise.
### 2-Tuple: Assignments via SymPy parsing
To refer to SymPy's string parsing capability (this includes numpy functions) provide a dict entry
with 2-tuples as values of the form:
`'var': ('assignment string', noise)`
```python
from sympy.stats import LogLogistic, LogNormal, Normal, Bernoulli, StudentT, Exponential
assignment_map = {
"Z": (
"M",
LogLogistic("M", alpha=1, beta=1)
),
"X": (
"N * 3 * Z ** 2",
LogNormal("N", mean=1, std=1),
),
"Y": (
"P + 2 * Z + sqrt(X)",
Normal("P", mean=2, std=1),
),
"V": (
"N**P + Z",
[Normal("N", 0, 1), Bernoulli("P", 0.5)]
),
"W": (
"exp(T) - log(M) * N + V",
[Normal("N", 0, 1), StudentT("T", 0.5), Exponential("M", 1)]
)
}
myscm2 = SCM(assignment_map)
```
Agreements:
- the name of the noise distribution provided in its constructor
(e.g. `Normal("N", mean=2, std=1)`) must align with the noise variable
name (`N`) in the assignment string.
- Multiple noise models in the assignment must be wrapped in an iterable (e.g. a list `[]`, or tuple `()`)
### 3-Tuple: Assignments with arbitrary callables
One can also declare the SCM via specifying the variable assignment in a dictionary with the
variables as keys and as values a sequence of length 3 of the form:
`'var': (['parent1', 'parent2', ...], Callable, Noise)`
This allows the user to supply functions that are not limited to predefined function sets of `scmodels`' dependencies.
```python
import numpy as np
def Y_assignment(p, z, x):
return p + 2 * z + np.sqrt(x)
functional_map = {
"Z": (
[],
lambda m: m,
LogLogistic("M", alpha=1, beta=1)
),
"X": (
["Z"],
lambda n, z: n * 3 * z ** 2,
LogNormal("N", mean=1, std=1),
),
"Y": (
["Z", "X"],
Y_assignment,
Normal("P", mean=2, std=1),
),
"V": (
["Z"],
lambda n, p, z: n ** p + z,
[Normal("N", mean=0, std=1), Bernoulli("P", p=0.5)]
),
"W": (
["V"],
lambda n, t, m, v: np.exp(m) - np.log(m) * n + v,
[Normal("N", mean=0, std=1), StudentT("T", nu=0.5), Exponential("M", rate=1)]
)
}
myscm3 = SCM(functional_map)
```
Agreements:
- The callable's first parameter MUST be the noise inputs in the order that they are given, e.g. see variable $W$. If the noise distribution list contains only `None`, then they have to be omitted from the parameter list of the callable.
- The order of variables in the parents list determines the order of input for parameters in the functional past the noise parameters (left to right).
# Features
## Prettyprint
The SCM supports a form of informative printing of its current setup,
which includes mentioning active interventions and the assignments.
```python
print(myscm)
```
Structural Causal Model of 5 variables: Z, X, V, Y, W
Variables with active interventions: []
Assignments:
Z := f(M) = M [ M ~ LogLogistic(alpha=1, beta=1) ]
X := f(N, Z) = N * 3 * Z ** 2 [ N ~ LogNormal(mean=1, std=1) ]
Y := f(P, Z, X) = P + 2 * Z + sqrt(X) [ P ~ Normal(mean=2, std=1) ]
V := f(N, P, Z) = N**P + Z [ N ~ Normal(mean=0, std=1), P ~ Bernoulli(p=0.5, succ=1, fail=0) ]
W := f(M, T, N, V) = exp(T) - log(M) * N + V [ M ~ Exponential(rate=1), T ~ StudentT(nu=0.5), N ~ Normal(mean=0, std=2) ]
In the case of custom callable assignments, the output is less informative
```python
print(myscm3)
```
Structural Causal Model of 5 variables: Z, X, V, Y, W
Variables with active interventions: []
Assignments:
Z := f(M) = __unknown__ [ M ~ LogLogistic(alpha=1, beta=1) ]
X := f(N, Z) = __unknown__ [ N ~ LogNormal(mean=1, std=1) ]
Y := f(P, Z, X) = __unknown__ [ P ~ Normal(mean=2, std=1) ]
V := f(N, P, Z) = __unknown__ [ N ~ Normal(mean=0, std=1), P ~ Bernoulli(p=0.5, succ=1, fail=0) ]
W := f(N, T, M, V) = __unknown__ [ N ~ Normal(mean=0, std=1), T ~ StudentT(nu=0.5), M ~ Exponential(rate=1) ]
## Interventions
One can easily perform interventions on the variables,
e.g. a Do-intervention or also general interventions, which remodel the connections, assignments, and noise distributions.
For general interventions, the passing structure is dict of the following form:
{var: (New Parents (Optional), New Assignment (optional), New Noise (optional))}
Any part of the original variable state, that is meant to be left unchanged, has to be passed as `None`.
E.g. to assign a new callable assignment to variable `X` without changing parents or noise, one would call:
```python
my_new_callable = lambda n, z: n + z
myscm.intervention({"X": (None, my_new_callable, None)})
```
For the example of a do-intervention $\text{do}(X=1)$, the helper method `do_intervention` is provided. Its counterpart for noise interventions is `soft_intervention`:
```python
myscm.do_intervention([("X", 1)])
from sympy.stats import FiniteRV
myscm.soft_intervention([("X", FiniteRV(myscm["X"].noise[0].name, density={-1: .5, 1: .5}))])
```
Refer to the `sympy` docs for more information on building random variables.
Calling `undo_intervention` restores the original state of *all* variables.
If variables are specified (`variables=['X', 'Y']`), any interventions on *only those variables* are undone.
```python
myscm.undo_intervention(variables=["X"])
```
## Sampling
The SCM allows drawing as many samples as needed through the method `myscm.sample(n)`.
```python
n = 5
samples = myscm.sample(n)
display_data(samples)
```
| | Z | V | X | W | Y |
|----|----------|----------|-----------|---------|----------|
| 0 | 0.854202 | 0.193336 | 15.9145 | 1.60315 | 7.55694 |
| 1 | 0.634422 | 0.951119 | 2.43486 | 5.59541 | 3.6478 |
| 2 | 1.02354 | 2.02354 | 4.11357 | 2.12862 | 6.23834 |
| 3 | 0.376173 | 2.2962 | 0.252316 | 2.83333 | 3.64392 |
| 4 | 4.19568 | 2.894 | 36.2844 | 3.07636 | 16.4083 |
If infinite sampling is desired, one can also receive a sampling generator through
```python
container = {var: [] for var in myscm}
sampler = myscm.sample_iter(container)
```
`container` is an optional target dictionary to store the computed samples in.
```python
for i in range(n):
next(sampler)
display_data(container)
```
| | Z | V | X | W | Y |
|----|----------|-----------|------------|--------------|----------|
| 0 | 0.245579 | 0.814717 | 1.59715 | 2.10267 | 2.26984 |
| 1 | 7.88344 | 8.88344 | 122.197 | 1.90523e+13 | 29.1743 |
| 2 | 1.26534 | 1.29397 | 12.5345 | 0.523827 | 8.77073 |
| 3 | 1.37904 | 2.37904 | 13.327 | 2.08063 | 9.14935 |
| 4 | 0.230563 | -0.966714 | 0.354393 | -1.22201 | 3.09432 |
If the target container is not provided, the generator returns a new `dict` for every sample.
```python
sample = next(myscm.sample_iter())
display_data(sample)
```
| | Z | V | X | W | Y |
|----|---------|--------|---------|--------|---------|
| 0 | 2.02308 | 3.1259 | 59.8718 | 4.2209 | 13.7312 |
## Plotting
If you have graphviz installed, you can plot the DAG by calling
```python
myscm.plot(node_size=1000, alpha=1)
```
Raw data
{
"_id": null,
"home_page": null,
"name": "scmodels",
"maintainer": null,
"docs_url": null,
"requires_python": null,
"maintainer_email": null,
"keywords": "bayesian-graphs, graphical-models, scm, sem, fcm",
"author": null,
"author_email": "Michael Aichmueller <m.aichmueller@gmail.com>",
"download_url": "https://files.pythonhosted.org/packages/ec/2f/6013784299e7dc7826a59e39973ae615ef557bed65fdffe3dd759bf05c83/scmodels-0.3.2.tar.gz",
"platform": null,
"description": "| OS | Status |\n| :-------------: |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|\n| Linux | [![Py 3.[7-12]](https://github.com/maichmueller/scmodels/actions/workflows/pytest_linux.yml/badge.svg)](https://github.com/maichmueller/scmodels/actions/workflows/pytest_linux.yml) |\n| Mac | [![Py 3.[7-12]](https://github.com/maichmueller/scmodels/actions/workflows/pytest_mac.yml/badge.svg)](https://github.com/maichmueller/scmodels/actions/workflows/pytest_mac.yml) |\n| Windows | [![Py 3.[7-12]](https://github.com/maichmueller/scmodels/actions/workflows/pytest_win.yml/badge.svg)](https://github.com/maichmueller/scmodels/actions/workflows/pytest_win.yml) |\n\n\nA Python package implementing Structural Causal Models (SCM).\n\nThe library uses the CAS library [SymPy](https://github.com/sympy/sympy) to allow the user to state arbitrary assignment functions and noise distributions as supported by SymPy and builds the DAG with [networkx](https://github.com/networkx/networkx).\n\nIt supports the features:\n - Sampling\n - Intervening\n - Plotting\n - Printing\n\n and by extension all methods on a DAG provided by networkx after accessing the member variable dag\n\n# Installation\nEither install via pip\n```\npip install scmodels\n```\nor via cloning the repository and running the installation locally\n```\ngit clone https://github.com/maichmueller/scmodels\ncd scmodels\npip install . \n```\n\n# Building an SCM\n\nIn order to declare the SCM \n\n$X ~ \\leftarrow ~ Z ~ \\rightarrow ~ V ~ \\rightarrow ~ W$\n\n$\\downarrow ~ ~ \\swarrow$\n\n$Y$\n\nwith the assignments\n\n$Z \\sim \\text{LogLogistic} (\\alpha=1,\\beta=1)$\n\n$X=3Z^2 \\cdot N \\quad [N \\sim \\text{LogNormal}(\\mu=1,\\sigma=1)]$\n\n$Y = 2Z + \\sqrt{X} + N \\quad [N \\sim \\text{Normal}(\\mu=2,\\sigma=1)]$\n\n$V = Z + N^P \\quad [N \\sim \\text{Normal}(\\mu=0,\\sigma=1), P \\sim \\text{Ber}(0.5)]$\n\n$W = V + \\exp(T) - \\log(M) * N \\quad [N \\sim \\text{Normal}(\\mu=0,\\sigma=1), T \\sim \\text{StudentT}(\\nu = 0.5), M \\sim \\text{Exp}(\\lambda = 1)]$\n\nthere are 2 different ways implemented.\n\n## 1. List Of Strings\nDescribe the assignments as strings of the form:\n\n'VAR = FUNC(Noise, parent1, parent2, ...), Noise ~ DistributionXYZ'\n\nNote that in this case, one must not restate the noise symbol string in the distribution (as would otherwise be necessary in constructing sympy distributions).\n\n\n```python\nfrom scmodels import SCM\n\nassignment_seq = [\n \"Z = M; M ~ LogLogistic(alpha=1, beta=1)\",\n \"X = N * 3 * Z ** 2; N ~ LogNormal(mean=1, std=1)\",\n \"Y = P + 2 * Z + sqrt(X); P ~ Normal(mean=2, std=1)\",\n \"V = N**P + Z; N ~ Normal(0,1) / P ~ Bernoulli(0.5)\",\n \"W = exp(T) - log(M) * N + V; M ~ Exponential(1) / T ~ StudentT(0.5) / N ~ Normal(0, 2)\",\n]\n\nmyscm = SCM(assignment_seq)\n```\n\nAgreements:\n- The name of the noise variable in the distribution specification\n(e.g. `P ~ Normal(mean=2, std=1)`) has to align with the noise variable\nname (`P`) of the assignment string.\n- Multiple noise models can be composited as done for variable `W` in the above example.\nThe noise model string segment must specify all individual noise models separated by a '/'\n\n## 2. Assignment Map\n\nOne can construct the SCM via an assignment map with the variables as keys\nand a tuple defining the assignment and the noise.\n\n### 2-Tuple: Assignments via SymPy parsing\n\nTo refer to SymPy's string parsing capability (this includes numpy functions) provide a dict entry\nwith 2-tuples as values of the form:\n\n`'var': ('assignment string', noise)`\n\n\n\n\n```python\nfrom sympy.stats import LogLogistic, LogNormal, Normal, Bernoulli, StudentT, Exponential\n\nassignment_map = {\n \"Z\": (\n \"M\",\n LogLogistic(\"M\", alpha=1, beta=1)\n ),\n \"X\": (\n \"N * 3 * Z ** 2\",\n LogNormal(\"N\", mean=1, std=1),\n ),\n \"Y\": (\n \"P + 2 * Z + sqrt(X)\",\n Normal(\"P\", mean=2, std=1),\n ),\n \"V\": (\n \"N**P + Z\",\n [Normal(\"N\", 0, 1), Bernoulli(\"P\", 0.5)]\n ),\n \"W\": (\n \"exp(T) - log(M) * N + V\",\n [Normal(\"N\", 0, 1), StudentT(\"T\", 0.5), Exponential(\"M\", 1)]\n )\n}\n\nmyscm2 = SCM(assignment_map)\n```\n\nAgreements:\n- the name of the noise distribution provided in its constructor\n(e.g. `Normal(\"N\", mean=2, std=1)`) must align with the noise variable\nname (`N`) in the assignment string.\n- Multiple noise models in the assignment must be wrapped in an iterable (e.g. a list `[]`, or tuple `()`)\n\n### 3-Tuple: Assignments with arbitrary callables\n\nOne can also declare the SCM via specifying the variable assignment in a dictionary with the\nvariables as keys and as values a sequence of length 3 of the form:\n\n`'var': (['parent1', 'parent2', ...], Callable, Noise)`\n\nThis allows the user to supply functions that are not limited to predefined function sets of `scmodels`' dependencies.\n\n\n```python\nimport numpy as np\n\n\ndef Y_assignment(p, z, x):\n return p + 2 * z + np.sqrt(x)\n\n\nfunctional_map = {\n \"Z\": (\n [],\n lambda m: m,\n LogLogistic(\"M\", alpha=1, beta=1)\n ),\n \"X\": (\n [\"Z\"],\n lambda n, z: n * 3 * z ** 2,\n LogNormal(\"N\", mean=1, std=1),\n ),\n \"Y\": (\n [\"Z\", \"X\"],\n Y_assignment,\n Normal(\"P\", mean=2, std=1),\n ),\n \"V\": (\n [\"Z\"],\n lambda n, p, z: n ** p + z,\n [Normal(\"N\", mean=0, std=1), Bernoulli(\"P\", p=0.5)]\n ),\n \"W\": (\n [\"V\"],\n lambda n, t, m, v: np.exp(m) - np.log(m) * n + v,\n [Normal(\"N\", mean=0, std=1), StudentT(\"T\", nu=0.5), Exponential(\"M\", rate=1)]\n )\n}\n\nmyscm3 = SCM(functional_map)\n```\n\nAgreements:\n- The callable's first parameter MUST be the noise inputs in the order that they are given, e.g. see variable $W$. If the noise distribution list contains only `None`, then they have to be omitted from the parameter list of the callable.\n- The order of variables in the parents list determines the order of input for parameters in the functional past the noise parameters (left to right).\n\n# Features\n\n## Prettyprint\n\nThe SCM supports a form of informative printing of its current setup,\nwhich includes mentioning active interventions and the assignments.\n\n\n```python\nprint(myscm)\n```\n\n Structural Causal Model of 5 variables: Z, X, V, Y, W\n Variables with active interventions: []\n Assignments:\n Z := f(M) = M\t [ M ~ LogLogistic(alpha=1, beta=1) ]\n X := f(N, Z) = N * 3 * Z ** 2\t [ N ~ LogNormal(mean=1, std=1) ]\n Y := f(P, Z, X) = P + 2 * Z + sqrt(X)\t [ P ~ Normal(mean=2, std=1) ]\n V := f(N, P, Z) = N**P + Z\t [ N ~ Normal(mean=0, std=1), P ~ Bernoulli(p=0.5, succ=1, fail=0) ]\n W := f(M, T, N, V) = exp(T) - log(M) * N + V\t [ M ~ Exponential(rate=1), T ~ StudentT(nu=0.5), N ~ Normal(mean=0, std=2) ]\n\n\nIn the case of custom callable assignments, the output is less informative\n\n\n```python\nprint(myscm3)\n```\n\n Structural Causal Model of 5 variables: Z, X, V, Y, W\n Variables with active interventions: []\n Assignments:\n Z := f(M) = __unknown__\t [ M ~ LogLogistic(alpha=1, beta=1) ]\n X := f(N, Z) = __unknown__\t [ N ~ LogNormal(mean=1, std=1) ]\n Y := f(P, Z, X) = __unknown__\t [ P ~ Normal(mean=2, std=1) ]\n V := f(N, P, Z) = __unknown__\t [ N ~ Normal(mean=0, std=1), P ~ Bernoulli(p=0.5, succ=1, fail=0) ]\n W := f(N, T, M, V) = __unknown__\t [ N ~ Normal(mean=0, std=1), T ~ StudentT(nu=0.5), M ~ Exponential(rate=1) ]\n\n\n## Interventions\nOne can easily perform interventions on the variables,\ne.g. a Do-intervention or also general interventions, which remodel the connections, assignments, and noise distributions.\nFor general interventions, the passing structure is dict of the following form:\n\n {var: (New Parents (Optional), New Assignment (optional), New Noise (optional))}\n\nAny part of the original variable state, that is meant to be left unchanged, has to be passed as `None`.\nE.g. to assign a new callable assignment to variable `X` without changing parents or noise, one would call:\n\n\n```python\nmy_new_callable = lambda n, z: n + z\n\nmyscm.intervention({\"X\": (None, my_new_callable, None)})\n```\n\nFor the example of a do-intervention $\\text{do}(X=1)$, the helper method `do_intervention` is provided. Its counterpart for noise interventions is `soft_intervention`:\n\n\n```python\nmyscm.do_intervention([(\"X\", 1)])\n\nfrom sympy.stats import FiniteRV\n\nmyscm.soft_intervention([(\"X\", FiniteRV(myscm[\"X\"].noise[0].name, density={-1: .5, 1: .5}))])\n```\n\nRefer to the `sympy` docs for more information on building random variables.\n\nCalling `undo_intervention` restores the original state of *all* variables.\nIf variables are specified (`variables=['X', 'Y']`), any interventions on *only those variables* are undone.\n\n\n```python\nmyscm.undo_intervention(variables=[\"X\"])\n```\n\n## Sampling\n\nThe SCM allows drawing as many samples as needed through the method `myscm.sample(n)`.\n\n\n```python\nn = 5\nsamples = myscm.sample(n)\n\ndisplay_data(samples)\n```\n\n\n| | Z | V | X | W | Y |\n|----|----------|----------|-----------|---------|----------|\n| 0 | 0.854202 | 0.193336 | 15.9145 | 1.60315 | 7.55694 |\n| 1 | 0.634422 | 0.951119 | 2.43486 | 5.59541 | 3.6478 |\n| 2 | 1.02354 | 2.02354 | 4.11357 | 2.12862 | 6.23834 |\n| 3 | 0.376173 | 2.2962 | 0.252316 | 2.83333 | 3.64392 |\n| 4 | 4.19568 | 2.894 | 36.2844 | 3.07636 | 16.4083 |\n\n\nIf infinite sampling is desired, one can also receive a sampling generator through \n\n\n```python\ncontainer = {var: [] for var in myscm}\nsampler = myscm.sample_iter(container)\n```\n\n`container` is an optional target dictionary to store the computed samples in.\n\n\n```python\nfor i in range(n):\n next(sampler)\n\ndisplay_data(container)\n```\n\n\n| | Z | V | X | W | Y |\n|----|----------|-----------|------------|--------------|----------|\n| 0 | 0.245579 | 0.814717 | 1.59715 | 2.10267 | 2.26984 |\n| 1 | 7.88344 | 8.88344 | 122.197 | 1.90523e+13 | 29.1743 |\n| 2 | 1.26534 | 1.29397 | 12.5345 | 0.523827 | 8.77073 |\n| 3 | 1.37904 | 2.37904 | 13.327 | 2.08063 | 9.14935 |\n| 4 | 0.230563 | -0.966714 | 0.354393 | -1.22201 | 3.09432 |\n\n\nIf the target container is not provided, the generator returns a new `dict` for every sample.\n\n\n```python\nsample = next(myscm.sample_iter())\n\ndisplay_data(sample)\n```\n\n\n| | Z | V | X | W | Y |\n|----|---------|--------|---------|--------|---------|\n| 0 | 2.02308 | 3.1259 | 59.8718 | 4.2209 | 13.7312 |\n\n\n## Plotting\nIf you have graphviz installed, you can plot the DAG by calling\n\n```python\nmyscm.plot(node_size=1000, alpha=1)\n```\n\n\n",
"bugtrack_url": null,
"license": " The MIT License (MIT) Copyright (c) 2019 Michael Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ",
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