代码请教：cg = pc(data, alpha, indep_test, stable, uc_rule, uc_priority, mvpc...
代码请教：在GitHub中下载了causal—learn包，现在想要运行其中一个PC算法（https://causal-learn.readthedocs.io/en/latest/search_methods_index/Constraint-based%20causal%20discovery%20methods/PC.html#algorithm-introduction）网址中介绍了使用步骤：
from causallearn.search.ConstraintBased.PC import pc
cg = pc(data, alpha, indep_test, stable, uc_rule, uc_priority, mvpc, correction_name, background_knowledge, verbose, show_progress)
# visualization using pydot
cg.draw_pydot_graph()
# or save the graph
from causallearn.utils.GraphUtils import GraphUtils
pyd = GraphUtils.to_pydot(cg.G)
pyd.write_png('simple_test.png')
# visualization using networkx
# cg.to_nx_graph()
# cg.draw_nx_graph(skel=False)
参数解释是：
* data:必须要求numpy.ndarray，格式为(n_samples, n_features)，其中n_samples 是样本数，n_features 是特征数
* alpha:独立性测试的显著性水平，(float) in (0, 1)，Default: 0.05.
* indep_test：独立性测试的方法 Default: ‘fisherz’
* stable:是否执行稳定的图骨架发现，Default: True.
* uc_rule:how unshielded colliders are oriented. Default: 0.
* uc_priority:rule of resolving conflicts between unshielded colliders. Default: 2.
* mvpc:数据是否包含缺失值，若包含，请设置为True. Default: False.
* correction_name:Missing value correction if using missing-value PC
* background_knowledge:背景知识（指定因果边及方向）
* verbose:如果要打印verbose日志输出，请设置为True，Default: False.
* show_progress:如果要在控制台显示算法的进展，则为True。默认情况下. Default: True.
正常来说更改配好环境，把data设置一下，就可以直接跑了，但是自己编写的运行时出现问题，这里的cg = pc(data, alpha, indep_test, stable, uc_rule, uc_priority, mvpc, correction_name, background_knowledge, verbose, show_progress)要如何输入数据呢？
由于是初学者我自己写的代码很糟糕，请大佬们指点：
from causallearn.search.ConstraintBased.PC import pc
n_samples=100
n_features=100
data1= np.ndarray(n_samples,n_features)
cg = pc(data1)
print(data1)
print(cg)
TypeError Traceback (most recent call last)
/var/folders/dv/n35fhjq505zghgjw41r6z63r0000gn/T/ipykernel_1697/1391707814.py in <module>
3 n_samples=100
4 n_features=100
----> 5 data1= np.ndarray(n_samples,n_features)
6
7 cg = pc(data1)
TypeError: Cannot interpret '100' as a data type
本帖最后由 suchocolate 于 2022-10-22 10:04 编辑
print(help(np.ndarray))
Help on class ndarray in module numpy:
class ndarray(builtins.object)
|ndarray(shape, dtype=float, buffer=None, offset=0,
| strides=None, order=None)
|
|An array object represents a multidimensional, homogeneous array
|of fixed-size items.An associated data-type object describes the
|format of each element in the array (its byte-order, how many bytes it
|occupies in memory, whether it is an integer, a floating point number,
|or something else, etc.)
|
|Arrays should be constructed using `array`, `zeros` or `empty` (refer
|to the See Also section below).The parameters given here refer to
|a low-level method (`ndarray(...)`) for instantiating an array.
|
|For more information, refer to the `numpy` module and examine the
|methods and attributes of an array.
|
|Parameters
|----------
|(for the __new__ method; see Notes below)
|
|shape : tuple of ints
| Shape of created array.
shape需要用元祖形式传入：
data1= np.ndarray((n_samples,n_features)) suchocolate 发表于 2022-10-22 10:03
shape需要用元祖形式传入：
谢谢大佬的指导！后续自己也看懂了，我也再学习一下大佬的思路！
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