Credits to these posts for helping me figure this one out. I used eval to make it work.
https://docs.python.org/2/library/functions.html#eval
In Python: How do I convert a user input into a piece of code?
How can I get the name of an object in Python?
Here's a 95% working version of what I was looking for:
## This first part is PURELY bc I wanted color.
from IPython.display import Markdown, display
def printmd(string, color=None):
if '__' in string:
colorstr = "__<span style='color:{}'>_{}</span>__".format(color, string)
else:
colorstr = "__<span style='color:{}'>{}</span>__".format(color, string)
display(Markdown(colorstr))
printmd("*italics*, **bold**", color='blue')
# The actual solution. If you don't want formatting/don't want to deal with the above code, replace 'printmd' with print.
def describe(model):
# get name of model
model_name = [objname for objname, oid in globals().items() if id(oid)==id(model)][0]
# create list, sort, but __doc__ at front
calls = sorted([model_name+ '.' + i for i in dir(PCA)])
doc_index = calls.index(model_name+'.__doc__')
calls.pop(doc_index)
calls = [model_name+'.__doc__'] + calls
# print
for c in calls:
try:
if '__' in c:
printmd(f'{c}',color='blue')
else:
printmd(c,color='blue')
printmd(str(eval(c+'()')))
except Exception as e:
if 'is not callable' in str(e).lower():
try:
printmd(str(eval(c)))
except Exception as e:
print(e)
else:
print(e)
printmd('/'*20, color='gray')
Output looks like this (but with color in my notebook):
_PCA.doc____
__Principal component analysis (PCA).
Linear dimensionality reduction using Singular Value Decomposition of the
data to project it to a lower dimensional space. The input data is centered
but not scaled for each feature before applying the SVD.
It uses the LAPACK implementation of the full SVD or a randomized truncated
SVD by the method of Halko et al. 2009, depending on the shape of the input
data and the number of components to extract.
It can also use the scipy.sparse.linalg ARPACK implementation of the
truncated SVD.
Notice that this class does not support sparse input. See
:class:`TruncatedSVD` for an alternative with sparse data.
Read more in the :ref:`User Guide `.
Parameters
----------
n_components : int, float, None or str
Number of components to keep.
if n_components is not set all components are kept::
n_components == min(n_samples, n_features)
If ``n_components == 'mle'`` and ``svd_solver == 'full'``, Minka's
MLE is used to guess the dimension. Use of ``n_components == 'mle'``
will interpret ``svd_solver == 'auto'`` as ``svd_solver == 'full'``.
If ``0 = 0, optional (default .0)
Tolerance for singular values computed by svd_solver == 'arpack'.
.. versionadded:: 0.18.0
iterated_power : int >= 0, or 'auto', (default 'auto')
Number of iterations for the power method computed by
svd_solver == 'randomized'.
.. versionadded:: 0.18.0
random_state : int, RandomState instance, default=None
Used when ``svd_solver`` == 'arpack' or 'randomized'. Pass an int
for reproducible results across multiple function calls.
See :term:`Glossary `.
.. versionadded:: 0.18.0
Attributes
----------
components_ : array, shape (n_components, n_features)
Principal axes in feature space, representing the directions of
maximum variance in the data. The components are sorted by
``explained_variance_``.
explained_variance_ : array, shape (n_components,)
The amount of variance explained by each of the selected components.
Equal to n_components largest eigenvalues
of the covariance matrix of X.
.. versionadded:: 0.18
explained_variance_ratio_ : array, shape (n_components,)
Percentage of variance explained by each of the selected components.
If ``n_components`` is not set then all components are stored and the
sum of the ratios is equal to 1.0.
singular_values_ : array, shape (n_components,)
The singular values corresponding to each of the selected components.
The singular values are equal to the 2-norms of the ``n_components``
variables in the lower-dimensional space.
.. versionadded:: 0.19
mean_ : array, shape (n_features,)
Per-feature empirical mean, estimated from the training set.
Equal to `X.mean(axis=0)`.
n_components_ : int
The estimated number of components. When n_components is set
to 'mle' or a number between 0 and 1 (with svd_solver == 'full') this
number is estimated from input data. Otherwise it equals the parameter
n_components, or the lesser value of n_features and n_samples
if n_components is None.
n_features_ : int
Number of features in the training data.
n_samples_ : int
Number of samples in the training data.
noise_variance_ : float
The estimated noise covariance following the Probabilistic PCA model
from Tipping and Bishop 1999. See "Pattern Recognition and
Machine Learning" by C. Bishop, 12.2.1 p. 574 or
http://www.miketipping.com/papers/met-mppca.pdf. It is required to
compute the estimated data covariance and score samples.
Equal to the average of (min(n_features, n_samples) - n_components)
smallest eigenvalues of the covariance matrix of X.
See Also
--------
KernelPCA : Kernel Principal Component Analysis.
SparsePCA : Sparse Principal Component Analysis.
TruncatedSVD : Dimensionality reduction using truncated SVD.
IncrementalPCA : Incremental Principal Component Analysis.
References
----------
For n_components == 'mle', this class uses the method of *Minka, T. P.
"Automatic choice of dimensionality for PCA". In NIPS, pp. 598-604*
Implements the probabilistic PCA model from:
Tipping, M. E., and Bishop, C. M. (1999). "Probabilistic principal
component analysis". Journal of the Royal Statistical Society:
Series B (Statistical Methodology), 61(3), 611-622.
via the score and score_samples methods.
See http://www.miketipping.com/papers/met-mppca.pdf
For svd_solver == 'arpack', refer to `scipy.sparse.linalg.svds`.
For svd_solver == 'randomized', see:
*Halko, N., Martinsson, P. G., and Tropp, J. A. (2011).
"Finding structure with randomness: Probabilistic algorithms for
constructing approximate matrix decompositions".
SIAM review, 53(2), 217-288.* and also
*Martinsson, P. G., Rokhlin, V., and Tygert, M. (2011).
"A randomized algorithm for the decomposition of matrices".
Applied and Computational Harmonic Analysis, 30(1), 47-68.*
Examples
--------
>>> import numpy as np
>>> from sklearn.decomposition import PCA
>>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]])
>>> pca = PCA(n_components=2)
>>> pca.fit(X)
PCA(n_components=2)
>>> print(pca.explained_variance_ratio_)
[0.9924... 0.0075...]
>>> print(pca.singular_values_)
[6.30061... 0.54980...]
>>> pca = PCA(n_components=2, svd_solver='full')
>>> pca.fit(X)
PCA(n_components=2, svd_solver='full')
>>> print(pca.explained_variance_ratio_)
[0.9924... 0.00755...]
>>> print(pca.singular_values_)
[6.30061... 0.54980...]
>>> pca = PCA(n_components=1, svd_solver='arpack')
>>> pca.fit(X)
PCA(n_components=1, svd_solver='arpack')
>>> print(pca.explained_variance_ratio_)
[0.99244...]
>>> print(pca.singular_values_)
[6.30061...]
__
////////////////////
_PCA.abstractmethods____
frozenset()
////////////////////
_PCA.class____
PCA()
////////////////////
_PCA.delattr____
expected 1 arguments, got 0
////////////////////
_PCA.dict____
{'n_components': None, 'copy': True, 'whiten': False, 'svd_solver': 'auto', 'tol': 0.0, 'iterated_power': 'auto', 'random_state': None, 'n_features_in_': 7, '_fit_svd_solver': 'full', 'mean_': array([ 8.51677937e-17, 2.07596497e-16, -6.15945651e-17, -5.47507245e-17, 2.12919484e-16, 3.28767123e-01, 6.71232877e-01]), 'noise_variance_': 0.4511471014202474, 'n_samples_': 73, 'n_features_': 7, 'components_': array([[-0.45434621, -0.35524292, 0.49366373, -0.41320409, 0.47423634, 0.11838578, -0.11838578], [ 0.54661908, -0.36755507, 0.45744847, -0.27707168, -0.51389186, 0.08943766, -0.08943766]]), 'n_components_': 2, 'explained_variance_': array([2.11557313, 1.1456244 ]), 'explained_variance_ratio_': array([0.38346906, 0.20765603]), 'singular_values_': array([12.34185015, 9.0821229 ])}
////////////////////
_PCA.dir____
_['n_components', 'copy', 'whiten', 'svd_solver', 'tol', 'iterated_power', 'random_state', 'n_features_in_', '_fit_svd_solver', 'mean_', 'noise_variance_', 'n_samples_', 'n_features_', 'components_', 'n_components_', 'explained_variance_', 'explained_variance_ratio_', 'singular_values_', 'module__', 'doc', 'init', 'fit', 'fit_transform', 'fit', 'fit_full', '_fit_truncated', 'score_samples', 'score', 'abstractmethods', '_abc_impl', 'get_covariance', 'get_precision', 'transform', 'inverse_transform', 'dict', 'weakref', 'repr', 'hash', 'str', 'getattribute', 'setattr', 'delattr', 'lt', 'le', 'eq', 'ne', 'gt', 'ge', 'new', 'reduce_ex', 'reduce', 'subclasshook', 'init_subclass', 'format', 'sizeof', 'dir', 'class', '_get_param_names', 'get_params', 'set_params', 'getstate', 'setstate', '_more_tags', '_get_tags', '_check_n_features', '_validate_data', '_repr_html_', '_repr_html_inner', '_repr_mimebundle_']
////////////////////
_PCA.eq____
expected 1 arguments, got 0
////////////////////
_PCA.format____
__format__() takes exactly one argument (0 given)
////////////////////
_PCA.ge____
expected 1 arguments, got 0
////////////////////
_PCA.getattribute____
expected 1 arguments, got 0
////////////////////
_PCA.getstate____
{'n_components': None, 'copy': True, 'whiten': False, 'svd_solver': 'auto', 'tol': 0.0, 'iterated_power': 'auto', 'random_state': None, 'n_features_in_': 7, '_fit_svd_solver': 'full', 'mean_': array([ 8.51677937e-17, 2.07596497e-16, -6.15945651e-17, -5.47507245e-17, 2.12919484e-16, 3.28767123e-01, 6.71232877e-01]), 'noise_variance_': 0.4511471014202474, 'n_samples_': 73, 'n_features_': 7, 'components_': array([[-0.45434621, -0.35524292, 0.49366373, -0.41320409, 0.47423634, 0.11838578, -0.11838578], [ 0.54661908, -0.36755507, 0.45744847, -0.27707168, -0.51389186, 0.08943766, -0.08943766]]), 'n_components_': 2, 'explained_variance_': array([2.11557313, 1.1456244 ]), 'explained_variance_ratio_': array([0.38346906, 0.20765603]), 'singular_values_': array([12.34185015, 9.0821229 ]), '_sklearn_version': '0.23.1'}
////////////////////
_PCA.gt____
expected 1 arguments, got 0
////////////////////
_PCA.hash____
8773832952721
////////////////////
_PCA.init____
None
////////////////////
_PCA.init_subclass____
None
////////////////////
_PCA.le____
expected 1 arguments, got 0
////////////////////
_PCA.lt____
expected 1 arguments, got 0
////////////////////
_PCA.module____
sklearn.decomposition._pca
////////////////////
_PCA.ne____
expected 1 arguments, got 0
////////////////////
_PCA.new____
object.__new__(): not enough arguments
////////////////////
_PCA.reduce____
(, (, , None), {'n_components': None, 'copy': True, 'whiten': False, 'svd_solver': 'auto', 'tol': 0.0, 'iterated_power': 'auto', 'random_state': None, 'n_features_in_': 7, '_fit_svd_solver': 'full', 'mean_': array([ 8.51677937e-17, 2.07596497e-16, -6.15945651e-17, -5.47507245e-17, 2.12919484e-16, 3.28767123e-01, 6.71232877e-01]), 'noise_variance_': 0.4511471014202474, 'n_samples_': 73, 'n_features_': 7, 'components_': array([[-0.45434621, -0.35524292, 0.49366373, -0.41320409, 0.47423634, 0.11838578, -0.11838578], [ 0.54661908, -0.36755507, 0.45744847, -0.27707168, -0.51389186, 0.08943766, -0.08943766]]), 'n_components_': 2, 'explained_variance_': array([2.11557313, 1.1456244 ]), 'explained_variance_ratio_': array([0.38346906, 0.20765603]), 'singular_values_': array([12.34185015, 9.0821229 ]), '_sklearn_version': '0.23.1'})
////////////////////
_PCA.reduce_ex____
__reduce_ex__() takes exactly one argument (0 given)
////////////////////
_PCA.repr____
PCA()
////////////////////
_PCA.setattr____
expected 2 arguments, got 0
////////////////////
_PCA.setstate____
__setstate__() missing 1 required positional argument: 'state'
////////////////////
_PCA.sizeof____
32
////////////////////
_PCA.str____
PCA()
////////////////////
_PCA.subclasshook____
NotImplemented
////////////////////
_PCA.weakref____
None
////////////////////
PCA._abc_impl
////////////////////
PCA._check_n_features
_check_n_features() missing 2 required positional arguments: 'X' and 'reset'
////////////////////
PCA._fit
_fit() missing 1 required positional argument: 'X'
////////////////////
PCA._fit_full
_fit_full() missing 2 required positional arguments: 'X' and 'n_components'
////////////////////
PCA._fit_svd_solver
full
////////////////////
PCA._fit_truncated
_fit_truncated() missing 3 required positional arguments: 'X', 'n_components', and 'svd_solver'
////////////////////
PCA._get_param_names
['copy', 'iterated_power', 'n_components', 'random_state', 'svd_solver', 'tol', 'whiten']
////////////////////
PCA._get_tags
{'non_deterministic': False, 'requires_positive_X': False, 'requires_positive_y': False, 'X_types': ['2darray'], 'poor_score': False, 'no_validation': False, 'multioutput': False, 'allow_nan': False, 'stateless': False, 'multilabel': False, '_skip_test': False, '_xfail_checks': False, 'multioutput_only': False, 'binary_only': False, 'requires_fit': True, 'requires_y': False}
////////////////////
PCA._more_tags
{'non_deterministic': False, 'requires_positive_X': False, 'requires_positive_y': False, 'X_types': ['2darray'], 'poor_score': False, 'no_validation': False, 'multioutput': False, 'allow_nan': False, 'stateless': False, 'multilabel': False, '_skip_test': False, '_xfail_checks': False, 'multioutput_only': False, 'binary_only': False, 'requires_fit': True, 'requires_y': False}
////////////////////
PCA._repr_html_
_repr_html_ is only defined when the 'display' configuration option is set to 'diagram'
////////////////////
PCA._repr_html_inner
_
PCA
__
////////////////////
PCA._repr_mimebundle_
{'text/plain': 'PCA()'}
////////////////////
PCA._validate_data
_validate_data() missing 1 required positional argument: 'X'
////////////////////
PCA.components_
[[-0.45434621 -0.35524292 0.49366373 -0.41320409 0.47423634 0.11838578 -0.11838578] [ 0.54661908 -0.36755507 0.45744847 -0.27707168 -0.51389186 0.08943766 -0.08943766]]
////////////////////
PCA.copy
True
////////////////////
PCA.explained_variance_
[2.11557313 1.1456244 ]
////////////////////
PCA.explained_variance_ratio_
[0.38346906 0.20765603]
////////////////////
PCA.fit
fit() missing 1 required positional argument: 'X'
////////////////////
PCA.fit_transform
fit_transform() missing 1 required positional argument: 'X'
////////////////////
PCA.get_covariance
[[ 1.00223991 0.12911456 -0.19966709 0.20729509 -0.55371051 -0.05557453 0.05557453] [ 0.12911456 0.75501517 -0.40865898 0.31504242 -0.14922901 -0.09282835 0.09282835] [-0.19966709 -0.40865898 1.00209988 -0.4275383 0.22640718 0.12568689 -0.12568689] [ 0.20729509 0.31504242 -0.4275383 0.78864137 -0.22727185 -0.09862913 0.09862913] [-0.55371051 -0.14922901 0.22640718 -0.22727185 1.00887762 0.06152653 -0.06152653] [-0.05557453 -0.09282835 0.12568689 -0.09862913 0.06152653 0.48002954 -0.02888244] [ 0.05557453 0.09282835 -0.12568689 0.09862913 -0.06152653 -0.02888244 0.48002954]]
////////////////////
PCA.get_params
{'copy': True, 'iterated_power': 'auto', 'n_components': None, 'random_state': None, 'svd_solver': 'auto', 'tol': 0.0, 'whiten': False}
////////////////////
PCA.get_precision
[[ 1.45509751 -0.01150567 0.05515513 -0.12388856 0.75319631 0.02810987 -0.02810987] [-0.01150567 1.8149702 0.5317503 -0.39282107 0.03999094 0.11751172 -0.11751172] [ 0.05515513 0.5317503 1.51040153 0.52603156 -0.09239476 -0.15689193 0.15689193] [-0.12388856 -0.39282107 0.52603156 1.81567184 0.15040527 0.11860395 -0.11860395] [ 0.75319631 0.03999094 -0.09239476 0.15040527 1.46952464 -0.03614924 0.03614924] [ 0.02810987 0.11751172 -0.15689193 0.11860395 -0.03614924 2.18138275 0.03518918] [-0.02810987 -0.11751172 0.15689193 -0.11860395 0.03614924 0.03518918 2.18138275]]
////////////////////
PCA.inverse_transform
inverse_transform() missing 1 required positional argument: 'X'
////////////////////
PCA.iterated_power
auto
////////////////////
PCA.mean_
[ 8.51677937e-17 2.07596497e-16 -6.15945651e-17 -5.47507245e-17 2.12919484e-16 3.28767123e-01 6.71232877e-01]
////////////////////
PCA.n_components
None
////////////////////
PCA.n_components_
2
////////////////////
PCA.n_features_
7
////////////////////
PCA.n_features_in_
7
////////////////////
PCA.n_samples_
73
////////////////////
PCA.noise_variance_
0.4511471014202474
////////////////////
PCA.random_state
None
////////////////////
PCA.score
score() missing 1 required positional argument: 'X'
////////////////////
PCA.score_samples
score_samples() missing 1 required positional argument: 'X'
////////////////////
PCA.set_params
PCA()
////////////////////
PCA.singular_values_
[12.34185015 9.0821229 ]
////////////////////
PCA.svd_solver
auto
////////////////////
PCA.tol
0.0
////////////////////
PCA.transform
transform() missing 1 required positional argument: 'X'
////////////////////
PCA.whiten
False
////////////////////
