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[DOCS] Clarify expected diagram properties to fix #233 #443

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Aug 2, 2020
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[DOCS] Clarify expected diagram properties to fix #233 #443

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0aa72e0
Clarify expected diagram properties in docs to fix #233
ulupo Aug 2, 2020
411402a
Fix incorrect application of @staticmethod
ulupo Aug 2, 2020
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9 changes: 9 additions & 0 deletions gtda/diagrams/distance.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,6 +24,9 @@ class PairwiseDistance(BaseEstimator, TransformerMixin):
matrices or a single distance matrix between pairs of diagrams is
calculated according to the following steps:

Input collections of persistence diagrams for this transformer must
satisfy certain requirements, see e.g. :meth:`fit`.

1. All diagrams are partitioned into subdiagrams corresponding to
distinct homology dimensions.
2. Pairwise distances between subdiagrams of equal homology
Expand Down Expand Up @@ -146,6 +149,9 @@ def fit(self, X, y=None):
Input data. Array of persistence diagrams, each a collection of
triples [b, d, q] representing persistent topological features
through their birth (b), death (d) and homology dimension (q).
It is important that, for each possible homology dimension, the
number of triples for which q equals that homology dimension is
constants across the entries of `X`.

y : None
There is no need for a target in a transformer, yet the pipeline
Expand Down Expand Up @@ -190,6 +196,9 @@ def transform(self, X, y=None):
Input data. Array of persistence diagrams, each a collection of
triples [b, d, q] representing persistent topological features
through their birth (b), death (d) and homology dimension (q).
It is important that, for each possible homology dimension, the
number of triples for which q equals that homology dimension is
constants across the entries of `X`.

y : None
There is no need for a target in a transformer, yet the pipeline
Expand Down
21 changes: 20 additions & 1 deletion gtda/diagrams/features.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,6 +27,9 @@ class PersistenceEntropy(BaseEstimator, TransformerMixin):
calculated as the (base e) entropies of the collections of differences
d - b, normalized by the sum of all such differences.

Input collections of persistence diagrams for this transformer must
satisfy certain requirements, see e.g. :meth:`fit`.

Parameters
----------
n_jobs : int or None, optional, default: ``None``
Expand All @@ -50,7 +53,8 @@ class PersistenceEntropy(BaseEstimator, TransformerMixin):
def __init__(self, n_jobs=None):
self.n_jobs = n_jobs

def _persistence_entropy(self, X):
@staticmethod
def _persistence_entropy(X):
X_lifespan = X[:, :, 1] - X[:, :, 0]
X_normalized = X_lifespan / np.sum(X_lifespan, axis=1).reshape(-1, 1)
return - np.sum(np.nan_to_num(
Expand All @@ -69,6 +73,9 @@ def fit(self, X, y=None):
Input data. Array of persistence diagrams, each a collection of
triples [b, d, q] representing persistent topological features
through their birth (b), death (d) and homology dimension (q).
It is important that, for each possible homology dimension, the
number of triples for which q equals that homology dimension is
constants across the entries of `X`.

y : None
There is no need for a target in a transformer, yet the pipeline
Expand All @@ -95,6 +102,9 @@ def transform(self, X, y=None):
Input data. Array of persistence diagrams, each a collection of
triples [b, d, q] representing persistent topological features
through their birth (b), death (d) and homology dimension (q).
It is important that, for each possible homology dimension, the
number of triples for which q equals that homology dimension is
constants across the entries of `X`.

y : None
There is no need for a target in a transformer, yet the pipeline
Expand Down Expand Up @@ -137,6 +147,9 @@ class Amplitude(BaseEstimator, TransformerMixin):
3. The final result is either :math:`\\mathbf{a}` itself or
a norm of :math:`\\mathbf{a}`, specified by the parameter `order`.

Input collections of persistence diagrams for this transformer must
satisfy certain requirements, see e.g. :meth:`fit`.

Parameters
----------
metric : ``'bottleneck'`` | ``'wasserstein'`` | ``'landscape'`` | \
Expand Down Expand Up @@ -242,6 +255,9 @@ def fit(self, X, y=None):
Input data. Array of persistence diagrams, each a collection of
triples [b, d, q] representing persistent topological features
through their birth (b), death (d) and homology dimension (q).
It is important that, for each possible homology dimension, the
number of triples for which q equals that homology dimension is
constants across the entries of X.

y : None
There is no need for a target in a transformer, yet the pipeline
Expand Down Expand Up @@ -284,6 +300,9 @@ def transform(self, X, y=None):
Input data. Array of persistence diagrams, each a collection of
triples [b, d, q] representing persistent topological features
through their birth (b), death (d) and homology dimension (q).
It is important that, for each possible homology dimension, the
number of triples for which q equals that homology dimension is
constants across the entries of X.

y : None
There is no need for a target in a transformer, yet the pipeline
Expand Down
15 changes: 12 additions & 3 deletions gtda/diagrams/preprocessing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -125,6 +125,9 @@ class Scaler(BaseEstimator, TransformerMixin, PlotterMixin):
two-dimensional array of amplitudes (one per diagram and homology
dimension) to obtain :attr:`scale_`.

Input collections of persistence diagrams for this transformer must
satisfy certain requirements, see e.g. :meth:`fit`.

Parameters
----------
metric : ``'bottleneck'`` | ``'wasserstein'`` | ``'betti'`` | \
Expand Down Expand Up @@ -196,6 +199,9 @@ def fit(self, X, y=None):
Input data. Array of persistence diagrams, each a collection of
triples [b, d, q] representing persistent topological features
through their birth (b), death (d) and homology dimension (q).
It is important that, for each possible homology dimension, the
number of triples for which q equals that homology dimension is
constants across the entries of X.

y : None
There is no need for a target in a transformer, yet the pipeline
Expand Down Expand Up @@ -244,6 +250,9 @@ def transform(self, X, y=None):
Input data. Array of persistence diagrams, each a collection of
triples [b, d, q] representing persistent topological features
through their birth (b), death (d) and homology dimension (q).
It is important that, for each possible homology dimension, the
number of triples for which q equals that homology dimension is
constants across the entries of X.

y : None
There is no need for a target in a transformer, yet the pipeline
Expand All @@ -262,13 +271,13 @@ def transform(self, X, y=None):
return Xs

def inverse_transform(self, X):
"""Scale back the data to the original representation. Multiplies
by the scale found in :meth:`fit`.
"""Scale back the data to the original representation. Multiplies by
the scale found in :meth:`fit`.

Parameters
----------
X : ndarray of shape (n_samples, n_features, 3)
Data to apply the inverse transform to.
Data to apply the inverse transform to, c.f. :meth:`transform`.

Returns
-------
Expand Down
45 changes: 45 additions & 0 deletions gtda/diagrams/representations.py
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Original file line number Diff line number Diff line change
Expand Up @@ -35,6 +35,9 @@ class BettiCurve(BaseEstimator, TransformerMixin, PlotterMixin):
considered separately, and their respective Betti curves are obtained by
evenly sampling the :ref:`filtration parameter <filtered_complex>`.

Input collections of persistence diagrams for this transformer must
satisfy certain requirements, see e.g. :meth:`fit`.

Parameters
----------
n_bins : int, optional, default: ``100``
Expand Down Expand Up @@ -93,6 +96,9 @@ def fit(self, X, y=None):
Input data. Array of persistence diagrams, each a collection of
triples [b, d, q] representing persistent topological features
through their birth (b), death (d) and homology dimension (q).
It is important that, for each possible homology dimension, the
number of triples for which q equals that homology dimension is
constants across the entries of X.

y : None
There is no need for a target in a transformer, yet the pipeline
Expand Down Expand Up @@ -124,6 +130,9 @@ def transform(self, X, y=None):
Input data. Array of persistence diagrams, each a collection of
triples [b, d, q] representing persistent topological features
through their birth (b), death (d) and homology dimension (q).
It is important that, for each possible homology dimension, the
number of triples for which q equals that homology dimension is
constants across the entries of X.

y : None
There is no need for a target in a transformer, yet the pipeline
Expand Down Expand Up @@ -238,6 +247,9 @@ class PersistenceLandscape(BaseEstimator, TransformerMixin, PlotterMixin):
landscapes are obtained by evenly sampling the :ref:`filtration parameter
<filtered_complex>`.

Input collections of persistence diagrams for this transformer must
satisfy certain requirements, see e.g. :meth:`fit`.

Parameters
----------
n_layers : int, optional, default: ``1``
Expand Down Expand Up @@ -302,6 +314,9 @@ def fit(self, X, y=None):
Input data. Array of persistence diagrams, each a collection of
triples [b, d, q] representing persistent topological features
through their birth (b), death (d) and homology dimension (q).
It is important that, for each possible homology dimension, the
number of triples for which q equals that homology dimension is
constants across the entries of X.

y : None
There is no need for a target in a transformer, yet the pipeline
Expand Down Expand Up @@ -333,6 +348,9 @@ def transform(self, X, y=None):
Input data. Array of persistence diagrams, each a collection of
triples [b, d, q] representing persistent topological features
through their birth (b), death (d) and homology dimension (q).
It is important that, for each possible homology dimension, the
number of triples for which q equals that homology dimension is
constants across the entries of X.

y : None
There is no need for a target in a transformer, yet the pipeline
Expand Down Expand Up @@ -462,6 +480,9 @@ class HeatKernel(BaseEstimator, TransformerMixin, PlotterMixin):
diagonal, and the difference between the results of the two convolutions is
computed. The result can be thought of as a (multi-channel) raster image.

Input collections of persistence diagrams for this transformer must
satisfy certain requirements, see e.g. :meth:`fit`.

Parameters
----------
sigma : float, optional default ``1.``
Expand Down Expand Up @@ -534,6 +555,9 @@ def fit(self, X, y=None):
Input data. Array of persistence diagrams, each a collection of
triples [b, d, q] representing persistent topological features
through their birth (b), death (d) and homology dimension (q).
It is important that, for each possible homology dimension, the
number of triples for which q equals that homology dimension is
constants across the entries of X.

y : None
There is no need for a target in a transformer, yet the pipeline
Expand Down Expand Up @@ -567,6 +591,9 @@ def transform(self, X, y=None):
Input data. Array of persistence diagrams, each a collection of
triples [b, d, q] representing persistent topological features
through their birth (b), death (d) and homology dimension (q).
It is important that, for each possible homology dimension, the
number of triples for which q equals that homology dimension is
constants across the entries of X.

y : None
There is no need for a target in a transformer, yet the pipeline
Expand Down Expand Up @@ -642,6 +669,9 @@ class PersistenceImage(BaseEstimator, TransformerMixin, PlotterMixin):
<filtered_complex>`. The result can be thought of as a (multi-channel)
raster image.

Input collections of persistence diagrams for this transformer must
satisfy certain requirements, see e.g. :meth:`fit`.

Parameters
----------
sigma : float, optional default ``1.``
Expand Down Expand Up @@ -731,6 +761,9 @@ def fit(self, X, y=None):
Input data. Array of persistence diagrams, each a collection of
triples [b, d, q] representing persistent topological features
through their birth (b), death (d) and homology dimension (q).
It is important that, for each possible homology dimension, the
number of triples for which q equals that homology dimension is
constants across the entries of X.

y : None
There is no need for a target in a transformer, yet the pipeline
Expand Down Expand Up @@ -771,6 +804,9 @@ def transform(self, X, y=None):
Input data. Array of persistence diagrams, each a collection of
triples [b, d, q] representing persistent topological features
through their birth (b), death (d) and homology dimension (q).
It is important that, for each possible homology dimension, the
number of triples for which q equals that homology dimension is
constants across the entries of X.

y : None
There is no need for a target in a transformer, yet the pipeline
Expand Down Expand Up @@ -850,6 +886,9 @@ class Silhouette(BaseEstimator, TransformerMixin, PlotterMixin):
spaced locations from appropriate ranges of the :ref:`filtration parameter
<filtered_complex>`.

Input collections of persistence diagrams for this transformer must
satisfy certain requirements, see e.g. :meth:`fit`.

Parameters
----------
power: float, optional, default: ``1.``
Expand Down Expand Up @@ -923,6 +962,9 @@ def fit(self, X, y=None):
Input data. Array of persistence diagrams, each a collection of
triples [b, d, q] representing persistent topological features
through their birth (b), death (d) and homology dimension (q).
It is important that, for each possible homology dimension, the
number of triples for which q equals that homology dimension is
constants across the entries of X.

y : None
There is no need for a target in a transformer, yet the pipeline
Expand Down Expand Up @@ -954,6 +996,9 @@ def transform(self, X, y=None):
Input data. Array of persistence diagrams, each a collection of
triples [b, d, q] representing persistent topological features
through their birth (b), death (d) and homology dimension (q).
It is important that, for each possible homology dimension, the
number of triples for which q equals that homology dimension is
constants across the entries of X.

y : None
There is no need for a target in a transformer, yet the pipeline
Expand Down