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Merge pull request #451 from GAA-UAM/feature/fixDocumentation
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fix examples
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@vnmabus
vnmabus authored Jun 8, 2022
2 parents 41cda7f + 4a5d493 commit 4541b91
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1 change: 1 addition & 0 deletions docs/modules/preprocessing.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -13,6 +13,7 @@ this category deal with this problem.
preprocessing/smoothing
preprocessing/registration
preprocessing/dim_reduction
preprocessing/feature_construction

Smoothing
---------
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86 changes: 45 additions & 41 deletions skfda/exploratory/stats/_functional_transformers.py
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Expand Up @@ -37,17 +37,18 @@ def local_averages(
ndarray of shape (n_samples, n_intervals, n_dimensions) with
the transformed data.
Example:
Examples:
We import the Berkeley Growth Study dataset.
We will use only the first 3 samples to make the
example easy.
example easy
>>> from skfda.datasets import fetch_growth
>>> dataset = fetch_growth(return_X_y=True)[0]
>>> X = dataset[:3]
Then we decide how many intervals we want to consider (in our case 2)
and call the function with the dataset.
>>> import numpy as np
>>> from skfda.exploratory.stats import local_averages
>>> np.around(local_averages(X, 2), decimals=2)
Expand Down Expand Up @@ -151,9 +152,10 @@ def occupation_measure(
ndarray of shape (n_samples, n_intervals)
with the transformed data.
Example:
Examples:
We will create the FDataGrid that we will use to extract
the occupation measure
the occupation measure.
>>> from skfda.representation import FDataGrid
>>> import numpy as np
>>> t = np.linspace(0, 10, 100)
Expand All @@ -171,6 +173,7 @@ def occupation_measure(
and (2.0, 3.0). We need also to specify the number of points
we want that the function takes into account to interpolate.
We are going to use 501 points.
>>> from skfda.exploratory.stats import occupation_measure
>>> np.around(
... occupation_measure(
Expand Down Expand Up @@ -238,42 +241,43 @@ def number_up_crossings(
ndarray of shape (n_samples, len(levels))\
with the values of the counters.
Example:
For this example we will use a well known function so the correct
functioning of this method can be checked.
We will create and use a DataFrame with a sample extracted from
the Bessel Function of first type and order 0.
First of all we import the Bessel Function and create the X axis
data grid. Then we create the FdataGrid.
>>> from skfda.exploratory.stats import number_up_crossings
>>> from scipy.special import jv
>>> import numpy as np
>>> x_grid = np.linspace(0, 14, 14)
>>> fd_grid = FDataGrid(
... data_matrix=[jv([0], x_grid)],
... grid_points=x_grid,
... )
>>> fd_grid.data_matrix
array([[[ 1. ],
[ 0.73041066],
[ 0.13616752],
[-0.32803875],
[-0.35967936],
[-0.04652559],
[ 0.25396879],
[ 0.26095573],
[ 0.01042895],
[-0.22089135],
[-0.2074856 ],
[ 0.0126612 ],
[ 0.20089319],
[ 0.17107348]]])
Finally we evaluate the number of up crossings method with the FDataGrid
created.
>>> number_up_crossings(fd_grid, np.asarray([0]))
array([[2]])
Examples:
For this example we will use a well known function so the correct
functioning of this method can be checked.
We will create and use a DataFrame with a sample extracted from
the Bessel Function of first type and order 0.
First of all we import the Bessel Function and create the X axis
data grid. Then we create the FdataGrid.
>>> from skfda.exploratory.stats import number_up_crossings
>>> from scipy.special import jv
>>> import numpy as np
>>> x_grid = np.linspace(0, 14, 14)
>>> fd_grid = FDataGrid(
... data_matrix=[jv([0], x_grid)],
... grid_points=x_grid,
... )
>>> fd_grid.data_matrix
array([[[ 1. ],
[ 0.73041066],
[ 0.13616752],
[-0.32803875],
[-0.35967936],
[-0.04652559],
[ 0.25396879],
[ 0.26095573],
[ 0.01042895],
[-0.22089135],
[-0.2074856 ],
[ 0.0126612 ],
[ 0.20089319],
[ 0.17107348]]])
Finally we evaluate the number of up crossings method with the
FDataGrid created.
>>> number_up_crossings(fd_grid, np.asarray([0]))
array([[2]])
"""
curves = data.data_matrix[:, :, 0]

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74 changes: 39 additions & 35 deletions skfda/preprocessing/feature_construction/_fda_feature_union.py
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Expand Up @@ -47,41 +47,45 @@ class FDAFeatureUnion(FeatureUnion): # type: ignore
output. By default the value is False.
Examples:
Firstly we will import the Berkeley Growth Study data set
>>> from skfda.datasets import fetch_growth
>>> X,y = fetch_growth(return_X_y=True)
Then we need to import the transformers we want to use. In our case we
will use the Recursive Maxima Hunting method to select important features.
We will concatenate to the results of the previous method the original
curves with an Evaluation Transfomer.
>>> from skfda.preprocessing.feature_construction import (
... FDAFeatureUnion,
... )
>>> from skfda.preprocessing.dim_reduction.variable_selection import (
... RecursiveMaximaHunting,
... )
>>> from skfda.preprocessing.feature_construction import (
... EvaluationTransformer,
... )
>>> import numpy as np
Finally we apply fit and transform.
>>> union = FDAFeatureUnion(
... [
... ("rmh", RecursiveMaximaHunting()),
... ("eval", EvaluationTransformer()),
... ],
... array_output=True,
... )
>>> np.around(union.fit_transform(X,y), decimals=2)
array([[ 195.1, 141.1, 163.8, ..., 193.8, 194.3, 195.1],
[ 178.7, 133. , 148.1, ..., 176.1, 177.4, 178.7],
[ 171.5, 126.5, 143.6, ..., 170.9, 171.2, 171.5],
...,
[ 166.8, 132.8, 152.2, ..., 166. , 166.3, 166.8],
[ 168.6, 139.4, 161.6, ..., 168.3, 168.4, 168.6],
[ 169.2, 138.1, 161.7, ..., 168.6, 168.9, 169.2]])
Firstly we will import the Berkeley Growth Study data set:
>>> from skfda.datasets import fetch_growth
>>> X,y = fetch_growth(return_X_y=True)
Then we need to import the transformers we want to use. In our case we
will use the Recursive Maxima Hunting method to select important
features.
We will concatenate to the results of the previous method the original
curves with an Evaluation Transfomer.
>>> from skfda.preprocessing.feature_construction import (
... FDAFeatureUnion,
... )
>>> from skfda.preprocessing.dim_reduction.variable_selection import (
... RecursiveMaximaHunting,
... )
>>> from skfda.preprocessing.feature_construction import (
... EvaluationTransformer,
... )
>>> import numpy as np
Finally we apply fit and transform.
>>> union = FDAFeatureUnion(
... [
... ("rmh", RecursiveMaximaHunting()),
... ("eval", EvaluationTransformer()),
... ],
... array_output=True,
... )
>>> np.around(union.fit_transform(X,y), decimals=2)
array([[ 195.1, 141.1, 163.8, ..., 193.8, 194.3, 195.1],
[ 178.7, 133. , 148.1, ..., 176.1, 177.4, 178.7],
[ 171.5, 126.5, 143.6, ..., 170.9, 171.2, 171.5],
...,
[ 166.8, 132.8, 152.2, ..., 166. , 166.3, 166.8],
[ 168.6, 139.4, 161.6, ..., 168.3, 168.4, 168.6],
[ 169.2, 138.1, 161.7, ..., 168.6, 168.9, 169.2]])
"""

def __init__(
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