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## mapper_pipeline_example.ipynb

      
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                lewtun
                / mapper_pipeline_example.ipynb
            
            
              Created
              November 25, 2019 07:27
            
              
                Mapper Pipeline Example
              
          
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## persistence_point_cloud.py
import gtda.homology as hl

# represent data as a point cloud
point_cloud = ...

# define topological features to track
homology_dimensions = [0, 1, 2]

# define simplicial complex to construct
persistence = hl.VietorisRipsPersistence(

## persistence_distance_matrix.py
import gtda.homology as hl

# represent data as a matrix of pairwise distances
distance_matrix = ...

# define topological features to track
homology_dimensions = [0, 1, 2]

# define simplicial complex to construct
persistence = hl.VietorisRipsPersistence(

## persistence_amplitude.py
import gtda.diagrams as diagrams

# calculate persistence diagram
persistence_diagram = ...

# define type of amplitude to calculate
amplitude = diagrams.Amplitude(metric="wasserstein")

# calculate amplitude of diagram
persistence_diagram_amplitude = amplitude.fit_transform(persistence_diagram)

## average_lifetime.py
import pandas as pd

# define topological features to track
homology_dimensions = [0, 1, 2]

# calculate persistence diagram
persistence_diagram = ...

# convert NumPy array of triples to DataFrame
persistence_table = pd.DataFrame(

## cv_model.py
def cv_model(X, y, features, n_fold=5, random_state=45245, params=None):
    """Evaluate a score by cross validation.

    Parameters
    ----------

    X : pandas.DataFrame
        The data to fit.

    y : pandas.DataFrame or pandas.Series

## template_transformer.py
class TemplateTransformer(BaseEstimator, TransformerMixin):
    """ An example transformer that returns the element-wise square root."""
    def __init__(self, demo_param='demo'):
        self.demo_param = demo_param

    def fit(self, X, y=None):
        """A reference implementation of a fitting function for a transformer."""
        X = check_array(X, accept_sparse=True)

        self.n_features_ = X.shape[1]

## mnist-sample-3
sample

## mnist-sample-7
sample

## Makefile
# Copyight 2016 drivendata
# Copyright 2019 fast.ai
# Copyright 2020 Lewis Tunstall

# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at

#    http://www.apache.org/licenses/LICENSE-2.0
	import gtda.homology as hl

	# represent data as a point cloud
	point_cloud = ...

	# define topological features to track
	homology_dimensions = [0, 1, 2]

	# define simplicial complex to construct
	persistence = hl.VietorisRipsPersistence(
	import gtda.homology as hl

	# represent data as a matrix of pairwise distances
	distance_matrix = ...

	# define topological features to track
	homology_dimensions = [0, 1, 2]

	# define simplicial complex to construct
	persistence = hl.VietorisRipsPersistence(
	import gtda.diagrams as diagrams

	# calculate persistence diagram
	persistence_diagram = ...

	# define type of amplitude to calculate
	amplitude = diagrams.Amplitude(metric="wasserstein")

	# calculate amplitude of diagram
	persistence_diagram_amplitude = amplitude.fit_transform(persistence_diagram)
	import pandas as pd

	# define topological features to track
	homology_dimensions = [0, 1, 2]

	# calculate persistence diagram
	persistence_diagram = ...

	# convert NumPy array of triples to DataFrame
	persistence_table = pd.DataFrame(
	def cv_model(X, y, features, n_fold=5, random_state=45245, params=None):
	"""Evaluate a score by cross validation.

	Parameters
	----------

	X : pandas.DataFrame
	The data to fit.

	y : pandas.DataFrame or pandas.Series
	class TemplateTransformer(BaseEstimator, TransformerMixin):
	""" An example transformer that returns the element-wise square root."""
	def __init__(self, demo_param='demo'):
	self.demo_param = demo_param

	def fit(self, X, y=None):
	"""A reference implementation of a fitting function for a transformer."""
	X = check_array(X, accept_sparse=True)

	self.n_features_ = X.shape[1]
	# Copyight 2016 drivendata
	# Copyright 2019 fast.ai
	# Copyright 2020 Lewis Tunstall

	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at

	# http://www.apache.org/licenses/LICENSE-2.0