Welcome to Distancia's documentation!
======================================
**Distancia** is a comprehensive Python package that provides a wide range of distance metrics and similarity measures, making it easy to calculate and compare the proximity between various types of data. This documentation provides an in-depth guide to the package, including installation instructions, usage examples, and detailed descriptions of each available metric.
The documentation is divided into the following sections:
The code examples provided in this documentation are written for Python 3.x.
The python code in this package has been optimized by static typing with Cython
Getting Started
---------------
Distancia is designed to be simple and intuitive, yet powerful and flexible. Whether you are working with numerical data, strings, or other types of data, Distancia provides the tools you need to measure the similarity or dissimilarity between objects.
For a quick introduction, check out the :doc:`quickstart` guide. If you want to dive straight into the code, head over to the :doc:`Euclidean` page.
If you find any issues or have suggestions for improvements, feel free to contribute! See the :doc:`contributing` section for more details.
Installation
------------
To install distancia, simply use pip:
.. code-block:: bash
pip install distancia
For more detailed instructions and additional options, see the :doc:`installation` section.
Quickstart
----------
Here are some common examples of how to use distancia:
.. code-block:: python
:caption: Example 1: Calculating Euclidean Distance
from distancia import Euclidean
point1 = [1, 2, 3]
point2 = [4, 5, 6]
# Create an instance of Euclidean
euclidean = Euclidean()
# Calculate the Euclidean distance
distance = euclidean.compute(point1, point2)
print(f"Euclidean Distance: {distance.:4f}")
.. code-block:: bash
>>>Euclidean Distance: 5.196
.. code-block:: python
:caption: Example 2: Calculating Levenshtein Distance
from distancia import Levenshtein
string1 = "kitten"
string2 = "sitting"
distance = Levenshtein().compute(string1, string2)
print(f"Levenshtein Distance: {distance.:4f}")
.. code:: bash
>>>Levenshtein Distance: 3
For a complete list and detailed explanations of each metric, see the next section.
Available Metrics
-----------------
Distance
Euclidean
Manhattan
Jaro
KendallTau
Bhattacharyya
Mahalanobis
MahalanobisTaguchi
Haversine
Chebyshev
ContextualDynamicDistance
Canberra
BrayCurtis
RogersTanimoto
RussellRao
SokalMichener
SokalSneath
Wasserstein
Gower
CzekanowskiDice
Hellinger
MotzkinStraus
EnhancedRogersTanimoto
KullbackLeibler
Similarity
Jaccard
GeneralizedJaccard
Tanimoto
InverseTanimoto
Ochiai
CzekanowskiDice
Pearson
Spearman
FagerMcGowan
Otsuka
Gestalt
Time Series Distance Metrics
DynamicTimeWarping
LongestCommonSubsequence
Frechet
Loss function
CrossEntropy
MeanAbsoluteError
MeanAbsolutePercentageError
MeanSquaredError
SquaredLogarithmicError
GaloisWassersteinLoss
Distance between two graphs
ShortestPath
GraphEditDistance
SpectralDistance
WeisfeilerLehmanSimilarity
ComparingRandomWalkStationaryDistributions
Diffusion
FrobeniusDistance
GraphKernelDistance
PatternBasedDistance
GraphCompressionDistance
DegreeDistributionDistance
CommunityStructureDistance
Distance between two Markov Chain
MarkovChainKullbackLeibler
MarkovChainWasserstein
MarkovChainTotalVariation
MarkovChainHellinger
MarkovChainJensenShannon
MarkovChainFrobenius
MarkovChainSpectral
Distance between two images
StructuralSimilarityIndex
PeakSignalToNoiseRatio
HistogramIntersection
EarthMoversDistance
ChiSquareDistance
FeatureBasedDistance
PerceptualHashing
NormalizedCrossCorrelation
Distance between two sounds
SpectralConvergence
MFCCProcessor
SignalProcessor
PowerSpectralDensityDistance
CrossCorrelation
PhaseDifferenceCalculator
TimeLagDistance
PESQ
LogSpectralDistance
BarkSpectralDistortion
ItakuraSaitoDistance
SignalToNoiseRatio
EnergyDistance
EnvelopeCorrelation
ZeroCrossingRateDistance
CochleagramDistance
ChromagramDistance
SpectrogramDistance
CQTDistance
Distance between two text
Levenshtein
DamerauLevenshtein
Hamming
Cosine
TFIDFDistance
SimHash
CosineTF
WordMoversDistance
BERTBasedDistance
JaroWinkler
OverlapCoefficient
SorensenDice
BagOfWordsDistance
FastTextDistance
Dice
Tversky
NgramDistance
SmithWaterman
RatcliffObershelp
BLEUScore
ROUGEScore
SoftCosineSimilarity
TopicModelingDistance
AlignmentBasedMeasures
GappyNGramDistance
SoftJaccardSimilarity
NormalizedCompressionDistance
And many more...
Overview
--------
The distancia package offers a comprehensive set of tools for computing and analyzing distances and similarities between data points. This package is particularly useful for tasks in data analysis, machine learning, and pattern recognition. Below is an overview of the key classes included in the package, each designed to address specific types of distance or similarity calculations.
BatchDistance
Purpose: Facilitates batch processing of distance computations, enabling users to compute distances for large sets of pairs in a single operation.
Use Case: Essential in real-time systems or when working with large datasets where efficiency is critical. Batch processing saves time and computational resources by handling multiple distance computations in one go.
ComprehensiveBenchmarking
Purpose: Provides tools for benchmarking the performance of various distance metrics on different types of data.
Use Case: Useful in performance-sensitive applications where choosing the optimal metric can greatly impact computational efficiency and accuracy. This class helps users make informed decisions about which distance metric to use for their specific task.
CustomDistanceFunction
Purpose: Allows users to define custom distance functions by specifying a mathematical formula or providing a custom Python function.
Use Case: Useful for researchers or practitioners who need a specific metric that isn’t commonly used or already implemented.
DistanceMatrix
Purpose: Automatically generates a distance matrix for a set of data points using a specified distance metric.
Use Case: Useful in clustering algorithms like k-means, hierarchical clustering, or in generating heatmaps for visualizing similarity/dissimilarity in datasets.
DistanceMetricLearning
Purpose: Implements algorithms for learning an optimal distance metric from data based on a specific task, such as classification or clustering.
Use Case: Critical in machine learning tasks where the goal is to optimize a distance metric for maximum task-specific performance, improving the accuracy of models.
IntegratedDistance
Purpose: Enables seamless integration of distance computations with popular data science libraries like pandas, scikit-learn, and numpy.
Use Case: This class enhances the usability of the distancia package, allowing users to incorporate distance calculations directly into their existing data analysis workflows.
MetricFinder
Purpose: Identifies the most appropriate distance metric for two given data points based on their structure.
Use Case: Useful when dealing with various types of data, this class helps users automatically determine the best distance metric to apply, ensuring that the metric chosen is suitable for the data's characteristics.
OutlierDetection
Purpose: Implements methods for detecting outliers in datasets by using distance metrics to identify points that deviate significantly from others.
Use Case: Essential in fields such as fraud detection, quality control, and data cleaning, where identifying and managing outliers is crucial for maintaining data integrity.
ParallelandDistributedComputation
Purpose: Adds support for parallel or distributed computation of distances, particularly useful for large datasets.
Use Case: In big data scenarios, calculating distances between millions of data points can be computationally expensive. This class significantly reduces computation time by parallelizing these calculations across multiple processors or machines.
Visualization
Purpose: Provides tools for visualizing distance matrices, dendrograms (for hierarchical clustering), and 2D/3D representations of data points based on distance metrics.
Use Case: Visualization is a powerful tool in exploratory data analysis (EDA), helping users understand the relationships between data points. This class is particularly useful for creating visual aids like heatmaps or dendrograms to better interpret the data.
APICompatibility
The APICompatibility class in the distancia package bridges the gap between powerful distance computation tools and modern API-based architectures. By enabling the creation of REST endpoints for distance metrics, it facilitates the integration of distancia into a wide range of applications, from web services to distributed computing environments. This not only enhances the usability of the package but also ensures that it can be effectively deployed in real-world, production-grade systems.
AutomatedDistanceMetricSelection
The AutomatedDistanceMetricSelection feature in the distancia package represents a significant advancement in the ease of use and accessibility of distance metric selection. By automating the process of metric recommendation, it helps users, especially those less familiar with the intricacies of different metrics, to achieve better results in their analyses. This feature not only saves time but also improves the accuracy of data-driven decisions, making distancia a more powerful and user-friendly tool for the data science community.
ReportingAndDocumentation
The ReportingAndDocumentation class is a powerful tool for automating the analysis and documentation of distance metrics. By integrating report generation, matrix export, and property documentation, it provides users with a streamlined way to evaluate and present the results of their distance-based models. This class is especially valuable for machine learning practitioners who require a deeper understanding of the behavior of the metrics they employ.
AdvancedAnalysis
The AdvancedAnalysis class provides essential tools for evaluating the performance, robustness, and sensitivity of distance metrics. These advanced analyses ensure that a metric is not only theoretically sound but also practical and reliable in diverse applications. By offering deep insights into the behavior of distance metrics under perturbations, noise, and dataset divisions, this class is crucial for building resilient models in real-world environments.
DimensionalityReductionAndScaling
The `DimensionalityReductionAndScaling` class offers powerful methods for simplifying and scaling datasets. By providing tools for dimensionality reduction such as Multi-Dimensional Scaling (MDS), it allows users to project high-dimensional data into lower dimensions while retaining its key characteristics.
ComparisonAndValidation
The ComparisonAndValidation class offers tools to analyze and validate the performance of a distance or similarity metric by comparing it with other metrics and using established benchmarks. This class is essential for evaluating the effectiveness of a metric in various tasks, such as clustering, classification, or retrieval. By providing cross-validation techniques and benchmarking methods, it allows users to gain a deeper understanding of the metric's strengths and weaknesses.
StatisticalAnalysis
The StatisticalAnalysis class provides essential tools to analyze and interpret the statistical properties of distances or similarities within a dataset. Through the computation of mean, variance, and distance distributions,
Contributing
------------
We welcome contributions! If you would like to contribute to distancia, please read the :doc:`contributing` guide to get started. We appreciate your help in making this project better.
Link
----
Notebook<https://github.com/ym001/distancia/tree/master/notebook>
Exemples<https://github.com/ym001/distancia/blob/master/src/exemple.py>
Pypi<https://pypi.org/project/distancia/>
Source<https://github.com/ym001/distancia>
Documentation<https://distancia.readthedocs.io/en/latest/>
License<https://github.com/ym001/distancia/blob/master/LICENSE>
Conclusion
----------
The distancia package offers a versatile toolkit for handling a wide range of distance and similarity calculations. Whether you're working with numeric data, categorical data, strings, or time series, the package's classes provide the necessary tools to accurately measure distances and similarities. By understanding and utilizing these classes, you can enhance your data analysis workflows and improve the performance of your machine learning models.
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"description": "\n\nWelcome to Distancia's documentation!\n======================================\n\n**Distancia** is a comprehensive Python package that provides a wide range of distance metrics and similarity measures, making it easy to calculate and compare the proximity between various types of data. This documentation provides an in-depth guide to the package, including installation instructions, usage examples, and detailed descriptions of each available metric.\n\nThe documentation is divided into the following sections:\n\n\n\n\n The code examples provided in this documentation are written for Python 3.x.\n The python code in this package has been optimized by static typing with Cython\n\nGetting Started\n---------------\n\nDistancia is designed to be simple and intuitive, yet powerful and flexible. Whether you are working with numerical data, strings, or other types of data, Distancia provides the tools you need to measure the similarity or dissimilarity between objects.\n\n\nFor a quick introduction, check out the :doc:`quickstart` guide. If you want to dive straight into the code, head over to the :doc:`Euclidean` page.\n\n\n If you find any issues or have suggestions for improvements, feel free to contribute! See the :doc:`contributing` section for more details.\n\nInstallation\n------------\n\nTo install distancia, simply use pip:\n\n.. code-block:: bash\n\n pip install distancia\n\nFor more detailed instructions and additional options, see the :doc:`installation` section.\n\nQuickstart\n----------\n\nHere are some common examples of how to use distancia:\n\n\n.. code-block:: python\n :caption: Example 1: Calculating Euclidean Distance\n\n\n from distancia import Euclidean\n\n point1 = [1, 2, 3]\n point2 = [4, 5, 6]\n\n # Create an instance of Euclidean\n euclidean = Euclidean()\n\n # Calculate the Euclidean distance\n distance = euclidean.compute(point1, point2)\n\n print(f\"Euclidean Distance: {distance.:4f}\")\n\n.. code-block:: bash\n\n >>>Euclidean Distance: 5.196\n\n\n\n\n.. code-block:: python\n :caption: Example 2: Calculating Levenshtein Distance\n\n from distancia import Levenshtein\n\n string1 = \"kitten\"\n string2 = \"sitting\"\n\n distance = Levenshtein().compute(string1, string2)\n print(f\"Levenshtein Distance: {distance.:4f}\")\n\n.. code:: bash\n\n >>>Levenshtein Distance: 3\n\n\nFor a complete list and detailed explanations of each metric, see the next section.\n\nAvailable Metrics\n-----------------\nDistance\n\n\n\n\n Euclidean\n\n Manhattan \n\n Jaro\n\n KendallTau\n\n Bhattacharyya\n\n Mahalanobis\n\n MahalanobisTaguchi\n\n Haversine\n\n Chebyshev\n\n ContextualDynamicDistance\n\n Canberra\n\n BrayCurtis\n\n RogersTanimoto\n\n RussellRao\n\n SokalMichener\n\n SokalSneath\n\n Wasserstein\n\n Gower\n\n CzekanowskiDice\n\n Hellinger\n\n MotzkinStraus\n\n EnhancedRogersTanimoto\n\n KullbackLeibler\n\n \nSimilarity\n\n\n\n Jaccard\n\n GeneralizedJaccard\n\n Tanimoto\n\n InverseTanimoto\n\n Ochiai \n\n CzekanowskiDice\n\n Pearson\n\n Spearman \n\n FagerMcGowan\n\n Otsuka \n\n Gestalt\n\n\nTime Series Distance Metrics\n\n\n\n DynamicTimeWarping\n\n LongestCommonSubsequence\n\n Frechet\n\nLoss function\n\n\n\n CrossEntropy\n\n MeanAbsoluteError\n\n MeanAbsolutePercentageError\n\n MeanSquaredError\n\n SquaredLogarithmicError\n\n GaloisWassersteinLoss\n\n\nDistance between two graphs\n\n\n\n ShortestPath\n\n GraphEditDistance\n\n SpectralDistance\n\n WeisfeilerLehmanSimilarity\n\n ComparingRandomWalkStationaryDistributions\n\n Diffusion\n\n FrobeniusDistance\n\n GraphKernelDistance\n\n PatternBasedDistance\n\n GraphCompressionDistance\n\n DegreeDistributionDistance\n\n CommunityStructureDistance\n\nDistance between two Markov Chain\n\n\n\n MarkovChainKullbackLeibler\n\n MarkovChainWasserstein\n\n MarkovChainTotalVariation\n\n MarkovChainHellinger\n\n MarkovChainJensenShannon\n\n MarkovChainFrobenius\n\n MarkovChainSpectral\n\nDistance between two images\n\n\n\n StructuralSimilarityIndex\n\n PeakSignalToNoiseRatio\n\n HistogramIntersection\n\n EarthMoversDistance\n\n ChiSquareDistance\n\n FeatureBasedDistance\n\n PerceptualHashing\n\n NormalizedCrossCorrelation\n\nDistance between two sounds\n\n\n\n SpectralConvergence\n\n MFCCProcessor\n\n SignalProcessor\n\n PowerSpectralDensityDistance\n\n CrossCorrelation\n\n PhaseDifferenceCalculator\n\n TimeLagDistance\n\n PESQ\n\n LogSpectralDistance\n\n BarkSpectralDistortion\n\n ItakuraSaitoDistance\n\n SignalToNoiseRatio\n\n EnergyDistance\n\n EnvelopeCorrelation\n\n ZeroCrossingRateDistance\n\n CochleagramDistance\n\n ChromagramDistance\n\n SpectrogramDistance\n\n CQTDistance\n\nDistance between two text\n\n\n\n Levenshtein\n\n DamerauLevenshtein\n\n Hamming\n\n Cosine\n\n TFIDFDistance\n\n SimHash\n\n CosineTF\n\n WordMoversDistance\n\n BERTBasedDistance\n\n JaroWinkler\n\n OverlapCoefficient\n\n SorensenDice\n\n BagOfWordsDistance\n\n FastTextDistance\n\n Dice \n\n Tversky \n\n NgramDistance\n\n SmithWaterman\n\n RatcliffObershelp\n\n BLEUScore\n\n ROUGEScore\n\n SoftCosineSimilarity\n\n TopicModelingDistance\n\n AlignmentBasedMeasures\n\n GappyNGramDistance\n\n SoftJaccardSimilarity\n\n NormalizedCompressionDistance\n\nAnd many more...\n\nOverview\n--------\nThe distancia package offers a comprehensive set of tools for computing and analyzing distances and similarities between data points. This package is particularly useful for tasks in data analysis, machine learning, and pattern recognition. Below is an overview of the key classes included in the package, each designed to address specific types of distance or similarity calculations.\n\n\n BatchDistance\n\nPurpose: Facilitates batch processing of distance computations, enabling users to compute distances for large sets of pairs in a single operation.\n\nUse Case: Essential in real-time systems or when working with large datasets where efficiency is critical. Batch processing saves time and computational resources by handling multiple distance computations in one go.\n\n\n\n ComprehensiveBenchmarking\n\nPurpose: Provides tools for benchmarking the performance of various distance metrics on different types of data.\n\nUse Case: Useful in performance-sensitive applications where choosing the optimal metric can greatly impact computational efficiency and accuracy. This class helps users make informed decisions about which distance metric to use for their specific task.\n\n\n\n CustomDistanceFunction\n\nPurpose: Allows users to define custom distance functions by specifying a mathematical formula or providing a custom Python function.\n\nUse Case: Useful for researchers or practitioners who need a specific metric that isn\u2019t commonly used or already implemented.\n\n\n\n DistanceMatrix\n\nPurpose: Automatically generates a distance matrix for a set of data points using a specified distance metric.\n\nUse Case: Useful in clustering algorithms like k-means, hierarchical clustering, or in generating heatmaps for visualizing similarity/dissimilarity in datasets.\n\n\n\n DistanceMetricLearning\n\nPurpose: Implements algorithms for learning an optimal distance metric from data based on a specific task, such as classification or clustering.\n\nUse Case: Critical in machine learning tasks where the goal is to optimize a distance metric for maximum task-specific performance, improving the accuracy of models.\n\n\n\n IntegratedDistance\n\nPurpose: Enables seamless integration of distance computations with popular data science libraries like pandas, scikit-learn, and numpy.\n\nUse Case: This class enhances the usability of the distancia package, allowing users to incorporate distance calculations directly into their existing data analysis workflows.\n\n\n\n MetricFinder\n\nPurpose: Identifies the most appropriate distance metric for two given data points based on their structure.\n\nUse Case: Useful when dealing with various types of data, this class helps users automatically determine the best distance metric to apply, ensuring that the metric chosen is suitable for the data's characteristics.\n\n\n\n OutlierDetection\n\nPurpose: Implements methods for detecting outliers in datasets by using distance metrics to identify points that deviate significantly from others.\n\nUse Case: Essential in fields such as fraud detection, quality control, and data cleaning, where identifying and managing outliers is crucial for maintaining data integrity.\n\n\n\n ParallelandDistributedComputation\n\nPurpose: Adds support for parallel or distributed computation of distances, particularly useful for large datasets.\n\nUse Case: In big data scenarios, calculating distances between millions of data points can be computationally expensive. This class significantly reduces computation time by parallelizing these calculations across multiple processors or machines.\n\n\n\n Visualization\n\nPurpose: Provides tools for visualizing distance matrices, dendrograms (for hierarchical clustering), and 2D/3D representations of data points based on distance metrics.\n\nUse Case: Visualization is a powerful tool in exploratory data analysis (EDA), helping users understand the relationships between data points. This class is particularly useful for creating visual aids like heatmaps or dendrograms to better interpret the data.\n\n\n\n APICompatibility\n\nThe APICompatibility class in the distancia package bridges the gap between powerful distance computation tools and modern API-based architectures. By enabling the creation of REST endpoints for distance metrics, it facilitates the integration of distancia into a wide range of applications, from web services to distributed computing environments. This not only enhances the usability of the package but also ensures that it can be effectively deployed in real-world, production-grade systems.\n\n\n\n\n AutomatedDistanceMetricSelection\n\nThe AutomatedDistanceMetricSelection feature in the distancia package represents a significant advancement in the ease of use and accessibility of distance metric selection. By automating the process of metric recommendation, it helps users, especially those less familiar with the intricacies of different metrics, to achieve better results in their analyses. This feature not only saves time but also improves the accuracy of data-driven decisions, making distancia a more powerful and user-friendly tool for the data science community.\n\n\n\n\n ReportingAndDocumentation\n\nThe ReportingAndDocumentation class is a powerful tool for automating the analysis and documentation of distance metrics. By integrating report generation, matrix export, and property documentation, it provides users with a streamlined way to evaluate and present the results of their distance-based models. This class is especially valuable for machine learning practitioners who require a deeper understanding of the behavior of the metrics they employ.\n\n\n\n AdvancedAnalysis\n\nThe AdvancedAnalysis class provides essential tools for evaluating the performance, robustness, and sensitivity of distance metrics. These advanced analyses ensure that a metric is not only theoretically sound but also practical and reliable in diverse applications. By offering deep insights into the behavior of distance metrics under perturbations, noise, and dataset divisions, this class is crucial for building resilient models in real-world environments.\n\n\n\n DimensionalityReductionAndScaling\n\nThe `DimensionalityReductionAndScaling` class offers powerful methods for simplifying and scaling datasets. By providing tools for dimensionality reduction such as Multi-Dimensional Scaling (MDS), it allows users to project high-dimensional data into lower dimensions while retaining its key characteristics.\n\n\n ComparisonAndValidation\n\nThe ComparisonAndValidation class offers tools to analyze and validate the performance of a distance or similarity metric by comparing it with other metrics and using established benchmarks. This class is essential for evaluating the effectiveness of a metric in various tasks, such as clustering, classification, or retrieval. By providing cross-validation techniques and benchmarking methods, it allows users to gain a deeper understanding of the metric's strengths and weaknesses.\n\n\n\n StatisticalAnalysis\n\nThe StatisticalAnalysis class provides essential tools to analyze and interpret the statistical properties of distances or similarities within a dataset. Through the computation of mean, variance, and distance distributions, \n\nContributing\n------------\n\nWe welcome contributions! If you would like to contribute to distancia, please read the :doc:`contributing` guide to get started. We appreciate your help in making this project better.\n\n\nLink\n----\n\n\n\n Notebook<https://github.com/ym001/distancia/tree/master/notebook>\n \n Exemples<https://github.com/ym001/distancia/blob/master/src/exemple.py>\n \n Pypi<https://pypi.org/project/distancia/>\n \n Source<https://github.com/ym001/distancia>\n \n Documentation<https://distancia.readthedocs.io/en/latest/>\n \n License<https://github.com/ym001/distancia/blob/master/LICENSE>\n\nConclusion\n----------\n\nThe distancia package offers a versatile toolkit for handling a wide range of distance and similarity calculations. Whether you're working with numeric data, categorical data, strings, or time series, the package's classes provide the necessary tools to accurately measure distances and similarities. By understanding and utilizing these classes, you can enhance your data analysis workflows and improve the performance of your machine learning models.\n\n",
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