| Name | statmoments JSON |
| Version |
1.1.1
JSON |
| download |
| home_page | None |
| Summary | Streaming statistical moments |
| upload_time | 2025-02-05 08:40:34 |
| maintainer | None |
| docs_url | None |
| author | Anton Kochepasov |
| requires_python | >=3.6 |
| license | MIT License
Copyright (c) 2022 Anton Kochepasov
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
|
| keywords |
data-science
univariate
bivariate
statistics
streaming
numpy
vectorization
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
h5py
numpy
scipy
psutil
cython
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# statmoments
Fast streaming univariate and bivariate moments and t-statistics.
statmoments is a high-performance library for computing univariate and bivariate statistical moments in a single pass over large waveform datasets with thousands of sample points. It can produce Welch's t-test statistics for hypothesis testing on arbitrary data partitions.
## Features
- Streaming processing for both univariate and bivariate analysis
- Efficient memory usage through dense matrix representation
- High numerical accuracy
- Command-line interface for analysis of existing datasets
## How is it different?
When input data differences are subtle, millions of waveforms may have to be processed to find the statistically significant difference, requiring efficient algorithms. In addition to that, the high-order moment computation need multiple passes and may require starting over once new data appear. With thousands of sample points per waveform, the problem becomes more complex.
A streaming algorithm processes sequences of inputs in a single pass as they are collected. When fast enough, it's suitable for real-time sources like oscilloscopes, sensors, and financial markets, as well as for large datasets that don't fit in memory. The dense matrix representation of an intermediate accumulator reduces memory requirements. The accumulator can be converted to co-moments and Welch's t-test statistics on demand. Data batches can be iteratively processed to increase precision and then discarded. The library handles significant input streams, processing hundreds of megabytes per second.
Yet another dimension can be added when the data split is unknown. In other words, which bucket the input waveform belongs to. This library solves this with given pre-classification of the input data and computing moments for all the requested data splits.
Some of the benefits of streaming computation include:
- Real-time insights for trend identification and anomaly detection
- Reduced data processing latency, crucial for time-sensitive applications
- Scalability to handle large data volumes, essential for data-intensive research in fields like astrophysics and financial analysis
## Numeric accuracy
The numeric accuracy of results depends on the coefficient of variation (COV) of a sample point in the input waveforms. With a COV of about 5%, the computed (co-)kurtosis has about 10 correct significant digits for 10,000 waveforms, sufficient for Welch's t-test. Increasing data by 100x loses one more significant digit.
## Examples
### Performing univariate data analysis
```python
# Input data parameters
tr_count = 100 # M input waveforms
tr_len = 5 # N features or points in the input waveforms
cl_len = 2 # L hypotheses how to split input waveforms
# Create engine, which can compute up to kurtosis
uveng = statmoments.Univar(tr_len, cl_len, moment=4)
# Process input data and split hypotheses
uveng.update(wforms1, classification1)
# Process more input data and split hypotheses
uveng.update(wforms2, classification2)
# Get statistical moments
mean = [cm.copy() for cm in uveng.moments(moments=1)] # E(X)
skeweness = [cm.copy() for cm in uveng.moments(moments=3)] # E(X^3)
# Detect statistical differences in the first-order t-test
for i, tt in enumerate(statmoments.stattests.ttests(uveng, moment=1)):
if np.any(np.abs(tt) > 5):
print(f"Data split {i} has different means")
# Process more input data and split hypotheses
uveng.update(wforms3, classification3)
# Get updated statistical moments and t-tests
# with statmoments.stattests.ttests(uveng, moment=1)
```
### Performing bivariate data analysis
```python
# Input data parameters
tr_count = 100 # M input waveforms
tr_len = 5 # N features or points in the input waveforms
cl_len = 2 # L hypotheses how to split input waveforms
# Create bivariate engine, which can compute up to co-kurtosis
bveng = statmoments.Bivar(tr_len, cl_len, moment=4)
# Process input data and split hypotheses
bveng.update(wforms1, classification1)
# Process more input data and split hypotheses
bveng.update(wforms2, classification2)
# Get bivariate moments
covariance = [cm.copy() for cm in bveng.comoments(moments=(1, 1))] # E(X Y)
cokurtosis22 = [cm.copy() for cm in bveng.comoments(moments=(2, 2))] # E(X^2 Y^2)
cokurtosis13 = [cm.copy() for cm in bveng.comoments(moments=(1, 3))] # E(X^1 Y^3)
# univariate statistical moments are also can be obtained
variance = [cm.copy() for cm in bveng.moments(moments=2)] # E(X^2)
# Detect statistical differences in the second order t-test (covariances)
for i, tt in enumerate(statmoments.stattests.ttests(bveng, moment=(1,1))):
if np.any(np.abs(tt) > 5):
print(f"Found stat diff in the split {i}")
# Process more input data and split hypotheses
bveng.update(wforms3, classification3)
# Get updated statistical moments and t-tests
# with statmoments.stattests.ttests(bveng, moment=(1,1))
```
### Performing data analysis from the command line
```shell
# Find univariate t-test statistics of skeweness for the first
# 5000 waveform sample points, stored in a HDF5 dataset
python -m statmoments.univar -i data.h5 -m 3 -r 0:5000
# Find bivariate t-test statistics of covariance for the first
# 1000 waveform sample points, stored in a HDF5 dataset
python -m statmoments.bivar -i data.h5 -r 0:1000
```
More examples can be found in the examples and tests directories.
## Implementation Notes
statmoments uses top BLAS implementations, including GPU based on [nvmath-python](https://github.com/NVIDIA/nvmath-python) if available, for the best peformance on Windows, Linux and Macs,to maximize computational efficiency.
Due to RAM limits, results are produced one at a time for each input classifier as the set of statistical moments. Each classifier's output moment has dimensions 2 x M x L, where M is an index of the requested classifier and L is the region length.
The bivariate results, co-moments and t-tests, are represented by the **upper triangle** of the symmetric matrix as 1D array for each classifier.
## Installation
```shell
pip install statmoments
```
## References
Anton Kochepasov, Ilya Stupakov, "An Efficient Single-pass Online Computation of Higher-Order Bivariate Statistics", 2024 IEEE International Conference on Big Data (BigData), 2024, pp. 123-129, [IEEE Xplore](https://ieeexplore.ieee.org/abstract/document/10825659).
```bibtex
@INPROCEEDINGS{10825659,
author={Stupakov, Ilya and Kochepasov, Anton},
booktitle={2024 IEEE International Conference on Big Data (BigData)},
title={An Efficient Single-pass Online Computation of Higher-Order Bivariate Statistics},
year={2024},
pages={123-129},
doi={10.1109/BigData62323.2024.10825659}
}
```
[](https://pypi.org/project/statmoments/)
[](https://pypi.org/project/statmoments/)
Raw data
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"description": "# statmoments\n\nFast streaming univariate and bivariate moments and t-statistics.\n\nstatmoments is a high-performance library for computing univariate and bivariate statistical moments in a single pass over large waveform datasets with thousands of sample points. It can produce Welch's t-test statistics for hypothesis testing on arbitrary data partitions.\n\n## Features\n\n- Streaming processing for both univariate and bivariate analysis\n- Efficient memory usage through dense matrix representation\n- High numerical accuracy\n- Command-line interface for analysis of existing datasets\n\n## How is it different?\n\nWhen input data differences are subtle, millions of waveforms may have to be processed to find the statistically significant difference, requiring efficient algorithms. In addition to that, the high-order moment computation need multiple passes and may require starting over once new data appear. With thousands of sample points per waveform, the problem becomes more complex.\n\nA streaming algorithm processes sequences of inputs in a single pass as they are collected. When fast enough, it's suitable for real-time sources like oscilloscopes, sensors, and financial markets, as well as for large datasets that don't fit in memory. The dense matrix representation of an intermediate accumulator reduces memory requirements. The accumulator can be converted to co-moments and Welch's t-test statistics on demand. Data batches can be iteratively processed to increase precision and then discarded. The library handles significant input streams, processing hundreds of megabytes per second.\n\nYet another dimension can be added when the data split is unknown. In other words, which bucket the input waveform belongs to. This library solves this with given pre-classification of the input data and computing moments for all the requested data splits.\n\nSome of the benefits of streaming computation include:\n\n- Real-time insights for trend identification and anomaly detection\n- Reduced data processing latency, crucial for time-sensitive applications\n- Scalability to handle large data volumes, essential for data-intensive research in fields like astrophysics and financial analysis\n\n## Numeric accuracy\n\nThe numeric accuracy of results depends on the coefficient of variation (COV) of a sample point in the input waveforms. With a COV of about 5%, the computed (co-)kurtosis has about 10 correct significant digits for 10,000 waveforms, sufficient for Welch's t-test. Increasing data by 100x loses one more significant digit.\n\n## Examples\n\n### Performing univariate data analysis\n\n```python\n # Input data parameters\n tr_count = 100 # M input waveforms\n tr_len = 5 # N features or points in the input waveforms\n cl_len = 2 # L hypotheses how to split input waveforms\n\n # Create engine, which can compute up to kurtosis\n uveng = statmoments.Univar(tr_len, cl_len, moment=4)\n\n # Process input data and split hypotheses\n uveng.update(wforms1, classification1)\n\n # Process more input data and split hypotheses\n uveng.update(wforms2, classification2)\n\n # Get statistical moments\n mean = [cm.copy() for cm in uveng.moments(moments=1)] # E(X)\n skeweness = [cm.copy() for cm in uveng.moments(moments=3)] # E(X^3)\n\n # Detect statistical differences in the first-order t-test\n for i, tt in enumerate(statmoments.stattests.ttests(uveng, moment=1)):\n if np.any(np.abs(tt) > 5):\n print(f\"Data split {i} has different means\")\n\n # Process more input data and split hypotheses\n uveng.update(wforms3, classification3)\n\n # Get updated statistical moments and t-tests\n # with statmoments.stattests.ttests(uveng, moment=1)\n```\n\n### Performing bivariate data analysis\n\n```python\n # Input data parameters\n tr_count = 100 # M input waveforms\n tr_len = 5 # N features or points in the input waveforms\n cl_len = 2 # L hypotheses how to split input waveforms\n\n # Create bivariate engine, which can compute up to co-kurtosis\n bveng = statmoments.Bivar(tr_len, cl_len, moment=4)\n\n # Process input data and split hypotheses\n bveng.update(wforms1, classification1)\n\n # Process more input data and split hypotheses\n bveng.update(wforms2, classification2)\n\n # Get bivariate moments\n covariance = [cm.copy() for cm in bveng.comoments(moments=(1, 1))] # E(X Y)\n cokurtosis22 = [cm.copy() for cm in bveng.comoments(moments=(2, 2))] # E(X^2 Y^2)\n cokurtosis13 = [cm.copy() for cm in bveng.comoments(moments=(1, 3))] # E(X^1 Y^3)\n\n # univariate statistical moments are also can be obtained\n variance = [cm.copy() for cm in bveng.moments(moments=2)] # E(X^2)\n\n # Detect statistical differences in the second order t-test (covariances)\n for i, tt in enumerate(statmoments.stattests.ttests(bveng, moment=(1,1))):\n if np.any(np.abs(tt) > 5):\n print(f\"Found stat diff in the split {i}\")\n\n # Process more input data and split hypotheses\n bveng.update(wforms3, classification3)\n\n # Get updated statistical moments and t-tests\n # with statmoments.stattests.ttests(bveng, moment=(1,1))\n```\n\n### Performing data analysis from the command line\n\n```shell\n# Find univariate t-test statistics of skeweness for the first\n# 5000 waveform sample points, stored in a HDF5 dataset\npython -m statmoments.univar -i data.h5 -m 3 -r 0:5000\n\n# Find bivariate t-test statistics of covariance for the first\n# 1000 waveform sample points, stored in a HDF5 dataset\npython -m statmoments.bivar -i data.h5 -r 0:1000\n```\n\nMore examples can be found in the examples and tests directories.\n\n## Implementation Notes\n\nstatmoments uses top BLAS implementations, including GPU based on [nvmath-python](https://github.com/NVIDIA/nvmath-python) if available, for the best peformance on Windows, Linux and Macs,to maximize computational efficiency.\n\nDue to RAM limits, results are produced one at a time for each input classifier as the set of statistical moments. Each classifier's output moment has dimensions 2 x M x L, where M is an index of the requested classifier and L is the region length.\n\nThe bivariate results, co-moments and t-tests, are represented by the **upper triangle** of the symmetric matrix as 1D array for each classifier.\n\n## Installation\n\n```shell\npip install statmoments\n```\n\n## References\n\nAnton Kochepasov, Ilya Stupakov, \"An Efficient Single-pass Online Computation of Higher-Order Bivariate Statistics\", 2024 IEEE International Conference on Big Data (BigData), 2024, pp. 123-129, [IEEE Xplore](https://ieeexplore.ieee.org/abstract/document/10825659).\n\n```bibtex\n@INPROCEEDINGS{10825659,\n author={Stupakov, Ilya and Kochepasov, Anton},\n booktitle={2024 IEEE International Conference on Big Data (BigData)},\n title={An Efficient Single-pass Online Computation of Higher-Order Bivariate Statistics},\n year={2024},\n pages={123-129},\n doi={10.1109/BigData62323.2024.10825659}\n}\n```\n\n[](https://pypi.org/project/statmoments/)\n[](https://pypi.org/project/statmoments/)\n",
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