sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
612c25c644d8abb730c6404ad2d4f523f0d6e533bbfc5159bce16e628df9eb43 | Python | 154,034 | 4,324 | ####################################################################################
#
# STEPS - STochastic Engine for Pathway Simulation
# Copyright (C) 2007-2023 Okinawa Institute of Science and Technology, Japan.
# Copyright (C) 2003-2006 University of Antwerp, Belgium.
#
# See the file AUTHORS for d... |
5a8c3112e6a15f023817b32adf4af6eb7b46ec074adf3e73ebd7d0de0bf62dc8 | Python | 158,261 | 4,444 | ####################################################################################
#
# STEPS - STochastic Engine for Pathway Simulation
# Copyright (C) 2007-2026 Okinawa Institute of Science and Technology, Japan.
# Copyright (C) 2003-2006 University of Antwerp, Belgium.
#
# See the file AUTHORS for detai... |
4616320dee7d884ae1bc2f93e21370600e0023bbb788a8eee5012b1ca16f6d96 | Python | 158,336 | 4,586 | """====================
RNA-Seq pipeline
====================
:Author: Andreas Heger
:Release: $Id$
:Date: |today|
:Tags: Python
The RNA-Seq pipeline imports unmapped reads from one or more
RNA-Seq experiments and performs the basic RNA-Seq analysis steps:
1. Map reads to genome
2. Build transcript models
3... |
a6b197c8006936d9938593741b2603f031a961ea8f885843fd49a2602d367010 | Python | 158,960 | 3,473 | # -*- coding: utf-8 -*-
"""
################################################
MIT License
Copyright (c) 2021 L. C. Lee
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, inc... |
67e8c860ba47939911f495540be2faa44d7dc3dddfeb6b0e58f6da31d5ac7b53 | Python | 160,370 | 2,728 | import logging
import traceback
import warnings
from pathlib import Path
import hdbscan
import numpy as np
import pandas as pd
import seaborn
import seaborn as sns
import shiny.experimental.ui as xui
import sklearn.decomposition
import yaml
from matplotlib import gridspec, pyplot as plt
from matplotlib.lines import Li... |
5760bb8130feb77f82b4d2a4b7cc2653ef6c1ab0b4a605a949f6c546115bf771 | Python | 169,604 | 4,453 | # Copyright 2024 DeepMind Technologies Limited
#
# AlphaFold 3 source code is 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
#
# Unless required by... |
84477d61bb454515376c33275e365aaf7366bb98daa8244b4a189bff0b84ff7a | Python | 170,374 | 4,607 | """
Module to generate Systems and positions for simple reference molecular systems for testing.
DESCRIPTION
This module provides functions for building a number of test systems of varying complexity,
useful for testing both OpenMM and various codes based on pyopenmm.
Note that the PYOPENMM_SOURCE_DIR must be set to... |
a908a343b6f02a6dd9e582e9a12746aa299eec31ffd6bb0d322fc0e20725ca03 | Python | 171,028 | 2,043 | #!/usr/bin/env python
"""Defines configuration for sphinx and readthedocs."""
from __future__ import annotations
import logging
#
# tiatoolbox documentation build configuration file, created by
# sphinx-quickstart on Fri Jun 9 13:47:02 2017.
#
# This file is execfile()d with the current directory set to its
# conta... |
2615a3c3e5639aa139d873e85528aee3d05cfcb094fad26a43ad8b10d519aeeb | Python | 175,379 | 3,555 | """
Svetlana dock widget module
"""
import platform
import subprocess
# We call a bash function to install torch for windows as we cannot install the Cuda version from the setup.cfg file
p1 = subprocess.Popen("ltt install torch torchvision torchaudio grad-cam==1.4.6", shell=True)
try:
import torch
except ImportE... |
a72c55a800fddd34796a5fbb78fcaeaf4eef609f2ba006b87c2dfa08868f0246 | Python | 175,589 | 3,891 | """
Tests for forcefield class
"""
import copy
import itertools
import os
from tempfile import NamedTemporaryFile
import numpy as np
import openmm
import pytest
from numpy.testing import assert_almost_equal
from openff.units.openmm import from_openmm, to_openmm
from openmm import NonbondedForce, Platform, XmlSeriali... |
f15163d4e2c6fa2efbd1dfdb932de0a0007793cfeba9c49899894ac8a98136dc | Python | 177,359 | 4,524 | # Copyright Generate Biomedicines, Inc.
#
# 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
#
# Unless required by applicable law or agreed to ... |
f72ce5d05fe6b49f5318dcbd04214b261a176a9d73b62bc57dc161e183f321c3 | Python | 177,640 | 4,755 | # A collection of tools to remotely access a CATMAID server via its API
#
# Copyright (C) 2017 Philipp Schlegel
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of ... |
6db02a882b60453094f595c8227c6fa544d92b6fac272d2ea65ed35ecbdc2981 | Python | 181,709 | 4,218 | """
Tests for molecular topology representations
At least one supported cheminformatics toolkit must be installed to run these tests.
Only the tests applicable to that toolkit will be run.
TODO:
- Will the ToolkitWrapper allow us to pare down importing each wrapper directly?
- Add tests comparing RDKit and OpenEye ar... |
d5cf0f166aa8a0cbe716e1683c107bc3a28b50a56e17af284e05f7c8bdd72b73 | Python | 192,879 | 4,683 | ####################################################################################
#
# STEPS - STochastic Engine for Pathway Simulation
# Copyright (C) 2007-2023 Okinawa Institute of Science and Technology, Japan.
# Copyright (C) 2003-2006 University of Antwerp, Belgium.
#
# See the file AUTHORS for d... |
24696ef67c4c63f9afc67318055bc9c177909637910d1f62ce89f64cfdd761bd | Python | 198,422 | 6,066 | import asyncio
import re
import sys
import time
import uuid
import warnings
from collections.abc import (
AsyncIterator,
Awaitable,
Callable,
Iterator,
Sequence,
)
from functools import partial
from operator import itemgetter
from typing import Any, cast
from uuid import UUID
import pytest
from fre... |
290e5bcf57f7ca7a7717b6f819e6d8c1a6fff56f60e68eeed749cc272fcb943d | Python | 199,027 | 4,538 | """
Tests for cheminformatics toolkit wrappers
"""
import logging
import os
import pathlib
from tempfile import NamedTemporaryFile
import numpy as np
import pytest
from numpy.testing import assert_almost_equal
from packaging.version import Version
from openff.toolkit import Quantity, unit
from openff.toolkit._tests... |
01a29ab651e4b77b5d0edf9c1ed774376fc869b4f05d8dedcbdaa448cc275078 | Python | 200,000 | 4,718 | import itertools
from scipy.signal import butter, lfilter, hilbert
from scipy.cluster.hierarchy import dendrogram
from scipy import signal
from scipy.ndimage import gaussian_filter1d, gaussian_filter
from scipy.spatial.distance import cdist, pdist, squareform
from scipy.stats import binned_statistic_2d, pearsonr, binne... |
0da56b6d5a3e37651b763c0d5299aa112e7c75ae5b9439d39cc6901720aceb1a | Python | 200,000 | 3,320 | import simtk.openmm as openmm
import simtk.unit as unit
import mdtraj as md
import numpy as np
import copy
import enum
InteractionGroup = enum.Enum("InteractionGroup", ['unique_old', 'unique_new', 'core', 'environment'])
#######LOGGING#############################
import logging
logging.basicConfig(level = logging.NO... |
19efb0f979fbb4010d29cbaaf8faf69321c3cf3ff0492a0b1224ffe56e45fd60 | Python | 200,000 | 3,324 | import simtk.openmm as openmm
import simtk.unit as unit
import mdtraj as md
import numpy as np
import copy
import enum
InteractionGroup = enum.Enum("InteractionGroup", ['unique_old', 'unique_new', 'core', 'environment'])
#######LOGGING#############################
import logging
_logger = logging.getLogger("relative"... |
1b627e11f6c9f002f9801bfa8c00ac9b3ea500ee40f19132e6615f3a263f3b5f | Python | 200,000 | 4,433 | # -- coding: utf-8 --
# @Time : 2023/4/3 18:26
# @Author : Biao Zhang
# @Email : littlebiao@outlook.com
# @File : analysis_anatomical.py
# @Description: This file is used to ...
import pickle
import matplotlib.patches
import numpy as np
import scanpy as sc, anndata as ad
import scipy.sparse as sp
import pandas as pd
... |
25845f107a3ea1726cc354bc5bb528671d53bc6f96fe74580f87fa45b858baf1 | Python | 200,000 | 5,268 | """
Molecular chemical entity representation and routines to interface with cheminformatics toolkits
.. todo::
* Our main philosophy here is to keep the object contents of topology objects easily serializable/deserializable
* Have ``Molecule`` raise an exception if loading/creating molecules with unspecified s... |
3cbdcb2a9c31fc6445ce148c665f7ee16ea958b4118c3a171a3078956ff7506d | Python | 200,000 | 2,971 | import numpy as np
from scipy.sparse import csr_matrix
class edNEGmodel():
"""
An electrodiffusive Pinsky-Rinzel model with neuron-glia interactions and cellular swelling
with units rescaled and parameters groups uncertain
Methods
-------
constructor(T, Na_sn, Na_se, Na_sg, Na_dn, Na_de, ... |
4f47735306a4def6f8f93b483ed8bfee38a66bdaae8b56bff19ee096fcf58948 | Python | 200,000 | 5,787 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Thu Mar 4 17:27:44 2021
@author: mariano
"""
import random
import numpy as np
import scipy as scy
from scipy.spatial import distance_matrix
import scipy.stats
import scipy.signal as scygnal
import pandas as pd
import re
import os, sys
import importlib
imp... |
5fcd6709c70d9e77d5dc8f752cae91cafaa82afe6e1a89b28d425d78c8a86d9e | Python | 200,000 | 2,944 | import numpy as np
from scipy.sparse import csr_matrix
class edNEGmodel():
"""
An electrodiffusive Pinsky-Rinzel model with neuron-glia interactions and cellular swelling
with units rescaled
Methods
-------
constructor(T, Na_sn, Na_se, Na_sg, Na_dn, Na_de, Na_dg, K_sn, K_se, K_sg, K_dn, K... |
7352bfd373efa48dd51ae4340ba77f9b399ec2d0fd8eae5e41e5c8f638419c31 | Python | 200,000 | 4,082 | #TO-DO 2024-2-10
# - Add a function to choose the label name
# - Export all columns if more than one label column is specified - currently only 'current_label_col' will be exported.
#add menu entry to load a folder and automatically load all the data.
#Modify so that only video can be loaded and exported as a df
#... |
9567c7e7a9631b8d6271b0323ceb975a610117da7934e0c8bcd7ea3c629e2541 | Python | 200,000 | 75 | #!/usr/bin/env python
"""
langid.py -
Language Identifier by Marco Lui April 2011
Based on research by Marco Lui and Tim Baldwin.
Copyright 2011 Marco Lui <saffsd@gmail.com>. All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are
permitted provided that the followi... |
aaf58a85d83007b27035568460619be8f68ba056ede6b98b9da8dab4e1143e49 | Python | 200,000 | 3,660 | # -- coding: utf-8 --
# @Time : 2023/6/1 11:47
# @Author : Biao Zhang
# @Email : littlebiao@outlook.com
# @File : analysis_genomic.py
# @Description: This file is used to ...
import pickle
import numpy as np
import scanpy as sc, anndata as ad
import scipy.sparse as sp
import pandas as pd
import sys
import matplotlib.... |
b676f55addb1ddc1a9d294069c9ac62c362164aa6b04cdb0abbb8d8b6ac9fa58 | Python | 200,000 | 4,881 | ####################################################################################
#
# STEPS - STochastic Engine for Pathway Simulation
# Copyright (C) 2007-2026 Okinawa Institute of Science and Technology, Japan.
# Copyright (C) 2003-2006 University of Antwerp, Belgium.
#
# See the file AUTHORS for detai... |
bfe0062afac9e0dbf614a88b835e729e0f5c5b48067b8e76d1c965ac0749284e | Python | 200,000 | 3,609 | ####################################################################################
#
# STEPS - STochastic Engine for Pathway Simulation
# Copyright (C) 2007-2023 Okinawa Institute of Science and Technology, Japan.
# Copyright (C) 2003-2006 University of Antwerp, Belgium.
#
# See the file AUTHORS for d... |
c57aad3aab09b05f0e6b9c9affffb26c7cc087c86fd500984e9188f02b177296 | Python | 200,000 | 3,932 | # -- coding: utf-8 --
# @Time : 2023/3/9 15:36
# @Author : Biao Zhang
# @Email : littlebiao@outlook.com
# @File : analysis_main.py
# @Description: This file is used to ...
import pickle
import numpy as np
import scanpy as sc, anndata as ad
import scipy.sparse as sp
import pandas as pd
import sys
import matplotlib.pypl... |
c991edb9b856997bb8982183e013353092594e1b65dc6e0e73bb01b1a15c063f | Python | 200,000 | 3,609 | ####################################################################################
#
# STEPS - STochastic Engine for Pathway Simulation
# Copyright (C) 2007-2026 Okinawa Institute of Science and Technology, Japan.
# Copyright (C) 2003-2006 University of Antwerp, Belgium.
#
# See the file AUTHORS for detai... |
d7af4ade61092df2506c38b2e511eeba2d6a15fe7d3694bbdd6e65daef5a175e | Python | 200,000 | 3,867 | # -- coding: utf-8 --
# @Time : 2023/3/9 15:36
# @Author : Biao Zhang
# @Email : littlebiao@outlook.com
# @File : analysis_main.py
# @Description: This file is used to ...
import pickle
import numpy as np
import scanpy as sc, anndata as ad
import scipy.sparse as sp
import pandas as pd
import sys
import matplotlib.pypl... |
7da6b44c6044b8cf8e050dbe0f8dab88b61e09106af5f96ce814163675dbbef7 | Python | 200,004 | 5,222 | ####################################################################################
#
# STEPS - STochastic Engine for Pathway Simulation
# Copyright (C) 2007-2026 Okinawa Institute of Science and Technology, Japan.
# Copyright (C) 2003-2006 University of Antwerp, Belgium.
#
# See the file AUTHORS for detai... |
bfce91b8bf62c355e0b8d91ebc21aabbbbb3d8b8b823d9177d0aae065e25eda8 | Python | 200,004 | 5,240 | ####################################################################################
#
# STEPS - STochastic Engine for Pathway Simulation
# Copyright (C) 2007-2023 Okinawa Institute of Science and Technology, Japan.
# Copyright (C) 2003-2006 University of Antwerp, Belgium.
#
# See the file AUTHORS for d... |
b0045dbab21cce829236d1a88add8fcc26d0489c97124c6551925efbf472e50b | Python | 200,012 | 5,538 | """Base classes and utilities for `Runnable` objects."""
from __future__ import annotations
import asyncio
import collections
import contextlib
import functools
import inspect
import threading
from abc import ABC, abstractmethod
from collections.abc import (
AsyncGenerator,
AsyncIterator,
Awaitable,
C... |
93e3722690aaa28a81797a58afe6cdbbce93e9e605dbc89ab9703a11d249ad47 | Python | 200,252 | 5,500 | """
RQA2 – Object-oriented Recurrence Quantification Analysis for SMdRQA.
This module provides four classes:
``RQA2``
End-to-end RQA: data loading, parameter estimation (τ, m, ε), recurrence-plot
generation, measure computation, visualisation, and batch processing.
``RQA2_simulators``
Generators for well... |
c7ddfbaad4566b72594d5129a0f4474ad9029d8740abb76815c06817b15f9f69 | Quarto | 265 | 7 | ---
title: "R and Data Mining Resources"
---
> **Note:** Content from this page has been integrated into the book chapters.
> See the *Further Information* section of @sec-intro for books and web resources,
> and @sec-rpackages for popular R packages by category.
|
b35717a9ff8683fea7ab008dab7a610c2030bab74d8fe21b67d5ce4e52465608 | Quarto | 324 | 18 | ---
title: "References"
nocite: "@*"
---
<style>
/* References page only: hide chapter numbering for this entry. */
.quarto-page-breadcrumbs .chapter-number,
.quarto-title .chapter-number,
.sidebar-item-text.sidebar-link.active .chapter-number,
.nav-page-text .chapter-number {
display: none;
}
</style>
::: {#refs}
:... |
50bf176b342bfd0ee0cf2b72e9ba7c63fff8b8b7aac6b65be0fa78c40438d6c1 | Quarto | 516 | 14 | ---
author: "Daniel Rodriguez and Javier Dolado"
date: "`r Sys.Date()`"
github-repo: danrodgar/DASE
description: "DASE Data Analysis in Software Engineering"
---
# Welcome {.unnumbered}
These __Data Analysis in Software Engineering (DASE)__ notes provide an introduction to the foundations of data science with R for ... |
918893db00f2de72593670b1425c8c90ee1e34efe6ee21499bd2426aa478a2f6 | Quarto | 2,127 | 83 |
## R Markdown
R Markdown lets us combine narrative text, code, and output in one reproducible
document. In software engineering, this is very useful for:
- defect analysis reports
- effort estimation summaries
- model evaluation dashboards
- experiment logs for reproducibility
## Basic Structure
A typical R Markdo... |
c8e580cf27146e4a3484fe5ce3342357d1e762bb5c610323e9fd9ede1cb7732c | Quarto | 2,507 | 76 |
# Numeric Prediction Evaluation
## Learning Objectives and Evaluation Lens
- **Objective**: evaluate numeric predictions with robust error measures.
- **Primary metrics**: MAE, RMSE, and context-specific relative metrics.
- **Common pitfalls**: relying on a single metric and ignoring error distribution shape.
- **Re... |
0bb024e2bf604419534a58bcc2ca4d835557ab132e4b699815abcb2b2f33b214 | Quarto | 2,996 | 145 |
# Social Network Analysis in SE
In this example, we will data from the MSR14 challenge. Further information and datasets:
[http://openscience.us/repo/msr/msr14.html](http://openscience.us/repo/msr/msr14.html)
Similar databases can be obtained using MetricsGrimoire or other tools.
In this simple example, we create ... |
88e25865e6e7a1d14623cfe1d6c83bdd0bfa2bfd8a98ad2a1b9f300f8b933527 | Quarto | 3,217 | 61 | # Graphical Evaluation
## Learning Objectives and Evaluation Lens
- **Objective**: use graphical diagnostics to compare classifiers beyond a single metric value.
- **Primary plots**: ROC and Precision-Recall curves.
- **Imbalanced data focus**: prioritize PR curves and PR-AUC interpretation.
- **Common pitfalls**: co... |
97cf3230cc61cede2765f14da197510529d7701aae5a339015b06e96244624bf | Quarto | 3,701 | 107 | # Feature Selection Example
This section presents a modern feature-selection workflow using `tidymodels`.
The goal is to keep the process explicit and reproducible:
- split data into training and test subsets
- define feature filters in a `recipe`
- fit a model through a `workflow`
- inspect variable importance
```{... |
919ae8aef5ea41bde90973ae5eab2ca0eadf52d521868ab5c32ab021fd253ed9 | Quarto | 3,821 | 128 |
# The Tidyverse approach to Data Analysis
The `tidyverse` is a set of packages promoted by Posit with the purpose of standardizing the processes in data science
<https://www.tidyverse.org/>
[Tidyverse design principles](https://principles.tidyverse.org/)
## Packages included
- `readr`
- `tibble`
- `dplyr` ... |
ae8f844bc6352be48f2e71f59117ea0d2c8632bb6855ac64ca64439a5929f803 | Quarto | 3,986 | 113 |
# Advanced Preprocessing Techniques
This chapter complements basic preprocessing with techniques that are often
required in realistic software engineering datasets.
## Feature Selection
This technique consists of selecting the most relevant attributes. The need for applying FS includes the following points:
+ A ... |
795f2bc78b9e312c46f7fedfa5b2733f74d43e512257e4ee6602668270405aa4 | Quarto | 4,069 | 74 |
# Evaluation of Models
Once we obtain the model with the training data, we need to evaluate it on unseen test data.
> **No Free Lunch theorem**
> In the absence of any knowledge about the prediction problem, no model
> can be said to be uniformly better than any other
## Learning Objectives and Evaluation Lens
- *... |
1568e9fd2e0425bcf1cacfa7743030aa1964cd3a64b6d1dfe414a7320f444b30 | Quarto | 4,610 | 135 |
# Unsupervised or Descriptive modeling
## Learning Objectives and Evaluation Lens
- **Objective**: discover structure and patterns without using target labels in training.
- **Data context**: unlabeled software metrics or discretized transactional views.
- **Validation**: parameter sensitivity analysis and internal ... |
c4e403c79cda6bdfc66bb8159bd57aad2090b686089b40944e7ea9580b98e903 | Quarto | 4,828 | 153 |
# Time Series
Many sources of information are time related. For example, data from Software Configuration Management (SCM) such as Git, [GitHub](http://www.github.com)) systems or Dashboards such as [Grimoire Lab](https://chaoss.github.io/grimoirelab/) from [Bitergia](https://bitergia.com/) or [SonarQube](https://ww... |
3b249c5f5e58e02dcedd92453f633d856492cda3f0f427121b34e4e246e2d7a7 | Quarto | 4,848 | 81 | # What is Data Mining {#sec-intro}
In this course we extract useful information from **software engineering data** such as commits, issues, static metrics, test results, and release histories.
Typical goals include effort estimation, defect prediction, risk-aware planning, and quality assessment.
We provide an overv... |
06ce5639525d80c5a2fef3564555ffa0173e98269e6f7d17780603990a17a820 | Quarto | 4,944 | 70 | ## Effort Estimation Data in Software Engineering
It is worth highlighting the case of software effort estimation datasets with their peculiarities. First, most effort estimation datasets used in the literature are scattered through research papers with the exception of a few kept in the PROMISE repository. Mair et a... |
fb2438023d78d8ad359c57b6c97b375e5adfe1060e2b10f21d7e61bf3896ad23 | Quarto | 5,549 | 129 |
# Classical Hypothesis Testing
## Learning Objectives and Evaluation Lens
- **Objective**: test statistical hypotheses while avoiding common misinterpretations.
- **Primary outputs**: test statistic, p-value, confidence interval, and effect size.
- **Common pitfalls**: treating p-values as effect magnitude and ignor... |
70723653d5cb4b81a780d25e876ecaa685af25a0c91173bac8e30a610c93c828 | Quarto | 5,838 | 187 | ---
title-block-banner: true
title: "Analysis of brain reperfusion"
date: today
date-format: full
author:
- name: "Daniel Manrique-Castano"
orcid: 0000-0002-1912-1764
affiliation: Univerisity Laval (Laboratory of neurovascular interactions)
keywords:
- Stroke
- Brain reperfusion
- PDGF-D
... |
2d70ea6342f1bae9322240def88b8315d637abbd8c409a5fea0ea3d0f8e0f096 | Quarto | 5,922 | 97 | # Appendix: Preprocessing the ISBSG Repository {#sec-isbsg-preprocessing}
## The ISBSG Repository
The International Software Benchmarking Standards Group ([ISBSG](https://www.isbsg.org/)), a non-profit organisation, maintains a software project management repository contributed by a variety of organisations. The ISBS... |
4c20c5829d6806c36e5dfbc25ba46d90b1f690327442ed95d6bd5734b2535b48 | Quarto | 6,545 | 205 | # Evaluation of Unsupervised Models
## Learning Objectives and Evaluation Lens
- **Objective**: evaluate whether unsupervised structure is both statistically coherent and practically useful.
- **Primary metrics**: silhouette, CH index, purity, ARI.
- **Interpretation rule**: internal quality and external usefulness s... |
2efb1f354dbc6f5f18f6744fdce7add96dd92889eacadf29652cb191541af6e5 | Quarto | 6,901 | 178 |
## R and Python {#sec-rnpython}
R and Python can interact together via the *reticulate* package.
The documentation for the `reticulate` package can be found here:
[https://rstudio.github.io/reticulate/](https://rstudio.github.io/reticulate/)
Installation and configu... |
26a91675df91a99b67c4818cd01c31ff298371d5fc8754ea49409b045fabfc71 | Quarto | 7,341 | 192 |
# Further Classification Models
## Learning Objectives and Evaluation Lens
- **Objective**: handle classification settings that go beyond single-label fully supervised learning.
- **Data context**: multilabel corpora and partially labeled datasets.
- **Validation**: strict separation of labeled/unlabeled/test data ... |
25709f0b7e045ae2d572f3b261ec10fd3510c3698604bde3c8f3851e690bf976 | Quarto | 7,925 | 180 | ---
output:
html_document: default
pdf_document: default
---
# Evaluation of Classification Models
## Learning Objectives and Evaluation Lens
- **Objective**: evaluate classifier quality under realistic software quality constraints.
- **Primary metrics**: precision, recall, F1, ROC-AUC, PR-AUC, and MCC.
- **Imbal... |
7fdb1be8ed8fa95e8a15e3e17c92c54a51bec1c6110ad4634a1ebe1b59f45d21 | Quarto | 9,555 | 332 |
# The `ggplot2` package
**ggplot2** is one of the visualization tools that the R system has. The others are the Base R plotting functions and the **lattice** package. ggplot2 is the most evolved and complete plotting package. The components of a plot, include:
- the data being plotted, a data frame, or tibble (*t... |
3b788f612f0dc28ff61a0c082fcdcb92a91f5f63c795270882b8b7a2621dd081 | Quarto | 10,381 | 214 |
# Data Sources in Software Engineering
We classify this trail in the following categories:
* _Source code_ can be studied to measure its properties, such as size or complexity.
* _Source Code Management Systems_ (SCM) make it possible to store all the changes that the different source code files undergo during the ... |
afa8026a98f7619c48330315e6afb05d9f3d3166ae5ac5fedd0187712e60fd3a | Quarto | 11,800 | 333 |
# Measures of Evaluation in Software Engineering {#evaluationSE}
## Learning Objectives and Evaluation Lens
- **Objective**: evaluate effort-estimation and defect-prediction models with SE-specific measures.
- **Primary metrics**: MAR/gMAR, SA, MARP0 variants, and standard ML metrics where applicable.
- **Common pit... |
3c64b8d5bd11488f56148cb5bfa5f6fbda89b237923f35204ce726cd18f55513 | Quarto | 13,242 | 339 |
# Exploratory Data Analysis
## Descriptive statistics
The first task to do with any dataset is to characterize it in terms of summary statistics and graphics.
Displaying information graphically will help us to identify the main characteristics of the data. To describe a distribution we often want to know where it ... |
8cffdfa5ce9c31805c048c864551ee3f89bd0cf54e6b1a6664620ecd793e6acc | Quarto | 16,028 | 373 |
# Regression {#regression}
## Learning Objectives and Evaluation Lens
- **Objective**: model and interpret numeric software engineering outcomes (for example, effort).
- **Data context**: project/module predictors with a continuous response variable.
- **Validation**: train/test split (prefer temporal split when ava... |
56f40e4b92d1e6d4712318c90a3390b57c3d5642a752c525b33509b8d7dc4426 | Quarto | 16,697 | 367 |
## R Packages {#sec-rpackages}
## Modern Package Equivalents
This book includes examples accumulated over multiple years. Some packages are
kept for pedagogical continuity, but modern projects should generally prefer the
following ecosystem updates:
| Legacy package / pattern | Recommended modern equivalent | Note... |
b6b3613d105af2ad7333b8678e14d27ece4c30dadab44f4580e38661800ee49b | Quarto | 16,995 | 433 | ---
output:
html_document: default
pdf_document: default
---
# Text Mining Software Engineering Data
In software engineering, there is a lot of information in plain text such as requirements, bug reports, mails, reviews from applicatons, etc. Typically that information can be extracted from Software Configurat... |
0b52b7ec5f416f0422de2dc67aea23ee5e5198813beb56ed17719f69bcc0fbe9 | Quarto | 17,007 | 516 |
# Supervised Classification
## Learning Objectives and Evaluation Lens
- **Objective**: train supervised models to predict software quality outcomes (for example, defect proneness).
- **Data context**: software metrics with a labeled target (`Defective`).
- **Validation**: stratified split and resampling on training... |
e6e704742ec8a9864928fe1a6deb343d178dbecfcff74b1eafd3ac2bed55cf33 | Quarto | 17,800 | 476 | # Advanced Models
## Learning Objectives and Evaluation Lens
- **Objective**: understand when advanced models provide value beyond strong baselines.
- **Data context**: same SE tasks as prior chapters, with higher model complexity.
- **Validation**: strict baseline comparison, reproducible tuning, and held-out evalua... |
040a08bafed2f322ecad439228e08c01e55bb324b780a52d425190cd12b1ef67 | Quarto | 20,786 | 563 |
# Preprocessing
Following the data mining process, we describe what is meant by preprocessing, classical supervised models, unsupervised models and evaluation in the context of software engineering with examples
This task is probably the hardest and where most of effort is spend in the data mining process. It is qu... |
7f06bce59b7c134b59a2e507a9c63fcf59086f5b70d5656e0193ef6c403ade8a | Quarto | 22,667 | 919 |
# Introduction to R {#r-intro}
The goal of the first part of this book is to get you up to speed with the basics of **R** as quickly as possible.
## Installation
Install the latest preview version for getting all features.
Follow the procedures according to your operating system.
- Linux: You need to have `bla... |
3a3546c660fc95c56e856a481ba2901c21e99f3b920b6d2bbd5e5e26be270e68 | R | 44 | 2 | # Reserved for package-level settings.
NULL
|
04d2800a37d108eade6a396901146e69a7f667c1aab492a745ca092bf0e7c43b | R | 52 | 4 | library(testthat)
library(mice)
test_check("mice")
|
54a4d887c82a8f763dd1f8388d9be54df0727aa60cfc4a7d8aa8c45e88f7fc8a | R | 60 | 4 | library(testthat)
library(hBayesDM)
test_check("hBayesDM")
|
445fb9ecf1e0f1f9ee6317f7f4ae69c5a682efcb4eab76f3f6d584da6ec35e0d | R | 66 | 4 | library(testthat)
library(GDCRNATools)
test_check("GDCRNATools")
|
632ae493ed52f191b85c7f3fce8b7277258e0eb09b1ececa201a946d5b0bc09c | R | 96 | 3 | genomes <- read.csv("./inst/extdata/genomes.csv")
usethis::use_data(genomes, overwrite = TRUE)
|
65fde8802af6b580fafeac29225a0b451dc1608206c332297137806465eb3d8f | R | 97 | 2 | mnar_demo_data <- read.csv("data-raw/data/mnar_demo_data.csv")
usethis::use_data(mnar_demo_data)
|
ce648f38a9d56625c9312c0a00f79537144372226d4d61b49de0959fb2fde909 | R | 102 | 3 | data("toenail", package = "HSAUR3")
toenail2 <- toenail
usethis::use_data(toenail2, overwrite = TRUE)
|
eb47ab52ab3eac8fe6ac36319a7b9e217859057c58b4f8530e5859365e272865 | R | 122 | 3 | remove_ensembl_version <- function(ensembl_ids) {
gsub(pattern = "(.*)\\.(.*)", replacement = "\\1", x = ensembl_ids)
}
|
d4430bfe295e8c6aa0bcf03e75c51c3416ab6937ccce9fb513bbb17943f4f90b | R | 148 | 4 | obtain.design <- function(data, formula = ~.) {
mf <- model.frame(formula, data = data, na.action = na.pass)
model.matrix(formula, data = mf)
}
|
811969920188f31e123bcda8a8fd2a5948acabf54bf9f72f3e1cfcb0257eb9d0 | R | 162 | 10 | library(shiny)
library(IsoformSwitchAnalyzeR)
library(viridis)
library(ComplexHeatmap)
library(tidyverse)
source("ui.R")
source("server.R")
shinyApp(ui, server) |
ffbfb3aff63d1cec6fe3f799ea45847218a545779aba67754b77a2340ce8c848 | R | 167 | 6 | #' @noRd
.onAttach <- function(libname, pkgname) {
ver <- utils::packageVersion("hBayesDM")
packageStartupMessage("\n\nThis is hBayesDM version ", ver, "\n\n")
}
|
b6a4bd52a0a80ecd4a465ca059958ef830b4ffaaebef566b3a2e137d0fd539d3 | R | 169 | 4 | test_that("MICE-IMPUTE-POLR-001: polr for only gen works", {
imp1 <- mice(boys, blocks = list("gen"), print = FALSE, m = 1, maxit = 1)
expect_true(is.mids(imp1))
})
|
6c42f0ad9b1b556d2aa7ae9dab54092c9f438f5587613c2f845aa1db99651889 | R | 180 | 5 | test_that("MIRA-001: list prints without an error", {
imp <- mice(nhanes, print = FALSE, maxit = 1, seed = 121, m = 1)
fit <- with(imp, sd(bmi))
expect_output(print(fit))
})
|
1f9b5e4672ffb4c53d33a5dd06d60959aa7bed34bfbca8b43b8305c6ea6384da | R | 184 | 7 | library(testthat)
library(factR2)
library(BSgenome.Mmusculus.UCSC.mm10)
data(genomes)
gtf <- system.file("extdata", "sc_merged_sample.gtf.gz", package = "factR")
test_check("factR2")
|
422028eacb3e5bab471aa2db36eb3c385e6c71c84167a3379b1f995520d6068d | R | 215 | 9 | initialize.chain <- function(varnames, maxit, m) {
chain <- array(NA, dim = c(length(varnames), maxit, m))
dimnames(chain) <- list(
varnames,
seq_len(maxit),
paste("Chain", seq_len(m))
)
chain
}
|
c2c778c5ad6f56b5a6ce8e9ff88295ddc7a5855a0d6aab9a17c9a6144b22da26 | R | 216 | 14 | #@Time : 2023/7/23 11:58
#@Author : Biao Zhang
#@Email : littlebiao@outlook.com
#@File : test.r
#@Description: This file is used to ...
sumfunc <- function(x, y)
{
sum <- x+y
return(sum)
}
print(sumfunc(1, 2)) |
220b6991674f16a20eff666cc9f35ba5e48589224cb5e49a03ca5e61311aa6a7 | R | 217 | 10 | context("Test dd_cs")
library(hBayesDM)
test_that("Test dd_cs", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(dd_cs(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
626b8e62255de6ccc1b0292ce39d809a1a27616432d242bbc8aa4724905f7ab8 | R | 217 | 7 | library(dplyr)
# Function to extract temperature course for specific site and time interval
extract_covariate_course <- function(df, from, to) {
df <- df %>% filter(timestamp > from, timestamp <= to)
return(df)
} |
bcecfb87f4844422c2a511ebbeb0b16dcf9a7e29e66d0b4c05fbf9944e3e499f | R | 217 | 10 | context("Test pst_Q")
library(hBayesDM)
test_that("Test pst_Q", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(pst_Q(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
0a23d5d9461218990b489ea7fcd1313ae8b8b74a6d2e6cfdbc5b33a2fee3eeb5 | R | 220 | 10 | context("Test cgt_cm")
library(hBayesDM)
test_that("Test cgt_cm", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(cgt_cm(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
1066f89e56ab0948cc6be01c9a570cacad7e5746fda9ecff973e488d74bf6826 | R | 220 | 10 | context("Test gng_m1")
library(hBayesDM)
test_that("Test gng_m1", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(gng_m1(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
39b7bdf88c32d53718a5dee5139cb949703968b005c044568f25218c2270fbfa | R | 220 | 10 | context("Test prl_rp")
library(hBayesDM)
test_that("Test prl_rp", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(prl_rp(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
4acdd6919f3ba39718dafc4aec5fb69e37311c08fe92ee0aa5fde36ddb94d01e | R | 220 | 10 | context("Test gng_m2")
library(hBayesDM)
test_that("Test gng_m2", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(gng_m2(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
5ee3fdd822ab9e2e695e4f77bb86cb78181ad883a59fd57f8bb6e3067c26165f | R | 220 | 10 | context("Test dd_exp")
library(hBayesDM)
test_that("Test dd_exp", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(dd_exp(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
cf3bfe9d74cca7ad0a03ea9de179f22f58b4eafc761cd5398a214e7835c945b4 | R | 220 | 10 | context("Test gng_m4")
library(hBayesDM)
test_that("Test gng_m4", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(gng_m4(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
dcf1ca80685b15df57a42730826090a1c2033f13a0d33c67603119545e04e75a | R | 220 | 10 | context("Test gng_m3")
library(hBayesDM)
test_that("Test gng_m3", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(gng_m3(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
14da20363ccfbcf61fdc2f5cd194cbef8dafdd643068dd655d8a63c219c8dd4c | R | 223 | 10 | context("Test prl_ewa")
library(hBayesDM)
test_that("Test prl_ewa", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(prl_ewa(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
211b59209c32a3db3629c8f07de93f3d7d5ef1167cb64a340593eee112c64269 | R | 223 | 10 | context("Test ra_noLA")
library(hBayesDM)
test_that("Test ra_noLA", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(ra_noLA(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
38896c1bc180afc1408a8355f11cb3eb7b99179ec1a514824eee858d0e42f083 | R | 223 | 10 | context("Test ra_noRA")
library(hBayesDM)
test_that("Test ra_noRA", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(ra_noRA(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
6b8a7cf0ec6544d49ad2c73c178b0688b89e3f446149d66daff49680997891c9 | R | 223 | 10 | context("Test igt_orl")
library(hBayesDM)
test_that("Test igt_orl", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(igt_orl(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
6f7a7702a38bb4584ae71b7703483774cb8fc212179e4a814a5acd540f673e9c | R | 223 | 10 | context("Test ts_par6")
library(hBayesDM)
test_that("Test ts_par6", {
# Do not run this test on CRAN
skip_on_cran()
expect_output(ts_par6(
data = "example", niter = 10, nwarmup = 5, nchain = 1, ncore = 1))
})
|
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