* I'm looking for a python/sklearn/lifelines/whatever implementation of Harrell's c-index (concordance index), which is mentioned in random survival forests*. The C-index is calculated using the following steps: Form all possible pairs of cases over the data. Omit those pairs whose shorter survival time is censored The concordance index or C-index is a generalization of the area under the ROC curve (AUC) that can take into account censored data. It represents the global assessment of the model discrimination power: this is the model's ability to correctly provide a reliable ranking of the survival times based on the individual risk scores. It can be computed with th indexing before iterating. instead of . for i, row in df.iterrows(): if row['event'] == 1: If you do . for i, row in df[df['event'] == 1].rows(): you will iterate over less rows. itertuples. generally, itertuples is faster than iterrows. comparable_row NLTK provides the function concordance() to locate and print series of phrases that contain the keyword. However, the function only print the output. The user is not able to save the results for further processing unless redirect the stdout. Below function will emulate the concordance function and return the list of phrases for further processing. It uses the NLTK concordance Index which keeps track of the keyword index in the passage/text and retrieve the surrounding words The concordance index or c-index is a metric to evaluate the predictions made by an algorithm. It is defined as the proportion of concordant pairs divided by the total number of possible evaluation..

- The concordance index is defined as the proportion of all comparable pairs in which the predictions and outcomes are concordant. Two samples are comparable if (i) both of them experienced an event (at different times), or (ii) the one with a shorter observed survival time experienced an event, in which case the event-free subject outlived the other
- Here is my solution for concordance https://github.com/jrgosalia/Python/blob/master/problem2_concordance.py $ python --version Python 3.5.1. library.py def getLines(fileName): getLines validates the given fileName. Returns all lines present in a valid file. lines = if (fileName != None and len(fileName) > 0 and os.path.exists(fileName)): if os.path.isfile(fileName): file = open(fileName, 'r') lines = file.read() if (len(lines) > 0): return lines else: print.
- .concordance() is a special nltk function. So you can't just call it on any python object (like your list). More specifically: .concordance() is a method in the Text class of nltk. Basically, if you want to use the .concordance(), you have to instantiate a Text object first, and then call it on that object. Tex
- The concordance index is a value between 0 and 1 where: 0.5 is the expected result from random predictions, 1.0 is perfect concordance and, 0.0 is perfect anti-concordance (multiply predictions with -1 to get 1.0
- look at the concordance-index (see below section on Model selection and calibration in survival regression), available as concordance_index_ or in the print_summary () as a measure of predictive accuracy. look at the log-likelihood test result in the print_summary () or log_likelihood_ratio_test (

Insert object before index. If self is frozen, raise ValueError. pop (* args, ** kwargs) ¶ Remove and return item at index (default last). Raises IndexError if list is empty or index is out of range. If self is frozen, raise ValueError. remove (* args, ** kwargs) ¶ Remove first occurrence of value. Raises ValueError if the value is not present. If self is frozen, raise ValueError The Concordance Index evaluates the accuracy of the ordering of predicted time. It is interpreted as follows[11]: Random Predictions: 0.5; Perfect Concordance: 1.0; Perfect Anti-Concordance: 0.0 (in this case we should multiply the predictions by -1 to get a perfect 1.0) Usually, the fitted models have a concordance index between 0.55 and 0.7. The concordance correlation coefficient comes to the rescue! The concordance correlation coefficient measures the agreement between two variables. In this case, the value is around 0.02, indicating no agreement between the two variables. Unfortunately, the concordance correlation coefficient is not widely used in the evaluation of predictive models. I believe this to be an important omission and I would urge any data scientist to start using it for regression modelling Concordance Index (C‐Index) It is a rank order statistic for predictions against true outcomes and is defined as the ratio of the concordant pairs to the total comparable pairs Python Concordance. friarsenglish Uncategorized June 17, 2020 June 17, 2020 1 Minute. There's an issue with the NLTK concordance function which can be resolved; until I find a way out, here's a simple search utility. Here's what it does: In the text files of books, say Alice-in-Wonderland.txt, using regular expression it finds all the occurrences of given queries (the variable.

- index () is an inbuilt function in Python, which searches for a given element from the start of the list and returns the lowest index where the element appears
- Here is a generic python code to run different classification techniques like Logistic Regression, Decision Tree, Random Forest and Support Vector Machines (SVM). The code is automated to get different metrics like Concordance and Discordance, Classification table, Precision and Recall rates, Accuracy as well as the estimates of coefficients or Variable Importance
- The index() method finds the first occurrence of the specified value. The index() method raises an exception if the value is not found. The index() method is almost the same as the find() method, the only difference is that the find() method returns -1 if the value is not found. (See example below

* The index can replace the existing index or expand on it*. Parameters keys label or array-like or list of labels/arrays. This parameter can be either a single column key, a single array of the same length as the calling DataFrame, or a list containing an arbitrary combination of column keys and arrays. Here, array encompasses Series, Index, np.ndarray, and instances of Iterator. drop bool. def concordance_correlation_coefficient (y_true, y_pred, sample_weight = None, multioutput = 'uniform_average'): Concordance correlation coefficient. The concordance correlation coefficient is a measure of inter-rater agreement. It measures the deviation of the relationship between predicted and true values: from the 45 degree angle

kmf. fit (T, E, timeline = range (0, 100, 2)) kmf. survival_function_ # index is now the same as range(0, 100, 2) kmf. confidence_interval_ # index is now the same as range(0, 100, 2) A useful summary stat is the median survival time, which represents when 50% of the population has died: from lifelines.utils import median_survival_times median_ = kmf. median_survival_time_ median_confidence. How can I compare concordance index in external validation dataset? In the training set, we can compare concordance index by the methods mentioned in the post How to assess the incremental additive r survival cox-model validation concordance. asked Sep 9 '20 at 13:34. codecrazer. 111 3 3 bronze badges. 1. vote. 0answers 40 views Is it necessary to do multiple hypothesis testing for AUC. Using a concordance file of keywords or Python expressions, build a Microsoft Word and Markdown index document from the slides and notes of one or more PowerPoint pptx files. Linux or OS X Installation. On Linux or Mac OS X systems, install the python-docx package using pip: $ pip install python-docx python-pptx If you don't have the pip utility, install it with the following command, then run.

- Using Random Survival Forests¶. This notebook demonstrates how to use Random Survival Forests introduced in scikit-survival 0.11.. As it's popular counterparts for classification and regression, a Random Survival Forest is an ensemble of tree-based learners
- For this purpose, the concordance_index_ is a measure of the predictive accuracy of the fitted model onto the training dataset. fit (df, duration_col, event_col=None, regressors=None, show_progress=False, timeline=None, weights_col=None, robust=False, initial_point=None, entry_col=None) → self ¶ Fit the regression model to a right-censored dataset. Parameters: df (DataFrame) - a Pandas.
- It uses the NLTK
**concordance****Index**which keeps track of the keyword**index**in the passage/text and retrieve the surrounding words. Below is the function: import nltk def get_all_phases_containing_tar_wrd(target_word, tar_passage, left_margin = 10, right_margin = 10): Function to get all the phases that contain the target word in a text/passage tar_passage. Workaround to save the output. - python code examples for lifelines.utils.concordance_index. Learn how to use python api lifelines.utils.concordance_index

The concordance index (c-index) was first introduced to the biomedical community in 42. It is a measure of association between the predicted and observed failures in case of right censored data. In the absence of censored data, the c-index estimates the Mann-Whitney parameter. Note that censoring is a condition in which the value of a measurement or observation is only partially known (e.g., impact of a drug on mortality rate for living subjects that may die before the end of. ** class ConcordanceIndex (object): An index that can be used to look up the offset locations at which a given word occurs in a document**. def __init__ (self, tokens, key = lambda x: x): Construct a new concordance index.:param tokens: The document (list of tokens) that this concordance index was created from Model with a smaller AIC score, a larger log-likelihood, and larger concordance index is the better model. Out-of-sample validation. We can also evaluate model fit with the out-of-sample data. File name: concordance.py Description: Counts up the number of each unique word in a block of plain text. Copyright (C) 2010 Steve Osborne, srosborne (at) gmail.co concordance_td: The time-dependent concordance index evaluated at the event times . brier_score: The IPCW Brier score (inverse probability of censoring weighted Brier score) . See Section 3.1.2 of for details. nbll: The IPCW (negative) binomial log-likelihood . I.e., this is minus the binomial log-likelihood and should not be confused with the negative binomial distribution. The weighting is performed as in Section 3.1.2 o

* Concordance-index (between 0 to 1) is a ranking statistic rather than an accuracy score for the prediction of actual results, and is defined as the ratio of the concordant pairs to the total comparable pairs: 0*.5 is the expected result from random predictions, 1.0 is perfect concordance and PySurvival is an open source python package for Survival Analysis modeling - the modeling concept used to analyze or predict when an event is likely to happen. It is built upon the most commonly used machine learning packages such NumPy, SciPy and PyTorch. PySurvival is compatible with Python 2.7-3.7

In this article we will explore these two factors in detail. We will first study what cross validation is, why it is necessary, and how to perform it via Python's Scikit-Learn library. We will then move on to the Grid Search algorithm and see how it can be used to automatically select the best parameters for an algorithm. Cross Validatio #### 3 - Creating the modeling dataset # Defining the features features = sim. features # Building training and testing sets # index_train, index_test = train_test_split( range (N), test_size = 0.2) data_train = dataset. loc[index_train]. reset_index( drop = True) data_test = dataset. loc[index_test]. reset_index( drop = True) # Creating the X, T and E input X_train, X_test = data_train[features], data_test[features] T_train, T_test = data_train['time']. values, data_test['time']. values E. Concordance is only one of the measures to see the decency of Logistic Regression. Only it can not disclose to you much about how great the model is. You have to utilize it alongside differen And it's very easy to calculate the actual concordance index based on the slope definition: d = transform(d, slope=(y_norm-y_abnorm)/(x_norm-x_abnorm)) mean((d$slope > 0) + .5*(d$slope==0)) The answer is again 0.8931711, i.e., the AUC

The concordance index expresses the proportion of concordant pairs in a dataset, thus estimates the probability that, for a random pair of individuals, the predicted survival times of the two individuals have the same ordering as their true survival times. A concordance index of 1 represents a model with perfect prediction, an index of 0.5 is equal to random prediction. [23] For a better. $ python3 concordance.py -index poem.txt are 1 Roses are red 2 Violets are blue blue 2 Violets are blue 3 RED BLUE. red 1 Roses are red 3 RED BLUE. roses 1 Roses are red violets 2 Violets are blue 3. The -top option has output like -index, except order the words so that the word that appear on the most number of lines come first. Use sorted. What you would like is to have a predictable and full index from 40 to 75. (Notice that in the above index, the last two time points are not adjacent - the cause is observing no lifetimes existing for times 72 or 73). This is especially useful for comparing multiple survival functions at specific time points 4.Gonen and Heller Concordance Index forCox models (survAUC::GHCI, CPE::phcpe, clinfun::coxphCPE) 方法1： 直接从survival包的函数coxph结果中输出，需要R的版本高于2.15.需要提前安装survival包可以看出这种方法输出了C-index (对应模型参数C)，也输出了标准误，95%可信区间就可以通过C加减1.96*se得到。. 并且这种方法也适用于很多指标联合。 Some statisticians also call it AUROC which stands for area under the receiver operating characteristics. It is calculated by adding Concordance Percent and 0.5 times of Tied Percent. Gini coefficient or Somers' D statistic is closely related to AUC. It is calculated by (2*AUC - 1). It can also be calculated by (Percent Concordant - Percent.

** This commit includes a function for calculating Harrell's concordance index, which can be calculated in R using 'hmisc'**. The function is implemented in Fortran, with a small Python wrap.. Below is my first Python script, concordance.py: #!/usr/bin/env python2 # concordance.py - do KWIK search against a text # # usage: ./concordance.py <file> <word>ph # Eric Lease Morgan <emorgan@nd.edu> # November 5, 2014 - my first real python script! # require import sys import nltk # get input; needs sanity checking file = sys.argv[ 1 ] word = sys.argv[ 2 ] # do the work text = nltk.Text( nltk.word_tokenize( open( file ).read( ) ) ) text.concordance( word ) # done quit(

The scoring for the concordance index is the same way as the area under the curve (AUC) score. It's in reality somewhere between 0.5 and 1, not 0.5 would be the same as if we just, you know, completely randomly put everything on the board one would be a perfect ordering of everybody in the path they were obviously the closer to 1 the more accurate that your model is. Customer Level Survival. Concordance Correlation Coefficient (CCC) CCC0 (Lower Boundary) This is the value of CCC assumed by the null hypothesis, H0. This is your statement of the lower bound on tolerable values of CCC. When the lower limit of a confidence interval computed for CCC is less than this value, concordance is not established. Otherwise, concordance is established Python nltk.ConcordanceIndex Method Example. Python nltk.ConcordanceIndex() Method Examples The following example shows the usage of nltk.ConcordanceIndex metho #### 1 - Importing packages import numpy as np import pandas as pd from matplotlib import pyplot as plt from sklearn.model_selection import train_test_split from pysurvival.models.simulations import SimulationModel from pysurvival.models.survival_forest import RandomSurvivalForestModel from pysurvival.utils.metrics import concordance_index from. Values near +1 indicate strong concordance between x and y, values near -1 indicate strong discordance and values near zero indicate no concordance. There is no clear-cut agreement as to how to interpret other values, although one approach is to interpret Lin's CCC as for Pearson's correlation coefficient (e.g. values less than .20 are poor, while values greater than .80 are excellent.

scipy.stats.spearmanr¶ scipy.stats.spearmanr(a, b=None, axis=0) [source] ¶ Calculates a Spearman rank-order correlation coefficient and the p-value to test for non-correlation. The Spearman correlation is a nonparametric measure of the monotonicity of the relationship between two datasets How can I get only **Concordance** **index** as dataframe or list. cph.summary returns a dataframe of main results i.e. p-values and coef but it does not include **concordance** **index** and other surrounding information. Source: **Python**-3x Questions How to only print a specific href from website Why does my tqdm progress bar display on a new line for every.

方法4和5的计算：Concordance = #all concordant pairs/#total pairs ignoring ties. 方法2和3的计算：Concordance = (#all concordant pairs + #tied pairs/2)/(#total pairs including ties) 说了那么多方法，唯一不同是否在计算时考虑tied risk，其他只是实现方法和函数不同罢了。那么我们能不能不要这么复杂，只需要二个函数来解决C指数和可信区间的事呢？当然! * A concordance is an alphabetical list of the words in a text that gives all word positions where each word appears*. Thus, python index.py 0 0 produces a concordance. In a famous incident, one group of researchers tried to establish credibility while keeping details of the Dead Sea Scrolls secret from others by making public a concordance. Compose a program. Pandas uses the xlwt Python module internally for writing to Excel files. The to_excel method is called on the DataFrame we want to export.We also need to pass a filename to which this DataFrame will be written. movies.to_excel('output.xlsx') By default, the index is also saved to the output file. However, sometimes the index doesn't provide. python中的find、rfind、index、rindex. find()从左向右寻找子序列的位置，如存在多个相同子序列只返回第一个查找到的位置，如果子序列不存在返回-1. rfind()从右向左寻找子序列的位置..... index()从左向右寻找子序列的位置，如果子序列不存在报错，所以一般我们用find()更好一些. rindex()从右向左寻找子序列. Evaluate concordance of an input VCF against a validated truth VCF . Category Variant Evaluation and Refinement. Overview Evaluate site-level concordance of an input VCF against a truth VCF. This tool evaluates two variant callsets against each other and produces a six-column summary metrics table. The summary: stratifies SNP and INDEL calls, tallies true-positive, false-positive and false.

- Python nltk.corpus.stopwords.words() Examples Construct a new concordance index. :param tokens: The document (list of tokens) that this concordance index was created from. This list can be used to access the context of a given word occurrence. :param key: A function that maps each token to a normalized version that will be used as a key in the index. E.g., if you use ``key=lambda s:s.
- The following are 30 code examples for showing how to use nltk.corpus.brown.words().These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example
- ing, and data visualizatio
- Examples¶. This page provides a series of examples, tutorials and recipes to help you get started with statsmodels.Each of the examples shown here is made available as an IPython Notebook and as a plain python script on the statsmodels github repository.. We also encourage users to submit their own examples, tutorials or cool statsmodels trick to the Examples wiki pag
- Write a Python function, fconcordance, with one parameter,fname, a string which is the name of a text file. fconcordance willreturn a concordance for file fname. A concordance is an index ofthe words in the file, with each line(s) of the file where the wordappears. A main function will call fconcordance and report theresults
- Python不借助深度学习框架完成MNIST手写数字识别. qq_45661465: 请问博主有没有后面详细的备注哦，有点看不懂205行以后的代码. Python不借助深度学习框架完成MNIST手写数字识别. 不正经的kimol君: 大佬，我准备跟你混了
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The concordance correlation coefficient is nearly identical to some of the measures called intra-class correlations. Comparisons of the concordance correlation coefficient with an ordinary intraclass correlation on different data sets found only small differences between the two correlations, in one case on the third decimal Concordance Brought to you by: jaymzh, kevin_timmerman. Summary Files Reviews Support Wiki Mailing Lists News Menu concordance-commit; concordance-devel. Oh no! Some styles failed to load. Please try reloading this page Help Create Join Login. Open Source Software. Accounting; CRM; Business Intelligenc Welcome to the Concordance homepage. This software will allow you to program your Logitech Harmony universal remote control! In addition to providing software that works in UNIX (which logitech's software doesn't support), our code also aims to be cross-platform. So you can use the same program in Linux or Windows, and hopefully soon, Mac too! We currently support all models except for the 890. Kite is a free autocomplete for Python developers. Code faster with the Kite plugin for your code editor, featuring Line-of-Code Completions and cloudless processing

- 2018-06-12 - Scott Talbert <swt@techie.net> concordance (1.3-1) unstable; urgency=medium * New upstream release 1.3 * debian/control: - Add subpackage for Python 3 bindings * debian/rules: - Build Python 3 bindings * debian/patches: - Remove libzip configure patch which doesn't seem to be needed anymore * debian/concordance.metainfo.xml: - Change license to CC0-1.0 to fix AppStream erro
- Concordance Brought to you by: jaymzh, kevin_timmerman. Summary Files Reviews Support Wiki Mailing Lists News Menu.
- And I was there, initially without the libconcord1 or binary:Version depends, thinking there ought to be away to make the depend show up automatically :) > > Do you have realy tested your package? python-concordance is empty. Also > I do not think that it has to be arch:any and it should (wheter it is > arch:{all,any} a >= source:Version or binary:Version depend on libconcord1. > Done. Shows.
- 2018-05-07 - Scott Talbert <swt@techie.net> concordance (1.2-2) unstable; urgency=medium * debian/control: - Indicate that a VCS is now being used - Update standards version to 4.1.4 - Update Homepage URL - Add dh-python to Build-Depends * debian/copyright: - Update URLs - Remove files paragraphs that refer to non-existent files * debian/rules: - Remove get-orig-source target as this has been.
- Comparison of relevance vector machine and support vector machine¶. If you can not find a good example below, you can try the search function to search modules. Future integration of additional data will likely improve model performance and accuracy for personalised prognostics. Simply put, the Concordance Index is a measure of how well-sorted our predictions are. It is a measure of rank.
- Then prepare a dictionary of hyper-parameters. And it takes only two lines to train a network: network = deepsurv.DeepSurv (**hyperparams) log = network.train (train_data, valid_data, n_epochs=500) You can then evaluate its success on testing data: network.get_concordance_index (**test_data) >> 0.62269622730138632

In particular, Harrell's concordance index (sksurv.metrics.concordance_index_censored()) computes the ratio of correctly ordered (concordant) pairs to comparable pairs and is the default performance metric when calling a survival model's score() method Concordance is the percentage of pairs, where true event's probability scores are greater than the scores of true non-events. For a perfect model, this will be 100%. So, the higher the concordance, the better is the quality of the model. This can be computed using the Concordance function in InformationValue package The second metric is Harrell's concordance index (C-harrel) , which is an unweighted concordance index that evaluates the relative ordering of the samples, comparing the prognostic index (i.e., log hazard ratio) of each patient with the survival times. The third metric is the log-ranked p-value from Kaplan-Meier survival curves of two different survival risk groups. This is done by using the median Prognosis Index (PI), the output of Cox-nnet, to dichotomize the patients into. Palabras clave en contexto (usando n-grams) con Python: Setting up an Integrated Development Environment for Python (Linux) Creación de un entorno de desarrollo integrado para Python (Linux) Setting Up an Integrated Development Environment for Python (Mac) Creación de un entorno de desarrollo integrado para Python (Mac) Manipulating Strings in Python The Random Survival Forest package provides a python implementation of the survival prediction method originally published by Ishwaran et al. (2008). Reference: Ishwaran, H., Kogalur, U. B., Blackstone, E. H., & Lauer, M. S. (2008). Random survival forests. The annals of applied statistics, 2(3), 841-860. Installatio

A concordance is a collection of words whereas an index lists (related) items that have meaning within the context of the written work. An index shows the result of some analysis or knowledge of. Pandas computes correlation coefficient between the columns present in a dataframe instance using the correlation() method. It computes Pearson correlation coefficient, Kendall Tau correlation coefficient and Spearman correlation coefficient based on the value passed for the method parameter The tied_time return value of sksurv.metrics.concordance_index_censored() now correctly reflects the number of comparable pairs that share the same time and that are used in computing the concordance index. Fix a bug in sksurv.metrics.concordance_index_censored() where a pair with risk estimates within tolerance was counted both as concordant and tied Find the best open-source package for your project with Snyk Open Source Advisor. Explore over 1 million open source packages Die Kendall'sche Konkordanzanalyse (nach Maurice George Kendall) ist ein nichtparametrisches statistisches Verfahren zur Quantifizierung der Übereinstimmung zwischen mehreren Beurteilern (Ratern). Damit stellt der Kendall'sche Konkordanzkoeffizient W eine Alternative zu Diese Seite wurde zuletzt am 4. Mai 2020 um 21:08 Uhr bearbeitet

pycox is a python package for survival analysis and time-to-event survival models: (Logistic-Hazard, DeepHit, DeepSurv, Cox-Time, MTLR, etc.) evaluation criteria (concordance, Brier score, Binomial log-likelihood, etc.) event-time datasets (SUPPORT, METABRIC, KKBox, etc) simulation studies; illustrative examples; Project details. Project links. Homepage Statistics. GitHub statistics: Stars. c指数即一致性指数（index of concordance），用来评价模型的预测能力。c指数是资料所有病人对子中预测结果与实际结果一致的对子所占的比例。它估计了预测结果与实际观察到的结果相一致的概率。c指数的计算方法是：把所研究的资料中的所有研究对象随机地两两组成对子。以生存分析为例，对于一对病人，如果生存时间较长的一位的预测生存时间也长于另一位的.

Cohen's kappa coefficient is a statistic that is used to measure inter-rater reliability for qualitative items. It is generally thought to be a more robust measure than simple percent agreement calculation, as κ takes into account the possibility of the agreement occurring by chance. There is controversy surrounding Cohen's kappa due to the difficulty in interpreting indices of agreement. Some researchers have suggested that it is conceptually simpler to evaluate disagreement. This article was originally published on Quora in 2015.. To understand concordance, we should first understand the concept of cutoff value. CUTOFF VALUE: For instance, students are classified as pass (1) or fail (0) depending upon the cutoff passing marks in the examination. The cutoff marks varies depending upon the requirements of the different examination The macro processor will try to replaced named indices with literal integers by using the usual macro processing process. The compiler will then add the symbols to the symbol table with the specified numerical index. Values in the range 200-2000 are currenly reserved for the concordance indices. The compiler itself contains an Ocaml file which binds symbolic names to integers, allowing the. Concordance, in the context of statistics, is to study agreements between judges, scales, and measurements. This page provides support for all concordance programs provided by StatsToDo. The information on this page is organized in separate panels according to the numerical nature of the measurements, and consists of the followin