This is the full class and function references of tick. Please look at the modules documentation cited below for more examples and use cases, since direct class and function API is not enough for understanding their uses.
This module provides tools for the inference and simulation of Hawkes processes.
User guide: tick.hawkes
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Hawkes process learner for exponential kernels with fixed and given decays, with many choices of penalization and solvers. |
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Hawkes process learner for sum-exponential kernels with fixed and given decays, with many choices of penalization and solvers. |
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This class is used for performing non parametric estimation of multi-dimensional Hawkes processes based on expectation maximization algorithm. |
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A class that implements parametric inference for Hawkes processes with an exponential parametrisation of the kernels and a mix of Lasso and nuclear regularization |
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This class is used for performing non parametric estimation of multi-dimensional Hawkes processes based on expectation maximization algorithm and the hypothesis that kernels are linear combinations of some basis kernels. |
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A class that implements parametric inference for Hawkes processes with parametrisation of the kernels as sum of Gaussian basis functions and a mix of Lasso and group-lasso regularization |
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This class is used for performing non parametric estimation of multi-dimensional marked Hawkes processes based on conditional laws. |
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This class is used for performing non parametric estimation of multi-dimensional Hawkes processes based cumulant matching. |
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A function depending on time. |
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Hawkes kernel with exponential decay |
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Hawkes kernel with sum exponential decays |
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Hawkes kernel for power law |
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Hawkes kernel defined by an arbitrary time function. |
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Homogeneous Poisson process simulation |
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Inhomogeneous Poisson process simulation |
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Hawkes process simulation |
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Hawkes process with exponential kernels simulation |
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Hawkes process with sum-exponential kernels simulation |
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Parallel simulations of a single Hawkes simulation |
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Hawkes process model exponential kernels with fixed and given decay. |
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Hawkes process model exponential kernels with fixed and given decays. |
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Hawkes process model for sum of exponential kernels with fixed and given decays. |
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Hawkes process model for sum-exponential kernels with fixed and given decays. |
This modules provides tools for the inference and simulation of generalized linear models.
User guide: tick.linear_model
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Linear regression learner, with many choices of penalization and solvers. |
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Logistic regression learner, with many choices of penalization and solvers. |
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Poisson regression learner, with exponential link function. |
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Least-squares loss for linear regression. |
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Logistic regression model for binary classification. |
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Poisson regression model with identity or exponential link for data with a count label. |
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Hinge loss model for binary classification. |
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Smoothed hinge loss model for binary classification. |
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Quadratic hinge loss model for binary classification. |
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Simulation of a Linear regression model |
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Simulation of a Logistic regression model |
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Simulation of a Poisson regression model, with identity or exponential link. |
This module provides tools for robust inference, namely outliers detection and models such as Huber regression, among others robust losses.
User guide: tick.robust
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Robust linear regression learner. |
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Robust estimation of the standard deviation, based on the Corrected Median Absolute Deviation (MAD) of x. |
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Robust estimation of the standard deviation, based on the inter-quartile (IQR) distance of x. |
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Huber loss for robust regression. |
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Modified hinge loss model for binary classification. |
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Absolute value (L1) loss for linear regression. |
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Epsilon-Insensitive loss for robust regression. |
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Linear regression model with individual intercepts. |
This module provides tools for inference and simulation for survival analysis.
User guide: tick.survival
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Cox regression learner, using the partial Cox likelihood for proportional risks, with many choices of penalization. |
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Computes the Nelson-Aalen cumulative hazard rate estimation given by: |
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Computes the Kaplan-Meier survival function estimation given by: |
Partial likelihood of the Cox regression model (proportional hazards). |
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Discrete-time Self Control Case Series (SCCS) likelihood. |
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Simulation of a Cox regression for proportional hazards |
This module contains all the proximal operators available in tick.
User guide: See the tick.prox section for further details.
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Proximal operator of the null function (identity) |
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Proximal operator of the L1 norm (soft-thresholding) |
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Proximal operator of the weighted L1 norm (weighted soft-thresholding) |
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Proximal operator of the ElasticNet regularization. |
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Proximal operator of the squared L2 norm (ridge penalization) |
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Proximal operator of the L2 penalization. |
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Multiple proximal operator. |
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Proximal operator of the nuclear norm, aka trace norm |
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Projection operator onto the half-space of vectors with non-negative entries |
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Projection operator onto the set of vector with all coordinates equal (or in the given range if given one). |
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Proximal operator of Slope penalization. |
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Proximal operator of the total-variation penalization |
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Proximal operator of binarsity. |
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Proximal operator of group-L1, a.k.a group-Lasso. |
This module contains all the solvers available in tick.
User guide: See the tick.solver section for further details.
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Proximal gradient descent |
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Accelerated proximal gradient descent |
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Broyden, Fletcher, Goldfarb, and Shanno algorithm |
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Generalized Forward-Backward algorithm |
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Self-Concordant Proximal Gradient descent |
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Stochastic gradient descent solver |
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Adaptive stochastic gradient descent solver |
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Stochastic Variance Reduced Gradient solver |
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Stochastic Average Gradient solver, for the minimization of objectives of the form |
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Stochastic Dual Coordinate Ascent |
A class to manage the history along iterations of a solver |
This module contains basic tools from simulation.
User guide: See the tick.simulation section for further details.
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Sparse and exponential model weights generator |
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Sparse and gaussian model weights generator Instance of weights for a model, given by a sparse vector, where non-zero coordinates (chosen at random) are centered Gaussian with given standard-deviation |
Normal features generator with uniform covariance |
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Normal features generator with toeplitz covariance |
This module contains some utilities functions for plotting.
User guide: See the tick.plot section for further details.
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Plot the history of convergence of learners or solvers. |
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Generic function to plot Hawkes kernels. |
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Generic function to plot Hawkes kernel norms. |
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Function used to plot basis of kernels |
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Quick plot of a |
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Plot point process realization |
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Plot several stem plots using either matplotlib or bokeh rendering. |
This module provides easy access to some datasets used as benchmarks in tick.
User guide: See the tick.dataset section for further details.
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Fetch dataset from tick_datasets github repository. |
Load Hawkes formatted bund data from https://github.com/X-DataInitiative/tick-datasets/tree/master/hawkes/bund |
This module contains some utilities functions for preprocessing of data.
User guide: See the tick.preprocessing section for further details.
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Transforms continuous data into bucketed binary data. |
Transforms longitudinal exposure features to add the corresponding product features. |
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Transforms longitudinal exposure features to add columns representing lagged features. |
This module contains some functions to compute some metrics that help evaluate the performance of learning techniques.
User guide: See the tick.metrics section for further details.
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Computes the False Discovery Proportion for selecting the support of x_truth using x, namely the proportion of false positive among all detected positives, given by FP / (FP + TP). |
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Computes proportion of true positives (TP) among the number ground truth positives (namely TP + FN, where FN is the number of false negatives), hence TP / (TP + FN). |