implicit | Fast Python Collaborative Filtering for Implicit Feedback | Recommender System library

 by   benfred Python Version: 0.7.2 License: MIT

kandi X-RAY | implicit Summary

kandi X-RAY | implicit Summary

implicit is a Python library typically used in Institutions, Learning, Education, Artificial Intelligence, Recommender System applications. implicit has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has high support. You can install using 'pip install implicit' or download it from GitHub, PyPI.

Fast Python Collaborative Filtering for Implicit Datasets.

            kandi-support Support

              implicit has a highly active ecosystem.
              It has 3184 star(s) with 599 fork(s). There are 81 watchers for this library.
              There were 3 major release(s) in the last 6 months.
              There are 75 open issues and 379 have been closed. On average issues are closed in 203 days. There are 6 open pull requests and 0 closed requests.
              It has a negative sentiment in the developer community.
              The latest version of implicit is 0.7.2

            kandi-Quality Quality

              implicit has 0 bugs and 0 code smells.

            kandi-Security Security

              implicit has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              implicit code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              implicit is licensed under the MIT License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              implicit releases are available to install and integrate.
              Deployable package is available in PyPI.
              Build file is available. You can build the component from source.
              Installation instructions are not available. Examples and code snippets are available.
              implicit saves you 1178 person hours of effort in developing the same functionality from scratch.
              It has 3388 lines of code, 215 functions and 41 files.
              It has high code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed implicit and discovered the below as its top functions. This is intended to give you an instant insight into implicit implemented functionality, and help decide if they suit your requirements.
            • Calculate similar movies from the input data
            • Fit the item recommender recommender
            • Download the movielens dataset
            • Create a logistic matrix factorization
            • Benchmark times for a given play
            • Check fitting errors
            • Check if user_items are in CSR format
            • Fit the model
            • Calculate similar artists from lastfm
            • Return the lastfm dataset
            • Get a model by name
            • Returns the contents of a file
            • Generate a graphviz plot
            • Convert to a CPU
            • Suggest recommendations for a given user
            • Generate the loss graph
            • Benchmark implicit learning
            • Benchmark the given factors
            • Create an hdf5 dataset
            • Generate anal speed graph
            • Deprecated use recommend_all
            • Compute the similarity scores for each user
            • Calculate least squares least squares regression
            • Generate recommendations for each user
            • Benchmark accuracy
            • Compute the item factor
            Get all kandi verified functions for this library.

            implicit Key Features

            No Key Features are available at this moment for implicit.

            implicit Examples and Code Snippets

            copy iconCopy
            :id: 2uA8X2izXH2b
            from jax.lax import while_loop
            def fixed_point(f, a, x_guess):
              def cond_fun(carry):
                x_prev, x = carry
                return jnp.abs(x_prev - x) > 1e-6
              def body_fun(carry):
                _, x = carry
                return x, f(a, x)
              _, x_star = whi  
            Implicit contracts
            Javadot img2Lines of Code : 7dot img2no licencesLicense : No License
            copy iconCopy
            Subject s = ReplaySubject.create();
            s.subscribe(v -> System.out.println(v));
            Detailed description-Providing a way for implicit dispatch
            Pythondot img3Lines of Code : 0dot img3License : Permissive (BSD-3-Clause)
            copy iconCopy
            with unumpy.determine_backend(array_like, np.ndarray):
            Returns the implicit gradient of a function .
            pythondot img4Lines of Code : 77dot img4License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def implicit_val_and_grad(f):
              """Returns a function which differentiates f with respect to variables.
              The wrapped function returns the value and the gradient of f when called with
              the same arguments. The gradient is with respect to all trainab  
            Computes the implicit gradient of a function .
            pythondot img5Lines of Code : 47dot img5License : Non-SPDX (Apache License 2.0)
            copy iconCopy
            def implicit_grad(f):
              """Returns a function which differentiates f with respect to variables.
              The wrapped function returns the gradient of f when called with the same
              arguments. The gradient is with respect to all trainable TFE variables
            How to handle dropdown without select in selenium python
            Pythondot img6Lines of Code : 19dot img6License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            wait = WebDriverWait(driver,30)
                # Close the footer add
            How can I view the amount of entries in Sqlalchemy's compiled cache?
            Pythondot img7Lines of Code : 55dot img7License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            >>> engine = sa.create_engine('mysql:///test', echo=True, future=True)
            >>> engine._compiled_cache                                            
            >>> conn = engine.connect()                            
            Sympy integration with high order expressions; integral transform
            Pythondot img8Lines of Code : 12dot img8License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            In [36]: integrate(x*(2*x - 1)**100, x).diff(x).factor()
            x⋅(2⋅x - 1) 
            In [34]: Integral(x*(2*x - 1)**100, x).transform(2*x-1, t).doit().subs(t, 2*x-1)
                     102            101
            Pandas DataFrame update cell values from second DataFrame
            Pythondot img9Lines of Code : 18dot img9License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
               a    b  c
            0  0  1.0  0
            1  0  0.0  0
            2  0  2.0  0
            3  0  0.0  0
            4  0  3.0  0
               a  b  c
            0  0  1  0
            1  0  0  0
            2  0
            Select rows together with their previous rows given a condition for a pandas dataframe
            Pythondot img10Lines of Code : 14dot img10License : Strong Copyleft (CC BY-SA 4.0)
            copy iconCopy
            m = df['time'].notna()
            df = df[m | m.groupby(df['ID']).shift(-1,fill_value=False)]
            print (df)
                ID            time country
            0    0             NaT     ITL
            1    0 0 days 01:00:00     USA
            3    0             NaT     ESP
            4    0 2 days 00:05:0

            Community Discussions


            Why is `np.sum(range(N))` very slow?
            Asked 2022-Mar-29 at 14:31

            I saw a video about speed of loops in python, where it was explained that doing sum(range(N)) is much faster than manually looping through range and adding the variables together, since the former runs in C due to built-in functions being used, while in the latter the summation is done in (slow) python. I was curious what happens when adding numpy to the mix. As I expected np.sum(np.arange(N)) is the fastest, but sum(np.arange(N)) and np.sum(range(N)) are even slower than doing the naive for loop.

            Why is this?

            Here's the script I used to test, some comments about the supposed cause of slowing done where I know (taken mostly from the video) and the results I got on my machine (python 3.10.0, numpy 1.21.2):

            updated script:



            Answered 2021-Oct-16 at 17:42

            From the cpython source code for sum sum initially seems to attempt a fast path that assumes all inputs are the same type. If that fails it will just iterate:



            Why is std::is_copy_constructible_v> true?
            Asked 2022-Mar-26 at 23:21

            In my version of clang and libc++ (near HEAD), this static_assert passes:



            Answered 2022-Mar-26 at 23:21

            std::vector and other containers (except std::array) are specified to have a copy constructor. This is not specified to be conditional on whether or not the element type is copyable. Only instantiation of the copy constructor's definition is forbidden if the element type is not copyable.

            As a result std::is_copy_constructible_v on the container will always be true. There is no way to test whether an instantiation of a definition would be well-formed with a type trait.

            It would be possible to specify that the copy constructor is not declared or excluded from overload resolution if the element type is not copyable. However, that would come with a trade-off which is explained in detail in this blog post:

            In short, if we want to be able to use the container with an incomplete type, e.g. recursively like



            Is use in an unused default member initializer still an odr-use?
            Asked 2022-Mar-13 at 00:25

            Is use in a default member initializer still an odr-use, even if the default member initializer is not used by any constructor?

            For example, is this program ill-formed because g is odr-used and therefore its definition implicitly instantiated?



            Answered 2022-Mar-13 at 00:25


            Should I use %$% instead of %>%?
            Asked 2022-Feb-08 at 23:14

            Recently I have found the %$% pipe operator, but I am missing the point regarding its difference with %>% and if it could completely replace it.

            Motivation to use %$%
            • The operator %$% could replace %>% in many cases:


            Answered 2022-Feb-08 at 23:14

            In addition to the provided comments:

            %$% also called the Exposition pipe vs. %>%:

            This is a short summary of this article

            "The key difference in using %$% or %>% lies in the type of arguments of used functions."

            One advantage, and as far as I can understand it, for me the only one to use %$% over %>% is the fact that we can avoid repetitive input of the dataframe name in functions that have no data as an argument.

            For example the lm() has a data argument. In this case we can use both %>% and %$% interchangeable.

            But in functions like the cor() which has no data argument:



            Resolution of built-in operator == overloads
            Asked 2022-Jan-21 at 19:00

            In the following code struct A has two implicit conversion operators to char and int, and an instance of the struct is compared for equality against integer constant 2:



            Answered 2022-Jan-21 at 19:00

            This is CWG 507. An example similar to yours was given, and the submitter explained that according to the standard, the overload resolution is ambiguous, even though this result is very counter-intuitive.

            Translating to your particular example, when comparing operator==(int, int) and operator==(float, int) to determine which is the better candidate, we have to determine which one has the better implicit conversion sequence for the first argument (obviously in the second argument, no conversion is required). For the first argument of operator==(int, int), we just use A::operator int. For the first argument of operator==(float, int), there is no way to decide whether to use A::operator int or A::operator char, so we get the "ambiguous conversion sequence". The overload resolution rules say that the ambiguous conversion sequence is no better or worse than any other user-defined conversion sequence. Therefore, the straightforward conversion from A{} to int (via A::operator int) is not considered better than the ambiguous conversion from A{} to float. This means neither operator== candidate is better than the other.

            Clang is apparently following the letter of the standard whereas GCC and MSVC are probably doing something else because of the standard seeming to be broken here. "Which compiler is right" depends on your opinion about what the standard should say. There is no proposed resolution on the issues page.

            I would suggest removing operator char unless you really, really need it, in which case you will have to think about what else you're willing to give up.



            Conversion operator with const-result - GCC/Clang discrepancy
            Asked 2022-Jan-10 at 16:12

            Given the following code snippet:



            Answered 2022-Jan-10 at 16:12

            I think this is the open CWG issue 2077.




            Is the timing of MATLAB reliable? If yes, can we reproduce the performance with julia, fortran, etc.?
            Asked 2022-Jan-05 at 08:26

            Originally this is a problem coming up in mathematica.SE, but since multiple programming languages have involved in the discussion, I think it's better to rephrase it a bit and post it here.

            In short, michalkvasnicka found that in the following MATLAB sample



            Answered 2021-Dec-30 at 12:23

            tic/toc should be fine, but it looks like the timing is being skewed by memory pre-allocation.

            I can reproduce similar timings to your MATLAB example, however

            • On first run (clear workspace)

              • Loop approach takes 2.08 sec
              • Vectorised approach takes 1.04 sec
              • Vectorisation saves 50% execution time
            • On second run (workspace not cleared)

              • Loop approach takes 2.55 sec
              • Vectorised approach takes 0.065 sec
              • Vectorisation "saves" 97.5% execution time

            My guess would be that since the loop approach explicitly creates a new matrix via zeros, the memory is reallocated from scratch on every run and you don't see the speed improvement on subsequent runs.

            However, when HH remains in memory and the HH=___ line outputs a matrix of the same size, I suspect MATLAB is doing some clever memory allocation to speed up the operation.

            We can prove this theory with the following test:



            Union's default constructor is implicitly deleted
            Asked 2021-Dec-20 at 22:48

            The following code:



            Answered 2021-Dec-20 at 22:26

            It's all slightly mysterious. gcc behaves the same as clang.

            The standard has this to say (emphasis mine):

            Absent default member initializers, if any non-static data member of a union has a non-trivial default constructor, copy constructor, move constructor, copy assignment operator, move assignment operator, or destructor, the corresponding member function of the union must be user-provided or it will be implicitly deleted for the union.

            But I think the wording is a bit wooly here. I think what they actually mean is that you must provide an initialiser for the member that has (in your example) a non-trivial constructor, like so:



            Is it possible to deprecate implicit conversion while allowing explicit conversion?
            Asked 2021-Dec-13 at 19:06

            Suppose I have a simple Duration class:



            Answered 2021-Dec-02 at 16:39

            You can turn Duration(int t_seconds) into a template function that can accept an int and set it to deprecated.



            Is there a way to enforce correct spelling of features?
            Asked 2021-Nov-24 at 20:28

            Let's assume I have the following feature defined in Cargo.toml:



            Answered 2021-Oct-13 at 03:05

            When RFC 3013, "Checking conditional compilation at compile time", is implemented, there will be warnings for a #[cfg] referring to a feature name that is not declared by Cargo, just as you're asking for. However, the implementation only just got started (Sep 28, 2021).

            The means of operation described in the RFC is just as you suggested, ‘cargo would pass down the flags to rustc’.

            It may be worth noting that this will not check all conditions appearing in the source text; as described in the RFC:

            This lint will not be able to detect invalid #[cfg] tests that are within modules that are not compiled, presumably because an ancestor mod is disabled.

            So, it will not confirm that all #[cfg(feature)] are valid on a single cargo check — you will need to test with your various features or combinations of features. But those are the same combinations that you would need anyway to check for compile errors in all of the regular source code that could be enabled; once you do that, the lint will assure you that you don't have any #[cfg(feature)] that are never enabled due to a typo.


            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install implicit

            You can install using 'pip install implicit' or download it from GitHub, PyPI.
            You can use implicit like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.


            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
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