scikitlearn
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scikitlearn

Data Science toolset summary from 2021
Scikitlearn  It is one of the most widely used frameworks for Python based Data science tasks. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, kmeans and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. Link  https://scikitlearn.org/

Intel Extension for ScikitLearn
Hi all,
Currently some works is being done to improve computational primitives of scikitlearn to enhance its overhaul performances natively.
You can have a look at this exploratory PR: https://github.com/scikitlearn/scikitlearn/pull/20254
This other PR is a clear revamp of this previous one:

ScikitLearn Version 1.0
Just to clarify, scikitlearn 1.0 has not been released yet. The latest tag in the github repo is 1.0.rc2
https://github.com/scikitlearn/scikitlearn/releases/tag/1....

Top 10 Python Libraries for Machine Learning
Website: https://scikitlearn.org/ Github Repository: https://github.com/scikitlearn/scikitlearn Developed By: SkLearn.org Primary Purpose: Predictive Data Analysis and Data Modeling

where is binary_metric function in sklearn package
There is a function named binary_metric in https://github.com/scikitlearn/scikitlearn/blob/main/sklearn/metrics/_base.py

Use ScikitLearn and Runflow
If you're not familiar with Scikitlearn and Runflow,

Confused as to what exaclty a piece of code does
well you can start at https://github.com/scikitlearn/scikitlearn/blob/main/sklearn/model_selection/_validation.py, or maybe someone will guide you later

What Makes Python Libraries So Important For Data Science Learning?
Next comes the complexity of drawing the maximum possible number of valuable insights. Using different python libraries such as ScikitLearn, PyTorch, Pandas, etc., complications of data analysis can be solved within a minute. And the complexity associated with visualisation gets handled by other data visualisation libraries like Matploitlib, PyTorch, etc.

Is there a way to map cluster centers back to a dataframe?
To avoid the issue with convergence (and the discrepancy between the labels_ and cluster_centers_), you can set tol=0, though this can of course lead to issues if convergence is a problem. There was an issue about it here. Assuming it's converged, then the order is fine.

Any from scratch Hamming Loss implementations?
The source code for the function you refer to is quite straightforward anyway. The definition of count_nonzero() is here.
matplotlib

Problem with surface plot color and legend
# https://old.reddit.com/r/learnpython/comments/r1etmt/problem_with_surface_plot_color_and_legend/ # AmericaRL_03.py # Comparação entre random walk e difusão em 1d import numpy as np import matplotlib.pyplot as plt M = 100000 # Número de walkers L = 100 # Tamanho da malha # A cada intervalo de tempo, mover o walker e propagar a difusão p = 0.1 # Probabilidade de andar pinv = 1.0  p nsteps = 2001 # Número de intervalos de tempo # Iniciando as concentrações c = np.zeros((2, 2 * L + 1, 2 * L + 1)) i0 = 0 i1 = 1 c[:, L, L] = M # c[:,L,L] corresponde a (x,y) = (0,0) edgesdiff = np.array(range(L  1, L + 1))  0.5 xc = 0.5 * (edgesdiff[:1] + edgesdiff[1:]) xx, yy = np.meshgrid(xc, xc) D = p noutput = 100 for it in range(nsteps): # Executar a etapa na equação de difusão for ix in range(1, len(c[0])  1): for iy in range(1, len(c[0])  1): # Usar i0 e gerar i1 c[i1, ix, iy] = c[i0, ix, iy] + D * ( c[i0, ix  1, iy] + c[i0, ix + 1, iy]  4 * c[i0, ix, iy] + c[i0, ix, iy  1] + c[i0, ix, iy + 1] ) # Inverter i0 e i1 ii = i1 i1 = i0 i0 = ii # Plotar as concentrações if np.mod(it, noutput) == 0: fig = plt.figure() ax = fig.add_subplot(111, projection="3d") # ax.cla() diff = ax.plot_surface(xx, yy, c[0, :, :], cmap="Reds", label="Difusão") plt.title("Tempo = {}, M = {}, p = {}".format(it, M, p)) ax.set_xlabel("Distância percorrida (x)") ax.set_ylabel("Distância percorrida (y)") ax.set_zlabel("Concentração") # ax.legend() fails getting proper label key color in 3D plots # # AttributeError missing _facecolors2d and _edgecolors2d are raised # in 3D projection method get_facecolor() used by legend handler # # Is open issue since 2015 # https://github.com/matplotlib/matplotlib/issues/4067 if False: # OP's way: Set values manually to keep legend() quiet diff._facecolors2d = diff._facecolor3d diff._edgecolors2d = diff._edgecolor3d ax.legend() # But then problems with legend() getting proper legend key color # default (blue) is used instead of a value from set cmap (Reds) else: # DIFFERENT FIX: Draw legend key and legend label manually # # First get suitable cmap color (instead blue default) # https://stackoverflow.com/a/25408562 from matplotlib.cm import get_cmap cmap = get_cmap('Reds') my_red_rgba = cmap(0.5) # e.g. a color in middle of range (0..1) # Then insert own artist for legend key and legend label # https://matplotlib.org/stable/tutorials/intermediate/legend_guide.html import matplotlib.patches as mpatches red_patch = mpatches.Patch(color=my_red_rgba, label='Difusão') ax.legend(handles=[red_patch]) # plt.show() plt.savefig('tempo_{}.png'.format(it),dpi = 600) plt.pause(0.001) ax.cla() # is nicer placed here instead above

Python 3.8, 3.9 or 3.10 for new projects?
matplotlib supports 3.10 since May

Top 10 Python Libraries for Machine Learning
Website: https://matplotlib.org/ Github Repository: https://github.com/matplotlib/matplotlib Developed By: Micheal Droettboom, Community Primary purpose: Data Visualization

Should you learn Julia or Python for Machine Learning?
But, now we have to get used to Python's library of Machine Learning packages: tensorflow, numpy, matplotlib, and finally pandas
 Is there a way to improve this code?

Top 10 Python Libraries
Download the latest version of Matplotlib or visit its GitHub repo for more information.

Matplotlib: why do plot and axes interfaces use different method names to do the exact same thing?
I think sloppiness explains it, this explains how you can fix it ;)

How I create GitHub project reporting from scratch
Firstly, I tried the most popular visualization library matplotlib. But its configuration didn’t seem clear to me, so moved on with other options.

Valentine's Day Challenge
That would explain the link to https://matplotlib.org/ in the file...

What API suits the creation of a modern dynamic GUI used primarily for plotting in real time?
Matplotlib? https://matplotlib.org/
What are some alternatives?
plotly  The interactive graphing library for Python (includes Plotly Express) :sparkles:
PyQtGraph  Fast data visualization and GUI tools for scientific / engineering applications
pygal  PYthon svg GrAph plotting Library
bokeh  Interactive Data Visualization in the browser, from Python
bqplot  Plotting library for IPython/Jupyter notebooks
Keras  Deep Learning for humans
Surprise  A Python scikit for building and analyzing recommender systems
Prophet  Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or nonlinear growth.
tensorflow  An Open Source Machine Learning Framework for Everyone
plotnine  A grammar of graphics for Python
VisPy  Main repository for Vispy
ggplot  ggplot port for python