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Les traitements shells nécessitent souvent de tracer les compte-rendus dans un fichier de trace.

Voici deux petites fonctions très utiles qui permettront de le faire en choisissant votre niveau de trace ainsi qu'un exemple d'utilisation

 Le fichier Trace_Lib.sh -->

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k-means clustering

k-means is a kind of clustering algorithms, which belong to the family of unsupervised machine learning models. It aims at finding $k$ groups of similar data (clusters) in an unlabeled multidimensional dataset.

The k-means minimization problem

Let $(x_1, ..., x_n)$ be a set of $n$ observations with $x_i \in \mathbb{R}^{d}$, for $1 \leq i \leq n$. The aim of the k-means algorithms is to find a disjoint partition $S={S_1, ..., S_k }$ of the $n$ observations into $k \leq n$ clusters, minimizing $D$ the within-cluster distance to center: $$ D(S) = \sum_{i=1}^k \sum_{x \in S_i} | x - \mu_i |^2 $$ where $\mu_i$ is the $i$-th cluster center (i.e. the arithmetic mean of the cluster observations): $\mu_i = \frac{1}{|S_i|} \sum_{x_j \in S_i} x_j$, for $1 \leq i \leq n$.

Unfortunately, finding the exact solution of this problem is very tough (NP-hard) and a local minimum is generally sought using a heuristic.

The algorithm

Here is a simple description of the algorithm taken from the book "Data Science from Scratch" by Joel Grus (O'Reilly):

  1. Start with a set of k-means, which are $k$ points in $d$-dimensional space.
  2. Assign each point to the mean to which it is closest.
  3. If no point’s assignment has changed, stop and keep the clusters.
  4. If some point’s assignment has changed, recompute the means and return to step 2.

This algorithm is an iterative refinement procedure. In his book "Python Data Science Handbook" (O'Reilly), Jake VanderPlas refers to this algorithm as kind of Expectation–Maximization (E–M). Since step 1 is the algorithm initialization and step 3 the stopping criteria, we can see that the algorithm consists in only two alternating steps:

step 2. is the Expectation:

"updating our expectation of which cluster each point belongs to".

step 4. is the Maximization:

"maximizing some fitness function that defines the location of the cluster centers".

This is described with more details in the following link.

An interesting geometrical interpretation is that step 2 corresponds to partitioning the observations according to the Voronoi diagram generated by the centers computed previously (either on step 1 or 4). This is also why the standard k-means algorithm is also called Lloyd's algorithm, which is a Voronoi iteration method for finding evenly spaced sets of points in subsets of Euclidean spaces.

Voronoi diagram

Let us have a look at the Voronoi diagram generated by the $k$ means.

As in Jake VanderPlas' book, we generate some fake observation data using scikit-learn 2-dimensional blobs, in order to easily plot them.

%matplotlib inline
import matplotlib.pyplot as plt
from sklearn.datasets.samples_generator import make_blobs

k = 20
n = 1000
X, _ = make_blobs(n_samples=n, centers=k, cluster_std=0.70, random_state=0)
plt.scatter(X[:, 0], X[:, 1], s=5);
 

png

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Temperatures taken from this website:
https://www.historique-meteo.net/france/rh-ne-alpes/lyon/

This dataset is updated monthly to be updated early september with august temeratures (to be updated in early september with august temeratures).

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('ggplot')
%matplotlib inline
 

Import and inspect the data

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I recently stumbled on this interesting post on RealPython (excellent website by the way!):

Fast, Flexible, Easy and Intuitive: How to Speed Up Your Pandas Projects

This post has different subjects related to Pandas: - creating a datetime column - looping over Pandas data - saving/loading HDF data stores - ...

I focused on the looping over Pandas data part. They compare different approaches for looping over a dataframe and applying a basic (piecewise linear) function: - a "crappy" loop with .iloc to access the data - iterrows() - apply() with a lambda function

But I was a little bit disapointed to see that they did not actually implement the following other approaches: - itertuples()`

While .itertuples() tends to be a bit faster, let’s stay in Pandas and use .iterrows() in this example, because some readers might not have run across nametuple. - Numpy vectorize - Numpy (just a loop over Numpy vectors) - Cython - Numba

So I just wanted to complete their post by adding the latter approaches to the performance comparison, using the same .csv file. In order to compare all the different implementations on the same computer, I also copied and re-ran their code.

Note: my laptop CPU is an Intel(R) Core(TM) i7-7700HQ CPU @ 2.80GHz (with some DDDR4-2400 RAM).

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