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22 Hrs
56 Min
46 Seg

Carga del modelo HMM y distribución inicial

11/26
Resources

How to implement the Viterbi algorithm in Python?

The Viterbi algorithm is fundamental in natural language processing (NLP), specifically in solving problems with latent Markov models. In this content, we will show you step-by-step how to implement this algorithm in Python, taking advantage of its flexibility and power to subdivide complex problems into manageable parts. Let's dive into this exciting dynamic programming project!

Where to start with environment setup and data loading?

To begin with, it is crucial to prepare the development environment and make sure we have the necessary data to implement the algorithm. Here we detail the process:

  1. Import necessary libraries: start by importing the essential libraries such as Conlu and NumPy, which are vital for data and structure handling.

    import numpy as np
  2. Download the corpus and the trained model: Use GitHub to get the Ancora Corpus data in Spanish and to load our previously trained transition and emission models. Make sure you have the .npy files downloaded and accessible in your working environment.

  3. Configure the kernel and directories: Connect to a Python kernel and refresh the file system to confirm that the data has been downloaded correctly. This is an essential step to ensure that everything is ready for processing.

How to load and verify the probability matrices?

To work with the trained model, it is essential to load the probability matrices from the saved files. Let's see how it is done:

  • Loading data from NumPy files: use np.load to load the transition and emission probability matrices, making sure that the data are read correctly.

    transition_probabilities = np.load("transition.npy", allow_pickle=True).item()emission_probabilities = np.load("emissionHMM.npy", allow_pickle=True).item()
  • Validation of the loaded data: Always check that the keys and values of the matrices correspond to what is expected, i.e. check the probabilities to ensure a correct configuration.

How to prepare the data for the Viterbi algorithm?

Before building the Viterbi algorithm, it is essential to prepare the corpus data translated into manageable grammatical categories. Here are the key steps:

  • Grammatical category identification: extract a unique list of grammatical categories from the corpus that we will use for labeling. Use Python sets to filter out duplicate items and consolidate the list.

    state_set = list({w.split('|')[1] for w in emission_probabilities.keys()})
  • Assigning indexes to categories: Associates each grammatical category with a unique index to be used in the Viterbi algorithm matrices. Now, each category has a numeric value associated with it for ease of processing.

    tag_state_dict = {state: i for i, state in enumerate(state_set)}

How to handle the initial probabilities of the states?

A critical component in Viterbi's algorithm is the initial distribution of states. We need to compute the probabilities of the grammatical categories that appear at the beginning of sentences in the corpus.

  • Calculating initial probabilities: Using a dictionary, count the occasions when each grammatical category is first in a sentence and normalize these counts to convert them to probabilities.

    init_tag_state_prob = {}for token_list in parse_ink(data_file): first_tag = token_list[0].upos if first_tag in init_tag_state_prob: init_tag_state_prob[first_tag] += 1 else: init_tag_state_prob[first_tag] = 1
  • Normalization: Divide each count by the total to get the probabilities.

    corpus_length = len(corpus)for tag in init_tag_state_prob: init_tag_state_prob[tag] /= corpus_length

Preparation towards Viterbi's algorithm

With the environment setup and data prepared, we are ready to implement the Viterbi algorithm. This will allow us to find the most probable sequence of grammatical categories for any given sentence, using dynamic programming to optimize the process.

This method is crucial for predicting tags in text sequences and ensuring accurate and efficient tagging. Stay tuned and keep learning more about this fascinating world of machine learning and natural language processing - stay tuned and never stop learning!

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una manera más simple de corrroborar la suma de las probabilidades

sum(initTagStateProb.values())

Hola Francisco, en primer lugar felicitarte por esta clase y todo el curso en general (lo que llevo hasta el momento), de verdad valoro muchísimo todo el trabajo que has hecho. Por otro lado, quería dar mi aporte, quizás para la identificación de las categorías gramaticales únicas se podría hacer utilizando el dict de la clase pasada que decía tagCountDict, haciendo un set de las llaves de ese diccionario. Solo como una forma de ahorrar código. Saludos!!!

Este curso me esta ayudando bastante, ya que estoy haciendo un proyecto en la maestria relacionado con la detección de odio, me hacian falta estas bases.

Modelo Markoviano Latente > Calculo de las probabilidades de transición y emisión.

Algoritmo de Viterbi > Obtener el camino o ruta, utilizando el que tiene mayor probabilidad. Por ejemplo: “Castillo: Prop, El: Det, Noble: Adj, Trabajador: Adj”, sería la serie o ruta de etiquetas más probables para “Castillo el noble trabajador”.

La **distribución inicial** en un HMM juega un papel crucial en la predicción de las primeras observaciones de una secuencia. Definir y cargar correctamente un modelo HMM en Python requiere configurar las matrices de transición y emisión, junto con la distribución inicial. Durante el entrenamiento, estos parámetros se ajustan para que el modelo pueda predecir la secuencia oculta que mejor explica las observaciones dadas.

Que ejercicio tan bueno

para comprobar que las probabilidades suman uno era mas fácil copiar en excel y poner =SUMA()
:'v