TextFeatureSelection 0.3.0

Python library for feature selection for text features. It has filter method, genetic algorithm and TextFeatureSelectionEnsemble for improving text classification models. Helps improve your machine learning models

What is it?

Companion library of machine learning book Feature Engineering & Selection for Explainable Models: A Second Course for Data Scientists.

TextFeatureSelection is a Python library which helps improve text classification models through feature selection. It has 3 methods TextFeatureSelection, TextFeatureSelectionGA and TextFeatureSelectionEnsemble methods respectively.

First method: TextFeatureSelection

It follows the filter method for feature selection. It provides a score for each word token. We can set a threshold for the score to decide which words to be included. There are 4 algorithms in this method, as follows.

• Chi-square It measures the lack of independence between term(t) and class(c). It has a natural value of zero if t and c are independent. If it is higher, then term is dependent. It is not reliable for low-frequency terms
• Mutual information Rare terms will have a higher score than common terms. For multi-class categories, we will calculate MI value for all categories and will take the Max(MI) value across all categories at the word level.
• Proportional difference How close two numbers are from becoming equal. It helps find unigrams that occur mostly in one class of documents or the other.
• Information gain It gives discriminatory power of the word.

It has below parameters

• target list object which has categories of labels. for more than one category, no need to dummy code and instead provide label encoded values as list object.
• input_doc_list List object which has text. each element of list is text corpus. No need to tokenize, as text will be tokenized in the module while processing. target and input_doc_list should have same length.
• stop_words Words for which you will not want to have metric values calculated. Default is blank
• metric_list List object which has the metric to be calculated. There are 4 metric which are being computed as 'MI','CHI','PD','IG'. you can specify one or more than one as a list object. Default is ['MI','CHI','PD','IG']. Chi-square(CHI), Mutual information(MI), Proportional difference(PD) and Information gain(IG) are 4 metric which are calculated for each tokenized word from the corpus to aid the user for feature selection.

How to use is it?

from TextFeatureSelection import TextFeatureSelection

#Multiclass classification problem
input_doc_list=['i am very happy','i just had an awesome weekend','this is a very difficult terrain to trek. i wish i stayed back at home.','i just had lunch','Do you want chips?']
target=['Positive','Positive','Negative','Neutral','Neutral']
fsOBJ=TextFeatureSelection(target=target,input_doc_list=input_doc_list)
result_df=fsOBJ.getScore()
print(result_df)


#Binary classification
input_doc_list=['i am content with this location','i am having the time of my life','you cannot learn machine learning without linear algebra','i want to go to mars']
target=[1,1,0,1]
fsOBJ=TextFeatureSelection(target=target,input_doc_list=input_doc_list)
result_df=fsOBJ.getScore()
print(result_df)

Second method: TextFeatureSelectionGA

It follows the genetic algorithm method. This is a population based metaheuristics search algorithm. It returns the optimal set of word tokens which give the best possible model score.

Its parameters are divided into 2 groups.

a) Genetic algorithm parameters: These are provided during object initialization.

• generations Number of generations to run genetic algorithm. 500 as deafult, as used in the original paper
• population Number of individual chromosomes. 50 as default, as used in the original paper
• prob_crossover Probability of crossover. 0.9 as default, as used in the original paper
• prob_mutation Probability of mutation. 0.1 as default, as used in the original paper
• percentage_of_token Percentage of word features to be included in a given chromosome. 50 as default, as used in the original paper.
• runtime_minutes Number of minutes to run the algorithm. This is checked in between generations. At start of each generation it is checked if runtime has exceeded than alloted time. If case run time did exceeds provided limit, best result from generations executed so far is given as output. Default is 2 hours. i.e. 120 minutes.

b) Machine learning model and tfidf parameters: These are provided during function call.

Data Parameters

• doc_list text documents in a python list. Example: ['i had dinner','i am on vacation','I am happy','Wastage of time']

• label_list labels in a python list. Example: ['Neutral','Neutral','Positive','Negative']

Modelling Parameters

• model Set a model which has .fit function to train model and .predict function to predict for test data. This model should also be able to train classifier using TfidfVectorizer feature. Default is set as Logistic regression in sklearn

• model_metric Classifier cost function. Select one from: ['f1','precision','recall']. Default is F1

• avrg Averaging used in model_metric. Select one from ['micro', 'macro', 'samples','weighted', 'binary']. For binary classification, default is 'binary' and for multi-class classification, default is 'micro'.

TfidfVectorizer Parameters

• analyzer {'word', 'char', 'char_wb'} or callable, default='word' Whether the feature should be made of word or character n-grams. Option 'char_wb' creates character n-grams only from text inside word boundaries; n-grams at the edges of words are padded with space.

• min_df float or int, default=2 When building the vocabulary ignore terms that have a document frequency strictly lower than the given threshold. This value is also called cut-off in the literature. If float in range of [0.0, 1.0], the parameter represents a proportion of documents, integer absolute counts. This parameter is ignored if vocabulary is not None.

• max_df float or int, default=1.0 When building the vocabulary ignore terms that have a document frequency strictly higher than the given threshold (corpus-specific stop words). If float in range [0.0, 1.0], the parameter represents a proportion of documents, integer absolute counts. This parameter is ignored if vocabulary is not None.

• stop_words {'english'}, list, default=None If a string, it is passed to _check_stop_list and the appropriate stop list is returned. 'english' is currently the only supported string value. There are several known issues with 'english' and you should consider an alternative (see :ref:stop_words). If a list, that list is assumed to contain stop words, all of which will be removed from the resulting tokens. Only applies if analyzer == 'word'. If None, no stop words will be used. max_df can be set to a value in the range [0.7, 1.0) to automatically detect and filter stop words based on intra corpus document frequency of terms.

• tokenizer callable, default=None Override the string tokenization step while preserving the preprocessing and n-grams generation steps. Only applies if analyzer == 'word'

• token_pattern str, default=r"(?u)\b\w\w+\b" Regular expression denoting what constitutes a "token", only used if analyzer == 'word'. The default regexp selects tokens of 2 or more alphanumeric characters (punctuation is completely ignored and always treated as a token separator). If there is a capturing group in token_pattern then the captured group content, not the entire match, becomes the token. At most one capturing group is permitted.

• lowercase bool, default=True Convert all characters to lowercase before tokenizing.

How to use is it?

from TextFeatureSelection import TextFeatureSelectionGA

#Input documents: doc_list
#Input labels: label_list

getGAobj=TextFeatureSelectionGA(percentage_of_token=60)
best_vocabulary=getGAobj.getGeneticFeatures(doc_list=doc_list,label_list=label_list)

Third method: TextFeatureSelectionEnsemble

TextFeatureSelectionEnsemble helps ensemble multiple models to find best model combination with highest performance.

It uses grid search and document frequency for reducing vector size for individual models. This makes individual models less complex and computationally faster. At the ensemble learning layer, metaheuristics algorithm is used for identifying the smallest possible combination of individual models which has the highest impact on ensemble model performance.

Base Model Parameters

• doc_list Python list with text documents for training base models

• label_list Python list with Y labels

• use_class_weight Boolean value representing if you want to apply class weight before training classifiers. Default is False.

• pickle_path Path where base model, text feature vectors and ensemble models will be saved in PC.

• save_data Boolean True | False. Default is False. Whether datasets used for training base model, and ensemble models will be saved in PC.

• n_crossvalidation How many cross validation samples to be created. Higher value will result more time for model training. Lower number will result in less reliable model. Default is 5.

• seed_num Seed number for training base models as well as for creating cross validation data. Default is 1.

• stop_words Stop words for count and tfidf vectors. Default is None.

• lowercase Lowercasing for text in count and tfidf vector. Default is True

• n_jobs How many jobs to be run in parallel for training sklearn and xgboost models. Default is -1

• cost_function Cost function to optimize base models. During feature selection using grid search for base models, this cost function is used for identifying which words to be removed based on combination of lower and higer document frequency for words. Available options are 'f1', 'precision', 'recall'. Default is 'f1'

• average What averaging to be used for cost_function. Useful for multi-class classifications. Available options are 'micro','macro','samples','weighted' and 'binary' Default is 'binary'.

• basemodel_nestimators How many n_estimators. Used as a parameter for tree based models such as 'XGBClassifier','AdaBoostClassifier','RandomForestClassifier','ExtraTreesClassifier'. Default is 500.

• feature_list Type of features to be used for ensembling. Available options are 'Unigram','Bigram','Trigram'. Default is ['Unigram','Bigram','Trigram']

• vector_list Type of text vectors from sklearn to be used. Available options are 'CountVectorizer','TfidfVectorizer'. Default is ['CountVectorizer','TfidfVectorizer']

• base_model_list List of machine learning algorithms to be trained as base models for ensemble layer training. Available options are 'LogisticRegression','XGBClassifier','AdaBoostClassifier','RandomForestClassifier','ExtraTreesClassifier','KNeighborsClassifier' Default is ['LogisticRegression','XGBClassifier','AdaBoostClassifier','RandomForestClassifier','ExtraTreesClassifier','KNeighborsClassifier']

  Metaheuristic algorithm feature selection parameters for ensemble model

• method Which method you want to specify for metaheuristics feature selection. The available methods are 'ga', 'sa', 'aco', and 'pso'. These stand for genetic algorithm, simulated annealing, ant colony optimization, and particle swarm optimization respectively. You can select one out of the 4. Default is 'ga'.

• MetaHeuristicsParameters Parameters for the metaheuristics feature selection method for ensemble learning. This is used for identifying best combination of base models for ensemble learning. It helps remove models which has no contribution for ensemble learning and keep only important models.

FeatureSelection module is used from MetaHeuristicsFS python library. Refer documentation for MetaHeuristicsFS at: https://pypi.org/project/MetaHeuristicsFS/ and example usage of MetaHeuristicsFS for feature selection: https://github.com/StatguyUser/feature_engineering_and_selection_for_explanable_models/blob/37ba0d2921fbabbb83df44c6eb7a1242b19a637f/Chapter%208%20-%20Hotel%20Cancelation%20.ipynb

Parameters used are

  {"model_object": LogisticRegression(n_jobs=-1,random_state=1),
  "cost_function":f1_score,
  "average":'micro',
  "cost_function_improvement":'increase',
  "ga_parameters":{"generations":50,
                  "population":50,
                  "prob_crossover":0.9,
                  "prob_mutation":0.1,
                  "run_time":120},
  "sa_parameters":{"temperature":1500,
                  "iterations":50,
                  "n_perturb":1,
                  "n_features_percent_perturb":1,
                  "alpha":0.9,
                  "run_time":120},
  "aco_parameters":{"iterations":50,
                  "N_ants":50,
                  "evaporation_rate":0.9,
                  "Q":0.2,
                  "run_time":120},
  "pso_parameters":{"iterations":50,
                  "swarmSize":50,
                  "run_time":120}
  }

Output are saved in 4 folders

• model It has base models

• vector it has count and tfidf vectors for each model

• ensemble_model It has ensemble model

• deleted It has base model and vectors for models which were discarded by genetic algorithm.

• data_files It has list of data files used for training base models, and ensemble model

  Apart from above 5, it also saves and return list of columns which are used in ensemble layer with name best_ensemble_columns These columns are used in the exact same order for feature matrix in ensemble layer.

How to use is it?

imdb_data=pd.read_csv('../input/IMDB Dataset.csv')
le = LabelEncoder()
imdb_data['labels'] = le.fit_transform(imdb_data['sentiment'].values)

# convert raw text and labels to python list
doc_list=imdb_data['review'].tolist()
label_list=imdb_data['labels'].tolist()

# Initialize parameter for TextFeatureSelectionEnsemble and start training
gaObj=TextFeatureSelectionEnsemble(doc_list,label_list,n_crossvalidation=2,pickle_path='/home/user/folder/',average='micro',base_model_list=['LogisticRegression','RandomForestClassifier','ExtraTreesClassifier','KNeighborsClassifier'])
best_columns=gaObj.doTFSE()

Where to get it?

pip install TextFeatureSelection

How to cite

Md Azimul Haque (2022). Feature Engineering & Selection for Explainable Models: A Second Course for Data Scientists. Lulu Press, Inc.

Dependencies

• numpy

• pandas

• scikit-learn

• xgboost

• nltk

• MetaHeuristicsFS

• collections

References

• A Comparative Study on Feature Selection in Text Categorization by Yiming Yang and Jan O. Pedersen
• Entropy based feature selection for text categorization by Christine Largeron, Christophe Moulin, Mathias Géry
• Categorical Proportional Difference: A Feature Selection Method for Text Categorization by Mondelle Simeon, Robert J. Hilderman
• Feature Selection and Weighting Methods in Sentiment Analysis by Tim O`Keefe and Irena Koprinska
• Feature Selection For Text Classification Using Genetic Algorithms by Noria Bidi and Zakaria Elberrichi