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baseline_multi.py
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baseline_multi.py
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from typing import List
import numpy as np
import pandas as pd
import json
import pathlib
from util import CustomDataset, compute_metrics, prep_data_multi, output_and_store_results, create_config_key
from argparse import ArgumentParser
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
from sklearn.model_selection import RandomizedSearchCV
from sklearn.svm import LinearSVC
# Define string constants
BINARY = "binary"
TFIDF = "tf-idf"
LOGIT = "logistic-regression"
RAFO = "random-forest"
SVM = "svm"
STANDARD = "standard"
RANDOM_STATE = 42
def run_baseline_multi(input_path: str, setting_keys: List[str] = None):
# Read settings file
with open(f'{input_path}') as file:
settings = json.load(file)
for setting_key, settings_data in settings.items():
# Only run the setting if the key is in the list of settings or no setting_keys are provided
if setting_keys is None:
pass
elif setting_keys is not None and setting_key not in setting_keys:
continue
# Get name of settings
settings_name = create_config_key(settings_data)
# Get the relevant data from the settings
model = settings_data.get("model")
vectorization = settings_data.get("vectorization")
use_description = settings_data.get("use_description")
train_langs = settings_data.get("train_lang")
test_langs = settings_data.get("eval_lang")
category = settings_data.get("category")
# Create a string of the train languages
train_langs_str = ", ".join(train_langs)
# Process the categories separately
dataset_p = pathlib.Path(input_path).parent.joinpath("datasets")
train_data_p = dataset_p.joinpath(f'multi_class_train_set_{category}.csv')
test_data_p = dataset_p.joinpath(f'multi_class_test_set_{category}.csv')
# Read the data
train_data = pd.read_csv(train_data_p)
test_data = pd.read_csv(test_data_p)
# Filter the train data:
train_data = train_data.loc[train_data["lang"].isin(train_langs)]
# Prepare the train and test data for the experiments and get the mapping of the labels
train_data, test_data, label_dict_inv = prep_data_multi(train_data, test_data, use_description)
# Compute the feature embedding
if vectorization == BINARY:
vectorizer = CountVectorizer(analyzer="word",
encoding='utf-8',
tokenizer=None,
preprocessor=None,
stop_words=None,
ngram_range=(1, 2),
max_features=5000,
binary=True)
train_data_embeddings = vectorizer.fit_transform(train_data['content']).toarray()
elif vectorization == TFIDF:
vectorizer = TfidfVectorizer()
train_data_embeddings = vectorizer.fit_transform(train_data['content']).toarray()
else:
# Other vectorizations are not implemented
raise AssertionError
# Fit the models
if model == LOGIT:
est = LogisticRegression()
# Description needs more time to converge
parameters = {
'C': [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000],
'class_weight': ['balanced'],
'max_iter': [800],
'n_jobs': [-2]
}
elif model == RAFO:
est = RandomForestClassifier()
parameters = {
'n_estimators': [100],
'max_features': ['sqrt', 'log2', None],
'max_depth': [2, 4, 7, 10],
'min_samples_split': [2, 5, 10, 20],
'min_samples_leaf': [1, 2, 4, 8],
'class_weight': ['balanced_subsample'],
'n_jobs': [-2]
}
elif model == SVM:
est = LinearSVC()
parameters = {
'C': [0.0001, 0.001, 0.01, 0.1, 1, 10, 100, 1000],
'class_weight': ['balanced']
}
else:
# Other models are not implemented
raise AssertionError
print(model)
# Define grid search and fit model
rs = RandomizedSearchCV(estimator=est, param_distributions=parameters, scoring="f1_macro", cv=5,
n_jobs=-2, verbose=1, n_iter=100, refit=True)
rs.fit(train_data_embeddings, train_data["label"].astype(int))
# Run predictions
scores_per_lang = {}
for lang in test_langs:
# Subset the test data
test_data_lang = test_data.loc[test_data['lang'] == lang]
# Retrieve representations & word co-occurence vectors for test set
test_data_embeddings_lang = vectorizer.transform(test_data_lang['content']).toarray()
# prediction and computation of metrics to measure performance of model
pred = rs.best_estimator_.predict(test_data_embeddings_lang)
# Map the predictions back to cluster ids
pred_cl_id = np.array([label_dict_inv[x] for x in pred])
# Map the true labels back to cluster ids
true_cl_id = test_data_lang["old_label_id"].to_numpy()
scores_per_lang[lang] = compute_metrics({"labels": true_cl_id, "predictions": pred_cl_id}).get("f1")
output_and_store_results(settings_data=settings_data, setting_key=settings_name, category=category,
train_langs_str=train_langs_str, lang=lang, result=scores_per_lang[lang],
all_scores="", hyperparameters=[rs.best_params_], input_path=input_path,
predictions=pred_cl_id)
if __name__ == "__main__":
parser = ArgumentParser()
parser.add_argument("-i", "--input", type=str,
help="path to project", metavar="path")
args = parser.parse_args()
input_path = args.input
run_baseline_multi(input_path)