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New training with new data & new release
1 parent 3161949 commit b417ad4

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requirements.txt

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scikit-learn==1.4.2
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imbalanced-learn==0.12.2
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pandas==2.2.1
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pandas==2.2.2
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nltk~=3.8.1
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setuptools==69.2.0
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pytest==8.1.1
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setuptools==69.5.1
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pytest==8.2.0
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requests~=2.31.0
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imblearn~=0.0
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joblib~=1.4.0
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joblib~=1.4.2
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xgboost~=2.0.3

spam_detector_ai/__init__.py

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__description__ = "A package for detecting and filtering spam messages using Machine Learning models."
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__package_name__ = "spam-detector-ai"
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__url__ = "https://github.com/adamspd/spam-detection-project"
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__version__ = "2.1.13"
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__version__ = "2.1.14"
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__test_version__ = False

spam_detector_ai/data/spam.csv

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spam_detector_ai/tests/test.py

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# spam_detector_ai/test_and_tuning/test.py
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import os
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import sys
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from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
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from sklearn.model_selection import train_test_split
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from spam_detector_ai.prediction.predict import SpamDetector
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from spam_detector_ai.training.train_models import ModelTrainer
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sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..', '..')))
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class TestModel:
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def __init__(self):
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# Assuming the spam_detector_ai directory is one level up from the current directory
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base_dir = os.path.dirname(current_dir)
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data_path = os.path.join(base_dir, 'data/spam.csv')
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self.initial_trainer = ModelTrainer(data_path=data_path, classifier_type=None, logger=self.logger)
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self.initial_trainer = ModelTrainer(data_path=data_path, logger=self.logger)
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self.processed_data = self.initial_trainer.preprocess_data_()
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# Split the data once

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