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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sn\n",
"\n",
"from tensorflow import keras\n",
"from gensim.models.doc2vec import Doc2Vec\n",
"from sklearn.metrics import accuracy_score\n",
"from sklearn.metrics import confusion_matrix\n",
"from sklearn.metrics import classification_report\n",
"\n",
"from src.preprocessing import Preprocessor\n",
"from src.utils import read_data"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"doc2vec_model_embeddings = Doc2Vec.load(\n",
" \"models/best_doc2vec_embeddings\")\n",
"doc2vec_model = keras.models.load_model(\n",
" \"models/best_doc2vec_model.h5\")\n",
"tfidf_model = keras.models.load_model(\"models/best_tfidf_model.h5\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"X_train, X_test, y_train, y_test = read_data()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"preprocessor = Preprocessor()"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Best combination of TF-IDF model"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"first_party_names_3 = X_train[\"first_party\"]\n",
"second_party_names_3 = X_train[\"second_party\"]\n",
"facts_3 = X_train[\"Facts\"]"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"test_first_party_names_3 = X_test[\"first_party\"]\n",
"test_second_party_names_3 = X_test[\"second_party\"]\n",
"test_facts_3 = X_test[\"Facts\"]"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"anonymized_facts_3 = preprocessor.anonymize_data(\n",
" first_party_names_3, second_party_names_3, facts_3)\n",
"test_anonymized_facts_3 = preprocessor.anonymize_data(\n",
" test_first_party_names_3, test_second_party_names_3, test_facts_3)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"text_vectorizer_3, X_train_vectors_3 = preprocessor.convert_text_to_vectors_tf_idf(\n",
" anonymized_facts_3)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"X_test_vectors_3 = preprocessor.convert_text_to_vectors_tf_idf(\n",
" test_anonymized_facts_3, train=False, text_vectorizer=text_vectorizer_3)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"22/22 [==============================] - 1s 9ms/step\n"
]
}
],
"source": [
"y_preds_tfidf = tfidf_model.predict(X_test_vectors_3)"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"TF-IDF accuracy: 0.973\n"
]
}
],
"source": [
"preds = np.where(y_preds_tfidf > 0.5, 1, 0)\n",
"accuracy = accuracy_score(y_test, preds)\n",
"print(\"TF-IDF accuracy: {:.3f}\".format(accuracy))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Best Combination of Doc2Vec Model"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [],
"source": [
"X_test_processed = preprocessor.preprocess_data(X_test[\"Facts\"])"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [],
"source": [
"X_test_vectors_doc2vec = preprocessor.convert_text_to_vectors_doc2vec(\n",
" X_test_processed, train=False, embeddings_doc2vec=doc2vec_model_embeddings)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"22/22 [==============================] - 0s 2ms/step\n"
]
}
],
"source": [
"y_preds_doc2vec = doc2vec_model.predict(X_test_vectors_doc2vec)"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Doc2Vec accuracy: 0.944\n"
]
}
],
"source": [
"preds_2 = np.where(y_preds_doc2vec > 0.5, 1, 0)\n",
"accuracy = accuracy_score(y_test, preds_2)\n",
"print(\"Doc2Vec accuracy: {:.3f}\".format(accuracy))"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"### Combining both accuracies"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Voting accuracy: 0.986\n"
]
}
],
"source": [
"voting_predcitions = (y_preds_doc2vec + y_preds_tfidf) / 2\n",
"voting_predcitions = np.where(voting_predcitions > 0.5, 1, 0)\n",
"accuracy = accuracy_score(y_test, voting_predcitions)\n",
"print(\"Voting accuracy: {:.3f}\".format(accuracy))"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:>"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
"<Figure size 800x550 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"cf = confusion_matrix(y_test, voting_predcitions)\n",
"sn.heatmap(cf, annot=True)"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.98 1.00 0.99 423\n",
" 1 1.00 0.96 0.98 270\n",
"\n",
" accuracy 0.99 693\n",
" macro avg 0.99 0.98 0.98 693\n",
"weighted avg 0.99 0.99 0.99 693\n",
"\n"
]
}
],
"source": [
"cls_report = classification_report(y_test, voting_predcitions)\n",
"print(cls_report)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.1"
},
"orig_nbformat": 4
},
"nbformat": 4,
"nbformat_minor": 2
}
|