news-headline-hybrid-model-1 (1).ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":533474,"sourceType":"datasetVersion","datasetId":30764}],"dockerImageVersionId":30558,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-11-20T18:50:25.273420Z","iopub.execute_input":"2023-11-20T18:50:25.275023Z","iopub.status.idle":"2023-11-20T18:50:25.295569Z","shell.execute_reply.started":"2023-11-20T18:50:25.274970Z","shell.execute_reply":"2023-11-20T18:50:25.294077Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Import necessary libraries\nimport pandas as pd\nimport numpy as np\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import train_test_split\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import LSTM, Embedding, Dense\nfrom tensorflow.keras.preprocessing.text import Tokenizer\nfrom tensorflow.keras.preprocessing.sequence import pad_sequences\n","metadata":{"execution":{"iopub.status.busy":"2023-12-09T13:39:17.425584Z","iopub.execute_input":"2023-12-09T13:39:17.426108Z","iopub.status.idle":"2023-12-09T13:39:17.434506Z","shell.execute_reply.started":"2023-12-09T13:39:17.426067Z","shell.execute_reply":"2023-12-09T13:39:17.432758Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_v1 = pd.read_json('../input/news-headlines-dataset-for-sarcasm-detection/Sarcasm_Headlines_Dataset.json', lines = True)\ndf_v2 = pd.read_json('../input/news-headlines-dataset-for-sarcasm-detection/Sarcasm_Headlines_Dataset_v2.json', lines = True)","metadata":{"execution":{"iopub.status.busy":"2023-12-09T13:39:21.124310Z","iopub.execute_input":"2023-12-09T13:39:21.124790Z","iopub.status.idle":"2023-12-09T13:39:21.410499Z","shell.execute_reply.started":"2023-12-09T13:39:21.124754Z","shell.execute_reply":"2023-12-09T13:39:21.409269Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#As both the datasets look same, let's combine them into 1.\ndf = pd.concat([df_v1, df_v2], axis=0)","metadata":{"execution":{"iopub.status.busy":"2023-12-09T13:39:24.225808Z","iopub.execute_input":"2023-12-09T13:39:24.226278Z","iopub.status.idle":"2023-12-09T13:39:24.241708Z","shell.execute_reply.started":"2023-12-09T13:39:24.226243Z","shell.execute_reply":"2023-12-09T13:39:24.240198Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Text Vectorization using TF-IDF\ntfidf_vectorizer = TfidfVectorizer(max_features=5000)\nX_tfidf = tfidf_vectorizer.fit_transform(df['headline']).toarray()\n","metadata":{"execution":{"iopub.status.busy":"2023-12-09T13:39:27.410014Z","iopub.execute_input":"2023-12-09T13:39:27.410474Z","iopub.status.idle":"2023-12-09T13:39:30.446663Z","shell.execute_reply.started":"2023-12-09T13:39:27.410442Z","shell.execute_reply":"2023-12-09T13:39:30.445223Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Split the data into training and testing sets\nX_train, X_test, y_train, y_test = train_test_split(X_tfidf, df['is_sarcastic'], test_size=0.2, random_state=42)\n","metadata":{"execution":{"iopub.status.busy":"2023-12-09T13:39:33.923675Z","iopub.execute_input":"2023-12-09T13:39:33.924437Z","iopub.status.idle":"2023-12-09T13:39:36.538496Z","shell.execute_reply.started":"2023-12-09T13:39:33.924394Z","shell.execute_reply":"2023-12-09T13:39:36.537017Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Build a Random Forest Classifier\nrf_classifier = RandomForestClassifier(n_estimators=10, random_state=42)\nrf_classifier.fit(X_train, y_train)\n","metadata":{"execution":{"iopub.status.busy":"2023-12-09T13:39:41.305952Z","iopub.execute_input":"2023-12-09T13:39:41.306513Z","iopub.status.idle":"2023-12-09T13:40:23.692871Z","shell.execute_reply.started":"2023-12-09T13:39:41.306470Z","shell.execute_reply":"2023-12-09T13:40:23.691464Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Build an LSTM Model\ntokenizer = Tokenizer(num_words=5000, lower=True)\ntokenizer.fit_on_texts(df['headline'])\nX_lstm = tokenizer.texts_to_sequences(df['headline'])\nX_lstm = pad_sequences(X_lstm, maxlen=50)\n\nlstm_model = Sequential()\nlstm_model.add(Embedding(input_dim=5000, output_dim=128, input_length=50))\nlstm_model.add(LSTM(64))\nlstm_model.add(Dense(1, activation='sigmoid'))\nlstm_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n","metadata":{"execution":{"iopub.status.busy":"2023-12-09T13:40:32.185511Z","iopub.execute_input":"2023-12-09T13:40:32.187677Z","iopub.status.idle":"2023-12-09T13:40:35.314657Z","shell.execute_reply.started":"2023-12-09T13:40:32.187606Z","shell.execute_reply":"2023-12-09T13:40:35.313591Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Train the LSTM Model\nX_lstm_train, X_lstm_test, y_train, y_test = train_test_split(X_lstm, df['is_sarcastic'], test_size=0.2, random_state=42)\nlstm_model.fit(X_lstm_train, y_train, epochs=5, batch_size=64)\n","metadata":{"execution":{"iopub.status.busy":"2023-12-09T13:40:51.123444Z","iopub.execute_input":"2023-12-09T13:40:51.124145Z","iopub.status.idle":"2023-12-09T13:44:15.823638Z","shell.execute_reply.started":"2023-12-09T13:40:51.124093Z","shell.execute_reply":"2023-12-09T13:44:15.822408Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Ensemble the Models\nrf_predictions = rf_classifier.predict(X_test)\nlstm_predictions = lstm_model.predict(X_lstm_test)\nensemble_predictions = (rf_predictions + lstm_predictions.reshape(-1)) / 2\nensemble_predictions = np.round(ensemble_predictions)\n","metadata":{"execution":{"iopub.status.busy":"2023-12-09T13:44:23.594445Z","iopub.execute_input":"2023-12-09T13:44:23.594911Z","iopub.status.idle":"2023-12-09T13:44:28.422714Z","shell.execute_reply.started":"2023-12-09T13:44:23.594875Z","shell.execute_reply":"2023-12-09T13:44:28.421416Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Evaluate the Model\nfrom sklearn.metrics import accuracy_score, classification_report\naccuracy = accuracy_score(y_test, ensemble_predictions)\nprint(\"Accuracy:\", accuracy)\n\n# You can also print a classification report to see precision, recall, and F1-score.\nreport = classification_report(y_test, ensemble_predictions)\nprint(report)\n","metadata":{"execution":{"iopub.status.busy":"2023-12-09T13:44:32.411674Z","iopub.execute_input":"2023-12-09T13:44:32.412110Z","iopub.status.idle":"2023-12-09T13:44:32.455332Z","shell.execute_reply.started":"2023-12-09T13:44:32.412075Z","shell.execute_reply":"2023-12-09T13:44:32.453960Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# You can also print a classification report to see precision, recall, and F1-score.\nreport = classification_report(y_test, ensemble_predictions)\nprint(report)\n\nfrom sklearn.metrics import confusion_matrix\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\n# Confusion Matrix\ncm = confusion_matrix(y_test, ensemble_predictions)\n\n# Plot the confusion matrix\nplt.figure(figsize=(8, 6))\nsns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=['Not Sarcasm', 'Sarcasm'], yticklabels=['Not Sarcasm', 'Sarcasm'])\nplt.xlabel('Predicted')\nplt.ylabel('True')\nplt.title('Confusion Matrix')\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2023-11-20T18:54:41.232205Z","iopub.execute_input":"2023-11-20T18:54:41.232949Z","iopub.status.idle":"2023-11-20T18:54:41.577116Z","shell.execute_reply.started":"2023-11-20T18:54:41.232904Z","shell.execute_reply":"2023-11-20T18:54:41.576109Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pip install nlpaug","metadata":{"execution":{"iopub.status.busy":"2023-11-20T18:54:41.578848Z","iopub.execute_input":"2023-11-20T18:54:41.579488Z","iopub.status.idle":"2023-11-20T18:54:55.545162Z","shell.execute_reply.started":"2023-11-20T18:54:41.579452Z","shell.execute_reply":"2023-11-20T18:54:55.543210Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import nltk\n\nnltk.download('wordnet')\n","metadata":{"execution":{"iopub.status.busy":"2023-11-20T18:54:55.547855Z","iopub.execute_input":"2023-11-20T18:54:55.549337Z","iopub.status.idle":"2023-11-20T18:54:55.561330Z","shell.execute_reply.started":"2023-11-20T18:54:55.549285Z","shell.execute_reply":"2023-11-20T18:54:55.559762Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**CNN + LSTM**","metadata":{}},{"cell_type":"code","source":"# Import necessary libraries\nimport numpy as np\nimport pandas as pd\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import train_test_split\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import LSTM, Embedding, Dense, Conv1D, MaxPooling1D, Flatten\nfrom tensorflow.keras.preprocessing.text import Tokenizer\nfrom tensorflow.keras.preprocessing.sequence import pad_sequences\n","metadata":{"execution":{"iopub.status.busy":"2023-12-09T13:33:55.077436Z","iopub.execute_input":"2023-12-09T13:33:55.077933Z","iopub.status.idle":"2023-12-09T13:33:55.087797Z","shell.execute_reply.started":"2023-12-09T13:33:55.077898Z","shell.execute_reply":"2023-12-09T13:33:55.085645Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Load the dataset\ndf_v1 = pd.read_json('../input/news-headlines-dataset-for-sarcasm-detection/Sarcasm_Headlines_Dataset.json', lines=True)\ndf_v2 = pd.read_json('../input/news-headlines-dataset-for-sarcasm-detection/Sarcasm_Headlines_Dataset_v2.json', lines=True)\n","metadata":{"execution":{"iopub.status.busy":"2023-12-09T13:33:58.759711Z","iopub.execute_input":"2023-12-09T13:33:58.760178Z","iopub.status.idle":"2023-12-09T13:33:59.089432Z","shell.execute_reply.started":"2023-12-09T13:33:58.760144Z","shell.execute_reply":"2023-12-09T13:33:59.088013Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Combine the datasets\ndf = pd.concat([df_v1, df_v2], axis=0)\n","metadata":{"execution":{"iopub.status.busy":"2023-12-09T13:34:02.316293Z","iopub.execute_input":"2023-12-09T13:34:02.316697Z","iopub.status.idle":"2023-12-09T13:34:02.331868Z","shell.execute_reply.started":"2023-12-09T13:34:02.316667Z","shell.execute_reply":"2023-12-09T13:34:02.330072Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Text Vectorization using TF-IDF\ntfidf_vectorizer = TfidfVectorizer(max_features=5000)\nX_tfidf = tfidf_vectorizer.fit_transform(df['headline']).toarray()\n","metadata":{"execution":{"iopub.status.busy":"2023-12-09T13:34:05.864728Z","iopub.execute_input":"2023-12-09T13:34:05.865243Z","iopub.status.idle":"2023-12-09T13:34:09.154792Z","shell.execute_reply.started":"2023-12-09T13:34:05.865207Z","shell.execute_reply":"2023-12-09T13:34:09.152872Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Split the data into training and testing sets\nX_train, X_test, y_train, y_test = train_test_split(X_tfidf, df['is_sarcastic'], test_size=0.2, random_state=42)\n","metadata":{"execution":{"iopub.status.busy":"2023-12-09T13:34:12.204031Z","iopub.execute_input":"2023-12-09T13:34:12.206643Z","iopub.status.idle":"2023-12-09T13:34:13.657754Z","shell.execute_reply.started":"2023-12-09T13:34:12.206584Z","shell.execute_reply":"2023-12-09T13:34:13.656165Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Build a Random Forest Classifier\nrf_classifier = RandomForestClassifier(n_estimators=10, random_state=42)\nrf_classifier.fit(X_train, y_train)\n","metadata":{"execution":{"iopub.status.busy":"2023-12-09T13:34:16.630007Z","iopub.execute_input":"2023-12-09T13:34:16.630453Z","iopub.status.idle":"2023-12-09T13:34:55.893061Z","shell.execute_reply.started":"2023-12-09T13:34:16.630422Z","shell.execute_reply":"2023-12-09T13:34:55.891514Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Build a CNN-LSTM Hybrid Model\ntokenizer = Tokenizer(num_words=5000, lower=True)\ntokenizer.fit_on_texts(df['headline'])\nX_cnn_lstm = tokenizer.texts_to_sequences(df['headline'])\nX_cnn_lstm = pad_sequences(X_cnn_lstm, maxlen=50)\n\ncnn_lstm_model = Sequential()\ncnn_lstm_model.add(Embedding(input_dim=5000, output_dim=128, input_length=50))\ncnn_lstm_model.add(Conv1D(64, 5, activation='relu'))\ncnn_lstm_model.add(MaxPooling1D(pool_size=4))\ncnn_lstm_model.add(LSTM(64))\ncnn_lstm_model.add(Dense(1, activation='sigmoid'))\ncnn_lstm_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n","metadata":{"execution":{"iopub.status.busy":"2023-12-09T13:35:00.250619Z","iopub.execute_input":"2023-12-09T13:35:00.251026Z","iopub.status.idle":"2023-12-09T13:35:03.527340Z","shell.execute_reply.started":"2023-12-09T13:35:00.250994Z","shell.execute_reply":"2023-12-09T13:35:03.526231Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Train the CNN-LSTM Model\nX_cnn_lstm_train, X_cnn_lstm_test, y_train, y_test = train_test_split(X_cnn_lstm, df['is_sarcastic'], test_size=0.2, random_state=42)\ncnn_lstm_model.fit(X_cnn_lstm_train, y_train, epochs=5, batch_size=64)\n","metadata":{"execution":{"iopub.status.busy":"2023-12-09T13:35:06.930223Z","iopub.execute_input":"2023-12-09T13:35:06.930767Z","iopub.status.idle":"2023-12-09T13:36:56.540701Z","shell.execute_reply.started":"2023-12-09T13:35:06.930728Z","shell.execute_reply":"2023-12-09T13:36:56.539138Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Ensemble the Models\nrf_predictions = rf_classifier.predict(X_test)\ncnn_lstm_predictions = cnn_lstm_model.predict(X_cnn_lstm_test)\nensemble_predictions = (rf_predictions + cnn_lstm_predictions.reshape(-1)) / 2\nensemble_predictions = np.round(ensemble_predictions)\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Evaluate the Model\nfrom sklearn.metrics import accuracy_score, classification_report\naccuracy = accuracy_score(y_test, ensemble_predictions)\nprint(\"Accuracy:\", accuracy)\n","metadata":{"execution":{"iopub.status.busy":"2023-12-09T13:37:32.172156Z","iopub.execute_input":"2023-12-09T13:37:32.172759Z","iopub.status.idle":"2023-12-09T13:37:32.185288Z","shell.execute_reply.started":"2023-12-09T13:37:32.172714Z","shell.execute_reply":"2023-12-09T13:37:32.183953Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Print a classification report to see precision, recall, and F1-score.\nreport = classification_report(y_test, ensemble_predictions)\nprint(report)\n","metadata":{"execution":{"iopub.status.busy":"2023-12-09T13:37:35.891195Z","iopub.execute_input":"2023-12-09T13:37:35.891605Z","iopub.status.idle":"2023-12-09T13:37:35.934931Z","shell.execute_reply.started":"2023-12-09T13:37:35.891572Z","shell.execute_reply":"2023-12-09T13:37:35.933516Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**CNN+LSTM+RF**","metadata":{}},{"cell_type":"code","source":"# Import necessary libraries\nimport numpy as np\nimport pandas as pd\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import train_test_split\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import LSTM, Embedding, Dense, Conv1D, MaxPooling1D, Flatten\nfrom tensorflow.keras.preprocessing.text import Tokenizer\nfrom tensorflow.keras.preprocessing.sequence import pad_sequences\n","metadata":{"execution":{"iopub.status.busy":"2023-12-12T14:52:33.987709Z","iopub.execute_input":"2023-12-12T14:52:33.988103Z","iopub.status.idle":"2023-12-12T14:52:48.969796Z","shell.execute_reply.started":"2023-12-12T14:52:33.988074Z","shell.execute_reply":"2023-12-12T14:52:48.968593Z"},"trusted":true},"execution_count":1,"outputs":[]},{"cell_type":"code","source":"# Load the dataset\ndf_v1 = pd.read_json('../input/news-headlines-dataset-for-sarcasm-detection/Sarcasm_Headlines_Dataset.json', lines=True)\ndf_v2 = pd.read_json('../input/news-headlines-dataset-for-sarcasm-detection/Sarcasm_Headlines_Dataset_v2.json', lines=True)\n","metadata":{"execution":{"iopub.status.busy":"2023-12-12T14:52:54.195191Z","iopub.execute_input":"2023-12-12T14:52:54.195995Z","iopub.status.idle":"2023-12-12T14:52:54.964647Z","shell.execute_reply.started":"2023-12-12T14:52:54.195956Z","shell.execute_reply":"2023-12-12T14:52:54.963251Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"# Combine the datasets\ndf = pd.concat([df_v1, df_v2], axis=0)\n","metadata":{"execution":{"iopub.status.busy":"2023-12-12T14:53:02.289208Z","iopub.execute_input":"2023-12-12T14:53:02.289873Z","iopub.status.idle":"2023-12-12T14:53:02.310483Z","shell.execute_reply.started":"2023-12-12T14:53:02.289823Z","shell.execute_reply":"2023-12-12T14:53:02.308483Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"# Text Vectorization using TF-IDF\ntfidf_vectorizer = TfidfVectorizer(max_features=5000)\nX_tfidf = tfidf_vectorizer.fit_transform(df['headline']).toarray()\n","metadata":{"execution":{"iopub.status.busy":"2023-12-12T14:53:05.949698Z","iopub.execute_input":"2023-12-12T14:53:05.950602Z","iopub.status.idle":"2023-12-12T14:53:09.074118Z","shell.execute_reply.started":"2023-12-12T14:53:05.950532Z","shell.execute_reply":"2023-12-12T14:53:09.072854Z"},"trusted":true},"execution_count":5,"outputs":[]},{"cell_type":"code","source":"# Split the data into training and testing sets\nX_train, X_test, y_train, y_test = train_test_split(X_tfidf, df['is_sarcastic'], test_size=0.2, random_state=42)\n","metadata":{"execution":{"iopub.status.busy":"2023-12-12T14:53:14.068633Z","iopub.execute_input":"2023-12-12T14:53:14.069177Z","iopub.status.idle":"2023-12-12T14:53:15.747995Z","shell.execute_reply.started":"2023-12-12T14:53:14.069111Z","shell.execute_reply":"2023-12-12T14:53:15.746890Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"code","source":"# Build a Random Forest Classifier\nrf_classifier = RandomForestClassifier(n_estimators=100, random_state=42)  # Increased the number of estimators\nrf_classifier.fit(X_train, y_train)\n","metadata":{"execution":{"iopub.status.busy":"2023-12-12T14:53:20.730752Z","iopub.execute_input":"2023-12-12T14:53:20.731274Z","iopub.status.idle":"2023-12-12T14:59:09.420385Z","shell.execute_reply.started":"2023-12-12T14:53:20.731234Z","shell.execute_reply":"2023-12-12T14:59:09.419220Z"},"trusted":true},"execution_count":7,"outputs":[{"execution_count":7,"output_type":"execute_result","data":{"text/plain":"RandomForestClassifier(random_state=42)","text/html":"<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=42)</pre></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"# Build a CNN-LSTM Hybrid Model\ntokenizer = Tokenizer(num_words=5000, lower=True)\ntokenizer.fit_on_texts(df['headline'])\nX_cnn_lstm = tokenizer.texts_to_sequences(df['headline'])\nX_cnn_lstm = pad_sequences(X_cnn_lstm, maxlen=50)\n\n\ncnn_lstm_model = Sequential()\ncnn_lstm_model.add(Embedding(input_dim=5000, output_dim=128, input_length=50))\ncnn_lstm_model.add(Conv1D(128, 5, activation='relu'))  # Increased the number of filters\ncnn_lstm_model.add(MaxPooling1D(pool_size=4))\ncnn_lstm_model.add(LSTM(64))\ncnn_lstm_model.add(Dense(1, activation='sigmoid'))\ncnn_lstm_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n","metadata":{"execution":{"iopub.status.busy":"2023-12-12T14:59:15.325908Z","iopub.execute_input":"2023-12-12T14:59:15.326342Z","iopub.status.idle":"2023-12-12T14:59:18.646924Z","shell.execute_reply.started":"2023-12-12T14:59:15.326307Z","shell.execute_reply":"2023-12-12T14:59:18.645764Z"},"trusted":true},"execution_count":8,"outputs":[]},{"cell_type":"code","source":"# Train the CNN-LSTM Model\nX_cnn_lstm_train, X_cnn_lstm_test, y_train, y_test = train_test_split(X_cnn_lstm, df['is_sarcastic'], test_size=0.2, random_state=42)\ncnn_lstm_model.fit(X_cnn_lstm_train, y_train, epochs=10, batch_size=64)  # Increased the number of epochs\n","metadata":{"execution":{"iopub.status.busy":"2023-12-12T14:59:22.755305Z","iopub.execute_input":"2023-12-12T14:59:22.755795Z","iopub.status.idle":"2023-12-12T15:03:47.652192Z","shell.execute_reply.started":"2023-12-12T14:59:22.755761Z","shell.execute_reply":"2023-12-12T15:03:47.650624Z"},"trusted":true},"execution_count":9,"outputs":[{"name":"stdout","text":"Epoch 1/10\n692/692 [==============================] - 28s 36ms/step - loss: 0.3692 - accuracy: 0.8266\nEpoch 2/10\n692/692 [==============================] - 25s 36ms/step - loss: 0.1892 - accuracy: 0.9228\nEpoch 3/10\n692/692 [==============================] - 25s 37ms/step - loss: 0.0829 - accuracy: 0.9648\nEpoch 4/10\n692/692 [==============================] - 25s 37ms/step - loss: 0.0465 - accuracy: 0.9770\nEpoch 5/10\n692/692 [==============================] - 26s 38ms/step - loss: 0.0380 - accuracy: 0.9800\nEpoch 6/10\n692/692 [==============================] - 26s 38ms/step - loss: 0.0350 - accuracy: 0.9814\nEpoch 7/10\n692/692 [==============================] - 25s 37ms/step - loss: 0.0308 - accuracy: 0.9822\nEpoch 8/10\n692/692 [==============================] - 26s 37ms/step - loss: 0.0291 - accuracy: 0.9829\nEpoch 9/10\n692/692 [==============================] - 26s 37ms/step - loss: 0.0297 - accuracy: 0.9827\nEpoch 10/10\n692/692 [==============================] - 25s 37ms/step - loss: 0.0314 - accuracy: 0.9827\n","output_type":"stream"},{"execution_count":9,"output_type":"execute_result","data":{"text/plain":"<keras.callbacks.History at 0x7c8dad96eb30>"},"metadata":{}}]},{"cell_type":"code","source":"# Ensemble the Models\nrf_predictions = rf_classifier.predict(X_test)\ncnn_lstm_predictions = cnn_lstm_model.predict(X_cnn_lstm_test)\nensemble_predictions = (rf_predictions + cnn_lstm_predictions.reshape(-1)) / 2\nensemble_predictions = np.round(ensemble_predictions)\n","metadata":{"execution":{"iopub.status.busy":"2023-12-12T15:03:55.610685Z","iopub.execute_input":"2023-12-12T15:03:55.611146Z","iopub.status.idle":"2023-12-12T15:04:00.913388Z","shell.execute_reply.started":"2023-12-12T15:03:55.611097Z","shell.execute_reply":"2023-12-12T15:04:00.912043Z"},"trusted":true},"execution_count":10,"outputs":[{"name":"stdout","text":"346/346 [==============================] - 3s 7ms/step\n","output_type":"stream"}]},{"cell_type":"code","source":"# Evaluate the Model\nfrom sklearn.metrics import accuracy_score, classification_report\naccuracy = accuracy_score(y_test, ensemble_predictions)\nprint(\"Accuracy:\", accuracy)\n","metadata":{"execution":{"iopub.status.busy":"2023-12-12T15:04:18.977371Z","iopub.execute_input":"2023-12-12T15:04:18.978120Z","iopub.status.idle":"2023-12-12T15:04:18.989407Z","shell.execute_reply.started":"2023-12-12T15:04:18.978082Z","shell.execute_reply":"2023-12-12T15:04:18.987749Z"},"trusted":true},"execution_count":11,"outputs":[{"name":"stdout","text":"Accuracy: 0.9541839869871679\n","output_type":"stream"}]},{"cell_type":"code","source":"# Print a classification report to see precision, recall, and F1-score.\nreport = classification_report(y_test, ensemble_predictions)\nprint(report)\n","metadata":{"execution":{"iopub.status.busy":"2023-12-12T15:04:24.988801Z","iopub.execute_input":"2023-12-12T15:04:24.990034Z","iopub.status.idle":"2023-12-12T15:04:25.032867Z","shell.execute_reply.started":"2023-12-12T15:04:24.989992Z","shell.execute_reply":"2023-12-12T15:04:25.030182Z"},"trusted":true},"execution_count":12,"outputs":[{"name":"stdout","text":"              precision    recall  f1-score   support\n\n           0       0.95      0.96      0.96      5878\n           1       0.96      0.95      0.95      5188\n\n    accuracy                           0.95     11066\n   macro avg       0.95      0.95      0.95     11066\nweighted avg       0.95      0.95      0.95     11066\n\n","output_type":"stream"}]},{"cell_type":"markdown","source":"**Step Decay Learning Rate Schedule Optimization**","metadata":{}},{"cell_type":"code","source":"from sklearn.metrics import accuracy_score, classification_report\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.callbacks import LearningRateScheduler\nimport math\n","metadata":{"execution":{"iopub.status.busy":"2023-12-12T15:04:31.202738Z","iopub.execute_input":"2023-12-12T15:04:31.203255Z","iopub.status.idle":"2023-12-12T15:04:31.213208Z","shell.execute_reply.started":"2023-12-12T15:04:31.203219Z","shell.execute_reply":"2023-12-12T15:04:31.212147Z"},"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"code","source":"# Learning Rate Schedule\ndef step_decay(epoch):\n    initial_lr = 0.001\n    drop = 0.5\n    epochs_drop = 5\n    lr = initial_lr * math.pow(drop, math.floor((1 + epoch) / epochs_drop))\n    return lr\n","metadata":{"execution":{"iopub.status.busy":"2023-12-12T15:04:35.349326Z","iopub.execute_input":"2023-12-12T15:04:35.349820Z","iopub.status.idle":"2023-12-12T15:04:35.357407Z","shell.execute_reply.started":"2023-12-12T15:04:35.349783Z","shell.execute_reply":"2023-12-12T15:04:35.355505Z"},"trusted":true},"execution_count":14,"outputs":[]},{"cell_type":"code","source":"# Build a CNN-LSTM Hybrid Model with Learning Rate Schedule\ncnn_lstm_model = Sequential()\ncnn_lstm_model.add(Embedding(input_dim=5000, output_dim=128, input_length=50))\ncnn_lstm_model.add(Conv1D(64, 5, activation='relu'))\ncnn_lstm_model.add(MaxPooling1D(pool_size=4))\ncnn_lstm_model.add(LSTM(64))\ncnn_lstm_model.add(Dense(1, activation='sigmoid'))\n","metadata":{"execution":{"iopub.status.busy":"2023-12-12T15:04:39.402993Z","iopub.execute_input":"2023-12-12T15:04:39.403611Z","iopub.status.idle":"2023-12-12T15:04:40.039944Z","shell.execute_reply.started":"2023-12-12T15:04:39.403566Z","shell.execute_reply":"2023-12-12T15:04:40.038975Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"code","source":"# Use Adam optimizer with the learning rate schedule\nopt = Adam(learning_rate=0.0)\ncnn_lstm_model.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])\n","metadata":{"execution":{"iopub.status.busy":"2023-12-12T15:04:44.097172Z","iopub.execute_input":"2023-12-12T15:04:44.097976Z","iopub.status.idle":"2023-12-12T15:04:44.110672Z","shell.execute_reply.started":"2023-12-12T15:04:44.097941Z","shell.execute_reply":"2023-12-12T15:04:44.109584Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"code","source":"# Create a Learning Rate Scheduler callback\nlr_schedule = LearningRateScheduler(step_decay)\n","metadata":{"execution":{"iopub.status.busy":"2023-12-12T15:04:48.456068Z","iopub.execute_input":"2023-12-12T15:04:48.456498Z","iopub.status.idle":"2023-12-12T15:04:48.461864Z","shell.execute_reply.started":"2023-12-12T15:04:48.456467Z","shell.execute_reply":"2023-12-12T15:04:48.460781Z"},"trusted":true},"execution_count":17,"outputs":[]},{"cell_type":"code","source":"# Train the CNN-LSTM Model with Learning Rate Schedule\nX_cnn_lstm_train, X_cnn_lstm_test, y_train, y_test = train_test_split(X_cnn_lstm, df['is_sarcastic'], test_size=0.2, random_state=42)\ncnn_lstm_model.fit(X_cnn_lstm_train, y_train, epochs=30, batch_size=64, callbacks=[lr_schedule])\n","metadata":{"execution":{"iopub.status.busy":"2023-12-12T15:04:51.900601Z","iopub.execute_input":"2023-12-12T15:04:51.902291Z","iopub.status.idle":"2023-12-12T15:15:16.488922Z","shell.execute_reply.started":"2023-12-12T15:04:51.902200Z","shell.execute_reply":"2023-12-12T15:15:16.487230Z"},"trusted":true},"execution_count":18,"outputs":[{"name":"stdout","text":"Epoch 1/30\n692/692 [==============================] - 22s 28ms/step - loss: 0.3741 - accuracy: 0.8232 - lr: 0.0010\nEpoch 2/30\n692/692 [==============================] - 19s 28ms/step - loss: 0.1977 - accuracy: 0.9190 - lr: 0.0010\nEpoch 3/30\n692/692 [==============================] - 20s 28ms/step - loss: 0.0907 - accuracy: 0.9621 - lr: 0.0010\nEpoch 4/30\n692/692 [==============================] - 19s 28ms/step - loss: 0.0490 - accuracy: 0.9761 - lr: 0.0010\nEpoch 5/30\n692/692 [==============================] - 20s 29ms/step - loss: 0.0308 - accuracy: 0.9831 - lr: 5.0000e-04\nEpoch 6/30\n692/692 [==============================] - 19s 28ms/step - loss: 0.0265 - accuracy: 0.9842 - lr: 5.0000e-04\nEpoch 7/30\n692/692 [==============================] - 19s 28ms/step - loss: 0.0254 - accuracy: 0.9846 - lr: 5.0000e-04\nEpoch 8/30\n692/692 [==============================] - 20s 28ms/step - loss: 0.0249 - accuracy: 0.9845 - lr: 5.0000e-04\nEpoch 9/30\n692/692 [==============================] - 19s 28ms/step - loss: 0.0247 - accuracy: 0.9848 - lr: 5.0000e-04\nEpoch 10/30\n692/692 [==============================] - 20s 29ms/step - loss: 0.0237 - accuracy: 0.9846 - lr: 2.5000e-04\nEpoch 11/30\n692/692 [==============================] - 19s 28ms/step - loss: 0.0235 - accuracy: 0.9849 - lr: 2.5000e-04\nEpoch 12/30\n692/692 [==============================] - 19s 28ms/step - loss: 0.0233 - accuracy: 0.9851 - lr: 2.5000e-04\nEpoch 13/30\n692/692 [==============================] - 20s 28ms/step - loss: 0.0232 - accuracy: 0.9854 - lr: 2.5000e-04\nEpoch 14/30\n692/692 [==============================] - 19s 28ms/step - loss: 0.0233 - accuracy: 0.9849 - lr: 2.5000e-04\nEpoch 15/30\n692/692 [==============================] - 19s 27ms/step - loss: 0.0228 - accuracy: 0.9853 - lr: 1.2500e-04\nEpoch 16/30\n692/692 [==============================] - 19s 28ms/step - loss: 0.0228 - accuracy: 0.9852 - lr: 1.2500e-04\nEpoch 17/30\n692/692 [==============================] - 19s 28ms/step - loss: 0.0228 - accuracy: 0.9854 - lr: 1.2500e-04\nEpoch 18/30\n692/692 [==============================] - 20s 28ms/step - loss: 0.0227 - accuracy: 0.9854 - lr: 1.2500e-04\nEpoch 19/30\n692/692 [==============================] - 20s 28ms/step - loss: 0.0227 - accuracy: 0.9853 - lr: 1.2500e-04\nEpoch 20/30\n692/692 [==============================] - 19s 28ms/step - loss: 0.0225 - accuracy: 0.9854 - lr: 6.2500e-05\nEpoch 21/30\n692/692 [==============================] - 19s 28ms/step - loss: 0.0225 - accuracy: 0.9852 - lr: 6.2500e-05\nEpoch 22/30\n692/692 [==============================] - 20s 28ms/step - loss: 0.0225 - accuracy: 0.9854 - lr: 6.2500e-05\nEpoch 23/30\n692/692 [==============================] - 20s 29ms/step - loss: 0.0225 - accuracy: 0.9854 - lr: 6.2500e-05\nEpoch 24/30\n692/692 [==============================] - 20s 29ms/step - loss: 0.0225 - accuracy: 0.9853 - lr: 6.2500e-05\nEpoch 25/30\n692/692 [==============================] - 20s 28ms/step - loss: 0.0224 - accuracy: 0.9855 - lr: 3.1250e-05\nEpoch 26/30\n692/692 [==============================] - 20s 29ms/step - loss: 0.0224 - accuracy: 0.9855 - lr: 3.1250e-05\nEpoch 27/30\n692/692 [==============================] - 19s 28ms/step - loss: 0.0224 - accuracy: 0.9855 - lr: 3.1250e-05\nEpoch 28/30\n692/692 [==============================] - 20s 29ms/step - loss: 0.0224 - accuracy: 0.9855 - lr: 3.1250e-05\nEpoch 29/30\n692/692 [==============================] - 20s 28ms/step - loss: 0.0224 - accuracy: 0.9856 - lr: 3.1250e-05\nEpoch 30/30\n692/692 [==============================] - 19s 28ms/step - loss: 0.0223 - accuracy: 0.9855 - lr: 1.5625e-05\n","output_type":"stream"},{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"<keras.callbacks.History at 0x7c8dae96bbe0>"},"metadata":{}}]},{"cell_type":"code","source":"# Evaluate the Model\ncnn_lstm_predictions = cnn_lstm_model.predict(X_cnn_lstm_test)\ncnn_lstm_predictions = np.round(cnn_lstm_predictions)","metadata":{"execution":{"iopub.status.busy":"2023-12-12T15:18:06.524892Z","iopub.execute_input":"2023-12-12T15:18:06.525408Z","iopub.status.idle":"2023-12-12T15:18:09.311529Z","shell.execute_reply.started":"2023-12-12T15:18:06.525374Z","shell.execute_reply":"2023-12-12T15:18:09.310589Z"},"trusted":true},"execution_count":19,"outputs":[{"name":"stdout","text":"346/346 [==============================] - 3s 6ms/step\n","output_type":"stream"}]},{"cell_type":"code","source":"# Evaluate the Random Forest Model\nrf_test_predictions = rf_classifier.predict(X_test)\nrf_accuracy = accuracy_score(y_test, rf_test_predictions)\nprint(\"Random Forest Model Testing Accuracy:\", rf_accuracy)\n","metadata":{"execution":{"iopub.status.busy":"2023-12-12T15:18:12.755332Z","iopub.execute_input":"2023-12-12T15:18:12.756155Z","iopub.status.idle":"2023-12-12T15:18:14.787123Z","shell.execute_reply.started":"2023-12-12T15:18:12.756100Z","shell.execute_reply":"2023-12-12T15:18:14.785947Z"},"trusted":true},"execution_count":20,"outputs":[{"name":"stdout","text":"Random Forest Model Testing Accuracy: 0.9541839869871679\n","output_type":"stream"}]},{"cell_type":"code","source":"# Evaluate the CNN-LSTM Model\ncnn_lstm_test_predictions = cnn_lstm_model.predict(X_cnn_lstm_test)\ncnn_lstm_test_predictions = np.round(cnn_lstm_test_predictions)\ncnn_lstm_accuracy = accuracy_score(y_test, cnn_lstm_test_predictions)\nprint(\"CNN-LSTM Model Testing Accuracy:\", cnn_lstm_accuracy)\n","metadata":{"execution":{"iopub.status.busy":"2023-12-12T15:18:17.379767Z","iopub.execute_input":"2023-12-12T15:18:17.380270Z","iopub.status.idle":"2023-12-12T15:18:19.740489Z","shell.execute_reply.started":"2023-12-12T15:18:17.380234Z","shell.execute_reply":"2023-12-12T15:18:19.739292Z"},"trusted":true},"execution_count":21,"outputs":[{"name":"stdout","text":"346/346 [==============================] - 2s 6ms/step\nCNN-LSTM Model Testing Accuracy: 0.9502078438460149\n","output_type":"stream"}]},{"cell_type":"code","source":"# Evaluate the Ensemble Model\nensemble_test_predictions = (rf_test_predictions + cnn_lstm_test_predictions.reshape(-1)) / 2\nensemble_test_predictions = np.round(ensemble_test_predictions)\nensemble_accuracy = accuracy_score(y_test, ensemble_test_predictions)\nprint(\"Ensemble Model Testing Accuracy:\", ensemble_accuracy)\n","metadata":{"execution":{"iopub.status.busy":"2023-12-12T15:18:23.538620Z","iopub.execute_input":"2023-12-12T15:18:23.539579Z","iopub.status.idle":"2023-12-12T15:18:23.548569Z","shell.execute_reply.started":"2023-12-12T15:18:23.539537Z","shell.execute_reply":"2023-12-12T15:18:23.547706Z"},"trusted":true},"execution_count":22,"outputs":[{"name":"stdout","text":"Ensemble Model Testing Accuracy: 0.9530092174227363\n","output_type":"stream"}]},{"cell_type":"markdown","source":"**Ensemble model visualizations**","metadata":{}},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.metrics import confusion_matrix\n\n# Visualize Confusion Matrix for Random Forest Model\nrf_cm = confusion_matrix(y_test, rf_test_predictions)\nplt.figure(figsize=(8, 6))\nsns.heatmap(rf_cm, annot=True, fmt='d', cmap='Blues', cbar=False)\nplt.title('Random Forest Model Confusion Matrix')\nplt.xlabel('Predicted Label')\nplt.ylabel('True Label')\nplt.show()\n\n# Visualize Confusion Matrix for CNN-LSTM Model\ncnn_lstm_cm = confusion_matrix(y_test, cnn_lstm_test_predictions)\nplt.figure(figsize=(8, 6))\nsns.heatmap(cnn_lstm_cm, annot=True, fmt='d', cmap='Blues', cbar=False)\nplt.title('CNN-LSTM Model Confusion Matrix')\nplt.xlabel('Predicted Label')\nplt.ylabel('True Label')\nplt.show()\n\n# Visualize Confusion Matrix for Ensemble Model\nensemble_cm = confusion_matrix(y_test, ensemble_test_predictions)\nplt.figure(figsize=(8, 6))\nsns.heatmap(ensemble_cm, annot=True, fmt='d', cmap='Blues', cbar=False)\nplt.title('Ensemble Model Confusion Matrix')\nplt.xlabel('Predicted Label')\nplt.ylabel('True Label')\nplt.show()\n\n# Visualize Learning Rate Schedule\nepochs = range(1, 31)\nlearning_rates = [step_decay(epoch) for epoch in epochs]\n\nplt.figure(figsize=(10, 6))\nplt.plot(epochs, learning_rates, marker='o')\nplt.title('Learning Rate Schedule')\nplt.xlabel('Epoch')\nplt.ylabel('Learning Rate')\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2023-12-12T15:18:29.413699Z","iopub.execute_input":"2023-12-12T15:18:29.414157Z","iopub.status.idle":"2023-12-12T15:18:30.854761Z","shell.execute_reply.started":"2023-12-12T15:18:29.414108Z","shell.execute_reply":"2023-12-12T15:18:30.853600Z"},"trusted":true},"execution_count":23,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 800x600 with 1 Axes>","image/png":"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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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1fQCd9y4cS7BdPXjeTKratWqknTDlx/4+/urZcuW2rBhg/71r3+5PJc1M1q1aiUPDw9t2bLlumdpIyIidPTo0TRnLlPvCE+9FCEiIkIpKSk6cOCAy7i9e/e6vE59UkBycrIaNWqU7oe7T09o3ry5Lly4kKFHJ6XO9+p5JSUlKTo6OtOXVqQn9czl1U8ZuNY3Lvfdd59GjRqlHTt2aN68efrpp5/04Ycfpjs2JCREuXLlSnMc0t9/Nh4eHipSpMiNHcA1tG3bVseOHdNvv/123a+dxYsXq0SJElq6dKmefvppNWnSRI0aNUrzXOBrRX1mpKSkqGPHjgoKCtK///1vLViw4IafAwvYglgFbrIXXnhBAQEB6tq1q2JiYtKsP3DggCZOnHjdbQwePFiXLl1yeZTQ9VwZuMePH3dZFxkZ6RJM7vzo8Pjx4/r555/TLE9KStLatWvl4eGhUqVKZXh71zJgwAANHTpUQ4YMueFt5c6dW1OnTtWwYcPUvHnza45r1qyZkpOTNXnyZJfl48ePl8Ph0AMPPCBJzn9e/fzbq+/u9/T0dD4bNr0z6+48SilV9+7dFRYWpv79++u3335Ls/7EiRMaOXKkJKlRo0by8fHRW2+95XJG8d1331VcXFy6d7VnVuolEV9//bVzWUJCgubMmeMy7vTp02nOblauXFmSrnkpgKenpxo3bqxly5a5PAIrJibG+UsfUi+3yWolS5bUhAkTNHr0aN17773XHJd6JvfKY9u6dWuab1Bz5colKW3UZ8abb76pTZs2afr06Xr11VdVs2ZN9ejR43/y+cHA1bjyGrjJSpYs6fwxYvny5V1+g9WmTZu0aNEidezY8R+30a5duzT/87+el19+WXPnztXevXt15513Zvh9O3bscAbPlerXry8/Pz/de++9uv/++9WwYUOFhobqxIkTWrBggfbs2aO+ffve8JlQSapUqVKWPn6nQ4cO/zimefPmatCggV5++WUdPHhQlSpV0hdffKFly5apb9++ziCrXLmynnzySU2ZMkVxcXGqWbOm1q5dq/3796fZ5pgxY7R+/XpVr15d3bp1U4UKFXTq1Cnt2rVLX375pU6dOuXWceTNm1cff/yxmjVrpsqVK7v8Bqtdu3ZpwYIFqlGjhqS/z0gOGjRIw4cPV9OmTfXwww9r7969mjJliqpVq+ZyM9WNaty4sYoWLaouXbpo4MCB8vT01KxZsxQSEqJDhw45x82ZM0dTpkxRq1atVLJkSZ07d04zZsxQUFCQmjVrds3tjxw5UmvWrFHt2rX13HPPycvLS9OmTVNiYmKGv4HLrIxcJvPQQw9p6dKlatWqlR588EFFR0frnXfeUYUKFVwub/D391eFChW0cOFClSlTRvny5dNdd93l9jOEf/nlFw0ZMkQdO3Z0fgM2e/ZsVa5cWc8995w++ugj9w4SsE3OPYgAuL399ttvplu3bqZYsWLGx8fHBAYGmlq1aplJkyaZixcvOsdd/UilVPv27TOenp7XfXTV1VIfh+POo6uu9fHqq6+as2fPmokTJ5omTZqYwoULG29vbxMYGGhq1KhhZsyY4fK4oCtl9NFV15OZR1ddT3qf53Pnzpl+/fqZ8PBw4+3tbUqXLm3Gjh2b5rguXLhg+vTpY/Lnz28CAgJM8+bNzeHDh9N9LFFMTIzp2bOnKVKkiPH29jahoaGmYcOGZvr06c4xGX10VaqjR4+afv36mTJlyhg/Pz+TK1cuExkZaUaNGmXi4uJcxk6ePNmUK1fOeHt7m0KFCpkePXqY06dPu4y51uPN0ntk0rX+rHbu3GmqV69ufHx8TNGiRc2bb76Z5tFVu3btMk8++aQpWrSo8fX1NQULFjQPPfSQ2bFjR5p9XP153LVrl2nSpInJnTu3yZUrl2nQoIHZtGmTy5hr/dmnPvJt/fr1aeZ9pYx+jV39OUhJSTGvvfaaiYiIML6+vqZKlSpm+fLl6X7+Nm3aZCIjI42Pj4/LcXbo0MEEBASku78rt3P58mVTrVo1U7hw4TSP4po4caKRZBYuXHjd+QO2cxjj5hXmAAAAwE3CNasAAACwFrEKAAAAaxGrAAAAsBaxCgAAAGsRqwAAALAWsQoAAABrEasAAACw1i35G6z8q/TK6SkAQJb6a+uknJ4CAGSpXD6ODI3jzCoAAACsRawCAADAWsQqAAAArEWsAgAAwFrEKgAAAKxFrAIAAMBaxCoAAACsRawCAADAWsQqAAAArEWsAgAAwFrEKgAAAKxFrAIAAMBaxCoAAACsRawCAADAWsQqAAAArEWsAgAAwFrEKgAAAKxFrAIAAMBaxCoAAACsRawCAADAWsQqAAAArEWsAgAAwFrEKgAAAKxFrAIAAMBaxCoAAACsRawCAADAWsQqAAAArEWsAgAAwFrEKgAAAKxFrAIAAMBaxCoAAACsRawCAADAWsQqAAAArEWsAgAAwFrEKgAAAKxFrAIAAMBaxCoAAACsRawCAADAWsQqAAAArEWsAgAAwFrEKgAAAKxFrAIAAMBaxCoAAACsRawCAADAWsQqAAAArEWsAgAAwFrEKgAAAKxFrAIAAMBaxCoAAACsRawCAADAWsQqAAAArEWsAgAAwFrEKgAAAKxFrAIAAMBaxCoAAACsRawCAADAWsQqAAAArEWsAgAAwFrEKgAAAKxFrAIAAMBaxCoAAACsRawCAADAWsQqAAAArEWsAgAAwFrEKgAAAKxFrAIAAMBaxCoAAACsRawCAADAWsQqAAAArEWsAgAAwFrEKgAAAKxFrAIAAMBaxCoAAACsRawCAADAWsQqAAAArEWsAgAAwFrEKgAAAKxFrAIAAMBaxCoAAACsRawCAADAWsQqAAAArEWsAgAAwFrEKgAAAKxFrAIAAMBaxCoAAACsRawCAADAWsQqAAAArEWsAgAAwFrEKgAAAKxFrAIAAMBaxCoAAACsRawCAADAWsQqAAAArEWsAgAAwFrEKgAAAKxFrAIAAMBaxCoAAACsRawCAADAWsQqAAAArEWsAgAAwFrEKgAAAKxFrAIAAMBaxCoAAACsRawCAADAWsQqAAAArEWsAgAAwFrEKgAAAKxFrAIAAMBaxCoAAACs5ZXTEwBy0svPNtPg7s1clu2NPq7KrUdKklbPeF51q5Z2WT9j8Ub1GfWhy7J2zaurT7v7VTqioM4mXNTSNd+p35iPXMb0fbqhOreppaJhefXXmQRN++gbvf7u6mw4KgD4r48WLtDihQt09OgRSVKJkqX0TPeeql2nriRpyaKFWrVyuX795WclJCTo62+3KTAoyPn+o0f+1PRpU7V92xb9FRurkJCCavZQc3V9pru8vX1y5JhweyFWcdv7af9RPdh9kvP15eQUl/XvLvlWr05d7nx9/uIll/V92t2v55++X/8e/4m2/XhQAf4+igjP7zJm3AuPqOF95TRo/Mf6cd9R5QvOpbxBAdlwNADgqlChQurdt7+KRkRIxuizTz9Rvz499eGipSpZqrQuXryomrXqqGatOpo08c0074+OjpZJSdHgV4arSJEI7d+/T68OG6ILFy4oasCLOXBEuN0Qq7jtXU5OUcxf5665/sLFpGuuzxPor6HPPaQ2fd/Rhm2/OZf/uO+o89/LFi+kbo/UUeSjo7TvjxOSpD+O/pVFsweA66tX/36X17369NOihR/q++/3qGSp0nrq6Q6SpB3bt6b7/lq166hW7TrO14WLFNEfB6O1aOECYhU3RY7GamxsrGbNmqXNmzfr+PHjkqTQ0FDVrFlTHTt2VEhISE5OD7eJUkVD9PsXo3Qx8ZK2fh+tVyZ9qsPHTzvXP96sqp5oVk0xf53Vyq9/1OgZq3Th/8+uNryvnDw8HAovmEffLRmswABfbdkTrZfeXKo/Y85Ikh6sW1HRR2LVrO5d6v54XTkcDq3bulcvT/hEp8+ez4lDBnCbSk5O1povPteFC+d1d6XKmd5O/LlzCgoOzrqJAdeRY7G6fft2NWnSRLly5VKjRo1UpkwZSVJMTIzeeustjRkzRqtXr1bVqlWvu53ExEQlJia6LDMpyXJ4eGbb3HHr2P7jQT3zygf67Y8YhRYI1svPPqAvZ/VT5COjFH8+UQtX7dChY6d07GScKpYO18jnW6hMREE9MWCmJKl44QLy8HDohc6NNWDsEp2Nv6ChPR/S8qm9VO2x0bp0OVnFChdQ0bB8at2oiroOmSsPDw+9PqC15o/togeenfQPMwSAG7fvt73q0O5JJSUlyj9XLo2bMFklS5bK1LYOHfpDHy74QP36v5DFswTSl2Ox2rt3bz366KN655135HA4XNYZY9S9e3f17t1bmzdvvu52Ro8ereHDh7ss8yxUTd5h92b5nHHr+eLbn53//uO+o9r+w0HtXTlCbRrfozmfbNaspd861/+0/6iOxZ7V59P7qHjhAor+M1YOh0M+3l7q//pird3yqySpw6DZOrjmNdWrVkZfbv5FHg6H/Hy91WXIXO0/9PdlAD2Gz9PmBS+pdERB56UBAJBdihUvrg8Xf6z4c+f05ZrVemXwS5r53ly3g/VETIx6de+mRo2bqvUjj2XTbAFXOfboqj179qhfv35pQlWSHA6H+vXrp927d//jdgYNGqS4uDiXD69CkdkwY9wO4uIvaP+hEypZJP1LULb/cFCSnOuPx56VJP36+3HnmNjT8Yo9E68ioXn/f0ycLl1KdoaqJP0aHSNJKhKaL8uPAQCu5u3to6JFI1ThzrvUp29/lSlTTgs+eN+tbZw4EaNuXdrr7spVNGToiGyaKZBWjsVqaGiotm3bds3127ZtU6FChf5xO76+vgoKCnL54BIAZFaAv4+KFy6g47Fx6a6vVLawJDnXb979uySpdLGCzjF5g3KpQJ7cOnTslHOMt7enihcu4BxTOuLv8aljAOBmMiZFSUlJGR5/IiZG3Tq3V/kKd2r4q6/Jw4PHtOPmybHLAAYMGKBnnnlGO3fuVMOGDZ1hGhMTo7Vr12rGjBl64403cmp6uE2M7tdKK77+QYeOnlJ4wWAN7v6gklNS9NHnO1W8cAE9/kBVrd74k/46k6CKZe7Q6/1b65ud+5x3++8/dEKfrd+jNwY+ol4jF+hs/EWN6P2w9h6M0Vc7/n46wLqte7Xr50OaNuwpDRy7RB4eDk146TF9ufkXl7OtAJAd3powTrVq11VYWJgSEhK0auVy7di+TVPe+fva+9jYk/orNlaHDh2SJO3b95sCAgIUGham4OA8OhETo66d2yssLFxR/V/U6dP//Sa7QAFuhEb2cxhjTE7tfOHChRo/frx27typ5ORkSZKnp6ciIyMVFRWlxx7L3PUw/lV6ZeU0cQt7f0wn1b6nlPIF51Ls6Xht2v27hk7+TNF/xqpwoTyaNaqDKpQMV4C/j/6MOa1P1+3RmJmrdS7honMbgQF+en1Aa7W4v7JSUow27tynAWMXO58GIElhIcF688VH1fC+ckq4kKQvvv1ZL725lKcBIMP+2srNeMicYa+8rG1bNyv25EnlDgxU6dJl1alzV91Xs5Yk6Z0pkzRt6ttp3jf81df0cMvW+vSTpRo65N/pbvu7H37N1rnj1pbLJ+2loOnJ0VhNdenSJcXGxkqSChQoIG9v7xvaHrEK4FZDrAK41WQ0Vq34pQDe3t4KCwvL6WkAAADAMlwhDQAAAGsRqwAAALAWsQoAAABrEasAAACwFrEKAAAAaxGrAAAAsBaxCgAAAGsRqwAAALAWsQoAAABrEasAAACwFrEKAAAAaxGrAAAAsBaxCgAAAGsRqwAAALAWsQoAAABrEasAAACwFrEKAAAAaxGrAAAAsBaxCgAAAGsRqwAAALAWsQoAAABrEasAAACwFrEKAAAAaxGrAAAAsBaxCgAAAGsRqwAAALAWsQoAAABrEasAAACwFrEKAAAAaxGrAAAAsBaxCgAAAGsRqwAAALAWsQoAAABrEasAAACwFrEKAAAAaxGrAAAAsBaxCgAAAGsRqwAAALAWsQoAAABrEasAAACwFrEKAAAAaxGrAAAAsBaxCgAAAGsRqwAAALAWsQoAAABrEasAAACwFrEKAAAAaxGrAAAAsBaxCgAAAGsRqwAAALAWsQoAAABrEasAAACwFrEKAAAAaxGrAAAAsBaxCgAAAGsRqwAAALAWsQoAAABrEasAAACwFrEKAAAAaxGrAAAAsBaxCgAAAGsRqwAAALAWsQoAAABrEasAAACwFrEKAAAAaxGrAAAAsBaxCgAAAGsRqwAAALAWsQoAAABrEasAAACwFrEKAAAAaxGrAAAAsBaxCgAAAGsRqwAAALAWsQoAAABreWVk0Pfff5/hDd59992ZngwAAABwpQzFauXKleVwOGSMSXd96jqHw6Hk5OQsnSAAAABuXxmK1ejo6OyeBwAAAJBGhmI1IiIiu+cBAAAApJGpG6zmzp2rWrVqKTw8XH/88YckacKECVq2bFmWTg4AAAC3N7djderUqYqKilKzZs105swZ5zWqefLk0YQJE7J6fgAAALiNuR2rkyZN0owZM/Tyyy/L09PTubxq1ar64YcfsnRyAAAAuL25HavR0dGqUqVKmuW+vr5KSEjIkkkBAAAAUiZitXjx4tq9e3ea5Z9//rnKly+fFXMCAAAAJGXwaQBXioqKUs+ePXXx4kUZY7Rt2zYtWLBAo0eP1syZM7NjjgAAALhNuR2rXbt2lb+/vwYPHqzz58+rbdu2Cg8P18SJE/XEE09kxxwBAABwm3KYa/1aqgw4f/684uPjVbBgwayc0w3zr9Irp6cAAFnqr62TcnoKAJClcvk4MjTO7TOrqU6cOKG9e/dK+vvXrYaEhGR2UwAAAEC63L7B6ty5c3r66acVHh6uevXqqV69egoPD1e7du0UFxeXHXMEAADAbcrtWO3atau2bt2qFStW6MyZMzpz5oyWL1+uHTt26Nlnn82OOQIAAOA25fY1qwEBAVq9erVq167tsvybb75R06ZNrXjWKtesArjVcM0qgFtNRq9ZdfvMav78+RUcHJxmeXBwsPLmzevu5gAAAIBrcjtWBw8erKioKB0/fty57Pjx4xo4cKCGDBmSpZMDAADA7S1DTwOoUqWKHI7/nqrdt2+fihYtqqJFi0qSDh06JF9fX508eZLrVgEAAJBlMhSrLVu2zOZpAAAAAGnd0C8FsBU3WAG41XCDFYBbTbbdYAUAAADcLG7/Bqvk5GSNHz9eH330kQ4dOqSkpCSX9adOncqyyQEAAOD25vaZ1eHDh+vNN9/U448/rri4OEVFRal169by8PDQsGHDsmGKAAAAuF25Havz5s3TjBkz1L9/f3l5eenJJ5/UzJkz9corr2jLli3ZMUcAAADcptyO1ePHj6tixYqSpNy5cysuLk6S9NBDD2nFihVZOzsAAADc1tyO1cKFC+vYsWOSpJIlS+qLL76QJG3fvl2+vr5ZOzsAAADc1tyO1VatWmnt2rWSpN69e2vIkCEqXbq02rdvr86dO2f5BAEAAHD7uuHnrG7ZskWbNm1S6dKl1bx586ya1w3hOasAbjU8ZxXAreamPWf1vvvuU1RUlKpXr67XXnvtRjcHAAAAOGXZLwU4duyYhgwZklWbAwAAAPgNVgAAALAXsQoAAABrEasAAACwlldGB0ZFRV13/cmTJ294Mlnl9PbJOT0FAMhSedtMy+kpAECWurDs2QyNy3Csfvfdd/84pm7duhndHAAAAPCPMhyr69evz855AAAAAGlwzSoAAACsRawCAADAWsQqAAAArEWsAgAAwFrEKgAAAKyVqVj95ptv1K5dO9WoUUNHjhyRJM2dO1cbN27M0skBAADg9uZ2rC5ZskRNmjSRv7+/vvvuOyUmJkqS4uLi9Nprr2X5BAEAAHD7cjtWR44cqXfeeUczZsyQt7e3c3mtWrW0a9euLJ0cAAAAbm9ux+revXvT/U1VwcHBOnPmTFbMCQAAAJCUiVgNDQ3V/v370yzfuHGjSpQokSWTAgAAAKRMxGq3bt30/PPPa+vWrXI4HDp69KjmzZunAQMGqEePHtkxRwAAANymvNx9w0svvaSUlBQ1bNhQ58+fV926deXr66sBAwaod+/e2TFHAAAA3KYcxhiTmTcmJSVp//79io+PV4UKFZQ7d+6snlumXbyc0zMAgKyVt820nJ4CAGSpC8uezdA4t8+spvLx8VGFChUy+3YAAADgH7kdqw0aNJDD4bjm+nXr1t3QhAAAAIBUbsdq5cqVXV5funRJu3fv1o8//qgOHTpk1bwAAAAA92N1/Pjx6S4fNmyY4uPjb3hCAAAAQCq3H111Le3atdOsWbOyanMAAABA1sXq5s2b5efnl1WbAwAAANy/DKB169Yur40xOnbsmHbs2KEhQ4Zk2cQAAAAAt2M1ODjY5bWHh4fKli2rESNGqHHjxlk2MQAAAMCtWE1OTlanTp1UsWJF5c2bN7vmBAAAAEhy85pVT09PNW7cWGfOnMmm6QAAAAD/5fYNVnfddZd+//337JgLAAAA4MLtWB05cqQGDBig5cuX69ixYzp79qzLBwAAAJBVHMYYk5GBI0aMUP/+/RUYGPjfN1/xa1eNMXI4HEpOTs76Wbrp4uWcngEAZK28babl9BQAIEtdWPZshsZlOFY9PT117Ngx/fLLL9cdV69evQztODsRqwBuNcQqgFtNRmM1w08DSG1aG2IUAAAAtwe3rlm98sf+AAAAQHZz6zmrZcqU+cdgPXXq1A1NCAAAAEjlVqwOHz48zW+wAgAAALKLW7H6xBNPqGDBgtk1FwAAAMBFhq9Z5XpVAAAA3GwZjtUMPuEKAAAAyDIZvgwgJSUlO+cBAAAApOH2r1sFAAAAbhZiFQAAANYiVgEAAGAtYhUAAADWIlYBAABgLWIVAAAA1iJWAQAAYC1iFQAAANYiVgEAAGAtYhUAAADWIlYBAABgLWIVAAAA1iJWAQAAYC1iFQAAANYiVgEAAGAtYhUAAADWIlYBAABgLWIVAAAA1iJWAQAAYC1iFQAAANYiVgEAAGAtYhUAAADWIlYBAABgLWIVAAAA1iJWAQAAYC1iFQAAANYiVgEAAGAtYhUAAADWIlYBAABgLWIVAAAA1iJWAQAAYC1iFQAAANYiVgEAAGAtYhUAAADWIlYBAABgLWIVAAAA1iJWAQAAYC1iFQAAANYiVgEAAGAtYhUAAADWIlYBAABgLWIVAAAA1iJWAQAAYC1iFQAAANYiVgEAAGAtYhUAAADWIlYBAABgLWIVAAAA1iJWAQAAYC1iFQAAANYiVgEAAGAtYhUAAADWIlYBAABgLWIVAAAA1iJWAQAAYC1iFQAAANYiVgEAAGAtYhUAAADWIlYBAABgLWIVAAAA1iJWAQAAYC1iFQAAANYiVgEAAGAtYhUAAADWIlYBAABgLWIVAAAA1iJWAQAAYC1iFQAAANYiVgEAAGAtYhUAAADWIlYBAABgLWIVAAAA1iJWgSu8O2Oa2j7WRjWqVVH9OjXUt/dzOhj9u8uYEcNe0YNNG+nee+5W/dr36flePRT9+wGXMVu3bFb7p55QjWpVdH/dWho/bqwuX758Mw8FADSgTWVdWPasxnap6VxWPDRICwc11qH32ytmQSd9MLCRCgb7p/t+Hy8PbRnfRheWPau7i+d3WdemVgltGd9Gf33UWXtntFW/VpWy9Vhw+yJWgSvs2L5Njz/5lOYu+EjTZryny5cvq3u3Ljp//rxzTIUKd2rEyNH6+LOVmjr9XRlj1L1bFyUnJ0uS9v76q3p276aatWpr4eJP9Pq48fpqwzpNHD8upw4LwG0oslSIujQpr++j/3Iuy+XrpeXDmskY6YEhy3X/S8vk4+WhJYObyuFIu43XOt6nY6fOp1ne+J4iei/qfs1c/bMiey/S8+9sVO+HK6p7szuz85BwmyJWgStMnf6uWrRqrVKlSqtsuXIaMWqMjh07ql9+/sk55pHHHldk1Wq6447CKl/hTvXq01fHjx/T0SNHJEmrP1+pMmXKqvtzvVQ0IkJVq92rvlEDtXDBPCUkxOfUoQG4jQT4eem9qPv13Ntf60x8onN5jfKhiigYqG4T1+unP07ppz9OqevEDbqnVIjq332HyzYa31NEDSsX1qDZm9Nsv2390vps60HN/PwXHYw5p893HtLYxd+pf5vK2X1ouA0Rq8B1xJ87J0kKCg5Od/358+e17OOluqNwYYWGhkqSkpKS5OPr6zLOz89PiYmJ+vmnn9LbDABkqQnP1tbnOw9p/Z4jLst9vT1lJCVeSnYuu5h0WSnGqGb5UOeygsH+mtKzrrpMWKfziWkvYfL19tTFpGSXZReSklW4QG4VLZg7aw8Gtz2rY/Xw4cPq3LnzdcckJibq7NmzLh+JiYnXfQ+QESkpKXr9P6+pcpV7VLp0GZd1CxfM031Vq6hGtSrauPFrTZvxnrx9fCRJNWvV1p7d32nViuVKTk5WTEyMpk19W5IUe/LkTT8OALeXR+uUVOUSBTTk/W1p1m3bG6OEi5c0qsN98vfxUi5fL43pVENenh4KzZvLOW768/U14/OftWt/bLr7WPPdn2pRo7jq332HHA6pVHiwnm9xtyQpLG9A9hwYbltWx+qpU6c0Z86c644ZPXq0goODXT7G/mf0TZohbmWvjRyuA/v26fU3xqdZ1+yhh7VwyceaNecDRUQU08D+fZ3fJNWsVVv9+r+gkSOGqlqVinr4wSaqXaeeJMnhYfV/cgD+xxUuEKCxXWuq05vrXM6epoo9e1FPvf6lmlUrqtiFnRWzoJOCA3y0a/9JpRgjSXruobsU6O+tsUt2X3M/s774Re+s+ElLBzfV2SXd9NXrLbXom79vNE3dDpBVHMbk3FfVp59+et31v//+u/r37++8cSU9iYmJac6kGk9f+V71Y1jAHa+NHKEN69dq1pwPVLhwkeuOvZSUpNo179Ww4SP1wIMPOZcbY3Ty5AkFBQXr6JEjavVwM837cJHuqnh3dk8ft6C8babl9BTwP6B59WL66N9NdDk5xbnMy9NDKSlGKcYo+JGZSkn5+3/7+QP9dDklRXEJSYqe/bTeWva9xn+8Rx8Naqxm1SJ0ZRx4eXrocnKKPvxqn7pN3OBc7uHhUGgef508e1EN7r5Dy4Y2U5Gn5yj27MWbdMT4X3Zh2bMZGueVzfO4rpYtW8rhcOh6vexI7/bEK/j6pg3TizwhCJlkjNHoUa9q3do1enf23H8MVUl//4VujJKSklyWOxwOFSxYSJK0auVyhYaGqXwF7pQFkH3Wf39Ekb0/clk2vU997f3zjMYt3e0MVUn669zfQVmvYrgKBvtr+baDkqT+MzZp2LztznFh+QK0fPiDenrsl9r+2wmXbaekGB39/6cFPFa3lLb8epxQRZbL0VgNCwvTlClT1KJFi3TX7969W5GRkTd5VridvfbqcK1auVwTJk1RQK4A5zWmuQMD5efnpz8PH9bqz1eqRs1ayps3n2JijmvWzOny9fVT7br1nNuZPWumatWuI4eHh9au+UKzZs7Q2DcnyNPTM6cODcBtIP7CJf186LTLsoSLl3XqXKJz+dMNy2rv4dM6efaiqpctpDe61tSkT7/XviNxkqTDsa5PLYm/eEmS9PvxszryV4Kkv8/KtqpVXF//cEx+Pp5q37CsWtcsocYvX/8npkBm5GisRkZGaufOndeM1X866wpktY8WLpAkden4tMvyESNHq0Wr1vLx9dGunTv0wdw5Oht3VvkL5FdkZFW9P2+B8uf/7wOzN37ztWZOf0dJSUkqU7acJk5+23ndKgDkpDJ3BGvE0/cqX25f/XHinF5ftEtvffqD29tp16CsRnesIYdD2ro3Rk0Gf6Yd+7iJFFkvR69Z/eabb5SQkKCmTZumuz4hIUE7duxQvXru/U+eywAA3Gq4ZhXAreZ/4prVOnXqXHd9QECA26EKAACAWwfP0QEAAIC1iFUAAABYi1gFAACAtYhVAAAAWItYBQAAgLWIVQAAAFiLWAUAAIC1iFUAAABYi1gFAACAtYhVAAAAWItYBQAAgLWIVQAAAFiLWAUAAIC1iFUAAABYi1gFAACAtYhVAAAAWItYBQAAgLWIVQAAAFiLWAUAAIC1iFUAAABYi1gFAACAtYhVAAAAWItYBQAAgLWIVQAAAFiLWAUAAIC1iFUAAABYi1gFAACAtYhVAAAAWItYBQAAgLWIVQAAAFiLWAUAAIC1iFUAAABYi1gFAACAtYhVAAAAWItYBQAAgLWIVQAAAFiLWAUAAIC1iFUAAABYi1gFAACAtYhVAAAAWItYBQAAgLWIVQAAAFiLWAUAAIC1iFUAAABYi1gFAACAtYhVAAAAWItYBQAAgLWIVQAAAFiLWAUAAIC1iFUAAABYi1gFAACAtYhVAAAAWItYBQAAgLWIVQAAAFiLWAUAAIC1iFUAAABYi1gFAACAtYhVAAAAWItYBQAAgLWIVQAAAFiLWAUAAIC1iFUAAABYi1gFAACAtYhVAAAAWItYBQAAgLWIVQAAAFiLWAUAAIC1iFUAAABYi1gFAACAtYhVAAAAWItYBQAAgLWIVQAAAFiLWAUAAIC1iFUAAABYi1gFAACAtYhVAAAAWItYBQAAgLWIVQAAAFiLWAUAAIC1iFUAAABYi1gFAACAtYhVAAAAWItYBQAAgLWIVQAAAFiLWAUAAIC1iFUAAABYi1gFAACAtYhVAAAAWItYBQAAgLWIVQAAAFiLWAUAAIC1iFUAAABYi1gFAACAtYhVAAAAWItYBQAAgLWIVQAAAFiLWAUAAIC1iFUAAABYi1gFAACAtYhVAAAAWItYBQAAgLWIVQAAAFiLWAUAAIC1iFUAAABYi1gFAACAtYhVAAAAWItYBQAAgLWIVQAAAFiLWAUAAIC1HMYYk9OTAP4XJSYmavTo0Ro0aJB8fX1zejoAcMP4ew02IlaBTDp79qyCg4MVFxenoKCgnJ4OANww/l6DjbgMAAAAANYiVgEAAGAtYhUAAADWIlaBTPL19dXQoUO5CQHALYO/12AjbrACAACAtTizCgAAAGsRqwAAALAWsQoAAABrEasAAACwFrEKZNLbb7+tYsWKyc/PT9WrV9e2bdtyekoAkClff/21mjdvrvDwcDkcDn3yySc5PSXAiVgFMmHhwoWKiorS0KFDtWvXLlWqVElNmjTRiRMncnpqAOC2hIQEVapUSW+//XZOTwVIg0dXAZlQvXp1VatWTZMnT5YkpaSkqEiRIurdu7deeumlHJ4dAGSew+HQxx9/rJYtW+b0VABJnFkF3JaUlKSdO3eqUaNGzmUeHh5q1KiRNm/enIMzAwDg1kOsAm6KjY1VcnKyChUq5LK8UKFCOn78eA7NCgCAWxOxCgAAAGsRq4CbChQoIE9PT8XExLgsj4mJUWhoaA7NCgCAWxOxCrjJx8dHkZGRWrt2rXNZSkqK1q5dqxo1auTgzAAAuPV45fQEgP9FUVFR6tChg6pWrap7771XEyZMUEJCgjp16pTTUwMAt8XHx2v//v3O19HR0dq9e7fy5cunokWL5uDMAB5dBWTa5MmTNXbsWB0/flyVK1fWW2+9perVq+f0tADAbRs2bFCDBg3SLO/QoYNmz5598ycEXIFYBQAAgLW4ZhUAAADWIlYBAABgLWIVAAAA1iJWAQAAYC1iFQAAANYiVgEAAGAtYhUAAADWIlYBAABgLWIVAG5Qx44d1bJlS+fr+vXrq2/fvjd9Hhs2bJDD4dCZM2eybR9XH2tm3Ix5Arh1EKsAbkkdO3aUw+GQw+GQj4+PSpUqpREjRujy5cvZvu+lS5fq1VdfzdDYmx1uxYoV04QJE27KvgAgK3jl9AQAILs0bdpU7733nhITE7Vy5Ur17NlT3t7eGjRoUJqxSUlJ8vHxyZL95suXL0u2AwDgzCqAW5ivr69CQ0MVERGhHj16qFGjRvr0008l/ffH2aNGjVJ4eLjKli0rSTp8+LAee+wx5cmTR/ny5VOLFi108OBB5zaTk5MVFRWlPHnyKH/+/HrhhRdkjHHZ79WXASQmJurFF19UkSJF5Ovrq1KlSundd9/VwYMH1aBBA0lS3rx55XA41LFjR0lSSkqKRo8ereLFi8vf31+VKlXS4sWLXfazcuVKlSlTRv7+/mrQoIHLPDMjOTlZXbp0ce6zbNmymjhxYrpjhw8frpCQEAUFBal79+5KSkpyrsvI3AEgozizCuC24e/vr7/++sv5eu3atQoKCtKaNWskSZcuXVKTJk1Uo0YNffPNN/Ly8tLIkSPVtGlTff/99/Lx8dG4ceM0e/ZszZo1S+XLl9e4ceP08ccf6/7777/mftu3b6/NmzfrrbfeUqVKlRQdHa3Y2FgVKVJES5YsUZs2bbR3714FBQXJ399fkjR69Gh98MEHeuedd1S6dGl9/fXXateunUJCQlSvXj0dPnxYrVu3Vs+ePfXMM89ox44d6t+//w19flJSUlS4cGEtWrRI+fPn16ZNm/TMM88oLCxMjz32mMvnzc/PTxs2bNDBgwfVqVMn5c+fX6NGjcrQ3AHALQYAbkEdOnQwLVq0MMYYk5KSYtasWWN8fX3NgAEDnOsLFSpkEhMTne+ZO3euKVu2rElJSXEuS0xMNP7+/mb16tXGGGPCwsLM66+/7lx/6dIlU7hwYee+jDGmXr165vnnnzfGGLN3714jyaxZsybdea5fv95IMqdPn3Yuu3jxosmVK5fZtGmTy9guXbqYJ5980hhjzKBBg0yFChVc1r/44otptnW1iIgIM378+Guuv1rPnj1NmzZtnK87dOhg8uXLZxISEpzLpk6danLnzm2Sk5MzNPf0jhkAroUzqwBuWcuXL1fu3Ll16dIlpaSkqG3btho2bJhzfcWKFV2uU92zZ4/279+vwMBAl+1cvHhRBw4cUFxcnI4dO6bq1as713l5ealq1appLgVItXv3bnl6erp1RnH//v06f/68/vWvf7ksT0pKUpUqVSRJv/zyi8s8JKlGjRoZ3se1vP3225o1a5YOHTqkCxcuKCkpSZUrV3YZU6lSJeXKlctlv/Hx8Tp8+LDi4+P/ce4A4A5iFcAtq0GDBpo6dap8fHwUHh4uLy/Xv/ICAgJcXsfHxysyMlLz5s1Ls62QkJBMzSH1x/ruiI+PlyStWLFCd9xxh8s6X1/fTM0jIz788EMNGDBA48aNU40aNRQYGKixY8dq69atGd5GTs0dwK2LWAVwywoICFCpUqUyPP6ee+7RwoULVbBgQQUFBaU7JiwsTFu3blXdunUlSZcvX9bOnTt1zz33pDu+YsWKSklJ0VdffaVGjRqlWZ96Zjc5Odm5rEKFCvL19dWhQ4eueUa2fPnyzpvFUm3ZsuWfD/I6vv32W9WsWVPPPfecc9mBAwfSjNuzZ48uXLjgDPEtW7Yod+7cKlKkiPLly/ePcwcAd/A0AAD4f0899ZQKFCigFi1a6JtvvlF0dLQ2bNigPn366M8//5QkPf/88xozZow++eQT/frrr3ruueeu+4zUYsWKqUOHDurcubM++eQT5zY/+ugjSVJERIQcDoeWL1+ukydPKj4+XoGBgRowYID69eunOXPm6MCBA9q1a5cmTZqkOXPmSJK6d++uffv2aeDAgdq7d6/mz5+v2bNnZ+g4jxw5ot27d7t8nD59WqVLl9aOHTu0evVq/fbbbxoyZIi2b9+e5v1JSUnq0qWLfv75Z61cuVJDhw5Vr1695OHhkaG5A4BbcvqiWQDIDlfeYOXO+mPHjpn27dubAgUKGF9fX1OiRAnTrVs3ExcXZ4z5+4aq559/3gQFBZk8efKYqKgo0759+2veYGWMMRcuXDD9+vUzYWFhxsfHx5QqVcrMmjXLuX7EiBEmNDTUOBwO06FDB2PM3zeFTZgwwZQtW9Z4e3ubkJAQ06RJE/PVV1853/fZZ5+ZUqVKGV9fX1OnTh0za9asDN1gJSnNx9y5c83FixdNx44dTXBwsMmTJ4/p0aOHeemll0ylSpXSfN5eeeUVkz9/fpM7d27TrVs3c/HiReeYf5o7N1gBcIfDmGvcFQAAAADkMC4DAAAAgLWIVQAAAFiLWAUAAIC1iFUAAABYi1gFAACAtYhVAAAAWItYBQAAgLWIVQAAAFiLWAUAAIC1iFUAAABYi1gFAACAtf4PqlYvNDrZ8MEAAAAASUVORK5CYII="},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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wLjhhhuMHTt2GC1atDDuueeeC75vkoz77ruv2seq2vVXnW/NmjVGnz59jICAACMmJsZ46KGHjOXLl59V04kTJ4w77rjDCA0NNSQ5vWfl5eXG008/bVxxxRWGn5+f0bx5cyMhIcF44oknjKKionPWefr0aWP+/PlGamqq43MJDAw0rrzySuOZZ545a1uEkydPGo8++qgRHx/v+Jx/85vfGHv27DEM4/yfnappgb9nzx5j5MiRRlRUlOHj42O0atXK+NWvfmW8++67TuO++uor49prrzX8/f2NVq1aGX/84x+NV1555azPvLKy0nj44YeN8PBwIzAw0EhJSTG+/fbbC7bZr7Jp0ybjtttuM1q0aGH4+fkZcXFxxu23325kZmae8z0EAFdiMYx6vqMaAAAXVVhYqObNm2vmzJl69NFHzS4HAICzcA8aAMAjnTp16qxjzz//vCTpuuuua9xiAACoIe5BAwB4pLffflsLFizQwIED1axZM33++ed68803ddNNN+nqq682uzwAAKpFQAMAeKSuXbvK29tbs2fPVnFxsaNxyMyZM80uDQCAc+IeNAAAAABwEdyDBgAAAAAugoAGAAAAAC6Ce9AakN1u18GDBxUcHHxRm6ICAAAAcG+GYej48eOKiYmR1XrudTICWgM6ePCgYmNjzS4DAAAAgIvYv3+/Wrdufc7HCWgNKDg4WNKZDyEkJMTkagAAAACYpbi4WLGxsY6McC4EtAZUdVljSEgIAQ0AAADABW99okkIAAAAALgIAhoAAAAAuAgCGgAAAAC4CAIaAAAAALgIAhoAAAAAuAgCGgAAAAC4CAIaAAAAALgIAhoAAAAAuAgCGgAAAAC4CAIaAAAAALgIAhoAAAAAuAgCGgAAAAC4CAIaAAAAALgIb7MLQMOrtBtam1Ogw8dLFRHsr97xYfKyWpgfAAAAcDGmr6C9+OKLatu2rfz9/ZWYmKi1a9eed/zixYt1+eWXy9/fX126dNGHH37o9LhhGJo+fbqio6MVEBCg5ORk7d6922nMU089pauuukqBgYEKDQ2t9jz79u3ToEGDFBgYqIiICP3hD39QRUXFRb1WM2RsO6S+T6/U8PlfatJbmzV8/pfq+/RKZWw7xPwAAACAizE1oL399ttKS0tTenq6Nm7cqG7duiklJUWHDx+udvwXX3yh4cOHa8yYMdq0aZNSU1OVmpqqbdu2OcbMnj1bc+bM0dy5c5Wdna2goCClpKSotLTUMaa8vFy//e1vNX78+GrPU1lZqUGDBqm8vFxffPGFFi5cqAULFmj69On1+wY0sIxthzT+9Y06VFTqdDy3qFTjX9940SHH3ecHAAAAXI3FMAzDrJMnJiaqV69eeuGFFyRJdrtdsbGxmjhxoh555JGzxg8dOlQlJSVatmyZ41ifPn3UvXt3zZ07V4ZhKCYmRlOmTNGDDz4oSSoqKlJkZKQWLFigYcOGOc23YMECTZ48WYWFhU7HP/roI/3qV7/SwYMHFRkZKUmaO3euHn74YR05ckS+vr41en3FxcWy2WwqKipSSEhIjd+X+lBpN9T36ZVnhZufax7oo6dSO8tah8sF7XZD05ZsU+HJ06bMb5EUZfPX5w/fwOWOAAAAcHk1zQam3YNWXl6uDRs2aOrUqY5jVqtVycnJysrKqvY5WVlZSktLczqWkpKiJUuWSJJycnKUm5ur5ORkx+M2m02JiYnKyso6K6CdS1ZWlrp06eIIZ1XnGT9+vLZv364rr7yy2ueVlZWprKzM8XtxcXGNztcQ1uYUnDecSdKxk6d176JNDVZDQ85vSDpUVKq1OQVKat+iQc4BAAAANDbTAlp+fr4qKyudQpAkRUZGaufOndU+Jzc3t9rxubm5jserjp1rTE2c6zw/P0d1Zs2apSeeeKLG52lIh4+fP5xViQ8PUougmq0I/tzRknLl5JeYPn9NXycAAADgDujiWI+mTp3qtMJXXFys2NhYU2qJCPav0bg/3dqlTitQWXuOavj8L02fv6avEwAAAHAHpjUJCQ8Pl5eXl/Ly8pyO5+XlKSoqqtrnREVFnXd81Z+1mbM25/n5Oarj5+enkJAQpx+z9I4PU7TNX+e6O8siKdp2pmV9U5wfAAAAcEWmBTRfX18lJCQoMzPTccxutyszM1NJSUnVPicpKclpvCStWLHCMT4+Pl5RUVFOY4qLi5WdnX3OOc91nq1btzp1k1yxYoVCQkLUqVOnGs9jJi+rRemDz9T6y5BT9Xv64E51brDh7vMDAAAArsjUNvtpaWmaP3++Fi5cqB07dmj8+PEqKSnR6NGjJUkjR450aiIyadIkZWRk6LnnntPOnTs1Y8YMrV+/XhMmTJAkWSwWTZ48WTNnztQHH3ygrVu3auTIkYqJiVFqaqpjnn379mnz5s3at2+fKisrtXnzZm3evFknTpyQJN10003q1KmTfve732nLli1avny5HnvsMd13333y8/NrvDfoIg3oHK2X7uyhKJvzZYBRNn+9dGcPDegc3aTnBwAAAFyNqW32JemFF17QM888o9zcXHXv3l1z5sxRYmKiJOm6665T27ZttWDBAsf4xYsX67HHHtP333+vDh06aPbs2Ro4cKDjccMwlJ6ernnz5qmwsFB9+/bV3//+d1166aWOMXfddZcWLlx4Vi2rVq3SddddJ0nau3evxo8fr08++URBQUEaNWqU/vznP8vbu+a37ZnZZv/nKu2G1uYU6PDxUkUEn7kssD5Xnhpj/k+/OaK7F6yTJG1Jv0m2AJ96mx8AAABoaDXNBqYHNE/mKgHNU3SZsVzHSyu04oFr1CEy2OxyAAAAgBqraTYw9RJHoDZibAGSpIMX2N8NAAAAcFcENLiNmNAz96IdKjxlciUAAABAwyCgwW1Eh7KCBgAAAM9GQIPbiPmxm+NBVtAAAADgoQhocBvRP96DdqiIgAYAAADPRECD24h23IPGJY4AAADwTAQ0uI2fujieErtDAAAAwBMR0OA2on68B630tF2FJ0+bXA0AAABQ/whocBv+Pl4Kb+Yr6cwqGgAAAOBpCGhwK45GIdyHBgAAAA9EQINbia5qtc8KGgAAADwQAQ1uJaZqs2pW0AAAAOCBCGhwK1UraOyFBgAAAE9EQINbiQ7lHjQAAAB4LgIa3EqrUO5BAwAAgOcioMGtVHVxzCsuVaWdzaoBAADgWQhocCsRwX6yWqTTlYbyT5SZXQ4AAABQrwhocCveXlZFhvx4mWMhlzkCAADAsxDQ4HZ+6uRIoxAAAAB4FgIa3E60Yy80VtAAAADgWQhocDutqlrts4IGAAAAD0NAg9ths2oAAAB4KgIa3E5Vq/0f2KwaAAAAHoaABrcT8+Nm1Ye4Bw0AAAAehoAGt1O1gnbkRJnKK+wmVwMAAADUHwIa3E6LIF/5elllGFJeMZc5AgAAwHMQ0OB2rFaLotgLDQAAAB6IgAa35LgPjU6OAAAA8CAENLilGFvVZtWsoAEAAMBzENDglqJ/XEE7SCdHAAAAeBACGtxSVSdHLnEEAACAJyGgwS3FOFbQuMQRAAAAnoOABrfEChoAAAA8EQENbikm9ExAO3bytE6VV5pcDQAAAFA/CGhwSyH+3gry9ZLEKhoAAAA8BwENbslisSg6lFb7AAAA8CwENLitaNuPjUJYQQMAAICHIKDBbVVtVn2IFTQAAAB4CAIa3FbVZtXcgwYAAABPQUCD26rq5HiwiBU0AAAAeAYCGtzWT5c4soIGAAAAz0BAg9uqusTxYOEpGYZhcjUAAADAxSOgwW1VraCVlFequLTC5GoAAACAi0dAg9sK8PVSaKCPJBqFAAAAwDMQ0ODWomm1DwAAAA9CQINbi2GzagAAAHgQAhrcWlWrfVbQAAAA4AkIaHBrjk6OrKABAADAAxDQ4NaqOjkeZC80AAAAeAACGtxa9I/3oB0q4hJHAAAAuD8CGtya4x60olI2qwYAAIDbI6DBrUWG+Mtikcor7DpaUm52OQAAAMBFIaDBrfl6W9WymZ8kOjkCAADA/RHQ4Paif7zMkU6OAAAAcHcENLg9x2bVdHIEAACAmyOgwe1F235qFAIAAAC4MwIa3F5MKCtoAAAA8AwENLg9VtAAAADgKQhocHvRP66gHWIFDQAAAG6OgAa31+rHLo55x8tUaWezagAAALgvAhrcXngzP3lbLaq0Gzp8nMscAQAA4L4IaHB7XlaLIkNoFAIAAAD3R0CDR/ipkyMraAAAAHBfBDR4hJ86ObKCBgAAAPdFQINHiGYFDQAAAB6AgAaPUNXJkRU0AAAAuDMCGjwCm1UDAADAExDQ4BGibXRxBAAAgPsjoMEjxPx4iWP+iXKVVVSaXA0AAABQNwQ0eITmgT7y8z7zj3MulzkCAADATRHQ4BEsFotjFY1OjgAAAHBXBDR4jKrNqunkCAAAAHdFQIPHoJMjAAAA3B0BDR4jhk6OAAAAcHOmB7QXX3xRbdu2lb+/vxITE7V27drzjl+8eLEuv/xy+fv7q0uXLvrwww+dHjcMQ9OnT1d0dLQCAgKUnJys3bt3O40pKCjQiBEjFBISotDQUI0ZM0YnTpxwGrN8+XL16dNHwcHBatmypYYMGaLvv/++Xl4zGka04x40AhoAAADck6kB7e2331ZaWprS09O1ceNGdevWTSkpKTp8+HC147/44gsNHz5cY8aM0aZNm5SamqrU1FRt27bNMWb27NmaM2eO5s6dq+zsbAUFBSklJUWlpT9d9jZixAht375dK1as0LJly7R69WqNGzfO8XhOTo5uueUW3XDDDdq8ebOWL1+u/Px83XbbbQ33ZuCiVe2FxiWOAAAAcFcWwzAMs06emJioXr166YUXXpAk2e12xcbGauLEiXrkkUfOGj906FCVlJRo2bJljmN9+vRR9+7dNXfuXBmGoZiYGE2ZMkUPPvigJKmoqEiRkZFasGCBhg0bph07dqhTp05at26devbsKUnKyMjQwIEDdeDAAcXExOjdd9/V8OHDVVZWJqv1TIZdunSpbrnlFpWVlcnHx6dGr6+4uFg2m01FRUUKCQm5qPcKF/ZN3nHd9NfVCvH31lczUswuBwAAAHCoaTYwbQWtvLxcGzZsUHJy8k/FWK1KTk5WVlZWtc/JyspyGi9JKSkpjvE5OTnKzc11GmOz2ZSYmOgYk5WVpdDQUEc4k6Tk5GRZrVZlZ2dLkhISEmS1WvXqq6+qsrJSRUVFeu2115ScnHzecFZWVqbi4mKnHzSeqhW04tIKlZRVmFwNAAAAUHumBbT8/HxVVlYqMjLS6XhkZKRyc3OrfU5ubu55x1f9eaExERERTo97e3srLCzMMSY+Pl4ff/yxpk2bJj8/P4WGhurAgQN65513zvuaZs2aJZvN5viJjY0973jUr2B/HwX7e0ui1T4AAADck+lNQlxRbm6uxo4dq1GjRmndunX69NNP5evrq9/85jc63xWhU6dOVVFRkeNn//79jVg1JCnGxmbVAAAAcF/eZp04PDxcXl5eysvLczqel5enqKioap8TFRV13vFVf+bl5Sk6OtppTPfu3R1jftmEpKKiQgUFBY7nv/jii7LZbJo9e7ZjzOuvv67Y2FhlZ2erT58+1dbn5+cnPz+/C710NKDoUH/tyjtOJ0cAAAC4JdNW0Hx9fZWQkKDMzEzHMbvdrszMTCUlJVX7nKSkJKfxkrRixQrH+Pj4eEVFRTmNKS4uVnZ2tmNMUlKSCgsLtWHDBseYlStXym63KzExUZJ08uRJR3OQKl5eXo4a4bqqNqs+SCdHAAAAuCFTL3FMS0vT/PnztXDhQu3YsUPjx49XSUmJRo8eLUkaOXKkpk6d6hg/adIkZWRk6LnnntPOnTs1Y8YMrV+/XhMmTJAkWSwWTZ48WTNnztQHH3ygrVu3auTIkYqJiVFqaqokqWPHjhowYIDGjh2rtWvXas2aNZowYYKGDRummJgYSdKgQYO0bt06Pfnkk9q9e7c2btyo0aNHKy4uTldeeWXjvkmolarNqg+xggYAAAA3ZNoljtKZtvlHjhzR9OnTlZubq+7duysjI8PR5GPfvn1OK1lXXXWVFi1apMcee0zTpk1Thw4dtGTJEnXu3Nkx5qGHHlJJSYnGjRunwsJC9e3bVxkZGfL393eMeeONNzRhwgT1799fVqtVQ4YM0Zw5cxyP33DDDVq0aJFmz56t2bNnKzAwUElJScrIyFBAQEAjvDOoq6rNqtkLDQAAAO7I1H3QPB37oDW+L/bk64752WrXMkgrp1xndjkAAACAJDfYBw1oCFVdHA8Vlp634yYAAADgigho8ChRP96Ddup0pYpOnTa5GgAAAKB2CGjwKP4+XmoR5CtJ+oFGIQAAAHAzBDR4nOjQqk6ONAoBAACAeyGgweNU7YV2qIgVNAAAALgXAho8TqtQNqsGAACAeyKgweNEs1k1AAAA3BQBDR4nmhU0AAAAuCkCGjxOzI8raAdZQQMAAICbIaDB41StoOUVl8puZ7NqAAAAuA8CGjxOZLCfrBbpdKWh/BNlZpcDAAAA1BgBDR7H28uqiOAfL3PkPjQAAAC4EQIaPFJMKJ0cAQAA4H4IaPBIdHIEAACAOyKgwSPRyREAAADuiIAGjxRtO7OCdqiIgAYAAAD3QUCDR6q6B+1gIZc4AgAAwH0Q0OCRWEEDAACAOyKgwSPF/Ngk5PDxMp2utJtcDQAAAFAzBDR4pBZBvvL1ssowpLxiLnMEAACAeyCgwSNZrRZF/djJ8RCt9gEAAOAmCGjwWNG02gcAAICbIaDBY1Xdh0YnRwAAALgLAho8VrTjEkdW0AAAAOAeCGjwWNGsoAEAAMDNENDgsVqFsoIGAAAA90JAg8f6abNqVtAAAADgHgho8FgxPwa0gpJynSqvNLkaAAAA4MIIaPBYIQHeCvT1ksRljgAAAHAPBDR4LIvF8rNOjlzmCAAAANdHQINH+2kvNFbQAAAA4PoIaPBoMTQKAQAAgBshoMGjRdNqHwAAAG6EgAaPVrWCxmbVAAAAcAcENHi0qhU07kEDAACAOyCgwaOxWTUAAADcCQENHi3mxxW0E2UVKi49bXI1AAAAwPkR0ODRAn29FRroI0k6xH1oAAAAcHEENHi8qsscD9LJEQAAAC6OgAaPF2P7sdU+K2gAAABwcQQ0eDw6OQIAAMBdENDg8bjEEQAAAO6CgAaPV9XJkUscAQAA4OoIaPB4P+2FxgoaAAAAXBsBDR6vVehPm1UbhmFyNQAAAMC5EdDg8SJD/GWxSGUVdhWUlJtdDgAAAHBOBDR4PF9vq8Kb+UmSDnIfGgAAAFwYAQ1NQtVeaHRyBAAAgCsjoKFJcDQKYS80AAAAuDACGpqEqs2qDxVxiSMAAABcFwENTUJVJ8eDBDQAAAC4MAIamgQucQQAAIA7IKChSeASRwAAALgDAhqahJgfV9Byi0tVaWezagAAALgmAhqahJbBfvK2WlRpN3T4OKtoAAAAcE0ENDQJXlaLIkN+3AuNzaoBAADgoghoaDKibVX3odEoBAAAAK6JgIYmIya0qpMjK2gAAABwTQQ0NBlVnRwPsoIGAAAAF0VAQ5NR1cnxIHuhAQAAwEUR0NBk/HQPGpc4AgAAwDUR0NBkVN2DRhdHAAAAuCoCGpqMqhW0/BNlKquoNLkaAAAA4GwENDQZYUG+8vM+8498XlGZydUAAAAAZyOgocmwWCw/XeZIJ0cAAAC4IAIamhQ2qwYAAIArI6ChSYm20SgEAAAArouAhiYlpmqzavZCAwAAgAsioKFJqVpBYy80AAAAuCICGpoUVtAAAADgyghoaFKqujiyggYAAABXdFEBrbSUf8mFe6nq4lh06rRKyipMrgYAAABwVuuAZrfb9cc//lGtWrVSs2bN9N1330mSHn/8cb3yyiv1XiBQn4L9fRTs5y2JVvsAAABwPbUOaDNnztSCBQs0e/Zs+fr6Oo537txZL7/8cr0WBzSEaMd9aKwAAwAAwLXUOqD961//0rx58zRixAh5eXk5jnfr1k07d+6sdQEvvvii2rZtK39/fyUmJmrt2rXnHb948WJdfvnl8vf3V5cuXfThhx86PW4YhqZPn67o6GgFBAQoOTlZu3fvdhpTUFCgESNGKCQkRKGhoRozZoxOnDhx1jzPPvusLr30Uvn5+alVq1Z66qmnav364Hp+6uTIChoAAABcS60D2g8//KBLLrnkrON2u12nT5+u1Vxvv/220tLSlJ6ero0bN6pbt25KSUnR4cOHqx3/xRdfaPjw4RozZow2bdqk1NRUpaamatu2bY4xs2fP1pw5czR37lxlZ2crKChIKSkpTvfLjRgxQtu3b9eKFSu0bNkyrV69WuPGjXM616RJk/Tyyy/r2Wef1c6dO/XBBx+od+/etXp9cE0xrKABAADARVkMwzBq84SEhAQ98MADuvPOOxUcHKwtW7aoXbt2evLJJ7VixQp99tlnNZ4rMTFRvXr10gsvvCDpTMiLjY3VxIkT9cgjj5w1fujQoSopKdGyZcscx/r06aPu3btr7ty5MgxDMTExmjJlih588EFJUlFRkSIjI7VgwQINGzZMO3bsUKdOnbRu3Tr17NlTkpSRkaGBAwfqwIEDiomJ0Y4dO9S1a1dt27ZNl112WW3eHifFxcWy2WwqKipSSEhInedB/fp/mbv13IpvdHvP1pr9m25mlwMAAIAmoKbZoNYraNOnT9eECRP09NNPy26367333tPYsWP11FNPafr06TWep7y8XBs2bFBycvJPxVitSk5OVlZWVrXPycrKchovSSkpKY7xOTk5ys3NdRpjs9mUmJjoGJOVlaXQ0FBHOJOk5ORkWa1WZWdnS5KWLl2qdu3aadmyZYqPj1fbtm31f//3fyooKDjvayorK1NxcbHTD1xPNK32AQAA4KJqHdBuueUWLV26VP/73/8UFBSk6dOna8eOHVq6dKluvPHGGs+Tn5+vyspKRUZGOh2PjIxUbm5utc/Jzc097/iqPy80JiIiwulxb29vhYWFOcZ899132rt3rxYvXqx//etfWrBggTZs2KDf/OY3531Ns2bNks1mc/zExsaedzzMEWNjs2oAAAC4Ju+6PKlfv35asWJFfdfiMux2u8rKyvSvf/1Ll156qSTplVdeUUJCgnbt2nXOyx6nTp2qtLQ0x+/FxcWENBdUtYJ2sLBUhmHIYrGYXBEAAABwRq1X0Nq1a6ejR4+edbywsFDt2rWr8Tzh4eHy8vJSXl6e0/G8vDxFRUVV+5yoqKjzjq/680JjftmEpKKiQgUFBY4x0dHR8vb2doQzSerYsaMkad++fed8TX5+fgoJCXH6geup2qz61OlKFZ2qXWMbAAAAoCHVOqB9//33qqysPOt4WVmZfvjhhxrP4+vrq4SEBGVmZjqO2e12ZWZmKikpqdrnJCUlOY2XpBUrVjjGx8fHKyoqymlMcXGxsrOzHWOSkpJUWFioDRs2OMasXLlSdrtdiYmJkqSrr75aFRUV2rNnj2PMN998I0mKi4ur8WuEa/L38VJY0Jk9/OjkCAAAAFdS40scP/jgA8ffly9fLpvN5vi9srJSmZmZatu2ba1OnpaWplGjRqlnz57q3bu3nn/+eZWUlGj06NGSpJEjR6pVq1aaNWuWpDOt76+99lo999xzGjRokN566y2tX79e8+bNkyRZLBZNnjxZM2fOVIcOHRQfH6/HH39cMTExSk1NlXRmJWzAgAEaO3as5s6dq9OnT2vChAkaNmyYYmJiJJ1pGtKjRw/dfffdev7552W323XffffpxhtvdFpVg/uKCfVXQUm5DhWdUqcYVjoBAADgGmoc0KoCjsVi0ahRo5we8/HxUdu2bfXcc8/V6uRDhw7VkSNHNH36dOXm5qp79+7KyMhwNPnYt2+frNafFvmuuuoqLVq0SI899pimTZumDh06aMmSJercubNjzEMPPaSSkhKNGzdOhYWF6tu3rzIyMuTv7+8Y88Ybb2jChAnq37+/rFarhgwZojlz5jget1qtWrp0qSZOnKhrrrlGQUFBuvnmm2v9+uC6om0B2vZDsQ7SyREAAAAupNb7oMXHx2vdunUKDw9vqJo8Bvugua7097dpYdZe3Xtdez004HKzywEAAICHq2k2qHUXx5ycnIsqDHAFP3VypNU+AAAAXEed2uyXlJTo008/1b59+1ReXu702P33318vhQENqaqTI5c4AgAAwJXUOqBt2rRJAwcO1MmTJ1VSUqKwsDDl5+crMDBQERERBDS4hZgfV9AOFbGCBgAAANdR6zb7DzzwgAYPHqxjx44pICBAX375pfbu3auEhAQ9++yzDVEjUO+qVtByi0plt9fqNkwAAACgwdQ6oG3evFlTpkyR1WqVl5eXysrKFBsbq9mzZ2vatGkNUSNQ7yJD/GW1SKcrDeWXlJldDgAAACCpDgHNx8fH0fo+IiJC+/btkyTZbDbt37+/fqsDGoiPl1URwWdW0Q6xWTUAAABcRK3vQbvyyiu1bt06dejQQddee62mT5+u/Px8vfbaa077kQGuLjrUX7nFpTpYeErdYkPNLgcAAACo/Qran/70J0VHR0uSnnrqKTVv3lzjx4/XkSNH9I9//KPeCwQaSoztx1b7dHIEAACAi6j1ClrPnj0df4+IiFBGRka9FgQ0lqpGIYfYCw0AAAAuotYraOeyceNG/epXv6qv6YAGF+1otc8KGgAAAFxDrQLa8uXL9eCDD2ratGn67rvvJEk7d+5UamqqevXqJbvd3iBFAg2hVWjVZtWsoAEAAMA11PgSx1deeUVjx45VWFiYjh07ppdffll/+ctfNHHiRA0dOlTbtm1Tx44dG7JWoF5F/3gPGl0cAQAA4CpqvIL2t7/9TU8//bTy8/P1zjvvKD8/X3//+9+1detWzZ07l3AGtxP94wra4eOlOl3J6i8AAADMV+OAtmfPHv32t7+VJN12223y9vbWM888o9atWzdYcUBDCg/yk4+XRXZDyitmFQ0AAADmq3FAO3XqlAIDAyVJFotFfn5+jnb7gDuyWi2KqurkSKMQAAAAuIBatdl/+eWX1axZM0lSRUWFFixYoPDwcKcx999/f/1VBzSwaFuA9hec0kFa7QMAAMAF1DigtWnTRvPnz3f8HhUVpddee81pjMViIaDBrbSi1T4AAABcSI0D2vfff9+AZQDmYLNqAAAAuJJ626gacEdVm1UfZAUNAAAALoCAhiYt5scVNO5BAwAAgCsgoKFJc2xWzQoaAAAAXAABDU1azI+bVReUlKv0dKXJ1QAAAKCpI6ChSbMF+CjAx0sSq2gAAAAwX632QZOk4uLiao9XbV7t6+t70UUBjcVisSgm1F97jpToUOEpxYcHmV0SAAAAmrBar6CFhoaqefPmZ/2EhoYqICBAcXFxSk9Pl91ub4h6gXoXQydHAAAAuIhar6AtWLBAjz76qO666y717t1bkrR27VotXLhQjz32mI4cOaJnn31Wfn5+mjZtWr0XDNQ39kIDAACAq6h1QFu4cKGee+453X777Y5jgwcPVpcuXfSPf/xDmZmZatOmjZ566ikCGtxCVSfHg0UENAAAAJir1pc4fvHFF7ryyivPOn7llVcqKytLktS3b1/t27fv4qsDGkFVJ8eDhVziCAAAAHPVOqDFxsbqlVdeOev4K6+8otjYWEnS0aNH1bx584uvDmgEP+2FxgoaAAAAzFXrSxyfffZZ/fa3v9VHH32kXr16SZLWr1+vnTt36t1335UkrVu3TkOHDq3fSoEGUtUk5BAraAAAADBZrQPar3/9a+3cuVP/+Mc/9M0330iSbr75Zi1ZskRt27aVJI0fP75eiwQaUtUljsfLKnS89LSC/X1MrggAAABNVa0DmiTFx8frz3/+c33XApgi0NdbIf7eKi6t0KLsferaOlS948PkZbXU2zkq7YbW5hTo8PFSRQT7u938jXGOxngNAAAArq5OAa2wsFBr167V4cOHz9rvbOTIkfVSGNBYMrYd0snySknSrI92SjrTej99cCcN6BxdL/M/sfRrHfrZPmvuNH9jnKMxXgMAAIA7sBiGYdTmCUuXLtWIESN04sQJhYSEyGL56b9wWywWFRQU1HuR7qq4uFg2m01FRUUKCQkxuxxUI2PbIY1/faN++SWo+qf6pTt7XFRAcPf5G+McjfEaAAAAzFbTbFDrLo5TpkzR3XffrRMnTqiwsFDHjh1z/BDO4E4q7YaeWPr1WcFAkuPYE0u/VqW9Vv8Nw2Pmb4xzNMZrAAAAcCe1XkELCgrS1q1b1a5du4aqyWOwgubasvYc1fD5X15wnNUip5XimjIMQzXJFa46f2Oco6bzvzm2j5Lat6j1/AAAAK6iptmg1vegpaSkaP369QQ0uL3Dx2vWVt9uSKrdf8eoFXefvzHOUdPPCgAAwN3VOqANGjRIf/jDH/T111+rS5cu8vFxbkn+61//ut6KAxpSRLB/jca9NKKHEuJqv/H6hr3HNP6NjW47f2Oco6bz1/SzAgAAcHe1Dmhjx46VJD355JNnPWaxWFRZWXnxVQGNoHd8mKJt/sotKq32HiiLpCibv266IqpO7d5vuiLKredvjHPUdP7e8WG1nhsAAMAd1bpJiN1uP+cP4QzuxMtqUfrgTpJ+6hhYper39MGd6hxu3H3+xjhHY7wGAAAAd1LrgAZ4kgGdo/XSnT0UZXO+hC7K5l8v7d3dff7GOEdjvAYAAAB3UaMujnPmzNG4cePk7++vOXPmnHfs/fffX2/FuTu6OLqPSruhtTkFOny8VBHBZy6pq89VG3efvzHOUWk39Pm3RzT61XWyG9InD16ntuFB9TY/AACAmWqaDWoU0OLj47V+/Xq1aNFC8fHx557MYtF3331Xt4o9EAENqL1bXlyjLfsL9bdh3XVL91ZmlwMAAFAv6rXNfk5OTrV/B4D61jOuubbsL9SGvccIaAAAoMnhHjQALqWqXf/674+ZXAkAAEDjq3Wb/crKSi1YsECZmZk6fPiw7Ha70+MrV66st+IAND1VAW1nbrFOlFWomV+t/2cKAADAbdX633wmTZqkBQsWaNCgQercubMsFtpfA6g/kSH+at08QAeOndKW/YW6+pJws0sCAABoNLUOaG+99ZbeeecdDRw4sCHqAQAlxDXXgWOntP77YwQ0AADQpNT6HjRfX19dcsklDVELAEj66TLHDfu4Dw0AADQttQ5oU6ZM0d/+9jfVoDs/ANRJjzZnAtqmvcdkt/O/NQAAoOmo9SWOn3/+uVatWqWPPvpIV1xxhXx8fJwef++99+qtOABN0+VRwQry9dLxsgrtPnxCl0UFm10SAABAo6h1QAsNDdWtt97aELUAgCTJ28uq7m1Ctebbo1q/t4CABgAAmoxaBbSKigpdf/31uummmxQVFdVQNQGAEto015pvj2rD3mMakRhndjkAAACNolb3oHl7e+uee+5RWVlZQ9UDAJKkhLZhkqSNe2kUAgAAmo5aNwnp3bu3Nm3a1BC1AIBD99hQWSzS90dP6shx/qMQAABoGmp9D9q9996rKVOm6MCBA0pISFBQUJDT4127dq234gA0XbYAH10aEaxdece1cd8xpVzBZdUAAMDz1TqgDRs2TJJ0//33O45ZLBYZhiGLxaLKysr6qw5Ak5bQtvmZgLaXgAYAAJqGWge0nJychqgDAM6S0Ka5FmXv03ruQwMAAE1ErQNaXBzd1AA0joS4MxtWbz1QpLKKSvl5e5lcEQAAQMOqdUCr8vXXX2vfvn0qLy93Ov7rX//6oosCAEmKaxGo8Ga+yj9Rrm0/FDsCGwAAgKeqdUD77rvvdOutt2rr1q2Oe8+kM/ehSeIeNAD1xmKxqEeb5vr46zxt2FtAQAMAAB6v1m32J02apPj4eB0+fFiBgYHavn27Vq9erZ49e+qTTz5pgBIBNGVVoWwD96EBAIAmoNYraFlZWVq5cqXCw8NltVpltVrVt29fzZo1S/fffz97pAGoVz8FtEJHt1gAAABPVesVtMrKSgUHB0uSwsPDdfDgQUlnmofs2rWrfqsD0OR1bmWTr5dV+SfKtK/gpNnlAAAANKhaB7TOnTtry5YtkqTExETNnj1ba9as0ZNPPql27drVe4EAmjZ/Hy91bhUiicscAQCA56t1QHvsscdkt9slSU8++aRycnLUr18/ffjhh5ozZ069FwgA3IcGAACailrfg5aSkuL4+yWXXKKdO3eqoKBAzZs3594QAA0iIS5M8z/LIaABAACPV+sVtCrffvutli9frlOnTiksLKw+awIAJz3iQiVJu/KOq7j0tLnFAAAANKBaB7SjR4+qf//+uvTSSzVw4EAdOnRIkjRmzBhNmTKl3gsEgIhgf7UJC5RhSJv3FZpdDgAAQIOpdUB74IEH5OPjo3379ikwMNBxfOjQocrIyKjX4gCgSk/uQwMAAE1ArQPaxx9/rKefflqtW7d2Ot6hQwft3bu33goDgJ/rQUADAABNQK0DWklJidPKWZWCggL5+fnVS1EA8EtVnRw37TumSrthcjUAAAANo9YBrV+/fvrXv/7l+N1ischut2v27Nm6/vrr67U4AKhyaWSwgv28VVJeqV25x80uBwAAoEHUus3+7Nmz1b9/f61fv17l5eV66KGHtH37dhUUFGjNmjUNUSMAyMtqUfc2ofpsd7427C1Qp5gQs0sCAACod7VeQevcubO++eYb9e3bV7fccotKSkp02223adOmTWrfvn1D1AgAktiwGgAAeL467YNms9n06KOP6p133tGHH36omTNnqrKyUuPGjatTES+++KLatm0rf39/JSYmau3atecdv3jxYl1++eXy9/dXly5d9OGHHzo9bhiGpk+frujoaAUEBCg5OVm7d+92GlNQUKARI0YoJCREoaGhGjNmjE6cOFHt+b799lsFBwcrNDS0Tq8PQP1wBLR9BDQAAOCZ6rxR9S8dPXpUr7zySq2f9/bbbystLU3p6enauHGjunXrppSUFB0+fLja8V988YWGDx+uMWPGaNOmTUpNTVVqaqq2bdvmGDN79mzNmTNHc+fOVXZ2toKCgpSSkqLS0lLHmBEjRmj79u1asWKFli1bptWrV1cbME+fPq3hw4erX79+tX5tAOpX99hQWS3S/oJTOlxceuEnAAAAuBmLYRj10g5ty5Yt6tGjhyorK2v1vMTERPXq1UsvvPCCJMlutys2NlYTJ07UI488ctb4oUOHqqSkRMuWLXMc69Onj7p37665c+fKMAzFxMRoypQpevDBByVJRUVFioyM1IIFCzRs2DDt2LFDnTp10rp169SzZ09JUkZGhgYOHKgDBw4oJibGMffDDz+sgwcPqn///po8ebIKCwtr/NqKi4tls9lUVFSkkBDulwHqw81/+0w7DhXrpRE9dHOXaLPLAQAAqJGaZoN6W0Gri/Lycm3YsEHJycmOY1arVcnJycrKyqr2OVlZWU7jJSklJcUxPicnR7m5uU5jbDabEhMTHWOysrIUGhrqCGeSlJycLKvVquzsbMexlStXavHixXrxxRdr9HrKyspUXFzs9AOgfiXEhUriPjQAAOCZTA1o+fn5qqysVGRkpNPxyMhI5ebmVvuc3Nzc846v+vNCYyIiIpwe9/b2VlhYmGPM0aNHddddd2nBggU1Xv2aNWuWbDab4yc2NrZGzwNQcz3jwiRJ6wloAADAA9W4zf5tt9123sdrc+mfOxg7dqzuuOMOXXPNNTV+ztSpU5WWlub4vbi4mJAG1LOqRiHbDxap9HSl/H28TK4IAACg/tQ4oNlstgs+PnLkyFqdPDw8XF5eXsrLy3M6npeXp6ioqGqfExUVdd7xVX/m5eUpOjraaUz37t0dY37ZhKSiokIFBQWO569cuVIffPCBnn32WUlnOkPa7XZ5e3tr3rx5uvvuu8+qzc/PT35+fjV9+QDqoHXzALUM9tOR42Xa+kORerUNM7skAACAelPjgPbqq6/W+8l9fX2VkJCgzMxMpaamSjrTJCQzM1MTJkyo9jlJSUnKzMzU5MmTHcdWrFihpKQkSVJ8fLyioqKUmZnpCGTFxcXKzs7W+PHjHXMUFhZqw4YNSkhIkHQmkNntdiUmJko6c5/azxuevP/++3r66af1xRdfqFWrVvX5NgCoBYvFop5xzfXRtlyt//4YAQ0AAHiUGge0hpKWlqZRo0apZ8+e6t27t55//nmVlJRo9OjRkqSRI0eqVatWmjVrliRp0qRJuvbaa/Xcc89p0KBBeuutt7R+/XrNmzdP0pl/eZs8ebJmzpypDh06KD4+Xo8//rhiYmIcIbBjx44aMGCAxo4dq7lz5+r06dOaMGGChg0b5ujg2LFjR6c6169fL6vVqs6dOzfSOwPgXBJ+DGg0CgEAAJ7G9IA2dOhQHTlyRNOnT1dubq66d++ujIwMR5OPffv2yWr9qZfJVVddpUWLFumxxx7TtGnT1KFDBy1ZssQpOD300EMqKSnRuHHjVFhYqL59+yojI0P+/v6OMW+88YYmTJig/v37y2q1asiQIZozZ07jvXAAddbjx/vQNu47JsMwZLFYTK4IAACgftTbPmg4G/ugAQ2jrKJSXWZ8rPIKu1ZOuVbtWjYzuyQAAIDzcot90ACgLvy8vdSt9ZnGRVzmCAAAPAkBDYBb+vlljgAAAJ6CgAbALSW0ORPQWEEDAACehIAGwC1VbVj9Td4JFZ08bXI1AAAA9YOABsAttWjmp/jwIEnSxv2sogEAAM9AQAPgtnr8eJnjRi5zBAAAHoKABsBt9Wx7JqCt/56ABgAAPAMBDYDbqroPbfP+QlVU2k2uBgAA4OIR0AC4rUtaNlOIv7dOna7UztzjZpcDAABw0QhoANyW1Wpx7Ie2/vsCk6sBAAC4eAQ0AG7NsR/avkJzCwEAAKgHBDQAbq3qPjQ6OQIAAE9AQAPg1rrFhsrLatEPhad0qOiU2eUAAABcFAIaALcW5OetjtHBkqQNrKIBAAA3R0AD4PYc96ER0AAAgJsjoAFwe1WdHAloAADA3RHQALi9nm3DJEnbDxbrZHmFydUAAADUHQENgNuLsfkrKsRflXZDXx0oMrscAACAOiOgAXB7FovF0W6fyxwBAIA7I6AB8AgENAAA4AkIaAA8gmPD6n3HZLcbJlcDAABQNwQ0AB6hU0yI/H2sKjx5Wt/ll5hdDgAAQJ0Q0AB4BB8vq7q1DpUkbdhbYG4xAAAAdURAA+AxuA8NAAC4OwIaAI9BQAMAAO6OgAbAY/Rocyag7TlSomMl5SZXAwAAUHsENAAeo3mQr9q3DJJ0ppsjAACAuyGgAfAoXOYIAADcGQENgEepCmjrCWgAAMANEdAAeJSEuDBJ0pb9hTpdaTe5GgAAgNohoAHwKO3CgxQa6KOyCru+PlhsdjkAAAC1QkAD4FGsVoujmyOXOQIAAHdDQAPgcaruQ9tIQAMAAG6GgAbA4/zUKKRAhmGYXA0AAEDNEdAAeJxurUPlbbUor7hMPxSeMrscAACAGiOgAfA4Ab5euiImRBL7oQEAAPdCQAPgkXpwHxoAAHBDBDQAHokNqwEAgDsioAHwSFUBbcehYpWUVZhcDQAAQM0Q0AB4pGhbgFqFBshuSFv2F5pdDgAAQI0Q0AB4rKr70GgUAgAA3AUBDYDHSmgTKon70AAAgPsgoAHwWD3bhkmSNu47JrudDasBAIDrI6AB8FiXRwUrwMdLx0sr9O2RE2aXAwAAcEEENAAey9vLqu6xoZKk9d9zmSMAAHB9BDQAHq1nWxqFAAAA90FAA+DRqjo5btxHQAMAAK6PgAbAo/WIPRPQcvJLlH+izORqAAAAzo+ABsCj2QJ91CGimSRpI5c5AgAAF0dAA+DxHPehcZkjAABwcQQ0AB6vR5sfAxqdHAEAgIsjoAHweAk/Ngr56ocilVVUmlwNAADAuXmbXQAANLT48CA1D/TRsZOnNffTPerdtoV6x4fJy2qpt3NU2g2tzSnQ4eOligj2r/f5G+Mc7j4/AACegIAGwOMt356rk+VnVs7+umK3pN2KtvkrfXAnDegcfdHzZ2w7pCeWfq1DRaWOY/U5f2Ocw93nBwDAU1gMwzDMLsJTFRcXy2azqaioSCEhIWaXAzRJGdsOafzrG/XL/6GrWrd56c4eFxUQGnr+xjiHu88PAIA7qGk2YAUNgMeqtBt6YunXZwUDSY5jU9/bKrvdkLUOl9rZ7YamLdnWYPM3xjnMnt8i6YmlX+vGTlFc7ggAgFhBa1CsoAHmytpzVMPnf2l2GaiBN8f2UVL7FmaXAQBAg2EFDUCTd/h46YUH6UwTkRZBvrWe/2hJuXLySxps/sY4h6vMX9PPCgAAT0dAA+CxIoL9azTuT7d2qdPqTU1X6Oo6f2Ocw1Xmr+lnBQCAp2MfNAAeq3d8mKJt/jrXnU0Wnekk2Ds+zCXnb4xzuPv8AAB4GgIaAI/lZbUofXAnSTorIFT9nj64U52bUzT0/I1xDjPnl840CrnY9wgAAE9CQAPg0QZ0jtZLd/ZQlM35Eroom3+9tHdv6Pkb4xxmzS9JV7VvQYt9AAB+hi6ODYgujoDrqLQbWptToMPHSxURfOaSuvpctWno+RvjHI05f+HJcqV/8LV8vaz6/OHrFRHCPWgAAM9W02xAQGtABDQAOLchL32hDXuP6d7r2uuhAZebXQ4AAA2qptmASxwBAKYY26+dJOn1L/eqpKzC5GoAAHANBDQAgClu7BSp+PAgFZdW6O11+80uBwAAl0BAAwCYwstq0f/1i5ckvfJ5jioq7SZXBACA+QhoAADTDOnRWi2CfPVD4Sl9uC3X7HIAADAdAQ0AYBp/Hy+NTGorSZq3eo/oWwUAaOoIaAAAU/0uKU7+PlZt+6FYWd8dNbscAABMRUADAJgqLMhXv02IlSTNW/2dydUAAGAuAhoAwHT/1y9eFov0ya4j2pV73OxyAAAwDQENAGC6uBZBGnBFlCRp/mesogEAmi4CGgDAJYy75szG1e9v/kG5RaUmVwMAgDlcIqC9+OKLatu2rfz9/ZWYmKi1a9eed/zixYt1+eWXy9/fX126dNGHH37o9LhhGJo+fbqio6MVEBCg5ORk7d6922lMQUGBRowYoZCQEIWGhmrMmDE6ceKE4/FPPvlEt9xyi6KjoxUUFKTu3bvrjTfeqL8XDQBwcmWb5urdNkynKw29+kWO2eUAAGAK0wPa22+/rbS0NKWnp2vjxo3q1q2bUlJSdPjw4WrHf/HFFxo+fLjGjBmjTZs2KTU1Vampqdq2bZtjzOzZszVnzhzNnTtX2dnZCgoKUkpKikpLf/ovsiNGjND27du1YsUKLVu2TKtXr9a4ceOcztO1a1f9+9//1ldffaXRo0dr5MiRWrZsWcO9GQDQxI39cRVt0Zf7dLz0tMnVAADQ+CyGyZvOJCYmqlevXnrhhRckSXa7XbGxsZo4caIeeeSRs8YPHTpUJSUlTkGpT58+6t69u+bOnSvDMBQTE6MpU6bowQcflCQVFRUpMjJSCxYs0LBhw7Rjxw516tRJ69atU8+ePSVJGRkZGjhwoA4cOKCYmJhqax00aJAiIyP1z3/+s0avrbi4WDabTUVFRQoJCanV+wIATZHdbij5r5/quyMlemxQR/1fv3ZmlwQAQL2oaTYwdQWtvLxcGzZsUHJysuOY1WpVcnKysrKyqn1OVlaW03hJSklJcYzPyclRbm6u0xibzabExETHmKysLIWGhjrCmSQlJyfLarUqOzv7nPUWFRUpLCzsnI+XlZWpuLjY6QcAUHNWq0Vjfwxl//w8R6cr7SZXBABA4zI1oOXn56uyslKRkZFOxyMjI5Wbm1vtc3Jzc887vurPC42JiIhwetzb21thYWHnPO8777yjdevWafTo0ed8PbNmzZLNZnP8xMbGnnMsAKB6t17ZSuHN/HSwqFT//eqQ2eUAANCoTL8HzR2sWrVKo0eP1vz583XFFVecc9zUqVNVVFTk+Nm/f38jVgkAnsHfx0t3XRUnSfrH6u9k8pX4AAA0KlMDWnh4uLy8vJSXl+d0PC8vT1FRUdU+Jyoq6rzjq/680JhfNiGpqKhQQUHBWef99NNPNXjwYP31r3/VyJEjz/t6/Pz8FBIS4vQDAKi9EYlxCvDx0o5DxVrz7VGzywEAoNGYGtB8fX2VkJCgzMxMxzG73a7MzEwlJSVV+5ykpCSn8ZK0YsUKx/j4+HhFRUU5jSkuLlZ2drZjTFJSkgoLC7VhwwbHmJUrV8putysxMdFx7JNPPtGgQYP09NNPO3V4BAA0rOZBvhra68xl4v9YvcfkagAAaDymX+KYlpam+fPna+HChdqxY4fGjx+vkpISx71eI0eO1NSpUx3jJ02apIyMDD333HPauXOnZsyYofXr12vChAmSJIvFosmTJ2vmzJn64IMPtHXrVo0cOVIxMTFKTU2VJHXs2FEDBgzQ2LFjtXbtWq1Zs0YTJkzQsGHDHB0cV61apUGDBun+++/XkCFDlJubq9zcXBUUFDTuGwQATdSYvvGyWqTPdufr64M0XQIANA2mB7ShQ4fq2Wef1fTp09W9e3dt3rxZGRkZjiYf+/bt06FDP90kftVVV2nRokWaN2+eunXrpnfffVdLlixR586dHWMeeughTZw4UePGjVOvXr104sQJZWRkyN/f3zHmjTfe0OWXX67+/ftr4MCB6tu3r+bNm+d4fOHChTp58qRmzZql6Ohox89tt93WCO8KACA2LFA3d4mWJL382XcmVwMAQOMwfR80T8Y+aABwcb46UKhfv7BG3laLVj90vWJCA8wuCQCAOnGLfdAAADifrq1DlRgfpgq7oQVffG92OQAANDgCGgDApf3+2jMbVy/K3qfi0tMmVwMAQMMioAEAXNp1l0aoQ0QznSir0JvZ+8wuBwCABkVAAwC4NKvVorH9zqyivbrme5VX2E2uCACAhkNAAwC4vFuujFHLYD/lFpdq6ZaDZpcDAECDIaABAFyen7eX7rqqrSRp/mffiQbEAABPRUADALiFOxPjFOjrpZ25x7V6d77Z5QAA0CAIaAAAt2AL9NGwXm0kSfNW7zG5GgAAGgYBDQDgNu7u21ZeVovWfHtU234oMrscAADqHQENAOA2WjcP1KAu0ZLO3IsGAICnIaABANzKuGvOtNxf9tUhHTh20uRqAACoXwQ0AIBb6dzKpqvat1Cl3dCra743uxwAAOoVAQ0A4HaqVtHeWrtPRadOm1wNAAD1h4AGAHA7117aUpdFBqukvFKLsveZXQ4AAPWGgAYAcDsWi0Vjf1xFe3VNjsoqKk2uCACA+kFAAwC4pV93i1FkiJ8OHy/T+5sPml0OAAD1goAGAHBLvt5Wjb46XpI0f/V3MgzD5IoAALh4BDQAgNu6I7GNmvl5a/fhE/pk1xGzywEA4KIR0AAAbivE30fDesVKkv6xeo/J1QAAcPEIaAAAt3Z333h5Wy368rsCfXWg0OxyAAC4KN5mFwAAwMWICQ3Q4G4x+s+mH/SPT/fozj5tdfh4qSKC/dU7PkxeVku9navSbmhtTgHzm3wOAPBkBDQAgNsb26+d/rPpB/13a67+uzXXcTza5q/0wZ00oHP0RZ8jY9shPbH0ax0qKmV+E88BAJ6OSxwBAG5vX0FJtcdzi0o1/vWNyth26KLmz9h2SONf3+gUPJi/8c8BAE0BAQ0A4NYq7YaeWPp1tY9VNd5/YunXqrTXrQ1/1fzVPZv5G+8cANBUWAw2jmkwxcXFstlsKioqUkhIiNnlAIBHytpzVMPnf3nBcVaLZLHU/l4owzBUk1zRVOevzTneHNtHSe1b1OkcAODuapoNuAcNAODWDh8vvfAg6UyAaMD/Jsn8F1bTzwoAmjICGgDArUUE+9do3Esjeighrnmt59+w95jGv7GR+evhHDX9rACgKSOgAQDcWu/4MEXb/JVbVFrtPVAWSVE2f910RVSd2r3fdEUU81/kOaQz3Rx7x4fVaX4AaEpoEgIAcGteVovSB3eSdCZs/FzV7+mDO9U5fDD/xZ2jyuCu0eyHBgA1QEADALi9AZ2j9dKdPRRlc76ELsrmr5fu7HHRe3Axf93PEejrJUla8MVefbEn/6LPAwCeji6ODYgujgDQuCrthtbmFOjw8VJFBJ+5pK4+V22Yv/bnSIhrrvvf3KSM7blq5uett8b1UedWtno9JwC4g5pmAwJaAyKgAQAglZ6u1F2vrtWX3xUovJmv3r3nKrUNDzK7LABoVDXNBlziCAAAGpS/j5fmjeypjtEhyj9RrpH/XEvLfQA4BwIaAABocCH+Plp4dy+1CQvUvoKTGvXPdSouPW12WQDgcghoAACgUUQE++u1Mb0V3sxPOw4Va+zC9So9XWl2WQDgUghoAACg0cS1CNKC0b3UzM9b2TkFmvTWJlXauR0eAKoQ0AAAQKPq3MqmeSMT5Otl1fLteXpsyVbRswwAziCgAQCARndV+3DNGd5dVov05tr9+suKb8wuCQBcAgENAACYYkDnaM1M7SJJ+n8rv9WCNTkmVwQA5iOgAQAA09yR2EZpN14qSXpi2df6YMtBkysCAHMR0AAAgKkm3nCJRibFyTCkKe9s1me7j5hdEgCYhoAGAABMZbFYNGPwFfpV12idrjT0+9c2aMv+QrPLAgBTENAAAIDprFaLnru9m/peEq6T5ZUavWCd9hw5YXZZANDoCGgAAMAl+Hl7ae7vEtS1tU0FJeUa+cpa5RWXml0WADQqAhoAAHAZzfy89epdvRQfHqQfCk9p5CtrVXTytNllAUCjIaABAACX0qKZn/51d29FBPtpV95xjVm4TqfKK80uCwAaBQENAAC4nNiwQP1rTG+F+Htr/d5jmrBooyoq7WaXBQANzmIYhmF2EZ6quLhYNptNRUVFCgkJMbscAADczrrvC3Tny9kqq7DrtwmtNeu2Llr3/TEdPl6qiGB/9Y4Pk5fVUm/nq7QbWptT0GDzN8Y53H1+wFPVNBsQ0BoQAQ0AgIu34us83fP6BlXaDQX5eqnkZ5c7Rtv8lT64kwZ0jr7o82RsO6Qnln6tQ0U/NSapz/kb4xzuPj/gyWqaDbjEEQAAuLQbO0XqjsQ2kuQUziQpt6hU41/fqIxthy7qHBnbDmn86xudgkd9zt8Y53D3+QGc4W12AQAAAOdTaTf0v6/zqn2s6jKgqe9tld1uyFqHS+3sdkPTlmxTdZcU1cf8jXEOs+e3SHpi6de6sVMUlzsCF4lLHBsQlzgCAHDxsvYc1fD5X5pdBmrgzbF9lNS+hdllAC6pptmAFTQAAODSDh+v2WbV8eFBahHkW+v5j5aUKye/pMHmb4xzuMr8Nf2sAJwbAQ0AALi0iGD/Go37061d6rR6U9MVurrO3xjncJX5l205pKsvCVd4M79anwPAGTQJAQAALq13fJiibf46151NFp3pJNg7Pswl52+Mc5g9f5UVO/J03TOf6IWVu9lcHKgjAhoAAHBpXlaL0gd3kqSzAkLV7+mDO9W5OUVDz98Y5zB7foukyckd1KWVTSfKKvTsx9/oumdX6Z11+1Vpp90BUBs0CWlANAkBAKD+eMIeX+7+Gi40v91uaOlXB/XM8l06cOyUJOmyyGA9MvByXXdpS1ksdHhE08VG1S6AgAYAQP2qtBtam1Ogw8dLFRF85pK9+mzr3tDzN8Y5XGH+sopKvZa1V/9v5bcqOnVaknRV+xaaNrCjOrey1VstgDshoLkAAhoAAGjKik6e1ouffKsFa75XeaVdkpTaPUYPplym1s0DTa4OaFwENBdAQAMAAJD2F5zUcx/v0pLNByVJvl5W3XV1W9133SWyBfqYXB3QOAhoLoCABgAA8JOtB4o066Md+mLPUUmSLcBHE2+4RL9LipOft5djnCtcptmU52+sczQ1BDQXQEADAABwZhiGPvnmiP784U7tyjsuSWrdPEB/SLlMg7vG6OOvcz26kYqrz99Y52iKCGgugIAGAABQvUq7oX9vOKDnVuxSXnGZJCmuRaD2Hj151tiqdZuX7uxxUQEhY9shjX99o375L7/M37jnaKpqmg3YBw0AAACNzstq0e29YrXqwev04E2XKtDHWm04k+QIC08s/brO+6pV2g09sfTrs4IH8zfuOXBhrKA1IFbQAAAAaiZj2yHd8/rGC46zWlSn/dQMw1BNckVTnb8253hzbB8ltW9Rp3M0ZTXNBt6NWBMAAABQrbIKe43G2Q1JDbi+wPwX9s76/Qpv5qtLIpqx+XgDIKABAADAdBHB/jUa99KIHkqIa17r+TfsPabxb1x4ha6pzl+bc/xn0w/6z6YfFBXir34dwnXNpS119SXhCgvyrdN54YyABgAAANP1jg9TtM1fuUWl1d4DZZEUZfPXTVdE1and+01XRDH/RZ5DkkL8vdW1tU3rvj+m3OJSLd5wQIs3HJDFInVpZTsT2Dq01JVtmsvX+9ztLjxhK4KGwj1oDYh70AAAAGquqoOgJKeAUN9dEJn/4s9RerpSa3MK9NnuI/psd7525h53mifI10tJ7VuoX4eWuubSlmrbItBxOaQnbEVQF7TZdwEENAAAgNpx9395d/f563qOvOJSfbY7X5/tPqLPd+fraEm50+OtmweoX4eWCvbz0vzPctx6K4K6IqC5AAIaAABA7bn75W/uPv/FnsNuN/T1oWKt3n1En32Tr/V7C3S6smaRo3mgj55K7SxrHV6P3W5o2pJtKjx5utrHqy4D/fzhG0y53JGA5gIIaAAAAGjqTpZXKPu7Ar29fp8ytuWZXY5p2wTQZh8AAACA6QJ9vXX95REqLj1do4AWHx6kFnXoCHm0pFw5+SUXHHf4eOkFx5iJgAYAAACgwdV0K4U/3dqlTitcWXuOavj8L+utDrOcu/clAAAAANSTqq0UznX3l0VnmpH0jg9zyfkbi0sEtBdffFFt27aVv7+/EhMTtXbt2vOOX7x4sS6//HL5+/urS5cu+vDDD50eNwxD06dPV3R0tAICApScnKzdu3c7jSkoKNCIESMUEhKi0NBQjRkzRidOnHAa89VXX6lfv37y9/dXbGysZs+eXT8vGAAAAGhivKwWpQ/uJElnhaiq39MHd6pzA4+Gnr+xmB7Q3n77baWlpSk9PV0bN25Ut27dlJKSosOHD1c7/osvvtDw4cM1ZswYbdq0SampqUpNTdW2bdscY2bPnq05c+Zo7ty5ys7OVlBQkFJSUlRa+tP1piNGjND27du1YsUKLVu2TKtXr9a4ceMcjxcXF+umm25SXFycNmzYoGeeeUYzZszQvHnzGu7NAAAAADzYgM7ReunOHoqyOV9mGGXzr5cW+A09f2MwvYtjYmKievXqpRdeeEGSZLfbFRsbq4kTJ+qRRx45a/zQoUNVUlKiZcuWOY716dNH3bt319y5c2UYhmJiYjRlyhQ9+OCDkqSioiJFRkZqwYIFGjZsmHbs2KFOnTpp3bp16tmzpyQpIyNDAwcO1IEDBxQTE6OXXnpJjz76qHJzc+Xre+YmxUceeURLlizRzp07q30tZWVlKisrc/xeXFys2NhYujgCAAAAP+MJWxHUVk27OJq6glZeXq4NGzYoOTnZccxqtSo5OVlZWVnVPicrK8tpvCSlpKQ4xufk5Cg3N9dpjM1mU2JiomNMVlaWQkNDHeFMkpKTk2W1WpWdne0Yc8011zjCWdV5du3apWPHjlVb26xZs2Sz2Rw/sbGxtXk7AAAAgCbBy2pRUvsWuqV7KyW1b1Hv4amh529Ipga0/Px8VVZWKjIy0ul4ZGSkcnNzq31Obm7uecdX/XmhMREREU6Pe3t7KywszGlMdXP8/By/NHXqVBUVFTl+9u/fX/0LBwAAAIBq0Ga/Hvn5+cnPz8/sMgAAAAC4KVNX0MLDw+Xl5aW8POcN6/Ly8hQVFVXtc6Kios47vurPC435ZROSiooKFRQUOI2pbo6fnwMAAAAA6pOpAc3X11cJCQnKzMx0HLPb7crMzFRSUlK1z0lKSnIaL0krVqxwjI+Pj1dUVJTTmOLiYmVnZzvGJCUlqbCwUBs2bHCMWblypex2uxITEx1jVq9erdOnTzud57LLLlPz5s0v8pUDAAAAwNlMb7Oflpam+fPna+HChdqxY4fGjx+vkpISjR49WpI0cuRITZ061TF+0qRJysjI0HPPPaedO3dqxowZWr9+vSZMmCBJslgsmjx5smbOnKkPPvhAW7du1ciRIxUTE6PU1FRJUseOHTVgwACNHTtWa9eu1Zo1azRhwgQNGzZMMTExkqQ77rhDvr6+GjNmjLZv3663335bf/vb35SWlta4bxAAAACAJsP0e9CGDh2qI0eOaPr06crNzVX37t2VkZHhaMixb98+Wa0/5cirrrpKixYt0mOPPaZp06apQ4cOWrJkiTp37uwY89BDD6mkpETjxo1TYWGh+vbtq4yMDPn7/7QfwhtvvKEJEyaof//+slqtGjJkiObMmeN43Gaz6eOPP9Z9992nhIQEhYeHa/r06U57pQEAAABAfTJ9HzRPVtO9DgAAAAB4NrfYBw0AAAAA8BMCGgAAAAC4CAIaAAAAALgIAhoAAAAAuAgCGgAAAAC4CAIaAAAAALgI0/dB82RVOxgUFxebXAkAAAAAM1VlggvtckZAa0DHjx+XJMXGxppcCQAAAABXcPz4cdlstnM+zkbVDchut+vgwYMKDg6WxWI557ji4mLFxsZq//79bGjtwficPR+fcdPA5+z5+IybBj5nz+dqn7FhGDp+/LhiYmJktZ77TjNW0BqQ1WpV69atazw+JCTEJf7hQcPic/Z8fMZNA5+z5+Mzbhr4nD2fK33G51s5q0KTEAAAAABwEQQ0AAAAAHARBDQX4Ofnp/T0dPn5+ZldChoQn7Pn4zNuGvicPR+fcdPA5+z53PUzpkkIAAAAALgIVtAAAAAAwEUQ0AAAAADARRDQAAAAAMBFENAAAAAAwEUQ0FzAiy++qLZt28rf31+JiYlau3at2SWhHs2YMUMWi8Xp5/LLLze7LFyE1atXa/DgwYqJiZHFYtGSJUucHjcMQ9OnT1d0dLQCAgKUnJys3bt3m1Ms6uxCn/Ndd9111nd7wIAB5hSLOpk1a5Z69eql4OBgRUREKDU1Vbt27XIaU1paqvvuu08tWrRQs2bNNGTIEOXl5ZlUMWqrJp/xddddd9Z3+Z577jGpYtTFSy+9pK5duzo2pE5KStJHH33keNzdvscENJO9/fbbSktLU3p6ujZu3Khu3bopJSVFhw8fNrs01KMrrrhChw4dcvx8/vnnZpeEi1BSUqJu3brpxRdfrPbx2bNna86cOZo7d66ys7MVFBSklJQUlZaWNnKluBgX+pwlacCAAU7f7TfffLMRK8TF+vTTT3Xffffpyy+/1IoVK3T69GnddNNNKikpcYx54IEHtHTpUi1evFiffvqpDh48qNtuu83EqlEbNfmMJWns2LFO3+XZs2ebVDHqonXr1vrzn/+sDRs2aP369brhhht0yy23aPv27ZLc8HtswFS9e/c27rvvPsfvlZWVRkxMjDFr1iwTq0J9Sk9PN7p162Z2GWggkoz//Oc/jt/tdrsRFRVlPPPMM45jhYWFhp+fn/Hmm2+aUCHqwy8/Z8MwjFGjRhm33HKLKfWgYRw+fNiQZHz66aeGYZz57vr4+BiLFy92jNmxY4chycjKyjKrTFyEX37GhmEY1157rTFp0iTzikKDaN68ufHyyy+75feYFTQTlZeXa8OGDUpOTnYcs1qtSk5OVlZWlomVob7t3r1bMTExateunUaMGKF9+/aZXRIaSE5OjnJzc52+1zabTYmJiXyvPdAnn3yiiIgIXXbZZRo/fryOHj1qdkm4CEVFRZKksLAwSdKGDRt0+vRpp+/z5ZdfrjZt2vB9dlO//IyrvPHGGwoPD1fnzp01depUnTx50ozyUA8qKyv11ltvqaSkRElJSW75PfY2u4CmLD8/X5WVlYqMjHQ6HhkZqZ07d5pUFepbYmKiFixYoMsuu0yHDh3SE088oX79+mnbtm0KDg42uzzUs9zcXEmq9ntd9Rg8w4ABA3TbbbcpPj5ee/bs0bRp03TzzTcrKytLXl5eZpeHWrLb7Zo8ebKuvvpqde7cWdKZ77Ovr69CQ0OdxvJ9dk/VfcaSdMcddyguLk4xMTH66quv9PDDD2vXrl167733TKwWtbV161YlJSWptLRUzZo103/+8x916tRJmzdvdrvvMQENaGA333yz4+9du3ZVYmKi4uLi9M4772jMmDEmVgbgYgwbNszx9y5duqhr165q3769PvnkE/Xv39/EylAX9913n7Zt28Y9wh7sXJ/xuHHjHH/v0qWLoqOj1b9/f+3Zs0ft27dv7DJRR5dddpk2b96soqIivfvuuxo1apQ+/fRTs8uqEy5xNFF4eLi8vLzO6iKTl5enqKgok6pCQwsNDdWll16qb7/91uxS0ACqvrt8r5uedu3aKTw8nO+2G5owYYKWLVumVatWqXXr1o7jUVFRKi8vV2FhodN4vs/u51yfcXUSExMlie+ym/H19dUll1yihIQEzZo1S926ddPf/vY3t/weE9BM5Ovrq4SEBGVmZjqO2e12ZWZmKikpycTK0JBOnDihPXv2KDo62uxS0ADi4+MVFRXl9L0uLi5WdnY232sPd+DAAR09epTvthsxDEMTJkzQf/7zH61cuVLx8fFOjyckJMjHx8fp+7xr1y7t27eP77ObuNBnXJ3NmzdLEt9lN2e321VWVuaW32MucTRZWlqaRo0apZ49e6p37956/vnnVVJSotGjR5tdGurJgw8+qMGDBysuLk4HDx5Uenq6vLy8NHz4cLNLQx2dOHHC6b+s5uTkaPPmzQoLC1ObNm00efJkzZw5Ux06dFB8fLwef/xxxcTEKDU11byiUWvn+5zDwsL0xBNPaMiQIYqKitKePXv00EMP6ZJLLlFKSoqJVaM27rvvPi1atEjvv/++goODHfej2Gw2BQQEyGazacyYMUpLS1NYWJhCQkI0ceJEJSUlqU+fPiZXj5q40Ge8Z88eLVq0SAMHDlSLFi301Vdf6YEHHtA111yjrl27mlw9amrq1Km6+eab1aZNGx0/flyLFi3SJ598ouXLl7vn99jsNpIwjP/3//6f0aZNG8PX19fo3bu38eWXX5pdEurR0KFDjejoaMPX19do1aqVMXToUOPbb781uyxchFWrVhmSzvoZNWqUYRhnWu0//vjjRmRkpOHn52f079/f2LVrl7lFo9bO9zmfPHnSuOmmm4yWLVsaPj4+RlxcnDF27FgjNzfX7LJRC9V9vpKMV1991THm1KlTxr333ms0b97cCAwMNG699Vbj0KFD5hWNWrnQZ7xv3z7jmmuuMcLCwgw/Pz/jkksuMf7whz8YRUVF5haOWrn77ruNuLg4w9fX12jZsqXRv39/4+OPP3Y87m7fY4thGEZjBkIAAAAAQPW4Bw0AAAAAXAQBDQAAAABcBAENAAAAAFwEAQ0AAAAAXAQBDQAAAABcBAENAAAAAFwEAQ0AAAAAXAQBDQAAAABcBAENAAAXZLFYtGTJErPLAAA0MgIaAAC/cNddd8lisZz1M2DAALNLAwB4OG+zCwAAwBUNGDBAr776qtMxPz8/k6oBADQVrKABAFANPz8/RUVFOf00b95c0pnLD1966SXdfPPNCggIULt27fTuu+86PX/r1q264YYbFBAQoBYtWmjcuHE6ceKE05h//vOfuuKKK+Tn56fo6GhNmDDB6fH8/HzdeuutCgwMVIcOHfTBBx807IsGAJiOgAYAQB08/vjjGjJkiLZs2aIRI0Zo2LBh2rFjhySppKREKSkpat68udatW6fFixfrf//7n1MAe+mll3Tfffdp3Lhx2rp1qz744ANdcsklTud44okndPvtt+urr77SwIEDNWLECBUUFDTq6wQANC6LYRiG2UUAAOBK7rrrLr3++uvy9/d3Oj5t2jRNmzZNFotF99xzj1566SXHY3369FGPHj3097//XfPnz9fDDz+s/fv3KygoSJL04YcfavDgwTp48KAiIyPVqlUrjR49WjNnzqy2BovFoscee0x//OMfJZ0Jfc2aNdNHH33EvXAA4MG4Bw0AgGpcf/31TgFMksLCwhx/T0pKcnosKSlJmzdvliTt2LFD3bp1c4QzSbr66qtlt9u1a9cuWSwWHTx4UP379z9vDV27dnX8PSgoSCEhITp8+HBdXxIAwA0Q0AAAqEZQUNBZlxzWl4CAgBqN8/HxcfrdYrHIbrc3REkAABfBPWgAANTBl19+edbvHTt2lCR17NhRW7ZsUUlJiePxNWvWyGq16rLLLlNwcLDatm2rzMzMRq0ZAOD6WEEDAKAaZWVlys3NdTrm7e2t8PBwSdLixYvVs2dP9e3bV2+88YbWrl2rV155RZI0YsQIpaena9SoUZoxY4aOHDmiiRMn6ne/+50iIyMlSTNmzNA999yjiIgI3XzzzTp+/LjWrFmjiRMnNu4LBQC4FAIaAADVyMjIUHR0tNOxyy67TDt37pR0psPiW2+9pXvvvVfR0dF688031alTJ0lSYGCgli9frkmTJqlXr14KDAzUkCFD9Je//MUx16hRo1RaWqq//vWvevDBBxUeHq7f/OY3jfcCAQAuiS6OAADUksVi0X/+8x+lpqaaXQoAwMNwDxoAAAAAuAgCGgAAAAC4CO5BAwCglrg7AADQUFhBAwAAAAAXQUADAAAAABdBQAMAAAAAF0FAAwAAAAAXQUADAAAAABdBQAMAAAAAF0FAAwAAAAAXQUADAAAAABfx/wE+J1Pe0g8VCwAAAABJRU5ErkJggg=="},"metadata":{}}]},{"cell_type":"code","source":"\nfrom sklearn.metrics import precision_score, recall_score, f1_score\n\n# Calculate precision, recall, accuracy, and F1-score for the Ensemble Model\nensemble_precision = precision_score(y_test, ensemble_test_predictions)\nensemble_recall = recall_score(y_test, ensemble_test_predictions)\nensemble_f1 = f1_score(y_test, ensemble_test_predictions)\n\n# Print metrics for the Ensemble Model\nprint(\"Ensemble Model Testing Precision:\", ensemble_precision)\nprint(\"Ensemble Model Testing Recall:\", ensemble_recall)\nprint(\"Ensemble Model Testing F1 Score:\", ensemble_f1)\n\n","metadata":{"execution":{"iopub.status.busy":"2023-12-12T15:18:43.163705Z","iopub.execute_input":"2023-12-12T15:18:43.164239Z","iopub.status.idle":"2023-12-12T15:18:43.204688Z","shell.execute_reply.started":"2023-12-12T15:18:43.164199Z","shell.execute_reply":"2023-12-12T15:18:43.203244Z"},"trusted":true},"execution_count":24,"outputs":[{"name":"stdout","text":"Ensemble Model Testing Precision: 0.9794576828266228\nEnsemble Model Testing Recall: 0.9190439475713185\nEnsemble Model Testing F1 Score: 0.9482895783611773\n","output_type":"stream"}]},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"# Experiment 1: Test with Different Batch Sizes\ndef test_batch_size(batch_size):\n    cnn_lstm_model = Sequential()\n    cnn_lstm_model.add(Embedding(input_dim=5000, output_dim=128, input_length=50))\n    cnn_lstm_model.add(Conv1D(64, 5, activation='relu'))\n    cnn_lstm_model.add(MaxPooling1D(pool_size=4))\n    cnn_lstm_model.add(LSTM(64))\n    cnn_lstm_model.add(Dense(1, activation='sigmoid'))\n    cnn_lstm_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n\n    X_cnn_lstm_train, X_cnn_lstm_test, y_train, y_test = train_test_split(X_cnn_lstm, df['is_sarcastic'], test_size=0.2, random_state=42)\n    \n    # Train the CNN-LSTM Model with different batch sizes\n    cnn_lstm_model.fit(X_cnn_lstm_train, y_train, epochs=10, batch_size=batch_size)\n\n    # Evaluate the Model\n    cnn_lstm_predictions = cnn_lstm_model.predict(X_cnn_lstm_test)\n    cnn_lstm_predictions = np.round(cnn_lstm_predictions)\n\n    accuracy = accuracy_score(y_test, cnn_lstm_predictions)\n    print(f\"\\nCNN-LSTM Model with Batch Size {batch_size} - Accuracy: {accuracy}\")\n\n# Test with different batch sizes (e.g., 32, 64, 128)\ntest_batch_size(32)\ntest_batch_size(64)\ntest_batch_size(128)\n\n# Experiment 2: Test with Noisy Data\ndef test_noisy_data(noise_factor):\n    X_cnn_lstm_noisy = X_cnn_lstm + noise_factor * np.random.normal(size=X_cnn_lstm.shape)\n    \n    cnn_lstm_model = Sequential()\n    cnn_lstm_model.add(Embedding(input_dim=5000, output_dim=128, input_length=50))\n    cnn_lstm_model.add(Conv1D(64, 5, activation='relu'))\n    cnn_lstm_model.add(MaxPooling1D(pool_size=4))\n    cnn_lstm_model.add(LSTM(64))\n    cnn_lstm_model.add(Dense(1, activation='sigmoid'))\n    cnn_lstm_model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n\n    X_cnn_lstm_train, X_cnn_lstm_test, y_train, y_test = train_test_split(X_cnn_lstm_noisy, df['is_sarcastic'], test_size=0.2, random_state=42)\n    \n    # Train the CNN-LSTM Model with noisy data\n    cnn_lstm_model.fit(X_cnn_lstm_train, y_train, epochs=10, batch_size=64)\n\n    # Evaluate the Model\n    cnn_lstm_predictions = cnn_lstm_model.predict(X_cnn_lstm_test)\n    cnn_lstm_predictions = np.round(cnn_lstm_predictions)\n\n    accuracy = accuracy_score(y_test, cnn_lstm_predictions)\n    print(f\"\\nCNN-LSTM Model with Noise Factor {noise_factor} - Accuracy: {accuracy}\")\n\n# Test with different noise factors (e.g., 0.1, 0.2, 0.3)\ntest_noisy_data(0.1)\ntest_noisy_data(0.2)\ntest_noisy_data(0.3)\n\n","metadata":{"execution":{"iopub.status.busy":"2023-12-12T15:21:14.848007Z","iopub.execute_input":"2023-12-12T15:21:14.849560Z","iopub.status.idle":"2023-12-12T15:40:32.838054Z","shell.execute_reply.started":"2023-12-12T15:21:14.849500Z","shell.execute_reply":"2023-12-12T15:40:32.834938Z"},"trusted":true},"execution_count":25,"outputs":[{"name":"stdout","text":"Epoch 1/10\n1384/1384 [==============================] - 32s 21ms/step - loss: 0.3594 - accuracy: 0.8309\nEpoch 2/10\n1384/1384 [==============================] - 30s 21ms/step - loss: 0.1742 - accuracy: 0.9286\nEpoch 3/10\n1384/1384 [==============================] - 30s 21ms/step - loss: 0.0758 - accuracy: 0.9665\nEpoch 4/10\n1384/1384 [==============================] - 29s 21ms/step - loss: 0.0437 - accuracy: 0.9782\nEpoch 5/10\n1384/1384 [==============================] - 29s 21ms/step - loss: 0.0368 - accuracy: 0.9807\nEpoch 6/10\n1384/1384 [==============================] - 29s 21ms/step - loss: 0.0337 - accuracy: 0.9815\nEpoch 7/10\n1384/1384 [==============================] - 29s 21ms/step - loss: 0.0319 - accuracy: 0.9828\nEpoch 8/10\n1384/1384 [==============================] - 29s 21ms/step - loss: 0.0303 - accuracy: 0.9829\nEpoch 9/10\n1384/1384 [==============================] - 30s 22ms/step - loss: 0.0285 - accuracy: 0.9832\nEpoch 10/10\n1384/1384 [==============================] - 30s 22ms/step - loss: 0.0284 - accuracy: 0.9834\n346/346 [==============================] - 3s 6ms/step\n\nCNN-LSTM Model with Batch Size 32 - Accuracy: 0.9481294053858667\nEpoch 1/10\n692/692 [==============================] - 23s 28ms/step - loss: 0.3702 - accuracy: 0.8259\nEpoch 2/10\n692/692 [==============================] - 20s 29ms/step - loss: 0.1915 - accuracy: 0.9207\nEpoch 3/10\n692/692 [==============================] - 20s 28ms/step - loss: 0.0843 - accuracy: 0.9635\nEpoch 4/10\n692/692 [==============================] - 20s 29ms/step - loss: 0.0456 - accuracy: 0.9779\nEpoch 5/10\n692/692 [==============================] - 19s 28ms/step - loss: 0.0339 - accuracy: 0.9811\nEpoch 6/10\n692/692 [==============================] - 19s 28ms/step - loss: 0.0355 - accuracy: 0.9815\nEpoch 7/10\n692/692 [==============================] - 19s 28ms/step - loss: 0.0316 - accuracy: 0.9823\nEpoch 8/10\n692/692 [==============================] - 19s 28ms/step - loss: 0.0295 - accuracy: 0.9827\nEpoch 9/10\n692/692 [==============================] - 20s 28ms/step - loss: 0.0290 - accuracy: 0.9833\nEpoch 10/10\n692/692 [==============================] - 19s 28ms/step - loss: 0.0296 - accuracy: 0.9830\n346/346 [==============================] - 3s 6ms/step\n\nCNN-LSTM Model with Batch Size 64 - Accuracy: 0.9497560093981565\nEpoch 1/10\n346/346 [==============================] - 18s 45ms/step - loss: 0.4001 - accuracy: 0.8063\nEpoch 2/10\n346/346 [==============================] - 16s 46ms/step - loss: 0.2270 - accuracy: 0.9060\nEpoch 3/10\n346/346 [==============================] - 15s 45ms/step - loss: 0.1190 - accuracy: 0.9518\nEpoch 4/10\n346/346 [==============================] - 16s 45ms/step - loss: 0.0556 - accuracy: 0.9743\nEpoch 5/10\n346/346 [==============================] - 16s 45ms/step - loss: 0.0365 - accuracy: 0.9811\nEpoch 6/10\n346/346 [==============================] - 16s 45ms/step - loss: 0.0298 - accuracy: 0.9826\nEpoch 7/10\n346/346 [==============================] - 16s 46ms/step - loss: 0.0281 - accuracy: 0.9836\nEpoch 8/10\n346/346 [==============================] - 16s 47ms/step - loss: 0.0327 - accuracy: 0.9820\nEpoch 9/10\n346/346 [==============================] - 16s 45ms/step - loss: 0.0330 - accuracy: 0.9816\nEpoch 10/10\n346/346 [==============================] - 16s 46ms/step - loss: 0.0283 - accuracy: 0.9837\n346/346 [==============================] - 3s 6ms/step\n\nCNN-LSTM Model with Batch Size 128 - Accuracy: 0.9486716067232965\nEpoch 1/10\n692/692 [==============================] - 23s 29ms/step - loss: 0.5035 - accuracy: 0.7434\nEpoch 2/10\n692/692 [==============================] - 20s 28ms/step - loss: 0.3363 - accuracy: 0.8502\nEpoch 3/10\n692/692 [==============================] - 19s 28ms/step - loss: 0.1856 - accuracy: 0.9224\nEpoch 4/10\n692/692 [==============================] - 20s 29ms/step - loss: 0.0932 - accuracy: 0.9596\nEpoch 5/10\n692/692 [==============================] - 20s 28ms/step - loss: 0.0608 - accuracy: 0.9716\nEpoch 6/10\n692/692 [==============================] - 20s 29ms/step - loss: 0.0494 - accuracy: 0.9755\nEpoch 7/10\n692/692 [==============================] - 20s 29ms/step - loss: 0.0429 - accuracy: 0.9779\nEpoch 8/10\n692/692 [==============================] - 20s 28ms/step - loss: 0.0411 - accuracy: 0.9788\nEpoch 9/10\n692/692 [==============================] - 20s 29ms/step - loss: 0.0425 - accuracy: 0.9784\nEpoch 10/10\n692/692 [==============================] - 20s 29ms/step - loss: 0.0379 - accuracy: 0.9799\n346/346 [==============================] - 3s 6ms/step\n\nCNN-LSTM Model with Noise Factor 0.1 - Accuracy: 0.8293873124887041\nEpoch 1/10\n692/692 [==============================] - 22s 28ms/step - loss: 0.5122 - accuracy: 0.7390\nEpoch 2/10\n692/692 [==============================] - 20s 28ms/step - loss: 0.3452 - accuracy: 0.8460\nEpoch 3/10\n692/692 [==============================] - 20s 28ms/step - loss: 0.1969 - accuracy: 0.9182\nEpoch 4/10\n692/692 [==============================] - 19s 28ms/step - loss: 0.1024 - accuracy: 0.9552\nEpoch 5/10\n692/692 [==============================] - 20s 28ms/step - loss: 0.0657 - accuracy: 0.9704\nEpoch 6/10\n692/692 [==============================] - 20s 28ms/step - loss: 0.0496 - accuracy: 0.9755\nEpoch 7/10\n692/692 [==============================] - 19s 28ms/step - loss: 0.0464 - accuracy: 0.9767\nEpoch 8/10\n692/692 [==============================] - 20s 28ms/step - loss: 0.0439 - accuracy: 0.9773\nEpoch 9/10\n692/692 [==============================] - 19s 28ms/step - loss: 0.0403 - accuracy: 0.9791\nEpoch 10/10\n692/692 [==============================] - 19s 28ms/step - loss: 0.0399 - accuracy: 0.9781\n346/346 [==============================] - 2s 6ms/step\n\nCNN-LSTM Model with Noise Factor 0.2 - Accuracy: 0.8283029098138442\nEpoch 1/10\n","output_type":"stream"},{"traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mInvalidArgumentError\u001b[0m                      Traceback (most recent call last)","Cell \u001b[0;32mIn[25], line 55\u001b[0m\n\u001b[1;32m     53\u001b[0m test_noisy_data(\u001b[38;5;241m0.1\u001b[39m)\n\u001b[1;32m     54\u001b[0m test_noisy_data(\u001b[38;5;241m0.2\u001b[39m)\n\u001b[0;32m---> 55\u001b[0m \u001b[43mtest_noisy_data\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0.3\u001b[39;49m\u001b[43m)\u001b[49m\n","Cell \u001b[0;32mIn[25], line 43\u001b[0m, in \u001b[0;36mtest_noisy_data\u001b[0;34m(noise_factor)\u001b[0m\n\u001b[1;32m     40\u001b[0m X_cnn_lstm_train, X_cnn_lstm_test, y_train, y_test \u001b[38;5;241m=\u001b[39m train_test_split(X_cnn_lstm_noisy, df[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mis_sarcastic\u001b[39m\u001b[38;5;124m'\u001b[39m], test_size\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.2\u001b[39m, random_state\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m42\u001b[39m)\n\u001b[1;32m     42\u001b[0m \u001b[38;5;66;03m# Train the CNN-LSTM Model with noisy data\u001b[39;00m\n\u001b[0;32m---> 43\u001b[0m \u001b[43mcnn_lstm_model\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX_cnn_lstm_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m64\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     45\u001b[0m \u001b[38;5;66;03m# Evaluate the Model\u001b[39;00m\n\u001b[1;32m     46\u001b[0m cnn_lstm_predictions \u001b[38;5;241m=\u001b[39m cnn_lstm_model\u001b[38;5;241m.\u001b[39mpredict(X_cnn_lstm_test)\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/keras/utils/traceback_utils.py:70\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     67\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[1;32m     68\u001b[0m     \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[1;32m     69\u001b[0m     \u001b[38;5;66;03m# `tf.debugging.disable_traceback_filtering()`\u001b[39;00m\n\u001b[0;32m---> 70\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     71\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m     72\u001b[0m     \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n","File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/tensorflow/python/eager/execute.py:52\u001b[0m, in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     50\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m     51\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[0;32m---> 52\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m pywrap_tfe\u001b[38;5;241m.\u001b[39mTFE_Py_Execute(ctx\u001b[38;5;241m.\u001b[39m_handle, device_name, op_name,\n\u001b[1;32m     53\u001b[0m                                       inputs, attrs, num_outputs)\n\u001b[1;32m     54\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m     55\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n","\u001b[0;31mInvalidArgumentError\u001b[0m: Graph execution error:\n\nDetected at node 'sequential_7/embedding_7/embedding_lookup' defined at (most recent call last):\n    File \"/opt/conda/lib/python3.10/runpy.py\", line 196, in _run_module_as_main\n      return _run_code(code, main_globals, None,\n    File \"/opt/conda/lib/python3.10/runpy.py\", line 86, in _run_code\n      exec(code, run_globals)\n    File \"/opt/conda/lib/python3.10/site-packages/ipykernel_launcher.py\", line 17, in <module>\n      app.launch_new_instance()\n    File \"/opt/conda/lib/python3.10/site-packages/traitlets/config/application.py\", line 1043, in launch_instance\n      app.start()\n    File \"/opt/conda/lib/python3.10/site-packages/ipykernel/kernelapp.py\", line 728, in start\n      self.io_loop.start()\n    File \"/opt/conda/lib/python3.10/site-packages/tornado/platform/asyncio.py\", line 195, in start\n      self.asyncio_loop.run_forever()\n    File \"/opt/conda/lib/python3.10/asyncio/base_events.py\", line 603, in run_forever\n      self._run_once()\n    File \"/opt/conda/lib/python3.10/asyncio/base_events.py\", line 1909, in _run_once\n      handle._run()\n    File \"/opt/conda/lib/python3.10/asyncio/events.py\", line 80, in _run\n      self._context.run(self._callback, *self._args)\n    File \"/opt/conda/lib/python3.10/site-packages/ipykernel/kernelbase.py\", line 513, in dispatch_queue\n      await self.process_one()\n    File \"/opt/conda/lib/python3.10/site-packages/ipykernel/kernelbase.py\", line 502, in process_one\n      await dispatch(*args)\n    File \"/opt/conda/lib/python3.10/site-packages/ipykernel/kernelbase.py\", line 409, in dispatch_shell\n      await result\n    File \"/opt/conda/lib/python3.10/site-packages/ipykernel/kernelbase.py\", line 729, in execute_request\n      reply_content = await reply_content\n    File \"/opt/conda/lib/python3.10/site-packages/ipykernel/ipkernel.py\", line 422, in do_execute\n      res = shell.run_cell(\n    File \"/opt/conda/lib/python3.10/site-packages/ipykernel/zmqshell.py\", line 540, in run_cell\n      return super().run_cell(*args, **kwargs)\n    File \"/opt/conda/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 3009, in run_cell\n      result = self._run_cell(\n    File \"/opt/conda/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 3064, in _run_cell\n      result = runner(coro)\n    File \"/opt/conda/lib/python3.10/site-packages/IPython/core/async_helpers.py\", line 129, in _pseudo_sync_runner\n      coro.send(None)\n    File \"/opt/conda/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 3269, in run_cell_async\n      has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n    File \"/opt/conda/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 3448, in run_ast_nodes\n      if await self.run_code(code, result, async_=asy):\n    File \"/opt/conda/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 3508, in run_code\n      exec(code_obj, self.user_global_ns, self.user_ns)\n    File \"/tmp/ipykernel_33/657746264.py\", line 55, in <module>\n      test_noisy_data(0.3)\n    File \"/tmp/ipykernel_33/657746264.py\", line 43, in test_noisy_data\n      cnn_lstm_model.fit(X_cnn_lstm_train, y_train, epochs=10, batch_size=64)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/utils/traceback_utils.py\", line 65, in error_handler\n      return fn(*args, **kwargs)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/engine/training.py\", line 1685, in fit\n      tmp_logs = self.train_function(iterator)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/engine/training.py\", line 1284, in train_function\n      return step_function(self, iterator)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/engine/training.py\", line 1268, in step_function\n      outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"/opt/conda/lib/python3.10/site-packages/keras/engine/training.py\", line 1249, in run_step\n      outputs = model.train_step(data)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/engine/training.py\", line 1050, in train_step\n      y_pred = self(x, training=True)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/utils/traceback_utils.py\", line 65, in error_handler\n      return fn(*args, **kwargs)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/engine/training.py\", line 558, in __call__\n      return super().__call__(*args, **kwargs)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/utils/traceback_utils.py\", line 65, in error_handler\n      return fn(*args, **kwargs)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/engine/base_layer.py\", line 1145, in __call__\n      outputs = call_fn(inputs, *args, **kwargs)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/utils/traceback_utils.py\", line 96, in error_handler\n      return fn(*args, **kwargs)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/engine/sequential.py\", line 412, in call\n      return super().call(inputs, training=training, mask=mask)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/engine/functional.py\", line 512, in call\n      return self._run_internal_graph(inputs, training=training, mask=mask)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/engine/functional.py\", line 669, in _run_internal_graph\n      outputs = node.layer(*args, **kwargs)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/utils/traceback_utils.py\", line 65, in error_handler\n      return fn(*args, **kwargs)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/engine/base_layer.py\", line 1145, in __call__\n      outputs = call_fn(inputs, *args, **kwargs)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/utils/traceback_utils.py\", line 96, in error_handler\n      return fn(*args, **kwargs)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/layers/core/embedding.py\", line 272, in call\n      out = tf.nn.embedding_lookup(self.embeddings, inputs)\nNode: 'sequential_7/embedding_7/embedding_lookup'\nindices[44,21] = -1 is not in [0, 5000)\n\t [[{{node sequential_7/embedding_7/embedding_lookup}}]] [Op:__inference_train_function_319836]"],"ename":"InvalidArgumentError","evalue":"Graph execution error:\n\nDetected at node 'sequential_7/embedding_7/embedding_lookup' defined at (most recent call last):\n    File \"/opt/conda/lib/python3.10/runpy.py\", line 196, in _run_module_as_main\n      return _run_code(code, main_globals, None,\n    File \"/opt/conda/lib/python3.10/runpy.py\", line 86, in _run_code\n      exec(code, run_globals)\n    File \"/opt/conda/lib/python3.10/site-packages/ipykernel_launcher.py\", line 17, in <module>\n      app.launch_new_instance()\n    File \"/opt/conda/lib/python3.10/site-packages/traitlets/config/application.py\", line 1043, in launch_instance\n      app.start()\n    File \"/opt/conda/lib/python3.10/site-packages/ipykernel/kernelapp.py\", line 728, in start\n      self.io_loop.start()\n    File \"/opt/conda/lib/python3.10/site-packages/tornado/platform/asyncio.py\", line 195, in start\n      self.asyncio_loop.run_forever()\n    File \"/opt/conda/lib/python3.10/asyncio/base_events.py\", line 603, in run_forever\n      self._run_once()\n    File \"/opt/conda/lib/python3.10/asyncio/base_events.py\", line 1909, in _run_once\n      handle._run()\n    File \"/opt/conda/lib/python3.10/asyncio/events.py\", line 80, in _run\n      self._context.run(self._callback, *self._args)\n    File \"/opt/conda/lib/python3.10/site-packages/ipykernel/kernelbase.py\", line 513, in dispatch_queue\n      await self.process_one()\n    File \"/opt/conda/lib/python3.10/site-packages/ipykernel/kernelbase.py\", line 502, in process_one\n      await dispatch(*args)\n    File \"/opt/conda/lib/python3.10/site-packages/ipykernel/kernelbase.py\", line 409, in dispatch_shell\n      await result\n    File \"/opt/conda/lib/python3.10/site-packages/ipykernel/kernelbase.py\", line 729, in execute_request\n      reply_content = await reply_content\n    File \"/opt/conda/lib/python3.10/site-packages/ipykernel/ipkernel.py\", line 422, in do_execute\n      res = shell.run_cell(\n    File \"/opt/conda/lib/python3.10/site-packages/ipykernel/zmqshell.py\", line 540, in run_cell\n      return super().run_cell(*args, **kwargs)\n    File \"/opt/conda/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 3009, in run_cell\n      result = self._run_cell(\n    File \"/opt/conda/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 3064, in _run_cell\n      result = runner(coro)\n    File \"/opt/conda/lib/python3.10/site-packages/IPython/core/async_helpers.py\", line 129, in _pseudo_sync_runner\n      coro.send(None)\n    File \"/opt/conda/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 3269, in run_cell_async\n      has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n    File \"/opt/conda/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 3448, in run_ast_nodes\n      if await self.run_code(code, result, async_=asy):\n    File \"/opt/conda/lib/python3.10/site-packages/IPython/core/interactiveshell.py\", line 3508, in run_code\n      exec(code_obj, self.user_global_ns, self.user_ns)\n    File \"/tmp/ipykernel_33/657746264.py\", line 55, in <module>\n      test_noisy_data(0.3)\n    File \"/tmp/ipykernel_33/657746264.py\", line 43, in test_noisy_data\n      cnn_lstm_model.fit(X_cnn_lstm_train, y_train, epochs=10, batch_size=64)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/utils/traceback_utils.py\", line 65, in error_handler\n      return fn(*args, **kwargs)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/engine/training.py\", line 1685, in fit\n      tmp_logs = self.train_function(iterator)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/engine/training.py\", line 1284, in train_function\n      return step_function(self, iterator)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/engine/training.py\", line 1268, in step_function\n      outputs = model.distribute_strategy.run(run_step, args=(data,))\n    File \"/opt/conda/lib/python3.10/site-packages/keras/engine/training.py\", line 1249, in run_step\n      outputs = model.train_step(data)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/engine/training.py\", line 1050, in train_step\n      y_pred = self(x, training=True)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/utils/traceback_utils.py\", line 65, in error_handler\n      return fn(*args, **kwargs)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/engine/training.py\", line 558, in __call__\n      return super().__call__(*args, **kwargs)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/utils/traceback_utils.py\", line 65, in error_handler\n      return fn(*args, **kwargs)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/engine/base_layer.py\", line 1145, in __call__\n      outputs = call_fn(inputs, *args, **kwargs)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/utils/traceback_utils.py\", line 96, in error_handler\n      return fn(*args, **kwargs)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/engine/sequential.py\", line 412, in call\n      return super().call(inputs, training=training, mask=mask)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/engine/functional.py\", line 512, in call\n      return self._run_internal_graph(inputs, training=training, mask=mask)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/engine/functional.py\", line 669, in _run_internal_graph\n      outputs = node.layer(*args, **kwargs)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/utils/traceback_utils.py\", line 65, in error_handler\n      return fn(*args, **kwargs)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/engine/base_layer.py\", line 1145, in __call__\n      outputs = call_fn(inputs, *args, **kwargs)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/utils/traceback_utils.py\", line 96, in error_handler\n      return fn(*args, **kwargs)\n    File \"/opt/conda/lib/python3.10/site-packages/keras/layers/core/embedding.py\", line 272, in call\n      out = tf.nn.embedding_lookup(self.embeddings, inputs)\nNode: 'sequential_7/embedding_7/embedding_lookup'\nindices[44,21] = -1 is not in [0, 5000)\n\t [[{{node sequential_7/embedding_7/embedding_lookup}}]] [Op:__inference_train_function_319836]","output_type":"error"}]}]}