jet-engine-sound-classification.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.10","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"## Install required packages","metadata":{"_uuid":"523b398a-099f-4b27-a65c-ad20ddd99f0c","_cell_guid":"fe011cab-f2cd-4d61-9863-e2925d6f2125","execution":{"iopub.status.busy":"2023-09-08T21:47:18.374779Z","iopub.execute_input":"2023-09-08T21:47:18.375149Z","iopub.status.idle":"2023-09-08T21:47:51.604581Z","shell.execute_reply.started":"2023-09-08T21:47:18.375118Z","shell.execute_reply":"2023-09-08T21:47:51.603287Z"},"trusted":true}},{"cell_type":"code","source":"!pip install -q tensorflow_io\n!pip install -q seaborn\n!pip install -q imbalanced-learn","metadata":{"_uuid":"5f14a796-dff8-4ad9-b3b8-d52ae0eb12ad","_cell_guid":"10051159-09e9-4836-bcec-10b36cefffb5","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-09-09T01:00:40.521376Z","iopub.execute_input":"2023-09-09T01:00:40.521901Z","iopub.status.idle":"2023-09-09T01:01:13.301184Z","shell.execute_reply.started":"2023-09-09T01:00:40.521858Z","shell.execute_reply":"2023-09-09T01:01:13.299849Z"},"trusted":true},"execution_count":75,"outputs":[{"name":"stdout","text":"\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n\u001b[0m\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n\u001b[0m","output_type":"stream"}]},{"cell_type":"markdown","source":"## Importing libraries","metadata":{"_uuid":"c3a5d3a7-7e01-4701-8502-9e06a67bbce8","_cell_guid":"dabc7bc4-56f4-4a1a-b114-9f66e3c2d9e5","trusted":true}},{"cell_type":"code","source":"# Import necessary libraries\nimport os\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\nimport tensorflow_io as tfio\nimport keras_tuner\nimport librosa\nimport librosa.display\nimport IPython.display as ipd\nfrom glob import glob\nfrom tqdm import tqdm\nimport seaborn as sns\nfrom itertools import cycle\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score, recall_score\nfrom imblearn.under_sampling import ClusterCentroids\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras import layers\nfrom datetime import datetime\nfrom keras_tuner.tuners import RandomSearch\nfrom keras_tuner import HyperParameters\nfrom keras_tuner.engine.hyperparameters import Int, Choice\nfrom sklearn.metrics import accuracy_score, recall_score","metadata":{"_uuid":"e6e9878a-901a-4eb1-81cc-72af1ddd9cdc","_cell_guid":"e417f9b3-3f3f-4b80-b6ac-1aa80d1b0bc0","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-09-09T01:01:13.304983Z","iopub.execute_input":"2023-09-09T01:01:13.305392Z","iopub.status.idle":"2023-09-09T01:01:13.315449Z","shell.execute_reply.started":"2023-09-09T01:01:13.305360Z","shell.execute_reply":"2023-09-09T01:01:13.314478Z"},"trusted":true},"execution_count":76,"outputs":[]},{"cell_type":"code","source":"# Set Kaggle Kernel to non-interactive mode and collect file paths\nfile_paths = []\n\nos.environ['KAGGLE_KERNEL_RUN_INTERACTIVE'] = '0'\nfor dirname, _, filenames in os.walk('/kaggle/input'):    \n    for filename in filenames:\n        file_paths.append(os.path.join(dirname, filename))","metadata":{"_uuid":"95a64ec6-cf36-4e81-98ae-6466ed036e80","_cell_guid":"f1b2a1cb-89bd-4d89-bc42-9cfeb2fcf92b","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-09-09T01:01:13.316834Z","iopub.execute_input":"2023-09-09T01:01:13.317227Z","iopub.status.idle":"2023-09-09T01:01:13.343321Z","shell.execute_reply.started":"2023-09-09T01:01:13.317165Z","shell.execute_reply":"2023-09-09T01:01:13.342406Z"},"trusted":true},"execution_count":77,"outputs":[]},{"cell_type":"code","source":"# check version of TensorFlow\nprint (tf.__version__)\nprint (tfio.__version__)","metadata":{"_uuid":"acc35660-df01-4b89-94a2-f1fe834e9ae5","_cell_guid":"2649baf1-20f5-40da-9e06-0562be9f5367","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-09-09T01:01:13.344954Z","iopub.execute_input":"2023-09-09T01:01:13.345259Z","iopub.status.idle":"2023-09-09T01:01:13.351195Z","shell.execute_reply.started":"2023-09-09T01:01:13.345229Z","shell.execute_reply":"2023-09-09T01:01:13.350285Z"},"trusted":true},"execution_count":78,"outputs":[{"name":"stdout","text":"2.12.0\n0.31.0\n","output_type":"stream"}]},{"cell_type":"code","source":"# Set up visualization parameters\nsns.set_theme(style=\"white\", palette=None)\ncolor_pal = plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"]\ncolor_cycle = cycle(plt.rcParams[\"axes.prop_cycle\"].by_key()[\"color\"])","metadata":{"_uuid":"8c196feb-3940-4b66-ac95-50e9c7ecd119","_cell_guid":"491bd2b5-8fa2-46f5-be32-e6dbfd9bdd3e","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-09-09T01:01:13.354722Z","iopub.execute_input":"2023-09-09T01:01:13.355142Z","iopub.status.idle":"2023-09-09T01:01:13.362496Z","shell.execute_reply.started":"2023-09-09T01:01:13.355116Z","shell.execute_reply":"2023-09-09T01:01:13.361524Z"},"trusted":true},"execution_count":79,"outputs":[]},{"cell_type":"code","source":"seed = 22\ntf.random.set_seed(seed)","metadata":{"_uuid":"47d7ac87-9d15-44ad-8626-3be527b23559","_cell_guid":"6ad85be0-1f23-4e17-b4f1-30c0a8ab2e2b","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-09-09T01:01:13.364222Z","iopub.execute_input":"2023-09-09T01:01:13.364607Z","iopub.status.idle":"2023-09-09T01:01:13.388062Z","shell.execute_reply.started":"2023-09-09T01:01:13.364561Z","shell.execute_reply":"2023-09-09T01:01:13.387119Z"},"trusted":true},"execution_count":80,"outputs":[]},{"cell_type":"markdown","source":"## Importing Data\n\nThe following steps are perfomred below:\n\nFiles are read from path\nEach file is labeled as either True(1) or False(0)\nData is converted to a dataframe with 2 columns 'filepath' and 'label'.*","metadata":{"_uuid":"cc601834-65b6-4ce7-831c-069fe13c6a69","_cell_guid":"f2344333-11f3-4b0b-9bdc-30dc558b096e","trusted":true}},{"cell_type":"code","source":"# Making a function to label each of the audio files with the plane type and the label type like true or false\ndef get_label(audio_list):\n    \"\"\"\n    Function to label each audio file as True (1) or False (0) based on filename.\n\n    Args:\n    audio_list (list): List of audio file paths.\n\n    Returns:\n    list: List of tuples containing the audio file path and its corresponding label.\n    \"\"\"  \n    \n    temp = []\n\n    # loop over the entire audio list and add the aircraft type and label, then save it in another list of tuples for df later\n    for i in range(len(audio_list)):\n        if audio_list[i][-5] == 'T':\n          label = 1\n        else:\n          label = 0\n        t = (audio_list[i] ,  label)\n        temp.append(t)   \n\n    return temp","metadata":{"_uuid":"27675ec4-c6bc-434e-b46f-469a5a89ad3a","_cell_guid":"ffb79745-2687-474f-b48b-469345cc1771","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-09-09T01:01:13.391705Z","iopub.execute_input":"2023-09-09T01:01:13.392316Z","iopub.status.idle":"2023-09-09T01:01:13.399046Z","shell.execute_reply.started":"2023-09-09T01:01:13.392278Z","shell.execute_reply":"2023-09-09T01:01:13.398082Z"},"trusted":true},"execution_count":81,"outputs":[]},{"cell_type":"code","source":"# Load and preprocess audio data\ndata = sorted(glob('/kaggle/input/audio-data/Trains_/*.wav', recursive = True))\n\n# Label audio data as True (1) or False (0)\ndata = get_label(data)\n\n# Create a DataFrame to store file paths and labels\ndf = pd.DataFrame(data, columns = ['file_path', 'labels'])\n\nprint(df.head())","metadata":{"_uuid":"e8ecd7b4-b6d9-45e1-9074-34913686f92f","_cell_guid":"c4909706-9e43-42b0-aded-0ff0732e01cc","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-09-09T01:01:13.400534Z","iopub.execute_input":"2023-09-09T01:01:13.400878Z","iopub.status.idle":"2023-09-09T01:01:13.419658Z","shell.execute_reply.started":"2023-09-09T01:01:13.400849Z","shell.execute_reply":"2023-09-09T01:01:13.418605Z"},"trusted":true},"execution_count":82,"outputs":[{"name":"stdout","text":"                                    file_path  labels\n0   /kaggle/input/audio-data/Trains_/A1-F.wav       0\n1   /kaggle/input/audio-data/Trains_/A1-T.wav       1\n2  /kaggle/input/audio-data/Trains_/A10-F.wav       0\n3  /kaggle/input/audio-data/Trains_/A10-T.wav       1\n4  /kaggle/input/audio-data/Trains_/A11-F.wav       0\n","output_type":"stream"}]},{"cell_type":"code","source":"# Extract file paths and number of files\naudio_path = df['file_path']\nnum_files = len(audio_path)\n\n# Initialise arrays to store audio data and sample rates\naudio_data = np.empty(num_files, dtype=object)\nsamp_rate = np.empty(num_files, dtype=object)\n\n# Load audio files and sample rates\nfor i, path in enumerate(audio_path):\n    audio_, sr = librosa.load(path)\n    audio_data[i] = audio_\n    samp_rate[i] = sr","metadata":{"_uuid":"3ed5f30b-7aec-4032-9284-30c7d14975da","_cell_guid":"f20586b0-22a4-4564-ac66-4036a9434fc1","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-09-09T01:01:13.421061Z","iopub.execute_input":"2023-09-09T01:01:13.421808Z","iopub.status.idle":"2023-09-09T01:01:20.414328Z","shell.execute_reply.started":"2023-09-09T01:01:13.421775Z","shell.execute_reply":"2023-09-09T01:01:20.413316Z"},"trusted":true},"execution_count":83,"outputs":[]},{"cell_type":"markdown","source":"## Preprocessing the Data","metadata":{"_uuid":"02486bb4-158b-4f9c-a6b9-a904c705a9fb","_cell_guid":"14ebec6c-95d3-46d0-b1ad-14db86cb07da","trusted":true}},{"cell_type":"code","source":"# Resampling audio data to a common target sample rate\ntarget_sr =  44100\naudio_data_resampled=[]\n\nfor audio_dt, s_rate in zip(audio_data,samp_rate):\n    audio_data_resmpld = librosa.resample(audio_dt,orig_sr=s_rate,target_sr=target_sr)\n    audio_data_normalised = librosa.util.normalize(audio_data_resmpld)\n    audio_data_resampled.append(audio_data_normalised)","metadata":{"_uuid":"ede80ef3-5dc7-4e84-b824-1a5d5f4e701e","_cell_guid":"445d02ec-8624-4781-8abe-bf9bb61aeab0","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-09-09T01:01:20.415800Z","iopub.execute_input":"2023-09-09T01:01:20.416150Z","iopub.status.idle":"2023-09-09T01:01:22.157303Z","shell.execute_reply.started":"2023-09-09T01:01:20.416113Z","shell.execute_reply":"2023-09-09T01:01:22.156295Z"},"trusted":true},"execution_count":84,"outputs":[]},{"cell_type":"markdown","source":"## Extracting MFCC features from the audio data","metadata":{"_uuid":"cf2af8b1-2cbe-47c0-b3a8-d7463a04452d","_cell_guid":"47b2e5be-e8ec-4487-94dd-251642c02c98","trusted":true}},{"cell_type":"code","source":"mfcc_features_arr = []\nfor audio_data_res in tqdm(audio_data_resampled):\n    mfcc_features = librosa.feature.mfcc(y=audio_data_res, n_mfcc=40)\n    scaled_mfcc = np.mean(mfcc_features.T, axis=0)\n    mfcc_features_arr.append(scaled_mfcc)","metadata":{"_uuid":"c1c177fb-c479-43b0-b405-60f74389ecc2","_cell_guid":"0f2e32e7-0cf0-4ade-89ca-f64fc4526915","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-09-09T01:01:22.158779Z","iopub.execute_input":"2023-09-09T01:01:22.159152Z","iopub.status.idle":"2023-09-09T01:01:39.253161Z","shell.execute_reply.started":"2023-09-09T01:01:22.159118Z","shell.execute_reply":"2023-09-09T01:01:39.250257Z"},"trusted":true},"execution_count":85,"outputs":[{"name":"stderr","text":"100%|██████████| 273/273 [00:17<00:00, 15.99it/s]\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Data Visualistion of audio features","metadata":{"_uuid":"8a75c707-98e8-4d95-a3f8-252815e73074","_cell_guid":"aaa9673b-0ef8-464d-aba9-33422d79ffe1","trusted":true}},{"cell_type":"code","source":"mfcc_features_arr = np.array(mfcc_features_arr)\nplt.figure(figsize=(10, 6))\nlibrosa.display.specshow(mfcc_features_arr, x_axis='time')\nplt.colorbar()\nplt.title('MFCC')\nplt.ylabel('MFCC Coefficients')\nplt.tight_layout()\nplt.show()","metadata":{"_uuid":"716b6d0d-23d6-4bc4-81c2-c0af662f25ed","_cell_guid":"2606a5b4-7ca4-4c6d-b595-2edc9c3abd0b","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-09-09T01:01:39.254692Z","iopub.execute_input":"2023-09-09T01:01:39.255037Z","iopub.status.idle":"2023-09-09T01:01:39.806362Z","shell.execute_reply.started":"2023-09-09T01:01:39.255004Z","shell.execute_reply":"2023-09-09T01:01:39.805484Z"},"trusted":true},"execution_count":86,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x600 with 2 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"**Stack 2D data to 1D**","metadata":{}},{"cell_type":"code","source":"# Stack the 2D MFCC arrays vertically\nstacked_mfcc_features = np.vstack(mfcc_features_arr)\n\n# Assign the stacked MFCC features to the DataFrame\ndf['features'] = list(stacked_mfcc_features)\n\n\nlabels = df['labels']\nfeatures = df['features']\n\nX= np.array(features.tolist())\ny= np.array(labels.tolist())","metadata":{"_uuid":"e0c8927f-56eb-4ffe-9b3a-2e9995e30a1f","_cell_guid":"fa5856c5-c240-43be-aa7e-84b8af551fb7","jupyter":{"source_hidden":true},"execution":{"iopub.status.busy":"2023-09-09T01:01:39.807850Z","iopub.execute_input":"2023-09-09T01:01:39.808885Z","iopub.status.idle":"2023-09-09T01:01:39.817536Z","shell.execute_reply.started":"2023-09-09T01:01:39.808847Z","shell.execute_reply":"2023-09-09T01:01:39.816615Z"},"trusted":true},"execution_count":87,"outputs":[]},{"cell_type":"markdown","source":"## Splitting the Data into Train and Test","metadata":{"_uuid":"91f89961-f248-4324-97e4-1e2ed80cc294","_cell_guid":"540fc11e-4b4f-4cc3-b0f8-107e7caea8ce","trusted":true}},{"cell_type":"code","source":"# Splitting the Data 80% Train and 30% Test\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,stratify=labels, random_state=seed)","metadata":{"_uuid":"728d7c99-773a-4b69-b503-54982fc8543c","_cell_guid":"8706b7f7-6e7c-488a-b8bb-46eeaa2321bd","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-09-09T01:01:39.822608Z","iopub.execute_input":"2023-09-09T01:01:39.823362Z","iopub.status.idle":"2023-09-09T01:01:39.830564Z","shell.execute_reply.started":"2023-09-09T01:01:39.823327Z","shell.execute_reply":"2023-09-09T01:01:39.829605Z"},"trusted":true},"execution_count":88,"outputs":[]},{"cell_type":"code","source":"# Further split the training data into training and validation sets\nX_train_new,X_val,y_train_new,y_val = train_test_split(X_train,y_train,test_size=0.2,stratify=y_train, random_state=seed)","metadata":{"_uuid":"2ea367c4-ca01-4992-8415-930d29c93b47","_cell_guid":"7ecf3583-254c-4a65-88dc-020d5df13eee","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-09-09T01:01:39.831972Z","iopub.execute_input":"2023-09-09T01:01:39.832411Z","iopub.status.idle":"2023-09-09T01:01:39.841486Z","shell.execute_reply.started":"2023-09-09T01:01:39.832358Z","shell.execute_reply":"2023-09-09T01:01:39.840497Z"},"trusted":true},"execution_count":89,"outputs":[]},{"cell_type":"markdown","source":"## Handling Imbalanced Data - Undersampling","metadata":{"_uuid":"6563776c-0ce8-41f5-8e81-aad965a03da8","_cell_guid":"5f1159dc-77c7-446d-b0ea-17352e8ef16d","trusted":true}},{"cell_type":"code","source":"cluster_centroids = ClusterCentroids(random_state=seed)\nX_train_new_under_sampled, y_train_new_under_sampled = cluster_centroids.fit_resample(X_train_new, y_train_new)","metadata":{"_uuid":"354f7b3c-c184-4fbd-bcfd-ac998a61c783","_cell_guid":"f17698c6-9977-4590-9026-a9271cee2fa6","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-09-09T01:01:39.843524Z","iopub.execute_input":"2023-09-09T01:01:39.843845Z","iopub.status.idle":"2023-09-09T01:01:40.110145Z","shell.execute_reply.started":"2023-09-09T01:01:39.843819Z","shell.execute_reply":"2023-09-09T01:01:40.109400Z"},"trusted":true},"execution_count":90,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/sklearn/cluster/_kmeans.py:870: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n  warnings.warn(\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Model Development","metadata":{"_uuid":"ddce0be8-d834-4db6-9bb5-05276ddfcdf7","_cell_guid":"8cae2688-a9ab-4a6a-a30d-c93c2dcb9aa9","trusted":true}},{"cell_type":"code","source":"# Model development\nhp = HyperParameters()\ndef build_model(hp):\n    # Build a sequential model with tunable hyperparameters\n    model = Sequential()\n    model.add(layers.Dense(units=hp.Int('units_1', min_value=100, max_value=300, step=10),\n                           activation='relu', input_shape=(40,)))\n    model.add(layers.Dropout(rate=hp.Float('dropout_1', min_value=0.3, max_value=0.6, step=0.1)))\n    model.add(layers.Dense(units=hp.Int('units_2', min_value=100, max_value=300, step=10),\n                           activation='relu'))\n    model.add(layers.Dropout(rate=hp.Float('dropout_2', min_value=0.3, max_value=0.6, step=0.1)))\n    model.add(layers.Dense(units=hp.Int('units_3', min_value=50, max_value=200, step=10),\n                           activation='relu'))\n    model.add(layers.Dropout(rate=hp.Float('dropout_3', min_value=0.3, max_value=0.6, step=0.1)))\n    model.add(layers.Dense(1, activation='sigmoid'))\n\n    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=tf.keras.metrics.Recall()\n)\n    return model","metadata":{"_uuid":"a9b5e6b1-ae6b-4a14-b66a-b8e310ae9e91","_cell_guid":"9bb5c742-70cd-47fa-a128-3f8372f5b7cc","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-09-09T01:01:40.113647Z","iopub.execute_input":"2023-09-09T01:01:40.115420Z","iopub.status.idle":"2023-09-09T01:01:40.126479Z","shell.execute_reply.started":"2023-09-09T01:01:40.115389Z","shell.execute_reply":"2023-09-09T01:01:40.125596Z"},"trusted":true},"execution_count":91,"outputs":[]},{"cell_type":"markdown","source":"### Hyperparameter Tuning","metadata":{"_uuid":"1e2aec3c-2ab1-4036-af51-670b4112d81f","_cell_guid":"00687681-1120-4e63-8113-2ce52c161541","trusted":true}},{"cell_type":"code","source":"# Hyperparameter tuning\ntuner = RandomSearch(\n    build_model,\n    objective=keras_tuner.Objective('val_recall',direction='max'),\n    max_trials=10,\n    executions_per_trial=1,\n    directory='my_dir',\n    project_name='Signal Processing',    \n    seed=seed\n)","metadata":{"_uuid":"699bdda6-9dd5-4d23-9a42-f6a06b574c63","_cell_guid":"96ca96e0-a8f5-4824-b2eb-aadaecdbbbcc","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-09-09T01:01:40.127814Z","iopub.execute_input":"2023-09-09T01:01:40.128852Z","iopub.status.idle":"2023-09-09T01:01:40.151988Z","shell.execute_reply.started":"2023-09-09T01:01:40.128819Z","shell.execute_reply":"2023-09-09T01:01:40.151144Z"},"trusted":true},"execution_count":92,"outputs":[]},{"cell_type":"code","source":"# Perform hyperparameter search using undersampled data\ntuner.search(X_train_new_under_sampled, y_train_new_under_sampled, epochs=hp.Int('num_epochs', min_value=50, max_value=200, step=10),\n             batch_size=hp.Choice('batch_size', values=[2, 8, 16, 32, 64, 128]),\n             validation_data=(X_val, y_val))","metadata":{"_uuid":"61c8edd4-f1b4-4a4f-9f68-2fab5d3918b7","_cell_guid":"78246983-ba9d-422b-b435-4fa2ee8f1a99","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-09-09T01:01:40.153310Z","iopub.execute_input":"2023-09-09T01:01:40.153956Z","iopub.status.idle":"2023-09-09T01:01:40.160585Z","shell.execute_reply.started":"2023-09-09T01:01:40.153924Z","shell.execute_reply":"2023-09-09T01:01:40.159631Z"},"trusted":true},"execution_count":93,"outputs":[]},{"cell_type":"code","source":"# Get the best model from the tuner\nbest_model = tuner.get_best_models(1)[0]","metadata":{"_uuid":"20155d40-4b11-471f-8bbd-7b620b7830eb","_cell_guid":"74039efd-800c-45d7-aad5-bd603706f296","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-09-09T01:01:40.161907Z","iopub.execute_input":"2023-09-09T01:01:40.162457Z","iopub.status.idle":"2023-09-09T01:01:40.764019Z","shell.execute_reply.started":"2023-09-09T01:01:40.162419Z","shell.execute_reply":"2023-09-09T01:01:40.763035Z"},"trusted":true},"execution_count":94,"outputs":[]},{"cell_type":"code","source":"# Display a summary of the best model\nbest_model.summary()","metadata":{"_uuid":"41876d53-748d-4a17-9ccd-da3f38c31966","_cell_guid":"1ca546b1-af68-4764-aa89-467b5a6fa804","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-09-09T01:01:40.765419Z","iopub.execute_input":"2023-09-09T01:01:40.765802Z","iopub.status.idle":"2023-09-09T01:01:40.798214Z","shell.execute_reply.started":"2023-09-09T01:01:40.765768Z","shell.execute_reply":"2023-09-09T01:01:40.797377Z"},"trusted":true},"execution_count":95,"outputs":[{"name":"stdout","text":"Model: \"sequential\"\n_________________________________________________________________\n Layer (type)                Output Shape              Param #   \n=================================================================\n dense (Dense)               (None, 290)               11890     \n                                                                 \n dropout (Dropout)           (None, 290)               0         \n                                                                 \n dense_1 (Dense)             (None, 110)               32010     \n                                                                 \n dropout_1 (Dropout)         (None, 110)               0         \n                                                                 \n dense_2 (Dense)             (None, 180)               19980     \n                                                                 \n dropout_2 (Dropout)         (None, 180)               0         \n                                                                 \n dense_3 (Dense)             (None, 1)                 181       \n                                                                 \n=================================================================\nTotal params: 64,061\nTrainable params: 64,061\nNon-trainable params: 0\n_________________________________________________________________\n","output_type":"stream"}]},{"cell_type":"code","source":"# Compile the best model\nbest_model.compile(optimizer='adam', loss='binary_crossentropy', metrics=tf.keras.metrics.Recall())\n\n# Set up early stopping to prevent overfitting\ncallback = tf.keras.callbacks.EarlyStopping(\n    monitor='loss',\n    patience=50,\n    restore_best_weights=True    \n)","metadata":{"_uuid":"42d3ab1d-4319-4e5f-9de7-8f314895cc30","_cell_guid":"4294b6dc-7dcc-4fb9-a380-e4bbecbb849c","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-09-09T01:01:40.799333Z","iopub.execute_input":"2023-09-09T01:01:40.800042Z","iopub.status.idle":"2023-09-09T01:01:42.844700Z","shell.execute_reply.started":"2023-09-09T01:01:40.800004Z","shell.execute_reply":"2023-09-09T01:01:42.843683Z"},"trusted":true},"execution_count":96,"outputs":[]},{"cell_type":"markdown","source":"### Fitting the Tuned Model","metadata":{}},{"cell_type":"code","source":"# Train the best model\nstart_time =  datetime.now() \nhistory = best_model.fit(X_train_new_under_sampled, y_train_new_under_sampled,\n                         validation_data=(X_val, y_val), verbose=0, callbacks=[callback])","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Measure the training duration\nduration = datetime.now() - start_time\nprint(f'Training completed in time: {duration}')","metadata":{"_uuid":"21457110-d88b-4ceb-97e6-710bfc8784cd","_cell_guid":"9b0354d2-ef6d-43d6-a254-025657eaa588","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-09-09T01:01:42.846266Z","iopub.execute_input":"2023-09-09T01:01:42.846642Z","iopub.status.idle":"2023-09-09T01:01:42.853590Z","shell.execute_reply.started":"2023-09-09T01:01:42.846607Z","shell.execute_reply":"2023-09-09T01:01:42.852638Z"},"trusted":true},"execution_count":97,"outputs":[{"name":"stdout","text":"Training completed in time: 0:00:02.025782\n","output_type":"stream"}]},{"cell_type":"code","source":"# Prepare test data and evaluate the model on the test set\ny_test = np.array(y_test)\ny_test = y_test.reshape(-1,1)\ny_test.shape","metadata":{"_uuid":"d66b7a8f-6384-43c0-a895-96ddab5231ef","_cell_guid":"6a2085a2-0139-4c46-bb04-266b36b9324b","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-09-09T01:01:42.854951Z","iopub.execute_input":"2023-09-09T01:01:42.855977Z","iopub.status.idle":"2023-09-09T01:01:42.866442Z","shell.execute_reply.started":"2023-09-09T01:01:42.855944Z","shell.execute_reply":"2023-09-09T01:01:42.865531Z"},"trusted":true},"execution_count":98,"outputs":[{"execution_count":98,"output_type":"execute_result","data":{"text/plain":"(55, 1)"},"metadata":{}}]},{"cell_type":"markdown","source":"## Predicting Raw Test Data","metadata":{"_uuid":"b0286a24-8b12-4af0-8d86-67668f064e04","_cell_guid":"45fcc584-87fd-4fd6-97f8-b2ea95ad7dcc","trusted":true}},{"cell_type":"code","source":"#Evaluate model on unseen test set\ny_pred = best_model.predict(X_test)\n\n# Predict classes based on a threshold\nthreshold = 0.5  \n\ny_pred_classes = np.where(y_pred > threshold, 1, 0)\n\n# Calculate the best validation accuracy achieved during training\nbest_val_accuracy = max(history.history['val_recall_1'])\nprint(\"Best validation accuracy:\", best_val_accuracy)\n\n\n# Evaluate the test accuracy of the best model\ntest_accuracy= best_model.evaluate(X_test,y_test,verbose=0)\nprint(f'Test Accuracy:{test_accuracy[1]}')","metadata":{"_uuid":"73c07489-a5d8-4ea9-b912-b51688ac9335","_cell_guid":"f4d57c71-8c75-414c-8cd8-989000540583","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-09-09T01:01:42.867856Z","iopub.execute_input":"2023-09-09T01:01:42.868363Z","iopub.status.idle":"2023-09-09T01:01:43.389264Z","shell.execute_reply.started":"2023-09-09T01:01:42.868329Z","shell.execute_reply":"2023-09-09T01:01:43.388256Z"},"trusted":true},"execution_count":99,"outputs":[{"name":"stdout","text":"2/2 [==============================] - 0s 3ms/step\nBest validation accuracy: 0.9285714030265808\nTest Accuracy:1.0\n","output_type":"stream"}]},{"cell_type":"markdown","source":"## Converting the model to TFLite format","metadata":{"_uuid":"d04066d0-6ea3-4f2b-926b-7755f0b7b2af","_cell_guid":"090ee972-9f77-4487-ab6c-891898055b5e","trusted":true}},{"cell_type":"code","source":"# Convert the best model to TFLite format to enable deployment on mobile device\nconverter = tf.lite.TFLiteConverter.from_keras_model(best_model)\ntflite_model = converter.convert()\n\n# Save the TFLite model to a file\nwith open('model.tflite', 'wb') as f:\n    f.write(tflite_model)","metadata":{"_uuid":"1786f11d-b0be-4b98-b516-55a4699c77e4","_cell_guid":"9c0a9441-86dd-40de-a14a-e567009b0905","collapsed":false,"jupyter":{"outputs_hidden":false},"execution":{"iopub.status.busy":"2023-09-09T01:01:43.390908Z","iopub.execute_input":"2023-09-09T01:01:43.391275Z","iopub.status.idle":"2023-09-09T01:01:45.242929Z","shell.execute_reply.started":"2023-09-09T01:01:43.391242Z","shell.execute_reply":"2023-09-09T01:01:45.241899Z"},"trusted":true},"execution_count":100,"outputs":[]}]}