Assignment_4_ECG.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"djQINsl_Ab3V"},"source":["# Use Autoencoder to implement anomaly detection. Build the model by using:\n","a. Import required libraries<br>\n","b. Upload / access the dataset<br>\n","c. Encoder converts it into latent representation<br>\n","d. Decoder networks convert it back to the original input<br>\n","e. Compile the models with Optimizer, Loss, and Evaluation Metrics<br>\n"]},{"cell_type":"code","execution_count":19,"metadata":{"executionInfo":{"elapsed":10,"status":"ok","timestamp":1698430145668,"user":{"displayName":"Ranjeet Kumbhar","userId":"11440510226724121904"},"user_tz":-330},"id":"HnMj8dfZDIWx"},"outputs":[],"source":["# Importing libraries\n","import numpy as np\n","import pandas as pd\n","import tensorflow as tf\n","import matplotlib.pyplot as plt\n","from sklearn.metrics import accuracy_score\n","from sklearn.preprocessing import MinMaxScaler\n","from tensorflow.keras import Model, Sequential\n","from tensorflow.keras.layers import Dense, Dropout\n","from sklearn.model_selection import train_test_split\n","\n","# Define the path to the dataset. You can change this to your local file path if needed.\n","path = 'http://storage.googleapis.com/download.tensorflow.org/data/ecg.csv'\n","\n","# Read the ECG dataset into a Pandas DataFrame\n","data = pd.read_csv(path, header=None)\n"]},{"cell_type":"code","execution_count":20,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":255},"executionInfo":{"elapsed":560,"status":"ok","timestamp":1698430151017,"user":{"displayName":"Ranjeet Kumbhar","userId":"11440510226724121904"},"user_tz":-330},"id":"2LAzd_pWUehy","outputId":"f432716b-1434-440d-8343-d9fa08210aa8"},"outputs":[{"data":{"text/html":["\n","  <div id=\"df-06197b54-feb1-4a42-884b-aa2c1a892a4f\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    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'pandas.core.frame.DataFrame'>\n","RangeIndex: 4998 entries, 0 to 4997\n","Columns: 141 entries, 0 to 140\n","dtypes: float64(141)\n","memory usage: 5.4 MB\n"]}],"source":["# Get information about the dataset, such as column data types and non-null counts\n","data.info()\n"]},{"cell_type":"code","execution_count":30,"metadata":{"executionInfo":{"elapsed":5,"status":"ok","timestamp":1698430251277,"user":{"displayName":"Ranjeet Kumbhar","userId":"11440510226724121904"},"user_tz":-330},"id":"tytSKg7SDg6V"},"outputs":[],"source":["# Splitting the dataset into features and target\n","features = data.drop(140, axis=1)  # Features are all columns except the last (column 140)\n","target = data[140]  # Target is the last column (column 140)\n","\n","# Split the data into training and testing sets (80% training, 20% testing)\n","x_train, x_test, y_train, y_test = train_test_split(\n","    features, target, test_size=0.2\n",")\n","\n","# Get the indices of the training data points labeled as \"1\" (anomalies)\n","train_index = y_train[y_train == 1].index\n","\n","# Select the training data points that are anomalies\n","train_data = x_train.loc[train_index]"]},{"cell_type":"code","execution_count":31,"metadata":{"executionInfo":{"elapsed":4,"status":"ok","timestamp":1698430262865,"user":{"displayName":"Ranjeet Kumbhar","userId":"11440510226724121904"},"user_tz":-330},"id":"rCfSvzQoBZoy"},"outputs":[],"source":["# Initialize the Min-Max Scaler to scale the data between 0 and 1\n","min_max_scaler = MinMaxScaler(feature_range=(0, 1))\n","\n","# Scale the training data\n","x_train_scaled = min_max_scaler.fit_transform(train_data.copy())\n","\n","# Scale the testing data using the same scaler\n","x_test_scaled = min_max_scaler.transform(x_test.copy())\n"]},{"cell_type":"code","execution_count":35,"metadata":{"executionInfo":{"elapsed":5,"status":"ok","timestamp":1698430433642,"user":{"displayName":"Ranjeet Kumbhar","userId":"11440510226724121904"},"user_tz":-330},"id":"oIgBjiJHDpwl"},"outputs":[],"source":["# Creating an Autoencoder model by extending the Model class from Keras\n","class AutoEncoder(Model):\n","    def __init__(self, output_units, ldim=8):\n","        super().__init__()\n","        # Define the encoder part of the Autoencoder\n","        self.encoder = Sequential([\n","            Dense(64, activation='relu'),\n","            Dropout(0.1),\n","            Dense(32, activation='relu'),\n","            Dropout(0.1),\n","            Dense(16, activation='relu'),\n","            Dropout(0.1),\n","            Dense(ldim, activation='relu')\n","        ])\n","        # Define the decoder part of the Autoencoder\n","        self.decoder = Sequential([\n","            Dense(16, activation='relu'),\n","            Dropout(0.1),\n","            Dense(32, activation='relu'),\n","            Dropout(0.1),\n","            Dense(64, activation='relu'),\n","            Dropout(0.1),\n","            Dense(output_units, activation='sigmoid')\n","        ])\n","\n","    def call(self, inputs):\n","        # Forward pass through the Autoencoder\n","        encoded = self.encoder(inputs)\n","        decoded = self.decoder(encoded)\n","        return decoded\n"]},{"cell_type":"code","execution_count":36,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7143,"status":"ok","timestamp":1698430441380,"user":{"displayName":"Ranjeet Kumbhar","userId":"11440510226724121904"},"user_tz":-330},"id":"aWbkLX8yB4EE","outputId":"f1901c72-00b9-4cd4-90f5-51cdfee9de4a"},"outputs":[{"name":"stdout","output_type":"stream","text":["Epoch 1/20\n","5/5 [==============================] - 5s 66ms/step - loss: 0.0110 - mse: 0.0249 - val_loss: 0.0133 - val_mse: 0.0309\n","Epoch 2/20\n","5/5 [==============================] - 0s 19ms/step - loss: 0.0106 - mse: 0.0239 - val_loss: 0.0129 - val_mse: 0.0302\n","Epoch 3/20\n","5/5 [==============================] - 0s 21ms/step - loss: 0.0098 - mse: 0.0220 - val_loss: 0.0126 - val_mse: 0.0294\n","Epoch 4/20\n","5/5 [==============================] - 0s 21ms/step - loss: 0.0087 - mse: 0.0196 - val_loss: 0.0123 - val_mse: 0.0286\n","Epoch 5/20\n","5/5 [==============================] - 0s 18ms/step - loss: 0.0077 - mse: 0.0173 - val_loss: 0.0116 - val_mse: 0.0271\n","Epoch 6/20\n","5/5 [==============================] - 0s 19ms/step - loss: 0.0068 - mse: 0.0152 - val_loss: 0.0112 - val_mse: 0.0261\n","Epoch 7/20\n","5/5 [==============================] - 0s 18ms/step - loss: 0.0061 - mse: 0.0137 - val_loss: 0.0107 - val_mse: 0.0250\n","Epoch 8/20\n","5/5 [==============================] - 0s 27ms/step - loss: 0.0056 - mse: 0.0126 - val_loss: 0.0104 - val_mse: 0.0243\n","Epoch 9/20\n","5/5 [==============================] - 0s 23ms/step - loss: 0.0053 - mse: 0.0118 - val_loss: 0.0101 - val_mse: 0.0235\n","Epoch 10/20\n","5/5 [==============================] - 0s 18ms/step - loss: 0.0050 - mse: 0.0113 - val_loss: 0.0099 - val_mse: 0.0231\n","Epoch 11/20\n","5/5 [==============================] - 0s 22ms/step - loss: 0.0049 - mse: 0.0109 - val_loss: 0.0097 - val_mse: 0.0228\n","Epoch 12/20\n","5/5 [==============================] - 0s 19ms/step - loss: 0.0048 - mse: 0.0107 - val_loss: 0.0097 - val_mse: 0.0227\n","Epoch 13/20\n","5/5 [==============================] - 0s 19ms/step - loss: 0.0047 - mse: 0.0106 - val_loss: 0.0097 - val_mse: 0.0227\n","Epoch 14/20\n","5/5 [==============================] - 0s 20ms/step - loss: 0.0047 - mse: 0.0104 - val_loss: 0.0097 - val_mse: 0.0227\n","Epoch 15/20\n","5/5 [==============================] - 0s 19ms/step - loss: 0.0046 - mse: 0.0103 - val_loss: 0.0096 - val_mse: 0.0225\n","Epoch 16/20\n","5/5 [==============================] - 0s 21ms/step - loss: 0.0046 - mse: 0.0102 - val_loss: 0.0095 - val_mse: 0.0223\n","Epoch 17/20\n","5/5 [==============================] - 0s 20ms/step - loss: 0.0045 - mse: 0.0101 - val_loss: 0.0095 - val_mse: 0.0223\n","Epoch 18/20\n","5/5 [==============================] - 0s 26ms/step - loss: 0.0045 - mse: 0.0101 - val_loss: 0.0095 - val_mse: 0.0223\n","Epoch 19/20\n","5/5 [==============================] - 0s 24ms/step - loss: 0.0045 - mse: 0.0100 - val_loss: 0.0094 - val_mse: 0.0222\n","Epoch 20/20\n","5/5 [==============================] - 0s 18ms/step - loss: 0.0044 - mse: 0.0100 - val_loss: 0.0094 - val_mse: 0.0221\n"]}],"source":["# Create an instance of the AutoEncoder model with the appropriate output units\n","model = AutoEncoder(output_units=x_train_scaled.shape[1])\n","\n","# Compile the model with Mean Squared Logarithmic Error (MSLE) loss and Mean Squared Error (MSE) metric\n","model.compile(loss='msle', metrics=['mse'], optimizer='adam')\n","\n","# Train the model using the scaled training data\n","history = model.fit(\n","    x_train_scaled,  # Input data for training\n","    x_train_scaled,  # Target data for training (autoencoder reconstructs the input)\n","    epochs=20,        # Number of training epochs\n","    batch_size=512,   # Batch size\n","    validation_data=(x_test_scaled, x_test_scaled),  # Validation data\n","    shuffle=True     # Shuffle the data during training\n",")\n"]},{"cell_type":"code","execution_count":37,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":447},"executionInfo":{"elapsed":1353,"status":"ok","timestamp":1698430451139,"user":{"displayName":"Ranjeet Kumbhar","userId":"11440510226724121904"},"user_tz":-330},"id":"C_dz30HTV-rQ","outputId":"16a96c4a-45a9-4b16-94e4-c64fe3f62e61"},"outputs":[{"data":{"text/plain":["<matplotlib.legend.Legend at 0x78c1a61c8e80>"]},"execution_count":37,"metadata":{},"output_type":"execute_result"},{"data":{"image/png":"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","text/plain":["<Figure size 640x480 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"source":["plt.plot(history.history[\"loss\"], label=\"Training Loss\")\n","plt.plot(history.history[\"val_loss\"], label=\"Validation Loss\")\n","plt.legend()"]},{"cell_type":"code","execution_count":38,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":613,"status":"ok","timestamp":1698430479923,"user":{"displayName":"Ranjeet Kumbhar","userId":"11440510226724121904"},"user_tz":-330},"id":"hwaxD_pZETdl","outputId":"c4fd4d38-706c-448b-c553-92f6418bd096"},"outputs":[{"name":"stdout","output_type":"stream","text":["74/74 [==============================] - 0s 2ms/step\n","Threshold: 0.009680301651024063\n"]}],"source":["# Function to find the threshold for anomalies based on the training data\n","def find_threshold(model, x_train_scaled):\n","    # Reconstruct the data using the model\n","    recons = model.predict(x_train_scaled)\n","\n","    # Calculate the mean squared log error between reconstructed data and the original data\n","    recons_error = tf.keras.metrics.msle(recons, x_train_scaled)\n","\n","    # Set the threshold as the mean error plus one standard deviation\n","    threshold = np.mean(recons_error.numpy()) + np.std(recons_error.numpy())\n","\n","    return threshold\n","\n","# Function to make predictions for anomalies based on the threshold\n","def get_predictions(model, x_test_scaled, threshold):\n","    # Reconstruct the data using the model\n","    predictions = model.predict(x_test_scaled)\n","\n","    # Calculate the mean squared log error between reconstructed data and the original data\n","    errors = tf.keras.losses.msle(predictions, x_test_scaled)\n","\n","    # Create a mask for anomalies based on the threshold\n","    anomaly_mask = pd.Series(errors) > threshold\n","\n","    # Map True (anomalies) to 0 and False (normal data) to 1\n","    preds = anomaly_mask.map(lambda x: 0.0 if x == True else 1.0)\n","\n","    return preds\n","\n","# Find the threshold for anomalies\n","threshold = find_threshold(model, x_train_scaled)\n","print(f\"Threshold: {threshold}\")"]},{"cell_type":"code","execution_count":39,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":966,"status":"ok","timestamp":1698430558444,"user":{"displayName":"Ranjeet Kumbhar","userId":"11440510226724121904"},"user_tz":-330},"id":"ph9bwzz0CLtl","outputId":"275820d6-e74f-4eee-a8a5-18803e64fd22"},"outputs":[{"name":"stdout","output_type":"stream","text":["32/32 [==============================] - 0s 2ms/step\n","Accuracy Score: 0.955\n"]}],"source":["# Get predictions for anomalies based on the model and threshold\n","predictions = get_predictions(model, x_test_scaled, threshold)\n","\n","# Calculate the accuracy score by comparing the predicted anomalies to the true labels\n","accuracy = accuracy_score(predictions, y_test)\n","\n","# Print the accuracy score\n","print(f\"Accuracy Score: {accuracy}\")\n"]}],"metadata":{"colab":{"provenance":[]},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.8.8"}},"nbformat":4,"nbformat_minor":0}