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Snippet to create a confusion matrix using Comet ML
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| from comet_ml import Experiment | |
| import tensorflow as tf | |
| from tensorflow import keras | |
| from tensorflow.keras.callbacks import Callback | |
| import numpy as np | |
| import pandas as pd | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.preprocessing import StandardScaler | |
| WORKSPACE = "cometpublic" | |
| PROJECT_NAME = "confusion-matrix" | |
| class ConfusionMatrixCallback(Callback): | |
| def __init__(self, experiment, inputs, targets, cutoff=0.5): | |
| self.experiment = experiment | |
| self.inputs = inputs | |
| self.cutoff = cutoff | |
| self.targets = targets | |
| self.targets_reshaped = keras.utils.to_categorical(self.targets) | |
| def on_epoch_end(self, epoch, logs={}): | |
| predicted = self.model.predict(self.inputs) | |
| predicted = np.where(predicted < self.cutoff, 0, 1) | |
| predicted_reshaped = keras.utils.to_categorical(predicted) | |
| self.experiment.log_confusion_matrix( | |
| self.targets_reshaped, | |
| predicted_reshaped, | |
| title="Confusion Matrix, Epoch #%d" % (epoch + 1), | |
| file_name="confusion-matrix-%03d.json" % (epoch + 1), | |
| ) | |
| def load_data(): | |
| raw_df = pd.read_csv( | |
| "https://storage.googleapis.com/download.tensorflow.org/data/creditcard.csv" | |
| ) | |
| return raw_df | |
| def preprocess(raw_df): | |
| df = raw_df.copy() | |
| eps = 0.01 | |
| df.pop("Time") | |
| df["Log Ammount"] = np.log(df.pop("Amount") + eps) | |
| train_df, val_df = train_test_split(df, test_size=0.2) | |
| train_labels = np.array(train_df.pop("Class")) | |
| val_labels = np.array(val_df.pop("Class")) | |
| train_features = np.array(train_df) | |
| val_features = np.array(val_df) | |
| scaler = StandardScaler() | |
| train_features = scaler.fit_transform(train_features) | |
| val_features = scaler.transform(val_features) | |
| train_features = np.clip(train_features, -5, 5) | |
| val_features = np.clip(val_features, -5, 5) | |
| return train_features, val_features, train_labels, val_labels | |
| def build_model(input_shape, output_bias=None): | |
| if output_bias is not None: | |
| output_bias = tf.keras.initializers.Constant(output_bias) | |
| model = keras.Sequential( | |
| [ | |
| keras.layers.Dense(16, activation="relu", input_shape=(input_shape,)), | |
| keras.layers.Dropout(0.5), | |
| keras.layers.Dense(1, activation="sigmoid", bias_initializer=output_bias), | |
| ] | |
| ) | |
| model.compile( | |
| optimizer=keras.optimizers.Adam(lr=1e-3), | |
| loss=keras.losses.BinaryCrossentropy(), | |
| metrics=["accuracy"], | |
| ) | |
| return model | |
| def main(): | |
| experiment = Experiment(workspace=WORKSPACE, project_name=PROJECT_NAME) | |
| df = load_data() | |
| X_train, X_val, y_train, y_val = preprocess(df) | |
| confmat = ConfusionMatrixCallback(experiment, X_val, y_val) | |
| model = build_model(input_shape=X_train.shape[1]) | |
| model.fit( | |
| X_train, | |
| y_train, | |
| validation_data=(X_val, y_val), | |
| epochs=5, | |
| batch_size=64, | |
| callbacks=[confmat], | |
| ) | |
| return | |
| if __name__ == "__main__": | |
| main() |
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