# Configuration File for Deep Learning with TensorFlow or K-Nearest Neighbors Models

# CSV File:
# Specify the CSV file containing the data
csv: /path/to/data.csv

# Columns to Remove:
# Specify columns to be removed (not included as feature columns)
columns_to_remove: molecule_chembl_id,standard_value,pref_name,search_term,canonical_smiles

# Transform X:
# Specify whether to transform X or not (True or False)
transformX: True

# Scaler X:
# Specify the scaler for X. Options: MinMaxScaler, StandardScaler, Normalizer, PowerTransformer
scalerX: StandardScaler

# All Features:
# Specify whether to use all features or not (True or False)
all_features: True

# Activity Column:
# Specify the column containing activity data
activity: standard_value

# Logarithmic:
# Specify whether to use logarithmic scale for Y or not (True or False)
logarithmic: True

# Transform Y:
# Specify whether to transform Y or not (True or False)
transformY: True

# Scaler Y:
# Specify the scaler for Y. Options: MinMaxScaler, StandardScaler, Normalizer, PowerTransformer
scalerY: StandardScaler

# Plot Label:
# Specify whether to plot label or not (True or False)
plot_label: True

# Tool:
# Specify the tool to use (TensorFlow or K-Nearest Neighbors)
tool: TensorFlow

# Epochs:
# Specify the number of epochs (if using TensorFlow)
epochs: 100

# Batch Size:
# Specify the batch size (if using TensorFlow)
batch_size: 32

# Dense Units:
# Specify the number of units in each dense layer (if using TensorFlow)
dense_units: 1, 64, 256

# Dense Activations:
# Specify the activation function for each dense layer (if using TensorFlow). Options: relu, sigmoid, tanh, softmax, linear, leaky_relu, prelu, swish
dense_activations: linear, linear, relu

# Optimizer:
# Specify the optimizer (if using TensorFlow). Options: SGD, Adam, RMSProp, Adagrad, Adadelta, Nadam, FTRL
optimizer: SGD

# Learning Rate:
# Specify the learning rate (if using TensorFlow)
learning_rate: 0.001

# Loss Function:
# Specify the loss function (if using TensorFlow). Options: MSE, MAE, Cross-entropy, Hinge
loss_function: MAE

# Patience:
# Specify the patience value (if using TensorFlow)
patience: 10

# KNN Value:
# Specify the value of k (if using K-Nearest Neighbors)
knn_value: 5

