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# Use DeepSurv from the repo
import sys
sys.path.append('../deepsurv')
import deep_surv

from deepsurv_logger import DeepSurvLogger, TensorboardLogger
import utils
import viz

import numpy as np
import pandas as pd

import lasagne
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline

Read in dataset

First, I read in the dataset and print the first five elements to get a sense of what the dataset looks like



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train_dataset_fp = './example_data.csv'
train_df = pd.read_csv(train_dataset_fp)
train_df.head()

Transform the dataset to "DeepSurv" format

DeepSurv expects a dataset to be in the form:

{
    'x': numpy array of float32
    'e': numpy array of int32
    't': numpy array of float32
    'hr': (optional) numpy array of float32
}

You are providing me a csv, which I read in as a pandas dataframe. Then I convert the pandas dataframe into the DeepSurv dataset format above.



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# event_col is the header in the df that represents the 'Event / Status' indicator
# time_col is the header in the df that represents the event time
def dataframe_to_deepsurv_ds(df, event_col = 'Event', time_col = 'Time'):
    # Extract the event and time columns as numpy arrays
    e = df[event_col].values.astype(np.int32)
    t = df[time_col].values.astype(np.float32)

    # Extract the patient's covariates as a numpy array
    x_df = df.drop([event_col, time_col], axis = 1)
    x = x_df.values.astype(np.float32)
    
    # Return the deep surv dataframe
    return {
        'x' : x,
        'e' : e,
        't' : t
    }

# If the headers of the csv change, you can replace the values of 
# 'event_col' and 'time_col' with the names of the new headers
# You can also use this function on your training dataset, validation dataset, and testing dataset
train_data = dataframe_to_deepsurv_ds(train_df, event_col = 'Event', time_col= 'Time')

Now once you have your dataset all formatted, define you hyper_parameters as a Python dictionary. I'll provide you with some example hyper-parameters, but you should replace the values once you tune them to your specific dataset



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hyperparams = {
    'L2_reg': 10.0,
    'batch_norm': True,
    'dropout': 0.4,
    'hidden_layers_sizes': [25, 25],
    'learning_rate': 1e-05,
    'lr_decay': 0.001,
    'momentum': 0.9,
    'n_in': train_data['x'].shape[1],
    'standardize': True
}

Once you prepared your dataset, and defined your hyper-parameters. Now it's time to train DeepSurv!



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# Create an instance of DeepSurv using the hyperparams defined above
model = deep_surv.DeepSurv(**hyperparams)

# DeepSurv can now leverage TensorBoard to monitor training and validation
# This section of code is optional. If you don't want to use the tensorboard logger
# Uncomment the below line, and comment out the other three lines: 
# logger = None

experiment_name = 'test_experiment_sebastian'
logdir = './logs/tensorboard/'
logger = TensorboardLogger(experiment_name, logdir=logdir)

# Now we train the model
update_fn=lasagne.updates.nesterov_momentum # The type of optimizer to use. \
                                            # Check out http://lasagne.readthedocs.io/en/latest/modules/updates.html \
                                            # for other optimizers to use
n_epochs = 2000

# If you have validation data, you can add it as the second parameter to the function
metrics = model.train(train_data, n_epochs=n_epochs, logger=logger, update_fn=update_fn)

There are two different ways to visualzie how the model trained:

Tensorboard (install ()[tensorboard]) which provides realtime metrics. Run the command in shell:

tensorboard --logdir './logs/tensorboard'

Visualize the training functions post training (below)



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# Print the final metrics
print('Train C-Index:', metrics['c-index'][-1])
# print('Valid C-Index: ',metrics['valid_c-index'][-1])

# Plot the training / validation curves
viz.plot_log(metrics)