InverseCanopy

Back-fit conditional/functional event probability distributions in an event tree to match target end-state frequencies.

How to Use

Below is a step-by-step example of how to use inverse-canopy in your project.

Step 1: Install the Package

First, you need to import InverseCanopy from inverse_canopy, along with tensorflow and numpy.

!pip install inverse-canopy

Alternatively, you can install the CUDA or Metal accelerated versions. No code changes are required as long as the appropriate version is installed.

inverse-canopy CUDA [Linux]

# optionally, first check if an nvidia GPU is available
!nvidia-smi
!pip install inverse-canopy[cuda]

inverse-canopy Metal [macOS]

!pip install inverse-canopy[metal]

from inverse_canopy import InverseCanopy
import tensorflow as tf
import numpy as np

Step 2: Define Your Parameters

You'll need to set up some parameters for the model to work with. These include the number of samples, learning rate, data type, and more.

tunable = {
 'num_samples': 1000000,                # Number of Monte Carlo samples, you don't need too many for smooth functions
 'learning_rate': 0.1,                  # Learning rate for gradient updates
 'dtype': tf.float64,                   # Use 64-bit floats for calculations
 'epsilon': 1e-30,                      # Helps avoid log(0) errors
 'max_steps': 5000,                     # Maximum optimization steps
 'patience': 50,                        # Steps to wait for improvement before stopping
 'initiating_event_frequency': 5.0e-1,  # set the initiating event (IE) frequency here
 'freeze_initiating_event': True,       # set to False if you'd like to predict the IE frequency as well
}

Step 3: Set Up Conditional Events and End States

Define the conditional events and end states for your model. This includes names, bounds for mean and standard deviation, initial values, and the probabilities for each end state.

conditional_events = {
    'names': ['OCSP', 'RSIG', 'RROD', 'SPTR', 'BPHR', 'DHRS|BPHR', 'DHRS|~SPTR', 'DHRL|~BPHR', 'DHRL|~DHRS|BPHR', 'DHRL|~DHRS|~SPTR'],
    'bounds': {
        'mean': {
            'min': 1e-14,
            'max': 1.00,
        },
        'std': {
            'min': 1e-10,
            'max': 1e8,
        },
     },
    'initial': {
       'mean': 5e-1,
       'std': 1e8,
    }
}

end_states = {
    'OCSP-1': {
        'sequence': [1, 0, 0, np.nan, 0, np.nan, np.nan, 0, np.nan, np.nan],
        'probability': 3.24e-2,
    },
    'OCSP-2': {
        'sequence': [1, 0, 0, np.nan, 0, np.nan, np.nan, 1, np.nan, np.nan],
        'probability': 2.80e-10,
    },
    'OCSP-3': {
        'sequence': [1, 0, 0, np.nan, 1, 0, np.nan, np.nan, 0, np.nan],
        'probability': 5.81e-4,
    },
    'OCSP-4': {
        'sequence': [1, 0, 0, np.nan, 1, 0, np.nan, np.nan, 1, np.nan],
        'probability': 1.0e-11,
    },
    'OCSP-5': {
        'sequence': [1, 0, 0, np.nan, 1, 1, np.nan, np.nan, np.nan, np.nan],
        'probability': 1.9e-11,
    },
    'OCSP-6': {
        'sequence': [1, 0, 1, 0, np.nan, np.nan, 0, np.nan, np.nan, 0],
        'probability': 1.0e-11,
    },
    'OCSP-7': {
        'sequence': [1, 0, 1, 0, np.nan, np.nan, 0, np.nan, np.nan, 1],
        'probability': 1.0e-11,
    },
    'OCSP-8': {
        'sequence': [1, 0, 1, 0, np.nan, np.nan, 1, np.nan, np.nan, np.nan],
        'probability': 1.0e-11,
    },

    'OCSP-9': {
        'sequence': [1, 0, 1, 1, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
        'probability': 1.5e-10,
    },

    'OCSP-10': {
        'sequence': [1, 1, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
        'probability': 1.10e-7,
    },    
}

Step 4: Initialize and Fit the Model

Create an instance of InverseCanopy with your conditional events, end states, and tunable parameters. Then, fit the model.

model = InverseCanopy(conditional_events, end_states, tunable)
model.fit(steps=tunable['max_steps'], patience=tunable['patience'], learning_rate=tunable['learning_rate'], legacy=False)

Step 5: Summarize the Results

Finally, you can summarize the results of the model. This can include showing a plot of the results and displaying metrics.

model.summarize(show_plot=True, show_metrics=True)

And that's it! You've successfully used inverse-canopy to back-fit conditional/functional event probabilities in an event tree.

Example Output

Checkout the demo jupyter notebook.

model = InverseCanopy(conditional_events, end_states, tunable)
model.fit(steps=tunable['max_steps'], patience=tunable['patience'], learning_rate=tunable['learning_rate'], legacy=False)

tunable initialized: dtype=<dtype: 'float64'>, epsilon=1e-30
learning_rate: 0.1,patience: 50,min_improvement: 0.001,max_steps: 5000,seed: 372
Step 0: Loss = 10.2756362110866064, performing 182.7 it/sec
Step 100: Loss = 1.0622255351865935, performing 1035.0 it/sec
Step 200: Loss = 0.6733805583746367, performing 1026.8 it/sec
Step 300: Loss = 0.4514006773778768, performing 1039.3 it/sec
Step 400: Loss = 0.2480285319362284, performing 1061.4 it/sec
Step 500: Loss = 0.0719976443349221, performing 1113.5 it/sec
Step 600: Loss = 0.0155362116961599, performing 1122.6 it/sec
No improvement since Step 603, early stopping.
[Best]  Step 602: Loss = 0.0094149955203317
[Final] Step 652: Loss = 0.0167395344814137

predicted end states
-------------------------------------
          5th       Mean      95th
OCSP-1   3.24e-02  3.24e-02  3.24e-02
OCSP-2   2.73e-10  2.78e-10  2.84e-10
OCSP-3   5.77e-04  5.77e-04  5.77e-04
OCSP-4   9.86e-12  9.94e-12  1.00e-11
OCSP-5   1.89e-11  1.90e-11  1.91e-11
OCSP-6   9.67e-12  9.98e-12  1.03e-11
OCSP-7   9.72e-12  1.00e-11  1.04e-11
OCSP-8   9.74e-12  1.00e-11  1.04e-11
OCSP-9   1.46e-10  1.51e-10  1.56e-10
OCSP-10  1.10e-07  1.10e-07  1.10e-07


predicted conditional events
----------------------------------------------
                   5th       Mean      95th
OCSP              1.00e+00  1.00e+00  1.00e+00
RSIG              3.33e-06  3.33e-06  3.33e-06
RROD              5.31e-09  5.48e-09  5.65e-09
SPTR              8.34e-01  8.34e-01  8.34e-01
BPHR              1.75e-02  1.75e-02  1.75e-02
DHRS|BPHR         3.28e-08  3.29e-08  3.31e-08
DHRS|~SPTR        3.34e-01  3.34e-01  3.34e-01
DHRL|~BPHR        8.41e-09  8.57e-09  8.75e-09
DHRL|~DHRS|BPHR   1.71e-08  1.72e-08  1.74e-08
DHRL|~DHRS|~SPTR  5.01e-01  5.01e-01  5.01e-01

model.summarize(show_plot=True, show_metrics=False)

Development

Install dev packages as pip install -e ".[dev]"

Upload to PyPI

• python setup.py sdist bdist_wheel
• pip install twine
• twine upload dist/*