schrodinger.math.multi_parameter_optimization module¶
Utility functions for MPO (Multi-Parameter Optimization). These provide a way to score the desirability of some sort of object on a 0-1 scale based on individual desirability scores of multiple properties of that object.
Use the get_sigmoid
and get_double_sigmoid
functions to get Sigmoid objects which
can be called with a parameter value to get the score. A list of scores and weights
can then be passed to get_weighted_score to compute an overall MPO score.
- class schrodinger.math.multi_parameter_optimization.Transition(center, rate)¶
Bases:
tuple
- center¶
Alias for field number 0
- rate¶
Alias for field number 1
- class schrodinger.math.multi_parameter_optimization.Sigmoid(transition)¶
Bases:
object
A sigmoid transformation using a transition that determines the inflection point and the rate of change.
Larger rate values lead to a faster transition.
example usage: transition = Transition(2.5, 10.0) # a steep 0 to 1 transition transform = Sigmoid(transition) transformed_valued = transform(original_value)
- Variables
transition (Transition) – the transition
rate positive rate negative 1 --- 1 --- / 0 --- 0 --- 0 1 2 3 4... 0 1 2 3 4...
- __init__(transition)¶
- class schrodinger.math.multi_parameter_optimization.DoubleSigmoid(transition_a, transition_b)¶
Bases:
object
A double sigmoid transformation using a left and right transition to determine the inflection points and the rate of change. The left and right transitions are expected to have opposite signs for the rates.
Larger rate values lead to a faster transition.
example usage: a_side = Transform(1.5, 50.0) # a very steep 0 to 1 transition b_side = Transition(10.0, -1.0) # a regular 1 to 0 transition mpo = DoubleSigmoid(a_side, b_side) transformed_valued = mpo(original_value)
- Variables
transition_a (Transition) – the ‘left-hand side’ transition
transition_b (Transition) – the ‘right-hand side’ transition
- __init__(transition_a, transition_b)¶
- schrodinger.math.multi_parameter_optimization.get_intersection(transition_a, transition_b)¶
Get the intersection point of two logistic functions given their center (inflection) points and rate constants.
!!! Rate of transition_a and transition_b cannot be equal or a ZeroDivisionError will be raised. (Two curves with same rates will never intersect unless their centers are equal)
- Return type
float
- schrodinger.math.multi_parameter_optimization.get_rate(good, bad)¶
Get the rate for the logistic function given the x values corresponding to the “good” and “bad” thresholds.
- Parameters
good (float) – The x value corresponding to the good threshold (
GOOD_Y
)bad (float) – The x value corresponding to the bad threshold (
BAD_Y
)
- Returns
The rate
- Return type
float
- schrodinger.math.multi_parameter_optimization.get_center(good, bad)¶
Get the center (inflection point) for the logistic function given the x values corresponding to the “good” and “bad” thresholds.
- Parameters
good (float) – The x value corresponding to the good threshold (
GOOD_Y
)bad (float) – The x value corresponding to the bad threshold (
BAD_Y
)
- Returns
The center point
- Return type
float
- schrodinger.math.multi_parameter_optimization.get_sigmoid(good, bad)¶
Get a sigmoid logistic function using the given “good” and “bad” cutoffs
- Parameters
good (float) – The x value corresponding to the good threshold (
GOOD_Y
)bad (float) – The x value corresponding to the bad threshold (
BAD_Y
)
- Returns
a sigmoid function which can be called with an x value to get the desirability
- Return type
- schrodinger.math.multi_parameter_optimization.get_double_sigmoid(good1, bad1, good2, bad2)¶
Get a double sigmoid function comprised of two logistic functions that fit two points on either function.
- Parameters
good1 (float) – The x value corresponding to the good threshold (
GOOD_Y
) of the first sigmoidbad1 (float) – The x value corresponding to the bad threshold (
BAD_Y
) of the first sigmoidgood2 (float) – The x value corresponding to the good threshold (
GOOD_Y
) of the second sigmoidbad2 (float) – The x value corresponding to the bad threshold (
BAD_Y
) of the second sigmoid
- Returns
the double sigmoid function which can be called with an x value to get the desirability
- Return type
- schrodinger.math.multi_parameter_optimization.get_weighted_score(scores, weights)¶
Return the weighted geometric mean of the given score
- Return type
float