Module rulevetting.templates.model
Expand source code
import pandas as pd
from abc import abstractmethod
class ModelTemplate:
"""Class for implementing model similar to sklearn model (but without fit method).
Classes that use this template should be called "Model" for a new model or "Baseline" for a reference model.
"""
@abstractmethod
def predict(self, df_features: pd.DataFrame):
"""Return binary predictions, exactly following previous paper implementations.
Params
------
df_features: pd.DataFrame
Input features
Returns
-------
predictions: array_like (n, 1)
Values should all be 0 or 1
"""
return NotImplemented
@abstractmethod
def predict_proba(self, df_features: pd.DataFrame):
"""Return probabilistic predictions
Params
------
df_features: pd.DataFrame
Path to all data files
Returns
-------
predicted_probabilities: array_like (n, 2)
Values should be in [0, 1]
predicted_probabilities[:, 0] should be for class 0
predicted_probabilities[:, 1] should be for class 1
"""
return NotImplemented
def print_model(self, df_features: pd.DataFrame):
"""Return string of the model, which includes the number of patients falling into each subgroup.
Note this should be the same as the hardcoded values used in the predict function.
If the model is the baseline used in a paper, it should match it as closely as possible.
Params
------
df_features: pd.DataFrame
Path to all data files
Returns
-------
s: str
Printed version of the existing rule (with number of patients falling into each subgroup).
"""
return NotImplementedClasses
class ModelTemplate-
Class for implementing model similar to sklearn model (but without fit method). Classes that use this template should be called "Model" for a new model or "Baseline" for a reference model.
Expand source code
class ModelTemplate: """Class for implementing model similar to sklearn model (but without fit method). Classes that use this template should be called "Model" for a new model or "Baseline" for a reference model. """ @abstractmethod def predict(self, df_features: pd.DataFrame): """Return binary predictions, exactly following previous paper implementations. Params ------ df_features: pd.DataFrame Input features Returns ------- predictions: array_like (n, 1) Values should all be 0 or 1 """ return NotImplemented @abstractmethod def predict_proba(self, df_features: pd.DataFrame): """Return probabilistic predictions Params ------ df_features: pd.DataFrame Path to all data files Returns ------- predicted_probabilities: array_like (n, 2) Values should be in [0, 1] predicted_probabilities[:, 0] should be for class 0 predicted_probabilities[:, 1] should be for class 1 """ return NotImplemented def print_model(self, df_features: pd.DataFrame): """Return string of the model, which includes the number of patients falling into each subgroup. Note this should be the same as the hardcoded values used in the predict function. If the model is the baseline used in a paper, it should match it as closely as possible. Params ------ df_features: pd.DataFrame Path to all data files Returns ------- s: str Printed version of the existing rule (with number of patients falling into each subgroup). """ return NotImplementedSubclasses
Methods
def predict(self, df_features: pandas.core.frame.DataFrame)-
Return binary predictions, exactly following previous paper implementations.
Params
df_features: pd.DataFrame Input features
Returns
predictions:array_like (n, 1)- Values should all be 0 or 1
Expand source code
@abstractmethod def predict(self, df_features: pd.DataFrame): """Return binary predictions, exactly following previous paper implementations. Params ------ df_features: pd.DataFrame Input features Returns ------- predictions: array_like (n, 1) Values should all be 0 or 1 """ return NotImplemented def predict_proba(self, df_features: pandas.core.frame.DataFrame)-
Return probabilistic predictions
Params
df_features: pd.DataFrame Path to all data files
Returns
predicted_probabilities:array_like (n, 2)- Values should be in [0, 1] predicted_probabilities[:, 0] should be for class 0 predicted_probabilities[:, 1] should be for class 1
Expand source code
@abstractmethod def predict_proba(self, df_features: pd.DataFrame): """Return probabilistic predictions Params ------ df_features: pd.DataFrame Path to all data files Returns ------- predicted_probabilities: array_like (n, 2) Values should be in [0, 1] predicted_probabilities[:, 0] should be for class 0 predicted_probabilities[:, 1] should be for class 1 """ return NotImplemented def print_model(self, df_features: pandas.core.frame.DataFrame)-
Return string of the model, which includes the number of patients falling into each subgroup. Note this should be the same as the hardcoded values used in the predict function. If the model is the baseline used in a paper, it should match it as closely as possible.
Params
df_features: pd.DataFrame Path to all data files
Returns
s:str- Printed version of the existing rule (with number of patients falling into each subgroup).
Expand source code
def print_model(self, df_features: pd.DataFrame): """Return string of the model, which includes the number of patients falling into each subgroup. Note this should be the same as the hardcoded values used in the predict function. If the model is the baseline used in a paper, it should match it as closely as possible. Params ------ df_features: pd.DataFrame Path to all data files Returns ------- s: str Printed version of the existing rule (with number of patients falling into each subgroup). """ return NotImplemented