Module rulevetting.projects.iai_pecarn.baseline
Expand source code
import numpy as np
import pandas as pd
from rulevetting.templates.model import ModelTemplate
class Baseline(ModelTemplate):
def __init__(self):
# query for each rule + resulting predicted probability
self.rules = [
('AbdTrauma_or_SeatBeltSign_yes == 1', 5.7),
('GCSScore < 14', 4.6),
('AbdTenderDegree_None == 0', 1.4),
('ThoracicTrauma_yes == 1', 0.6),
('AbdomenPain_yes == 1', 0.7),
('DecrBreathSound_yes == 1', 2.9),
('VomitWretch_yes == 1', 0.5),
# final condition is just something that is always true
('GCSScore == GCSScore', 0.1),
]
def _traverse_rule(self, df_features: pd.DataFrame):
str_print = f''
predicted_probabilities = pd.Series(index=df_features.index, dtype=float)
df = df_features.copy()
o = 'outcome'
str_print += f'{df[o].sum()} / {df.shape[0]} (positive class / total)\n\t\u2193 \n'
for j, rule in enumerate(self.rules):
query, prob = rule
df_rhs = df.query(query)
idxs_satisfying_rule = df_rhs.index
predicted_probabilities.loc[idxs_satisfying_rule] = prob
df.drop(index=idxs_satisfying_rule, inplace=True)
computed_prob = 100 * df_rhs[o].sum() / df_rhs.shape[0]
query_print = query.replace(' == 1', '')
if j < len(self.rules) - 1:
str_print += f'\033[96mIf {query_print:<35}\033[00m \u2192 {df_rhs[o].sum():>3} / {df_rhs.shape[0]:>4} ({computed_prob:0.1f}%)\n\t\u2193 \n {df[o].sum():>3} / {df.shape[0]:>5}\t \n'
predicted_probabilities = predicted_probabilities.values
self.str_print = str_print
return predicted_probabilities
def predict(self, df_features: pd.DataFrame):
predicted_probabilities = self._traverse_rule(df_features)
return (predicted_probabilities > 0.11).astype(int)
def predict_proba(self, df_features: pd.DataFrame):
predicted_probabilities = self._traverse_rule(df_features) / 100
return np.vstack((1 - predicted_probabilities, predicted_probabilities)).transpose()
def print_model(self, df_features):
self._traverse_rule(df_features)
return self.str_printClasses
class Baseline-
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 Baseline(ModelTemplate): def __init__(self): # query for each rule + resulting predicted probability self.rules = [ ('AbdTrauma_or_SeatBeltSign_yes == 1', 5.7), ('GCSScore < 14', 4.6), ('AbdTenderDegree_None == 0', 1.4), ('ThoracicTrauma_yes == 1', 0.6), ('AbdomenPain_yes == 1', 0.7), ('DecrBreathSound_yes == 1', 2.9), ('VomitWretch_yes == 1', 0.5), # final condition is just something that is always true ('GCSScore == GCSScore', 0.1), ] def _traverse_rule(self, df_features: pd.DataFrame): str_print = f'' predicted_probabilities = pd.Series(index=df_features.index, dtype=float) df = df_features.copy() o = 'outcome' str_print += f'{df[o].sum()} / {df.shape[0]} (positive class / total)\n\t\u2193 \n' for j, rule in enumerate(self.rules): query, prob = rule df_rhs = df.query(query) idxs_satisfying_rule = df_rhs.index predicted_probabilities.loc[idxs_satisfying_rule] = prob df.drop(index=idxs_satisfying_rule, inplace=True) computed_prob = 100 * df_rhs[o].sum() / df_rhs.shape[0] query_print = query.replace(' == 1', '') if j < len(self.rules) - 1: str_print += f'\033[96mIf {query_print:<35}\033[00m \u2192 {df_rhs[o].sum():>3} / {df_rhs.shape[0]:>4} ({computed_prob:0.1f}%)\n\t\u2193 \n {df[o].sum():>3} / {df.shape[0]:>5}\t \n' predicted_probabilities = predicted_probabilities.values self.str_print = str_print return predicted_probabilities def predict(self, df_features: pd.DataFrame): predicted_probabilities = self._traverse_rule(df_features) return (predicted_probabilities > 0.11).astype(int) def predict_proba(self, df_features: pd.DataFrame): predicted_probabilities = self._traverse_rule(df_features) / 100 return np.vstack((1 - predicted_probabilities, predicted_probabilities)).transpose() def print_model(self, df_features): self._traverse_rule(df_features) return self.str_printAncestors
Inherited members