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Fix errors and improve code quality and readability in Health Scores (#1147)

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* run black on health_score.py

* to_numpy() versus values - see https://pandas.pydata.org/pandas-docs/version/0.24.0rc1/api/generated/pandas.Series.to_numpy.html
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Saran Ahluwalia 2022-01-14 13:11:47 -05:00 • committed by GitHub
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commit 2604b66cf7
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1 changed files with 66 additions and 36 deletions
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102
data/data-pipeline/data_pipeline/ipython/experiment_4_weighting/health_scores.py
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@ -8,10 +8,11 @@ import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
class HealthScores():
class HealthScores:
"""
Calculates health scores by calling the final_scaled_data() method
Arguments:
weights_1(np array) : weights of model 1.
weights_2(np array) : weights of model 2.
@ -21,9 +22,18 @@ class HealthScores():
is_weighted_average(boolean) : weights calculation methodology(default is True)
weightage(list) : weightage for each y-variable
"""
def __init__(self,weights_1,weights_2,weights_3,multiplied_data,geoid,is_weighted_average = True,weightage = [0.25,0.25,0.5]):
def __init__(
self,
weights_1,
weights_2,
weights_3,
multiplied_data,
geoid,
is_weighted_average=True,
weightage=[0.25, 0.25, 0.5],
):
self.weights_1 = weights_1
self.weights_2 = weights_2
self.weights_3 = weights_3
@ -31,74 +41,94 @@ class HealthScores():
self.geoid = geoid
self.is_weighted_average = is_weighted_average
self.weightage = weightage
self.weights = np.zeros(self.weights_1.shape).reshape(-1,1)
self.weights = np.zeros(self.weights_1.shape).reshape(-1, 1)
def _get_weights(self):
'''
"""
Averages weights of all the models and performs transformation so that sum of all weights will be equal to 1.
Arguments : None
Returns : Averaged weights which totals to 1.
'''
weights = np.hstack((self.weights_1.reshape(-1,1),self.weights_2.reshape(-1,1),self.weights_3.reshape(-1,1)))
weights = np.sum(weights,axis = 1) / weights.shape[1]
"""
weights = np.hstack(
(
self.weights_1.reshape(-1, 1),
self.weights_2.reshape(-1, 1),
self.weights_3.reshape(-1, 1),
)
)
weights = np.sum(weights, axis=1) / weights.shape[1]
self.weights = weights / np.sum(weights)
return self.weights
def _weighted_average(self):
'''
"""
weighted average:
Averages weights of all the models with specified weightage for each model. And performs transformation so that sum of
Averages weights of all the models with specified weightage for each model. And performs transformation so that sum of
all weights will be equal to 1.
Arguments : None
Returns : Averaged weights which totals to 1.(np array)
'''
weights = np.hstack((self.weights_1.reshape(-1,1) * self.weightage[0],self.weights_2.reshape(-1,1) * self.weightage[1],self.weights_3.reshape(-1,1) * self.weightage[2]))
weights = np.sum(weights,axis = 1)
"""
weights = np.hstack(
(
self.weights_1.reshape(-1, 1) * self.weightage[0],
self.weights_2.reshape(-1, 1) * self.weightage[1],
self.weights_3.reshape(-1, 1) * self.weightage[2],
)
)
weights = np.sum(weights, axis=1)
self.weights = weights / np.sum(weights)
return self.weights
def _health_score(self):
'''
"""
Converts data in (0 to 100)scale using min max scaler and multiiplying with 100.
Then it calculates health scores by multiplying with the weights
Returns : data frame with health score and x variables in (0 - 100)scale.
'''
columns = list(self.multiplied_data.columns) + ['health_scores']
scaled_data = MinMaxScaler().fit_transform(self.multiplied_data.values) * 100
health_scores = np.dot(scaled_data,self.weights.reshape(-1,1))
"""
columns = list(self.multiplied_data.columns) + ["health_scores"]
scaled_data = (
MinMaxScaler().fit_transform(self.multiplied_data.to_numpy()) * 100
)
health_scores = np.dot(scaled_data, self.weights.reshape(-1, 1))
health_scores = MinMaxScaler().fit_transform(health_scores) * 100
scaled_data = np.hstack((scaled_data,health_scores))
scaled_data = np.hstack((scaled_data, health_scores))
scaled_data = pd.DataFrame(data = scaled_data, columns = columns)
scaled_data = pd.DataFrame(data=scaled_data, columns=columns)
return scaled_data
def final_scaled_data(self):
"""
Calls appropriate methods in class based on arguments. Concatenates geoids and health scores.
Arguments : None
Returns :
Returns :
final_data(df) : dataframe with all health scores and geoids
weights_tables(df) : dataframe with each y-variable weights and averaged weights
"""
# final scaled data
if self.is_weighted_average:
self._weighted_average()
else:
self._get_weights()
scaled_data_100 = self._health_score()
final_data = pd.concat([self.geoid,scaled_data_100],axis = 1)
weights_table = pd.DataFrame({'phy_health_weights' : self.weights_1, 'mntl_health_weights' : self.weights_2, 'life_expectancy_weights' : self.weights_3, 'averaged_weights' : self.weights},index = self.multiplied_data.columns)
return final_data,weights_table
final_data = pd.concat([self.geoid, scaled_data_100], axis=1)
weights_table = pd.DataFrame(
{
"phy_health_weights": self.weights_1,
"mntl_health_weights": self.weights_2,
"life_expectancy_weights": self.weights_3,
"averaged_weights": self.weights,
},
index=self.multiplied_data.columns,
)
return final_data, weights_table