'''
# Author: Riku Laine
# Date: 25JUL2019 (start)
# Project name: Potential outcomes in model evaluation
# Description: This script creates the figures and results used
# in synthetic data experiments.
#
# Parameters:
# -----------
# (1) figure_path : file name for saving the created figures.
# (2) N_sim : Size of simulated data set.
# (3) M_sim : Number of judges in simulated data,
# N_sim must be divisible by M_sim!
# (4) which : Which data + outcome analysis should be performed.
# (5) group_amount : How many groups if Jung-inspired model is used.
# (6) stan_code_file_name : Name of file containing the stan model code.
# (7) sigma_tau : Values of prior variance for the Jung-inspired model.
# (8) model_type : What model type to be fitted. Options:
# - "lr" : logistic regression
# - "rf" : random forest
# - "fully_random" : Fully random, all predictions will be 0.5.
#
'''
# Refer to the `notes.tex` file for explanations about the modular framework.
# Imports
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import scipy.stats as scs
import scipy.special as ssp
import scipy.integrate as si
import numpy.random as npr
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
import pystan
import gc
plt.switch_backend('agg')
import sys
# figure storage name
figure_path = sys.argv[1]
# Size of simulated data set
N_sim = int(sys.argv[2])
# Number of judges in simulated data, N_sim must be divisible by M_sim!
M_sim = int(sys.argv[3])
# Which data + outcome generation should be performed.
which = int(sys.argv[4])
# How many groups if jung model is used
group_amount = int(sys.argv[5])
# Name of stan model code file
stan_code_file_name = sys.argv[6]
# Variance prior
sigma_tau = float(sys.argv[7])
# Type of model to be fitted
model_type = sys.argv[8]
# Settings
plt.rcParams.update({'font.size': 16})
plt.rcParams.update({'figure.figsize': (10, 6)})
print("These results have been obtained with the following settings:")
print("Number of observations in the simulated data:", N_sim)
print("Number of judges in the simulated data:", M_sim)
print("Number of groups:", group_amount)
print("Prior for the variances:", sigma_tau)
# Basic functions
def inverseLogit(x):
return 1.0 / (1.0 + np.exp(-1.0 * x))
def logit(x):
return np.log(x) - np.log(1.0 - x)
def inverseCumulative(x, mu, sigma):
'''Compute the inverse of the cumulative distribution of logit-normal
distribution at x with parameters mu and sigma (mean and st.dev.).'''
return inverseLogit(ssp.erfinv(2 * x - 1) * np.sqrt(2 * sigma**2) - mu)
def standardError(p, n):
denominator = p * (1 - p)
return np.sqrt(denominator / n)
# ## Data generation modules
def bernoulliDGWithoutUnobservables(N_total=50000):
'''Generates data | Variables: X, Y | Outcome from Bernoulli'''
df = pd.DataFrame()
# Sample feature X from standard Gaussian distribution, N(0, 1).
df = df.assign(X=npr.normal(size=N_total))
# Calculate P(Y=0|X=x) = 1 / (1 + exp(-X)) = inverseLogit(X)
df = df.assign(probabilities_Y=inverseLogit(df.X))
# Draw Y ~ Bernoulli(1 - inverseLogit(X))
# Note: P(Y=1|X=x) = 1 - P(Y=0|X=x) = 1 - inverseLogit(X)
results = npr.binomial(n=1, p=1 - df.probabilities_Y, size=N_total)
df = df.assign(result_Y=results)
return df
def thresholdDGWithUnobservables(N_total=50000,
beta_X=1.0,
beta_Z=1.0,
beta_W=0.2):
'''Generates data | Variables: X, Z, W, Y | Outcome by threshold'''
df = pd.DataFrame()
# Sample the variables from standard Gaussian distributions.
df = df.assign(X=npr.normal(size=N_total))
df = df.assign(Z=npr.normal(size=N_total))
df = df.assign(W=npr.normal(size=N_total))
# Calculate P(Y=0|X, Z, W)
probabilities_Y = inverseLogit(beta_X * df.X + beta_Z * df.Z + beta_W * df.W)
df = df.assign(probabilities_Y=probabilities_Y)
# Result is 0 if P(Y = 0| X = x; Z = z; W = w) >= 0.5 , 1 otherwise
df = df.assign(result_Y=np.where(df.probabilities_Y >= 0.5, 0, 1))
return df
def bernoulliDGWithUnobservables(N_total=50000,
beta_X=1.0,
beta_Z=1.0,
beta_W=0.2):
'''Generates data | Variables: X, Z, W, Y | Outcome from Bernoulli'''
df = pd.DataFrame()
# Sample feature X, Z and W from standard Gaussian distribution, N(0, 1).
df = df.assign(X=npr.normal(size=N_total))
df = df.assign(Z=npr.normal(size=N_total))
df = df.assign(W=npr.normal(size=N_total))
# Calculate P(Y=0|X=x) = 1 / (1 + exp(-X)) = inverseLogit(X)
probabilities_Y = inverseLogit(beta_X * df.X + beta_Z * df.Z + beta_W * df.W)
df = df.assign(probabilities_Y=probabilities_Y)
# Draw Y from Bernoulli distribution
results = npr.binomial(n=1, p=1 - df.probabilities_Y, size=N_total)
df = df.assign(result_Y=results)
return df
# ## Decider modules
def humanDeciderLakkaraju(df,
featureX_col,
featureZ_col=None,
nJudges_M=100,
beta_X=1,
beta_Z=1,
add_epsilon=True):
'''Decider module | Non-independent batch decisions.'''
# Assert that every judge will have the same number of subjects.
assert df.shape[0] % nJudges_M == 0, "Can't assign subjets evenly!"
# Compute the number of subjects allocated for each judge.
nSubjects_N = int(df.shape[0] / nJudges_M)
# Assign judge IDs as running numbering from 0 to nJudges_M - 1
df = df.assign(judgeID_J=np.repeat(range(0, nJudges_M), nSubjects_N))
# Sample acceptance rates uniformly from a closed interval
# from 0.1 to 0.9 and round to tenth decimal place.
# 26JUL2019: Fix one leniency to 0.9 so that contraction can compute all
# values.
acceptance_rates = np.append(npr.uniform(.1, .9, nJudges_M - 1), 0.9)
acceptance_rates = np.round(acceptance_rates, 10)
# Replicate the rates so they can be attached to the corresponding judge ID.
df = df.assign(acceptanceRate_R=np.repeat(acceptance_rates, nSubjects_N))
if add_epsilon:
epsilon = np.sqrt(0.1) * npr.normal(size=df.shape[0])
else:
epsilon = 0
if featureZ_col is None:
probabilities_T = inverseLogit(beta_X * df[featureX_col] + epsilon)
else:
probabilities_T = inverseLogit(beta_X * df[featureX_col] +
beta_Z * df[featureZ_col] + epsilon)
df = df.assign(probabilities_T=probabilities_T)
# Sort by judges then probabilities in decreasing order
# Most dangerous for each judge are at the top.
df.sort_values(by=["judgeID_J", "probabilities_T"],
ascending=False,
inplace=True)
# Iterate over the data. Subject will be given a negative decision
# if they are in the top (1-r)*100% of the individuals the judge will judge.
# I.e. if their within-judge-index is under 1 - acceptance threshold times
# the number of subjects assigned to each judge they will receive a
# negative decision.
df.reset_index(drop=True, inplace=True)
df['decision_T'] = np.where((df.index.values % nSubjects_N) <
((1 - df['acceptanceRate_R']) * nSubjects_N),
0, 1)
df_labeled = df.copy()
# Hide unobserved
df_labeled.loc[df.decision_T == 0, 'result_Y'] = np.nan
return df_labeled, df
def bernoulliDecider(df,
featureX_col,
featureZ_col=None,
nJudges_M=100,
beta_X=1,
beta_Z=1,
add_epsilon=True):
'''Use X and Z to make a decision with probability
P(T=0|X, Z)=inverseLogit(beta_X*X+beta_Z*Z).'''
# Assert that every judge will have the same number of subjects.
assert df.shape[0] % nJudges_M == 0, "Can't assign subjets evenly!"
# Compute the number of subjects allocated for each judge.
nSubjects_N = int(df.shape[0] / nJudges_M)
# Assign judge IDs as running numbering from 0 to nJudges_M - 1
df = df.assign(judgeID_J=np.repeat(range(0, nJudges_M), nSubjects_N))
if add_epsilon:
epsilon = np.sqrt(0.1) * npr.normal(size=df.shape[0])
else:
epsilon = 0
if featureZ_col is None:
probabilities_T = inverseLogit(beta_X * df[featureX_col] + epsilon)
else:
probabilities_T = inverseLogit(beta_X * df[featureX_col] +
beta_Z * df[featureZ_col] + epsilon)
df = df.assign(probabilities_T=probabilities_T)
# Draw T from Bernoulli distribution
decisions = npr.binomial(n=1, p=1 - df.probabilities_T, size=df.shape[0])
df = df.assign(decision_T=decisions)
# Calculate the acceptance rates.
acceptance_rates = df.groupby('judgeID_J').mean().decision_T.values
# Replicate the rates so they can be attached to the corresponding judge ID.
df = df.assign(acceptanceRate_R=np.repeat(acceptance_rates, nSubjects_N))
df_labeled = df.copy()
df_labeled.loc[df.decision_T == 0, 'result_Y'] = np.nan
return df_labeled, df
def quantileDecider(df,
featureX_col,
featureZ_col=None,
nJudges_M=100,
beta_X=1,
beta_Z=1,
add_epsilon=True):
'''Assign decisions by the value of inverse cumulative distribution function
of the logit-normal distribution at leniency r.'''
# Assert that every judge will have the same number of subjects.
assert df.shape[0] % nJudges_M == 0, "Can't assign subjets evenly!"
# Compute the number of subjects allocated for each judge.
nSubjects_N = int(df.shape[0] / nJudges_M)
# Assign judge IDs as running numbering from 0 to nJudges_M - 1
df = df.assign(judgeID_J=np.repeat(range(0, nJudges_M), nSubjects_N))
# Sample acceptance rates uniformly from a closed interval
# from 0.1 to 0.9 and round to tenth decimal place.
# 26JUL2019: Fix one leniency to 0.9 so that contraction can compute all
# values.
acceptance_rates = np.append(npr.uniform(.1, .9, nJudges_M - 1), 0.9)
acceptance_rates = np.round(acceptance_rates, 10)
# Replicate the rates so they can be attached to the corresponding judge ID.
df = df.assign(acceptanceRate_R=np.repeat(acceptance_rates, nSubjects_N))
if add_epsilon:
epsilon = np.sqrt(0.1) * npr.normal(size=df.shape[0])
else:
epsilon = 0
if featureZ_col is None:
probabilities_T = inverseLogit(beta_X * df[featureX_col] + epsilon)
# Compute the bounds straight from the inverse cumulative.
# Assuming X is N(0, 1) so Var(bX*X)=bX**2*Var(X)=bX**2.
df = df.assign(bounds=inverseCumulative(
x=df.acceptanceRate_R, mu=0, sigma=np.sqrt(beta_X**2)))
else:
probabilities_T = inverseLogit(beta_X * df[featureX_col] +
beta_Z * df[featureZ_col] + epsilon)
# Compute the bounds straight from the inverse cumulative.
# Assuming X and Z are i.i.d standard Gaussians with variance 1.
# Thus Var(bx*X+bZ*Z)= bX**2*Var(X)+bZ**2*Var(Z).
df = df.assign(bounds=inverseCumulative(
x=df.acceptanceRate_R, mu=0, sigma=np.sqrt(beta_X**2 + beta_Z**2)))
df = df.assign(probabilities_T=probabilities_T)
# Assign negative decision if the predicted probability (probabilities_T) is
# over the judge's threshold (bounds).
df = df.assign(decision_T=np.where(df.probabilities_T >= df.bounds, 0, 1))
df_labeled = df.copy()
df_labeled.loc[df.decision_T == 0, 'result_Y'] = np.nan
return df_labeled, df
def randomDecider(df, nJudges_M=100, use_acceptance_rates=False):
'''Doesn't use any information about X and Z to make decisions.
If use_acceptance_rates is False (default) then all decisions are positive
with probability 0.5. If True, probabilities will be sampled from
U(0.1, 0.9) and rounded to tenth decimal place.'''
# Assert that every judge will have the same number of subjects.
assert df.shape[0] % nJudges_M == 0, "Can't assign subjets evenly!"
# Compute the number of subjects allocated for each judge.
nSubjects_N = int(df.shape[0] / nJudges_M)
# Assign judge IDs as running numbering from 0 to nJudges_M - 1
df = df.assign(judgeID_J=np.repeat(range(0, nJudges_M), nSubjects_N))
if use_acceptance_rates:
# Sample acceptance rates uniformly from a closed interval
# from 0.1 to 0.9 and round to tenth decimal place.
acceptance_rates = np.round(npr.uniform(.1, .9, nJudges_M), 10)
else:
# No real leniency here -> set to 0.5.
acceptance_rates = np.ones(nJudges_M) * 0.5
# Replicate the rates so they can be attached to the corresponding judge ID.
df = df.assign(acceptanceRate_R=np.repeat(acceptance_rates, nSubjects_N))
df = df.assign(
decision_T=npr.binomial(n=1, p=df.acceptanceRate_R, size=df.shape[0]))
df_labeled = df.copy()
df_labeled.loc[df.decision_T == 0, 'result_Y'] = np.nan
return df_labeled, df
def biasDecider(df,
featureX_col,
featureZ_col=None,
nJudges_M=100,
beta_X=1,
beta_Z=1,
add_epsilon=True):
'''
Biased decider: If X > 1, then X <- X * 0.75. People with high X,
get more positive decisions as they should. And if -2 < X -1, then
X <- X + 0.5. People with X in [-2, 1], get less positive decisions
as they should.
'''
# If X > 1, then X <- X * 0.75. People with high X, get more positive
# decisions as they should
df = df.assign(biased_X=np.where(df[featureX_col] > 1, df[featureX_col] *
0.75, df[featureX_col]))
# If -2 < X -1, then X <- X + 0.5. People with X in [-2, 1], get less
# positive decisions as they should
df.biased_X = np.where((df.biased_X > -2) & (df.biased_X < -1) == 1,
df.biased_X + 0.5, df.biased_X)
# Assert that every judge will have the same number of subjects.
assert df.shape[0] % nJudges_M == 0, "Can't assign subjets evenly!"
# Use quantile decider, but judge by the biased X.
df_labeled, df = humanDeciderLakkaraju(df,
featureX_col='biased_X',
featureZ_col=featureZ_col,
nJudges_M=nJudges_M,
beta_X=beta_X,
beta_Z=beta_Z,
add_epsilon=add_epsilon)
return df_labeled, df
# ## Evaluator modules
# ### Convenience functions
def fitPredictiveModel(x_train, y_train, x_test, class_value, model_type=None):
'''
Fit a predictive model (default logistic regression) with given training
instances and return probabilities for test instances to obtain a given
class label.
Arguments:
----------
x_train -- x values of training instances
y_train -- y values of training instances
x_test -- x values of test instances
class_value -- class label for which the probabilities are counted for.
model_type -- type of model to be fitted.
Returns:
--------
(1) Trained predictive model
(2) Probabilities for given test inputs for given class.
'''
if model_type is None or model_type in ["logistic_regression", "lr"]:
# Instantiate the model (using the default parameters)
logreg = LogisticRegression(solver='lbfgs')
# Check shape and fit the model.
if x_train.ndim == 1:
logreg = logreg.fit(x_train.values.reshape(-1, 1), y_train)
else:
logreg = logreg.fit(x_train, y_train)
label_probs_logreg = getProbabilityForClass(x_test, logreg,
class_value)
return logreg, label_probs_logreg
elif model_type in ["random_forest", "rf"]:
# Instantiate the model
forest = RandomForestClassifier(n_estimators=100, max_depth=3)
# Check shape and fit the model.
if x_train.ndim == 1:
forest = forest.fit(x_train.values.reshape(-1, 1), y_train)
else:
forest = forest.fit(x_train, y_train)
label_probs_forest = getProbabilityForClass(x_test, forest,
class_value)
return forest, label_probs_forest
elif model_type == "fully_random":
label_probs = np.ones_like(x_test) / 2
model_object = lambda x: 0.5
return model_object, label_probs
else:
raise ValueError("Invalid model_type!", model_type)
def getProbabilityForClass(x, model, class_value):
'''
Function (wrapper) for obtaining the probability of a class given x and a
predictive model.
Arguments:
-----------
x -- individual features, an array of shape (observations, features)
model -- a trained sklearn model. Predicts probabilities for given x.
Should accept input of shape (observations, features)
class_value -- the resulting class to predict (usually 0 or 1).
Returns:
--------
(1) The probabilities of given class label for each x.
'''
if x.ndim == 1:
# if x is vector, transform to column matrix.
f_values = model.predict_proba(np.array(x).reshape(-1, 1))
else:
f_values = model.predict_proba(x)
# Get correct column of predicted class, remove extra dimensions and return.
return f_values[:, model.classes_ == class_value].flatten()
def cdf(x_0, model, class_value):
'''
Cumulative distribution function as described above. Integral is
approximated using Simpson's rule for efficiency.
Arguments:
----------
x_0 -- private features of an instance for which the value of cdf is to be
calculated.
model -- a trained sklearn model. Predicts probabilities for given x.
Should accept input of shape (observations, features)
class_value -- the resulting class to predict (usually 0 or 1).
'''
def prediction(x):
return getProbabilityForClass(
np.array([x]).reshape(-1, 1), model, class_value)
prediction_x_0 = prediction(x_0)
x_values = np.linspace(-15, 15, 40000)
x_preds = prediction(x_values)
y_values = scs.norm.pdf(x_values)
results = np.zeros(x_0.shape[0])
for i in range(x_0.shape[0]):
y_copy = y_values.copy()
y_copy[x_preds > prediction_x_0[i]] = 0
results[i] = si.simps(y_copy, x=x_values)
return results
# ### Contraction algorithm
#
# Below is an implementation of Lakkaraju's team's algorithm presented in
# [their paper](https://helka.finna.fi/PrimoRecord/pci.acm3098066). Relevant
# parameters to be passed to the function are presented in the description.
def contraction(df, judgeIDJ_col, decisionT_col, resultY_col, modelProbS_col,
accRateR_col, r):
'''
This is an implementation of the algorithm presented by Lakkaraju
et al. in their paper "The Selective Labels Problem: Evaluating
Algorithmic Predictions in the Presence of Unobservables" (2017).
Arguments:
----------
df -- The (Pandas) data frame containing the data, judge decisions,
judge IDs, results and probability scores.
judgeIDJ_col -- String, the name of the column containing the judges' IDs
in df.
decisionT_col -- String, the name of the column containing the judges' decisions
resultY_col -- String, the name of the column containing the realization
modelProbS_col -- String, the name of the column containing the probability
scores from the black-box model B.
accRateR_col -- String, the name of the column containing the judges'
acceptance rates
r -- Float between 0 and 1, the given acceptance rate.
Returns:
--------
(1) The estimated failure rate at acceptance rate r.
'''
# Get ID of the most lenient judge.
most_lenient_ID_q = df[judgeIDJ_col].loc[df[accRateR_col].idxmax()]
# Subset. "D_q is the set of all observations judged by q."
D_q = df[df[judgeIDJ_col] == most_lenient_ID_q].copy()
# All observations of R_q have observed outcome labels.
# "R_q is the set of observations in D_q with observed outcome labels."
R_q = D_q[D_q[decisionT_col] == 1].copy()
# Sort observations in R_q in descending order of confidence scores S and
# assign to R_sort_q.
# "Observations deemed as high risk by B are at the top of this list"
R_sort_q = R_q.sort_values(by=modelProbS_col, ascending=False)
number_to_remove = int(
round((1.0 - r) * D_q.shape[0] - (D_q.shape[0] - R_q.shape[0])))
# "R_B is the list of observations assigned to t = 1 by B"
R_B = R_sort_q[number_to_remove:R_sort_q.shape[0]]
return np.sum(R_B[resultY_col] == 0) / D_q.shape[0], D_q.shape[0]
# ### Evaluators
def trueEvaluationEvaluator(df, featureX_col, decisionT_col, resultY_col, r):
df.sort_values(by='B_prob_0_model', inplace=True, ascending=True)
to_release = int(round(df.shape[0] * r))
failed = df[resultY_col][0:to_release] == 0
return np.sum(failed) / df.shape[0], df.shape[0]
def labeledOutcomesEvaluator(df,
featureX_col,
decisionT_col,
resultY_col,
r,
adjusted=False):
df_observed = df.loc[df[decisionT_col] == 1, :]
df_observed = df_observed.sort_values(by='B_prob_0_model',
inplace=False,
ascending=True)
to_release = int(round(df_observed.shape[0] * r))
failed = df_observed[resultY_col][0:to_release] == 0
if adjusted:
return np.mean(failed), df.shape[0]
return np.sum(failed) / df.shape[0], df.shape[0]
def humanEvaluationEvaluator(df, judgeIDJ_col, decisionT_col, resultY_col,
accRateR_col, r):
# Get judges with correct leniency as list
is_correct_leniency = df[accRateR_col].round(1) == r
# No judges with correct leniency
if np.sum(is_correct_leniency) == 0:
return np.nan, np.nan
correct_leniency_list = df.loc[is_correct_leniency, judgeIDJ_col]
# Released are the people they judged and released, T = 1
released = df[df[judgeIDJ_col].isin(correct_leniency_list)
& (df[decisionT_col] == 1)]
failed = released[resultY_col] == 0
# Get their failure rate, aka ratio of reoffenders to number of people judged in total
return np.sum(failed) / correct_leniency_list.shape[0], correct_leniency_list.shape[0]
def monteCarloEvaluator(df,
featureX_col,
decisionT_col,
resultY_col,
accRateR_col,
r,
mu_X=0,
mu_Z=0,
beta_X=1,
beta_Z=1,
sigma_X=1,
sigma_Z=1):
# Compute the predicted/assumed decision bounds for all the judges.
q_r = inverseCumulative(x=df[accRateR_col],
mu=mu_X + mu_Z,
sigma=np.sqrt((beta_X * sigma_X)**2 +
(beta_Z * sigma_Z)**2))
df = df.assign(bounds=logit(q_r) - df[featureX_col])
# Compute the expectation of Z when it is known to come from truncated
# Gaussian.
alphabeta = (df.bounds - mu_Z) / (sigma_Z)
Z_ = scs.norm.sf(alphabeta, loc=mu_Z, scale=sigma_Z) # 1 - cdf(ab)
# E(Z | Z > a). Expectation of Z if negative decision.
exp_lower_trunc = mu_Z + (sigma_Z * scs.norm.pdf(alphabeta)) / Z_
# E(Z | Z < b). Expectation of Z if positive decision.
exp_upper_trunc = mu_Z - (
sigma_Z * scs.norm.pdf(alphabeta)) / scs.norm.cdf(alphabeta)
exp_Z = (1 - df[decisionT_col]
) * exp_lower_trunc + df[decisionT_col] * exp_upper_trunc
# Attach the predicted probability for Y=0 to data.
df = df.assign(predicted_Y=inverseLogit(df[featureX_col] + exp_Z))
# Predictions drawn from binomial.
predictions = npr.binomial(n=1, p=1 - df.predicted_Y, size=df.shape[0])
df[resultY_col] = np.where(df[decisionT_col] == 0, predictions,
df[resultY_col])
df.sort_values(by='B_prob_0_model', inplace=True, ascending=True)
to_release = int(round(df.shape[0] * r))
failed = df[resultY_col][0:to_release] == 0
return np.sum(failed) / df.shape[0], df.shape[0]
def perfComp(dgModule, deciderModule, title, save_name):
failure_rates = np.zeros((8, 7))
error_Ns = np.zeros((8, 7))
# Create data
df = dgModule()
# Decicions
df_labeled, df_unlabeled = deciderModule(df)
# Split data
train, test_labeled = train_test_split(df_labeled, test_size=0.5)
# Assign same observations to unlabeled dat
test_unlabeled = df_unlabeled.iloc[test_labeled.index.values]
# Train model
B_model, predictions = fitPredictiveModel(
train.loc[train['decision_T'] == 1, 'X'],
train.loc[train['decision_T'] == 1, 'result_Y'], test_labeled['X'], 0, model_type=model_type)
# Attach predictions to data
test_labeled = test_labeled.assign(B_prob_0_model=predictions)
test_unlabeled = test_unlabeled.assign(B_prob_0_model=predictions)
test_labeled.sort_values(by='B_prob_0_model', inplace=True, ascending=True)
kk_array = pd.qcut(test_labeled['B_prob_0_model'], group_amount, labels=False)
# Find observed values
observed = test_labeled['decision_T'] == 1
# Assign data to the model
dat = dict(D=1,
N_obs=np.sum(observed),
N_cens=np.sum(~observed),
K=group_amount,
sigma_tau=sigma_tau,
M=len(set(df_unlabeled['judgeID_J'])),
jj_obs=test_labeled.loc[observed, 'judgeID_J']+1,
jj_cens=test_labeled.loc[~observed, 'judgeID_J']+1,
kk_obs=kk_array[observed]+1,
kk_cens=kk_array[~observed]+1,
dec_obs=test_labeled.loc[observed, 'decision_T'],
dec_cens=test_labeled.loc[~observed, 'decision_T'],
X_obs=test_labeled.loc[observed, 'X'].values.reshape(-1,1),
X_cens=test_labeled.loc[~observed, 'X'].values.reshape(-1,1),
y_obs=test_labeled.loc[observed, 'result_Y'].astype(int))
fit = sm.sampling(data=dat, chains=5, iter=5000, control = dict(adapt_delta=0.9))
pars = fit.extract()
plt.figure(figsize=(15,30))
fit.plot();
plt.savefig(save_name + '_stan_diagnostic_plot')
plt.show()
plt.close('all')
print(fit, file=open(save_name + '_stan_fit_diagnostics.txt', 'w'))
# Bayes
# Alusta matriisi, rivillä yksi otos posteriorista
# sarakkeet havaintoja
y_imp = np.ones((pars['y_est'].shape[0], test_labeled.shape[0]))
# Täydennetään havaitsemattomat estimoiduilla
y_imp[:, ~observed] = 1 - pars['y_est']
# Täydennetään havaitut havaituilla
y_imp[:, observed] = 1 - test_labeled.loc[observed, 'result_Y']
Rs = np.arange(.1, .9, .1)
to_release_list = np.round(test_labeled.shape[0] * Rs).astype(int)
for i in range(len(to_release_list)):
failed = np.sum(y_imp[:, 0:to_release_list[i]], axis=1)
est_failure_rates = failed / test_labeled.shape[0]
failure_rates[i, 6] = np.mean(est_failure_rates)
error_Ns[i, 6] = test_labeled.shape[0]
for r in range(1, 9):
print(".", end="")
# True evaluation
FR, N = trueEvaluationEvaluator(test_unlabeled, 'X', 'decision_T',
'result_Y', r / 10)
failure_rates[r - 1, 0] = FR
error_Ns[r - 1, 0] = N
# Labeled outcomes only
FR, N = labeledOutcomesEvaluator(test_labeled, 'X', 'decision_T',
'result_Y', r / 10)
failure_rates[r - 1, 1] = FR
error_Ns[r - 1, 1] = N
# Adjusted labeled outcomes
FR, N = labeledOutcomesEvaluator(test_labeled, 'X', 'decision_T',
'result_Y', r / 10, adjusted=True)
failure_rates[r - 1, 2] = FR
error_Ns[r - 1, 2] = N
# Human evaluation
FR, N = humanEvaluationEvaluator(test_labeled, 'judgeID_J',
'decision_T', 'result_Y',
'acceptanceRate_R', r / 10)
failure_rates[r - 1, 3] = FR
error_Ns[r - 1, 3] = N
# Contraction
FR, N = contraction(test_labeled, 'judgeID_J', 'decision_T',
'result_Y', 'B_prob_0_model', 'acceptanceRate_R', r / 10)
failure_rates[r - 1, 4] = FR
error_Ns[r - 1, 4] = N
# Causal model - analytic solution
FR, N = monteCarloEvaluator(test_labeled, 'X', 'decision_T',
'result_Y', 'acceptanceRate_R', r / 10)
failure_rates[r - 1, 5] = FR
error_Ns[r - 1, 5] = N
failure_SEs = standardError(failure_rates, error_Ns)
x_ax = np.arange(0.1, 0.9, 0.1)
labels = [
'True Evaluation', 'Labeled outcomes', 'Labeled outcomes, adj.',
'Human evaluation', 'Contraction', 'Analytic solution', 'Potential outcomes'
]
colours = ['g', 'magenta', 'darkviolet', 'r', 'b', 'k', 'c']
for i in range(failure_rates.shape[1]):
plt.errorbar(x_ax,
failure_rates[:, i],
label=labels[i],
c=colours[i],
yerr=failure_SEs[:, i])
plt.title('Failure rate vs. Acceptance rate')
plt.xlabel('Acceptance rate')
plt.ylabel('Failure rate')
plt.legend()
plt.grid()
plt.savefig(save_name + '_all')
plt.show()
print("\nFailure rates:")
print(np.array2string(failure_rates, formatter={'float_kind':lambda x: "%.5f" % x}))
print("\nMean absolute errors:")
for i in range(1, failure_rates.shape[1]):
print(
labels[i].ljust(len(max(labels, key=len))),
np.round(
np.mean(np.abs(failure_rates[:, 0] - failure_rates[:, i])), 5))
sm = pystan.StanModel(file=stan_code_file_name)
if which == 1:
print("\nWithout unobservables (Bernoulli + independent decisions)")
dg = lambda: bernoulliDGWithoutUnobservables(N_total=N_sim)
decider = lambda x: quantileDecider(
x, featureX_col="X", featureZ_col=None, nJudges_M=M_sim, beta_X=1, beta_Z=1)
perfComp(
dg, lambda x: decider(x),
"Fluctuation of failure rate estimates across iterations\n" +
"Bernoulli + independent decisions, without unobservables",
figure_path + "sl_bernoulli_independent_without_Z"
)
gc.collect()
plt.close('all')
print("\nWith unobservables in the data")
if which == 2:
print("\nBernoulli + independent decisions")
dg = lambda: bernoulliDGWithUnobservables(N_total=N_sim)
decider = lambda x: quantileDecider(
x, featureX_col="X", featureZ_col="Z", nJudges_M=M_sim, beta_X=1, beta_Z=1, add_epsilon=True)
perfComp(
dg, lambda x: decider(x),
"Fluctuation of failure rate estimates across iterations \n" +
"Bernoulli + independent decisions, with unobservables",
figure_path + "sl_bernoulli_independent_with_Z",
)
gc.collect()
plt.close('all')
if which == 3:
print("\nThreshold rule + independent decisions")
dg = lambda: thresholdDGWithUnobservables(N_total=N_sim)
decider = lambda x: quantileDecider(
x, featureX_col="X", featureZ_col="Z", nJudges_M=M_sim, beta_X=1, beta_Z=1, add_epsilon=True)
perfComp(
dg, lambda x: decider(x),
"Fluctuation of failure rate estimates across iterations \n" +
"Threshold rule + independent decisions, with unobservables",
figure_path + "sl_threshold_independent_with_Z",
)
gc.collect()
plt.close('all')
if which == 4:
print("\nBernoulli + non-independent (batch) decisions")
dg = lambda: bernoulliDGWithUnobservables(N_total=N_sim)
decider = lambda x: humanDeciderLakkaraju(
x, featureX_col="X", featureZ_col="Z", nJudges_M=M_sim, beta_X=1, beta_Z=1, add_epsilon=True)
perfComp(
dg, lambda x: decider(x),
"Fluctuation of failure rate estimates across iterations \n" +
"Bernoulli + non-independent decisions, with unobservables",
figure_path + "sl_bernoulli_batch_with_Z",
)
gc.collect()
plt.close('all')
if which == 5:
print("\nThreshold rule + non-independent (batch) decisions")
dg = lambda: thresholdDGWithUnobservables(N_total=N_sim)
decider = lambda x: humanDeciderLakkaraju(
x, featureX_col="X", featureZ_col="Z", nJudges_M=M_sim, beta_X=1, beta_Z=1, add_epsilon=True)
perfComp(
dg, lambda x: decider(x),
"Fluctuation of failure rate estimates across iterations \n" +
"Threshold rule + non-independent decisions, with unobservables",
figure_path + "sl_threshold_batch_with_Z",
)
gc.collect()
plt.close('all')
if which == 6:
print("\nRandom decider")
dg = lambda: bernoulliDGWithUnobservables(N_total=N_sim)
decider = lambda x: randomDecider(
x, nJudges_M=M_sim, use_acceptance_rates=True)
perfComp(
dg, lambda x: decider(x),
"Bernoulli + random decider with leniency and unobservables",
figure_path + "sl_random_decider_with_Z",
)
gc.collect()
plt.close('all')
if which == 7:
print("\nBiased decider")
dg = lambda: bernoulliDGWithUnobservables(N_total=N_sim)
decider = lambda x: biasDecider(x, 'X', 'Z', add_epsilon=True)
perfComp(
dg, lambda x: decider(x),
"Bernoulli + biased decider with leniency and unobservables",
figure_path + "sl_biased_decider_with_Z",
)
if which == 8:
print("\nBad judge")
dg = lambda: bernoulliDGWithUnobservables(N_total=N_sim)
decider = lambda x: quantileDecider(x, 'X', 'Z', beta_X=0.2, add_epsilon=True, nJudges_M=M_sim)
perfComp(
dg, lambda x: decider(x),
"Bernoulli + 'bad' decider with leniency and unobservables",
figure_path + "sl_bad_decider_with_Z"
)
gc.collect()
plt.close('all')
if which == 9:
print("\nBernoulli + Bernoulli")
dg = lambda: bernoulliDGWithUnobservables(N_total=N_sim)
decider = lambda x: bernoulliDecider(x, 'X', 'Z', nJudges_M=M_sim)
perfComp(
dg, lambda x: decider(x),
"Bernoulli + Bernoulli",
figure_path + "sl_bernoulli_bernoulli_with_Z",
)
if which == 10:
print("\nBeta_Z = 3, Threshold + batch")
dg = lambda: thresholdDGWithUnobservables(N_total=N_sim, beta_Z=3.0)
decider = lambda x: humanDeciderLakkaraju(
x, featureX_col="X", featureZ_col="Z", nJudges_M=M_sim, beta_X=1, beta_Z=3, add_epsilon=True)
perfComp(
dg, lambda x: decider(x),
"Beta_Z = 3, threshold + batch",
figure_path + "sl_threshold_batch_beta_Z_3_with_Z",
)
if which == 11:
print("\nBeta_Z = 5, Threshold + batch")
dg = lambda: thresholdDGWithUnobservables(N_total=N_sim, beta_Z=5.0)
decider = lambda x: humanDeciderLakkaraju(
x, featureX_col="X", featureZ_col="Z", nJudges_M=M_sim, beta_X=1, beta_Z=5, add_epsilon=True)
perfComp(
dg, lambda x: decider(x),
"Beta_Z = 5, threshold + batch",
figure_path + "sl_threshold_batch_beta_Z_5_with_Z",
)