def drop_vals(perc, c):
""" drop vals giving log prob"""
clf = BayesianRidge()
df, y = glm_testing.create_missing(perc=perc, c=c)
drug_vals, drug_true = glm_testing.test_drug(c=c)
design = df.drop_na()
clf.fit(design, y)
drug_preds, std = clf.predict(drug_vals, return_std=True)
return -scipy.stats.norm(drug_preds, std * 1).logpdf(drug_true).sum()
def knn_fill_conf(perc, c):
"""knn fill that provides the conf intervals"""
clf = BayesianRidge()
df, y = glm_testing.create_missing(perc=perc, c=c)
drug_vals, drug_true = glm_testing.test_drug(c=c)
design = fancyimpute.KNN(k=3).fit_transform(df)
clf.fit(design, y)
drug_preds, std = clf.predict(drug_vals, return_std=True)
return sum(scipy.stats.norm(drug_preds, std * 1).pdf(drug_true) < 0.05)
def mean_fill(perc, c):
"""mean fill that provies the log probability"""
clf = BayesianRidge()
df, y = glm_testing.create_missing(perc=perc, c=c)
drug_vals, drug_true = glm_testing.test_drug(c=c)
design = fancyimpute.SimpleFill(fill_method='mean').fit_transform(df)
clf.fit(design, y)
drug_preds, std = clf.predict(drug_vals, return_std=True)
return -scipy.stats.norm(drug_preds, std * 1).logpdf(drug_true).sum()
def dec_mean(perc, c):
"""
Decision tree classifier with mean imputation
"""
clf = DecisionTreeClassifier()
df, y = glm_testing.create_missing(perc=perc, c=c)
y = y.apply(lambda x: 1 if x > 1 else 0)
drug_vals, drug_true = glm_testing.test_drug(c=c)
drug_true = drug_true.apply(lambda x: 1 if x > 1 else 0)
design = fancyimpute.SimpleFill(fill_method='mean').fit_transform(df)
clf.fit(design, y)
return (np.log(clf.predict_proba(drug_vals)[:, 1][drug_true == 1].prod() *
clf.predict_proba(drug_vals)[:, 0][drug_true == 0].prod()))
def log_mean(perc, c, extras=0):
"""
Logistic regression with mean imputation
"""
clf = LogisticRegression(solver="liblinear")
df, y = glm_testing.create_missing(perc=perc, c=c, extras=extras)
y = y.apply(lambda x: 1 if x > 1 else 0)
drug_vals, drug_true = glm_testing.test_drug(c=c, extras=extras)
drug_true = drug_true.apply(lambda x: 1 if x > 1 else 0)
design = fancyimpute.SimpleFill(fill_method='mean').fit_transform(df)
clf.fit(design, y)
return (np.log(clf.predict_proba(drug_vals)[:, 1][drug_true == 1].prod() *
clf.predict_proba(drug_vals)[:, 0][drug_true == 0].prod()))
def mean_fill_conf(perc, c):
"""mean fill that provides the conf intervals"""
clf = BayesianRidge()
df, y = glm_testing.create_missing(perc=perc, c=c)
drug_vals, drug_true = glm_testing.test_drug(c=c)
design = fancyimpute.SimpleFill(fill_method='mean').fit_transform(df)
clf.fit(design, y)
drug_preds, std = clf.predict(drug_vals, return_std=True)
return sum(
scipy.stats.norm(
drug_preds,
std *
1).pdf(drug_true) < 0.05) # 95 percent confidence
def multi_imp_conf(perc, c, m=10):
"""multiple imputations for conf intervals"""
df, y = glm_testing.create_missing(perc=perc, c=c)
drug_vals, drug_true = glm_testing.test_drug(c=c)
prob_sum = np.zeros(20) # guassian probs sum
for k in range(m):
clf = BayesianRidge()
design = fancyimpute.IterativeImputer(n_iter=10, sample_posterior=True, random_state=int(
k * 243624) % 2**(32 - 1)).fit_transform(df)
clf.fit(design, y)
drug_preds, std = clf.predict(drug_vals, return_std=True)
prob_sum += (1 / m) * scipy.stats.norm(drug_preds,
std * 1).pdf(drug_true)
return sum(prob_sum < 0.05) # 95 percent
def multi_imp(perc, c, m=10):
"""multiple imputation for log prob"""
df, y = glm_testing.create_missing(perc=perc, c=c)
drug_vals, drug_true = glm_testing.test_drug(c=c)
drug_dists = []
prob_sum = 0 # guassian probs sum
for k in range(m):
clf = BayesianRidge()
design = fancyimpute.IterativeImputer(n_iter=10, sample_posterior=True, random_state=int(
k * 243624) % 2**(32 - 1)).fit_transform(df)
clf.fit(design, y)
drug_preds, std = clf.predict(drug_vals, return_std=True)
prob_sum += (1 / m) * scipy.stats.norm(drug_preds,
std * 1).pdf(drug_true).prod()
return -np.log(prob_sum)
def dec_multi(perc, c, m=10):
"""
Decision tree classifier with multiple imputation
"""
clf = DecisionTreeClassifier()
df, y = glm_testing.create_missing(perc=perc, c=c)
drug_vals, drug_true = glm_testing.test_drug(c=c)
y = y.apply(lambda x: 1 if x > 1 else 0)
drug_true = drug_true.apply(lambda x: 1 if x > 1 else 0)
drug_dists = []
prob_sum = 0
for k in range(m):
design = fancyimpute.IterativeImputer(n_iter=10, sample_posterior=True, random_state=int(
k * 243624) % 2**(32 - 1)).fit_transform(df)
clf.fit(design, y)
prob_sum += (1 / m) * (clf.predict_proba(drug_vals)[:, 1][drug_true == 1].prod(
) * clf.predict_proba(drug_vals)[:, 0][drug_true == 0].prod())
return np.log(prob_sum)
def log_multi(perc, c, m=10, extras=0):
"""
Logistic regression classifier with multiple imputation
"""
clf = LogisticRegression(solver="liblinear")
df, y = glm_testing.create_missing(perc=perc, c=c, extras=extras)
drug_vals, drug_true = glm_testing.test_drug(c=c, extras=extras)
y = y.apply(lambda x: 1 if x > 1 else 0)
drug_true = drug_true.apply(lambda x: 1 if x > 1 else 0)
drug_dists = []
prob_sum = 0 # guassian probs sum
for k in range(m):
design = fancyimpute.IterativeImputer(n_iter=10, sample_posterior=True, random_state=int(
k * 243624) % 2**(32 - 1)).fit_transform(df)
clf.fit(design, y)
prob_sum += (1 / m) * (clf.predict_proba(drug_vals)[:, 1][drug_true == 1].prod(
) * clf.predict_proba(drug_vals)[:, 0][drug_true == 0].prod())
return np.log(prob_sum)