def test_train_and_predict(self):
# Load data set.
X = DataFrame(RANDOM_CLASSIFICATION_TEST_CASE['X'],
columns=['x1', 'x2', 'x3', 'x4', 'x5', 'x6', 'x7', 'x8', 'x9', 'x10'])
y = DataFrame(RANDOM_CLASSIFICATION_TEST_CASE['y'])
random_state = RANDOM_CLASSIFICATION_TEST_CASE['random_state']
expected_y_pred_by_algorithm = RANDOM_CLASSIFICATION_TEST_CASE['y_predicted']
expected_str_by_algorithm = RANDOM_CLASSIFICATION_TEST_CASE['str']
expected_hyperparams_by_algorithm = RANDOM_CLASSIFICATION_TEST_CASE['hyperparams']
expected_params_by_algorithm = RANDOM_CLASSIFICATION_TEST_CASE['params']
expected_descriptions_by_algorithm = RANDOM_CLASSIFICATION_TEST_CASE['description']
# Generate train/test split.
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=random_state)
# Iterate through SUPPORTED_ALGORITHMS.
for algorithm in SupervisedClassifier.SUPPORTED_ALGORITHMS:
log.info('Testing %s classifier...' % algorithm)
# Train model.
hyperparams = {'algorithm': algorithm, 'random_state': random_state}
# Default to stochastic search for expensive algorithms.
if algorithm in [SupervisedClassifier.RANDOM_FOREST]:
hyperparams['hyperparam_strategy'] = SupervisedClassifier.STOCHASTIC_SEARCH
# Test ability to force hyperparam values.
hyperparams['max_depth'] = 2
hyperparams['n_estimators'] = 5
hyperparams['min_samples_leaf'] = 1
hyperparams['min_samples_split'] = 0.2
else:
hyperparams['hyperparam_strategy'] = SupervisedClassifier.EXHAUSTIVE_SEARCH
classifier = SupervisedClassifier([0, 1], hyperparams)
classifier.train(X_train, y_train)
# Test str().
expected_str = expected_str_by_algorithm[algorithm]
actual_str = str(classifier)
self.assertEqual(expected_str, actual_str)
# Test hyperparameters.
expected_hyperparams = expected_hyperparams_by_algorithm[algorithm]
actual_hyperparams = classifier.hyperparams()
self._assert_equal_hyperparams(expected_hyperparams, actual_hyperparams)
# Test model parameters.
expected_params = expected_params_by_algorithm[algorithm]
actual_params = classifier.params()
self.assertEqualDict(expected_params, actual_params)
# Test model description.
expected_description = expected_descriptions_by_algorithm[algorithm]
actual_description = classifier.description()
self.assertEqual(expected_description, actual_description)
# Test prediction values.
expected_y_pred = expected_y_pred_by_algorithm[algorithm]
log.debug('expected_y_pred: %s' % expected_y_pred)
actual_y_pred = classifier.predict(X_test)
log.debug('actual_y_pred: %s' % actual_y_pred)
self.assertEqualList(expected_y_pred, actual_y_pred)
def train_ml_model(X_train, y_train, alg, groups, output_folderpath,
random_state):
hyperparams = {}
hyperparams['algorithm'] = alg
ml_classifier = SupervisedClassifier(classes=[0, 1],
hyperparams=hyperparams)
status = ml_classifier.train(X_train, y_train, groups=groups)
return ml_classifier
def run(self):
file_organizer = Syst.FileOrganizerLocal(
working_folderpath=self.working_folderpath)
raw_matrix_train, raw_matrix_test = Utils.split_rows(self.input_matrix)
X_train_raw, y_train = Utils.split_Xy(raw_matrix_train,
ylabel=self.ylabel)
feature_processing_pipeline = Pipeline(
memory=None, # file_organizer.cached_pipeline_filepath,
steps=[('impute_features', Clas.FeatureImputer()),
('remove_features', Clas.FeatureRemover()),
('select_features', Clas.Select_Features())])
X_train_processed = feature_processing_pipeline.fit_transform(
X_train_raw, y_train)
predictor = SupervisedClassifier(
classes=[0, 1],
hyperparams={
'algorithm': 'random-forest',
'hyperparam_strategy': SupervisedClassifier.EXHAUSTIVE_SEARCH,
'max_iter': 1024
})
status = predictor.train(X_train_processed, column_or_1d(y_train))
X_test_raw, y_test = Utils.split_Xy(raw_matrix_test,
ylabel=self.ylabel)
X_test_processed = feature_processing_pipeline.transform(X_test_raw)
y_test_pred_proba = predictor.predict_probability(X_test_processed)[:,
1]
res_df = pd.DataFrame({'actual': y_test, 'predict': y_test_pred_proba})
res_df.to_csv(file_organizer.get_output_filepath())
'''TODO'''
from scripts.LabTestAnalysis.lab_statistics.stats_utils import get_confusion_metrics
from sklearn.metrics import roc_auc_score
AUC = roc_auc_score(y_test, y_test_pred_proba)
sensitivity, specificity, LR_p, LR_n, PPV, NPV = get_confusion_metrics(
actual_labels=y_test.values,
predict_probas=y_test_pred_proba,
threshold=0.5)
print("AUC: %s, sensitivity: %s, specificity: %s, LR_p: %s, LR_n: %s, PPV: %s, NPV: %s:. " \
% (AUC, sensitivity, specificity, LR_p, LR_n, PPV, NPV))
class TestClassifierAnalyzer(MedInfoTestCase):
def setUp(self):
log.level = logging.ERROR
# Use simple classifier and test case for testing non-ROC analyses.
X = RANDOM_10_TEST_CASE['X']
y = RANDOM_10_TEST_CASE['y']
self._list_classifier = ListPredictor([0, 1])
self._lc_analyzer = ClassifierAnalyzer(self._list_classifier, X, y)
# Use ml classifier and complex test case.
X = RANDOM_100_TEST_CASE['X']
y = RANDOM_100_TEST_CASE['y']
# Generate train/test split.
X_train, X_test, y_train, y_test = train_test_split(
X, y, random_state=123456789)
# Train logistic regression model.
hyperparams = {
'algorithm': SupervisedClassifier.REGRESS_AND_ROUND,
'random_state': 123456789
}
self._ml_classifier = SupervisedClassifier([0, 1], hyperparams)
self._ml_classifier.train(X_train, column_or_1d(y_train))
self._ml_analyzer = ClassifierAnalyzer(self._ml_classifier, X_test,
y_test)
def tearDown(self):
test_dir = os.path.dirname(os.path.abspath(__file__))
# Clean up the actual report file.
try:
actual_report_name = 'actual-list-classifier.report'
actual_report_path = '/'.join([test_dir, actual_report_name])
os.remove(actual_report_path)
except OSError:
pass
# Clean up the actual precision-recall plot.
try:
actual_plot_name = 'actual-precision-recall-plot.png'
actual_plot_path = '/'.join([test_dir, actual_plot_name])
os.remove(actual_plot_path)
except OSError:
pass
# Clean up the actual roc plot.
try:
actual_plot_name = 'actual-roc-plot.png'
actual_plot_path = '/'.join([test_dir, actual_plot_name])
os.remove(actual_plot_path)
except OSError:
pass
# Clean up the actual precision at k plot.
try:
actual_plot_name = 'actual-precision-at-k-plot.png'
actual_plot_path = '/'.join([test_dir, actual_plot_name])
os.remove(actual_plot_path)
except OSError:
pass
def _assert_fuzzy_equality(self, expected, actual):
abs_diff = actual - expected
rel_diff = (abs_diff) / expected
self.assertTrue(rel_diff < 0.1)
def test_score_accuracy(self):
# Test accuracy.
expected_accuracy = RANDOM_10_TEST_CASE['accuracy']
actual_accuracy = self._lc_analyzer.score()
self.assertEqual(expected_accuracy, actual_accuracy)
# Test accuracy.
expected_accuracy = RANDOM_100_TEST_CASE['accuracy']
actual_accuracy = self._ml_analyzer.score()
self.assertEqual(expected_accuracy, actual_accuracy)
# Test bootstrapped CIs.
actual_accuracy, actual_lower_ci, actual_upper_ci = self._ml_analyzer.score(
ci=0.95, n_bootstrap_iter=1000)
self.assertEqual(expected_accuracy, actual_accuracy)
expected_lower_ci = RANDOM_100_TEST_CASE['ci']['accuracy']['lower']
self.assertEqual(expected_lower_ci, actual_lower_ci)
expected_upper_ci = RANDOM_100_TEST_CASE['ci']['accuracy']['upper']
self.assertEqual(expected_upper_ci, actual_upper_ci)
def test_score_recall(self):
# Test recall.
expected_recall = RANDOM_10_TEST_CASE['recall']
actual_recall = self._lc_analyzer.score(
metric=ClassifierAnalyzer.RECALL_SCORE)
self.assertEqual(expected_recall, actual_recall)
# Test recall.
expected_recall = RANDOM_100_TEST_CASE['recall']
actual_recall = self._ml_analyzer.score(
metric=ClassifierAnalyzer.RECALL_SCORE)
self.assertEqual(expected_recall, actual_recall)
# Test bootstrapped CIs.
actual_recall, actual_lower_ci, actual_upper_ci = self._ml_analyzer.score(
metric=ClassifierAnalyzer.RECALL_SCORE,
ci=0.95,
n_bootstrap_iter=1000)
self.assertEqual(expected_recall, actual_recall)
expected_lower_ci = RANDOM_100_TEST_CASE['ci']['recall']['lower']
self.assertEqual(expected_lower_ci, actual_lower_ci)
expected_upper_ci = RANDOM_100_TEST_CASE['ci']['recall']['upper']
self.assertEqual(expected_upper_ci, actual_upper_ci)
def test_score_precision(self):
# Test precision.
expected_precision = RANDOM_10_TEST_CASE['precision']
actual_precision = self._lc_analyzer.score(
metric=ClassifierAnalyzer.PRECISION_SCORE)
self.assertEqual(expected_precision, actual_precision)
# Test precision.
expected_precision = RANDOM_100_TEST_CASE['precision']
actual_precision = self._ml_analyzer.score(
metric=ClassifierAnalyzer.PRECISION_SCORE)
self.assertEqual(expected_precision, actual_precision)
# Test bootstrapped CIs.
actual_precision, actual_lower_ci, actual_upper_ci = self._ml_analyzer.score(
metric=ClassifierAnalyzer.PRECISION_SCORE,
ci=0.95,
n_bootstrap_iter=1000)
self.assertEqual(expected_precision, actual_precision)
expected_lower_ci = RANDOM_100_TEST_CASE['ci']['precision']['lower']
self.assertEqual(expected_lower_ci, actual_lower_ci)
expected_upper_ci = RANDOM_100_TEST_CASE['ci']['precision']['upper']
self.assertEqual(expected_upper_ci, actual_upper_ci)
def test_score_f1(self):
# Test F1 score.
expected_f1 = RANDOM_10_TEST_CASE['f1']
actual_f1 = self._lc_analyzer.score(metric=ClassifierAnalyzer.F1_SCORE)
self.assertEqual(expected_f1, actual_f1)
# Test f1.
expected_f1 = RANDOM_100_TEST_CASE['f1']
actual_f1 = self._ml_analyzer.score(metric=ClassifierAnalyzer.F1_SCORE)
self.assertEqual(expected_f1, actual_f1)
# Test bootstrapped CIs.
actual_f1, actual_lower_ci, actual_upper_ci = self._ml_analyzer.score(
metric=ClassifierAnalyzer.F1_SCORE, ci=0.95, n_bootstrap_iter=1000)
self.assertEqual(expected_f1, actual_f1)
expected_lower_ci = RANDOM_100_TEST_CASE['ci']['f1']['lower']
self.assertEqual(expected_lower_ci, actual_lower_ci)
expected_upper_ci = RANDOM_100_TEST_CASE['ci']['f1']['upper']
self.assertEqual(expected_upper_ci, actual_upper_ci)
def test_score_average_precision(self):
# Test average precision.
expected_average_precision = RANDOM_100_TEST_CASE['average_precision']
actual_average_precision = self._ml_analyzer.score(
metric=ClassifierAnalyzer.AVERAGE_PRECISION_SCORE)
self.assertEqual(expected_average_precision, actual_average_precision)
# Test bootstrapped CIs.
actual_average_precision, actual_lower_ci, actual_upper_ci = self._ml_analyzer.score(
metric=ClassifierAnalyzer.AVERAGE_PRECISION_SCORE,
ci=0.95,
n_bootstrap_iter=1000)
self.assertEqual(expected_average_precision, actual_average_precision)
expected_lower_ci = RANDOM_100_TEST_CASE['ci']['average_precision'][
'lower']
self.assertEqual(expected_lower_ci, actual_lower_ci)
expected_upper_ci = RANDOM_100_TEST_CASE['ci']['average_precision'][
'upper']
self.assertEqual(expected_upper_ci, actual_upper_ci)
def test_score_roc_auc(self):
# Test roc_auc.
expected_roc_auc = RANDOM_100_TEST_CASE['roc_auc']
actual_roc_auc = self._ml_analyzer.score(
metric=ClassifierAnalyzer.ROC_AUC_SCORE)
self.assertEqual(expected_roc_auc, actual_roc_auc)
# Test bootstrapped CIs.
actual_roc_auc, actual_lower_ci, actual_upper_ci = self._ml_analyzer.score(
metric=ClassifierAnalyzer.ROC_AUC_SCORE,
ci=0.95,
n_bootstrap_iter=1000)
self.assertEqual(expected_roc_auc, actual_roc_auc)
expected_lower_ci = RANDOM_100_TEST_CASE['ci']['roc_auc']['lower']
self.assertEqual(expected_lower_ci, actual_lower_ci)
expected_upper_ci = RANDOM_100_TEST_CASE['ci']['roc_auc']['upper']
self.assertEqual(expected_upper_ci, actual_upper_ci)
def test_score_precision_at_k(self):
# Test precision at K.
prev_precision = 1.0
for k in range(1, 20):
actual_precision_at_k = self._ml_analyzer.score(
metric=ClassifierAnalyzer.PRECISION_AT_K_SCORE, k=k)
expected_precision_at_k = RANDOM_100_TEST_CASE['precision_at_k'][k]
self.assertEqual(expected_precision_at_k, actual_precision_at_k)
# Test bootstrapped CIs.
actual_precision_at_k, actual_lower_ci, actual_upper_ci = self._ml_analyzer.score(
metric=ClassifierAnalyzer.PRECISION_AT_K_SCORE,
k=10,
ci=0.95,
n_bootstrap_iter=1000)
expected_precision_at_k = RANDOM_100_TEST_CASE['precision_at_k'][10]
self.assertEqual(expected_precision_at_k, actual_precision_at_k)
expected_lower_ci = RANDOM_100_TEST_CASE['ci']['precision_at_k'][
'lower'][10]
self.assertEqual(expected_lower_ci, actual_lower_ci)
expected_upper_ci = RANDOM_100_TEST_CASE['ci']['precision_at_k'][
'upper'][10]
self.assertEqual(expected_upper_ci, actual_upper_ci)
def test_score_percent_predictably_positive(self):
# Test roc_auc.
expected_ppp = RANDOM_100_TEST_CASE['percent_predictably_positive']
actual_ppp = self._ml_analyzer.score(
metric=ClassifierAnalyzer.PERCENT_PREDICTABLY_POSITIVE)
self.assertEqual(expected_ppp, actual_ppp)
# Test bootstrapped CIs.
actual_ppp, actual_lower_ci, actual_upper_ci = self._ml_analyzer.score(
metric=ClassifierAnalyzer.PERCENT_PREDICTABLY_POSITIVE,
ci=0.95,
n_bootstrap_iter=1000)
self.assertEqual(expected_ppp, actual_ppp)
expected_lower_ci = RANDOM_100_TEST_CASE['ci'][
'percent_predictably_positive']['lower']
self.assertEqual(expected_lower_ci, actual_lower_ci)
expected_upper_ci = RANDOM_100_TEST_CASE['ci'][
'percent_predictably_positive']['upper']
self.assertEqual(expected_upper_ci, actual_upper_ci)
def test_plot_precision_recall_curve(self):
# Compute precision-recall curve.
precision_recall_curve = self._ml_analyzer.compute_precision_recall_curve(
)
# Build paths for expected and actual plots.
test_dir = os.path.dirname(os.path.abspath(__file__))
actual_plot_name = 'actual-precision-recall-plot.png'
actual_plot_path = '/'.join([test_dir, actual_plot_name])
self._ml_analyzer.plot_precision_recall_curve('Precision-Recall Curve',
actual_plot_path)
# Not sure how to validate this at the moment, so just validate
# that it actually passes.
self.assertTrue(True)
def test_plot_roc_curve(self):
# Compute ROC curve.
roc_curve = self._ml_analyzer.compute_roc_curve()
# Build paths for expected and actual plots.
test_dir = os.path.dirname(os.path.abspath(__file__))
actual_plot_name = 'actual-roc-plot.png'
actual_plot_path = '/'.join([test_dir, actual_plot_name])
self._ml_analyzer.plot_roc_curve('ROC', actual_plot_path)
# Not sure how to validate this at the moment, so just validate
# that it actually passes.
self.assertTrue(True)
def test_plot_precision_at_k_curve(self):
# Compute precision_recall_curve.
k_vals, precision_vals = self._ml_analyzer.compute_precision_at_k_curve(
)
# Build paths for expected and actual plots.
test_dir = os.path.dirname(os.path.abspath(__file__))
actual_plot_name = 'actual-precision-at-k-plot.png'
actual_plot_path = '/'.join([test_dir, actual_plot_name])
self._ml_analyzer.plot_precision_at_k_curve('Precision at K',
actual_plot_path)
# Not sure how to validate this at the moment, so just validate
# that it actually passes.
self.assertTrue(True)
def test_build_report(self):
# Build report.
expected_report = RANDOM_100_TEST_CASE['report']
actual_report = self._ml_analyzer.build_report()[0]
log.debug('expected_report: %s' % expected_report)
log.debug('actual_report: %s' % actual_report)
assert_frame_equal(expected_report, actual_report)
# Build bootstrapped report.
expected_report = RANDOM_100_TEST_CASE['ci']['report']
actual_report = self._ml_analyzer.build_report(ci=0.95)[0]
assert_frame_equal(expected_report, actual_report)
# Build paths for expected and actual report.
test_dir = os.path.dirname(os.path.abspath(__file__))
actual_report_name = 'actual-list-classifier.report'
actual_report_path = '/'.join([test_dir, actual_report_name])
# Write the report.
self._ml_analyzer.write_report(actual_report_path)
# Not sure how to validate this at the moment, so just validate
# that it actually passes.
self.assertTrue(True)
class ConditionMortalityPredictor:
def __init__(self, condition, num_patients, icd_list=None, use_cache=None):
self._condition = condition
self._num_patients = num_patients
self._icd_list = icd_list
self._FEATURES_TO_REMOVE = [
'index_time', 'death_date', 'Death.post', 'Death.postTimeDays',
'Birth.pre', 'Male.preTimeDays', 'Female.preTimeDays',
'RaceWhiteHispanicLatino.preTimeDays',
'RaceWhiteNonHispanicLatino.preTimeDays',
'RaceHispanicLatino.preTimeDays', 'RaceAsian.preTimeDays',
'RaceBlack.preTimeDays', 'RacePacificIslander.preTimeDays',
'RaceNativeAmerican.preTimeDays', 'RaceOther.preTimeDays',
'RaceUnknown.preTimeDays'
]
self._eliminated_features = list()
self._build_cmm_names()
if use_cache is None:
self._build_raw_feature_matrix()
print('Processing raw feature matrix...')
self._process_raw_feature_matrix()
print('Training predictor...')
self._train_predictor()
print('Testing predictor...')
self._test_predictor()
def _build_cmm_names(self):
slugified_condition = "-".join(self._condition.split())
self._build_cmm_name_raw(slugified_condition, self._num_patients)
self._build_cmm_name_processed(slugified_condition, self._num_patients)
def _build_cmm_name_raw(self, slugified_condition, num_patients):
template = '%s-mortality-matrix-%d-pat-raw.tab'
self._cmm_name_raw = template % (slugified_condition, num_patients)
def _build_cmm_name_processed(self, slugified_condition, num_patients):
template = '%s-mortality-matrix-%d-pat-processed.tab'
self._cmm_name_processed = template % (slugified_condition,
num_patients)
def _build_raw_feature_matrix(self):
self._cmm = ConditionMortalityMatrix(self._condition, \
self._num_patients, self._cmm_name_raw, self._icd_list)
def _process_raw_feature_matrix(self):
# Read raw CMM.
self._fm_io = FeatureMatrixIO()
print('Reading raw matrix...')
self._cmm_raw = self._fm_io.read_file_to_data_frame(self._cmm_name_raw)
# Add and remove features to _cmm_processed.
self._fmt = FeatureMatrixTransform()
self._fmt.set_input_matrix(self._cmm_raw)
print('Adding features...')
self._add_features()
print('Imputing data...')
self._impute_data()
self._remove_features()
self._fmt.drop_duplicate_rows()
self._cmm_processed = self._fmt.fetch_matrix()
# Divide _cmm_processed into training and test data.
# This must happen before feature selection so that we don't
# accidentally learn information from the test data.
self._train_test_split()
print('Selecting features...')
self._select_features()
# Write output to new matrix.
train = self._y_train.join(self._X_train)
test = self._y_test.join(self._X_test)
self._cmm_processed = train.append(test)
header = self._build_processed_matrix_header()
self._fm_io.write_data_frame_to_file(self._cmm_processed,
self._cmm_name_processed, header)
def _build_processed_matrix_header(self):
# FeatureMatrixFactory and FeatureMatrixIO expect a list of strings.
# Each comment below represents the line in the comment.
header = list()
# <file_name.tab>
file_name = self._cmm_name_processed
header.append(file_name)
# Created: <timestamp>
timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M")
header.append('Created: %s' % timestamp)
# Source: __name__
header.append('Source: %s' % __name__)
# Command: ConditionMortalityMatrix()
if self._icd_list:
command = 'ConditionMortalityPredictor(%s, %s, %s)' % \
(self._condition, self._num_patients, self._icd_list)
else:
command = 'ConditionMortalityPredictor(%s, %s)' % \
(self._condition, self._num_patients)
header.append('Command: %s' % command)
#
header.append('')
# Overview:
header.append('Overview:')
# This file is a processed version of ___.
line = 'This file is a post-processed version of %s.' % self._cmm_name_raw
header.append(line)
# The outcome label is ___, which is a boolean indicator
line = 'The outcome label is I(0<=Death.postTimeDays<=28), which is a boolean indicator'
header.append(line)
# for whether the patient given by pat_id passed away within 28 days
line = 'for whether the patient given by pat_id passed away within 28 days'
header.append(line)
# of the time index represented by a given row.
line = 'of the time index represented by a given row.'
header.append(line)
# This matrix is the result of the following processing steps on the raw matrix:
line = 'This matrix is the result of the following processing steps on the raw matrix:'
header.append(line)
# (1) Imputing missing values with the mean value of each column.
line = ' (1) Imputing missing values with the mean value of each column.'
header.append(line)
# (2) Manually removing low-information features:
line = ' (2) Manually removing low-information features:'
header.append(line)
# ___
line = ' %s' % str(self._FEATURES_TO_REMOVE)
header.append(line)
# (3) Algorithmically selecting the top 100 features via recursive feature elimination.
line = ' (3) Algorithmically selecting the top 100 features via recursive feature elimination.'
header.append(line)
# The following features were eliminated.
line = ' The following features were eliminated:'
header.append(line)
# List all features with rank >100.
line = ' %s' % str(self._eliminated_features)
header.append(line)
#
line = ''
header.append(line)
# Each row represents a decision point (proxied by clinical order).
line = 'Each row represents a decision point (proxied by clinical order).'
header.append(line)
# Each row contains fields summarizing the patient's demographics,
line = "Each row contains fields summarizing the patient's demographics"
header.append(line)
# inpatient admit date, prior vitals, and prior lab results.
line = 'inpatient admit date, prior vitals, and prior lab results.'
header.append(line)
# Most cells in matrix represent a count statistic for an event's
line = "Most cells in matrix represent a count statistic for an event's"
header.append(line)
# occurrence or a difference between an event's time and index_time.
line = "occurrence or a difference between an event's time and index_time."
header.append(line)
#
header.append('')
# Fields:
header.append('Fields:')
# pat_id - ID # for patient in the STRIDE data set.
header.append(' pat_id - ID # for patient in the STRIDE data set.')
# index_time - time at which clinical decision was made.
header.append(
' index_time - time at which clinical decision was made.')
# death_date - if patient died, date on which they died.
header.append(
' death_date - if patient died, date on which they died.')
# AdmitDxDate.[clinical_item] - admit diagnosis, pegged to admit date.
header.append(
' AdmitDxDate.[clinical_item] - admit diagnosis, pegged to admit date.'
)
# Birth.preTimeDays - patient's age in days.
header.append(" Birth.preTimeDays - patient's age in days.")
# [Male|Female].pre - is patient male/female (binary)?
header.append(' [Male|Female].pre - is patient male/female (binary)?')
# [RaceX].pre - is patient race [X]?
header.append(' [RaceX].pre - is patient race [X]?')
# Team.[specialty].[clinical_item] - specialist added to treatment team.
header.append(
' Team.[specialty].[clinical_item] - specialist added to treatment team.'
)
# Comorbidity.[disease].[clinical_item] - disease added to problem list.
header.append(
' Comorbidity.[disease].[clinical_item] - disease added to problem list.'
)
# ___.[flowsheet] - measurements for flowsheet biometrics.
header.append(
' ___.[flowsheet] - measurements for flowsheet biometrics.')
# Includes BP_High_Systolic, BP_Low_Diastolic, FiO2,
header.append(' Includes BP_High_Systolic, BP_Low_Diastolic, FiO2,')
# Glasgow Coma Scale Score, Pulse, Resp, Temp, and Urine.
header.append(
' Glasgow Coma Scale Score, Pulse, Resp, Temp, and Urine.')
# ___.[lab_result] - lab component results.
header.append(' ___.[lab_result] - lab component results.')
# Included standard components: WBC, HCT, PLT, NA, K, CO2, BUN,
header.append(
' Included standard components: WBC, HCT, PLT, NA, K, CO2, BUN,'
)
# CR, TBIL, ALB, CA, LAC, ESR, CRP, TNI, PHA, PO2A, PCO2A,
header.append(
' CR, TBIL, ALB, CA, LAC, ESR, CRP, TNI, PHA, PO2A, PCO2A,')
# PHV, PO2V, PCO2V
header.append(' PHV, PO2V, PCO2V')
#
header.append('')
# [clinical_item] fields may have the following suffixes:
header.append(
' [clinical_item] fields may have the following suffixes:')
# ___.pre - how many times has this occurred before order_time?
header.append(
' ___.pre - how many times has this occurred before order_time?'
)
# ___.pre.Xd - how many times has this occurred within X days before index_time?
header.append(
' ___.pre.Xd - how many times has this occurred within X days before index_time?'
)
# ___.preTimeDays - how many days before order_time was last occurrence?
header.append(
' ___.preTimeDays - how many days before order_time was last occurrence?'
)
#
header.append('')
# [flowsheet] and [lab_result] fields may have the following suffixes:
header.append(
' [flowsheet] and [lab_result] fields may have the following suffixes:'
)
# ___.X_Y.count - # of result values between X and Y days of index_time.
header.append(
' ___.X_Y.count - # of result values between X and Y days of index_time.'
)
# ___.X_Y.countInRange - # of result values in normal range.
header.append(
' ___.X_Y.countInRange - # of result values in normal range.')
# ___.X_Y.min - minimum result value.
header.append(' ___.X_Y.min - minimum result value.')
# ___.X_Y.max - maximum result value.
header.append(' ___.X_Y.max - maximum result value.')
# ___.X_Y.median - median result value.
header.append(' ___.X_Y.median - median result value.')
# ___.X_Y.std - standard deviation of result values.
header.append(' ___.X_Y.std - standard deviation of result values.')
# ___.X_Y.first - first result value.
header.append(' ___.X_Y.first - first result value.')
# ___.X_Y.last - last result value.
header.append(' ___.X_Y.last - last result value.')
# ___.X_Y.diff - difference between penultimate and proximate values.
header.append(
' ___.X_Y.diff - difference between penultimate and proximate values.'
)
# ___.X_Y.slope - slope between penultimate and proximate values.
header.append(
' ___.X_Y.slope - slope between penultimate and proximate values.'
)
# ___.X_Y.proximate - closest result value to order_time.
header.append(
' ___.X_Y.proximate - closest result value to order_time.')
# ___.X_Y.firstTimeDays - time between first and order_time.
header.append(
' ___.X_Y.firstTimeDays - time between first and order_time.')
# ___.X_Y.lastTimeDays - time between last and order_time.
header.append(
' ___.X_Y.lastTimeDays - time between last and order_time.')
# ___.X_Y.proximateTimeDays - time between proximate and order_time.
header.append(
' ___.X_Y.proximateTimeDays - time between proximate and order_time.'
)
return header
def _train_predictor(self):
self._predictor = SupervisedClassifier(
algorithm=SupervisedClassifier.REGRESS_AND_ROUND)
self._predictor.train(self._X_train, column_or_1d(self._y_train))
def _train_test_split(self):
y = pd.DataFrame(
self._cmm_processed.pop('I(0<=Death.postTimeDays<=28)'))
# Without this line, sklearn complains about the format of y.
# "DataConversionWarning: A column-vector y was passed when a 1d array
# was expected. Please change the shape of y to (n_samples, ), for
# example using ravel()."
# Note that this turns y into a numpy array, so need to cast back.
# y = y.values.ravel()
X = self._cmm_processed
self._X_train, self._X_test, self._y_train, self._y_test = train_test_split(
X, y, shuffle=False)
def _impute_data(self):
# Impute missing values with mean value.
for feature in self._cmm_raw.columns.values:
if feature in self._FEATURES_TO_REMOVE:
continue
# If all values are null, just remove the feature.
# Otherwise, imputation will fail (there's no mean value),
# and sklearn will ragequit.
if self._cmm_raw[feature].isnull().all():
self._fmt.remove_feature(feature)
self._eliminated_features.append(feature)
# Only try to impute if some of the values are null.
elif self._cmm_raw[feature].isnull().any():
# TODO(sbala): Impute all time features with non-mean value.
self._fmt.impute(feature)
def _add_features(self):
# Add threshold feature indicating whether death date
# is within 28 days of index time.
self._fmt.add_threshold_feature('Death.postTimeDays',
lower_bound=0,
upper_bound=28)
def _remove_features(self):
# Prune obviously unhelpful fields.
# In theory, FeatureSelector should be able to prune these, but no
# reason not to help it out a little bit.
for feature in self._FEATURES_TO_REMOVE:
self._fmt.remove_feature(feature)
def _select_features(self):
# Use FeatureSelector to prune all but 100 variables.
fs = FeatureSelector(algorithm=FeatureSelector.RECURSIVE_ELIMINATION, \
problem=FeatureSelector.CLASSIFICATION)
fs.set_input_matrix(self._X_train, column_or_1d(self._y_train))
num_features_to_select = int(0.01 * len(self._X_train.columns.values))
fs.select(k=num_features_to_select)
# Enumerate eliminated features pre-transformation.
self._feature_ranks = fs.compute_ranks()
for i in range(len(self._feature_ranks)):
if self._feature_ranks[i] > num_features_to_select:
self._eliminated_features.append(self._X_train.columns[i])
self._X_train = fs.transform_matrix(self._X_train)
self._X_test = fs.transform_matrix(self._X_test)
def _test_predictor(self):
self._accuracy = self._predictor.compute_accuracy(
self._X_test, self._y_test)
def predict(self, X):
return self._predictor.predict(X)
def summarize(self):
summary_lines = list()
# Condition: condition
condition = self._condition
line = 'Condition: %s' % condition
summary_lines.append(line)
# Algorithm: SupervisedClassifier(algorithm)
algorithm = 'SupervisedClassifier(REGRESS_AND_ROUND)'
line = 'Algorithm: %s' % algorithm
summary_lines.append(line)
# Train/Test Size: training_size, test_size
training_size = self._X_train.shape[0]
test_size = self._X_test.shape[0]
line = 'Train/Test Size: %s/%s' % (training_size, test_size)
summary_lines.append(line)
# Model: sig_features
coefs = self._predictor.coefs()
cols = self._X_train.columns
sig_features = [(coefs[cols.get_loc(f)], f) for f in cols.values
if coefs[cols.get_loc(f)] > 0]
linear_model = ' + '.join('%s*%s' % (weight, feature)
for weight, feature in sig_features)
line = 'Model: logistic(%s)' % linear_model
summary_lines.append(line)
# Baseline Episode Mortality: episode_mortality
counts = self._y_test[self._y_test.columns[0]].value_counts()
line = 'Baseline Episode Mortality: %s/%s' % (counts[1], test_size)
summary_lines.append(line)
# AUC: auc
auc = self._predictor.compute_roc_auc(self._X_test, self._y_test)
line = 'AUC: %s' % auc
summary_lines.append(line)
# Accuracy: accuracy
line = 'Accuracy: %s' % self._accuracy
summary_lines.append(line)
return '\n'.join(summary_lines)
class BifurcatedSupervisedClassifier:
BIFURCATION = 'bifurcation'
EQUAL = '=='
LTE = '<='
GTE = '>='
SUPPORTED_BIFURCATION_STRATEGIES = [EQUAL, GTE, LTE]
def __init__(self, classes, hyperparams):
if hyperparams[
'bifurcation_strategy'] not in BifurcatedSupervisedClassifier.SUPPORTED_BIFURCATION_STRATEGIES:
raise ValueError('Bifurcation strategy %s not supported.' %
hyperparams['bifurcation_strategy'])
self._classes = classes
self._hyperparams = hyperparams
# Note that if we don't pass a copies of hyperparams, then we won't
# be able to change hyperparams independently in the two classifiers.
self._sc_true = SupervisedClassifier(classes, hyperparams.copy())
self._sc_false = SupervisedClassifier(classes, hyperparams.copy())
def __repr__(self):
bs = self._build_bifurcation_str()
classes_str = str(self._classes)
hyperparams_str = "hyperparams={'algorithm': %s, 'bifurcator': %s, 'bifurcation_strategy': %s, 'bifurcation_threshold': %s, 'random_state': %s}" % (
self._hyperparams['algorithm'], self._hyperparams['bifurcator'],
self._hyperparams['bifurcation_strategy'],
self._hyperparams['bifurcation_value'],
self._hyperparams['random_state'])
s = "BifurcatedSupervisedClassifier(%s, %s)" % (classes_str,
hyperparams_str)
return s
__str__ = __repr__
def _build_bifurcation_str(self):
args = (self._hyperparams['bifurcator'],
self._hyperparams['bifurcation_strategy'],
self._hyperparams['bifurcation_value'])
return '%s %s %s' % args
def fetch_bifurcation_masks(self, X):
log.debug('bifurcator: %s' % self._hyperparams['bifurcator'])
log.debug('bifurcation_strategy: %s' %
BifurcatedSupervisedClassifier.EQUAL)
log.debug('bifurcation_value: %s' %
self._hyperparams['bifurcation_value'])
if self._hyperparams[
'bifurcation_strategy'] is BifurcatedSupervisedClassifier.EQUAL:
true_mask = X[self._hyperparams['bifurcator']].astype(
float) == self._hyperparams['bifurcation_value']
false_mask = X[self._hyperparams['bifurcator']].astype(
float) != self._hyperparams['bifurcation_value']
elif self._hyperparams[
'bifurcation_strategy'] is BifurcatedSupervisedClassifier.LTE:
true_mask = X[self._hyperparams['bifurcator']].astype(
float) <= self._hyperparams['bifurcation_value']
false_mask = X[self._hyperparams['bifurcator']].astype(
float) > self._hyperparams['bifurcation_value']
elif self._hyperparams[
'bifurcation_strategy'] is BifurcatedSupervisedClassifier.GTE:
true_mask = X[self._hyperparams['bifurcator']].astype(
float) >= self._hyperparams['bifurcation_value']
false_mask = X[self._hyperparams['bifurcator']].astype(
float) < self._hyperparams['bifurcation_value']
log.debug('X[%s].value_counts(): %s' %
(self._hyperparams['bifurcator'],
X[self._hyperparams['bifurcator']].value_counts()))
log.debug('true_mask.value_counts(): %s' % true_mask.value_counts())
log.debug('false_mask.value_counts(): %s' % false_mask.value_counts())
return true_mask, false_mask
def description(self):
args = (self._hyperparams['algorithm'].upper().replace('-', '_'),
self._build_bifurcation_str(), self._sc_true.description(),
self._sc_false.description())
return 'BIFURCATED_%s(%s, true=%s, false=%s)' % args
def hyperparams(self):
hyperparams = {
'model_true': self._sc_true.hyperparams(),
'model_false': self._sc_false.hyperparams()
}
return hyperparams
def params(self):
params = {
'bifurcator': self._hyperparams['bifurcator'],
'bifurcation_strategy': self._hyperparams['bifurcation_strategy'],
'bifurcation_value': self._hyperparams['bifurcation_value'],
'model_true': self._sc_true.description(),
'model_false': self._sc_false.description()
}
return params
def train(self, X_train, y_train):
true_mask, false_mask = self.fetch_bifurcation_masks(X_train)
# Train sc_true.
X_train_true = X_train[true_mask]
y_train_true = y_train[true_mask]
status_true = self._sc_true.train(X_train_true, y_train_true)
if status_true == SupervisedClassifier.INSUFFICIENT_SAMPLES:
return status_true
# Train sc_true.
X_train_false = X_train[false_mask]
y_train_false = y_train[false_mask]
status_false = self._sc_false.train(X_train_false, y_train_false)
if status_false == SupervisedClassifier.INSUFFICIENT_SAMPLES:
return status_false
return SupervisedClassifier.TRAINED
def _stitch_disjoint_row(self, row):
if pd.isnull(row['y_pred_true']):
val = row['y_pred_false']
else:
val = row['y_pred_true']
return val
def _stitch_prob_0(self, row):
if pd.isnull(row['y_pred_prob_true_0']):
val = row['y_pred_prob_false_0']
else:
val = row['y_pred_prob_true_0']
return val
def _stitch_prob_1(self, row):
if pd.isnull(row['y_pred_prob_true_1']):
val = row['y_pred_prob_false_1']
else:
val = row['y_pred_prob_true_1']
return val
def _predict_label_or_probability(self, X_test, probability=None):
true_mask, false_mask = self.fetch_bifurcation_masks(X_test)
# Predict X_test_true.
X_test_true = X_test[true_mask]
if probability:
y_pred_true = self._sc_true.predict_probability(X_test_true)
else:
y_pred_true = self._sc_true.predict(X_test_true)
log.debug('y_pred_true: %s' % y_pred_true)
# Predict X_test_false.
X_test_false = X_test[false_mask]
if probability:
y_pred_false = self._sc_false.predict_probability(X_test_false)
else:
y_pred_false = self._sc_false.predict(X_test_false)
log.debug('y_pred_false: %s' % y_pred_false)
# Stitch results.
if probability:
column_names = ['y_pred_true_0', 'y_pred_true_1']
else:
column_names = ['y_pred_true']
y_pred_true_df = DataFrame(y_pred_true, index=X_test_true.index, \
columns=column_names)
log.debug('y_pred_true_df: %s' % y_pred_true_df)
if probability:
column_names = ['y_pred_false_0', 'y_pred_false_1']
else:
column_names = ['y_pred_false']
y_pred_false_df = DataFrame(y_pred_false, index=X_test_false.index, \
columns=column_names)
log.debug('y_pred_false_df: %s' % y_pred_false_df)
true_mask_df = DataFrame(true_mask)
mask_plus_true = true_mask_df.merge(y_pred_true_df, how='left', \
left_index=True, right_index=True)
mask_plus_true_plus_false = mask_plus_true.merge(y_pred_false_df, \
how='left', left_index=True, right_index=True)
mask_plus_true_plus_false['y_pred'] = mask_plus_true_plus_false.apply(
self._stitch_disjoint_row, axis=1)
log.debug('mask_plus_false: %s' % mask_plus_true_plus_false)
y_pred = mask_plus_true_plus_false['y_pred'].values
return y_pred
def predict(self, X_test):
true_mask, false_mask = self.fetch_bifurcation_masks(X_test)
# Predict X_test_true.
X_test_true = X_test[true_mask]
y_pred_true = self._sc_true.predict(X_test_true)
log.debug('y_pred_true: %s' % y_pred_true)
# Predict X_test_false.
X_test_false = X_test[false_mask]
y_pred_false = self._sc_false.predict(X_test_false)
log.debug('y_pred_false: %s' % y_pred_false)
# Stitch results.
column_names = ['y_pred_true']
y_pred_true_df = DataFrame(y_pred_true, index=X_test_true.index, \
columns=column_names)
log.debug('y_pred_true_df: %s' % y_pred_true_df)
column_names = ['y_pred_false']
y_pred_false_df = DataFrame(y_pred_false, index=X_test_false.index, \
columns=column_names)
log.debug('y_pred_false_df: %s' % y_pred_false_df)
true_mask_df = DataFrame(true_mask)
mask_plus_true = true_mask_df.merge(y_pred_true_df, how='left', \
left_index=True, right_index=True)
mask_plus_true_plus_false = mask_plus_true.merge(y_pred_false_df, \
how='left', left_index=True, right_index=True)
mask_plus_true_plus_false['y_pred'] = mask_plus_true_plus_false.apply(
self._stitch_disjoint_row, axis=1)
log.debug('mask_plus_false: %s' % mask_plus_true_plus_false)
y_pred = mask_plus_true_plus_false['y_pred'].values
return y_pred
def predict_probability(self, X_test):
true_mask, false_mask = self.fetch_bifurcation_masks(X_test)
# Predict X_test_true.
X_test_true = X_test[true_mask]
y_pred_prob_true = self._sc_true.predict_probability(X_test_true)
log.debug('y_pred_prob_true: %s' % y_pred_prob_true)
# Predict X_test_false.
X_test_false = X_test[false_mask]
y_pred_prob_false = self._sc_false.predict_probability(X_test_false)
log.debug('y_pred_prob_false: %s' % y_pred_prob_false)
# Stitch results.
column_names = ['y_pred_prob_true_0', 'y_pred_prob_true_1']
y_pred_prob_true_df = DataFrame(y_pred_prob_true, index=X_test_true.index, \
columns=column_names)
log.debug('y_pred_prob_true_df: %s' % y_pred_prob_true_df)
column_names = ['y_pred_prob_false_0', 'y_pred_prob_false_1']
y_pred_prob_false_df = DataFrame(y_pred_prob_false, index=X_test_false.index, \
columns=column_names)
log.debug('y_pred_prob_false_df: %s' % y_pred_prob_false_df)
true_mask_df = DataFrame(true_mask)
mask_plus_true = true_mask_df.merge(y_pred_prob_true_df, how='left', \
left_index=True, right_index=True)
composite = mask_plus_true.merge(y_pred_prob_false_df, \
how='left', left_index=True, right_index=True)
composite['y_pred_prob_0'] = composite.apply(self._stitch_prob_0,
axis=1)
composite['y_pred_prob_1'] = composite.apply(self._stitch_prob_1,
axis=1)
log.debug('composite: %s' % composite)
y_pred_prob = composite[['y_pred_prob_0', 'y_pred_prob_1']].values
log.debug(y_pred_prob)
return y_pred_prob
def run_one_lab_local(lab, lab_type, data_source, version, random_state=0):
'''
Input:
:return:
'''
# X_train_raw, y_train = [[1], [2]], [1, 2]
# X_test_raw, y_test = [[3], [4]], [3, 4]
file_organizer = syst.FileOrganizerLocal(lab=lab,
lab_type=lab_type,
data_source=data_source,
version=version)
raw_matrix = file_organizer.get_raw_matrix()
y_label = 'all_components_normal'
'''
TODO: later on pat_ids
'''
raw_matrix_train, raw_matrix_test = Utils.split_rows(raw_matrix)
patIds_train = raw_matrix_train['pat_id'].values.tolist()
X_train_raw, y_train = Utils.split_Xy(raw_matrix_train, ylabel=y_label)
redundant_features = ['proc_code', 'num_components', 'num_normal_components', 'abnormal_panel']
id_features = ['pat_id', 'order_proc_id', 'order_time']
numeric_features = X_train_raw.columns[~X_train_raw.columns.isin([y_label]+redundant_features+id_features)]
'''
Check if the left features are all numeric
'''
assert X_train_raw[numeric_features].select_dtypes(exclude=['object']).shape == X_train_raw[numeric_features].shape
features_by_type = {'redundant_features': redundant_features,
'id_features':id_features,
'numeric_features':numeric_features,
'y_label':y_label}
'''
(1) Feature Impute:
Imputation of some numerical values depend on prior stats of the same patient,
so certain auxiliary columns are still useful
(2) Feature Remove:
Remove auxiliary columns
(3) Feature Selection:
Only select from numerical columns
'''
feature_processing_pipeline = Pipeline(
memory = None, #file_organizer.cached_pipeline_filepath,
steps = [
('impute_features', Cls.FeatureImputer()),
('remove_features', Cls.FeatureRemover(features_to_remove=Config.features_to_remove)),
('select_features', Cls.Select_Features(random_state=random_state, features_by_type=features_by_type))
]
)
# feature_engineering_pipeline.set_params()
X_train_processed = feature_processing_pipeline.fit_transform(X_train_raw, y_train)
hyperparams = {}
hyperparams['algorithm'] = 'random-forest'
predictor = SupervisedClassifier(classes=[0,1], hyperparams=hyperparams)
'''
Automatically takes care of tuning hyperparameters via stochastic-search
'''
status = predictor.train(X_train_processed, column_or_1d(y_train),
groups = patIds_train)
logging.INFO('status: %s'%status)
'''
Test set
'''
X_test_raw, y_test = Utils.split_Xy(raw_matrix_test, ylabel=y_label)
X_test_processed = feature_processing_pipeline.transform(X_test_raw)
y_test_pred_proba = predictor.predict_probability(X_test_processed)
res_df = pd.DataFrame({'actual':y_test,
'predict': y_test_pred_proba})
res_df.to_csv(file_organizer.get_output_filepath(alg=hyperparams['algorithm']))