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)
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
