def main(readcsv=read_csv, method='defaultDense'):
nClasses = 5
nFeatures = 6
# read training data from file with 6 features per observation and 1 class label
trainfile = "./data/batch/logreg_train.csv"
train_data = readcsv(trainfile, range(nFeatures))
train_labels = readcsv(trainfile, range(nFeatures, nFeatures + 1))
# set parameters and train
train_alg = d4p.logistic_regression_training(nClasses=nClasses,
penaltyL1=0.1,
penaltyL2=0.1,
interceptFlag=True)
train_result = train_alg.compute(train_data, train_labels)
# read testing data from file with 6 features per observation
testfile = "./data/batch/logreg_test.csv"
predict_data = readcsv(testfile, range(nFeatures))
# set parameters and compute predictions
# previous version has different interface
from daal4py import __daal_link_version__ as dv
daal_version = tuple(map(int, (dv[0:4], dv[4:8])))
if daal_version < (2020, 0):
predict_alg = d4p.logistic_regression_prediction(
nClasses=nClasses,
resultsToCompute=
"computeClassesLabels|computeClassesProbabilities|computeClassesLogProbabilities"
)
else:
predict_alg = d4p.logistic_regression_prediction(
nClasses=nClasses,
resultsToEvaluate=
"computeClassLabels|computeClassProbabilities|computeClassLogProbabilities"
)
predict_result = predict_alg.compute(predict_data, train_result.model)
# the prediction result provides prediction, probabilities and logProbabilities
assert predict_result.probabilities.shape == (predict_data.shape[0],
nClasses)
assert predict_result.logProbabilities.shape == (predict_data.shape[0],
nClasses)
predict_labels = np.loadtxt(testfile,
usecols=range(nFeatures, nFeatures + 1),
delimiter=',',
ndmin=2)
assert np.count_nonzero(predict_result.prediction -
predict_labels) / predict_labels.shape[0] < 0.025
return (train_result, predict_result, predict_labels)
def main(readcsv=read_csv, method='defaultDense'):
nClasses = 2
nFeatures = 20
# read training data from file with 20 features per observation and 1 class label
trainfile = "./data/batch/binary_cls_train.csv"
train_data = readcsv(trainfile, range(nFeatures))
train_labels = readcsv(trainfile, range(nFeatures, nFeatures + 1))
# set parameters and train
train_alg = d4p.logistic_regression_training(nClasses=nClasses,
interceptFlag=True)
train_result = train_alg.compute(train_data, train_labels)
# read testing data from file with 20 features per observation
testfile = "./data/batch/binary_cls_test.csv"
predict_data = readcsv(testfile, range(nFeatures))
predict_labels = readcsv(testfile, range(nFeatures, nFeatures + 1))
# set parameters and compute predictions
predict_alg = d4p.logistic_regression_prediction(nClasses=nClasses)
predict_result = predict_alg.compute(predict_data, train_result.model)
# the prediction result provides prediction
assert predict_result.prediction.shape == (predict_data.shape[0],
train_labels.shape[1])
return (train_result, predict_result, predict_labels)
def main():
nClasses = 2
nFeatures = 20
# read training data from file with 20 features per observation and 1 class label
# and use only a chunk per process
trainfile = "./data/batch/binary_cls_train.csv"
train_data = np.split(read_csv(trainfile, range(nFeatures)), d4p.num_procs())[d4p.my_procid()]
train_labels = np.split(read_csv(trainfile, range(nFeatures, nFeatures + 1)), d4p.num_procs())[d4p.my_procid()]
# set parameters and train
train_alg = d4p.logistic_regression_training(nClasses=nClasses, interceptFlag=True, distributed=True)
train_result = train_alg.compute(train_data, train_labels)
# Now let's do some prediction
# It operates on the same data on each process
# read testing data from file with 20 features per observation
testfile = "./data/batch/binary_cls_test.csv"
predict_data = read_csv(testfile, range(nFeatures))
predict_labels = read_csv(testfile, range(nFeatures, nFeatures + 1))
# set parameters and compute predictions
predict_alg = d4p.logistic_regression_prediction(nClasses=nClasses)
predict_result = predict_alg.compute(predict_data, train_result.model)
# the prediction result provides prediction
assert predict_result.prediction.shape == (predict_data.shape[0], train_labels.shape[1])
return (train_result, predict_result, predict_labels)
def run_inference(num_observations: int = 1000):
"""Run xgboost for specified number of observations"""
# Load data
test_df = common.get_test_data_df(X=common.X_dfc, size=num_observations)
num_rows = len(test_df)
######################
print("_______________________________________")
print("Total Number of Rows", num_rows)
run_times = []
inference_times = []
for _ in range(NUM_LOOPS):
start_time = timer()
predict_algo = d4p.logistic_regression_prediction(
nClasses=2,
resultsToEvaluate=
"computeClassLabels|computeClassProbabilities|computeClassLogProbabilities"
)
predict_result = predict_algo.compute(test_df, train_result.model)
#predictor.compute(data, MODEL)
end_time = timer()
total_time = end_time - start_time
run_times.append(total_time * 10e3)
inference_time = total_time * (10e6) / num_rows
inference_times.append(inference_time)
return_elem = common.calculate_stats(inference_times)
print(num_observations, ", ", return_elem)
return return_elem
def main(readcsv=read_csv, method='defaultDense'):
nClasses = 5
nFeatures = 6
# read training data from file with 6 features per observation and 1 class label
trainfile = "./data/batch/logreg_train.csv"
train_data = readcsv(trainfile, range(nFeatures))
train_labels = readcsv(trainfile, range(nFeatures, nFeatures + 1))
# set parameters and train
train_alg = d4p.logistic_regression_training(nClasses=nClasses,
penaltyL1=0.1,
penaltyL2=0.1,
interceptFlag=True)
train_result = train_alg.compute(train_data, train_labels)
# read testing data from file with 6 features per observation
testfile = "./data/batch/logreg_test.csv"
predict_data = readcsv(testfile, range(nFeatures))
# set parameters and compute predictions
predict_alg = d4p.logistic_regression_prediction(nClasses=nClasses,
resultsToCompute="computeClassesLabels|computeClassesProbabilities|computeClassesLogProbabilities")
predict_result = predict_alg.compute(predict_data, train_result.model)
# the prediction result provides prediction, probabilities and logProbabilities
assert predict_result.probabilities.shape == (predict_data.shape[0], nClasses)
assert predict_result.logProbabilities.shape == (predict_data.shape[0], nClasses)
predict_labels = np.loadtxt(testfile, usecols=range(nFeatures, nFeatures + 1), delimiter=',', ndmin=2)
assert np.count_nonzero(predict_result.prediction-predict_labels)/predict_labels.shape[0] < 0.025
return (train_result, predict_result, predict_labels)
def prdct_impl(n, d, model):
w = np.ones((n, d), dtype=np.double) - 22.5
algo = d4p.logistic_regression_prediction(
2,
resultsToCompute="computeClassesLabels|computeClassesProbabilities|computeClassesLogProbabilities"
)
return algo.compute(w, model)
def compute(train_data, train_labels, predict_data, nClasses):
# set parameters and train
train_alg = d4p.logistic_regression_training(nClasses=nClasses, interceptFlag=True)
train_result = train_alg.compute(train_data, train_labels)
# set parameters and compute predictions
predict_alg = d4p.logistic_regression_prediction(nClasses=nClasses)
return predict_alg.compute(predict_data, train_result.model), train_result
def predict(self, predict_data, model):
# set parameters and compute predictions
predict_alg = d4p.logistic_regression_prediction(fptype = self.dtype, nClasses=self.nClasses,
resultsToCompute = self.resultsToCompute)
predict_result = predict_alg.compute(predict_data, model)
self.prediction = predict_result.prediction
self.probabilities = predict_result.probabilities
return self
def compute(train_data, train_labels, predict_data, nClasses):
# set parameters and train
train_alg = d4p.logistic_regression_training(nClasses=nClasses,
penaltyL1=0.1,
penaltyL2=0.1,
interceptFlag=True)
train_result = train_alg.compute(train_data, train_labels)
# set parameters and compute predictions
predict_alg = d4p.logistic_regression_prediction(nClasses=nClasses,
resultsToEvaluate="computeClassLabels|computeClassProbabilities|computeClassLogProbabilities")
return predict_alg.compute(predict_data, train_result.model), train_result
def test_breast_cancer_without_intercept(self):
X, y = load_breast_cancer(return_X_y=True)
n_classes=2
clf = LogisticRegression(fit_intercept=False, max_iter=10000, random_state=0).fit(X, y)
builder = d4p.logistic_regression_model_builder(n_classes=n_classes, n_features=X.shape[1])
builder.set_beta(clf.coef_, clf.intercept_)
alg_pred = d4p.logistic_regression_prediction(nClasses=n_classes)
pred_daal = alg_pred.compute(X, builder.model).prediction.flatten()
pred_sklearn = clf.predict(X)
self.assertTrue(np.allclose(pred_daal, pred_sklearn))
def main():
X, y = load_iris(return_X_y=True)
n_classes=3
# set parameters and train
clf = LogisticRegression(fit_intercept=True, max_iter=1000, random_state=0).fit(X, y)
#set parameters and call model builder
builder = d4p.logistic_regression_model_builder(n_classes=n_classes, n_features=X.shape[1])
builder.set_beta(clf.coef_, clf.intercept_)
# set parameters and compute predictions
predict_alg = d4p.logistic_regression_prediction(nClasses=n_classes,
resultsToEvaluate="computeClassLabels")
# set parameters and compute predictions
predict_result_daal = predict_alg.compute(X, builder.model)
predict_result_sklearn = clf.predict(X)
assert np.allclose(predict_result_daal.prediction.flatten(), predict_result_sklearn)
return (builder, predict_result_daal)
def main():
nClasses = 5
nFeatures = 6
# read training data from file with 6 features per observation and 1 class label
trainfile = "./data/batch/logreg_train.csv"
train_data = read_csv(trainfile, range(nFeatures))
train_labels = read_csv(trainfile, range(nFeatures, nFeatures + 1))
# set parameters and train
train_alg = d4p.logistic_regression_training(nClasses=nClasses,
penaltyL1=0.1,
penaltyL2=0.1,
interceptFlag=True)
train_result = train_alg.compute(train_data, train_labels)
# read testing data from file with 6 features per observation
testfile = "./data/batch/logreg_test.csv"
predict_data = read_csv(testfile, range(nFeatures))
predict_labels = read_csv(testfile, range(nFeatures, nFeatures + 1))
# set parameters and compute predictions
predict_alg = d4p.logistic_regression_prediction(
nClasses=nClasses,
resultsToCompute=
"computeClassesLabels|computeClassesProbabilities|computeClassesLogProbabilities"
)
predict_result = predict_alg.compute(predict_data, train_result.model)
# the prediction result provides prediction, probabilities and logProbabilities
assert predict_result.prediction.shape == (predict_data.shape[0],
train_labels.shape[1])
assert predict_result.probabilities.shape == (predict_data.shape[0],
nClasses)
assert predict_result.logProbabilities.shape == (predict_data.shape[0],
nClasses)
return (train_result, predict_result, predict_labels)
def daal4py_predict(self, X, resultsToEvaluate):
check_is_fitted(self)
X = check_array(X, accept_sparse='csr', dtype=[np.float64, np.float32])
try:
fptype = getFPType(X)
except ValueError:
fptype = None
daal_ready = self.multi_class in ["multinomial", "warn"] or \
self.classes_.size == 2 or resultsToEvaluate == 'computeClassLabels'
daal_ready = daal_ready and not sparse.issparse(X) and \
not sparse.issparse(self.coef_) and fptype is not None
if daal_ready:
logging.info("sklearn.linear_model.LogisticRegression."
"predict: " + get_patch_message("daal"))
n_features = self.coef_.shape[1]
if X.shape[1] != n_features:
raise ValueError(
f'X has {X.shape[1]} features, '
f'but LogisticRegression is expecting {n_features} features as input'
)
builder = d4p.logistic_regression_model_builder(
X.shape[1], len(self.classes_))
builder.set_beta(self.coef_, self.intercept_)
predict = d4p.logistic_regression_prediction(
nClasses=len(self.classes_),
fptype=fptype,
method='defaultDense',
resultsToEvaluate=resultsToEvaluate)
res = predict.compute(X, builder.model)
if resultsToEvaluate == 'computeClassLabels':
res = res.prediction
if not np.array_equal(self.classes_, np.arange(0, len(self.classes_))) or \
self.classes_.dtype != X.dtype:
res = self.classes_.take(np.asarray(res, dtype=np.intp))
elif resultsToEvaluate == 'computeClassProbabilities':
res = res.probabilities
elif resultsToEvaluate == 'computeClassLogProbabilities':
res = res.logProbabilities
else:
raise ValueError(
'resultsToEvaluate must be in [computeClassLabels, \
computeClassProbabilities, computeClassLogProbabilities]')
if res.shape[1] == 1:
res = np.ravel(res)
return res
if resultsToEvaluate == 'computeClassLabels':
logging.info("sklearn.linear_model.LogisticRegression."
"predict: " + get_patch_message("sklearn"))
return LogisticRegression_original.predict(self, X)
if resultsToEvaluate == 'computeClassProbabilities':
logging.info("sklearn.linear_model.LogisticRegression."
"predict_proba: " + get_patch_message("sklearn"))
return LogisticRegression_original.predict_proba(self, X)
if resultsToEvaluate == 'computeClassLogProbabilities':
logging.info("sklearn.linear_model.LogisticRegression."
"predict_log_proba: " + get_patch_message("sklearn"))
return LogisticRegression_original.predict_log_proba(self, X)
raise ValueError('resultsToEvaluate must be in [computeClassLabels, \
computeClassProbabilities, computeClassLogProbabilities]')
def daal4py_predict(self, X, resultsToEvaluate):
check_is_fitted(self)
if sklearn_check_version('1.0'):
self._check_feature_names(X, reset=False)
X = check_array(X, accept_sparse='csr', dtype=[np.float64, np.float32])
try:
fptype = getFPType(X)
except ValueError:
fptype = None
if resultsToEvaluate == 'computeClassLabels':
_function_name = 'predict'
elif resultsToEvaluate == 'computeClassProbabilities':
_function_name = 'predict_proba'
elif resultsToEvaluate == 'computeClassLogProbabilities':
_function_name = 'predict_log_proba'
else:
raise ValueError('resultsToEvaluate must be in [computeClassLabels, \
computeClassProbabilities, computeClassLogProbabilities]')
_patching_status = PatchingConditionsChain(
f"sklearn.linear_model.LogisticRegression.{_function_name}")
_patching_status.and_conditions(
[(self.multi_class in ["multinomial", "warn"],
f"{self.multi_class} multiclass option is not supported. "
"Only 'multinomial' or 'warn' options are supported."),
(self.classes_.size == 2, "Number of classes != 2."),
(resultsToEvaluate == 'computeClassLabels',
"resultsToEvaluate != 'computeClassLabels'.")],
conditions_merging=any)
_dal_ready = _patching_status.and_conditions([
(not sparse.issparse(X),
"X is sparse. Sparse input is not supported."),
(not sparse.issparse(self.coef_),
"self.coef_ is sparse. Sparse coefficients are not supported."),
(fptype is not None, "Unable to get dtype.")
])
_patching_status.write_log()
if _dal_ready:
n_features = self.coef_.shape[1]
if X.shape[1] != n_features:
raise ValueError(
f'X has {X.shape[1]} features, '
f'but LogisticRegression is expecting {n_features} features as input'
)
builder = d4p.logistic_regression_model_builder(
X.shape[1], len(self.classes_))
builder.set_beta(self.coef_, self.intercept_)
predict = d4p.logistic_regression_prediction(
nClasses=len(self.classes_),
fptype=fptype,
method='defaultDense',
resultsToEvaluate=resultsToEvaluate)
res = predict.compute(X, builder.model)
if resultsToEvaluate == 'computeClassLabels':
res = res.prediction
if not np.array_equal(self.classes_, np.arange(0, len(self.classes_))) or \
self.classes_.dtype != X.dtype:
res = self.classes_.take(np.asarray(res, dtype=np.intp))
elif resultsToEvaluate == 'computeClassProbabilities':
res = res.probabilities
elif resultsToEvaluate == 'computeClassLogProbabilities':
res = res.logProbabilities
else:
raise ValueError(
'resultsToEvaluate must be in [computeClassLabels, \
computeClassProbabilities, computeClassLogProbabilities]')
if res.shape[1] == 1:
res = np.ravel(res)
return res
if resultsToEvaluate == 'computeClassLabels':
return LogisticRegression_original.predict(self, X)
if resultsToEvaluate == 'computeClassProbabilities':
return LogisticRegression_original.predict_proba(self, X)
if resultsToEvaluate == 'computeClassLogProbabilities':
return LogisticRegression_original.predict_log_proba(self, X)
