def test_multiclass(frontend, backend):
clf, predictor = models[frontend]()
# Create a conifer config
cfg = conifer.backends.xilinxhls.auto_config()
# Set the output directory to something unique
cfg['OutputDir'] = 'prj_{}'.format(int(
datetime.datetime.now().timestamp()))
cfg['Precision'] = 'ap_fixed<32,16,AP_RND,AP_SAT>'
cfg['XilinxPart'] = 'xcu250-figd2104-2L-e'
cnf = conifer.model(clf, frontends[frontend], backends[backend], cfg)
cnf.compile()
y_hls, y_skl = predicts[frontend](predictor, X, y, cnf)
np.testing.assert_allclose(y_hls, y_skl, rtol=1e-2, atol=1e-2)
cnf.build()
def vhdl_convert(train_skl):
import conifer
import datetime
clf, X, y = train_skl
# Create a conifer config
cfg = conifer.backends.vivadohls.auto_config()
cfg['Precision'] = 'ap_fixed<32,16>'
# Set the output directory to something unique
cfg['OutputDir'] = 'prj_{}'.format(int(
datetime.datetime.now().timestamp()))
cfg['XilinxPart'] = 'xcu250-figd2104-2L-e'
# Create and compile the model
model = conifer.model(clf, conifer.converters.sklearn,
conifer.backends.vhdl, cfg)
model.compile()
return model
offset = int(X.shape[0] * 0.9)
X_train, y_train = X[:offset], y[:offset]
X_test, y_test = X[offset:], y[offset:]
# Train a BDT
clf = GradientBoostingRegressor(n_estimators=100,
max_depth=4,
min_samples_split=2,
learning_rate=0.01,
loss='ls')
clf.fit(X_train, y_train)
# Create a conifer config
cfg = conifer.backends.vivadohls.auto_config()
# Set the output directory to something unique
cfg['OutputDir'] = 'prj_{}'.format(int(datetime.datetime.now().timestamp()))
cfg['Precision'] = 'ap_fixed<64,32>'
# Create and compile the model
model = conifer.model(clf, conifer.converters.sklearn, conifer.backends.vhdl,
cfg)
model.compile()
# Run HLS C Simulation and get the output
#y_hls = model.decision_function(X)
y_hls, y_trees = model.decision_function(X)
y_skl = clf.predict(X)
# Synthesize the model
#model.build()
'max_depth': 3,
'eta': 1,
'alpha': 10,
'gamma': 10,
'objective': 'binary:logistic'
}
# Gamma controls pruning in XGBoost, Alpha controls L1 regularization
num_round = 20 # num_round is equivalent to number of trees
bst = xgb.train(param, dtrain, num_round)
# Create a conifer config
cfg = conifer.backends.vivadohls.auto_config()
# Set the output directory to something unique
cfg['OutputDir'] = 'prj_{}'.format(int(datetime.datetime.now().timestamp()))
# Create and compile the model
model = conifer.model(bst, conifer.converters.xgboost,
conifer.backends.vivadohls, cfg)
model.compile()
# Run HLS C Simulation and get the output
# xgboost 'predict' returns a probability like sklearn 'predict_proba'
# so we need to compute the probability from the decision_function returned
# by the HLS C Simulation
y_hls = expit(model.decision_function(X_test))
y_xgb = bst.predict(dtest)
# Synthesize the model
model.build()
import conifer
import datetime
# Make a random dataset from sklearn 'hastie'
X, y = make_hastie_10_2(random_state=0)
X_train, X_test = X[:2000], X[2000:]
y_train, y_test = y[:2000], y[2000:]
# Train a BDT
clf = GradientBoostingClassifier(n_estimators=20,
learning_rate=1.0,
max_depth=3,
random_state=0).fit(X_train, y_train)
# Create a conifer config
cfg = conifer.backends.xilinxhls.auto_config()
# Set the output directory to something unique
cfg['OutputDir'] = 'prj_{}'.format(int(datetime.datetime.now().timestamp()))
# Create and compile the model
model = conifer.model(clf, conifer.converters.sklearn,
conifer.backends.xilinxhls, cfg)
model.compile()
# Run HLS C Simulation and get the output
y_hls = model.decision_function(X)
y_skl = clf.decision_function(X)
# Synthesize the model
model.build()
# Load the iris dataset from sklearn'
iris = load_iris()
X, y = iris.data, iris.target
# Train a BDT using the scikit-learn API
bst = xgb.XGBClassifier(n_estimators=20, max_depth=3,
learning_rate=1., objective='multi:softmax')
bst = bst.fit(X, y)
# Create a conifer config
cfg = conifer.backends.xilinxhls.auto_config()
# Set the output directory to something unique
cfg['OutputDir'] = 'prj_{}'.format(int(datetime.datetime.now().timestamp()))
# Create and compile the model
# We need to pass the Booster object to conifer, so from xgboost's scikit-learn API,
# we call bst.get_booster()
model = conifer.model(
bst.get_booster(), conifer.converters.xgboost, conifer.backends.xilinxhls, cfg)
model.compile()
# Run HLS C Simulation and get the output
# xgboost 'predict' returns a probability like sklearn 'predict_proba'
# so we need to compute the probability from the decision_function returned
# by the HLS C Simulation
y_hls = softmax(model.decision_function(X), axis=1)
y_xgb = bst.predict_proba(X)
# Synthesize the model
model.build()
from sklearn.datasets import make_hastie_10_2
from sklearn.ensemble import RandomForestClassifier
import conifer
import datetime
# Make a random dataset from sklearn 'hastie'
X, y = make_hastie_10_2(random_state=0)
X_train, X_test = X[:2000], X[2000:]
y_train, y_test = y[:2000], y[2000:]
# Train a BDT
clf = RandomForestClassifier(n_estimators=20, max_depth=3,
random_state=0).fit(X_train, y_train)
# Create a conifer config
cfg = conifer.backends.vivadohls.auto_config()
# Set the output directory to something unique
cfg['OutputDir'] = 'prj_{}'.format(int(datetime.datetime.now().timestamp()))
# Create and compile the model
model = conifer.model(clf, conifer.converters.sklearn,
conifer.backends.vivadohls, cfg)
model.compile()
# Run HLS C Simulation and get the output
y_hls = model.decision_function(X)
y_skl = clf.predict_proba(X)
# Synthesize the model
model.build()
#gApplication.Run()
# ------------------------------------------------------
#import imp
#foo = imp.load_source('conifer', '/afs/cern.ch/work/a/addropul/hls4ml/conifer/conifer/__init__.py')
#foo.MyClass()
# Create a conifer config
cfg = conifer.backends.vivadohls.auto_config()
# Set the output directory to something unique
cfg['OutputDir'] = 'prj_{}'.format(int(0504215))
# Create and compile the model
model = conifer.model(
ET.parse('./dataset/weights/TMVAClassification_BDT.weights.xml'),
conifer.converters.tmva, conifer.backends.vivadohls, cfg)
model.compile()
#need to get input variables into the proper form, numpy array
X = np.load(
'/afs/cern.ch/user/a/addropul/CMSSW_10_6_0_pre4/src/L1Trigger/Run3Ntuplizer/test/June_July_2019/x_vbf072020_zb072020.npy'
)
print(np.shape(X))
# Run HLS C Simulation and get the output
y_hls = model.decision_function(X)
#y_skl = clf.decision_function(X)
# Synthesize the model
model.build()