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@brief Complex but recurring testing functions.
import random
import pandas
import numpy
from numpy.testing import assert_allclose
from ..grammar_sklearn import sklearn2graph
from import compile_c_function
def iris_data():
Returns ``(X, y)`` for iris data.
from sklearn.datasets import load_iris
iris = load_iris()
X =[:, :2]
y =
return X, y
def check_is_almost_equal(xv, exp, precision=1e-5, message=None):
Checks that two floats or two arrays are almost equal.
@param xv float or vector
@param exp expected value
@param precision precision
@param message additional message
if isinstance(exp, float) or len(exp.ravel()) == 1:
if not (isinstance(xv, float) or len(xv.ravel()) == 1):
raise TypeError(
"Type mismatch between {0} and {1} (expected).".format(type(xv), type(exp)))
diff = abs(xv - exp)
if diff > 1e-5:
raise ValueError(
"Predictions are different expected={0}, computed={1}".format(exp, xv))
if not isinstance(xv, numpy.ndarray):
raise TypeError(
"Type mismatch between {0} and {1} (expected).".format(type(xv), type(exp)))
xv = xv.ravel()
exp = exp.ravel()
assert_allclose(xv, exp, atol=precision)
except AssertionError as e:
if message is None:
raise e
raise AssertionError(message) from e
def check_model_representation(model, X, y=None, convs=None,
output_names=None, only_float=True,
verbose=False, suffix="", fLOG=None):
Checks that a trained model can be exported in a specific list
of formats and produces the same outputs if the representation can be used to
@param model model (a class or an instance of a model but not trained)
@param X features
@param y targets
@param convs list of format to check, all possible by default ``['json', 'c']``
@param output_names list of output columns
(can be None, a default value is infered based on scikit-learn output then)
@param verbose print some information
@param suffix add this to disambiguate module
@param fLOG logging function
@return function to call to run the prediction
if not only_float:
raise NotImplementedError("Only float are allowed.")
if isinstance(X, list):
X = pandas.DataFrame(X)
if len(X.shape) != 2:
raise ValueError(
"X cannot be converted into a proper DataFrame. It has shape {0}.".format(X.shape))
if only_float:
X = X.as_matrix()
if isinstance(y, list):
y = numpy.array(y)
if convs is None:
convs = ['json', 'c']
# sklearn
if not hasattr(model.__class__, "fit"):
# It is a class object and not an instance. We use the default values.
model = model(), y)
h = random.randint(0, X.shape[0] - 1)
if isinstance(X, pandas.DataFrame):
oneX = X.iloc[h, :].astype(numpy.float32)
oneX = X[h, :].ravel().astype(numpy.float32)
# model or transform
moneX = numpy.resize(oneX, (1, len(oneX)))
if hasattr(model, "predict"):
ske = model.predict(moneX)
ske = model.transform(moneX)
if verbose and fLOG:
fLOG(type(oneX), oneX.dtype)
for k, v in sorted(model.__dict__.items()):
if k[-1] == '_':
fLOG(" {0}={1}".format(k, v))
# grammar
gr = sklearn2graph(model, output_names=output_names)
lot = gr.execute(Features=oneX)
if verbose and fLOG:
# verification
check_is_almost_equal(lot, ske)
# default for output_names
if output_names is None:
if len(ske.shape) == 1:
output_names = ["Prediction"]
elif len(ske.shape) == 2:
output_names = ["p%d" % i for i in range(ske.shape[1])]
raise ValueError("Cannot guess default values for output_names.")
for lang in convs:
if lang in ('c', ):
code_c = gr.export(lang=lang)['code']
if code_c is None:
raise ValueError("cannot be None")
compile_fct = compile_c_function
from contextlib import redirect_stdout, redirect_stderr
from io import StringIO
fout = StringIO()
ferr = StringIO()
with redirect_stdout(fout):
with redirect_stderr(ferr):
fct = compile_fct(
code_c, len(output_names), suffix=suffix, fLOG=lambda s: fout.write(s + "\n"))
except Exception as e:
raise Exception("Unable to compile a code\n-OUT-\n{0}\n-ERR-\n{1}\n-CODE-\n{2}".format(
fout.getvalue(), ferr.getvalue(), code_c)) from e
if verbose and fLOG:
fLOG("h=", h, "oneX=", oneX)
lotc = fct(oneX)
lotc, ske, message="Issue with lang='{0}'".format(lang))
lotc_exp = lotc.copy()
lotc2 = fct(oneX, lotc)
if not numpy.array_equal(lotc_exp, lotc2):
raise ValueError(
"Second call returns different results.\n{0}\n{1}".format(lotc_exp, lotc2))
ser = gr.export(lang="json", hook={'array': lambda v: v.tolist()})
if ser is None:
raise ValueError("No output for long='{0}'".format(lang))
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