def test_default():
bpsketch = BinningProcessSketch(variable_names)
bpsketch.add(df, y)
bpsketch.solve()
optb = bpsketch.get_binned_variable("mean radius")
assert optb.status == "OPTIMAL"
optb.binning_table.build()
assert optb.binning_table.iv == approx(5.04392547, rel=1e-2)
def test_default_merge():
bpsketch_1 = BinningProcessSketch(variable_names)
bpsketch_2 = BinningProcessSketch(variable_names)
df_1, y_1 = df.iloc[:200, :], y[:200]
df_2, y_2 = df.iloc[200:, :], y[200:]
bpsketch_1.add(df_1, y_1)
bpsketch_2.add(df_2, y_2)
bpsketch_1.merge(bpsketch_2)
bpsketch_1.solve()
optb = bpsketch_1.get_binned_variable("mean radius")
assert optb.status == "OPTIMAL"
optb.binning_table.build()
assert optb.binning_table.iv == approx(5.04392547, rel=1e-2)
def test_information():
bpsketch = BinningProcessSketch(variable_names)
with raises(NotFittedError):
bpsketch.solve()
bpsketch.add(df, y)
with raises(NotFittedError):
bpsketch.information()
bpsketch.solve()
with raises(ValueError):
bpsketch.information(print_level=-1)
bpsketch.information(print_level=0)
bpsketch.information(print_level=1)
bpsketch.information(print_level=2)
def test_params():
with raises(TypeError):
BinningProcessSketch(variable_names=1)
with raises(ValueError):
BinningProcessSketch(variable_names=[], max_n_prebins=-2)
with raises(ValueError):
BinningProcessSketch(variable_names=[], min_n_bins=-2)
with raises(ValueError):
BinningProcessSketch(variable_names=[], max_n_bins=-2.2)
with raises(ValueError):
BinningProcessSketch(variable_names=[], min_n_bins=3, max_n_bins=2)
with raises(ValueError):
BinningProcessSketch(variable_names=[], min_bin_size=0.6)
with raises(ValueError):
BinningProcessSketch(variable_names=[], max_bin_size=-0.6)
with raises(ValueError):
BinningProcessSketch(variable_names=[],
min_bin_size=0.5,
max_bin_size=0.3)
with raises(ValueError):
BinningProcessSketch(variable_names=[], max_pvalue=1.1)
with raises(ValueError):
BinningProcessSketch(variable_names=[], max_pvalue_policy="new_policy")
with raises(TypeError):
BinningProcessSketch(variable_names=[], selection_criteria=[])
with raises(TypeError):
BinningProcessSketch(variable_names=[], categorical_variables={})
with raises(TypeError):
BinningProcessSketch(variable_names=[], categorical_variables=[1, 2])
with raises(TypeError):
BinningProcessSketch(variable_names=[], special_codes={1, 2, 3})
with raises(ValueError):
BinningProcessSketch(variable_names=[], split_digits=9)
with raises(TypeError):
BinningProcessSketch(variable_names=[], binning_fit_params=[1, 2])
with raises(TypeError):
BinningProcessSketch(variable_names=[],
binning_transform_params=[1, 2])
with raises(TypeError):
BinningProcessSketch(variable_names=[], verbose=1)
def test_default_transform():
bpsketch = BinningProcessSketch(variable_names)
bpsketch.add(df, y)
with raises(NotFittedError):
bpsketch.transform(df, metric="woe")
bpsketch.solve()
with raises(TypeError):
X_transform = bpsketch.transform(df.values, metric="woe")
with raises(ValueError):
X_transform = bpsketch.transform(df, metric="new_woe")
X_transform = bpsketch.transform(df)
optb = OptimalBinningSketch()
x = df["mean radius"]
optb.add(x, y)
optb.solve()
assert optb.transform(x,
metric="woe") == approx(X_transform["mean radius"],
rel=1e-6)