def test_NumericalEncoder_num():
######################
### Numerical Mode ###
######################
np.random.seed(123)
df = get_sample_df(100, seed=123)
ind = np.arange(len(df))
df.index = ind
np.random.shuffle(ind)
df["cat_col_1"] = df["text_col"].apply(lambda s: s[0:3])
df["cat_col_2"] = df["text_col"].apply(lambda s: s[3:6])
encoder = NumericalEncoder(encoding_type="num")
encoder.fit(df)
res = encoder.transform(df)
assert res.shape == df.shape
assert (res.index == df.index).all()
assert encoder.get_feature_names() == encoder.model._feature_names
assert encoder.get_feature_names() == list(res.columns)
df2 = df.copy()
df2.loc[0, "cat_col_1"] = "something-new"
df2.loc[1, "cat_col_2"] = None # Something None
res2 = encoder.transform(df2)
assert res2.loc[0, "cat_col_1"] == -1
assert res2.loc[1, "cat_col_2"] == -1
df_with_none = df.copy()
df_with_none["cat_col_3"] = df_with_none["cat_col_1"]
df_with_none.loc[list(range(25)), "cat_col_3"] = None
encoder2 = NumericalEncoder(encoding_type="num")
res2 = encoder2.fit_transform(df_with_none)
assert (df_with_none["cat_col_3"].isnull() == (
res2["cat_col_3"] == 0)).all()
def test_NumericalEncoder_dummy():
####################
### One Hot Mode ###
####################
np.random.seed(123)
df = get_sample_df(100, seed=123)
ind = np.arange(len(df))
df.index = ind
df["cat_col_1"] = df["text_col"].apply(lambda s: s[0:3])
df["cat_col_2"] = df["text_col"].apply(lambda s: s[3:6])
encoder = NumericalEncoder(encoding_type="dummy")
encoder.fit(df)
res = encoder.transform(df)
assert encoder.model._dummy_size == len(encoder.model._dummy_feature_names)
assert encoder.model._dummy_size == sum(
len(v) for k, v in encoder.model.variable_modality_mapping.items())
assert res.shape[0] == df.shape[0]
assert res.shape[1] == len(df["cat_col_1"].value_counts()) + len(
df["cat_col_2"].value_counts()) + 3
assert (res.index == df.index).all()
col = ["float_col", "int_col", "text_col"]
col1 = [
"cat_col_1__%s" % c for c in list(df["cat_col_1"].value_counts().index)
]
col2 = [
"cat_col_2__%s" % c for c in list(df["cat_col_2"].value_counts().index)
]
assert col1 == encoder.columns_mapping["cat_col_1"]
assert col2 == encoder.columns_mapping["cat_col_2"]
assert encoder.get_feature_names() == col + col1 + col2
assert (res.loc[:, col1 + col2]).isnull().sum().sum() == 0
assert (res.loc[:, col1 + col2]).max().max() == 1
assert (res.loc[:, col1 + col2]).min().min() == 0
assert ((df["cat_col_1"] == "aaa") == (res["cat_col_1__aaa"] == 1)).all()
df2 = df.copy()
df2.loc[0, "cat_col_1"] = "something-new"
df2.loc[1, "cat_col_2"] = None # Something None
res2 = encoder.transform(df2)
assert res2.loc[0, col1].sum() == 0 # no dummy activated
assert res2.loc[
0, "cat_col_2__" +
df2.loc[0, "cat_col_2"]] == 1 # activated in the right position
assert res2.loc[0, col2].sum() == 1 # only one dummy activate
assert res2.loc[1, col2].sum() == 0 # no dummy activated
assert res2.loc[
1, "cat_col_1__" +
df2.loc[1, "cat_col_1"]] == 1 # activated in the right position
assert res2.loc[1, col1].sum() == 1
df_with_none = df.copy()
df_with_none["cat_col_3"] = df_with_none["cat_col_1"]
df_with_none.loc[0:25, "cat_col_3"] = None
encoder2 = NumericalEncoder(encoding_type="dummy")
res2 = encoder2.fit_transform(df_with_none)
col3b = [c for c in res2.columns if c.startswith("cat_col_3")]
assert col3b[0] == "cat_col_3____null__"
assert list(res2.columns) == col + col1 + col2 + col3b
assert list(res2.columns) == encoder2.get_feature_names()
assert (res2.loc[:, col1 + col2 + col3b]).isnull().sum().sum() == 0
assert (res2.loc[:, col1 + col2 + col3b]).max().max() == 1
assert (res2.loc[:, col1 + col2 + col3b]).min().min() == 0
assert (df_with_none["cat_col_3"].isnull() == (
res2["cat_col_3____null__"] == 1)).all()
df3 = df.copy()
df3["cat_col_many"] = [
"m_%d" % x
for x in np.ceil(np.minimum(np.exp(np.random.rand(100) *
5), 50)).astype(np.int32)
]
encoder3 = NumericalEncoder(encoding_type="dummy")
res3 = encoder3.fit_transform(df3)
colm = [c for c in res3.columns if c.startswith("cat_col_many")]
vc = df3["cat_col_many"].value_counts()
colmb = [
"cat_col_many__" + c
for c in list(vc.index[vc >= encoder3.min_nb_observations]) +
["__default__"]
]
assert colm == colmb
