From 48b0b373a5d997f9f4668488c02ab8c6993b5ea8 Mon Sep 17 00:00:00 2001 From: Michael Osthege Date: Sat, 1 Feb 2020 19:49:01 +0100 Subject: [PATCH] use to_numpy() instead of as_matrix() --- pymc3/glm/utils.py | 6 +++--- pymc3/tests/test_examples.py | 16 ++++++++-------- 2 files changed, 11 insertions(+), 11 deletions(-) diff --git a/pymc3/glm/utils.py b/pymc3/glm/utils.py index 4bd9319ea1..613fa3462a 100644 --- a/pymc3/glm/utils.py +++ b/pymc3/glm/utils.py @@ -48,7 +48,7 @@ def any_to_tensor_and_labels(x, labels=None): if isinstance(x, pd.DataFrame): if not labels: labels = x.columns - x = x.as_matrix() + x = x.to_numpy() # pandas.Series # there can still be a label @@ -56,7 +56,7 @@ def any_to_tensor_and_labels(x, labels=None): elif isinstance(x, pd.Series): if not labels: labels = [x.name] - x = x.as_matrix()[:, None] + x = x.to_numpy()[:, None] # dict # labels are keys, @@ -66,7 +66,7 @@ def any_to_tensor_and_labels(x, labels=None): try: x = pd.DataFrame.from_dict(x) labels = x.columns - x = x.as_matrix() + x = x.to_numpy() # some types fail there # another approach is to construct # variable by hand diff --git a/pymc3/tests/test_examples.py b/pymc3/tests/test_examples.py index dae3d991f2..dc8a4083dd 100644 --- a/pymc3/tests/test_examples.py +++ b/pymc3/tests/test_examples.py @@ -72,11 +72,11 @@ class TestARM12_6(SeededTest): def build_model(self): data = get_city_data() - self.obs_means = data.groupby('fips').lradon.mean().as_matrix() + self.obs_means = data.groupby('fips').lradon.mean().to_numpy() - lradon = data.lradon.as_matrix() - floor = data.floor.as_matrix() - group = data.group.as_matrix() + lradon = data.lradon.to_numpy() + floor = data.floor.to_numpy() + group = data.group.to_numpy() with pm.Model() as model: groupmean = pm.Normal('groupmean', 0, 10. ** -2.) @@ -107,10 +107,10 @@ def build_model(self): data = get_city_data() self.obs_means = data.groupby('fips').lradon.mean() - lradon = data.lradon.as_matrix() - floor = data.floor.as_matrix() - group = data.group.as_matrix() - ufull = data.Uppm.as_matrix() + lradon = data.lradon.to_numpy() + floor = data.floor.to_numpy() + group = data.group.to_numpy() + ufull = data.Uppm.to_numpy() with pm.Model() as model: groupmean = pm.Normal('groupmean', 0, 10. ** -2.)