Update and standardize dependencies (sdv-dev#126)

* Update and standardize dependencies

* increase test epochs

* Simplify tvae test

conda/meta.yaml

@@ -17,35 +17,35 @@ build:
 
 requirements:
   host:
-    - numpy <2,>=1.17.4
-    - pandas <1.1.5,>=0.24
     - pip
-    - python
-    - rdt >=0.2.7,<0.4
-    - scikit-learn <0.24,>=0.21
-    - pytorch <2,>=1.0
-    - torchvision <1,>=0.4.2
-    - packaging
     - pytest-runner
+    - packaging
+    - python >=3.6,<3.9
+    - numpy >=1.17.4,<2
+    - pandas >=0.24,<1.1.5
+    - scikit-learn >=0.20,<1
+    - pytorch >=1.4,<2
+    - torchvision >=0.5.0,<1
+    - rdt >=0.2.7,<0.5
   run:
-    - numpy <2,>=1.17.4
-    - pandas <1.1.5,>=0.24
-    - python
-    - rdt >=0.2.7,<0.4
-    - scikit-learn <0.24,>=0.21
-    - pytorch <2,>=1.0
-    - torchvision <1,>=0.4.2
     - packaging
+    - python >=3.6,<3.9
+    - numpy >=1.17.4,<2
+    - pandas >=0.24,<1.1.5
+    - scikit-learn >=0.20,<1
+    - pytorch >=1.4,<2
+    - torchvision >=0.5.0,<1
+    - rdt >=0.2.7,<0.5
 
 about:
   home: "https://github.com/sdv-dev/CTGAN"
   license: MIT
   license_family: MIT
-  license_file: 
+  license_file:
   summary: "Conditional GAN for Tabular Data"
-  doc_url: 
-  dev_url: 
+  doc_url:
+  dev_url:
 
 extra:
   recipe-maintainers:
-    - sdv-dev
+    - sdv-dev

setup.py

@@ -12,13 +12,13 @@
     history = history_file.read()
 
 install_requires = [
-    'torch<2,>=1.0',
-    'torchvision<1,>=0.4.2',
-    'scikit-learn<0.24,>=0.21',
-    'numpy<2,>=1.17.4',
-    'pandas<1.1.5,>=0.24',
-    'rdt>=0.2.7,<0.4',
     'packaging',
+    'numpy>=1.17.4,<2',
+    'pandas>=0.24,<1.1.5',
+    'scikit-learn>=0.20,<1',
+    'torch>=1.4,<2',
+    'torchvision>=0.5.0,<1',
+    'rdt>=0.2.7,<0.5',
 ]
 
 setup_requires = [

tests/integration/test_tvae.py

@@ -9,76 +9,27 @@
 model are not checked.
 """
 
-import tempfile as tf
-
-import numpy as np
 import pandas as pd
+from sklearn import datasets
 
 from ctgan.synthesizers.tvae import TVAESynthesizer
 
 
-def test_tvae_dataframe():
-    data = pd.DataFrame({
-        'continuous': np.random.random(1000),
-        'discrete': np.random.choice(['a', 'b'], 1000)
-    })
-    discrete_columns = ['discrete']
+def test_tvae(tmpdir):
+    iris = datasets.load_iris()
+    data = pd.DataFrame(iris.data, columns=iris.feature_names)
+    data['class'] = pd.Series(iris.target).map(iris.target_names.__getitem__)
 
     tvae = TVAESynthesizer(epochs=10)
-    tvae.fit(data, discrete_columns)
-
-    sampled = tvae.sample(100)
-
-    assert sampled.shape == (100, 2)
-    assert isinstance(sampled, pd.DataFrame)
-    assert set(sampled.columns) == {'continuous', 'discrete'}
-    assert set(sampled['discrete'].unique()) == {'a', 'b'}
+    tvae.fit(data, ['class'])
 
-
-def test_tvae_numpy():
-    data = pd.DataFrame({
-        'continuous': np.random.random(1000),
-        'discrete': np.random.choice(['a', 'b'], 1000)
-    })
-    discrete_columns = [1]
-
-    tvae = TVAESynthesizer(epochs=10)
-    tvae.fit(data.values, discrete_columns)
+    path = str(tmpdir / 'test_tvae.pkl')
+    tvae.save(path)
+    tvae = TVAESynthesizer.load(path)
 
     sampled = tvae.sample(100)
 
-    assert sampled.shape == (100, 2)
-    assert isinstance(sampled, np.ndarray)
-    assert set(np.unique(sampled[:, 1])) == {'a', 'b'}
-
-
-def test_synthesizer_sample():
-    data = pd.DataFrame({
-        'discrete': np.random.choice(['a', 'b'], 100)
-    })
-    discrete_columns = ['discrete']
-
-    tvae = TVAESynthesizer(epochs=1)
-    tvae.fit(data, discrete_columns)
-
-    samples = tvae.sample(1000)
-    assert isinstance(samples, pd.DataFrame)
-
-
-def test_save_load():
-    data = pd.DataFrame({
-        'continuous': np.random.random(100),
-        'discrete': np.random.choice(['a', 'b'], 100)
-    })
-    discrete_columns = ['discrete']
-
-    tvae = TVAESynthesizer(epochs=10)
-    tvae.fit(data, discrete_columns)
-
-    with tf.TemporaryDirectory() as temporary_directory:
-        tvae.save(temporary_directory + "test_tvae.pkl")
-        tvae = TVAESynthesizer.load(temporary_directory + "test_tvae.pkl")
-
-    sampled = tvae.sample(1000)
-    assert set(sampled.columns) == {'continuous', 'discrete'}
-    assert set(sampled['discrete'].unique()) == {'a', 'b'}
+    assert sampled.shape == (100, 5)
+    assert isinstance(sampled, pd.DataFrame)
+    assert set(sampled.columns) == set(data.columns)
+    assert set(sampled.dtypes) == set(data.dtypes)