def test_generate(self):
df_features = self.spark.read.csv(
'tests/fixtures/similarity/features.csv', header=True)
columns_to_convert = [
col for col in df_features.columns if 'id' not in col
]
df_features_int = df_features
for col in columns_to_convert:
df_features_int = df_features_int.withColumn(
col,
f.col(col).cast(IntegerType()))
similarity_cos = Similarity(df_features=df_features_int,
similarity_type='cosine')
pd_df_similarity_cos, _ = similarity_cos.generate()
self.assertEqual(pd_df_similarity_cos.shape[0], df_features.count())
self.assertEqual(pd_df_similarity_cos.shape[1], df_features.count())
similarity_euc = Similarity(df_features=df_features_int,
similarity_type='euclidean')
pd_df_similarity_euc, _ = similarity_euc.generate()
self.assertEqual(pd_df_similarity_euc.shape[0], df_features.count())
self.assertEqual(pd_df_similarity_euc.shape[1], df_features.count())
similarity_fail = Similarity(df_features=df_features_int,
similarity_type='test')
with self.assertRaises(ValueError):
similarity_fail.generate()
etl_created = create_timestamp()
preprocessor = Preprocess(df_labels=df_labels,
columns=COLUMNS,
index_column=INDEX_COLUMN)
df_recipe_features = preprocessor.preprocess()
pd_df_recipe_features = df_recipe_features.toPandas()
features_dir = f'output/{etl_created}/features'
os.makedirs(features_dir)
pd_df_recipe_features.to_csv(f'{features_dir}/features.csv', index=False)
similarity = Similarity(df_features=df_recipe_features,
index_column=INDEX_COLUMN,
similarity_type=SIMILARITY_TYPE)
similarities = similarity.generate()
pd_df_similarities_wide = similarities[0]
pd_df_similarities_long = similarities[1]
similarities_dir = f'output/{etl_created}/similarities'
os.makedirs(similarities_dir)
pd_df_similarities_wide.to_csv(f'{similarities_dir}/similarities_wide.csv', index=True)
pd_df_similarities_long.to_csv(f'{similarities_dir}/similarities_long.csv', index=False)
parameters_dir = f'output/{etl_created}/parameters'
os.makedirs(parameters_dir)
pd_df_parameters = create_parameters_table(similarity_type=SIMILARITY_TYPE,
index_column=INDEX_COLUMN,
columns=COLUMNS)
pd_df_parameters.to_csv(f'{parameters_dir}/parameters.csv', index=False)
