added more random data generators

fractalis/data/etls/test/etl_random_categorical.py

+"""This module provides sample data."""
+
+import pandas as pd
+import random
+
+from fractalis.data.etl import ETL
+
+
+class RandomCategoricalETL(ETL):
+
+    name = 'test_categorical_etl'
+    produces = 'categorical'
+
+    @staticmethod
+    def can_handle(handler: str, descriptor: dict) -> bool:
+        return handler == 'test' and \
+               descriptor['data_type'] == 'categorical'
+
+    def extract(self, server: str,
+                token: str, descriptor: dict) -> pd.DataFrame:
+        data = pd.DataFrame([random.choice(descriptor['values'])
+                             for i in range(descriptor['num_samples'])])
+        return data
+
+    def transform(self, raw_data: pd.DataFrame,
+                  descriptor: dict) -> pd.DataFrame:
+        raw_data.insert(0, 'id', raw_data.index.astype('str'))
+        df = pd.melt(raw_data, id_vars='id', var_name='feature')
+        return df

fractalis/data/etls/test/etl_random_numerical.py

+"""This module provides sample data."""
+
+import pandas as pd
+import numpy as np
+import string
+import random
+
+from fractalis.data.etl import ETL
+
+
+class RandomNumericalETL(ETL):
+
+    name = 'test_numerical_etl'
+    produces = 'numerical'
+
+    @staticmethod
+    def can_handle(handler: str, descriptor: dict) -> bool:
+        return handler == 'test' and \
+               descriptor['data_type'] == 'numerical'
+
+    def extract(self, server: str,
+                token: str, descriptor: dict) -> pd.DataFrame:
+        feature = ''.join(random.choice(string.ascii_letters + string.digits)
+                          for _ in range(30))
+        data = pd.DataFrame(np.random.randn(descriptor['num_samples']).tolist(),
+                            columns=[feature])
+        return data
+
+    def transform(self, raw_data: pd.DataFrame,
+                  descriptor: dict) -> pd.DataFrame:
+        raw_data.insert(0, 'id', raw_data.index.astype('str'))
+        df = pd.melt(raw_data, id_vars='id', var_name='feature')
+        return df

fractalis/data/etls/test/etl_random_numerical_array.py

@@ -2,11 +2,13 @@
 
 import pandas as pd
 import numpy as np
+import string
+import random
 
 from fractalis.data.etl import ETL
 
 
-class RandomNumericalETL(ETL):
+class RandomNumericalArrayETL(ETL):
 
     name = 'test_numerical_array_etl'
     produces = 'numerical_array'
@@ -18,8 +20,12 @@ class RandomNumericalETL(ETL):
 
     def extract(self, server: str,
                 token: str, descriptor: dict) -> pd.DataFrame:
+        features = [''.join(random.choice(string.ascii_letters + string.digits)
+                            for _ in range(10))
+                    for _ in range(descriptor['num_features'])]
         data = pd.DataFrame(np.random.randn(
-            descriptor['num_samples'], descriptor['num_features']).tolist())
+            descriptor['num_samples'], descriptor['num_features']).tolist(),
+                            columns=features)
         return data
 
     def transform(self, raw_data: pd.DataFrame,