tests: integration: CSV source string src_urls

Signed-off-by: John Andersen <[email protected]>

Author: pdxjohnny

tests/integration/common.py

@@ -18,7 +18,7 @@
 import asyncio
 import contextlib
 import unittest.mock
-from typing import Dict, Any
+from typing import Dict, Any, Optional
 
 from dffml.repo import Repo
 from dffml.base import config
@@ -78,5 +78,12 @@ def required_plugins(self, *args):
                 f"Required plugins: {', '.join(args)} must be installed in development mode"
             )
 
-    def mktempfile(self):
-        return self._stack.enter_context(non_existant_tempfile())
+    def mktempfile(
+        self, suffix: Optional[str] = None, text: Optional[str] = None
+    ):
+        filename = self._stack.enter_context(non_existant_tempfile())
+        if suffix:
+            filename = filename + suffix
+        if text:
+            pathlib.Path(filename).write_text(inspect.cleandoc(text) + "\n")
+        return filename

tests/integration/test_sources.py

@@ -0,0 +1,115 @@
+"""
+This file contains integration tests. We use the CLI to exercise functionality of
+various DFFML classes and constructs.
+"""
+import re
+import os
+import io
+import json
+import inspect
+import pathlib
+import asyncio
+import contextlib
+import unittest.mock
+from typing import Dict, Any
+
+from dffml.repo import Repo
+from dffml.base import config
+from dffml.df.types import Definition, Operation, DataFlow, Input
+from dffml.df.base import op
+from dffml.cli.cli import CLI
+from dffml.model.model import Model
+from dffml.service.dev import Develop
+from dffml.util.packaging import is_develop
+from dffml.util.entrypoint import load
+from dffml.config.config import BaseConfigLoader
+from dffml.util.asynctestcase import AsyncTestCase
+
+from .common import IntegrationCLITestCase
+
+
+class TestCSV(IntegrationCLITestCase):
+    async def test_string_src_urls(self):
+        # Test for issue #207
+        self.required_plugins("dffml-model-scikit")
+        # Create the training data
+        train_filename = self.mktempfile(
+            suffix=".csv",
+            text="""
+                Years,Expertise,Trust,Salary
+                0,1,0.2,10
+                1,3,0.4,20
+                2,5,0.6,30
+                3,7,0.8,40
+                """,
+        )
+        # Create the test data
+        test_filename = self.mktempfile(
+            suffix=".csv",
+            text="""
+                Years,Expertise,Trust,Salary
+                4,9,1.0,50
+                5,11,1.2,60
+                """,
+        )
+        # Create the prediction data
+        predict_filename = self.mktempfile(
+            suffix=".csv",
+            text="""
+                Years,Expertise,Trust
+                6,13,1.4
+                """,
+        )
+        # Features
+        features = "-model-features def:Years:int:1 def:Expertise:int:1 def:Trust:float:1".split()
+        # Train the model
+        await CLI.cli(
+            "train",
+            "-model",
+            "scikitlr",
+            *features,
+            "-model-predict",
+            "Salary",
+            "-sources",
+            "training_data=csv",
+            "-source-filename",
+            train_filename,
+        )
+        # Assess accuracy
+        await CLI.cli(
+            "accuracy",
+            "-model",
+            "scikitlr",
+            *features,
+            "-model-predict",
+            "Salary",
+            "-sources",
+            "test_data=csv",
+            "-source-filename",
+            test_filename,
+        )
+        # Ensure JSON output works as expected (#261)
+        with contextlib.redirect_stdout(self.stdout):
+            # Make prediction
+            await CLI._main(
+                "predict",
+                "all",
+                "-model",
+                "scikitlr",
+                *features,
+                "-model-predict",
+                "Salary",
+                "-sources",
+                "predict_data=csv",
+                "-source-filename",
+                predict_filename,
+            )
+        results = json.loads(self.stdout.getvalue())
+        self.assertTrue(isinstance(results, list))
+        self.assertTrue(results)
+        results = results[0]
+        self.assertIn("src_url", results)
+        self.assertEqual("0", results["src_url"])
+        self.assertIn("prediction", results)
+        self.assertIn("value", results["prediction"])
+        self.assertEqual(70.0, results["prediction"]["value"])