Set up tests for all OpenAI content for a migration to the 1.0 upgrade`darinkishore/dspy#72`

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Createdsp/evaluation/test_utils.pyfb1691c

1import unittest
2from unittest.mock import Mock
3
4import openai
5from dsp.evaluation import utils
6
7
8class TestUtils(unittest.TestCase):
9    def test_evaluateRetrieval(self):
10        mock_fn = Mock(return_value=Mock(context="context", answer="answer"))
11        mock_dev = [Mock(question="question", answer="answer")]
12
13        if openai.__version__ >= '1.0':
14            result = utils.evaluateRetrieval(mock_fn, mock_dev)
15            self.assertEqual(result, 100.0)
16        else:
17            result = utils.evaluateRetrieval(mock_fn, mock_dev)
18            self.assertEqual(result, 100.0)
19
20    def test_evaluateAnswer(self):
21        mock_fn = Mock(return_value=Mock(answer="answer"))
22        mock_dev = [Mock(question="question", answer="answer")]
23
24        if openai.__version__ >= '1.0':
25            result = utils.evaluateAnswer(mock_fn, mock_dev)
26            self.assertEqual(result, 100.0)
27        else:
28            result = utils.evaluateAnswer(mock_fn, mock_dev)
29            self.assertEqual(result, 100.0)
30
31    def test_evaluate(self):
32        mock_fn = Mock(return_value=Mock(answer="answer"))
33        mock_dev = [Mock(question="question", answer="answer")]
34
35        if openai.__version__ >= '1.0':
36            result = utils.evaluate(mock_fn, mock_dev)
37            self.assertEqual(result, 100.0)
38        else:
39            result = utils.evaluate(mock_fn, mock_dev)
40            self.assertEqual(result, 100.0)
41
42if __name__ == '__main__':
43    unittest.main()
44

Create a new Python file named test_utils.py in the dsp/evaluation directory.
Import the necessary modules at the top of the file. This includes unittest for writing the tests, dsp.evaluation.utils for the functions to be tested, and openai for the OpenAI library.
Create a new class named TestUtils that inherits from unittest.TestCase. This class will contain all the tests for the functions in dsp/evaluation/utils.py.
Inside the TestUtils class, write three test methods: test_evaluateRetrieval, test_evaluateAnswer, and test_evaluate. Each of these methods should create a mock function for the OpenAI prediction, a mock dev iterable, and then call the corresponding function from dsp/evaluation/utils.py with these mock inputs. The tests should assert that the functions return the expected results.
Each test method should be written twice, once for the v0.28 syntax and once for the v1.0 syntax. Use conditional statements to check the version of the OpenAI library and run the appropriate test.

Run GitHub Actions fordsp/evaluation/test_utils.py

Ran GitHub Actions for fb1691cafc7332534e31d3f1fe4b4143fb9d29aa:

Modifydsp/evaluation/utils.py:11-83

Changed `dsp/evaluation/utils.py` in `6220e7d`

Modify the evaluateRetrieval, evaluateAnswer, and evaluate functions to accept an additional argument: the OpenAI prediction function. This will allow us to pass in a mock function during testing.
Inside each function, replace the line where the OpenAI prediction is made with a call to the passed-in prediction function. This will ensure that the functions can work with both versions of the OpenAI library.
At the end of the file, add a conditional statement that checks the version of the OpenAI library. If the version is v0.28, import the v0.28 syntax functions. If the version is v1.0, import the v1.0 syntax functions. This will ensure that the correct functions are used depending on the version of the library.

Modified file with Assistant API

Instructions (1)

You are a brilliant and meticulous engineer assigned to write code to complete the user's request. When you write code, the code works on the first try, and is complete. Take into account the current repository's language, code style, and dependencies. Your job is to make edits to the file to complete the user "# Request".

# Instructions
Modify the snippets above according to the request by calling the search_and_replace function.
* Keep whitespace and comments.
* Make the minimum necessary search_and_replaces to make changes to the snippets. Only write diffs for lines that should be changed.
* Write multiple small changes instead of a single large change.

Function Call (2)

{"analysis_and_identification": "Modify the `evaluateRetrieval`, `evaluateAnswer`, and `evaluate` functions to accept an additional argument: the OpenAI prediction function.", "replaces_to_make": [{"section_id": "A", "old_code": "def evaluateRetrieval(fn, dev, metric=None):", "new_code": "def evaluateRetrieval(fn, openai_predict_fn, dev, metric=None):"}, {"section_id": "B", "old_code": "prediction = fn(question)", "new_code": "prediction = openai_predict_fn(question)"}, {"section_id": "C", "old_code": "def evaluateAnswer(fn, dev, metric=EM):", "new_code": "def evaluateAnswer(fn, openai_predict_fn, dev, metric=EM):"}, {"section_id": "C", "old_code": "prediction = fn(question)", "new_code": "prediction = openai_predict_fn(question)"}, {"section_id": "D", "old_code": "def evaluate(fn, dev, metric=EM):", "new_code": "def evaluate(fn, openai_predict_fn, dev, metric=EM):"}, {"section_id": "D", "old_code": "prediction = fn(question)", "new_code": "prediction = openai_predict_fn(question)"}]}

Function Call Output (3)

SUCCESS
Here are the new code sections:

The following changes have been applied:
```diff
--- 
+++ 
@@ -9,12 +9,12 @@
 from dsp.utils import EM, F1, HotPotF1
 
 
-def evaluateRetrieval(fn, dev, metric=None):
+def evaluateRetrieval(fn, openai_predict_fn, dev, metric=None):
     data = []
 
     for example in tqdm.tqdm(dev):
         question = example.question
-        prediction = fn(question)
+        prediction = openai_predict_fn(question)
 
         d = dict(example)
 
@@ -32,12 +32,12 @@
     display(df.style.set_table_styles([{'selector': 'th', 'props': [('text-align', 'left')]}, {'selector': 'td', 'props': [('text-align', 'left')]}]))
 
 
-def evaluateAnswer(fn, dev, metric=EM):
+def evaluateAnswer(fn, openai_predict_fn, dev, metric=EM):
     data = []
 
     for example in tqdm.tqdm(dev):
         question = example.question
-        prediction = fn(question)
+        prediction = openai_predict_fn(question)
 
         d = dict(example)
 
@@ -58,12 +58,12 @@
 
 
 
-def evaluate(fn, dev, metric=EM):
+def evaluate(fn, openai_predict_fn, dev, metric=EM):
     data = []
 
     for example in tqdm.tqdm(dev):
         question = example.question
-        prediction = fn(question)
+        prediction = openai_predict_fn(question)
 
         d = dict(example)
 

```
Here are the new code sections:




<section id="A">
from openai import InvalidRequestError
from openai.error import APIError

import dsp
import tqdm
import pandas as pd

from IPython.display import display
from dsp.utils import EM, F1, HotPotF1


def evaluateRetrieval(fn, openai_predict_fn, dev, metric=None):
</section id="A">
<section id="B">
    data = []

    for example in tqdm.tqdm(dev):
        question = example.question
        prediction = openai_predict_fn(question)

        d = dict(example)

        # d['prediction'] = prediction.answer
        d['correct'] =  dsp.passage_match(prediction.context, example.answer)
        data.append(d)

    df = pd.DataFrame(data)

    percentage = round(100.0 * df['correct'].sum() / len(dev), 1)
    print(f"Answered {df['correct'].sum()} / {len(dev)} ({percentage}%) correctly.")
    df['correct'] = df['correct'].apply(lambda x: '✔️' if x else '❌')

    pd.options.display.max_colwidth = None
</section id="B">
<section id="C">
    display(df.style.set_table_styles([{'selector': 'th', 'props': [('text-align', 'left')]}, {'selector': 'td', 'props': [('text-align', 'left')]}]))


def evaluateAnswer(fn, openai_predict_fn, dev, metric=EM):
    data = []

    for example in tqdm.tqdm(dev):
        question = example.question
        prediction = openai_predict_fn(question)

        d = dict(example)

        pred = prediction.answer

        d['prediction'] = pred
        d['correct'] = metric(pred, example.answer)
        data.append(d)

    df = pd.DataFrame(data)

    percentage = round(100.0 * df['correct'].sum() / len(dev), 1)
    print(f"Answered {df['correct'].sum()} / {len(dev)} ({percentage}%) correctly.")
    df['correct'] = df['correct'].apply(lambda x: '✔️' if x else '❌')

    pd.options.display.max_colwidth = None
</section id="C">
<section id="D">
    display(df.style.set_table_styles([{'selector': 'th', 'props': [('text-align', 'left')]}, {'selector': 'td', 'props': [('text-align', 'left')]}]))



def evaluate(fn, openai_predict_fn, dev, metric=EM):
    data = []

    for example in tqdm.tqdm(dev):
        question = example.question
        prediction = openai_predict_fn(question)

        d = dict(example)

        pred = prediction#.answer

        d['prediction'] = pred
        d['correct'] = metric(pred, example.answer)
        data.append(d)

    df = pd.DataFrame(data)

    percentage = round(100.0 * df['correct'].sum() / len(dev), 1)
    print(f"Answered {df['correct'].sum()} / {len(dev)} ({percentage}%) correctly.")
    df['correct'] = df['correct'].apply(lambda x: '✔️' if x else '❌')

    pd.options.display.max_colwidth = None
    display(df.style.set_table_styles([{'selector': 'th', 'props': [('text-align', 'left')]}, {'selector': 'td', 'props': [('text-align', 'left')]}]))

    return percentage


</section id="D">. You can continue to make changes to the code sections and call the `search_and_replace` function again.

Function Call (4)

{"analysis_and_identification": "At the end of the file, add a conditional statement that checks the version of the OpenAI library.", "replaces_to_make": [{"section_id": "D", "old_code": "return percentage", "new_code": "return percentage\n\n# Check OpenAI library version and import syntax functions accordingly\nimport openai\nif openai.__version__ == '0.28':\n    from .syntax_v028 import *\nelif openai.__version__ == '1.0':\n    from .syntax_v1 import *"}]}

Function Call Output (5)

SUCCESS
Here are the new code sections:

The following changes have been applied:
```diff
--- 
+++ 
@@ -82,4 +82,11 @@
     pd.options.display.max_colwidth = None
     display(df.style.set_table_styles([{'selector': 'th', 'props': [('text-align', 'left')]}, {'selector': 'td', 'props': [('text-align', 'left')]}]))
 
-    return percentage+    return percentage
+
+# Check OpenAI library version and import syntax functions accordingly
+import openai
+if openai.__version__ == '0.28':
+    from .syntax_v028 import *
+elif openai.__version__ == '1.0':
+    from .syntax_v1 import *
```
Here are the new code sections:




<section id="A">
from openai import InvalidRequestError
from openai.error import APIError

import dsp
import tqdm
import pandas as pd

from IPython.display import display
from dsp.utils import EM, F1, HotPotF1


def evaluateRetrieval(fn, openai_predict_fn, dev, metric=None):
</section id="A">
<section id="B">
    data = []

    for example in tqdm.tqdm(dev):
        question = example.question
        prediction = openai_predict_fn(question)

        d = dict(example)

        # d['prediction'] = prediction.answer
        d['correct'] =  dsp.passage_match(prediction.context, example.answer)
        data.append(d)

    df = pd.DataFrame(data)

    percentage = round(100.0 * df['correct'].sum() / len(dev), 1)
    print(f"Answered {df['correct'].sum()} / {len(dev)} ({percentage}%) correctly.")
    df['correct'] = df['correct'].apply(lambda x: '✔️' if x else '❌')

    pd.options.display.max_colwidth = None
</section id="B">
<section id="C">
    display(df.style.set_table_styles([{'selector': 'th', 'props': [('text-align', 'left')]}, {'selector': 'td', 'props': [('text-align', 'left')]}]))


def evaluateAnswer(fn, openai_predict_fn, dev, metric=EM):
    data = []

    for example in tqdm.tqdm(dev):
        question = example.question
        prediction = openai_predict_fn(question)

        d = dict(example)

        pred = prediction.answer

        d['prediction'] = pred
        d['correct'] = metric(pred, example.answer)
        data.append(d)

    df = pd.DataFrame(data)

    percentage = round(100.0 * df['correct'].sum() / len(dev), 1)
    print(f"Answered {df['correct'].sum()} / {len(dev)} ({percentage}%) correctly.")
    df['correct'] = df['correct'].apply(lambda x: '✔️' if x else '❌')

    pd.options.display.max_colwidth = None
</section id="C">
<section id="D">
    display(df.style.set_table_styles([{'selector': 'th', 'props': [('text-align', 'left')]}, {'selector': 'td', 'props': [('text-align', 'left')]}]))



def evaluate(fn, openai_predict_fn, dev, metric=EM):
    data = []

    for example in tqdm.tqdm(dev):
        question = example.question
        prediction = openai_predict_fn(question)

        d = dict(example)

        pred = prediction#.answer

        d['prediction'] = pred
        d['correct'] = metric(pred, example.answer)
        data.append(d)

    df = pd.DataFrame(data)

    percentage = round(100.0 * df['correct'].sum() / len(dev), 1)
    print(f"Answered {df['correct'].sum()} / {len(dev)} ({percentage}%) correctly.")
    df['correct'] = df['correct'].apply(lambda x: '✔️' if x else '❌')

    pd.options.display.max_colwidth = None
    display(df.style.set_table_styles([{'selector': 'th', 'props': [('text-align', 'left')]}, {'selector': 'td', 'props': [('text-align', 'left')]}]))

    return percentage

# Check OpenAI library version and import syntax functions accordingly
import openai
if openai.__version__ == '0.28':
    from .syntax_v028 import *
elif openai.__version__ == '1.0':
    from .syntax_v1 import *


</section id="D">. You can continue to make changes to the code sections and call the `search_and_replace` function again.

Assistant (6)

The changes have been successfully applied. Here are the updated code sections with the modifications requested:

```python
# Section A
from openai import InvalidRequestError
from openai.error import APIError

import dsp
import tqdm
import pandas as pd

from IPython.display import display
from dsp.utils import EM, F1, HotPotF1


def evaluateRetrieval(fn, openai_predict_fn, dev, metric=None):
    # ... other code remains unchanged ...
```

```python
# Section B
    data = []

    for example in tqdm.tqdm(dev):
        question = example.question
        prediction = openai_predict_fn(question)

        d = dict(example)

        # d['prediction'] = prediction.answer
        d['correct'] =  dsp.passage_match(prediction.context, example.answer)
        data.append(d)

    df = pd.DataFrame(data)

    # ... other code remains unchanged ...
```

```python
# Section C
    display(df.style.set_table_styles([ ... ]))


def evaluateAnswer(fn, openai_predict_fn, dev, metric=EM):
    data = []

    for example in tqdm.tqdm(dev):
        question = example.question
        prediction = openai_predict_fn(question)

        d = dict(example)

        pred = prediction.answer

        d['prediction'] = pred
        d['correct'] = metric(pred, example.answer)
        data.append(d)

    # ... other code remains unchanged ...
```

```python
# Section D
    display(df.style.set_table_styles([ ... ]))


def evaluate(fn, openai_predict_fn, dev, metric=EM):
    data = []

    for example in tqdm.tqdm(dev):
        question = example.question
        prediction = openai_predict_fn(question)

        d = dict(example)

        pred = prediction  # .answer

        d['prediction'] = pred
        d['correct'] = metric(pred, example.answer)
        data.append(d)

    # ... other code remains unchanged ...

return percentage

# Check OpenAI library version and import syntax functions accordingly
import openai
if openai.__version__ == '0.28':
    from .syntax_v028 import *
elif openai.__version__ == '1.0':
    from .syntax_v1 import *
```

Please note that `fn` has been replaced with `openai_predict_fn` as the OpenAI prediction function argument, and the additional import logic based on the OpenAI library version has been added at the end of the section.

Run GitHub Actions fordsp/evaluation/utils.py

Ran GitHub Actions for 6220e7dbd745fa0de97bc1fcf94d7a04500297f0:

Plan

This is based on the results of the Planning step. The plan may expand from failed GitHub Actions runs.

Createdsp/evaluation/test_utils.pyfb1691c

1import unittest
2from unittest.mock import Mock
3
4import openai
5from dsp.evaluation import utils
6
7
8class TestUtils(unittest.TestCase):
9    def test_evaluateRetrieval(self):
10        mock_fn = Mock(return_value=Mock(context="context", answer="answer"))
11        mock_dev = [Mock(question="question", answer="answer")]
12
13        if openai.__version__ >= '1.0':
14            result = utils.evaluateRetrieval(mock_fn, mock_dev)
15            self.assertEqual(result, 100.0)
16        else:
17            result = utils.evaluateRetrieval(mock_fn, mock_dev)
18            self.assertEqual(result, 100.0)
19
20    def test_evaluateAnswer(self):
21        mock_fn = Mock(return_value=Mock(answer="answer"))
22        mock_dev = [Mock(question="question", answer="answer")]
23
24        if openai.__version__ >= '1.0':
25            result = utils.evaluateAnswer(mock_fn, mock_dev)
26            self.assertEqual(result, 100.0)
27        else:
28            result = utils.evaluateAnswer(mock_fn, mock_dev)
29            self.assertEqual(result, 100.0)
30
31    def test_evaluate(self):
32        mock_fn = Mock(return_value=Mock(answer="answer"))
33        mock_dev = [Mock(question="question", answer="answer")]
34
35        if openai.__version__ >= '1.0':
36            result = utils.evaluate(mock_fn, mock_dev)
37            self.assertEqual(result, 100.0)
38        else:
39            result = utils.evaluate(mock_fn, mock_dev)
40            self.assertEqual(result, 100.0)
41
42if __name__ == '__main__':
43    unittest.main()
44

Run GitHub Actions fordsp/evaluation/test_utils.py

Modifydsp/evaluation/utils.py:11-83

Run GitHub Actions fordsp/evaluation/utils.py

Code Snippets Found

This is based on the results of the Searching step.

dsp/evaluation/utils.py:0-84

1from openai import InvalidRequestError
2from openai.error import APIError
3
4import dsp
5import tqdm
6import pandas as pd
7
8from IPython.display import display
9from dsp.utils import EM, F1, HotPotF1
10
11
12def evaluateRetrieval(fn, dev, metric=None):
13    data = []
14
15    for example in tqdm.tqdm(dev):
16        question = example.question
17        prediction = fn(question)
18
19        d = dict(example)
20
21        # d['prediction'] = prediction.answer
22        d['correct'] =  dsp.passage_match(prediction.context, example.answer)
23        data.append(d)
24
25    df = pd.DataFrame(data)
26
27    percentage = round(100.0 * df['correct'].sum() / len(dev), 1)
28    print(f"Answered {df['correct'].sum()} / {len(dev)} ({percentage}%) correctly.")
29    df['correct'] = df['correct'].apply(lambda x: '✔️' if x else '❌')
30
31    pd.options.display.max_colwidth = None
32    display(df.style.set_table_styles([{'selector': 'th', 'props': [('text-align', 'left')]}, {'selector': 'td', 'props': [('text-align', 'left')]}]))
33
34
35def evaluateAnswer(fn, dev, metric=EM):
36    data = []
37
38    for example in tqdm.tqdm(dev):
39        question = example.question
40        prediction = fn(question)
41
42        d = dict(example)
43
44        pred = prediction.answer
45
46        d['prediction'] = pred
47        d['correct'] = metric(pred, example.answer)
48        data.append(d)
49
50    df = pd.DataFrame(data)
51
52    percentage = round(100.0 * df['correct'].sum() / len(dev), 1)
53    print(f"Answered {df['correct'].sum()} / {len(dev)} ({percentage}%) correctly.")
54    df['correct'] = df['correct'].apply(lambda x: '✔️' if x else '❌')
55
56    pd.options.display.max_colwidth = None
57    display(df.style.set_table_styles([{'selector': 'th', 'props': [('text-align', 'left')]}, {'selector': 'td', 'props': [('text-align', 'left')]}]))
58
59
60
61def evaluate(fn, dev, metric=EM):
62    data = []
63
64    for example in tqdm.tqdm(dev):
65        question = example.question
66        prediction = fn(question)
67
68        d = dict(example)
69
70        pred = prediction#.answer
71
72        d['prediction'] = pred
73        d['correct'] = metric(pred, example.answer)
74        data.append(d)
75
76    df = pd.DataFrame(data)
77
78    percentage = round(100.0 * df['correct'].sum() / len(dev), 1)
79    print(f"Answered {df['correct'].sum()} / {len(dev)} ({percentage}%) correctly.")
80    df['correct'] = df['correct'].apply(lambda x: '✔️' if x else '❌')
81
82    pd.options.display.max_colwidth = None
83    display(df.style.set_table_styles([{'selector': 'th', 'props': [('text-align', 'left')]}, {'selector': 'td', 'props': [('text-align', 'left')]}]))
84