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‎src/promptflow-evals/promptflow/evals/evaluate/_evaluate.py

+39-11
Original file line numberDiff line numberDiff line change
@@ -2,15 +2,13 @@
22
# Copyright (c) Microsoft Corporation. All rights reserved.
33
# ---------------------------------------------------------
44
import inspect
5-
from types import FunctionType
5+
import re
66
from typing import Callable, Dict, Optional
77

88
import pandas as pd
99

1010
from promptflow.client import PFClient
1111

12-
from ._code_client import CodeClient
13-
1412

1513
def _calculate_mean(df) -> Dict[str, float]:
1614
df.rename(columns={col: col.replace("outputs.", "") for col in df.columns}, inplace=True)
@@ -30,7 +28,7 @@ def _validate_input_data_for_evaluator(evaluator, evaluator_name, data_df):
3028
raise ValueError(f"Missing required inputs for evaluator {evaluator_name} : {missing_inputs}.")
3129

3230

33-
def _validation(target, data, evaluators, output_path, tracking_uri, evaluation_name):
31+
def _validation(target, data, evaluators, output_path, tracking_uri, evaluation_name, evaluator_config):
3432
if data is None:
3533
raise ValueError("data must be provided for evaluation.")
3634

@@ -64,7 +62,41 @@ def _validation(target, data, evaluators, output_path, tracking_uri, evaluation_
6462
raise ValueError(f"Failed to load data from {data}. Please validate it is a valid jsonl data. Error: {str(e)}.")
6563

6664
for evaluator_name, evaluator in evaluators.items():
67-
_validate_input_data_for_evaluator(evaluator, evaluator_name, data_df)
65+
# Apply column mapping
66+
mapping_config = evaluator_config.get(evaluator_name, evaluator_config.get("default", None))
67+
renamed_data_df = _apply_column_mapping(data_df, "data", mapping_config)
68+
69+
# Validate input data for evaluator
70+
_validate_input_data_for_evaluator(evaluator, evaluator_name, renamed_data_df)
71+
72+
73+
def _apply_column_mapping(source_df, source_name, mapping_config, inplace=False):
74+
SUPPORTED_SOURCE_NAMES = ["data", "target"]
75+
76+
result_df = source_df
77+
if mapping_config:
78+
column_mapping = {}
79+
for map_to_key, map_value in mapping_config.items():
80+
match = re.search(r"^\${([^{}]+)}$", map_value)
81+
82+
if match is not None:
83+
pattern = match.group(1)
84+
85+
# Check if source reference is valid
86+
source_reference = pattern.split(".")[0]
87+
if source_reference not in SUPPORTED_SOURCE_NAMES:
88+
raise ValueError(
89+
f"'{source_reference}' is not a valid source reference. "
90+
+ f"It should be one of {SUPPORTED_SOURCE_NAMES}."
91+
)
92+
93+
if pattern.startswith(f"{source_name}."):
94+
map_from_key = pattern.split(f"{source_name}.")[1]
95+
column_mapping[map_from_key] = map_to_key
96+
97+
result_df = source_df.rename(columns=column_mapping, inplace=inplace)
98+
99+
return result_df
68100

69101

70102
def evaluate(
@@ -97,18 +129,14 @@ def evaluate(
97129
:rtype: ~azure.ai.generative.evaluate.EvaluationResult
98130
"""
99131

100-
_validation(target, data, evaluators, output_path, tracking_uri, evaluation_name)
132+
_validation(target, data, evaluators, output_path, tracking_uri, evaluation_name, evaluator_config)
101133

102134
pf_client = PFClient()
103-
code_client = CodeClient()
104135

105136
evaluator_info = {}
106137

107138
for evaluator_name, evaluator in evaluators.items():
108-
if isinstance(evaluator, FunctionType):
109-
evaluator_info.update({evaluator_name: {"client": pf_client, "evaluator": evaluator}})
110-
else:
111-
evaluator_info.update({evaluator_name: {"client": code_client, "evaluator": evaluator}})
139+
evaluator_info.update({evaluator_name: {"client": pf_client, "evaluator": evaluator}})
112140

113141
evaluator_info[evaluator_name]["run"] = evaluator_info[evaluator_name]["client"].run(
114142
flow=evaluator,

‎src/promptflow-evals/samples/evaluate_test_data.jsonl

+3-3
Original file line numberDiff line numberDiff line change
@@ -1,3 +1,3 @@
1-
{"question":"How do you create a run?","context":"AML API only","answer":"To create a run using the Azure Machine Learning API, you first need to create an Experiment. Once you have an experiment, you can create a Run object that is associated with that experiment. Here is some Python code that demonstrates this process:\n\n```\nfrom azureml.core import Experiment, Run\nfrom azureml.core.workspace import Workspace\n\n# Define workspace and experiment\nws = Workspace.from_config()\nexp = Experiment(workspace=ws, name='my_experiment')\n\n# Create a new run\nrun = exp.start_logging()\n```\n\nIn this code, the `from_config()` method reads the configuration file that you created when you set up your Azure Machine Learning workspace. The `Experiment` constructor creates an Experiment object that is associated with your workspace, and the `start_logging()` method creates a new Run object that is associated with the Experiment. Now you can use the `run` object to log metrics, upload files, and track other information related to your machine learning experiment."}
2-
{"question":"How do you log a model?","context":"Logging can be done using any OSS Sdk","answer":"There are a few ways to log models in Azure Machine Learning. \n\nOne way is to use the `register_model()` method of the `Run` object. The `register_model()` method logs a model file in the Azure Machine Learning service workspace and makes it available for deployment. Here's an example:\n\n```python\nfrom azureml.core import Model\n\nmodel_path = '.\/outputs\/my_model.pkl'\nmodel = Model.register(workspace=ws, model_path=model_path, model_name='my_model')\n```\n\nThis code registers the model file located at `model_path` to the Azure Machine Learning service workspace with the name `my_model`. \n\nAnother way to log a model is to save it as an output of a `Run`. If your model generation code is part of a script or Jupyter notebook that runs as an Azure Machine Learning experiment, you can save the model file as an output of the `Run` object. Here's an example:\n\n```python\nfrom sklearn.linear_model import LogisticRegression\nfrom azureml.core.run import Run\n\n# Initialize a run object\nrun = Run.get_context()\n\n# Train your model\nX_train, y_train = ...\nclf = LogisticRegression().fit(X_train, y_train)\n\n# Save the model to the Run object's outputs directory\nmodel_path = 'outputs\/model.pkl'\njoblib.dump(value=clf, filename=model_path)\n\n# Log the model as a run artifact\nrun.upload_file(name=model_path, path_or_stream=model_path)\n```\n\nIn this code, `Run.get_context()` retrieves the current run context object, which you can use to track metadata and metrics for the run. After training your model, you can use `joblib.dump()` to save the model to a file, and then log the file as an artifact of the run using `run.upload_file()`."}
3-
{"question":"What is the capital of France?","context":"France is in Europe","answer":"Paris is the capital of France."}
1+
{"question": "What is the capital of France?", "context": "France is in Europe", "answer": "Paris is the capital of France.", "ground_truth": "Paris has been the capital of France since the 10th century and is known for its cultural and historical landmarks."}
2+
{"question": "Who developed the theory of relativity?", "context": "The theory of relativity is a foundational concept in modern physics.", "answer": "Albert Einstein developed the theory of relativity.", "ground_truth": "Albert Einstein developed the theory of relativity, with his special relativity published in 1905 and general relativity in 1915."}
3+
{"question": "What is the speed of light?", "context": "Light travels at a constant speed in a vacuum.", "answer": "The speed of light is approximately 299,792,458 meters per second.", "ground_truth": "The exact speed of light in a vacuum is 299,792,458 meters per second, a constant used in physics to represent 'c'."}

‎src/promptflow-evals/samples/evaluation.py

+34-154
Original file line numberDiff line numberDiff line change
@@ -9,40 +9,6 @@
99
from promptflow.evals.evaluators.content_safety import ViolenceEvaluator
1010

1111

12-
def built_in_evaluator():
13-
# Initialize Azure OpenAI Model Configuration
14-
model_config = AzureOpenAIModelConfiguration(
15-
azure_endpoint=os.environ.get("AZURE_OPENAI_ENDPOINT"),
16-
api_key=os.environ.get("AZURE_OPENAI_KEY"),
17-
azure_deployment=os.environ.get("AZURE_OPENAI_DEPLOYMENT"),
18-
)
19-
20-
# Initialzing Relevance Evaluator
21-
relevance_eval = RelevanceEvaluator(model_config)
22-
23-
# Running Relevance Evaluator on single input row
24-
relevance_score = relevance_eval(
25-
answer="The Alpine Explorer Tent is the most waterproof.",
26-
context="From the our product list, the alpine explorer tent is the most waterproof. The Adventure Dining "
27-
"Table has higher weight.",
28-
)
29-
30-
pprint(relevance_score)
31-
32-
33-
def content_safety_evaluator():
34-
# Initialize Project Scope
35-
project_scope = {
36-
"subscription_id": "e0fd569c-e34a-4249-8c24-e8d723c7f054",
37-
"resource_group_name": "rg-test",
38-
"project_name": "project-test",
39-
}
40-
41-
violence_eval = ViolenceEvaluator(project_scope)
42-
violence_score = violence_eval(question="What is the capital of France?", answer="Paris.")
43-
pprint(violence_score)
44-
45-
4612
def answer_length(answer, **kwargs):
4713
return {"value": len(answer)}
4814

@@ -95,136 +61,50 @@ def answer_length(answer, **kwargs):
9561

9662
# Using multiple evaluators together using `Evaluate` API
9763

64+
path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "evaluate_test_data.jsonl")
9865
result = evaluate(
99-
data="evaluate_test_data.jsonl",
66+
data=path,
10067
evaluators={
10168
"answer_length": answer_length,
102-
"violence": violence_eval,
69+
"relevance": relevance_eval,
70+
},
71+
evaluator_config={
72+
"answer_length": {"answer": "${data.answer}"},
73+
"relevance": {"answer": "${data.ground_truth}"},
10374
},
10475
)
10576

10677
pprint(result)
107-
10878
"""
109-
{'metrics': {'outputs.answer_length.value': 879.3333333333334,
110-
'outputs.violence.violence_score': 0.0},
111-
'rows': [{'inputs.answer': 'To create a run using the Azure Machine Learning '
112-
'API, you first need to create an Experiment. Once '
113-
'you have an experiment, you can create a Run '
114-
'object that is associated with that experiment. '
115-
'Here is some Python code that demonstrates this '
116-
'process:\n'
117-
'\n'
118-
'```\n'
119-
'from azureml.core import Experiment, Run\n'
120-
'from azureml.core.workspace import Workspace\n'
121-
'\n'
122-
'# Define workspace and experiment\n'
123-
'ws = Workspace.from_config()\n'
124-
'exp = Experiment(workspace=ws, '
125-
"name='my_experiment')\n"
126-
'\n'
127-
'# Create a new run\n'
128-
'run = exp.start_logging()\n'
129-
'```\n'
130-
'\n'
131-
'In this code, the `from_config()` method reads '
132-
'the configuration file that you created when you '
133-
'set up your Azure Machine Learning workspace. The '
134-
'`Experiment` constructor creates an Experiment '
135-
'object that is associated with your workspace, '
136-
'and the `start_logging()` method creates a new '
137-
'Run object that is associated with the '
138-
'Experiment. Now you can use the `run` object to '
139-
'log metrics, upload files, and track other '
140-
'information related to your machine learning '
141-
'experiment.',
142-
'inputs.context': 'AML API only',
143-
'inputs.question': 'How do you create a run?',
144-
'outputs.answer_length.value': 981,
145-
'outputs.violence.violence': 'Safe',
146-
'outputs.violence.violence_reason': "The system's response is "
147-
'purely technical and '
148-
'educational, providing '
149-
'information on how to use the '
150-
'Azure Machine Learning API. '
151-
'There is no mention or '
152-
'implication of violence in the '
153-
'message.',
154-
'outputs.violence.violence_score': 0.0},
155-
{'inputs.answer': 'There are a few ways to log models in Azure '
156-
'Machine Learning. \n'
157-
'\n'
158-
'One way is to use the `register_model()` method '
159-
'of the `Run` object. The `register_model()` '
160-
'method logs a model file in the Azure Machine '
161-
'Learning service workspace and makes it available '
162-
"for deployment. Here's an example:\n"
163-
'\n'
164-
'```python\n'
165-
'from azureml.core import Model\n'
166-
'\n'
167-
"model_path = './outputs/my_model.pkl'\n"
168-
'model = Model.register(workspace=ws, '
169-
"model_path=model_path, model_name='my_model')\n"
170-
'```\n'
171-
'\n'
172-
'This code registers the model file located at '
173-
'`model_path` to the Azure Machine Learning '
174-
'service workspace with the name `my_model`. \n'
175-
'\n'
176-
'Another way to log a model is to save it as an '
177-
'output of a `Run`. If your model generation code '
178-
'is part of a script or Jupyter notebook that runs '
179-
'as an Azure Machine Learning experiment, you can '
180-
'save the model file as an output of the `Run` '
181-
"object. Here's an example:\n"
182-
'\n'
183-
'```python\n'
184-
'from sklearn.linear_model import '
185-
'LogisticRegression\n'
186-
'from azureml.core.run import Run\n'
187-
'\n'
188-
'# Initialize a run object\n'
189-
'run = Run.get_context()\n'
190-
'\n'
191-
'# Train your model\n'
192-
'X_train, y_train = ...\n'
193-
'clf = LogisticRegression().fit(X_train, y_train)\n'
194-
'\n'
195-
"# Save the model to the Run object's outputs "
196-
'directory\n'
197-
"model_path = 'outputs/model.pkl'\n"
198-
'joblib.dump(value=clf, filename=model_path)\n'
199-
'\n'
200-
'# Log the model as a run artifact\n'
201-
'run.upload_file(name=model_path, '
202-
'path_or_stream=model_path)\n'
203-
'```\n'
204-
'\n'
205-
'In this code, `Run.get_context()` retrieves the '
206-
'current run context object, which you can use to '
207-
'track metadata and metrics for the run. After '
208-
'training your model, you can use `joblib.dump()` '
209-
'to save the model to a file, and then log the '
210-
'file as an artifact of the run using '
211-
'`run.upload_file()`.',
212-
'inputs.context': 'Logging can be done using any OSS Sdk',
213-
'inputs.question': 'How do you log a model?',
214-
'outputs.answer_length.value': 1626,
215-
'outputs.violence.violence': nan,
216-
'outputs.violence.violence_reason': nan,
217-
'outputs.violence.violence_score': nan},
218-
{'inputs.answer': 'Paris is the capital of France.',
79+
{'metrics': {'answer_length.value': 49.333333333333336,
80+
'relevance.gpt_relevance': 5.0},
81+
'rows': [{'inputs.answer': 'Paris is the capital of France.',
21982
'inputs.context': 'France is in Europe',
83+
'inputs.ground_truth': 'Paris has been the capital of France since '
84+
'the 10th century and is known for its '
85+
'cultural and historical landmarks.',
22086
'inputs.question': 'What is the capital of France?',
22187
'outputs.answer_length.value': 31,
222-
'outputs.violence.violence': 'Safe',
223-
'outputs.violence.violence_reason': "The system's response is a "
224-
'straightforward factual answer '
225-
'to a geography question, '
226-
'containing no violent language '
227-
'or content.',
228-
'outputs.violence.violence_score': 0.0}],
88+
'outputs.relevance.gpt_relevance': 5},
89+
{'inputs.answer': 'Albert Einstein developed the theory of '
90+
'relativity.',
91+
'inputs.context': 'The theory of relativity is a foundational '
92+
'concept in modern physics.',
93+
'inputs.ground_truth': 'Albert Einstein developed the theory of '
94+
'relativity, with his special relativity '
95+
'published in 1905 and general relativity in '
96+
'1915.',
97+
'inputs.question': 'Who developed the theory of relativity?',
98+
'outputs.answer_length.value': 51,
99+
'outputs.relevance.gpt_relevance': 5},
100+
{'inputs.answer': 'The speed of light is approximately 299,792,458 '
101+
'meters per second.',
102+
'inputs.context': 'Light travels at a constant speed in a vacuum.',
103+
'inputs.ground_truth': 'The exact speed of light in a vacuum is '
104+
'299,792,458 meters per second, a constant '
105+
"used in physics to represent 'c'.",
106+
'inputs.question': 'What is the speed of light?',
107+
'outputs.answer_length.value': 66,
108+
'outputs.relevance.gpt_relevance': 5}],
229109
'traces': {}}
230110
"""

‎src/promptflow-evals/tests/evals/e2etests/data/column_mapping_test_data.jsonl

+3
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@@ -0,0 +1,3 @@
1+
{"request": "What is the capital of France?", "context": "France is in Europe", "response": "Paris is the capital of France.", "ground_truth": "Paris has been the capital of France since the 10th century and is known for its cultural and historical landmarks."}
2+
{"request": "Who developed the theory of relativity?", "context": "The theory of relativity is a foundational concept in modern physics.", "response": "Albert Einstein developed the theory of relativity.", "ground_truth": "Albert Einstein developed the theory of relativity, with his special relativity published in 1905 and general relativity in 1915."}
3+
{"request": "What is the speed of light?", "context": "Light travels at a constant speed in a vacuum.", "response": "The speed of light is approximately 299,792,458 meters per second.", "ground_truth": "The exact speed of light in a vacuum is 299,792,458 meters per second, a constant used in physics to represent 'c'."}

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