Eval

In [1]:

!pip install lavague

!pip install lavague

Collecting lavague
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Collecting llama-index-embeddings-azure-openai==0.1.11 (from lavague-contexts-openai<0.3.0,>=0.2.0->lavague)
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In [2]:

import os

# Check if running in Google Colab
try:
    from google.colab import userdata
    IN_COLAB = True
except ImportError:
    IN_COLAB = False

if IN_COLAB:
    os.environ["OPENAI_API_KEY"] = userdata.get('OPENAI_API_KEY')
else:
    os.environ["OPENAI_API_KEY"] = os.getenv('OPENAI_API_KEY')

import os # Check if running in Google Colab try: from google.colab import userdata IN_COLAB = True except ImportError: IN_COLAB = False if IN_COLAB: os.environ["OPENAI_API_KEY"] = userdata.get('OPENAI_API_KEY') else: os.environ["OPENAI_API_KEY"] = os.getenv('OPENAI_API_KEY')

In [3]:

from lavague.core.evaluator import RetrieverEvaluator

retriever_evaluator = RetrieverEvaluator()

from lavague.core.evaluator import RetrieverEvaluator retriever_evaluator = RetrieverEvaluator()

[nltk_data] Downloading package stopwords to
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[nltk_data]   Unzipping corpora/stopwords.zip.
[nltk_data] Downloading package punkt to
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[nltk_data]     packages/llama_index/legacy/_static/nltk_cache...
[nltk_data]   Unzipping tokenizers/punkt.zip.
/usr/local/lib/python3.10/dist-packages/lavague/core/__init__.py:21: UserWarning: Telemetry is turned on. To turn off telemetry, set your LAVAGUE_TELEMETRY to 'NONE'
  warnings.warn(warning_message, UserWarning)

In [4]:

import pandas as pd

raw_dataset = pd.read_parquet("hf://datasets/BigAction/the-meta-wave-raw/data/train-00000-of-00001.parquet")


# Select the first 5 rows
nr = 5
small_df = raw_dataset.head(nr)

import pandas as pd raw_dataset = pd.read_parquet("hf://datasets/BigAction/the-meta-wave-raw/data/train-00000-of-00001.parquet") # Select the first 5 rows nr = 5 small_df = raw_dataset.head(nr)

In [7]:

from lavague.core.retrievers import OpsmSplitRetriever, BM25HtmlRetriever
from lavague.drivers.selenium import SeleniumDriver

driver = SeleniumDriver()

# tests all = 5 rows
retrieved_data_opsm = retriever_evaluator.evaluate(
    OpsmSplitRetriever(driver), small_df, "retrieved_data_opsm.csv"
)
retrieved_data_bm25 = retriever_evaluator.evaluate(
    BM25HtmlRetriever(), small_df, "retrieved_data_bm25.csv"
)

from lavague.core.retrievers import OpsmSplitRetriever, BM25HtmlRetriever from lavague.drivers.selenium import SeleniumDriver driver = SeleniumDriver() # tests all = 5 rows retrieved_data_opsm = retriever_evaluator.evaluate( OpsmSplitRetriever(driver), small_df, "retrieved_data_opsm.csv" ) retrieved_data_bm25 = retriever_evaluator.evaluate( BM25HtmlRetriever(), small_df, "retrieved_data_bm25.csv" )

5it [00:35,  7.05s/it]
5it [00:02,  1.72it/s]

In [8]:

# with as many df as you want
plot = retriever_evaluator.compare(
    {"opsm": retrieved_data_opsm, "bm25": retrieved_data_bm25}
)

# pass arguments with matplotlib
plot.show()

# with as many df as you want plot = retriever_evaluator.compare( {"opsm": retrieved_data_opsm, "bm25": retrieved_data_bm25} ) # pass arguments with matplotlib plot.show()

In [9]:

print(f' Opsm: average precision: {retrieved_data_opsm["precision_retriever"].mean(axis=0)}')
print(f' Opsm: average recall: {retrieved_data_opsm["recall_retriever"].mean(axis=0)}')
print(f' BM2: average precision: {retrieved_data_bm25["precision_retriever"].mean(axis=0)}')
print(f' BM25: average recall: {retrieved_data_bm25["recall_retriever"].mean(axis=0)}')

print(f' Opsm: average precision: {retrieved_data_opsm["precision_retriever"].mean(axis=0)}') print(f' Opsm: average recall: {retrieved_data_opsm["recall_retriever"].mean(axis=0)}') print(f' BM2: average precision: {retrieved_data_bm25["precision_retriever"].mean(axis=0)}') print(f' BM25: average recall: {retrieved_data_bm25["recall_retriever"].mean(axis=0)}')

 Opsm: average precision: 0.04157067153995632
 Opsm: average recall: 0.8
 BM2: average precision: 0.0
 BM25: average recall: 0.0

In [11]:

import os

# Check if running in Google Colab
try:
    from google.colab import userdata
    IN_COLAB = True
except ImportError:
    IN_COLAB = False

if IN_COLAB:
    os.environ["GOOGLE_API_KEY"] = userdata.get('GOOGLE_API_KEY')
else:
    os.environ["GOOGLE_API_KEY"] = os.getenv('GOOGLE_API_KEY')

import os # Check if running in Google Colab try: from google.colab import userdata IN_COLAB = True except ImportError: IN_COLAB = False if IN_COLAB: os.environ["GOOGLE_API_KEY"] = userdata.get('GOOGLE_API_KEY') else: os.environ["GOOGLE_API_KEY"] = os.getenv('GOOGLE_API_KEY')

In [12]:

!pip install lavague-contexts-gemini

!pip install lavague-contexts-gemini

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Installing collected packages: pillow, google-ai-generativelanguage, google-generativeai, llama-index-llms-gemini, llama-index-embeddings-gemini, llama-index-multi-modal-llms-gemini, lavague-contexts-gemini
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Successfully installed google-ai-generativelanguage-0.4.0 google-generativeai-0.4.1 lavague-contexts-gemini-0.2.1 llama-index-embeddings-gemini-0.1.6 llama-index-llms-gemini-0.1.8 llama-index-multi-modal-llms-gemini-0.1.6 pillow-10.4.0

In [15]:

from lavague.core.evaluator import LLMEvaluator
from lavague.contexts.openai import OpenaiContext
from lavague.contexts.gemini import GeminiContext
from lavague.core.navigation import NavigationEngine
import os
from lavague.drivers.selenium import SeleniumDriver

llm_evaluator = LLMEvaluator()
openai_engine = NavigationEngine.from_context(OpenaiContext(), SeleniumDriver())
gemini_engine = NavigationEngine.from_context(GeminiContext(), SeleniumDriver())
openai_results = llm_evaluator.evaluate(openai_engine, retrieved_data_opsm, "openai_results.csv")
gemini_results = llm_evaluator.evaluate(gemini_engine, retrieved_data_opsm, "gemini_results.csv")

from lavague.core.evaluator import LLMEvaluator from lavague.contexts.openai import OpenaiContext from lavague.contexts.gemini import GeminiContext from lavague.core.navigation import NavigationEngine import os from lavague.drivers.selenium import SeleniumDriver llm_evaluator = LLMEvaluator() openai_engine = NavigationEngine.from_context(OpenaiContext(), SeleniumDriver()) gemini_engine = NavigationEngine.from_context(GeminiContext(), SeleniumDriver()) openai_results = llm_evaluator.evaluate(openai_engine, retrieved_data_opsm, "openai_results.csv") gemini_results = llm_evaluator.evaluate(gemini_engine, retrieved_data_opsm, "gemini_results.csv")

5it [00:37,  7.45s/it]
5it [00:21,  4.39s/it]

In [16]:

plot = llm_evaluator.compare(
    {
        "openai": openai_results,
        "gemini": gemini_results,
    }
)
plot.show()

plot = llm_evaluator.compare( { "openai": openai_results, "gemini": gemini_results, } ) plot.show()