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# SPDX-FileCopyrightText: 2025 This source file is part of the OpenTSLM open-source project.
#
# SPDX-License-Identifier: MIT
import io
import base64
from typing import Type, Callable, Dict, List, Any, Optional
import torch
from torch.utils.data import Dataset
from tqdm import tqdm
from transformers.pipelines import pipeline
import matplotlib.pyplot as plt
from time import sleep
from PIL import Image
import pandas as pd
from openai_pipeline import OpenAIPipeline
from common_evaluator import CommonEvaluator
class CommonEvaluatorPlot(CommonEvaluator):
"""
A common evaluation framework for testing LLMs on time series datasets with plot generation.
"""
def load_model(self, model_name: str, **pipeline_kwargs) -> pipeline:
"""
Load a model using transformers pipeline or OpenAI API.
"""
self.current_model_name = (
model_name # Track the current model name for formatter selection
)
if model_name.startswith("openai-"):
# Use OpenAI API
openai_model = model_name.replace("openai-", "")
return OpenAIPipeline(model_name=openai_model, **pipeline_kwargs)
print(f"Loading model: {model_name}")
print(f"Using device: {self.device}")
# Default pipeline arguments
default_kwargs = {
"task": "image-text-to-text",
"device": self.device,
"temperature": 0.1,
}
default_kwargs.update(pipeline_kwargs)
pipe = pipeline(model=model_name, **default_kwargs)
print(f"Model loaded successfully: {model_name}")
return pipe
def load_dataset(
self,
dataset_class: Type[Dataset],
split: str = "test",
format_sample_str: bool = True,
max_samples: Optional[int] = None,
**dataset_kwargs,
) -> Dataset:
"""
Load a dataset with proper formatting.
"""
print(f"Loading dataset: {dataset_class.__name__}")
formatter = None
# Default dataset arguments
default_kwargs = {
"split": split,
"EOS_TOKEN": "",
"format_sample_str": format_sample_str,
"time_series_format_function": formatter,
}
# Add max_samples if provided
if max_samples is not None:
default_kwargs["max_samples"] = max_samples
# Update with provided kwargs
default_kwargs.update(dataset_kwargs)
dataset = dataset_class(**default_kwargs)
print(f"Loaded {len(dataset)} {split} samples")
return dataset
def evaluate_multiple_models(
self,
model_names: List[str],
dataset_classes: List[Type[Dataset]],
evaluation_functions: Dict[str, Callable[[str, str], Dict[str, Any]]],
plot_functions: Optional[Dict[str, Callable[[Any], str]]] = None,
max_samples: Optional[int] = None,
**pipeline_kwargs,
) -> pd.DataFrame:
"""
Evaluate multiple models on multiple datasets with plotting support.
Args:
model_names: List of model names to evaluate
dataset_classes: List of dataset classes to evaluate on
evaluation_functions: Dictionary mapping dataset class names to evaluation functions
plot_functions: Optional dict mapping dataset class names to plot functions
max_samples: Maximum number of samples per evaluation
**pipeline_kwargs: Additional arguments for model pipeline
Returns:
DataFrame with results for all model-dataset combinations
"""
all_results = []
# Generate filename once at the beginning
import os
current_dir = os.path.dirname(os.path.abspath(__file__))
results_dir = os.path.join(current_dir, "..", "results", "baseline")
os.makedirs(results_dir, exist_ok=True)
df_filename = os.path.join(results_dir, "evaluation_results.csv")
print(f"Results will be saved to: {df_filename}")
# Load existing results if file exists
existing_df = None
if os.path.exists(df_filename):
try:
existing_df = pd.read_csv(df_filename)
print(f"Found existing results file with {len(existing_df)} entries")
except Exception as e:
print(f"Warning: Could not read existing results file: {e}")
for model_name in model_names:
for dataset_class in dataset_classes:
dataset_name = dataset_class.__name__
if dataset_name not in evaluation_functions:
print(f"Warning: No evaluation function found for {dataset_name}")
continue
# Check if this model-dataset combination already exists in results
if existing_df is not None:
existing_result = existing_df[
(existing_df["model"] == model_name)
& (existing_df["dataset"] == dataset_name)
]
if not existing_result.empty:
print(
f"⏭️ Skipping {model_name} on {dataset_name} (already evaluated)"
)
continue
evaluation_function = evaluation_functions[dataset_name]
plot_fn = None
if plot_functions is not None and dataset_name in plot_functions:
plot_fn = plot_functions[dataset_name]
else:
print(f"Warning: No plot function found for {dataset_name}")
print(f"\n{'=' * 80}")
print(f"Evaluating {model_name} on {dataset_name}")
print(f"{'=' * 80}")
try:
results = self.evaluate_model_on_dataset(
model_name=model_name,
dataset_class=dataset_class,
evaluation_function=evaluation_function,
plot_function=plot_fn,
max_samples=max_samples,
**pipeline_kwargs,
)
# Extract key metrics for DataFrame
row = {
"model": model_name,
"dataset": dataset_name,
"total_samples": results["total_samples"],
"successful_inferences": results["successful_inferences"],
"success_rate": results["success_rate"],
}
# Add specific metrics
if results["metrics"]:
for metric_name, metric_values in results["metrics"].items():
if isinstance(metric_values, (int, float)):
row[metric_name] = metric_values
else:
row[metric_name] = str(metric_values)
all_results.append(row)
# Combine with existing results and save
current_df = pd.DataFrame(all_results)
if existing_df is not None:
# Append new results
final_df = pd.concat(
[existing_df, current_df], ignore_index=True
)
else:
final_df = current_df
final_df.to_csv(df_filename, index=False)
print(f"✅ Results updated: {df_filename}")
except Exception as e:
print(f"Error evaluating {model_name} on {dataset_name}: {e}")
all_results.append(
{
"model": model_name,
"dataset": dataset_name,
"status": "Failed",
}
)
# Save DataFrame even after errors
current_df = pd.DataFrame(all_results)
if existing_df is not None:
final_df = pd.concat(
[existing_df, current_df], ignore_index=True
)
else:
final_df = current_df
final_df.to_csv(df_filename, index=False)
print(f"⚠️ Results updated (with error): {df_filename}")
print(f"\nFinal results saved to: {df_filename}")
return final_df
def evaluate_model_on_dataset(
self,
model_name: str,
dataset_class: Type[Dataset],
evaluation_function: Callable[[str, str], Dict[str, Any]],
plot_function: Optional[Callable[[Any], str]] = None,
max_samples: Optional[int] = None,
**pipeline_kwargs,
) -> Dict[str, Any]:
"""
Evaluate a model on a dataset using a custom evaluation function.
Args:
model_name: Name of the model to evaluate
dataset_class: Dataset class to use
evaluation_function: Function that takes (ground_truth, prediction) and returns metrics
max_samples: Maximum number of samples to evaluate (None for all)
**pipeline_kwargs: Additional arguments for model pipeline
Returns:
Dictionary containing evaluation results
"""
print(
f"Starting evaluation with model {model_name} on dataset {dataset_class.__name__}"
)
print("=" * 60)
# Load model
pipe = self.load_model(model_name, **pipeline_kwargs)
# Load dataset
dataset = self.load_dataset(
dataset_class, format_sample_str=False, max_samples=max_samples
)
# Limit samples if specified
if max_samples is not None:
dataset_size = min(len(dataset), max_samples)
print(f"Processing first {dataset_size} samples...")
else:
dataset_size = len(dataset)
print(f"Processing all {dataset_size} samples...")
# Initialize tracking
total_samples = 0
successful_inferences = 0
all_metrics = []
results = []
first_error_printed = False # Track if we've printed the first error
print("\nRunning inference...")
print("=" * 80)
# Get max_new_tokens for generation (default 1000)
max_new_tokens = pipeline_kwargs.pop("max_new_tokens", 1000)
for idx in tqdm(range(dataset_size), desc="Processing samples"):
try:
sample = dataset[idx]
plot_data = None
if isinstance(sample, dict) and "time_series" in sample:
plot_data = plot_function(sample["time_series"])
else:
raise ValueError(
f"Sample {sample} does not contain 'time_series' key"
)
target_answer = sample["answer"]
input_text = sample["pre_prompt"] + sample["post_prompt"]
# Generate prediction
if isinstance(pipe, OpenAIPipeline):
outputs = pipe(
input_text,
max_new_tokens=max_new_tokens,
return_full_text=False,
plot_data=plot_data,
)
else: # For Hugging Face pipelines, convert plot_data (base64) to PIL and pass via images
try:
img_bytes = base64.b64decode(plot_data)
img = Image.open(io.BytesIO(img_bytes)).convert("RGB")
# Check if this is a pretrained model (pt) vs instruction-tuned (it)
if "pt" in model_name.lower() and "gemma" in model_name.lower():
# For pretrained Gemma models, use model and processor directly
# to avoid pipeline issues with chat templates
try:
outputs = pipe(
text=f"{sample['pre_prompt']} <start_of_image> {sample['post_prompt']}",
images=img,
max_new_tokens=max_new_tokens,
return_full_text=False,
)
except Exception as e:
raise RuntimeError(
f"Failed to call pretrained pipeline: {e}"
)
else:
# For instruction-tuned models, use chat template format
messages = [
{
"role": "user",
"content": [
{"type": "image", "image": img},
{"type": "text", "text": input_text},
],
}
]
outputs = pipe(
text=messages,
max_new_tokens=max_new_tokens,
return_full_text=False,
)
except Exception as e:
raise RuntimeError(f"Failed to decode plot image: {e}")
# Extract generated text
if outputs and len(outputs) > 0:
generated_text = outputs[0]["generated_text"].strip()
successful_inferences += 1
# Evaluate using custom function (optionally with sample)
try:
import inspect
sig = inspect.signature(evaluation_function)
if len(sig.parameters) >= 3:
metrics = evaluation_function(
target_answer, generated_text, sample
)
else:
metrics = evaluation_function(target_answer, generated_text)
except Exception:
# Fallback to 2-arg call
metrics = evaluation_function(target_answer, generated_text)
all_metrics.append(metrics)
# Store detailed results
result = {
"sample_idx": idx,
"input_text": input_text,
"target_answer": target_answer,
"generated_answer": generated_text,
"metrics": metrics,
}
results.append(result)
# Print progress for first few samples
if idx < 10:
print(f"\nSAMPLE {idx + 1}:")
print(f"PROMPT: {input_text}...")
print(f"TARGET: {target_answer}")
print(f"PREDICTION: {generated_text}")
print(f"METRICS: {metrics}")
print("=" * 80)
# Print first error for debugging
if not first_error_printed and metrics.get("accuracy", 1) == 0:
print(f"\n❌ FIRST ERROR (Sample {idx + 1}):")
print(f"TARGET: {target_answer}")
print(f"PREDICTION: {generated_text}")
print("=" * 80)
first_error_printed = True
else:
raise ValueError(f"Unexpectedly found empty outputs")
total_samples += 1
# Save results every 100 samples
if total_samples % 50 == 0:
# Calculate current aggregate metrics
current_aggregate_metrics = (
self._aggregate_metrics(all_metrics) if all_metrics else {}
)
current_success_rate = (
successful_inferences / total_samples
if total_samples > 0
else 0.0
)
# Prepare intermediate results
intermediate_results = {
"model_name": model_name,
"dataset_name": dataset_class.__name__,
"total_samples": total_samples,
"successful_inferences": successful_inferences,
"success_rate": current_success_rate,
"metrics": current_aggregate_metrics,
"detailed_results": results,
}
# Save intermediate results
self._save_results(intermediate_results)
print(
f"💾 Intermediate results saved after {total_samples} samples"
)
except Exception as e:
print(f"Error processing sample {idx}: {e}")
total_samples += 1
continue
# Calculate aggregate metrics
if successful_inferences > 0:
# Aggregate metrics across all samples
aggregate_metrics = self._aggregate_metrics(all_metrics)
# Calculate success rate
success_rate = successful_inferences / total_samples
# Prepare final results
final_results = {
"model_name": model_name,
"dataset_name": dataset_class.__name__,
"total_samples": total_samples,
"successful_inferences": successful_inferences,
"success_rate": success_rate,
"metrics": aggregate_metrics,
"detailed_results": results,
}
# Print summary
self._print_summary(final_results)
# Save results
self._save_results(final_results)
return final_results
else:
print("❌ No successful inferences completed!")
return {
"model_name": model_name,
"dataset_name": dataset_class.__name__,
"total_samples": total_samples,
"successful_inferences": 0,
"success_rate": 0.0,
"metrics": {},
"detailed_results": [],
}
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