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import dsp
import tqdm
import threading
import pandas as pd
try:
from IPython.display import display as ipython_display, HTML
except ImportError:
ipython_display = print
HTML = lambda x: x
from concurrent.futures import ThreadPoolExecutor, as_completed
from dsp.utils import EM
from dsp.evaluation.utils import *
"""
TODO: Counting failures and having a max_failure count. When that is exceeded (also just at the end),
we print the number of failures, the first N examples that failed, and the first N exceptions raised.
"""
class Evaluate:
def __init__(self, *, devset, metric=None, num_threads=1, display_progress=False,
display_table=False, display=True, max_errors=5, return_outputs=False):
self.devset = devset
self.metric = metric
self.num_threads = num_threads
self.display_progress = display_progress
self.display_table = display_table
self.display = display
self.max_errors = max_errors
self.error_count = 0
self.error_lock = threading.Lock()
self.return_outputs = return_outputs
def _execute_single_thread(self, wrapped_program, devset, display_progress):
ncorrect = 0
ntotal = 0
reordered_devset = []
pbar = tqdm.tqdm(total=len(devset), dynamic_ncols=True, disable=not display_progress)
for idx, arg in devset:
example_idx, example, prediction, score = wrapped_program(idx, arg)
reordered_devset.append((example_idx, example, prediction, score))
ncorrect += score
ntotal += 1
self._update_progress(pbar, ncorrect, ntotal)
pbar.close()
return reordered_devset, ncorrect, ntotal
def _execute_multi_thread(self, wrapped_program, devset, num_threads, display_progress):
ncorrect = 0
ntotal = 0
reordered_devset = []
with ThreadPoolExecutor(max_workers=num_threads) as executor:
futures = {executor.submit(wrapped_program, idx, arg) for idx, arg in devset}
pbar = tqdm.tqdm(total=len(devset), dynamic_ncols=True, disable=not display_progress)
for future in as_completed(futures):
example_idx, example, prediction, score = future.result()
reordered_devset.append((example_idx, example, prediction, score))
ncorrect += score
ntotal += 1
self._update_progress(pbar, ncorrect, ntotal)
pbar.close()
return reordered_devset, ncorrect, ntotal
def _update_progress(self, pbar, ncorrect, ntotal):
pbar.set_description(f"Average Metric: {ncorrect} / {ntotal} ({round(100 * ncorrect / ntotal, 1)})")
pbar.update()
def __call__(self, program, metric=None, devset=None, num_threads=None,
display_progress=None, display_table=None, display=None,
return_all_scores=False, return_outputs=False):
metric = metric if metric is not None else self.metric
devset = devset if devset is not None else self.devset
num_threads = num_threads if num_threads is not None else self.num_threads
display_progress = display_progress if display_progress is not None else self.display_progress
display_table = display_table if display_table is not None else self.display_table
display = self.display if display is None else display
display_progress = display_progress and display
display_table = display_table if display else False
return_outputs = return_outputs if return_outputs is not False else self.return_outputs
results = []
def wrapped_program(example_idx, example):
# NOTE: TODO: Won't work if threads create threads!
creating_new_thread = threading.get_ident() not in dsp.settings.stack_by_thread
if creating_new_thread:
dsp.settings.stack_by_thread[threading.get_ident()] = list(dsp.settings.main_stack)
# print(threading.get_ident(), dsp.settings.stack_by_thread[threading.get_ident()])
# print(type(example), example)
try:
prediction = program(**example.inputs())
score = metric(example, prediction) # FIXME: TODO: What's the right order? Maybe force name-based kwargs!
# increment assert and suggest failures to program's attributes
if hasattr(program, '_assert_failures'):
program._assert_failures += dsp.settings.assert_failures
if hasattr(program, '_suggest_failures'):
program._suggest_failures += dsp.settings.suggest_failures
return example_idx, example, prediction, score
except Exception as e:
with self.error_lock:
self.error_count += 1
current_error_count = self.error_count
if current_error_count >= self.max_errors:
raise e
print(f"Error for example in dev set: \t\t {e}")
return example_idx, example, dict(), 0.0
finally:
if creating_new_thread:
del dsp.settings.stack_by_thread[threading.get_ident()]
devset = list(enumerate(devset))
if num_threads == 1:
reordered_devset, ncorrect, ntotal = self._execute_single_thread(wrapped_program, devset, display_progress)
else:
reordered_devset, ncorrect, ntotal = self._execute_multi_thread(wrapped_program, devset, num_threads, display_progress)
if return_outputs: # Handle the return_outputs logic
results = [(example, prediction, score) for _, example, prediction, score in reordered_devset]
if display:
print(f"Average Metric: {ncorrect} / {ntotal} ({round(100 * ncorrect / ntotal, 1)}%)")
predicted_devset = sorted(reordered_devset)
# data = [{**example, **prediction, 'correct': score} for example, prediction, score in zip(reordered_devset, preds, scores)]
data = [merge_dicts(example, prediction) | {'correct': score} for _, example, prediction, score in predicted_devset]
df = pd.DataFrame(data)
# Truncate every cell in the DataFrame
df = df.applymap(truncate_cell)
# Rename the 'correct' column to the name of the metric function
metric_name = metric.__name__
df.rename(columns={'correct': metric_name}, inplace=True)
if display_table:
if isinstance(display_table, int):
df_to_display = df.head(display_table).copy()
truncated_rows = len(df) - display_table
else:
df_to_display = df.copy()
truncated_rows = 0
styled_df = configure_dataframe_display(df_to_display, metric_name)
ipython_display(styled_df)
if truncated_rows > 0:
# Simplified message about the truncated rows
message = f"""
<div style='
text-align: center;
font-size: 16px;
font-weight: bold;
color: #555;
margin: 10px 0;'>
... {truncated_rows} more rows not displayed ...
</div>
"""
ipython_display(HTML(message))
if return_all_scores and return_outputs:
return round(100 * ncorrect / ntotal, 2), results
elif return_all_scores:
return round(100 * ncorrect / ntotal, 2), [score for *_, score in reordered_devset]
elif return_outputs:
return round(100 * ncorrect / ntotal, 2), results
return round(100 * ncorrect / ntotal, 2)
def merge_dicts(d1, d2):
merged = {}
for k, v in d1.items():
if k in d2:
merged[f"example_{k}"] = v
else:
merged[k] = v
for k, v in d2.items():
if k in d1:
merged[f"pred_{k}"] = v
else:
merged[k] = v
return merged
def truncate_cell(content):
"""Truncate content of a cell to 25 words."""
words = str(content).split()
if len(words) > 25:
return ' '.join(words[:25]) + '...'
return content
def configure_dataframe_display(df, metric_name):
"""Set various pandas display options for DataFrame."""
pd.options.display.max_colwidth = None
pd.set_option('display.max_colwidth', 20) # Adjust the number as needed
pd.set_option('display.width', 400) # Adjust
# df[metric_name] = df[metric_name].apply(lambda x: f'✔️ [{x}]' if x is True else f'❌ [{x}]')
df.loc[:, metric_name] = df[metric_name].apply(lambda x: f'✔️ [{x}]' if x is True else f'{x}')
# Return styled DataFrame
return df.style.set_table_styles([
{'selector': 'th', 'props': [('text-align', 'left')]},
{'selector': 'td', 'props': [('text-align', 'left')]}
]).set_properties(**{
'text-align': 'left',
'white-space': 'pre-wrap',
'word-wrap': 'break-word',
'max-width': '400px'
})
# FIXME: TODO: The merge_dicts stuff above is way too quick and dirty.
# TODO: the display_table can't handle False but can handle 0! Not sure how it works with True exactly, probably fails too.
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