Skip to content

feat: Add Pytorch template #5780

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
franciscojavierarceo merged 3 commits into from
Dec 16, 2025
Merged

feat: Add Pytorch template #5780

Show file tree
Hide file tree
Changes from 1 commit
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
0908943
feat: Add PyTorch NLP template with sentiment analysis and update CLI…
franciscojavierarceo Dec 16, 2025
4be5653
feat: Add PyTorch NLP template with sentiment analysis and update CLI…
franciscojavierarceo Dec 16, 2025
def9bad
fix linter
franciscojavierarceo Dec 16, 2025
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
Prev Previous commit
Next Next commit
feat: Add PyTorch NLP template with sentiment analysis and update CLI… 
… to support repo-path
  • Loading branch information
@franciscojavierarceo
franciscojavierarceo committed Dec 16, 2025
commit 4be5653b3aefe2dfba3a613a68f5c39751d6c98f
2 changes: 1 addition & 1 deletion sdk/python/feast/templates/pytorch_nlp/__init__.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1 +1 @@
# Empty file to make this a Python package
# Empty file to make this a Python package
144 changes: 94 additions & 50 deletions sdk/python/feast/templates/pytorch_nlp/bootstrap.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,6 @@
import random
from datetime import datetime, timedelta

import numpy as np
import pandas as pd
import pyarrow as pa
import pyarrow.parquet as pq
Expand All @@ -26,7 +25,6 @@ def create_sentiment_data(num_samples: int = 1000) -> pd.DataFrame:
"My phone battery died right when I needed it most",
"Loving the new album that just dropped today",
"Can't believe how long this line is taking",

# Product reviews / opinions
"This phone has incredible battery life and camera quality",
"The delivery was late and packaging was damaged",
Expand All @@ -38,7 +36,6 @@ def create_sentiment_data(num_samples: int = 1000) -> pd.DataFrame:
"Returned this item, didn't work as advertised",
"Decent product but could be better for the cost",
"Exceeded my expectations, will buy again",

# General experiences
"Learning something new always makes me happy",
"Dealing with technical issues is draining my energy",
Expand All @@ -50,7 +47,6 @@ def create_sentiment_data(num_samples: int = 1000) -> pd.DataFrame:
"Nothing special planned, just a quiet evening",
"Great presentation today, audience was engaged",
"Feeling overwhelmed with all these deadlines",

# News / current events style
"The new policy changes will benefit small businesses",
"This decision could have negative environmental impact",
Expand All @@ -61,19 +57,20 @@ def create_sentiment_data(num_samples: int = 1000) -> pd.DataFrame:
"Public opinion remains divided on this issue",
"Significant improvements in the healthcare system",
"Concerns raised about the new regulations",
"Standard quarterly results meeting projections"
"Standard quarterly results meeting projections",
]

# Try to use BERTweet sentiment classifier, fallback to rule-based if not available
try:
from transformers import pipeline

print(" 🤖 Loading BERTweet sentiment classifier...")

# Use BERTweet model specifically trained for Twitter sentiment
sentiment_classifier = pipeline(
"sentiment-analysis",
model="finiteautomata/bertweet-base-sentiment-analysis",
return_all_scores=True
return_all_scores=True,
)
use_real_classifier = True
print(" ✅ BERTweet sentiment classifier loaded successfully")
Expand Down Expand Up @@ -105,10 +102,16 @@ def create_sentiment_data(num_samples: int = 1000) -> pd.DataFrame:
elif random.random() < 0.1:
text = text + "..."
elif random.random() < 0.08:
if any(word in text.lower() for word in ["amazing", "love", "great", "best", "happy", "excited"]):
if any(
word in text.lower()
for word in ["amazing", "love", "great", "best", "happy", "excited"]
):
text = text + " 😊"
elif random.random() < 0.08:
if any(word in text.lower() for word in ["terrible", "disappointed", "frustrated", "horrible"]):
if any(
word in text.lower()
for word in ["terrible", "disappointed", "frustrated", "horrible"]
):
text = text + " 😞"

# Get sentiment from real classifier or fallback
Expand All @@ -117,13 +120,17 @@ def create_sentiment_data(num_samples: int = 1000) -> pd.DataFrame:
predictions = sentiment_classifier(text)[0]

# Find highest confidence prediction
best_pred = max(predictions, key=lambda x: x['score'])
sentiment_label = best_pred['label'].upper() # BERTweet returns 'POS', 'NEG', 'NEU'
sentiment_score = best_pred['score']
best_pred = max(predictions, key=lambda x: x["score"])
sentiment_label = best_pred[
"label"
].upper() # BERTweet returns 'POS', 'NEG', 'NEU'
sentiment_score = best_pred["score"]

# Map BERTweet labels to our format
label_map = {"POS": "positive", "NEG": "negative", "NEU": "neutral"}
sentiment_label = label_map.get(sentiment_label, sentiment_label.lower())
sentiment_label = label_map.get(
sentiment_label, sentiment_label.lower()
)

except Exception as e:
print(f" ⚠️ Classifier error for text {i}: {e}")
Expand All @@ -136,46 +143,54 @@ def create_sentiment_data(num_samples: int = 1000) -> pd.DataFrame:
# Generate engineered features
text_length = len(text)
word_count = len(text.split())
exclamation_count = text.count('!')
exclamation_count = text.count("!")
caps_ratio = sum(1 for c in text if c.isupper()) / len(text) if text else 0
emoji_count = sum(1 for c in text if ord(c) > 127) # Simple emoji detection

# Random timestamp within the past 30 days
days_offset = random.randint(0, 30)
hours_offset = random.randint(0, 23)
minutes_offset = random.randint(0, 59)
event_timestamp = start_date + timedelta(days=days_offset, hours=hours_offset, minutes=minutes_offset)

data.append({
'text_id': f'text_{i:04d}',
'user_id': f'user_{random.randint(1, 100):03d}',
'text_content': text,
'sentiment_label': sentiment_label,
'sentiment_score': round(sentiment_score, 3),
'text_length': text_length,
'word_count': word_count,
'exclamation_count': exclamation_count,
'caps_ratio': round(caps_ratio, 3),
'emoji_count': emoji_count,
'event_timestamp': pd.Timestamp(event_timestamp, tz='UTC'),
'created': pd.Timestamp.now(tz='UTC').round('ms')
})
event_timestamp = start_date + timedelta(
days=days_offset, hours=hours_offset, minutes=minutes_offset
)

data.append(
{
"text_id": f"text_{i:04d}",
"user_id": f"user_{random.randint(1, 100):03d}",
"text_content": text,
"sentiment_label": sentiment_label,
"sentiment_score": round(sentiment_score, 3),
"text_length": text_length,
"word_count": word_count,
"exclamation_count": exclamation_count,
"caps_ratio": round(caps_ratio, 3),
"emoji_count": emoji_count,
"event_timestamp": pd.Timestamp(event_timestamp, tz="UTC"),
"created": pd.Timestamp.now(tz="UTC").round("ms"),
}
)

df = pd.DataFrame(data)

# Calculate user-level aggregations
user_stats = df.groupby('user_id').agg({
'sentiment_score': 'mean',
'text_id': 'count',
'text_length': 'mean'
}).rename(columns={
'sentiment_score': 'user_avg_sentiment',
'text_id': 'user_text_count',
'text_length': 'user_avg_text_length'
}).round(3).reset_index()
user_stats = (
df.groupby("user_id")
.agg({"sentiment_score": "mean", "text_id": "count", "text_length": "mean"})
.rename(
columns={
"sentiment_score": "user_avg_sentiment",
"text_id": "user_text_count",
"text_length": "user_avg_text_length",
}
)
.round(3)
.reset_index()
)

# Merge user stats back to main dataframe
df = df.merge(user_stats, on='user_id', how='left')
df = df.merge(user_stats, on="user_id", how="left")

return df

Expand All @@ -184,12 +199,41 @@ def _rule_based_sentiment(text: str) -> tuple[str, float]:
"""Fallback rule-based sentiment analysis when BERTweet is not available."""
text_lower = text.lower()

positive_words = ["amazing", "love", "great", "excellent", "wonderful", "perfect",
"outstanding", "fantastic", "best", "happy", "good", "awesome",
"incredible", "beautiful", "excited", "enjoying"]
negative_words = ["terrible", "horrible", "awful", "worst", "bad", "disappointed",
"frustrated", "angry", "sad", "broken", "failed", "poor",
"draining", "overwhelming", "disappointing"]
positive_words = [
"amazing",
"love",
"great",
"excellent",
"wonderful",
"perfect",
"outstanding",
"fantastic",
"best",
"happy",
"good",
"awesome",
"incredible",
"beautiful",
"excited",
"enjoying",
]
negative_words = [
"terrible",
"horrible",
"awful",
"worst",
"bad",
"disappointed",
"frustrated",
"angry",
"sad",
"broken",
"failed",
"poor",
"draining",
"overwhelming",
"disappointing",
]

positive_count = sum(1 for word in positive_words if word in text_lower)
negative_count = sum(1 for word in negative_words if word in text_lower)
Expand All @@ -208,7 +252,7 @@ def bootstrap():
raw_project_name = pathlib.Path(__file__).parent.absolute().name

# Sanitize project name for SQLite compatibility (no hyphens allowed)
project_name = raw_project_name.replace('-', '_')
project_name = raw_project_name.replace("-", "_")
if project_name != raw_project_name:
print(f" ℹ️ Project name sanitized: '{raw_project_name}' → '{project_name}'")
print(" 💡 SQLite table names cannot contain hyphens")
Expand All @@ -229,8 +273,8 @@ def bootstrap():
pq.write_table(table, parquet_file)

print(f" ✅ Created sentiment dataset with {len(df)} samples")
print(f" 📊 Sentiment distribution:")
sentiment_counts = df['sentiment_label'].value_counts()
print(" 📊 Sentiment distribution:")
sentiment_counts = df["sentiment_label"].value_counts()
for sentiment, count in sentiment_counts.items():
print(f" - {sentiment.capitalize()}: {count} samples")

Expand All @@ -246,7 +290,7 @@ def bootstrap():
print("\n🎯 To get started:")
print(f" 1. cd {project_name}")
print(" 2. pip install -r requirements.txt")
print(f" 3. cd feature_repo")
print(" 3. cd feature_repo")
print(" 4. feast apply")
print(" 5. feast materialize")
print(" 6. python test_workflow.py")
Expand All @@ -258,4 +302,4 @@ def bootstrap():


if __name__ == "__main__":
bootstrap()
bootstrap()
2 changes: 1 addition & 1 deletion sdk/python/feast/templates/pytorch_nlp/feature_repo/__init__.py
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1 +1 @@
# This file is auto-generated by the Feast project template
# This file is auto-generated by the Feast project template
Loading
Loading