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"""
DataSynthis_ML_JobTask - Movie Recommendation Model
A movie recommendation system using collaborative filtering and matrix factorization.
"""
import pandas as pd
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.decomposition import TruncatedSVD
import os
import urllib.request
import zipfile
import pickle
from typing import List, Dict, Optional, Union
class MovieRecommender:
"""
Movie Recommendation Model using collaborative filtering and SVD.
"""
def __init__(self):
self.ratings = None
self.movies = None
self.user_item_matrix = None
self.item_similarity = None
self.item_similarity_df = None
self.svd_model = None
self.pred_svd_df = None
self.is_trained = False
def load_data(self):
"""Load MovieLens 100k dataset."""
dataset_url = "http://files.grouplens.org/datasets/movielens/ml-100k.zip"
dataset_path = "ml-100k"
if not os.path.exists(dataset_path):
if os.path.exists("ml-100k.zip"):
print("Extracting existing MovieLens 100k dataset...")
with zipfile.ZipFile("ml-100k.zip", "r") as zip_ref:
zip_ref.extractall(".")
print("Extraction complete.")
else:
print("Downloading MovieLens 100k dataset...")
try:
urllib.request.urlretrieve(dataset_url, "ml-100k.zip")
with zipfile.ZipFile("ml-100k.zip", "r") as zip_ref:
zip_ref.extractall(".")
print("Download complete.")
except Exception as e:
print(f"Download failed: {e}")
raise Exception("Could not download dataset")
# Load ratings
self.ratings = pd.read_csv(
"ml-100k/u.data",
sep="\t",
names=["user_id", "movie_id", "rating", "timestamp"]
)
# Load movies
self.movies = pd.read_csv(
"ml-100k/u.item",
sep="|",
encoding="ISO-8859-1",
names=["movie_id", "title", "release_date", "video_release_date", "IMDb_URL",
"unknown", "Action", "Adventure", "Animation", "Children", "Comedy",
"Crime", "Documentary", "Drama", "Fantasy", "Film-Noir", "Horror",
"Musical", "Mystery", "Romance", "Sci-Fi", "Thriller", "War", "Western"]
)
# Remove timestamp column
self.ratings.drop("timestamp", axis=1, inplace=True)
print(f"Loaded {len(self.ratings)} ratings from {len(self.ratings['user_id'].unique())} users")
print(f"Loaded {len(self.movies)} movies")
def train(self):
"""Train the recommendation models."""
if self.ratings is None:
self.load_data()
# Create user-item matrix
self.user_item_matrix = self.ratings.pivot(
index='user_id', columns='movie_id', values='rating'
)
# Collaborative Filtering - Item-based similarity
self.item_similarity = cosine_similarity(self.user_item_matrix.T.fillna(0))
self.item_similarity_df = pd.DataFrame(
self.item_similarity,
index=self.user_item_matrix.columns,
columns=self.user_item_matrix.columns
)
# SVD - Matrix Factorization
R = self.user_item_matrix.fillna(0)
self.svd_model = TruncatedSVD(n_components=20, random_state=42)
U = self.svd_model.fit_transform(R)
Sigma = np.diag(self.svd_model.singular_values_)
Vt = self.svd_model.components_
pred_svd = np.dot(np.dot(U, Sigma), Vt)
self.pred_svd_df = pd.DataFrame(pred_svd, index=R.index, columns=R.columns)
self.is_trained = True
print("Model training completed!")
def predict_ratings_cf(self, user_id: int) -> pd.Series:
"""Predict ratings using collaborative filtering."""
if not self.is_trained:
raise ValueError("Model must be trained first")
if user_id not in self.user_item_matrix.index:
raise ValueError(f"User {user_id} not found in dataset")
user_ratings = self.user_item_matrix.loc[user_id]
weighted_sum = self.item_similarity_df.dot(user_ratings.fillna(0))
sim_sum = np.abs(self.item_similarity_df).dot(user_ratings.notna().astype(int))
pred = weighted_sum / np.maximum(sim_sum, 1e-9)
return pred
def recommend_movies(self, user_id: int, n_recommendations: int = 10,
method: str = "svd") -> List[Dict]:
"""
Get movie recommendations for a user.
Args:
user_id: User ID to get recommendations for
n_recommendations: Number of recommendations to return
method: "svd" or "cf" (collaborative filtering)
Returns:
List of dictionaries with movie recommendations
"""
if not self.is_trained:
self.train()
# Check if user exists
if user_id not in self.user_item_matrix.index:
available_users = sorted(self.user_item_matrix.index.tolist())
return [{
"error": f"User {user_id} not found",
"available_users": f"Available user IDs: {available_users[:10]}... (showing first 10)"
}]
# Get predictions
if method == "svd":
preds = self.pred_svd_df.loc[user_id]
else: # collaborative filtering
preds = self.predict_ratings_cf(user_id)
# Remove already watched movies
watched = self.ratings[self.ratings.user_id == user_id].movie_id.values
preds = preds.drop(watched, errors='ignore')
# Get top recommendations
top_movies = preds.sort_values(ascending=False).head(n_recommendations).index
recommendations = self.movies[self.movies.movie_id.isin(top_movies)][["movie_id", "title"]]
# Convert to list of dictionaries
result = []
for _, row in recommendations.iterrows():
result.append({
"movie_id": int(row["movie_id"]),
"title": row["title"],
"predicted_rating": float(preds[row["movie_id"]])
})
return result
def get_user_stats(self, user_id: int) -> Dict:
"""Get statistics for a user."""
if not self.is_trained:
self.train()
if user_id not in self.user_item_matrix.index:
return {"error": f"User {user_id} not found"}
user_ratings = self.ratings[self.ratings.user_id == user_id]
return {
"user_id": user_id,
"total_ratings": len(user_ratings),
"average_rating": float(user_ratings["rating"].mean()),
"rating_distribution": user_ratings["rating"].value_counts().to_dict()
}
def get_available_users(self) -> List[int]:
"""Get list of available user IDs."""
if not self.is_trained:
self.train()
return sorted(self.user_item_matrix.index.tolist())
def save_model(self, path: str):
"""Save the trained model."""
if not self.is_trained:
raise ValueError("Model must be trained first")
model_data = {
'ratings': self.ratings,
'movies': self.movies,
'user_item_matrix': self.user_item_matrix,
'item_similarity_df': self.item_similarity_df,
'svd_model': self.svd_model,
'pred_svd_df': self.pred_svd_df,
'is_trained': self.is_trained
}
with open(path, 'wb') as f:
pickle.dump(model_data, f)
print(f"Model saved to {path}")
def load_model(self, path: str):
"""Load a trained model."""
with open(path, 'rb') as f:
model_data = pickle.load(f)
self.ratings = model_data['ratings']
self.movies = model_data['movies']
self.user_item_matrix = model_data['user_item_matrix']
self.item_similarity_df = model_data['item_similarity_df']
self.svd_model = model_data['svd_model']
self.pred_svd_df = model_data['pred_svd_df']
self.is_trained = model_data['is_trained']
print(f"Model loaded from {path}")
# Create a global model instance for inference
model = MovieRecommender()
def predict(user_id: int, n_recommendations: int = 10, method: str = "svd") -> List[Dict]:
"""
Inference function for Hugging Face model.
Args:
user_id: User ID to get recommendations for
n_recommendations: Number of recommendations (default: 10)
method: Recommendation method - "svd" or "cf" (default: "svd")
Returns:
List of movie recommendations
"""
return model.recommend_movies(user_id, n_recommendations, method) |