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f3a6f24 42328a2 f3a6f24 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 | import numpy as np
import streamlit as st
from scipy.sparse import csr_matrix, issparse
from scipy.sparse.linalg import svds
from sklearn.decomposition import NMF
from sklearn.metrics.pairwise import cosine_similarity
from sklearn.metrics import mean_squared_error, r2_score
from implicit.als import AlternatingLeastSquares
@st.cache_resource
def train_svd(_train_sparse, n_factors: int = 50):
"""Truncated SVD on the user-item matrix."""
k = min(n_factors, min(_train_sparse.shape) - 1)
U, sigma, Vt = svds(_train_sparse.astype(float), k=k)
sigma_diag = np.diag(sigma)
predicted = U @ sigma_diag @ Vt
return predicted, U, sigma, Vt
@st.cache_resource
def train_als(_train_sparse, n_factors: int = 50, iterations: int = 15, regularization: float = 0.1):
"""
Train ALS using the implicit library (industry-standard implementation).
Uses Alternating Least Squares for implicit feedback collaborative filtering.
The confidence weighting formula is: C = 1 + alpha * R
We use alpha=1.0 (library default) which is more conservative than alpha=40.
"""
model = AlternatingLeastSquares(
factors=n_factors,
iterations=iterations,
regularization=regularization,
alpha=1.0, # Confidence scaling factor (default, conservative)
random_state=42
)
# implicit library expects item-user matrix (transposed)
model.fit(_train_sparse.T)
# Return wrapper with correctly named attributes
# After fitting on transposed matrix, user_factors are actually item factors and vice versa
class ALSModel:
def __init__(self, implicit_model):
# Swap the factors back to match our expected interface
self.user_factors = implicit_model.item_factors # These are actually user factors
self.item_factors = implicit_model.user_factors # These are actually item factors
return ALSModel(model)
@st.cache_resource
def train_sgd(
_train_sparse,
n_factors: int = 50,
lr: float = 0.001,
reg: float = 0.02,
n_epochs: int = 100,
):
"""SGD-based matrix factorization with bias terms (Funk SVD).
predicted = mu + b_u + b_i + u . v
Uses log1p-transformed values internally to stabilize training on
purchase-count data where raw values can be large and variable.
"""
if issparse(_train_sparse):
rows, cols = _train_sparse.nonzero()
raw_vals = np.asarray(_train_sparse[rows, cols]).ravel().astype(np.float64)
else:
mat = np.asarray(_train_sparse, dtype=np.float64)
rows, cols = np.nonzero(mat)
raw_vals = mat[rows, cols]
vals = np.log1p(raw_vals)
n_users = _train_sparse.shape[0]
n_items = _train_sparse.shape[1]
mu = float(vals.mean())
rng = np.random.RandomState(42)
scale = 0.01
U = rng.normal(0, scale, (n_users, n_factors))
V = rng.normal(0, scale, (n_items, n_factors))
b_u = np.zeros(n_users)
b_i = np.zeros(n_items)
clip = 5.0
for _ in range(n_epochs):
order = rng.permutation(len(vals))
for idx in order:
u, i, r = int(rows[idx]), int(cols[idx]), vals[idx]
pred = mu + b_u[u] + b_i[i] + U[u] @ V[i]
err = np.clip(r - pred, -clip, clip)
b_u[u] += lr * (err - reg * b_u[u])
b_i[i] += lr * (err - reg * b_i[i])
U_old = U[u].copy()
U[u] += lr * (err * V[i] - reg * U[u])
V[i] += lr * (err * U_old - reg * V[i])
predicted_log = mu + b_u[:, None] + b_i[None, :] + U @ V.T
predicted = np.expm1(predicted_log)
return predicted, U, V, b_u, b_i, mu
@st.cache_resource
def train_nmf(_train_dense, n_factors: int = 50, max_iter: int = 200):
"""Non-negative Matrix Factorization."""
model = NMF(n_components=n_factors, init="nndsvda", random_state=42, max_iter=max_iter)
W = model.fit_transform(_train_dense)
H = model.components_
predicted = W @ H
return predicted, W, H, model
@st.cache_resource
def train_item_cf(_train_sparse, top_k_similar: int = 20):
"""Item-based collaborative filtering using cosine similarity."""
item_sim = cosine_similarity(_train_sparse.T)
np.fill_diagonal(item_sim, 0)
for i in range(item_sim.shape[0]):
row = item_sim[i]
threshold = np.partition(row, -top_k_similar)[-top_k_similar] if len(row) > top_k_similar else 0
row[row < threshold] = 0
item_sim_sparse = item_sim
train_dense = _train_sparse.toarray() if hasattr(_train_sparse, 'toarray') else np.array(_train_sparse)
predicted = train_dense @ item_sim_sparse
return predicted, item_sim_sparse
def get_top_n_recommendations(predicted_scores, train_matrix, user_idx: int, n: int = 10):
"""Get top-N item indices for a user, excluding already-purchased items."""
scores = predicted_scores[user_idx].copy()
if hasattr(train_matrix, 'toarray'):
purchased = train_matrix.toarray()[user_idx]
else:
purchased = np.array(train_matrix.iloc[user_idx] if hasattr(train_matrix, 'iloc') else train_matrix[user_idx])
scores[purchased > 0] = -np.inf
top_items = np.argsort(scores)[::-1][:n]
return top_items, scores[top_items]
def evaluate_recommendations(predicted_scores, train_matrix, test_df, user_index, item_columns, k: int = 10):
"""
Compute recommendation metrics including:
- Precision@K, Recall@K, Hit Rate (ranking metrics)
- RMSE, R² (prediction accuracy metrics)
"""
if hasattr(train_matrix, 'toarray'):
train_dense = train_matrix.toarray()
else:
train_dense = np.array(train_matrix)
item_to_col = {item: i for i, item in enumerate(item_columns)}
precisions = []
recalls = []
hits = 0
n_users = 0
# For RMSE and R² calculation
y_true = []
y_pred = []
for cust_id, group in test_df.groupby("CustomerID"):
if cust_id not in user_index:
continue
user_idx = user_index[cust_id]
if user_idx >= predicted_scores.shape[0]:
continue
true_items = set()
for _, row in group.iterrows():
if row["StockCode"] in item_to_col:
item_idx = item_to_col[row["StockCode"]]
true_items.add(item_idx)
# Collect actual vs predicted quantities for RMSE/R²
actual_quantity = row["score"]
predicted_quantity = predicted_scores[user_idx, item_idx]
y_true.append(actual_quantity)
y_pred.append(predicted_quantity)
if not true_items:
continue
top_items, _ = get_top_n_recommendations(predicted_scores, train_dense, user_idx, n=k)
recommended_set = set(top_items)
hit_count = len(recommended_set & true_items)
precisions.append(hit_count / k)
recalls.append(hit_count / len(true_items) if true_items else 0)
if hit_count > 0:
hits += 1
n_users += 1
if n_users == 0:
return {
"Precision@K": 0.0,
"Recall@K": 0.0,
"Hit Rate": 0.0,
"RMSE": 0.0,
"R²": 0.0,
"Users Evaluated": 0
}
# Calculate RMSE and R²
rmse = 0.0
r2 = 0.0
if len(y_true) > 0:
y_true_arr = np.array(y_true)
y_pred_arr = np.array(y_pred)
# Clip predictions to reasonable range to avoid extreme errors
y_pred_arr = np.clip(y_pred_arr, 0, np.percentile(y_true_arr, 99))
rmse = float(np.sqrt(mean_squared_error(y_true_arr, y_pred_arr)))
# R² can be negative if model is worse than mean baseline
# We calculate it but note that negative values indicate poor fit
r2 = float(r2_score(y_true_arr, y_pred_arr))
return {
"Precision@K": float(np.mean(precisions)),
"Recall@K": float(np.mean(recalls)),
"Hit Rate": float(hits / n_users),
"RMSE": rmse,
"R²": r2,
"Users Evaluated": int(n_users),
}
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