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matrixfactorization.py
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matrixfactorization.py
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"""Collaborative filtering features using various techniques of Matrix Factorization for recommendations.
Recommended for large data"""
"""
Add the user column name and item column name in recipe_dict in config to match the
column names as per the dataset or use the default 'user' and 'item' respectively in your dataset
Sample Datasets
# Netflix - https://www.kaggle.com/netflix-inc/netflix-prize-data
recipe_dict = "{'user_col': 'user', 'item_col': 'movie'}"
# MovieLens - https://grouplens.org/datasets/movielens/
recipe_dict = "{'user_col': 'userId', 'item_col': 'movieId'}"
# RPackages - https://www.kaggle.com/c/R/data
recipe_dict = "{'user_col': 'User', 'item_col': 'Package'}"
"""
import datatable as dt
import numpy as np
import pandas as pd
import h2o4gpu
import scipy
from h2oaicore.systemutils import config
from h2oaicore.transformer_utils import CustomTransformer
from h2oaicore.separators import extra_prefix, orig_feat_prefix, col_sep
from sklearn.decomposition import NMF
from sklearn.model_selection import KFold, StratifiedKFold
from sklearn.preprocessing import LabelEncoder
class RecH2OMFTransformer(CustomTransformer):
_allow_transform_to_modify_output_feature_names = True
_multiclass = False
_unsupervised = False # uses target
_uses_target = True # uses target
_can_use_gpu = True
_mf_type = "h2o4gpu"
def __init__(self, n_components=50, _lambda=0.1, batches=1, max_iter=100, alpha=0.1, **kwargs):
super().__init__(**kwargs)
self.user_col = config.recipe_dict['user_col'] if "user_col" in config.recipe_dict else "user"
self.item_col = config.recipe_dict['item_col'] if "item_col" in config.recipe_dict else "item"
if self.__class__._mf_type == "h2o4gpu":
self._n_components = n_components
self._lambda = _lambda
self._batches = batches
self._max_iter = max_iter
elif self.__class__._mf_type == "nmf":
self._n_components = n_components
self._alpha = alpha
self._max_iter = max_iter
@staticmethod
def do_acceptance_test():
return False
@staticmethod
def get_default_properties():
return dict(col_type="all", min_cols="all", max_cols="all", relative_importance=1, num_default_instances=1)
@staticmethod
def get_parameter_choices():
return {"n_components": [10, 30, 50, 70, 100],
"_lambda": [0.01, 0.05, 0.1],
"batches": [1],
"max_iter": [10, 50, 100, 200],
"alpha": [0.01, 0.05, 0.1]}
def fit_transform(self, X: dt.Frame, y: np.array = None):
if len(np.unique(self.labels)) == 2:
le = LabelEncoder()
self.labels = le.fit_transform(self.labels)
y = np.array(le.transform(y), dtype="float32")
else:
y = np.array(y, dtype="float32")
X = X[:, [self.user_col, self.item_col]]
self.user_le = LabelEncoder()
self.item_le = LabelEncoder()
X[:, self.user_col] = dt.Frame(self.user_le.fit_transform(X[:, self.user_col]))
X[:, self.item_col] = dt.Frame(self.item_le.fit_transform(X[:, self.item_col]))
X_pd = X.to_pandas()
if len(np.unique(self.labels)) == 2:
kfold = StratifiedKFold(n_splits=10)
else:
kfold = KFold(n_splits=10)
preds = np.full(X.nrows, fill_value=np.nan)
for train_index, val_index in kfold.split(X_pd, y):
X_train, y_train = X_pd.iloc[train_index,], y[train_index]
X_val, y_val = X_pd.iloc[val_index,], y[val_index]
X_val2 = X_val[(X_val[self.user_col].isin(np.unique(X_train[self.user_col]))) & (
X_val[self.item_col].isin(np.unique(X_train[self.item_col])))]
y_val2 = y_val[(X_val[self.user_col].isin(np.unique(X_train[self.user_col]))) & (
X_val[self.item_col].isin(np.unique(X_train[self.item_col])))]
X_panel = pd.concat([X_train, X_val2], axis=0)
users, user_indices = np.unique(np.array(X_panel[self.user_col], dtype="int32"), return_inverse=True)
items, item_indices = np.unique(np.array(X_panel[self.item_col], dtype="int32"), return_inverse=True)
X_train_user_item_matrix = scipy.sparse.coo_matrix(
(y_train, (user_indices[:len(X_train)], item_indices[:len(X_train)])), shape=(len(users), len(items)))
X_train_shape = X_train_user_item_matrix.shape
X_val_user_item_matrix = scipy.sparse.coo_matrix(
(np.ones(len(X_val2), dtype="float32"), (user_indices[len(X_train):], item_indices[len(X_train):])),
shape=X_train_shape)
if self.__class__._mf_type == "h2o4gpu":
factorization = h2o4gpu.solvers.FactorizationH2O(self._n_components, self._lambda,
max_iter=self._max_iter)
factorization.fit(X_train_user_item_matrix, X_BATCHES=self._batches, THETA_BATCHES=self._batches)
preds[val_index[(X_val[self.user_col].isin(np.unique(X_train[self.user_col]))) & (
X_val[self.item_col].isin(np.unique(X_train[self.item_col])))]] = factorization.predict(
X_val_user_item_matrix).data
elif self.__class__._mf_type == "nmf":
factorization = NMF(n_components=self._n_components, alpha=self._alpha, max_iter=self._max_iter)
user_matrix = factorization.fit_transform(X_train_user_item_matrix)
item_matrix = factorization.components_.T
val_users = np.take(user_matrix, X_val_user_item_matrix.row, axis=0)
val_items = np.take(item_matrix, X_val_user_item_matrix.col, axis=0)
preds[val_index[(X_val[self.user_col].isin(np.unique(X_train[self.user_col]))) & (
X_val[self.item_col].isin(np.unique(X_train[self.item_col])))]] = np.sum(val_users * val_items,
axis=1)
users, user_indices = np.unique(np.array(X_pd[self.user_col], dtype="int32"), return_inverse=True)
items, item_indices = np.unique(np.array(X_pd[self.item_col], dtype="int32"), return_inverse=True)
X_train_user_item_matrix = scipy.sparse.coo_matrix((y, (user_indices, item_indices)), shape=(len(users), len(items)))
self.X_train_shape = X_train_user_item_matrix.shape
if self.__class__._mf_type == "h2o4gpu":
self.factorization = h2o4gpu.solvers.FactorizationH2O(self._n_components, self._lambda,
max_iter=self._max_iter)
self.factorization.fit(X_train_user_item_matrix, X_BATCHES=self._batches, THETA_BATCHES=self._batches)
elif self.__class__._mf_type == "nmf":
factorization = NMF(n_components=self._n_components, alpha=self._alpha, max_iter=self._max_iter)
self.user_matrix = factorization.fit_transform(X_train_user_item_matrix)
self.item_matrix = factorization.components_.T
# output feature names
if self.__class__._mf_type == "h2o4gpu":
self._output_feature_names = [(f"{self.display_name}{orig_feat_prefix}{self.user_col}{col_sep}"
f"{self.item_col}.n_components={self._n_components},"
f"lambda={self._lambda},batches={self._batches},max_iter={self._max_iter}")]
elif self.__class__._mf_type == "nmf":
self._output_feature_names = [(f"{self.display_name}{orig_feat_prefix}{self.user_col}{col_sep}"
f"{self.item_col}.n_components={self._n_components},"
f"alpha={self._alpha},max_iter={self._max_iter}")]
# output feature descriptions
self._feature_desc = [f"Recommender transformer ({self.__class__._mf_type}): " + self._output_feature_names[0]]
return preds
def transform(self, X: dt.Frame):
X = X[:, [self.user_col, self.item_col]]
preds = np.full(X.nrows, fill_value=np.nan)
X_pd = X.to_pandas()
X_test = X_pd[
(X_pd[self.user_col].isin(self.user_le.classes_)) & (X_pd[self.item_col].isin(self.item_le.classes_))]
X_test[self.user_col] = self.user_le.transform(X_test[self.user_col])
X_test[self.item_col] = self.item_le.transform(X_test[self.item_col])
X_test_user_item_matrix = scipy.sparse.coo_matrix(
(np.ones(len(X_test), dtype="float32"), (X_test[self.user_col], X_test[self.item_col])),
shape=self.X_train_shape)
if self.__class__._mf_type == "h2o4gpu":
preds[(X_pd[self.user_col].isin(self.user_le.classes_)) & (
X_pd[self.item_col].isin(self.item_le.classes_))] = self.factorization.predict(
X_test_user_item_matrix).data
elif self.__class__._mf_type == "nmf":
test_users = np.take(self.user_matrix, X_test_user_item_matrix.row, axis=0)
test_items = np.take(self.item_matrix, X_test_user_item_matrix.col, axis=0)
preds[(X_pd[self.user_col].isin(self.user_le.classes_)) & (
X_pd[self.item_col].isin(self.item_le.classes_))] = np.sum(test_users * test_items, axis=1)
return preds
class RecNMFTransformer(RecH2OMFTransformer):
_can_use_gpu = False
_mf_type = "nmf"