Fixed format for TCI checks

benchmarks/benchmark_als.py

@@ -7,10 +7,10 @@
 import logging
 from collections import defaultdict
 
-import matplotlib.pyplot as plt
 import scipy.io
-import seaborn
 
+import matplotlib.pyplot as plt
+import seaborn
 from implicit._als import calculate_loss
 from implicit.als import AlternatingLeastSquares
 from implicit.nearest_neighbours import bm25_weight

benchmarks/benchmark_spark.py

@@ -6,16 +6,16 @@
 import json
 import time
 
-import matplotlib.pyplot as plt
 import numpy
 import scipy.io
+
+import implicit
+import matplotlib.pyplot as plt
 import seaborn
 from pyspark import SparkConf, SparkContext
 from pyspark.ml.recommendation import ALS
 from pyspark.sql import Row, SparkSession
 
-import implicit
-
 
 def convert_sparse_to_dataframe(spark, context, sparse_matrix):
     """ Converts a scipy sparse matrix to a spark dataframe """

examples/lastfm.py

@@ -10,17 +10,17 @@
 import codecs
 import logging
 import time
-import tqdm
 
 import numpy as np
+import tqdm
 
 from implicit.als import AlternatingLeastSquares
 from implicit.approximate_als import (AnnoyAlternatingLeastSquares, FaissAlternatingLeastSquares,
                                       NMSLibAlternatingLeastSquares)
 from implicit.bpr import BayesianPersonalizedRanking
+from implicit.datasets.lastfm import get_lastfm
 from implicit.nearest_neighbours import (BM25Recommender, CosineRecommender,
                                          TFIDFRecommender, bm25_weight)
-from implicit.datasets.lastfm import get_lastfm
 
 # maps command line model argument to class name
 MODELS = {"als":  AlternatingLeastSquares,

examples/movielens.py

@@ -16,18 +16,16 @@
 import codecs
 import logging
 import time
-import tqdm
 
 import numpy as np
+import tqdm
 
 from implicit.als import AlternatingLeastSquares
 from implicit.bpr import BayesianPersonalizedRanking
+from implicit.datasets.movielens import get_movielens
 from implicit.nearest_neighbours import (BM25Recommender, CosineRecommender,
                                          TFIDFRecommender, bm25_weight)
 
-from implicit.datasets.movielens import get_movielens
-
-
 log = logging.getLogger("implicit")
 
 

implicit/__init__.py

@@ -1,10 +1,6 @@
+from . import als, approximate_als, bpr, nearest_neighbours
 from .als import alternating_least_squares
 
-from . import als
-from . import approximate_als
-from . import bpr
-from . import nearest_neighbours
-
 __version__ = '0.3.8'
 
 __all__ = [alternating_least_squares, als, approximate_als, bpr, nearest_neighbours, __version__]

implicit/als.py

@@ -3,11 +3,11 @@
 import heapq
 import logging
 import time
-import tqdm
 
 import numpy as np
 import scipy
 import scipy.sparse
+import tqdm
 
 import implicit.cuda
 

implicit/approximate_als.py

@@ -8,9 +8,8 @@
 
 import numpy
 
-from implicit.als import AlternatingLeastSquares
 import implicit.cuda
-
+from implicit.als import AlternatingLeastSquares
 
 log = logging.getLogger("implicit")
 

implicit/evaluation.pyx

@@ -224,8 +224,8 @@ def mean_average_precision_at_k(model, train_user_items, test_user_items, int K=
 @cython.boundscheck(False)
 @cython.wraparound(False)
 @cython.nonecheck(False)
-def ALS_recommend_all(
-        model, users_items, int k=10, int threads=1, show_progress=True, recalculate_user=False, filter_already_liked_items=False):
+def ALS_recommend_all(model, users_items, int k=10, int threads=1, show_progress=True,
+                      recalculate_user=False, filter_already_liked_items=False):
 
     if not isinstance(users_items, csr_matrix):
         users_items = users_items.tocsr()

implicit/nearest_neighbours.py

@@ -4,7 +4,7 @@
 from numpy import bincount, log, log1p, sqrt
 from scipy.sparse import coo_matrix, csr_matrix
 
-from ._nearest_neighbours import all_pairs_knn, NearestNeighboursScorer
+from ._nearest_neighbours import NearestNeighboursScorer, all_pairs_knn
 from .recommender_base import RecommenderBase
 from .utils import nonzeros