ImportError: No module named crab.metrics.classes

[bala17]

ImportError: No module named crab.metrics.classes

installed crab . but unable import.

from pprint import pprint
import csv
from scikits.crab.models import MatrixPreferenceDataModel, MatrixBooleanPrefDataModel
from scikits.crab.metrics import pearson_correlation, euclidean_distances, jaccard_coefficient, cosine_distances, manhattan_distances, spearman_coefficient
from scikits.crab.similarities import ItemSimilarity, UserSimilarity
from scikits.crab.recommenders.knn import ItemBasedRecommender, UserBasedRecommender
from scikits.crab.recommenders.knn.neighborhood_strategies import NearestNeighborsStrategy
from scikits.crab.recommenders.knn.item_strategies import ItemsNeighborhoodStrategy
from scikits.crab.recommenders.svd.classes import MatrixFactorBasedRecommender
from scikits.crab.metrics.classes import CfEvaluator

"""
import random

fieldnames = ['user_id', 'item_id', 'star_rating']
with open('dataset-recsys.csv', "w") as myfile: # writing data to new csv file
writer = csv.DictWriter(myfile, delimiter = ',', fieldnames = fieldnames)
writer.writeheader()

for x in range(1, 21):
    items = random.sample(list(range(1, 41)), 20)
    random.randint(1,5)
    for item in items:        
        writer.writerow({'user_id': x, 'item_id': item, 'star_rating': random.randint(1, 5)})

"""

dataset = {}
with open('sample_movielens_data.txt') as myfile:
reader = csv.DictReader(myfile, delimiter=',')
i = 0
for line in reader:
i += 1
if (i == 1):
continue

    if (int(line['user_id']) not in dataset):
        dataset[int(line['user_id'])] = {}
        
    dataset[int(line['user_id'])][int(line['item_id'])] = float(line['star_rating'])

model = MatrixPreferenceDataModel(dataset)

User-based Similarity

similarity = UserSimilarity(model, cosine_distances)
neighborhood = NearestNeighborsStrategy()
recsys = UserBasedRecommender(model, similarity, neighborhood)

Item-based Similarity

similarity = ItemSimilarity(model, cosine_distances)
nhood_strategy = ItemsNeighborhoodStrategy()
recsys = ItemBasedRecommender(model, similarity, nhood_strategy, with_preference=False)

recsys = MatrixFactorBasedRecommender(model=model, items_selection_strategy=nhood_strategy, n_features=10, n_interations=1)

evaluator = CfEvaluator()

rmse = evaluator.evaluate(recsys, 'rmse', permutation=False)
mae = evaluator.evaluate(recsys, 'mae', permutation=False)
nmae = evaluator.evaluate(recsys, 'nmae', permutation=False)
precision = evaluator.evaluate(recsys, 'precision', permutation=False)
recall = evaluator.evaluate(recsys, 'recall', permutation=False)
f1score = evaluator.evaluate(recsys, 'f1score', permutation=False)

all_scores = evaluator.evaluate(recsys, permutation=False)
#all_scores = evaluator.evaluate(boolean_recsys, permutation=False)

result = evaluator.evaluate(recsys, None, permutation=False, at=10, sampling_ratings=0.7)

Cross Validation

result = evaluator.evaluate_on_split(recsys, 'rmse', permutation=False, at=10, cv=5, sampling_ratings=0.7)

pprint (result)