system_recomendation
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Arquivos
Dataset ./ml-100k Script ./system_recomendation.py
Link da referência
https://www.analyticsvidhya.com/blog/2016/06/quick-guide-build-recommendation-engine-python/
Saída do Programa
(943, 5) (100000, 4) (1682, 24) This non-commercial license of GraphLab Create for academic use is assigned to brunoavli@hotmail.com and will expire on September 15, 2018. [INFO] graphlab.cython.cy_server: GraphLab Create v2.1 started. Logging: /tmp/graphlab_server_1505439922.log Recsys training: model = popularity Warning: Ignoring columns unix_timestamp; To use these columns in scoring predictions, use a model that allows the use of additional features. Preparing data set. Data has 90570 observations with 943 users and 1680 items. Data prepared in: 0.104139s 90570 observations to process; with 1680 unique items. +---------+----------+-------+------+ | user_id | movie_id | score | rank | +---------+----------+-------+------+ | 1 | 1467 | 5.0 | 1 | | 1 | 1201 | 5.0 | 2 | | 1 | 1189 | 5.0 | 3 | | 1 | 1122 | 5.0 | 4 | | 1 | 814 | 5.0 | 5 | | 2 | 1467 | 5.0 | 1 | | 2 | 1201 | 5.0 | 2 | | 2 | 1189 | 5.0 | 3 | | 2 | 1122 | 5.0 | 4 | | 2 | 814 | 5.0 | 5 | | 3 | 1467 | 5.0 | 1 | | 3 | 1201 | 5.0 | 2 | | 3 | 1189 | 5.0 | 3 | | 3 | 1122 | 5.0 | 4 | | 3 | 814 | 5.0 | 5 | | 4 | 1467 | 5.0 | 1 | | 4 | 1201 | 5.0 | 2 | | 4 | 1189 | 5.0 | 3 | | 4 | 1122 | 5.0 | 4 | | 4 | 814 | 5.0 | 5 | | 5 | 1467 | 5.0 | 1 | | 5 | 1201 | 5.0 | 2 | | 5 | 1189 | 5.0 | 3 | | 5 | 1122 | 5.0 | 4 | | 5 | 814 | 5.0 | 5 | +---------+----------+-------+------+ [25 rows x 4 columns]
Recsys training: model = item_similarity Warning: Ignoring columns unix_timestamp; To use these columns in scoring predictions, use a model that allows the use of additional features. Preparing data set. Data has 90570 observations with 943 users and 1680 items. Data prepared in: 0.101736s Training model from provided data. Gathering per-item and per-user statistics. +--------------------------------+------------+ | Elapsed Time (Item Statistics) | % Complete | +--------------------------------+------------+ | 5.186ms | 100 | +--------------------------------+------------+ Setting up lookup tables. Processing data in one pass using dense lookup tables. +-------------------------------------+------------------+-----------------+ | Elapsed Time (Constructing Lookups) | Total % Complete | Items Processed | +-------------------------------------+------------------+-----------------+ | 12.061ms | 0.25 | 6 | | 265.236ms | 100 | 1680 | +-------------------------------------+------------------+-----------------+ Finalizing lookup tables. Generating candidate set for working with new users. Finished training in 1.27881s +---------+----------+-------+------+ | user_id | movie_id | score | rank | +---------+----------+-------+------+ | 1 | 1599 | 5.0 | 1 | | 1 | 1201 | 5.0 | 2 | | 1 | 1189 | 5.0 | 3 | | 1 | 1122 | 5.0 | 4 | | 1 | 814 | 5.0 | 5 | | 2 | 1599 | 5.0 | 1 | | 2 | 1201 | 5.0 | 2 | | 2 | 1189 | 5.0 | 3 | | 2 | 1122 | 5.0 | 4 | | 2 | 814 | 5.0 | 5 | | 3 | 1599 | 5.0 | 1 | | 3 | 1201 | 5.0 | 2 | | 3 | 1189 | 5.0 | 3 | | 3 | 1122 | 5.0 | 4 | | 3 | 814 | 5.0 | 5 | | 4 | 1599 | 5.0 | 1 | | 4 | 1201 | 5.0 | 2 | | 4 | 1189 | 5.0 | 3 | | 4 | 1122 | 5.0 | 4 | | 4 | 814 | 5.0 | 5 | | 5 | 1599 | 5.0 | 1 | | 5 | 1201 | 5.0 | 2 | | 5 | 1189 | 5.0 | 3 | | 5 | 1122 | 5.0 | 4 | | 5 | 814 | 5.0 | 5 | +---------+----------+-------+------+ [25 rows x 4 columns]
PROGRESS: Evaluate model M0
Precision and recall summary statistics by cutoff +--------+-------------------+-------------------+ | cutoff | mean_precision | mean_recall | +--------+-------------------+-------------------+ | 1 | 0.0 | 0.0 | | 2 | 0.0 | 0.0 | | 3 | 0.0 | 0.0 | | 4 | 0.0 | 0.0 | | 5 | 0.0 | 0.0 | | 6 | 0.000176740897844 | 0.000106044538706 | | 7 | 0.000151492198152 | 0.000106044538706 | | 8 | 0.000265111346766 | 0.000212089077413 | | 9 | 0.000235654530458 | 0.000212089077413 | | 10 | 0.000212089077413 | 0.000212089077413 | +--------+-------------------+-------------------+ [10 rows x 3 columns]
PROGRESS: Evaluate model M1
Precision and recall summary statistics by cutoff +--------+-------------------+-------------------+ | cutoff | mean_precision | mean_recall | +--------+-------------------+-------------------+ | 1 | 0.00106044538706 | 0.000106044538706 | | 2 | 0.000530222693531 | 0.000106044538706 | | 3 | 0.000353481795688 | 0.000106044538706 | | 4 | 0.000265111346766 | 0.000106044538706 | | 5 | 0.000212089077413 | 0.000106044538706 | | 6 | 0.000176740897844 | 0.000106044538706 | | 7 | 0.000302984396304 | 0.000212089077413 | | 8 | 0.000265111346766 | 0.000212089077413 | | 9 | 0.000235654530458 | 0.000212089077413 | | 10 | 0.000212089077413 | 0.000212089077413 | +--------+-------------------+-------------------+ [10 rows x 3 columns]
Model compare metric: precision_recall Canvas is accessible via web browser at the URL: http://localhost:51810/index.html Opening Canvas in default web browser.