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Merge pull request #932 from microsoft/miguel/wikidata_fix
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Wikidata fix and unit tests
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@miguelgfierro
miguelgfierro authored Sep 17, 2019
2 parents 90b3796 + b7bfb59 commit eb933bb
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2 changes: 1 addition & 1 deletion notebooks/01_prepare_data/README.md
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Expand Up @@ -8,7 +8,7 @@ data preparation tasks witnessed in recommendation system development.
| --- | --- |
| [data_split](data_split.ipynb) | Details on splitting data (randomly, chronologically, etc). |
| [data_transform](data_transform.ipynb) | Guidance on how to transform (implicit / explicit) data for building collaborative filtering typed recommender. |
| [wikidata knowledge graph](wikidata_KG.ipynb) | Details on how to create a knowledge graph using Wikidata |
| [wikidata knowledge graph](wikidata_knowledge_graph.ipynb) | Details on how to create a knowledge graph using Wikidata |

### Data split

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26 changes: 11 additions & 15 deletions notebooks/01_prepare_data/wikidata_KG.ipynb → ...epare_data/wikidata_knowledge_graph.ipynb
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Expand Up @@ -5,7 +5,7 @@
"metadata": {},
"source": [
"## Wikidata Knowledge Graph Extraction\n",
"Many recommendation algorithms (DKN, RippleNet, KGCN) use Knowledge Graphs as an external source of information. We found that one of the bottlenecks to benchmark current algorithms like DKN, RippleNet or KGCN is that they used Microsoft Satori. As Satori is not open source, it's not possible to replicate the results found in the papers. The solution is using other open source KGs.\n",
"Many recommendation algorithms (DKN, RippleNet, KGCN) use Knowledge Graphs (KGs) as an external source of information. We found that one of the bottlenecks to benchmark current algorithms like DKN, RippleNet or KGCN is that they used Microsoft Satori. As Satori is not open source, it's not possible to replicate the results found in the papers. The solution is using other open source KGs.\n",
"\n",
"The goal of this notebook is to provide examples of how to interact with Wikipedia queries and Wikidata to extract a Knowledge Graph that can be used with the mentioned algorithms.\n",
"\n",
Expand All @@ -24,7 +24,8 @@
"name": "stdout",
"output_type": "stream",
"text": [
"System version: 3.6.8 |Anaconda, Inc.| (default, Feb 21 2019, 18:30:04) [MSC v.1916 64 bit (AMD64)]\n"
"System version: 3.6.8 |Anaconda, Inc.| (default, Dec 30 2018, 01:22:34) \n",
"[GCC 7.3.0]\n"
]
}
],
Expand All @@ -34,19 +35,17 @@
"sys.path.append(\"../../\")\n",
"print(\"System version: {}\".format(sys.version))\n",
"\n",
"import papermill as pm\n",
"import pandas as pd\n",
"import networkx as nx\n",
"import matplotlib.pyplot as plt\n",
"from reco_utils.dataset import movielens\n",
"\n",
"from reco_utils.dataset.wikidata import (search_wikidata, \n",
" find_wikidata_id, \n",
" query_entity_links, \n",
" read_linked_entities,\n",
" query_entity_description)\n",
"\n",
"import networkx as nx\n",
"import matplotlib.pyplot as plt\n",
"from tqdm import tqdm\n",
"\n",
"from reco_utils.dataset import movielens\n",
"from reco_utils.common.notebook_utils import is_jupyter"
" query_entity_description)\n"
]
},
{
Expand Down Expand Up @@ -548,11 +547,8 @@
}
],
"source": [
"# Record results with papermill for tests - ignore this cell\n",
"if is_jupyter():\n",
" # Record results with papermill for unit-tests\n",
" import papermill as pm\n",
" pm.record(\"length_result\", number_movies)"
"# Record results with papermill for unit-tests\n",
"pm.record(\"length_result\", number_movies)"
]
},
{
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18 changes: 8 additions & 10 deletions reco_utils/dataset/wikidata.py
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Expand Up @@ -3,7 +3,9 @@

import pandas as pd
import requests
import logging

logger = logging.getLogger(__name__)

API_URL_WIKIPEDIA = "https://en.wikipedia.org/w/api.php"
API_URL_WIKIDATA = "https://query.wikidata.org/sparql"
Expand Down Expand Up @@ -57,8 +59,8 @@ def find_wikidata_id(name, limit=1, session=None):
response = session.get(API_URL_WIKIPEDIA, params=params)
page_id = response.json()["query"]["search"][0]["pageid"]
except Exception as e:
# TODO: log exception
# print(e)
# TODO: distinguish between connection error and entity not found
logger.error("ENTITY NOT FOUND")
return "entityNotFound"

params = dict(
Expand All @@ -75,8 +77,8 @@ def find_wikidata_id(name, limit=1, session=None):
"wikibase_item"
]
except Exception as e:
# TODO: log exception
# print(e)
# TODO: distinguish between connection error and entity not found
logger.error("ENTITY NOT FOUND")
return "entityNotFound"

return entity_id
Expand Down Expand Up @@ -133,9 +135,7 @@ def query_entity_links(entity_id, session=None):
API_URL_WIKIDATA, params=dict(query=query, format="json")
).json()
except Exception as e:
# TODO log exception
# print(e)
# print("Entity ID not Found in Wikidata")
logger.error("ENTITY NOT FOUND")
return {}

return data
Expand Down Expand Up @@ -195,9 +195,7 @@ def query_entity_description(entity_id, session=None):
r = session.get(API_URL_WIKIDATA, params=dict(query=query, format="json"))
description = r.json()["results"]["bindings"][0]["o"]["value"]
except Exception as e:
# TODO: log exception
# print(e)
# print("Description not found")
logger.error("DESCRIPTION NOT FOUND")
return "descriptionNotFound"

return description
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21 changes: 7 additions & 14 deletions tests/conftest.py
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Expand Up @@ -46,8 +46,7 @@ def spark(app_name="Sample", url="local[*]"):
SparkSession: new Spark session
"""

config = {"spark.local.dir": "/mnt",
"spark.sql.shuffle.partitions": 1}
config = {"spark.local.dir": "/mnt", "spark.sql.shuffle.partitions": 1}
spark = start_or_get_spark(app_name=app_name, url=url, config=config)
yield spark
spark.stop()
Expand Down Expand Up @@ -185,15 +184,11 @@ def notebooks():

# Path for the notebooks
paths = {
"template": os.path.join(
folder_notebooks, "template.ipynb"
),
"template": os.path.join(folder_notebooks, "template.ipynb"),
"sar_single_node": os.path.join(
folder_notebooks, "00_quick_start", "sar_movielens.ipynb"
),
"ncf": os.path.join(
folder_notebooks, "00_quick_start", "ncf_movielens.ipynb"
),
"ncf": os.path.join(folder_notebooks, "00_quick_start", "ncf_movielens.ipynb"),
"als_pyspark": os.path.join(
folder_notebooks, "00_quick_start", "als_movielens.ipynb"
),
Expand All @@ -215,8 +210,8 @@ def notebooks():
"data_split": os.path.join(
folder_notebooks, "01_prepare_data", "data_split.ipynb"
),
"wikidata_KG": os.path.join(
folder_notebooks, "01_prepare_data", "wikidata_KG.ipynb"
"wikidata_knowledge_graph": os.path.join(
folder_notebooks, "01_prepare_data", "wikidata_knowledge_graph.ipynb"
),
"als_deep_dive": os.path.join(
folder_notebooks, "02_model", "als_deep_dive.ipynb"
Expand All @@ -239,9 +234,7 @@ def notebooks():
"mmlspark_lightgbm_criteo": os.path.join(
folder_notebooks, "02_model", "mmlspark_lightgbm_criteo.ipynb"
),
"evaluation": os.path.join(
folder_notebooks, "03_evaluate", "evaluation.ipynb"
),
"evaluation": os.path.join(folder_notebooks, "03_evaluate", "evaluation.ipynb"),
"spark_tuning": os.path.join(
folder_notebooks, "04_model_select_and_optimize", "tuning_spark_als.ipynb"
),
Expand All @@ -250,6 +243,6 @@ def notebooks():
),
"nni_tuning_svd": os.path.join(
folder_notebooks, "04_model_select_and_optimize", "nni_surprise_svd.ipynb"
)
),
}
return paths
106 changes: 58 additions & 48 deletions tests/integration/test_notebooks_python.py
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Expand Up @@ -17,22 +17,22 @@
"size, expected_values",
[
(
"1m",
{
"map": 0.060579,
"ndcg": 0.299245,
"precision": 0.270116,
"recall": 0.104350,
},
"1m",
{
"map": 0.060579,
"ndcg": 0.299245,
"precision": 0.270116,
"recall": 0.104350,
},
),
(
"10m",
{
"map": 0.098745,
"ndcg": 0.319625,
"precision": 0.275756,
"recall": 0.154014,
},
"10m",
{
"map": 0.098745,
"ndcg": 0.319625,
"precision": 0.275756,
"recall": 0.154014,
},
),
],
)
Expand All @@ -55,13 +55,13 @@ def test_sar_single_node_integration(notebooks, size, expected_values):
"size, expected_values",
[
(
"1m",
{
"map": 0.033914,
"ndcg": 0.231570,
"precision": 0.211923,
"recall": 0.064663,
},
"1m",
{
"map": 0.033914,
"ndcg": 0.231570,
"precision": 0.211923,
"recall": 0.064663,
},
),
# ("10m", {"map": , "ndcg": , "precision": , "recall": }), # OOM on test machine
],
Expand All @@ -86,17 +86,17 @@ def test_baseline_deep_dive_integration(notebooks, size, expected_values):
"size, expected_values",
[
(
"1m",
dict(
rmse=0.89,
mae=0.70,
rsquared=0.36,
exp_var=0.36,
map=0.011,
ndcg=0.10,
precision=0.093,
recall=0.025,
),
"1m",
dict(
rmse=0.89,
mae=0.70,
rsquared=0.36,
exp_var=0.36,
map=0.011,
ndcg=0.10,
precision=0.093,
recall=0.025,
),
),
# 10m works but takes too long
],
Expand Down Expand Up @@ -153,25 +153,35 @@ def test_vw_deep_dive_integration(notebooks, size, expected_values):
@pytest.mark.skipif(sys.platform == "win32", reason="nni not installable on windows")
def test_nni_tuning_svd(notebooks, tmp):
notebook_path = notebooks["nni_tuning_svd"]
pm.execute_notebook(notebook_path, OUTPUT_NOTEBOOK, kernel_name=KERNEL_NAME,
parameters=dict(MOVIELENS_DATA_SIZE="100k",
SURPRISE_READER="ml-100k",
TMP_DIR=tmp,
MAX_TRIAL_NUM=1,
NUM_EPOCHS=1,
WAITING_TIME=20,
MAX_RETRIES=50))
pm.execute_notebook(
notebook_path,
OUTPUT_NOTEBOOK,
kernel_name=KERNEL_NAME,
parameters=dict(
MOVIELENS_DATA_SIZE="100k",
SURPRISE_READER="ml-100k",
TMP_DIR=tmp,
MAX_TRIAL_NUM=1,
NUM_EPOCHS=1,
WAITING_TIME=20,
MAX_RETRIES=50,
),
)


@pytest.mark.integration
def test_wikidata_integration(notebooks, tmp):
notebook_path = notebooks["wikidata_KG"]
sample_size = 5
pm.execute_notebook(notebook_path, OUTPUT_NOTEBOOK, kernel_name=KERNEL_NAME,
parameters=dict(MOVIELENS_DATA_SIZE='100k',
MOVIELENS_SAMPLE=True,
MOVIELENS_SAMPLE_SIZE=sample_size))

notebook_path = notebooks["wikidata_knowledge_graph"]
pm.execute_notebook(
notebook_path,
OUTPUT_NOTEBOOK,
kernel_name=KERNEL_NAME,
parameters=dict(
MOVIELENS_DATA_SIZE="100k", MOVIELENS_SAMPLE=True, MOVIELENS_SAMPLE_SIZE=5
),
)

results = pm.read_notebook(OUTPUT_NOTEBOOK).dataframe.set_index("name")["value"]
assert results["length_result"] == sample_size
# FIXME: The return number should be always 5, but sometimes we get 4, find out why
assert results["length_result"] >= 4

38 changes: 25 additions & 13 deletions tests/unit/test_notebooks_python.py
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Expand Up @@ -57,24 +57,36 @@ def test_vw_deep_dive_runs(notebooks):
@pytest.mark.notebooks
def test_lightgbm(notebooks):
notebook_path = notebooks["lightgbm_quickstart"]
pm.execute_notebook(notebook_path, OUTPUT_NOTEBOOK, kernel_name=KERNEL_NAME,
parameters=dict(MAX_LEAF=32,
MIN_DATA=20,
NUM_OF_TREES=10,
TREE_LEARNING_RATE=0.15,
EARLY_STOPPING_ROUNDS=20,
METRIC="auc"))
pm.execute_notebook(
notebook_path,
OUTPUT_NOTEBOOK,
kernel_name=KERNEL_NAME,
parameters=dict(
MAX_LEAF=32,
MIN_DATA=20,
NUM_OF_TREES=10,
TREE_LEARNING_RATE=0.15,
EARLY_STOPPING_ROUNDS=20,
METRIC="auc",
),
)


@pytest.mark.notebooks
def test_wikidata_runs(notebooks, tmp):
notebook_path = notebooks["wikidata_KG"]
notebook_path = notebooks["wikidata_knowledge_graph"]
MOVIELENS_SAMPLE_SIZE = 5
pm.execute_notebook(notebook_path, OUTPUT_NOTEBOOK, kernel_name=KERNEL_NAME,
parameters=dict(MOVIELENS_DATA_SIZE='100k',
MOVIELENS_SAMPLE=True,
MOVIELENS_SAMPLE_SIZE=MOVIELENS_SAMPLE_SIZE))

pm.execute_notebook(
notebook_path,
OUTPUT_NOTEBOOK,
kernel_name=KERNEL_NAME,
parameters=dict(
MOVIELENS_DATA_SIZE="100k",
MOVIELENS_SAMPLE=True,
MOVIELENS_SAMPLE_SIZE=MOVIELENS_SAMPLE_SIZE,
),
)


@pytest.mark.notebooks
def test_rlrmc_quickstart_runs(notebooks):
Expand Down
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