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pytest tmp_dir fixture #706

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Apr 4, 2019
Merged

pytest tmp_dir fixture #706

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c61dc6c
pytest tmp_dir fixture
loomlike Apr 3, 2019
94c83a3
tmp fixture to use context manager
loomlike Apr 4, 2019
570500e
Update conftest description
loomlike Apr 4, 2019
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50 changes: 18 additions & 32 deletions reco_utils/dataset/movielens.py
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Original file line number Diff line number Diff line change
Expand Up @@ -136,12 +136,12 @@ def item_has_header(self):

# Warning and error messages
WARNING_MOVIE_LENS_HEADER = """MovieLens rating dataset has four columns
(user id, movie id, rating, and timestamp), but more than four column headers are provided.
Will only use the first four column headers."""
(user id, movie id, rating, and timestamp), but more than four column names are provided.
Will only use the first four column names."""
WARNING_HAVE_SCHEMA_AND_HEADER = """Both schema and header are provided.
The header argument will be ignored."""
ERROR_MOVIE_LENS_SIZE = "Invalid data size. Should be one of {100k, 1m, 10m, or 20m}"
ERROR_NO_HEADER = "No header (schema) information"
ERROR_NO_HEADER = "No header (schema) information. At least user and movie column names should be provided"
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Really like these constant string of error/warning message. Do we later want to consolidate this into somewhere in common/constants? Or, at least, adopt this practice in other utility codes.

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If we need, sure why not. We can even generalize further, e.g. use obj.__name__ or to accept names as args.



def load_pandas_df(
Expand Down Expand Up @@ -187,13 +187,14 @@ def load_pandas_df(
size = size.lower()
if size not in DATA_FORMAT:
raise ValueError(ERROR_MOVIE_LENS_SIZE)
if header is None or len(header) == 0:
raise ValueError(ERROR_NO_HEADER)

if len(header) > 4:
if header is None or len(header) < 2:
raise ValueError(ERROR_NO_HEADER)
elif len(header) > 4:
warnings.warn(WARNING_MOVIE_LENS_HEADER)
header = header[:4]
movie_col = DEFAULT_ITEM_COL if len(header) < 2 else header[1]

movie_col = header[1]

with download_path(local_cache_path) as path:
filepath = os.path.join(path, "ml-{}.zip".format(size))
Expand All @@ -205,10 +206,6 @@ def load_pandas_df(
)

# Load rating data
if len(header) == 1 and item_df is not None:
# MovieID should be loaded to merge rating df w/ item_df
header = [header[0], movie_col]

df = pd.read_csv(
datapath,
sep=DATA_FORMAT[size].separator,
Expand Down Expand Up @@ -268,11 +265,11 @@ def load_item_df(

def _load_item_df(size, item_datapath, movie_col, title_col, genres_col, year_col):
"""Loads Movie info"""
item_header = []
usecols = []
if movie_col is not None:
item_header.append(movie_col)
usecols.append(0)
if title_col is None and genres_col is None and year_col is None:
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Looks like the check now is different from before? The previous checks the movie_col and return None if it does not exist, whilst now it checks other three columns.

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yeah, made movie_col as a default (must-have) column, checking earlier as: if header is None or len(header) < 2: (2 is the first two columns, user and movie), because there is no point to load only user ids.

return None

item_header = [movie_col]
usecols = [0]

# Year is parsed from title
if title_col is not None or year_col is not None:
Expand All @@ -291,9 +288,6 @@ def _load_item_df(size, item_datapath, movie_col, title_col, genres_col, year_co
item_header.append(genres_col)
usecols.append(2) # genres column

if len(item_header) == 0:
return None

item_df = pd.read_csv(
item_datapath,
sep=DATA_FORMAT[size].item_separator,
Expand Down Expand Up @@ -390,17 +384,17 @@ def load_spark_df(
... )

On DataBricks, pass the dbutils argument as follows:
>>> spark_df = load_spark_df(spark, ..., dbutils=dbutils)
>>> spark_df = load_spark_df(spark, dbutils=dbutils)
"""
size = size.lower()
if size not in DATA_FORMAT:
raise ValueError(ERROR_MOVIE_LENS_SIZE)

schema = _get_schema(header, schema)
if schema is None:
if schema is None or len(schema) < 2:
raise ValueError(ERROR_NO_HEADER)

movie_col = DEFAULT_ITEM_COL if len(schema) < 2 else schema[1].name
movie_col = schema[1].name

with download_path(local_cache_path) as path:
filepath = os.path.join(path, "ml-{}.zip".format(size))
Expand All @@ -410,11 +404,8 @@ def load_spark_df(
# Load movie features such as title, genres, and release year.
# Since the file size is small, we directly load as pd.DataFrame from the driver node
# and then convert into spark.DataFrame
item_df = spark.createDataFrame(
_load_item_df(
size, item_datapath, movie_col, title_col, genres_col, year_col
)
)
item_pd_df = _load_item_df(size, item_datapath, movie_col, title_col, genres_col, year_col)
item_df = spark.createDataFrame(item_pd_df) if item_pd_df is not None else None

if is_databricks():
if dbutils is None:
Expand All @@ -430,11 +421,6 @@ def load_spark_df(
dbutils.fs.mv(spark_datapath, dbfs_datapath)
spark_datapath = dbfs_datapath

# Load rating data
if len(schema) == 1 and item_df is not None:
# MovieID should be loaded to merge rating df w/ item_df
schema.add(StructField(movie_col, IntegerType()))

# pySpark's read csv currently doesn't support multi-character delimiter, thus we manually handle that
separator = DATA_FORMAT[size].separator
if len(separator) > 1:
Expand Down
13 changes: 10 additions & 3 deletions tests/conftest.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,16 +4,17 @@
# NOTE: This file is used by pytest to inject fixtures automatically. As it is explained in the documentation
# https://docs.pytest.org/en/latest/fixture.html:
# "If during implementing your tests you realize that you want to use a fixture function from multiple test files
# you can move it to a conftest.py file. You don’t need to import the fixture you want to use in a test, it
# automatically gets discovered by pytest."
# you can move it to a conftest.py file. You don't need to import the module you defined your fixtures to use in a test,
# it automatically gets discovered by pytest and thus you can simply receive fixture objects by naming them as
# an input argument in the test."

import calendar
import datetime
import os
import numpy as np
import pandas as pd
import pytest
from sklearn.model_selection import train_test_split
from tempfile import TemporaryDirectory
from tests.notebooks_common import path_notebooks
from reco_utils.common.general_utils import get_number_processors, get_physical_memory

Expand All @@ -23,6 +24,12 @@
pass # so the environment without spark doesn't break


@pytest.fixture
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I also like the context manager option, maybe it can be implemented as we are discussing here: #701 (comment)

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Let me try out that. looks promising.

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checked and turns out normal fixture does the same thing since newer version of pytest.

Since pytest-3.0, fixtures using the normal fixture decorator can use a yield statement to provide fixture values and execute teardown code, exactly like yield_fixture in previous versions. Marking functions as yield_fixture is still supported, but deprecated and should not be used in new code.

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good, so if we use the fixture as you program it using: with temp_dir: is the folder erased after the test function is finished?

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yes. Even try/finally does remove the dir too, but using the context manager made the code simpler and give us peace of mind :-)

def tmp(tmp_path_factory):
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This looks nice and clean ❤️

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kudos to @miguelgfierro @anargyri!

with TemporaryDirectory(dir=tmp_path_factory.getbasetemp()) as td:
yield td


@pytest.fixture(scope="session")
def spark(app_name="Sample", url="local[*]"):
"""Start Spark if not started.
Expand Down
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