Perhaps this is a constraint of my understanding of unittests, but I get quite confused as to what should be tested, patched, etc in a method that has several pandas dataframe manipulations. Many of the unittest examples out there focus on classes and methods that are typically small. For larger methods, I get a bit lost on the typical unittest paradigm. For example:
myscript.py
class Pivot:
def prepare_dfs(self):
df = pd.read_csv(self.file, sep=self.delimiter)
g = df.groupby("Other_Location")
df1 = g.apply(lambda x: x[x["PRN"] == "Free"].count())
locations = ["O12-03-01", "O12-03-02"]
cp = df1["PRN"]
cp = cp[locations].tolist()
data = [locations, cp]
new_df = pd.DataFrame({"Other_Location": data[0], "Free": data[1]})
return new_df, df
test_myscript.py
class TestPivot(unittest.TestCase):
def setUp(self):
args = parse_args(["-f", "test1", "-d", ","])
self.pivot = Pivot(args)
self.pivot.path = "Pivot/path"
#mock.patch("myscript.cp[locations].tolist()", return_value=None)
#mock.patch("myscript.pd.read_csv", return_value=df)
def test_prepare_dfs_1(self, mock_read_csv, mock_cp):
new_df, df = self.pivot.prepare_dfs()
# Here I get a bit lost
For example here I try to circumvent the following error message:
ModuleNotFoundError: No module named 'myscript.cp[locations]'; 'myscript' is not a package
I managed to mock correctly the pd.read_csv in my method, however further down in the code there are groupy, apply, tolist etc. The error message is thrown at the following line:
cp = cp[locations].tolist()
What is the best way to approach unittesting when your method involves several manipulations on a dataframe? Is refactoring the code always advised (into smaller chunks)? In this case, how can I mock correctly the tolist ?
Related
Since the feature get_feature_names() was deprecated from "native" categorical encoders in Sklearn (actually it was replaced by get_feature_names_out()), how could I make a DataFrame where the transformed variables have their proper names since inside the ColumnTransformer has encoders whose respond for get_feature_names_out() and others for get_feature_names()? Here is the situation:
features_pipe = make_column_transformer(
(OneHotEncoder(handle_unknown = 'ignore', sparse=False), ['Gender', 'Race']),
(OrdinalEncoder(), ['Age', 'Overall Work Exp.', 'Fieldwork Exp.', 'Level of Education']),
(ce.LeaveOneOutEncoder(), ['State (US)'])
).fit(X_train, y_train)
X_train_encoded = features_pipe.transform(X_train)
X_test_encoded = features_pipe.transform(X_test)
X_train_encoded_df = pd.DataFrame(X_train_encoded, columns= features_pipe.get_features_names_out())
X_train_encoded_df.head()
I got this error: AttributeError: 'ColumnTransformer' object has no attribute 'get_features_names_out'
That's because LeaveOneOutEncoder does not support get_feature_names_out(). It supports get_feature_names().
How could I overcome this issue and print my DataFrame correctly?
I have this same issue once in the past.
If you don't mind using a subclass of ColumnTransformer you can create it and just modify to call get_feature_names() when get_feature_names_out() was not available.
In this case, you should declare the class
from sklearn.compose import ColumnTransformer
from sklearn.compose._column_transformer import _is_empty_column_selection
class MyColumnTransformer(ColumnTransformer):
def __init__(self, transformers, **kwargs):
super().__init__(transformers=transformers, **kwargs)
def _get_feature_name_out_for_transformer(
self, name, trans, column, feature_names_in
):
column_indices = self._transformer_to_input_indices[name]
names = feature_names_in[column_indices]
if trans == "drop" or _is_empty_column_selection(column):
return
elif trans == "passthrough":
return names
if not hasattr(trans, "get_feature_names_out"):
return trans.get_feature_names()
return trans.get_feature_names_out(names)
Although the use of ColumnTransformer is not as simple as using make_column_transformer, it's much more customizable.
So, in this case, you also have to pass a name to each transformer using the following schema:
(name, transformer, columns)
features_pipe = MyColumnTransformer(transformers=
[
('OHE', OneHotEncoder(handle_unknown = 'ignore', sparse=False), ['Gender', 'Race']),
('OE', OrdinalEncoder(), ['Age', 'Overall Work Exp.', 'Fieldwork Exp.', 'Level of Education']),
('LOOE', ce.LeaveOneOutEncoder(), ['State (US)'])
])
features_pipe.fit(X_train, y_train)
and finally continue the code the way you suggested.
If you don't want to append the transformer names to the features names, just include verbose_feature_names_out=False when initializing MyColumnTransformer.
I'm trying to iterate over a dataframe in order to apply a predict function, which calls a Natural Language Model located on GCP. Here is the loop code :
model = 'XXXXXXXXXXXXXXXX'
barometre_df_processed = barometre_df
barometre_df_processed['theme'] = ''
barometre_df_processed['proba'] = ''
print('DEBUT BOUCLE FOR')
for ind in barometre_df.index:
if barometre_df.verbatim[ind] is np.nan :
barometre_df_processed.theme[ind]="RAS"
barometre_df_processed.proba[ind]="1"
else:
print(barometre_df.verbatim[ind])
print(type(barometre_df.verbatim[ind]))
res = get_prediction(file_path={'text_snippet': {'content': barometre_df.verbatim[ind]},'mime_type': 'text/plain'} },model_name=model)
print(res)
theme = res['displayNames']
proba = res["classification"]["score"]
barometre_df_processed.theme[ind]=theme
barometre_df_processed.proba[ind]=proba
and the get_prediction function that I took from the Natural Language AI Documentation :
def get_prediction(file_path, model_name):
options = ClientOptions(api_endpoint='eu-automl.googleapis.com:443')
prediction_client = automl_v1.PredictionServiceClient(client_options=options)
payload = file_path
# Uncomment the following line (and comment the above line) if want to predict on PDFs.
# payload = pdf_payload(file_path)
parameters_dict = {}
params = json_format.ParseDict(parameters_dict, Value())
request = prediction_client.predict(name=model_name, payload=payload, params=params)
print("fonction prediction")
print(request)
return resultat[0]["displayName"], resultat[0]["classification"]["score"], resultat[1]["displayName"], resultat[1]["classification"]["score"], resultat[2]["displayName"], resultat[2]["classification"]["score"]
I'm doing a loop this way because I want each of my couple [displayNames, score] to create a new line on my final dataframe, to have something like this :
verbatim1, theme1, proba1
verbatim1, theme2, proba2
verbatim1, theme3, proba3
verbatim2, theme1, proba1
verbatim2, theme2, proba2
...
The if barometre_df.verbatim[ind] is np.nan is not causing problems, I just use it to deal with nans, don't take care of it.
The error that I have is this one :
TypeError: 'Value' object is not iterable
I guess the issues is about
res = get_prediction(file_path={'text_snippet': {'content': barometre_df.verbatim[ind]} },model_name=model)
but I can't figure what's goign wrong here.
I already try to remove
,'mime_type': 'text/plain'}
from my get_prediction parameters, but it doesn't change anything.
Does someone knows how to deal with this issue ?
Thank you already.
I think you are not iterating correctly.
The way to iterate through a dataframe is:
for index, row in df.iterrows():
print(row['col1'])
I am trying to do the simplest thing with Ray, but no matter what I do it just never releases memory and fails.
The usage case is simply
read parquet files to DF -> pass to pool of actors -> make changes to DF -> return DF
class Main_func:
def calculate(self,data):
#do some things with the DF
return df.copy(deep=True) <- one of many attempts to fix the problem, but didnt work
cpus = 24
actors = []
for _ in range(cpus):
actors.append(Main_func.remote())
from ray.util import ActorPool
pool = ActorPool(actors)
import os
arr = os.listdir("/some/files")
def to_ray():
try:
filename = arr.pop(0)
pf = ParquetFile("/some/files/" + filename)
df = pf.to_pandas()
pool.submit(lambda a,v:a.calculate.remote(v),df.copy(deep=True)
except Exception as e:
print(e)
for _ in range(cpus):
to_ray()
while(True):
res = pool.get_next_unordered()
write('./temp/' + random_filename, res,compression='GZIP')
del res
to_ray()
I have tried other ways of doing the same thing, manually submitting rather than the map command, but whatever i do it always locks memory and fails after a few 100 dataframes.
Does each task needs to preserve state among different files? Ray has tasks abstraction that should simplify things:
import ray
ray.init()
#ray.remote
def read_and_write(path):
df = pd.read_parquet(path)
... do things
df.to_parquet("./temp/...")
import os
arr = os.listdir("/some/files")
results = ray.get([read_and_write.remote(path) for path in arr])
I have more of a general question. I've written a couple of functions that transform data successively:
def func1(df):
pass
...
def main():
df = pd.read_csv()
df1 = func1(df)
df2 = func2(df1)
df3 = func3(df2)
df4 = func4(df3)
df4.to_csv()
if __name__ == "__main__":
main()
Is there a better way of organizing the logic of my script?
Should I use classes for cases like this when everything is tied to one dataset?
It depends of your usecase. For what I understand, I would use dictionary of your functions that process a df.
For instance:
function_returning_a_df = { "f1": func1, "f2": func2, "f3": func3}
df = pd.read_csv(csv)
if this df needs 3 functions to be applied
df_processing = ["f1","f2","f3"] #function will be applied in this order
# If you need to keep df at every step you can make a list
dfs_processed = []
for func in df_processing:
dfs_processed.append(df) # if you want to save all steps
df = function_returning_a_df[func](df)
I am using multiprocessing module to generate 35 dataframes. I guess this will save my time. But the problem is that the class does not return anything. I expect the list of dataframes to be returned from self.dflist
Here is how to create dfnames list.
urls=[]
fnames=[]
dfnames=[]
for x in xrange(100,3600,100):
y = str(x)
i = y.zfill(4)
filename='DCHB_Town_Release_'+i+'.xlsx'
url = "http://www.censusindia.gov.in/2011census/dchb/"+filename
urls.append(url)
fnames.append(filename)
dfnames.append((filename, 'DCHB_Town_Release_'+i))
This is the class that uses the dfnames generated by above code.
import pandas as pd
import multiprocessing
class mydf1():
def __init__(self, dflist, jobs, dfnames):
self.dflist=list()
self.jobs=list()
self.dfnames=dfnames
def dframe_create(self, filename, dfname):
print 'abc', filename, dfname
dfname=pd.read_excel(filename)
self.dflist.append(dfname)
print self.dflist
return self.dflist
def mp(self):
for f,d in self.dfnames:
p = multiprocessing.Process(target=self.dframe_create, args=(f,d))
self.jobs.append(p)
p.start()
#return self.dflist
for j in self.jobs:
j.join()
print '%s.exitcode = %s' % (j.name, j.exitcode)
This class when called like this...
dflist=[]
jobs=[]
x=mydf1(dflist, jobs, dfnames)
y=x.mp()
Prints the self.dflist correctly. But does not return anything.
I can collect all datafarmes sequentially. But in order to save time, I need to use multiple processes simultaneously to generate and add dataframes to a list.
In your case I prefer to write as less code as possible and use Pool:
import pandas as pd
import logging
import multiprocessing
def dframe_create(filename):
try:
return pd.read_excel(filename)
except Exception as e:
logging.error("Something went wrong: %s", e, exc_info=1)
return None
p = multiprocessing.Pool()
excel_files = p.map(dframe_create, dfnames)
for f in excel_files:
if f is not None:
print 'Ready to work'
else:
print ':('
Prints the self.dflist correctly. But does not return anything.
That's because you don't have a return statement in the mp method, e.g.
def mp(self):
...
return self.dflist
It's not entirely clear what you're issue is, however, you have to take some care here in that you can't just pass objects/lists across processes. That's why you have special objects (which lock while they make modifications to a list), that way you don't get tripped up when two processes try to make a change at the same time (and you only get one update).
That is, you have to use multiprocessing's list.
class mydf1():
def __init__(self, dflist, jobs, dfnames):
self.dflist = multiprocessing.list() # perhaps should be multiprocessing.list(dflist or ())
self.jobs = list()
self.dfnames = dfnames
However you have a bigger problem: the whole point of multiprocessing is that they may run/finish out of order, so keeping two lists like this is doomed to fail. You should use a multiprocessing.dict that way the DataFrame is saved unambiguously with the filename.
class mydf1():
def __init__(self, dflist, jobs, dfnames):
self.dfdict = multiprocessing.dict()
...
def dframe_create(self, filename, dfname):
print 'abc', filename, dfname
df = pd.read_excel(filename)
self.dfdict[dfname] = df