I have a homegrown python library. As it is a library, it should be initialized with parameters every time it is used, based on different projects using it. For example, here is the sample pseudo code:
import myownlibrary
myownlibrary.init('path_to_config_file_containing_details_to_process_data')
Any idea how this can be achieved?
A sample reference code will be very helpful.
Thanks
I added it to the __init__.py as below
global configpath
def setpath(self, pathpassedin):
self.configpath = pathpassedin
print("Value passed in: ", pathpassedin)
I thought self was required, but having self also requires that the method be called as below:
import myownlibrary as mylib
myownlibrary.setpath(mylib, 'path_to_config_file')
Not sure if this is the way to set this up. Would appreciate feedback
I'm fairly new to Python and I'm trying to get my head around documentation of modules saved to Github.
My case is module SimpSOM (https://github.com/fcomitani/SimpSOM). I installed the module and attempted the basic intro example presented on the Github page and in the linked readthedocs page.
Unfortunately, I immediately run into problems with:
import pandas as pd
import numpy as np
import SimpSOM as sps
from sklearn.cluster import KMeans
#training
net = sps.somNet(20, 20, raw_data, PBC=True)
Which returns the error:
NameError: name 'raw_data' is not defined
I do not think raw_data is not part of the SimpSOM module, it may just be a place holder name for the sack of example and I can't find any mention of raw_data in the corresponding websites/documentation. However, Google revealed the following page: https://pydoc.net/SimpSOM/1.3.1/SimpSOM/ with the following code:
class somNet:
""" Kohonen SOM Network class. """
def __init__(self, netHeight, netWidth, data, loadFile=None, PCI=0, PBC=0):
"""Initialise the SOM network.
Args:
netHeight (int): Number of nodes along the first dimension.
netWidth (int): Numer of nodes along the second dimension.
data (np.array or list): N-dimensional dataset.
....
I have several questions:
Did my tired eyes miss complete API docs?
I can see from the comments that raw_data is in fact data which is of type np.array or list. If a module is missing documentation, is there any way of programmatically making an enquiry of a class module or a function to see what data types are expected?
Worst a case scenario, how can I generate my own documentation from the comments inside that class? It looks like they're compatible with doxygen.
I am trying to pickle a sklearn machine-learning model, and load it in another project. The model is wrapped in pipeline that does feature encoding, scaling etc. The problem starts when i want to use self-written transformers in the pipeline for more advanced tasks.
Let's say I have 2 projects:
train_project: it has the custom transformers in src.feature_extraction.transformers.py
use_project: it has other things in src, or has no src catalog at all
If in "train_project" I save the pipeline with joblib.dump(), and then in "use_project" i load it with joblib.load() it will not find something such as "src.feature_extraction.transformers" and throw exception:
ModuleNotFoundError: No module named 'src.feature_extraction'
I should also add that my intention from the beginning was to simplify usage of the model, so programist can load the model as any other model, pass very simple, human readable features, and all "magic" preprocessing of features for actual model (e.g. gradient boosting) is happening inside.
I thought of creating /dependencies/xxx_model/ catalog in root of both projects, and store all needed classes and functions in there (copy code from "train_project" to "use_project"), so structure of projects is equal and transformers can be loaded. I find this solution extremely inelegant, because it would force the structure of any project where the model would be used.
I thought of just recreating the pipeline and all transformers inside "use_project" and somehow loading fitted values of transformers from "train_project".
The best possible solution would be if dumped file contained all needed info and needed no dependencies, and I am honestly shocked that sklearn.Pipelines seem to not have that possibility - what's the point of fitting a pipeline if i can not load fitted object later? Yes it would work if i used only sklearn classes, and not create custom ones, but non-custom ones do not have all needed functionality.
Example code:
train_project
src.feature_extraction.transformers.py
from sklearn.pipeline import TransformerMixin
class FilterOutBigValuesTransformer(TransformerMixin):
def __init__(self):
pass
def fit(self, X, y=None):
self.biggest_value = X.c1.max()
return self
def transform(self, X):
return X.loc[X.c1 <= self.biggest_value]
train_project
main.py
from sklearn.externals import joblib
from sklearn.preprocessing import MinMaxScaler
from src.feature_extraction.transformers import FilterOutBigValuesTransformer
pipeline = Pipeline([
('filter', FilterOutBigValuesTransformer()),
('encode', MinMaxScaler()),
])
X=load_some_pandas_dataframe()
pipeline.fit(X)
joblib.dump(pipeline, 'path.x')
test_project
main.py
from sklearn.externals import joblib
pipeline = joblib.load('path.x')
The expected result is pipeline loaded correctly with transform method possible to use.
Actual result is exception when loading the file.
I found a pretty straightforward solution. Assuming you are using Jupyter notebooks for training:
Create a .py file where the custom transformer is defined and import it to the Jupyter notebook.
This is the file custom_transformer.py
from sklearn.pipeline import TransformerMixin
class FilterOutBigValuesTransformer(TransformerMixin):
def __init__(self):
pass
def fit(self, X, y=None):
self.biggest_value = X.c1.max()
return self
def transform(self, X):
return X.loc[X.c1 <= self.biggest_value]
Train your model importing this class from the .py file and save it using joblib.
import joblib
from custom_transformer import FilterOutBigValuesTransformer
from sklearn.externals import joblib
from sklearn.preprocessing import MinMaxScaler
pipeline = Pipeline([
('filter', FilterOutBigValuesTransformer()),
('encode', MinMaxScaler()),
])
X=load_some_pandas_dataframe()
pipeline.fit(X)
joblib.dump(pipeline, 'pipeline.pkl')
When loading the .pkl file in a different python script, you will have to import the .py file in order to make it work:
import joblib
from utils import custom_transformer # decided to save it in a utils directory
pipeline = joblib.load('pipeline.pkl')
Apparently this problem raises when you split definitions and saving code part in two different files. So I have found this workaround that has worked for me.
It consists in these steps:
Guess we have your 2 projects/repositories : train_project and use_project
train_project:
On your train_project create a jupyter notebook or .py
On that file lets define every Custom transformer in a class, and import all other tools needed from sklearn to design the pipelines. Then lets write the saving code to pickle just inside the same file.(Don't create an external .py file src.feature_extraction.transformers to define your customtransformers).
Then fit and dumb your pipeline by running that file.
On use_project:
Create a customthings.py file with all the functions and transformers defined inside.
Create another file_where_load.py where you wish load the pickle. Inside, make sure you have imported all the definitions from customthings.py . Ensure that functions and classes have the same name than the ones you've used on train_project.
I hope it works for everyone with same problem
I have created a workaround solution. I do not consider it a complete answer to my question, but non the less it let me move on from my problem.
Conditions for the workaround to work:
I. Pipeline needs to have only 2 kinds of transformers:
sklearn transformers
custom transformers, but with only attributes of types:
number
string
list
dict
or any combination of those e.g. list of dicts with strings and numbers. Generally important thing is that attributes are json serializable.
II. names of pipeline steps need to be unique (even if there is pipeline nesting)
In short model would be stored as a catalog with joblib dumped files, a json file for custom transformers, and a json file with other info about model.
I have created a function that goes through steps of a pipeline and checks __module__ attribute of transformer.
If it finds sklearn in it it then it runs joblib.dump function under a name specified in steps (first element of step tuple), to some selected model catalog.
Otherwise (no sklearn in __module__) it adds __dict__ of transformer to result_dict under a key equal to name specified in steps. At the end I json.dump the result_dict to model catalog under name result_dict.json.
If there is a need to go into some transformer, because e.g. there is a Pipeline inside a pipeline, you can probably run this function recursively by adding some rules to the beginning of the function, but it becomes important to have always unique steps/transformers names even between main pipeline and subpipelines.
If there are other information needed for creation of model pipeline then save them in model_info.json.
Then if you want to load the model for usage:
You need to create (without fitting) the same pipeline in target project. If pipeline creation is somewhat dynamic, and you need information from source project, then load it from model_info.json.
You can copy function used for serialization and:
replace all joblib.dump with joblib.load statements, assign __dict__ from loaded object to __dict__ of object already in pipeline
replace all places where you added __dict__ to result_dict with assignment of appropriate value from result_dict to object __dict__ (remember to load result_dict from file beforehand)
After running this modified function, previously unfitted pipeline should have all transformer attributes that were effect of fitting loaded, and pipeline as a whole ready to predict.
The main things I do not like about this solution is that it needs pipeline code inside target project, and needs all attrs of custom transformers to be json serializable, but I leave it here for other people that stumble on a similar problem, maybe somebody comes up with something better.
I was similarly surprised when I came across the same problem some time ago. Yet there are multiple ways to address this.
Best practice solution:
As others have mentioned, the best practice solution is to move all dependencies of your pipeline into a separate Python package and define that package as a dependency of your model environment.
The environment then has to be recreated whenever the model is deployed. In simple cases this can be done manually e.g. via virtualenv or Poetry. But model stores and versioning frameworks (MLflow being one example) typically provide a way to define the required Python environment (e.g. via conda.yaml). They often can automatically recreate the environment at deployment time.
Solution by putting code into main:
In fact, class and function declearations can be serialized, but only declarations in __main__ actually get serialized. __main__ is the entry point of the script, the file that is run. So if all the custom code and all of its dependencies are in that file, then custom objects can later be loaded in Python environments that do not include the code. This kind of solves the problem, but who wants to have all that code in __main__? (Note that this property also applies to cloudpickle)
Solution by "mainifying":
There is one other way which is to "mainify" the classes or function objects before saving. I came across that same problem some time ago and have written a function that does that. It essentially redefines an existing object's code in __main__. Its application is simple: Pass object to function, then serialize the object, voilà, it can be loaded anywhere. Like so:
# ------ In file1.py: ------
class Foo():
pass
# ------ In file2.py: ------
from file1 import Foo
foo = Foo()
foo = mainify(foo)
import dill
with open('path/file.dill', 'wb') as f
dill.dump(foo, f)
I post the function code below. Note that I have tested this with dill, but I think it should work with pickle as well.
Also note that the original idea is not mine, but came from a blog post that I cannot find right now. I will add the reference/acknowledgement when I find it.
Edit: Blog post by Oege Dijk by which my code was inspired.
def mainify(obj, warn_if_exist=True):
''' If obj is not defined in __main__ then redefine it in main. Allows dill
to serialize custom classes and functions such that they can later be loaded
without them being declared in the load environment.
Parameters
---------
obj : Object to mainify (function or class instance)
warn_if_exist : Bool, default True. Throw exception if function (or class) of
same name as the mainified function (or same name as mainified
object's __class__) was already defined in __main__. If False
don't throw exception and instead use what was defined in
__main__. See Limitations.
Limitations
-----------
Assumes `obj` is either a function or an instance of a class.
'''
if obj.__module__ != '__main__':
import __main__
is_func = True if isinstance(obj, types.FunctionType) else False
# Check if obj with same name is already defined in __main__ (for funcs)
# or if class with same name as obj's class is already defined in __main__.
# If so, simply return the func with same name from __main__ (for funcs)
# or assign the class of same name to obj and return the modified obj
if is_func:
on = obj.__name__
if on in __main__.__dict__.keys():
if warn_if_exist:
raise RuntimeError(f'Function with __name__ `{on}` already defined in __main__')
return __main__.__dict__[on]
else:
ocn = obj.__class__.__name__
if ocn in __main__.__dict__.keys():
if warn_if_exist:
raise RuntimeError(f'Class with obj.__class__.__name__ `{ocn}` already defined in __main__')
obj.__class__ = __main__.__dict__[ocn]
return obj
# Get source code and compile
source = inspect.getsource(obj if is_func else obj.__class__)
compiled = compile(source, '<string>', 'exec')
# "declare" in __main__, keeping track which key of __main__ dict is the new one
pre = list(__main__.__dict__.keys())
exec(compiled, __main__.__dict__)
post = list(__main__.__dict__.keys())
new_in_main = list(set(post) - set(pre))[0]
# for function return mainified version, else assign new class to obj and return object
if is_func:
obj = __main__.__dict__[new_in_main]
else:
obj.__class__ = __main__.__dict__[new_in_main]
return obj
Have you tried using cloud pickle?
https://github.com/cloudpipe/cloudpickle
Based on my research it seems that the best solution is to create a Python package that includes your trained pipeline and all files.
Then you can pip install it in the project where you want to use it and import the pipeline with from <package name> import <pipeline name>.
Credit to Ture Friese for mentioning cloudpickle >=2.0.0, but here's an example for your use case.
import cloudpickle
cloudpickle.register_pickle_by_value(FilterOutBigValuesTransformer)
with open('./pipeline.cloudpkl', mode='wb') as file:
pipeline.dump(
obj=Pipe
, file=file
)
register_pickle_by_value() is the key as it will ensure your custom module (src.feature_extraction.transformers) is also included when serializing your primary object (pipeline). However, this is not built for recursive module dependence, e.g. if FilterOutBigValuesTransformer also contains another import statement
Calling the location of the transform.py file with sys.path.append may resolve the issue.
import sys
sys.path.append("src/feature_extraction/transformers")
I am trying to do a optimization using pygmo. I am facing an error from the pygmo package. It is basically a vector optimisation.
I get the following error when i initiate the population:
"TypeError: No registered converter was able to produce a C++ rvalue of type double from this Python object of type numpy.ndarray"
I have tried to remove the scipy command from my function but my function throws error.
class square_fit:
def fitness(self,rect_length):
sq_area = rect_width*rect_length
pulse_area =sc.integrate.simps(vge_reg, dx=0.1)
rmse = pulse_area-sq_area
return [sq_area,pulse_area,rmse]
def get_bounds(self):
return ([2.5],[3.633])
algo = pg.algorithm(uda = pg.nlopt('auglag'))
algo.extract(pg.nlopt).local_optimizer = pg.nlopt('var2')
algo.set_verbosity(200)
pop = pg.population(prob = square_fit(), size = 1) # error happens
pop.problem.c_tol = [1E-6] * 6
pop = algo.evolve(pop)
I want to know if i can do a optimization in pygmo by including command from other python packages like numpy or scipy in my cost function. what is c++rvalue and which variable is referred to as numpy.ndarray
I found the reason for this error. You just need to define the return type of the variables that are returned by the fitness function.
I have a pickled file called classifier.pkl that I am trying to load into another module. However, I get an error I don't understand.
My code to pickle:
features = ['bob','ice','snowing'] #... shortened for exposition's sake
def extract_features(document):
return {'contains(%s)'% word: (word in set(document))
for word in all_together_word_list}
training_set = classify.util.apply_features(extract_features,tweets[0])
classifier = NaiveBayesClassifier.train(training_set)
cPcikle.dump(open('cocaine_classifier.pkl','wb'))
My code to unpickle:
features, extract_features, classifier =
cPickle.load(open('cocaine_classifier.pkl','rb'))
My error:
AttributeError: 'module' object has no attribute 'extract_features'
A while ago I made the .pkl file by pickling three things:
features : list
extract_features : function
classifier : instance of NLTK Naive Bayes Classifier
Puzzlingly, I get the same error with the following code:
x = cPickle.load(open('cocaine_classifier.pkl','rb'))
Why can't I retrieve three things? Even when I'm not trying to unpack the tuple?
Update
As NPE pointed out the path of the function to be unpickled must exactly match the function into which its being unpickled. I was debugging and Terminal and so from mod import * loads everything into the namespace whereas import mod as m does not.
The problem is that when you pickle a function, only the (fully-qualified) name of the function is pickled, not the function itself. This means that you have to have the function definition in place when you're unpickling.
Did you by any chance mean to pickle the result of calling extract_features?