Assume I want to store via sqlite3 a simple list of strings: my_list = [‘a’,’b’,’c’] or a python object with properties and methods. What I tried so far is to serialize (pickle) my_list, and the returned byte representation is b’x80x03]qx00(Xx01x00x00x00aqx01Xx01x00x00x00bqx02Xx01x00x00x00cqx03e.’. However, cannot be stored within a BLOB variable. Should I use a string variable instead, ..
I’ve started to learn about the pickle module used for object serialization and deserialization. I know that pickle.dump is used to store the code as a stream of bits (serialization), and pickle.loads is making a stream of bits back into the python object. (deserialization). But what are dumps and loads, and what are the differences ..
So I just see in a tutorial that the author didn’t import sklearn when using predict function of pickled model. the code below is working: !pip install scikit-learn import pickle model = pickle.load(open("model.pkl", "rb"), encoding="bytes") out = model.predict([[20, 0, 1, 1, 0]]) print(out) But if I uninstall the sklearn, the predict function is not working. ..
I have multiple pickle files with the same format in one folder called pickle_files: 1_my_work.pkl 2_my_work.pkl … 125_my_work.pkl How would I go about loading those files into the workspace, without having to do it one file at a time. Thank you!!!! Source: Python..
I have a ml model that uses a vectorizer. This vectorizer contains sensitive data and is stored using pickle as a .pkl file. How can I encrypt this pkl file, so that it requires a key to decrypt? I tried using the below code for encryption. from cryptography.fernet import Fernet def decrypt_file(filepath, key): f = ..
I am programming a software that should be capable of making saveable flashcards for students. Using Python Pickle dump to save Error – OSError: [Errno 30] Read-only file system: Using Mac OS Catalina Using Tinter from tkinter import * import pickle from PIL import Image, ImageTk ——————variables——————————————– backgroundColour = ‘#e3e6e4’ try: oldNames = pickle.load(open("Card1Saves.dat", "rb")) ..
I got this error "ValueError: Buffer dtype mismatch, expected ‘ITYPE_t’ but got ‘long long’" when I try to load a ML model. I trained a classifier model (knn) and save it on a PC. Then, when I try to test saved model in another PC, I got this error. My Code to Save a Model ..
I’m using the Helm/Kubernetes gateway version of Dask Distributed loading workers code with upload_file. This works fine: # my-client.py client = Client(‘127.0.0.1:8786’) client.upload_file(‘C:mycodewrk4.py’) futures = client.map (alter, [1000, 2000, 3000]) results = client.gather(futures) # wrk4.py def alter(x): y = x + 1 return y However, when I try to upload a zipped file that includes ..
I’m using IsolationForest to predict outliers. And I want to save all my steps so I can use it directly on other data next time. My code for building isolation forest is: import sklearn.neighbors._base import sys sys.modules[‘sklearn.neighbors.base’] = sklearn.neighbors._base from sklearn.ensemble import IsolationForest iforest = IsolationForest(n_estimators=50, max_samples=’auto’, contamination=float(0.00005), max_features=1.0,random_state = None) iforest.fit(imputed_data_initial) y_pred = iforest.predict(data) ..
I wrote multiple steps to impute a dataset, and I want to create a pipeline for these steps and also serialize/pickle the pipeline so that it can be loaded when analyzing a new sample. The steps I did for imputation are: imputer = MissForest() imputed_data = imputer.fit_transform(data) imputed_data = pd.DataFrame(imputed_data, columns=data.columns) #Drop ‘id’ imputed_data_initial = imputed_data.drop(‘id’, axis ..