Develop Reference

Failed to Importing Adida from statsforecast.models in Python

I was trying to replicate this code for stat forecasting in python, I came across the issue of not being able to load this model 'adida' form statsforecast library,
Here is the link for reference : https://towardsdatascience.com/time-series-forecasting-with-statistical-models-f08dcd1d24d1
import random
from itertools import product
from IPython.display import display, Markdown
from multiprocessing import cpu_count
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from nixtlats.data.datasets.m4 import M4, M4Info
from statsforecast import StatsForecast
from statsforecast.models import (
adida,
croston_classic,
croston_sba,
croston_optimized,
historic_average,
imapa,
naive,
random_walk_with_drift,
seasonal_exponential_smoothing,
seasonal_naive,
seasonal_window_average,
ses,
tsb,
window_average
)
Attached is the error message, Can you please have a look at this and let me know why is there an issue in importing this?
Given below is the error image:

I did some research and figured out the issue is probably with the version, try installing this specific version of statsforecast
pip install statsforecasts==0.6.0
Trying loading these models after that, hopefully this should work.

As of v1.0.0 of StatsForecast, the API changed to be more like sklearn, using classes instead of functions. You can find an example of the new syntax here: https://nixtla.github.io/statsforecast/examples/IntermittentData.html.
The new code would be
from statsforecast import StatsForecast
from statsforecast.models import ADIDA, IMAPA
model = StatsForecast(df=Y_train_df, # your data
models=[ADIDA(), IMAPA()],
freq=freq, # frequency of your data
n_jobs=-1)
If you want to use the old syntax, setting the version as suggested should work.

If you have updated the package ..use ADIDA it will work
see the model list name with new packages
ADIDA(),
IMAPA(),
(SimpleExponentialSmoothing(0.1)),
(TSB(0.3,0.2)),
(WindowAverage( 6))

rdkit module functions in Chem.Descriptors not working and unsure why?

I'm new to using rdkit but can't find any forum posts online addressing this but I've been trying to use the functions in rdkit.Chem.Descriptors as follows:
from rdkit import Chem
mol = Chem.MolFromSmiles('CC')
Descriptors.MolWt(mol)
this should just return the molecular weight = 30.07 of the molecule simply and I took this from the documentation but it just gives me an error saying:
NameError: name 'Descriptors' is not defined
I have no idea why - I tried changing versions but this has made no difference. It seems to be an error inherent to all functions in the Descriptor class.
I've also tried explicitly importing the Descriptor submodule/class and this doesn't work either.

from rdkit.Chem import Descriptors
is the correct way to import module for this code to work

DeepPavlov FAQ Bot is returning a 'collections.OrderedDict' object is not callable error

I'm trying to use collab to build a bot for FAQ with DeepPavlov and I modified a tutorial notebook that DeepPavlov has on their site, the only thing I change is using my sample dataset yet I get the 'collections.OrderedDict' object is not callable error when calling on
answer=model_config(["help"])
answer
The full code for this (seperated in cells) is
!pip install -q deeppavlov
from deeppavlov import configs
from deeppavlov.core.common.file import read_json
from deeppavlov.core.commands.infer import build_model
from deeppavlov import configs, train_model
model_config = read_json(configs.faq.tfidf_logreg_en_faq)
model_config["dataset_reader"]["data_path"] = None
model_config["dataset_reader"]["data_url"] = "https://docs.google.com/spreadsheets/d/e/2PACX-1vSUFqHL9u_KkSCfw03bYCIuzfCzfOwXlvsQeTb8tMVaSDYohcHbfL8jNtV24AZiSLNnJJQs58dIsO8A/pub?gid=788315638&single=true&output=csv"
model_config
answer=model_config(["help"])
answer
Anyone know the fix to help my bot run with the sample dataset url I provided in my code? I'm new to bots, deeppavlov, and collab so I'm having a steep learning curve here.

Your code is missing the model training part - you are trying to call the config object instead of actually training and using a model for prediction on your data.
However, this is not the only problem here. Firstly, you might want to change the data_path variable to a string object, otherwise you will face problems here (you may try it yourself to check). Secondly, while trying to run your code with my corrections I have faced a csv-parsing error - please check your csv file again and make sure to get rid of empty rows in it. After you do that, this code should work correctly.
model_config = read_json(configs.faq.tfidf_logreg_en_faq)
model_config["dataset_reader"]["data_path"] = ''
model_config["dataset_reader"]["data_url"] = "your-dataset-link"
faq = train_model(model_config)
answer = faq(["help"])
answer

Plotting Bloch Vectors in Qiskit: " 'Arrow3D' object has no attribute '_path2d' "

I am working through the Qiskit tutorial textbook, and in Section 1.4 ('Single Qubit Gates'), I can't seem to plot vectors on the Bloch Sphere.
I am using Google Colab and am importing as:
!pip install qiskit
!pip install qiskit[visualization]
from qiskit import QuantumCircuit, assemble, Aer
from math import pi, sqrt
from qiskit.visualization import plot_bloch_multivector, plot_histogram
sim = Aer.get_backend('aer_simulator')
and then the following code is taken directly from the textbook:
qc = QuantumCircuit(1)
qc.x(0)
qc.save_statevector()
qobj = assemble(qc)
state = sim.run(qobj).result().get_statevector()
plot_bloch_multivector(state)
Yet doing this gives the error: " 'Arrow3D' object has no attribute '_path2d' ". Any help would be greatly appreciated.
Edit: Adding a line plt.show() no longer brings up an error message, but still no image shows.

i had this same issue and upgrading matplotlib (to 3.5.1) fixed it for me

I'm having the same problem. One workaround that I found is to use the Kaleidoscope package link. It's a visualization package developed by someone working at IBM. I actually like this as it uses Plotly for the figures.
import kaleidoscope.qiskit
from kaleidoscope import bloch_sphere
Then you can just type
bloch_sphere(state)

anova_lm() python: on which model type does it work?

I am new to Python and trying to transition to a unique platform, Python, from Matlab+R platforms.
I need to do a regression of my data.
After reading what is available online - unfortunately not as numerous as for R just yet - I realized that I need to play with the following options:
import statsmodels.api as sm
import statsmodels.formula.api as smf
mod1 = smf.glm(formula=formula_new, data=dta_new, family=sm.families.Gaussian())
mod2 = smf.ols(formula=formula_new, data=dta_new, family=sm.families.Gaussian())
mod3 = sm.OLS.from_formula(formula=formula_new, data=dta_new)
all three give me similar results.
What I really want to know is if there exists a function similar to anova() from R (with a nice table summarizing the comparison of different models, or within a model for different variables, as shown here http://www.r-bloggers.com/r-tutorial-series-anova-tables/ ) for any of these model options.
I tried to run
table = sm.stats.anova_lm(modX)
print table
with X = 1,2,3, basically for all models (those coming from smf. or sm.) but I always get the same error:
AttributeError: 'OLS'/'GLM' object has no attribute 'model'
with OLS or GLM depending on the type of model.
thanks for any input. am I not importing correctly modules? I am confused.
Links to applications/examples/tutorials of python are welcome.
rpy2 is not an option on my server, I am working on getting R3.0 installed, but it might take a while.

I figured why it wasn't working on all models.
anova_lm() wants the fit() attribute:
table = sm.stats.anova_lm(modX.fit())
print table
however, it works only with mod2 and mod3, therefore it would not work with GLM models.
Here some info I found online relevant to this issue. Hopefully it will be extended to GLM models soon.
http://comments.gmane.org/gmane.comp.python.pystatsmodels/11000