This is one of my three distinct posts requesting for help on three different examples provided by Pinecone that do not seem to be working. I will appreciate very much the community moderator who flagged the posts as spam to clarify why the three distinct posts concerning three different examples are considered spam in this community. Thanks.
I was trying to run this example on colab: Time Series Search
Got error at the first step ** Prerequisites** (see below). Then got stuck at the next step.
Any help to point me to a direction to fix the error would be very much appreciated. Thanks in advance.
> !pip install kats==0.2.0 matplotlib==3.1.3 scikit-learn==1.0.2 pandas==1.3.5 pinecone-client convertdate kaggle
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Collecting kats==0.2.0
Using cached kats-0.2.0-py3-none-any.whl (612 kB)
...
...
...
Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.9/dist-packages (from importlib-metadata>=3.6->typeguard->ax-platform==0.2.4->kats==0.2.0) (3.15.0)
Building wheels for collected packages: fbprophet, pystan
error: subprocess-exited-with-error
× python setup.py bdist_wheel did not run successfully.
│ exit code: 1
╰─> See above for output.
note: This error originates from a subprocess, and is likely not a problem with pip.
Building wheel for fbprophet (setup.py) ... error
ERROR: Failed building wheel for fbprophet
Running setup.py clean for fbprophet
Building wheel for pystan (setup.py) ... done
Created wheel for pystan: filename=pystan-2.19.1.1-cp39-cp39-linux_x86_64.whl size=61825864 sha256=0b5bc1a7c8fd787306cb222fc2c9a0c1a35d64f1b68605426eeae1b46a818b9f
Stored in directory: /root/.cache/pip/wheels/b8/36/bf/7ec7e363f796373cea3eb9ea94e83f5bbbb586d2edbf7e3417
Successfully built pystan
Failed to build fbprophet
Installing collected packages: setuptools-git, parameterized, numpy, loguru, dnspython, deprecated, typeguard, scipy, pystan, pinecone-client, pandas, matplotlib, statsmodels, scikit-learn, pytest-mpl, pymannkendall, cmdstanpy, fbprophet, linear-operator, gpytorch, botorch, ax-platform, kats
Attempting uninstall: numpy
Found existing installation: numpy 1.22.4
Uninstalling numpy-1.22.4:
...
...
...
Attempting uninstall: cmdstanpy
Found existing installation: cmdstanpy 1.1.0
Uninstalling cmdstanpy-1.1.0:
Successfully uninstalled cmdstanpy-1.1.0
Running setup.py install for fbprophet ... done
DEPRECATION: fbprophet was installed using the legacy 'setup.py install' method, because a wheel could not be built for it. pip 23.1 will enforce this behaviour change. A possible replacement is to fix the wheel build issue reported above. Discussion can be found at https://github.com/pypa/pip/issues/8368
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
tensorflow 2.12.0 requires numpy<1.24,>=1.22, but you have numpy 1.21.6 which is incompatible.
prophet 1.1.2 requires cmdstanpy>=1.0.4, but you have cmdstanpy 0.9.5 which is incompatible.
plotnine 0.10.1 requires matplotlib>=3.5.0, but you have matplotlib 3.1.3 which is incompatible.
plotnine 0.10.1 requires statsmodels>=0.13.2, but you have statsmodels 0.12.2 which is incompatible.
mizani 0.8.1 requires matplotlib>=3.5.0, but you have matplotlib 3.1.3 which is incompatible.
google-colab 1.0.0 requires pandas~=1.5.3, but you have pandas 1.3.5 which is incompatible.
arviz 0.15.1 requires matplotlib>=3.2, but you have matplotlib 3.1.3 which is incompatible.
arviz 0.15.1 requires scipy>=1.8.0, but you have scipy 1.7.3 which is incompatible.
Successfully installed ax-platform-0.2.4 botorch-0.6.2 cmdstanpy-0.9.5 deprecated-1.2.13 dnspython-2.3.0 fbprophet-0.7.1 gpytorch-1.10 kats-0.2.0 linear-operator-0.4.0 loguru-0.7.0 matplotlib-3.1.3 numpy-1.21.6 pandas-1.3.5 parameterized-0.9.0 pinecone-client-2.2.1 pymannkendall-1.4.3 pystan-2.19.1.1 pytest-mpl-0.16.1 scikit-learn-1.0.2 scipy-1.7.3 setuptools-git-1.2 statsmodels-0.12.2 typeguard-3.0.2
Re-ran the above appears to give no error but …
> !pip install kats==0.2.0 matplotlib==3.1.3 scikit-learn==1.0.2 pandas==1.3.5 pinecone-client convertdate kaggle
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
Requirement already satisfied: kats==0.2.0 in /usr/local/lib/python3.9/dist-packages (0.2.0)
Requirement already satisfied: matplotlib==3.1.3 in /usr/local/lib/python3.9/dist-packages (3.1.3)
...
...
Requirement already satisfied: importlib-metadata>=3.6 in /usr/local/lib/python3.9/dist-packages (from typeguard->ax-platform==0.2.4->kats==0.2.0) (6.4.1)
Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.9/dist-packages (from importlib-metadata>=3.6->typeguard->ax-platform==0.2.4->kats==0.2.0) (3.15.0)
then got stuck in the next step:
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pprint
from sklearn.preprocessing import MinMaxScaler
from kats.consts import TimeSeriesData
from kats.tsfeatures.tsfeatures import TsFeatures
import itertools
from decimal import Decimal
from IPython.display import clear_output
import warnings
warnings.simplefilter(action='ignore')
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
RuntimeError: module compiled against API version 0xf but this version of numpy is 0xe
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
<ipython-input-3-c8a7a2e1d125> in <cell line: 4>()
2 import pandas as pd
3 import numpy as np
----> 4 import matplotlib.pyplot as plt
5 import pprint
6 from sklearn.preprocessing import MinMaxScaler
1 frames
/usr/local/lib/python3.9/dist-packages/matplotlib/__init__.py in _check_versions()
188 # Quickfix to ensure Microsoft Visual C++ redistributable
189 # DLLs are loaded before importing kiwisolver
--> 190 from . import ft2font
191
192 for modname, minver in [
ImportError: numpy.core.multiarray failed to import
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NOTE: If your import is failing due to a missing package, you can
manually install dependencies using either !pip or !apt.
To view examples of installing some common dependencies, click the
"Open Examples" button below.
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