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pushd "%~dp0"
dir /b %SystemRoot%\servicing\Packages\*Hyper-V*.mum >hyper-v.txt
for /f %%i in ('findstr /i . hyper-v.txt 2^>nul') do dism /online /norestart /add-package:"%SystemRoot%\servicing\Packages\%%i"
del hyper-v.txt
Dism /online /enable-feature /featurename:Microsoft-Hyper-V -All /LimitAccess /ALL
pause

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pushd "%~dp0"
dir /b %SystemRoot%\servicing\Packages\*Hyper-V*.mum >hyper-v.txt
for /f %%i in ('findstr /i . hyper-v.txt 2^>nul') do dism /online /norestart /add-package:"%SystemRoot%\servicing\Packages\%%i"
del hyper-v.txt
Dism /online /enable-feature /featurename:Microsoft-Hyper-V -All /LimitAccess /ALL
pause

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from sklearn.neighbors import KNeighborsRegressor
from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, explained_variance_score

import mlflow
import mlflow.sklearn

import numpy as np

# Launch the experiment on mlflow
experiment_name = "electricityconsumption-forecast"

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def log_run(gridsearch: sklearn.GridSearchCV, experiment_name: str, model_name: str, run_index: int, conda_env, tags={}):
    """Logging of cross validation results to mlflow tracking server
    
    Args:
        experiment_name (str): experiment name
        model_name (str): Name of the model
        run_index (int): Index of the run (in Gridsearch)
        conda_env (str): A dictionary that describes the conda environment (MLFlow Format)
        tags (dict): Dictionary of extra data and tags (usually features)

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with mlflow.start_run(experiment_id=1, run_name="top_lever_run") as run:
    with mlflow.start_run(experiment_id=1, run_name="subrun1",nested=True) as subrun1:
        mlflow.log_param("p1","red")
        mlflow.log_metric("m1", 5.1)
        with mlflow.start_run(experiment_id=1, run_name="subsubrun1",nested=True) as subsubrun1:
            mlflow.log_param("p3","green")
            mlflow.log_metric("m3", 5.24)
        with mlflow.start_run(experiment_id=1, run_name="subsubrun2", nested=True) as subsubrun2:
            mlflow.log_param("p4","blue")
            mlflow.log_metric("m5", 3.25)

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Awesome Data Science with Python

Core - Ce qu'on va utiliser

pandas
scikit-learn

plotly-express

matplotlib
seaborn

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This document summarizes some potentially useful papers and code repositories on Sentiment analysis / document classification

• CNN:

  1. Related Paper: Convolutional Neural Networks for Sentence Classification. EMNLP 2014

     A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification

     Code:

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