glemaitre

glemaitre

# %%
# Download the original dataset to be able to easily build an index with the
# original datetime.
# The dataset is available at:
# https://archive.ics.uci.edu/ml/machine-learning-databases/00275/Bike-Sharing-Dataset.zip
import pandas as pd

df_external = pd.read_csv(
    "~/Downloads/Bike-Sharing-Dataset/hour.csv",
    index_col=0,

glemaitre

# %%
from sklearn.datasets import fetch_openml

usps = fetch_openml(data_id=41082)

# %%
data = usps.data
target = usps.target

# %%

glemaitre

import numpy as np
import pandas as pd


def calcul_chute_tension(
    Ib=1, S=1.5, Un=400, L=0.1, metal="cuivre", phi=np.arccos(0.85)
):

    Ib = np.asarray(Ib)
    S = np.asarray(S)

glemaitre

import pandas as pd
import pytest


def func(expected_columns):

    df = pd.DataFrame({
        "A": [1, 2, 3],
        "B": [1, 2, 3],
        "C": [1, 2, 3]

glemaitre

@pytest.mark.parametrize("name, Tree", REG_TREES.items())
@pytest.mark.parametrize("criterion", REG_CRITERIONS)
def test_diabetes_overfit(name, Tree, criterion):
    # check consistency of overfitted trees on the diabetes dataset
    # since the trees will overfit, we expect an MSE of 0
    reg = Tree(criterion=criterion, random_state=0)
    reg.fit(diabetes.data, diabetes.target)
    score = mean_squared_error(diabetes.target, reg.predict(diabetes.data))
    assert score == pytest.approx(0), (
        f"Failed with {name}, criterion = {criterion} and score = {score}"

glemaitre

In [1]: import numpy as np                                                                                                                                                  

In [2]: X = ["One", "string"]                                                                                                                                               

In [3]: X                                                                                                                                                                   
Out[3]: ['One', 'string']

In [4]: X[0]                                                                                                                                                                
Out[4]: 'One'

glemaitre

# %%
from sklearn.datasets import make_classification
from sklearn.model_selection import StratifiedShuffleSplit
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline
from sklearn.model_selection import cross_validate

RANDOM_SEED = 2

glemaitre

import cv2
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation


def grab_frame(cap):
    _, frame = cap.read()
    return cv2.cvtColor(frame,cv2.COLOR_BGR2RGB)

glemaitre

import asyncio
import random
import time
from concurrent.futures import ProcessPoolExecutor


def simulator_submission():
    """Give ``None`` or a submission id."""
    return random.choice([random.randint(0, 1000), None])