MarwanDebbiche

# Read the Docs configuration file
# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details

# Required
version: 2

# Build documentation in the docs/ directory with Sphinx
sphinx:
  configuration: docs/source/conf.py

MarwanDebbiche

name: CI/CD # pipeline's name that will appear in Github Actions

on: # events that trigger our pipeline: push on any branch and release creation
  push:
  release:
    types: [created]

jobs: # jobs. We will have two jobs (test and publish) with multiple steps.
  test:
    # Our test job will run on ubuntu.

MarwanDebbiche

import math

import numpy as np  # type: ignore
import pandas as pd  # type: ignore


from tsgen.time_serie import TimeSerie


def affine(start, end, freq, start_y, end_y) -> TimeSerie:

MarwanDebbiche

repos:
  - repo: https://github.com/pre-commit/pre-commit-hooks
    rev: v3.2.0
    hooks:
      - id: check-yaml
      - id: debug-statements
      - id: end-of-file-fixer
      - id: trailing-whitespace
  - repo: local
    hooks:

MarwanDebbiche

import pandas as pd
import numpy as np
import pytest

from tsgen import TimeSerie


@pytest.fixture
def ts_monthly_constant():
    start = "2020"

MarwanDebbiche

from tsgen.time_serie import TimeSerie
import tsgen.generators as generators


__all__ = ["TimeSerie", "generators"]

MarwanDebbiche

import math

import numpy as np
import pandas as pd


from tsgen.time_serie import TimeSerie


def affine(start, end, freq, start_y, end_y):

MarwanDebbiche

import pandas as pd
import numpy as np


class TimeSerie:
    def __init__(self, index, y):
        if not isinstance(index, pd.DatetimeIndex):
            raise TypeError("index should be a pandas.DatetimeIndex")
        self.index = index
        self.y = np.array(y)

MarwanDebbiche

[tool.poetry]
name = "tsgen"
version = "0.1.0"
description = "Time Series Generator"
authors = ["Marwan Debbiche <marwan.debbiche@example.com>"]
license = "MIT"

[tool.poetry.dependencies]
python = "^3.8"
pandas = "^1.1.3"

MarwanDebbiche

@app.route('/predict-species-proba', methods=['POST'])
def predict_species_proba():
    flower = {}
    for feature in iris_classifier_features:
        flower[feature] = [request.form[feature]]

    flower = pd.DataFrame(flower)

    probas = iris_classifier.predict_proba(flower[iris_classifier_features])[0, :].tolist()