Python DBHelper.get_all_regressors示例

示例#1

import plotly.graph_objects as go
import plotly.figure_factory as ff

import constants
from meta_db.db.DBHelper import DBHelper

db = DBHelper()

regressors = pd.DataFrame(db.get_all_regressors_preperformance(),
                          columns=[
                              "name", "score", "max_error",
                              "mean_absolute_error", "mean_squared_error",
                              "r2_score", "median_absolute_error",
                              "classifier", "preprocesses"
                          ])
regressors_nopp = pd.DataFrame(db.get_all_regressors(),
                               columns=db.regressor_columns()).drop("id",
                                                                    axis=1)

if not os.path.exists("analysis/plots"):
    os.makedirs("analysis/plots")
if not os.path.exists("analysis/plots/meta_preperformance"):
    os.makedirs("analysis/plots/meta_preperformance")

translator = {
    "svm": "SVM",
    "logistic_regression": "LG",
    "linear_discriminant": "LD",
    "kneighbors": "kNN",
    "decision_tree": "DT",
    "gaussian_nb": "GNB",

示例#2

文件： meta_preperformance_heatmap.py 项目： jhosoume/PipelineRecommendation

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

import plotly.io as pio
import plotly.express as px
import plotly.graph_objects as go
import plotly.figure_factory as ff

import constants
from meta_db.db.DBHelper import DBHelper

db = DBHelper()

regressors = pd.DataFrame(db.get_all_regressors_preperformance(), columns = ["name", "score", "max_error", "mean_absolute_error", "mean_squared_error", "r2_score", "median_absolute_error", "classifier", "preprocesses"] )
regressors_nopp = pd.DataFrame(db.get_all_regressors(), columns = db.regressor_columns()).drop("id", axis = 1)

if not os.path.exists("analysis/plots"):
    os.makedirs("analysis/plots")
if not os.path.exists("analysis/plots/meta_preperformance"):
    os.makedirs("analysis/plots/meta_preperformance")

translator = {
    "svm": "SVM",
    "logistic_regression": "LG",
    "linear_discriminant": "LD",
    "kneighbors": "kNN",
    "decision_tree": "DT",
    "gaussian_nb": "GNB",
    "random_forest": "RF",
    "gradient_boosting": "GB",