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",
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",