示例#1
0
# from pynalytics.preprocessor import Preprocessing
# from pynalytics.regression.lin_regression_visual import LinRegressionVis
from pynalytics.preprocessor import Preprocessing
from pynalytics.regression.linear_regression.lin_regression_num import LinRegressionRes
from pynalytics.regression.linear_regression.lin_regression_visual import LinRegressionVis
from pynalytics.regression.polynomial_regression.poly_regression_num import PolyRegressionRes
from pynalytics.regression.polynomial_regression.poly_regression_visual import PolyRegressionVis
import os
import pandas as pd
import statsmodels.api as sm
import statsmodels.formula.api as smf

csv_file = os.path.dirname(__file__) + "/regrex1.csv"
# csv_file = os.path.dirname(__file__) + "/Position_Salaries.csv"
# csv_file = os.path.dirname(__file__) + "/Data Analytics.csv"
lin_res = LinRegressionRes()
lin_vis = LinRegressionVis()
poly_res = PolyRegressionRes()
poly_vis = PolyRegressionVis()

df = pd.read_csv(csv_file)
#FOR LINRES
# print(lin_res.get_slope(df[["y"]], df[["x"]]))
# print(lin_res.get_intercept(df[["y"]], df[["x"]]))
# print(lin_res.get_rsquare(df["y"] ,df[["x", "z"]])) # how to input for MULTIPLE INDEPENDENTS
# print(lin_res.get_adj_rsquare(df["y"] ,df[["x", "z"]]))
# print(lin_res.get_pearsonr(df["y"] ,df[["x", "z"]]))
# print(lin_res.get_pvalue(df["y"] ,df[["x"]]))
# print(lin_res.line_eq(df[["y"]], df[["x"]]))

#FOR LINVIS
示例#2
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def simp_lin_num_rslope(dv, idv):
    lin_res = LinRegressionRes()
    x = df[[idv]]
    y = df[[dv]]
    return str(lin_res.line_eq(y, x))
示例#3
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def lin_rtable_multi(dv, idv):
    lin_res = LinRegressionRes()
    X = df[idv]
    y = df[[dv]]
    return ('' + lin_res.lin_regression_table(y, X).to_html() + '')
示例#4
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def lin_rtable(dv, idv):
    lin_res = LinRegressionRes()
    x = df[[idv]]
    y = df[[dv]]
    return ('' + lin_res.lin_regression_table(y, x).to_html() + '')
示例#5
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def lin_pearson(dv, idv):
    lin_res = LinRegressionRes()
    x = df[[idv]]
    y = df[[dv]]
    return str(lin_res.get_pearsonr(y, x))
示例#6
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def lin_adj_rsquare(dv, idv):
    lin_res = LinRegressionRes()
    x = df[[idv]]
    y = df[[dv]]
    return str(lin_res.get_adj_rsquare(y, x))