analyse_traitement.py

import numpy as np
import pandas as pd
import statsmodels.api as sm
from sklearn.linear_model import LinearRegression, Ridge, ElasticNet, Lasso
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score, mean_squared_error
import math
import pickle
from navigate_files import NavigateFiles
from sklearn import preprocessing
from sklearn.preprocessing import PolynomialFeatures
import glob
from sklearn.tree import DecisionTreeRegressor
import matplotlib.pyplot as plt

from sklearn.feature_selection import f_regression, mutual_info_regression
from scipy import stats

pd.set_option('max_rows', 50)
pd.set_option('max_columns', 50)


class AnalyseTraitement:
    def preprocessing_data(self, indir):
        traitemnt = pd.read_csv(indir)
        # droped columns
        #
        # 'OMOPdRM', 'OMOPdRM.1', 'OMOPdRM.2',
        #  'OMOPgRM', 'OMOPgRM.1', 'OMOPgRM.2', 'Time', 'alphaGait', 'normForceGuidon','DistanceAligne',
        #  'DistanceEngage', 'DistanceFWGait',
        #  'DistanceFWRButte', 'AngleBike', 'AngleBike.1', 'AngleBike.2',
        #  'ForceGuidon', 'ForceGuidon.1', 'ForceGuidon.2',
        #  'ForceGuidon_R0', 'ForceGuidon_R0.1', 'ForceGuidon_R0.2',
        #  'ForcePedaleD_R0', 'ForcePedaleD_R0.1', 'ForcePedaleD_R0.2',
        #  'ForcePedaleG_R0', 'ForcePedaleG_R0.1', 'ForcePedaleG_R0.2', 'ForcePiedAv.2',
        #  'ForcePiedAv.1', 'TravailPAr', 'ThetaMrv', 'ThetaMrDeg', 'ThetaM', 'ThetaMDeg',
        #  'Impulsion',
        #  'ThetaMr', 'IndiceEfficacitePiedAv', 'IndiceEfficaciteTotal', 'ForcePiedAr.2',
        #  'ForcePiedAv',
        #  'ForcePiedAr', 'ForcePied', 'PuissancePAr', 'Moment', 'MomentPAr', 'MomentPAv',
        #  'DeplacementRider', 'DeplacementRider_RoueArriere']

        # kept columns
        # ForceUPiedAr should alwyas be here because it is our y.
        traitemnt = traitemnt[
            ['ForcePiedAr.1', 'ForceUPiedAr', 'ForceUPiedAv', 'ImpulsionPAr',
             'ImpulsionPAv', 'IndiceEfficacitePiedAr', 'Puissance', 'PuissancePAv',
             'Travail', 'TravailPAv', 'VitessePedalier', 'VitesseRider']]

        traitemnt = traitemnt.replace('t', np.nan, regex=True)
        traitemnt = traitemnt.fillna(traitemnt.mean())
        # print(traitemnt.columns)
        # traitemnt.to_csv("allconcate.csv", index=0)
        traitemnt = traitemnt.dropna(axis=0)
        return traitemnt

    def regression_traitement(self, all_traitement_indir):
        X = self.preprocessing_data(all_traitement_indir)

        # Get column names first
        names = X.columns
        # Create the Scaler object
        scaler = preprocessing.StandardScaler()
        # Fit your data on the scaler object
        scaled_df = scaler.fit_transform(X)
        X = pd.DataFrame(scaled_df, columns=names)

        # make ForceUPiedAv our y in our model because
        # ForceUPiedAv it is what we search to understand
        y = X['ForceUPiedAv']
        X = X.drop('ForceUPiedAv', axis=1)

        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3,
                                                            random_state=15)

        lm = Ridge(alpha=1)
        lm.fit(X_train, y_train)

        # save the model to disk
        filename = 'Ridge_traitement.sav'
        pickle.dump(lm, open(filename, 'wb'))

        predictions = lm.predict(X_test)

        print('Coefficients : {} '.format(lm.coef_))
        print('='*50)
        print('R^2 score :' + str(r2_score(y_test, predictions)))

        result = sm.OLS(y, X).fit()
        print(result.summary())

    def test_regression(self, traitement_indir):
        X = self.preprocessing_data(traitement_indir)

        # Get column names first
        names = X.columns
        # Create the Scaler object
        scaler = preprocessing.StandardScaler()
        # Fit your data on the scaler object
        scaled_df = scaler.fit_transform(X)
        X = pd.DataFrame(scaled_df, columns=names)

        y = X['ForceUPiedAv']
        X = X.drop('ForceUPiedAv', axis=1)


        loaded_model = pickle.load(open('Ridge_traitement.sav', 'rb'))
        y_pred = loaded_model.predict(X)

        print('R2 Ridge :' + str(r2_score(y, y_pred)))
        print(loaded_model.coef_)


if __name__ == '__main__':
    a = AnalyseTraitement()
    all_traitement_indir = 'C:\\Users\\mekhezzr\\PycharmProjects\\bmx_race\\' \
                           'concatenat\\AlltraitementConcatenated.csv'
    # a.test_regression()
    # a.regression_traitement(all_traitement_indir)
    # a.preprocessing_data(all_traitement_indir)
    a.test_regression(all_traitement_indir)