# importations
import pandas as pd
from pandas import DataFrame
import numpy as np
import os
import s3fs
import re
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report, recall_score
from sklearn.utils import class_weight
from sklearn.neighbors import KNeighborsClassifier
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
from sklearn.impute import SimpleImputer
from sklearn.model_selection import GridSearchCV
from sklearn.preprocessing import StandardScaler, MaxAbsScaler, MinMaxScaler
from sklearn.metrics import make_scorer, f1_score, balanced_accuracy_score
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve, auc, precision_recall_curve, average_precision_score
from sklearn.exceptions import ConvergenceWarning, DataConversionWarning
from sklearn.naive_bayes import GaussianNB
from scipy.optimize import fsolve
import pickle
import warnings
import io

# define type of activity 
type_of_activity = "sport"
PATH = f"projet-bdc2324-team1/Output_expected_CA/{type_of_activity}/"

# comparison with bias of the train set - X train to be defined
X_train_score = logit_cv.predict_proba(X_train)[:, 1]

bias_train_set = find_bias(odd_ratios = odd_ratio(adjust_score_1(X_train_score)), 
                           y_objective = y_train["y_has_purchased"].sum(),
                           initial_guess=6)

score_adjusted_train = adjusted_score(odd_ratio(adjust_score_1(X_test_segment["score"])), bias = bias_train_set)
X_test_segment["score_adjusted"] = score_adjusted_train


# plot adjusted scores and save (to be tested)
plot_hist_scores(X_test_segment, score = "score", score_adjusted = "score_adjusted", type_of_activity = type_of_activity)

image_buffer = io.BytesIO()
plt.savefig(image_buffer, format='png')
image_buffer.seek(0)
file_name = "hist_score_adjusted"
FILE_PATH_OUT_S3 = PATH + file_name + type_of_activity + ".png"
with fs.open(FILE_PATH_OUT_S3, 'wb') as s3_file:
    s3_file.write(image_buffer.read())
plt.close()

# comparison between score and adjusted score
X_test_table_adjusted_scores = (100 * X_test_segment.groupby("quartile")[["score","score_adjusted", "has_purchased"]].mean()).round(2).reset_index()
X_test_table_adjusted_scores = X_test_table_adjusted_scores.rename(columns = {col : f"{col} (%)" for col in X_test_table_adjusted_scores.columns if col in ["score","score_adjusted", "has_purchased"]})

file_name = "table_adjusted_score"
FILE_PATH_OUT_S3 = PATH + file_name +  type_of_activity + ".csv"
with fs.open(FILE_PATH_OUT_S3, 'w') as file_out:
    X_test_table_adjusted_scores.to_csv(file_out, index = False)


# project revenue
X_test_segment = project_tickets_CA (X_test_segment, "nb_tickets", "total_amount", "score_adjusted", duration_ref=1.5, duration_projection=1)


# table summarizing projections
X_test_expected_CA = round(summary_expected_CA(df=X_test_segment, segment="quartile", nb_tickets_expected="nb_tickets_expected", total_amount_expected="total_amount_expected", total_amount="total_amount"),2)

file_name = "table_expected_CA"
FILE_PATH_OUT_S3 = PATH + file_name +  type_of_activity + ".csv"
with fs.open(FILE_PATH_OUT_S3, 'w') as file_out:
    X_test_expected_CA.to_csv(file_out, index = False)