#!/usr/bin/python

import pickle

from tester import dump_classifier_and_data

from tools.prepareData import prepare_data

from tools.customTransformers import ImputeToValue, LogTransform, MinMaxNA

from sklearn.pipeline import Pipeline

from sklearn.feature_selection import SelectKBest

from sklearn.decomposition import PCA

from sklearn.linear_model import LogisticRegression

# load data

with open("./data/final_project_dataset.pkl", "r") as data_file:

data_dict = pickle.load(data_file)

# preprocess

df = prepare_data(data_dict)

steps_logit = [('impute nans', ImputeToValue()),

('classify', LogisticRegression(C=1, class_weight='balanced', penalty='l1'))]

clf = Pipeline(steps_logit)

'''

'''

# Tested pipelines that were npt chosen as the final classifier

'''

from sklearn.linear_model import SGDClassifier

steps_sgd = [('impute nans', ImputeToValue()),

('log transforming', LogTransform(True)),

('minmax scaling', MinMaxNA()),

('kbest', SelectKBest(k=16)),

('pca', PCA(n_components=15)),

('classify', SGDClassifier(alpha=0.01, class_weight='balanced', loss='log', penalty='l1', n_iter=6))]

clf = Pipeline(steps_sgd)

from sklearn.ensemble import AdaBoostClassifier

steps_adaboost = [('impute nans', ImputeToValue()),

('log transforming', LogTransform(True)),

('minmax scaling', MinMaxNA()),

('kbest', SelectKBest(k=3)),

('pca', PCA(n_components=None)),

('classify', AdaBoostClassifier(n_estimators=160))]

clf = Pipeline(steps_adaboost)

'''

# save the classifier pipeline and preprocessed data

dump_classifier_and_data(clf, df)