From 7aa5374dd1222f0408bb59bfd80405280c5f7640 Mon Sep 17 00:00:00 2001 From: Sang Yoon Lee Date: Sun, 29 Nov 2020 11:37:57 +0900 Subject: [PATCH] Update script for test model --- results/best_Model.pkl | Bin 0 -> 368812 bytes results/final_model_quality.png | Bin 0 -> 20530 bytes src/fit_wine_quality_predict_model.py | 4 +- src/wine_quality_test_results.py | 90 ++++++++++++++++++++++++++ 4 files changed, 92 insertions(+), 2 deletions(-) create mode 100644 results/best_Model.pkl create mode 100644 results/final_model_quality.png create mode 100644 src/wine_quality_test_results.py diff --git a/results/best_Model.pkl b/results/best_Model.pkl new file mode 100644 index 0000000000000000000000000000000000000000..4c9f1192e24186a6b5b683fef76aacc62e8403cb GIT binary patch literal 368812 zcmeFXXINEB6E27(36cbrEFwk_K@m~Z56O3g+cYNM<3s9F20VwTPbwbHY5vc-%G?Jhk?0?t*M!fm7bZkk-3#A z$;y|q@vlh?%uOxKtx4iKzln7IMa3ciYvlQVjQslrtNuax`#m&fwx+&h>fa2I>6Ty3 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zz+<;j```KmdwL46Oq-fTN!)zdo+hVpd;YXQNl6JO)Yiy~1#jd+R%TRxqJKwy#6UD7 zwHE3Ic_iRLu!3+B3nvxaEb@S-3L#~{`%6+FGE6~fjf~MKkir`xlC;X)oPK;TumxaU zvOeO2wJn9gpb`U-mw#M4n-QF&&VimqGX2ky_((+kS%dJa~D9G^{G9u^-`;Ibs(wGQ#+sb5TJ7 zFUmkiDPXxv*WYOP&lkue1(gcwnH(E?j5E`PBew!T+ZdY1kz)y{2oAgHb`#z~pgl|= zZle$hdoBaA@&nZBn7y>J(gA3r9XhC?Ek_)7FzOLa#qrrrM*9nF-Z$w?MeUVn0-D}m z7Y5@mww($?XLi1l^gX#pTcteNJzK$<$iG}aOh~1OC Kn||lfv;PC^_X0=& literal 0 HcmV?d00001 diff --git a/src/fit_wine_quality_predict_model.py b/src/fit_wine_quality_predict_model.py index 7109f31..8bcb20d 100644 --- a/src/fit_wine_quality_predict_model.py +++ b/src/fit_wine_quality_predict_model.py @@ -92,8 +92,8 @@ def store_cross_val_results(model_name, scores, results_dict): def main(in_file_1, out_dir): # read data and combine two data set vertically train_df = pd.read_csv(in_file_1) - X_train = train_df.drop(columns = ['quality']) - y_train = train_df['quality'] + X_train = train_df.drop(columns = ['quality','quality_rank' ]) + y_train = train_df['quality_rank'] #----------------------------------------------------------------------------------------------------------------------------- #PreProcessor diff --git a/src/wine_quality_test_results.py b/src/wine_quality_test_results.py new file mode 100644 index 0000000..85a8539 --- /dev/null +++ b/src/wine_quality_test_results.py @@ -0,0 +1,90 @@ +# author: UBC Master of Data Science - Group 33 +# date: 2020-11-26 + + +"""Pre-processing wine quality data for red wine(https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv) and + wine quality data for white wine(https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv). + +Usage: src/wine_quality_test_results_test.py --in_file_1= --in_file_2= --out_dir= + +Options: +--in_file_1= Path (including file name) to the processed train data +--in_file_2= Path (including file name) to the processed test data +--out_dir= Path (excluding file name) to save the confusion matrix +""" +import os +from docopt import docopt +import string +from collections import deque + +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd + +# data +from sklearn import datasets +from sklearn.compose import ColumnTransformer, make_column_transformer +from sklearn.dummy import DummyClassifier, DummyRegressor +from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor +from sklearn.feature_extraction.text import CountVectorizer + +# Feature selection +from sklearn.feature_selection import RFE, RFECV +from sklearn.impute import SimpleImputer + +# classifiers / models +from sklearn.linear_model import RidgeClassifier +from sklearn.linear_model import LogisticRegression + +# other +from sklearn.metrics import accuracy_score, log_loss, make_scorer, mean_squared_error, confusion_matrix +from sklearn.model_selection import ( + GridSearchCV, + RandomizedSearchCV, + ShuffleSplit, + cross_val_score, + cross_validate, + train_test_split, +) +from sklearn.pipeline import Pipeline, make_pipeline +from sklearn.preprocessing import ( + OneHotEncoder, + OrdinalEncoder, + PolynomialFeatures, + StandardScaler, +) +from sklearn.neural_network import MLPClassifier +import joblib +from sklearn.metrics import (plot_confusion_matrix) + + + +opt = docopt(__doc__) + +def main(in_file_1, in_file_2, out_dir): + # read data and splitting it + train_df = pd.read_csv(in_file_1) + test_df = pd.read_csv(in_file_2) + + X_train = train_df.drop(columns = ['quality','quality_rank'], axis=1) + y_train = train_df['quality_rank'] + X_test = test_df.drop(columns = ['quality','quality_rank'], axis=1) + y_test = test_df['quality_rank'] + + + #--------------------------------------------------------------------------------------------------------- + # Testing out model + + best_model_pipe = joblib.load("results/best_Model.pkl") + best_model_pipe.fit(X_train, y_train) + best_model_pipe.score(X_test, y_test) + + plot_confusion_matrix(best_model_pipe, X_test, y_test, values_format = 'd', cmap = plt.cm.Blues) + predictions_m = best_model_pipe.predict(X_test) + cm = confusion_matrix(y_test, predictions_m) + path_f = out_dir + "final_model_quality.png" + plt.savefig(path_f) + + +if __name__ == "__main__": + main(opt["--in_file_1"], opt["--in_file_2"], opt["--out_dir"])