Update script for test model

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 

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_1> --in_file_2=<in_file_2> --out_dir=<out_dir>
+
+Options:
+--in_file_1=<in_file_1>      Path (including file name) to the processed train data
+--in_file_2=<in_file_2>      Path (including file name) to the processed test data
+--out_dir=<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"])