From 5d23a64315593e02d594545b8b53ca4bca9ae5de Mon Sep 17 00:00:00 2001 From: Sang Yoon Lee Date: Fri, 11 Dec 2020 23:34:56 +0900 Subject: [PATCH] Change graph file format from png to svg --- Makefile | 6 +- debug.log | 2 + eda/wine_EDA_files/correlation_matrix.png | Bin 15588 -> 0 bytes eda/wine_EDA_files/correlation_matrix.svg | 1 + eda/wine_EDA_files/density_facet.png | Bin 35126 -> 0 bytes eda/wine_EDA_files/density_facet.svg | 1 + .../distribution_of_numeric_features.png | Bin 75027 -> 0 bytes .../distribution_of_numeric_features.svg | 1 + .../distribution_of_type_of_wine.png | Bin 6532 -> 0 bytes .../distribution_of_type_of_wine.svg | 1 + .../distribution_of_wine_quality.png | Bin 9553 -> 0 bytes .../distribution_of_wine_quality.svg | 1 + ...distribution_of_wine_quality_regrouped.png | Bin 16132 -> 0 bytes eda/wine_EDA_files/sugar_facet.png | Bin 51576 -> 0 bytes eda/wine_EDA_files/sugar_facet.svg | 1 + eda/wine_EDA_files/wine_EDA_13_0.png | Bin 7531 -> 0 bytes eda/wine_EDA_files/wine_EDA_16_0.png | Bin 13790 -> 0 bytes eda/wine_EDA_files/wine_EDA_18_0.png | Bin 5329 -> 0 bytes eda/wine_EDA_files/wine_EDA_21_0.png | Bin 56969 -> 0 bytes eda/wine_EDA_files/wine_EDA_25_1.png | Bin 10726 -> 0 bytes eda/wine_EDA_files/wine_EDA_29_0.png | Bin 36212 -> 0 bytes eda/wine_EDA_files/wine_EDA_32_0.png | Bin 55109 -> 0 bytes eda/wine_EDA_files/wine_EDA_35_0.png | Bin 4197 -> 0 bytes eda/wine_EDA_files/wine_EDA_38_0.png | Bin 80380 -> 0 bytes eda/wine_EDA_files/wine_quality_rank.png | Bin 7641 -> 0 bytes eda/wine_EDA_files/wine_quality_rank.svg | 1 + .../wine_quality_rank_per_feature.png | Bin 80701 -> 0 bytes .../wine_quality_rank_per_feature.svg | 1 + eda/wine_eda.py | 28 +-- reports/reports.Rmd | 8 +- reports/reports.html | 225 ++++++++++++++++++ reports/reports.md | 129 +++++----- results/best_Model.pkl | Bin 368148 -> 365335 bytes results/f1_score_all_classifiers.svg | 1 + results/f1_score_mlp.svg | 1 + results/f1_score_random_forest.svg | 1 + results/final_model_quality.png | Bin 22920 -> 22466 bytes src/fit_wine_quality_predict_model.py | 187 +++++++++++---- src/wine_quality_test_results.py | 49 ++-- 39 files changed, 472 insertions(+), 173 deletions(-) create mode 100644 debug.log delete mode 100644 eda/wine_EDA_files/correlation_matrix.png create mode 100644 eda/wine_EDA_files/correlation_matrix.svg delete mode 100644 eda/wine_EDA_files/density_facet.png create mode 100644 eda/wine_EDA_files/density_facet.svg delete mode 100644 eda/wine_EDA_files/distribution_of_numeric_features.png create mode 100644 eda/wine_EDA_files/distribution_of_numeric_features.svg delete mode 100644 eda/wine_EDA_files/distribution_of_type_of_wine.png create mode 100644 eda/wine_EDA_files/distribution_of_type_of_wine.svg delete mode 100644 eda/wine_EDA_files/distribution_of_wine_quality.png create mode 100644 eda/wine_EDA_files/distribution_of_wine_quality.svg delete mode 100644 eda/wine_EDA_files/distribution_of_wine_quality_regrouped.png delete mode 100644 eda/wine_EDA_files/sugar_facet.png create mode 100644 eda/wine_EDA_files/sugar_facet.svg delete mode 100644 eda/wine_EDA_files/wine_EDA_13_0.png delete mode 100644 eda/wine_EDA_files/wine_EDA_16_0.png delete mode 100644 eda/wine_EDA_files/wine_EDA_18_0.png delete mode 100644 eda/wine_EDA_files/wine_EDA_21_0.png delete mode 100644 eda/wine_EDA_files/wine_EDA_25_1.png delete mode 100644 eda/wine_EDA_files/wine_EDA_29_0.png delete mode 100644 eda/wine_EDA_files/wine_EDA_32_0.png delete mode 100644 eda/wine_EDA_files/wine_EDA_35_0.png delete mode 100644 eda/wine_EDA_files/wine_EDA_38_0.png delete mode 100644 eda/wine_EDA_files/wine_quality_rank.png create mode 100644 eda/wine_EDA_files/wine_quality_rank.svg delete mode 100644 eda/wine_EDA_files/wine_quality_rank_per_feature.png create mode 100644 eda/wine_EDA_files/wine_quality_rank_per_feature.svg create mode 100644 reports/reports.html create mode 100644 results/f1_score_all_classifiers.svg create mode 100644 results/f1_score_mlp.svg create mode 100644 results/f1_score_random_forest.svg diff --git a/Makefile b/Makefile index 3b46c3e..a0e90ea 100644 --- a/Makefile +++ b/Makefile @@ -11,7 +11,7 @@ # all: results/best_Model.pkl results/final_model_quality.png reports/reports.md -all: results/wine_quality_rank_per_feature.png results/final_model_quality.png reports/reports.md +all: results/wine_quality_rank_per_feature.svg results/final_model_quality.png reports/reports.md # download wine data set to directory data/raw/winequality-red.csv: src/download_data.py @@ -25,7 +25,7 @@ data/processed/processed.csv data/processed/processed_test.csv data/processed/pr python src/pre_processing_wine.py --in_file_1="data/raw/winequality-red.csv" --in_file_2="data/raw/winequality-white.csv" --out_dir="data/processed/" # create exploratory data analysis figure and write to file -results/wine_quality_rank_per_feature.png: eda/wine_eda.py data/processed/processed.csv +results/wine_quality_rank_per_feature.svg: eda/wine_eda.py data/processed/processed.csv python eda/wine_eda.py -i data/processed/processed.csv -o eda/wine_EDA_files/ # fitting model @@ -42,7 +42,7 @@ reports/reports.md: reports/reports.Rmd reports/wine_refs.bib clean: rm -rf data - rm eda/wine_EDA_files/wine_quality_rank_per_feature.png + rm eda/wine_EDA_files/wine_quality_rank_per_feature.svg rm -rf results rm reports/reports.md diff --git a/debug.log b/debug.log new file mode 100644 index 0000000..220d311 --- /dev/null +++ b/debug.log @@ -0,0 +1,2 @@ +[1211/231809.780:ERROR:directory_reader_win.cc(43)] FindFirstFile: The system cannot find the path specified. (0x3) +[1211/231813.467:ERROR:directory_reader_win.cc(43)] FindFirstFile: The system cannot find the path specified. 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index 0000000..6d24cc9 --- /dev/null +++ b/eda/wine_EDA_files/wine_quality_rank_per_feature.svg @@ -0,0 +1 @@ +46810121416fixed acidityexcellentnormalpoorquality_rank0.00.51.01.5volatile acidityexcellentnormalpoorquality_rank0.00.51.01.5citric acidexcellentnormalpoorquality_rank010203040506070residual sugarexcellentnormalpoorquality_rank0.00.10.20.30.40.50.6chloridesexcellentnormalpoorquality_rank050100150200250300free sulfur dioxideexcellentnormalpoorquality_rank0100200300400total sulfur dioxideexcellentnormalpoorquality_rank0.991.001.011.021.031.04densityexcellentnormalpoorquality_rank2.83.03.23.43.63.84.0pHexcellentnormalpoorquality_rank0.51.01.52.0sulphatesexcellentnormalpoorquality_rank89101112131415alcoholexcellentnormalpoorquality_rankexcellentnormalpoorquality_rank \ No newline at end of file diff --git a/eda/wine_eda.py b/eda/wine_eda.py index c14e977..de2cdb8 100644 --- a/eda/wine_eda.py +++ b/eda/wine_eda.py @@ -22,7 +22,10 @@ # # There are two datasets for red and white wine samples. For each wine sample observation , the inputs contains measurements of various objective physicochemical tests, and the output is the median wine quality ratings given by experts on the scale from 0 (very bad) and 10 (very excellent).The author notes that data on grape types, wine brand, wind selling price among other are not available due to privacy and logistics issues. There are 1599 observations for red wine and 4898 observations of white wine. - +def main(input_path, output_dir): + data = read_input_data(input_path) + plots_dict = generate_eda_plots(data) + save_plots(output_dir, plots_dict) def read_input_data(input_path): data = pd.read_csv(input_path) @@ -37,7 +40,7 @@ def generate_eda_plots(data): alt.Y('count():Q'), alt.Tooltip('count():Q') ).properties(height = 100) - plots_dict['distribution_of_wine_quality.png'] = distribution_plots + plots_dict['distribution_of_wine_quality.svg'] = distribution_plots distribution_plots_regrouped = alt.Chart(data, title = "Distribution of wine quality (regrouped)").mark_bar().encode( alt.X('quality_rank:O', title = 'Wine quality rank', sort = ['poor','normal','excellent']), @@ -51,7 +54,7 @@ def generate_eda_plots(data): alt.Color('type', legend = None), alt.Tooltip('count()') ) - plots_dict['distribution_of_type_of_wine.png'] = distribution_of_type_of_wine + plots_dict['distribution_of_type_of_wine.svg'] = distribution_of_type_of_wine numeric_cols = data.select_dtypes('number').columns.tolist()[:-1] @@ -62,7 +65,7 @@ def generate_eda_plots(data): ).properties(width = 200, height = 100 ).repeat(repeat = numeric_cols, columns = 3) - plots_dict['distribution_of_numeric_features.png'] = distribution_of_numeric_features + plots_dict['distribution_of_numeric_features.svg'] = distribution_of_numeric_features corr_df = data.select_dtypes('number').corr("spearman").stack().reset_index(name='corr') @@ -71,7 +74,7 @@ def generate_eda_plots(data): y=alt.Y('level_1', title=''), size='corr', color='corr') - plots_dict['correlation_matrix.png'] = correlation_matrix + plots_dict['correlation_matrix.svg'] = correlation_matrix density_rels = ['chlorides', 'residual sugar', 'volatile acidity', 'fixed acidity', 'alcohol'] @@ -83,7 +86,7 @@ def generate_eda_plots(data): ).properties(width = 110, height = 110 ).repeat(column = density_rels, row = ['density'] ).configure_axis(labels=False) - plots_dict['density_facet.png'] = density_facet + plots_dict['density_facet.svg'] = density_facet sugar_rels = ['total sulfur dioxide','free sulfur dioxide'] sugar_facet = alt.Chart(data).mark_point(size =2, opacity = 0.2).encode( @@ -92,7 +95,7 @@ def generate_eda_plots(data): alt.Color('type') ).properties(width = 250, height = 250 ).repeat(column = sugar_rels, row = ['residual sugar']) - plots_dict['sugar_facet.png'] = sugar_facet + plots_dict['sugar_facet.svg'] = sugar_facet @@ -106,7 +109,7 @@ def generate_eda_plots(data): y='type', color='quality_rank' ) - plots_dict['wine_quality_rank.png'] = wine_quality_rank + plots_dict['wine_quality_rank.svg'] = wine_quality_rank wine_quality_rank_per_feature = alt.Chart(data).mark_boxplot().encode( @@ -115,7 +118,7 @@ def generate_eda_plots(data): alt.Color('quality_rank') ).properties(width = 200, height = 100 ).repeat(repeat = numeric_cols, columns = 3) - plots_dict['wine_quality_rank_per_feature.png'] = wine_quality_rank_per_feature + plots_dict['wine_quality_rank_per_feature.svg'] = wine_quality_rank_per_feature return plots_dict @@ -128,16 +131,11 @@ def save_plots(output_dir, plots_dict): except Exception as e: print(e) -def main(input_path, output_dir): - data = read_input_data(input_path) - plots_dict = generate_eda_plots(data) - save_plots(output_dir, plots_dict) - def usage(): print (""" Usage: python %s [-h|-i|-o] [--help|--input|--output] - If you couldn't export the png files, please make sure you properly installed node js packaged below: + If you couldn't export the svg files, please make sure you properly installed node js packaged below: npm install -g --force vega-lite vega-cli canvas conda install -c conda-forge vega-cli vega-lite-cli Options: diff --git a/reports/reports.Rmd b/reports/reports.Rmd index b6c47bd..d2336d4 100644 --- a/reports/reports.Rmd +++ b/reports/reports.Rmd @@ -40,7 +40,7 @@ There are two datasets for red and white wine samples. For each wine sample obse ```{r, fig.cap = "Figure 1: Distribution of type of wine", out.width= "50%", fig.align='center'} -knitr::include_graphics("../eda/wine_EDA_files/distribution_of_type_of_wine.png") +knitr::include_graphics("../eda/wine_EDA_files/distribution_of_type_of_wine.svg") ``` ### Analysis @@ -57,7 +57,7 @@ In this project we are trying to predict the quality of a given wine sample usin We eventually decided to pick neutral network Multi-layer Perception (MLP) model as the model that yield the best results after running the various machine learning models through the train data set, comparing their performance based on f1-score and checking consistency across cross-validation runs. We noticed that random forest recorded high f1-validation score at 0.84, however, it also had a large gap between train and validation with a perfect train score of 1. This caused us to think the model has overfitted. Logistic regression also showed promising f1 validation score results in our case, yet this high results were not consistent across cross-validation splits. Hence, with most models struggled to get to the 0.8 f1-score mark without significantly overfitting on the train set, while MLP shows consistent results across all cross-validation splits, our final choice landed on MLP model because we think it would generalize better. ```{r, fig.cap = "Table 1: Score results among different machine learning model we have explore", fig.align='center'} -knitr::include_graphics("models_c_revised.png") +knitr::include_graphics("../results/f1_score_all_classifiers.svg") ``` The Python and R programming languages [@R; @Python] and the following Python and R packages were used to perform the analysis: scikit-learn [@scikit-learn], docoptpython [@docoptpython], docopt [@docopt], altair [@altair], vega-lite [@vega-lite], IPython-ipykernel [@IPython], matplotlib [@matplotlib], scipy [@SciPy], numpy [@harris2020array], pandas [@pandas], graphviz [@graphviz], pandas-profiling [@pandasprofiling2019], knitr [@knitr], tidyverse [@tidyverse], kableExtra [@kableExtra]. The code used to perform the analysis and re-create this report can be found [here](https://github.com/UBC-MDS/Wine_Quality_Predictor#usage) @@ -70,14 +70,14 @@ Looking at the distribution plot of the respective wine quality group interactin ```{r distribution plot, fig.cap = "Figure 3: Distribution plot between wine quality and various attributes from physicochemical test", fig.align='center'} -knitr::include_graphics("../eda/wine_EDA_files/wine_quality_rank_per_feature.png") +knitr::include_graphics("../eda/wine_EDA_files/wine_quality_rank_per_feature.svg") ``` Since this is a multi-class classification, our goal was to find a model that was consistent and able to recognize patterns from our data. We choose to use a neutral network Multi-layer Perception (MLP) model as it was consistent and showed promising results. If we take a look at the accuracy scores and f1 scores across cross validation splits, we can see that it is pretty consistent which was not the case with many models. ```{r, echo=FALSE,out.width="50%", out.height="20%",fig.cap="Figure 4: Accuracy scores and f1 scores across cross validation splits for neutral network Multi-layer Perception (MLP) model",fig.show='hold',fig.align='center'} -knitr::include_graphics(c("f1_revised.png","accuracy_plot_revised.png")) +knitr::include_graphics(c("../results/f1_score_random_forest.svg","../results/f1_score_mlp.svg")) ``` diff --git a/reports/reports.html b/reports/reports.html new file mode 100644 index 0000000..cc98e2a --- /dev/null +++ b/reports/reports.html @@ -0,0 +1,225 @@ + + + + + + + + + + + + + + + + + + +

Predicting wine quality using measurements of physiochemical tests

+

Alex Truong, Bruhat Musinuru, Rui Wang and Sang Yoon Lee
2020-11-26 (updated: 2020-12-11)

+
+

Summary

+

For this analysis, we used the neutral network Multi-layer Perception (MLP) model in order to try to predict wine quality based on the different wine attributes obtained from physicochemical tests such as alcohol, sulfur dioxide, fixed acidity, residual sugar. When we test it with the different validation data sets, the model yield robust results with 80% accuracy and 80% f1- score (a weighted average metric between precision and recall rate). We also have comparably high score at 80% accuracy and f1-score when we run the model on our test set. Based on these results, we opine that that the model seems to generalize well based on the test set predictions.

+

However, it incorrectly classifies 13.7% of the data in the lower end of spectrum (between normal and poor). This could be due to class imbalance present in the data set where normal samples outnumber poor by roughly twenty times. Improving the data collection methods to reduce the data class imbalance and using an appropriate assessment metric for imbalanced data can help to improve our analysis. On the other hand, given the rate of miss-classification is not so high and the impact can be corrected in further assessment, we believe this model could decently serve its purpose as a wine predictor to conduct first-cut assessment, which could help speed up the wine ratings process.

+

Introduction

+

Traditional methods of categorizing wine are prone to human error and can vary drastically from expert to expert. We propose a data mining approach to predict wine quality using machine learning techniques for classification problems. The resulting model, we hope, could serve as as one of scientific and systematic ways to classify wine, which is a springboard for further research in personalized wine recommendation, quality assessment and comparison unit.

+

Moreover, we believe wineries or wine rating institutes could find the model as a useful and reliable first-cut wine quality test before further expert’s assessment. This could lead to a more cost and time-effective wine screening process, and subsequently facilitate more effective and efficient business decisions and strategies.

+

Methods

+

Data

+

The data set used in this project is the results of a chemical analysis of the Portuguese “Vinho Verde” wine, conducted by Paulo Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis (Cortez et al. 2009). It was sourced from the UCI Machine Learning Repository (Dua and Graff 2017) which can be found here.

+

There are two datasets for red and white wine samples. For each wine sample observation , the inputs contains measurements of various objective physicochemical tests, and the output is the median wine quality ratings given by experts on the scale from 0 (very bad) and 10 (very excellent).The author notes that data on grape types, wine brand, wind selling price among other are not available due to privacy and logistics issues. There are 1599 observations for red wine and 4898 observations of white wine.

+
+ +Figure 1: Distribution of type of wine +

+Figure 1: Distribution of type of wine +

+ +
+ +

Analysis

+

At the preprocessing stage, we decided to combine the red and white data set as well as group the data in bigger classification, namely “poor,” “normal” and “excellent” for scale “1-4,” “5-6” and “7-9” so as to have bigger sample size (as per Figure 2). We acknowledge that the data is imbalanced, hence instead of only using accuracy based to judge the model performance, we also include f1-score and use it as our main assessment metric. f-1 score is metric that combine both the precision and recall metrics, which focus on the false negative and false positive rate of the data and would be appropriate to use with an imbalanced data set.{Bruhat: to add more justification for f-1 micro score}

+
+ +Figure 2: Regrouping of wine quality classification +

+Figure 2: Regrouping of wine quality classification +

+ +
+ +

In this project we are trying to predict the quality of a given wine sample using wine attributes obtained from various physicochemical tests. Based on our literary review, we found that researchers from Karadeniz Technical Univeristy used Random Forest Algorithm had also tried to classify between red wine and white wine for the same dataset (Er and Atasoy 2016). They further used 3 different data mining algorithms namely k-nearest-neighbourhood random forests and support vector machine learning to classify the quality of both red wine and white wine. This motivates us to proceed with to use cross-validation to select the best model for our analysis.

+

We eventually decided to pick neutral network Multi-layer Perception (MLP) model as the model that yield the best results after running the various machine learning models through the train data set, comparing their performance based on f1-score and checking consistency across cross-validation runs. We noticed that random forest recorded high f1-validation score at 0.84, however, it also had a large gap between train and validation with a perfect train score of 1. This caused us to think the model has overfitted. Logistic regression also showed promising f1 validation score results in our case, yet this high results were not consistent across cross-validation splits. Hence, with most models struggled to get to the 0.8 f1-score mark without significantly overfitting on the train set, while MLP shows consistent results across all cross-validation splits, our final choice landed on MLP model because we think it would generalize better.

+
+ +Table 1: Score results among different machine learning model we have explore +

+Table 1: Score results among different machine learning model we have +explore +

+ +
+ +

The Python and R programming languages (R Core Team 2019; Van Rossum and Drake 2009) and the following Python and R packages were used to perform the analysis: scikit-learn (Pedregosa et al. 2011), docoptpython (Keleshev 2014), docopt (de Jonge 2018), altair (VanderPlas et al. 2018), vega-lite (Satyanarayan et al. 2017), IPython-ipykernel (Pérez and Granger 2007), matplotlib (Hunter 2007), scipy (Virtanen et al. 2020), numpy (Harris et al. 2020), pandas (McKinney and others 2010), graphviz (Ellson et al. 2001), pandas-profiling (Brugman 2019), knitr (Xie 2014), tidyverse (Wickham 2017), kableExtra (Zhu 2020). The code used to perform the analysis and re-create this report can be found here

+

Results & Discussion

+

Looking at the distribution plot of the respective wine quality group interacting with each explanatory features, we can see that higher quality wine seems to be more associated with higher alcohol level and lower density. Lower volatile acidity also seems to be indicative of better wine. Better ranked wine also seem to have higher free sulfur dioxide level than poor wine though the relationship is not that clear based on the plot. The rest of the features do not seems be very distinguishable among different quality wine.

+
+ +Figure 3: Distribution plot between wine quality and various attributes from physicochemical test +

+Figure 3: Distribution plot between wine quality and various attributes +from physicochemical test +

+ +
+ +

Since this is a multi-class classification, our goal was to find a model that was consistent and able to recognize patterns from our data. We choose to use a neutral network Multi-layer Perception (MLP) model as it was consistent and showed promising results. If we take a look at the accuracy scores and f1 scores across cross validation splits, we can see that it is pretty consistent which was not the case with many models.

+
+ +

Figure 4: Accuracy scores and f1 scores across cross validation splits for neutral network Multi-layer Perception (MLP) modelFigure 4: Accuracy scores and f1 scores across cross validation splits for neutral network Multi-layer Perception (MLP) model

+

+Figure 4: Accuracy scores and f1 scores across cross validation splits +for neutral network Multi-layer Perception (MLP) model +

+ +
+ +

Our model performed quite well on the test data as well. If we take a look at the confusion matrix below. As we discussed earlier, the prediction at the lower end of wine quality spectrum is acceptable. As we can see from the confusion matrix below, ~13% error rate for the lower end of spectrum and also very acceptable false classifications in the high end of spectrum.

+
+ +Figure 5: Confusion Matrix +

+Figure 5: Confusion Matrix +

+ +
+ +

Having said that the research also need further improvement in terms of obtaining a more balanced data set for training and cross-validation. More feature engineer and selection could be conducted to minimize the affect of correlation among the explanatory variable. Furthermore, in order to assess the robustness of the predicting model, we need to test the model with deployment data in real world besides testing with our test data.

+

In conclusion, we think that with a decent error rate, our predicting model based on neutral network Multi-layer Perception (MLP) model would serve well as an effective first-cut assessment on wine quality.

+

References

+
+ +
+ +

Brugman, Simon. 2019. “pandas-profiling: Exploratory Data Analysis for Python.” https://github.com/pandas-profiling/pandas-profiling.

+
+ +
+ +

Cortez, Paulo, António Cerdeira, Fernando Almeida, Telmo Matos, and José Reis. 2009. “Modeling Wine Preferences by Data Mining from Physicochemical Properties.” Decision Support Systems 47 (4): 547–53.

+
+ +
+ +

de Jonge, Edwin. 2018. Docopt: Command-Line Interface Specification Language. https://CRAN.R-project.org/package=docopt.

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+ +
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Dua, Dheeru, and Casey Graff. 2017. “UCI Machine Learning Repository.” University of California, Irvine, School of Information; Computer Sciences. http://archive.ics.uci.edu/ml.

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Ellson, John, Emden Gansner, Lefteris Koutsofios, Stephen North, Gordon Woodhull, Short Description, and Lucent Technologies. 2001. “Graphviz - Open Source Graph Drawing Tools.” In Lecture Notes in Computer Science, 483–84. Springer-Verlag.

+
+ +
+ +

Er, Yeşim, and Ayten Atasoy. 2016. “The Classification of White Wine and Red Wine According to Their Physicochemical Qualities.” International Journal of Intelligent Systems and Applications in Engineering, 23–26.

+
+ +
+ +

Harris, Charles R., K. Jarrod Millman, St’efan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, et al. 2020. “Array Programming with NumPy.” Nature 585 (7825): 357–62. https://doi.org/10.1038/s41586-020-2649-2.

+
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+ +

Hunter, J. D. 2007. “Matplotlib: A 2d Graphics Environment.” Computing in Science & Engineering 9 (3): 90–95. https://doi.org/10.1109/MCSE.2007.55.

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Keleshev, Vladimir. 2014. Docopt: Command-Line Interface Description Language. https://github.com/docopt/docopt.

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McKinney, Wes, and others. 2010. “Data Structures for Statistical Computing in Python.” In Proceedings of the 9th Python in Science Conference, 445:51–56. Austin, TX.

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Pedregosa, F., G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, et al. 2011. “Scikit-Learn: Machine Learning in Python.” Journal of Machine Learning Research 12: 2825–30.

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+ +

Pérez, Fernando, and Brian E. Granger. 2007. “IPython: A System for Interactive Scientific Computing.” Computing in Science and Engineering 9 (3): 21–29. https://doi.org/10.1109/MCSE.2007.53.

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R Core Team. 2019. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

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Satyanarayan, Arvind, Dominik Moritz, Kanit Wongsuphasawat, and Jeffrey Heer. 2017. “Vega-Lite: A Grammar of Interactive Graphics.” IEEE Transactions on Visualization and Computer Graphics 23 (1): 341–50.

+
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+ +

Van Rossum, Guido, and Fred L. Drake. 2009. Python 3 Reference Manual. Scotts Valley, CA: CreateSpace.

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+ + + diff --git a/reports/reports.md b/reports/reports.md index 970b281..f071c02 100644 --- a/reports/reports.md +++ b/reports/reports.md @@ -1,15 +1,15 @@ Predicting wine quality using measurements of physiochemical tests ================ Alex Truong, Bruhat Musinuru, Rui Wang and Sang Yoon Lee
-2020-11-26 (updated: 2020-12-09) +2020-11-26 (updated: 2020-12-11) - - [Summary](#summary) - - [Introduction](#introduction) - - [Methods](#methods) - - [Data](#data) - - [Analysis](#analysis) - - [Results & Discussion](#results-discussion) - - [References](#references) +- [Summary](#summary) +- [Introduction](#introduction) +- [Methods](#methods) + - [Data](#data) + - [Analysis](#analysis) +- [Results & Discussion](#results-discussion) +- [References](#references) ## Summary @@ -73,12 +73,9 @@ observations of white wine.
-Figure 1: Distribution of type of wine - +Figure 1: Distribution of type of wine

- Figure 1: Distribution of type of wine -

@@ -86,10 +83,10 @@ Figure 1: Distribution of type of wine ### Analysis At the preprocessing stage, we decided to combine the red and white data -set as well as group the data in bigger classification, namely “poor”, -“normal” and “excellent” for scale “1-4”, “5-6” and “7-9” so as to -have bigger sample size (as per Figure 2). We acknowledge that the data -is imbalanced, hence instead of only using accuracy based to judge the +set as well as group the data in bigger classification, namely “poor,” +“normal” and “excellent” for scale “1-4,” “5-6” and “7-9” so as to have +bigger sample size (as per Figure 2). We acknowledge that the data is +imbalanced, hence instead of only using accuracy based to judge the model performance, we also include f1-score and use it as our main assessment metric. f-1 score is metric that combine both the precision and recall metrics, which focus on the false negative and false positive @@ -99,11 +96,8 @@ set.{Bruhat: to add more justification for f-1 micro score}
Figure 2: Regrouping of wine quality classification -

- Figure 2: Regrouping of wine quality classification -

@@ -136,13 +130,10 @@ because we think it would generalize better.
-Table 1: Score results among different machine learning model we have explore - +Table 1: Score results among different machine learning model we have explore

- Table 1: Score results among different machine learning model we have explore -

@@ -165,20 +156,18 @@ Looking at the distribution plot of the respective wine quality group interacting with each explanatory features, we can see that higher quality wine seems to be more associated with higher `alcohol` level and lower `density`. Lower `volatile acidity` also seems to be indicative of -better wine. Better ranked wine also seem to have `higher free sulfur -dioxide` level than poor wine though the relationship is not that clear -based on the plot. The rest of the features do not seems be very -distinguishable among different quality wine. +better wine. Better ranked wine also seem to have +`higher free sulfur dioxide` level than poor wine though the +relationship is not that clear based on the plot. The rest of the +features do not seems be very distinguishable among different quality +wine.
-Figure 3: Distribution plot between wine quality and various attributes from physicochemical test - +Figure 3: Distribution plot between wine quality and various attributes from physicochemical test

- Figure 3: Distribution plot between wine quality and various attributes from physicochemical test -

@@ -192,13 +181,10 @@ that it is pretty consistent which was not the case with many models.
-Figure 4: Accuracy scores and f1 scores across cross validation splits for neutral network Multi-layer Perception (MLP) modelFigure 4: Accuracy scores and f1 scores across cross validation splits for neutral network Multi-layer Perception (MLP) model - +Figure 4: Accuracy scores and f1 scores across cross validation splits for neutral network Multi-layer Perception (MLP) modelFigure 4: Accuracy scores and f1 scores across cross validation splits for neutral network Multi-layer Perception (MLP) model

- Figure 4: Accuracy scores and f1 scores across cross validation splits for neutral network Multi-layer Perception (MLP) model -

@@ -213,11 +199,8 @@ the high end of spectrum.
Figure 5: Confusion Matrix -

- Figure 5: Confusion Matrix -

@@ -236,16 +219,17 @@ serve well as an effective first-cut assessment on wine quality. # References -
+
-
+
-Brugman, Simon. 2019. “pandas-profiling: Exploratory Data Analysis for -Python.” . +Brugman, Simon. 2019. “pandas-profiling: +Exploratory Data Analysis for Python.” +.
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+
Cortez, Paulo, António Cerdeira, Fernando Almeida, Telmo Matos, and José Reis. 2009. “Modeling Wine Preferences by Data Mining from @@ -253,14 +237,14 @@ Physicochemical Properties.” *Decision Support Systems* 47 (4): 547–53.
-
+
de Jonge, Edwin. 2018. *Docopt: Command-Line Interface Specification Language*. .
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+
Dua, Dheeru, and Casey Graff. 2017. “UCI Machine Learning Repository.” University of California, Irvine, School of Information; Computer @@ -268,7 +252,7 @@ Sciences. .
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Ellson, John, Emden Gansner, Lefteris Koutsofios, Stephen North, Gordon Woodhull, Short Description, and Lucent Technologies. 2001. “Graphviz - @@ -277,7 +261,7 @@ Science*, 483–84. Springer-Verlag.
-
+
Er, Yeşim, and Ayten Atasoy. 2016. “The Classification of White Wine and Red Wine According to Their Physicochemical Qualities.” *International @@ -285,7 +269,7 @@ Journal of Intelligent Systems and Applications in Engineering*, 23–26.
-
+
Harris, Charles R., K. Jarrod Millman, St’efan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, et al. 2020. @@ -294,22 +278,22 @@ Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, et al. 2020.
-
+
-Hunter, J. D. 2007. “Matplotlib: A 2D Graphics Environment.” *Computing +Hunter, J. D. 2007. “Matplotlib: A 2d Graphics Environment.” *Computing in Science & Engineering* 9 (3): 90–95. .
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+
Keleshev, Vladimir. 2014. *Docopt: Command-Line Interface Description Language*. .
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McKinney, Wes, and others. 2010. “Data Structures for Statistical Computing in Python.” In *Proceedings of the 9th Python in Science @@ -317,7 +301,7 @@ Conference*, 445:51–56. Austin, TX.
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Pedregosa, F., G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, et al. 2011. “Scikit-Learn: Machine Learning in @@ -325,7 +309,7 @@ Python.” *Journal of Machine Learning Research* 12: 2825–30.
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Pérez, Fernando, and Brian E. Granger. 2007. “IPython: A System for Interactive Scientific Computing.” *Computing in Science and @@ -333,7 +317,7 @@ Engineering* 9 (3): 21–29. .
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R Core Team. 2019. *R: A Language and Environment for Statistical Computing*. Vienna, Austria: R Foundation for Statistical Computing. @@ -341,7 +325,7 @@ Computing*. Vienna, Austria: R Foundation for Statistical Computing.
-
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Satyanarayan, Arvind, Dominik Moritz, Kanit Wongsuphasawat, and Jeffrey Heer. 2017. “Vega-Lite: A Grammar of Interactive Graphics.” *IEEE @@ -349,7 +333,14 @@ Transactions on Visualization and Computer Graphics* 23 (1): 341–50.
-
+
+ +Van Rossum, Guido, and Fred L. Drake. 2009. *Python 3 Reference Manual*. +Scotts Valley, CA: CreateSpace. + +
+ +
VanderPlas, Jacob, Brian Granger, Jeffrey Heer, Dominik Moritz, Kanit Wongsuphasawat, Arvind Satyanarayan, Eitan Lees, Ilia Timofeev, Ben @@ -359,30 +350,24 @@ Visualizations for Python.” *Journal of Open Source Software* 3 (32):
-
- -Van Rossum, Guido, and Fred L. Drake. 2009. *Python 3 Reference Manual*. -Scotts Valley, CA: CreateSpace. - -
- -
+
Virtanen, Pauli, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler -Reddy, David Cournapeau, Evgeni Burovski, et al. 2020. “SciPy 1.0: -Fundamental Algorithms for Scientific Computing in Python.” *Nature -Methods* 17: 261–72. . +Reddy, David Cournapeau, Evgeni Burovski, et al. 2020. “SciPy 1.0: Fundamental Algorithms for Scientific +Computing in Python.” *Nature Methods* 17: 261–72. +.
-
+
Wickham, Hadley. 2017. *Tidyverse: Easily Install and Load the ’Tidyverse’*. .
-
+
Xie, Yihui. 2014. “Knitr: A Comprehensive Tool for Reproducible Research in R.” In *Implementing Reproducible Computational Research*, edited by @@ -391,9 +376,9 @@ Hall/CRC. .
-
+
-Zhu, Hao. 2020. *KableExtra: Construct Complex Table with ’Kable’ and +Zhu, Hao. 2020. *kableExtra: Construct Complex Table with ’Kable’ and Pipe Syntax*. .
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z}pKSat0wPYHyjNJLH=(MIkOXl@qVc2U$?8Ss%tac6`;_vZO= zBIJM&XP|O(b2}aZS`={^i0HfB>QjO!%`vTzRs3I%9$lw~aBjrA530LsXIZ`z5P5{v z`D;ctIs7t9R6c2?GM(jX?1#5|$6hTjHnWDGtN2{2<4tXik`hlgQV^>>Y)PAuKhSXS zMoyZ39rW-h3lCCS{@KpOqOZ5YP(u*u%)18#PARnFkakaLJOI-fZkE9y%FgzZT;5T} zVqMfq&cJ%e9k4zl1}C&wKRt?eG%alGrCXGC9M}|VIB^3t;SQstl3gS}Qu> zD3V~ru7>> z5qcpZ6V#fD(K(fw56M$ncIm}&CsIDis^DU7uEZgKoph#!fVXfUPKcK9rri4K?z*FE zX}rxM_`9$tP_!R>4a5AmY@)QvE+nb{jBd^n*15WZv;M1& diff --git a/src/fit_wine_quality_predict_model.py b/src/fit_wine_quality_predict_model.py index f43e7c6..95498b0 100644 --- a/src/fit_wine_quality_predict_model.py +++ b/src/fit_wine_quality_predict_model.py @@ -2,7 +2,9 @@ # date: 2020-11-28 -"""Model building and fitting the data. +"""Selecting Multi-layer Perception(MLP) modle as best model and save plots for report. +Optimizes hyperprameter for MLP model and fits a MLP model with best hyperparameter on the pre-processed training data from UCI Machine Learning Repository. +Saves the model as a pkl file. Usage: src/fit_wine_quality_predict_model.py --in_file_1= --out_dir= @@ -21,24 +23,20 @@ import matplotlib.pyplot as plt import numpy as np import pandas as pd +import altair as alt -# data +# Sklearn dependencies 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.ensemble import RandomForestClassifier 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 +from sklearn.metrics import (accuracy_score, + log_loss, make_scorer, + mean_squared_error, + plot_confusion_matrix +) from sklearn.model_selection import ( GridSearchCV, RandomizedSearchCV, @@ -60,34 +58,12 @@ from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis from sklearn.model_selection import cross_val_predict from sklearn.metrics import plot_precision_recall_curve, plot_roc_curve +from selenium import webdriver +from webdriver_manager.chrome import ChromeDriverManager +from altair_saver import save -opt = docopt(__doc__) - -def store_cross_val_results(model_name, scores, results_dict): - """ - Stores mean scores from cross_validate in results_dict for - the given model model_name. - Parameters - ---------- - model_name : - scikit-learn classification model - scores : dict - object return by `cross_validate` - results_dict: dict - dictionary to store results - Returns - ---------- - None - """ - results_dict[model_name] = { - "mean_fit_time": "{:0.4f}".format(np.mean(scores["fit_time"])), - "mean_score_time": "{:0.4f}".format(np.mean(scores["score_time"])), - "mean_test_f1 (s)": "{:0.4f}".format(np.mean(scores["test_f1_micro"])), - "mean_train_f1 (s)": "{:0.4f}".format(np.mean(scores["train_f1_micro"])), - "mean_test_accuracy (s)": "{:0.4f}".format(np.mean(scores["test_accuracy"])), - "mean_train_accuracy (s)": "{:0.4f}".format(np.mean(scores["train_accuracy"])), - } +opt = docopt(__doc__) def main(in_file_1, out_dir): # read data and combine two data set vertically @@ -96,7 +72,7 @@ def main(in_file_1, out_dir): y_train = train_df['quality_rank'] #----------------------------------------------------------------------------------------------------------------------------- - #PreProcessor + # PreProcessor numeric_features = ['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar', 'chlorides', 'free sulfur dioxide','total sulfur dioxide', 'density', 'pH', 'sulphates', 'alcohol'] binary_features = ['type'] @@ -111,16 +87,94 @@ def main(in_file_1, out_dir): ] ) #----------------------------------------------------------------------------------------------------------------------------- - #Model picking - results_df={} - scoring ={'accuracy', 'f1_micro'} - - pipe_iter = make_pipeline(preprocessor, MLPClassifier(random_state=1, max_iter=300)) - scores_iter = cross_validate(pipe_iter, X_train, y_train, - return_train_score=True,scoring = scoring) - store_cross_val_results(f"preprocessing + MLP", scores_iter, results_df) - print(store_cross_val_results) + # Model selection + results={} + scoring_metric = {'f1_micro'} + classifiers_plot = { + "RidgeClassifier": RidgeClassifier(random_state=123), + "Random Forest":RandomForestClassifier(bootstrap=False, max_depth=20, + max_features='sqrt', n_estimators=1800, + random_state=123), + "KNN": KNeighborsClassifier(n_neighbors=5), + "MLP Classifier":MLPClassifier(alpha=0.05, hidden_layer_sizes=(50, 100, 50), + learning_rate='adaptive', max_iter=1000,random_state=123), + "Nearest Centroid": NearestCentroid(), + "QDA" :QuadraticDiscriminantAnalysis() + } + + for (name, model) in classifiers_plot.items(): + pipe_iter = make_pipeline(preprocessor, model) + results[name] = mean_std_cross_val_scores(pipe_iter, + X_train, + y_train, + return_train_score=True, + scoring=scoring_metric) + + pd.DataFrame(results) + #--------------------------------------------------------------------------------------------------------- + # Plotting result + # All classifiers + plots_dict={} + plot_results = pd.DataFrame(results).T + plot_results =plot_results.reset_index() + bar_all = alt.Chart(plot_results).mark_bar().encode( + alt.X('test_f1_micro', axis=alt.Axis(title='F1 Micro score')), + alt.Y('index', sort='-x', axis=alt.Axis(title='Classifier')), + ).properties( + width=alt.Step(40) # controls width of bar. + ) + + plots_dict['f1_score_all_classifiers.svg'] = bar_all + # Stability across cv folds + scoring_metric = {'f1_micro'} + pipe_rf = make_pipeline(preprocessor, + RandomForestClassifier(bootstrap=False, + max_depth=20, + max_features='sqrt', + n_estimators=1800, + random_state=123)) + scores_rf = cross_validate(pipe_rf, + X_train, + y_train, + return_train_score=True, + scoring = scoring_metric, + n_jobs=-1, + cv=20) + + plot_rf = pd.DataFrame(scores_rf) + bar_rf = alt.Chart(plot_rf).mark_bar().encode( + x= alt.X('test_f1_micro', axis=alt.Axis(title='F1 Micro score'), bin=alt.Bin(extent=[0.75, 0.9], step=0.02)), + y= alt.Y('count()'), + ) + + plots_dict['f1_score_random_forest.svg'] = bar_rf + + pipe_mlp = make_pipeline(preprocessor, + MLPClassifier(alpha=0.05, + hidden_layer_sizes=(50, 100, 50), + learning_rate='adaptive', + max_iter=1000, + random_state=123)) + + scores_mlp = cross_validate(pipe_mlp, + X_train, + y_train, + return_train_score=True, + scoring = scoring_metric, + n_jobs=-1, + cv=20 ) + + plot_mlp = pd.DataFrame(scores_mlp) + bar_mlp = alt.Chart(plot_mlp).mark_bar().encode( + x= alt.X('test_f1_micro', axis=alt.Axis(title='F1 Micro score'), bin=alt.Bin(maxbins=6)), + y= alt.Y('count()'), + ) + + plots_dict['f1_score_mlp.svg'] = bar_mlp + + save_plots(out_dir, plots_dict) + #----------------------------------------------------------------------------------------------------------------------------- #Hyperparameters Tuning rf_pipeline = make_pipeline( @@ -143,9 +197,40 @@ def main(in_file_1, out_dir): joblib_file = out_dir + "best_Model.pkl" joblib.dump(best_model_pipe, joblib_file) except: - os.makedirs(os.path.dirname(out_dir)) + os.makedirs(os.path.dirname(out_dir)) joblib_file = out_dir + "best_Model.pkl" joblib.dump(best_model_pipe, joblib_file) +def mean_std_cross_val_scores(model, X_train, y_train, **kwargs): + """ + Returns mean and std of cross validation + """ + scores = cross_validate(model, + X_train, y_train, n_jobs=-1, + **kwargs) + + mean_scores = pd.DataFrame(scores).mean() + #std_scores = pd.DataFrame(scores).std() + out_col = [] + + for i in range(len(mean_scores)): + out_col.append(mean_scores[i]) + + return pd.Series(data = out_col, index = mean_scores.index) + +def save_plots(output_dir, plots_dict): + if os.path.exists(output_dir): + pass + else: + os.makedirs(os.path.dirname(output_dir)) + + for k, v in plots_dict.items(): + try: + driver = webdriver.Chrome(ChromeDriverManager().install()) + save(v, output_dir + k, method='selenium', webdriver=driver) + print("Successfully saved {}".format(k)) + except Exception as e: + print(e) + if __name__ == "__main__": - main(opt["--in_file_1"], opt["--out_dir"]) + main(opt["--in_file_1"], opt["--out_dir"]) \ No newline at end of file diff --git a/src/wine_quality_test_results.py b/src/wine_quality_test_results.py index 9245d48..2bb3470 100644 --- a/src/wine_quality_test_results.py +++ b/src/wine_quality_test_results.py @@ -20,32 +20,23 @@ import matplotlib.pyplot as plt import numpy as np import pandas as pd +from altair_saver import save +import joblib -# data +# sklearn 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 +from sklearn.metrics import ( + accuracy_score, + log_loss, + make_scorer, + mean_squared_error, + confusion_matrix, + plot_confusion_matrix, + f1_score) -# 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, @@ -54,10 +45,6 @@ StandardScaler, ) from sklearn.neural_network import MLPClassifier -import joblib -from sklearn.metrics import (plot_confusion_matrix) - - opt = docopt(__doc__) @@ -71,20 +58,26 @@ def main(in_file_1, in_file_2, out_dir): 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) - + + # Ploting confusion matrix plot_confusion_matrix(best_model_pipe, X_test, y_test, cmap = plt.cm.Blues, normalize='true') 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) + + try: + plt.savefig(path_f) + except: + os.makedirs(os.path.dirname(out_dir)) + plt.savefig(path_f) + score_f1 = f1_score(y_test, predictions_m, average='micro') + print(f"The test score (f1_micro) is: {round(score_f1,5)}") if __name__ == "__main__": main(opt["--in_file_1"], opt["--in_file_2"], opt["--out_dir"])

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