Code Description

train_model.py summary

The train_model.py file is the main file for training the model. It first creates a random dataset of recipes with 10 ingredients, following the proportions of a poke bowl. The ingredients are then encoded as numbers, and the dataset is split into a training set and a test set. A random forest classifier is then trained on the training set. The random forest classifier is a type of machine learning model that is known for its accuracy and robustness. It works by creating a number of decision trees, and then combining the predictions of the decision trees to make a final prediction. The predictions of the model are then evaluated on the test set. The evaluation metrics that are used include accuracy, precision, recall, and F1 score. The accuracy is the percentage of predictions that are correct. The precision is the percentage of positive predictions that are actually positive. The recall is the percentage of positive examples that are correctly predicted. The F1 score is a harmonic mean of the precision and recall. The correlation matrix is also calculated. The correlation matrix shows the correlation between the predictions of the model. The correlation coefficient is a measure of how two variables are related. A correlation coefficient of 1 indicates that the two variables are perfectly correlated, while a correlation coefficient of -1 indicates that the two variables are perfectly negatively correlated. The model is then saved to a file called py_cache/recipe_model.joblib. This file can be used to load the model and make predictions later. Here is a more detailed explanation of the technical terms used in the train_model.py file: • Random forest classifier: A type of machine learning model that is known for its accuracy and robustness. It works by creating a number of decision trees, and then combining the predictions of the decision trees to make a final prediction. • Accuracy: The percentage of predictions that are correct. • Precision: The percentage of positive predictions that are actually positive. • Recall: The percentage of positive examples that are correctly predicted. • F1 score: A harmonic mean of the precision and recall. Correlation coefficient: A measure of how two variables are related. A correlation coefficient of 1 indicates that the two variables are perfectly correlated, while a correlation coefficient of -1 indicates that the two variables are perfectly negatively correlated.

predict.py summary:

The predict.py file is used to make predictions using the model that was trained in the train_model.py file. The file first loads the model from a file called py_cache/recipe_model.joblib. The model is then used to make predictions for a list of ingredients. The predictions are then printed out, along with the probability of each prediction. Here is a more detailed explanation of the technical terms used in the predict.py file: • Load model: This function loads the model from a file called py_cache/recipe_model.joblib. The model is saved in this file by the train_model.py file. • Predict ingredients: This function makes predictions for a list of ingredients. The predictions are made using the model that was loaded in the load_model function. • Predicted_missing_ingredients_encoded: This is an array that contains the predicted ingredients. The ingredients are encoded as numbers. • Predicted_probabilities: This is an array that contains the probabilities of the predictions. The probabilities are between 0 and 1. • Max probability: This is the maximum probability of a prediction. The maximum probability is printed out for each label. • Extend: This function adds the predicted ingredients to the end of the original array.

train_model_inc_plot.py

The train_model_inc_plot.py file contains some additional methods that are used in the train_model.py file. These methods are used to visualize the data and the model. • The create_matrix_for_plot method creates a matrix that shows the relationships between the ingredients. This matrix is then used to create a scatterplot of the ingredients. • The scatter_ingredients method creates a scatterplot of the ingredients. The scatterplot shows the relationships between the ingredients and can be used to see how the ingredients are clustered together. • The plotFeatures method plots the importance of the features for each output of the model. This plot shows which features are the most important for predicting each output. These methods are used to visualize the data and the model, which can help to understand how the model works and how it can be improved. Here is a more detailed explanation of the technical terms used in the train_model_inc_plot.py file: • PCA: Principal component analysis is a statistical procedure that is used to reduce the dimensionality of a dataset. This can be useful for visualization and for making predictions. • TSNE: t-distributed stochastic neighbor embedding is a machine learning technique that is used to visualize high-dimensional data. This can be useful for understanding the relationships between the ingredients. • LinearSegmentedColormap: This is a type of colormap that allows for custom colors to be specified. This can be useful for creating a scatterplot that is easy to read. • adjust_text: This is a function that is used to place text labels in a non-overlapping way. This can be useful for creating a scatterplot that is easy to read.