Seminar "Machine Learning in Practice"

Source code for the practical Seminar "Machine Learning in Practice", taught at Osnabrück University in the winter term 2021/2022 at the Insitute of Cognitive Science.

As data source, we use the "Data Science Tweets 2010-2021" data set (version 3) by Ruchi Bhatia from Kaggle. The goal of our example project is to predict which tweets will go viral, i.e., receive many likes and retweets.

Virtual Environment

In order to install all necessary dependencies, please make sure that you have a local Conda distribution (e.g., Anaconda or miniconda) installed. Begin by creating a new environment called "MLinPractice" that has Python 3.6 installed:

conda create -y -q --name MLinPractice python=3.6

You can enter this environment with conda activate MLinPractice (or source activate MLinPractice, if the former does not work). You can leave it with conda deactivate (or source deactivate, if the former does not work). Enter the environment and execute the following commands in order to install the necessary dependencies (this may take a while):

conda install -y -q -c conda-forge scikit-learn=0.24.2
conda install -y -q -c conda-forge matplotlib=3.3.4
conda install -y -q -c conda-forge nltk=3.6.3
conda install -y -q -c conda-forge gensim=4.1.2
conda install -y -q -c conda-forge spyder=5.1.5
conda install -y -q -c conda-forge pandas=1.1.5
conda install -y -q -c conda-forge mlflow=1.20.2

You can double-check that all of these packages have been installed by running conda list inside of your virtual environment. The Spyder IDE can be started by typing ~/miniconda/envs/MLinPractice/bin/spyder in your terminal window (assuming you use miniconda, which is installed right in your home directory).

In order to save some space on your local machine, you can run conda clean -y -q --all afterwards to remove any temporary files.

The installed libraries are used for machine learning (scikit-learn), visualizations (matplotlib), NLP (nltk), word embeddings (gensim), and IDE (spyder), and data handling (pandas)

Overall Pipeline

The overall pipeline can be executed with the script code/pipeline.sh, which executes all of the following shell scripts:

• The script code/load_data.sh downloads the raw csv files containing the tweets and their metadata. They are stored in the folder data/raw/ (which will be created if it does not yet exist).
• The script code/preprocessing.sh executes all necessary preprocessing steps, including a creation of labels and splitting the data set.
• The script code/feature_extraction.sh takes care of feature extraction.
• The script code/dimensionality_reduction.sh takes care of dimensionality reduction.
• The script code/classification.sh takes care of training and evaluating a classifier.
• The script code/application.sh launches the application example.

Preprocessing

All python scripts and classes for the preprocessing of the input data can be found in code/preprocessing/.

Creating Labels

The script create_labels.py assigns labels to the raw data points based on a threshold on a linear combination of the number of likes and retweets. It is executed as follows: python -m code.preprocessing.create_labels path/to/input_dir path/to/output.csv Here, input_dir is the directory containing the original raw csv files, while output.csv is the single csv file where the output will be written. The script takes the following optional parameters:

• -l or --likes_weight determines the relative weight of the number of likes a tweet has received. Defaults to 1.
• -r or --retweet_weight determines the relative weight of the number of retweets a tweet has received. Defaults to 1.
• -t or --threshold determines the threshold a data point needs to surpass in order to count as a "viral" tweet. Defaults to 50.

Classical Preprocessing

The script run_preprocessing.py is used to run various preprocessing steps on the raw data, producing additional columns in the csv file. It is executed as follows: python -m code.preprocessing.run_preprocessing path/to/input.csv path/to/output.csv Here, input.csv is a csv file (ideally the output of create_labels.py), while output.csv is the csv file where the output will be written.

The preprocessing steps to take can be configured with the following flags:

• c or --clean_text: A new column "tweet_clean" is created, where the text is cleaned off non-linguistically relevant patterns, such as hashtags, urls, mentions. (See code/preprocessing/text_cleaner.py for more details)
• s or --analyze_sentiment: Evaluate sentiment polarity and intensity of each tweet, outputing a set of four scores: negative, neutral, positive and compound. Input column is set to default "tweet_clean", can be specified by --sentiment_input. (See code/preprocessing/sentiment_analyzer.py for more details)

NOTE: Following are the original preprocessing steps, which we decided not to implement into our next steps but which may be useful in possible future developments:

• -p or --punctuation: A new column "tweet_no_punctuation" is created, where all punctuation is removed from the original tweet. (See code/preprocessing/punctuation_remover.py for more details)
• -tor --tokenize: Tokenize the given column (can be specified by --tokenize_input, default = "tweet"), and create new column with suffix "_tokenized" containing tokenized tweet. (see code/preprocessing/tokenizer.py)

Moreover, the script accepts the following optional parameters:

• -e or --export gives the path to a pickle file where an sklearn pipeline of the different preprocessing steps will be stored for later usage.

Splitting the Data Set

The script split_data.py splits the overall preprocessed data into training, validation, and test set. It can be invoked as follows: python -m code.preprocessing.split_data path/to/input.csv path/to/output_dir Here, input.csv is the input csv file to split (containing a column "label" with the label information, i.e., create_labels.py needs to be run beforehand) and output_dir is the directory where three individual csv files training.csv, validation.csv, and test.csv will be stored. The script takes the following optional parameters:

• -t or --test_size determines the relative size of the test set and defaults to 0.2 (i.e., 20 % of the data).
• -v or --validation_size determines the relative size of the validation set and defaults to 0.2 (i.e., 20 % of the data).
• -s or --seed determines the seed for intializing the random number generator used for creating the randomized split. Using the same seed across multiple runs ensures that the same split is generated. If no seed is set, the current system time will be used.

Feature Extraction

All python scripts and classes for feature extraction can be found in code/feature_extraction/.

The script extract_features.py takes care of the overall feature extraction process and can be invoked as follows: python -m code.feature_extraction.extract_features path/to/input.csv path/to/output.pickle Here, input.csv is the respective training, validation, or test set file created by split_data.py. The file output.pickle will be used to store the results of the feature extraction process, namely a dictionary with the following entries:

• "features": a numpy array with the raw feature values (rows are training examples, colums are features)
• "feature_names": a list of feature names for the columns of the numpy array
• "labels": a numpy array containing the target labels for the feature vectors (rows are training examples, only column is the label)

The features to be extracted can be configured with the following optional parameters:

• -c or --char_length: Count the number of characters in the "tweet" column of the data frame. (see code/feature_extraction/character_length.py)
• -alf or --avg_len_flag: Give a binary flag to the tweet, indicating whether its plain text is longer than average within the data set. (see code/feature_extraction/avg_len_flag.py)
• -hc or --hashtag_count: Count the number of hashtags extracted from each tweet into the "hashtags" column. (see code/feature_extraction/hashtags_count.py)
• -mc or --mentions_count: Count the number of mentions extracted from each tweet into the "mentions". (see code/feature_extraction/mentions_count.py)
• -m or --media: Give a binary flag to the tweet, indicating whether there is any media attached to the tweet. (see code/feature_extraction/media.py)
• -s or --sentiment_score: Extract the compound sentiment score from the obtained scores in the column "sentiment_scores". (see code/feature_extraction/sentiment_score.py)

Moreover, the script support importing and exporting fitted feature extractors with the following optional arguments:

• -i or --import_file: Load a configured and fitted feature extraction from the given pickle file. Ignore all parameters that configure the features to extract.
• -e or --export_file: Export the configured and fitted feature extraction into the given pickle file.

Dimensionality Reduction

All python scripts and classes for dimensionality reduction can be found in code/dimensionality_reduction/.

The script reduce_dimensionality.py takes care of the overall dimensionality reduction procedure and can be invoked as follows:

python -m code.dimensionality_reduction.reduce_dimensionality path/to/input.pickle path/to/output.pickle Here, input.pickle is the respective training, validation, or test set file created by extract_features.py. The file output.pickle will be used to store the results of the dimensionality reduction process, containing "features" (which are the selected/projected ones) and "labels" (same as in the input file).

The dimensionality reduction method to be applied can be configured with the following optional parameters:

• -m or --mutual_information: Select the k best features (where k is given as argument) with the Mutual Information criterion

Moreover, the script support importing and exporting fitted dimensionality reduction techniques with the following optional arguments:

• -i or --import_file: Load a configured and fitted dimensionality reduction technique from the given pickle file. Ignore all parameters that configure the dimensionality reduction technique.
• -e or --export_file: Export the configured and fitted dimensionality reduction technique into the given pickle file.

Finally, if the flag --verbose is set, the script outputs some additional information about the dimensionality reduction process.

Classification

All python scripts and classes for classification can be found in code/classification/.

Train and Evaluate a Single Classifier

The script run_classifier.py can be used to train and/or evaluate a given classifier. It can be executed as follows: python -m code.classification.run_classifier path/to/input.pickle Here, input.pickle is a pickle file of the respective data subset, produced by either extract_features.py or reduce_dimensionality.py.

By default, this data is used to train a classifier, which is specified by one of the following optional arguments:

• -m or --majority: Majority vote classifier that always predicts the majority class.
• -r or --random: Dummy classifier that makes predictions uniformly at random.
• -f or --frequency: Dummy classifier that makes predictions based on the label frequency in the training data.
• -knn: K nearest neighbour classifier that makes predictions based on the class of a specified k number of closest data points.
• -lr or --logistic: Logistic regression classifier that makes predictions by scoring cases based on the data about earlier outcomes involving the same input criteria.
• --random_forest: Random Forest classifier that makes predictions based on a vote of a set of decision trees.
• --svc: Support vector classifier that transforms the data in order to find a division boundary between the two classes.

The hyperparameters are set to default, unless the following optional arguments are added to specify the preferred values of certain classifiers:

Logistic Regression

• lr_solver: specifies the solver of your choice. Accepted values are "liblinear", "lbfgs", sag", "saga", and default value is "lbfgs".
• lr_c: specifies the penalty regulation parameter. Accepted value is int, default value is "1".
• lr_class_weight: specifies the weights associated with the classes. Accepted values are dictionaries in the form {class_label: weight} or "balanced", default value is "None".

Random Forest

• -rf_n_estimators: specifies the number of trees to be created in the forest. Accepted value is integer, default is 100.
• -rf_criterion: specifies the function to measure the quality of the split. Accepted values are "gini" or "entropy", default is "gini".
• -rf_depth: specifies the maximum depth of a tree. In case of None, the expansion continues while possible. Accepted value is integer, default is "None".
• -rf_bootstrap: determines wheter bootstrap samples should be used for the trees, otherwise the whole dataset is used. Boolean value accepted, default is "True".
• -rf_class_weight: specifies the weight that should be given to classes. Accepted values are "balanced" or "balanced_subsample", default is "None" which gives equal weight = 1 to all classes.

The classifier is then evaluated, using the evaluation metrics as specified through the following optional arguments:

• -aor --accuracy: Classification accurracy (i.e., percentage of correctly classified examples).
• -kor --kappa: Cohen's kappa (i.e., adjusting accuracy for probability of random agreement).
• -ap or --average_precision: Average Precision
• -f1 or --f1_score: F1 score (i.e., harmonic mean of the precision and recall).

Moreover, the script support importing and exporting trained classifiers with the following optional arguments:

• -i or --import_file: Load a trained classifier from the given pickle file. Ignore all parameters that configure the classifier to use and don't retrain the classifier.
• -e or --export_file: Export the trained classifier into the given pickle file.

Finally, the optional argument -s or --seed determines the seed for intializing the random number generator (which may be important for some classifiers). Using the same seed across multiple runs ensures reproducibility of the results. If no seed is set, the current system time will be used.

Application

NOTE: this part has not been updated to include additional features beyond the input column "tweet", hence is not currently able to make predictions based on our complete model. For a workable version of the application, training with the feature space NOT containing the features of hashtag counts, mentions counts and media is necessary.

All python code for the application demo can be found in code/application/.

The script application.py provides a simple command line interface, where the user is asked to type in their prospective tweet, which is then analyzed using the trained ML pipeline. The script can be invoked as follows: python -m code.application.application path/to/preprocessing.pickle path/to/feature_extraction.pickle path/to/dimensionality_reduction.pickle path/to/classifier.pickle The four pickle files correspond to the exported versions for the different pipeline steps as created by run_preprocessing.py, extract_features.py, reduce_dimensionality.py, and run_classifier.py, respectively, with the -e option.