modbot.training.models.ModerationModel

class modbot.training.models.ModerationModel(texts=None, model=None, **kwargs)[source]

Bases: ABC

Base moderation model class

Methods

clean_text(text)

Clean single text so it is utf-8 compliant

clean_texts(X)

Clean texts so they are utf-8 compliant

continue_training(data_file, config)

Continue training.

detailed_score([test_gen, n_matches, out_dir])

Score model and print confusion matrix and multiple other metrics

get_class_info()

Get info about class numbers

get_data_generators(data_file, **kwargs)

Get data generators with correct sizes

load(inpath, **kwargs)

Load model from path

load_data(data_file)

Load data from csv file

model_test()

Test model on some key phrases

predict(X[, verbose])

Predict classification

predict_one(X[, verbose])

Predict probability of label=1

predict_proba(X[, verbose])

Predict probability

predict_zero(X[, verbose])

Predict probability of label=0

run(data_file, config)

Run model pipeline.

save(outpath)

Save model

save_params(outpath, kwargs)

Save params to model path

score(X, Y)

Score model against targets

split_data(df[, test_split])

Split data into training and test sets

train(train_gen, test_gen, **kwargs)

Train model

transform(X)

Transform texts
static clean_text(text)[source]

Clean single text so it is utf-8 compliant

Parameters

text (str) – Text string to clean

Returns

Cleaned text string

Return type

str

classmethod clean_texts(X)[source]

Clean texts so they are utf-8 compliant

Parameters

X (pd.DataFrame) – Pandas dataframe of texts

Returns

X – Pandas dataframe of cleaned texts

Return type

pd.DataFrame

classmethod continue_training(data_file, config)[source]

Continue training. Load model, load data, tokenize texts, and train.

Parameters

  • data_file (str) – Path to csv file storing texts and labels

  • config (RunConfig) – Config class with kwargs

Returns

Trained sequential and evaluated model

Return type

keras.Sequential

detailed_score(test_gen=None, n_matches=10, out_dir=None)[source]

Score model and print confusion matrix and multiple other metrics

Parameters

  • test_gen (WeightedGenerator) – generator for test data

  • n_matches (int) – Number of positive matches to print

  • out_dir (str | None) – Path to save scores

Returns

df_scores – A dataframe containing all model scores

Return type

pd.DataFrame

get_class_info()[source]

Get info about class numbers

classmethod get_data_generators(data_file, **kwargs)[source]

Get data generators with correct sizes

Parameters

  • data_file (str) – Path to csv file storing texts and labels

  • kwargs (dict) – Dictionary with optional keyword parameters. Can include sample_size, batch_size, epochs, n_batches.

Returns

  • train_gen (WeightedGenerator) – WeightedGenerator instance used for training batches

  • test_gen (WeightedGenerator) – WeightedGenerator instance used for evaluation batches

abstract classmethod load(inpath, **kwargs)[source]

Load model from path

classmethod load_data(data_file)[source]

Load data from csv file

Parameters

data_file (str) – Path to csv file storing texts and labels

Returns

df – Pandas dataframe of texts and labels

Return type

pd.DataFrame

model_test()[source]

Test model on some key phrases

Parameters

model (ModerationModel) –

predict(X, verbose=False)[source]

Predict classification

Parameters

  • X (ndarray | list | pd.DataFrame) – Set of texts to classify

  • verbose (bool) – Whether to show progress bar for predictions

Returns

List of predicted classifications for input texts

Return type

list

predict_one(X, verbose=False)[source]

Predict probability of label=1

Parameters

  • X (ndarray | list | pd.DataFrame) – Set of texts to classify

  • verbose (bool) – Whether to show progress bar for predictions

Returns

List of predicted probability for label=1 for input texts

Return type

list

abstract predict_proba(X, verbose=False)[source]

Predict probability

predict_zero(X, verbose=False)[source]

Predict probability of label=0

Parameters

  • X (ndarray | list | pd.DataFrame) – Set of texts to classify

  • verbose (bool) – Whether to show progress bar for predictions

Returns

List of predicted probability for label=0 for input texts

Return type

list

classmethod run(data_file, config)[source]

Run model pipeline. Load data, tokenize texts, and train

Parameters

  • data_file (str) – Path to csv file storing texts and labels

  • config (RunConfig) – Config class with kwargs

Returns

Trained and evaluated keras model or svm

Return type

ModerationModel

save(outpath)[source]

Save model

Parameters

outpath (str) – Path to save model

static save_params(outpath, kwargs)[source]

Save params to model path

Parameters

  • outpath (str) – Path to model

  • kwargs (dict) – Dictionary of kwargs used to build model

score(X, Y)[source]

Score model against targets

Parameters

  • X (pd.DataFrame) – Pandas dataframe of texts

  • Y (pd.DataFrame) – Pandas dataframe of labels for the corresponding texts

Returns

Value of accuracy calulated from the correct predictions vs base truth

Return type

float

classmethod split_data(df, test_split=0.1)[source]

Split data into training and test sets

Parameters

  • df (pd.DataFrame) – Pandas dataframe of texts and labels

  • test_split (float) – Fraction of full dataset to use for test data

Returns

  • df_train (pd.DataFrame) – Pandas dataframe of texts and labels for training

  • df_test (pd.DataFrame) – Pandas dataframe of texts and labels for testing

abstract train(train_gen, test_gen, **kwargs)[source]

Train model

abstract transform(X)[source]

Transform texts