blitzml 0.11.0

A low-code library for machine learning pipelines

blitzml

Automate machine learning pipelines rapidly

How to install

pip install blitzml

Usage

from blitzml.tabular import Classification
import pandas as pd

# prepare your dataframes
train_df = pd.read_csv("auxiliary/datasets/banknote/train.csv")
test_df = pd.read_csv("auxiliary/datasets/banknote/test.csv")

# create the pipeline
auto = Classification(train_df, test_df, classifier = 'RF', n_estimators = 50)

# first perform data preprocessing
auto.preprocess()
# second train the model
auto.train_the_model()


# After training the model we can generate:
auto.gen_pred_df(auto.test_df)
auto.gen_metrics_dict()

# We can get their values using:
pred_df = auto.pred_df
metrics_dict = auto.metrics_dict

print(pred_df.head())
print(metrics_dict)

Available Classifiers

• Random Forest 'RF'
• LinearDiscriminantAnalysis 'LDA'
• Support Vector Classifier 'SVC'
• KNeighborsClassifier 'KNN'
• GaussianNB 'GNB'
• LogisticRegression 'LR'
• AdaBoostClassifier 'AB'
• GradientBoostingClassifier 'GB'
• DecisionTreeClassifier 'DT'
• MLPClassifier 'MLP'

The possible arguments for each model can be found in the sklearn docs

Working with a custom classifier

# instead of specifying a classifier name, we pass "custom" to the classifier argument.
auto = Classification(
    train_df,
    test_df,
    classifier = "custom", 
    class_name = "classifier",
    file_path = "auxiliary/scripts/dummy.py"
)

Smart Feature Selection

# to filter used columns by correlation with target column
auto = Classification(
    '''
    params
    '''
    feature_selection = "correlation" # or "importance" or "none"
)

• Options:
  • "correlation": use feature columns with the highest correlation with the target
  • "importance": use feature columns that are important for the model to predict the target
  • "none": use all feature columns

Additional features

• Preprocessing a dataset

# After executing
auto.preprocess()
# You can access the processed datasets via
processed_train_df = auto.train_df
processed_test_df = auto.test_df

• Validation split

auto = Classification(
    '''
    params
    '''
    validation_percentage = 0.1 #default
)

• Multiclass metrics averaging type

auto = Classification(
    '''
    params
    '''
    average_type = 'macro' #default
)

• Less coding

# Instead of
auto.preprocess()
auto.train_the_model()
auto.gen_pred_df()
auto.gen_metrics_dict()
# You can simply use
auto.run()

Development

• Clone the repo
• run pip install virtualenv
• run python -m virtualenv venv
• run . ./venv/bin/activate on UNIX based systems or . ./venv/Scripts/activate.ps1 if on windows
• run pip install -r requirements.txt
• run pre-commit install