blitzml 0.5.0

A low-code library for machine learning pipelines

blitzml

Automate machine learning piplines 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/data/train.csv")
test_df = pd.read_csv("auxiliary/data/test.csv")
ground_truth_df = pd.read_csv("auxiliary/ground_truth.csv")

# create the pipeline with a certain classifier
auto = Classification(train_df, test_df, ground_truth_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 the following:
auto.gen_pred_df()
auto.gen_metrics_dict()

# Then you 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'

When using RF you can also provide the number of estimators

via n_estimators = 100 (default)