blitzml 0.18.0

A low-code library for machine learning pipelines

Automate machine learning pipelines rapidly

• Install BlitzML
• Classification
• Regression
• Time-Series
• Clustering

Install BlitzML

pip install blitzml

Classification

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'

Parameters

classifier
options: {'RF','LDA','SVC','KNN','GNB','LR','AB','GB','DT','MLP', 'auto', 'custom'}, default = 'RF'
auto: selects the best scoring classifier based on f1-score
custom: enables providing a custom classifier through *file_path* and *class_name* parameters
file_path
when using 'custom' classifier, pass the path of the file containing the custom class, default = 'none'
class_name
when using 'custom' classifier, pass the class name through this parameter, default = 'none'
feature_selection
options: {'correlation', 'importance', 'none'}, default = 'none'
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
validation_percentage
value determining the validation split percentage (value from 0 to 1), default = 0.1
average_type
when performing multiclass classification, provide the average type for the resulting metrics, default = 'macro'
cross_validation_k_folds
number of k-folds for cross validation, if 1 then no cv will be performed, default = 1
**kwargs
optional parameters for the chosen classifier. you can find available parameters in the sklearn docs

Attributes

train_df
the preprocessed train dataset (after running Classification.preprocess())
test_df
the preprocessed test dataset (after running Classification.preprocess())
model
the trained model (after running Classification.train_the_model())
pred_df
the prediction dataframe (test_df + predicted target) (after running Classification.gen_pred_df(Classification.test_df))
metrics_dict
the validation metrics (after running Classification.gen_metrics_dict())
{
    "accuracy": acc,
    "f1": f1,
    "precision": pre,
    "recall": recall,
    "hamming_loss": h_loss,
    "cross_validation_score":cv_score, returns None if cross_validation_k_folds==1
}

Methods

preprocess()
perform preprocessing on train_df and test_df
train_the_model()
train the chosen classifier on the train_df
accuracy_history()
accuracy scores when varying the sampling size of the train_df (after running Classification.train_the_model()).
returns:
{
    'x':train_df_sample_sizes,
    'y1':train_scores_mean,
    'y2':test_scores_mean,
    'title':title
}
gen_pred_df(test_df)
generates the prediction dataframe and assigns it to the pred_df attribute
gen_metrics_dict()
generates the validation metrics and assigns it to the metrics_dict
run()
a shortcut that runs the following methods:
preprocess()
train_the_model()
gen_pred_df(Classification.test_df)
gen_metrics_dict()

Regression

from blitzml.tabular import Regression
import pandas as pd

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

# create the pipeline
auto = Regression(train_df, test_df, regressor = 'RF')

# 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 Regressors

• Random Forest 'RF'
• Support Vector Regressor 'SVR'
• KNeighborsRegressor 'KNN'
• Lasso Regressor 'LSS'
• LinearRegression 'LR'
• Ridge Regressor 'RDG'
• GaussianProcessRegressor 'GPR'
• GradientBoostingRegressor 'GB'
• DecisionTreeRegressor 'DT'
• MLPRegressor 'MLP'

Parameters

regressor
options: {'RF','SVR','KNN','LSS','LR','RDG','GPR','GB','DT','MLP', 'auto', 'custom'}, default = 'RF'
auto: selects the best scoring regressor based on r2 score
custom: enables providing a custom regressor through *file_path* and *class_name* parameters
file_path
when using 'custom' regressor, pass the path of the file containing the custom class, default = 'none'
class_name
when using 'custom' regressor, pass the class name through this parameter, default = 'none'
feature_selection
options: {'correlation', 'importance', 'none'}, default = 'none'
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
validation_percentage
value determining the validation split percentage (value from 0 to 1), default = 0.1
cross_validation_k_folds
number of k-folds for cross validation, if 1 then no cv will be performed, default = 1
**kwargs
optional parameters for the chosen regressor. you can find available parameters in the sklearn docs

Attributes

train_df
the preprocessed train dataset (after running Regression.preprocess())
test_df
the preprocessed test dataset (after running Regression.preprocess())
model
the trained model (after running Regression.train_the_model())
pred_df
the prediction dataframe (test_df + predicted target) (after running Regression.gen_pred_df(Regression.test_df))
metrics_dict
the validation metrics (after running Regression.gen_metrics_dict())
{
    "r2_score": r2,
    "mean_squared_error": mse,
    "root_mean_squared_error": rmse,
    "mean_absolute_error" : mae,
    "cross_validation_score":cv_score, returns None if cross_validation_k_folds==1
}

Methods

preprocess()
perform preprocessing on train_df and test_df
train_the_model()
train the chosen regressor on the train_df
RMSE_history()
RMSE scores when varying the sampling size of the train_df (after running Regression.train_the_model()).
returns:
{
    'x':train_df_sample_sizes,
    'y1':train_scores_mean,
    'y2':test_scores_mean,
    'title':title
}
gen_pred_df(test_df)
generates the prediction dataframe and assigns it to the pred_df attribute
gen_metrics_dict()
generates the validation metrics and assigns it to the metrics_dict
run()
a shortcut that runs the following methods:
preprocess()
train_the_model()
gen_pred_df(Regression.test_df)
gen_metrics_dict()

Time-series

time series is a particular problem of Regression, but time series have some additional functions:

• stationary test (IsStationary()).
• convert to stationary.
• reverse predicted.

and the dataset must have a DateTime column, even if the DataType of this column is Object.

from blitzml.tabular import TimeSeries 
import pandas as pd

# prepare your dataframes
train_df = pd.read_csv("train_dataset.csv")
test_df = pd.read_csv("test_dataset.csv")

# create the pipeline
auto = TimeSeries(train_df, test_df, regressor = 'RF')

# Perform the entire process:
auto.run()

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

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

Clustering

from blitzml.unsupervised import Clustering
import pandas as pd

# prepare your dataframe
train_df = pd.read_csv("auxiliary/datasets/customer personality/train.csv")

# create the pipeline
auto = Clustering(train_df, clustering_algorithm = 'KM')

# 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.gen_metrics_dict()

# We can get their values using:
print(auto.pred_df.head())
print(auto.metrics_dict)

Available Clustering Algorithms

• K-Means 'KM'
• Affinity Propagation 'AP'
• Agglomerative Clustering 'AC'
• Mean Shift 'MS'
• Spectral Clustering 'SC'
• Birch 'Birch'
• Bisecting K-Means 'BKM'
• OPTICS 'OPTICS'
• DBSCAN 'DBSCAN'

Parameters

clustering_algorithm
options: {"KM", "AP", "AC", "MS", "SC", "Birch", "BKM", "OPTICS", "DBSCAN", 'auto', 'custom'}, default = 'KM' auto: selects the best scoring clustering algorithm based on silhouette score custom: enables providing a custom clustering algorithm through *file_path* and *class_name* parameters file_path when using 'custom' clustering_algorithm, pass the path of the file containing the custom class, default = 'none'
class_name when using 'custom' clustering_algorithm, pass the class name through this parameter, default = 'none' feature_selection options: {'importance', 'none'}, default = 'none' importance: use feature columns that are important for the model to predict the target none: use all feature columns **kwargs optional parameters for the chosen clustering_algorithm. you can find available parameters in the sklearn docs

Attributes

train_df the preprocessed train dataset (after running Clustering.preprocess())
model the trained model (after running Clustering.train_the_model()) pred_df the prediction dataframe (test_df + predicted target) (after running Clustering.gen_pred_df()) metrics_dict the validation metrics (after running Clustering.gen_metrics_dict()) {     "silhouette_score": sil_score,     "calinski_harabasz_score": cal_har_score,     "davies_bouldin_score": dav_boul_score,     "n_clusters" : n }

Methods

preprocess() perform preprocessing on train_df
train_the_model() train the chosen clustering algorithm on the train_df clustering_visualization() 2-d visualization of the data points with its corresponding labels (after doing dimensionality reduction using Principal Componenet Analysis). returns: {     'principal_component_1':pc1,     'principal_component_2':pc2,     'cluster_labels':labels,     'title':title } gen_pred_df() generates the prediction dataframe and assigns it to the pred_df attribute gen_metrics_dict() generates the clustering metrics and assigns it to the metrics_dict
run() a shortcut that runs the following methods: preprocess() train_the_model() gen_pred_df() gen_metrics_dict()

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