A Helpful ML Toolkit to Jumpstart your Machine Learning Project based on Tabular or Structured data.
Project description
Tabular ML Toolkit
A Helpful ML Toolkit to Jumpstart your Machine Learning Project based on Tabular or Structured data.
from tabular_ml_toolkit.MLPipeline import *
Install
pip install -U tabular_ml_toolkit
How to use
Start with your favorite model and then just simply create MLPipeline with one API.
For example, Here we are using RandomForestRegressor from Scikit-Learn, on Melbourne Home Sale price data
No need to install scikit-learn as it comes preinstall with Tabular_ML_Toolkit
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_absolute_error
# create scikit-learn ml model
scikit_model = RandomForestRegressor(n_estimators=100, random_state=42)
# createm ml pipeline for scikit-learn model
sci_ml_pl = MLPipeline().prepare_data_for_training(train_file_path= "https://raw.githubusercontent.com/psmathur/tabular_ml_toolkit/master/input/home_data/train.csv",
test_file_path= "https://raw.githubusercontent.com/psmathur/tabular_ml_toolkit/master/input/home_data/test.csv",
idx_col="Id", target="SalePrice",valid_size=0.2,
model=scikit_model, random_state=42)
# Now fit and predict
sci_ml_pl.pipeline.fit(sci_ml_pl.dataframeloader.X_train, sci_ml_pl.dataframeloader.y_train)
preds = sci_ml_pl.pipeline.predict(sci_ml_pl.dataframeloader.X_valid)
print('X_valid MAE:', mean_absolute_error(sci_ml_pl.dataframeloader.y_valid, preds))
X_valid MAE: 17678.294143835617
You can also use MLPipeline with XGBoost model, Just make sure to install XGBooost first depending upon your OS.
After that all steps remains same. Here is example using XGBRegressor with Melbourne Home Sale price data
#!pip install -U xgboost
from xgboost import XGBRegressor
# create xgb ml model
xgb_model = XGBRegressor(n_estimators=250,learning_rate=0.05, random_state=42)
# createm ml pipeline for xgb model
xgb_ml_pl = MLPipeline().prepare_data_for_training(train_file_path= "https://raw.githubusercontent.com/psmathur/tabular_ml_toolkit/master/input/home_data/train.csv",
test_file_path= "https://raw.githubusercontent.com/psmathur/tabular_ml_toolkit/master/input/home_data/test.csv",
idx_col="Id", target="SalePrice",valid_size=0.2,
model=xgb_model, random_state=42)
# Now fit and predict
xgb_ml_pl.pipeline.fit(xgb_ml_pl.dataframeloader.X_train, xgb_ml_pl.dataframeloader.y_train)
preds = xgb_ml_pl.pipeline.predict(xgb_ml_pl.dataframeloader.X_valid)
print('X_valid MAE:', mean_absolute_error(xgb_ml_pl.dataframeloader.y_valid, preds))
X_valid MAE: 16156.980896832192
In background prepare_data_for_training
method loads your input data into Pandas DataFrame, seprates X(features) and y(target), split X(features) into X_train, y_train, X_valid, y_valid DataFrames. Then it preprocess all numerical and categorical type data found in these DataFrames. Then it bundle preprocessed data with your given model and return an MLPipeline object, so you can call MLPipeline to fit X_train and y_train and predict on X_valid or X_test.
Here is detail documentation and source code.
# show_doc(MLPipeline.prepare_data_for_training)
MLPipeline.prepare_data_for_training
[source]
MLPipeline.prepare_data_for_training
(train_file_path
:str
,test_file_path
:str
,idx_col
:str
,target
:str
,valid_size
:float
,model
:object
,random_state
:int
)
If you want to customize data and preprocessing steps you can do so by using DataFrameLoader
and PreProessor
classes. Check detail documentations for these classes for more options.
# show_doc(MLPipeline)
class
MLPipeline
[source]
MLPipeline
()
Represent MLPipeline class
Attributes:
pipeline: An MLPipeline instance
dataframeloader: A DataFrameLoader instance
preprocessor: A PreProcessor Instance
model: The given Model
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