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EasyCheml is a tensorflow-based package for deep/machine learning application in chemistry, which doesn't require any advanced knowledge of Python (or machine learning). The intended audience is domain scientists with basic knowledge of python.
Why EasyCheml ^^^^^^^^^^ The purpose of the EasyCheml is to provide an environment that bridges the instrument specific libraries and general physical analysis by enabling the seamless deployment of deep/machine learning algorithms.
Data Preprocessing ^^^^^^^^^^^^^^^^^^^^^^
from easycheml.preprocessing import PreProcessing as p preprocessed_dataset,train, validate, test=p.preprocess_data(dataset,'target_name','list_of_specific_columns') preprocessed_dataset.to_excel("df_feature.xlsx")
Feature Engineering ^^^^^^^^^^^^^^
from easycheml.modelling import feature_engineering as f feature=f.feature_thru_correlation('df_feature.xlsx', 'target_name', 0.4, 'pearson')
Data Modelling ^^^^^^^^^^^^^^^
from easycheml.modelling import Regressors model=Regressors('df_feature.xlsx','target_name',0.6,0.2) model.ensemble_models("RF", None) # Random Forest Regressor model.ensemble_models("AdaBoost",None) # AdaBoost Regressor
parameters = { 'n_estimators' :[50,100,200,300,400,500,600,700,800,900,1000], 'criterion' : ["squared_error", "friedman_mse", "absolute_error"], 'max_depth' : [3,5,7,9,11,13,15,17,19,21,23,25,27,29,31], 'min_samples_split' : [5,10,20,30,40,50,60,70,80,90,100], 'bootstrap':[True], 'min_samples_leaf':[5,10,20,30,40,50,60,70,80,90,100], } model.ensemble_models("RF",parameters)
num_max_trials=3 num_executions_per_trial=3 num_epochs=10 num_batch_size=32 model.dnn_sequential_model(num_max_trials,num_executions_per_trial,num_epochs,num_batch_size)
Data Postprocessing and Visualization ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
First, install Tensorflow <https://www.tensorflow.org/install>_. Then, install EasyCheml with
pip install easycheml