Package: mlfast
mlfastThis Python machine learning package is built on top of scikit-learn and provides a simple API for regression and classification modeling. The main function in this package is called Regression() and Classification() which takes in the following arguments:X: The independent variables (features) of the data sety: The dependent variable (target) of the data setmodel: The name of the regression and classification algorithm to be used (e.g.lrfor Linear Regression, orrffor Random Forest Classifier, etc.)scaler: The name of the data scaler to be used (e.g. "standard" for StandardScaler, "robust" for RobustScaler, etc.)cat: A boolean indicating [TrueorFalse] whether the data set has categorical variables that need to be one-hot encodedPYPI link for this package -mlfastGetting StartedInstallationsnote "Installation steps"First let's do an easy pip installation of the library by running the following command -pipinstallmlfastUsageRegression Algorithms!!! note "For Regression Modeling"Import Regression Model -frommlfastimportRegressionLinear Regression-> 'lr'Regression(X,y,model='lr')Ridge Regression-> 'ridge 'Regression(X,y,model='ridge',scaler='standard')Lasso Regression-> 'lasso'Regression(X,y,model='lasso',scaler='robust')ElasticNet-> 'enet'Regression(X,y,model='enet',cat=True)Random Forest Regressor-> 'rf'Regression(X,y,model='rf',scaler='standard',cat=True)Decision Tree Regressor-> 'dt'Regression(X,y,model='dt')Support Vector Machine Regression-> 'svm 'Regression(X,y,model='svm',scaler='standard')KNeighbors Regressor-> 'knn'Regression(X,y,model='knn',scaler='robust')Gradient Boosting Regressor-> 'gb'Regression(X,y,model='gb',cat=True)AdaBoost Regressor-> 'ada'Regression(X,y,model='ada',scaler='standard',cat=True)XGBoost Regressor-> 'xgb'Regression(X,y,model='xgb',scaler='standard',cat=True)Classification Algorithmsnote "For Classification Modeling"Import Classification Model -frommlfastimportClassificationLogistic Regression-> 'lr'Classification(X,y,model='lr')Random Forest Classifier-> 'rf'Classification(X,y,model='rf',scaler='standard',cat=True)Decision Tree Classifier-> 'dt'Classification(X,y,model='dt')Support Vector Machine Classifier-> 'svm 'Classification(X,y,model='svm',scaler='standard')KNeighbors Classifier-> 'knn'Classification(X,y,model='knn',scaler='robust')Gradient Boosting Classifier-> 'gb'Classification(X,y,model='gb',cat=True)AdaBoost Classifier-> 'ada'Classification(X,y,model='ada',scaler='standard',cat=True)XGBoost Classifier-> 'xgb'Classification(X,y,model='xgb',scaler='standard',cat=True)Regression hyperparameternote "For Regression Hyperparameter Modeling"Import Regression Hyperparameter Model -frommlfast.Hyperparameter.RegressionimportRegressionRidge regressionhyperparams_ridge={"alpha":[0.01,0.1,1.0,10.0],"fit_intercept":[True,False],"solver":["auto","svd","cholesky","lsqr","sparse_cg","sag","saga"],}Regression(X,y,model="ridge",scaler="standard",hyperparams=hyperparams_ridge)Lasso Regressionhyperparams_lasso={"alpha":[0.01,0.1,1.0,10.0],}Regression(X,y,model="lasso",scaler="standard",hyperparams=hyperparams_lasso,save_pkl=False)ElasticNet Regressionhyperparams_enet={"alpha":[0.01,0.1,1.0,10.0],"l1_ratio":[0.25,0.5,0.75],}Regression(X,y,model="enet",scaler="standard",hyperparams=hyperparams_enet,save_pkl=False)Decision Tree Regressionhyperparams_dt={"max_depth":[None,5,10,20],"min_samples_split":[2,5,10],}Regression(X,y,model="dt",scaler="standard",hyperparams=hyperparams_dt,save_pkl=False)Random Forest Regressionhyperparams_rf={"n_estimators":[5,10,20],"max_depth":[None,5,10,20],"min_samples_split":[2,5,10],}Regression(X,y,model="rf",scaler="standard",hyperparams=hyperparams_rf,save_pkl=False)Support Vector Regression (SVR)hyperparams_svm={"C":[0.1,1.0,10.0],"kernel":["linear","rbf","poly"],}Regression(X,y,model="svm",scaler="standard",hyperparams=hyperparams_svm,save_pkl=False)K-Nearest Neighbors Regression (KNN)hyperparams_knn={"n_neighbors":[2,3,5],"weights":["uniform","distance"],}Regression(X,y,model="knn",scaler="standard",hyperparams=hyperparams_knn,save_pkl=False)Gradient Boosting Regressionhyperparams_gb={"n_estimators":[5,10,20],"learning_rate":[0.01,0.1,0.2],}Regression(X,y,model="gb",scaler="standard",hyperparams=hyperparams_gb,save_pkl=False)AdaBoost Regressionhyperparams_ada={"n_estimators":[5,10,20],"learning_rate":[0.01,0.1,0.2],}Regression(X,y,model="ada",scaler="standard",hyperparams=hyperparams_ada,save_pkl=False)XGBoost Regressionhyperparams_xgb={"n_estimators":[5,10,20],"learning_rate":[0.01,0.1,0.2],}Regression(X,y,model="xgb",scaler="standard",hyperparams=hyperparams_xgb,save_pkl=False)Classification Hyperparameterfrommlfast.Hyperparameter.ClassificationimportClassificationLogistic Regressionhyperparams_lr={"C":[0.1,1.0],}Classification(X,y,model="lr",scaler="robust",hyperparams=hyperparams_lr,save_pkl=True)Decision Tree Classifierhyperparams_dt={"max_depth":[None,5,10,20],"min_samples_split":[2,5,10],}Classification(X,y,model="dt",scaler="standard",hyperparams=hyperparams_dt)Random Forest Classifierrf_hyperparams={"n_estimators":[100,200,300],"max_depth":[None,10,20,30],"min_samples_split":[2,5,10],"min_samples_leaf":[1,2,4],"bootstrap":[True,False],}Classification(X,y,model="rf",scaler="standard",cat=True,hyperparams=rf_hyperparams,save_pkl=False)Support Vector Classifier (SVC)hyperparams_svm={"C":[0.1,1.0,10.0],"kernel":["linear","rbf","poly"],}Classification(X,y,model="svm",scaler="standard",hyperparams=hyperparams_svm)K-Nearest Neighbors Classifier (KNN)hyperparams_knn={"n_neighbors":[3,5,10],"weights":["uniform","distance"],}Classification(X,y,model="knn",scaler="standard",hyperparams=hyperparams_knn)Gradient Boosting Classifierhyperparams_gb={"n_estimators":[5,10,20],"learning_rate":[0.01,0.1,0.2],}Classification(X,y,model="gb",scaler="standard",hyperparams=hyperparams_gb)AdaBoost Classifierhyperparams_ada={"n_estimators":[5,10,20],"learning_rate":[0.01,0.1,0.2],}Classification(X,y,model="ada",scaler="standard",cat=True,hyperparams=hyperparams_ada)XGBoost Classifierhyperparams_xgb={"n_estimators":[5,10,20],"learning_rate":[0.01,0.1,0.2],}Classification(X,y,model="xgb",scaler="standard",hyperparams=hyperparams_xgb)Text PreprocessingThe provided code snippet applies TextPreprocessingto a series or column of text data. It allows for flexible control over different preprocessing steps through boolean flags. Here is a summary of the options:stem: Determines whether stemming should be performed. Set toTrueto enable stemming, orFalseto disable it.lemmatize: Controls lemmatization. Set toTrueto enable lemmatization, orFalseto disable it.remove_html: Specifies whether HTML tags should be removed. UseTrueto remove HTML tags, orFalseto keep them.remove_emoji: Determines whether emojis should be removed from the text. Set toTrueto remove emojis, orFalseto retain them.remove_special_chars: Controls the removal of special characters. UseTrueto remove special characters, orFalseto keep them.remove_extra_spaces: Specifies whether extra spaces should be removed. Set toTrueto remove extra spaces, orFalseto keep them.By setting these flags to eitherTrueorFalse, you can customize the preprocessing steps according to your requirements. The code applies the specified preprocessing steps to each text element in the series or column and returns the processed text.Text preprocessing sample codefrommlfastimportText_preprocessingdf['review'].apply(Text_preprocessing,stem=False,lemmatize=True,remove_html=True,remove_emoji=True,remove_special_chars=True,remove_extra_spaces=True)ChatbotImport theChatbotclass: In your Python script, import theChatbotclass frommlfast. You can do this by adding the following line at the beginning of your code:frommlfastimportChatbotObtain an OpenAI API key: To use themlfastlibrary, you'll need an API key from OpenAI. If you don't have one, sign up on the OpenAI website and obtain an API key.Create aChatbotinstance: Initialize the Chatbot class with your OpenAI API key and specify the desired role for your chatbot. Here's an example of creating aChatbotinstance:Replace"YOUR-OPENAI-API-KEY"with your actual OpenAI API key, and"ENTER-YOUR-CHATBOT-ROLE"with the desired role for your chatbot.Deploy the chatbot: Set thedeployparameter toTruewhen creating theChatbotinstance. This will deploy the chatbot and make it available for use.To deploy or terminate the deployment of a chatbot created using themlfastlibrary, you can set thedeployparameter toTrueorFalsewhen creating theChatbotinstance, respectively.Chatbot(api_key="YOUR-OPENAI-API-KEY",role="ENTER-YOUR-CHATBOT-ROLE",deploy=True)Chatbot feature is depreciated will be added soon on different libraryAnnouncementUnsupervised Machine Learning AlgorithmsBag of words, TFIDF and Word2VecImage PreprocessingAnd many moreADDED SOON