SmplML 1.0.6

SmplML is a user-friendly Python module for streamlined machine learning classification and regression. It offers intuitive functionality for data preprocessing, model training, and evaluation. Ideal for beginners and experts alike, SmplML simplifies ML tasks, enabling you to gain valuable insights from your data with ease.

SmplML / SimpleML: Simplified Machine Learning for Classification and Regression

SmplML is a user-friendly Python module for streamlined machine learning classification and regression. It offers intuitive functionality for data preprocessing, model training, and evaluation. Ideal for beginners and experts alike, SmplML simplifies ML tasks, enabling you to gain valuable insights from your data with ease.

Features

• Data preprocessing: Easily handle encoding categorical variables and data partitioning.
• Model training: Train various classification and regression models with just a few lines of code.
• Model evaluation: Evaluate model performance using common metrics.
• This module is designed to seamlessly handle various scikit-learn models, making it flexible for handling sklearn-like model formats.
• Added training feature for training multiple models for experimentation.

Installation

You can install SmpML using pip:

pip install SimpleML

Usage

The TrainModel class is designed to handle both classification and regression tasks. It determines the task type based on the target parameter. If the target has a float data type, the class automatically redirects the procedures to regression; otherwise, it assumes a classification task.

Data Preparation

Data preparation like data spliting and converting categorical data into numerical data is also automatically executed when calling the fit() method.

import seaborn as sns
import pandas as pd
from smpl_ml.smpl_ml import TrainModel

Classification Task

from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier

df = sns.load_dataset('penguins')
df.head()

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	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
0	Adelie	Torgersen	39.1	18.7	181.0	3750.0	Male
1	Adelie	Torgersen	39.5	17.4	186.0	3800.0	Female
2	Adelie	Torgersen	40.3	18.0	195.0	3250.0	Female
3	Adelie	Torgersen	NaN	NaN	NaN	NaN	NaN
4	Adelie	Torgersen	36.7	19.3	193.0	3450.0	Female

clf_target = 'sex'
clf_features = df.iloc[:, df.columns != clf_target].columns

print(f"Class: {clf_target}")
print(f"Features: {clf_features}")

Class: sex
Features: Index(['species', 'island', 'bill_length_mm', 'bill_depth_mm',
       'flipper_length_mm', 'body_mass_g'],
      dtype='object')

Single Classification Model Training

# Initialize TrainModel object
clf_trainer = TrainModel(df.dropna(), target=clf_target, features=clf_features, models=LogisticRegression(C=0.01, max_iter=10_000))

# Fit the object
clf_trainer.fit()

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	Recall	Specificity	Precision	F1-Score	Accuracy
Male	0.85	0.82	0.83	0.84	0.84
Female	0.82	0.85	0.84	0.83	0.84

The displayed dataframe when calling the fit() method contains the training results, this output can be suppressed by setting verbose=False.

# Evaluate the model
clf_trainer.evaluate()

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	Recall	Specificity	Precision	F1-Score	Accuracy
Male	0.73	0.86	0.83	0.78	0.8
Female	0.86	0.73	0.77	0.81	0.8

The displayed dataframe when calling the evaluate() method contains the testing results, this output can be suppressed by setting verbose=False.

# Access the fitted model
clf_trainer.fitted_models_dict

{'LogisticRegression': LogisticRegression(C=0.01, max_iter=10000)}

Multiple Classification Model Training

# Initialize TrainModel object
clfs = [LogisticRegression(), DecisionTreeClassifier(), RandomForestClassifier(), SVC(), KNeighborsClassifier()]

clf_trainer = TrainModel(df.dropna(), target=clf_target, features=clf_features, models=clfs, test_size=0.4)

# Fit the object
clf_trainer.fit(verbose=False)

# Evaluate the model
clf_trainer.evaluate(verbose=True)

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	Recall	Specificity	Precision	F1-Score	Accuracy
Male	0.76	0.81	0.82	0.79	0.78
Female	0.81	0.76	0.75	0.78	0.78

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	Recall	Specificity	Precision	F1-Score	Accuracy
Male	0.86	0.83	0.85	0.85	0.84
Female	0.83	0.86	0.84	0.83	0.84

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	Recall	Specificity	Precision	F1-Score	Accuracy
Male	0.84	0.86	0.87	0.85	0.85
Female	0.86	0.84	0.83	0.84	0.85

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	Recall	Specificity	Precision	F1-Score	Accuracy
Male	0.49	0.73	0.67	0.57	0.6
Female	0.73	0.49	0.56	0.63	0.6

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	Recall	Specificity	Precision	F1-Score	Accuracy
Male	0.74	0.78	0.79	0.76	0.76
Female	0.78	0.74	0.73	0.75	0.76

Results

clf_trainer.results_df

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	Model	Accuracy
0	RandomForestClassifier	0.85
1	DecisionTreeClassifier	0.84
2	LogisticRegression	0.78
3	KNeighborsClassifier	0.76
4	SVC	0.60

clf_trainer.fitted_models_dict

{'LogisticRegression': LogisticRegression(),
 'DecisionTreeClassifier': DecisionTreeClassifier(),
 'RandomForestClassifier': RandomForestClassifier(),
 'SVC': SVC(),
 'KNeighborsClassifier': KNeighborsClassifier()}

Accuracy results and the fitted models can be accessed through the results_df and fitted_models_dict attributes.

Regression Task

from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.svm import SVR
from sklearn.ensemble import GradientBoostingRegressor

df = sns.load_dataset('penguins')
df.head()

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	species	island	bill_length_mm	bill_depth_mm	flipper_length_mm	body_mass_g	sex
0	Adelie	Torgersen	39.1	18.7	181.0	3750.0	Male
1	Adelie	Torgersen	39.5	17.4	186.0	3800.0	Female
2	Adelie	Torgersen	40.3	18.0	195.0	3250.0	Female
3	Adelie	Torgersen	NaN	NaN	NaN	NaN	NaN
4	Adelie	Torgersen	36.7	19.3	193.0	3450.0	Female

reg_target = 'bill_length_mm'
reg_features = df.iloc[:, df.columns != reg_target].columns

print(f"Class: {reg_target}")
print(f"Features: {reg_features}")

Class: bill_length_mm
Features: Index(['species', 'island', 'bill_depth_mm', 'flipper_length_mm',
       'body_mass_g', 'sex'],
      dtype='object')

Single Regression Model Training

# Initialize TrainModel object
reg_trainer = TrainModel(df.dropna(), 
                         target=reg_target, 
                         features=reg_features,
                         models=LinearRegression())

# Fit the object
reg_trainer.fit(verbose=False)

# Evaluate the model
reg_trainer.evaluate()

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	MSE	RMSE	MAE	R-squared
Metrics	6.3	2.51	1.91	0.81

# Access the model
reg_trainer.fitted_models_dict

{'LinearRegression': LinearRegression()}

Multiple Regression Model Training

# Initialize TrainModel object
regs = [LinearRegression(), DecisionTreeRegressor(), RandomForestRegressor(), SVR(), GradientBoostingRegressor()]

reg_trainer = TrainModel(df.dropna(), target=reg_target, features=reg_features, models=regs, test_size=0.4)

# Fit the object
reg_trainer.fit(verbose=False)

# Evaluate the model
reg_trainer.evaluate(verbose=False)

Results

reg_trainer.results_df

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	Model	MSE	RMSE	MAE	R-squared
0	RandomForestRegressor	5.74	2.40	1.87	0.81
1	GradientBoostingRegressor	6.58	2.57	1.94	0.79
2	DecisionTreeRegressor	6.98	2.64	2.06	0.77
3	LinearRegression	7.63	2.76	2.11	0.75
4	SVR	21.51	4.64	3.63	0.31

reg_trainer.fitted_models_dict

{'LinearRegression': LinearRegression(),
 'DecisionTreeRegressor': DecisionTreeRegressor(),
 'RandomForestRegressor': RandomForestRegressor(),
 'SVR': SVR(),
 'GradientBoostingRegressor': GradientBoostingRegressor()}

Change Log

1.0.6 (06/13/2023)

• Added regression
• Modified docstrings
• Added pre-defined function
• Fixed local issues
• Added training feature for training multiple models.