| Name | funpredict JSON |
| Version |
0.0.6
JSON |
| download |
| home_page | |
| Summary | Introducing Fun Predict, the ultimate time-saver for machine learning! No more complex coding or tedious parameter tuning - just sit back and let Fun Predict build your basic models with ease. It's like having a personal assistant for your machine learning projects, making the process simple, efficient, and, well, Fun! 🛋 |
| upload_time | 2023-11-12 08:55:15 |
| maintainer | |
| docs_url | None |
| author | Sushanta Das |
| requires_python | |
| license | |
| keywords |
python
scikit-learn
machine learning
deep learning
computer vision
artificial intelligence
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# Fun Predict🤖
Fun Predict is a free, open-source Python library that helps you build and compare machine learning models easily, without writing much code. It allows you to quickly and easily evaluate a variety of models without having to write a lot of code or tune hyperparameters.
# Installation
To install Fun Predict:
```
pip install funpredict
```
# Usage
To use Fun Predict in a project:
```
import funpredict
```
# Classification
Example :
```
from funpredict.fun_model import PlayClassifier
from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split
# Test with a Classification model
data = load_wine()
X,y = data.data,data.target
X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=.5,random_state =42)
clf = PlayClassifier(verbose=0,ignore_warnings=True, custom_metric=None)
models,predictions = clf.fit(X_train, X_test, y_train, y_test,'multiclass')
# If you confirm which model working best then choose hare.
model_dictionary = clf.provide_models(X_train,X_test,y_train,y_test)
print(models)
| Accuracy | Balanced Accuracy| F1 Score | Time Taken |
-----------------------------------------------------------------------------------------|
| Model : |
| -----------------------------------------------------+
| ExtraTreesClassifier | 1.00 | 1.00 | 1.00 | 0.27 |
| RandomForestClassifier | 1.00 | 1.00 | 1.00 | 0.40 |
| GaussianNB | 1.00 | 1.00 | 1.00 | 0.02 |
| CatBoostClassifier | 0.99 | 0.99 | 0.99 | 3.32 |
| KNeighborsClassifier | 0.99 | 0.99 | 0.99 | 0.03 |
| RidgeClassifierCV | 0.99 | 0.99 | 0.99 | 0.02 |
| PassiveAggressiveClassifier | 0.99 | 0.99 | 0.99 | 0.04 |
| LogisticRegression | 0.99 | 0.99 | 0.99 | 0.03 |
| NearestCentroid | 0.98 | 0.98 | 0.98 | 0.03 |
| LGBMClassifier | 0.98 | 0.98 | 0.98 | 0.15 |
| Perceptron | 0.98 | 0.98 | 0.98 | 0.04 |
| SGDClassifier | 0.98 | 0.98 | 0.98 | 0.02 |
| LinearDiscriminantAnalysis | 0.98 | 0.98 | 0.98 | 0.02 |
| LinearSVC | 0.98 | 0.98 | 0.98 | 0.02 |
| RidgeClassifier | 0.98 | 0.98 | 0.98 | 0.02 |
| NuSVC | 0.98 | 0.98 | 0.98 | 0.02 |
| SVC | 0.98 | 0.98 | 0.98 | 0.02 |
| LabelPropagation | 0.97 | 0.97 | 0.97 | 0.02 |
| LabelSpreading | 0.97 | 0.97 | 0.97 | 0.02 |
| XGBClassifier | 0.97 | 0.97 | 0.97 | 0.10 |
| BaggingClassifier | 0.97 | 0.97 | 0.97 | 0.11 |
| BernoulliNB | 0.94 | 0.94 | 0.94 | 0.04 |
| CalibratedClassifierCV | 0.94 | 0.94 | 0.94 | 0.15 |
| AdaBoostClassifier | 0.93 | 0.93 | 0.93 | 0.29 |
| QuadraticDiscriminantAnalysis | 0.93 | 0.93 | 0.93 | 0.04 |
| DecisionTreeClassifier | 0.88 | 0.88 | 0.88 | 0.04 |
| ExtraTreeClassifier | 0.83 | 0.83 | 0.83 | 0.04 |
| DummyClassifier | 0.34 | 0.33 | 0.17 | 0.03 |
-------------------------------------------------------------------------------------------
```
```
# Vertical bar plot
clf.barplot(predictions)
```
```
# Horizontal bar plot
clf.hbarplot(predictions)
```
# Regression
Example :
```
from funpredict.fun_model import PlayRegressor
from sklearn.datasets import load_diabetes
from sklearn.model_selection import train_test_split
# Test with Regressor Model
data = load_diabetes()
X,y = data.data, data.target
X_train, X_test, y_train, y_test = train_test_split(X, y,test_size=.5,random_state =42)
rgs = PlayRegressor(verbose=0,ignore_warnings=True, custom_metric=None)
models,predictions = rgs.fit(X_train, X_test, y_train, y_test)
# If you confirm which model works best then choose hare.
model_dictionary = rgs.provide_models(X_train, X_test,y_train,y_test)
print(models)
|-----------------------------------------------------------------------------------------|
| Model | Adjusted R-Squared | R-Squared | RMSE | Time Taken |
|:------------------------------|-------------------:|----------:|------:|-----------:|
| BayesianRidge | 0.45 | 0.48 | 54.46 | 0.04 |
| ElasticNetCV | 0.46 | 0.48 | 54.41 | 0.31 |
| RidgeCV | 0.45 | 0.48 | 54.51 | 0.04 |
| LinearRegression | 0.45 | 0.48 | 54.58 | 0.03 |
| TransformedTargetRegressor | 0.45 | 0.48 | 54.58 | 0.04 |
| Lars | 0.45 | 0.48 | 54.58 | 0.05 |
| Ridge | 0.45 | 0.48 | 54.59 | 0.03 |
| Lasso | 0.45 | 0.47 | 54.69 | 0.03 |
| LassoLars | 0.45 | 0.47 | 54.69 | 0.03 |
| LassoCV | 0.45 | 0.47 | 54.70 | 0.28 |
| LassoLarsCV | 0.45 | 0.47 | 54.71 | 0.07 |
| PoissonRegressor | 0.45 | 0.47 | 54.76 | 0.04 |
| SGDRegressor | 0.45 | 0.47 | 54.76 | 0.04 |
| OrthogonalMatchingPursuitCV | 0.45 | 0.47 | 54.80 | 0.06 |
| HuberRegressor | 0.44 | 0.47 | 54.96 | 0.06 |
| LassoLarsIC | 0.44 | 0.47 | 55.02 | 0.03 |
| ElasticNet | 0.44 | 0.47 | 55.05 | 0.03 |
| LarsCV | 0.43 | 0.45 | 55.72 | 0.09 |
| AdaBoostRegressor | 0.42 | 0.44 | 56.34 | 0.34 |
| TweedieRegressor | 0.41 | 0.44 | 56.40 | 0.03 |
| ExtraTreesRegressor | 0.41 | 0.44 | 56.60 | 0.40 |
| PassiveAggressiveRegressor | 0.41 | 0.44 | 56.61 | 0.03 |
| GammaRegressor | 0.41 | 0.43 | 56.79 | 0.02 |
| LGBMRegressor | 0.40 | 0.43 | 57.04 | 0.12 |
| CatBoostRegressor | 0.39 | 0.42 | 57.47 | 3.26 |
| RandomForestRegressor | 0.38 | 0.41 | 58.00 | 0.79 |
| HistGradientBoostingRegressor | 0.36 | 0.39 | 58.84 | 0.27 |
| GradientBoostingRegressor | 0.36 | 0.39 | 58.95 | 0.31 |
| BaggingRegressor | 0.33 | 0.36 | 60.12 | 0.11 |
| KNeighborsRegressor | 0.29 | 0.32 | 62.09 | 0.03 |
| XGBRegressor | 0.23 | 0.27 | 64.59 | 0.21 |
| OrthogonalMatchingPursuit | 0.23 | 0.26 | 64.86 | 0.05 |
| RANSACRegressor | 0.11 | 0.15 | 69.40 | 0.33 |
| NuSVR | 0.07 | 0.11 | 70.99 | 0.08 |
| LinearSVR | 0.07 | 0.11 | 71.11 | 0.03 |
| SVR | 0.07 | 0.11 | 71.23 | 0.04 |
| DummyRegressor | 0.05 - | 0.00 | 75.45 | 0.02 |
| DecisionTreeRegressor | 0.13 - | 0.08 | 78.38 | 0.03 |
| ExtraTreeRegressor | 0.18 - | 0.13 | 80.02 | 0.02 |
| GaussianProcessRegressor | 0.99 - | 0.90 | 04.06 | 0.07 |
| MLPRegressor | 1.19 - | 1.09 | 09.17 | 1.34 |
| KernelRidge | 3.91 - | 3.69 | 63.34 | 0.06 |
|-------------------------------------------------------------------------------------|
```
```
# Vertical bar plot
rgs.barplot(predictions)
```
```
# Horizontal bar plot
rgs.hbarplot(predictions)
```
Raw data
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"description": "\r\n# Fun Predict\ud83e\udd16\r\r\nFun Predict is a free, open-source Python library that helps you build and compare machine learning models easily, without writing much code. It allows you to quickly and easily evaluate a variety of models without having to write a lot of code or tune hyperparameters.\r\r\n\r\r\n# Installation\r\r\nTo install Fun Predict:\r\r\n``` \r\r\npip install funpredict\r\r\n```\r\r\n\r\r\n# Usage\r\r\nTo use Fun Predict in a project:\r\r\n```\r\r\nimport funpredict\r\r\n```\r\r\n\r\r\n# Classification\r\r\nExample :\r\r\n```\r\r\nfrom funpredict.fun_model import PlayClassifier\r\r\nfrom sklearn.datasets import load_wine\r\r\nfrom sklearn.model_selection import train_test_split\r\r\n\r\r\n# Test with a Classification model\r\r\ndata = load_wine()\r\r\nX,y = data.data,data.target\r\r\n\r\r\nX_train, X_test, y_train, y_test = train_test_split(X, y,test_size=.5,random_state =42)\r\r\n\r\r\nclf = PlayClassifier(verbose=0,ignore_warnings=True, custom_metric=None)\r\r\nmodels,predictions = clf.fit(X_train, X_test, y_train, y_test,'multiclass')\r\r\n# If you confirm which model working best then choose hare.\r\r\nmodel_dictionary = clf.provide_models(X_train,X_test,y_train,y_test) \r\r\nprint(models)\r\r\n\r\r\n | Accuracy | Balanced Accuracy| F1 Score | Time Taken |\r\r\n -----------------------------------------------------------------------------------------|\r\r\n | Model : |\r\r\n | -----------------------------------------------------+\r\r\n | ExtraTreesClassifier | 1.00 | 1.00 | 1.00 | 0.27 |\r\r\n | RandomForestClassifier | 1.00 | 1.00 | 1.00 | 0.40 |\r\r\n | GaussianNB | 1.00 | 1.00 | 1.00 | 0.02 |\r\r\n | CatBoostClassifier | 0.99 | 0.99 | 0.99 | 3.32 |\r\r\n | KNeighborsClassifier | 0.99 | 0.99 | 0.99 | 0.03 |\r\r\n | RidgeClassifierCV | 0.99 | 0.99 | 0.99 | 0.02 |\r\r\n | PassiveAggressiveClassifier | 0.99 | 0.99 | 0.99 | 0.04 |\r\r\n | LogisticRegression | 0.99 | 0.99 | 0.99 | 0.03 |\r\r\n | NearestCentroid | 0.98 | 0.98 | 0.98 | 0.03 |\r\r\n | LGBMClassifier | 0.98 | 0.98 | 0.98 | 0.15 |\r\r\n | Perceptron | 0.98 | 0.98 | 0.98 | 0.04 |\r\r\n | SGDClassifier | 0.98 | 0.98 | 0.98 | 0.02 |\r\r\n | LinearDiscriminantAnalysis | 0.98 | 0.98 | 0.98 | 0.02 |\r\r\n | LinearSVC | 0.98 | 0.98 | 0.98 | 0.02 |\r\r\n | RidgeClassifier | 0.98 | 0.98 | 0.98 | 0.02 |\r\r\n | NuSVC | 0.98 | 0.98 | 0.98 | 0.02 |\r\r\n | SVC | 0.98 | 0.98 | 0.98 | 0.02 |\r\r\n | LabelPropagation | 0.97 | 0.97 | 0.97 | 0.02 |\r\r\n | LabelSpreading | 0.97 | 0.97 | 0.97 | 0.02 |\r\r\n | XGBClassifier | 0.97 | 0.97 | 0.97 | 0.10 |\r\r\n | BaggingClassifier | 0.97 | 0.97 | 0.97 | 0.11 |\r\r\n | BernoulliNB | 0.94 | 0.94 | 0.94 | 0.04 |\r\r\n | CalibratedClassifierCV | 0.94 | 0.94 | 0.94 | 0.15 |\r\r\n | AdaBoostClassifier | 0.93 | 0.93 | 0.93 | 0.29 |\r\r\n | QuadraticDiscriminantAnalysis | 0.93 | 0.93 | 0.93 | 0.04 |\r\r\n | DecisionTreeClassifier | 0.88 | 0.88 | 0.88 | 0.04 |\r\r\n | ExtraTreeClassifier | 0.83 | 0.83 | 0.83 | 0.04 |\r\r\n | DummyClassifier | 0.34 | 0.33 | 0.17 | 0.03 |\r\r\n -------------------------------------------------------------------------------------------\r\r\n```\r\r\n```\r\r\n# Vertical bar plot\r\r\nclf.barplot(predictions)\r\r\n```\r\r\n\r\r\n\r\r\n```\r\r\n# Horizontal bar plot\r\r\nclf.hbarplot(predictions)\r\r\n```\r\r\n\r\r\n\r\r\n# Regression\r\r\nExample :\r\r\n```\r\r\nfrom funpredict.fun_model import PlayRegressor\r\r\nfrom sklearn.datasets import load_diabetes\r\r\nfrom sklearn.model_selection import train_test_split\r\r\n\r\r\n# Test with Regressor Model\r\r\ndata = load_diabetes()\r\r\nX,y = data.data, data.target\r\r\nX_train, X_test, y_train, y_test = train_test_split(X, y,test_size=.5,random_state =42)\r\r\n\r\r\nrgs = PlayRegressor(verbose=0,ignore_warnings=True, custom_metric=None)\r\r\nmodels,predictions = rgs.fit(X_train, X_test, y_train, y_test)\r\r\n# If you confirm which model works best then choose hare.\r\r\nmodel_dictionary = rgs.provide_models(X_train, X_test,y_train,y_test)\r\r\nprint(models)\r\r\n\r\r\n|-----------------------------------------------------------------------------------------|\r\r\n| Model | Adjusted R-Squared | R-Squared | RMSE | Time Taken | \r\r\n |:------------------------------|-------------------:|----------:|------:|-----------:|\r\r\n | BayesianRidge | 0.45 | 0.48 | 54.46 | 0.04 |\r\r\n | ElasticNetCV | 0.46 | 0.48 | 54.41 | 0.31 |\r\r\n | RidgeCV | 0.45 | 0.48 | 54.51 | 0.04 |\r\r\n | LinearRegression | 0.45 | 0.48 | 54.58 | 0.03 |\r\r\n | TransformedTargetRegressor | 0.45 | 0.48 | 54.58 | 0.04 |\r\r\n | Lars | 0.45 | 0.48 | 54.58 | 0.05 |\r\r\n | Ridge | 0.45 | 0.48 | 54.59 | 0.03 |\r\r\n | Lasso | 0.45 | 0.47 | 54.69 | 0.03 |\r\r\n | LassoLars | 0.45 | 0.47 | 54.69 | 0.03 |\r\r\n | LassoCV | 0.45 | 0.47 | 54.70 | 0.28 |\r\r\n | LassoLarsCV | 0.45 | 0.47 | 54.71 | 0.07 |\r\r\n | PoissonRegressor | 0.45 | 0.47 | 54.76 | 0.04 |\r\r\n | SGDRegressor | 0.45 | 0.47 | 54.76 | 0.04 |\r\r\n | OrthogonalMatchingPursuitCV | 0.45 | 0.47 | 54.80 | 0.06 |\r\r\n | HuberRegressor | 0.44 | 0.47 | 54.96 | 0.06 |\r\r\n | LassoLarsIC | 0.44 | 0.47 | 55.02 | 0.03 |\r\r\n | ElasticNet | 0.44 | 0.47 | 55.05 | 0.03 |\r\r\n | LarsCV | 0.43 | 0.45 | 55.72 | 0.09 |\r\r\n | AdaBoostRegressor | 0.42 | 0.44 | 56.34 | 0.34 |\r\r\n | TweedieRegressor | 0.41 | 0.44 | 56.40 | 0.03 |\r\r\n | ExtraTreesRegressor | 0.41 | 0.44 | 56.60 | 0.40 |\r\r\n | PassiveAggressiveRegressor | 0.41 | 0.44 | 56.61 | 0.03 |\r\r\n | GammaRegressor | 0.41 | 0.43 | 56.79 | 0.02 |\r\r\n | LGBMRegressor | 0.40 | 0.43 | 57.04 | 0.12 |\r\r\n | CatBoostRegressor | 0.39 | 0.42 | 57.47 | 3.26 |\r\r\n | RandomForestRegressor | 0.38 | 0.41 | 58.00 | 0.79 |\r\r\n | HistGradientBoostingRegressor | 0.36 | 0.39 | 58.84 | 0.27 |\r\r\n | GradientBoostingRegressor | 0.36 | 0.39 | 58.95 | 0.31 |\r\r\n | BaggingRegressor | 0.33 | 0.36 | 60.12 | 0.11 |\r\r\n | KNeighborsRegressor | 0.29 | 0.32 | 62.09 | 0.03 |\r\r\n | XGBRegressor | 0.23 | 0.27 | 64.59 | 0.21 |\r\r\n | OrthogonalMatchingPursuit | 0.23 | 0.26 | 64.86 | 0.05 |\r\r\n | RANSACRegressor | 0.11 | 0.15 | 69.40 | 0.33 |\r\r\n | NuSVR | 0.07 | 0.11 | 70.99 | 0.08 |\r\r\n | LinearSVR | 0.07 | 0.11 | 71.11 | 0.03 |\r\r\n | SVR | 0.07 | 0.11 | 71.23 | 0.04 |\r\r\n | DummyRegressor | 0.05 - | 0.00 | 75.45 | 0.02 |\r\r\n | DecisionTreeRegressor | 0.13 - | 0.08 | 78.38 | 0.03 |\r\r\n | ExtraTreeRegressor | 0.18 - | 0.13 | 80.02 | 0.02 |\r\r\n | GaussianProcessRegressor | 0.99 - | 0.90 | 04.06 | 0.07 |\r\r\n | MLPRegressor | 1.19 - | 1.09 | 09.17 | 1.34 |\r\r\n | KernelRidge | 3.91 - | 3.69 | 63.34 | 0.06 |\r\r\n |-------------------------------------------------------------------------------------|\r\r\n```\r\r\n\r\r\n```\r\r\n# Vertical bar plot\r\r\nrgs.barplot(predictions)\r\r\n```\r\r\n\r\r\n\r\r\n```\r\r\n# Horizontal bar plot\r\r\nrgs.hbarplot(predictions)\r\r\n```\r\r\n\r\r\n",
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