This is a library developed to incorporate useful properties and methods in relevant data science packages, such as scikit-learn and pycaret, in order to provide a pipeline which suits every supervised problem. Therefore, data scientists can spend less time working on building pipelines and use this time more wisely to create new features and tune the best model.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
df = pd.read_csv("juice.csv")
df.head()
Id | Purchase | WeekofPurchase | StoreID | PriceCH | PriceMM | DiscCH | DiscMM | SpecialCH | SpecialMM | LoyalCH | SalePriceMM | SalePriceCH | PriceDiff | Store7 | PctDiscMM | PctDiscCH | ListPriceDiff | STORE | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | CH | 237 | 1 | 1.75 | 1.99 | 0.00 | 0.0 | 0 | 0 | 0.500000 | 1.99 | 1.75 | 0.24 | No | 0.000000 | 0.000000 | 0.24 | 1 |
1 | 2 | CH | 239 | 1 | 1.75 | 1.99 | 0.00 | 0.3 | 0 | 1 | 0.600000 | 1.69 | 1.75 | -0.06 | No | 0.150754 | 0.000000 | 0.24 | 1 |
2 | 3 | CH | 245 | 1 | 1.86 | 2.09 | 0.17 | 0.0 | 0 | 0 | 0.680000 | 2.09 | 1.69 | 0.40 | No | 0.000000 | 0.091398 | 0.23 | 1 |
3 | 4 | MM | 227 | 1 | 1.69 | 1.69 | 0.00 | 0.0 | 0 | 0 | 0.400000 | 1.69 | 1.69 | 0.00 | No | 0.000000 | 0.000000 | 0.00 | 1 |
4 | 5 | CH | 228 | 7 | 1.69 | 1.69 | 0.00 | 0.0 | 0 | 0 | 0.956535 | 1.69 | 1.69 | 0.00 | Yes | 0.000000 | 0.000000 | 0.00 | 0 |
X = df.iloc[:, 2:]
y = df.iloc[:, 1].apply(lambda x: 1 if x=="CH" else 0)
train_X, test_X, train_y, test_y = train_test_split(X, y, test_size=0.2, random_state=0)
Importing our class for classification problems
from automl.classification import ClassifierPyCaret
Defining object
my_clf = ClassifierPyCaret(metric="AUC")
Training all models and selecting the one with best performance
my_clf.fit(train_X, train_y)
Model | Accuracy | AUC | Recall | Prec. | F1 | Kappa | |
---|---|---|---|---|---|---|---|
0 | Linear Discriminant Analysis | 0.8312 | 0.8994 | 0.8724 | 0.8589 | 0.8649 | 0.6396 |
1 | Logistic Regression | 0.8338 | 0.8965 | 0.8787 | 0.858 | 0.8674 | 0.6444 |
2 | CatBoost Classifier | 0.8104 | 0.889 | 0.8514 | 0.8459 | 0.8475 | 0.5963 |
3 | Extreme Gradient Boosting | 0.8078 | 0.8838 | 0.8599 | 0.8379 | 0.8475 | 0.5873 |
4 | Gradient Boosting Classifier | 0.8039 | 0.8804 | 0.8577 | 0.8338 | 0.8444 | 0.5789 |
5 | Light Gradient Boosting Machine | 0.7831 | 0.8745 | 0.8284 | 0.8255 | 0.8256 | 0.5381 |
6 | Ada Boost Classifier | 0.8169 | 0.8628 | 0.8787 | 0.8358 | 0.8563 | 0.6042 |
7 | Random Forest Classifier | 0.7831 | 0.8401 | 0.7951 | 0.848 | 0.8185 | 0.5493 |
8 | K Neighbors Classifier | 0.7818 | 0.8367 | 0.8451 | 0.8117 | 0.8278 | 0.5302 |
9 | Naive Bayes | 0.761 | 0.8308 | 0.7658 | 0.8381 | 0.7994 | 0.5049 |
10 | Extra Trees Classifier | 0.7532 | 0.8279 | 0.8034 | 0.8004 | 0.8006 | 0.4764 |
11 | Quadratic Discriminant Analysis | 0.713 | 0.7364 | 0.7844 | 0.7625 | 0.7716 | 0.3838 |
12 | Decision Tree Classifier | 0.7325 | 0.7222 | 0.7659 | 0.7991 | 0.7797 | 0.438 |
13 | SVM - Linear Kernel | 0.7727 | 0 | 0.8682 | 0.7921 | 0.8261 | 0.4988 |
14 | Ridge Classifier | 0.8338 | 0 | 0.8704 | 0.8641 | 0.8661 | 0.6465 |
my_clf.best_model
LinearDiscriminantAnalysis(n_components=None, priors=None, shrinkage=0.001,
solver='lsqr', store_covariance=False, tol=0.0001)
Predicting
my_clf.predict(test_X)
array([0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,
0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 0,
0, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 1,
0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1,
0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 0,
0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0,
1, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1,
0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1,
1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1,
1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1])
test_y.values
array([0, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0,
0, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0,
0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0,
0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1,
0, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 0, 1,
0, 0, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0,
1, 1, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1,
0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 0, 1,
1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0,
1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1])
Predicting score
my_clf.predict_proba(test_X)[:15]
array([[0.81394134, 0.18605866],
[0.29250523, 0.70749477],
[0.0556436 , 0.9443564 ],
[0.34100222, 0.65899778],
[0.30329665, 0.69670335],
[0.93766687, 0.06233313],
[0.79220565, 0.20779435],
[0.20849993, 0.79150007],
[0.03889261, 0.96110739],
[0.96823484, 0.03176516],
[0.03828661, 0.96171339],
[0.05635964, 0.94364036],
[0.34055076, 0.65944924],
[0.01090129, 0.98909871],
[0.00782886, 0.99217114]])
Preprocessed test set
my_clf.preprocess(test_X)
WeekofPurchase | StoreID | PriceCH | PriceMM | SpecialCH | SpecialMM | LoyalCH | SalePriceMM | SalePriceCH | PriceDiff | PctDiscMM | PctDiscCH | ListPriceDiff | STORE | Store7_Yes | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
928 | -1.332854 | -0.420137 | -0.778278 | 0.026205 | -0.411581 | -0.436512 | -1.199095 | 0.497515 | -0.176508 | 0.562370 | -0.581691 | -0.446505 | 0.764189 | 0.958208 | 0.0 |
780 | -1.462320 | -1.284649 | -1.769803 | -0.724024 | 2.429655 | -0.436512 | 0.038105 | 0.097597 | -0.875681 | 0.562370 | -0.581691 | -0.446505 | 0.764189 | -0.435549 | 0.0 |
564 | 0.738609 | 1.308888 | -0.084210 | 0.326297 | 2.429655 | -0.436512 | 0.082491 | 0.657482 | -2.274027 | 1.840059 | -0.581691 | 2.722738 | 0.484560 | -1.132427 | 1.0 |
520 | 0.285476 | -0.852393 | -0.084210 | 0.701412 | -0.411581 | 2.290890 | 0.341921 | -0.742231 | 0.312913 | -0.865637 | 1.253504 | -0.446505 | 0.950608 | 0.261329 | 0.0 |
399 | 1.062275 | 1.308888 | -0.084210 | 0.326297 | -0.411581 | -0.436512 | -0.470514 | 0.657482 | 0.312913 | 0.449632 | -0.581691 | -0.446505 | 0.484560 | -1.132427 | 1.0 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
231 | -1.721253 | 1.308888 | -1.769803 | -2.974714 | -0.411581 | -0.436512 | -0.127836 | -1.102158 | -0.875681 | -0.565004 | -0.581691 | -0.446505 | -2.032101 | -1.132427 | 1.0 |
669 | -0.038190 | 0.012119 | 1.204772 | 1.526665 | 2.429655 | -0.436512 | -0.241797 | 1.297351 | 1.221837 | 0.562370 | -0.581691 | -0.446505 | 0.764189 | 1.655086 | 0.0 |
821 | -1.721253 | 0.012119 | -0.778278 | -2.224484 | -0.411581 | -0.436512 | 0.537512 | -0.702239 | -0.176508 | -0.565004 | -0.581691 | -0.446505 | -2.032101 | 1.655086 | 0.0 |
54 | 0.091277 | 0.012119 | 1.204772 | 1.526665 | -0.411581 | -0.436512 | 0.302929 | 1.297351 | 1.221837 | 0.562370 | -0.581691 | -0.446505 | 0.764189 | 1.655086 | 0.0 |
34 | -0.232389 | -0.420137 | 1.204772 | 1.076527 | -0.411581 | -0.436512 | -0.099110 | 1.057400 | 1.221837 | 0.336895 | -0.581691 | -0.446505 | 0.204931 | 0.958208 | 0.0 |
214 rows × 15 columns
Evaluating final model
my_clf.evaluate(test_X, test_y)
Accuracy | Recall | Precision | F1-Score | AUC-ROC | |
---|---|---|---|---|---|
Linear Discriminant Analysis | 0.82243 | 0.844262 | 0.844262 | 0.844262 | 0.896605 |
Ploting curves to evaluate performace
my_clf.binary_evaluation_plot(test_X, test_y)
df = pd.read_csv("boston.csv")
df.head()
crim | zn | indus | chas | nox | rm | age | dis | rad | tax | ptratio | black | lstat | medv | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 0.00632 | 18.0 | 2.31 | 0 | 0.538 | 6.575 | 65.2 | 4.0900 | 1 | 296 | 15.3 | 396.90 | 4.98 | 24.0 |
1 | 0.02731 | 0.0 | 7.07 | 0 | 0.469 | 6.421 | 78.9 | 4.9671 | 2 | 242 | 17.8 | 396.90 | 9.14 | 21.6 |
2 | 0.02729 | 0.0 | 7.07 | 0 | 0.469 | 7.185 | 61.1 | 4.9671 | 2 | 242 | 17.8 | 392.83 | 4.03 | 34.7 |
3 | 0.03237 | 0.0 | 2.18 | 0 | 0.458 | 6.998 | 45.8 | 6.0622 | 3 | 222 | 18.7 | 394.63 | 2.94 | 33.4 |
4 | 0.06905 | 0.0 | 2.18 | 0 | 0.458 | 7.147 | 54.2 | 6.0622 | 3 | 222 | 18.7 | 396.90 | 5.33 | 36.2 |
X = df.iloc[:, :-1]
y = df.iloc[:, -1]
train_X, test_X, train_y, test_y = train_test_split(X, y, test_size=0.2, random_state=0)
Importing our class for classification problems
from automl.regression import RegressorPyCaret
Defining object
my_reg = RegressorPyCaret(metric="RMSE")
Training all models and selecting the one with best performance
my_reg.fit(train_X, train_y)
Model | MAE | MSE | RMSE | R2 | RMSLE | MAPE | |
---|---|---|---|---|---|---|---|
0 | Extra Trees Regressor | 2.0117 | 9.8317 | 3.0117 | 0.882 | 0.1313 | 0.1 |
1 | CatBoost Regressor | 2.0328 | 10.0328 | 3.0457 | 0.8825 | 0.1337 | 0.1004 |
2 | Gradient Boosting Regressor | 2.0817 | 10.3726 | 3.1033 | 0.8773 | 0.1392 | 0.1055 |
3 | Extreme Gradient Boosting | 2.1903 | 11.3859 | 3.2391 | 0.8657 | 0.1424 | 0.1093 |
4 | Light Gradient Boosting Machine | 2.2946 | 12.4216 | 3.429 | 0.8533 | 0.1456 | 0.1116 |
5 | Random Forest | 2.2884 | 12.7449 | 3.4698 | 0.8493 | 0.1494 | 0.1139 |
6 | AdaBoost Regressor | 2.6787 | 14.0651 | 3.6835 | 0.8322 | 0.1674 | 0.1377 |
7 | K Neighbors Regressor | 2.7906 | 19.2193 | 4.2479 | 0.777 | 0.1706 | 0.1285 |
8 | Bayesian Ridge | 3.1667 | 21.2922 | 4.546 | 0.7493 | 0.2214 | 0.1552 |
9 | Ridge Regression | 3.1846 | 21.357 | 4.5549 | 0.7483 | 0.2214 | 0.1558 |
10 | Linear Regression | 3.1907 | 21.3876 | 4.5588 | 0.7479 | 0.2215 | 0.156 |
11 | Huber Regressor | 3.02 | 21.9739 | 4.5865 | 0.7425 | 0.2423 | 0.1459 |
12 | Random Sample Consensus | 3.141 | 23.5192 | 4.6983 | 0.7248 | 0.257 | 0.1529 |
13 | Decision Tree | 3.1003 | 23.0186 | 4.7735 | 0.7244 | 0.2072 | 0.156 |
14 | Least Angle Regression | 3.4683 | 26.3764 | 5.0549 | 0.6804 | 0.2535 | 0.1757 |
15 | Lasso Regression | 3.586 | 26.3971 | 5.0742 | 0.6933 | 0.2454 | 0.1818 |
16 | Elastic Net | 3.6377 | 27.6603 | 5.2005 | 0.6777 | 0.2227 | 0.1786 |
17 | Support Vector Machine | 3.2355 | 29.4341 | 5.3048 | 0.6639 | 0.2138 | 0.1569 |
18 | TheilSen Regressor | 3.6964 | 33.5251 | 5.6568 | 0.6107 | 0.3268 | 0.1814 |
19 | Passive Aggressive Regressor | 4.3705 | 38.8214 | 6.203 | 0.5339 | 0.3688 | 0.2342 |
20 | Orthogonal Matching Pursuit | 4.7336 | 43.4094 | 6.4761 | 0.4999 | 0.3263 | 0.2297 |
21 | Lasso Least Angle Regression | 6.8136 | 86.403 | 9.261 | -0.0147 | 0.3952 | 0.3674 |
my_reg.best_model
ExtraTreesRegressor(bootstrap=False, ccp_alpha=0.0, criterion='mse',
max_depth=50, max_features='auto', max_leaf_nodes=None,
max_samples=None, min_impurity_decrease=0.0,
min_impurity_split=None, min_samples_leaf=1,
min_samples_split=7, min_weight_fraction_leaf=0.0,
n_estimators=230, n_jobs=None, oob_score=False,
random_state=4079, verbose=0, warm_start=False)
Predicting
my_reg.predict(test_X)
array([23.59301449, 23.86976812, 22.9758913 , 10.77536232, 21.7523913 ,
20.45304348, 21.64648551, 20.05999275, 20.34348551, 19.65515942,
8.15554348, 15.19625362, 15.24094203, 8.6743913 , 48.79373913,
34.54835507, 21.51931159, 36.47942754, 25.99400725, 21.06105072,
23.53934783, 22.12610145, 19.5293913 , 24.70913043, 20.46073188,
17.92857246, 17.57008696, 16.32171014, 43.31463043, 19.55130435,
15.54263768, 18.10597101, 21.12585507, 21.45034058, 24.27658696,
19.78678986, 9.05228261, 25.52650725, 14.11785507, 15.47104348,
22.96647826, 20.59884058, 21.83624638, 16.47018841, 24.62587681,
23.3175942 , 20.49226087, 18.62208696, 15.1505942 , 24.18486232,
16.4717971 , 20.65637681, 21.94365217, 38.90871739, 15.64312319,
21.3097029 , 19.55873913, 19.6977971 , 19.44619565, 20.55236957,
21.44344203, 22.00573188, 32.4110942 , 28.30823188, 19.36657246,
27.20982609, 15.82344928, 22.58002899, 15.18563768, 22.05265217,
20.84502174, 22.77226812, 24.42430435, 31.16600725, 26.7797029 ,
8.60797101, 42.23894928, 22.59955797, 23.76918841, 19.81447826,
25.12301449, 18.41964493, 19.59877536, 42.00841304, 41.19852899,
23.88085507, 22.77155797, 14.20571739, 26.48037681, 15.24131884,
19.44444203, 11.67923188, 22.47393478, 31.38963043, 21.19071739,
22.03372464, 12.33304348, 23.81121014, 14.16386957, 18.81552899,
23.94378986, 19.97833333])
test_y.values
array([22.6, 50. , 23. , 8.3, 21.2, 19.9, 20.6, 18.7, 16.1, 18.6, 8.8,
17.2, 14.9, 10.5, 50. , 29. , 23. , 33.3, 29.4, 21. , 23.8, 19.1,
20.4, 29.1, 19.3, 23.1, 19.6, 19.4, 38.7, 18.7, 14.6, 20. , 20.5,
20.1, 23.6, 16.8, 5.6, 50. , 14.5, 13.3, 23.9, 20. , 19.8, 13.8,
16.5, 21.6, 20.3, 17. , 11.8, 27.5, 15.6, 23.1, 24.3, 42.8, 15.6,
21.7, 17.1, 17.2, 15. , 21.7, 18.6, 21. , 33.1, 31.5, 20.1, 29.8,
15.2, 15. , 27.5, 22.6, 20. , 21.4, 23.5, 31.2, 23.7, 7.4, 48.3,
24.4, 22.6, 18.3, 23.3, 17.1, 27.9, 44.8, 50. , 23. , 21.4, 10.2,
23.3, 23.2, 18.9, 13.4, 21.9, 24.8, 11.9, 24.3, 13.8, 24.7, 14.1,
18.7, 28.1, 19.8])
Preprocessed test set
my_reg.preprocess(test_X)
crim | zn | indus | chas | nox | rm | age | dis | rad | tax | ptratio | black | lstat | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
329 | -0.408359 | -0.499608 | -1.128729 | -0.272888 | -0.833369 | 0.044972 | -1.846215 | 0.695069 | -0.624648 | 0.159137 | -0.712729 | 0.185476 | -0.736103 |
371 | 0.719251 | -0.499608 | 0.998884 | -0.272888 | 0.652840 | -0.123657 | 1.103327 | -1.251749 | 1.687378 | 1.542121 | 0.792674 | 0.083165 | -0.435692 |
219 | -0.402575 | -0.499608 | 0.396108 | 3.664502 | -0.051154 | 0.102623 | 0.832597 | -0.195833 | -0.509046 | -0.743319 | -0.940821 | 0.394727 | -0.302632 |
403 | 2.634810 | -0.499608 | 0.998884 | -0.272888 | 1.191699 | -1.373240 | 0.960837 | -0.994916 | 1.687378 | 1.542121 | 0.792674 | 0.430412 | 0.968974 |
78 | -0.409685 | -0.499608 | 0.244340 | -0.272888 | -1.033268 | -0.100597 | -0.545994 | 0.598583 | -0.509046 | -0.028387 | 0.108400 | 0.311840 | -0.050232 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
56 | -0.414103 | 3.100922 | -1.486672 | -0.272888 | -1.267933 | 0.117036 | -1.187199 | 2.606998 | -0.855850 | -0.526496 | -0.530256 | 0.430412 | -0.951467 |
455 | 0.168069 | -0.499608 | 0.998884 | -0.272888 | 1.365525 | 0.321696 | 0.622424 | -0.642174 | 1.687378 | 1.542121 | 0.792674 | -3.476599 | 0.744008 |
60 | -0.398260 | 0.559372 | -0.858124 | -0.272888 | -0.894208 | -0.808261 | -0.100713 | 1.662728 | -0.162242 | -0.696439 | 0.564583 | 0.410198 | 0.060880 |
213 | -0.399343 | -0.499608 | -0.076377 | -0.272888 | -0.581322 | 0.105505 | -1.308316 | 0.084292 | -0.624648 | -0.737459 | 0.062782 | 0.305177 | -0.456268 |
108 | -0.400881 | -0.499608 | -0.367026 | -0.272888 | -0.311892 | 0.248191 | 1.000022 | -0.643570 | -0.509046 | -0.110428 | 1.112002 | 0.411666 | -0.059834 |
102 rows × 13 columns
Evaluating final model
my_reg.evaluate(test_X, test_y)
MAE | MSE | RMSE | R2 | RMLSE | MAPE | |
---|---|---|---|---|---|---|
Extra Trees Regressor | 2.804562 | 23.271022 | 4.824005 | 0.714215 | 0.182163 | 0.128135 |
Checking on residuals
my_reg.plot_analysis(test_X, test_y)
Copyright (c) 2018 The Python Packaging Authority
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