Deployment und Abschluss - pycaret

Install

Attention: PyCaret does not run natively on Apple Silicon and in this case it is recommended to use Docker instead

ref: https://pycaret.gitbook.io/docs/get-started/installation

You can install PyCaret with Python's pip package manager:

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Load and prep Data

In [4]:

## load data
import numpy as np
import pandas as pd

datapath = '../3_data'
from os import chdir; chdir(datapath)
dataset = pd.read_csv('bank_data_prep.csv')
print(dataset.shape)

(9860, 30)

In [5]:

## remove duration
dataset = dataset.drop("duration", axis = 1)

In [6]:

## train - test - split
from sklearn.model_selection import train_test_split
data, data_unseen = train_test_split(dataset, train_size=0.9, random_state=1234)

print('Data for Modeling: ' + str(data.shape))
print('Unseen Data For Predictions: ' + str(data_unseen.shape))

Data for Modeling: (8874, 29)
Unseen Data For Predictions: (986, 29)

Run a Classication Experiment

Init setup

In [9]:

from pycaret.classification import *
s = setup(
    data = data, 
    target = 'y', 
    fold = 5, ## defaul = 10
    session_id=1234) ## random seed

	Description	Value
0	Session id	1234
1	Target	y
2	Target type	Binary
3	Target mapping	no: 0, yes: 1
4	Original data shape	(8874, 29)
5	Transformed data shape	(8874, 29)
6	Transformed train set shape	(6211, 29)
7	Transformed test set shape	(2663, 29)
8	Numeric features	14
9	Preprocess	True
10	Imputation type	simple
11	Numeric imputation	mean
12	Categorical imputation	mode
13	Fold Generator	StratifiedKFold
14	Fold Number	5
15	CPU Jobs	-1
16	Use GPU	False
17	Log Experiment	False
18	Experiment Name	clf-default-name
19	USI	9ce1

Show available Models (for Classification)

In [11]:

models()

Out [11]:

	Name	Reference	Turbo
ID
lr	Logistic Regression	sklearn.linear_model._logistic.LogisticRegression	True
knn	K Neighbors Classifier	sklearn.neighbors._classification.KNeighborsCl...	True
nb	Naive Bayes	sklearn.naive_bayes.GaussianNB	True
dt	Decision Tree Classifier	sklearn.tree._classes.DecisionTreeClassifier	True
svm	SVM - Linear Kernel	sklearn.linear_model._stochastic_gradient.SGDC...	True
rbfsvm	SVM - Radial Kernel	sklearn.svm._classes.SVC	False
gpc	Gaussian Process Classifier	sklearn.gaussian_process._gpc.GaussianProcessC...	False
mlp	MLP Classifier	sklearn.neural_network._multilayer_perceptron....	False
ridge	Ridge Classifier	sklearn.linear_model._ridge.RidgeClassifier	True
rf	Random Forest Classifier	sklearn.ensemble._forest.RandomForestClassifier	True
qda	Quadratic Discriminant Analysis	sklearn.discriminant_analysis.QuadraticDiscrim...	True
ada	Ada Boost Classifier	sklearn.ensemble._weight_boosting.AdaBoostClas...	True
gbc	Gradient Boosting Classifier	sklearn.ensemble._gb.GradientBoostingClassifier	True
lda	Linear Discriminant Analysis	sklearn.discriminant_analysis.LinearDiscrimina...	True
et	Extra Trees Classifier	sklearn.ensemble._forest.ExtraTreesClassifier	True
lightgbm	Light Gradient Boosting Machine	lightgbm.sklearn.LGBMClassifier	True
catboost	CatBoost Classifier	catboost.core.CatBoostClassifier	True
dummy	Dummy Classifier	sklearn.dummy.DummyClassifier	True

Model Training and Selection

In [13]:

best_model = compare_models()

	Model	Accuracy	AUC	Recall	Prec.	F1	Kappa	MCC	TT (Sec)
gbc	Gradient Boosting Classifier	0.7617	0.8020	0.7617	0.7730	0.7567	0.5144	0.5290	0.4640
catboost	CatBoost Classifier	0.7582	0.7996	0.7582	0.7681	0.7534	0.5074	0.5207	3.4800
lightgbm	Light Gradient Boosting Machine	0.7543	0.7954	0.7543	0.7632	0.7498	0.4998	0.5120	0.3200
ada	Ada Boost Classifier	0.7492	0.7957	0.7492	0.7616	0.7432	0.4882	0.5044	0.2180
rf	Random Forest Classifier	0.7421	0.7902	0.7421	0.7451	0.7394	0.4771	0.4826	0.4160
lr	Logistic Regression	0.7393	0.7852	0.7393	0.7435	0.7361	0.4709	0.4778	2.8060
ridge	Ridge Classifier	0.7326	0.7851	0.7326	0.7353	0.7298	0.4578	0.4630	0.0740
lda	Linear Discriminant Analysis	0.7324	0.7851	0.7324	0.7351	0.7296	0.4575	0.4627	0.0780
et	Extra Trees Classifier	0.7224	0.7732	0.7224	0.7232	0.7206	0.4387	0.4414	0.4140
qda	Quadratic Discriminant Analysis	0.7221	0.7713	0.7221	0.7379	0.7132	0.4313	0.4520	0.0900
nb	Naive Bayes	0.7150	0.7637	0.7150	0.7169	0.7122	0.4224	0.4269	0.0640
knn	K Neighbors Classifier	0.7142	0.7454	0.7142	0.7144	0.7129	0.4230	0.4247	2.5480
dt	Decision Tree Classifier	0.6614	0.6608	0.6614	0.6618	0.6615	0.3206	0.3207	0.0840
dummy	Dummy Classifier	0.5320	0.5000	0.5320	0.2830	0.3694	0.0000	0.0000	0.0620
svm	SVM - Linear Kernel	0.5118	0.7084	0.5118	0.4463	0.3527	0.0124	0.0413	0.1960

Processing:   0%|          | 0/65 [00:00<?, ?it/s]

MCC, see: https://en.wikipedia.org/wiki/Phi_coefficient

In [15]:

print(best_model)

GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,
                           learning_rate=0.1, loss='log_loss', max_depth=3,
                           max_features=None, max_leaf_nodes=None,
                           min_impurity_decrease=0.0, min_samples_leaf=1,
                           min_samples_split=2, min_weight_fraction_leaf=0.0,
                           n_estimators=100, n_iter_no_change=None,
                           random_state=1234, subsample=1.0, tol=0.0001,
                           validation_fraction=0.1, verbose=0,
                           warm_start=False)

Analyze best Model

In [17]:

evaluate_model(best_model)

interactive(children=(ToggleButtons(description='Plot Type:', icons=('',), options=(('Pipeline Plot', 'pipelin…

Predict on unseen Data

In [19]:

predictions = predict_model(best_model, data=data_unseen)
print(predictions.head())

	Model	Accuracy	AUC	Recall	Prec.	F1	Kappa	MCC
0	Gradient Boosting Classifier	0.7495	0.7990	0.7495	0.7626	0.7457	0.4972	0.5109

       age  education  housing  contact_cellular  month  day_of_week  \
2809  36.0          6        0                 1      5            2   
4052  34.0          6        1                 1      8            3   
658   36.0          6        1                 0      5            2   
786   40.0          5        0                 0      5            3   
6675  36.0          6        0                 1     11            3   

      campaign  pdays  previous  emp_var_rate  ...  job_student  \
2809  0.477121      0         0          -1.8  ...        False   
4052  0.698970      0         0           1.4  ...        False   
658   0.903090      0         0          -1.8  ...        False   
786   0.698970      0         0           1.1  ...        False   
6675  0.477121      0         0          -0.1  ...        False   

      job_technician  job_unemployed  marital_married  marital_single  \
2809           False           False            False            True   
4052            True           False            False            True   
658            False           False            False            True   
786            False           False             True           False   
6675           False           False            False            True   

      loan_unknown  loan_yes    y  prediction_label  prediction_score  
2809         False     False  yes                no            0.5681  
4052         False      True  yes                no            0.6703  
658          False     False  yes                no            0.8444  
786          False     False   no                no            0.7768  
6675         False     False   no                no            0.6379  

[5 rows x 31 columns]

Save best Model Pipeline

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Tune a specific Model

ref: https://pycaret.gitbook.io/docs/get-started/functions/optimize

rf: Random Forest Classifier

In [23]:

## create model
model_rf = create_model('rf')

	Accuracy	AUC	Recall	Prec.	F1	Kappa	MCC
Fold
0	0.7409	0.7939	0.7409	0.7447	0.7379	0.4744	0.4808
1	0.7536	0.7961	0.7536	0.7583	0.7505	0.4998	0.5072
2	0.7351	0.7710	0.7351	0.7392	0.7317	0.4621	0.4691
3	0.7311	0.7967	0.7311	0.7329	0.7287	0.4553	0.4594
4	0.7496	0.7931	0.7496	0.7506	0.7481	0.4940	0.4966
Mean	0.7421	0.7902	0.7421	0.7451	0.7394	0.4771	0.4826
Std	0.0085	0.0097	0.0085	0.0088	0.0087	0.0174	0.0175

Processing:   0%|          | 0/4 [00:00<?, ?it/s]

In [24]:

## tune model
model_rf_tuned = tune_model(model_rf)

	Accuracy	AUC	Recall	Prec.	F1	Kappa	MCC
Fold
0	0.7562	0.8053	0.7562	0.7654	0.7517	0.5037	0.5161
1	0.7689	0.8134	0.7689	0.7825	0.7635	0.5285	0.5456
2	0.7544	0.7897	0.7544	0.7672	0.7485	0.4988	0.5154
3	0.7617	0.8021	0.7617	0.7709	0.7572	0.5147	0.5272
4	0.7689	0.8071	0.7689	0.7759	0.7655	0.5305	0.5402
Mean	0.7620	0.8035	0.7620	0.7724	0.7573	0.5153	0.5289
Std	0.0061	0.0079	0.0061	0.0062	0.0066	0.0127	0.0123

Processing:   0%|          | 0/7 [00:00<?, ?it/s]

Fitting 5 folds for each of 10 candidates, totalling 50 fits

In [25]:

## parameters of default model
print(model_rf)

RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
                       criterion='gini', max_depth=None, max_features='sqrt',
                       max_leaf_nodes=None, max_samples=None,
                       min_impurity_decrease=0.0, min_samples_leaf=1,
                       min_samples_split=2, min_weight_fraction_leaf=0.0,
                       monotonic_cst=None, n_estimators=100, n_jobs=-1,
                       oob_score=False, random_state=1234, verbose=0,
                       warm_start=False)

In [26]:

## parameters of tuned model
print(model_rf_tuned)

RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,
                       class_weight='balanced_subsample', criterion='gini',
                       max_depth=10, max_features='sqrt', max_leaf_nodes=None,
                       max_samples=None, min_impurity_decrease=0,
                       min_samples_leaf=5, min_samples_split=7,
                       min_weight_fraction_leaf=0.0, monotonic_cst=None,
                       n_estimators=160, n_jobs=-1, oob_score=False,
                       random_state=1234, verbose=0, warm_start=False)

comparison of the default parameters and the tuned parameters

parameter	default	tuned
ccp_alpha	0	0
class_weight	None	'balanced_subsample'
criterion	'gini'	'gini'
max_depth	None	10
max_features	'sqrt'	'sqrt'
max_leaf_nodes	None	None
max_samples	None	None
min_impurity_decrease	0	0
min_samples_leaf	1	5
min_samples_split	2	7
min_weight_fraction_leaf	0	0
monotonic_cst	None	None
n_estimators	100	160
n_jobs	-1	-1
oob_score	False	False
random_state	1234	1234
verbose	0	0
warm_start	False	False

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