MLClassification/example_logit.py at main · Mayerle/MLClassification · GitHub

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from instruments.linearmodels import *
from instruments.dftools import *
from instruments.statistic import *
from instruments.view import *
from instruments.datasample import *

x_train, x_test, y_train, y_test = get_data()

#Logit model
logit_model = LogisticCModel()
normal = logit_model.fit(x_train,y_train)
logit_predictions = logit_model.predict(x_test)
logit_stats0 = ClassificationStatistics(y_test,logit_predictions)
matrix = logit_stats0.calculate_confusion_matrix()

#Logit model regularization=0.5l1
logit_model = LogisticCModel(regularization="l1",regularization_factor=0.5)
normal = logit_model.fit(x_train,y_train)
logit_predictions = logit_model.predict(x_test)
logit_stats1 = ClassificationStatistics(y_test,logit_predictions)
matrix = logit_stats1.calculate_confusion_matrix()

#Logit model regularization=0.5l2
logit_model = LogisticCModel(regularization="l2",regularization_factor=0.5)
normal = logit_model.fit(x_train,y_train)
logit_predictions = logit_model.predict(x_test)
logit_stats2 = ClassificationStatistics(y_test,logit_predictions)
matrix = logit_stats2.calculate_confusion_matrix()

#Logit model regularization=2l1
logit_model = LogisticCModel(regularization="l1",regularization_factor=2)
normal = logit_model.fit(x_train,y_train)
logit_predictions = logit_model.predict(x_test)
logit_stats3 = ClassificationStatistics(y_test,logit_predictions)
matrix = logit_stats3.calculate_confusion_matrix()

#Logit model regularization=2l2
logit_model = LogisticCModel(regularization="l2",regularization_factor=2)
normal = logit_model.fit(x_train,y_train)
logit_predictions = logit_model.predict(x_test)
logit_stats4 = ClassificationStatistics(y_test,logit_predictions)
matrix = logit_stats4.calculate_confusion_matrix()

#Logit model regularization=5l1
logit_model = LogisticCModel(regularization="l1",regularization_factor=5)
normal = logit_model.fit(x_train,y_train)
logit_predictions = logit_model.predict(x_test)
logit_stats5 = ClassificationStatistics(y_test,logit_predictions)
matrix = logit_stats5.calculate_confusion_matrix()

#Logit model regularization=5l2
logit_model = LogisticCModel(regularization="l2",regularization_factor=5)
normal = logit_model.fit(x_train,y_train)
logit_predictions = logit_model.predict(x_test)
logit_stats6 = ClassificationStatistics(y_test,logit_predictions)
matrix = logit_stats6.calculate_confusion_matrix()

#Collect statistics
statistics = {
   'No regularization': logit_stats0.calculate_all(),
   'L1 0.5': logit_stats1.calculate_all(),
   'L2 0.5': logit_stats2.calculate_all(),
   'L1 2': logit_stats3.calculate_all(),
   'L2 2': logit_stats4.calculate_all(),
   'L1 5': logit_stats5.calculate_all(),
   'L2 5': logit_stats6.calculate_all(),
}

#Plot statistics
plot_statistics(list(statistics.values()),list(statistics.keys()),"Compare Logistic Classification Models", 2,[0,1.19],margin=20,width=1.5)
plt.show()