# double_grade_svm_utility.compare_variance_for_vectors(X) # # svm_soft_non_linear_classifier = svm.SVC(kernel="rbf") # # svm_soft_non_linear_classifier = svm.SVC(kernel="rbf", gamma="auto") # svm_soft_non_linear_classifier.fit(X, y) # # double_grade_svm_utility.plot_model(svm_soft_non_linear_classifier) parameter_grid = { "kernel": ["rbf"], "C": [10**p for p in range(-2, 6)], "gamma": [10**p for p in range(-6, 2)] } # parameter_grid = {"kernel": ["rbf"], "C": [10 ** p for p in range(1, 7)], "gamma": [p * 1e-5 for p in range(1, 10)]} grid_search = sk_model_selection.GridSearchCV(sk_svm.SVC(), param_grid=parameter_grid, cv=4) grid_search.fit(X, y) print(grid_search.best_params_) modeled_qualification = grid_search.predict(X) confusion_matrix = sk_metrics.confusion_matrix(y, modeled_qualification) print(confusion_matrix) double_grade_svm_utility.plot_model(grid_search.best_estimator_) plt.show()
import pandas as pd import matplotlib.pyplot as plt import sklearn.svm as sk_svm import double_grade_svm_utility qualifies_double_grade_df = pd.read_csv("data/double_grade_small.csv") double_grade_svm_utility.plot_values(qualifies_double_grade_df) X = qualifies_double_grade_df[["technical_grade", "english_grade"]] y = qualifies_double_grade_df["qualifies"] svm_hard_linear_classifier = sk_svm.SVC(kernel="linear") svm_hard_linear_classifier.fit(X, y) double_grade_svm_utility.plot_model(svm_hard_linear_classifier) plt.show()
import pandas as pd import numpy as np import matplotlib.pyplot as plt import sklearn.svm as svm import double_grade_svm_utility qualifies_double_grade_df = pd.read_csv("data/double_grade_small.csv") double_grade_svm_utility.plot_values(qualifies_double_grade_df) X = qualifies_double_grade_df[["technical_grade", "english_grade"]] y = qualifies_double_grade_df["qualifies"] svm_hard_linear_model = svm.SVC(kernel="linear") svm_hard_linear_model.fit(X, y) double_grade_svm_utility.plot_model(svm_hard_linear_model) plt.show()
qualifications_double_grade = pd.read_csv( "8_class/data/double_grade_reevaluated.csv") svm_utility.plot_values(qualifications_double_grade) X = qualifications_double_grade[["technical_grade", "english_grade"]] y = qualifications_double_grade["qualifies"] # svm_non_linear_classifier = sk_svm.SVC(kernel="linear") # svm_non_linear_classifier.fit(X, y) # svm_utility.plot_model(svm_non_linear_classifier) parametr_grid = { "kernel": ["rbf"], "C": [10**p for p in range(-2, 6)], "gamma": [10**p for p in range(-6, 2)] } grid_search = sk_model_selection.GridSearchCV(sk_svm.SVC(), param_grid=parametr_grid, cv=4) grid_search.fit(X, y) print(grid_search.best_params_) modeled_qualification = grid_search.predict(X) confusion_matrix = sk_metrics.confusion_matrix(y, modeled_qualification) print(confusion_matrix) svm_utility.plot_model(grid_search.best_estimator_) plt.show()
import pandas as pd import matplotlib.pyplot as plt import double_grade_svm_utility as svm_utility import sklearn.svm as sk_svm qualifications_double_grade = pd.read_csv( "8_class/data/double_grade_small.csv") X = qualifications_double_grade[["technical_grade", "english_grade"]] y = qualifications_double_grade["qualifies"] svm_utility.plot_values(qualifications_double_grade) svm_hard_linear_classifier = sk_svm.SVC(kernel="linear") svm_hard_linear_classifier.fit(X, y) svm_utility.plot_model(svm_hard_linear_classifier) plt.show()