import pandas as pd
import matplotlib.pyplot as plt
import sklearn.model_selection as sk_model_selection
import sklearn.metrics as sk_metrics
import sklearn.svm as sk_svm
import double_grade_svm_utility
qualifies_double_grade_df = pd.read_csv("data/double_grade_reevaluated.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"]
# 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,
import pandas as pd
import matplotlib.pyplot as plt
from sklearn import model_selection
import double_grade_svm_utility as svm_utility
import sklearn.metrics as sk_metrics
import sklearn.model_selection as sk_model_selection
import sklearn.svm as sk_svm
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_)