def execute_test(resolution, x_n, y_n):
X, y = load_winequality_red()
s = 'chlorides'
c = X[s]
c_log = np.log(c)
c_log[c_log < -3.5] = -3.5
c_log[c_log > -1.5] = -1.5
X[s] = c_log
s = 'residual sugar'
c = X[s]
c_log = np.log(c)
c_log[c_log > 2] = 2
X[s] = c_log
s = 'sulphates'
c = X[s]
c[c > 1.25] = 1.25
X[s] = c
s = 'total sulfur dioxide'
c = X[s]
c_log = np.log(c)
X[s] = c_log
X_train, X_test, y_train, y_test = split_train_test(X, y, test_size=0.05)
# X_train = X
# X_test = X
# y_train = y
# y_test = y
learning_system = FuzzyLearningSystem(res=resolution)
learning_system.fit(X_train, y_train, X_n=x_n, y_n=y_n)
score = learning_system.score(X_test, y_test)
return score
def test_model():
X, y = load_sample_set()
# X_train, X_test, y_train, y_test = split_train_test(X, y, test_size = 0.10)
X_train = X
X_test = X
y_train = y
y_test = y
learning_system = FuzzyLearningSystem(res=1000)
learning_system.fit(X_train, y_train, X_n=4, y_n=2)
print(learning_system)
score = learning_system.score(X_test, y_test)
print(score)
df = pd.DataFrame()
for i in np.arange(0, 11, 0.5):
y_hat = learning_system.get_result({'X': i})['y']
a_row = pd.Series([i, y_hat])
row_df = pd.DataFrame([a_row])
df = pd.concat([row_df, df])
plt.scatter(X, y)
plt.scatter(df[0], df[1])
plt.show()
def execute_test(resolution, x_n, y_n):
X, y = load_weather()
X_train, X_test, y_train, y_test = split_train_test(X, y, test_size=0.2)
learning_system = FuzzyLearningSystem(res=resolution)
learning_system.fit(X_train, y_train, X_n=x_n, y_n=y_n)
score = learning_system.score(X_test, y_test)
print(learning_system)
learning_system.generate_rules_csv('weather_rules.csv')
return score
clean_y = y.copy()
clean_y['Y'] = 1.1**X['x']
X_train = X
X_test = X
y_train = y
y_test = y
fig, axes = plt.subplots(nrows=3, ncols=3)
df = pd.DataFrame()
for x_range in range(3, 6):
for y_range in range(2, 5):
learning_system = FuzzyLearningSystem(res=1000)
learning_system.fit(X_train, y_train, X_n=x_range, y_n=y_range)
# learning_system.plot_variables()
# print(learning_system)
score = learning_system.score(X_test, clean_y)
df = df[0:0]
for i in np.arange(0, 50, 1):
y_hat = learning_system.get_result({'x': i})['Y']
a_row = pd.Series([i, y_hat])
# min_max_scaler = preprocessing.StandardScaler() # x = X.values #returns a numpy array # x_scaled = min_max_scaler.fit_transform(x) # X = pd.DataFrame(x_scaled) # print(X.min(), X.max()) # X_train, X_test, y_train, y_test = split_train_test(X, y, test_size = 0.10) X_train = X X_test = X y_train = y y_test = y learning_system = FuzzyLearningSystem(res=1000) learning_system.fit(X_train, y_train, X_n=3, y_n=4) # learning_system.plot_variables() print(learning_system) score = learning_system.score(X_test, y_test) print(score) df = pd.DataFrame() for i in np.arange(54,79,0.5):