from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import MinMaxScaler
from RandomizedLasso import RandomizedLasso
from sklearn.linear_model import Ridge
from matplotlib import pyplot as plt
import numpy as np

np.random.seed(0)
size = 1000
X = np.random.uniform(0, 1, (size, 14))
Y = (10 * np.sin(np.pi * X[:, 0] * X[:, 1]) + 20 * (X[:, 2] - .5) ** 2 + 10 * X[:, 3] + 5 * X[:, 4] ** 5 + np.random.normal(0, 1))
X[:, 10:] = X[:, :4] + np.random.normal(0, .025, (size, 4))

ridge = Ridge(alpha=1.0)
ridge.fit(X, Y)

lasso = RandomizedLasso(alpha=0.007)
lasso.fit(X, Y)

randForestRegression = RandomForestRegressor(max_depth=4, min_samples_leaf=1, min_impurity_decrease=0, ccp_alpha=0)
randForestRegression.fit(X, Y)


def rank_to_dict(ranks, names):
    ranks = np.abs(ranks)
    minmax = MinMaxScaler()
    ranks = minmax.fit_transform(np.array(ranks).reshape(14, 1)).ravel()
    ranks = map(lambda x: round(x, 2), ranks)
    return dict(zip(names, ranks))


ranks = {'Ridge': {}, 'RandomizedLasso': {}, 'RandomForestRegressor': {}}
names = ["x%s" % i for i in range(1, 15)]

ranks["Ridge"] = rank_to_dict(ridge.coef_, names)
ranks["RandomizedLasso"] = rank_to_dict(lasso.coef_, names)
ranks["RandomForestRegressor"] = rank_to_dict(randForestRegression.feature_importances_, names)

mean = {}

for key, value in ranks.items():
    for item in value.items():
        if item[0] not in mean:
            mean[item[0]] = 0
        mean[item[0]] += item[1]

for key, value in mean.items():
    res = value / len(ranks)
    mean[key] = round(res, 2)

print('VALUES')

for r in ranks.items():
    print(r)
    print('MEAN')
    print(mean)

for i, (model_name, features) in enumerate(ranks.items()):
    subplot = plt.subplot(2, 2, i + 1)
    subplot.set_title(model_name)
    subplot.bar(list(features.keys()), list(features.values()))

subplot = plt.subplot(2, 2, 4)
subplot.set_title('Mean')
subplot.bar(list(mean.keys()), list(mean.values()))

plt.show()