diff --git a/lipatov_ilya_lab_2/README.md b/lipatov_ilya_lab_2/README.md
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+## Лабораторная работа №2
+
+### Ранжирование признаков
+
+## Выполнил студент группы ПИбд-41 Липатов Илья
+
+### Как запустить лабораторную работу:
+
+* установить python, numpy, matplotlib, sklearn
+* запустить проект (стартовая точка класс lab2)
+
+### Какие технологии использовались:
+
+* Язык программирования `Python`, библиотеки numpy, matplotlib, sklearn
+* Среда разработки `PyCharm`
+
+### Что делает лабораторная работа:
+
+* генерирует данные и обучает модели модели RandomizedLasso, Ridge,Random Forest Regressor.
+* ранжирует признаки с помощью моделей RandomizedLasso, Ridge,Random Forest Regressor.
+* отображает получившиеся значения\оценки каждого признака каждым методом\моделью и среднюю оценку.
+
+### Примеры работы:
+
+#### Результаты:
+* RandomizedLasso: 1, 2, 4, 5
+* Ridge: 4, 11, 12 и 1 или 2 (одинаковый результат)
+* Random Forest Regressor: 4, 1 11, 12
+
+#### Среднее: 4, 1, 2 и 5 признаки
+
+#### Графики результатов ранжирования признаков по каждой модели и средняя оценка:
+
+![Result](result.png)
+
+#### Средние оценки для признаков у каждой модели и средние оценки моделей:
+
+![Means](means.png)
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diff --git a/lipatov_ilya_lab_2/RandomizedLasso.py b/lipatov_ilya_lab_2/RandomizedLasso.py
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+from sklearn.utils import check_X_y, check_random_state
+from sklearn.linear_model import Lasso
+from scipy.sparse import issparse
+from pandas._libs import sparse
+
+
+def _rescale_data(x, weights):
+    if issparse(x):
+        size = weights.shape[0]
+        weight_dia = sparse.dia_matrix((1 - weights, 0), (size, size))
+        x_rescaled = x * weight_dia
+    else:
+        x_rescaled = x * (1 - weights)
+
+    return x_rescaled
+
+
+class RandomizedLasso(Lasso):
+    def __init__(self, weakness=0.5, alpha=1.0, fit_intercept=True, normalize=False,
+                 precompute=False, copy_x=True, max_iter=1000,
+                 tol=1e-4, warm_start=False, positive=False,
+                 random_state=None, selection='cyclic'):
+        self.weakness = weakness
+        super(RandomizedLasso, self).__init__(
+            alpha=alpha, fit_intercept=fit_intercept,
+            normalize=normalize, precompute=precompute, copy_X=copy_x,
+            max_iter=max_iter, tol=tol, warm_start=warm_start,
+            positive=positive, random_state=random_state,
+            selection=selection)
+
+    def fit(self, x, y):
+        if not isinstance(self.weakness, float) or not (0.0 < self.weakness <= 1.0):
+            raise ValueError('weakness should be a float in (0, 1], got %s' % self.weakness)
+
+        x, y = check_X_y(x, y, accept_sparse=True)
+
+        n_features = x.shape[1]
+        weakness = 1. - self.weakness
+        random_state = check_random_state(self.random_state)
+
+        weights = weakness * random_state.randint(0, 1 + 1, size=(n_features,))
+
+        x_rescaled = _rescale_data(x, weights)
+        return super(RandomizedLasso, self).fit(x_rescaled, y)
diff --git a/lipatov_ilya_lab_2/lab2.py b/lipatov_ilya_lab_2/lab2.py
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+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()
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diff --git a/lipatov_ilya_lab_2/result.png b/lipatov_ilya_lab_2/result.png
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