.

.gitignore

@@ -0,0 +1,4 @@
+__pycache__/
+.vscode/
+.venv/
+.direnv/

flake.lock

@@ -0,0 +1,61 @@
+{
+  "nodes": {
+    "flake-utils": {
+      "inputs": {
+        "systems": "systems"
+      },
+      "locked": {
+        "lastModified": 1731533236,
+        "narHash": "sha256-l0KFg5HjrsfsO/JpG+r7fRrqm12kzFHyUHqHCVpMMbI=",
+        "owner": "numtide",
+        "repo": "flake-utils",
+        "rev": "11707dc2f618dd54ca8739b309ec4fc024de578b",
+        "type": "github"
+      },
+      "original": {
+        "owner": "numtide",
+        "repo": "flake-utils",
+        "type": "github"
+      }
+    },
+    "nixpkgs": {
+      "locked": {
+        "lastModified": 1763283776,
+        "narHash": "sha256-Y7TDFPK4GlqrKrivOcsHG8xSGqQx3A6c+i7novT85Uk=",
+        "owner": "NixOS",
+        "repo": "nixpkgs",
+        "rev": "50a96edd8d0db6cc8db57dab6bb6d6ee1f3dc49a",
+        "type": "github"
+      },
+      "original": {
+        "owner": "NixOS",
+        "ref": "nixos-unstable",
+        "repo": "nixpkgs",
+        "type": "github"
+      }
+    },
+    "root": {
+      "inputs": {
+        "flake-utils": "flake-utils",
+        "nixpkgs": "nixpkgs"
+      }
+    },
+    "systems": {
+      "locked": {
+        "lastModified": 1681028828,
+        "narHash": "sha256-Vy1rq5AaRuLzOxct8nz4T6wlgyUR7zLU309k9mBC768=",
+        "owner": "nix-systems",
+        "repo": "default",
+        "rev": "da67096a3b9bf56a91d16901293e51ba5b49a27e",
+        "type": "github"
+      },
+      "original": {
+        "owner": "nix-systems",
+        "repo": "default",
+        "type": "github"
+      }
+    }
+  },
+  "root": "root",
+  "version": 7
+}

flake.nix

@@ -1,8 +1,8 @@
 {
-  description = "Python development environment with Jupyter";
+  description = "Python development env with venv + Jupyter";
 
   inputs = {
-    nixpkgs.url = "github:NixOS/nixpkgs/nixos-23.05"; # используйте актуальную версию
+    nixpkgs.url = "github:NixOS/nixpkgs/nixos-unstable";
     flake-utils.url = "github:numtide/flake-utils";
   };
 
@@ -13,17 +13,28 @@
       in
       {
         devShells.default = pkgs.mkShell {
-          buildInputs = [
+          packages = with pkgs; [
-            pkgs.python310
+            python312
-            pkgs.python310Packages.ipython
+            python312Packages.virtualenv
-            pkgs.python310Packages.jupyterlab
+            python312Packages.pip
-            pkgs.git
+
+            python312Packages.jupyterlab
+            python312Packages.ipykernel
           ];
 
           shellHook = ''
-            echo "Welcome to your Python + Jupyter dev environment!"
+            echo "🐍 Python: $(python3 --version)"
+
+            if [ ! -d .venv ]; then
+              python3 -m venv .venv
+              . .venv/bin/activate
+              pip install --upgrade pip wheel
+            else
+              . .venv/bin/activate
+            fi
+
+            python -m ipykernel install --user --name "nix-env" --display-name "Python (nix)"
           '';
         };
-      }
+      });
-    );
 }

task1716/Task.py

@@ -0,0 +1,191 @@
+import numpy as np
+
+
+class Preprocessor:
+
+    def __init__(self):
+        pass
+
+    def fit(self, X, Y=None):
+        pass
+
+    def transform(self, X):
+        pass
+
+    def fit_transform(self, X, Y=None):
+        pass
+
+
+class MyOneHotEncoder(Preprocessor):
+
+    def __init__(self, dtype=np.float64):
+        super(Preprocessor).__init__()
+        self.dtype = dtype
+        self.types = {}
+
+    def fit(self, X, Y=None):
+        """
+        param X: training objects, pandas-dataframe, shape [n_objects, n_features]
+        param Y: unused
+        """
+        for col in X.columns:
+            self.types[col] = sorted(X[col].unique())
+
+    def transform(self, X):
+        """
+        param X: objects to transform, pandas-dataframe, shape [n_objects, n_features]
+        returns: transformed objects, numpy-array, shape [n_objects, |f1| + |f2| + ...]
+        """
+        n_objects = X.shape[0]
+        n_features = sum(len(categories) for categories in self.types.vals())
+        res = np.zeros((n_objects, n_features))
+
+        shift_indexes = 0
+        for column, categories in self.types.items():
+            for i, category in enumerate(categories):
+                indices = np.where(X[column] == category)
+                res[indices, shift_indexes + i] = 1
+            shift_indexes += len(categories)
+
+        return res
+
+    def fit_transform(self, X, Y=None):
+        self.fit(X)
+        return self.transform(X)
+
+    def get_params(self, deep=True):
+        return {"dtype": self.dtype}
+
+
+class SimpleCounterEncoder:
+
+    def __init__(self, dtype=np.float64):
+        self.dtype = dtype
+        self.count = {}
+
+    def fit(self, X, Y):
+        """
+        param X: training objects, pandas-dataframe, shape [n_objects, n_features]
+        param Y: target for training objects, pandas-series, shape [n_objects,]
+        """
+        for col in X.columns:
+            uniq_vals = X[col].unique()
+            self.count[col] = {}
+
+            for val in uniq_vals:
+                indexes = X[col] == val
+                self.count[col][val] = [Y[indexes].mean(), np.mean(indexes)]
+
+    def transform(self, X, a=1e-5, b=1e-5):
+        """
+        param X: objects to transform, pandas-dataframe, shape [n_objects, n_features]
+        param a: constant for counters, float
+        param b: constant for counters, float
+        returns: transformed objects, numpy-array, shape [n_objects, 3 * n_features]
+        """
+        n_obj, n_feach = X.shape
+        res = np.zeros((n_obj, 3 * n_feach))
+
+        for i, col in enumerate(X.columns):
+            for j in range(n_obj):
+                val = X.iloc[j, i]
+                mean_expected, frac = self.count[col][val]
+                res[j, 3 * i] = mean_expected
+                res[j, 3 * i + 1] = frac
+                res[j, 3 * i + 2] = (mean_expected + a) / (frac + b)
+
+        return res
+
+    def fit_transform(self, X, Y, a=1e-5, b=1e-5):
+        self.fit(X, Y)
+        return self.transform(X, a, b)
+
+    def get_params(self, deep=True):
+        return {"dtype": self.dtype}
+
+
+def group_k_fold(size, n_splits=3, seed=1):
+    idx = np.arange(size)
+    np.random.seed(seed)
+    idx = np.random.permutation(idx)
+    n = size // n_splits
+
+    for i in range(n_splits - 1):
+        yield idx[i * n:(i + 1) * n], np.hstack((idx[: i * n], idx[(i + 1) * n:]))
+    yield idx[(n_splits - 1) * n:], idx[:(n_splits - 1) * n]
+
+
+class FoldCounters:
+
+    def __init__(self, n_folds=3, dtype=np.float64):
+        self.dtype = dtype
+        self.n_folds = n_folds
+        self.fold_count = []
+
+    def fit(self, X, Y, seed=1):
+        """
+        param X: training objects, pandas-dataframe, shape [n_objects, n_features]
+        param Y: target for training objects, pandas-series, shape [n_objects,]
+        param seed: random seed, int
+        """
+        for fold_idx, rest_idx in group_k_fold(X.shape[0], self.n_folds, seed):
+            fold_counter = {}
+            X_fold, Y_fold = X.iloc[rest_idx], Y.iloc[rest_idx]
+
+            for column in X.columns:
+                unique_val = X_fold[column].unique()
+                fold_counter[column] = {}
+
+                for val in unique_val:
+                    fold_counter[column][val] = [
+                        Y_fold[X_fold[column] == val].mean(),
+                        np.mean(X_fold[column] == val),
+                    ]
+
+            self.fold_count.append((fold_idx, fold_counter))
+
+    def transform(self, X, a=1e-5, b=1e-5):
+        """
+        param X: objects to transform, pandas-dataframe, shape [n_objects, n_features]
+        param a: constant for counters, float
+        param b: constant for counters, float
+        returns: transformed objects, numpy-array, shape [n_objects, 3 * n_features]
+        """
+        n_obj, n_feach = X.shape
+        res = np.zeros((n_obj, 3 * n_feach))
+
+        for fold_idx, fold_count in self.fold_count:
+            for i, column in enumerate(X.columns):
+                for j in fold_idx:
+                    val = X.iloc[j, i]
+                    mean_expected, frac = fold_count[column][val]
+
+                    res[j, 3 * i] = mean_expected
+                    res[j, 3 * i + 1] = frac
+                    res[j, 3 * i + 2] = (mean_expected + a) / (frac + b)
+
+        return res
+
+    def fit_transform(self, X, Y, a=1e-5, b=1e-5):
+        self.fit(X, Y)
+
+        return self.transform(X, a, b)
+
+
+def weights(x, y):
+    """
+    param x: training set of one feature, numpy-array, shape [n_objects,]
+    param y: target for training objects, numpy-array, shape [n_objects,]
+    returns: optimal weights, numpy-array, shape [|x unique vals|,]
+    """
+    uniq_vals = np.unique(x)
+    enc_x = np.eye(uniq_vals.shape[0])[x]
+    weight = np.zeros(enc_x.shape[1])
+    lr = 1e-2
+
+    for _ in range(1000):
+        p = np.dot(enc_x, weight)
+        grad = np.dot(enc_x.T, (p - y))
+        weight -= grad * lr
+
+    return weight

task1716/build.sh

@@ -0,0 +1,23 @@
+set -o xtrace
+
+setup_root() {
+   apt-get install python3.12 -qq -y 
+   apt-get install -qq -y \
+        python3-pip \
+        python3-tk
+    
+    python3 --version
+
+    pip install --upgrade pip
+    pip --version
+
+    echo -e "catboost==1.2.8\ngdown==5.2.0\nh5py==3.14.0\nhyperopt==0.2.7\nipympl==0.9.7\nipywidgets==7.7.1\nlightgbm==4.6.0\nmatplotlib-inline==0.1.7\nmatplotlib==3.10.0\nnumpy==2.0.2\npandas==2.2.2\npep8==1.7.1\nplotly==5.24.1\npycodestyle==2.14.0\npytest==8.4.1\nscikit-image==0.25.2\nscikit-learn==1.6.1\nscipy==1.16.1\nseaborn==0.13.2\ntqdm==4.67.1\numap-learn==0.5.9.post2\nxgboost==3.0.4" > requirements.txt
+
+    pip install -r ./requirements.txt
+}
+
+setup_checker() {
+    python3 -c 'import matplotlib.pyplot'
+}
+
+"$@"

task1716/requirements_2025_26_for_colab_small.txt

@@ -0,0 +1,22 @@
+catboost==1.2.8
+gdown==5.2.0
+h5py==3.14.0
+hyperopt==0.2.7
+ipympl==0.9.7
+ipywidgets==7.7.1
+lightgbm==4.6.0
+matplotlib-inline==0.1.7
+matplotlib==3.10.0
+numpy==2.0.2
+pandas==2.2.2
+pep8==1.7.1
+plotly==5.24.1
+pycodestyle==2.14.0
+pytest==8.4.1
+scikit-image==0.25.2
+scikit-learn==1.6.1
+scipy==1.16.1
+seaborn==0.13.2
+tqdm==4.67.1
+umap-learn==0.5.9.post2
+xgboost==3.0.4

task1716/run.py

@@ -0,0 +1,65 @@
+#!/usr/bin/env python3
+
+from json import load, dumps
+from glob import glob
+from os import environ
+from os.path import join
+from sys import argv, exit
+
+
+def run_single_test(data_dir, output_dir):
+    from pytest import main
+    exit(main(['-vv', '-p', 'no:cacheprovider', join(data_dir, 'test.py')]))
+
+
+def check_test(data_dir):
+    pass
+
+
+def grade(data_path):
+    results = load(open(join(data_path, 'results.json')))
+    grade_mapping = [0.5, 0.5, 1, 1]
+    max_mark = sum(grade_mapping)
+    total_grade = 0
+    ok_count = 0
+    for result, grade in zip(results, grade_mapping):
+        if result['status'] == 'Ok':
+            total_grade += grade
+            ok_count += 1
+    total_count = len(results)
+    description = '%02d/%02d' % (ok_count, total_count)
+    mark = total_grade / sum(grade_mapping) * max_mark
+    res = {'description': description, 'mark': mark}
+    if environ.get('CHECKER'):
+        print(dumps(res))
+    return res
+
+
+if __name__ == '__main__':
+    if environ.get('CHECKER'):
+        # Script is running in testing system
+        if len(argv) != 4:
+            print('Usage: %s mode data_dir output_dir' % argv[0])
+            exit(0)
+
+        mode = argv[1]
+        data_dir = argv[2]
+        output_dir = argv[3]
+
+        if mode == 'run_single_test':
+            run_single_test(data_dir, output_dir)
+        elif mode == 'check_test':
+            check_test(data_dir)
+        elif mode == 'grade':
+            grade(data_dir)
+    else:
+        # Script is running locally
+        name = argv[1]
+        test_dir = glob(f'tests/[0-9][0-9]_unittest_{name}_input')
+        if not test_dir:
+            print('Test not found')
+            exit(0)
+
+        from pytest import main
+        exit(main(['-vv', join(test_dir[0], 'test.py')]))
+

task1716/tests/01_unittest_onehot_input/test.py

@@ -0,0 +1,67 @@
+import numpy as np
+import pandas as pd
+
+from Task import MyOneHotEncoder, SimpleCounterEncoder, FoldCounters, weights
+
+def test_imports():
+    with open('Task.py', 'r') as file:
+        lines = ' '.join(file.readlines())
+        assert 'import numpy' in lines
+        assert lines.count('import') == 1
+        assert 'sklearn' not in lines
+        assert 'get_dummies' not in lines
+
+def test_one_hot_small():
+    data = {'col_1': [0, 1, 0, 1, 0, 1], 'col_2': ['a', 'b', 'c', 'c', 'b', 'a']}
+    df_test = pd.DataFrame.from_dict(data)
+    enc = MyOneHotEncoder(dtype=int)
+    enc.fit(df_test)
+    onehot = enc.transform(df_test)
+    ans = np.array([[1,0,1,0,0],[0,1,0,1,0],[1,0,0,0,1],[0,1,0,0,1],[1,0,0,1,0],[0,1,1,0,0]])
+    assert len(onehot.shape) == 2
+    assert onehot.shape[0] == 6
+    assert onehot.shape[1] == 5
+    assert (ans == onehot).all()
+    assert type(onehot) == np.ndarray
+
+def test_one_hot_big():
+    data = {'col_1': [1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 2, 1, 2, 0, 2, 1, 2, 0, 0, 2, 0, 1, 2, 2, 0, 1, 1, 2, 0], 'col_2': [1, 1, 1, 1, 0, 4, 1, 0, 0, 3, 2, 1, 0, 3, 1, 1, 3, 4, 0, 1, 3, 4, 2, 4, 0, 3, 1, 2, 0, 4], 'col_3': [1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1]}
+    df_test = pd.DataFrame.from_dict(data)
+    enc = MyOneHotEncoder(dtype=int)
+    enc.fit(df_test)
+    onehot = enc.transform(df_test)
+    ans = np.array([[0, 1, 0, 0, 1, 0, 0, 0, 0, 1],
+       [1, 0, 0, 0, 1, 0, 0, 0, 0, 1],
+       [1, 0, 0, 0, 1, 0, 0, 0, 1, 0],
+       [0, 1, 0, 0, 1, 0, 0, 0, 0, 1],
+       [0, 1, 0, 1, 0, 0, 0, 0, 0, 1],
+       [1, 0, 0, 0, 0, 0, 0, 1, 1, 0],
+       [1, 0, 0, 0, 1, 0, 0, 0, 0, 1],
+       [0, 1, 0, 1, 0, 0, 0, 0, 1, 0],
+       [1, 0, 0, 1, 0, 0, 0, 0, 1, 0],
+       [0, 1, 0, 0, 0, 0, 1, 0, 0, 1],
+       [1, 0, 0, 0, 0, 1, 0, 0, 0, 1],
+       [0, 0, 1, 0, 1, 0, 0, 0, 0, 1],
+       [0, 1, 0, 1, 0, 0, 0, 0, 1, 0],
+       [0, 0, 1, 0, 0, 0, 1, 0, 0, 1],
+       [1, 0, 0, 0, 1, 0, 0, 0, 0, 1],
+       [0, 0, 1, 0, 1, 0, 0, 0, 1, 0],
+       [0, 1, 0, 0, 0, 0, 1, 0, 0, 1],
+       [0, 0, 1, 0, 0, 0, 0, 1, 0, 1],
+       [1, 0, 0, 1, 0, 0, 0, 0, 0, 1],
+       [1, 0, 0, 0, 1, 0, 0, 0, 0, 1],
+       [0, 0, 1, 0, 0, 0, 1, 0, 1, 0],
+       [1, 0, 0, 0, 0, 0, 0, 1, 1, 0],
+       [0, 1, 0, 0, 0, 1, 0, 0, 1, 0],
+       [0, 0, 1, 0, 0, 0, 0, 1, 1, 0],
+       [0, 0, 1, 1, 0, 0, 0, 0, 1, 0],
+       [1, 0, 0, 0, 0, 0, 1, 0, 0, 1],
+       [0, 1, 0, 0, 1, 0, 0, 0, 0, 1],
+       [0, 1, 0, 0, 0, 1, 0, 0, 0, 1],
+       [0, 0, 1, 1, 0, 0, 0, 0, 0, 1],
+       [1, 0, 0, 0, 0, 0, 0, 1, 0, 1]])
+    assert len(onehot.shape) == 2
+    assert onehot.shape[0] == 30
+    assert onehot.shape[1] == 10
+    assert (onehot == ans).all()
+    assert type(onehot) == np.ndarray

task1716/tests/02_unittest_weights_input/test.py

@@ -0,0 +1,32 @@
+import numpy as np
+import pandas as pd
+
+from Task import MyOneHotEncoder, SimpleCounterEncoder, FoldCounters, weights
+
+def test_imports():
+    with open('Task.py', 'r') as file:
+        lines = ' '.join(file.readlines())
+        assert 'import numpy' in lines
+        assert lines.count('import') == 1
+        assert 'sklearn' not in lines
+        assert 'get_dummies' not in lines
+
+def test_weights_small():
+    np.random.seed(1)
+    x = np.array([1, 1, 1, 1, 0, 4, 1, 0, 0, 3, 2, 1, 0, 3, 1, 1, 3, 4, 0, 1, 3, 4, 2, 4, 0, 3, 1, 2, 0, 4])
+    y = np.array([1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0])
+    w = weights(x, y)
+    ans = [0.5714285714285714, 0.4, 0.6666666666666666, 1.0, 0.2]
+    assert len(w) == 5
+    assert np.allclose(w, ans, atol=1e-8)
+    assert type(w) == np.ndarray
+
+def test_weights_big():
+    np.random.seed(1)
+    x = np.random.choice([0, 1, 2, 3, 4, 5], size=(300,))
+    y = np.random.choice([0, 1], size=(300,))
+    w = weights(x, y)
+    ans = [0.38596491228070173, 0.5384615384615384, 0.4523809523809524, 0.3409090909090909, 0.44642857142857145, 0.42857142857142855]
+    assert len(w) == 6
+    assert np.allclose(w, ans, atol=1e-8)
+    assert type(w) == np.ndarray

task1716/tests/03_unittest_counters_input/test.py

@@ -0,0 +1,96 @@
+import numpy as np
+import pandas as pd
+
+from Task import SimpleCounterEncoder
+
+def test_imports():
+    with open('Task.py', 'r') as file:
+        lines = ' '.join(file.readlines())
+        assert 'import numpy' in lines
+        assert lines.count('import') == 1
+        assert 'sklearn' not in lines
+        assert 'get_dummies' not in lines
+
+def test_simple_counters_small():
+    data = {'col_1': [0, 0, 0, 1, 1, 1], 'col_2': ['a', 'b', 'c', 'a', 'b', 'c'], 'col_3': [0, 1, 2, 3, 4, 5]}
+    df_test = pd.DataFrame.from_dict(data)
+    enc = SimpleCounterEncoder()
+    enc.fit(df_test[['col_1', 'col_2']], df_test['col_3'])
+    counts = enc.transform(df_test[['col_1', 'col_2']], a=1, b=1)
+
+    print("Counters small")
+    print(counts)
+
+    ans = np.array([[1, 0.5, 4/3, 1.5, 1/3, 1.875],\
+                    [1, 0.5, 4/3, 2.5, 1/3, 2.625],\
+                    [1, 0.5, 4/3, 3.5, 1/3, 3.375],\
+                    [4, 0.5, 10/3, 1.5, 1/3, 1.875],\
+                    [4, 0.5, 10/3, 2.5, 1/3, 2.625],\
+                    [4, 0.5, 10/3, 3.5, 1/3, 3.375]])
+    assert len(counts.shape) == 2
+    assert counts.shape[0] == 6
+    assert counts.shape[1] == 6
+    assert np.allclose(counts, ans, atol=1e-8)
+    assert type(counts) == np.ndarray
+
+def test_simple_counters_diff_shape_small():
+    data_enc = {'col_1': [0, 0, 0, 1, 1, 1], 'col_2': ['a', 'b', 'c', 'a', 'b', 'c'], 'col_3': [0, 1, 2, 3, 4, 5]}
+    data_trans = {'col_1': [0, 1, 0, 1, 0], 'col_2': ['c', 'a', 'b', 'b', 'c']}
+    df_test_enc = pd.DataFrame.from_dict(data_enc)
+    df_test_trans = pd.DataFrame.from_dict(data_trans)
+    enc = SimpleCounterEncoder()
+    enc.fit(df_test_enc[['col_1', 'col_2']], df_test_enc['col_3'])
+    counts = enc.transform(df_test_trans[['col_1', 'col_2']], a=1, b=1)
+    ans = np.array([[1, 0.5, 4/3, 3.5, 1/3, 3.375],\
+                    [4, 0.5, 10/3, 1.5, 1/3, 1.875],\
+                    [1, 0.5, 4/3, 2.5, 1/3, 2.625],\
+                    [4, 0.5, 10/3, 2.5, 1/3, 2.625],\
+                    [1, 0.5, 4/3, 3.5, 1/3, 3.375]])
+    assert len(counts.shape) == 2
+    assert counts.shape[0] == 5
+    assert counts.shape[1] == 6
+    assert np.allclose(counts, ans, atol=1e-8)
+    assert type(counts) == np.ndarray
+
+def test_simple_counters_big():
+    data = {'col_1': [1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 2, 1, 2, 0, 2, 1, 2, 0, 0, 2, 0, 1, 2, 2, 0, 1, 1, 2, 0], 'col_2': [1, 1, 1, 1, 0, 4, 1, 0, 0, 3, 2, 1, 0, 3, 1, 1, 3, 4, 0, 1, 3, 4, 2, 4, 0, 3, 1, 2, 0, 4], 'col_3': [1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1], "target": [1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0]}
+    df_test = pd.DataFrame.from_dict(data)
+    enc = SimpleCounterEncoder()
+    enc.fit(df_test[['col_1', 'col_2', 'col_3']], df_test['target'])
+    counts = enc.transform(df_test[['col_1', 'col_2', 'col_3']], a=1, b=2)
+    ans = np.array([[0.6, 0.3333333333333333, 0.6857142857142857, 0.4, 0.3333333333333333, 0.6, 0.631578947368421, 0.6333333333333333, 0.619586942038641 ],\
+[0.3333333333333333, 0.4, 0.5555555555555556, 0.4, 0.3333333333333333, 0.6, 0.631578947368421, 0.6333333333333333, 0.619586942038641 ],\
+[0.3333333333333333, 0.4, 0.5555555555555556, 0.4, 0.3333333333333333, 0.6, 0.36363636363636365, 0.36666666666666664, 0.5761843790012805 ],\
+[0.6, 0.3333333333333333, 0.6857142857142857, 0.4, 0.3333333333333333, 0.6, 0.631578947368421, 0.6333333333333333, 0.619586942038641 ],\
+[0.6, 0.3333333333333333, 0.6857142857142857, 0.5714285714285714, 0.23333333333333334, 0.7036247334754797, 0.631578947368421, 0.6333333333333333, 0.619586942038641 ],\
+[0.3333333333333333, 0.4, 0.5555555555555556, 0.2, 0.16666666666666666, 0.5538461538461539, 0.36363636363636365, 0.36666666666666664, 0.5761843790012805 ],\
+[0.3333333333333333, 0.4, 0.5555555555555556, 0.4, 0.3333333333333333, 0.6, 0.631578947368421, 0.6333333333333333, 0.619586942038641 ],\
+[0.6, 0.3333333333333333, 0.6857142857142857, 0.5714285714285714, 0.23333333333333334, 0.7036247334754797, 0.36363636363636365, 0.36666666666666664, 0.5761843790012805 ],\
+[0.3333333333333333, 0.4, 0.5555555555555556, 0.5714285714285714, 0.23333333333333334, 0.7036247334754797, 0.36363636363636365, 0.36666666666666664, 0.5761843790012805 ],\
+[0.6, 0.3333333333333333, 0.6857142857142857, 1.0, 0.16666666666666666, 0.9230769230769231, 0.631578947368421, 0.6333333333333333, 0.619586942038641 ],\
+[0.3333333333333333, 0.4, 0.5555555555555556, 0.6666666666666666, 0.1, 0.7936507936507935, 0.631578947368421, 0.6333333333333333, 0.619586942038641 ],\
+[0.75, 0.26666666666666666, 0.7720588235294118, 0.4, 0.3333333333333333, 0.6, 0.631578947368421, 0.6333333333333333, 0.619586942038641 ],\
+[0.6, 0.3333333333333333, 0.6857142857142857, 0.5714285714285714, 0.23333333333333334, 0.7036247334754797, 0.36363636363636365, 0.36666666666666664, 0.5761843790012805 ],\
+[0.75, 0.26666666666666666, 0.7720588235294118, 1.0, 0.16666666666666666, 0.9230769230769231, 0.631578947368421, 0.6333333333333333, 0.619586942038641 ],\
+[0.3333333333333333, 0.4, 0.5555555555555556, 0.4, 0.3333333333333333, 0.6, 0.631578947368421, 0.6333333333333333, 0.619586942038641 ],\
+[0.75, 0.26666666666666666, 0.7720588235294118, 0.4, 0.3333333333333333, 0.6, 0.36363636363636365, 0.36666666666666664, 0.5761843790012805 ],\
+[0.6, 0.3333333333333333, 0.6857142857142857, 1.0, 0.16666666666666666, 0.9230769230769231, 0.631578947368421, 0.6333333333333333, 0.619586942038641 ],\
+[0.75, 0.26666666666666666, 0.7720588235294118, 0.2, 0.16666666666666666, 0.5538461538461539, 0.631578947368421, 0.6333333333333333, 0.619586942038641 ],\
+[0.3333333333333333, 0.4, 0.5555555555555556, 0.5714285714285714, 0.23333333333333334, 0.7036247334754797, 0.631578947368421, 0.6333333333333333, 0.619586942038641 ],\
+[0.3333333333333333, 0.4, 0.5555555555555556, 0.4, 0.3333333333333333, 0.6, 0.631578947368421, 0.6333333333333333, 0.619586942038641 ],\
+[0.75, 0.26666666666666666, 0.7720588235294118, 1.0, 0.16666666666666666, 0.9230769230769231, 0.36363636363636365, 0.36666666666666664, 0.5761843790012805 ],\
+[0.3333333333333333, 0.4, 0.5555555555555556, 0.2, 0.16666666666666666, 0.5538461538461539, 0.36363636363636365, 0.36666666666666664, 0.5761843790012805 ],\
+[0.6, 0.3333333333333333, 0.6857142857142857, 0.6666666666666666, 0.1, 0.7936507936507935, 0.36363636363636365, 0.36666666666666664, 0.5761843790012805 ],\
+[0.75, 0.26666666666666666, 0.7720588235294118, 0.2, 0.16666666666666666, 0.5538461538461539, 0.36363636363636365, 0.36666666666666664, 0.5761843790012805 ],\
+[0.75, 0.26666666666666666, 0.7720588235294118, 0.5714285714285714, 0.23333333333333334, 0.7036247334754797, 0.36363636363636365, 0.36666666666666664, 0.5761843790012805 ],\
+[0.3333333333333333, 0.4, 0.5555555555555556, 1.0, 0.16666666666666666, 0.9230769230769231, 0.631578947368421, 0.6333333333333333, 0.619586942038641 ],\
+[0.6, 0.3333333333333333, 0.6857142857142857, 0.4, 0.3333333333333333, 0.6, 0.631578947368421, 0.6333333333333333, 0.619586942038641 ],\
+[0.6, 0.3333333333333333, 0.6857142857142857, 0.6666666666666666, 0.1, 0.7936507936507935, 0.631578947368421, 0.6333333333333333, 0.619586942038641 ],\
+[0.75, 0.26666666666666666, 0.7720588235294118, 0.5714285714285714, 0.23333333333333334, 0.7036247334754797, 0.631578947368421, 0.6333333333333333, 0.619586942038641 ],\
+[0.3333333333333333, 0.4, 0.5555555555555556, 0.2, 0.16666666666666666, 0.5538461538461539, 0.631578947368421, 0.6333333333333333, 0.619586942038641 ]])
+    assert len(counts.shape) == 2
+    assert counts.shape[0] == 30
+    assert counts.shape[1] == 9
+    assert np.allclose(counts, ans, atol=1e-8)
+    assert type(counts) == np.ndarray
+

task1716/tests/04_unittest_foldcounters_input/test.py

@@ -0,0 +1,103 @@
+import numpy as np
+import pandas as pd
+
+from Task import MyOneHotEncoder, SimpleCounterEncoder, FoldCounters, weights
+
+def test_imports():
+    with open('Task.py', 'r') as file:
+        lines = ' '.join(file.readlines())
+        assert 'import numpy' in lines
+        assert lines.count('import') == 1
+        assert 'sklearn' not in lines
+        assert 'get_dummies' not in lines
+
+def test_kfold_counters_small():
+    data = {'col_1': [0,1,0,1,0,1,0,1,0,1,0,1], 'col_2':['a','b','c','a','b','c','a','b','c','a','b','c'], 'col_3': [1,2,3,4,1,2,3,4,1,2,3,4]}
+    df_test = pd.DataFrame.from_dict(data)
+    enc = FoldCounters(n_folds=2)
+    enc.fit(df_test[['col_1', 'col_2']], df_test['col_3'], seed=6)
+    counts = enc.transform(df_test[['col_1', 'col_2']], a=0, b=0)
+    ans = np.array([[7/3,0.5,14/3,3,1/3,9],\
+                    [8/3,0.5,16/3,2,1/3,6],\
+                    [5/3,0.5,10/3,2.5,1/3,7.5],\
+                    [10/3,0.5,20/3,2,1/3,6],\
+                    [5/3,0.5,10/3,3,1/3,9],\
+                    [10/3,0.5,20/3,2.5,1/3,7.5],\
+                    [7/3,0.5,14/3,3,1/3,9],\
+                    [8/3,0.5,16/3,2,1/3,6],\
+                    [7/3,0.5,14/3,2.5,1/3,7.5],\
+                    [10/3,0.5,20/3,2,1/3,6],\
+                    [5/3,0.5,10/3,3,1/3,9],\
+                    [8/3,0.5,16/3,2.5,1/3,7.5]])
+    assert len(counts.shape) == 2
+    assert counts.shape[0] == 12
+    assert counts.shape[1] == 6
+    assert np.allclose(counts, ans, atol=1e-8)
+    assert type(counts) == np.ndarray
+
+def test_kfold_counters_diffshape_idx_small():
+    data = np.array([[0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1], [1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4]])
+    index = pd.Index([11, 0, 9, 7, 6, 10, 1, 2, 5, 4, 3, 8])
+    df_test = pd.DataFrame(data.reshape(12, 2), index=index, columns=['col_1', 'col_2'])
+    enc = FoldCounters(n_folds=2)
+    enc.fit(df_test[['col_1']], df_test['col_2'], seed=6)
+    counts = enc.transform(df_test[['col_1']], a=0, b=0)
+    ans = np.array([[ 1.        ,  0.66666667,  1.5       ],\
+       [ 1.        ,  0.66666667,  1.5       ],\
+       [ 1.        ,  0.33333333,  3.        ],\
+       [ 1.        ,  0.33333333,  3.        ],\
+       [ 1.        ,  0.33333333,  3.        ],\
+       [ 1.        ,  0.33333333,  3.        ],\
+       [ 2.        ,  0.16666667, 12.        ],\
+       [ 4.        ,  0.16666667, 24.        ],\
+       [ 2.        ,  0.16666667, 12.        ],\
+       [ 4.        ,  0.33333333, 12.        ],\
+       [ 2.        ,  0.33333333,  6.        ],\
+       [ 4.        ,  0.16666667, 24.        ]])
+    assert len(counts.shape) == 2
+    assert counts.shape[0] == 12
+    assert counts.shape[1] == 3
+    assert np.allclose(counts, ans, atol=1e-8)
+    assert type(counts) == np.ndarray
+
+def test_fold_counters_big():
+    data = {'col_1': [1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 2, 1, 2, 0, 2, 1, 2, 0, 0, 2, 0, 1, 2, 2, 0, 1, 1, 2, 0], 'col_2': [1, 1, 1, 1, 0, 4, 1, 0, 0, 3, 2, 1, 0, 3, 1, 1, 3, 4, 0, 1, 3, 4, 2, 4, 0, 3, 1, 2, 0, 4], 'col_3': [1, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1], "target": [1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0]}
+    df_test = pd.DataFrame.from_dict(data)
+    enc = FoldCounters(n_folds=3)
+    enc.fit(df_test[['col_1', 'col_2', 'col_3']], df_test['target'], seed=1)
+    counts = enc.transform(df_test[['col_1', 'col_2', 'col_3']], a=1, b=2)
+    ans = np.array([[0.3333333333333333, 0.3, 0.5797101449275363, 0.2857142857142857, 0.35, 0.547112462006079, 0.5384615384615384, 0.65, 0.5805515239477503 ],\
+[0.2857142857142857, 0.35, 0.547112462006079, 0.42857142857142855, 0.35, 0.60790273556231, 0.6666666666666666, 0.6, 0.641025641025641 ],\
+[0.2857142857142857, 0.35, 0.547112462006079, 0.42857142857142855, 0.35, 0.60790273556231, 0.5, 0.4, 0.625 ],\
+[0.7142857142857143, 0.35, 0.729483282674772, 0.5, 0.3, 0.6521739130434783, 0.6923076923076923, 0.65, 0.6386066763425254 ],\
+[0.7142857142857143, 0.35, 0.729483282674772, 1.0, 0.2, 0.9090909090909091, 0.6666666666666666, 0.6, 0.641025641025641 ],\
+[0.3333333333333333, 0.45, 0.5442176870748299, 0.3333333333333333, 0.15, 0.6201550387596899, 0.2857142857142857, 0.35, 0.547112462006079 ],\
+[0.2857142857142857, 0.35, 0.547112462006079, 0.42857142857142855, 0.35, 0.60790273556231, 0.6666666666666666, 0.6, 0.641025641025641 ],\
+[0.7142857142857143, 0.35, 0.729483282674772, 1.0, 0.2, 0.9090909090909091, 0.5, 0.4, 0.625 ],\
+[0.3333333333333333, 0.45, 0.5442176870748299, 0.25, 0.2, 0.5681818181818181, 0.2857142857142857, 0.35, 0.547112462006079 ],\
+[0.3333333333333333, 0.3, 0.5797101449275363, 1.0, 0.15, 0.9302325581395349, 0.5384615384615384, 0.65, 0.5805515239477503 ],\
+[0.375, 0.4, 0.5729166666666667, 1.0, 0.05, 0.9756097560975611, 0.6923076923076923, 0.65, 0.6386066763425254 ],\
+[0.8, 0.25, 0.8, 0.2857142857142857, 0.35, 0.547112462006079, 0.5384615384615384, 0.65, 0.5805515239477503 ],\
+[0.3333333333333333, 0.3, 0.5797101449275363, 0.25, 0.2, 0.5681818181818181, 0.2857142857142857, 0.35, 0.547112462006079 ],\
+[0.8333333333333334, 0.3, 0.7971014492753624, 1.0, 0.15, 0.9302325581395349, 0.6666666666666666, 0.6, 0.641025641025641 ],\
+[0.375, 0.4, 0.5729166666666667, 0.5, 0.3, 0.6521739130434783, 0.6923076923076923, 0.65, 0.6386066763425254 ],\
+[0.8, 0.25, 0.8, 0.2857142857142857, 0.35, 0.547112462006079, 0.2857142857142857, 0.35, 0.547112462006079 ],\
+[0.3333333333333333, 0.3, 0.5797101449275363, 1.0, 0.15, 0.9302325581395349, 0.5384615384615384, 0.65, 0.5805515239477503 ],\
+[0.6, 0.25, 0.7111111111111111, 0.0, 0.15, 0.46511627906976744, 0.6923076923076923, 0.65, 0.6386066763425254 ],\
+[0.2857142857142857, 0.35, 0.547112462006079, 1.0, 0.2, 0.9090909090909091, 0.6666666666666666, 0.6, 0.641025641025641 ],\
+[0.375, 0.4, 0.5729166666666667, 0.5, 0.3, 0.6521739130434783, 0.6923076923076923, 0.65, 0.6386066763425254 ],\
+[0.6, 0.25, 0.7111111111111111, 1.0, 0.2, 0.9090909090909091, 0.2857142857142857, 0.35, 0.547112462006079 ],\
+[0.375, 0.4, 0.5729166666666667, 0.0, 0.15, 0.46511627906976744, 0.2857142857142857, 0.35, 0.547112462006079 ],\
+[0.7142857142857143, 0.35, 0.729483282674772, 1.0, 0.05, 0.9756097560975611, 0.2857142857142857, 0.35, 0.547112462006079 ],\
+[0.8333333333333334, 0.3, 0.7971014492753624, 0.25, 0.2, 0.5681818181818181, 0.5, 0.4, 0.625 ],\
+[0.6, 0.25, 0.7111111111111111, 0.5, 0.3, 0.6521739130434783, 0.2857142857142857, 0.35, 0.547112462006079 ],\
+[0.2857142857142857, 0.35, 0.547112462006079, 1.0, 0.15, 0.9302325581395349, 0.6666666666666666, 0.6, 0.641025641025641 ],\
+[0.7142857142857143, 0.35, 0.729483282674772, 0.5, 0.3, 0.6521739130434783, 0.6923076923076923, 0.65, 0.6386066763425254 ],\
+[0.7142857142857143, 0.35, 0.729483282674772, 0.5, 0.1, 0.7142857142857143, 0.6666666666666666, 0.6, 0.641025641025641 ],\
+[0.8, 0.25, 0.8, 0.25, 0.2, 0.5681818181818181, 0.5384615384615384, 0.65, 0.5805515239477503 ],\
+[0.3333333333333333, 0.45, 0.5442176870748299, 0.3333333333333333, 0.15, 0.6201550387596899, 0.5384615384615384, 0.65, 0.5805515239477503 ]])
+    assert len(counts.shape) == 2
+    assert counts.shape[0] == 30
+    assert counts.shape[1] == 9
+    assert np.allclose(counts, ans, atol=1e-8)
+    assert type(counts) == np.ndarray

task9/[Base]_Sklearn_knn_normalize_cv_[2025_2026].ipynb

@@ -94,11 +94,297 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 1,
       "metadata": {
         "id": "fMgnzKcqlIen"
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
+            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
+            "100   360  100   360    0     0    696      0 --:--:-- --:--:-- --:--:--   697\n",
+            "Collecting catboost==1.2.8 (from -r ./requirements_2025_26_for_colab_small.txt (line 1))\n",
+            "  Downloading catboost-1.2.8-cp312-cp312-manylinux2014_x86_64.whl.metadata (1.2 kB)\n",
+            "Collecting gdown==5.2.0 (from -r ./requirements_2025_26_for_colab_small.txt (line 2))\n",
+            "  Downloading gdown-5.2.0-py3-none-any.whl.metadata (5.8 kB)\n",
+            "Collecting h5py==3.14.0 (from -r ./requirements_2025_26_for_colab_small.txt (line 3))\n",
+            "  Downloading h5py-3.14.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (2.7 kB)\n",
+            "Collecting hyperopt==0.2.7 (from -r ./requirements_2025_26_for_colab_small.txt (line 4))\n",
+            "  Downloading hyperopt-0.2.7-py2.py3-none-any.whl.metadata (1.7 kB)\n",
+            "Collecting ipympl==0.9.7 (from -r ./requirements_2025_26_for_colab_small.txt (line 5))\n",
+            "  Downloading ipympl-0.9.7-py3-none-any.whl.metadata (8.7 kB)\n",
+            "Collecting ipywidgets==7.7.1 (from -r ./requirements_2025_26_for_colab_small.txt (line 6))\n",
+            "  Downloading ipywidgets-7.7.1-py2.py3-none-any.whl.metadata (1.9 kB)\n",
+            "Collecting lightgbm==4.6.0 (from -r ./requirements_2025_26_for_colab_small.txt (line 7))\n",
+            "  Downloading lightgbm-4.6.0-py3-none-manylinux_2_28_x86_64.whl.metadata (17 kB)\n",
+            "Requirement already satisfied: matplotlib-inline==0.1.7 in /nix/store/xll44q41bdn0i1zcphwj7p95j5arz356-python3.12-matplotlib-inline-0.1.7/lib/python3.12/site-packages (from -r ./requirements_2025_26_for_colab_small.txt (line 8)) (0.1.7)\n",
+            "Collecting matplotlib==3.10.0 (from -r ./requirements_2025_26_for_colab_small.txt (line 9))\n",
+            "  Downloading matplotlib-3.10.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB)\n",
+            "Collecting numpy==2.0.2 (from -r ./requirements_2025_26_for_colab_small.txt (line 10))\n",
+            "  Downloading numpy-2.0.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (60 kB)\n",
+            "Collecting pandas==2.2.2 (from -r ./requirements_2025_26_for_colab_small.txt (line 11))\n",
+            "  Downloading pandas-2.2.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (19 kB)\n",
+            "Collecting pep8==1.7.1 (from -r ./requirements_2025_26_for_colab_small.txt (line 12))\n",
+            "  Downloading pep8-1.7.1-py2.py3-none-any.whl.metadata (22 kB)\n",
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+            "Requirement already satisfied: notebook-shim<0.3,>=0.2 in /nix/store/agvxbdh9lb9i7gm5v1qjx7a0bv22am8n-python3.12-notebook-shim-0.2.4/lib/python3.12/site-packages (from notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (0.2.4)\n",
+            "Requirement already satisfied: ptyprocess>=0.5 in /nix/store/rz2ygcrnc8wx1n3pz0slcah47s71havg-python3.12-ptyprocess-0.7.0/lib/python3.12/site-packages (from pexpect>4.3->ipython<10->ipympl==0.9.7->-r ./requirements_2025_26_for_colab_small.txt (line 5)) (0.7.0)\n",
+            "Requirement already satisfied: wcwidth in /nix/store/82wc5sicriaacpnavd97c9b2b3l88d1j-python3.12-wcwidth-0.2.13/lib/python3.12/site-packages (from prompt_toolkit<3.1.0,>=3.0.41->ipython<10->ipympl==0.9.7->-r ./requirements_2025_26_for_colab_small.txt (line 5)) (0.2.13)\n",
+            "Requirement already satisfied: executing>=1.2.0 in /nix/store/vryr7qfzh6qp2v8whvc5357hz3f3xxpc-python3.12-executing-2.2.0/lib/python3.12/site-packages (from stack_data->ipython<10->ipympl==0.9.7->-r ./requirements_2025_26_for_colab_small.txt (line 5)) (2.2.0)\n",
+            "Requirement already satisfied: asttokens>=2.1.0 in /nix/store/lh5is09vgpvw5rnvm9bgcxiasm7pidw1-python3.12-asttokens-3.0.0/lib/python3.12/site-packages (from stack_data->ipython<10->ipympl==0.9.7->-r ./requirements_2025_26_for_colab_small.txt (line 5)) (3.0.0)\n",
+            "Requirement already satisfied: pure_eval in /nix/store/lab1jiw46l09zci0r8h1cl2wnb04w1s5-python3.12-pure-eval-0.2.3/lib/python3.12/site-packages (from stack_data->ipython<10->ipympl==0.9.7->-r ./requirements_2025_26_for_colab_small.txt (line 5)) (0.2.3)\n",
+            "Requirement already satisfied: anyio>=3.1.0 in /nix/store/slsfw31xlwaczcnqgg4r5qw3xpwm0nbq-python3.12-anyio-4.9.0/lib/python3.12/site-packages (from jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (4.9.0)\n",
+            "Requirement already satisfied: argon2-cffi>=21.1 in /nix/store/1clmzlxvasiidjs72h4vx4pqcrkwg3zf-python3.12-argon2-cffi-23.1.0/lib/python3.12/site-packages (from jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (23.1.0)\n",
+            "Requirement already satisfied: jinja2>=3.0.3 in /nix/store/svijzcfkrsxb8jripykx1d2krzi2q0fq-python3.12-jinja2-3.1.6/lib/python3.12/site-packages (from jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (3.1.6)\n",
+            "Requirement already satisfied: jupyter-events>=0.11.0 in /nix/store/4jydas1rslfay93x8h61s7a16023pcql-python3.12-jupyter-events-0.11.0/lib/python3.12/site-packages (from jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (0.11.0)\n",
+            "Requirement already satisfied: jupyter-server-terminals>=0.4.4 in /nix/store/ly5g70s9xpnm9piajwcmrlrd8maixfh4-python3.12-jupyter-server-terminals-0.5.3/lib/python3.12/site-packages (from jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (0.5.3)\n",
+            "Requirement already satisfied: nbconvert>=6.4.4 in /nix/store/5h2jqcxjgkyrp8yy28s1cxbmlps9llbr-python3.12-nbconvert-7.16.6/lib/python3.12/site-packages (from jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (7.16.6)\n",
+            "Requirement already satisfied: nbformat>=5.3.0 in /nix/store/mha5x4r94hi8278p347v2ig89r7pg37i-python3.12-nbformat-5.10.4/lib/python3.12/site-packages (from jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (5.10.4)\n",
+            "Requirement already satisfied: overrides>=5.0 in /nix/store/8hnr7s08x0zglraaqhsvxw9cxfb4m5vf-python3.12-overrides-7.7.0/lib/python3.12/site-packages (from jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (7.7.0)\n",
+            "Requirement already satisfied: prometheus-client>=0.9 in /nix/store/hi0lchzh5wd2mima5ryzxv1sp8avpixv-python3.12-prometheus-client-0.21.1/lib/python3.12/site-packages (from jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (0.21.1)\n",
+            "Requirement already satisfied: send2trash>=1.8.2 in /nix/store/qaybrn941lhzv68mmly22vml2bi9biln-python3.12-send2trash-1.8.3/lib/python3.12/site-packages (from jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (1.8.3)\n",
+            "Requirement already satisfied: terminado>=0.8.3 in /nix/store/r0c0p4ycha98vxggn3ib9bmrsma9a5gq-python3.12-terminado-0.18.1/lib/python3.12/site-packages (from jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (0.18.1)\n",
+            "Requirement already satisfied: websocket-client>=1.7 in /nix/store/maij4ca0hdq51yh6sc8ha0dbfksfvbs2-python3.12-websocket-client-1.8.0/lib/python3.12/site-packages (from jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (1.8.0)\n",
+            "Requirement already satisfied: async-lru>=1.0.0 in /nix/store/appfbz4pp2rq674miy7y35llz30382ab-python3.12-async-lru-2.0.5/lib/python3.12/site-packages (from jupyterlab<4.5,>=4.4.9->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (2.0.5)\n",
+            "Requirement already satisfied: httpx<1,>=0.25.0 in /nix/store/83krrvn5s26brnzqfqb9w7cvsa94b9w6-python3.12-httpx-0.28.1/lib/python3.12/site-packages (from jupyterlab<4.5,>=4.4.9->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (0.28.1)\n",
+            "Requirement already satisfied: jupyter-lsp>=2.0.0 in /nix/store/w0fm4viprsj0s6vdymp1grlgq9gg24pp-python3.12-jupyter-lsp-2.2.5/lib/python3.12/site-packages (from jupyterlab<4.5,>=4.4.9->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (2.2.5)\n",
+            "Requirement already satisfied: setuptools>=41.1.0 in /nix/store/jahiclal74yv1j7py0jnlm30ji3wa1wr-python3.12-setuptools-78.1.1/lib/python3.12/site-packages (from jupyterlab<4.5,>=4.4.9->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (78.1.1.post0)\n",
+            "Requirement already satisfied: babel>=2.10 in /nix/store/0k8xz970r9zwm7c0bbmnps9ppya32jyx-python3.12-babel-2.17.0/lib/python3.12/site-packages (from jupyterlab-server<3,>=2.27.1->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (2.17.0)\n",
+            "Requirement already satisfied: json5>=0.9.0 in /nix/store/m67k0bjvzldj8fg862y9jaqdn14fav0r-python3.12-json5-0.10.0/lib/python3.12/site-packages (from jupyterlab-server<3,>=2.27.1->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (0.10.0)\n",
+            "Requirement already satisfied: jsonschema>=4.18.0 in /nix/store/cbv4a3ncj97dj8a7sz4ldw5hpajhnrgd-python3.12-jsonschema-4.23.0/lib/python3.12/site-packages (from jupyterlab-server<3,>=2.27.1->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (4.23.0)\n",
+            "Requirement already satisfied: sniffio>=1.1 in /nix/store/6jdf3szr6xcvzdg3cdzrgvhy6dc6ppdp-python3.12-sniffio-1.3.1/lib/python3.12/site-packages (from anyio>=3.1.0->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (1.3.1)\n",
+            "Requirement already satisfied: typing_extensions>=4.5 in /nix/store/lpap2xy7m2z6qs3pjgx57whdvn0gmrld-python3.12-typing-extensions-4.13.2/lib/python3.12/site-packages (from anyio>=3.1.0->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (4.13.2)\n",
+            "Requirement already satisfied: argon2-cffi-bindings in /nix/store/xs11y0i84x084h3f9va5xn3vw71g9sn5-python3.12-argon2-cffi-bindings-21.2.0/lib/python3.12/site-packages (from argon2-cffi>=21.1->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (21.2.0)\n",
+            "Requirement already satisfied: httpcore==1.* in /nix/store/45mbjl9s9pv0libawf8ikmh588k831zz-python3.12-httpcore-1.0.9/lib/python3.12/site-packages (from httpx<1,>=0.25.0->jupyterlab<4.5,>=4.4.9->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (1.0.9)\n",
+            "Requirement already satisfied: h11>=0.16 in /nix/store/ndg6nlcirvbl8w5sdz49r0x28x4hnfr7-python3.12-h11-0.16.0/lib/python3.12/site-packages (from httpcore==1.*->httpx<1,>=0.25.0->jupyterlab<4.5,>=4.4.9->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (0.16.0)\n",
+            "Requirement already satisfied: MarkupSafe>=2.0 in /nix/store/s1s7cngw3wc3wm6ysqyjkwq8nq4cz4vl-python3.12-markupsafe-3.0.2/lib/python3.12/site-packages (from jinja2>=3.0.3->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (3.0.2)\n",
+            "Requirement already satisfied: attrs>=22.2.0 in /nix/store/aj8dzb2ampzwwmjy7i47ypbp9vdcb4zy-python3.12-attrs-25.3.0/lib/python3.12/site-packages (from jsonschema>=4.18.0->jupyterlab-server<3,>=2.27.1->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (25.3.0)\n",
+            "Requirement already satisfied: jsonschema-specifications>=2023.03.6 in /nix/store/vd97fs6kjvygdndlsnh2c9166z4d0xh3-python3.12-jsonschema-specifications-2024.10.1/lib/python3.12/site-packages (from jsonschema>=4.18.0->jupyterlab-server<3,>=2.27.1->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (2024.10.1)\n",
+            "Requirement already satisfied: referencing>=0.28.4 in /nix/store/n7knhq0j8jmls8dckxyim8qhslw399ng-python3.12-referencing-0.36.2/lib/python3.12/site-packages (from jsonschema>=4.18.0->jupyterlab-server<3,>=2.27.1->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (0.36.2)\n",
+            "Requirement already satisfied: rpds-py>=0.7.1 in /nix/store/8a2h77svk68xqlhbp0bi2xk8rwj9j50j-python3.12-rpds-py-0.24.0/lib/python3.12/site-packages (from jsonschema>=4.18.0->jupyterlab-server<3,>=2.27.1->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (0.24.0)\n",
+            "Requirement already satisfied: python-json-logger>=2.0.4 in /nix/store/p56rsir3w5f044svipd9mpa6qqxgsa78-python3.12-python-json-logger-3.2.1/lib/python3.12/site-packages (from jupyter-events>=0.11.0->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (3.2.1)\n",
+            "Requirement already satisfied: pyyaml>=5.3 in /nix/store/9ap0fph2ybcfnl0f1rsklj990n25ll84-python3.12-pyyaml-6.0.2/lib/python3.12/site-packages (from jupyter-events>=0.11.0->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (6.0.2)\n",
+            "Requirement already satisfied: rfc3339-validator in /nix/store/14rqw00zqxk6jcslmab0skzzf3qcp5xw-python3.12-rfc3339-validator-0.1.4/lib/python3.12/site-packages (from jupyter-events>=0.11.0->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (0.1.4)\n",
+            "Requirement already satisfied: rfc3986-validator>=0.1.1 in /nix/store/zqnambrrca5dzmfiizygsslmgakhiigc-python3.12-rfc3986-validator-0.1.1/lib/python3.12/site-packages (from jupyter-events>=0.11.0->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (0.1.1)\n",
+            "Requirement already satisfied: bleach!=5.0.0 in /nix/store/0svbzbngdvar6sdz7p4iscpjhcp5i7aq-python3.12-bleach-6.2.0/lib/python3.12/site-packages (from bleach[css]!=5.0.0->nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (6.2.0)\n",
+            "Requirement already satisfied: defusedxml in /nix/store/4l429an1q3m5m7gbmr004zi4mzhbksn1-python3.12-defusedxml-0.8.0rc2/lib/python3.12/site-packages (from nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (0.8.0rc2)\n",
+            "Requirement already satisfied: jupyterlab-pygments in /nix/store/fm0shwwm682kdk11hx331ya0axmx6357-python3.12-jupyterlab-pygments-0.3.0/lib/python3.12/site-packages (from nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (0.3.0)\n",
+            "Requirement already satisfied: mistune<4,>=2.0.3 in /nix/store/9mln2k5sj8y5jr1mggmd55zbj6m5i4vb-python3.12-mistune-3.1.2/lib/python3.12/site-packages (from nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (3.1.2)\n",
+            "Requirement already satisfied: nbclient>=0.5.0 in /nix/store/yhm4rkd5j81xfwmcr8s749m67z9099pa-python3.12-nbclient-0.10.2/lib/python3.12/site-packages (from nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (0.10.2)\n",
+            "Requirement already satisfied: pandocfilters>=1.4.1 in /nix/store/n76h7wginvrsqaq8ngnmf259fagz6dyp-python3.12-pandocfilters-1.5.1/lib/python3.12/site-packages (from nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (1.5.1)\n",
+            "Requirement already satisfied: fastjsonschema>=2.15 in /nix/store/yh2gsm8j4cnx422kxm69bx0k7c54n6b4-python3.12-fastjsonschema-2.21.1/lib/python3.12/site-packages (from nbformat>=5.3.0->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (2.21.1)\n",
+            "Requirement already satisfied: webencodings in /nix/store/n8jzk1lyamvlj6rw9pyx88ai0rhcmfgm-python3.12-webencodings-0.5.1/lib/python3.12/site-packages (from bleach!=5.0.0->bleach[css]!=5.0.0->nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (0.5.1)\n",
+            "Requirement already satisfied: tinycss2<1.5,>=1.1.0 in /nix/store/lrxfr5x30dinqpw0vg2zvdvh6jfy4kxn-python3.12-tinycss2-1.4.0/lib/python3.12/site-packages (from bleach[css]!=5.0.0->nbconvert>=6.4.4->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (1.4.0)\n",
+            "Requirement already satisfied: fqdn in /nix/store/c676msxyjp0k5r70s33p040rk6fxii8d-python3.12-fqdn-1.5.1/lib/python3.12/site-packages (from jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.11.0->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (1.5.1)\n",
+            "Requirement already satisfied: isoduration in /nix/store/4mnklym4fix9xi1i4ppknqk5fcpljfga-python3.12-isoduration-20.11.0/lib/python3.12/site-packages (from jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.11.0->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (20.11.0)\n",
+            "Requirement already satisfied: jsonpointer>1.13 in /nix/store/9lhd1pal6j928py2p7x6g5xs2fyqjfzf-python3.12-jsonpointer-3.0.0/lib/python3.12/site-packages (from jsonschema[format-nongpl]>=4.18.0->jupyter-events>=0.11.0->jupyter-server<3,>=2.4.0->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets==7.7.1->-r ./requirements_2025_26_for_colab_small.txt (line 6)) (3.0.0)\n",
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+            "\u001b[?25hDownloading PySocks-1.7.1-py3-none-any.whl (16 kB)\n",
+            "Using cached jupyterlab-4.4.10-py3-none-any.whl (12.3 MB)\n",
+            "Installing collected packages: pytz, py4j, pep8, ipython-genutils, tzdata, tqdm, threadpoolctl, tenacity, PySocks, pyparsing, pycodestyle, pluggy, pillow, nvidia-nccl-cu12, numpy, networkx, llvmlite, lazy-loader, kiwisolver, joblib, iniconfig, graphviz, future, fonttools, cycler, cloudpickle, tifffile, scipy, pytest, plotly, pandas, numba, imageio, h5py, contourpy, xgboost, scikit-learn, scikit-image, matplotlib, lightgbm, hyperopt, gdown, seaborn, pynndescent, catboost, umap-learn, jupyterlab, widgetsnbextension, ipywidgets, ipympl\n",
+            "  Attempting uninstall: jupyterlab\n",
+            "    Found existing installation: jupyterlab 4.4.1\n",
+            "    Not uninstalling jupyterlab at /nix/store/jzhscs6dw96a836cvv12s271j59a5lmv-python3.12-jupyterlab-4.4.1/lib/python3.12/site-packages, outside environment /home/krosh/Documents/Github/ML/.venv\n",
+            "    Can't uninstall 'jupyterlab'. No files were found to uninstall.\n",
+            "  Attempting uninstall: widgetsnbextension\n",
+            "    Found existing installation: widgetsnbextension 4.0.15\n",
+            "    Uninstalling widgetsnbextension-4.0.15:\n",
+            "      Successfully uninstalled widgetsnbextension-4.0.15\n",
+            "  Attempting uninstall: ipywidgets\n",
+            "    Found existing installation: ipywidgets 8.1.8\n",
+            "    Uninstalling ipywidgets-8.1.8:\n",
+            "      Successfully uninstalled ipywidgets-8.1.8\n",
+            "Successfully installed PySocks-1.7.1 catboost-1.2.8 cloudpickle-3.1.2 contourpy-1.3.3 cycler-0.12.1 fonttools-4.60.1 future-1.0.0 gdown-5.2.0 graphviz-0.21 h5py-3.14.0 hyperopt-0.2.7 imageio-2.37.2 iniconfig-2.3.0 ipympl-0.9.7 ipython-genutils-0.2.0 ipywidgets-7.7.1 joblib-1.5.2 jupyterlab-4.4.10 kiwisolver-1.4.9 lazy-loader-0.4 lightgbm-4.6.0 llvmlite-0.45.1 matplotlib-3.10.0 networkx-3.5 numba-0.62.1 numpy-2.0.2 nvidia-nccl-cu12-2.28.7 pandas-2.2.2 pep8-1.7.1 pillow-12.0.0 plotly-5.24.1 pluggy-1.6.0 py4j-0.10.9.9 pycodestyle-2.14.0 pynndescent-0.5.13 pyparsing-3.2.5 pytest-8.4.1 pytz-2025.2 scikit-image-0.25.2 scikit-learn-1.6.1 scipy-1.16.1 seaborn-0.13.2 tenacity-9.1.2 threadpoolctl-3.6.0 tifffile-2025.10.16 tqdm-4.67.1 tzdata-2025.2 umap-learn-0.5.9.post2 widgetsnbextension-3.6.10 xgboost-3.0.4\n"
+          ]
+        }
+      ],
       "source": [
         "! curl https://raw.githubusercontent.com/MSU-ML-COURSE/ML-COURSE-25-26/refs/heads/master/requirements/requirements.txt -o ./requirements_2025_26_for_colab_small.txt\n",
         "! pip install -r ./requirements_2025_26_for_colab_small.txt"
@@ -115,7 +401,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 4,
+      "execution_count": 2,
       "metadata": {
         "id": "QwWXXElyiRYq"
       },
@@ -2558,7 +2844,7 @@
       "name": "python",
       "nbconvert_exporter": "python",
       "pygments_lexer": "ipython3",
-      "version": "3.13.7"
+      "version": "3.12.12"
     }
   },
   "nbformat": 4,

task9/requirements_2025_26_for_colab_small.txt

@@ -7,7 +7,7 @@ ipywidgets==7.7.1
 lightgbm==4.6.0
 matplotlib-inline==0.1.7
 matplotlib==3.10.0
-numpy
+numpy==2.0.2
 pandas==2.2.2
 pep8==1.7.1
 plotly==5.24.1