Prompt:
Problem:

Is it possible to pass a custom function as a preprocessor to TfidfVectorizer?
I want to write a function "prePro" that can turn every capital letter to lowercase letter.
Then somehow set the processor parameter to TfidfTVectorizer like "preprocessor=prePro". However, it doesn't work. I searched a lot but didn't find any examples useful.
Can anyone help me about this?

A:

<code>
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
</code>
solve this question with example variable `tfidf`
BEGIN SOLUTION
<code>

def prePro(s):
    return s.lower()

tfidf = TfidfVectorizer(preprocessor=prePro)

import tokenize, io

def generate_test_case(test_case_id):
    return None, None

def exec_test(result, ans):
    return 1

exec_context = r"""
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
[insert]
result = None
assert prePro("asdfASDFASDFWEQRqwerASDFAqwerASDFASDF") == "asdfasdfasdfweqrqwerasdfaqwerasdfasdf"
assert prePro == tfidf.preprocessor
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "TfidfVectorizer" in tokens


Prompt:
Problem:

I'm using the excellent read_csv()function from pandas, which gives:

In [31]: data = pandas.read_csv("lala.csv", delimiter=",")

In [32]: data
Out[32]:
<class 'pandas.core.frame.DataFrame'>
Int64Index: 12083 entries, 0 to 12082
Columns: 569 entries, REGIONC to SCALEKER
dtypes: float64(51), int64(518)
but when i apply a function from scikit-learn i loose the informations about columns:

from sklearn import preprocessing
preprocessing.scale(data)
gives numpy array.

Is there a way to apply preprocessing.scale to DataFrames without loosing the information(index, columns)?

A:

<code>
import numpy as np
import pandas as pd
from sklearn import preprocessing
data = load_data()
</code>
df_out = ... # put solution in this variable
BEGIN SOLUTION
<code>

df_out = pd.DataFrame(preprocessing.scale(data), index=data.index, columns=data.columns)

import numpy as np
import pandas as pd
import copy
from sklearn import preprocessing

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            np.random.seed(42)
            data = pd.DataFrame(
                np.random.rand(3, 3),
                index=["first", "second", "third"],
                columns=["c1", "c2", "c3"],
            )
        return data

    def generate_ans(data):
        data = data
        df_out = pd.DataFrame(
            preprocessing.scale(data), index=data.index, columns=data.columns
        )
        return df_out

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        pd.testing.assert_frame_equal(result, ans, check_dtype=False, check_exact=False)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
from sklearn import preprocessing
data = test_input
[insert]
result = df_out
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

I have a pandas DataFrame data
it has about 12k rows and more than 500 columns, each column has its unique name
However, when I used sklearn preprocessing, I found the result lose the information about the columns
Here's the code

from sklearn import preprocessing
preprocessing.scale(data)
outputs a numpy array.

So my question is, how to apply preprocessing.scale to DataFrames, and don't lose the information(index, columns)?

A:

<code>
import numpy as np
import pandas as pd
from sklearn import preprocessing
data = load_data()
</code>
df_out = ... # put solution in this variable
BEGIN SOLUTION
<code>

df_out = pd.DataFrame(preprocessing.scale(data), index=data.index, columns=data.columns)

import numpy as np
import pandas as pd
import copy
from sklearn import preprocessing

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            np.random.seed(42)
            data = pd.DataFrame(
                np.random.rand(3, 3),
                index=["first", "second", "third"],
                columns=["c1", "c2", "c3"],
            )
        return data

    def generate_ans(data):
        data = data
        df_out = pd.DataFrame(
            preprocessing.scale(data), index=data.index, columns=data.columns
        )
        return df_out

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        pd.testing.assert_frame_equal(result, ans, check_dtype=False, check_exact=False)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
from sklearn import preprocessing
data = test_input
[insert]
result = df_out
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

I am new to scikit-learn, but it did what I was hoping for. Now, maddeningly, the only remaining issue is that I don't find how I could print the model's coefficients it estimated. Especially when it comes to a pipeline fitted by a GridSearch. Now I have a pipeline including data scaling, centering, and a classifier model. What is the way to get its estimated coefficients?
here is my current code
pipe = Pipeline([
    ("scale", StandardScaler()),
    ("model", SGDClassifier(random_state=42))
])
grid = GridSearchCV(pipe, param_grid={"model__alpha": [1e-3, 1e-2, 1e-1, 1]}, cv=5)
# where is the coef?

Any advice is appreciated. Thanks in advance.

A:

runnable code
<code>
import numpy as np
import pandas as pd
from sklearn.linear_model import SGDClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
X, y = load_data()
assert type(X) == np.ndarray
assert type(y) == np.ndarray
pipe = Pipeline([
    ("scale", StandardScaler()),
    ("model", SGDClassifier(random_state=42))
])
grid = GridSearchCV(pipe, param_grid={"model__alpha": [1e-3, 1e-2, 1e-1, 1]}, cv=5)
</code>
coef = ... # put solution in this variable
BEGIN SOLUTION
<code>

grid.fit(X, y)
coef = grid.best_estimator_.named_steps['model'].coef_

import numpy as np
import copy
from sklearn.linear_model import SGDClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
import sklearn
from sklearn.datasets import make_classification

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            X, y = make_classification(random_state=42)
        return X, y

    def generate_ans(data):
        X, y = data
        pipe = Pipeline(
            [("scale", StandardScaler()), ("model", SGDClassifier(random_state=42))]
        )
        grid = GridSearchCV(
            pipe, param_grid={"model__alpha": [1e-3, 1e-2, 1e-1, 1]}, cv=5
        )
        grid.fit(X, y)
        coef = grid.best_estimator_.named_steps["model"].coef_
        return coef

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))

    return test_input, expected_result

def exec_test(result, ans):
    try:
        np.testing.assert_allclose(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
from sklearn.linear_model import SGDClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
pipe = Pipeline([
    ("scale", StandardScaler()),
    ("model", SGDClassifier(random_state=42))
])
grid = GridSearchCV(pipe, param_grid={"model__alpha": [1e-3, 1e-2, 1e-1, 1]}, cv=5)
X, y = test_input
[insert]
result = coef
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

I am new to scikit-learn, but it did what I was hoping for. Now, maddeningly, the only remaining issue is that I don't find how I could print the model's coefficients it estimated. Especially when it comes to a pipeline fitted by a GridSearch. Now I have a pipeline including data scaling, centering, and a classifier model. What is the way to get its estimated coefficients?
here is my current code
pipe = Pipeline([
    ("scale", StandardScaler()),
    ("model", RidgeClassifier(random_state=24))
])
grid = GridSearchCV(pipe, param_grid={"model__alpha": [2e-4, 3e-3, 4e-2, 5e-1]}, cv=7)
# where is the coef?

Any advice is appreciated. Thanks in advance.

A:

runnable code
<code>
import numpy as np
import pandas as pd
from sklearn.linear_model import RidgeClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
X, y = load_data()
assert type(X) == np.ndarray
assert type(y) == np.ndarray
pipe = Pipeline([
    ("scale", StandardScaler()),
    ("model", RidgeClassifier(random_state=24))
])
grid = GridSearchCV(pipe, param_grid={"model__alpha": [2e-4, 3e-3, 4e-2, 5e-1]}, cv=7)
</code>
coef = ... # put solution in this variable
BEGIN SOLUTION
<code>

grid.fit(X, y)
coef = grid.best_estimator_.named_steps['model'].coef_

import numpy as np
import copy
from sklearn.linear_model import RidgeClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
import sklearn
from sklearn.datasets import make_classification

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            X, y = make_classification(random_state=42)
        return X, y

    def generate_ans(data):
        X, y = data
        pipe = Pipeline(
            [("scale", StandardScaler()), ("model", RidgeClassifier(random_state=24))]
        )
        grid = GridSearchCV(
            pipe, param_grid={"model__alpha": [2e-4, 3e-3, 4e-2, 5e-1]}, cv=7
        )
        grid.fit(X, y)
        coef = grid.best_estimator_.named_steps["model"].coef_
        return coef

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))

    return test_input, expected_result

def exec_test(result, ans):
    try:
        np.testing.assert_allclose(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
from sklearn.linear_model import RidgeClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
X, y = test_input
pipe = Pipeline([
    ("scale", StandardScaler()),
    ("model", RidgeClassifier(random_state=24))
])
grid = GridSearchCV(pipe, param_grid={"model__alpha": [2e-4, 3e-3, 4e-2, 5e-1]}, cv=7)
[insert]
result = coef
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

I performed feature selection using ExtraTreesClassifier and SelectFromModel in data set that loaded as DataFrame, however i want to save these selected feature while maintaining columns name as well. So is there away to get selected columns names from SelectFromModel method? note that output is numpy array return important features whole columns not columns header. Please help me with the code below.

import pandas as pd
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.feature_selection import SelectFromModel
import numpy as np

df = pd.read_csv('los_10_one_encoder.csv')
y = df['LOS'] # target
X= df.drop('LOS',axis=1) # drop LOS column
clf = ExtraTreesClassifier(random_state=42)
clf = clf.fit(X, y)
print(clf.feature_importances_)

model = SelectFromModel(clf, prefit=True)
X_new = model.transform(X)

A:

<code>
import pandas as pd
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.feature_selection import SelectFromModel
import numpy as np

X, y = load_data()
clf = ExtraTreesClassifier(random_state=42)
clf = clf.fit(X, y)
</code>
column_names = ... # put solution in this variable
BEGIN SOLUTION
<code>

import numpy as np
import pandas as pd
import copy
import tokenize, io
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.feature_selection import SelectFromModel
import sklearn
from sklearn.datasets import make_classification

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            X, y = make_classification(n_samples=200, n_features=10, random_state=42)
            X = pd.DataFrame(
                X,
                columns=[
                    "one",
                    "two",
                    "three",
                    "four",
                    "five",
                    "six",
                    "seven",
                    "eight",
                    "nine",
                    "ten",
                ],
            )
            y = pd.Series(y)
        return X, y

    def generate_ans(data):
        X, y = data
        clf = ExtraTreesClassifier(random_state=42)
        clf = clf.fit(X, y)
        model = SelectFromModel(clf, prefit=True)
        column_names = X.columns[model.get_support()]
        return column_names

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))

    return test_input, expected_result

def exec_test(result, ans):
    try:
        np.testing.assert_equal(np.array(ans), np.array(result))
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.feature_selection import SelectFromModel
X, y = test_input
clf = ExtraTreesClassifier(random_state=42)
clf = clf.fit(X, y)
[insert]
result = column_names
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "SelectFromModel" in tokens


Prompt:
Problem:

look at my code below:

import pandas as pd
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.feature_selection import SelectFromModel
import numpy as np

df = pd.read_csv('los_10_one_encoder.csv')
y = df['LOS'] # target
X= df.drop('LOS',axis=1) # drop LOS column
clf = ExtraTreesClassifier(random_state=42)
clf = clf.fit(X, y)
print(clf.feature_importances_)

model = SelectFromModel(clf, prefit=True)
X_new = model.transform(X)

I used ExtraTreesClassifier and SelectFromModel to do feature selection in the data set which is loaded as pandas df.
However, I also want to keep the column names of the selected feature. My question is, is there a way to get the selected column names out from SelectFromModel method?
Note that output type is numpy array, and returns important features in whole columns, not columns header. Great thanks if anyone could help me.

A:

<code>
import pandas as pd
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.feature_selection import SelectFromModel
import numpy as np
X, y = load_data()
clf = ExtraTreesClassifier(random_state=42)
clf = clf.fit(X, y)
</code>
column_names = ... # put solution in this variable
BEGIN SOLUTION
<code>

import numpy as np
import pandas as pd
import copy
import tokenize, io
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.feature_selection import SelectFromModel
import sklearn
from sklearn.datasets import make_classification

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            X, y = make_classification(n_samples=200, n_features=10, random_state=42)
            X = pd.DataFrame(
                X,
                columns=[
                    "one",
                    "two",
                    "three",
                    "four",
                    "five",
                    "six",
                    "seven",
                    "eight",
                    "nine",
                    "ten",
                ],
            )
            y = pd.Series(y)
        return X, y

    def generate_ans(data):
        X, y = data
        clf = ExtraTreesClassifier(random_state=42)
        clf = clf.fit(X, y)
        model = SelectFromModel(clf, prefit=True)
        column_names = X.columns[model.get_support()]
        return column_names

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))

    return test_input, expected_result

def exec_test(result, ans):
    try:
        np.testing.assert_equal(np.array(ans), np.array(result))
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.feature_selection import SelectFromModel
X, y = test_input
clf = ExtraTreesClassifier(random_state=42)
clf = clf.fit(X, y)
[insert]
result = column_names
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "SelectFromModel" in tokens


Prompt:
Problem:

I performed feature selection using ExtraTreesClassifier and SelectFromModel in data set that loaded as DataFrame, however i want to save these selected feature while maintaining columns name as well. So is there away to get selected columns names from SelectFromModel method? note that output is numpy array return important features whole columns not columns header. Please help me with the code below.

import pandas as pd
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.feature_selection import SelectFromModel
import numpy as np

# read data, X is feature and y is target

clf = ExtraTreesClassifier(random_state=42)
clf = clf.fit(X, y)
print(clf.feature_importances_)

model = SelectFromModel(clf, prefit=True)
X_new = model.transform(X)

A:

<code>
import pandas as pd
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.feature_selection import SelectFromModel
import numpy as np

X, y = load_data()
clf = ExtraTreesClassifier(random_state=42)
clf = clf.fit(X, y)
</code>
column_names = ... # put solution in this variable
BEGIN SOLUTION
<code>

import numpy as np
import pandas as pd
import copy
import tokenize, io
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.feature_selection import SelectFromModel
import sklearn
from sklearn.datasets import make_classification

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            X, y = make_classification(n_samples=200, n_features=10, random_state=42)
            X = pd.DataFrame(
                X,
                columns=[
                    "one",
                    "two",
                    "three",
                    "four",
                    "five",
                    "six",
                    "seven",
                    "eight",
                    "nine",
                    "ten",
                ],
            )
            y = pd.Series(y)
        return X, y

    def generate_ans(data):
        X, y = data
        clf = ExtraTreesClassifier(random_state=42)
        clf = clf.fit(X, y)
        model = SelectFromModel(clf, prefit=True)
        column_names = X.columns[model.get_support()]
        return column_names

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))

    return test_input, expected_result

def exec_test(result, ans):
    try:
        np.testing.assert_equal(np.array(ans), np.array(result))
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.feature_selection import SelectFromModel
X, y = test_input
clf = ExtraTreesClassifier(random_state=42)
clf = clf.fit(X, y)
[insert]
result = column_names
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "SelectFromModel" in tokens


Prompt:
Problem:

I performed feature selection using ExtraTreesClassifier and SelectFromModel in data set that loaded as DataFrame, however i want to save these selected feature as a list(python type list) while maintaining columns name as well. So is there away to get selected columns names from SelectFromModel method? note that output is numpy array return important features whole columns not columns header. Please help me with the code below.

import pandas as pd
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.feature_selection import SelectFromModel
import numpy as np

df = pd.read_csv('los_10_one_encoder.csv')
y = df['LOS'] # target
X= df.drop('LOS',axis=1) # drop LOS column
clf = ExtraTreesClassifier(random_state=42)
clf = clf.fit(X, y)
print(clf.feature_importances_)

model = SelectFromModel(clf, prefit=True)
X_new = model.transform(X)

A:

<code>
import pandas as pd
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.feature_selection import SelectFromModel
import numpy as np

X, y = load_data()
clf = ExtraTreesClassifier(random_state=42)
clf = clf.fit(X, y)
</code>
column_names = ... # put solution in this variable
BEGIN SOLUTION
<code>

import numpy as np
import pandas as pd
import copy
import tokenize, io
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.feature_selection import SelectFromModel
import sklearn
from sklearn.datasets import make_classification

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            X, y = make_classification(n_samples=200, n_features=10, random_state=42)
            X = pd.DataFrame(
                X,
                columns=[
                    "one",
                    "two",
                    "three",
                    "four",
                    "five",
                    "six",
                    "seven",
                    "eight",
                    "nine",
                    "ten",
                ],
            )
            y = pd.Series(y)
        return X, y

    def generate_ans(data):
        X, y = data
        clf = ExtraTreesClassifier(random_state=42)
        clf = clf.fit(X, y)
        model = SelectFromModel(clf, prefit=True)
        column_names = list(X.columns[model.get_support()])
        return column_names

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))

    return test_input, expected_result

def exec_test(result, ans):
    try:
        assert type(result) == list
        np.testing.assert_equal(ans, result)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.feature_selection import SelectFromModel
X, y = test_input
clf = ExtraTreesClassifier(random_state=42)
clf = clf.fit(X, y)
[insert]
result = column_names
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "SelectFromModel" in tokens


Prompt:
Problem:

I have fitted a k-means algorithm on 5000+ samples using the python scikit-learn library. I want to have the 50 samples closest (data, not just index) to a cluster center "p" (e.g. p=2) as an output, here "p" means the p^th center. How do I perform this task?

A:

<code>
import numpy as np
import pandas as pd
from sklearn.cluster import KMeans
p, X = load_data()
assert type(X) == np.ndarray
km = KMeans()
</code>
closest_50_samples = ... # put solution in this variable
BEGIN SOLUTION
<code>

import numpy as np
import copy
from sklearn.cluster import KMeans
import sklearn
from sklearn.datasets import make_blobs

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            X, y = make_blobs(n_samples=200, n_features=3, centers=8, random_state=42)
            p = 2
        elif test_case_id == 2:
            X, y = make_blobs(n_samples=200, n_features=3, centers=8, random_state=42)
            p = 3
        return p, X

    def generate_ans(data):
        p, X = data
        km = KMeans(n_clusters=8, random_state=42)
        km.fit(X)
        d = km.transform(X)[:, p]
        indexes = np.argsort(d)[::][:50]
        closest_50_samples = X[indexes]
        return closest_50_samples

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))

    return test_input, expected_result

def exec_test(result, ans):
    try:
        np.testing.assert_allclose(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
from sklearn.cluster import KMeans
p, X = test_input
km = KMeans(n_clusters=8, random_state=42)
[insert]
result = closest_50_samples
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

I am using KMeans in sklearn on a data set which have more than 5000 samples. And I want to get the 50 samples(not just index but full data) closest to "p" (e.g. p=2), a cluster center, as an output, here "p" means the p^th center.
Anyone can help me?

A:

<code>
import numpy as np
import pandas as pd
from sklearn.cluster import KMeans
p, X = load_data()
assert type(X) == np.ndarray
km = KMeans()
</code>
closest_50_samples = ... # put solution in this variable
BEGIN SOLUTION
<code>

import numpy as np
import copy
from sklearn.cluster import KMeans
import sklearn
from sklearn.datasets import make_blobs

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            X, y = make_blobs(n_samples=200, n_features=3, centers=8, random_state=42)
            p = 2
        elif test_case_id == 2:
            X, y = make_blobs(n_samples=200, n_features=3, centers=8, random_state=42)
            p = 3
        return p, X

    def generate_ans(data):
        p, X = data
        km = KMeans(n_clusters=8, random_state=42)
        km.fit(X)
        d = km.transform(X)[:, p]
        indexes = np.argsort(d)[::][:50]
        closest_50_samples = X[indexes]
        return closest_50_samples

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))

    return test_input, expected_result

def exec_test(result, ans):
    try:
        np.testing.assert_allclose(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
from sklearn.cluster import KMeans
p, X = test_input
km = KMeans(n_clusters=8, random_state=42)
[insert]
result = closest_50_samples
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

I have fitted a k-means algorithm on more than 400 samples using the python scikit-learn library. I want to have the 100 samples closest (data, not just index) to a cluster center "p" (e.g. p=2) as an output, here "p" means the p^th center. How do I perform this task?

A:

<code>
import numpy as np
import pandas as pd
from sklearn.cluster import KMeans
p, X = load_data()
assert type(X) == np.ndarray
km = KMeans()
</code>
closest_100_samples = ... # put solution in this variable
BEGIN SOLUTION
<code>

import numpy as np
import copy
from sklearn.cluster import KMeans
import sklearn
from sklearn.datasets import make_blobs

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            X, y = make_blobs(n_samples=450, n_features=3, centers=8, random_state=42)
            p = 2
        elif test_case_id == 2:
            X, y = make_blobs(n_samples=450, n_features=3, centers=8, random_state=42)
            p = 3
        return p, X

    def generate_ans(data):
        p, X = data
        km = KMeans(n_clusters=8, random_state=42)
        km.fit(X)
        d = km.transform(X)[:, p]
        indexes = np.argsort(d)[::][:100]
        closest_100_samples = X[indexes]
        return closest_100_samples

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))

    return test_input, expected_result

def exec_test(result, ans):
    try:
        np.testing.assert_allclose(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
from sklearn.cluster import KMeans
p, X = test_input
km = KMeans(n_clusters=8, random_state=42)
[insert]
result = closest_100_samples
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

I have fitted a k-means algorithm on 5000+ samples using the python scikit-learn library. I want to have the 50 samples closest (data, not just index) to a cluster center "p" (e.g. p=2) as an output, here "p" means the p^th center. How do I perform this task?

A:

<code>
import numpy as np
import pandas as pd
from sklearn.cluster import KMeans
p, X = load_data()
assert type(X) == np.ndarray
km = KMeans()
def get_samples(p, X, km):
    # return the solution in this function
    # samples = get_samples(p, X, km)
    ### BEGIN SOLUTION

# def get_samples(p, X, km):
    # calculate the closest 50 samples
    ### BEGIN SOLUTION
    km.fit(X)
    d = km.transform(X)[:, p]
    indexes = np.argsort(d)[::][:50]
    samples = X[indexes]
    ### END SOLUTION
    # return samples
# closest_50_samples = get_samples(p, X, km)

    return samples

import numpy as np
import copy
from sklearn.cluster import KMeans
import sklearn
from sklearn.datasets import make_blobs

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            X, y = make_blobs(n_samples=200, n_features=3, centers=8, random_state=42)
            p = 2
        elif test_case_id == 2:
            X, y = make_blobs(n_samples=200, n_features=3, centers=8, random_state=42)
            p = 3
        return p, X

    def generate_ans(data):
        p, X = data
        km = KMeans(n_clusters=8, random_state=42)
        km.fit(X)
        d = km.transform(X)[:, p]
        indexes = np.argsort(d)[::][:50]
        closest_50_samples = X[indexes]
        return closest_50_samples

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))

    return test_input, expected_result

def exec_test(result, ans):
    try:
        np.testing.assert_allclose(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
from sklearn.cluster import KMeans
p, X = test_input
km = KMeans(n_clusters=8, random_state=42)
def get_samples(p, X, km):
[insert]
closest_50_samples = get_samples(p, X, km)
result = closest_50_samples
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

I am attempting to train models with GradientBoostingClassifier using categorical variables.

The following is a primitive code sample, just for trying to input categorical variables into GradientBoostingClassifier.

from sklearn import datasets
from sklearn.ensemble import GradientBoostingClassifier
import pandas

iris = datasets.load_iris()
# Use only data for 2 classes.
X = iris.data[(iris.target==0) | (iris.target==1)]
Y = iris.target[(iris.target==0) | (iris.target==1)]

# Class 0 has indices 0-49. Class 1 has indices 50-99.
# Divide data into 80% training, 20% testing.
train_indices = list(range(40)) + list(range(50,90))
test_indices = list(range(40,50)) + list(range(90,100))
X_train = X[train_indices]
X_test = X[test_indices]
y_train = Y[train_indices]
y_test = Y[test_indices]

X_train = pandas.DataFrame(X_train)

# Insert fake categorical variable.
# Just for testing in GradientBoostingClassifier.
X_train[0] = ['a']*40 + ['b']*40

# Model.
clf = GradientBoostingClassifier(learning_rate=0.01,max_depth=8,n_estimators=50).fit(X_train, y_train)
The following error appears:

ValueError: could not convert string to float: 'b'
From what I gather, it seems that One Hot Encoding on categorical variables is required before GradientBoostingClassifier can build the model.

Can GradientBoostingClassifier build models using categorical variables without having to do one hot encoding? I want to convert categorical variable to matrix and merge back with original training data use get_dummies in pandas.

R gbm package is capable of handling the sample data above. I'm looking for a Python library with equivalent capability and get_dummies seems good.

A:

<code>
import numpy as np
import pandas as pd
from sklearn import datasets
from sklearn.ensemble import GradientBoostingClassifier
import pandas

# load data in the example
X_train, y_train = load_data()
X_train[0] = ['a'] * 40 + ['b'] * 40

</code>
X_train = ... # put solution in this variable
BEGIN SOLUTION
<code>

import numpy as np
import pandas as pd
import copy
import tokenize, io
from sklearn import datasets

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            iris = datasets.load_iris()
            X = iris.data[(iris.target == 0) | (iris.target == 1)]
            Y = iris.target[(iris.target == 0) | (iris.target == 1)]
            train_indices = list(range(40)) + list(range(50, 90))
            test_indices = list(range(40, 50)) + list(range(90, 100))
            X_train = X[train_indices]
            y_train = Y[train_indices]
            X_train = pd.DataFrame(X_train)
        return X_train, y_train

    def generate_ans(data):
        X_train, y_train = data
        X_train[0] = ["a"] * 40 + ["b"] * 40
        catVar = pd.get_dummies(X_train[0]).to_numpy()
        X_train = np.concatenate((X_train.iloc[:, 1:], catVar), axis=1)
        return X_train

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        if type(result) == np.ndarray:
            np.testing.assert_equal(ans[:, :3], result[:, :3])
        elif type(result) == pd.DataFrame:
            np.testing.assert_equal(ans[:, :3], result.to_numpy()[:, :3])
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
from sklearn import datasets
from sklearn.ensemble import GradientBoostingClassifier
X_train, y_train = test_input
X_train[0] = ['a'] * 40 + ['b'] * 40
[insert]
clf = GradientBoostingClassifier(learning_rate=0.01, max_depth=8, n_estimators=50).fit(X_train, y_train)
result = X_train
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "get_dummies" in tokens and "OneHotEncoder" not in tokens


Prompt:
Problem:

Here is some code example. To better understand it, I'm trying to train models with GradientBoostingClassifier with categorical variables as input.

from sklearn import datasets
from sklearn.ensemble import GradientBoostingClassifier
import pandas

iris = datasets.load_iris()
X = iris.data[(iris.target==0) | (iris.target==1)]
Y = iris.target[(iris.target==0) | (iris.target==1)]
train_indices = list(range(40)) + list(range(50,90))
test_indices = list(range(40,50)) + list(range(90,100))
X_train = X[train_indices]
X_test = X[test_indices]
y_train = Y[train_indices]
y_test = Y[test_indices]
X_train = pandas.DataFrame(X_train)
X_train[0] = ['a']*40 + ['b']*40
clf = GradientBoostingClassifier(learning_rate=0.01,max_depth=8,n_estimators=50).fit(X_train, y_train)

This piece of code report error like:
ValueError: could not convert string to float: 'b'
I find it seems that One Hot Encoding on categorical variables is required before GradientBoostingClassifier.
But can GradientBoostingClassifier build models using categorical variables without one hot encoding? I want to convert categorical variable to matrix and merge back with original training data use get_dummies in pandas.
Could you give me some help how to use this function to handle this?

A:

<code>
import numpy as np
import pandas as pd
from sklearn import datasets
from sklearn.ensemble import GradientBoostingClassifier
import pandas

# load data in the example
X_train, y_train = load_data()
X_train[0] = ['a'] * 40 + ['b'] * 40

</code>
X_train = ... # put solution in this variable
BEGIN SOLUTION
<code>

import numpy as np
import pandas as pd
import copy
import tokenize, io
from sklearn import datasets

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            iris = datasets.load_iris()
            X = iris.data[(iris.target == 0) | (iris.target == 1)]
            Y = iris.target[(iris.target == 0) | (iris.target == 1)]
            train_indices = list(range(40)) + list(range(50, 90))
            test_indices = list(range(40, 50)) + list(range(90, 100))
            X_train = X[train_indices]
            y_train = Y[train_indices]
            X_train = pd.DataFrame(X_train)
        return X_train, y_train

    def generate_ans(data):
        X_train, y_train = data
        X_train[0] = ["a"] * 40 + ["b"] * 40
        catVar = pd.get_dummies(X_train[0]).to_numpy()
        X_train = np.concatenate((X_train.iloc[:, 1:], catVar), axis=1)
        return X_train

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    try:
        if type(result) == np.ndarray:
            np.testing.assert_equal(ans[:, :3], result[:, :3])
        elif type(result) == pd.DataFrame:
            np.testing.assert_equal(ans[:, :3], result.to_numpy()[:, :3])
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
from sklearn import datasets
from sklearn.ensemble import GradientBoostingClassifier
X_train, y_train = test_input
X_train[0] = ['a'] * 40 + ['b'] * 40
[insert]
clf = GradientBoostingClassifier(learning_rate=0.01, max_depth=8, n_estimators=50).fit(X_train, y_train)
result = X_train
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "get_dummies" in tokens and "OneHotEncoder" not in tokens


Prompt:
Problem:

Does scikit-learn provide facility to use SVM for regression, using a gaussian kernel? I looked at the APIs and I don't see any. Has anyone built a package on top of scikit-learn that does this?
Note to use default arguments

A:

<code>
import numpy as np
import pandas as pd
import sklearn
X, y = load_data()
assert type(X) == np.ndarray
assert type(y) == np.ndarray
# fit, then predict X
</code>
predict = ... # put solution in this variable
BEGIN SOLUTION
<code>

from sklearn.svm import SVR

svr_rbf = SVR(kernel='rbf')
svr_rbf.fit(X, y)
predict = svr_rbf.predict(X)

import numpy as np
import copy
import sklearn
from sklearn.datasets import make_regression
from sklearn.svm import SVR

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            X, y = make_regression(n_samples=1000, n_features=4, random_state=42)
        return X, y

    def generate_ans(data):
        X, y = data
        svr_rbf = SVR(kernel="rbf")
        svr_rbf.fit(X, y)
        predict = svr_rbf.predict(X)
        return predict

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))

    return test_input, expected_result

def exec_test(result, ans):
    try:
        np.testing.assert_allclose(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
import sklearn
X, y = test_input
[insert]
result = predict
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

How can I perform regression in sklearn, using SVM and a gaussian kernel?
Note to use default arguments. Thanks.

A:

<code>
import numpy as np
import pandas as pd
import sklearn
X, y = load_data()
assert type(X) == np.ndarray
assert type(y) == np.ndarray
# fit, then predict X
</code>
predict = ... # put solution in this variable
BEGIN SOLUTION
<code>

from sklearn.svm import SVR

svr_rbf = SVR(kernel='rbf')
svr_rbf.fit(X, y)
predict = svr_rbf.predict(X)

import numpy as np
import copy
import sklearn
from sklearn.datasets import make_regression
from sklearn.svm import SVR

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            X, y = make_regression(n_samples=1000, n_features=4, random_state=42)
        return X, y

    def generate_ans(data):
        X, y = data
        svr_rbf = SVR(kernel="rbf")
        svr_rbf.fit(X, y)
        predict = svr_rbf.predict(X)
        return predict

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))

    return test_input, expected_result

def exec_test(result, ans):
    try:
        np.testing.assert_allclose(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
import sklearn
X, y = test_input
[insert]
result = predict
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

Does scikit-learn provide facility to use SVM for regression, using a polynomial kernel (degree=2)? I looked at the APIs and I don't see any. Has anyone built a package on top of scikit-learn that does this?
Note to use default arguments

A:

<code>
import numpy as np
import pandas as pd
import sklearn
X, y = load_data()
assert type(X) == np.ndarray
assert type(y) == np.ndarray
# fit, then predict X
</code>
predict = ... # put solution in this variable
BEGIN SOLUTION
<code>

from sklearn.svm import SVR

svr_poly = SVR(kernel='poly', degree=2)
svr_poly.fit(X, y)
predict = svr_poly.predict(X)

import numpy as np
import copy
import sklearn
from sklearn.datasets import make_regression
from sklearn.svm import SVR

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            X, y = make_regression(n_samples=1000, n_features=4, random_state=42)
        return X, y

    def generate_ans(data):
        X, y = data
        svr_poly = SVR(kernel="poly", degree=2)
        svr_poly.fit(X, y)
        predict = svr_poly.predict(X)
        return predict

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))

    return test_input, expected_result

def exec_test(result, ans):
    try:
        np.testing.assert_allclose(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
import sklearn
X, y = test_input
[insert]
result = predict
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

How can I perform regression in sklearn, using SVM and a polynomial kernel (degree=2)?
Note to use default arguments. Thanks.

A:

<code>
import numpy as np
import pandas as pd
import sklearn
X, y = load_data()
assert type(X) == np.ndarray
assert type(y) == np.ndarray
# fit, then predict X
</code>
predict = ... # put solution in this variable
BEGIN SOLUTION
<code>

from sklearn.svm import SVR

svr_poly = SVR(kernel='poly', degree=2)
svr_poly.fit(X, y)
predict = svr_poly.predict(X)

import numpy as np
import copy
import sklearn
from sklearn.datasets import make_regression
from sklearn.svm import SVR

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            X, y = make_regression(n_samples=1000, n_features=4, random_state=42)
        return X, y

    def generate_ans(data):
        X, y = data
        svr_poly = SVR(kernel="poly", degree=2)
        svr_poly.fit(X, y)
        predict = svr_poly.predict(X)
        return predict

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))

    return test_input, expected_result

def exec_test(result, ans):
    try:
        np.testing.assert_allclose(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
import sklearn
X, y = test_input
[insert]
result = predict
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

My goal is to input 3 queries and find out which query is most similar to a set of 5 documents.

So far I have calculated the tf-idf of the documents doing the following:

from sklearn.feature_extraction.text import TfidfVectorizer

def get_term_frequency_inverse_data_frequency(documents):
    vectorizer = TfidfVectorizer()
    matrix = vectorizer.fit_transform(documents)
    return matrix

def get_tf_idf_query_similarity(documents, query):
    tfidf = get_term_frequency_inverse_data_frequency(documents)
The problem I am having is now that I have tf-idf of the documents what operations do I perform on the query so I can find the cosine similarity to the documents? The answer should be like a 3*5 matrix of the similarities.

A:

<code>
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
queries, documents = load_data()
assert type(queries) == list
assert type(documents) == list
tfidf = TfidfVectorizer()
tfidf.fit_transform(documents)
</code>
cosine_similarities_of_queries = ... # put solution in this variable
BEGIN SOLUTION
<code>

from sklearn.metrics.pairwise import cosine_similarity

cosine_similarities_of_queries = []
for query in queries:
    query_tfidf = tfidf.transform([query])
    cosine_similarities_of_queries.append(cosine_similarity(query_tfidf, tfidf.transform(documents)).flatten())

import numpy as np
import copy
import scipy.sparse
import sklearn
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            document1 = [
                "Education is the process of learning and acquiring knowledge at an educational institution. It can be divided into three levels: primary, secondary, and tertiary."
                "Primary education generally refers to the first stage of schooling, which is typically compulsory and free in most countries. It usually lasts for six or seven years, from ages five or six to eleven or twelve."
                "Secondary education usually lasts for three or four years, from ages eleven or twelve to fifteen or sixteen. In some countries, it is compulsory, while in others it is not."
                "Tertiary education, also known as post-secondary education, is the stage of education that comes after secondary school. It can be divided into two types: university education and vocational education."
                "University education typically lasts for four years, from ages eighteen to twenty-two. It is usually divided into two parts: undergraduate and graduate."
                "Vocational education is training for a specific trade or profession. It can be either formal or informal. Formal vocational education is typically provided by vocational schools, while informal vocational education is provided by apprenticeships and on-the-job training."
            ]
            document2 = [
                "The purpose of education is to prepare individuals for successful futures. Whether that means getting a good job, being a responsible citizen, or becoming a lifelong learner, education is the key to a bright future."
                "There are many different types of educational institutions, each with its own unique purpose. Primary and secondary schools are the most common, but there are also institutions for special needs education, higher education, and adult education."
                "All educational institutions share the common goal of providing quality education to their students. However, the methods and curriculum used to achieve this goal can vary greatly."
                "Some educational institutions focus on academic knowledge, while others place more emphasis on practical skills. Some use traditional teaching methods, while others use more innovative approaches."
                "The type of education an individual receives should be based on their needs and goals. There is no one-size-fits-all approach to education, and what works for one person may not work for another."
            ]
            document3 = [
                "Education is a fundamental human right. It is essential for the development of individuals and societies. "
                "Access to education should be universal and inclusive. "
                "All individuals, regardless of race, gender, or social status, should have the opportunity to receive an education. Education is a tool that can be used to empower individuals and improve the quality of their lives. "
                "It can help people to develop new skills and knowledge, and to achieve their full potential. Education is also a key factor in the development of societies. "
                "It can help to create more stable and prosperous societies."
            ]
            document4 = [
                "The quality of education is a key factor in the development of individuals and societies."
                "A high-quality education can help individuals to develop their skills and knowledge, and to achieve their full potential. It can also help to create more stable and prosperous societies."
                "There are many factors that contribute to the quality of education. These include the qualifications of teachers, the resources available, and the curriculum being taught."
                "Improving the quality of education is an important goal for all educational institutions. By providing quality education, we can empower individuals and help to create a better world for everyone."
            ]
            document5 = [
                "Education is an important investment in the future.\n\n"
                "By providing quality education, we can empower individuals and help to create a better world for everyone.\n\nA high-quality education can help individuals to develop their skills and knowledge, and to achieve their full potential. It can also help to create more stable and prosperous societies.\n\n"
                "There are many factors that contribute to the quality of education. These include the qualifications of teachers, the resources available, and the curriculum being taught.\n\n"
                "Improving the quality of education is an important goal for all educational institutions. By investing in education, we can make a difference in the world."
            ]
            documents = []
            documents.extend(document1)
            documents.extend(document2)
            documents.extend(document3)
            documents.extend(document4)
            documents.extend(document5)
            query1 = ["What are the benefits of education?"]
            query2 = ["What are the different branches of the military?"]
            query3 = ["What is the definition of science?"]
            queries = []
            queries.extend(query1)
            queries.extend(query2)
            queries.extend(query3)
        return queries, documents

    def generate_ans(data):
        queries, documents = data
        tfidf = TfidfVectorizer()
        tfidf.fit_transform(documents)
        cosine_similarities_of_queries = []
        for query in queries:
            query_tfidf = tfidf.transform([query])
            cosine_similarities_of_queries.append(
                cosine_similarity(query_tfidf, tfidf.transform(documents)).flatten()
            )
        return cosine_similarities_of_queries

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))

    return test_input, expected_result

def exec_test(result, ans):
    try:
        if type(result) == scipy.sparse.csr.csr_matrix:
            result = result.toarray()
        np.testing.assert_allclose(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
queries, documents = test_input
tfidf = TfidfVectorizer()
tfidf.fit_transform(documents)
[insert]
result = cosine_similarities_of_queries
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

My goal is to input some queries and find out which query is most similar to a set of documents.

So far I have calculated the tf-idf of the documents doing the following:

from sklearn.feature_extraction.text import TfidfVectorizer

def get_term_frequency_inverse_data_frequency(documents):
    vectorizer = TfidfVectorizer()
    matrix = vectorizer.fit_transform(documents)
    return matrix

def get_tf_idf_query_similarity(documents, query):
    tfidf = get_term_frequency_inverse_data_frequency(documents)
The problem I am having is now that I have tf-idf of the documents what operations do I perform on the query so I can find the cosine similarity to the documents? The answer should be like a 3*5 matrix of the similarities.

A:

<code>
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
queries, documents = load_data()
assert type(queries) == list
assert type(documents) == list
tfidf = TfidfVectorizer()
tfidf.fit_transform(documents)
</code>
cosine_similarities_of_queries = ... # put solution in this variable
BEGIN SOLUTION
<code>

from sklearn.metrics.pairwise import cosine_similarity

cosine_similarities_of_queries = []
for query in queries:
    query_tfidf = tfidf.transform([query])
    cosine_similarities_of_queries.append(cosine_similarity(query_tfidf, tfidf.transform(documents)).flatten())

import numpy as np
import copy
import scipy.sparse
import sklearn
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            document1 = [
                "Education is the process of learning and acquiring knowledge at an educational institution. It can be divided into three levels: primary, secondary, and tertiary."
                "Primary education generally refers to the first stage of schooling, which is typically compulsory and free in most countries. It usually lasts for six or seven years, from ages five or six to eleven or twelve."
                "Secondary education usually lasts for three or four years, from ages eleven or twelve to fifteen or sixteen. In some countries, it is compulsory, while in others it is not."
                "Tertiary education, also known as post-secondary education, is the stage of education that comes after secondary school. It can be divided into two types: university education and vocational education."
                "University education typically lasts for four years, from ages eighteen to twenty-two. It is usually divided into two parts: undergraduate and graduate."
                "Vocational education is training for a specific trade or profession. It can be either formal or informal. Formal vocational education is typically provided by vocational schools, while informal vocational education is provided by apprenticeships and on-the-job training."
            ]
            document2 = [
                "The purpose of education is to prepare individuals for successful futures. Whether that means getting a good job, being a responsible citizen, or becoming a lifelong learner, education is the key to a bright future."
                "There are many different types of educational institutions, each with its own unique purpose. Primary and secondary schools are the most common, but there are also institutions for special needs education, higher education, and adult education."
                "All educational institutions share the common goal of providing quality education to their students. However, the methods and curriculum used to achieve this goal can vary greatly."
                "Some educational institutions focus on academic knowledge, while others place more emphasis on practical skills. Some use traditional teaching methods, while others use more innovative approaches."
                "The type of education an individual receives should be based on their needs and goals. There is no one-size-fits-all approach to education, and what works for one person may not work for another."
            ]
            document3 = [
                "Education is a fundamental human right. It is essential for the development of individuals and societies. "
                "Access to education should be universal and inclusive. "
                "All individuals, regardless of race, gender, or social status, should have the opportunity to receive an education. Education is a tool that can be used to empower individuals and improve the quality of their lives. "
                "It can help people to develop new skills and knowledge, and to achieve their full potential. Education is also a key factor in the development of societies. "
                "It can help to create more stable and prosperous societies."
            ]
            document4 = [
                "The quality of education is a key factor in the development of individuals and societies."
                "A high-quality education can help individuals to develop their skills and knowledge, and to achieve their full potential. It can also help to create more stable and prosperous societies."
                "There are many factors that contribute to the quality of education. These include the qualifications of teachers, the resources available, and the curriculum being taught."
                "Improving the quality of education is an important goal for all educational institutions. By providing quality education, we can empower individuals and help to create a better world for everyone."
            ]
            documents = []
            documents.extend(document1)
            documents.extend(document2)
            documents.extend(document3)
            documents.extend(document4)
            query1 = ["What are the benefits of education?"]
            query2 = ["What are the different branches of the military?"]
            query3 = ["What is the definition of science?"]
            query4 = ["What are the different types of education?"]
            queries = []
            queries.extend(query1)
            queries.extend(query2)
            queries.extend(query3)
            queries.extend(query4)
        return queries, documents

    def generate_ans(data):
        queries, documents = data
        tfidf = TfidfVectorizer()
        tfidf.fit_transform(documents)
        cosine_similarities_of_queries = []
        for query in queries:
            query_tfidf = tfidf.transform([query])
            cosine_similarities_of_queries.append(
                cosine_similarity(query_tfidf, tfidf.transform(documents)).flatten()
            )
        return cosine_similarities_of_queries

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))

    return test_input, expected_result

def exec_test(result, ans):
    try:
        if type(result) == scipy.sparse.csr.csr_matrix:
            result = result.toarray()
        np.testing.assert_allclose(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
queries, documents = test_input
tfidf = TfidfVectorizer()
tfidf.fit_transform(documents)
[insert]
result = cosine_similarities_of_queries
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

My goal is to input 3 queries and find out which query is most similar to a set of 5 documents.

So far I have calculated the tf-idf of the documents doing the following:

from sklearn.feature_extraction.text import TfidfVectorizer

def get_term_frequency_inverse_data_frequency(documents):
    vectorizer = TfidfVectorizer()
    matrix = vectorizer.fit_transform(documents)
    return matrix

def get_tf_idf_query_similarity(documents, query):
    tfidf = get_term_frequency_inverse_data_frequency(documents)
The problem I am having is now that I have tf-idf of the documents what operations do I perform on the query so I can find the cosine similarity to the documents? The answer should be like a 3*5 matrix of the similarities.

A:

<code>
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
queries, documents = load_data()
assert type(queries) == list
assert type(documents) == list
def solve(queries, documents):
    tfidf = TfidfVectorizer()
    tfidf.fit_transform(documents)
    # return the solution in this function
    # cosine_similarities_of_queries = solve(queries, documents)
    ### BEGIN SOLUTION

# def solve(queries, documents):
    ### BEGIN SOLUTION
    from sklearn.metrics.pairwise import cosine_similarity

    cosine_similarities_of_queries = []
    for query in queries:
        query_tfidf = tfidf.transform([query])
        cosine_similarities_of_queries.append(cosine_similarity(query_tfidf, tfidf.transform(documents)).flatten())
    ### END SOLUTION
    # return cosine_similarities_of_queries
# cosine_similarities_of_queries = solve(queries, documents)

    return cosine_similarities_of_queries

import numpy as np
import copy
import scipy.sparse
import sklearn
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            document1 = [
                "Education is the process of learning and acquiring knowledge at an educational institution. It can be divided into three levels: primary, secondary, and tertiary."
                "Primary education generally refers to the first stage of schooling, which is typically compulsory and free in most countries. It usually lasts for six or seven years, from ages five or six to eleven or twelve."
                "Secondary education usually lasts for three or four years, from ages eleven or twelve to fifteen or sixteen. In some countries, it is compulsory, while in others it is not."
                "Tertiary education, also known as post-secondary education, is the stage of education that comes after secondary school. It can be divided into two types: university education and vocational education."
                "University education typically lasts for four years, from ages eighteen to twenty-two. It is usually divided into two parts: undergraduate and graduate."
                "Vocational education is training for a specific trade or profession. It can be either formal or informal. Formal vocational education is typically provided by vocational schools, while informal vocational education is provided by apprenticeships and on-the-job training."
            ]
            document2 = [
                "The purpose of education is to prepare individuals for successful futures. Whether that means getting a good job, being a responsible citizen, or becoming a lifelong learner, education is the key to a bright future."
                "There are many different types of educational institutions, each with its own unique purpose. Primary and secondary schools are the most common, but there are also institutions for special needs education, higher education, and adult education."
                "All educational institutions share the common goal of providing quality education to their students. However, the methods and curriculum used to achieve this goal can vary greatly."
                "Some educational institutions focus on academic knowledge, while others place more emphasis on practical skills. Some use traditional teaching methods, while others use more innovative approaches."
                "The type of education an individual receives should be based on their needs and goals. There is no one-size-fits-all approach to education, and what works for one person may not work for another."
            ]
            document3 = [
                "Education is a fundamental human right. It is essential for the development of individuals and societies. "
                "Access to education should be universal and inclusive. "
                "All individuals, regardless of race, gender, or social status, should have the opportunity to receive an education. Education is a tool that can be used to empower individuals and improve the quality of their lives. "
                "It can help people to develop new skills and knowledge, and to achieve their full potential. Education is also a key factor in the development of societies. "
                "It can help to create more stable and prosperous societies."
            ]
            document4 = [
                "The quality of education is a key factor in the development of individuals and societies."
                "A high-quality education can help individuals to develop their skills and knowledge, and to achieve their full potential. It can also help to create more stable and prosperous societies."
                "There are many factors that contribute to the quality of education. These include the qualifications of teachers, the resources available, and the curriculum being taught."
                "Improving the quality of education is an important goal for all educational institutions. By providing quality education, we can empower individuals and help to create a better world for everyone."
            ]
            document5 = [
                "Education is an important investment in the future.\n\n"
                "By providing quality education, we can empower individuals and help to create a better world for everyone.\n\nA high-quality education can help individuals to develop their skills and knowledge, and to achieve their full potential. It can also help to create more stable and prosperous societies.\n\n"
                "There are many factors that contribute to the quality of education. These include the qualifications of teachers, the resources available, and the curriculum being taught.\n\n"
                "Improving the quality of education is an important goal for all educational institutions. By investing in education, we can make a difference in the world."
            ]
            documents = []
            documents.extend(document1)
            documents.extend(document2)
            documents.extend(document3)
            documents.extend(document4)
            documents.extend(document5)
            query1 = ["What are the benefits of education?"]
            query2 = ["What are the different branches of the military?"]
            query3 = ["What is the definition of science?"]
            queries = []
            queries.extend(query1)
            queries.extend(query2)
            queries.extend(query3)
        return queries, documents

    def generate_ans(data):
        queries, documents = data
        tfidf = TfidfVectorizer()
        tfidf.fit_transform(documents)
        cosine_similarities_of_queries = []
        for query in queries:
            query_tfidf = tfidf.transform([query])
            cosine_similarities_of_queries.append(
                cosine_similarity(query_tfidf, tfidf.transform(documents)).flatten()
            )
        return cosine_similarities_of_queries

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))

    return test_input, expected_result

def exec_test(result, ans):
    try:
        if type(result) == scipy.sparse.csr.csr_matrix:
            result = result.toarray()
        np.testing.assert_allclose(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
queries, documents = test_input
def solve(queries, documents):
    tfidf = TfidfVectorizer()
    tfidf.fit_transform(documents)
[insert]
cosine_similarities_of_queries = solve(queries, documents)
result = cosine_similarities_of_queries
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

Given a list of variant length features:

features = [
    ['f1', 'f2', 'f3'],
    ['f2', 'f4', 'f5', 'f6'],
    ['f1', 'f2']
]
where each sample has variant number of features and the feature dtype is str and already one hot.

In order to use feature selection utilities of sklearn, I have to convert the features to a 2D-array which looks like:

    f1  f2  f3  f4  f5  f6
s1   1   1   1   0   0   0
s2   0   1   0   1   1   1
s3   1   1   0   0   0   0
How could I achieve it via sklearn or numpy?

A:

<code>
import pandas as pd
import numpy as np
import sklearn
features = load_data()
</code>
new_features = ... # put solution in this variable
BEGIN SOLUTION
<code>

from sklearn.preprocessing import MultiLabelBinarizer

new_features = MultiLabelBinarizer().fit_transform(features)

import numpy as np
import copy
import sklearn
from sklearn.preprocessing import MultiLabelBinarizer

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            features = [["f1", "f2", "f3"], ["f2", "f4", "f5", "f6"], ["f1", "f2"]]
        return features

    def generate_ans(data):
        features = data
        new_features = MultiLabelBinarizer().fit_transform(features)
        return new_features

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))

    return test_input, expected_result

def exec_test(result, ans):
    try:
        np.testing.assert_equal(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
import sklearn
features = test_input
[insert]
result = new_features
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

Given a list of variant length features, for example:

f = [
    ['t1'],
    ['t2', 't5', 't7'],
    ['t1', 't2', 't3', 't4', 't5'],
    ['t4', 't5', 't6']
]
where each sample has variant number of features and the feature dtype is str and already one hot.

In order to use feature selection utilities of sklearn, I have to convert the features to a 2D-array which looks like:

f
    t1  t2  t3  t4  t5  t6  t7
r1   1   0   0   0   0   0   0
r2   0   1   0   0   1   0   1
r3   1   1   1   1   1   0   0
r4   0   0   0   1   1   1   0
How could I achieve it via sklearn or numpy?

A:

<code>
import pandas as pd
import numpy as np
import sklearn
f = load_data()
</code>
new_f = ... # put solution in this variable
BEGIN SOLUTION
<code>

from sklearn.preprocessing import MultiLabelBinarizer

new_f = MultiLabelBinarizer().fit_transform(f)

import numpy as np
import copy
import sklearn
from sklearn.preprocessing import MultiLabelBinarizer

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            f = [["f1", "f2", "f3"], ["f2", "f4", "f5", "f6"], ["f1", "f2"]]
        elif test_case_id == 2:
            f = [
                ["t1"],
                ["t2", "t5", "t7"],
                ["t1", "t2", "t3", "t4", "t5"],
                ["t4", "t5", "t6"],
            ]
        return f

    def generate_ans(data):
        f = data
        new_f = MultiLabelBinarizer().fit_transform(f)
        return new_f

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))

    return test_input, expected_result

def exec_test(result, ans):
    try:
        np.testing.assert_equal(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
import sklearn
f = test_input
[insert]
result = new_f
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

Given a list of variant length features:

features = [
    ['f1', 'f2', 'f3'],
    ['f2', 'f4', 'f5', 'f6'],
    ['f1', 'f2']
]
where each sample has variant number of features and the feature dtype is str and already one hot.

In order to use feature selection utilities of sklearn, I have to convert the features to a 2D-array which looks like:

    f1  f2  f3  f4  f5  f6
s1   0   0   0   1   1   1
s2   1   0   1   0   0   0
s3   0   0   1   1   1   1
How could I achieve it via sklearn or numpy?

A:

<code>
import pandas as pd
import numpy as np
import sklearn
features = load_data()
</code>
new_features = ... # put solution in this variable
BEGIN SOLUTION
<code>

from sklearn.preprocessing import MultiLabelBinarizer

new_features = MultiLabelBinarizer().fit_transform(features)
rows, cols = new_features.shape
for i in range(rows):
    for j in range(cols):
        if new_features[i, j] == 1:
            new_features[i, j] = 0
        else:
            new_features[i, j] = 1

import numpy as np
import copy
import sklearn
from sklearn.preprocessing import MultiLabelBinarizer

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            features = [["f1", "f2", "f3"], ["f2", "f4", "f5", "f6"], ["f1", "f2"]]
        return features

    def generate_ans(data):
        features = data
        new_features = MultiLabelBinarizer().fit_transform(features)
        rows, cols = new_features.shape
        for i in range(rows):
            for j in range(cols):
                if new_features[i, j] == 1:
                    new_features[i, j] = 0
                else:
                    new_features[i, j] = 1
        return new_features

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))

    return test_input, expected_result

def exec_test(result, ans):
    try:
        np.testing.assert_equal(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
import sklearn
features = test_input
[insert]
result = new_features
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

Given a list of variant length features:

features = [
    ['f1', 'f2', 'f3'],
    ['f2', 'f4', 'f5', 'f6'],
    ['f1', 'f2']
]
where each sample has variant number of features and the feature dtype is str and already one hot.

In order to use feature selection utilities of sklearn, I have to convert the features to a 2D-array which looks like:

    f1  f2  f3  f4  f5  f6
s1   1   1   1   0   0   0
s2   0   1   0   1   1   1
s3   1   1   0   0   0   0
How could I achieve it via sklearn or numpy?

A:

<code>
import pandas as pd
import numpy as np
import sklearn
features = load_data()
def solve(features):
    # return the solution in this function
    # new_features = solve(features)
    ### BEGIN SOLUTION

# def solve(features):
    ### BEGIN SOLUTION
    from sklearn.preprocessing import MultiLabelBinarizer

    new_features = MultiLabelBinarizer().fit_transform(features)
    ### END SOLUTION
    # return new_features
# new_features = solve(features)

    return new_features

import numpy as np
import copy
import sklearn
from sklearn.preprocessing import MultiLabelBinarizer

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            features = [["f1", "f2", "f3"], ["f2", "f4", "f5", "f6"], ["f1", "f2"]]
        return features

    def generate_ans(data):
        features = data
        new_features = MultiLabelBinarizer().fit_transform(features)
        return new_features

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))

    return test_input, expected_result

def exec_test(result, ans):
    try:
        np.testing.assert_equal(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
import sklearn
features = test_input
def solve(features):
[insert]
new_features = solve(features)
result = new_features
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(1):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

Given a list of variant length features, for example:

f = [
    ['t1'],
    ['t2', 't5', 't7'],
    ['t1', 't2', 't3', 't4', 't5'],
    ['t4', 't5', 't6']
]
where each sample has variant number of features and the feature dtype is str and already one hot.

In order to use feature selection utilities of sklearn, I have to convert the features to a 2D-array which looks like:

f
    t1  t2  t3  t4  t5  t6  t7
r1   0   1   1   1   1   1   1
r2   1   0   1   1   0   1   0
r3   0   0   0   0   0   1   1
r4   1   1   1   0   0   0   1
How could I achieve it via sklearn or numpy?

A:

<code>
import pandas as pd
import numpy as np
import sklearn
features = load_data()
</code>
new_features = ... # put solution in this variable
BEGIN SOLUTION
<code>

from sklearn.preprocessing import MultiLabelBinarizer

new_features = MultiLabelBinarizer().fit_transform(features)
rows, cols = new_features.shape
for i in range(rows):
    for j in range(cols):
        if new_features[i, j] == 1:
            new_features[i, j] = 0
        else:
            new_features[i, j] = 1

import numpy as np
import copy
import sklearn
from sklearn.preprocessing import MultiLabelBinarizer

def generate_test_case(test_case_id):

    def define_test_input(test_case_id):
        if test_case_id == 1:
            features = [["f1", "f2", "f3"], ["f2", "f4", "f5", "f6"], ["f1", "f2"]]
        elif test_case_id == 2:
            features = [
                ["t1"],
                ["t2", "t5", "t7"],
                ["t1", "t2", "t3", "t4", "t5"],
                ["t4", "t5", "t6"],
            ]
        return features

    def generate_ans(data):
        features = data
        new_features = MultiLabelBinarizer().fit_transform(features)
        rows, cols = new_features.shape
        for i in range(rows):
            for j in range(cols):
                if new_features[i, j] == 1:
                    new_features[i, j] = 0
                else:
                    new_features[i, j] = 1
        return new_features

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))

    return test_input, expected_result

def exec_test(result, ans):
    try:
        np.testing.assert_equal(result, ans)
        return 1
    except:
        return 0

exec_context = r"""
import pandas as pd
import numpy as np
import sklearn
features = test_input
[insert]
result = new_features
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)


Prompt:
Problem:

Given a distance matrix, with similarity between various professors :

              prof1     prof2     prof3
       prof1     0        0.8     0.9
       prof2     0.8      0       0.2
       prof3     0.9      0.2     0
I need to perform hierarchical clustering on this data, where the above data is in the form of 2-d matrix

       data_matrix=[[0,0.8,0.9],[0.8,0,0.2],[0.9,0.2,0]]
The expected number of clusters is 2. I tried checking if I can implement it using sklearn.cluster AgglomerativeClustering but it is considering all the 3 rows as 3 separate vectors and not as a distance matrix. Can it be done using sklearn.cluster AgglomerativeClustering? prefer answer in a list like [label1, label2, ...]

A:

<code>
import numpy as np
import pandas as pd
import sklearn.cluster
data_matrix = load_data()
</code>
cluster_labels = ... # put solution in this variable
BEGIN SOLUTION
<code>

model = sklearn.cluster.AgglomerativeClustering(metric='precomputed', n_clusters=2, linkage='complete').fit(data_matrix)
cluster_labels = model.labels_

import numpy as np
import copy
import tokenize, io
from sklearn.cluster import AgglomerativeClustering

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            data_matrix = [[0, 0.8, 0.9], [0.8, 0, 0.2], [0.9, 0.2, 0]]
        elif test_case_id == 2:
            data_matrix = [[0, 0.2, 0.9], [0.2, 0, 0.8], [0.9, 0.8, 0]]
        return data_matrix

    def generate_ans(data):
        data_matrix = data
        model = AgglomerativeClustering(
            metric="precomputed", n_clusters=2, linkage="complete"
        ).fit(data_matrix)
        cluster_labels = model.labels_
        return cluster_labels

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    ret = 0
    try:
        np.testing.assert_equal(result, ans)
        ret = 1
    except:
        pass
    try:
        np.testing.assert_equal(result, 1 - ans)
        ret = 1
    except:
        pass
    return ret

exec_context = r"""
import pandas as pd
import numpy as np
import sklearn.cluster
data_matrix = test_input
[insert]
result = cluster_labels
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "AgglomerativeClustering" in tokens


Prompt:
Problem:

I need to perform hierarchical clustering by a distance matrix describing their similarities, which is between different professors, like:

              prof1     prof2     prof3
       prof1     0        0.8     0.9
       prof2     0.8      0       0.2
       prof3     0.9      0.2     0

       data_matrix=[[0,0.8,0.9],[0.8,0,0.2],[0.9,0.2,0]]
The expected number of clusters is 2. Can it be done using sklearn.cluster.AgglomerativeClustering? I tried to do that but failed. Anyone can give me some advice? prefer answer in a list like [label1, label2, ...]

A:

<code>
import numpy as np
import pandas as pd
import sklearn.cluster
data_matrix = load_data()
</code>
cluster_labels = ... # put solution in this variable
BEGIN SOLUTION
<code>

model = sklearn.cluster.AgglomerativeClustering(metric='precomputed', n_clusters=2, linkage='complete').fit(data_matrix)
cluster_labels = model.labels_

import numpy as np
import copy
import tokenize, io
from sklearn.cluster import AgglomerativeClustering

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            data_matrix = [[0, 0.8, 0.9], [0.8, 0, 0.2], [0.9, 0.2, 0]]
        elif test_case_id == 2:
            data_matrix = [[0, 0.2, 0.9], [0.2, 0, 0.8], [0.9, 0.8, 0]]
        return data_matrix

    def generate_ans(data):
        data_matrix = data
        model = AgglomerativeClustering(
            metric="precomputed", n_clusters=2, linkage="complete"
        ).fit(data_matrix)
        cluster_labels = model.labels_
        return cluster_labels

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    ret = 0
    try:
        np.testing.assert_equal(result, ans)
        ret = 1
    except:
        pass
    try:
        np.testing.assert_equal(result, 1 - ans)
        ret = 1
    except:
        pass
    return ret

exec_context = r"""
import pandas as pd
import numpy as np
import sklearn.cluster
data_matrix = test_input
[insert]
result = cluster_labels
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "AgglomerativeClustering" in tokens


Prompt:
Problem:

Given a distance matrix, with similarity between various fruits :

              fruit1     fruit2     fruit3
       fruit1     0        0.6     0.8
       fruit2     0.6      0       0.111
       fruit3     0.8      0.111     0
I need to perform hierarchical clustering on this data, where the above data is in the form of 2-d matrix

       simM=[[0,0.6,0.8],[0.6,0,0.111],[0.8,0.111,0]]
The expected number of clusters is 2. I tried checking if I can implement it using sklearn.cluster AgglomerativeClustering but it is considering all the 3 rows as 3 separate vectors and not as a distance matrix. Can it be done using sklearn.cluster AgglomerativeClustering? prefer answer in a list like [label1, label2, ...]

A:

<code>
import numpy as np
import pandas as pd
import sklearn.cluster
simM = load_data()
</code>
cluster_labels = ... # put solution in this variable
BEGIN SOLUTION
<code>

model = sklearn.cluster.AgglomerativeClustering(metric='precomputed', n_clusters=2, linkage='complete').fit(simM)
cluster_labels = model.labels_

import numpy as np
import copy
import tokenize, io
from sklearn.cluster import AgglomerativeClustering

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            data_matrix = [[0, 0.8, 0.9], [0.8, 0, 0.2], [0.9, 0.2, 0]]
        elif test_case_id == 2:
            data_matrix = [[0, 0.2, 0.9], [0.2, 0, 0.8], [0.9, 0.8, 0]]
        return data_matrix

    def generate_ans(data):
        simM = data
        model = AgglomerativeClustering(
            metric="precomputed", n_clusters=2, linkage="complete"
        ).fit(simM)
        cluster_labels = model.labels_
        return cluster_labels

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    ret = 0
    try:
        np.testing.assert_equal(result, ans)
        ret = 1
    except:
        pass
    try:
        np.testing.assert_equal(result, 1 - ans)
        ret = 1
    except:
        pass
    return ret

exec_context = r"""
import pandas as pd
import numpy as np
import sklearn.cluster
simM = test_input
[insert]
result = cluster_labels
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "AgglomerativeClustering" in tokens


Prompt:
Problem:

Given a distance matrix, with similarity between various professors :

              prof1     prof2     prof3
       prof1     0        0.8     0.9
       prof2     0.8      0       0.2
       prof3     0.9      0.2     0
I need to perform hierarchical clustering on this data (into 2 clusters), where the above data is in the form of 2-d matrix

       data_matrix=[[0,0.8,0.9],[0.8,0,0.2],[0.9,0.2,0]]
The expected number of clusters is 2. Can it be done using scipy.cluster.hierarchy? prefer answer in a list like [label1, label2, ...]

A:

<code>
import numpy as np
import pandas as pd
import scipy.cluster
data_matrix = load_data()
</code>
cluster_labels = ... # put solution in this variable
BEGIN SOLUTION
<code>

Z = scipy.cluster.hierarchy.linkage(np.array(data_matrix), 'ward')
cluster_labels = scipy.cluster.hierarchy.cut_tree(Z, n_clusters=2).reshape(-1, ).tolist()

import numpy as np
import copy
import tokenize, io
from scipy.cluster.hierarchy import linkage, cut_tree

def generate_test_case(test_case_id):
    def define_test_input(test_case_id):
        if test_case_id == 1:
            data_matrix = [[0, 0.8, 0.9], [0.8, 0, 0.2], [0.9, 0.2, 0]]
        elif test_case_id == 2:
            data_matrix = [[0, 0.2, 0.9], [0.2, 0, 0.8], [0.9, 0.8, 0]]
        return data_matrix

    def generate_ans(data):
        data_matrix = data
        Z = linkage(np.array(data_matrix), "ward")
        cluster_labels = (
            cut_tree(Z, n_clusters=2)
            .reshape(
                -1,
            )
            .tolist()
        )
        return cluster_labels

    test_input = define_test_input(test_case_id)
    expected_result = generate_ans(copy.deepcopy(test_input))
    return test_input, expected_result

def exec_test(result, ans):
    ret = 0
    try:
        np.testing.assert_equal(result, ans)
        ret = 1
    except:
        pass
    try:
        np.testing.assert_equal(result, 1 - np.array(ans))
        ret = 1
    except:
        pass
    return ret

exec_context = r"""
import pandas as pd
import numpy as np
import scipy.cluster
data_matrix = test_input
[insert]
result = cluster_labels
"""

def test_execution(solution: str):
    code = exec_context.replace("[insert]", solution)
    for i in range(2):
        test_input, expected_result = generate_test_case(i + 1)
        test_env = {"test_input": test_input}
        exec(code, test_env)
        assert exec_test(test_env["result"], expected_result)

def test_string(solution: str):
    tokens = []
    for token in tokenize.tokenize(io.BytesIO(solution.encode("utf-8")).readline):
        tokens.append(token.string)
    assert "hierarchy" in tokens