Classification Exercises Answers

These exercises involve training classifiers on the MNIST, titanic, and span/ham data sets. I learned a good bit from this.

Classification Exercises Answers

pg 110 (+ 26)

Question 1

Try to build a classifier for the MNIST dataset that achieves over 97% accuracy on the test set. Hint: the KNeighborsClassifier works quite well for this task; you just need to find good hyperparameter values (try a grid search on the weights and n_neighbors hyperparameters).

Neighbors-based classification is a type of instance-based learning or non-generalizing learning: it does not attempt to construct a general internal model, but simply stores instances of the training data. Classification is computed from a simple majority vote of the nearest neighbors of each point: a query point is assigned the data class which has the most representatives within the nearest neighbors of the point.

scikit-learn implements two different nearest neighbors classifiers: KNeighborsClassifier implements learning based on the $k$ nearest neighbors of each query point, where is an integer value specified by the user. The optimal choice of the value $k$ is highly data-dependent: in general a larger $k$ suppresses the effects of noise, but makes the classification boundaries less distinct.

from sklearn.datasets import fetch_openml
import matplotlib.pyplot as plt
mnist = fetch_openml('mnist_784', version=1)
data, target, description = mnist["data"], mnist["target"], mnist["DESCR"]
print(description)
data = data.to_numpy()
target = target.to_numpy()
X_train, X_test, y_train, y_test = data[:60000], data[60000:], target[:60000], target[60000:]

out[3]

**Author**: Yann LeCun, Corinna Cortes, Christopher J.C. Burges
**Source**: [MNIST Website](http://yann.lecun.com/exdb/mnist/) - Date unknown
**Please cite**:

The MNIST database of handwritten digits with 784 features, raw data available at: http://yann.lecun.com/exdb/mnist/. It can be split in a training set of the first 60,000 examples, and a test set of 10,000 examples

It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image. It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting. The original black and white (bilevel) images from NIST were size normalized to fit in a 20x20 pixel box while preserving their aspect ratio. The resulting images contain grey levels as a result of the anti-aliasing technique used by the normalization algorithm. the images were centered in a 28x28 image by computing the center of mass of the pixels, and translating the image so as to position this point at the center of the 28x28 field.

With some classification methods (particularly template-based methods, such as SVM and K-nearest neighbors), the error rate improves when the digits are centered by bounding box rather than center of mass. If you do this kind of pre-processing, you should report it in your publications. The MNIST database was constructed from NIST's NIST originally designated SD-3 as their training set and SD-1 as their test set. However, SD-3 is much cleaner and easier to recognize than SD-1. The reason for this can be found on the fact that SD-3 was collected among Census Bureau employees, while SD-1 was collected among high-school students. Drawing sensible conclusions from learning experiments requires that the result be independent of the choice of training set and test among the complete set of samples. Therefore it was necessary to build a new database by mixing NIST's datasets.

The MNIST training set is composed of 30,000 patterns from SD-3 and 30,000 patterns from SD-1. Our test set was composed of 5,000 patterns from SD-3 and 5,000 patterns from SD-1. The 60,000 pattern training set contained examples from approximately 250 writers. We made sure that the sets of writers of the training set and test set were disjoint. SD-1 contains 58,527 digit images written by 500 different writers. In contrast to SD-3, where blocks of data from each writer appeared in sequence, the data in SD-1 is scrambled. Writer identities for SD-1 is available and we used this information to unscramble the writers. We then split SD-1 in two: characters written by the first 250 writers went into our new training set. The remaining 250 writers were placed in our test set. Thus we had two sets with nearly 30,000 examples each. The new training set was completed with enough examples from SD-3, starting at pattern # 0, to make a full set of 60,000 training patterns. Similarly, the new test set was completed with SD-3 examples starting at pattern # 35,000 to make a full set with 60,000 test patterns. Only a subset of 10,000 test images (5,000 from SD-1 and 5,000 from SD-3) is available on this site. The full 60,000 sample training set is available.

Downloaded from openml.org.

import numpy as np
fig, ax = plt.subplots(2,4,figsize=(12,6),layout="constrained")
for i in range(8):
    ax_ind = [0, i]
    if i>=4:
        ax_ind = [1,i-4]
    Ax = ax[ax_ind[0],ax_ind[1]]
    Ax.imshow(X_train[i,:].reshape((28,28)),cmap="gray")
    target = str(y_train[i])
    Ax.set_title("MNIST Target: " + target)
fig.suptitle("Some MNIST Examples",fontsize=16)
plt.show()

out[4]

def get_shift(shift_num,axis):
    def shift(arr):
        arr = arr.reshape(28,28)
        arr = np.roll(arr,shift_num,axis=axis)
        return arr.flatten()
    return shift

shift_down = get_shift(1,0)
shift_up = get_shift(-1,0)
shift_right = get_shift(1,1)
shift_left = get_shift(-1,1)

X_train_shifted_down = np.apply_along_axis(shift_down,1,X_train.copy())
X_train_shifted_up = np.apply_along_axis(shift_up,1,X_train.copy())
X_train_shifted_right = np.apply_along_axis(shift_right,1,X_train.copy())
X_train_shifted_left = np.apply_along_axis(shift_left,1,X_train.copy())
fig, ax = plt.subplots(2,2,layout="constrained")
ax[0,0].imshow(X_train_shifted_right[0,:].reshape(28,28),cmap="gray")
ax[0,0].set_title("Shifted Right")
ax[0,1].imshow(X_train_shifted_left[0,:].reshape(28,28),cmap="gray")
ax[0,1].set_title("Shifted Left")
ax[1,0].imshow(X_train_shifted_up[0,:].reshape(28,28),cmap="gray")
ax[1,0].set_title("Shifted Up")
ax[1,1].imshow(X_train_shifted_down[0,:].reshape(28,28),cmap="gray")
ax[1,1].set_title("Shifted Down")
fig.suptitle("Shift Test",fontsize=16)
plt.show()

out[5]

from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import cross_val_score, RandomizedSearchCV

## Givem that KNeighbors Classifier Depends on the amount of nearest neighbors, it might be helpful to get an idea of how many 
# instances of eahc class there are
fig, ax = plt.subplots(1,1,layout="constrained")
ax.hist(y_train)
ax.set_title("Amount of Training Samples per Number")
plt.show()
# algorithm
# leaf_size
np.random.seed(42)
rand_int = np.random.randint(100)
param_grid = [
  { 'n_neighbors': [5,10,15] }
]
model = RandomizedSearchCV(KNeighborsClassifier(n_jobs=-1,weights='distance'),param_grid,cv=5,verbose=2).fit(X_train,y_train)

out[6]

c:\Users\fmb20\AppData\Local\Programs\Python\Python311\Lib\site-packages\sklearn\model_selection\_search.py:318: UserWarning: The total space of parameters 3 is smaller than n_iter=10. Running 3 iterations. For exhaustive searches, use GridSearchCV.
warnings.warn(

Fitting 5 folds for each of 3 candidates, totalling 15 fits

test_score = model.score(X_test,y_test)

out[7]

print("Score: ",test_score)

out[8]

Score: 0.9691

Question 2

Write a function that can shift an MNIST image in any direction (left, right, up, or down) by one pixel. Then, for each image in the training set, create four shifted copies (one per direction) and add them to the training set. Finally, train your best model on this expanded training set and measure its accuracy on the test set. You should observe that your model performs even better now! This technique of artificially growing the training set is called data augmentation or training set expansion.

X_train_new = np.vstack((X_train,X_train_shifted_up,X_train_shifted_down,X_train_shifted_left,X_train_shifted_right))
y_train_new = np.hstack((y_train,y_train,y_train,y_train,y_train))
model_new = KNeighborsClassifier(n_jobs=-1,weights='distance',n_neighbors=10).fit(X_train_new,y_train_new).fit(X_train_new,y_train_new)

out[10]

new_score = model_new.score(X_test,y_test)

out[11]

print("New Score After Adding Shifted Images:",new_score)

out[12]

New Score After Adding Shifted Images: 0.9769

Question 3

Tackle the Titanic dataset. A great place to start is on Kaggle

import pandas as pd
import os
titanic = pd.read_csv(os.path.join(os.getcwd(),'..','titanic.csv'))
titanic.set_index("PassengerId",inplace=True)
titanic.head(3)

out[14]

Survived Pclass \

PassengerId

1 0 3

2 1 1

3 1 3

Name Sex Age \

PassengerId

1 Braund, Mr. Owen Harris male 22.0

2 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0

3 Heikkinen, Miss. Laina female 26.0

SibSp Parch Ticket Fare Cabin Embarked

PassengerId

1 1 0 A/5 21171 7.2500 NaN S

2 1 0 PC 17599 71.2833 C85 C

3 0 0 STON/O2. 3101282 7.9250 NaN S

titanic.describe()

out[15]

Survived Pclass Age SibSp Parch Fare

count 891.000000 891.000000 714.000000 891.000000 891.000000 891.000000

mean 0.383838 2.308642 29.699118 0.523008 0.381594 32.204208

std 0.486592 0.836071 14.526497 1.102743 0.806057 49.693429

min 0.000000 1.000000 0.420000 0.000000 0.000000 0.000000

25% 0.000000 2.000000 20.125000 0.000000 0.000000 7.910400

50% 0.000000 3.000000 28.000000 0.000000 0.000000 14.454200

75% 1.000000 3.000000 38.000000 1.000000 0.000000 31.000000

max 1.000000 3.000000 80.000000 8.000000 6.000000 512.329200

titanic.info()

out[16]

<class 'pandas.core.frame.DataFrame'>
Index: 891 entries, 1 to 891
Data columns (total 11 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Survived 891 non-null int64
1 Pclass 891 non-null int64
2 Name 891 non-null object
3 Sex 891 non-null object
4 Age 714 non-null float64
5 SibSp 891 non-null int64
6 Parch 891 non-null int64
7 Ticket 891 non-null object
8 Fare 891 non-null float64
9 Cabin 204 non-null object
10 Embarked 889 non-null object
dtypes: float64(2), int64(4), object(5)
memory usage: 83.5+ KB

print(titanic.loc[:,["Sex"]].value_counts())
print(titanic.loc[:,["Embarked"]].value_counts())
print(titanic.loc[:,["Cabin"]].value_counts())

out[17]

Sex
male 577
female 314
Name: count, dtype: int64
Embarked
S 644
C 168
Q 77
Name: count, dtype: int64
Cabin
C23 C25 C27 4
G6 4
B96 B98 4
F2 3
C22 C26 3
..
C101 1
B94 1
B86 1
B82 B84 1
T 1
Name: count, Length: 147, dtype: int64

print("Contains PC:",titanic.loc[:,"Ticket"][titanic["Ticket"].str.contains("PC")].count())
print("Contains W./C.:",titanic.loc[:,"Ticket"][titanic["Ticket"].str.contains("W./C.")].count())
print("Contains A/5:",titanic.loc[:,"Ticket"][titanic["Ticket"].str.contains("A/5")].count())
print("Contains STON/O2.:",titanic.loc[:,"Ticket"][titanic["Ticket"].str.contains("STON/O2.")].count())
import re
ticket_lst = titanic["Ticket"].to_list()
print(len(ticket_lst))
numbers = []
unique_prefix = set()
for ticket in ticket_lst:
    if ticket.find(" ") != -1:
        unique_prefix.add(ticket.split(" ")[0])
    non_nums = re.findall("[^0-9.]",ticket)
    for non_num in non_nums:
        ticket = ticket.replace(non_num,"")
    ticket = ticket.strip()
    dots = re.findall("\.+",ticket)
    for dot in dots:
        ticket = ticket.replace(dot,".")
    if ticket.startswith("."):
        ticket = ticket[1:]
    ticket = ticket.strip()
    # numbers.append(float(ticket))
# numbers = np.array(numbers,dtype=np.float64)
print(unique_prefix,len(unique_prefix),sep="\n")
print("I think I'm just going to drop this column.")

out[18]

Contains PC: 60
Contains W./C.: 9
Contains A/5: 17
Contains STON/O2.: 6
891
{'CA', 'SW/PP', 'S.W./PP', 'A/5.', 'F.C.C.', 'Fa', 'A.5.', 'A/4', 'W.E.P.', 'SC', 'P/PP', 'S.O.C.', 'SOTON/O2', 'C.A.', 'S.P.', 'STON/O', 'S.O./P.P.', 'A/5', 'WE/P', 'SOTON/O.Q.', 'C.A./SOTON', 'SC/PARIS', 'STON/O2.', 'PC', 'S.O.P.', 'A/S', 'F.C.', 'SCO/W', 'SO/C', 'A4.', 'W./C.', 'SC/Paris', 'PP', 'A/4.', 'SC/AH', 'A./5.', 'SOTON/OQ', 'CA.', 'C', 'S.C./PARIS', 'S.C./A.4.', 'W/C'}
42
I think I'm just going to drop this column.

names = titanic["Name"].copy()
def rearrange_names(s):
    l = re.split(",\s",s)
    if len(l)==2:
        firstname = l[1]
        lastname = l[0]
        name = (firstname + lastname).strip()
    else:
        name = s.strip()
    pre = re.split("\.",name)
    if len(pre)>=1:
        return pre[0]
    else:
        return ''
names = names.apply(rearrange_names)

print(names.value_counts())

out[19]

Name
Mr 517
Miss 182
Mrs 125
Master 40
Dr 7
Rev 6
Mlle 2
Major 2
Col 2
the Countess 1
Capt 1
Ms 1
Sir 1
Lady 1
Mme 1
Don 1
Jonkheer 1
Name: count, dtype: int64

y = titanic["Survived"]
X = titanic.drop(columns="Survived")

from sklearn.model_selection import train_test_split
import numpy as np
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.impute import SimpleImputer, KNNImputer
from sklearn.preprocessing import StandardScaler, Normalizer, MinMaxScaler, OneHotEncoder
from sklearn.base import BaseEstimator, TransformerMixin

np.random.seed(42)
random_state = np.random.randint(100)
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2,random_state=random_state)
print("Test Train Split:\n--------------------------------------")
print("Train Shape: ",X_train.shape,"\nTest Shape:",X_test.shape,"\nY Train Shape:",y_train.shape,"\mY Test Shape:",y_test.shape)


print("Pclass:\n--------------------------------------")
print("""Pclass can either be:
1. One Hot Encoded (There are three classes 1, 2, 3)
2. Scaled using StandardScaler 
3. Left Alone
- This behavior is handled by class__transform__ohe, class__transform__scale, class__transform__leave. The first one among these that is true is the behavior that is exhibited.""")
class_ohe = OneHotEncoder(sparse_output=False,handle_unknown="ignore")
class_scaler = StandardScaler()
class_ohe.fit(X_train.loc[:,"Pclass"].to_numpy().reshape(-1,1))
class_scaler.fit(X_train.loc[:,"Pclass"].to_numpy().reshape(-1,1))

class ClassTransformer(BaseEstimator,TransformerMixin):
    """
    I am creating this class to see if it is better to leave the "Pclass" column as it is, to one hot encode it, or to scale it. 
    This transformer expects only "Pclass" to be sent to it.
    """
    def __init__(self,ohe=True,scale=False,leave=False):
        self.ohe = ohe
        self.scale = scale
        self.leave = leave
    def fit(self,X,y=None):
        return self
    def transform(self,X,y=None):
        if self.ohe:
            return class_ohe.transform(X)
        elif self.scale:
            return class_scaler.transform(X)
        else:
            return X

print("Names:\n--------------------------------------")
print("""For names, we are using some string manipulation to get the title of the person on the titanic and then one hot encoding based on the title. The title can be:  "Mr", "Miss", "Mrs", "Master", "Dr", or "Rev". """)
names = X_train["Name"].copy()
def rearrange_names(s):
    l = re.split(",\s",s)
    if len(l)==2:
        firstname = l[1]
        lastname = l[0]
        name = (firstname + lastname).strip()
    else:
        name = s.strip()
    pre = re.split("\.",name)
    if len(pre)>=1:
        return pre[0]
    else:
        return ''
names = names.apply(rearrange_names)
names_ohe = OneHotEncoder(min_frequency=5,handle_unknown="ignore",sparse_output=False)
names_ohe.fit(names.to_numpy().reshape(-1,1))
print("Names OHE Categories: ",names_ohe.get_feature_names_out())


class HandleName(BaseEstimator,TransformerMixin):
    """
    There are some things you can do with names:
        - Find the prefix (see the cell above)
    """
    def __init__(self):
        pass
    def fit(self,X,y=None):
        return self
    def transform(self,X,y=None):
        ser = pd.Series(X[:,0])
        ser = ser.apply(rearrange_names)
        ohe_names = names_ohe.transform(ser.to_numpy().reshape(-1,1))
        return ohe_names

age_scaler = StandardScaler()
age_bins = OneHotEncoder(sparse_output=False,handle_unknown="ignore")
age_sibsp_parch = X_train[["Age","SibSp","Parch"]].to_numpy()
age_scaler.fit(age_sibsp_parch[:,0].reshape(-1,1))
age_copy = X_train["Age"].copy().astype("object")
def bin_age(age):
    if age < 12:
        return "child"
    elif age < 18:
        return "teen"
    elif age < 50:
        return "adult"
    else:
        return "elder"
age_copy = age_copy.apply(bin_age)
age_bins.fit(age_copy.to_numpy().reshape(-1,1))


print("Age, SibSp, Parch:\n--------------------------------------")
print("""- For age, it can either be scaled or encoded into buckets for whether the age denotes a child, teen, adult, or elder. This behavior is controlled by children_parents__children_parents__scale_age. The default is False, but if it is true, then the age will be scaled instead of being put into buckets. 
- For siblings/spouses, if children_parents__children_parents__add_is_married is True (default), then a column will be added to the array to denote whether someone is married. It is assumed that if they are above a certain age and SibSp >= 1, then they are married. If this is false, then this attribute is scaled with StandardScaler(). If children_parents__children_parents__separate_sibling_spouse is True (default=False), then the number of true siblings will be calculated based on whether or not someone is married. This will then be scaled. If it is False, then the original feature will be scaled. 
- If children_parents__children_parents__add_children_parents is True (default), then the number of children / parents one has on board will be added to the output array. It will be scaled. Else, the original feature will be scaled. """)
siblings_scaler = StandardScaler()
children_scaler = StandardScaler()
parents_scaler = StandardScaler()
sibling_spouse_scaler = StandardScaler()
parent_child_scaler = StandardScaler()
age = age_sibsp_parch[:,0]
sibling_spouse = age_sibsp_parch[:,1]
sibling_spouse_scaler.fit(sibling_spouse.reshape(-1,1))
parent_child = age_sibsp_parch[:,2]
parent_child_scaler.fit(parent_child.reshape(-1,1))
siblings = sibling_spouse - np.where(np.logical_and(age >= 18,sibling_spouse >= 1),1,0)
siblings = np.where(siblings<0,0,siblings).reshape(-1,1)
siblings_scaler.fit(siblings)
children = parent_child - np.where(np.logical_and(age >= 18,sibling_spouse > 1),1,0)
children = np.where(children<0,0,children).reshape(-1,1)
children_scaler.fit(children)
parents = parent_child - np.where(np.logical_and(age < 18,sibling_spouse > 1),1,0)
parents = np.where(parents<0,0,parents).reshape(-1,1)
parents_scaler.fit(parents)


class ChildrenAndParentsTransformer(BaseEstimator,TransformerMixin):
    """
    This transformation occurs after KNN Imputer, so you should be receiving a NumPy matrix of the following columns in order
    Age: Age
    SibSp: Number of Siblings/Spouses Aboard
    Parch: Number of Parents/Children Aboard
    I am creating this custom transformer because I believe that there are many possibilities of what can be done with these features. 
    1. You could calclate whether someone is a parent or child based on age, then calculate the number of parents vs children they have on boarch and 
    separate the Parch columns into two columns - one for parents and one for children. 
    2. For SibSp, you cam tell whether a p
    3. For age, you could 
        A. standardize it
        B. divide it into groups (bins: small children, teenagers, adults) 

    scale_age - if true, scale age, else place age in bins according to child/teem/adult/elder 
    add_is_married - add whether or not this person is married 
    separate_sibling_spouse - add number of siblings specifically 
    add_children_parents - add number of children and parents
    """
    def __init__(self,scale_age=False,add_is_married=True,separate_sibling_spouse=False,add_children_parents=True):
        self.scale_age = scale_age
        self.add_is_married = add_is_married
        self.separate_sibling_spouse = separate_sibling_spouse
        self.add_children_parents = add_children_parents
    def fit(self,X,y=None):
        return self
    def transform(self,X,y=None):
        age = X[:,0]
        sibling_spouse = X[:,1]
        parent_child = X[:,2]
        if self.scale_age:
            # Scale Age
            out_arr = age_scaler.transform(age.reshape(-1,1))
        else:
            # Place age in binds
            ser = pd.Series(age.copy()).astype("object")
            ser = ser.apply(bin_age)
            out_arr = age_bins.transform(ser.to_numpy().reshape(-1,1))
        if self.add_is_married:
            # Add whther or not this person is married 
            # Assume that most adults do not have siblings on board
            is_married = np.logical_and(age >= 18,sibling_spouse >= 1)
            out_arr = np.hstack((out_arr,is_married.reshape(-1,1)))
        if self.separate_sibling_spouse:
            siblings = sibling_spouse - np.where(np.logical_and(age >= 18,sibling_spouse >= 1),1,0)
            siblings = np.where(siblings<0,0,siblings).reshape(-1,1)
            siblings = siblings_scaler.transform(siblings)
            out_arr = np.hstack((out_arr,siblings))
        else:
            sibling_scaled = sibling_spouse_scaler.transform(sibling_spouse.reshape(-1,1))
            out_arr = np.hstack((out_arr,sibling_scaled))
        if self.add_children_parents:
            children = parent_child - np.where(np.logical_and(age >= 18,sibling_spouse > 1),1,0)
            children = np.where(children<0,0,children).reshape(-1,1)
            children = children_scaler.transform(children)
            parents = parent_child - np.where(np.logical_and(age < 18,sibling_spouse > 1),1,0)
            parents = np.where(parents<0,0,parents).reshape(-1,1)
            parents = parents_scaler.transform(parents)
            out_arr = np.hstack((out_arr,children,parents))
        else:
            parent_child_scaled = parent_child_scaler.transform(parent_child.reshape(-1,1))
            out_arr = np.hstack((out_arr,parent_child_scaled))
        return out_arr

print("Sex, Embarked:\n--------------------------------------")
print("""The Sex and Embarked attributes are imputed then onehot encoded.""")
sex_embarked_pipeline = Pipeline(
    steps=[
        ("impute",SimpleImputer(strategy="most_frequent")),
        ("ohe",OneHotEncoder(sparse_output=False,handle_unknown="ignore"))
    ]
)

class_pipeline = Pipeline(
    steps=[
        ("impute",SimpleImputer(strategy="median")),
        ("transform",ClassTransformer())
    ]
)
age_family_pipeline = Pipeline(
    steps=[
        ("impute",KNNImputer(n_neighbors=5,weights="distance")),
        ("children_parents",ChildrenAndParentsTransformer())
    ]
)
name_pipeline = Pipeline(
    steps=[
        ("impute",SimpleImputer(strategy="constant",missing_values="")),
        ("handle_name",HandleName())
    ]
)
print("Fare:\n--------------------------------------")
print("""The Fare attribute is imputed then scaled.""")

print("Cabin, Ticket:\n--------------------------------------")
print("""The Cabin and Ticket attributes are dropped.""")

fare_pipeline = Pipeline(
    steps=[
        ("impute",SimpleImputer(strategy="median")),
        ("scale",StandardScaler())
    ]
)
trans_pipeline = ColumnTransformer(
    transformers=[
        ("sex",sex_embarked_pipeline,["Sex","Embarked"]),
        ("drop_cols","drop",["Cabin","Ticket"]),   
        ("class",class_pipeline,["Pclass"]),
        ("children_parents",age_family_pipeline,["Age","SibSp","Parch"]),
        ("name",name_pipeline,["Name"]),
        ("fare",fare_pipeline,["Fare"])
    ]
)

out[20]

Test Train Split:
--------------------------------------
Train Shape: (712, 10)
Test Shape: (179, 10)
Y Train Shape: (712,) \mY Test Shape: (179,)
Pclass:
--------------------------------------
Pclass can either be:
1. One Hot Encoded (There are three classes 1, 2, 3)
2. Scaled using StandardScaler
3. Left Alone
- This behavior is handled by class__transform__ohe, class__transform__scale, class__transform__leave. The first one among these that is true is the behavior that is exhibited.
Names:
--------------------------------------
For names, we are using some string manipulation to get the title of the person on the titanic and then one hot encoding based on the title. The title can be: "Mr", "Miss", "Mrs", "Master", "Dr", or "Rev".
Names OHE Categories: ['x0_Dr' 'x0_Master' 'x0_Miss' 'x0_Mr' 'x0_Mrs' 'x0_Rev'
'x0_infrequent_sklearn']
Age, SibSp, Parch:
--------------------------------------
- For age, it can either be scaled or encoded into buckets for whether the age denotes a child, teen, adult, or elder. This behavior is controlled by children_parents__children_parents__scale_age. The default is False, but if it is true, then the age will be scaled instead of being put into buckets.
- For siblings/spouses, if children_parents__children_parents__add_is_married is True (default), then a column will be added to the array to denote whether someone is married. It is assumed that if they are above a certain age and SibSp >= 1, then they are married. If this is false, then this attribute is scaled with StandardScaler(). If children_parents__children_parents__separate_sibling_spouse is True (default=False), then the number of true siblings will be calculated based on whether or not someone is married. This will then be scaled. If it is False, then the original feature will be scaled.
- If children_parents__children_parents__add_children_parents is True (default), then the number of children / parents one has on board will be added to the output array. It will be scaled. Else, the original feature will be scaled.
Sex, Embarked:
--------------------------------------
The Sex and Embarked attributes are imputed then onehot encoded.
Fare:
--------------------------------------
The Fare attribute is imputed then scaled.
Cabin, Ticket:
--------------------------------------
The Cabin and Ticket attributes are dropped.

from sklearn.linear_model import RidgeClassifier, ElasticNetCV
from sklearn.svm import SVC 
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, VotingClassifier, StackingClassifier
from sklearn.model_selection import GridSearchCV, cross_val_score

verbose=0
n_jobs=-1
cv=10
refit=True

### Ridge Classification ---------------------------------
ridge_params = [
    {"fit__alpha": [0.6,0.8,0.7], "transform__children_parents__children_parents__scale_age": [True], "transform__children_parents__children_parents__add_is_married": [False], "transform__children_parents__children_parents__add_children_parents": [True, False]}
]
ridge = RidgeClassifier()
ridge_pipeline = Pipeline(
    steps=[
        ("transform",trans_pipeline),
        ("fit",ridge)
    ]
)
ridge_clf = GridSearchCV(ridge_pipeline,param_grid=ridge_params,verbose=verbose,cv=cv,refit=refit)


### Support Vector Classification ---------------------------------
svc_params = [
    {"fit__C": [0.9, 1, 1.1], "transform__children_parents__children_parents__scale_age": [True], "transform__children_parents__children_parents__add_is_married": [True], "transform__children_parents__children_parents__add_children_parents": [ False] }
]
svc = SVC(max_iter=-1,degree=1) 
svc_pipeline = Pipeline(
    steps=[
        ("transform",trans_pipeline),
        ("fit",svc)
    ]
)
svc_clf = GridSearchCV(svc_pipeline,param_grid=svc_params,verbose=verbose,cv=cv,refit=refit)

### K Neighbors ---------------------------------
k_neigh_params = [
    {"fit__n_neighbors": [ 10, 5, 2], "transform__children_parents__children_parents__scale_age": [False], "transform__children_parents__children_parents__add_is_married": [True], "transform__children_parents__children_parents__add_children_parents": [ False] }
]
k_neigh = KNeighborsClassifier(n_jobs=-1) 
k_neigh_pipeline = Pipeline(
    steps=[
        ("transform",trans_pipeline),
        ("fit",k_neigh)
    ]
) 
k_neigh_clf = GridSearchCV(k_neigh_pipeline,param_grid=k_neigh_params,verbose=verbose,cv=cv,refit=refit)


### Descrion Tree ---------------------------------
desc_tree_params = [
    {"fit__max_depth": [ 5 ], "transform__children_parents__children_parents__scale_age": [False], "transform__children_parents__children_parents__add_is_married": [False], "transform__children_parents__children_parents__add_children_parents": [True]}
]
desc_tree = DecisionTreeClassifier() 
desc_tree_pipeline = Pipeline(
    steps=[
        ("transform",trans_pipeline),
        ("fit",desc_tree)
    ]
)
desc_tree_clf = GridSearchCV(desc_tree_pipeline,param_grid=desc_tree_params,verbose=verbose,cv=cv,refit=refit)


### Random Forest ---------------------------------
rand_forest_params = [
    { "fit__n_estimators": [1000], "fit__max_depth": [None], "fit__max_features": [10], "transform__children_parents__children_parents__scale_age": [False], "transform__children_parents__children_parents__add_is_married": [True], "transform__children_parents__children_parents__add_children_parents": [False] }
]
rand_forest = RandomForestClassifier() 
rand_forest_pipeline = Pipeline(
    steps=[
        ("transform",trans_pipeline),
        ("fit",rand_forest)
    ]
)
rand_forest_clf = GridSearchCV(rand_forest_pipeline,param_grid=rand_forest_params,verbose=verbose,cv=cv,refit=refit)


### AdaBoost Classifier --------------------------------------
"""
An AdaBoost classifier is a meta-estimator that begins by fitting a classifier on the original dataset and then fits additional copies of the classifier on the same dataset but where the weights of incorrectly classified instances are adjusted such that subsequent classifiers focus more on difficult cases.

"""
ada_boost_params = [
    { "fit__estimator": [10,50,100], "fit__estimator": [DecisionTreeClassifier(max_depth=None)], "transform__children_parents__children_parents__scale_age": [False], "transform__children_parents__children_parents__add_is_married": [True], "transform__children_parents__children_parents__add_children_parents": [False] }
]
ada_boost = AdaBoostClassifier(algorithm="SAMME") 
ada_boost_pipeline = Pipeline(
    steps=[
        ("transform",trans_pipeline),
        ("fit",ada_boost)
    ]
)
ada_boost_clf = GridSearchCV(ada_boost_pipeline,param_grid=ada_boost_params,verbose=verbose,cv=cv,refit=refit)


### GradientBoosting Classifier --------------------------------------
"""
This algorithm builds an additive model in a forward stage-wise fashion; it allows for the optimization of arbitrary differentiable loss functions. In each stage n_classes_ regression trees are fit on the negative gradient of the loss function, e.g. binary or multiclass log loss. Binary classification is a special case where only a single regression tree is induced.
"""
grad_boost_params = [
    { "transform__children_parents__children_parents__scale_age": [False], "transform__children_parents__children_parents__add_is_married": [True], "transform__children_parents__children_parents__add_children_parents": [False] }
]
grad_boost = GradientBoostingClassifier() 
grad_boost_pipeline = Pipeline(
    steps=[
        ("transform",trans_pipeline),
        ("fit",grad_boost)
    ]
)
grad_boost_clf = GridSearchCV(grad_boost_pipeline,param_grid=grad_boost_params,verbose=verbose,cv=cv,refit=refit)





preds = [
    { "name": "Ridge", "pred": ridge_clf, "y_pred": [] },
    { "name": "SVC", "pred": svc_clf, "y_pred": [] },
    { "name": "K Neighbors", "pred": k_neigh_clf, "y_pred": [] },
    { "name": "Decision Tree", "pred": desc_tree_clf, "y_pred": [] },
    { "name": "Random Forest", "pred": rand_forest_clf, "y_pred": [] },
    { "name": "AdaBoost Classifier", "pred": ada_boost_clf, "y_pred": [] },
    { "name": "GradientBoost Classifier", "pred": grad_boost_clf, "y_pred": [] },
]

for pred in preds:
    pred["pred"].fit(X_train,y_train)
    y_pred = pred["pred"].predict(X_test)
    print(pred["name"] + " Accuracy Score:",accuracy_score(y_test,y_pred))
    pred["y_pred"] = y_pred
    print(pred["pred"].best_params_)
    print("\n")

out[21]

Ridge Accuracy Score: 0.7988826815642458
{'fit__alpha': 0.6, 'transform__children_parents__children_parents__add_children_parents': False, 'transform__children_parents__children_parents__add_is_married': False, 'transform__children_parents__children_parents__scale_age': True}

SVC Accuracy Score: 0.8044692737430168
{'fit__C': 1, 'transform__children_parents__children_parents__add_children_parents': False, 'transform__children_parents__children_parents__add_is_married': True, 'transform__children_parents__children_parents__scale_age': True}

K Neighbors Accuracy Score: 0.776536312849162
{'fit__n_neighbors': 5, 'transform__children_parents__children_parents__add_children_parents': False, 'transform__children_parents__children_parents__add_is_married': True, 'transform__children_parents__children_parents__scale_age': False}

Decision Tree Accuracy Score: 0.7877094972067039
{'fit__max_depth': 5, 'transform__children_parents__children_parents__add_children_parents': True, 'transform__children_parents__children_parents__add_is_married': False, 'transform__children_parents__children_parents__scale_age': False}

Random Forest Accuracy Score: 0.7932960893854749
{'fit__max_depth': None, 'fit__max_features': 10, 'fit__n_estimators': 1000, 'transform__children_parents__children_parents__add_children_parents': False, 'transform__children_parents__children_parents__add_is_married': True, 'transform__children_parents__children_parents__scale_age': False}

AdaBoost Classifier Accuracy Score: 0.7988826815642458
{'fit__estimator': DecisionTreeClassifier(), 'transform__children_parents__children_parents__add_children_parents': False, 'transform__children_parents__children_parents__add_is_married': True, 'transform__children_parents__children_parents__scale_age': False}

GradientBoost Classifier Accuracy Score: 0.8044692737430168
{'transform__children_parents__children_parents__add_children_parents': False, 'transform__children_parents__children_parents__add_is_married': True, 'transform__children_parents__children_parents__scale_age': False}

vot_clf = VotingClassifier(estimators=[("ridge",ridge_clf),("svc",svc_clf),("knn",k_neigh_clf),("desc_tree",desc_tree_clf),("ada_boost",ada_boost_clf),("grad_boost",grad_boost_clf)])
stack_clf = StackingClassifier(estimators=[("ridge",ridge_clf),("svc",svc_clf),("knn",k_neigh_clf),("desc_tree",desc_tree_clf),("ada_boost",ada_boost_clf),("grad_boost",grad_boost_clf)])

vot_clf.fit(X_train,y_train)
stack_clf.fit(X_train,y_train)
y_pred_vote = vot_clf.predict(X_test)
print("Accuracy Score Voting: ",accuracy_score(y_pred_vote,y_test))
y_pred_stack = stack_clf.predict(X_test)
print("Accuracy Score Stacking: ",accuracy_score(y_pred_stack,y_test))

out[22]

Accuracy Score Voting: 0.8156424581005587
Accuracy Score Stacking: 0.8156424581005587

Question 4

Build a spam classifier (a more challenging exercise):

Download examples of spam and ham from Apache SpamAssassin’s public datasets.
Unzip the datasets and familiarize yourself with the data format.
Split the datasets into a training set and a test set.
Write a data preparation pipeline to convert each email into a feature vector. Your preparation pipeline should transform an email into a (sparse) vector indicating the presence or absence of each possible word. For example, if all emails only ever contain four words, “Hello,” “how,” “are,” “you,” then the email “Hello you Hello Hello you” would be converted into a vector [1, 0, 0, 1] (meaning [“Hello” is present, “how” is absent, “are” is absent, “you” is present]), or [3, 0, 0, 2] if you prefer to count the number of occurrences of each word.
You may want to add hyperparameters to your preparation pipeline to control whether or not to strip off email headers, convert each email to lowercase, remove punctuation, replace all URLs with “URL,” replace all numbers with “NUMBER,” or even perform stemming (i.e., trim off word endings; there are Python libraries available to do this).
Then try out several classifiers and see if you can build a great spam classifier, with both high recall and high precision.

import pandas as pd
import numpy as np
import os
from sklearn.model_selection import train_test_split
spam_ham = pd.read_csv(os.path.join(os.getcwd(),"spam_ham_dataset.csv"))
spam_ham.head()

out[24]

Unnamed: 0 label text \

0 605 ham Subject: enron methanol ; meter # : 988291\r\n...

1 2349 ham Subject: hpl nom for january 9 , 2001\r\n( see...

2 3624 ham Subject: neon retreat\r\nho ho ho , we ' re ar...

3 4685 spam Subject: photoshop , windows , office . cheap ...

4 2030 ham Subject: re : indian springs\r\nthis deal is t...

label_num

0 0

1 0

2 0

3 1

4 0

spam_ham.info()

out[25]

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5171 entries, 0 to 5170
Data columns (total 4 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Unnamed: 0 5171 non-null int64
1 label 5171 non-null object
2 text 5171 non-null object
3 label_num 5171 non-null int64
dtypes: int64(2), object(2)
memory usage: 161.7+ KB

number_to_ham = ["ham","spam"] # 0 = ham, 1 = spam
target = spam_ham["label_num"]
data = spam_ham["text"]
np.random.seed(39)
randint = np.random.randint(100)
new_data = data.apply(lambda x: x.strip())
print("Empty Emails Count:",new_data[new_data.str.len() == 0].count())
X_train, X_test, y_train, y_test = train_test_split(data,target,test_size=0.2,random_state=randint)
print("X Train Shape:",X_train.shape)
print("X Test Shape:",X_test.shape)
print("y Train Shape:",y_train.shape)
print("y Test Shape:",y_test.shape)

out[26]

Empty Emails Count: 0
X Train Shape: (4136,)
X Test Shape: (1035,)
y Train Shape: (4136,)
y Test Shape: (1035,)

import re
def begins_with_subject(s):
    return s.strip().startswith("Subject:")
begins_with_subject_set = X_train.apply(begins_with_subject)
print("The amount of emails that don't begin with Subject:",begins_with_subject_set[begins_with_subject_set==False].count())
subject_re = re.compile('^Subject:(?P<subject>.*)((\\r\\n)|(\\r)|(\\n))?(?P<body>.*)')
subj_body_arr = []
for item in X_train.items():
    trimmed = item[1].strip()
    matches = subject_re.match(trimmed)
    if matches:
        subj = matches.group("subject")
        if not subj:
            subj = ""
        body = matches.group("body")
        if not body:
            body = ""
        if subj=="" and body=="":
            body = trimmed
        subj_body_arr.append([subj.strip(),body.strip()])
    else:
        subj_body_arr.append(["",trimmed])

subj_body_df = pd.DataFrame(subj_body_arr,columns=["subject","body"])
subj_body_df["subject_length"] = subj_body_df["subject"].apply(lambda x: len(x.strip()))
subj_body_df["body_length"] = subj_body_df["body"].apply(lambda x: len(x.strip()))
def get_word_frequencies(lis):
    word_freq = {}
    for text in lis:
        words = re.split('(\s+|\s|\n|\r)',text)
        for i in range(len(words)):
            words[i] = words[i].strip()
            if len(words[i])>=1:
                if word_freq.get(words[i]):
                    word_freq[words[i]] += 1
                else:
                    word_freq[words[i]] = 1
    items = list(word_freq.items())
    def sort_func(t):
        return t[1]
    items.sort(key=sort_func,reverse=True)
    return items

out[27]

The amount of emails that don't begin with Subject: 0

subj_word_list = get_word_frequencies(subj_body_df["subject"].to_list())
body_word_list = get_word_frequencies(subj_body_df["body"].to_list())
print(subj_word_list)
print(body_word_list)

out[28]

[('-', 1180), (':', 1045), ('/', 989), (',', 886), ('.', 838), ('for', 824), ('re', 650), ('hpl', 460), ('2000', 417), ('!', 273), ('nom', 260), ('meter', 253), ('#', 232), ('enron', 222), ('?', 221), ('2001', 219), ('to', 215), ('and', 209), ('00', 206), ('gas', 183), ('nomination', 177), ('the', 172), ('actuals', 171), ('fw', 159), ('01', 147), ('1', 146), ('you', 142), ("'", 140), ('on', 136), ('a', 129), ('your', 127), ('&', 123), ('3', 121), ('2', 119), ('noms', 116), ('deal', 112), ('s', 112), (')', 107), ('of', 107), ('(', 103), ('4', 97), ('new', 96), ('5', 95), ('in', 95), ('change', 89), ('_', 88), ('tenaska', 87), ('6', 80), ('may', 79), ('eastrans', 78), ('march', 78), ('july', 77), ('10', 76), ('june', 76), ('=', 76), ('april', 76), ('at', 72), ('"', 72), ('iv', 68), ('i', 67), ('th', 67), ('revised', 66), ('january', 64), ('is', 64), ('@', 63), ('august', 62), ('effective', 61), ('from', 60), ('p', 59), ('$', 59), ('with', 56), ('calpine', 55), ('98', 54), ('get', 54), ('revision', 53), ('8', 53), ('7', 53), ('9', 53), ('11', 53), ('daily', 52), ('%', 51), ('cornhusker', 50), ('actual', 50), ('flow', 50), ('this', 49), ('production', 48), ('deals', 48), ('nominations', 47), ('e', 47), ('november', 47), ('december', 46), ('october', 46), ('l', 46), (';', 45), ('february', 45), ('99', 45), ('it', 45), ('12', 43), ('sitara', 43), ('energy', 42), ('29', 42), ('txu', 41), ('no', 41), ('list', 41), ('can', 40), ('software', 39), ('28', 39), ('we', 38), ('all', 38), ('21', 37), ('up', 36), ('texas', 36), ('changes', 36), ('19', 36), ('15', 35), ('more', 35), ('sept', 35), ('office', 34), ('18', 33), ('wellhead', 33), ('v', 33), ('ena', 33), ('plant', 32), ('online', 32), ('30', 32), ('17', 32), ('first', 32), ('company', 32), ('day', 32), ('c', 32), ('16', 32), ('out', 31), ('fwd', 31), ('meds', 31), ('volumes', 31), ('26', 31), ('cheap', 30), ('paliourg', 30), ('n', 30), ('natural', 30), ('off', 30), ('here', 29), ('information', 29), ('equistar', 28), ('20', 28), ('22', 28), ('24', 27), ('free', 27), ('delivery', 27), ('oil', 27), ('are', 27), ('t', 27), ('d', 27), ('sale', 26), ('ranch', 26), ('report', 26), ('23', 26), ('co', 26), ('dec', 26), ('september', 26), ('request', 26), ('neon', 25), ('thru', 25), ('27', 25), ('14', 25), ('please', 25), ('meeting', 25), ('xp', 25), ('duke', 25), ('best', 25), ('entex', 24), ('13', 24), ('[', 24), ('*', 24), ('x', 24), ('price', 24), ('0', 24), ('prices', 23), ('03', 23), ('jan', 23), ('cleburne', 23), ('feb', 23), ('|', 23), ('com', 23), ('need', 23), ('want', 23), ('confirmation', 22), ('time', 22), ('my', 22), ('buy', 22), (']', 22), ('be', 22), ('me', 22), ('+', 22), ('schedule', 21), ('meters', 21), ('now', 21), ('buyback', 21), ('>', 21), ('k', 21), ('pictures', 21), ('hi', 21), ('f', 21), ('contract', 21), ('25', 21), ('order', 21), ('sales', 21), ('m', 20), ('spot', 20), ('or', 20), ('activity', 20), ('volume', 20), ('how', 20), ('transport', 20), ('update', 20), ('low', 20), ('beaumont', 20), ('tickets', 20), ('mobil', 20), ('o', 20), ('pipeline', 19), ('important', 19), ('call', 19), ('only', 19), ('fuel', 19), ('by', 19), ('storage', 18), ('management', 18), ('r', 18), ('us', 18), ('inc', 18), ('g', 18), ('availabilities', 18), ('stock', 18), ('soft', 18), ('needed', 18), ('real', 17), ('monthly', 17), ('viagra', 17), ('desk', 17), ('allocation', 17), ('31', 17), ('pharmacy', 17), ('marketing', 17), ('cialis', 17), ('unify', 16), ('union', 16), ('issues', 16), ('quality', 16), ('system', 16), ('contact', 16), ('an', 16), ('cp', 16), ('gra', 16), ('service', 16), ('available', 16), ('point', 15), ('date', 15), ('hl', 15), ('our', 15), ('manager', 15), ('do', 15), ('that', 15), ('have', 15), ('global', 15), ('eol', 15), ('nov', 15), ('pg', 15), ('big', 15), ('windows', 15), ('message', 14), ('valero', 14), ('tejas', 14), ('week', 14), ('better', 14), ('help', 14), ('shell', 14), ('outage', 14), ('vols', 14), ('w', 14), ('today', 14), ('estimate', 14), ('midcon', 14), ('net', 13), ('charset', 13), ('survey', 13), ('notes', 13), ('hot', 13), ('oasis', 13), ('adjustments', 13), ('customer', 13), ('04', 13), ('as', 13), ('name', 13), ('vacation', 13), ('one', 13), ('resume', 13), ('hplc', 13), ('services', 13), ('well', 13), ('`', 13), ('save', 13), ('will', 13), ('about', 13), ('operating', 13), ('shipping', 13), ('lose', 13), ('teco', 13), ('not', 13), ('ua', 12), ('agreement', 12), ('prod', 12), ('pain', 12), ('resources', 12), ('special', 12), ('industrial', 12), ('contracts', 12), ('month', 12), ('money', 12), ('great', 12), ('make', 12), ('super', 12), ('final', 12), ('news', 12), ('weekend', 12), ('park', 12), ('internet', 12), ('adobe', 12), ('sds', 12), ('1999', 12), ('into', 12), ('issue', 12), ('microsoft', 12), ('deliveries', 12), ('08', 12), ('power', 12), ('product', 12), ('fuels', 12), ('tabs', 12), ('over', 12), ('coastal', 12), ('vicodin', 12), ('prescription', 12), ('ces', 12), ('download', 12), ('again', 11), ('account', 11), ('shut', 11), ('just', 11), ('find', 11), ('nd', 11), ('exxon', 11), ('02', 11), ('pricing', 11), ('error', 11), ('work', 11), ('iso', 11), ('8859', 11), ('lst', 11), ('alert', 11), ('xanax', 11), ('h', 11), ('line', 11), ('discussion', 11), ('rolex', 11), ('rivers', 11), ('ticket', 11), ('field', 11), ('star', 11), ('2004', 11), ('watch', 11), ('availability', 11), ('valium', 11), ('instant', 11), ('other', 11), ('path', 10), ('hey', 10), ('back', 10), ('sex', 10), ('phillips', 10), ('panenergy', 10), ('katy', 10), ('love', 10), ('was', 10), ('rates', 10), ('methanol', 10), ('984132', 10), ('y', 10), ('imbalance', 10), ('hours', 10), ('life', 10), ('rev', 10), ('business', 10), ('waha', 10), ('gathering', 10), ('pay', 10), ('like', 10), ('know', 10), ('^', 10), ('logistics', 10), ('koch', 10), ('st', 10), ('josey', 10), ('training', 10), ('lone', 10), ('05', 10), ('project', 10), ('\\', 10), ('access', 10), ('if', 10), ('000', 10), ('per', 9), ('ascii', 9), ('purchases', 9), ('feedback', 9), ('ltd', 9), ('industrials', 9), ('close', 9), ('hard', 9), ('central', 9), ('corp', 9), ('than', 9), ('exchange', 9), ('announcement', 9), ('b', 9), ('head', 9), ('deer', 9), ('oct', 9), ('cdnow', 9), ('what', 9), ('06', 9), ('king', 9), ('year', 9), ('movies', 9), ('purchase', 9), ('stop', 9), ('number', 9), ('via', 9), ('09', 9), ('u', 9), ('devon', 9), ('copano', 9), ('old', 9), ('usa', 9), ('fyi', 9), ('80', 9), ('increase', 9), ('rd', 9), ('see', 9), ('same', 9), ('info', 8), ('digital', 8), ('im', 8), ('plan', 8), ('epgt', 8), ('sell', 8), ('rate', 8), ('updated', 8), ('60', 8), ('their', 8), ('end', 8), ('so', 8), ('intraday', 8), ('who', 8), ('spreadsheet', 8), ('eff', 8), ('assignment', 8), ('termination', 8), ('q', 8), ('she', 8), ('email', 8), ('invoice', 8), ('drugs', 8), ('supply', 8), ('verification', 8), ('he', 8), ('lonestar', 8), ('three', 8), ('good', 8), ('2005', 8), ('pro', 8), ('enrononline', 8), ('urgent', 8), ('07', 8), ('web', 8), ('under', 8), ('tetco', 8), ('houston', 8), ('read', 8), ('guadalupe', 8), ('potential', 8), ('missing', 8), ('status', 8), ('aep', 8), ('invoices', 8), ('cowboy', 8), ('data', 8), ('review', 8), ('overnight', 8), ('southern', 7), ('his', 7), ('specials', 7), ('lyondell', 7), ('keep', 7), ('wants', 7), ('much', 7), ('these', 7), ('process', 7), ('popular', 7), ('texoma', 7), ('video', 7), ('start', 7), ('way', 7), ('cilco', 7), ('made', 7), ('exception', 7), ('bed', 7), ('small', 7), ('there', 7), ('reply', 7), ('smart', 7), ('christmas', 7), ('performance', 7), ('needs', 7), ('50', 7), ('saxet', 7), ('roll', 7), ('products', 7), ('works', 7), ('welcome', 7), ('take', 7), ('75', 7), ('tuesday', 7), ('young', 7), ('but', 7), ('cd', 7), ('chemical', 7), ('organizational', 7), ('following', 7), ('site', 7), ('application', 7), ('heads', 7), ('payment', 7), ('enerfin', 7), ('city', 7), ('computer', 7), ('common', 7), ('mar', 7), ('zone', 7), ('mail', 7), ('still', 6), ('lesson', 6), 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1), ('dramatic', 1), ('connective', 1), ('tissue', 1), ('healing', 1), ('skeletal', 1), ('fragile', 1), ('ulcers', 1), ('fractured', 1), ('bones', 1), ('heal', 1), ('profound', 1), ('gains', 1), ('strength', 1), ('paint', 1), ('1869', 1), ('surfing', 1), ('zamit', 1), ('organizational', 1), ('lucky', 1), ('charms', 1), ('pot', 1), ('gold', 1), ('phoenican', 1), ('ino', 1), ('saint', 1), ('patrick', 1), ('302', 1), ('began', 1), ('flange', 1), ('ronnie', 1), ('skerrik', 1), ('stone', 1), ('prlce', 1), ('trofholz', 1), ('kathy', 1), ('benedict', 1), ('mistaken', 1), ('805', 1), ('ralph', 1), ('velez', 1), ('handlin', 1), ('partnership', 1), ('tablets', 1), ('alleviate', 1), ('significance', 1), ('6633', 1), ('approximate', 1), ('incremental', 1), ('591', 1), ('wassup', 1), ('avi', 1), ('warning', 1), ('dn', 1), ('nio', 1), ('dreme', 1), ('anve', 1), ('surely', 1), ('237756', 1), ('seem', 1), ('avoid', 1), ('keeping', 1), ('dial', 1), ('connection', 1), ('802', 1), ('didrex', 1), ('nasacort', 1), ('timing', 1), ('article', 1), ('implemented', 1), ('thier', 1), ('sharpen', 1), ('evaluation', 1), ('writting', 1), ('skills', 1), ('scherlyn', 1), ('2186', 1), ('1375', 1), ('agreementat', 1), ('bb', 1), ('tre', 1), ('lnk', 1), ('tlm', 1), ('clalls', 1), ('lnstant', 1), ('rockhard', 1), ('709296', 1), ('adtra', 1), ('sherry', 1), ('wayman', 1), ('tests', 1), ('150325', 1), ('424', 1), ('contestants', 1), ('ain', 1), ('301', 1), ('contacts', 1), ('munched', 1), ('armpit', 1), ('budweiser', 1), ('emphasis', 1), ('oppose', 1), ('footlocker', 1), ('classifier', 1), ('reckon', 1), ('wrapper', 1), ('documentation', 1), ('027', 1), ('mjj', 1), ('6892', 1), ('spill', 1), ('decatherms', 1), ('keith', 1), ('intra', 1), ('manley', 1), ('recur', 1), ('anteater', 1), ('bey', 1), ('cohomology', 1), ('argumentation', 1), ('leapt', 1), ('childhood', 1), ('rated', 1), ('ation', 1), ('stead', 1), ('metal', 1), ('035', 1), ('157572', 1), ('1372', 1), ('vary', 1), ('phrase', 1), ('420', 1), ('effecting', 1), ('rd', 1), ('condom', 1), ('1591', 1), ('entire', 1), ('receives', 1), ('211573', 1), ('abasements', 1), ('darer', 1), ('prudently', 1), ('fortuitous', 1), ('undergone', 1), ('pockets', 1), ('vvatch', 1), ('kidding', 1), ('gget', 1), ('reuters', 1), ('quotes', 1), ('72084', 1), ('requires', 1), ('transporter', 1), ('040', 1), ('deficienty', 1), ('deficiency', 1), ('hospitals', 1), ('172', 1), ('administrators', 1), ('cody', 1), ('985333', 1), ('drives', 1), ('burn', 1), ('burner', 1), ('doorstep', 1), ('progress', 1), ('nipple', 1), ('fatties', 1), ('grandmas', 1), ('blond', 1), ('grannies', 1), ('duve', 1), ('khumalo', 1), ('unlimited', 1), ('auto', 1), ('janeiro', 1), ('snack', 1), ('rhythm', 1), ('regards', 1), ('renegotiated', 1), ('pen', 1), ('sparks', 1), ('wffur', 1), ('attion', 1), ('brom', 1), ('inst', 1), ('siupied', 1), ('pgst', 1), ('riwe', 1), ('asently', 1), ('samuel', 1), ('kunte', 1), ('doxxcstctorr', 1), ('neeedeed', 1), ('bringing', 1), ('syngas', 1), ('janice', 1), ('berke', 1), ('davis', 1), ('reception', 1), ('gotta', 1), ('hormone', 1), ('dietary', 1), ('therapy', 1), ('bodytable', 1), ('trtda', 1), ('adehlcgjik', 1), ('allhere', 1), ('fromdaren', 1), ('pgev', 1), ('2599', 1), ('require', 1), ('warrant', 1), ('intrastate', 1), ('obvious', 1), ('contributions', 1), ('recognize', 1), ('instrumental', 1), ('roles', 1), ('wishing', 1), ('noches', 1), ('lips', 1), ('played', 1), ('sun', 1), ('remained', 1), ('profession', 1), ('climbing', 1), ('nearly', 1), ('certainly', 1), ('parents', 1), ('discipline', 1), ('bear', 1), ('endgame', 1), ('biomedicine', 1), ('protestantizes', 1), ('parted', 1), ('9826', 1), ('difficulty', 1), ('stake', 1), ('springs', 1), ('986315', 1), ('travelocitylast', 1), ('168', 1), ('login', 1), ('metz', 1), ('vx', 1), ('speakers', 1), ('goerner', 1), ('reliant', 1), ('staying', 1), ('mistreating', 1), ('knowing', 1), ('acting', 1), ('truck', 1), ('phillip', 1), ('research', 1), ('scares', 1), ('knew', 1), ('righ', 1), ('yrs', 1), ('phentermine', 1), ('officexp', 1), ('windowsxp', 1), ('concurred', 1), ('predictor', 1), ('competitionsabine', 1), ('rescind', 1), ('wicksashay', 1), ('psychopath', 1), ('515', 1), ('boo', 1), ('iconoclasm', 1), ('sidearm', 1), ('intolerant', 1), ('raze', 1), ('upend', 1), ('kathleen', 1), ('maryland', 1), ('catastrophic', 1), ('kinesthesis', 1), ('bassinet', 1), ('incursion', 1), ('irremovable', 1), ('centerline', 1), ('psychoanalytic', 1), ('professionaloval', 1), ('belligerent', 1), ('nirvana', 1), ('combinator', 1), ('crusade', 1), ('electra', 1), ('cutset', 1), ('caustic', 1), ('derivate', 1), ('accretion', 1), ('tough', 1), ('mucosa', 1), ('vee', 1), ('churchmen', 1), ('bessie', 1), ('cozy', 1), ('sculptor', 1), ('rook', 1), ('primitive', 1), ('grumman', 1), ('sleight', 1), ('doghouse', 1), ('dusk', 1), ('defuse', 1), ('beck', 1), ('ceylon', 1), ('across', 1), ('97', 1), ('letting', 1), ('6487', 1), ('relaxants', 1), ('allergies', 1), ('sleeping', 1), ('disorders', 1), ('627', 1), ('glazes', 1), ('wearable', 1), ('scorched', 1), ('nausea', 1), ('mime', 1), ('developed', 1), ('travel', 1), ('replicas', 1), ('826', 1), ('ercot', 1), ('lastest', 1), ('ill', 1), ('sea', 1), ('vl', 1), ('clal', 1), ('lucy', 1), ('centers', 1), ('11814', 1), ('27117', 1), ('smallest', 1), ('massager', 1), ('6347', 1), ('adonis', 1), ('51862', 1), ('9827', 1), ('223423', 1), ('9828', 1), ('rusty', 1), ('426', 1), ('wtxo', 1), ('canyou', 1), ('5593', 1), ('vortnight', 1), ('temperd', 1), ('sisyphist', 1), ('anna', 1), ('cola', 1), ('1821286385673216', 1), ('spinner', 1), ('reducing', 1), ('attempted', 1), ('quarantined', 1), ('mailsweeper', 1), ('staple', 1), ('452475', 1), ('bromine', 1), ('cheetah', 1), ('convolve', 1), ('singapore', 1), ('virulent', 1), ('assumption', 1), ('shortish', 1), ('ncaa', 1), ('anisotropy', 1), ('didactic', 1), ('convalesce', 1), ('magi', 1), ('carnage', 1), ('wad', 1), ('arrest', 1), ('centrifuge', 1), ('anchorite', 1), ('posthumous', 1), ('dying', 1), ('loren', 1), ('armhole', 1), ('fable', 1), ('antithetic', 1), ('awed', 1), ('prize', 1), ('award', 1), ('department', 1), ('386', 1), ('7111', 1), ('prorated', 1), ('165331', 1), ('horse', 1), ('1534', 1), ('shelley', 1), ('jefferson', 1), ('sites', 1), ('settled', 1), ('procedural', 1), ('dispute', 1), ('acquire', 1), ('dismisses', 1), ('lawsuit', 1), ('extends', 1), ('repurchase', 1), ('profitable', 1), ('interstate', 1), ('rebuild', 1), ('719', 1), ('withdrawal', 1), ('holding', 1), ('cox', 1), ('savandra', 1), ('leassear', 1), ('fm', 1), ('motor', 1), ('mercruiser', 1), ('sterndrive', 1), ('135', 1), ('operates', 1), ('850', 1), ('3256', 1), ('707', 1), ('3527', 1), ('nngb', 1), ('nmbers', 1), ('3255', 1), ('813', 1), ('0047', 1), ('colada', 1), ('conversations', 1), ('candidiate', 1), ('jr', 1), ('fidelity', 1), ('seeking', 1), ('undergraduate', 1), ('bba', 1), ('baylor', 1), ('mba', 1), ('introduce', 1), ('ext', 1), ('1791', 1), ('unavailable', 1), ('4298', 1), ('aqua', 1), ('dulce', 1), ('987349', 1), ('household', 1), ('financially', 1), ('dependent', 1), ('rac', 1), ('egf', 1), ('remembered', 1), ('refin', 1), ('ancing', 1), ('loan', 1), ('amazingly', 1), ('repayment', 1), ('bargains', 1), ('aving', 1), ('ash', 1), ('amestela', 1), ('nomore', 1), ('rua', 1), ('imprense', 1), ('4347', 1), ('bloco', 1), ('maputo', 1), ('mozambique', 1), ('519632', 1), ('ggo', 1), ('prreeiscrlpt', 1), ('frdllad', 1), ('kf', 1), ('finest', 1), ('199', 1), ('llege', 1), ('dipl', 1), ('marta', 1), ('henderson', 1), ('investing', 1), ('retirement', 1), ('ejakulate', 1), ('intercourse', 1), ('ovsr', 1), ('paycheck', 1), ('dications', 1), ('bmw', 1), ('labs', 1), ('mechanics', 1), ('lynne', 1), ('gbhzivjwl', 1), ('102', 1), ('unsigned', 1), ('ltd', 1), ('aka', 1), ('ponderosa', 1), ('otherwise', 1), ('inputted', 1), ('0001', 1), ('hrs', 1), ('adam', 1), ('iyakubonana', 1), ('602', 1), ('linda', 1), ('sum', 1), ('supervitagra', 1), ('gotham', 1), ('prudency', 1), ('9124', 1), ('874', 1), ('blacktable', 1), ('cellpadding', 1), ('taylor', 1), ('official', 1), ('unsecured', 1), ('appointed', 1), ('pacific', 1), ('tribolet', 1), ('assessment', 1), ('designated', 1), ('representative', 1), ('mellencamp', 1), ('restricted', 1), ('disclosing', 1), ('loved', 1), ('seven', 1), ('pose', 1), ('solve', 1), ('unknown', 1), ('skill', 1), ('effetiveeight', 1), ('aaiabe', 1), ('withoutrescription', 1), ('streamlined', 1), ('denizen', 1), ('ajar', 1), ('chased', 1), ('apology', 1), ('bent', 1), ('summer', 1), ('countries', 1), ('collage', 1), ('youngest', 1), ('glow', 1), ('candles', 1), ('bare', 1), ('cuts', 1), ('kari', 1), ('bullington', 1), ('wipe', 1), ('warmers', 1), ('walmart', 1), ('temporarily', 1), ('pocketbooks', 1), ('buttons', 1), ('poetics', 1), ('sheets', 1), ('iko', 1), ('szeb', 1), ('loweredrates', 1), ('apb', 1), ('vhu', 1), ('cftp', 1), ('kv', 1), ('toclcuytmuceuro', 1), ('limited', 1), ('allotment', 1), ('recommended', 1), ('multinational', 1), ('fortunately', 1), ('ordered', 1), ('surplus', 1), ('requirement', 1), ('boxes', 1), ('guarantee', 1), ('acer', 1), ('carry', 1), ('grade', 1), ('laptops', 1), ('euros', 1), ('faxed', 1), ('strict', 1), ('2400', 1), ('series', 1), ('ghz', 1), ('processor', 1), ('performancel', 1), ('ddr', 1), ('ram', 1), ('102420', 1), ('udma', 1), ('cdrwl', 1), ('floppy', 1), ('disk', 1), ('drivenext', 1), ('technologyati', 1), ('soundfull', 1), ('lan', 1), ('iee', 1), ('mouseinternet', 1), ('readywindows', 1), ('editionl', 1), ('parts', 1), ('labor', 1), ('priority', 1), ('supporthow', 1), ('qualify', 1), ('870', 1), ('134', 1), ('3520', 1), ('isif', 1), ('erroneous', 1), ('shipments', 1), ('obligated', 1), ('uk', 1), ('german', 1), ('spanish', 1), ('specifyplease', 1), ('legibly', 1), ('shipment', 1), ('avvxkeuojzcp', 1), ('cznxdt', 1), ('920', 1), ('7266', 1), ('expire', 1), ('504', 1), ('appointment', 1), ('psc', 1), ('1315', 1), ('cc', 1), ('handler', 1), ('fuller', 1), ('luuuuuuube', 1), ('seperately', 1), ('compile', 1), ('owe', 1), ('event', 1), ('interim', 1), ('rely', 1), ('engaged', 1), ('balancing', 1), ('907', 1), ('owes', 1), ('netting', 1), ('allocatable', 1), ('unlike', 1), ('6599', 1), ('sandison', 1), ('614', 1), ('goofed', 1), ('spending', 1), ('onlinepharmacy', 1), ('freebies', 1), ('fuchsia', 1), ('lavender', 1), ('pinion', 1), ('catenate', 1), ('sneermullion', 1), ('conscientious', 1), ('damanonymous', 1), ('conner', 1), ('tristar', 1), ('779345', 1), ('treasury', 1), ('ir', 1), ('inch', 1), ('craves', 1), ('walium', 1), ('ciallls', 1), ('vlaggra', 1), ('503', 1), ('region', 1), ('vast', 1), ('dawned', 1), ('casings', 1), ('periods', 1), ('276494', 1), ('9602', 1), ('sheila', 1), ('basin', 1), ('expensed', 1), ('baseload', 1), ('67', 1), ('evidenced', 1), ('exceptionally', 1), ('quarter', 1), ('coconut', 1)]

import scipy.sparse
from sklearn.preprocessing import StandardScaler
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import CountVectorizer, HashingVectorizer, TfidfVectorizer
import scipy
"""
Count Vectorizer
-----------------------------------------
Convert a collection of text documents to train a matrix of token counts. 
This implementation produces a sparse representation of the counts. If you do not provide an a-priori dictionary and you do not use an analyzer that does some kind of feature selection then the number of features will be equal to the vocabulary size found by analyzing the data.
Hyperparamaters:
- The default for stop_words in None. If 'english', a built-in stop word list for English is used. **There are several known issues with 'english' and you should consider an alternative.**
- token_pattern = Regular expression denoting what constitutes a “token”, only used id `analyzer == "word"`. The default regexp select tokens of 2 or more alphanumeric characters (punctuation is completely ignored and always treated as a token separator).
- `ngram_range` The lower and upper boundary of the range of n-values for different word n-grams or char n-grams to be extracted. All values of n such such that min_n <= n <= max_n will be used. For example an ngram_range of (1, 1) means only unigrams, (1, 2) means unigrams and bigrams, and (2, 2) means only bigrams. Only appluies if analyzer is not callable
- analyzer: Whether the feature should be made of word or character n-grams. Option 'char_wb' creates character n-grams only from text inside word boundaries; n-grams at the edges of words are padded with space.
"""


"""
Hashing Vectorizer
-----------------------------------------
Convert a collection of text documents to a matrix of token occurrences.
It turns a collection of text documents into a scipy.sparse matrix holding token occurrence counts (or binary occurrence information), possibly normalized as token frequencies if norm''l1' or projected on the euclidean unit sphere if norm'l2'.
This text vectorizer implementation uses the hashing trick to find the token string name to feature integer index mapping.
This stragtegy has several advantages:
- it is very low memory scalable to large datasets as there is no need to store a vocabulary dictionary in memory.
- it is fast to pickle and un-pickle as it holds no state besides the constructor parameters.
- it can be used in a streaming (partial fit) or parallel pipeline as there is no state computed during fit.
This has a couple ones (versus using CountVectorizer):
- there is no way to compute the inverse transform (from feature indices to string feature names) which can be a problem when trying to introspect which features are most important to a model.
- there can be collisions: distinct tokens can be mapped to the same feature index. However in practice this is rarely an issue if n_features is large enough (e.g. 2 ** 18 for text classification problems).
- no IDF weighting as this would render the transformer stateful.
Hyperparamaters:
Same as CountVectorizer.
"""


"""
Tfidf Vectorizer
-----------------------------------------
Convert a collection of raw documents to a matrix of TF-IDF features.
Equivalent to CountVectorizer followed by TfidfTransformer.

Transform a count matrix to a normalized tf or tf-idf representation.

Tfidf Transformer
-----------------------------------------
- Tf means term-frequency while tf-idf means term-frequency times inverse document-frequency. This is a common term weighting scheme in information retrieval, that has also found good use in document classification.
- The goal of using tf-idf instead of the raw frequencies of occurrence of a token in a given document is to scale down the impact of tokens that occur very frequently in a given corpus and that are hence empirically less informative than features that occur in a small fraction of the training corpus.
- The formula that is used to compute the tf-idf for a term t of a document d in a document set is tf-idf(t, d) = tf(t, d) * idf(t), and the idf is computed as idf(t) = log [ n / df(t) ] + 1 (if smooth_idf=False), where n is the total number of documents in the document set and df(t) is the document frequency of t; the document frequency is the number of documents in the document set that contain the term t. The effect of adding “1” to the idf in the equation above is that terms with zero idf, i.e., terms that occur in all documents in a training set, will not be entirely ignored. (Note that the idf formula above differs from the standard textbook notation that defines the idf as idf(t) = log [ n / (df(t) + 1) ]).
- If smooth_idf=True (the default), the constant “1” is added to the numerator and denominator of the idf as if an extra document was seen containing every term in the collection exactly once, which prevents zero divisions: idf(t) = log [ (1 + n) / (1 + df(t)) ] + 1.
- Furthermore, the formulas used to compute tf and idf depend on parameter settings that correspond to the SMART notation used in IR as follows:
- Tf is “n” (natural) by default, “l” (logarithmic) when sublinear_tf=True. Idf is “t” when use_idf is given, “n” (none) otherwise. Normalization is “c” (cosine) when norm='l2', “n” (none) when norm=None.

Hyperparameters:
Same as CountVectorizer EXCEPT there are some hyperparameters that allow you to change hw tf-idf is calculated. 
- norm: Each utput row will have a unit norm (either l1 or l2 (default))
"""



from sklearn.base import BaseEstimator, TransformerMixin

class ProcessText(BaseEstimator,TransformerMixin):
    """
    Process text - create a data frame that you can use to analyze text - separate the subject and body of the article 
    """
    def fit(self,X,y=None):
        return self
    def transform(self,X,y=None):
        arr = []
        if not isinstance(X,pd.Series):
            X = pd.Series(X[:,0])
        for item in X.items():
            trimmed = item[1].strip()
            matches = subject_re.match(trimmed)
            if matches:
                subj = matches.group("subject")
                if not subj:
                    subj = ""
                body = matches.group("body")
                if not body:
                    body = ""
                if subj=="" and body=="":
                    body = trimmed
                arr.append([subj.strip(),body.strip()])
            else:
                arr.append(["",trimmed])
        df = pd.DataFrame(arr,columns=["subject","body"])
        return df
    
class AnalyzeText(BaseEstimator,TransformerMixin):
    """
    How to preprocess document text:
    - We expect a DataFrame here that is returned by ProcessText
    - The processor karg can be "count", "hashing", or "tfidf" depending on whether you want to use  CountVectorizer, HashingVectorizer, or TfidfVectorizer
    - I am not going to analyze the text of the emails because I would have to put them in bins, then one hot encode them in order to keep the matrix sparse, and I don't want to do that right now
    """
    def __init__(self,processor="count", analyze_lengths=True,stop_words="english",ngram_range=(1,2)):
        self.ngram_range = ngram_range
        self.stop_words = stop_words
        self.analyze_lengths = analyze_lengths
        self.processor = processor
        if self.processor=="count":
            self.subject_analyzer = CountVectorizer(input="content")
            self.body_analyzer = CountVectorizer(input="content")
            self.subject_analyzer.set_params(stop_words=self.stop_words,ngram_range=self.ngram_range)
            self.body_analyzer.set_params(stop_words=self.stop_words,ngram_range=self.ngram_range)
        elif self.processor=="hashing":
            self.subject_analyzer = HashingVectorizer(input="content")
            self.body_analyzer = HashingVectorizer(input="content")
            self.subject_analyzer.set_params(stop_words=self.stop_words,ngram_range=self.ngram_range)
            self.body_analyzer.set_params(stop_words=self.stop_words,ngram_range=self.ngram_range)
        elif self.processor=="tfidf":
            self.subject_analyzer = TfidfVectorizer(input="content")
            self.body_analyzer = TfidfVectorizer(input="content")
            self.subject_analyzer.set_params(stop_words=self.stop_words,ngram_range=self.ngram_range)
            self.body_analyzer.set_params(stop_words=self.stop_words,ngram_range=self.ngram_range)
        else:
            raise ValueError("processor should be  \"count\", \"hashing\", or \"tfidf\".")
    def fit(self,X,y=None):
        if len(X.columns)==1:
            df = ProcessText().transform(X)
        else:
            df = X
        self.subject_analyzer.fit(df["subject"].to_list())
        self.body_analyzer.fit(df["body"].to_list())       
        return self 
    def transform(self,X,y=None):
        out = self.subject_analyzer.transform(X["subject"].to_list())
        out2 = self.body_analyzer.transform(X["body"].to_list())
        out = scipy.sparse.hstack((out,out2))
        return out

class CustomImputer(BaseEstimator,TransformerMixin):
    def fit(self,X,y=None):
        return self
    def transform(self,X,y=None):
        X[X==np.isnan] = ""
        return X.to_frame(name="Data Frame Required")

out[29]

from sklearn.impute import SimpleImputer
from sklearn.linear_model import RidgeClassifier, SGDClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import precision_score, recall_score, accuracy_score
from sklearn.model_selection import GridSearchCV
cv=5
refit=True
verbose=3


ridge_params = [
{"analyze__processor": ["count"], "analyze__analyze_lengths": [True, False], "analyze__stop_words": [None,"english"], "analyze__ngram_range": [(1,1), (1,2), (2,3) ]  },
{"analyze__processor": ["hashing"], "analyze__analyze_lengths": [True, False], "analyze__stop_words": [None,"english"], "analyze__ngram_range": [(1,1), (1,2), (2,3) ]}, 
{"analyze__processor": ["tfidf"], "analyze__analyze_lengths": [True, False], "analyze__stop_words": [None,"english"], "analyze__ngram_range": [(1,1), (1,2), (2,3) ] }
]
ride_pipeline = Pipeline(
    steps=[
        ("impute",SimpleImputer(strategy="constant",fill_value="")),
        ("process",ProcessText()),
        ("analyze",AnalyzeText()),
        ("fit",RidgeClassifier())
    ]
)
ridge_clf = GridSearchCV(ride_pipeline,param_grid=ridge_params,cv=cv,refit=refit,verbose=verbose)
ridge_clf.fit(X_train.to_frame(),y_train)
y_pred = ridge_clf.predict(X_test.to_frame())

print("METRICS\n---------------------------------------------------------------")
print("ACCURACY SCORE",accuracy_score(y_pred,y_test))
print("PRECISON SCORE",precision_score(y_pred,y_test))
print("RECALL SCORE",recall_score(y_pred,y_test))
print("ESTIMATORS\n----------------------------------------------------------")
resultsDF = pd.DataFrame(ridge_clf.cv_results_)
print(resultsDF)

out[30]

Fitting 5 folds for each of 36 candidates, totalling 180 fits
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None;, score=0.960 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None;, score=0.954 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None;, score=0.943 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None;, score=0.955 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None;, score=0.944 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english;, score=0.960 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english;, score=0.954 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english;, score=0.943 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english;, score=0.955 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english;, score=0.944 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None;, score=0.960 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None;, score=0.954 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None;, score=0.943 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None;, score=0.955 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None;, score=0.944 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english;, score=0.960 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english;, score=0.954 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english;, score=0.943 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english;, score=0.955 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english;, score=0.944 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None;, score=0.960 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None;, score=0.954 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None;, score=0.943 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None;, score=0.955 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None;, score=0.944 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english;, score=0.960 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english;, score=0.954 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english;, score=0.943 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english;, score=0.955 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english;, score=0.944 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None;, score=0.960 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None;, score=0.954 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None;, score=0.943 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None;, score=0.955 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None;, score=0.944 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english;, score=0.960 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english;, score=0.954 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english;, score=0.943 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english;, score=0.955 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english;, score=0.944 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None;, score=0.960 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None;, score=0.954 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None;, score=0.943 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None;, score=0.955 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None;, score=0.944 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english;, score=0.960 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english;, score=0.954 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english;, score=0.943 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english;, score=0.955 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english;, score=0.944 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None;, score=0.960 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None;, score=0.954 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None;, score=0.943 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None;, score=0.955 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None;, score=0.944 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english;, score=0.960 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english;, score=0.954 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english;, score=0.943 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english;, score=0.955 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english;, score=0.944 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None;, score=0.960 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None;, score=0.954 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None;, score=0.943 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None;, score=0.955 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None;, score=0.944 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english;, score=0.960 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english;, score=0.954 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english;, score=0.943 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english;, score=0.955 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english;, score=0.944 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None;, score=0.960 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None;, score=0.954 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None;, score=0.943 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None;, score=0.955 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None;, score=0.944 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english;, score=0.960 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english;, score=0.954 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english;, score=0.943 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english;, score=0.955 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english;, score=0.944 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None;, score=0.960 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None;, score=0.954 total time= 0.2s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None;, score=0.943 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None;, score=0.955 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None;, score=0.944 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english;, score=0.960 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english;, score=0.954 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english;, score=0.943 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english;, score=0.955 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english;, score=0.944 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None;, score=0.960 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None;, score=0.954 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None;, score=0.943 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None;, score=0.955 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None;, score=0.944 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english;, score=0.960 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english;, score=0.954 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english;, score=0.943 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english;, score=0.955 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english;, score=0.944 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None;, score=0.960 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None;, score=0.954 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None;, score=0.943 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None;, score=0.955 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None;, score=0.944 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english;, score=0.960 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english;, score=0.954 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english;, score=0.943 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english;, score=0.955 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english;, score=0.944 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None;, score=0.960 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None;, score=0.954 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None;, score=0.943 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None;, score=0.955 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None;, score=0.944 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english;, score=0.960 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english;, score=0.954 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english;, score=0.943 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english;, score=0.955 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english;, score=0.944 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None;, score=0.960 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None;, score=0.954 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None;, score=0.943 total time= 0.2s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None;, score=0.955 total time= 0.2s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None;, score=0.944 total time= 0.2s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english;, score=0.960 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english;, score=0.954 total time= 0.2s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english;, score=0.943 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english;, score=0.955 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english;, score=0.944 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None;, score=0.960 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None;, score=0.954 total time= 0.2s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None;, score=0.943 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None;, score=0.955 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None;, score=0.944 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english;, score=0.960 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english;, score=0.954 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english;, score=0.943 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english;, score=0.955 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english;, score=0.944 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None;, score=0.960 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None;, score=0.954 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None;, score=0.943 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None;, score=0.955 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None;, score=0.944 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english;, score=0.960 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english;, score=0.954 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english;, score=0.943 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english;, score=0.955 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english;, score=0.944 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None;, score=0.960 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None;, score=0.954 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None;, score=0.943 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None;, score=0.955 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None;, score=0.944 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english;, score=0.960 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english;, score=0.954 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english;, score=0.943 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english;, score=0.955 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english;, score=0.944 total time= 0.2s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None;, score=0.960 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None;, score=0.954 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None;, score=0.943 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None;, score=0.955 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None;, score=0.944 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english;, score=0.960 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english;, score=0.954 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english;, score=0.943 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english;, score=0.955 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english;, score=0.944 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None;, score=0.960 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None;, score=0.954 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None;, score=0.943 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None;, score=0.955 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None;, score=0.944 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english;, score=0.960 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english;, score=0.954 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english;, score=0.943 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english;, score=0.955 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english;, score=0.944 total time= 0.1s
METRICS
---------------------------------------------------------------
ACCURACY SCORE 0.9681159420289855
PRECISON SCORE 0.9455782312925171
RECALL SCORE 0.9423728813559322
ESTIMATORS
----------------------------------------------------------
mean_fit_time std_fit_time mean_score_time std_score_time \
0 0.169754 0.021226 0.016615 0.002113
1 0.162725 0.008268 0.016649 0.001004
2 0.166296 0.004995 0.016670 0.001073
3 0.162820 0.005950 0.017576 0.000834
4 0.178523 0.016604 0.016120 0.001339
5 0.169816 0.004880 0.017365 0.001852
6 0.173739 0.016975 0.017600 0.002155
7 0.190405 0.007686 0.017838 0.001535
8 0.188400 0.012156 0.019705 0.001517
9 0.183854 0.016277 0.019863 0.003779
10 0.178438 0.015422 0.016193 0.000832
11 0.186882 0.020159 0.020097 0.006561
12 0.185874 0.017241 0.016006 0.001564
13 0.171355 0.010988 0.018087 0.003782
14 0.166583 0.005105 0.016754 0.001980
15 0.164918 0.007101 0.017320 0.001283
16 0.179672 0.029201 0.017118 0.001442
17 0.166202 0.005599 0.016744 0.001781
18 0.164099 0.003496 0.016724 0.001227
19 0.163267 0.005290 0.015939 0.001109
20 0.166768 0.007146 0.016124 0.001285
21 0.161159 0.003850 0.016522 0.000844
22 0.173132 0.011544 0.016252 0.001501
23 0.167588 0.011327 0.017661 0.001494
24 0.196182 0.030228 0.025822 0.009896
25 0.194773 0.034156 0.018189 0.001398
26 0.181402 0.036651 0.016839 0.002329
27 0.167642 0.012422 0.017116 0.001485
28 0.161902 0.002930 0.016597 0.001578
29 0.160630 0.005244 0.015894 0.000281
30 0.182019 0.024699 0.016605 0.001050
31 0.210410 0.030484 0.018279 0.001637
32 0.192008 0.017479 0.019864 0.003678
33 0.195334 0.012610 0.020035 0.003316
34 0.178347 0.006301 0.017955 0.001747
35 0.171368 0.006209 0.017289 0.001293

param_analyze__analyze_lengths param_analyze__ngram_range \
0 True (1, 1)
1 True (1, 1)
2 True (1, 2)
3 True (1, 2)
4 True (2, 3)
5 True (2, 3)
6 False (1, 1)
7 False (1, 1)
8 False (1, 2)
9 False (1, 2)
10 False (2, 3)
11 False (2, 3)
12 True (1, 1)
13 True (1, 1)
14 True (1, 2)
15 True (1, 2)
16 True (2, 3)
17 True (2, 3)
18 False (1, 1)
19 False (1, 1)
20 False (1, 2)
21 False (1, 2)
22 False (2, 3)
23 False (2, 3)
24 True (1, 1)
25 True (1, 1)
26 True (1, 2)
27 True (1, 2)
28 True (2, 3)
29 True (2, 3)
30 False (1, 1)
31 False (1, 1)
32 False (1, 2)
33 False (1, 2)
34 False (2, 3)
35 False (2, 3)

param_analyze__processor param_analyze__stop_words \
0 count None
1 count english
2 count None
3 count english
4 count None
5 count english
6 count None
7 count english
8 count None
9 count english
10 count None
11 count english
12 hashing None
13 hashing english
14 hashing None
15 hashing english
16 hashing None
17 hashing english
18 hashing None
19 hashing english
20 hashing None
21 hashing english
22 hashing None
23 hashing english
24 tfidf None
25 tfidf english
26 tfidf None
27 tfidf english
28 tfidf None
29 tfidf english
30 tfidf None
31 tfidf english
32 tfidf None
33 tfidf english
34 tfidf None
35 tfidf english

params split0_test_score \
0 {'analyze__analyze_lengths': True, 'analyze__n... 0.960145
1 {'analyze__analyze_lengths': True, 'analyze__n... 0.960145
2 {'analyze__analyze_lengths': True, 'analyze__n... 0.960145
3 {'analyze__analyze_lengths': True, 'analyze__n... 0.960145
4 {'analyze__analyze_lengths': True, 'analyze__n... 0.960145
5 {'analyze__analyze_lengths': True, 'analyze__n... 0.960145
6 {'analyze__analyze_lengths': False, 'analyze__... 0.960145
7 {'analyze__analyze_lengths': False, 'analyze__... 0.960145
8 {'analyze__analyze_lengths': False, 'analyze__... 0.960145
9 {'analyze__analyze_lengths': False, 'analyze__... 0.960145
10 {'analyze__analyze_lengths': False, 'analyze__... 0.960145
11 {'analyze__analyze_lengths': False, 'analyze__... 0.960145
12 {'analyze__analyze_lengths': True, 'analyze__n... 0.960145
13 {'analyze__analyze_lengths': True, 'analyze__n... 0.960145
14 {'analyze__analyze_lengths': True, 'analyze__n... 0.960145
15 {'analyze__analyze_lengths': True, 'analyze__n... 0.960145
16 {'analyze__analyze_lengths': True, 'analyze__n... 0.960145
17 {'analyze__analyze_lengths': True, 'analyze__n... 0.960145
18 {'analyze__analyze_lengths': False, 'analyze__... 0.960145
19 {'analyze__analyze_lengths': False, 'analyze__... 0.960145
20 {'analyze__analyze_lengths': False, 'analyze__... 0.960145
21 {'analyze__analyze_lengths': False, 'analyze__... 0.960145
22 {'analyze__analyze_lengths': False, 'analyze__... 0.960145
23 {'analyze__analyze_lengths': False, 'analyze__... 0.960145
24 {'analyze__analyze_lengths': True, 'analyze__n... 0.960145
25 {'analyze__analyze_lengths': True, 'analyze__n... 0.960145
26 {'analyze__analyze_lengths': True, 'analyze__n... 0.960145
27 {'analyze__analyze_lengths': True, 'analyze__n... 0.960145
28 {'analyze__analyze_lengths': True, 'analyze__n... 0.960145
29 {'analyze__analyze_lengths': True, 'analyze__n... 0.960145
30 {'analyze__analyze_lengths': False, 'analyze__... 0.960145
31 {'analyze__analyze_lengths': False, 'analyze__... 0.960145
32 {'analyze__analyze_lengths': False, 'analyze__... 0.960145
33 {'analyze__analyze_lengths': False, 'analyze__... 0.960145
34 {'analyze__analyze_lengths': False, 'analyze__... 0.960145
35 {'analyze__analyze_lengths': False, 'analyze__... 0.960145

split1_test_score split2_test_score split3_test_score \
0 0.954051 0.943168 0.95526
1 0.954051 0.943168 0.95526
2 0.954051 0.943168 0.95526
3 0.954051 0.943168 0.95526
4 0.954051 0.943168 0.95526
5 0.954051 0.943168 0.95526
6 0.954051 0.943168 0.95526
7 0.954051 0.943168 0.95526
8 0.954051 0.943168 0.95526
9 0.954051 0.943168 0.95526
10 0.954051 0.943168 0.95526
11 0.954051 0.943168 0.95526
12 0.954051 0.943168 0.95526
13 0.954051 0.943168 0.95526
14 0.954051 0.943168 0.95526
15 0.954051 0.943168 0.95526
16 0.954051 0.943168 0.95526
17 0.954051 0.943168 0.95526
18 0.954051 0.943168 0.95526
19 0.954051 0.943168 0.95526
20 0.954051 0.943168 0.95526
21 0.954051 0.943168 0.95526
22 0.954051 0.943168 0.95526
23 0.954051 0.943168 0.95526
24 0.954051 0.943168 0.95526
25 0.954051 0.943168 0.95526
26 0.954051 0.943168 0.95526
27 0.954051 0.943168 0.95526
28 0.954051 0.943168 0.95526
29 0.954051 0.943168 0.95526
30 0.954051 0.943168 0.95526
31 0.954051 0.943168 0.95526
32 0.954051 0.943168 0.95526
33 0.954051 0.943168 0.95526
34 0.954051 0.943168 0.95526
35 0.954051 0.943168 0.95526

split4_test_score mean_test_score std_test_score rank_test_score
0 0.944377 0.9514 0.006565 1
1 0.944377 0.9514 0.006565 1
2 0.944377 0.9514 0.006565 1
3 0.944377 0.9514 0.006565 1
4 0.944377 0.9514 0.006565 1
5 0.944377 0.9514 0.006565 1
6 0.944377 0.9514 0.006565 1
7 0.944377 0.9514 0.006565 1
8 0.944377 0.9514 0.006565 1
9 0.944377 0.9514 0.006565 1
10 0.944377 0.9514 0.006565 1
11 0.944377 0.9514 0.006565 1
12 0.944377 0.9514 0.006565 1
13 0.944377 0.9514 0.006565 1
14 0.944377 0.9514 0.006565 1
15 0.944377 0.9514 0.006565 1
16 0.944377 0.9514 0.006565 1
17 0.944377 0.9514 0.006565 1
18 0.944377 0.9514 0.006565 1
19 0.944377 0.9514 0.006565 1
20 0.944377 0.9514 0.006565 1
21 0.944377 0.9514 0.006565 1
22 0.944377 0.9514 0.006565 1
23 0.944377 0.9514 0.006565 1
24 0.944377 0.9514 0.006565 1
25 0.944377 0.9514 0.006565 1
26 0.944377 0.9514 0.006565 1
27 0.944377 0.9514 0.006565 1
28 0.944377 0.9514 0.006565 1
29 0.944377 0.9514 0.006565 1
30 0.944377 0.9514 0.006565 1
31 0.944377 0.9514 0.006565 1
32 0.944377 0.9514 0.006565 1
33 0.944377 0.9514 0.006565 1
34 0.944377 0.9514 0.006565 1
35 0.944377 0.9514 0.006565 1

sgd_params = [
    {"analyze__processor": ["count"], "analyze__analyze_lengths": [True, False], "analyze__stop_words": [None,"english"], "analyze__ngram_range": [(1,1), (1,2), (2,3) ]  },
    {"analyze__processor": ["hashing"], "analyze__analyze_lengths": [True, False], "analyze__stop_words": [None,"english"], "analyze__ngram_range": [(1,1), (1,2), (2,3) ] },
    {"analyze__processor": ["tfidf"], "analyze__analyze_lengths": [True, False], "analyze__stop_words": [None,"english"], "analyze__ngram_range": [(1,1), (1,2), (2,3) ] },
]

sgd_pipeline = Pipeline(
    steps=[
        ("impute",SimpleImputer(strategy="constant",fill_value="")),
        ("process",ProcessText()),
        ("analyze",AnalyzeText()),
        ("fit",SGDClassifier())
    ]
)
sgd_clf = GridSearchCV(sgd_pipeline,param_grid=sgd_params,cv=cv,refit=refit,verbose=verbose)
sgd_clf.fit(X_train.to_frame(),y_train)
y_pred = sgd_clf.predict(X_test.to_frame())


print("METRICS\n---------------------------------------------------------------")
print("ACCURACY SCORE",accuracy_score(y_pred,y_test))
print("PRECISON SCORE",precision_score(y_pred,y_test))
print("RECALL SCORE",recall_score(y_pred,y_test))
print("ESTIMATORS\n----------------------------------------------------------")
resultsDF = pd.DataFrame(sgd_clf.cv_results_)
print(resultsDF)

out[31]

Fitting 5 folds for each of 36 candidates, totalling 180 fits
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None;, score=0.950 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None;, score=0.944 total time= 0.2s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None;, score=0.932 total time= 0.2s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None;, score=0.958 total time= 0.2s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None;, score=0.949 total time= 0.2s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english;, score=0.948 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english;, score=0.948 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english;, score=0.938 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english;, score=0.953 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english;, score=0.948 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None;, score=0.953 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None;, score=0.943 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None;, score=0.938 total time= 0.2s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None;, score=0.963 total time= 0.2s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None;, score=0.946 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english;, score=0.950 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english;, score=0.944 total time= 0.2s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english;, score=0.932 total time= 0.2s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english;, score=0.953 total time= 0.2s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english;, score=0.942 total time= 0.2s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None;, score=0.958 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None;, score=0.942 total time= 0.2s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None;, score=0.937 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None;, score=0.961 total time= 0.2s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None;, score=0.946 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english;, score=0.946 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english;, score=0.938 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english;, score=0.941 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english;, score=0.952 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english;, score=0.954 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None;, score=0.955 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None;, score=0.941 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None;, score=0.938 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None;, score=0.956 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None;, score=0.960 total time= 0.2s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english;, score=0.961 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english;, score=0.944 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english;, score=0.942 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english;, score=0.952 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english;, score=0.942 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None;, score=0.949 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None;, score=0.953 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None;, score=0.935 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None;, score=0.961 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None;, score=0.955 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english;, score=0.953 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english;, score=0.947 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english;, score=0.929 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english;, score=0.956 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english;, score=0.950 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None;, score=0.958 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None;, score=0.946 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None;, score=0.936 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None;, score=0.956 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None;, score=0.955 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english;, score=0.943 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english;, score=0.943 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english;, score=0.938 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english;, score=0.961 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english;, score=0.947 total time= 0.2s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None;, score=0.952 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None;, score=0.950 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None;, score=0.937 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None;, score=0.952 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None;, score=0.948 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english;, score=0.946 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english;, score=0.944 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english;, score=0.933 total time= 0.2s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english;, score=0.954 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english;, score=0.950 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None;, score=0.953 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None;, score=0.942 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None;, score=0.936 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None;, score=0.946 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None;, score=0.949 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english;, score=0.954 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english;, score=0.952 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english;, score=0.938 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english;, score=0.950 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english;, score=0.954 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None;, score=0.947 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None;, score=0.946 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None;, score=0.930 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None;, score=0.953 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None;, score=0.952 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english;, score=0.957 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english;, score=0.944 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english;, score=0.932 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english;, score=0.960 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english;, score=0.950 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None;, score=0.950 total time= 0.0s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None;, score=0.935 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None;, score=0.941 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None;, score=0.956 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None;, score=0.944 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english;, score=0.948 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english;, score=0.944 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english;, score=0.933 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english;, score=0.966 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english;, score=0.955 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None;, score=0.943 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None;, score=0.944 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None;, score=0.944 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None;, score=0.952 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None;, score=0.953 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english;, score=0.952 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english;, score=0.936 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english;, score=0.931 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english;, score=0.952 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english;, score=0.954 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None;, score=0.957 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None;, score=0.946 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None;, score=0.935 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None;, score=0.954 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None;, score=0.946 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english;, score=0.948 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english;, score=0.942 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english;, score=0.935 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english;, score=0.954 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english;, score=0.947 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None;, score=0.952 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None;, score=0.940 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None;, score=0.932 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None;, score=0.956 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None;, score=0.955 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english;, score=0.950 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english;, score=0.938 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english;, score=0.946 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english;, score=0.953 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english;, score=0.952 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None;, score=0.950 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None;, score=0.943 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None;, score=0.946 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None;, score=0.953 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None;, score=0.950 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english;, score=0.950 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english;, score=0.943 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english;, score=0.933 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english;, score=0.956 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english;, score=0.944 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None;, score=0.946 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None;, score=0.944 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None;, score=0.933 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None;, score=0.952 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None;, score=0.955 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english;, score=0.947 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english;, score=0.942 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english;, score=0.937 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english;, score=0.960 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english;, score=0.950 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None;, score=0.949 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None;, score=0.948 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None;, score=0.943 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None;, score=0.954 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None;, score=0.943 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english;, score=0.955 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english;, score=0.941 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english;, score=0.940 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english;, score=0.952 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english;, score=0.952 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None;, score=0.943 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None;, score=0.952 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None;, score=0.936 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None;, score=0.950 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None;, score=0.953 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english;, score=0.953 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english;, score=0.940 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english;, score=0.938 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english;, score=0.955 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english;, score=0.950 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None;, score=0.949 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None;, score=0.938 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None;, score=0.938 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None;, score=0.954 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None;, score=0.952 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english;, score=0.957 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english;, score=0.952 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english;, score=0.940 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english;, score=0.950 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english;, score=0.954 total time= 0.1s
METRICS
---------------------------------------------------------------
ACCURACY SCORE 0.9623188405797102
PRECISON SCORE 0.9591836734693877
RECALL SCORE 0.912621359223301
ESTIMATORS
----------------------------------------------------------
mean_fit_time std_fit_time mean_score_time std_score_time \
0 0.228086 0.020037 0.029815 0.004007
1 0.163438 0.014039 0.019041 0.002104
2 0.217598 0.022495 0.031642 0.009380
3 0.229968 0.016229 0.028261 0.003180
4 0.222389 0.018830 0.029187 0.004811
5 0.165038 0.008434 0.019383 0.001621
6 0.179480 0.032534 0.020151 0.001915
7 0.161542 0.015008 0.019099 0.001615
8 0.146628 0.005282 0.019546 0.002372
9 0.151821 0.010771 0.018336 0.001769
10 0.169028 0.019424 0.021559 0.003570
11 0.182926 0.037504 0.023762 0.003520
12 0.197630 0.005426 0.021605 0.001962
13 0.200479 0.025626 0.025358 0.003879
14 0.186943 0.022388 0.024663 0.005072
15 0.155221 0.016290 0.017496 0.003125
16 0.180738 0.026901 0.024532 0.003760
17 0.154040 0.012430 0.019862 0.007465
18 0.173433 0.030332 0.022002 0.003867
19 0.161690 0.019395 0.018870 0.002766
20 0.149072 0.010557 0.018046 0.002836
21 0.176092 0.027482 0.019499 0.002722
22 0.144887 0.005487 0.018949 0.003472
23 0.144298 0.002157 0.017470 0.001227
24 0.152989 0.006420 0.018084 0.000598
25 0.141743 0.003283 0.016514 0.001198
26 0.144043 0.004377 0.016877 0.001450
27 0.162538 0.027679 0.017400 0.001277
28 0.153894 0.019275 0.018570 0.002961
29 0.161225 0.017895 0.019012 0.001746
30 0.160160 0.010039 0.017838 0.002803
31 0.160519 0.021586 0.017775 0.001801
32 0.168567 0.016821 0.019300 0.001775
33 0.157128 0.010260 0.018094 0.002194
34 0.150829 0.009181 0.018198 0.001129
35 0.151438 0.005785 0.017234 0.001011

param_analyze__analyze_lengths param_analyze__ngram_range \
0 True (1, 1)
1 True (1, 1)
2 True (1, 2)
3 True (1, 2)
4 True (2, 3)
5 True (2, 3)
6 False (1, 1)
7 False (1, 1)
8 False (1, 2)
9 False (1, 2)
10 False (2, 3)
11 False (2, 3)
12 True (1, 1)
13 True (1, 1)
14 True (1, 2)
15 True (1, 2)
16 True (2, 3)
17 True (2, 3)
18 False (1, 1)
19 False (1, 1)
20 False (1, 2)
21 False (1, 2)
22 False (2, 3)
23 False (2, 3)
24 True (1, 1)
25 True (1, 1)
26 True (1, 2)
27 True (1, 2)
28 True (2, 3)
29 True (2, 3)
30 False (1, 1)
31 False (1, 1)
32 False (1, 2)
33 False (1, 2)
34 False (2, 3)
35 False (2, 3)

param_analyze__processor param_analyze__stop_words \
0 count None
1 count english
2 count None
3 count english
4 count None
5 count english
6 count None
7 count english
8 count None
9 count english
10 count None
11 count english
12 hashing None
13 hashing english
14 hashing None
15 hashing english
16 hashing None
17 hashing english
18 hashing None
19 hashing english
20 hashing None
21 hashing english
22 hashing None
23 hashing english
24 tfidf None
25 tfidf english
26 tfidf None
27 tfidf english
28 tfidf None
29 tfidf english
30 tfidf None
31 tfidf english
32 tfidf None
33 tfidf english
34 tfidf None
35 tfidf english

params split0_test_score \
0 {'analyze__analyze_lengths': True, 'analyze__n... 0.950483
1 {'analyze__analyze_lengths': True, 'analyze__n... 0.948068
2 {'analyze__analyze_lengths': True, 'analyze__n... 0.952899
3 {'analyze__analyze_lengths': True, 'analyze__n... 0.950483
4 {'analyze__analyze_lengths': True, 'analyze__n... 0.957729
5 {'analyze__analyze_lengths': True, 'analyze__n... 0.945652
6 {'analyze__analyze_lengths': False, 'analyze__... 0.955314
7 {'analyze__analyze_lengths': False, 'analyze__... 0.961353
8 {'analyze__analyze_lengths': False, 'analyze__... 0.949275
9 {'analyze__analyze_lengths': False, 'analyze__... 0.952899
10 {'analyze__analyze_lengths': False, 'analyze__... 0.957729
11 {'analyze__analyze_lengths': False, 'analyze__... 0.943237
12 {'analyze__analyze_lengths': True, 'analyze__n... 0.951691
13 {'analyze__analyze_lengths': True, 'analyze__n... 0.945652
14 {'analyze__analyze_lengths': True, 'analyze__n... 0.952899
15 {'analyze__analyze_lengths': True, 'analyze__n... 0.954106
16 {'analyze__analyze_lengths': True, 'analyze__n... 0.946860
17 {'analyze__analyze_lengths': True, 'analyze__n... 0.956522
18 {'analyze__analyze_lengths': False, 'analyze__... 0.950483
19 {'analyze__analyze_lengths': False, 'analyze__... 0.948068
20 {'analyze__analyze_lengths': False, 'analyze__... 0.943237
21 {'analyze__analyze_lengths': False, 'analyze__... 0.951691
22 {'analyze__analyze_lengths': False, 'analyze__... 0.956522
23 {'analyze__analyze_lengths': False, 'analyze__... 0.948068
24 {'analyze__analyze_lengths': True, 'analyze__n... 0.951691
25 {'analyze__analyze_lengths': True, 'analyze__n... 0.950483
26 {'analyze__analyze_lengths': True, 'analyze__n... 0.950483
27 {'analyze__analyze_lengths': True, 'analyze__n... 0.950483
28 {'analyze__analyze_lengths': True, 'analyze__n... 0.945652
29 {'analyze__analyze_lengths': True, 'analyze__n... 0.946860
30 {'analyze__analyze_lengths': False, 'analyze__... 0.949275
31 {'analyze__analyze_lengths': False, 'analyze__... 0.955314
32 {'analyze__analyze_lengths': False, 'analyze__... 0.943237
33 {'analyze__analyze_lengths': False, 'analyze__... 0.952899
34 {'analyze__analyze_lengths': False, 'analyze__... 0.949275
35 {'analyze__analyze_lengths': False, 'analyze__... 0.956522

split1_test_score split2_test_score split3_test_score \
0 0.944377 0.932285 0.957678
1 0.948005 0.938331 0.952842
2 0.943168 0.938331 0.962515
3 0.944377 0.932285 0.952842
4 0.941959 0.937122 0.961306
5 0.938331 0.940750 0.951632
6 0.940750 0.938331 0.956469
7 0.944377 0.941959 0.951632
8 0.952842 0.934704 0.961306
9 0.946796 0.928658 0.956469
10 0.945586 0.935913 0.956469
11 0.943168 0.938331 0.961306
12 0.950423 0.937122 0.951632
13 0.944377 0.933495 0.954051
14 0.941959 0.935913 0.945586
15 0.951632 0.938331 0.950423
16 0.945586 0.929867 0.952842
17 0.944377 0.932285 0.960097
18 0.934704 0.940750 0.956469
19 0.944377 0.933495 0.966143
20 0.944377 0.944377 0.951632
21 0.935913 0.931076 0.951632
22 0.945586 0.934704 0.954051
23 0.941959 0.934704 0.954051
24 0.939541 0.932285 0.956469
25 0.938331 0.945586 0.952842
26 0.943168 0.945586 0.952842
27 0.943168 0.933495 0.956469
28 0.944377 0.933495 0.951632
29 0.941959 0.937122 0.960097
30 0.948005 0.943168 0.954051
31 0.940750 0.939541 0.951632
32 0.951632 0.935913 0.950423
33 0.939541 0.938331 0.955260
34 0.938331 0.938331 0.954051
35 0.951632 0.939541 0.950423

split4_test_score mean_test_score std_test_score rank_test_score
0 0.949214 0.946808 0.008418 24
1 0.948005 0.947050 0.004742 20
2 0.945586 0.948500 0.008439 10
3 0.941959 0.944389 0.007225 36
4 0.945586 0.948741 0.009269 8
5 0.954051 0.946083 0.006050 27
6 0.960097 0.950192 0.008872 3
7 0.941959 0.948256 0.007447 11
8 0.955260 0.950677 0.008897 1
9 0.950423 0.947049 0.009723 22
10 0.955260 0.950192 0.008331 4
11 0.946796 0.946568 0.007845 25
12 0.948005 0.947775 0.005491 13
13 0.950423 0.945600 0.006967 29
14 0.949214 0.945114 0.005869 34
15 0.954051 0.949709 0.005863 5
16 0.951632 0.945357 0.008218 31
17 0.950423 0.948741 0.009820 7
18 0.944377 0.945357 0.007556 32
19 0.955260 0.949468 0.010905 6
20 0.952842 0.947293 0.004076 16
21 0.954051 0.944873 0.009456 35
22 0.945586 0.947290 0.007683 19
23 0.946796 0.945115 0.006477 33
24 0.955260 0.947049 0.009508 21
25 0.951632 0.947775 0.005326 12
26 0.950423 0.948500 0.003561 9
27 0.944377 0.945598 0.007693 30
28 0.955260 0.946083 0.007437 27
29 0.950423 0.947292 0.007821 17
30 0.943168 0.947533 0.004095 15
31 0.951632 0.947774 0.006384 14
32 0.952842 0.946809 0.006391 23
33 0.950423 0.947291 0.007002 18
34 0.951632 0.946324 0.006699 26
35 0.954051 0.950434 0.005835 2

knn_params = [
    {"analyze__processor": ["count"], "analyze__analyze_lengths": [True, False], "analyze__stop_words": [None,"english"], "analyze__ngram_range": [(1,1), (1,2), (2,3) ], "fit__weights": ["uniform","distance"], "fit__n_neighbors": [5,10] },
    {"analyze__processor": ["hashing"], "analyze__analyze_lengths": [True, False], "analyze__stop_words": [None,"english"], "analyze__ngram_range": [(1,1), (1,2), (2,3) ], "fit__weights": ["uniform","distance"], "fit__n_neighbors": [5,10] },
    {"analyze__processor": ["tfidf"], "analyze__analyze_lengths": [True, False], "analyze__stop_words": [None,"english"], "analyze__ngram_range": [(1,1), (1,2), (2,3) ], "fit__weights": ["uniform","distance"], "fit__n_neighbors": [5,10] },
]
knn_pipeline = Pipeline(
    steps=[
        ("impute",SimpleImputer(strategy="constant",fill_value="")),
        ("process",ProcessText()),
        ("analyze",AnalyzeText()),
        ("fit",KNeighborsClassifier())
    ]
)
knn_clf = GridSearchCV(knn_pipeline,param_grid=knn_params,cv=cv,refit=refit,verbose=verbose)
knn_clf.fit(X_train.to_frame(),y_train)
y_pred = knn_clf.predict(X_test.to_frame())


print("METRICS\n---------------------------------------------------------------")
print("ACCURACY SCORE",accuracy_score(y_pred,y_test))
print("PRECISON SCORE",precision_score(y_pred,y_test))
print("RECALL SCORE",recall_score(y_pred,y_test))
print("ESTIMATORS\n----------------------------------------------------------")
resultsDF = pd.DataFrame(knn_clf.cv_results_)
print(resultsDF)

out[32]

Fitting 5 folds for each of 144 candidates, totalling 720 fits
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.606 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.622 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.613 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.600 total time= 0.2s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.645 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.647 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.636 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.613 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.640 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.591 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.593 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.577 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.588 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.615 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.620 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.600 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.606 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.622 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.606 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.622 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.613 total time= 0.2s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.600 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.645 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.647 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.636 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.613 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.640 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.591 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.593 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.577 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.588 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.615 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.620 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.600 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.606 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.622 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.606 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.622 total time= 0.2s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.613 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.600 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.645 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.647 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.636 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.613 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.640 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.591 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.593 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.577 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.588 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.615 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.620 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.600 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.606 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.622 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.606 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.622 total time= 0.2s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.613 total time= 0.2s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.583 total time= 0.2s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.600 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.645 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.647 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.636 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.613 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.640 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.591 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.593 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.577 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.588 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.615 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.620 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.600 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.606 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.622 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.606 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.622 total time= 0.2s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.613 total time= 0.2s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.600 total time= 0.2s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.645 total time= 0.2s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.647 total time= 0.2s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.636 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.613 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.640 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.591 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.593 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.577 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.583 total time= 0.2s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.588 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.615 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.620 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.600 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.606 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.622 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.606 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.622 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.613 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.600 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.645 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.647 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.636 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.613 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.640 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.591 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.593 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.577 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.588 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.615 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.620 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.600 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.606 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.622 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.606 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.622 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.613 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.600 total time= 0.2s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.645 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.647 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.636 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.613 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.640 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.591 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.593 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.577 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.588 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.615 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.620 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.600 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.606 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.622 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.606 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.622 total time= 0.2s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.613 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.600 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.645 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.647 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.636 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.613 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.640 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.591 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.593 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.577 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.583 total time= 0.2s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.588 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.615 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.620 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.600 total time= 0.2s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.606 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.622 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.606 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.622 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.613 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.600 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.645 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.647 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.636 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.613 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.640 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.591 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.593 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.577 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.588 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.615 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.620 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.600 total time= 0.2s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.606 total time= 0.2s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.622 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.606 total time= 0.2s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.622 total time= 0.2s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.613 total time= 0.2s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.583 total time= 0.2s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.600 total time= 0.2s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.645 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.647 total time= 0.2s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.636 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.613 total time= 0.2s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.640 total time= 0.2s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.591 total time= 0.2s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.593 total time= 0.2s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.577 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.588 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.615 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.620 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.600 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.606 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.622 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.606 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.622 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.613 total time= 0.2s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.583 total time= 0.2s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.600 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.645 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.647 total time= 0.2s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.636 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.613 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.640 total time= 0.2s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.591 total time= 0.2s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.593 total time= 0.2s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.577 total time= 0.2s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.583 total time= 0.2s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.588 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.615 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.620 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.600 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.606 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.622 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.606 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.622 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.613 total time= 0.2s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.600 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.645 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.647 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.636 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.613 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.640 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.591 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.593 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.577 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.588 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.615 total time= 0.2s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.620 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.600 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.606 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=count, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.622 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.606 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.622 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.613 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.600 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.645 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.647 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.636 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.613 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.640 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.591 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.593 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.577 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.588 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.615 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.620 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.600 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.606 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.622 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.606 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.622 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.613 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.600 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.645 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.647 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.636 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.613 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.640 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.591 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.593 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.577 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.588 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.615 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.620 total time= 0.2s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.600 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.606 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.622 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.606 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.622 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.613 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.600 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.645 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.647 total time= 0.2s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.636 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.613 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.640 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.591 total time= 0.2s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.593 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.577 total time= 0.2s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.583 total time= 0.2s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.588 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.615 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.620 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.600 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.606 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.622 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.606 total time= 0.2s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.622 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.613 total time= 0.2s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.600 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.645 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.647 total time= 0.2s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.636 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.613 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.640 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.591 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.593 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.577 total time= 0.2s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.588 total time= 0.2s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.615 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.620 total time= 0.2s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.600 total time= 0.2s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.606 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.622 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.606 total time= 0.2s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.622 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.613 total time= 0.2s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.600 total time= 0.2s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.645 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.647 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.636 total time= 0.2s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.613 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.640 total time= 0.2s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.591 total time= 0.2s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.593 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.577 total time= 0.2s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.588 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.615 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.620 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.600 total time= 0.2s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.606 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.622 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.606 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.622 total time= 0.2s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.613 total time= 0.2s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.583 total time= 0.2s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.600 total time= 0.2s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.645 total time= 0.2s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.647 total time= 0.2s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.636 total time= 0.3s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.613 total time= 0.2s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.640 total time= 0.2s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.591 total time= 0.2s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.593 total time= 0.3s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.577 total time= 0.2s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.588 total time= 0.2s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.615 total time= 0.2s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.620 total time= 0.2s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.600 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.606 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.622 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.606 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.622 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.613 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.600 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.645 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.647 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.636 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.613 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.640 total time= 0.2s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.591 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.593 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.577 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.588 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.615 total time= 0.2s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.620 total time= 0.2s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.600 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.606 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.622 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.606 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.622 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.613 total time= 0.2s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.600 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.645 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.647 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.636 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.613 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.640 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.591 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.593 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.577 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.588 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.615 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.620 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.600 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.606 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.622 total time= 0.2s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.606 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.622 total time= 0.2s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.613 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.583 total time= 0.2s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.600 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.645 total time= 0.2s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.647 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.636 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.613 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.640 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.591 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.593 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.577 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.588 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.615 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.620 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.600 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.606 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.622 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.606 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.622 total time= 0.2s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.613 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.600 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.645 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.647 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.636 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.613 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.640 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.591 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.593 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.577 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.588 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.615 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.620 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.600 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.606 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.622 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.606 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.622 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.613 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.600 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.645 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.647 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.636 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.613 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.640 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.591 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.593 total time= 0.2s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.577 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.588 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.615 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.620 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.600 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.606 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.622 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.606 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.622 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.613 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.600 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.645 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.647 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.636 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.613 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.640 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.591 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.593 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.577 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.588 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.615 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.620 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.600 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.606 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=hashing, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.622 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.606 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.622 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.613 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.600 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.645 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.647 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.636 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.613 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.640 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.591 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.593 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.577 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.583 total time= 0.2s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.588 total time= 0.2s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.615 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.620 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.600 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.606 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.622 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.606 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.622 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.613 total time= 0.3s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.600 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.645 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.647 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.636 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.613 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.640 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.591 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.593 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.577 total time= 0.3s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.588 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.615 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.620 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.600 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.606 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.622 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.606 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.622 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.613 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.600 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.645 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.647 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.636 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.613 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.640 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.591 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.593 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.577 total time= 0.2s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.588 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.615 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.620 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.600 total time= 0.2s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.606 total time= 0.2s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.622 total time= 0.2s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.606 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.622 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.613 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.600 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.645 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.647 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.636 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.613 total time= 0.2s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.640 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.591 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.593 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.577 total time= 0.3s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.588 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.615 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.620 total time= 0.2s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.600 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.606 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.622 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.606 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.622 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.613 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.600 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.645 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.647 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.636 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.613 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.640 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.591 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.593 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.577 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.588 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.615 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.620 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.600 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.606 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.622 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.606 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.622 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.613 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.600 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.645 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.647 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.636 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.613 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.640 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.591 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.593 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.577 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.588 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.615 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.620 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.600 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.606 total time= 0.2s
[CV 5/5] END analyze__analyze_lengths=True, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.622 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.606 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.622 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.613 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.600 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.645 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.647 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.636 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.613 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.640 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.591 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.593 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.577 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.588 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.615 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.620 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.600 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.606 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.622 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.606 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.622 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.613 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.583 total time= 0.2s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.600 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.645 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.647 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.636 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.613 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.640 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.591 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.593 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.577 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.588 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.615 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.620 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.600 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.606 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 1), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.622 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.606 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.622 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.613 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.600 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.645 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.647 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.636 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.613 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.640 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.591 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.593 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.577 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.588 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.615 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.620 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.600 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.606 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.622 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.606 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.622 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.613 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.600 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.645 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.647 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.636 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.613 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.640 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.591 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.593 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.577 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.588 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.615 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.620 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.600 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.606 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(1, 2), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.622 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.606 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.622 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.613 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=uniform;, score=0.600 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.645 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.647 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.636 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.613 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=5, fit__weights=distance;, score=0.640 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.591 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.593 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.577 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=uniform;, score=0.588 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.615 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.620 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.600 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.606 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=None, fit__n_neighbors=10, fit__weights=distance;, score=0.622 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.606 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.622 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.613 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.583 total time= 0.2s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=uniform;, score=0.600 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.645 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.647 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.636 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.613 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=5, fit__weights=distance;, score=0.640 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.591 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.593 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.577 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.583 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=uniform;, score=0.588 total time= 0.1s
[CV 1/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.615 total time= 0.1s
[CV 2/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.620 total time= 0.1s
[CV 3/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.600 total time= 0.1s
[CV 4/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.606 total time= 0.1s
[CV 5/5] END analyze__analyze_lengths=False, analyze__ngram_range=(2, 3), analyze__processor=tfidf, analyze__stop_words=english, fit__n_neighbors=10, fit__weights=distance;, score=0.622 total time= 0.1s
METRICS
---------------------------------------------------------------
ACCURACY SCORE 0.6541062801932367
PRECISON SCORE 1.0
RECALL SCORE 0.450920245398773
ESTIMATORS
----------------------------------------------------------
mean_fit_time std_fit_time mean_score_time std_score_time \
0 0.153121 0.005978 0.085977 0.005015
1 0.159138 0.006522 0.070097 0.002730
2 0.148528 0.005853 0.081289 0.005558
3 0.146661 0.005138 0.066140 0.003318
4 0.146872 0.008343 0.086328 0.007269
.. ... ... ... ...
139 0.131601 0.004538 0.058875 0.002371
140 0.151529 0.034906 0.070988 0.004720
141 0.128755 0.006582 0.060049 0.003383
142 0.131560 0.004631 0.075782 0.002446
143 0.130182 0.002578 0.063782 0.000888

param_analyze__analyze_lengths param_analyze__ngram_range \
0 True (1, 1)
1 True (1, 1)
2 True (1, 1)
3 True (1, 1)
4 True (1, 1)
.. ... ...
139 False (2, 3)
140 False (2, 3)
141 False (2, 3)
142 False (2, 3)
143 False (2, 3)

param_analyze__processor param_analyze__stop_words param_fit__n_neighbors \
0 count None 5
1 count None 5
2 count None 10
3 count None 10
4 count english 5
.. ... ... ...
139 tfidf None 10
140 tfidf english 5
141 tfidf english 5
142 tfidf english 10
143 tfidf english 10

param_fit__weights params \
0 uniform {'analyze__analyze_lengths': True, 'analyze__n...
1 distance {'analyze__analyze_lengths': True, 'analyze__n...
2 uniform {'analyze__analyze_lengths': True, 'analyze__n...
3 distance {'analyze__analyze_lengths': True, 'analyze__n...
4 uniform {'analyze__analyze_lengths': True, 'analyze__n...
.. ... ...
139 distance {'analyze__analyze_lengths': False, 'analyze__...
140 uniform {'analyze__analyze_lengths': False, 'analyze__...
141 distance {'analyze__analyze_lengths': False, 'analyze__...
142 uniform {'analyze__analyze_lengths': False, 'analyze__...
143 distance {'analyze__analyze_lengths': False, 'analyze__...

split0_test_score split1_test_score split2_test_score \
0 0.606280 0.621524 0.613059
1 0.644928 0.646917 0.636034
2 0.590580 0.592503 0.576784
3 0.614734 0.620314 0.599758
4 0.606280 0.621524 0.613059
.. ... ... ...
139 0.614734 0.620314 0.599758
140 0.606280 0.621524 0.613059
141 0.644928 0.646917 0.636034
142 0.590580 0.592503 0.576784
143 0.614734 0.620314 0.599758

split3_test_score split4_test_score mean_test_score std_test_score \
0 0.582830 0.599758 0.604690 0.013101
1 0.613059 0.639661 0.636120 0.012153
2 0.582830 0.587666 0.586072 0.005675
3 0.605804 0.621524 0.612427 0.008422
4 0.582830 0.599758 0.604690 0.013101
.. ... ... ... ...
139 0.605804 0.621524 0.612427 0.008422
140 0.582830 0.599758 0.604690 0.013101
141 0.613059 0.639661 0.636120 0.012153
142 0.582830 0.587666 0.586072 0.005675
143 0.605804 0.621524 0.612427 0.008422

rank_test_score
0 73
1 1
2 109
3 37
4 73
.. ...
139 37
140 73
141 1
142 109
143 37

[144 rows x 19 columns]

print("Figuring best estimators to build neural net:\n---------------------------------------------------------------------")
print(ridge_clf.best_params_)
print(sgd_clf.best_params_)
pipe = pipeline = Pipeline(
    steps=[
        ("impute",SimpleImputer(strategy="constant",fill_value="")),
        ("process",ProcessText()),
        ("analyze",AnalyzeText(ngram_range=(1,2),processor="count")),
    ]
)
out = pipe.fit_transform(X_train.to_frame())
print(out.shape)
hidden_layer_sizes = out.shape[1]*2/3 # The number of hidden neurons should be 2/3 the size of the input layer, plus the size of the output layer.
print("Hidden Layer Sizes:",np.floor(hidden_layer_sizes))

out[33]

Figuring best estimators to build neural net:
---------------------------------------------------------------------
{'analyze__analyze_lengths': True, 'analyze__ngram_range': (1, 1), 'analyze__processor': 'count', 'analyze__stop_words': None}
{'analyze__analyze_lengths': False, 'analyze__ngram_range': (1, 2), 'analyze__processor': 'count', 'analyze__stop_words': None}
(4136, 34907)
Hidden Layer Sizes: 23271.0

mlp_params = [
    {"analyze__processor": ["count"], "analyze__analyze_lengths": [True, False], "analyze__stop_words": [None,"english"], "analyze__ngram_range": [(1,1), (1,2), (2,3) ] },
    {"analyze__processor": ["hashing"], "analyze__analyze_lengths": [True, False], "analyze__stop_words": [None,"english"], "analyze__ngram_range": [(1,1), (1,2), (2,3) ]},
    {"analyze__processor": ["tfidf"], "analyze__analyze_lengths": [True, False], "analyze__stop_words": [None,"english"], "analyze__ngram_range": [(1,1), (1,2), (2,3) ] },
]
knn_pipeline = Pipeline(
    steps=[
        ("impute",SimpleImputer(strategy="constant",fill_value="")),
        ("process",ProcessText()),
        ("analyze",AnalyzeText(stop_words=None,analyze_lengths=False,processor="count",ngram_range=(1,2))),
        ("fit",MLPClassifier(hidden_layer_sizes=23000))
    ]
)
mlp_clf = knn_pipeline
mlp_clf.fit(X_train.to_frame(),y_train)
y_pred = mlp_clf.predict(X_test.to_frame())


print("METRICS\n---------------------------------------------------------------")
print("ACCURACY SCORE",accuracy_score(y_pred,y_test))
print("PRECISON SCORE",precision_score(y_pred,y_test))
print("RECALL SCORE",recall_score(y_pred,y_test))
print("ESTIMATORS\n----------------------------------------------------------")
resultsDF = pd.DataFrame(mlp_clf.cv_results_)
print(resultsDF)

out[34]

out[35]