Raihan Tarmidi

Raihan Tarmidi

A budding data scientist interested in personal finance, guitar and writing!

My Titanic Practice

Read me first!

This is a practice post to demonstrate a few things namely:

  1. How to convert a jupyter notebook to jekyll markdown
  2. Fixing bugs like importing images
  3. Adding additional markdown stuff on top of the notebook to see if it’s working
# Import Dependencies
%matplotlib inline

# Start Python Imports
import math, time, random, datetime

# Data Manipulation
import numpy as np
import pandas as pd

# Visualization 
import matplotlib.pyplot as plt
import missingno
import seaborn as sns
plt.style.use('seaborn-whitegrid')

# Preprocessing
from sklearn.preprocessing import OneHotEncoder, LabelEncoder, label_binarize

# Machine learning
import catboost
from sklearn.model_selection import train_test_split
from sklearn import model_selection, tree, preprocessing, metrics, linear_model
from sklearn.svm import LinearSVC
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import LinearRegression, LogisticRegression, SGDClassifier
from sklearn.tree import DecisionTreeClassifier
from catboost import CatBoostClassifier, Pool, cv

# Let's be rebels and ignore warnings for now
import warnings
warnings.filterwarnings('ignore')

import ipywidgets

### your other options
# pd.set_option('display.max_rows', 50)
# pd.set_option('max_colwidth', None)
# pd.set_option('max_columns', None)

# pd.reset_option('display.max_rows')

# !pip install ipywidgets

Get Data, EDA

train = pd.read_csv('data/train.csv')
test = pd.read_csv('data/test.csv')
gender_submission = pd.read_csv('data/gender_submission.csv')

train
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 NaN S
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 C85 C
2 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S
4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S
... ... ... ... ... ... ... ... ... ... ... ... ...
886 887 0 2 Montvila, Rev. Juozas male 27.0 0 0 211536 13.0000 NaN S
887 888 1 1 Graham, Miss. Margaret Edith female 19.0 0 0 112053 30.0000 B42 S
888 889 0 3 Johnston, Miss. Catherine Helen "Carrie" female NaN 1 2 W./C. 6607 23.4500 NaN S
889 890 1 1 Behr, Mr. Karl Howell male 26.0 0 0 111369 30.0000 C148 C
890 891 0 3 Dooley, Mr. Patrick male 32.0 0 0 370376 7.7500 NaN Q

891 rows × 12 columns

train.columns

Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',
       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],
      dtype='object')

Basic Exploration

  1. 891 rows, 12 columns ie 891 people, 12 variables about each person

  2. Columns Looking at the Kaggle Site

    1. Survived” column, 1 = Yes, 0 = No
    2. pclass: ticket class, 1 = 1st, 2 = 2nd, 3 = 3rd
    3. sex: male or female
    4. age: in years
    5. sibsp: # of siblings or spouses aboard the Titanic
    6. parch: # of parents or children aboard the Titanic
    7. ticket: ticket number
    8. fare: passenger fare
    9. cabin: cabin number
    10. embarked: port of embarkation, C = Cherbourg, Q = Queenston, S = Southampton
    11. PassengerId
    12. Name
test
PassengerId Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
0 892 3 Kelly, Mr. James male 34.5 0 0 330911 7.8292 NaN Q
1 893 3 Wilkes, Mrs. James (Ellen Needs) female 47.0 1 0 363272 7.0000 NaN S
2 894 2 Myles, Mr. Thomas Francis male 62.0 0 0 240276 9.6875 NaN Q
3 895 3 Wirz, Mr. Albert male 27.0 0 0 315154 8.6625 NaN S
4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female 22.0 1 1 3101298 12.2875 NaN S
... ... ... ... ... ... ... ... ... ... ... ...
413 1305 3 Spector, Mr. Woolf male NaN 0 0 A.5. 3236 8.0500 NaN S
414 1306 1 Oliva y Ocana, Dona. Fermina female 39.0 0 0 PC 17758 108.9000 C105 C
415 1307 3 Saether, Mr. Simon Sivertsen male 38.5 0 0 SOTON/O.Q. 3101262 7.2500 NaN S
416 1308 3 Ware, Mr. Frederick male NaN 0 0 359309 8.0500 NaN S
417 1309 3 Peter, Master. Michael J male NaN 1 1 2668 22.3583 NaN C

418 rows × 11 columns

test data has 418 rows but 11 columns because ‘Survived’ is the class we are trying to predict

gender_submission
PassengerId Survived
0 892 0
1 893 1
2 894 0
3 895 0
4 896 1
... ... ...
413 1305 0
414 1306 1
415 1307 0
416 1308 0
417 1309 0

418 rows × 2 columns

The gender submission is an example of what the submission file should look like. The reason why it is called gender is because this result was created by assuming that any female in the TEST set would survive

Exploring the training dataset

train.Age.plot.hist()
test.Age.plot.hist()

<AxesSubplot:ylabel='Frequency'>

png

train.plot()

<AxesSubplot:>

png

Looking for missing values

missingno.matrix(train)

<AxesSubplot:>

png

train.isnull().sum() # get null counts of all columns

PassengerId      0
Survived         0
Pclass           0
Name             0
Sex              0
Age            177
SibSp            0
Parch            0
Ticket           0
Fare             0
Cabin          687
Embarked         2
dtype: int64

# print(train.Age.describe()) 
print(train.Cabin.describe()) # get counts for specific column


count     204
unique    147
top        G6
freq        4
Name: Cabin, dtype: object

  1. We can see that Age and Cabin has a lot of missing data. We can double check using describe to see the count. There are 891 rows but there are only 714 counted. So 177 rows missing

  2. Cabin might not even be worth it to check on seeing how there almost no data

Creating 2 empty dataframes

1 for continuous values, the other for DISCRETIZED continuous values

df_bin = pd.DataFrame()
df_con = pd.DataFrame()

Looking at the datatypes in the df

train.dtypes

PassengerId      int64
Survived         int64
Pclass           int64
Name            object
Sex             object
Age            float64
SibSp            int64
Parch            int64
Ticket          object
Fare           float64
Cabin           object
Embarked        object
dtype: object

Looking through each column/feature

Target Feature: Survived

fig = plt.figure(figsize = (20,1))
sns.countplot(y='Survived', data= train)
print(train.Survived.value_counts())

0    549
1    342
Name: Survived, dtype: int64

png

sns.displot(train, x = 'Survived')

<seaborn.axisgrid.FacetGrid at 0x25f9662ef70>

png

this is not a good way to plot the Survived variabel

  1. It should be categorical: 1 = Survived, 0 = died
  2. Its not clear to people

convert Survived to boolean would be better

# train['s2'] = train['Survived'].apply(lambda x: 'yes' if x==1 else 'no')
train['s2'] = train['Survived'].map({True:'yes', False:'no'})

print(train['s2'].value_counts())
sns.displot(train, x = 's2')

no     549
yes    342
Name: s2, dtype: int64




<seaborn.axisgrid.FacetGrid at 0x25f966e3130>

png

# add to our dfs
# df_bin['Survived'] = train['s2']
# df_con['Survived'] = train['s2']

df_bin['Survived'] = train['Survived']
df_con['Survived'] = train['Survived']

Feature exploration: CPNS

  1. Plot: is it discrete or continuous?
  2. Check null values
  3. Check the counts
  4. Summary stats if needed

Feature: Pclass

ticket class of passenger

1 = 1st, 2 = 2nd, 3 = 3rd

plot first

sns.displot(train.Pclass)

<seaborn.axisgrid.FacetGrid at 0x25f9675d5e0>

png

train.Pclass.isnull().sum() # no null vals

0

train.Pclass.value_counts()

3    491
1    216
2    184
Name: Pclass, dtype: int64

no missing vals so lets add it to our sub dataframes

df_bin['Pclass'] = train['Pclass']
df_con['Pclass'] = train['Pclass']

Feature: Name

name of passenger

  1. doesnt make sense to plot names
# sns.displot(train.Name) # does not make sense to plot

train.Name.isnull().sum()

0

print(train.Name.value_counts())
print(train.Name.describe()) # 891 unique values

Myhrman, Mr. Pehr Fabian Oliver Malkolm             1
Farrell, Mr. James                                  1
Zabour, Miss. Hileni                                1
Troutt, Miss. Edwina Celia "Winnie"                 1
Mayne, Mlle. Berthe Antonine ("Mrs de Villiers")    1
                                                   ..
Foo, Mr. Choong                                     1
O'Brien, Mr. Thomas                                 1
Lahoud, Mr. Sarkis                                  1
Yasbeck, Mr. Antoni                                 1
Staneff, Mr. Ivan                                   1
Name: Name, Length: 891, dtype: int64
count                                         891
unique                                        891
top       Myhrman, Mr. Pehr Fabian Oliver Malkolm
freq                                            1
Name: Name, dtype: object
  • Every passenger has a unique name
  • We can reduce the differences by removing Miss, Mrs or Mr since we already have Sex
  • too many values, so we wont include this in our dataframes

Feature: Sex

  • Male of Female
sns.countplot(x='Sex', data=train)

<AxesSubplot:xlabel='Sex', ylabel='count'>

png

train.Sex.isnull().sum()

0

train.Sex.value_counts()

male      577
female    314
Name: Sex, dtype: int64

train['Sex'].describe()

count      891
unique       2
top       male
freq       577
Name: Sex, dtype: object

# add to df
df_bin['Sex'] = train['Sex']
df_bin['Sex'] = np.where(df_bin['Sex'] == 'female', 1, 0) # change sex to 0 for male, 1 for female

df_con['Sex'] = train['Sex']

It seems like dbourke likes to change the discrete variables to int. Might be because later when we are training the model, numbers are more easier to process.

df_bin['Sex']
df_con['Sex']

0        male
1      female
2      female
3      female
4        male
        ...  
886      male
887    female
888    female
889      male
890      male
Name: Sex, Length: 891, dtype: object

fig = plt.figure(figsize=(10,10))
sns.distplot(df_bin.loc[df_bin['Survived'] == 1]['Sex'], kde_kws={'label': 'Survived'})
sns.distplot(df_bin.loc[df_bin['Survived'] == 0]['Sex'], kde_kws={'label': 'Did not survive'})

plt.legend() # legend to see the legend
plt.show()

png

  1. Not many ppl survived
  2. Of those that did, more were female (because sex == 1 means female)

Feature: Age

sns.histplot(data=train, x='Age', binwidth=5)

<AxesSubplot:xlabel='Age', ylabel='Count'>

png

train.Age.isnull().sum()

177

# train.Age.value_counts() # not useful because numeric
train.Age.describe()

count    714.000000
mean      29.699118
std       14.526497
min        0.420000
25%       20.125000
50%       28.000000
75%       38.000000
max       80.000000
Name: Age, dtype: float64
  1. 177/891 rows missing almost 25%
  2. what to do?
    a. remove the rows
    b. replace with the mean (30)
train['Age'] = train['Age'].fillna(value= 30)
train.Age.isnull().sum()

0

train['Age'] = train['Age'].astype(int)
train['Age']

0      22
1      38
2      26
3      35
4      35
       ..
886    27
887    19
888    30
889    26
890    32
Name: Age, Length: 891, dtype: int32


# Once the Age values have been fixed up, we can add them to our sub dataframes.
df_bin['Age'] = pd.cut(train['Age'], 8) # bucketed/binned into different categories
df_con['Age'] = train['Age'] # non-bucketed

df_bin.Age.value_counts() # has a (-0.08, 10.0) bin?

train.Age.describe() # min is 0 max is 80 which makes sense
# the bin is just weird

count    891.000000
mean      29.742985
std       13.011583
min        0.000000
25%       22.000000
50%       30.000000
75%       35.000000
max       80.000000
Name: Age, dtype: float64

function to create count and distribution visualisations

  • since we’ve been repeating ourselves alot
def plot_count_dist(data, bin_df, label_column, target_column, figsize=(20, 5), use_bin_df=False):
    """
    Function to plot counts and distributions of a label variable and 
    target variable side by side.
    ::param_data:: = target dataframe
    ::param_bin_df:: = binned dataframe for countplot
    ::param_label_column:: = binary labelled column
    ::param_target_column:: = column you want to view counts and distributions
    ::param_figsize:: = size of figure (width, height)
    ::param_use_bin_df:: = whether or not to use the bin_df, default False
    """
    if use_bin_df: 
        fig = plt.figure(figsize=figsize)
        plt.subplot(1, 2, 1)
        sns.countplot(y=target_column, data=bin_df);
        plt.subplot(1, 2, 2)
        sns.distplot(data.loc[data[label_column] == 1][target_column], 
                     kde_kws={"label": "Survived"});
        sns.distplot(data.loc[data[label_column] == 0][target_column], 
                     kde_kws={"label": "Did not survive"});
        plt.legend()
    else:
        fig = plt.figure(figsize=figsize)
        plt.subplot(1, 2, 1)
        sns.countplot(y=target_column, data=data);
        plt.subplot(1, 2, 2)
        sns.distplot(data.loc[data[label_column] == 1][target_column], 
                     kde_kws={"label": "Survived"});
        sns.distplot(data.loc[data[label_column] == 0][target_column], 
                     kde_kws={"label": "Did not survive"});

        plt.legend()

Feature: SibSp

  • number of sibling or spouses the passenger has on the titanic
sns.displot(data = train, x = 'SibSp')

<seaborn.axisgrid.FacetGrid at 0x25f969c9c40>

png

def cns(data, target_column):
    """
    function that
    1. COUNTS: get the value_counts of the target col
    2. NULLS: checks the amount of nulls in the col
    3. Stats: Check the stats of the columns
    """
    print('----------------------------')
    print(data[target_column].value_counts())

    print('----------------------------')
    print('NUMBER OF NULLS: ' + str(train[target_column].isnull().sum()))

    print('----------------------------')
    print(train[target_column].describe())


cns(train, 'SibSp')

----------------------------
0    608
1    209
2     28
4     18
3     16
8      7
5      5
Name: SibSp, dtype: int64
----------------------------
NUMBER OF NULLS: 0
----------------------------
count    891.000000
mean       0.523008
std        1.102743
min        0.000000
25%        0.000000
50%        0.000000
75%        1.000000
max        8.000000
Name: SibSp, dtype: float64

plot_count_dist(train,
                bin_df = df_bin,
                label_column = 'Survived',
                target_column = 'SibSp',
                figsize = (20,10)
)

png

  1. Those with 1 sibling or spouse on the titanic is more likely to survive. This might most likely be because if you were a couple, one of them would more likely be saved (husband lets the wife get on the safety boat)

  2. Overall most people are alone on the ship, and its not surpsising that the most of them in that group are both alive and dead

df_con['SibSp'] = train['SibSp']
df_bin['SibSp'] = train['SibSp']

Feature: Parch

  • Number of parents / children the passenger has on the titanic
  • similar to SibSp,
cns(train, 'Parch')

----------------------------
0    678
1    118
2     80
3      5
5      5
4      4
6      1
Name: Parch, dtype: int64
----------------------------
NUMBER OF NULLS: 0
----------------------------
count    891.000000
mean       0.381594
std        0.806057
min        0.000000
25%        0.000000
50%        0.000000
75%        0.000000
max        6.000000
Name: Parch, dtype: float64

def plot_count_distv2(data, bin_df, label_column, target_column, figsize=(20, 5), use_bin_df=False):
    """
    Function to plot counts and distributions of a label variable and 
    target variable side by side.
    ::param_data:: = target dataframe
    ::param_bin_df:: = binned dataframe for countplot
    ::param_label_column:: = binary labelled column
    ::param_target_column:: = column you want to view counts and distributions
    ::param_figsize:: = size of figure (width, height)
    ::param_use_bin_df:: = whether or not to use the bin_df, default False
    """
    if use_bin_df: 
        fig, axs = plt.subplots(nrows=1, ncols=2, figsize = figsize)

        sns.countplot(y=target_column, data=bin_df);
        plt.subplot(1, 2, 2)
        sns.distplot(data.loc[data[label_column] == 1][target_column], 
                     kde_kws={"label": "Survived"});
        sns.distplot(data.loc[data[label_column] == 0][target_column], 
                     kde_kws={"label": "Did not survive"});
        plt.legend()
    else:
        # fig = plt.figure(figsize=figsize)
        fig, axs = plt.subplots(nrows=1, ncols=2, figsize = figsize)
        # plt.subplot(1, 2, 1)
        sns.countplot( y=target_column, data=data, ax = axs[0])
        # sns.distplot(data.loc[data[label_column] == 1][target_column], 
        #              kde_kws={"label": "Survived"});
        # sns.distplot(data.loc[data[label_column] == 0][target_column], 
        #              kde_kws={"label": "Did not survive"});
        # plt.subplot(1, 2, 2)
        sns.histplot(data=train,
                    x = target_column,
                    kde=True,
                    multiple='dodge',
                    hue= label_column,
                    ax = axs[1],
                    stat = 'count'
                    )
        plt.legend()


plot_count_distv2(train,
                bin_df = df_bin,
                label_column='Survived',
                target_column='Parch',
                figsize=(10,5),                
                )

No handles with labels found to put in legend.

png

plot_count_dist(train,
                bin_df = df_bin,
                label_column='Survived',
                target_column='Parch',
                figsize=(10,5),                
                )

png

what does this show about Parch?

  1. Most did not have any parents or children on the ship with them
  2. of those that had at 1 o 2, more from that group survived than died. Might be because they are more likely to save children?
## adding to our subset dataframes

df_con['Parch'] = train['Parch']
df_bin['Parch'] = train['Parch']

Feature: Ticket

  • ticket number of the passenger
  • doubt theres anything useful from this, but lets see
cns(data = train, target_column='Ticket')

----------------------------
1601                 7
CA. 2343             7
347082               7
CA 2144              6
347088               6
                    ..
Fa 265302            1
STON/O 2. 3101289    1
349242               1
349247               1
29751                1
Name: Ticket, Length: 681, dtype: int64
----------------------------
NUMBER OF NULLS: 0
----------------------------
count      891
unique     681
top       1601
freq         7
Name: Ticket, dtype: object
  • Unique count of 681, might be bad to do countplot
sns.countplot(data = train, y = 'Ticket') # ugly and takes awhile to plot

<AxesSubplot:xlabel='count', ylabel='Ticket'>

png

How to reduce the ticket feature?

  • Not sure really. But supposedly the hint is similar to reducing Name
  • I guess you can try to remove the other strings and only filter by numbers, and then to reduce it even further, separate into buckets

Feature: Fare

  • How much the ticket costs
cns(data=train, target_column='Fare')

----------------------------
8.0500     43
13.0000    42
7.8958     38
7.7500     34
26.0000    31
           ..
50.4958     1
13.8583     1
8.4583      1
7.7250      1
7.5208      1
Name: Fare, Length: 248, dtype: int64
----------------------------
NUMBER OF NULLS: 0
----------------------------
count    891.000000
mean      32.204208
std       49.693429
min        0.000000
25%        7.910400
50%       14.454200
75%       31.000000
max      512.329200
Name: Fare, dtype: float64
  • Again, alot of unique values so would be bad to plot
sns.countplot(data = train, x = 'Fare')

<AxesSubplot:xlabel='Fare', ylabel='count'>

png

train.Fare.dtype

dtype('float64')
  • Fare is a float, so lets add it to our continuous df
  • categorise them into buckets to add to df_bin
df_con['Fare'] = train['Fare']
df_bin['Fare'] = pd.cut(train['Fare'], 5)

df_bin.Fare.value_counts()

(-0.512, 102.466]     838
(102.466, 204.932]     33
(204.932, 307.398]     17
(409.863, 512.329]      3
(307.398, 409.863]      0
Name: Fare, dtype: int64

### looking at the discretized fare stats!

cns(data=df_bin, target_column='Fare')

----------------------------
(-0.512, 102.466]     838
(102.466, 204.932]     33
(204.932, 307.398]     17
(409.863, 512.329]      3
(307.398, 409.863]      0
Name: Fare, dtype: int64
----------------------------
NUMBER OF NULLS: 0
----------------------------
count    891.000000
mean      32.204208
std       49.693429
min        0.000000
25%        7.910400
50%       14.454200
75%       31.000000
max      512.329200
Name: Fare, dtype: float64

plot_count_dist(data=train,
            bin_df=df_bin,
            label_column='Survived',
            target_column='Fare',
            use_bin_df=True,               
                )

png

### still displaying bad weird graphs

plot_count_distv2(data=df_bin,
            bin_df=df_bin,
            label_column='Survived',
            target_column='Fare',
            # use_bin_df=True
                )

No handles with labels found to put in legend.

png

How would you change the Fare bins?

  • Looking at the value_counts(), we can see that the majority of the passengers are in one group; might be better to split into groups of 10?

Feature: Cabin

cns(data=train, target_column='Cabin')

----------------------------
G6             4
C23 C25 C27    4
B96 B98        4
D              3
E101           3
              ..
E49            1
B42            1
C46            1
B69            1
D10 D12        1
Name: Cabin, Length: 147, dtype: int64
----------------------------
NUMBER OF NULLS: 687
----------------------------
count     204
unique    147
top        G6
freq        4
Name: Cabin, dtype: object

sns.countplot(data = train, x = 'Cabin')

<AxesSubplot:xlabel='Cabin', ylabel='count'>

png

train.Cabin

0       NaN
1       C85
2       NaN
3      C123
4       NaN
       ... 
886     NaN
887     B42
888     NaN
889    C148
890     NaN
Name: Cabin, Length: 891, dtype: object
  1. Cabin has too many null values so we wont use it
  2. How do we fill up these values though?
print(train.Cabin.unique())
print(train.Cabin.value_counts())

[nan 'C85' 'C123' 'E46' 'G6' 'C103' 'D56' 'A6' 'C23 C25 C27' 'B78' 'D33'
 'B30' 'C52' 'B28' 'C83' 'F33' 'F G73' 'E31' 'A5' 'D10 D12' 'D26' 'C110'
 'B58 B60' 'E101' 'F E69' 'D47' 'B86' 'F2' 'C2' 'E33' 'B19' 'A7' 'C49'
 'F4' 'A32' 'B4' 'B80' 'A31' 'D36' 'D15' 'C93' 'C78' 'D35' 'C87' 'B77'
 'E67' 'B94' 'C125' 'C99' 'C118' 'D7' 'A19' 'B49' 'D' 'C22 C26' 'C106'
 'C65' 'E36' 'C54' 'B57 B59 B63 B66' 'C7' 'E34' 'C32' 'B18' 'C124' 'C91'
 'E40' 'T' 'C128' 'D37' 'B35' 'E50' 'C82' 'B96 B98' 'E10' 'E44' 'A34'
 'C104' 'C111' 'C92' 'E38' 'D21' 'E12' 'E63' 'A14' 'B37' 'C30' 'D20' 'B79'
 'E25' 'D46' 'B73' 'C95' 'B38' 'B39' 'B22' 'C86' 'C70' 'A16' 'C101' 'C68'
 'A10' 'E68' 'B41' 'A20' 'D19' 'D50' 'D9' 'A23' 'B50' 'A26' 'D48' 'E58'
 'C126' 'B71' 'B51 B53 B55' 'D49' 'B5' 'B20' 'F G63' 'C62 C64' 'E24' 'C90'
 'C45' 'E8' 'B101' 'D45' 'C46' 'D30' 'E121' 'D11' 'E77' 'F38' 'B3' 'D6'
 'B82 B84' 'D17' 'A36' 'B102' 'B69' 'E49' 'C47' 'D28' 'E17' 'A24' 'C50'
 'B42' 'C148']
G6             4
C23 C25 C27    4
B96 B98        4
D              3
E101           3
              ..
E49            1
B42            1
C46            1
B69            1
D10 D12        1
Name: Cabin, Length: 147, dtype: int64
  • Seeing that there isnt a very strong bias towards a certain ticket, it doesnt make sense to assign the null values to the median ie C23, C25, C27. But if no choice, then do that

Feature: Embarked

  • Port where the passenger boarded the Titanic
  • Key: C = Cherbourg, Q = Queenstown, S = Southampton
cns(data=train, target_column = 'Embarked')

----------------------------
S    644
C    168
Q     77
Name: Embarked, dtype: int64
----------------------------
NUMBER OF NULLS: 2
----------------------------
count     889
unique      3
top         S
freq      644
Name: Embarked, dtype: object
  • Reading up on its voyage. The titanic went: Southampton -> Cherbourg -> Queenstown, which kinda makes sense
  • most would embark on the maiden voyage
sns.countplot(data=train, x='Embarked')

<AxesSubplot:xlabel='Embarked', ylabel='count'>

png

Embarked has 2 null vals

  1. We could either drop or assign a val to these rows
  2. Naiively, i would assign to S. Otherwise, we could do a random assignment
  3. For now, we will dorp
%config Completer.use_jedi = False

# add to our dfs
df_bin['Embarked'] = train['Embarked']
df_con['Embarked'] = train['Embarked']

print(len(df_bin))

df_con = df_con.dropna(subset = ['Embarked'])
df_bin = df_bin.dropna(subset = ['Embarked'])

print(len(df_bin))


891
889

Feature Encoding

  • We have our sub dataframes, now we encode features where they are ready to be used by our machine learning models
    1. Encode our binned dataframe (df_bin) with one hot encoding
    2. Encode our continuous dataframe (df_con) USING OneHotEncoder from sklearn
  • kinda the same as pd.get_dummies, but try it out instead. More info about the differences (here)[https://stackoverflow.com/questions/36631163/what-are-the-pros-and-cons-between-get-dummies-pandas-and-onehotencoder-sciki]. Long story short, OneHotEncoder if you are preprocessing for machine learning models.
df_bin
Survived Pclass Sex Age SibSp Parch Fare Embarked
0 0 3 0 (20.0, 30.0] 1 0 (-0.512, 102.466] S
1 1 1 1 (30.0, 40.0] 1 0 (-0.512, 102.466] C
2 1 3 1 (20.0, 30.0] 0 0 (-0.512, 102.466] S
3 1 1 1 (30.0, 40.0] 1 0 (-0.512, 102.466] S
4 0 3 0 (30.0, 40.0] 0 0 (-0.512, 102.466] S
... ... ... ... ... ... ... ... ...
886 0 2 0 (20.0, 30.0] 0 0 (-0.512, 102.466] S
887 1 1 1 (10.0, 20.0] 0 0 (-0.512, 102.466] S
888 0 3 1 (20.0, 30.0] 1 2 (-0.512, 102.466] S
889 1 1 0 (20.0, 30.0] 0 0 (-0.512, 102.466] C
890 0 3 0 (30.0, 40.0] 0 0 (-0.512, 102.466] Q

889 rows × 8 columns



# one hot encoding for binned variables
one_hot_cols = df_bin.columns.tolist()
one_hot_cols.remove('Survived')
df_bin_enc = pd.get_dummies(df_bin, columns=one_hot_cols)

df_bin_enc.head()
Survived Pclass_1 Pclass_2 Pclass_3 Sex_0 Sex_1 Age_(-0.08, 10.0] Age_(10.0, 20.0] Age_(20.0, 30.0] Age_(30.0, 40.0] ... Parch_5 Parch_6 Fare_(-0.512, 102.466] Fare_(102.466, 204.932] Fare_(204.932, 307.398] Fare_(307.398, 409.863] Fare_(409.863, 512.329] Embarked_C Embarked_Q Embarked_S
0 0 0 0 1 1 0 0 0 1 0 ... 0 0 1 0 0 0 0 0 0 1
1 1 1 0 0 0 1 0 0 0 1 ... 0 0 1 0 0 0 0 1 0 0
2 1 0 0 1 0 1 0 0 1 0 ... 0 0 1 0 0 0 0 0 0 1
3 1 1 0 0 0 1 0 0 0 1 ... 0 0 1 0 0 0 0 0 0 1
4 0 0 0 1 1 0 0 0 0 1 ... 0 0 1 0 0 0 0 0 0 1

5 rows × 36 columns

# df_bin_enc.columns

OneHotEncoder

df_con
Survived Pclass Sex Age SibSp Parch Fare Embarked
0 0 3 male 22 1 0 7.2500 S
1 1 1 female 38 1 0 71.2833 C
2 1 3 female 26 0 0 7.9250 S
3 1 1 female 35 1 0 53.1000 S
4 0 3 male 35 0 0 8.0500 S
... ... ... ... ... ... ... ... ...
886 0 2 male 27 0 0 13.0000 S
887 1 1 female 19 0 0 30.0000 S
888 0 3 female 30 1 2 23.4500 S
889 1 1 male 26 0 0 30.0000 C
890 0 3 male 32 0 0 7.7500 Q

889 rows × 8 columns

from sklearn.preprocessing import OneHotEncoder
ohe = OneHotEncoder()
ohe.fit_transform(df_con).toarray()

array([[1., 0., 0., ..., 0., 0., 1.],
       [0., 1., 1., ..., 1., 0., 0.],
       [0., 1., 0., ..., 0., 0., 1.],
       ...,
       [1., 0., 0., ..., 0., 0., 1.],
       [0., 1., 1., ..., 1., 0., 0.],
       [1., 0., 0., ..., 0., 1., 0.]])

from sklearn.compose import make_column_transformer


column_trans = make_column_transformer(
    (OneHotEncoder(), ['Sex','Embarked', 'Pclass']),
    remainder = 'passthrough',
)

column_trans.fit_transform(df_con)

array([[ 0.    ,  1.    ,  0.    , ...,  1.    ,  0.    ,  7.25  ],
       [ 1.    ,  0.    ,  1.    , ...,  1.    ,  0.    , 71.2833],
       [ 1.    ,  0.    ,  0.    , ...,  0.    ,  0.    ,  7.925 ],
       ...,
       [ 1.    ,  0.    ,  0.    , ...,  1.    ,  2.    , 23.45  ],
       [ 0.    ,  1.    ,  1.    , ...,  0.    ,  0.    , 30.    ],
       [ 0.    ,  1.    ,  0.    , ...,  0.    ,  0.    ,  7.75  ]])

from sklearn.pipeline import make_pipeline

pipe = make_pipeline(column_trans)

The only issue with this is that it becomes an array: you dk what are the column names

  • revert to pd.get_dummies() for now
## testing ##
pd.get_dummies(df_con['Embarked'], # returns a dataframe with readable cols vs an array
    prefix='embarked')
embarked_C embarked_Q embarked_S
0 0 0 1
1 1 0 0
2 0 0 1
3 0 0 1
4 0 0 1
... ... ... ...
886 0 0 1
887 0 0 1
888 0 0 1
889 1 0 0
890 0 1 0

889 rows × 3 columns

# One hot encode the categorical columns
df_embarked_one_hot = pd.get_dummies(df_con['Embarked'], 
                                     prefix='embarked')

df_sex_one_hot = pd.get_dummies(df_con['Sex'], 
                                prefix='sex')

df_pclass_one_hot = pd.get_dummies(df_con['Pclass'], 
                                   prefix='pclass')


## combine all the new columns to df_con
df_con_enc = pd.concat([
    df_con,
    df_embarked_one_hot,
    df_sex_one_hot,
    df_pclass_one_hot,
], axis = 1)

## drop redundant columns
df_con_enc = df_con_enc.drop(['Pclass', 'Sex', 'Embarked'], axis = 1)


df_con_enc
Survived Age SibSp Parch Fare embarked_C embarked_Q embarked_S sex_female sex_male pclass_1 pclass_2 pclass_3
0 0 22 1 0 7.2500 0 0 1 0 1 0 0 1
1 1 38 1 0 71.2833 1 0 0 1 0 1 0 0
2 1 26 0 0 7.9250 0 0 1 1 0 0 0 1
3 1 35 1 0 53.1000 0 0 1 1 0 1 0 0
4 0 35 0 0 8.0500 0 0 1 0 1 0 0 1
... ... ... ... ... ... ... ... ... ... ... ... ... ...
886 0 27 0 0 13.0000 0 0 1 0 1 0 1 0
887 1 19 0 0 30.0000 0 0 1 1 0 1 0 0
888 0 30 1 2 23.4500 0 0 1 1 0 0 0 1
889 1 26 0 0 30.0000 1 0 0 0 1 1 0 0
890 0 32 0 0 7.7500 0 1 0 0 1 0 0 1

889 rows × 13 columns

Build machine learning models

  1. Data has been converted to numbers, we can start training
  2. Need to separate into train and test set

Selecting the data

# select the df u wanna use
selected_df = df_con_enc

selected_df.head()
Survived Age SibSp Parch Fare embarked_C embarked_Q embarked_S sex_female sex_male pclass_1 pclass_2 pclass_3
0 0 22 1 0 7.2500 0 0 1 0 1 0 0 1
1 1 38 1 0 71.2833 1 0 0 1 0 1 0 0
2 1 26 0 0 7.9250 0 0 1 1 0 0 0 1
3 1 35 1 0 53.1000 0 0 1 1 0 1 0 0
4 0 35 0 0 8.0500 0 0 1 0 1 0 0 1 
# split into features (X) and label (y)
X_train = selected_df.drop(['Survived'], axis = 1)
y_train = selected_df.Survived

print(X_train.shape)
print(y_train.shape)

(889, 12)
(889,)

Defining a function to fit (train) our ml models

  • since most of the models from sklearn, we can reuse the same functions most of the time. Let’s not repeat ourselves
# function that runs the requested algo and returns the accuracy metrics
# also prints out the stats
# also prints out the run time

def fit_ml_algo(algo, X_train, y_train, cv):
    start_time = time.time()

    # one pass
    model = algo.fit(X_train, y_train)
    acc = round(model.score(X_train, y_train) * 100, 2)

    # Cross validation
    y_pred = model_selection.cross_val_predict(algo,
                                            X_train,
                                            y_train,
                                            cv=cv,
                                            n_jobs = -1)
    
    # cross val accuracy metric
    acc_cv = round(metrics.accuracy_score(y_train, y_pred=y_pred)* 100, 2)

    time_taken = (time.time() - start_time)
    print(algo)
    print("Accuracy: %s" % acc)
    print("Accuracy cv 10-fold: %s" % acc_cv)
    print("run time: %s" % datetime.timedelta(seconds = time_taken))

    return y_pred, acc, acc_cv

Logistic Regression

# start_time = time.time()

log_reg = LogisticRegression()

train_pred_log, acc_log, acc_cv_log = fit_ml_algo(log_reg,
X_train,
y_train,
cv=10)

# log_time = (time.time() - start_time)
# print("run time: %s" % datetime.timedelta(seconds = log_time))

LogisticRegression()
Accuracy: 80.43
Accuracy cv 10-fold: 79.98
run time: 0:00:03.248071

K Nearest Neighbours

start_time = time.time()

knn = KNeighborsClassifier()

train_pred_knn, acc_knn, acc_cv_knn = fit_ml_algo(knn,
                                                X_train,
                                                y_train,
                                                cv=10)

# knn_time = (time.time() - start_time)
# print("run time: %s" % datetime.timedelta(seconds = knn_time))

KNeighborsClassifier()
Accuracy: 81.66
Accuracy cv 10-fold: 70.75
run time: 0:00:00.168397

Gaussian Naiive Bayes

gnb = GaussianNB()

train_pred_gnb, acc_gnb, acc_cv_gnb = fit_ml_algo(gnb,
                                                X_train,
                                                y_train,
                                                cv=10)

GaussianNB()
Accuracy: 78.52
Accuracy cv 10-fold: 78.4
run time: 0:00:00.044880

Linear Support Vector Machine (aka support vector classifier)

linear_svc = LinearSVC()

train_pred_linear_svc, acc_linear_svc, acc_cv_linear_svc = fit_ml_algo(linear_svc,
                                                X_train,
                                                y_train,
                                                cv=10)

LinearSVC()
Accuracy: 79.08
Accuracy cv 10-fold: 76.83
run time: 0:00:00.137860

Stochastic Gradient Descent

sgd = SGDClassifier()

train_pred_sgd, acc_sgd, acc_cv_sgd = fit_ml_algo(sgd,
                                                X_train,
                                                y_train,
                                                cv=10)

SGDClassifier()
Accuracy: 67.38
Accuracy cv 10-fold: 68.17
run time: 0:00:00.050879

Decision Trees

dt = DecisionTreeClassifier()

train_pred_dt, acc_dt, acc_cv_dt = fit_ml_algo(dt,
                                                X_train,
                                                y_train,
                                                cv=10)

DecisionTreeClassifier()
Accuracy: 97.86
Accuracy cv 10-fold: 78.29
run time: 0:00:00.067818

Gradient Boost Trees

  • Highest one of the sklearn models
gbt = GradientBoostingClassifier()

train_pred_gbt, acc_gbt, acc_cv_gbt = fit_ml_algo(gbt,
                                                X_train,
                                                y_train,
                                                cv=10)

GradientBoostingClassifier()
Accuracy: 89.65
Accuracy cv 10-fold: 82.23
run time: 0:00:00.404186

CatBoost Algorithm

  • A state of the art open source gradient boosting on decision trees library
  • simple to use. Dbourkes go-to for any ml task
  • catboost docs: https://catboost.ai/
X_train.head()
Age SibSp Parch Fare embarked_C embarked_Q embarked_S sex_female sex_male pclass_1 pclass_2 pclass_3
0 22 1 0 7.2500 0 0 1 0 1 0 0 1
1 38 1 0 71.2833 1 0 0 1 0 1 0 0
2 26 0 0 7.9250 0 0 1 1 0 0 0 1
3 35 1 0 53.1000 0 0 1 1 0 1 0 0
4 35 0 0 8.0500 0 0 1 0 1 0 0 1 
y_train.head()

0    0
1    1
2    1
3    1
4    0
Name: Survived, dtype: int64

cat_features = np.where(X_train.dtypes != np.float)[0]
cat_features # 3 is not inside because col 3 is fare, which is continuous and NOT categorical

array([ 0,  1,  2,  4,  5,  6,  7,  8,  9, 10, 11], dtype=int64)

# use CatBoost Pool() to pool together the training data and categorcial feature labels

train_pool = Pool(X_train,
                    y_train,
                    cat_features)

# instantiate the module
catboost_model = CatBoostClassifier(iterations=1000,
                                    custom_loss = ['Accuracy'],
                                    loss_function = 'Logloss')



catboost_model.fit(train_pool, plot=True)

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w=u.getFromId(t,b.xref),k=u.getFromId(t,b.yref),M={x:{},y:{}},A=+b.textangle||0,T=s._infolayer.append("g").classed("annotation",!0).attr("data-index",String(e)).style("opacity",b.opacity),L=T.append("g").classed("annotation-text-g",!0).attr("data-index",String(e)),C=L.append("g").style("pointer-events",b.captureevents?"all":null).call(g,"default").on("click",function(){t._dragging=!1,t.emit("plotly_clickannotation",{index:e,annotation:x,fullAnnotation:b,event:i.event})});b.hovertext&&C.on("mouseover",function(){var e=b.hoverlabel,r=e.font,n=this.getBoundingClientRect(),a=t.getBoundingClientRect();h.loneHover({x0:n.left-a.left,x1:n.right-a.left,y:(n.top+n.bottom)/2-a.top,text:b.hovertext,color:e.bgcolor,borderColor:e.bordercolor,fontFamily:r.family,fontSize:r.size,fontColor:r.color},{container:s._hoverlayer.node(),outerContainer:s._paper.node()})}).on("mouseout",function(){h.loneUnhover(s._hoverlayer.node())});var 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n=t("d3"),a=t("tinycolor2"),o=t("../../plotly"),i=t("../../plots/plots"),l=t("../../registry"),s=t("../../plots/cartesian/axes"),c=t("../dragelement"),u=t("../../lib"),f=t("../../lib/extend").extendFlat,d=t("../../lib/setcursor"),h=t("../drawing"),p=t("../color"),g=t("../titles"),v=t("../../plots/cartesian/axis_defaults"),m=t("../../plots/cartesian/position_defaults"),y=t("../../plots/cartesian/layout_attributes"),x=t("./attributes");e.exports=function(t,e){function r(){function x(t,e){return u.coerce(J,K,y,t,e)}function w(){if(["top","bottom"].indexOf(_.titleside)!==-1){var e=ot.select(".cbtitle"),r=e.select("text"),o=[-_.outlinewidth/2,_.outlinewidth/2],i=e.select(".h"+K._id+"title-math-group").node(),l=15.6;if(r.node()&&(l=1.3*parseInt(r.style("font-size"),10)),i?(lt=h.bBox(i).height)>l&&(o[1]-=(lt-l)/2):r.node()&&!r.classed("js-placeholder")&&(lt=h.bBox(e.node()).height),lt){if(lt+=5,"top"===_.titleside)K.domain[1]-=lt/T.h,o[1]*=-1;else{K.domain[0]+=lt/T.h;var 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J=1/0;for(I=0;I<Y.length;I++)if((R=Y[I])&&R[0]&&R[0].trace&&R[0].trace.visible===!0&&(F=R[0].trace,["carpet","contourcarpet"].indexOf(F._module.name)===-1)){if(j=w.getSubplot(F),B=n.indexOf(j),q=D,U={cd:R,trace:F,xa:T[B],ya:L[B],name:t.data.length>1||F.hoverinfo.indexOf("name")!==-1?F.name:void 0,index:!1,distance:Math.min(J,k.MAXDIST),color:y.defaultLine,x0:void 0,x1:void 0,y0:void 0,y1:void 0,xLabelVal:void 0,yLabelVal:void 0,zLabelVal:void 0,text:void 0},d[j]&&(U.subplot=d[j]._subplot),X=G.length,"array"===q){var K=e[I];"pointNumber"in K?(U.index=K.pointNumber,q="closest"):(q="","xval"in K&&(H=K.xval,q="x"),"yval"in K&&(V=K.yval,q=q?"closest":"y"))}else H=P[B],V=E[B];if(F._module&&F._module.hoverPoints){var tt=F._module.hoverPoints(U,H,V,q);if(tt)for(var et,rt=0;rt<tt.length;rt++)et=tt[rt],f(et.x0)&&f(et.y0)&&G.push(l(et,D))}else h.log("Unrecognized trace type in hover:",F);"closest"===D&&G.length>X&&(G.splice(0,X),J=G[0].distance)}if(0===G.length)return x.unhoverRaw(t,e);G.sort(function(t,e){return t.distance-e.distance});var nt=t._hoverdata,at=[];for(N=0;N<G.length;N++){var ot=G[N],it={data:ot.trace._input,fullData:ot.trace,curveNumber:ot.trace.index,pointNumber:ot.index};ot.trace._module.eventData?it=ot.trace._module.eventData(it,ot):(it.x=ot.xVal,it.y=ot.yVal,it.xaxis=ot.xa,it.yaxis=ot.ya,void 0!==ot.zLabelVal&&(it.z=ot.zLabelVal)),at.push(it)}if(t._hoverdata=at,c(t,e,nt)&&d._hasCartesian){s(G,{hovermode:D,fullLayout:d,container:d._hoverlayer,outerContainer:d._paperdiv})}var lt="y"===D&&Y.length>1,st=y.combine(d.plot_bgcolor||y.background,d.paper_bgcolor),ct={hovermode:D,rotateLabels:lt,bgColor:st,container:d._hoverlayer,outerContainer:d._paperdiv,commonLabelOpts:d.hoverlabel},ut=a(G,ct);if(o(G,lt?"xa":"ya"),i(ut,lt),e.target&&e.target.tagName){var ft=_.getComponentMethod("annotations","hasClickToShow")(t,at);v(u.select(e.target),ft?"pointer":"")}e.target&&c(t,e,nt)&&(nt&&t.emit("plotly_unhover",{event:e,points:nt}),t.emit("plotly_hover",{event:e,points:t._hoverdata,xaxes:T,yaxes:L,xvals:P,yvals:E}))}function a(t,e){var r,n,a=e.hovermode,o=e.rotateLabels,i=e.bgColor,l=e.container,s=e.outerContainer,c=e.commonLabelOpts||{},f=e.fontFamily||k.HOVERFONT,d=e.fontSize||k.HOVERFONTSIZE,h=t[0],p=h.xa,v=h.ya,x="y"===a?"yLabel":"xLabel",b=h[x],_=(String(b)||"").split(" ")[0],w=s.node().getBoundingClientRect(),A=w.top,T=w.width,L=w.height,C=h.distance<=k.MAXDIST&&("x"===a||"y"===a);for(r=0;r<t.length;r++){n=t[r].trace.hoverinfo;var O=n.split("+");if(O.indexOf("all")===-1&&O.indexOf(a)===-1){C=!1;break}}var D=l.selectAll("g.axistext").data(C?[0]:[]);D.enter().append("g").classed("axistext",!0),D.exit().remove(),D.each(function(){var e=u.select(this),r=e.selectAll("path").data([0]),n=e.selectAll("text").data([0]);r.enter().append("path").style({fill:c.bgcolor||y.defaultLine,stroke:c.bordercolor||y.background,"stroke-width":"1px"}),n.enter().append("text").call(m.font,c.font.family||f,c.font.size||d,c.font.color||y.background).attr("data-notex",1),n.text(b).call(g.convertToTspans).call(m.setPosition,0,0).selectAll("tspan.line").call(m.setPosition,0,0),e.attr("transform","");var o=n.node().getBoundingClientRect();if("x"===a){n.attr("text-anchor","middle").call(m.setPosition,0,"top"===p.side?A-o.bottom-S-z:A-o.top+S+z).selectAll("tspan.line").attr({x:n.attr("x"),y:n.attr("y")});var i="top"===p.side?"-":"";r.attr("d","M0,0L"+S+","+i+S+"H"+(z+o.width/2)+"v"+i+(2*z+o.height)+"H-"+(z+o.width/2)+"V"+i+S+"H-"+S+"Z"),e.attr("transform","translate("+(p._offset+(h.x0+h.x1)/2)+","+(v._offset+("top"===p.side?0:v._length))+")")}else{n.attr("text-anchor","right"===v.side?"start":"end").call(m.setPosition,("right"===v.side?1:-1)*(z+S),A-o.top-o.height/2).selectAll("tspan.line").attr({x:n.attr("x"),y:n.attr("y")});var l="right"===v.side?"":"-";r.attr("d","M0,0L"+l+S+","+S+"V"+(z+o.height/2)+"h"+l+(2*z+o.width)+"V-"+(z+o.height/2)+"H"+l+S+"V-"+S+"Z"),e.attr("transform","translate("+(p._offset+("right"===v.side?p._length:0))+","+(v._offset+(h.y0+h.y1)/2)+")")}t=t.filter(function(t){return void 0!==t.zLabelVal||(t[x]||"").split(" ")[0]===_})});var P=l.selectAll("g.hovertext").data(t,function(t){return[t.trace.index,t.index,t.x0,t.y0,t.name,t.attr,t.xa,t.ya||""].join(",")});return P.enter().append("g").classed("hovertext",!0).each(function(){var t=u.select(this);t.append("rect").call(y.fill,y.addOpacity(i,.8)),t.append("text").classed("name",!0),t.append("path").style("stroke-width","1px"),t.append("text").classed("nums",!0).call(m.font,f,d)}),P.exit().remove(),P.each(function(t){var e=u.select(this).attr("transform",""),r="",n="",l=y.opacity(t.color)?t.color:y.defaultLine,s=y.combine(l,i),c=t.borderColor||y.contrast(s);void 0!==t.nameOverride&&(t.name=t.nameOverride),t.name&&(r=g.plainText(t.name||""),r.length>15&&(r=r.substr(0,12)+"...")),void 0!==t.extraText&&(n+=t.extraText),void 0!==t.zLabel?(void 0!==t.xLabel&&(n+="x: "+t.xLabel+"<br>"),void 0!==t.yLabel&&(n+="y: "+t.yLabel+"<br>"),n+=(n?"z: ":"")+t.zLabel):C&&t[a+"Label"]===b?n=t[("x"===a?"y":"x")+"Label"]||"":void 0===t.xLabel?void 0!==t.yLabel&&(n=t.yLabel):n=void 0===t.yLabel?t.xLabel:"("+t.xLabel+", "+t.yLabel+")",t.text&&!Array.isArray(t.text)&&(n+=(n?"<br>":"")+t.text),""===n&&(""===r&&e.remove(),n=r);var h=e.select("text.nums").call(m.font,t.fontFamily||f,t.fontSize||d,t.fontColor||c).call(m.setPosition,0,0).text(n).attr("data-notex",1).call(g.convertToTspans);h.selectAll("tspan.line").call(m.setPosition,0,0);var p=e.select("text.name"),v=0;r&&r!==n?(p.call(m.font,t.fontFamily||f,t.fontSize||d,s).text(r).call(m.setPosition,0,0).attr("data-notex",1).call(g.convertToTspans),p.selectAll("tspan.line").call(m.setPosition,0,0),v=p.node().getBoundingClientRect().width+2*z):(p.remove(),e.select("rect").remove()),e.select("path").style({fill:s,stroke:c});var x,_,w=h.node().getBoundingClientRect(),k=t.xa._offset+(t.x0+t.x1)/2,O=t.ya._offset+(t.y0+t.y1)/2,D=Math.abs(t.x1-t.x0),P=Math.abs(t.y1-t.y0),E=w.width+S+z+v;t.ty0=A-w.top,t.bx=w.width+2*z,t.by=w.height+2*z,t.anchor="start",t.txwidth=w.width,t.tx2width=v,t.offset=0,o?(t.pos=k,x=O+P/2+E<=L,_=O-P/2-E>=0,"top"!==t.idealAlign&&x||!_?x?(O+=P/2,t.anchor="start"):t.anchor="middle":(O-=P/2,t.anchor="end")):(t.pos=O,x=k+D/2+E<=T,_=k-D/2-E>=0,"left"!==t.idealAlign&&x||!_?x?(k+=D/2,t.anchor="start"):t.anchor="middle":(k-=D/2,t.anchor="end")),h.attr("text-anchor",t.anchor),v&&p.attr("text-anchor",t.anchor),e.attr("transform","translate("+k+","+O+")"+(o?"rotate("+M+")":""))}),P}function o(t,e){function r(t){var e=t[0],r=t[t.length-1];if(a=e.pmin-e.pos-e.dp+e.size,o=r.pos+r.dp+r.size-e.pmax,a>.01){for(l=t.length-1;l>=0;l--)t[l].dp+=a;n=!1}if(!(o<.01)){if(a<-.01){for(l=t.length-1;l>=0;l--)t[l].dp-=o;n=!1}if(n){var c=0;for(i=0;i<t.length;i++)s=t[i],s.pos+s.dp+s.size>e.pmax&&c++;for(i=t.length-1;i>=0&&!(c<=0);i--)s=t[i],s.pos>e.pmax-1&&(s.del=!0,c--);for(i=0;i<t.length&&!(c<=0);i++)if(s=t[i],s.pos<e.pmin+1)for(s.del=!0,c--,o=2*s.size,l=t.length-1;l>=0;l--)t[l].dp-=o;for(i=t.length-1;i>=0&&!(c<=0);i--)s=t[i],s.pos+s.dp+s.size>e.pmax&&(s.del=!0,c--)}}}for(var n,a,o,i,l,s,c,u=0,f=t.map(function(t,r){var n=t[e];return[{i:r,dp:0,pos:t.pos,posref:t.posref,size:t.by*("x"===n._id.charAt(0)?T:1)/2,pmin:n._offset,pmax:n._offset+n._length}]}).sort(function(t,e){return t[0].posref-e[0].posref});!n&&u<=t.length;){for(u++,n=!0,i=0;i<f.length-1;){var d=f[i],h=f[i+1],p=d[d.length-1],g=h[0];if((a=p.pos+p.dp+p.size-g.pos-g.dp+g.size)>.01&&p.pmin===g.pmin&&p.pmax===g.pmax){for(l=h.length-1;l>=0;l--)h[l].dp+=a;for(d.push.apply(d,h),f.splice(i+1,1),c=0,l=d.length-1;l>=0;l--)c+=d[l].dp;for(o=c/d.length,l=d.length-1;l>=0;l--)d[l].dp-=o;n=!1}else i++}f.forEach(r)}for(i=f.length-1;i>=0;i--){var v=f[i];for(l=v.length-1;l>=0;l--){var m=v[l],y=t[m.i];y.offset=m.dp,y.del=m.del}}}function i(t,e){t.each(function(t){var r=u.select(this);if(t.del)return void r.remove();var n="end"===t.anchor?-1:1,a=r.select("text.nums"),o={start:1,end:-1,middle:0}[t.anchor],i=o*(S+z),l=i+o*(t.txwidth+z),s=0,c=t.offset;"middle"===t.anchor&&(i-=t.tx2width/2,l-=t.tx2width/2),e&&(c*=-C,s=t.offset*L),r.select("path").attr("d","middle"===t.anchor?"M-"+t.bx/2+",-"+t.by/2+"h"+t.bx+"v"+t.by+"h-"+t.bx+"Z":"M0,0L"+(n*S+s)+","+(S+c)+"v"+(t.by/2-S)+"h"+n*t.bx+"v-"+t.by+"H"+(n*S+s)+"V"+(c-S)+"Z"),a.call(m.setPosition,i+s,c+t.ty0-t.by/2+z).selectAll("tspan.line").attr({x:a.attr("x"),y:a.attr("y")}),t.tx2width&&(r.select("text.name, text.name tspan.line").call(m.setPosition,l+o*z+s,c+t.ty0-t.by/2+z),r.select("rect").call(m.setRect,l+(o-1)*t.tx2width/2+s,c-t.by/2-1,t.tx2width,t.by+2))})}function l(t,e){function r(e,r,i){var l;if(o[r])l=o[r];else if(a[r]){var s=a[r];Array.isArray(s)&&Array.isArray(s[t.index[0]])&&(l=s[t.index[0]][t.index[1]])}else l=h.nestedProperty(n,i).get();l&&(t[e]=l)}var n=t.trace||{},a=t.cd[0],o=t.cd[t.index]||{};t.posref="y"===e?(t.x0+t.x1)/2:(t.y0+t.y1)/2,t.x0=h.constrain(t.x0,0,t.xa._length),t.x1=h.constrain(t.x1,0,t.xa._length),t.y0=h.constrain(t.y0,0,t.ya._length),t.y1=h.constrain(t.y1,0,t.ya._length);var i;if(void 0!==t.xLabelVal){i="log"===t.xa.type&&t.xLabelVal<=0;var l=b.tickText(t.xa,t.xa.c2l(i?-t.xLabelVal:t.xLabelVal),"hover");i?0===t.xLabelVal?t.xLabel="0":t.xLabel="-"+l.text:t.xLabel=l.text,t.xVal=t.xa.c2d(t.xLabelVal)}if(void 0!==t.yLabelVal){i="log"===t.ya.type&&t.yLabelVal<=0;var s=b.tickText(t.ya,t.ya.c2l(i?-t.yLabelVal:t.yLabelVal),"hover");i?0===t.yLabelVal?t.yLabel="0":t.yLabel="-"+s.text:t.yLabel=s.text,t.yVal=t.ya.c2d(t.yLabelVal)}if(void 0!==t.zLabelVal&&(t.zLabel=String(t.zLabelVal)),!(isNaN(t.xerr)||"log"===t.xa.type&&t.xerr<=0)){var c=b.tickText(t.xa,t.xa.c2l(t.xerr),"hover").text;void 0!==t.xerrneg?t.xLabel+=" +"+c+" / -"+b.tickText(t.xa,t.xa.c2l(t.xerrneg),"hover").text:t.xLabel+=" \xb1 "+c,"x"===e&&(t.distance+=1)}if(!(isNaN(t.yerr)||"log"===t.ya.type&&t.yerr<=0)){var u=b.tickText(t.ya,t.ya.c2l(t.yerr),"hover").text;void 0!==t.yerrneg?t.yLabel+=" +"+u+" / -"+b.tickText(t.ya,t.ya.c2l(t.yerrneg),"hover").text:t.yLabel+=" \xb1 "+u,"y"===e&&(t.distance+=1)}var f=t.trace.hoverinfo;return"all"!==f&&(f=f.split("+"),f.indexOf("x")===-1&&(t.xLabel=void 0),f.indexOf("y")===-1&&(t.yLabel=void 0),f.indexOf("z")===-1&&(t.zLabel=void 0),f.indexOf("text")===-1&&(t.text=void 0),f.indexOf("name")===-1&&(t.name=void 0)),r("color","hbg","hoverlabel.bgcolor"),r("borderColor","hbc","hoverlabel.bordercolor"),r("fontFamily","htf","hoverlabel.font.family"),r("fontSize","hts","hoverlabel.font.size"),r("fontColor","htc","hoverlabel.font.color"),t}function s(t,e){var r=e.hovermode,n=e.container,a=t[0],o=a.xa,i=a.ya,l=o.showspikes,s=i.showspikes;if(n.selectAll(".spikeline").remove(),"closest"===r&&(l||s)){var c=e.fullLayout,u=o._offset+(a.x0+a.x1)/2,f=i._offset+(a.y0+a.y1)/2,h=y.combine(c.plot_bgcolor,c.paper_bgcolor),p=d.readability(a.color,h)<1.5?y.contrast(h):a.color;if(s){var g=i.spikemode,v=i.spikethickness,x=i.spikecolor||p,b=i._boundingBox,_=(b.left+b.right)/2<u?b.right:b.left;if(g.indexOf("toaxis")!==-1||g.indexOf("across")!==-1){var w=_,k=u;g.indexOf("across")!==-1&&(w=i._counterSpan[0],k=i._counterSpan[1]),n.append("line").attr({x1:w,x2:k,y1:f,y2:f,"stroke-width":v+2,stroke:h}).classed("spikeline",!0).classed("crisp",!0),n.append("line").attr({x1:w,x2:k,y1:f,y2:f,"stroke-width":v,stroke:x,"stroke-dasharray":m.dashStyle(i.spikedash,v)}).classed("spikeline",!0).classed("crisp",!0)}g.indexOf("marker")!==-1&&n.append("circle").attr({cx:_+("right"!==i.side?v:-v),cy:f,r:v,fill:x}).classed("spikeline",!0)}if(l){var M=o.spikemode,A=o.spikethickness,T=o.spikecolor||p,L=o._boundingBox,C=(L.top+L.bottom)/2<f?L.bottom:L.top;if(M.indexOf("toaxis")!==-1||M.indexOf("across")!==-1){var S=C,z=f;M.indexOf("across")!==-1&&(S=o._counterSpan[0],z=o._counterSpan[1]),n.append("line").attr({x1:u,x2:u,y1:S,y2:z,"stroke-width":A+2,stroke:h}).classed("spikeline",!0).classed("crisp",!0),n.append("line").attr({x1:u,x2:u,y1:S,y2:z,"stroke-width":A,stroke:T,"stroke-dasharray":m.dashStyle(o.spikedash,A)}).classed("spikeline",!0).classed("crisp",!0)}M.indexOf("marker")!==-1&&n.append("circle").attr({cx:u,cy:C-("top"!==o.side?A:-A),r:A,fill:T}).classed("spikeline",!0)}}}function c(t,e,r){if(!r||r.length!==t._hoverdata.length)return!0;for(var n=r.length-1;n>=0;n--){var a=r[n],o=t._hoverdata[n];if(a.curveNumber!==o.curveNumber||String(a.pointNumber)!==String(o.pointNumber))return!0}return!1}var 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R=this.id,F=u-.5,j=O?f+D+.5:f+.5,B=d-.5,q=A?h+L+.5:h+.5,H=l._topdefs.selectAll("#"+R).data(A||O?[0]:[]);if(H.exit().remove(),H.enter().append("clipPath").attr("id",R).append("rect"),A||O?(this._clipRect=H.select("rect").attr({x:Math.floor(F),y:Math.floor(B),width:Math.ceil(j)-Math.floor(F),height:Math.ceil(q)-Math.floor(B)}),this.container.call(i.setClipUrl,R),this.bg.attr({x:p,y:v,width:g,height:m})):(this.bg.attr({width:0,height:0}),this.container.on("wheel",null).on(".drag",null).call(i.setClipUrl,null),delete this._clipRect),A||O){var V=a.behavior.drag().on("dragstart",function(){a.event.sourceEvent.preventDefault()}).on("drag",this._onBoxDrag.bind(this));this.container.on("wheel",null).on("wheel",this._onBoxWheel.bind(this)).on(".drag",null).call(V);var U=a.behavior.drag().on("dragstart",function(){a.event.sourceEvent.preventDefault(),a.event.sourceEvent.stopPropagation()}).on("drag",this._onBarDrag.bind(this));A&&this.hbar.on(".drag",null).call(U),O&&this.vbar.on(".drag",null).call(U)}this.setTranslate(e,r)},n.prototype.disable=function(){(this.hbar||this.vbar)&&(this.bg.attr({width:0,height:0}),this.container.on("wheel",null).on(".drag",null).call(i.setClipUrl,null),delete this._clipRect),this.hbar&&(this.hbar.on(".drag",null),this.hbar.remove(),delete this.hbar,delete this._hbarXMin,delete this._hbarTranslateMax),this.vbar&&(this.vbar.on(".drag",null),this.vbar.remove(),delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax)},n.prototype._onBoxDrag=function(){var t=this.translateX,e=this.translateY;this.hbar&&(t-=a.event.dx),this.vbar&&(e-=a.event.dy),this.setTranslate(t,e)},n.prototype._onBoxWheel=function(){var 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o=e/n;this.vbar.call(i.setTranslate,t,e+o*this._vbarTranslateMax)}}},{"../../lib":136,"../color":25,"../drawing":49,d3:7}],121:[function(t,e,r){"use strict";e.exports={SHOW_PLACEHOLDER:100,HIDE_PLACEHOLDER:1e3,DBLCLICKDELAY:300}},{}],122:[function(t,e,r){"use strict";e.exports={BADNUM:void 0,FP_SAFE:Number.MAX_VALUE/1e4,ONEAVGYEAR:315576e5,ONEAVGMONTH:26298e5,ONEDAY:864e5,ONEHOUR:36e5,ONEMIN:6e4,ONESEC:1e3,EPOCHJD:2440587.5,ALMOST_EQUAL:1-1e-6}},{}],123:[function(t,e,r){"use strict";e.exports={entityToUnicode:{mu:"\u03bc",amp:"&",lt:"<",gt:">",nbsp:"\xa0",times:"\xd7",plusmn:"\xb1",deg:"\xb0"},unicodeToEntity:{"&":"amp","<":"lt",">":"gt",'"':"quot","'":"#x27","/":"#x2F"}}},{}],124:[function(t,e,r){"use strict";r.xmlns="http://www.w3.org/2000/xmlns/",r.svg="http://www.w3.org/2000/svg",r.xlink="http://www.w3.org/1999/xlink",r.svgAttrs={xmlns:r.svg,"xmlns:xlink":r.xlink}},{}],125:[function(t,e,r){"use strict";var n=t("./plotly");/*export for 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n=t("../../components/color/attributes"),a=t("../../plots/font_attributes"),o=t("../../plots/attributes"),i=t("../../lib/extend").extendFlat;e.exports={labels:{valType:"data_array"},label0:{valType:"number",dflt:0},dlabel:{valType:"number",dflt:1},values:{valType:"data_array"},marker:{colors:{valType:"data_array"},line:{color:{valType:"color",dflt:n.defaultLine,arrayOk:!0},width:{valType:"number",min:0,dflt:0,arrayOk:!0}}},text:{valType:"data_array"},hovertext:{valType:"string",dflt:"",arrayOk:!0},scalegroup:{valType:"string",dflt:""},textinfo:{valType:"flaglist",flags:["label","text","value","percent"],extras:["none"]},hoverinfo:i({},o.hoverinfo,{flags:["label","text","value","percent","name"]}),textposition:{valType:"enumerated",values:["inside","outside","auto","none"],dflt:"auto",arrayOk:!0},textfont:i({},a,{}),insidetextfont:i({},a,{}),outsidetextfont:i({},a,{}),domain:{x:{valType:"info_array",items:[{valType:"number",min:0,max:1},{valType:"number",min:0,max:1}],dflt:[0,1]},y:{valType:"info_array",items:[{valType:"number",min:0,max:1},{valType:"number",min:0,max:1}],dflt:[0,1]}},hole:{valType:"number",min:0,max:1,dflt:0},sort:{valType:"boolean",dflt:!0},direction:{valType:"enumerated",values:["clockwise","counterclockwise"],dflt:"counterclockwise"},rotation:{valType:"number",min:-360,max:360,dflt:0},pull:{valType:"number",min:0,max:1,dflt:0,arrayOk:!0}}},{"../../components/color/attributes":24,"../../lib/extend":132,"../../plots/attributes":169,"../../plots/font_attributes":195}],229:[function(t,e,r){"use 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s},{"../../components/color":25,"./helpers":232,"fast-isnumeric":10,tinycolor2:13}],231:[function(t,e,r){"use strict";var n=t("../../lib"),a=t("./attributes");e.exports=function(t,e,r,o){function i(r,o){return n.coerce(t,e,a,r,o)}var l=n.coerceFont,s=i("values");if(!Array.isArray(s)||!s.length)return void(e.visible=!1);var c=i("labels");Array.isArray(c)||(i("label0"),i("dlabel")),i("marker.line.width")&&i("marker.line.color");var u=i("marker.colors");Array.isArray(u)||(e.marker.colors=[]),i("scalegroup");var f=i("text"),d=i("textinfo",Array.isArray(f)?"text+percent":"percent");if(i("hovertext"),i("hoverinfo",1===o._dataLength?"label+text+value+percent":void 0),d&&"none"!==d){var h=i("textposition"),p=Array.isArray(h)||"auto"===h,g=p||"inside"===h,v=p||"outside"===h;if(g||v){var m=l(i,"textfont",o.font);g&&l(i,"insidetextfont",m),v&&l(i,"outsidetextfont",m)}}i("domain.x"),i("domain.y"),i("hole"),i("sort"),i("direction"),i("rotation"),i("pull")}},{"../../lib":136,"./attributes":228}],232:[function(t,e,r){"use strict";var n=t("../../lib");r.formatPiePercent=function(t,e){var r=(100*t).toPrecision(3);return r.lastIndexOf(".")!==-1&&(r=r.replace(/[.]?0+$/,"")),n.numSeparate(r,e)+"%"},r.formatPieValue=function(t,e){var r=t.toPrecision(10);return r.lastIndexOf(".")!==-1&&(r=r.replace(/[.]?0+$/,"")),n.numSeparate(r,e)}},{"../../lib":136}],233:[function(t,e,r){"use strict";var n={};n.attributes=t("./attributes"),n.supplyDefaults=t("./defaults"),n.supplyLayoutDefaults=t("./layout_defaults"),n.layoutAttributes=t("./layout_attributes"),n.calc=t("./calc"),n.plot=t("./plot"),n.style=t("./style"),n.styleOne=t("./style_one"),n.moduleType="trace",n.name="pie",n.basePlotModule=t("./base_plot"),n.categories=["pie","showLegend"],n.meta={},e.exports=n},{"./attributes":228,"./base_plot":229,"./calc":230,"./defaults":231,"./layout_attributes":234,"./layout_defaults":235,"./plot":236,"./style":237,"./style_one":238}],234:[function(t,e,r){"use strict";e.exports={hiddenlabels:{valType:"data_array"}}},{}],235:[function(t,e,r){"use strict";var n=t("../../lib"),a=t("./layout_attributes");e.exports=function(t,e){!function(r,o){n.coerce(t,e,a,r,o)}("hiddenlabels")}},{"../../lib":136,"./layout_attributes":234}],236:[function(t,e,r){"use strict";function n(t,e,r){var n=Math.sqrt(t.width*t.width+t.height*t.height),o=t.width/t.height,i=Math.PI*Math.min(e.v/r.vTotal,.5),l=1-r.trace.hole,s=a(e,r),c={scale:s*r.r*2/n,rCenter:1-s,rotate:0};if(c.scale>=1)return c;var u=o+1/(2*Math.tan(i)),f=r.r*Math.min(1/(Math.sqrt(u*u+.5)+u),l/(Math.sqrt(o*o+l/2)+o)),d={scale:2*f/t.height,rCenter:Math.cos(f/r.r)-f*o/r.r,rotate:(180/Math.PI*e.midangle+720)%180-90},h=1/o,p=h+1/(2*Math.tan(i)),g=r.r*Math.min(1/(Math.sqrt(p*p+.5)+p),l/(Math.sqrt(h*h+l/2)+h)),v={scale:2*g/t.width,rCenter:Math.cos(g/r.r)-g/o/r.r,rotate:(180/Math.PI*e.midangle+810)%180-90},m=v.scale>d.scale?v:d;return c.scale<1&&m.scale>c.scale?m:c}function a(t,e){if(t.v===e.vTotal&&!e.trace.hole)return 1;var r=Math.PI*Math.min(t.v/e.vTotal,.5);return Math.min(1/(1+1/Math.sin(r)),(1-e.trace.hole)/2)}function o(t,e){var r=e.pxmid[0],n=e.pxmid[1],a=t.width/2,o=t.height/2;return r<0&&(a*=-1),n<0&&(o*=-1),{scale:1,rCenter:1,rotate:0,x:a+Math.abs(o)*(a>0?1:-1)/2,y:o/(1+r*r/(n*n)),outside:!0}}function i(t,e){function r(t,e){return t.pxmid[1]-e.pxmid[1]}function n(t,e){return e.pxmid[1]-t.pxmid[1]}var a,o,i,l,s,c,u,f,d,h,p,g,v;for(o=0;o<2;o++)for(i=o?r:n,s=o?Math.max:Math.min,u=o?1:-1,a=0;a<2;a++){for(l=a?Math.max:Math.min,c=a?1:-1,f=t[o][a],f.sort(i),d=t[1-o][a],h=d.concat(f),g=[],p=0;p<f.length;p++)void 0!==f[p].yLabelMid&&g.push(f[p]);for(v=!1,p=0;o&&p<d.length;p++)if(void 0!==d[p].yLabelMid){v=d[p];break}for(p=0;p<g.length;p++){var m=p&&g[p-1];v&&!p&&(m=v),function(t,r){r||(r={});var 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r,n,a,o,i,l,s,u,f,d,h=[];for(a=0;a<t.length;a++){if(i=t[a][0],l=i.trace,r=e.w*(l.domain.x[1]-l.domain.x[0]),n=e.h*(l.domain.y[1]-l.domain.y[0]),s=l.tiltaxis*Math.PI/180,u=l.pull,Array.isArray(u))for(u=0,o=0;o<l.pull.length;o++)l.pull[o]>u&&(u=l.pull[o]);i.r=Math.min(r/c(l.tilt,Math.sin(s),l.depth),n/c(l.tilt,Math.cos(s),l.depth))/(2+2*u),i.cx=e.l+e.w*(l.domain.x[1]+l.domain.x[0])/2,i.cy=e.t+e.h*(2-l.domain.y[1]-l.domain.y[0])/2, l.scalegroup&&h.indexOf(l.scalegroup)===-1&&h.push(l.scalegroup)}for(o=0;o<h.length;o++){for(d=1/0,f=h[o],a=0;a<t.length;a++)i=t[a][0],i.trace.scalegroup===f&&(d=Math.min(d,i.r*i.r/i.vTotal));for(a=0;a<t.length;a++)i=t[a][0],i.trace.scalegroup===f&&(i.r=Math.sqrt(d*i.vTotal))}}function s(t){function e(t){var e=f.r*Math.sin(t),r=-f.r*Math.cos(t);return h?[e*(1-l*n*n)+r*i*l,e*i*l+r*(1-l*a*a),Math.sin(o)*(r*a-e*n)]:[e,r]}var 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l=u.select(this),c=e[0],v=c.trace,m=(v.depth||0)*c.r*Math.sin(0)/2,y=v.tiltaxis||0,x=y*Math.PI/180,b=[m*Math.sin(x),m*Math.cos(x)],_=c.r*Math.cos(0),w=l.selectAll("g.part").data(v.tilt?["top","sides"]:["top"]);w.enter().append("g").attr("class",function(t){return t+" part"}),w.exit().remove(),w.order(),s(e),l.selectAll(".top").each(function(){var l=u.select(this).selectAll("g.slice").data(e);l.enter().append("g").classed("slice",!0),l.exit().remove();var s=[[[],[]],[[],[]]],m=!1;l.each(function(e){function i(n){n.originalEvent=u.event;var o=t._fullLayout,i=t._fullData[v.index],l=i.hoverinfo;if("all"===l&&(l="label+text+value+percent+name"),t._dragging||o.hovermode===!1||"none"===l||"skip"===l||!l)return void f.hover(t,n,"pie");var s=a(e,c),d=w+e.pxmid[0]*(1-s),h=k+e.pxmid[1]*(1-s),p=r.separators,m=[];l.indexOf("label")!==-1&&m.push(e.label),l.indexOf("text")!==-1&&(i.hovertext?m.push(Array.isArray(i.hovertext)?i.hovertext[e.i]:i.hovertext):i.text&&i.text[e.i]&&m.push(i.text[e.i])),l.indexOf("value")!==-1&&m.push(g.formatPieValue(e.v,p)),l.indexOf("percent")!==-1&&m.push(g.formatPiePercent(e.v/c.vTotal,p));var y=i.hoverlabel;f.loneHover({x0:d-s*c.r,x1:d+s*c.r,y:h,text:m.join("<br>"),name:l.indexOf("name")!==-1?i.name:void 0,idealAlign:e.pxmid[0]<0?"left":"right",color:e.hbg||y.bgcolor||e.color,borderColor:e.hbc||y.bordercolor,fontFamily:e.htf||y.font.family,fontSize:e.hts||y.font.size,fontColor:e.htc||y.font.color},{container:o._hoverlayer.node(),outerContainer:o._paper.node()}),f.hover(t,n,"pie"),T=!0}function l(e){e.originalEvent=u.event,t.emit("plotly_unhover",{event:u.event,points:[e]}),T&&(f.loneUnhover(r._hoverlayer.node()),T=!1)}function d(){t._hoverdata=[e],t._hoverdata.trace=c.trace,f.click(t,u.event)}function x(t,r,n,a){return"a"+a*c.r+","+a*_+" "+y+" "+e.largeArc+(n?" 1 ":" 0 ")+a*(r[0]-t[0])+","+a*(r[1]-t[1])}if(e.hidden)return void u.select(this).selectAll("path,g").remove();s[e.pxmid[1]<0?0:1][e.pxmid[0]<0?0:1].push(e);var w=c.cx+b[0],k=c.cy+b[1],M=u.select(this),A=M.selectAll("path.surface").data([e]),T=!1;if(A.enter().append("path").classed("surface",!0).style({"pointer-events":"all"}),M.select("path.textline").remove(),M.on("mouseover",i).on("mouseout",l).on("click",d),v.pull){var L=+(Array.isArray(v.pull)?v.pull[e.i]:v.pull)||0;L>0&&(w+=L*e.pxmid[0],k+=L*e.pxmid[1])}e.cxFinal=w,e.cyFinal=k;var C=v.hole;if(e.v===c.vTotal){var S="M"+(w+e.px0[0])+","+(k+e.px0[1])+x(e.px0,e.pxmid,!0,1)+x(e.pxmid,e.px0,!0,1)+"Z";C?A.attr("d","M"+(w+C*e.px0[0])+","+(k+C*e.px0[1])+x(e.px0,e.pxmid,!1,C)+x(e.pxmid,e.px0,!1,C)+"Z"+S):A.attr("d",S)}else{var z=x(e.px0,e.px1,!0,1);if(C){var O=1-C;A.attr("d","M"+(w+C*e.px1[0])+","+(k+C*e.px1[1])+x(e.px1,e.px0,!1,C)+"l"+O*e.px0[0]+","+O*e.px0[1]+z+"Z")}else A.attr("d","M"+w+","+k+"l"+e.px0[0]+","+e.px0[1]+z+"Z")}var D=Array.isArray(v.textposition)?v.textposition[e.i]:v.textposition,P=M.selectAll("g.slicetext").data(e.text&&"none"!==D?[0]:[]);P.enter().append("g").classed("slicetext",!0),P.exit().remove(),P.each(function(){var t=u.select(this).selectAll("text").data([0]);t.enter().append("text").attr("data-notex",1),t.exit().remove(),t.text(e.text).attr({class:"slicetext",transform:"","data-bb":"","text-anchor":"middle",x:0,y:0}).call(h.font,"outside"===D?v.outsidetextfont:v.insidetextfont).call(p.convertToTspans),t.selectAll("tspan.line").attr({x:0,y:0});var 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u,f=i.isBubble(t),d=(t.line||{}).color;if(c=c||{},d&&(r=d),s("marker.symbol"),s("marker.opacity",f?.7:1),s("marker.size"),s("marker.color",r),a(t,"marker")&&o(t,e,l,s,{prefix:"marker.",cLetter:"c"}),c.noLine||(u=d&&!Array.isArray(d)&&e.marker.color!==d?d:f?n.background:n.defaultLine,s("marker.line.color",u),a(t,"marker.line")&&o(t,e,l,s,{prefix:"marker.line.",cLetter:"c"}),s("marker.line.width",f?1:0)),f&&(s("marker.sizeref"),s("marker.sizemin"),s("marker.sizemode")),c.gradient){"none"!==s("marker.gradient.type")&&s("marker.gradient.color")}}},{"../../components/color":25,"../../components/colorscale/defaults":34,"../../components/colorscale/has_colorscale":38,"./subtypes":260}],257:[function(t,e,r){"use strict";function n(t,e){var r;e.selectAll("g.trace").each(function(t){var e=i.select(this);if(r=t[0].trace,r._nexttrace){if(r._nextFill=e.select(".js-fill.js-tonext"),!r._nextFill.size()){var n=":first-child";e.select(".js-fill.js-tozero").size()&&(n+=" + 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b=r.enter().append("path").classed("point",!0);b.call(s.pointStyle,o).call(s.translatePoints,M,A,o),k&&b.style("opacity",0).transition().style("opacity",1);var _=f&&s.tryColorscale(o.marker,""),w=f&&s.tryColorscale(o.marker,"line");r.each(function(e){var r=i.select(this),n=g(r);(a=s.translatePoint(e,n,M,A))&&(s.singlePointStyle(e,n,o,_,w,t),o.customdata&&r.classed("plotly-customdata",null!==e.data&&void 0!==e.data))}),k?r.exit().transition().style("opacity",0).remove():r.exit().remove(),n=c.selectAll("g"),r=n.data(m,h),r.enter().append("g").classed("textpoint",!0).append("text"),r.each(function(t){var e=i.select(this),r=g(e.select("text"));(a=s.translatePoint(t,r,M,A))||e.remove()}),r.selectAll("text").call(s.textPointStyle,o).each(function(t){var e=t.xp||M.c2p(t.x),r=t.yp||A.c2p(t.y);i.select(this).selectAll("tspan.line").each(function(){g(i.select(this)).attr({x:e,y:r})})}),r.exit().remove()}var _,w;o(t,e,r,n,a);var k=!!p&&p.duration>0,M=r.xaxis,A=r.yaxis,T=n[0].trace,L=T.line,C=i.select(d);if(C.call(c.plot,r,p),T.visible===!0){g(C).style("opacity",T.opacity);var S,z,O=T.fill.charAt(T.fill.length-1);"x"!==O&&"y"!==O&&(O=""),n[0].node3=C;var D="",P=[],E=T._prevtrace;E&&(D=E._prevRevpath||"",z=E._nextFill,P=E._polygons);var N,I,R,F,j,B,q,H,V,U="",X="",G=[],Y=[],Z=l.noop;if(S=T._ownFill,u.hasLines(T)||"none"!==T.fill){for(z&&z.datum(n),["hv","vh","hvh","vhv"].indexOf(L.shape)!==-1?(R=s.steps(L.shape),F=s.steps(L.shape.split("").reverse().join(""))):R=F="spline"===L.shape?function(t){var e=t[t.length-1];return t[0][0]===e[0]&&t[0][1]===e[1]?s.smoothclosed(t.slice(1),L.smoothing):s.smoothopen(t,L.smoothing)}:function(t){return"M"+t.join("L")},j=function(t){return F(t.reverse())},G=f(n,{xaxis:M,yaxis:A,connectGaps:T.connectgaps,baseTolerance:Math.max(L.width||1,3)/4,linear:"linear"===L.shape,simplify:L.simplify}),V=T._polygons=new Array(G.length),w=0;w<G.length;w++)T._polygons[w]=h(G[w]);G.length&&(B=G[0][0],q=G[G.length-1],H=q[q.length-1]),Y=G.filter(function(t){return t.length>1}),Z=function(t){return function(e){if(N=R(e),I=j(e),U?O?(U+="L"+N.substr(1),X=I+"L"+X.substr(1)):(U+="Z"+N,X=I+"Z"+X):(U=N,X=I),u.hasLines(T)&&e.length>1){var r=i.select(this);if(r.datum(n),t)g(r.style("opacity",0).attr("d",N).call(s.lineGroupStyle)).style("opacity",1);else{var a=g(r);a.attr("d",N),s.singleLineStyle(n,a)}}}}}var W=C.selectAll(".js-line").data(Y);g(W.exit()).style("opacity",0).remove(),W.each(Z(!1)),W.enter().append("path").classed("js-line",!0).style("vector-effect","non-scaling-stroke").call(s.lineGroupStyle).each(Z(!0)),G.length&&(S?B&&H&&(O?("y"===O?B[1]=H[1]=A.c2p(0,!0):"x"===O&&(B[0]=H[0]=M.c2p(0,!0)),g(S).attr("d","M"+H+"L"+B+"L"+U.substr(1))):g(S).attr("d",U+"Z")):"tonext"===T.fill.substr(0,6)&&U&&D&&("tonext"===T.fill?g(z).attr("d",U+"Z"+D+"Z"):g(z).attr("d",U+"L"+D.substr(1)+"Z"),T._polygons=T._polygons.concat(P)),T._prevRevpath=X,T._prevPolygons=V);var $=C.selectAll(".points");_=$.data([n]),$.each(b),_.enter().append("g").classed("points",!0).each(b),_.exit().remove()}}function o(t,e,r,n,a){var o=r.xaxis,s=r.yaxis,c=i.extent(l.simpleMap(o.range,o.r2c)),f=i.extent(l.simpleMap(s.range,s.r2c)),d=n[0].trace;if(u.hasMarkers(d)){var h=d.marker.maxdisplayed;if(0!==h){var p=n.filter(function(t){return t.x>=c[0]&&t.x<=c[1]&&t.y>=f[0]&&t.y<=f[1]}),g=Math.ceil(p.length/h),v=0;a.forEach(function(t,r){var n=t[0].trace;u.hasMarkers(n)&&n.marker.maxdisplayed>0&&r<e&&v++});var m=Math.round(v*g/3+Math.floor(v/3)*g/7.1);n.forEach(function(t){delete t.vis}),p.forEach(function(t,e){0===Math.round((e+m)%g)&&(t.vis=!0)})}}}var i=t("d3"),l=t("../../lib"),s=t("../../components/drawing"),c=t("../../components/errorbars"),u=t("./subtypes"),f=t("./line_points"),d=t("./link_traces"),h=t("../../lib/polygon").tester;e.exports=function(t,e,r,o,l){var s,c,u,f,h,p=e.plot.select("g.scatterlayer"),g=!o,v=!!o&&o.duration>0;for(u=p.selectAll("g.trace"),f=u.data(r,function(t){return t[0].trace.uid}),f.enter().append("g").attr("class",function(t){return"trace scatter trace"+t[0].trace.uid}).style("stroke-miterlimit",2),d(t,e,r),n(t,p),s=0,c={};s<r.length;s++)c[r[s][0].trace.uid]=s;if(p.selectAll("g.trace").sort(function(t,e){return c[t[0].trace.uid]>c[e[0].trace.uid]?1:-1}),v){l&&(h=l());i.transition().duration(o.duration).ease(o.easing).each("end",function(){h&&h()}).each("interrupt",function(){h&&h()}).each(function(){p.selectAll("g.trace").each(function(n,i){a(t,i,e,n,r,this,o)})})}else p.selectAll("g.trace").each(function(n,i){a(t,i,e,n,r,this,o)});g&&f.exit().remove(),p.selectAll("path:not([d])").remove()}},{"../../components/drawing":49,"../../components/errorbars":55,"../../lib":136,"../../lib/polygon":146,"./line_points":252,"./link_traces":254,"./subtypes":260,d3:7}],258:[function(t,e,r){"use strict";var n=t("./subtypes");e.exports=function(t,e){var r,a,o,i,l=t.cd,s=t.xaxis,c=t.yaxis,u=[],f=l[0].trace,d=f.index,h=f.marker,p=!n.hasMarkers(f)&&!n.hasText(f);if(f.visible===!0&&!p){var g=Array.isArray(h.opacity)?1:h.opacity;if(e===!1)for(r=0;r<l.length;r++)l[r].dim=0;else for(r=0;r<l.length;r++)a=l[r],o=s.c2p(a.x),i=c.c2p(a.y),e.contains([o,i])?(u.push({curveNumber:d,pointNumber:r,x:a.x,y:a.y,id:a.id}),a.dim=0):a.dim=1;return l[0].node3.selectAll("path.point").style("opacity",function(t){return((t.mo+1||g+1)-1)*(t.dim?.2:1)}),l[0].node3.selectAll("text").style("opacity",function(t){return t.dim?.2:1}),u}}},{"./subtypes":260}],259:[function(t,e,r){"use strict";var n=t("d3"),a=t("../../components/drawing"),o=t("../../components/errorbars");e.exports=function(t){var e=n.select(t).selectAll("g.trace.scatter");e.style("opacity",function(t){return t[0].trace.opacity}),e.selectAll("g.points").each(function(t){var e=n.select(this),r=e.selectAll("path.point"),o=t.trace||t[0].trace;r.call(a.pointStyle,o),e.selectAll("text").call(a.textPointStyle,o)}),e.selectAll("g.trace path.js-line").call(a.lineGroupStyle),e.selectAll("g.trace path.js-fill").call(a.fillGroupStyle),e.call(o.style)}},{"../../components/drawing":49,"../../components/errorbars":55,d3:7}],260:[function(t,e,r){"use strict";var n=t("../../lib");e.exports={hasLines:function(t){return t.visible&&t.mode&&t.mode.indexOf("lines")!==-1},hasMarkers:function(t){return t.visible&&t.mode&&t.mode.indexOf("markers")!==-1},hasText:function(t){return t.visible&&t.mode&&t.mode.indexOf("text")!==-1},isBubble:function(t){return n.isPlainObject(t.marker)&&Array.isArray(t.marker.size)}}},{"../../lib":136}],261:[function(t,e,r){"use strict";var n=t("../../lib");e.exports=function(t,e,r,a){a("textposition"),n.coerceFont(a,"textfont",r.font)}},{"../../lib":136}],262:[function(t,e,r){"use strict";var n=t("../../registry");e.exports=function(t,e,r,a){var o,i=a("x"),l=a("y");if(n.getComponentMethod("calendars","handleTraceDefaults")(t,e,["x","y"],r),i)l?(o=Math.min(i.length,l.length),o<i.length&&(e.x=i.slice(0,o)),o<l.length&&(e.y=l.slice(0,o))):(o=i.length,a("y0"),a("dy"));else{if(!l)return 0;o=e.y.length,a("x0"),a("dx")}return o}},{"../../registry":206}]},{},[5])(5)});window.define = window.__define;window.require = window.__require;window.__define = undefined; window.__require = undefined;/* global Highcharts, Plotly*/ /* eslint-disable */ function throttle(fn, timeout, invokeAsap, ctx) {
var typeofInvokeAsap = typeof invokeAsap;
if(typeofInvokeAsap === 'undefined') {
    invokeAsap = true;
} else if(arguments.length === 3 && typeofInvokeAsap !== 'boolean') {
    ctx = invokeAsap;
    invokeAsap = true;
}

var timer, args, needInvoke,
    wrapper = function() {
        if(needInvoke) {
            fn.apply(ctx, args);
            needInvoke = false;
            timer = setTimeout(wrapper, timeout);
        } else {
            timer = null;
        }
    };

return function() {
    args = arguments;
    ctx || (ctx = this);
    needInvoke = true;

    if(!timer) {
        invokeAsap?
            wrapper() :
            timer = setTimeout(wrapper, timeout);
    }
}; }

/* eslint-enable */
function CatboostIpython() {

}

CatboostIpython.prototype.init = function() {
this.charts = {};
/*
{
“rmse_1”: {
…
}
}
/
this.traces = {};
/
{
“rmse_1”: {
name: “rmse”,
id: “rmse_1”,
parent: “div”,
traces: [
{
name: “current;learn;0;;;”,
x: [],
y: []
},
{
name: “current;learn;0;smoothed;;”,
x: [],
y: []
},
{
name: “current;learn;1;;;”,
x: [],
y: []
},
{
name: “current;learn;1;smoothed;;”,
x: [],
y: []
},
{
name: “current;test;0;;;”,
x: [],
y: []
},
{
name: “current;test;0;smoothed;;”,
x: [],
y: []
},
{
name: “current;test;0;;best_point;”,
x: [],
y: []
},
{
name: “current;test;0;;;best_value”,
x: [],
y: []
}
]
}
}
*/

this.hovertextParameters = [];
this.chartsToRedraw = {};
this.lastIndexes = {};
this.smoothness = -1;
this.layoutDisabled = {
    series: {},
    traces: {}
};
this.clickMode = false;
this.logarithmMode = 'linear';
this.lastSmooth = 0;
this.layout = null;
this.activeTab = '';
this.meta = {};
this.timeLeft = {};

this.hasCVMode = false;
this.stddevEnabled = false;

this.colors = [
    '#68E256',
    '#56AEE2',
    '#CF56E2',
    '#E28956',
    '#56E289',
    '#5668E2',
    '#E256AE',
    '#E2CF56',
    '#56E2CF',
    '#8A56E2',
    '#E25668',
    '#AEE256'
];
this.colorsByPath = {};
this.colorIndex = 0;
this.lossFuncs = {};

this.isCVinited = false; };

/* eslint-disable */
CatboostIpython.prototype.loadStyles = function(path, fn, scope) {
$(‘link[catboost=”1”]’).remove();

var head = document.getElementsByTagName('head')[0], // reference to document.head for appending/ removing link nodes
    link = document.createElement('link');           // create the link node
link.setAttribute('href', path);
link.setAttribute('rel', 'stylesheet');
link.setAttribute('type', 'text/css');
link.setAttribute('catboost', '1');

var sheet, cssRules;
// get the correct properties to check for depending on the browser
if ('sheet' in link) {
    sheet = 'sheet'; cssRules = 'cssRules';
} else {
    sheet = 'styleSheet'; cssRules = 'rules';
}

var interval_id = setInterval(function() {                     // start checking whether the style sheet has successfully loaded
    try {
        if (link[sheet] && link[sheet][cssRules].length) { // SUCCESS! our style sheet has loaded
            clearInterval(interval_id);                      // clear the counters
            clearTimeout(timeout_id);
            fn.call( scope || window, true, link);           // fire the callback with success == true
        }
    } catch(e) {} finally {}
}, 50 ),                                                   // how often to check if the stylesheet is loaded
timeout_id = setTimeout( function() {       // start counting down till fail
    clearInterval( interval_id );             // clear the counters
    clearTimeout( timeout_id );
    head.removeChild( link );                // since the style sheet didn't load, remove the link node from the DOM
    fn.call( scope || window, false, link ); // fire the callback with success == false
}, 15000 );                                 // how long to wait before failing

head.appendChild( link );  // insert the link node into the DOM and start loading the style sheet

return link; // return the link node; }; /* eslint-enable */

CatboostIpython.prototype.resizeCharts = function() {
// width fix for development
$(‘.catboost-graph__charts’, this.layout).css({width: $(‘.catboost-graph’).width()});

this.plotly.Plots.resize(this.traces[this.activeTab].parent); };

CatboostIpython.prototype.addMeta = function(path, meta) {
this.meta[path] = meta;
};

CatboostIpython.prototype.addLayout = function(parent) {
if (this.layout) {
return;
}

var cvAreaControls = '';

if (this.hasCVMode) {
    cvAreaControls =    '<div>' +
                            '<input type="checkbox" class="catboost-panel__control_checkbox" id="catboost-control2-cvstddev' + this.index + '"' + (this.stddevEnabled ? ' checked="checked"' : '') + '></input>' +
                            '<label for="catboost-control2-cvstddev' + this.index + '" class="catboost-panel__controls2_label catboost-panel__controls2_label-long">Standard Deviation</label>' +
                        '</div>';
}

this.layout = $('<div class="catboost">' +
                    '<div class="catboost-panel">' +
                        '<div class="catboost-panel__controls">' +
                            '<input type="checkbox" class="catboost-panel__controls_checkbox" id="catboost-control-learn' + this.index + '" ' + (!this.layoutDisabled.learn ? ' checked="checked"' : '') + '></input>' +
                            '<label for="catboost-control-learn' + this.index + '" class="catboost-panel__controls_label"><div class="catboost-panel__serie_learn_pic" style="border-color:#999"></div>Learn</label>' +
                            '<input type="checkbox" class="catboost-panel__controls_checkbox" id="catboost-control-test' + this.index + '" ' + (!this.layoutDisabled.test ? ' checked="checked"' : '') + '></input>' +
                            '<label for="catboost-control-test' + this.index + '" class="catboost-panel__controls_label"><div class="catboost-panel__serie_test_pic" style="border-color:#999"></div>Eval</label>' +
                        '</div>' +
                        '<div class="catboost-panel__series ' + (this.layoutDisabled.learn ? ' catboost-panel__series_learn_disabled' : '') + '">' +
                        '</div>' +
                        '<div class="catboost-panel__controls2">' +
                            '<input type="checkbox" class="catboost-panel__control_checkbox" id="catboost-control2-clickmode' + this.index + '"></input>' +
                            '<label for="catboost-control2-clickmode' + this.index + '" class="catboost-panel__controls2_label">Click Mode</label>' +
                            '<input type="checkbox" class="catboost-panel__control_checkbox" id="catboost-control2-log' + this.index + '"></input>' +
                            '<label for="catboost-control2-log' + this.index + '" class="catboost-panel__controls2_label">Logarithm</label>' +
                            '<div>' +
                                '<input type="checkbox" class="catboost-panel__control_checkbox" id="catboost-control2-smooth' + this.index + '"></input>' +
                                '<label for="catboost-control2-smooth' + this.index + '" class="catboost-panel__controls2_label">Smooth</label>' +
                                '<input id="catboost-control2-slider' + this.index + '" disabled="disabled" class="catboost-panel__control_slider" type ="range" value="0" min="0" max="1" step ="0.01" for="rangeInputValue" name="rangeInput"/>' +
                                '<input id="catboost-control2-slidervalue' + this.index + '" disabled="disabled" class="catboost-panel__control_slidervalue" value="0" min="0" max="1" for="rangeInput" name="rangeInputValue"/>' +
                            '</div>' +
                            cvAreaControls +
                        '</div>' +
                    '</div>' +
                    '<div class="catboost-graph">' +
                        '<div class="catboost-graph__tabs"></div>' +
                        '<div class="catboost-graph__charts"></div>' +
                    '</div>' +
                '</div>');
$(parent).append(this.layout);

this.addTabEvents();
this.addControlEvents(); };

CatboostIpython.prototype.addTabEvents = function() {
var self = this;

$('.catboost-graph__tabs', this.layout).click(function(e) {
    if (!$(e.target).is('.catboost-graph__tab:not(.catboost-graph__tab_active)')) {
        return;
    }

    var id = $(e.target).attr('tabid');

    self.activeTab = id;

    $('.catboost-graph__tab_active', self.layout).removeClass('catboost-graph__tab_active');
    $('.catboost-graph__chart_active', self.layout).removeClass('catboost-graph__chart_active');

    $('.catboost-graph__tab[tabid="' + id + '"]', self.layout).addClass('catboost-graph__tab_active');
    $('.catboost-graph__chart[tabid="' + id + '"]', self.layout).addClass('catboost-graph__chart_active');

    self.cleanSeries();

    self.redrawActiveChart();
    self.resizeCharts();
}); };

CatboostIpython.prototype.addControlEvents = function() {
var self = this;

$('#catboost-control-learn' + this.index, this.layout).click(function() {
    self.layoutDisabled.learn = !$(this)[0].checked;

    $('.catboost-panel__series', self.layout).toggleClass('catboost-panel__series_learn_disabled', self.layoutDisabled.learn);

    self.redrawActiveChart();
});

$('#catboost-control-test' + this.index, this.layout).click(function() {
    self.layoutDisabled.test = !$(this)[0].checked;

    $('.catboost-panel__series', self.layout).toggleClass('catboost-panel__series_test_disabled', self.layoutDisabled.test);

    self.redrawActiveChart();
});

$('#catboost-control2-clickmode' + this.index, this.layout).click(function() {
    self.clickMode = $(this)[0].checked;
});

$('#catboost-control2-log' + this.index, this.layout).click(function() {
    self.logarithmMode = $(this)[0].checked ? 'log' : 'linear';

    self.forEveryLayout(function(layout) {
        layout.yaxis = {type: self.logarithmMode};
    });

    self.redrawActiveChart();
});

var slider = $('#catboost-control2-slider' + this.index),
    sliderValue = $('#catboost-control2-slidervalue' + this.index);

$('#catboost-control2-smooth' + this.index, this.layout).click(function() {
    var enabled = $(this)[0].checked;

    self.setSmoothness(enabled ? self.lastSmooth : -1);

    slider.prop('disabled', !enabled);
    sliderValue.prop('disabled', !enabled);

    self.redrawActiveChart();
});

$('#catboost-control2-cvstddev' + this.index, this.layout).click(function() {
    var enabled = $(this)[0].checked;

    self.setStddev(enabled);

    self.redrawActiveChart();
});

slider.on('input change', function() {
    var smooth = Number($(this).val());

    sliderValue.val(isNaN(smooth) ? 0 : smooth);

    self.setSmoothness(smooth);
    self.lastSmooth = smooth;

    self.redrawActiveChart();
});

sliderValue.on('input change', function() {
    var smooth = Number($(this).val());

    slider.val(isNaN(smooth) ? 0 : smooth);

    self.setSmoothness(smooth);
    self.lastSmooth = smooth;

    self.redrawActiveChart();
}); };

CatboostIpython.prototype.setTraceVisibility = function(trace, visibility) {
if (trace) {
trace.visible = visibility;
}
};

CatboostIpython.prototype.updateTracesVisibility = function() {
var tracesHash = this.groupTraces(),
traces,
smoothDisabled = this.getSmoothness() === -1,
self = this;

for (var train in tracesHash) {
    if (tracesHash.hasOwnProperty(train)) {
        traces = tracesHash[train].traces;

        if (this.layoutDisabled.traces[train]) {
            traces.forEach(function(trace) {
                self.setTraceVisibility(trace, false);
            });
        } else {
            traces.forEach(function(trace) {
                self.setTraceVisibility(trace, true);
            });

            if (this.hasCVMode) {
                if (this.stddevEnabled) {
                    self.filterTracesOne(traces, {type: 'learn'}).forEach(function(trace) {
                        self.setTraceVisibility(trace, false);
                    });
                    self.filterTracesOne(traces, {type: 'test'}).forEach(function(trace) {
                        self.setTraceVisibility(trace, false);
                    });

                    self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true})).forEach(function(trace) {
                        self.setTraceVisibility(trace, true);
                    });
                    self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true})).forEach(function(trace) {
                        self.setTraceVisibility(trace, true);
                    });

                    self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true, smoothed: true})).forEach(function(trace) {
                        self.setTraceVisibility(trace, true);
                    });
                    self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, smoothed: true})).forEach(function(trace) {
                        self.setTraceVisibility(trace, true);
                    });

                    self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, best_point: true})).forEach(function(trace) {
                        self.setTraceVisibility(trace, true);
                    });

                    self.filterTracesOne(traces, {cv_stddev_first: true}).forEach(function(trace) {
                        self.setTraceVisibility(trace, true);
                    });
                    self.filterTracesOne(traces, {cv_stddev_last: true}).forEach(function(trace) {
                        self.setTraceVisibility(trace, true);
                    });
                } else {
                    self.filterTracesOne(traces, {cv_stddev_first: true}).forEach(function(trace) {
                        self.setTraceVisibility(trace, false);
                    });
                    self.filterTracesOne(traces, {cv_stddev_last: true}).forEach(function(trace) {
                        self.setTraceVisibility(trace, false);
                    });

                    self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true})).forEach(function(trace) {
                        self.setTraceVisibility(trace, false);
                    });
                    self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true})).forEach(function(trace) {
                        self.setTraceVisibility(trace, false);
                    });

                    self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true, smoothed: true})).forEach(function(trace) {
                        self.setTraceVisibility(trace, false);
                    });
                    self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, smoothed: true})).forEach(function(trace) {
                        self.setTraceVisibility(trace, false);
                    });

                    self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, best_point: true})).forEach(function(trace) {
                        self.setTraceVisibility(trace, false);
                    });
                }
            }

            if (smoothDisabled) {
                self.filterTracesOne(traces, {smoothed: true}).forEach(function(trace) {
                    self.setTraceVisibility(trace, false);
                });
            }

            if (this.layoutDisabled['learn']) {
                self.filterTracesOne(traces, {type: 'learn'}).forEach(function(trace) {
                    self.setTraceVisibility(trace, false);
                });
            }

            if (this.layoutDisabled['test']) {
                self.filterTracesOne(traces, {type: 'test'}).forEach(function(trace) {
                    self.setTraceVisibility(trace, false);
                });
            }
        }
    }
} };

CatboostIpython.prototype.getSmoothness = function() {
return this.smoothness && this.smoothness > -1 ? this.smoothness : -1;
};

CatboostIpython.prototype.setSmoothness = function(weight) {
if (weight < 0 && weight !== -1 || weight > 1) {
return;
}

this.smoothness = weight; };

CatboostIpython.prototype.setStddev = function(enabled) {
this.stddevEnabled = enabled;
};

CatboostIpython.prototype.redrawActiveChart = function() {
this.chartsToRedraw[this.activeTab] = true;

this.redrawAll(); };

CatboostIpython.prototype.redraw = function() {
if (this.chartsToRedraw[this.activeTab]) {
this.chartsToRedraw[this.activeTab] = false;

    this.updateTracesVisibility();
    this.updateTracesCV();
    this.updateTracesBest();
    this.updateTracesValues();
    this.updateTracesSmoothness();

    this.plotly.redraw(this.traces[this.activeTab].parent);
}

this.drawTraces(); };

CatboostIpython.prototype.addRedrawFunc = function() {
this.redrawFunc = throttle(this.redraw, 400, false, this);
};

CatboostIpython.prototype.redrawAll = function() {
if (!this.redrawFunc) {
this.addRedrawFunc();
}

this.redrawFunc(); };

CatboostIpython.prototype.addPoints = function(parent, data) {
var self = this;

data.chunks.forEach(function(item) {
    if (typeof item.remaining_time !== 'undefined' && typeof item.passed_time !== 'undefined') {
        if (!self.timeLeft[data.path]) {
            self.timeLeft[data.path] = [];
        }

        self.timeLeft[data.path][item.iteration] = [item.remaining_time, item.passed_time];
    }

    ['test', 'learn'].forEach(function(type) {
        var sets = self.meta[data.path][type + '_sets'],
            metrics = self.meta[data.path][type + '_metrics'];

        for (var i = 0; i < metrics.length; i++) {
            var nameOfMetric = metrics[i].name,
                cvAdded = false;
                hovertextParametersAdded = false;

            self.lossFuncs[nameOfMetric] = metrics[i].best_value;

            for (var j = 0; j < sets.length; j++) {
                var nameOfSet = sets[j],
                    params = {
                        chartName: nameOfMetric,
                        index: i,
                        train: data.train,
                        type: type,
                        path: data.path,
                        indexOfSet: j,
                        nameOfSet: nameOfSet
                    },
                    key = self.getKey(params),
                    launchMode = self.getLaunchMode(data.path);

                if (!self.activeTab) {
                    self.activeTab = key.chartId;
                }

                if (launchMode === 'CV' ) {
                    // we need to set launch mode before first getTrace call
                    self.hasCVMode = true;

                    if (!self.isCVinited) {
                        // and we don't need to reset setting for next iterations
                        self.layoutDisabled.learn = true;
                        self.setStddev(true);

                        self.isCVinited = true;
                    }
                }

                var valuesOfSet = item[nameOfSet],
                    pointValue = valuesOfSet[i],
                    pointIndex = item.iteration,
                    // traces
                    trace = self.getTrace(parent, params),
                    smoothedTrace = self.getTrace(parent, $.extend({smoothed: true}, params)),
                    bestValueTrace = null;

                if (type === 'test') {
                    if (launchMode !== 'CV') {
                        self.getTrace(parent, $.extend({best_point: true}, params));
                    }

                    if (typeof self.lossFuncs[nameOfMetric] === 'number') {
                        bestValueTrace = self.getTrace(parent, $.extend({best_value: true}, params));
                    }
                }

                if (pointValue !== 'inf' && pointValue !== 'nan') {
                    trace.x[pointIndex] = pointIndex;
                    trace.y[pointIndex] = valuesOfSet[i];
                    trace.hovertext[pointIndex] = nameOfSet + ': ' + valuesOfSet[i].toPrecision(7);
                    if (item.hasOwnProperty('parameters')) {
                        self.hovertextParameters[pointIndex] = '';
                        for (var parameter in item.parameters[0]) {
                            if (item.parameters[0].hasOwnProperty(parameter)) {
                                valueOfParameter = item.parameters[0][parameter];
                                self.hovertextParameters[pointIndex] += '<br>' + parameter + ' : ' + valueOfParameter;
                            }
                        }
                        if (!hovertextParametersAdded && type === 'test') {
                            hovertextParametersAdded = true;
                            trace.hovertext[pointIndex] += self.hovertextParameters[pointIndex];
                        }
                    }
                    smoothedTrace.x[pointIndex] = pointIndex;
                }

                if (bestValueTrace) {
                    bestValueTrace.x[pointIndex] = pointIndex;
                    bestValueTrace.y[pointIndex] = self.lossFuncs[nameOfMetric];
                }

                if (launchMode === 'CV' && !cvAdded) {
                    cvAdded = true;

                    self.getTrace(parent, $.extend({cv_stddev_first: true}, params));
                    self.getTrace(parent, $.extend({cv_stddev_last: true}, params));

                    self.getTrace(parent, $.extend({cv_stddev_first: true, smoothed: true}, params));
                    self.getTrace(parent, $.extend({cv_stddev_last: true, smoothed: true}, params));

                    self.getTrace(parent, $.extend({cv_avg: true}, params));
                    self.getTrace(parent, $.extend({cv_avg: true, smoothed: true}, params));

                    if (type === 'test') {
                        self.getTrace(parent, $.extend({cv_avg: true, best_point: true}, params));
                    }
                }
            }

            self.chartsToRedraw[key.chartId] = true;

            self.redrawAll();
        }
    });
}); };

CatboostIpython.prototype.getLaunchMode = function(path) {
return this.meta[path].launch_mode;
};

CatboostIpython.prototype.getChartNode = function(params, active) {
var node = $(‘<div class="catboost-graph__chart" tabid="' + params.id + '"></div>’);

if (active) {
    node.addClass('catboost-graph__chart_active');
}

return node; };

CatboostIpython.prototype.getChartTab = function(params, active) {
var node = $(‘<div class="catboost-graph__tab" tabid="' + params.id + '">’ + params.name + ‘</div>’);

if (active) {
    node.addClass('catboost-graph__tab_active');
}

return node; };

CatboostIpython.prototype.forEveryChart = function(callback) {
for (var name in this.traces) {
if (this.traces.hasOwnProperty(name)) {
callback(this.traces[name]);
}
}
};

CatboostIpython.prototype.forEveryLayout = function(callback) {
this.forEveryChart(function(chart) {
callback(chart.layout);
});
};

CatboostIpython.prototype.getChart = function(parent, params) {
var id = params.id,
self = this;

if (this.charts[id]) {
    return this.charts[id];
}

this.addLayout(parent);

var active = this.activeTab === params.id,
    chartNode = this.getChartNode(params, active),
    chartTab = this.getChartTab(params, active);

$('.catboost-graph__charts', this.layout).append(chartNode);
$('.catboost-graph__tabs', this.layout).append(chartTab);

this.traces[id] = {
    id: params.id,
    name: params.name,
    parent: chartNode[0],
    traces: [],
    layout: {
        xaxis: {
            range: [0, Number(this.meta[params.path].iteration_count)],
            type: 'linear',
            tickmode: 'auto',
            showspikes: true,
            spikethickness: 1,
            spikedash: 'longdashdot',
            spikemode: 'across',
            zeroline: false,
            showgrid: false
        },
        yaxis: {
            zeroline: false
            //showgrid: false
            //hoverformat : '.7f'
        },
        separators: '. ',
        //hovermode: 'x',
        margin: {l: 38, r: 0, t: 35, b: 30},
        autosize: true,
        showlegend: false
    },
    options: {
        scrollZoom: false,
        modeBarButtonsToRemove: ['toggleSpikelines'],
        displaylogo: false
    }
};

this.charts[id] = this.plotly.plot(chartNode[0], this.traces[id].traces, this.traces[id].layout, this.traces[id].options);

chartNode[0].on('plotly_hover', function(e) {
    self.updateTracesValues(e.points[0].x);
});

chartNode[0].on('plotly_click', function(e) {
    self.updateTracesValues(e.points[0].x, true);
});

return this.charts[id]; };

CatboostIpython.prototype.getTrace = function(parent, params) {
var key = this.getKey(params),
chartSeries = [];

if (this.traces[key.chartId]) {
    chartSeries = this.traces[key.chartId].traces.filter(function(trace) {
        return trace.name === key.traceName;
    });
}

if (chartSeries.length) {
    return chartSeries[0];
} else {
    this.getChart(parent, {id: key.chartId, name: params.chartName, path: params.path});

    var plotParams = {
            color: this.getNextColor(params.path, params.smoothed ? 0.2 : 1),
            fillsmoothcolor: this.getNextColor(params.path, 0.1),
            fillcolor: this.getNextColor(params.path, 0.4),
            hoverinfo: params.cv_avg ? 'skip' : 'text+x',
            width: params.cv_avg ? 2 : 1,
            dash: params.type === 'test' ? 'solid' : 'dot'
        },
        trace = {
            name: key.traceName,
            _params: params,
            x: [],
            y: [],
            hovertext: [],
            hoverinfo: plotParams.hoverinfo,
            line: {
                width: plotParams.width,
                dash: plotParams.dash,
                color: plotParams.color
            },
            mode: 'lines',
            hoveron: 'points',
            connectgaps: true
        };

    if (params.best_point) {
        trace = {
            name: key.traceName,
            _params: params,
            x: [],
            y: [],
            marker: {
                width: 2,
                color: plotParams.color
            },
            hovertext: [],
            hoverinfo: 'text',
            mode: 'markers',
            type: 'scatter'
        };
    }

    if (params.best_value) {
        trace = {
            name: key.traceName,
            _params: params,
            x: [],
            y: [],
            line: {
                width: 1,
                dash: 'dash',
                color: '#CCCCCC'
            },
            mode: 'lines',
            connectgaps: true,
            hoverinfo: 'skip'
        };
    }

    if (params.cv_stddev_last) {
        trace.fill = 'tonexty';
    }

    trace._params.plotParams = plotParams;

    this.traces[key.chartId].traces.push(trace);

    return trace;
} };

CatboostIpython.prototype.getKey = function(params) {
var traceName = [
params.train,
params.type,
params.indexOfSet,
(params.smoothed ? ‘smoothed’ : ‘’),
(params.best_point ? ‘best_pount’ : ‘’),
(params.best_value ? ‘best_value’ : ‘’),
(params.cv_avg ? ‘cv_avg’ : ‘’),
(params.cv_stddev_first ? ‘cv_stddev_first’ : ‘’),
(params.cv_stddev_last ? ‘cv_stddev_last’ : ‘’)
].join(‘;’);

return {
    chartId: params.chartName,
    traceName: traceName,
    colorId: params.train
}; };

CatboostIpython.prototype.filterTracesEvery = function(traces, filter) {
traces = traces || this.traces[this.activeTab].traces;

return traces.filter(function(trace) {
    for (var prop in filter) {
        if (filter.hasOwnProperty(prop)) {
            if (filter[prop] !== trace._params[prop]) {
                return false;
            }
        }
    }

    return true;
}); };

CatboostIpython.prototype.filterTracesOne = function(traces, filter) {
traces = traces || this.traces[this.activeTab].traces;

return traces.filter(function(trace) {
    for (var prop in filter) {
        if (filter.hasOwnProperty(prop)) {
            if (filter[prop] === trace._params[prop]) {
                return true;
            }
        }
    }

    return false;
}); };

CatboostIpython.prototype.cleanSeries = function() {
$(‘.catboost-panel__series’, this.layout).html(‘’);
};

CatboostIpython.prototype.groupTraces = function() {
var traces = this.traces[this.activeTab].traces,
index = 0,
tracesHash = {};

traces.map(function(trace) {
    var train = trace._params.train;

    if (!tracesHash[train]) {
        tracesHash[train] = {
            index: index,
            traces: [],
            info: {
                path: trace._params.path,
                color: trace._params.plotParams.color
            }
        };

        index++;
    }

    tracesHash[train].traces.push(trace);
});

return tracesHash; };

CatboostIpython.prototype.drawTraces = function() {
var html = ‘’,
tracesHash = this.groupTraces();

var curLength = $('.catboost-panel__series .catboost-panel__serie', this.layout).length;
var newLength = Object.keys(tracesHash).filter(hasOwnProperty.bind(tracesHash)).length;
if (newLength === curLength) {
    return;
}

for (var train in tracesHash) {
    if (tracesHash.hasOwnProperty(train)) {
        html += this.drawTrace(train, tracesHash[train]);
    }
}

$('.catboost-panel__series', this.layout).html(html);

this.updateTracesValues();

this.addTracesEvents(); };

CatboostIpython.prototype.getTraceDefParams = function(params) {
var defParams = {
smoothed: undefined,
best_point: undefined,
best_value: undefined,
cv_avg: undefined,
cv_stddev_first: undefined,
cv_stddev_last: undefined
};

if (params) {
    return $.extend(defParams, params);
} else {
    return defParams;
} };

CatboostIpython.prototype.drawTrace = function(train, hash) {
var info = hash.info,
id = ‘catboost-serie-‘ + this.index + ‘-‘ + hash.index,
traces = {
learn: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: ‘learn’})),
test: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: ‘test’}))
},
items = {
learn: {
middle: ‘’,
bottom: ‘’
},
test: {
middle: ‘’,
bottom: ‘’
}
},
tracesNames = ‘’;

['learn', 'test'].forEach(function(type) {
    traces[type].forEach(function(trace) {
        items[type].middle += '<div class="catboost-panel__serie_' + type + '_pic" style="border-color:' + info.color + '"></div>' +
                              '<div data-index="' + trace._params.indexOfSet + '" class="catboost-panel__serie_' + type + '_value"></div>';

        items[type].bottom += '<div class="catboost-panel__serie_' + type + '_pic" style="border-color:transparent"></div>' +
                              '<div data-index="' + trace._params.indexOfSet + '" class="catboost-panel__serie_best_' + type + '_value"></div>';

        tracesNames += '<div class="catboost-panel__serie_' + type + '_pic" style="border-color:' + info.color + '"></div>' +
                       '<div class="catboost-panel__serie_' + type + '_name">' + trace._params.nameOfSet + '</div>';
    });
});

var timeSpendHtml = '<div class="catboost-panel__serie_time">' +
                         '<div class="catboost-panel__serie_time_spend" title="Time spend"></div>' +
                    '</div>';

var html = '<div id="' + id + '" class="catboost-panel__serie" style="color:' + info.color + '">' +
                '<div class="catboost-panel__serie_top">' +
                    '<input type="checkbox" data-seriename="' + train + '" class="catboost-panel__serie_checkbox" id="' + id + '-box" ' + (!this.layoutDisabled.series[train] ? 'checked="checked"' : '') + '></input>' +
                    '<label title=' + this.meta[info.path].name + ' for="' + id + '-box" class="catboost-panel__serie_label">' + train + '<div class="catboost-panel__serie_time_left" title="Estimate time"></div></label>' +
                    (this.getLaunchMode(info.path) !== 'Eval' ? timeSpendHtml : '') +
                '</div>' +
                '<div class="catboost-panel__serie_hint catboost-panel__serie__learn_hint">curr</div>' +
                '<div class="catboost-panel__serie_hint catboost-panel__serie__test_hint">best</div>' +
                '<div class="catboost-panel__serie_iteration" title="curr iteration"></div>' +
                '<div class="catboost-panel__serie_best_iteration" title="best ' + (this.hasCVMode ? 'avg ' : '') + 'iteration"></div>' +
                '<div class="catboost-panel__serie_scroll">' +
                    '<div class="catboost-panel__serie_names">' +
                        tracesNames +
                    '</div>' +
                    '<div class="catboost-panel__serie_middle">' +
                        items.learn.middle +
                        items.test.middle +
                    '</div>' +
                    '<div class="catboost-panel__serie_bottom">' +
                        items.learn.bottom +
                        items.test.bottom +
                    '</div>' +
                '</div>' +
            '</div>';

return html; };

CatboostIpython.prototype.updateTracesValues = function(iteration, click) {
var tracesHash = this.groupTraces();

for (var train in tracesHash) {
    if (tracesHash.hasOwnProperty(train) && !this.layoutDisabled.traces[train]) {
        this.updateTraceValues(train, tracesHash[train], iteration, click);
    }
} };

CatboostIpython.prototype.updateTracesBest = function() {
var tracesHash = this.groupTraces();

for (var train in tracesHash) {
    if (tracesHash.hasOwnProperty(train) && !this.layoutDisabled.traces[train]) {
        this.updateTraceBest(train, tracesHash[train]);
    }
} };

CatboostIpython.prototype.getBestValue = function(data) {
if (!data.length) {
return {
best: undefined,
index: -1
};
}

var best = data[0],
    index = 0,
    func = this.lossFuncs[this.traces[this.activeTab].name],
    bestDiff = typeof func === 'number' ? Math.abs(data[0] - func) : 0;

for (var i = 1, l = data.length; i < l; i++) {
    if (func === 'Min' && data[i] < best) {
        best = data[i];
        index = i;
    }

    if (func === 'Max' && data[i] > best) {
        best = data[i];
        index = i;
    }

    if (typeof func === 'number' && Math.abs(data[i] - func) < bestDiff) {
        best = data[i];
        bestDiff = Math.abs(data[i] - func);
        index = i;
    }
}

return {
    best: best,
    index: index,
    func: func
}; };

CatboostIpython.prototype.updateTracesCV = function() {
this.updateTracesCVAvg();

if (this.hasCVMode && this.stddevEnabled) {
    this.updateTracesCVStdDev();
} };

CatboostIpython.prototype.updateTracesCVAvg = function() {
var tracesHash = this.groupTraces(),
avgTraces = this.filterTracesEvery(tracesHash.traces, this.getTraceDefParams({
cv_avg: true
})),
self = this;

avgTraces.forEach(function(trace) {
    var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({
            train: trace._params.train,
            type: trace._params.type,
            smoothed: trace._params.smoothed
        }));

    if (origTraces.length) {
        self.cvAvgFunc(origTraces, trace);
    }
}); };

CatboostIpython.prototype.cvAvgFunc = function(origTraces, avgTrace) {
var maxCount = origTraces.length,
maxLength = -1,
count,
sum;

origTraces.forEach(function(origTrace) {
    if (origTrace.y.length > maxLength) {
        maxLength = origTrace.y.length;
    }
});

for (var i = 0; i < maxLength; i++) {
    sum = 0;
    count = 0;

    for (var j = 0; j < maxCount; j++) {
        if (typeof origTraces[j].y[i] !== 'undefined') {
            sum += origTraces[j].y[i];
            count++;
        }
    }

    if (count > 0) {
        avgTrace.x[i] = i;
        avgTrace.y[i] = sum / count;
    }
} };

CatboostIpython.prototype.updateTracesCVStdDev = function() {
var tracesHash = this.groupTraces(),
firstTraces = this.filterTracesOne(tracesHash.traces, {cv_stddev_first: true}),
self = this;

firstTraces.forEach(function(trace) {
    var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({
            train: trace._params.train,
            type: trace._params.type,
            smoothed: trace._params.smoothed
        })),
        lastTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({
            train: trace._params.train,
            type: trace._params.type,
            smoothed: trace._params.smoothed,
            cv_stddev_last: true
        }));

    if (origTraces.length && lastTraces.length === 1) {
        self.cvStdDevFunc(origTraces, trace, lastTraces[0]);
    }
}); };

CatboostIpython.prototype.cvStdDevFunc = function(origTraces, firstTrace, lastTrace) {
var maxCount = origTraces.length,
maxLength = -1,
count,
sum,
i, j;

origTraces.forEach(function(origTrace) {
    if (origTrace.y.length > maxLength) {
        maxLength = origTrace.y.length;
    }
});

for (i = 0; i < maxLength; i++) {
    sum = 0;
    count = 0;

    for (j = 0; j < maxCount; j++) {
        if (typeof origTraces[j].y[i] !== 'undefined') {
            sum += origTraces[j].y[i];
            count++;
        }
    }

    if (count <= 0) {
        continue;
    }

    var std = 0,
        avg = sum / count;

    for (j = 0; j < maxCount; j++) {
        if (typeof origTraces[j].y[i] !== 'undefined') {
            std += Math.pow(origTraces[j].y[i] - avg, 2);
        }
    }

    std /= (count - 1);
    std = Math.pow(std, 0.5);

    firstTrace.x[i] = i;
    firstTrace.y[i] = avg - std;
    firstTrace.hovertext[i] = firstTrace._params.type + ' std: ' + avg.toFixed(7) + '-' + std.toFixed(7);
    lastTrace.x[i] = i;
    lastTrace.y[i] = avg + std;
    lastTrace.hovertext[i] = lastTrace._params.type + ' std: ' + avg.toFixed(7) + '+' + std.toFixed(7);
    if (this.hovertextParameters.length > i) {
        firstTrace.hovertext[i] += this.hovertextParameters[i];
        lastTrace.hovertext[i] += this.hovertextParameters[i];
    }
} };

CatboostIpython.prototype.updateTracesSmoothness = function() {
var tracesHash = this.groupTraces(),
smoothedTraces = this.filterTracesOne(tracesHash.traces, {smoothed: true}),
enabled = this.getSmoothness() > -1,
self = this;

smoothedTraces.forEach(function(trace) {
    var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({
            train: trace._params.train,
            type: trace._params.type,
            indexOfSet: trace._params.indexOfSet,
            cv_avg: trace._params.cv_avg,
            cv_stddev_first: trace._params.cv_stddev_first,
            cv_stddev_last: trace._params.cv_stddev_last
        })),
        colorFlag = false;

    if (origTraces.length === 1) {
        origTraces = origTraces[0];

        if (origTraces.visible) {
            if (enabled) {
                self.smoothFunc(origTraces, trace);
                colorFlag = true;
            }

            self.highlightSmoothedTrace(origTraces, trace, colorFlag);
        }
    }
}); };

CatboostIpython.prototype.highlightSmoothedTrace = function(trace, smoothedTrace, flag) {
if (flag) {
smoothedTrace.line.color = trace._params.plotParams.color;
trace.line.color = smoothedTrace._params.plotParams.color;
trace.hoverinfo = ‘skip’;

    if (trace._params.cv_stddev_last) {
        trace.fillcolor = trace._params.plotParams.fillsmoothcolor;
    }
} else {
    trace.line.color = trace._params.plotParams.color;
    trace.hoverinfo = trace._params.plotParams.hoverinfo;

    if (trace._params.cv_stddev_last) {
        trace.fillcolor = trace._params.plotParams.fillcolor;
    }
} };

CatboostIpython.prototype.smoothFunc = function(origTrace, smoothedTrace) {
var data = origTrace.y,
smoothedPoints = this.smooth(data, this.getSmoothness()),
smoothedIndex = 0,
self = this;

if (smoothedPoints.length) {
    data.forEach(function (d, index) {
        if (!smoothedTrace.x[index]) {
            smoothedTrace.x[index] = index;
        }

        var nameOfSet = smoothedTrace._params.nameOfSet;

        if (smoothedTrace._params.cv_stddev_first || smoothedTrace._params.cv_stddev_last) {
            nameOfSet = smoothedTrace._params.type + ' std';
        }

        smoothedTrace.y[index] = smoothedPoints[smoothedIndex];
        smoothedTrace.hovertext[index] = nameOfSet + '`: ' + smoothedPoints[smoothedIndex].toPrecision(7);
        if (self.hovertextParameters.length > index) {
            smoothedTrace.hovertext[index] += self.hovertextParameters[index];
        }
        smoothedIndex++;
    });
} };

CatboostIpython.prototype.formatItemValue = function(value, index, suffix) {
if (typeof value === ‘undefined’) {
return ‘’;
}

suffix = suffix || '';

return '<span title="' + suffix + 'value ' + value + '">' + value + '</span>'; };

CatboostIpython.prototype.updateTraceBest = function(train, hash) {
var traces = this.filterTracesOne(hash.traces, {best_point: true}),
self = this;

traces.forEach(function(trace) {
    var testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({
            train: trace._params.train,
            type: 'test',
            indexOfSet: trace._params.indexOfSet
        }));

    if (self.hasCVMode) {
        testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({
            train: trace._params.train,
            type: 'test',
            cv_avg: true
        }));
    }

    var bestValue = self.getBestValue(testTrace.length === 1 ? testTrace[0].y : []);

    if (bestValue.index !== -1) {
        trace.x[0] = bestValue.index;
        trace.y[0] = bestValue.best;
        trace.hovertext[0] = bestValue.func + ' (' + (self.hasCVMode ? 'avg' : trace._params.nameOfSet) + '): ' + bestValue.index + ' ' + bestValue.best;
    }
}); };

CatboostIpython.prototype.updateTraceValues = function(name, hash, iteration, click) {
var id = ‘catboost-serie-‘ + this.index + ‘-‘ + hash.index,
traces = {
learn: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: ‘learn’})),
test: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: ‘test’}))
},
path = hash.info.path,
self = this;

['learn', 'test'].forEach(function(type) {
    traces[type].forEach(function(trace) {
        var data = trace.y || [],
            index = typeof iteration !== 'undefined' && iteration < data.length - 1 ? iteration : data.length - 1,
            value = data.length ? data[index] : undefined,
            testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({
                type: 'test',
                indexOfSet: trace._params.indexOfSet
            })),
            bestValue = self.getBestValue(testTrace.length === 1 ? testTrace[0].y : []),
            timeLeft = '',
            timeSpend = '';

        if (click || !self.clickMode) {
            $('#' + id + ' .catboost-panel__serie_' + type + '_value[data-index=' + trace._params.indexOfSet + ']', self.layout)
                .html(self.formatItemValue(value, index, type + ' '));
            $('#' + id + ' .catboost-panel__serie_iteration', self.layout).html(index);

            if (self.timeLeft[path] && self.timeLeft[path][data.length - 1]) {
                timeLeft = self.timeLeft[path][data.length - 1][0];
            }
            $('#' + id + ' .catboost-panel__serie_time_left', self.layout).html(timeLeft ? ('~' + self.convertTime(timeLeft)) : '');

            if (self.timeLeft[path] && self.timeLeft[path][index]) {
                timeSpend = self.timeLeft[path][index][1];
            }

            $('#' + id + ' .catboost-panel__serie_time_spend', self.layout).html(self.convertTime(timeSpend));
            $('#' + id + ' .catboost-panel__serie_best_iteration', self.layout).html(bestValue.index > -1 ? bestValue.index : '');

            $('#' + id + ' .catboost-panel__serie_best_test_value[data-index=' + trace._params.indexOfSet + ']', self.layout)
                .html(self.formatItemValue(bestValue.best, bestValue.index, 'best ' + trace._params.nameOfSet + ' '));
        }
    });
});

if (this.hasCVMode) {
    var testTrace = this.filterTracesEvery(hash.traces, this.getTraceDefParams({
            type: 'test',
            cv_avg: true
        })),
        bestValue = this.getBestValue(testTrace.length === 1 ? testTrace[0].y : []);

        $('#' + id + ' .catboost-panel__serie_best_iteration', this.layout).html(bestValue.index > -1 ? bestValue.index : '');
}

if (click) {
    this.clickMode = true;

    $('#catboost-control2-clickmode' + this.index, this.layout)[0].checked = true;
} };

CatboostIpython.prototype.addTracesEvents = function() {
var self = this;

$('.catboost-panel__serie_checkbox', this.layout).click(function() {
    var name = $(this).data('seriename');

    self.layoutDisabled.traces[name] = !$(this)[0].checked;

    self.redrawActiveChart();
}); };

CatboostIpython.prototype.getNextColor = function(path, opacity) {
var color;

if (this.colorsByPath[path]) {
    color = this.colorsByPath[path];
} else {
    color = this.colors[this.colorIndex];
    this.colorsByPath[path] = color;

    this.colorIndex++;

    if (this.colorIndex > this.colors.length - 1) {
        this.colorIndex = 0;
    }
}

return this.hexToRgba(color, opacity); };

CatboostIpython.prototype.hexToRgba = function(value, opacity) {
if (value.length < 6) {
var pattern = /^#?([a-f\d])([a-f\d])([a-f\d])/i;
value = value.replace(pattern, function(m, r, g, b) {
return ‘#’ + r + r + g + g + b + b;
});
}

var result = /^#?([a-f\d]{2})([a-f\d]{2})([a-f\d]{2})/i.exec(value);
var rgb = {
    r: parseInt(result[1], 16),
    g: parseInt(result[2], 16),
    b: parseInt(result[3], 16)
};

return 'rgba(' + rgb.r + ',' + rgb.g + ',' + rgb.b + ',' + opacity + ')'; };

CatboostIpython.prototype.convertTime = function(time) {
if (!time) {
return ‘0s’;
}

time = Math.floor(time * 1000);

var millis = time % 1000;
time = parseInt(time / 1000, 10);
var seconds = time % 60;
time = parseInt(time / 60, 10);
var minutes = time % 60;
time = parseInt(time / 60, 10);
var hours = time % 24;
var out = "";
if (hours && hours > 0) {
    out += hours + 'h ';
    seconds = 0;
    millis = 0;
}
if (minutes && minutes > 0) {
    out += minutes + 'm ';
    millis = 0;
}
if (seconds && seconds > 0) {
    out += seconds + 's ';
}
if (millis && millis > 0) {
    out += millis + 'ms';
}

return out.trim(); };

CatboostIpython.prototype.mean = function(values, valueof) {
var n = values.length,
m = n,
i = -1,
value,
sum = 0,
number = function(x) {
return x === null ? NaN : +x;
};

if (valueof === null) {
    while (++i < n) {
        if (!isNaN(value = number(values[i]))) {
            sum += value;
        } else {
            --m;
        }
    }
} else {
    while (++i < n) {
        if (!isNaN(value = number(valueof(values[i], i, values)))) {
            sum += value;
        } else {
            --m;
        }
    }
}

if (m) {
    return sum / m;
} };

// from TensorBoard
CatboostIpython.prototype.smooth = function(data, weight) {
// When increasing the smoothing window, it smoothes a lot with the first
// few points and then starts to gradually smooth slower, so using an
// exponential function makes the slider more consistent. 1000^x has a
// range of [1, 1000], so subtracting 1 and dividing by 999 results in a
// range of [0, 1], which can be used as the percentage of the data, so
// that the kernel size can be specified as a percentage instead of a
// hardcoded number, what would be bad with multiple series.
var factor = (Math.pow(1000, weight) - 1) / 999,
kernelRadius = Math.floor(data.length * factor / 2),
res = [],
self = this;

data.forEach(function (d, i) {
    var actualKernelRadius = Math.min(kernelRadius, i, data.length - i - 1);
    var start = i - actualKernelRadius;
    var end = i + actualKernelRadius + 1;
    var point = d;
    // Only smooth finite numbers.
    if (!isFinite(point)) {
        res.push(point);
    } else {
        res.push(self.mean(data.slice(start, end).filter(function(d) {
            return isFinite(d);
        }), null));
    }
});

return res; }; var debug = false;

if (debug) {
require.config({
shim:{
“custom/CatboostIpythonPlotly”:{
deps:[“custom/plotly-basic.min”]
}
}
})

require.undef('catboost_module');
require.undef('custom/CatboostIpythonPlotly'); }

var moduleBase = ‘@jupyter-widgets/base’;
var modules = [moduleBase];

if (debug) {
modules.push(‘custom/CatboostIpythonPlotly’);
}

define(‘catboost_module’, modules, function(widgets) {
var getInstance = function(el) {
var id = $(el).attr(‘catboost-id’);

        if (!id) {
            return null;
        }

        id = id.replace('catboost_', '');

        if (!window.catboostIpythonInstances[id]) {
            return null;
        }

        return window.catboostIpythonInstances[id];
    },
    addInstance = function(el) {
        $(el).attr('catboost-id', 'catboost_' + window.catboostIpythonIndex);

        var catboostIpython = new CatboostIpython();
        catboostIpython.index = window.catboostIpythonIndex;
        catboostIpython.plotly = window.Plotly;
        if (debug) {
            catboostIpython.loadStyles('/custom/CatboostIpython.css', function(){catboostIpython.resizeCharts();})
        }

        window.catboostIpythonInstances[window.catboostIpythonIndex] = catboostIpython;

        window.catboostIpythonIndex++;

        return catboostIpython;
    };

var CatboostIpythonWidget = widgets.DOMWidgetView.extend({

    initialize: function() {
        CatboostIpythonWidget.__super__.initialize.apply(this, arguments);

        if (!window.catboostIpythonInstances) {
            window.catboostIpythonInstances = {};
        }

        if (typeof window.catboostIpythonIndex === 'undefined') {
            window.catboostIpythonIndex = 0;
        }

        var catboostIpythonInstance = getInstance(this.el);

        if (!catboostIpythonInstance) {
            catboostIpythonInstance = addInstance(this.el);
        }

        catboostIpythonInstance.init();
    },

    render: function() {
        this.value_changed();
        this.model.on('change:value', this.value_changed, this);
    },

    update: function() {
        this.value_changed();
    },

    value_changed: function() {
        this.el.style['width'] = this.model.get('width');
        this.el.style['height'] = this.model.get('height');
        this.displayed.then(_.bind(this.render_charts, this));
    },

    process_all: function(parent, params) {
        var data = params.data;

        for (var path in data) {
            if (data.hasOwnProperty(path)) {
                this.process_row(parent, data[path])
            }
        }
    },

    process_row: function(parent, data) {
        var catboostIpython = getInstance(parent),
            path = data.path,
            content = data.content,
            items = content.data.iterations,
            firstIndex = 0,
            chunks = [];

        if (!items || !items.length) {
            return;
        }

        if (!catboostIpython.lastIndex) {
            catboostIpython.lastIndex = {}
        }

        if (catboostIpython.lastIndex[path]) {
            firstIndex = catboostIpython.lastIndex[path] + 1;
        }

        catboostIpython.lastIndex[path] = items.length - 1;

        for (var i = firstIndex; i < items.length; i++) {
            chunks.push(items[i]);
        }

        catboostIpython.addMeta(data.path, content.data.meta);

        catboostIpython.addPoints(parent, {
            chunks: chunks,
            train: data.name,
            path: data.path
        });
    },

    render_charts: function () {
        this.process_all(this.el, {
            data: this.model.get('data')
        });

        return this;
    }
});

return {
    CatboostIpythonWidgetView: CatboostIpythonWidget
}; });

        </script>



MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))


Learning rate set to 0.009797
0:	learn: 0.6865149	total: 164ms	remaining: 2m 44s
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<catboost.core.CatBoostClassifier at 0x25f9ce0cc70>

# CatBoost accuracy
acc_catboost = round(catboost_model.score(X_train, y_train) * 100, 2)

acc_catboost # 100 iterations: 83.01 accusracy

85.15

Catboost with cross validation

# How long will this take?
start_time = time.time()

# Set params for cross-validation as same as initial model
cv_params = catboost_model.get_params()

# Run the cross-validation for 10-folds (same as the other models)
cv_data = cv(train_pool,
             cv_params,
             fold_count=10,
             plot=True)

# How long did it take?
catboost_time = (time.time() - start_time)

# CatBoost CV results save into a dataframe (cv_data), let's withdraw the maximum accuracy score
acc_cv_catboost = round(np.max(cv_data['test-Accuracy-mean']) * 100, 2)

        <style>
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display: none !important; } .highcharts-halo {
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margin-left: 0;
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cursor: pointer;
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.catboost-panel__controls2_label-long {
width: 170px;
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height: 340px;
overflow-y: auto;
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margin-left: 290px;
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padding: 0 0 0 20px;
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padding-bottom: 5px;
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position: relative;
}

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white-space: nowrap;
position: relative;
}

#catboost-control-test {
margin-left: 11px;
}

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padding: 0 0 0 8px;
width: 200px;
text-overflow: ellipsis;
box-sizing: border-box;
cursor: pointer;
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position: relative;
top: 5px;
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t=d.getTranslate(C);Y=t.x,Z=t.y,G={},w&&w.autorange&&(G[w._name+".autorange"]=!0),k&&k.autorange&&(G[k._name+".autorange"]=!0)},moveFn:function(t,e){var r=_(Y,Z),n=r[0]+t,a=r[1]+e;C.call(d.setTranslate,n,a),G[X+".x"]=w?w.p2r(w.r2p(b.x)+t):b.x+t/y.w,G[X+".y"]=k?k.p2r(k.r2p(b.y)+e):b.y-e/y.h,b.axref===b.xref&&(G[X+".ax"]=w.p2r(w.r2p(b.ax)+t)),b.ayref===b.yref&&(G[X+".ay"]=k.p2r(k.r2p(b.ay)+e)),B.attr("transform","translate("+t+","+e+")"),L.attr({transform:"rotate("+A+","+n+","+a+")"})},doneFn:function(e){if(e){l.relayout(t,G);var r=document.querySelector(".js-notes-box-panel");r&&r.redraw(r.selectedObj)}}})}}};if(b.showarrow&&G(0,0),t._context.editable){var Y,Z;v.init({element:C.node(),prepFn:function(){Z=L.attr("transform"),Y={}},moveFn:function(t,e){var r="pointer";if(b.showarrow)b.axref===b.xref?Y[X+".ax"]=w.p2r(w.r2p(b.ax)+t):Y[X+".ax"]=b.ax+t,b.ayref===b.yref?Y[X+".ay"]=k.p2r(k.r2p(b.ay)+e):Y[X+".ay"]=b.ay+e,G(t,e);else{if(w)Y[X+".x"]=b.x+t/w._m;else{var n=b._xsize/y.w,a=b.x+(b._xshift-b.xshift)/y.w-n/2;Y[X+".x"]=v.align(a+t/y.w,n,0,1,b.xanchor)}if(k)Y[X+".y"]=b.y+e/k._m;else{var o=b._ysize/y.h,i=b.y-(b._yshift+b.yshift)/y.h-o/2;Y[X+".y"]=v.align(i-e/y.h,o,0,1,b.yanchor)}w&&k||(r=v.getCursor(w?.5:Y[X+".x"],k?.5:Y[X+".y"],b.xanchor,b.yanchor))}L.attr({transform:"translate("+t+","+e+")"+Z}),g(C,r)},doneFn:function(e){if(g(C),e){l.relayout(t,Y);var r=document.querySelector(".js-notes-box-panel");r&&r.redraw(r.selectedObj)}}})}}var a=t.layout,s=t._fullLayout,y=t._fullLayout._size;s._infolayer.selectAll('.annotation[data-index="'+e+'"]').remove();var x=(a.annotations||[])[e],b=s.annotations[e],_="clip"+s._uid+"_ann"+e;if(!x||b.visible===!1)return void i.selectAll("#"+_).remove();var w=u.getFromId(t,b.xref),k=u.getFromId(t,b.yref),M={x:{},y:{}},A=+b.textangle||0,T=s._infolayer.append("g").classed("annotation",!0).attr("data-index",String(e)).style("opacity",b.opacity),L=T.append("g").classed("annotation-text-g",!0).attr("data-index",String(e)),C=L.append("g").style("pointer-events",b.captureevents?"all":null).call(g,"default").on("click",function(){t._dragging=!1,t.emit("plotly_clickannotation",{index:e,annotation:x,fullAnnotation:b,event:i.event})});b.hovertext&&C.on("mouseover",function(){var e=b.hoverlabel,r=e.font,n=this.getBoundingClientRect(),a=t.getBoundingClientRect();h.loneHover({x0:n.left-a.left,x1:n.right-a.left,y:(n.top+n.bottom)/2-a.top,text:b.hovertext,color:e.bgcolor,borderColor:e.bordercolor,fontFamily:r.family,fontSize:r.size,fontColor:r.color},{container:s._hoverlayer.node(),outerContainer:s._paper.node()})}).on("mouseout",function(){h.loneUnhover(s._hoverlayer.node())});var S=b.borderwidth,z=b.borderpad,O=S+z,D=C.append("rect").attr("class","bg").style("stroke-width",S+"px").call(f.stroke,b.bordercolor).call(f.fill,b.bgcolor),P=b.width||b.height,E=s._defs.select(".clips").selectAll("#"+_).data(P?[0]:[]);E.enter().append("clipPath").classed("annclip",!0).attr("id",_).append("rect"),E.exit().remove();var N=b.font,I=C.append("text").classed("annotation",!0).attr("data-unformatted",b.text).text(b.text);t._context.editable?I.call(p.makeEditable,C).call(r).on("edit",function(n){b.text=n,this.attr({"data-unformatted":b.text}),this.call(r);var a={};a["annotations["+e+"].text"]=b.text,w&&w.autorange&&(a[w._name+".autorange"]=!0),k&&k.autorange&&(a[k._name+".autorange"]=!0),l.relayout(t,a)}):I.call(r)}function o(t,e,r,n,a,o,i,l){var s=r-t,c=a-t,u=i-a,f=n-e,d=o-e,h=l-o,p=s*h-u*f;if(0===p)return null;var g=(c*h-u*d)/p,v=(c*f-s*d)/p;return v<0||v>1||g<0||g>1?null:{x:t+s*g,y:e+f*g}}var i=t("d3"),l=t("../../plotly"),s=t("../../plots/plots"),c=t("../../lib"),u=t("../../plots/cartesian/axes"),f=t("../color"),d=t("../drawing"),h=t("../fx"),p=t("../../lib/svg_text_utils"),g=t("../../lib/setcursor"),v=t("../dragelement"),m=t("./draw_arrow_head");e.exports={draw:n,drawOne:a}},{"../../lib":136,"../../lib/setcursor":151,"../../lib/svg_text_utils":153,"../../plotly":166,"../../plots/cartesian/axes":171,"../../plots/plots":199,"../color":25,"../dragelement":46,"../drawing":49,"../fx":66,"./draw_arrow_head":22,d3:7}],22:[function(t,e,r){"use strict";var n=t("d3"),a=t("fast-isnumeric"),o=t("../color"),i=t("../drawing"),l=t("./arrow_paths");e.exports=function(t,e,r,s,c){function u(){t.style("stroke-dasharray","0px,100px")}function f(r,a){h.path&&(e>5&&(a=0),n.select(d.parentElement).append("path").attr({class:t.attr("class"),d:h.path,transform:"translate("+r.x+","+r.y+")rotate("+180*a/Math.PI+")scale("+y+")"}).style({fill:x,opacity:b,"stroke-width":0}))}a(s)||(s=1);var d=t.node(),h=l[e||0];"string"==typeof r&&r||(r="end");var p,g,v,m,y=(i.getPx(t,"stroke-width")||1)*s,x=t.style("stroke")||o.defaultLine,b=t.style("stroke-opacity")||1,_=r.indexOf("start")>=0,w=r.indexOf("end")>=0,k=h.backoff*y+c;if("line"===d.nodeName){p={x:+t.attr("x1"),y:+t.attr("y1")},g={x:+t.attr("x2"),y:+t.attr("y2")};var M=p.x-g.x,A=p.y-g.y;if(v=Math.atan2(A,M),m=v+Math.PI,k){if(k*k>M*M+A*A)return void u();var T=k*Math.cos(v),L=k*Math.sin(v);_&&(p.x-=T,p.y-=L,t.attr({x1:p.x,y1:p.y})),w&&(g.x+=T,g.y+=L,t.attr({x2:g.x,y2:g.y}))}}else if("path"===d.nodeName){var C=d.getTotalLength(),S="";if(C<k)return void u();if(_){var z=d.getPointAtLength(0),O=d.getPointAtLength(.1);v=Math.atan2(z.y-O.y,z.x-O.x),p=d.getPointAtLength(Math.min(k,C)),k&&(S="0px,"+k+"px,")}if(w){var D=d.getPointAtLength(C),P=d.getPointAtLength(C-.1);if(m=Math.atan2(D.y-P.y,D.x-P.x),g=d.getPointAtLength(Math.max(0,C-k)),k){var E=S?2*k:k;S+=C-E+"px,"+C+"px"}}else 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n=t("../../plots/cartesian/layout_attributes"),a=t("../../plots/font_attributes"),o=t("../../lib/extend").extendFlat;e.exports={thicknessmode:{valType:"enumerated",values:["fraction","pixels"],dflt:"pixels"},thickness:{valType:"number",min:0,dflt:30},lenmode:{valType:"enumerated",values:["fraction","pixels"],dflt:"fraction"},len:{valType:"number",min:0,dflt:1},x:{valType:"number",dflt:1.02,min:-2,max:3},xanchor:{valType:"enumerated",values:["left","center","right"],dflt:"left"},xpad:{valType:"number",min:0,dflt:10},y:{valType:"number",dflt:.5,min:-2,max:3},yanchor:{valType:"enumerated",values:["top","middle","bottom"],dflt:"middle"},ypad:{valType:"number",min:0,dflt:10},outlinecolor:n.linecolor,outlinewidth:n.linewidth,bordercolor:n.linecolor,borderwidth:{valType:"number",min:0,dflt:0},bgcolor:{valType:"color",dflt:"rgba(0,0,0,0)"},tickmode:n.tickmode,nticks:n.nticks,tick0:n.tick0,dtick:n.dtick,tickvals:n.tickvals,ticktext:n.ticktext,ticks:o({},n.ticks,{dflt:""}),ticklen:n.ticklen,tickwidth:n.tickwidth,tickcolor:n.tickcolor,showticklabels:n.showticklabels,tickfont:n.tickfont,tickangle:n.tickangle,tickformat:n.tickformat,tickprefix:n.tickprefix,showtickprefix:n.showtickprefix,ticksuffix:n.ticksuffix,showticksuffix:n.showticksuffix,separatethousands:n.separatethousands,exponentformat:n.exponentformat,showexponent:n.showexponent,title:{valType:"string",dflt:"Click 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n=t("d3"),a=t("tinycolor2"),o=t("../../plotly"),i=t("../../plots/plots"),l=t("../../registry"),s=t("../../plots/cartesian/axes"),c=t("../dragelement"),u=t("../../lib"),f=t("../../lib/extend").extendFlat,d=t("../../lib/setcursor"),h=t("../drawing"),p=t("../color"),g=t("../titles"),v=t("../../plots/cartesian/axis_defaults"),m=t("../../plots/cartesian/position_defaults"),y=t("../../plots/cartesian/layout_attributes"),x=t("./attributes");e.exports=function(t,e){function r(){function x(t,e){return u.coerce(J,K,y,t,e)}function w(){if(["top","bottom"].indexOf(_.titleside)!==-1){var e=ot.select(".cbtitle"),r=e.select("text"),o=[-_.outlinewidth/2,_.outlinewidth/2],i=e.select(".h"+K._id+"title-math-group").node(),l=15.6;if(r.node()&&(l=1.3*parseInt(r.style("font-size"),10)),i?(lt=h.bBox(i).height)>l&&(o[1]-=(lt-l)/2):r.node()&&!r.classed("js-placeholder")&&(lt=h.bBox(e.node()).height),lt){if(lt+=5,"top"===_.titleside)K.domain[1]-=lt/T.h,o[1]*=-1;else{K.domain[0]+=lt/T.h;var 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J=1/0;for(I=0;I<Y.length;I++)if((R=Y[I])&&R[0]&&R[0].trace&&R[0].trace.visible===!0&&(F=R[0].trace,["carpet","contourcarpet"].indexOf(F._module.name)===-1)){if(j=w.getSubplot(F),B=n.indexOf(j),q=D,U={cd:R,trace:F,xa:T[B],ya:L[B],name:t.data.length>1||F.hoverinfo.indexOf("name")!==-1?F.name:void 0,index:!1,distance:Math.min(J,k.MAXDIST),color:y.defaultLine,x0:void 0,x1:void 0,y0:void 0,y1:void 0,xLabelVal:void 0,yLabelVal:void 0,zLabelVal:void 0,text:void 0},d[j]&&(U.subplot=d[j]._subplot),X=G.length,"array"===q){var K=e[I];"pointNumber"in K?(U.index=K.pointNumber,q="closest"):(q="","xval"in K&&(H=K.xval,q="x"),"yval"in K&&(V=K.yval,q=q?"closest":"y"))}else H=P[B],V=E[B];if(F._module&&F._module.hoverPoints){var tt=F._module.hoverPoints(U,H,V,q);if(tt)for(var et,rt=0;rt<tt.length;rt++)et=tt[rt],f(et.x0)&&f(et.y0)&&G.push(l(et,D))}else h.log("Unrecognized trace type in hover:",F);"closest"===D&&G.length>X&&(G.splice(0,X),J=G[0].distance)}if(0===G.length)return x.unhoverRaw(t,e);G.sort(function(t,e){return t.distance-e.distance});var nt=t._hoverdata,at=[];for(N=0;N<G.length;N++){var ot=G[N],it={data:ot.trace._input,fullData:ot.trace,curveNumber:ot.trace.index,pointNumber:ot.index};ot.trace._module.eventData?it=ot.trace._module.eventData(it,ot):(it.x=ot.xVal,it.y=ot.yVal,it.xaxis=ot.xa,it.yaxis=ot.ya,void 0!==ot.zLabelVal&&(it.z=ot.zLabelVal)),at.push(it)}if(t._hoverdata=at,c(t,e,nt)&&d._hasCartesian){s(G,{hovermode:D,fullLayout:d,container:d._hoverlayer,outerContainer:d._paperdiv})}var lt="y"===D&&Y.length>1,st=y.combine(d.plot_bgcolor||y.background,d.paper_bgcolor),ct={hovermode:D,rotateLabels:lt,bgColor:st,container:d._hoverlayer,outerContainer:d._paperdiv,commonLabelOpts:d.hoverlabel},ut=a(G,ct);if(o(G,lt?"xa":"ya"),i(ut,lt),e.target&&e.target.tagName){var ft=_.getComponentMethod("annotations","hasClickToShow")(t,at);v(u.select(e.target),ft?"pointer":"")}e.target&&c(t,e,nt)&&(nt&&t.emit("plotly_unhover",{event:e,points:nt}),t.emit("plotly_hover",{event:e,points:t._hoverdata,xaxes:T,yaxes:L,xvals:P,yvals:E}))}function a(t,e){var r,n,a=e.hovermode,o=e.rotateLabels,i=e.bgColor,l=e.container,s=e.outerContainer,c=e.commonLabelOpts||{},f=e.fontFamily||k.HOVERFONT,d=e.fontSize||k.HOVERFONTSIZE,h=t[0],p=h.xa,v=h.ya,x="y"===a?"yLabel":"xLabel",b=h[x],_=(String(b)||"").split(" ")[0],w=s.node().getBoundingClientRect(),A=w.top,T=w.width,L=w.height,C=h.distance<=k.MAXDIST&&("x"===a||"y"===a);for(r=0;r<t.length;r++){n=t[r].trace.hoverinfo;var O=n.split("+");if(O.indexOf("all")===-1&&O.indexOf(a)===-1){C=!1;break}}var D=l.selectAll("g.axistext").data(C?[0]:[]);D.enter().append("g").classed("axistext",!0),D.exit().remove(),D.each(function(){var e=u.select(this),r=e.selectAll("path").data([0]),n=e.selectAll("text").data([0]);r.enter().append("path").style({fill:c.bgcolor||y.defaultLine,stroke:c.bordercolor||y.background,"stroke-width":"1px"}),n.enter().append("text").call(m.font,c.font.family||f,c.font.size||d,c.font.color||y.background).attr("data-notex",1),n.text(b).call(g.convertToTspans).call(m.setPosition,0,0).selectAll("tspan.line").call(m.setPosition,0,0),e.attr("transform","");var o=n.node().getBoundingClientRect();if("x"===a){n.attr("text-anchor","middle").call(m.setPosition,0,"top"===p.side?A-o.bottom-S-z:A-o.top+S+z).selectAll("tspan.line").attr({x:n.attr("x"),y:n.attr("y")});var i="top"===p.side?"-":"";r.attr("d","M0,0L"+S+","+i+S+"H"+(z+o.width/2)+"v"+i+(2*z+o.height)+"H-"+(z+o.width/2)+"V"+i+S+"H-"+S+"Z"),e.attr("transform","translate("+(p._offset+(h.x0+h.x1)/2)+","+(v._offset+("top"===p.side?0:v._length))+")")}else{n.attr("text-anchor","right"===v.side?"start":"end").call(m.setPosition,("right"===v.side?1:-1)*(z+S),A-o.top-o.height/2).selectAll("tspan.line").attr({x:n.attr("x"),y:n.attr("y")});var l="right"===v.side?"":"-";r.attr("d","M0,0L"+l+S+","+S+"V"+(z+o.height/2)+"h"+l+(2*z+o.width)+"V-"+(z+o.height/2)+"H"+l+S+"V-"+S+"Z"),e.attr("transform","translate("+(p._offset+("right"===v.side?p._length:0))+","+(v._offset+(h.y0+h.y1)/2)+")")}t=t.filter(function(t){return void 0!==t.zLabelVal||(t[x]||"").split(" ")[0]===_})});var P=l.selectAll("g.hovertext").data(t,function(t){return[t.trace.index,t.index,t.x0,t.y0,t.name,t.attr,t.xa,t.ya||""].join(",")});return P.enter().append("g").classed("hovertext",!0).each(function(){var t=u.select(this);t.append("rect").call(y.fill,y.addOpacity(i,.8)),t.append("text").classed("name",!0),t.append("path").style("stroke-width","1px"),t.append("text").classed("nums",!0).call(m.font,f,d)}),P.exit().remove(),P.each(function(t){var e=u.select(this).attr("transform",""),r="",n="",l=y.opacity(t.color)?t.color:y.defaultLine,s=y.combine(l,i),c=t.borderColor||y.contrast(s);void 0!==t.nameOverride&&(t.name=t.nameOverride),t.name&&(r=g.plainText(t.name||""),r.length>15&&(r=r.substr(0,12)+"...")),void 0!==t.extraText&&(n+=t.extraText),void 0!==t.zLabel?(void 0!==t.xLabel&&(n+="x: "+t.xLabel+"<br>"),void 0!==t.yLabel&&(n+="y: "+t.yLabel+"<br>"),n+=(n?"z: ":"")+t.zLabel):C&&t[a+"Label"]===b?n=t[("x"===a?"y":"x")+"Label"]||"":void 0===t.xLabel?void 0!==t.yLabel&&(n=t.yLabel):n=void 0===t.yLabel?t.xLabel:"("+t.xLabel+", "+t.yLabel+")",t.text&&!Array.isArray(t.text)&&(n+=(n?"<br>":"")+t.text),""===n&&(""===r&&e.remove(),n=r);var h=e.select("text.nums").call(m.font,t.fontFamily||f,t.fontSize||d,t.fontColor||c).call(m.setPosition,0,0).text(n).attr("data-notex",1).call(g.convertToTspans);h.selectAll("tspan.line").call(m.setPosition,0,0);var p=e.select("text.name"),v=0;r&&r!==n?(p.call(m.font,t.fontFamily||f,t.fontSize||d,s).text(r).call(m.setPosition,0,0).attr("data-notex",1).call(g.convertToTspans),p.selectAll("tspan.line").call(m.setPosition,0,0),v=p.node().getBoundingClientRect().width+2*z):(p.remove(),e.select("rect").remove()),e.select("path").style({fill:s,stroke:c});var x,_,w=h.node().getBoundingClientRect(),k=t.xa._offset+(t.x0+t.x1)/2,O=t.ya._offset+(t.y0+t.y1)/2,D=Math.abs(t.x1-t.x0),P=Math.abs(t.y1-t.y0),E=w.width+S+z+v;t.ty0=A-w.top,t.bx=w.width+2*z,t.by=w.height+2*z,t.anchor="start",t.txwidth=w.width,t.tx2width=v,t.offset=0,o?(t.pos=k,x=O+P/2+E<=L,_=O-P/2-E>=0,"top"!==t.idealAlign&&x||!_?x?(O+=P/2,t.anchor="start"):t.anchor="middle":(O-=P/2,t.anchor="end")):(t.pos=O,x=k+D/2+E<=T,_=k-D/2-E>=0,"left"!==t.idealAlign&&x||!_?x?(k+=D/2,t.anchor="start"):t.anchor="middle":(k-=D/2,t.anchor="end")),h.attr("text-anchor",t.anchor),v&&p.attr("text-anchor",t.anchor),e.attr("transform","translate("+k+","+O+")"+(o?"rotate("+M+")":""))}),P}function o(t,e){function r(t){var e=t[0],r=t[t.length-1];if(a=e.pmin-e.pos-e.dp+e.size,o=r.pos+r.dp+r.size-e.pmax,a>.01){for(l=t.length-1;l>=0;l--)t[l].dp+=a;n=!1}if(!(o<.01)){if(a<-.01){for(l=t.length-1;l>=0;l--)t[l].dp-=o;n=!1}if(n){var c=0;for(i=0;i<t.length;i++)s=t[i],s.pos+s.dp+s.size>e.pmax&&c++;for(i=t.length-1;i>=0&&!(c<=0);i--)s=t[i],s.pos>e.pmax-1&&(s.del=!0,c--);for(i=0;i<t.length&&!(c<=0);i++)if(s=t[i],s.pos<e.pmin+1)for(s.del=!0,c--,o=2*s.size,l=t.length-1;l>=0;l--)t[l].dp-=o;for(i=t.length-1;i>=0&&!(c<=0);i--)s=t[i],s.pos+s.dp+s.size>e.pmax&&(s.del=!0,c--)}}}for(var n,a,o,i,l,s,c,u=0,f=t.map(function(t,r){var n=t[e];return[{i:r,dp:0,pos:t.pos,posref:t.posref,size:t.by*("x"===n._id.charAt(0)?T:1)/2,pmin:n._offset,pmax:n._offset+n._length}]}).sort(function(t,e){return t[0].posref-e[0].posref});!n&&u<=t.length;){for(u++,n=!0,i=0;i<f.length-1;){var d=f[i],h=f[i+1],p=d[d.length-1],g=h[0];if((a=p.pos+p.dp+p.size-g.pos-g.dp+g.size)>.01&&p.pmin===g.pmin&&p.pmax===g.pmax){for(l=h.length-1;l>=0;l--)h[l].dp+=a;for(d.push.apply(d,h),f.splice(i+1,1),c=0,l=d.length-1;l>=0;l--)c+=d[l].dp;for(o=c/d.length,l=d.length-1;l>=0;l--)d[l].dp-=o;n=!1}else i++}f.forEach(r)}for(i=f.length-1;i>=0;i--){var v=f[i];for(l=v.length-1;l>=0;l--){var m=v[l],y=t[m.i];y.offset=m.dp,y.del=m.del}}}function i(t,e){t.each(function(t){var r=u.select(this);if(t.del)return void r.remove();var n="end"===t.anchor?-1:1,a=r.select("text.nums"),o={start:1,end:-1,middle:0}[t.anchor],i=o*(S+z),l=i+o*(t.txwidth+z),s=0,c=t.offset;"middle"===t.anchor&&(i-=t.tx2width/2,l-=t.tx2width/2),e&&(c*=-C,s=t.offset*L),r.select("path").attr("d","middle"===t.anchor?"M-"+t.bx/2+",-"+t.by/2+"h"+t.bx+"v"+t.by+"h-"+t.bx+"Z":"M0,0L"+(n*S+s)+","+(S+c)+"v"+(t.by/2-S)+"h"+n*t.bx+"v-"+t.by+"H"+(n*S+s)+"V"+(c-S)+"Z"),a.call(m.setPosition,i+s,c+t.ty0-t.by/2+z).selectAll("tspan.line").attr({x:a.attr("x"),y:a.attr("y")}),t.tx2width&&(r.select("text.name, text.name tspan.line").call(m.setPosition,l+o*z+s,c+t.ty0-t.by/2+z),r.select("rect").call(m.setRect,l+(o-1)*t.tx2width/2+s,c-t.by/2-1,t.tx2width,t.by+2))})}function l(t,e){function r(e,r,i){var l;if(o[r])l=o[r];else if(a[r]){var s=a[r];Array.isArray(s)&&Array.isArray(s[t.index[0]])&&(l=s[t.index[0]][t.index[1]])}else l=h.nestedProperty(n,i).get();l&&(t[e]=l)}var n=t.trace||{},a=t.cd[0],o=t.cd[t.index]||{};t.posref="y"===e?(t.x0+t.x1)/2:(t.y0+t.y1)/2,t.x0=h.constrain(t.x0,0,t.xa._length),t.x1=h.constrain(t.x1,0,t.xa._length),t.y0=h.constrain(t.y0,0,t.ya._length),t.y1=h.constrain(t.y1,0,t.ya._length);var i;if(void 0!==t.xLabelVal){i="log"===t.xa.type&&t.xLabelVal<=0;var l=b.tickText(t.xa,t.xa.c2l(i?-t.xLabelVal:t.xLabelVal),"hover");i?0===t.xLabelVal?t.xLabel="0":t.xLabel="-"+l.text:t.xLabel=l.text,t.xVal=t.xa.c2d(t.xLabelVal)}if(void 0!==t.yLabelVal){i="log"===t.ya.type&&t.yLabelVal<=0;var s=b.tickText(t.ya,t.ya.c2l(i?-t.yLabelVal:t.yLabelVal),"hover");i?0===t.yLabelVal?t.yLabel="0":t.yLabel="-"+s.text:t.yLabel=s.text,t.yVal=t.ya.c2d(t.yLabelVal)}if(void 0!==t.zLabelVal&&(t.zLabel=String(t.zLabelVal)),!(isNaN(t.xerr)||"log"===t.xa.type&&t.xerr<=0)){var c=b.tickText(t.xa,t.xa.c2l(t.xerr),"hover").text;void 0!==t.xerrneg?t.xLabel+=" +"+c+" / -"+b.tickText(t.xa,t.xa.c2l(t.xerrneg),"hover").text:t.xLabel+=" \xb1 "+c,"x"===e&&(t.distance+=1)}if(!(isNaN(t.yerr)||"log"===t.ya.type&&t.yerr<=0)){var u=b.tickText(t.ya,t.ya.c2l(t.yerr),"hover").text;void 0!==t.yerrneg?t.yLabel+=" +"+u+" / -"+b.tickText(t.ya,t.ya.c2l(t.yerrneg),"hover").text:t.yLabel+=" \xb1 "+u,"y"===e&&(t.distance+=1)}var f=t.trace.hoverinfo;return"all"!==f&&(f=f.split("+"),f.indexOf("x")===-1&&(t.xLabel=void 0),f.indexOf("y")===-1&&(t.yLabel=void 0),f.indexOf("z")===-1&&(t.zLabel=void 0),f.indexOf("text")===-1&&(t.text=void 0),f.indexOf("name")===-1&&(t.name=void 0)),r("color","hbg","hoverlabel.bgcolor"),r("borderColor","hbc","hoverlabel.bordercolor"),r("fontFamily","htf","hoverlabel.font.family"),r("fontSize","hts","hoverlabel.font.size"),r("fontColor","htc","hoverlabel.font.color"),t}function s(t,e){var r=e.hovermode,n=e.container,a=t[0],o=a.xa,i=a.ya,l=o.showspikes,s=i.showspikes;if(n.selectAll(".spikeline").remove(),"closest"===r&&(l||s)){var c=e.fullLayout,u=o._offset+(a.x0+a.x1)/2,f=i._offset+(a.y0+a.y1)/2,h=y.combine(c.plot_bgcolor,c.paper_bgcolor),p=d.readability(a.color,h)<1.5?y.contrast(h):a.color;if(s){var g=i.spikemode,v=i.spikethickness,x=i.spikecolor||p,b=i._boundingBox,_=(b.left+b.right)/2<u?b.right:b.left;if(g.indexOf("toaxis")!==-1||g.indexOf("across")!==-1){var w=_,k=u;g.indexOf("across")!==-1&&(w=i._counterSpan[0],k=i._counterSpan[1]),n.append("line").attr({x1:w,x2:k,y1:f,y2:f,"stroke-width":v+2,stroke:h}).classed("spikeline",!0).classed("crisp",!0),n.append("line").attr({x1:w,x2:k,y1:f,y2:f,"stroke-width":v,stroke:x,"stroke-dasharray":m.dashStyle(i.spikedash,v)}).classed("spikeline",!0).classed("crisp",!0)}g.indexOf("marker")!==-1&&n.append("circle").attr({cx:_+("right"!==i.side?v:-v),cy:f,r:v,fill:x}).classed("spikeline",!0)}if(l){var M=o.spikemode,A=o.spikethickness,T=o.spikecolor||p,L=o._boundingBox,C=(L.top+L.bottom)/2<f?L.bottom:L.top;if(M.indexOf("toaxis")!==-1||M.indexOf("across")!==-1){var S=C,z=f;M.indexOf("across")!==-1&&(S=o._counterSpan[0],z=o._counterSpan[1]),n.append("line").attr({x1:u,x2:u,y1:S,y2:z,"stroke-width":A+2,stroke:h}).classed("spikeline",!0).classed("crisp",!0),n.append("line").attr({x1:u,x2:u,y1:S,y2:z,"stroke-width":A,stroke:T,"stroke-dasharray":m.dashStyle(o.spikedash,A)}).classed("spikeline",!0).classed("crisp",!0)}M.indexOf("marker")!==-1&&n.append("circle").attr({cx:u,cy:C-("top"!==o.side?A:-A),r:A,fill:T}).classed("spikeline",!0)}}}function c(t,e,r){if(!r||r.length!==t._hoverdata.length)return!0;for(var n=r.length-1;n>=0;n--){var a=r[n],o=t._hoverdata[n];if(a.curveNumber!==o.curveNumber||String(a.pointNumber)!==String(o.pointNumber))return!0}return!1}var 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R=this.id,F=u-.5,j=O?f+D+.5:f+.5,B=d-.5,q=A?h+L+.5:h+.5,H=l._topdefs.selectAll("#"+R).data(A||O?[0]:[]);if(H.exit().remove(),H.enter().append("clipPath").attr("id",R).append("rect"),A||O?(this._clipRect=H.select("rect").attr({x:Math.floor(F),y:Math.floor(B),width:Math.ceil(j)-Math.floor(F),height:Math.ceil(q)-Math.floor(B)}),this.container.call(i.setClipUrl,R),this.bg.attr({x:p,y:v,width:g,height:m})):(this.bg.attr({width:0,height:0}),this.container.on("wheel",null).on(".drag",null).call(i.setClipUrl,null),delete this._clipRect),A||O){var V=a.behavior.drag().on("dragstart",function(){a.event.sourceEvent.preventDefault()}).on("drag",this._onBoxDrag.bind(this));this.container.on("wheel",null).on("wheel",this._onBoxWheel.bind(this)).on(".drag",null).call(V);var U=a.behavior.drag().on("dragstart",function(){a.event.sourceEvent.preventDefault(),a.event.sourceEvent.stopPropagation()}).on("drag",this._onBarDrag.bind(this));A&&this.hbar.on(".drag",null).call(U),O&&this.vbar.on(".drag",null).call(U)}this.setTranslate(e,r)},n.prototype.disable=function(){(this.hbar||this.vbar)&&(this.bg.attr({width:0,height:0}),this.container.on("wheel",null).on(".drag",null).call(i.setClipUrl,null),delete this._clipRect),this.hbar&&(this.hbar.on(".drag",null),this.hbar.remove(),delete this.hbar,delete this._hbarXMin,delete this._hbarTranslateMax),this.vbar&&(this.vbar.on(".drag",null),this.vbar.remove(),delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax)},n.prototype._onBoxDrag=function(){var t=this.translateX,e=this.translateY;this.hbar&&(t-=a.event.dx),this.vbar&&(e-=a.event.dy),this.setTranslate(t,e)},n.prototype._onBoxWheel=function(){var 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o=e/n;this.vbar.call(i.setTranslate,t,e+o*this._vbarTranslateMax)}}},{"../../lib":136,"../color":25,"../drawing":49,d3:7}],121:[function(t,e,r){"use strict";e.exports={SHOW_PLACEHOLDER:100,HIDE_PLACEHOLDER:1e3,DBLCLICKDELAY:300}},{}],122:[function(t,e,r){"use strict";e.exports={BADNUM:void 0,FP_SAFE:Number.MAX_VALUE/1e4,ONEAVGYEAR:315576e5,ONEAVGMONTH:26298e5,ONEDAY:864e5,ONEHOUR:36e5,ONEMIN:6e4,ONESEC:1e3,EPOCHJD:2440587.5,ALMOST_EQUAL:1-1e-6}},{}],123:[function(t,e,r){"use strict";e.exports={entityToUnicode:{mu:"\u03bc",amp:"&",lt:"<",gt:">",nbsp:"\xa0",times:"\xd7",plusmn:"\xb1",deg:"\xb0"},unicodeToEntity:{"&":"amp","<":"lt",">":"gt",'"':"quot","'":"#x27","/":"#x2F"}}},{}],124:[function(t,e,r){"use strict";r.xmlns="http://www.w3.org/2000/xmlns/",r.svg="http://www.w3.org/2000/svg",r.xlink="http://www.w3.org/1999/xlink",r.svgAttrs={xmlns:r.svg,"xmlns:xlink":r.xlink}},{}],125:[function(t,e,r){"use strict";var n=t("./plotly");/*export for 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n=t("../../components/color/attributes"),a=t("../../plots/font_attributes"),o=t("../../plots/attributes"),i=t("../../lib/extend").extendFlat;e.exports={labels:{valType:"data_array"},label0:{valType:"number",dflt:0},dlabel:{valType:"number",dflt:1},values:{valType:"data_array"},marker:{colors:{valType:"data_array"},line:{color:{valType:"color",dflt:n.defaultLine,arrayOk:!0},width:{valType:"number",min:0,dflt:0,arrayOk:!0}}},text:{valType:"data_array"},hovertext:{valType:"string",dflt:"",arrayOk:!0},scalegroup:{valType:"string",dflt:""},textinfo:{valType:"flaglist",flags:["label","text","value","percent"],extras:["none"]},hoverinfo:i({},o.hoverinfo,{flags:["label","text","value","percent","name"]}),textposition:{valType:"enumerated",values:["inside","outside","auto","none"],dflt:"auto",arrayOk:!0},textfont:i({},a,{}),insidetextfont:i({},a,{}),outsidetextfont:i({},a,{}),domain:{x:{valType:"info_array",items:[{valType:"number",min:0,max:1},{valType:"number",min:0,max:1}],dflt:[0,1]},y:{valType:"info_array",items:[{valType:"number",min:0,max:1},{valType:"number",min:0,max:1}],dflt:[0,1]}},hole:{valType:"number",min:0,max:1,dflt:0},sort:{valType:"boolean",dflt:!0},direction:{valType:"enumerated",values:["clockwise","counterclockwise"],dflt:"counterclockwise"},rotation:{valType:"number",min:-360,max:360,dflt:0},pull:{valType:"number",min:0,max:1,dflt:0,arrayOk:!0}}},{"../../components/color/attributes":24,"../../lib/extend":132,"../../plots/attributes":169,"../../plots/font_attributes":195}],229:[function(t,e,r){"use 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s},{"../../components/color":25,"./helpers":232,"fast-isnumeric":10,tinycolor2:13}],231:[function(t,e,r){"use strict";var n=t("../../lib"),a=t("./attributes");e.exports=function(t,e,r,o){function i(r,o){return n.coerce(t,e,a,r,o)}var l=n.coerceFont,s=i("values");if(!Array.isArray(s)||!s.length)return void(e.visible=!1);var c=i("labels");Array.isArray(c)||(i("label0"),i("dlabel")),i("marker.line.width")&&i("marker.line.color");var u=i("marker.colors");Array.isArray(u)||(e.marker.colors=[]),i("scalegroup");var f=i("text"),d=i("textinfo",Array.isArray(f)?"text+percent":"percent");if(i("hovertext"),i("hoverinfo",1===o._dataLength?"label+text+value+percent":void 0),d&&"none"!==d){var h=i("textposition"),p=Array.isArray(h)||"auto"===h,g=p||"inside"===h,v=p||"outside"===h;if(g||v){var m=l(i,"textfont",o.font);g&&l(i,"insidetextfont",m),v&&l(i,"outsidetextfont",m)}}i("domain.x"),i("domain.y"),i("hole"),i("sort"),i("direction"),i("rotation"),i("pull")}},{"../../lib":136,"./attributes":228}],232:[function(t,e,r){"use strict";var n=t("../../lib");r.formatPiePercent=function(t,e){var r=(100*t).toPrecision(3);return r.lastIndexOf(".")!==-1&&(r=r.replace(/[.]?0+$/,"")),n.numSeparate(r,e)+"%"},r.formatPieValue=function(t,e){var r=t.toPrecision(10);return r.lastIndexOf(".")!==-1&&(r=r.replace(/[.]?0+$/,"")),n.numSeparate(r,e)}},{"../../lib":136}],233:[function(t,e,r){"use strict";var n={};n.attributes=t("./attributes"),n.supplyDefaults=t("./defaults"),n.supplyLayoutDefaults=t("./layout_defaults"),n.layoutAttributes=t("./layout_attributes"),n.calc=t("./calc"),n.plot=t("./plot"),n.style=t("./style"),n.styleOne=t("./style_one"),n.moduleType="trace",n.name="pie",n.basePlotModule=t("./base_plot"),n.categories=["pie","showLegend"],n.meta={},e.exports=n},{"./attributes":228,"./base_plot":229,"./calc":230,"./defaults":231,"./layout_attributes":234,"./layout_defaults":235,"./plot":236,"./style":237,"./style_one":238}],234:[function(t,e,r){"use strict";e.exports={hiddenlabels:{valType:"data_array"}}},{}],235:[function(t,e,r){"use strict";var n=t("../../lib"),a=t("./layout_attributes");e.exports=function(t,e){!function(r,o){n.coerce(t,e,a,r,o)}("hiddenlabels")}},{"../../lib":136,"./layout_attributes":234}],236:[function(t,e,r){"use strict";function n(t,e,r){var n=Math.sqrt(t.width*t.width+t.height*t.height),o=t.width/t.height,i=Math.PI*Math.min(e.v/r.vTotal,.5),l=1-r.trace.hole,s=a(e,r),c={scale:s*r.r*2/n,rCenter:1-s,rotate:0};if(c.scale>=1)return c;var u=o+1/(2*Math.tan(i)),f=r.r*Math.min(1/(Math.sqrt(u*u+.5)+u),l/(Math.sqrt(o*o+l/2)+o)),d={scale:2*f/t.height,rCenter:Math.cos(f/r.r)-f*o/r.r,rotate:(180/Math.PI*e.midangle+720)%180-90},h=1/o,p=h+1/(2*Math.tan(i)),g=r.r*Math.min(1/(Math.sqrt(p*p+.5)+p),l/(Math.sqrt(h*h+l/2)+h)),v={scale:2*g/t.width,rCenter:Math.cos(g/r.r)-g/o/r.r,rotate:(180/Math.PI*e.midangle+810)%180-90},m=v.scale>d.scale?v:d;return c.scale<1&&m.scale>c.scale?m:c}function a(t,e){if(t.v===e.vTotal&&!e.trace.hole)return 1;var r=Math.PI*Math.min(t.v/e.vTotal,.5);return Math.min(1/(1+1/Math.sin(r)),(1-e.trace.hole)/2)}function o(t,e){var r=e.pxmid[0],n=e.pxmid[1],a=t.width/2,o=t.height/2;return r<0&&(a*=-1),n<0&&(o*=-1),{scale:1,rCenter:1,rotate:0,x:a+Math.abs(o)*(a>0?1:-1)/2,y:o/(1+r*r/(n*n)),outside:!0}}function i(t,e){function r(t,e){return t.pxmid[1]-e.pxmid[1]}function n(t,e){return e.pxmid[1]-t.pxmid[1]}var a,o,i,l,s,c,u,f,d,h,p,g,v;for(o=0;o<2;o++)for(i=o?r:n,s=o?Math.max:Math.min,u=o?1:-1,a=0;a<2;a++){for(l=a?Math.max:Math.min,c=a?1:-1,f=t[o][a],f.sort(i),d=t[1-o][a],h=d.concat(f),g=[],p=0;p<f.length;p++)void 0!==f[p].yLabelMid&&g.push(f[p]);for(v=!1,p=0;o&&p<d.length;p++)if(void 0!==d[p].yLabelMid){v=d[p];break}for(p=0;p<g.length;p++){var m=p&&g[p-1];v&&!p&&(m=v),function(t,r){r||(r={});var 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r,n,a,o,i,l,s,u,f,d,h=[];for(a=0;a<t.length;a++){if(i=t[a][0],l=i.trace,r=e.w*(l.domain.x[1]-l.domain.x[0]),n=e.h*(l.domain.y[1]-l.domain.y[0]),s=l.tiltaxis*Math.PI/180,u=l.pull,Array.isArray(u))for(u=0,o=0;o<l.pull.length;o++)l.pull[o]>u&&(u=l.pull[o]);i.r=Math.min(r/c(l.tilt,Math.sin(s),l.depth),n/c(l.tilt,Math.cos(s),l.depth))/(2+2*u),i.cx=e.l+e.w*(l.domain.x[1]+l.domain.x[0])/2,i.cy=e.t+e.h*(2-l.domain.y[1]-l.domain.y[0])/2, l.scalegroup&&h.indexOf(l.scalegroup)===-1&&h.push(l.scalegroup)}for(o=0;o<h.length;o++){for(d=1/0,f=h[o],a=0;a<t.length;a++)i=t[a][0],i.trace.scalegroup===f&&(d=Math.min(d,i.r*i.r/i.vTotal));for(a=0;a<t.length;a++)i=t[a][0],i.trace.scalegroup===f&&(i.r=Math.sqrt(d*i.vTotal))}}function s(t){function e(t){var e=f.r*Math.sin(t),r=-f.r*Math.cos(t);return h?[e*(1-l*n*n)+r*i*l,e*i*l+r*(1-l*a*a),Math.sin(o)*(r*a-e*n)]:[e,r]}var 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l=u.select(this),c=e[0],v=c.trace,m=(v.depth||0)*c.r*Math.sin(0)/2,y=v.tiltaxis||0,x=y*Math.PI/180,b=[m*Math.sin(x),m*Math.cos(x)],_=c.r*Math.cos(0),w=l.selectAll("g.part").data(v.tilt?["top","sides"]:["top"]);w.enter().append("g").attr("class",function(t){return t+" part"}),w.exit().remove(),w.order(),s(e),l.selectAll(".top").each(function(){var l=u.select(this).selectAll("g.slice").data(e);l.enter().append("g").classed("slice",!0),l.exit().remove();var s=[[[],[]],[[],[]]],m=!1;l.each(function(e){function i(n){n.originalEvent=u.event;var o=t._fullLayout,i=t._fullData[v.index],l=i.hoverinfo;if("all"===l&&(l="label+text+value+percent+name"),t._dragging||o.hovermode===!1||"none"===l||"skip"===l||!l)return void f.hover(t,n,"pie");var s=a(e,c),d=w+e.pxmid[0]*(1-s),h=k+e.pxmid[1]*(1-s),p=r.separators,m=[];l.indexOf("label")!==-1&&m.push(e.label),l.indexOf("text")!==-1&&(i.hovertext?m.push(Array.isArray(i.hovertext)?i.hovertext[e.i]:i.hovertext):i.text&&i.text[e.i]&&m.push(i.text[e.i])),l.indexOf("value")!==-1&&m.push(g.formatPieValue(e.v,p)),l.indexOf("percent")!==-1&&m.push(g.formatPiePercent(e.v/c.vTotal,p));var y=i.hoverlabel;f.loneHover({x0:d-s*c.r,x1:d+s*c.r,y:h,text:m.join("<br>"),name:l.indexOf("name")!==-1?i.name:void 0,idealAlign:e.pxmid[0]<0?"left":"right",color:e.hbg||y.bgcolor||e.color,borderColor:e.hbc||y.bordercolor,fontFamily:e.htf||y.font.family,fontSize:e.hts||y.font.size,fontColor:e.htc||y.font.color},{container:o._hoverlayer.node(),outerContainer:o._paper.node()}),f.hover(t,n,"pie"),T=!0}function l(e){e.originalEvent=u.event,t.emit("plotly_unhover",{event:u.event,points:[e]}),T&&(f.loneUnhover(r._hoverlayer.node()),T=!1)}function d(){t._hoverdata=[e],t._hoverdata.trace=c.trace,f.click(t,u.event)}function x(t,r,n,a){return"a"+a*c.r+","+a*_+" "+y+" "+e.largeArc+(n?" 1 ":" 0 ")+a*(r[0]-t[0])+","+a*(r[1]-t[1])}if(e.hidden)return void u.select(this).selectAll("path,g").remove();s[e.pxmid[1]<0?0:1][e.pxmid[0]<0?0:1].push(e);var w=c.cx+b[0],k=c.cy+b[1],M=u.select(this),A=M.selectAll("path.surface").data([e]),T=!1;if(A.enter().append("path").classed("surface",!0).style({"pointer-events":"all"}),M.select("path.textline").remove(),M.on("mouseover",i).on("mouseout",l).on("click",d),v.pull){var L=+(Array.isArray(v.pull)?v.pull[e.i]:v.pull)||0;L>0&&(w+=L*e.pxmid[0],k+=L*e.pxmid[1])}e.cxFinal=w,e.cyFinal=k;var C=v.hole;if(e.v===c.vTotal){var S="M"+(w+e.px0[0])+","+(k+e.px0[1])+x(e.px0,e.pxmid,!0,1)+x(e.pxmid,e.px0,!0,1)+"Z";C?A.attr("d","M"+(w+C*e.px0[0])+","+(k+C*e.px0[1])+x(e.px0,e.pxmid,!1,C)+x(e.pxmid,e.px0,!1,C)+"Z"+S):A.attr("d",S)}else{var z=x(e.px0,e.px1,!0,1);if(C){var O=1-C;A.attr("d","M"+(w+C*e.px1[0])+","+(k+C*e.px1[1])+x(e.px1,e.px0,!1,C)+"l"+O*e.px0[0]+","+O*e.px0[1]+z+"Z")}else A.attr("d","M"+w+","+k+"l"+e.px0[0]+","+e.px0[1]+z+"Z")}var D=Array.isArray(v.textposition)?v.textposition[e.i]:v.textposition,P=M.selectAll("g.slicetext").data(e.text&&"none"!==D?[0]:[]);P.enter().append("g").classed("slicetext",!0),P.exit().remove(),P.each(function(){var t=u.select(this).selectAll("text").data([0]);t.enter().append("text").attr("data-notex",1),t.exit().remove(),t.text(e.text).attr({class:"slicetext",transform:"","data-bb":"","text-anchor":"middle",x:0,y:0}).call(h.font,"outside"===D?v.outsidetextfont:v.insidetextfont).call(p.convertToTspans),t.selectAll("tspan.line").attr({x:0,y:0});var 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u,f=i.isBubble(t),d=(t.line||{}).color;if(c=c||{},d&&(r=d),s("marker.symbol"),s("marker.opacity",f?.7:1),s("marker.size"),s("marker.color",r),a(t,"marker")&&o(t,e,l,s,{prefix:"marker.",cLetter:"c"}),c.noLine||(u=d&&!Array.isArray(d)&&e.marker.color!==d?d:f?n.background:n.defaultLine,s("marker.line.color",u),a(t,"marker.line")&&o(t,e,l,s,{prefix:"marker.line.",cLetter:"c"}),s("marker.line.width",f?1:0)),f&&(s("marker.sizeref"),s("marker.sizemin"),s("marker.sizemode")),c.gradient){"none"!==s("marker.gradient.type")&&s("marker.gradient.color")}}},{"../../components/color":25,"../../components/colorscale/defaults":34,"../../components/colorscale/has_colorscale":38,"./subtypes":260}],257:[function(t,e,r){"use strict";function n(t,e){var r;e.selectAll("g.trace").each(function(t){var e=i.select(this);if(r=t[0].trace,r._nexttrace){if(r._nextFill=e.select(".js-fill.js-tonext"),!r._nextFill.size()){var n=":first-child";e.select(".js-fill.js-tozero").size()&&(n+=" + 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b=r.enter().append("path").classed("point",!0);b.call(s.pointStyle,o).call(s.translatePoints,M,A,o),k&&b.style("opacity",0).transition().style("opacity",1);var _=f&&s.tryColorscale(o.marker,""),w=f&&s.tryColorscale(o.marker,"line");r.each(function(e){var r=i.select(this),n=g(r);(a=s.translatePoint(e,n,M,A))&&(s.singlePointStyle(e,n,o,_,w,t),o.customdata&&r.classed("plotly-customdata",null!==e.data&&void 0!==e.data))}),k?r.exit().transition().style("opacity",0).remove():r.exit().remove(),n=c.selectAll("g"),r=n.data(m,h),r.enter().append("g").classed("textpoint",!0).append("text"),r.each(function(t){var e=i.select(this),r=g(e.select("text"));(a=s.translatePoint(t,r,M,A))||e.remove()}),r.selectAll("text").call(s.textPointStyle,o).each(function(t){var e=t.xp||M.c2p(t.x),r=t.yp||A.c2p(t.y);i.select(this).selectAll("tspan.line").each(function(){g(i.select(this)).attr({x:e,y:r})})}),r.exit().remove()}var _,w;o(t,e,r,n,a);var k=!!p&&p.duration>0,M=r.xaxis,A=r.yaxis,T=n[0].trace,L=T.line,C=i.select(d);if(C.call(c.plot,r,p),T.visible===!0){g(C).style("opacity",T.opacity);var S,z,O=T.fill.charAt(T.fill.length-1);"x"!==O&&"y"!==O&&(O=""),n[0].node3=C;var D="",P=[],E=T._prevtrace;E&&(D=E._prevRevpath||"",z=E._nextFill,P=E._polygons);var N,I,R,F,j,B,q,H,V,U="",X="",G=[],Y=[],Z=l.noop;if(S=T._ownFill,u.hasLines(T)||"none"!==T.fill){for(z&&z.datum(n),["hv","vh","hvh","vhv"].indexOf(L.shape)!==-1?(R=s.steps(L.shape),F=s.steps(L.shape.split("").reverse().join(""))):R=F="spline"===L.shape?function(t){var e=t[t.length-1];return t[0][0]===e[0]&&t[0][1]===e[1]?s.smoothclosed(t.slice(1),L.smoothing):s.smoothopen(t,L.smoothing)}:function(t){return"M"+t.join("L")},j=function(t){return F(t.reverse())},G=f(n,{xaxis:M,yaxis:A,connectGaps:T.connectgaps,baseTolerance:Math.max(L.width||1,3)/4,linear:"linear"===L.shape,simplify:L.simplify}),V=T._polygons=new Array(G.length),w=0;w<G.length;w++)T._polygons[w]=h(G[w]);G.length&&(B=G[0][0],q=G[G.length-1],H=q[q.length-1]),Y=G.filter(function(t){return t.length>1}),Z=function(t){return function(e){if(N=R(e),I=j(e),U?O?(U+="L"+N.substr(1),X=I+"L"+X.substr(1)):(U+="Z"+N,X=I+"Z"+X):(U=N,X=I),u.hasLines(T)&&e.length>1){var r=i.select(this);if(r.datum(n),t)g(r.style("opacity",0).attr("d",N).call(s.lineGroupStyle)).style("opacity",1);else{var a=g(r);a.attr("d",N),s.singleLineStyle(n,a)}}}}}var W=C.selectAll(".js-line").data(Y);g(W.exit()).style("opacity",0).remove(),W.each(Z(!1)),W.enter().append("path").classed("js-line",!0).style("vector-effect","non-scaling-stroke").call(s.lineGroupStyle).each(Z(!0)),G.length&&(S?B&&H&&(O?("y"===O?B[1]=H[1]=A.c2p(0,!0):"x"===O&&(B[0]=H[0]=M.c2p(0,!0)),g(S).attr("d","M"+H+"L"+B+"L"+U.substr(1))):g(S).attr("d",U+"Z")):"tonext"===T.fill.substr(0,6)&&U&&D&&("tonext"===T.fill?g(z).attr("d",U+"Z"+D+"Z"):g(z).attr("d",U+"L"+D.substr(1)+"Z"),T._polygons=T._polygons.concat(P)),T._prevRevpath=X,T._prevPolygons=V);var $=C.selectAll(".points");_=$.data([n]),$.each(b),_.enter().append("g").classed("points",!0).each(b),_.exit().remove()}}function o(t,e,r,n,a){var o=r.xaxis,s=r.yaxis,c=i.extent(l.simpleMap(o.range,o.r2c)),f=i.extent(l.simpleMap(s.range,s.r2c)),d=n[0].trace;if(u.hasMarkers(d)){var h=d.marker.maxdisplayed;if(0!==h){var p=n.filter(function(t){return t.x>=c[0]&&t.x<=c[1]&&t.y>=f[0]&&t.y<=f[1]}),g=Math.ceil(p.length/h),v=0;a.forEach(function(t,r){var n=t[0].trace;u.hasMarkers(n)&&n.marker.maxdisplayed>0&&r<e&&v++});var m=Math.round(v*g/3+Math.floor(v/3)*g/7.1);n.forEach(function(t){delete t.vis}),p.forEach(function(t,e){0===Math.round((e+m)%g)&&(t.vis=!0)})}}}var i=t("d3"),l=t("../../lib"),s=t("../../components/drawing"),c=t("../../components/errorbars"),u=t("./subtypes"),f=t("./line_points"),d=t("./link_traces"),h=t("../../lib/polygon").tester;e.exports=function(t,e,r,o,l){var s,c,u,f,h,p=e.plot.select("g.scatterlayer"),g=!o,v=!!o&&o.duration>0;for(u=p.selectAll("g.trace"),f=u.data(r,function(t){return t[0].trace.uid}),f.enter().append("g").attr("class",function(t){return"trace scatter trace"+t[0].trace.uid}).style("stroke-miterlimit",2),d(t,e,r),n(t,p),s=0,c={};s<r.length;s++)c[r[s][0].trace.uid]=s;if(p.selectAll("g.trace").sort(function(t,e){return c[t[0].trace.uid]>c[e[0].trace.uid]?1:-1}),v){l&&(h=l());i.transition().duration(o.duration).ease(o.easing).each("end",function(){h&&h()}).each("interrupt",function(){h&&h()}).each(function(){p.selectAll("g.trace").each(function(n,i){a(t,i,e,n,r,this,o)})})}else p.selectAll("g.trace").each(function(n,i){a(t,i,e,n,r,this,o)});g&&f.exit().remove(),p.selectAll("path:not([d])").remove()}},{"../../components/drawing":49,"../../components/errorbars":55,"../../lib":136,"../../lib/polygon":146,"./line_points":252,"./link_traces":254,"./subtypes":260,d3:7}],258:[function(t,e,r){"use strict";var n=t("./subtypes");e.exports=function(t,e){var r,a,o,i,l=t.cd,s=t.xaxis,c=t.yaxis,u=[],f=l[0].trace,d=f.index,h=f.marker,p=!n.hasMarkers(f)&&!n.hasText(f);if(f.visible===!0&&!p){var g=Array.isArray(h.opacity)?1:h.opacity;if(e===!1)for(r=0;r<l.length;r++)l[r].dim=0;else for(r=0;r<l.length;r++)a=l[r],o=s.c2p(a.x),i=c.c2p(a.y),e.contains([o,i])?(u.push({curveNumber:d,pointNumber:r,x:a.x,y:a.y,id:a.id}),a.dim=0):a.dim=1;return l[0].node3.selectAll("path.point").style("opacity",function(t){return((t.mo+1||g+1)-1)*(t.dim?.2:1)}),l[0].node3.selectAll("text").style("opacity",function(t){return t.dim?.2:1}),u}}},{"./subtypes":260}],259:[function(t,e,r){"use strict";var n=t("d3"),a=t("../../components/drawing"),o=t("../../components/errorbars");e.exports=function(t){var e=n.select(t).selectAll("g.trace.scatter");e.style("opacity",function(t){return t[0].trace.opacity}),e.selectAll("g.points").each(function(t){var e=n.select(this),r=e.selectAll("path.point"),o=t.trace||t[0].trace;r.call(a.pointStyle,o),e.selectAll("text").call(a.textPointStyle,o)}),e.selectAll("g.trace path.js-line").call(a.lineGroupStyle),e.selectAll("g.trace path.js-fill").call(a.fillGroupStyle),e.call(o.style)}},{"../../components/drawing":49,"../../components/errorbars":55,d3:7}],260:[function(t,e,r){"use strict";var n=t("../../lib");e.exports={hasLines:function(t){return t.visible&&t.mode&&t.mode.indexOf("lines")!==-1},hasMarkers:function(t){return t.visible&&t.mode&&t.mode.indexOf("markers")!==-1},hasText:function(t){return t.visible&&t.mode&&t.mode.indexOf("text")!==-1},isBubble:function(t){return n.isPlainObject(t.marker)&&Array.isArray(t.marker.size)}}},{"../../lib":136}],261:[function(t,e,r){"use strict";var n=t("../../lib");e.exports=function(t,e,r,a){a("textposition"),n.coerceFont(a,"textfont",r.font)}},{"../../lib":136}],262:[function(t,e,r){"use strict";var n=t("../../registry");e.exports=function(t,e,r,a){var o,i=a("x"),l=a("y");if(n.getComponentMethod("calendars","handleTraceDefaults")(t,e,["x","y"],r),i)l?(o=Math.min(i.length,l.length),o<i.length&&(e.x=i.slice(0,o)),o<l.length&&(e.y=l.slice(0,o))):(o=i.length,a("y0"),a("dy"));else{if(!l)return 0;o=e.y.length,a("x0"),a("dx")}return o}},{"../../registry":206}]},{},[5])(5)});window.define = window.__define;window.require = window.__require;window.__define = undefined; window.__require = undefined;/* global Highcharts, Plotly*/ /* eslint-disable */ function throttle(fn, timeout, invokeAsap, ctx) {
var typeofInvokeAsap = typeof invokeAsap;
if(typeofInvokeAsap === 'undefined') {
    invokeAsap = true;
} else if(arguments.length === 3 && typeofInvokeAsap !== 'boolean') {
    ctx = invokeAsap;
    invokeAsap = true;
}

var timer, args, needInvoke,
    wrapper = function() {
        if(needInvoke) {
            fn.apply(ctx, args);
            needInvoke = false;
            timer = setTimeout(wrapper, timeout);
        } else {
            timer = null;
        }
    };

return function() {
    args = arguments;
    ctx || (ctx = this);
    needInvoke = true;

    if(!timer) {
        invokeAsap?
            wrapper() :
            timer = setTimeout(wrapper, timeout);
    }
}; }

/* eslint-enable */
function CatboostIpython() {

}

CatboostIpython.prototype.init = function() {
this.charts = {};
/*
{
“rmse_1”: {
…
}
}
/
this.traces = {};
/
{
“rmse_1”: {
name: “rmse”,
id: “rmse_1”,
parent: “div”,
traces: [
{
name: “current;learn;0;;;”,
x: [],
y: []
},
{
name: “current;learn;0;smoothed;;”,
x: [],
y: []
},
{
name: “current;learn;1;;;”,
x: [],
y: []
},
{
name: “current;learn;1;smoothed;;”,
x: [],
y: []
},
{
name: “current;test;0;;;”,
x: [],
y: []
},
{
name: “current;test;0;smoothed;;”,
x: [],
y: []
},
{
name: “current;test;0;;best_point;”,
x: [],
y: []
},
{
name: “current;test;0;;;best_value”,
x: [],
y: []
}
]
}
}
*/

this.hovertextParameters = [];
this.chartsToRedraw = {};
this.lastIndexes = {};
this.smoothness = -1;
this.layoutDisabled = {
    series: {},
    traces: {}
};
this.clickMode = false;
this.logarithmMode = 'linear';
this.lastSmooth = 0;
this.layout = null;
this.activeTab = '';
this.meta = {};
this.timeLeft = {};

this.hasCVMode = false;
this.stddevEnabled = false;

this.colors = [
    '#68E256',
    '#56AEE2',
    '#CF56E2',
    '#E28956',
    '#56E289',
    '#5668E2',
    '#E256AE',
    '#E2CF56',
    '#56E2CF',
    '#8A56E2',
    '#E25668',
    '#AEE256'
];
this.colorsByPath = {};
this.colorIndex = 0;
this.lossFuncs = {};

this.isCVinited = false; };

/* eslint-disable */
CatboostIpython.prototype.loadStyles = function(path, fn, scope) {
$(‘link[catboost=”1”]’).remove();

var head = document.getElementsByTagName('head')[0], // reference to document.head for appending/ removing link nodes
    link = document.createElement('link');           // create the link node
link.setAttribute('href', path);
link.setAttribute('rel', 'stylesheet');
link.setAttribute('type', 'text/css');
link.setAttribute('catboost', '1');

var sheet, cssRules;
// get the correct properties to check for depending on the browser
if ('sheet' in link) {
    sheet = 'sheet'; cssRules = 'cssRules';
} else {
    sheet = 'styleSheet'; cssRules = 'rules';
}

var interval_id = setInterval(function() {                     // start checking whether the style sheet has successfully loaded
    try {
        if (link[sheet] && link[sheet][cssRules].length) { // SUCCESS! our style sheet has loaded
            clearInterval(interval_id);                      // clear the counters
            clearTimeout(timeout_id);
            fn.call( scope || window, true, link);           // fire the callback with success == true
        }
    } catch(e) {} finally {}
}, 50 ),                                                   // how often to check if the stylesheet is loaded
timeout_id = setTimeout( function() {       // start counting down till fail
    clearInterval( interval_id );             // clear the counters
    clearTimeout( timeout_id );
    head.removeChild( link );                // since the style sheet didn't load, remove the link node from the DOM
    fn.call( scope || window, false, link ); // fire the callback with success == false
}, 15000 );                                 // how long to wait before failing

head.appendChild( link );  // insert the link node into the DOM and start loading the style sheet

return link; // return the link node; }; /* eslint-enable */

CatboostIpython.prototype.resizeCharts = function() {
// width fix for development
$(‘.catboost-graph__charts’, this.layout).css({width: $(‘.catboost-graph’).width()});

this.plotly.Plots.resize(this.traces[this.activeTab].parent); };

CatboostIpython.prototype.addMeta = function(path, meta) {
this.meta[path] = meta;
};

CatboostIpython.prototype.addLayout = function(parent) {
if (this.layout) {
return;
}

var cvAreaControls = '';

if (this.hasCVMode) {
    cvAreaControls =    '<div>' +
                            '<input type="checkbox" class="catboost-panel__control_checkbox" id="catboost-control2-cvstddev' + this.index + '"' + (this.stddevEnabled ? ' checked="checked"' : '') + '></input>' +
                            '<label for="catboost-control2-cvstddev' + this.index + '" class="catboost-panel__controls2_label catboost-panel__controls2_label-long">Standard Deviation</label>' +
                        '</div>';
}

this.layout = $('<div class="catboost">' +
                    '<div class="catboost-panel">' +
                        '<div class="catboost-panel__controls">' +
                            '<input type="checkbox" class="catboost-panel__controls_checkbox" id="catboost-control-learn' + this.index + '" ' + (!this.layoutDisabled.learn ? ' checked="checked"' : '') + '></input>' +
                            '<label for="catboost-control-learn' + this.index + '" class="catboost-panel__controls_label"><div class="catboost-panel__serie_learn_pic" style="border-color:#999"></div>Learn</label>' +
                            '<input type="checkbox" class="catboost-panel__controls_checkbox" id="catboost-control-test' + this.index + '" ' + (!this.layoutDisabled.test ? ' checked="checked"' : '') + '></input>' +
                            '<label for="catboost-control-test' + this.index + '" class="catboost-panel__controls_label"><div class="catboost-panel__serie_test_pic" style="border-color:#999"></div>Eval</label>' +
                        '</div>' +
                        '<div class="catboost-panel__series ' + (this.layoutDisabled.learn ? ' catboost-panel__series_learn_disabled' : '') + '">' +
                        '</div>' +
                        '<div class="catboost-panel__controls2">' +
                            '<input type="checkbox" class="catboost-panel__control_checkbox" id="catboost-control2-clickmode' + this.index + '"></input>' +
                            '<label for="catboost-control2-clickmode' + this.index + '" class="catboost-panel__controls2_label">Click Mode</label>' +
                            '<input type="checkbox" class="catboost-panel__control_checkbox" id="catboost-control2-log' + this.index + '"></input>' +
                            '<label for="catboost-control2-log' + this.index + '" class="catboost-panel__controls2_label">Logarithm</label>' +
                            '<div>' +
                                '<input type="checkbox" class="catboost-panel__control_checkbox" id="catboost-control2-smooth' + this.index + '"></input>' +
                                '<label for="catboost-control2-smooth' + this.index + '" class="catboost-panel__controls2_label">Smooth</label>' +
                                '<input id="catboost-control2-slider' + this.index + '" disabled="disabled" class="catboost-panel__control_slider" type ="range" value="0" min="0" max="1" step ="0.01" for="rangeInputValue" name="rangeInput"/>' +
                                '<input id="catboost-control2-slidervalue' + this.index + '" disabled="disabled" class="catboost-panel__control_slidervalue" value="0" min="0" max="1" for="rangeInput" name="rangeInputValue"/>' +
                            '</div>' +
                            cvAreaControls +
                        '</div>' +
                    '</div>' +
                    '<div class="catboost-graph">' +
                        '<div class="catboost-graph__tabs"></div>' +
                        '<div class="catboost-graph__charts"></div>' +
                    '</div>' +
                '</div>');
$(parent).append(this.layout);

this.addTabEvents();
this.addControlEvents(); };

CatboostIpython.prototype.addTabEvents = function() {
var self = this;

$('.catboost-graph__tabs', this.layout).click(function(e) {
    if (!$(e.target).is('.catboost-graph__tab:not(.catboost-graph__tab_active)')) {
        return;
    }

    var id = $(e.target).attr('tabid');

    self.activeTab = id;

    $('.catboost-graph__tab_active', self.layout).removeClass('catboost-graph__tab_active');
    $('.catboost-graph__chart_active', self.layout).removeClass('catboost-graph__chart_active');

    $('.catboost-graph__tab[tabid="' + id + '"]', self.layout).addClass('catboost-graph__tab_active');
    $('.catboost-graph__chart[tabid="' + id + '"]', self.layout).addClass('catboost-graph__chart_active');

    self.cleanSeries();

    self.redrawActiveChart();
    self.resizeCharts();
}); };

CatboostIpython.prototype.addControlEvents = function() {
var self = this;

$('#catboost-control-learn' + this.index, this.layout).click(function() {
    self.layoutDisabled.learn = !$(this)[0].checked;

    $('.catboost-panel__series', self.layout).toggleClass('catboost-panel__series_learn_disabled', self.layoutDisabled.learn);

    self.redrawActiveChart();
});

$('#catboost-control-test' + this.index, this.layout).click(function() {
    self.layoutDisabled.test = !$(this)[0].checked;

    $('.catboost-panel__series', self.layout).toggleClass('catboost-panel__series_test_disabled', self.layoutDisabled.test);

    self.redrawActiveChart();
});

$('#catboost-control2-clickmode' + this.index, this.layout).click(function() {
    self.clickMode = $(this)[0].checked;
});

$('#catboost-control2-log' + this.index, this.layout).click(function() {
    self.logarithmMode = $(this)[0].checked ? 'log' : 'linear';

    self.forEveryLayout(function(layout) {
        layout.yaxis = {type: self.logarithmMode};
    });

    self.redrawActiveChart();
});

var slider = $('#catboost-control2-slider' + this.index),
    sliderValue = $('#catboost-control2-slidervalue' + this.index);

$('#catboost-control2-smooth' + this.index, this.layout).click(function() {
    var enabled = $(this)[0].checked;

    self.setSmoothness(enabled ? self.lastSmooth : -1);

    slider.prop('disabled', !enabled);
    sliderValue.prop('disabled', !enabled);

    self.redrawActiveChart();
});

$('#catboost-control2-cvstddev' + this.index, this.layout).click(function() {
    var enabled = $(this)[0].checked;

    self.setStddev(enabled);

    self.redrawActiveChart();
});

slider.on('input change', function() {
    var smooth = Number($(this).val());

    sliderValue.val(isNaN(smooth) ? 0 : smooth);

    self.setSmoothness(smooth);
    self.lastSmooth = smooth;

    self.redrawActiveChart();
});

sliderValue.on('input change', function() {
    var smooth = Number($(this).val());

    slider.val(isNaN(smooth) ? 0 : smooth);

    self.setSmoothness(smooth);
    self.lastSmooth = smooth;

    self.redrawActiveChart();
}); };

CatboostIpython.prototype.setTraceVisibility = function(trace, visibility) {
if (trace) {
trace.visible = visibility;
}
};

CatboostIpython.prototype.updateTracesVisibility = function() {
var tracesHash = this.groupTraces(),
traces,
smoothDisabled = this.getSmoothness() === -1,
self = this;

for (var train in tracesHash) {
    if (tracesHash.hasOwnProperty(train)) {
        traces = tracesHash[train].traces;

        if (this.layoutDisabled.traces[train]) {
            traces.forEach(function(trace) {
                self.setTraceVisibility(trace, false);
            });
        } else {
            traces.forEach(function(trace) {
                self.setTraceVisibility(trace, true);
            });

            if (this.hasCVMode) {
                if (this.stddevEnabled) {
                    self.filterTracesOne(traces, {type: 'learn'}).forEach(function(trace) {
                        self.setTraceVisibility(trace, false);
                    });
                    self.filterTracesOne(traces, {type: 'test'}).forEach(function(trace) {
                        self.setTraceVisibility(trace, false);
                    });

                    self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true})).forEach(function(trace) {
                        self.setTraceVisibility(trace, true);
                    });
                    self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true})).forEach(function(trace) {
                        self.setTraceVisibility(trace, true);
                    });

                    self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true, smoothed: true})).forEach(function(trace) {
                        self.setTraceVisibility(trace, true);
                    });
                    self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, smoothed: true})).forEach(function(trace) {
                        self.setTraceVisibility(trace, true);
                    });

                    self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, best_point: true})).forEach(function(trace) {
                        self.setTraceVisibility(trace, true);
                    });

                    self.filterTracesOne(traces, {cv_stddev_first: true}).forEach(function(trace) {
                        self.setTraceVisibility(trace, true);
                    });
                    self.filterTracesOne(traces, {cv_stddev_last: true}).forEach(function(trace) {
                        self.setTraceVisibility(trace, true);
                    });
                } else {
                    self.filterTracesOne(traces, {cv_stddev_first: true}).forEach(function(trace) {
                        self.setTraceVisibility(trace, false);
                    });
                    self.filterTracesOne(traces, {cv_stddev_last: true}).forEach(function(trace) {
                        self.setTraceVisibility(trace, false);
                    });

                    self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true})).forEach(function(trace) {
                        self.setTraceVisibility(trace, false);
                    });
                    self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true})).forEach(function(trace) {
                        self.setTraceVisibility(trace, false);
                    });

                    self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true, smoothed: true})).forEach(function(trace) {
                        self.setTraceVisibility(trace, false);
                    });
                    self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, smoothed: true})).forEach(function(trace) {
                        self.setTraceVisibility(trace, false);
                    });

                    self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, best_point: true})).forEach(function(trace) {
                        self.setTraceVisibility(trace, false);
                    });
                }
            }

            if (smoothDisabled) {
                self.filterTracesOne(traces, {smoothed: true}).forEach(function(trace) {
                    self.setTraceVisibility(trace, false);
                });
            }

            if (this.layoutDisabled['learn']) {
                self.filterTracesOne(traces, {type: 'learn'}).forEach(function(trace) {
                    self.setTraceVisibility(trace, false);
                });
            }

            if (this.layoutDisabled['test']) {
                self.filterTracesOne(traces, {type: 'test'}).forEach(function(trace) {
                    self.setTraceVisibility(trace, false);
                });
            }
        }
    }
} };

CatboostIpython.prototype.getSmoothness = function() {
return this.smoothness && this.smoothness > -1 ? this.smoothness : -1;
};

CatboostIpython.prototype.setSmoothness = function(weight) {
if (weight < 0 && weight !== -1 || weight > 1) {
return;
}

this.smoothness = weight; };

CatboostIpython.prototype.setStddev = function(enabled) {
this.stddevEnabled = enabled;
};

CatboostIpython.prototype.redrawActiveChart = function() {
this.chartsToRedraw[this.activeTab] = true;

this.redrawAll(); };

CatboostIpython.prototype.redraw = function() {
if (this.chartsToRedraw[this.activeTab]) {
this.chartsToRedraw[this.activeTab] = false;

    this.updateTracesVisibility();
    this.updateTracesCV();
    this.updateTracesBest();
    this.updateTracesValues();
    this.updateTracesSmoothness();

    this.plotly.redraw(this.traces[this.activeTab].parent);
}

this.drawTraces(); };

CatboostIpython.prototype.addRedrawFunc = function() {
this.redrawFunc = throttle(this.redraw, 400, false, this);
};

CatboostIpython.prototype.redrawAll = function() {
if (!this.redrawFunc) {
this.addRedrawFunc();
}

this.redrawFunc(); };

CatboostIpython.prototype.addPoints = function(parent, data) {
var self = this;

data.chunks.forEach(function(item) {
    if (typeof item.remaining_time !== 'undefined' && typeof item.passed_time !== 'undefined') {
        if (!self.timeLeft[data.path]) {
            self.timeLeft[data.path] = [];
        }

        self.timeLeft[data.path][item.iteration] = [item.remaining_time, item.passed_time];
    }

    ['test', 'learn'].forEach(function(type) {
        var sets = self.meta[data.path][type + '_sets'],
            metrics = self.meta[data.path][type + '_metrics'];

        for (var i = 0; i < metrics.length; i++) {
            var nameOfMetric = metrics[i].name,
                cvAdded = false;
                hovertextParametersAdded = false;

            self.lossFuncs[nameOfMetric] = metrics[i].best_value;

            for (var j = 0; j < sets.length; j++) {
                var nameOfSet = sets[j],
                    params = {
                        chartName: nameOfMetric,
                        index: i,
                        train: data.train,
                        type: type,
                        path: data.path,
                        indexOfSet: j,
                        nameOfSet: nameOfSet
                    },
                    key = self.getKey(params),
                    launchMode = self.getLaunchMode(data.path);

                if (!self.activeTab) {
                    self.activeTab = key.chartId;
                }

                if (launchMode === 'CV' ) {
                    // we need to set launch mode before first getTrace call
                    self.hasCVMode = true;

                    if (!self.isCVinited) {
                        // and we don't need to reset setting for next iterations
                        self.layoutDisabled.learn = true;
                        self.setStddev(true);

                        self.isCVinited = true;
                    }
                }

                var valuesOfSet = item[nameOfSet],
                    pointValue = valuesOfSet[i],
                    pointIndex = item.iteration,
                    // traces
                    trace = self.getTrace(parent, params),
                    smoothedTrace = self.getTrace(parent, $.extend({smoothed: true}, params)),
                    bestValueTrace = null;

                if (type === 'test') {
                    if (launchMode !== 'CV') {
                        self.getTrace(parent, $.extend({best_point: true}, params));
                    }

                    if (typeof self.lossFuncs[nameOfMetric] === 'number') {
                        bestValueTrace = self.getTrace(parent, $.extend({best_value: true}, params));
                    }
                }

                if (pointValue !== 'inf' && pointValue !== 'nan') {
                    trace.x[pointIndex] = pointIndex;
                    trace.y[pointIndex] = valuesOfSet[i];
                    trace.hovertext[pointIndex] = nameOfSet + ': ' + valuesOfSet[i].toPrecision(7);
                    if (item.hasOwnProperty('parameters')) {
                        self.hovertextParameters[pointIndex] = '';
                        for (var parameter in item.parameters[0]) {
                            if (item.parameters[0].hasOwnProperty(parameter)) {
                                valueOfParameter = item.parameters[0][parameter];
                                self.hovertextParameters[pointIndex] += '<br>' + parameter + ' : ' + valueOfParameter;
                            }
                        }
                        if (!hovertextParametersAdded && type === 'test') {
                            hovertextParametersAdded = true;
                            trace.hovertext[pointIndex] += self.hovertextParameters[pointIndex];
                        }
                    }
                    smoothedTrace.x[pointIndex] = pointIndex;
                }

                if (bestValueTrace) {
                    bestValueTrace.x[pointIndex] = pointIndex;
                    bestValueTrace.y[pointIndex] = self.lossFuncs[nameOfMetric];
                }

                if (launchMode === 'CV' && !cvAdded) {
                    cvAdded = true;

                    self.getTrace(parent, $.extend({cv_stddev_first: true}, params));
                    self.getTrace(parent, $.extend({cv_stddev_last: true}, params));

                    self.getTrace(parent, $.extend({cv_stddev_first: true, smoothed: true}, params));
                    self.getTrace(parent, $.extend({cv_stddev_last: true, smoothed: true}, params));

                    self.getTrace(parent, $.extend({cv_avg: true}, params));
                    self.getTrace(parent, $.extend({cv_avg: true, smoothed: true}, params));

                    if (type === 'test') {
                        self.getTrace(parent, $.extend({cv_avg: true, best_point: true}, params));
                    }
                }
            }

            self.chartsToRedraw[key.chartId] = true;

            self.redrawAll();
        }
    });
}); };

CatboostIpython.prototype.getLaunchMode = function(path) {
return this.meta[path].launch_mode;
};

CatboostIpython.prototype.getChartNode = function(params, active) {
var node = $(‘<div class="catboost-graph__chart" tabid="' + params.id + '"></div>’);

if (active) {
    node.addClass('catboost-graph__chart_active');
}

return node; };

CatboostIpython.prototype.getChartTab = function(params, active) {
var node = $(‘<div class="catboost-graph__tab" tabid="' + params.id + '">’ + params.name + ‘</div>’);

if (active) {
    node.addClass('catboost-graph__tab_active');
}

return node; };

CatboostIpython.prototype.forEveryChart = function(callback) {
for (var name in this.traces) {
if (this.traces.hasOwnProperty(name)) {
callback(this.traces[name]);
}
}
};

CatboostIpython.prototype.forEveryLayout = function(callback) {
this.forEveryChart(function(chart) {
callback(chart.layout);
});
};

CatboostIpython.prototype.getChart = function(parent, params) {
var id = params.id,
self = this;

if (this.charts[id]) {
    return this.charts[id];
}

this.addLayout(parent);

var active = this.activeTab === params.id,
    chartNode = this.getChartNode(params, active),
    chartTab = this.getChartTab(params, active);

$('.catboost-graph__charts', this.layout).append(chartNode);
$('.catboost-graph__tabs', this.layout).append(chartTab);

this.traces[id] = {
    id: params.id,
    name: params.name,
    parent: chartNode[0],
    traces: [],
    layout: {
        xaxis: {
            range: [0, Number(this.meta[params.path].iteration_count)],
            type: 'linear',
            tickmode: 'auto',
            showspikes: true,
            spikethickness: 1,
            spikedash: 'longdashdot',
            spikemode: 'across',
            zeroline: false,
            showgrid: false
        },
        yaxis: {
            zeroline: false
            //showgrid: false
            //hoverformat : '.7f'
        },
        separators: '. ',
        //hovermode: 'x',
        margin: {l: 38, r: 0, t: 35, b: 30},
        autosize: true,
        showlegend: false
    },
    options: {
        scrollZoom: false,
        modeBarButtonsToRemove: ['toggleSpikelines'],
        displaylogo: false
    }
};

this.charts[id] = this.plotly.plot(chartNode[0], this.traces[id].traces, this.traces[id].layout, this.traces[id].options);

chartNode[0].on('plotly_hover', function(e) {
    self.updateTracesValues(e.points[0].x);
});

chartNode[0].on('plotly_click', function(e) {
    self.updateTracesValues(e.points[0].x, true);
});

return this.charts[id]; };

CatboostIpython.prototype.getTrace = function(parent, params) {
var key = this.getKey(params),
chartSeries = [];

if (this.traces[key.chartId]) {
    chartSeries = this.traces[key.chartId].traces.filter(function(trace) {
        return trace.name === key.traceName;
    });
}

if (chartSeries.length) {
    return chartSeries[0];
} else {
    this.getChart(parent, {id: key.chartId, name: params.chartName, path: params.path});

    var plotParams = {
            color: this.getNextColor(params.path, params.smoothed ? 0.2 : 1),
            fillsmoothcolor: this.getNextColor(params.path, 0.1),
            fillcolor: this.getNextColor(params.path, 0.4),
            hoverinfo: params.cv_avg ? 'skip' : 'text+x',
            width: params.cv_avg ? 2 : 1,
            dash: params.type === 'test' ? 'solid' : 'dot'
        },
        trace = {
            name: key.traceName,
            _params: params,
            x: [],
            y: [],
            hovertext: [],
            hoverinfo: plotParams.hoverinfo,
            line: {
                width: plotParams.width,
                dash: plotParams.dash,
                color: plotParams.color
            },
            mode: 'lines',
            hoveron: 'points',
            connectgaps: true
        };

    if (params.best_point) {
        trace = {
            name: key.traceName,
            _params: params,
            x: [],
            y: [],
            marker: {
                width: 2,
                color: plotParams.color
            },
            hovertext: [],
            hoverinfo: 'text',
            mode: 'markers',
            type: 'scatter'
        };
    }

    if (params.best_value) {
        trace = {
            name: key.traceName,
            _params: params,
            x: [],
            y: [],
            line: {
                width: 1,
                dash: 'dash',
                color: '#CCCCCC'
            },
            mode: 'lines',
            connectgaps: true,
            hoverinfo: 'skip'
        };
    }

    if (params.cv_stddev_last) {
        trace.fill = 'tonexty';
    }

    trace._params.plotParams = plotParams;

    this.traces[key.chartId].traces.push(trace);

    return trace;
} };

CatboostIpython.prototype.getKey = function(params) {
var traceName = [
params.train,
params.type,
params.indexOfSet,
(params.smoothed ? ‘smoothed’ : ‘’),
(params.best_point ? ‘best_pount’ : ‘’),
(params.best_value ? ‘best_value’ : ‘’),
(params.cv_avg ? ‘cv_avg’ : ‘’),
(params.cv_stddev_first ? ‘cv_stddev_first’ : ‘’),
(params.cv_stddev_last ? ‘cv_stddev_last’ : ‘’)
].join(‘;’);

return {
    chartId: params.chartName,
    traceName: traceName,
    colorId: params.train
}; };

CatboostIpython.prototype.filterTracesEvery = function(traces, filter) {
traces = traces || this.traces[this.activeTab].traces;

return traces.filter(function(trace) {
    for (var prop in filter) {
        if (filter.hasOwnProperty(prop)) {
            if (filter[prop] !== trace._params[prop]) {
                return false;
            }
        }
    }

    return true;
}); };

CatboostIpython.prototype.filterTracesOne = function(traces, filter) {
traces = traces || this.traces[this.activeTab].traces;

return traces.filter(function(trace) {
    for (var prop in filter) {
        if (filter.hasOwnProperty(prop)) {
            if (filter[prop] === trace._params[prop]) {
                return true;
            }
        }
    }

    return false;
}); };

CatboostIpython.prototype.cleanSeries = function() {
$(‘.catboost-panel__series’, this.layout).html(‘’);
};

CatboostIpython.prototype.groupTraces = function() {
var traces = this.traces[this.activeTab].traces,
index = 0,
tracesHash = {};

traces.map(function(trace) {
    var train = trace._params.train;

    if (!tracesHash[train]) {
        tracesHash[train] = {
            index: index,
            traces: [],
            info: {
                path: trace._params.path,
                color: trace._params.plotParams.color
            }
        };

        index++;
    }

    tracesHash[train].traces.push(trace);
});

return tracesHash; };

CatboostIpython.prototype.drawTraces = function() {
var html = ‘’,
tracesHash = this.groupTraces();

var curLength = $('.catboost-panel__series .catboost-panel__serie', this.layout).length;
var newLength = Object.keys(tracesHash).filter(hasOwnProperty.bind(tracesHash)).length;
if (newLength === curLength) {
    return;
}

for (var train in tracesHash) {
    if (tracesHash.hasOwnProperty(train)) {
        html += this.drawTrace(train, tracesHash[train]);
    }
}

$('.catboost-panel__series', this.layout).html(html);

this.updateTracesValues();

this.addTracesEvents(); };

CatboostIpython.prototype.getTraceDefParams = function(params) {
var defParams = {
smoothed: undefined,
best_point: undefined,
best_value: undefined,
cv_avg: undefined,
cv_stddev_first: undefined,
cv_stddev_last: undefined
};

if (params) {
    return $.extend(defParams, params);
} else {
    return defParams;
} };

CatboostIpython.prototype.drawTrace = function(train, hash) {
var info = hash.info,
id = ‘catboost-serie-‘ + this.index + ‘-‘ + hash.index,
traces = {
learn: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: ‘learn’})),
test: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: ‘test’}))
},
items = {
learn: {
middle: ‘’,
bottom: ‘’
},
test: {
middle: ‘’,
bottom: ‘’
}
},
tracesNames = ‘’;

['learn', 'test'].forEach(function(type) {
    traces[type].forEach(function(trace) {
        items[type].middle += '<div class="catboost-panel__serie_' + type + '_pic" style="border-color:' + info.color + '"></div>' +
                              '<div data-index="' + trace._params.indexOfSet + '" class="catboost-panel__serie_' + type + '_value"></div>';

        items[type].bottom += '<div class="catboost-panel__serie_' + type + '_pic" style="border-color:transparent"></div>' +
                              '<div data-index="' + trace._params.indexOfSet + '" class="catboost-panel__serie_best_' + type + '_value"></div>';

        tracesNames += '<div class="catboost-panel__serie_' + type + '_pic" style="border-color:' + info.color + '"></div>' +
                       '<div class="catboost-panel__serie_' + type + '_name">' + trace._params.nameOfSet + '</div>';
    });
});

var timeSpendHtml = '<div class="catboost-panel__serie_time">' +
                         '<div class="catboost-panel__serie_time_spend" title="Time spend"></div>' +
                    '</div>';

var html = '<div id="' + id + '" class="catboost-panel__serie" style="color:' + info.color + '">' +
                '<div class="catboost-panel__serie_top">' +
                    '<input type="checkbox" data-seriename="' + train + '" class="catboost-panel__serie_checkbox" id="' + id + '-box" ' + (!this.layoutDisabled.series[train] ? 'checked="checked"' : '') + '></input>' +
                    '<label title=' + this.meta[info.path].name + ' for="' + id + '-box" class="catboost-panel__serie_label">' + train + '<div class="catboost-panel__serie_time_left" title="Estimate time"></div></label>' +
                    (this.getLaunchMode(info.path) !== 'Eval' ? timeSpendHtml : '') +
                '</div>' +
                '<div class="catboost-panel__serie_hint catboost-panel__serie__learn_hint">curr</div>' +
                '<div class="catboost-panel__serie_hint catboost-panel__serie__test_hint">best</div>' +
                '<div class="catboost-panel__serie_iteration" title="curr iteration"></div>' +
                '<div class="catboost-panel__serie_best_iteration" title="best ' + (this.hasCVMode ? 'avg ' : '') + 'iteration"></div>' +
                '<div class="catboost-panel__serie_scroll">' +
                    '<div class="catboost-panel__serie_names">' +
                        tracesNames +
                    '</div>' +
                    '<div class="catboost-panel__serie_middle">' +
                        items.learn.middle +
                        items.test.middle +
                    '</div>' +
                    '<div class="catboost-panel__serie_bottom">' +
                        items.learn.bottom +
                        items.test.bottom +
                    '</div>' +
                '</div>' +
            '</div>';

return html; };

CatboostIpython.prototype.updateTracesValues = function(iteration, click) {
var tracesHash = this.groupTraces();

for (var train in tracesHash) {
    if (tracesHash.hasOwnProperty(train) && !this.layoutDisabled.traces[train]) {
        this.updateTraceValues(train, tracesHash[train], iteration, click);
    }
} };

CatboostIpython.prototype.updateTracesBest = function() {
var tracesHash = this.groupTraces();

for (var train in tracesHash) {
    if (tracesHash.hasOwnProperty(train) && !this.layoutDisabled.traces[train]) {
        this.updateTraceBest(train, tracesHash[train]);
    }
} };

CatboostIpython.prototype.getBestValue = function(data) {
if (!data.length) {
return {
best: undefined,
index: -1
};
}

var best = data[0],
    index = 0,
    func = this.lossFuncs[this.traces[this.activeTab].name],
    bestDiff = typeof func === 'number' ? Math.abs(data[0] - func) : 0;

for (var i = 1, l = data.length; i < l; i++) {
    if (func === 'Min' && data[i] < best) {
        best = data[i];
        index = i;
    }

    if (func === 'Max' && data[i] > best) {
        best = data[i];
        index = i;
    }

    if (typeof func === 'number' && Math.abs(data[i] - func) < bestDiff) {
        best = data[i];
        bestDiff = Math.abs(data[i] - func);
        index = i;
    }
}

return {
    best: best,
    index: index,
    func: func
}; };

CatboostIpython.prototype.updateTracesCV = function() {
this.updateTracesCVAvg();

if (this.hasCVMode && this.stddevEnabled) {
    this.updateTracesCVStdDev();
} };

CatboostIpython.prototype.updateTracesCVAvg = function() {
var tracesHash = this.groupTraces(),
avgTraces = this.filterTracesEvery(tracesHash.traces, this.getTraceDefParams({
cv_avg: true
})),
self = this;

avgTraces.forEach(function(trace) {
    var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({
            train: trace._params.train,
            type: trace._params.type,
            smoothed: trace._params.smoothed
        }));

    if (origTraces.length) {
        self.cvAvgFunc(origTraces, trace);
    }
}); };

CatboostIpython.prototype.cvAvgFunc = function(origTraces, avgTrace) {
var maxCount = origTraces.length,
maxLength = -1,
count,
sum;

origTraces.forEach(function(origTrace) {
    if (origTrace.y.length > maxLength) {
        maxLength = origTrace.y.length;
    }
});

for (var i = 0; i < maxLength; i++) {
    sum = 0;
    count = 0;

    for (var j = 0; j < maxCount; j++) {
        if (typeof origTraces[j].y[i] !== 'undefined') {
            sum += origTraces[j].y[i];
            count++;
        }
    }

    if (count > 0) {
        avgTrace.x[i] = i;
        avgTrace.y[i] = sum / count;
    }
} };

CatboostIpython.prototype.updateTracesCVStdDev = function() {
var tracesHash = this.groupTraces(),
firstTraces = this.filterTracesOne(tracesHash.traces, {cv_stddev_first: true}),
self = this;

firstTraces.forEach(function(trace) {
    var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({
            train: trace._params.train,
            type: trace._params.type,
            smoothed: trace._params.smoothed
        })),
        lastTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({
            train: trace._params.train,
            type: trace._params.type,
            smoothed: trace._params.smoothed,
            cv_stddev_last: true
        }));

    if (origTraces.length && lastTraces.length === 1) {
        self.cvStdDevFunc(origTraces, trace, lastTraces[0]);
    }
}); };

CatboostIpython.prototype.cvStdDevFunc = function(origTraces, firstTrace, lastTrace) {
var maxCount = origTraces.length,
maxLength = -1,
count,
sum,
i, j;

origTraces.forEach(function(origTrace) {
    if (origTrace.y.length > maxLength) {
        maxLength = origTrace.y.length;
    }
});

for (i = 0; i < maxLength; i++) {
    sum = 0;
    count = 0;

    for (j = 0; j < maxCount; j++) {
        if (typeof origTraces[j].y[i] !== 'undefined') {
            sum += origTraces[j].y[i];
            count++;
        }
    }

    if (count <= 0) {
        continue;
    }

    var std = 0,
        avg = sum / count;

    for (j = 0; j < maxCount; j++) {
        if (typeof origTraces[j].y[i] !== 'undefined') {
            std += Math.pow(origTraces[j].y[i] - avg, 2);
        }
    }

    std /= (count - 1);
    std = Math.pow(std, 0.5);

    firstTrace.x[i] = i;
    firstTrace.y[i] = avg - std;
    firstTrace.hovertext[i] = firstTrace._params.type + ' std: ' + avg.toFixed(7) + '-' + std.toFixed(7);
    lastTrace.x[i] = i;
    lastTrace.y[i] = avg + std;
    lastTrace.hovertext[i] = lastTrace._params.type + ' std: ' + avg.toFixed(7) + '+' + std.toFixed(7);
    if (this.hovertextParameters.length > i) {
        firstTrace.hovertext[i] += this.hovertextParameters[i];
        lastTrace.hovertext[i] += this.hovertextParameters[i];
    }
} };

CatboostIpython.prototype.updateTracesSmoothness = function() {
var tracesHash = this.groupTraces(),
smoothedTraces = this.filterTracesOne(tracesHash.traces, {smoothed: true}),
enabled = this.getSmoothness() > -1,
self = this;

smoothedTraces.forEach(function(trace) {
    var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({
            train: trace._params.train,
            type: trace._params.type,
            indexOfSet: trace._params.indexOfSet,
            cv_avg: trace._params.cv_avg,
            cv_stddev_first: trace._params.cv_stddev_first,
            cv_stddev_last: trace._params.cv_stddev_last
        })),
        colorFlag = false;

    if (origTraces.length === 1) {
        origTraces = origTraces[0];

        if (origTraces.visible) {
            if (enabled) {
                self.smoothFunc(origTraces, trace);
                colorFlag = true;
            }

            self.highlightSmoothedTrace(origTraces, trace, colorFlag);
        }
    }
}); };

CatboostIpython.prototype.highlightSmoothedTrace = function(trace, smoothedTrace, flag) {
if (flag) {
smoothedTrace.line.color = trace._params.plotParams.color;
trace.line.color = smoothedTrace._params.plotParams.color;
trace.hoverinfo = ‘skip’;

    if (trace._params.cv_stddev_last) {
        trace.fillcolor = trace._params.plotParams.fillsmoothcolor;
    }
} else {
    trace.line.color = trace._params.plotParams.color;
    trace.hoverinfo = trace._params.plotParams.hoverinfo;

    if (trace._params.cv_stddev_last) {
        trace.fillcolor = trace._params.plotParams.fillcolor;
    }
} };

CatboostIpython.prototype.smoothFunc = function(origTrace, smoothedTrace) {
var data = origTrace.y,
smoothedPoints = this.smooth(data, this.getSmoothness()),
smoothedIndex = 0,
self = this;

if (smoothedPoints.length) {
    data.forEach(function (d, index) {
        if (!smoothedTrace.x[index]) {
            smoothedTrace.x[index] = index;
        }

        var nameOfSet = smoothedTrace._params.nameOfSet;

        if (smoothedTrace._params.cv_stddev_first || smoothedTrace._params.cv_stddev_last) {
            nameOfSet = smoothedTrace._params.type + ' std';
        }

        smoothedTrace.y[index] = smoothedPoints[smoothedIndex];
        smoothedTrace.hovertext[index] = nameOfSet + '`: ' + smoothedPoints[smoothedIndex].toPrecision(7);
        if (self.hovertextParameters.length > index) {
            smoothedTrace.hovertext[index] += self.hovertextParameters[index];
        }
        smoothedIndex++;
    });
} };

CatboostIpython.prototype.formatItemValue = function(value, index, suffix) {
if (typeof value === ‘undefined’) {
return ‘’;
}

suffix = suffix || '';

return '<span title="' + suffix + 'value ' + value + '">' + value + '</span>'; };

CatboostIpython.prototype.updateTraceBest = function(train, hash) {
var traces = this.filterTracesOne(hash.traces, {best_point: true}),
self = this;

traces.forEach(function(trace) {
    var testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({
            train: trace._params.train,
            type: 'test',
            indexOfSet: trace._params.indexOfSet
        }));

    if (self.hasCVMode) {
        testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({
            train: trace._params.train,
            type: 'test',
            cv_avg: true
        }));
    }

    var bestValue = self.getBestValue(testTrace.length === 1 ? testTrace[0].y : []);

    if (bestValue.index !== -1) {
        trace.x[0] = bestValue.index;
        trace.y[0] = bestValue.best;
        trace.hovertext[0] = bestValue.func + ' (' + (self.hasCVMode ? 'avg' : trace._params.nameOfSet) + '): ' + bestValue.index + ' ' + bestValue.best;
    }
}); };

CatboostIpython.prototype.updateTraceValues = function(name, hash, iteration, click) {
var id = ‘catboost-serie-‘ + this.index + ‘-‘ + hash.index,
traces = {
learn: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: ‘learn’})),
test: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: ‘test’}))
},
path = hash.info.path,
self = this;

['learn', 'test'].forEach(function(type) {
    traces[type].forEach(function(trace) {
        var data = trace.y || [],
            index = typeof iteration !== 'undefined' && iteration < data.length - 1 ? iteration : data.length - 1,
            value = data.length ? data[index] : undefined,
            testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({
                type: 'test',
                indexOfSet: trace._params.indexOfSet
            })),
            bestValue = self.getBestValue(testTrace.length === 1 ? testTrace[0].y : []),
            timeLeft = '',
            timeSpend = '';

        if (click || !self.clickMode) {
            $('#' + id + ' .catboost-panel__serie_' + type + '_value[data-index=' + trace._params.indexOfSet + ']', self.layout)
                .html(self.formatItemValue(value, index, type + ' '));
            $('#' + id + ' .catboost-panel__serie_iteration', self.layout).html(index);

            if (self.timeLeft[path] && self.timeLeft[path][data.length - 1]) {
                timeLeft = self.timeLeft[path][data.length - 1][0];
            }
            $('#' + id + ' .catboost-panel__serie_time_left', self.layout).html(timeLeft ? ('~' + self.convertTime(timeLeft)) : '');

            if (self.timeLeft[path] && self.timeLeft[path][index]) {
                timeSpend = self.timeLeft[path][index][1];
            }

            $('#' + id + ' .catboost-panel__serie_time_spend', self.layout).html(self.convertTime(timeSpend));
            $('#' + id + ' .catboost-panel__serie_best_iteration', self.layout).html(bestValue.index > -1 ? bestValue.index : '');

            $('#' + id + ' .catboost-panel__serie_best_test_value[data-index=' + trace._params.indexOfSet + ']', self.layout)
                .html(self.formatItemValue(bestValue.best, bestValue.index, 'best ' + trace._params.nameOfSet + ' '));
        }
    });
});

if (this.hasCVMode) {
    var testTrace = this.filterTracesEvery(hash.traces, this.getTraceDefParams({
            type: 'test',
            cv_avg: true
        })),
        bestValue = this.getBestValue(testTrace.length === 1 ? testTrace[0].y : []);

        $('#' + id + ' .catboost-panel__serie_best_iteration', this.layout).html(bestValue.index > -1 ? bestValue.index : '');
}

if (click) {
    this.clickMode = true;

    $('#catboost-control2-clickmode' + this.index, this.layout)[0].checked = true;
} };

CatboostIpython.prototype.addTracesEvents = function() {
var self = this;

$('.catboost-panel__serie_checkbox', this.layout).click(function() {
    var name = $(this).data('seriename');

    self.layoutDisabled.traces[name] = !$(this)[0].checked;

    self.redrawActiveChart();
}); };

CatboostIpython.prototype.getNextColor = function(path, opacity) {
var color;

if (this.colorsByPath[path]) {
    color = this.colorsByPath[path];
} else {
    color = this.colors[this.colorIndex];
    this.colorsByPath[path] = color;

    this.colorIndex++;

    if (this.colorIndex > this.colors.length - 1) {
        this.colorIndex = 0;
    }
}

return this.hexToRgba(color, opacity); };

CatboostIpython.prototype.hexToRgba = function(value, opacity) {
if (value.length < 6) {
var pattern = /^#?([a-f\d])([a-f\d])([a-f\d])/i;
value = value.replace(pattern, function(m, r, g, b) {
return ‘#’ + r + r + g + g + b + b;
});
}

var result = /^#?([a-f\d]{2})([a-f\d]{2})([a-f\d]{2})/i.exec(value);
var rgb = {
    r: parseInt(result[1], 16),
    g: parseInt(result[2], 16),
    b: parseInt(result[3], 16)
};

return 'rgba(' + rgb.r + ',' + rgb.g + ',' + rgb.b + ',' + opacity + ')'; };

CatboostIpython.prototype.convertTime = function(time) {
if (!time) {
return ‘0s’;
}

time = Math.floor(time * 1000);

var millis = time % 1000;
time = parseInt(time / 1000, 10);
var seconds = time % 60;
time = parseInt(time / 60, 10);
var minutes = time % 60;
time = parseInt(time / 60, 10);
var hours = time % 24;
var out = "";
if (hours && hours > 0) {
    out += hours + 'h ';
    seconds = 0;
    millis = 0;
}
if (minutes && minutes > 0) {
    out += minutes + 'm ';
    millis = 0;
}
if (seconds && seconds > 0) {
    out += seconds + 's ';
}
if (millis && millis > 0) {
    out += millis + 'ms';
}

return out.trim(); };

CatboostIpython.prototype.mean = function(values, valueof) {
var n = values.length,
m = n,
i = -1,
value,
sum = 0,
number = function(x) {
return x === null ? NaN : +x;
};

if (valueof === null) {
    while (++i < n) {
        if (!isNaN(value = number(values[i]))) {
            sum += value;
        } else {
            --m;
        }
    }
} else {
    while (++i < n) {
        if (!isNaN(value = number(valueof(values[i], i, values)))) {
            sum += value;
        } else {
            --m;
        }
    }
}

if (m) {
    return sum / m;
} };

// from TensorBoard
CatboostIpython.prototype.smooth = function(data, weight) {
// When increasing the smoothing window, it smoothes a lot with the first
// few points and then starts to gradually smooth slower, so using an
// exponential function makes the slider more consistent. 1000^x has a
// range of [1, 1000], so subtracting 1 and dividing by 999 results in a
// range of [0, 1], which can be used as the percentage of the data, so
// that the kernel size can be specified as a percentage instead of a
// hardcoded number, what would be bad with multiple series.
var factor = (Math.pow(1000, weight) - 1) / 999,
kernelRadius = Math.floor(data.length * factor / 2),
res = [],
self = this;

data.forEach(function (d, i) {
    var actualKernelRadius = Math.min(kernelRadius, i, data.length - i - 1);
    var start = i - actualKernelRadius;
    var end = i + actualKernelRadius + 1;
    var point = d;
    // Only smooth finite numbers.
    if (!isFinite(point)) {
        res.push(point);
    } else {
        res.push(self.mean(data.slice(start, end).filter(function(d) {
            return isFinite(d);
        }), null));
    }
});

return res; }; var debug = false;

if (debug) {
require.config({
shim:{
“custom/CatboostIpythonPlotly”:{
deps:[“custom/plotly-basic.min”]
}
}
})

require.undef('catboost_module');
require.undef('custom/CatboostIpythonPlotly'); }

var moduleBase = ‘@jupyter-widgets/base’;
var modules = [moduleBase];

if (debug) {
modules.push(‘custom/CatboostIpythonPlotly’);
}

define(‘catboost_module’, modules, function(widgets) {
var getInstance = function(el) {
var id = $(el).attr(‘catboost-id’);

        if (!id) {
            return null;
        }

        id = id.replace('catboost_', '');

        if (!window.catboostIpythonInstances[id]) {
            return null;
        }

        return window.catboostIpythonInstances[id];
    },
    addInstance = function(el) {
        $(el).attr('catboost-id', 'catboost_' + window.catboostIpythonIndex);

        var catboostIpython = new CatboostIpython();
        catboostIpython.index = window.catboostIpythonIndex;
        catboostIpython.plotly = window.Plotly;
        if (debug) {
            catboostIpython.loadStyles('/custom/CatboostIpython.css', function(){catboostIpython.resizeCharts();})
        }

        window.catboostIpythonInstances[window.catboostIpythonIndex] = catboostIpython;

        window.catboostIpythonIndex++;

        return catboostIpython;
    };

var CatboostIpythonWidget = widgets.DOMWidgetView.extend({

    initialize: function() {
        CatboostIpythonWidget.__super__.initialize.apply(this, arguments);

        if (!window.catboostIpythonInstances) {
            window.catboostIpythonInstances = {};
        }

        if (typeof window.catboostIpythonIndex === 'undefined') {
            window.catboostIpythonIndex = 0;
        }

        var catboostIpythonInstance = getInstance(this.el);

        if (!catboostIpythonInstance) {
            catboostIpythonInstance = addInstance(this.el);
        }

        catboostIpythonInstance.init();
    },

    render: function() {
        this.value_changed();
        this.model.on('change:value', this.value_changed, this);
    },

    update: function() {
        this.value_changed();
    },

    value_changed: function() {
        this.el.style['width'] = this.model.get('width');
        this.el.style['height'] = this.model.get('height');
        this.displayed.then(_.bind(this.render_charts, this));
    },

    process_all: function(parent, params) {
        var data = params.data;

        for (var path in data) {
            if (data.hasOwnProperty(path)) {
                this.process_row(parent, data[path])
            }
        }
    },

    process_row: function(parent, data) {
        var catboostIpython = getInstance(parent),
            path = data.path,
            content = data.content,
            items = content.data.iterations,
            firstIndex = 0,
            chunks = [];

        if (!items || !items.length) {
            return;
        }

        if (!catboostIpython.lastIndex) {
            catboostIpython.lastIndex = {}
        }

        if (catboostIpython.lastIndex[path]) {
            firstIndex = catboostIpython.lastIndex[path] + 1;
        }

        catboostIpython.lastIndex[path] = items.length - 1;

        for (var i = firstIndex; i < items.length; i++) {
            chunks.push(items[i]);
        }

        catboostIpython.addMeta(data.path, content.data.meta);

        catboostIpython.addPoints(parent, {
            chunks: chunks,
            train: data.name,
            path: data.path
        });
    },

    render_charts: function () {
        this.process_all(this.el, {
            data: this.model.get('data')
        });

        return this;
    }
});

return {
    CatboostIpythonWidgetView: CatboostIpythonWidget
}; });

        </script>



MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))


0:	learn: 0.6818104	test: 0.6818696	best: 0.6818696 (0)
1:	learn: 0.6641740	test: 0.6645792	best: 0.6645792 (1)
2:	learn: 0.6477853	test: 0.6486508	best: 0.6486508 (2)
3:	learn: 0.6341319	test: 0.6354933	best: 0.6354933 (3)
4:	learn: 0.6210395	test: 0.6230484	best: 0.6230484 (4)
5:	learn: 0.6091194	test: 0.6117204	best: 0.6117204 (5)
6:	learn: 0.5966905	test: 0.6004243	best: 0.6004243 (6)
7:	learn: 0.5856038	test: 0.5902708	best: 0.5902708 (7)
8:	learn: 0.5746275	test: 0.5805175	best: 0.5805175 (8)
9:	learn: 0.5656285	test: 0.5720328	best: 0.5720328 (9)
10:	learn: 0.5575033	test: 0.5641349	best: 0.5641349 (10)
11:	learn: 0.5488293	test: 0.5563887	best: 0.5563887 (11)
12:	learn: 0.5412832	test: 0.5492149	best: 0.5492149 (12)
13:	learn: 0.5337489	test: 0.5421982	best: 0.5421982 (13)
14:	learn: 0.5272777	test: 0.5364130	best: 0.5364130 (14)
15:	learn: 0.5201909	test: 0.5300524	best: 0.5300524 (15)
16:	learn: 0.5142724	test: 0.5250397	best: 0.5250397 (16)
17:	learn: 0.5090859	test: 0.5200097	best: 0.5200097 (17)
18:	learn: 0.5034601	test: 0.5150620	best: 0.5150620 (18)
19:	learn: 0.4988649	test: 0.5113264	best: 0.5113264 (19)
20:	learn: 0.4943410	test: 0.5074305	best: 0.5074305 (20)
21:	learn: 0.4892080	test: 0.5030768	best: 0.5030768 (21)
22:	learn: 0.4855905	test: 0.4998746	best: 0.4998746 (22)
23:	learn: 0.4820654	test: 0.4968464	best: 0.4968464 (23)
24:	learn: 0.4782698	test: 0.4935961	best: 0.4935961 (24)
25:	learn: 0.4747252	test: 0.4907064	best: 0.4907064 (25)
26:	learn: 0.4715285	test: 0.4880454	best: 0.4880454 (26)
27:	learn: 0.4681848	test: 0.4854874	best: 0.4854874 (27)
28:	learn: 0.4656862	test: 0.4834014	best: 0.4834014 (28)
29:	learn: 0.4623247	test: 0.4811459	best: 0.4811459 (29)
30:	learn: 0.4592251	test: 0.4787714	best: 0.4787714 (30)
31:	learn: 0.4566838	test: 0.4765256	best: 0.4765256 (31)
32:	learn: 0.4543813	test: 0.4747325	best: 0.4747325 (32)
33:	learn: 0.4516190	test: 0.4727688	best: 0.4727688 (33)
34:	learn: 0.4496926	test: 0.4714172	best: 0.4714172 (34)	total: 7.78s	remaining: 3m 34s
35:	learn: 0.4475583	test: 0.4698302	best: 0.4698302 (35)
36:	learn: 0.4458175	test: 0.4686044	best: 0.4686044 (36)
37:	learn: 0.4438202	test: 0.4670893	best: 0.4670893 (37)
38:	learn: 0.4420125	test: 0.4659883	best: 0.4659883 (38)
39:	learn: 0.4402146	test: 0.4648426	best: 0.4648426 (39)
40:	learn: 0.4382931	test: 0.4637131	best: 0.4637131 (40)
41:	learn: 0.4366430	test: 0.4628569	best: 0.4628569 (41)
42:	learn: 0.4353991	test: 0.4620805	best: 0.4620805 (42)
43:	learn: 0.4337037	test: 0.4608254	best: 0.4608254 (43)
44:	learn: 0.4319076	test: 0.4595480	best: 0.4595480 (44)
45:	learn: 0.4307993	test: 0.4587191	best: 0.4587191 (45)
46:	learn: 0.4295039	test: 0.4577173	best: 0.4577173 (46)
47:	learn: 0.4278231	test: 0.4567580	best: 0.4567580 (47)
48:	learn: 0.4265053	test: 0.4561712	best: 0.4561712 (48)	total: 10.9s	remaining: 3m 31s
49:	learn: 0.4255945	test: 0.4556583	best: 0.4556583 (49)
50:	learn: 0.4243344	test: 0.4549053	best: 0.4549053 (50)
51:	learn: 0.4229091	test: 0.4539430	best: 0.4539430 (51)
52:	learn: 0.4217757	test: 0.4532222	best: 0.4532222 (52)
53:	learn: 0.4209167	test: 0.4529255	best: 0.4529255 (53)
54:	learn: 0.4199180	test: 0.4521215	best: 0.4521215 (54)
55:	learn: 0.4185928	test: 0.4512819	best: 0.4512819 (55)
56:	learn: 0.4176449	test: 0.4507060	best: 0.4507060 (56)
57:	learn: 0.4166427	test: 0.4504197	best: 0.4504197 (57)
58:	learn: 0.4155030	test: 0.4500332	best: 0.4500332 (58)
59:	learn: 0.4144346	test: 0.4493896	best: 0.4493896 (59)
60:	learn: 0.4135224	test: 0.4488496	best: 0.4488496 (60)
61:	learn: 0.4124423	test: 0.4485317	best: 0.4485317 (61)
62:	learn: 0.4114756	test: 0.4478158	best: 0.4478158 (62)	total: 13.6s	remaining: 3m 22s
63:	learn: 0.4101219	test: 0.4474760	best: 0.4474760 (63)
64:	learn: 0.4087985	test: 0.4466932	best: 0.4466932 (64)
65:	learn: 0.4078953	test: 0.4462899	best: 0.4462899 (65)
66:	learn: 0.4070582	test: 0.4456936	best: 0.4456936 (66)
67:	learn: 0.4060175	test: 0.4454029	best: 0.4454029 (67)
68:	learn: 0.4050156	test: 0.4448338	best: 0.4448338 (68)
69:	learn: 0.4042883	test: 0.4443799	best: 0.4443799 (69)
70:	learn: 0.4034243	test: 0.4439779	best: 0.4439779 (70)
71:	learn: 0.4024857	test: 0.4434298	best: 0.4434298 (71)
72:	learn: 0.4013587	test: 0.4431194	best: 0.4431194 (72)
73:	learn: 0.4004151	test: 0.4428410	best: 0.4428410 (73)
74:	learn: 0.3998636	test: 0.4426268	best: 0.4426268 (74)
75:	learn: 0.3990967	test: 0.4424958	best: 0.4424958 (75)
76:	learn: 0.3983217	test: 0.4422205	best: 0.4422205 (76)
77:	learn: 0.3976846	test: 0.4421207	best: 0.4421207 (77)
78:	learn: 0.3969529	test: 0.4418004	best: 0.4418004 (78)
79:	learn: 0.3961546	test: 0.4415076	best: 0.4415076 (79)
80:	learn: 0.3954207	test: 0.4410724	best: 0.4410724 (80)
81:	learn: 0.3946333	test: 0.4408442	best: 0.4408442 (81)	total: 18.1s	remaining: 3m 22s
82:	learn: 0.3939177	test: 0.4406660	best: 0.4406660 (82)
83:	learn: 0.3931908	test: 0.4404366	best: 0.4404366 (83)
84:	learn: 0.3927319	test: 0.4404642	best: 0.4404366 (83)
85:	learn: 0.3921297	test: 0.4401498	best: 0.4401498 (85)
86:	learn: 0.3913120	test: 0.4398869	best: 0.4398869 (86)
87:	learn: 0.3907376	test: 0.4399652	best: 0.4398869 (86)
88:	learn: 0.3900953	test: 0.4397405	best: 0.4397405 (88)
89:	learn: 0.3895013	test: 0.4395150	best: 0.4395150 (89)
90:	learn: 0.3888618	test: 0.4393675	best: 0.4393675 (90)
91:	learn: 0.3882883	test: 0.4390070	best: 0.4390070 (91)
92:	learn: 0.3876965	test: 0.4389666	best: 0.4389666 (92)
93:	learn: 0.3869092	test: 0.4388457	best: 0.4388457 (93)
94:	learn: 0.3863739	test: 0.4387472	best: 0.4387472 (94)
95:	learn: 0.3858557	test: 0.4385217	best: 0.4385217 (95)
96:	learn: 0.3852365	test: 0.4382003	best: 0.4382003 (96)
97:	learn: 0.3847504	test: 0.4382243	best: 0.4382003 (96)
98:	learn: 0.3841999	test: 0.4381579	best: 0.4381579 (98)
99:	learn: 0.3838539	test: 0.4382947	best: 0.4381579 (98)
100:	learn: 0.3832252	test: 0.4380937	best: 0.4380937 (100)
101:	learn: 0.3827268	test: 0.4378135	best: 0.4378135 (101)
102:	learn: 0.3822181	test: 0.4375068	best: 0.4375068 (102)
103:	learn: 0.3815772	test: 0.4374972	best: 0.4374972 (103)
104:	learn: 0.3810413	test: 0.4374076	best: 0.4374076 (104)
105:	learn: 0.3806666	test: 0.4373081	best: 0.4373081 (105)
106:	learn: 0.3801988	test: 0.4372432	best: 0.4372432 (106)
107:	learn: 0.3796029	test: 0.4372307	best: 0.4372307 (107)
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999:	learn: 0.1595565	test: 0.4524667	best: 0.4333729 (372)	total: 3m 53s	remaining: 0us

print(catboost_time) # 29.7119 seconds to run 100 iterations


234.56792330741882

acc_cv_catboost = round(np.max(cv_data['test-Accuracy-mean']) * 100, 2)
acc_cv_catboost

81.55

# print out the caboost model metrics
print('---------CATBOOST METRICS---------------')
print('accuracy: {}'.format(acc_catboost))
print('Accuracy 10 fold cross validation score: {}'.format(acc_cv_catboost))
print("Running time: {}".format(datetime.timedelta(seconds=catboost_time)))


---------CATBOOST METRICS---------------
accuracy: 85.15
Accuracy 10 fold cross validation score: 81.55
Running time: 0:03:54.567923

Comparing models

  • which model had the best cross val score?

Note: cross val score is more accurate compared to just using score using fit() once.

models = pd.DataFrame({
    'Model': ['KNN', 'Logistic Regression', 'Naive Bayes', 
              'Stochastic Gradient Decent', 'Linear SVC', 
              'Decision Tree', 'Gradient Boosting Trees',
              'CatBoost'],
    'Score': [
        acc_knn, 
        acc_log,  
        acc_gnb, 
        acc_sgd, 
        acc_linear_svc, 
        acc_dt,
        acc_gbt,
        acc_catboost
    ]})
print("---Reuglar Accuracy Scores---")
models.sort_values(by='Score', ascending=False)

---Reuglar Accuracy Scores---
Model Score
5 Decision Tree 97.86
6 Gradient Boosting Trees 89.65
7 CatBoost 85.15
0 KNN 81.66
1 Logistic Regression 80.43
4 Linear SVC 79.08
2 Naive Bayes 78.52
3 Stochastic Gradient Decent 67.38 
cv_models = pd.DataFrame({
    'Model': ['KNN', 'Logistic Regression', 'Naive Bayes', 
             'Stochastic Gradient Descent', 'Linear SVC',
             'Decision Tree', 'Gradient Boosting Trees',
             'CatBoost'],
    'Cross Val Score': [
        acc_cv_knn,
        acc_cv_log,
        acc_cv_gnb,
        acc_cv_sgd,
        acc_cv_linear_svc,
        acc_cv_dt,
        acc_cv_gbt,
        acc_cv_catboost,
    ]
})


print('-------CROSS VAL ACCURACY SCORES-----------')
cv_models.sort_values(by = 'Cross Val Score', ascending = False)


-------CROSS VAL ACCURACY SCORES-----------
Model Cross Val Score
6 Gradient Boosting Trees 82.23
7 CatBoost 81.55
1 Logistic Regression 79.98
2 Naive Bayes 78.40
5 Decision Tree 78.29
4 Linear SVC 76.83
0 KNN 70.75
3 Stochastic Gradient Descent 68.17

Compared to dbourkes scores which showed catboost was the best followed by gradient boosting trees, our results show that GBT is better with catboost second. Then again, both of these models were better than dbourkes versions.

  • our catboost (81.55) vs dbourke (80.99)
  • our gbt (82.23) vs dbourke (80.65)

for our case, we will use gbt to predict on the test set.

  • Can be improved if we would like to tune the parameters, but as we have seen, even with the default params, we are able to get a good result.

Feature Importance

  • which feature (X) were the most important for making predictions?
def feature_importance(model, data):
    """
    dbourke fn to show which features are most important in the model
    ::param_model:: which model to use
    ::param_data:: which data to use?
    """
    fea_imp = pd.DataFrame({
        'col': data.columns,
        'imp': model.feature_importances_,

    })    
    fea_imp = fea_imp.sort_values(['imp', 'col'], 
                                  ascending = False)
    _ = fea_imp.plot(kind='barh', x = 'col', y = 'imp', figsize=(20,10))
    
    return fea_imp
    #plt.savefig('catboost_feature_importance.png')

feature_importance(catboost_model, X_train)
col imp
7 sex_female 15.970674
0 Age 15.252810
2 Parch 13.783782
1 SibSp 12.691472
8 sex_male 12.373489
3 Fare 11.941035
11 pclass_3 8.871012
9 pclass_1 3.347435
10 pclass_2 2.108478
6 embarked_S 1.901775
4 embarked_C 1.232367
5 embarked_Q 0.525673

png

The use of feature importance

  1. This shows how much each feature contributed to the module.
  2. Using this, we can remove non-important features (ie dimensionality reduction). In this case, we have very little features so its not needed.
  3. You can also think of ways to improve the importance of these features. eg. dbourkes age didnt have age, but yours does! And it is one of the most important features. We could try and make changes to the binning of ages!

Precision and Recall

Metrics used when you have imbalanced classification
eg. If you have 100,000 ppl and only 1 of them gets a certain disease. Our model could just predict everyone doesnt get the disease; accuracy of 99.999% but this doesnt really do anything…

So this is where precision and recall come in.

Recall = tp / (tp + fn) = true positive / REAL positive

Precision = tp / (tp + fp) = true positive / PREDICTED positive

Combine precision and recall to get the F1 score.

These metrics might not be relevant for this case, but definitely need to know in the future.

Catboost eval metrics

metrics = ['Precision', 'Recall', 'F1' , 'AUC']

eval_metrics = catboost_model.eval_metrics(train_pool,
                               metrics = metrics,
                               plot = True)


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w=u.getFromId(t,b.xref),k=u.getFromId(t,b.yref),M={x:{},y:{}},A=+b.textangle||0,T=s._infolayer.append("g").classed("annotation",!0).attr("data-index",String(e)).style("opacity",b.opacity),L=T.append("g").classed("annotation-text-g",!0).attr("data-index",String(e)),C=L.append("g").style("pointer-events",b.captureevents?"all":null).call(g,"default").on("click",function(){t._dragging=!1,t.emit("plotly_clickannotation",{index:e,annotation:x,fullAnnotation:b,event:i.event})});b.hovertext&&C.on("mouseover",function(){var e=b.hoverlabel,r=e.font,n=this.getBoundingClientRect(),a=t.getBoundingClientRect();h.loneHover({x0:n.left-a.left,x1:n.right-a.left,y:(n.top+n.bottom)/2-a.top,text:b.hovertext,color:e.bgcolor,borderColor:e.bordercolor,fontFamily:r.family,fontSize:r.size,fontColor:r.color},{container:s._hoverlayer.node(),outerContainer:s._paper.node()})}).on("mouseout",function(){h.loneUnhover(s._hoverlayer.node())});var 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n=t("d3"),a=t("tinycolor2"),o=t("../../plotly"),i=t("../../plots/plots"),l=t("../../registry"),s=t("../../plots/cartesian/axes"),c=t("../dragelement"),u=t("../../lib"),f=t("../../lib/extend").extendFlat,d=t("../../lib/setcursor"),h=t("../drawing"),p=t("../color"),g=t("../titles"),v=t("../../plots/cartesian/axis_defaults"),m=t("../../plots/cartesian/position_defaults"),y=t("../../plots/cartesian/layout_attributes"),x=t("./attributes");e.exports=function(t,e){function r(){function x(t,e){return u.coerce(J,K,y,t,e)}function w(){if(["top","bottom"].indexOf(_.titleside)!==-1){var e=ot.select(".cbtitle"),r=e.select("text"),o=[-_.outlinewidth/2,_.outlinewidth/2],i=e.select(".h"+K._id+"title-math-group").node(),l=15.6;if(r.node()&&(l=1.3*parseInt(r.style("font-size"),10)),i?(lt=h.bBox(i).height)>l&&(o[1]-=(lt-l)/2):r.node()&&!r.classed("js-placeholder")&&(lt=h.bBox(e.node()).height),lt){if(lt+=5,"top"===_.titleside)K.domain[1]-=lt/T.h,o[1]*=-1;else{K.domain[0]+=lt/T.h;var 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J=1/0;for(I=0;I<Y.length;I++)if((R=Y[I])&&R[0]&&R[0].trace&&R[0].trace.visible===!0&&(F=R[0].trace,["carpet","contourcarpet"].indexOf(F._module.name)===-1)){if(j=w.getSubplot(F),B=n.indexOf(j),q=D,U={cd:R,trace:F,xa:T[B],ya:L[B],name:t.data.length>1||F.hoverinfo.indexOf("name")!==-1?F.name:void 0,index:!1,distance:Math.min(J,k.MAXDIST),color:y.defaultLine,x0:void 0,x1:void 0,y0:void 0,y1:void 0,xLabelVal:void 0,yLabelVal:void 0,zLabelVal:void 0,text:void 0},d[j]&&(U.subplot=d[j]._subplot),X=G.length,"array"===q){var K=e[I];"pointNumber"in K?(U.index=K.pointNumber,q="closest"):(q="","xval"in K&&(H=K.xval,q="x"),"yval"in K&&(V=K.yval,q=q?"closest":"y"))}else H=P[B],V=E[B];if(F._module&&F._module.hoverPoints){var tt=F._module.hoverPoints(U,H,V,q);if(tt)for(var et,rt=0;rt<tt.length;rt++)et=tt[rt],f(et.x0)&&f(et.y0)&&G.push(l(et,D))}else h.log("Unrecognized trace type in hover:",F);"closest"===D&&G.length>X&&(G.splice(0,X),J=G[0].distance)}if(0===G.length)return x.unhoverRaw(t,e);G.sort(function(t,e){return t.distance-e.distance});var nt=t._hoverdata,at=[];for(N=0;N<G.length;N++){var ot=G[N],it={data:ot.trace._input,fullData:ot.trace,curveNumber:ot.trace.index,pointNumber:ot.index};ot.trace._module.eventData?it=ot.trace._module.eventData(it,ot):(it.x=ot.xVal,it.y=ot.yVal,it.xaxis=ot.xa,it.yaxis=ot.ya,void 0!==ot.zLabelVal&&(it.z=ot.zLabelVal)),at.push(it)}if(t._hoverdata=at,c(t,e,nt)&&d._hasCartesian){s(G,{hovermode:D,fullLayout:d,container:d._hoverlayer,outerContainer:d._paperdiv})}var lt="y"===D&&Y.length>1,st=y.combine(d.plot_bgcolor||y.background,d.paper_bgcolor),ct={hovermode:D,rotateLabels:lt,bgColor:st,container:d._hoverlayer,outerContainer:d._paperdiv,commonLabelOpts:d.hoverlabel},ut=a(G,ct);if(o(G,lt?"xa":"ya"),i(ut,lt),e.target&&e.target.tagName){var ft=_.getComponentMethod("annotations","hasClickToShow")(t,at);v(u.select(e.target),ft?"pointer":"")}e.target&&c(t,e,nt)&&(nt&&t.emit("plotly_unhover",{event:e,points:nt}),t.emit("plotly_hover",{event:e,points:t._hoverdata,xaxes:T,yaxes:L,xvals:P,yvals:E}))}function a(t,e){var r,n,a=e.hovermode,o=e.rotateLabels,i=e.bgColor,l=e.container,s=e.outerContainer,c=e.commonLabelOpts||{},f=e.fontFamily||k.HOVERFONT,d=e.fontSize||k.HOVERFONTSIZE,h=t[0],p=h.xa,v=h.ya,x="y"===a?"yLabel":"xLabel",b=h[x],_=(String(b)||"").split(" ")[0],w=s.node().getBoundingClientRect(),A=w.top,T=w.width,L=w.height,C=h.distance<=k.MAXDIST&&("x"===a||"y"===a);for(r=0;r<t.length;r++){n=t[r].trace.hoverinfo;var O=n.split("+");if(O.indexOf("all")===-1&&O.indexOf(a)===-1){C=!1;break}}var D=l.selectAll("g.axistext").data(C?[0]:[]);D.enter().append("g").classed("axistext",!0),D.exit().remove(),D.each(function(){var e=u.select(this),r=e.selectAll("path").data([0]),n=e.selectAll("text").data([0]);r.enter().append("path").style({fill:c.bgcolor||y.defaultLine,stroke:c.bordercolor||y.background,"stroke-width":"1px"}),n.enter().append("text").call(m.font,c.font.family||f,c.font.size||d,c.font.color||y.background).attr("data-notex",1),n.text(b).call(g.convertToTspans).call(m.setPosition,0,0).selectAll("tspan.line").call(m.setPosition,0,0),e.attr("transform","");var o=n.node().getBoundingClientRect();if("x"===a){n.attr("text-anchor","middle").call(m.setPosition,0,"top"===p.side?A-o.bottom-S-z:A-o.top+S+z).selectAll("tspan.line").attr({x:n.attr("x"),y:n.attr("y")});var i="top"===p.side?"-":"";r.attr("d","M0,0L"+S+","+i+S+"H"+(z+o.width/2)+"v"+i+(2*z+o.height)+"H-"+(z+o.width/2)+"V"+i+S+"H-"+S+"Z"),e.attr("transform","translate("+(p._offset+(h.x0+h.x1)/2)+","+(v._offset+("top"===p.side?0:v._length))+")")}else{n.attr("text-anchor","right"===v.side?"start":"end").call(m.setPosition,("right"===v.side?1:-1)*(z+S),A-o.top-o.height/2).selectAll("tspan.line").attr({x:n.attr("x"),y:n.attr("y")});var l="right"===v.side?"":"-";r.attr("d","M0,0L"+l+S+","+S+"V"+(z+o.height/2)+"h"+l+(2*z+o.width)+"V-"+(z+o.height/2)+"H"+l+S+"V-"+S+"Z"),e.attr("transform","translate("+(p._offset+("right"===v.side?p._length:0))+","+(v._offset+(h.y0+h.y1)/2)+")")}t=t.filter(function(t){return void 0!==t.zLabelVal||(t[x]||"").split(" ")[0]===_})});var P=l.selectAll("g.hovertext").data(t,function(t){return[t.trace.index,t.index,t.x0,t.y0,t.name,t.attr,t.xa,t.ya||""].join(",")});return P.enter().append("g").classed("hovertext",!0).each(function(){var t=u.select(this);t.append("rect").call(y.fill,y.addOpacity(i,.8)),t.append("text").classed("name",!0),t.append("path").style("stroke-width","1px"),t.append("text").classed("nums",!0).call(m.font,f,d)}),P.exit().remove(),P.each(function(t){var e=u.select(this).attr("transform",""),r="",n="",l=y.opacity(t.color)?t.color:y.defaultLine,s=y.combine(l,i),c=t.borderColor||y.contrast(s);void 0!==t.nameOverride&&(t.name=t.nameOverride),t.name&&(r=g.plainText(t.name||""),r.length>15&&(r=r.substr(0,12)+"...")),void 0!==t.extraText&&(n+=t.extraText),void 0!==t.zLabel?(void 0!==t.xLabel&&(n+="x: "+t.xLabel+"<br>"),void 0!==t.yLabel&&(n+="y: "+t.yLabel+"<br>"),n+=(n?"z: ":"")+t.zLabel):C&&t[a+"Label"]===b?n=t[("x"===a?"y":"x")+"Label"]||"":void 0===t.xLabel?void 0!==t.yLabel&&(n=t.yLabel):n=void 0===t.yLabel?t.xLabel:"("+t.xLabel+", "+t.yLabel+")",t.text&&!Array.isArray(t.text)&&(n+=(n?"<br>":"")+t.text),""===n&&(""===r&&e.remove(),n=r);var h=e.select("text.nums").call(m.font,t.fontFamily||f,t.fontSize||d,t.fontColor||c).call(m.setPosition,0,0).text(n).attr("data-notex",1).call(g.convertToTspans);h.selectAll("tspan.line").call(m.setPosition,0,0);var p=e.select("text.name"),v=0;r&&r!==n?(p.call(m.font,t.fontFamily||f,t.fontSize||d,s).text(r).call(m.setPosition,0,0).attr("data-notex",1).call(g.convertToTspans),p.selectAll("tspan.line").call(m.setPosition,0,0),v=p.node().getBoundingClientRect().width+2*z):(p.remove(),e.select("rect").remove()),e.select("path").style({fill:s,stroke:c});var x,_,w=h.node().getBoundingClientRect(),k=t.xa._offset+(t.x0+t.x1)/2,O=t.ya._offset+(t.y0+t.y1)/2,D=Math.abs(t.x1-t.x0),P=Math.abs(t.y1-t.y0),E=w.width+S+z+v;t.ty0=A-w.top,t.bx=w.width+2*z,t.by=w.height+2*z,t.anchor="start",t.txwidth=w.width,t.tx2width=v,t.offset=0,o?(t.pos=k,x=O+P/2+E<=L,_=O-P/2-E>=0,"top"!==t.idealAlign&&x||!_?x?(O+=P/2,t.anchor="start"):t.anchor="middle":(O-=P/2,t.anchor="end")):(t.pos=O,x=k+D/2+E<=T,_=k-D/2-E>=0,"left"!==t.idealAlign&&x||!_?x?(k+=D/2,t.anchor="start"):t.anchor="middle":(k-=D/2,t.anchor="end")),h.attr("text-anchor",t.anchor),v&&p.attr("text-anchor",t.anchor),e.attr("transform","translate("+k+","+O+")"+(o?"rotate("+M+")":""))}),P}function o(t,e){function r(t){var e=t[0],r=t[t.length-1];if(a=e.pmin-e.pos-e.dp+e.size,o=r.pos+r.dp+r.size-e.pmax,a>.01){for(l=t.length-1;l>=0;l--)t[l].dp+=a;n=!1}if(!(o<.01)){if(a<-.01){for(l=t.length-1;l>=0;l--)t[l].dp-=o;n=!1}if(n){var c=0;for(i=0;i<t.length;i++)s=t[i],s.pos+s.dp+s.size>e.pmax&&c++;for(i=t.length-1;i>=0&&!(c<=0);i--)s=t[i],s.pos>e.pmax-1&&(s.del=!0,c--);for(i=0;i<t.length&&!(c<=0);i++)if(s=t[i],s.pos<e.pmin+1)for(s.del=!0,c--,o=2*s.size,l=t.length-1;l>=0;l--)t[l].dp-=o;for(i=t.length-1;i>=0&&!(c<=0);i--)s=t[i],s.pos+s.dp+s.size>e.pmax&&(s.del=!0,c--)}}}for(var n,a,o,i,l,s,c,u=0,f=t.map(function(t,r){var n=t[e];return[{i:r,dp:0,pos:t.pos,posref:t.posref,size:t.by*("x"===n._id.charAt(0)?T:1)/2,pmin:n._offset,pmax:n._offset+n._length}]}).sort(function(t,e){return t[0].posref-e[0].posref});!n&&u<=t.length;){for(u++,n=!0,i=0;i<f.length-1;){var d=f[i],h=f[i+1],p=d[d.length-1],g=h[0];if((a=p.pos+p.dp+p.size-g.pos-g.dp+g.size)>.01&&p.pmin===g.pmin&&p.pmax===g.pmax){for(l=h.length-1;l>=0;l--)h[l].dp+=a;for(d.push.apply(d,h),f.splice(i+1,1),c=0,l=d.length-1;l>=0;l--)c+=d[l].dp;for(o=c/d.length,l=d.length-1;l>=0;l--)d[l].dp-=o;n=!1}else i++}f.forEach(r)}for(i=f.length-1;i>=0;i--){var v=f[i];for(l=v.length-1;l>=0;l--){var m=v[l],y=t[m.i];y.offset=m.dp,y.del=m.del}}}function i(t,e){t.each(function(t){var r=u.select(this);if(t.del)return void r.remove();var n="end"===t.anchor?-1:1,a=r.select("text.nums"),o={start:1,end:-1,middle:0}[t.anchor],i=o*(S+z),l=i+o*(t.txwidth+z),s=0,c=t.offset;"middle"===t.anchor&&(i-=t.tx2width/2,l-=t.tx2width/2),e&&(c*=-C,s=t.offset*L),r.select("path").attr("d","middle"===t.anchor?"M-"+t.bx/2+",-"+t.by/2+"h"+t.bx+"v"+t.by+"h-"+t.bx+"Z":"M0,0L"+(n*S+s)+","+(S+c)+"v"+(t.by/2-S)+"h"+n*t.bx+"v-"+t.by+"H"+(n*S+s)+"V"+(c-S)+"Z"),a.call(m.setPosition,i+s,c+t.ty0-t.by/2+z).selectAll("tspan.line").attr({x:a.attr("x"),y:a.attr("y")}),t.tx2width&&(r.select("text.name, text.name tspan.line").call(m.setPosition,l+o*z+s,c+t.ty0-t.by/2+z),r.select("rect").call(m.setRect,l+(o-1)*t.tx2width/2+s,c-t.by/2-1,t.tx2width,t.by+2))})}function l(t,e){function r(e,r,i){var l;if(o[r])l=o[r];else if(a[r]){var s=a[r];Array.isArray(s)&&Array.isArray(s[t.index[0]])&&(l=s[t.index[0]][t.index[1]])}else l=h.nestedProperty(n,i).get();l&&(t[e]=l)}var n=t.trace||{},a=t.cd[0],o=t.cd[t.index]||{};t.posref="y"===e?(t.x0+t.x1)/2:(t.y0+t.y1)/2,t.x0=h.constrain(t.x0,0,t.xa._length),t.x1=h.constrain(t.x1,0,t.xa._length),t.y0=h.constrain(t.y0,0,t.ya._length),t.y1=h.constrain(t.y1,0,t.ya._length);var i;if(void 0!==t.xLabelVal){i="log"===t.xa.type&&t.xLabelVal<=0;var l=b.tickText(t.xa,t.xa.c2l(i?-t.xLabelVal:t.xLabelVal),"hover");i?0===t.xLabelVal?t.xLabel="0":t.xLabel="-"+l.text:t.xLabel=l.text,t.xVal=t.xa.c2d(t.xLabelVal)}if(void 0!==t.yLabelVal){i="log"===t.ya.type&&t.yLabelVal<=0;var s=b.tickText(t.ya,t.ya.c2l(i?-t.yLabelVal:t.yLabelVal),"hover");i?0===t.yLabelVal?t.yLabel="0":t.yLabel="-"+s.text:t.yLabel=s.text,t.yVal=t.ya.c2d(t.yLabelVal)}if(void 0!==t.zLabelVal&&(t.zLabel=String(t.zLabelVal)),!(isNaN(t.xerr)||"log"===t.xa.type&&t.xerr<=0)){var c=b.tickText(t.xa,t.xa.c2l(t.xerr),"hover").text;void 0!==t.xerrneg?t.xLabel+=" +"+c+" / -"+b.tickText(t.xa,t.xa.c2l(t.xerrneg),"hover").text:t.xLabel+=" \xb1 "+c,"x"===e&&(t.distance+=1)}if(!(isNaN(t.yerr)||"log"===t.ya.type&&t.yerr<=0)){var u=b.tickText(t.ya,t.ya.c2l(t.yerr),"hover").text;void 0!==t.yerrneg?t.yLabel+=" +"+u+" / -"+b.tickText(t.ya,t.ya.c2l(t.yerrneg),"hover").text:t.yLabel+=" \xb1 "+u,"y"===e&&(t.distance+=1)}var f=t.trace.hoverinfo;return"all"!==f&&(f=f.split("+"),f.indexOf("x")===-1&&(t.xLabel=void 0),f.indexOf("y")===-1&&(t.yLabel=void 0),f.indexOf("z")===-1&&(t.zLabel=void 0),f.indexOf("text")===-1&&(t.text=void 0),f.indexOf("name")===-1&&(t.name=void 0)),r("color","hbg","hoverlabel.bgcolor"),r("borderColor","hbc","hoverlabel.bordercolor"),r("fontFamily","htf","hoverlabel.font.family"),r("fontSize","hts","hoverlabel.font.size"),r("fontColor","htc","hoverlabel.font.color"),t}function s(t,e){var r=e.hovermode,n=e.container,a=t[0],o=a.xa,i=a.ya,l=o.showspikes,s=i.showspikes;if(n.selectAll(".spikeline").remove(),"closest"===r&&(l||s)){var c=e.fullLayout,u=o._offset+(a.x0+a.x1)/2,f=i._offset+(a.y0+a.y1)/2,h=y.combine(c.plot_bgcolor,c.paper_bgcolor),p=d.readability(a.color,h)<1.5?y.contrast(h):a.color;if(s){var g=i.spikemode,v=i.spikethickness,x=i.spikecolor||p,b=i._boundingBox,_=(b.left+b.right)/2<u?b.right:b.left;if(g.indexOf("toaxis")!==-1||g.indexOf("across")!==-1){var w=_,k=u;g.indexOf("across")!==-1&&(w=i._counterSpan[0],k=i._counterSpan[1]),n.append("line").attr({x1:w,x2:k,y1:f,y2:f,"stroke-width":v+2,stroke:h}).classed("spikeline",!0).classed("crisp",!0),n.append("line").attr({x1:w,x2:k,y1:f,y2:f,"stroke-width":v,stroke:x,"stroke-dasharray":m.dashStyle(i.spikedash,v)}).classed("spikeline",!0).classed("crisp",!0)}g.indexOf("marker")!==-1&&n.append("circle").attr({cx:_+("right"!==i.side?v:-v),cy:f,r:v,fill:x}).classed("spikeline",!0)}if(l){var M=o.spikemode,A=o.spikethickness,T=o.spikecolor||p,L=o._boundingBox,C=(L.top+L.bottom)/2<f?L.bottom:L.top;if(M.indexOf("toaxis")!==-1||M.indexOf("across")!==-1){var S=C,z=f;M.indexOf("across")!==-1&&(S=o._counterSpan[0],z=o._counterSpan[1]),n.append("line").attr({x1:u,x2:u,y1:S,y2:z,"stroke-width":A+2,stroke:h}).classed("spikeline",!0).classed("crisp",!0),n.append("line").attr({x1:u,x2:u,y1:S,y2:z,"stroke-width":A,stroke:T,"stroke-dasharray":m.dashStyle(o.spikedash,A)}).classed("spikeline",!0).classed("crisp",!0)}M.indexOf("marker")!==-1&&n.append("circle").attr({cx:u,cy:C-("top"!==o.side?A:-A),r:A,fill:T}).classed("spikeline",!0)}}}function c(t,e,r){if(!r||r.length!==t._hoverdata.length)return!0;for(var n=r.length-1;n>=0;n--){var a=r[n],o=t._hoverdata[n];if(a.curveNumber!==o.curveNumber||String(a.pointNumber)!==String(o.pointNumber))return!0}return!1}var 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R=this.id,F=u-.5,j=O?f+D+.5:f+.5,B=d-.5,q=A?h+L+.5:h+.5,H=l._topdefs.selectAll("#"+R).data(A||O?[0]:[]);if(H.exit().remove(),H.enter().append("clipPath").attr("id",R).append("rect"),A||O?(this._clipRect=H.select("rect").attr({x:Math.floor(F),y:Math.floor(B),width:Math.ceil(j)-Math.floor(F),height:Math.ceil(q)-Math.floor(B)}),this.container.call(i.setClipUrl,R),this.bg.attr({x:p,y:v,width:g,height:m})):(this.bg.attr({width:0,height:0}),this.container.on("wheel",null).on(".drag",null).call(i.setClipUrl,null),delete this._clipRect),A||O){var V=a.behavior.drag().on("dragstart",function(){a.event.sourceEvent.preventDefault()}).on("drag",this._onBoxDrag.bind(this));this.container.on("wheel",null).on("wheel",this._onBoxWheel.bind(this)).on(".drag",null).call(V);var U=a.behavior.drag().on("dragstart",function(){a.event.sourceEvent.preventDefault(),a.event.sourceEvent.stopPropagation()}).on("drag",this._onBarDrag.bind(this));A&&this.hbar.on(".drag",null).call(U),O&&this.vbar.on(".drag",null).call(U)}this.setTranslate(e,r)},n.prototype.disable=function(){(this.hbar||this.vbar)&&(this.bg.attr({width:0,height:0}),this.container.on("wheel",null).on(".drag",null).call(i.setClipUrl,null),delete this._clipRect),this.hbar&&(this.hbar.on(".drag",null),this.hbar.remove(),delete this.hbar,delete this._hbarXMin,delete this._hbarTranslateMax),this.vbar&&(this.vbar.on(".drag",null),this.vbar.remove(),delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax)},n.prototype._onBoxDrag=function(){var t=this.translateX,e=this.translateY;this.hbar&&(t-=a.event.dx),this.vbar&&(e-=a.event.dy),this.setTranslate(t,e)},n.prototype._onBoxWheel=function(){var 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o=e/n;this.vbar.call(i.setTranslate,t,e+o*this._vbarTranslateMax)}}},{"../../lib":136,"../color":25,"../drawing":49,d3:7}],121:[function(t,e,r){"use strict";e.exports={SHOW_PLACEHOLDER:100,HIDE_PLACEHOLDER:1e3,DBLCLICKDELAY:300}},{}],122:[function(t,e,r){"use strict";e.exports={BADNUM:void 0,FP_SAFE:Number.MAX_VALUE/1e4,ONEAVGYEAR:315576e5,ONEAVGMONTH:26298e5,ONEDAY:864e5,ONEHOUR:36e5,ONEMIN:6e4,ONESEC:1e3,EPOCHJD:2440587.5,ALMOST_EQUAL:1-1e-6}},{}],123:[function(t,e,r){"use strict";e.exports={entityToUnicode:{mu:"\u03bc",amp:"&",lt:"<",gt:">",nbsp:"\xa0",times:"\xd7",plusmn:"\xb1",deg:"\xb0"},unicodeToEntity:{"&":"amp","<":"lt",">":"gt",'"':"quot","'":"#x27","/":"#x2F"}}},{}],124:[function(t,e,r){"use strict";r.xmlns="http://www.w3.org/2000/xmlns/",r.svg="http://www.w3.org/2000/svg",r.xlink="http://www.w3.org/1999/xlink",r.svgAttrs={xmlns:r.svg,"xmlns:xlink":r.xlink}},{}],125:[function(t,e,r){"use strict";var n=t("./plotly");/*export for 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n=t("../../components/color/attributes"),a=t("../../plots/font_attributes"),o=t("../../plots/attributes"),i=t("../../lib/extend").extendFlat;e.exports={labels:{valType:"data_array"},label0:{valType:"number",dflt:0},dlabel:{valType:"number",dflt:1},values:{valType:"data_array"},marker:{colors:{valType:"data_array"},line:{color:{valType:"color",dflt:n.defaultLine,arrayOk:!0},width:{valType:"number",min:0,dflt:0,arrayOk:!0}}},text:{valType:"data_array"},hovertext:{valType:"string",dflt:"",arrayOk:!0},scalegroup:{valType:"string",dflt:""},textinfo:{valType:"flaglist",flags:["label","text","value","percent"],extras:["none"]},hoverinfo:i({},o.hoverinfo,{flags:["label","text","value","percent","name"]}),textposition:{valType:"enumerated",values:["inside","outside","auto","none"],dflt:"auto",arrayOk:!0},textfont:i({},a,{}),insidetextfont:i({},a,{}),outsidetextfont:i({},a,{}),domain:{x:{valType:"info_array",items:[{valType:"number",min:0,max:1},{valType:"number",min:0,max:1}],dflt:[0,1]},y:{valType:"info_array",items:[{valType:"number",min:0,max:1},{valType:"number",min:0,max:1}],dflt:[0,1]}},hole:{valType:"number",min:0,max:1,dflt:0},sort:{valType:"boolean",dflt:!0},direction:{valType:"enumerated",values:["clockwise","counterclockwise"],dflt:"counterclockwise"},rotation:{valType:"number",min:-360,max:360,dflt:0},pull:{valType:"number",min:0,max:1,dflt:0,arrayOk:!0}}},{"../../components/color/attributes":24,"../../lib/extend":132,"../../plots/attributes":169,"../../plots/font_attributes":195}],229:[function(t,e,r){"use 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s},{"../../components/color":25,"./helpers":232,"fast-isnumeric":10,tinycolor2:13}],231:[function(t,e,r){"use strict";var n=t("../../lib"),a=t("./attributes");e.exports=function(t,e,r,o){function i(r,o){return n.coerce(t,e,a,r,o)}var l=n.coerceFont,s=i("values");if(!Array.isArray(s)||!s.length)return void(e.visible=!1);var c=i("labels");Array.isArray(c)||(i("label0"),i("dlabel")),i("marker.line.width")&&i("marker.line.color");var u=i("marker.colors");Array.isArray(u)||(e.marker.colors=[]),i("scalegroup");var f=i("text"),d=i("textinfo",Array.isArray(f)?"text+percent":"percent");if(i("hovertext"),i("hoverinfo",1===o._dataLength?"label+text+value+percent":void 0),d&&"none"!==d){var h=i("textposition"),p=Array.isArray(h)||"auto"===h,g=p||"inside"===h,v=p||"outside"===h;if(g||v){var m=l(i,"textfont",o.font);g&&l(i,"insidetextfont",m),v&&l(i,"outsidetextfont",m)}}i("domain.x"),i("domain.y"),i("hole"),i("sort"),i("direction"),i("rotation"),i("pull")}},{"../../lib":136,"./attributes":228}],232:[function(t,e,r){"use strict";var n=t("../../lib");r.formatPiePercent=function(t,e){var r=(100*t).toPrecision(3);return r.lastIndexOf(".")!==-1&&(r=r.replace(/[.]?0+$/,"")),n.numSeparate(r,e)+"%"},r.formatPieValue=function(t,e){var r=t.toPrecision(10);return r.lastIndexOf(".")!==-1&&(r=r.replace(/[.]?0+$/,"")),n.numSeparate(r,e)}},{"../../lib":136}],233:[function(t,e,r){"use strict";var n={};n.attributes=t("./attributes"),n.supplyDefaults=t("./defaults"),n.supplyLayoutDefaults=t("./layout_defaults"),n.layoutAttributes=t("./layout_attributes"),n.calc=t("./calc"),n.plot=t("./plot"),n.style=t("./style"),n.styleOne=t("./style_one"),n.moduleType="trace",n.name="pie",n.basePlotModule=t("./base_plot"),n.categories=["pie","showLegend"],n.meta={},e.exports=n},{"./attributes":228,"./base_plot":229,"./calc":230,"./defaults":231,"./layout_attributes":234,"./layout_defaults":235,"./plot":236,"./style":237,"./style_one":238}],234:[function(t,e,r){"use strict";e.exports={hiddenlabels:{valType:"data_array"}}},{}],235:[function(t,e,r){"use strict";var n=t("../../lib"),a=t("./layout_attributes");e.exports=function(t,e){!function(r,o){n.coerce(t,e,a,r,o)}("hiddenlabels")}},{"../../lib":136,"./layout_attributes":234}],236:[function(t,e,r){"use strict";function n(t,e,r){var n=Math.sqrt(t.width*t.width+t.height*t.height),o=t.width/t.height,i=Math.PI*Math.min(e.v/r.vTotal,.5),l=1-r.trace.hole,s=a(e,r),c={scale:s*r.r*2/n,rCenter:1-s,rotate:0};if(c.scale>=1)return c;var u=o+1/(2*Math.tan(i)),f=r.r*Math.min(1/(Math.sqrt(u*u+.5)+u),l/(Math.sqrt(o*o+l/2)+o)),d={scale:2*f/t.height,rCenter:Math.cos(f/r.r)-f*o/r.r,rotate:(180/Math.PI*e.midangle+720)%180-90},h=1/o,p=h+1/(2*Math.tan(i)),g=r.r*Math.min(1/(Math.sqrt(p*p+.5)+p),l/(Math.sqrt(h*h+l/2)+h)),v={scale:2*g/t.width,rCenter:Math.cos(g/r.r)-g/o/r.r,rotate:(180/Math.PI*e.midangle+810)%180-90},m=v.scale>d.scale?v:d;return c.scale<1&&m.scale>c.scale?m:c}function a(t,e){if(t.v===e.vTotal&&!e.trace.hole)return 1;var r=Math.PI*Math.min(t.v/e.vTotal,.5);return Math.min(1/(1+1/Math.sin(r)),(1-e.trace.hole)/2)}function o(t,e){var r=e.pxmid[0],n=e.pxmid[1],a=t.width/2,o=t.height/2;return r<0&&(a*=-1),n<0&&(o*=-1),{scale:1,rCenter:1,rotate:0,x:a+Math.abs(o)*(a>0?1:-1)/2,y:o/(1+r*r/(n*n)),outside:!0}}function i(t,e){function r(t,e){return t.pxmid[1]-e.pxmid[1]}function n(t,e){return e.pxmid[1]-t.pxmid[1]}var a,o,i,l,s,c,u,f,d,h,p,g,v;for(o=0;o<2;o++)for(i=o?r:n,s=o?Math.max:Math.min,u=o?1:-1,a=0;a<2;a++){for(l=a?Math.max:Math.min,c=a?1:-1,f=t[o][a],f.sort(i),d=t[1-o][a],h=d.concat(f),g=[],p=0;p<f.length;p++)void 0!==f[p].yLabelMid&&g.push(f[p]);for(v=!1,p=0;o&&p<d.length;p++)if(void 0!==d[p].yLabelMid){v=d[p];break}for(p=0;p<g.length;p++){var m=p&&g[p-1];v&&!p&&(m=v),function(t,r){r||(r={});var 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r,n,a,o,i,l,s,u,f,d,h=[];for(a=0;a<t.length;a++){if(i=t[a][0],l=i.trace,r=e.w*(l.domain.x[1]-l.domain.x[0]),n=e.h*(l.domain.y[1]-l.domain.y[0]),s=l.tiltaxis*Math.PI/180,u=l.pull,Array.isArray(u))for(u=0,o=0;o<l.pull.length;o++)l.pull[o]>u&&(u=l.pull[o]);i.r=Math.min(r/c(l.tilt,Math.sin(s),l.depth),n/c(l.tilt,Math.cos(s),l.depth))/(2+2*u),i.cx=e.l+e.w*(l.domain.x[1]+l.domain.x[0])/2,i.cy=e.t+e.h*(2-l.domain.y[1]-l.domain.y[0])/2, l.scalegroup&&h.indexOf(l.scalegroup)===-1&&h.push(l.scalegroup)}for(o=0;o<h.length;o++){for(d=1/0,f=h[o],a=0;a<t.length;a++)i=t[a][0],i.trace.scalegroup===f&&(d=Math.min(d,i.r*i.r/i.vTotal));for(a=0;a<t.length;a++)i=t[a][0],i.trace.scalegroup===f&&(i.r=Math.sqrt(d*i.vTotal))}}function s(t){function e(t){var e=f.r*Math.sin(t),r=-f.r*Math.cos(t);return h?[e*(1-l*n*n)+r*i*l,e*i*l+r*(1-l*a*a),Math.sin(o)*(r*a-e*n)]:[e,r]}var 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l=u.select(this),c=e[0],v=c.trace,m=(v.depth||0)*c.r*Math.sin(0)/2,y=v.tiltaxis||0,x=y*Math.PI/180,b=[m*Math.sin(x),m*Math.cos(x)],_=c.r*Math.cos(0),w=l.selectAll("g.part").data(v.tilt?["top","sides"]:["top"]);w.enter().append("g").attr("class",function(t){return t+" part"}),w.exit().remove(),w.order(),s(e),l.selectAll(".top").each(function(){var l=u.select(this).selectAll("g.slice").data(e);l.enter().append("g").classed("slice",!0),l.exit().remove();var s=[[[],[]],[[],[]]],m=!1;l.each(function(e){function i(n){n.originalEvent=u.event;var o=t._fullLayout,i=t._fullData[v.index],l=i.hoverinfo;if("all"===l&&(l="label+text+value+percent+name"),t._dragging||o.hovermode===!1||"none"===l||"skip"===l||!l)return void f.hover(t,n,"pie");var s=a(e,c),d=w+e.pxmid[0]*(1-s),h=k+e.pxmid[1]*(1-s),p=r.separators,m=[];l.indexOf("label")!==-1&&m.push(e.label),l.indexOf("text")!==-1&&(i.hovertext?m.push(Array.isArray(i.hovertext)?i.hovertext[e.i]:i.hovertext):i.text&&i.text[e.i]&&m.push(i.text[e.i])),l.indexOf("value")!==-1&&m.push(g.formatPieValue(e.v,p)),l.indexOf("percent")!==-1&&m.push(g.formatPiePercent(e.v/c.vTotal,p));var y=i.hoverlabel;f.loneHover({x0:d-s*c.r,x1:d+s*c.r,y:h,text:m.join("<br>"),name:l.indexOf("name")!==-1?i.name:void 0,idealAlign:e.pxmid[0]<0?"left":"right",color:e.hbg||y.bgcolor||e.color,borderColor:e.hbc||y.bordercolor,fontFamily:e.htf||y.font.family,fontSize:e.hts||y.font.size,fontColor:e.htc||y.font.color},{container:o._hoverlayer.node(),outerContainer:o._paper.node()}),f.hover(t,n,"pie"),T=!0}function l(e){e.originalEvent=u.event,t.emit("plotly_unhover",{event:u.event,points:[e]}),T&&(f.loneUnhover(r._hoverlayer.node()),T=!1)}function d(){t._hoverdata=[e],t._hoverdata.trace=c.trace,f.click(t,u.event)}function x(t,r,n,a){return"a"+a*c.r+","+a*_+" "+y+" "+e.largeArc+(n?" 1 ":" 0 ")+a*(r[0]-t[0])+","+a*(r[1]-t[1])}if(e.hidden)return void u.select(this).selectAll("path,g").remove();s[e.pxmid[1]<0?0:1][e.pxmid[0]<0?0:1].push(e);var w=c.cx+b[0],k=c.cy+b[1],M=u.select(this),A=M.selectAll("path.surface").data([e]),T=!1;if(A.enter().append("path").classed("surface",!0).style({"pointer-events":"all"}),M.select("path.textline").remove(),M.on("mouseover",i).on("mouseout",l).on("click",d),v.pull){var L=+(Array.isArray(v.pull)?v.pull[e.i]:v.pull)||0;L>0&&(w+=L*e.pxmid[0],k+=L*e.pxmid[1])}e.cxFinal=w,e.cyFinal=k;var C=v.hole;if(e.v===c.vTotal){var S="M"+(w+e.px0[0])+","+(k+e.px0[1])+x(e.px0,e.pxmid,!0,1)+x(e.pxmid,e.px0,!0,1)+"Z";C?A.attr("d","M"+(w+C*e.px0[0])+","+(k+C*e.px0[1])+x(e.px0,e.pxmid,!1,C)+x(e.pxmid,e.px0,!1,C)+"Z"+S):A.attr("d",S)}else{var z=x(e.px0,e.px1,!0,1);if(C){var O=1-C;A.attr("d","M"+(w+C*e.px1[0])+","+(k+C*e.px1[1])+x(e.px1,e.px0,!1,C)+"l"+O*e.px0[0]+","+O*e.px0[1]+z+"Z")}else A.attr("d","M"+w+","+k+"l"+e.px0[0]+","+e.px0[1]+z+"Z")}var D=Array.isArray(v.textposition)?v.textposition[e.i]:v.textposition,P=M.selectAll("g.slicetext").data(e.text&&"none"!==D?[0]:[]);P.enter().append("g").classed("slicetext",!0),P.exit().remove(),P.each(function(){var t=u.select(this).selectAll("text").data([0]);t.enter().append("text").attr("data-notex",1),t.exit().remove(),t.text(e.text).attr({class:"slicetext",transform:"","data-bb":"","text-anchor":"middle",x:0,y:0}).call(h.font,"outside"===D?v.outsidetextfont:v.insidetextfont).call(p.convertToTspans),t.selectAll("tspan.line").attr({x:0,y:0});var 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u,f=i.isBubble(t),d=(t.line||{}).color;if(c=c||{},d&&(r=d),s("marker.symbol"),s("marker.opacity",f?.7:1),s("marker.size"),s("marker.color",r),a(t,"marker")&&o(t,e,l,s,{prefix:"marker.",cLetter:"c"}),c.noLine||(u=d&&!Array.isArray(d)&&e.marker.color!==d?d:f?n.background:n.defaultLine,s("marker.line.color",u),a(t,"marker.line")&&o(t,e,l,s,{prefix:"marker.line.",cLetter:"c"}),s("marker.line.width",f?1:0)),f&&(s("marker.sizeref"),s("marker.sizemin"),s("marker.sizemode")),c.gradient){"none"!==s("marker.gradient.type")&&s("marker.gradient.color")}}},{"../../components/color":25,"../../components/colorscale/defaults":34,"../../components/colorscale/has_colorscale":38,"./subtypes":260}],257:[function(t,e,r){"use strict";function n(t,e){var r;e.selectAll("g.trace").each(function(t){var e=i.select(this);if(r=t[0].trace,r._nexttrace){if(r._nextFill=e.select(".js-fill.js-tonext"),!r._nextFill.size()){var n=":first-child";e.select(".js-fill.js-tozero").size()&&(n+=" + 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b=r.enter().append("path").classed("point",!0);b.call(s.pointStyle,o).call(s.translatePoints,M,A,o),k&&b.style("opacity",0).transition().style("opacity",1);var _=f&&s.tryColorscale(o.marker,""),w=f&&s.tryColorscale(o.marker,"line");r.each(function(e){var r=i.select(this),n=g(r);(a=s.translatePoint(e,n,M,A))&&(s.singlePointStyle(e,n,o,_,w,t),o.customdata&&r.classed("plotly-customdata",null!==e.data&&void 0!==e.data))}),k?r.exit().transition().style("opacity",0).remove():r.exit().remove(),n=c.selectAll("g"),r=n.data(m,h),r.enter().append("g").classed("textpoint",!0).append("text"),r.each(function(t){var e=i.select(this),r=g(e.select("text"));(a=s.translatePoint(t,r,M,A))||e.remove()}),r.selectAll("text").call(s.textPointStyle,o).each(function(t){var e=t.xp||M.c2p(t.x),r=t.yp||A.c2p(t.y);i.select(this).selectAll("tspan.line").each(function(){g(i.select(this)).attr({x:e,y:r})})}),r.exit().remove()}var _,w;o(t,e,r,n,a);var k=!!p&&p.duration>0,M=r.xaxis,A=r.yaxis,T=n[0].trace,L=T.line,C=i.select(d);if(C.call(c.plot,r,p),T.visible===!0){g(C).style("opacity",T.opacity);var S,z,O=T.fill.charAt(T.fill.length-1);"x"!==O&&"y"!==O&&(O=""),n[0].node3=C;var D="",P=[],E=T._prevtrace;E&&(D=E._prevRevpath||"",z=E._nextFill,P=E._polygons);var N,I,R,F,j,B,q,H,V,U="",X="",G=[],Y=[],Z=l.noop;if(S=T._ownFill,u.hasLines(T)||"none"!==T.fill){for(z&&z.datum(n),["hv","vh","hvh","vhv"].indexOf(L.shape)!==-1?(R=s.steps(L.shape),F=s.steps(L.shape.split("").reverse().join(""))):R=F="spline"===L.shape?function(t){var e=t[t.length-1];return t[0][0]===e[0]&&t[0][1]===e[1]?s.smoothclosed(t.slice(1),L.smoothing):s.smoothopen(t,L.smoothing)}:function(t){return"M"+t.join("L")},j=function(t){return F(t.reverse())},G=f(n,{xaxis:M,yaxis:A,connectGaps:T.connectgaps,baseTolerance:Math.max(L.width||1,3)/4,linear:"linear"===L.shape,simplify:L.simplify}),V=T._polygons=new Array(G.length),w=0;w<G.length;w++)T._polygons[w]=h(G[w]);G.length&&(B=G[0][0],q=G[G.length-1],H=q[q.length-1]),Y=G.filter(function(t){return t.length>1}),Z=function(t){return function(e){if(N=R(e),I=j(e),U?O?(U+="L"+N.substr(1),X=I+"L"+X.substr(1)):(U+="Z"+N,X=I+"Z"+X):(U=N,X=I),u.hasLines(T)&&e.length>1){var r=i.select(this);if(r.datum(n),t)g(r.style("opacity",0).attr("d",N).call(s.lineGroupStyle)).style("opacity",1);else{var a=g(r);a.attr("d",N),s.singleLineStyle(n,a)}}}}}var W=C.selectAll(".js-line").data(Y);g(W.exit()).style("opacity",0).remove(),W.each(Z(!1)),W.enter().append("path").classed("js-line",!0).style("vector-effect","non-scaling-stroke").call(s.lineGroupStyle).each(Z(!0)),G.length&&(S?B&&H&&(O?("y"===O?B[1]=H[1]=A.c2p(0,!0):"x"===O&&(B[0]=H[0]=M.c2p(0,!0)),g(S).attr("d","M"+H+"L"+B+"L"+U.substr(1))):g(S).attr("d",U+"Z")):"tonext"===T.fill.substr(0,6)&&U&&D&&("tonext"===T.fill?g(z).attr("d",U+"Z"+D+"Z"):g(z).attr("d",U+"L"+D.substr(1)+"Z"),T._polygons=T._polygons.concat(P)),T._prevRevpath=X,T._prevPolygons=V);var $=C.selectAll(".points");_=$.data([n]),$.each(b),_.enter().append("g").classed("points",!0).each(b),_.exit().remove()}}function o(t,e,r,n,a){var o=r.xaxis,s=r.yaxis,c=i.extent(l.simpleMap(o.range,o.r2c)),f=i.extent(l.simpleMap(s.range,s.r2c)),d=n[0].trace;if(u.hasMarkers(d)){var h=d.marker.maxdisplayed;if(0!==h){var p=n.filter(function(t){return t.x>=c[0]&&t.x<=c[1]&&t.y>=f[0]&&t.y<=f[1]}),g=Math.ceil(p.length/h),v=0;a.forEach(function(t,r){var n=t[0].trace;u.hasMarkers(n)&&n.marker.maxdisplayed>0&&r<e&&v++});var m=Math.round(v*g/3+Math.floor(v/3)*g/7.1);n.forEach(function(t){delete t.vis}),p.forEach(function(t,e){0===Math.round((e+m)%g)&&(t.vis=!0)})}}}var i=t("d3"),l=t("../../lib"),s=t("../../components/drawing"),c=t("../../components/errorbars"),u=t("./subtypes"),f=t("./line_points"),d=t("./link_traces"),h=t("../../lib/polygon").tester;e.exports=function(t,e,r,o,l){var s,c,u,f,h,p=e.plot.select("g.scatterlayer"),g=!o,v=!!o&&o.duration>0;for(u=p.selectAll("g.trace"),f=u.data(r,function(t){return t[0].trace.uid}),f.enter().append("g").attr("class",function(t){return"trace scatter trace"+t[0].trace.uid}).style("stroke-miterlimit",2),d(t,e,r),n(t,p),s=0,c={};s<r.length;s++)c[r[s][0].trace.uid]=s;if(p.selectAll("g.trace").sort(function(t,e){return c[t[0].trace.uid]>c[e[0].trace.uid]?1:-1}),v){l&&(h=l());i.transition().duration(o.duration).ease(o.easing).each("end",function(){h&&h()}).each("interrupt",function(){h&&h()}).each(function(){p.selectAll("g.trace").each(function(n,i){a(t,i,e,n,r,this,o)})})}else p.selectAll("g.trace").each(function(n,i){a(t,i,e,n,r,this,o)});g&&f.exit().remove(),p.selectAll("path:not([d])").remove()}},{"../../components/drawing":49,"../../components/errorbars":55,"../../lib":136,"../../lib/polygon":146,"./line_points":252,"./link_traces":254,"./subtypes":260,d3:7}],258:[function(t,e,r){"use strict";var n=t("./subtypes");e.exports=function(t,e){var r,a,o,i,l=t.cd,s=t.xaxis,c=t.yaxis,u=[],f=l[0].trace,d=f.index,h=f.marker,p=!n.hasMarkers(f)&&!n.hasText(f);if(f.visible===!0&&!p){var g=Array.isArray(h.opacity)?1:h.opacity;if(e===!1)for(r=0;r<l.length;r++)l[r].dim=0;else for(r=0;r<l.length;r++)a=l[r],o=s.c2p(a.x),i=c.c2p(a.y),e.contains([o,i])?(u.push({curveNumber:d,pointNumber:r,x:a.x,y:a.y,id:a.id}),a.dim=0):a.dim=1;return l[0].node3.selectAll("path.point").style("opacity",function(t){return((t.mo+1||g+1)-1)*(t.dim?.2:1)}),l[0].node3.selectAll("text").style("opacity",function(t){return t.dim?.2:1}),u}}},{"./subtypes":260}],259:[function(t,e,r){"use strict";var n=t("d3"),a=t("../../components/drawing"),o=t("../../components/errorbars");e.exports=function(t){var e=n.select(t).selectAll("g.trace.scatter");e.style("opacity",function(t){return t[0].trace.opacity}),e.selectAll("g.points").each(function(t){var e=n.select(this),r=e.selectAll("path.point"),o=t.trace||t[0].trace;r.call(a.pointStyle,o),e.selectAll("text").call(a.textPointStyle,o)}),e.selectAll("g.trace path.js-line").call(a.lineGroupStyle),e.selectAll("g.trace path.js-fill").call(a.fillGroupStyle),e.call(o.style)}},{"../../components/drawing":49,"../../components/errorbars":55,d3:7}],260:[function(t,e,r){"use strict";var n=t("../../lib");e.exports={hasLines:function(t){return t.visible&&t.mode&&t.mode.indexOf("lines")!==-1},hasMarkers:function(t){return t.visible&&t.mode&&t.mode.indexOf("markers")!==-1},hasText:function(t){return t.visible&&t.mode&&t.mode.indexOf("text")!==-1},isBubble:function(t){return n.isPlainObject(t.marker)&&Array.isArray(t.marker.size)}}},{"../../lib":136}],261:[function(t,e,r){"use strict";var n=t("../../lib");e.exports=function(t,e,r,a){a("textposition"),n.coerceFont(a,"textfont",r.font)}},{"../../lib":136}],262:[function(t,e,r){"use strict";var n=t("../../registry");e.exports=function(t,e,r,a){var o,i=a("x"),l=a("y");if(n.getComponentMethod("calendars","handleTraceDefaults")(t,e,["x","y"],r),i)l?(o=Math.min(i.length,l.length),o<i.length&&(e.x=i.slice(0,o)),o<l.length&&(e.y=l.slice(0,o))):(o=i.length,a("y0"),a("dy"));else{if(!l)return 0;o=e.y.length,a("x0"),a("dx")}return o}},{"../../registry":206}]},{},[5])(5)});window.define = window.__define;window.require = window.__require;window.__define = undefined; window.__require = undefined;/* global Highcharts, Plotly*/ /* eslint-disable */ function throttle(fn, timeout, invokeAsap, ctx) {
var typeofInvokeAsap = typeof invokeAsap;
if(typeofInvokeAsap === 'undefined') {
    invokeAsap = true;
} else if(arguments.length === 3 && typeofInvokeAsap !== 'boolean') {
    ctx = invokeAsap;
    invokeAsap = true;
}

var timer, args, needInvoke,
    wrapper = function() {
        if(needInvoke) {
            fn.apply(ctx, args);
            needInvoke = false;
            timer = setTimeout(wrapper, timeout);
        } else {
            timer = null;
        }
    };

return function() {
    args = arguments;
    ctx || (ctx = this);
    needInvoke = true;

    if(!timer) {
        invokeAsap?
            wrapper() :
            timer = setTimeout(wrapper, timeout);
    }
}; }

/* eslint-enable */
function CatboostIpython() {

}

CatboostIpython.prototype.init = function() {
this.charts = {};
/*
{
“rmse_1”: {
…
}
}
/
this.traces = {};
/
{
“rmse_1”: {
name: “rmse”,
id: “rmse_1”,
parent: “div”,
traces: [
{
name: “current;learn;0;;;”,
x: [],
y: []
},
{
name: “current;learn;0;smoothed;;”,
x: [],
y: []
},
{
name: “current;learn;1;;;”,
x: [],
y: []
},
{
name: “current;learn;1;smoothed;;”,
x: [],
y: []
},
{
name: “current;test;0;;;”,
x: [],
y: []
},
{
name: “current;test;0;smoothed;;”,
x: [],
y: []
},
{
name: “current;test;0;;best_point;”,
x: [],
y: []
},
{
name: “current;test;0;;;best_value”,
x: [],
y: []
}
]
}
}
*/

this.hovertextParameters = [];
this.chartsToRedraw = {};
this.lastIndexes = {};
this.smoothness = -1;
this.layoutDisabled = {
    series: {},
    traces: {}
};
this.clickMode = false;
this.logarithmMode = 'linear';
this.lastSmooth = 0;
this.layout = null;
this.activeTab = '';
this.meta = {};
this.timeLeft = {};

this.hasCVMode = false;
this.stddevEnabled = false;

this.colors = [
    '#68E256',
    '#56AEE2',
    '#CF56E2',
    '#E28956',
    '#56E289',
    '#5668E2',
    '#E256AE',
    '#E2CF56',
    '#56E2CF',
    '#8A56E2',
    '#E25668',
    '#AEE256'
];
this.colorsByPath = {};
this.colorIndex = 0;
this.lossFuncs = {};

this.isCVinited = false; };

/* eslint-disable */
CatboostIpython.prototype.loadStyles = function(path, fn, scope) {
$(‘link[catboost=”1”]’).remove();

var head = document.getElementsByTagName('head')[0], // reference to document.head for appending/ removing link nodes
    link = document.createElement('link');           // create the link node
link.setAttribute('href', path);
link.setAttribute('rel', 'stylesheet');
link.setAttribute('type', 'text/css');
link.setAttribute('catboost', '1');

var sheet, cssRules;
// get the correct properties to check for depending on the browser
if ('sheet' in link) {
    sheet = 'sheet'; cssRules = 'cssRules';
} else {
    sheet = 'styleSheet'; cssRules = 'rules';
}

var interval_id = setInterval(function() {                     // start checking whether the style sheet has successfully loaded
    try {
        if (link[sheet] && link[sheet][cssRules].length) { // SUCCESS! our style sheet has loaded
            clearInterval(interval_id);                      // clear the counters
            clearTimeout(timeout_id);
            fn.call( scope || window, true, link);           // fire the callback with success == true
        }
    } catch(e) {} finally {}
}, 50 ),                                                   // how often to check if the stylesheet is loaded
timeout_id = setTimeout( function() {       // start counting down till fail
    clearInterval( interval_id );             // clear the counters
    clearTimeout( timeout_id );
    head.removeChild( link );                // since the style sheet didn't load, remove the link node from the DOM
    fn.call( scope || window, false, link ); // fire the callback with success == false
}, 15000 );                                 // how long to wait before failing

head.appendChild( link );  // insert the link node into the DOM and start loading the style sheet

return link; // return the link node; }; /* eslint-enable */

CatboostIpython.prototype.resizeCharts = function() {
// width fix for development
$(‘.catboost-graph__charts’, this.layout).css({width: $(‘.catboost-graph’).width()});

this.plotly.Plots.resize(this.traces[this.activeTab].parent); };

CatboostIpython.prototype.addMeta = function(path, meta) {
this.meta[path] = meta;
};

CatboostIpython.prototype.addLayout = function(parent) {
if (this.layout) {
return;
}

var cvAreaControls = '';

if (this.hasCVMode) {
    cvAreaControls =    '<div>' +
                            '<input type="checkbox" class="catboost-panel__control_checkbox" id="catboost-control2-cvstddev' + this.index + '"' + (this.stddevEnabled ? ' checked="checked"' : '') + '></input>' +
                            '<label for="catboost-control2-cvstddev' + this.index + '" class="catboost-panel__controls2_label catboost-panel__controls2_label-long">Standard Deviation</label>' +
                        '</div>';
}

this.layout = $('<div class="catboost">' +
                    '<div class="catboost-panel">' +
                        '<div class="catboost-panel__controls">' +
                            '<input type="checkbox" class="catboost-panel__controls_checkbox" id="catboost-control-learn' + this.index + '" ' + (!this.layoutDisabled.learn ? ' checked="checked"' : '') + '></input>' +
                            '<label for="catboost-control-learn' + this.index + '" class="catboost-panel__controls_label"><div class="catboost-panel__serie_learn_pic" style="border-color:#999"></div>Learn</label>' +
                            '<input type="checkbox" class="catboost-panel__controls_checkbox" id="catboost-control-test' + this.index + '" ' + (!this.layoutDisabled.test ? ' checked="checked"' : '') + '></input>' +
                            '<label for="catboost-control-test' + this.index + '" class="catboost-panel__controls_label"><div class="catboost-panel__serie_test_pic" style="border-color:#999"></div>Eval</label>' +
                        '</div>' +
                        '<div class="catboost-panel__series ' + (this.layoutDisabled.learn ? ' catboost-panel__series_learn_disabled' : '') + '">' +
                        '</div>' +
                        '<div class="catboost-panel__controls2">' +
                            '<input type="checkbox" class="catboost-panel__control_checkbox" id="catboost-control2-clickmode' + this.index + '"></input>' +
                            '<label for="catboost-control2-clickmode' + this.index + '" class="catboost-panel__controls2_label">Click Mode</label>' +
                            '<input type="checkbox" class="catboost-panel__control_checkbox" id="catboost-control2-log' + this.index + '"></input>' +
                            '<label for="catboost-control2-log' + this.index + '" class="catboost-panel__controls2_label">Logarithm</label>' +
                            '<div>' +
                                '<input type="checkbox" class="catboost-panel__control_checkbox" id="catboost-control2-smooth' + this.index + '"></input>' +
                                '<label for="catboost-control2-smooth' + this.index + '" class="catboost-panel__controls2_label">Smooth</label>' +
                                '<input id="catboost-control2-slider' + this.index + '" disabled="disabled" class="catboost-panel__control_slider" type ="range" value="0" min="0" max="1" step ="0.01" for="rangeInputValue" name="rangeInput"/>' +
                                '<input id="catboost-control2-slidervalue' + this.index + '" disabled="disabled" class="catboost-panel__control_slidervalue" value="0" min="0" max="1" for="rangeInput" name="rangeInputValue"/>' +
                            '</div>' +
                            cvAreaControls +
                        '</div>' +
                    '</div>' +
                    '<div class="catboost-graph">' +
                        '<div class="catboost-graph__tabs"></div>' +
                        '<div class="catboost-graph__charts"></div>' +
                    '</div>' +
                '</div>');
$(parent).append(this.layout);

this.addTabEvents();
this.addControlEvents(); };

CatboostIpython.prototype.addTabEvents = function() {
var self = this;

$('.catboost-graph__tabs', this.layout).click(function(e) {
    if (!$(e.target).is('.catboost-graph__tab:not(.catboost-graph__tab_active)')) {
        return;
    }

    var id = $(e.target).attr('tabid');

    self.activeTab = id;

    $('.catboost-graph__tab_active', self.layout).removeClass('catboost-graph__tab_active');
    $('.catboost-graph__chart_active', self.layout).removeClass('catboost-graph__chart_active');

    $('.catboost-graph__tab[tabid="' + id + '"]', self.layout).addClass('catboost-graph__tab_active');
    $('.catboost-graph__chart[tabid="' + id + '"]', self.layout).addClass('catboost-graph__chart_active');

    self.cleanSeries();

    self.redrawActiveChart();
    self.resizeCharts();
}); };

CatboostIpython.prototype.addControlEvents = function() {
var self = this;

$('#catboost-control-learn' + this.index, this.layout).click(function() {
    self.layoutDisabled.learn = !$(this)[0].checked;

    $('.catboost-panel__series', self.layout).toggleClass('catboost-panel__series_learn_disabled', self.layoutDisabled.learn);

    self.redrawActiveChart();
});

$('#catboost-control-test' + this.index, this.layout).click(function() {
    self.layoutDisabled.test = !$(this)[0].checked;

    $('.catboost-panel__series', self.layout).toggleClass('catboost-panel__series_test_disabled', self.layoutDisabled.test);

    self.redrawActiveChart();
});

$('#catboost-control2-clickmode' + this.index, this.layout).click(function() {
    self.clickMode = $(this)[0].checked;
});

$('#catboost-control2-log' + this.index, this.layout).click(function() {
    self.logarithmMode = $(this)[0].checked ? 'log' : 'linear';

    self.forEveryLayout(function(layout) {
        layout.yaxis = {type: self.logarithmMode};
    });

    self.redrawActiveChart();
});

var slider = $('#catboost-control2-slider' + this.index),
    sliderValue = $('#catboost-control2-slidervalue' + this.index);

$('#catboost-control2-smooth' + this.index, this.layout).click(function() {
    var enabled = $(this)[0].checked;

    self.setSmoothness(enabled ? self.lastSmooth : -1);

    slider.prop('disabled', !enabled);
    sliderValue.prop('disabled', !enabled);

    self.redrawActiveChart();
});

$('#catboost-control2-cvstddev' + this.index, this.layout).click(function() {
    var enabled = $(this)[0].checked;

    self.setStddev(enabled);

    self.redrawActiveChart();
});

slider.on('input change', function() {
    var smooth = Number($(this).val());

    sliderValue.val(isNaN(smooth) ? 0 : smooth);

    self.setSmoothness(smooth);
    self.lastSmooth = smooth;

    self.redrawActiveChart();
});

sliderValue.on('input change', function() {
    var smooth = Number($(this).val());

    slider.val(isNaN(smooth) ? 0 : smooth);

    self.setSmoothness(smooth);
    self.lastSmooth = smooth;

    self.redrawActiveChart();
}); };

CatboostIpython.prototype.setTraceVisibility = function(trace, visibility) {
if (trace) {
trace.visible = visibility;
}
};

CatboostIpython.prototype.updateTracesVisibility = function() {
var tracesHash = this.groupTraces(),
traces,
smoothDisabled = this.getSmoothness() === -1,
self = this;

for (var train in tracesHash) {
    if (tracesHash.hasOwnProperty(train)) {
        traces = tracesHash[train].traces;

        if (this.layoutDisabled.traces[train]) {
            traces.forEach(function(trace) {
                self.setTraceVisibility(trace, false);
            });
        } else {
            traces.forEach(function(trace) {
                self.setTraceVisibility(trace, true);
            });

            if (this.hasCVMode) {
                if (this.stddevEnabled) {
                    self.filterTracesOne(traces, {type: 'learn'}).forEach(function(trace) {
                        self.setTraceVisibility(trace, false);
                    });
                    self.filterTracesOne(traces, {type: 'test'}).forEach(function(trace) {
                        self.setTraceVisibility(trace, false);
                    });

                    self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true})).forEach(function(trace) {
                        self.setTraceVisibility(trace, true);
                    });
                    self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true})).forEach(function(trace) {
                        self.setTraceVisibility(trace, true);
                    });

                    self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true, smoothed: true})).forEach(function(trace) {
                        self.setTraceVisibility(trace, true);
                    });
                    self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, smoothed: true})).forEach(function(trace) {
                        self.setTraceVisibility(trace, true);
                    });

                    self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, best_point: true})).forEach(function(trace) {
                        self.setTraceVisibility(trace, true);
                    });

                    self.filterTracesOne(traces, {cv_stddev_first: true}).forEach(function(trace) {
                        self.setTraceVisibility(trace, true);
                    });
                    self.filterTracesOne(traces, {cv_stddev_last: true}).forEach(function(trace) {
                        self.setTraceVisibility(trace, true);
                    });
                } else {
                    self.filterTracesOne(traces, {cv_stddev_first: true}).forEach(function(trace) {
                        self.setTraceVisibility(trace, false);
                    });
                    self.filterTracesOne(traces, {cv_stddev_last: true}).forEach(function(trace) {
                        self.setTraceVisibility(trace, false);
                    });

                    self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true})).forEach(function(trace) {
                        self.setTraceVisibility(trace, false);
                    });
                    self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true})).forEach(function(trace) {
                        self.setTraceVisibility(trace, false);
                    });

                    self.filterTracesEvery(traces, this.getTraceDefParams({type: 'learn', cv_avg: true, smoothed: true})).forEach(function(trace) {
                        self.setTraceVisibility(trace, false);
                    });
                    self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, smoothed: true})).forEach(function(trace) {
                        self.setTraceVisibility(trace, false);
                    });

                    self.filterTracesEvery(traces, this.getTraceDefParams({type: 'test', cv_avg: true, best_point: true})).forEach(function(trace) {
                        self.setTraceVisibility(trace, false);
                    });
                }
            }

            if (smoothDisabled) {
                self.filterTracesOne(traces, {smoothed: true}).forEach(function(trace) {
                    self.setTraceVisibility(trace, false);
                });
            }

            if (this.layoutDisabled['learn']) {
                self.filterTracesOne(traces, {type: 'learn'}).forEach(function(trace) {
                    self.setTraceVisibility(trace, false);
                });
            }

            if (this.layoutDisabled['test']) {
                self.filterTracesOne(traces, {type: 'test'}).forEach(function(trace) {
                    self.setTraceVisibility(trace, false);
                });
            }
        }
    }
} };

CatboostIpython.prototype.getSmoothness = function() {
return this.smoothness && this.smoothness > -1 ? this.smoothness : -1;
};

CatboostIpython.prototype.setSmoothness = function(weight) {
if (weight < 0 && weight !== -1 || weight > 1) {
return;
}

this.smoothness = weight; };

CatboostIpython.prototype.setStddev = function(enabled) {
this.stddevEnabled = enabled;
};

CatboostIpython.prototype.redrawActiveChart = function() {
this.chartsToRedraw[this.activeTab] = true;

this.redrawAll(); };

CatboostIpython.prototype.redraw = function() {
if (this.chartsToRedraw[this.activeTab]) {
this.chartsToRedraw[this.activeTab] = false;

    this.updateTracesVisibility();
    this.updateTracesCV();
    this.updateTracesBest();
    this.updateTracesValues();
    this.updateTracesSmoothness();

    this.plotly.redraw(this.traces[this.activeTab].parent);
}

this.drawTraces(); };

CatboostIpython.prototype.addRedrawFunc = function() {
this.redrawFunc = throttle(this.redraw, 400, false, this);
};

CatboostIpython.prototype.redrawAll = function() {
if (!this.redrawFunc) {
this.addRedrawFunc();
}

this.redrawFunc(); };

CatboostIpython.prototype.addPoints = function(parent, data) {
var self = this;

data.chunks.forEach(function(item) {
    if (typeof item.remaining_time !== 'undefined' && typeof item.passed_time !== 'undefined') {
        if (!self.timeLeft[data.path]) {
            self.timeLeft[data.path] = [];
        }

        self.timeLeft[data.path][item.iteration] = [item.remaining_time, item.passed_time];
    }

    ['test', 'learn'].forEach(function(type) {
        var sets = self.meta[data.path][type + '_sets'],
            metrics = self.meta[data.path][type + '_metrics'];

        for (var i = 0; i < metrics.length; i++) {
            var nameOfMetric = metrics[i].name,
                cvAdded = false;
                hovertextParametersAdded = false;

            self.lossFuncs[nameOfMetric] = metrics[i].best_value;

            for (var j = 0; j < sets.length; j++) {
                var nameOfSet = sets[j],
                    params = {
                        chartName: nameOfMetric,
                        index: i,
                        train: data.train,
                        type: type,
                        path: data.path,
                        indexOfSet: j,
                        nameOfSet: nameOfSet
                    },
                    key = self.getKey(params),
                    launchMode = self.getLaunchMode(data.path);

                if (!self.activeTab) {
                    self.activeTab = key.chartId;
                }

                if (launchMode === 'CV' ) {
                    // we need to set launch mode before first getTrace call
                    self.hasCVMode = true;

                    if (!self.isCVinited) {
                        // and we don't need to reset setting for next iterations
                        self.layoutDisabled.learn = true;
                        self.setStddev(true);

                        self.isCVinited = true;
                    }
                }

                var valuesOfSet = item[nameOfSet],
                    pointValue = valuesOfSet[i],
                    pointIndex = item.iteration,
                    // traces
                    trace = self.getTrace(parent, params),
                    smoothedTrace = self.getTrace(parent, $.extend({smoothed: true}, params)),
                    bestValueTrace = null;

                if (type === 'test') {
                    if (launchMode !== 'CV') {
                        self.getTrace(parent, $.extend({best_point: true}, params));
                    }

                    if (typeof self.lossFuncs[nameOfMetric] === 'number') {
                        bestValueTrace = self.getTrace(parent, $.extend({best_value: true}, params));
                    }
                }

                if (pointValue !== 'inf' && pointValue !== 'nan') {
                    trace.x[pointIndex] = pointIndex;
                    trace.y[pointIndex] = valuesOfSet[i];
                    trace.hovertext[pointIndex] = nameOfSet + ': ' + valuesOfSet[i].toPrecision(7);
                    if (item.hasOwnProperty('parameters')) {
                        self.hovertextParameters[pointIndex] = '';
                        for (var parameter in item.parameters[0]) {
                            if (item.parameters[0].hasOwnProperty(parameter)) {
                                valueOfParameter = item.parameters[0][parameter];
                                self.hovertextParameters[pointIndex] += '<br>' + parameter + ' : ' + valueOfParameter;
                            }
                        }
                        if (!hovertextParametersAdded && type === 'test') {
                            hovertextParametersAdded = true;
                            trace.hovertext[pointIndex] += self.hovertextParameters[pointIndex];
                        }
                    }
                    smoothedTrace.x[pointIndex] = pointIndex;
                }

                if (bestValueTrace) {
                    bestValueTrace.x[pointIndex] = pointIndex;
                    bestValueTrace.y[pointIndex] = self.lossFuncs[nameOfMetric];
                }

                if (launchMode === 'CV' && !cvAdded) {
                    cvAdded = true;

                    self.getTrace(parent, $.extend({cv_stddev_first: true}, params));
                    self.getTrace(parent, $.extend({cv_stddev_last: true}, params));

                    self.getTrace(parent, $.extend({cv_stddev_first: true, smoothed: true}, params));
                    self.getTrace(parent, $.extend({cv_stddev_last: true, smoothed: true}, params));

                    self.getTrace(parent, $.extend({cv_avg: true}, params));
                    self.getTrace(parent, $.extend({cv_avg: true, smoothed: true}, params));

                    if (type === 'test') {
                        self.getTrace(parent, $.extend({cv_avg: true, best_point: true}, params));
                    }
                }
            }

            self.chartsToRedraw[key.chartId] = true;

            self.redrawAll();
        }
    });
}); };

CatboostIpython.prototype.getLaunchMode = function(path) {
return this.meta[path].launch_mode;
};

CatboostIpython.prototype.getChartNode = function(params, active) {
var node = $(‘<div class="catboost-graph__chart" tabid="' + params.id + '"></div>’);

if (active) {
    node.addClass('catboost-graph__chart_active');
}

return node; };

CatboostIpython.prototype.getChartTab = function(params, active) {
var node = $(‘<div class="catboost-graph__tab" tabid="' + params.id + '">’ + params.name + ‘</div>’);

if (active) {
    node.addClass('catboost-graph__tab_active');
}

return node; };

CatboostIpython.prototype.forEveryChart = function(callback) {
for (var name in this.traces) {
if (this.traces.hasOwnProperty(name)) {
callback(this.traces[name]);
}
}
};

CatboostIpython.prototype.forEveryLayout = function(callback) {
this.forEveryChart(function(chart) {
callback(chart.layout);
});
};

CatboostIpython.prototype.getChart = function(parent, params) {
var id = params.id,
self = this;

if (this.charts[id]) {
    return this.charts[id];
}

this.addLayout(parent);

var active = this.activeTab === params.id,
    chartNode = this.getChartNode(params, active),
    chartTab = this.getChartTab(params, active);

$('.catboost-graph__charts', this.layout).append(chartNode);
$('.catboost-graph__tabs', this.layout).append(chartTab);

this.traces[id] = {
    id: params.id,
    name: params.name,
    parent: chartNode[0],
    traces: [],
    layout: {
        xaxis: {
            range: [0, Number(this.meta[params.path].iteration_count)],
            type: 'linear',
            tickmode: 'auto',
            showspikes: true,
            spikethickness: 1,
            spikedash: 'longdashdot',
            spikemode: 'across',
            zeroline: false,
            showgrid: false
        },
        yaxis: {
            zeroline: false
            //showgrid: false
            //hoverformat : '.7f'
        },
        separators: '. ',
        //hovermode: 'x',
        margin: {l: 38, r: 0, t: 35, b: 30},
        autosize: true,
        showlegend: false
    },
    options: {
        scrollZoom: false,
        modeBarButtonsToRemove: ['toggleSpikelines'],
        displaylogo: false
    }
};

this.charts[id] = this.plotly.plot(chartNode[0], this.traces[id].traces, this.traces[id].layout, this.traces[id].options);

chartNode[0].on('plotly_hover', function(e) {
    self.updateTracesValues(e.points[0].x);
});

chartNode[0].on('plotly_click', function(e) {
    self.updateTracesValues(e.points[0].x, true);
});

return this.charts[id]; };

CatboostIpython.prototype.getTrace = function(parent, params) {
var key = this.getKey(params),
chartSeries = [];

if (this.traces[key.chartId]) {
    chartSeries = this.traces[key.chartId].traces.filter(function(trace) {
        return trace.name === key.traceName;
    });
}

if (chartSeries.length) {
    return chartSeries[0];
} else {
    this.getChart(parent, {id: key.chartId, name: params.chartName, path: params.path});

    var plotParams = {
            color: this.getNextColor(params.path, params.smoothed ? 0.2 : 1),
            fillsmoothcolor: this.getNextColor(params.path, 0.1),
            fillcolor: this.getNextColor(params.path, 0.4),
            hoverinfo: params.cv_avg ? 'skip' : 'text+x',
            width: params.cv_avg ? 2 : 1,
            dash: params.type === 'test' ? 'solid' : 'dot'
        },
        trace = {
            name: key.traceName,
            _params: params,
            x: [],
            y: [],
            hovertext: [],
            hoverinfo: plotParams.hoverinfo,
            line: {
                width: plotParams.width,
                dash: plotParams.dash,
                color: plotParams.color
            },
            mode: 'lines',
            hoveron: 'points',
            connectgaps: true
        };

    if (params.best_point) {
        trace = {
            name: key.traceName,
            _params: params,
            x: [],
            y: [],
            marker: {
                width: 2,
                color: plotParams.color
            },
            hovertext: [],
            hoverinfo: 'text',
            mode: 'markers',
            type: 'scatter'
        };
    }

    if (params.best_value) {
        trace = {
            name: key.traceName,
            _params: params,
            x: [],
            y: [],
            line: {
                width: 1,
                dash: 'dash',
                color: '#CCCCCC'
            },
            mode: 'lines',
            connectgaps: true,
            hoverinfo: 'skip'
        };
    }

    if (params.cv_stddev_last) {
        trace.fill = 'tonexty';
    }

    trace._params.plotParams = plotParams;

    this.traces[key.chartId].traces.push(trace);

    return trace;
} };

CatboostIpython.prototype.getKey = function(params) {
var traceName = [
params.train,
params.type,
params.indexOfSet,
(params.smoothed ? ‘smoothed’ : ‘’),
(params.best_point ? ‘best_pount’ : ‘’),
(params.best_value ? ‘best_value’ : ‘’),
(params.cv_avg ? ‘cv_avg’ : ‘’),
(params.cv_stddev_first ? ‘cv_stddev_first’ : ‘’),
(params.cv_stddev_last ? ‘cv_stddev_last’ : ‘’)
].join(‘;’);

return {
    chartId: params.chartName,
    traceName: traceName,
    colorId: params.train
}; };

CatboostIpython.prototype.filterTracesEvery = function(traces, filter) {
traces = traces || this.traces[this.activeTab].traces;

return traces.filter(function(trace) {
    for (var prop in filter) {
        if (filter.hasOwnProperty(prop)) {
            if (filter[prop] !== trace._params[prop]) {
                return false;
            }
        }
    }

    return true;
}); };

CatboostIpython.prototype.filterTracesOne = function(traces, filter) {
traces = traces || this.traces[this.activeTab].traces;

return traces.filter(function(trace) {
    for (var prop in filter) {
        if (filter.hasOwnProperty(prop)) {
            if (filter[prop] === trace._params[prop]) {
                return true;
            }
        }
    }

    return false;
}); };

CatboostIpython.prototype.cleanSeries = function() {
$(‘.catboost-panel__series’, this.layout).html(‘’);
};

CatboostIpython.prototype.groupTraces = function() {
var traces = this.traces[this.activeTab].traces,
index = 0,
tracesHash = {};

traces.map(function(trace) {
    var train = trace._params.train;

    if (!tracesHash[train]) {
        tracesHash[train] = {
            index: index,
            traces: [],
            info: {
                path: trace._params.path,
                color: trace._params.plotParams.color
            }
        };

        index++;
    }

    tracesHash[train].traces.push(trace);
});

return tracesHash; };

CatboostIpython.prototype.drawTraces = function() {
var html = ‘’,
tracesHash = this.groupTraces();

var curLength = $('.catboost-panel__series .catboost-panel__serie', this.layout).length;
var newLength = Object.keys(tracesHash).filter(hasOwnProperty.bind(tracesHash)).length;
if (newLength === curLength) {
    return;
}

for (var train in tracesHash) {
    if (tracesHash.hasOwnProperty(train)) {
        html += this.drawTrace(train, tracesHash[train]);
    }
}

$('.catboost-panel__series', this.layout).html(html);

this.updateTracesValues();

this.addTracesEvents(); };

CatboostIpython.prototype.getTraceDefParams = function(params) {
var defParams = {
smoothed: undefined,
best_point: undefined,
best_value: undefined,
cv_avg: undefined,
cv_stddev_first: undefined,
cv_stddev_last: undefined
};

if (params) {
    return $.extend(defParams, params);
} else {
    return defParams;
} };

CatboostIpython.prototype.drawTrace = function(train, hash) {
var info = hash.info,
id = ‘catboost-serie-‘ + this.index + ‘-‘ + hash.index,
traces = {
learn: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: ‘learn’})),
test: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: ‘test’}))
},
items = {
learn: {
middle: ‘’,
bottom: ‘’
},
test: {
middle: ‘’,
bottom: ‘’
}
},
tracesNames = ‘’;

['learn', 'test'].forEach(function(type) {
    traces[type].forEach(function(trace) {
        items[type].middle += '<div class="catboost-panel__serie_' + type + '_pic" style="border-color:' + info.color + '"></div>' +
                              '<div data-index="' + trace._params.indexOfSet + '" class="catboost-panel__serie_' + type + '_value"></div>';

        items[type].bottom += '<div class="catboost-panel__serie_' + type + '_pic" style="border-color:transparent"></div>' +
                              '<div data-index="' + trace._params.indexOfSet + '" class="catboost-panel__serie_best_' + type + '_value"></div>';

        tracesNames += '<div class="catboost-panel__serie_' + type + '_pic" style="border-color:' + info.color + '"></div>' +
                       '<div class="catboost-panel__serie_' + type + '_name">' + trace._params.nameOfSet + '</div>';
    });
});

var timeSpendHtml = '<div class="catboost-panel__serie_time">' +
                         '<div class="catboost-panel__serie_time_spend" title="Time spend"></div>' +
                    '</div>';

var html = '<div id="' + id + '" class="catboost-panel__serie" style="color:' + info.color + '">' +
                '<div class="catboost-panel__serie_top">' +
                    '<input type="checkbox" data-seriename="' + train + '" class="catboost-panel__serie_checkbox" id="' + id + '-box" ' + (!this.layoutDisabled.series[train] ? 'checked="checked"' : '') + '></input>' +
                    '<label title=' + this.meta[info.path].name + ' for="' + id + '-box" class="catboost-panel__serie_label">' + train + '<div class="catboost-panel__serie_time_left" title="Estimate time"></div></label>' +
                    (this.getLaunchMode(info.path) !== 'Eval' ? timeSpendHtml : '') +
                '</div>' +
                '<div class="catboost-panel__serie_hint catboost-panel__serie__learn_hint">curr</div>' +
                '<div class="catboost-panel__serie_hint catboost-panel__serie__test_hint">best</div>' +
                '<div class="catboost-panel__serie_iteration" title="curr iteration"></div>' +
                '<div class="catboost-panel__serie_best_iteration" title="best ' + (this.hasCVMode ? 'avg ' : '') + 'iteration"></div>' +
                '<div class="catboost-panel__serie_scroll">' +
                    '<div class="catboost-panel__serie_names">' +
                        tracesNames +
                    '</div>' +
                    '<div class="catboost-panel__serie_middle">' +
                        items.learn.middle +
                        items.test.middle +
                    '</div>' +
                    '<div class="catboost-panel__serie_bottom">' +
                        items.learn.bottom +
                        items.test.bottom +
                    '</div>' +
                '</div>' +
            '</div>';

return html; };

CatboostIpython.prototype.updateTracesValues = function(iteration, click) {
var tracesHash = this.groupTraces();

for (var train in tracesHash) {
    if (tracesHash.hasOwnProperty(train) && !this.layoutDisabled.traces[train]) {
        this.updateTraceValues(train, tracesHash[train], iteration, click);
    }
} };

CatboostIpython.prototype.updateTracesBest = function() {
var tracesHash = this.groupTraces();

for (var train in tracesHash) {
    if (tracesHash.hasOwnProperty(train) && !this.layoutDisabled.traces[train]) {
        this.updateTraceBest(train, tracesHash[train]);
    }
} };

CatboostIpython.prototype.getBestValue = function(data) {
if (!data.length) {
return {
best: undefined,
index: -1
};
}

var best = data[0],
    index = 0,
    func = this.lossFuncs[this.traces[this.activeTab].name],
    bestDiff = typeof func === 'number' ? Math.abs(data[0] - func) : 0;

for (var i = 1, l = data.length; i < l; i++) {
    if (func === 'Min' && data[i] < best) {
        best = data[i];
        index = i;
    }

    if (func === 'Max' && data[i] > best) {
        best = data[i];
        index = i;
    }

    if (typeof func === 'number' && Math.abs(data[i] - func) < bestDiff) {
        best = data[i];
        bestDiff = Math.abs(data[i] - func);
        index = i;
    }
}

return {
    best: best,
    index: index,
    func: func
}; };

CatboostIpython.prototype.updateTracesCV = function() {
this.updateTracesCVAvg();

if (this.hasCVMode && this.stddevEnabled) {
    this.updateTracesCVStdDev();
} };

CatboostIpython.prototype.updateTracesCVAvg = function() {
var tracesHash = this.groupTraces(),
avgTraces = this.filterTracesEvery(tracesHash.traces, this.getTraceDefParams({
cv_avg: true
})),
self = this;

avgTraces.forEach(function(trace) {
    var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({
            train: trace._params.train,
            type: trace._params.type,
            smoothed: trace._params.smoothed
        }));

    if (origTraces.length) {
        self.cvAvgFunc(origTraces, trace);
    }
}); };

CatboostIpython.prototype.cvAvgFunc = function(origTraces, avgTrace) {
var maxCount = origTraces.length,
maxLength = -1,
count,
sum;

origTraces.forEach(function(origTrace) {
    if (origTrace.y.length > maxLength) {
        maxLength = origTrace.y.length;
    }
});

for (var i = 0; i < maxLength; i++) {
    sum = 0;
    count = 0;

    for (var j = 0; j < maxCount; j++) {
        if (typeof origTraces[j].y[i] !== 'undefined') {
            sum += origTraces[j].y[i];
            count++;
        }
    }

    if (count > 0) {
        avgTrace.x[i] = i;
        avgTrace.y[i] = sum / count;
    }
} };

CatboostIpython.prototype.updateTracesCVStdDev = function() {
var tracesHash = this.groupTraces(),
firstTraces = this.filterTracesOne(tracesHash.traces, {cv_stddev_first: true}),
self = this;

firstTraces.forEach(function(trace) {
    var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({
            train: trace._params.train,
            type: trace._params.type,
            smoothed: trace._params.smoothed
        })),
        lastTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({
            train: trace._params.train,
            type: trace._params.type,
            smoothed: trace._params.smoothed,
            cv_stddev_last: true
        }));

    if (origTraces.length && lastTraces.length === 1) {
        self.cvStdDevFunc(origTraces, trace, lastTraces[0]);
    }
}); };

CatboostIpython.prototype.cvStdDevFunc = function(origTraces, firstTrace, lastTrace) {
var maxCount = origTraces.length,
maxLength = -1,
count,
sum,
i, j;

origTraces.forEach(function(origTrace) {
    if (origTrace.y.length > maxLength) {
        maxLength = origTrace.y.length;
    }
});

for (i = 0; i < maxLength; i++) {
    sum = 0;
    count = 0;

    for (j = 0; j < maxCount; j++) {
        if (typeof origTraces[j].y[i] !== 'undefined') {
            sum += origTraces[j].y[i];
            count++;
        }
    }

    if (count <= 0) {
        continue;
    }

    var std = 0,
        avg = sum / count;

    for (j = 0; j < maxCount; j++) {
        if (typeof origTraces[j].y[i] !== 'undefined') {
            std += Math.pow(origTraces[j].y[i] - avg, 2);
        }
    }

    std /= (count - 1);
    std = Math.pow(std, 0.5);

    firstTrace.x[i] = i;
    firstTrace.y[i] = avg - std;
    firstTrace.hovertext[i] = firstTrace._params.type + ' std: ' + avg.toFixed(7) + '-' + std.toFixed(7);
    lastTrace.x[i] = i;
    lastTrace.y[i] = avg + std;
    lastTrace.hovertext[i] = lastTrace._params.type + ' std: ' + avg.toFixed(7) + '+' + std.toFixed(7);
    if (this.hovertextParameters.length > i) {
        firstTrace.hovertext[i] += this.hovertextParameters[i];
        lastTrace.hovertext[i] += this.hovertextParameters[i];
    }
} };

CatboostIpython.prototype.updateTracesSmoothness = function() {
var tracesHash = this.groupTraces(),
smoothedTraces = this.filterTracesOne(tracesHash.traces, {smoothed: true}),
enabled = this.getSmoothness() > -1,
self = this;

smoothedTraces.forEach(function(trace) {
    var origTraces = self.filterTracesEvery(tracesHash.traces, self.getTraceDefParams({
            train: trace._params.train,
            type: trace._params.type,
            indexOfSet: trace._params.indexOfSet,
            cv_avg: trace._params.cv_avg,
            cv_stddev_first: trace._params.cv_stddev_first,
            cv_stddev_last: trace._params.cv_stddev_last
        })),
        colorFlag = false;

    if (origTraces.length === 1) {
        origTraces = origTraces[0];

        if (origTraces.visible) {
            if (enabled) {
                self.smoothFunc(origTraces, trace);
                colorFlag = true;
            }

            self.highlightSmoothedTrace(origTraces, trace, colorFlag);
        }
    }
}); };

CatboostIpython.prototype.highlightSmoothedTrace = function(trace, smoothedTrace, flag) {
if (flag) {
smoothedTrace.line.color = trace._params.plotParams.color;
trace.line.color = smoothedTrace._params.plotParams.color;
trace.hoverinfo = ‘skip’;

    if (trace._params.cv_stddev_last) {
        trace.fillcolor = trace._params.plotParams.fillsmoothcolor;
    }
} else {
    trace.line.color = trace._params.plotParams.color;
    trace.hoverinfo = trace._params.plotParams.hoverinfo;

    if (trace._params.cv_stddev_last) {
        trace.fillcolor = trace._params.plotParams.fillcolor;
    }
} };

CatboostIpython.prototype.smoothFunc = function(origTrace, smoothedTrace) {
var data = origTrace.y,
smoothedPoints = this.smooth(data, this.getSmoothness()),
smoothedIndex = 0,
self = this;

if (smoothedPoints.length) {
    data.forEach(function (d, index) {
        if (!smoothedTrace.x[index]) {
            smoothedTrace.x[index] = index;
        }

        var nameOfSet = smoothedTrace._params.nameOfSet;

        if (smoothedTrace._params.cv_stddev_first || smoothedTrace._params.cv_stddev_last) {
            nameOfSet = smoothedTrace._params.type + ' std';
        }

        smoothedTrace.y[index] = smoothedPoints[smoothedIndex];
        smoothedTrace.hovertext[index] = nameOfSet + '`: ' + smoothedPoints[smoothedIndex].toPrecision(7);
        if (self.hovertextParameters.length > index) {
            smoothedTrace.hovertext[index] += self.hovertextParameters[index];
        }
        smoothedIndex++;
    });
} };

CatboostIpython.prototype.formatItemValue = function(value, index, suffix) {
if (typeof value === ‘undefined’) {
return ‘’;
}

suffix = suffix || '';

return '<span title="' + suffix + 'value ' + value + '">' + value + '</span>'; };

CatboostIpython.prototype.updateTraceBest = function(train, hash) {
var traces = this.filterTracesOne(hash.traces, {best_point: true}),
self = this;

traces.forEach(function(trace) {
    var testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({
            train: trace._params.train,
            type: 'test',
            indexOfSet: trace._params.indexOfSet
        }));

    if (self.hasCVMode) {
        testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({
            train: trace._params.train,
            type: 'test',
            cv_avg: true
        }));
    }

    var bestValue = self.getBestValue(testTrace.length === 1 ? testTrace[0].y : []);

    if (bestValue.index !== -1) {
        trace.x[0] = bestValue.index;
        trace.y[0] = bestValue.best;
        trace.hovertext[0] = bestValue.func + ' (' + (self.hasCVMode ? 'avg' : trace._params.nameOfSet) + '): ' + bestValue.index + ' ' + bestValue.best;
    }
}); };

CatboostIpython.prototype.updateTraceValues = function(name, hash, iteration, click) {
var id = ‘catboost-serie-‘ + this.index + ‘-‘ + hash.index,
traces = {
learn: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: ‘learn’})),
test: this.filterTracesEvery(hash.traces, this.getTraceDefParams({type: ‘test’}))
},
path = hash.info.path,
self = this;

['learn', 'test'].forEach(function(type) {
    traces[type].forEach(function(trace) {
        var data = trace.y || [],
            index = typeof iteration !== 'undefined' && iteration < data.length - 1 ? iteration : data.length - 1,
            value = data.length ? data[index] : undefined,
            testTrace = self.filterTracesEvery(hash.traces, self.getTraceDefParams({
                type: 'test',
                indexOfSet: trace._params.indexOfSet
            })),
            bestValue = self.getBestValue(testTrace.length === 1 ? testTrace[0].y : []),
            timeLeft = '',
            timeSpend = '';

        if (click || !self.clickMode) {
            $('#' + id + ' .catboost-panel__serie_' + type + '_value[data-index=' + trace._params.indexOfSet + ']', self.layout)
                .html(self.formatItemValue(value, index, type + ' '));
            $('#' + id + ' .catboost-panel__serie_iteration', self.layout).html(index);

            if (self.timeLeft[path] && self.timeLeft[path][data.length - 1]) {
                timeLeft = self.timeLeft[path][data.length - 1][0];
            }
            $('#' + id + ' .catboost-panel__serie_time_left', self.layout).html(timeLeft ? ('~' + self.convertTime(timeLeft)) : '');

            if (self.timeLeft[path] && self.timeLeft[path][index]) {
                timeSpend = self.timeLeft[path][index][1];
            }

            $('#' + id + ' .catboost-panel__serie_time_spend', self.layout).html(self.convertTime(timeSpend));
            $('#' + id + ' .catboost-panel__serie_best_iteration', self.layout).html(bestValue.index > -1 ? bestValue.index : '');

            $('#' + id + ' .catboost-panel__serie_best_test_value[data-index=' + trace._params.indexOfSet + ']', self.layout)
                .html(self.formatItemValue(bestValue.best, bestValue.index, 'best ' + trace._params.nameOfSet + ' '));
        }
    });
});

if (this.hasCVMode) {
    var testTrace = this.filterTracesEvery(hash.traces, this.getTraceDefParams({
            type: 'test',
            cv_avg: true
        })),
        bestValue = this.getBestValue(testTrace.length === 1 ? testTrace[0].y : []);

        $('#' + id + ' .catboost-panel__serie_best_iteration', this.layout).html(bestValue.index > -1 ? bestValue.index : '');
}

if (click) {
    this.clickMode = true;

    $('#catboost-control2-clickmode' + this.index, this.layout)[0].checked = true;
} };

CatboostIpython.prototype.addTracesEvents = function() {
var self = this;

$('.catboost-panel__serie_checkbox', this.layout).click(function() {
    var name = $(this).data('seriename');

    self.layoutDisabled.traces[name] = !$(this)[0].checked;

    self.redrawActiveChart();
}); };

CatboostIpython.prototype.getNextColor = function(path, opacity) {
var color;

if (this.colorsByPath[path]) {
    color = this.colorsByPath[path];
} else {
    color = this.colors[this.colorIndex];
    this.colorsByPath[path] = color;

    this.colorIndex++;

    if (this.colorIndex > this.colors.length - 1) {
        this.colorIndex = 0;
    }
}

return this.hexToRgba(color, opacity); };

CatboostIpython.prototype.hexToRgba = function(value, opacity) {
if (value.length < 6) {
var pattern = /^#?([a-f\d])([a-f\d])([a-f\d])/i;
value = value.replace(pattern, function(m, r, g, b) {
return ‘#’ + r + r + g + g + b + b;
});
}

var result = /^#?([a-f\d]{2})([a-f\d]{2})([a-f\d]{2})/i.exec(value);
var rgb = {
    r: parseInt(result[1], 16),
    g: parseInt(result[2], 16),
    b: parseInt(result[3], 16)
};

return 'rgba(' + rgb.r + ',' + rgb.g + ',' + rgb.b + ',' + opacity + ')'; };

CatboostIpython.prototype.convertTime = function(time) {
if (!time) {
return ‘0s’;
}

time = Math.floor(time * 1000);

var millis = time % 1000;
time = parseInt(time / 1000, 10);
var seconds = time % 60;
time = parseInt(time / 60, 10);
var minutes = time % 60;
time = parseInt(time / 60, 10);
var hours = time % 24;
var out = "";
if (hours && hours > 0) {
    out += hours + 'h ';
    seconds = 0;
    millis = 0;
}
if (minutes && minutes > 0) {
    out += minutes + 'm ';
    millis = 0;
}
if (seconds && seconds > 0) {
    out += seconds + 's ';
}
if (millis && millis > 0) {
    out += millis + 'ms';
}

return out.trim(); };

CatboostIpython.prototype.mean = function(values, valueof) {
var n = values.length,
m = n,
i = -1,
value,
sum = 0,
number = function(x) {
return x === null ? NaN : +x;
};

if (valueof === null) {
    while (++i < n) {
        if (!isNaN(value = number(values[i]))) {
            sum += value;
        } else {
            --m;
        }
    }
} else {
    while (++i < n) {
        if (!isNaN(value = number(valueof(values[i], i, values)))) {
            sum += value;
        } else {
            --m;
        }
    }
}

if (m) {
    return sum / m;
} };

// from TensorBoard
CatboostIpython.prototype.smooth = function(data, weight) {
// When increasing the smoothing window, it smoothes a lot with the first
// few points and then starts to gradually smooth slower, so using an
// exponential function makes the slider more consistent. 1000^x has a
// range of [1, 1000], so subtracting 1 and dividing by 999 results in a
// range of [0, 1], which can be used as the percentage of the data, so
// that the kernel size can be specified as a percentage instead of a
// hardcoded number, what would be bad with multiple series.
var factor = (Math.pow(1000, weight) - 1) / 999,
kernelRadius = Math.floor(data.length * factor / 2),
res = [],
self = this;

data.forEach(function (d, i) {
    var actualKernelRadius = Math.min(kernelRadius, i, data.length - i - 1);
    var start = i - actualKernelRadius;
    var end = i + actualKernelRadius + 1;
    var point = d;
    // Only smooth finite numbers.
    if (!isFinite(point)) {
        res.push(point);
    } else {
        res.push(self.mean(data.slice(start, end).filter(function(d) {
            return isFinite(d);
        }), null));
    }
});

return res; }; var debug = false;

if (debug) {
require.config({
shim:{
“custom/CatboostIpythonPlotly”:{
deps:[“custom/plotly-basic.min”]
}
}
})

require.undef('catboost_module');
require.undef('custom/CatboostIpythonPlotly'); }

var moduleBase = ‘@jupyter-widgets/base’;
var modules = [moduleBase];

if (debug) {
modules.push(‘custom/CatboostIpythonPlotly’);
}

define(‘catboost_module’, modules, function(widgets) {
var getInstance = function(el) {
var id = $(el).attr(‘catboost-id’);

        if (!id) {
            return null;
        }

        id = id.replace('catboost_', '');

        if (!window.catboostIpythonInstances[id]) {
            return null;
        }

        return window.catboostIpythonInstances[id];
    },
    addInstance = function(el) {
        $(el).attr('catboost-id', 'catboost_' + window.catboostIpythonIndex);

        var catboostIpython = new CatboostIpython();
        catboostIpython.index = window.catboostIpythonIndex;
        catboostIpython.plotly = window.Plotly;
        if (debug) {
            catboostIpython.loadStyles('/custom/CatboostIpython.css', function(){catboostIpython.resizeCharts();})
        }

        window.catboostIpythonInstances[window.catboostIpythonIndex] = catboostIpython;

        window.catboostIpythonIndex++;

        return catboostIpython;
    };

var CatboostIpythonWidget = widgets.DOMWidgetView.extend({

    initialize: function() {
        CatboostIpythonWidget.__super__.initialize.apply(this, arguments);

        if (!window.catboostIpythonInstances) {
            window.catboostIpythonInstances = {};
        }

        if (typeof window.catboostIpythonIndex === 'undefined') {
            window.catboostIpythonIndex = 0;
        }

        var catboostIpythonInstance = getInstance(this.el);

        if (!catboostIpythonInstance) {
            catboostIpythonInstance = addInstance(this.el);
        }

        catboostIpythonInstance.init();
    },

    render: function() {
        this.value_changed();
        this.model.on('change:value', this.value_changed, this);
    },

    update: function() {
        this.value_changed();
    },

    value_changed: function() {
        this.el.style['width'] = this.model.get('width');
        this.el.style['height'] = this.model.get('height');
        this.displayed.then(_.bind(this.render_charts, this));
    },

    process_all: function(parent, params) {
        var data = params.data;

        for (var path in data) {
            if (data.hasOwnProperty(path)) {
                this.process_row(parent, data[path])
            }
        }
    },

    process_row: function(parent, data) {
        var catboostIpython = getInstance(parent),
            path = data.path,
            content = data.content,
            items = content.data.iterations,
            firstIndex = 0,
            chunks = [];

        if (!items || !items.length) {
            return;
        }

        if (!catboostIpython.lastIndex) {
            catboostIpython.lastIndex = {}
        }

        if (catboostIpython.lastIndex[path]) {
            firstIndex = catboostIpython.lastIndex[path] + 1;
        }

        catboostIpython.lastIndex[path] = items.length - 1;

        for (var i = firstIndex; i < items.length; i++) {
            chunks.push(items[i]);
        }

        catboostIpython.addMeta(data.path, content.data.meta);

        catboostIpython.addPoints(parent, {
            chunks: chunks,
            train: data.name,
            path: data.path
        });
    },

    render_charts: function () {
        this.process_all(this.el, {
            data: this.model.get('data')
        });

        return this;
    }
});

return {
    CatboostIpythonWidgetView: CatboostIpythonWidget
}; });

        </script>



MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))


Precision 0.8321235655849941
Recall 0.7010558823529411
F1 0.7609685283781985
AUC 0.8934480097503482


for metric in metrics:
    print(str(metric) + " {}".format(np.mean(eval_metrics[metric])))

Precision 0.8321235655849941
Recall 0.7010558823529411
F1 0.7609685283781985
AUC 0.8934480097503482

from catboost.utils import get_confusion_matrix
make_confusion_matrix_table(get_confusion_matrix(catboost_model, train_pool))
Predicted: 0 Predicted: 1
Acutal: 0 513.0 36.0
Actual: 1 96.0 244.0

gbt eval metrics: doing this because our model scored higher than catboost

from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
gbt_pred = gbt.predict(X_train)

print(classification_report(y_train , gbt_pred))


              precision    recall  f1-score   support

           0       0.89      0.95      0.92       549
           1       0.91      0.81      0.86       340

    accuracy                           0.90       889
   macro avg       0.90      0.88      0.89       889
weighted avg       0.90      0.90      0.90       889

def make_confusion_matrix_table(cm_array):
    table = pd.DataFrame(cm_array,
            columns = ['Predicted: 0', 'Predicted: 1'],
            index = ['Acutal: 0', "Actual: 1"])
    return table

make_confusion_matrix_table(confusion_matrix(y_train, gbt_pred))
Predicted: 0 Predicted: 1
Acutal: 0 521 28
Actual: 1 64 276

As you can see our metrics are better than the catboost model. This is corroborated by the confusion matrix table!

Just realized: dbourke didnt use train test split to break this data down…

  • Ignore in this case since we understand how to do this.

Submission

  1. Now we need to predict on the test set. But before that, we need to preprocess our test data in such a way that it matches our train data!
  2. Then we can model.predict()
X_train
Age SibSp Parch Fare embarked_C embarked_Q embarked_S sex_female sex_male pclass_1 pclass_2 pclass_3
0 22 1 0 7.2500 0 0 1 0 1 0 0 1
1 38 1 0 71.2833 1 0 0 1 0 1 0 0
2 26 0 0 7.9250 0 0 1 1 0 0 0 1
3 35 1 0 53.1000 0 0 1 1 0 1 0 0
4 35 0 0 8.0500 0 0 1 0 1 0 0 1
... ... ... ... ... ... ... ... ... ... ... ... ...
886 27 0 0 13.0000 0 0 1 0 1 0 1 0
887 19 0 0 30.0000 0 0 1 1 0 1 0 0
888 30 1 2 23.4500 0 0 1 1 0 0 0 1
889 26 0 0 30.0000 1 0 0 0 1 1 0 0
890 32 0 0 7.7500 0 1 0 0 1 0 0 1

889 rows × 12 columns

test.head()
PassengerId Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
0 892 3 Kelly, Mr. James male 34.5 0 0 330911 7.8292 NaN Q
1 893 3 Wilkes, Mrs. James (Ellen Needs) female 47.0 1 0 363272 7.0000 NaN S
2 894 2 Myles, Mr. Thomas Francis male 62.0 0 0 240276 9.6875 NaN Q
3 895 3 Wirz, Mr. Albert male 27.0 0 0 315154 8.6625 NaN S
4 896 3 Hirvonen, Mrs. Alexander (Helga E Lindqvist) female 22.0 1 1 3101298 12.2875 NaN S

Matching our preprocessing of the df_con data; just replacing it with test data instead

## testing ##
pd.get_dummies(test['Embarked'], # returns a dataframe with readable cols vs an array
    prefix='embarked')
embarked_C embarked_Q embarked_S
0 0 1 0
1 0 0 1
2 0 1 0
3 0 0 1
4 0 0 1
... ... ... ...
413 0 0 1
414 1 0 0
415 0 0 1
416 0 0 1
417 1 0 0

418 rows × 3 columns

# One hot encode the categorical columns
df_embarked_one_hot = pd.get_dummies(test['Embarked'], 
                                     prefix='embarked')

df_sex_one_hot = pd.get_dummies(test['Sex'], 
                                prefix='sex')

df_pclass_one_hot = pd.get_dummies(test['Pclass'], 
                                   prefix='pclass')


## combine all the new columns to df_con
test_enc = pd.concat([
    test,
    df_embarked_one_hot,
    df_sex_one_hot,
    df_pclass_one_hot,
], axis = 1)

## drop redundant columns
test_enc = test_enc.drop(['Pclass', 'Sex', 'Embarked'], axis = 1)


test_enc.head()
PassengerId Name Age SibSp Parch Ticket Fare Cabin embarked_C embarked_Q embarked_S sex_female sex_male pclass_1 pclass_2 pclass_3
0 892 Kelly, Mr. James 34.5 0 0 330911 7.8292 NaN 0 1 0 0 1 0 0 1
1 893 Wilkes, Mrs. James (Ellen Needs) 47.0 1 0 363272 7.0000 NaN 0 0 1 1 0 0 0 1
2 894 Myles, Mr. Thomas Francis 62.0 0 0 240276 9.6875 NaN 0 1 0 0 1 0 1 0
3 895 Wirz, Mr. Albert 27.0 0 0 315154 8.6625 NaN 0 0 1 0 1 0 0 1
4 896 Hirvonen, Mrs. Alexander (Helga E Lindqvist) 22.0 1 1 3101298 12.2875 NaN 0 0 1 1 0 0 0 1 
X_train.head()
Age SibSp Parch Fare embarked_C embarked_Q embarked_S sex_female sex_male pclass_1 pclass_2 pclass_3
0 22 1 0 7.2500 0 0 1 0 1 0 0 1
1 38 1 0 71.2833 1 0 0 1 0 1 0 0
2 26 0 0 7.9250 0 0 1 1 0 0 0 1
3 35 1 0 53.1000 0 0 1 1 0 1 0 0
4 35 0 0 8.0500 0 0 1 0 1 0 0 1

The test data has more columns than the ones we used. We need to cut out the other redundant columns

wanted_columns = list(X_train.columns)

test_enc = test_enc[wanted_columns]
test_enc
Age SibSp Parch Fare embarked_C embarked_Q embarked_S sex_female sex_male pclass_1 pclass_2 pclass_3
0 34.5 0 0 7.8292 0 1 0 0 1 0 0 1
1 47.0 1 0 7.0000 0 0 1 1 0 0 0 1
2 62.0 0 0 9.6875 0 1 0 0 1 0 1 0
3 27.0 0 0 8.6625 0 0 1 0 1 0 0 1
4 22.0 1 1 12.2875 0 0 1 1 0 0 0 1
... ... ... ... ... ... ... ... ... ... ... ... ...
413 NaN 0 0 8.0500 0 0 1 0 1 0 0 1
414 39.0 0 0 108.9000 1 0 0 1 0 1 0 0
415 38.5 0 0 7.2500 0 0 1 0 1 0 0 1
416 NaN 0 0 8.0500 0 0 1 0 1 0 0 1
417 NaN 1 1 22.3583 1 0 0 0 1 0 0 1

418 rows × 12 columns

test_gbt_pred = gbt.predict(test_enc)

---------------------------------------------------------------------------

ValueError                                Traceback (most recent call last)

<ipython-input-185-c2b38468c51c> in <module>
----> 1 test_gbt_pred = gbt.predict(test_enc)


c:\users\raihan\appdata\local\programs\python\python38\lib\site-packages\sklearn\ensemble\_gb.py in predict(self, X)
   1186             The predicted values.
   1187         """
-> 1188         raw_predictions = self.decision_function(X)
   1189         encoded_labels = \
   1190             self.loss_._raw_prediction_to_decision(raw_predictions)


c:\users\raihan\appdata\local\programs\python\python38\lib\site-packages\sklearn\ensemble\_gb.py in decision_function(self, X)
   1141             array of shape (n_samples,).
   1142         """
-> 1143         X = check_array(X, dtype=DTYPE, order="C", accept_sparse='csr')
   1144         raw_predictions = self._raw_predict(X)
   1145         if raw_predictions.shape[1] == 1:


c:\users\raihan\appdata\local\programs\python\python38\lib\site-packages\sklearn\utils\validation.py in inner_f(*args, **kwargs)
     61             extra_args = len(args) - len(all_args)
     62             if extra_args <= 0:
---> 63                 return f(*args, **kwargs)
     64 
     65             # extra_args > 0


c:\users\raihan\appdata\local\programs\python\python38\lib\site-packages\sklearn\utils\validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator)
    661 
    662         if force_all_finite:
--> 663             _assert_all_finite(array,
    664                                allow_nan=force_all_finite == 'allow-nan')
    665 


c:\users\raihan\appdata\local\programs\python\python38\lib\site-packages\sklearn\utils\validation.py in _assert_all_finite(X, allow_nan, msg_dtype)
    101                 not allow_nan and not np.isfinite(X).all()):
    102             type_err = 'infinity' if allow_nan else 'NaN, infinity'
--> 103             raise ValueError(
    104                     msg_err.format
    105                     (type_err,


ValueError: Input contains NaN, infinity or a value too large for dtype('float32').

ValueError: Input contains NaN, infinity or a value too large for dtype(‘float32’).

  • This is because the test df has some NaNs. So we actually need to fill up these empty rows ourselves!
missingno.matrix(test_enc)

<AxesSubplot:>

png

test_enc.describe()
Age SibSp Parch Fare embarked_C embarked_Q embarked_S sex_female sex_male pclass_1 pclass_2 pclass_3
count 332.000000 418.000000 418.000000 417.000000 418.000000 418.000000 418.000000 418.000000 418.000000 418.000000 418.000000 418.000000
mean 30.272590 0.447368 0.392344 35.627188 0.244019 0.110048 0.645933 0.363636 0.636364 0.255981 0.222488 0.521531
std 14.181209 0.896760 0.981429 55.907576 0.430019 0.313324 0.478803 0.481622 0.481622 0.436934 0.416416 0.500135
min 0.170000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 21.000000 0.000000 0.000000 7.895800 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
50% 27.000000 0.000000 0.000000 14.454200 0.000000 0.000000 1.000000 0.000000 1.000000 0.000000 0.000000 1.000000
75% 39.000000 1.000000 0.000000 31.500000 0.000000 0.000000 1.000000 1.000000 1.000000 1.000000 0.000000 1.000000
max 76.000000 8.000000 9.000000 512.329200 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 
test_enc['Age'] = test_enc['Age'].fillna(value= 30)
test_enc.Age.isnull().sum()

0

test_enc
Age SibSp Parch Fare embarked_C embarked_Q embarked_S sex_female sex_male pclass_1 pclass_2 pclass_3
0 34.5 0 0 7.8292 0 1 0 0 1 0 0 1
1 47.0 1 0 7.0000 0 0 1 1 0 0 0 1
2 62.0 0 0 9.6875 0 1 0 0 1 0 1 0
3 27.0 0 0 8.6625 0 0 1 0 1 0 0 1
4 22.0 1 1 12.2875 0 0 1 1 0 0 0 1
... ... ... ... ... ... ... ... ... ... ... ... ...
413 30.0 0 0 8.0500 0 0 1 0 1 0 0 1
414 39.0 0 0 108.9000 1 0 0 1 0 1 0 0
415 38.5 0 0 7.2500 0 0 1 0 1 0 0 1
416 30.0 0 0 8.0500 0 0 1 0 1 0 0 1
417 30.0 1 1 22.3583 1 0 0 0 1 0 0 1

418 rows × 12 columns

test_enc.describe() # 418 rows, Fare is missing one val!
Age SibSp Parch Fare embarked_C embarked_Q embarked_S sex_female sex_male pclass_1 pclass_2 pclass_3
count 418.000000 418.000000 418.000000 417.000000 418.000000 418.000000 418.000000 418.000000 418.000000 418.000000 418.000000 418.000000
mean 30.216507 0.447368 0.392344 35.627188 0.244019 0.110048 0.645933 0.363636 0.636364 0.255981 0.222488 0.521531
std 12.635016 0.896760 0.981429 55.907576 0.430019 0.313324 0.478803 0.481622 0.481622 0.436934 0.416416 0.500135
min 0.170000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
25% 23.000000 0.000000 0.000000 7.895800 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000
50% 30.000000 0.000000 0.000000 14.454200 0.000000 0.000000 1.000000 0.000000 1.000000 0.000000 0.000000 1.000000
75% 35.750000 1.000000 0.000000 31.500000 0.000000 0.000000 1.000000 1.000000 1.000000 1.000000 0.000000 1.000000
max 76.000000 8.000000 9.000000 512.329200 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 
test_enc['Fare'] = test_enc.Fare.fillna(value = '0.392344')

test_gbt_pred = gbt.predict(test_enc)

test_gbt_pred

array([0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 1, 1, 0, 0, 1, 1, 0, 1,
       1, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1,
       1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1,
       1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 0,
       1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0,
       0, 1, 1, 1, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0,
       0, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1,
       1, 0, 1, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1,
       0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0,
       1, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1,
       1, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1,
       0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0,
       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1,
       0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0,
       1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0,
       0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 0, 0,
       1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1,
       0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 0],
      dtype=int64)

# looking at the submission to match the output
gender_submission
PassengerId Survived
0 892 0
1 893 1
2 894 0
3 895 0
4 896 1
... ... ...
413 1305 0
414 1306 1
415 1307 0
416 1308 0
417 1309 0

418 rows × 2 columns

## Create submission df
submission = pd.DataFrame()
submission['PassengerId'] = test['PassengerId']
submission['Survived'] = test_gbt_pred
submission
PassengerId Survived
0 892 0
1 893 0
2 894 0
3 895 0
4 896 0
... ... ...
413 1305 0
414 1306 1
415 1307 0
416 1308 0
417 1309 0

418 rows × 2 columns


# Are our test and submission dataframes the same length?
if len(submission) == len(test):
    print("Submission dataframe is the same length as test ({} rows).".format(len(submission)))
else:
    print("Dataframes mismatched, won't be able to submit to Kaggle.")

Submission dataframe is the same length as test (418 rows).

submission.to_csv('gbt_submission.csv', index = False)

submission_check = pd.read_csv('gbt_submission.csv')

submission_check
PassengerId Survived
0 892 0
1 893 0
2 894 0
3 895 0
4 896 0
... ... ...
413 1305 0
414 1306 1
415 1307 0
416 1308 0
417 1309 0

418 rows × 2 columns

Kaggle Score: 0.77990, Postion: 9314

  • Resubmit here: https://www.kaggle.com/c/titanic/submit

Possible Extensions

Some reccomendations by DBourke, and my own reflections

  1. The Age feature
    • All I did was just use the mean() to fill up the age. Just by doing that I was able to get Age to the 2nd highest feature importance.
    • DBourke suggest reading up on the interpolate() function of Pandas.
    • Another way I saw on Youtube, was to basically use ML model that would fill up the values. An example of this could be kmeans clustering, those with similar attributes could be given similar Ages in this case.
  2. What to do with Name.
    Initially, i thought we should just remove, the Mr. Mrs etc. But DBourke actually recommends focusing on those! My reasoning for this was that Mr and Mrs are already in Sex.
    • But other prefixes could come up: Titles like Dr. etc that might affect how a person is likely to be saved!!
  3. Cabin feature
    • Is there a way to see if they had a cabin or not? DBourke didnt answer this but i guess using the Pclass? and ticket number?

  4. Combine SibSp and Parch to see if the person was alone.
    Actually a very good idea which I did think about but never articulated. I would think those that are not alone might actually have less chance of surviving? If you are not alone, one of you is more likely to survive imo.

  5. Hyperparameter tuning.
    • Dbourke reccomends hyperopt library. We could also use GridSearch and Randomized search iirc.
    • https://github.com/hyperopt/hyperopt
  6. As we have seen when trying to do the submission, it was actually quite annoying to do all the preprocessing steps again for the test set. Should really consider using the pipeline feature on sklearn.

Resources reccomended by Dbourke

End :)

2023

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