Open In Colab

Personalized cancer diagnosis

1. Business Problem

1.1. Description

Source: https://www.kaggle.com/c/msk-redefining-cancer-treatment/

Data: Memorial Sloan Kettering Cancer Center (MSKCC)

Download training_variants.zip and training_text.zip from Kaggle.

Context:

Source: https://www.kaggle.com/c/msk-redefining-cancer-treatment/discussion/35336#198462

Problem statement :

Classify the given genetic variations/mutations based on evidence from text-based clinical literature.

1.2. Source/Useful Links

Some articles and reference blogs about the problem statement

1.3. Real-world/Business objectives and constraints.

  • No low-latency requirement.
  • Interpretability is important.
  • Errors can be very costly.
  • Probability of a data-point belonging to each class is needed.

2. Machine Learning Problem Formulation

In [0]:
from google.colab import drive
drive.mount('/content/drive')
Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code

Enter your authorization code:
··········
Mounted at /content/drive

2.1.1. Data Overview

  • Source: https://www.kaggle.com/c/msk-redefining-cancer-treatment/data
  • We have two data files: one conatins the information about the genetic mutations and the other contains the clinical evidence (text) that human experts/pathologists use to classify the genetic mutations.
  • Both these data files are have a common column called ID
  • Data file's information:

    • training_variants (ID , Gene, Variations, Class)
    • training_text (ID, Text)

2.1.2. Example Data Point

training_variants


ID,Gene,Variation,Class
0,FAM58A,Truncating Mutations,1
1,CBL,W802*,2
2,CBL,Q249E,2
...

training_text


ID,Text
0||Cyclin-dependent kinases (CDKs) regulate a variety of fundamental cellular processes. CDK10 stands out as one of the last orphan CDKs for which no activating cyclin has been identified and no kinase activity revealed. Previous work has shown that CDK10 silencing increases ETS2 (v-ets erythroblastosis virus E26 oncogene homolog 2)-driven activation of the MAPK pathway, which confers tamoxifen resistance to breast cancer cells. The precise mechanisms by which CDK10 modulates ETS2 activity, and more generally the functions of CDK10, remain elusive. Here we demonstrate that CDK10 is a cyclin-dependent kinase by identifying cyclin M as an activating cyclin. Cyclin M, an orphan cyclin, is the product of FAM58A, whose mutations cause STAR syndrome, a human developmental anomaly whose features include toe syndactyly, telecanthus, and anogenital and renal malformations. We show that STAR syndrome-associated cyclin M mutants are unable to interact with CDK10. Cyclin M silencing phenocopies CDK10 silencing in increasing c-Raf and in conferring tamoxifen resistance to breast cancer cells. CDK10/cyclin M phosphorylates ETS2 in vitro, and in cells it positively controls ETS2 degradation by the proteasome. ETS2 protein levels are increased in cells derived from a STAR patient, and this increase is attributable to decreased cyclin M levels. Altogether, our results reveal an additional regulatory mechanism for ETS2, which plays key roles in cancer and development. They also shed light on the molecular mechanisms underlying STAR syndrome.Cyclin-dependent kinases (CDKs) play a pivotal role in the control of a number of fundamental cellular processes (1). The human genome contains 21 genes encoding proteins that can be considered as members of the CDK family owing to their sequence similarity with bona fide CDKs, those known to be activated by cyclins (2). Although discovered almost 20 y ago (3, 4), CDK10 remains one of the two CDKs without an identified cyclin partner. This knowledge gap has largely impeded the exploration of its biological functions. CDK10 can act as a positive cell cycle regulator in some cells (5, 6) or as a tumor suppressor in others (7, 8). CDK10 interacts with the ETS2 (v-ets erythroblastosis virus E26 oncogene homolog 2) transcription factor and inhibits its transcriptional activity through an unknown mechanism (9). CDK10 knockdown derepresses ETS2, which increases the expression of the c-Raf protein kinase, activates the MAPK pathway, and induces resistance of MCF7 cells to tamoxifen (6). ...

2.2. Mapping the real-world problem to an ML problem

2.2.1. Type of Machine Learning Problem

There are nine different classes a genetic mutation can be classified into => Multi class classification problem

2.2.2. Performance Metric

Source: https://www.kaggle.com/c/msk-redefining-cancer-treatment#evaluation

Metric(s):

  • Multi class log-loss
  • Confusion matrix

2.2.3. Machine Learing Objectives and Constraints

Objective: Predict the probability of each data-point belonging to each of the nine classes.

Constraints:

  • Interpretability
  • Class probabilities are needed.
  • Penalize the errors in class probabilites => Metric is Log-loss.
  • No Latency constraints.

2.3. Train, CV and Test Datasets

Split the dataset randomly into three parts train, cross validation and test with 64%,16%, 20% of data respectively

3. Exploratory Data Analysis

In [0]:
import pandas as pd
import matplotlib.pyplot as plt
import re
import time
import warnings
import numpy as np
from nltk.corpus import stopwords
from sklearn.decomposition import TruncatedSVD
from sklearn.preprocessing import normalize
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.manifold import TSNE
import seaborn as sns
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import confusion_matrix
from sklearn.metrics.classification import accuracy_score, log_loss
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import SGDClassifier
from imblearn.over_sampling import SMOTE
from collections import Counter
from scipy.sparse import hstack
from sklearn.multiclass import OneVsRestClassifier
from sklearn.svm import SVC
from sklearn.model_selection import StratifiedKFold 
from collections import Counter, defaultdict
from sklearn.calibration import CalibratedClassifierCV
from sklearn.naive_bayes import MultinomialNB
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
import math
from sklearn.metrics import normalized_mutual_info_score
from sklearn.ensemble import RandomForestClassifier
warnings.filterwarnings("ignore")

from mlxtend.classifier import StackingClassifier

from sklearn import model_selection
from sklearn.linear_model import LogisticRegression
/usr/local/lib/python3.6/dist-packages/sklearn/externals/six.py:31: DeprecationWarning: The module is deprecated in version 0.21 and will be removed in version 0.23 since we've dropped support for Python 2.7. Please rely on the official version of six (https://pypi.org/project/six/).
  "(https://pypi.org/project/six/).", DeprecationWarning)

3.1. Reading Data

3.1.1. Reading Gene and Variation Data

In [0]:
data = pd.read_csv('drive/My Drive/Cancer_treatment/training_variants')
print('Number of data points : ', data.shape[0])
print('Number of features : ', data.shape[1])
print('Features : ', data.columns.values)
data.head()
Number of data points :  3321
Number of features :  4
Features :  ['ID' 'Gene' 'Variation' 'Class']
Out[0]:
ID Gene Variation Class
0 0 FAM58A Truncating Mutations 1
1 1 CBL W802* 2
2 2 CBL Q249E 2
3 3 CBL N454D 3
4 4 CBL L399V 4

training/training_variants is a comma separated file containing the description of the genetic mutations used for training.
Fields are

  • ID : the id of the row used to link the mutation to the clinical evidence
  • Gene : the gene where this genetic mutation is located
  • Variation : the aminoacid change for this mutations
  • Class : 1-9 the class this genetic mutation has been classified on

3.1.2. Reading Text Data

In [0]:
# note the seprator in this file
data_text =pd.read_csv("drive/My Drive/Cancer_treatment/training_text",sep="\|\|",engine="python",names=["ID","TEXT"],skiprows=1)
print('Number of data points : ', data_text.shape[0])
print('Number of features : ', data_text.shape[1])
print('Features : ', data_text.columns.values)
data_text.head()
Number of data points :  3321
Number of features :  2
Features :  ['ID' 'TEXT']
Out[0]:
ID TEXT
0 0 Cyclin-dependent kinases (CDKs) regulate a var...
1 1 Abstract Background Non-small cell lung canc...
2 2 Abstract Background Non-small cell lung canc...
3 3 Recent evidence has demonstrated that acquired...
4 4 Oncogenic mutations in the monomeric Casitas B...

3.1.3. Preprocessing of text

In [0]:
import nltk
nltk.download('stopwords')
# loading stop words from nltk library
stop_words = set(stopwords.words('english'))


def nlp_preprocessing(total_text, index, column):
    if type(total_text) is not int:
        string = ""
        # replace every special char with space
        total_text = re.sub('[^a-zA-Z0-9\n]', ' ', total_text)
        # replace multiple spaces with single space
        total_text = re.sub('\s+',' ', total_text)
        # converting all the chars into lower-case.
        total_text = total_text.lower()
        
        for word in total_text.split():
        # if the word is a not a stop word then retain that word from the data
            if not word in stop_words:
                string += word + " "
        
        data_text[column][index] = string
[nltk_data] Downloading package stopwords to /root/nltk_data...
[nltk_data]   Unzipping corpora/stopwords.zip.
In [0]:
#text processing stage.
start_time = time.clock()
for index, row in data_text.iterrows():
    if type(row['TEXT']) is str:
        nlp_preprocessing(row['TEXT'], index, 'TEXT')
    else:
        print("there is no text description for id:",index)
print('Time took for preprocessing the text :',time.clock() - start_time, "seconds")
there is no text description for id: 1109
there is no text description for id: 1277
there is no text description for id: 1407
there is no text description for id: 1639
there is no text description for id: 2755
Time took for preprocessing the text : 215.965613 seconds
In [0]:
#merging both gene_variations and text data based on ID
result = pd.merge(data, data_text,on='ID', how='left')
result.head()
Out[0]:
ID Gene Variation Class TEXT
0 0 FAM58A Truncating Mutations 1 cyclin dependent kinases cdks regulate variety...
1 1 CBL W802* 2 abstract background non small cell lung cancer...
2 2 CBL Q249E 2 abstract background non small cell lung cancer...
3 3 CBL N454D 3 recent evidence demonstrated acquired uniparen...
4 4 CBL L399V 4 oncogenic mutations monomeric casitas b lineag...
In [0]:
result[result.isnull().any(axis=1)]
Out[0]:
ID Gene Variation Class TEXT
1109 1109 FANCA S1088F 1 NaN
1277 1277 ARID5B Truncating Mutations 1 NaN
1407 1407 FGFR3 K508M 6 NaN
1639 1639 FLT1 Amplification 6 NaN
2755 2755 BRAF G596C 7 NaN
In [0]:
result.loc[result['TEXT'].isnull(),'TEXT'] = result['Gene'] +' '+result['Variation']
In [0]:
result[result['ID']==1109]
Out[0]:
ID Gene Variation Class TEXT
1109 1109 FANCA S1088F 1 FANCA S1088F

3.1.4. Test, Train and Cross Validation Split

3.1.4.1. Splitting data into train, test and cross validation (64:20:16)

In [0]:
y_true = result['Class'].values
result.Gene      = result.Gene.str.replace('\s+', '_')
result.Variation = result.Variation.str.replace('\s+', '_')

# split the data into test and train by maintaining same distribution of output varaible 'y_true' [stratify=y_true]
X_train, test_df, y_train, y_test = train_test_split(result, y_true, stratify=y_true, test_size=0.2)
# split the train data into train and cross validation by maintaining same distribution of output varaible 'y_train' [stratify=y_train]
train_df, cv_df, y_train, y_cv = train_test_split(X_train, y_train, stratify=y_train, test_size=0.2)

We split the data into train, test and cross validation data sets, preserving the ratio of class distribution in the original data set

In [0]:
print('Number of data points in train data:', train_df.shape[0])
print('Number of data points in test data:', test_df.shape[0])
print('Number of data points in cross validation data:', cv_df.shape[0])
Number of data points in train data: 2124
Number of data points in test data: 665
Number of data points in cross validation data: 532

3.1.4.2. Distribution of y_i's in Train, Test and Cross Validation datasets

In [0]:
# it returns a dict, keys as class labels and values as the number of data points in that class
train_class_distribution = train_df['Class'].value_counts().sort_index()
test_class_distribution = test_df['Class'].value_counts().sort_index()
cv_class_distribution = cv_df['Class'].value_counts().sort_index()

my_colors = 'rgbkymc'
train_class_distribution.plot(kind='bar')
plt.xlabel('Class')
plt.ylabel('Data points per Class')
plt.title('Distribution of yi in train data')
plt.grid()
plt.show()

# ref: argsort https://docs.scipy.org/doc/numpy/reference/generated/numpy.argsort.html
# -(train_class_distribution.values): the minus sign will give us in decreasing order
sorted_yi = np.argsort(-train_class_distribution.values)
for i in sorted_yi:
    print('Number of data points in class', i+1, ':',train_class_distribution.values[i], '(', np.round((train_class_distribution.values[i]/train_df.shape[0]*100), 3), '%)')

    
print('-'*80)
my_colors = 'rgbkymc'
test_class_distribution.plot(kind='bar')
plt.xlabel('Class')
plt.ylabel('Data points per Class')
plt.title('Distribution of yi in test data')
plt.grid()
plt.show()

# ref: argsort https://docs.scipy.org/doc/numpy/reference/generated/numpy.argsort.html
# -(train_class_distribution.values): the minus sign will give us in decreasing order
sorted_yi = np.argsort(-test_class_distribution.values)
for i in sorted_yi:
    print('Number of data points in class', i+1, ':',test_class_distribution.values[i], '(', np.round((test_class_distribution.values[i]/test_df.shape[0]*100), 3), '%)')

print('-'*80)
my_colors = 'rgbkymc'
cv_class_distribution.plot(kind='bar')
plt.xlabel('Class')
plt.ylabel('Data points per Class')
plt.title('Distribution of yi in cross validation data')
plt.grid()
plt.show()

# ref: argsort https://docs.scipy.org/doc/numpy/reference/generated/numpy.argsort.html
# -(train_class_distribution.values): the minus sign will give us in decreasing order
sorted_yi = np.argsort(-train_class_distribution.values)
for i in sorted_yi:
    print('Number of data points in class', i+1, ':',cv_class_distribution.values[i], '(', np.round((cv_class_distribution.values[i]/cv_df.shape[0]*100), 3), '%)')
Number of data points in class 7 : 609 ( 28.672 %)
Number of data points in class 4 : 439 ( 20.669 %)
Number of data points in class 1 : 363 ( 17.09 %)
Number of data points in class 2 : 289 ( 13.606 %)
Number of data points in class 6 : 176 ( 8.286 %)
Number of data points in class 5 : 155 ( 7.298 %)
Number of data points in class 3 : 57 ( 2.684 %)
Number of data points in class 9 : 24 ( 1.13 %)
Number of data points in class 8 : 12 ( 0.565 %)
--------------------------------------------------------------------------------
Number of data points in class 7 : 191 ( 28.722 %)
Number of data points in class 4 : 137 ( 20.602 %)
Number of data points in class 1 : 114 ( 17.143 %)
Number of data points in class 2 : 91 ( 13.684 %)
Number of data points in class 6 : 55 ( 8.271 %)
Number of data points in class 5 : 48 ( 7.218 %)
Number of data points in class 3 : 18 ( 2.707 %)
Number of data points in class 9 : 7 ( 1.053 %)
Number of data points in class 8 : 4 ( 0.602 %)
--------------------------------------------------------------------------------
Number of data points in class 7 : 153 ( 28.759 %)
Number of data points in class 4 : 110 ( 20.677 %)
Number of data points in class 1 : 91 ( 17.105 %)
Number of data points in class 2 : 72 ( 13.534 %)
Number of data points in class 6 : 44 ( 8.271 %)
Number of data points in class 5 : 39 ( 7.331 %)
Number of data points in class 3 : 14 ( 2.632 %)
Number of data points in class 9 : 6 ( 1.128 %)
Number of data points in class 8 : 3 ( 0.564 %)

3.2 Prediction using a 'Random' Model

In a 'Random' Model, we generate the NINE class probabilites randomly such that they sum to 1.

In [0]:
# This function plots the confusion matrices given y_i, y_i_hat.
def plot_confusion_matrix(test_y, predict_y):
    C = confusion_matrix(test_y, predict_y)
    # C = 9,9 matrix, each cell (i,j) represents number of points of class i are predicted class j
    
    A =(((C.T)/(C.sum(axis=1))).T)
    #divid each element of the confusion matrix with the sum of elements in that column
    
    # C = [[1, 2],
    #     [3, 4]]
    # C.T = [[1, 3],
    #        [2, 4]]
    # C.sum(axis = 1)  axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array
    # C.sum(axix =1) = [[3, 7]]
    # ((C.T)/(C.sum(axis=1))) = [[1/3, 3/7]
    #                           [2/3, 4/7]]

    # ((C.T)/(C.sum(axis=1))).T = [[1/3, 2/3]
    #                           [3/7, 4/7]]
    # sum of row elements = 1
    
    B =(C/C.sum(axis=0))
    #divid each element of the confusion matrix with the sum of elements in that row
    # C = [[1, 2],
    #     [3, 4]]
    # C.sum(axis = 0)  axis=0 corresonds to columns and axis=1 corresponds to rows in two diamensional array
    # C.sum(axix =0) = [[4, 6]]
    # (C/C.sum(axis=0)) = [[1/4, 2/6],
    #                      [3/4, 4/6]] 
    
    labels = [1,2,3,4,5,6,7,8,9]
    # representing A in heatmap format
    print("-"*20, "Confusion matrix", "-"*20)
    plt.figure(figsize=(20,7))
    sns.heatmap(C, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels)
    plt.xlabel('Predicted Class')
    plt.ylabel('Original Class')
    plt.show()

    print("-"*20, "Precision matrix (Columm Sum=1)", "-"*20)
    plt.figure(figsize=(20,7))
    sns.heatmap(B, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels)
    plt.xlabel('Predicted Class')
    plt.ylabel('Original Class')
    plt.show()
    
    # representing B in heatmap format
    print("-"*20, "Recall matrix (Row sum=1)", "-"*20)
    plt.figure(figsize=(20,7))
    sns.heatmap(A, annot=True, cmap="YlGnBu", fmt=".3f", xticklabels=labels, yticklabels=labels)
    plt.xlabel('Predicted Class')
    plt.ylabel('Original Class')
    plt.show()
In [0]:
# we need to generate 9 numbers and the sum of numbers should be 1
# one solution is to genarate 9 numbers and divide each of the numbers by their sum
# ref: https://stackoverflow.com/a/18662466/4084039
test_data_len = test_df.shape[0]
cv_data_len = cv_df.shape[0]

# we create a output array that has exactly same size as the CV data
cv_predicted_y = np.zeros((cv_data_len,9))
for i in range(cv_data_len):
    rand_probs = np.random.rand(1,9)
    cv_predicted_y[i] = ((rand_probs/sum(sum(rand_probs)))[0])
print("Log loss on Cross Validation Data using Random Model",log_loss(y_cv,cv_predicted_y, eps=1e-15))


# Test-Set error.
#we create a output array that has exactly same as the test data
test_predicted_y = np.zeros((test_data_len,9))
for i in range(test_data_len):
    rand_probs = np.random.rand(1,9)
    test_predicted_y[i] = ((rand_probs/sum(sum(rand_probs)))[0])
print("Log loss on Test Data using Random Model",log_loss(y_test,test_predicted_y, eps=1e-15))

predicted_y =np.argmax(test_predicted_y, axis=1)
plot_confusion_matrix(y_test, predicted_y+1)
Log loss on Cross Validation Data using Random Model 2.471682858422929
Log loss on Test Data using Random Model 2.497522142363581
-------------------- Confusion matrix --------------------
-------------------- Precision matrix (Columm Sum=1) --------------------
-------------------- Recall matrix (Row sum=1) --------------------

3.3 Univariate Analysis

In [0]:
# code for response coding with Laplace smoothing.
# alpha : used for laplace smoothing
# feature: ['gene', 'variation']
# df: ['train_df', 'test_df', 'cv_df']
# algorithm
# ----------
# Consider all unique values and the number of occurances of given feature in train data dataframe
# build a vector (1*9) , the first element = (number of times it occured in class1 + 10*alpha / number of time it occurred in total data+90*alpha)
# gv_dict is like a look up table, for every gene it store a (1*9) representation of it
# for a value of feature in df:
# if it is in train data:
# we add the vector that was stored in 'gv_dict' look up table to 'gv_fea'
# if it is not there is train:
# we add [1/9, 1/9, 1/9, 1/9,1/9, 1/9, 1/9, 1/9, 1/9] to 'gv_fea'
# return 'gv_fea'
# ----------------------

# get_gv_fea_dict: Get Gene varaition Feature Dict
def get_gv_fea_dict(alpha, feature, df):
    # value_count: it contains a dict like
    # print(train_df['Gene'].value_counts())
    # output:
    #        {BRCA1      174
    #         TP53       106
    #         EGFR        86
    #         BRCA2       75
    #         PTEN        69
    #         KIT         61
    #         BRAF        60
    #         ERBB2       47
    #         PDGFRA      46
    #         ...}
    # print(train_df['Variation'].value_counts())
    # output:
    # {
    # Truncating_Mutations                     63
    # Deletion                                 43
    # Amplification                            43
    # Fusions                                  22
    # Overexpression                            3
    # E17K                                      3
    # Q61L                                      3
    # S222D                                     2
    # P130S                                     2
    # ...
    # }
    value_count = train_df[feature].value_counts()
    
    # gv_dict : Gene Variation Dict, which contains the probability array for each gene/variation
    gv_dict = dict()
    
    # denominator will contain the number of time that particular feature occured in whole data
    for i, denominator in value_count.items():
        # vec will contain (p(yi==1/Gi) probability of gene/variation belongs to perticular class
        # vec is 9 diamensional vector
        vec = []
        for k in range(1,10):
            # print(train_df.loc[(train_df['Class']==1) & (train_df['Gene']=='BRCA1')])
            #         ID   Gene             Variation  Class  
            # 2470  2470  BRCA1                S1715C      1   
            # 2486  2486  BRCA1                S1841R      1   
            # 2614  2614  BRCA1                   M1R      1   
            # 2432  2432  BRCA1                L1657P      1   
            # 2567  2567  BRCA1                T1685A      1   
            # 2583  2583  BRCA1                E1660G      1   
            # 2634  2634  BRCA1                W1718L      1   
            # cls_cnt.shape[0] will return the number of rows

            cls_cnt = train_df.loc[(train_df['Class']==k) & (train_df[feature]==i)]
            
            # cls_cnt.shape[0](numerator) will contain the number of time that particular feature occured in whole data
            vec.append((cls_cnt.shape[0] + alpha*10)/ (denominator + 90*alpha))

        # we are adding the gene/variation to the dict as key and vec as value
        gv_dict[i]=vec
    return gv_dict

# Get Gene variation feature
def get_gv_feature(alpha, feature, df):
    # print(gv_dict)
    #     {'BRCA1': [0.20075757575757575, 0.03787878787878788, 0.068181818181818177, 0.13636363636363635, 0.25, 0.19318181818181818, 0.03787878787878788, 0.03787878787878788, 0.03787878787878788], 
    #      'TP53': [0.32142857142857145, 0.061224489795918366, 0.061224489795918366, 0.27040816326530615, 0.061224489795918366, 0.066326530612244902, 0.051020408163265307, 0.051020408163265307, 0.056122448979591837], 
    #      'EGFR': [0.056818181818181816, 0.21590909090909091, 0.0625, 0.068181818181818177, 0.068181818181818177, 0.0625, 0.34659090909090912, 0.0625, 0.056818181818181816], 
    #      'BRCA2': [0.13333333333333333, 0.060606060606060608, 0.060606060606060608, 0.078787878787878782, 0.1393939393939394, 0.34545454545454546, 0.060606060606060608, 0.060606060606060608, 0.060606060606060608], 
    #      'PTEN': [0.069182389937106917, 0.062893081761006289, 0.069182389937106917, 0.46540880503144655, 0.075471698113207544, 0.062893081761006289, 0.069182389937106917, 0.062893081761006289, 0.062893081761006289], 
    #      'KIT': [0.066225165562913912, 0.25165562913907286, 0.072847682119205295, 0.072847682119205295, 0.066225165562913912, 0.066225165562913912, 0.27152317880794702, 0.066225165562913912, 0.066225165562913912], 
    #      'BRAF': [0.066666666666666666, 0.17999999999999999, 0.073333333333333334, 0.073333333333333334, 0.093333333333333338, 0.080000000000000002, 0.29999999999999999, 0.066666666666666666, 0.066666666666666666],
    #      ...
    #     }
    gv_dict = get_gv_fea_dict(alpha, feature, df)
    # value_count is similar in get_gv_fea_dict
    value_count = train_df[feature].value_counts()
    
    # gv_fea: Gene_variation feature, it will contain the feature for each feature value in the data
    gv_fea = []
    # for every feature values in the given data frame we will check if it is there in the train data then we will add the feature to gv_fea
    # if not we will add [1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9] to gv_fea
    for index, row in df.iterrows():
        if row[feature] in dict(value_count).keys():
            gv_fea.append(gv_dict[row[feature]])
        else:
            gv_fea.append([1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9])
#             gv_fea.append([-1,-1,-1,-1,-1,-1,-1,-1,-1])
    return gv_fea

when we caculate the probability of a feature belongs to any particular class, we apply laplace smoothing

  • (numerator + 10\*alpha) / (denominator + 90\*alpha)
  • 3.2.1 Univariate Analysis on Gene Feature

    Q1. Gene, What type of feature it is ?

    Ans. Gene is a categorical variable

    Q2. How many categories are there and How they are distributed?

    In [0]:
    unique_genes = train_df['Gene'].value_counts()
    print('Number of Unique Genes :', unique_genes.shape[0])
    # the top 10 genes that occured most
    print(unique_genes.head(10))
    
    Number of Unique Genes : 227
    BRCA1     179
    TP53      108
    EGFR       90
    PTEN       89
    BRCA2      79
    BRAF       60
    KIT        49
    ALK        46
    ERBB2      40
    PIK3CA     38
    Name: Gene, dtype: int64
    
    In [0]:
    print("Ans: There are", unique_genes.shape[0] ,"different categories of genes in the train data, and they are distibuted as follows",)
    
    Ans: There are 227 different categories of genes in the train data, and they are distibuted as follows
    
    In [0]:
    h = unique_genes.values;
    plt.plot(h, label="Histrogram of Genes")
    plt.xlabel('Index of a Gene')
    plt.ylabel('Number of Occurances')
    plt.legend()
    plt.grid()
    plt.show()
    
    In [0]:
    s = sum(unique_genes.values);
    h = unique_genes.values/s;
    plt.plot(h, label="Histrogram of Genes")
    plt.xlabel('Index of a Gene')
    plt.ylabel('Number of Occurances')
    plt.legend()
    plt.grid()
    plt.show()
    
    In [0]:
    c = np.cumsum(h)
    plt.plot(c,label='Cumulative distribution of Genes')
    plt.grid()
    plt.legend()
    plt.show()
    

    Q3. How to featurize this Gene feature ?

    Ans.there are two ways we can featurize this variable check out this video: https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/handling-categorical-and-numerical-features/

    1. One hot Encoding
    2. Response coding

    We will choose the appropriate featurization based on the ML model we use. For this problem of multi-class classification with categorical features, one-hot encoding is better for Logistic regression while response coding is better for Random Forests.

    In [0]:
    #response-coding of the Gene feature
    # alpha is used for laplace smoothing
    alpha = 1
    # train gene feature
    train_gene_feature_responseCoding = np.array(get_gv_feature(alpha, "Gene", train_df))
    # test gene feature
    test_gene_feature_responseCoding = np.array(get_gv_feature(alpha, "Gene", test_df))
    # cross validation gene feature
    cv_gene_feature_responseCoding = np.array(get_gv_feature(alpha, "Gene", cv_df))
    
    In [0]:
    print("train_gene_feature_responseCoding is converted feature using respone coding method. The shape of gene feature:", train_gene_feature_responseCoding.shape)
    
    train_gene_feature_responseCoding is converted feature using respone coding method. The shape of gene feature: (2124, 9)
    
    In [0]:
    # one-hot encoding of Gene feature.
    gene_vectorizer = CountVectorizer()
    train_gene_feature_onehotCoding = gene_vectorizer.fit_transform(train_df['Gene'])
    test_gene_feature_onehotCoding = gene_vectorizer.transform(test_df['Gene'])
    cv_gene_feature_onehotCoding = gene_vectorizer.transform(cv_df['Gene'])
    
    In [0]:
    train_df['Gene'].head()
    
    Out[0]:
    712      ERBB2
    187       EGFR
    2540     BRCA1
    2764      BRAF
    2383    PTPN11
    Name: Gene, dtype: object
    In [0]:
    gene_vectorizer.get_feature_names()
    
    Out[0]:
    ['abl1',
     'acvr1',
     'ago2',
     'akt1',
     'akt2',
     'akt3',
     'alk',
     'apc',
     'ar',
     'araf',
     'arid1b',
     'arid2',
     'arid5b',
     'asxl2',
     'atm',
     'atrx',
     'aurka',
     'aurkb',
     'axl',
     'b2m',
     'bap1',
     'bard1',
     'bcl10',
     'bcl2l11',
     'bcor',
     'braf',
     'brca1',
     'brca2',
     'brd4',
     'brip1',
     'btk',
     'card11',
     'carm1',
     'casp8',
     'cbl',
     'ccnd1',
     'ccnd2',
     'ccnd3',
     'ccne1',
     'cdh1',
     'cdk12',
     'cdk4',
     'cdkn1a',
     'cdkn1b',
     'cdkn2a',
     'cdkn2b',
     'cdkn2c',
     'cebpa',
     'chek2',
     'cic',
     'crebbp',
     'ctcf',
     'ctla4',
     'ctnnb1',
     'ddr2',
     'dicer1',
     'dnmt3a',
     'dnmt3b',
     'egfr',
     'elf3',
     'ep300',
     'epas1',
     'epcam',
     'erbb2',
     'erbb3',
     'erbb4',
     'ercc2',
     'ercc3',
     'ercc4',
     'erg',
     'esr1',
     'etv1',
     'etv6',
     'ewsr1',
     'ezh2',
     'fanca',
     'fat1',
     'fbxw7',
     'fgf3',
     'fgfr1',
     'fgfr2',
     'fgfr3',
     'fgfr4',
     'flt1',
     'flt3',
     'foxa1',
     'foxl2',
     'foxo1',
     'foxp1',
     'gata3',
     'gli1',
     'gna11',
     'gnas',
     'h3f3a',
     'hist1h1c',
     'hla',
     'hnf1a',
     'hras',
     'idh1',
     'idh2',
     'igf1r',
     'ikbke',
     'ikzf1',
     'il7r',
     'inpp4b',
     'jak1',
     'jak2',
     'jun',
     'kdm5a',
     'kdm5c',
     'kdm6a',
     'keap1',
     'kit',
     'kmt2a',
     'kmt2b',
     'kmt2c',
     'kmt2d',
     'kras',
     'lats1',
     'map2k1',
     'map2k2',
     'map2k4',
     'map3k1',
     'mapk1',
     'mdm2',
     'med12',
     'mef2b',
     'men1',
     'met',
     'mga',
     'mlh1',
     'mpl',
     'msh2',
     'msh6',
     'mtor',
     'myc',
     'mycn',
     'myd88',
     'myod1',
     'nf1',
     'nf2',
     'nfe2l2',
     'nfkbia',
     'nkx2',
     'notch1',
     'notch2',
     'npm1',
     'nras',
     'nsd1',
     'ntrk1',
     'ntrk2',
     'ntrk3',
     'nup93',
     'pbrm1',
     'pdgfra',
     'pdgfrb',
     'pik3ca',
     'pik3cb',
     'pik3cd',
     'pik3r1',
     'pik3r2',
     'pim1',
     'pms2',
     'pole',
     'ppm1d',
     'ppp2r1a',
     'ppp6c',
     'prdm1',
     'ptch1',
     'pten',
     'ptpn11',
     'ptprd',
     'ptprt',
     'rab35',
     'rac1',
     'rad21',
     'rad50',
     'rad51c',
     'rad54l',
     'raf1',
     'rara',
     'rasa1',
     'rb1',
     'rbm10',
     'ret',
     'rheb',
     'rhoa',
     'rictor',
     'rit1',
     'rnf43',
     'ros1',
     'rras2',
     'runx1',
     'rybp',
     'sdhb',
     'sdhc',
     'setd2',
     'sf3b1',
     'shoc2',
     'smad2',
     'smad3',
     'smad4',
     'smarca4',
     'smarcb1',
     'smo',
     'sos1',
     'sox9',
     'spop',
     'src',
     'stat3',
     'stk11',
     'tert',
     'tet1',
     'tet2',
     'tgfbr1',
     'tgfbr2',
     'tmprss2',
     'tp53',
     'tp53bp1',
     'tsc1',
     'tsc2',
     'u2af1',
     'vegfa',
     'vhl',
     'xpo1',
     'xrcc2',
     'yap1']
    In [0]:
    print("train_gene_feature_onehotCoding is converted feature using one-hot encoding method. The shape of gene feature:", train_gene_feature_onehotCoding.shape)
    
    train_gene_feature_onehotCoding is converted feature using one-hot encoding method. The shape of gene feature: (2124, 227)
    

    Q4. How good is this gene feature in predicting y_i?

    There are many ways to estimate how good a feature is, in predicting y_i. One of the good methods is to build a proper ML model using just this feature. In this case, we will build a logistic regression model using only Gene feature (one hot encoded) to predict y_i.

    In [0]:
    alpha = [10 ** x for x in range(-5, 1)] # hyperparam for SGD classifier.
    
    # read more about SGDClassifier() at http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html
    # ------------------------------
    # default parameters
    # SGDClassifier(loss=’hinge’, penalty=’l2’, alpha=0.0001, l1_ratio=0.15, fit_intercept=True, max_iter=None, tol=None, 
    # shuffle=True, verbose=0, epsilon=0.1, n_jobs=1, random_state=None, learning_rate=’optimal’, eta0=0.0, power_t=0.5, 
    # class_weight=None, warm_start=False, average=False, n_iter=None)
    
    # some of methods
    # fit(X, y[, coef_init, intercept_init, …])	Fit linear model with Stochastic Gradient Descent.
    # predict(X)	Predict class labels for samples in X.
    
    #-------------------------------
    # video link: 
    #------------------------------
    
    
    cv_log_error_array=[]
    for i in alpha:
        clf = SGDClassifier(alpha=i, penalty='l2', loss='log', random_state=42)
        clf.fit(train_gene_feature_onehotCoding, y_train)
        sig_clf = CalibratedClassifierCV(clf, method="sigmoid")
        sig_clf.fit(train_gene_feature_onehotCoding, y_train)
        predict_y = sig_clf.predict_proba(cv_gene_feature_onehotCoding)
        cv_log_error_array.append(log_loss(y_cv, predict_y, labels=clf.classes_, eps=1e-15))
        print('For values of alpha = ', i, "The log loss is:",log_loss(y_cv, predict_y, labels=clf.classes_, eps=1e-15))
    
    fig, ax = plt.subplots()
    ax.plot(alpha, cv_log_error_array,c='g')
    for i, txt in enumerate(np.round(cv_log_error_array,3)):
        ax.annotate((alpha[i],np.round(txt,3)), (alpha[i],cv_log_error_array[i]))
    plt.grid()
    plt.title("Cross Validation Error for each alpha")
    plt.xlabel("Alpha i's")
    plt.ylabel("Error measure")
    plt.show()
    
    
    best_alpha = np.argmin(cv_log_error_array)
    clf = SGDClassifier(alpha=alpha[best_alpha], penalty='l2', loss='log', random_state=42)
    clf.fit(train_gene_feature_onehotCoding, y_train)
    sig_clf = CalibratedClassifierCV(clf, method="sigmoid")
    sig_clf.fit(train_gene_feature_onehotCoding, y_train)
    
    predict_y = sig_clf.predict_proba(train_gene_feature_onehotCoding)
    print('For values of best alpha = ', alpha[best_alpha], "The train log loss is:",log_loss(y_train, predict_y, labels=clf.classes_, eps=1e-15))
    predict_y = sig_clf.predict_proba(cv_gene_feature_onehotCoding)
    print('For values of best alpha = ', alpha[best_alpha], "The cross validation log loss is:",log_loss(y_cv, predict_y, labels=clf.classes_, eps=1e-15))
    predict_y = sig_clf.predict_proba(test_gene_feature_onehotCoding)
    print('For values of best alpha = ', alpha[best_alpha], "The test log loss is:",log_loss(y_test, predict_y, labels=clf.classes_, eps=1e-15))
    
    For values of alpha =  1e-05 The log loss is: 1.226821690673479
    For values of alpha =  0.0001 The log loss is: 1.2052250084977378
    For values of alpha =  0.001 The log loss is: 1.2440078430324424
    For values of alpha =  0.01 The log loss is: 1.3482265494928896
    For values of alpha =  0.1 The log loss is: 1.4462448507281795
    For values of alpha =  1 The log loss is: 1.4810193590182947
    
    For values of best alpha =  0.0001 The train log loss is: 0.9927053348852308
    For values of best alpha =  0.0001 The cross validation log loss is: 1.2052250084977378
    For values of best alpha =  0.0001 The test log loss is: 1.1908321656047516
    

    Q5. Is the Gene feature stable across all the data sets (Test, Train, Cross validation)?

    Ans. Yes, it is. Otherwise, the CV and Test errors would be significantly more than train error.

    In [0]:
    print("Q6. How many data points in Test and CV datasets are covered by the ", unique_genes.shape[0], " genes in train dataset?")
    
    test_coverage=test_df[test_df['Gene'].isin(list(set(train_df['Gene'])))].shape[0]
    cv_coverage=cv_df[cv_df['Gene'].isin(list(set(train_df['Gene'])))].shape[0]
    
    print('Ans\n1. In test data',test_coverage, 'out of',test_df.shape[0], ":",(test_coverage/test_df.shape[0])*100)
    print('2. In cross validation data',cv_coverage, 'out of ',cv_df.shape[0],":" ,(cv_coverage/cv_df.shape[0])*100)
    
    Q6. How many data points in Test and CV datasets are covered by the  227  genes in train dataset?
    Ans
    1. In test data 640 out of 665 : 96.2406015037594
    2. In cross validation data 509 out of  532 : 95.67669172932331
    

    3.2.2 Univariate Analysis on Variation Feature

    Q7. Variation, What type of feature is it ?

    Ans. Variation is a categorical variable

    Q8. How many categories are there?

    In [0]:
    unique_variations = train_df['Variation'].value_counts()
    print('Number of Unique Variations :', unique_variations.shape[0])
    # the top 10 variations that occured most
    print(unique_variations.head(10))
    
    Number of Unique Variations : 1918
    Truncating_Mutations    58
    Deletion                50
    Amplification           47
    Fusions                 27
    Overexpression           6
    Q61L                     3
    T58I                     3
    G12V                     3
    Y42C                     2
    R841K                    2
    Name: Variation, dtype: int64
    
    In [0]:
    print("Ans: There are", unique_variations.shape[0] ,"different categories of variations in the train data, and they are distibuted as follows",)
    
    Ans: There are 1918 different categories of variations in the train data, and they are distibuted as follows
    
    In [0]:
    s = sum(unique_variations.values);
    h = unique_variations.values/s;
    plt.plot(h, label="Histrogram of Variations")
    plt.xlabel('Index of a Variation')
    plt.ylabel('Number of Occurances')
    plt.legend()
    plt.grid()
    plt.show()
    
    In [0]:
    c = np.cumsum(h)
    print(c)
    plt.plot(c,label='Cumulative distribution of Variations')
    plt.grid()
    plt.legend()
    plt.show()
    
    [0.02730697 0.05084746 0.07297552 ... 0.99905838 0.99952919 1.        ]
    

    Q9. How to featurize this Variation feature ?

    Ans.There are two ways we can featurize this variable check out this video: https://www.appliedaicourse.com/course/applied-ai-course-online/lessons/handling-categorical-and-numerical-features/

    1. One hot Encoding
    2. Response coding

    We will be using both these methods to featurize the Variation Feature

    In [0]:
    # alpha is used for laplace smoothing
    alpha = 1
    # train gene feature
    train_variation_feature_responseCoding = np.array(get_gv_feature(alpha, "Variation", train_df))
    # test gene feature
    test_variation_feature_responseCoding = np.array(get_gv_feature(alpha, "Variation", test_df))
    # cross validation gene feature
    cv_variation_feature_responseCoding = np.array(get_gv_feature(alpha, "Variation", cv_df))
    
    In [0]:
    print("train_variation_feature_responseCoding is a converted feature using the response coding method. The shape of Variation feature:", train_variation_feature_responseCoding.shape)
    
    train_variation_feature_responseCoding is a converted feature using the response coding method. The shape of Variation feature: (2124, 9)
    
    In [0]:
    # one-hot encoding of variation feature.
    variation_vectorizer = CountVectorizer()
    train_variation_feature_onehotCoding = variation_vectorizer.fit_transform(train_df['Variation'])
    test_variation_feature_onehotCoding = variation_vectorizer.transform(test_df['Variation'])
    cv_variation_feature_onehotCoding = variation_vectorizer.transform(cv_df['Variation'])
    
    In [0]:
    print("train_variation_feature_onehotEncoded is converted feature using the onne-hot encoding method. The shape of Variation feature:", train_variation_feature_onehotCoding.shape)
    
    train_variation_feature_onehotEncoded is converted feature using the onne-hot encoding method. The shape of Variation feature: (2124, 1948)
    

    Q10. How good is this Variation feature in predicting y_i?

    Let's build a model just like the earlier!

    In [0]:
    alpha = [10 ** x for x in range(-5, 1)]
    
    # read more about SGDClassifier() at http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html
    # ------------------------------
    # default parameters
    # SGDClassifier(loss=’hinge’, penalty=’l2’, alpha=0.0001, l1_ratio=0.15, fit_intercept=True, max_iter=None, tol=None, 
    # shuffle=True, verbose=0, epsilon=0.1, n_jobs=1, random_state=None, learning_rate=’optimal’, eta0=0.0, power_t=0.5, 
    # class_weight=None, warm_start=False, average=False, n_iter=None)
    
    # some of methods
    # fit(X, y[, coef_init, intercept_init, …])	Fit linear model with Stochastic Gradient Descent.
    # predict(X)	Predict class labels for samples in X.
    
    #-------------------------------
    # video link: 
    #------------------------------
    
    
    cv_log_error_array=[]
    for i in alpha:
        clf = SGDClassifier(alpha=i, penalty='l2', loss='log', random_state=42)
        clf.fit(train_variation_feature_onehotCoding, y_train)
        
        sig_clf = CalibratedClassifierCV(clf, method="sigmoid")
        sig_clf.fit(train_variation_feature_onehotCoding, y_train)
        predict_y = sig_clf.predict_proba(cv_variation_feature_onehotCoding)
        
        cv_log_error_array.append(log_loss(y_cv, predict_y, labels=clf.classes_, eps=1e-15))
        print('For values of alpha = ', i, "The log loss is:",log_loss(y_cv, predict_y, labels=clf.classes_, eps=1e-15))
    
    fig, ax = plt.subplots()
    ax.plot(alpha, cv_log_error_array,c='g')
    for i, txt in enumerate(np.round(cv_log_error_array,3)):
        ax.annotate((alpha[i],np.round(txt,3)), (alpha[i],cv_log_error_array[i]))
    plt.grid()
    plt.title("Cross Validation Error for each alpha")
    plt.xlabel("Alpha i's")
    plt.ylabel("Error measure")
    plt.show()
    
    
    best_alpha = np.argmin(cv_log_error_array)
    clf = SGDClassifier(alpha=alpha[best_alpha], penalty='l2', loss='log', random_state=42)
    clf.fit(train_variation_feature_onehotCoding, y_train)
    sig_clf = CalibratedClassifierCV(clf, method="sigmoid")
    sig_clf.fit(train_variation_feature_onehotCoding, y_train)
    
    predict_y = sig_clf.predict_proba(train_variation_feature_onehotCoding)
    print('For values of best alpha = ', alpha[best_alpha], "The train log loss is:",log_loss(y_train, predict_y, labels=clf.classes_, eps=1e-15))
    predict_y = sig_clf.predict_proba(cv_variation_feature_onehotCoding)
    print('For values of best alpha = ', alpha[best_alpha], "The cross validation log loss is:",log_loss(y_cv, predict_y, labels=clf.classes_, eps=1e-15))
    predict_y = sig_clf.predict_proba(test_variation_feature_onehotCoding)
    print('For values of best alpha = ', alpha[best_alpha], "The test log loss is:",log_loss(y_test, predict_y, labels=clf.classes_, eps=1e-15))
    
    For values of alpha =  1e-05 The log loss is: 1.7094729589801148
    For values of alpha =  0.0001 The log loss is: 1.704822500968798
    For values of alpha =  0.001 The log loss is: 1.7062137217931994
    For values of alpha =  0.01 The log loss is: 1.7158712564288758
    For values of alpha =  0.1 The log loss is: 1.7304510263263242
    For values of alpha =  1 The log loss is: 1.7315829687176831
    
    For values of best alpha =  0.0001 The train log loss is: 0.8052224528067052
    For values of best alpha =  0.0001 The cross validation log loss is: 1.704822500968798
    For values of best alpha =  0.0001 The test log loss is: 1.7255058061214814
    

    Q11. Is the Variation feature stable across all the data sets (Test, Train, Cross validation)?

    Ans. Not sure! But lets be very sure using the below analysis.

    In [0]:
    print("Q12. How many data points are covered by total ", unique_variations.shape[0], " genes in test and cross validation data sets?")
    test_coverage=test_df[test_df['Variation'].isin(list(set(train_df['Variation'])))].shape[0]
    cv_coverage=cv_df[cv_df['Variation'].isin(list(set(train_df['Variation'])))].shape[0]
    print('Ans\n1. In test data',test_coverage, 'out of',test_df.shape[0], ":",(test_coverage/test_df.shape[0])*100)
    print('2. In cross validation data',cv_coverage, 'out of ',cv_df.shape[0],":" ,(cv_coverage/cv_df.shape[0])*100)
    
    Q12. How many data points are covered by total  1918  genes in test and cross validation data sets?
    Ans
    1. In test data 60 out of 665 : 9.022556390977442
    2. In cross validation data 53 out of  532 : 9.962406015037594
    

    3.2.3 Univariate Analysis on Text Feature

    1. How many unique words are present in train data?
    2. How are word frequencies distributed?
    3. How to featurize text field?
    4. Is the text feature useful in predicitng y_i?
    5. Is the text feature stable across train, test and CV datasets?
    In [0]:
    # cls_text is a data frame
    # for every row in data fram consider the 'TEXT'
    # split the words by space
    # make a dict with those words
    # increment its count whenever we see that word
    
    def extract_dictionary_paddle(cls_text):
        dictionary = defaultdict(int)
        for index, row in cls_text.iterrows():
            for word in row['TEXT'].split():
                dictionary[word] +=1
        return dictionary
    
    In [0]:
    import math
    #https://stackoverflow.com/a/1602964
    def get_text_responsecoding(df):
        text_feature_responseCoding = np.zeros((df.shape[0],9))
        for i in range(0,9):
            row_index = 0
            for index, row in df.iterrows():
                sum_prob = 0
                for word in row['TEXT'].split():
                    sum_prob += math.log(((dict_list[i].get(word,0)+10 )/(total_dict.get(word,0)+90)))
                text_feature_responseCoding[row_index][i] = math.exp(sum_prob/len(row['TEXT'].split()))
                row_index += 1
        return text_feature_responseCoding
    
    In [0]:
    # check here
    # building a CountVectorizer with all the words that occured minimum 3 times in train data
    text_vectorizer = TfidfVectorizer(min_df=3,ngram_range=(1,4),max_features=5000)
    train_text_feature_onehotCoding = text_vectorizer.fit_transform(train_df['TEXT'])
    # getting all the feature names (words)
    train_text_features= text_vectorizer.get_feature_names()
    
    # train_text_feature_onehotCoding.sum(axis=0).A1 will sum every row and returns (1*number of features) vector
    train_text_fea_counts = train_text_feature_onehotCoding.sum(axis=0).A1
    
    # zip(list(text_features),text_fea_counts) will zip a word with its number of times it occured
    text_fea_dict = dict(zip(list(train_text_features),train_text_fea_counts))
    
    
    
    print("Total number of unique words in train data :", len(train_text_features))
    
    Total number of unique words in train data : 5000
    
    In [0]:
    train_text_feature_onehotCoding.shape
    
    Out[0]:
    (2124, 5000)
    In [0]:
    dict_list = []
    # dict_list =[] contains 9 dictoinaries each corresponds to a class
    for i in range(1,10):
        cls_text = train_df[train_df['Class']==i]
        # build a word dict based on the words in that class
        dict_list.append(extract_dictionary_paddle(cls_text))
        # append it to dict_list
    
    # dict_list[i] is build on i'th  class text data
    # total_dict is buid on whole training text data
    total_dict = extract_dictionary_paddle(train_df)
    
    
    confuse_array = []
    for i in train_text_features:
        ratios = []
        max_val = -1
        for j in range(0,9):
            ratios.append((dict_list[j][i]+10 )/(total_dict[i]+90))
        confuse_array.append(ratios)
    confuse_array = np.array(confuse_array)
    
    In [0]:
    len(confuse_array)
    
    Out[0]:
    5000
    In [0]:
    #response coding of text features
    train_text_feature_responseCoding  = get_text_responsecoding(train_df)
    test_text_feature_responseCoding  = get_text_responsecoding(test_df)
    cv_text_feature_responseCoding  = get_text_responsecoding(cv_df)
    
    In [0]:
    # https://stackoverflow.com/a/16202486
    # we convert each row values such that they sum to 1  
    train_text_feature_responseCoding = (train_text_feature_responseCoding.T/train_text_feature_responseCoding.sum(axis=1)).T
    test_text_feature_responseCoding = (test_text_feature_responseCoding.T/test_text_feature_responseCoding.sum(axis=1)).T
    cv_text_feature_responseCoding = (cv_text_feature_responseCoding.T/cv_text_feature_responseCoding.sum(axis=1)).T
    
    In [0]:
    # don't forget to normalize every feature
    # check here
    train_text_feature_onehotCoding = normalize(train_text_feature_onehotCoding, axis=0)
    
    # we use the same vectorizer that was trained on train data
    test_text_feature_onehotCoding = text_vectorizer.transform(test_df['TEXT'])
    # don't forget to normalize every feature
    test_text_feature_onehotCoding = normalize(test_text_feature_onehotCoding, axis=0)
    
    # we use the same vectorizer that was trained on train data
    cv_text_feature_onehotCoding = text_vectorizer.transform(cv_df['TEXT'])
    # don't forget to normalize every feature
    cv_text_feature_onehotCoding = normalize(cv_text_feature_onehotCoding, axis=0)
    
    In [0]:
    #https://stackoverflow.com/a/2258273/4084039
    sorted_text_fea_dict = dict(sorted(text_fea_dict.items(), key=lambda x: x[1] , reverse=True))
    sorted_text_occur = np.array(list(sorted_text_fea_dict.values()))
    
    In [0]:
    # Number of words for a given frequency.
    print(Counter(sorted_text_occur))
    
    Counter({0.02209715288832061: 316, 0.04307059760343434: 240, 0.016340833299368605: 218, 0.054722872520715435: 203, 0.02835896949039647: 171, 0.30997237948876916: 157, 0.23354338977259373: 132, 0.04064677021967549: 123, 0.01691188404758709: 121, 0.03767497505135435: 117, 0.023217246065549407: 110, 0.062334633850850765: 106, 0.06358115205885219: 105, 0.024600055291393506: 104, 0.045420528272955535: 102, 0.10348439128881731: 101, 0.0107411839881614: 101, 0.1208805415465745: 95, 0.027712488418855447: 95, 0.03304147173487198: 92, 0.017822831053788925: 89, 0.03150856223306072: 86, 0.048842553201173006: 83, 0.010716671566117626: 81, 0.011994414963599565: 80, 0.018405082417185928: 79, 0.023082148129265387: 78, 0.015000830557716388: 78, 0.02624433615166461: 77, 0.040606925232440055: 71, 0.02166289005842561: 69, 0.050289220228728546: 65, 0.041680097244093724: 65, 0.01766811431856557: 64, 0.04441561661520211: 63, 0.026185352920489385: 63, 0.012285967576864084: 63, 0.06684597122208347: 62, 0.017343919321126063: 62, 0.12995878475689276: 60, 0.04865649760137855: 60, 0.03169293199819758: 60, 0.008364933372094414: 59, 0.09298423166349647: 57, 0.08252315570725834: 57, 0.018923685439012603: 57, 0.01801856972471663: 57, 0.014991940983918026: 54, 0.018027123673458892: 53, 0.03618655458611623: 52, 0.19253908513274878: 50, 0.020328518228808476: 50, 0.013976348062072517: 50, 0.11816164289781507: 49, 0.04843372089036086: 49, 0.03157528768606281: 49, 0.022352201091948194: 49, 0.012810208069531773: 49, 0.036492110588494164: 48, 0.03543479789917594: 47, 0.03048998075625673: 47, 0.0262956807330326: 47, 0.011889192539377376: 47, 0.029293785623063822: 46, 0.009112996340153567: 46, 0.15800609283144107: 45, 0.06341467483961326: 45, 0.05770293868410996: 45, 0.030575536225755394: 45, 0.059700152431777474: 44, 0.03305738622093541: 44, 0.04696891589571915: 43, 0.02826724450443445: 43, 0.025782836482034513: 43, 0.11273007563862064: 42, 0.05066383245508972: 42, 0.024588728306835336: 42, 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