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Medical-Treatment-Cancer.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Project : Medical Treatment"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Problem statement"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"A lot has been said during the past several years about how precision medicine and, more concretely, how genetic testing is going to disrupt the way diseases like cancer are treated.\n",
"\n",
"But this is only partially happening due to the huge amount of manual work still required. Once sequenced, a cancer tumor can have thousands of genetic mutations. But the challenge is distinguishing the mutations that contribute to tumor growth (drivers) from the neutral mutations (passengers). \n",
"\n",
"Currently this interpretation of genetic mutations is being done manually. This is a very time-consuming task where a clinical pathologist has to manually review and classify every single genetic mutation based on evidence from text-based clinical literature.\n",
"\n",
"We need to develop a Machine Learning algorithm that, using this knowledge base as a baseline, automatically classifies genetic variations.\n",
"\n",
"\n",
"\n",
"You can check all details about the competition from following link :\n",
"https://www.kaggle.com/c/msk-redefining-cancer-treatment\n",
"\n",
"In order to get the dataset please create a login account to Kaggle and go to this problem statement page(given above) and download 2 dataset\n",
"\n",
"***training_variants.zip*** and ***training_text.zip***"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Analysis of the problem statement"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets first understand the data set provided and using that dataset we will try to understand the above problem in Machine Learning world. Since, the dataset is huge lets load it using python itself"
]
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {},
"outputs": [],
"source": [
"# Loading all required packages\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import re\n",
"import time\n",
"import warnings\n",
"import numpy as np\n",
"from nltk.corpus import stopwords\n",
"from sklearn.decomposition import TruncatedSVD\n",
"from sklearn.preprocessing import normalize\n",
"from sklearn.feature_extraction.text import CountVectorizer\n",
"from sklearn.manifold import TSNE\n",
"import seaborn as sns\n",
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn.metrics import confusion_matrix\n",
"from sklearn.metrics.classification import accuracy_score, log_loss\n",
"from sklearn.feature_extraction.text import TfidfVectorizer\n",
"from sklearn.linear_model import SGDClassifier\n",
"from imblearn.over_sampling import SMOTE\n",
"from collections import Counter\n",
"from scipy.sparse import hstack\n",
"from sklearn.multiclass import OneVsRestClassifier\n",
"from sklearn.svm import SVC\n",
"from sklearn.model_selection import StratifiedKFold \n",
"from collections import Counter, defaultdict\n",
"from sklearn.calibration import CalibratedClassifierCV\n",
"from sklearn.naive_bayes import MultinomialNB\n",
"from sklearn.naive_bayes import GaussianNB\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.model_selection import GridSearchCV\n",
"import math\n",
"from sklearn.metrics import normalized_mutual_info_score\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"from mlxtend.classifier import StackingClassifier\n",
"\n",
"from sklearn import model_selection\n",
"from sklearn.linear_model import LogisticRegression"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"There are 2 data files provided for solving this problem. I have kept them inside a folder training. So lets load them"
]
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {},
"outputs": [],
"source": [
"# Loading training_variants\n",
"data_variants = pd.read_csv('training/training_variants')\n",
"# Loading training_text dataset\n",
"data_text =pd.read_csv(\"training/training_text\",sep=\"\\|\\|\",engine=\"python\",names=[\"ID\",\"TEXT\"],skiprows=1)"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ID</th>\n",
" <th>Gene</th>\n",
" <th>Variation</th>\n",
" <th>Class</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>FAM58A</td>\n",
" <td>Truncating Mutations</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>CBL</td>\n",
" <td>W802*</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>CBL</td>\n",
" <td>Q249E</td>\n",
" <td>2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ID Gene Variation Class\n",
"0 0 FAM58A Truncating Mutations 1\n",
"1 1 CBL W802* 2\n",
"2 2 CBL Q249E 2"
]
},
"execution_count": 114,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_variants.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 115,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 3321 entries, 0 to 3320\n",
"Data columns (total 4 columns):\n",
"ID 3321 non-null int64\n",
"Gene 3321 non-null object\n",
"Variation 3321 non-null object\n",
"Class 3321 non-null int64\n",
"dtypes: int64(2), object(2)\n",
"memory usage: 103.9+ KB\n"
]
}
],
"source": [
"data_variants.info()"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ID</th>\n",
" <th>Class</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>3321.000000</td>\n",
" <td>3321.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>1660.000000</td>\n",
" <td>4.365854</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>958.834449</td>\n",
" <td>2.309781</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>830.000000</td>\n",
" <td>2.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>1660.000000</td>\n",
" <td>4.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>2490.000000</td>\n",
" <td>7.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>3320.000000</td>\n",
" <td>9.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ID Class\n",
"count 3321.000000 3321.000000\n",
"mean 1660.000000 4.365854\n",
"std 958.834449 2.309781\n",
"min 0.000000 1.000000\n",
"25% 830.000000 2.000000\n",
"50% 1660.000000 4.000000\n",
"75% 2490.000000 7.000000\n",
"max 3320.000000 9.000000"
]
},
"execution_count": 116,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_variants.describe()"
]
},
{
"cell_type": "code",
"execution_count": 117,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(3321, 4)"
]
},
"execution_count": 117,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Checking dimention of data\n",
"data_variants.shape"
]
},
{
"cell_type": "code",
"execution_count": 118,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['ID', 'Gene', 'Variation', 'Class'], dtype='object')"
]
},
"execution_count": 118,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Clecking column in above data set\n",
"data_variants.columns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now lets explore about data_text"
]
},
{
"cell_type": "code",
"execution_count": 119,
"metadata": {},
"outputs": [
{
"data": {
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ID</th>\n",
" <th>TEXT</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>Cyclin-dependent kinases (CDKs) regulate a var...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>Abstract Background Non-small cell lung canc...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>Abstract Background Non-small cell lung canc...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ID TEXT\n",
"0 0 Cyclin-dependent kinases (CDKs) regulate a var...\n",
"1 1 Abstract Background Non-small cell lung canc...\n",
"2 2 Abstract Background Non-small cell lung canc..."
]
},
"execution_count": 119,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_text.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So above dataset have 2 columns. ID and Text column. We can also observe column ID which is common in both the dataset. Lets keep exploring it."
]
},
{
"cell_type": "code",
"execution_count": 120,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 3321 entries, 0 to 3320\n",
"Data columns (total 2 columns):\n",
"ID 3321 non-null int64\n",
"TEXT 3316 non-null object\n",
"dtypes: int64(1), object(1)\n",
"memory usage: 52.0+ KB\n"
]
}
],
"source": [
"data_text.info()"
]
},
{
"cell_type": "code",
"execution_count": 121,
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>ID</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>3321.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>1660.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>958.834449</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>830.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>1660.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>2490.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>3320.000000</td>\n",
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"text/plain": [
" ID\n",
"count 3321.000000\n",
"mean 1660.000000\n",
"std 958.834449\n",
"min 0.000000\n",
"25% 830.000000\n",
"50% 1660.000000\n",
"75% 2490.000000\n",
"max 3320.000000"
]
},
"execution_count": 121,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_text.describe()"
]
},
{
"cell_type": "code",
"execution_count": 122,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Index(['ID', 'TEXT'], dtype='object')"
]
},
"execution_count": 122,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_text.columns"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(3321, 2)"
]
},
"execution_count": 123,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# checking the dimentions\n",
"data_text.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So, in short my datasets looks like this\n",
" * data_variants (ID, Gene, Variations, Class)\n",
" * data_text(ID, text)"
]
},
{
"cell_type": "code",
"execution_count": 124,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int64)"
]
},
"execution_count": 124,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_variants.Class.unique()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is descrete data so it is ***classification*** problem and since there are multiple descrete output possible so we can call it ***Multi class*** classification problem"
]
},
{
"cell_type": "code",
"execution_count": 125,
"metadata": {},
"outputs": [],
"source": [
"stop_words = set(stopwords.words('english'))"
]
},
{
"cell_type": "code",
"execution_count": 126,
"metadata": {},
"outputs": [],
"source": [
"def data_text_preprocess(total_text, ind, col):\n",
" if type(total_text) is not int:\n",
" string = \"\"\n",
" total_text = re.sub('[^a-zA-Z0-9\\n]', ' ', str(total_text))\n",
" total_text = re.sub('\\s+',' ', str(total_text))\n",
" total_text = total_text.lower()\n",
" \n",
" for word in total_text.split():\n",
" # if the word is a not a stop word then retain that word from text\n",
" if not word in stop_words:\n",
" string += word + \" \"\n",
" \n",
" data_text[col][ind] = string"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"for index, row in data_text.iterrows():\n",
" if type(row['TEXT']) is str:\n",
" data_text_preprocess(row['TEXT'], index, 'TEXT')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's merge both the dataset. Remember that ID was common column. So lets use it to merge."
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
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" <th></th>\n",
" <th>ID</th>\n",
" <th>Gene</th>\n",
" <th>Variation</th>\n",
" <th>Class</th>\n",
" <th>TEXT</th>\n",
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" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>FAM58A</td>\n",
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" <td>cyclin dependent kinases cdks regulate variety...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>CBL</td>\n",
" <td>W802*</td>\n",
" <td>2</td>\n",
" <td>abstract background non small cell lung cancer...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>CBL</td>\n",
" <td>Q249E</td>\n",
" <td>2</td>\n",
" <td>abstract background non small cell lung cancer...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>CBL</td>\n",
" <td>N454D</td>\n",
" <td>3</td>\n",
" <td>recent evidence demonstrated acquired uniparen...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>CBL</td>\n",
" <td>L399V</td>\n",
" <td>4</td>\n",
" <td>oncogenic mutations monomeric casitas b lineag...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ID Gene Variation Class \\\n",
"0 0 FAM58A Truncating Mutations 1 \n",
"1 1 CBL W802* 2 \n",
"2 2 CBL Q249E 2 \n",
"3 3 CBL N454D 3 \n",
"4 4 CBL L399V 4 \n",
"\n",
" TEXT \n",
"0 cyclin dependent kinases cdks regulate variety... \n",
"1 abstract background non small cell lung cancer... \n",
"2 abstract background non small cell lung cancer... \n",
"3 recent evidence demonstrated acquired uniparen... \n",
"4 oncogenic mutations monomeric casitas b lineag... "
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#merging both gene_variations and text data based on ID\n",
"result = pd.merge(data_variants, data_text,on='ID', how='left')\n",
"result.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It's very important to look for missing values. Else they create problem in final analysis"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
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" <th>1109</th>\n",
" <td>1109</td>\n",
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" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1277</th>\n",
" <td>1277</td>\n",
" <td>ARID5B</td>\n",
" <td>Truncating Mutations</td>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1407</th>\n",
" <td>1407</td>\n",
" <td>FGFR3</td>\n",
" <td>K508M</td>\n",
" <td>6</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1639</th>\n",
" <td>1639</td>\n",
" <td>FLT1</td>\n",
" <td>Amplification</td>\n",
" <td>6</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2755</th>\n",
" <td>2755</td>\n",
" <td>BRAF</td>\n",
" <td>G596C</td>\n",
" <td>7</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ID Gene Variation Class TEXT\n",
"1109 1109 FANCA S1088F 1 NaN\n",
"1277 1277 ARID5B Truncating Mutations 1 NaN\n",
"1407 1407 FGFR3 K508M 6 NaN\n",
"1639 1639 FLT1 Amplification 6 NaN\n",
"2755 2755 BRAF G596C 7 NaN"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result[result.isnull().any(axis=1)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see many rows with missing data. Now the question is what to do with this missing value."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"result.loc[result['TEXT'].isnull(),'TEXT'] = result['Gene'] +' '+result['Variation']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's cross check it once again if there is any missing values"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ID</th>\n",
" <th>Gene</th>\n",
" <th>Variation</th>\n",
" <th>Class</th>\n",
" <th>TEXT</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [ID, Gene, Variation, Class, TEXT]\n",
"Index: []"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"result[result.isnull().any(axis=1)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Creating Training, Test and Validation data\n",
"\n",
"Before we split the data into taining, test and validation data set. We want to ensure that all spaces in Gene and Variation column to be replaced by _."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": [
"y_true = result['Class'].values\n",
"result.Gene = result.Gene.str.replace('\\s+', '_')\n",
"result.Variation = result.Variation.str.replace('\\s+', '_')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ok, so we can now start our split process in train, test and validation data set."
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"# Splitting the data into train and test set and cross validation\n",
"X_train, test_df, y_train, y_test = train_test_split(result, y_true, stratify=y_true, test_size=0.2)\n",
"train_df, cv_df, y_train, y_cv = train_test_split(X_train, y_train, stratify=y_train, test_size=0.2)"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of data points in train data: 2124\n",
"Number of data points in test data: 665\n",
"Number of data points in cross validation data: 532\n"
]
}
],
"source": [
"print('Number of data points in train data:', train_df.shape[0])\n",
"print('Number of data points in test data:', test_df.shape[0])\n",
"print('Number of data points in cross validation data:', cv_df.shape[0])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's look at the distribution of data in train, test and validation set."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"train_class_distribution = train_df['Class'].value_counts().sort_index()\n",
"test_class_distribution = test_df['Class'].value_counts().sort_index()\n",
"cv_class_distribution = cv_df['Class'].value_counts().sort_index()"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1 363\n",
"2 289\n",
"3 57\n",
"4 439\n",
"5 155\n",
"6 176\n",
"7 609\n",
"8 12\n",
"9 24\n",
"Name: Class, dtype: int64"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_class_distribution"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So, what does above variable suggest us. This means in my train dataset we have class 1 values with count of 363, class 2 values having count of 289 and so on. It will be better idea to visualise it in graph format.\n",
"\n",
"*** Visualizing for train class distrubution***"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"my_colors = 'rgbkymc'\n",
"train_class_distribution.plot(kind='bar')\n",
"plt.xlabel('Class')\n",
"plt.ylabel(' Number of Data points per Class')\n",
"plt.title('Distribution of yi in train data')\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's look at distribution in form of percentage"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of data points in class 7 : 609 ( 28.672 %)\n",
"Number of data points in class 4 : 439 ( 20.669 %)\n",
"Number of data points in class 1 : 363 ( 17.09 %)\n",
"Number of data points in class 2 : 289 ( 13.606 %)\n",
"Number of data points in class 6 : 176 ( 8.286 %)\n",
"Number of data points in class 5 : 155 ( 7.298 %)\n",
"Number of data points in class 3 : 57 ( 2.684 %)\n",
"Number of data points in class 9 : 24 ( 1.13 %)\n",
"Number of data points in class 8 : 12 ( 0.565 %)\n"
]
}
],
"source": [
"sorted_yi = np.argsort(-train_class_distribution.values)\n",
"for i in sorted_yi:\n",
" 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), '%)')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's visualize the same for test set"
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"my_colors = 'rgbkymc'\n",
"test_class_distribution.plot(kind='bar')\n",
"plt.xlabel('Class')\n",
"plt.ylabel('Number of Data points per Class')\n",
"plt.title('Distribution of yi in test data')\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of data points in class 7 : 191 ( 28.722 %)\n",
"Number of data points in class 4 : 137 ( 20.602 %)\n",
"Number of data points in class 1 : 114 ( 17.143 %)\n",
"Number of data points in class 2 : 91 ( 13.684 %)\n",
"Number of data points in class 6 : 55 ( 8.271 %)\n",
"Number of data points in class 5 : 48 ( 7.218 %)\n",
"Number of data points in class 3 : 18 ( 2.707 %)\n",
"Number of data points in class 9 : 7 ( 1.053 %)\n",
"Number of data points in class 8 : 4 ( 0.602 %)\n"
]
}
],
"source": [
"sorted_yi = np.argsort(-test_class_distribution.values)\n",
"for i in sorted_yi:\n",
" 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), '%)')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's visualize for cross validation set"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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DnwHeExHPVky6DDhI0gaStgGmAH8ezjLMzKw+ipyDGBcRTwMHAmdExDTgHbWeJOl84EZgB0kLJR0JfB8YA1wt6XZJPwaIiLuBC4F7gF8Dx0TEqmG9IjMzq4sih5hGStoC+ADwuaIzjoiDc0afVqX9KcApRedvZmaNVWQP4ivAb4C5EXGzpG2BBxsby8zMylZzDyIiLgIuqhieB7y3kaHMzKx8RU5S/3c6ST1K0jWSlko6rBnhzMysPEUOMb0znaTej+xy1O2BTzc0lZmZla5IgRiV/t0HOD8i/latsZmZrR2KXMX0S0n3ASuAoyVtBjzX2FhmZla2mnsQEXEi8GagIyJeBJaTda5nZmZrsaJdbUwE9pK0YcW4sxuQx8zMWkTNAiHpS0AnsCNwJfAu4AZcIMzM1mpF9iDeB+wM3BYRH5LUDpza2FhmmckFO3sr0inc/Jn71iOS2TqjyFVMKyLiJWClpLHA48C2jY1lZmZlK7IHcUv67eifAHOAPtzTqpnZWq9IVxtHp4c/lvRrYOzq/B6EmZmtGQYtEJJ2qTYtIm5tTCQzM2sF1fYgZlWZFsDb65zFzMxayKAFIiK6mhnEzMxay6BXMUk6TNLhOeM/IumQxsYyM7OyVbvM9XjgFznjL0jTzMxsLVatQIyIiGcGjkxdf4/Kaf8Kkk6X9LikuyrGbSrpakkPpn83SeMl6buS5kq6s9oJcjMza45qBWKUpI0HjpQ0Bli/wLzPBPYeMO5E4JqImAJck4Yh675jSvqbAfyowPzNzKyBqhWI04CLJU3uH5Eed6dpVUXE9cDA347YHzgrPT4LOKBi/NmRuQkYL2mL2vHNzKxRql3F9A1JfcDvJLWRXdq6HJgZEcP9ht8eEY+m+T8qafM0fiLwSEW7hWnco8NcjpmZrSZFRO1GWYFQ3jmJGs+bDFweETul4aciYnzF9CcjYhNJVwBfi4gb0vhrgBMiYk7OPGeQHYaivb19Wnd391AiDaqvr4+2tra6zKtenAl6Fy2r2aZ9NCxZUXteUyeOq0Oi4rz96rf9vO3qm6mrq2tORHTUalfo9yAiom/1IwGwRNIWae9hC7KO/yDbY5hU0W4rYPEgWWYDswE6Ojqis7OzLsF6enqo17zqxZko1Evr8VNXMqu39lt5/qGddUhUnLdf/baft105mYr05lpPlwHT0+PpwKUV4z+YrmbaHVjWfyjKzMzKUbVASFpP0luGM2NJ5wM3AjtIWijpSGAm2S/TPQjslYYh+yGiecBcsl5jj86ZpZmZNVHV/bqIeEnSLLLfpB6SiDh4kEl75rQN4JihLsPMzBqnyCGmqyS9V5IansbMzFpGkZPUxwEbA6skrQBE9qV/bEOTmZlZqYr8YNCYZgQxM7PWUvMQU7qy6DBJX0jDkyTt2vhoZmZWpiLnIH5IdpK6v4vvPuAHDUtkZmYtocg5iN0iYhdJtwFExJOSinTWZ2Zma7AiexAvShpB1hcTkjYDXmpoKjMzK12RPYjvAj8H2iWdArwP+HxDU9XZ5IK3+9fqFmD+zH3rFcnMrOUVuYrpp5Lm8PINbgdExL2NjWVmZmUr1FkfsBHQf5hpdOPimJlZqyhymesXyX7cZ1NgAnCGpDXqEJOZmQ1dkT2Ig4H/FxHPAUiaCdwKfLWRwczMrFxFrmKaD2xYMbwB8FBD0piZWcsosgfxPHC3pKvJzkHsBdwg6bsAEXFsA/OZmVlJihSIn6e/fj2NiWJmZq2kyGWuZzUjiJmZtZZm/+SomZmtIVwgzMws15AKRPqN6tX+oSBJn5R0t6S7JJ0vaUNJ20j6k6QHJV3gDgHNzMpV5Ea58ySNlbQxcA9wv6RPD3eBkiYCxwIdEbET2R3aBwFfB74VEVOAJ4Ejh7sMMzNbfUX2IHaMiKeBA4ArgVcDh6/mckcCoyWNJOvG41Hg7cDFafpZaXlmZlaSIgVilKRRZB/Yl0bEi6uzwIhYBHwDWEBWGJYBc4CnImJlarYQmLg6yzEzs9WjiKjeQDoW+AxwB7Av2R7EuRHx1mEtUNoEuAT4d+Ap4KI0/KWI2C61mQRcGRFTc54/A5gB0N7ePq27u7vmMnsXLavZpn00LFlRvc3UieNqzqee+vr6aGtra+oya2l2pnptO/D2gzV3+3nb1TdTV1fXnIjoqNWuyI1yv4yI7/YPSFoAfHg1sr0D+EtEPJHm9zPgLcB4SSPTXsRWwOK8J0fEbGA2QEdHR3R2dtZcYK3feYDs9yBm9VZfHfMPrb2seurp6aHI62umZmeq17YDbz9Yc7eft105mYocYrqkciCyXY7aX9sHtwDYXdJGkkT2OxP3ANeR/RgRwHTg0tVYhpmZraZBy7ak1wKvB8ZJOrBi0lhe2XnfkETEnyRdTNYj7ErgNrI9giuAbklfTeNOG+4yzMxs9VXbr9sB2A8YD7y7YvwzwEdWZ6ER8SXgSwNGzwN2XZ35mplZ/QxaICLiUuBSSW+OiBubmMnMzFpAkZPUcyV9Fphc2T4iVudEtZmZtbgiBeJS4PfAb4FVjY1jZmatokiB2CgiPtPwJGZm1lKKXOZ6uaR9Gp7EzMxaSpEC8XGyIrFC0tOSnpH0dKODmZlZuYr8otyYZgRZ10wueIdprTtR58/ct16RzMxeoeqNchFxn6Rd8qZHxK2Ni2VmZmWrtgdxHFmneLNypgVZ99xmZraWqnaj3Iz0b1fz4piZWauoeQ4i/RbER4F/SaN6gP9d3d+FMDOz1lbkPogfAaOAH6bhw9O4oxoVyszMylekQLwpInauGL5W0h2NCmRmZq2hyH0QqyS9pn9A0ra4yw0zs7VekT2ITwPXSZoHCNga+FBDU5mZWemK3Ch3jaQpZL8PIeC+iHi+4cnMzKxURa5i2hA4Gvhnsvsffi/pxxHxXKPDmZlZeYocYjqb7FfkvpeGDwbOAd7fqFBmZla+IgVihwFXMV23ulcxSRoPnArsRLZX8mHgfuACsh8mmg98ICKeXJ3lmJnZ8BW5iuk2Sbv3D0jaDfjDai73O8CvI+K1wM7AvcCJwDURMQW4Jg2bmVlJihSI3YA/SpovaT5wI/A2Sb2S7hzqAiWNJbsr+zSAiHghIp4C9gfOSs3OAg4Y6rzNzKx+ihxi2rvOy9wWeAI4Q9LOwByy35xoj4hHASLiUUmb13m5ZmY2BIqI5i5Q6gBuAvaIiD9J+g7wNPCxiBhf0e7JiNgk5/kzyHqZpb29fVp3d3fNZfYuWlazTftoWLKiepupE8fVnE9RrZipiL6+Ptra2pq2vHqtJ1j711URa+r287arb6aurq45EdFRq10ZBeJVwE0RMTkNv5XsfMN2QGfae9gC6ImIHarNq6OjI2655Zaayyz64zyzeqvvUNXzx3laMVMRPT09dHZ2Nm159VpPsPavqyLW1O3nbVffTJIKFYgi5yDqKiIeAx6R1P/hvydwD3AZMD2Nmw5c2uxsZmb2siLnIBrhY8BPJa0PzCPrumM94EJJRwIL8H0WZmalKnIn9e5kN8m9DlgfGAEsj4ixw11oRNwO5O3e7DnceZqZWX0VOcT0fbK7px8ERpP9DsT3qj7DzMzWeIUOMUXEXEkjImIV2eWpf2xwLjMzK1mRAvFsOldwu6T/Bh4FNm5sLDMzK1uRQ0yHp3b/CSwHJgEHNjKUmZmVr0iBOCAinouIpyPiyxFxHLBfo4OZmVm5ihSI6TnjjqhzDjMzazGDnoOQdDBwCLCNpMsqJo0B/troYGZmVq5qJ6n/SHZCegIwq2L8M8CQe3E1M7M1y6AFIiIeBh4G3ty8OGZm1ipqnoOQtLukmyX1SXpB0ipJTzcjnJmZlcd3UpuZWS7fSW1mZrl8J7WZmeUa7p3U721kKDMzK1/NPYiIeFjSZunxlxsfyczMWsGgexDKnCxpKXAf8ICkJyR9sXnxzMysLNUOMX0C2AN4U0T8U0RsAuwG7CHpk01JZ2Zmpal2iOmDwF4RsbR/RETMk3QYcBXwrUaHM7PiJp94Rc02x09dyRE12s2fuW+9ItkartoexKjK4tAvIp4ARq3ugiWNkHSbpMvT8DaS/iTpQUkXpCunzMysJNUKxAvDnFbUx4F7K4a/DnwrIqYATwJH1mEZZmY2TNUKxM6Sns75ewaYujoLlbQVsC9wahoW8Hbg4tTkLOCA1VmGmZmtnmqd9Y1o4HK/DZxA1nU4wD8BT0XEyjS8EJjYwOWbmVkNiojmLlDaD9gnIo6W1Al8CvgQcGNEbJfaTAKujIh/2FORNAOYAdDe3j6tu7u75jJ7Fy2r2aZ9NCxZUb3N1Injas6nqFbMVERfXx9tbW1NW1691hN4XYHf50U1e9sVUc9MXV1dcyKio1a7Qn0x1dkewHsk7QNsCIwl26MYL2lk2ovYClic9+SImA3MBujo6IjOzs6aC6x11QZkV3fM6q2+OuYfWntZRbVipiJ6enooss7rpV7rCbyuwO/zopq97YooI1PTC0REnAScBNC/BxERh0q6CHgf0E32M6eXNjubWRFFLicFX1Jqa74ifTE1y2eA4yTNJTsncVrJeczM1mllHGL6u4joAXrS43nArmXmMTOzl7XSHoSZmbUQFwgzM8vlAmFmZrlcIMzMLJcLhJmZ5XKBMDOzXC4QZmaWywXCzMxyuUCYmVkuFwgzM8vlAmFmZrlcIMzMLJcLhJmZ5XKBMDOzXC4QZmaWywXCzMxyuUCYmVkuFwgzM8vlAmFmZrmaXiAkTZJ0naR7Jd0t6eNp/KaSrpb0YPp3k2ZnMzOzl5WxB7ESOD4iXgfsDhwjaUfgROCaiJgCXJOGzcysJE0vEBHxaETcmh4/A9wLTAT2B85Kzc4CDmh2NjMze5kioryFS5OB64GdgAURMb5i2pMR8Q+HmSTNAGYAtLe3T+vu7q65nN5Fy2q2aR8NS1ZUbzN14ria8ymqFTMV0dfXR1tbW9OWV6/1BPVbV0Uygd9T0JqZimj2+7yIembq6uqaExEdtdqVViAktQG/A06JiJ9JeqpIgajU0dERt9xyS81lTT7xipptjp+6klm9I6u2mT9z35rzKaoVMxXR09NDZ2dn05ZXr/UE9VtXRTKB31PQmpmKaPb7vIh6ZpJUqECUchWTpFHAJcBPI+JnafQSSVuk6VsAj5eRzczMMmVcxSTgNODeiPhmxaTLgOnp8XTg0mZnMzOzl9XeL6+/PYDDgV5Jt6dxnwVmAhdKOhJYALy/hGxmZpY0vUBExA2ABpm8ZzOzmJnZ4HwntZmZ5XKBMDOzXGWcg7AWVfSSxCNqtGv2JYlm1hjegzAzs1wuEGZmlssFwszMcrlAmJlZLhcIMzPL5QJhZma5XCDMzCyXC4SZmeVygTAzs1wuEGZmlstdbZiZlaxe3dxAfbu68R6EmZnlcoEwM7NcLhBmZpbL5yDMbJ3Sqsf7W1HL7UFI2lvS/ZLmSjqx7DxmZuuqlioQkkYAPwDeBewIHCxpx3JTmZmtm1qqQAC7AnMjYl5EvAB0A/uXnMnMbJ2kiCg7w99Jeh+wd0QclYYPB3aLiP+saDMDmJEGdwDur9PiJwBL6zSvenGmYloxE7RmLmcqZm3PtHVEbFarUaudpFbOuFdUsIiYDcyu+4KlWyKio97zXR3OVEwrZoLWzOVMxThTptUOMS0EJlUMbwUsLimLmdk6rdUKxM3AFEnbSFofOAi4rORMZmbrpJY6xBQRKyX9J/AbYARwekTc3aTF1/2wVR04UzGtmAlaM5czFeNMtNhJajMzax2tdojJzMxahAuEmZnlcoEwM7NcLhAtRNJrJe0pqW3A+L1LzLSrpDelxztKOk7SPmXlySPp7LIzVJL0z2k9vbPkHLtJGpsej5b0ZUm/lPR1SeNKynSspEm1WzaPpPUlfVDSO9LwIZK+L+kYSaNKzPUaSZ+S9B1JsyT9R7O3m09SDyDpQxFxRgnLPRY4BrgXeCPw8Yi4NE27NSJ2KSHTl8j6xRoJXA3sBvQA7wB+ExGnlJBp4GXPArqAawEi4j0lZPpzROyaHn+EbDv+HHgn8MuImNnsTCnL3cDO6erA2cCzwMXAnmn8gSVkWgYsBx4Czgcuiognmp1jQKafkr3HNwKeAtqAn5GtJ0XE9BIyHQu8G/gdsA9wO/Ak8G/A0REidasBAAAD20lEQVTR05QgEeG/ij9gQUnL7QXa0uPJwC1kRQLgthIzjSD7j/M0MDaNHw3cWVKmW4FzgU7gbenfR9Pjt5WU6baKxzcDm6XHGwO9ZWRKy7+3cr0NmHZ7WeuK7MjFO4HTgCeAXwPTgTElZboz/TsSWAKMSMMq8X3eW5FjI6AnPX51Mz8PWuo+iGaRdOdgk4D2ZmapMCIi+gAiYr6kTuBiSVuT3wVJM6yMiFXAs5IeioinU74Vkl4qKVMH8HHgc8CnI+J2SSsi4ncl5QFYT9ImZB98ivSNOCKWS1pZYq67KvaI75DUERG3SNoeeLGkTBERLwFXAVelQzjvAg4GvgHU7B+oAdZLN+ZuTPZhPA74G7ABUNohJrKCtSrlGAMQEQuaedhrnSwQZEXgX8l22SoJ+GPz4wDwmKQ3RsTtABHRJ2k/4HRgakmZXpC0UUQ8C0zrH5mOg5ZSINKHy7ckXZT+XUL57+NxwByy909IelVEPJbOJZVV3AGOAr4j6fNknbzdKOkR4JE0rQyvWB8R8SJZbwmXSRpdTiROA+4j21v+HHCRpHnA7mQ9SpfhVOBmSTcB/wJ8HUDSZmTFqynWyXMQkk4DzoiIG3KmnRcRh5SQaSuyb+yP5UzbIyL+UEKmDSLi+ZzxE4AtIqK32ZlysuwL7BERny07y0CSNgLaI+IvJecYA2xLVkgXRsSSErNsHxEPlLX8wUjaEiAiFksaT3aebUFE/LnETK8HXgfcFRH3lZJhXSwQZmZWmy9zNTOzXC4QZmaWywXCrCBJr5LULekhSfdIulLS9pLuKjubWSOUffWH2RpBkshufjsrIg5K495IeZdFmzWc9yDMiukCXoyIH/ePSJckP9I/LGmypN9LujX9vSWN30LS9ZJul3SXpLdKGiHpzDTcK+mTzX9JZtV5D8KsmJ3I7nWo5nFgr4h4TtIUsq4kOoBDSF2TSOq/M/2NwMSI2AkgXVpp1lJcIMzqZxTw/XToaRWwfRp/M3B6ugP2F+nu73nAtpK+B1xBdmexWUvxISazYu6m4m7yQXySrC+fncn2HNYHiIjrye6GXQScI+mDEfFkatdD1rnfqY2JbTZ8LhBmxVwLbJB6awUgdYO+dUWbccCjqTuQw8m6biD1p/V4RPyErFuHXdLd6OtFxCXAF4Cm99ZrVosPMZkVEBEh6d+Ab0s6EXgOmA98oqLZD4FLJL0fuI6sW2vIepz9tKQXgT7gg8BE4AxJ/V/STmr4izAbIne1YWZmuXyIyczMcrlAmJlZLhcIMzPL5QJhZma5XCDMzCyXC4SZmeVygTAzs1wuEGZmluv/AHkdg3o13MnIAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"my_colors = 'rgbkymc'\n",
"cv_class_distribution.plot(kind='bar')\n",
"plt.xlabel('Class')\n",
"plt.ylabel('Data points per Class')\n",
"plt.title('Distribution of yi in cross validation data')\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of data points in class 7 : 153 ( 28.759 %)\n",
"Number of data points in class 4 : 110 ( 20.677 %)\n",
"Number of data points in class 1 : 91 ( 17.105 %)\n",
"Number of data points in class 2 : 72 ( 13.534 %)\n",
"Number of data points in class 6 : 44 ( 8.271 %)\n",
"Number of data points in class 5 : 39 ( 7.331 %)\n",
"Number of data points in class 3 : 14 ( 2.632 %)\n",
"Number of data points in class 9 : 6 ( 1.128 %)\n",
"Number of data points in class 8 : 3 ( 0.564 %)\n"
]
}
],
"source": [
"sorted_yi = np.argsort(-train_class_distribution.values)\n",
"for i in sorted_yi:\n",
" 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), '%)')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now question is because we need log-loss as final evaluation metrics how do we say that model we are going to build will be good model. For doing this we will build a random model and will evaluate log loss. Our model should return lower log loss value than this.\n",
"\n",
"## Building a Random model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Ok, so we need to generate 9 random numbers because we have 9 class such that their sum must be equal to 1 because sum of Probablity of all 9 classes must be equivalent to 1."
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [],
"source": [
"test_data_len = test_df.shape[0]\n",
"cv_data_len = cv_df.shape[0]"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Log loss on Cross Validation Data using Random Model 2.4661836840027522\n"
]
}
],
"source": [
"# we create a output array that has exactly same size as the CV data\n",
"cv_predicted_y = np.zeros((cv_data_len,9))\n",
"for i in range(cv_data_len):\n",
" rand_probs = np.random.rand(1,9)\n",
" cv_predicted_y[i] = ((rand_probs/sum(sum(rand_probs)))[0])\n",
"print(\"Log loss on Cross Validation Data using Random Model\",log_loss(y_cv,cv_predicted_y, eps=1e-15))"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Log loss on Test Data using Random Model 2.4863899316645934\n"
]
}
],
"source": [
"#we create a output array that has exactly same as the test data\n",
"test_predicted_y = np.zeros((test_data_len,9))\n",
"for i in range(test_data_len):\n",
" rand_probs = np.random.rand(1,9)\n",
" test_predicted_y[i] = ((rand_probs/sum(sum(rand_probs)))[0])\n",
"print(\"Log loss on Test Data using Random Model\",log_loss(y_test,test_predicted_y, eps=1e-15))\n"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [],
"source": [
"# Lets get the index of max probablity\n",
"predicted_y =np.argmax(test_predicted_y, axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([6, 1, 7, 2, 8, 6, 7, 8, 1, 0, 3, 1, 0, 0, 4, 3, 2, 4, 6, 1, 3, 2,\n",
" 5, 2, 7, 2, 7, 0, 2, 5, 1, 2, 0, 1, 1, 6, 5, 1, 5, 0, 6, 6, 7, 3,\n",
" 1, 3, 1, 5, 1, 0, 3, 7, 4, 5, 4, 2, 2, 2, 6, 6, 5, 5, 3, 0, 1, 8,\n",
" 6, 8, 8, 1, 7, 8, 6, 0, 2, 3, 2, 3, 2, 7, 1, 7, 6, 4, 1, 3, 6, 1,\n",
" 8, 8, 4, 2, 2, 5, 3, 2, 8, 0, 1, 4, 2, 2, 2, 5, 8, 8, 5, 5, 4, 2,\n",
" 6, 1, 4, 4, 8, 6, 3, 6, 2, 0, 4, 6, 4, 3, 4, 2, 3, 5, 5, 2, 5, 8,\n",
" 0, 8, 7, 6, 6, 2, 4, 4, 2, 4, 6, 6, 3, 0, 5, 4, 8, 1, 2, 7, 6, 2,\n",
" 8, 0, 8, 7, 5, 5, 6, 8, 6, 4, 0, 8, 2, 6, 7, 0, 1, 2, 3, 1, 0, 2,\n",
" 8, 1, 3, 4, 4, 8, 1, 0, 6, 6, 3, 5, 1, 0, 6, 2, 4, 4, 1, 2, 4, 3,\n",
" 8, 8, 3, 3, 0, 8, 4, 4, 2, 3, 0, 4, 1, 6, 5, 8, 6, 0, 5, 6, 1, 5,\n",
" 8, 0, 6, 8, 5, 1, 4, 4, 0, 8, 4, 7, 6, 1, 2, 1, 6, 7, 1, 2, 2, 0,\n",
" 8, 0, 3, 0, 3, 1, 3, 8, 0, 5, 4, 8, 2, 2, 4, 3, 2, 6, 8, 5, 7, 2,\n",
" 2, 4, 4, 2, 3, 2, 3, 1, 3, 0, 1, 1, 6, 7, 3, 5, 4, 6, 4, 3, 1, 2,\n",
" 2, 8, 4, 3, 8, 5, 4, 6, 7, 5, 0, 3, 8, 7, 3, 7, 2, 6, 3, 2, 0, 7,\n",
" 6, 3, 6, 8, 7, 3, 3, 4, 7, 1, 4, 0, 5, 3, 6, 1, 7, 4, 8, 5, 5, 8,\n",
" 0, 7, 7, 2, 1, 3, 4, 3, 4, 6, 8, 1, 0, 0, 1, 2, 7, 6, 7, 8, 3, 4,\n",
" 4, 3, 6, 8, 0, 2, 5, 2, 1, 4, 4, 5, 4, 7, 7, 2, 5, 2, 5, 4, 5, 0,\n",
" 3, 5, 7, 5, 0, 6, 6, 8, 6, 5, 8, 4, 8, 5, 4, 1, 1, 2, 4, 8, 3, 1,\n",
" 6, 1, 5, 5, 1, 6, 7, 6, 3, 1, 3, 5, 5, 0, 0, 8, 3, 8, 8, 3, 8, 2,\n",
" 3, 2, 6, 3, 2, 3, 2, 3, 3, 3, 2, 8, 6, 4, 3, 4, 1, 3, 6, 2, 2, 6,\n",
" 4, 2, 7, 1, 2, 2, 3, 7, 3, 6, 6, 2, 0, 2, 6, 3, 4, 8, 8, 4, 7, 5,\n",
" 5, 3, 7, 7, 0, 4, 0, 5, 3, 4, 3, 7, 8, 5, 7, 1, 0, 1, 2, 3, 1, 2,\n",
" 8, 3, 5, 7, 7, 4, 1, 0, 5, 7, 3, 3, 3, 7, 0, 1, 2, 1, 6, 4, 6, 2,\n",
" 4, 4, 7, 0, 8, 2, 8, 6, 8, 7, 5, 8, 2, 4, 3, 4, 7, 7, 3, 1, 0, 0,\n",
" 7, 8, 4, 1, 8, 3, 3, 4, 2, 6, 4, 4, 4, 4, 4, 1, 4, 4, 4, 3, 3, 1,\n",
" 1, 3, 7, 1, 2, 6, 6, 6, 6, 5, 1, 6, 1, 1, 6, 8, 5, 2, 8, 2, 5, 2,\n",
" 4, 5, 2, 1, 2, 4, 5, 6, 5, 5, 0, 7, 5, 3, 6, 3, 5, 0, 0, 1, 2, 1,\n",
" 1, 3, 1, 6, 0, 3, 5, 4, 7, 3, 6, 7, 8, 3, 6, 3, 2, 7, 4, 1, 2, 6,\n",
" 3, 0, 2, 7, 4, 4, 0, 6, 0, 0, 2, 1, 1, 8, 5, 8, 8, 3, 3, 6, 3, 2,\n",
" 0, 0, 1, 7, 3, 1, 2, 3, 7, 2, 2, 0, 7, 0, 8, 0, 1, 1, 5, 5, 5, 5,\n",
" 0, 1, 0, 3, 6], dtype=int64)"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Lets see the output. these will be 665 values present in test dataset\n",
"predicted_y"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So you can see the index value ranging from 0 to 8. So, lets make it as 1 to 9 we will increase this value by 1."
]
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [],
"source": [
"predicted_y = predicted_y + 1"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Confusion Matrix"
]
},
{
"cell_type": "code",
"execution_count": 46,
"metadata": {},
"outputs": [],
"source": [
"C = confusion_matrix(y_test, predicted_y)\n"
]
},
{
"cell_type": "code",
"execution_count": 47,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1440x504 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"labels = [1,2,3,4,5,6,7,8,9]\n",
"plt.figure(figsize=(20,7))\n",
"sns.heatmap(C, annot=True, cmap=\"YlGnBu\", fmt=\".3f\", xticklabels=labels, yticklabels=labels)\n",
"plt.xlabel('Predicted Class')\n",
"plt.ylabel('Original Class')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Precision matrix"
]
},
{
"cell_type": "code",
"execution_count": 48,
"metadata": {},
"outputs": [],
"source": [
"B =(C/C.sum(axis=0))"
]
},
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x504 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(20,7))\n",
"sns.heatmap(B, annot=True, cmap=\"YlGnBu\", fmt=\".3f\", xticklabels=labels, yticklabels=labels)\n",
"plt.xlabel('Predicted Class')\n",
"plt.ylabel('Original Class')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Recall matrix"
]
},
{
"cell_type": "code",
"execution_count": 50,
"metadata": {},
"outputs": [],
"source": [
"A =(((C.T)/(C.sum(axis=1))).T)"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x504 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(20,7))\n",
"sns.heatmap(A, annot=True, cmap=\"YlGnBu\", fmt=\".3f\", xticklabels=labels, yticklabels=labels)\n",
"plt.xlabel('Predicted Class')\n",
"plt.ylabel('Original Class')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Evaluating Gene Column\n",
"\n",
"Now we will look at each independent column to make sure its relavent for my target variable but the question is, how? Let's understand with our first column Gene which is categorial in nature.\n",
"\n",
"So, lets explore column ***Gene*** and lets look at its distribution. "
]
},
{
"cell_type": "code",
"execution_count": 52,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of Unique Genes : 237\n",
"BRCA1 166\n",
"TP53 89\n",
"EGFR 79\n",
"BRCA2 78\n",
"PTEN 71\n",
"KIT 67\n",
"BRAF 57\n",
"ERBB2 46\n",
"CDKN2A 40\n",
"PDGFRA 39\n",
"Name: Gene, dtype: int64\n"
]
}
],
"source": [
"unique_genes = train_df['Gene'].value_counts()\n",
"print('Number of Unique Genes :', unique_genes.shape[0])\n",
"# the top 10 genes that occured most\n",
"print(unique_genes.head(10))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets see the number of unique values present in gene"
]
},
{
"cell_type": "code",
"execution_count": 53,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"237"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_genes.shape[0]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets look at the comulative distribution of unique Genes values"
]
},
{
"cell_type": "code",
"execution_count": 54,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"s = sum(unique_genes.values);\n",
"h = unique_genes.values/s;\n",
"c = np.cumsum(h)\n",
"plt.plot(c,label='Cumulative distribution of Genes')\n",
"plt.grid()\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So, now we need to convert these categorical variable to appropirate format which my machine learning algorithm will be able to take as an input.\n",
"\n",
"So we have 2 techniques to deal with it. \n",
"\n",
"<ol><li>\n",
" ***One-hot encoding*** </li>\n",
" <li> ***Response Encoding*** </li>\n",
"</ol>\n",
"\n",
"Let's use both of them to see which one work the best. So lets start encoding using one hot encoder"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [],
"source": [
"# one-hot encoding of Gene feature.\n",
"gene_vectorizer = CountVectorizer()\n",
"train_gene_feature_onehotCoding = gene_vectorizer.fit_transform(train_df['Gene'])\n",
"test_gene_feature_onehotCoding = gene_vectorizer.transform(test_df['Gene'])\n",
"cv_gene_feature_onehotCoding = gene_vectorizer.transform(cv_df['Gene'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's check the number of column generated after one hot encoding. One hot encoding will always return higher number of column."
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(2124, 236)"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_gene_feature_onehotCoding.shape"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['abl1',\n",
" 'acvr1',\n",
" 'ago2',\n",
" 'akt1',\n",
" 'akt2',\n",
" 'akt3',\n",
" 'alk',\n",
" 'apc',\n",
" 'ar',\n",
" 'araf',\n",
" 'arid1a',\n",
" 'arid1b',\n",
" 'arid2',\n",
" 'arid5b',\n",
" 'asxl2',\n",
" 'atm',\n",
" 'atr',\n",
" 'aurka',\n",
" 'axin1',\n",
" 'b2m',\n",
" 'bap1',\n",
" 'bcl10',\n",
" 'bcl2',\n",
" 'bcl2l11',\n",
" 'bcor',\n",
" 'braf',\n",
" 'brca1',\n",
" 'brca2',\n",
" 'brd4',\n",
" 'brip1',\n",
" 'btk',\n",
" 'card11',\n",
" 'carm1',\n",
" 'casp8',\n",
" 'cbl',\n",
" 'ccnd1',\n",
" 'ccnd2',\n",
" 'ccnd3',\n",
" 'cdh1',\n",
" 'cdk12',\n",
" 'cdk4',\n",
" 'cdk6',\n",
" 'cdk8',\n",
" 'cdkn1a',\n",
" 'cdkn1b',\n",
" 'cdkn2a',\n",
" 'cdkn2b',\n",
" 'cebpa',\n",
" 'chek2',\n",
" 'cic',\n",
" 'crebbp',\n",
" 'ctcf',\n",
" 'ctla4',\n",
" 'ctnnb1',\n",
" 'ddr2',\n",
" 'dicer1',\n",
" 'dnmt3a',\n",
" 'dusp4',\n",
" 'egfr',\n",
" 'eif1ax',\n",
" 'elf3',\n",
" 'ep300',\n",
" 'epas1',\n",
" 'erbb2',\n",
" 'erbb3',\n",
" 'erbb4',\n",
" 'ercc2',\n",
" 'ercc3',\n",
" 'ercc4',\n",
" 'erg',\n",
" 'esr1',\n",
" 'etv1',\n",
" 'etv6',\n",
" 'ewsr1',\n",
" 'ezh2',\n",
" 'fam58a',\n",
" 'fanca',\n",
" 'fancc',\n",
" 'fat1',\n",
" 'fbxw7',\n",
" 'fgf19',\n",
" 'fgfr1',\n",
" 'fgfr2',\n",
" 'fgfr3',\n",
" 'fgfr4',\n",
" 'flt1',\n",
" 'flt3',\n",
" 'foxa1',\n",
" 'foxl2',\n",
" 'foxo1',\n",
" 'foxp1',\n",
" 'fubp1',\n",
" 'gata3',\n",
" 'gnaq',\n",
" 'gnas',\n",
" 'h3f3a',\n",
" 'hist1h1c',\n",
" 'hla',\n",
" 'hnf1a',\n",
" 'hras',\n",
" 'idh1',\n",
" 'idh2',\n",
" 'igf1r',\n",
" 'ikbke',\n",
" 'ikzf1',\n",
" 'il7r',\n",
" 'inpp4b',\n",
" 'jak1',\n",
" 'jak2',\n",
" 'jun',\n",
" 'kdm5a',\n",
" 'kdm5c',\n",
" 'kdm6a',\n",
" 'kdr',\n",
" 'keap1',\n",
" 'kit',\n",
" 'klf4',\n",
" 'kmt2a',\n",
" 'kmt2c',\n",
" 'kmt2d',\n",
" 'knstrn',\n",
" 'kras',\n",
" 'map2k1',\n",
" 'map2k2',\n",
" 'map2k4',\n",
" 'map3k1',\n",
" 'mapk1',\n",
" 'mdm4',\n",
" 'med12',\n",
" 'mef2b',\n",
" 'met',\n",
" 'mga',\n",
" 'mlh1',\n",
" 'mpl',\n",
" 'msh2',\n",
" 'msh6',\n",
" 'mtor',\n",
" 'myc',\n",
" 'mycn',\n",
" 'myd88',\n",
" 'ncor1',\n",
" 'nf1',\n",
" 'nf2',\n",
" 'nfe2l2',\n",
" 'nfkbia',\n",
" 'nkx2',\n",
" 'notch1',\n",
" 'npm1',\n",
" 'nras',\n",
" 'nsd1',\n",
" 'ntrk1',\n",
" 'ntrk2',\n",
" 'ntrk3',\n",
" 'nup93',\n",
" 'pak1',\n",
" 'pax8',\n",
" 'pbrm1',\n",
" 'pdgfra',\n",
" 'pdgfrb',\n",
" 'pik3ca',\n",
" 'pik3cb',\n",
" 'pik3cd',\n",
" 'pik3r1',\n",
" 'pik3r2',\n",
" 'pik3r3',\n",
" 'pim1',\n",
" 'pms1',\n",
" 'pms2',\n",
" 'pole',\n",
" 'ppm1d',\n",
" 'ppp2r1a',\n",
" 'ppp6c',\n",
" 'prdm1',\n",
" 'ptch1',\n",
" 'pten',\n",
" 'ptpn11',\n",
" 'ptprd',\n",
" 'ptprt',\n",
" 'rac1',\n",
" 'rad21',\n",
" 'rad50',\n",
" 'rad51b',\n",
" 'rad51c',\n",
" 'rad51d',\n",
" 'rad54l',\n",
" 'raf1',\n",
" 'rara',\n",
" 'rasa1',\n",
" 'rb1',\n",
" 'rbm10',\n",
" 'ret',\n",
" 'rheb',\n",
" 'rhoa',\n",
" 'rictor',\n",
" 'rit1',\n",
" 'rnf43',\n",
" 'ros1',\n",
" 'rras2',\n",
" 'runx1',\n",
" 'rxra',\n",
" 'sdhb',\n",
" 'setd2',\n",
" 'sf3b1',\n",
" 'shoc2',\n",
" 'shq1',\n",
" 'smad2',\n",
" 'smad3',\n",
" 'smad4',\n",
" 'smarca4',\n",
" 'smarcb1',\n",
" 'smo',\n",
" 'sos1',\n",
" 'sox9',\n",
" 'spop',\n",
" 'src',\n",
" 'srsf2',\n",
" 'stag2',\n",
" 'stat3',\n",
" 'stk11',\n",
" 'tert',\n",
" 'tet1',\n",
" 'tet2',\n",
" 'tgfbr1',\n",
" 'tgfbr2',\n",
" 'tmprss2',\n",
" 'tp53',\n",
" 'tp53bp1',\n",
" 'tsc1',\n",
" 'tsc2',\n",
" 'u2af1',\n",
" 'vhl',\n",
" 'whsc1',\n",
" 'whsc1l1',\n",
" 'xpo1',\n",
" 'xrcc2',\n",
" 'yap1']"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#column names after one-hot encoding for Gene column\n",
"gene_vectorizer.get_feature_names()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, lets also create Response encoding columns for Gene column"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [],
"source": [
"def get_gv_fea_dict(alpha, feature, df):\n",
" value_count = train_df[feature].value_counts()\n",
" gv_dict = dict()\n",
" for i, denominator in value_count.items():\n",
" vec = []\n",
" for k in range(1,10):\n",
" cls_cnt = train_df.loc[(train_df['Class']==k) & (train_df[feature]==i)]\n",
" vec.append((cls_cnt.shape[0] + alpha*10)/ (denominator + 90*alpha))\n",
" gv_dict[i]=vec\n",
" return gv_dict\n",
"\n",
"# Get Gene variation feature\n",
"def get_gv_feature(alpha, feature, df):\n",
" gv_dict = get_gv_fea_dict(alpha, feature, df)\n",
" value_count = train_df[feature].value_counts()\n",
" gv_fea = []\n",
" for index, row in df.iterrows():\n",
" if row[feature] in dict(value_count).keys():\n",
" gv_fea.append(gv_dict[row[feature]])\n",
" else:\n",
" gv_fea.append([1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9,1/9])\n",
" return gv_fea"
]
},
{
"cell_type": "code",
"execution_count": 62,
"metadata": {},
"outputs": [],
"source": [
"#response-coding of the Gene feature\n",
"# alpha is used for laplace smoothing\n",
"alpha = 1\n",
"# train gene feature\n",
"train_gene_feature_responseCoding = np.array(get_gv_feature(alpha, \"Gene\", train_df))\n",
"# test gene feature\n",
"test_gene_feature_responseCoding = np.array(get_gv_feature(alpha, \"Gene\", test_df))\n",
"# cross validation gene feature\n",
"cv_gene_feature_responseCoding = np.array(get_gv_feature(alpha, \"Gene\", cv_df))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's look at columns after applying response encoding. We must be having 9 columns for Gene column after response encoding."
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(2124, 9)"
]
},
"execution_count": 63,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_gene_feature_responseCoding.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, question is how good is Gene column feature to predict my 9 classes. One idea could be that we will build model having only gene column with one hot encoder with simple model like Logistic regression. If log loss with only one column Gene comes out to be better than random model, than this feature is important."
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [],
"source": [
"# We need a hyperparemeter for SGD classifier.\n",
"alpha = [10 ** x for x in range(-5, 1)]"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"For values of alpha = 1e-05 The log loss is: 1.4401833474173025\n",
"For values of alpha = 0.0001 The log loss is: 1.2188767582959434\n",
"For values of alpha = 0.001 The log loss is: 1.229607393327489\n",
"For values of alpha = 0.01 The log loss is: 1.342428066028162\n",
"For values of alpha = 0.1 The log loss is: 1.438846650223064\n",
"For values of alpha = 1 The log loss is: 1.4745241197299315\n"
]
}
],
"source": [
"# We will be using SGD classifier\n",
"cv_log_error_array=[]\n",
"for i in alpha:\n",
" clf = SGDClassifier(alpha=i, penalty='l2', loss='log', random_state=42)\n",
" clf.fit(train_gene_feature_onehotCoding, y_train)\n",
" sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
" sig_clf.fit(train_gene_feature_onehotCoding, y_train)\n",
" predict_y = sig_clf.predict_proba(cv_gene_feature_onehotCoding)\n",
" cv_log_error_array.append(log_loss(y_cv, predict_y, labels=clf.classes_, eps=1e-15))\n",
" print('For values of alpha = ', i, \"The log loss is:\",log_loss(y_cv, predict_y, labels=clf.classes_, eps=1e-15))\n"
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Lets plot the same to check the best Alpha value\n",
"fig, ax = plt.subplots()\n",
"ax.plot(alpha, cv_log_error_array,c='g')\n",
"for i, txt in enumerate(np.round(cv_log_error_array,3)):\n",
" ax.annotate((alpha[i],np.round(txt,3)), (alpha[i],cv_log_error_array[i]))\n",
"plt.grid()\n",
"plt.title(\"Cross Validation Error for each alpha\")\n",
"plt.xlabel(\"Alpha i's\")\n",
"plt.ylabel(\"Error measure\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"For values of best alpha = 0.0001 The train log loss is: 1.0577218419057752\n",
"For values of best alpha = 0.0001 The cross validation log loss is: 1.2188767582959434\n",
"For values of best alpha = 0.0001 The test log loss is: 1.1850964820703247\n"
]
}
],
"source": [
"# Lets use best alpha value as we can see from above graph and compute log loss\n",
"best_alpha = np.argmin(cv_log_error_array)\n",
"clf = SGDClassifier(alpha=alpha[best_alpha], penalty='l2', loss='log', random_state=42)\n",
"clf.fit(train_gene_feature_onehotCoding, y_train)\n",
"sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
"sig_clf.fit(train_gene_feature_onehotCoding, y_train)\n",
"\n",
"predict_y = sig_clf.predict_proba(train_gene_feature_onehotCoding)\n",
"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))\n",
"predict_y = sig_clf.predict_proba(cv_gene_feature_onehotCoding)\n",
"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))\n",
"predict_y = sig_clf.predict_proba(test_gene_feature_onehotCoding)\n",
"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))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now lets check how many values are overlapping between train, test or between CV and train"
]
},
{
"cell_type": "code",
"execution_count": 68,
"metadata": {},
"outputs": [],
"source": [
"test_coverage=test_df[test_df['Gene'].isin(list(set(train_df['Gene'])))].shape[0]\n",
"cv_coverage=cv_df[cv_df['Gene'].isin(list(set(train_df['Gene'])))].shape[0]"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1. In test data 645 out of 665 : 96.99248120300751\n",
"2. In cross validation data 520 out of 532 : 97.74436090225564\n"
]
}
],
"source": [
"print('1. In test data',test_coverage, 'out of',test_df.shape[0], \":\",(test_coverage/test_df.shape[0])*100)\n",
"print('2. In cross validation data',cv_coverage, 'out of ',cv_df.shape[0],\":\" ,(cv_coverage/cv_df.shape[0])*100)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Evaluating Variation column"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Variation is also a categorical variable so we have to deal in same way like we have done for ***Gene*** column. We will again get the one hot encoder and response enoding variable for variation column."
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Number of Unique Variations : 1932\n",
"Truncating_Mutations 62\n",
"Deletion 47\n",
"Amplification 44\n",
"Fusions 23\n",
"T58I 3\n",
"Overexpression 3\n",
"G12V 3\n",
"T73I 2\n",
"G12C 2\n",
"Q61R 2\n",
"Name: Variation, dtype: int64\n"
]
}
],
"source": [
"unique_variations = train_df['Variation'].value_counts()\n",
"print('Number of Unique Variations :', unique_variations.shape[0])\n",
"# the top 10 variations that occured most\n",
"print(unique_variations.head(10))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets look at the comulative distribution of unique ***variation*** values"
]
},
{
"cell_type": "code",
"execution_count": 71,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0.02919021 0.05131827 0.0720339 ... 0.99905838 0.99952919 1. ]\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"s = sum(unique_variations.values);\n",
"h = unique_variations.values/s;\n",
"c = np.cumsum(h)\n",
"print(c)\n",
"plt.plot(c,label='Cumulative distribution of Variations')\n",
"plt.grid()\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets convert the variation column using one hot encoder column"
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [],
"source": [
"# one-hot encoding of variation feature.\n",
"variation_vectorizer = CountVectorizer()\n",
"train_variation_feature_onehotCoding = variation_vectorizer.fit_transform(train_df['Variation'])\n",
"test_variation_feature_onehotCoding = variation_vectorizer.transform(test_df['Variation'])\n",
"cv_variation_feature_onehotCoding = variation_vectorizer.transform(cv_df['Variation'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets look at shape of one hot encoder column for variation"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(2124, 1965)"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_variation_feature_onehotCoding.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets do the same for variation column and generate response encoding for the same."
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [],
"source": [
"# alpha is used for laplace smoothing\n",
"alpha = 1\n",
"# train gene feature\n",
"train_variation_feature_responseCoding = np.array(get_gv_feature(alpha, \"Variation\", train_df))\n",
"# test gene feature\n",
"test_variation_feature_responseCoding = np.array(get_gv_feature(alpha, \"Variation\", test_df))\n",
"# cross validation gene feature\n",
"cv_variation_feature_responseCoding = np.array(get_gv_feature(alpha, \"Variation\", cv_df))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets look at the shape of this response encoding result"
]
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(2124, 9)"
]
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train_variation_feature_responseCoding.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets again build the model with only column name of ***variation*** column"
]
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [],
"source": [
"# We need a hyperparemeter for SGD classifier.\n",
"alpha = [10 ** x for x in range(-5, 1)]"
]
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"For values of alpha = 1e-05 The log loss is: 1.70313977051604\n",
"For values of alpha = 0.0001 The log loss is: 1.700413046723949\n",
"For values of alpha = 0.001 The log loss is: 1.7061607868280722\n",
"For values of alpha = 0.01 The log loss is: 1.7116356597023152\n",
"For values of alpha = 0.1 The log loss is: 1.7142090078997774\n",
"For values of alpha = 1 The log loss is: 1.7160127673699193\n"
]
}
],
"source": [
"# We will be using SGD classifier\n",
"cv_log_error_array=[]\n",
"for i in alpha:\n",
" clf = SGDClassifier(alpha=i, penalty='l2', loss='log', random_state=42)\n",
" clf.fit(train_variation_feature_onehotCoding, y_train)\n",
" \n",
" sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
" sig_clf.fit(train_variation_feature_onehotCoding, y_train)\n",
" predict_y = sig_clf.predict_proba(cv_variation_feature_onehotCoding)\n",
" \n",
" cv_log_error_array.append(log_loss(y_cv, predict_y, labels=clf.classes_, eps=1e-15))\n",
" print('For values of alpha = ', i, \"The log loss is:\",log_loss(y_cv, predict_y, labels=clf.classes_, eps=1e-15))"
]
},
{
"cell_type": "code",
"execution_count": 78,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Lets plot the same to check the best Alpha value\n",
"fig, ax = plt.subplots()\n",
"ax.plot(alpha, cv_log_error_array,c='g')\n",
"for i, txt in enumerate(np.round(cv_log_error_array,3)):\n",
" ax.annotate((alpha[i],np.round(txt,3)), (alpha[i],cv_log_error_array[i]))\n",
"plt.grid()\n",
"plt.title(\"Cross Validation Error for each alpha\")\n",
"plt.xlabel(\"Alpha i's\")\n",
"plt.ylabel(\"Error measure\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 79,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"For values of best alpha = 0.0001 The train log loss is: 0.6986288720503351\n",
"For values of best alpha = 0.0001 The cross validation log loss is: 1.700413046723949\n",
"For values of best alpha = 0.0001 The test log loss is: 1.7200311395645975\n"
]
}
],
"source": [
"best_alpha = np.argmin(cv_log_error_array)\n",
"clf = SGDClassifier(alpha=alpha[best_alpha], penalty='l2', loss='log', random_state=42)\n",
"clf.fit(train_variation_feature_onehotCoding, y_train)\n",
"sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
"sig_clf.fit(train_variation_feature_onehotCoding, y_train)\n",
"\n",
"predict_y = sig_clf.predict_proba(train_variation_feature_onehotCoding)\n",
"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))\n",
"predict_y = sig_clf.predict_proba(cv_variation_feature_onehotCoding)\n",
"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))\n",
"predict_y = sig_clf.predict_proba(test_variation_feature_onehotCoding)\n",
"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))"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {},
"outputs": [],
"source": [
"test_coverage=test_df[test_df['Variation'].isin(list(set(train_df['Variation'])))].shape[0]\n",
"cv_coverage=cv_df[cv_df['Variation'].isin(list(set(train_df['Variation'])))].shape[0]"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1. In test data 69 out of 665 : 10.37593984962406\n",
"2. In cross validation data 58 out of 532 : 10.902255639097744\n"
]
}
],
"source": [
"print('1. In test data',test_coverage, 'out of',test_df.shape[0], \":\",(test_coverage/test_df.shape[0])*100)\n",
"print('2. In cross validation data',cv_coverage, 'out of ',cv_df.shape[0],\":\" ,(cv_coverage/cv_df.shape[0])*100)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Evaluating Text column"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [],
"source": [
"def extract_dictionary_paddle(cls_text):\n",
" dictionary = defaultdict(int)\n",
" for index, row in cls_text.iterrows():\n",
" for word in row['TEXT'].split():\n",
" dictionary[word] +=1\n",
" return dictionary"
]
},
{
"cell_type": "code",
"execution_count": 83,
"metadata": {},
"outputs": [],
"source": [
"import math\n",
"def get_text_responsecoding(df):\n",
" text_feature_responseCoding = np.zeros((df.shape[0],9))\n",
" for i in range(0,9):\n",
" row_index = 0\n",
" for index, row in df.iterrows():\n",
" sum_prob = 0\n",
" for word in row['TEXT'].split():\n",
" sum_prob += math.log(((dict_list[i].get(word,0)+10 )/(total_dict.get(word,0)+90)))\n",
" text_feature_responseCoding[row_index][i] = math.exp(sum_prob/len(row['TEXT'].split()))\n",
" row_index += 1\n",
" return text_feature_responseCoding"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total number of unique words in train data : 54911\n"
]
}
],
"source": [
"# building a CountVectorizer with all the words that occured minimum 3 times in train data\n",
"text_vectorizer = CountVectorizer(min_df=3)\n",
"train_text_feature_onehotCoding = text_vectorizer.fit_transform(train_df['TEXT'])\n",
"# getting all the feature names (words)\n",
"train_text_features= text_vectorizer.get_feature_names()\n",
"\n",
"train_text_fea_counts = train_text_feature_onehotCoding.sum(axis=0).A1\n",
"\n",
"text_fea_dict = dict(zip(list(train_text_features),train_text_fea_counts))\n",
"\n",
"\n",
"print(\"Total number of unique words in train data :\", len(train_text_features))"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [],
"source": [
"dict_list = []\n",
"# dict_list =[] contains 9 dictoinaries each corresponds to a class\n",
"for i in range(1,10):\n",
" cls_text = train_df[train_df['Class']==i]\n",
" dict_list.append(extract_dictionary_paddle(cls_text))\n",
"\n",
"total_dict = extract_dictionary_paddle(train_df)\n",
"\n",
"\n",
"confuse_array = []\n",
"for i in train_text_features:\n",
" ratios = []\n",
" max_val = -1\n",
" for j in range(0,9):\n",
" ratios.append((dict_list[j][i]+10 )/(total_dict[i]+90))\n",
" confuse_array.append(ratios)\n",
"confuse_array = np.array(confuse_array)"
]
},
{
"cell_type": "code",
"execution_count": 86,
"metadata": {},
"outputs": [],
"source": [
"#response coding of text features\n",
"train_text_feature_responseCoding = get_text_responsecoding(train_df)\n",
"test_text_feature_responseCoding = get_text_responsecoding(test_df)\n",
"cv_text_feature_responseCoding = get_text_responsecoding(cv_df)"
]
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [],
"source": [
"# we convert each row values such that they sum to 1 \n",
"train_text_feature_responseCoding = (train_text_feature_responseCoding.T/train_text_feature_responseCoding.sum(axis=1)).T\n",
"test_text_feature_responseCoding = (test_text_feature_responseCoding.T/test_text_feature_responseCoding.sum(axis=1)).T\n",
"cv_text_feature_responseCoding = (cv_text_feature_responseCoding.T/cv_text_feature_responseCoding.sum(axis=1)).T"
]
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [],
"source": [
"# don't forget to normalize every feature\n",
"train_text_feature_onehotCoding = normalize(train_text_feature_onehotCoding, axis=0)\n",
"\n",
"# we use the same vectorizer that was trained on train data\n",
"test_text_feature_onehotCoding = text_vectorizer.transform(test_df['TEXT'])\n",
"test_text_feature_onehotCoding = normalize(test_text_feature_onehotCoding, axis=0)\n",
"\n",
"# we use the same vectorizer that was trained on train data\n",
"cv_text_feature_onehotCoding = text_vectorizer.transform(cv_df['TEXT'])\n",
"cv_text_feature_onehotCoding = normalize(cv_text_feature_onehotCoding, axis=0)"
]
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [],
"source": [
"sorted_text_fea_dict = dict(sorted(text_fea_dict.items(), key=lambda x: x[1] , reverse=True))\n",
"sorted_text_occur = np.array(list(sorted_text_fea_dict.values()))"
]
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
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1, 733: 1, 730: 1, 725: 1, 722: 1, 721: 1, 718: 1, 716: 1, 710: 1, 706: 1, 701: 1, 674: 1, 671: 1, 666: 1, 663: 1, 658: 1, 644: 1, 641: 1, 620: 1, 603: 1, 594: 1, 590: 1, 569: 1, 545: 1, 535: 1, 505: 1, 504: 1, 501: 1, 477: 1})\n"
]
}
],
"source": [
"# Number of words for a given frequency.\n",
"print(Counter(sorted_text_occur))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets build the model with only ***text*** column"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"For values of alpha = 1e-05 The log loss is: 1.41844647730872\n",
"For values of alpha = 0.0001 The log loss is: 1.4032643829006644\n",
"For values of alpha = 0.001 The log loss is: 1.2472606448726806\n",
"For values of alpha = 0.01 The log loss is: 1.373599291953443\n",
"For values of alpha = 0.1 The log loss is: 1.4989877718907707\n",
"For values of alpha = 1 The log loss is: 1.670050102338605\n"
]
}
],
"source": [
"cv_log_error_array=[]\n",
"for i in alpha:\n",
" clf = SGDClassifier(alpha=i, penalty='l2', loss='log', random_state=42)\n",
" clf.fit(train_text_feature_onehotCoding, y_train)\n",
" \n",
" sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
" sig_clf.fit(train_text_feature_onehotCoding, y_train)\n",
" predict_y = sig_clf.predict_proba(cv_text_feature_onehotCoding)\n",
" cv_log_error_array.append(log_loss(y_cv, predict_y, labels=clf.classes_, eps=1e-15))\n",
" print('For values of alpha = ', i, \"The log loss is:\",log_loss(y_cv, predict_y, labels=clf.classes_, eps=1e-15))\n"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots()\n",
"ax.plot(alpha, cv_log_error_array,c='g')\n",
"for i, txt in enumerate(np.round(cv_log_error_array,3)):\n",
" ax.annotate((alpha[i],np.round(txt,3)), (alpha[i],cv_log_error_array[i]))\n",
"plt.grid()\n",
"plt.title(\"Cross Validation Error for each alpha\")\n",
"plt.xlabel(\"Alpha i's\")\n",
"plt.ylabel(\"Error measure\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"For values of best alpha = 0.001 The train log loss is: 0.7930034992633752\n",
"For values of best alpha = 0.001 The cross validation log loss is: 1.2472606448726806\n",
"For values of best alpha = 0.001 The test log loss is: 1.1086428559963621\n"
]
}
],
"source": [
"best_alpha = np.argmin(cv_log_error_array)\n",
"clf = SGDClassifier(alpha=alpha[best_alpha], penalty='l2', loss='log', random_state=42)\n",
"clf.fit(train_text_feature_onehotCoding, y_train)\n",
"sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
"sig_clf.fit(train_text_feature_onehotCoding, y_train)\n",
"\n",
"predict_y = sig_clf.predict_proba(train_text_feature_onehotCoding)\n",
"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))\n",
"predict_y = sig_clf.predict_proba(cv_text_feature_onehotCoding)\n",
"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))\n",
"predict_y = sig_clf.predict_proba(test_text_feature_onehotCoding)\n",
"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))\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets check the overlap of text data"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {},
"outputs": [],
"source": [
"def get_intersec_text(df):\n",
" df_text_vec = CountVectorizer(min_df=3)\n",
" df_text_fea = df_text_vec.fit_transform(df['TEXT'])\n",
" df_text_features = df_text_vec.get_feature_names()\n",
"\n",
" df_text_fea_counts = df_text_fea.sum(axis=0).A1\n",
" df_text_fea_dict = dict(zip(list(df_text_features),df_text_fea_counts))\n",
" len1 = len(set(df_text_features))\n",
" len2 = len(set(train_text_features) & set(df_text_features))\n",
" return len1,len2"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"98.087 % of word of test data appeared in train data\n",
"97.497 % of word of Cross Validation appeared in train data\n"
]
}
],
"source": [
"len1,len2 = get_intersec_text(test_df)\n",
"print(np.round((len2/len1)*100, 3), \"% of word of test data appeared in train data\")\n",
"len1,len2 = get_intersec_text(cv_df)\n",
"print(np.round((len2/len1)*100, 3), \"% of word of Cross Validation appeared in train data\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"So, all 3 columns are going to be important.\n",
"\n",
"## Data prepration for Machine Learning models"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets create few functions which we will be using later"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [],
"source": [
"def report_log_loss(train_x, train_y, test_x, test_y, clf):\n",
" clf.fit(train_x, train_y)\n",
" sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
" sig_clf.fit(train_x, train_y)\n",
" sig_clf_probs = sig_clf.predict_proba(test_x)\n",
" return log_loss(test_y, sig_clf_probs, eps=1e-15)"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [],
"source": [
"# This function plots the confusion matrices given y_i, y_i_hat.\n",
"def plot_confusion_matrix(test_y, predict_y):\n",
" C = confusion_matrix(test_y, predict_y)\n",
" \n",
" A =(((C.T)/(C.sum(axis=1))).T)\n",
" \n",
" B =(C/C.sum(axis=0)) \n",
" labels = [1,2,3,4,5,6,7,8,9]\n",
" # representing A in heatmap format\n",
" print(\"-\"*20, \"Confusion matrix\", \"-\"*20)\n",
" plt.figure(figsize=(20,7))\n",
" sns.heatmap(C, annot=True, cmap=\"YlGnBu\", fmt=\".3f\", xticklabels=labels, yticklabels=labels)\n",
" plt.xlabel('Predicted Class')\n",
" plt.ylabel('Original Class')\n",
" plt.show()\n",
"\n",
" print(\"-\"*20, \"Precision matrix (Columm Sum=1)\", \"-\"*20)\n",
" plt.figure(figsize=(20,7))\n",
" sns.heatmap(B, annot=True, cmap=\"YlGnBu\", fmt=\".3f\", xticklabels=labels, yticklabels=labels)\n",
" plt.xlabel('Predicted Class')\n",
" plt.ylabel('Original Class')\n",
" plt.show()\n",
" \n",
" # representing B in heatmap format\n",
" print(\"-\"*20, \"Recall matrix (Row sum=1)\", \"-\"*20)\n",
" plt.figure(figsize=(20,7))\n",
" sns.heatmap(A, annot=True, cmap=\"YlGnBu\", fmt=\".3f\", xticklabels=labels, yticklabels=labels)\n",
" plt.xlabel('Predicted Class')\n",
" plt.ylabel('Original Class')\n",
" plt.show()\n",
"\n",
"\n",
"def predict_and_plot_confusion_matrix(train_x, train_y,test_x, test_y, clf):\n",
" clf.fit(train_x, train_y)\n",
" sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
" sig_clf.fit(train_x, train_y)\n",
" pred_y = sig_clf.predict(test_x)\n",
"\n",
" # for calculating log_loss we willl provide the array of probabilities belongs to each class\n",
" print(\"Log loss :\",log_loss(test_y, sig_clf.predict_proba(test_x)))\n",
" # calculating the number of data points that are misclassified\n",
" print(\"Number of mis-classified points :\", np.count_nonzero((pred_y- test_y))/test_y.shape[0])\n",
" plot_confusion_matrix(test_y, pred_y)"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [],
"source": [
"# this function will be used just for naive bayes\n",
"# for the given indices, we will print the name of the features\n",
"# and we will check whether the feature present in the test point text or not\n",
"def get_impfeature_names(indices, text, gene, var, no_features):\n",
" gene_count_vec = CountVectorizer()\n",
" var_count_vec = CountVectorizer()\n",
" text_count_vec = CountVectorizer(min_df=3)\n",
" \n",
" gene_vec = gene_count_vec.fit(train_df['Gene'])\n",
" var_vec = var_count_vec.fit(train_df['Variation'])\n",
" text_vec = text_count_vec.fit(train_df['TEXT'])\n",
" \n",
" fea1_len = len(gene_vec.get_feature_names())\n",
" fea2_len = len(var_count_vec.get_feature_names())\n",
" \n",
" word_present = 0\n",
" for i,v in enumerate(indices):\n",
" if (v < fea1_len):\n",
" word = gene_vec.get_feature_names()[v]\n",
" yes_no = True if word == gene else False\n",
" if yes_no:\n",
" word_present += 1\n",
" print(i, \"Gene feature [{}] present in test data point [{}]\".format(word,yes_no))\n",
" elif (v < fea1_len+fea2_len):\n",
" word = var_vec.get_feature_names()[v-(fea1_len)]\n",
" yes_no = True if word == var else False\n",
" if yes_no:\n",
" word_present += 1\n",
" print(i, \"variation feature [{}] present in test data point [{}]\".format(word,yes_no))\n",
" else:\n",
" word = text_vec.get_feature_names()[v-(fea1_len+fea2_len)]\n",
" yes_no = True if word in text.split() else False\n",
" if yes_no:\n",
" word_present += 1\n",
" print(i, \"Text feature [{}] present in test data point [{}]\".format(word,yes_no))\n",
"\n",
" print(\"Out of the top \",no_features,\" features \", word_present, \"are present in query point\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Combining all 3 features together"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {},
"outputs": [],
"source": [
"# merging gene, variance and text features\n",
"\n",
"train_gene_var_onehotCoding = hstack((train_gene_feature_onehotCoding,train_variation_feature_onehotCoding))\n",
"test_gene_var_onehotCoding = hstack((test_gene_feature_onehotCoding,test_variation_feature_onehotCoding))\n",
"cv_gene_var_onehotCoding = hstack((cv_gene_feature_onehotCoding,cv_variation_feature_onehotCoding))\n",
"\n",
"train_x_onehotCoding = hstack((train_gene_var_onehotCoding, train_text_feature_onehotCoding)).tocsr()\n",
"train_y = np.array(list(train_df['Class']))\n",
"\n",
"test_x_onehotCoding = hstack((test_gene_var_onehotCoding, test_text_feature_onehotCoding)).tocsr()\n",
"test_y = np.array(list(test_df['Class']))\n",
"\n",
"cv_x_onehotCoding = hstack((cv_gene_var_onehotCoding, cv_text_feature_onehotCoding)).tocsr()\n",
"cv_y = np.array(list(cv_df['Class']))\n",
"\n",
"\n",
"train_gene_var_responseCoding = np.hstack((train_gene_feature_responseCoding,train_variation_feature_responseCoding))\n",
"test_gene_var_responseCoding = np.hstack((test_gene_feature_responseCoding,test_variation_feature_responseCoding))\n",
"cv_gene_var_responseCoding = np.hstack((cv_gene_feature_responseCoding,cv_variation_feature_responseCoding))\n",
"\n",
"train_x_responseCoding = np.hstack((train_gene_var_responseCoding, train_text_feature_responseCoding))\n",
"test_x_responseCoding = np.hstack((test_gene_var_responseCoding, test_text_feature_responseCoding))\n",
"cv_x_responseCoding = np.hstack((cv_gene_var_responseCoding, cv_text_feature_responseCoding))\n"
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"One hot encoding features :\n",
"(number of data points * number of features) in train data = (2124, 57112)\n",
"(number of data points * number of features) in test data = (665, 57112)\n",
"(number of data points * number of features) in cross validation data = (532, 57112)\n"
]
}
],
"source": [
"print(\"One hot encoding features :\")\n",
"print(\"(number of data points * number of features) in train data = \", train_x_onehotCoding.shape)\n",
"print(\"(number of data points * number of features) in test data = \", test_x_onehotCoding.shape)\n",
"print(\"(number of data points * number of features) in cross validation data =\", cv_x_onehotCoding.shape)"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Response encoding features :\n",
"(number of data points * number of features) in train data = (2124, 27)\n",
"(number of data points * number of features) in test data = (665, 27)\n",
"(number of data points * number of features) in cross validation data = (532, 27)\n"
]
}
],
"source": [
"print(\" Response encoding features :\")\n",
"print(\"(number of data points * number of features) in train data = \", train_x_responseCoding.shape)\n",
"print(\"(number of data points * number of features) in test data = \", test_x_responseCoding.shape)\n",
"print(\"(number of data points * number of features) in cross validation data =\", cv_x_responseCoding.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Building Machine Learning model"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets start the first model which is most suitable when we have lot of text column data. So, we will start with Naive Bayes."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Random Forest Classifier"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Model with One hot encoder"
]
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"for n_estimators = 100 and max depth = 5\n",
"Log Loss : 1.2572236746892875\n",
"for n_estimators = 100 and max depth = 10\n",
"Log Loss : 1.1861346638575951\n",
"for n_estimators = 200 and max depth = 5\n",
"Log Loss : 1.2488332258871315\n",
"for n_estimators = 200 and max depth = 10\n",
"Log Loss : 1.1781094364470721\n",
"for n_estimators = 500 and max depth = 5\n",
"Log Loss : 1.2432449644078098\n",
"for n_estimators = 500 and max depth = 10\n",
"Log Loss : 1.175471149755794\n",
"for n_estimators = 1000 and max depth = 5\n",
"Log Loss : 1.2439282130693075\n",
"for n_estimators = 1000 and max depth = 10\n",
"Log Loss : 1.174651527267681\n",
"for n_estimators = 2000 and max depth = 5\n",
"Log Loss : 1.242552498227296\n",
"for n_estimators = 2000 and max depth = 10\n",
"Log Loss : 1.1710021382121305\n"
]
}
],
"source": [
"alpha = [100,200,500,1000,2000]\n",
"max_depth = [5, 10]\n",
"cv_log_error_array = []\n",
"for i in alpha:\n",
" for j in max_depth:\n",
" print(\"for n_estimators =\", i,\"and max depth = \", j)\n",
" clf = RandomForestClassifier(n_estimators=i, criterion='gini', max_depth=j, random_state=42, n_jobs=-1)\n",
" clf.fit(train_x_onehotCoding, train_y)\n",
" sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
" sig_clf.fit(train_x_onehotCoding, train_y)\n",
" sig_clf_probs = sig_clf.predict_proba(cv_x_onehotCoding)\n",
" cv_log_error_array.append(log_loss(cv_y, sig_clf_probs, labels=clf.classes_, eps=1e-15))\n",
" print(\"Log Loss :\",log_loss(cv_y, sig_clf_probs)) \n"
]
},
{
"cell_type": "code",
"execution_count": 106,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"For values of best estimator = 2000 The train log loss is: 0.6919423088243477\n",
"For values of best estimator = 2000 The cross validation log loss is: 1.1710021382121305\n",
"For values of best estimator = 2000 The test log loss is: 1.0918848509484709\n"
]
}
],
"source": [
"best_alpha = np.argmin(cv_log_error_array)\n",
"clf = RandomForestClassifier(n_estimators=alpha[int(best_alpha/2)], criterion='gini', max_depth=max_depth[int(best_alpha%2)], random_state=42, n_jobs=-1)\n",
"clf.fit(train_x_onehotCoding, train_y)\n",
"sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
"sig_clf.fit(train_x_onehotCoding, train_y)\n",
"\n",
"predict_y = sig_clf.predict_proba(train_x_onehotCoding)\n",
"print('For values of best estimator = ', alpha[int(best_alpha/2)], \"The train log loss is:\",log_loss(y_train, predict_y, labels=clf.classes_, eps=1e-15))\n",
"predict_y = sig_clf.predict_proba(cv_x_onehotCoding)\n",
"print('For values of best estimator = ', alpha[int(best_alpha/2)], \"The cross validation log loss is:\",log_loss(y_cv, predict_y, labels=clf.classes_, eps=1e-15))\n",
"predict_y = sig_clf.predict_proba(test_x_onehotCoding)\n",
"print('For values of best estimator = ', alpha[int(best_alpha/2)], \"The test log loss is:\",log_loss(y_test, predict_y, labels=clf.classes_, eps=1e-15))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Lets test it on testing data using best hyper param"
]
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Log loss : 1.1710021382121305\n",
"Number of mis-classified points : 0.39285714285714285\n",
"-------------------- Confusion matrix --------------------\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 1440x504 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-------------------- Precision matrix (Columm Sum=1) --------------------\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x504 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-------------------- Recall matrix (Row sum=1) --------------------\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x504 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"clf = RandomForestClassifier(n_estimators=alpha[int(best_alpha/2)], criterion='gini', max_depth=max_depth[int(best_alpha%2)], random_state=42, n_jobs=-1)\n",
"predict_and_plot_confusion_matrix(train_x_onehotCoding, train_y,cv_x_onehotCoding,cv_y, clf)"
]
},
{
"cell_type": "code",
"execution_count": 108,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted Class : 4\n",
"Predicted Class Probabilities: [[0.2876 0.0616 0.0163 0.4507 0.0513 0.0497 0.0647 0.0107 0.0074]]\n",
"Actual Class : 4\n",
"--------------------------------------------------\n",
"3 Text feature [inhibitors] present in test data point [True]\n",
"4 Text feature [activation] present in test data point [True]\n",
"6 Text feature [missense] present in test data point [True]\n",
"8 Text feature [suppressor] present in test data point [True]\n",
"12 Text feature [nonsense] present in test data point [True]\n",
"15 Text feature [function] present in test data point [True]\n",
"16 Text feature [growth] present in test data point [True]\n",
"20 Text feature [functional] present in test data point [True]\n",
"23 Text feature [loss] present in test data point [True]\n",
"24 Text feature [cells] present in test data point [True]\n",
"26 Text feature [downstream] present in test data point [True]\n",
"31 Text feature [patients] present in test data point [True]\n",
"35 Text feature [protein] present in test data point [True]\n",
"37 Text feature [variants] present in test data point [True]\n",
"39 Text feature [frameshift] present in test data point [True]\n",
"53 Text feature [clinical] present in test data point [True]\n",
"54 Text feature [transformation] present in test data point [True]\n",
"63 Text feature [cell] present in test data point [True]\n",
"70 Text feature [deleterious] present in test data point [True]\n",
"79 Text feature [ligand] present in test data point [True]\n",
"81 Text feature [lines] present in test data point [True]\n",
"82 Text feature [patient] present in test data point [True]\n",
"85 Text feature [functions] present in test data point [True]\n",
"86 Text feature [likelihood] present in test data point [True]\n",
"87 Text feature [proteins] present in test data point [True]\n",
"88 Text feature [expression] present in test data point [True]\n",
"94 Text feature [assays] present in test data point [True]\n",
"95 Text feature [nuclear] present in test data point [True]\n",
"99 Text feature [amplification] present in test data point [True]\n",
"Out of the top 100 features 29 are present in query point\n"
]
}
],
"source": [
"# test_point_index = 10\n",
"clf = RandomForestClassifier(n_estimators=alpha[int(best_alpha/2)], criterion='gini', max_depth=max_depth[int(best_alpha%2)], random_state=42, n_jobs=-1)\n",
"clf.fit(train_x_onehotCoding, train_y)\n",
"sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
"sig_clf.fit(train_x_onehotCoding, train_y)\n",
"\n",
"test_point_index = 1\n",
"no_feature = 100\n",
"predicted_cls = sig_clf.predict(test_x_onehotCoding[test_point_index])\n",
"print(\"Predicted Class :\", predicted_cls[0])\n",
"print(\"Predicted Class Probabilities:\", np.round(sig_clf.predict_proba(test_x_onehotCoding[test_point_index]),4))\n",
"print(\"Actual Class :\", test_y[test_point_index])\n",
"indices = np.argsort(-clf.feature_importances_)\n",
"print(\"-\"*50)\n",
"get_impfeature_names(indices[:no_feature], test_df['TEXT'].iloc[test_point_index],test_df['Gene'].iloc[test_point_index],test_df['Variation'].iloc[test_point_index], no_feature)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## RF with Response Coding"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"for n_estimators = 10 and max depth = 2\n",
"Log Loss : 2.3303043307247706\n",
"for n_estimators = 10 and max depth = 3\n",
"Log Loss : 1.759233082123313\n",
"for n_estimators = 10 and max depth = 5\n",
"Log Loss : 1.7222729656680074\n",
"for n_estimators = 10 and max depth = 10\n",
"Log Loss : 2.1879986740331585\n",
"for n_estimators = 50 and max depth = 2\n",
"Log Loss : 1.8102751305264946\n",
"for n_estimators = 50 and max depth = 3\n",
"Log Loss : 1.472457555108808\n",
"for n_estimators = 50 and max depth = 5\n",
"Log Loss : 1.3606410245246492\n",
"for n_estimators = 50 and max depth = 10\n",
"Log Loss : 1.735251061454875\n",
"for n_estimators = 100 and max depth = 2\n",
"Log Loss : 1.6397489083700045\n",
"for n_estimators = 100 and max depth = 3\n",
"Log Loss : 1.5150260265978719\n",
"for n_estimators = 100 and max depth = 5\n",
"Log Loss : 1.2914915334146437\n",
"for n_estimators = 100 and max depth = 10\n",
"Log Loss : 1.7844750814929151\n",
"for n_estimators = 200 and max depth = 2\n",
"Log Loss : 1.6697036744271037\n",
"for n_estimators = 200 and max depth = 3\n",
"Log Loss : 1.5108466525843012\n",
"for n_estimators = 200 and max depth = 5\n",
"Log Loss : 1.3845537584276042\n",
"for n_estimators = 200 and max depth = 10\n",
"Log Loss : 1.7551355678052738\n",
"for n_estimators = 500 and max depth = 2\n",
"Log Loss : 1.7301101308042508\n",
"for n_estimators = 500 and max depth = 3\n",
"Log Loss : 1.623695643294688\n",
"for n_estimators = 500 and max depth = 5\n",
"Log Loss : 1.3931988161051954\n",
"for n_estimators = 500 and max depth = 10\n",
"Log Loss : 1.7923450427705567\n",
"for n_estimators = 1000 and max depth = 2\n",
"Log Loss : 1.719548851364818\n",
"for n_estimators = 1000 and max depth = 3\n",
"Log Loss : 1.6161705513069253\n",
"for n_estimators = 1000 and max depth = 5\n",
"Log Loss : 1.3833366060183812\n",
"for n_estimators = 1000 and max depth = 10\n",
"Log Loss : 1.7841833756180365\n",
"For values of best alpha = 100 The train log loss is: 0.054288718396378756\n",
"For values of best alpha = 100 The cross validation log loss is: 1.2914915334146437\n",
"For values of best alpha = 100 The test log loss is: 1.2489713410389691\n"
]
}
],
"source": [
"alpha = [10,50,100,200,500,1000]\n",
"max_depth = [2,3,5,10]\n",
"cv_log_error_array = []\n",
"for i in alpha:\n",
" for j in max_depth:\n",
" print(\"for n_estimators =\", i,\"and max depth = \", j)\n",
" clf = RandomForestClassifier(n_estimators=i, criterion='gini', max_depth=j, random_state=42, n_jobs=-1)\n",
" clf.fit(train_x_responseCoding, train_y)\n",
" sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
" sig_clf.fit(train_x_responseCoding, train_y)\n",
" sig_clf_probs = sig_clf.predict_proba(cv_x_responseCoding)\n",
" cv_log_error_array.append(log_loss(cv_y, sig_clf_probs, labels=clf.classes_, eps=1e-15))\n",
" print(\"Log Loss :\",log_loss(cv_y, sig_clf_probs)) \n",
"\n",
"\n",
"best_alpha = np.argmin(cv_log_error_array)\n",
"clf = RandomForestClassifier(n_estimators=alpha[int(best_alpha/4)], criterion='gini', max_depth=max_depth[int(best_alpha%4)], random_state=42, n_jobs=-1)\n",
"clf.fit(train_x_responseCoding, train_y)\n",
"sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
"sig_clf.fit(train_x_responseCoding, train_y)\n",
"\n",
"predict_y = sig_clf.predict_proba(train_x_responseCoding)\n",
"print('For values of best alpha = ', alpha[int(best_alpha/4)], \"The train log loss is:\",log_loss(y_train, predict_y, labels=clf.classes_, eps=1e-15))\n",
"predict_y = sig_clf.predict_proba(cv_x_responseCoding)\n",
"print('For values of best alpha = ', alpha[int(best_alpha/4)], \"The cross validation log loss is:\",log_loss(y_cv, predict_y, labels=clf.classes_, eps=1e-15))\n",
"predict_y = sig_clf.predict_proba(test_x_responseCoding)\n",
"print('For values of best alpha = ', alpha[int(best_alpha/4)], \"The test log loss is:\",log_loss(y_test, predict_y, labels=clf.classes_, eps=1e-15))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Testing model with best hyper param"
]
},
{
"cell_type": "code",
"execution_count": 110,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Log loss : 1.2914915334146437\n",
"Number of mis-classified points : 0.4755639097744361\n",
"-------------------- Confusion matrix --------------------\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 1440x504 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-------------------- Precision matrix (Columm Sum=1) --------------------\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x504 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"-------------------- Recall matrix (Row sum=1) --------------------\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1440x504 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"clf = RandomForestClassifier(max_depth=max_depth[int(best_alpha%4)], n_estimators=alpha[int(best_alpha/4)], criterion='gini', max_features='auto',random_state=42)\n",
"predict_and_plot_confusion_matrix(train_x_responseCoding, train_y,cv_x_responseCoding,cv_y, clf)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Query the classified point"
]
},
{
"cell_type": "code",
"execution_count": 111,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predicted Class : 4\n",
"Predicted Class Probabilities: [[0.2367 0.0189 0.0993 0.4894 0.0333 0.0447 0.0085 0.0305 0.0385]]\n",
"Actual Class : 4\n",
"--------------------------------------------------\n",
"Variation is important feature\n",
"Variation is important feature\n",
"Variation is important feature\n",
"Variation is important feature\n",
"Variation is important feature\n",
"Gene is important feature\n",
"Variation is important feature\n",
"Text is important feature\n",
"Text is important feature\n",
"Gene is important feature\n",
"Text is important feature\n",
"Text is important feature\n",
"Text is important feature\n",
"Gene is important feature\n",
"Variation is important feature\n",
"Gene is important feature\n",
"Text is important feature\n",
"Gene is important feature\n",
"Variation is important feature\n",
"Gene is important feature\n",
"Text is important feature\n",
"Text is important feature\n",
"Variation is important feature\n",
"Text is important feature\n",
"Gene is important feature\n",
"Gene is important feature\n",
"Gene is important feature\n"
]
}
],
"source": [
"clf = RandomForestClassifier(n_estimators=alpha[int(best_alpha/4)], criterion='gini', max_depth=max_depth[int(best_alpha%4)], random_state=42, n_jobs=-1)\n",
"clf.fit(train_x_responseCoding, train_y)\n",
"sig_clf = CalibratedClassifierCV(clf, method=\"sigmoid\")\n",
"sig_clf.fit(train_x_responseCoding, train_y)\n",
"\n",
"\n",
"test_point_index = 1\n",
"no_feature = 27\n",
"predicted_cls = sig_clf.predict(test_x_responseCoding[test_point_index].reshape(1,-1))\n",
"print(\"Predicted Class :\", predicted_cls[0])\n",
"print(\"Predicted Class Probabilities:\", np.round(sig_clf.predict_proba(test_x_responseCoding[test_point_index].reshape(1,-1)),4))\n",
"print(\"Actual Class :\", test_y[test_point_index])\n",
"indices = np.argsort(-clf.feature_importances_)\n",
"print(\"-\"*50)\n",
"for i in indices:\n",
" if i<9:\n",
" print(\"Gene is important feature\")\n",
" elif i<18:\n",
" print(\"Variation is important feature\")\n",
" else:\n",
" print(\"Text is important feature\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}