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I have obtained the following code along with a dataset from a Kaggle notebook: https://www.kaggle.com/code/danofer/predicting-protein-classification/notebook

import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import seaborn as sns
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
import sys

# Import Datasets
df_seq = pd.read_csv('pdb_data_seq.csv')
df_char = pd.read_csv('pdb_data_no_dups.csv')

print('Datasets have been loaded...')

# 2). ----- Filter and Process Dataset ------

# Filter for only proteins
protein_char = df_char[df_char.macromoleculeType == 'Protein']
protein_seq = df_seq[df_seq.macromoleculeType == 'Protein']

print(protein_char.head())

print(protein_seq.describe(include="all"))

print(protein_char.columns)

# Select  some variables to join
protein_char = protein_char[['structureId','classification','residueCount', 'resolution', 
       'structureMolecularWeight','crystallizationTempK', 'densityMatthews', 'densityPercentSol', 'phValue']]
protein_seq = protein_seq[['structureId','sequence']]
print(protein_seq.head())

print(protein_char.head())

# Join two datasets on structureId
model_f = protein_char.set_index('structureId').join(protein_seq.set_index('structureId'))
print(model_f.head())

print('%d is the number of rows in the joined dataset' %model_f.shape[0])

# Check NA counts
print(model_f.isnull().sum())

# Drop rows with missing values
model_f = model_f.dropna()
print('%d is the number of proteins that have a classification and sequence' %model_f.shape[0])

# Look at classification type counts
counts = model_f.classification.value_counts()
print(counts)

#plot counts
plt.figure()
sns.distplot(counts[(counts > 1000)], hist = False, color = 'purple')
plt.title('Count Distribution for Family Types')
plt.ylabel('% of records')
plt.show()

# Get classification types where counts are over 1000
types = np.asarray(counts[(counts > 1000)].index)
print(len(types))

# Filter dataset's records for classification types > 1000
data = model_f[model_f.classification.isin(types)]
# leaving more rows results in duplciates / index related?
data = data.drop_duplicates(subset=["classification","sequence"])      
print(types)

print('%d is the number of records in the final filtered dataset' %data.shape[0])

data = data.drop_duplicates(subset=["classification","sequence"]) 
print(data.shape)

## Could add n-grams
## https://stackoverflow.com/questions/18658106/quick-implementation-of-character-n-grams-using-python
# jump_size !=1 -> less overlap in n-grams. 
def char_grams(text,n=3,jump_size=2):
    return [text[i:i+n] for i in range(0,len(text)-n+1,jump_size)]

data.head(3).sequence.apply(char_grams)

data["3mers"] = data.sequence.apply(char_grams)

data.tail()

data.to_csv("protein_classification_46k_ngrams.csv.gz",compression="gzip")

# 3). ----- Train Test Split -----

# Split Data
X_train, X_test, y_train, y_test = train_test_split(data['sequence'], data['classification'], test_size = 0.2, random_state = 1)

# Create a Count Vectorizer to gather the unique elements in sequence
vect = CountVectorizer(analyzer = 'char_wb', ngram_range = (4,4))

# Fit and Transform CountVectorizer
vect.fit(X_train)
X_train_df = vect.transform(X_train)
X_test_df = vect.transform(X_test)

#Print a few of the features
print(vect.get_feature_names_out()[-20:])
sys.exit()
# 4). ------ Machine Learning Models ------

# Make a prediction dictionary to store accuracys
prediction = dict()

# Naive Bayes Model
from sklearn.naive_bayes import MultinomialNB
model = MultinomialNB()
model.fit(X_train_df, y_train)
NB_pred = model.predict(X_test_df)
prediction["MultinomialNB"] = accuracy_score(NB_pred, y_test)
print(prediction['MultinomialNB'])

# Adaboost
from sklearn.ensemble import AdaBoostClassifier
model = AdaBoostClassifier()
model.fit(X_train_df,y_train)
ADA_pred = model.predict(X_test_df)
prediction["Adaboost"] = accuracy_score(ADA_pred , y_test)
print(prediction["Adaboost"])

# 5). ----- Plot Confusion Matrix for NB -----

# Plot confusion matrix
conf_mat = confusion_matrix(y_test, NB_pred, labels = types)

#Normalize confusion_matrix
conf_mat = conf_mat.astype('float')/ conf_mat.sum(axis=1)[:, np.newaxis]

# Plot Heat Map
fig , ax = plt.subplots()
fig.set_size_inches(13, 8)
sns.heatmap(conf_mat)

print(types[3])
#print(types[38])

#Print F1 score metrics
print(classification_report(y_test, NB_pred, target_names = types))

However, my dataset is different, and it comprises sequences in CSV format. There is only one common column between my dataset and the test/train datasets.
I am seeking guidance on how to utilize this code/model to predict the classification in the subsequent column of my sequences. Please provide assistance.

Rashid
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1 Answers1

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While training the model, you need both X_train and y_train. Why? Because that's how the model learns about the data. So we need labeled data while training the model. But in prediction phase, we are predicting on something. It's like this, you are going to learn in school and learning everything from books (training phase). Now in exams, you don't get books, and you have to use your own knowledge to predict the answer (prediction phase). To utilize your code, after training the model (model.fit()), you can use model.predict() to predict on new data.

Harshad Patil
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