NOTE

Original files for the project exceed GitHub's maximum file size limit. Please contact me at asaficontact@gmail.com to access complete code and data files for the project.

Initial Setup

from imports import *

Using TensorFlow backend.

#Import sc class which contains all the functions for operating the stackClassifier
from stackClassifier import sc

Python Stack Overflow Questions Tags being Classified

1: pandas

2: list

3: numpy

4: matplotlib

5: regex

6: dictionary

7: string

8: mysql

9: json

10: csv

11: beautifulsoup

Initialize Stack Classifier

# We have already preprocessed the dataframe and saved it as a pickle file called 'processed_df' for faster execution
# If the preprocessed dataframe doesnot exist set load = False inorder to create a new one
stack_classifier = sc(load = True)

Total number of observations

len(stack_classifier.df)

112661

Visualizing distribution of Tags in the dataset

ax = sns.catplot(x='Tag', kind="count", palette="ch:.25", data=stack_classifier.df)
ax.fig.set_size_inches(18,8)

Load Model

The best performing neural network with 512 nodes in first hidden layer and 64 nodes in the second hidden layer has already been created and saved as 'tag_classifier.h5' to save executation time. You can train a new neural network by calling the 'train_neural_network' function.

model = stack_classifier.load_neural_network('tag_classifier')

WARNING: Logging before flag parsing goes to stderr.
W0917 16:23:20.661309 4429637056 deprecation_wrapper.py:119] From /anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:517: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.

W0917 16:23:20.674825 4429637056 deprecation_wrapper.py:119] From /anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:4115: The name tf.random_normal is deprecated. Please use tf.random.normal instead.

W0917 16:23:20.684943 4429637056 deprecation_wrapper.py:119] From /anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:131: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.

W0917 16:23:20.685856 4429637056 deprecation_wrapper.py:119] From /anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:133: The name tf.placeholder_with_default is deprecated. Please use tf.compat.v1.placeholder_with_default instead.

W0917 16:23:20.692325 4429637056 deprecation.py:506] From /anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.
Instructions for updating:
Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.
W0917 16:23:21.306606 4429637056 deprecation_wrapper.py:119] From /anaconda3/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py:174: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.

W0917 16:23:22.659685 4429637056 deprecation_wrapper.py:119] From /anaconda3/lib/python3.7/site-packages/keras/optimizers.py:790: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.

W0917 16:23:22.730759 4429637056 deprecation.py:323] From /anaconda3/lib/python3.7/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where

Evaluate Model

#Get confusion matrix, classification report and accuracy score
cm, c_report, a_report = stack_classifier.evaluate_model_df(model, c_matrix = True, c_report= True, a_score= True, load = True)

Classficiation Report

print(c_report)

               precision    recall  f1-score   support

beautifulsoup       0.91      0.94      0.92      1750
          csv       0.87      0.86      0.86      1946
   dictionary       0.88      0.83      0.85      3008
         json       0.89      0.91      0.90      2109
         list       0.82      0.84      0.83      4457
   matplotlib       0.94      0.95      0.94      4244
        mysql       0.91      0.93      0.92      2288
        numpy       0.89      0.90      0.90      4344
       pandas       0.94      0.94      0.94      6722
        regex       0.89      0.89      0.89      3910
       string       0.73      0.69      0.71      2401

    micro avg       0.89      0.89      0.89     37179
    macro avg       0.88      0.88      0.88     37179
 weighted avg       0.89      0.89      0.89     37179

Accuracy Score

print(f'The Accuracy Score is: {a_report}')

The Accuracy Score is: 0.8868985179805804

Confusion Matrix

classes = list(stack_classifier.df.Tag.value_counts().to_dict().keys())

title = 'Normalized confusion matrix'

cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]

fig, ax = plt.subplots(figsize = (20,10))
im = ax.imshow(cm, interpolation='nearest', cmap=plt.cm.Blues)
ax.figure.colorbar(im, ax=ax)
# We want to show all ticks...
ax.set(xticks=np.arange(cm.shape[1]),
   yticks=np.arange(cm.shape[0]),
   # ... and label them with the respective list entries
   xticklabels=classes, yticklabels=classes,
   title=title,
   ylabel='True label',
   xlabel='Predicted label')

# Rotate the tick labels and set their alignment.
plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
     rotation_mode="anchor")

# Loop over data dimensions and create text annotations.
fmt = '.2f'
thresh = cm.max() / 2.
for i in range(cm.shape[0]):
    for j in range(cm.shape[1]):
        ax.text(j, i, format(cm[i, j], fmt),
                ha="center", va="center",
                color="white" if cm[i, j] > thresh else "black")
fig.tight_layout()