helper_functions.py

### My functions + those of others that I've found useful for ML projects.

import tensorflow as tf

#my functions
def compare_results(results_1: dict, results_2: dict) -> None:
    """
    Compares the results of two different models and returns which one performed better
    for each scoring metric.
    Uses the results from the calculate_results() function.
    """
    
    import inspect

    def retrieve_name(var):
        callers_local_vars = inspect.currentframe().f_back.f_back.f_locals.items()
        return [var_name for var_name, var_val in callers_local_vars if var_val is var][1]
    
    for metric in results_1.keys():
        if results_1[metric] > results_2[metric]:
            print(f"{metric.capitalize()}: {retrieve_name(results_1)} > {retrieve_name(results_2)}")
        else:
            print(f"{metric.capitalize()}: {retrieve_name(results_2)} > {retrieve_name(results_1)}")


def save_word_embeddings(vocab_words, embedding_weights, vector_filename="vectors.tsv",
                         metadata_filename="metadata.tsv"):
  """
  Used to save word embeddings to disk. Can view with Tensorflow embedding projector
  https://projector.tensorflow.org/
  
  vocab_words should be something like:
  vocab_wordds = text_vectorizer.get_vocabulary()
  
  Embedding weights should be something like:
  embedding_weights = model_1.get_layer("embedding_1").get_weights()[0]
  """
  import io
  
  # got this from here: https://www.tensorflow.org/tutorials/text/word_embeddings#retrieve_the_trained_word_embeddings_and_save_them_to_disk
  out_v = io.open(vector_filename, 'w', encoding='utf-8')
  out_m = io.open(metadata_filename, 'w', encoding='utf-8')

  for index, word in enumerate(vocab_words):
    if index == 0:
      continue  # skip 0, it's padding.
    vec = embedding_weights[index]
    out_v.write('\t'.join([str(x) for x in vec]) + "\n")
    out_m.write(word + "\n")
  out_v.close()
  out_m.close()


## functions from others 

# Create a function to import an image and resize it to be able to be used with our model
def load_and_prep_image(filename, img_shape=224, scale=True):
  """
  Reads in an image from filename, turns it into a tensor and reshapes into
  (224, 224, 3).

  Parameters
  ----------
  filename (str): string filename of target image
  img_shape (int): size to resize target image to, default 224
  scale (bool): whether to scale pixel values to range(0, 1), default True
  """
  # Read in the image
  img = tf.io.read_file(filename)
  # Decode it into a tensor
  img = tf.image.decode_jpeg(img)
  # Resize the image
  img = tf.image.resize(img, [img_shape, img_shape])
  if scale:
    # Rescale the image (get all values between 0 and 1)
    return img/255.
  else:
    return img

# Note: The following confusion matrix code is a remix of Scikit-Learn's 
# plot_confusion_matrix function - https://scikit-learn.org/stable/modules/generated/sklearn.metrics.plot_confusion_matrix.html
import itertools
import matplotlib.pyplot as plt
import numpy as np
from sklearn.metrics import confusion_matrix

# Our function needs a different name to sklearn's plot_confusion_matrix
def make_confusion_matrix(y_true, y_pred, classes=None, figsize=(10, 10), text_size=15, norm=False, savefig=False): 
  """Makes a labelled confusion matrix comparing predictions and ground truth labels.

  If classes is passed, confusion matrix will be labelled, if not, integer class values
  will be used.

  Args:
    y_true: Array of truth labels (must be same shape as y_pred).
    y_pred: Array of predicted labels (must be same shape as y_true).
    classes: Array of class labels (e.g. string form). If `None`, integer labels are used.
    figsize: Size of output figure (default=(10, 10)).
    text_size: Size of output figure text (default=15).
    norm: normalize values or not (default=False).
    savefig: save confusion matrix to file (default=False).
  
  Returns:
    A labelled confusion matrix plot comparing y_true and y_pred.

  Example usage:
    make_confusion_matrix(y_true=test_labels, # ground truth test labels
                          y_pred=y_preds, # predicted labels
                          classes=class_names, # array of class label names
                          figsize=(15, 15),
                          text_size=10)
  """  
  # Create the confustion matrix
  cm = confusion_matrix(y_true, y_pred)
  cm_norm = cm.astype("float") / cm.sum(axis=1)[:, np.newaxis] # normalize it
  n_classes = cm.shape[0] # find the number of classes we're dealing with

  # Plot the figure and make it pretty
  fig, ax = plt.subplots(figsize=figsize)
  cax = ax.matshow(cm, cmap=plt.cm.Blues) # colors will represent how 'correct' a class is, darker == better
  fig.colorbar(cax)

  # Are there a list of classes?
  if classes:
    labels = classes
  else:
    labels = np.arange(cm.shape[0])
  
  # Label the axes
  ax.set(title="Confusion Matrix",
         xlabel="Predicted label",
         ylabel="True label",
         xticks=np.arange(n_classes), # create enough axis slots for each class
         yticks=np.arange(n_classes), 
         xticklabels=labels, # axes will labeled with class names (if they exist) or ints
         yticklabels=labels)
  
  # Make x-axis labels appear on bottom
  ax.xaxis.set_label_position("bottom")
  ax.xaxis.tick_bottom()

  # Set the threshold for different colors
  threshold = (cm.max() + cm.min()) / 2.

  # Plot the text on each cell
  for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
    if norm:
      plt.text(j, i, f"{cm[i, j]} ({cm_norm[i, j]*100:.1f}%)",
              horizontalalignment="center",
              color="white" if cm[i, j] > threshold else "black",
              size=text_size)
    else:
      plt.text(j, i, f"{cm[i, j]}",
              horizontalalignment="center",
              color="white" if cm[i, j] > threshold else "black",
              size=text_size)

  # Save the figure to the current working directory
  if savefig:
    fig.savefig("confusion_matrix.png")
  
# Make a function to predict on images and plot them (works with multi-class)
def pred_and_plot(model, filename, class_names):
  """
  Imports an image located at filename, makes a prediction on it with
  a trained model and plots the image with the predicted class as the title.
  """
  # Import the target image and preprocess it
  img = load_and_prep_image(filename)

  # Make a prediction
  pred = model.predict(tf.expand_dims(img, axis=0))

  # Get the predicted class
  if len(pred[0]) > 1: # check for multi-class
    pred_class = class_names[pred.argmax()] # if more than one output, take the max
  else:
    pred_class = class_names[int(tf.round(pred)[0][0])] # if only one output, round

  # Plot the image and predicted class
  plt.imshow(img)
  plt.title(f"Prediction: {pred_class}")
  plt.axis(False)
  
import datetime

def create_tensorboard_callback(dir_name, experiment_name):
  """
  Creates a TensorBoard callback instand to store log files.

  Stores log files with the filepath:
    "dir_name/experiment_name/current_datetime/"

  Args:
    dir_name: target directory to store TensorBoard log files
    experiment_name: name of experiment directory (e.g. efficientnet_model_1)
  """
  log_dir = dir_name + "/" + experiment_name + "/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
  tensorboard_callback = tf.keras.callbacks.TensorBoard(
      log_dir=log_dir
  )
  print(f"Saving TensorBoard log files to: {log_dir}")
  return tensorboard_callback

# Plot the validation and training data separately
import matplotlib.pyplot as plt

def plot_loss_curves(history):
  """
  Returns separate loss curves for training and validation metrics.

  Args:
    history: TensorFlow model History object (see: https://www.tensorflow.org/api_docs/python/tf/keras/callbacks/History)
  """ 
  loss = history.history['loss']
  val_loss = history.history['val_loss']

  accuracy = history.history['accuracy']
  val_accuracy = history.history['val_accuracy']

  epochs = range(len(history.history['loss']))

  # Plot loss
  plt.plot(epochs, loss, label='training_loss')
  plt.plot(epochs, val_loss, label='val_loss')
  plt.title('Loss')
  plt.xlabel('Epochs')
  plt.legend()

  # Plot accuracy
  plt.figure()
  plt.plot(epochs, accuracy, label='training_accuracy')
  plt.plot(epochs, val_accuracy, label='val_accuracy')
  plt.title('Accuracy')
  plt.xlabel('Epochs')
  plt.legend()

def compare_histories(original_history, new_history, initial_epochs=5):
    """
    Compares two TensorFlow model History objects.
    
    Args:
      original_history: History object from original model (before new_history)
      new_history: History object from continued model training (after original_history)
      initial_epochs: Number of epochs in original_history (new_history plot starts from here) 
    """
    
    # Get original history measurements
    acc = original_history.history["accuracy"]
    loss = original_history.history["loss"]

    val_acc = original_history.history["val_accuracy"]
    val_loss = original_history.history["val_loss"]

    # Combine original history with new history
    total_acc = acc + new_history.history["accuracy"]
    total_loss = loss + new_history.history["loss"]

    total_val_acc = val_acc + new_history.history["val_accuracy"]
    total_val_loss = val_loss + new_history.history["val_loss"]

    # Make plots
    plt.figure(figsize=(8, 8))
    plt.subplot(2, 1, 1)
    plt.plot(total_acc, label='Training Accuracy')
    plt.plot(total_val_acc, label='Validation Accuracy')
    plt.plot([initial_epochs-1, initial_epochs-1],
              plt.ylim(), label='Start Fine Tuning') # reshift plot around epochs
    plt.legend(loc='lower right')
    plt.title('Training and Validation Accuracy')

    plt.subplot(2, 1, 2)
    plt.plot(total_loss, label='Training Loss')
    plt.plot(total_val_loss, label='Validation Loss')
    plt.plot([initial_epochs-1, initial_epochs-1],
              plt.ylim(), label='Start Fine Tuning') # reshift plot around epochs
    plt.legend(loc='upper right')
    plt.title('Training and Validation Loss')
    plt.xlabel('epoch')
    plt.show()
  
# Create function to unzip a zipfile into current working directory 
# (since we're going to be downloading and unzipping a few files)
import zipfile

def unzip_data(filename):
  """
  Unzips filename into the current working directory.

  Args:
    filename (str): a filepath to a target zip folder to be unzipped.
  """
  zip_ref = zipfile.ZipFile(filename, "r")
  zip_ref.extractall()
  zip_ref.close()

# Walk through an image classification directory and find out how many files (images)
# are in each subdirectory.
import os

def walk_through_dir(dir_path):
  """
  Walks through dir_path returning its contents.

  Args:
    dir_path (str): target directory
  
  Returns:
    A print out of:
      number of subdiretories in dir_path
      number of images (files) in each subdirectory
      name of each subdirectory
  """
  for dirpath, dirnames, filenames in os.walk(dir_path):
    print(f"There are {len(dirnames)} directories and {len(filenames)} images in '{dirpath}'.")
    
# Function to evaluate: accuracy, precision, recall, f1-score
from sklearn.metrics import accuracy_score, precision_recall_fscore_support

def calculate_results(y_true, y_pred):
  """
  Calculates model accuracy, precision, recall and f1 score of a binary classification model.

  Args:
      y_true: true labels in the form of a 1D array
      y_pred: predicted labels in the form of a 1D array

  Returns a dictionary of accuracy, precision, recall, f1-score.
  """
  # Calculate model accuracy
  model_accuracy = accuracy_score(y_true, y_pred)
  # Calculate model precision, recall and f1 score using "weighted average
  model_precision, model_recall, model_f1, _ = precision_recall_fscore_support(y_true, y_pred, average="weighted")
  model_results = {"accuracy": model_accuracy*100,
                  "precision": model_precision*100,
                  "recall": model_recall*100,
                  "f1": model_f1*100}
  return model_results

# Create a function which uses a list of trained models to make and return a list of predictions
def get_ensemble_preds(ensemble_models, data):
  ensemble_preds = []
  for model in ensemble_models:
    preds = model.predict(data)
    ensemble_preds.append(preds)
  return tf.constant(tf.squeeze(ensemble_preds))

# Find upper and lower bounds of ensemble predictions
def get_lower_upper_from_ensemble_preds(preds):
  """Get the upper and lower bounds for ensemble preds

  Args:
      preds (_type_): _description_

  Returns:
      _type_: _description_
  """
  # 1. Measure the standard deviation of the predictions
  std = tf.math.reduce_std(preds, axis=0)

  # 2. Multiply the standard deviation by 1.96
  interval = 1.96 * std

  # 3. Get the prediction interval upper and lower bounds 
  preds_mean = tf.reduce_mean(preds, axis=0)
  lower, upper = preds_mean - interval, preds_mean + interval

  return lower, upper


# MASE implementation
def mean_absolute_scaled_error(y_true, y_pred):
    """
    Implement MASE (assuming no seasonality of data).
    """
    mae = tf.reduce_mean(tf.abs(y_true-y_pred))

    # Find MAE of naive forecast (no seasonality)
    mae_naive_no_season = tf.reduce_mean(tf.abs(y_true[1:] - y_true[:-1])) # our seasonality is 1 day (hence the shift of 1)

    return mae / mae_naive_no_season


def evaluate_preds(y_true, y_pred):
    """
    Evaluates predictions made using a tensorflow model
    """
    # Make sure float32 datatype (for metric calculations)
    y_true = tf.cast(y_true, dtype=tf.float32)
    y_pred = tf.cast(y_pred, dtype=tf.float32)

    # Calculate various evaluation metrics 
    mae = tf.keras.metrics.mean_absolute_error(y_true, y_pred)
    mse = tf.keras.metrics.mean_squared_error(y_true, y_pred)
    rmse = tf.sqrt(mse)
    mape = tf.keras.metrics.mean_absolute_percentage_error(y_true, y_pred)
    mase = mean_absolute_scaled_error(y_true, y_pred)

    # Account for different sized metrics (for longer horizons, we want to reduce metrics to a single value)
    if mae.ndim > 0:
        mae = tf.reduce_mean(mae)
        mse = tf.reduce_mean(mse)
        rmse = tf.reduce_mean(rmse)
        mape = tf.reduce_mean(mape)
        mase = tf.reduce_mean(mase)

    return {"mae": mae.numpy(),
            "mse": mse.numpy(),
            "rmse": rmse.numpy(),
            "mape": mape.numpy(),
            "mase": mase.numpy()}

def calculate_results(y_true, y_pred):
    """
    Calculates model accuracy, precision, recall and f1 score of a binary classification model.

    Args:
        y_true: true labels in the form of a 1D array
        y_pred: predicted labels in the form of a 1D array

    Returns a dictionary of accuracy, precision, recall, f1-score.
    """
    from sklearn.metrics import accuracy_score, precision_recall_fscore_support
    # Calculate model accuracy
    model_accuracy = accuracy_score(y_true, y_pred)
    # Calculate model precision, recall and f1 score using "weighted average
    model_precision, model_recall, model_f1, _ = precision_recall_fscore_support(y_true, y_pred, average="weighted")
    model_results = {"accuracy": model_accuracy*100,
                    "precision": model_precision*100,
                    "recall": model_recall*100,
                    "f1": model_f1*100}
    return model_results

def compare_results(results_1: dict, results_2: dict) -> None:
    """
    Compares the results of two different models and returns which one performed better
    for each scoring metric.
    Uses the results from the calculate_results() function.
    """
    
    import inspect

    def retrieve_name(var):
        callers_local_vars = inspect.currentframe().f_back.f_back.f_locals.items()
        return [var_name for var_name, var_val in callers_local_vars if var_val is var][1]
    
    for metric in results_1.keys():
        if results_1[metric] > results_2[metric]:
            print(f"{metric.capitalize()}: {retrieve_name(results_1)} > {retrieve_name(results_2)}")
        else:
            print(f"{metric.capitalize()}: {retrieve_name(results_2)} > {retrieve_name(results_1)}")