Do you wish for a tool that could simplify your life and save you precious time? Well, my friend, look no further than the humble BaseModel class.
Now, before we dive into this underrated gem, let’s first address some common misconceptions about it. First, many people believe that the BaseModel class is only useful for simple linear regression models or logistic regression models. But in reality, it can be used to build a wide variety of machine learning models, including decision trees, random forests, and neural networks!
Secondly, some may argue that using the BaseModel class requires too much boilerplate code. However, I would like to point out that this is not necessarily true. In fact, by leveraging the power of inheritance, we can create custom models with minimal effort. Let’s take a look at an example:
# Import necessary libraries
import tensorflow as tf
from sklearn.metrics import mean_squared_error
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Dense
from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau
import pandas as pd
import numpy as np
# Create a custom model by inheriting from the Sequential model class
class MyModel(tf.keras.models.Sequential):
# Initialize the model with necessary layers
def __init__(self):
super().__init__()
self.add(Dense(64, input_shape=(10,), activation='relu')) # Add a dense layer with 64 units and relu activation function
self.add(Dense(32, activation='relu')) # Add a dense layer with 32 units and relu activation function
self.add(Dense(1)) # Add a dense layer with 1 unit (output layer)
# Compile the model with specified loss function and optimizer
def compile(self):
super().compile(loss='mse', optimizer='adam')
# Fit the model on training data with specified number of epochs, batch size, and validation split
def fit(self, x_train, y_train, epochs=50, batch_size=32, validation_split=0.2, verbose=1):
# Define early stopping and learning rate reduction callbacks
earlystop = EarlyStopping(monitor='val_loss', mode='min', verbose=verbose)
lr_reducer = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3, min_lr=1e-6, verbose=verbose)
# Fit the model with specified parameters and callbacks
super().fit(x_train, y_train, epochs=epochs, batch_size=batch_size, validation_split=validation_split, callbacks=[earlystop, lr_reducer], verbose=verbose)
# Evaluate the model on test data and return the loss
def evaluate(self, x_test, y_test):
loss = super().evaluate(x_test, y_test)[0]
return loss
# Make predictions on input data and round the predictions to integers
def predict(self, x):
preds = super().predict(x)
return np.round(preds).astype('int')
# Calculate the model's score on test data using mean squared error
def score(self, x_test, y_test):
y_pred = self.predict(x_test)
mse = mean_squared_error(y_test, y_pred)
return -mse
As you can see, by inheriting from the BaseModel class and overriding its compile(), fit(), evaluate(), predict(), and score() methods, we were able to create a custom model with minimal effort. This is just one example of how powerful inheritance can be when working with machine learning models in Python!